diff --git a/.readthedocs.yml b/.readthedocs.yml index 827ed1117..229a16285 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -1,3 +1,10 @@ +version: 2 + +build: + os: "ubuntu-20.04" + tools: + python: "3.7" + python: install: - requirements: docs/requirements.txt diff --git a/.travis.yml b/.travis.yml index 551eea7b6..af0d4232c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,4 +1,9 @@ language: python +env: + global: + - PYTEST_SETTINGS="not requires_gpu and not memory_intense and not slow and not travis_slow" + - MODIFIES_PLUGIN="False" + - PLUGIN_ONLY="False" matrix: include: - name: 3.7 public @@ -16,13 +21,28 @@ before_install: - pip install pytest # download large files - pip install awscli -- bash test_setup.sh install: -- pip install . +- pip install -e ".[test]" +# install conda for plugin runner +- wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh +- bash miniconda.sh -b -p $HOME/miniconda +- source "$HOME/miniconda/etc/profile.d/conda.sh" +- hash -r +- conda config --set always_yes yes --set changeps1 no +- conda update -q conda +- conda info -a - pip list # list installed package versions script: -- if [ "$PRIVATE_ACCESS" = 1 ] && [ "$TRAVIS_PULL_REQUEST" = "false" ]; then pytest -m "not requires_gpu and not memory_intense and not slow and not travis_slow"; fi -- if [ "$PRIVATE_ACCESS" != 1 ]; then pytest -m "not requires_gpu and not memory_intense and not private_access and not slow and not travis_slow"; fi + # if ONLY plugin files changed, ONLY run tests for those plugins; otherwise, run full test suite + - | + if [ ! -z "$TRAVIS_PULL_REQUEST_BRANCH" ]; then + CHANGED_FILES=$( git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && git fetch && echo $(git diff --name-only origin/$TRAVIS_PULL_REQUEST_BRANCH origin/$TRAVIS_BRANCH -C $TRAVIS_BUILD_DIR) | tr '\n' ' ' ) && + TESTING_NEEDED=$( python -c "from brainscore_core.plugin_management.parse_plugin_changes import get_testing_info; get_testing_info(\"${CHANGED_FILES}\", 'brainscore_vision')" ) && + read MODIFIES_PLUGIN PLUGIN_ONLY <<< $TESTING_NEEDED && echo MODIFIES_PLUGIN: $MODIFIES_PLUGIN && echo PLUGIN_ONLY: $PLUGIN_ONLY; + fi + - if [ "$MODIFIES_PLUGIN" = "True" ]; then python -c "from brainscore_core.plugin_management.parse_plugin_changes import run_changed_plugin_tests; run_changed_plugin_tests(\"${CHANGED_FILES}\", 'brainscore_vision')"; fi + - if [ "$PRIVATE_ACCESS" = 1 ] && [ -n "${GITHUB_TOKEN}" ] && [ "$PLUGIN_ONLY" = "False" ]; then pytest -m "private_access and $PYTEST_SETTINGS"; fi + - if [ "$PRIVATE_ACCESS" != 1 ] && [ "$PLUGIN_ONLY" = "False" ]; then pytest -m "not private_access and $PYTEST_SETTINGS" --ignore "tests/test_submission"; fi notifications: slack: diff --git a/MANIFEST.in b/MANIFEST.in index 3b860798b..25bed5f12 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,6 +1,8 @@ include LICENSE include README.md -include brainscore/lookup.csv +include brainscore_vision/lookup.csv +include brainscore_vision/model_helpers/brain_transformation/imagenet_classes.txt +include brainscore_vision/model_helpers/check_submission/images/*.png recursive-exclude * __pycache__ recursive-exclude * *.py[co] diff --git a/README.md b/README.md index 0e5ca0597..dd4476ade 100644 --- a/README.md +++ b/README.md @@ -6,10 +6,10 @@ on their match to brain measurements in primate vision. The intent of Brain-Score is to adopt many (ideally all) the experimental benchmarks in the field for the purpose of model testing, falsification, and comparison. To that end, Brain-Score operationalizes experimental data into quantitative benchmarks -that any model candidate following the [`BrainModel`](brainscore/model_interface.py) interface can be scored on. +that any model candidate following the [`BrainModel`](brainscore_vision/model_interface.py) interface can be scored on. See the [Documentation](https://brain-score.readthedocs.io) for more details -and the [Tutorial](https://brain-score.readthedocs.io/en/latest/modules/tutorial.html) +and the [Tutorial](https://brain-score.readthedocs.io/en/latest/modules/model_tutorial.html) and [Examples](https://github.com/brain-score/candidate_models/blob/master/examples/score-model.ipynb) for submitting a model to Brain-Score. @@ -19,28 +19,29 @@ To contribute, please [send in a pull request](https://github.com/brain-score/br ## Local installation -You will need Python >= 3.7 and pip >= 18.1. +You will need Python = 3.7 and pip >= 18.1. Note that you can only access public benchmarks when running locally. To score a model on all benchmarks, submit it via the [brain-score.org website](http://www.brain-score.org). `pip install git+https://github.com/brain-score/brain-score` Score a model on a public benchmark: + ```python -from brainscore.benchmarks import public_benchmark_pool +from brainscore_vision.benchmarks import public_benchmark_pool benchmark = public_benchmark_pool['dicarlo.MajajHong2015public.IT-pls'] model = my_model() score = benchmark(model) -#> -#> array([0.32641998, 0.0207475]) -#> Coordinates: -#> * aggregation (aggregation) Attributes: -#> raw: \narray([0.4278365 ... -#> ceiling: \narray([0.7488407 ... -#> model_identifier: my-model -#> benchmark_identifier: dicarlo.MajajHong2015public.IT-pls +# > +# > array([0.32641998, 0.0207475]) +# > Coordinates: +# > * aggregation (aggregation) Attributes: +# > raw: \narray([0.4278365 ... +# > ceiling: \narray([0.7488407 ... +# > model_identifier: my-model +# > benchmark_identifier: dicarlo.MajajHong2015public.IT-pls ``` Some steps may take minutes because data has to be downloaded during first-time use. diff --git a/brainscore/__init__.py b/brainscore/__init__.py deleted file mode 100644 index 08a849b67..000000000 --- a/brainscore/__init__.py +++ /dev/null @@ -1,53 +0,0 @@ -import logging - -# The following imports provide convenience methods. -# noinspection PyUnresolvedReferences -from brainio import list_stimulus_sets, list_assemblies -# noinspection PyUnresolvedReferences -from brainio.fetch import get_assembly as brainio_get_assembly, get_stimulus_set -from brainscore.benchmarks import benchmark_pool -from result_caching import store - -_logger = logging.getLogger(__name__) - - -def get_assembly(name): - assembly = brainio_get_assembly(name) - assert hasattr(assembly.stimulus_set, 'identifier') - assert assembly.stimulus_set.identifier == assembly.stimulus_set_identifier - return assembly - - -@store(identifier_ignore=['model', 'benchmark']) -def score_model(model_identifier, benchmark_identifier, model): - """ - Score a given model on a given benchmark. - The model needs to implement the :class:`~brainscore.model_interface.BrainModel` interface so that the benchmark can - interact with it. - The benchmark will be looked up from the :data:`~brainscore.benchmarks.benchmark_pool` and evaluates the model - on how brain-like it is under that benchmark's experimental paradigm, primate measurements, comparison metric, and - ceiling. This results in a quantitative :class:`~brainscore.metrics.Score` ranging from 0 (least brain-like) to 1 - (most brain-like under this benchmark). - - The results of this method are cached by default (according to the identifiers), calling it twice with the same - identifiers will only invoke once. - - :param model_identifier: a unique identifier for this model - :param model: the model implementation following the :class:`~brainscore.model_interface.BrainModel` interface - :param benchmark_identifier: the identifier of the benchmark to test the model against - :return: a :class:`~brainscore.metrics.Score` of how brain-like the candidate model is under this benchmark. The - score is normalized by this benchmark's ceiling such that 1 means the model matches the data to ceiling - level. - - :seealso: :class:`brainscore.benchmarks.Benchmark` - :seealso: :class:`brainscore.model_interface.BrainModel` - """ - # model_identifier variable is not unused, the result caching component uses it to identify the cached results - assert model is not None - _logger.debug("retrieving benchmark") - benchmark = benchmark_pool[benchmark_identifier] - _logger.debug("scoring model") - score = benchmark(model) - score.attrs['model_identifier'] = model_identifier - score.attrs['benchmark_identifier'] = benchmark_identifier - return score diff --git a/brainscore/benchmarks/__init__.py b/brainscore/benchmarks/__init__.py deleted file mode 100644 index c45af140b..000000000 --- a/brainscore/benchmarks/__init__.py +++ /dev/null @@ -1,336 +0,0 @@ -""" -A :class:`~brainscore.benchmarks.Benchmark` runs an experiment on a :class:`~brainscore.model_interface.BrainModel` -and tests the resulting measurements against primate `data `_. -This comparison is done by a :class:`~brainscore.metrics.Metric` which outputs a score of how well model and data match. -This score is normalized with data ceilings and the benchmark returns this ceiled score. -""" -import itertools -from abc import ABC - -from result_caching import cache, store - -from brainscore.metrics import Score -from brainscore.model_interface import BrainModel -from brainscore.utils import LazyLoad - - -class Benchmark(ABC): - """ - Standard Benchmark interface defining the method interfaces. - """ - - def __call__(self, candidate: BrainModel) -> Score: - """ - Evaluate a candidate `BrainModel` and return a :class:`~brainscore.metrics.Score` denoting the brain-likeness of - the model under this benchmark. Typically this involves reproducing the experiment on the model and then - comparing model measurements (e.g. neural/behavioral) against recordings from biological subjects (e.g. - primates) using a :class:`~brainscore.metrics.Metric`. The output of this method is a normalized score between 0 - and 1 where 0 means the model does not match the measurements at all and 1 means the model matches the - measurements at ceiling level (e.g. if the model obtains a score of 0.8 and the data ceiling is also 0.8, the - score output by this method should be 1). - - :param candidate: a candidate model implementing the `BrainModel` interface. Benchmarks are agnostic of the - exact implementation and only interact with models through the methods defined in the interface. - :return: a :class:`~brainscore.metrics.Score` of how brain-like the candidate model is under this benchmark. The - score is normalized by this benchmark's ceiling such that 1 means the model matches the data to ceiling - level. - """ - raise NotImplementedError() - - @property - def bibtex(self) -> str: - """ - bibtex string to build the reference. - Should include an `url` to build a proper link. - """ - raise NotImplementedError() - - @property - def identifier(self) -> str: - """ - Unique identifier for this benchmark. - Standard format is `-`, e.g. `dicarlo.Rajalingham2018-i2n`. - - :return: a unique identifier for this benchmark - """ - raise NotImplementedError() - - @property - def version(self) -> str: - """ - Optional, but strongly encouraged. - - :return: a version number that is increased every time the model scores for this benchmark change - (but not for code changes that do not change scores). - """ - raise NotImplementedError() - - @property - def ceiling(self) -> Score: - """ - The ceiling of this benchmark. Scores need to be normalized by this value. - Typically this represents the signal in the data and how well we expect the best possible model to score. - - :return: a Score object, denoting the ceiling of this benchmark. - Typically has two values indexed by an `aggregation` coordinate: - `center` for the averaged ceiling value, and `error` for the uncertainty. - """ - raise NotImplementedError() - - -class BenchmarkBase(Benchmark): - """ - Helper class for implementing standard functions of the `Benchmark` interface. - """ - - def __init__(self, identifier, ceiling_func, version, parent=None, bibtex=None): - self._identifier = identifier - self._ceiling_func = ceiling_func - self._version = version - self.parent = parent - self._bibtex = bibtex - - @property - def bibtex(self): - return self._bibtex - - @property - def identifier(self): - return self._identifier - - @property - def version(self): - return self._version - - @property - def ceiling(self): - return self._ceiling(identifier=self.identifier) - - @store() - def _ceiling(self, identifier): - return self._ceiling_func() - - -def ceil_score(score, ceiling): - ceiled_center = score.sel(aggregation='center').values / ceiling.sel(aggregation='center').values - ceiled_score = type(score)([ceiled_center, score.sel(aggregation='error').values], - coords=score.coords, dims=score.dims) - ceiled_score.attrs[Score.RAW_VALUES_KEY] = score - ceiled_score.attrs['ceiling'] = ceiling - return ceiled_score - - -# define functions creating the benchmark pools, with local imports to avoid circular imports - -def _evaluation_benchmark_pool(): - """" - Standard benchmarks that are evaluated for the website. - """ - pool = {} - - # neural benchmarks - # MajajHong2015 - from .majajhong2015 import DicarloMajajHong2015V4PLS, DicarloMajajHong2015ITPLS - pool['dicarlo.MajajHong2015.V4-pls'] = LazyLoad(DicarloMajajHong2015V4PLS) - pool['dicarlo.MajajHong2015.IT-pls'] = LazyLoad(DicarloMajajHong2015ITPLS) - # FreemanZiemba2013 - from .freemanziemba2013 import MovshonFreemanZiemba2013V1PLS, MovshonFreemanZiemba2013V2PLS - pool['movshon.FreemanZiemba2013.V1-pls'] = LazyLoad(MovshonFreemanZiemba2013V1PLS) - pool['movshon.FreemanZiemba2013.V2-pls'] = LazyLoad(MovshonFreemanZiemba2013V2PLS) - from .kar2019 import DicarloKar2019OST - pool['dicarlo.Kar2019-ost'] = LazyLoad(DicarloKar2019OST) - # V1 properties benchmarks: orientation - from .marques2020_ringach2002 import MarquesRingach2002V1CircularVariance, MarquesRingach2002V1Bandwidth, \ - MarquesRingach2002V1OrthogonalPreferredRatio, MarquesRingach2002V1OrientationSelective, \ - MarquesRingach2002V1CircularVarianceBandwidthRatio, \ - MarquesRingach2002V1OrthogonalPrefferredRatioCircularVarianceDifference - from .marques2020_devalois1982a import MarquesDeValois1982V1PreferredOrientation - pool['dicarlo.Marques2020_Ringach2002-circular_variance'] = LazyLoad(MarquesRingach2002V1CircularVariance) - pool['dicarlo.Marques2020_Ringach2002-or_bandwidth'] = LazyLoad(MarquesRingach2002V1Bandwidth) - pool['dicarlo.Marques2020_Ringach2002-orth_pref_ratio'] = LazyLoad(MarquesRingach2002V1OrthogonalPreferredRatio) - pool['dicarlo.Marques2020_Ringach2002-or_selective'] = LazyLoad(MarquesRingach2002V1OrientationSelective) - pool['dicarlo.Marques2020_Ringach2002-cv_bandwidth_ratio'] = \ - LazyLoad(MarquesRingach2002V1CircularVarianceBandwidthRatio) - pool['dicarlo.Marques2020_Ringach2002-opr_cv_diff'] = \ - LazyLoad(MarquesRingach2002V1OrthogonalPrefferredRatioCircularVarianceDifference) - pool['dicarlo.Marques2020_DeValois1982-pref_or'] = LazyLoad(MarquesDeValois1982V1PreferredOrientation) - # V1 properties benchmarks: spatial frequency - from .marques2020_devalois1982b import MarquesDeValois1982V1PeakSpatialFrequency - from .marques2020_schiller1976 import MarquesSchiller1976V1SpatialFrequencyBandwidth, \ - MarquesSchiller1976V1SpatialFrequencySelective - pool['dicarlo.Marques2020_DeValois1982-peak_sf'] = LazyLoad(MarquesDeValois1982V1PeakSpatialFrequency) - pool['dicarlo.Marques2020_Schiller1976-sf_selective'] = LazyLoad(MarquesSchiller1976V1SpatialFrequencySelective) - pool['dicarlo.Marques2020_Schiller1976-sf_bandwidth'] = LazyLoad(MarquesSchiller1976V1SpatialFrequencyBandwidth) - # V1 properties benchmarks: surround - from .marques2020_cavanaugh2002a import MarquesCavanaugh2002V1GratingSummationField, \ - MarquesCavanaugh2002V1SurroundDiameter, MarquesCavanaugh2002V1SurroundSuppressionIndex - pool['dicarlo.Marques2020_Cavanaugh2002-grating_summation_field'] = \ - LazyLoad(MarquesCavanaugh2002V1GratingSummationField) - pool['dicarlo.Marques2020_Cavanaugh2002-surround_diameter'] = \ - LazyLoad(MarquesCavanaugh2002V1SurroundDiameter) - pool['dicarlo.Marques2020_Cavanaugh2002-surround_suppression_index'] = \ - LazyLoad(MarquesCavanaugh2002V1SurroundSuppressionIndex) - # V1 properties benchmarks: texture modulation - from .marques2020_freemanZiemba2013 import MarquesFreemanZiemba2013V1TextureModulationIndex, \ - MarquesFreemanZiemba2013V1AbsoluteTextureModulationIndex - pool['dicarlo.Marques2020_FreemanZiemba2013-texture_modulation_index'] = \ - LazyLoad(MarquesFreemanZiemba2013V1TextureModulationIndex) - pool['dicarlo.Marques2020_FreemanZiemba2013-abs_texture_modulation_index'] = \ - LazyLoad(MarquesFreemanZiemba2013V1AbsoluteTextureModulationIndex) - # V1 properties benchmarks: selectivity - from .marques2020_freemanZiemba2013 import MarquesFreemanZiemba2013V1TextureSelectivity, \ - MarquesFreemanZiemba2013V1TextureSparseness, MarquesFreemanZiemba2013V1VarianceRatio - pool['dicarlo.Marques2020_FreemanZiemba2013-texture_selectivity'] = \ - LazyLoad(MarquesFreemanZiemba2013V1TextureSelectivity) - pool['dicarlo.Marques2020_FreemanZiemba2013-texture_sparseness'] = \ - LazyLoad(MarquesFreemanZiemba2013V1TextureSparseness) - pool['dicarlo.Marques2020_FreemanZiemba2013-texture_variance_ratio'] = \ - LazyLoad(MarquesFreemanZiemba2013V1VarianceRatio) - # V1 properties benchmarks: magnitude - from .marques2020_ringach2002 import MarquesRingach2002V1MaxDC, MarquesRingach2002V1ModulationRatio - from .marques2020_freemanZiemba2013 import MarquesFreemanZiemba2013V1MaxTexture, MarquesFreemanZiemba2013V1MaxNoise - pool['dicarlo.Marques2020_Ringach2002-max_dc'] = LazyLoad(MarquesRingach2002V1MaxDC) - pool['dicarlo.Marques2020_Ringach2002-modulation_ratio'] = LazyLoad(MarquesRingach2002V1ModulationRatio) - pool['dicarlo.Marques2020_FreemanZiemba2013-max_texture'] = LazyLoad(MarquesFreemanZiemba2013V1MaxTexture) - pool['dicarlo.Marques2020_FreemanZiemba2013-max_noise'] = LazyLoad(MarquesFreemanZiemba2013V1MaxNoise) - # Sanghavi2020 benchmarks - from .sanghavi2020 import DicarloSanghavi2020V4PLS, DicarloSanghavi2020ITPLS - pool['dicarlo.Sanghavi2020.V4-pls'] = LazyLoad(DicarloSanghavi2020V4PLS) - pool['dicarlo.Sanghavi2020.IT-pls'] = LazyLoad(DicarloSanghavi2020ITPLS) - from .sanghavijozwik2020 import DicarloSanghaviJozwik2020V4PLS, DicarloSanghaviJozwik2020ITPLS - pool['dicarlo.SanghaviJozwik2020.V4-pls'] = LazyLoad(DicarloSanghaviJozwik2020V4PLS) - pool['dicarlo.SanghaviJozwik2020.IT-pls'] = LazyLoad(DicarloSanghaviJozwik2020ITPLS) - from .sanghavimurty2020 import DicarloSanghaviMurty2020V4PLS, DicarloSanghaviMurty2020ITPLS - pool['dicarlo.SanghaviMurty2020.V4-pls'] = LazyLoad(DicarloSanghaviMurty2020V4PLS) - pool['dicarlo.SanghaviMurty2020.IT-pls'] = LazyLoad(DicarloSanghaviMurty2020ITPLS) - - # behavioral benchmarks - # Rajalingham2018 - from .rajalingham2018 import DicarloRajalingham2018I2n - pool['dicarlo.Rajalingham2018-i2n'] = LazyLoad(DicarloRajalingham2018I2n) - # Geirhos2021-error_consistency - from . import geirhos2021 - for dataset in geirhos2021.DATASETS: - assembly_identifier = f'Geirhos2021{dataset}'.replace('-', '') - benchmark_ctr = getattr(geirhos2021, f"{assembly_identifier}ErrorConsistency") - pool[f"brendel.{assembly_identifier}-error_consistency"] = LazyLoad( - # use lambda parameter-binding to avoid `benchmark_ctr` being re-assigned in the next loop iteration - lambda benchmark_ctr=benchmark_ctr: benchmark_ctr()) - - return pool - - -def _engineering_benchmark_pool(): - """ - Additional engineering (ML) benchmarks. These benchmarks are public, but are also be evaluated for the website. - """ - pool = {} - - # ImageNet - from .imagenet import Imagenet2012 - pool['fei-fei.Deng2009-top1'] = LazyLoad(Imagenet2012) - - # ImageNet-C - from .imagenet_c import Imagenet_C_Noise, Imagenet_C_Blur, Imagenet_C_Weather, Imagenet_C_Digital - pool['dietterich.Hendrycks2019-noise-top1'] = LazyLoad(Imagenet_C_Noise) - pool['dietterich.Hendrycks2019-blur-top1'] = LazyLoad(Imagenet_C_Blur) - pool['dietterich.Hendrycks2019-weather-top1'] = LazyLoad(Imagenet_C_Weather) - pool['dietterich.Hendrycks2019-digital-top1'] = LazyLoad(Imagenet_C_Digital) - - # ObjectNet - from .objectnet import Objectnet - pool['katz.BarbuMayo2019-top1'] = LazyLoad(Objectnet) - - # Geirhos2021 - from . import geirhos2021 - for dataset in geirhos2021.DATASETS: - assembly_identifier = f'Geirhos2021{dataset}'.replace('-', '') - benchmark_ctr = getattr(geirhos2021, f"{assembly_identifier}Accuracy") - pool[f"brendel.{assembly_identifier}-top1"] = LazyLoad( - # use lambda parameter-binding to avoid `benchmark_ctr` being re-assigned in the next loop iteration - lambda benchmark_ctr=benchmark_ctr: benchmark_ctr()) - - # Hermann2020 - from .hermann2020 import Hermann2020cueconflictShapeBias, Hermann2020cueconflictShapeMatch - pool['kornblith.Hermann2020cueconflict-shape_bias'] = LazyLoad(Hermann2020cueconflictShapeBias) - pool['kornblith.Hermann2020cueconflict-shape_match'] = LazyLoad(Hermann2020cueconflictShapeMatch) - - return pool - - -def _experimental_benchmark_pool(): - """ - Benchmarks that can be used, but are not evaluated for the website. - """ - pool = {} - # neural benchmarks - from .majajhong2015 import DicarloMajajHong2015V4Mask, DicarloMajajHong2015ITMask, \ - DicarloMajajHong2015V4RDM, DicarloMajajHong2015ITRDM - pool['dicarlo.MajajHong2015.V4-mask'] = LazyLoad(DicarloMajajHong2015V4Mask) - pool['dicarlo.MajajHong2015.IT-mask'] = LazyLoad(DicarloMajajHong2015ITMask) - pool['dicarlo.MajajHong2015.V4-rdm'] = LazyLoad(DicarloMajajHong2015V4RDM) - pool['dicarlo.MajajHong2015.IT-rdm'] = LazyLoad(DicarloMajajHong2015ITRDM) - from .freemanziemba2013 import MovshonFreemanZiemba2013V1RDM, MovshonFreemanZiemba2013V2RDM, \ - MovshonFreemanZiemba2013V1Single - pool['movshon.FreemanZiemba2013.V1-rdm'] = LazyLoad(MovshonFreemanZiemba2013V1RDM) - pool['movshon.FreemanZiemba2013.V2-rdm'] = LazyLoad(MovshonFreemanZiemba2013V2RDM) - pool['movshon.FreemanZiemba2013.V1-single'] = LazyLoad(MovshonFreemanZiemba2013V1Single) - from .cadena2017 import ToliasCadena2017PLS, ToliasCadena2017Mask - pool['tolias.Cadena2017-pls'] = LazyLoad(ToliasCadena2017PLS) - pool['tolias.Cadena2017-mask'] = LazyLoad(ToliasCadena2017Mask) - from .rajalingham2020 import DicarloRajalingham2020ITPLS - pool['dicarlo.Rajalingham2020.IT-pls'] = LazyLoad(DicarloRajalingham2020ITPLS) - - return pool - - -def _public_benchmark_pool(): - """ - Benchmarks that are publicly usable, but are not used for the website. - """ - pool = {} - # neural benchmarks - from .public_benchmarks import FreemanZiembaV1PublicBenchmark, FreemanZiembaV2PublicBenchmark, \ - MajajHongV4PublicBenchmark, MajajHongITPublicBenchmark - pool['movshon.FreemanZiemba2013public.V1-pls'] = LazyLoad(FreemanZiembaV1PublicBenchmark) - pool['movshon.FreemanZiemba2013public.V2-pls'] = LazyLoad(FreemanZiembaV2PublicBenchmark) - pool['dicarlo.MajajHong2015public.V4-pls'] = LazyLoad(MajajHongV4PublicBenchmark) - pool['dicarlo.MajajHong2015public.IT-pls'] = LazyLoad(MajajHongITPublicBenchmark) - - # behavioral benchmarks - from .public_benchmarks import RajalinghamMatchtosamplePublicBenchmark - pool['dicarlo.Rajalingham2018public-i2n'] = LazyLoad(RajalinghamMatchtosamplePublicBenchmark) - - return pool - - -evaluation_benchmark_pool = _evaluation_benchmark_pool() -engineering_benchmark_pool = _engineering_benchmark_pool() -experimental_benchmark_pool = _experimental_benchmark_pool() -public_benchmark_pool = _public_benchmark_pool() - - -# make sure no identifiers overlap -def check_all_disjoint(*pools): - union = list(itertools.chain([pool.keys() for pool in pools])) - duplicates = set([identifier for identifier in union if union.count(identifier) > 1]) - if duplicates: - raise ValueError(f"Duplicate identifiers in pools: {duplicates}") - - -check_all_disjoint(evaluation_benchmark_pool, engineering_benchmark_pool, - experimental_benchmark_pool, public_benchmark_pool) - -# engineering benchmarks are part of both the public as well as the private evaluation pools -public_benchmark_pool = {**public_benchmark_pool, **engineering_benchmark_pool} -evaluation_benchmark_pool = {**evaluation_benchmark_pool} -# provide unifying pool -benchmark_pool = {**public_benchmark_pool, **engineering_benchmark_pool, - **experimental_benchmark_pool, **evaluation_benchmark_pool} - - -@cache() -def load(name): - if name not in benchmark_pool: - raise ValueError(f"Unknown benchmark '{name}' - must choose from {list(benchmark_pool.keys())}") - return benchmark_pool[name] diff --git a/brainscore/benchmarks/freemanziemba2013.py b/brainscore/benchmarks/freemanziemba2013.py deleted file mode 100644 index 1b3e93f4f..000000000 --- a/brainscore/benchmarks/freemanziemba2013.py +++ /dev/null @@ -1,100 +0,0 @@ -import brainscore -from brainscore.benchmarks._neural_common import NeuralBenchmark, average_repetition -from brainscore.metrics.ceiling import InternalConsistency, RDMConsistency -from brainscore.metrics.rdm import RDMCrossValidated -from brainscore.metrics.regression import CrossRegressedCorrelation, pls_regression, pearsonr_correlation, \ - single_regression -from brainscore.utils import LazyLoad -from result_caching import store - -VISUAL_DEGREES = 4 -NUMBER_OF_TRIALS = 20 - -BIBTEX = """@Article{Freeman2013, - author={Freeman, Jeremy - and Ziemba, Corey M. - and Heeger, David J. - and Simoncelli, Eero P. - and Movshon, J. Anthony}, - title={A functional and perceptual signature of the second visual area in primates}, - journal={Nature Neuroscience}, - year={2013}, - month={Jul}, - day={01}, - volume={16}, - number={7}, - pages={974-981}, - abstract={The authors examined neuronal responses in V1 and V2 to synthetic texture stimuli that replicate higher-order statistical dependencies found in natural images. V2, but not V1, responded differentially to these textures, in both macaque (single neurons) and human (fMRI). Human detection of naturalistic structure in the same images was predicted by V2 responses, suggesting a role for V2 in representing natural image structure.}, - issn={1546-1726}, - doi={10.1038/nn.3402}, - url={https://doi.org/10.1038/nn.3402} - } - """ - - -def _MovshonFreemanZiemba2013Region(region, identifier_metric_suffix, similarity_metric, ceiler): - assembly_repetition = LazyLoad(lambda region=region: load_assembly(False, region=region)) - assembly = LazyLoad(lambda region=region: load_assembly(True, region=region)) - return NeuralBenchmark(identifier=f'movshon.FreemanZiemba2013.{region}-{identifier_metric_suffix}', version=2, - assembly=assembly, similarity_metric=similarity_metric, parent=region, - ceiling_func=lambda: ceiler(assembly_repetition), - visual_degrees=VISUAL_DEGREES, number_of_trials=NUMBER_OF_TRIALS, - bibtex=BIBTEX) - - -def MovshonFreemanZiemba2013V1PLS(): - return _MovshonFreemanZiemba2013Region('V1', identifier_metric_suffix='pls', - similarity_metric=CrossRegressedCorrelation( - regression=pls_regression(), correlation=pearsonr_correlation(), - crossvalidation_kwargs=dict(stratification_coord='texture_type')), - ceiler=InternalConsistency()) - - -def MovshonFreemanZiemba2013V1Single(): - return _MovshonFreemanZiemba2013Region('V1', identifier_metric_suffix='single', - similarity_metric=CrossRegressedCorrelation( - regression=single_regression(), correlation=pearsonr_correlation(), - crossvalidation_kwargs=dict(stratification_coord='texture_type')), - ceiler=InternalConsistency()) - - -def MovshonFreemanZiemba2013V1RDM(): - return _MovshonFreemanZiemba2013Region('V1', identifier_metric_suffix='rdm', - similarity_metric=RDMCrossValidated( - crossvalidation_kwargs=dict(stratification_coord='texture_type')), - ceiler=RDMConsistency()) - - -def MovshonFreemanZiemba2013V2PLS(): - return _MovshonFreemanZiemba2013Region('V2', identifier_metric_suffix='pls', - similarity_metric=CrossRegressedCorrelation( - regression=pls_regression(), correlation=pearsonr_correlation(), - crossvalidation_kwargs=dict(stratification_coord='texture_type')), - ceiler=InternalConsistency()) - - -def MovshonFreemanZiemba2013V2RDM(): - return _MovshonFreemanZiemba2013Region('V2', identifier_metric_suffix='rdm', - similarity_metric=RDMCrossValidated( - crossvalidation_kwargs=dict(stratification_coord='texture_type')), - ceiler=RDMConsistency()) - - -@store() -def load_assembly(average_repetitions, region, access='private'): - assembly = brainscore.get_assembly(f'movshon.FreemanZiemba2013.{access}') - assembly = assembly.sel(region=region) - assembly = assembly.stack(neuroid=['neuroid_id']) # work around xarray multiindex issues - assembly['region'] = 'neuroid', [region] * len(assembly['neuroid']) - assembly.load() - time_window = (50, 200) - assembly = assembly.sel(time_bin=[(t, t + 1) for t in range(*time_window)]) - assembly = assembly.mean(dim='time_bin', keep_attrs=True) - assembly = assembly.expand_dims('time_bin_start').expand_dims('time_bin_end') - assembly['time_bin_start'], assembly['time_bin_end'] = [time_window[0]], [time_window[1]] - assembly = assembly.stack(time_bin=['time_bin_start', 'time_bin_end']) - assembly = assembly.squeeze('time_bin') - assembly = assembly.transpose('presentation', 'neuroid') - if average_repetitions: - assembly = average_repetition(assembly) - return assembly diff --git a/brainscore/benchmarks/objectnet.py b/brainscore/benchmarks/objectnet.py deleted file mode 100644 index 222c955ae..000000000 --- a/brainscore/benchmarks/objectnet.py +++ /dev/null @@ -1,59 +0,0 @@ -import os -import logging - -import numpy as np -import pandas as pd - -import brainscore -from brainio.stimuli import StimulusSet -from brainscore.benchmarks import BenchmarkBase -from brainscore.metrics import Score -from brainscore.metrics.accuracy import Accuracy -from brainscore.model_interface import BrainModel -from brainio.fetch import StimulusSetLoader -from brainio.lookup import lookup_stimulus_set - -NUMBER_OF_TRIALS = 10 - -_logger = logging.getLogger(__name__) -LOCAL_STIMULUS_DIRECTORY = '/braintree/data2/active/common/objectnet-stimuli/' - -class Objectnet(BenchmarkBase): - def __init__(self): - self._stimulus_set = brainscore.get_stimulus_set('katz.BarbuMayo2019') - self._similarity_metric = Accuracy() - ceiling = Score([1, np.nan], coords={'aggregation': ['center', 'error']}, dims=['aggregation']) - - super(Objectnet, self).__init__(identifier='katz.BarbuMayo2019-top1', version=1, - ceiling_func=lambda: ceiling, - parent='engineering', - bibtex="""@inproceedings{DBLP:conf/nips/BarbuMALWGTK19, - author = {Andrei Barbu and - David Mayo and - Julian Alverio and - William Luo and - Christopher Wang and - Dan Gutfreund and - Josh Tenenbaum and - Boris Katz}, - title = {ObjectNet: {A} large-scale bias-controlled dataset for pushing the - limits of object recognition models}, - booktitle = {NeurIPS 2019}, - pages = {9448--9458}, - year = {2019}, - url = {https://proceedings.neurips.cc/paper/2019/hash/97af07a14cacba681feacf3012730892-Abstract.html}, - }""") - - - def __call__(self, candidate): - # The proper `fitting_stimuli` to pass to the candidate would be the imagenet training set. - # For now, since almost all models in our hands were trained with imagenet, we'll just short-cut this - # by telling the candidate to use its pre-trained imagenet weights. - candidate.start_task(BrainModel.Task.label, 'imagenet') - stimulus_set = self._stimulus_set[list(set(self._stimulus_set.columns) - {'synset'})] # do not show label - predictions = candidate.look_at(stimulus_set, number_of_trials=NUMBER_OF_TRIALS) - score = self._similarity_metric( - predictions.sortby('filename'), - self._stimulus_set.sort_values('filename')['synset'].values - ) - return score diff --git a/brainscore/benchmarks/public_benchmarks.py b/brainscore/benchmarks/public_benchmarks.py deleted file mode 100644 index 66a0128bf..000000000 --- a/brainscore/benchmarks/public_benchmarks.py +++ /dev/null @@ -1,110 +0,0 @@ -""" -The purpose of this file is to provide benchmarks based on publicly accessible data that can be run on candidate models -without restrictions. As opposed to the private benchmarks hosted on www.Brain-Score.org, models can be evaluated -without having to submit them to the online platform. -This allows for quick local prototyping, layer commitment, etc. -For the final model evaluation, candidate models should still be sent to www.Brain-Score.org to evaluate them on -held-out private data. -""" -import functools -import logging - -import boto3 -from botocore import UNSIGNED -from botocore.config import Config -from botocore.exceptions import ClientError - -import brainio -from brainio.fetch import BotoFetcher -from brainscore.benchmarks._neural_common import NeuralBenchmark -from brainscore.metrics.ceiling import InternalConsistency -from brainscore.metrics.regression import CrossRegressedCorrelation, pls_regression, pearsonr_correlation -from brainscore.utils import LazyLoad -from .freemanziemba2013 import load_assembly as load_freemanziemba2013, VISUAL_DEGREES as freemanziemba2013_degrees, \ - NUMBER_OF_TRIALS as freemanziemba2013_trials -from .majajhong2015 import load_assembly as load_majajhong2015, VISUAL_DEGREES as majajhong2015_degrees, \ - NUMBER_OF_TRIALS as majajhong2015_trials -from .freemanziemba2013 import load_assembly as load_freemanziemba2013, VISUAL_DEGREES as freemanziemba2013_degrees, \ - BIBTEX as freemanziemba2013_bibtex -from .majajhong2015 import load_assembly as load_majajhong2015, VISUAL_DEGREES as majajhong2015_degrees, \ - BIBTEX as majajhong2015_bibtex -from .rajalingham2018 import load_assembly as load_rajalingham2018, _DicarloRajalingham2018 -from brainscore.metrics.image_level_behavior import I2n - -_logger = logging.getLogger(__name__) - - -def _standard_benchmark(identifier, load_assembly, visual_degrees, number_of_trials, stratification_coord, bibtex): - assembly_repetition = LazyLoad(lambda: load_assembly(average_repetitions=False)) - assembly = LazyLoad(lambda: load_assembly(average_repetitions=True)) - similarity_metric = CrossRegressedCorrelation( - regression=pls_regression(), correlation=pearsonr_correlation(), - crossvalidation_kwargs=dict(stratification_coord=stratification_coord)) - ceiler = InternalConsistency() - return NeuralBenchmark(identifier=f"{identifier}-pls", version=1, - assembly=assembly, similarity_metric=similarity_metric, - visual_degrees=visual_degrees, number_of_trials=number_of_trials, - ceiling_func=lambda: ceiler(assembly_repetition), - parent=None, - bibtex=bibtex) - - -def FreemanZiembaV1PublicBenchmark(): - return _standard_benchmark('movshon.FreemanZiemba2013.V1.public', - load_assembly=functools.partial(load_freemanziemba2013, region='V1', access='public'), - visual_degrees=freemanziemba2013_degrees, number_of_trials=freemanziemba2013_trials, - stratification_coord='texture_type', bibtex=freemanziemba2013_bibtex) - - -def FreemanZiembaV2PublicBenchmark(): - return _standard_benchmark('movshon.FreemanZiemba2013.V2.public', - load_assembly=functools.partial(load_freemanziemba2013, region='V2', access='public'), - visual_degrees=freemanziemba2013_degrees, number_of_trials=freemanziemba2013_trials, - stratification_coord='texture_type', bibtex=freemanziemba2013_bibtex) - - -def MajajHongV4PublicBenchmark(): - return _standard_benchmark('dicarlo.MajajHong2015.V4.public', - load_assembly=functools.partial(load_majajhong2015, region='V4', access='public'), - visual_degrees=majajhong2015_degrees, number_of_trials=majajhong2015_trials, - stratification_coord='object_name', bibtex=majajhong2015_bibtex) - - -def MajajHongITPublicBenchmark(): - return _standard_benchmark('dicarlo.MajajHong2015.IT.public', - load_assembly=functools.partial(load_majajhong2015, region='IT', access='public'), - visual_degrees=majajhong2015_degrees, number_of_trials=majajhong2015_trials, - stratification_coord='object_name', bibtex=majajhong2015_bibtex) - - -class RajalinghamMatchtosamplePublicBenchmark(_DicarloRajalingham2018): - def __init__(self): - super(RajalinghamMatchtosamplePublicBenchmark, self).__init__(metric=I2n(), metric_identifier='i2n') - self._assembly = LazyLoad(lambda: load_rajalingham2018(access='public')) - self._ceiling_func = lambda: self._metric.ceiling(self._assembly, skipna=True) - - -def list_public_assemblies(): - all_assemblies = brainio.list_assemblies() - public_assemblies = [] - for assembly in all_assemblies: - assy_model = brainio.lookup.lookup_assembly(assembly) - if assy_model['location_type'] != 'S3': - _logger.warning(f"Unknown location_type in assembly {assy_model}") - continue - probe_fetcher = _ProbeBotoFetcher(location=assy_model['location'], local_filename='probe') # filename is unused - if probe_fetcher.has_access(): - public_assemblies.append(assembly) - return public_assemblies - - -class _ProbeBotoFetcher(BotoFetcher): - def has_access(self): - s3 = boto3.resource('s3', config=Config(signature_version=UNSIGNED)) - obj = s3.Object(self.bucketname, self.relative_path) - try: - # noinspection PyStatementEffect - obj.content_length # probe - return True - except ClientError: - return False diff --git a/brainscore/benchmarks/sanghavi2020.py b/brainscore/benchmarks/sanghavi2020.py deleted file mode 100644 index c61f222d8..000000000 --- a/brainscore/benchmarks/sanghavi2020.py +++ /dev/null @@ -1,53 +0,0 @@ -import numpy as np - -import brainscore -from brainscore.benchmarks._neural_common import NeuralBenchmark, average_repetition -from brainscore.metrics.ceiling import InternalConsistency -from brainscore.metrics.regression import CrossRegressedCorrelation, pls_regression, pearsonr_correlation -from brainscore.utils import LazyLoad - - -VISUAL_DEGREES = 8 -NUMBER_OF_TRIALS = 28 -BIBTEX = """""" - - -def _DicarloSanghavi2020Region(region, identifier_metric_suffix, similarity_metric, ceiler): - assembly_repetition = LazyLoad(lambda region=region: load_assembly(average_repetitions=False, region=region)) - assembly = LazyLoad(lambda region=region: load_assembly(average_repetitions=True, region=region)) - return NeuralBenchmark(identifier=f'dicarlo.Sanghavi2020.{region}-{identifier_metric_suffix}', version=1, - assembly=assembly, similarity_metric=similarity_metric, - visual_degrees=VISUAL_DEGREES, number_of_trials=NUMBER_OF_TRIALS, - ceiling_func=lambda: ceiler(assembly_repetition), - parent=region, - bibtex=BIBTEX) - - -def DicarloSanghavi2020V4PLS(): - return _DicarloSanghavi2020Region('V4', identifier_metric_suffix='pls', - similarity_metric=CrossRegressedCorrelation( - regression=pls_regression(), correlation=pearsonr_correlation(), - crossvalidation_kwargs=dict(stratification_coord='object_name')), - ceiler=InternalConsistency()) - - -def DicarloSanghavi2020ITPLS(): - return _DicarloSanghavi2020Region('IT', identifier_metric_suffix='pls', - similarity_metric=CrossRegressedCorrelation( - regression=pls_regression(), correlation=pearsonr_correlation(), - crossvalidation_kwargs=dict(stratification_coord='object_name')), - ceiler=InternalConsistency()) - - -def load_assembly(average_repetitions, region): - assembly = brainscore.get_assembly(name=f'dicarlo.Sanghavi2020') - assembly = assembly.sel(region=region) - assembly['region'] = 'neuroid', [region] * len(assembly['neuroid']) - assembly.load() - assembly = assembly.sel(time_bin_id=0) # 70-170ms - assembly = assembly.squeeze('time_bin') - assert NUMBER_OF_TRIALS == len(np.unique(assembly.coords['repetition'])) - assert VISUAL_DEGREES == assembly.attrs['image_size_degree'] - if average_repetitions: - assembly = average_repetition(assembly) - return assembly diff --git a/brainscore/benchmarks/sanghavijozwik2020.py b/brainscore/benchmarks/sanghavijozwik2020.py deleted file mode 100644 index 60b3df6a1..000000000 --- a/brainscore/benchmarks/sanghavijozwik2020.py +++ /dev/null @@ -1,53 +0,0 @@ -import numpy as np - -import brainscore -from brainscore.benchmarks._neural_common import NeuralBenchmark, average_repetition -from brainscore.metrics.ceiling import InternalConsistency -from brainscore.metrics.regression import CrossRegressedCorrelation, pls_regression, pearsonr_correlation -from brainscore.utils import LazyLoad - - -VISUAL_DEGREES = 8 -NUMBER_OF_TRIALS = 37 -BIBTEX = """""" - - -def _DicarloSanghaviJozwik2020Region(region, identifier_metric_suffix, similarity_metric, ceiler): - assembly_repetition = LazyLoad(lambda region=region: load_assembly(average_repetitions=False, region=region)) - assembly = LazyLoad(lambda region=region: load_assembly(average_repetitions=True, region=region)) - return NeuralBenchmark(identifier=f'dicarlo.SanghaviJozwik2020.{region}-{identifier_metric_suffix}', version=1, - assembly=assembly, similarity_metric=similarity_metric, - visual_degrees=VISUAL_DEGREES, number_of_trials=NUMBER_OF_TRIALS, - ceiling_func=lambda: ceiler(assembly_repetition), - parent=region, - bibtex=BIBTEX) - - -def DicarloSanghaviJozwik2020V4PLS(): - return _DicarloSanghaviJozwik2020Region('V4', identifier_metric_suffix='pls', - similarity_metric=CrossRegressedCorrelation( - regression=pls_regression(), correlation=pearsonr_correlation(), - crossvalidation_kwargs=dict(stratification_coord=None)), - ceiler=InternalConsistency()) - - -def DicarloSanghaviJozwik2020ITPLS(): - return _DicarloSanghaviJozwik2020Region('IT', identifier_metric_suffix='pls', - similarity_metric=CrossRegressedCorrelation( - regression=pls_regression(), correlation=pearsonr_correlation(), - crossvalidation_kwargs=dict(stratification_coord=None)), - ceiler=InternalConsistency()) - - -def load_assembly(average_repetitions, region): - assembly = brainscore.get_assembly(name=f'dicarlo.SanghaviJozwik2020') - assembly = assembly.sel(region=region) - assembly['region'] = 'neuroid', [region] * len(assembly['neuroid']) - assembly.load() - assembly = assembly.sel(time_bin_id=0) # 70-170ms - assembly = assembly.squeeze('time_bin') - assert NUMBER_OF_TRIALS == len(np.unique(assembly.coords['repetition'])) - assert VISUAL_DEGREES == assembly.attrs['image_size_degree'] - if average_repetitions: - assembly = average_repetition(assembly) - return assembly diff --git a/brainscore/benchmarks/sanghavimurty2020.py b/brainscore/benchmarks/sanghavimurty2020.py deleted file mode 100644 index 4dfe588a8..000000000 --- a/brainscore/benchmarks/sanghavimurty2020.py +++ /dev/null @@ -1,53 +0,0 @@ -import numpy as np - -import brainscore -from brainscore.benchmarks._neural_common import NeuralBenchmark, average_repetition -from brainscore.metrics.ceiling import InternalConsistency -from brainscore.metrics.regression import CrossRegressedCorrelation, pls_regression, pearsonr_correlation -from brainscore.utils import LazyLoad - - -VISUAL_DEGREES = 5 -NUMBER_OF_TRIALS = 50 -BIBTEX = """""" - - -def _DicarloSanghaviMurty2020Region(region, identifier_metric_suffix, similarity_metric, ceiler): - assembly_repetition = LazyLoad(lambda region=region: load_assembly(average_repetitions=False, region=region)) - assembly = LazyLoad(lambda region=region: load_assembly(average_repetitions=True, region=region)) - return NeuralBenchmark(identifier=f'dicarlo.SanghaviMurty2020.{region}-{identifier_metric_suffix}', version=1, - assembly=assembly, similarity_metric=similarity_metric, - visual_degrees=VISUAL_DEGREES, number_of_trials=NUMBER_OF_TRIALS, - ceiling_func=lambda: ceiler(assembly_repetition), - parent=region, - bibtex=BIBTEX) - - -def DicarloSanghaviMurty2020V4PLS(): - return _DicarloSanghaviMurty2020Region('V4', identifier_metric_suffix='pls', - similarity_metric=CrossRegressedCorrelation( - regression=pls_regression(), correlation=pearsonr_correlation(), - crossvalidation_kwargs=dict(stratification_coord=None)), - ceiler=InternalConsistency()) - - -def DicarloSanghaviMurty2020ITPLS(): - return _DicarloSanghaviMurty2020Region('IT', identifier_metric_suffix='pls', - similarity_metric=CrossRegressedCorrelation( - regression=pls_regression(), correlation=pearsonr_correlation(), - crossvalidation_kwargs=dict(stratification_coord=None)), - ceiler=InternalConsistency()) - - -def load_assembly(average_repetitions, region): - assembly = brainscore.get_assembly(name=f'dicarlo.SanghaviMurty2020') - assembly = assembly.sel(region=region) - assembly['region'] = 'neuroid', [region] * len(assembly['neuroid']) - assembly.load() - assembly = assembly.sel(time_bin_id=0) # 70-170ms - assembly = assembly.squeeze('time_bin') - assert NUMBER_OF_TRIALS == len(np.unique(assembly.coords['repetition'])) - assert VISUAL_DEGREES == assembly.attrs['image_size_degree'] - if average_repetitions: - assembly = average_repetition(assembly) - return assembly diff --git a/brainscore/metrics/__init__.py b/brainscore/metrics/__init__.py deleted file mode 100644 index 1759b6a85..000000000 --- a/brainscore/metrics/__init__.py +++ /dev/null @@ -1,115 +0,0 @@ -""" -A :class:`~brainscore.metrics.Metric` is part of a :class:`~brainscore.benchmarks.Benchmark` -and scores how similar two sets of data are. -Typically these two sets are model and primate measurements, but metrics are agnostic of the data source -and can also be used to compare two primate measurements (e.g. for ceiling estimates). -""" - -import warnings - -import logging - -from brainio.assemblies import DataAssembly, merge_data_arrays - - -class Metric: - """ - Metric interface. - A metric compares two sets of data and outputs a score of how well they match (1 = identical, 0 = no match). - """ - - def __call__(self, assembly1, assembly2): - """ - Compare two assemblies on their similarity. - These assemblies are typically neural or behavioral measurements, e.g. model and primate recordings. - - :param assembly1: the first assembly to compare against the second - :param assembly2: the second assembly to compare against the first - :return: a :class:`~brainscore.metrics.Score` denoting the match between the two assemblies - (1 = identical, 0 = no match). - """ - raise NotImplementedError() - - -_logger = logging.getLogger(__name__) # cannot set directly on Score object - - -class Score(DataAssembly): - """ - Scores are used as the outputs of metrics, benchmarks, and ceilings. They indicate similarity or goodness-of-fit - of sets of data. The high-level score is typically an aggregate of many smaller scores, e.g. the median of neuroid - correlations. To keep records of these smaller scores, a score can store "raw" scores in its attributes - (`score.attrs['raw']`). - """ - - RAW_VALUES_KEY = 'raw' - - def sel(self, *args, _apply_raw=True, **kwargs): - return self._preserve_raw('sel', *args, **kwargs, _apply_raw=_apply_raw) - - def isel(self, *args, _apply_raw=True, **kwargs): - return self._preserve_raw('isel', *args, **kwargs, _apply_raw=_apply_raw) - - def squeeze(self, *args, _apply_raw=True, **kwargs): - return self._preserve_raw('squeeze', *args, **kwargs, _apply_raw=_apply_raw) - - def expand_dims(self, *args, _apply_raw=True, **kwargs): - return self._preserve_raw('expand_dims', *args, **kwargs, _apply_raw=_apply_raw) - - def mean(self, *args, _apply_raw=False, **kwargs): - return self._preserve_raw('mean', *args, **kwargs, _apply_raw=_apply_raw) - - def sum(self, *args, _apply_raw=False, **kwargs): - return self._preserve_raw('sum', *args, **kwargs, _apply_raw=_apply_raw) - - def std(self, *args, _apply_raw=False, **kwargs): - return self._preserve_raw('std', *args, **kwargs, _apply_raw=_apply_raw) - - def min(self, *args, _apply_raw=False, **kwargs): - return self._preserve_raw('min', *args, **kwargs, _apply_raw=_apply_raw) - - def reduce(self, *args, _apply_raw=False, **kwargs): - return self._preserve_raw('reduce', *args, **kwargs, _apply_raw=_apply_raw) - - def _preserve_raw(self, operation, *args, _apply_raw=False, _ignore_errors=True, **kwargs): - result = getattr(super(Score, self), operation)(*args, **kwargs) - if self.RAW_VALUES_KEY in self.attrs: - raw = self.attrs[self.RAW_VALUES_KEY] - if _apply_raw: - try: - raw = getattr(raw, operation)(*args, **kwargs) - except Exception as e: - if _ignore_errors: - # ignore errors with warning. most users will likely only want to access the main score - _logger.debug(f"{operation} on raw values failed: {repr(e)}") - else: - raise e - result.attrs[self.RAW_VALUES_KEY] = raw - return result - - def __setitem__(self, key, value, _apply_raw=True): - super(Score, self).__setitem__(key, value) - if _apply_raw and self.RAW_VALUES_KEY in self.attrs: - try: - self.attrs[self.RAW_VALUES_KEY].__setitem__(key, value) - except Exception as e: - _logger.debug(f"failed to set {key}={value} on raw values: " + (repr(e))) - - @classmethod - def merge(cls, *scores, ignore_exceptions=False): - """ - Merges the raw values in addition to the score assemblies. - """ - try: - result = merge_data_arrays(scores) - raws = [score.attrs[cls.RAW_VALUES_KEY] for score in scores if cls.RAW_VALUES_KEY in score.attrs] - if len(raws) > 0: - raw = Score.merge(*raws, ignore_exceptions=True) - result.attrs[cls.RAW_VALUES_KEY] = raw - except Exception as e: - if ignore_exceptions: - warnings.warn("failed to merge raw values: " + str(e)) - return None - else: - raise e - return result diff --git a/brainscore/metrics/accuracy.py b/brainscore/metrics/accuracy.py deleted file mode 100644 index 253ef2cfa..000000000 --- a/brainscore/metrics/accuracy.py +++ /dev/null @@ -1,14 +0,0 @@ -import numpy as np - -from brainscore.metrics import Score - - -class Accuracy: - def __call__(self, source, target): - values = source == target - center = np.mean(values) - error = np.std(values) - - score = Score([center, error], coords={'aggregation': ['center', 'error']}, dims=('aggregation',)) - score.attrs[Score.RAW_VALUES_KEY] = values - return score diff --git a/brainscore/metrics/anatomy.py b/brainscore/metrics/anatomy.py deleted file mode 100644 index 9014c6d08..000000000 --- a/brainscore/metrics/anatomy.py +++ /dev/null @@ -1,30 +0,0 @@ -import networkx as nx - -from brainscore.metrics import Metric - -ventral_stream = nx.DiGraph() # derived from Felleman & van Essen -ventral_stream.add_edge('input', 'V1') -ventral_stream.add_edge('V1', 'V2') -ventral_stream.add_edge('V1', 'V4') -ventral_stream.add_edge('V2', 'V1') -ventral_stream.add_edge('V2', 'V4') -ventral_stream.add_edge('V4', 'V1') -ventral_stream.add_edge('V4', 'V2') -ventral_stream.add_edge('V4', 'pIT') -ventral_stream.add_edge('V4', 'cIT') -ventral_stream.add_edge('V4', 'aIT') -ventral_stream.add_edge('pIT', 'V4') -ventral_stream.add_edge('pIT', 'cIT') -ventral_stream.add_edge('pIT', 'aIT') -ventral_stream.add_edge('cIT', 'V4') -ventral_stream.add_edge('cIT', 'pIT') -ventral_stream.add_edge('cIT', 'aIT') -ventral_stream.add_edge('aIT', 'V4') -ventral_stream.add_edge('aIT', 'pIT') -ventral_stream.add_edge('aIT', 'cIT') - - -class EdgeRatioMetric(Metric): - def __call__(self, source_graph, target_graph): - unmatched = [edge for edge in target_graph.edges() if edge not in source_graph.edges()] - return 1 - len(unmatched) / len(target_graph.edges()) diff --git a/brainscore/metrics/correlation.py b/brainscore/metrics/correlation.py deleted file mode 100644 index ec959e423..000000000 --- a/brainscore/metrics/correlation.py +++ /dev/null @@ -1,33 +0,0 @@ -from scipy.stats import pearsonr - -from brainscore.metrics.xarray_utils import XarrayCorrelation, Defaults as XarrayDefaults -from brainscore.metrics.transformations import TestOnlyCrossValidation - - -class CrossCorrelation: - def __init__(self, stimulus_coord=XarrayDefaults.stimulus_coord, neuroid_coord=XarrayDefaults.neuroid_coord, - neuroid_dim=XarrayDefaults.neuroid_dim, - test_size=.8, splits=5): - self._correlation = XarrayCorrelation(pearsonr, correlation_coord=stimulus_coord, neuroid_coord=neuroid_coord) - self._cross_validation = TestOnlyCrossValidation(test_size=test_size, splits=splits) - self._neuroid_dim = neuroid_dim - - def __call__(self, source, target): - return self._cross_validation(source, target, apply=self._correlation, aggregate=self.aggregate) - - def aggregate(self, scores): - return scores.median(dim=self._neuroid_dim) - - -class Correlation: - def __init__(self, stimulus_coord=XarrayDefaults.stimulus_coord, neuroid_coord=XarrayDefaults.neuroid_coord, - neuroid_dim=XarrayDefaults.neuroid_dim): - self._correlation = XarrayCorrelation(pearsonr, correlation_coord=stimulus_coord, neuroid_coord=neuroid_coord) - self._neuroid_dim = neuroid_dim - - def __call__(self, source, target): - correlation = self._correlation(source, target) - return self.aggregate(correlation) - - def aggregate(self, scores): - return scores.median(dim=self._neuroid_dim) diff --git a/brainscore/metrics/temporal.py b/brainscore/metrics/temporal.py deleted file mode 100644 index b2f7d2a43..000000000 --- a/brainscore/metrics/temporal.py +++ /dev/null @@ -1,81 +0,0 @@ -from xarray import DataArray - -from brainio.assemblies import merge_data_arrays, walk_coords -from brainscore.metrics.transformations import apply_aggregate - - -class TemporalRegressionAcrossTime: - """ - Fits a regression with weights shared across the time bins. - """ - - def __init__(self, regression): - self._regression = regression - - def fit(self, source, target): - assert (source['time_bin'] == target['time_bin']).all() - source = self._stack_timebins(source) - target = self._stack_timebins(target) - self._regression.fit(source, target) - - def predict(self, source): - predictions = [] - for time_bin_start, time_bin_end in source['time_bin'].values: - time_source = source.sel(time_bin=(time_bin_start, time_bin_end)) - prediction = self._regression.predict(time_source) - prediction = prediction.expand_dims('time_bin_start').expand_dims('time_bin_end') - prediction['time_bin_start'] = [time_bin_start] - prediction['time_bin_end'] = [time_bin_end] - prediction = prediction.stack(time_bin=['time_bin_start', 'time_bin_end']) - predictions.append(prediction) - return merge_data_arrays(predictions) - - def _stack_timebins(self, assembly): - assembly_type = type(assembly) - assembly = DataArray(assembly) # xarray cannot deal with stacking MultiIndex (pydata/xarray#1554) - assembly = assembly.reset_index(['presentation', 'time_bin']) - assembly = assembly.rename({'presentation': '_presentation'}) # we'll call stacked timebins "presentation" - assembly = assembly.stack(presentation=['_presentation', 'time_bin']) - return assembly_type(assembly) - - -class TemporalCorrelationAcrossImages: - """ - per time-bin, computes the correlation across images, then takes the mean across time-bins - """ - - def __init__(self, correlation): - self._correlation = correlation - - def __call__(self, prediction, target): - return cross_correlation(prediction, target, 'time_bin', self._correlation) - - -class TemporalCorrelationAcrossTime: - """ - per image, computes the correlation across time-bins, then takes the mean across images - """ - - def __init__(self, correlation): - self._correlation = correlation - - def __call__(self, prediction, target): - return cross_correlation(prediction, target, 'presentation', self._correlation) - - -def cross_correlation(prediction, target, cross, correlation): - assert (prediction[cross] == target[cross]).all() - scores = [] - coords = [coord for coord, dims, values in walk_coords(target[cross])] - for cross_value in target[cross].values: - _prediction = prediction.sel(**{cross: cross_value}) - _target = target.sel(**{cross: cross_value}) - score = correlation(_prediction, _target) - for coord, coord_value in zip(coords, cross_value): - score = score.expand_dims(coord) - score[coord] = [coord_value] - score = score.stack(**{cross: coords}) - scores.append(score) - score = merge_data_arrays(scores) - score = apply_aggregate(lambda score: score.mean(cross), score) - return score diff --git a/brainscore/model_interface.py b/brainscore/model_interface.py deleted file mode 100644 index c1bdd85e9..000000000 --- a/brainscore/model_interface.py +++ /dev/null @@ -1,95 +0,0 @@ -""" -The :class:`~brainscore.model_interface.BrainModel` interface is the central communication point -between benchmarks and models. -""" - -from enum import Enum - -from brainio.stimuli import StimulusSet -from typing import List, Tuple, Union - - -class BrainModel: - """ - The BrainModel interface defines an API for models to follow. - Benchmarks will use this interface to treat models as an experimental subject - without needing to know about the details of the model implementation. - """ - - RecordingTarget = Enum('RecordingTarget', " ".join(['V1', 'V2', 'V4', 'IT'])) - """ - location to record from - """ - - Task = Enum('Task', " ".join(['passive', 'probabilities', 'label'])) - """ - task to perform - """ - - @property - def identifier(self) -> str: - """ - The unique identifier for this model. - - :return: e.g. `'CORnet-S'`, or `'alexnet'` - """ - raise NotImplementedError() - - def visual_degrees(self) -> int: - """ - The visual degrees this model covers as a single scalar. - - :return: e.g. `8`, or `10` - """ - raise NotImplementedError() - - def look_at(self, stimuli: Union[StimulusSet, List[str]], number_of_trials=1): - """ - Digest a set of stimuli and return requested outputs. Which outputs to return is instructed by the - :meth:`~brainscore.model_interface.BrainMode.start_task` and - :meth:`~brainscore.model_interface.BrainModel.start_recording` methods. - - :param stimuli: A set of stimuli, passed as either a :class:`~brainio.stimuli.StimulusSet` - or a list of image file paths - :param number_of_trials: The number of repeated trials of the stimuli that the model should average over. - E.g. 10 or 35. Non-stochastic models can likely ignore this parameter. - :return: recordings or task behaviors as instructed - """ - raise NotImplementedError() - - def start_task(self, task: Task, fitting_stimuli): - """ - Instructs the model to begin one of the tasks specified in :data:`~brainscore.model_interface.BrainModel.Task`. - For all followings call of :meth:`~brainscore.model_interface.BrainModel.look_at`, the model returns the - expected outputs for the specified task. - - :param task: The task the model should perform, and thus which outputs it should return - :param fitting_stimuli: A set of stimuli for the model to learn on, e.g. image-label pairs - """ - raise NotImplementedError() - - def start_recording(self, recording_target: RecordingTarget, time_bins=List[Tuple[int]]): - """ - Instructs the model to begin recording in a specified - :data:`~brainscore.model_interface.BrainModel.RecordingTarget` and return the specified `time_bins`. - For all followings call of :meth:`~brainscore.model_interface.BrainModel.look_at`, the model returns the - corresponding recordings. These recordings are a :class:`~brainio.assemblies.NeuroidAssembly` with exactly - 3 dimensions: - - - `presentation`: the presented stimuli (cf. stimuli argument of - :meth:`~brainscore.model_interface.BrainModel.look_at`). If a :class:`~brainio.stimuli.StimulusSet` - was passed, the recordings should contain all of the :class:`~brainio.stimuli.StimulusSet` columns as - coordinates on this dimension. The `stimulus_id` coordinate is required in either case. - - `neuroid`: the recorded neuroids (neurons or mixtures thereof). They should all be part of the - specified :data:`~brainscore.model_interface.BrainModel.RecordingTarget`. The coordinates of this - dimension should again include as much information as is available, at the very least a `neuroid_id`. - - `time_bins`: the time bins of each recording slice. This dimension should contain at least 2 coordinates: - `time_bin_start` and `time_bin_end`, where one `time_bin` is the bin between start and end. - For instance, a 70-170ms time_bin would be marked as `time_bin_start=70` and `time_bin_end=170`. - If only one time_bin is requested, the model may choose to omit this dimension. - - :param recording_target: which location to record from - :param time_bins: which time_bins to record as a list of integer tuples, - e.g. `[(50, 100), (100, 150), (150, 200)]` or `[(70, 170)]` - """ - raise NotImplementedError() diff --git a/brainscore_vision/__init__.py b/brainscore_vision/__init__.py new file mode 100644 index 000000000..403f2801e --- /dev/null +++ b/brainscore_vision/__init__.py @@ -0,0 +1,102 @@ +import logging +from typing import Dict, Any, Union, Callable + + +from brainio.assemblies import DataAssembly +from brainio.stimuli import StimulusSet +from brainscore_core.benchmarks import Benchmark +from brainscore_core.metrics import Metric, Score +from brainscore_core.plugin_management.conda_score import wrap_score +from brainscore_core.plugin_management.import_plugin import import_plugin +from brainscore_vision.metrics import Ceiling +from brainscore_vision.model_interface import BrainModel + +_logger = logging.getLogger(__name__) + +data_registry: Dict[str, Callable[[], Union[DataAssembly, Any]]] = {} +""" Pool of available data """ + +stimulus_set_registry: Dict[str, Callable[[], StimulusSet]] = {} +""" Pool of available stimulus sets""" + +metric_registry: Dict[str, Callable[[], Metric]] = {} +""" Pool of available metrics """ + +benchmark_registry: Dict[str, Callable[[], Benchmark]] = {} +""" Pool of available benchmarks """ + +model_registry: Dict[str, Callable[[], BrainModel]] = {} +""" Pool of available models """ + + +def load_dataset(identifier: str) -> Union[DataAssembly, Any]: + import_plugin('brainscore_vision', 'data', identifier) + + return data_registry[identifier]() + + +def load_stimulus_set(identifier: str) -> Any: + # dataset and stimulus_set are kept in the same directory + import_plugin('brainscore_vision', 'data', identifier, registry_prefix='stimulus_set') + + return stimulus_set_registry[identifier]() + + +def load_metric(identifier: str, *args, **kwargs) -> Metric: + import_plugin('brainscore_vision', 'metrics', identifier) + + return metric_registry[identifier](*args, **kwargs) + + +def load_ceiling(identifier: str, *args, **kwargs) -> Ceiling: + """ + Convenience method to load ceilings with the correct type -- internally uses :meth:`~brainscore_vision.load_metric`, + i.e. the ceiling has to be registered as a metric. + """ + return load_metric(identifier, *args, **kwargs) + + +def load_benchmark(identifier: str) -> Benchmark: + import_plugin('brainscore_vision', 'benchmarks', identifier) + + return benchmark_registry[identifier]() + + +def load_model(identifier: str) -> BrainModel: + import_plugin('brainscore_vision', 'models', identifier) + + return model_registry[identifier]() + + +def _run_score(model_identifier: str, benchmark_identifier: str) -> Score: + """ + Score the model referenced by the `model_identifier` on the benchmark referenced by the `benchmark_identifier`. + """ + model: BrainModel = load_model(model_identifier) + benchmark: Benchmark = load_benchmark(benchmark_identifier) + score: Score = benchmark(model) + score.attrs['model_identifier'] = model_identifier + score.attrs['benchmark_identifier'] = benchmark_identifier + return score + + +def score(model_identifier: str, benchmark_identifier: str) -> Score: + """ + Score the model referenced by the `model_identifier` on the benchmark referenced by the `benchmark_identifier`. + The model needs to implement the :class:`~brainscore.model_interface.BrainModel` interface + so that the benchmark can interact with it. + The benchmark will be looked up from the :data:`~brainscore_vision.benchmarks` and evaluates the model + (looked up from :data:`~brainscore_vision.models`) on how brain-like it is under that benchmark's + experimental paradigm, primate measurements, comparison metric, and ceiling. + This results in a quantitative + `Score `_ + ranging from 0 (least brain-like) to 1 (most brain-like under this benchmark). + + :param model_identifier: the identifier for the model + :param benchmark_identifier: the identifier for the benchmark to test the model against + :return: a Score of how brain-like the candidate model is under this benchmark. The score is normalized by + this benchmark's ceiling such that 1 means the model matches the data to ceiling level. + """ + return wrap_score(__file__, + model_identifier=model_identifier, benchmark_identifier=benchmark_identifier, + score_function=_run_score) diff --git a/brainscore_vision/__main__.py b/brainscore_vision/__main__.py new file mode 100644 index 000000000..f4214b2ee --- /dev/null +++ b/brainscore_vision/__main__.py @@ -0,0 +1,20 @@ +from pathlib import Path + +import fire +from brainscore_core.plugin_management.test_plugins import run_args as _core_test_plugins + +from brainscore_vision import score as _score_function + + +def score(model_identifier: str, benchmark_identifier: str): + result = _score_function(model_identifier, benchmark_identifier) + print(result) # print instead of return because fire has issues with xarray objects + + +def test_plugins(*args, **kwargs): + root_directory = Path(__file__).parent + _core_test_plugins(*args, root_directory=root_directory, **kwargs) + + +if __name__ == '__main__': + fire.Fire() diff --git a/tests/test_benchmarks/__init__.py b/brainscore_vision/benchmark_helpers/__init__.py similarity index 97% rename from tests/test_benchmarks/__init__.py rename to brainscore_vision/benchmark_helpers/__init__.py index 8ca554e5e..e818b6c91 100644 --- a/tests/test_benchmarks/__init__.py +++ b/brainscore_vision/benchmark_helpers/__init__.py @@ -1,8 +1,9 @@ -import numpy as np from typing import Union +import numpy as np + from brainio.assemblies import NeuroidAssembly, DataAssembly -from brainscore.model_interface import BrainModel +from brainscore_vision.model_interface import BrainModel class PrecomputedFeatures(BrainModel): diff --git a/brainscore/benchmarks/_neural_common.py b/brainscore_vision/benchmark_helpers/neural_common.py similarity index 78% rename from brainscore/benchmarks/_neural_common.py rename to brainscore_vision/benchmark_helpers/neural_common.py index ea8f4d4f2..bb2b3cdc8 100644 --- a/brainscore/benchmarks/_neural_common.py +++ b/brainscore_vision/benchmark_helpers/neural_common.py @@ -1,9 +1,10 @@ import numpy as np from brainio.assemblies import array_is_element, walk_coords -from brainscore.benchmarks import BenchmarkBase, ceil_score -from brainscore.benchmarks.screen import place_on_screen -from brainscore.model_interface import BrainModel +from brainscore_core import Score +from brainscore_vision.benchmarks import BenchmarkBase +from brainscore_vision.model_interface import BrainModel +from .screen import place_on_screen class NeuralBenchmark(BenchmarkBase): @@ -27,7 +28,8 @@ def __call__(self, candidate: BrainModel): if 'time_bin' in source_assembly.dims: source_assembly = source_assembly.squeeze('time_bin') # static case for these benchmarks raw_score = self._similarity_metric(source_assembly, self._assembly) - return explained_variance(raw_score, self.ceiling) + ceiled_score = explained_variance(raw_score, self.ceiling) + return ceiled_score def timebins_from_assembly(assembly): @@ -37,16 +39,18 @@ def timebins_from_assembly(assembly): return timebins -def explained_variance(score, ceiling): - ceiled_score = ceil_score(score, ceiling) +def explained_variance(score: Score, ceiling: Score) -> Score: # ro(X, Y) # = (r(X, Y) / sqrt(r(X, X) * r(Y, Y)))^2 # = (r(X, Y) / sqrt(r(Y, Y) * r(Y, Y)))^2 # assuming that r(Y, Y) ~ r(X, X) following Yamins 2014 # = (r(X, Y) / r(Y, Y))^2 - r_square = np.power(ceiled_score.raw.sel(aggregation='center').values / - ceiled_score.ceiling.sel(aggregation='center').values, 2) - ceiled_score.__setitem__({'aggregation': score['aggregation'] == 'center'}, r_square, - _apply_raw=False) + r_square = np.power(score.values / + ceiling.values, 2) + ceiled_score = Score(r_square) + if 'error' in score.attrs: + ceiled_score.attrs['error'] = score.attrs['error'] + ceiled_score.attrs[Score.RAW_VALUES_KEY] = score + ceiled_score.attrs['ceiling'] = ceiling return ceiled_score diff --git a/brainscore/benchmarks/_properties_common.py b/brainscore_vision/benchmark_helpers/properties_common.py similarity index 98% rename from brainscore/benchmarks/_properties_common.py rename to brainscore_vision/benchmark_helpers/properties_common.py index b115b138d..c0b6a60e7 100644 --- a/brainscore/benchmarks/_properties_common.py +++ b/brainscore_vision/benchmark_helpers/properties_common.py @@ -1,10 +1,10 @@ import numpy as np +import brainscore_vision from brainio.assemblies import DataAssembly -from brainscore import get_stimulus_set -from brainscore.benchmarks import BenchmarkBase, ceil_score -from brainscore.benchmarks.screen import place_on_screen -from brainscore.model_interface import BrainModel +from brainscore_vision.benchmarks import BenchmarkBase, ceil_score +from .screen import place_on_screen +from brainscore_vision.model_interface import BrainModel from result_caching import store BLANK_STIM_NAME = 'dicarlo.Marques2020_blank' @@ -79,7 +79,7 @@ def get_firing_rates(model_identifier, model, region, stimulus_identifier, numbe def record_from_model(model: BrainModel, stimulus_identifier, number_of_trials): - stimulus_set = get_stimulus_set(stimulus_identifier) + stimulus_set = brainscore_vision.load_stimulus_set(stimulus_identifier) stimulus_set = place_on_screen(stimulus_set, target_visual_degrees=model.visual_degrees()) activations = model.look_at(stimulus_set, number_of_trials) if 'time_bin' in activations.dims: diff --git a/brainscore/benchmarks/screen.py b/brainscore_vision/benchmark_helpers/screen.py similarity index 100% rename from brainscore/benchmarks/screen.py rename to brainscore_vision/benchmark_helpers/screen.py diff --git a/brainscore_vision/benchmark_helpers/test_helper.py b/brainscore_vision/benchmark_helpers/test_helper.py new file mode 100644 index 000000000..13fb23879 --- /dev/null +++ b/brainscore_vision/benchmark_helpers/test_helper.py @@ -0,0 +1,157 @@ +from pathlib import Path +from typing import List, Tuple + +import numpy as np +from PIL import Image + +from brainio.assemblies import NeuroidAssembly, PropertyAssembly +from brainscore_vision import load_benchmark +from brainscore_vision.model_interface import BrainModel +from . import PrecomputedFeatures + + +class StandardizedTests: + def ceilings_test(self, benchmark: str, expected: float): + benchmark = load_benchmark(benchmark) + ceiling = benchmark.ceiling + assert ceiling == expected + + def self_regression_test(self, benchmark: str, visual_degrees: int, expected: float): + benchmark = load_benchmark(benchmark) + score = benchmark(PrecomputedFeatures(benchmark._assembly, visual_degrees=visual_degrees)).raw + assert score == expected + raw_values = score.attrs['raw'] + assert hasattr(raw_values, 'neuroid') + assert hasattr(raw_values, 'split') + assert len(raw_values['split']) == 10 + + def self_rdm_test(self, benchmark: str, visual_degrees: int, expected: float): + benchmark = load_benchmark(benchmark) + score = benchmark(PrecomputedFeatures(benchmark._assembly, visual_degrees=visual_degrees)).raw + assert score == expected + raw_values = score.attrs['raw'] + assert hasattr(raw_values, 'split') + assert len(raw_values['split']) == 10 + + +class PrecomputedTests: + def run_test(self, benchmark: str, precomputed_features_filepath: str, expected: float): + benchmark = load_benchmark(benchmark) + precomputed_features = NeuroidAssembly.from_files( + precomputed_features_filepath, + stimulus_set_identifier=benchmark._assembly.stimulus_set.identifier, + stimulus_set=None) + precomputed_features = precomputed_features.stack(presentation=['stimulus_path']) + precomputed_paths = list(map(lambda f: Path(f).name, precomputed_features['stimulus_path'].values)) + # attach stimulus set meta + stimulus_set = benchmark._assembly.stimulus_set + expected_stimulus_paths = [stimulus_set.get_stimulus(image_id) for image_id in stimulus_set['stimulus_id']] + expected_stimulus_paths = list(map(lambda f: Path(f).name, expected_stimulus_paths)) + assert set(precomputed_paths) == set(expected_stimulus_paths) + for column in stimulus_set.columns: + precomputed_features[column] = 'presentation', stimulus_set[column].values + precomputed_features = PrecomputedFeatures(precomputed_features, + visual_degrees=10, # doesn't matter, features are already computed + ) + # score + score = benchmark(precomputed_features).raw + assert score == expected + + def run_test_properties(self, benchmark: str, files: dict, expected: float): + benchmark = load_benchmark(benchmark) + from brainscore_vision import load_stimulus_set + + stimulus_identifiers = np.unique(np.array(['dicarlo.Marques2020_blank', 'dicarlo.Marques2020_receptive_field', + 'dicarlo.Marques2020_orientation', + benchmark._assembly.stimulus_set.identifier])) + precomputed_features = {} + for current_stimulus in stimulus_identifiers: + stimulus_set = load_stimulus_set(current_stimulus) + path = Path(__file__).parent / files[current_stimulus] + features = PropertyAssembly.from_files(path, + stimulus_set_identifier=stimulus_set.identifier, + stimulus_set=stimulus_set) + features = features.stack(presentation=['stimulus_path']) + precomputed_features[current_stimulus] = features + precomputed_paths = [Path(f).name for f in precomputed_features[current_stimulus]['stimulus_path'].values] + # attach stimulus set meta + expected_stimulus_paths = [stimulus_set.get_stimulus(stimulus_id) + for stimulus_id in stimulus_set['stimulus_id']] + expected_stimulus_paths = list(map(lambda f: Path(f).name, expected_stimulus_paths)) + assert set(precomputed_paths) == set(expected_stimulus_paths) + for column in stimulus_set.columns: + precomputed_features[current_stimulus][column] = 'presentation', stimulus_set[column].values + + precomputed_features = PrecomputedFeatures(precomputed_features, visual_degrees=8) + # score + score = benchmark(precomputed_features).raw + assert score == expected + + +class VisualDegreesTests: + def amount_gray_test(self, benchmark: str, candidate_degrees: int, image_id: str, expected: float): + benchmark = load_benchmark(benchmark) + + class DummyCandidate(BrainModel): + class StopException(Exception): + pass + + def visual_degrees(self): + return candidate_degrees + + def look_at(self, stimuli, number_of_trials=1): + image = stimuli.get_stimulus(image_id) + image = Image.open(image) + image = np.array(image) + amount_gray = 0 + for index in np.ndindex(image.shape[:2]): + color = image[index] + gray = [128, 128, 128] + if (color == gray).all(): + amount_gray += 1 + assert amount_gray / image.size == expected + raise self.StopException() + + def start_task(self, task: BrainModel.Task, fitting_stimuli): + pass + + def start_recording(self, recording_target: BrainModel.RecordingTarget, time_bins=List[Tuple[int]]): + pass + + candidate = DummyCandidate() + try: + benchmark(candidate) # just call to get the stimuli + except DummyCandidate.StopException: # but stop early + pass + + +class NumberOfTrialsTests: + def repetitions_test(self, benchmark_identifier: str): + """ Tests that benchmarks have repetitions in the stimulus_set """ + benchmark = load_benchmark(benchmark_identifier) + + class AssertRepeatCandidate(BrainModel): + class StopException(Exception): + pass + + def identifier(self) -> str: + return 'assert-repeat-candidate' + + def visual_degrees(self): + return 8 + + def look_at(self, stimuli, number_of_trials=1): + assert number_of_trials > 1 + raise self.StopException() + + def start_task(self, task: BrainModel.Task, fitting_stimuli): + pass + + def start_recording(self, recording_target: BrainModel.RecordingTarget, time_bins=List[Tuple[int]]): + pass + + candidate = AssertRepeatCandidate() + try: + benchmark(candidate) # just call to get the stimuli + except AssertRepeatCandidate.StopException: # but stop early + pass diff --git a/brainscore/benchmarks/README.md b/brainscore_vision/benchmarks/README.md similarity index 100% rename from brainscore/benchmarks/README.md rename to brainscore_vision/benchmarks/README.md diff --git a/brainscore_vision/benchmarks/__init__.py b/brainscore_vision/benchmarks/__init__.py new file mode 100644 index 000000000..66950699f --- /dev/null +++ b/brainscore_vision/benchmarks/__init__.py @@ -0,0 +1,122 @@ +# Importing individual benchmarks directly is discouraged. +# Use `brainscore_vision.load_benchmark` instead which provides dependency support. + +""" +A :class:`~brainscore.benchmarks.Benchmark` runs an experiment on a :class:`~brainscore.model_interface.BrainModel` +and tests the resulting measurements against primate `data `_. +This comparison is done by a :class:`~brainscore.metrics.Metric` which outputs a score of how well model and data match. +This score is normalized with data ceilings and the benchmark returns this ceiled score. +""" +from abc import ABC + +from result_caching import store + +from brainscore_core.metrics import Score +from brainscore_vision.model_interface import BrainModel + + +class Benchmark(ABC): + """ + Standard Benchmark interface defining the method interfaces. + """ + + def __call__(self, candidate: BrainModel) -> Score: + """ + Evaluate a candidate `BrainModel` and return a :class:`~brainscore.metrics.Score` denoting the brain-likeness of + the model under this benchmark. Typically this involves reproducing the experiment on the model and then + comparing model measurements (e.g. neural/behavioral) against recordings from biological subjects (e.g. + primates) using a :class:`~brainscore.metrics.Metric`. The output of this method is a normalized score between 0 + and 1 where 0 means the model does not match the measurements at all and 1 means the model matches the + measurements at ceiling level (e.g. if the model obtains a score of 0.8 and the data ceiling is also 0.8, the + score output by this method should be 1). + + :param candidate: a candidate model implementing the `BrainModel` interface. Benchmarks are agnostic of the + exact implementation and only interact with models through the methods defined in the interface. + :return: a :class:`~brainscore.metrics.Score` of how brain-like the candidate model is under this benchmark. The + score is normalized by this benchmark's ceiling such that 1 means the model matches the data to ceiling + level. + """ + raise NotImplementedError() + + @property + def bibtex(self) -> str: + """ + bibtex string to build the reference. + Should include an `url` to build a proper link. + """ + raise NotImplementedError() + + @property + def identifier(self) -> str: + """ + Unique identifier for this benchmark. + Standard format is `-`, e.g. `dicarlo.Rajalingham2018-i2n`. + + :return: a unique identifier for this benchmark + """ + raise NotImplementedError() + + @property + def version(self) -> str: + """ + Optional, but strongly encouraged. + + :return: a version number that is increased every time the model scores for this benchmark change + (but not for code changes that do not change scores). + """ + raise NotImplementedError() + + @property + def ceiling(self) -> Score: + """ + The ceiling of this benchmark. Scores need to be normalized by this value. + Typically this represents the signal in the data and how well we expect the best possible model to score. + + :return: a Score object, denoting the ceiling of this benchmark. + The Score values itself typically consist of just a scalar between zero and one. + Many ceilers also include the error estimate and raw values, + available in `ceiling.attrs['error']` and `ceiling.attrs['raw']` respectively. + """ + raise NotImplementedError() + + +class BenchmarkBase(Benchmark): + """ + Helper class for implementing standard functions of the `Benchmark` interface. + """ + + def __init__(self, identifier, ceiling_func, version, parent=None, bibtex=None): + self._identifier = identifier + self._ceiling_func = ceiling_func + self._version = version + self.parent = parent + self._bibtex = bibtex + + @property + def bibtex(self): + return self._bibtex + + @property + def identifier(self): + return self._identifier + + @property + def version(self): + return self._version + + @property + def ceiling(self): + return self._ceiling(identifier=self.identifier) + + @store() + def _ceiling(self, identifier): + return self._ceiling_func() + + +def ceil_score(score: Score, ceiling: Score) -> Score: + ceiled_score = score / ceiling + if 'error' in score.attrs: + ceiled_score.attrs['error'] = score.attrs['error'] + ceiled_score.attrs[Score.RAW_VALUES_KEY] = score + ceiled_score.attrs['ceiling'] = ceiling + return ceiled_score diff --git a/brainscore_vision/benchmarks/cadena2017/__init__.py b/brainscore_vision/benchmarks/cadena2017/__init__.py new file mode 100644 index 000000000..2f2e31921 --- /dev/null +++ b/brainscore_vision/benchmarks/cadena2017/__init__.py @@ -0,0 +1,5 @@ +from brainscore_vision import benchmark_registry +from .benchmark import ToliasCadena2017PLS, ToliasCadena2017Mask + +benchmark_registry['tolias.Cadena2017-pls'] = ToliasCadena2017PLS +benchmark_registry['tolias.Cadena2017-mask'] = ToliasCadena2017Mask diff --git a/brainscore/benchmarks/cadena2017.py b/brainscore_vision/benchmarks/cadena2017/benchmark.py similarity index 79% rename from brainscore/benchmarks/cadena2017.py rename to brainscore_vision/benchmarks/cadena2017/benchmark.py index 57a757b53..e1077bd0c 100644 --- a/brainscore/benchmarks/cadena2017.py +++ b/brainscore_vision/benchmarks/cadena2017/benchmark.py @@ -1,9 +1,7 @@ -import brainscore +import brainscore_vision +from brainscore_vision import load_metric, load_ceiling from brainio.assemblies import walk_coords, array_is_element -from brainscore.benchmarks._neural_common import NeuralBenchmark -from brainscore.metrics.ceiling import InternalConsistency -from brainscore.metrics.regression import CrossRegressedCorrelation, mask_regression, pls_regression, \ - pearsonr_correlation +from brainscore_vision.benchmark_helpers.neural_common import NeuralBenchmark VISUAL_DEGREES = 2 NUMBER_OF_TRIALS = 2 @@ -26,12 +24,8 @@ def ToliasCadena2017PLS(): assembly = loader(average_repetition=True) assembly.stimulus_set.identifier = assembly.stimulus_set_identifier - similarity_metric = CrossRegressedCorrelation( - regression=pls_regression(), - correlation=pearsonr_correlation(), - crossvalidation_kwargs={'stratification_coord': None}) - identifier = f'tolias.Cadena2017-pls' - ceiler = InternalConsistency(split_coord='repetition_id') + similarity_metric = load_metric('pls', crossvalidation_kwargs={'stratification_coord': None}) + ceiler = load_ceiling('internal_consistency', split_coord='repetition_id') def ceiling(): # This assembly has many stimuli that are only shown to a subset of the neurons. @@ -44,7 +38,7 @@ def ceiling(): assembly_nonan, stimuli = loader.dropna(assembly_repetition, assembly_repetition.attrs['stimulus_set']) return ceiler(assembly_nonan) - return NeuralBenchmark(identifier=identifier, version=1, + return NeuralBenchmark(identifier='tolias.Cadena2017-pls', version=1, assembly=assembly, similarity_metric=similarity_metric, visual_degrees=VISUAL_DEGREES, number_of_trials=NUMBER_OF_TRIALS, parent='V1', bibtex=BIBTEX, @@ -57,13 +51,10 @@ def ToliasCadena2017Mask(): assembly = loader(average_repetition=True) assembly.stimulus_set.identifier = assembly.stimulus_set_identifier - similarity_metric = CrossRegressedCorrelation( - regression=mask_regression(), - correlation=pearsonr_correlation(), - crossvalidation_kwargs={'splits': 4, 'stratification_coord': None}) - identifier = f'tolias.Cadena2017-mask' - ceiler = InternalConsistency(split_coord='repetition_id') - return NeuralBenchmark(identifier=identifier, version=1, + similarity_metric = load_metric('mask_regression', + crossvalidation_kwargs={'splits': 4, 'stratification_coord': None}) + ceiler = load_ceiling('internal_consistency', split_coord='repetition_id') + return NeuralBenchmark(identifier='tolias.Cadena2017-mask', version=1, assembly=assembly, similarity_metric=similarity_metric, visual_degrees=VISUAL_DEGREES, number_of_trials=NUMBER_OF_TRIALS, parent='V1', bibtex=BIBTEX, @@ -74,7 +65,7 @@ class AssemblyLoader: name = 'tolias.Cadena2017' def __call__(self, average_repetition=True): - assembly = brainscore.get_assembly(name='tolias.Cadena2017') + assembly = brainscore_vision.load_dataset(identifier='tolias.Cadena2017') assembly = assembly.rename({'neuroid': 'neuroid_id'}).stack(neuroid=['neuroid_id']) assembly.load() assembly['region'] = 'neuroid', ['V1'] * len(assembly['neuroid']) diff --git a/brainscore_vision/benchmarks/cadena2017/test.py b/brainscore_vision/benchmarks/cadena2017/test.py new file mode 100644 index 000000000..85c520f04 --- /dev/null +++ b/brainscore_vision/benchmarks/cadena2017/test.py @@ -0,0 +1,35 @@ +import pytest +from pytest import approx + +from brainscore_vision.benchmark_helpers import check_standard_format +from brainscore_vision.benchmark_helpers.test_helper import StandardizedTests, VisualDegreesTests +from brainscore_vision.benchmarks.cadena2017.benchmark import AssemblyLoader + +standardized_tests = StandardizedTests() +visual_degrees_test = VisualDegreesTests() + + +@pytest.mark.private_access +class TestAssembly: + def test(self): + loader = AssemblyLoader() + assembly = loader() + check_standard_format(assembly) + assert assembly.attrs['stimulus_set_identifier'] == 'tolias.Cadena2017' + assert len(assembly['presentation']) == 6249 + assert len(assembly['neuroid']) == 166 + + +@pytest.mark.private_access +def test_self_regression(): + standardized_tests.self_regression_test( + benchmark='tolias.Cadena2017-pls', visual_degrees=2, expected=approx(.577474, abs=.005)) + + +@pytest.mark.private_access +@pytest.mark.parametrize('benchmark, candidate_degrees, image_id, expected', [ + ('tolias.Cadena2017-pls', 14, '0fe27ddd5b9ea701e380063dc09b91234eba3551', approx(.32655, abs=.0001)), + ('tolias.Cadena2017-pls', 6, '0fe27ddd5b9ea701e380063dc09b91234eba3551', approx(.29641, abs=.0001)), +]) +def test_amount_gray(benchmark: str, candidate_degrees: int, image_id: str, expected: float): + visual_degrees_test.amount_gray_test(benchmark, candidate_degrees, image_id, expected) diff --git a/brainscore_vision/benchmarks/freemanziemba2013/__init__.py b/brainscore_vision/benchmarks/freemanziemba2013/__init__.py new file mode 100644 index 000000000..1b4e04bbc --- /dev/null +++ b/brainscore_vision/benchmarks/freemanziemba2013/__init__.py @@ -0,0 +1,10 @@ +from brainscore_vision import benchmark_registry +from .benchmarks.benchmark import MovshonFreemanZiemba2013V1PLS, MovshonFreemanZiemba2013V2PLS +from .benchmarks.public_benchmarks import FreemanZiembaV1PublicBenchmark, FreemanZiembaV2PublicBenchmark + +benchmark_registry['movshon.FreemanZiemba2013.V1-pls'] = MovshonFreemanZiemba2013V1PLS +benchmark_registry['movshon.FreemanZiemba2013.V2-pls'] = MovshonFreemanZiemba2013V2PLS + +# public benchmarks +benchmark_registry['movshon.FreemanZiemba2013public.V1-pls'] = FreemanZiembaV1PublicBenchmark +benchmark_registry['movshon.FreemanZiemba2013public.V2-pls'] = FreemanZiembaV2PublicBenchmark diff --git a/brainscore_vision/benchmarks/freemanziemba2013/benchmarks/benchmark.py b/brainscore_vision/benchmarks/freemanziemba2013/benchmarks/benchmark.py new file mode 100644 index 000000000..b8d939397 --- /dev/null +++ b/brainscore_vision/benchmarks/freemanziemba2013/benchmarks/benchmark.py @@ -0,0 +1,53 @@ +from result_caching import store + +from brainscore_vision import load_dataset, load_metric, load_ceiling +from brainscore_vision.benchmark_helpers.neural_common import NeuralBenchmark, average_repetition +from brainscore_vision.data.freemanziemba2013 import BIBTEX +from brainscore_vision.utils import LazyLoad + +VISUAL_DEGREES = 4 +NUMBER_OF_TRIALS = 20 + + +def _MovshonFreemanZiemba2013Region(region, identifier_metric_suffix, similarity_metric, ceiler): + assembly_repetition = LazyLoad(lambda region=region: load_assembly(False, region=region)) + assembly = LazyLoad(lambda region=region: load_assembly(True, region=region)) + return NeuralBenchmark(identifier=f'movshon.FreemanZiemba2013.{region}-{identifier_metric_suffix}', version=2, + assembly=assembly, similarity_metric=similarity_metric, parent=region, + ceiling_func=lambda: ceiler(assembly_repetition), + visual_degrees=VISUAL_DEGREES, number_of_trials=NUMBER_OF_TRIALS, + bibtex=BIBTEX) + + +def MovshonFreemanZiemba2013V1PLS(): + metric = load_metric('pls', crossvalidation_kwargs=dict(stratification_coord='texture_type')) + ceiler = load_ceiling('internal_consistency') + return _MovshonFreemanZiemba2013Region( + 'V1', identifier_metric_suffix='pls', similarity_metric=metric, ceiler=ceiler) + + +def MovshonFreemanZiemba2013V2PLS(): + metric = load_metric('pls', crossvalidation_kwargs=dict(stratification_coord='texture_type')) + ceiler = load_ceiling('internal_consistency') + return _MovshonFreemanZiemba2013Region( + 'V2', identifier_metric_suffix='pls', similarity_metric=metric, ceiler=ceiler) + + +@store() +def load_assembly(average_repetitions, region, access='private'): + assembly = load_dataset(f'movshon.FreemanZiemba2013.{access}') + assembly = assembly.sel(region=region) + assembly = assembly.stack(neuroid=['neuroid_id']) # work around xarray multiindex issues + assembly['region'] = 'neuroid', [region] * len(assembly['neuroid']) + assembly.load() + time_window = (50, 200) + assembly = assembly.sel(time_bin=[(t, t + 1) for t in range(*time_window)]) + assembly = assembly.mean(dim='time_bin', keep_attrs=True) + assembly = assembly.expand_dims('time_bin_start').expand_dims('time_bin_end') + assembly['time_bin_start'], assembly['time_bin_end'] = [time_window[0]], [time_window[1]] + assembly = assembly.stack(time_bin=['time_bin_start', 'time_bin_end']) + assembly = assembly.squeeze('time_bin') + assembly = assembly.transpose('presentation', 'neuroid') + if average_repetitions: + assembly = average_repetition(assembly) + return assembly diff --git a/brainscore_vision/benchmarks/freemanziemba2013/benchmarks/public_benchmarks.py b/brainscore_vision/benchmarks/freemanziemba2013/benchmarks/public_benchmarks.py new file mode 100644 index 000000000..47f66c8fd --- /dev/null +++ b/brainscore_vision/benchmarks/freemanziemba2013/benchmarks/public_benchmarks.py @@ -0,0 +1,37 @@ +""" +The purpose of this file is to provide benchmarks based on publicly accessible data that can be run on candidate models +without restrictions. As opposed to the private benchmarks hosted on www.Brain-Score.org, models can be evaluated +without having to submit them to the online platform. +This allows for quick local prototyping, layer commitment, etc. +For the final model evaluation, candidate models should still be sent to www.Brain-Score.org to evaluate them on +held-out private data. +""" +import logging + +from brainscore_vision import load_metric, load_ceiling +from brainscore_vision.benchmark_helpers.neural_common import NeuralBenchmark +from brainscore_vision.utils import LazyLoad +from .benchmark import load_assembly, NUMBER_OF_TRIALS, VISUAL_DEGREES, BIBTEX + +_logger = logging.getLogger(__name__) + + +def _freemanziemba2013_public_benchmark(region: str): + assembly_repetition = LazyLoad(lambda: load_assembly(region=region, access='public', average_repetitions=False)) + assembly = LazyLoad(lambda: load_assembly(region=region, access='public', average_repetitions=True)) + similarity_metric = load_metric('pls', crossvalidation_kwargs=dict(stratification_coord='texture_type')) + ceiler = load_ceiling('internal_consistency') + return NeuralBenchmark(identifier=f"movshon.FreemanZiemba2013.{region}.public-pls", version=1, + assembly=assembly, similarity_metric=similarity_metric, + visual_degrees=VISUAL_DEGREES, number_of_trials=NUMBER_OF_TRIALS, + ceiling_func=lambda: ceiler(assembly_repetition), + parent=None, + bibtex=BIBTEX) + + +def FreemanZiembaV1PublicBenchmark(): + return _freemanziemba2013_public_benchmark('V1') + + +def FreemanZiembaV2PublicBenchmark(): + return _freemanziemba2013_public_benchmark('V2') diff --git a/brainscore_vision/benchmarks/freemanziemba2013/test.py b/brainscore_vision/benchmarks/freemanziemba2013/test.py new file mode 100644 index 000000000..41d6e4290 --- /dev/null +++ b/brainscore_vision/benchmarks/freemanziemba2013/test.py @@ -0,0 +1,98 @@ +from pathlib import Path + +import pytest +from pytest import approx + +from brainscore_vision import benchmark_registry, load_benchmark +from brainscore_vision.benchmark_helpers import PrecomputedFeatures +from brainscore_vision.benchmark_helpers.test_helper import StandardizedTests, PrecomputedTests, NumberOfTrialsTests, \ + VisualDegreesTests +from brainscore_vision.data_helpers import s3 + +standardized_tests = StandardizedTests() +precomputed_test = PrecomputedTests() +num_trials_test = NumberOfTrialsTests() +visual_degrees_test = VisualDegreesTests() + + +@pytest.mark.parametrize('benchmark', [ + 'movshon.FreemanZiemba2013.V1-pls', + 'movshon.FreemanZiemba2013.V2-pls', # are these what should be checked for? what about public vs. private +]) +def test_benchmark_registry(benchmark): + assert benchmark in benchmark_registry + + +@pytest.mark.memory_intense +@pytest.mark.parametrize('benchmark, expected', [ + ('movshon.FreemanZiemba2013.V1-pls', approx(.873345, abs=.001)), + ('movshon.FreemanZiemba2013.V2-pls', approx(.824836, abs=.001)), +]) +def test_ceilings(benchmark, expected): + standardized_tests.ceilings_test(benchmark, expected) + + +@pytest.mark.memory_intense +@pytest.mark.parametrize('benchmark, visual_degrees, expected', [ + ('movshon.FreemanZiemba2013.V1-pls', 4, approx(.668491, abs=.001)), + ('movshon.FreemanZiemba2013.V2-pls', 4, approx(.553155, abs=.001)), +]) +def test_self_regression(benchmark, visual_degrees, expected): + standardized_tests.self_regression_test(benchmark, visual_degrees, expected) + + +@pytest.mark.memory_intense +@pytest.mark.parametrize('benchmark, expected', [ + ('movshon.FreemanZiemba2013.V1-pls', approx(.466222, abs=.005)), + ('movshon.FreemanZiemba2013.V2-pls', approx(.459283, abs=.005)), +]) +def test_FreemanZiemba2013(benchmark, expected): + filename = 'alexnet-freemanziemba2013.aperture-private.nc' + filepath = Path(__file__).parent / filename + s3.download_file_if_not_exists(local_path=filepath, + bucket='brain-score-tests', remote_filepath=f'tests/test_benchmarks/{filename}') + precomputed_test.run_test(benchmark=benchmark, precomputed_features_filepath=filename, expected=expected) + + +@pytest.mark.parametrize('benchmark, candidate_degrees, image_id, expected', [ + pytest.param('movshon.FreemanZiemba2013.V1-pls', 14, 'c3a633a13e736394f213ddf44bf124fe80cabe07', + approx(.31429, abs=.0001), marks=[pytest.mark.private_access]), + pytest.param('movshon.FreemanZiemba2013.V1-pls', 6, 'c3a633a13e736394f213ddf44bf124fe80cabe07', + approx(.22966, abs=.0001), marks=[pytest.mark.private_access]), + pytest.param('movshon.FreemanZiemba2013public.V1-pls', 14, '21041db1f26c142812a66277c2957fb3e2070916', + approx(.314561, abs=.0001), marks=[]), + pytest.param('movshon.FreemanZiemba2013public.V1-pls', 6, '21041db1f26c142812a66277c2957fb3e2070916', + approx(.23113, abs=.0001), marks=[]), + pytest.param('movshon.FreemanZiemba2013.V2-pls', 14, 'c3a633a13e736394f213ddf44bf124fe80cabe07', + approx(.31429, abs=.0001), marks=[pytest.mark.private_access]), + pytest.param('movshon.FreemanZiemba2013.V2-pls', 6, 'c3a633a13e736394f213ddf44bf124fe80cabe07', + approx(.22966, abs=.0001), marks=[pytest.mark.private_access]), + pytest.param('movshon.FreemanZiemba2013public.V2-pls', 14, '21041db1f26c142812a66277c2957fb3e2070916', + approx(.314561, abs=.0001), marks=[]), + pytest.param('movshon.FreemanZiemba2013public.V2-pls', 6, '21041db1f26c142812a66277c2957fb3e2070916', + approx(.23113, abs=.0001), marks=[]), +]) +def test_amount_gray(benchmark: str, candidate_degrees: int, image_id: str, expected: float): + visual_degrees_test.amount_gray_test(benchmark, candidate_degrees, image_id, expected) + + +@pytest.mark.private_access +@pytest.mark.parametrize('benchmark_identifier', [ + 'movshon.FreemanZiemba2013.V1-pls', + 'movshon.FreemanZiemba2013.V2-pls', +]) +def test_repetitions(benchmark_identifier): + num_trials_test.repetitions_test(benchmark_identifier) + + +@pytest.mark.memory_intense +@pytest.mark.parametrize('benchmark, visual_degrees, expected', [ + ('movshon.FreemanZiemba2013public.V1-pls', 4, approx(.679954, abs=.001)), + ('movshon.FreemanZiemba2013public.V2-pls', 4, approx(.577498, abs=.001)), +]) +def test_self(benchmark, visual_degrees, expected): + benchmark = load_benchmark(benchmark) + source = benchmark._assembly.copy() + source = {benchmark._assembly.stimulus_set.identifier: source} + score = benchmark(PrecomputedFeatures(source, visual_degrees=visual_degrees)).raw + assert score == expected diff --git a/brainscore_vision/benchmarks/geirhos2021/__init__.py b/brainscore_vision/benchmarks/geirhos2021/__init__.py new file mode 100644 index 000000000..18dda9146 --- /dev/null +++ b/brainscore_vision/benchmarks/geirhos2021/__init__.py @@ -0,0 +1,59 @@ +from brainscore_vision import benchmark_registry +from . import benchmark + +DATASETS = ['colour', 'contrast', 'cue-conflict', 'edge', + 'eidolonI', 'eidolonII', 'eidolonIII', + 'false-colour', 'high-pass', 'low-pass', 'phase-scrambling', 'power-equalisation', + 'rotation', 'silhouette', 'sketch', 'stylized', 'uniform-noise'] + +benchmark_registry['brendel.Geirhos2021colour-top1'] = getattr(benchmark, "Geirhos2021colourAccuracy") +benchmark_registry['brendel.Geirhos2021colour-error_consistency'] = getattr(benchmark, "Geirhos2021colourErrorConsistency") + +benchmark_registry['brendel.Geirhos2021contrast-top1'] = getattr(benchmark, "Geirhos2021contrastAccuracy") +benchmark_registry['brendel.Geirhos2021contrast-error_consistency'] = getattr(benchmark, "Geirhos2021contrastErrorConsistency") + +benchmark_registry['brendel.Geirhos2021cueconflict-top1'] = getattr(benchmark, "Geirhos2021cueconflictAccuracy") +benchmark_registry['brendel.Geirhos2021cueconflict-error_consistency'] = getattr(benchmark, "Geirhos2021cueconflictErrorConsistency") + +benchmark_registry['brendel.Geirhos2021edge-top1'] = getattr(benchmark, "Geirhos2021edgeAccuracy") +benchmark_registry['brendel.Geirhos2021edge-error_consistency'] = getattr(benchmark, "Geirhos2021edgeErrorConsistency") + +benchmark_registry['brendel.Geirhos2021eidolonI-top1'] = getattr(benchmark, "Geirhos2021eidolonIAccuracy") +benchmark_registry['brendel.Geirhos2021eidolonI-error_consistency'] = getattr(benchmark, "Geirhos2021eidolonIErrorConsistency") + +benchmark_registry['brendel.Geirhos2021eidolonII-top1'] = getattr(benchmark, "Geirhos2021eidolonIIAccuracy") +benchmark_registry['brendel.Geirhos2021eidolonII-error_consistency'] = getattr(benchmark, "Geirhos2021eidolonIIErrorConsistency") + +benchmark_registry['brendel.Geirhos2021eidolonIII-top1'] = getattr(benchmark, "Geirhos2021eidolonIIIAccuracy") +benchmark_registry['brendel.Geirhos2021eidolonIII-error_consistency'] = getattr(benchmark, "Geirhos2021eidolonIIIErrorConsistency") + +benchmark_registry['brendel.Geirhos2021falsecolour-top1'] = getattr(benchmark, "Geirhos2021falsecolourAccuracy") +benchmark_registry['brendel.Geirhos2021falsecolour-error_consistency'] = getattr(benchmark, "Geirhos2021falsecolourErrorConsistency") + +benchmark_registry['brendel.Geirhos2021highpass-top1'] = getattr(benchmark, "Geirhos2021highpassAccuracy") +benchmark_registry['brendel.Geirhos2021highpass-error_consistency'] = getattr(benchmark, "Geirhos2021highpassErrorConsistency") + +benchmark_registry['brendel.Geirhos2021lowpass-top1'] = getattr(benchmark, "Geirhos2021lowpassAccuracy") +benchmark_registry['brendel.Geirhos2021lowpass-error_consistency'] = getattr(benchmark, "Geirhos2021lowpassErrorConsistency") + +benchmark_registry['brendel.Geirhos2021phasescrambling-top1'] = getattr(benchmark, "Geirhos2021phasescramblingAccuracy") +benchmark_registry['brendel.Geirhos2021phasescrambling-error_consistency'] = getattr(benchmark, "Geirhos2021phasescramblingErrorConsistency") + +benchmark_registry['brendel.Geirhos2021powerequalisation-top1'] = getattr(benchmark, "Geirhos2021powerequalisationAccuracy") +benchmark_registry['brendel.Geirhos2021powerequalisation-error_consistency'] = getattr(benchmark, "Geirhos2021powerequalisationErrorConsistency") + +benchmark_registry['brendel.Geirhos2021rotation-top1'] = getattr(benchmark, "Geirhos2021rotationAccuracy") +benchmark_registry['brendel.Geirhos2021rotation-error_consistency'] = getattr(benchmark, "Geirhos2021rotationErrorConsistency") + +benchmark_registry['brendel.Geirhos2021silhouette-top1'] = getattr(benchmark, "Geirhos2021silhouetteAccuracy") +benchmark_registry['brendel.Geirhos2021silhouette-error_consistency'] = getattr(benchmark, "Geirhos2021silhouetteErrorConsistency") + +benchmark_registry['brendel.Geirhos2021sketch-top1'] = getattr(benchmark, "Geirhos2021sketchAccuracy") +benchmark_registry['brendel.Geirhos2021sketch-error_consistency'] = getattr(benchmark, "Geirhos2021sketchErrorConsistency") + +benchmark_registry['brendel.Geirhos2021stylized-top1'] = getattr(benchmark, "Geirhos2021stylizedAccuracy") +benchmark_registry['brendel.Geirhos2021stylized-error_consistency'] = getattr(benchmark, "Geirhos2021stylizedErrorConsistency") + +benchmark_registry['brendel.Geirhos2021uniformnoise-top1'] = getattr(benchmark, "Geirhos2021uniformnoiseAccuracy") +benchmark_registry['brendel.Geirhos2021uniformnoise-error_consistency'] = getattr(benchmark, "Geirhos2021uniformnoiseErrorConsistency") + diff --git a/brainscore/benchmarks/geirhos2021.py b/brainscore_vision/benchmarks/geirhos2021/benchmark.py similarity index 89% rename from brainscore/benchmarks/geirhos2021.py rename to brainscore_vision/benchmarks/geirhos2021/benchmark.py index 4a1df5b82..0b0f9b623 100644 --- a/brainscore/benchmarks/geirhos2021.py +++ b/brainscore_vision/benchmarks/geirhos2021/benchmark.py @@ -1,14 +1,12 @@ import numpy as np -import brainscore from brainio.assemblies import walk_coords -from brainscore.benchmarks import BenchmarkBase -from brainscore.benchmarks.screen import place_on_screen -from brainscore.metrics import Score -from brainscore.metrics.accuracy import Accuracy -from brainscore.metrics.error_consistency import ErrorConsistency -from brainscore.model_interface import BrainModel -from brainscore.utils import LazyLoad +from brainscore_vision import load_dataset, load_metric +from brainscore_vision.benchmark_helpers.screen import place_on_screen +from brainscore_vision.benchmarks import BenchmarkBase +from brainscore_vision.metrics import Score +from brainscore_vision.model_interface import BrainModel +from brainscore_vision.utils import LazyLoad BIBTEX = """@article{geirhos2021partial, title={Partial success in closing the gap between human and machine vision}, @@ -55,7 +53,7 @@ class _Geirhos2021ErrorConsistency(BenchmarkBase): # behavioral benchmark def __init__(self, dataset): - self._metric = ErrorConsistency() + self._metric = load_metric('error_consistency') self._assembly = LazyLoad(lambda: load_assembly(dataset)) self._visual_degrees = 3 @@ -76,7 +74,7 @@ def __call__(self, candidate: BrainModel): labels = candidate.look_at(stimulus_set, number_of_trials=self._number_of_trials) raw_score = self._metric(labels, self._assembly) ceiling = self.ceiling - score = raw_score / ceiling.sel(aggregation='center') + score = raw_score / ceiling score.attrs['raw'] = raw_score score.attrs['ceiling'] = ceiling return score @@ -85,11 +83,11 @@ def __call__(self, candidate: BrainModel): class _Geirhos2021Accuracy(BenchmarkBase): # engineering/ML benchmark def __init__(self, dataset): - self._metric = Accuracy() + self._metric = load_metric('accuracy') self._stimulus_set = LazyLoad(lambda: load_assembly(dataset).stimulus_set) super(_Geirhos2021Accuracy, self).__init__( identifier=f'brendel.Geirhos2021{dataset}-accuracy', version=1, - ceiling_func=lambda: Score([1, np.nan], coords={'aggregation': ['center', 'error']}, dims=['aggregation']), + ceiling_func=lambda: Score(1), parent='brendel.Geirhos2021-top1', bibtex=BIBTEX) @@ -103,7 +101,7 @@ def __call__(self, candidate: BrainModel): def load_assembly(dataset): - assembly = brainscore.get_assembly(f'brendel.Geirhos2021_{dataset}') + assembly = load_dataset(f'brendel.Geirhos2021_{dataset}') # exclude conditions following the paper if dataset in EXCLUDE_CONDITIONS: diff --git a/tests/test_benchmarks/test_geirhos2021.py b/brainscore_vision/benchmarks/geirhos2021/test.py similarity index 51% rename from tests/test_benchmarks/test_geirhos2021.py rename to brainscore_vision/benchmarks/geirhos2021/test.py index 85fbfbfcd..020ad10d9 100644 --- a/tests/test_benchmarks/test_geirhos2021.py +++ b/brainscore_vision/benchmarks/geirhos2021/test.py @@ -5,9 +5,50 @@ from pytest import approx from brainio.assemblies import BehavioralAssembly -from brainscore import benchmark_pool -from brainscore.benchmarks.geirhos2021 import DATASETS, cast_coordinate_type -from tests.test_benchmarks import PrecomputedFeatures +from brainscore_vision import benchmark_registry, load_benchmark +from brainscore_vision.benchmark_helpers import PrecomputedFeatures +from brainscore_vision.benchmarks.geirhos2021.benchmark import DATASETS, cast_coordinate_type +from brainscore_vision.data_helpers import s3 + + +@pytest.mark.parametrize('benchmark', [ + 'brendel.Geirhos2021colour-error_consistency', + 'brendel.Geirhos2021contrast-error_consistency', + 'brendel.Geirhos2021cueconflict-error_consistency', + 'brendel.Geirhos2021edge-error_consistency', + 'brendel.Geirhos2021eidolonI-error_consistency', + 'brendel.Geirhos2021eidolonII-error_consistency', + 'brendel.Geirhos2021eidolonIII-error_consistency', + 'brendel.Geirhos2021falsecolour-error_consistency', + 'brendel.Geirhos2021highpass-error_consistency', + 'brendel.Geirhos2021lowpass-error_consistency', + 'brendel.Geirhos2021phasescrambling-error_consistency', + 'brendel.Geirhos2021powerequalisation-error_consistency', + 'brendel.Geirhos2021rotation-error_consistency', + 'brendel.Geirhos2021silhouette-error_consistency', + 'brendel.Geirhos2021stylized-error_consistency', + 'brendel.Geirhos2021sketch-error_consistency', + 'brendel.Geirhos2021uniformnoise-error_consistency', + 'brendel.Geirhos2021colour-top1', + 'brendel.Geirhos2021contrast-top1', + 'brendel.Geirhos2021cueconflict-top1', + 'brendel.Geirhos2021edge-top1', + 'brendel.Geirhos2021eidolonI-top1', + 'brendel.Geirhos2021eidolonII-top1', + 'brendel.Geirhos2021eidolonIII-top1', + 'brendel.Geirhos2021falsecolour-top1', + 'brendel.Geirhos2021highpass-top1', + 'brendel.Geirhos2021lowpass-top1', + 'brendel.Geirhos2021phasescrambling-top1', + 'brendel.Geirhos2021powerequalisation-top1', + 'brendel.Geirhos2021rotation-top1', + 'brendel.Geirhos2021silhouette-top1', + 'brendel.Geirhos2021stylized-top1', + 'brendel.Geirhos2021sketch-top1', + 'brendel.Geirhos2021uniformnoise-top1', +]) +def test_benchmark_registry(benchmark): + assert benchmark in benchmark_registry class TestBehavioral: @@ -17,12 +58,12 @@ def test_count(self): @pytest.mark.parametrize('dataset', DATASETS) def test_in_pool(self, dataset): identifier = f"brendel.Geirhos2021{dataset.replace('-', '')}-error_consistency" - assert identifier in benchmark_pool + assert identifier in benchmark_registry def test_mean_ceiling(self): benchmarks = [f"brendel.Geirhos2021{dataset.replace('-', '')}-error_consistency" for dataset in DATASETS] - benchmarks = [benchmark_pool[benchmark] for benchmark in benchmarks] - ceilings = [benchmark.ceiling.sel(aggregation='center') for benchmark in benchmarks] + benchmarks = [load_benchmark(benchmark) for benchmark in benchmarks] + ceilings = [benchmark.ceiling.item() for benchmark in benchmarks] mean_ceiling = np.mean(ceilings) assert mean_ceiling == approx(0.43122, abs=0.001) @@ -47,46 +88,47 @@ def test_mean_ceiling(self): ]) def test_dataset_ceiling(self, dataset, expected_ceiling): benchmark = f"brendel.Geirhos2021{dataset.replace('-', '')}-error_consistency" - benchmark = benchmark_pool[benchmark] + benchmark = load_benchmark(benchmark) ceiling = benchmark.ceiling - assert ceiling.sel(aggregation='center').values.item() == expected_ceiling + assert ceiling == expected_ceiling - @pytest.mark.parametrize('dataset, model, expected_raw_score', [ - ('colour', 'resnet-50-pytorch', approx(0.21135, abs=0.001)), - ('contrast', 'resnet-50-pytorch', approx(0.22546, abs=0.001)), - ('cue-conflict', 'resnet-50-pytorch', approx(0.06800, abs=0.001)), - ('edge', 'resnet-50-pytorch', approx(0.03626, abs=0.001)), - ('eidolonI', 'resnet-50-pytorch', approx(0.21029, abs=0.001)), - ('eidolonII', 'resnet-50-pytorch', approx(0.25090, abs=0.001)), - ('eidolonIII', 'resnet-50-pytorch', approx(0.15806, abs=0.001)), - ('false-colour', 'resnet-50-pytorch', approx(0.29431, abs=0.001)), - ('high-pass', 'resnet-50-pytorch', approx(0.20300, abs=0.001)), - ('low-pass', 'resnet-50-pytorch', approx(0.16741, abs=0.001)), - ('phase-scrambling', 'resnet-50-pytorch', approx(0.26879, abs=0.001)), - ('power-equalisation', 'resnet-50-pytorch', approx(0.29293, abs=0.001)), - ('rotation', 'resnet-50-pytorch', approx(0.16892, abs=0.001)), - ('silhouette', 'resnet-50-pytorch', approx(0.42805, abs=0.001)), - ('sketch', 'resnet-50-pytorch', approx(0.10537, abs=0.001)), - ('stylized', 'resnet-50-pytorch', approx(0.25430, abs=0.001)), - ('uniform-noise', 'resnet-50-pytorch', approx(0.19839, abs=0.001)), + @pytest.mark.parametrize('dataset, expected_raw_score', [ + ('colour', approx(0.21135, abs=0.001)), + ('contrast', approx(0.22546, abs=0.001)), + ('cue-conflict', approx(0.06800, abs=0.001)), + ('edge', approx(0.03626, abs=0.001)), + ('eidolonI', approx(0.21029, abs=0.001)), + ('eidolonII', approx(0.25090, abs=0.001)), + ('eidolonIII', approx(0.15806, abs=0.001)), + ('false-colour', approx(0.29431, abs=0.001)), + ('high-pass', approx(0.20300, abs=0.001)), + ('low-pass', approx(0.16741, abs=0.001)), + ('phase-scrambling', approx(0.26879, abs=0.001)), + ('power-equalisation', approx(0.29293, abs=0.001)), + ('rotation', approx(0.16892, abs=0.001)), + ('silhouette', approx(0.42805, abs=0.001)), + ('sketch', approx(0.10537, abs=0.001)), + ('stylized', approx(0.25430, abs=0.001)), + ('uniform-noise', approx(0.19839, abs=0.001)), ]) - def test_model_3degrees(self, dataset, model, expected_raw_score): - benchmark = benchmark_pool[f"brendel.Geirhos2021{dataset.replace('-', '')}-error_consistency"] + def test_model_3degrees(self, dataset, expected_raw_score): + benchmark = load_benchmark(f"brendel.Geirhos2021{dataset.replace('-', '')}-error_consistency") # load features - precomputed_features = Path(__file__).parent / f'{model}-3deg-Geirhos2021_{dataset}.nc' + filename = f'resnet-50-pytorch-3deg-Geirhos2021_{dataset}.nc' + precomputed_features = Path(__file__).parent / filename + s3.download_file_if_not_exists(precomputed_features, + bucket='brain-score-tests', remote_filepath=f'tests/test_benchmarks/{filename}') precomputed_features = BehavioralAssembly.from_files(file_path=precomputed_features) # these features were packaged with condition as int/float. Current xarray versions have trouble when # selecting for a float coordinate however, so we had to change the type to string. precomputed_features = cast_coordinate_type(precomputed_features, 'condition', newtype=str) - precomputed_features = PrecomputedFeatures(precomputed_features, - visual_degrees=8, # doesn't matter, features are already computed - ) + precomputed_features = PrecomputedFeatures(precomputed_features, visual_degrees=8) # score score = benchmark(precomputed_features) raw_score = score.raw # division by ceiling <= 1 should result in higher score - assert score.sel(aggregation='center') >= raw_score.sel(aggregation='center') - assert raw_score.sel(aggregation='center') == expected_raw_score + assert score >= raw_score + assert raw_score == expected_raw_score @pytest.mark.parametrize('model, expected_raw_score', [ ('resnet-50-pytorch-3deg', approx(0.20834, abs=0.001)), @@ -95,20 +137,25 @@ def test_model_3degrees(self, dataset, model, expected_raw_score): def test_model_mean(self, model, expected_raw_score): scores = [] for dataset in DATASETS: - benchmark = benchmark_pool[f"brendel.Geirhos2021{dataset.replace('-', '')}-error_consistency"] - precomputed_features = Path(__file__).parent / f'{model}-Geirhos2021_{dataset}.nc' + benchmark = load_benchmark(f"brendel.Geirhos2021{dataset.replace('-', '')}-error_consistency") + filename = f'{model}-Geirhos2021_{dataset}.nc' + precomputed_features = Path(__file__).parent / filename + s3.download_file_if_not_exists(precomputed_features, + bucket='brain-score-tests', + remote_filepath=f'tests/test_benchmarks/{filename}') precomputed_features = BehavioralAssembly.from_files(file_path=precomputed_features) # these features were packaged with condition as int/float. Current xarray versions have trouble when # selecting for a float coordinate however, so we had to change the type to string. precomputed_features = cast_coordinate_type(precomputed_features, 'condition', newtype=str) precomputed_features = PrecomputedFeatures(precomputed_features, visual_degrees=8) score = benchmark(precomputed_features).raw - scores.append(score.sel(aggregation='center')) + scores.append(score.item()) mean_score = np.mean(scores) assert mean_score == expected_raw_score class TestEngineering: + @pytest.mark.private_access @pytest.mark.parametrize('dataset, model, expected_accuracy', [ ('colour', 'resnet-50-pytorch', approx(0.96875, abs=0.001)), ('contrast', 'resnet-50-pytorch', approx(0.81625, abs=0.001)), @@ -129,11 +176,14 @@ class TestEngineering: ('uniform-noise', 'resnet-50-pytorch', approx(0.44500, abs=0.001)), ]) def test_accuracy(self, dataset, model, expected_accuracy): - benchmark = benchmark_pool[f"brendel.Geirhos2021{dataset.replace('-', '')}-top1"] + benchmark = load_benchmark(f"brendel.Geirhos2021{dataset.replace('-', '')}-top1") # load features - precomputed_features = Path(__file__).parent / f'{model}-3deg-Geirhos2021_{dataset}.nc' + filename = f'{model}-3deg-Geirhos2021_{dataset}.nc' + precomputed_features = Path(__file__).parent / filename + s3.download_file_if_not_exists(precomputed_features, + bucket='brain-score-tests', remote_filepath=f'tests/test_benchmarks/{filename}') precomputed_features = BehavioralAssembly.from_files(file_path=precomputed_features) precomputed_features = PrecomputedFeatures(precomputed_features, visual_degrees=None) # score score = benchmark(precomputed_features) - assert score.sel(aggregation='center') == expected_accuracy + assert score == expected_accuracy diff --git a/brainscore_vision/benchmarks/hermann2020/__init__.py b/brainscore_vision/benchmarks/hermann2020/__init__.py new file mode 100644 index 000000000..1e40b92ac --- /dev/null +++ b/brainscore_vision/benchmarks/hermann2020/__init__.py @@ -0,0 +1,6 @@ +from brainscore_vision import benchmark_registry +from .benchmark import Hermann2020cueconflictShapeBias, Hermann2020cueconflictShapeMatch + + +benchmark_registry['kornblith.Hermann2020cueconflict-shape_bias'] = Hermann2020cueconflictShapeBias +benchmark_registry['kornblith.Hermann2020cueconflict-shape_match'] = Hermann2020cueconflictShapeMatch diff --git a/brainscore/benchmarks/hermann2020.py b/brainscore_vision/benchmarks/hermann2020/benchmark.py similarity index 82% rename from brainscore/benchmarks/hermann2020.py rename to brainscore_vision/benchmarks/hermann2020/benchmark.py index 4bd6b7175..862b7a999 100644 --- a/brainscore/benchmarks/hermann2020.py +++ b/brainscore_vision/benchmarks/hermann2020/benchmark.py @@ -1,9 +1,8 @@ -import numpy as np +from brainscore_core import Score -import brainscore -from brainscore.benchmarks import BenchmarkBase, Score -from brainscore.metrics.accuracy import Accuracy -from brainscore.model_interface import BrainModel +from brainscore_vision import load_stimulus_set, load_metric +from brainscore_vision.benchmarks import BenchmarkBase +from brainscore_vision.model_interface import BrainModel BIBTEX = """@article{hermann2020origins, title={The origins and prevalence of texture bias in convolutional neural networks}, @@ -20,13 +19,13 @@ class _Hermann2020Match(BenchmarkBase): def __init__(self, metric_identifier, stimulus_column): assert metric_identifier in ["shape_match", "texture_match"] assert stimulus_column in ["original_image_category", "conflict_image_category"] - self._stimulus_set = brainscore.get_stimulus_set("brendel.Geirhos2021_cue-conflict") - self._metric = Accuracy() + self._stimulus_set = load_stimulus_set("brendel.Geirhos2021_cue-conflict") + self._metric = load_metric('accuracy') self._number_of_trials = 1 self._stimulus_column = stimulus_column super(_Hermann2020Match, self).__init__( identifier=f'kornblith.Hermann2020-{metric_identifier}', version=1, - ceiling_func=lambda: Score([1, np.nan], coords={'aggregation': ['center', 'error']}, dims=['aggregation']), + ceiling_func=lambda: Score(1), parent='kornblith.Hermann2020', bibtex=BIBTEX) @@ -50,7 +49,7 @@ def __init__(self): self.texture_benchmark = _Hermann2020Match("texture_match", "conflict_image_category") super(Hermann2020cueconflictShapeBias, self).__init__( identifier=f'brendel.Hermann2020-shape_bias', version=1, - ceiling_func=lambda: Score([1, np.nan], coords={'aggregation': ['center', 'error']}, dims=['aggregation']), + ceiling_func=lambda: Score(1), parent='brendel.Hermann2020', bibtex=BIBTEX) diff --git a/brainscore_vision/benchmarks/hermann2020/test.py b/brainscore_vision/benchmarks/hermann2020/test.py new file mode 100644 index 000000000..f62ad98e8 --- /dev/null +++ b/brainscore_vision/benchmarks/hermann2020/test.py @@ -0,0 +1,28 @@ +from pathlib import Path + +import pytest +from pytest import approx + +from brainio.assemblies import BehavioralAssembly +from brainscore_vision import load_benchmark +from brainscore_vision.benchmark_helpers import PrecomputedFeatures +from brainscore_vision.data_helpers import s3 + + +class TestEngineering: + @pytest.mark.parametrize('model, benchmark, expected_shape_bias', [ + ('resnet-50-pytorch', "kornblith.Hermann2020cueconflict-shape_bias", approx(0.21392405, abs=0.001)), + ('resnet-50-pytorch', "kornblith.Hermann2020cueconflict-shape_match", approx(0.14083333, abs=0.001)), + ]) + def test_score(self, model, benchmark, expected_shape_bias): + benchmark = load_benchmark(benchmark) + # load features + filename = f'{model}-3deg-Geirhos2021_cue-conflict.nc' + filepath = Path(__file__).parent / filename + s3.download_file_if_not_exists(local_path=filepath, + bucket='brain-score-tests', remote_filepath=f'tests/test_benchmarks/{filename}') + precomputed_features = BehavioralAssembly.from_files(file_path=filepath) + precomputed_features = PrecomputedFeatures(precomputed_features, visual_degrees=None) + # score + score = benchmark(precomputed_features) + assert score == expected_shape_bias diff --git a/brainscore_vision/benchmarks/imagenet/__init__.py b/brainscore_vision/benchmarks/imagenet/__init__.py new file mode 100644 index 000000000..5f4b797ae --- /dev/null +++ b/brainscore_vision/benchmarks/imagenet/__init__.py @@ -0,0 +1,4 @@ +from brainscore_vision import benchmark_registry +from .benchmark import Imagenet2012 + +benchmark_registry['fei-fei.Deng2009-top1'] = Imagenet2012 \ No newline at end of file diff --git a/brainscore/benchmarks/imagenet.py b/brainscore_vision/benchmarks/imagenet/benchmark.py similarity index 84% rename from brainscore/benchmarks/imagenet.py rename to brainscore_vision/benchmarks/imagenet/benchmark.py index 4ca5e777f..e160698a0 100644 --- a/brainscore/benchmarks/imagenet.py +++ b/brainscore_vision/benchmarks/imagenet/benchmark.py @@ -1,13 +1,12 @@ -import os +from pathlib import Path -import numpy as np import pandas as pd from brainio.stimuli import StimulusSet, StimulusSetLoader -from brainscore.benchmarks import BenchmarkBase -from brainscore.metrics import Score -from brainscore.metrics.accuracy import Accuracy -from brainscore.model_interface import BrainModel +from brainscore_core import Score +from brainscore_vision import load_metric +from brainscore_vision.benchmarks import BenchmarkBase +from brainscore_vision.model_interface import BrainModel NUMBER_OF_TRIALS = 10 @@ -16,13 +15,13 @@ class Imagenet2012(BenchmarkBase): def __init__(self): # Not a valid StimulusSet if the files are not present on the filesystem. # We bypass StimulusSet.from_files() here. - stimulus_set = pd.read_csv(os.path.join(os.path.dirname(__file__), 'imagenet2012.csv')) + stimulus_set = pd.read_csv(Path(__file__).parent / 'imagenet2012.csv') StimulusSetLoader.correct_stimulus_id_name(stimulus_set) stimulus_set = StimulusSet(stimulus_set) stimulus_set.stimulus_paths = {row.stimulus_id: row.filepath for row in stimulus_set.itertuples()} self._stimulus_set = stimulus_set - self._similarity_metric = Accuracy() - ceiling = Score([1, np.nan], coords={'aggregation': ['center', 'error']}, dims=['aggregation']) + self._similarity_metric = load_metric('accuracy') + ceiling = Score(1) super(Imagenet2012, self).__init__(identifier='fei-fei.Deng2009-top1', version=1, ceiling_func=lambda: ceiling, parent='ImageNet', diff --git a/brainscore/benchmarks/imagenet2012.csv b/brainscore_vision/benchmarks/imagenet/imagenet2012.csv similarity index 100% rename from brainscore/benchmarks/imagenet2012.csv rename to brainscore_vision/benchmarks/imagenet/imagenet2012.csv diff --git a/tests/test_benchmarks/test_imagenet.py b/brainscore_vision/benchmarks/imagenet/test.py similarity index 88% rename from tests/test_benchmarks/test_imagenet.py rename to brainscore_vision/benchmarks/imagenet/test.py index a8c713621..be55f9b6b 100644 --- a/tests/test_benchmarks/test_imagenet.py +++ b/brainscore_vision/benchmarks/imagenet/test.py @@ -2,8 +2,8 @@ from pytest import approx from brainio.assemblies import BehavioralAssembly -from brainscore.benchmarks.imagenet import Imagenet2012 -from brainscore.model_interface import BrainModel +from brainscore_vision.benchmarks.imagenet import Imagenet2012 +from brainscore_vision.model_interface import BrainModel class TestImagenet2012: @@ -29,4 +29,4 @@ def look_at(self, stimuli, number_of_trials=1): candidate = GroundTruth() score = benchmark(candidate) - assert score.sel(aggregation='center') == approx(1) + assert score == approx(1) diff --git a/brainscore_vision/benchmarks/imagenet_c/__init__.py b/brainscore_vision/benchmarks/imagenet_c/__init__.py new file mode 100644 index 000000000..8dd3d325e --- /dev/null +++ b/brainscore_vision/benchmarks/imagenet_c/__init__.py @@ -0,0 +1,7 @@ +from brainscore_vision import benchmark_registry +from .benchmark import Imagenet_C_Noise, Imagenet_C_Blur, Imagenet_C_Weather, Imagenet_C_Digital + +benchmark_registry['dietterich.Hendrycks2019-noise-top1'] = Imagenet_C_Noise +benchmark_registry['dietterich.Hendrycks2019-blur-top1'] = Imagenet_C_Blur +benchmark_registry['dietterich.Hendrycks2019-weather-top1'] = Imagenet_C_Weather +benchmark_registry['dietterich.Hendrycks2019-digital-top1'] = Imagenet_C_Digital diff --git a/brainscore/benchmarks/imagenet_c.py b/brainscore_vision/benchmarks/imagenet_c/benchmark.py similarity index 89% rename from brainscore/benchmarks/imagenet_c.py rename to brainscore_vision/benchmarks/imagenet_c/benchmark.py index 3914c0959..71dbfca88 100644 --- a/brainscore/benchmarks/imagenet_c.py +++ b/brainscore_vision/benchmarks/imagenet_c/benchmark.py @@ -1,19 +1,18 @@ -import os import logging +import os from pathlib import Path import numpy as np import pandas as pd import xarray as xr -import brainscore -from brainscore.benchmarks import BenchmarkBase -from brainscore.benchmarks.imagenet import NUMBER_OF_TRIALS -from brainscore.metrics import Score -from brainscore.metrics.accuracy import Accuracy -from brainscore.model_interface import BrainModel -from brainio.stimuli import StimulusSet from brainio.fetch import StimulusSetLoader +from brainio.stimuli import StimulusSet +from brainscore_core import Score +from brainscore_vision import load_stimulus_set, load_metric +from brainscore_vision.benchmarks import BenchmarkBase +from brainscore_vision.benchmarks.imagenet.benchmark import NUMBER_OF_TRIALS +from brainscore_vision.model_interface import BrainModel _logger = logging.getLogger(__name__) LOCAL_STIMULUS_DIRECTORY = '/braintree/data2/active/common/imagenet-c-brainscore-stimuli/' @@ -77,13 +76,13 @@ class Imagenet_C_Category(BenchmarkBase): def __init__(self, noise_category, sampling_factor=10): self.noise_category = noise_category self.stimulus_set_name = f'dietterich.Hendrycks2019.{noise_category}' - + self.sampling_factor = sampling_factor self.stimulus_set = self.load_stimulus_set() self.noise_types = self.noise_category_map[noise_category] - ceiling = Score([1, np.nan], coords={'aggregation': ['center', 'error']}, dims=['aggregation']) - super(Imagenet_C_Category, self).__init__(identifier=f'dietterich.Hendrycks2019-{noise_category}-top1', + ceiling = Score(1) + super(Imagenet_C_Category, self).__init__(identifier=f'dietterich.Hendrycks2019-{noise_category}-top1', version=2, ceiling_func=lambda: ceiling, parent='dietterich.Hendrycks2019-top1', @@ -98,21 +97,21 @@ def load_stimulus_set(self): try: _logger.debug(f'Loading local Imagenet-C {self.noise_category}') category_path = os.path.join( - LOCAL_STIMULUS_DIRECTORY, + LOCAL_STIMULUS_DIRECTORY, f'image_dietterich_Hendrycks2019_{self.noise_category}' ) loader = SampledStimulusSetLoader( cls=StimulusSet, - csv_path=os.path.join(category_path, f'image_dietterich_Hendrycks2019_{self.noise_category}.csv'), - stimuli_directory=category_path, + csv_path=os.path.join(category_path, f'image_dietterich_Hendrycks2019_{self.noise_category}.csv'), + stimuli_directory=category_path, sampling_factor=self.sampling_factor ) return loader.load() - + except OSError as error: _logger.debug(f'Excepted {error}. Attempting to access {self.stimulus_set_name} through Brainscore.') - return brainscore.get_stimulus_set(self.stimulus_set_name) + return load_stimulus_set(self.stimulus_set_name) def __call__(self, candidate): scores = xr.concat([ @@ -122,7 +121,8 @@ def __call__(self, candidate): assert len(set(scores['noise_type'].values)) == len(self.noise_types) center = np.mean(scores) error = np.std(scores) - score = Score([center, error], coords={'aggregation': ['center', 'error']}, dims=('aggregation',)) + score = Score(center) + score.attrs['error'] = error score.attrs[Score.RAW_VALUES_KEY] = scores return score @@ -135,7 +135,7 @@ def __init__(self, stimulus_set, noise_type, noise_category): self.stimulus_set = stimulus_set[stimulus_set['noise_type'] == noise_type] self.noise_type = noise_type self.noise_category = noise_category - ceiling = Score([1, np.nan], coords={'aggregation': ['center', 'error']}, dims=['aggregation']) + ceiling = Score(1) super(Imagenet_C_Type, self).__init__(identifier=f'dietterich.Hendrycks2019-{noise_category}-{noise_type}-top1', version=2, ceiling_func=lambda: ceiling, @@ -160,8 +160,8 @@ def __init__(self, stimulus_set, noise_level, noise_type, noise_category): self.noise_level = noise_level self.noise_type = noise_type self.benchmark_name = f'dietterich.Hendrycks2019-{noise_category}-{noise_type}-{noise_level}-top1' - self._similarity_metric = Accuracy() - ceiling = Score([1, np.nan], coords={'aggregation': ['center', 'error']}, dims=['aggregation']) + self._similarity_metric = load_metric('accuracy') + ceiling = Score(1) super(Imagenet_C_Individual, self).__init__(identifier=self.benchmark_name, version=2, ceiling_func=lambda: ceiling, parent=f'dietterich.Hendrycks2019-{noise_category}-{noise_type}-top1', diff --git a/tests/test_benchmarks/test_imagenet_c.py b/brainscore_vision/benchmarks/imagenet_c/test.py similarity index 93% rename from tests/test_benchmarks/test_imagenet_c.py rename to brainscore_vision/benchmarks/imagenet_c/test.py index cc3debe46..733cac59d 100644 --- a/tests/test_benchmarks/test_imagenet_c.py +++ b/brainscore_vision/benchmarks/imagenet_c/test.py @@ -3,9 +3,9 @@ from pytest import approx from brainio.assemblies import BehavioralAssembly -from brainscore.benchmarks.imagenet_c import Imagenet_C_Individual, Imagenet_C_Noise, Imagenet_C_Blur, \ +from brainscore_vision.benchmarks.imagenet_c import Imagenet_C_Noise, Imagenet_C_Blur, \ Imagenet_C_Weather, Imagenet_C_Digital -from brainscore.model_interface import BrainModel +from brainscore_vision.model_interface import BrainModel # downloads all ImageNet C benchmarks (50.3G) and runs with default downsampling by a factor of 10 @pytest.mark.slow diff --git a/brainscore_vision/benchmarks/kar2019/__init__.py b/brainscore_vision/benchmarks/kar2019/__init__.py new file mode 100644 index 000000000..c7cf74de7 --- /dev/null +++ b/brainscore_vision/benchmarks/kar2019/__init__.py @@ -0,0 +1,4 @@ +from brainscore_vision import benchmark_registry +from .benchmark import DicarloKar2019OST + +benchmark_registry['dicarlo.Kar2019-ost'] = DicarloKar2019OST diff --git a/brainscore/benchmarks/kar2019.py b/brainscore_vision/benchmarks/kar2019/benchmark.py similarity index 87% rename from brainscore/benchmarks/kar2019.py rename to brainscore_vision/benchmarks/kar2019/benchmark.py index 5075d4dca..ec1b8bb0b 100644 --- a/brainscore/benchmarks/kar2019.py +++ b/brainscore_vision/benchmarks/kar2019/benchmark.py @@ -1,11 +1,10 @@ import numpy as np -import brainscore -from brainscore.benchmarks import BenchmarkBase, ceil_score -from brainscore.benchmarks.screen import place_on_screen -from brainscore.metrics import Score -from brainscore.metrics.ost import OSTCorrelation -from brainscore.model_interface import BrainModel +from brainscore_core import Score +from brainscore_vision import load_dataset, load_metric +from brainscore_vision.benchmark_helpers.screen import place_on_screen +from brainscore_vision.benchmarks import BenchmarkBase, ceil_score +from brainscore_vision.model_interface import BrainModel BIBTEX = """@Article{Kar2019, author={Kar, Kohitij @@ -33,13 +32,12 @@ class DicarloKar2019OST(BenchmarkBase): def __init__(self): - ceiling = Score([.79, np.nan], # following private conversation with Kohitij Kar - coords={'aggregation': ['center', 'error']}, dims=['aggregation']) + ceiling = Score(.79) # computed offline by Kohitij Kar super(DicarloKar2019OST, self).__init__(identifier='dicarlo.Kar2019-ost', version=2, ceiling_func=lambda: ceiling, parent='IT', bibtex=BIBTEX) - assembly = brainscore.get_assembly('dicarlo.Kar2019') + assembly = load_dataset('dicarlo.Kar2019') # drop duplicate images _, index = np.unique(assembly['stimulus_id'], return_index=True) assembly = assembly.isel(presentation=index) @@ -51,13 +49,13 @@ def __init__(self): self._assembly['truth'] = self._assembly['image_label'] self._assembly.stimulus_set['truth'] = self._assembly.stimulus_set['image_label'] - self._similarity_metric = OSTCorrelation() + self._similarity_metric = load_metric('ost') self._visual_degrees = VISUAL_DEGREES self._number_of_trials = NUMBER_OF_TRIALS self._time_bins = TIME_BINS - def __call__(self, candidate: BrainModel): + def __call__(self, candidate: BrainModel) -> Score: candidate.start_recording('IT', time_bins=self._time_bins) stimulus_set = place_on_screen(self._assembly.stimulus_set, target_visual_degrees=candidate.visual_degrees(), source_visual_degrees=self._visual_degrees) @@ -70,7 +68,7 @@ def __call__(self, candidate: BrainModel): check_stimulus_set.identifier = None # unset identifier to avoid storing (interferes with actual stimulus_set) check_recordings = candidate.look_at(check_stimulus_set, number_of_trials=self._number_of_trials) if not temporally_varying(check_recordings): - score = Score([np.nan, np.nan], coords={'aggregation': ['center', 'error']}, dims=['aggregation']) + score = Score(np.nan) else: recordings = candidate.look_at(stimulus_set, number_of_trials=self._number_of_trials) score = self._similarity_metric(recordings, self._assembly) diff --git a/brainscore_vision/benchmarks/kar2019/test.py b/brainscore_vision/benchmarks/kar2019/test.py new file mode 100644 index 000000000..f48222ec1 --- /dev/null +++ b/brainscore_vision/benchmarks/kar2019/test.py @@ -0,0 +1,94 @@ +from pathlib import Path + +import numpy as np +import pytest +from numpy.random.mtrand import RandomState +from pytest import approx + +from brainio.assemblies import NeuroidAssembly, DataAssembly +from brainscore_vision import load_benchmark +from brainscore_vision.benchmark_helpers import PrecomputedFeatures +from brainscore_vision.benchmark_helpers.test_helper import VisualDegreesTests, NumberOfTrialsTests +from brainscore_vision.benchmarks.kar2019 import DicarloKar2019OST +from brainscore_vision.data_helpers import s3 + +visual_degrees = VisualDegreesTests() +number_trials = NumberOfTrialsTests() + + +@pytest.mark.memory_intense +@pytest.mark.private_access +@pytest.mark.slow +def test_Kar2019ost_cornet_s(): + benchmark = load_benchmark('dicarlo.Kar2019-ost') + filename = 'cornet_s-kar2019.nc' + filepath = Path(__file__).parent / filename + s3.download_file_if_not_exists(local_path=filepath, + bucket='brainio-brainscore', remote_filepath=f'tests/test_benchmarks/{filename}') + precomputed_features = NeuroidAssembly.from_files( + filepath, + stimulus_set_identifier=benchmark._assembly.stimulus_set.identifier, + stimulus_set=benchmark._assembly.stimulus_set) + precomputed_features = PrecomputedFeatures(precomputed_features, visual_degrees=8) + # score + score = benchmark(precomputed_features).raw + assert score == approx(.316, abs=.005) + + +@pytest.mark.private_access +@pytest.mark.parametrize('benchmark, candidate_degrees, image_id, expected', [ + pytest.param('dicarlo.Kar2019-ost', 14, '6d19b24c29832dfb28360e7731e3261c13a4287f', + approx(.225021, abs=.0001), marks=[pytest.mark.private_access]), + pytest.param('dicarlo.Kar2019-ost', 6, '6d19b24c29832dfb28360e7731e3261c13a4287f', + approx(.001248, abs=.0001), marks=[pytest.mark.private_access]), +]) +def test_amount_gray(benchmark: str, candidate_degrees: int, image_id: str, expected: float): + visual_degrees.amount_gray_test(benchmark, candidate_degrees, image_id, expected) + + +@pytest.mark.private_access +@pytest.mark.parametrize('benchmark_identifier', ['dicarlo.Kar2019-ost']) +def test_repetitions(benchmark_identifier): + number_trials.repetitions_test(benchmark_identifier) + + +@pytest.mark.memory_intense +@pytest.mark.private_access +def test_no_time(): + benchmark = DicarloKar2019OST() + rnd = RandomState(0) + stimuli = benchmark._assembly.stimulus_set + source = DataAssembly(rnd.rand(len(stimuli), 5, 1), coords={ + 'stimulus_id': ('presentation', stimuli['stimulus_id']), + 'image_label': ('presentation', stimuli['image_label']), + 'truth': ('presentation', stimuli['truth']), + 'neuroid_id': ('neuroid', list(range(5))), + 'layer': ('neuroid', ['test'] * 5), + 'time_bin_start': ('time_bin', [70]), + 'time_bin_end': ('time_bin', [170]), + }, dims=['presentation', 'neuroid', 'time_bin']) + source.name = __name__ + ".test_notime" + score = benchmark(PrecomputedFeatures(source, visual_degrees=8)) + assert np.isnan(score) # not a temporal model + assert np.isnan(score.raw) # not a temporal model + assert score.attrs['ceiling'] == approx(.79) + + +@pytest.mark.memory_intense +@pytest.mark.private_access +def test_random_time(): + benchmark = DicarloKar2019OST() + rnd = RandomState(0) + stimuli = benchmark._assembly.stimulus_set + source = DataAssembly(rnd.rand(len(stimuli), 5, 5), coords={ + 'stimulus_id': ('presentation', stimuli['stimulus_id']), + 'image_label': ('presentation', stimuli['image_label']), + 'truth': ('presentation', stimuli['truth']), + 'neuroid_id': ('neuroid', list(range(5))), + 'layer': ('neuroid', ['test'] * 5), + 'time_bin_start': ('time_bin', [70, 90, 110, 130, 150]), + 'time_bin_end': ('time_bin', [90, 110, 130, 150, 170]), + }, dims=['presentation', 'neuroid', 'time_bin']) + source.name = __name__ + ".test_notime" + score = benchmark(PrecomputedFeatures(source, visual_degrees=8)) + assert np.isnan(score) # not a good temporal model diff --git a/brainscore_vision/benchmarks/majajhong2015/__init__.py b/brainscore_vision/benchmarks/majajhong2015/__init__.py new file mode 100644 index 000000000..0bf46da86 --- /dev/null +++ b/brainscore_vision/benchmarks/majajhong2015/__init__.py @@ -0,0 +1,13 @@ +from brainscore_vision import benchmark_registry + +# neural predictivity +from .benchmark import DicarloMajajHong2015V4PLS, DicarloMajajHong2015ITPLS + +benchmark_registry['dicarlo.MajajHong2015.V4-pls'] = DicarloMajajHong2015V4PLS +benchmark_registry['dicarlo.MajajHong2015.IT-pls'] = DicarloMajajHong2015ITPLS + +# public +from .benchmark import MajajHongV4PublicBenchmark, MajajHongITPublicBenchmark + +benchmark_registry['dicarlo.MajajHong2015public.V4-pls'] = MajajHongV4PublicBenchmark +benchmark_registry['dicarlo.MajajHong2015public.IT-pls'] = MajajHongITPublicBenchmark diff --git a/brainscore/benchmarks/majajhong2015.py b/brainscore_vision/benchmarks/majajhong2015/benchmark.py similarity index 54% rename from brainscore/benchmarks/majajhong2015.py rename to brainscore_vision/benchmarks/majajhong2015/benchmark.py index 476243463..76b8bd9c4 100644 --- a/brainscore/benchmarks/majajhong2015.py +++ b/brainscore_vision/benchmarks/majajhong2015/benchmark.py @@ -1,10 +1,7 @@ -import brainscore -from brainscore.benchmarks._neural_common import NeuralBenchmark, average_repetition -from brainscore.metrics.ceiling import InternalConsistency, RDMConsistency -from brainscore.metrics.rdm import RDMCrossValidated -from brainscore.metrics.regression import CrossRegressedCorrelation, mask_regression, ScaledCrossRegressedCorrelation, \ - pls_regression, pearsonr_correlation -from brainscore.utils import LazyLoad +from brainscore_core import Metric + +from brainscore_vision import load_metric, Ceiling, load_ceiling, load_dataset +from brainscore_vision.benchmark_helpers.neural_common import NeuralBenchmark, average_repetition VISUAL_DEGREES = 8 NUMBER_OF_TRIALS = 50 @@ -23,11 +20,15 @@ eprint = {https://www.jneurosci.org/content/35/39/13402.full.pdf}, journal = {Journal of Neuroscience}}""" +pls_metric = lambda: load_metric('pls', crossvalidation_kwargs=dict(stratification_coord='object_name')) + -def _DicarloMajajHong2015Region(region, identifier_metric_suffix, similarity_metric, ceiler): - assembly_repetition = LazyLoad(lambda region=region: load_assembly(average_repetitions=False, region=region)) - assembly = LazyLoad(lambda region=region: load_assembly(average_repetitions=True, region=region)) - return NeuralBenchmark(identifier=f'dicarlo.MajajHong2015.{region}-{identifier_metric_suffix}', version=3, +def _DicarloMajajHong2015Region(region: str, access: str, identifier_metric_suffix: str, + similarity_metric: Metric, ceiler: Ceiling): + assembly_repetition = load_assembly(average_repetitions=False, region=region, access=access) + assembly = load_assembly(average_repetitions=True, region=region, access=access) + benchmark_identifier = f'dicarlo.MajajHong2015.{region}' + ('.public' if access == 'public' else '') + return NeuralBenchmark(identifier=f'{benchmark_identifier}-{identifier_metric_suffix}', version=3, assembly=assembly, similarity_metric=similarity_metric, visual_degrees=VISUAL_DEGREES, number_of_trials=NUMBER_OF_TRIALS, ceiling_func=lambda: ceiler(assembly_repetition), @@ -36,53 +37,31 @@ def _DicarloMajajHong2015Region(region, identifier_metric_suffix, similarity_met def DicarloMajajHong2015V4PLS(): - return _DicarloMajajHong2015Region('V4', identifier_metric_suffix='pls', - similarity_metric=CrossRegressedCorrelation( - regression=pls_regression(), correlation=pearsonr_correlation(), - crossvalidation_kwargs=dict(stratification_coord='object_name')), - ceiler=InternalConsistency()) + return _DicarloMajajHong2015Region(region='V4', access='private', identifier_metric_suffix='pls', + similarity_metric=pls_metric(), + ceiler=load_ceiling('internal_consistency')) def DicarloMajajHong2015ITPLS(): - return _DicarloMajajHong2015Region('IT', identifier_metric_suffix='pls', - similarity_metric=CrossRegressedCorrelation( - regression=pls_regression(), correlation=pearsonr_correlation(), - crossvalidation_kwargs=dict(stratification_coord='object_name')), - ceiler=InternalConsistency()) - - -def DicarloMajajHong2015V4Mask(): - return _DicarloMajajHong2015Region('V4', identifier_metric_suffix='mask', - similarity_metric=ScaledCrossRegressedCorrelation( - regression=mask_regression(), correlation=pearsonr_correlation(), - crossvalidation_kwargs=dict(splits=2, stratification_coord='object_name')), - ceiler=InternalConsistency()) - - -def DicarloMajajHong2015ITMask(): - return _DicarloMajajHong2015Region('IT', identifier_metric_suffix='mask', - similarity_metric=ScaledCrossRegressedCorrelation( - regression=mask_regression(), correlation=pearsonr_correlation(), - crossvalidation_kwargs=dict(splits=2, stratification_coord='object_name')), - ceiler=InternalConsistency()) + return _DicarloMajajHong2015Region(region='IT', access='private', identifier_metric_suffix='pls', + similarity_metric=pls_metric(), + ceiler=load_ceiling('internal_consistency')) -def DicarloMajajHong2015V4RDM(): - return _DicarloMajajHong2015Region('V4', identifier_metric_suffix='rdm', - similarity_metric=RDMCrossValidated( - crossvalidation_kwargs=dict(stratification_coord='object_name')), - ceiler=RDMConsistency()) +def MajajHongV4PublicBenchmark(): + return _DicarloMajajHong2015Region(region='V4', access='public', identifier_metric_suffix='pls', + similarity_metric=pls_metric(), + ceiler=load_ceiling('internal_consistency')) -def DicarloMajajHong2015ITRDM(): - return _DicarloMajajHong2015Region('IT', identifier_metric_suffix='rdm', - similarity_metric=RDMCrossValidated( - crossvalidation_kwargs=dict(stratification_coord='object_name')), - ceiler=RDMConsistency()) +def MajajHongITPublicBenchmark(): + return _DicarloMajajHong2015Region(region='IT', access='public', identifier_metric_suffix='pls', + similarity_metric=pls_metric(), + ceiler=load_ceiling('internal_consistency')) def load_assembly(average_repetitions, region, access='private'): - assembly = brainscore.get_assembly(name=f'dicarlo.MajajHong2015.{access}') + assembly = load_dataset(f'dicarlo.MajajHong2015.{access}') assembly = assembly.sel(region=region) assembly['region'] = 'neuroid', [region] * len(assembly['neuroid']) assembly = assembly.squeeze("time_bin") diff --git a/brainscore_vision/benchmarks/majajhong2015/test.py b/brainscore_vision/benchmarks/majajhong2015/test.py new file mode 100644 index 000000000..6f3a58a91 --- /dev/null +++ b/brainscore_vision/benchmarks/majajhong2015/test.py @@ -0,0 +1,103 @@ +from pathlib import Path + +import pytest +from pytest import approx + +from brainscore_vision import benchmark_registry +from brainscore_vision.benchmark_helpers import PrecomputedFeatures +from brainscore_vision.benchmark_helpers.test_helper import StandardizedTests, PrecomputedTests, NumberOfTrialsTests, \ + VisualDegreesTests +from brainscore_vision.benchmarks.majajhong2015.benchmark import MajajHongV4PublicBenchmark, MajajHongITPublicBenchmark +from brainscore_vision.data_helpers import s3 + +standardized_tests = StandardizedTests() +precomputed_test = PrecomputedTests() +num_trials_test = NumberOfTrialsTests() +visual_degrees = VisualDegreesTests() + + +@pytest.mark.parametrize('benchmark', [ + +]) +def test_benchmark_registry(benchmark): + assert benchmark in benchmark_registry + + +@pytest.mark.private_access +@pytest.mark.parametrize('benchmark, expected', [ + pytest.param('dicarlo.MajajHong2015.V4-pls', approx(.89503, abs=.001), + marks=pytest.mark.memory_intense), + pytest.param('dicarlo.MajajHong2015.IT-pls', approx(.821841, abs=.001), + marks=pytest.mark.memory_intense), +]) +def test_ceilings(benchmark, expected): + standardized_tests.ceilings_test(benchmark, expected) + + +@pytest.mark.private_access +@pytest.mark.memory_intense +@pytest.mark.parametrize('benchmark, visual_degrees, expected', [ + ('dicarlo.MajajHong2015.V4-pls', 8, approx(.923713, abs=.001)), + ('dicarlo.MajajHong2015.IT-pls', 8, approx(.823433, abs=.001)), +]) +def test_self_regression(benchmark, visual_degrees, expected): + standardized_tests.self_regression_test(benchmark, visual_degrees, expected) + + +@pytest.mark.private_access +@pytest.mark.memory_intense +@pytest.mark.parametrize('benchmark, expected', [ + ('dicarlo.MajajHong2015.V4-pls', approx(.490236, abs=.005)), + ('dicarlo.MajajHong2015.IT-pls', approx(.584053, abs=.005)), +]) +def test_MajajHong2015(benchmark, expected): + filename = 'alexnet-majaj2015.private-features.12.nc' + filepath = Path(__file__).parent / filename + s3.download_file_if_not_exists(local_path=filepath, + bucket='brainio-brainscore', remote_filepath=f'tests/test_benchmarks/{filename}') + precomputed_test.run_test(benchmark=benchmark, precomputed_features_filepath=filepath, expected=expected) + + +@pytest.mark.parametrize('benchmark, candidate_degrees, image_id, expected', [ + pytest.param('dicarlo.MajajHong2015.V4-pls', 14, '40a786ed8e13db10185ddfdbe07759d83a589e1c', + approx(.251345, abs=.0001), marks=[pytest.mark.private_access]), + pytest.param('dicarlo.MajajHong2015.V4-pls', 6, '40a786ed8e13db10185ddfdbe07759d83a589e1c', + approx(.0054886, abs=.0001), marks=[pytest.mark.private_access]), + pytest.param('dicarlo.MajajHong2015public.V4-pls', 14, '8a72e2bfdb8c267b57232bf96f069374d5b21832', + approx(.25071, abs=.0001), marks=[]), + pytest.param('dicarlo.MajajHong2015public.V4-pls', 6, '8a72e2bfdb8c267b57232bf96f069374d5b21832', + approx(.00460, abs=.0001), marks=[]), + pytest.param('dicarlo.MajajHong2015.IT-pls', 14, '40a786ed8e13db10185ddfdbe07759d83a589e1c', + approx(.251345, abs=.0001), marks=[pytest.mark.private_access]), + pytest.param('dicarlo.MajajHong2015.IT-pls', 6, '40a786ed8e13db10185ddfdbe07759d83a589e1c', + approx(.0054886, abs=.0001), marks=[pytest.mark.private_access]), + pytest.param('dicarlo.MajajHong2015public.IT-pls', 14, '8a72e2bfdb8c267b57232bf96f069374d5b21832', + approx(.25071, abs=.0001), marks=[]), + pytest.param('dicarlo.MajajHong2015public.IT-pls', 6, '8a72e2bfdb8c267b57232bf96f069374d5b21832', + approx(.00460, abs=.0001), marks=[]), +]) +def test_amount_gray(benchmark: str, candidate_degrees: int, image_id: str, expected: float): + visual_degrees.amount_gray_test(benchmark, candidate_degrees, image_id, expected) + + +@pytest.mark.private_access +@pytest.mark.parametrize('benchmark_identifier', [ + 'dicarlo.MajajHong2015.V4-pls', + 'dicarlo.MajajHong2015.IT-pls', +]) +def test_repetitions(benchmark_identifier): + num_trials_test.repetitions_test(benchmark_identifier) + + +@pytest.mark.parametrize('benchmark_ctr, visual_degrees, expected', [ + pytest.param(MajajHongV4PublicBenchmark, 8, approx(.897956, abs=.001), + marks=pytest.mark.memory_intense), + pytest.param(MajajHongITPublicBenchmark, 8, approx(.816251, abs=.001), + marks=pytest.mark.memory_intense), +]) +def test_self(benchmark_ctr, visual_degrees, expected): + benchmark = benchmark_ctr() + source = benchmark._assembly.copy() + source = {benchmark._assembly.stimulus_set.identifier: source} + score = benchmark(PrecomputedFeatures(source, visual_degrees=visual_degrees)).raw + assert score == expected diff --git a/brainscore_vision/benchmarks/marques2020/__init__.py b/brainscore_vision/benchmarks/marques2020/__init__.py new file mode 100644 index 000000000..7466a5b36 --- /dev/null +++ b/brainscore_vision/benchmarks/marques2020/__init__.py @@ -0,0 +1,76 @@ +from brainscore_vision import benchmark_registry +from .benchmarks.cavanaugh2002a_benchmark import MarquesCavanaugh2002V1SurroundSuppressionIndex, \ + MarquesCavanaugh2002V1GratingSummationField, MarquesCavanaugh2002V1SurroundDiameter + +benchmark_registry['dicarlo.Marques2020_Cavanaugh2002-grating_summation_field'] = \ + MarquesCavanaugh2002V1GratingSummationField +benchmark_registry['dicarlo.Marques2020_Cavanaugh2002-surround_diameter'] = \ + MarquesCavanaugh2002V1SurroundDiameter +benchmark_registry['dicarlo.Marques2020_Cavanaugh2002-surround_suppression_index'] = \ + MarquesCavanaugh2002V1SurroundSuppressionIndex + + +from .benchmarks.devalois1982a_benchmark import MarquesDeValois1982V1PreferredOrientation + +benchmark_registry['dicarlo.Marques2020_DeValois1982-pref_or'] = \ + MarquesDeValois1982V1PreferredOrientation + + +from .benchmarks.devalois1982b_benchmark import MarquesDeValois1982V1PeakSpatialFrequency + +benchmark_registry['dicarlo.Marques2020_DeValois1982-peak_sf'] = \ + MarquesDeValois1982V1PeakSpatialFrequency + + +from .benchmarks.freemanZiemba2013_benchmark import MarquesFreemanZiemba2013V1TextureModulationIndex, \ + MarquesFreemanZiemba2013V1AbsoluteTextureModulationIndex, MarquesFreemanZiemba2013V1TextureSelectivity, \ + MarquesFreemanZiemba2013V1TextureSparseness, MarquesFreemanZiemba2013V1VarianceRatio, \ + MarquesFreemanZiemba2013V1MaxTexture, MarquesFreemanZiemba2013V1MaxNoise + +# V1 properties benchmarks: texture modulation +benchmark_registry['dicarlo.Marques2020_FreemanZiemba2013-texture_modulation_index'] = \ + MarquesFreemanZiemba2013V1TextureModulationIndex +benchmark_registry['dicarlo.Marques2020_FreemanZiemba2013-abs_texture_modulation_index'] = \ + MarquesFreemanZiemba2013V1AbsoluteTextureModulationIndex + +# V1 properties benchmarks: selectivity +benchmark_registry['dicarlo.Marques2020_FreemanZiemba2013-texture_selectivity'] = \ + MarquesFreemanZiemba2013V1TextureSelectivity +benchmark_registry['dicarlo.Marques2020_FreemanZiemba2013-texture_sparseness'] = \ + MarquesFreemanZiemba2013V1TextureSparseness +benchmark_registry['dicarlo.Marques2020_FreemanZiemba2013-texture_variance_ratio'] = \ + MarquesFreemanZiemba2013V1VarianceRatio + +# V1 properties benchmarks: magnitude +benchmark_registry['dicarlo.Marques2020_FreemanZiemba2013-max_texture'] = MarquesFreemanZiemba2013V1MaxTexture +benchmark_registry['dicarlo.Marques2020_FreemanZiemba2013-max_noise'] = MarquesFreemanZiemba2013V1MaxNoise + + +from .benchmarks.ringach2002_benchmark import MarquesRingach2002V1CircularVariance, MarquesRingach2002V1Bandwidth, \ + MarquesRingach2002V1OrthogonalPreferredRatio, MarquesRingach2002V1OrientationSelective, \ + MarquesRingach2002V1CircularVarianceBandwidthRatio, \ + MarquesRingach2002V1OrthogonalPrefferredRatioCircularVarianceDifference, MarquesRingach2002V1MaxDC, \ + MarquesRingach2002V1ModulationRatio + +# V1 properties benchmarks: orientation +benchmark_registry['dicarlo.Marques2020_Ringach2002-circular_variance'] = MarquesRingach2002V1CircularVariance +benchmark_registry['dicarlo.Marques2020_Ringach2002-or_bandwidth'] = MarquesRingach2002V1Bandwidth +benchmark_registry['dicarlo.Marques2020_Ringach2002-orth_pref_ratio'] = MarquesRingach2002V1OrthogonalPreferredRatio +benchmark_registry['dicarlo.Marques2020_Ringach2002-or_selective'] = MarquesRingach2002V1OrientationSelective +benchmark_registry['dicarlo.Marques2020_Ringach2002-cv_bandwidth_ratio'] = \ + MarquesRingach2002V1CircularVarianceBandwidthRatio +benchmark_registry['dicarlo.Marques2020_Ringach2002-opr_cv_diff'] = \ + MarquesRingach2002V1OrthogonalPrefferredRatioCircularVarianceDifference + +# V1 properties benchmarks: magnitude +benchmark_registry['dicarlo.Marques2020_Ringach2002-max_dc'] = MarquesRingach2002V1MaxDC +benchmark_registry['dicarlo.Marques2020_Ringach2002-modulation_ratio'] = MarquesRingach2002V1ModulationRatio + + +from .benchmarks.schiller1976_benchmark import MarquesSchiller1976V1SpatialFrequencyBandwidth, \ + MarquesSchiller1976V1SpatialFrequencySelective + +# V1 properties benchmarks: spatial frequency +benchmark_registry['dicarlo.Marques2020_Schiller1976-sf_selective'] = MarquesSchiller1976V1SpatialFrequencySelective +benchmark_registry['dicarlo.Marques2020_Schiller1976-sf_bandwidth'] = MarquesSchiller1976V1SpatialFrequencyBandwidth + diff --git a/brainscore/benchmarks/marques2020_cavanaugh2002a.py b/brainscore_vision/benchmarks/marques2020/benchmarks/cavanaugh2002a_benchmark.py similarity index 89% rename from brainscore/benchmarks/marques2020_cavanaugh2002a.py rename to brainscore_vision/benchmarks/marques2020/benchmarks/cavanaugh2002a_benchmark.py index 78c7e2707..dd9d5f293 100644 --- a/brainscore/benchmarks/marques2020_cavanaugh2002a.py +++ b/brainscore_vision/benchmarks/marques2020/benchmarks/cavanaugh2002a_benchmark.py @@ -1,12 +1,11 @@ import numpy as np +from result_caching import store -import brainscore from brainio.assemblies import DataAssembly -from brainscore.benchmarks._properties_common import PropertiesBenchmark, _assert_grating_activations -from brainscore.benchmarks._properties_common import calc_size_tuning -from brainscore.metrics.ceiling import NeuronalPropertyCeiling -from brainscore.metrics.distribution_similarity import BootstrapDistributionSimilarity, ks_similarity -from result_caching import store +from brainscore_vision import load_dataset, load_metric +from brainscore_vision.benchmark_helpers.properties_common import PropertiesBenchmark, _assert_grating_activations, \ + calc_size_tuning +from brainscore_vision.metrics.distribution_similarity import NeuronalPropertyCeiling ASSEMBLY_NAME = 'movshon.Cavanaugh2002a' REGION = 'V1' @@ -38,8 +37,8 @@ def _MarquesCavanaugh2002V1Property(property_name, parent): - assembly = brainscore.get_assembly(ASSEMBLY_NAME) - similarity_metric = BootstrapDistributionSimilarity(similarity_func=ks_similarity, property_name=property_name) + assembly = load_dataset(ASSEMBLY_NAME) + similarity_metric = load_metric('ks_similarity', property_name=property_name) ceil_func = NeuronalPropertyCeiling(similarity_metric) return PropertiesBenchmark(identifier=f'dicarlo.Marques_cavanaugh2002-{property_name}', assembly=assembly, neuronal_property=cavanaugh2002_properties, similarity_metric=similarity_metric, @@ -103,7 +102,7 @@ def cavanaugh2002_properties(model_identifier, responses, baseline): size_curve = responses[neur, :, pref_spatial_frequency, pref_orientation, pref_component] grating_summation_field[neur], surround_diameter[neur], surround_grating_summation_field_ratio[neur], \ - surround_suppression_index[neur] = calc_size_tuning(size_curve, radius) + surround_suppression_index[neur] = calc_size_tuning(size_curve, radius) strongly_suppressed[surround_suppression_index >= 0.1] = 1 diff --git a/brainscore/benchmarks/marques2020_devalois1982a.py b/brainscore_vision/benchmarks/marques2020/benchmarks/devalois1982a_benchmark.py similarity index 87% rename from brainscore/benchmarks/marques2020_devalois1982a.py rename to brainscore_vision/benchmarks/marques2020/benchmarks/devalois1982a_benchmark.py index ab9ed29c2..31c8e8ab0 100644 --- a/brainscore/benchmarks/marques2020_devalois1982a.py +++ b/brainscore_vision/benchmarks/marques2020/benchmarks/devalois1982a_benchmark.py @@ -1,12 +1,11 @@ import numpy as np +from result_caching import store -import brainscore from brainio.assemblies import DataAssembly -from brainscore.benchmarks._properties_common import PropertiesBenchmark, _assert_grating_activations -from brainscore.benchmarks._properties_common import calc_bandwidth -from brainscore.metrics.ceiling import NeuronalPropertyCeiling -from brainscore.metrics.distribution_similarity import BootstrapDistributionSimilarity, ks_similarity -from result_caching import store +from brainscore_vision import load_dataset, load_metric +from brainscore_vision.benchmark_helpers.properties_common import PropertiesBenchmark, _assert_grating_activations +from brainscore_vision.benchmark_helpers.properties_common import calc_bandwidth +from brainscore_vision.metrics.distribution_similarity import NeuronalPropertyCeiling ASSEMBLY_NAME = 'devalois.DeValois1982a' REGION = 'V1' @@ -30,10 +29,10 @@ def MarquesDeValois1982V1PreferredOrientation(): - assembly = brainscore.get_assembly(ASSEMBLY_NAME) + assembly = load_dataset(ASSEMBLY_NAME) property_name = 'preferred_orientation' parent = PARENT - similarity_metric = BootstrapDistributionSimilarity(similarity_func=ks_similarity, property_name=property_name) + similarity_metric = load_metric('ks_similarity', property_name=property_name) ceil_func = NeuronalPropertyCeiling(similarity_metric) return PropertiesBenchmark(identifier=f'dicarlo.Marques_devalois1982-{property_name}', assembly=assembly, neuronal_property=devalois1982a_properties, similarity_metric=similarity_metric, diff --git a/brainscore/benchmarks/marques2020_devalois1982b.py b/brainscore_vision/benchmarks/marques2020/benchmarks/devalois1982b_benchmark.py similarity index 87% rename from brainscore/benchmarks/marques2020_devalois1982b.py rename to brainscore_vision/benchmarks/marques2020/benchmarks/devalois1982b_benchmark.py index 0c6573823..a89307fa2 100644 --- a/brainscore/benchmarks/marques2020_devalois1982b.py +++ b/brainscore_vision/benchmarks/marques2020/benchmarks/devalois1982b_benchmark.py @@ -1,12 +1,11 @@ import numpy as np +from result_caching import store -import brainscore from brainio.assemblies import DataAssembly -from brainscore.benchmarks._properties_common import PropertiesBenchmark, _assert_grating_activations -from brainscore.benchmarks._properties_common import calc_spatial_frequency_tuning -from brainscore.metrics.ceiling import NeuronalPropertyCeiling -from brainscore.metrics.distribution_similarity import BootstrapDistributionSimilarity, ks_similarity -from result_caching import store +from brainscore_vision import load_dataset, load_metric +from brainscore_vision.benchmark_helpers.properties_common import PropertiesBenchmark, _assert_grating_activations +from brainscore_vision.benchmark_helpers.properties_common import calc_spatial_frequency_tuning +from brainscore_vision.metrics.distribution_similarity import NeuronalPropertyCeiling ASSEMBLY_NAME = 'devalois.DeValois1982b' REGION = 'V1' @@ -29,10 +28,10 @@ def MarquesDeValois1982V1PeakSpatialFrequency(): - assembly = brainscore.get_assembly(ASSEMBLY_NAME) + assembly = load_dataset(ASSEMBLY_NAME) property_name = 'peak_spatial_frequency' parent = PARENT - similarity_metric = BootstrapDistributionSimilarity(similarity_func=ks_similarity, property_name=property_name) + similarity_metric = load_metric('ks_similarity', property_name=property_name) ceil_func = NeuronalPropertyCeiling(similarity_metric) return PropertiesBenchmark(identifier=f'dicarlo.Marques_devalois1982-{property_name}', assembly=assembly, neuronal_property=devalois1982b_properties, similarity_metric=similarity_metric, diff --git a/brainscore/benchmarks/marques2020_freemanZiemba2013.py b/brainscore_vision/benchmarks/marques2020/benchmarks/freemanZiemba2013_benchmark.py similarity index 82% rename from brainscore/benchmarks/marques2020_freemanZiemba2013.py rename to brainscore_vision/benchmarks/marques2020/benchmarks/freemanZiemba2013_benchmark.py index 830aa9a88..6b9fdb5fa 100644 --- a/brainscore/benchmarks/marques2020_freemanZiemba2013.py +++ b/brainscore_vision/benchmarks/marques2020/benchmarks/freemanZiemba2013_benchmark.py @@ -1,12 +1,13 @@ import numpy as np +from result_caching import store -import brainscore from brainio.assemblies import DataAssembly -from brainscore.benchmarks._properties_common import PropertiesBenchmark, _assert_texture_activations -from brainscore.benchmarks._properties_common import calc_texture_modulation, calc_sparseness, calc_variance_ratio -from brainscore.metrics.ceiling import NeuronalPropertyCeiling -from brainscore.metrics.distribution_similarity import BootstrapDistributionSimilarity, ks_similarity -from result_caching import store +from brainscore_vision import load_dataset, load_metric +from brainscore_vision.benchmark_helpers.properties_common import PropertiesBenchmark, _assert_texture_activations +from brainscore_vision.benchmark_helpers.properties_common import calc_texture_modulation, calc_sparseness, \ + calc_variance_ratio +from brainscore_vision.data.freemanziemba2013 import BIBTEX +from brainscore_vision.metrics.distribution_similarity import NeuronalPropertyCeiling ASSEMBLY_NAME = 'movshon.FreemanZiemba2013_V1_properties' REGION = 'V1' @@ -19,28 +20,12 @@ 'noise_selectivity', 'texture_sparseness', 'noise_sparseness', 'variance_ratio', 'sample_variance', 'family_variance', 'max_texture', 'max_noise'] -BIBTEX = """@article{Freeman2013, - author = {Freeman, Jeremy and Ziemba, Corey M. and Heeger, David J. and Simoncelli, E. P. and Movshon, J. A.}, - doi = {10.1038/nn.3402}, - issn = {10976256}, - journal = {Nature Neuroscience}, - number = {7}, - pages = {974--981}, - pmid = {23685719}, - publisher = {Nature Publishing Group}, - title = {{A functional and perceptual signature of the second visual area in primates}}, - url = {http://dx.doi.org/10.1038/nn.3402}, - volume = {16}, - year = {2013} - } - """ - RESPONSE_THRESHOLD = 5 def _MarquesFreemanZiemba2013V1Property(property_name, parent): - assembly = brainscore.get_assembly(ASSEMBLY_NAME) - similarity_metric = BootstrapDistributionSimilarity(similarity_func=ks_similarity, property_name=property_name) + assembly = load_dataset(ASSEMBLY_NAME) + similarity_metric = load_metric('ks_similarity', property_name=property_name) ceil_func = NeuronalPropertyCeiling(similarity_metric) return PropertiesBenchmark(identifier=f'dicarlo.Marques_freemanziemba2013-{property_name}', assembly=assembly, neuronal_property=freemanziemba2013_properties, similarity_metric=similarity_metric, diff --git a/brainscore/benchmarks/marques2020_ringach2002.py b/brainscore_vision/benchmarks/marques2020/benchmarks/ringach2002_benchmark.py similarity index 92% rename from brainscore/benchmarks/marques2020_ringach2002.py rename to brainscore_vision/benchmarks/marques2020/benchmarks/ringach2002_benchmark.py index 5d01dab62..e4e1d65cb 100644 --- a/brainscore/benchmarks/marques2020_ringach2002.py +++ b/brainscore_vision/benchmarks/marques2020/benchmarks/ringach2002_benchmark.py @@ -1,13 +1,12 @@ import numpy as np +from result_caching import store -import brainscore from brainio.assemblies import DataAssembly -from brainscore.benchmarks._properties_common import PropertiesBenchmark, _assert_grating_activations -from brainscore.benchmarks._properties_common import calc_circular_variance, calc_bandwidth, \ +from brainscore_vision import load_dataset, load_metric +from brainscore_vision.benchmark_helpers.properties_common import PropertiesBenchmark, _assert_grating_activations +from brainscore_vision.benchmark_helpers.properties_common import calc_circular_variance, calc_bandwidth, \ calc_orthogonal_preferred_ratio -from brainscore.metrics.ceiling import NeuronalPropertyCeiling -from brainscore.metrics.distribution_similarity import BootstrapDistributionSimilarity, ks_similarity -from result_caching import store +from brainscore_vision.metrics.distribution_similarity import NeuronalPropertyCeiling ASSEMBLY_NAME = 'shapley.Ringach2002' REGION = 'V1' @@ -38,8 +37,8 @@ def _MarquesRingach2002V1Property(property_name, parent): - assembly = brainscore.get_assembly(ASSEMBLY_NAME) - similarity_metric = BootstrapDistributionSimilarity(similarity_func=ks_similarity, property_name=property_name) + assembly = load_dataset(ASSEMBLY_NAME) + similarity_metric = load_metric('ks_similarity', property_name=property_name) ceil_func = NeuronalPropertyCeiling(similarity_metric) return PropertiesBenchmark(identifier=f'dicarlo.Marques_ringach2002-{property_name}', assembly=assembly, neuronal_property=ringach2002_properties, similarity_metric=similarity_metric, diff --git a/brainscore/benchmarks/marques2020_schiller1976.py b/brainscore_vision/benchmarks/marques2020/benchmarks/schiller1976_benchmark.py similarity index 88% rename from brainscore/benchmarks/marques2020_schiller1976.py rename to brainscore_vision/benchmarks/marques2020/benchmarks/schiller1976_benchmark.py index 36f1856b8..4d82333c2 100644 --- a/brainscore/benchmarks/marques2020_schiller1976.py +++ b/brainscore_vision/benchmarks/marques2020/benchmarks/schiller1976_benchmark.py @@ -1,12 +1,11 @@ import numpy as np +from result_caching import store -import brainscore from brainio.assemblies import DataAssembly -from brainscore.benchmarks._properties_common import PropertiesBenchmark, _assert_grating_activations -from brainscore.benchmarks._properties_common import calc_spatial_frequency_tuning -from brainscore.metrics.ceiling import NeuronalPropertyCeiling -from brainscore.metrics.distribution_similarity import BootstrapDistributionSimilarity, ks_similarity -from result_caching import store +from brainscore_vision import load_metric, load_dataset +from brainscore_vision.benchmark_helpers.properties_common import PropertiesBenchmark, _assert_grating_activations +from brainscore_vision.benchmark_helpers.properties_common import calc_spatial_frequency_tuning +from brainscore_vision.metrics.distribution_similarity import NeuronalPropertyCeiling ASSEMBLY_NAME = 'schiller.Schiller1976c' REGION = 'V1' @@ -34,8 +33,8 @@ def _MarquesSchiller1976V1Property(property_name): - assembly = brainscore.get_assembly(ASSEMBLY_NAME) - similarity_metric = BootstrapDistributionSimilarity(similarity_func=ks_similarity, property_name=property_name) + assembly = load_dataset(ASSEMBLY_NAME) + similarity_metric = load_metric('ks_similarity', property_name=property_name) ceil_func = NeuronalPropertyCeiling(similarity_metric) parent = PARENT return PropertiesBenchmark(identifier=f'dicarlo.Marques_schiller1976-{property_name}', assembly=assembly, diff --git a/brainscore_vision/benchmarks/marques2020/test.py b/brainscore_vision/benchmarks/marques2020/test.py new file mode 100644 index 000000000..4825f44a1 --- /dev/null +++ b/brainscore_vision/benchmarks/marques2020/test.py @@ -0,0 +1,135 @@ +import pytest +from pytest import approx + +from brainscore_vision import benchmark_registry +from brainscore_vision.benchmark_helpers.test_helper import StandardizedTests, PrecomputedTests, NumberOfTrialsTests + +standardized_tests = StandardizedTests() +precomputed_test = PrecomputedTests() +num_trials_test = NumberOfTrialsTests() + + +@pytest.mark.parametrize('benchmark', [ + 'dicarlo.Marques2020_Cavanaugh2002-grating_summation_field', + 'dicarlo.Marques2020_Cavanaugh2002-surround_diameter', + 'dicarlo.Marques2020_Cavanaugh2002-surround_suppression_index', + 'dicarlo.Marques2020_DeValois1982-pref_or', + 'dicarlo.Marques2020_DeValois1982-peak_sf', + 'dicarlo.Marques2020_FreemanZiemba2013-texture_sparseness', + 'dicarlo.Marques2020_FreemanZiemba2013-texture_selectivity', + 'dicarlo.Marques2020_FreemanZiemba2013-texture_variance_ratio', + 'dicarlo.Marques2020_FreemanZiemba2013-texture_modulation_index', + 'dicarlo.Marques2020_FreemanZiemba2013-abs_texture_modulation_index', + 'dicarlo.Marques2020_FreemanZiemba2013-max_noise', + 'dicarlo.Marques2020_FreemanZiemba2013-max_texture', + 'dicarlo.Marques2020_Ringach2002-or_bandwidth', + 'dicarlo.Marques2020_Ringach2002-or_selective', + 'dicarlo.Marques2020_Ringach2002-circular_variance', + 'dicarlo.Marques2020_Ringach2002-orth_pref_ratio', + 'dicarlo.Marques2020_Ringach2002-cv_bandwidth_ratio', + 'dicarlo.Marques2020_Ringach2002-opr_cv_diff', + 'dicarlo.Marques2020_Ringach2002-modulation_ratio', + 'dicarlo.Marques2020_Ringach2002-max_dc', + 'dicarlo.Marques2020_Schiller1976-sf_bandwidth', + 'dicarlo.Marques2020_Schiller1976-sf_selective', +]) +def test_benchmark_registry(benchmark): + assert benchmark in benchmark_registry + + +@pytest.mark.private_access +@pytest.mark.parametrize('benchmark, expected', [ + ('dicarlo.Marques2020_Cavanaugh2002-grating_summation_field', approx(0.956, abs=.005)), + ('dicarlo.Marques2020_Cavanaugh2002-surround_diameter', approx(0.955, abs=.005)), + ('dicarlo.Marques2020_Cavanaugh2002-surround_suppression_index', approx(0.958, abs=.005)), + ('dicarlo.Marques2020_DeValois1982-pref_or', approx(0.962, abs=.005)), + ('dicarlo.Marques2020_DeValois1982-peak_sf', approx(0.967, abs=.005)), + ('dicarlo.Marques2020_FreemanZiemba2013-texture_modulation_index', approx(0.948, abs=.005)), + ('dicarlo.Marques2020_FreemanZiemba2013-abs_texture_modulation_index', approx(0.958, abs=.005)), + ('dicarlo.Marques2020_FreemanZiemba2013-texture_selectivity', approx(0.940, abs=.005)), + ('dicarlo.Marques2020_FreemanZiemba2013-texture_sparseness', approx(0.935, abs=.005)), + ('dicarlo.Marques2020_FreemanZiemba2013-texture_variance_ratio', approx(0.939, abs=.005)), + ('dicarlo.Marques2020_FreemanZiemba2013-max_texture', approx(0.946, abs=.005)), + ('dicarlo.Marques2020_FreemanZiemba2013-max_noise', approx(0.945, abs=.005)), + ('dicarlo.Marques2020_Ringach2002-circular_variance', approx(0.959, abs=.005)), + ('dicarlo.Marques2020_Ringach2002-or_bandwidth', approx(0.964, abs=.005)), + ('dicarlo.Marques2020_Ringach2002-orth_pref_ratio', approx(0.962, abs=.005)), + ('dicarlo.Marques2020_Ringach2002-or_selective', approx(0.994, abs=.005)), + ('dicarlo.Marques2020_Ringach2002-cv_bandwidth_ratio', approx(0.968, abs=.005)), + ('dicarlo.Marques2020_Ringach2002-opr_cv_diff', approx(0.967, abs=.005)), + ('dicarlo.Marques2020_Ringach2002-modulation_ratio', approx(0.959, abs=.005)), + ('dicarlo.Marques2020_Ringach2002-max_dc', approx(0.968, abs=.005)), + ('dicarlo.Marques2020_Schiller1976-sf_selective', approx(0.963, abs=.005)), + ('dicarlo.Marques2020_Schiller1976-sf_bandwidth', approx(0.933, abs=.005)), +]) +def test_ceilings(benchmark, expected): + standardized_tests.ceilings_test(benchmark, expected) + + +@pytest.mark.private_access +@pytest.mark.memory_intense +@pytest.mark.slow +@pytest.mark.parametrize('benchmark, expected', [ + ('dicarlo.Marques2020_Cavanaugh2002-grating_summation_field', approx(.599, abs=.01)), + ('dicarlo.Marques2020_Cavanaugh2002-surround_diameter', approx(.367, abs=.01)), + ('dicarlo.Marques2020_Cavanaugh2002-surround_suppression_index', approx(.365, abs=.01)), + ('dicarlo.Marques2020_DeValois1982-pref_or', approx(.895, abs=.01)), + ('dicarlo.Marques2020_DeValois1982-peak_sf', approx(.775, abs=.01)), + ('dicarlo.Marques2020_FreemanZiemba2013-texture_modulation_index', approx(.636, abs=.01)), + ('dicarlo.Marques2020_FreemanZiemba2013-abs_texture_modulation_index', approx(.861, abs=.01)), + ('dicarlo.Marques2020_FreemanZiemba2013-texture_selectivity', approx(.646, abs=.01)), + ('dicarlo.Marques2020_FreemanZiemba2013-texture_sparseness', approx(.508, abs=.01)), + ('dicarlo.Marques2020_FreemanZiemba2013-texture_variance_ratio', approx(.827, abs=.01)), + ('dicarlo.Marques2020_FreemanZiemba2013-max_texture', approx(.823, abs=.01)), + ('dicarlo.Marques2020_FreemanZiemba2013-max_noise', approx(.684, abs=.01)), + ('dicarlo.Marques2020_Ringach2002-circular_variance', approx(.830, abs=.01)), + ('dicarlo.Marques2020_Ringach2002-or_bandwidth', approx(.844, abs=.01)), + ('dicarlo.Marques2020_Ringach2002-orth_pref_ratio', approx(.876, abs=.01)), + ('dicarlo.Marques2020_Ringach2002-or_selective', approx(.895, abs=.01)), + ('dicarlo.Marques2020_Ringach2002-cv_bandwidth_ratio', approx(.841, abs=.01)), + ('dicarlo.Marques2020_Ringach2002-opr_cv_diff', approx(.909, abs=.01)), + ('dicarlo.Marques2020_Ringach2002-modulation_ratio', approx(.371, abs=.01)), + ('dicarlo.Marques2020_Ringach2002-max_dc', approx(.904, abs=.01)), + ('dicarlo.Marques2020_Schiller1976-sf_selective', approx(.808, abs=.01)), + ('dicarlo.Marques2020_Schiller1976-sf_bandwidth', approx(.869, abs=.01)), +]) +def test_Marques2020(benchmark, expected): + precomputed_test.run_test_properties( + benchmark=benchmark, + files={'dicarlo.Marques2020_blank': 'alexnet-dicarlo.Marques2020_blank.nc', + 'dicarlo.Marques2020_receptive_field': 'alexnet-dicarlo.Marques2020_receptive_field.nc', + 'dicarlo.Marques2020_orientation': 'alexnet-dicarlo.Marques2020_orientation.nc', + 'dicarlo.Marques2020_spatial_frequency': 'alexnet-dicarlo.Marques2020_spatial_frequency.nc', + 'dicarlo.Marques2020_size': 'alexnet-dicarlo.Marques2020_size.nc', + 'movshon.FreemanZiemba2013_properties': 'alexnet-movshon.FreemanZiemba2013_properties.nc', + }, + expected=expected) + + +@pytest.mark.private_access +@pytest.mark.parametrize('benchmark_identifier', [ + 'dicarlo.Marques2020_Cavanaugh2002-grating_summation_field', + 'dicarlo.Marques2020_Cavanaugh2002-surround_diameter', + 'dicarlo.Marques2020_Cavanaugh2002-surround_suppression_index', + 'dicarlo.Marques2020_DeValois1982-pref_or', + 'dicarlo.Marques2020_DeValois1982-peak_sf', + 'dicarlo.Marques2020_FreemanZiemba2013-texture_sparseness', + 'dicarlo.Marques2020_FreemanZiemba2013-texture_selectivity', + 'dicarlo.Marques2020_FreemanZiemba2013-texture_variance_ratio', + 'dicarlo.Marques2020_FreemanZiemba2013-texture_modulation_index', + 'dicarlo.Marques2020_FreemanZiemba2013-abs_texture_modulation_index', + 'dicarlo.Marques2020_FreemanZiemba2013-max_noise', + 'dicarlo.Marques2020_FreemanZiemba2013-max_texture', + 'dicarlo.Marques2020_Ringach2002-or_bandwidth', + 'dicarlo.Marques2020_Ringach2002-or_selective', + 'dicarlo.Marques2020_Ringach2002-circular_variance', + 'dicarlo.Marques2020_Ringach2002-orth_pref_ratio', + 'dicarlo.Marques2020_Ringach2002-cv_bandwidth_ratio', + 'dicarlo.Marques2020_Ringach2002-opr_cv_diff', + 'dicarlo.Marques2020_Ringach2002-modulation_ratio', + 'dicarlo.Marques2020_Ringach2002-max_dc', + 'dicarlo.Marques2020_Schiller1976-sf_bandwidth', + 'dicarlo.Marques2020_Schiller1976-sf_selective', +]) +def test_repetitions(benchmark_identifier): + num_trials_test.repetitions_test(benchmark_identifier) diff --git a/brainscore_vision/benchmarks/objectnet/__init__.py b/brainscore_vision/benchmarks/objectnet/__init__.py new file mode 100644 index 000000000..a9bec842e --- /dev/null +++ b/brainscore_vision/benchmarks/objectnet/__init__.py @@ -0,0 +1,4 @@ +from brainscore_vision import benchmark_registry +from .benchmark import Objectnet + +benchmark_registry['katz.BarbuMayo2019-top1'] = Objectnet \ No newline at end of file diff --git a/brainscore_vision/benchmarks/objectnet/benchmark.py b/brainscore_vision/benchmarks/objectnet/benchmark.py new file mode 100644 index 000000000..13e29da0a --- /dev/null +++ b/brainscore_vision/benchmarks/objectnet/benchmark.py @@ -0,0 +1,53 @@ +import logging + +from brainscore_core import Score +from brainscore_vision import load_stimulus_set, load_metric +from brainscore_vision.benchmarks import BenchmarkBase +from brainscore_vision.model_interface import BrainModel + +BIBTEX = """@inproceedings{DBLP:conf/nips/BarbuMALWGTK19, + author = {Andrei Barbu and + David Mayo and + Julian Alverio and + William Luo and + Christopher Wang and + Dan Gutfreund and + Josh Tenenbaum and + Boris Katz}, + title = {ObjectNet: {A} large-scale bias-controlled dataset for pushing the + limits of object recognition models}, + booktitle = {NeurIPS 2019}, + pages = {9448--9458}, + year = {2019}, + url = {https://proceedings.neurips.cc/paper/2019/hash/97af07a14cacba681feacf3012730892-Abstract.html}, + }""" + +NUMBER_OF_TRIALS = 10 + +_logger = logging.getLogger(__name__) +LOCAL_STIMULUS_DIRECTORY = '/braintree/data2/active/common/objectnet-stimuli/' + + +class Objectnet(BenchmarkBase): + def __init__(self): + self._stimulus_set = load_stimulus_set('katz.BarbuMayo2019') + self._similarity_metric = load_metric('accuracy') + ceiling = Score(1) + + super(Objectnet, self).__init__(identifier='katz.BarbuMayo2019-top1', version=1, + ceiling_func=lambda: ceiling, + parent='engineering', + bibtex=BIBTEX) + + def __call__(self, candidate): + # The proper `fitting_stimuli` to pass to the candidate would be the imagenet training set. + # For now, since almost all models in our hands were trained with imagenet, we'll just short-cut this + # by telling the candidate to use its pre-trained imagenet weights. + candidate.start_task(BrainModel.Task.label, 'imagenet') + stimulus_set = self._stimulus_set[list(set(self._stimulus_set.columns) - {'synset'})] # do not show label + predictions = candidate.look_at(stimulus_set, number_of_trials=NUMBER_OF_TRIALS) + score = self._similarity_metric( + predictions.sortby('filename'), + self._stimulus_set.sort_values('filename')['synset'].values + ) + return score diff --git a/tests/test_benchmarks/test_objectnet.py b/brainscore_vision/benchmarks/objectnet/test.py similarity index 87% rename from tests/test_benchmarks/test_objectnet.py rename to brainscore_vision/benchmarks/objectnet/test.py index ffc0f1867..628fdc491 100644 --- a/tests/test_benchmarks/test_objectnet.py +++ b/brainscore_vision/benchmarks/objectnet/test.py @@ -2,8 +2,9 @@ import pytest from brainio.assemblies import BehavioralAssembly -from brainscore.benchmarks.objectnet import Objectnet -from brainscore.model_interface import BrainModel +from brainscore_vision.benchmarks.objectnet import Objectnet +from brainscore_vision.model_interface import BrainModel + @pytest.mark.private_access class TestObjectnet: @@ -29,4 +30,4 @@ def look_at(self, stimuli, number_of_trials=1): candidate = GroundTruth() score = benchmark(candidate) - assert score.sel(aggregation='center') == pytest.approx(1) + assert score == pytest.approx(1) diff --git a/brainscore_vision/benchmarks/rajalingham2018/__init__.py b/brainscore_vision/benchmarks/rajalingham2018/__init__.py new file mode 100644 index 000000000..397ed14c1 --- /dev/null +++ b/brainscore_vision/benchmarks/rajalingham2018/__init__.py @@ -0,0 +1,10 @@ +from brainscore_vision import benchmark_registry +from .benchmarks.benchmark import DicarloRajalingham2018I2n + +benchmark_registry['dicarlo.Rajalingham2018-i2n'] = DicarloRajalingham2018I2n + + +# public benchmark +from.benchmarks.public_benchmark import RajalinghamMatchtosamplePublicBenchmark + +benchmark_registry['dicarlo.Rajalingham2018public-i2n'] = RajalinghamMatchtosamplePublicBenchmark diff --git a/brainscore/benchmarks/rajalingham2018.py b/brainscore_vision/benchmarks/rajalingham2018/benchmarks/benchmark.py similarity index 89% rename from brainscore/benchmarks/rajalingham2018.py rename to brainscore_vision/benchmarks/rajalingham2018/benchmarks/benchmark.py index 6c2dff64e..9acad241c 100644 --- a/brainscore/benchmarks/rajalingham2018.py +++ b/brainscore_vision/benchmarks/rajalingham2018/benchmarks/benchmark.py @@ -1,13 +1,12 @@ import numpy as np -import brainscore -from brainscore.benchmarks import BenchmarkBase -from brainscore.benchmarks.screen import place_on_screen -from brainscore.metrics import Score -from brainscore.metrics.image_level_behavior import I2n -from brainscore.metrics.transformations import apply_aggregate -from brainscore.model_interface import BrainModel -from brainscore.utils import LazyLoad +from brainscore_core import Score +from brainscore_vision import load_metric, load_stimulus_set, load_dataset +from brainscore_vision.benchmark_helpers.screen import place_on_screen +from brainscore_vision.benchmarks import BenchmarkBase +from brainscore_vision.metric_helpers.transformations import apply_aggregate +from brainscore_vision.model_interface import BrainModel +from brainscore_vision.utils import LazyLoad BIBTEX = """@article {Rajalingham240614, author = {Rajalingham, Rishi and Issa, Elias B. and Bashivan, Pouya and Kar, Kohitij and Schmidt, Kailyn and DiCarlo, James J.}, @@ -26,7 +25,7 @@ class _DicarloRajalingham2018(BenchmarkBase): def __init__(self, metric, metric_identifier): self._metric = metric - self._fitting_stimuli = brainscore.get_stimulus_set('dicarlo.objectome.public') + self._fitting_stimuli = load_stimulus_set('dicarlo.objectome.public') self._assembly = LazyLoad(lambda: load_assembly('private')) self._visual_degrees = 8 self._number_of_trials = 2 @@ -36,7 +35,7 @@ def __init__(self, metric, metric_identifier): parent='behavior', bibtex=BIBTEX) - def __call__(self, candidate: BrainModel): + def __call__(self, candidate: BrainModel) -> Score: fitting_stimuli = place_on_screen(self._fitting_stimuli, target_visual_degrees=candidate.visual_degrees(), source_visual_degrees=self._visual_degrees) candidate.start_task(BrainModel.Task.probabilities, fitting_stimuli) @@ -66,10 +65,10 @@ def ceil_score(self, score, ceiling): def DicarloRajalingham2018I2n(): - return _DicarloRajalingham2018(metric=I2n(), metric_identifier='i2n') + return _DicarloRajalingham2018(metric=load_metric('i2n'), metric_identifier='i2n') def load_assembly(access='private'): - assembly = brainscore.get_assembly(f'dicarlo.Rajalingham2018.{access}') + assembly = load_dataset(f'dicarlo.Rajalingham2018.{access}') assembly['correct'] = assembly['choice'] == assembly['sample_obj'] return assembly diff --git a/brainscore_vision/benchmarks/rajalingham2018/benchmarks/public_benchmark.py b/brainscore_vision/benchmarks/rajalingham2018/benchmarks/public_benchmark.py new file mode 100644 index 000000000..00e0679fe --- /dev/null +++ b/brainscore_vision/benchmarks/rajalingham2018/benchmarks/public_benchmark.py @@ -0,0 +1,10 @@ +from brainscore_vision import load_metric +from .benchmark import load_assembly as load_rajalingham2018, _DicarloRajalingham2018 + + +class RajalinghamMatchtosamplePublicBenchmark(_DicarloRajalingham2018): + def __init__(self): + super(RajalinghamMatchtosamplePublicBenchmark, self).__init__( + metric=load_metric('i2n'), metric_identifier='i2n') + self._assembly = load_rajalingham2018(access='public') + self._ceiling_func = lambda: self._metric.ceiling(self._assembly, skipna=True) diff --git a/brainscore_vision/benchmarks/rajalingham2018/test.py b/brainscore_vision/benchmarks/rajalingham2018/test.py new file mode 100644 index 000000000..34780c850 --- /dev/null +++ b/brainscore_vision/benchmarks/rajalingham2018/test.py @@ -0,0 +1,160 @@ +import os +from pathlib import Path + +import numpy as np +import pytest +import xarray as xr +from pytest import approx + +from brainio.assemblies import BehavioralAssembly +from brainscore_vision import benchmark_registry, load_benchmark, load_metric +from brainscore_vision.benchmark_helpers import PrecomputedFeatures +from brainscore_vision.benchmark_helpers.test_helper import VisualDegreesTests, NumberOfTrialsTests +from brainscore_vision.benchmarks.rajalingham2018 import DicarloRajalingham2018I2n +from brainscore_vision.benchmarks.rajalingham2018.benchmarks.benchmark import _DicarloRajalingham2018 +from brainscore_vision.data_helpers import s3 +from brainscore_vision.model_helpers.brain_transformation import ProbabilitiesMapping +from brainscore_vision.model_interface import BrainModel + +visual_degrees = VisualDegreesTests() +number_trials = NumberOfTrialsTests() + + +@pytest.mark.parametrize('benchmark', [ + 'dicarlo.Rajalingham2018-i2n', + 'dicarlo.Rajalingham2018public-i2n', +]) +def test_benchmark_registry(benchmark): + assert benchmark in benchmark_registry + + +@pytest.mark.private_access +class TestRajalingham2018: + def test_ceiling(self): + benchmark = DicarloRajalingham2018I2n() + ceiling = benchmark.ceiling + assert ceiling == approx(.479, abs=.0064) + + @pytest.mark.parametrize(['model', 'expected_score'], + [ + ('alexnet', .253), + ('resnet34', .37787), + ('resnet18', .3638), + ]) + def test_precomputed(self, model, expected_score): + benchmark = DicarloRajalingham2018I2n() + probabilities = Path(__file__).parent.parent / 'test_metrics' / f'{model}-probabilities.nc' + probabilities = BehavioralAssembly.from_files(probabilities, + stimulus_set_identifier=benchmark._assembly.stimulus_set.identifier, + stimulus_set=benchmark._assembly.stimulus_set) + candidate = PrecomputedProbabilities(probabilities) + score = benchmark(candidate) + assert score.raw == approx(expected_score, abs=.005) + assert score == approx(expected_score / np.sqrt(.479), abs=.005) + + +class PrecomputedProbabilities(BrainModel): + def __init__(self, probabilities): + self.probabilities = probabilities + + def visual_degrees(self) -> int: + return 8 + + def start_task(self, task: BrainModel.Task, fitting_stimuli): + assert task == BrainModel.Task.probabilities + assert len(fitting_stimuli) == 2160 + + def look_at(self, stimuli, number_of_trials=1): + assert set(self.probabilities['stimulus_id'].values) == set(stimuli['stimulus_id'].values) + image_ids = self.probabilities['stimulus_id'].values.tolist() + probabilities = self.probabilities[[image_ids.index(image_id) for image_id in stimuli['stimulus_id'].values], :] + assert all(probabilities['stimulus_id'].values == stimuli['stimulus_id'].values) + return probabilities + + +def test_Rajalingham2018public(): + benchmark = load_benchmark('dicarlo.Rajalingham2018public-i2n') + # load features + filename = 'CORnetZ-rajalingham2018public.nc' + filepath = Path(__file__).parent / filename + s3.download_file_if_not_exists(filepath, + bucket='brain-score-tests', remote_filepath=f'tests/test_benchmarks/{filename}') + precomputed_features = BehavioralAssembly.from_files( + filepath, + stimulus_set_identifier=benchmark._assembly.stimulus_set.identifier, + stimulus_set=benchmark._assembly.stimulus_set) + precomputed_features = PrecomputedFeatures(precomputed_features, + visual_degrees=8, # doesn't matter, features are already computed + ) + # score + score = benchmark(precomputed_features).raw + assert score == approx(.136923, abs=.005) + + +@pytest.mark.parametrize('benchmark, candidate_degrees, image_id, expected', [ + pytest.param('dicarlo.Rajalingham2018-i2n', 14, '0223bf9e5db0edad21976b16494fe9396a5ef145', + approx(.225023, abs=.0001), marks=[pytest.mark.private_access]), + pytest.param('dicarlo.Rajalingham2018-i2n', 6, '0223bf9e5db0edad21976b16494fe9396a5ef145', + approx(.002244, abs=.0001), marks=[pytest.mark.private_access]), + pytest.param('dicarlo.Rajalingham2018public-i2n', 14, '0020cef91bd626e9fbbabd853494ee444e5c9ecb', + approx(.22486, abs=.0001), marks=[]), + pytest.param('dicarlo.Rajalingham2018public-i2n', 6, '0020cef91bd626e9fbbabd853494ee444e5c9ecb', + approx(.00097, abs=.0001), marks=[]), +]) +def test_amount_gray(benchmark: str, candidate_degrees: int, image_id: str, expected: float): + visual_degrees.amount_gray_test(benchmark, candidate_degrees, image_id, expected) + + +@pytest.mark.private_access +@pytest.mark.parametrize('benchmark_identifier', ['dicarlo.Rajalingham2018-i2n']) +def test_repetitions(benchmark_identifier): + number_trials.repetitions_test(benchmark_identifier) + + +@pytest.mark.private_access +class TestMetricScore: + @pytest.mark.parametrize(['model', 'expected_score'], + [ + ('alexnet', .253), + ('resnet34', .37787), + ('resnet18', .3638), + ]) + def test_model(self, model, expected_score): + class UnceiledBenchmark(_DicarloRajalingham2018): + def __init__(self): + metric = load_metric('i2n') + super(UnceiledBenchmark, self).__init__(metric=metric, metric_identifier='i2n') + + def __call__(self, candidate: BrainModel): + candidate.start_task(BrainModel.Task.probabilities, self._fitting_stimuli) + probabilities = candidate.look_at(self._assembly.stimulus_set) + score = self._metric(probabilities, self._assembly) + return score + + benchmark = UnceiledBenchmark() + # features + feature_responses = xr.load_dataarray(Path(__file__).parent / 'test_resources' / + f'identifier={model},stimuli_identifier=objectome-240.nc') + feature_responses['stimulus_id'] = 'stimulus_path', [os.path.splitext(os.path.basename(path))[0] + for path in feature_responses['stimulus_path'].values] + feature_responses = feature_responses.stack(presentation=['stimulus_path']) + assert len(np.unique(feature_responses['layer'])) == 1 # only penultimate layer + + class PrecomputedFeatures: + def __init__(self, precomputed_features): + self.features = precomputed_features + + def __call__(self, stimuli, layers): + np.testing.assert_array_equal(layers, ['behavioral-layer']) + self_stimulus_ids = self.features['stimulus_id'].values.tolist() + indices = [self_stimulus_ids.index(stimulus_id) for stimulus_id in stimuli['stimulus_id'].values] + features = self.features[{'presentation': indices}] + return features + + # evaluate candidate + transformation = ProbabilitiesMapping(identifier=f'TestI2N.{model}', + activations_model=PrecomputedFeatures(feature_responses), + layer='behavioral-layer') + score = benchmark(transformation) + score = score.sel(aggregation='center') + assert score == approx(expected_score, abs=0.005), f"expected {expected_score}, but got {score}" diff --git a/tests/alexnet-pca200.pkl b/brainscore_vision/benchmarks/rajalingham2018/test_resources/identifier=alexnet,stimuli_identifier=objectome-240.nc similarity index 66% rename from tests/alexnet-pca200.pkl rename to brainscore_vision/benchmarks/rajalingham2018/test_resources/identifier=alexnet,stimuli_identifier=objectome-240.nc index 9eb722a63..cfbf89543 100644 Binary files a/tests/alexnet-pca200.pkl and b/brainscore_vision/benchmarks/rajalingham2018/test_resources/identifier=alexnet,stimuli_identifier=objectome-240.nc differ diff --git a/brainscore_vision/benchmarks/rajalingham2018/test_resources/identifier=resnet18,stimuli_identifier=objectome-240.nc b/brainscore_vision/benchmarks/rajalingham2018/test_resources/identifier=resnet18,stimuli_identifier=objectome-240.nc new file mode 100644 index 000000000..135fb69bb Binary files /dev/null and b/brainscore_vision/benchmarks/rajalingham2018/test_resources/identifier=resnet18,stimuli_identifier=objectome-240.nc differ diff --git a/brainscore_vision/benchmarks/rajalingham2018/test_resources/identifier=resnet34,stimuli_identifier=objectome-240.nc b/brainscore_vision/benchmarks/rajalingham2018/test_resources/identifier=resnet34,stimuli_identifier=objectome-240.nc new file mode 100644 index 000000000..cd68a9e43 Binary files /dev/null and b/brainscore_vision/benchmarks/rajalingham2018/test_resources/identifier=resnet34,stimuli_identifier=objectome-240.nc differ diff --git a/brainscore_vision/benchmarks/rajalingham2020/__init__.py b/brainscore_vision/benchmarks/rajalingham2020/__init__.py new file mode 100644 index 000000000..12e4d219e --- /dev/null +++ b/brainscore_vision/benchmarks/rajalingham2020/__init__.py @@ -0,0 +1,4 @@ +from brainscore_vision import benchmark_registry +from .benchmark import DicarloRajalingham2020ITPLS + +benchmark_registry['dicarlo.Rajalingham2020.IT-pls'] = DicarloRajalingham2020ITPLS diff --git a/brainscore/benchmarks/rajalingham2020.py b/brainscore_vision/benchmarks/rajalingham2020/benchmark.py similarity index 80% rename from brainscore/benchmarks/rajalingham2020.py rename to brainscore_vision/benchmarks/rajalingham2020/benchmark.py index 2011275b2..c64d1d342 100644 --- a/brainscore/benchmarks/rajalingham2020.py +++ b/brainscore_vision/benchmarks/rajalingham2020/benchmark.py @@ -1,11 +1,8 @@ import numpy as np -import brainscore -from brainscore.benchmarks._neural_common import NeuralBenchmark, average_repetition -from brainscore.metrics.ceiling import InternalConsistency -from brainscore.metrics.regression import CrossRegressedCorrelation, pls_regression, pearsonr_correlation -from brainscore.utils import LazyLoad - +from brainscore_vision import load_metric, load_ceiling, load_dataset +from brainscore_vision.benchmark_helpers.neural_common import NeuralBenchmark, average_repetition +from brainscore_vision.utils import LazyLoad VISUAL_DEGREES = 8 NUMBER_OF_TRIALS = 33 @@ -37,15 +34,14 @@ def _DicarloRajalingham2020Region(region, identifier_metric_suffix, similarity_m def DicarloRajalingham2020ITPLS(): + metric = load_metric('pls', crossvalidation_kwargs=dict(stratification_coord=None)) + ceiler = load_ceiling('internal_consistency') return _DicarloRajalingham2020Region('IT', identifier_metric_suffix='pls', - similarity_metric=CrossRegressedCorrelation( - regression=pls_regression(), correlation=pearsonr_correlation(), - crossvalidation_kwargs=dict(stratification_coord=None)), - ceiler=InternalConsistency()) + similarity_metric=metric, ceiler=ceiler) def load_assembly(average_repetitions, region): - assembly = brainscore.get_assembly(name=f'dicarlo.Rajalingham2020') + assembly = load_dataset(identifier=f'dicarlo.Rajalingham2020') assembly = assembly.sel(region=region) assembly['region'] = 'neuroid', [region] * len(assembly['neuroid']) assembly.load() diff --git a/brainscore_vision/benchmarks/rajalingham2020/test.py b/brainscore_vision/benchmarks/rajalingham2020/test.py new file mode 100644 index 000000000..033495dad --- /dev/null +++ b/brainscore_vision/benchmarks/rajalingham2020/test.py @@ -0,0 +1,39 @@ +from pathlib import Path + +import pytest +from pytest import approx + +from brainscore_vision.benchmark_helpers.test_helper import StandardizedTests, PrecomputedTests +from brainscore_vision.data_helpers import s3 + +standardized_tests = StandardizedTests() +precomputed_test = PrecomputedTests() + + +@pytest.mark.parametrize('benchmark, expected', [ + pytest.param('dicarlo.Rajalingham2020.IT-pls', approx(.561013, abs=.001), + marks=[pytest.mark.memory_intense, pytest.mark.slow]), +]) +def test_ceilings(benchmark, expected): + standardized_tests.ceilings_test(benchmark, expected) + + +@pytest.mark.parametrize('benchmark, visual_degrees, expected', [ + pytest.param('dicarlo.Rajalingham2020.IT-pls', 8, approx(.693463, abs=.005), + marks=[pytest.mark.memory_intense, pytest.mark.slow]), +]) +def test_self_regression(benchmark, visual_degrees, expected): + standardized_tests.self_regression_test(benchmark, visual_degrees, expected) + + +@pytest.mark.memory_intense +@pytest.mark.slow +@pytest.mark.parametrize('benchmark, expected', [ + ('dicarlo.Rajalingham2020.IT-pls', approx(.147549, abs=.01)), +]) +def test_Rajalingham2020(benchmark, expected): + filename = 'alexnet-rajalingham2020-features.12.nc' + filepath = Path(__file__).parent / filename + s3.download_file_if_not_exists(local_path=filepath, + bucket='brainio-brainscore', remote_filepath=f'tests/test_benchmarks/{filename}') + precomputed_test.run_test(benchmark=benchmark, precomputed_features_filepath=filepath, expected=expected) diff --git a/brainscore_vision/benchmarks/sanghavi2020/__init__.py b/brainscore_vision/benchmarks/sanghavi2020/__init__.py new file mode 100644 index 000000000..ac78823b5 --- /dev/null +++ b/brainscore_vision/benchmarks/sanghavi2020/__init__.py @@ -0,0 +1,17 @@ +from brainscore_vision import benchmark_registry +from .benchmarks.sanghavi2020_benchmark import DicarloSanghavi2020V4PLS, DicarloSanghavi2020ITPLS + +benchmark_registry['dicarlo.Sanghavi2020.V4-pls'] = DicarloSanghavi2020V4PLS +benchmark_registry['dicarlo.Sanghavi2020.IT-pls'] = DicarloSanghavi2020ITPLS + + +from .benchmarks.sanghavijozwik2020_benchmark import DicarloSanghaviJozwik2020V4PLS, DicarloSanghaviJozwik2020ITPLS + +benchmark_registry['dicarlo.SanghaviJozwik2020.V4-pls'] = DicarloSanghaviJozwik2020V4PLS +benchmark_registry['dicarlo.SanghaviJozwik2020.IT-pls'] = DicarloSanghaviJozwik2020ITPLS + + +from .benchmarks.sanghavimurty2020_benchmark import DicarloSanghaviMurty2020V4PLS, DicarloSanghaviMurty2020ITPLS + +benchmark_registry['dicarlo.SanghaviMurty2020.V4-pls'] = DicarloSanghaviMurty2020V4PLS +benchmark_registry['dicarlo.SanghaviMurty2020.IT-pls'] = DicarloSanghaviMurty2020ITPLS diff --git a/brainscore_vision/benchmarks/sanghavi2020/benchmarks/sanghavi2020_benchmark.py b/brainscore_vision/benchmarks/sanghavi2020/benchmarks/sanghavi2020_benchmark.py new file mode 100644 index 000000000..7a8fc8792 --- /dev/null +++ b/brainscore_vision/benchmarks/sanghavi2020/benchmarks/sanghavi2020_benchmark.py @@ -0,0 +1,44 @@ +import numpy as np + +from brainscore_vision import load_ceiling, load_metric, load_dataset +from brainscore_vision.benchmark_helpers.neural_common import NeuralBenchmark, average_repetition +from brainscore_vision.data.sanghavi2020 import BIBTEX_SANGHAVI +from brainscore_vision.utils import LazyLoad + +VISUAL_DEGREES = 8 +NUMBER_OF_TRIALS = 28 + + +def _DicarloSanghavi2020Region(region): + assembly_repetition = LazyLoad(lambda region=region: load_assembly(average_repetitions=False, region=region)) + assembly = LazyLoad(lambda region=region: load_assembly(average_repetitions=True, region=region)) + metric = load_metric('pls', crossvalidation_kwargs=dict(stratification_coord='object_name')) + ceiler = load_ceiling('internal_consistency') + return NeuralBenchmark(identifier=f'dicarlo.Sanghavi2020.{region}-pls', version=1, + assembly=assembly, similarity_metric=metric, + visual_degrees=VISUAL_DEGREES, number_of_trials=NUMBER_OF_TRIALS, + ceiling_func=lambda: ceiler(assembly_repetition), + parent=region, + bibtex=BIBTEX_SANGHAVI) + + +def DicarloSanghavi2020V4PLS(): + return _DicarloSanghavi2020Region('V4') + + +def DicarloSanghavi2020ITPLS(): + return _DicarloSanghavi2020Region('IT') + + +def load_assembly(average_repetitions, region): + assembly = load_dataset(f'dicarlo.Sanghavi2020') + assembly = assembly.sel(region=region) + assembly['region'] = 'neuroid', [region] * len(assembly['neuroid']) + assembly.load() + assembly = assembly.sel(time_bin_id=0) # 70-170ms + assembly = assembly.squeeze('time_bin') + assert NUMBER_OF_TRIALS == len(np.unique(assembly.coords['repetition'])) + assert VISUAL_DEGREES == assembly.attrs['image_size_degree'] + if average_repetitions: + assembly = average_repetition(assembly) + return assembly diff --git a/brainscore_vision/benchmarks/sanghavi2020/benchmarks/sanghavijozwik2020_benchmark.py b/brainscore_vision/benchmarks/sanghavi2020/benchmarks/sanghavijozwik2020_benchmark.py new file mode 100644 index 000000000..282a6c234 --- /dev/null +++ b/brainscore_vision/benchmarks/sanghavi2020/benchmarks/sanghavijozwik2020_benchmark.py @@ -0,0 +1,44 @@ +import numpy as np + +from brainscore_vision import load_ceiling, load_metric, load_dataset +from brainscore_vision.benchmark_helpers.neural_common import NeuralBenchmark, average_repetition +from brainscore_vision.data.sanghavi2020 import BIBTEX_SANGHAVIJOZWIK +from brainscore_vision.utils import LazyLoad + +VISUAL_DEGREES = 8 +NUMBER_OF_TRIALS = 37 + + +def _DicarloSanghaviJozwik2020Region(region): + assembly_repetition = LazyLoad(lambda region=region: load_assembly(average_repetitions=False, region=region)) + assembly = LazyLoad(lambda region=region: load_assembly(average_repetitions=True, region=region)) + metric = load_metric('pls', crossvalidation_kwargs=dict(stratification_coord=None)) + ceiler = load_ceiling('internal_consistency') + return NeuralBenchmark(identifier=f'dicarlo.SanghaviJozwik2020.{region}-pls', version=1, + assembly=assembly, similarity_metric=metric, + visual_degrees=VISUAL_DEGREES, number_of_trials=NUMBER_OF_TRIALS, + ceiling_func=lambda: ceiler(assembly_repetition), + parent=region, + bibtex=BIBTEX_SANGHAVIJOZWIK) + + +def DicarloSanghaviJozwik2020V4PLS(): + return _DicarloSanghaviJozwik2020Region('V4') + + +def DicarloSanghaviJozwik2020ITPLS(): + return _DicarloSanghaviJozwik2020Region('IT') + + +def load_assembly(average_repetitions, region): + assembly = load_dataset(f'dicarlo.SanghaviJozwik2020') + assembly = assembly.sel(region=region) + assembly['region'] = 'neuroid', [region] * len(assembly['neuroid']) + assembly.load() + assembly = assembly.sel(time_bin_id=0) # 70-170ms + assembly = assembly.squeeze('time_bin') + assert NUMBER_OF_TRIALS == len(np.unique(assembly.coords['repetition'])) + assert VISUAL_DEGREES == assembly.attrs['image_size_degree'] + if average_repetitions: + assembly = average_repetition(assembly) + return assembly diff --git a/brainscore_vision/benchmarks/sanghavi2020/benchmarks/sanghavimurty2020_benchmark.py b/brainscore_vision/benchmarks/sanghavi2020/benchmarks/sanghavimurty2020_benchmark.py new file mode 100644 index 000000000..b7cbf7049 --- /dev/null +++ b/brainscore_vision/benchmarks/sanghavi2020/benchmarks/sanghavimurty2020_benchmark.py @@ -0,0 +1,44 @@ +import numpy as np + +from brainscore_vision import load_metric, load_ceiling, load_dataset +from brainscore_vision.benchmark_helpers.neural_common import NeuralBenchmark, average_repetition +from brainscore_vision.data.sanghavi2020 import BIBTEX_SANGHAVIMURTY +from brainscore_vision.utils import LazyLoad + +VISUAL_DEGREES = 5 +NUMBER_OF_TRIALS = 50 + + +def _DicarloSanghaviMurty2020Region(region): + assembly_repetition = LazyLoad(lambda region=region: load_assembly(average_repetitions=False, region=region)) + assembly = LazyLoad(lambda region=region: load_assembly(average_repetitions=True, region=region)) + metric = load_metric('pls', crossvalidation_kwargs=dict(stratification_coord=None)) + ceiler = load_ceiling('internal_consistency') + return NeuralBenchmark(identifier=f'dicarlo.SanghaviMurty2020.{region}-pls', version=1, + assembly=assembly, similarity_metric=metric, + visual_degrees=VISUAL_DEGREES, number_of_trials=NUMBER_OF_TRIALS, + ceiling_func=lambda: ceiler(assembly_repetition), + parent=region, + bibtex=BIBTEX_SANGHAVIMURTY) + + +def DicarloSanghaviMurty2020V4PLS(): + return _DicarloSanghaviMurty2020Region('V4') + + +def DicarloSanghaviMurty2020ITPLS(): + return _DicarloSanghaviMurty2020Region('IT') + + +def load_assembly(average_repetitions, region): + assembly = load_dataset(f'dicarlo.SanghaviMurty2020') + assembly = assembly.sel(region=region) + assembly['region'] = 'neuroid', [region] * len(assembly['neuroid']) + assembly.load() + assembly = assembly.sel(time_bin_id=0) # 70-170ms + assembly = assembly.squeeze('time_bin') + assert NUMBER_OF_TRIALS == len(np.unique(assembly.coords['repetition'])) + assert VISUAL_DEGREES == assembly.attrs['image_size_degree'] + if average_repetitions: + assembly = average_repetition(assembly) + return assembly diff --git a/brainscore_vision/benchmarks/sanghavi2020/test.py b/brainscore_vision/benchmarks/sanghavi2020/test.py new file mode 100644 index 000000000..77b35df10 --- /dev/null +++ b/brainscore_vision/benchmarks/sanghavi2020/test.py @@ -0,0 +1,83 @@ +from pathlib import Path + +import pytest +from pytest import approx + +from brainscore_vision import benchmark_registry +from brainscore_vision.benchmark_helpers.test_helper import StandardizedTests, PrecomputedTests, NumberOfTrialsTests +from brainscore_vision.data_helpers import s3 + +standardized_tests = StandardizedTests() +precomputed_test = PrecomputedTests() +num_trials_test = NumberOfTrialsTests() + + +@pytest.mark.parametrize('benchmark', [ + 'dicarlo.Sanghavi2020.V4-pls', + 'dicarlo.Sanghavi2020.IT-pls', + 'dicarlo.SanghaviJozwik2020.V4-pls', + 'dicarlo.SanghaviJozwik2020.IT-pls', + 'dicarlo.SanghaviMurty2020.V4-pls', + 'dicarlo.SanghaviMurty2020.IT-pls', +]) +def test_benchmark_registry(benchmark): + assert benchmark in benchmark_registry + + +@pytest.mark.memory_intense +@pytest.mark.private_access +@pytest.mark.parametrize('benchmark, expected', [ + ('dicarlo.Sanghavi2020.V4-pls', approx(.8892049, abs=.001)), + ('dicarlo.Sanghavi2020.IT-pls', approx(.868293, abs=.001)), + ('dicarlo.SanghaviJozwik2020.V4-pls', approx(.9630336, abs=.001)), + ('dicarlo.SanghaviJozwik2020.IT-pls', approx(.860352, abs=.001)), + ('dicarlo.SanghaviMurty2020.V4-pls', approx(.9666086, abs=.001)), + ('dicarlo.SanghaviMurty2020.IT-pls', approx(.875714, abs=.001)), +]) +def test_ceilings(benchmark, expected): + standardized_tests.ceilings_test(benchmark, expected) + + +@pytest.mark.memory_intense +@pytest.mark.private_access +@pytest.mark.parametrize('benchmark, visual_degrees, expected', [ + ('dicarlo.Sanghavi2020.V4-pls', 8, approx(.9727137, abs=.001)), + ('dicarlo.Sanghavi2020.IT-pls', 8, approx(.890062, abs=.001)), + ('dicarlo.SanghaviJozwik2020.V4-pls', 8, approx(.9739177, abs=.001)), + ('dicarlo.SanghaviJozwik2020.IT-pls', 8, approx(.9999779, abs=.001)), + ('dicarlo.SanghaviMurty2020.V4-pls', 5, approx(.978581, abs=.001)), + ('dicarlo.SanghaviMurty2020.IT-pls', 5, approx(.9997532, abs=.001)), +]) +def test_self_regression(benchmark, visual_degrees, expected): + standardized_tests.self_regression_test(benchmark, visual_degrees, expected) + + +@pytest.mark.memory_intense +@pytest.mark.private_access +@pytest.mark.slow +@pytest.mark.parametrize('benchmark, filename, expected', [ + ('dicarlo.Sanghavi2020.V4-pls', 'alexnet-sanghavi2020-features.12.nc', approx(.551135, abs=.015)), + ('dicarlo.Sanghavi2020.IT-pls', 'alexnet-sanghavi2020-features.12.nc', approx(.611347, abs=.015)), + ('dicarlo.SanghaviJozwik2020.V4-pls', 'alexnet-sanghavijozwik2020-features.12.nc', approx(.49235, abs=.005)), + ('dicarlo.SanghaviJozwik2020.IT-pls', 'alexnet-sanghavijozwik2020-features.12.nc', approx(.590543, abs=.005)), + ('dicarlo.SanghaviMurty2020.V4-pls', 'alexnet-sanghavimurty2020-features.12.nc', approx(.357461, abs=.015)), + ('dicarlo.SanghaviMurty2020.IT-pls', 'alexnet-sanghavimurty2020-features.12.nc', approx(.53006, abs=.015)), +]) +def test_model_features(benchmark, filename, expected): + filepath = Path(__file__).parent / filename + s3.download_file_if_not_exists(local_path=filepath, + bucket='brainio-brainscore', remote_filepath=f'tests/test_benchmarks/{filename}') + precomputed_test.run_test(benchmark=benchmark, precomputed_features_filepath=filepath, expected=expected) + + +@pytest.mark.private_access +@pytest.mark.parametrize('benchmark_identifier', [ + 'dicarlo.Sanghavi2020.V4-pls', + 'dicarlo.Sanghavi2020.IT-pls', + 'dicarlo.SanghaviJozwik2020.V4-pls', + 'dicarlo.SanghaviJozwik2020.IT-pls', + 'dicarlo.SanghaviMurty2020.V4-pls', + 'dicarlo.SanghaviMurty2020.IT-pls', +]) +def test_repetitions(benchmark_identifier): + num_trials_test.repetitions_test(benchmark_identifier) diff --git a/packaging/dicarlo/BashivanKar2019/__init__.py b/brainscore_vision/data/__init__.py similarity index 100% rename from packaging/dicarlo/BashivanKar2019/__init__.py rename to brainscore_vision/data/__init__.py diff --git a/packaging/katz/BarbuMayo2019.py b/brainscore_vision/data/barbumayo2019/BarbuMayo2019.py similarity index 100% rename from packaging/katz/BarbuMayo2019.py rename to brainscore_vision/data/barbumayo2019/BarbuMayo2019.py diff --git a/brainscore_vision/data/barbumayo2019/__init__.py b/brainscore_vision/data/barbumayo2019/__init__.py new file mode 100644 index 000000000..923b399de --- /dev/null +++ b/brainscore_vision/data/barbumayo2019/__init__.py @@ -0,0 +1,25 @@ +from brainscore_vision import stimulus_set_registry +from brainscore_vision.data_helpers.s3 import load_stimulus_set_from_s3 + +BIBTEX = """@inproceedings{NEURIPS2019_97af07a1, + author = {Barbu, Andrei and Mayo, David and Alverio, Julian and Luo, William and Wang, Christopher and Gutfreund, Dan and Tenenbaum, Josh and Katz, Boris}, + booktitle = {Advances in Neural Information Processing Systems}, + editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett}, + pages = {}, + publisher = {Curran Associates, Inc.}, + title = {ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models}, + url = {https://proceedings.neurips.cc/paper/2019/file/97af07a14cacba681feacf3012730892-Paper.pdf}, + volume = {32}, + year = {2019} +}""" + + +# stimulus set +stimulus_set_registry['katz.BarbuMayo2019'] = lambda: load_stimulus_set_from_s3( + identifier="katz.BarbuMayo2019", + bucket="brainio-brainscore", + csv_sha1="e4d8888ccb6beca28636e6698e7beb130e278e12", + zip_sha1="1365eb2a7231516806127a7d2a908343a7ac9464", + csv_version_id="z2_yFBqD9rtNv3S5l3BFN_Ijk6Vj_oGz", + zip_version_id="SS3vLphOlh23KoNpYBEC89h3aCrsOARM") + diff --git a/brainscore_vision/data/barbumayo2019/test.py b/brainscore_vision/data/barbumayo2019/test.py new file mode 100644 index 000000000..fbf9a0c67 --- /dev/null +++ b/brainscore_vision/data/barbumayo2019/test.py @@ -0,0 +1,9 @@ +import pytest +from brainscore_vision import load_stimulus_set + + +@pytest.mark.private_access +def test_Katz_BarbuMayo2019(): + stimulus_set = load_stimulus_set('katz.BarbuMayo2019') + assert len(stimulus_set) == 17261 + assert len(set(stimulus_set['synset'])) == 104 diff --git a/brainscore_vision/data/bashivankar2019/__init__.py b/brainscore_vision/data/bashivankar2019/__init__.py new file mode 100644 index 000000000..58930eaf4 --- /dev/null +++ b/brainscore_vision/data/bashivankar2019/__init__.py @@ -0,0 +1,52 @@ +from brainio.assemblies import NeuronRecordingAssembly +from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set +from brainscore_vision.data_helpers.s3 import load_assembly_from_s3, load_stimulus_set_from_s3 + +BIBTEX = """@article{bashivan2019neural, + title={Neural population control via deep image synthesis}, + author={Bashivan, Pouya and Kar, Kohitij and DiCarlo, James J}, + journal={Science}, + volume={364}, + number={6439}, + pages={eaav9436}, + year={2019}, + publisher={American Association for the Advancement of Science} +}""" + +# assemblies +data_registry['dicarlo.BashivanKar2019.naturalistic'] = lambda: load_assembly_from_s3( + identifier="dicarlo.BashivanKar2019.naturalistic", + version_id="jDlQnpuhORCtzITHfOFcJiogR7jg8ihJ", + sha1="1ec2f32ef800f0c6e15879d883be1d55b51b8b67", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.BashivanKar2019.naturalistic'), +) + +data_registry['dicarlo.BashivanKar2019.synthetic'] = lambda: load_assembly_from_s3( + identifier="dicarlo.BashivanKar2019.synthetic", + version_id="30CNHSBYS_HN5tEuM1aa5hDFlFKq6gbI", + sha1="f687c8d26f8943dc379dbcbe94d3feb148400c6b", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.BashivanKar2019.synthetic'), +) + +# stimulus sets +# naturalistic +stimulus_set_registry['dicarlo.BashivanKar2019.naturalistic'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.BashivanKar2019.naturalistic", + bucket="brainio-brainscore", + csv_sha1="48ef84282552b8796142ffe7d0d2c632f8ef061a", + zip_sha1="d7b71b431cf23d435395205f1e38036a9e10acca", + csv_version_id="DVRPJT0fKdeRTu6FIjOWClzB9kyyg63R", + zip_version_id="ZxlD_lHE23VzgSCnYypZAHRzO7oblr4h") + +# synthetic +stimulus_set_registry['dicarlo.BashivanKar2019.synthetic'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.BashivanKar2019.synthetic", + bucket="brainio-brainscore", + csv_sha1="81da195e9b2a128b228fc4867e23ae6b21bd7abd", + zip_sha1="e2de33f25c5c19bcfb400055c1db399d553487e5", + csv_version_id="tV0YP8QZ5DahblYrItWsURtUxwMnPXOV", + zip_version_id="5YI9FrlUvtVMJETKmThsLJV2IOQlfzvm") diff --git a/packaging/dicarlo/BashivanKar2019/2020-08-17_npc_v4_data.h5.png b/brainscore_vision/data/bashivankar2019/data_packaging/2020-08-17_npc_v4_data.h5.png similarity index 100% rename from packaging/dicarlo/BashivanKar2019/2020-08-17_npc_v4_data.h5.png rename to brainscore_vision/data/bashivankar2019/data_packaging/2020-08-17_npc_v4_data.h5.png diff --git a/packaging/dicarlo/BashivanKar2019/requirements.txt b/brainscore_vision/data/bashivankar2019/data_packaging/requirements.txt similarity index 100% rename from packaging/dicarlo/BashivanKar2019/requirements.txt rename to brainscore_vision/data/bashivankar2019/data_packaging/requirements.txt diff --git a/packaging/dicarlo/BashivanKar2019/synthetic.py b/brainscore_vision/data/bashivankar2019/data_packaging/synthetic.py similarity index 97% rename from packaging/dicarlo/BashivanKar2019/synthetic.py rename to brainscore_vision/data/bashivankar2019/data_packaging/synthetic.py index 2c30ad4a0..a13b7f296 100644 --- a/packaging/dicarlo/BashivanKar2019/synthetic.py +++ b/brainscore_vision/data/bashivankar2019/data_packaging/synthetic.py @@ -132,7 +132,7 @@ def main(): stimuli_synth.identifier = "dicarlo.BashivanKar2019.synthetic" _logger.debug('Packaging naturalistic stimuli') - package_stimulus_set(stimuli_nat, stimulus_set_identifier=stimuli_nat.identifier, bucket_name='brainio.dicarlo') + package_stimulus_set(stimuli_nat, stimulus_set_identifier=stimuli_nat.identifier, bucket_name='brainio-brainscore') _logger.debug('Packaging naturalistic assembly') responses_nat_concat = xr.concat(responses_nat_d.values(), dim="neuroid") @@ -141,11 +141,11 @@ def main(): responses_nat_concat, assembly_identifier="dicarlo.BashivanKar2019.naturalistic", stimulus_set_identifier=stimuli_nat.identifier, - bucket_name='brainio.dicarlo' + bucket_name='brainio-dicarlo' ) _logger.debug('Packaging synthetic stimuli') - package_stimulus_set(stimuli_synth, stimulus_set_identifier=stimuli_synth.identifier, bucket_name='brainio.dicarlo') + package_stimulus_set(stimuli_synth, stimulus_set_identifier=stimuli_synth.identifier, bucket_name='brainio-dicarlo') _logger.debug('Packaging synthetic assembly') responses_synth_concat = xr.concat(responses_synth_d.values(), dim="presentation") @@ -154,7 +154,7 @@ def main(): responses_synth_concat, assembly_identifier="dicarlo.BashivanKar2019.synthetic", stimulus_set_identifier=stimuli_synth.identifier, - bucket_name='brainio.dicarlo' + bucket_name='brainio-dicarlo' ) diff --git a/brainscore_vision/data/bashivankar2019/test.py b/brainscore_vision/data/bashivankar2019/test.py new file mode 100644 index 000000000..3184d1118 --- /dev/null +++ b/brainscore_vision/data/bashivankar2019/test.py @@ -0,0 +1,22 @@ +import brainscore_vision +import brainio +import pytest +import numpy as np + + +@pytest.mark.parametrize('assembly_identifier', [ + pytest.param('dicarlo.BashivanKar2019.naturalistic', marks=[pytest.mark.private_access]), + pytest.param('dicarlo.BashivanKar2019.synthetic', marks=[pytest.mark.private_access]), +]) +def test_existence(assembly_identifier): + assert brainscore_vision.load_dataset(assembly_identifier) is not None + + +@pytest.mark.parametrize('assembly,shape,nans', [ + pytest.param('dicarlo.BashivanKar2019.naturalistic', (24320, 233, 1), 309760, marks=[pytest.mark.private_access]), + pytest.param('dicarlo.BashivanKar2019.synthetic', (21360, 233, 1), 4319940, marks=[pytest.mark.private_access]), +]) +def test_assembly_shape(assembly, shape, nans): + assy = brainio.get_assembly(assembly) + assert assy.shape == shape + assert np.count_nonzero(np.isnan(assy)) == nans diff --git a/brainscore_vision/data/cadena2017/__init__.py b/brainscore_vision/data/cadena2017/__init__.py new file mode 100644 index 000000000..2f0adec83 --- /dev/null +++ b/brainscore_vision/data/cadena2017/__init__.py @@ -0,0 +1,40 @@ +import logging + +from brainio.assemblies import NeuronRecordingAssembly +from brainscore_vision import data_registry, load_stimulus_set, stimulus_set_registry +from brainscore_vision.data_helpers.s3 import load_assembly_from_s3, load_stimulus_set_from_s3 + +_logger = logging.getLogger(__name__) + +BIBTEX = """@article {Cadena201764, + author = {Cadena, Santiago A. and Denfield, George H. and Walker, Edgar Y. and Gatys, Leon A. and Tolias, Andreas S. and Bethge, Matthias and Ecker, Alexander S.}, + title = {Deep convolutional models improve predictions of macaque V1 responses to natural images}, + elocation-id = {201764}, + year = {2017}, + doi = {10.1101/201764}, + publisher = {Cold Spring Harbor Laboratory}, + abstract = {Despite great efforts over several decades, our best models of primary visual cortex (V1) still predict neural responses quite poorly when probed with natural stimuli, highlighting our limited understanding of the nonlinear computations in V1. At the same time, recent advances in machine learning have shown that deep neural networks can learn highly nonlinear functions for visual information processing. Two approaches based on deep learning have recently been successfully applied to neural data: transfer learning for predicting neural activity in higher areas of the primate ventral stream and data-driven models to predict retina and V1 neural activity of mice. However, so far there exists no comparison between the two approaches and neither of them has been used to model the early primate visual system. Here, we test the ability of both approaches to predict neural responses to natural images in V1 of awake monkeys. We found that both deep learning approaches outperformed classical linear-nonlinear and wavelet-based feature representations building on existing V1 encoding theories. On our dataset, transfer learning and data-driven models performed similarly, while the data-driven model employed a much simpler architecture. Thus, multi-layer CNNs set the new state of the art for predicting neural responses to natural images in primate V1. Having such good predictive in-silico models opens the door for quantitative studies of yet unknown nonlinear computations in V1 without being limited by the available experimental time.}, + URL = {https://www.biorxiv.org/content/early/2017/10/11/201764}, + eprint = {https://www.biorxiv.org/content/early/2017/10/11/201764.full.pdf}, + journal = {bioRxiv} +} +""" + +# assembly +data_registry['tolias.Cadena2017'] = lambda: load_assembly_from_s3( + identifier="tolias.Cadena2017", + version_id="94KHymrNxUzoF6q56DYWg6n0vCdkLUEU", + sha1="69bcaaa9370dceb0027beaa06235ef418c3d7063", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('tolias.Cadena2017'), +) + +# stimulus set +stimulus_set_registry['tolias.Cadena2017'] = lambda: load_stimulus_set_from_s3( + identifier="tolias.Cadena2017", + bucket="brainio-brainscore", + csv_sha1="f55b174cc4540e5612cfba5e695324328064b051", + zip_sha1="88cc2ce3ef5e197ffd1477144a2e6a68d424ef6c", + csv_version_id="qdO206S_7GBQUHUgrMFvZw7wGcJM0phU", + zip_version_id="G7kqboBHCS33fA_MYWD0TfXTWQjW7rgk") diff --git a/packaging/notebooks/2018-08-07_tolias_v1.ipynb b/brainscore_vision/data/cadena2017/data_packaging/2018-08-07_tolias_v1.ipynb similarity index 100% rename from packaging/notebooks/2018-08-07_tolias_v1.ipynb rename to brainscore_vision/data/cadena2017/data_packaging/2018-08-07_tolias_v1.ipynb diff --git a/packaging/tolias/__init__.py b/brainscore_vision/data/cadena2017/data_packaging/analysis.py similarity index 100% rename from packaging/tolias/__init__.py rename to brainscore_vision/data/cadena2017/data_packaging/analysis.py diff --git a/brainscore_vision/data/cadena2017/test.py b/brainscore_vision/data/cadena2017/test.py new file mode 100644 index 000000000..d18091244 --- /dev/null +++ b/brainscore_vision/data/cadena2017/test.py @@ -0,0 +1,29 @@ +import pytest + +from brainscore_vision import load_dataset, load_stimulus_set +from brainscore_vision.benchmark_helpers import check_standard_format + + +@pytest.mark.private_access +def test_existence(): + assert load_dataset('tolias.Cadena2017') is not None + + +@pytest.mark.private_access +def test_format(): + assembly = load_dataset('tolias.Cadena2017') + check_standard_format(assembly) + + +@pytest.mark.private_access +def test_num_items(): + assembly = load_dataset('tolias.Cadena2017') + assert assembly.attrs['stimulus_set_identifier'] == 'tolias.Cadena2017' + assert len(assembly['presentation']) == 29000 + assert len(assembly['neuroid']) == 166 + + +@pytest.mark.private_access +def test_stimulus_set(): + stimulus_set = load_stimulus_set('tolias.Cadena2017') + assert len(stimulus_set) == 7249 diff --git a/brainscore_vision/data/cichy2019/__init__.py b/brainscore_vision/data/cichy2019/__init__.py new file mode 100644 index 000000000..9a0a21a4f --- /dev/null +++ b/brainscore_vision/data/cichy2019/__init__.py @@ -0,0 +1,38 @@ +from brainio.assemblies import DataAssembly +from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set +from brainscore_vision.data_helpers.s3 import load_assembly_from_s3, load_stimulus_set_from_s3 + +BIBTEX = """@article{CICHY201912, +title = {The spatiotemporal neural dynamics underlying perceived similarity for real-world objects}, +journal = {NeuroImage}, +volume = {194}, +pages = {12-24}, +year = {2019}, +issn = {1053-8119}, +doi = {https://doi.org/10.1016/j.neuroimage.2019.03.031}, +url = {https://www.sciencedirect.com/science/article/pii/S1053811919302083}, +author = {Radoslaw M. Cichy and Nikolaus Kriegeskorte and Kamila M. Jozwik and Jasper J.F. {van den Bosch} and Ian Charest}, +keywords = {fMRI, MEG, Object recognition, Perceived similarity, Visual perception}, +abstract = {The degree to which we perceive real-world objects as similar or dissimilar structures our perception and guides categorization behavior. Here, we investigated the neural representations enabling perceived similarity using behavioral judgments, fMRI and MEG. As different object dimensions co-occur and partly correlate, to understand the relationship between perceived similarity and brain activity it is necessary to assess the unique role of multiple object dimensions. We thus behaviorally assessed perceived object similarity in relation to shape, function, color and background. We then used representational similarity analyses to relate these behavioral judgments to brain activity. We observed a link between each object dimension and representations in visual cortex. These representations emerged rapidly within 200 ms of stimulus onset. Assessing the unique role of each object dimension revealed partly overlapping and distributed representations: while color-related representations distinctly preceded shape-related representations both in the processing hierarchy of the ventral visual pathway and in time, several dimensions were linked to high-level ventral visual cortex. Further analysis singled out the shape dimension as neither fully accounted for by supra-category membership, nor a deep neural network trained on object categorization. Together our results comprehensively characterize the relationship between perceived similarity of key object dimensions and neural activity.} +}""" + + +# assembly +data_registry['aru.Cichy2019'] = lambda: load_assembly_from_s3( + identifier="aru.Cichy2019", + version_id="83yFgsx5rc6pgpra4UiMxDDvgaV6ytU8", + sha1="701e63be62b642082d476244d0d91d510b3ff05d", + bucket="brainio-brainscore", + cls=DataAssembly, + stimulus_set_loader=lambda: load_stimulus_set('aru.Cichy2019'), +) + + +# stimulus set +stimulus_set_registry['aru.Cichy2019'] = lambda: load_stimulus_set_from_s3( + identifier="aru.Cichy2019", + bucket="brainio-brainscore", + csv_sha1="281c4d9d0dd91a2916674638098fe94afb87d29a", + zip_sha1="d2166dd9c2720cb24bc520f5041e6830779c0240", + csv_version_id="ZtjaClAgoeJPA24WVCsd2IywsML5_pBc", + zip_version_id="UmONlzEKwa7MaLWIKpWNONe9QSXM8NI6") diff --git a/brainscore_vision/data/cichy2019/test.py b/brainscore_vision/data/cichy2019/test.py new file mode 100644 index 000000000..4b86f7360 --- /dev/null +++ b/brainscore_vision/data/cichy2019/test.py @@ -0,0 +1,9 @@ +import pytest +from brainscore_vision import load_dataset + + +@pytest.mark.parametrize('assembly_identifier', [ + pytest.param('aru.Cichy2019', marks=[pytest.mark.private_access]), +]) +def test_existence(assembly_identifier): + assert load_dataset(assembly_identifier) is not None diff --git a/brainscore_vision/data/david2004/__init__.py b/brainscore_vision/data/david2004/__init__.py new file mode 100644 index 000000000..8b07e8e4d --- /dev/null +++ b/brainscore_vision/data/david2004/__init__.py @@ -0,0 +1,34 @@ +from brainio.assemblies import NeuronRecordingAssembly + +from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set +from brainscore_vision.data_helpers.s3 import load_assembly_from_s3, load_stimulus_set_from_s3 + +BIBTEX = """@article{david2004evaluation, + title={Evaluation of different measures of functional connectivity using a neural mass model}, + author={David, Olivier and Cosmelli, Diego and Friston, Karl J}, + journal={Neuroimage}, + volume={21}, + number={2}, + pages={659--673}, + year={2004}, + publisher={Elsevier} +}""" + +# assembly +data_registry['gallant.David2004'] = lambda: load_assembly_from_s3( + identifier="gallant.David2004", + version_id="8getDVrrr1iT0DA385T8ZdSzCcuM3_m0", + sha1="d2ed9834c054da2333f5d894285c9841a1f27313", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('gallant.David2004'), +) + +# stimulus set +stimulus_set_registry['gallant.David2004'] = lambda: load_stimulus_set_from_s3( + identifier="gallant.David2004", + bucket="brainio-brainscore", + csv_sha1="8ec76338b998cadcdf1e57edd2dd992e2ab2355b", + zip_sha1="0200421d66a0613946d39cab64c00b561160016e", + csv_version_id="0Ks2opc6t_IpZdB.kylVCbYgXV6ADB4O", + zip_version_id="hFTILhFSGqL95wp1li1aBzzbqCyuAlb_") diff --git a/packaging/notebooks/2018-05-10_gallant_data.ipynb b/brainscore_vision/data/david2004/data_packaging/2018-05-10_gallant_data.ipynb similarity index 100% rename from packaging/notebooks/2018-05-10_gallant_data.ipynb rename to brainscore_vision/data/david2004/data_packaging/2018-05-10_gallant_data.ipynb diff --git a/packaging/notebooks/2018-05-23_gallant_data.ipynb b/brainscore_vision/data/david2004/data_packaging/2018-05-23_gallant_data.ipynb similarity index 100% rename from packaging/notebooks/2018-05-23_gallant_data.ipynb rename to brainscore_vision/data/david2004/data_packaging/2018-05-23_gallant_data.ipynb diff --git a/packaging/notebooks/2018-06-05_gallant_data.ipynb b/brainscore_vision/data/david2004/data_packaging/2018-06-05_gallant_data.ipynb similarity index 100% rename from packaging/notebooks/2018-06-05_gallant_data.ipynb rename to brainscore_vision/data/david2004/data_packaging/2018-06-05_gallant_data.ipynb diff --git a/packaging/gallant/__init__.py b/brainscore_vision/data/david2004/data_packaging/__init__.py similarity index 100% rename from packaging/gallant/__init__.py rename to brainscore_vision/data/david2004/data_packaging/__init__.py diff --git a/packaging/gallant/convertGallant.m b/brainscore_vision/data/david2004/data_packaging/convertGallant.m similarity index 100% rename from packaging/gallant/convertGallant.m rename to brainscore_vision/data/david2004/data_packaging/convertGallant.m diff --git a/packaging/gallant/convertGallantV1Aligned.m b/brainscore_vision/data/david2004/data_packaging/convertGallantV1Aligned.m similarity index 100% rename from packaging/gallant/convertGallantV1Aligned.m rename to brainscore_vision/data/david2004/data_packaging/convertGallantV1Aligned.m diff --git a/packaging/gallant/lib/DataHash_20160618/DataHash.m b/brainscore_vision/data/david2004/data_packaging/lib/DataHash_20160618/DataHash.m similarity index 100% rename from packaging/gallant/lib/DataHash_20160618/DataHash.m rename to brainscore_vision/data/david2004/data_packaging/lib/DataHash_20160618/DataHash.m diff --git a/packaging/gallant/lib/DataHash_20160618/license.txt b/brainscore_vision/data/david2004/data_packaging/lib/DataHash_20160618/license.txt similarity index 100% rename from packaging/gallant/lib/DataHash_20160618/license.txt rename to brainscore_vision/data/david2004/data_packaging/lib/DataHash_20160618/license.txt diff --git a/packaging/gallant/lib/GetMD5/GetMD5.c b/brainscore_vision/data/david2004/data_packaging/lib/GetMD5/GetMD5.c similarity index 100% rename from packaging/gallant/lib/GetMD5/GetMD5.c rename to brainscore_vision/data/david2004/data_packaging/lib/GetMD5/GetMD5.c diff --git a/packaging/gallant/lib/GetMD5/GetMD5.m b/brainscore_vision/data/david2004/data_packaging/lib/GetMD5/GetMD5.m similarity index 100% rename from packaging/gallant/lib/GetMD5/GetMD5.m rename to brainscore_vision/data/david2004/data_packaging/lib/GetMD5/GetMD5.m diff --git a/packaging/gallant/lib/GetMD5/GetMD5.mexw64 b/brainscore_vision/data/david2004/data_packaging/lib/GetMD5/GetMD5.mexw64 similarity index 100% rename from packaging/gallant/lib/GetMD5/GetMD5.mexw64 rename to brainscore_vision/data/david2004/data_packaging/lib/GetMD5/GetMD5.mexw64 diff --git a/packaging/gallant/lib/GetMD5/GetMD5_helper.m b/brainscore_vision/data/david2004/data_packaging/lib/GetMD5/GetMD5_helper.m similarity index 100% rename from packaging/gallant/lib/GetMD5/GetMD5_helper.m rename to brainscore_vision/data/david2004/data_packaging/lib/GetMD5/GetMD5_helper.m diff --git a/packaging/gallant/lib/GetMD5/InstallMex.m b/brainscore_vision/data/david2004/data_packaging/lib/GetMD5/InstallMex.m similarity index 100% rename from packaging/gallant/lib/GetMD5/InstallMex.m rename to brainscore_vision/data/david2004/data_packaging/lib/GetMD5/InstallMex.m diff --git a/packaging/gallant/lib/GetMD5/license.txt b/brainscore_vision/data/david2004/data_packaging/lib/GetMD5/license.txt similarity index 100% rename from packaging/gallant/lib/GetMD5/license.txt rename to brainscore_vision/data/david2004/data_packaging/lib/GetMD5/license.txt diff --git a/packaging/gallant/lib/GetMD5/uTest_GetMD5.m b/brainscore_vision/data/david2004/data_packaging/lib/GetMD5/uTest_GetMD5.m similarity index 100% rename from packaging/gallant/lib/GetMD5/uTest_GetMD5.m rename to brainscore_vision/data/david2004/data_packaging/lib/GetMD5/uTest_GetMD5.m diff --git a/packaging/gallant/lib/glob/glob.m b/brainscore_vision/data/david2004/data_packaging/lib/glob/glob.m similarity index 100% rename from packaging/gallant/lib/glob/glob.m rename to brainscore_vision/data/david2004/data_packaging/lib/glob/glob.m diff --git a/packaging/gallant/lib/glob/license.txt b/brainscore_vision/data/david2004/data_packaging/lib/glob/license.txt similarity index 100% rename from packaging/gallant/lib/glob/license.txt rename to brainscore_vision/data/david2004/data_packaging/lib/glob/license.txt diff --git a/packaging/gallant/xr_align_debug.py b/brainscore_vision/data/david2004/data_packaging/xr_align_debug.py similarity index 100% rename from packaging/gallant/xr_align_debug.py rename to brainscore_vision/data/david2004/data_packaging/xr_align_debug.py diff --git a/brainscore_vision/data/david2004/test.py b/brainscore_vision/data/david2004/test.py new file mode 100644 index 000000000..2bfffd026 --- /dev/null +++ b/brainscore_vision/data/david2004/test.py @@ -0,0 +1,9 @@ +import pytest +from brainscore_vision import load_dataset + + +@pytest.mark.parametrize('assembly_identifier', [ + pytest.param('gallant.David2004', marks=[pytest.mark.private_access]), +]) +def test_existence(assembly_identifier): + assert load_dataset(assembly_identifier) is not None diff --git a/brainscore_vision/data/deng2009/__init__.py b/brainscore_vision/data/deng2009/__init__.py new file mode 100644 index 000000000..b3218db37 --- /dev/null +++ b/brainscore_vision/data/deng2009/__init__.py @@ -0,0 +1,22 @@ +from brainscore_vision import stimulus_set_registry +from brainscore_vision.data_helpers.s3 import load_stimulus_set_from_s3 + +BIBTEX = """@INPROCEEDINGS{5206848, + author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Kai Li and Li Fei-Fei}, + booktitle={2009 IEEE Conference on Computer Vision and Pattern Recognition}, + title={ImageNet: A large-scale hierarchical image database}, + year={2009}, + volume={}, + number={}, + pages={248-255}, + doi={10.1109/CVPR.2009.5206848}}""" + + +# stimulus set +stimulus_set_registry['fei-fei.Deng2009'] = lambda: load_stimulus_set_from_s3( + identifier="fei-fei.Deng2009", + bucket="brainio-brainscore", + csv_sha1="ff79dcf6b0d115e6e8aa8d0fbba3af11dc649e57", + zip_sha1="78172d752d8216a00833cfa34be67c8532ad7330", + csv_version_id="6cwRpiWNvf1iiYeg6jr444c8NIE4GTee", + zip_version_id="UX_CZSIoOrsFtWAK0j4vYdsprniIhgJB") diff --git a/packaging/fei-fei/deng2009imagenet.py b/brainscore_vision/data/deng2009/deng2009imagenet.py similarity index 100% rename from packaging/fei-fei/deng2009imagenet.py rename to brainscore_vision/data/deng2009/deng2009imagenet.py diff --git a/brainscore_vision/data/deng2009/test.py b/brainscore_vision/data/deng2009/test.py new file mode 100644 index 000000000..ccaf50a93 --- /dev/null +++ b/brainscore_vision/data/deng2009/test.py @@ -0,0 +1,9 @@ +import pytest +from brainscore_vision import load_stimulus_set + + +@pytest.mark.private_access +def test_feifei_Deng2009(): + stimulus_set = load_stimulus_set('fei-fei.Deng2009') + assert len(stimulus_set) == 50_000 + assert len(set(stimulus_set['label'])) == 1_000 diff --git a/brainscore_vision/data/freemanziemba2013/__init__.py b/brainscore_vision/data/freemanziemba2013/__init__.py new file mode 100644 index 000000000..95b374cf1 --- /dev/null +++ b/brainscore_vision/data/freemanziemba2013/__init__.py @@ -0,0 +1,133 @@ +from brainio.assemblies import NeuronRecordingAssembly + +from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set +from brainscore_vision.data_helpers.s3 import load_stimulus_set_from_s3, load_assembly_from_s3 + +BIBTEX = """@article{freeman2013functional, + title={A functional and perceptual signature of the second visual area in primates}, + author={Freeman, Jeremy and Ziemba, Corey M and Heeger, David J and Simoncelli, Eero P and Movshon, J Anthony}, + journal={Nature neuroscience}, + volume={16}, + number={7}, + pages={974--981}, + year={2013}, + publisher={Nature Publishing Group} +}""" + +# assembly: movshon.FreemanZiemba2013.public +data_registry['movshon.FreemanZiemba2013.public'] = lambda: load_assembly_from_s3( + identifier="movshon.FreemanZiemba2013.public", + version_id=None, # "Ff3BssTYdRYSXv2hO8ByirYIxsbW1__s", + sha1="761c08f796db4e342555cdb60eef23a4f19ead43", + bucket="brainio.contrib", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('movshon.FreemanZiemba2013.aperture-public'), +) + +# assembly: movshon.FreemanZiemba2013.private +data_registry['movshon.FreemanZiemba2013.private'] = lambda: load_assembly_from_s3( + identifier="movshon.FreemanZiemba2013.private", + version_id=None, # "3t5ehf.WHJWKX2vUuESVI3PY7ZIUDBkY", + sha1="63f636fa2e2b51b47a676768a69b06ce95efdd8f", + bucket="brainio.contrib", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('movshon.FreemanZiemba2013.aperture-private'), +) + +# stimulus set: movshon.FreemanZiemba2013.aperture-public +stimulus_set_registry['movshon.FreemanZiemba2013.aperture-public'] = lambda: load_stimulus_set_from_s3( + identifier="movshon.FreemanZiemba2013.aperture-public", + bucket="brainio.contrib", + csv_sha1="194c9b301a4e3e9cff02481d4c19b39331d52660", + zip_sha1="ad3c6c237491485c863acd2f4d2f219f737e424c", + csv_version_id=None, # "eAzVlM2C0Fv_9EfFZw7uuljFvG0d9sCw", + zip_version_id=None, # "DcP1CnztWfy9a4kIoQIzEdrmYfiFQxAZ" +) + +# stimulus set: movshon.FreemanZiemba2013.aperture-private +stimulus_set_registry['movshon.FreemanZiemba2013.aperture-private'] = lambda: load_stimulus_set_from_s3( + identifier="movshon.FreemanZiemba2013.aperture-private", + bucket="brainio.contrib", + csv_sha1="65bee31483dd743ae2a19c6af03b9abe5f4d5a41", + zip_sha1="0015c94e01d037994cdde1b2e3d169ab99f380dc", + csv_version_id=None, # "ch8CiI4n2AsQUGO88tHqzrXy9NpQaEO_", + zip_version_id=None, # "_EkMn3M3c_Yz8QyL6IweWSsillKxC7HI" +) + +### secondary assemblies and stimulus sets + +# assembly: movshon.FreemanZiemba2013 +data_registry['movshon.FreemanZiemba2013'] = lambda: load_assembly_from_s3( + identifier="movshon.FreemanZiemba2013", + version_id="knfzP5wWG3BRWgD0PGy1CXCadExElV0f", + sha1="f03f1630f0ab1e2dbd51816b47fbf2916876134e", + bucket="brainio.contriib", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('movshon.FreemanZiemba2013.aperture'), +) + +# assembly: movshon.FreemanZiemba2013.noaperture +data_registry['movshon.FreemanZiemba2013.noaperture'] = lambda: load_assembly_from_s3( + identifier="movshon.FreemanZiemba2013.noaperture", + version_id="3Icsm_HjYeVfzfvsaUJ5qdUT94EaXa5A", + sha1="6176fd435ab840d284c2c426742c8211622739b5", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('movshon.FreemanZiemba2013'), +) + +# assembly: movshon.FreemanZiemba2013.noaperture.public +data_registry['movshon.FreemanZiemba2013.noaperture.public'] = lambda: load_assembly_from_s3( + identifier="movshon.FreemanZiemba2013.noaperture.public", + version_id="ggkwDaFLsKesxL0MZY7fMpHuw3gwaEjs", + sha1="68dd9e1da7207dc2ae17dbbb1adf628e922d15fa", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('movshon.FreemanZiemba2013-public'), +) + +# assembly: movshon.FreemanZiemba2013.noaperture.private +data_registry['movshon.FreemanZiemba2013.noaperture.private'] = lambda: load_assembly_from_s3( + identifier="movshon.FreemanZiemba2013.noaperture.private", + version_id="SBtg_vxBS_7VsFOy5lBs_.6K_pQnyM40", + sha1="9ffa772ce61754f6f5b7b391436680ebc25bb8dd", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('movshon.FreemanZiemba2013-private'), +) + +# stimulus set: movshon.FreemanZiemba2013 +stimulus_set_registry['movshon.FreemanZiemba2013'] = lambda: load_stimulus_set_from_s3( + identifier="movshon.FreemanZiemba2013", + bucket="brainio-brainscore", + csv_sha1="de0e65a25c7de4c1740f48ac9b1ff513dcfa5caf", + zip_sha1="bdfc3ba2d878d6aeaa842f9de6abeae50922f2f2", + csv_version_id="DbcMe82xZez8vTPdeCF.BV9dkW0vLICP", + zip_version_id="nGleGCtI3K4_GiqH1ZB30i38YrfaNa9Q") + +# stimulus set: movshon.FreemanZiemba2013.public +stimulus_set_registry['movshon.FreemanZiemba2013.public'] = lambda: load_stimulus_set_from_s3( + identifier="movshon.FreemanZiemba2013-public", + bucket="brainio-brainscore", + csv_sha1="b4fee824f361fa0b78d7247ed4192b04cd675d4f", + zip_sha1="e14d691db081ace829f76bb24dfc055a4fa2eaf9", + csv_version_id="uUEhRWa1r3UqlishT48F35opJhGW1bLY", + zip_version_id="qkNdZrXfYvItVlx1x9mMJgkIyJfXFm8j") + +# stimulus set: movshon.FreemanZiemba2013.private +stimulus_set_registry['movshon.FreemanZiemba2013.private'] = lambda: load_stimulus_set_from_s3( + identifier="movshon.FreemanZiemba2013-private", + bucket="brainio-brainscore", + csv_sha1="91bab3340410ff5171490a337c5931545f29da82", + zip_sha1="c2adb4c0f2f0fbbc6006a879234740131ed2dcbb", + csv_version_id="WSssn5zVuXVSmtywgMUOMosIzU5RAxX9", + zip_version_id="K8yB0pGfrsTbfFFMMPRwy_XY7wvOXjmQ") + +# stimulus set: movshon.FreemanZiemba2013.aperture +stimulus_set_registry['movshon.FreemanZiemba2013.aperture'] = lambda: load_stimulus_set_from_s3( + identifier="movshon.FreemanZiemba2013.aperture", + bucket="brainio-brainscore", + csv_sha1="4205eca54974da46accba7812fce25c1b6df58e0", + zip_sha1="ab07880a0770bd73f68bcd5fd34e6cd945ee17fc", + csv_version_id="peK1zN9yHKI9uEwolD.Wgxk6YRA8X1xi", + zip_version_id="eCuC1NINJJyQIHVBz.Mzb1m111rNLe6m") diff --git a/packaging/notebooks/2018-10-05_movshon.ipynb b/brainscore_vision/data/freemanziemba2013/data_packaging/2018-10-05_movshon.ipynb similarity index 100% rename from packaging/notebooks/2018-10-05_movshon.ipynb rename to brainscore_vision/data/freemanziemba2013/data_packaging/2018-10-05_movshon.ipynb diff --git a/packaging/notebooks/2020-02-21_movshon_aperture.ipynb b/brainscore_vision/data/freemanziemba2013/data_packaging/2020-02-21_movshon_aperture.ipynb similarity index 100% rename from packaging/notebooks/2020-02-21_movshon_aperture.ipynb rename to brainscore_vision/data/freemanziemba2013/data_packaging/2020-02-21_movshon_aperture.ipynb diff --git a/packaging/notebooks/2020-02-26_movshon_aperture_test.ipynb b/brainscore_vision/data/freemanziemba2013/data_packaging/2020-02-26_movshon_aperture_test.ipynb similarity index 100% rename from packaging/notebooks/2020-02-26_movshon_aperture_test.ipynb rename to brainscore_vision/data/freemanziemba2013/data_packaging/2020-02-26_movshon_aperture_test.ipynb diff --git a/packaging/movshon/aperture_correct.py b/brainscore_vision/data/freemanziemba2013/data_packaging/aperture_correct.py similarity index 100% rename from packaging/movshon/aperture_correct.py rename to brainscore_vision/data/freemanziemba2013/data_packaging/aperture_correct.py diff --git a/brainscore_vision/data/freemanziemba2013/data_packaging/data_packaging.py b/brainscore_vision/data/freemanziemba2013/data_packaging/data_packaging.py new file mode 100644 index 000000000..fa4082c65 --- /dev/null +++ b/brainscore_vision/data/freemanziemba2013/data_packaging/data_packaging.py @@ -0,0 +1,57 @@ +import numpy as np +from numpy.random.mtrand import RandomState +from sklearn.model_selection import StratifiedShuffleSplit + +from brainio_collection.fetch import fetch_assembly, get_assembly +from brainio_contrib.packaging import package_data_assembly, package_stimulus_set + + +def adapt_stimulus_set(assembly, name_suffix): + stimulus_set_name = f"{assembly.stimulus_set.name}-{name_suffix}" + assembly.attrs['stimulus_set'] = assembly.stimulus_set[ + assembly.stimulus_set['image_id'].isin(assembly['image_id'].values)] + assembly.stimulus_set.name = stimulus_set_name + assembly.attrs['stimulus_set_name'] = stimulus_set_name + + +def load_assembly(assembly_name): + assembly = get_assembly(assembly_name) + + if not hasattr(assembly.stimulus_set, 'name'): + assembly.stimulus_set.name = assembly.stimulus_set_name + + stimulus_set_degrees = {'dicarlo.hvm': 8, 'movshon.FreemanZiemba2013': 4} + if assembly.stimulus_set.name in stimulus_set_degrees: + assembly.stimulus_set['degrees'] = stimulus_set_degrees[assembly.stimulus_set.name] + return assembly + + +def package_Movshon_datasets(name): + assembly = load_assembly(name) + assembly.load() + base_assembly = assembly + _, unique_indices = np.unique(base_assembly['image_id'].values, return_index=True) + unique_indices = np.sort(unique_indices) # preserve order + image_ids = base_assembly['image_id'].values[unique_indices] + stratification_values = base_assembly['texture_type'].values[unique_indices] + rng = RandomState(seed=12) + splitter = StratifiedShuffleSplit(n_splits=1, train_size=.3, test_size=None, random_state=rng) + split = next(splitter.split(np.zeros(len(image_ids)), stratification_values)) + access_indices = {assembly_type: image_indices + for assembly_type, image_indices in zip(['public', 'private'], split)} + for access in ['public', 'private']: + indices = access_indices[access] + subset_image_ids = image_ids[indices] + assembly = base_assembly[ + {'presentation': [image_id in subset_image_ids for image_id in base_assembly['image_id'].values]}] + adapt_stimulus_set(assembly, access) + package_stimulus_set(assembly.attrs['stimulus_set'], stimulus_set_name=assembly.attrs['stimulus_set_name']) + del assembly.attrs['stimulus_set'] + package_data_assembly(assembly, f"{name}.{access}", stimulus_set_name=assembly.attrs['stimulus_set_name']) + + # not really sure if this is necessary + return assembly + + +if __name__ == '__main__': + package_Movshon_datasets(name='movshon.FreemanZiemba2013') diff --git a/packaging/movshon/movshon.py b/brainscore_vision/data/freemanziemba2013/data_packaging/movshon.py similarity index 96% rename from packaging/movshon/movshon.py rename to brainscore_vision/data/freemanziemba2013/data_packaging/movshon.py index 9ccf2e32e..a6b119e37 100644 --- a/packaging/movshon/movshon.py +++ b/brainscore_vision/data/freemanziemba2013/data_packaging/movshon.py @@ -3,16 +3,16 @@ import zipfile from glob import glob -import brainscore +import brainscore_vision import h5py import numpy as np import pandas as pd import xarray as xr -from brainscore.knownfile import KnownFile as kf -from brainscore.lookup import pwdb -from brainscore.assemblies import AssemblyModel, AssemblyStoreMap, AssemblyStoreModel -from brainscore.stimuli import ImageModel, AttributeModel, ImageMetaModel, StimulusSetModel, ImageStoreModel, \ +from brainscore_vision.knownfile import KnownFile as kf +from brainscore_vision.lookup import pwdb +from brainscore_vision.assemblies import AssemblyModel, AssemblyStoreMap, AssemblyStoreModel +from brainscore_vision.stimuli import ImageModel, AttributeModel, ImageMetaModel, StimulusSetModel, ImageStoreModel, \ StimulusSetImageMap, ImageStoreMap # from FreemanZiemba2013_V1V2data_readme.m @@ -87,7 +87,7 @@ def load_responses(response_file, stimuli_directory): sha1s.append(im_kf.sha1) assembly["image_id"] = ("presentation", sha1s) - return brainscore.assemblies.NeuronRecordingAssembly(assembly) + return brainscore_vision.assemblies.NeuronRecordingAssembly(assembly) def write_netcdf(assembly, target_netcdf_file): diff --git a/brainscore_vision/data/freemanziemba2013/test.py b/brainscore_vision/data/freemanziemba2013/test.py new file mode 100644 index 000000000..bee043098 --- /dev/null +++ b/brainscore_vision/data/freemanziemba2013/test.py @@ -0,0 +1,96 @@ +import pytest +import numpy as np +from pathlib import Path +from PIL import Image +from pytest import approx + +from brainscore_vision.benchmarks.freemanziemba2013.benchmarks.benchmark import load_assembly +from brainscore_vision import load_dataset +from brainscore_vision.benchmark_helpers import check_standard_format + + +@pytest.mark.parametrize('assembly_identifier', [ + pytest.param('movshon.FreemanZiemba2013', marks=[pytest.mark.private_access, pytest.mark.memory_intense]), + pytest.param('movshon.FreemanZiemba2013.public', marks=[pytest.mark.memory_intense]), + pytest.param('movshon.FreemanZiemba2013.private', marks=[pytest.mark.private_access, pytest.mark.memory_intense]), +]) +def test_existence(assembly_identifier): + assert load_dataset(assembly_identifier) is not None + + +@pytest.mark.memory_intense +@pytest.mark.private_access +class TestAssembly: + def test_V1(self): + assembly = load_assembly(region='V1', average_repetitions=True) + check_standard_format(assembly) + assert assembly.attrs['stimulus_set_identifier'] == 'movshon.FreemanZiemba2013.aperture-private' + assert set(assembly['region'].values) == {'V1'} + assert len(assembly['presentation']) == 315 + assert len(assembly['neuroid']) == 102 + + def test_V2(self): + assembly = load_assembly(region='V2', average_repetitions=True) + check_standard_format(assembly) + assert assembly.attrs['stimulus_set_identifier'] == 'movshon.FreemanZiemba2013.aperture-private' + assert set(assembly['region'].values) == {'V2'} + assert len(assembly['presentation']) == 315 + assert len(assembly['neuroid']) == 103 + + +class TestFreemanZiemba: + @pytest.mark.parametrize('identifier', [ + pytest.param('movshon.FreemanZiemba2013.public', marks=[]), + pytest.param('movshon.FreemanZiemba2013.private', marks=[pytest.mark.private_access]), + ]) + def test_v1_v2_alignment(self, identifier): + assembly = load_dataset(identifier) + v1 = assembly[{'neuroid': [region == 'V1' for region in assembly['region'].values]}] + v2 = assembly[{'neuroid': [region == 'V2' for region in assembly['region'].values]}] + assert len(v1['presentation']) == len(v2['presentation']) + assert set(v1['stimulus_id'].values) == set(v2['stimulus_id'].values) + + @pytest.mark.parametrize('identifier', [ + pytest.param('movshon.FreemanZiemba2013.public', marks=[]), + pytest.param('movshon.FreemanZiemba2013.private', marks=[pytest.mark.private_access]), + ]) + def test_num_neurons(self, identifier): + assembly = load_dataset(identifier) + assert len(assembly['neuroid']) == 205 + v1 = assembly[{'neuroid': [region == 'V1' for region in assembly['region'].values]}] + assert len(v1['neuroid']) == 102 + v2 = assembly[{'neuroid': [region == 'V2' for region in assembly['region'].values]}] + assert len(v2['neuroid']) == 103 + + @pytest.mark.parametrize('identifier', [ + pytest.param('movshon.FreemanZiemba2013.public', marks=[]), + pytest.param('movshon.FreemanZiemba2013.private', marks=[pytest.mark.private_access]), + ]) + def test_nonzero(self, identifier): + assembly = load_dataset(identifier) + nonzero = np.count_nonzero(assembly) + assert nonzero > 0 + + @pytest.mark.parametrize('identifier, image_id, expected_amount_gray, ratio_gray', [ + pytest.param('movshon.FreemanZiemba2013.public', '21041db1f26c142812a66277c2957fb3e2070916', + 31756, .3101171875, marks=[]), + pytest.param('movshon.FreemanZiemba2013.private', 'bfd26c127f8ba028cc95cdc95f00c45c8884b365', + 31585, .308447265625, marks=[pytest.mark.private_access]), + ]) + def test_aperture(self, identifier, image_id, expected_amount_gray, ratio_gray): + """ test a random image for the correct amount of gray pixels """ + assembly = load_dataset(identifier) + stimulus_set = assembly.stimulus_set + image_path = Path(stimulus_set.get_stimulus(image_id)) + assert image_path.is_file() + # count number of gray pixels in image + image = Image.open(image_path) + image = np.array(image) + amount_gray = 0 + for index in np.ndindex(image.shape[:2]): + color = image[index] + gray = [128, 128, 128] + if (color == gray).all(): + amount_gray += 1 + assert amount_gray / image.size == approx(ratio_gray, abs=.0001) + assert amount_gray == expected_amount_gray diff --git a/brainscore_vision/data/geirhos2021/__init__.py b/brainscore_vision/data/geirhos2021/__init__.py new file mode 100644 index 000000000..6b45be81a --- /dev/null +++ b/brainscore_vision/data/geirhos2021/__init__.py @@ -0,0 +1,360 @@ +from brainio.assemblies import BehavioralAssembly + +from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set +from brainscore_vision.data_helpers.s3 import load_assembly_from_s3, load_stimulus_set_from_s3 + +BIBTEX = """@article{geirhos2021partial, + title={Partial success in closing the gap between human and machine vision}, + author={Geirhos, Robert and Narayanappa, Kantharaju and Mitzkus, Benjamin and Thieringer, Tizian and Bethge, Matthias and Wichmann, Felix A and Brendel, Wieland}, + journal={Advances in Neural Information Processing Systems}, + volume={34}, + pages={23885--23899}, + year={2021} +}""" + +DATASETS = ['colour', 'contrast', 'cue-conflict', 'edge', + 'eidolonI', 'eidolonII', 'eidolonIII', + 'false-colour', 'high-pass', 'low-pass', 'phase-scrambling', 'power-equalisation', + 'rotation', 'silhouette', 'sketch', 'stylized', 'uniform-noise'] + + +# 'colour' +# assembly +data_registry['brendel.Geirhos2021_colour'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_colour', + version_id="RDjCFAFt_J5mMwFBN9Ifo0OyNPKlToqf", + sha1="258862d82467614e45cc1e488a5ac909eb6e122d", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_colour'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_colour'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_colour', + bucket="brainio-brainscore", + csv_sha1="9c97c155fd6039a95978be89eb604c6894c5fa16", + zip_sha1="d166f1d3dc3d00c4f51a489e6fcf96dbbe778d2c", + csv_version_id="1ZaFYwHPBkDOrgdrwGHYqMfJJBCWei21", + zip_version_id="X62ivk_UuHgh7Sd7VwDxgnB8tWPK06gt") + +# 'contrast' +# assembly +data_registry['brendel.Geirhos2021_contrast'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_contrast', + version_id="JMyi4jtdirYkdIQEIZRJ2024NQ1McQXc", + sha1="1e114c987dc035ccca43781ff8cee9689acd3c3f", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_contrast'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_contrast'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_contrast', + bucket="brainio-brainscore", + csv_sha1="f6ef69a2e8937e1d0d83c8a21b325b4273494cb5", + zip_sha1="ebeeef2f9c6a7282e20ef2026dc77eefa026957b", + csv_version_id="OsunVV3gPXsgB3lp_kUirl6t.Qy9Xzu9", + zip_version_id="Gezu9w0T8jL0BHIACTy3VBoR2nleOTK0") + +# 'cue-conflict' +# assembly +data_registry['brendel.Geirhos2021_cue-conflict'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_cue-conflict', + version_id="WCGAQqS86x2z5CXGlegLPHu3XsH.tEk3", + sha1="cc214e3595d34565b13963c5f56049769a39a5c9", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_cue-conflict'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_cue-conflict'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_cue-conflict', + bucket="brainio-brainscore", + csv_sha1="8d3ae89d8870fb7d7c5d5ff387085b1f0116e2b7", + zip_sha1="7e601186b181102939cd0b43a4e8a3ca95c18259", + csv_version_id="fNwBooRV2kLU9HenaHhr7RSV_9RRt.DF", + zip_version_id="Zox6nvmZim8DBEx2uLmu6KQBIvv5wLis") + +# 'edge' +# assembly +data_registry['brendel.Geirhos2021_edge'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_edge', + version_id="gTmCANJ797ZNcfKzecCnwlnUYcgl1.xa", + sha1="ab1dc9e188e248da07215b375eb3dbcc58fde7fb", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_edge'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_edge'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_edge', + bucket="brainio-brainscore", + csv_sha1="fb57005ecb80e4e37b01e084f1f176fe7f59ff7f", + zip_sha1="6c1199d90836a26be454aa799864a63c5efacaa1", + csv_version_id="3zqzUnK1hHlYh9tRNMHpuAP9bnDLtxTp", + zip_version_id="6OQpha9nx0GBrLYaYCJ89S2vKaLALKeY") + +# 'eidolonI' +# assembly +data_registry['brendel.Geirhos2021_eidolonI'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_eidolonI', + version_id="rVmuuTXAi6XqVI3hdyhe7WqSJNFOsFA4", + sha1="0f01f351ae19eafc2cb5e504d98e5cd01b4c07b4", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_eidolonI'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_eidolonI'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_eidolonI', + bucket="brainio-brainscore", + csv_sha1="049adbed36fed52c609d38d5230e6084336df6b9", + zip_sha1="abaa45a225628dd66e424ec7d8e2b10a0c88bc0d", + csv_version_id="q4QRehm2cIAi0E1erEQCBfyxMbqbNItt", + zip_version_id="3AGu.Q8jBbJh5iSiH5Mpq9QZDu3itfiI") + +# 'eidolonII' +# assembly +data_registry['brendel.Geirhos2021_eidolonII'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_eidolonII', + version_id="yOXPugtdV6fx0_BAEY4NeYlzwfCChIaR", + sha1="499eea0f0c0817d02b5b97d2ebab89bc4c40a153", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_eidolonII'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_eidolonII'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_eidolonII', + bucket="brainio-brainscore", + csv_sha1="1806ada128c260ab54db570d2e73aea71d679754", + zip_sha1="2654ba55291f8ab972f18b36565f9ead80a45339", + csv_version_id="4kXnYMC1rTdUZ8jy1ga29dTbhWuVuRCb", + zip_version_id="okHEb16F_gxXl9EgMRZpUy87.zziTA90") + +# 'eidolonIII' +# assembly +data_registry['brendel.Geirhos2021_eidolonIII'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_eidolonIII', + version_id="zkbswpduS0z51_OrUF_XA7UaKb_4bYkN", + sha1="e7c9a49e729f8666f8aedc6e47c746fbbe2ebe36", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_eidolonIII'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_eidolonIII'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_eidolonIII', + bucket="brainio-brainscore", + csv_sha1="ba0173b315f02df16d418dc3ff1df7dc498b4893", + zip_sha1="d0304c0c0024d0f493ea9c0c47ae0221da391016", + csv_version_id="JVa2jLVBG9_EoFe3dV8kQmBAvXQxb4AA", + zip_version_id="SJRD2F8CsfHwrpppz.FGLClBAAhOV2wT") + +# 'false-colour' +# assembly +data_registry['brendel.Geirhos2021_false-colour'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_false-colour', + version_id="GmYh1SBFUiEBLzkf1eRvA_GdXs1kCqtO", + sha1="4dc072264651c81575564ba4818a12b8e8039c65", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_false-colour'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_false-colour'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_false-colour', + bucket="brainio-brainscore", + csv_sha1="8a09a7af8ec44339bcae5500ae5900d9c4309042", + zip_sha1="ec0ba347fc14d0c0587d38bfa96e4ab5d2f7979a", + csv_version_id="jOj2gCxQDPkWk8.CcpILCHAdyxDVgGog", + zip_version_id="H61i.jgHll7KOh8CJUKPMI3hyp.0C9o1") + +# 'high-pass' +# assembly +data_registry['brendel.Geirhos2021_high-pass'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_high-pass', + version_id="FB_uDoX3nMZR1Qb6vcR4Y.PdEKKa1eKo", + sha1="5df45c69127758f1ba3391671c521711050e3b4d", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_high-pass'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_high-pass'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_high-pass', + bucket="brainio-brainscore", + csv_sha1="ddf523dcf43398cc15894c7b51c436d526e6c992", + zip_sha1="12322bb17270a5dde206314fcdc125c4bb235e3b", + csv_version_id="J9v3TDGWx0dqdNYh2Vz8BctY2EpQ7ndd", + zip_version_id="L.6uThKdjIE3U0WxH.7IehrjiBLdbkQ6") + +# 'low-pass' +# assembly +data_registry['brendel.Geirhos2021_low-pass'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_low-pass', + version_id="qUX5BRBV1rWK75m4QKH5aL1CVKs8x7u7", + sha1="75ab628d9e6d0d634290567b1cb261d7f8dc61e2", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_low-pass'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_low-pass'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_low-pass', + bucket="brainio-brainscore", + csv_sha1="1264f9be407c7d428cf3d62a7bb1b1bb45a821bc", + zip_sha1="ad087676e04e51adadea7c7c5c1fa28e4dd6360c", + csv_version_id="yI1wYlc4y3DvtXGFb81saMOHgDP_t75r", + zip_version_id="WUd5KNK.lV05AObHabGXQYKdrJ3F9YL4") + +# 'phase-scrambling' +# assembly +data_registry['brendel.Geirhos2021_phase-scrambling'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_phase-scrambling', + version_id="Dg4Je4uHy4pYe7_CIN27fWgeVNonHlZP", + sha1="4124f9f5b86fb6ed82c98197d292eef50b608aba", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_phase-scrambling'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_phase-scrambling'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_phase-scrambling', + bucket="brainio-brainscore", + csv_sha1="0cc87f7ac42c2266f98d3a08783f7173499ec2fc", + zip_sha1="462e77ab9533072b7036118f1f697e8c9bf30ae4", + csv_version_id="VWItIr6fMdSSP1yfNRSkMDZ4uuxMxrgb", + zip_version_id="xguK90WVWgOHUmgYFcoe13qo8BJ6z6MW") + +# 'power-equalisation' +# assembly +data_registry['brendel.Geirhos2021_power-equalisation'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_power-equalisation', + version_id="XKaUgVEpoBaTNL0qpFFRNyTq3WRcPQJY", + sha1="0aba1b50a7e0802d76c41d332a121a3f96ef4f7d", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_power-equalisation'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_power-equalisation'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_power-equalisation', + bucket="brainio-brainscore", + csv_sha1="743935476b1fe4b7bd4d01a4eed24cd9ed5b3a22", + zip_sha1="a0d5307525bccf8da8f3d293e7e324b9b20248c6", + csv_version_id="cJ82v9s6U1CF9ZlQwlYy3f8cUqP3oA3W", + zip_version_id="gzdXAFvWg777viFJhkE_9h1lxSda.SRH") + +# 'rotation' +# assembly +data_registry['brendel.Geirhos2021_rotation'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_rotation', + version_id="zkIC2EQlN5oDwhtLDbnl_THME74mtFfc", + sha1="e51a5c3bc95ade159e71aa602232063730bcd57b", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_rotation'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_rotation'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_rotation', + bucket="brainio-brainscore", + csv_sha1="2577831e9ead905669613fa17cb2651d0c48a455", + zip_sha1="8f5d9cb217807e96ace61337144e429d0d4ba04c", + csv_version_id="gWUg4SS.OtrtbV14RXemhMgjQeGw5M64", + zip_version_id="VoHUYi_3g0awBpC2k8WV.2lCuxBQvdoT") + +# 'silhouette' +# assembly +data_registry['brendel.Geirhos2021_silhouette'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_silhouette', + version_id="xlpXfo7iy8_nssKR4OwM4199E6wCA0nA", + sha1="7dc94991465fe8009244e0d6fb8283419a1f9885", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_silhouette'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_silhouette'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_silhouette', + bucket="brainio-brainscore", + csv_sha1="fb57005ecb80e4e37b01e084f1f176fe7f59ff7f", + zip_sha1="36c8a481f7876a2af2ad4fe80890b302fe3ae91e", + csv_version_id="7zg2Ex3miPaGvktK7kjIfB5e9DNhVS6P", + zip_version_id="RFDrz2.PR7A5DfjeQpmejx3R4orIZM4q") + +# 'sketch' +# assembly +data_registry['brendel.Geirhos2021_sketch'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_sketch', + version_id="w7_3YJYTmVMYWbNY6SQvzRvYb45mRqte", + sha1="6709850864cea16d99a29fb31ae3c4a489983562", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_sketch'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_sketch'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_sketch', + bucket="brainio-brainscore", + csv_sha1="a5741b2f7bd08541a0dbefd7fb2d6a3845ca800b", + zip_sha1="8e8712f08a5ad3655ea2bd8cd675db8cdf65129a", + csv_version_id="Z.EJlugAmsHoy1kwR9TSI6uCmldgn747", + zip_version_id="8pw3EY5H4oLJVhygvrUcAQc05lj4JllD") + +# 'stylized' +# assembly +data_registry['brendel.Geirhos2021_stylized'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_stylized', + version_id="0ESwWyMc5wODloonsSkzURncuYYJMxql", + sha1="dcf15f292e787a88e1e0f271e6b2838d6bdadfd3", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_stylized'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_stylized'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_stylized', + bucket="brainio-brainscore", + csv_sha1="2265a540dee6915150bf7c61143eaf788f603866", + zip_sha1="75d273e8de643b0d814fbe60cd237c31ebe19c44", + csv_version_id="r5ESVPnObUmVhhHbHCIXs4gKSvT2Ezu5", + zip_version_id="p4lxCUU.4WG.xEPGx3gsILhjhcapZiBS") + +# 'uniform-noise' +# assembly +data_registry['brendel.Geirhos2021_uniform-noise'] = lambda: load_assembly_from_s3( + identifier='brendel.Geirhos2021_uniform-noise', + version_id="89aJ7jLIB68QCNCEcQsBjchKbv.31YWH", + sha1="f5e8b2636738f978c71591b8df6f8a21a66b72d1", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('brendel.Geirhos2021_uniform-noise'), +) + +# stimulus set +stimulus_set_registry['brendel.Geirhos2021_uniform-noise'] = lambda: load_stimulus_set_from_s3( + identifier='brendel.Geirhos2021_uniform-noise', + bucket="brainio-brainscore", + csv_sha1="89b62a6af878974d388278ed0e23e8ed1c2fd855", + zip_sha1="ff4566542d65056028660293e2409b532e887714", + csv_version_id="8edSpj0OAnnBdG3IgRNVTff6x3v6i7aS", + zip_version_id="XRXqDJpQHZLZKERvNOevdDsqRESnNuSU") + diff --git a/brainscore_vision/data/geirhos2021/creating_geirhos_ids.ipynb b/brainscore_vision/data/geirhos2021/creating_geirhos_ids.ipynb new file mode 100644 index 000000000..918a7150f --- /dev/null +++ b/brainscore_vision/data/geirhos2021/creating_geirhos_ids.ipynb @@ -0,0 +1,468 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "outputs": [], + "source": [ + "DATASETS = ['colour', 'contrast', 'cue-conflict', 'edge',\n", + " 'eidolonI', 'eidolonII', 'eidolonIII',\n", + " 'false-colour', 'high-pass', 'low-pass', 'phase-scrambling', 'power-equalisation',\n", + " 'rotation', 'silhouette', 'sketch', 'stylized', 'uniform-noise']" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "lookup_csv = pd.read_csv('lookup.csv')" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": " identifier lookup_type class \\\n0 dicarlo.hvm stimulus_set StimulusSet \n1 dicarlo.hvm stimulus_set NaN \n2 gallant.David2004 stimulus_set StimulusSet \n3 gallant.David2004 stimulus_set NaN \n4 tolias.Cadena2017 stimulus_set StimulusSet \n.. ... ... ... \n164 brendel.Geirhos2021_sketch assembly BehavioralAssembly \n165 brendel.Geirhos2021_stylized assembly BehavioralAssembly \n166 brendel.Geirhos2021_uniform-noise assembly BehavioralAssembly \n167 katz.BarbuMayo2019 stimulus_set StimulusSet \n168 katz.BarbuMayo2019 stimulus_set NaN \n\n location_type location \\\n0 S3 https://brainio-brainscore.s3.amazonaws.com/image... \n1 S3 https://brainio-brainscore.s3.amazonaws.com/image... \n2 S3 https://brainio-brainscore.s3.amazonaws.com/image... \n3 S3 https://brainio-brainscore.s3.amazonaws.com/image... \n4 S3 https://brainio-brainscore.s3.amazonaws.com/image... \n.. ... ... \n164 S3 https://brainio-brainscore.s3.amazonaws.com/as... \n165 S3 https://brainio-brainscore.s3.amazonaws.com/as... \n166 S3 https://brainio-brainscore.s3.amazonaws.com/as... \n167 S3 https://brainio-brainscore.s3.amazonaws.com/im... \n168 S3 https://brainio-brainscore.s3.amazonaws.com/im... \n\n sha1 \\\n0 a56f55205904d5fb5ead4d0dc7bfad5cc4083b94 \n1 6fd5080deccfb061699909ffcb86a26209516811 \n2 8ec76338b998cadcdf1e57edd2dd992e2ab2355b \n3 0200421d66a0613946d39cab64c00b561160016e \n4 f55b174cc4540e5612cfba5e695324328064b051 \n.. ... \n164 6709850864cea16d99a29fb31ae3c4a489983562 \n165 dcf15f292e787a88e1e0f271e6b2838d6bdadfd3 \n166 f5e8b2636738f978c71591b8df6f8a21a66b72d1 \n167 e4d8888ccb6beca28636e6698e7beb130e278e12 \n168 1365eb2a7231516806127a7d2a908343a7ac9464 \n\n stimulus_set_identifier lookup_source \n0 NaN brainio_brainscore \n1 NaN brainio_brainscore \n2 NaN brainio_brainscore \n3 NaN brainio_brainscore \n4 NaN brainio_brainscore \n.. ... ... \n164 brendel.Geirhos2021_sketch brainio_brainscore \n165 brendel.Geirhos2021_stylized brainio_brainscore \n166 brendel.Geirhos2021_uniform-noise brainio_brainscore \n167 NaN brainio_brainscore \n168 NaN brainio_brainscore \n\n[169 rows x 8 columns]", + "text/html": "
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identifierlookup_typeclasslocation_typelocationsha1stimulus_set_identifierlookup_source
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3gallant.David2004stimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/image...0200421d66a0613946d39cab64c00b561160016eNaNbrainio_brainscore
4tolias.Cadena2017stimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/image...f55b174cc4540e5612cfba5e695324328064b051NaNbrainio_brainscore
...........................
164brendel.Geirhos2021_sketchassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...6709850864cea16d99a29fb31ae3c4a489983562brendel.Geirhos2021_sketchbrainio_brainscore
165brendel.Geirhos2021_stylizedassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...dcf15f292e787a88e1e0f271e6b2838d6bdadfd3brendel.Geirhos2021_stylizedbrainio_brainscore
166brendel.Geirhos2021_uniform-noiseassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...f5e8b2636738f978c71591b8df6f8a21a66b72d1brendel.Geirhos2021_uniform-noisebrainio_brainscore
167katz.BarbuMayo2019stimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...e4d8888ccb6beca28636e6698e7beb130e278e12NaNbrainio_brainscore
168katz.BarbuMayo2019stimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...1365eb2a7231516806127a7d2a908343a7ac9464NaNbrainio_brainscore
\n

169 rows × 8 columns

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" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lookup_csv" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 26, + "outputs": [ + { + "data": { + "text/plain": " identifier lookup_type class \\\n116 brendel.Geirhos2021_colour stimulus_set StimulusSet \n117 brendel.Geirhos2021_colour stimulus_set NaN \n118 brendel.Geirhos2021_sketch stimulus_set StimulusSet \n119 brendel.Geirhos2021_sketch stimulus_set NaN \n120 brendel.Geirhos2021_contrast stimulus_set StimulusSet \n121 brendel.Geirhos2021_contrast stimulus_set NaN \n122 brendel.Geirhos2021_eidolonI stimulus_set StimulusSet \n123 brendel.Geirhos2021_eidolonI stimulus_set NaN \n124 brendel.Geirhos2021_eidolonIII stimulus_set StimulusSet \n125 brendel.Geirhos2021_eidolonIII stimulus_set NaN \n126 brendel.Geirhos2021_high-pass stimulus_set StimulusSet \n127 brendel.Geirhos2021_high-pass stimulus_set NaN \n128 brendel.Geirhos2021_low-pass stimulus_set StimulusSet \n129 brendel.Geirhos2021_low-pass stimulus_set NaN \n130 brendel.Geirhos2021_phase-scrambling stimulus_set StimulusSet \n131 brendel.Geirhos2021_phase-scrambling stimulus_set NaN \n132 brendel.Geirhos2021_rotation stimulus_set StimulusSet \n133 brendel.Geirhos2021_rotation stimulus_set NaN \n134 brendel.Geirhos2021_uniform-noise stimulus_set StimulusSet \n135 brendel.Geirhos2021_uniform-noise stimulus_set NaN \n136 brendel.Geirhos2021_eidolonII stimulus_set StimulusSet \n137 brendel.Geirhos2021_eidolonII stimulus_set NaN \n138 brendel.Geirhos2021_power-equalisation stimulus_set StimulusSet \n139 brendel.Geirhos2021_power-equalisation stimulus_set NaN \n140 brendel.Geirhos2021_stylized stimulus_set StimulusSet \n141 brendel.Geirhos2021_stylized stimulus_set NaN \n142 brendel.Geirhos2021_edge stimulus_set StimulusSet \n143 brendel.Geirhos2021_edge stimulus_set NaN \n144 brendel.Geirhos2021_silhouette stimulus_set StimulusSet \n145 brendel.Geirhos2021_silhouette stimulus_set NaN \n146 brendel.Geirhos2021_false-colour stimulus_set StimulusSet \n147 brendel.Geirhos2021_false-colour stimulus_set NaN \n148 brendel.Geirhos2021_cue-conflict stimulus_set StimulusSet \n149 brendel.Geirhos2021_cue-conflict stimulus_set NaN \n150 brendel.Geirhos2021_colour assembly BehavioralAssembly \n151 brendel.Geirhos2021_contrast assembly BehavioralAssembly \n152 brendel.Geirhos2021_cue-conflict assembly BehavioralAssembly \n153 brendel.Geirhos2021_edge assembly BehavioralAssembly \n154 brendel.Geirhos2021_eidolonI assembly BehavioralAssembly \n155 brendel.Geirhos2021_eidolonII assembly BehavioralAssembly \n156 brendel.Geirhos2021_eidolonIII assembly BehavioralAssembly \n157 brendel.Geirhos2021_false-colour assembly BehavioralAssembly \n158 brendel.Geirhos2021_high-pass assembly BehavioralAssembly \n159 brendel.Geirhos2021_low-pass assembly BehavioralAssembly \n160 brendel.Geirhos2021_phase-scrambling assembly BehavioralAssembly \n161 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brainio_brainscore \n119 NaN brainio_brainscore \n120 NaN brainio_brainscore \n121 NaN brainio_brainscore \n122 NaN brainio_brainscore \n123 NaN brainio_brainscore \n124 NaN brainio_brainscore \n125 NaN brainio_brainscore \n126 NaN brainio_brainscore \n127 NaN brainio_brainscore \n128 NaN brainio_brainscore \n129 NaN brainio_brainscore \n130 NaN brainio_brainscore \n131 NaN brainio_brainscore \n132 NaN brainio_brainscore \n133 NaN brainio_brainscore \n134 NaN brainio_brainscore \n135 NaN brainio_brainscore \n136 NaN brainio_brainscore \n137 NaN brainio_brainscore \n138 NaN brainio_brainscore \n139 NaN brainio_brainscore \n140 NaN brainio_brainscore \n141 NaN brainio_brainscore \n142 NaN brainio_brainscore \n143 NaN brainio_brainscore \n144 NaN brainio_brainscore \n145 NaN brainio_brainscore \n146 NaN brainio_brainscore \n147 NaN brainio_brainscore \n148 NaN brainio_brainscore \n149 NaN brainio_brainscore \n150 brendel.Geirhos2021_colour brainio_brainscore \n151 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identifierlookup_typeclasslocation_typelocationsha1stimulus_set_identifierlookup_source
116brendel.Geirhos2021_colourstimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...9c97c155fd6039a95978be89eb604c6894c5fa16NaNbrainio_brainscore
117brendel.Geirhos2021_colourstimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...d166f1d3dc3d00c4f51a489e6fcf96dbbe778d2cNaNbrainio_brainscore
118brendel.Geirhos2021_sketchstimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...a5741b2f7bd08541a0dbefd7fb2d6a3845ca800bNaNbrainio_brainscore
119brendel.Geirhos2021_sketchstimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...8e8712f08a5ad3655ea2bd8cd675db8cdf65129aNaNbrainio_brainscore
120brendel.Geirhos2021_contraststimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...f6ef69a2e8937e1d0d83c8a21b325b4273494cb5NaNbrainio_brainscore
121brendel.Geirhos2021_contraststimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...ebeeef2f9c6a7282e20ef2026dc77eefa026957bNaNbrainio_brainscore
122brendel.Geirhos2021_eidolonIstimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...049adbed36fed52c609d38d5230e6084336df6b9NaNbrainio_brainscore
123brendel.Geirhos2021_eidolonIstimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...abaa45a225628dd66e424ec7d8e2b10a0c88bc0dNaNbrainio_brainscore
124brendel.Geirhos2021_eidolonIIIstimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...ba0173b315f02df16d418dc3ff1df7dc498b4893NaNbrainio_brainscore
125brendel.Geirhos2021_eidolonIIIstimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...d0304c0c0024d0f493ea9c0c47ae0221da391016NaNbrainio_brainscore
126brendel.Geirhos2021_high-passstimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...ddf523dcf43398cc15894c7b51c436d526e6c992NaNbrainio_brainscore
127brendel.Geirhos2021_high-passstimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...12322bb17270a5dde206314fcdc125c4bb235e3bNaNbrainio_brainscore
128brendel.Geirhos2021_low-passstimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...1264f9be407c7d428cf3d62a7bb1b1bb45a821bcNaNbrainio_brainscore
129brendel.Geirhos2021_low-passstimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...ad087676e04e51adadea7c7c5c1fa28e4dd6360cNaNbrainio_brainscore
130brendel.Geirhos2021_phase-scramblingstimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...0cc87f7ac42c2266f98d3a08783f7173499ec2fcNaNbrainio_brainscore
131brendel.Geirhos2021_phase-scramblingstimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...462e77ab9533072b7036118f1f697e8c9bf30ae4NaNbrainio_brainscore
132brendel.Geirhos2021_rotationstimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...2577831e9ead905669613fa17cb2651d0c48a455NaNbrainio_brainscore
133brendel.Geirhos2021_rotationstimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...8f5d9cb217807e96ace61337144e429d0d4ba04cNaNbrainio_brainscore
134brendel.Geirhos2021_uniform-noisestimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...89b62a6af878974d388278ed0e23e8ed1c2fd855NaNbrainio_brainscore
135brendel.Geirhos2021_uniform-noisestimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...ff4566542d65056028660293e2409b532e887714NaNbrainio_brainscore
136brendel.Geirhos2021_eidolonIIstimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...1806ada128c260ab54db570d2e73aea71d679754NaNbrainio_brainscore
137brendel.Geirhos2021_eidolonIIstimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...2654ba55291f8ab972f18b36565f9ead80a45339NaNbrainio_brainscore
138brendel.Geirhos2021_power-equalisationstimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...743935476b1fe4b7bd4d01a4eed24cd9ed5b3a22NaNbrainio_brainscore
139brendel.Geirhos2021_power-equalisationstimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...a0d5307525bccf8da8f3d293e7e324b9b20248c6NaNbrainio_brainscore
140brendel.Geirhos2021_stylizedstimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...2265a540dee6915150bf7c61143eaf788f603866NaNbrainio_brainscore
141brendel.Geirhos2021_stylizedstimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...75d273e8de643b0d814fbe60cd237c31ebe19c44NaNbrainio_brainscore
142brendel.Geirhos2021_edgestimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...fb57005ecb80e4e37b01e084f1f176fe7f59ff7fNaNbrainio_brainscore
143brendel.Geirhos2021_edgestimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...6c1199d90836a26be454aa799864a63c5efacaa1NaNbrainio_brainscore
144brendel.Geirhos2021_silhouettestimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...fb57005ecb80e4e37b01e084f1f176fe7f59ff7fNaNbrainio_brainscore
145brendel.Geirhos2021_silhouettestimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...36c8a481f7876a2af2ad4fe80890b302fe3ae91eNaNbrainio_brainscore
146brendel.Geirhos2021_false-colourstimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...8a09a7af8ec44339bcae5500ae5900d9c4309042NaNbrainio_brainscore
147brendel.Geirhos2021_false-colourstimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...ec0ba347fc14d0c0587d38bfa96e4ab5d2f7979aNaNbrainio_brainscore
148brendel.Geirhos2021_cue-conflictstimulus_setStimulusSetS3https://brainio-brainscore.s3.amazonaws.com/im...8d3ae89d8870fb7d7c5d5ff387085b1f0116e2b7NaNbrainio_brainscore
149brendel.Geirhos2021_cue-conflictstimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...7e601186b181102939cd0b43a4e8a3ca95c18259NaNbrainio_brainscore
150brendel.Geirhos2021_colourassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...258862d82467614e45cc1e488a5ac909eb6e122dbrendel.Geirhos2021_colourbrainio_brainscore
151brendel.Geirhos2021_contrastassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...1e114c987dc035ccca43781ff8cee9689acd3c3fbrendel.Geirhos2021_contrastbrainio_brainscore
152brendel.Geirhos2021_cue-conflictassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...cc214e3595d34565b13963c5f56049769a39a5c9brendel.Geirhos2021_cue-conflictbrainio_brainscore
153brendel.Geirhos2021_edgeassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...ab1dc9e188e248da07215b375eb3dbcc58fde7fbbrendel.Geirhos2021_edgebrainio_brainscore
154brendel.Geirhos2021_eidolonIassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...0f01f351ae19eafc2cb5e504d98e5cd01b4c07b4brendel.Geirhos2021_eidolonIbrainio_brainscore
155brendel.Geirhos2021_eidolonIIassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...499eea0f0c0817d02b5b97d2ebab89bc4c40a153brendel.Geirhos2021_eidolonIIbrainio_brainscore
156brendel.Geirhos2021_eidolonIIIassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...e7c9a49e729f8666f8aedc6e47c746fbbe2ebe36brendel.Geirhos2021_eidolonIIIbrainio_brainscore
157brendel.Geirhos2021_false-colourassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...4dc072264651c81575564ba4818a12b8e8039c65brendel.Geirhos2021_false-colourbrainio_brainscore
158brendel.Geirhos2021_high-passassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...5df45c69127758f1ba3391671c521711050e3b4dbrendel.Geirhos2021_high-passbrainio_brainscore
159brendel.Geirhos2021_low-passassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...75ab628d9e6d0d634290567b1cb261d7f8dc61e2brendel.Geirhos2021_low-passbrainio_brainscore
160brendel.Geirhos2021_phase-scramblingassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...4124f9f5b86fb6ed82c98197d292eef50b608ababrendel.Geirhos2021_phase-scramblingbrainio_brainscore
161brendel.Geirhos2021_power-equalisationassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...0aba1b50a7e0802d76c41d332a121a3f96ef4f7dbrendel.Geirhos2021_power-equalisationbrainio_brainscore
162brendel.Geirhos2021_rotationassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...e51a5c3bc95ade159e71aa602232063730bcd57bbrendel.Geirhos2021_rotationbrainio_brainscore
163brendel.Geirhos2021_silhouetteassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...7dc94991465fe8009244e0d6fb8283419a1f9885brendel.Geirhos2021_silhouettebrainio_brainscore
164brendel.Geirhos2021_sketchassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...6709850864cea16d99a29fb31ae3c4a489983562brendel.Geirhos2021_sketchbrainio_brainscore
165brendel.Geirhos2021_stylizedassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...dcf15f292e787a88e1e0f271e6b2838d6bdadfd3brendel.Geirhos2021_stylizedbrainio_brainscore
166brendel.Geirhos2021_uniform-noiseassemblyBehavioralAssemblyS3https://brainio-brainscore.s3.amazonaws.com/as...f5e8b2636738f978c71591b8df6f8a21a66b72d1brendel.Geirhos2021_uniform-noisebrainio_brainscore
\n
" + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brendel = lookup_csv[116:167]\n", + "brendel" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 41, + "outputs": [], + "source": [ + "sorted_brendel = brendel.sort_values(by=['identifier', 'class'])\n", + "sorted_brendel.reset_index(drop=True, inplace=True)\n", + "sorted_brendel.to_csv('geirhos_hashes.csv', index=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 34, + "outputs": [ + { + "data": { + "text/plain": "str" + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_brendel.loc[0, 'sha1']" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 50, + "outputs": [ + { + "data": { + "text/plain": " identifier lookup_type class \\\n0 brendel.Geirhos2021_colour assembly BehavioralAssembly \n1 brendel.Geirhos2021_colour stimulus_set StimulusSet \n2 brendel.Geirhos2021_colour stimulus_set NaN \n3 brendel.Geirhos2021_contrast assembly BehavioralAssembly \n4 brendel.Geirhos2021_contrast stimulus_set StimulusSet \n5 brendel.Geirhos2021_contrast stimulus_set NaN \n6 brendel.Geirhos2021_cue-conflict assembly BehavioralAssembly \n7 brendel.Geirhos2021_cue-conflict stimulus_set StimulusSet \n8 brendel.Geirhos2021_cue-conflict stimulus_set NaN \n9 brendel.Geirhos2021_edge assembly BehavioralAssembly \n10 brendel.Geirhos2021_edge stimulus_set StimulusSet \n11 brendel.Geirhos2021_edge stimulus_set NaN \n12 brendel.Geirhos2021_eidolonI assembly BehavioralAssembly \n13 brendel.Geirhos2021_eidolonI stimulus_set StimulusSet \n14 brendel.Geirhos2021_eidolonI stimulus_set NaN \n15 brendel.Geirhos2021_eidolonII assembly BehavioralAssembly \n16 brendel.Geirhos2021_eidolonII stimulus_set StimulusSet \n17 brendel.Geirhos2021_eidolonII stimulus_set NaN \n18 brendel.Geirhos2021_eidolonIII assembly BehavioralAssembly \n19 brendel.Geirhos2021_eidolonIII stimulus_set StimulusSet \n20 brendel.Geirhos2021_eidolonIII stimulus_set NaN \n21 brendel.Geirhos2021_false-colour assembly BehavioralAssembly \n22 brendel.Geirhos2021_false-colour stimulus_set StimulusSet \n23 brendel.Geirhos2021_false-colour stimulus_set NaN \n24 brendel.Geirhos2021_high-pass assembly BehavioralAssembly \n25 brendel.Geirhos2021_high-pass stimulus_set StimulusSet \n26 brendel.Geirhos2021_high-pass stimulus_set NaN \n27 brendel.Geirhos2021_low-pass assembly BehavioralAssembly \n28 brendel.Geirhos2021_low-pass stimulus_set StimulusSet \n29 brendel.Geirhos2021_low-pass stimulus_set NaN \n30 brendel.Geirhos2021_phase-scrambling assembly BehavioralAssembly \n31 brendel.Geirhos2021_phase-scrambling stimulus_set StimulusSet \n32 brendel.Geirhos2021_phase-scrambling stimulus_set NaN \n33 brendel.Geirhos2021_power-equalisation assembly BehavioralAssembly \n34 brendel.Geirhos2021_power-equalisation stimulus_set StimulusSet \n35 brendel.Geirhos2021_power-equalisation stimulus_set NaN \n36 brendel.Geirhos2021_rotation assembly BehavioralAssembly \n37 brendel.Geirhos2021_rotation stimulus_set StimulusSet \n38 brendel.Geirhos2021_rotation stimulus_set NaN \n39 brendel.Geirhos2021_silhouette assembly BehavioralAssembly \n40 brendel.Geirhos2021_silhouette stimulus_set StimulusSet \n41 brendel.Geirhos2021_silhouette stimulus_set NaN \n42 brendel.Geirhos2021_sketch assembly BehavioralAssembly \n43 brendel.Geirhos2021_sketch stimulus_set StimulusSet \n44 brendel.Geirhos2021_sketch stimulus_set NaN \n45 brendel.Geirhos2021_stylized assembly BehavioralAssembly \n46 brendel.Geirhos2021_stylized stimulus_set StimulusSet \n47 brendel.Geirhos2021_stylized stimulus_set NaN \n48 brendel.Geirhos2021_uniform-noise assembly BehavioralAssembly \n49 brendel.Geirhos2021_uniform-noise stimulus_set StimulusSet \n50 brendel.Geirhos2021_uniform-noise stimulus_set NaN \n\n location_type location \\\n0 S3 https://brainio-brainscore.s3.amazonaws.com/as... \n1 S3 https://brainio-brainscore.s3.amazonaws.com/im... \n2 S3 https://brainio-brainscore.s3.amazonaws.com/im... \n3 S3 https://brainio-brainscore.s3.amazonaws.com/as... \n4 S3 https://brainio-brainscore.s3.amazonaws.com/im... \n5 S3 https://brainio-brainscore.s3.amazonaws.com/im... \n6 S3 https://brainio-brainscore.s3.amazonaws.com/as... \n7 S3 https://brainio-brainscore.s3.amazonaws.com/im... \n8 S3 https://brainio-brainscore.s3.amazonaws.com/im... \n9 S3 https://brainio-brainscore.s3.amazonaws.com/as... \n10 S3 https://brainio-brainscore.s3.amazonaws.com/im... \n11 S3 https://brainio-brainscore.s3.amazonaws.com/im... \n12 S3 https://brainio-brainscore.s3.amazonaws.com/as... \n13 S3 https://brainio-brainscore.s3.amazonaws.com/im... \n14 S3 https://brainio-brainscore.s3.amazonaws.com/im... \n15 S3 https://brainio-brainscore.s3.amazonaws.com/as... \n16 S3 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50brendel.Geirhos2021_uniform-noisestimulus_setNaNS3https://brainio-brainscore.s3.amazonaws.com/im...ff4566542d65056028660293e2409b532e887714NaNbrainio_brainscore
\n
" + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "geirhos_csv = pd.read_csv('geirhos_hashes.csv')\n", + "geirhos_csv" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 68, + "outputs": [ + { + "data": { + "text/plain": "'colour'" + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "geirhos_csv['identifier'].str.split('_')[:][-1]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 78, + "outputs": [], + "source": [ + "geirhos_csv[['general', 'dataset_id']] = geirhos_csv['identifier'].str.split('_', expand=True)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 119, + "outputs": [], + "source": [ + "class HashCollection:\n", + " def __init__(self, dataset_identifier, assembly_sha1, stim_csv_sha1, stim_zip_sha1):\n", + " self.dataset_identifier = dataset_identifier\n", + " self.assembly_sha1 = assembly_sha1\n", + " self.stim_csv_sha1 = stim_csv_sha1\n", + " self.stim_zip_sha1 = stim_zip_sha1" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 122, + "outputs": [ + { + "data": { + "text/plain": "'colour'" + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hash_dict = {}\n", + "for dataset_id in DATASETS:\n", + " dataset_subset = geirhos_csv[geirhos_csv['dataset_id']==dataset_id]\n", + "\n", + " # use classes (Assembly vs. Stim Set vs. Nan) to determine file type\n", + " assembly_hash = dataset_subset[dataset_subset['class']=='BehavioralAssembly']['sha1'].item()\n", + " stim_csv_hash = dataset_subset[dataset_subset['class']=='StimulusSet']['sha1'].item()\n", + " stim_zip_hash = dataset_subset[dataset_subset['class'].isnull()]['sha1'].item()\n", + " hash_dict[dataset_id] = HashCollection(dataset_id, assembly_hash, stim_csv_hash, stim_zip_hash)\n", + "\n", + "hash_dict['colour'].dataset_identifier" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 113, + "outputs": [ + { + "data": { + "text/plain": "{'colour': ('258862d82467614e45cc1e488a5ac909eb6e122d',\n '9c97c155fd6039a95978be89eb604c6894c5fa16',\n 'd166f1d3dc3d00c4f51a489e6fcf96dbbe778d2c'),\n 'contrast': ('1e114c987dc035ccca43781ff8cee9689acd3c3f',\n 'f6ef69a2e8937e1d0d83c8a21b325b4273494cb5',\n 'ebeeef2f9c6a7282e20ef2026dc77eefa026957b'),\n 'cue-conflict': ('cc214e3595d34565b13963c5f56049769a39a5c9',\n '8d3ae89d8870fb7d7c5d5ff387085b1f0116e2b7',\n '7e601186b181102939cd0b43a4e8a3ca95c18259'),\n 'edge': ('ab1dc9e188e248da07215b375eb3dbcc58fde7fb',\n 'fb57005ecb80e4e37b01e084f1f176fe7f59ff7f',\n '6c1199d90836a26be454aa799864a63c5efacaa1'),\n 'eidolonI': ('0f01f351ae19eafc2cb5e504d98e5cd01b4c07b4',\n '049adbed36fed52c609d38d5230e6084336df6b9',\n 'abaa45a225628dd66e424ec7d8e2b10a0c88bc0d'),\n 'eidolonII': ('499eea0f0c0817d02b5b97d2ebab89bc4c40a153',\n '1806ada128c260ab54db570d2e73aea71d679754',\n '2654ba55291f8ab972f18b36565f9ead80a45339'),\n 'eidolonIII': ('e7c9a49e729f8666f8aedc6e47c746fbbe2ebe36',\n 'ba0173b315f02df16d418dc3ff1df7dc498b4893',\n 'd0304c0c0024d0f493ea9c0c47ae0221da391016'),\n 'false-colour': ('4dc072264651c81575564ba4818a12b8e8039c65',\n '8a09a7af8ec44339bcae5500ae5900d9c4309042',\n 'ec0ba347fc14d0c0587d38bfa96e4ab5d2f7979a'),\n 'high-pass': ('5df45c69127758f1ba3391671c521711050e3b4d',\n 'ddf523dcf43398cc15894c7b51c436d526e6c992',\n '12322bb17270a5dde206314fcdc125c4bb235e3b'),\n 'low-pass': ('75ab628d9e6d0d634290567b1cb261d7f8dc61e2',\n '1264f9be407c7d428cf3d62a7bb1b1bb45a821bc',\n 'ad087676e04e51adadea7c7c5c1fa28e4dd6360c'),\n 'phase-scrambling': ('4124f9f5b86fb6ed82c98197d292eef50b608aba',\n '0cc87f7ac42c2266f98d3a08783f7173499ec2fc',\n '462e77ab9533072b7036118f1f697e8c9bf30ae4'),\n 'power-equalisation': ('0aba1b50a7e0802d76c41d332a121a3f96ef4f7d',\n '743935476b1fe4b7bd4d01a4eed24cd9ed5b3a22',\n 'a0d5307525bccf8da8f3d293e7e324b9b20248c6'),\n 'rotation': ('e51a5c3bc95ade159e71aa602232063730bcd57b',\n '2577831e9ead905669613fa17cb2651d0c48a455',\n '8f5d9cb217807e96ace61337144e429d0d4ba04c'),\n 'silhouette': ('7dc94991465fe8009244e0d6fb8283419a1f9885',\n 'fb57005ecb80e4e37b01e084f1f176fe7f59ff7f',\n '36c8a481f7876a2af2ad4fe80890b302fe3ae91e'),\n 'sketch': ('6709850864cea16d99a29fb31ae3c4a489983562',\n 'a5741b2f7bd08541a0dbefd7fb2d6a3845ca800b',\n '8e8712f08a5ad3655ea2bd8cd675db8cdf65129a'),\n 'stylized': ('dcf15f292e787a88e1e0f271e6b2838d6bdadfd3',\n '2265a540dee6915150bf7c61143eaf788f603866',\n '75d273e8de643b0d814fbe60cd237c31ebe19c44'),\n 'uniform-noise': ('f5e8b2636738f978c71591b8df6f8a21a66b72d1',\n '89b62a6af878974d388278ed0e23e8ed1c2fd855',\n 'ff4566542d65056028660293e2409b532e887714')}" + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hash_dict = {}\n", + "for dataset_id in DATASETS:\n", + " dataset_subset = geirhos_csv[geirhos_csv['dataset_id']==dataset_id]\n", + "\n", + " # use classes (Assembly vs. Stim Set vs. Nan) to determine file type\n", + " assembly_hash = dataset_subset[dataset_subset['class']=='BehavioralAssembly']['sha1'].item()\n", + " stim_csv_hash = dataset_subset[dataset_subset['class']=='StimulusSet']['sha1'].item()\n", + " stim_zip_hash = dataset_subset[dataset_subset['class'].isnull()]['sha1'].item()\n", + " hash_dict[dataset_id] = (assembly_hash, stim_csv_hash, stim_zip_hash)\n", + "\n", + "hash_dict" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 114, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "colour\n", + "258862d82467614e45cc1e488a5ac909eb6e122d\n", + "9c97c155fd6039a95978be89eb604c6894c5fa16\n", + "d166f1d3dc3d00c4f51a489e6fcf96dbbe778d2c\n", + "contrast\n", + "1e114c987dc035ccca43781ff8cee9689acd3c3f\n", + "f6ef69a2e8937e1d0d83c8a21b325b4273494cb5\n", + "ebeeef2f9c6a7282e20ef2026dc77eefa026957b\n", + "cue-conflict\n", + "cc214e3595d34565b13963c5f56049769a39a5c9\n", + "8d3ae89d8870fb7d7c5d5ff387085b1f0116e2b7\n", + "7e601186b181102939cd0b43a4e8a3ca95c18259\n", + "edge\n", + "ab1dc9e188e248da07215b375eb3dbcc58fde7fb\n", + "fb57005ecb80e4e37b01e084f1f176fe7f59ff7f\n", + "6c1199d90836a26be454aa799864a63c5efacaa1\n", + "eidolonI\n", + "0f01f351ae19eafc2cb5e504d98e5cd01b4c07b4\n", + "049adbed36fed52c609d38d5230e6084336df6b9\n", + "abaa45a225628dd66e424ec7d8e2b10a0c88bc0d\n", + "eidolonII\n", + "499eea0f0c0817d02b5b97d2ebab89bc4c40a153\n", + "1806ada128c260ab54db570d2e73aea71d679754\n", + "2654ba55291f8ab972f18b36565f9ead80a45339\n", + "eidolonIII\n", + "e7c9a49e729f8666f8aedc6e47c746fbbe2ebe36\n", + "ba0173b315f02df16d418dc3ff1df7dc498b4893\n", + "d0304c0c0024d0f493ea9c0c47ae0221da391016\n", + "false-colour\n", + "4dc072264651c81575564ba4818a12b8e8039c65\n", + "8a09a7af8ec44339bcae5500ae5900d9c4309042\n", + "ec0ba347fc14d0c0587d38bfa96e4ab5d2f7979a\n", + "high-pass\n", + "5df45c69127758f1ba3391671c521711050e3b4d\n", + "ddf523dcf43398cc15894c7b51c436d526e6c992\n", + "12322bb17270a5dde206314fcdc125c4bb235e3b\n", + "low-pass\n", + "75ab628d9e6d0d634290567b1cb261d7f8dc61e2\n", + "1264f9be407c7d428cf3d62a7bb1b1bb45a821bc\n", + "ad087676e04e51adadea7c7c5c1fa28e4dd6360c\n", + "phase-scrambling\n", + "4124f9f5b86fb6ed82c98197d292eef50b608aba\n", + "0cc87f7ac42c2266f98d3a08783f7173499ec2fc\n", + "462e77ab9533072b7036118f1f697e8c9bf30ae4\n", + "power-equalisation\n", + "0aba1b50a7e0802d76c41d332a121a3f96ef4f7d\n", + "743935476b1fe4b7bd4d01a4eed24cd9ed5b3a22\n", + "a0d5307525bccf8da8f3d293e7e324b9b20248c6\n", + "rotation\n", + "e51a5c3bc95ade159e71aa602232063730bcd57b\n", + "2577831e9ead905669613fa17cb2651d0c48a455\n", + "8f5d9cb217807e96ace61337144e429d0d4ba04c\n", + "silhouette\n", + "7dc94991465fe8009244e0d6fb8283419a1f9885\n", + "fb57005ecb80e4e37b01e084f1f176fe7f59ff7f\n", + "36c8a481f7876a2af2ad4fe80890b302fe3ae91e\n", + "sketch\n", + "6709850864cea16d99a29fb31ae3c4a489983562\n", + "a5741b2f7bd08541a0dbefd7fb2d6a3845ca800b\n", + "8e8712f08a5ad3655ea2bd8cd675db8cdf65129a\n", + "stylized\n", + "dcf15f292e787a88e1e0f271e6b2838d6bdadfd3\n", + "2265a540dee6915150bf7c61143eaf788f603866\n", + "75d273e8de643b0d814fbe60cd237c31ebe19c44\n", + "uniform-noise\n", + "f5e8b2636738f978c71591b8df6f8a21a66b72d1\n", + "89b62a6af878974d388278ed0e23e8ed1c2fd855\n", + "ff4566542d65056028660293e2409b532e887714\n" + ] + } + ], + "source": [ + "for dataset, (assembly_hash, stim_csv_hash, stim_zip_hash) in hash_dict.items():\n", + " print(dataset)\n", + " print(assembly_hash)\n", + " print(stim_csv_hash)\n", + " print(stim_zip_hash)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 43, + "outputs": [ + { + "data": { + "text/plain": "[['258862d82467614e45cc1e488a5ac909eb6e122d',\n '9c97c155fd6039a95978be89eb604c6894c5fa16',\n 'd166f1d3dc3d00c4f51a489e6fcf96dbbe778d2c'],\n ['1e114c987dc035ccca43781ff8cee9689acd3c3f',\n 'f6ef69a2e8937e1d0d83c8a21b325b4273494cb5',\n 'ebeeef2f9c6a7282e20ef2026dc77eefa026957b'],\n ['cc214e3595d34565b13963c5f56049769a39a5c9',\n '8d3ae89d8870fb7d7c5d5ff387085b1f0116e2b7',\n '7e601186b181102939cd0b43a4e8a3ca95c18259'],\n ['ab1dc9e188e248da07215b375eb3dbcc58fde7fb',\n 'fb57005ecb80e4e37b01e084f1f176fe7f59ff7f',\n '6c1199d90836a26be454aa799864a63c5efacaa1'],\n ['0f01f351ae19eafc2cb5e504d98e5cd01b4c07b4',\n '049adbed36fed52c609d38d5230e6084336df6b9',\n 'abaa45a225628dd66e424ec7d8e2b10a0c88bc0d'],\n ['499eea0f0c0817d02b5b97d2ebab89bc4c40a153',\n '1806ada128c260ab54db570d2e73aea71d679754',\n '2654ba55291f8ab972f18b36565f9ead80a45339'],\n ['e7c9a49e729f8666f8aedc6e47c746fbbe2ebe36',\n 'ba0173b315f02df16d418dc3ff1df7dc498b4893',\n 'd0304c0c0024d0f493ea9c0c47ae0221da391016'],\n ['4dc072264651c81575564ba4818a12b8e8039c65',\n '8a09a7af8ec44339bcae5500ae5900d9c4309042',\n 'ec0ba347fc14d0c0587d38bfa96e4ab5d2f7979a'],\n ['5df45c69127758f1ba3391671c521711050e3b4d',\n 'ddf523dcf43398cc15894c7b51c436d526e6c992',\n '12322bb17270a5dde206314fcdc125c4bb235e3b'],\n ['75ab628d9e6d0d634290567b1cb261d7f8dc61e2',\n '1264f9be407c7d428cf3d62a7bb1b1bb45a821bc',\n 'ad087676e04e51adadea7c7c5c1fa28e4dd6360c'],\n ['4124f9f5b86fb6ed82c98197d292eef50b608aba',\n '0cc87f7ac42c2266f98d3a08783f7173499ec2fc',\n '462e77ab9533072b7036118f1f697e8c9bf30ae4'],\n ['0aba1b50a7e0802d76c41d332a121a3f96ef4f7d',\n '743935476b1fe4b7bd4d01a4eed24cd9ed5b3a22',\n 'a0d5307525bccf8da8f3d293e7e324b9b20248c6'],\n ['e51a5c3bc95ade159e71aa602232063730bcd57b',\n '2577831e9ead905669613fa17cb2651d0c48a455',\n '8f5d9cb217807e96ace61337144e429d0d4ba04c'],\n ['7dc94991465fe8009244e0d6fb8283419a1f9885',\n 'fb57005ecb80e4e37b01e084f1f176fe7f59ff7f',\n '36c8a481f7876a2af2ad4fe80890b302fe3ae91e'],\n ['6709850864cea16d99a29fb31ae3c4a489983562',\n 'a5741b2f7bd08541a0dbefd7fb2d6a3845ca800b',\n '8e8712f08a5ad3655ea2bd8cd675db8cdf65129a'],\n ['dcf15f292e787a88e1e0f271e6b2838d6bdadfd3',\n '2265a540dee6915150bf7c61143eaf788f603866',\n '75d273e8de643b0d814fbe60cd237c31ebe19c44'],\n ['f5e8b2636738f978c71591b8df6f8a21a66b72d1',\n '89b62a6af878974d388278ed0e23e8ed1c2fd855',\n 'ff4566542d65056028660293e2409b532e887714']]" + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# grouping the three hashes together for each assembly/stimulus set pairing\n", + "hashes = []\n", + "for i in range(0, len(geirhos_csv), 3):\n", + " assembly_hash = geirhos_csv.loc[i, 'sha1']\n", + " stim_csv_hash = geirhos_csv.loc[i+1, 'sha1']\n", + " stim_zip_hash = geirhos_csv.loc[i+2, 'sha1']\n", + " hashes.append([assembly_hash, stim_csv_hash, stim_zip_hash])\n", + "hashes\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 44, + "outputs": [], + "source": [ + "DATASETS = ['colour', 'contrast', 'cue-conflict', 'edge',\n", + " 'eidolonI', 'eidolonII', 'eidolonIII',\n", + " 'false-colour', 'high-pass', 'low-pass', 'phase-scrambling', 'power-equalisation',\n", + " 'rotation', 'silhouette', 'sketch', 'stylized', 'uniform-noise']" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 45, + "outputs": [ + { + "data": { + "text/plain": "[('colour',\n ['258862d82467614e45cc1e488a5ac909eb6e122d',\n '9c97c155fd6039a95978be89eb604c6894c5fa16',\n 'd166f1d3dc3d00c4f51a489e6fcf96dbbe778d2c']),\n ('contrast',\n ['1e114c987dc035ccca43781ff8cee9689acd3c3f',\n 'f6ef69a2e8937e1d0d83c8a21b325b4273494cb5',\n 'ebeeef2f9c6a7282e20ef2026dc77eefa026957b']),\n ('cue-conflict',\n ['cc214e3595d34565b13963c5f56049769a39a5c9',\n '8d3ae89d8870fb7d7c5d5ff387085b1f0116e2b7',\n '7e601186b181102939cd0b43a4e8a3ca95c18259']),\n ('edge',\n ['ab1dc9e188e248da07215b375eb3dbcc58fde7fb',\n 'fb57005ecb80e4e37b01e084f1f176fe7f59ff7f',\n '6c1199d90836a26be454aa799864a63c5efacaa1']),\n ('eidolonI',\n ['0f01f351ae19eafc2cb5e504d98e5cd01b4c07b4',\n '049adbed36fed52c609d38d5230e6084336df6b9',\n 'abaa45a225628dd66e424ec7d8e2b10a0c88bc0d']),\n ('eidolonII',\n ['499eea0f0c0817d02b5b97d2ebab89bc4c40a153',\n '1806ada128c260ab54db570d2e73aea71d679754',\n '2654ba55291f8ab972f18b36565f9ead80a45339']),\n ('eidolonIII',\n ['e7c9a49e729f8666f8aedc6e47c746fbbe2ebe36',\n 'ba0173b315f02df16d418dc3ff1df7dc498b4893',\n 'd0304c0c0024d0f493ea9c0c47ae0221da391016']),\n ('false-colour',\n ['4dc072264651c81575564ba4818a12b8e8039c65',\n '8a09a7af8ec44339bcae5500ae5900d9c4309042',\n 'ec0ba347fc14d0c0587d38bfa96e4ab5d2f7979a']),\n ('high-pass',\n ['5df45c69127758f1ba3391671c521711050e3b4d',\n 'ddf523dcf43398cc15894c7b51c436d526e6c992',\n '12322bb17270a5dde206314fcdc125c4bb235e3b']),\n ('low-pass',\n ['75ab628d9e6d0d634290567b1cb261d7f8dc61e2',\n '1264f9be407c7d428cf3d62a7bb1b1bb45a821bc',\n 'ad087676e04e51adadea7c7c5c1fa28e4dd6360c']),\n ('phase-scrambling',\n ['4124f9f5b86fb6ed82c98197d292eef50b608aba',\n '0cc87f7ac42c2266f98d3a08783f7173499ec2fc',\n '462e77ab9533072b7036118f1f697e8c9bf30ae4']),\n ('power-equalisation',\n ['0aba1b50a7e0802d76c41d332a121a3f96ef4f7d',\n '743935476b1fe4b7bd4d01a4eed24cd9ed5b3a22',\n 'a0d5307525bccf8da8f3d293e7e324b9b20248c6']),\n ('rotation',\n ['e51a5c3bc95ade159e71aa602232063730bcd57b',\n '2577831e9ead905669613fa17cb2651d0c48a455',\n '8f5d9cb217807e96ace61337144e429d0d4ba04c']),\n ('silhouette',\n ['7dc94991465fe8009244e0d6fb8283419a1f9885',\n 'fb57005ecb80e4e37b01e084f1f176fe7f59ff7f',\n '36c8a481f7876a2af2ad4fe80890b302fe3ae91e']),\n ('sketch',\n ['6709850864cea16d99a29fb31ae3c4a489983562',\n 'a5741b2f7bd08541a0dbefd7fb2d6a3845ca800b',\n '8e8712f08a5ad3655ea2bd8cd675db8cdf65129a']),\n ('stylized',\n ['dcf15f292e787a88e1e0f271e6b2838d6bdadfd3',\n '2265a540dee6915150bf7c61143eaf788f603866',\n '75d273e8de643b0d814fbe60cd237c31ebe19c44']),\n ('uniform-noise',\n ['f5e8b2636738f978c71591b8df6f8a21a66b72d1',\n '89b62a6af878974d388278ed0e23e8ed1c2fd855',\n 'ff4566542d65056028660293e2409b532e887714'])]" + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "zipped = list(zip(DATASETS, hashes))\n", + "zipped" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 47, + "outputs": [ + { + "data": { + "text/plain": "'colour'" + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "zipped[0][0]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 49, + "outputs": [ + { + "data": { + "text/plain": "'258862d82467614e45cc1e488a5ac909eb6e122d'" + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "zipped[0][1][0]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/packaging/geirhos2021/colour/colour_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/colour/colour_data_assembly.py similarity index 100% rename from packaging/geirhos2021/colour/colour_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/colour/colour_data_assembly.py diff --git a/packaging/geirhos2021/colour/colour_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/colour/colour_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/colour/colour_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/colour/colour_stimulus_set.py diff --git a/packaging/geirhos2021/contrast/contrast_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/contrast/contrast_data_assembly.py similarity index 100% rename from packaging/geirhos2021/contrast/contrast_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/contrast/contrast_data_assembly.py diff --git a/packaging/geirhos2021/contrast/contrast_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/contrast/contrast_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/contrast/contrast_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/contrast/contrast_stimulus_set.py diff --git a/packaging/geirhos2021/cue-conflict/cue-conflict_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/cue-conflict/cue-conflict_data_assembly.py similarity index 100% rename from packaging/geirhos2021/cue-conflict/cue-conflict_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/cue-conflict/cue-conflict_data_assembly.py diff --git a/packaging/geirhos2021/cue-conflict/cue-conflict_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/cue-conflict/cue-conflict_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/cue-conflict/cue-conflict_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/cue-conflict/cue-conflict_stimulus_set.py diff --git a/packaging/geirhos2021/edge/edge_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/edge/edge_data_assembly.py similarity index 100% rename from packaging/geirhos2021/edge/edge_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/edge/edge_data_assembly.py diff --git a/packaging/geirhos2021/edge/edge_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/edge/edge_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/edge/edge_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/edge/edge_stimulus_set.py diff --git a/packaging/geirhos2021/eidolonI/eidolonI_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/eidolonI/eidolonI_data_assembly.py similarity index 100% rename from packaging/geirhos2021/eidolonI/eidolonI_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/eidolonI/eidolonI_data_assembly.py diff --git a/packaging/geirhos2021/eidolonI/eidolonI_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/eidolonI/eidolonI_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/eidolonI/eidolonI_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/eidolonI/eidolonI_stimulus_set.py diff --git a/packaging/geirhos2021/eidolonII/eidolonII_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/eidolonII/eidolonII_data_assembly.py similarity index 100% rename from packaging/geirhos2021/eidolonII/eidolonII_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/eidolonII/eidolonII_data_assembly.py diff --git a/packaging/geirhos2021/eidolonII/eidolonII_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/eidolonII/eidolonII_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/eidolonII/eidolonII_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/eidolonII/eidolonII_stimulus_set.py diff --git a/packaging/geirhos2021/eidolonIII/eidolonIII_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/eidolonIII/eidolonIII_data_assembly.py similarity index 100% rename from packaging/geirhos2021/eidolonIII/eidolonIII_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/eidolonIII/eidolonIII_data_assembly.py diff --git a/packaging/geirhos2021/eidolonIII/eidolonIII_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/eidolonIII/eidolonIII_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/eidolonIII/eidolonIII_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/eidolonIII/eidolonIII_stimulus_set.py diff --git a/packaging/geirhos2021/false-colour/false-colour_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/false-colour/false-colour_data_assembly.py similarity index 100% rename from packaging/geirhos2021/false-colour/false-colour_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/false-colour/false-colour_data_assembly.py diff --git a/packaging/geirhos2021/false-colour/false-colour_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/false-colour/false-colour_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/false-colour/false-colour_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/false-colour/false-colour_stimulus_set.py diff --git a/packaging/geirhos2021/high-pass/high-pass_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/high-pass/high-pass_data_assembly.py similarity index 100% rename from packaging/geirhos2021/high-pass/high-pass_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/high-pass/high-pass_data_assembly.py diff --git a/packaging/geirhos2021/high-pass/high-pass_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/high-pass/high-pass_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/high-pass/high-pass_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/high-pass/high-pass_stimulus_set.py diff --git a/packaging/geirhos2021/low-pass/low-pass_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/low-pass/low-pass_data_assembly.py similarity index 100% rename from packaging/geirhos2021/low-pass/low-pass_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/low-pass/low-pass_data_assembly.py diff --git a/packaging/geirhos2021/low-pass/low-pass_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/low-pass/low-pass_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/low-pass/low-pass_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/low-pass/low-pass_stimulus_set.py diff --git a/packaging/geirhos2021/phase-scrambling/phase-scrambling_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/phase-scrambling/phase-scrambling_data_assembly.py similarity index 100% rename from packaging/geirhos2021/phase-scrambling/phase-scrambling_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/phase-scrambling/phase-scrambling_data_assembly.py diff --git a/packaging/geirhos2021/phase-scrambling/phase-scrambling_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/phase-scrambling/phase-scrambling_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/phase-scrambling/phase-scrambling_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/phase-scrambling/phase-scrambling_stimulus_set.py diff --git a/packaging/geirhos2021/power-equalisation/power-equalisation_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/power-equalisation/power-equalisation_data_assembly.py similarity index 100% rename from packaging/geirhos2021/power-equalisation/power-equalisation_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/power-equalisation/power-equalisation_data_assembly.py diff --git a/packaging/geirhos2021/power-equalisation/power-equalisation_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/power-equalisation/power-equalisation_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/power-equalisation/power-equalisation_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/power-equalisation/power-equalisation_stimulus_set.py diff --git a/packaging/geirhos2021/rotation/rotation_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/rotation/rotation_data_assembly.py similarity index 100% rename from packaging/geirhos2021/rotation/rotation_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/rotation/rotation_data_assembly.py diff --git a/packaging/geirhos2021/rotation/rotation_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/rotation/rotation_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/rotation/rotation_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/rotation/rotation_stimulus_set.py diff --git a/packaging/geirhos2021/silhouette/silhouette_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/silhouette/silhouette_data_assembly.py similarity index 100% rename from packaging/geirhos2021/silhouette/silhouette_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/silhouette/silhouette_data_assembly.py diff --git a/packaging/geirhos2021/silhouette/silhouette_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/silhouette/silhouette_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/silhouette/silhouette_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/silhouette/silhouette_stimulus_set.py diff --git a/packaging/geirhos2021/sketch/sketch_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/sketch/sketch_data_assembly.py similarity index 100% rename from packaging/geirhos2021/sketch/sketch_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/sketch/sketch_data_assembly.py diff --git a/packaging/geirhos2021/sketch/sketch_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/sketch/sketch_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/sketch/sketch_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/sketch/sketch_stimulus_set.py diff --git a/packaging/geirhos2021/stylized/stylized_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/stylized/stylized_data_assembly.py similarity index 100% rename from packaging/geirhos2021/stylized/stylized_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/stylized/stylized_data_assembly.py diff --git a/packaging/geirhos2021/stylized/stylized_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/stylized/stylized_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/stylized/stylized_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/stylized/stylized_stimulus_set.py diff --git a/packaging/geirhos2021/uniform-noise/uniform-noise_data_assembly.py b/brainscore_vision/data/geirhos2021/data_packaging/uniform-noise/uniform-noise_data_assembly.py similarity index 100% rename from packaging/geirhos2021/uniform-noise/uniform-noise_data_assembly.py rename to brainscore_vision/data/geirhos2021/data_packaging/uniform-noise/uniform-noise_data_assembly.py diff --git a/packaging/geirhos2021/uniform-noise/uniform-noise_stimulus_set.py b/brainscore_vision/data/geirhos2021/data_packaging/uniform-noise/uniform-noise_stimulus_set.py similarity index 100% rename from packaging/geirhos2021/uniform-noise/uniform-noise_stimulus_set.py rename to brainscore_vision/data/geirhos2021/data_packaging/uniform-noise/uniform-noise_stimulus_set.py diff --git a/brainscore_vision/data/geirhos2021/geirhos_hashes.csv b/brainscore_vision/data/geirhos2021/geirhos_hashes.csv new file mode 100644 index 000000000..e62775a4e --- /dev/null +++ b/brainscore_vision/data/geirhos2021/geirhos_hashes.csv @@ -0,0 +1,52 @@ +identifier,lookup_type,class,location_type,location,sha1,stimulus_set_identifier,lookup_source +brendel.Geirhos2021_colour,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_colour.nc,258862d82467614e45cc1e488a5ac909eb6e122d,brendel.Geirhos2021_colour,brainio_brainscore +brendel.Geirhos2021_colour,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_colour.csv,9c97c155fd6039a95978be89eb604c6894c5fa16,,brainio_brainscore +brendel.Geirhos2021_colour,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_colour.zip,d166f1d3dc3d00c4f51a489e6fcf96dbbe778d2c,,brainio_brainscore +brendel.Geirhos2021_contrast,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_contrast.nc,1e114c987dc035ccca43781ff8cee9689acd3c3f,brendel.Geirhos2021_contrast,brainio_brainscore +brendel.Geirhos2021_contrast,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_contrast.csv,f6ef69a2e8937e1d0d83c8a21b325b4273494cb5,,brainio_brainscore +brendel.Geirhos2021_contrast,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_contrast.zip,ebeeef2f9c6a7282e20ef2026dc77eefa026957b,,brainio_brainscore +brendel.Geirhos2021_cue-conflict,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_cue-conflict.nc,cc214e3595d34565b13963c5f56049769a39a5c9,brendel.Geirhos2021_cue-conflict,brainio_brainscore +brendel.Geirhos2021_cue-conflict,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_cue-conflict.csv,8d3ae89d8870fb7d7c5d5ff387085b1f0116e2b7,,brainio_brainscore +brendel.Geirhos2021_cue-conflict,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_cue-conflict.zip,7e601186b181102939cd0b43a4e8a3ca95c18259,,brainio_brainscore +brendel.Geirhos2021_edge,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_edge.nc,ab1dc9e188e248da07215b375eb3dbcc58fde7fb,brendel.Geirhos2021_edge,brainio_brainscore +brendel.Geirhos2021_edge,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_edge.csv,fb57005ecb80e4e37b01e084f1f176fe7f59ff7f,,brainio_brainscore 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+brendel.Geirhos2021_eidolonIII,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_eidolonIII.csv,ba0173b315f02df16d418dc3ff1df7dc498b4893,,brainio_brainscore +brendel.Geirhos2021_eidolonIII,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_eidolonIII.zip,d0304c0c0024d0f493ea9c0c47ae0221da391016,,brainio_brainscore +brendel.Geirhos2021_false-colour,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_false-colour.nc,4dc072264651c81575564ba4818a12b8e8039c65,brendel.Geirhos2021_false-colour,brainio_brainscore +brendel.Geirhos2021_false-colour,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_false-colour.csv,8a09a7af8ec44339bcae5500ae5900d9c4309042,,brainio_brainscore +brendel.Geirhos2021_false-colour,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_false-colour.zip,ec0ba347fc14d0c0587d38bfa96e4ab5d2f7979a,,brainio_brainscore +brendel.Geirhos2021_high-pass,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_high-pass.nc,5df45c69127758f1ba3391671c521711050e3b4d,brendel.Geirhos2021_high-pass,brainio_brainscore +brendel.Geirhos2021_high-pass,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_high-pass.csv,ddf523dcf43398cc15894c7b51c436d526e6c992,,brainio_brainscore +brendel.Geirhos2021_high-pass,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_high-pass.zip,12322bb17270a5dde206314fcdc125c4bb235e3b,,brainio_brainscore +brendel.Geirhos2021_low-pass,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_low-pass.nc,75ab628d9e6d0d634290567b1cb261d7f8dc61e2,brendel.Geirhos2021_low-pass,brainio_brainscore +brendel.Geirhos2021_low-pass,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_low-pass.csv,1264f9be407c7d428cf3d62a7bb1b1bb45a821bc,,brainio_brainscore +brendel.Geirhos2021_low-pass,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_low-pass.zip,ad087676e04e51adadea7c7c5c1fa28e4dd6360c,,brainio_brainscore +brendel.Geirhos2021_phase-scrambling,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_phase-scrambling.nc,4124f9f5b86fb6ed82c98197d292eef50b608aba,brendel.Geirhos2021_phase-scrambling,brainio_brainscore +brendel.Geirhos2021_phase-scrambling,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_phase-scrambling.csv,0cc87f7ac42c2266f98d3a08783f7173499ec2fc,,brainio_brainscore +brendel.Geirhos2021_phase-scrambling,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_phase-scrambling.zip,462e77ab9533072b7036118f1f697e8c9bf30ae4,,brainio_brainscore +brendel.Geirhos2021_power-equalisation,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_power-equalisation.nc,0aba1b50a7e0802d76c41d332a121a3f96ef4f7d,brendel.Geirhos2021_power-equalisation,brainio_brainscore +brendel.Geirhos2021_power-equalisation,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_power-equalisation.csv,743935476b1fe4b7bd4d01a4eed24cd9ed5b3a22,,brainio_brainscore +brendel.Geirhos2021_power-equalisation,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_power-equalisation.zip,a0d5307525bccf8da8f3d293e7e324b9b20248c6,,brainio_brainscore +brendel.Geirhos2021_rotation,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_rotation.nc,e51a5c3bc95ade159e71aa602232063730bcd57b,brendel.Geirhos2021_rotation,brainio_brainscore +brendel.Geirhos2021_rotation,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_rotation.csv,2577831e9ead905669613fa17cb2651d0c48a455,,brainio_brainscore +brendel.Geirhos2021_rotation,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_rotation.zip,8f5d9cb217807e96ace61337144e429d0d4ba04c,,brainio_brainscore +brendel.Geirhos2021_silhouette,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_silhouette.nc,7dc94991465fe8009244e0d6fb8283419a1f9885,brendel.Geirhos2021_silhouette,brainio_brainscore +brendel.Geirhos2021_silhouette,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_silhouette.csv,fb57005ecb80e4e37b01e084f1f176fe7f59ff7f,,brainio_brainscore +brendel.Geirhos2021_silhouette,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_silhouette.zip,36c8a481f7876a2af2ad4fe80890b302fe3ae91e,,brainio_brainscore +brendel.Geirhos2021_sketch,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_sketch.nc,6709850864cea16d99a29fb31ae3c4a489983562,brendel.Geirhos2021_sketch,brainio_brainscore +brendel.Geirhos2021_sketch,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_sketch.csv,a5741b2f7bd08541a0dbefd7fb2d6a3845ca800b,,brainio_brainscore +brendel.Geirhos2021_sketch,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_sketch.zip,8e8712f08a5ad3655ea2bd8cd675db8cdf65129a,,brainio_brainscore +brendel.Geirhos2021_stylized,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_stylized.nc,dcf15f292e787a88e1e0f271e6b2838d6bdadfd3,brendel.Geirhos2021_stylized,brainio_brainscore +brendel.Geirhos2021_stylized,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_stylized.csv,2265a540dee6915150bf7c61143eaf788f603866,,brainio_brainscore +brendel.Geirhos2021_stylized,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_stylized.zip,75d273e8de643b0d814fbe60cd237c31ebe19c44,,brainio_brainscore +brendel.Geirhos2021_uniform-noise,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_uniform-noise.nc,f5e8b2636738f978c71591b8df6f8a21a66b72d1,brendel.Geirhos2021_uniform-noise,brainio_brainscore +brendel.Geirhos2021_uniform-noise,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_uniform-noise.csv,89b62a6af878974d388278ed0e23e8ed1c2fd855,,brainio_brainscore +brendel.Geirhos2021_uniform-noise,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_uniform-noise.zip,ff4566542d65056028660293e2409b532e887714,,brainio_brainscore diff --git a/brainscore_vision/data/geirhos2021/test.py b/brainscore_vision/data/geirhos2021/test.py new file mode 100644 index 000000000..45c404450 --- /dev/null +++ b/brainscore_vision/data/geirhos2021/test.py @@ -0,0 +1,330 @@ +import numpy as np +import pytest + +from brainscore_vision import load_stimulus_set, load_dataset +from brainscore_vision.benchmarks.geirhos2021.benchmark import DATASETS + + +def test_count(): + assert len(DATASETS) == 12 + 5 + + +@pytest.mark.parametrize('assembly_identifier', [ + pytest.param('brendel.Geirhos2021_colour', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_contrast', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_cue-conflict', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_edge', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_eidolonI', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_eidolonII', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_eidolonIII', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_false-colour', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_high-pass', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_low-pass', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_phase-scrambling', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_power-equalisation', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_rotation', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_silhouette', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_stylized', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_sketch', marks=[pytest.mark.private_access]), + pytest.param('brendel.Geirhos2021_uniform-noise', marks=[pytest.mark.private_access]), +]) +def test_existence(assembly_identifier): + assert load_dataset(assembly_identifier) is not None + + +class TestAssemblies: + # test stimulus_set data alignment with assembly: + @pytest.mark.parametrize('identifier', [ + 'colour', + 'contrast', + 'cue-conflict', + 'edge', + 'eidolonI', + 'eidolonII', + 'eidolonIII', + 'false-colour', + 'high-pass', + 'low-pass', + 'phase-scrambling', + 'power-equalisation', + 'rotation', + 'silhouette', + 'stylized', + 'sketch', + 'uniform-noise', + ]) + @pytest.mark.parametrize('field', [ + 'image_id', + 'condition', + 'truth', + ]) + def test_stimulus_set_assembly_alignment(self, identifier, field): + full_name = f"brendel.Geirhos2021_{identifier}" + assembly = load_dataset(full_name) + assert assembly.stimulus_set is not None + assert assembly.stimulus_set.identifier == full_name + assert set(assembly.stimulus_set[field]) == set(assembly[field].values) + + # test the number of subjects: + @pytest.mark.parametrize('identifier, num_subjects', [ + ('colour', 4), + ('contrast', 4), + ('cue-conflict', 10), + ('edge', 10), + ('eidolonI', 4), + ('eidolonII', 4), + ('eidolonIII', 4), + ('false-colour', 4), + ('high-pass', 4), + ('low-pass', 4), + ('phase-scrambling', 4), + ('power-equalisation', 4), + ('rotation', 4), + ('silhouette', 10), + ('stylized', 5), + ('sketch', 7), + ('uniform-noise', 4), + ]) + def test_num_subjects(self, identifier, num_subjects): + assembly = load_dataset(f"brendel.Geirhos2021_{identifier}") + assert len(np.unique(assembly['subject'].values)) == num_subjects + + # test the number of images + @pytest.mark.parametrize('identifier, num_images', [ + ('colour', 1280), + ('contrast', 1280), + ('cue-conflict', 1280), + ('edge', 160), + ('eidolonI', 1280), + ('eidolonII', 1280), + ('eidolonIII', 1280), + ('false-colour', 1120), + ('high-pass', 1280), + ('low-pass', 1280), + ('phase-scrambling', 1120), + ('power-equalisation', 1120), + ('rotation', 1280), + ('silhouette', 160), + ('stylized', 800), + ('sketch', 800), + ('uniform-noise', 1280), + ]) + def test_num_images(self, identifier, num_images): + assembly = load_dataset(f"brendel.Geirhos2021_{identifier}") + assert len(np.unique(assembly['image_id'].values)) == num_images + + # tests assembly dim for ALL 17 sets: + @pytest.mark.parametrize('identifier, length', [ + ('colour', 5120), + ('contrast', 5120), + ('cue-conflict', 12800), + ('edge', 1600), + ('eidolonI', 5120), + ('eidolonII', 5120), + ('eidolonIII', 5120), + ('false-colour', 4480), + ('high-pass', 5120), + ('low-pass', 5120), + ('phase-scrambling', 4480), + ('power-equalisation', 4480), + ('rotation', 5120), + ('silhouette', 1600), + ('stylized', 4000), + ('sketch', 5600), + ('uniform-noise', 5120), + ]) + def test_length(self, identifier, length): + assembly = load_dataset(f"brendel.Geirhos2021_{identifier}") + assert len(assembly['presentation']) == length + + # test assembly coords present in ALL 17 sets: + @pytest.mark.parametrize('identifier', [ + 'colour', + 'contrast', + 'cue-conflict', + 'edge', + 'eidolonI', + 'eidolonII', + 'eidolonIII', + 'false-colour', + 'high-pass', + 'low-pass', + 'phase-scrambling', + 'power-equalisation', + 'rotation', + 'silhouette', + 'stylized', + 'sketch', + 'uniform-noise', + ]) + @pytest.mark.parametrize('field', [ + 'image_id', + 'image_id_long', + 'choice', + 'truth', + 'condition', + 'response_time', + 'trial', + 'subject', + 'session', + ]) + def test_fields_present(self, identifier, field): + assembly = load_dataset(f"brendel.Geirhos2021_{identifier}") + assert hasattr(assembly, field) + + # tests assembly coords for the 2 "abnormal" sets: + @pytest.mark.parametrize('identifier', [ + 'edge', + 'silhouette', + ]) + @pytest.mark.parametrize('field', [ + 'image_id', + 'image_category', + 'truth', + 'image_variation', + 'condition', + ]) + def test_fields_present_abnormal_sets(self, identifier, field): + assembly = load_dataset(f"brendel.Geirhos2021_{identifier}") + assert hasattr(assembly, field) + + # tests assembly coords for the cue-conflict different set: + @pytest.mark.parametrize('identifier', [ + 'cue-conflict', + ]) + @pytest.mark.parametrize('field', [ + 'image_id', + 'original_image', + 'truth', + 'category', + 'conflict_image', + 'original_image_category', + 'original_image_variation', + 'conflict_image_category', + 'conflict_image_variation', + 'condition', + ]) + def test_fields_present_cue_conflict(self, identifier, field): + assembly = load_dataset(f"brendel.Geirhos2021_{identifier}") + assert hasattr(assembly, field) + + +# testing stimulus sets +@pytest.mark.slow +class TestStimulusSets: + # test stimulus_set data: + @pytest.mark.parametrize('identifier', [ + 'colour', + 'contrast', + 'cue-conflict', + 'edge', + 'eidolonI', + 'eidolonII', + 'eidolonIII', + 'false-colour', + 'high-pass', + 'low-pass', + 'phase-scrambling', + 'power-equalisation', + 'rotation', + 'silhouette', + 'stylized', + 'sketch', + 'uniform-noise', + ]) + def test_stimulus_set_exist(self, identifier): + full_name = f"brendel.Geirhos2021_{identifier}" + stimulus_set = load_stimulus_set(full_name) + assert stimulus_set is not None + assert stimulus_set.identifier == full_name + + # test the number of images + @pytest.mark.parametrize('identifier, num_images', [ + ('colour', 1280), + ('contrast', 1280), + ('cue-conflict', 1280), + ('edge', 160), + ('eidolonI', 1280), + ('eidolonII', 1280), + ('eidolonIII', 1280), + ('false-colour', 1120), + ('high-pass', 1280), + ('low-pass', 1280), + ('phase-scrambling', 1120), + ('power-equalisation', 1120), + ('rotation', 1280), + ('silhouette', 160), + ('stylized', 800), + ('sketch', 800), + ('uniform-noise', 1280), + ]) + def test_num_images(self, identifier, num_images): + stimulus_set = load_stimulus_set(f"brendel.Geirhos2021_{identifier}") + assert len(np.unique(stimulus_set['image_id'].values)) == num_images + + # tests stimulus_set coords for the 14 "normal" sets: + @pytest.mark.parametrize('identifier', [ + 'colour', + 'contrast', + 'eidolonI', + 'eidolonII', + 'eidolonIII', + 'false-colour', + 'high-pass', + 'low-pass', + 'phase-scrambling', + 'power-equalisation', + 'rotation', + 'stylized', + 'sketch', + 'uniform-noise', + ]) + @pytest.mark.parametrize('field', [ + 'image_id', + 'image_id_long', + 'image_number', + 'experiment_code', + 'condition', + 'truth', + 'category_ground_truth', + 'random_number', + ]) + def test_fields_present(self, identifier, field): + stimulus_set = load_stimulus_set(f"brendel.Geirhos2021_{identifier}") + assert hasattr(stimulus_set, field) + + # tests assembly coords for the 2 "abnormal" sets: + @pytest.mark.parametrize('identifier', [ + 'edge', + 'silhouette', + ]) + @pytest.mark.parametrize('field', [ + 'image_id', + 'image_category', + 'truth', + 'image_variation', + 'condition', + ]) + def test_fields_present2(self, identifier, field): + stimulus_set = load_dataset(f"brendel.Geirhos2021_{identifier}") + assert hasattr(stimulus_set, field) + + # test assembly fields for cue-conflict's odd stimulus_set: + @pytest.mark.parametrize('identifier', [ + 'cue-conflict', + ]) + @pytest.mark.parametrize('field', [ + 'image_id', + 'original_image', + 'truth', + 'category', + 'conflict_image', + 'original_image_category', + 'original_image_variation', + 'conflict_image_category', + 'conflict_image_variation', + 'condition', + ]) + def test_fields_present3(self, identifier, field): + stimulus_set = load_dataset(f"brendel.Geirhos2021_{identifier}") + assert hasattr(stimulus_set, field) diff --git a/brainscore_vision/data/hendrycks2019/__init__.py b/brainscore_vision/data/hendrycks2019/__init__.py new file mode 100644 index 000000000..c7ee51172 --- /dev/null +++ b/brainscore_vision/data/hendrycks2019/__init__.py @@ -0,0 +1,45 @@ +from brainscore_vision import stimulus_set_registry +from brainscore_vision.data_helpers.s3 import load_stimulus_set_from_s3 + +BIBTEX = """@article{hendrycks2019benchmarking, + title={Benchmarking neural network robustness to common corruptions and perturbations}, + author={Hendrycks, Dan and Dietterich, Thomas}, + journal={arXiv preprint arXiv:1903.12261}, + year={2019} +}""" + +# stimulus set: dietterich.Hendrycks2019.noise +stimulus_set_registry['dietterich.Hendrycks2019.noise'] = lambda: load_stimulus_set_from_s3( + identifier="dietterich.Hendrycks2019.noise", + bucket="brainio-brainscore", + csv_sha1="56f445e058b4d825e7731711c824918812ed2d2d", + zip_sha1="e3c46b81bfd8a522cadcd8a4bb0c67bb5ccb4c6a", + csv_version_id="oR0_anEdzvyqgLSWz8.uLbzYeMQR2uem", + zip_version_id="Hcbdp9m83Ajoxc5nqWDB0zi9UFplB1w7") + +# stimulus set: dietterich.Hendrycks2019.blur +stimulus_set_registry['dietterich.Hendrycks2019.blur'] = lambda: load_stimulus_set_from_s3( + identifier="dietterich.Hendrycks2019.blur", + bucket="brainio-brainscore", + csv_sha1="e7a537bb2f3f94b9cd3819a529de9f4349e58bd2", + zip_sha1="85bac6d7c9b9646b22480f65cf6f1486fcf4b488", + csv_version_id="WegyPp9lBJ5jw_hQDhRN8mKQ8aRsCIzv", + zip_version_id="IIF74vl5z75b65umHvgn5nds8XHz05Jd") + +# stimulus set: dietterich.Hendrycks2019.weather +stimulus_set_registry['dietterich.Hendrycks2019.weather'] = lambda: load_stimulus_set_from_s3( + identifier="dietterich.Hendrycks2019.weather", + bucket="brainio-brainscore", + csv_sha1="02fa92430be754163ecd06ee9211c1edd8984207", + zip_sha1="18ef85343b10a4a326b5fc0758b9ae1da58f7d90", + csv_version_id=".2f.Ze0pLG0.qgPxiVAjMQAI.QMzSg5S", + zip_version_id="hy.5apU1C13feZSHOR90daDHDc5oRtVk") + +# stimulus set: dietterich.Hendrycks2019.digital +stimulus_set_registry['dietterich.Hendrycks2019.digital'] = lambda: load_stimulus_set_from_s3( + identifier="dietterich.Hendrycks2019.digital", + bucket="brainio-brainscore", + csv_sha1="1e9611b560333989d4673bfd019160a27842f89b", + zip_sha1="a10e8d7eabce191d1f8e92983c08b4f0fe0f435d", + csv_version_id="f_xxGK_dwzOyy8tM4hmSnnIlRa1RwCiF", + zip_version_id="3eblycjaIWN7E4lCcQwMRtRBSotiCxun") diff --git a/brainscore_vision/data/hendrycks2019/test.py b/brainscore_vision/data/hendrycks2019/test.py new file mode 100644 index 000000000..c33827ada --- /dev/null +++ b/brainscore_vision/data/hendrycks2019/test.py @@ -0,0 +1,26 @@ +import pytest +from brainscore_vision import load_stimulus_set + + +@pytest.mark.private_access +@pytest.mark.slow +class TestDietterichHendrycks2019: + def test_noise(self): + stimulus_set = load_stimulus_set('dietterich.Hendrycks2019.noise') + assert len(stimulus_set) == 3 * 5 * 50000 + assert len(set(stimulus_set['synset'])) == 1000 + + def test_blur(self): + stimulus_set = load_stimulus_set('dietterich.Hendrycks2019.blur') + assert len(stimulus_set) == 4 * 5 * 50000 + assert len(set(stimulus_set['synset'])) == 1000 + + def test_weather(self): + stimulus_set = load_stimulus_set('dietterich.Hendrycks2019.weather') + assert len(stimulus_set) == 4 * 5 * 50000 + assert len(set(stimulus_set['synset'])) == 1000 + + def test_digital(self): + stimulus_set = load_stimulus_set('dietterich.Hendrycks2019.digital') + assert len(stimulus_set) == 4 * 5 * 50000 + assert len(set(stimulus_set['synset'])) == 1000 \ No newline at end of file diff --git a/packaging/dicarlo/ImageNetSlim15000.py b/brainscore_vision/data/imagenetslim15000/ImageNetSlim15000.py similarity index 94% rename from packaging/dicarlo/ImageNetSlim15000.py rename to brainscore_vision/data/imagenetslim15000/ImageNetSlim15000.py index ac748e990..78b4d214a 100644 --- a/packaging/dicarlo/ImageNetSlim15000.py +++ b/brainscore_vision/data/imagenetslim15000/ImageNetSlim15000.py @@ -23,7 +23,7 @@ def main(): def package(stimulus_set): print("Packaging stimuli") - package_stimulus_set(stimulus_set, stimulus_set_identifier=stimulus_set.identifier, bucket_name = 'brainio.requested') + package_stimulus_set(stimulus_set, stimulus_set_identifier=stimulus_set.identifier, bucket_name= 'brainio.requested') if __name__ == '__main__': diff --git a/brainscore_vision/data/imagenetslim15000/__init__.py b/brainscore_vision/data/imagenetslim15000/__init__.py new file mode 100644 index 000000000..33c2cd078 --- /dev/null +++ b/brainscore_vision/data/imagenetslim15000/__init__.py @@ -0,0 +1,14 @@ +from brainscore_vision import stimulus_set_registry +from brainscore_vision.data_helpers.s3 import load_stimulus_set_from_s3 + +BIBTEX = """""" + + +# stimulus set +stimulus_set_registry['dicarlo.ImageNetSlim15000'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.ImageNetSlim15000", + bucket="brainio-brainscore", + csv_sha1="56f79de36060147bc43ae89778e26d50f73c3a63", + zip_sha1="633d08474e3067add834f4ddf552642c538db112", + csv_version_id="GJ7u6Aq6nbjMAPL1YeHOxrGhhZVfJ4Uh", + zip_version_id="oKEfsCxamcNmJpo7IvEsWok81ODaeIUW") diff --git a/brainscore_vision/data/kar2018/__init__.py b/brainscore_vision/data/kar2018/__init__.py new file mode 100644 index 000000000..6ddd8250c --- /dev/null +++ b/brainscore_vision/data/kar2018/__init__.py @@ -0,0 +1,58 @@ +from brainio.assemblies import NeuronRecordingAssembly + +from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set +from brainscore_vision.metrics.internal_consistency.ceiling import InternalConsistency +from brainscore_vision.metric_helpers.transformations import CrossValidation +from brainscore_vision.data_helpers.s3 import load_assembly_from_s3, load_stimulus_set_from_s3 + +BIBTEX = """@article {19, + title = {Evidence that recurrent circuits are critical to the ventral stream{\textquoteright}s execution of core object recognition behavior}, + journal = {bioRxiv}, + year = {2018}, + month = {06/2018}, + type = {preprint}, + abstract = {

Non-recurrent deep convolutional neural networks (DCNNs) are currently the best models of core object recognition; a behavior supported by the densely recurrent primate ventral stream, culminating in the inferior temporal (IT) cortex. Are these recurrent circuits critical to ventral stream\&$\#$39;s execution of this behavior? We reasoned that, if recurrence is critical, then primates should outperform feedforward-only DCNNs for some images, and that these images should require additional processing time beyond the feedforward IT response. Here we first used behavioral methods to discover hundreds of these \"challenge\" images. Second, using large-scale IT electrophysiology in animals performing core recognition tasks, we observed that behaviorally-sufficient, linearly-decodable object identity solutions emerged ~30ms (on average) later in IT for challenge images compared to DCNN and primate performance-matched \"control\" images. We observed these same late solutions even during passive viewing. Third, consistent with a failure of feedforward computations, the behaviorally-critical late-phase IT population response patterns evoked by the challenge images were poorly predicted by DCNN activations. Interestingly, deeper CNNs better predicted these late IT responses, suggesting a functional equivalence between recurrence and additional nonlinear transformations. Our results argue that automatically-evoked recurrent circuits are critical even for rapid object identification. By precisely comparing current DCNNs, primate behavior and IT population dynamics, we provide guidance for future recurrent model development.

+}, + doi = {https://doi.org/10.1101/354753}, + url = {https://www.biorxiv.org/content/10.1101/354753v1.full.pdf}, + author = {Kar, Kohitij and Kubilius, Jonas and Schmidt, Kailyn and Issa, Elias B and DiCarlo, James J.} +}""" + +# assemblies: hvm +data_registry['dicarlo.Kar2018hvm'] = lambda: load_assembly_from_s3( + identifier="dicarlo.Kar2018hvm", + version_id="4sytUtSGiyB.G0oBmPCVnnQ6l4FChj8z", + sha1="96ccacc76c5fa30ee68bdc8736d1d43ace93f3e7", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.hvm'), +) + +# assemblies: cocogray +data_registry['dicarlo.Kar2018cocogray'] = lambda: load_assembly_from_s3( + identifier="dicarlo.Kar2018cocogray", + version_id="RxiK296HHAe2ql_STmZ2K..uEsfCuHtF", + sha1="4202cb3992a5d71f71a7ca9e28ba3f8b27937b43", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.Kar2018cocogray'), +) + +# stimulus set: cocogray +stimulus_set_registry['dicarlo.Kar2018cocogray'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.Kar2018cocogray", + bucket="brainio-brainscore", + csv_sha1="be9bb267b80fd7ee36a88d025b73ae8a849165da", + zip_sha1="1457003ee9b27ed51c018237009fe148c6e71fd3", + csv_version_id="3Y1o11OuW8dmEyqJ7WHxrKCTqacBC20_", + zip_version_id="bnYW48AQ7DoLBzWY5Bx0IZ9r8_bwvWRC") + + +def filter_neuroids(assembly, threshold): + ceiler = InternalConsistency() + ceiling = ceiler(assembly) + ceiling = ceiling.raw + ceiling = CrossValidation().aggregate(ceiling) + pass_threshold = ceiling >= threshold + assembly = assembly[{'neuroid': pass_threshold}] + return assembly diff --git a/packaging/dicarlo/kar2018/kar_coco.py b/brainscore_vision/data/kar2018/data_packaging/kar_coco.py similarity index 100% rename from packaging/dicarlo/kar2018/kar_coco.py rename to brainscore_vision/data/kar2018/data_packaging/kar_coco.py diff --git a/packaging/dicarlo/kar2018/kar_hvm.py b/brainscore_vision/data/kar2018/data_packaging/kar_hvm.py similarity index 100% rename from packaging/dicarlo/kar2018/kar_hvm.py rename to brainscore_vision/data/kar2018/data_packaging/kar_hvm.py diff --git a/packaging/dicarlo/kar2018/requirements.txt b/brainscore_vision/data/kar2018/data_packaging/requirements.txt similarity index 100% rename from packaging/dicarlo/kar2018/requirements.txt rename to brainscore_vision/data/kar2018/data_packaging/requirements.txt diff --git a/brainscore_vision/data/kar2018/test.py b/brainscore_vision/data/kar2018/test.py new file mode 100644 index 000000000..ba44b0af3 --- /dev/null +++ b/brainscore_vision/data/kar2018/test.py @@ -0,0 +1,10 @@ +import brainscore_vision +import pytest + + +@pytest.mark.parametrize('assembly_identifier', [ + pytest.param('dicarlo.Kar2018hvm', marks=[pytest.mark.private_access]), + pytest.param('dicarlo.Kar2018cocogray', marks=[pytest.mark.private_access]), +]) +def test_existence(assembly_identifier): + assert brainscore_vision.load_dataset(assembly_identifier) is not None \ No newline at end of file diff --git a/brainscore_vision/data/kar2019/__init__.py b/brainscore_vision/data/kar2019/__init__.py new file mode 100644 index 000000000..cbf257ab8 --- /dev/null +++ b/brainscore_vision/data/kar2019/__init__.py @@ -0,0 +1,43 @@ +from brainio.assemblies import DataAssembly + +from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set +from brainscore_vision.data_helpers.s3 import load_assembly_from_s3, load_stimulus_set_from_s3 + +BIBTEX = """@Article{Kar2019, + author={Kar, Kohitij + and Kubilius, Jonas + and Schmidt, Kailyn + and Issa, Elias B. + and DiCarlo, James J.}, + title={Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior}, + journal={Nature Neuroscience}, + year={2019}, + month={Jun}, + day={01}, + volume={22}, + number={6}, + pages={974-983}, + abstract={Non-recurrent deep convolutional neural networks (CNNs) are currently the best at modeling core object recognition, a behavior that is supported by the densely recurrent primate ventral stream, culminating in the inferior temporal (IT) cortex. If recurrence is critical to this behavior, then primates should outperform feedforward-only deep CNNs for images that require additional recurrent processing beyond the feedforward IT response. Here we first used behavioral methods to discover hundreds of these `challenge' images. Second, using large-scale electrophysiology, we observed that behaviorally sufficient object identity solutions emerged {\textasciitilde}30{\thinspace}ms later in the IT cortex for challenge images compared with primate performance-matched `control' images. Third, these behaviorally critical late-phase IT response patterns were poorly predicted by feedforward deep CNN activations. Notably, very-deep CNNs and shallower recurrent CNNs better predicted these late IT responses, suggesting that there is a functional equivalence between additional nonlinear transformations and recurrence. Beyond arguing that recurrent circuits are critical for rapid object identification, our results provide strong constraints for future recurrent model development.}, + issn={1546-1726}, + doi={10.1038/s41593-019-0392-5}, + url={https://doi.org/10.1038/s41593-019-0392-5} + }""" + +# assembly +data_registry['dicarlo.Kar2019'] = lambda: load_assembly_from_s3( + identifier="dicarlo.Kar2019", + version_id="mQZ1nNRfE_m_IjFbox.xvlJvbudFwsWo", + sha1="147717ce397e11d56164d472063a84a83bbcbb94", + bucket="brainio-brainscore", + cls=DataAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.Kar2019'), +) + +# stimulus set +stimulus_set_registry['dicarlo.Kar2019'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.Kar2019", + bucket="brainio-brainscore", + csv_sha1="7f705bdea02c0a72a76d7f5e7b6963531df818a6", + zip_sha1="75ab7b8b499fc8e86c813f717b79d268bcb986be", + csv_version_id="CkijGNImwMjh2uTQ2iLzkPSlDCJcURHn", + zip_version_id="0JL0S_SGuysrnJ4zuGH_pYo8kmIvksFJ") diff --git a/packaging/dicarlo/kar2019ost.py b/brainscore_vision/data/kar2019/data_packaging.py similarity index 90% rename from packaging/dicarlo/kar2019ost.py rename to brainscore_vision/data/kar2019/data_packaging.py index dc40b32b7..76e0cae4b 100644 --- a/packaging/dicarlo/kar2019ost.py +++ b/brainscore_vision/data/kar2019/data_packaging.py @@ -14,6 +14,12 @@ from brainio_base.stimuli import StimulusSet from brainio_collection.knownfile import KnownFile from brainio_contrib.packaging import package_stimulus_set, package_data_assembly +from brainscore_vision.data_helpers.lookup_legacy import version_id_df, build_filename + +# extract version ids from version_ids csv +assembly_version = version_id_df.at[build_filename('dicarlo.Kar2019', '.nc'), 'version_id'] +csv_version = version_id_df.at[build_filename('dicarlo.Kar2019', '.csv'), 'version_id'] +zip_version = version_id_df.at[build_filename('dicarlo.Kar2019', '.zip'), 'version_id'] object_lookup = { 1: 'bear', diff --git a/brainscore_vision/data/kar2019/test.py b/brainscore_vision/data/kar2019/test.py new file mode 100644 index 000000000..7ce52a39b --- /dev/null +++ b/brainscore_vision/data/kar2019/test.py @@ -0,0 +1,8 @@ +import pytest + +import brainscore_vision + + +@pytest.mark.private_access +def test_existence(): + assert brainscore_vision.load_dataset('dicarlo.Kar2019') is not None diff --git a/brainscore_vision/data/kuzovkin2018/__init__.py b/brainscore_vision/data/kuzovkin2018/__init__.py new file mode 100644 index 000000000..d010044e8 --- /dev/null +++ b/brainscore_vision/data/kuzovkin2018/__init__.py @@ -0,0 +1,35 @@ +from brainio.assemblies import NeuronRecordingAssembly + +from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set +from brainscore_vision.data_helpers.s3 import load_stimulus_set_from_s3, load_assembly_from_s3 + +BIBTEX = """@article{kuzovkin2018activations, + title={Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex}, + author={Kuzovkin, Ilya and Vicente, Raul and Petton, Mathilde and Lachaux, Jean-Philippe and Baciu, Monica and Kahane, Philippe and Rheims, Sylvain and Vidal, Juan R and Aru, Jaan}, + journal={Communications biology}, + volume={1}, + number={1}, + pages={1--12}, + year={2018}, + publisher={Nature Publishing Group} +}""" + + +# assembly +data_registry['aru.Kuzovkin2018'] = lambda: load_assembly_from_s3( + identifier="aru.Kuzovkin2018", + version_id="nk5w.m3N1D4fWg2PeY9_AJb5yY6UtPeM", + sha1="5fae8b283a043562ce9925d48ad99db151f39067", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('aru.Kuzovkin2018'), +) + +# stimulus set +stimulus_set_registry['aru.Kuzovkin2018'] = lambda: load_stimulus_set_from_s3( + identifier="aru.Kuzovkin2018", + bucket="brainio-brainscore", + csv_sha1="a5990b24aea3e453756141cbe69a83304db72d0b", + zip_sha1="cca4d819d7743bdd4bf65c1cb2439fd0ec97543a", + csv_version_id="XcZTPe65cRJ_vMibAAQOpKyXin0MDWnG", + zip_version_id="zg5gixyw1mI3e.45CH.O81v8Jqb3dgJp") diff --git a/packaging/aru/Kuzovkin2018/createAssembliesBrainScore.py b/brainscore_vision/data/kuzovkin2018/createAssembliesBrainScore.py similarity index 100% rename from packaging/aru/Kuzovkin2018/createAssembliesBrainScore.py rename to brainscore_vision/data/kuzovkin2018/createAssembliesBrainScore.py diff --git a/brainscore_vision/data/kuzovkin2018/test.py b/brainscore_vision/data/kuzovkin2018/test.py new file mode 100644 index 000000000..247e422e8 --- /dev/null +++ b/brainscore_vision/data/kuzovkin2018/test.py @@ -0,0 +1,9 @@ +import brainscore_vision +import pytest + + +@pytest.mark.parametrize('assembly_identifier', [ + pytest.param('aru.Kuzovkin2018', marks=[pytest.mark.private_access]), +]) +def test_existence(assembly_identifier): + assert brainscore_vision.load_dataset(assembly_identifier) is not None \ No newline at end of file diff --git a/brainscore_vision/data/majajhong2015/__init__.py b/brainscore_vision/data/majajhong2015/__init__.py new file mode 100644 index 000000000..f2b7b61b5 --- /dev/null +++ b/brainscore_vision/data/majajhong2015/__init__.py @@ -0,0 +1,113 @@ +from brainio.assemblies import NeuronRecordingAssembly + +import brainscore_vision +from brainscore_vision import data_registry, stimulus_set_registry +from brainscore_vision.data_helpers.s3 import load_stimulus_set_from_s3, load_assembly_from_s3 + +BIBTEX = """@article{majajhong2015simple, + title={Simple learned weighted sums of inferior temporal neuronal firing rates accurately predict human core object recognition performance}, + author={Majaj, Najib J and Hong, Ha and Solomon, Ethan A and DiCarlo, James J}, + journal={Journal of Neuroscience}, + volume={35}, + number={39}, + pages={13402--13418}, + year={2015}, + publisher={Soc Neuroscience} +}""" + +# assembly: dicarlo.MajajHong2015 +data_registry['dicarlo.MajajHong2015'] = lambda: load_assembly_from_s3( + identifier="dicarlo.MajajHong2015", + version_id="H8u9POg3GffsLpQM6XdLUwdgUIp25eIm", + sha1="bf8f8d01010d727e3db3f85a9bd5f95f9456b7ec", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: brainscore_vision.load_stimulus_set('dicarlo.hvm'), +) + +# assembly: dicarlo.MajajHong2015.temporal +data_registry['dicarlo.MajajHong2015.temporal'] = lambda: load_assembly_from_s3( + identifier="dicarlo.MajajHong2015.temporal", + version_id="32To3cMj5i.KgcN46AYZg4D_Yd.Sn.GM", + sha1="4c5cfe25ad53162c5c716d64004de269162eff38", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: brainscore_vision.load_stimulus_set('dicarlo.hvm'), +) + +# assembly: dicarlo.MajajHong2015.temporal-10ms +data_registry['dicarlo.MajajHong2015.temporal-10ms'] = lambda: load_assembly_from_s3( + identifier="dicarlo.MajajHong2015.temporal-10ms", + version_id="9CVpFWSgb0er45fAfEsWzyt1r71jhlHj", + sha1="3a43db05db722b456d156f53b7215413c994e5b5", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: brainscore_vision.load_stimulus_set('dicarlo.hvm'), +) + +# assembly: dicarlo.MajajHong2015.public +data_registry['dicarlo.MajajHong2015.public'] = lambda: load_assembly_from_s3( + identifier="dicarlo.MajajHong2015.public", + version_id="LZWj2qjAwZKdmHdTGftpBPSO6oq.y9Q7", + sha1="13d28ca0ce88ee550b54db3004374ae19096e9b9", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: brainscore_vision.load_stimulus_set('dicarlo.hvm-public'), +) + +# assembly: dicarlo.MajajHong2015.private +data_registry['dicarlo.MajajHong2015.private'] = lambda: load_assembly_from_s3( + identifier="dicarlo.MajajHong2015.private", + version_id="te22BOyPgmV_MOnV5l0J3EqjqTIkX3s4", + sha1="7a40d16148d6f82939155f0bd976d310857fb156", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: brainscore_vision.load_stimulus_set('dicarlo.hvm-private'), +) + +# assembly: dicarlo.MajajHong2015.temporal.public +data_registry['dicarlo.MajajHong2015.temporal.public'] = lambda: load_assembly_from_s3( + identifier="dicarlo.MajajHong2015.temporal.public", + version_id="7LRAZtOgYeYZIWc3UaJ.lY0AgjtlyV8G", + sha1="093ac35b3e8464c676d24cf38238415d4d6a9448", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: brainscore_vision.load_stimulus_set('dicarlo.hvm-public'), +) + +# assembly: dicarlo.MajajHong2015.temporal.private +data_registry['dicarlo.MajajHong2015.temporal.private'] = lambda: load_assembly_from_s3( + identifier="dicarlo.MajajHong2015.temporal.private", + version_id="BmN.WZG7sq0bvsC_hH_KFXbTLsyylrHN", + sha1="804ea9e7c08ae9ab7a7d705c9c7e68582750e2ea", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: brainscore_vision.load_stimulus_set('dicarlo.hvm-private'), +) + +# stimulus set: dicarlo.hvm - majajhong2015 +stimulus_set_registry['dicarlo.hvm'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.hvm", + bucket="brainio-brainscore", + csv_sha1="a56f55205904d5fb5ead4d0dc7bfad5cc4083b94", + zip_sha1="6fd5080deccfb061699909ffcb86a26209516811", + csv_version_id="8hlPT.aceeOTOB9ri0KbSslJJx.mNR3y", + zip_version_id="VeCwvgVpj.UEDgu3TwiTqxn713a6TT15") + +# stimulus set: dicarlo.hvm-public +stimulus_set_registry['dicarlo.hvm-public'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.hvm-public", + bucket="brainio-brainscore", + csv_sha1="5ca7a3da00d8e9c694a9cd725df5ba0ad6d735af", + zip_sha1="8aa44e038d7b551efa8077467622f9d48d72e473", + csv_version_id="PxkMLFgPqRM7wOe3tBfwsXI3pfB49RPR", + zip_version_id="UzgkNOtIWMXaMN7vUA0FVemXLTvdtH13") + +# stimulus set: dicarlo.hvm-private +stimulus_set_registry['dicarlo.hvm-private'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.hvm-private", + bucket="brainio-brainscore", + csv_sha1="6ff4981722fa05feb73a2bd26bbbba8b50dc29a6", + zip_sha1="d7b1ca1876dad87e15b0242b4c82c0203ff3cbd3", + csv_version_id="AjHL0UBtYui_PwXV0zISNSilWJEWgL4a", + zip_version_id="0gLBR1UgyfhIuE5wHSK6Ctb4PBPIRUJN") diff --git a/packaging/dicarlo/darren10ms.py b/brainscore_vision/data/majajhong2015/data_packaging/darren10ms.py similarity index 100% rename from packaging/dicarlo/darren10ms.py rename to brainscore_vision/data/majajhong2015/data_packaging/darren10ms.py diff --git a/packaging/separate_datasets.py b/brainscore_vision/data/majajhong2015/data_packaging/data_packaging.py similarity index 64% rename from packaging/separate_datasets.py rename to brainscore_vision/data/majajhong2015/data_packaging/data_packaging.py index c7d0cbffe..eb67d7c57 100644 --- a/packaging/separate_datasets.py +++ b/brainscore_vision/data/majajhong2015/data_packaging/data_packaging.py @@ -1,11 +1,4 @@ -import numpy as np -from brainio_base.assemblies import walk_coords, array_is_element -from numpy.random.mtrand import RandomState -from sklearn.model_selection import StratifiedShuffleSplit -from xarray import DataArray - from brainio_collection.fetch import fetch_assembly, get_assembly -from brainio_collection.transform import subset from brainio_contrib.packaging import package_data_assembly, package_stimulus_set @@ -36,33 +29,6 @@ def apply_keep_attrs(assembly, fnc): # workaround to keeping attrs return assembly -def package_Movshon_datasets(name): - assembly = load_assembly(name) - assembly.load() - base_assembly = assembly - _, unique_indices = np.unique(base_assembly['image_id'].values, return_index=True) - unique_indices = np.sort(unique_indices) # preserve order - image_ids = base_assembly['image_id'].values[unique_indices] - stratification_values = base_assembly['texture_type'].values[unique_indices] - rng = RandomState(seed=12) - splitter = StratifiedShuffleSplit(n_splits=1, train_size=.3, test_size=None, random_state=rng) - split = next(splitter.split(np.zeros(len(image_ids)), stratification_values)) - access_indices = {assembly_type: image_indices - for assembly_type, image_indices in zip(['public', 'private'], split)} - for access in ['public', 'private']: - indices = access_indices[access] - subset_image_ids = image_ids[indices] - assembly = base_assembly[ - {'presentation': [image_id in subset_image_ids for image_id in base_assembly['image_id'].values]}] - adapt_stimulus_set(assembly, access) - package_stimulus_set(assembly.attrs['stimulus_set'], stimulus_set_name=assembly.attrs['stimulus_set_name']) - del assembly.attrs['stimulus_set'] - package_data_assembly(assembly, f"{name}.{access}", stimulus_set_name=assembly.attrs['stimulus_set_name']) - - # not really sure if this is necessary - return assembly - - def _filter_erroneous_neuroids(assembly): err_neuroids = ['Tito_L_P_8_5', 'Tito_L_P_7_3', 'Tito_L_P_7_5', 'Tito_L_P_5_1', 'Tito_L_P_9_3', 'Tito_L_P_6_3', 'Tito_L_P_7_4', 'Tito_L_P_5_0', 'Tito_L_P_5_4', 'Tito_L_P_9_6', @@ -97,4 +63,3 @@ def package_dicarlo_datasets(name): if __name__ == '__main__': package_dicarlo_datasets(name='dicarlo.Majaj2015') package_dicarlo_datasets(name='dicarlo.Majaj2015.temporal') - package_Movshon_datasets(name='movshon.FreemanZiemba2013') diff --git a/brainscore_vision/data/majajhong2015/test.py b/brainscore_vision/data/majajhong2015/test.py new file mode 100644 index 000000000..806e32271 --- /dev/null +++ b/brainscore_vision/data/majajhong2015/test.py @@ -0,0 +1,38 @@ +import pytest + +from brainscore_vision.benchmarks.majajhong2015.benchmark import load_assembly +from brainscore_vision import load_dataset +from brainscore_vision.benchmark_helpers import check_standard_format + + +@pytest.mark.private_access +class TestAssembly: + def test_majajhong2015_V4(self): + assembly = load_assembly(average_repetitions=True, region='V4') + check_standard_format(assembly) + assert assembly.attrs['stimulus_set_identifier'] == 'dicarlo.hvm-private' + assert set(assembly['region'].values) == {'V4'} + assert len(assembly['presentation']) == 2560 + assert len(assembly['neuroid']) == 88 + + def test_majajhong2015_IT(self): + assembly = load_assembly(average_repetitions=True, region='IT') + check_standard_format(assembly) + assert assembly.attrs['stimulus_set_identifier'] == 'dicarlo.hvm-private' + assert set(assembly['region'].values) == {'IT'} + assert len(assembly['presentation']) == 2560 + assert len(assembly['neuroid']) == 168 + + +@pytest.mark.parametrize('assembly_identifier', [ + pytest.param('dicarlo.MajajHong2015', marks=[pytest.mark.private_access]), + pytest.param('dicarlo.MajajHong2015.public', marks=[]), + pytest.param('dicarlo.MajajHong2015.private', marks=[pytest.mark.private_access]), + pytest.param('dicarlo.MajajHong2015.temporal', marks=[pytest.mark.private_access, pytest.mark.memory_intense]), + pytest.param('dicarlo.MajajHong2015.temporal.public', marks=[pytest.mark.memory_intense]), + pytest.param('dicarlo.MajajHong2015.temporal.private', + marks=[pytest.mark.private_access, pytest.mark.memory_intense]), + pytest.param('dicarlo.MajajHong2015.temporal-10ms', marks=[pytest.mark.private_access, pytest.mark.memory_intense]), +]) +def test_existence(assembly_identifier): + assert load_dataset(assembly_identifier) is not None diff --git a/brainscore_vision/data/marques2020/__init__.py b/brainscore_vision/data/marques2020/__init__.py new file mode 100644 index 000000000..fda71fa25 --- /dev/null +++ b/brainscore_vision/data/marques2020/__init__.py @@ -0,0 +1,123 @@ +from brainio.assemblies import PropertyAssembly + +from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set +from brainscore_vision.data_helpers.s3 import load_stimulus_set_from_s3, load_assembly_from_s3 + +BIBTEX = """@article{dapello2020simulating, + title={Simulating a primary visual cortex at the front of CNNs improves robustness to image perturbations}, + author={Dapello, Joel and Marques, Tiago and Schrimpf, Martin and Geiger, Franziska and Cox, David and DiCarlo, James J}, + journal={Advances in Neural Information Processing Systems}, + volume={33}, + pages={13073--13087}, + year={2020} +}""" + +# --- datasets --- +data_registry['movshon.Cavanaugh2002a'] = lambda: load_assembly_from_s3( + identifier="movshon.Cavanaugh2002a", + version_id="2vAgf8I9mx0dAZ6E.v9_wQ1qjqXlBIlG", + sha1="d4b24f4bbf8a14138a8e98391ab03796c2c05e7d", + bucket="brainio-brainscore", + cls=PropertyAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.Marques2020_size'), + merge_stimulus_set_meta=False, +) + +data_registry['movshon.FreemanZiemba2013_V1_properties'] = lambda: load_assembly_from_s3( + identifier="movshon.FreemanZiemba2013_V1_properties", + version_id="fmhJH37ozlGbGPW16B0NSgdm2fz17aEc", + sha1="e665522d9a32e6cd6c3ebc61ff65fd3899c8d3e6", + bucket="brainio-brainscore", + cls=PropertyAssembly, + stimulus_set_loader=lambda: load_stimulus_set('movshon.FreemanZiemba2013_properties'), + merge_stimulus_set_meta=False, +) + +data_registry['devalois.DeValois1982a'] = lambda: load_assembly_from_s3( + identifier="devalois.DeValois1982a", + version_id="d_IU4t6LbkvxsaEEsu9VzhTm9jX3AhSI", + sha1="8f4fd70e987c3c566b5b6c6bf15ef27637618b2c", + bucket="brainio-brainscore", + cls=PropertyAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.Marques2020_orientation'), + merge_stimulus_set_meta=False, +) + +data_registry['devalois.DeValois1982b'] = lambda: load_assembly_from_s3( + identifier="devalois.DeValois1982b", + version_id="jG7loVtkn3vsH0hT6wl4Qv24EsJJJSDt", + sha1="611176eefdac09feaa4c07f081784c24067629e1", + bucket="brainio-brainscore", + cls=PropertyAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.Marques2020_spatial_frequency'), + merge_stimulus_set_meta=False, +) + +data_registry['shapley.Ringach2002'] = lambda: load_assembly_from_s3( + identifier="shapley.Ringach2002", + version_id="aMkWQinb7JEhJCJb0RhfGJegTwbya6Yw", + sha1="0f3e19140bd9f930109879a38e574471d9576cf5", + bucket="brainio-brainscore", + cls=PropertyAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.Marques2020_orientation'), + merge_stimulus_set_meta=False, +) + +data_registry['schiller.Schiller1976c'] = lambda: load_assembly_from_s3( + identifier="schiller.Schiller1976c", + version_id="tJUZfDfz5G1E3UtEjptQeI7Esbg2aArD", + sha1="58daa02ba2680e75dcb11b37eb6085c4afb6576e", + bucket="brainio-brainscore", + cls=PropertyAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.Marques2020_spatial_frequency'), + merge_stimulus_set_meta=False, +) + +# --- stimulus sets --- +stimulus_set_registry['dicarlo.Marques2020_blank'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.Marques2020_blank", + bucket="brainio-brainscore", + csv_sha1="c02d44b0d3e157b29c6b96c5c5a478a8b37dc70b", + zip_sha1="5c4eef94691d6d63f8b1610918728b8531fc861e", + csv_version_id="TPtJnOoJAKEnWSDrQ_1X9bvx_jUQkTvM", + zip_version_id="4Zv7Mt0cGuKCV5i0uwGipVa91so_6U.V") + +stimulus_set_registry['dicarlo.Marques2020_receptive_field'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.Marques2020_receptive_field", + bucket="brainio-brainscore", + csv_sha1="e3681c69122b20ff3dd22486f546a26b3a97e057", + zip_sha1="fc21a9302bb0fca85350049ab740af80f2307ece", + csv_version_id="o6JVmVyn015uKDSqwH1Qfaek8I4AH0A2", + zip_version_id="wFfNAHvgptFmzn58VdlABHeEPlnh54EO") + +stimulus_set_registry['dicarlo.Marques2020_orientation'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.Marques2020_orientation", + bucket="brainio-brainscore", + csv_sha1="b316c6d9c6ddb58f8f21e79ae2b59b03933c0068", + zip_sha1="6a02466e221ac314da2db2969c89688dff64ab1b", + csv_version_id="FuItfnJQzVkbXuC6oDZYiIa6Ye.ZrtHp", + zip_version_id="HAlNBCxxrzdFy6g9ofCM1rG49KXMgMjH") + +stimulus_set_registry['dicarlo.Marques2020_spatial_frequency'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.Marques2020_spatial_frequency", + bucket="brainio-brainscore", + csv_sha1="31d0293c8aa1590f4680bb8a9446f56135b8d646", + zip_sha1="6fdeee4366514b9a0ff9c52e52c13762a62ceaab", + csv_version_id="xtKupFJyMLIqtI64H9bFikeeyycg0esU", + zip_version_id="cPxm7TfKmFPFigPdzJjOf3gHcdKDkVXi") + +stimulus_set_registry['dicarlo.Marques2020_size'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.Marques2020_size", + bucket="brainio-brainscore", + csv_sha1="0fd0aeea8fa6ff2b30ee9a6a684d4600590d631f", + zip_sha1="11122de2784218b7dd4cd0b557e62583ca7ee283", + csv_version_id="VQsd1qLhSKqd5Wfz6tmnmypgs038.hoT", + zip_version_id="RAfJJhR9.KQlwdiuZa6PiEiJTs62Jk.4") + +stimulus_set_registry['movshon.FreemanZiemba2013_properties'] = lambda: load_stimulus_set_from_s3( + identifier="movshon.FreemanZiemba2013_properties", + bucket="brainio-brainscore", + csv_sha1="6f7bcb5d0c01e81c9fbdcec9bf586cbb579a9b02", + zip_sha1="c64a0d6b447676a6d5cff3d3625a5f294e62ff97", + csv_version_id="cXG5bRIhKTFX.36oAHk3zvmC.0TreoH8", + zip_version_id="PxoZZYyCCKJDbaoluOC.8nHZKWQ0.m8C") diff --git a/packaging/dicarlo/marques/marques_cavanaugh2002a.py b/brainscore_vision/data/marques2020/data_packaging/marques_cavanaugh2002a.py similarity index 100% rename from packaging/dicarlo/marques/marques_cavanaugh2002a.py rename to brainscore_vision/data/marques2020/data_packaging/marques_cavanaugh2002a.py diff --git a/packaging/dicarlo/marques/marques_devalois1982a.py b/brainscore_vision/data/marques2020/data_packaging/marques_devalois1982a.py similarity index 100% rename from packaging/dicarlo/marques/marques_devalois1982a.py rename to brainscore_vision/data/marques2020/data_packaging/marques_devalois1982a.py diff --git a/packaging/dicarlo/marques/marques_devalois1982b.py b/brainscore_vision/data/marques2020/data_packaging/marques_devalois1982b.py similarity index 100% rename from packaging/dicarlo/marques/marques_devalois1982b.py rename to brainscore_vision/data/marques2020/data_packaging/marques_devalois1982b.py diff --git a/packaging/dicarlo/marques/marques_freemanZiemba2013.py b/brainscore_vision/data/marques2020/data_packaging/marques_freemanZiemba2013.py similarity index 100% rename from packaging/dicarlo/marques/marques_freemanZiemba2013.py rename to brainscore_vision/data/marques2020/data_packaging/marques_freemanZiemba2013.py diff --git a/packaging/dicarlo/marques/marques_gen_stim.py b/brainscore_vision/data/marques2020/data_packaging/marques_gen_stim.py similarity index 100% rename from packaging/dicarlo/marques/marques_gen_stim.py rename to brainscore_vision/data/marques2020/data_packaging/marques_gen_stim.py diff --git a/packaging/dicarlo/marques/marques_ringach2002.py b/brainscore_vision/data/marques2020/data_packaging/marques_ringach2002.py similarity index 99% rename from packaging/dicarlo/marques/marques_ringach2002.py rename to brainscore_vision/data/marques2020/data_packaging/marques_ringach2002.py index 7685bf893..e4e3a4ef4 100644 --- a/packaging/dicarlo/marques/marques_ringach2002.py +++ b/brainscore_vision/data/marques2020/data_packaging/marques_ringach2002.py @@ -87,7 +87,7 @@ def main(): assembly.name = ASSEMBLY_NAME print('Packaging assembly') package_data_assembly(xarray.DataArray(assembly), assembly_identifier=assembly.name, stimulus_set_identifier=ORIENTATION_STIM_NAME, - assembly_class='PropertyAssembly', bucket_name='brainio.contrib') + assembly_class='PropertyAssembly', bucket_name='brainio.contriib') if __name__ == '__main__': diff --git a/packaging/dicarlo/marques/marques_schiller1976c.py b/brainscore_vision/data/marques2020/data_packaging/marques_schiller1976c.py similarity index 100% rename from packaging/dicarlo/marques/marques_schiller1976c.py rename to brainscore_vision/data/marques2020/data_packaging/marques_schiller1976c.py diff --git a/packaging/dicarlo/marques/marques_stim_common.py b/brainscore_vision/data/marques2020/data_packaging/marques_stim_common.py similarity index 100% rename from packaging/dicarlo/marques/marques_stim_common.py rename to brainscore_vision/data/marques2020/data_packaging/marques_stim_common.py diff --git a/packaging/dicarlo/marques/marques_utils.py b/brainscore_vision/data/marques2020/data_packaging/marques_utils.py similarity index 100% rename from packaging/dicarlo/marques/marques_utils.py rename to brainscore_vision/data/marques2020/data_packaging/marques_utils.py diff --git a/packaging/dicarlo/marques/setup.py b/brainscore_vision/data/marques2020/data_packaging/setup.py similarity index 100% rename from packaging/dicarlo/marques/setup.py rename to brainscore_vision/data/marques2020/data_packaging/setup.py diff --git a/brainscore_vision/data/marques2020/test.py b/brainscore_vision/data/marques2020/test.py new file mode 100644 index 000000000..ed84c7c95 --- /dev/null +++ b/brainscore_vision/data/marques2020/test.py @@ -0,0 +1,55 @@ +import pytest +import brainscore_vision +from brainscore_vision import load_stimulus_set + + +@pytest.mark.private_access +class TestMarques2020V1Properties: + @pytest.mark.parametrize('identifier,num_neuroids,properties,stimulus_set_identifier', [ + ('movshon.Cavanaugh2002a', 190, [ + 'surround_suppression_index', 'strongly_suppressed', 'grating_summation_field', 'surround_diameter', + 'surround_grating_summation_field_ratio'], + 'dicarlo.Marques2020_size'), + ('devalois.DeValois1982a', 385, [ + 'preferred_orientation'], + 'dicarlo.Marques2020_orientation'), + ('devalois.DeValois1982b', 363, [ + 'peak_spatial_frequency'], + 'dicarlo.Marques2020_spatial_frequency'), + ('movshon.FreemanZiemba2013_V1_properties', 102, [ + 'absolute_texture_modulation_index', 'family_variance', 'max_noise', 'max_texture', 'noise_selectivity', + 'noise_sparseness', 'sample_variance', 'texture_modulation_index', 'texture_selectivity', + 'texture_sparseness', 'variance_ratio'], + 'movshon.FreemanZiemba2013_properties'), + ('shapley.Ringach2002', 304, [ + 'bandwidth', 'baseline', 'circular_variance', 'circular_variance_bandwidth_ratio', 'max_ac', 'max_dc', + 'min_dc', 'modulation_ratio', 'orientation_selective', 'orthogonal_preferred_ratio', + 'orthogonal_preferred_ratio_bandwidth_ratio', 'orthogonal_preferred_ratio_circular_variance_difference'], + 'dicarlo.Marques2020_orientation'), + ('schiller.Schiller1976c', 87, [ + 'spatial_frequency_selective', 'spatial_frequency_bandwidth'], + 'dicarlo.Marques2020_spatial_frequency'), + ]) + def test_assembly(self, identifier, num_neuroids, properties, stimulus_set_identifier): + assembly = brainscore_vision.load_dataset(identifier) + assert set(assembly.dims) == {'neuroid', 'neuronal_property'} + assert len(assembly['neuroid']) == num_neuroids + assert set(assembly['neuronal_property'].values) == set(properties) + assert assembly.stimulus_set is not None + assert assembly.stimulus_set.identifier == stimulus_set_identifier + + +@pytest.mark.private_access +class TestMarques2020V1Properties: + @pytest.mark.parametrize('identifier,num_stimuli', [ + ('dicarlo.Marques2020_blank', 1), + ('dicarlo.Marques2020_receptive_field', 3528), + ('dicarlo.Marques2020_orientation', 1152), + ('dicarlo.Marques2020_spatial_frequency', 2112), + ('dicarlo.Marques2020_size', 2304), + ('movshon.FreemanZiemba2013_properties', 450), + ]) + def test_num_stimuli(self, identifier, num_stimuli): + stimulus_set = load_stimulus_set(identifier) + assert len(stimulus_set) == num_stimuli + diff --git a/brainscore_vision/data/rajalingham2018/__init__.py b/brainscore_vision/data/rajalingham2018/__init__.py new file mode 100644 index 000000000..cb112cf10 --- /dev/null +++ b/brainscore_vision/data/rajalingham2018/__init__.py @@ -0,0 +1,56 @@ +from brainio.assemblies import BehavioralAssembly + +from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set +from brainscore_vision.data_helpers.s3 import load_assembly_from_s3, load_stimulus_set_from_s3 + + +BIBTEX = """@article{rajalingham2018large, + title={Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks}, + author={Rajalingham, Rishi and Issa, Elias B and Bashivan, Pouya and Kar, Kohitij and Schmidt, Kailyn and DiCarlo, James J}, + journal={Journal of Neuroscience}, + volume={38}, + number={33}, + pages={7255--7269}, + year={2018}, + publisher={Soc Neuroscience} +}""" + + +# public assembly: uses dicarlo.objectome.public stimuli +data_registry['dicarlo.Rajalingham2018.public'] = lambda: load_assembly_from_s3( + identifier="dicarlo.Rajalingham2018.public", + version_id="WEBNb7Azz4CWpzO25JanNjdPSLArltS2", + sha1="34c6a8b6f7c523589c1861e4123232e5f7c7df4c", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.objectome.public'), +) + +# private assembly: uses dicarlo.objectome.private stimuli +data_registry['dicarlo.Rajalingham2018.private'] = lambda: load_assembly_from_s3( + identifier="dicarlo.Rajalingham2018.private", + version_id="gwBpHTT2al4FN35Yje7MU2d_ByA_HphX", + sha1="516f13793d1c5b72bb445bb4008448ce97a02d23", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.objectome.private'), +) + + +# stimulus set: dicarlo.objectome.public - rajalingham2018 +stimulus_set_registry['dicarlo.objectome.public'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.objectome.public", + bucket="brainio-brainscore", + csv_sha1="47884e17106a3be471d6481279cab33889b80850", + zip_sha1="064f2955f98e63867755fee2e3ead8cddf6bfab8", + csv_version_id="4X7B_5lax3hDOCD2TfBz.DlMFIRP9YLF", + zip_version_id="4AvWG8JceGt1dpeP7EWYdGce.xj_Acgr") + +# stimulus set: dicarlo.objectome.private - same +stimulus_set_registry['dicarlo.objectome.private'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.objectome.private", + bucket="brainio-brainscore", + csv_sha1="ac38e8f7c08fa8294ed25a3bf84a6adb108bf3fc", + zip_sha1="ccd39f7f9b8b4a92da06e3960d06225e46208593", + csv_version_id="QgE.v4ph5Xu6QlcMITL80MGIMPBRai.D", + zip_version_id="esF_WWa.X8Qtl1j3HkUxUu3ZZcs8074H") diff --git a/packaging/dicarlo/rajalingham2018objectome.py b/brainscore_vision/data/rajalingham2018/rajalingham2018objectome.py similarity index 100% rename from packaging/dicarlo/rajalingham2018objectome.py rename to brainscore_vision/data/rajalingham2018/rajalingham2018objectome.py diff --git a/brainscore_vision/data/rajalingham2018/test.py b/brainscore_vision/data/rajalingham2018/test.py new file mode 100644 index 000000000..3f711e00b --- /dev/null +++ b/brainscore_vision/data/rajalingham2018/test.py @@ -0,0 +1,10 @@ +import brainscore_vision +import pytest + + +@pytest.mark.parametrize('assembly_identifier', [ + pytest.param('dicarlo.Rajalingham2018.public', marks=[]), + pytest.param('dicarlo.Rajalingham2018.private', marks=[pytest.mark.private_access]), +]) +def test_existence(assembly_identifier): + assert brainscore_vision.load_dataset(assembly_identifier) is not None diff --git a/brainscore_vision/data/rajalingham2020/__init__.py b/brainscore_vision/data/rajalingham2020/__init__.py new file mode 100644 index 000000000..19dbc2eb2 --- /dev/null +++ b/brainscore_vision/data/rajalingham2020/__init__.py @@ -0,0 +1,39 @@ +import logging + +from brainio.assemblies import NeuronRecordingAssembly + +from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set +from brainscore_vision.data_helpers.s3 import load_assembly_from_s3, load_stimulus_set_from_s3 + +_logger = logging.getLogger(__name__) + +BIBTEX = """@article{rajalingham2020inferior, + title={The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys}, + author={Rajalingham, Rishi and Kar, Kohitij and Sanghavi, Sachi and Dehaene, Stanislas and DiCarlo, James J}, + journal={Nature communications}, + volume={11}, + number={1}, + pages={1--13}, + year={2020}, + publisher={Nature Publishing Group} +}""" + +# assembly: uses below stimulus set +data_registry['dicarlo.Rajalingham2020'] = lambda: load_assembly_from_s3( + identifier="dicarlo.Rajalingham2020", + version_id="L4YlA5o2gToDj4sbXE0Utn362sPyy_GW", + sha1="ab95ae6c9907438f87b9b13b238244049f588680", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.Rajalingham2020'), +) + +# stimulus set +stimulus_set_registry['dicarlo.Rajalingham2020'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.Rajalingham2020", + bucket="brainio-brainscore", + csv_sha1="9a9a6b3115d2d8ce5d54ec2522093d8a87ed13a0", + zip_sha1="6097086901032e20f8ae764e9cc06e0a891a3e18", + csv_version_id="kPZEA.xmFrtYesjID0KbTZxuuD_LCu4M", + zip_version_id="eTXlzNCSlwK.Lm6Smh.YJe2KmiaW_QNY") + diff --git a/packaging/dicarlo/rajalingham2020orthographic_IT.py b/brainscore_vision/data/rajalingham2020/rajalingham2020orthographic_IT.py similarity index 100% rename from packaging/dicarlo/rajalingham2020orthographic_IT.py rename to brainscore_vision/data/rajalingham2020/rajalingham2020orthographic_IT.py diff --git a/brainscore_vision/data/rajalingham2020/test.py b/brainscore_vision/data/rajalingham2020/test.py new file mode 100644 index 000000000..8b85bb84b --- /dev/null +++ b/brainscore_vision/data/rajalingham2020/test.py @@ -0,0 +1,9 @@ +import brainscore_vision +import pytest + + +@pytest.mark.parametrize('assembly_identifier', [ + pytest.param('dicarlo.Rajalingham2020', marks=[pytest.mark.private_access]), +]) +def test_existence(assembly_identifier): + assert brainscore_vision.load_dataset(assembly_identifier) is not None diff --git a/packaging/notebooks/2020-12-28_rust.ipynb b/brainscore_vision/data/rust2012/2020-12-28_rust.ipynb similarity index 100% rename from packaging/notebooks/2020-12-28_rust.ipynb rename to brainscore_vision/data/rust2012/2020-12-28_rust.ipynb diff --git a/brainscore_vision/data/rust2012/__init__.py b/brainscore_vision/data/rust2012/__init__.py new file mode 100644 index 000000000..91a717520 --- /dev/null +++ b/brainscore_vision/data/rust2012/__init__.py @@ -0,0 +1,45 @@ +from brainio.assemblies import NeuronRecordingAssembly + +from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set +from brainscore_vision.data_helpers.s3 import load_stimulus_set_from_s3, load_assembly_from_s3 + +BIBTEX = """@article{rust2012balanced, + title={Balanced increases in selectivity and tolerance produce constant sparseness along the ventral visual stream}, + author={Rust, Nicole C and DiCarlo, James J}, + journal={Journal of Neuroscience}, + volume={32}, + number={30}, + pages={10170--10182}, + year={2012}, + publisher={Soc Neuroscience} +}""" + +# dicarlo.Rust2012.single assembly +data_registry['dicarlo.Rust2012.single'] = lambda: load_assembly_from_s3( + identifier="dicarlo.Rust2012.single", + version_id="8T7Pt1LwHPRoxCC9c9xBfMNGDSJ0b3e4", + sha1="4ef420e70fbd0de3745df5be7c83dfc0a8f2e528", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.Rust2012'), +) + +# dicarlo.Rust2012.array assembly +data_registry['dicarlo.Rust2012.array'] = lambda: load_assembly_from_s3( + identifier="dicarlo.Rust2012.array", + version_id="O_8qaKqFhzRNOIvVx8djTQDUE0a6g.w9", + sha1="6709b641751370acfccd9567e3d75b71865a71ab", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.Rust2012'), +) + + +# stimulus set +stimulus_set_registry['dicarlo.Rust2012'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.Rust2012", + bucket="brainio-brainscore", + csv_sha1="482da1f9f4a0ab5433c3b7b57073ad30e45c2bf1", + zip_sha1="7cbf5dcec235f7705eaad1cfae202eda77e261a2", + csv_version_id="wtjzAmRyAgzZUHcn_ThuAnlIbr5W7o3y", + zip_version_id=".lwNwrP6vLz3cnOOb6eqf8atHSOKoL6X") diff --git a/packaging/dicarlo/rust305.py b/brainscore_vision/data/rust2012/rust305.py similarity index 100% rename from packaging/dicarlo/rust305.py rename to brainscore_vision/data/rust2012/rust305.py diff --git a/brainscore_vision/data/rust2012/test.py b/brainscore_vision/data/rust2012/test.py new file mode 100644 index 000000000..922a278a9 --- /dev/null +++ b/brainscore_vision/data/rust2012/test.py @@ -0,0 +1,47 @@ +import pytest +from brainscore_vision import load_dataset + + +@pytest.mark.parametrize('assembly_identifier', [ + pytest.param('dicarlo.Rust2012.single', marks=[pytest.mark.private_access]), + pytest.param('dicarlo.Rust2012.array', marks=[pytest.mark.private_access]), +]) +def test_existence(assembly_identifier): + assert load_dataset(assembly_identifier) is not None + + +class TestRustSingle: + @pytest.mark.private_access + def test_dims(self): + assembly = load_dataset('dicarlo.Rust2012.single') + # (neuroid: 285, presentation: 1500, time_bin: 1) + assert assembly.dims == ("neuroid", "presentation", "time_bin") + assert len(assembly['neuroid']) == 285 + assert len(assembly['presentation']) == 1500 + assert len(assembly['time_bin']) == 1 + + @pytest.mark.private_access + def test_coords(self): + assembly = load_dataset('dicarlo.Rust2012.single') + assert len(set(assembly['stimulus_id'].values)) == 300 + assert len(set(assembly['neuroid_id'].values)) == 285 + assert len(set(assembly['region'].values)) == 2 + + +class TestRustArray: + @pytest.mark.private_access + def test_dims(self): + assembly = load_dataset('dicarlo.Rust2012.array') + # (neuroid: 296, presentation: 53700, time_bin: 6) + assert assembly.dims == ("neuroid", "presentation", "time_bin") + assert len(assembly['neuroid']) == 296 + assert len(assembly['presentation']) == 53700 + assert len(assembly['time_bin']) == 6 + + @pytest.mark.private_access + def test_coords(self): + assembly = load_dataset('dicarlo.Rust2012.array') + assert len(set(assembly['stimulus_id'].values)) == 300 + assert len(set(assembly['neuroid_id'].values)) == 296 + assert len(set(assembly['animal'].values)) == 2 + assert len(set(assembly['region'].values)) == 2 \ No newline at end of file diff --git a/brainscore_vision/data/sanghavi2020/__init__.py b/brainscore_vision/data/sanghavi2020/__init__.py new file mode 100644 index 000000000..d7369bf47 --- /dev/null +++ b/brainscore_vision/data/sanghavi2020/__init__.py @@ -0,0 +1,119 @@ +from brainio.assemblies import NeuronRecordingAssembly +from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set, load_ceiling +from brainscore_vision.data_helpers.s3 import load_stimulus_set_from_s3, load_assembly_from_s3 +from brainscore_vision.metric_helpers.transformations import CrossValidation + +BIBTEX_SANGHAVI = """ @misc{Sanghavi2020, + title={SanghaviMurty2020}, + url={osf.io/CHWDK}, + DOI={10.17605/OSF.IO/CHWDK}, + publisher={OSF}, + author={Sanghavi, Sachi and DiCarlo, James J}, + year={2020}, + month={Nov} +}""" # 'HvM' images from (Majaj et al., 2015) +BIBTEX_SANGHAVIMURTY = """ @misc{SanghaviMurty2020, + title={SanghaviMurty2020}, + url={osf.io/fchme}, + DOI={10.17605/OSF.IO/FCHME}, + publisher={OSF}, + author={Sanghavi, Sachi and Murty, N A R and DiCarlo, James J}, + year={2020}, + month={Nov} +}""" # BOLD5000 images +BIBTEX_SANGHAVIJOZWIK = """ @misc{SanghaviJozwik2020, + title={SanghaviJozwik2020}, + url={osf.io/FHY36}, + DOI={10.17605/OSF.IO/FHY36}, + publisher={OSF}, + author={Sanghavi, Sachi and Jozwik, K M and DiCarlo, James J}, + year={2020}, + month={Nov} +}""" # images from (Rust & DiCarlo, 2012) + +# assemblies: dicarlo.Sanghavi2020 uses dicarlo.hvm +data_registry['dicarlo.Sanghavi2020'] = lambda: load_assembly_from_s3( + identifier="dicarlo.Sanghavi2020", + version_id="RZz2m5wUm.wgYyMEDY9UCMuyYjZKDuzw", + sha1="12e94e9dcda797c851021dfe818b64615c785866", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.hvm'), +) + +# assemblies: dicarlo.SanghaviJozwik2020 uses dicarlo.BOLD5000 +data_registry['dicarlo.SanghaviJozwik2020'] = lambda: load_assembly_from_s3( + identifier="dicarlo.SanghaviJozwik2020", + version_id="j5AiLVh8xbchFP2CxVxFoifAeJy1vwHA", + sha1="c5841f1e7d2cf0544a6ee010e56e4e2eb0994ee0", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.BOLD5000'), +) + +# assemblies: dicarlo.SanghaviMurty2020 uses dicarlo.Rust2012 +data_registry['dicarlo.SanghaviMurty2020'] = lambda: load_assembly_from_s3( + identifier="dicarlo.SanghaviMurty2020", + version_id="yvyTo2fM8kLsa7h7WMWC8jcqC2uAx.kp", + sha1="6cb8e054688066d1d86d4944e1385efc6a69ebd4", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.Rust2012'), +) + +# assemblies: dicarlo.SanghaviMurty2020THINGS1 uses dicarlo.THINGS1 +data_registry['dicarlo.SanghaviMurty2020THINGS1'] = lambda: load_assembly_from_s3( + identifier="dicarlo.SanghaviMurty2020THINGS1", + version_id=".n3o2r4SKG4fO829jJPl9zz1UaSM7okH", + sha1="718def227d38c8425f449512e47a2df81c04de62", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.THINGS1'), +) + +# assemblies: dicarlo.SanghaviMurty2020THINGS2 uses dicarlo.THINGS2 +data_registry['dicarlo.SanghaviMurty2020THINGS2'] = lambda: load_assembly_from_s3( + identifier="dicarlo.SanghaviMurty2020THINGS2", + version_id="z_MWZd12fk.AIcQzwRh6.vOIHhxInWRl", + sha1="80962139823cb145e2385c344e3945e99ed97fa2", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.THINGS2'), +) + +# stimulus set: dicarlo.BOLD5000 - can put in sanghavi +stimulus_set_registry['dicarlo.BOLD5000'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.BOLD5000", + bucket="brainio-brainscore", + csv_sha1="51ac47ea03ed2e72864a95065494c2fabe779f6e", + zip_sha1="1a2e6d782dcd21bbe60460d85d83b13fa76a9543", + csv_version_id="2gHs8J9iH7PzBOO24USSwOmmZZZez0K8", + zip_version_id="r4gfAtAHaSj2WPc8kC5xkBpR2u4vkj_o") + +# stimulus set: dicarlo.THINGS1 +stimulus_set_registry['dicarlo.THINGS1'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.THINGS1", + bucket="brainio-brainscore", + csv_sha1="e02469d805e1b9c1a18403e9b0b8a37ee6da5130", + zip_sha1="fb716eb58c303157dd577a70caad5c8079e74e9b", + csv_version_id="PvdnGZPINuidsDNMZ3M2sZ4A3otjev1M", + zip_version_id="c7UP4sqx2LsayqNxhScM.Omad5cUhw8g") + +# stimulus set: dicarlo.THINGS2 +stimulus_set_registry['dicarlo.THINGS2'] = lambda: load_stimulus_set_from_s3( + identifier="dicarlo.THINGS2", + bucket="brainio-brainscore", + csv_sha1="86c8beda8f495cd69ed047d3457902dd98e4904c", + zip_sha1="e7918dd10102b67464bc652fdb3ced25ee1fbe7a", + csv_version_id="uZK7NNq7mfVFYTw2tdwrmJs7LivGnLRx", + zip_version_id="xibNW6tiYOUZqhxO0XkeXammNehAjQY2") + + +def filter_neuroids(assembly, threshold): + ceiler = load_ceiling('internal_consistency') + ceiling = ceiler(assembly) + ceiling = ceiling.raw + ceiling = CrossValidation().aggregate(ceiling) + pass_threshold = ceiling >= threshold + assembly = assembly[{'neuroid': pass_threshold}] + return assembly diff --git a/packaging/dicarlo/sanghavi/environment.yml b/brainscore_vision/data/sanghavi2020/data_packaging/environment.yml similarity index 100% rename from packaging/dicarlo/sanghavi/environment.yml rename to brainscore_vision/data/sanghavi2020/data_packaging/environment.yml diff --git a/packaging/dicarlo/sanghavi/requirements.txt b/brainscore_vision/data/sanghavi2020/data_packaging/requirements.txt similarity index 100% rename from packaging/dicarlo/sanghavi/requirements.txt rename to brainscore_vision/data/sanghavi2020/data_packaging/requirements.txt diff --git a/packaging/dicarlo/sanghavi/sanghavi2020.py b/brainscore_vision/data/sanghavi2020/data_packaging/sanghavi2020.py similarity index 100% rename from packaging/dicarlo/sanghavi/sanghavi2020.py rename to brainscore_vision/data/sanghavi2020/data_packaging/sanghavi2020.py diff --git a/packaging/dicarlo/sanghavi/sanghavijozwik2020.py b/brainscore_vision/data/sanghavi2020/data_packaging/sanghavijozwik2020.py similarity index 100% rename from packaging/dicarlo/sanghavi/sanghavijozwik2020.py rename to brainscore_vision/data/sanghavi2020/data_packaging/sanghavijozwik2020.py diff --git a/packaging/dicarlo/sanghavi/sanghavikar2020.py b/brainscore_vision/data/sanghavi2020/data_packaging/sanghavikar2020.py similarity index 100% rename from packaging/dicarlo/sanghavi/sanghavikar2020.py rename to brainscore_vision/data/sanghavi2020/data_packaging/sanghavikar2020.py diff --git a/packaging/dicarlo/sanghavi/sanghavimurty2020.py b/brainscore_vision/data/sanghavi2020/data_packaging/sanghavimurty2020.py similarity index 100% rename from packaging/dicarlo/sanghavi/sanghavimurty2020.py rename to brainscore_vision/data/sanghavi2020/data_packaging/sanghavimurty2020.py diff --git a/packaging/dicarlo/sanghavi/sanghavimurty2020things.py b/brainscore_vision/data/sanghavi2020/data_packaging/sanghavimurty2020things.py similarity index 100% rename from packaging/dicarlo/sanghavi/sanghavimurty2020things.py rename to brainscore_vision/data/sanghavi2020/data_packaging/sanghavimurty2020things.py diff --git a/packaging/dicarlo/sanghavi/sanghavimurty2020things1.py b/brainscore_vision/data/sanghavi2020/data_packaging/sanghavimurty2020things1.py similarity index 100% rename from packaging/dicarlo/sanghavi/sanghavimurty2020things1.py rename to brainscore_vision/data/sanghavi2020/data_packaging/sanghavimurty2020things1.py diff --git a/packaging/dicarlo/sanghavi/sanghavimurty2020things2.py b/brainscore_vision/data/sanghavi2020/data_packaging/sanghavimurty2020things2.py similarity index 100% rename from packaging/dicarlo/sanghavi/sanghavimurty2020things2.py rename to brainscore_vision/data/sanghavi2020/data_packaging/sanghavimurty2020things2.py diff --git a/brainscore_vision/data/sanghavi2020/test.py b/brainscore_vision/data/sanghavi2020/test.py new file mode 100644 index 000000000..965fbd955 --- /dev/null +++ b/brainscore_vision/data/sanghavi2020/test.py @@ -0,0 +1,13 @@ +import brainscore_vision +import pytest + + +@pytest.mark.parametrize('assembly_identifier', [ + pytest.param('dicarlo.SanghaviMurty2020', marks=[pytest.mark.private_access]), + pytest.param('dicarlo.SanghaviJozwik2020', marks=[pytest.mark.private_access]), + pytest.param('dicarlo.Sanghavi2020', marks=[pytest.mark.private_access]), + pytest.param('dicarlo.SanghaviMurty2020THINGS1', marks=[pytest.mark.private_access]), + pytest.param('dicarlo.SanghaviMurty2020THINGS2', marks=[pytest.mark.private_access]), +]) +def test_existence(assembly_identifier): + assert brainscore_vision.load_dataset(assembly_identifier) is not None diff --git a/brainscore_vision/data/seibert2019/__init__.py b/brainscore_vision/data/seibert2019/__init__.py new file mode 100644 index 000000000..1a793fe7f --- /dev/null +++ b/brainscore_vision/data/seibert2019/__init__.py @@ -0,0 +1,25 @@ +from brainio.assemblies import NeuronRecordingAssembly + +from brainscore_vision import data_registry, load_stimulus_set +from brainscore_vision.data_helpers.s3 import load_assembly_from_s3 + +BIBTEX = """@article{lee2019sensitivity, + title={Sensitivity of inferotemporal cortex to naturalistic image statistics in developing macaques}, + author={Lee, Gerick M and Seibert, Darren A and Majaj, Najib J and Movshon, J Anthony and Kiorpes, Lynne}, + journal={Journal of Vision}, + volume={19}, + number={10}, + pages={124--124}, + year={2019}, + publisher={The Association for Research in Vision and Ophthalmology} +}""" + +# assembly: uses dicarlo.hvm +data_registry['dicarlo.Seibert2019'] = lambda: load_assembly_from_s3( + identifier="dicarlo.Seibert2019", + version_id="vSUte2bpVhGxPRKocfk_MP0s3LAPElBi", + sha1="eef41bb1f3d83c0e60ebf0e91511ce71ef5fee32", + bucket="brainio-brainscore", + cls=NeuronRecordingAssembly, + stimulus_set_loader=lambda: load_stimulus_set('dicarlo.hvm'), +) diff --git a/packaging/notebooks/2020-10-13_juvenile.ipynb b/brainscore_vision/data/seibert2019/data_packaging/2020-10-13_juvenile.ipynb similarity index 100% rename from packaging/notebooks/2020-10-13_juvenile.ipynb rename to brainscore_vision/data/seibert2019/data_packaging/2020-10-13_juvenile.ipynb diff --git a/packaging/notebooks/2020-11-18_juvenile_scratch.txt b/brainscore_vision/data/seibert2019/data_packaging/2020-11-18_juvenile_scratch.txt similarity index 100% rename from packaging/notebooks/2020-11-18_juvenile_scratch.txt rename to brainscore_vision/data/seibert2019/data_packaging/2020-11-18_juvenile_scratch.txt diff --git a/packaging/notebooks/2020-11-22_juvenile_dldata.ipynb b/brainscore_vision/data/seibert2019/data_packaging/2020-11-22_juvenile_dldata.ipynb similarity index 100% rename from packaging/notebooks/2020-11-22_juvenile_dldata.ipynb rename to brainscore_vision/data/seibert2019/data_packaging/2020-11-22_juvenile_dldata.ipynb diff --git a/packaging/dicarlo/juvenile.py b/brainscore_vision/data/seibert2019/data_packaging/juvenile.py similarity index 100% rename from packaging/dicarlo/juvenile.py rename to brainscore_vision/data/seibert2019/data_packaging/juvenile.py diff --git a/brainscore_vision/data/seibert2019/test.py b/brainscore_vision/data/seibert2019/test.py new file mode 100644 index 000000000..1a881f519 --- /dev/null +++ b/brainscore_vision/data/seibert2019/test.py @@ -0,0 +1,38 @@ +import pytest +import numpy as np + +from brainscore_vision import load_dataset + + +@pytest.mark.parametrize('assembly_identifier', [ + pytest.param('dicarlo.Seibert2019', marks=[pytest.mark.private_access]), +]) +def test_existence(assembly_identifier): + assert load_dataset(assembly_identifier) is not None + +class TestSeibert: + @pytest.mark.private_access + def test_dims(self): + assembly = load_dataset('dicarlo.Seibert2019') + # neuroid: 258 presentation: 286080 time_bin: 1 + assert assembly.dims == ("neuroid", "presentation", "time_bin") + assert len(assembly['neuroid']) == 258 + assert len(assembly['presentation']) == 286080 + assert len(assembly['time_bin']) == 1 + + @pytest.mark.private_access + def test_coords(self): + assembly = load_dataset('dicarlo.Seibert2019') + assert len(set(assembly['stimulus_id'].values)) == 5760 + assert len(set(assembly['neuroid_id'].values)) == 258 + assert len(set(assembly['animal'].values)) == 3 + assert len(set(assembly['region'].values)) == 2 + assert len(set(assembly['variation'].values)) == 3 + + @pytest.mark.private_access + def test_content(self): + assembly = load_dataset('dicarlo.Seibert2019') + assert np.count_nonzero(np.isnan(assembly)) == 19118720 + assert assembly.stimulus_set_identifier == "dicarlo.hvm" + hvm = assembly.stimulus_set + assert hvm.shape == (5760, 19) \ No newline at end of file diff --git a/brainscore_vision/data/zhang2018/__init__.py b/brainscore_vision/data/zhang2018/__init__.py new file mode 100644 index 000000000..9f8893fa5 --- /dev/null +++ b/brainscore_vision/data/zhang2018/__init__.py @@ -0,0 +1,25 @@ +from brainio.assemblies import BehavioralAssembly + +from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set +from brainscore_vision.data_helpers.s3 import load_assembly_from_s3, load_stimulus_set_from_s3 + +BIBTEX = """""" + +# assembly: klab.Zhang2018search_obj_array +data_registry['klab.Zhang2018search_obj_array'] = lambda: load_assembly_from_s3( + identifier="klab.Zhang2018search_obj_array", + version_id="EDRblSpQTmoaKRdiY4JEoQ.1UO8mo0Tk", + sha1="84400dc814b79df8d8eca99a557df5f741cce4b9", + bucket="brainio-brainscore", + cls=BehavioralAssembly, + stimulus_set_loader=lambda: load_stimulus_set('klab.Zhang2018.search_obj_array'), +) + +# stimulus set: klab.Zhang2018.search_obj_array +stimulus_set_registry['klab.Zhang2018.search_obj_array'] = lambda: load_stimulus_set_from_s3( + identifier="klab.Zhang2018.search_obj_array", + bucket="brainio-brainscore", + csv_sha1="e9c2f6b35b84256242d257e8d36261aa26d3ed4a", + zip_sha1="d92dfb20a87bb9a015c77c2862a215e8fa4f2cc3", + csv_version_id="HC_2UVxrTqbKtBtMny2YqYjm6cjdx6JI", + zip_version_id="B2uxHg7zx9Zx4dnAD2Lk_TxaQZPB6p_P") diff --git a/brainscore_vision/data/zhang2018/test.py b/brainscore_vision/data/zhang2018/test.py new file mode 100644 index 000000000..abeb05a6d --- /dev/null +++ b/brainscore_vision/data/zhang2018/test.py @@ -0,0 +1,32 @@ +import pytest +from brainscore_vision import load_dataset, load_stimulus_set + + +@pytest.mark.parametrize('assembly_identifier', [ + pytest.param('klab.Zhang2018search_obj_array', marks=[pytest.mark.private_access]), +]) +def test_existence(assembly_identifier): + assert load_dataset(assembly_identifier) is not None + +@pytest.mark.private_access +def test_klab_Zhang2018search(): + assembly = load_dataset('klab.Zhang2018search_obj_array') + assert set(assembly.dims) == {'presentation', 'fixation', 'position'} + assert len(assembly['presentation']) == 4500 + assert len(set(assembly['stimulus_id'].values)) == 300 + assert len(set(assembly['subjects'].values)) == 15 + assert len(assembly['fixation']) == 8 + assert len(assembly['position']) == 2 + assert assembly.stimulus_set is not None + + +@pytest.mark.private_access +def test_klab_Zhang2018search(): + stimulus_set = load_stimulus_set('klab.Zhang2018.search_obj_array') + # There are 300 presentation images in the assembly but 606 in the StimulusSet (explanation from @shashikg follows). + # For each of the visual search task out of total 300, you need two images (one - the target image, + # second - the search space image) plus there are 6 different mask images to mask objects + # present at 6 different locations in a specified search image. + # Therefore, a total of 300 * 2 + 6 images are there in the stimulus set. + assert len(stimulus_set) == 606 + assert len(set(stimulus_set['stimulus_id'])) == 606 diff --git a/packaging/dicarlo/__init__.py b/brainscore_vision/data_helpers/__init__.py similarity index 100% rename from packaging/dicarlo/__init__.py rename to brainscore_vision/data_helpers/__init__.py diff --git a/brainscore_vision/data_helpers/lookup_legacy.csv b/brainscore_vision/data_helpers/lookup_legacy.csv new file mode 100644 index 000000000..595b9248f --- /dev/null +++ b/brainscore_vision/data_helpers/lookup_legacy.csv @@ -0,0 +1,168 @@ +,version_id +image_dicarlo_hvm.csv,8hlPT.aceeOTOB9ri0KbSslJJx.mNR3y +image_dicarlo_hvm.zip,VeCwvgVpj.UEDgu3TwiTqxn713a6TT15 +image_gallant_David2004.csv,0Ks2opc6t_IpZdB.kylVCbYgXV6ADB4O +image_gallant_David2004.zip,hFTILhFSGqL95wp1li1aBzzbqCyuAlb_ +image_tolias_Cadena2017.csv,qdO206S_7GBQUHUgrMFvZw7wGcJM0phU 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b/brainscore_vision/data_helpers/lookup_legacy.py new file mode 100644 index 000000000..b12b5836e --- /dev/null +++ b/brainscore_vision/data_helpers/lookup_legacy.py @@ -0,0 +1,15 @@ +import pandas as pd +from pathlib import Path + +version_id_path = Path(__file__).parent / 'version_ids.csv' +version_id_df = pd.read_csv(version_id_path, index_col='Unnamed: 0') + + +def build_filename(identifier: str, file_type: str): + if file_type == '.nc': + filename = f'assy_{identifier.replace(".", "_")}.nc' + elif file_type == '.csv': + filename = f'image_{identifier.replace(".", "_")}.csv' + else: + filename = f'image_{identifier.replace(".", "_")}.zip' + return filename diff --git a/brainscore_vision/data_helpers/s3.py b/brainscore_vision/data_helpers/s3.py new file mode 100644 index 000000000..e5cd6a7e0 --- /dev/null +++ b/brainscore_vision/data_helpers/s3.py @@ -0,0 +1,87 @@ +""" +Will contain functions for loading data assemblies and stimuli sets from s3, +and whatever helpers are needed. + +Should provide similar functionality to brainio/lookup.py and brainio/fetch.py, +but without use of lookup.csv +""" +import functools +import logging +import os +from pathlib import Path +from typing import Callable, Union + +import boto3 +from botocore import UNSIGNED +from botocore.config import Config + +from brainio.assemblies import DataAssembly, AssemblyLoader, StimulusMergeAssemblyLoader, \ + StimulusReferenceAssemblyLoader +from brainio.fetch import fetch_file, unzip, resolve_stimulus_set_class +from brainio.stimuli import StimulusSetLoader, StimulusSet + +_logger = logging.getLogger(__name__) + + +def get_path(identifier: str, file_type: str, bucket: str, version_id: str, sha1: str): + """ + Finds path of desired file (for .csvs, .zips, and .ncs). + """ + # for stimuli sets + if file_type == 'csv' or file_type == 'zip': + assembly_prefix = 'image_' + # for data assemblies + else: + assembly_prefix = 'assy_' + + filename = f"{assembly_prefix}{identifier.replace('.', '_')}.{file_type}" + file_path = fetch_file(location_type="S3", + location=f"https://{bucket}.s3.amazonaws.com/{filename}", + version_id=version_id, + sha1=sha1) + return file_path + + +def load_assembly_from_s3(identifier: str, version_id: str, sha1: str, bucket: str, cls: type, + stimulus_set_loader: Callable[[], StimulusSet] = None, + merge_stimulus_set_meta: bool = True) -> DataAssembly: + file_path = get_path(identifier, 'nc', bucket, version_id, sha1) + if stimulus_set_loader: # merge stimulus set meta into assembly if `stimulus_set_loader` is passed + stimulus_set = stimulus_set_loader() + loader_base_class = StimulusMergeAssemblyLoader if merge_stimulus_set_meta else StimulusReferenceAssemblyLoader + loader_class = functools.partial(loader_base_class, + stimulus_set_identifier=stimulus_set.identifier, stimulus_set=stimulus_set) + else: # if no `stimulus_set_loader` passed, just load assembly + loader_class = AssemblyLoader + loader = loader_class(cls=cls, file_path=file_path) + assembly = loader.load() + assembly.attrs['identifier'] = identifier + return assembly + + +def load_stimulus_set_from_s3(identifier: str, bucket: str, csv_sha1: str, zip_sha1: str, + csv_version_id: str, zip_version_id: str): + csv_path = get_path(identifier, 'csv', bucket, csv_version_id, csv_sha1) + zip_path = get_path(identifier, 'zip', bucket, zip_version_id, zip_sha1) + stimuli_directory = unzip(zip_path) + loader = StimulusSetLoader( + csv_path=csv_path, + stimuli_directory=stimuli_directory, + cls=resolve_stimulus_set_class('StimulusSet') + ) + stimulus_set = loader.load() + stimulus_set.identifier = identifier + # ensure perfect overlap + stimuli_paths = [Path(stimuli_directory) / local_path for local_path in os.listdir(stimuli_directory) + if not local_path.endswith('.zip') and not local_path.endswith('.csv')] + assert set(stimulus_set.stimulus_paths.values()) == set(stimuli_paths), \ + "Inconsistency: unzipped stimuli paths do not match csv paths" + return stimulus_set + + +def download_file_if_not_exists(local_path: Union[str, Path], bucket: str, remote_filepath: str): + if local_path.is_file(): + return # nothing to do, file already exists + unsigned_config = Config(signature_version=UNSIGNED) # do not attempt to look up credentials + s3 = boto3.client('s3', config=unsigned_config) + s3.download_file(bucket, remote_filepath, str(local_path)) diff --git a/brainscore/entrypoint.py b/brainscore_vision/entrypoint.py similarity index 100% rename from brainscore/entrypoint.py rename to brainscore_vision/entrypoint.py diff --git a/brainscore/lookup.csv b/brainscore_vision/lookup.csv similarity index 73% rename from brainscore/lookup.csv rename to brainscore_vision/lookup.csv index 523b659c7..4a51f919c 100644 --- a/brainscore/lookup.csv +++ b/brainscore_vision/lookup.csv @@ -1,170 +1,172 @@ -identifier,lookup_type,class,location_type,location,sha1,stimulus_set_identifier,lookup_source -dicarlo.hvm,stimulus_set,StimulusSet,S3,https://brainio.dicarlo.s3.amazonaws.com/image_dicarlo_hvm.csv,a56f55205904d5fb5ead4d0dc7bfad5cc4083b94,,brainio_brainscore -dicarlo.hvm,stimulus_set,,S3,https://brainio.dicarlo.s3.amazonaws.com/image_dicarlo_hvm.zip,6fd5080deccfb061699909ffcb86a26209516811,,brainio_brainscore -gallant.David2004,stimulus_set,StimulusSet,S3,https://brainio.contrib.s3.amazonaws.com/image_gallant_David2004.csv,8ec76338b998cadcdf1e57edd2dd992e2ab2355b,,brainio_brainscore -gallant.David2004,stimulus_set,,S3,https://brainio.contrib.s3.amazonaws.com/image_gallant_David2004.zip,0200421d66a0613946d39cab64c00b561160016e,,brainio_brainscore -tolias.Cadena2017,stimulus_set,StimulusSet,S3,https://brainio.contrib.s3.amazonaws.com/image_tolias_Cadena2017.csv,f55b174cc4540e5612cfba5e695324328064b051,,brainio_brainscore 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+brendel.Geirhos2021_silhouette,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_silhouette.csv,fb57005ecb80e4e37b01e084f1f176fe7f59ff7f, +brendel.Geirhos2021_silhouette,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_silhouette.zip,36c8a481f7876a2af2ad4fe80890b302fe3ae91e, +brendel.Geirhos2021_false-colour,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_false-colour.csv,8a09a7af8ec44339bcae5500ae5900d9c4309042, +brendel.Geirhos2021_false-colour,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_false-colour.zip,ec0ba347fc14d0c0587d38bfa96e4ab5d2f7979a, +brendel.Geirhos2021_cue-conflict,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_cue-conflict.csv,8d3ae89d8870fb7d7c5d5ff387085b1f0116e2b7, +brendel.Geirhos2021_cue-conflict,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_brendel_Geirhos2021_cue-conflict.zip,7e601186b181102939cd0b43a4e8a3ca95c18259, +brendel.Geirhos2021_colour,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_colour.nc,258862d82467614e45cc1e488a5ac909eb6e122d,brendel.Geirhos2021_colour +brendel.Geirhos2021_contrast,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_contrast.nc,1e114c987dc035ccca43781ff8cee9689acd3c3f,brendel.Geirhos2021_contrast +brendel.Geirhos2021_cue-conflict,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_cue-conflict.nc,cc214e3595d34565b13963c5f56049769a39a5c9,brendel.Geirhos2021_cue-conflict +brendel.Geirhos2021_edge,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_edge.nc,ab1dc9e188e248da07215b375eb3dbcc58fde7fb,brendel.Geirhos2021_edge +brendel.Geirhos2021_eidolonI,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_eidolonI.nc,0f01f351ae19eafc2cb5e504d98e5cd01b4c07b4,brendel.Geirhos2021_eidolonI +brendel.Geirhos2021_eidolonII,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_eidolonII.nc,499eea0f0c0817d02b5b97d2ebab89bc4c40a153,brendel.Geirhos2021_eidolonII +brendel.Geirhos2021_eidolonIII,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_eidolonIII.nc,e7c9a49e729f8666f8aedc6e47c746fbbe2ebe36,brendel.Geirhos2021_eidolonIII +brendel.Geirhos2021_false-colour,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_false-colour.nc,4dc072264651c81575564ba4818a12b8e8039c65,brendel.Geirhos2021_false-colour +brendel.Geirhos2021_high-pass,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_high-pass.nc,5df45c69127758f1ba3391671c521711050e3b4d,brendel.Geirhos2021_high-pass +brendel.Geirhos2021_low-pass,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_low-pass.nc,75ab628d9e6d0d634290567b1cb261d7f8dc61e2,brendel.Geirhos2021_low-pass +brendel.Geirhos2021_phase-scrambling,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_phase-scrambling.nc,4124f9f5b86fb6ed82c98197d292eef50b608aba,brendel.Geirhos2021_phase-scrambling +brendel.Geirhos2021_power-equalisation,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_power-equalisation.nc,0aba1b50a7e0802d76c41d332a121a3f96ef4f7d,brendel.Geirhos2021_power-equalisation +brendel.Geirhos2021_rotation,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_rotation.nc,e51a5c3bc95ade159e71aa602232063730bcd57b,brendel.Geirhos2021_rotation +brendel.Geirhos2021_silhouette,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_silhouette.nc,7dc94991465fe8009244e0d6fb8283419a1f9885,brendel.Geirhos2021_silhouette +brendel.Geirhos2021_sketch,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_sketch.nc,6709850864cea16d99a29fb31ae3c4a489983562,brendel.Geirhos2021_sketch +brendel.Geirhos2021_stylized,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_stylized.nc,dcf15f292e787a88e1e0f271e6b2838d6bdadfd3,brendel.Geirhos2021_stylized +brendel.Geirhos2021_uniform-noise,assembly,BehavioralAssembly,S3,https://brainio-brainscore.s3.amazonaws.com/assy_brendel_Geirhos2021_uniform-noise.nc,f5e8b2636738f978c71591b8df6f8a21a66b72d1,brendel.Geirhos2021_uniform-noise +katz.BarbuMayo2019,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/image_katz_BarbuMayo2019.csv,e4d8888ccb6beca28636e6698e7beb130e278e12, +katz.BarbuMayo2019,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/image_katz_BarbuMayo2019.zip,1365eb2a7231516806127a7d2a908343a7ac9464, +Islam2021,stimulus_set,StimulusSet,S3,https://brainio-brainscore.s3.amazonaws.com/stimulus_neil_Islam2021.csv,93ab0f6386d8d5fb56640da45980a819c7dd6efc, +Islam2021,stimulus_set,,S3,https://brainio-brainscore.s3.amazonaws.com/stimulus_neil_Islam2021.zip,e55b673117a472f463e0705ac3e330ef8dfd938b, diff --git a/brainscore_vision/metric_helpers/__init__.py b/brainscore_vision/metric_helpers/__init__.py new file mode 100644 index 000000000..18cfa1dde --- /dev/null +++ b/brainscore_vision/metric_helpers/__init__.py @@ -0,0 +1,5 @@ +class Defaults: + expected_dims = ('presentation', 'neuroid') + stimulus_coord = 'stimulus_id' + neuroid_dim = 'neuroid' + neuroid_coord = 'neuroid_id' diff --git a/brainscore/metrics/transformations.py b/brainscore_vision/metric_helpers/transformations.py similarity index 96% rename from brainscore/metrics/transformations.py rename to brainscore_vision/metric_helpers/transformations.py index 5f10d65d3..0438cfd7a 100644 --- a/brainscore/metrics/transformations.py +++ b/brainscore_vision/metric_helpers/transformations.py @@ -1,18 +1,18 @@ -from collections import OrderedDict - import itertools import logging import math +from collections import OrderedDict + import numpy as np import xarray as xr -from brainio.assemblies import DataAssembly, walk_coords -from brainio.transform import subset from sklearn.model_selection import StratifiedShuffleSplit, ShuffleSplit, KFold, StratifiedKFold from tqdm import tqdm -from brainscore.metrics import Score -from brainscore.metrics.utils import unique_ordered -from brainscore.utils import fullname +from brainio.assemblies import DataAssembly +from brainio.transform import subset +from brainscore_vision.metric_helpers.utils import unique_ordered +from brainscore_vision.metrics import Score +from brainscore_vision.utils import fullname def apply_aggregate(aggregate_fnc, values): @@ -210,12 +210,9 @@ def build_splits(self, assembly): @classmethod def aggregate(cls, values): - center = values.mean('split') - error = standard_error_of_the_mean(values, 'split') - return Score([center, error], - coords={**{'aggregation': ['center', 'error']}, - **{coord: (dims, values) for coord, dims, values in walk_coords(center)}}, - dims=('aggregation',) + center.dims) + score = apply_aggregate(lambda scores: scores.mean('split'), values) + score.attrs['error'] = standard_error_of_the_mean(values, 'split') + return score def extract_coord(assembly, coord, unique=False): @@ -303,7 +300,8 @@ def pipe(self, source_assembly, target_assembly): # check only for equal values, alignment is given by metadata assert sorted(source_assembly[self._split_coord].values) == sorted(target_assembly[self._split_coord].values) if self._split.do_stratify: - assert hasattr(source_assembly, self._stratification_coord) + assert hasattr(source_assembly, self._stratification_coord), \ + f"Expected stratification coordinate {self._stratification_coord}" assert sorted(source_assembly[self._stratification_coord].values) == \ sorted(target_assembly[self._stratification_coord].values) cross_validation_values, splits = self._split.build_splits(target_assembly) diff --git a/brainscore/metrics/utils.py b/brainscore_vision/metric_helpers/utils.py similarity index 100% rename from brainscore/metrics/utils.py rename to brainscore_vision/metric_helpers/utils.py diff --git a/brainscore/metrics/xarray_utils.py b/brainscore_vision/metric_helpers/xarray_utils.py similarity index 95% rename from brainscore/metrics/xarray_utils.py rename to brainscore_vision/metric_helpers/xarray_utils.py index 742baeb5b..ce67654ff 100644 --- a/brainscore/metrics/xarray_utils.py +++ b/brainscore_vision/metric_helpers/xarray_utils.py @@ -1,14 +1,8 @@ import numpy as np from brainio.assemblies import NeuroidAssembly, array_is_element, walk_coords -from brainscore.metrics import Score - - -class Defaults: - expected_dims = ('presentation', 'neuroid') - stimulus_coord = 'stimulus_id' - neuroid_dim = 'neuroid' - neuroid_coord = 'neuroid_id' +from brainscore_vision.metric_helpers import Defaults +from brainscore_vision.metrics import Score class XarrayRegression: diff --git a/brainscore_vision/metrics/__init__.py b/brainscore_vision/metrics/__init__.py new file mode 100644 index 000000000..8b172ec8f --- /dev/null +++ b/brainscore_vision/metrics/__init__.py @@ -0,0 +1,7 @@ +from brainio.assemblies import DataAssembly +from brainscore_core import Score + + +class Ceiling: + def __call__(self, assembly: DataAssembly) -> Score: + raise NotImplementedError() diff --git a/brainscore_vision/metrics/accuracy/__init__.py b/brainscore_vision/metrics/accuracy/__init__.py new file mode 100644 index 000000000..db1870c8b --- /dev/null +++ b/brainscore_vision/metrics/accuracy/__init__.py @@ -0,0 +1,4 @@ +from brainscore_vision import metric_registry +from .metric import Accuracy + +metric_registry['accuracy'] = Accuracy diff --git a/brainscore_vision/metrics/accuracy/metric.py b/brainscore_vision/metrics/accuracy/metric.py new file mode 100644 index 000000000..bb655bb8a --- /dev/null +++ b/brainscore_vision/metrics/accuracy/metric.py @@ -0,0 +1,16 @@ +import numpy as np + +from brainscore_core import Metric +from brainscore_vision.metrics import Score + + +class Accuracy(Metric): + def __call__(self, source, target) -> Score: + values = source == target + center = np.mean(values) + error = np.std(values) + + score = Score(center) + score.attrs['error'] = error + score.attrs[Score.RAW_VALUES_KEY] = values + return score diff --git a/brainscore_vision/metrics/accuracy/test.py b/brainscore_vision/metrics/accuracy/test.py new file mode 100644 index 000000000..00c4b6dc9 --- /dev/null +++ b/brainscore_vision/metrics/accuracy/test.py @@ -0,0 +1,11 @@ +import numpy as np +from numpy.random import RandomState + +from brainio.assemblies import DataAssembly +from brainscore_vision import load_metric + + +def test_fifty_percent(): + metric = load_metric('accuracy') + accuracy = metric(np.array([5, 3, 6, 7, 1, 8]), np.array([5, 2, 1, 7, 1, 9])) + assert accuracy == 0.5 diff --git a/brainscore_vision/metrics/cka/__init__.py b/brainscore_vision/metrics/cka/__init__.py new file mode 100644 index 000000000..e97c7a671 --- /dev/null +++ b/brainscore_vision/metrics/cka/__init__.py @@ -0,0 +1,14 @@ +from brainscore_vision import metric_registry +from .metric import CKA, CKACrossValidated + +metric_registry['cka'] = CKA +metric_registry['cka_cv'] = CKACrossValidated + +BIBTEX = """@inproceedings{kornblith2019similarity, + title={Similarity of neural network representations revisited}, + author={Kornblith, Simon and Norouzi, Mohammad and Lee, Honglak and Hinton, Geoffrey}, + booktitle={International conference on machine learning}, + pages={3519--3529}, + year={2019}, + organization={PMLR} +}""" diff --git a/brainscore/metrics/cka.py b/brainscore_vision/metrics/cka/metric.py similarity index 84% rename from brainscore/metrics/cka.py rename to brainscore_vision/metrics/cka/metric.py index e0bfca9d3..67ccd2931 100644 --- a/brainscore/metrics/cka.py +++ b/brainscore_vision/metrics/cka/metric.py @@ -2,12 +2,14 @@ import numpy as np -from brainscore.metrics import Score -from brainscore.metrics.transformations import TestOnlyCrossValidation -from brainscore.metrics.xarray_utils import Defaults as XarrayDefaults +from brainio.assemblies import DataAssembly +from brainscore_core import Metric +from brainscore_vision.metric_helpers import Defaults as XarrayDefaults +from brainscore_vision.metric_helpers.transformations import TestOnlyCrossValidation, apply_aggregate +from brainscore_vision.metrics import Score -class CKACrossValidated: +class CKACrossValidated(Metric): """ Computes a cross-validated similarity index for the similarity between two assemblies with centered kernel alignment (CKA). @@ -16,22 +18,16 @@ class CKACrossValidated: """ def __init__(self, comparison_coord=XarrayDefaults.stimulus_coord, crossvalidation_kwargs=None): - self._metric = CKAMetric(comparison_coord=comparison_coord) + self._metric = CKA(comparison_coord=comparison_coord) crossvalidation_defaults = dict(test_size=.9) # leave 10% out crossvalidation_kwargs = {**crossvalidation_defaults, **(crossvalidation_kwargs or {})} self._cross_validation = TestOnlyCrossValidation(**crossvalidation_kwargs) - def __call__(self, assembly1, assembly2): - """ - :param brainio.assemblies.NeuroidAssembly assembly1: - :param brainio.assemblies.NeuroidAssembly assembly2: - :return: brainscore.metrics.Score - """ - + def __call__(self, assembly1: DataAssembly, assembly2: DataAssembly) -> Score: return self._cross_validation(assembly1, assembly2, apply=self._metric) -class CKAMetric: +class CKA(Metric): """ Computes a similarity index for the similarity between two assemblies with centered kernel alignment (CKA). @@ -41,7 +37,7 @@ class CKAMetric: def __init__(self, comparison_coord=XarrayDefaults.stimulus_coord): self._comparison_coord = comparison_coord - def __call__(self, assembly1, assembly2): + def __call__(self, assembly1: DataAssembly, assembly2: DataAssembly) -> Score: """ :param brainscore.assemblies.NeuroidAssembly assembly1: :param brainscore.assemblies.NeuroidAssembly assembly2: diff --git a/brainscore_vision/metrics/cka/test.py b/brainscore_vision/metrics/cka/test.py new file mode 100644 index 000000000..3ba876834 --- /dev/null +++ b/brainscore_vision/metrics/cka/test.py @@ -0,0 +1,28 @@ +import numpy as np +from numpy.random import RandomState +from pytest import approx + +from brainio.assemblies import DataAssembly +from brainscore_vision import load_metric + + +def _make_data(): + rnd = RandomState(1) + return DataAssembly(rnd.uniform(size=(300, 10)), coords={ + 'stimulus_id': ('presentation', np.arange(300)), 'category': ('presentation', ['dummy'] * 300), + 'neuroid_id': ('neuroid', np.arange(10)), 'region': ('neuroid', ['Vdummy'] * 10)}, + dims=['presentation', 'neuroid']) + + +def test_identity(): + cka = load_metric('cka') + assembly = _make_data() + similarity = cka(assembly, assembly) + assert similarity == approx(1) + + +def test_crossvalidated_has_error(): + assembly = _make_data() + ckacv = load_metric('cka_cv', crossvalidation_kwargs=dict(stratification_coord=None)) + similarity = ckacv(assembly, assembly) + assert hasattr(similarity, 'error') diff --git a/brainscore_vision/metrics/distribution_similarity/__init__.py b/brainscore_vision/metrics/distribution_similarity/__init__.py new file mode 100644 index 000000000..94d24e3e7 --- /dev/null +++ b/brainscore_vision/metrics/distribution_similarity/__init__.py @@ -0,0 +1,14 @@ +from brainscore_vision import metric_registry +from .metric import BootstrapDistributionSimilarity, NeuronalPropertyCeiling + +metric_registry['ks_similarity'] = BootstrapDistributionSimilarity +metric_registry['property_ceiling'] = NeuronalPropertyCeiling + +BIBTEX = """@article{marques2021multi, + title={Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior}, + author={Marques, Tiago and Schrimpf, Martin and DiCarlo, James J}, + journal={bioRxiv}, + pages={2021--03}, + year={2021}, + publisher={Cold Spring Harbor Laboratory} +}""" diff --git a/brainscore/metrics/distribution_similarity.py b/brainscore_vision/metrics/distribution_similarity/metric.py similarity index 68% rename from brainscore/metrics/distribution_similarity.py rename to brainscore_vision/metrics/distribution_similarity/metric.py index 4ed160d32..f106c786e 100644 --- a/brainscore/metrics/distribution_similarity.py +++ b/brainscore_vision/metrics/distribution_similarity/metric.py @@ -1,17 +1,34 @@ import numpy as np -from brainscore.metrics import Metric, Score + +from brainio.assemblies import PropertyAssembly +from brainscore_core.metrics import Metric, Score NSAMPLES = 1000 +def ks_similarity(p, q): + z1 = np.zeros_like(p) + z2 = np.zeros_like(p) + z1[0] = 1 + z2[-1] = 1 + + kolm_raw = np.max(np.abs(np.cumsum(p) - np.cumsum(q))) + kolm_max1 = np.max(np.abs(np.cumsum(z1) - np.cumsum(q))) + kolm_max2 = np.max(np.abs(np.cumsum(z2) - np.cumsum(q))) + kolm_max = np.max(np.array([kolm_max1, kolm_max2])) + kolm_ceiled = kolm_raw / kolm_max + + return 1 - kolm_ceiled + + class BootstrapDistributionSimilarity(Metric): - def __init__(self, similarity_func, property_name, ns=NSAMPLES): + def __init__(self, property_name, similarity_func=ks_similarity, ns=NSAMPLES): self.similarity_func = similarity_func self.property_name = property_name self.ns = ns def __call__(self, model_property, data_property): - bins = data_property.attrs[self.property_name+'_bins'] + bins = data_property.attrs[self.property_name + '_bins'] data_property = data_property.loc[:, self.property_name].values model_property = model_property.loc[:, self.property_name].values @@ -41,7 +58,8 @@ def __call__(self, model_property, data_property): center = np.nanmean(dist_similarity) error = np.nanstd(dist_similarity) - score = Score([center, error], coords={'aggregation': ['center', 'error']}, dims=('aggregation',)) + score = Score(center) + score.attrs['error'] = error score.attrs[Score.RAW_VALUES_KEY] = dist_similarity self.data_hist = data_hist @@ -54,16 +72,13 @@ def get_dists(self): return self.data_hist, self.model_hist, self.bins -def ks_similarity(p, q): - z1 = np.zeros_like(p) - z2 = np.zeros_like(p) - z1[0] = 1 - z2[-1] = 1 +class NeuronalPropertyCeiling: + def __init__(self, similarity_metric: Metric): + self.similarity_metric = similarity_metric - kolm_raw = np.max(np.abs(np.cumsum(p)-np.cumsum(q))) - kolm_max1 = np.max(np.abs(np.cumsum(z1)-np.cumsum(q))) - kolm_max2 = np.max(np.abs(np.cumsum(z2)-np.cumsum(q))) - kolm_max = np.max(np.array([kolm_max1, kolm_max2])) - kolm_ceiled = kolm_raw / kolm_max + def __call__(self, assembly: PropertyAssembly) -> Score: + self.assembly = assembly + return self._ceiling(self.similarity_metric.property_name) - return 1 - kolm_ceiled + def _ceiling(self, identifier): + return self.similarity_metric(self.assembly, self.assembly) diff --git a/brainscore_vision/metrics/distribution_similarity/test.py b/brainscore_vision/metrics/distribution_similarity/test.py new file mode 100644 index 000000000..79dcf5cef --- /dev/null +++ b/brainscore_vision/metrics/distribution_similarity/test.py @@ -0,0 +1,10 @@ +from pytest import approx + +from brainscore_vision import load_metric, load_dataset + + +def test_identity_circularvariance(): + assembly = load_dataset('shapley.Ringach2002') + metric = load_metric('ks_similarity', property_name='circular_variance') + score = metric(assembly, assembly) + assert score == approx(1, abs=0.05) diff --git a/brainscore_vision/metrics/error_consistency/__init__.py b/brainscore_vision/metrics/error_consistency/__init__.py new file mode 100644 index 000000000..74c00829f --- /dev/null +++ b/brainscore_vision/metrics/error_consistency/__init__.py @@ -0,0 +1,13 @@ +from brainscore_vision import metric_registry +from .metric import ErrorConsistency + +metric_registry['error_consistency'] = ErrorConsistency + +BIBTEX = """@article{geirhos2020beyond, + title={Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency}, + author={Geirhos, Robert and Meding, Kristof and Wichmann, Felix A}, + journal={Advances in Neural Information Processing Systems}, + volume={33}, + pages={13890--13902}, + year={2020} +}""" diff --git a/brainscore/metrics/error_consistency.py b/brainscore_vision/metrics/error_consistency/metric.py similarity index 83% rename from brainscore/metrics/error_consistency.py rename to brainscore_vision/metrics/error_consistency/metric.py index e82f50cae..f68e7a5da 100644 --- a/brainscore/metrics/error_consistency.py +++ b/brainscore_vision/metrics/error_consistency/metric.py @@ -2,8 +2,8 @@ import numpy as np -from brainscore.metrics import Metric, Score -from brainscore.metrics.transformations import apply_aggregate +from brainscore_core.metrics import Metric, Score +from brainscore_vision.metric_helpers.transformations import apply_aggregate class ErrorConsistency(Metric): @@ -12,8 +12,8 @@ class ErrorConsistency(Metric): implemented in Geirhos et al., 2020 https://arxiv.org/abs/2006.16736 """ - def __call__(self, source, target): - assert len(set(source['image_id'].values)) == len(set(target['image_id'].values)) + def __call__(self, source, target) -> Score: + assert len(set(source['stimulus_id'].values)) == len(set(target['stimulus_id'].values)) # https://github.com/bethgelab/model-vs-human/blob/745046c4d82ff884af618756bd6a5f47b6f36c45/modelvshuman/plotting/analyses.py#L161 subject_scores = [] for subject in self.extract_subjects(target): @@ -31,13 +31,14 @@ def __call__(self, source, target): @classmethod def aggregate(cls, scores): - center = scores.mean('condition').mean('subject') - error = scores.std(['condition', 'subject']) # note that the original paper did not have error estimates + mean = scores.mean('condition').mean('subject') # This deviates from the original paper which did not deal with scores < 0 # (neither for a single subject/condition, nor in the aggregate). # We here avoid negative scores, so that we comply with all 0 <= scores <= 1. - center = np.abs(center) - return Score([center, error], coords={'aggregation': ['center', 'error']}, dims=['aggregation']) + score = abs(mean) + # estimate error; but note that the original paper did not have error estimates + score.attrs['error'] = scores.std(['condition', 'subject']) + return score def ceiling(self, assembly): """ @@ -63,10 +64,10 @@ def extract_subjects(self, assembly): return list(sorted(set(assembly['subject'].values))) def compare_single_subject(self, source, target): - assert len(source['presentation']) == len(target['presentation']) - source = source.sortby('image_id') - target = target.sortby('image_id') - assert all(source['image_id'].values == target['image_id'].values) + assert len(source['stimulus_id']) == len(target['stimulus_id']) + source = source.sortby('stimulus_id') + target = target.sortby('stimulus_id') + assert all(source['stimulus_id'].values == target['stimulus_id'].values) correct_source = source.values == source['truth'].values correct_target = target.values == target['truth'].values diff --git a/brainscore_vision/metrics/error_consistency/test.py b/brainscore_vision/metrics/error_consistency/test.py new file mode 100644 index 000000000..b1bf272c5 --- /dev/null +++ b/brainscore_vision/metrics/error_consistency/test.py @@ -0,0 +1,39 @@ +import numpy as np +from pytest import approx + +from brainio.assemblies import BehavioralAssembly +from brainscore_vision import load_metric + + +def test_score(): + assembly = _make_data() + metric = load_metric('error_consistency') + score = metric(assembly.sel(subject='A'), assembly) + assert score == approx(0.2) + + +def test_has_error(): + assembly = _make_data() + metric = load_metric('error_consistency') + score = metric(assembly.sel(subject='A'), assembly) + assert hasattr(score, 'error') + + +def test_raw_subjects(): + assembly = _make_data() + metric = load_metric('error_consistency') + score = metric(assembly.sel(subject='A'), assembly) + subject_scores = score.raw.mean('condition') + assert subject_scores.sel(subject='A') == 1 + + +def _make_data(): + return BehavioralAssembly(['dog', 'cat', 'chair', 'cat', 'dog', 'dog', 'dog', 'dog', 'chair', # subject A + 'cat', 'cat', 'chair', 'cat', 'dog', 'cat', 'chair', 'cat', 'cat', # subject B + 'dog', 'cat', 'chair', 'dog', 'cat', 'chair', 'dog', 'cat', 'chair' # subject C + ], + coords={'stimulus_id': ('presentation', np.resize(np.arange(9), 9 * 3)), + 'truth': ('presentation', np.resize(['dog', 'cat', 'chair'], 9 * 3)), + 'condition': ('presentation', np.resize([1, 1, 1, 2, 2, 2, 3, 3, 3], 9 * 3)), + 'subject': ('presentation', ['A'] * 9 + ['B'] * 9 + ['C'] * 9)}, + dims=['presentation']) diff --git a/brainscore_vision/metrics/i1i2/__init__.py b/brainscore_vision/metrics/i1i2/__init__.py new file mode 100644 index 000000000..6c000fa25 --- /dev/null +++ b/brainscore_vision/metrics/i1i2/__init__.py @@ -0,0 +1,16 @@ +from brainscore_vision import metric_registry +from .metric import I1, I2n + +metric_registry['i1'] = I1 +metric_registry['i2n'] = I2n + +BIBTEX = """@article{rajalingham2018large, + title={Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks}, + author={Rajalingham, Rishi and Issa, Elias B and Bashivan, Pouya and Kar, Kohitij and Schmidt, Kailyn and DiCarlo, James J}, + journal={Journal of Neuroscience}, + volume={38}, + number={33}, + pages={7255--7269}, + year={2018}, + publisher={Soc Neuroscience} +}""" diff --git a/brainscore/metrics/image_level_behavior.py b/brainscore_vision/metrics/i1i2/metric.py similarity index 96% rename from brainscore/metrics/image_level_behavior.py rename to brainscore_vision/metrics/i1i2/metric.py index de64e8a9b..c5ee969e3 100644 --- a/brainscore/metrics/image_level_behavior.py +++ b/brainscore_vision/metrics/i1i2/metric.py @@ -1,7 +1,7 @@ +import itertools import logging from collections import Counter -import itertools import numpy as np import scipy.stats import xarray @@ -9,11 +9,13 @@ from scipy.stats import pearsonr from brainio.assemblies import walk_coords, DataAssembly -from brainscore.metrics import Metric, Score -from brainscore.metrics.transformations import apply_aggregate -from brainscore.utils import fullname +from brainscore_core.metrics import Metric, Score +from brainscore_vision.metric_helpers.transformations import apply_aggregate +from brainscore_vision.utils import fullname +# only the implementation for i2n has been verified. + def I1(*args, **kwargs): return _I(*args, collapse_distractors=True, normalize=False, **kwargs) @@ -38,7 +40,6 @@ def O2(*args, **kwargs): return _O(*args, collapse_distractors=False, normalize=False, **kwargs) -# From Martin's fork def _o2(assembly): i2n = I2n() false_alarm_rates = i2n.target_distractor_scores(assembly) @@ -61,6 +62,7 @@ class _Behavior_Metric(Metric): - for computing dprime scores, Rajalingham et al. computed the false-alarms rate across the flat vector of all distractor images. This implementation computes the false-alarms rate per object, and then takes the mean. """ + def __init__(self, collapse_distractors, normalize=False, repetitions=2): super().__init__() self._collapse_distractors = collapse_distractors @@ -68,7 +70,7 @@ def __init__(self, collapse_distractors, normalize=False, repetitions=2): self._repetitions = repetitions self._logger = logging.getLogger(fullname(self)) - def __call__(self, source_probabilities, target): + def __call__(self, source_probabilities, target) -> Score: return self._repeat(lambda random_state: self._call_single(source_probabilities, target, random_state=random_state)) @@ -104,7 +106,8 @@ def compute_ceiling(self, assembly, random_state, skipna=False): return self.correlate(*dprime_halves, skipna=skipna, collapse_distractors=self._collapse_distractors) def build_response_matrix_from_responses(self, responses): - num_choices = [(image_id, choice) for image_id, choice in zip(responses['stimulus_id'].values, responses.values)] + num_choices = [(image_id, choice) for image_id, choice in + zip(responses['stimulus_id'].values, responses.values)] num_choices = Counter(num_choices) num_objects = [[(image_id, sample_obj), (image_id, dist_obj)] for image_id, sample_obj, dist_obj in zip( responses['stimulus_id'].values, responses['sample_obj'].values, responses['dist_obj'].values)] @@ -164,9 +167,9 @@ def subtract_mean(self, scores): @classmethod def aggregate(cls, scores): - center = scores.mean('split') - error = scores.std('split') - return Score([center, error], coords={'aggregation': ['center', 'error']}, dims=['aggregation']) + score = scores.mean('split') + score.attrs['error'] = scores.std('split') + return score def generate_halves(self, assembly, random_state): indices = list(range(len(assembly))) diff --git a/brainscore_vision/metrics/i1i2/requirements.txt b/brainscore_vision/metrics/i1i2/requirements.txt new file mode 100644 index 000000000..5576e19fe --- /dev/null +++ b/brainscore_vision/metrics/i1i2/requirements.txt @@ -0,0 +1,2 @@ +numpy +scipy \ No newline at end of file diff --git a/brainscore_vision/metrics/i1i2/test.py b/brainscore_vision/metrics/i1i2/test.py new file mode 100644 index 000000000..ca9496ade --- /dev/null +++ b/brainscore_vision/metrics/i1i2/test.py @@ -0,0 +1,36 @@ +from pathlib import Path + +import pytest +from pytest import approx + +from brainio.assemblies import BehavioralAssembly +from brainscore_vision import load_metric +from brainscore_vision.benchmarks.rajalingham2018.benchmarks.benchmark import load_assembly + + +@pytest.mark.private_access +class TestI2N: + @pytest.mark.parametrize(['model', 'expected_score'], [ + ('alexnet', .253), + ('resnet34', .37787), + ('resnet18', .3638), + ]) + def test_model(self, model, expected_score): + # assemblies + objectome = load_assembly() + probabilities = Path(__file__).parent / 'test_resources' / f'{model}-probabilities.nc' + probabilities = BehavioralAssembly.from_files( + probabilities, + stimulus_set_identifier=objectome.attrs['stimulus_set_identifier'], + stimulus_set=objectome.attrs['stimulus_set']) + # metric + i2n = load_metric('i2n') + score = i2n(probabilities, objectome) + assert score == approx(expected_score, abs=0.005), f"expected {expected_score}, but got {score}" + + def test_ceiling(self): + objectome = load_assembly() + i2n = load_metric('i2n') + ceiling = i2n.ceiling(objectome) + assert ceiling == approx(.4786, abs=.0064) + assert ceiling == approx(.00537, abs=.0015) diff --git a/tests/test_metrics/alexnet-probabilities.nc b/brainscore_vision/metrics/i1i2/test_resources/alexnet-probabilities.nc similarity index 100% rename from tests/test_metrics/alexnet-probabilities.nc rename to brainscore_vision/metrics/i1i2/test_resources/alexnet-probabilities.nc diff --git a/tests/test_metrics/resnet18-probabilities.nc b/brainscore_vision/metrics/i1i2/test_resources/resnet18-probabilities.nc similarity index 100% rename from tests/test_metrics/resnet18-probabilities.nc rename to brainscore_vision/metrics/i1i2/test_resources/resnet18-probabilities.nc diff --git a/tests/test_metrics/resnet34-probabilities.nc b/brainscore_vision/metrics/i1i2/test_resources/resnet34-probabilities.nc similarity index 100% rename from tests/test_metrics/resnet34-probabilities.nc rename to brainscore_vision/metrics/i1i2/test_resources/resnet34-probabilities.nc diff --git a/brainscore_vision/metrics/internal_consistency/__init__.py b/brainscore_vision/metrics/internal_consistency/__init__.py new file mode 100644 index 000000000..bd71776be --- /dev/null +++ b/brainscore_vision/metrics/internal_consistency/__init__.py @@ -0,0 +1,4 @@ +from brainscore_vision import metric_registry +from .ceiling import InternalConsistency + +metric_registry['internal_consistency'] = InternalConsistency diff --git a/brainscore/metrics/ceiling.py b/brainscore_vision/metrics/internal_consistency/ceiling.py similarity index 55% rename from brainscore/metrics/ceiling.py rename to brainscore_vision/metrics/internal_consistency/ceiling.py index da1a70fa5..f344b1e23 100644 --- a/brainscore/metrics/ceiling.py +++ b/brainscore_vision/metrics/internal_consistency/ceiling.py @@ -2,24 +2,12 @@ import xarray as xr from tqdm import tqdm -from brainio.assemblies import walk_coords -from brainscore.metrics import Score -from brainscore.metrics.rdm import RDMMetric -from brainscore.metrics.cka import CKAMetric -from brainscore.metrics.transformations import CrossValidationSingle -from brainscore.metrics.xarray_utils import Defaults as XarrayDefaults -from brainscore.metrics.xarray_utils import XarrayCorrelation -from result_caching import store - - -class Ceiling: - def __call__(self, *args, **kwargs): - raise NotImplementedError() - - -class NoCeiling(Ceiling): - def __call__(self): - return Score(1) +from brainio.assemblies import walk_coords, DataAssembly +from brainscore_core import Metric, Score +from brainscore_vision.metric_helpers import Defaults as XarrayDefaults +from brainscore_vision.metric_helpers.transformations import CrossValidationSingle +from brainscore_vision.metric_helpers.xarray_utils import XarrayCorrelation +from brainscore_vision.metrics import Ceiling class _SplitHalvesConsistency(Ceiling): @@ -31,10 +19,11 @@ class _SplitHalvesConsistency(Ceiling): class Defaults: split_coord = 'repetition' - def __init__(self, consistency, split_coord=Defaults.split_coord, cross_validation_kwargs=None, aggregate=None): + def __init__(self, consistency_metric, aggregate=None, + split_coord=Defaults.split_coord, cross_validation_kwargs=None): correction = SpearmanBrownCorrection() self._consistency = self.SplitHalfWrapper(split_coord=split_coord, - consistency=consistency, correction=correction) + consistency_metric=consistency_metric, correction=correction) self._aggregate = aggregate cross_validation_defaults = dict(train_size=0.5, split_coord=split_coord, stratification_coord=None, unique_split_values=True) @@ -45,16 +34,16 @@ def __call__(self, assembly): return self._cross_validation(assembly, apply=self._consistency, aggregate=self._aggregate) class SplitHalfWrapper: - def __init__(self, split_coord, consistency, correction): + def __init__(self, split_coord, consistency_metric: Metric, correction): self._split_coord = split_coord - self._consistency = consistency + self._consistency_metric = consistency_metric self._correction = correction def __call__(self, half1, half2): half1, half2 = self._average_repetitions(half1), self._average_repetitions(half2) - consistency = self._consistency(half1, half2) - consistency = self._correction(consistency, n=2) - return consistency + consistency = self._consistency_metric(half1, half2) + corrected_consistency = self._correction(consistency, n=2) + return corrected_consistency def _average_repetitions(self, assembly): repetition_dims = assembly[self._split_coord].dims @@ -64,22 +53,9 @@ def _average_repetitions(self, assembly): return average -class InternalConsistency(Ceiling): - def __init__(self, - split_coord=_SplitHalvesConsistency.Defaults.split_coord, stimulus_coord=XarrayDefaults.stimulus_coord, - neuroid_dim=XarrayDefaults.neuroid_dim, neuroid_coord=XarrayDefaults.neuroid_coord): - consistency = SplitHalfConsistency(stimulus_coord=stimulus_coord, neuroid_dim=neuroid_dim, - neuroid_coord=neuroid_coord) - self._consistency = _SplitHalvesConsistency(consistency=consistency, split_coord=split_coord, - aggregate=consistency.aggregate) - - def __call__(self, assembly): - return self._consistency(assembly) - - -class SplitHalfConsistency: +class PearsonCorrelation: """ - Computes the consistency between two halves of an assembly. + Computes the Pearson r between two halves of an assembly. """ def __init__(self, stimulus_coord=XarrayDefaults.stimulus_coord, @@ -96,21 +72,28 @@ def aggregate(self, scores): return scores.median(dim=self._neuroid_dim) -class RDMConsistency(Ceiling): - def __init__(self): - rdm = RDMMetric() - self._consistency = _SplitHalvesConsistency(consistency=rdm) - - def __call__(self, assembly): - return self._consistency(assembly) - - -class CKAConsistency(Ceiling): - def __init__(self): - cka = CKAMetric() - self._consistency = _SplitHalvesConsistency(consistency=cka) +class InternalConsistency(Ceiling): + def __init__(self, + consistency_metric: Metric = None, + aggregate=None, + split_coord=_SplitHalvesConsistency.Defaults.split_coord): + """ + Creates a class to estimate the ceiling for a given assembly, based on split-half consistencies. - def __call__(self, assembly): + :param consistency_metric: The metric to compare two halves. :class:`~.PearsonCorrelation` by default. + :param aggregate: An optional function to aggregate the output of the `consistency_metric`. + :param split_coord: Over which coordinate to split to obtain the two halves. + """ + if not consistency_metric: + consistency_metric = PearsonCorrelation( + stimulus_coord=XarrayDefaults.stimulus_coord, neuroid_dim=XarrayDefaults.neuroid_dim, + neuroid_coord=XarrayDefaults.neuroid_coord) + aggregate = consistency_metric.aggregate + self._consistency = _SplitHalvesConsistency(consistency_metric=consistency_metric, + aggregate=aggregate, + split_coord=split_coord) + + def __call__(self, assembly: DataAssembly) -> Score: return self._consistency(assembly) @@ -133,7 +116,7 @@ def __init__(self, ceiling): """ self.ceiling = ceiling - def __call__(self, assembly): + def __call__(self, assembly: DataAssembly) -> Score: ceilings = [] for time_bin in tqdm(assembly['time_bin'].values, desc='time-ceiling'): ceiling = self.ceiling(assembly.sel(time_bin=time_bin)) @@ -142,21 +125,3 @@ def __call__(self, assembly): ceilings.append(ceiling) ceiling = Score.merge(*ceilings) return ceiling - - -class NeuronalPropertyCeiling: - def __init__(self, similarity_metric): - self.similarity_metric = similarity_metric - - def __call__(self, assembly): - self.assembly = assembly - return self._ceiling(self.similarity_metric.property_name) - - @store() - def _ceiling(self, identifier): - return self.similarity_metric(self.assembly, self.assembly) - -ceilings = { - 'cons': InternalConsistency, - None: NoCeiling, -} diff --git a/brainscore_vision/metrics/internal_consistency/requirements.txt b/brainscore_vision/metrics/internal_consistency/requirements.txt new file mode 100644 index 000000000..9c61c7363 --- /dev/null +++ b/brainscore_vision/metrics/internal_consistency/requirements.txt @@ -0,0 +1 @@ +scipy \ No newline at end of file diff --git a/tests/test_metrics/test_ceiling.py b/brainscore_vision/metrics/internal_consistency/test.py similarity index 71% rename from tests/test_metrics/test_ceiling.py rename to brainscore_vision/metrics/internal_consistency/test.py index ef7363472..6ccd597c3 100644 --- a/tests/test_metrics/test_ceiling.py +++ b/brainscore_vision/metrics/internal_consistency/test.py @@ -1,19 +1,11 @@ from string import ascii_lowercase as alphabet import numpy as np -import pytest from pytest import approx from brainio.assemblies import NeuroidAssembly, DataAssembly -from brainscore.benchmarks.majajhong2015 import load_assembly -from brainscore.metrics.ceiling import NoCeiling, InternalConsistency, SplitHalfConsistency - - -class TestNoCeiling: - def test(self): - ceiling = NoCeiling() - ceiling_score = ceiling() - assert ceiling_score == 1 +from brainscore_vision import load_ceiling +from .ceiling import PearsonCorrelation class TestInternalConsistency: @@ -25,16 +17,9 @@ def test_dummy_data(self): 'neuroid_id': ('neuroid', np.arange(10)), 'neuroid_meta': ('neuroid', np.arange(10))}, dims=['presentation', 'neuroid']) - ceiler = InternalConsistency() + ceiler = load_ceiling('internal_consistency') ceiling = ceiler(data) - assert ceiling.sel(aggregation='center') == 1 - - @pytest.mark.private_access - def test_majajhong2015_it(self): - assembly_repetitions = load_assembly(average_repetitions=False, region='IT') - ceiler = InternalConsistency() - ceiling = ceiler(assembly_repetitions) - assert ceiling.sel(aggregation='center') == approx(.82, abs=.01) + assert ceiling == 1 class TestSplitHalfConsistency: @@ -46,7 +31,7 @@ def test(self): 'neuroid_id': ('neuroid', np.arange(10)), 'neuroid_meta': ('neuroid', np.arange(10))}, dims=['presentation', 'neuroid']) - ceiler = SplitHalfConsistency() + ceiler = PearsonCorrelation() ceiling = ceiler(data, data) assert all(ceiling == DataAssembly([approx(1)] * 10, coords={'neuroid_id': ('neuroid', np.arange(10)), diff --git a/brainscore_vision/metrics/mask_regression/__init__.py b/brainscore_vision/metrics/mask_regression/__init__.py new file mode 100644 index 000000000..ed8c2e725 --- /dev/null +++ b/brainscore_vision/metrics/mask_regression/__init__.py @@ -0,0 +1,16 @@ +from brainscore_vision import metric_registry +from brainscore_vision.metrics.regression_correlation.metric import CrossRegressedCorrelation, pearsonr_correlation +from .metric import mask_regression + +metric_registry['mask_regression'] = lambda *args, **kwargs: CrossRegressedCorrelation( + regression=mask_regression(), + correlation=pearsonr_correlation(), + *args, **kwargs) + +BIBTEX = """@article{klindt2017neural, + title={Neural system identification for large populations separating “what” and “where”}, + author={Klindt, David and Ecker, Alexander S and Euler, Thomas and Bethge, Matthias}, + journal={Advances in neural information processing systems}, + volume={30}, + year={2017} +}""" diff --git a/brainscore_vision/metrics/mask_regression/metric.py b/brainscore_vision/metrics/mask_regression/metric.py new file mode 100644 index 000000000..b0bc917de --- /dev/null +++ b/brainscore_vision/metrics/mask_regression/metric.py @@ -0,0 +1,242 @@ +import logging +from typing import Union + +import numpy as np +from tqdm import tqdm + +from brainscore_vision.metric_helpers.xarray_utils import XarrayRegression +from brainscore_vision.utils import fullname + + +# do not import tensorflow at top level to avoid forcing users to install it if they don't use this metric +def mask_regression(): + regression = MaskRegression() + regression = XarrayRegression(regression) + return regression + + +class MaskRegression: + """ + Klindt et. al, NIPS 2017 + https://papers.nips.cc/paper/6942-neural-system-identification-for-large-populations-separating-what-and-where + + TF implementation of Receptive Field factorized regression + """ + + def __init__(self, init_lr=0.01, + max_epochs=40, tol=0.1, batch_size=50, ls=.1, ld=.1, decay_rate=25, + inits: Union[None, dict] = None, log_rate=10, gpu_options=None): + """ + mapping function class. + :param batch_size: batch size + :param init_lr: initial learning rate + :param ls: regularization coefficient for spatial parameters (spatial convolution) + :param ld: regularization coefficient for depth parameters (depth convolution) + :param tol: tolerance - stops the optimization if reaches below tol + :param max_epochs: maximum number of epochs to train + :param inits: initial values for the mapping function parameters. A dictionary containing + any of the following keys ['s_w', 'd_w', 'bias'] + :param log_rate: rate of logging the loss values + :param decay_rate: rate of decay for learning rate (#epochs) + """ + self._ld = ld + self._ls = ls + self._tol = tol + self._batch_size = batch_size + self._lr = init_lr + self._max_epochs = max_epochs + self._inits = inits + self._log_rate = log_rate + self._decay_rate = decay_rate + self._gpu_options = gpu_options + + self._graph = None + self._lr_ph = None + self._opt = None + self._logger = logging.getLogger(fullname(self)) + + def _iterate_minibatches(self, inputs, targets=None, batchsize=128, shuffle=False): + """ + Iterates over inputs with minibatches + :param inputs: input dataset, first dimension should be examples + :param targets: [n_examples, n_neurons] response values, first dimension should be examples + :param batchsize: batch size + :param shuffle: flag indicating whether to shuffle the data while making minibatches + :return: minibatch of (X, Y) + """ + input_len = inputs.shape[0] + if shuffle: + indices = np.arange(input_len) + np.random.shuffle(indices) + for start_idx in range(0, input_len, batchsize): + if shuffle: + excerpt = indices[start_idx:start_idx + batchsize] + else: + excerpt = slice(start_idx, start_idx + batchsize) + if targets is None: + yield inputs[excerpt] + else: + yield inputs[excerpt], targets[excerpt] + + def fit(self, X, Y): + """ + Fits the parameters to the data + :param X: Source data, first dimension is examples + :param Y: Target data, first dimension is examples + """ + assert not np.isnan(X).any() and not np.isnan(Y).any() + self.setup() + X = self.reindex(X) + assert X.ndim == 4, 'Input matrix rank should be 4.' + with self._graph.as_default(): + self._init_mapper(X, Y) + lr = self._lr + for epoch in tqdm(range(self._max_epochs), desc='mask epochs'): + for counter, batch in enumerate( + self._iterate_minibatches(X, Y, batchsize=self._batch_size, shuffle=True)): + feed_dict = {self._input_placeholder: batch[0], + self._target_placeholder: batch[1], + self._lr_ph: lr} + _, loss_value, reg_loss_value = self._sess.run([self.train_op, self.l2_error, self.reg_loss], + feed_dict=feed_dict) + if epoch % self._log_rate == 0: + self._logger.debug(f'Epoch: {epoch}, Err Loss: {loss_value:.2f}, Reg Loss: {reg_loss_value:.2f}') + if epoch % self._decay_rate == 0 and epoch != 0: + lr /= 10. + if loss_value < self._tol: + self._logger.debug('Converged.') + break + + def predict(self, X): + """ + Predicts the responses to the give input X + :param X: Input data, first dimension is examples + :return: predictions + """ + assert not np.isnan(X).any() + X = self.reindex(X) + with self._graph.as_default(): + preds = [] + for batch in self._iterate_minibatches(X, batchsize=self._batch_size, shuffle=False): + feed_dict = {self._input_placeholder: batch} + preds.append(np.squeeze(self._sess.run([self._predictions], feed_dict=feed_dict))) + return np.concatenate(preds, axis=0) + + def setup(self): + import tensorflow as tf + self._graph = tf.Graph() + with self._graph.as_default(): + self._lr_ph = tf.compat.v1.placeholder(dtype=tf.float32) + self._opt = tf.compat.v1.train.AdamOptimizer(learning_rate=self._lr_ph) + + def reindex(self, X): + channel_names = ['channel', 'channel_x', 'channel_y'] + assert all(hasattr(X, coord) for coord in channel_names) + shapes = [len(set(X[channel].values)) for channel in channel_names] + X = np.reshape(X.values, [X.shape[0]] + shapes) + X = np.transpose(X, axes=[0, 2, 3, 1]) + return X + + def _make_separable_map(self): + """ + Makes the mapping function computational graph + """ + import tensorflow as tf + with self._graph.as_default(): + with tf.compat.v1.variable_scope('mapping'): + input_shape = self._input_placeholder.shape + preds = [] + for n in range(self._target_placeholder.shape[1]): + with tf.compat.v1.variable_scope('N_{}'.format(n)): + # for all variables, either use pre-defined initial value or initialize randomly + if self._inits is not None and 's_w' in self._inits: + s_w = tf.Variable(initial_value= + self._inits['s_w'][n].reshape((1, input_shape[1], input_shape[2], 1)), + dtype=tf.float32) + else: + s_w = tf.Variable(initial_value=np.random.randn(1, input_shape[1], input_shape[2], 1), + dtype=tf.float32) + if self._inits is not None and 'd_w' in self._inits: + d_w = tf.Variable(initial_value=self._inits['d_w'][n].reshape(1, 1, input_shape[-1], 1), + dtype=tf.float32) + else: + d_w = tf.Variable(initial_value=np.random.randn(1, 1, input_shape[-1], 1), + dtype=tf.float32) + if self._inits is not None and 'bias' in self._inits: + bias = tf.Variable(initial_value=self._inits['bias'][n].reshape(1, 1, 1, 1), + dtype=tf.float32) + else: + bias = tf.Variable(initial_value=np.zeros((1, 1, 1, 1)), dtype=tf.float32) + + tf.compat.v1.add_to_collection('s_w', s_w) + out = s_w * self._input_placeholder + + tf.compat.v1.add_to_collection('d_w', d_w) + out = tf.reduce_sum(input_tensor=out, axis=[1, 2], keepdims=True) + out = tf.nn.conv2d(input=out, filters=d_w, strides=[1, 1, 1, 1], padding='SAME') + + tf.compat.v1.add_to_collection('bias', bias) + preds.append(tf.squeeze(out, axis=[1, 2]) + bias) + + self._predictions = tf.concat(preds, -1) + + def _make_loss(self): + """ + Makes the loss computational graph + """ + import tensorflow as tf + with self._graph.as_default(): + with tf.compat.v1.variable_scope('loss'): + self.l2_error = tf.norm(tensor=self._predictions - self._target_placeholder, ord=2) + # For separable mapping + self._s_vars = tf.compat.v1.get_collection('s_w') + self._d_vars = tf.compat.v1.get_collection('d_w') + self._biases = tf.compat.v1.get_collection('bias') + + # Laplacian loss + laplace_filter = tf.constant(np.array([0, -1, 0, -1, 4, -1, 0, -1, 0]).reshape((3, 3, 1, 1)), + dtype=tf.float32) + laplace_loss = tf.reduce_sum( + input_tensor=[tf.norm( + tensor=tf.nn.conv2d(input=t, filters=laplace_filter, strides=[1, 1, 1, 1], padding='SAME')) for + t in self._s_vars]) + l2_loss = tf.reduce_sum(input_tensor=[tf.reduce_sum(input_tensor=tf.pow(t, 2)) for t in self._s_vars]) + self.reg_loss = self._ls * (l2_loss + laplace_loss) + \ + self._ld * tf.reduce_sum( + input_tensor=[tf.reduce_sum(input_tensor=tf.pow(t, 2)) for t in self._d_vars]) + + self.total_loss = self.l2_error + self.reg_loss + self.tvars = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES) + self.train_op = self._opt.minimize(self.total_loss, var_list=self.tvars, + global_step=tf.compat.v1.train.get_or_create_global_step()) + + def _init_mapper(self, X, Y): + """ + Initializes the mapping function graph + :param X: input data + """ + import tensorflow as tf + assert len(Y.shape) == 2 + with self._graph.as_default(): + self._input_placeholder = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None] + list(X.shape[1:])) + self._target_placeholder = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, Y.shape[1]]) + # Build the model graph + self._make_separable_map() + self._make_loss() + + # initialize graph + self._logger.debug('Initializing mapper') + init_op = tf.compat.v1.variables_initializer( + tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES)) + self._sess = tf.compat.v1.Session( + config=tf.compat.v1.ConfigProto( + gpu_options=self._gpu_options) if self._gpu_options is not None else None) + self._sess.run(init_op) + + def close(self): + """ + Closes occupied resources + """ + import tensorflow as tf + tf.compat.v1.reset_default_graph() + self._sess.close() diff --git a/brainscore_vision/metrics/mask_regression/requirements.txt b/brainscore_vision/metrics/mask_regression/requirements.txt new file mode 100644 index 000000000..b3a60b6da --- /dev/null +++ b/brainscore_vision/metrics/mask_regression/requirements.txt @@ -0,0 +1 @@ +tensorflow \ No newline at end of file diff --git a/packaging/dicarlo/marques/__init__.py b/brainscore_vision/metrics/mask_regression/test.py similarity index 100% rename from packaging/dicarlo/marques/__init__.py rename to brainscore_vision/metrics/mask_regression/test.py diff --git a/brainscore_vision/metrics/ost/__init__.py b/brainscore_vision/metrics/ost/__init__.py new file mode 100644 index 000000000..c9da7e195 --- /dev/null +++ b/brainscore_vision/metrics/ost/__init__.py @@ -0,0 +1,23 @@ +from brainscore_vision import metric_registry +from .metric import OSTCorrelation + +metric_registry['ost'] = OSTCorrelation + +BIBTEX = """@article{kubiliusschrimpf2019brain, + title={Brain-like object recognition with high-performing shallow recurrent ANNs}, + author={Kubilius, Jonas and Schrimpf, Martin and Kar, Kohitij and Rajalingham, Rishi and Hong, Ha and Majaj, Najib and Issa, Elias and Bashivan, Pouya and Prescott-Roy, Jonathan and Schmidt, Kailyn and others}, + journal={Advances in neural information processing systems}, + volume={32}, + year={2019} +}""" + +BIBTEX_CHARACTERIZATION = """@article{kar2019evidence, + title={Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior}, + author={Kar, Kohitij and Kubilius, Jonas and Schmidt, Kailyn and Issa, Elias B and DiCarlo, James J}, + journal={Nature neuroscience}, + volume={22}, + number={6}, + pages={974--983}, + year={2019}, + publisher={Nature Publishing Group US New York} +}""" diff --git a/brainscore/metrics/ost.py b/brainscore_vision/metrics/ost/metric.py similarity index 97% rename from brainscore/metrics/ost.py rename to brainscore_vision/metrics/ost/metric.py index e0272dff0..7093781e7 100644 --- a/brainscore/metrics/ost.py +++ b/brainscore_vision/metrics/ost/metric.py @@ -8,11 +8,11 @@ from scipy.stats import spearmanr from tqdm import tqdm -from brainio.assemblies import walk_coords, array_is_element, BehavioralAssembly -from brainscore.metrics import Metric, Score -from brainscore.metrics.image_level_behavior import I1 -from brainscore.metrics.transformations import CrossValidation -from brainscore.utils import fullname +from brainio.assemblies import walk_coords, array_is_element, BehavioralAssembly, DataAssembly +from brainscore_core.metrics import Metric, Score +from brainscore_vision import load_metric +from brainscore_vision.metric_helpers.transformations import CrossValidation +from brainscore_vision.utils import fullname class OSTCorrelation(Metric): @@ -22,10 +22,10 @@ class OSTCorrelation(Metric): """ def __init__(self): self._cross_validation = CrossValidation(stratification_coord=None, splits=10, test_size=0.1) - self._i1 = I1() + self._i1 = load_metric('i1') self._predicted_osts, self._target_osts = [], [] - def __call__(self, source_recordings, target_osts): + def __call__(self, source_recordings: DataAssembly, target_osts: DataAssembly) -> Score: score = self._cross_validation(source_recordings, target_osts, apply=self.apply) return score diff --git a/brainscore_vision/metrics/ost/requirements.txt b/brainscore_vision/metrics/ost/requirements.txt new file mode 100644 index 000000000..5afca24a3 --- /dev/null +++ b/brainscore_vision/metrics/ost/requirements.txt @@ -0,0 +1,2 @@ +tensorflow +scipy \ No newline at end of file diff --git a/packaging/fei-fei/__init__.py b/brainscore_vision/metrics/ost/test.py similarity index 100% rename from packaging/fei-fei/__init__.py rename to brainscore_vision/metrics/ost/test.py diff --git a/brainscore_vision/metrics/rdm/__init__.py b/brainscore_vision/metrics/rdm/__init__.py new file mode 100644 index 000000000..0fcf5d333 --- /dev/null +++ b/brainscore_vision/metrics/rdm/__init__.py @@ -0,0 +1,14 @@ +from brainscore_vision import metric_registry +from .metric import RDMMetric, RDMCrossValidated + +metric_registry['rdm'] = RDMMetric +metric_registry['rdm_cv'] = RDMCrossValidated + +BIBTEX = """@article{kriegeskorte2008representational, + title={Representational similarity analysis-connecting the branches of systems neuroscience}, + author={Kriegeskorte, Nikolaus and Mur, Marieke and Bandettini, Peter A}, + journal={Frontiers in systems neuroscience}, + pages={4}, + year={2008}, + publisher={Frontiers} +}""" diff --git a/brainscore/metrics/rdm.py b/brainscore_vision/metrics/rdm/metric.py similarity index 75% rename from brainscore/metrics/rdm.py rename to brainscore_vision/metrics/rdm/metric.py index 5dc623d79..f9cfdad00 100644 --- a/brainscore/metrics/rdm.py +++ b/brainscore_vision/metrics/rdm/metric.py @@ -1,14 +1,13 @@ import numpy as np from scipy.stats import spearmanr -from brainio.assemblies import DataAssembly, walk_coords +from brainio.assemblies import DataAssembly, walk_coords, NeuroidAssembly +from brainscore_core.metrics import Metric, Score +from brainscore_vision.metric_helpers import Defaults as XarrayDefaults +from brainscore_vision.metric_helpers.transformations import TestOnlyCrossValidation -from brainscore.metrics import Score -from brainscore.metrics.transformations import TestOnlyCrossValidation -from brainscore.metrics.xarray_utils import Defaults as XarrayDefaults - -class RDMCrossValidated: +class RDMCrossValidated(Metric): """ Computes a coefficient for the similarity between two `RDM`s, using the upper triangular regions @@ -22,17 +21,11 @@ def __init__(self, neuroid_dim=XarrayDefaults.neuroid_dim, comparison_coord=Xarr crossvalidation_kwargs = {**crossvalidation_defaults, **(crossvalidation_kwargs or {})} self._cross_validation = TestOnlyCrossValidation(**crossvalidation_kwargs) - def __call__(self, assembly1, assembly2): - """ - :param brainscore.assemblies.NeuroidAssembly assembly1: - :param brainscore.assemblies.NeuroidAssembly assembly2: - :return: brainscore.assemblies.DataAssembly - """ - + def __call__(self, assembly1: NeuroidAssembly, assembly2: NeuroidAssembly) -> Score: return self._cross_validation(assembly1, assembly2, apply=self._metric) -class RDMMetric: +class RDMMetric(Metric): """ Computes a coefficient for the similarity between two `RDM`s, using the upper triangular regions @@ -44,23 +37,17 @@ def __init__(self, neuroid_dim=XarrayDefaults.neuroid_dim, comparison_coord=Xarr self._rdm = RDM(neuroid_dim=neuroid_dim) self._similarity = RDMSimilarity(comparison_coord=comparison_coord) - def __call__(self, assembly1, assembly2): - """ - :param brainio.assemblies.NeuroidAssembly assembly1: - :param brainio.assemblies.NeuroidAssembly assembly2: - :return: brainscore.metrics.Score - """ - + def __call__(self, assembly1: NeuroidAssembly, assembly2: NeuroidAssembly) -> Score: rdm1 = self._rdm(assembly1) rdm2 = self._rdm(assembly2) similarity = self._similarity(rdm1, rdm2) return Score(similarity) -class RSA: +class RDM: """ - Representational Similarity Analysis. - Converts an assembly of `presentation x neuroid` into a `neuroid x neuroid` RSA matrix. + Representational Dissimilarity Matrix. + Converts an assembly of `presentation x neuroid` into a `neuroid x neuroid` RDM. Kriegeskorte et al., 2008 https://doi.org/10.3389/neuro.06.004.2008 """ @@ -74,26 +61,11 @@ def __call__(self, assembly): coords = {coord: coord_value for coord, coord_value in assembly.coords.items() if coord != self._neuroid_dim} dims = [dim if dim != self._neuroid_dim else assembly.dims[(i - 1) % len(assembly.dims)] for i, dim in enumerate(assembly.dims)] - return DataAssembly(correlations, coords=coords, dims=dims) - - -class RDM: - """ - Representational Dissimilarity Matrix. - Converts an assembly of `presentation x neuroid` into a `neuroid x neuroid` RDM. - - Kriegeskorte et al., 2008 https://doi.org/10.3389/neuro.06.004.2008 - """ - - def __init__(self, *args, **kwargs): - self._rsa = RSA(*args, **kwargs) - - def __call__(self, assembly): - rsa = self._rsa(assembly) - return 1 - rsa + similarities = DataAssembly(correlations, coords=coords, dims=dims) + return 1 - similarities -class RDMSimilarity(object): +class RDMSimilarity: def __init__(self, comparison_coord=XarrayDefaults.stimulus_coord): self._comparison_coord = comparison_coord diff --git a/brainscore_vision/metrics/rdm/requirements.txt b/brainscore_vision/metrics/rdm/requirements.txt new file mode 100644 index 000000000..5576e19fe --- /dev/null +++ b/brainscore_vision/metrics/rdm/requirements.txt @@ -0,0 +1,2 @@ +numpy +scipy \ No newline at end of file diff --git a/tests/test_metrics/test_rdm.py b/brainscore_vision/metrics/rdm/test.py similarity index 57% rename from tests/test_metrics/test_rdm.py rename to brainscore_vision/metrics/rdm/test.py index b88214605..02bfdbe3d 100644 --- a/tests/test_metrics/test_rdm.py +++ b/brainscore_vision/metrics/rdm/test.py @@ -1,60 +1,12 @@ -import os - import numpy as np -import pytest -from brainio.assemblies import NeuroidAssembly, DataAssembly from pytest import approx -from brainscore.metrics.rdm import RSA, RDMSimilarity, RDMMetric, RDMCrossValidated -from tests.test_metrics import load_hvm - - -class TestRDMCrossValidated: - def test_small(self): - assembly = NeuroidAssembly((np.arange(30 * 25) + np.random.standard_normal(30 * 25)).reshape((30, 25)), - coords={'stimulus_id': ('presentation', np.arange(30)), - 'object_name': ('presentation', ['a', 'b', 'c'] * 10), - 'neuroid_id': ('neuroid', np.arange(25)), - 'region': ('neuroid', [None] * 25)}, - dims=['presentation', 'neuroid']) - metric = RDMCrossValidated() - score = metric(assembly1=assembly, assembly2=assembly) - assert score.sel(aggregation='center') == approx(1) - - -class TestRSA: - @pytest.mark.private_access - def test_equal_hvm(self): - hvm = load_hvm().sel(region='IT') - metric = RDMMetric() - score = metric(hvm, hvm) - assert score == approx(1.) - - @pytest.mark.skip(reason="loaded ordering likely incorrect") - def test_hvm(self): - hvm_it_v6_obj = self._load_neural_data() - assert hvm_it_v6_obj.shape == (64, 168) - self._test_hvm(hvm_it_v6_obj) - - @pytest.mark.skip(reason="loaded ordering likely incorrect") - def test_hvm_T(self): - hvm_it_v6_obj = self._load_neural_data().T - assert hvm_it_v6_obj.shape == (168, 64) - self._test_hvm(hvm_it_v6_obj) - - def _test_hvm(self, hvm_it_v6_obj): - loaded = np.load(os.path.join(os.path.dirname(__file__), "it_rdm.p"), encoding="latin1") - rsa_characterization = RSA() - rsa = rsa_characterization(hvm_it_v6_obj) - np.testing.assert_array_equal(rsa.shape, [64, 64]) - np.testing.assert_array_equal(rsa.dims, ['presentation', 'presentation']) - assert rsa.values == approx(loaded, abs=1e-6) +from brainio.assemblies import NeuroidAssembly, DataAssembly +from brainscore_vision import load_metric +from .metric import RDM - def _load_neural_data(self): - data = load_hvm(group=lambda hvm: hvm.multi_groupby(["category_name", "object_name"])) - data = data.sel(region='IT') - return data +class TestCharacterization: def test_alignment(self): assembly = NeuroidAssembly([[1, 2], [1, 2], [4, 3], [4, 3]], coords={'stimulus_id': ('presentation', list(range(4))), @@ -62,28 +14,41 @@ def test_alignment(self): 'neuroid_id': ('neuroid', list(range(2))), 'neuroid_meta': ('neuroid', list(range(2)))}, dims=['presentation', 'neuroid']) - matrix = RSA()(assembly) - assert np.all(np.diag(matrix) == approx(1., abs=.001)) + matrix = RDM()(assembly) + assert np.all(np.diag(matrix) == approx(0, abs=.001)) assert all(matrix.values[np.triu_indices(matrix.shape[0], k=1)] == matrix.values[np.tril_indices(matrix.shape[0], k=-1)]), "upper and lower triangular need to be equal" - expected = DataAssembly([[1., 1., -1., -1.], - [1., 1., -1., -1.], - [-1., -1., 1., 1.], - [-1., -1., 1., 1.]], + expected = DataAssembly([[0, 0, 2, 2], + [0, 0, 2, 2], + [2, 2, 0, 0], + [2, 2, 0, 0]], coords={'stimulus_id': ('presentation', list(range(4))), 'image_meta': ('presentation', list(range(4)))}, dims=['presentation', 'presentation']) np.testing.assert_array_almost_equal(matrix.values, expected.values) # does not take ordering into account -class TestRDMSimilarity(object): +class TestRDMCrossValidated: + def test_small(self): + assembly = NeuroidAssembly((np.arange(30 * 25) + np.random.standard_normal(30 * 25)).reshape((30, 25)), + coords={'stimulus_id': ('presentation', np.arange(30)), + 'object_name': ('presentation', ['a', 'b', 'c'] * 10), + 'neuroid_id': ('neuroid', np.arange(25)), + 'region': ('neuroid', [None] * 25)}, + dims=['presentation', 'neuroid']) + metric = load_metric('rdm_cv') + score = metric(assembly1=assembly, assembly2=assembly) + assert score == approx(1) + + +class TestRDMSimilarity: def test_2d_equal20(self): rdm = np.random.rand(20, 20) # not mirrored across diagonal, but fine for unit test np.fill_diagonal(rdm, 0) rdm = NeuroidAssembly(rdm, coords={'stimulus_id': ('presentation', list(range(20))), 'object_name': ('presentation', ['A', 'B'] * 10)}, dims=['presentation', 'presentation']) - similarity = RDMSimilarity() + similarity = load_metric('rdm') score = similarity(rdm, rdm) assert score == approx(1.) @@ -93,12 +58,6 @@ def test_2d_equal100(self): rdm = NeuroidAssembly(rdm, coords={'stimulus_id': ('presentation', list(range(100))), 'object_name': ('presentation', ['A', 'B'] * 50)}, dims=['presentation', 'presentation']) - similarity = RDMSimilarity() + similarity = load_metric('rdm') score = similarity(rdm, rdm) assert score == approx(1.) - - -def test_np_load(): - p_path = os.path.join(os.path.dirname(__file__), "it_rdm.p") - it_rdm = np.load(p_path, encoding="latin1", allow_pickle=True) - assert it_rdm.shape == (64, 64) diff --git a/brainscore_vision/metrics/regression_correlation/__init__.py b/brainscore_vision/metrics/regression_correlation/__init__.py new file mode 100644 index 000000000..2f8019b3f --- /dev/null +++ b/brainscore_vision/metrics/regression_correlation/__init__.py @@ -0,0 +1,39 @@ +from brainscore_vision import metric_registry +from .metric import CrossRegressedCorrelation, pls_regression, ridge_regression, single_regression, linear_regression,\ + pearsonr_correlation + +metric_registry['pls'] = lambda *args, **kwargs: CrossRegressedCorrelation( + regression=pls_regression(), correlation=pearsonr_correlation(), *args, **kwargs) +metric_registry['ridge'] = lambda *args, **kwargs: CrossRegressedCorrelation( + regression=ridge_regression(), correlation=pearsonr_correlation(), *args, **kwargs) +metric_registry['neuron_to_neuron'] = lambda *args, **kwargs: CrossRegressedCorrelation( + regression=single_regression(), correlation=pearsonr_correlation(), *args, **kwargs) +metric_registry['linear_predictivity'] = lambda *args, **kwargs: CrossRegressedCorrelation( + regression=linear_regression(), correlation=pearsonr_correlation(), *args, **kwargs) + +BIBTEX = """@article{schrimpf2018brain, + title={Brain-score: Which artificial neural network for object recognition is most brain-like?}, + author={Schrimpf, Martin and Kubilius, Jonas and Hong, Ha and Majaj, Najib J and Rajalingham, Rishi and Issa, Elias B and Kar, Kohitij and Bashivan, Pouya and Prescott-Roy, Jonathan and Geiger, Franziska and others}, + journal={BioRxiv}, + pages={407007}, + year={2018}, + publisher={Cold Spring Harbor Laboratory} +}""" + +BIBTEX_PLS = """@article{yamins2014performance, + title={Performance-optimized hierarchical models predict neural responses in higher visual cortex}, + author={Yamins, Daniel LK and Hong, Ha and Cadieu, Charles F and Solomon, Ethan A and Seibert, Darren and DiCarlo, James J}, + journal={Proceedings of the national academy of sciences}, + volume={111}, + number={23}, + pages={8619--8624}, + year={2014}, + publisher={National Acad Sciences} +}""" + +BIBTEX_NEURONTONEURON = """@techreport{arend2018single, + title={Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results}, + author={Arend, Luke and Han, Yena and Schrimpf, Martin and Bashivan, Pouya and Kar, Kohitij and Poggio, Tomaso and DiCarlo, James J and Boix, Xavier}, + year={2018}, + institution={Center for Brains, Minds and Machines (CBMM)} +}""" diff --git a/brainscore/metrics/mask_regression.py b/brainscore_vision/metrics/regression_correlation/mask_regression.py similarity index 99% rename from brainscore/metrics/mask_regression.py rename to brainscore_vision/metrics/regression_correlation/mask_regression.py index 6376d9343..adfb4dbfe 100644 --- a/brainscore/metrics/mask_regression.py +++ b/brainscore_vision/metrics/regression_correlation/mask_regression.py @@ -4,7 +4,7 @@ from tqdm import tqdm from typing import Union -from brainscore.utils import fullname +from brainscore_vision.utils import fullname # do not import tensorflow at top level to avoid forcing users to install it if they don't use this metric diff --git a/brainscore/metrics/regression.py b/brainscore_vision/metrics/regression_correlation/metric.py similarity index 83% rename from brainscore/metrics/regression.py rename to brainscore_vision/metrics/regression_correlation/metric.py index 1675ab8d2..365f63868 100644 --- a/brainscore/metrics/regression.py +++ b/brainscore_vision/metrics/regression_correlation/metric.py @@ -1,16 +1,16 @@ -import scipy.stats import numpy as np +import scipy.stats from sklearn.cross_decomposition import PLSRegression from sklearn.linear_model import LinearRegression, Ridge from sklearn.preprocessing import scale -from brainio.assemblies import walk_coords -from brainscore.metrics.mask_regression import MaskRegression -from brainscore.metrics.transformations import CrossValidation -from .xarray_utils import XarrayRegression, XarrayCorrelation +from brainio.assemblies import walk_coords, DataAssembly +from brainscore_core.metrics import Metric, Score +from brainscore_vision.metric_helpers.transformations import CrossValidation +from brainscore_vision.metric_helpers.xarray_utils import XarrayRegression, XarrayCorrelation -class CrossRegressedCorrelation: +class CrossRegressedCorrelation(Metric): def __init__(self, regression, correlation, crossvalidation_kwargs=None): regression = regression or pls_regression() crossvalidation_defaults = dict(train_size=.9, test_size=None) @@ -20,7 +20,7 @@ def __init__(self, regression, correlation, crossvalidation_kwargs=None): self.regression = regression self.correlation = correlation - def __call__(self, source, target): + def __call__(self, source: DataAssembly, target: DataAssembly) -> Score: return self.cross_validation(source, target, apply=self.apply, aggregate=self.aggregate) def apply(self, source_train, target_train, source_test, target_test): @@ -33,19 +33,19 @@ def aggregate(self, scores): return scores.median(dim='neuroid') -class ScaledCrossRegressedCorrelation: +class ScaledCrossRegressedCorrelation(Metric): def __init__(self, *args, **kwargs): self.cross_regressed_correlation = CrossRegressedCorrelation(*args, **kwargs) self.aggregate = self.cross_regressed_correlation.aggregate - def __call__(self, source, target): + def __call__(self, source: DataAssembly, target: DataAssembly) -> Score: scaled_values = scale(target, copy=True) target = target.__class__(scaled_values, coords={ coord: (dims, value) for coord, dims, value in walk_coords(target)}, dims=target.dims) return self.cross_regressed_correlation(source, target) -class SingleRegression(): +class SingleRegression: def __init__(self): self.mapping = [] @@ -56,7 +56,7 @@ def fit(self, X, Y): _, n_neuron = Y.shape r = np.zeros((n_neuron, n_neuroid)) for neuron in range(n_neuron): - r[neuron, :] = pearsonr(X, Y[:, neuron:neuron+1]) + r[neuron, :] = pearsonr(X, Y[:, neuron:neuron + 1]) self.mapping = np.nanargmax(r, axis=1) def predict(self, X): @@ -65,12 +65,6 @@ def predict(self, X): return Ypred -def mask_regression(): - regression = MaskRegression() - regression = XarrayRegression(regression) - return regression - - def pls_regression(regression_kwargs=None, xarray_kwargs=None): regression_defaults = dict(n_components=25, scale=False) regression_kwargs = {**regression_defaults, **(regression_kwargs or {})} @@ -86,6 +80,7 @@ def linear_regression(xarray_kwargs=None): regression = XarrayRegression(regression, **xarray_kwargs) return regression + def ridge_regression(xarray_kwargs=None): regression = Ridge() xarray_kwargs = xarray_kwargs or {} @@ -93,11 +88,6 @@ def ridge_regression(xarray_kwargs=None): return regression -def pearsonr_correlation(xarray_kwargs=None): - xarray_kwargs = xarray_kwargs or {} - return XarrayCorrelation(scipy.stats.pearsonr, **xarray_kwargs) - - def single_regression(xarray_kwargs=None): regression = SingleRegression() xarray_kwargs = xarray_kwargs or {} @@ -105,6 +95,11 @@ def single_regression(xarray_kwargs=None): return regression +def pearsonr_correlation(xarray_kwargs=None): + xarray_kwargs = xarray_kwargs or {} + return XarrayCorrelation(scipy.stats.pearsonr, **xarray_kwargs) + + def pearsonr(x, y): xmean = x.mean(axis=0, keepdims=True) ymean = y.mean(axis=0, keepdims=True) @@ -115,6 +110,6 @@ def pearsonr(x, y): normxm = scipy.linalg.norm(xm, axis=0, keepdims=True) normym = scipy.linalg.norm(ym, axis=0, keepdims=True) - r = ((xm/normxm)*(ym/normym)).sum(axis=0) + r = ((xm / normxm) * (ym / normym)).sum(axis=0) return r diff --git a/brainscore_vision/metrics/regression_correlation/requirements.txt b/brainscore_vision/metrics/regression_correlation/requirements.txt new file mode 100644 index 000000000..0f3b3811c --- /dev/null +++ b/brainscore_vision/metrics/regression_correlation/requirements.txt @@ -0,0 +1,3 @@ +numpy +scipy +scikit-learn \ No newline at end of file diff --git a/tests/test_metrics/test_regression.py b/brainscore_vision/metrics/regression_correlation/test.py similarity index 84% rename from tests/test_metrics/test_regression.py rename to brainscore_vision/metrics/regression_correlation/test.py index d7441955b..a2fac04ec 100644 --- a/tests/test_metrics/test_regression.py +++ b/brainscore_vision/metrics/regression_correlation/test.py @@ -1,10 +1,10 @@ import numpy as np import pytest -from brainio.assemblies import NeuroidAssembly from pytest import approx -from brainscore.metrics.regression import CrossRegressedCorrelation, pls_regression, linear_regression, \ - pearsonr_correlation, ridge_regression +from brainio.assemblies import NeuroidAssembly +from brainscore_vision import load_metric +from .metric import pls_regression, linear_regression, ridge_regression class TestCrossRegressedCorrelation: @@ -15,9 +15,9 @@ def test_small(self): 'neuroid_id': ('neuroid', np.arange(25)), 'region': ('neuroid', ['some_region'] * 25)}, dims=['presentation', 'neuroid']) - metric = CrossRegressedCorrelation(regression=pls_regression(), correlation=pearsonr_correlation()) + metric = load_metric('pls') score = metric(source=assembly, target=assembly) - assert score.sel(aggregation='center') == approx(1, abs=.00001) + assert score == approx(1, abs=.00001) class TestRegression: diff --git a/brainscore_vision/model_helpers/README.md b/brainscore_vision/model_helpers/README.md new file mode 100644 index 000000000..ef97c5e4d --- /dev/null +++ b/brainscore_vision/model_helpers/README.md @@ -0,0 +1,16 @@ +# Brain-Score Model Helpers + +Utility for generic models to interact with brain data. + + +## Environment variables + +Environment variables are prefixed with `MT_`. + +| Variable | Description | +|------------------------|--------------------------------------------------------------| +| MT_MULTITHREAD | whether or not to use multi-threading | +| MT_HOME | path to framework root | +| MT_IMAGENET_PATH | path to ImageNet file containing the validation image set | + + diff --git a/packaging/geirhos2021/__init__.py b/brainscore_vision/model_helpers/__init__.py similarity index 100% rename from packaging/geirhos2021/__init__.py rename to brainscore_vision/model_helpers/__init__.py diff --git a/brainscore_vision/model_helpers/activations/__init__.py b/brainscore_vision/model_helpers/activations/__init__.py new file mode 100644 index 000000000..10f514697 --- /dev/null +++ b/brainscore_vision/model_helpers/activations/__init__.py @@ -0,0 +1,3 @@ +from brainscore_vision.model_helpers.activations.keras import KerasWrapper, preprocess as preprocess_keras +from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper, preprocess_images as preprocess_pytorch +from brainscore_vision.model_helpers.activations.tensorflow import TensorflowWrapper, TensorflowSlimWrapper diff --git a/brainscore_vision/model_helpers/activations/core.py b/brainscore_vision/model_helpers/activations/core.py new file mode 100644 index 000000000..3d982b5e2 --- /dev/null +++ b/brainscore_vision/model_helpers/activations/core.py @@ -0,0 +1,320 @@ +import copy +import os + +import functools +import logging +from collections import OrderedDict +from multiprocessing.pool import ThreadPool + +import numpy as np +from tqdm.auto import tqdm + +from brainio.assemblies import NeuroidAssembly, walk_coords +from brainio.stimuli import StimulusSet +from brainscore_vision.model_helpers.utils import fullname +from result_caching import store_xarray + + +class Defaults: + batch_size = 64 + + +class ActivationsExtractorHelper: + def __init__(self, get_activations, preprocessing, identifier=False, batch_size=Defaults.batch_size): + """ + :param identifier: an activations identifier for the stored results file. False to disable saving. + """ + self._logger = logging.getLogger(fullname(self)) + + self._batch_size = batch_size + self.identifier = identifier + self.get_activations = get_activations + self.preprocess = preprocessing or (lambda x: x) + self._stimulus_set_hooks = {} + self._batch_activations_hooks = {} + + def __call__(self, stimuli, layers, stimuli_identifier=None): + """ + :param stimuli_identifier: a stimuli identifier for the stored results file. False to disable saving. + """ + if isinstance(stimuli, StimulusSet): + return self.from_stimulus_set(stimulus_set=stimuli, layers=layers, stimuli_identifier=stimuli_identifier) + else: + return self.from_paths(stimuli_paths=stimuli, layers=layers, stimuli_identifier=stimuli_identifier) + + def from_stimulus_set(self, stimulus_set, layers, stimuli_identifier=None): + """ + :param stimuli_identifier: a stimuli identifier for the stored results file. + False to disable saving. None to use `stimulus_set.identifier` + """ + if stimuli_identifier is None and hasattr(stimulus_set, 'identifier'): + stimuli_identifier = stimulus_set.identifier + for hook in self._stimulus_set_hooks.copy().values(): # copy to avoid stale handles + stimulus_set = hook(stimulus_set) + stimuli_paths = [str(stimulus_set.get_stimulus(stimulus_id)) for stimulus_id in stimulus_set['stimulus_id']] + activations = self.from_paths(stimuli_paths=stimuli_paths, layers=layers, stimuli_identifier=stimuli_identifier) + activations = attach_stimulus_set_meta(activations, stimulus_set) + return activations + + def from_paths(self, stimuli_paths, layers, stimuli_identifier=None): + if layers is None: + layers = ['logits'] + if self.identifier and stimuli_identifier: + fnc = functools.partial(self._from_paths_stored, + identifier=self.identifier, stimuli_identifier=stimuli_identifier) + else: + self._logger.debug(f"self.identifier `{self.identifier}` or stimuli_identifier {stimuli_identifier} " + f"are not set, will not store") + fnc = self._from_paths + # In case stimuli paths are duplicates (e.g. multiple trials), we first reduce them to only the paths that need + # to be run individually, compute activations for those, and then expand the activations to all paths again. + # This is done here, before storing, so that we only store the reduced activations. + reduced_paths = self._reduce_paths(stimuli_paths) + activations = fnc(layers=layers, stimuli_paths=reduced_paths) + activations = self._expand_paths(activations, original_paths=stimuli_paths) + return activations + + @store_xarray(identifier_ignore=['stimuli_paths', 'layers'], combine_fields={'layers': 'layer'}) + def _from_paths_stored(self, identifier, layers, stimuli_identifier, stimuli_paths): + return self._from_paths(layers=layers, stimuli_paths=stimuli_paths) + + def _from_paths(self, layers, stimuli_paths): + if len(layers) == 0: + raise ValueError("No layers passed to retrieve activations from") + self._logger.info('Running stimuli') + layer_activations = self._get_activations_batched(stimuli_paths, layers=layers, batch_size=self._batch_size) + self._logger.info('Packaging into assembly') + return self._package(layer_activations, stimuli_paths) + + def _reduce_paths(self, stimuli_paths): + return list(set(stimuli_paths)) + + def _expand_paths(self, activations, original_paths): + activations_paths = activations['stimulus_path'].values + argsort_indices = np.argsort(activations_paths) + sorted_x = activations_paths[argsort_indices] + sorted_index = np.searchsorted(sorted_x, original_paths) + index = [argsort_indices[i] for i in sorted_index] + return activations[{'stimulus_path': index}] + + def register_batch_activations_hook(self, hook): + r""" + The hook will be called every time a batch of activations is retrieved. + The hook should have the following signature:: + + hook(batch_activations) -> batch_activations + + The hook should return new batch_activations which will be used in place of the previous ones. + """ + + handle = HookHandle(self._batch_activations_hooks) + self._batch_activations_hooks[handle.id] = hook + return handle + + def register_stimulus_set_hook(self, hook): + r""" + The hook will be called every time before a stimulus set is processed. + The hook should have the following signature:: + + hook(stimulus_set) -> stimulus_set + + The hook should return a new stimulus_set which will be used in place of the previous one. + """ + + handle = HookHandle(self._stimulus_set_hooks) + self._stimulus_set_hooks[handle.id] = hook + return handle + + def _get_activations_batched(self, paths, layers, batch_size): + layer_activations = None + for batch_start in tqdm(range(0, len(paths), batch_size), unit_scale=batch_size, desc="activations"): + batch_end = min(batch_start + batch_size, len(paths)) + batch_inputs = paths[batch_start:batch_end] + batch_activations = self._get_batch_activations(batch_inputs, layer_names=layers, batch_size=batch_size) + for hook in self._batch_activations_hooks.copy().values(): # copy to avoid handle re-enabling messing with the loop + batch_activations = hook(batch_activations) + + if layer_activations is None: + layer_activations = copy.copy(batch_activations) + else: + for layer_name, layer_output in batch_activations.items(): + layer_activations[layer_name] = np.concatenate((layer_activations[layer_name], layer_output)) + + return layer_activations + + def _get_batch_activations(self, inputs, layer_names, batch_size): + inputs, num_padding = self._pad(inputs, batch_size) + preprocessed_inputs = self.preprocess(inputs) + activations = self.get_activations(preprocessed_inputs, layer_names) + assert isinstance(activations, OrderedDict) + activations = self._unpad(activations, num_padding) + return activations + + def _pad(self, batch_images, batch_size): + num_images = len(batch_images) + if num_images % batch_size == 0: + return batch_images, 0 + num_padding = batch_size - (num_images % batch_size) + padding = np.repeat(batch_images[-1:], repeats=num_padding, axis=0) + return np.concatenate((batch_images, padding)), num_padding + + def _unpad(self, layer_activations, num_padding): + return change_dict(layer_activations, lambda values: values[:-num_padding or None]) + + def _package(self, layer_activations, stimuli_paths): + shapes = [a.shape for a in layer_activations.values()] + self._logger.debug(f"Activations shapes: {shapes}") + self._logger.debug("Packaging individual layers") + layer_assemblies = [self._package_layer(single_layer_activations, layer=layer, stimuli_paths=stimuli_paths) for + layer, single_layer_activations in tqdm(layer_activations.items(), desc='layer packaging')] + # merge manually instead of using merge_data_arrays since `xarray.merge` is very slow with these large arrays + # complication: (non)neuroid_coords are taken from the structure of layer_assemblies[0] i.e. the 1st assembly; + # using these names/keys for all assemblies results in KeyError if the first layer contains flatten_coord_names + # (see _package_layer) not present in later layers, e.g. first layer = conv, later layer = transformer layer + self._logger.debug(f"Merging {len(layer_assemblies)} layer assemblies") + model_assembly = np.concatenate([a.values for a in layer_assemblies], + axis=layer_assemblies[0].dims.index('neuroid')) + nonneuroid_coords = {coord: (dims, values) for coord, dims, values in walk_coords(layer_assemblies[0]) + if set(dims) != {'neuroid'}} + neuroid_coords = {coord: [dims, values] for coord, dims, values in walk_coords(layer_assemblies[0]) + if set(dims) == {'neuroid'}} + for layer_assembly in layer_assemblies[1:]: + for coord in neuroid_coords: + neuroid_coords[coord][1] = np.concatenate((neuroid_coords[coord][1], layer_assembly[coord].values)) + assert layer_assemblies[0].dims == layer_assembly.dims + for dim in set(layer_assembly.dims) - {'neuroid'}: + for coord in layer_assembly[dim].coords: + assert (layer_assembly[coord].values == nonneuroid_coords[coord][1]).all() + neuroid_coords = {coord: (dims_values[0], dims_values[1]) # re-package as tuple instead of list for xarray + for coord, dims_values in neuroid_coords.items()} + model_assembly = type(layer_assemblies[0])(model_assembly, coords={**nonneuroid_coords, **neuroid_coords}, + dims=layer_assemblies[0].dims) + return model_assembly + + def _package_layer(self, layer_activations, layer, stimuli_paths): + assert layer_activations.shape[0] == len(stimuli_paths) + activations, flatten_indices = flatten(layer_activations, return_index=True) # collapse for single neuroid dim + flatten_coord_names = None + if flatten_indices.shape[1] == 1: # fully connected, e.g. classifier + # see comment in _package for an explanation why we cannot simply have 'channel' for the FC layer + flatten_coord_names = ['channel', 'channel_x', 'channel_y'] + elif flatten_indices.shape[1] == 2: # Transformer, e.g. ViT + flatten_coord_names = ['channel', 'embedding'] + elif flatten_indices.shape[1] == 3: # 2DConv, e.g. resnet + flatten_coord_names = ['channel', 'channel_x', 'channel_y'] + elif flatten_indices.shape[1] == 4: # temporal sliding window, e.g. omnivron + flatten_coord_names = ['channel_temporal', 'channel_x', 'channel_y', 'channel'] + else: + # we still package the activations, but are unable to provide channel information + self._logger.debug(f"Unknown layer activations shape {layer_activations.shape}, not inferring channels") + + # build assembly + coords = {'stimulus_path': stimuli_paths, + 'neuroid_num': ('neuroid', list(range(activations.shape[1]))), + 'model': ('neuroid', [self.identifier] * activations.shape[1]), + 'layer': ('neuroid', [layer] * activations.shape[1]), + } + if flatten_coord_names: + flatten_coords = {flatten_coord_names[i]: [sample_index[i] if i < flatten_indices.shape[1] else np.nan + for sample_index in flatten_indices] + for i in range(len(flatten_coord_names))} + coords = {**coords, **{coord: ('neuroid', values) for coord, values in flatten_coords.items()}} + layer_assembly = NeuroidAssembly(activations, coords=coords, dims=['stimulus_path', 'neuroid']) + neuroid_id = [".".join([f"{value}" for value in values]) for values in zip(*[ + layer_assembly[coord].values for coord in ['model', 'layer', 'neuroid_num']])] + layer_assembly['neuroid_id'] = 'neuroid', neuroid_id + return layer_assembly + + def insert_attrs(self, wrapper): + wrapper.from_stimulus_set = self.from_stimulus_set + wrapper.from_paths = self.from_paths + wrapper.register_batch_activations_hook = self.register_batch_activations_hook + wrapper.register_stimulus_set_hook = self.register_stimulus_set_hook + + +def change_dict(d, change_function, keep_name=False, multithread=False): + if not multithread: + map_fnc = map + else: + pool = ThreadPool() + map_fnc = pool.map + + def apply_change(layer_values): + layer, values = layer_values + values = change_function(values) if not keep_name else change_function(layer, values) + return layer, values + + results = map_fnc(apply_change, d.items()) + results = OrderedDict(results) + if multithread: + pool.close() + return results + + +def lstrip_local(path): + parts = path.split(os.sep) + try: + start_index = parts.index('.brainio') + except ValueError: # not in list -- perhaps custom directory + return path + path = os.sep.join(parts[start_index:]) + return path + + +def attach_stimulus_set_meta(assembly, stimulus_set): + stimulus_paths = [str(stimulus_set.get_stimulus(stimulus_id)) for stimulus_id in stimulus_set['stimulus_id']] + stimulus_paths = [lstrip_local(path) for path in stimulus_paths] + assembly_paths = [lstrip_local(path) for path in assembly['stimulus_path'].values] + assert (np.array(assembly_paths) == np.array(stimulus_paths)).all() + assembly['stimulus_path'] = stimulus_set['stimulus_id'].values + assembly = assembly.rename({'stimulus_path': 'stimulus_id'}) + for column in stimulus_set.columns: + assembly[column] = 'stimulus_id', stimulus_set[column].values + assembly = assembly.stack(presentation=('stimulus_id',)) + return assembly + + +class HookHandle: + next_id = 0 + + def __init__(self, hook_dict): + self.hook_dict = hook_dict + self.id = HookHandle.next_id + HookHandle.next_id += 1 + self._saved_hook = None + + def remove(self): + hook = self.hook_dict[self.id] + del self.hook_dict[self.id] + return hook + + def disable(self): + self._saved_hook = self.remove() + + def enable(self): + self.hook_dict[self.id] = self._saved_hook + self._saved_hook = None + + +def flatten(layer_output, return_index=False): + flattened = layer_output.reshape(layer_output.shape[0], -1) + if not return_index: + return flattened + + def cartesian_product_broadcasted(*arrays): + """ + http://stackoverflow.com/a/11146645/190597 + """ + broadcastable = np.ix_(*arrays) + broadcasted = np.broadcast_arrays(*broadcastable) + dtype = np.result_type(*arrays) + rows, cols = functools.reduce(np.multiply, broadcasted[0].shape), len(broadcasted) + out = np.empty(rows * cols, dtype=dtype) + start, end = 0, rows + for a in broadcasted: + out[start:end] = a.reshape(-1) + start, end = end, end + rows + return out.reshape(cols, rows).T + + index = cartesian_product_broadcasted(*[np.arange(s, dtype='int') for s in layer_output.shape[1:]]) + return flattened, index diff --git a/brainscore_vision/model_helpers/activations/keras.py b/brainscore_vision/model_helpers/activations/keras.py new file mode 100644 index 000000000..066e2003d --- /dev/null +++ b/brainscore_vision/model_helpers/activations/keras.py @@ -0,0 +1,84 @@ +from collections import OrderedDict + +import numpy as np + +from brainscore_vision.model_helpers.activations.core import ActivationsExtractorHelper + + +class KerasWrapper: + def __init__(self, model, preprocessing, identifier=None, *args, **kwargs): + """ + :param model: a keras model with a function `preprocess_input` + that will later be called on the loaded numpy image + """ + self._model = model + identifier = identifier or model.name + self._extractor = ActivationsExtractorHelper( + identifier=identifier, get_activations=self.get_activations, preprocessing=preprocessing, + *args, **kwargs) + self._extractor.insert_attrs(self) + + @property + def identifier(self): + return self._extractor.identifier + + @identifier.setter + def identifier(self, value): + self._extractor.identifier = value + + def __call__(self, *args, **kwargs): # cannot assign __call__ as attribute due to Python convention + return self._extractor(*args, **kwargs) + + def get_activations(self, images, layer_names): + from keras import backend as K + input_tensor = self._model.input + layers = [layer for layer in self._model.layers if layer.name in layer_names] + layers = sorted(layers, key=lambda layer: layer_names.index(layer.name)) + if 'logits' in layer_names: + layers.insert(layer_names.index('logits'), self._model.layers[-1]) + assert len(layers) == len(layer_names) + layer_out_tensors = [layer.output for layer in layers] + functor = K.function([input_tensor] + [K.learning_phase()], layer_out_tensors) # evaluate all tensors at once + layer_outputs = functor([images, 0.]) # 0 to signal testing phase + return OrderedDict([(layer_name, layer_output) for layer_name, layer_output in zip(layer_names, layer_outputs)]) + + def __repr__(self): + return repr(self._model) + + def graph(self): + import networkx as nx + g = nx.DiGraph() + for layer in self._model.layers: + g.add_node(layer.name, object=layer, type=type(layer)) + for outbound_node in layer._outbound_nodes: + g.add_edge(layer.name, outbound_node.outbound_layer.name) + return g + + +def load_images(image_filepaths, image_size): + images = [load_image(image_filepath) for image_filepath in image_filepaths] + images = [scale_image(image, image_size) for image in images] + return np.array(images) + + +def load_image(image_filepath): + from keras.preprocessing import image + img = image.load_img(image_filepath) + x = image.img_to_array(img) + return x + + +def scale_image(img, image_size): + from PIL import Image + from keras.preprocessing import image + img = Image.fromarray(img.astype(np.uint8)) + img = img.resize((image_size, image_size)) + img = image.img_to_array(img) + return img + + +def preprocess(image_filepaths, image_size, *args, **kwargs): + # only a wrapper to avoid top-level keras imports + from keras.applications.imagenet_utils import preprocess_input + images = load_images(image_filepaths, image_size=image_size) + return preprocess_input(images, *args, **kwargs) diff --git a/brainscore_vision/model_helpers/activations/pca.py b/brainscore_vision/model_helpers/activations/pca.py new file mode 100644 index 000000000..71ddcc561 --- /dev/null +++ b/brainscore_vision/model_helpers/activations/pca.py @@ -0,0 +1,117 @@ +import h5py +import logging +import numpy as np +import os +from PIL import Image +from result_caching import store_dict +from sklearn.decomposition import PCA +from tqdm import tqdm + +from brainscore_vision.model_helpers.activations.core import flatten, change_dict +from brainscore_vision.model_helpers.utils import fullname, s3 + + +class LayerPCA: + def __init__(self, activations_extractor, n_components): + self._logger = logging.getLogger(fullname(self)) + self._extractor = activations_extractor + self._n_components = n_components + self._layer_pcas = {} + + def __call__(self, batch_activations): + self._ensure_initialized(batch_activations.keys()) + + def apply_pca(layer, activations): + pca = self._layer_pcas[layer] + activations = flatten(activations) + if pca is None: + return activations + return pca.transform(activations) + + return change_dict(batch_activations, apply_pca, keep_name=True, + multithread=os.getenv('MT_MULTITHREAD', '1') == '1') + + def _ensure_initialized(self, layers): + missing_layers = [layer for layer in layers if layer not in self._layer_pcas] + if len(missing_layers) == 0: + return + layer_pcas = self._pcas(identifier=self._extractor.identifier, layers=missing_layers, + n_components=self._n_components) + self._layer_pcas = {**self._layer_pcas, **layer_pcas} + + @store_dict(dict_key='layers', identifier_ignore=['layers']) + def _pcas(self, identifier, layers, n_components): + self._logger.debug('Retrieving ImageNet activations') + imagenet_paths = _get_imagenet_val(num_images=n_components) + self.handle.disable() + imagenet_activations = self._extractor(imagenet_paths, layers=layers) + imagenet_activations = {layer: imagenet_activations.sel(layer=layer).values + for layer in np.unique(imagenet_activations['layer'])} + assert len(set(activations.shape[0] for activations in imagenet_activations.values())) == 1, "stimuli differ" + self.handle.enable() + + self._logger.debug('Computing ImageNet principal components') + progress = tqdm(total=len(imagenet_activations), desc="layer principal components") + + def init_and_progress(layer, activations): + activations = flatten(activations) + if activations.shape[1] <= n_components: + self._logger.debug(f"Not computing principal components for {layer} " + f"activations {activations.shape} as shape is small enough already") + pca = None + else: + pca = PCA(n_components=n_components, random_state=0) + pca.fit(activations) + progress.update(1) + return pca + + layer_pcas = change_dict(imagenet_activations, init_and_progress, keep_name=True, + multithread=os.getenv('MT_MULTITHREAD', '1') == '1') + progress.close() + return layer_pcas + + @classmethod + def hook(cls, activations_extractor, n_components): + hook = LayerPCA(activations_extractor=activations_extractor, n_components=n_components) + assert not cls.is_hooked(activations_extractor), "PCA already hooked" + handle = activations_extractor.register_batch_activations_hook(hook) + hook.handle = handle + return handle + + @classmethod + def is_hooked(cls, activations_extractor): + return any(isinstance(hook, cls) for hook in + activations_extractor._extractor._batch_activations_hooks.values()) + + +def _get_imagenet_val(num_images): + _logger = logging.getLogger(fullname(_get_imagenet_val)) + num_classes = 1000 + num_images_per_class = (num_images - 1) // num_classes + base_indices = np.arange(num_images_per_class).astype(int) + indices = [] + for i in range(num_classes): + indices.extend(50 * i + base_indices) + for i in range((num_images - 1) % num_classes + 1): + indices.extend(50 * i + np.array([num_images_per_class]).astype(int)) + + framework_home = os.path.expanduser(os.getenv('MT_HOME', '~/.model-tools')) + imagenet_filepath = os.getenv('MT_IMAGENET_PATH', os.path.join(framework_home, 'imagenet2012.hdf5')) + imagenet_dir = f"{imagenet_filepath}-files" + os.makedirs(imagenet_dir, exist_ok=True) + + if not os.path.isfile(imagenet_filepath): + os.makedirs(os.path.dirname(imagenet_filepath), exist_ok=True) + _logger.debug(f"Downloading ImageNet validation to {imagenet_filepath}") + s3.download_file("imagenet2012-val.hdf5", imagenet_filepath) + + filepaths = [] + with h5py.File(imagenet_filepath, 'r') as f: + for index in indices: + imagepath = os.path.join(imagenet_dir, f"{index}.png") + if not os.path.isfile(imagepath): + image = np.array(f['val/images'][index]) + Image.fromarray(image).save(imagepath) + filepaths.append(imagepath) + + return filepaths diff --git a/brainscore_vision/model_helpers/activations/pytorch.py b/brainscore_vision/model_helpers/activations/pytorch.py new file mode 100644 index 000000000..92ba6a857 --- /dev/null +++ b/brainscore_vision/model_helpers/activations/pytorch.py @@ -0,0 +1,152 @@ +from collections import OrderedDict + +import logging +import numpy as np +from PIL import Image + +from brainscore_vision.model_helpers.activations.core import ActivationsExtractorHelper +from brainscore_vision.model_helpers.utils import fullname + +SUBMODULE_SEPARATOR = '.' + + +class PytorchWrapper: + def __init__(self, model, preprocessing, identifier=None, forward_kwargs=None, *args, **kwargs): + import torch + logger = logging.getLogger(fullname(self)) + self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + logger.debug(f"Using device {self._device}") + self._model = model + self._model = self._model.to(self._device) + identifier = identifier or model.__class__.__name__ + self._extractor = self._build_extractor( + identifier=identifier, preprocessing=preprocessing, get_activations=self.get_activations, *args, **kwargs) + self._extractor.insert_attrs(self) + self._forward_kwargs = forward_kwargs or {} + + def _build_extractor(self, identifier, preprocessing, get_activations, *args, **kwargs): + return ActivationsExtractorHelper( + identifier=identifier, get_activations=get_activations, preprocessing=preprocessing, + *args, **kwargs) + + @property + def identifier(self): + return self._extractor.identifier + + @identifier.setter + def identifier(self, value): + self._extractor.identifier = value + + def __call__(self, *args, **kwargs): # cannot assign __call__ as attribute due to Python convention + return self._extractor(*args, **kwargs) + + def get_activations(self, images, layer_names): + import torch + from torch.autograd import Variable + images = [torch.from_numpy(image) if not isinstance(image, torch.Tensor) else image for image in images] + images = Variable(torch.stack(images)) + images = images.to(self._device) + self._model.eval() + + layer_results = OrderedDict() + hooks = [] + + for layer_name in layer_names: + layer = self.get_layer(layer_name) + hook = self.register_hook(layer, layer_name, target_dict=layer_results) + hooks.append(hook) + + with torch.no_grad(): + self._model(images, **self._forward_kwargs) + for hook in hooks: + hook.remove() + return layer_results + + def get_layer(self, layer_name): + if layer_name == 'logits': + return self._output_layer() + module = self._model + for part in layer_name.split(SUBMODULE_SEPARATOR): + module = module._modules.get(part) + assert module is not None, f"No submodule found for layer {layer_name}, at part {part}" + return module + + def _output_layer(self): + module = self._model + while module._modules: + module = module._modules[next(reversed(module._modules))] + return module + + @classmethod + def _tensor_to_numpy(cls, output): + return output.cpu().data.numpy() + + def register_hook(self, layer, layer_name, target_dict): + def hook_function(_layer, _input, output, name=layer_name): + target_dict[name] = PytorchWrapper._tensor_to_numpy(output) + + hook = layer.register_forward_hook(hook_function) + return hook + + def __repr__(self): + return repr(self._model) + + def layers(self): + for name, module in self._model.named_modules(): + if len(list(module.children())) > 0: # this module only holds other modules + continue + yield name, module + + def graph(self): + import networkx as nx + g = nx.DiGraph() + for layer_name, layer in self.layers(): + g.add_node(layer_name, object=layer, type=type(layer)) + return g + + +def load_preprocess_images(image_filepaths, image_size, **kwargs): + images = load_images(image_filepaths) + images = preprocess_images(images, image_size=image_size, **kwargs) + return images + + +def load_images(image_filepaths): + return [load_image(image_filepath) for image_filepath in image_filepaths] + + +def load_image(image_filepath): + with Image.open(image_filepath) as pil_image: + if 'L' not in pil_image.mode.upper() and 'A' not in pil_image.mode.upper() \ + and 'P' not in pil_image.mode.upper(): # not binary and not alpha and not palletized + # work around to https://github.com/python-pillow/Pillow/issues/1144, + # see https://stackoverflow.com/a/30376272/2225200 + return pil_image.copy() + else: # make sure potential binary images are in RGB + rgb_image = Image.new("RGB", pil_image.size) + rgb_image.paste(pil_image) + return rgb_image + + +def preprocess_images(images, image_size, **kwargs): + preprocess = torchvision_preprocess_input(image_size, **kwargs) + images = [preprocess(image) for image in images] + images = np.concatenate(images) + return images + + +def torchvision_preprocess_input(image_size, **kwargs): + from torchvision import transforms + return transforms.Compose([ + transforms.Resize((image_size, image_size)), + torchvision_preprocess(**kwargs), + ]) + + +def torchvision_preprocess(normalize_mean=(0.485, 0.456, 0.406), normalize_std=(0.229, 0.224, 0.225)): + from torchvision import transforms + return transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize(mean=normalize_mean, std=normalize_std), + lambda img: img.unsqueeze(0) + ]) diff --git a/brainscore_vision/model_helpers/activations/tensorflow.py b/brainscore_vision/model_helpers/activations/tensorflow.py new file mode 100644 index 000000000..d5e4864d5 --- /dev/null +++ b/brainscore_vision/model_helpers/activations/tensorflow.py @@ -0,0 +1,71 @@ +from collections import OrderedDict + +from brainscore_vision.model_helpers.activations.core import ActivationsExtractorHelper + + +class TensorflowWrapper: + def __init__(self, identifier, inputs, endpoints: dict, session, *args, **kwargs): + import tensorflow as tf + self._inputs = inputs + self._endpoints = endpoints + self._session = session or tf.compat.v1.Session() + self._extractor = ActivationsExtractorHelper(identifier=identifier, get_activations=self.get_activations, + preprocessing=None, *args, **kwargs) + self._extractor.insert_attrs(self) + + @property + def identifier(self): + return self._extractor.identifier + + @identifier.setter + def identifier(self, value): + self._extractor.identifier = value + + def __call__(self, *args, **kwargs): # cannot assign __call__ as attribute due to Python convention + return self._extractor(*args, **kwargs) + + def get_activations(self, images, layer_names): + layer_tensors = OrderedDict((layer, self._endpoints[ + layer if (layer != 'logits' or layer in self._endpoints) else next(reversed(self._endpoints))]) + for layer in layer_names) + layer_outputs = self._session.run(layer_tensors, feed_dict={self._inputs: images}) + return layer_outputs + + def graph(self): + import networkx as nx + g = nx.DiGraph() + for name, layer in self._endpoints.items(): + g.add_node(name, object=layer, type=type(layer)) + g.add_node("logits", object=self.logits, type=type(self.logits)) + return g + + +class TensorflowSlimWrapper(TensorflowWrapper): + def __init__(self, *args, labels_offset=1, **kwargs): + super(TensorflowSlimWrapper, self).__init__(*args, **kwargs) + self._labels_offset = labels_offset + + def get_activations(self, images, layer_names): + layer_outputs = super(TensorflowSlimWrapper, self).get_activations(images, layer_names) + if 'logits' in layer_outputs: + layer_outputs['logits'] = layer_outputs['logits'][:, self._labels_offset:] + return layer_outputs + + +def load_image(image_filepath): + import tensorflow as tf + image = tf.io.read_file(image_filepath) + image = tf.image.decode_png(image, channels=3) + return image + + +def resize_image(image, image_size): + import tensorflow as tf + image = tf.image.resize(image, (image_size, image_size)) + return image + + +def load_resize_image(image_path, image_size): + image = load_image(image_path) + image = resize_image(image, image_size) + return image diff --git a/brainscore_vision/model_helpers/brain_transformation/__init__.py b/brainscore_vision/model_helpers/brain_transformation/__init__.py new file mode 100644 index 000000000..d4e1a7a98 --- /dev/null +++ b/brainscore_vision/model_helpers/brain_transformation/__init__.py @@ -0,0 +1,89 @@ +from brainscore_vision import load_benchmark +from brainscore_vision.model_helpers.brain_transformation.temporal import TemporalIgnore +from brainscore_vision.model_interface import BrainModel +from brainscore_vision.utils import LazyLoad +from .behavior import BehaviorArbiter, LabelBehavior, ProbabilitiesMapping +from .neural import LayerMappedModel, LayerSelection, LayerScores + +STANDARD_REGION_BENCHMARKS = { + 'V1': LazyLoad(lambda: load_benchmark('movshon.FreemanZiemba2013.V1.public')), + 'V2': LazyLoad(lambda: load_benchmark('movshon.FreemanZiemba2013.V2.public')), + 'V4': LazyLoad(lambda: load_benchmark('dicarlo.MajajHong2015public.V4-pls')), + 'IT': LazyLoad(lambda: load_benchmark('dicarlo.MajajHong2015public.IT-pls')), +} + + +class ModelCommitment(BrainModel): + """ + Standard process to convert a BaseModel (e.g. standard Machine Learning/Computer Vision model) + into a BrainModel (e.g. commitments on layer-to-region, pixel-to-degrees, ...). + """ + + def __init__(self, identifier, + activations_model, layers, behavioral_readout_layer=None, region_layer_map=None, + visual_degrees=8): + self.layers = layers + self.activations_model = activations_model + self._visual_degrees = visual_degrees + # region-layer mapping + if region_layer_map is None: + layer_selection = LayerSelection(model_identifier=identifier, + activations_model=activations_model, layers=layers, + visual_degrees=visual_degrees) + region_layer_map = RegionLayerMap(layer_selection=layer_selection, + region_benchmarks=STANDARD_REGION_BENCHMARKS) + # neural + layer_model = LayerMappedModel(identifier=identifier, activations_model=activations_model, + region_layer_map=region_layer_map) + self.layer_model = TemporalIgnore(layer_model) + logits_behavior = LabelBehavior(identifier=identifier, activations_model=activations_model) + behavioral_readout_layer = behavioral_readout_layer or layers[-1] + probabilities_behavior = ProbabilitiesMapping(identifier=identifier, activations_model=activations_model, + layer=behavioral_readout_layer) + self.behavior_model = BehaviorArbiter({BrainModel.Task.label: logits_behavior, + BrainModel.Task.probabilities: probabilities_behavior}) + self.do_behavior = False + + def visual_degrees(self) -> int: + return self._visual_degrees + + def start_task(self, task: BrainModel.Task, *args, **kwargs): + if task != BrainModel.Task.passive: + self.behavior_model.start_task(task, *args, **kwargs) + self.do_behavior = True + else: + self.do_behavior = False + + def look_at(self, stimuli, number_of_trials=1): + if self.do_behavior: + return self.behavior_model.look_at(stimuli, number_of_trials=number_of_trials) + else: + return self.layer_model.look_at(stimuli, number_of_trials=number_of_trials) + + def start_recording(self, recording_target, time_bins): + return self.layer_model.start_recording(recording_target, time_bins) + + @property + def identifier(self): + return self.layer_model.identifier + + +class RegionLayerMap(dict): + """ + mapping of regions to layers that only evaluates lazily and only once. + """ + + def __init__(self, layer_selection, region_benchmarks): + super(RegionLayerMap, self).__init__() + self.layer_selection = layer_selection + self.region_benchmarks = region_benchmarks + + def __getitem__(self, region): + if region not in self: # not yet committed + self.commit_region(region) + return super(RegionLayerMap, self).__getitem__(region) + + def commit_region(self, region): + benchmark = self.region_benchmarks[region] + best_layer = self.layer_selection(selection_identifier=region, benchmark=benchmark) + self[region] = best_layer diff --git a/brainscore_vision/model_helpers/brain_transformation/behavior.py b/brainscore_vision/model_helpers/brain_transformation/behavior.py new file mode 100644 index 000000000..f85126414 --- /dev/null +++ b/brainscore_vision/model_helpers/brain_transformation/behavior.py @@ -0,0 +1,217 @@ +from collections import OrderedDict + +import numpy as np +import os +import sklearn.linear_model +import sklearn.multioutput + +from brainio.assemblies import walk_coords, array_is_element, BehavioralAssembly +from brainscore_vision.model_helpers.utils import make_list +from brainscore_vision.model_interface import BrainModel + + +class BehaviorArbiter(BrainModel): + def __init__(self, mapping): + self.mapping = mapping + self.current_executor = None + + def start_task(self, task: BrainModel.Task, *args, **kwargs): + self.current_executor = self.mapping[task] + return self.current_executor.start_task(task, *args, **kwargs) + + def look_at(self, stimuli, *args, **kwargs): + return self.current_executor.look_at(stimuli, *args, **kwargs) + + +class LabelBehavior(BrainModel): + def __init__(self, identifier, activations_model): + self._identifier = identifier + self.activations_model = activations_model + self.current_task = None + self.choice_labels = None + + @property + def identifier(self): + return self._identifier + + def start_task(self, task: BrainModel.Task, choice_labels): + assert task == BrainModel.Task.label + self.current_task = task + self.choice_labels = choice_labels + + def look_at(self, stimuli, number_of_trials=1): + assert self.current_task == BrainModel.Task.label + logits = self.activations_model(stimuli, layers=['logits']) + choices = self.logits_to_choice(logits) + return choices + + def logits_to_choice(self, logits): + assert len(logits['neuroid']) == 1000 + logits = logits.transpose(..., 'neuroid') # move neuroid dimension last + extra_coords = {} + if self.choice_labels == 'imagenet': + # assuming the model was already trained on those labels, we just need to convert to synsets + prediction_indices = logits.values.argmax(axis=1) + with open(os.path.join(os.path.dirname(__file__), 'imagenet_classes.txt')) as f: + synsets = f.read().splitlines() + choices = [synsets[index] for index in prediction_indices] + extra_coords['synset'] = ('presentation', choices) + extra_coords['logit'] = ('presentation', prediction_indices) + else: + probabilities = softmax(logits) + assert len(probabilities.dims) == 2 and probabilities.dims[-1] == 'neuroid' + # map imagenet labels to target labels + # from https://github.com/bethgelab/model-vs-human/blob/745046c4d82ff884af618756bd6a5f47b6f36c45/modelvshuman/datasets/decision_mappings.py#L30 + aggregated_class_probabilities = [] + for label in self.choice_labels: + indices = LabelToImagenetIndices.label_to_indices(label) + values = np.take(probabilities.values, indices, axis=-1) + aggregated_value = np.mean(values, axis=-1) + aggregated_class_probabilities.append(aggregated_value) + aggregated_class_probabilities = np.transpose(aggregated_class_probabilities) # now presentation x p(label) + top_indices = np.argmax(aggregated_class_probabilities, axis=1) + choices = [self.choice_labels[top_index] for top_index in top_indices] + + coords = {**{coord: (dims, values) for coord, dims, values in walk_coords(logits['presentation'])}, + **{'label': ('presentation', choices)}, + **extra_coords} + return BehavioralAssembly([choices], coords=coords, dims=['choice', 'presentation']) + + +LogitsBehavior = LabelBehavior # LogitsBehavior is deprecated. Use LabelBehavior instead. +""" +legacy support; still used in old candidate_models submissions +https://github.com/brain-score/candidate_models/blob/fa965c452bd17c6bfcca5b991fdbb55fd5db618f/candidate_models/model_commitments/cornets.py#L13 +""" + + +class LabelToImagenetIndices: + airplane_indices = [404] + bear_indices = [294, 295, 296, 297] + bicycle_indices = [444, 671] + bird_indices = [8, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 22, 23, + 24, 80, 81, 82, 83, 87, 88, 89, 90, 91, 92, 93, + 94, 95, 96, 98, 99, 100, 127, 128, 129, 130, 131, + 132, 133, 135, 136, 137, 138, 139, 140, 141, 142, + 143, 144, 145] + boat_indices = [472, 554, 625, 814, 914] + bottle_indices = [440, 720, 737, 898, 899, 901, 907] + car_indices = [436, 511, 817] + cat_indices = [281, 282, 283, 284, 285, 286] + chair_indices = [423, 559, 765, 857] + clock_indices = [409, 530, 892] + dog_indices = [152, 153, 154, 155, 156, 157, 158, 159, 160, 161, + 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, + 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, + 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, + 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, + 203, 205, 206, 207, 208, 209, 210, 211, 212, 213, + 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, + 224, 225, 226, 228, 229, 230, 231, 232, 233, 234, + 235, 236, 237, 238, 239, 240, 241, 243, 244, 245, + 246, 247, 248, 249, 250, 252, 253, 254, 255, 256, + 257, 259, 261, 262, 263, 265, 266, 267, 268] + elephant_indices = [385, 386] + keyboard_indices = [508, 878] + knife_indices = [499] + oven_indices = [766] + truck_indices = [555, 569, 656, 675, 717, 734, 864, 867] + + # added from Baker et al. 2022: + # cat and elephant indices as defined in Baker et al. 2022 are not used, instead we stick to the definition by Geirhos et al. 2021. + # cat_indices = [281, 282, 283, 284, 285] + # elephant_indices = [101, 385, 386] + frog_indices = [30, 31, 32] + lizard_indices = [38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48] + bunny_indices = [330, 331, 332] + tiger_indices = [286, 287, 288, 289, 290, 291, 292, 293] + turtle_indices = [33, 34, 35, 36, 37] + wolf_indices = [269, 270, 271, 272, 273, 274, 275] + + # added from Zhu et al. 2019: + aeroplane_indices = [404, 895] + # car indices as defined in Zhu et al. 2019 are not used, instead we stick to the definition by Geirhos et al. 2021. + # car_indices = [407, 436, 468, 511, 609, 627, 656, 661, 751, 817] + motorbike_indices = [670, 665] + bus_indices = [779, 874, 654] + + @classmethod + def label_to_indices(cls, label): + synset_indices = getattr(cls, f"{label}_indices") + return synset_indices + + +def softmax(x): + return np.exp(x) / np.exp(x).sum(dim='neuroid') + + +class ProbabilitiesMapping(BrainModel): + def __init__(self, identifier, activations_model, layer): + """ + :param identifier: a string to identify the model + :param activations_model: the model from which to retrieve representations for stimuli + :param layer: the single behavioral readout layer or a list of layers to read out of. + """ + self._identifier = identifier + self.activations_model = activations_model + self.readout = make_list(layer) + self.classifier = ProbabilitiesMapping.ProbabilitiesClassifier() + self.current_task = None + + @property + def identifier(self): + return self._identifier + + def start_task(self, task: BrainModel.Task, fitting_stimuli): + assert task in [BrainModel.Task.passive, BrainModel.Task.probabilities] + self.current_task = task + + fitting_features = self.activations_model(fitting_stimuli, layers=self.readout) + fitting_features = fitting_features.transpose('presentation', 'neuroid') + assert all(fitting_features['stimulus_id'].values == fitting_stimuli['stimulus_id'].values), \ + "stimulus_id ordering is incorrect" + self.classifier.fit(fitting_features, fitting_stimuli['image_label']) + + def look_at(self, stimuli, number_of_trials=1): + if self.current_task is BrainModel.Task.passive: + return + features = self.activations_model(stimuli, layers=self.readout) + features = features.transpose('presentation', 'neuroid') + prediction = self.classifier.predict_proba(features) + return prediction + + class ProbabilitiesClassifier: + def __init__(self, classifier_c=1e-3): + self._classifier = sklearn.linear_model.LogisticRegression( + multi_class='multinomial', solver='newton-cg', C=classifier_c) + self._label_mapping = None + self._scaler = None + + def fit(self, X, Y): + self._scaler = sklearn.preprocessing.StandardScaler().fit(X) + X = self._scaler.transform(X) + Y, self._label_mapping = self.labels_to_indices(Y.values) + self._classifier.fit(X, Y) + return self + + def predict_proba(self, X): + assert len(X.shape) == 2, "expected 2-dimensional input" + scaled_X = self._scaler.transform(X) + proba = self._classifier.predict_proba(scaled_X) + # we take only the 0th dimension because the 1st dimension is just the features + X_coords = {coord: (dims, value) for coord, dims, value in walk_coords(X) + if array_is_element(dims, X.dims[0])} + proba = BehavioralAssembly(proba, + coords={**X_coords, **{'choice': list(self._label_mapping.values())}}, + dims=[X.dims[0], 'choice']) + return proba + + def labels_to_indices(self, labels): + label2index = OrderedDict() + indices = [] + for label in labels: + if label not in label2index: + label2index[label] = (max(label2index.values()) + 1) if len(label2index) > 0 else 0 + indices.append(label2index[label]) + index2label = OrderedDict((index, label) for label, index in label2index.items()) + return indices, index2label diff --git a/brainscore_vision/model_helpers/brain_transformation/imagenet_categories.txt b/brainscore_vision/model_helpers/brain_transformation/imagenet_categories.txt new file mode 100644 index 000000000..a9e8c7f50 --- /dev/null +++ b/brainscore_vision/model_helpers/brain_transformation/imagenet_categories.txt @@ -0,0 +1,1000 @@ +n01440764 tench, Tinca tinca +n01443537 goldfish, Carassius auratus +n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +n01491361 tiger shark, Galeocerdo cuvieri +n01494475 hammerhead, hammerhead shark +n01496331 electric ray, crampfish, numbfish, torpedo +n01498041 stingray +n01514668 cock +n01514859 hen +n01518878 ostrich, Struthio camelus +n01530575 brambling, Fringilla montifringilla +n01531178 goldfinch, Carduelis carduelis +n01532829 house finch, linnet, Carpodacus mexicanus +n01534433 junco, snowbird +n01537544 indigo bunting, indigo finch, indigo bird, Passerina cyanea +n01558993 robin, American robin, Turdus migratorius +n01560419 bulbul +n01580077 jay +n01582220 magpie +n01592084 chickadee +n01601694 water ouzel, dipper +n01608432 kite +n01614925 bald eagle, American eagle, Haliaeetus leucocephalus +n01616318 vulture +n01622779 great grey owl, great gray owl, Strix nebulosa +n01629819 European fire salamander, Salamandra salamandra +n01630670 common newt, Triturus vulgaris +n01631663 eft +n01632458 spotted salamander, Ambystoma maculatum +n01632777 axolotl, mud puppy, Ambystoma mexicanum +n01641577 bullfrog, Rana catesbeiana +n01644373 tree frog, tree-frog +n01644900 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +n01664065 loggerhead, loggerhead turtle, Caretta caretta +n01665541 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +n01667114 mud turtle +n01667778 terrapin +n01669191 box turtle, box tortoise +n01675722 banded gecko +n01677366 common iguana, iguana, Iguana iguana +n01682714 American chameleon, anole, Anolis carolinensis +n01685808 whiptail, whiptail lizard +n01687978 agama +n01688243 frilled lizard, Chlamydosaurus kingi +n01689811 alligator lizard +n01692333 Gila monster, Heloderma suspectum +n01693334 green lizard, Lacerta viridis +n01694178 African chameleon, Chamaeleo chamaeleon +n01695060 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +n01697457 African crocodile, Nile crocodile, Crocodylus niloticus +n01698640 American alligator, Alligator mississipiensis +n01704323 triceratops +n01728572 thunder snake, worm snake, Carphophis amoenus +n01728920 ringneck snake, ring-necked snake, ring snake +n01729322 hognose snake, puff adder, sand viper +n01729977 green snake, grass snake +n01734418 king snake, kingsnake +n01735189 garter snake, grass snake +n01737021 water snake +n01739381 vine snake +n01740131 night snake, Hypsiglena torquata +n01742172 boa constrictor, Constrictor constrictor +n01744401 rock python, rock snake, Python sebae +n01748264 Indian cobra, Naja naja +n01749939 green mamba +n01751748 sea snake +n01753488 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +n01755581 diamondback, diamondback rattlesnake, Crotalus adamanteus +n01756291 sidewinder, horned rattlesnake, Crotalus cerastes +n01768244 trilobite +n01770081 harvestman, daddy longlegs, Phalangium opilio +n01770393 scorpion +n01773157 black and gold garden spider, Argiope aurantia +n01773549 barn spider, Araneus cavaticus +n01773797 garden spider, Aranea diademata +n01774384 black widow, Latrodectus mactans +n01774750 tarantula +n01775062 wolf spider, hunting spider +n01776313 tick +n01784675 centipede +n01795545 black grouse +n01796340 ptarmigan +n01797886 ruffed grouse, partridge, Bonasa umbellus +n01798484 prairie chicken, prairie grouse, prairie fowl +n01806143 peacock +n01806567 quail +n01807496 partridge +n01817953 African grey, African gray, Psittacus erithacus +n01818515 macaw +n01819313 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +n01820546 lorikeet +n01824575 coucal +n01828970 bee eater +n01829413 hornbill +n01833805 hummingbird +n01843065 jacamar +n01843383 toucan +n01847000 drake +n01855032 red-breasted merganser, Mergus serrator +n01855672 goose +n01860187 black swan, Cygnus atratus +n01871265 tusker +n01872401 echidna, spiny anteater, anteater +n01873310 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +n01877812 wallaby, brush kangaroo +n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +n01883070 wombat +n01910747 jellyfish +n01914609 sea anemone, anemone +n01917289 brain coral +n01924916 flatworm, platyhelminth +n01930112 nematode, nematode worm, roundworm +n01943899 conch +n01944390 snail +n01945685 slug +n01950731 sea slug, nudibranch +n01955084 chiton, coat-of-mail shell, sea cradle, polyplacophore +n01968897 chambered nautilus, pearly nautilus, nautilus +n01978287 Dungeness crab, Cancer magister +n01978455 rock crab, Cancer irroratus +n01980166 fiddler crab +n01981276 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +n01983481 American lobster, Northern lobster, Maine lobster, Homarus americanus +n01984695 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +n01985128 crayfish, crawfish, crawdad, crawdaddy +n01986214 hermit crab +n01990800 isopod +n02002556 white stork, Ciconia ciconia +n02002724 black stork, Ciconia nigra +n02006656 spoonbill +n02007558 flamingo +n02009229 little blue heron, Egretta caerulea +n02009912 American egret, great white heron, Egretta albus +n02011460 bittern +n02012849 crane +n02013706 limpkin, Aramus pictus +n02017213 European gallinule, Porphyrio porphyrio +n02018207 American coot, marsh hen, mud hen, water hen, Fulica americana +n02018795 bustard +n02025239 ruddy turnstone, Arenaria interpres +n02027492 red-backed sandpiper, dunlin, Erolia alpina +n02028035 redshank, Tringa totanus +n02033041 dowitcher +n02037110 oystercatcher, oyster catcher +n02051845 pelican +n02056570 king penguin, Aptenodytes patagonica +n02058221 albatross, mollymawk +n02066245 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +n02071294 killer whale, killer, orca, grampus, sea wolf, Orcinus orca +n02074367 dugong, Dugong dugon +n02077923 sea lion +n02085620 Chihuahua +n02085782 Japanese spaniel +n02085936 Maltese dog, Maltese terrier, Maltese +n02086079 Pekinese, Pekingese, Peke +n02086240 Shih-Tzu +n02086646 Blenheim spaniel +n02086910 papillon +n02087046 toy terrier +n02087394 Rhodesian ridgeback +n02088094 Afghan hound, Afghan +n02088238 basset, basset hound +n02088364 beagle +n02088466 bloodhound, sleuthhound +n02088632 bluetick +n02089078 black-and-tan coonhound +n02089867 Walker hound, Walker foxhound +n02089973 English foxhound +n02090379 redbone +n02090622 borzoi, Russian wolfhound +n02090721 Irish wolfhound +n02091032 Italian greyhound +n02091134 whippet +n02091244 Ibizan hound, Ibizan Podenco +n02091467 Norwegian elkhound, elkhound +n02091635 otterhound, otter hound +n02091831 Saluki, gazelle hound +n02092002 Scottish deerhound, deerhound +n02092339 Weimaraner +n02093256 Staffordshire bullterrier, Staffordshire bull terrier +n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +n02093647 Bedlington terrier +n02093754 Border terrier +n02093859 Kerry blue terrier +n02093991 Irish terrier +n02094114 Norfolk terrier +n02094258 Norwich terrier +n02094433 Yorkshire terrier +n02095314 wire-haired fox terrier +n02095570 Lakeland terrier +n02095889 Sealyham terrier, Sealyham +n02096051 Airedale, Airedale terrier +n02096177 cairn, cairn terrier +n02096294 Australian terrier +n02096437 Dandie Dinmont, Dandie Dinmont terrier +n02096585 Boston bull, Boston terrier +n02097047 miniature schnauzer +n02097130 giant schnauzer +n02097209 standard schnauzer +n02097298 Scotch terrier, Scottish terrier, Scottie +n02097474 Tibetan terrier, chrysanthemum dog +n02097658 silky terrier, Sydney silky +n02098105 soft-coated wheaten terrier +n02098286 West Highland white terrier +n02098413 Lhasa, Lhasa apso +n02099267 flat-coated retriever +n02099429 curly-coated retriever +n02099601 golden retriever +n02099712 Labrador retriever +n02099849 Chesapeake Bay retriever +n02100236 German short-haired pointer +n02100583 vizsla, Hungarian pointer +n02100735 English setter +n02100877 Irish setter, red setter +n02101006 Gordon setter +n02101388 Brittany spaniel +n02101556 clumber, clumber spaniel +n02102040 English springer, English springer spaniel +n02102177 Welsh springer spaniel +n02102318 cocker spaniel, English cocker spaniel, cocker +n02102480 Sussex spaniel +n02102973 Irish water spaniel +n02104029 kuvasz +n02104365 schipperke +n02105056 groenendael +n02105162 malinois +n02105251 briard +n02105412 kelpie +n02105505 komondor +n02105641 Old English sheepdog, bobtail +n02105855 Shetland sheepdog, Shetland sheep dog, Shetland +n02106030 collie +n02106166 Border collie +n02106382 Bouvier des Flandres, Bouviers des Flandres +n02106550 Rottweiler +n02106662 German shepherd, German shepherd dog, German police dog, alsatian +n02107142 Doberman, Doberman pinscher +n02107312 miniature pinscher +n02107574 Greater Swiss Mountain dog +n02107683 Bernese mountain dog +n02107908 Appenzeller +n02108000 EntleBucher +n02108089 boxer +n02108422 bull mastiff +n02108551 Tibetan mastiff +n02108915 French bulldog +n02109047 Great Dane +n02109525 Saint Bernard, St Bernard +n02109961 Eskimo dog, husky +n02110063 malamute, malemute, Alaskan malamute +n02110185 Siberian husky +n02110341 dalmatian, coach dog, carriage dog +n02110627 affenpinscher, monkey pinscher, monkey dog +n02110806 basenji +n02110958 pug, pug-dog +n02111129 Leonberg +n02111277 Newfoundland, Newfoundland dog +n02111500 Great Pyrenees +n02111889 Samoyed, Samoyede +n02112018 Pomeranian +n02112137 chow, chow chow +n02112350 keeshond +n02112706 Brabancon griffon +n02113023 Pembroke, Pembroke Welsh corgi +n02113186 Cardigan, Cardigan Welsh corgi +n02113624 toy poodle +n02113712 miniature poodle +n02113799 standard poodle +n02113978 Mexican hairless +n02114367 timber wolf, grey wolf, gray wolf, Canis lupus +n02114548 white wolf, Arctic wolf, Canis lupus tundrarum +n02114712 red wolf, maned wolf, Canis rufus, Canis niger +n02114855 coyote, prairie wolf, brush wolf, Canis latrans +n02115641 dingo, warrigal, warragal, Canis dingo +n02115913 dhole, Cuon alpinus +n02116738 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +n02117135 hyena, hyaena +n02119022 red fox, Vulpes vulpes +n02119789 kit fox, Vulpes macrotis +n02120079 Arctic fox, white fox, Alopex lagopus +n02120505 grey fox, gray fox, Urocyon cinereoargenteus +n02123045 tabby, tabby cat +n02123159 tiger cat +n02123394 Persian cat +n02123597 Siamese cat, Siamese +n02124075 Egyptian cat +n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +n02127052 lynx, catamount +n02128385 leopard, Panthera pardus +n02128757 snow leopard, ounce, Panthera uncia +n02128925 jaguar, panther, Panthera onca, Felis onca +n02129165 lion, king of beasts, Panthera leo +n02129604 tiger, Panthera tigris +n02130308 cheetah, chetah, Acinonyx jubatus +n02132136 brown bear, bruin, Ursus arctos +n02133161 American black bear, black bear, Ursus americanus, Euarctos americanus +n02134084 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +n02134418 sloth bear, Melursus ursinus, Ursus ursinus +n02137549 mongoose +n02138441 meerkat, mierkat +n02165105 tiger beetle +n02165456 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +n02167151 ground beetle, carabid beetle +n02168699 long-horned beetle, longicorn, longicorn beetle +n02169497 leaf beetle, chrysomelid +n02172182 dung beetle +n02174001 rhinoceros beetle +n02177972 weevil +n02190166 fly +n02206856 bee +n02219486 ant, emmet, pismire +n02226429 grasshopper, hopper +n02229544 cricket +n02231487 walking stick, walkingstick, stick insect +n02233338 cockroach, roach +n02236044 mantis, mantid +n02256656 cicada, cicala +n02259212 leafhopper +n02264363 lacewing, lacewing fly +n02268443 dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +n02268853 damselfly +n02276258 admiral +n02277742 ringlet, ringlet butterfly +n02279972 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +n02280649 cabbage butterfly +n02281406 sulphur butterfly, sulfur butterfly +n02281787 lycaenid, lycaenid butterfly +n02317335 starfish, sea star +n02319095 sea urchin +n02321529 sea cucumber, holothurian +n02325366 wood rabbit, cottontail, cottontail rabbit +n02326432 hare +n02328150 Angora, Angora rabbit +n02342885 hamster +n02346627 porcupine, hedgehog +n02356798 fox squirrel, eastern fox squirrel, Sciurus niger +n02361337 marmot +n02363005 beaver +n02364673 guinea pig, Cavia cobaya +n02389026 sorrel +n02391049 zebra +n02395406 hog, pig, grunter, squealer, Sus scrofa +n02396427 wild boar, boar, Sus scrofa +n02397096 warthog +n02398521 hippopotamus, hippo, river horse, Hippopotamus amphibius +n02403003 ox +n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +n02410509 bison +n02412080 ram, tup +n02415577 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +n02417914 ibex, Capra ibex +n02422106 hartebeest +n02422699 impala, Aepyceros melampus +n02423022 gazelle +n02437312 Arabian camel, dromedary, Camelus dromedarius +n02437616 llama +n02441942 weasel +n02442845 mink +n02443114 polecat, fitch, foulmart, foumart, Mustela putorius +n02443484 black-footed ferret, ferret, Mustela nigripes +n02444819 otter +n02445715 skunk, polecat, wood pussy +n02447366 badger +n02454379 armadillo +n02457408 three-toed sloth, ai, Bradypus tridactylus +n02480495 orangutan, orang, orangutang, Pongo pygmaeus +n02480855 gorilla, Gorilla gorilla +n02481823 chimpanzee, chimp, Pan troglodytes +n02483362 gibbon, Hylobates lar +n02483708 siamang, Hylobates syndactylus, Symphalangus syndactylus +n02484975 guenon, guenon monkey +n02486261 patas, hussar monkey, Erythrocebus patas +n02486410 baboon +n02487347 macaque +n02488291 langur +n02488702 colobus, colobus monkey +n02489166 proboscis monkey, Nasalis larvatus +n02490219 marmoset +n02492035 capuchin, ringtail, Cebus capucinus +n02492660 howler monkey, howler +n02493509 titi, titi monkey +n02493793 spider monkey, Ateles geoffroyi +n02494079 squirrel monkey, Saimiri sciureus +n02497673 Madagascar cat, ring-tailed lemur, Lemur catta +n02500267 indri, indris, Indri indri, Indri brevicaudatus +n02504013 Indian elephant, Elephas maximus +n02504458 African elephant, Loxodonta africana +n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +n02510455 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +n02514041 barracouta, snoek +n02526121 eel +n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +n02606052 rock beauty, Holocanthus tricolor +n02607072 anemone fish +n02640242 sturgeon +n02641379 gar, garfish, garpike, billfish, Lepisosteus osseus +n02643566 lionfish +n02655020 puffer, pufferfish, blowfish, globefish +n02666196 abacus +n02667093 abaya +n02669723 academic gown, academic robe, judge's robe +n02672831 accordion, piano accordion, squeeze box +n02676566 acoustic guitar +n02687172 aircraft carrier, carrier, flattop, attack aircraft carrier +n02690373 airliner +n02692877 airship, dirigible +n02699494 altar +n02701002 ambulance +n02704792 amphibian, amphibious vehicle +n02708093 analog clock +n02727426 apiary, bee house +n02730930 apron +n02747177 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +n02749479 assault rifle, assault gun +n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack +n02776631 bakery, bakeshop, bakehouse +n02777292 balance beam, beam +n02782093 balloon +n02783161 ballpoint, ballpoint pen, ballpen, Biro +n02786058 Band Aid +n02787622 banjo +n02788148 bannister, banister, balustrade, balusters, handrail +n02790996 barbell +n02791124 barber chair +n02791270 barbershop +n02793495 barn +n02794156 barometer +n02795169 barrel, cask +n02797295 barrow, garden cart, lawn cart, wheelbarrow +n02799071 baseball +n02802426 basketball +n02804414 bassinet +n02804610 bassoon +n02807133 bathing cap, swimming cap +n02808304 bath towel +n02808440 bathtub, bathing tub, bath, tub +n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +n02814860 beacon, lighthouse, beacon light, pharos +n02815834 beaker +n02817516 bearskin, busby, shako +n02823428 beer bottle +n02823750 beer glass +n02825657 bell cote, bell cot +n02834397 bib +n02835271 bicycle-built-for-two, tandem bicycle, tandem +n02837789 bikini, two-piece +n02840245 binder, ring-binder +n02841315 binoculars, field glasses, opera glasses +n02843684 birdhouse +n02859443 boathouse +n02860847 bobsled, bobsleigh, bob +n02865351 bolo tie, bolo, bola tie, bola +n02869837 bonnet, poke bonnet +n02870880 bookcase +n02871525 bookshop, bookstore, bookstall +n02877765 bottlecap +n02879718 bow +n02883205 bow tie, bow-tie, bowtie +n02892201 brass, memorial tablet, plaque +n02892767 brassiere, bra, bandeau +n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty +n02895154 breastplate, aegis, egis +n02906734 broom +n02909870 bucket, pail +n02910353 buckle +n02916936 bulletproof vest +n02917067 bullet train, bullet +n02927161 butcher shop, meat market +n02930766 cab, hack, taxi, taxicab +n02939185 caldron, cauldron +n02948072 candle, taper, wax light +n02950826 cannon +n02951358 canoe +n02951585 can opener, tin opener +n02963159 cardigan +n02965783 car mirror +n02966193 carousel, carrousel, merry-go-round, roundabout, whirligig +n02966687 carpenter's kit, tool kit +n02971356 carton +n02974003 car wheel +n02977058 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +n02978881 cassette +n02979186 cassette player +n02980441 castle +n02981792 catamaran +n02988304 CD player +n02992211 cello, violoncello +n02992529 cellular telephone, cellular phone, cellphone, cell, mobile phone +n02999410 chain +n03000134 chainlink fence +n03000247 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +n03000684 chain saw, chainsaw +n03014705 chest +n03016953 chiffonier, commode +n03017168 chime, bell, gong +n03018349 china cabinet, china closet +n03026506 Christmas stocking +n03028079 church, church building +n03032252 cinema, movie theater, movie theatre, movie house, picture palace +n03041632 cleaver, meat cleaver, chopper +n03042490 cliff dwelling +n03045698 cloak +n03047690 clog, geta, patten, sabot +n03062245 cocktail shaker +n03063599 coffee mug +n03063689 coffeepot +n03065424 coil, spiral, volute, whorl, helix +n03075370 combination lock +n03085013 computer keyboard, keypad +n03089624 confectionery, confectionary, candy store +n03095699 container ship, containership, container vessel +n03100240 convertible +n03109150 corkscrew, bottle screw +n03110669 cornet, horn, trumpet, trump +n03124043 cowboy boot +n03124170 cowboy hat, ten-gallon hat +n03125729 cradle +n03126707 crane +n03127747 crash helmet +n03127925 crate +n03131574 crib, cot +n03133878 Crock Pot +n03134739 croquet ball +n03141823 crutch +n03146219 cuirass +n03160309 dam, dike, dyke +n03179701 desk +n03180011 desktop computer +n03187595 dial telephone, dial phone +n03188531 diaper, nappy, napkin +n03196217 digital clock +n03197337 digital watch +n03201208 dining table, board +n03207743 dishrag, dishcloth +n03207941 dishwasher, dish washer, dishwashing machine +n03208938 disk brake, disc brake +n03216828 dock, dockage, docking facility +n03218198 dogsled, dog sled, dog sleigh +n03220513 dome +n03223299 doormat, welcome mat +n03240683 drilling platform, offshore rig +n03249569 drum, membranophone, tympan +n03250847 drumstick +n03255030 dumbbell +n03259280 Dutch oven +n03271574 electric fan, blower +n03272010 electric guitar +n03272562 electric locomotive +n03290653 entertainment center +n03291819 envelope +n03297495 espresso maker +n03314780 face powder +n03325584 feather boa, boa +n03337140 file, file cabinet, filing cabinet +n03344393 fireboat +n03345487 fire engine, fire truck +n03347037 fire screen, fireguard +n03355925 flagpole, flagstaff +n03372029 flute, transverse flute +n03376595 folding chair +n03379051 football helmet +n03384352 forklift +n03388043 fountain +n03388183 fountain pen +n03388549 four-poster +n03393912 freight car +n03394916 French horn, horn +n03400231 frying pan, frypan, skillet +n03404251 fur coat +n03417042 garbage truck, dustcart +n03424325 gasmask, respirator, gas helmet +n03425413 gas pump, gasoline pump, petrol pump, island dispenser +n03443371 goblet +n03444034 go-kart +n03445777 golf ball +n03445924 golfcart, golf cart +n03447447 gondola +n03447721 gong, tam-tam +n03450230 gown +n03452741 grand piano, grand +n03457902 greenhouse, nursery, glasshouse +n03459775 grille, radiator grille +n03461385 grocery store, grocery, food market, market +n03467068 guillotine +n03476684 hair slide +n03476991 hair spray +n03478589 half track +n03481172 hammer +n03482405 hamper +n03483316 hand blower, blow dryer, blow drier, hair dryer, hair drier +n03485407 hand-held computer, hand-held microcomputer +n03485794 handkerchief, hankie, hanky, hankey +n03492542 hard disc, hard disk, fixed disk +n03494278 harmonica, mouth organ, harp, mouth harp +n03495258 harp +n03496892 harvester, reaper +n03498962 hatchet +n03527444 holster +n03529860 home theater, home theatre +n03530642 honeycomb +n03532672 hook, claw +n03534580 hoopskirt, crinoline +n03535780 horizontal bar, high bar +n03538406 horse cart, horse-cart +n03544143 hourglass +n03584254 iPod +n03584829 iron, smoothing iron +n03590841 jack-o'-lantern +n03594734 jean, blue jean, denim +n03594945 jeep, landrover +n03595614 jersey, T-shirt, tee shirt +n03598930 jigsaw puzzle +n03599486 jinrikisha, ricksha, rickshaw +n03602883 joystick +n03617480 kimono +n03623198 knee pad +n03627232 knot +n03630383 lab coat, laboratory coat +n03633091 ladle +n03637318 lampshade, lamp shade +n03642806 laptop, laptop computer +n03649909 lawn mower, mower +n03657121 lens cap, lens cover +n03658185 letter opener, paper knife, paperknife +n03661043 library +n03662601 lifeboat +n03666591 lighter, light, igniter, ignitor +n03670208 limousine, limo +n03673027 liner, ocean liner +n03676483 lipstick, lip rouge +n03680355 Loafer +n03690938 lotion +n03691459 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +n03692522 loupe, jeweler's loupe +n03697007 lumbermill, sawmill +n03706229 magnetic compass +n03709823 mailbag, postbag +n03710193 mailbox, letter box +n03710637 maillot +n03710721 maillot, tank suit +n03717622 manhole cover +n03720891 maraca +n03721384 marimba, xylophone +n03724870 mask +n03729826 matchstick +n03733131 maypole +n03733281 maze, labyrinth +n03733805 measuring cup +n03742115 medicine chest, medicine cabinet +n03743016 megalith, megalithic structure +n03759954 microphone, mike +n03761084 microwave, microwave oven +n03763968 military uniform +n03764736 milk can +n03769881 minibus +n03770439 miniskirt, mini +n03770679 minivan +n03773504 missile +n03775071 mitten +n03775546 mixing bowl +n03776460 mobile home, manufactured home +n03777568 Model T +n03777754 modem +n03781244 monastery +n03782006 monitor +n03785016 moped +n03786901 mortar +n03787032 mortarboard +n03788195 mosque +n03788365 mosquito net +n03791053 motor scooter, scooter +n03792782 mountain bike, all-terrain bike, off-roader +n03792972 mountain tent +n03793489 mouse, computer mouse +n03794056 mousetrap +n03796401 moving van +n03803284 muzzle +n03804744 nail +n03814639 neck brace +n03814906 necklace +n03825788 nipple +n03832673 notebook, notebook computer +n03837869 obelisk +n03838899 oboe, hautboy, hautbois +n03840681 ocarina, sweet potato +n03841143 odometer, hodometer, mileometer, milometer +n03843555 oil filter +n03854065 organ, pipe organ +n03857828 oscilloscope, scope, cathode-ray oscilloscope, CRO +n03866082 overskirt +n03868242 oxcart +n03868863 oxygen mask +n03871628 packet +n03873416 paddle, boat paddle +n03874293 paddlewheel, paddle wheel +n03874599 padlock +n03876231 paintbrush +n03877472 pajama, pyjama, pj's, jammies +n03877845 palace +n03884397 panpipe, pandean pipe, syrinx +n03887697 paper towel +n03888257 parachute, chute +n03888605 parallel bars, bars +n03891251 park bench +n03891332 parking meter +n03895866 passenger car, coach, carriage +n03899768 patio, terrace +n03902125 pay-phone, pay-station +n03903868 pedestal, plinth, footstall +n03908618 pencil box, pencil case +n03908714 pencil sharpener +n03916031 perfume, essence +n03920288 Petri dish +n03924679 photocopier +n03929660 pick, plectrum, plectron +n03929855 pickelhaube +n03930313 picket fence, paling +n03930630 pickup, pickup truck +n03933933 pier +n03935335 piggy bank, penny bank +n03937543 pill bottle +n03938244 pillow +n03942813 ping-pong ball +n03944341 pinwheel +n03947888 pirate, pirate ship +n03950228 pitcher, ewer +n03954731 plane, carpenter's plane, woodworking plane +n03956157 planetarium +n03958227 plastic bag +n03961711 plate rack +n03967562 plow, plough +n03970156 plunger, plumber's helper +n03976467 Polaroid camera, Polaroid Land camera +n03976657 pole +n03977966 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +n03980874 poncho +n03982430 pool table, billiard table, snooker table +n03983396 pop bottle, soda bottle +n03991062 pot, flowerpot +n03992509 potter's wheel +n03995372 power drill +n03998194 prayer rug, prayer mat +n04004767 printer +n04005630 prison, prison house +n04008634 projectile, missile +n04009552 projector +n04019541 puck, hockey puck +n04023962 punching bag, punch bag, punching ball, punchball +n04026417 purse +n04033901 quill, quill pen +n04033995 quilt, comforter, comfort, puff +n04037443 racer, race car, racing car +n04039381 racket, racquet +n04040759 radiator +n04041544 radio, wireless +n04044716 radio telescope, radio reflector +n04049303 rain barrel +n04065272 recreational vehicle, RV, R.V. +n04067472 reel +n04069434 reflex camera +n04070727 refrigerator, icebox +n04074963 remote control, remote +n04081281 restaurant, eating house, eating place, eatery +n04086273 revolver, six-gun, six-shooter +n04090263 rifle +n04099969 rocking chair, rocker +n04111531 rotisserie +n04116512 rubber eraser, rubber, pencil eraser +n04118538 rugby ball +n04118776 rule, ruler +n04120489 running shoe +n04125021 safe +n04127249 safety pin +n04131690 saltshaker, salt shaker +n04133789 sandal +n04136333 sarong +n04141076 sax, saxophone +n04141327 scabbard +n04141975 scale, weighing machine +n04146614 school bus +n04147183 schooner +n04149813 scoreboard +n04152593 screen, CRT screen +n04153751 screw +n04154565 screwdriver +n04162706 seat belt, seatbelt +n04179913 sewing machine +n04192698 shield, buckler +n04200800 shoe shop, shoe-shop, shoe store +n04201297 shoji +n04204238 shopping basket +n04204347 shopping cart +n04208210 shovel +n04209133 shower cap +n04209239 shower curtain +n04228054 ski +n04229816 ski mask +n04235860 sleeping bag +n04238763 slide rule, slipstick +n04239074 sliding door +n04243546 slot, one-armed bandit +n04251144 snorkel +n04252077 snowmobile +n04252225 snowplow, snowplough +n04254120 soap dispenser +n04254680 soccer ball +n04254777 sock +n04258138 solar dish, solar collector, solar furnace +n04259630 sombrero +n04263257 soup bowl +n04264628 space bar +n04265275 space heater +n04266014 space shuttle +n04270147 spatula +n04273569 speedboat +n04275548 spider web, spider's web +n04277352 spindle +n04285008 sports car, sport car +n04286575 spotlight, spot +n04296562 stage +n04310018 steam locomotive +n04311004 steel arch bridge +n04311174 steel drum +n04317175 stethoscope +n04325704 stole +n04326547 stone wall +n04328186 stopwatch, stop watch +n04330267 stove +n04332243 strainer +n04335435 streetcar, tram, tramcar, trolley, trolley car +n04336792 stretcher +n04344873 studio couch, day bed +n04346328 stupa, tope +n04347754 submarine, pigboat, sub, U-boat +n04350905 suit, suit of clothes +n04355338 sundial +n04355933 sunglass +n04356056 sunglasses, dark glasses, shades +n04357314 sunscreen, sunblock, sun blocker +n04366367 suspension bridge +n04367480 swab, swob, mop +n04370456 sweatshirt +n04371430 swimming trunks, bathing trunks +n04371774 swing +n04372370 switch, electric switch, electrical switch +n04376876 syringe +n04380533 table lamp +n04389033 tank, army tank, armored combat vehicle, armoured combat vehicle +n04392985 tape player +n04398044 teapot +n04399382 teddy, teddy bear +n04404412 television, television system +n04409515 tennis ball +n04417672 thatch, thatched roof +n04418357 theater curtain, theatre curtain +n04423845 thimble +n04428191 thresher, thrasher, threshing machine +n04429376 throne +n04435653 tile roof +n04442312 toaster +n04443257 tobacco shop, tobacconist shop, tobacconist +n04447861 toilet seat +n04456115 torch +n04458633 totem pole +n04461696 tow truck, tow car, wrecker +n04462240 toyshop +n04465501 tractor +n04467665 trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +n04476259 tray +n04479046 trench coat +n04482393 tricycle, trike, velocipede +n04483307 trimaran +n04485082 tripod +n04486054 triumphal arch +n04487081 trolleybus, trolley coach, trackless trolley +n04487394 trombone +n04493381 tub, vat +n04501370 turnstile +n04505470 typewriter keyboard +n04507155 umbrella +n04509417 unicycle, monocycle +n04515003 upright, upright piano +n04517823 vacuum, vacuum cleaner +n04522168 vase +n04523525 vault +n04525038 velvet +n04525305 vending machine +n04532106 vestment +n04532670 viaduct +n04536866 violin, fiddle +n04540053 volleyball +n04542943 waffle iron +n04548280 wall clock +n04548362 wallet, billfold, notecase, pocketbook +n04550184 wardrobe, closet, press +n04552348 warplane, military plane +n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin +n04554684 washer, automatic washer, washing machine +n04557648 water bottle +n04560804 water jug +n04562935 water tower +n04579145 whiskey jug +n04579432 whistle +n04584207 wig +n04589890 window screen +n04590129 window shade +n04591157 Windsor tie +n04591713 wine bottle +n04592741 wing +n04596742 wok +n04597913 wooden spoon +n04599235 wool, woolen, woollen +n04604644 worm fence, snake fence, snake-rail fence, Virginia fence +n04606251 wreck +n04612504 yawl +n04613696 yurt +n06359193 web site, website, internet site, site +n06596364 comic book +n06785654 crossword puzzle, crossword +n06794110 street sign +n06874185 traffic light, traffic signal, stoplight +n07248320 book jacket, dust cover, dust jacket, dust wrapper +n07565083 menu +n07579787 plate +n07583066 guacamole +n07584110 consomme +n07590611 hot pot, hotpot +n07613480 trifle +n07614500 ice cream, icecream +n07615774 ice lolly, lolly, lollipop, popsicle +n07684084 French loaf +n07693725 bagel, beigel +n07695742 pretzel +n07697313 cheeseburger +n07697537 hotdog, hot dog, red hot +n07711569 mashed potato +n07714571 head cabbage +n07714990 broccoli +n07715103 cauliflower +n07716358 zucchini, courgette +n07716906 spaghetti squash +n07717410 acorn squash +n07717556 butternut squash +n07718472 cucumber, cuke +n07718747 artichoke, globe artichoke +n07720875 bell pepper +n07730033 cardoon +n07734744 mushroom +n07742313 Granny Smith +n07745940 strawberry +n07747607 orange +n07749582 lemon +n07753113 fig +n07753275 pineapple, ananas +n07753592 banana +n07754684 jackfruit, jak, jack +n07760859 custard apple +n07768694 pomegranate +n07802026 hay +n07831146 carbonara +n07836838 chocolate sauce, chocolate syrup +n07860988 dough +n07871810 meat loaf, meatloaf +n07873807 pizza, pizza pie +n07875152 potpie +n07880968 burrito +n07892512 red wine +n07920052 espresso +n07930864 cup +n07932039 eggnog +n09193705 alp +n09229709 bubble +n09246464 cliff, drop, drop-off +n09256479 coral reef +n09288635 geyser +n09332890 lakeside, lakeshore +n09399592 promontory, headland, head, foreland +n09421951 sandbar, sand bar +n09428293 seashore, coast, seacoast, sea-coast +n09468604 valley, vale +n09472597 volcano +n09835506 ballplayer, baseball player +n10148035 groom, bridegroom +n10565667 scuba diver +n11879895 rapeseed +n11939491 daisy +n12057211 yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +n12144580 corn +n12267677 acorn +n12620546 hip, rose hip, rosehip +n12768682 buckeye, horse chestnut, conker +n12985857 coral fungus +n12998815 agaric +n13037406 gyromitra +n13040303 stinkhorn, carrion fungus +n13044778 earthstar +n13052670 hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +n13054560 bolete +n13133613 ear, spike, capitulum +n15075141 toilet tissue, toilet paper, bathroom tissue diff --git a/brainscore_vision/model_helpers/brain_transformation/imagenet_classes.txt b/brainscore_vision/model_helpers/brain_transformation/imagenet_classes.txt new file mode 100644 index 000000000..c0ccfb003 --- /dev/null +++ b/brainscore_vision/model_helpers/brain_transformation/imagenet_classes.txt @@ -0,0 +1,1000 @@ +n01440764 +n01443537 +n01484850 +n01491361 +n01494475 +n01496331 +n01498041 +n01514668 +n01514859 +n01518878 +n01530575 +n01531178 +n01532829 +n01534433 +n01537544 +n01558993 +n01560419 +n01580077 +n01582220 +n01592084 +n01601694 +n01608432 +n01614925 +n01616318 +n01622779 +n01629819 +n01630670 +n01631663 +n01632458 +n01632777 +n01641577 +n01644373 +n01644900 +n01664065 +n01665541 +n01667114 +n01667778 +n01669191 +n01675722 +n01677366 +n01682714 +n01685808 +n01687978 +n01688243 +n01689811 +n01692333 +n01693334 +n01694178 +n01695060 +n01697457 +n01698640 +n01704323 +n01728572 +n01728920 +n01729322 +n01729977 +n01734418 +n01735189 +n01737021 +n01739381 +n01740131 +n01742172 +n01744401 +n01748264 +n01749939 +n01751748 +n01753488 +n01755581 +n01756291 +n01768244 +n01770081 +n01770393 +n01773157 +n01773549 +n01773797 +n01774384 +n01774750 +n01775062 +n01776313 +n01784675 +n01795545 +n01796340 +n01797886 +n01798484 +n01806143 +n01806567 +n01807496 +n01817953 +n01818515 +n01819313 +n01820546 +n01824575 +n01828970 +n01829413 +n01833805 +n01843065 +n01843383 +n01847000 +n01855032 +n01855672 +n01860187 +n01871265 +n01872401 +n01873310 +n01877812 +n01882714 +n01883070 +n01910747 +n01914609 +n01917289 +n01924916 +n01930112 +n01943899 +n01944390 +n01945685 +n01950731 +n01955084 +n01968897 +n01978287 +n01978455 +n01980166 +n01981276 +n01983481 +n01984695 +n01985128 +n01986214 +n01990800 +n02002556 +n02002724 +n02006656 +n02007558 +n02009229 +n02009912 +n02011460 +n02012849 +n02013706 +n02017213 +n02018207 +n02018795 +n02025239 +n02027492 +n02028035 +n02033041 +n02037110 +n02051845 +n02056570 +n02058221 +n02066245 +n02071294 +n02074367 +n02077923 +n02085620 +n02085782 +n02085936 +n02086079 +n02086240 +n02086646 +n02086910 +n02087046 +n02087394 +n02088094 +n02088238 +n02088364 +n02088466 +n02088632 +n02089078 +n02089867 +n02089973 +n02090379 +n02090622 +n02090721 +n02091032 +n02091134 +n02091244 +n02091467 +n02091635 +n02091831 +n02092002 +n02092339 +n02093256 +n02093428 +n02093647 +n02093754 +n02093859 +n02093991 +n02094114 +n02094258 +n02094433 +n02095314 +n02095570 +n02095889 +n02096051 +n02096177 +n02096294 +n02096437 +n02096585 +n02097047 +n02097130 +n02097209 +n02097298 +n02097474 +n02097658 +n02098105 +n02098286 +n02098413 +n02099267 +n02099429 +n02099601 +n02099712 +n02099849 +n02100236 +n02100583 +n02100735 +n02100877 +n02101006 +n02101388 +n02101556 +n02102040 +n02102177 +n02102318 +n02102480 +n02102973 +n02104029 +n02104365 +n02105056 +n02105162 +n02105251 +n02105412 +n02105505 +n02105641 +n02105855 +n02106030 +n02106166 +n02106382 +n02106550 +n02106662 +n02107142 +n02107312 +n02107574 +n02107683 +n02107908 +n02108000 +n02108089 +n02108422 +n02108551 +n02108915 +n02109047 +n02109525 +n02109961 +n02110063 +n02110185 +n02110341 +n02110627 +n02110806 +n02110958 +n02111129 +n02111277 +n02111500 +n02111889 +n02112018 +n02112137 +n02112350 +n02112706 +n02113023 +n02113186 +n02113624 +n02113712 +n02113799 +n02113978 +n02114367 +n02114548 +n02114712 +n02114855 +n02115641 +n02115913 +n02116738 +n02117135 +n02119022 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a/brainscore_vision/model_helpers/brain_transformation/neural.py b/brainscore_vision/model_helpers/brain_transformation/neural.py new file mode 100644 index 000000000..51cff1eb3 --- /dev/null +++ b/brainscore_vision/model_helpers/brain_transformation/neural.py @@ -0,0 +1,150 @@ +import logging + +from result_caching import store_xarray, store +from tqdm import tqdm + +from brainscore_vision.metrics import Score +from brainscore_vision.model_helpers.activations.pca import LayerPCA +from brainscore_vision.model_helpers.brain_transformation import TemporalIgnore +from brainscore_vision.model_helpers.utils import make_list +from brainscore_vision.model_interface import BrainModel +from brainscore_vision.utils import fullname + + +class LayerMappedModel(BrainModel): + def __init__(self, identifier, activations_model, region_layer_map, visual_degrees=None): + self._identifier = identifier + self.activations_model = activations_model + self._visual_degrees = visual_degrees + self.region_layer_map = region_layer_map + self.recorded_regions = [] + + @property + def identifier(self): + return self._identifier + + def look_at(self, stimuli, number_of_trials=1): + layer_regions = {} + for region in self.recorded_regions: + layers = self.region_layer_map[region] + layers = make_list(layers) + for layer in layers: + assert layer not in layer_regions, f"layer {layer} has already been assigned for {layer_regions[layer]}" + layer_regions[layer] = region + activations = self.run_activations( + stimuli, layers=list(layer_regions.keys()), number_of_trials=number_of_trials) + activations['region'] = 'neuroid', [layer_regions[layer] for layer in activations['layer'].values] + return activations + + def run_activations(self, stimuli, layers, number_of_trials=1): + activations = self.activations_model(stimuli, layers=layers) + return activations + + def start_task(self, task): + if task != BrainModel.Task.passive: + raise NotImplementedError() + + def start_recording(self, recording_target: BrainModel.RecordingTarget): + self.recorded_regions = [recording_target] + + def visual_degrees(self) -> int: + return self._visual_degrees + + +class LayerSelection: + def __init__(self, model_identifier, activations_model, layers, visual_degrees): + """ + :param model_identifier: this is separate from the container name because it only refers to + the combination of (model, preprocessing), i.e. no mapping. + """ + self.model_identifier = model_identifier + self._layer_scoring = LayerScores(model_identifier=model_identifier, activations_model=activations_model, + visual_degrees=visual_degrees) + self.layers = layers + self._logger = logging.getLogger(fullname(self)) + + def __call__(self, selection_identifier, benchmark): + # for layer-mapping, attach LayerPCA so that we can cache activations + model_identifier = self.model_identifier + pca_hooked = LayerPCA.is_hooked(self._layer_scoring._activations_model) + if not pca_hooked: + pca_handle = LayerPCA.hook(self._layer_scoring._activations_model, n_components=1000) + identifier = self._layer_scoring._activations_model.identifier + self._layer_scoring._activations_model.identifier = identifier + "-pca_1000" + model_identifier += "-pca_1000" + + result = self._call(model_identifier=model_identifier, selection_identifier=selection_identifier, + benchmark=benchmark) + + if not pca_hooked: + pca_handle.remove() + self._layer_scoring._activations_model.identifier = identifier + return result + + @store(identifier_ignore=['benchmark', 'benchmark']) + def _call(self, model_identifier, selection_identifier, benchmark): + self._logger.debug("Finding best layer") + layer_scores = self._layer_scoring(benchmark=benchmark, benchmark_identifier=selection_identifier, + layers=self.layers, prerun=True) + + self._logger.debug("Layer scores (unceiled): " + ", ".join([ + f"{layer} -> {layer_scores.raw.sel(layer=layer).item():.3f}" + f"+-{layer_scores.raw.sel(layer=layer).attrs['error'].item():.3f}" + for layer in layer_scores['layer'].values])) + best_layer = layer_scores['layer'].values[layer_scores.argmax()] + return best_layer + + +class LayerScores: + def __init__(self, model_identifier, activations_model, visual_degrees): + self.model_identifier = model_identifier + self._activations_model = activations_model + self._visual_degrees = visual_degrees + self._logger = logging.getLogger(fullname(self)) + + def __call__(self, benchmark, layers, benchmark_identifier=None, prerun=False): + return self._call(model_identifier=self.model_identifier, + benchmark_identifier=benchmark_identifier or benchmark.identifier, + visual_degrees=self._visual_degrees, + model=self._activations_model, benchmark=benchmark, layers=layers, prerun=prerun) + + @store_xarray(identifier_ignore=['model', 'benchmark', 'layers', 'prerun'], combine_fields={'layers': 'layer'}) + def _call(self, model_identifier, benchmark_identifier, visual_degrees, # storage fields + model, benchmark, layers, prerun=False): + layer_scores = [] + for i, layer in enumerate(tqdm(layers, desc="layers")): + layer_model = self._create_mapped_model(region=benchmark.region, layer=layer, model=model, + model_identifier=model_identifier, visual_degrees=visual_degrees) + layer_model = TemporalIgnore(layer_model) + if i == 0 and prerun: # pre-run activations together to avoid running every layer separately + # we can only pre-run stimuli in response to the benchmark, since we might otherwise be missing + # visual_degrees resizing. + layer_model = PreRunLayers(model=model, layers=layers, forward=layer_model) + score = benchmark(layer_model) + score = score.expand_dims('layer') + score['layer'] = [layer] + layer_scores.append(score) + layer_scores = Score.merge(*layer_scores) + layer_scores = layer_scores.sel(layer=layers) # preserve layer ordering + return layer_scores + + def _create_mapped_model(self, region, layer, model, model_identifier, visual_degrees): + return LayerMappedModel(identifier=f"{model_identifier}-{layer}", visual_degrees=visual_degrees, + # per-layer identifier to avoid overlap + activations_model=model, region_layer_map={region: layer}) + + +class PreRunLayers: + def __init__(self, model, layers, forward): + self._model = model + self._layers = layers + self._forward = forward + + def look_at(self, stimuli, number_of_trials=1): + self._model(layers=self._layers, stimuli=stimuli) + return self._forward.look_at(stimuli, number_of_trials=number_of_trials) + + def __getattr__(self, item): + if item in ['look_at']: + return super(PreRunLayers, self).__getattr__(item) + return getattr(self._forward, item) diff --git a/brainscore_vision/model_helpers/brain_transformation/temporal.py b/brainscore_vision/model_helpers/brain_transformation/temporal.py new file mode 100644 index 000000000..c9030156f --- /dev/null +++ b/brainscore_vision/model_helpers/brain_transformation/temporal.py @@ -0,0 +1,66 @@ +import logging +import numpy as np +from typing import List, Tuple + +from brainio.assemblies import merge_data_arrays, walk_coords +from brainscore_vision.model_helpers.utils import fullname +from brainscore_vision.model_interface import BrainModel + + +class TemporalIgnore(BrainModel): + """ + Always output the same prediction, regardless of time-bins. + Duplicates the LayerMappedModel prediction across time. + """ + + def __init__(self, layer_model): + self._logger = logging.getLogger(fullname(self)) + self._layer_model = layer_model + self.region_layer_map = self._layer_model.region_layer_map + self.activations_model = self._layer_model.activations_model + self.start_task = self._layer_model.start_task + self._time_bins = None + + def start_recording(self, recording_target: BrainModel.RecordingTarget, time_bins=List[Tuple[int]]): + self._layer_model.start_recording(recording_target) + self._time_bins = time_bins + + def visual_degrees(self) -> int: + return self._layer_model.visual_degrees() + + def look_at(self, stimuli, number_of_trials=1): + responses = self._layer_model.look_at(stimuli, number_of_trials=number_of_trials) + time_responses = [] + self._logger.debug(f'Repeating single assembly across time bins {self._time_bins}') + for time_bin in self._time_bins: + time_bin = time_bin if not isinstance(time_bin, np.ndarray) else time_bin.tolist() + time_bin_start, time_bin_end = time_bin + bin_responses = responses.expand_dims('time_bin_start').expand_dims('time_bin_end') + bin_responses['time_bin_start'] = [time_bin_start] + bin_responses['time_bin_end'] = [time_bin_end] + bin_responses = bin_responses.stack(time_bin=['time_bin_start', 'time_bin_end']) + time_responses.append(bin_responses) + responses = merge_data_arrays(time_responses) + if len(self._time_bins) == 1: + responses = responses.squeeze('time_bin') + responses = fix_timebin_naming(responses) + return responses + + @property + def identifier(self): + return self._layer_model.identifier + + +def fix_timebin_naming(assembly): + """ + renames coordinate time_bin_level_0 to time_bin_start and time_bin_level_1 to time_bin_end + to work around bug introduced in xarray 0.16.2 (and still present in 0.17.0). + """ + # jjpr had found that xarray 0.16.2 introduced a bug where xarray.core.alignment._get_joiner assumes Index when the + # object is a MultiIndex. + # xarray.rename for some reason does not work for some reason, it cannot find the coords + rename = dict(time_bin_level_0='time_bin_start', time_bin_level_1='time_bin_end') + assembly = type(assembly)(assembly.values, coords={ + rename[coord] if coord in rename else coord: (dims, values) + for coord, dims, values in walk_coords(assembly)}, dims=assembly.dims) + return assembly diff --git a/packaging/katz/__init__.py b/brainscore_vision/model_helpers/check_submission/__init__.py similarity index 100% rename from packaging/katz/__init__.py rename to brainscore_vision/model_helpers/check_submission/__init__.py diff --git a/brainscore_vision/model_helpers/check_submission/check_models.py b/brainscore_vision/model_helpers/check_submission/check_models.py new file mode 100644 index 000000000..7ed729042 --- /dev/null +++ b/brainscore_vision/model_helpers/check_submission/check_models.py @@ -0,0 +1,118 @@ +import numpy as np +import os + +import brainscore_vision.metric_helpers +from brainio.assemblies import NeuroidAssembly +from brainio.stimuli import StimulusSet +from brainscore_vision.benchmark_helpers.neural_common import average_repetition, timebins_from_assembly +from brainscore_vision.benchmarks import BenchmarkBase, ceil_score +from brainscore_vision.metrics.ceiling import InternalConsistency +from brainscore_vision.metrics.regression import CrossRegressedCorrelation, pls_regression, pearsonr_correlation +from brainscore_vision.metrics.transformations import Split +from brainscore_vision.model_helpers.brain_transformation import ModelCommitment, LayerSelection, RegionLayerMap +from brainscore_vision.model_interface import BrainModel + + +def check_brain_models(module): + module = __import__(module) + for model in module.get_model_list(): + model = module.get_model(model) + assert model is not None + assert isinstance(model, BrainModel) + check_brain_model_processing(model) + print('Test successful, you are ready to submit!') + + +def check_brain_model_processing(model): + benchmark = _MockBenchmark() + score = benchmark(model, do_behavior=True) + assert score is not None + + +def check_base_models(module): + module = __import__(module) + for model in module.get_model_list(): + layers = module.get_layers(model) + assert layers is not None + assert isinstance(layers, list) + assert len(layers) > 0 + assert module.get_model(model) is not None + check_processing(model, module) + print('Test successful, you are ready to submit!') + + +def check_processing(model_identifier, module): + os.environ['RESULTCACHING_DISABLE'] = '1' + model_instance = module.get_model(model_identifier) + layers = module.get_layers(model_identifier) + benchmark = _MockBenchmark() + layer_selection = LayerSelection(model_identifier=model_identifier, + activations_model=model_instance, layers=layers, + visual_degrees=8) + region_layer_map = RegionLayerMap(layer_selection=layer_selection, + region_benchmarks={'IT': benchmark}) + + brain_model = ModelCommitment(identifier=model_identifier, activations_model=model_instance, + layers=layers, region_layer_map=region_layer_map) + score = benchmark(brain_model, do_behavior=True) + assert score is not None + + +class _MockBenchmark(BenchmarkBase): + def __init__(self): + assembly_repetition = get_assembly() + assert len(np.unique(assembly_repetition['region'])) == 1 + assert hasattr(assembly_repetition, 'repetition') + self.region = 'IT' + self.assembly = average_repetition(assembly_repetition) + self._assembly = self.assembly + self.timebins = timebins_from_assembly(self.assembly) + + self._similarity_metric = CrossRegressedCorrelation( + regression=pls_regression(), correlation=pearsonr_correlation(), + crossvalidation_kwargs=dict(stratification_coord=brainscore_vision.metric_helpers.Defaults.stratification_coord + if hasattr(self.assembly, brainscore_vision.metric_helpers.Defaults.stratification_coord) else None)) + identifier = f'{assembly_repetition.name}-layer_selection' + ceiler = InternalConsistency() + super(_MockBenchmark, self).__init__(identifier=identifier, + ceiling_func=lambda: ceiler(assembly_repetition), + version='1.0') + + def __call__(self, candidate: BrainModel, do_behavior=False): + # Check neural recordings + candidate.start_recording(self.region, time_bins=self.timebins) + source_assembly = candidate.look_at(self.assembly.stimulus_set) + # Check behavioral tasks + if do_behavior: + candidate.start_task(BrainModel.Task.probabilities, self.assembly.stimulus_set) + candidate.look_at(self.assembly.stimulus_set) + raw_score = self._similarity_metric(source_assembly, self.assembly) + return ceil_score(raw_score, self.ceiling) + + +def get_assembly(): + image_names = [] + for i in range(1, 21): + image_names.append(f'images/{i}.png') + assembly = NeuroidAssembly((np.arange(40 * 5) + np.random.standard_normal(40 * 5)).reshape((5, 40, 1)), + coords={'stimulus_id': ( + 'presentation', + image_names * 2), + 'object_name': ('presentation', ['a'] * 40), + 'repetition': ('presentation', ([1] * 20 + [2] * 20)), + 'neuroid_id': ('neuroid', np.arange(5)), + 'region': ('neuroid', ['IT'] * 5), + 'time_bin_start': ('time_bin', [70]), + 'time_bin_end': ('time_bin', [170]) + }, + dims=['neuroid', 'presentation', 'time_bin']) + labels = ['a'] * 10 + ['b'] * 10 + stimulus_set = StimulusSet([{'stimulus_id': image_names[i], 'object_name': 'a', 'image_label': labels[i]} + for i in range(20)]) + stimulus_set.stimulus_paths = {image_name: os.path.join(os.path.dirname(__file__), image_name) + for image_name in image_names} + stimulus_set.identifier = 'test' + assembly.attrs['stimulus_set'] = stimulus_set + assembly.attrs['stimulus_set_name'] = stimulus_set.identifier + assembly = assembly.squeeze("time_bin") + return assembly.transpose('presentation', 'neuroid') diff --git a/brainscore_vision/model_helpers/check_submission/images/1.png b/brainscore_vision/model_helpers/check_submission/images/1.png new file mode 100644 index 000000000..3c48afd5c Binary files /dev/null and b/brainscore_vision/model_helpers/check_submission/images/1.png differ diff --git a/brainscore_vision/model_helpers/check_submission/images/10.png b/brainscore_vision/model_helpers/check_submission/images/10.png new file mode 100644 index 000000000..3c48afd5c Binary files /dev/null and b/brainscore_vision/model_helpers/check_submission/images/10.png differ diff --git a/brainscore_vision/model_helpers/check_submission/images/11.png 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mode 100644 index 000000000..3c48afd5c Binary files /dev/null and b/brainscore_vision/model_helpers/check_submission/images/9.png differ diff --git a/brainscore_vision/model_helpers/utils/__init__.py b/brainscore_vision/model_helpers/utils/__init__.py new file mode 100644 index 000000000..cc7a1984b --- /dev/null +++ b/brainscore_vision/model_helpers/utils/__init__.py @@ -0,0 +1,15 @@ +import inspect + +from result_caching import is_iterable + + +def fullname(obj): + module = obj.__module__ + name = obj.__name__ if inspect.isfunction(obj) else obj.__class__.__name__ + return module + "." + name + + +def make_list(element): + if not is_iterable(element) or isinstance(element, (str, bytes)): + return [element] + return element diff --git a/brainscore_vision/model_helpers/utils/s3.py b/brainscore_vision/model_helpers/utils/s3.py new file mode 100644 index 000000000..030a06eb1 --- /dev/null +++ b/brainscore_vision/model_helpers/utils/s3.py @@ -0,0 +1,42 @@ +import logging +import os +import sys + +import boto3 +from botocore import UNSIGNED +from botocore.config import Config +from tqdm import tqdm + +_logger = logging.getLogger(__name__) + +_DEFAULT_BUCKET = 'brain-score-models' +_DEFAULT_REGION = 'us-east-1' +_NO_SIGNATURE = Config(signature_version=UNSIGNED) + + +def download_folder(folder_key, target_directory, bucket=_DEFAULT_BUCKET, region=_DEFAULT_REGION): + if not folder_key.endswith('/'): + folder_key = folder_key + '/' + s3 = boto3.resource('s3', region_name=region, config=_NO_SIGNATURE) + bucket = s3.Bucket(bucket) + bucket_contents = list(bucket.objects.all()) + files = [obj.key for obj in bucket_contents if obj.key.startswith(folder_key)] + _logger.debug(f"Found {len(files)} files") + for file in tqdm(files): + # get part of file after given folder_key + filename = file[len(folder_key):] + target_path = os.path.join(target_directory, filename) + temp_path = target_path + '.filepart' + bucket.download_file(file, temp_path) + os.rename(temp_path, target_path) + + +def download_file(key, target_path, bucket=_DEFAULT_BUCKET, region=_DEFAULT_REGION): + s3 = boto3.resource('s3', region_name=region, config=_NO_SIGNATURE) + obj = s3.Object(bucket, key) + # show progress. see https://gist.github.com/wy193777/e7607d12fad13459e8992d4f69b53586 + with tqdm(total=obj.content_length, unit='B', unit_scale=True, desc=key, file=sys.stdout) as progress_bar: + def progress_hook(bytes_amount): + progress_bar.update(bytes_amount) + + obj.download_file(target_path, Callback=progress_hook) diff --git a/brainscore_vision/model_interface.py b/brainscore_vision/model_interface.py new file mode 100644 index 000000000..316ba0760 --- /dev/null +++ b/brainscore_vision/model_interface.py @@ -0,0 +1,179 @@ +""" +The :class:`~brainscore_vision.model_interface.BrainModel` interface is the central communication point +between benchmarks and models. +""" + +from typing import List, Tuple, Union + +from brainio.assemblies import BehavioralAssembly, NeuroidAssembly +from brainio.stimuli import StimulusSet + + +class BrainModel: + """ + The BrainModel interface defines an API for models to follow. + Benchmarks will use this interface to treat models as an experimental subject + without needing to know about the details of the model implementation. + """ + + @property + def identifier(self) -> str: + """ + The unique identifier for this model. + + :return: e.g. `'CORnet-S'`, or `'alexnet'` + """ + raise NotImplementedError() + + def visual_degrees(self) -> int: + """ + The visual degrees this model covers as a single scalar. + + :return: e.g. `8`, or `10` + """ + raise NotImplementedError() + + class Task: + """ task to perform """ + + passive = 'passive' + """ + Passive fixation, i.e. do not perform any task, but fixate on the center of the screen. + Does not output anything, but can be useful to fully specify the experimental setup. + + Example: + + Setting up passive fixation with `start_task(BrainModel.Task.passive)` and calling `look_at(...)` could output + + .. code-block:: python + + None + """ + + label = 'label' + """ + Predict the label for each stimulus. + Output a :class:`~brainio.assemblies.BehavioralAssembly` with labels as the values. + + The labeling domain can be specified in the second argument, e.g. `'imagenet'` for 1,000 ImageNet synsets, + or an explicit list of label strings. The model choices must be part of the labeling domain. + + Example: + + Setting up a labeling task for ImageNet synsets with `start_task(BrainModel.Task.label, 'imagenet')` + and calling `look_at(...)` could output + + .. code-block:: python + + + array([['n02107574'], ['n02123045'], ['n02804414']]), # the ImageNet synsets + Coordinates: + * presentation (presentation) MultiIndex + - stimulus_id (presentation) object 'hash1' 'hash2' 'hash3' + - stimulus_path (presentation) object '/home/me/.brainio/demo_stimuli/image1.png' ... + - logit (presentation) int64 239 282 432 + - synset (presentation) object 'n02107574' 'n02123045' 'n02804414' + + Example: + + Setting up a labeling task for 2 custom labels with `start_task(BrainModel.Task.label, ['dog', 'cat'])` + and calling `look_at(...)` could output + + .. code-block:: python + + + array([['dog'], ['cat'], ['cat']]), # the labels + Coordinates: + * presentation (presentation) MultiIndex + - stimulus_id (presentation) object 'hash1' 'hash2' 'hash3' + - stimulus_path (presentation) object '/home/me/.brainio/demo_stimuli/image1.png' ... + """ + + probabilities = 'probabilities' + """ + Predict the per-label probabilities for each stimulus. + Output a :class:`~brainio.assemblies.BehavioralAssembly` with probabilities as the values. + + The model must be supplied with `fitting_stimuli` in the second argument which allow it to train a readout + for a particular set of labels and image distribution. + The `fitting_stimuli` are a :class:`~brainio.stimuli.StimulusSet` and must include an `image_label` column + which is used as the labels to fit to. + + Example: + + Setting up a probabilities task `start_task(BrainModel.Task.probabilities, )` + (where `fitting_stimuli` includes 5 distinct labels) + and calling `look_at()` could output + + .. code-block:: python + + + array([[0.9 0.1 0.0 0.0 0.0] + [0.0 0.0 0.8 0.0 0.2] + [0.0 0.0 0.0 1.0 0.0]]), # the probabilities + Coordinates: + * presentation (presentation) MultiIndex + - stimulus_id (presentation) object 'hash1' 'hash2' 'hash3' + - stimulus_path (presentation) object '/home/me/.brainio/demo_stimuli/image1.png' ... + - choice (choice) object 'dog' 'cat' 'chair' 'flower' 'plane' + """ + + def start_task(self, task: Task, fitting_stimuli) -> None: + """ + Instructs the model to begin one of the tasks specified in + :data:`~brainscore_vision.model_interface.BrainModel.Task`. + For all followings call of :meth:`~brainscore_vision.model_interface.BrainModel.look_at`, + the model returns the expected outputs for the specified task. + + :param task: The task the model should perform, and thus which outputs it should return + :param fitting_stimuli: A set of stimuli for the model to learn on, e.g. image-label pairs + """ + raise NotImplementedError() + + class RecordingTarget: + """ location to record from """ + V1 = 'V1' + V2 = 'V2' + V4 = 'V4' + IT = 'IT' + + def start_recording(self, recording_target: RecordingTarget, time_bins: List[Tuple[int]]) -> None: + """ + Instructs the model to begin recording in a specified + :data:`~brainscore_vision.model_interface.BrainModel.RecordingTarget` and return the specified `time_bins`. + For all followings call of :meth:`~brainscore_vision.model_interface.BrainModel.look_at`, the model returns the + corresponding recordings. These recordings are a :class:`~brainio.assemblies.NeuroidAssembly` with exactly + 3 dimensions: + + - `presentation`: the presented stimuli (cf. stimuli argument of + :meth:`~brainscore_vision.model_interface.BrainModel.look_at`). If a :class:`~brainio.stimuli.StimulusSet` + was passed, the recordings should contain all of the :class:`~brainio.stimuli.StimulusSet` columns as + coordinates on this dimension. The `stimulus_id` coordinate is required in either case. + - `neuroid`: the recorded neuroids (neurons or mixtures thereof). They should all be part of the + specified :data:`~brainscore_vision.model_interface.BrainModel.RecordingTarget`. The coordinates of this + dimension should again include as much information as is available, at the very least a `neuroid_id`. + - `time_bins`: the time bins of each recording slice. This dimension should contain at least 2 coordinates: + `time_bin_start` and `time_bin_end`, where one `time_bin` is the bin between start and end. + For instance, a 70-170ms time_bin would be marked as `time_bin_start=70` and `time_bin_end=170`. + If only one time_bin is requested, the model may choose to omit this dimension. + + :param recording_target: which location to record from + :param time_bins: which time_bins to record as a list of integer tuples, + e.g. `[(50, 100), (100, 150), (150, 200)]` or `[(70, 170)]` + """ + raise NotImplementedError() + + def look_at(self, stimuli: Union[StimulusSet, List[str]], number_of_trials=1) \ + -> Union[BehavioralAssembly, NeuroidAssembly]: + """ + Digest a set of stimuli and return requested outputs. Which outputs to return is instructed by the + :meth:`~brainscore_vision.model_interface.BrainMode.start_task` and + :meth:`~brainscore_vision.model_interface.BrainModel.start_recording` methods. + + :param stimuli: A set of stimuli, passed as either a :class:`~brainio.stimuli.StimulusSet` + or a list of image file paths + :param number_of_trials: The number of repeated trials of the stimuli that the model should average over. + E.g. 10 or 35. Non-stochastic models can likely ignore this parameter. + :return: task behaviors or recordings as instructed + """ + raise NotImplementedError() diff --git a/packaging/movshon/__init__.py b/brainscore_vision/models/__init__.py similarity index 100% rename from packaging/movshon/__init__.py rename to brainscore_vision/models/__init__.py diff --git a/brainscore/submission/__init__.py b/brainscore_vision/submission/__init__.py similarity index 100% rename from brainscore/submission/__init__.py rename to brainscore_vision/submission/__init__.py diff --git a/brainscore/submission/__main__.py b/brainscore_vision/submission/__main__.py similarity index 90% rename from brainscore/submission/__main__.py rename to brainscore_vision/submission/__main__.py index 1f68881d8..bf379ac8b 100644 --- a/brainscore/submission/__main__.py +++ b/brainscore_vision/submission/__main__.py @@ -5,7 +5,7 @@ import fire -from brainscore.submission.evaluation import run_evaluation +from brainscore_vision.submission.evaluation import run_evaluation logger = logging.getLogger(__name__) @@ -18,7 +18,7 @@ parser.add_argument('--db_secret', type=str, help='The name of the database credential secret loaded from AWS', default='brainscore-1-ohio-cred') parser.add_argument('--models', type=str, nargs='*', default=None, - help='An optional list of the models to benchmark, if it does not exist all models are socred') + help='An optional list of the models to benchmark, if it does not exist all models are scored') parser.add_argument('--benchmarks', type=str, nargs='*', default=None, help='An optional list of the benchmarks to run, if it does not exist all benchmarks are run') args, remaining_args = parser.parse_known_args() @@ -36,7 +36,7 @@ def score_model_console(): assert isinstance(args.jenkins_id, int) logger.info(f'Benchmarks configured: {args.benchmarks}') logger.info(f'Models configured: {args.models}') - run_evaluation(args.config_dir, args.work_dir, args.jenkins_id, db_secret=args.db_secret, + run_evaluation(args.config_dir, args.work_dir, jenkins_id=args.jenkins_id, db_secret=args.db_secret, models=args.models, benchmarks=args.benchmarks) diff --git a/brainscore/submission/brainscore_submission.png b/brainscore_vision/submission/brainscore_submission.png similarity index 100% rename from brainscore/submission/brainscore_submission.png rename to brainscore_vision/submission/brainscore_submission.png diff --git a/brainscore/submission/configuration.py b/brainscore_vision/submission/configuration.py similarity index 92% rename from brainscore/submission/configuration.py rename to brainscore_vision/submission/configuration.py index 422f8d3a0..7677e6db8 100644 --- a/brainscore/submission/configuration.py +++ b/brainscore_vision/submission/configuration.py @@ -1,7 +1,7 @@ import datetime import distutils -from brainscore.submission.models import Model, Submission +from brainscore_vision.submission.models import Model, Submission class BaseConfig: @@ -42,8 +42,8 @@ class SubmissionConfig(BaseConfig): def __init__(self, model_type, user_id, jenkins_id, public: bool, competition_submission: str, **kwargs): super(SubmissionConfig, self).__init__(jenkins_id=jenkins_id, **kwargs) - self.submission = Submission.create(id=jenkins_id, submitter=user_id, timestamp=datetime.datetime.now(), - model_type=model_type, status='running') + self.submission = Submission.create(submitter=user_id, timestamp=datetime.datetime.now(), + model_type=model_type, status='running', jenkins_id=jenkins_id) self.competition_submission = competition_submission self.public = public diff --git a/brainscore/submission/database.py b/brainscore_vision/submission/database.py similarity index 84% rename from brainscore/submission/database.py rename to brainscore_vision/submission/database.py index 9b5b2a9aa..5e5d2b549 100644 --- a/brainscore/submission/database.py +++ b/brainscore_vision/submission/database.py @@ -3,8 +3,8 @@ from peewee import PostgresqlDatabase, SqliteDatabase -from brainscore.submission.models import database -from brainscore.submission.utils import get_secret +from brainscore_vision.submission.models import database +from brainscore_vision.submission.utils import get_secret def connect_db(db_secret): diff --git a/brainscore/submission/db_schema.uml b/brainscore_vision/submission/db_schema.uml similarity index 100% rename from brainscore/submission/db_schema.uml rename to brainscore_vision/submission/db_schema.uml diff --git a/brainscore/submission/developers_guide.md b/brainscore_vision/submission/developers_guide.md similarity index 100% rename from brainscore/submission/developers_guide.md rename to brainscore_vision/submission/developers_guide.md diff --git a/brainscore/submission/evaluation.py b/brainscore_vision/submission/evaluation.py similarity index 90% rename from brainscore/submission/evaluation.py rename to brainscore_vision/submission/evaluation.py index c0d151ec3..9f1fe0a22 100644 --- a/brainscore/submission/evaluation.py +++ b/brainscore_vision/submission/evaluation.py @@ -7,17 +7,19 @@ import pandas as pd from peewee import DoesNotExist -from brainscore import score_model -from brainscore.benchmarks import evaluation_benchmark_pool, benchmark_pool -from brainscore.submission.configuration import object_decoder, MultiConfig -from brainscore.submission.database import connect_db -from brainscore.submission.ml_pool import MLBrainPool, ModelLayers -from brainscore.submission.models import Model, Score, BenchmarkInstance, BenchmarkType, Reference -from brainscore.submission.repository import prepare_module, deinstall_project -from brainscore.utils import LazyLoad +from brainscore_vision import score as _score_function +from brainscore_vision.benchmarks import evaluation_benchmark_pool, benchmark_pool +from brainscore_vision import load_benchmark +from brainscore_vision.submission.configuration import object_decoder, MultiConfig +from brainscore_vision.submission.database import connect_db +from brainscore_vision.submission.ml_pool import MLBrainPool, ModelLayers +from brainscore_vision.submission.models import Model, Score, BenchmarkInstance, BenchmarkType, Reference +from brainscore_vision.submission.repository import prepare_module, deinstall_project +from brainscore_vision.utils import LazyLoad logger = logging.getLogger(__name__) +# TODO: Leave for Martin all_benchmarks_list = [benchmark for benchmark in evaluation_benchmark_pool.keys()] SCORE_COMMENT_MAX_LENGTH = 1000 @@ -129,18 +131,18 @@ def run_submission(module, test_models, test_benchmarks, submission_entry): model_entry.visual_degrees = model.visual_degrees() model_entry.save() # run model on benchmark - score = score_model(model_id, benchmark_name, model) + score = _score_function(model_id, benchmark_name) logger.info(f'Running benchmark {benchmark_name} on model {model_id} (id {model_entry.id}) ' f'produced this score: {score}') if not hasattr(score, 'ceiling'): # many engineering benchmarks do not have a primate ceiling - raw = score.sel(aggregation='center').item(0) + raw = score.item() ceiled = None error = None else: # score has a ceiling. Store ceiled as well as raw value - assert score.raw.sel(aggregation='center') is not None - raw = score.raw.sel(aggregation='center').item(0) - ceiled = score.sel(aggregation='center').item(0) - error = score.sel(aggregation='error').item(0) + assert score.raw is not None + ceiled = score.item() + raw = score.raw.item() + error = score.attrs['error'].item() if 'error' in score.attrs else None finished = datetime.datetime.now() comment = f"layers: {model.layer_model.region_layer_map}" \ if submission_entry.model_type == 'BaseModel' else '' @@ -209,7 +211,7 @@ def get_ml_pool(test_models, module, submission): def get_benchmark_instance(benchmark_name): - benchmark = benchmark_pool[benchmark_name] + benchmark = load_benchmark(benchmark_name) benchmark_type, created = BenchmarkType.get_or_create(identifier=benchmark_name, defaults=dict(order=999)) if created: try: @@ -229,8 +231,8 @@ def get_benchmark_instance(benchmark_name): if created: # the version has changed and the benchmark instance was not yet in the database ceiling = benchmark.ceiling - bench_inst.ceiling = ceiling.sel(aggregation='center') - bench_inst.ceiling_error = ceiling.sel(aggregation='error') + bench_inst.ceiling = ceiling + bench_inst.ceiling_error = ceiling.attrs['error'] if 'error' in ceiling.attrs else None bench_inst.save() return bench_inst diff --git a/brainscore/submission/ml_pool.py b/brainscore_vision/submission/ml_pool.py similarity index 93% rename from brainscore/submission/ml_pool.py rename to brainscore_vision/submission/ml_pool.py index 651dd71f1..9128ca3cf 100644 --- a/brainscore/submission/ml_pool.py +++ b/brainscore_vision/submission/ml_pool.py @@ -1,7 +1,7 @@ import warnings -from brainscore.utils import LazyLoad -from brainscore.submission.utils import UniqueKeyDict +from brainscore_vision.utils import LazyLoad +from brainscore_vision.submission.utils import UniqueKeyDict class ModelLayers(UniqueKeyDict): diff --git a/brainscore/submission/models.py b/brainscore_vision/submission/models.py similarity index 93% rename from brainscore/submission/models.py rename to brainscore_vision/submission/models.py index f6680e74f..99b7bebe8 100644 --- a/brainscore/submission/models.py +++ b/brainscore_vision/submission/models.py @@ -26,6 +26,7 @@ class BenchmarkType(PeeweeBase): order = IntegerField() parent = ForeignKeyField(column_name='parent_id', field='identifier', model='self', null=True) visible = BooleanField(default=False, null=False) + domain = CharField(max_length=200, default="vision") class Meta: table_name = 'brainscore_benchmarktype' @@ -69,13 +70,16 @@ class Meta: class Submission(PeeweeBase): - id = PrimaryKeyField() # We use jenkins id as id for the submission. + id = PrimaryKeyField() # IDs will not be incremental when resubmitting models submitter = ForeignKeyField(column_name='submitter_id', field='id', model=User) timestamp = DateTimeField(null=True) model_type = CharField() status = CharField() + # equivalent to ID until language changes were added: (ID 6756) + jenkins_id = IntegerField() + class Meta: table_name = 'brainscore_submission' schema = 'public' @@ -89,6 +93,7 @@ class Model(PeeweeBase): visual_degrees = IntegerField(null=True) # null during creation of new model without having model object loaded public = BooleanField() competition = CharField(max_length=200, default=None, null=True) + domain = CharField(max_length=200, default="vision") class Meta: table_name = 'brainscore_model' @@ -105,8 +110,6 @@ class Score(PeeweeBase): start_timestamp = DateTimeField(null=True) comment = CharField(null=True) - # jenkins_id = IntegerField(null=True) - class Meta: table_name = 'brainscore_score' schema = 'public' diff --git a/brainscore/submission/repository.py b/brainscore_vision/submission/repository.py similarity index 93% rename from brainscore/submission/repository.py rename to brainscore_vision/submission/repository.py index b85ed3d05..3dcb7e4e1 100644 --- a/brainscore/submission/repository.py +++ b/brainscore_vision/submission/repository.py @@ -6,16 +6,16 @@ from importlib import import_module from pathlib import Path -from brainscore.submission.configuration import BaseConfig -from brainscore.submission.models import Submission +from brainscore_vision.submission.configuration import BaseConfig +from brainscore_vision.submission.models import Submission logger = logging.getLogger(__name__) def prepare_module(submission: Submission, config: BaseConfig): config_path = config.config_path - logger.info('Start executing models in repo submission_%s' % submission.id) - repo = extract_zip_file(submission.id, config_path, config.work_dir) + logger.info('Start executing models in repo submission_%s' % submission.jenkins_id) + repo = extract_zip_file(submission.jenkins_id, config_path, config.work_dir) package = 'models.brain_models' if submission.model_type == 'BrainModel' else 'models.base_models' logger.info(f'We work with {submission.model_type} and access {package} in the submission folder') return install_project(repo, package), os.path.basename(repo) diff --git a/brainscore/submission/utils.py b/brainscore_vision/submission/utils.py similarity index 100% rename from brainscore/submission/utils.py rename to brainscore_vision/submission/utils.py diff --git a/brainscore/utils/__init__.py b/brainscore_vision/utils/__init__.py similarity index 100% rename from brainscore/utils/__init__.py rename to brainscore_vision/utils/__init__.py diff --git a/docs/aws_user b/docs/aws_user new file mode 100644 index 000000000..7a9f7aea3 --- /dev/null +++ b/docs/aws_user @@ -0,0 +1,10 @@ +1. get Martin's approval to add the user +2. go to https://us-east-1.console.aws.amazon.com/iamv2/home?region=us-east-1#/users +3. click "Add users" +4. set user name to the email username, e.g. mferg@mit.edu -> mferg +5. add to groups: "Brain-Score_read" (and if necessary "Brain-Score_write") +6. create +7. navigate to newly created user account, scroll down to "Access keys" +8. click "Create access key" (Command Line Interface CLI) +9. no tag, click Create access key +10. Download .csv file and send to user, point them at https://github.com/brain-score/brainio/blob/main/docs/aws_access.md diff --git a/docs/requirements.txt b/docs/requirements.txt index c62b785b7..0f71f0a9f 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,3 +1,4 @@ -e . -Sphinx>=4 \ No newline at end of file +Sphinx>=4 +recommonmark \ No newline at end of file diff --git a/docs/source/modules/benchmark_tutorial.rst b/docs/source/modules/benchmark_tutorial.rst index 0e9de40ff..70f6cf0f7 100644 --- a/docs/source/modules/benchmark_tutorial.rst +++ b/docs/source/modules/benchmark_tutorial.rst @@ -283,7 +283,7 @@ and can be maintained. We ask that all benchmarks test at least two things: #. The ceiling value of the benchmark for which the benchmark identifier and expected ceiling can simply be added to - the :meth:`tests.test_benchmarks.test___init__.test_ceilings` method + the :meth:`tests.test_benchmarks.test___init__.ceilings_test` method #. The score of a couple of models with precomputed features: The idea for scores of precomputed features is to run a few models on the benchmark, store their features, and test that diff --git a/packaging/dicarlo/kar2018/__init__.py b/packaging/dicarlo/kar2018/__init__.py deleted file mode 100644 index db7ee69f9..000000000 --- a/packaging/dicarlo/kar2018/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from brainscore.metrics.ceiling import InternalConsistency -from brainscore.metrics.transformations import CrossValidation - - -def filter_neuroids(assembly, threshold): - ceiler = InternalConsistency() - ceiling = ceiler(assembly) - ceiling = ceiling.raw - ceiling = CrossValidation().aggregate(ceiling) - ceiling = ceiling.sel(aggregation='center') - pass_threshold = ceiling >= threshold - assembly = assembly[{'neuroid': pass_threshold}] - return assembly diff --git a/packaging/dicarlo/sanghavi/__init__.py b/packaging/dicarlo/sanghavi/__init__.py deleted file mode 100644 index db7ee69f9..000000000 --- a/packaging/dicarlo/sanghavi/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from brainscore.metrics.ceiling import InternalConsistency -from brainscore.metrics.transformations import CrossValidation - - -def filter_neuroids(assembly, threshold): - ceiler = InternalConsistency() - ceiling = ceiler(assembly) - ceiling = ceiling.raw - ceiling = CrossValidation().aggregate(ceiling) - ceiling = ceiling.sel(aggregation='center') - pass_threshold = ceiling >= threshold - assembly = assembly[{'neuroid': pass_threshold}] - return assembly diff --git a/packaging/notebooks/2018-01-29_blackrock_xls.ipynb b/packaging/notebooks/2018-01-29_blackrock_xls.ipynb deleted file mode 100644 index 7673634bf..000000000 --- a/packaging/notebooks/2018-01-29_blackrock_xls.ipynb +++ /dev/null @@ -1,18451 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [], - "source": [ - "import os\n", - "import datetime\n", - "import pprint\n", - "import hashlib\n", - "import xlrd\n", - "import dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [], - "source": [ - "def dated_file_name(extension, prefix):\n", - " filename = prefix + \"_\" + datetime.datetime.now().isoformat() + \".\" + extension\n", - " return filename" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "def hash(path, buffer_size=64*2**10):\n", - " sha1 = hashlib.sha1()\n", - " with open(path, \"rb\") as f:\n", - " buffer = f.read(buffer_size)\n", - " while len(buffer) > 0:\n", - " sha1.update(buffer)\n", - " buffer = f.read(buffer_size)\n", - " return sha1.hexdigest()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "class KnownFile(object):\n", - " def __init__(self, path, persister=None):\n", - " self.path = path\n", - " self.exists = os.path.exists(path)\n", - " if self.exists:\n", - " self.realpath = os.path.realpath(path)\n", - " self.isfile = os.path.isfile(self.realpath)\n", - " if self.isfile:\n", - " self.sha1 = hash(self.realpath)\n", - " if persister is not None:\n", - " self.persister = persister\n", - " persister.persist(self)\n", - " \n", - " def __repr__(self):\n", - " return str((self.__class__.__name__, vars(self)))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "def kf(path):\n", - " return KnownFile(path).realpath" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [], - "source": [ - "class MappingFile(object):\n", - " def __repr__(self):\n", - " return str((self.__class__.__name__, vars(self)))" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'/braintree/home/jjpr/dev/mkgu_packaging/data'" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_dir = os.path.expanduser(\"~/dev/mkgu_packaging/data\")\n", - "data_dir" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[('KnownFile', {'path': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-000936/5602-2 SN1025-000936.xls', 'exists': True, 'realpath': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-000936/5602-2 SN1025-000936.xls', 'isfile': True, 'sha1': '6f31b351f851c75364be07386c3120ea87b0b2d2'}),\n", - " ('KnownFile', {'path': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0547,548,549/090609B-5E.xls', 'exists': True, 'realpath': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0547,548,549/090609B-5E.xls', 'isfile': True, 'sha1': 'fc0d9e2aa9887d06adc52b2dd6760b93bff6fc38'}),\n", - " ('KnownFile', {'path': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0547,548,549/090609B-5F.xls', 'exists': True, 'realpath': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0547,548,549/090609B-5F.xls', 'isfile': True, 'sha1': 'd86f820617a121268bafecb3e5ee7845be6e81ef'}),\n", - " ('KnownFile', {'path': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0547,548,549/090609B-5H.xls', 'exists': True, 'realpath': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0547,548,549/090609B-5H.xls', 'isfile': True, 'sha1': 'b59002bc965969c43a31215a07837b1cc40ea6e8'}),\n", - " ('KnownFile', {'path': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-5-SN0656.xls', 'exists': True, 'realpath': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-5-SN0656.xls', 'isfile': True, 'sha1': 'e412043376d8bbebf64c468ae84ea9370e9bf202'}),\n", - " ('KnownFile', {'path': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls', 'exists': True, 'realpath': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls', 'isfile': True, 'sha1': '7d705186a726776e9517269dafd01d6407d0ce2e'}),\n", - " ('KnownFile', {'path': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2466-1-SN0658.xls', 'exists': True, 'realpath': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2466-1-SN0658.xls', 'isfile': True, 'sha1': '16286f12fff8d02d009134045db3b5f01b4c340b'}),\n", - " ('KnownFile', {'path': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-552,559,649/090609B-4F.xls', 'exists': True, 'realpath': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-552,559,649/090609B-4F.xls', 'isfile': True, 'sha1': '4d3961bf430fdda3626a1a805de74546235dea22'}),\n", - " ('KnownFile', {'path': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-552,559,649/090609B-8E.xls', 'exists': True, 'realpath': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-552,559,649/090609B-8E.xls', 'isfile': True, 'sha1': '8cf488c5af297ca03416f0c7f8b8eecf18365a70'}),\n", - " ('KnownFile', {'path': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-552,559,649/2467-6_SN0649.xls', 'exists': True, 'realpath': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-552,559,649/2467-6_SN0649.xls', 'isfile': True, 'sha1': '6eede08c96af9c4619a02d0065e927e78a595007'})]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# mf_paths = [os.path.join(data_dir, x) for x in os.listdir(data_dir) if x.endswith(\".xls\")]\n", - "mf_paths = [\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-000936/5602-2 SN1025-000936.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0547,548,549/090609B-5E.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0547,548,549/090609B-5F.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0547,548,549/090609B-5H.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-5-SN0656.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2466-1-SN0658.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-552,559,649/090609B-4F.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-552,559,649/090609B-8E.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-552,559,649/2467-6_SN0649.xls\" \n", - "]\n", - "mf_knownfiles = [KnownFile(x) for x in mf_paths]\n", - "mf_knownfiles" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [], - "source": [ - "def is_comment_row(row):\n", - " return str(row[0].value).lstrip().startswith(\"//\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [], - "source": [ - "def row_col_valid(cell):\n", - " if cell.ctype != 2: return False\n", - " if type(cell.value) != float: return False\n", - " if cell.value % 1 != 0: return False\n", - " if cell.value < 0: return False\n", - " if cell.value > 9: return False\n", - " return True" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [], - "source": [ - "def get_mapping(i, row):\n", - " if not (row_col_valid(row[0]) and row_col_valid(row[1])):\n", - " return None\n", - " result = {\n", - " \"electrode_column\": int(row[0].value), \n", - " \"electrode_row\": int(row[1].value), \n", - " \"bank_name\": row[2].value,\n", - " \"electrode_number_in_bank\": int(row[3].value),\n", - " \"electrode_label\": row[4].value,\n", - " \"electrode_label_numeric\": int(row[4].value[4:]),\n", - " \"index_in_mapping_sheet\": i\n", - " }\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "def jump_over(input_str, to_jump):\n", - " if input_str.startswith(to_jump):\n", - " return input_str[len(to_jump):]\n", - " else:\n", - " raise ValueError(\"'{0}' does not start with '{1}'\".format(input_str, to_jump))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [], - "source": [ - "def get_array_id(row):\n", - " stripped = row[0].value.strip()\n", - " chopped = jump_over(stripped, \"Cerebus mapping for array \")\n", - " squoze = ''.join(chopped.split())\n", - " return squoze" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "inputHidden": false, - "outputExpanded": false, - "outputHidden": false - }, - "outputs": [], - "source": [ - "def get_mapping_file(path):\n", - " mf = MappingFile()\n", - " mf.xls_path = path\n", - " mf.workbook = xlrd.open_workbook(path)\n", - " mf.mapping_worksheet = mf.workbook.sheet_by_name(\"Cerebus mapping\")\n", - " mf.comment_lines = set()\n", - " mf.description_row = None\n", - " mf.mappings = []\n", - " for i, row in enumerate(mf.mapping_worksheet.get_rows()):\n", - " if is_comment_row(row):\n", - " mf.comment_lines.add(i)\n", - " elif mf.description_row is None:\n", - " mf.description_row = row\n", - " else:\n", - " mapping = get_mapping(i, row)\n", - " if mapping is not None:\n", - " mf.mappings.append(mapping)\n", - " mf.array_id = get_array_id(mf.description_row)\n", - " return mf" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "inputHidden": false, - "outputExpanded": false, - "outputHidden": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[{'array_id': '5602-2/SN1025-000936',\n", - " 'comment_lines': {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},\n", - " 'description_row': [text:'Cerebus mapping for array 5602-2/SN1025-000936',\n", - " empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " text:'elec',\n", - " empty:''],\n", - " 'mapping_worksheet': ,\n", - " 'mappings': [{'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec99',\n", - " 'electrode_label_numeric': 99,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 13},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec98',\n", - " 'electrode_label_numeric': 98,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 14},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec97',\n", - " 'electrode_label_numeric': 97,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 15},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec96',\n", - " 'electrode_label_numeric': 96,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 16},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec95',\n", - " 'electrode_label_numeric': 95,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 17},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec94',\n", - " 'electrode_label_numeric': 94,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 18},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec93',\n", - " 'electrode_label_numeric': 93,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 19},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec92',\n", - " 'electrode_label_numeric': 92,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 20},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec91',\n", - " 'electrode_label_numeric': 91,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 21},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec90',\n", - " 'electrode_label_numeric': 90,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 22},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec89',\n", - " 'electrode_label_numeric': 89,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 23},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec88',\n", - " 'electrode_label_numeric': 88,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 24},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec87',\n", - " 'electrode_label_numeric': 87,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 25},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec86',\n", - " 'electrode_label_numeric': 86,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 26},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec85',\n", - " 'electrode_label_numeric': 85,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 27},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec84',\n", - " 'electrode_label_numeric': 84,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 28},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec83',\n", - " 'electrode_label_numeric': 83,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 29},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec82',\n", - " 'electrode_label_numeric': 82,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 30},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec81',\n", - " 'electrode_label_numeric': 81,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 31},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec80',\n", - " 'electrode_label_numeric': 80,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 32},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec79',\n", - " 'electrode_label_numeric': 79,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 33},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec78',\n", - " 'electrode_label_numeric': 78,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 34},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec77',\n", - " 'electrode_label_numeric': 77,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 35},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec76',\n", - " 'electrode_label_numeric': 76,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 36},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec75',\n", - " 'electrode_label_numeric': 75,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 37},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec74',\n", - " 'electrode_label_numeric': 74,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 38},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec73',\n", - " 'electrode_label_numeric': 73,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 39},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec72',\n", - " 'electrode_label_numeric': 72,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 40},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec71',\n", - " 'electrode_label_numeric': 71,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 41},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec70',\n", - " 'electrode_label_numeric': 70,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 42},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec69',\n", - " 'electrode_label_numeric': 69,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 43},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec68',\n", - " 'electrode_label_numeric': 68,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 44},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec67',\n", - " 'electrode_label_numeric': 67,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 45},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec66',\n", - " 'electrode_label_numeric': 66,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 46},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec65',\n", - " 'electrode_label_numeric': 65,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 47},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec64',\n", - " 'electrode_label_numeric': 64,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 48},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec63',\n", - " 'electrode_label_numeric': 63,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 49},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec62',\n", - " 'electrode_label_numeric': 62,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 50},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec61',\n", - " 'electrode_label_numeric': 61,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 51},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec60',\n", - " 'electrode_label_numeric': 60,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 52},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec59',\n", - " 'electrode_label_numeric': 59,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 53},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec58',\n", - " 'electrode_label_numeric': 58,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 54},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec57',\n", - " 'electrode_label_numeric': 57,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 55},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec56',\n", - " 'electrode_label_numeric': 56,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 56},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec55',\n", - " 'electrode_label_numeric': 55,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 57},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec54',\n", - " 'electrode_label_numeric': 54,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 58},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec53',\n", - " 'electrode_label_numeric': 53,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 59},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec51',\n", - " 'electrode_label_numeric': 51,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 60},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec50',\n", - " 'electrode_label_numeric': 50,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 61},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec49',\n", - " 'electrode_label_numeric': 49,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 62},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec48',\n", - " 'electrode_label_numeric': 48,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 63},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec47',\n", - " 'electrode_label_numeric': 47,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 64},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec46',\n", - " 'electrode_label_numeric': 46,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 65},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec45',\n", - " 'electrode_label_numeric': 45,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 66},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec44',\n", - " 'electrode_label_numeric': 44,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 67},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec43',\n", - " 'electrode_label_numeric': 43,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 68},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec42',\n", - " 'electrode_label_numeric': 42,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 69},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec41',\n", - " 'electrode_label_numeric': 41,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 70},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec40',\n", - " 'electrode_label_numeric': 40,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 71},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec39',\n", - " 'electrode_label_numeric': 39,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 72},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec38',\n", - " 'electrode_label_numeric': 38,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 73},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec37',\n", - " 'electrode_label_numeric': 37,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 74},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec36',\n", - " 'electrode_label_numeric': 36,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 75},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec35',\n", - " 'electrode_label_numeric': 35,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 76},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec34',\n", - " 'electrode_label_numeric': 34,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 77},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec33',\n", - " 'electrode_label_numeric': 33,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 78},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec32',\n", - " 'electrode_label_numeric': 32,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 79},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec31',\n", - " 'electrode_label_numeric': 31,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 80},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec30',\n", - " 'electrode_label_numeric': 30,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 81},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec29',\n", - " 'electrode_label_numeric': 29,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 82},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec28',\n", - " 'electrode_label_numeric': 28,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 83},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec27',\n", - " 'electrode_label_numeric': 27,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 84},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec26',\n", - " 'electrode_label_numeric': 26,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 85},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec25',\n", - " 'electrode_label_numeric': 25,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 86},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec24',\n", - " 'electrode_label_numeric': 24,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 87},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec23',\n", - " 'electrode_label_numeric': 23,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 88},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec22',\n", - " 'electrode_label_numeric': 22,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 89},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec20',\n", - " 'electrode_label_numeric': 20,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 90},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec19',\n", - " 'electrode_label_numeric': 19,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 91},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec18',\n", - " 'electrode_label_numeric': 18,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 92},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec17',\n", - " 'electrode_label_numeric': 17,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 93},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec16',\n", - " 'electrode_label_numeric': 16,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 94},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec15',\n", - " 'electrode_label_numeric': 15,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 95},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec14',\n", - " 'electrode_label_numeric': 14,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 96},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec13',\n", - " 'electrode_label_numeric': 13,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 97},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec12',\n", - " 'electrode_label_numeric': 12,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 98},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec11',\n", - " 'electrode_label_numeric': 11,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 99},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec10',\n", - " 'electrode_label_numeric': 10,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 100},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec9',\n", - " 'electrode_label_numeric': 9,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 101},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec8',\n", - " 'electrode_label_numeric': 8,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 102},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec7',\n", - " 'electrode_label_numeric': 7,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 103},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec6',\n", - " 'electrode_label_numeric': 6,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 104},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec5',\n", - " 'electrode_label_numeric': 5,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 105},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec4',\n", - " 'electrode_label_numeric': 4,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 106},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec3',\n", - " 'electrode_label_numeric': 3,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 107},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec2',\n", - " 'electrode_label_numeric': 2,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 108}],\n", - " 'workbook': ,\n", - " 'xls_path': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping '\n", - " 'and Impedance - 1025-000936/5602-2 SN1025-000936.xls'},\n", - " {'array_id': '090609B-5E/SN00547',\n", - " 'comment_lines': {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},\n", - " 'description_row': [text:'Cerebus mapping for array 090609B-5E/SN00547',\n", - " empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " text:'elec',\n", - " empty:''],\n", - " 'mapping_worksheet': ,\n", - " 'mappings': [{'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec99',\n", - " 'electrode_label_numeric': 99,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 13},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec98',\n", - " 'electrode_label_numeric': 98,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 14},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec97',\n", - " 'electrode_label_numeric': 97,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 15},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec96',\n", - " 'electrode_label_numeric': 96,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 16},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec95',\n", - " 'electrode_label_numeric': 95,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 17},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec94',\n", - " 'electrode_label_numeric': 94,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 18},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec93',\n", - " 'electrode_label_numeric': 93,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 19},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec92',\n", - " 'electrode_label_numeric': 92,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 20},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec90',\n", - " 'electrode_label_numeric': 90,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 21},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec89',\n", - " 'electrode_label_numeric': 89,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 22},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec88',\n", - " 'electrode_label_numeric': 88,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 23},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec87',\n", - " 'electrode_label_numeric': 87,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 24},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec86',\n", - " 'electrode_label_numeric': 86,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 25},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec85',\n", - " 'electrode_label_numeric': 85,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 26},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec84',\n", - " 'electrode_label_numeric': 84,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 27},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec83',\n", - " 'electrode_label_numeric': 83,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 28},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec82',\n", - " 'electrode_label_numeric': 82,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 29},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec81',\n", - " 'electrode_label_numeric': 81,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 30},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec80',\n", - " 'electrode_label_numeric': 80,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 31},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec79',\n", - " 'electrode_label_numeric': 79,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 32},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec78',\n", - " 'electrode_label_numeric': 78,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 33},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec77',\n", - " 'electrode_label_numeric': 77,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 34},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec76',\n", - " 'electrode_label_numeric': 76,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 35},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec75',\n", - " 'electrode_label_numeric': 75,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 36},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec74',\n", - " 'electrode_label_numeric': 74,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 37},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec73',\n", - " 'electrode_label_numeric': 73,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 38},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec72',\n", - " 'electrode_label_numeric': 72,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 39},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec71',\n", - " 'electrode_label_numeric': 71,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 40},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec70',\n", - " 'electrode_label_numeric': 70,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 41},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec69',\n", - " 'electrode_label_numeric': 69,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 42},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec68',\n", - " 'electrode_label_numeric': 68,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 43},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec67',\n", - " 'electrode_label_numeric': 67,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 44},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec66',\n", - " 'electrode_label_numeric': 66,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 45},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec65',\n", - " 'electrode_label_numeric': 65,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 46},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec64',\n", - " 'electrode_label_numeric': 64,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 47},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec63',\n", - " 'electrode_label_numeric': 63,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 48},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec62',\n", - " 'electrode_label_numeric': 62,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 49},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec61',\n", - " 'electrode_label_numeric': 61,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 50},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec60',\n", - " 'electrode_label_numeric': 60,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 51},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec59',\n", - " 'electrode_label_numeric': 59,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 52},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec58',\n", - " 'electrode_label_numeric': 58,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 53},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec57',\n", - " 'electrode_label_numeric': 57,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 54},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec56',\n", - " 'electrode_label_numeric': 56,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 55},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec55',\n", - " 'electrode_label_numeric': 55,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 56},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec54',\n", - " 'electrode_label_numeric': 54,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 57},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec53',\n", - " 'electrode_label_numeric': 53,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 58},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec52',\n", - " 'electrode_label_numeric': 52,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 59},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec51',\n", - " 'electrode_label_numeric': 51,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 60},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec50',\n", - " 'electrode_label_numeric': 50,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 61},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec49',\n", - " 'electrode_label_numeric': 49,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 62},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec48',\n", - " 'electrode_label_numeric': 48,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 63},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec47',\n", - " 'electrode_label_numeric': 47,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 64},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec46',\n", - " 'electrode_label_numeric': 46,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 65},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec45',\n", - " 'electrode_label_numeric': 45,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 66},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec44',\n", - 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" 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 75},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec35',\n", - " 'electrode_label_numeric': 35,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 76},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec34',\n", - " 'electrode_label_numeric': 34,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 77},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec33',\n", - " 'electrode_label_numeric': 33,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 78},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec32',\n", - " 'electrode_label_numeric': 32,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 79},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec31',\n", - " 'electrode_label_numeric': 31,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 80},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec30',\n", - " 'electrode_label_numeric': 30,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 81},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec29',\n", - " 'electrode_label_numeric': 29,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 82},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec28',\n", - " 'electrode_label_numeric': 28,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 83},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec27',\n", - " 'electrode_label_numeric': 27,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 84},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec26',\n", - " 'electrode_label_numeric': 26,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 85},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec25',\n", - " 'electrode_label_numeric': 25,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 86},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec24',\n", - " 'electrode_label_numeric': 24,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 87},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec23',\n", - 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" 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 96},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec14',\n", - " 'electrode_label_numeric': 14,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 97},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec13',\n", - " 'electrode_label_numeric': 13,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 98},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec12',\n", - " 'electrode_label_numeric': 12,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 99},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec11',\n", - " 'electrode_label_numeric': 11,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 100},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec9',\n", - " 'electrode_label_numeric': 9,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 101},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec8',\n", - " 'electrode_label_numeric': 8,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 102},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec7',\n", - " 'electrode_label_numeric': 7,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 103},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec6',\n", - " 'electrode_label_numeric': 6,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 104},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 4,\n", - 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" 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 23},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec87',\n", - " 'electrode_label_numeric': 87,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 24},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec86',\n", - " 'electrode_label_numeric': 86,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 25},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec85',\n", - " 'electrode_label_numeric': 85,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 26},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec84',\n", - " 'electrode_label_numeric': 84,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 27},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec83',\n", - " 'electrode_label_numeric': 83,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 28},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec82',\n", - " 'electrode_label_numeric': 82,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 29},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec81',\n", - " 'electrode_label_numeric': 81,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 30},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec80',\n", - " 'electrode_label_numeric': 80,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 31},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec79',\n", - " 'electrode_label_numeric': 79,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 32},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec78',\n", - " 'electrode_label_numeric': 78,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 33},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec77',\n", - " 'electrode_label_numeric': 77,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 34},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec76',\n", - " 'electrode_label_numeric': 76,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 35},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec75',\n", - " 'electrode_label_numeric': 75,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 36},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec74',\n", - " 'electrode_label_numeric': 74,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 37},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec73',\n", - " 'electrode_label_numeric': 73,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 38},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec72',\n", - " 'electrode_label_numeric': 72,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 39},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec71',\n", - " 'electrode_label_numeric': 71,\n", - 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" 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 44},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec66',\n", - " 'electrode_label_numeric': 66,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 45},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec65',\n", - " 'electrode_label_numeric': 65,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 46},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec64',\n", - " 'electrode_label_numeric': 64,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 47},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec63',\n", - " 'electrode_label_numeric': 63,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 48},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec62',\n", - " 'electrode_label_numeric': 62,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 49},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec61',\n", - " 'electrode_label_numeric': 61,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 50},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec60',\n", - " 'electrode_label_numeric': 60,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 51},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec59',\n", - " 'electrode_label_numeric': 59,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 52},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec58',\n", - " 'electrode_label_numeric': 58,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 53},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec57',\n", - " 'electrode_label_numeric': 57,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 54},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec56',\n", - " 'electrode_label_numeric': 56,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 55},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec55',\n", - " 'electrode_label_numeric': 55,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 56},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec54',\n", - " 'electrode_label_numeric': 54,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 57},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec53',\n", - " 'electrode_label_numeric': 53,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 58},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec52',\n", - " 'electrode_label_numeric': 52,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 59},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec51',\n", - " 'electrode_label_numeric': 51,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 60},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec50',\n", - " 'electrode_label_numeric': 50,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 61},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec49',\n", - " 'electrode_label_numeric': 49,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 62},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec48',\n", - " 'electrode_label_numeric': 48,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 63},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec47',\n", - " 'electrode_label_numeric': 47,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 64},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec46',\n", - " 'electrode_label_numeric': 46,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 65},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec45',\n", - " 'electrode_label_numeric': 45,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 66},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec44',\n", - " 'electrode_label_numeric': 44,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 67},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec43',\n", - " 'electrode_label_numeric': 43,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 68},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec42',\n", - " 'electrode_label_numeric': 42,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 69},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec41',\n", - " 'electrode_label_numeric': 41,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 70},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec40',\n", - " 'electrode_label_numeric': 40,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 71},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec39',\n", - " 'electrode_label_numeric': 39,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 72},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec38',\n", - " 'electrode_label_numeric': 38,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 73},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec37',\n", - 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" 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 82},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec28',\n", - " 'electrode_label_numeric': 28,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 83},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec27',\n", - " 'electrode_label_numeric': 27,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 84},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec26',\n", - " 'electrode_label_numeric': 26,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 85},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec25',\n", - " 'electrode_label_numeric': 25,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 86},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec24',\n", - " 'electrode_label_numeric': 24,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 87},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec23',\n", - " 'electrode_label_numeric': 23,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 88},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec22',\n", - " 'electrode_label_numeric': 22,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 89},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec21',\n", - " 'electrode_label_numeric': 21,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 90},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 9,\n", - 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" {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec2',\n", - " 'electrode_label_numeric': 2,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 108}],\n", - " 'workbook': ,\n", - " 'xls_path': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping '\n", - " 'and Impedance - 1025-0547,548,549/090609B-5F.xls'},\n", - " {'array_id': '090609B-5H/SN0549',\n", - " 'comment_lines': {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},\n", - " 'description_row': [text:'Cerebus mapping for array 090609B-5H/SN0549',\n", - " empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " text:'elec',\n", - " empty:''],\n", - " 'mapping_worksheet': ,\n", - " 'mappings': [{'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec99',\n", - " 'electrode_label_numeric': 99,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 13},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec98',\n", - " 'electrode_label_numeric': 98,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 14},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec97',\n", - " 'electrode_label_numeric': 97,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 15},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec96',\n", - " 'electrode_label_numeric': 96,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 16},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec95',\n", - " 'electrode_label_numeric': 95,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 17},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec94',\n", - " 'electrode_label_numeric': 94,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 18},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec93',\n", - " 'electrode_label_numeric': 93,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 19},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec92',\n", - " 'electrode_label_numeric': 92,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 20},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec91',\n", - " 'electrode_label_numeric': 91,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 21},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec90',\n", - " 'electrode_label_numeric': 90,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 22},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec89',\n", - " 'electrode_label_numeric': 89,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 23},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec88',\n", - " 'electrode_label_numeric': 88,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 24},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec87',\n", - " 'electrode_label_numeric': 87,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 25},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec86',\n", - " 'electrode_label_numeric': 86,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 26},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec85',\n", - " 'electrode_label_numeric': 85,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 27},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec84',\n", - " 'electrode_label_numeric': 84,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 28},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec83',\n", - " 'electrode_label_numeric': 83,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 29},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec82',\n", - " 'electrode_label_numeric': 82,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 30},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec81',\n", - " 'electrode_label_numeric': 81,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 31},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec80',\n", - " 'electrode_label_numeric': 80,\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 32},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec79',\n", - " 'electrode_label_numeric': 79,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 33},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec78',\n", - " 'electrode_label_numeric': 78,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 34},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec77',\n", - " 'electrode_label_numeric': 77,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 35},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec76',\n", - " 'electrode_label_numeric': 76,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 36},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec75',\n", - " 'electrode_label_numeric': 75,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 37},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec74',\n", - " 'electrode_label_numeric': 74,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 38},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec73',\n", - " 'electrode_label_numeric': 73,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 39},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec72',\n", - " 'electrode_label_numeric': 72,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 40},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec71',\n", - " 'electrode_label_numeric': 71,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 41},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec70',\n", - " 'electrode_label_numeric': 70,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 42},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec69',\n", - " 'electrode_label_numeric': 69,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 43},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec68',\n", - " 'electrode_label_numeric': 68,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 44},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec67',\n", - " 'electrode_label_numeric': 67,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 45},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec66',\n", - " 'electrode_label_numeric': 66,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 46},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec65',\n", - " 'electrode_label_numeric': 65,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 47},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec64',\n", - " 'electrode_label_numeric': 64,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 48},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec63',\n", - " 'electrode_label_numeric': 63,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 49},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec62',\n", - " 'electrode_label_numeric': 62,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 50},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec61',\n", - " 'electrode_label_numeric': 61,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 51},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec60',\n", - " 'electrode_label_numeric': 60,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 52},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec59',\n", - " 'electrode_label_numeric': 59,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 53},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec58',\n", - " 'electrode_label_numeric': 58,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 54},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec57',\n", - " 'electrode_label_numeric': 57,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 55},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec56',\n", - " 'electrode_label_numeric': 56,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 56},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec55',\n", - " 'electrode_label_numeric': 55,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 57},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec54',\n", - " 'electrode_label_numeric': 54,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 58},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec53',\n", - " 'electrode_label_numeric': 53,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 59},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec52',\n", - " 'electrode_label_numeric': 52,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 60},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec51',\n", - " 'electrode_label_numeric': 51,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 61},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec50',\n", - " 'electrode_label_numeric': 50,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 62},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec49',\n", - " 'electrode_label_numeric': 49,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 63},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec48',\n", - " 'electrode_label_numeric': 48,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 64},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec47',\n", - " 'electrode_label_numeric': 47,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 65},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec46',\n", - " 'electrode_label_numeric': 46,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 66},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec45',\n", - " 'electrode_label_numeric': 45,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 67},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec44',\n", - " 'electrode_label_numeric': 44,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 68},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec43',\n", - " 'electrode_label_numeric': 43,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 69},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec42',\n", - " 'electrode_label_numeric': 42,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 70},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec41',\n", - " 'electrode_label_numeric': 41,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 71},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec40',\n", - " 'electrode_label_numeric': 40,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 72},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec39',\n", - " 'electrode_label_numeric': 39,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 73},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec38',\n", - " 'electrode_label_numeric': 38,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 74},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec37',\n", - " 'electrode_label_numeric': 37,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 75},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec36',\n", - " 'electrode_label_numeric': 36,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 76},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec35',\n", - " 'electrode_label_numeric': 35,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 77},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec34',\n", - " 'electrode_label_numeric': 34,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 78},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec33',\n", - " 'electrode_label_numeric': 33,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 79},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec32',\n", - " 'electrode_label_numeric': 32,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 80},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec31',\n", - " 'electrode_label_numeric': 31,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 81},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec30',\n", - " 'electrode_label_numeric': 30,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 82},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec29',\n", - " 'electrode_label_numeric': 29,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 83},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec28',\n", - " 'electrode_label_numeric': 28,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 84},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec27',\n", - " 'electrode_label_numeric': 27,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 85},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec26',\n", - " 'electrode_label_numeric': 26,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 86},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec25',\n", - " 'electrode_label_numeric': 25,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 87},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec24',\n", - " 'electrode_label_numeric': 24,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 88},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec23',\n", - " 'electrode_label_numeric': 23,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 89},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec22',\n", - " 'electrode_label_numeric': 22,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 90},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec21',\n", - " 'electrode_label_numeric': 21,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 91},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec20',\n", - " 'electrode_label_numeric': 20,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 92},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec19',\n", - " 'electrode_label_numeric': 19,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 93},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec18',\n", - " 'electrode_label_numeric': 18,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 94},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec17',\n", - " 'electrode_label_numeric': 17,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 95},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec16',\n", - " 'electrode_label_numeric': 16,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 96},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec15',\n", - " 'electrode_label_numeric': 15,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 97},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec14',\n", - " 'electrode_label_numeric': 14,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 98},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec13',\n", - " 'electrode_label_numeric': 13,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 99},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec12',\n", - " 'electrode_label_numeric': 12,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 100},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec9',\n", - " 'electrode_label_numeric': 9,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 101},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec8',\n", - " 'electrode_label_numeric': 8,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 102},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec7',\n", - " 'electrode_label_numeric': 7,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 103},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec6',\n", - " 'electrode_label_numeric': 6,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 104},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec5',\n", - " 'electrode_label_numeric': 5,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 105},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec4',\n", - " 'electrode_label_numeric': 4,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 106},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec3',\n", - " 'electrode_label_numeric': 3,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 107},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec2',\n", - " 'electrode_label_numeric': 2,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 108}],\n", - " 'workbook': ,\n", - " 'xls_path': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping '\n", - " 'and Impedance - 1025-0547,548,549/090609B-5H.xls'},\n", - " {'array_id': '2132-5/SN0656',\n", - " 'comment_lines': {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},\n", - " 'description_row': [text:'Cerebus mapping for array 2132-5/SN0656',\n", - " empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " text:'elec',\n", - " empty:''],\n", - " 'mapping_worksheet': ,\n", - " 'mappings': [{'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec100',\n", - " 'electrode_label_numeric': 100,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 13},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec99',\n", - " 'electrode_label_numeric': 99,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 14},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec98',\n", - " 'electrode_label_numeric': 98,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 15},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec97',\n", - " 'electrode_label_numeric': 97,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 16},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec96',\n", - " 'electrode_label_numeric': 96,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 17},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec95',\n", - " 'electrode_label_numeric': 95,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 18},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec94',\n", - " 'electrode_label_numeric': 94,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 19},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec93',\n", - " 'electrode_label_numeric': 93,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 20},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec92',\n", - " 'electrode_label_numeric': 92,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 21},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec90',\n", - " 'electrode_label_numeric': 90,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 22},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec88',\n", - " 'electrode_label_numeric': 88,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 23},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec87',\n", - " 'electrode_label_numeric': 87,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 24},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec86',\n", - " 'electrode_label_numeric': 86,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 25},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec85',\n", - " 'electrode_label_numeric': 85,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 26},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec84',\n", - " 'electrode_label_numeric': 84,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 27},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec83',\n", - " 'electrode_label_numeric': 83,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 28},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec82',\n", - " 'electrode_label_numeric': 82,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 29},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec81',\n", - " 'electrode_label_numeric': 81,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 30},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec80',\n", - " 'electrode_label_numeric': 80,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 31},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec79',\n", - " 'electrode_label_numeric': 79,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 32},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec78',\n", - " 'electrode_label_numeric': 78,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 33},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec77',\n", - " 'electrode_label_numeric': 77,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 34},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec76',\n", - " 'electrode_label_numeric': 76,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 35},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec75',\n", - " 'electrode_label_numeric': 75,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 36},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec74',\n", - " 'electrode_label_numeric': 74,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 37},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec73',\n", - " 'electrode_label_numeric': 73,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 38},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec72',\n", - " 'electrode_label_numeric': 72,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 39},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec71',\n", - " 'electrode_label_numeric': 71,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 40},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec70',\n", - " 'electrode_label_numeric': 70,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 41},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec69',\n", - " 'electrode_label_numeric': 69,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 42},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec68',\n", - " 'electrode_label_numeric': 68,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 43},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec67',\n", - " 'electrode_label_numeric': 67,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 44},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec66',\n", - " 'electrode_label_numeric': 66,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 45},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec65',\n", - " 'electrode_label_numeric': 65,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 46},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec64',\n", - " 'electrode_label_numeric': 64,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 47},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec63',\n", - " 'electrode_label_numeric': 63,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 48},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec62',\n", - " 'electrode_label_numeric': 62,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 49},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec61',\n", - " 'electrode_label_numeric': 61,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 50},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec60',\n", - " 'electrode_label_numeric': 60,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 51},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec59',\n", - " 'electrode_label_numeric': 59,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 52},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec58',\n", - " 'electrode_label_numeric': 58,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 53},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec57',\n", - " 'electrode_label_numeric': 57,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 54},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec56',\n", - " 'electrode_label_numeric': 56,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 55},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec55',\n", - " 'electrode_label_numeric': 55,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 56},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec54',\n", - " 'electrode_label_numeric': 54,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 57},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec53',\n", - " 'electrode_label_numeric': 53,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 58},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec52',\n", - " 'electrode_label_numeric': 52,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 59},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec51',\n", - " 'electrode_label_numeric': 51,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 60},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec49',\n", - " 'electrode_label_numeric': 49,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 61},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec48',\n", - " 'electrode_label_numeric': 48,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 62},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec47',\n", - " 'electrode_label_numeric': 47,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 63},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec46',\n", - " 'electrode_label_numeric': 46,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 64},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec45',\n", - " 'electrode_label_numeric': 45,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 65},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec44',\n", - " 'electrode_label_numeric': 44,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 66},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec43',\n", - " 'electrode_label_numeric': 43,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 67},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec42',\n", - " 'electrode_label_numeric': 42,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 68},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec41',\n", - " 'electrode_label_numeric': 41,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 69},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec40',\n", - " 'electrode_label_numeric': 40,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 70},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec39',\n", - " 'electrode_label_numeric': 39,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 71},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec38',\n", - " 'electrode_label_numeric': 38,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 72},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec37',\n", - " 'electrode_label_numeric': 37,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 73},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec36',\n", - " 'electrode_label_numeric': 36,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 74},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec35',\n", - " 'electrode_label_numeric': 35,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 75},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec34',\n", - " 'electrode_label_numeric': 34,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 76},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec33',\n", - " 'electrode_label_numeric': 33,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 77},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec32',\n", - " 'electrode_label_numeric': 32,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 78},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec31',\n", - " 'electrode_label_numeric': 31,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 79},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec30',\n", - " 'electrode_label_numeric': 30,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 80},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec29',\n", - " 'electrode_label_numeric': 29,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 81},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec28',\n", - " 'electrode_label_numeric': 28,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 82},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec27',\n", - " 'electrode_label_numeric': 27,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 83},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec26',\n", - " 'electrode_label_numeric': 26,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 84},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec25',\n", - " 'electrode_label_numeric': 25,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 85},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec24',\n", - " 'electrode_label_numeric': 24,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 86},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec23',\n", - " 'electrode_label_numeric': 23,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 87},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec22',\n", - " 'electrode_label_numeric': 22,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 88},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec21',\n", - " 'electrode_label_numeric': 21,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 89},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec20',\n", - " 'electrode_label_numeric': 20,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 90},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec19',\n", - " 'electrode_label_numeric': 19,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 91},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec18',\n", - " 'electrode_label_numeric': 18,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 92},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec17',\n", - " 'electrode_label_numeric': 17,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 93},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec16',\n", - " 'electrode_label_numeric': 16,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 94},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec15',\n", - " 'electrode_label_numeric': 15,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 95},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec14',\n", - " 'electrode_label_numeric': 14,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 96},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec13',\n", - " 'electrode_label_numeric': 13,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 97},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec12',\n", - " 'electrode_label_numeric': 12,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 98},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec11',\n", - " 'electrode_label_numeric': 11,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 99},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec10',\n", - " 'electrode_label_numeric': 10,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 100},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec9',\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 'electrode_label_numeric': 9,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 101},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec8',\n", - " 'electrode_label_numeric': 8,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 102},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec7',\n", - " 'electrode_label_numeric': 7,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 103},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec6',\n", - 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" 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 18},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec93',\n", - " 'electrode_label_numeric': 93,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 19},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec92',\n", - " 'electrode_label_numeric': 92,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 20},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec91',\n", - " 'electrode_label_numeric': 91,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 21},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec90',\n", - " 'electrode_label_numeric': 90,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 22},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec89',\n", - " 'electrode_label_numeric': 89,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 23},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec88',\n", - " 'electrode_label_numeric': 88,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 24},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec87',\n", - " 'electrode_label_numeric': 87,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 25},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec86',\n", - " 'electrode_label_numeric': 86,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 26},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec85',\n", - " 'electrode_label_numeric': 85,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 27},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec84',\n", - " 'electrode_label_numeric': 84,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 28},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec83',\n", - " 'electrode_label_numeric': 83,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 29},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec82',\n", - " 'electrode_label_numeric': 82,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 30},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec81',\n", - " 'electrode_label_numeric': 81,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 31},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec80',\n", - " 'electrode_label_numeric': 80,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 32},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec79',\n", - " 'electrode_label_numeric': 79,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 33},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec78',\n", - " 'electrode_label_numeric': 78,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 34},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec77',\n", - 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" 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 43},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec67',\n", - " 'electrode_label_numeric': 67,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 44},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec66',\n", - " 'electrode_label_numeric': 66,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 45},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec65',\n", - " 'electrode_label_numeric': 65,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 46},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec64',\n", - " 'electrode_label_numeric': 64,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 47},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec63',\n", - " 'electrode_label_numeric': 63,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 48},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec62',\n", - " 'electrode_label_numeric': 62,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 49},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec61',\n", - " 'electrode_label_numeric': 61,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 50},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec60',\n", - " 'electrode_label_numeric': 60,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 51},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec59',\n", - " 'electrode_label_numeric': 59,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 52},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec58',\n", - " 'electrode_label_numeric': 58,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 53},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec57',\n", - " 'electrode_label_numeric': 57,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 54},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec56',\n", - " 'electrode_label_numeric': 56,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 55},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec55',\n", - " 'electrode_label_numeric': 55,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 56},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec54',\n", - " 'electrode_label_numeric': 54,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 57},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec53',\n", - " 'electrode_label_numeric': 53,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 58},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec52',\n", - " 'electrode_label_numeric': 52,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 59},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec51',\n", - " 'electrode_label_numeric': 51,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 60},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec50',\n", - " 'electrode_label_numeric': 50,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 61},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec49',\n", - " 'electrode_label_numeric': 49,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 62},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec48',\n", - " 'electrode_label_numeric': 48,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 63},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec47',\n", - " 'electrode_label_numeric': 47,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 64},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec46',\n", - " 'electrode_label_numeric': 46,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 65},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec45',\n", - " 'electrode_label_numeric': 45,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 66},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec44',\n", - " 'electrode_label_numeric': 44,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 67},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec43',\n", - " 'electrode_label_numeric': 43,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 68},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec42',\n", - " 'electrode_label_numeric': 42,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 69},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec41',\n", - " 'electrode_label_numeric': 41,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 70},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec40',\n", - " 'electrode_label_numeric': 40,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 71},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec39',\n", - " 'electrode_label_numeric': 39,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 72},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec38',\n", - " 'electrode_label_numeric': 38,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 73},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec37',\n", - " 'electrode_label_numeric': 37,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 74},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec36',\n", - " 'electrode_label_numeric': 36,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 75},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec35',\n", - " 'electrode_label_numeric': 35,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 76},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec34',\n", - " 'electrode_label_numeric': 34,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 77},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec33',\n", - " 'electrode_label_numeric': 33,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 78},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec32',\n", - " 'electrode_label_numeric': 32,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 79},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec31',\n", - " 'electrode_label_numeric': 31,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 80},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec30',\n", - " 'electrode_label_numeric': 30,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 81},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec29',\n", - " 'electrode_label_numeric': 29,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 82},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec28',\n", - " 'electrode_label_numeric': 28,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 83},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec27',\n", - " 'electrode_label_numeric': 27,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 84},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec26',\n", - " 'electrode_label_numeric': 26,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 85},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec25',\n", - " 'electrode_label_numeric': 25,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 86},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec24',\n", - " 'electrode_label_numeric': 24,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 87},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec23',\n", - " 'electrode_label_numeric': 23,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 88},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec22',\n", - " 'electrode_label_numeric': 22,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 89},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec21',\n", - " 'electrode_label_numeric': 21,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 90},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec20',\n", - " 'electrode_label_numeric': 20,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 91},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec19',\n", - " 'electrode_label_numeric': 19,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 92},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec18',\n", - " 'electrode_label_numeric': 18,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 93},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec17',\n", - " 'electrode_label_numeric': 17,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 94},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec16',\n", - " 'electrode_label_numeric': 16,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 95},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec15',\n", - " 'electrode_label_numeric': 15,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 96},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec14',\n", - " 'electrode_label_numeric': 14,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 97},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec13',\n", - " 'electrode_label_numeric': 13,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 98},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec12',\n", - " 'electrode_label_numeric': 12,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 99},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec11',\n", - " 'electrode_label_numeric': 11,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 100},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec9',\n", - " 'electrode_label_numeric': 9,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 101},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec8',\n", - " 'electrode_label_numeric': 8,\n", - 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" empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " text:'elec',\n", - " empty:''],\n", - " 'mapping_worksheet': ,\n", - " 'mappings': [{'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec100',\n", - " 'electrode_label_numeric': 100,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 13},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec99',\n", - " 'electrode_label_numeric': 99,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 14},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec98',\n", - " 'electrode_label_numeric': 98,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 15},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec97',\n", - " 'electrode_label_numeric': 97,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 16},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec95',\n", - " 'electrode_label_numeric': 95,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 17},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec94',\n", - " 'electrode_label_numeric': 94,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 18},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec93',\n", - " 'electrode_label_numeric': 93,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 19},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec92',\n", - " 'electrode_label_numeric': 92,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 20},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec91',\n", - " 'electrode_label_numeric': 91,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 21},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec90',\n", - " 'electrode_label_numeric': 90,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 22},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec89',\n", - " 'electrode_label_numeric': 89,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 23},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec88',\n", - " 'electrode_label_numeric': 88,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 24},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec87',\n", - " 'electrode_label_numeric': 87,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 25},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec86',\n", - " 'electrode_label_numeric': 86,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 26},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec85',\n", - " 'electrode_label_numeric': 85,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 27},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec84',\n", - " 'electrode_label_numeric': 84,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 28},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec83',\n", - " 'electrode_label_numeric': 83,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 29},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec82',\n", - " 'electrode_label_numeric': 82,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 30},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec81',\n", - " 'electrode_label_numeric': 81,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 31},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec80',\n", - " 'electrode_label_numeric': 80,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 32},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec79',\n", - " 'electrode_label_numeric': 79,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 33},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec78',\n", - " 'electrode_label_numeric': 78,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 34},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec77',\n", - " 'electrode_label_numeric': 77,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 35},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec76',\n", - " 'electrode_label_numeric': 76,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 36},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec75',\n", - " 'electrode_label_numeric': 75,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 37},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec74',\n", - " 'electrode_label_numeric': 74,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 38},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec73',\n", - " 'electrode_label_numeric': 73,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 39},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec72',\n", - " 'electrode_label_numeric': 72,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 40},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec71',\n", - " 'electrode_label_numeric': 71,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 41},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec70',\n", - " 'electrode_label_numeric': 70,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 42},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec69',\n", - " 'electrode_label_numeric': 69,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 43},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec68',\n", - " 'electrode_label_numeric': 68,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 44},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec67',\n", - " 'electrode_label_numeric': 67,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 45},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec66',\n", - " 'electrode_label_numeric': 66,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 46},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec65',\n", - " 'electrode_label_numeric': 65,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 47},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec64',\n", - " 'electrode_label_numeric': 64,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 48},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec63',\n", - " 'electrode_label_numeric': 63,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 49},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec62',\n", - " 'electrode_label_numeric': 62,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 50},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec61',\n", - " 'electrode_label_numeric': 61,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 51},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec60',\n", - " 'electrode_label_numeric': 60,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 52},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec59',\n", - " 'electrode_label_numeric': 59,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 53},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec58',\n", - " 'electrode_label_numeric': 58,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 54},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec56',\n", - " 'electrode_label_numeric': 56,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 55},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec55',\n", - " 'electrode_label_numeric': 55,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 56},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec54',\n", - " 'electrode_label_numeric': 54,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 57},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec53',\n", - " 'electrode_label_numeric': 53,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 58},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec52',\n", - " 'electrode_label_numeric': 52,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 59},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec51',\n", - " 'electrode_label_numeric': 51,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 60},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec50',\n", - " 'electrode_label_numeric': 50,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 61},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec49',\n", - " 'electrode_label_numeric': 49,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 62},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec48',\n", - " 'electrode_label_numeric': 48,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 63},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec47',\n", - " 'electrode_label_numeric': 47,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 64},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec46',\n", - " 'electrode_label_numeric': 46,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 65},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec45',\n", - " 'electrode_label_numeric': 45,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 66},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec44',\n", - " 'electrode_label_numeric': 44,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 67},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec43',\n", - " 'electrode_label_numeric': 43,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 68},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec42',\n", - " 'electrode_label_numeric': 42,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 69},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec41',\n", - " 'electrode_label_numeric': 41,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 70},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec40',\n", - " 'electrode_label_numeric': 40,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 71},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec39',\n", - " 'electrode_label_numeric': 39,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 72},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec38',\n", - " 'electrode_label_numeric': 38,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 73},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec37',\n", - " 'electrode_label_numeric': 37,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 74},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec36',\n", - " 'electrode_label_numeric': 36,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 75},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec35',\n", - " 'electrode_label_numeric': 35,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 76},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec34',\n", - " 'electrode_label_numeric': 34,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 77},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec33',\n", - " 'electrode_label_numeric': 33,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 78},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec32',\n", - " 'electrode_label_numeric': 32,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 79},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec31',\n", - " 'electrode_label_numeric': 31,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 80},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec30',\n", - " 'electrode_label_numeric': 30,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 81},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec29',\n", - " 'electrode_label_numeric': 29,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 82},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec28',\n", - " 'electrode_label_numeric': 28,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 83},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec27',\n", - " 'electrode_label_numeric': 27,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 84},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec26',\n", - " 'electrode_label_numeric': 26,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 85},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec25',\n", - " 'electrode_label_numeric': 25,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 86},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec24',\n", - " 'electrode_label_numeric': 24,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 87},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec23',\n", - " 'electrode_label_numeric': 23,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 88},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec22',\n", - " 'electrode_label_numeric': 22,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 89},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec20',\n", - " 'electrode_label_numeric': 20,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 90},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec19',\n", - " 'electrode_label_numeric': 19,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 91},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec18',\n", - " 'electrode_label_numeric': 18,\n", - 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" 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 96},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec13',\n", - " 'electrode_label_numeric': 13,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 97},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec11',\n", - " 'electrode_label_numeric': 11,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 98},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec10',\n", - " 'electrode_label_numeric': 10,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 99},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec9',\n", - " 'electrode_label_numeric': 9,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 100},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec8',\n", - " 'electrode_label_numeric': 8,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 101},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec7',\n", - " 'electrode_label_numeric': 7,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 102},\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " {'bank_name': 'C',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec6',\n", - " 'electrode_label_numeric': 6,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 103},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec5',\n", - " 'electrode_label_numeric': 5,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 104},\n", - 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" 'and Impedance - 1025-0658, 0656, 0657/2466-1-SN0658.xls'},\n", - " {'array_id': '090609B-4F/SN0559',\n", - " 'comment_lines': {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},\n", - " 'description_row': [text:'Cerebus mapping for array 090609B-4F/SN0559',\n", - " empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " text:'elec',\n", - " empty:''],\n", - " 'mapping_worksheet': ,\n", - " 'mappings': [{'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec99',\n", - " 'electrode_label_numeric': 99,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 13},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec98',\n", - " 'electrode_label_numeric': 98,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 14},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec97',\n", - " 'electrode_label_numeric': 97,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 15},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec96',\n", - " 'electrode_label_numeric': 96,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 16},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec95',\n", - " 'electrode_label_numeric': 95,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 17},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec94',\n", - " 'electrode_label_numeric': 94,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 18},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec93',\n", - " 'electrode_label_numeric': 93,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 19},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec92',\n", - " 'electrode_label_numeric': 92,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 20},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec90',\n", - " 'electrode_label_numeric': 90,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 21},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec89',\n", - " 'electrode_label_numeric': 89,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 22},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec88',\n", - " 'electrode_label_numeric': 88,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 23},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec87',\n", - " 'electrode_label_numeric': 87,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 24},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec86',\n", - " 'electrode_label_numeric': 86,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 25},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec85',\n", - " 'electrode_label_numeric': 85,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 26},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec84',\n", - " 'electrode_label_numeric': 84,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 27},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec83',\n", - " 'electrode_label_numeric': 83,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 28},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec82',\n", - " 'electrode_label_numeric': 82,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 29},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec81',\n", - " 'electrode_label_numeric': 81,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 30},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec80',\n", - " 'electrode_label_numeric': 80,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 31},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec79',\n", - " 'electrode_label_numeric': 79,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 32},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec78',\n", - " 'electrode_label_numeric': 78,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 33},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec77',\n", - " 'electrode_label_numeric': 77,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 34},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec76',\n", - " 'electrode_label_numeric': 76,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 35},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec75',\n", - " 'electrode_label_numeric': 75,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 36},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec74',\n", - " 'electrode_label_numeric': 74,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 37},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec73',\n", - " 'electrode_label_numeric': 73,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 38},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec72',\n", - " 'electrode_label_numeric': 72,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 39},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec71',\n", - " 'electrode_label_numeric': 71,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 40},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec70',\n", - " 'electrode_label_numeric': 70,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 41},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec69',\n", - " 'electrode_label_numeric': 69,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 42},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec68',\n", - " 'electrode_label_numeric': 68,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 43},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec67',\n", - " 'electrode_label_numeric': 67,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 44},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec66',\n", - " 'electrode_label_numeric': 66,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 45},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec65',\n", - " 'electrode_label_numeric': 65,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 46},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec64',\n", - " 'electrode_label_numeric': 64,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 47},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec63',\n", - " 'electrode_label_numeric': 63,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 48},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec62',\n", - " 'electrode_label_numeric': 62,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 49},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec61',\n", - " 'electrode_label_numeric': 61,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 50},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec60',\n", - " 'electrode_label_numeric': 60,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 51},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec59',\n", - " 'electrode_label_numeric': 59,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 52},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec58',\n", - " 'electrode_label_numeric': 58,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 53},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec57',\n", - " 'electrode_label_numeric': 57,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 54},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec56',\n", - " 'electrode_label_numeric': 56,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 55},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec55',\n", - " 'electrode_label_numeric': 55,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 56},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec54',\n", - " 'electrode_label_numeric': 54,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 57},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec53',\n", - " 'electrode_label_numeric': 53,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 58},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec52',\n", - " 'electrode_label_numeric': 52,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 59},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec51',\n", - " 'electrode_label_numeric': 51,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 60},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec50',\n", - " 'electrode_label_numeric': 50,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 61},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec49',\n", - " 'electrode_label_numeric': 49,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 62},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec48',\n", - " 'electrode_label_numeric': 48,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 63},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec47',\n", - " 'electrode_label_numeric': 47,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 64},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec46',\n", - " 'electrode_label_numeric': 46,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 65},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec45',\n", - 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" {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec32',\n", - " 'electrode_label_numeric': 32,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 79},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec31',\n", - " 'electrode_label_numeric': 31,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 80},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec30',\n", - " 'electrode_label_numeric': 30,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 81},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec29',\n", - " 'electrode_label_numeric': 29,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 82},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - 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" {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec11',\n", - " 'electrode_label_numeric': 11,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 100},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec9',\n", - " 'electrode_label_numeric': 9,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 101},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec8',\n", - " 'electrode_label_numeric': 8,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 102},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec7',\n", - " 'electrode_label_numeric': 7,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 103},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 5,\n", - 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" 'electrode_label': 'elec98',\n", - " 'electrode_label_numeric': 98,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 14},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec97',\n", - " 'electrode_label_numeric': 97,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 15},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec96',\n", - " 'electrode_label_numeric': 96,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 16},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec95',\n", - " 'electrode_label_numeric': 95,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 17},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec94',\n", - " 'electrode_label_numeric': 94,\n", - 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" 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 22},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec88',\n", - " 'electrode_label_numeric': 88,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 23},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec87',\n", - " 'electrode_label_numeric': 87,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 24},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec86',\n", - " 'electrode_label_numeric': 86,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 25},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec85',\n", - " 'electrode_label_numeric': 85,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 26},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec84',\n", - " 'electrode_label_numeric': 84,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 27},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec83',\n", - " 'electrode_label_numeric': 83,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 28},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec82',\n", - " 'electrode_label_numeric': 82,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 29},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec81',\n", - " 'electrode_label_numeric': 81,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 30},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec80',\n", - " 'electrode_label_numeric': 80,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 31},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec79',\n", - " 'electrode_label_numeric': 79,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 32},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec78',\n", - " 'electrode_label_numeric': 78,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 33},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec77',\n", - " 'electrode_label_numeric': 77,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 34},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec76',\n", - " 'electrode_label_numeric': 76,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 35},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec75',\n", - " 'electrode_label_numeric': 75,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 36},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec74',\n", - " 'electrode_label_numeric': 74,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 37},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec73',\n", - " 'electrode_label_numeric': 73,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 38},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec72',\n", - " 'electrode_label_numeric': 72,\n", - 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" 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 43},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec67',\n", - " 'electrode_label_numeric': 67,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 44},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec66',\n", - " 'electrode_label_numeric': 66,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 45},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec65',\n", - " 'electrode_label_numeric': 65,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 46},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec64',\n", - " 'electrode_label_numeric': 64,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 47},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec63',\n", - " 'electrode_label_numeric': 63,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 48},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec62',\n", - " 'electrode_label_numeric': 62,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 49},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec61',\n", - " 'electrode_label_numeric': 61,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 50},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec60',\n", - " 'electrode_label_numeric': 60,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 51},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec59',\n", - " 'electrode_label_numeric': 59,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 52},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec58',\n", - " 'electrode_label_numeric': 58,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 53},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec57',\n", - " 'electrode_label_numeric': 57,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 54},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec56',\n", - " 'electrode_label_numeric': 56,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 55},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec55',\n", - " 'electrode_label_numeric': 55,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 56},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec54',\n", - " 'electrode_label_numeric': 54,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 57},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec53',\n", - " 'electrode_label_numeric': 53,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 58},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec52',\n", - " 'electrode_label_numeric': 52,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 59},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec51',\n", - " 'electrode_label_numeric': 51,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 60},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec50',\n", - " 'electrode_label_numeric': 50,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 61},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec49',\n", - " 'electrode_label_numeric': 49,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 62},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec48',\n", - " 'electrode_label_numeric': 48,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 63},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec47',\n", - " 'electrode_label_numeric': 47,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 64},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec46',\n", - " 'electrode_label_numeric': 46,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 65},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec45',\n", - " 'electrode_label_numeric': 45,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 66},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec44',\n", - " 'electrode_label_numeric': 44,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 67},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec43',\n", - " 'electrode_label_numeric': 43,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 68},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec42',\n", - " 'electrode_label_numeric': 42,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 69},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec41',\n", - " 'electrode_label_numeric': 41,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 70},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec40',\n", - " 'electrode_label_numeric': 40,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 71},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec39',\n", - " 'electrode_label_numeric': 39,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 72},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec38',\n", - " 'electrode_label_numeric': 38,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 73},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec37',\n", - " 'electrode_label_numeric': 37,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 74},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec36',\n", - " 'electrode_label_numeric': 36,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 75},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec35',\n", - " 'electrode_label_numeric': 35,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 76},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec34',\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 'electrode_label_numeric': 34,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 77},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec33',\n", - " 'electrode_label_numeric': 33,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 78},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec32',\n", - " 'electrode_label_numeric': 32,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 79},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec31',\n", - " 'electrode_label_numeric': 31,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 80},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec30',\n", - " 'electrode_label_numeric': 30,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 81},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec29',\n", - " 'electrode_label_numeric': 29,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 82},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec28',\n", - " 'electrode_label_numeric': 28,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 83},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec27',\n", - " 'electrode_label_numeric': 27,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 84},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec26',\n", - " 'electrode_label_numeric': 26,\n", - 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" 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 89},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec21',\n", - " 'electrode_label_numeric': 21,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 90},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec20',\n", - " 'electrode_label_numeric': 20,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 91},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec19',\n", - " 'electrode_label_numeric': 19,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 92},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec18',\n", - " 'electrode_label_numeric': 18,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 93},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec17',\n", - " 'electrode_label_numeric': 17,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 94},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec16',\n", - " 'electrode_label_numeric': 16,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 95},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec15',\n", - " 'electrode_label_numeric': 15,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 96},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec14',\n", - " 'electrode_label_numeric': 14,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 97},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec13',\n", - " 'electrode_label_numeric': 13,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 98},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec12',\n", - " 'electrode_label_numeric': 12,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 99},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec11',\n", - " 'electrode_label_numeric': 11,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 100},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec9',\n", - " 'electrode_label_numeric': 9,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 101},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec8',\n", - 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" empty:'',\n", - " empty:'',\n", - " empty:'',\n", - " text:'elec',\n", - " empty:''],\n", - " 'mapping_worksheet': ,\n", - " 'mappings': [{'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec99',\n", - " 'electrode_label_numeric': 99,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 13},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec98',\n", - " 'electrode_label_numeric': 98,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 14},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec97',\n", - " 'electrode_label_numeric': 97,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 15},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec96',\n", - " 'electrode_label_numeric': 96,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 16},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec95',\n", - " 'electrode_label_numeric': 95,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 17},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec94',\n", - " 'electrode_label_numeric': 94,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 18},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec93',\n", - " 'electrode_label_numeric': 93,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 19},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec92',\n", - " 'electrode_label_numeric': 92,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 9,\n", - " 'index_in_mapping_sheet': 20},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec90',\n", - " 'electrode_label_numeric': 90,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 21},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec89',\n", - " 'electrode_label_numeric': 89,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 22},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec88',\n", - " 'electrode_label_numeric': 88,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 23},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec87',\n", - " 'electrode_label_numeric': 87,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 24},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec86',\n", - " 'electrode_label_numeric': 86,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 25},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec85',\n", - " 'electrode_label_numeric': 85,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 26},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec84',\n", - " 'electrode_label_numeric': 84,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 27},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec83',\n", - " 'electrode_label_numeric': 83,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 28},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec82',\n", - " 'electrode_label_numeric': 82,\n", - " 'electrode_number_in_bank': 2,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 29},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec81',\n", - " 'electrode_label_numeric': 81,\n", - " 'electrode_number_in_bank': 1,\n", - " 'electrode_row': 8,\n", - " 'index_in_mapping_sheet': 30},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec80',\n", - " 'electrode_label_numeric': 80,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 31},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec79',\n", - " 'electrode_label_numeric': 79,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 32},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec78',\n", - " 'electrode_label_numeric': 78,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 33},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec77',\n", - " 'electrode_label_numeric': 77,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 34},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec76',\n", - " 'electrode_label_numeric': 76,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 35},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec75',\n", - " 'electrode_label_numeric': 75,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 36},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec74',\n", - " 'electrode_label_numeric': 74,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 37},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec73',\n", - " 'electrode_label_numeric': 73,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 38},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec72',\n", - " 'electrode_label_numeric': 72,\n", - " 'electrode_number_in_bank': 4,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 39},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec71',\n", - " 'electrode_label_numeric': 71,\n", - " 'electrode_number_in_bank': 3,\n", - " 'electrode_row': 7,\n", - " 'index_in_mapping_sheet': 40},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec70',\n", - " 'electrode_label_numeric': 70,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 41},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec69',\n", - " 'electrode_label_numeric': 69,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 42},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec68',\n", - " 'electrode_label_numeric': 68,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 43},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec67',\n", - " 'electrode_label_numeric': 67,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 44},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec66',\n", - " 'electrode_label_numeric': 66,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 45},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec65',\n", - " 'electrode_label_numeric': 65,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 46},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec64',\n", - " 'electrode_label_numeric': 64,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 47},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec63',\n", - " 'electrode_label_numeric': 63,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 48},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec62',\n", - " 'electrode_label_numeric': 62,\n", - " 'electrode_number_in_bank': 6,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 49},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec61',\n", - " 'electrode_label_numeric': 61,\n", - " 'electrode_number_in_bank': 5,\n", - " 'electrode_row': 6,\n", - " 'index_in_mapping_sheet': 50},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec60',\n", - " 'electrode_label_numeric': 60,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 51},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec59',\n", - " 'electrode_label_numeric': 59,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 52},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec58',\n", - " 'electrode_label_numeric': 58,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 53},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec57',\n", - " 'electrode_label_numeric': 57,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 54},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec56',\n", - " 'electrode_label_numeric': 56,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 55},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec55',\n", - " 'electrode_label_numeric': 55,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 56},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec54',\n", - " 'electrode_label_numeric': 54,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 57},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec53',\n", - " 'electrode_label_numeric': 53,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 58},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec52',\n", - " 'electrode_label_numeric': 52,\n", - " 'electrode_number_in_bank': 8,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 59},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec51',\n", - " 'electrode_label_numeric': 51,\n", - " 'electrode_number_in_bank': 7,\n", - " 'electrode_row': 5,\n", - " 'index_in_mapping_sheet': 60},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec50',\n", - " 'electrode_label_numeric': 50,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 61},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec49',\n", - " 'electrode_label_numeric': 49,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 62},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec48',\n", - " 'electrode_label_numeric': 48,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 63},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec47',\n", - " 'electrode_label_numeric': 47,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 64},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec46',\n", - " 'electrode_label_numeric': 46,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 65},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec45',\n", - " 'electrode_label_numeric': 45,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 66},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec44',\n", - " 'electrode_label_numeric': 44,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 67},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec43',\n", - " 'electrode_label_numeric': 43,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 68},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec42',\n", - " 'electrode_label_numeric': 42,\n", - " 'electrode_number_in_bank': 10,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 69},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec41',\n", - " 'electrode_label_numeric': 41,\n", - " 'electrode_number_in_bank': 9,\n", - " 'electrode_row': 4,\n", - " 'index_in_mapping_sheet': 70},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec40',\n", - " 'electrode_label_numeric': 40,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 71},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec39',\n", - " 'electrode_label_numeric': 39,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 72},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec38',\n", - " 'electrode_label_numeric': 38,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 73},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec37',\n", - " 'electrode_label_numeric': 37,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 74},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec36',\n", - " 'electrode_label_numeric': 36,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 75},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec35',\n", - " 'electrode_label_numeric': 35,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 76},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec34',\n", - " 'electrode_label_numeric': 34,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 77},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec33',\n", - " 'electrode_label_numeric': 33,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 78},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec32',\n", - " 'electrode_label_numeric': 32,\n", - " 'electrode_number_in_bank': 12,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 79},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec31',\n", - " 'electrode_label_numeric': 31,\n", - " 'electrode_number_in_bank': 11,\n", - " 'electrode_row': 3,\n", - " 'index_in_mapping_sheet': 80},\n", - " {'bank_name': 'A',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec30',\n", - " 'electrode_label_numeric': 30,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 81},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec29',\n", - " 'electrode_label_numeric': 29,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 82},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec28',\n", - " 'electrode_label_numeric': 28,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 83},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec27',\n", - " 'electrode_label_numeric': 27,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 84},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec26',\n", - " 'electrode_label_numeric': 26,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 85},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec25',\n", - " 'electrode_label_numeric': 25,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 86},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec24',\n", - " 'electrode_label_numeric': 24,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 87},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec23',\n", - " 'electrode_label_numeric': 23,\n", - " 'electrode_number_in_bank': 18,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 88},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec22',\n", - " 'electrode_label_numeric': 22,\n", - " 'electrode_number_in_bank': 14,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 89},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec21',\n", - " 'electrode_label_numeric': 21,\n", - " 'electrode_number_in_bank': 13,\n", - " 'electrode_row': 2,\n", - " 'index_in_mapping_sheet': 90},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 9,\n", - " 'electrode_label': 'elec20',\n", - " 'electrode_label_numeric': 20,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 91},\n", - " {'bank_name': 'B',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec19',\n", - " 'electrode_label_numeric': 19,\n", - " 'electrode_number_in_bank': 31,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 92},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec18',\n", - " 'electrode_label_numeric': 18,\n", - " 'electrode_number_in_bank': 30,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 93},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec17',\n", - " 'electrode_label_numeric': 17,\n", - " 'electrode_number_in_bank': 27,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 94},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec16',\n", - " 'electrode_label_numeric': 16,\n", - " 'electrode_number_in_bank': 25,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 95},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec15',\n", - " 'electrode_label_numeric': 15,\n", - " 'electrode_number_in_bank': 23,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 96},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec14',\n", - " 'electrode_label_numeric': 14,\n", - " 'electrode_number_in_bank': 22,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 97},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec13',\n", - " 'electrode_label_numeric': 13,\n", - " 'electrode_number_in_bank': 20,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 98},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec12',\n", - " 'electrode_label_numeric': 12,\n", - " 'electrode_number_in_bank': 16,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 99},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 0,\n", - " 'electrode_label': 'elec11',\n", - " 'electrode_label_numeric': 11,\n", - " 'electrode_number_in_bank': 15,\n", - " 'electrode_row': 1,\n", - " 'index_in_mapping_sheet': 100},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 8,\n", - " 'electrode_label': 'elec9',\n", - " 'electrode_label_numeric': 9,\n", - " 'electrode_number_in_bank': 32,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 101},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 7,\n", - " 'electrode_label': 'elec8',\n", - " 'electrode_label_numeric': 8,\n", - " 'electrode_number_in_bank': 29,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 102},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 6,\n", - " 'electrode_label': 'elec7',\n", - " 'electrode_label_numeric': 7,\n", - " 'electrode_number_in_bank': 28,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 103},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 5,\n", - " 'electrode_label': 'elec6',\n", - " 'electrode_label_numeric': 6,\n", - " 'electrode_number_in_bank': 26,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 104},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 4,\n", - " 'electrode_label': 'elec5',\n", - " 'electrode_label_numeric': 5,\n", - " 'electrode_number_in_bank': 24,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 105},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 3,\n", - " 'electrode_label': 'elec4',\n", - " 'electrode_label_numeric': 4,\n", - " 'electrode_number_in_bank': 21,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 106},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 2,\n", - " 'electrode_label': 'elec3',\n", - " 'electrode_label_numeric': 3,\n", - " 'electrode_number_in_bank': 19,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 107},\n", - " {'bank_name': 'C',\n", - " 'electrode_column': 1,\n", - " 'electrode_label': 'elec2',\n", - " 'electrode_label_numeric': 2,\n", - " 'electrode_number_in_bank': 17,\n", - " 'electrode_row': 0,\n", - " 'index_in_mapping_sheet': 108}],\n", - " 'workbook': ,\n", - " 'xls_path': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping '\n", - " 'and Impedance - 1025-552,559,649/2467-6_SN0649.xls'}]\n" - ] - } - ], - "source": [ - "mfs = []\n", - "\n", - "for path in mf_paths:\n", - " mf = get_mapping_file(kf(path))\n", - " mfs.append(mf)\n", - "\n", - "pprint.pprint([vars(mf) for mf in mfs])\n", - "# mfs" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['5602-2/SN1025-000936',\n", - " '090609B-5E/SN00547',\n", - " '090609B-5F/SN0548',\n", - " '090609B-5H/SN0549',\n", - " '2132-5/SN0656',\n", - " '2132-8/SN0657',\n", - " '2466-1/SN0658',\n", - " '090609B-4F/SN0559',\n", - " '090609B-8E/SN0552',\n", - " '2467-6/SN0649']" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[x.array_id for x in mfs]" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [], - "source": [ - "try: \n", - " db\n", - "except NameError:\n", - " db_file = os.path.join(data_dir, dated_file_name(\"db\", \"array_meta\"))\n", - " db_url = \"sqlite:///\" + db_file\n", - " db = dataset.connect(db_url)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# del db\n", - "db" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n" - ] - } - ], - "source": [ - "tbl_array = db[\"array\"]\n", - "tbl_mapping = db[\"mapping\"]\n", - "for mf in mfs:\n", - " array_dict = {\"blackrock_id_from_xls\": mf.array_id, \"mapping_xls\":mf.xls_path}\n", - " array_id = tbl_array.upsert(array_dict, keys=[\"blackrock_id_from_xls\"])\n", - " if type(array_id) == bool:\n", - " array_id = tbl_array.find_one(blackrock_id_from_xls=mf.array_id)[\"id\"]\n", - " print(array_id)\n", - " for mapping in mf.mappings:\n", - " insert = dict(mapping)\n", - " insert[\"array_id\"] = array_id\n", - " tbl_mapping.upsert(insert, keys=[\"array_id\", \"electrode_column\", \"electrode_row\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [], - "source": [ - "# tbl_mapping.drop()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [], - "source": [ - "animals = [\n", - " {\n", - " \"name\": \"Tito\",\n", - " \"implants\": [\n", - " {\"hemisphere\": \"L\", \"position\": \"A\", \"blackrock_id_from_surgery_note\": \"2132-8/SN0657\",\n", - " \"info_source\": \"/braintree/data1/archive/animals/Tito/mindhive/dicarlolab/data/Animals/07-01 Tito/20110628_implant_left_hemisphere/surgery_note/surgery_note.pages\"},\n", - " {\"hemisphere\": \"L\", \"position\": \"M\", \"blackrock_id_from_surgery_note\": \"2467-6/SN0649\",\n", - " \"info_source\": \"/braintree/data1/archive/animals/Tito/mindhive/dicarlolab/data/Animals/07-01 Tito/20110628_implant_left_hemisphere/surgery_note/surgery_note.pages\"},\n", - " {\"hemisphere\": \"L\", \"position\": \"P\", \"blackrock_id_from_surgery_note\": \"2132-5/SN0656\",\n", - " \"info_source\": \"/braintree/data1/archive/animals/Tito/mindhive/dicarlolab/data/Animals/07-01 Tito/20110628_implant_left_hemisphere/surgery_note/surgery_note.pages\"},\n", - " ]\n", - " },\n", - " {\n", - " \"name\": \"Chabo\",\n", - " \"implants\": [\n", - " {\"hemisphere\": \"L\", \"position\": \"A\", \"blackrock_id_from_surgery_note\": \"SN1024-0547\",\n", - " \"info_source\": \"/braintree/data1/archive/animals/Chabo/mindhive/dicarlolab/data/Animals/07-02 Chabo/CHABO_010511_surgery_left/surgery_note/surgery_note.pages\"},\n", - " {\"hemisphere\": \"L\", \"position\": \"M\", \"blackrock_id_from_surgery_note\": \"SN1025-0549\",\n", - " \"info_source\": \"/braintree/data1/archive/animals/Chabo/mindhive/dicarlolab/data/Animals/07-02 Chabo/CHABO_010511_surgery_left/surgery_note/surgery_note.pages\"},\n", - " {\"hemisphere\": \"L\", \"position\": \"P\", \"blackrock_id_from_surgery_note\": \"SN1025-0552\",\n", - " \"info_source\": \"/braintree/data1/archive/animals/Chabo/mindhive/dicarlolab/data/Animals/07-02 Chabo/CHABO_010511_surgery_left/surgery_note/surgery_note.pages\"},\n", - " ]\n", - " }\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [], - "source": [ - "# I am just guessing.\n", - "blackrock_id_map = [\n", - " {\"blackrock_id_from_surgery_note\": \"2132-8/SN0657\", \"blackrock_id_from_xls\": \"2132-8/SN0657\"},\n", - " {\"blackrock_id_from_surgery_note\": \"2467-6/SN0649\", \"blackrock_id_from_xls\": \"2467-6/SN0649\"},\n", - " {\"blackrock_id_from_surgery_note\": \"2132-5/SN0656\", \"blackrock_id_from_xls\": \"2132-5/SN0656\"},\n", - " {\"blackrock_id_from_surgery_note\": \"SN1024-0547\", \"blackrock_id_from_xls\": \"090609B-5E/SN00547\"},\n", - " {\"blackrock_id_from_surgery_note\": \"SN1025-0549\", \"blackrock_id_from_xls\": \"090609B-5H/SN0549\"},\n", - " {\"blackrock_id_from_surgery_note\": \"SN1025-0552\", \"blackrock_id_from_xls\": \"090609B-8E/SN0552\"}\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n", - "2\n" - ] - } - ], - "source": [ - "tbl_animal = db[\"animal\"]\n", - "tbl_implant = db[\"implant\"]\n", - "for animal in animals:\n", - " animal_id = tbl_animal.upsert({\"name\": animal[\"name\"]}, keys=[\"name\"])\n", - " if type(animal_id) == bool:\n", - " animal_id = tbl_animal.find_one(name=animal[\"name\"])[\"id\"]\n", - " print(animal_id)\n", - " for implant in animal[\"implants\"]:\n", - " id_map = next(x for x in blackrock_id_map if x[\"blackrock_id_from_surgery_note\"]==implant[\"blackrock_id_from_surgery_note\"])\n", - " array_id = tbl_array.find_one(blackrock_id_from_xls=id_map[\"blackrock_id_from_xls\"])[\"id\"]\n", - " insert = {\n", - " \"animal_id\": animal_id, \n", - " \"array_id\": array_id, \n", - " \"hemisphere\": implant[\"hemisphere\"], \n", - " \"position\": implant[\"position\"],\n", - " \"info_source\": implant[\"info_source\"],\n", - " \"blackrock_id_from_surgery_note\": implant[\"blackrock_id_from_surgery_note\"]\n", - " }\n", - " tbl_implant.upsert(insert, keys=[\"animal_id\", \"array_id\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[OrderedDict([('id', 576),\n", - " ('name', 'Tito'),\n", - " ('animal_id', 1),\n", - " ('array_id', 6),\n", - " ('hemisphere', 'L'),\n", - " ('position', 'A'),\n", - " ('info_source',\n", - " '/braintree/data1/archive/animals/Tito/mindhive/dicarlolab/data/Animals/07-01 Tito/20110628_implant_left_hemisphere/surgery_note/surgery_note.pages'),\n", - " ('blackrock_id_from_surgery_note', '2132-8/SN0657'),\n", - " ('blackrock_id_from_xls', '2132-8/SN0657'),\n", - " ('mapping_xls',\n", - " '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls'),\n", - " ('electrode_column', 0),\n", - " ('electrode_row', 0),\n", - " ('bank_name', 'C'),\n", - " ('electrode_number_in_bank', 17),\n", - " ('electrode_label', 'elec1'),\n", - " ('electrode_label_numeric', 1),\n", - " ('index_in_mapping_sheet', 108)]),\n", - " OrderedDict([('id', 568),\n", - " ('name', 'Tito'),\n", - " ('animal_id', 1),\n", - " ('array_id', 6),\n", - " ('hemisphere', 'L'),\n", - " ('position', 'A'),\n", - " ('info_source',\n", - " '/braintree/data1/archive/animals/Tito/mindhive/dicarlolab/data/Animals/07-01 Tito/20110628_implant_left_hemisphere/surgery_note/surgery_note.pages'),\n", - " ('blackrock_id_from_surgery_note', '2132-8/SN0657'),\n", - " ('blackrock_id_from_xls', '2132-8/SN0657'),\n", - " ('mapping_xls',\n", - " '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls'),\n", - " ('electrode_column', 0),\n", - " ('electrode_row', 1),\n", - " ('bank_name', 'C'),\n", - " ('electrode_number_in_bank', 15),\n", - " ('electrode_label', 'elec11'),\n", - " ('electrode_label_numeric', 11),\n", - " ('index_in_mapping_sheet', 100)]),\n", - " OrderedDict([('id', 558),\n", - " ('name', 'Tito'),\n", - " ('animal_id', 1),\n", - " ('array_id', 6),\n", - " ('hemisphere', 'L'),\n", - " ('position', 'A'),\n", - " ('info_source',\n", - " '/braintree/data1/archive/animals/Tito/mindhive/dicarlolab/data/Animals/07-01 Tito/20110628_implant_left_hemisphere/surgery_note/surgery_note.pages'),\n", - " ('blackrock_id_from_surgery_note', '2132-8/SN0657'),\n", - " ('blackrock_id_from_xls', '2132-8/SN0657'),\n", - " ('mapping_xls',\n", - " '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls'),\n", - " ('electrode_column', 0),\n", - " ('electrode_row', 2),\n", - " ('bank_name', 'C'),\n", - " ('electrode_number_in_bank', 13),\n", - " ('electrode_label', 'elec21'),\n", - " ('electrode_label_numeric', 21),\n", - " ('index_in_mapping_sheet', 90)]),\n", - " OrderedDict([('id', 548),\n", - " ('name', 'Tito'),\n", - " ('animal_id', 1),\n", - " ('array_id', 6),\n", - " ('hemisphere', 'L'),\n", - " ('position', 'A'),\n", - " ('info_source',\n", - " '/braintree/data1/archive/animals/Tito/mindhive/dicarlolab/data/Animals/07-01 Tito/20110628_implant_left_hemisphere/surgery_note/surgery_note.pages'),\n", - " ('blackrock_id_from_surgery_note', '2132-8/SN0657'),\n", - " ('blackrock_id_from_xls', '2132-8/SN0657'),\n", - " ('mapping_xls',\n", - " '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls'),\n", - " ('electrode_column', 0),\n", - " ('electrode_row', 3),\n", - " ('bank_name', 'C'),\n", - " ('electrode_number_in_bank', 11),\n", - " ('electrode_label', 'elec31'),\n", - " ('electrode_label_numeric', 31),\n", - " ('index_in_mapping_sheet', 80)]),\n", - " OrderedDict([('id', 538),\n", - " ('name', 'Tito'),\n", - " ('animal_id', 1),\n", - " ('array_id', 6),\n", - " ('hemisphere', 'L'),\n", - " ('position', 'A'),\n", - " ('info_source',\n", - " '/braintree/data1/archive/animals/Tito/mindhive/dicarlolab/data/Animals/07-01 Tito/20110628_implant_left_hemisphere/surgery_note/surgery_note.pages'),\n", - " ('blackrock_id_from_surgery_note', '2132-8/SN0657'),\n", - " ('blackrock_id_from_xls', '2132-8/SN0657'),\n", - " ('mapping_xls',\n", - " '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls'),\n", - " ('electrode_column', 0),\n", - " ('electrode_row', 4),\n", - " ('bank_name', 'C'),\n", - " ('electrode_number_in_bank', 9),\n", - " ('electrode_label', 'elec41'),\n", - " ('electrode_label_numeric', 41),\n", - " ('index_in_mapping_sheet', 70)]),\n", - " OrderedDict([('id', 528),\n", - " ('name', 'Tito'),\n", - " ('animal_id', 1),\n", - " ('array_id', 6),\n", - " ('hemisphere', 'L'),\n", - " ('position', 'A'),\n", - " ('info_source',\n", - " '/braintree/data1/archive/animals/Tito/mindhive/dicarlolab/data/Animals/07-01 Tito/20110628_implant_left_hemisphere/surgery_note/surgery_note.pages'),\n", - " ('blackrock_id_from_surgery_note', '2132-8/SN0657'),\n", - " ('blackrock_id_from_xls', '2132-8/SN0657'),\n", - " ('mapping_xls',\n", - " '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls'),\n", - " ('electrode_column', 0),\n", - " ('electrode_row', 5),\n", - " ('bank_name', 'C'),\n", - " ('electrode_number_in_bank', 7),\n", - " ('electrode_label', 'elec51'),\n", - " ('electrode_label_numeric', 51),\n", - " ('index_in_mapping_sheet', 60)]),\n", - " OrderedDict([('id', 518),\n", - " ('name', 'Tito'),\n", - " ('animal_id', 1),\n", - " ('array_id', 6),\n", - " ('hemisphere', 'L'),\n", - " ('position', 'A'),\n", - " ('info_source',\n", - " '/braintree/data1/archive/animals/Tito/mindhive/dicarlolab/data/Animals/07-01 Tito/20110628_implant_left_hemisphere/surgery_note/surgery_note.pages'),\n", - " ('blackrock_id_from_surgery_note', '2132-8/SN0657'),\n", - " ('blackrock_id_from_xls', '2132-8/SN0657'),\n", - " ('mapping_xls',\n", - " '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls'),\n", - " ('electrode_column', 0),\n", - " ('electrode_row', 6),\n", - " ('bank_name', 'C'),\n", - " ('electrode_number_in_bank', 5),\n", - " ('electrode_label', 'elec61'),\n", - " ('electrode_label_numeric', 61),\n", - " ('index_in_mapping_sheet', 50)]),\n", - " OrderedDict([('id', 499),\n", - " ('name', 'Tito'),\n", - " ('animal_id', 1),\n", - " ('array_id', 6),\n", - " ('hemisphere', 'L'),\n", - " ('position', 'A'),\n", - " ('info_source',\n", - " '/braintree/data1/archive/animals/Tito/mindhive/dicarlolab/data/Animals/07-01 Tito/20110628_implant_left_hemisphere/surgery_note/surgery_note.pages'),\n", - " ('blackrock_id_from_surgery_note', '2132-8/SN0657'),\n", - " ('blackrock_id_from_xls', '2132-8/SN0657'),\n", - " ('mapping_xls',\n", - " '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls'),\n", - " ('electrode_column', 0),\n", - " ('electrode_row', 8),\n", - " ('bank_name', 'C'),\n", - " ('electrode_number_in_bank', 1),\n", - " ('electrode_label', 'elec81'),\n", - " ('electrode_label_numeric', 81),\n", - " ('index_in_mapping_sheet', 31)]),\n", - " OrderedDict([('id', 489),\n", - " ('name', 'Tito'),\n", - " ('animal_id', 1),\n", - " ('array_id', 6),\n", - " ('hemisphere', 'L'),\n", - " ('position', 'A'),\n", - " ('info_source',\n", - " '/braintree/data1/archive/animals/Tito/mindhive/dicarlolab/data/Animals/07-01 Tito/20110628_implant_left_hemisphere/surgery_note/surgery_note.pages'),\n", - " ('blackrock_id_from_surgery_note', '2132-8/SN0657'),\n", - " ('blackrock_id_from_xls', '2132-8/SN0657'),\n", - " ('mapping_xls',\n", - " '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls'),\n", - " ('electrode_column', 0),\n", - " ('electrode_row', 9),\n", - " ('bank_name', 'C'),\n", - " ('electrode_number_in_bank', 3),\n", - " ('electrode_label', 'elec91'),\n", - " ('electrode_label_numeric', 91),\n", - " ('index_in_mapping_sheet', 21)]),\n", - " OrderedDict([('id', 567),\n", - " ('name', 'Tito'),\n", - " ('animal_id', 1),\n", - " ('array_id', 6),\n", - " ('hemisphere', 'L'),\n", - 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" ('name', 'Chabo'),\n", - " ('animal_id', 2),\n", - " ('array_id', 9),\n", - " ('hemisphere', 'L'),\n", - " ('position', 'P'),\n", - " ('info_source',\n", - " '/braintree/data1/archive/animals/Chabo/mindhive/dicarlolab/data/Animals/07-02 Chabo/CHABO_010511_surgery_left/surgery_note/surgery_note.pages'),\n", - " ('blackrock_id_from_surgery_note', 'SN1025-0552'),\n", - " ('blackrock_id_from_xls', '090609B-8E/SN0552'),\n", - " ('mapping_xls',\n", - " '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-552,559,649/090609B-8E.xls'),\n", - " ('electrode_column', 9),\n", - " ('electrode_row', 6),\n", - " ('bank_name', 'A'),\n", - " ('electrode_number_in_bank', 24),\n", - " ('electrode_label', 'elec70'),\n", - " ('electrode_label_numeric', 70),\n", - " ('index_in_mapping_sheet', 41)]),\n", - " OrderedDict([('id', 787),\n", - " ('name', 'Chabo'),\n", - " ('animal_id', 2),\n", - " ('array_id', 9),\n", - " ('hemisphere', 'L'),\n", - " ('position', 'P'),\n", - " ('info_source',\n", - " '/braintree/data1/archive/animals/Chabo/mindhive/dicarlolab/data/Animals/07-02 Chabo/CHABO_010511_surgery_left/surgery_note/surgery_note.pages'),\n", - " ('blackrock_id_from_surgery_note', 'SN1025-0552'),\n", - " ('blackrock_id_from_xls', '090609B-8E/SN0552'),\n", - " ('mapping_xls',\n", - " '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-552,559,649/090609B-8E.xls'),\n", - " ('electrode_column', 9),\n", - " ('electrode_row', 7),\n", - " ('bank_name', 'A'),\n", - " ('electrode_number_in_bank', 22),\n", - " ('electrode_label', 'elec80'),\n", - " ('electrode_label_numeric', 80),\n", - " ('index_in_mapping_sheet', 31)]),\n", - " OrderedDict([('id', 777),\n", - " ('name', 'Chabo'),\n", - " ('animal_id', 2),\n", - " ('array_id', 9),\n", - " ('hemisphere', 'L'),\n", - " ('position', 'P'),\n", - " ('info_source',\n", - " '/braintree/data1/archive/animals/Chabo/mindhive/dicarlolab/data/Animals/07-02 Chabo/CHABO_010511_surgery_left/surgery_note/surgery_note.pages'),\n", - " ('blackrock_id_from_surgery_note', 'SN1025-0552'),\n", - " ('blackrock_id_from_xls', '090609B-8E/SN0552'),\n", - " ('mapping_xls',\n", - " '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-552,559,649/090609B-8E.xls'),\n", - " ('electrode_column', 9),\n", - " ('electrode_row', 8),\n", - " ('bank_name', 'A'),\n", - " ('electrode_number_in_bank', 18),\n", - " ('electrode_label', 'elec90'),\n", - " ('electrode_label_numeric', 90),\n", - " ('index_in_mapping_sheet', 21)])]" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sql_overview = (\n", - " \"SELECT \"\n", - " \"* \"\n", - " \"FROM \"\n", - " \"animal an \"\n", - " \"JOIN implant i ON i.animal_id = an.id \"\n", - " \"JOIN array ar ON i.array_id = ar.id \"\n", - " \"JOIN mapping m ON m.array_id = ar.id\"\n", - " \";\"\n", - ")\n", - "big_overview = list(db.query(sql_overview))\n", - "big_overview" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "576" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# should be 6 implanted arrays * 96 electrodes = 576\n", - "len(big_overview)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "inputHidden": false, - "outputHidden": false - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernel_info": { - "name": "mkgu_packaging" - }, - "kernelspec": { - "display_name": "mkgu_packaging", - "language": "python", - "name": "mkgu_packaging" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - }, - "nteract": { - "version": "0.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/packaging/notebooks/2018-02-13_Ha_data_from_Darren.ipynb b/packaging/notebooks/2018-02-13_Ha_data_from_Darren.ipynb deleted file mode 100644 index 3ff6796e0..000000000 --- a/packaging/notebooks/2018-02-13_Ha_data_from_Darren.ipynb +++ /dev/null @@ -1,15902 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr\n", - "import scipy.io as sio\n", - "import matplotlib.pyplot as plt\n", - "import mkgu" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dims_original = [\"images\", \"reps\", \"time\", \"units\"] # per Darren Seibert, email 2017-10-17\n", - "dims = [\"stimulus\", \"repetition\", \"time_bin\", \"neuroid\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Paths" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "for_jonas_dir = \"/braintree/data2/active/common/for_jonas\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "for_jjpr_dir = \"/braintree/data2/active/common/for_jjpr\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "data_dir = \"/braintree/home/jjpr/dev/scratch/mkgu_scratch/data\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load .mat files" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['/braintree/data2/active/common/for_jonas/Chabo_IT_A_HVM0.mat',\n", - " '/braintree/data2/active/common/for_jonas/Chabo_IT_A_HVM3.mat',\n", - " '/braintree/data2/active/common/for_jonas/Tito_IT_A_HVM0.mat',\n", - " '/braintree/data2/active/common/for_jonas/Tito_IT_A_HVM3.mat',\n", - " '/braintree/data2/active/common/for_jonas/Chabo_IT_A_HVM6.mat',\n", - " '/braintree/data2/active/common/for_jonas/Tito_IT_A_HVM6.mat',\n", - " '/braintree/data2/active/common/for_jonas/Chabo_IT_M_HVM0.mat',\n", - " '/braintree/data2/active/common/for_jonas/Chabo_IT_M_HVM3.mat',\n", - " '/braintree/data2/active/common/for_jonas/Tito_IT_M_HVM0.mat',\n", - " '/braintree/data2/active/common/for_jonas/Chabo_IT_M_HVM6.mat',\n", - " '/braintree/data2/active/common/for_jonas/Tito_IT_M_HVM3.mat',\n", - " '/braintree/data2/active/common/for_jonas/Tito_IT_M_HVM6.mat',\n", - " '/braintree/data2/active/common/for_jonas/TitoR_IT_A_HVM0.mat',\n", - " '/braintree/data2/active/common/for_jonas/Chabo_V4_HVM0.mat',\n", - " '/braintree/data2/active/common/for_jonas/Chabo_V4_HVM3.mat',\n", - " '/braintree/data2/active/common/for_jonas/Chabo_V4_HVM6.mat',\n", - " '/braintree/data2/active/common/for_jonas/TitoR_IT_A_HVM3.mat',\n", - " '/braintree/data2/active/common/for_jonas/TitoR_IT_A_HVM6.mat',\n", - " '/braintree/data2/active/common/for_jonas/TitoR_IT_M_HVM0.mat',\n", - " '/braintree/data2/active/common/for_jonas/TitoR_IT_M_HVM3.mat',\n", - " '/braintree/data2/active/common/for_jonas/TitoR_IT_M_HVM6.mat',\n", - " '/braintree/data2/active/common/for_jonas/TitoR_V4_HVM0.mat',\n", - " '/braintree/data2/active/common/for_jonas/TitoR_V4_HVM3.mat',\n", - " '/braintree/data2/active/common/for_jonas/TitoR_V4_HVM6.mat',\n", - " '/braintree/data2/active/common/for_jonas/Tito_V4_HVM0.mat',\n", - " '/braintree/data2/active/common/for_jonas/Tito_V4_HVM3.mat',\n", - " '/braintree/data2/active/common/for_jonas/Tito_V4_HVM6.mat']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "for_jonas_dir_ls = [os.path.join(for_jonas_dir, x) for x in os.listdir(for_jonas_dir)]\n", - "for_jonas_dir_ls" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "mats = {os.path.basename(mat_file)[:-4]: sio.loadmat(mat_file) for mat_file in for_jonas_dir_ls if mat_file.endswith(\".mat\")}" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Chabo_IT_A_HVM0': {'__globals__': [],\n", - " '__header__': b'MATLAB 5.0 MAT-file Platform: posix, Created on: Fri Jan 6 17:04:41 2017',\n", - " '__version__': '1.0',\n", - " 'bins': array([[[[1, 1, 0, ..., 1, 2, 1],\n", - " [0, 1, 1, ..., 2, 1, 0],\n", - " [1, 1, 1, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 2, ..., 2, 0, 1],\n", - " [1, 0, 1, ..., 1, 1, 0],\n", - " [0, 0, 1, ..., 0, 0, 1]],\n", - " \n", - " [[1, 1, 0, ..., 1, 0, 2],\n", - " [0, 0, 1, ..., 0, 1, 0],\n", - " [2, 0, 1, ..., 0, 1, 1],\n", - " ...,\n", - " [2, 1, 2, ..., 1, 0, 2],\n", - " [0, 0, 1, ..., 1, 0, 0],\n", - " [1, 0, 0, ..., 1, 0, 1]],\n", - " \n", - " [[1, 1, 1, ..., 2, 0, 3],\n", - " [1, 2, 0, ..., 1, 0, 0],\n", - " [1, 0, 1, ..., 2, 0, 2],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 1, 1],\n", - " [2, 0, 1, ..., 2, 0, 0],\n", - " [1, 0, 0, ..., 1, 0, 0]],\n", - " \n", - " ...,\n", - " \n", - " [[0, 0, 1, ..., 0, 2, 0],\n", - " [0, 0, 1, ..., 1, 0, 0],\n", - " [0, 0, 0, ..., 1, 0, 0],\n", - " ...,\n", - " [2, 1, 1, ..., 2, 0, 1],\n", - " [1, 0, 2, ..., 0, 0, 1],\n", - " [0, 1, 0, ..., 0, 1, 1]],\n", - " \n", - " [[1, 2, 1, ..., 1, 0, 0],\n", - " [1, 0, 1, ..., 0, 2, 1],\n", - " [0, 0, 0, ..., 0, 1, 1],\n", - " ...,\n", - " [0, 3, 2, ..., 2, 2, 2],\n", - " [0, 4, 1, ..., 1, 1, 2],\n", - " [1, 3, 0, ..., 0, 0, 1]],\n", - " \n", - " [[0, 2, 0, ..., 1, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 1],\n", - " [0, 1, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [2, 0, 2, ..., 1, 1, 0],\n", - " [0, 0, 0, ..., 0, 2, 1],\n", - " [1, 0, 0, ..., 2, 1, 0]]],\n", - " \n", - " \n", - " [[[2, 1, 0, ..., 1, 0, 0],\n", - " [0, 2, 0, ..., 1, 1, 1],\n", - " [1, 0, 1, ..., 0, 0, 0],\n", - " ...,\n", - " [1, 1, 0, ..., 1, 1, 1],\n", - " [0, 1, 1, ..., 0, 2, 1],\n", - " [0, 2, 2, ..., 0, 1, 0]],\n", - " \n", - " [[0, 0, 0, ..., 2, 0, 0],\n", - " [2, 0, 1, ..., 1, 0, 2],\n", - " [2, 0, 0, ..., 0, 1, 0],\n", - " ...,\n", - " [1, 1, 1, ..., 0, 1, 1],\n", - " [1, 0, 1, ..., 1, 0, 0],\n", - " [0, 2, 0, ..., 1, 1, 1]],\n", - " \n", - " [[0, 1, 0, ..., 1, 2, 0],\n", - " [1, 1, 1, ..., 1, 0, 2],\n", - " [0, 0, 1, ..., 0, 2, 0],\n", - " ...,\n", - " [1, 0, 2, ..., 1, 0, 1],\n", - " [1, 0, 0, ..., 1, 2, 0],\n", - " [0, 0, 1, ..., 0, 2, 0]],\n", - " \n", - " ...,\n", - " \n", - " [[0, 0, 0, ..., 1, 0, 1],\n", - " [1, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 1],\n", - " ...,\n", - " [2, 1, 0, ..., 0, 1, 0],\n", - " [0, 1, 0, ..., 0, 0, 0],\n", - " [1, 1, 0, ..., 0, 1, 0]],\n", - " \n", - " [[2, 2, 0, ..., 0, 1, 2],\n", - " [2, 2, 0, ..., 0, 1, 1],\n", - " [0, 2, 1, ..., 2, 1, 1],\n", - " ...,\n", - " [1, 1, 2, ..., 1, 0, 0],\n", - " [0, 0, 0, ..., 2, 0, 0],\n", - " [0, 0, 1, ..., 0, 1, 2]],\n", - " \n", - " [[0, 2, 1, ..., 0, 1, 0],\n", - " [1, 2, 1, ..., 0, 0, 2],\n", - " [0, 0, 0, ..., 1, 0, 1],\n", - " ...,\n", - " [0, 0, 2, ..., 0, 0, 1],\n", - " [0, 1, 0, ..., 0, 2, 0],\n", - " [0, 2, 0, ..., 1, 1, 1]]],\n", - " \n", - " \n", - " [[[1, 0, 0, ..., 0, 1, 1],\n", - " [0, 1, 1, ..., 1, 1, 0],\n", - " [0, 0, 0, ..., 1, 0, 0],\n", - " ...,\n", - " [1, 1, 1, ..., 0, 1, 1],\n", - " [3, 1, 0, ..., 1, 0, 1],\n", - " [2, 1, 0, ..., 1, 2, 0]],\n", - " \n", - " [[0, 0, 0, ..., 0, 1, 0],\n", - " [1, 0, 2, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 1, 0, ..., 1, 0, 2],\n", - " [1, 0, 1, ..., 1, 1, 0],\n", - " [0, 1, 0, ..., 1, 0, 2]],\n", - " \n", - " [[2, 1, 0, ..., 1, 0, 1],\n", - " [0, 0, 0, ..., 0, 1, 0],\n", - " [1, 0, 1, ..., 0, 0, 2],\n", - " ...,\n", - " [1, 1, 3, ..., 0, 0, 1],\n", - " [0, 0, 2, ..., 0, 2, 2],\n", - " [0, 1, 0, ..., 1, 1, 0]],\n", - " \n", - " ...,\n", - " \n", - " [[0, 0, 0, ..., 0, 1, 0],\n", - " [0, 0, 1, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [1, 0, 1, ..., 1, 1, 2],\n", - " [1, 1, 1, ..., 1, 0, 0],\n", - " [1, 0, 2, ..., 0, 0, 1]],\n", - " \n", - " [[0, 1, 0, ..., 0, 1, 2],\n", - " [0, 2, 1, ..., 1, 0, 1],\n", - " [0, 0, 1, ..., 1, 1, 0],\n", - " ...,\n", - " [1, 0, 0, ..., 0, 0, 0],\n", - " [0, 1, 1, ..., 0, 1, 1],\n", - " [0, 2, 0, ..., 0, 0, 1]],\n", - " \n", - " [[2, 1, 2, ..., 0, 1, 2],\n", - " [0, 1, 1, ..., 1, 1, 1],\n", - " [2, 0, 0, ..., 1, 1, 1],\n", - " ...,\n", - " [1, 0, 2, ..., 0, 1, 1],\n", - " [1, 0, 0, ..., 0, 1, 0],\n", - " [1, 0, 0, ..., 2, 0, 0]]],\n", - " \n", - " \n", - " ...,\n", - " \n", - " \n", - " [[[2, 0, 0, ..., 1, 0, 0],\n", - " [2, 1, 2, ..., 0, 0, 1],\n", - " [2, 1, 2, ..., 2, 1, 1],\n", - " ...,\n", - " [0, 1, 0, ..., 2, 1, 0],\n", - " [1, 0, 0, ..., 1, 1, 0],\n", - " [1, 1, 1, ..., 0, 0, 1]],\n", - " \n", - " [[1, 1, 2, ..., 1, 1, 0],\n", - " [2, 0, 1, ..., 1, 2, 1],\n", - " [0, 0, 0, ..., 0, 1, 0],\n", - " ...,\n", - " [0, 0, 1, ..., 1, 1, 1],\n", - " [2, 1, 0, ..., 0, 2, 0],\n", - " [0, 1, 0, ..., 1, 1, 2]],\n", - " \n", - " [[1, 0, 0, ..., 2, 0, 1],\n", - " [0, 0, 0, ..., 2, 0, 0],\n", - " [1, 1, 0, ..., 0, 1, 1],\n", - " ...,\n", - " [1, 0, 3, ..., 1, 0, 2],\n", - " [1, 0, 1, ..., 1, 1, 0],\n", - 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" [1, 2, 1, ..., 2, 0, 2]]],\n", - " \n", - " \n", - " [[[1, 0, 0, ..., 1, 1, 2],\n", - " [1, 1, 0, ..., 1, 0, 2],\n", - " [0, 1, 0, ..., 0, 0, 1],\n", - " ...,\n", - " [0, 0, 0, ..., 2, 1, 1],\n", - " [0, 0, 1, ..., 1, 0, 0],\n", - " [0, 1, 0, ..., 0, 2, 0]],\n", - " \n", - " [[0, 0, 0, ..., 0, 0, 2],\n", - " [1, 2, 1, ..., 1, 0, 1],\n", - " [0, 1, 1, ..., 0, 0, 2],\n", - " ...,\n", - " [1, 1, 1, ..., 0, 1, 1],\n", - " [0, 1, 0, ..., 0, 0, 1],\n", - " [2, 1, 2, ..., 0, 2, 0]],\n", - " \n", - " [[0, 3, 2, ..., 2, 2, 0],\n", - " [1, 1, 1, ..., 0, 1, 1],\n", - " [1, 3, 0, ..., 0, 1, 0],\n", - " ...,\n", - " [2, 1, 2, ..., 0, 1, 1],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [1, 2, 1, ..., 0, 1, 0]],\n", - " \n", - " ...,\n", - " \n", - " [[1, 1, 2, ..., 2, 1, 2],\n", - " [0, 0, 2, ..., 0, 0, 0],\n", - " [0, 1, 1, ..., 0, 1, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 2, 1, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 1, 1, ..., 1, 0, 2]],\n", - " \n", - " [[0, 0, 0, ..., 0, 0, 0],\n", - " [0, 1, 1, ..., 1, 1, 0],\n", - " [1, 0, 1, ..., 1, 0, 1],\n", - " ...,\n", - " [2, 1, 1, ..., 0, 0, 0],\n", - " [0, 1, 1, ..., 1, 0, 0],\n", - " [0, 0, 0, ..., 2, 2, 1]],\n", - " \n", - " [[1, 0, 1, ..., 0, 1, 1],\n", - " [1, 0, 0, ..., 1, 0, 0],\n", - " [0, 0, 2, ..., 1, 1, 0],\n", - " ...,\n", - " [0, 0, 1, ..., 2, 0, 0],\n", - " [1, 0, 1, ..., 0, 0, 1],\n", - " [1, 0, 0, ..., 0, 0, 1]]]], dtype=uint8),\n", - " 'orig_elecs': array([[16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 40,\n", - " 41, 42, 44, 48, 47, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,\n", - " 61, 62, 64, 87, 88, 91, 92, 94]]),\n", - " 'rep_file_names': array(['/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110304_RSVPVar00a_on100_off100_S110204_RefAA_001.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110304_RSVPVar00a_on100_off100_S110204_RefAA_002.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110305_RSVPVar00a_on100_off100_S110204_RefAA_001.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110305_RSVPVar00a_on100_off100_S110204_RefAA_002.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110305_RSVPVar00a_on100_off100_S110204_RefAA_003.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110306_RSVPVar00a_on100_off100_S110204_RefAA_001.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110306_RSVPVar00a_on100_off100_S110204_RefAA_002.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110308_RSVPVar00a_on100_off100_S110204_RefAA_001.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110308_RSVPVar00a_on100_off100_S110204_RefAA_002.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110308_RSVPVar00a_on100_off100_S110204_RefAA_003.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110308_RSVPVar00a_on100_off100_S110204_RefAA_004.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110313_RSVPVar00a_S110204_RefAA_001.mat ',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110313_RSVPVar00a_S110204_RefAA_002.mat ',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110318_RSVPVar00a_on100_off100_S110204_RefAA_001.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110318_RSVPVar00a_on100_off100_S110204_RefAA_002.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110318_RSVPVar00a_on100_off100_S110204_RefAA_003.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110318_RSVPVar00a_on100_off100_S110204_RefAA_004.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110318_RSVPVar00a_on100_off100_S110204_RefAA_005.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110318_RSVPVar00a_on100_off100_S110204_RefAA_006.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110318_RSVPVar00a_on100_off100_S110204_RefAA_007.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110318_RSVPVar00a_on100_off100_S110204_RefAA_008.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110318_RSVPVar00a_on100_off100_S110204_RefAA_009.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110318_RSVPVar00a_on100_off100_S110204_RefAA_010.mat',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110331_RSVPVar00a_S110204_RefAA_001.mat ',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110331_RSVPVar00a_S110204_RefAA_002.mat ',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110226_RSVPVar00a_S110204_001.mat ',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110226_RSVPVar00a_S110204_002.mat ',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110226_RSVPVar00a_S110204_003.mat ',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110226_RSVPVar00a_S110204_004.mat ',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110226_RSVPVar00a_S110204_005.mat ',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110226_RSVPVar00a_S110204_006.mat ',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110301_RSVPVar00a_S110204_001.mat ',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110301_RSVPVar00a_S110204_002.mat ',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110301_RSVPVar00a_S110204_003.mat ',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110301_RSVPVar00a_S110204_004.mat ',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110301_RSVPVar00a_S110204_005.mat ',\n", - " '/mindhive/dicarlolab/u/darren/binned_data/Chabo_20110301_RSVPVar00a_S110204_006.mat '],\n", - " dtype='\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mhashlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'dataset'" - ] - } - ], - "source": [ - "import dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def hash(path, buffer_size=64*2**10):\n", - " sha1 = hashlib.sha1()\n", - " with open(path, \"rb\") as f:\n", - " buffer = f.read(buffer_size)\n", - " while len(buffer) > 0:\n", - " sha1.update(buffer)\n", - " buffer = f.read(buffer_size)\n", - " return sha1.hexdigest()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "class KnownFile(object):\n", - " def __init__(self, path, persister=None):\n", - " self.path = path\n", - " self.exists = os.path.exists(path)\n", - " if self.exists:\n", - " self.realpath = os.path.realpath(path)\n", - " self.isfile = os.path.isfile(self.realpath)\n", - " if self.isfile:\n", - " self.sha1 = hash(self.realpath)\n", - " if persister is not None:\n", - " self.persister = persister\n", - " persister.persist(self)\n", - " \n", - " def __repr__(self):\n", - " return str((self.__class__.__name__, vars(self)))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "test_dir = \"/braintree/data2/active/common/for_jjpr\"" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "test_not_exists = \"/braintree/data2/active/common/for_jjpr/foobar\"" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "xls_files = [\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-000936/5602-2 SN1025-000936.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0547,548,549/090609B-5E.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0547,548,549/090609B-5F.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0547,548,549/090609B-5H.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-5-SN0656.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2466-1-SN0658.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-552,559,649/090609B-4F.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-552,559,649/090609B-8E.xls\",\n", - " \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-552,559,649/2467-6_SN0649.xls\" \n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Tito IT A is 657 per /braintree/data1/archive/animals/Tito/mindhive/dicarlolab/data/Animals/07-01 Tito/20110628_implant_left_hemisphere/surgery_note/surgery_note.pages\n", - "tito_it_a_xls_file = \"/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls\"" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "kf_xls = KnownFile(tito_it_a_xls_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('KnownFile', {'path': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls', 'exists': True, 'realpath': '/braintree/data2/active/users/darren/mindhive/dicarlolab/u/darren/mworks_array_xmls/preproc/orig_hvm/array_maps_from_blackrock/Mapping and Impedance - 1025-0658, 0656, 0657/2132-8-SN0657.xls', 'isfile': True, 'sha1': '7d705186a726776e9517269dafd01d6407d0ce2e'})" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kf_xls" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "def persister_setup(persister_class):\n", - " db = persister_db()\n", - " return persister_class(db)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def persister_db(db_file_path=None): \n", - " if db_file_path is None:\n", - " db_file_path = os.path.join(os.path.dirname(mkgu.__file__), \"knownfile.db\")\n", - " db_url = \"sqlite:///\" + db_file_path\n", - " db = dataset.connect(db_url)\n", - " return db" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "class Persister(object):\n", - " def __init__(self, db):\n", - " self.db = db\n", - " \n", - " def persist(self):\n", - " raise NotImplementedError()\n", - "\n", - " def keyed_upsert(self, tbl_name, keys, data):\n", - " tbl = self.db[tbl_name]\n", - " record_id = tbl.upsert(data, keys=keys)\n", - " if type(record_id) == bool:\n", - " record_id = tbl.find_one(**{key: data[key] for key in keys})[\"id\"]\n", - " return record_id " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "class KnownFilePersister(Persister):\n", - " def persist(self, knownfile):\n", - " data = {\n", - " \"realpath\": knownfile.realpath,\n", - " \"sha1\": knownfile.sha1\n", - " }\n", - " self.id = self.keyed_upsert(\"file\", [\"sha1\"], data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mkgu_packaging", - "language": "python", - "name": "mkgu_packaging" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/packaging/notebooks/2018-03-07_image_meta.ipynb b/packaging/notebooks/2018-03-07_image_meta.ipynb deleted file mode 100644 index 9d6d59fb8..000000000 --- a/packaging/notebooks/2018-03-07_image_meta.ipynb +++ /dev/null @@ -1,849 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "source": [ - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr\n", - "import scipy.io as sio\n", - "import matplotlib.pyplot as plt\n", - "import mkgu\n", - "import dataset\n", - "import peewee" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "hvm = mkgu.get_assembly(\"HvM\")\n", - "hvm" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "scrolled": true - } - }, - { - "cell_type": "code", - "source": [ - "hvm.indexes[\"presentation\"].names" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "hvm_stripped = hvm.copy()\n", - "hvm_stripped" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true - } - }, - { - "cell_type": "code", - "source": [ - "hvm_stripped.reset_index(\"object\", inplace=True)\n", - "hvm_stripped" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "scrolled": true - } - }, - { - "cell_type": "code", - "source": [ - "hvm_stripped.reset_coords(\"object\", inplace=True, drop=True)\n", - "hvm_stripped" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "scrolled": true - } - }, - { - "cell_type": "code", - "source": [ - "hvm_stripped.indexes[\"presentation\"].names" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "to_remove = ['ryz_semantic', 'variation', 'image_file_name', 'rxy_semantic', 'size', 'category', 'object', 'tz', 'ty', 'rxz_semantic', 'rxy', 'background_id', 's', 'rxz', 'ryz']\n", - "for level in to_remove:\n", - " if level in hvm_stripped.indexes[\"presentation\"].names:\n", - " hvm_stripped.reset_index(level, inplace=True)\n", - " hvm_stripped.reset_coords(level, inplace=True, drop=True)\n", - "hvm_stripped" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true - } - }, - { - "cell_type": "code", - "source": [ - "presentation_index = hvm.indexes[\"presentation\"]" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "presentation_index" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true - } - }, - { - "cell_type": "code", - "source": [ - "type(presentation_index)" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "to_keep = set(presentation_index.names).difference(to_remove)\n", - "to_keep" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "lookup = mkgu.fetch.SQLiteLookup()\n", - "lookup" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "lookup.db_file" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "def resolve(table, record_dict, keys):\n", - " record_id = table.upsert(record_dict, keys=keys)\n", - " if type(record_id) == bool:\n", - " record_id = table.find_one(**{key: record_dict[key] for key in keys})[\"id\"]\n", - " return record_id" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "pwd" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "pwdb = peewee.SqliteDatabase(\"image_meta_scratch.db\")" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "class Foo(peewee.Model):\n", - " bar = peewee.CharField()\n", - " baz = peewee.IntegerField()\n", - " class Meta:\n", - " database = pwdb" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "pwdb.connect()" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "pwdb.create_tables([Foo])" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "pwdb.database" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "foo_first = Foo(bar=\"Wow!\", baz=4)\n", - "foo_first.save()" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "for fields in [\n", - " (\"Zoom!\", 27),\n", - " (\"Bam!\", 777),\n", - " (\"Whoosh!\", 1000000002)\n", - "]:\n", - " Foo(**dict(zip((\"bar\", \"baz\"), fields))).save()" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "got = Foo.select()\n", - "got" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "[(x.id, x.bar, x.baz) for x in got]" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "wow = got[0]" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "wow.id" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "class Image(peewee.Model):\n", - " hash_id = peewee.CharField()\n", - " object_name = peewee.CharField()\n", - " category_name = peewee.CharField()\n", - " background_id = peewee.CharField()\n", - " image_file_name = peewee.CharField()\n", - " variation = peewee.IntegerField()\n", - " ty = peewee.FloatField()\n", - " tz = peewee.FloatField()\n", - " rxy = peewee.FloatField()\n", - " rxz = peewee.FloatField()\n", - " ryz = peewee.FloatField()\n", - " rxy_semantic = peewee.FloatField()\n", - " rxz_semantic = peewee.FloatField()\n", - " ryz_semantic = peewee.FloatField()\n", - " size = peewee.FloatField()\n", - " s = peewee.FloatField()\n", - "\n", - " class Meta:\n", - " database = pwdb" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "class StimulusSet(peewee.Model):\n", - " name = peewee.CharField()\n", - " \n", - " class Meta:\n", - " database = pwdb" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "class ImageStore(peewee.Model):\n", - " location = peewee.CharField()\n", - " location_type = peewee.CharField()\n", - " store_type = peewee.CharField()\n", - " \n", - " class Meta:\n", - " database = pwdb" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "class StimulusSetImageMap(peewee.Model):\n", - " stimulus_set = peewee.ForeignKeyField(StimulusSet, backref=\"stimulus_set_image_maps\")\n", - " image = peewee.ForeignKeyField(Image, backref=\"stimulus_set_image_maps\")\n", - " \n", - " class Meta:\n", - " database = pwdb" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "class ImageImageStoreMap(peewee.Model):\n", - " image_store = peewee.ForeignKeyField(ImageStore, backref=\"image_image_store_maps\")\n", - " image = peewee.ForeignKeyField(Image, backref=\"image_image_store_maps\")\n", - " path = peewee.CharField()\n", - " \n", - " class Meta:\n", - " database = pwdb " - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "pw_classes = [Image, StimulusSet, ImageStore, StimulusSetImageMap, ImageImageStoreMap]" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "pwdb.drop_tables(pw_classes)" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "pwdb.create_tables(pw_classes)" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "hvm_images = StimulusSet(name=\"HvM\")\n", - "hvm_images.save()" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "df_stimulus = hvm.indexes[\"presentation\"].to_frame()\n", - "df_stimulus" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "scrolled": true - } - }, - { - "cell_type": "code", - "source": [ - "import hashlib\n", - "fn = b\"TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.318_ty-01.053_s+00.921_5068064aee7a1831c3a5fe0f023b05cffca511e3_256x256\"\n", - "# fn = b\"TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.318_ty-01.053_s+00.921_5068064aee7a1831c3a5fe0f023b05cffca511e3\"\n", - "# fn = b\"TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.318_ty-01.053_s+00.921_5068064aee7a1831c3a5fe0f023b05cffca511e3_256x256.png\"\n", - "fhash = \"d15aa059b0180b200fb82fb8039247c3b53f9d66\"\n", - "hashlib.sha1(fn).hexdigest()" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "fpath = \"/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.318_ty-01.053_s+00.921_5068064aee7a1831c3a5fe0f023b05cffca511e3_256x256.png\"\n", - "hashlib.sha1(open(fpath, \"rb\").read()).hexdigest()" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "ls /braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "path_map = {\"V0\": \"Variation00_20110203\", \"V3\": \"Variation03_20110128\", \"V6\": \"Variation06_20110131\"}" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "hvm_image_store = ImageStore(location_type=\"S3\", store_type=\"zip\", location=\"http://dicarlocox-datasets.s3.amazonaws.com/HvM_with_discfade.zip\")\n", - "hvm_image_store.save()" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "scrolled": true - } - }, - { - "cell_type": "code", - "source": [ - "# pwdb.drop_tables([ImageStore])" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "df_images = df_stimulus[df_stimulus[\"variation\"]!=\"nan\"].drop(columns=[\"stimulus\", \"repetition\"]).drop_duplicates()\n", - "df_images" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "len(df_images[\"image_id\"].unique())" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "pw_images = []\n", - "for image in df_images.itertuples():\n", - " pw_image = Image(\n", - " hash_id=image.image_id,\n", - " object_name=image.object,\n", - " category_name=image.category,\n", - " background_id=image.background_id,\n", - " image_file_name=image.image_file_name,\n", - " variation=int(image.variation[-1]),\n", - " ty=image.ty,\n", - " tz=image.tz,\n", - " rxy=image.rxy,\n", - " rxz=image.rxz,\n", - " ryz=image.ryz,\n", - " rxy_semantic=image.rxy_semantic,\n", - " rxz_semantic=image.rxz_semantic,\n", - " ryz_semantic=image.ryz_semantic,\n", - " size=image.size,\n", - " s=image.s\n", - " )\n", - " pw_images.append(pw_image)\n", - " pw_image.save()\n", - " print(image.image_file_name, image.variation)" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true - } - }, - { - "cell_type": "code", - "source": [ - "images_query = Image.select()\n", - "images_query" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "image_query_0 = images_query[0]" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "len(images_query)" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "image_query_0.object_name" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "df_images.columns" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "df_images[\"variation\"].unique()" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "df_stimulus[df_stimulus[\"variation\"]!=\"nan\"]" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true - } - }, - { - "cell_type": "code", - "source": [ - "hvm.sel(variation=\"nan\")" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true - } - }, - { - "cell_type": "code", - "source": [ - "np.unique(hvm[\"variation\"].values)" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "hvm.loc[xr.ufuncs.logical_or(hvm[\"region\"] == \"V4\", hvm[\"region\"] == \"IT\")]\n", - "# xr.ufuncs.logical_or(hvm[\"region\"] == \"V4\", hvm[\"region\"] == \"IT\")" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true - } - }, - { - "cell_type": "code", - "source": [ - "Image.drop_table()\n", - "list(Image.select())" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true - } - }, - { - "cell_type": "code", - "source": [ - "Image.create_table()\n", - "list(Image.select())" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "pw_images = []\n", - "pw_stimulus_set_image_maps = []\n", - "pw_image_image_store_maps = []\n", - "for image in df_images.itertuples():\n", - " pw_image = Image(\n", - " hash_id=image.image_id,\n", - " object_name=image.object,\n", - " category_name=image.category,\n", - " background_id=image.background_id,\n", - " image_file_name=image.image_file_name,\n", - " variation=int(image.variation[-1]),\n", - " ty=image.ty,\n", - " tz=image.tz,\n", - " rxy=image.rxy,\n", - " rxz=image.rxz,\n", - " ryz=image.ryz,\n", - " rxy_semantic=image.rxy_semantic,\n", - " rxz_semantic=image.rxz_semantic,\n", - " ryz_semantic=image.ryz_semantic,\n", - " size=image.size,\n", - " s=image.s\n", - " )\n", - " pw_stimulus_set_image_map = StimulusSetImageMap(stimulus_set=hvm_images, image=pw_image)\n", - " pw_image_image_store_map = ImageImageStoreMap(image=pw_image, image_store=hvm_image_store, \n", - " path=path_map[image.variation])\n", - " \n", - " pw_images.append(pw_image)\n", - " pw_image.save()\n", - " \n", - " pw_stimulus_set_image_maps.append(pw_stimulus_set_image_map)\n", - " pw_stimulus_set_image_map.save()\n", - " \n", - " pw_image_image_store_maps.append(pw_image_image_store_map)\n", - " pw_image_image_store_map.save()\n", - " \n", - " print(image.image_file_name, image.variation)" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "pw_query = (StimulusSet\n", - " .select()\n", - " .join(StimulusSetImageMap)\n", - " .join(Image)\n", - " .join(ImageImageStoreMap)\n", - " .join(ImageStore))" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "list(pw_query)" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "scrolled": true - } - }, - { - "cell_type": "code", - "source": [ - "len(pw_query)" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "pw_hvm = StimulusSet.get(StimulusSet.name == \"HvM\")\n", - "pw_hvm" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "[(x.image.hash_id, x.image.image_image_store_maps[0].image_store.location) for x in pw_hvm.stimulus_set_image_maps]" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "len(pw_hvm.stimulus_set_image_maps)" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "[(x.__name__, x.select().count()) for x in pw_classes]" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "pw_query.sql()" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "dir()" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [ - "df_images.to_pickle(\"image_meta_dataframe.pkl\")" - ], - "outputs": [], - "execution_count": null, - "metadata": {} - }, - { - "cell_type": "code", - "source": [], - "outputs": [], - "execution_count": null, - "metadata": {} - } - ], - "metadata": { - "kernelspec": { - "display_name": "mkgu_packaging", - "language": "python", - "name": "mkgu_packaging" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - }, - "nteract": { - "version": "0.28.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/packaging/notebooks/2018-03-23_merge_meta.ipynb b/packaging/notebooks/2018-03-23_merge_meta.ipynb deleted file mode 100644 index ef0ad19d5..000000000 --- a/packaging/notebooks/2018-03-23_merge_meta.ipynb +++ /dev/null @@ -1,15723 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr\n", - "import mkgu\n", - "import peewee" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[[ 0.060929],\n", - " [-0.686162],\n", - " ...,\n", - " [-0.968256],\n", - " [ 0.183887]],\n", - "\n", - " [[-0.725592],\n", - " [ 0.292777],\n", - " ...,\n", - " [ 2.449372],\n", - " [ 0.401197]],\n", - "\n", - " ...,\n", - "\n", - " [[ 1.121319],\n", - " [ 1.719423],\n", - " ...,\n", - " [ 0.800551],\n", - " [-0.019874]],\n", - "\n", - " [[-0.518903],\n", - " [ 0.696196],\n", - " ...,\n", - " [-0.603347],\n", - " [-0.175979]]], dtype=float32)\n", - "Coordinates:\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object 'Chabo_L_M_5_9' 'Chabo_L_M_6_9' ...\n", - " - arr (neuroid) object 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' ...\n", - " - col (neuroid) int64 9 9 8 9 8 8 7 7 5 6 4 9 9 9 9 9 8 7 9 6 ...\n", - " - hemisphere (neuroid) object 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' ...\n", - " - subregion (neuroid) object 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' ...\n", - " - animal (neuroid) object 'Chabo' 'Chabo' 'Chabo' 'Chabo' ...\n", - " - y (neuroid) float64 0.2 0.6 0.2 1.0 0.6 1.0 1.8 1.0 1.8 ...\n", - " - x (neuroid) float64 1.8 1.8 1.4 1.8 1.4 1.4 1.0 1.0 0.2 ...\n", - " - row (neuroid) int64 5 6 5 7 6 7 9 7 9 8 9 2 1 3 6 7 6 6 8 5 ...\n", - " - region (neuroid) object 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' ...\n", - " * presentation (presentation) MultiIndex\n", - " - ryz_semantic (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " - object (presentation) object 'car_astra' 'table3' 'face2' ...\n", - " - variation (presentation) object 'V0' 'V0' 'V0' 'V0' 'V0' 'V0' ...\n", - " - image_id (presentation) object '8a72e2bfdb8c267b57232bf96f069374d5b21832' ...\n", - " - image_file_name (presentation) object 'astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_ecd40f3f6d7a4d6d88134d648884e0b9b364efc9_256x256.png' ...\n", - " - rxy_semantic (presentation) float64 90.0 -0.0 -0.0 -0.0 -0.0 90.0 ...\n", - " - size (presentation) float64 256.0 256.0 256.0 256.0 256.0 ...\n", - " - category (presentation) object 'Cars' 'Tables' 'Faces' 'Fruits' ...\n", - " - tz (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - ty (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - rxz_semantic (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - rxy (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " - background_id (presentation) object 'ecd40f3f6d7a4d6d88134d648884e0b9b364efc9' ...\n", - " - s (presentation) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ...\n", - " - rxz (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - ryz (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " - repetition (presentation) int64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...\n", - " - stimulus (presentation) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 ...\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_end (time_bin) int64 170\n", - " - time_bin_start (time_bin) int64 70" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hvm = mkgu.get_assembly(\"dicarlolab.hong.2011.macaque\")\n", - "hvm" - ] - }, - { - "cell_type": "code", - "execution_count": 210, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[[ 0.060929],\n", - " [-0.686162],\n", - " ...,\n", - " [-0.968256],\n", - " [ 0.183887]],\n", - "\n", - " [[-0.725592],\n", - " [ 0.292777],\n", - " ...,\n", - " [ 2.449372],\n", - " [ 0.401197]],\n", - "\n", - " ...,\n", - "\n", - " [[ 1.121319],\n", - " [ 1.719423],\n", - " ...,\n", - " [ 0.800551],\n", - " [-0.019874]],\n", - "\n", - " [[-0.518903],\n", - " [ 0.696196],\n", - " ...,\n", - " [-0.603347],\n", - " [-0.175979]]], dtype=float32)\n", - "Coordinates:\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object 'Chabo_L_M_5_9' 'Chabo_L_M_6_9' ...\n", - " - arr (neuroid) object 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' ...\n", - " - col (neuroid) int64 9 9 8 9 8 8 7 7 5 6 4 9 9 9 9 9 8 7 9 6 ...\n", - " - hemisphere (neuroid) object 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' ...\n", - " - subregion (neuroid) object 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' ...\n", - " - animal (neuroid) object 'Chabo' 'Chabo' 'Chabo' 'Chabo' ...\n", - " - y (neuroid) float64 0.2 0.6 0.2 1.0 0.6 1.0 1.8 1.0 1.8 ...\n", - " - x (neuroid) float64 1.8 1.8 1.4 1.8 1.4 1.4 1.0 1.0 0.2 ...\n", - " - row (neuroid) int64 5 6 5 7 6 7 9 7 9 8 9 2 1 3 6 7 6 6 8 5 ...\n", - " - region (neuroid) object 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' ...\n", - " * presentation (presentation) MultiIndex\n", - " - ryz_semantic (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " - object (presentation) object 'car_astra' 'table3' 'face2' ...\n", - " - variation (presentation) object 'V0' 'V0' 'V0' 'V0' 'V0' 'V0' ...\n", - " - image_id (presentation) object '8a72e2bfdb8c267b57232bf96f069374d5b21832' ...\n", - " - image_file_name (presentation) object 'astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_ecd40f3f6d7a4d6d88134d648884e0b9b364efc9_256x256.png' ...\n", - " - rxy_semantic (presentation) float64 90.0 -0.0 -0.0 -0.0 -0.0 90.0 ...\n", - " - size (presentation) float64 256.0 256.0 256.0 256.0 256.0 ...\n", - " - category (presentation) object 'Cars' 'Tables' 'Faces' 'Fruits' ...\n", - " - tz (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - ty (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - rxz_semantic (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - rxy (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " - background_id (presentation) object 'ecd40f3f6d7a4d6d88134d648884e0b9b364efc9' ...\n", - " - s (presentation) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ...\n", - " - rxz (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - ryz (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " - repetition (presentation) int64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...\n", - " - stimulus (presentation) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 ...\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_end (time_bin) int64 170\n", - " - time_bin_start (time_bin) int64 70" - ] - }, - "execution_count": 210, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hvm_stripped = hvm.copy()\n", - "hvm_stripped" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "to_remove = ['ryz_semantic', 'variation', 'image_file_name', 'rxy_semantic', 'size', \n", - " 'category', 'object', 'tz', 'ty', 'rxz_semantic', 'rxy', 'background_id', 's', 'rxz', 'ryz']" - ] - }, - { - "cell_type": "code", - "execution_count": 211, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[[ 0.060929],\n", - " [-0.686162],\n", - " ...,\n", - " [-0.968256],\n", - " [ 0.183887]],\n", - "\n", - " [[-0.725592],\n", - " [ 0.292777],\n", - " ...,\n", - " [ 2.449372],\n", - " [ 0.401197]],\n", - "\n", - " ...,\n", - "\n", - " [[ 1.121319],\n", - " [ 1.719423],\n", - " ...,\n", - " [ 0.800551],\n", - " [-0.019874]],\n", - "\n", - " [[-0.518903],\n", - " [ 0.696196],\n", - " ...,\n", - " [-0.603347],\n", - " [-0.175979]]], dtype=float32)\n", - "Coordinates:\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object 'Chabo_L_M_5_9' 'Chabo_L_M_6_9' ...\n", - " - arr (neuroid) object 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' ...\n", - " - col (neuroid) int64 9 9 8 9 8 8 7 7 5 6 4 9 9 9 9 9 8 7 9 6 ...\n", - " - hemisphere (neuroid) object 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' ...\n", - " - subregion (neuroid) object 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' ...\n", - " - animal (neuroid) object 'Chabo' 'Chabo' 'Chabo' 'Chabo' 'Chabo' ...\n", - " - y (neuroid) float64 0.2 0.6 0.2 1.0 0.6 1.0 1.8 1.0 1.8 ...\n", - " - x (neuroid) float64 1.8 1.8 1.4 1.8 1.4 1.4 1.0 1.0 0.2 ...\n", - " - row (neuroid) int64 5 6 5 7 6 7 9 7 9 8 9 2 1 3 6 7 6 6 8 5 ...\n", - " - region (neuroid) object 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' ...\n", - " * presentation (presentation) MultiIndex\n", - " - image_id (presentation) object '8a72e2bfdb8c267b57232bf96f069374d5b21832' ...\n", - " - repetition (presentation) int64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...\n", - " - stimulus (presentation) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 ...\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_end (time_bin) int64 170\n", - " - time_bin_start (time_bin) int64 70" - ] - }, - "execution_count": 211, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "for level in to_remove:\n", - " if level in hvm_stripped.indexes[\"presentation\"].names:\n", - " hvm_stripped.reset_index(level, inplace=True)\n", - " hvm_stripped.reset_coords(level, inplace=True, drop=True)\n", - "hvm_stripped" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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ryz_semanticobjectvariationimage_idimage_file_namerxy_semanticsizecategorytztyrxz_semanticrxybackground_idsrxzryzrepetitionstimulus
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dogV0a17db26a610f96215d94cd673fd982c5bb3fd524breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_8f388437a015e5358e27dad6f0d60e023db40e60_256x256.png90.000000256.0Animals0.0000.0000.000000-0.0000008f388437a015e5358e27dad6f0d60e023db40e601.0000000.000000-0.0000000138f388437a015e5358e27dad6f0d60e023db40e60Animalsbreed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00....a17db26a610f96215d94cd673fd982c5bb3fd524dog0-0.00000090.0000000.0000000.000000-0.000000-0.0000001.000000256.0130.0000.000V0
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watermelonV0f680c591330f26793f4de603e26cc584ae19ddb2watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+00.700_ee94a3eb3df3de74988b6021a4a4644b6a8349a2_256x256.png-0.000000256.0Fruits0.0000.0000.000000-0.000000ee94a3eb3df3de74988b6021a4a4644b6a8349a21.0000000.000000-0.000000020ee94a3eb3df3de74988b6021a4a4644b6a8349a2Fruitswatermelon_obj_rx-90.000_ry+00.000_rz+00.000_t...f680c591330f26793f4de603e26cc584ae19ddb2watermelon0-0.000000-0.0000000.0000000.000000-0.000000-0.0000001.000000256.0200.0000.000V0
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270060 rows × 18 columns

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" car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 087d94504c098c5ebf0ad52164ffd70020644e6e \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 8001b24ed42c36be17da6ca4213b4d8be30be63a \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 90f464d4725214ddae0e9d2dd2e759db0561cb94 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 23957035c1e23cd4e8480b9aae74eca53837d835 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 824c7a6bd9bd2d9db1a239a90330ee4547249439 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 4252b5aec4714cde9351afcc18f9c4f1a7b260ae \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 eb58d84e32e97638191725980511005bc3c4298f \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 4647133f7784fbd40525ea113de132186f16667e \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 840b0badda8be10e1e3d5eff5cedce384f8021bc \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 3f072352f8336529aba839aa86fe0114d11886fc \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 771e5ab1f204905bf03d06936b01bb5f4de92788 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 6dec52041309894f4d4da45448fc6c9a4cd4ba87 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 b1a3abb743a6380929584173dcbbb5956bc0d216 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 828850cf3e0695ec1d4d32219904cf5e199210e2 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 cb40eea4d91e603380e46852251ad642dfd0ba75 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 9b87c8be1440ce9626bcfe656700ff3ac8f909fe \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 62a27decc64031d42ac8b683fb1e8c0ac0b616cf \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 57102e072f4cac897d8089085ca932a40a1aa1d5 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 f934a940602d18a719e8a3026c201301958142af \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 5e13ca12b89cd9631656c619a3a0c88b69b75b8e \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 46 2557 5068064aee7a1831c3a5fe0f023b05cffca511e3 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 46 2558 d6e2227592c00e5fa2607781d8a5096854058f7b \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 46 2559 52b8677d036765ba6c05d8035b641e3c55993f9a \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 46 2560 5a38b191f3aaa9592b8909777441e9dab3913997 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 46 2561 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 46 2562 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 46 2563 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 46 2564 0405dd6bf7747d33a030f46c86eabda0bd95e00a \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 821565cb10250ce762b8c594185fc810f52d38ea \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 524d21ed6255411c1f1531e075b1098eb2cda2e6 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 2363c8c3859603ee9db7f83dd7ba5b6989154258 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 \n", - "\n", - " category \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 Cars \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 Tables \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 Faces \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 Fruits \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 Fruits \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 Planes \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 Fruits \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 Fruits \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 Faces \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 Planes \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 Boats \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 Boats \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 Cars \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 Animals \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 Fruits \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 Cars \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 Tables \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 Cars \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 Planes \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 Boats \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 Fruits \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 Boats \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 Boats \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 Tables \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 Faces \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 Boats \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 Tables \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 Fruits \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 Fruits \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 Faces \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 Animals \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 Planes \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 Boats \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 Cars \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 Animals \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 Fruits \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 Fruits \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 Chairs \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 Chairs \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 Chairs \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 Chairs \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 Faces \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 Fruits \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 Cars \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 Chairs \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 Boats \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 Tables \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 46 2557 Animals \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 46 2558 Tables \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 46 2559 Chairs \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 46 2560 Planes \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 46 2561 Faces \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 46 2562 Fruits \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 46 2563 Cars \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 46 2564 Planes \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 Faces \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 Tables \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 Fruits \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 Faces \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 Fruits \n", - "\n", - " image_file_name \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.00... \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0... \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_t... \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.... \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000... \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.... \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000... \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.00... \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_t... \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.00... \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0... \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0... \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.5... \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.5... \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 support_rx-35.196_ry+40.372_rz+72.620_tx-00.22... \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546... \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.04... \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00... \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00... \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_t... \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_t... \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_t... \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_t... \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.5... \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00... \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.40... \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_t... \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.43... \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty... \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 46 2557 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.3... \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 46 2558 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty... \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 46 2559 _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_t... \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 46 2560 _01_Airliner_2jetEngines_rx+49.355_ry+95.933_r... \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 46 2561 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.5... \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 46 2562 walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+0... \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 46 2563 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.42... \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 46 2564 _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00... \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.5... \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty... \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_... \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.3... \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+... \n", - "\n", - " image_id \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 8a72e2bfdb8c267b57232bf96f069374d5b21832 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 27f69468c9d6019ed0d22b9583c94c5b58198c1c \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 d0f7a45b377d4920c3466ec7a20dce9437a150d6 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 ae2b8aac003c867a772647197dcd3c050b75a06e \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 a7a31e65e2770c7a503988835c24aeab689abf47 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 09d2924723bc4677df666072b6e25e988d5f0668 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 1df484a50ff7fbd85112990b7f8fe616c49adb28 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 fbef152524d2e593206fd976afde5e0506f477a8 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 a17db26a610f96215d94cd673fd982c5bb3fd524 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 41e376452bcb4a61646304b4fd397f11e329ebb2 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 36b1652f43aedf23e39f351fd931ab0cb4c410b5 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 6a8b93cd59587cc2969a126361bce879774e7f4f \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 05954905d976ab1cf247c49fc31e534a8a047ba1 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 97757502b8c2e32e47a79a266f2da46bbb7165d1 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 45b221ea1ed093c991ba6503c57093f6bea2c9d7 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 f680c591330f26793f4de603e26cc584ae19ddb2 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 3d31939c0fbbb695d865f3e010d9860b65c674f2 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 88429363542db8c918d7112a7335fcca62966a3e \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 a533e519b553cdf360ae134f7c9f161134ddaf00 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 fd135a1c5e2b157b4557ef8473681ca6d03b0188 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 6b87003f02400681dc545c7cd57cde747c1cb98a \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 a051580c645ed432381d24beb6e93ee7952c8334 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 a8648457a08c568c907c68d058e2d33bb276a71e \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 241a33fd5221263ac3d350bd82ddeab2d0104eab \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 51ba24371f3c0683da1978d332184732bbec5b96 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 88f954276bb53915429cc2d117e57c08bc8aaddd \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 d2af56088e557fbafc9815e9a7486f3efe61823e \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 d4dd9a086994a89bf6e2d32efab254bdd4648888 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 30912859b0dfac9c6531061a796fb96f16510fa8 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 518d256ccedbcda049049c1183bc30ff2aa93258 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 98cc0d8f063fae1b0c22645f384875ec0f73212c \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 fd18b1f8ca6d961c163aa56772d813f6cba28336 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 46 2557 d15aa059b0180b200fb82fb8039247c3b53f9d66 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 46 2558 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 46 2559 af8325d2d77b8475a00eb9a5296689adb4a8f39b \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 46 2560 5cbd24a0ac1881551f356940236e021bd29cd1de \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 46 2561 feef7d8cf74ce5f8a8d0612e015e0de363587e93 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 46 2562 6f61f7e3130d2fef3deeb310e08a927521cb932d \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 46 2563 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 46 2564 3fe99701f03c85d5b2017d4ec33eb227a695386e \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 2c381c66bf457942bf4818e54b4b5172b3cc0cac \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 d6a597127092d38ed37bcb19967075698acb35ee \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 cc975ea997317b74a79b9f5568f8008acdec341e \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 e35071a43f7a0dafc21b18229ca36f1940c2c567 \n", - "\n", - " object \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 car_astra \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 table3 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 face2 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 walnut \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 walnut \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 airplane7 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 apricot \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 strawberry \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 face1 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 airplane4 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 ship1 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 ship3 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 car_clio \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 dog \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 peach \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 car_astra \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 table5 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 car_beetle \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 airplane7 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 ship3 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 watermelon \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 ship5 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 ship4 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 table7 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 face0 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 ship3 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 table4 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 apricot \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 apricot \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 face0 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 turtle \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 airplane6 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 ship7 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 car_celica \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 gorilla \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 walnut \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 walnut \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 chair5 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 chair2 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 chair0 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 chair3 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 face7 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 peach \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 car_alfa \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 chair7 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 ship5 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 table3 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 46 2557 turtle \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 46 2558 table6 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 46 2559 chair1 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 46 2560 airplane0 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 46 2561 face1 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 46 2562 walnut \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 46 2563 car_alfa \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 46 2564 airplane1 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 face3 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 table0 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 strawberry \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 face3 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 apricot \n", - "\n", - " repetition \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 0 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 0 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 0 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 0 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 0 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 0 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 0 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 0 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 0 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 0 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 0 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 0 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 0 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 0 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 0 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 0 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 0 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 0 \n", - 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" table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 0 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 0 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 0 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 0 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 0 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 0 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 0 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 46 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 46 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 46 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 46 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 46 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 46 \n", - 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"\n", - " rxy \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 -0.000000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 -0.000000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 -0.000000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 -0.000000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 -0.000000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 -0.000000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 -0.000000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 -0.000000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 -0.000000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 -0.000000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 -0.000000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 -0.000000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 -0.000000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 -0.000000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 -0.000000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 -0.000000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 -0.000000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 -0.000000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 -0.000000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 -0.000000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 -0.000000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 -0.000000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 -0.000000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 -0.000000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 -0.000000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 -0.000000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 -0.000000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 -0.000000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 -0.000000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 -0.000000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 -84.231000 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 -66.132000 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 -49.628000 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 -7.294000 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 85.828000 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 86.257574 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 4.176806 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 -46.840000 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 45.285000 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 -11.238000 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 -2.935000 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 22.427000 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 33.515544 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 -28.605000 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 55.200000 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 -0.809000 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 -65.300000 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 46 2557 61.236000 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 46 2558 5.591000 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 46 2559 -16.072000 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 46 2560 49.507029 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 46 2561 64.386000 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 46 2562 -18.446784 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 46 2563 45.261000 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 46 2564 9.955137 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 48.146000 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 -70.967000 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 37.334164 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 -75.159000 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 -67.467580 \n", - "\n", - " rxy_semantic \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 90.000000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 -0.000000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 -0.000000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 -0.000000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 -0.000000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 90.000000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 -0.000000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 -0.000000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 -0.000000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 90.000000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 90.000000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 -90.000000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 90.000000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 90.000000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 -0.000000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 90.000000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 -0.000000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 90.000000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 90.000000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 -90.000000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 -0.000000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 90.000000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 90.000000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 -0.000000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 -0.000000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 -90.000000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 -0.000000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 -0.000000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 -0.000000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 -0.000000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 5.769000 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 23.868000 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 -139.628000 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 82.706000 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 175.828000 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 86.257574 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 4.176806 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 -46.840000 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 45.285000 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 -11.238000 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 -2.935000 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 22.427000 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 33.515544 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 61.395000 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 55.200000 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 89.191000 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 -65.300000 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 46 2557 151.236000 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 46 2558 5.591000 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 46 2559 -16.072000 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 46 2560 139.507029 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 46 2561 64.386000 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 46 2562 -18.446784 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 46 2563 135.261000 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 46 2564 99.955137 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 48.146000 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 -70.967000 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 37.334164 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 -75.159000 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 -67.467580 \n", - "\n", - " rxz \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 0.000000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 0.000000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 0.000000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 0.000000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 0.000000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 0.000000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 0.000000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 0.000000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 0.000000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 0.000000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 0.000000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 0.000000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 0.000000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 0.000000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 0.000000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 0.000000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 0.000000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 0.000000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 0.000000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 0.000000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 0.000000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 0.000000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 0.000000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 0.000000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 0.000000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 0.000000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 0.000000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 0.000000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 0.000000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 0.000000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 -89.052000 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 -67.801000 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 -35.196000 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 -71.878000 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 -73.304000 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 1.580624 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 2.259833 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 16.090000 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 37.122000 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 25.949000 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 49.021000 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 45.258000 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 -9.180214 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 -3.829000 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 -67.932000 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 -71.145000 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 -84.440000 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 46 2557 69.658000 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 46 2558 -55.612000 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 46 2559 86.864000 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 46 2560 -3.860598 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 46 2561 42.742000 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 46 2562 -10.719291 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 46 2563 -43.867000 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 46 2564 24.193423 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 -41.898000 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 11.985000 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 -67.238762 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 29.466000 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 -25.152591 \n", - "\n", - " rxz_semantic \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 0.000000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 0.000000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 0.000000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 0.000000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 0.000000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 0.000000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 0.000000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 0.000000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 0.000000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 0.000000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 0.000000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 0.000000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 0.000000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 0.000000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 0.000000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 0.000000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 0.000000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 0.000000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 0.000000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 0.000000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 0.000000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 0.000000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 0.000000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 0.000000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 0.000000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 0.000000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 0.000000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 0.000000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 0.000000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 0.000000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 -89.052000 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 -67.801000 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 -35.196000 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 -71.878000 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 -73.304000 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 1.580624 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 2.259833 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 16.090000 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 37.122000 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 25.949000 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 49.021000 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 45.258000 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 -9.180214 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 -3.829000 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 -67.932000 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 -71.145000 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 -84.440000 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 46 2557 69.658000 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 46 2558 -55.612000 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 46 2559 86.864000 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 46 2560 -3.860598 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 46 2561 42.742000 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 46 2562 -10.719291 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 46 2563 -43.867000 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 46 2564 24.193423 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 -41.898000 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 11.985000 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 -67.238762 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 29.466000 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 -25.152591 \n", - "\n", - " ryz \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 -0.000000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 -0.000000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 -0.000000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 -0.000000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 -0.000000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 -0.000000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 -0.000000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 -0.000000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 -0.000000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 -0.000000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 -0.000000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 -0.000000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 -0.000000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 -0.000000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 -0.000000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 -0.000000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 -0.000000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 -0.000000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 -0.000000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 -0.000000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 -0.000000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 -0.000000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 -0.000000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 -0.000000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 -0.000000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 -0.000000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 -0.000000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 -0.000000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 -0.000000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 -0.000000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 74.978000 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 -6.454000 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 72.620000 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 -3.863000 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 -50.242000 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 -147.908623 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 24.748583 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 24.070000 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 21.814000 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 49.199000 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 -52.625000 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 -17.151000 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 -2.873322 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 62.157000 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 -1.643000 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 -30.805000 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 -13.491000 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 46 2557 7.161000 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 46 2558 -84.185000 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 46 2559 30.443000 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 46 2560 18.531554 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 46 2561 87.404000 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 46 2562 -96.632598 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 46 2563 -21.184000 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 46 2564 -19.936269 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 51.346000 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 -45.818000 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 10.426955 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 12.283000 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 7.598457 \n", - "\n", - " ryz_semantic \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 -0.000000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 -0.000000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 -0.000000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 -0.000000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 -0.000000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 -0.000000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 -0.000000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 -0.000000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 -0.000000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 -0.000000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 -0.000000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 -0.000000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 -0.000000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 -0.000000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 -0.000000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 -0.000000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 -0.000000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 -0.000000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 -0.000000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 -0.000000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 -0.000000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 -0.000000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 -0.000000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 -0.000000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 -0.000000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 -0.000000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 -0.000000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 -0.000000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 -0.000000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 -0.000000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 74.978000 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 -6.454000 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 72.620000 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 -3.863000 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 -50.242000 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 -147.908623 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 24.748583 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 24.070000 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 21.814000 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 49.199000 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 -52.625000 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 -17.151000 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 -2.873322 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 62.157000 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 -1.643000 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 -30.805000 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 -13.491000 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 46 2557 7.161000 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 46 2558 -84.185000 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 46 2559 30.443000 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 46 2560 18.531554 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 46 2561 87.404000 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 46 2562 -96.632598 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 46 2563 -21.184000 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 46 2564 -19.936269 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 51.346000 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 -45.818000 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 10.426955 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 12.283000 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 7.598457 \n", - "\n", - " s \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 1.000000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 1.000000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 1.000000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 1.000000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 1.000000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 1.000000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 1.000000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 1.000000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 1.000000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 1.000000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 1.000000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 1.000000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 1.000000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 1.000000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 1.000000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 1.000000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 1.000000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 1.000000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 1.000000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 1.000000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 1.000000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 1.000000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 1.000000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 1.000000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 1.000000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 1.000000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 1.000000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 1.000000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 1.000000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 1.000000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 1.578750 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 0.820769 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 0.853333 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 0.860000 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 0.753750 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 0.920000 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 1.187143 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 0.752857 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 1.418571 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 1.305714 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 1.410000 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 1.487500 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 0.637143 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 1.273000 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 1.251429 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 1.073333 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 1.051429 \n", - 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" 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 0.923750 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 0.802857 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 1.478571 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 0.791250 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 1.050000 \n", - "\n", - " size \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 256.0 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 256.0 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 256.0 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 256.0 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 256.0 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 256.0 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 256.0 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 256.0 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 256.0 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 256.0 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 256.0 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 256.0 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 256.0 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 256.0 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 256.0 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 256.0 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 256.0 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 256.0 \n", - 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" table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 256.0 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 256.0 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 256.0 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 256.0 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 256.0 \n", - 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" 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 256.0 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 256.0 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 256.0 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 256.0 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 256.0 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 256.0 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 256.0 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 256.0 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 256.0 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 256.0 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 256.0 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 256.0 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 256.0 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 256.0 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 256.0 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 46 2557 256.0 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 46 2558 256.0 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 46 2559 256.0 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 46 2560 256.0 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 46 2561 256.0 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 46 2562 256.0 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 46 2563 256.0 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 46 2564 256.0 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 256.0 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 256.0 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 256.0 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 256.0 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 256.0 \n", - "\n", - " stimulus \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 0 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 1 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 2 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 3 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 4 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 5 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 6 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 7 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 8 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 9 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 10 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 11 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 12 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 13 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 14 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 15 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 16 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 17 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 18 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 19 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 20 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 21 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 22 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 23 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 24 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 25 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 26 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 27 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 28 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 29 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 2540 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 2541 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 2542 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 2543 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 2544 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 2545 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 2546 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 2547 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 2548 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 2549 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 2550 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 2551 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 2552 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 2553 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 2554 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 2555 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 2556 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 46 2557 2557 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 46 2558 2558 \n", - 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"-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 46 2563 2563 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 46 2564 2564 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 2565 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 2566 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 2567 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 2568 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 2569 \n", - "\n", - " ty \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 0.000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 0.000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 0.000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 0.000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 0.000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 0.000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 0.000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 0.000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 0.000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 0.000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 0.000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 0.000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 0.000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 0.000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 0.000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 0.000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 0.000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 0.000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 0.000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 0.000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 0.000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 0.000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 0.000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 0.000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 0.000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 0.000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 0.000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 0.000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 0.000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 0.000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 -0.598 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 0.542 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 -0.222 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 0.546 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 0.044 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 -0.493 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 -0.087 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 -0.160 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 0.060 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 -0.428 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 0.510 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 -0.587 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 0.308 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 0.409 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 -0.215 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 0.430 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 0.149 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 46 2557 0.318 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 46 2558 0.148 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 46 2559 0.597 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 46 2560 0.009 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 46 2561 -0.517 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 46 2562 0.251 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 46 2563 -0.423 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 46 2564 -0.284 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 0.526 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 0.371 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 -0.354 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 0.391 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 0.049 \n", - "\n", - " tz \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 0.000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 0.000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 0.000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 0.000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 0.000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 0.000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 0.000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 0.000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 0.000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 0.000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 0.000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 0.000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 0.000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 0.000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 0.000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 0.000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 0.000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 0.000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 0.000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 0.000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 0.000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 0.000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 0.000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 0.000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 0.000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 0.000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 0.000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 0.000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 0.000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 0.000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 46 2540 -0.709 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 46 2541 -0.870 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 46 2542 -0.015 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 46 2543 0.724 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 46 2544 0.943 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 46 2545 0.308 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 46 2546 -0.916 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 46 2547 -0.693 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 46 2548 1.199 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 46 2549 0.113 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 46 2550 -0.423 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 46 2551 0.976 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 46 2552 0.949 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 46 2553 0.252 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 46 2554 1.179 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 46 2555 0.209 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 46 2556 -1.000 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 46 2557 -1.053 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 46 2558 0.380 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 46 2559 -0.560 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 46 2560 -0.564 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 46 2561 -0.577 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 46 2562 -0.830 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 46 2563 1.095 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 46 2564 -0.537 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 46 2565 -0.511 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 46 2566 0.205 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 46 2567 0.466 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 46 2568 -0.280 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 46 2569 0.187 \n", - "\n", - " variation \n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 V0 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 V0 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 V0 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 V0 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 V0 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 V0 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 V0 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 V0 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 V0 \n", - 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" - ], - "text/plain": [ - " background_id \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 006d66c207c6417574f62f0560c6b2b40a9ec5a1 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 3b3c1d65865028d0fad0b0bf8f305098db717e7f \n", - 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" strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 03f46b6acdb2fc42f934c5ef67792026de49d530 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 2164801f3646e5e82eaa4373f8d249ce17edf1ac \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 8f388437a015e5358e27dad6f0d60e023db40e60 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 0c0d7402273188a7ed2d91dbef4baf75554c2017 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 087d94504c098c5ebf0ad52164ffd70020644e6e \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 8001b24ed42c36be17da6ca4213b4d8be30be63a \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 90f464d4725214ddae0e9d2dd2e759db0561cb94 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 23957035c1e23cd4e8480b9aae74eca53837d835 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 824c7a6bd9bd2d9db1a239a90330ee4547249439 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 4252b5aec4714cde9351afcc18f9c4f1a7b260ae \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 eb58d84e32e97638191725980511005bc3c4298f \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 4647133f7784fbd40525ea113de132186f16667e \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 840b0badda8be10e1e3d5eff5cedce384f8021bc \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 3f072352f8336529aba839aa86fe0114d11886fc \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 771e5ab1f204905bf03d06936b01bb5f4de92788 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 0 2542 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 0 2543 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 0 2544 6dec52041309894f4d4da45448fc6c9a4cd4ba87 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 0 2545 b1a3abb743a6380929584173dcbbb5956bc0d216 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 0 2546 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 0 2547 828850cf3e0695ec1d4d32219904cf5e199210e2 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 0 2548 cb40eea4d91e603380e46852251ad642dfd0ba75 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 0 2549 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 0 2550 9b87c8be1440ce9626bcfe656700ff3ac8f909fe \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 0 2551 62a27decc64031d42ac8b683fb1e8c0ac0b616cf \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 0 2552 57102e072f4cac897d8089085ca932a40a1aa1d5 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 0 2553 f934a940602d18a719e8a3026c201301958142af \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 0 2554 5e13ca12b89cd9631656c619a3a0c88b69b75b8e \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 0 2555 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 0 2556 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 0 2557 5068064aee7a1831c3a5fe0f023b05cffca511e3 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 d6e2227592c00e5fa2607781d8a5096854058f7b \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 52b8677d036765ba6c05d8035b641e3c55993f9a \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 5a38b191f3aaa9592b8909777441e9dab3913997 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 0 2562 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 0 2563 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 0 2564 0405dd6bf7747d33a030f46c86eabda0bd95e00a \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 0 2565 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 821565cb10250ce762b8c594185fc810f52d38ea \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 524d21ed6255411c1f1531e075b1098eb2cda2e6 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 2363c8c3859603ee9db7f83dd7ba5b6989154258 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 \n", - "\n", - " category \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 Cars \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 Tables \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 Faces \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 Fruits \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 Fruits \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 Planes \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 Fruits \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 Fruits \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 Faces \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 Planes \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 Boats \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 Boats \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 Cars \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 Animals \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 Fruits \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 Cars \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 Tables \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 Cars \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 Planes \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 Boats \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 Fruits \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 Boats \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 Boats \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 Tables \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 Faces \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 Boats \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 Tables \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 Fruits \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 Fruits \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 Faces \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 Animals \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 Planes \n", - 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"-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 Tables \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 Chairs \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 Planes \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 Faces \n", - 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"-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 Tables \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 Fruits \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 Faces \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 Fruits \n", - "\n", - " image_file_name \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.00... \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0... \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_t... \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.... \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000... \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.... \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000... \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.00... \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_t... \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.00... \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0... \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0... \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.0... \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.5... \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.5... \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 0 2542 support_rx-35.196_ry+40.372_rz+72.620_tx-00.22... \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 0 2543 celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546... \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 0 2544 GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.04... \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 0 2545 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00... \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 0 2546 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00... \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 0 2547 _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_t... \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 0 2548 _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_t... \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 0 2549 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_t... \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 0 2550 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_t... \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 0 2551 face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.5... \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 0 2552 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00... \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 0 2553 alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.40... \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 0 2554 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_t... \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 0 2555 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.43... \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 0 2556 _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty... \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 0 2557 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.3... \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty... \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_t... \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 _01_Airliner_2jetEngines_rx+49.355_ry+95.933_r... \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.5... \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 0 2562 walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+0... \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 0 2563 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.42... \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 0 2564 _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00... \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 0 2565 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.5... \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty... \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_... \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.3... \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+... \n", - "\n", - " image_id \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 8a72e2bfdb8c267b57232bf96f069374d5b21832 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 27f69468c9d6019ed0d22b9583c94c5b58198c1c \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 d0f7a45b377d4920c3466ec7a20dce9437a150d6 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 ae2b8aac003c867a772647197dcd3c050b75a06e \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 a7a31e65e2770c7a503988835c24aeab689abf47 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 09d2924723bc4677df666072b6e25e988d5f0668 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 1df484a50ff7fbd85112990b7f8fe616c49adb28 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 fbef152524d2e593206fd976afde5e0506f477a8 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 a17db26a610f96215d94cd673fd982c5bb3fd524 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 41e376452bcb4a61646304b4fd397f11e329ebb2 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 36b1652f43aedf23e39f351fd931ab0cb4c410b5 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 6a8b93cd59587cc2969a126361bce879774e7f4f \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 05954905d976ab1cf247c49fc31e534a8a047ba1 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 97757502b8c2e32e47a79a266f2da46bbb7165d1 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 45b221ea1ed093c991ba6503c57093f6bea2c9d7 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 f680c591330f26793f4de603e26cc584ae19ddb2 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 3d31939c0fbbb695d865f3e010d9860b65c674f2 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 88429363542db8c918d7112a7335fcca62966a3e \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 a533e519b553cdf360ae134f7c9f161134ddaf00 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 fd135a1c5e2b157b4557ef8473681ca6d03b0188 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 6b87003f02400681dc545c7cd57cde747c1cb98a \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 0 2542 a051580c645ed432381d24beb6e93ee7952c8334 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 0 2543 a8648457a08c568c907c68d058e2d33bb276a71e \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 0 2544 241a33fd5221263ac3d350bd82ddeab2d0104eab \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 0 2545 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 0 2546 51ba24371f3c0683da1978d332184732bbec5b96 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 0 2547 88f954276bb53915429cc2d117e57c08bc8aaddd \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 0 2548 d2af56088e557fbafc9815e9a7486f3efe61823e \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 0 2549 d4dd9a086994a89bf6e2d32efab254bdd4648888 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 0 2550 30912859b0dfac9c6531061a796fb96f16510fa8 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 0 2551 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 0 2552 518d256ccedbcda049049c1183bc30ff2aa93258 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 0 2553 98cc0d8f063fae1b0c22645f384875ec0f73212c \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 0 2554 fd18b1f8ca6d961c163aa56772d813f6cba28336 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 0 2555 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 0 2556 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 0 2557 d15aa059b0180b200fb82fb8039247c3b53f9d66 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 af8325d2d77b8475a00eb9a5296689adb4a8f39b \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 5cbd24a0ac1881551f356940236e021bd29cd1de \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 feef7d8cf74ce5f8a8d0612e015e0de363587e93 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 0 2562 6f61f7e3130d2fef3deeb310e08a927521cb932d \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 0 2563 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 0 2564 3fe99701f03c85d5b2017d4ec33eb227a695386e \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 0 2565 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 2c381c66bf457942bf4818e54b4b5172b3cc0cac \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 d6a597127092d38ed37bcb19967075698acb35ee \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 cc975ea997317b74a79b9f5568f8008acdec341e \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 e35071a43f7a0dafc21b18229ca36f1940c2c567 \n", - "\n", - " object \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 car_astra \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 table3 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 face2 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 walnut \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 walnut \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 airplane7 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 apricot \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 strawberry \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 face1 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 airplane4 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 ship1 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 ship3 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 car_clio \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 dog \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 peach \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 car_astra \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 table5 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 car_beetle \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 airplane7 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 ship3 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 watermelon \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 ship5 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 ship4 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 table7 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 face0 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 ship3 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 table4 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 apricot \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 apricot \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 face0 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 turtle \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 airplane6 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 0 2542 ship7 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 0 2543 car_celica \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 0 2544 gorilla \n", - 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" 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 0 2549 chair0 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 0 2550 chair3 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 0 2551 face7 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 0 2552 peach \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 0 2553 car_alfa \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 0 2554 chair7 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 0 2555 ship5 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 0 2556 table3 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 0 2557 turtle \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 table6 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 chair1 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 airplane0 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 face1 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 0 2562 walnut \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 0 2563 car_alfa \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 0 2564 airplane1 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 0 2565 face3 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 table0 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 strawberry \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 face3 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 apricot \n", - "\n", - " rxy \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 -0.000000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 -0.000000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 -0.000000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 -0.000000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 -0.000000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 -0.000000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 -0.000000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 -0.000000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 -0.000000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 -0.000000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 -0.000000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 -0.000000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 -0.000000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 -0.000000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 -0.000000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 -0.000000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 -0.000000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 -0.000000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 -0.000000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 -0.000000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 -0.000000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 -0.000000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 -0.000000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 -0.000000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 -0.000000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 -0.000000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 -0.000000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 -0.000000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 -0.000000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 -0.000000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 -84.231000 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 -66.132000 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 0 2542 -49.628000 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 0 2543 -7.294000 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 0 2544 85.828000 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 0 2545 86.257574 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 0 2546 4.176806 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 0 2547 -46.840000 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 0 2548 45.285000 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 0 2549 -11.238000 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 0 2550 -2.935000 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 0 2551 22.427000 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 0 2552 33.515544 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 0 2553 -28.605000 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 0 2554 55.200000 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 0 2555 -0.809000 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 0 2556 -65.300000 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 0 2557 61.236000 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 5.591000 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 -16.072000 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 49.507029 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 64.386000 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 0 2562 -18.446784 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 0 2563 45.261000 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 0 2564 9.955137 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 0 2565 48.146000 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 -70.967000 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 37.334164 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 -75.159000 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 -67.467580 \n", - "\n", - " rxy_semantic \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 90.000000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 -0.000000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 -0.000000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 -0.000000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 -0.000000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 90.000000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 -0.000000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 -0.000000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 -0.000000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 90.000000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 90.000000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 -90.000000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 90.000000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 90.000000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 -0.000000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 90.000000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 -0.000000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 90.000000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 90.000000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 -90.000000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 -0.000000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 90.000000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 90.000000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 -0.000000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 -0.000000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 -90.000000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 -0.000000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 -0.000000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 -0.000000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 -0.000000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 5.769000 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 23.868000 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 0 2542 -139.628000 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 0 2543 82.706000 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 0 2544 175.828000 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 0 2545 86.257574 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 0 2546 4.176806 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 0 2547 -46.840000 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 0 2548 45.285000 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 0 2549 -11.238000 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 0 2550 -2.935000 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 0 2551 22.427000 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 0 2552 33.515544 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 0 2553 61.395000 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 0 2554 55.200000 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 0 2555 89.191000 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 0 2556 -65.300000 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 0 2557 151.236000 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 5.591000 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 -16.072000 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 139.507029 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 64.386000 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 0 2562 -18.446784 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 0 2563 135.261000 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 0 2564 99.955137 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 0 2565 48.146000 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 -70.967000 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 37.334164 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 -75.159000 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 -67.467580 \n", - "\n", - " rxz \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 0.000000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 0.000000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 0.000000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 0.000000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 0.000000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 0.000000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 0.000000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 0.000000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 0.000000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 0.000000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 0.000000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 0.000000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 0.000000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 0.000000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 0.000000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 0.000000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 0.000000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 0.000000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 0.000000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 0.000000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 0.000000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 0.000000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 0.000000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 0.000000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 0.000000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 0.000000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 0.000000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 0.000000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 0.000000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 0.000000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 -89.052000 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 -67.801000 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 0 2542 -35.196000 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 0 2543 -71.878000 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 0 2544 -73.304000 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 0 2545 1.580624 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 0 2546 2.259833 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 0 2547 16.090000 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 0 2548 37.122000 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 0 2549 25.949000 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 0 2550 49.021000 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 0 2551 45.258000 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 0 2552 -9.180214 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 0 2553 -3.829000 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 0 2554 -67.932000 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 0 2555 -71.145000 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 0 2556 -84.440000 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 0 2557 69.658000 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 -55.612000 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 86.864000 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 -3.860598 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 42.742000 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 0 2562 -10.719291 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 0 2563 -43.867000 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 0 2564 24.193423 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 0 2565 -41.898000 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 11.985000 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 -67.238762 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 29.466000 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 -25.152591 \n", - "\n", - " rxz_semantic \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 0.000000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 0.000000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 0.000000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 0.000000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 0.000000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 0.000000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 0.000000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 0.000000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 0.000000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 0.000000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 0.000000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 0.000000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 0.000000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 0.000000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 0.000000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 0.000000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 0.000000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 0.000000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 0.000000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 0.000000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 0.000000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 0.000000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 0.000000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 0.000000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 0.000000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 0.000000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 0.000000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 0.000000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 0.000000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 0.000000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 -89.052000 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 -67.801000 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 0 2542 -35.196000 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 0 2543 -71.878000 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 0 2544 -73.304000 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 0 2545 1.580624 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 0 2546 2.259833 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 0 2547 16.090000 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 0 2548 37.122000 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 0 2549 25.949000 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 0 2550 49.021000 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 0 2551 45.258000 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 0 2552 -9.180214 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 0 2553 -3.829000 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 0 2554 -67.932000 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 0 2555 -71.145000 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 0 2556 -84.440000 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 0 2557 69.658000 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 -55.612000 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 86.864000 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 -3.860598 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 42.742000 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 0 2562 -10.719291 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 0 2563 -43.867000 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 0 2564 24.193423 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 0 2565 -41.898000 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 11.985000 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 -67.238762 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 29.466000 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 -25.152591 \n", - "\n", - " ryz \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 -0.000000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 -0.000000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 -0.000000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 -0.000000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 -0.000000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 -0.000000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 -0.000000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 -0.000000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 -0.000000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 -0.000000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 -0.000000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 -0.000000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 -0.000000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 -0.000000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 -0.000000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 -0.000000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 -0.000000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 -0.000000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 -0.000000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 -0.000000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 -0.000000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 -0.000000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 -0.000000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 -0.000000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 -0.000000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 -0.000000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 -0.000000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 -0.000000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 -0.000000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 -0.000000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 74.978000 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 -6.454000 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 0 2542 72.620000 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 0 2543 -3.863000 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 0 2544 -50.242000 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 0 2545 -147.908623 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 0 2546 24.748583 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 0 2547 24.070000 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 0 2548 21.814000 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 0 2549 49.199000 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 0 2550 -52.625000 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 0 2551 -17.151000 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 0 2552 -2.873322 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 0 2553 62.157000 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 0 2554 -1.643000 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 0 2555 -30.805000 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 0 2556 -13.491000 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 0 2557 7.161000 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 -84.185000 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 30.443000 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 18.531554 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 87.404000 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 0 2562 -96.632598 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 0 2563 -21.184000 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 0 2564 -19.936269 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 0 2565 51.346000 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 -45.818000 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 10.426955 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 12.283000 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 7.598457 \n", - "\n", - " ryz_semantic \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 -0.000000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 -0.000000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 -0.000000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 -0.000000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 -0.000000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 -0.000000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 -0.000000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 -0.000000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 -0.000000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 -0.000000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 -0.000000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 -0.000000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 -0.000000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 -0.000000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 -0.000000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 -0.000000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 -0.000000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 -0.000000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 -0.000000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 -0.000000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 -0.000000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 -0.000000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 -0.000000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 -0.000000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 -0.000000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 -0.000000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 -0.000000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 -0.000000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 -0.000000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 -0.000000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 74.978000 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 -6.454000 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 0 2542 72.620000 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 0 2543 -3.863000 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 0 2544 -50.242000 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 0 2545 -147.908623 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 0 2546 24.748583 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 0 2547 24.070000 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 0 2548 21.814000 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 0 2549 49.199000 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 0 2550 -52.625000 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 0 2551 -17.151000 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 0 2552 -2.873322 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 0 2553 62.157000 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 0 2554 -1.643000 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 0 2555 -30.805000 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 0 2556 -13.491000 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 0 2557 7.161000 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 -84.185000 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 30.443000 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 18.531554 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 87.404000 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 0 2562 -96.632598 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 0 2563 -21.184000 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 0 2564 -19.936269 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 0 2565 51.346000 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 -45.818000 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 10.426955 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 12.283000 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 7.598457 \n", - "\n", - " s \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 1.000000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 1.000000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 1.000000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 1.000000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 1.000000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 1.000000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 1.000000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 1.000000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 1.000000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 1.000000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 1.000000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 1.000000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 1.000000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 1.000000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 1.000000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 1.000000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 1.000000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 1.000000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 1.000000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 1.000000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 1.000000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 1.000000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 1.000000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 1.000000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 1.000000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 1.000000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 1.000000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 1.000000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 1.000000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 1.000000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 1.578750 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 0.820769 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 0 2542 0.853333 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 0 2543 0.860000 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 0 2544 0.753750 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 0 2545 0.920000 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 0 2546 1.187143 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 0 2547 0.752857 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 0 2548 1.418571 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 0 2549 1.305714 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 0 2550 1.410000 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 0 2551 1.487500 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 0 2552 0.637143 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 0 2553 1.273000 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 0 2554 1.251429 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 0 2555 1.073333 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 0 2556 1.051429 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 0 2557 1.151250 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 0.800000 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 0.657143 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 0.641538 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 0.830000 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 0 2562 1.407143 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 0 2563 1.297000 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 0 2564 0.856154 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 0 2565 0.923750 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 0.802857 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 1.478571 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 0.791250 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 1.050000 \n", - "\n", - " size \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 256.0 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 256.0 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 256.0 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 256.0 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 256.0 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 256.0 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 256.0 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 256.0 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 256.0 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 256.0 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 256.0 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 256.0 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 256.0 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 256.0 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 256.0 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 256.0 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 256.0 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 256.0 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 256.0 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 256.0 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 256.0 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 256.0 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 256.0 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 256.0 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 256.0 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 256.0 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 256.0 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 256.0 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 256.0 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 256.0 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 256.0 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 256.0 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 0 2542 256.0 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 0 2543 256.0 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 0 2544 256.0 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 0 2545 256.0 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 0 2546 256.0 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 0 2547 256.0 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 0 2548 256.0 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 0 2549 256.0 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 0 2550 256.0 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 0 2551 256.0 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 0 2552 256.0 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 0 2553 256.0 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 0 2554 256.0 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 0 2555 256.0 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 0 2556 256.0 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 0 2557 256.0 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 256.0 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 256.0 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 256.0 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 256.0 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 0 2562 256.0 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 0 2563 256.0 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 0 2564 256.0 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 0 2565 256.0 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 256.0 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 256.0 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 256.0 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 256.0 \n", - "\n", - " ty \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 0.000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 0.000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 0.000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 0.000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 0.000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 0.000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 0.000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 0.000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 0.000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 0.000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 0.000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 0.000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 0.000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 0.000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 0.000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 0.000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 0.000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 0.000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 0.000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 0.000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 0.000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 0.000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 0.000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 0.000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 0.000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 0.000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 0.000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 0.000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 0.000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 0.000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 -0.598 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 0.542 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 0 2542 -0.222 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 0 2543 0.546 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 0 2544 0.044 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 0 2545 -0.493 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 0 2546 -0.087 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 0 2547 -0.160 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 0 2548 0.060 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 0 2549 -0.428 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 0 2550 0.510 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 0 2551 -0.587 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 0 2552 0.308 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 0 2553 0.409 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 0 2554 -0.215 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 0 2555 0.430 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 0 2556 0.149 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 0 2557 0.318 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 0.148 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 0.597 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 0.009 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 -0.517 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 0 2562 0.251 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 0 2563 -0.423 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 0 2564 -0.284 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 0 2565 0.526 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 0.371 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 -0.354 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 0.391 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 0.049 \n", - "\n", - " tz \\\n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 0.000 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 0.000 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 0.000 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 0.000 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 0.000 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 0.000 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 0.000 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 0.000 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 0.000 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 0.000 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 0.000 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 0.000 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 0.000 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 0.000 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 0.000 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 0.000 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 0.000 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 0.000 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 0.000 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 0.000 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 0.000 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 0.000 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 0.000 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 0.000 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 0.000 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 0.000 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 0.000 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 0.000 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 0.000 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 0.000 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 -0.709 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 -0.870 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 0 2542 -0.015 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 0 2543 0.724 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 0 2544 0.943 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 0 2545 0.308 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 0 2546 -0.916 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 0 2547 -0.693 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 0 2548 1.199 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 0 2549 0.113 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 0 2550 -0.423 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 0 2551 0.976 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 0 2552 0.949 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 0 2553 0.252 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 0 2554 1.179 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 0 2555 0.209 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 0 2556 -1.000 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 0 2557 -1.053 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 0.380 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 -0.560 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 -0.564 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 -0.577 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 0 2562 -0.830 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 0 2563 1.095 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 0 2564 -0.537 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 0 2565 -0.511 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 0.205 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 0.466 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 -0.280 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 0.187 \n", - "\n", - " variation \n", - "ryz_semantic object variation image_id image_file_name rxy_semantic size category tz ty rxz_semantic rxy background_id s rxz ryz repetition stimulus \n", - "-0.000000 car_astra V0 8a72e2bfdb8c267b57232bf96f069374d5b21832 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 ecd40f3f6d7a4d6d88134d648884e0b9b364efc9 1.000000 0.000000 -0.000000 0 0 V0 \n", - " table3 V0 27f69468c9d6019ed0d22b9583c94c5b58198c1c _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 006d66c207c6417574f62f0560c6b2b40a9ec5a1 1.000000 0.000000 -0.000000 0 1 V0 \n", - " face2 V0 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 3b3c1d65865028d0fad0b0bf8f305098db717e7f 1.000000 0.000000 -0.000000 0 2 V0 \n", - " walnut V0 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 687ade2f9ee4d52af9705865395471a24ba38d5f 1.000000 0.000000 -0.000000 0 3 V0 \n", - " d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 724e5703cc42aa2c3ff135e3508038a90e4ebcb3 1.000000 0.000000 -0.000000 0 4 V0 \n", - " airplane7 V0 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 d6399fdb5cfe58a1c1908762cfe82406e2510231 1.000000 0.000000 -0.000000 0 5 V0 \n", - " apricot V0 ae2b8aac003c867a772647197dcd3c050b75a06e Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 7b44fb43755d315da8e94d73a1ab04e5db592a04 1.000000 0.000000 -0.000000 0 6 V0 \n", - " strawberry V0 a7a31e65e2770c7a503988835c24aeab689abf47 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 03f46b6acdb2fc42f934c5ef67792026de49d530 1.000000 0.000000 -0.000000 0 7 V0 \n", - " face1 V0 09d2924723bc4677df666072b6e25e988d5f0668 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 0e3b948ff7c95fcdd6bd07dc0d74b8e2c6ea0e25 1.000000 0.000000 -0.000000 0 8 V0 \n", - " airplane4 V0 1df484a50ff7fbd85112990b7f8fe616c49adb28 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.0... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 2164801f3646e5e82eaa4373f8d249ce17edf1ac 1.000000 0.000000 -0.000000 0 9 V0 \n", - " ship1 V0 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9 1.000000 0.000000 -0.000000 0 10 V0 \n", - " ship3 V0 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 87b9dcc6b79133dc602f3a1c3f9a7cbbbb3a6456 1.000000 0.000000 -0.000000 0 11 V0 \n", - " car_clio V0 fbef152524d2e593206fd976afde5e0506f477a8 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 fa3aff9af6b31c0527d07d84a9f49b95f265f1fb 1.000000 0.000000 -0.000000 0 12 V0 \n", - " dog V0 a17db26a610f96215d94cd673fd982c5bb3fd524 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.0... 90.000000 256.0 Animals 0.000 0.000 0.000000 -0.000000 8f388437a015e5358e27dad6f0d60e023db40e60 1.000000 0.000000 -0.000000 0 13 V0 \n", - " peach V0 41e376452bcb4a61646304b4fd397f11e329ebb2 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.0... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 0c0d7402273188a7ed2d91dbef4baf75554c2017 1.000000 0.000000 -0.000000 0 14 V0 \n", - " car_astra V0 36b1652f43aedf23e39f351fd931ab0cb4c410b5 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 087d94504c098c5ebf0ad52164ffd70020644e6e 1.000000 0.000000 -0.000000 0 15 V0 \n", - " table5 V0 6a8b93cd59587cc2969a126361bce879774e7f4f _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 8001b24ed42c36be17da6ca4213b4d8be30be63a 1.000000 0.000000 -0.000000 0 16 V0 \n", - " car_beetle V0 05954905d976ab1cf247c49fc31e534a8a047ba1 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... 90.000000 256.0 Cars 0.000 0.000 0.000000 -0.000000 90f464d4725214ddae0e9d2dd2e759db0561cb94 1.000000 0.000000 -0.000000 0 17 V0 \n", - " airplane7 V0 97757502b8c2e32e47a79a266f2da46bbb7165d1 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.000... 90.000000 256.0 Planes 0.000 0.000 0.000000 -0.000000 23957035c1e23cd4e8480b9aae74eca53837d835 1.000000 0.000000 -0.000000 0 18 V0 \n", - " ship3 V0 45b221ea1ed093c991ba6503c57093f6bea2c9d7 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 78944dce513d8a9bd023e4b4e0e7c53f69437ea6 1.000000 0.000000 -0.000000 0 19 V0 \n", - " watermelon V0 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_tx... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 ee94a3eb3df3de74988b6021a4a4644b6a8349a2 1.000000 0.000000 -0.000000 0 20 V0 \n", - " ship5 V0 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 824c7a6bd9bd2d9db1a239a90330ee4547249439 1.000000 0.000000 -0.000000 0 21 V0 \n", - " ship4 V0 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... 90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 0ab2c4be3b78a75e6e4b9cceac1b982e14546335 1.000000 0.000000 -0.000000 0 22 V0 \n", - " table7 V0 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 4252b5aec4714cde9351afcc18f9c4f1a7b260ae 1.000000 0.000000 -0.000000 0 23 V0 \n", - " face0 V0 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 eb58d84e32e97638191725980511005bc3c4298f 1.000000 0.000000 -0.000000 0 24 V0 \n", - " ship3 V0 88429363542db8c918d7112a7335fcca62966a3e MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -90.000000 256.0 Boats 0.000 0.000 0.000000 -0.000000 9bafe6f72571fcf11f6fc8f72bbc1f5bc2f8c0e0 1.000000 0.000000 -0.000000 0 25 V0 \n", - " table4 V0 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+... -0.000000 256.0 Tables 0.000 0.000 0.000000 -0.000000 f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2 1.000000 0.000000 -0.000000 0 26 V0 \n", - " apricot V0 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 4647133f7784fbd40525ea113de132186f16667e 1.000000 0.000000 -0.000000 0 27 V0 \n", - " 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... -0.000000 256.0 Fruits 0.000 0.000 0.000000 -0.000000 36d608bfa09d2d58f42f1cd12a27a2b80a3b8d02 1.000000 0.000000 -0.000000 0 28 V0 \n", - " face0 V0 a533e519b553cdf360ae134f7c9f161134ddaf00 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.00... -0.000000 256.0 Faces 0.000 0.000 0.000000 -0.000000 840b0badda8be10e1e3d5eff5cedce384f8021bc 1.000000 0.000000 -0.000000 0 29 V0 \n", - "... ... \n", - " 74.978000 turtle V6 fd135a1c5e2b157b4557ef8473681ca6d03b0188 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.59... 5.769000 256.0 Animals -0.709 -0.598 -89.052000 -84.231000 3f072352f8336529aba839aa86fe0114d11886fc 1.578750 -89.052000 74.978000 0 2540 V6 \n", - "-6.454000 airplane6 V6 6b87003f02400681dc545c7cd57cde747c1cb98a rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.54... 23.868000 256.0 Planes -0.870 0.542 -67.801000 -66.132000 771e5ab1f204905bf03d06936b01bb5f4de92788 0.820769 -67.801000 -6.454000 0 2541 V6 \n", - " 72.620000 ship7 V6 a051580c645ed432381d24beb6e93ee7952c8334 support_rx-35.196_ry+40.372_rz+72.620_tx-00.222... -139.628000 256.0 Boats -0.015 -0.222 -35.196000 -49.628000 f0cd72ef854ffa497fc53ef0e179bce45e8c2743 0.853333 -35.196000 72.620000 0 2542 V6 \n", - "-3.863000 car_celica V6 a8648457a08c568c907c68d058e2d33bb276a71e celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_... 82.706000 256.0 Cars 0.724 0.546 -71.878000 -7.294000 f2e18d3ca4f6e9899a519b5d74d2be7c4577a401 0.860000 -71.878000 -3.863000 0 2543 V6 \n", - "-50.242000 gorilla V6 241a33fd5221263ac3d350bd82ddeab2d0104eab GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044... 175.828000 256.0 Animals 0.943 0.044 -73.304000 85.828000 6dec52041309894f4d4da45448fc6c9a4cd4ba87 0.753750 -73.304000 -50.242000 0 2544 V6 \n", - "-147.908623 walnut V6 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00.... 86.257574 256.0 Fruits 0.308 -0.493 1.580624 86.257574 b1a3abb743a6380929584173dcbbb5956bc0d216 0.920000 1.580624 -147.908623 0 2545 V6 \n", - " 24.748583 walnut V6 51ba24371f3c0683da1978d332184732bbec5b96 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00.... 4.176806 256.0 Fruits -0.916 -0.087 2.259833 4.176806 2bc87202ff78a3f635e52fc82bf5bad487aaa1bc 1.187143 2.259833 24.748583 0 2546 V6 \n", - " 24.070000 chair5 V6 88f954276bb53915429cc2d117e57c08bc8aaddd _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty... -46.840000 256.0 Chairs -0.693 -0.160 16.090000 -46.840000 828850cf3e0695ec1d4d32219904cf5e199210e2 0.752857 16.090000 24.070000 0 2547 V6 \n", - " 21.814000 chair2 V6 d2af56088e557fbafc9815e9a7486f3efe61823e _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty... 45.285000 256.0 Chairs 1.199 0.060 37.122000 45.285000 cb40eea4d91e603380e46852251ad642dfd0ba75 1.418571 37.122000 21.814000 0 2548 V6 \n", - " 49.199000 chair0 V6 d4dd9a086994a89bf6e2d32efab254bdd4648888 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty... -11.238000 256.0 Chairs 0.113 -0.428 25.949000 -11.238000 f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6 1.305714 25.949000 49.199000 0 2549 V6 \n", - "-52.625000 chair3 V6 30912859b0dfac9c6531061a796fb96f16510fa8 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty... -2.935000 256.0 Chairs -0.423 0.510 49.021000 -2.935000 9b87c8be1440ce9626bcfe656700ff3ac8f909fe 1.410000 49.021000 -52.625000 0 2550 V6 \n", - "-17.151000 face7 V6 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.58... 22.427000 256.0 Faces 0.976 -0.587 45.258000 22.427000 62a27decc64031d42ac8b683fb1e8c0ac0b616cf 1.487500 45.258000 -17.151000 0 2551 V6 \n", - "-2.873322 peach V6 518d256ccedbcda049049c1183bc30ff2aa93258 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00.... 33.515544 256.0 Fruits 0.949 0.308 -9.180214 33.515544 57102e072f4cac897d8089085ca932a40a1aa1d5 0.637143 -9.180214 -2.873322 0 2552 V6 \n", - " 62.157000 car_alfa V6 98cc0d8f063fae1b0c22645f384875ec0f73212c alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409... 61.395000 256.0 Cars 0.252 0.409 -3.829000 -28.605000 f934a940602d18a719e8a3026c201301958142af 1.273000 -3.829000 62.157000 0 2553 V6 \n", - "-1.643000 chair7 V6 fd18b1f8ca6d961c163aa56772d813f6cba28336 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty... 55.200000 256.0 Chairs 1.179 -0.215 -67.932000 55.200000 5e13ca12b89cd9631656c619a3a0c88b69b75b8e 1.251429 -67.932000 -1.643000 0 2554 V6 \n", - "-30.805000 ship5 V6 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430... 89.191000 256.0 Boats 0.209 0.430 -71.145000 -0.809000 4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b 1.073333 -71.145000 -30.805000 0 2555 V6 \n", - "-13.491000 table3 V6 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty-... -65.300000 256.0 Tables -1.000 0.149 -84.440000 -65.300000 20a8031d6c172b2f92dbd5d08b41d5dcd3f4d21b 1.051429 -84.440000 -13.491000 0 2556 V6 \n", - " 7.161000 turtle V6 d15aa059b0180b200fb82fb8039247c3b53f9d66 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.31... 151.236000 256.0 Animals -1.053 0.318 69.658000 61.236000 5068064aee7a1831c3a5fe0f023b05cffca511e3 1.151250 69.658000 7.161000 0 2557 V6 \n", - "-84.185000 table6 V6 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty+... 5.591000 256.0 Tables 0.380 0.148 -55.612000 5.591000 d6e2227592c00e5fa2607781d8a5096854058f7b 0.800000 -55.612000 -84.185000 0 2558 V6 \n", - " 30.443000 chair1 V6 af8325d2d77b8475a00eb9a5296689adb4a8f39b _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty... -16.072000 256.0 Chairs -0.560 0.597 86.864000 -16.072000 52b8677d036765ba6c05d8035b641e3c55993f9a 0.657143 86.864000 30.443000 0 2559 V6 \n", - " 18.531554 airplane0 V6 5cbd24a0ac1881551f356940236e021bd29cd1de _01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz... 139.507029 256.0 Planes -0.564 0.009 -3.860598 49.507029 5a38b191f3aaa9592b8909777441e9dab3913997 0.641538 -3.860598 18.531554 0 2560 V6 \n", - " 87.404000 face1 V6 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.51... 64.386000 256.0 Faces -0.577 -0.517 42.742000 64.386000 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 0.830000 42.742000 87.404000 0 2561 V6 \n", - "-96.632598 walnut V6 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+00... -18.446784 256.0 Fruits -0.830 0.251 -10.719291 -18.446784 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 1.407143 -10.719291 -96.632598 0 2562 V6 \n", - "-21.184000 car_alfa V6 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423... 135.261000 256.0 Cars 1.095 -0.423 -43.867000 45.261000 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 1.297000 -43.867000 -21.184000 0 2563 V6 \n", - "-19.936269 airplane1 V6 3fe99701f03c85d5b2017d4ec33eb227a695386e _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.... 99.955137 256.0 Planes -0.537 -0.284 24.193423 9.955137 0405dd6bf7747d33a030f46c86eabda0bd95e00a 0.856154 24.193423 -19.936269 0 2564 V6 \n", - " 51.346000 face3 V6 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.52... 48.146000 256.0 Faces -0.511 0.526 -41.898000 48.146000 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d 0.923750 -41.898000 51.346000 0 2565 V6 \n", - "-45.818000 table0 V6 2c381c66bf457942bf4818e54b4b5172b3cc0cac _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty+... -70.967000 256.0 Tables 0.205 0.371 11.985000 -70.967000 821565cb10250ce762b8c594185fc810f52d38ea 0.802857 11.985000 -45.818000 0 2566 V6 \n", - " 10.426955 strawberry V6 d6a597127092d38ed37bcb19967075698acb35ee Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_t... 37.334164 256.0 Fruits 0.466 -0.354 -67.238762 37.334164 524d21ed6255411c1f1531e075b1098eb2cda2e6 1.478571 -67.238762 10.426955 0 2567 V6 \n", - " 12.283000 face3 V6 cc975ea997317b74a79b9f5568f8008acdec341e face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.39... -75.159000 256.0 Faces -0.280 0.391 29.466000 -75.159000 2363c8c3859603ee9db7f83dd7ba5b6989154258 0.791250 29.466000 12.283000 0 2568 V6 \n", - " 7.598457 apricot V6 e35071a43f7a0dafc21b18229ca36f1940c2c567 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+0... -67.467580 256.0 Fruits 0.187 0.049 -25.152591 -67.467580 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 1.050000 -25.152591 7.598457 0 2569 V6 \n", - "\n", - "[5760 rows x 16 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_images = df_stimulus[df_stimulus[\"variation\"]!=\"nan\"].drop(columns=[\"stimulus\", \"repetition\"]).drop_duplicates()\n", - "df_images" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5760, 16)" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_images.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(296, 270060, 1)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hvm_stripped.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[[ 0.060929],\n", - " [-0.686162],\n", - " ...,\n", - " [-0.968256],\n", - " [ 0.183887]],\n", - "\n", - " [[-0.725592],\n", - " [ 0.292777],\n", - " ...,\n", - " [ 2.449372],\n", - " [ 0.401197]],\n", - "\n", - " ...,\n", - "\n", - " [[ 1.121319],\n", - " [ 1.719423],\n", - " ...,\n", - " [ 0.800551],\n", - " [-0.019874]],\n", - "\n", - " [[-0.518903],\n", - " [ 0.696196],\n", - " ...,\n", - " [-0.603347],\n", - " [-0.175979]]], dtype=float32)\n", - "Coordinates:\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object 'Chabo_L_M_5_9' 'Chabo_L_M_6_9' ...\n", - " - arr (neuroid) object 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' ...\n", - " - col (neuroid) int64 9 9 8 9 8 8 7 7 5 6 4 9 9 9 9 9 8 7 9 6 ...\n", - " - hemisphere (neuroid) object 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' ...\n", - " - subregion (neuroid) object 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' ...\n", - " - animal (neuroid) object 'Chabo' 'Chabo' 'Chabo' 'Chabo' 'Chabo' ...\n", - " - y (neuroid) float64 0.2 0.6 0.2 1.0 0.6 1.0 1.8 1.0 1.8 ...\n", - " - x (neuroid) float64 1.8 1.8 1.4 1.8 1.4 1.4 1.0 1.0 0.2 ...\n", - " - row (neuroid) int64 5 6 5 7 6 7 9 7 9 8 9 2 1 3 6 7 6 6 8 5 ...\n", - " - region (neuroid) object 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' ...\n", - " * presentation (presentation) MultiIndex\n", - " - image_id (presentation) object '8a72e2bfdb8c267b57232bf96f069374d5b21832' ...\n", - " - repetition (presentation) int64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...\n", - " - stimulus (presentation) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 ...\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_end (time_bin) int64 170\n", - " - time_bin_start (time_bin) int64 70" - ] - }, - "execution_count": 167, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hvm_stripped_copy = hvm_stripped.copy()\n", - "hvm_stripped_copy" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": {}, - "outputs": [], - "source": [ - "def add_joined_levels(assy, meta_frame, join_key, axis_name, columns):\n", - " df_of_multi_index = assy.indexes[axis_name].to_frame()\n", - " merged = df_of_multi_index.merge(meta_frame, on=join_key, how=\"left\")\n", - " for col in columns:\n", - " assy[col] = (axis_name, merged[col])\n", - " assy.set_index(append=True, inplace=True, **{axis_name: [col]})" - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[[ 0.060929],\n", - " [-0.686162],\n", - " ...,\n", - " [-0.968256],\n", - " [ 0.183887]],\n", - "\n", - " [[-0.725592],\n", - " [ 0.292777],\n", - " ...,\n", - " [ 2.449372],\n", - " [ 0.401197]],\n", - "\n", - " ...,\n", - "\n", - " [[ 1.121319],\n", - " [ 1.719423],\n", - " ...,\n", - " [ 0.800551],\n", - " [-0.019874]],\n", - "\n", - " [[-0.518903],\n", - " [ 0.696196],\n", - " ...,\n", - " [-0.603347],\n", - " [-0.175979]]], dtype=float32)\n", - "Coordinates:\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object 'Chabo_L_M_5_9' 'Chabo_L_M_6_9' ...\n", - " - arr (neuroid) object 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' ...\n", - " - col (neuroid) int64 9 9 8 9 8 8 7 7 5 6 4 9 9 9 9 9 8 7 9 6 ...\n", - " - hemisphere (neuroid) object 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' ...\n", - " - subregion (neuroid) object 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' ...\n", - " - animal (neuroid) object 'Chabo' 'Chabo' 'Chabo' 'Chabo' ...\n", - " - y (neuroid) float64 0.2 0.6 0.2 1.0 0.6 1.0 1.8 1.0 1.8 ...\n", - " - x (neuroid) float64 1.8 1.8 1.4 1.8 1.4 1.4 1.0 1.0 0.2 ...\n", - " - row (neuroid) int64 5 6 5 7 6 7 9 7 9 8 9 2 1 3 6 7 6 6 8 5 ...\n", - " - region (neuroid) object 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' ...\n", - " * presentation (presentation) MultiIndex\n", - " - image_id (presentation) object '8a72e2bfdb8c267b57232bf96f069374d5b21832' ...\n", - " - repetition (presentation) int64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...\n", - " - stimulus (presentation) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 ...\n", - " - ryz_semantic (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " - variation (presentation) object 'V0' 'V0' 'V0' 'V0' 'V0' 'V0' ...\n", - " - image_file_name (presentation) object 'astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_ecd40f3f6d7a4d6d88134d648884e0b9b364efc9_256x256.png' ...\n", - " - rxy_semantic (presentation) float64 90.0 -0.0 -0.0 -0.0 -0.0 90.0 ...\n", - " - size (presentation) float64 256.0 256.0 256.0 256.0 256.0 ...\n", - " - category (presentation) object 'Cars' 'Tables' 'Faces' 'Fruits' ...\n", - " - object (presentation) object 'car_astra' 'table3' 'face2' ...\n", - " - tz (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - ty (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - rxz_semantic (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - rxy (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " - background_id (presentation) object 'ecd40f3f6d7a4d6d88134d648884e0b9b364efc9' ...\n", - " - s (presentation) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ...\n", - " - rxz (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - ryz (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_end (time_bin) int64 170\n", - " - time_bin_start (time_bin) int64 70" - ] - }, - "execution_count": 168, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "add_joined_levels(hvm_stripped_copy, df_images, \"image_id\", \"presentation\", to_remove)\n", - "hvm_stripped_copy" - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "metadata": {}, - "outputs": [], - "source": [ - "assert hvm_stripped_copy.shape == hvm.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "metadata": {}, - "outputs": [], - "source": [ - "nanless = lambda da: da.loc[:, da[\"image_id\"]!=\"nan\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "metadata": {}, - "outputs": [], - "source": [ - "assert all(nanless(hvm_stripped_copy)[\"image_id\"].values == nanless(hvm)[\"image_id\"].values)\n", - "assert all(nanless(hvm_stripped_copy)[\"background_id\"].values == nanless(hvm)[\"background_id\"].values)\n", - "assert all(nanless(hvm_stripped_copy)[\"object\"].values == nanless(hvm)[\"object\"].values)\n", - "assert all(nanless(hvm_stripped_copy)[\"variation\"].values == nanless(hvm)[\"variation\"].values)" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "metadata": {}, - "outputs": [], - "source": [ - "pwdb = peewee.SqliteDatabase(\"image_meta_scratch.db\")" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['foo',\n", - " 'image',\n", - " 'imageimagestoremap',\n", - " 'imagestore',\n", - " 'stimulusset',\n", - " 'stimulussetimagemap']" - ] - }, - "execution_count": 173, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pwdb.get_tables()" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": {}, - "outputs": [], - "source": [ - "class Image(peewee.Model):\n", - " hash_id = peewee.CharField()\n", - " object_name = peewee.CharField()\n", - " category_name = peewee.CharField()\n", - " background_id = peewee.CharField()\n", - " image_file_name = peewee.CharField()\n", - " variation = peewee.IntegerField()\n", - " ty = peewee.FloatField()\n", - " tz = peewee.FloatField()\n", - " rxy = peewee.FloatField()\n", - " rxz = peewee.FloatField()\n", - " ryz = peewee.FloatField()\n", - " rxy_semantic = peewee.FloatField()\n", - " rxz_semantic = peewee.FloatField()\n", - " ryz_semantic = peewee.FloatField()\n", - " size = peewee.FloatField()\n", - " s = peewee.FloatField()\n", - "\n", - " class Meta:\n", - " database = pwdb" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": {}, - "outputs": [], - "source": [ - "class StimulusSet(peewee.Model):\n", - " name = peewee.CharField()\n", - " \n", - " class Meta:\n", - " database = pwdb" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [], - "source": [ - "class ImageStore(peewee.Model):\n", - " location = peewee.CharField()\n", - " location_type = peewee.CharField()\n", - " store_type = peewee.CharField()\n", - " \n", - " class Meta:\n", - " database = pwdb" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [], - "source": [ - "class StimulusSetImageMap(peewee.Model):\n", - " stimulus_set = peewee.ForeignKeyField(StimulusSet, backref=\"stimulus_set_image_maps\")\n", - " image = peewee.ForeignKeyField(Image, backref=\"stimulus_set_image_maps\")\n", - " \n", - " class Meta:\n", - " database = pwdb" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": {}, - "outputs": [], - "source": [ - "class ImageImageStoreMap(peewee.Model):\n", - " image_store = peewee.ForeignKeyField(ImageStore, backref=\"image_image_store_maps\")\n", - " image = peewee.ForeignKeyField(Image, backref=\"image_image_store_maps\")\n", - " path = peewee.CharField()\n", - " \n", - " class Meta:\n", - " database = pwdb " - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": {}, - "outputs": [], - "source": [ - "pw_classes = [Image, StimulusSet, ImageStore, StimulusSetImageMap, ImageImageStoreMap]" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [], - "source": [ - "pw_query = (StimulusSet\n", - " .select()\n", - " .join(StimulusSetImageMap)\n", - " .join(Image)\n", - " .join(ImageImageStoreMap)\n", - " .join(ImageStore))" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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"5751 fc7f1dc814e56de2c6e1a715a83c9f9a70b3a1f5 Faces \n", - "5752 9ce9ffa3b4e08ee91c5b5488dd80a62973e7e280 Fruits \n", - "5753 f1bed88aa0fa49ce67ca15a82eb37c003e95cb56 Cars \n", - "5754 0405dd6bf7747d33a030f46c86eabda0bd95e00a Planes \n", - "5755 f0e2cf213ecf2fa5b8f1d733bd80ba3037f9042d Faces \n", - "5756 821565cb10250ce762b8c594185fc810f52d38ea Tables \n", - "5757 524d21ed6255411c1f1531e075b1098eb2cda2e6 Fruits \n", - "5758 2363c8c3859603ee9db7f83dd7ba5b6989154258 Faces \n", - "5759 a7fc954d2bd08fc2c4d3ade347f41a03a5850131 Fruits \n", - "\n", - " hash_id id \\\n", - "0 8a72e2bfdb8c267b57232bf96f069374d5b21832 1 \n", - "1 27f69468c9d6019ed0d22b9583c94c5b58198c1c 2 \n", - "2 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 3 \n", - "3 d0f7a45b377d4920c3466ec7a20dce9437a150d6 4 \n", - "4 d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 5 \n", - "5 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 6 \n", - "6 ae2b8aac003c867a772647197dcd3c050b75a06e 7 \n", - "7 a7a31e65e2770c7a503988835c24aeab689abf47 8 \n", - "8 09d2924723bc4677df666072b6e25e988d5f0668 9 \n", - "9 1df484a50ff7fbd85112990b7f8fe616c49adb28 10 \n", - "10 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 11 \n", - "11 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 12 \n", - "12 fbef152524d2e593206fd976afde5e0506f477a8 13 \n", - "13 a17db26a610f96215d94cd673fd982c5bb3fd524 14 \n", - "14 41e376452bcb4a61646304b4fd397f11e329ebb2 15 \n", - "15 36b1652f43aedf23e39f351fd931ab0cb4c410b5 16 \n", - "16 6a8b93cd59587cc2969a126361bce879774e7f4f 17 \n", - "17 05954905d976ab1cf247c49fc31e534a8a047ba1 18 \n", - "18 97757502b8c2e32e47a79a266f2da46bbb7165d1 19 \n", - "19 45b221ea1ed093c991ba6503c57093f6bea2c9d7 20 \n", - "20 f680c591330f26793f4de603e26cc584ae19ddb2 21 \n", - "21 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 22 \n", - "22 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 23 \n", - "23 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 24 \n", - "24 3d31939c0fbbb695d865f3e010d9860b65c674f2 25 \n", - "25 88429363542db8c918d7112a7335fcca62966a3e 26 \n", - "26 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 27 \n", - "27 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d 28 \n", - "28 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 29 \n", - "29 a533e519b553cdf360ae134f7c9f161134ddaf00 30 \n", - "... ... ... \n", - "5730 fd135a1c5e2b157b4557ef8473681ca6d03b0188 5731 \n", - "5731 6b87003f02400681dc545c7cd57cde747c1cb98a 5732 \n", - "5732 a051580c645ed432381d24beb6e93ee7952c8334 5733 \n", - "5733 a8648457a08c568c907c68d058e2d33bb276a71e 5734 \n", - "5734 241a33fd5221263ac3d350bd82ddeab2d0104eab 5735 \n", - "5735 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 5736 \n", - "5736 51ba24371f3c0683da1978d332184732bbec5b96 5737 \n", - "5737 88f954276bb53915429cc2d117e57c08bc8aaddd 5738 \n", - "5738 d2af56088e557fbafc9815e9a7486f3efe61823e 5739 \n", - "5739 d4dd9a086994a89bf6e2d32efab254bdd4648888 5740 \n", - "5740 30912859b0dfac9c6531061a796fb96f16510fa8 5741 \n", - "5741 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e 5742 \n", - "5742 518d256ccedbcda049049c1183bc30ff2aa93258 5743 \n", - "5743 98cc0d8f063fae1b0c22645f384875ec0f73212c 5744 \n", - "5744 fd18b1f8ca6d961c163aa56772d813f6cba28336 5745 \n", - "5745 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 5746 \n", - "5746 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec 5747 \n", - "5747 d15aa059b0180b200fb82fb8039247c3b53f9d66 5748 \n", - "5748 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 5749 \n", - "5749 af8325d2d77b8475a00eb9a5296689adb4a8f39b 5750 \n", - "5750 5cbd24a0ac1881551f356940236e021bd29cd1de 5751 \n", - "5751 feef7d8cf74ce5f8a8d0612e015e0de363587e93 5752 \n", - "5752 6f61f7e3130d2fef3deeb310e08a927521cb932d 5753 \n", - "5753 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 5754 \n", - "5754 3fe99701f03c85d5b2017d4ec33eb227a695386e 5755 \n", - "5755 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 5756 \n", - "5756 2c381c66bf457942bf4818e54b4b5172b3cc0cac 5757 \n", - "5757 d6a597127092d38ed37bcb19967075698acb35ee 5758 \n", - "5758 cc975ea997317b74a79b9f5568f8008acdec341e 5759 \n", - "5759 e35071a43f7a0dafc21b18229ca36f1940c2c567 5760 \n", - "\n", - " image_file_name object_name \\\n", - "0 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... car_astra \n", - "1 _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... table3 \n", - "2 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.0... face2 \n", - "3 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... walnut \n", - "4 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... walnut \n", - "5 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.00... airplane7 \n", - "6 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0... apricot \n", - "7 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_t... strawberry \n", - "8 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.0... face1 \n", - "9 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.... airplane4 \n", - "10 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000... ship1 \n", - "11 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... ship3 \n", - "12 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... car_clio \n", - "13 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.... dog \n", - "14 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... peach \n", - "15 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... car_astra \n", - "16 _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... table5 \n", - "17 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000... car_beetle \n", - "18 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.00... airplane7 \n", - "19 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... ship3 \n", - "20 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_t... watermelon \n", - "21 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.00... ship5 \n", - "22 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... ship4 \n", - "23 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... table7 \n", - "24 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.0... face0 \n", - "25 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... ship3 \n", - "26 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... table4 \n", - "27 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0... apricot \n", - "28 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0... apricot \n", - "29 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.0... face0 \n", - "... ... ... \n", - "5730 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.5... turtle \n", - "5731 rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.5... airplane6 \n", - "5732 support_rx-35.196_ry+40.372_rz+72.620_tx-00.22... ship7 \n", - "5733 celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546... car_celica \n", - "5734 GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.04... gorilla \n", - "5735 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00... walnut \n", - "5736 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00... walnut \n", - "5737 _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_t... chair5 \n", - "5738 _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_t... chair2 \n", - "5739 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_t... chair0 \n", - "5740 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_t... chair3 \n", - "5741 face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.5... face7 \n", - "5742 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00... peach \n", - "5743 alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.40... car_alfa \n", - "5744 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_t... chair7 \n", - "5745 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.43... ship5 \n", - "5746 _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty... table3 \n", - "5747 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.3... turtle \n", - "5748 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty... table6 \n", - "5749 _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_t... chair1 \n", - "5750 _01_Airliner_2jetEngines_rx+49.355_ry+95.933_r... airplane0 \n", - "5751 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.5... face1 \n", - "5752 walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+0... walnut \n", - "5753 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.42... car_alfa \n", - "5754 _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00... airplane1 \n", - "5755 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.5... face3 \n", - "5756 _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty... table0 \n", - "5757 Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_... strawberry \n", - "5758 face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.3... face3 \n", - "5759 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+... apricot \n", - "\n", - " rxy rxy_semantic rxz rxz_semantic ryz \\\n", - "0 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "1 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "2 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "3 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "4 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "5 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "6 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "7 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "8 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "9 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "10 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "11 0.000000 -90.000000 0.000000 0.000000 0.000000 \n", - "12 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "13 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "14 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "15 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "16 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "17 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "18 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "19 0.000000 -90.000000 0.000000 0.000000 0.000000 \n", - "20 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "21 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "22 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "23 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "24 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "25 0.000000 -90.000000 0.000000 0.000000 0.000000 \n", - "26 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "27 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "28 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "29 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "... ... ... ... ... ... \n", - "5730 -84.231000 5.769000 -89.052000 -89.052000 74.978000 \n", - "5731 -66.132000 23.868000 -67.801000 -67.801000 -6.454000 \n", - "5732 -49.628000 -139.628000 -35.196000 -35.196000 72.620000 \n", - "5733 -7.294000 82.706000 -71.878000 -71.878000 -3.863000 \n", - "5734 85.828000 175.828000 -73.304000 -73.304000 -50.242000 \n", - "5735 86.257574 86.257574 1.580624 1.580624 -147.908623 \n", - "5736 4.176806 4.176806 2.259833 2.259833 24.748583 \n", - "5737 -46.840000 -46.840000 16.090000 16.090000 24.070000 \n", - "5738 45.285000 45.285000 37.122000 37.122000 21.814000 \n", - "5739 -11.238000 -11.238000 25.949000 25.949000 49.199000 \n", - "5740 -2.935000 -2.935000 49.021000 49.021000 -52.625000 \n", - "5741 22.427000 22.427000 45.258000 45.258000 -17.151000 \n", - "5742 33.515544 33.515544 -9.180214 -9.180214 -2.873322 \n", - "5743 -28.605000 61.395000 -3.829000 -3.829000 62.157000 \n", - "5744 55.200000 55.200000 -67.932000 -67.932000 -1.643000 \n", - "5745 -0.809000 89.191000 -71.145000 -71.145000 -30.805000 \n", - "5746 -65.300000 -65.300000 -84.440000 -84.440000 -13.491000 \n", - "5747 61.236000 151.236000 69.658000 69.658000 7.161000 \n", - "5748 5.591000 5.591000 -55.612000 -55.612000 -84.185000 \n", - "5749 -16.072000 -16.072000 86.864000 86.864000 30.443000 \n", - "5750 49.507029 139.507029 -3.860598 -3.860598 18.531554 \n", - 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26f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2Tables1d832e0abdd6266fb3dcacfa05df1725d6b08fb727_18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty...table40.0000000.0000000.0000000.0000000.0000000.0000001.000000256.00.0000.0000
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2836d608bfa09d2d58f42f1cd12a27a2b80a3b8d02Fruits6e2ddadd2ca093f7638282e4851b60d5b87b8ca429Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0...apricot0.0000000.0000000.0000000.0000000.0000000.0000001.000000256.00.0000.0000
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57582363c8c3859603ee9db7f83dd7ba5b6989154258Facescc975ea997317b74a79b9f5568f8008acdec341e5759face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.3...face3-75.159000-75.15900029.46600029.46600012.28300012.2830000.791250256.00.391-0.2806
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5760 rows × 17 columns

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"5745 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 5746 \n", - "5746 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec 5747 \n", - "5747 d15aa059b0180b200fb82fb8039247c3b53f9d66 5748 \n", - "5748 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 5749 \n", - "5749 af8325d2d77b8475a00eb9a5296689adb4a8f39b 5750 \n", - "5750 5cbd24a0ac1881551f356940236e021bd29cd1de 5751 \n", - "5751 feef7d8cf74ce5f8a8d0612e015e0de363587e93 5752 \n", - "5752 6f61f7e3130d2fef3deeb310e08a927521cb932d 5753 \n", - "5753 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 5754 \n", - "5754 3fe99701f03c85d5b2017d4ec33eb227a695386e 5755 \n", - "5755 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 5756 \n", - "5756 2c381c66bf457942bf4818e54b4b5172b3cc0cac 5757 \n", - "5757 d6a597127092d38ed37bcb19967075698acb35ee 5758 \n", - "5758 cc975ea997317b74a79b9f5568f8008acdec341e 5759 \n", - "5759 e35071a43f7a0dafc21b18229ca36f1940c2c567 5760 \n", - "\n", - " image_file_name object_name \\\n", - "0 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... car_astra \n", - "1 _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... table3 \n", - "2 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.0... face2 \n", - "3 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... walnut \n", - "4 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... walnut \n", - "5 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.00... airplane7 \n", - "6 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0... apricot \n", - "7 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_t... strawberry \n", - "8 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.0... face1 \n", - "9 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.... airplane4 \n", - "10 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000... ship1 \n", - "11 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... ship3 \n", - "12 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... car_clio \n", - "13 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.... dog \n", - "14 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... peach \n", - "15 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... car_astra \n", - "16 _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... table5 \n", - "17 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000... car_beetle \n", - "18 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.00... airplane7 \n", - "19 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... ship3 \n", - "20 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_t... watermelon \n", - "21 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.00... ship5 \n", - "22 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... ship4 \n", - "23 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... table7 \n", - "24 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.0... face0 \n", - "25 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... ship3 \n", - "26 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... table4 \n", - "27 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0... apricot \n", - "28 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0... apricot \n", - "29 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.0... face0 \n", - "... ... ... \n", - "5730 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.5... turtle \n", - "5731 rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.5... airplane6 \n", - "5732 support_rx-35.196_ry+40.372_rz+72.620_tx-00.22... ship7 \n", - "5733 celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546... car_celica \n", - "5734 GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.04... gorilla \n", - "5735 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00... walnut \n", - "5736 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00... walnut \n", - "5737 _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_t... chair5 \n", - "5738 _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_t... chair2 \n", - "5739 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_t... chair0 \n", - "5740 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_t... chair3 \n", - "5741 face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.5... face7 \n", - "5742 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00... peach \n", - "5743 alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.40... car_alfa \n", - "5744 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_t... chair7 \n", - "5745 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.43... ship5 \n", - "5746 _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty... table3 \n", - "5747 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.3... turtle \n", - "5748 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty... table6 \n", - "5749 _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_t... chair1 \n", - "5750 _01_Airliner_2jetEngines_rx+49.355_ry+95.933_r... airplane0 \n", - "5751 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.5... face1 \n", - "5752 walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+0... walnut \n", - "5753 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.42... car_alfa \n", - "5754 _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00... airplane1 \n", - "5755 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.5... face3 \n", - "5756 _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty... table0 \n", - "5757 Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_... strawberry \n", - "5758 face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.3... face3 \n", - "5759 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+... apricot \n", - "\n", - " rxy rxy_semantic rxz rxz_semantic ryz \\\n", - "0 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "1 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "2 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "3 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "4 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "5 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "6 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "7 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "8 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "9 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "10 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "11 0.000000 -90.000000 0.000000 0.000000 0.000000 \n", - "12 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "13 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "14 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "15 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - "16 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "17 0.000000 90.000000 0.000000 0.000000 0.000000 \n", - 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"metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<__main__.StimulusSet at 0x7ff6d638dd30>" - ] - }, - "execution_count": 182, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pw_hvm = StimulusSet.get(StimulusSet.name == \"HvM\")\n", - "pw_hvm" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[<__main__.Image at 0x7ff6d5662cc0>,\n", - " <__main__.Image at 0x7ff6d56626a0>,\n", - " <__main__.Image at 0x7ff6d5662748>,\n", - " <__main__.Image at 0x7ff6d5662828>,\n", - " <__main__.Image at 0x7ff6d5662908>,\n", - " <__main__.Image at 0x7ff6d5662d30>,\n", - " <__main__.Image at 0x7ff6d5662da0>,\n", - " <__main__.Image at 0x7ff6d5662e80>,\n", - " <__main__.Image at 0x7ff6d5662fd0>,\n", - " <__main__.Image at 0x7ff6d5662a58>,\n", - " <__main__.Image at 0x7ff6d5662550>,\n", - " <__main__.Image at 0x7ff6d5662dd8>,\n", - " <__main__.Image at 0x7ff6d5662ba8>,\n", - " <__main__.Image at 0x7ff6d5662f28>,\n", - 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"270055 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.5... \n", - "270056 _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty... \n", - "270057 Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_... \n", - "270058 face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.3... \n", - "270059 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+... \n", - "\n", - " image_id object repetition \\\n", - "0 8a72e2bfdb8c267b57232bf96f069374d5b21832 car_astra 0 \n", - "1 27f69468c9d6019ed0d22b9583c94c5b58198c1c table3 0 \n", - "2 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 face2 0 \n", - "3 d0f7a45b377d4920c3466ec7a20dce9437a150d6 walnut 0 \n", - "4 d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 walnut 0 \n", - "5 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 airplane7 0 \n", - "6 ae2b8aac003c867a772647197dcd3c050b75a06e apricot 0 \n", - "7 a7a31e65e2770c7a503988835c24aeab689abf47 strawberry 0 \n", - "8 09d2924723bc4677df666072b6e25e988d5f0668 face1 0 \n", - "9 1df484a50ff7fbd85112990b7f8fe616c49adb28 airplane4 0 \n", - "10 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 ship1 0 \n", - "11 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 ship3 0 \n", - "12 fbef152524d2e593206fd976afde5e0506f477a8 car_clio 0 \n", - "13 a17db26a610f96215d94cd673fd982c5bb3fd524 dog 0 \n", - "14 41e376452bcb4a61646304b4fd397f11e329ebb2 peach 0 \n", - "15 36b1652f43aedf23e39f351fd931ab0cb4c410b5 car_astra 0 \n", - "16 6a8b93cd59587cc2969a126361bce879774e7f4f table5 0 \n", - "17 05954905d976ab1cf247c49fc31e534a8a047ba1 car_beetle 0 \n", - "18 97757502b8c2e32e47a79a266f2da46bbb7165d1 airplane7 0 \n", - "19 45b221ea1ed093c991ba6503c57093f6bea2c9d7 ship3 0 \n", - "20 f680c591330f26793f4de603e26cc584ae19ddb2 watermelon 0 \n", - "21 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 ship5 0 \n", - "22 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 ship4 0 \n", - "23 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 table7 0 \n", - "24 3d31939c0fbbb695d865f3e010d9860b65c674f2 face0 0 \n", - "25 88429363542db8c918d7112a7335fcca62966a3e ship3 0 \n", - "26 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 table4 0 \n", - "27 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d apricot 0 \n", - "28 6e2ddadd2ca093f7638282e4851b60d5b87b8ca4 apricot 0 \n", - "29 a533e519b553cdf360ae134f7c9f161134ddaf00 face0 0 \n", - "... ... ... ... \n", - "270030 fd135a1c5e2b157b4557ef8473681ca6d03b0188 turtle 46 \n", - "270031 6b87003f02400681dc545c7cd57cde747c1cb98a airplane6 46 \n", - "270032 a051580c645ed432381d24beb6e93ee7952c8334 ship7 46 \n", - "270033 a8648457a08c568c907c68d058e2d33bb276a71e car_celica 46 \n", - "270034 241a33fd5221263ac3d350bd82ddeab2d0104eab gorilla 46 \n", - "270035 81445f4ea46ae937d1b94f93f5e9a475dc6e0817 walnut 46 \n", - "270036 51ba24371f3c0683da1978d332184732bbec5b96 walnut 46 \n", - "270037 88f954276bb53915429cc2d117e57c08bc8aaddd chair5 46 \n", - "270038 d2af56088e557fbafc9815e9a7486f3efe61823e chair2 46 \n", - "270039 d4dd9a086994a89bf6e2d32efab254bdd4648888 chair0 46 \n", - "270040 30912859b0dfac9c6531061a796fb96f16510fa8 chair3 46 \n", - "270041 8b85fdd6295e9fa10c1230edd6ea63a038ceb15e face7 46 \n", - "270042 518d256ccedbcda049049c1183bc30ff2aa93258 peach 46 \n", - "270043 98cc0d8f063fae1b0c22645f384875ec0f73212c car_alfa 46 \n", - "270044 fd18b1f8ca6d961c163aa56772d813f6cba28336 chair7 46 \n", - "270045 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 ship5 46 \n", - "270046 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec table3 46 \n", - "270047 d15aa059b0180b200fb82fb8039247c3b53f9d66 turtle 46 \n", - "270048 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 table6 46 \n", - "270049 af8325d2d77b8475a00eb9a5296689adb4a8f39b chair1 46 \n", - "270050 5cbd24a0ac1881551f356940236e021bd29cd1de airplane0 46 \n", - "270051 feef7d8cf74ce5f8a8d0612e015e0de363587e93 face1 46 \n", - "270052 6f61f7e3130d2fef3deeb310e08a927521cb932d walnut 46 \n", - "270053 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 car_alfa 46 \n", - "270054 3fe99701f03c85d5b2017d4ec33eb227a695386e airplane1 46 \n", - "270055 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 face3 46 \n", - "270056 2c381c66bf457942bf4818e54b4b5172b3cc0cac table0 46 \n", - "270057 d6a597127092d38ed37bcb19967075698acb35ee strawberry 46 \n", - "270058 cc975ea997317b74a79b9f5568f8008acdec341e face3 46 \n", - "270059 e35071a43f7a0dafc21b18229ca36f1940c2c567 apricot 46 \n", - "\n", - " rxy_x rxy_semantic_x rxz_x rxz_semantic_x ... \\\n", - "0 -0.000000 90.000000 0.000000 0.000000 ... \n", - "1 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "2 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "3 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "4 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "5 -0.000000 90.000000 0.000000 0.000000 ... \n", - "6 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "7 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "8 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "9 -0.000000 90.000000 0.000000 0.000000 ... \n", - "10 -0.000000 90.000000 0.000000 0.000000 ... \n", - "11 -0.000000 -90.000000 0.000000 0.000000 ... \n", - "12 -0.000000 90.000000 0.000000 0.000000 ... \n", - "13 -0.000000 90.000000 0.000000 0.000000 ... \n", - "14 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "15 -0.000000 90.000000 0.000000 0.000000 ... \n", - "16 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "17 -0.000000 90.000000 0.000000 0.000000 ... \n", - "18 -0.000000 90.000000 0.000000 0.000000 ... \n", - "19 -0.000000 -90.000000 0.000000 0.000000 ... \n", - "20 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "21 -0.000000 90.000000 0.000000 0.000000 ... \n", - "22 -0.000000 90.000000 0.000000 0.000000 ... \n", - "23 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "24 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "25 -0.000000 -90.000000 0.000000 0.000000 ... \n", - "26 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "27 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "28 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "29 -0.000000 -0.000000 0.000000 0.000000 ... \n", - "... ... ... ... ... ... \n", - "270030 -84.231000 5.769000 -89.052000 -89.052000 ... \n", - "270031 -66.132000 23.868000 -67.801000 -67.801000 ... \n", - "270032 -49.628000 -139.628000 -35.196000 -35.196000 ... \n", - "270033 -7.294000 82.706000 -71.878000 -71.878000 ... \n", - "270034 85.828000 175.828000 -73.304000 -73.304000 ... \n", - "270035 86.257574 86.257574 1.580624 1.580624 ... \n", - "270036 4.176806 4.176806 2.259833 2.259833 ... \n", - "270037 -46.840000 -46.840000 16.090000 16.090000 ... \n", - "270038 45.285000 45.285000 37.122000 37.122000 ... \n", - "270039 -11.238000 -11.238000 25.949000 25.949000 ... \n", - "270040 -2.935000 -2.935000 49.021000 49.021000 ... \n", - "270041 22.427000 22.427000 45.258000 45.258000 ... \n", - "270042 33.515544 33.515544 -9.180214 -9.180214 ... \n", - "270043 -28.605000 61.395000 -3.829000 -3.829000 ... \n", - "270044 55.200000 55.200000 -67.932000 -67.932000 ... \n", - "270045 -0.809000 89.191000 -71.145000 -71.145000 ... \n", - "270046 -65.300000 -65.300000 -84.440000 -84.440000 ... \n", - "270047 61.236000 151.236000 69.658000 69.658000 ... \n", - "270048 5.591000 5.591000 -55.612000 -55.612000 ... \n", - "270049 -16.072000 -16.072000 86.864000 86.864000 ... \n", - "270050 49.507029 139.507029 -3.860598 -3.860598 ... \n", - "270051 64.386000 64.386000 42.742000 42.742000 ... \n", - "270052 -18.446784 -18.446784 -10.719291 -10.719291 ... \n", - "270053 45.261000 135.261000 -43.867000 -43.867000 ... \n", - "270054 9.955137 99.955137 24.193423 24.193423 ... \n", - "270055 48.146000 48.146000 -41.898000 -41.898000 ... \n", - "270056 -70.967000 -70.967000 11.985000 11.985000 ... \n", - "270057 37.334164 37.334164 -67.238762 -67.238762 ... \n", - "270058 -75.159000 -75.159000 29.466000 29.466000 ... \n", - "270059 -67.467580 -67.467580 -25.152591 -25.152591 ... \n", - "\n", - " rxy_semantic_y rxz_y rxz_semantic_y ryz_y ryz_semantic_y \\\n", - "0 90.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "2 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "3 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "4 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "5 90.000000 0.000000 0.000000 0.000000 0.000000 \n", - "6 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "7 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "8 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "9 90.000000 0.000000 0.000000 0.000000 0.000000 \n", - "10 90.000000 0.000000 0.000000 0.000000 0.000000 \n", - "11 -90.000000 0.000000 0.000000 0.000000 0.000000 \n", - "12 90.000000 0.000000 0.000000 0.000000 0.000000 \n", - "13 90.000000 0.000000 0.000000 0.000000 0.000000 \n", - "14 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "15 90.000000 0.000000 0.000000 0.000000 0.000000 \n", - "16 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "17 90.000000 0.000000 0.000000 0.000000 0.000000 \n", - "18 90.000000 0.000000 0.000000 0.000000 0.000000 \n", - "19 -90.000000 0.000000 0.000000 0.000000 0.000000 \n", - "20 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "21 90.000000 0.000000 0.000000 0.000000 0.000000 \n", - "22 90.000000 0.000000 0.000000 0.000000 0.000000 \n", - "23 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "24 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "25 -90.000000 0.000000 0.000000 0.000000 0.000000 \n", - "26 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "27 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "28 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "29 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "... ... ... ... ... ... \n", - "270030 5.769000 -89.052000 -89.052000 74.978000 74.978000 \n", - "270031 23.868000 -67.801000 -67.801000 -6.454000 -6.454000 \n", - "270032 -139.628000 -35.196000 -35.196000 72.620000 72.620000 \n", - "270033 82.706000 -71.878000 -71.878000 -3.863000 -3.863000 \n", - "270034 175.828000 -73.304000 -73.304000 -50.242000 -50.242000 \n", - "270035 86.257574 1.580624 1.580624 -147.908623 -147.908623 \n", - "270036 4.176806 2.259833 2.259833 24.748583 24.748583 \n", - "270037 -46.840000 16.090000 16.090000 24.070000 24.070000 \n", - "270038 45.285000 37.122000 37.122000 21.814000 21.814000 \n", - "270039 -11.238000 25.949000 25.949000 49.199000 49.199000 \n", - "270040 -2.935000 49.021000 49.021000 -52.625000 -52.625000 \n", - "270041 22.427000 45.258000 45.258000 -17.151000 -17.151000 \n", - "270042 33.515544 -9.180214 -9.180214 -2.873322 -2.873322 \n", - "270043 61.395000 -3.829000 -3.829000 62.157000 62.157000 \n", - "270044 55.200000 -67.932000 -67.932000 -1.643000 -1.643000 \n", - "270045 89.191000 -71.145000 -71.145000 -30.805000 -30.805000 \n", - "270046 -65.300000 -84.440000 -84.440000 -13.491000 -13.491000 \n", - "270047 151.236000 69.658000 69.658000 7.161000 7.161000 \n", - "270048 5.591000 -55.612000 -55.612000 -84.185000 -84.185000 \n", - "270049 -16.072000 86.864000 86.864000 30.443000 30.443000 \n", - "270050 139.507029 -3.860598 -3.860598 18.531554 18.531554 \n", - "270051 64.386000 42.742000 42.742000 87.404000 87.404000 \n", - "270052 -18.446784 -10.719291 -10.719291 -96.632598 -96.632598 \n", - "270053 135.261000 -43.867000 -43.867000 -21.184000 -21.184000 \n", - "270054 99.955137 24.193423 24.193423 -19.936269 -19.936269 \n", - "270055 48.146000 -41.898000 -41.898000 51.346000 51.346000 \n", - "270056 -70.967000 11.985000 11.985000 -45.818000 -45.818000 \n", - "270057 37.334164 -67.238762 -67.238762 10.426955 10.426955 \n", - "270058 -75.159000 29.466000 29.466000 12.283000 12.283000 \n", - "270059 -67.467580 -25.152591 -25.152591 7.598457 7.598457 \n", - "\n", - " s_y size_y ty_y tz_y variation_y \n", - "0 1.000000 256.0 0.000 0.000 0.0 \n", - "1 1.000000 256.0 0.000 0.000 0.0 \n", - "2 1.000000 256.0 0.000 0.000 0.0 \n", - "3 1.000000 256.0 0.000 0.000 0.0 \n", - "4 1.000000 256.0 0.000 0.000 0.0 \n", - "5 1.000000 256.0 0.000 0.000 0.0 \n", - "6 1.000000 256.0 0.000 0.000 0.0 \n", - "7 1.000000 256.0 0.000 0.000 0.0 \n", - "8 1.000000 256.0 0.000 0.000 0.0 \n", - "9 1.000000 256.0 0.000 0.000 0.0 \n", - "10 1.000000 256.0 0.000 0.000 0.0 \n", - "11 1.000000 256.0 0.000 0.000 0.0 \n", - "12 1.000000 256.0 0.000 0.000 0.0 \n", - "13 1.000000 256.0 0.000 0.000 0.0 \n", - "14 1.000000 256.0 0.000 0.000 0.0 \n", - "15 1.000000 256.0 0.000 0.000 0.0 \n", - "16 1.000000 256.0 0.000 0.000 0.0 \n", - 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" 'ryz_semantic', 's', 'size', 'ty', 'tz', 'variation'],\n", - " dtype='object')" - ] - }, - "execution_count": 204, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_reconstructed.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 212, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[[ 0.060929],\n", - " [-0.686162],\n", - " ...,\n", - " [-0.968256],\n", - " [ 0.183887]],\n", - "\n", - " [[-0.725592],\n", - " [ 0.292777],\n", - " ...,\n", - " [ 2.449372],\n", - " [ 0.401197]],\n", - "\n", - " ...,\n", - "\n", - " [[ 1.121319],\n", - " [ 1.719423],\n", - " ...,\n", - " [ 0.800551],\n", - " [-0.019874]],\n", - "\n", - " [[-0.518903],\n", - " [ 0.696196],\n", - " ...,\n", - " [-0.603347],\n", - " [-0.175979]]], dtype=float32)\n", - "Coordinates:\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object 'Chabo_L_M_5_9' 'Chabo_L_M_6_9' ...\n", - " - arr (neuroid) object 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' ...\n", - 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" - ty (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - tz (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - variation (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_end (time_bin) int64 170\n", - " - time_bin_start (time_bin) int64 70" - ] - }, - "execution_count": 212, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "add_image_meta(hvm_stripped, df_reconstructed, \n", - " join_key_assy=\"image_id\", join_key_frame=\"hash_id\", \n", - " axis_name=\"presentation\", columns=df_reconstructed.columns)\n", - "hvm_stripped" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mkgu_packaging", - "language": "python", - "name": "mkgu_packaging" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/packaging/notebooks/2018-04-18_coord_strip.ipynb b/packaging/notebooks/2018-04-18_coord_strip.ipynb deleted file mode 100644 index a74f66ce1..000000000 --- a/packaging/notebooks/2018-04-18_coord_strip.ipynb +++ /dev/null @@ -1,431 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "source": [ - "import os\n", - "import xarray as xr" - ], - "outputs": [], - "execution_count": 2, - "metadata": { - "collapsed": false, - "outputHidden": false, - "inputHidden": false - } - }, - { - "cell_type": "code", - "source": [ - "path = \"/Users/jjpr/.mkgu/data/dicarlo.Hong2011/hvm_neuronal_features.nc\"" - ], - "outputs": [], - "execution_count": 3, - "metadata": { - "collapsed": false, - "outputHidden": false, - "inputHidden": false - } - }, - { - "cell_type": "code", - "source": [ - 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" animal (neuroid) object 'Chabo' 'Chabo' 'Chabo' 'Chabo' 'Chabo' ...\n", - " x (neuroid) float64 1.8 1.8 1.4 1.8 1.4 1.4 1.0 1.0 0.2 ...\n", - " region (neuroid) object 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' ...\n", - " repetition (presentation) int64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...\n", - " stimulus (presentation) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 ...\n", - " image_id (presentation) object 'f1ae7c2160c6cbb309b940a0dc127a8aa1060e5d' ...\n", - "Dimensions without coordinates: neuroid, presentation, time_bin" - ] - }, - "metadata": {} - } - ], - "execution_count": 12, - "metadata": { - "collapsed": false, - "outputHidden": false, - "inputHidden": false - } - }, - { - "cell_type": "code", - "source": [ - "da.to_netcdf(path_out)" - ], - "outputs": [], - "execution_count": 13, - "metadata": { - "collapsed": false, - "outputHidden": false, - "inputHidden": false - } - }, - { - "cell_type": "code", - "source": [], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": false, - "outputHidden": false, - "inputHidden": false - } - } - ], - "metadata": { - "kernel_info": { - "name": "mkgu_packaging" - }, - "kernelspec": { - "name": "mkgu_packaging", - "language": "python", - "display_name": "mkgu_packaging" - }, - "language_info": { - "name": "python", - "version": "3.6.3", - "mimetype": "text/x-python", - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "pygments_lexer": "ipython3", - "nbconvert_exporter": "python", - "file_extension": ".py" - }, - "nteract": { - "version": "0.8.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/packaging/notebooks/2018-04-25_dldata_mkgu_comparison.ipynb b/packaging/notebooks/2018-04-25_dldata_mkgu_comparison.ipynb deleted file mode 100644 index 74aed3f74..000000000 --- a/packaging/notebooks/2018-04-25_dldata_mkgu_comparison.ipynb +++ /dev/null @@ -1,28060 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Can't import h5py\n", - "Can't import separate mcc package\n", - "Can't import asgd.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jjpr/anaconda/envs/dldata/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", - " \"This module will be removed in 0.20.\", DeprecationWarning)\n", - "/Users/jjpr/anaconda/envs/dldata/lib/python2.7/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", - " DeprecationWarning)\n", - "/Users/jjpr/anaconda/envs/dldata/lib/python2.7/site-packages/sklearn/lda.py:6: DeprecationWarning: lda.LDA has been moved to discriminant_analysis.LinearDiscriminantAnalysis in 0.17 and will be removed in 0.19\n", - " \"in 0.17 and will be removed in 0.19\", DeprecationWarning)\n", - "/Users/jjpr/anaconda/envs/dldata/lib/python2.7/site-packages/sklearn/qda.py:6: DeprecationWarning: qda.QDA has been moved to discriminant_analysis.QuadraticDiscriminantAnalysis in 0.17 and will be removed in 0.19.\n", - " \"in 0.17 and will be removed in 0.19.\", DeprecationWarning)\n" - ] - } - ], - "source": [ - "import dldata.stimulus_sets.hvm as hvm" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import OrderedDict" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from pytest import approx" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "dl_hvm = hvm.HvMWithDiscfade()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jjpr/anaconda/envs/dldata/lib/python2.7/site-packages/tabular/utils.py:81: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", - " if s != None:\n", - "/Users/jjpr/anaconda/envs/dldata/lib/python2.7/site-packages/tabular/spreadsheet.py:1467: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", - " for k in Names if deletecols(L[k][0:0], keycols) != None])\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "common attributes, forcing a renaming ...\n", - "Using default renamer ...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jjpr/anaconda/envs/dldata/lib/python2.7/site-packages/tabular/spreadsheet.py:1493: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", - " if deletecols(L[j][D[i]], keycols) != None])\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('Replacing columns', ['rxz', 'rxy', 'ryz'])\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jjpr/anaconda/envs/dldata/lib/python2.7/site-packages/tables/leaf.py:394: PerformanceWarning: The Leaf ``/spk`` is exceeding the maximum recommended rowsize (104857600 bytes);\n", - "be ready to see PyTables asking for *lots* of memory and possibly slow\n", - "I/O. You may want to reduce the rowsize by trimming the value of\n", - "dimensions that are orthogonal (and preferably close) to the *main*\n", - "dimension of this leave. Alternatively, in case you have specified a\n", - "very small/large chunksize, you may want to increase/decrease it.\n", - " PerformanceWarning)\n" - ] - } - ], - "source": [ - "dl_hvm_neu = dl_hvm.get_neuronal_feature_reps(sort_by_meta=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "dl_meta = dl_hvm.meta" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "dl_meta_neu = dl_hvm.meta_neu" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "xr_hvm = xr.open_dataarray(\"/Users/jjpr/.mkgu/data/dicarlo.Hong2011/hvm_neuronal_features.nc\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "[79564800 values with dtype=float32]\n", - "Coordinates:\n", - " neuroid_id (neuroid) object u'Chabo_L_M_5_9' u'Chabo_L_M_6_9' ...\n", - 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] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr_hvm" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(28, 640, 296), (51, 2560, 296), (47, 2560, 296)]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[s.shape for s in dl_hvm_neu.values()]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "dl_axes = [\"repetition\", \"image\", \"neuroid\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['V0', 'V3', 'V6']" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dl_hvm_neu.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dtype((numpy.record, [('obj', 'S24'), ('rxz', '\n", - "array([[[-0.996739]]], dtype=float32)\n", - "Coordinates:\n", - " * stimulus (stimulus) int64 41\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def get_xr_sample(xr_hvm, sample):\n", - " xr_sample = xr_hvm.sel(image_id=sample[\"dl_image_id\"], \n", - " repetition=sample[\"dl_repetition_idx\"],\n", - " animal=sample[\"dl_neuroid_animal\"],\n", - " hemisphere=sample[\"dl_neuroid_hemisphere\"],\n", - " arr=sample[\"dl_neuroid_arr\"],\n", - " row=sample[\"dl_neuroid_row\"],\n", - " col=sample[\"dl_neuroid_col\"])\n", - " return xr_sample\n", - "xr_sample = get_xr_sample(xr_hvm, sample)\n", - "xr_sample" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr_sample.values[0, 0, 0] == approx(sample[\"dl_value\"], abs=1e-6)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[[ 0.060929],\n", - " [-0.686162],\n", - " ..., \n", - " [-0.968256],\n", - " [ 0.183887]],\n", - "\n", - " [[-0.725592],\n", - " [ 0.292777],\n", - " ..., \n", - " [ 2.449372],\n", - " [ 0.401197]],\n", - "\n", - " ..., \n", - " [[ 1.121319],\n", - " [ 1.719423],\n", - " ..., \n", - " [ 0.800551],\n", - " [-0.019874]],\n", - "\n", - " [[-0.518903],\n", - " [ 0.696196],\n", - " ..., \n", - " [-0.603347],\n", - 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" - row (neuroid) int64 5 6 5 7 6 7 9 7 9 8 9 2 1 3 6 7 6 6 8 5 ...\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr_hvm.load()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "samples = []\n", - "for i in range(1000):\n", - " sample = get_dl_sample(dl_hvm_neu, dl_meta, dl_meta_neu) \n", - " xr_sample = get_xr_sample(xr_hvm, sample)\n", - " sample[\"xr_sample\"] = xr_sample\n", - " samples.append(sample)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(-0.61939675, -0.61939675)\n", - "(0.51070434, 0.51070434)\n", - "(0.10322264, 0.10322264)\n", - "(-0.69525141, -0.69525141)\n", - "(-1.3130871, -1.3130871)\n", - "(-1.872653, -1.872653)\n", - 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" errors.append(sample)\n", - " except IndexError:\n", - " print(dl_value)\n", - " errors.append(sample)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(errors)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(-0.61939675, array([[[-0.61939675]]], dtype=float32)),\n", - " (0.51070434, array([[[ 0.51070434]]], dtype=float32)),\n", - " (0.10322264, array([[[ 0.10322264]]], dtype=float32)),\n", - " (-0.69525141, array([[[-0.69525141]]], dtype=float32)),\n", - " (-1.3130871, array([[[-1.31308711]]], dtype=float32)),\n", - " (-1.872653, array([[[-1.87265301]]], dtype=float32)),\n", - " (-1.5418377, array([[[-1.54183769]]], dtype=float32)),\n", - " (-0.36553365, array([[[-0.36553365]]], dtype=float32)),\n", - 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] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'dl_image_id': '9d04359644201352037782246614a060beb35bdf',\n", - " 'dl_image_idx': 367,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 127,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -0.61939675,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.619397]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 191\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_8_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b5e3a8c2745ebec3513201aaa9f66682669f3d4f',\n", - " 'dl_image_idx': 352,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 139,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': 0.51070434,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.510704]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 397\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '658f7d1177b489f25fe7d04431fa2ed9dfcdedad',\n", - " 'dl_image_idx': 1783,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 113,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 0.10322264,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.103223]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2005\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e1572564e168319bac9ca70c5af9bfa4c7b00adb',\n", - " 'dl_image_idx': 2493,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 97,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': -0.69525141,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.695251]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2091\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5fa0286f0fab1b226b4446fd7f3b9d3582507f26',\n", - " 'dl_image_idx': 2476,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 41,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -1.3130871,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.313087]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 771\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e4b8cceb327705b56e28bce4484f80b328013b1b',\n", - " 'dl_image_idx': 1631,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 51,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -1.872653,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.872653]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1718\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_7'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6ebbc94d38b1b128c633162ecfe8f44505006b17',\n", - " 'dl_image_idx': 1076,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 190,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -1.5418377,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.541838]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 525\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_5'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4e05f442f98dceb778e7de1f2d0f700241461756',\n", - " 'dl_image_idx': 428,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 137,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -0.36553365,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.365534]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 67\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5eaef9fb2fa650f6cb8a3f34fd02e76f9901d691',\n", - " 'dl_image_idx': 779,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 218,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': 1.9079622,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.907962]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1121\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3517359b503bf6675d30a7baa99323e6007a91ba',\n", - " 'dl_image_idx': 2259,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 9,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 47,\n", - " 'dl_value': 0.32348523,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.323485]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1716\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_8_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6adfd37ba742c6ac0ae5339e5c85234d773e86b4',\n", - " 'dl_image_idx': 466,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 152,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 35,\n", - " 'dl_value': -0.05386496,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.053865]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 203\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1d832e0abdd6266fb3dcacfa05df1725d6b08fb7',\n", - " 'dl_image_idx': 299,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 194,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -0.65768099,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.657681]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 26\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'dd90edce90cea974a0c66be1a341dedbed970075',\n", - " 'dl_image_idx': 1402,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 219,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 0.46570408,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.465704]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 622\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4ef4aeddf2c39009efcf64a0ced878b2f60a39c9',\n", - " 'dl_image_idx': 648,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 266,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': -0.064900592,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.064901]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1704\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4b6b3ff7c72d6a40777007a3628ed66aa9f99fd0',\n", - " 'dl_image_idx': 1008,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 163,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 29,\n", - " 'dl_value': 0.39230689,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.392307]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2428\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8ea30ba0736418d7282258d1f1c8c7e20c990a0c',\n", - " 'dl_image_idx': 661,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 207,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': 2.8037624,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 2.803762]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2512\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4b6b3ff7c72d6a40777007a3628ed66aa9f99fd0',\n", - " 'dl_image_idx': 1008,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 276,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': -0.72313088,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.723131]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2428\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'cb0f5a5c8b9a0d6e2f080c296cb07e3bfd9e3d1a',\n", - " 'dl_image_idx': 7,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 139,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 46,\n", - " 'dl_value': 0.14842062,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.148421]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1782\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6d7d9476633ec36a39211c87d8b3d937c184e4c5',\n", - " 'dl_image_idx': 2511,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 129,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': 0.4484697,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.44847]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2191\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_8'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '82781cb673bf7b3227996c7c72e0c6d6873e898b',\n", - " 'dl_image_idx': 190,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 38,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': 0.49917316,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.499173]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 616\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'becd4dd66a0ea7e008c5bcd8440e68a64bb60af0',\n", - " 'dl_image_idx': 715,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 201,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -0.44880751,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.448808]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1123\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_0_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '426bcf9e3fa5f68c494b77e7252e59dd1c882b9c',\n", - " 'dl_image_idx': 976,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 157,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -1.4149538,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.414954]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1944\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '404fccc6f01c221472c7e2cecd878dac901dc887',\n", - " 'dl_image_idx': 831,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 40,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': -0.82885963,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.82886]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2306\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c766632574f6d86f0a048f2e5817826c1888699b',\n", - " 'dl_image_idx': 3,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 231,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -0.014211657,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.014212]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 530\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '264d5d6b8b4adf5414dc8312eac30ddc62abe736',\n", - " 'dl_image_idx': 301,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 235,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': -0.18886924,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.188869]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2147\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0a7cbcd3ba4adefa4d29a8b03cd9b4202e6336b9',\n", - " 'dl_image_idx': 2351,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 181,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': 0.21405879,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.214059]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1795\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ab95a960460df15a0deea8afb130519e408fd6c1',\n", - " 'dl_image_idx': 749,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 188,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 37,\n", - " 'dl_value': 0.57301897,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.573019]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2399\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_5'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd74fb40d88d6b4139346e0be3236da980a880c3c',\n", - " 'dl_image_idx': 147,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 195,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.79684258,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.796843]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1323\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '37f9c6598c80292978764100f0e7dead690490e0',\n", - " 'dl_image_idx': 1071,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 184,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': 0.30699074,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.306991]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 755\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1d832e0abdd6266fb3dcacfa05df1725d6b08fb7',\n", - " 'dl_image_idx': 299,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 287,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': 1.0789884,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.078988]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 26\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1d832e0abdd6266fb3dcacfa05df1725d6b08fb7',\n", - " 'dl_image_idx': 299,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 45,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 0.47498477,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.474985]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 26\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_8'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1b9692fde06e390fabd92f042a2eccd75f1f17fd',\n", - " 'dl_image_idx': 2152,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 253,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': 0.39601612,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.396016]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 217\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '994a36fd65165c01a75c856d72ea425975dfd514',\n", - " 'dl_image_idx': 1162,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 207,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 45,\n", - " 'dl_value': -2.586072,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-2.586072]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1763\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '458e8bb4724af0a51f4678d7bda4da9cdca56d07',\n", - " 'dl_image_idx': 2550,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 175,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 34,\n", - " 'dl_value': -0.62541872,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.625419]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 112\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4e739f70e377feadad7b6603c3a630190fe917d7',\n", - " 'dl_image_idx': 1132,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 236,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 1.4002517,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.400252]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 580\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7d214ed5978d571b26e2516a2d8b86bb0b99a91e',\n", - " 'dl_image_idx': 1972,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 48,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 45,\n", - " 'dl_value': -0.071719624,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.07172]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 658\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_7'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '81dc96c85f453d46d0bf0ba840def85612be76b0',\n", - " 'dl_image_idx': 1964,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 0,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -1.1646674,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.164667]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1101\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_5_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5e9b110f55717ced0d7eafc3e172d94ccdbd1018',\n", - " 'dl_image_idx': 360,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 169,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': -0.60391086,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.603911]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 256\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b6d53358a8319bce64797bdbe7de6d2cbedb39db',\n", - " 'dl_image_idx': 608,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 268,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 0.90325874,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.903259]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 159\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b94a210a5a7a0a708e701aa2744732a537aa5568',\n", - " 'dl_image_idx': 1640,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 204,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 49,\n", - " 'dl_value': 0.009348874,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.009349]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 570\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_4_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e3190d96a931d3e146d201dca7155d87100f4870',\n", - " 'dl_image_idx': 684,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 214,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 45,\n", - " 'dl_value': -0.43040285,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.430403]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 588\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '84b637c9fb1b8a75479285ba04a42222c9a4e8eb',\n", - " 'dl_image_idx': 416,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 242,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': 0.04927627,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.049276]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 522\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'aba817fb3853bdef0df5faf83c6104c2b4bff4a3',\n", - " 'dl_image_idx': 1186,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 15,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 33,\n", - " 'dl_value': 0.047460683,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.047461]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1667\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1baeb092108331c68b0ca97bd8d1c82255b19986',\n", - " 'dl_image_idx': 783,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 135,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': -0.23606426,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.236064]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2369\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bb8310bb5d08a7b1c14ea5aba6ba385c2827e387',\n", - " 'dl_image_idx': 145,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 294,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': -1.7013937,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.701394]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 529\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fa5ab85db45baf14a1c008c9eddd68cd6aaf3695',\n", - " 'dl_image_idx': 500,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 11,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': 0.75220633,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.752206]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2099\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7abe9c3fc55ee246b851237ef53aab9948c2c60b',\n", - " 'dl_image_idx': 2428,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 159,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 1.1845785,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.184579]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1602\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c3eeddec8ecef080a146864c55f7897a81b7f3dc',\n", - " 'dl_image_idx': 2319,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 86,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': 0.81880909,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.818809]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2113\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4f573b353112b590f8855cd2064b32c66dfff29f',\n", - " 'dl_image_idx': 60,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 64,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 43,\n", - " 'dl_value': 1.3209893,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.320989]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1674\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_6'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e134c3d8fa8be11c8eb0b8d55e7f534286f44736',\n", - " 'dl_image_idx': 46,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 197,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': -0.35564306,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.355643]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 10\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6f4db169691f5cfa68a43920fa2bae2cb4673af6',\n", - " 'dl_image_idx': 2338,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 117,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.67953193,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.679532]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 828\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5d5020ab0e7f631a665d8a086745e37373421503',\n", - " 'dl_image_idx': 1658,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 138,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': 2.1499519,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 2.149952]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1510\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0cd8940a98f37a09526560b6ec70a5c263be6f4f',\n", - " 'dl_image_idx': 518,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 119,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': 0.70187223,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.701872]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 607\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '649f44d1355797aa0d9413a53446cc19728929f1',\n", - " 'dl_image_idx': 2389,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 113,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': -0.71930707,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.719307]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 441\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6dd25168b9b884acaf7302587b0d63626762f41b',\n", - " 'dl_image_idx': 1293,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 66,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 43,\n", - " 'dl_value': 0.36009064,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.360091]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2033\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_5_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ba21a086db4cad7ed508999ff6b9a22437e95d81',\n", - " 'dl_image_idx': 1772,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 116,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': -1.208267,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.208267]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1342\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '202e13540b85d535b6fcabe430647ec11dcc67a5',\n", - " 'dl_image_idx': 9,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 170,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -0.77377439,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.773774]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 60\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6d7d9476633ec36a39211c87d8b3d937c184e4c5',\n", - " 'dl_image_idx': 2511,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 182,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': -0.2797738,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.279774]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2191\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '341e8449d194399f36babd0bb9025368eb4d6717',\n", - " 'dl_image_idx': 567,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 266,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 0.25889286,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.258893]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 570\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3f8a42c8ba189d22e17a1e514b7590b40e58cde8',\n", - " 'dl_image_idx': 1476,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 39,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': -0.62301332,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.623013]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2121\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'eee9e203f6b4cf64226f56f1a14692e92d815b70',\n", - " 'dl_image_idx': 758,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 196,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 49,\n", - " 'dl_value': 1.7334917,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.733492]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1564\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a9d92386017708513a75d78bf5c20e8bd696bf3f',\n", - " 'dl_image_idx': 1363,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 179,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 37,\n", - " 'dl_value': 0.23016927,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.230169]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1628\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_8_8'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd396b6f0fe952cf09516d902e464bc287e28c3c4',\n", - " 'dl_image_idx': 2117,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 87,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 1.0472587,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.047259]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 190\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'eb5de25682e7f39ff10bffc345c8994bb0d223fc',\n", - " 'dl_image_idx': 248,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 213,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': -1.7361348,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.736135]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 604\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1b6608fce8a8d7014e0fb6a99ae4fa34b4497145',\n", - " 'dl_image_idx': 1757,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 69,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 0.20678759,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.206788]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1590\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9d89aedf86af6292d4d6fe3774a51c92fb715c4e',\n", - " 'dl_image_idx': 306,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 224,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -0.72633284,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.726333]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 248\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'be6ca4ff51f7c30c93d56932d8b5b1db66340efa',\n", - " 'dl_image_idx': 19,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 213,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': 1.3368816,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.336882]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 157\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7594f5ffa75dbd65b5be6a5aa08f4055b67bd35d',\n", - " 'dl_image_idx': 2452,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 13,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 47,\n", - " 'dl_value': -1.4586078,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.458608]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1529\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fe2d1dba9e1fcb37cf0aea154c85857c93a34e4e',\n", - " 'dl_image_idx': 349,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 134,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 35,\n", - " 'dl_value': 1.1776103,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.17761]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1307\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_8'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd36ac99e25ac524b1ad7bc256be9abca07b4dc4f',\n", - " 'dl_image_idx': 1012,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 204,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 46,\n", - " 'dl_value': -0.38374323,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.383743]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1912\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_4_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '27bff527337a1df152eefdcd45325cf5daaf76d5',\n", - " 'dl_image_idx': 102,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 107,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': -0.54780954,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.54781]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 298\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '439bf39da907411240ddc3521b54c4e2c86a47a2',\n", - " 'dl_image_idx': 1340,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 16,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': 2.0109947,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 2.010995]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2343\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '598e7ecdf43514f1781b6045721d9684a67543f8',\n", - " 'dl_image_idx': 1730,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 184,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': -1.4418846,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.441885]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1928\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c9fefeb594597b2951c55d352fae0555062d5fce',\n", - " 'dl_image_idx': 243,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 222,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -0.80770457,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.807705]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 408\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c502687e1be45fa82af913e6023c1cd62280326a',\n", - " 'dl_image_idx': 358,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 234,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': -0.70745438,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.707454]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 37\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4d1697209942afa1fc5a3d1394af394a8c6bf89b',\n", - " 'dl_image_idx': 600,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 122,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -1.0733067,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.073307]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 526\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_8_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c43e08b1ee755c49cfd72d289f4a679c00c9198d',\n", - " 'dl_image_idx': 595,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 144,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': -0.98701239,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.987012]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 515\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b6f1217a499a05258bd83edea6383f3959383d87',\n", - " 'dl_image_idx': 156,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 197,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': -1.7299254,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.729925]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 513\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c22036d9bb146dba19759e2773aed47ac0f54e59',\n", - " 'dl_image_idx': 172,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 21,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': -1.8123519,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.812352]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 675\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bbc7c1da1c0ec4cdaffffd7859dc5438d793ecd3',\n", - " 'dl_image_idx': 1203,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 199,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': -1.7091327,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.709133]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 715\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_8_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'afb7c387453bace279dbdf9e06c40ea39624768a',\n", - " 'dl_image_idx': 22,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 273,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 0.46363381,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.463634]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 209\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '90e89b3f1ebba126f8a4b9c37bba8e4f3a1c56ef',\n", - " 'dl_image_idx': 669,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 136,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': 0.64206755,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.642068]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 140\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_8'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0e70fd2ffcf384fb39e2966912621d7a6e32cdfc',\n", - " 'dl_image_idx': 43,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 272,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': -0.35453996,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.35454]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2203\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a767d5e8e64f7366e95a79cfa9fea1d12727c422',\n", - " 'dl_image_idx': 1559,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 141,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 46,\n", - " 'dl_value': -0.74393868,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.743939]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 188\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a83e8966183bb4c97ec34baf54f944cc9e6e7c1c',\n", - " 'dl_image_idx': 1497,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 292,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 46,\n", - " 'dl_value': -0.85347795,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.853478]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1317\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7f5b6e12c535d0bc76c742d5ad8dd97f84aa5fcc',\n", - " 'dl_image_idx': 1131,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 18,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': 0.55884612,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.558846]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2204\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd01f439df6611116402c549e2ee2469398c39430',\n", - " 'dl_image_idx': 2289,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 119,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': 0.91634923,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.916349]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 236\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3d21794fdb2c4b33469d23e8e9667f1bd33a1f91',\n", - " 'dl_image_idx': 379,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 177,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': 0.77300817,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.773008]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 305\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_8_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd2f6f4528745cf8da42037601f788926a29ae780',\n", - " 'dl_image_idx': 411,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 83,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': 1.6976851,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.697685]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1512\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7d2ef68436087322166581dc8c50942a0f617a6f',\n", - " 'dl_image_idx': 870,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 135,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': 0.023136767,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.023137]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2145\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'db4980098b33e166d68a5dc062326441d05758b8',\n", - " 'dl_image_idx': 72,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 109,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 47,\n", - " 'dl_value': -0.30261078,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.302611]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 333\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3ca722d16116e5bcc00515e1440e6f4aec40cb6f',\n", - " 'dl_image_idx': 828,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 143,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -0.47360697,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.473607]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 979\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6452961bcb80c87fb9d151e36f8404399801ee42',\n", - " 'dl_image_idx': 292,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 263,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': 0.62047958,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.62048]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 167\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '93c0130c3257249f500171d131f6f800cb0eb775',\n", - " 'dl_image_idx': 2369,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 265,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': 1.3626978,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.362698]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1954\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6f6ccb2c2c6c9f3bd53fa1cfbf4063496345117f',\n", - " 'dl_image_idx': 2112,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 92,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': 1.1431593,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.143159]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1785\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '612929286e43af83b7c3663adadab6f05655351c',\n", - " 'dl_image_idx': 192,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 144,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 1.7352998,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.7353]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 150\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a17db26a610f96215d94cd673fd982c5bb3fd524',\n", - " 'dl_image_idx': 405,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 183,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': 1.126025,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.126025]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 13\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_5'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6f4db169691f5cfa68a43920fa2bae2cb4673af6',\n", - " 'dl_image_idx': 2338,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 243,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -0.0064639631,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.006464]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 828\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '532b98e5518927658dc8e404090f34a1e249a9fb',\n", - " 'dl_image_idx': 631,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 44,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -0.7091701,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.70917]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1126\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_4_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '70097b22e557f2016f4f2a6345a98ce4591a11eb',\n", - " 'dl_image_idx': 958,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 92,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 43,\n", - " 'dl_value': 1.066487,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.066487]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 295\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4e92b9f23b7facce68e88bfc8c94453ab1a311e6',\n", - " 'dl_image_idx': 2074,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 273,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -0.41168359,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.411684]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1864\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '88429363542db8c918d7112a7335fcca62966a3e',\n", - " 'dl_image_idx': 97,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 24,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 0.92776132,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.927761]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 25\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6d72c71bae63fd2a5fb1811d115f99eaa8a55c75',\n", - " 'dl_image_idx': 1891,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 282,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': 0.34004149,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.340041]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1873\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6bc94a19365c74d79e7d31f4e04f6fd2837706a2',\n", - " 'dl_image_idx': 395,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 220,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': -0.081374049,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.081374]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 131\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '270e480f1261762ec597cfd789b9cca981afa1df',\n", - " 'dl_image_idx': 885,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 155,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': 1.2138125,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.213812]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 981\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '683be441e92215f37052ee70c5b6f8a4ba45c4ca',\n", - " 'dl_image_idx': 2013,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 68,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': -0.10193997,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.10194]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 837\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ac4da60f7e95c2738a64be6e5a33b530c4316529',\n", - " 'dl_image_idx': 38,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 57,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 42,\n", - " 'dl_value': -0.4787004,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.4787]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 5\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '61948663f80dada76e9d7273b87707cd85669ce9',\n", - " 'dl_image_idx': 613,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 3,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -0.52272153,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.522722]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 216\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_7_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2b75c4efd20520d4fd57a0933e8e303a960aa0da',\n", - " 'dl_image_idx': 1763,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 80,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': -0.41026768,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.410268]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2281\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fd3ac01c3251eba6d1e899a2a9bf31ad319e2d71',\n", - " 'dl_image_idx': 2172,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 197,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 46,\n", - " 'dl_value': -0.88408017,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.88408]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2417\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '01a4f2015bed02d4fd49b66c4bbd92681d371197',\n", - " 'dl_image_idx': 1794,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 174,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': -0.39025915,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.390259]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 53\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ae316112871c3af31460036e9bdee3ce4cf1da28',\n", - " 'dl_image_idx': 509,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 137,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': -2.0478821,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-2.047882]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1173\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8738485a0c850d0a063ca412260d2664f57ed4e2',\n", - " 'dl_image_idx': 1154,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 207,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': 2.4244742,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 2.424474]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1645\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '17bdaaa93078fd7ff0c7d2a29a0515c3a8abd7fb',\n", - " 'dl_image_idx': 197,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 8,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': -0.77572316,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.775723]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 370\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_5'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a48861cea5d6e7711670a221f9e6b7e6c3214da9',\n", - " 'dl_image_idx': 394,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 284,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': 0.026309334,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.026309]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 540\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '989c39f54b6ef541b6a95fce146bb56f80d24006',\n", - " 'dl_image_idx': 174,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 287,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 0.11826988,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.11827]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 418\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2107e92abdb1a393fd3f5ef049b1b8c7c98b4b0a',\n", - " 'dl_image_idx': 1976,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 152,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': 0.82897019,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.82897]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 542\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f1a49d8df9b3535a95dda95b88c0d8a9cb50f676',\n", - " 'dl_image_idx': 139,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 284,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': 0.27296272,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.272963]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 34\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '13591ef06b18eb1c9c3ab0f10a825bbcb83914f1',\n", - " 'dl_image_idx': 241,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 177,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': 0.49704254,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.497043]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 186\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_8_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2a58e3c7db29eea47533b810271e5450f1b7cfdd',\n", - " 'dl_image_idx': 2163,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 84,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 29,\n", - " 'dl_value': -1.0099359,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.009936]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1553\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '68e5c98c6f24273fd3dc272253468d4c4e1e035b',\n", - " 'dl_image_idx': 1495,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 254,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': 1.6886554,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.688655]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1382\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '48f045664b5c5245b9a570c99c80d06b9cab26a9',\n", - " 'dl_image_idx': 1712,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 143,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -0.3991181,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.399118]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 702\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '52488e57442f85a9c6a518c02b2929c1dfd7d9d4',\n", - " 'dl_image_idx': 447,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 261,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 2.4560244,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 2.456024]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 286\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6686e061066b54983314dcb015d875ce2e85c55a',\n", - " 'dl_image_idx': 2168,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 84,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -0.37658268,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.376583]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2172\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '53830ff234deec1d589cbd145f59970b982af530',\n", - " 'dl_image_idx': 769,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 72,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -1.4390459,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.439046]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 496\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_5_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '868c221ea86c2861f7374eb052837b1acaa19ef3',\n", - " 'dl_image_idx': 1176,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 60,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 45,\n", - " 'dl_value': 1.241292,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.241292]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1769\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_3_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'aea392bf3d5d8080ed5333eb6c561cdad7a2a1a5',\n", - " 'dl_image_idx': 2415,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 177,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -0.2865575,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.286557]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 696\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_8_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1ebc83ccd7761e34bcc1207456cc920f6d0103cf',\n", - " 'dl_image_idx': 117,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 48,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.25336879,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.253369]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 476\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_7'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7cd382ea155d67227b21efb511eb457d9a5d186d',\n", - " 'dl_image_idx': 514,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 275,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': -0.63259089,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.632591]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 229\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '216a40c814de023e751714f13f357b214fcfcf4a',\n", - " 'dl_image_idx': 378,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 179,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': -1.2261446,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.226145]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 551\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_8_8'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '207af40ae3cdf86f513e8e3e705c6cea1d53f648',\n", - " 'dl_image_idx': 30,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 288,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -0.17392139,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.173921]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 381\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8a63355f30050fda7ab28c2da5c882c9b2cbb672',\n", - " 'dl_image_idx': 553,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 7,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': -1.6376977,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.637698]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 644\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_7_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '645a4df10a75d7b90b6381c8f5e2f020012a80f1',\n", - " 'dl_image_idx': 1637,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 291,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': 0.76908457,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.769085]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 490\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'dee78dd46699a3bbac0d9e032b588b1ff7363608',\n", - " 'dl_image_idx': 83,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 195,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': 1.6284453,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.628445]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 462\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '81858e98a903cf39de1d802e22a03f9f5104b7e4',\n", - " 'dl_image_idx': 1066,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 239,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 34,\n", - " 'dl_value': -0.64596331,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.645963]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 582\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '58f5461c0f0461e8508aa9d5092f481149d6c294',\n", - " 'dl_image_idx': 485,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 295,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': -0.98058176,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.980582]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 361\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a27cf5cde1fc5af661dcc7a6eb30512d69609ecf',\n", - " 'dl_image_idx': 548,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 253,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': 0.2571927,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.257193]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 115\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fda763c76129d1eecce32cb33f7a210bdcab0221',\n", - " 'dl_image_idx': 2481,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 101,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -0.76765174,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.767652]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2258\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4a482bcb2f940d16f0edbb357e23a11812a304ba',\n", - " 'dl_image_idx': 461,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 82,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': 0.94814992,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.94815]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 578\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '02875e1f3e0e7995a499fd82e2a3b1a2f5ea5935',\n", - " 'dl_image_idx': 1648,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 206,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': 1.8448166,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.844817]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1397\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a163eac439bb7e4562e126db042ff2dcf9786817',\n", - " 'dl_image_idx': 1042,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 101,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': 0.49280247,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.492802]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 509\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a7725cd5f1302899b89e9dcae00e5d62d5a82824',\n", - " 'dl_image_idx': 520,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 290,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': 0.25294024,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.25294]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 636\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '98e879585a0d4e2ad125d00ac1719ccbe99215bd',\n", - " 'dl_image_idx': 91,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 61,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 35,\n", - " 'dl_value': 2.058521,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 2.058521]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1274\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_2_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a9d65487ae80c284ea7dc12b274d74f08f8b122f',\n", - " 'dl_image_idx': 225,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 215,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 0.48533896,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.485339]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 467\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '83153248e90e931b3ff0f60626a479954e80f79d',\n", - " 'dl_image_idx': 1010,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 77,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -0.26427665,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.264277]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 250\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '937b0c65830bfa8fc1245e628ad13fe4981513b8',\n", - " 'dl_image_idx': 554,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 232,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': 1.3463595,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.346359]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 257\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '179d3bb2e365c92a7ea45aeda2049302f8b25fd6',\n", - " 'dl_image_idx': 82,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 176,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': -1.3147215,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.314721]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2180\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0a810eaa24904b841faf94bfac19283e5d00568a',\n", - " 'dl_image_idx': 1929,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 258,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 50,\n", - " 'dl_value': -1.9049078,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.904908]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 608\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c97af9d95d54aaa102a0f99491e996c9519e2f6d',\n", - " 'dl_image_idx': 326,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 169,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 49,\n", - " 'dl_value': -0.86637568,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.866376]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 611\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd9a754ac383ef26bd69ebc2b6506fd3e74207e9a',\n", - " 'dl_image_idx': 338,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 169,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': 0.50729758,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.507298]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1572\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3f3d28ffc95b4de627d5e27af448db3c86d94074',\n", - " 'dl_image_idx': 722,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 236,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': -1.19005,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.19005]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1731\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '30e28e1a41f9aaee0024af7236d1bd31ac77bfc4',\n", - " 'dl_image_idx': 55,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 229,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -0.11660187,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.116602]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 176\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b325ababf628584cb7f674c3772589c107e0a9cb',\n", - " 'dl_image_idx': 1271,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 279,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': -1.1923896,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.19239]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2459\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ed64d4d591145813455365333cfec9e2e2a89a1c',\n", - " 'dl_image_idx': 1044,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 152,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 28,\n", - " 'dl_value': -1.201332,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.201332]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 653\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8d57a417ed6901a5b327040b279cfc005a1a2e2d',\n", - " 'dl_image_idx': 780,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 81,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 46,\n", - " 'dl_value': -0.4836165,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.483617]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 878\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '36d91ade7ba22a86b2c0d043bd93691ed2333ea1',\n", - " 'dl_image_idx': 2,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 63,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': 0.14097157,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.140972]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1772\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2c381c66bf457942bf4818e54b4b5172b3cc0cac',\n", - " 'dl_image_idx': 1003,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 203,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -1.3375878,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.337588]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2566\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_3_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0440deec7566bcebd19def477af4e9ab37157cb5',\n", - " 'dl_image_idx': 344,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 253,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 37,\n", - " 'dl_value': -0.30279091,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.302791]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 474\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '28e31e574f90690adc228f38e4fff0a192bf8645',\n", - " 'dl_image_idx': 592,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 82,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': 2.1599162,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 2.159916]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 401\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1bac8d5cd7394e4ad430478690c4cda39882da6c',\n", - " 'dl_image_idx': 725,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 223,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 34,\n", - " 'dl_value': -0.42284364,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.422844]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 636\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ae8321bcb28458f76a75b9753f9a998acfe26071',\n", - " 'dl_image_idx': 911,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 112,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': 2.8510518,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 2.851052]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1063\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_4_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'feb2281e5fd2d7c5046a8e8ebb2d6b72b0558ddc',\n", - " 'dl_image_idx': 173,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 233,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': -2.0709476,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-2.070948]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2538\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c0f9f1388636765b1cbd6ac69cfa3263165c3a39',\n", - " 'dl_image_idx': 1477,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 89,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': -0.056008726,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.056009]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 368\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4bef068b1301b9176ced85dd742b1d07fa35c5ce',\n", - " 'dl_image_idx': 335,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 198,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 21,\n", - " 'dl_value': -0.22614774,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.226148]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 612\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_8_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'eb5533e147d78342f664e4f3f1968a62ada7acb9',\n", - " 'dl_image_idx': 13,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 266,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 0.055559121,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.055559]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 126\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bffb5a5531aabefe3a9f0eee48963e1dbfc2f5f2',\n", - " 'dl_image_idx': 549,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 65,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 0.37568837,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.375688]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 368\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_3_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3817eb87e05b9686609783a1de9b091d05f5fcd9',\n", - " 'dl_image_idx': 263,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 256,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': 0.60076129,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.600761]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 563\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a7725cd5f1302899b89e9dcae00e5d62d5a82824',\n", - " 'dl_image_idx': 520,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 36,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': 0.012022465,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.012022]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 636\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3c4ae5900917dfaf467078639a7c8144dc213ec5',\n", - " 'dl_image_idx': 598,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 174,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': 0.93732411,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.937324]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 345\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fddca81a7bc6cdd899d04c49cf5cd1bccedd4bdb',\n", - " 'dl_image_idx': 371,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 39,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -0.037140332,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.03714]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 600\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3191ee64ec40c85a35b3d36082040825e23bf0d7',\n", - " 'dl_image_idx': 902,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 139,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': 1.4820846,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.482085]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 769\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1966fae5ead4e8e4fe167539157152e244eb935f',\n", - " 'dl_image_idx': 583,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 181,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': -0.2781446,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.278145]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1348\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '533a60f820ec3403ef68e1f2ee79ab656a41ede9',\n", - " 'dl_image_idx': 1558,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 70,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': -0.082460418,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.08246]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 556\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8568b2410502fdc3607a472e030b315acbbf522a',\n", - " 'dl_image_idx': 99,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 250,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': 0.29541507,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.295415]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2308\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3fdcdba2fbb63bb4f96cc36b70bf819c0bebe81f',\n", - " 'dl_image_idx': 16,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 42,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -0.81200165,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.812002]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2193\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '97f1b203512ff007f1e2ebc918538588aabd9660',\n", - " 'dl_image_idx': 586,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 104,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 0.20717491,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.207175]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2071\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_0_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e3190d96a931d3e146d201dca7155d87100f4870',\n", - " 'dl_image_idx': 684,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 234,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': 0.81519037,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.81519]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 588\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6dd25168b9b884acaf7302587b0d63626762f41b',\n", - " 'dl_image_idx': 1293,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 237,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 1.168449,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.168449]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2033\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a960f3091ec9c6f61c2e816b532e2717499216c4',\n", - " 'dl_image_idx': 1205,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 123,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 1.4846382,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.484638]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 614\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_0_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '80fbd80c8b7bdf10065abd99340a2ba4c9dd0aa6',\n", - " 'dl_image_idx': 31,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 40,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': -2.2852786,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-2.285279]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1967\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6432700f9baabdd7ae95943b38a60a52dd1b3790',\n", - " 'dl_image_idx': 572,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 280,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 0.81099218,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.810992]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 65\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_1_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b363da6dc06f83d682f04ee0592211cd1c41a082',\n", - " 'dl_image_idx': 205,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 17,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': -1.5354688,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.535469]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 308\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2907decd824f71c40d223b38479c483b6c618540',\n", - " 'dl_image_idx': 200,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 169,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': 1.2088631,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.208863]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 202\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7ec08dc0b1e679e715344bf81bb4043b0708263d',\n", - " 'dl_image_idx': 2175,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 260,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -0.8468197,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.84682]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 287\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9a9c5874073f12154f5e24cc2da2345931d7f948',\n", - " 'dl_image_idx': 557,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 184,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': 0.39561263,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.395613]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 982\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4069cf701dee7c47ba55d9af9b561197710aee47',\n", - " 'dl_image_idx': 137,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 228,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': 0.37733912,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.377339]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1900\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f6080ce3ce18ced41ad468647ed4131b681dce7f',\n", - " 'dl_image_idx': 742,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 113,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 29,\n", - " 'dl_value': -0.95466942,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.954669]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 23\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '743ddb16c9f7e3c8f1d71bb33f09714384e0a1ff',\n", - " 'dl_image_idx': 428,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 123,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': 2.0458467,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 2.045847]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1928\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_0_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c9234bb0f070ec7dd222f7b39a968aba0fb0ee18',\n", - " 'dl_image_idx': 1365,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 219,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 47,\n", - " 'dl_value': 1.9079962,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.907996]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 506\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '13c06fb326cd4d18c3dc840a088c0cbc81a82925',\n", - " 'dl_image_idx': 17,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 225,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 43,\n", - " 'dl_value': 1.8310231,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.831023]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 478\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4a482bcb2f940d16f0edbb357e23a11812a304ba',\n", - " 'dl_image_idx': 461,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 291,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': 0.12642287,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.126423]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 578\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7c3322049581ee7d8063494c020cc85acbb4f9a7',\n", - " 'dl_image_idx': 118,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 184,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': 1.6449629,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.644963]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 882\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '608ae427ae0d8c40513409edd4f46cf08cfaf852',\n", - " 'dl_image_idx': 404,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 239,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 1.1404536,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.140454]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 685\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '080e4b315de5e0a804629ba46bf3ead4faa8f67c',\n", - " 'dl_image_idx': 1062,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 218,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': 0.55605561,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.556056]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1656\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'cc67fe89474be6c5ae653843a75d9ed0b343ca52',\n", - " 'dl_image_idx': 466,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 90,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': -1.7166737,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.716674]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 365\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_4_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '287900f6b85bc275c7de6c7d9ea22cd09893c3d2',\n", - " 'dl_image_idx': 1692,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 178,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 37,\n", - " 'dl_value': -0.14084323,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.140843]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 838\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f799d0b6d59776a9f46316c599d073e913aa2074',\n", - " 'dl_image_idx': 1229,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 81,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -0.91562593,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.915626]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2422\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c5cbc021aa6b07fca48064d531e2328110394e4e',\n", - " 'dl_image_idx': 2090,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 7,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': -0.86166394,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.861664]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1481\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_7_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c6ec4e024ed8e5221194805bd79842d6f5fe7c7b',\n", - " 'dl_image_idx': 307,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 233,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': -0.17130609,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.171306]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 478\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd44eb5addee61d495f192b1901b92076aa5ca001',\n", - " 'dl_image_idx': 596,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 272,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -0.42553693,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.425537]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 446\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '29f45a15c1e708be856edeecebe46e9c2a64ab3c',\n", - " 'dl_image_idx': 196,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 25,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -0.10464281,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.104643]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 50\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9ba9272560486bebb9d7667fb9e2c5200bffc33d',\n", - " 'dl_image_idx': 493,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 121,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 1.846253,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.846253]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 398\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd4c3b4d4aefd29fd168a2c3c9a9962d99653a715',\n", - " 'dl_image_idx': 615,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 249,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': -0.25645292,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.256453]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 4\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2c04ceaaaf704f50ab8b4db6868d29fbbf1da44c',\n", - " 'dl_image_idx': 90,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 276,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -0.21582747,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.215827]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 514\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0d2af38aace2dc666a26f16464caf9fbd9b69515',\n", - " 'dl_image_idx': 536,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 151,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -0.3285594,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.328559]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1616\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ab8af1ed063cf39ebcb0b7e233a0933fdba8cc44',\n", - " 'dl_image_idx': 1407,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 58,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': -0.35580531,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.355805]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 661\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1ecae184dd77b9d5be0367b7e6acce0ac8a44b37',\n", - " 'dl_image_idx': 709,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 151,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 0.95836663,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.958367]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1737\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '48b967fbf8fe4828e0bf76c0cbc598d2dca4e146',\n", - " 'dl_image_idx': 503,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 130,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 1.3208258,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.320826]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 566\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_9'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a38eaebb855209eaf96ddc35f27bd35fe2c8f0ae',\n", - " 'dl_image_idx': 17,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 136,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': -0.087830454,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.08783]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 53\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_8'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f4d1fcd59b3be48361986028f3f7f4c0a2bf6a36',\n", - " 'dl_image_idx': 2416,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 16,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 47,\n", - " 'dl_value': -0.40870494,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.408705]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1576\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2ba5a992fc991d89599ee947da8064c4717d7327',\n", - " 'dl_image_idx': 452,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 6,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': 0.6127857,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.612786]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 468\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f0cf8cf6870f21aca24e43be8a945889e4c176a7',\n", - " 'dl_image_idx': 1529,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 256,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -1.4641235,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.464123]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1727\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3f8a42c8ba189d22e17a1e514b7590b40e58cde8',\n", - " 'dl_image_idx': 1476,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 99,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': -0.4932825,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.493282]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2121\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ff8a65a46f8d125520a70e1027f397474c3a4bfc',\n", - " 'dl_image_idx': 342,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 286,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': -0.25936621,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.259366]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 493\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_1_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a93d08e9bdd0c5d299d2c413ff78be92a22907d1',\n", - " 'dl_image_idx': 2121,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 60,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 0.3013331,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.301333]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2019\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_3_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5faf88ac7aa9ad8b6da8b2a53fcebbaf9cf7f304',\n", - " 'dl_image_idx': 2254,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 9,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': -0.018573292,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.018573]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1784\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_8_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b10ce76c97f7bfd449de1d972602d9f82e1c70b7',\n", - " 'dl_image_idx': 1318,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 77,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': -0.36945835,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.369458]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2181\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '89d1140fe4f71a039ffe51b594cd8dc2df2d221f',\n", - " 'dl_image_idx': 516,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 242,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.04927627,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.049276]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 574\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '935c9abd0ad616ed8123efa242380ab08ea45d8b',\n", - " 'dl_image_idx': 2320,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 276,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -0.74728972,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.74729]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1321\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '28635942a16fe0edfd12f47bab045ced9608ba10',\n", - " 'dl_image_idx': 1133,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 183,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': 0.79916805,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.799168]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2165\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_5'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e9a28caa6ce9bf706d4c289e72716265917ecb7d',\n", - " 'dl_image_idx': 1825,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 279,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': -1.1847801,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.18478]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 832\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f3f1621d700f3c595eef6c6f2fba7d62fc7d8b0e',\n", - " 'dl_image_idx': 251,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 295,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': -0.50123495,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.501235]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1884\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3c6aef6a6f57297cbfc732a0205bb2f442387399',\n", - " 'dl_image_idx': 521,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 106,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': 1.4300846,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.430085]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 124\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a54adb0bf98892078d2bcdfd89688fe6a4bc3e84',\n", - " 'dl_image_idx': 146,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 244,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -1.1207433,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.120743]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 546\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e597858b58777da6c6e59d7a165238c6789b4524',\n", - " 'dl_image_idx': 1102,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 268,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 46,\n", - " 'dl_value': 1.1747186,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.174719]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1524\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '213b8645b1f34eb37c26d6c76bbdfd964b13c8a1',\n", - " 'dl_image_idx': 1960,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 121,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -0.27003726,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.270037]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2133\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1aec43446141b723ba67712bc584a9c74ccd7eaa',\n", - " 'dl_image_idx': 217,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 136,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': -0.20854729,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.208547]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 554\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_8'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ed607cf11034a1e2ae41c9f5212a3c0957035876',\n", - " 'dl_image_idx': 2280,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 150,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': -0.093516223,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.093516]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 373\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9d45a078279f2196684ba4212a08f11498e5519a',\n", - " 'dl_image_idx': 1161,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 233,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 43,\n", - " 'dl_value': -0.82583845,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.825838]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 384\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2b4ab2b5c3f7169602cc8357e9ed1228b8005b8f',\n", - " 'dl_image_idx': 245,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 81,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -0.096562199,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.096562]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2092\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '081915103308e25fe6b0cf97679cd8aea2990b20',\n", - " 'dl_image_idx': 2452,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 90,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 0.10289949,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.102899]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1985\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_4_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '53dbb118f8004950713cecc0c94d657bcea1ad88',\n", - " 'dl_image_idx': 2502,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 274,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -0.56406838,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.564068]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 296\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '43ad1d3b582af8487069dd9a59004087c0a46172',\n", - " 'dl_image_idx': 1965,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 0,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': 1.6455349,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.645535]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2144\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_5_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd895e0064508619619c288a415f518f91cba2481',\n", - " 'dl_image_idx': 1787,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 269,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': -1.9954761,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.995476]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1940\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0636ae836e587b417057435d3cb5c26304e077db',\n", - " 'dl_image_idx': 1137,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 10,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': 1.2173316,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.217332]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 127\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '93e17d12b458132139b7b05949ea861e73a9ca10',\n", - " 'dl_image_idx': 2425,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 54,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': -1.1466185,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.146618]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2432\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '340d0a924d5783ef4a1ef71ba9350f5a6f0751f8',\n", - " 'dl_image_idx': 419,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 187,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': -1.1719999,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.172]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 584\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_4_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '959b54690b7fb079b3ef249f9bb0e88526f7b01f',\n", - " 'dl_image_idx': 2501,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 281,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -1.092172,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.092172]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 716\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '55b01bdb9913974a5cac13fad10ff33a99d45551',\n", - " 'dl_image_idx': 112,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 113,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': -0.65937179,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.659372]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 107\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0e85d25bd85eaf1ccc1f3bcb864b6951a45f5138',\n", - " 'dl_image_idx': 1107,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 40,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': 0.43739966,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.4374]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1369\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fd1c8070be2eaf90ee17bed9094e9bb06eb7bb4e',\n", - " 'dl_image_idx': 232,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 176,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -0.96102887,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.961029]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 419\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ac20dde3b00e596602621513c561eb1bb146343c',\n", - " 'dl_image_idx': 408,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 55,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 1.6635768,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.663577]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 407\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_6'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '216f5eec6dd0c9e2249a69e85ffdf5a687387ef5',\n", - " 'dl_image_idx': 795,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 247,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': 0.40872565,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.408726]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1729\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_4_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '87275f29cfdc88f7c1534451ade975fa351c51bd',\n", - " 'dl_image_idx': 1228,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 265,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 0.29424751,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.294248]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1418\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e63a9ce54ff6aa0f00fb5cac2d5b3d76f2255506',\n", - " 'dl_image_idx': 361,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 106,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': 0.91364205,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.913642]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 495\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f4a11c226cb9b32c1bdd9f23002ff117790db970',\n", - " 'dl_image_idx': 262,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 135,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': 0.68012714,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.680127]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1545\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5f4ea554c5cf965520a54fbb0a18fcee3931c954',\n", - " 'dl_image_idx': 25,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 3,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': 0.020014834,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.020015]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 475\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_7_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b5b88d5dcf9eaa99660f7f4f1b576a726eaa7ac9',\n", - " 'dl_image_idx': 864,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 34,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -0.10836252,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.108363]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 209\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '216a40c814de023e751714f13f357b214fcfcf4a',\n", - " 'dl_image_idx': 378,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 279,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': 0.034742605,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.034743]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 551\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ba75a486df6b3979b579e6591580180ec2115101',\n", - " 'dl_image_idx': 565,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 144,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': -0.66859621,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.668596]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 312\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9ca849f71cc9925cda661e71370a9bd90af451fa',\n", - " 'dl_image_idx': 683,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 127,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': -0.039622236,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.039622]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1473\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_8_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '66465e9692efa310ce2aa1f017abf4a884c8b368',\n", - " 'dl_image_idx': 377,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 173,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 0.30760792,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.307608]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 541\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e6546bd2e3b92013873e5fa551700dffef3d1eb1',\n", - " 'dl_image_idx': 1871,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 197,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': 1.6832108,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.683211]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 197\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '693a69da4e861bf5320d965239302f56ec3107d0',\n", - " 'dl_image_idx': 624,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 221,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -1.5492193,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.549219]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2285\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_4_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '017326e7b521f50d84eda0ca44da0f9ba040f4ef',\n", - " 'dl_image_idx': 1983,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 207,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -0.05860848,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.058608]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1701\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '36e98035edd4d4ad7c20836ad5bb92b3a7379cc5',\n", - " 'dl_image_idx': 1318,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 185,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -0.27324799,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.273248]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1467\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6659b97645aaf6354462c744b718ca4c39f3bcb1',\n", - " 'dl_image_idx': 2524,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 121,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': -0.60332608,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.603326]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 155\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3366e4d04a6b851e0847f201726cdde944fcbaaa',\n", - " 'dl_image_idx': 1925,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 279,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': 0.13505572,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.135056]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 282\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '12396212017e175831c1d4b2b79eea74484e65fa',\n", - " 'dl_image_idx': 655,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 17,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 49,\n", - " 'dl_value': -1.0402066,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.040207]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 187\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '79787e3f9f7fa80185d918d4525c8b1c0d9632c7',\n", - " 'dl_image_idx': 1874,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 129,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': 0.33067751,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.330678]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 379\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_8'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'af9b99f3f659ea1a99d6f376b34c48c0cfa457c5',\n", - " 'dl_image_idx': 2116,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 0,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': -2.1856627,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-2.185663]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2387\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_5_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7e598a09d09264b2c8858fdcb4c806a8f436dd94',\n", - " 'dl_image_idx': 1934,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 26,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -0.61722022,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.61722]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1810\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a4cf10c60a263f788f8de75e0f482321e3c370c0',\n", - " 'dl_image_idx': 233,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 75,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 21,\n", - " 'dl_value': 0.81062418,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.810624]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 98\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1781f4e7f81a00db23d6948a04bae0e2f1a5141b',\n", - " 'dl_image_idx': 2297,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 244,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': 0.27006215,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.270062]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1268\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1b5664eaf77aec87cac2942fe3c9581f5fefa6d4',\n", - " 'dl_image_idx': 199,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 230,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 45,\n", - " 'dl_value': -1.6234235,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.623423]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 885\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '67b7751405161838545fcba5a429dd57acd96e8d',\n", - " 'dl_image_idx': 1847,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 92,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': 1.5085933,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.508593]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1218\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5430832f6295870805b2199e6b4c14bde57fd4f2',\n", - " 'dl_image_idx': 891,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 81,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 37,\n", - " 'dl_value': 0.022535987,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.022536]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2385\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5cf9cfcee30e143a8bfdf05bf3c73a2e61459d60',\n", - " 'dl_image_idx': 1474,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 87,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': -1.2555974,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.255597]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1559\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '28e31e574f90690adc228f38e4fff0a192bf8645',\n", - " 'dl_image_idx': 592,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 167,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': -0.21693946,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.216939]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 401\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'dd8ff3ddeac872a646b02b8fdcf286c87cdd7720',\n", - " 'dl_image_idx': 333,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 258,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -1.1122477,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.112248]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 23\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1c84323ee5dfe23c09d3d7965212f42104f0c336',\n", - " 'dl_image_idx': 2037,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 37,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 0.72238016,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.72238]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1774\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5fdb734f23e86d227ebaaae0f3f7afb1f07e9582',\n", - " 'dl_image_idx': 579,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 153,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': -1.580587,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.580587]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 395\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fddca81a7bc6cdd899d04c49cf5cd1bccedd4bdb',\n", - " 'dl_image_idx': 371,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 255,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': 0.57024568,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.570246]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 600\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4375b8a2c35dc88b531ba45b6e98b2fcb9fbe099',\n", - " 'dl_image_idx': 425,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 120,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': 0.85326105,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.853261]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2058\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7cc53b502f4edb4c80316733868a33894016aa53',\n", - " 'dl_image_idx': 7,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 236,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': -2.2714553,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-2.271455]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 131\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8d522d871e23b9dc8d2e5976641f9359b3836f69',\n", - " 'dl_image_idx': 867,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 129,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': -0.48955384,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.489554]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2192\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_8'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '190b8b051124eff06af2a62eafa471d7a275e566',\n", - " 'dl_image_idx': 2328,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 245,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': 0.96017182,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.960172]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1370\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e98ebb0ccba23965aaae45d1b758d5cbd1b48678',\n", - " 'dl_image_idx': 1762,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 2,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -0.87200111,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.872001]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 953\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_5_8'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c574efdbf0f57b47a7adf3d20feafcc3c974e689',\n", - " 'dl_image_idx': 105,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 54,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 1.0802515,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.080251]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 396\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '45564088770cdc6e87cdd8bfd161465ba653163a',\n", - " 'dl_image_idx': 848,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 122,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 29,\n", - " 'dl_value': 0.31704226,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.317042]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 66\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_8_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c67d7efde5072bc2f69be892288a1cc7047bdb68',\n", - " 'dl_image_idx': 1440,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 29,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 0.070989348,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.070989]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1952\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '573d8817cb924388dccd124d796d278b1d84c85a',\n", - " 'dl_image_idx': 951,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 141,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -0.84785765,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.847858]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2379\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'cc40c4b5ba4d0732ba0195ec8543666b1540f8c6',\n", - " 'dl_image_idx': 1021,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 288,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 37,\n", - " 'dl_value': -0.21968265,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.219683]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2315\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '81138f29185f738b2400b6a195391cc86413365c',\n", - " 'dl_image_idx': 345,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 154,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 21,\n", - " 'dl_value': -0.84607506,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.846075]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 233\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_4_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '97902a27365f4ff34936d97a7dc83d7dfec9d47b',\n", - " 'dl_image_idx': 149,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 18,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': -0.5244199,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.52442]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 332\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'cb7a24ad896ab2fad1b36aab4acaf2ae96d471b2',\n", - " 'dl_image_idx': 1829,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 275,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 35,\n", - " 'dl_value': -1.3132068,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.313207]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 342\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5bda6d668e8aadb8b36d39b4bc81cfe18e0f6ca2',\n", - " 'dl_image_idx': 163,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 169,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -0.29425234,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.294252]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 425\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '70abbcadfa9d1dfe0ce4341ac62c9e0fea2b7cde',\n", - " 'dl_image_idx': 256,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 228,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': -0.49636146,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.496361]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 69\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '79d8e812eae9ecf647eb1eff72d613dff34b1062',\n", - " 'dl_image_idx': 433,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 143,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': -1.1434437,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.143444]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 337\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1051b3f06d801cc12053171a4d682f4c96c99432',\n", - " 'dl_image_idx': 2390,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 230,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': 0.31886628,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.318866]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1587\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4d8155de2a438548cee4e50ff899fb22acc0959a',\n", - " 'dl_image_idx': 85,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 256,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -0.65478498,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.654785]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 288\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '633b5511dc21d2188a70545663fba9b2d21437d5',\n", - " 'dl_image_idx': 511,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 172,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -1.27214,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.27214]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1040\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_4_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '86b01ce334731450a39839c9452370186d44a902',\n", - " 'dl_image_idx': 250,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 203,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 21,\n", - " 'dl_value': -0.72023594,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.720236]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2176\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_3_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'dd2ba0808f0a1f1bb40d5f1b11dbcb7146400d5d',\n", - " 'dl_image_idx': 1644,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 57,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': 1.408337,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.408337]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2157\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3ff5c84ce4aa301a3789cc0971769a75ef6970d0',\n", - " 'dl_image_idx': 1471,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 208,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': 0.66588843,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.665888]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 672\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '04786bf649a119e4674e50836cf11532695a9e68',\n", - " 'dl_image_idx': 125,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 266,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -0.76111615,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.761116]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 213\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ddbd34db122aaddd44cc9adb76594ea7774a1186',\n", - " 'dl_image_idx': 946,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 120,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': -0.25208828,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.252088]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1666\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '47a898eec4d4a29f821e914b15ddcf310c4047fc',\n", - " 'dl_image_idx': 752,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 35,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': 0.41653815,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.416538]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2548\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'cd273ac84173183902480a22d8b6c35615827c91',\n", - " 'dl_image_idx': 451,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 267,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': -0.72698385,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.726984]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1297\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '68eb5f2ecc17d6e19c5e1c48923d858c920ad4b3',\n", - " 'dl_image_idx': 2117,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 110,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': 0.2693266,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.269327]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1455\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5433fff211b3b7c964e77fb6ff6422271f8cc1e2',\n", - " 'dl_image_idx': 294,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 217,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -0.24023326,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.240233]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 649\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7e598a09d09264b2c8858fdcb4c806a8f436dd94',\n", - " 'dl_image_idx': 1934,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 234,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': -0.22514267,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.225143]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1810\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2a58e3c7db29eea47533b810271e5450f1b7cfdd',\n", - " 'dl_image_idx': 2163,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 263,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': 1.981795,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.981795]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1553\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c1b2b71d50b9aeb2320b068e724f873e4b4e7061',\n", - " 'dl_image_idx': 1273,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 151,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': 0.97354192,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.973542]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 596\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '86ab8e2a2e24ee9083bbec184e23122e3a910f2c',\n", - " 'dl_image_idx': 1408,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 120,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': 0.032676734,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.032677]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1416\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bc164e2c7bf2c4a62b386cfeed0bdce60f7ac2f9',\n", - " 'dl_image_idx': 219,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 87,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': -0.2369711,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.236971]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 422\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'dd2ba0808f0a1f1bb40d5f1b11dbcb7146400d5d',\n", - " 'dl_image_idx': 1644,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 175,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 49,\n", - " 'dl_value': -0.63288474,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.632885]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2157\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '43d6b4b241d46151fe27217c6344dc45b06c5ab7',\n", - " 'dl_image_idx': 486,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 62,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': -0.14188753,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.141888]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 568\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'de4e903025aa6fc8cd4bf070cdbfe40590183a9d',\n", - " 'dl_image_idx': 2262,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 228,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 0.34830356,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.348304]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1309\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b76fe48ae5d3b2bedcfbd9895292f362a87dce8d',\n", - " 'dl_image_idx': 1565,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 7,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 0.34910777,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.349108]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2284\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_7_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd792f0ad021b3baeed06535f6077cc36f76762ac',\n", - " 'dl_image_idx': 560,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 261,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': 0.91737777,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.917378]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 358\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ba7c9a8ac12491e8b207d918b2e91080f53a29df',\n", - " 'dl_image_idx': 2396,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 282,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': -0.53130704,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.531307]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 674\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3711359e0be611b77882060a722b750b6b88834c',\n", - " 'dl_image_idx': 644,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 232,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -0.23588218,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.235882]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1671\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '56dc8a87c18bc177b0457cbc0808a9caa9c8ddd7',\n", - " 'dl_image_idx': 1969,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 198,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': -0.38272393,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.382724]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2510\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_8_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e6535de78b5da0e96d305a757470cf8c1156dd96',\n", - " 'dl_image_idx': 1012,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 100,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': 0.39267796,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.392678]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 306\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_0_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '937b0c65830bfa8fc1245e628ad13fe4981513b8',\n", - " 'dl_image_idx': 554,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 68,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -0.72894728,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.728947]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 257\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '102ad9d68eb9bbddda01c779a825fe26f7c2cbe8',\n", - " 'dl_image_idx': 269,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 195,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': -0.79737067,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.797371]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1655\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c86a6066face6419245c292840ccc948143bae2b',\n", - " 'dl_image_idx': 1983,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 270,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -1.6338681,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.633868]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 165\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4cf6940da3ad2e22021dee943edcc5534f75fee9',\n", - " 'dl_image_idx': 164,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 174,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': 0.050308242,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.050308]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 444\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'efe722969dc2a201c997acd68e15a056b7a27a63',\n", - " 'dl_image_idx': 353,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 192,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 36,\n", - " 'dl_value': -1.7793286,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.779329]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1796\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6d9c14edcba8c8984c2dfe473c658cea652f738b',\n", - " 'dl_image_idx': 1662,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 67,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 1.7411677,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.741168]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2396\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '54b3cf73f551f2a1181dff1070315f3b6540108a',\n", - " 'dl_image_idx': 680,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 268,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -1.1380644,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.138064]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2401\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '28dd77c20bd15ea22e298ec34351859658b2f4ac',\n", - " 'dl_image_idx': 2010,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 171,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 47,\n", - " 'dl_value': -0.27323905,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.273239]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1835\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_2_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '417626cc1b313ef1526259340148bbaddfa16427',\n", - " 'dl_image_idx': 623,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 125,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -0.80856621,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.808566]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 488\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_0_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5bdae948d3959146a154546a4aecc6830850669c',\n", - " 'dl_image_idx': 1321,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 181,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -0.19869231,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.198692]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1955\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a81099ef02fef8baf40af4532767d40b6642b1f9',\n", - " 'dl_image_idx': 124,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 234,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': 0.31732056,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.317321]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 290\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '825da98e19db692986892a033d192d6461cc02b3',\n", - " 'dl_image_idx': 1807,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 260,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 28,\n", - " 'dl_value': 1.058413,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.058413]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 644\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b60384819711fc95782f46796b012916b33e1107',\n", - " 'dl_image_idx': 2200,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 11,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': 1.0032171,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.003217]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 319\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '098ee0801ca0d21ff9dbc791ee8c5aa1af81d010',\n", - " 'dl_image_idx': 450,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 231,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': 0.14224973,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.14225]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1525\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '662d717d565949994af7addb082139d42d74847d',\n", - " 'dl_image_idx': 362,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 234,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': -0.077895738,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.077896]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1272\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0015b49a190e9bce70b108b28dc1a0674d3c9e66',\n", - " 'dl_image_idx': 54,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 159,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -0.91532785,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.915328]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 212\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '841c5c004495a075f2e169b1017718ff75a58727',\n", - " 'dl_image_idx': 1980,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 256,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': 0.48891443,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.488914]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2258\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a22aa0af2fef1f0a648f79aba5fa22bedc6c1c95',\n", - " 'dl_image_idx': 1316,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 148,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.058601711,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.058602]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1942\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '54d60562fddc03b581fbf1836efc9850320ee372',\n", - " 'dl_image_idx': 708,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 33,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': -0.0069388319,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.006939]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1066\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3c80253c5a82f8c92a190a9a43deb35250e9591c',\n", - " 'dl_image_idx': 620,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 104,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': 1.2506033,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.250603]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 461\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_0_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '69a43aff5f1cad38763b1c0fe49f20fcfbff4924',\n", - " 'dl_image_idx': 281,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 107,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': 0.59853828,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.598538]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 84\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b66671aed7c38cf4ecf869694d3532ae09802c55',\n", - " 'dl_image_idx': 277,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 159,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -0.39678785,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.396788]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 267\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '01783960847fba9881ee9a9a91e499fecb568dfa',\n", - " 'dl_image_idx': 445,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 148,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': -0.69233596,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.692336]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1212\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3d58efb5640c84b49cf3b69efc60d28ddf416cda',\n", - " 'dl_image_idx': 2051,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 286,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': 2.0234187,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 2.023419]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 924\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_1_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '61e8c4048907cf530e7ef06d467fcb6410f9da8d',\n", - " 'dl_image_idx': 1128,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 178,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': 0.4905206,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.490521]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2057\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a980e2e48259862db8690a7bb2db8f85e055809d',\n", - " 'dl_image_idx': 471,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 191,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -1.019274,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.019274]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 247\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_3_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5c6eeb5d4c274c0cd3f5a7efafce1abb1ef9b8d1',\n", - " 'dl_image_idx': 32,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 196,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 33,\n", - " 'dl_value': 0.10650942,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.106509]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1859\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '40482aba4c64c2b8f1ef95e85f1357fb6ed3c421',\n", - " 'dl_image_idx': 1024,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 88,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': -0.21381025,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.21381]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1712\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '688906e64f78f25a29c75ca4ae2fc209b5d6c682',\n", - " 'dl_image_idx': 1028,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 235,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 0.56461167,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.564612]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1024\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fe2d1dba9e1fcb37cf0aea154c85857c93a34e4e',\n", - " 'dl_image_idx': 349,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 165,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -2.5513825,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-2.551383]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1307\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a0d9bcbfba90b850fdea78906fd46e7b3f753576',\n", - " 'dl_image_idx': 2190,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 75,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': 0.18267445,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.182674]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1698\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2f0d06c7369a26179b4ff0ea5349b4c12b865fd5',\n", - " 'dl_image_idx': 555,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 45,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': 0.58082098,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.580821]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 36\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_8'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '43db501c48ea28e6dd2f103d96a174e63a32648e',\n", - " 'dl_image_idx': 594,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 109,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -1.4585366,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.458537]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1046\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '42fdcd99feed3f444514e91c073b491cd150ba95',\n", - " 'dl_image_idx': 1466,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 289,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': 0.26867196,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.268672]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 856\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7cc53b502f4edb4c80316733868a33894016aa53',\n", - " 'dl_image_idx': 7,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 119,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -0.48028719,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.480287]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 131\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e6ca1530c4ee03190ed600a1eade791b23c889d8',\n", - " 'dl_image_idx': 673,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 161,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 0.42040995,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.42041]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1686\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '348af1f8745e58ca3c8b0836bb74c489c4cf487d',\n", - " 'dl_image_idx': 1063,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 39,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 29,\n", - " 'dl_value': 2.4442203,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 2.44422]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 780\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '115d2b285cd3edecb3d47e911cb310bbec14f096',\n", - " 'dl_image_idx': 569,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 259,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': 0.26792079,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.267921]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 317\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ffefd7bb06be3b40589a3487d14991ca925dc618',\n", - " 'dl_image_idx': 1023,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 132,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': 0.49571708,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.495717]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 323\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_8'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4e9ae959fef7a32d23e4415342748528abb7fd93',\n", - " 'dl_image_idx': 330,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 295,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': -0.51890254,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.518903]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 383\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '635d332a373d812605eacd37d28ff6363c8be7f7',\n", - " 'dl_image_idx': 102,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 190,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 49,\n", - " 'dl_value': -0.4250499,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.42505]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2102\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_5'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '54518d1b71a70514c0a5eab07604218bfd77e6ee',\n", - " 'dl_image_idx': 159,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 120,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 2.2414448,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 2.241445]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 168\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c492ea48033ab65ff6780fd1ff650359ef649f0a',\n", - " 'dl_image_idx': 158,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 259,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.62681264,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.626813]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 63\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5db2b0d55bc4e3dc550e92fc36a29d555584751e',\n", - " 'dl_image_idx': 540,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 163,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -1.2964019,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.296402]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 386\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '33012311d43c0473fc2ddd2cbfa1780717d896c5',\n", - " 'dl_image_idx': 1272,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 45,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': -0.47620052,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.476201]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1034\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_8'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '43db5e53fdec7a0a36ab64838d7a6838af228ba2',\n", - " 'dl_image_idx': 2276,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 272,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 34,\n", - " 'dl_value': 0.25655636,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.256556]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2322\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1ee27075d74daa37d250bf2ccbb59154db4671a6',\n", - " 'dl_image_idx': 117,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 294,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': 0.61436701,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.614367]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2430\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0518faeff8fa12123279428b01de32915b40cc18',\n", - " 'dl_image_idx': 311,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 140,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': 2.003067,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 2.003067]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1540\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c04049b7f36b9e1c7861b01418d0a83cbe196c8d',\n", - " 'dl_image_idx': 463,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 121,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -0.92128187,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.921282]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 118\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ba75a486df6b3979b579e6591580180ec2115101',\n", - " 'dl_image_idx': 565,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 153,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': 0.52383113,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.523831]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 312\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '90530c200d2a68d723b572a6d8ec6b9db05b3aa0',\n", - " 'dl_image_idx': 207,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 110,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': 0.1734695,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.173469]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2510\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5acc4ebcc8fb76e5d5295549b4f958f403a5cce5',\n", - " 'dl_image_idx': 520,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 291,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': 1.4895995,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.489599]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 703\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '21e4ca161c749ae15400c27a3df9f1b7b43f6c97',\n", - " 'dl_image_idx': 1831,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 39,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 29,\n", - " 'dl_value': -0.34241211,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.342412]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 235\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ad45277dfaa58f13b4439f8dd3f472bcd82814d7',\n", - " 'dl_image_idx': 101,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 36,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': -0.10242725,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.102427]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1869\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e5299009d6f51172a23387c10b66c2eef72ff01a',\n", - " 'dl_image_idx': 1694,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 279,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': -0.17037797,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.170378]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 909\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e613c4c1ac61f70e051706b15cae911b8b1d895b',\n", - " 'dl_image_idx': 616,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 269,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 49,\n", - " 'dl_value': 0.46456581,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.464566]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1282\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6152ff6bb752038381e8b01dd957c30decb3d929',\n", - " 'dl_image_idx': 1437,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 71,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 1.0492339,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.049234]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1168\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_5_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'edddba71edc8c8de26798c60ffc31b08b99c3c29',\n", - " 'dl_image_idx': 54,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 1,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 0.26820126,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.268201]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 444\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_6_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '958be4ebc61d752e0b743700777c58afe2c228db',\n", - " 'dl_image_idx': 1789,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 78,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 0.61725968,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.61726]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2318\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd988e7a7ecaa21b6975ed376c5f06a5f4b2432a5',\n", - " 'dl_image_idx': 2342,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 27,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': 2.2550304,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 2.25503]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 693\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9ba9272560486bebb9d7667fb9e2c5200bffc33d',\n", - " 'dl_image_idx': 493,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 108,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': 0.3924374,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.392437]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 398\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4dd258fe76d583a4d654443e726abd354e87acd1',\n", - " 'dl_image_idx': 1739,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 58,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': 0.23671363,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.236714]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1325\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f33d654b7a5a7eccb761e76fde64ce6afb2b0727',\n", - " 'dl_image_idx': 2230,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 170,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -1.1456401,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.14564]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1954\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd4207a8fb0ba51bcfab7a14b3444c98ce8e37e00',\n", - " 'dl_image_idx': 484,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 287,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': 0.74203211,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.742032]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 297\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2bf8bbea63301e9854c36367110e30245611e528',\n", - " 'dl_image_idx': 1349,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 199,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': 0.21109517,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.211095]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1021\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_8_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '09568a09517d0fa847e444be119f022c72b02e22',\n", - " 'dl_image_idx': 628,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 172,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -0.77285284,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.772853]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 194\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_4_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '115d2b285cd3edecb3d47e911cb310bbec14f096',\n", - " 'dl_image_idx': 569,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 193,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -0.53766447,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.537664]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 317\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '991a2bed6d91bc5e820e903af697ce4c16b4adda',\n", - " 'dl_image_idx': 915,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 286,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': 0.96149272,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.961493]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 310\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_1_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '40df6569fe38b63932bc35c49910151c8d353a5f',\n", - " 'dl_image_idx': 278,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 295,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 35,\n", - " 'dl_value': -0.21164708,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.211647]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 312\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fd88e731055dbc0db46841fa8f0018c5ea29a557',\n", - " 'dl_image_idx': 417,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 92,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': 0.092068873,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.092069]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 564\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c4faebe54c3f433ea41f33a6623bdd38d37c72e1',\n", - " 'dl_image_idx': 1310,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 84,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': -1.5194408,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.519441]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1887\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1404933023737389101972f9c1a67fcd50c95c75',\n", - " 'dl_image_idx': 431,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 53,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -1.0880456,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.088046]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1842\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_6'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd21ae807aba391cf203b01c75220f0e9cb840dd4',\n", - " 'dl_image_idx': 462,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 145,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 45,\n", - " 'dl_value': -1.530267,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.530267]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 422\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7d17a8017ef0165ce115fa8ada01e6739fc29ccb',\n", - " 'dl_image_idx': 1123,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 109,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 29,\n", - " 'dl_value': 0.85094184,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.850942]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2293\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '775570462ec9b39f637c75cf48be4a0ffee4dba5',\n", - " 'dl_image_idx': 2539,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 76,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': -1.0882481,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.088248]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1889\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '396e6d51d53460719adbd2c47cdaff2df89875e6',\n", - " 'dl_image_idx': 773,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 4,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': 0.014509227,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.014509]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 367\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_6_8'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '899438b59ff8d678ed2aba9ed86a78c5d2832926',\n", - " 'dl_image_idx': 453,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 234,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': 0.14234735,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.142347]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1451\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7dfb48564b62d0bf96dc5a442e3017c171902f75',\n", - " 'dl_image_idx': 1430,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 275,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': 0.59853339,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.598533]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 500\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5047e997f1566499a135fcd77a0db9be82fcb91c',\n", - " 'dl_image_idx': 537,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 229,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': -0.77588505,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.775885]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 593\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a54adb0bf98892078d2bcdfd89688fe6a4bc3e84',\n", - " 'dl_image_idx': 146,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 137,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -0.4214561,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.421456]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 546\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8797d64f15126967f0957809fb96b16e36ec83a1',\n", - " 'dl_image_idx': 445,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 214,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -1.3005649,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.300565]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1893\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ad1c1f1c804c90df5dd7fc12b17b9217efd577f3',\n", - " 'dl_image_idx': 1308,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 246,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': 3.1886857,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 3.188686]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1428\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_4_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e09fac1cc9735914d7c10e092567c5c372a4867c',\n", - " 'dl_image_idx': 1198,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 29,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': -0.30833513,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.308335]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2226\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a89655c5a6539e1d650f838c1af6b284531ada33',\n", - " 'dl_image_idx': 1873,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 161,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': 0.0,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2210\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '35dc232cd57907aa56f7dd614fdd76e596b049ac',\n", - " 'dl_image_idx': 301,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 249,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': 0.056817569,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.056818]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 51\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '72f70be71d0ab0dfe5c8f73be730a288530e70ec',\n", - " 'dl_image_idx': 0,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 49,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': -0.63101351,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.631014]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 515\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '01029fbfd12c28ee82360690ce2ef95ed797f6ee',\n", - " 'dl_image_idx': 264,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 56,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 0.5168395,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.51684]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1870\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5bda6d668e8aadb8b36d39b4bc81cfe18e0f6ca2',\n", - " 'dl_image_idx': 163,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 170,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': -0.87949377,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.879494]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 425\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '97f456e89f7a29e81a886b7c7cce97bbb58370d8',\n", - " 'dl_image_idx': 691,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 61,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': 0.85664773,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.856648]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1060\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_2_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b7d7ea7158f656f3cfc5158bc87bffef696b4b7e',\n", - " 'dl_image_idx': 2346,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 251,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 0.78080553,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.780806]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 472\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '04f30486e5d1941b8c56a23a4e44b75a72362f80',\n", - " 'dl_image_idx': 2052,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 267,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 0.41052744,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.410527]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2050\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b30a30e12637a885c346688e72454191d77b4fde',\n", - " 'dl_image_idx': 497,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 234,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 0.36226043,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.36226]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 360\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3038b9bee2962ee3a006e156ca7ce2f06372dfcc',\n", - " 'dl_image_idx': 134,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 166,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': 1.6495016,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.649502]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 496\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ad09855a7755701a8d9c11fde725ad76044b0cdd',\n", - " 'dl_image_idx': 427,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 115,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': 0.23535158,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.235352]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 457\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '81b744f089e25237e73e7e0005cbdb1e3d99f214',\n", - " 'dl_image_idx': 2556,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 195,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 0.68952399,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.689524]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1137\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd5877727110b5a28953d32b41b920d1e96f7d81d',\n", - " 'dl_image_idx': 170,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 106,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -0.76544851,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.765449]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 366\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '199f30ca4e44cf169bc2398cc945e328788af843',\n", - " 'dl_image_idx': 1324,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 10,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': -1.6367577,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.636758]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 866\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '430e4a8dd16ae2d662ab80050571e88b8dfaad7b',\n", - " 'dl_image_idx': 2496,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 288,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 1.4376531,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.437653]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2053\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '82cd6da8eedaee6d3fa4428c6c35dcfe751cc3b8',\n", - " 'dl_image_idx': 463,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 270,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': 0.16168264,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.161683]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 133\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '51be5851436f583c4075bf637d7ef0587231d560',\n", - " 'dl_image_idx': 346,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 98,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': 0.10602866,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.106029]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 110\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5546dc0575a1e6477db5f4accd1d17184deb405b',\n", - " 'dl_image_idx': 79,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 83,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 1.8174436,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.817444]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 234\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '45b221ea1ed093c991ba6503c57093f6bea2c9d7',\n", - " 'dl_image_idx': 91,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 291,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': 0.52156675,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.521567]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 19\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '28ca0bdf69e90394d5ac801e4788bf5dea88ed95',\n", - " 'dl_image_idx': 1206,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 18,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': -1.9351184,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.935118]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1397\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '58f5461c0f0461e8508aa9d5092f481149d6c294',\n", - " 'dl_image_idx': 485,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 276,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': -0.40807894,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.408079]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 361\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'aea392bf3d5d8080ed5333eb6c561cdad7a2a1a5',\n", - " 'dl_image_idx': 2415,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 269,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 1.5887117,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.588712]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 696\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9fe081bc760039626e0cfb6c27077399b35124d4',\n", - " 'dl_image_idx': 2471,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 282,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -0.58095789,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.580958]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1730\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5cde85cc63e677623c606ebf6d21a6b028b50baf',\n", - " 'dl_image_idx': 6,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 123,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': -0.44520375,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.445204]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 431\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_0_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ead49df0df4de73a9e81b91f76fcd5d8dcfcd44e',\n", - " 'dl_image_idx': 343,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 10,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': -0.51565945,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.515659]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 550\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3d066f477d2517966a0d4ff62b3da849dc654520',\n", - " 'dl_image_idx': 1614,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 117,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': -0.76204324,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.762043]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 214\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a533e519b553cdf360ae134f7c9f161134ddaf00',\n", - " 'dl_image_idx': 455,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 214,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 21,\n", - " 'dl_value': -0.49399674,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.493997]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 29\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a808566454c04f4b3215dc3db3a5edf07318e72a',\n", - " 'dl_image_idx': 563,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 194,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 42,\n", - " 'dl_value': 0.29787502,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.297875]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 735\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2a58e3c7db29eea47533b810271e5450f1b7cfdd',\n", - " 'dl_image_idx': 2163,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 26,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': -0.46946993,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.46947]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1553\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1669a5b77cf71a4708647d426284e62842bf85ce',\n", - " 'dl_image_idx': 123,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 135,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': -1.5888429,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.588843]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2025\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ecfe40f2837099fd3727b64c06e1d1557bd2be76',\n", - " 'dl_image_idx': 499,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 200,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': 0.50042337,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.500423]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2246\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_1_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ab30d538e3039a64957a3d869f489370a036ff05',\n", - " 'dl_image_idx': 1886,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 244,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': 1.8072118,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.807212]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1520\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3f96324d77f768281de908ea6b64e120b1bfe5ae',\n", - " 'dl_image_idx': 1741,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 202,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -2.3151953,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-2.315195]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 751\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_2_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ea05353ce655648f5d42ba28abb9ede34cd56a5a',\n", - " 'dl_image_idx': 1237,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 124,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': 0.49573591,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.495736]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 436\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_0_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7f6e65cfd1689f05acd5a3341c173b05f3e377cb',\n", - " 'dl_image_idx': 105,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 274,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': 0.4551397,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.45514]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 433\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '49fba01e9ea45da87749863cd96b910d49779094',\n", - " 'dl_image_idx': 564,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 146,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -0.51052201,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.510522]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1999\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6878a93a7d6f98d336fc9f7bf99449350bae05c3',\n", - " 'dl_image_idx': 638,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 160,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': 1.8434029,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.843403]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 251\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '61948663f80dada76e9d7273b87707cd85669ce9',\n", - " 'dl_image_idx': 613,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 218,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -0.089693144,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.089693]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 216\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '937b0c65830bfa8fc1245e628ad13fe4981513b8',\n", - " 'dl_image_idx': 554,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 181,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -1.3242824,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.324282]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 257\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'cf1f1948b5e55815a11b72c52831275bababaeea',\n", - " 'dl_image_idx': 423,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 96,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': -0.011860407,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.01186]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 204\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6ced6395e4d3029120d6050c831ae2f505f55a0e',\n", - " 'dl_image_idx': 1664,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 174,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': 1.1029826,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.102983]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 860\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ea031b2517f62a70576712cf9050d9384f2bb057',\n", - " 'dl_image_idx': 407,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 73,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': -1.3738885,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.373888]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 154\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'de6005b6c4eae85070ca2674466739b213404481',\n", - " 'dl_image_idx': 426,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 233,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': 1.7574147,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.757415]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 148\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6e47b0e6d20d5c94430d0c94db5c52750534b97e',\n", - " 'dl_image_idx': 1995,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 276,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -0.55388105,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.553881]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1141\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5b6d80499aff015b8b0f186ef2c2fbbac42743fb',\n", - " 'dl_image_idx': 177,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 77,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': -0.092732944,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.092733]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 588\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ba75a486df6b3979b579e6591580180ec2115101',\n", - " 'dl_image_idx': 565,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 105,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -0.079035081,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.079035]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 312\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3a4cbf872eca1bf28804720b54c43cb4e6018255',\n", - " 'dl_image_idx': 129,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 78,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': -1.2600266,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.260027]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 275\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0015b49a190e9bce70b108b28dc1a0674d3c9e66',\n", - " 'dl_image_idx': 54,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 17,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -0.83740664,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.837407]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 212\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '87715dcb057f13a26e8d20b055e8cde90cbef79d',\n", - " 'dl_image_idx': 185,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 252,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': -0.057049636,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.05705]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 769\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5c6991ab1e435498f0b5b43cb82c6743342c91d2',\n", - " 'dl_image_idx': 830,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 115,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 0.4531405,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.45314]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2112\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '16c3865bc1a7e558630e3cb68fbd2f402f634d3d',\n", - " 'dl_image_idx': 581,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 89,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': 0.050373539,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.050374]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 438\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c4afd0ef1f0c04b457c75877c799e9de51eb8e60',\n", - " 'dl_image_idx': 1302,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 101,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': -0.1928916,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.192892]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 799\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '66b9c91b56c4ac6090b9676093337a52829ad087',\n", - " 'dl_image_idx': 1079,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 214,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 0.58023059,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.580231]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 560\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '66cb7c674151fb388f7dbd9604242148ac0506b6',\n", - " 'dl_image_idx': 446,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 295,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 34,\n", - " 'dl_value': 0.48253936,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.482539]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 831\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1781f4e7f81a00db23d6948a04bae0e2f1a5141b',\n", - " 'dl_image_idx': 2297,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 32,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -0.63378054,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.633781]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1268\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2dfaf099e44ad9cd82ccef7f538655a3fd6b7f25',\n", - " 'dl_image_idx': 643,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 41,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': 1.3449812,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.344981]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 480\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3b100678d4b27b37d4eaf694846295b0fa84ad24',\n", - " 'dl_image_idx': 197,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 155,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': 0.12851395,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.128514]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 892\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5c4ec697afb663d56b6f40720a70af86ab45508d',\n", - " 'dl_image_idx': 2504,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 192,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 28,\n", - " 'dl_value': 2.9775951,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 2.977595]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2214\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ed0bdf5e2955ec27fc6fd71886feb20254288706',\n", - " 'dl_image_idx': 1674,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 93,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 45,\n", - " 'dl_value': 0.32399198,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.323992]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 714\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ff5636d96d736401911d6ffc1c120a57908c1ffa',\n", - " 'dl_image_idx': 737,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 68,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -0.28464559,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.284646]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2039\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f4496cbc18f2ca81900c8d21be42e43f67260c07',\n", - " 'dl_image_idx': 530,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 183,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 1.4014143,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.401414]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2127\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_5'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '67a529a890994764632eca29355962aa510fd097',\n", - " 'dl_image_idx': 811,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 277,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': 0.76947659,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.769477]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 845\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '93b44ec3e448aca841208dc38002a7a79cbce8cb',\n", - " 'dl_image_idx': 1168,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 178,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -1.1098906,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.109891]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 711\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '06f64c25bee65d916b32d2d7c419b0b920f8cd8a',\n", - " 'dl_image_idx': 558,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 180,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 0.47878075,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.478781]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 75\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_8'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5db47a1ca0a1273604e4e25e17bd9bf8617177d5',\n", - " 'dl_image_idx': 894,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 205,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -1.1771184,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.177118]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2014\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a73f569d036d55bd9c3c0782e462a465e208a9f3',\n", - " 'dl_image_idx': 1713,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 275,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': 0.26724693,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.267247]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 252\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ad3d07e66fcc064aec0f3237169b172b236be200',\n", - " 'dl_image_idx': 242,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 25,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': -1.5091863,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.509186]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2468\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9ee68e859bb4f6def4bc2391f6f1cf5b1648c67d',\n", - " 'dl_image_idx': 1790,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 77,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': 0.27355379,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.273554]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1602\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'dad4750747e2aa8ebecdeae985cb9751adb8151b',\n", - " 'dl_image_idx': 177,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 262,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 1.3546048,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.354605]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 662\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'cdcb0c2661ae9d532610127969418de7ed8bc853',\n", - " 'dl_image_idx': 140,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 45,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': 0.5855394,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.585539]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 524\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_8'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'faaf58e8e19adf1aea26a01a3f0e792ccc5c088b',\n", - " 'dl_image_idx': 2114,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 217,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': 1.1851773,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.185177]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2166\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'dae21e06e59b3769649ab71e133ba8fda4cfb184',\n", - " 'dl_image_idx': 601,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 143,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -1.0628439,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.062844]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1862\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '706d56e197f6eb3a2e3d5ecbd3fe4ac9ac4f554e',\n", - " 'dl_image_idx': 977,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 106,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 0.74837595,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.748376]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1444\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4f5db4cadfd733acc4036c12fb3b052c20270e8d',\n", - " 'dl_image_idx': 154,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 76,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': -0.92891222,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.928912]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 220\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '62f1a2b9b22cd4a3586e273b9ac26d730745cb61',\n", - " 'dl_image_idx': 2252,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 216,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': 0.45247865,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.452479]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1017\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '97bd63eab14f1dc852c1bdcc69ec5a54b4ae9315',\n", - " 'dl_image_idx': 1413,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 89,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 0.11323906,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.113239]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1857\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'cbe3110d88f1cbac1ddfbb52e8f7f04b0af913d4',\n", - " 'dl_image_idx': 464,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 239,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': 1.5488684,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.548868]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 276\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd7fa5925f28bf3c55c65c8be3105ec4e00fbfc21',\n", - " 'dl_image_idx': 1682,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 49,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 34,\n", - " 'dl_value': 0.5586071,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.558607]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 283\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1a561177cff5c817c49a41978db66bc4b59f3dcc',\n", - " 'dl_image_idx': 1332,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 281,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': -1.0753125,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.075312]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1988\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e3846aa28ed4a3f459f671f672e4e882c0724b39',\n", - " 'dl_image_idx': 961,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 40,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': 0.9841038,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.984104]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 494\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e694f853b9ac008fe4d7d983dcecc0b042de783a',\n", - " 'dl_image_idx': 707,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 49,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': 0.64237714,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.642377]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1616\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4143239bcdd84fafd6f10336719b06fb429b6284',\n", - " 'dl_image_idx': 644,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 142,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -0.41218448,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.412184]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1456\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b5e3a8c2745ebec3513201aaa9f66682669f3d4f',\n", - " 'dl_image_idx': 352,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 245,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': -0.37023011,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.37023]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 397\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '85f19973baecf51a3943211f636b0683f7e6c568',\n", - " 'dl_image_idx': 1367,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 157,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 35,\n", - " 'dl_value': 0.052236728,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.052237]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2263\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f44970fba947e3c860f9136489194c0636c0451e',\n", - " 'dl_image_idx': 1870,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 54,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 0.13801564,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.138016]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1660\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '27f6daff11ee65e99329aad921732d0558e21157',\n", - " 'dl_image_idx': 15,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 32,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': -1.7342701,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.73427]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 444\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e8956cd4f6209c24d0d00e998388d31f36d34630',\n", - " 'dl_image_idx': 694,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 122,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -1.7767406,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.776741]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1644\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_8_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'cbaf03c0455b7756585b694dad2f1e82830104ed',\n", - " 'dl_image_idx': 1361,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 141,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': -0.76961404,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.769614]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 368\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '23d5ca58c1150f5ce84b9cc160443c4c77ef07fa',\n", - " 'dl_image_idx': 2508,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 29,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -0.96657503,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.966575]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1071\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0af5067081455fb4af8c2504cfaf4748dcac6c9e',\n", - " 'dl_image_idx': 2150,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 294,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -1.0462811,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.046281]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1292\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '160ecebc7024ecb064a57b4c7cd9cef15ad62cb1',\n", - " 'dl_image_idx': 133,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 235,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': -0.087354429,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.087354]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 329\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '32c11449c5f303008dee8783edbcca236341d97b',\n", - " 'dl_image_idx': 358,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 241,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 42,\n", - " 'dl_value': 0.20169941,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.201699]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 82\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f354f0fee3334dcce8fe9cab5c5caaee1c6e9ac2',\n", - " 'dl_image_idx': 2123,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 64,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': -0.74241722,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.742417]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1612\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_6'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '37ccafd8c964c9b2dabdb9169c12b6b282612284',\n", - " 'dl_image_idx': 66,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 202,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -0.69985336,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.699853]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1362\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_2_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '565fbf41a0293ec36b650609a96e05a5c1384aba',\n", - " 'dl_image_idx': 805,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 184,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 34,\n", - " 'dl_value': 0.78491622,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.784916]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2432\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0732c3ae2b18016897aeafbcfa7f4d9ce39cf51e',\n", - " 'dl_image_idx': 1327,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 264,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 36,\n", - " 'dl_value': 1.1777363,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.177736]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 747\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3e52a117766d348d8bcafa3f6b7bb2fc7b86a69d',\n", - " 'dl_image_idx': 380,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 231,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -1.3882965,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.388296]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 180\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'eb19b80166abae0f38333b0e2265914239250d85',\n", - " 'dl_image_idx': 1804,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 181,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': 0.69327521,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.693275]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1408\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '66b99499afeffac919bc77613d3a1d797c1e51fc',\n", - " 'dl_image_idx': 376,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 214,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': 0.37115994,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.37116]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 363\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9d415ca65e4c7dc746e2f16d71921ed68dd2a928',\n", - " 'dl_image_idx': 2333,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 165,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': 0.33724737,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.337247]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1476\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '857413489e88fa74b5b8a3bca7eb55ddfc374c08',\n", - " 'dl_image_idx': 267,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 100,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': 0.59030324,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.590303]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 33\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_0_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '015282073827c7400796d7f8ef3fc41ca5c0cf5b',\n", - " 'dl_image_idx': 907,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 207,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': -1.3384081,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.338408]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 183\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '51e0715b1ca0d386c9affa88f4ea80298c7b693f',\n", - " 'dl_image_idx': 1702,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 295,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': 1.046276,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.046276]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 187\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b96335167612e2825c34d55cd6159e85487e0e44',\n", - " 'dl_image_idx': 2203,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 50,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 37,\n", - " 'dl_value': 2.0730138,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 2.073014]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 844\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_7'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'cf454e2e542e705bd68ea78071e2d957de8f2666',\n", - " 'dl_image_idx': 2261,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 271,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': -0.54940814,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.549408]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2240\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0e2cf197edd4d469ec335e57d9c6aef9816d1684',\n", - " 'dl_image_idx': 1555,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 16,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': -1.6523156,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.652316]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2346\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '762e9530d9f9e0cc845e6aee6a5cd35408d5e7b7',\n", - " 'dl_image_idx': 1267,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 165,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 28,\n", - " 'dl_value': 0.31679434,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.316794]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1087\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2c4a5edadfa76efee8ccbf811bc99ace424e45c9',\n", - " 'dl_image_idx': 1408,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 11,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 34,\n", - " 'dl_value': -1.3749206,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.374921]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 871\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '746595e3638609e9dec83e65dd3c1fbd1b08e141',\n", - " 'dl_image_idx': 183,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 273,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -0.34779996,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.3478]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 156\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0e33235307b35c29342667a1b40e76d9fec3b54f',\n", - " 'dl_image_idx': 395,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 74,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 0.36042759,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.360428]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 436\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bb8310bb5d08a7b1c14ea5aba6ba385c2827e387',\n", - " 'dl_image_idx': 145,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 178,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': 0.98850673,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.988507]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 529\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bb3a615fa3c42ba2a346e5b55ccf4305cd468aa2',\n", - " 'dl_image_idx': 96,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 78,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': -0.28846073,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.288461]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1494\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5ed14e1d4d5903a63dce041560bbe2a36c01218c',\n", - " 'dl_image_idx': 939,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 162,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -0.10703465,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.107035]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2562\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e6dd036a9c8723c39661789c31fd7c893c1e8f16',\n", - " 'dl_image_idx': 329,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 42,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': -1.9213996,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.9214]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 236\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4a373781b7be1ae0ab8b271835b73ad162f1a8d3',\n", - " 'dl_image_idx': 2119,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 79,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -1.0972644,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.097264]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 136\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bd453136425ee94d5de363318e6a27d946b08219',\n", - " 'dl_image_idx': 2006,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 130,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 45,\n", - " 'dl_value': 2.312947,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 2.312947]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1387\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_9'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2566a1c12ecdcf73798abf728c5380a02c671f3d',\n", - " 'dl_image_idx': 137,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 174,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 0.37412214,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.374122]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 502\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '15497393c472705abc00d20b714ee4cfff93e657',\n", - " 'dl_image_idx': 1804,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 165,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 50,\n", - " 'dl_value': 2.661855,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 2.661855]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 376\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3a4cbf872eca1bf28804720b54c43cb4e6018255',\n", - " 'dl_image_idx': 129,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 100,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': 0.28519735,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.285197]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 275\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_0_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '04a8e163f51f5daf5789ad1d228b7bc81f07da60',\n", - " 'dl_image_idx': 1504,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 172,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 0.78808594,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.788086]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1495\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_4_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1a8f65bba37c41c9bbe5d2317b843dd9bebb18db',\n", - " 'dl_image_idx': 209,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 242,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': 0.24558532,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.245585]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 654\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8befe867ef4259e50e248ce6a9d14e4ce0ddd6af',\n", - " 'dl_image_idx': 2053,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 249,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 50,\n", - " 'dl_value': 2.5272088,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 2.527209]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1820\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2970df86fb9706eb1ef066a7120049eb7bb8656e',\n", - " 'dl_image_idx': 95,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 285,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.25249669,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.252497]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 200\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f83c06fd872642b2b78c6621b02563e06ed41021',\n", - " 'dl_image_idx': 2268,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 206,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': 0.86368787,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.863688]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 307\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '86b13198dab64971420055a092ac20c110f92ea8',\n", - " 'dl_image_idx': 719,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 77,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -0.083211906,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.083212]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1222\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '129b3dcb5eee92cc062a594fe3776bc098c957d7',\n", - " 'dl_image_idx': 441,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 173,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': -0.67686641,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.676866]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 417\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2b24f5e5253efe4b49b7306dca3985d04da35145',\n", - " 'dl_image_idx': 689,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 37,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -1.6529568,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.652957]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 89\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '565abd7e393f66440fe4fbc7e6d2f7d94b3bb3d1',\n", - " 'dl_image_idx': 1067,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 48,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 28,\n", - " 'dl_value': -0.34619048,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.34619]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 42\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_7'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7d4aba53b7acaa043de966c692d739313c5167f0',\n", - " 'dl_image_idx': 2220,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 93,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -1.0164614,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.016461]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2376\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e616a367b21e8333a380f5dd7aace101779d3659',\n", - " 'dl_image_idx': 1605,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 179,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': -1.2201461,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.220146]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 957\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_8_8'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bdb301c0c70f435581e10abf0a89beaef1e8d0d8',\n", - " 'dl_image_idx': 56,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 44,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': 1.3325459,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.332546]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 128\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_4_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2c8ea663efbcde8ca5f0e4e3f953b7dfe772c1b0',\n", - " 'dl_image_idx': 2475,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 185,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 3.5739944,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 3.573994]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 676\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'db30a94ef488d557c4af2c08920c1ffbb4ae2ce4',\n", - " 'dl_image_idx': 2287,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 142,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': -1.1444376,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.144438]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1252\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8395675b3b373e3bf88312bea07db925b754d4b6',\n", - " 'dl_image_idx': 836,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 73,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': -0.97342896,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.973429]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1720\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '989c39f54b6ef541b6a95fce146bb56f80d24006',\n", - " 'dl_image_idx': 174,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 149,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 0.027320763,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.027321]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 418\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '61ed411a932849f4caee5f14504ff89d485511fa',\n", - " 'dl_image_idx': 1179,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 148,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': -0.24656215,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.246562]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 431\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '757987832aad66022a88cf1f7dce6441a4ca9960',\n", - " 'dl_image_idx': 2483,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 166,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': -1.0121408,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.012141]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1231\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '383a0e1e9bbe692861e94f9653e956d05c60f228',\n", - " 'dl_image_idx': 336,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 123,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': 0.52997607,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.529976]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 533\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_0_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0d51a613255f289464e7d6c7b0b0afbf991135ec',\n", - " 'dl_image_idx': 360,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 196,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': 0.81039858,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.810399]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1556\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7f697fe4bfe29bf834b6898561819a06b3c3c9e5',\n", - " 'dl_image_idx': 905,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 209,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': -0.97036898,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.970369]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 117\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '921338650fd057e9a76a99335d8e6f3782df3cfb',\n", - " 'dl_image_idx': 1277,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 211,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 21,\n", - " 'dl_value': -0.42584023,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.42584]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2269\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2e420f3ab732d45578daf754725202d03ff2d6d2',\n", - " 'dl_image_idx': 2036,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 127,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 1.9543046,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.954305]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 168\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_8_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '056f1926e188b83b4b0b22aaf420ccbfe6c61c12',\n", - " 'dl_image_idx': 136,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 66,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': 1.4174961,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.417496]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 285\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_5_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5f00a62f84cb27f57ebaf020357891c549ee441f',\n", - " 'dl_image_idx': 462,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 189,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -1.8986882,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.898688]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 289\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_4_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a74b009a732329436483917f07b9244b1dfd9986',\n", - " 'dl_image_idx': 697,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 34,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 34,\n", - " 'dl_value': -0.95575529,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.955755]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1156\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0779043e4b8c04fcfca74eae5124f71d13d4bb71',\n", - " 'dl_image_idx': 385,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 130,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': 1.3834895,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.383489]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 114\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_9'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '14ced10c6b130de90052b0dbb6c28de354e66b1c',\n", - " 'dl_image_idx': 150,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 186,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -1.3512526,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.351253]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 163\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0a2cd7aa262a43ca22500d5e0e8db1785c133687',\n", - " 'dl_image_idx': 298,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 238,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 0.056972615,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.056973]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 555\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9b56b66e8e46e7e7b51684db87a9c563f540502a',\n", - " 'dl_image_idx': 128,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 87,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -1.5863857,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.586386]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 438\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '395f530107da06b0369bed2091c061c0ec897def',\n", - " 'dl_image_idx': 1403,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 123,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': -0.12379128,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.123791]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1608\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_0_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '33b22ddb82cd1e4175b9c2fdef23e51b2b700876',\n", - " 'dl_image_idx': 1724,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 101,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': 0.19488667,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.194887]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2087\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '48c621bed18d56770d77b061e01ca33b4548f539',\n", - " 'dl_image_idx': 582,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 235,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -1.3880713,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.388071]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 260\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3a4cbf872eca1bf28804720b54c43cb4e6018255',\n", - " 'dl_image_idx': 129,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 82,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': -0.31705078,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.317051]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 275\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8395675b3b373e3bf88312bea07db925b754d4b6',\n", - " 'dl_image_idx': 836,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 174,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': -0.82430643,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.824306]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1720\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '97d2757946971047f406eb562e947cce75e1d1e3',\n", - " 'dl_image_idx': 10,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 243,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 1.3616799,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.36168]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 379\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fd8a636f7034ce6cb3915d883ab9f3b11a6c83bf',\n", - " 'dl_image_idx': 633,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 228,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': -0.42077085,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.420771]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 539\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8eed25c5862f18ee3ababd9564a7e960472a899a',\n", - " 'dl_image_idx': 294,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 213,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': -1.1876751,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.187675]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 75\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '256c8638d0d8a190bb1ec2228aee9fe05c3ec503',\n", - " 'dl_image_idx': 1782,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 108,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': -0.40540218,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.405402]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 359\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9d1086bb37cd2ce96d0c70a21914202c7af4a480',\n", - " 'dl_image_idx': 2340,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 15,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 1.4740633,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.474063]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1630\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9d31d2f0a7774da7118ebc2a41eb379a8bc9f0e3',\n", - " 'dl_image_idx': 284,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 264,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': 0.59865576,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.598656]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2501\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5735a5e04f897ad09a2c63f2182fa537284b4eac',\n", - " 'dl_image_idx': 2094,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 145,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 28,\n", - " 'dl_value': 0.48461571,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.484616]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1336\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8795ffe469864d654d09ffb9f6776f3924f4f303',\n", - " 'dl_image_idx': 1454,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 124,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': -1.1434512,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.143451]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1649\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_0_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '47a5b4d3542b88d3a456f92e4c0ff9a652f9450e',\n", - " 'dl_image_idx': 436,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 206,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 1.217041,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.217041]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 70\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1debc56cd8bd329ce05a9bc888c9fc26c2999c33',\n", - " 'dl_image_idx': 559,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 65,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': -0.16346787,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.163468]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 133\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_3_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ac5c0ce0ccd09be4976c15077f6ea87ef9c6661e',\n", - " 'dl_image_idx': 317,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 116,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': 1.619306,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.619306]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 62\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bb05d073c756e040967c2a62737e50517ebfc95e',\n", - " 'dl_image_idx': 619,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 80,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -1.6796391,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.679639]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 48\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '71e38af8bc6efaf6a5b93ccdcaaec2c132fc2481',\n", - " 'dl_image_idx': 212,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 154,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -0.044742264,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.044742]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2513\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_4_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '15c904022ed2c411273640af6e6b7477f1e1651d',\n", - " 'dl_image_idx': 436,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 91,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': 0.18597986,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.18598]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 241\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f65ac63eb6a63ef8c611254b9050f6079c93ab5c',\n", - " 'dl_image_idx': 804,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 187,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 43,\n", - " 'dl_value': 0.017784333,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.017784]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1422\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_4_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3767864a1f4f34fdfc3499dca07d2184a3652b1b',\n", - " 'dl_image_idx': 2361,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 171,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 48,\n", - " 'dl_value': 0.67308289,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.673083]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 82\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_2_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '691aa8eb2ab9e0bc88a0c1abaa3c19e50f4865e7',\n", - " 'dl_image_idx': 503,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 204,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 28,\n", - " 'dl_value': 0.30425826,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.304258]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1491\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_4_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3a2e8aeaae50f81d1d4fc716969bd2c8e169b9d9',\n", - " 'dl_image_idx': 5,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 293,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': -0.47375688,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.473757]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 344\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fd88e731055dbc0db46841fa8f0018c5ea29a557',\n", - " 'dl_image_idx': 417,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 122,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -1.3246161,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.324616]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 564\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_8_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e752adec08995d6360b2ee41a1ae8bc6ceb9e0ed',\n", - " 'dl_image_idx': 420,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 222,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 0.91046745,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.910467]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 255\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '23d471245643bdf463e9cd6e392ebf781266ae8c',\n", - " 'dl_image_idx': 534,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 175,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 0.54944831,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.549448]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 184\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '54518d1b71a70514c0a5eab07604218bfd77e6ee',\n", - " 'dl_image_idx': 159,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 10,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -0.84315372,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.843154]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 168\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '27fdfa5f664007355d67760f1beabe6d3aaec166',\n", - " 'dl_image_idx': 2302,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 7,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -0.21226123,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.212261]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2524\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_7_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1904009146d7aaecf7a06dec4bbe1f6a118a257e',\n", - " 'dl_image_idx': 184,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 253,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': 0.63273454,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.632735]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 516\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '95a672a596bfeb6e5f057b8d2537e4b6c7479018',\n", - " 'dl_image_idx': 415,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 147,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -0.08412002,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.08412]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2397\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '044b86ce88cd8bbb047c076d4427a59050c5897a',\n", - " 'dl_image_idx': 39,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 103,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -1.5155413,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.515541]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 111\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_0_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '667b71cd68fea190537411ae4b176d1f77fdac53',\n", - " 'dl_image_idx': 556,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 289,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -1.9169034,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.916903]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 274\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4bef068b1301b9176ced85dd742b1d07fa35c5ce',\n", - " 'dl_image_idx': 335,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 170,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': -0.23313317,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.233133]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 612\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bc164e2c7bf2c4a62b386cfeed0bdce60f7ac2f9',\n", - " 'dl_image_idx': 219,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 206,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': 1.2551693,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.255169]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 422\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2f95ff072713b48ca5a841a0e40b6776d50ce299',\n", - " 'dl_image_idx': 48,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 188,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 0.25440872,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.254409]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 448\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_5'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '869c3410bdcd88105c745d395d68067a65a28c52',\n", - " 'dl_image_idx': 2212,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 261,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': -0.40563768,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.405638]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 486\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e35c3e9e26b8daa585cfe30ec06f3be4a12b6d45',\n", - " 'dl_image_idx': 496,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 175,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -0.79160243,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.791602]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 152\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b88a0479a73daf769bc59e13f0e1572e50d7fbac',\n", - " 'dl_image_idx': 279,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 247,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': 0.15929635,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.159296]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 342\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_4_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e752adec08995d6360b2ee41a1ae8bc6ceb9e0ed',\n", - " 'dl_image_idx': 420,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 268,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': -1.071674,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.071674]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 255\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4d169648f88280a79910c7b743b416b3b1a979ea',\n", - " 'dl_image_idx': 1578,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 10,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 21,\n", - " 'dl_value': -0.92588079,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.925881]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2231\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9aff5944dc10faee32f94f1b89ad34a662e02d44',\n", - " 'dl_image_idx': 545,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 139,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': 0.39130577,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.391306]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2467\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd5969faed9a76f3acadb83b3410ccfa067202071',\n", - " 'dl_image_idx': 411,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 26,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': 1.5777199,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.57772]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 439\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1193084cdc1497500b4212ab86b525d157cf5f87',\n", - " 'dl_image_idx': 2072,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 69,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 33,\n", - " 'dl_value': -1.0407012,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.040701]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2419\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '748fa7762811f17d80ff114f38533a5d1cbff871',\n", - " 'dl_image_idx': 967,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 16,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 34,\n", - " 'dl_value': 2.2526405,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 2.25264]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2418\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '02c9a0822fff87b6dbd6018319986d75b7308901',\n", - " 'dl_image_idx': 1469,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 208,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 29,\n", - " 'dl_value': -0.54517514,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.545175]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1016\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c67a787c296b3ae06da664016b24395868555aba',\n", - " 'dl_image_idx': 542,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 246,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': -1.1822604,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.18226]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1900\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_4_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7feef9674b68fd46787ee6fe9a1dcca27a9ac6bf',\n", - " 'dl_image_idx': 289,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 285,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 47,\n", - " 'dl_value': 1.0160245,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.016024]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2509\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b56ad897a946612f84f512dfefbbc25bc2a9d381',\n", - " 'dl_image_idx': 2108,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 101,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': -0.41377509,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.413775]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1586\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b9a41acb5c2696d25d6bcd92a28a8d07b0839bf2',\n", - " 'dl_image_idx': 437,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 196,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -0.69376993,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.69377]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 643\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5cbd24a0ac1881551f356940236e021bd29cd1de',\n", - " 'dl_image_idx': 850,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 195,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 45,\n", - " 'dl_value': -1.2778859,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.277886]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2560\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '20464b1160ac73b1fa19ff3bf8991dd66ae88af0',\n", - " 'dl_image_idx': 526,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 33,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -0.98236287,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.982363]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 102\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6cb2d6398cc64a29af1fd0891022b3816a8791dd',\n", - " 'dl_image_idx': 1445,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 106,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': 1.0232505,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.02325]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1262\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '490fe507cc08895368cb50af0a47635a8b308d41',\n", - " 'dl_image_idx': 690,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 176,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 42,\n", - " 'dl_value': 0.21351987,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.21352]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1964\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'abf9433dba4d11d47375c6a80634036e426673d2',\n", - " 'dl_image_idx': 1331,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 74,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 2.4356272,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 2.435627]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1297\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '19c40120cac1978b722e16be7aa4f4665df2a81c',\n", - " 'dl_image_idx': 475,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 253,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': 0.38910154,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.389102]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 147\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '39c5025638044c81ec392cda142ad4e4be09b370',\n", - " 'dl_image_idx': 510,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 101,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': 0.0065505863,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.006551]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 507\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd792f0ad021b3baeed06535f6077cc36f76762ac',\n", - " 'dl_image_idx': 560,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 265,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': 1.0824099,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.08241]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 358\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '775186bcdfc79e095454e12be37ad5b984bc2919',\n", - " 'dl_image_idx': 2251,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 126,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 48,\n", - " 'dl_value': -1.3409699,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.34097]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1865\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1b7b2f8ad699a70dd4cbb651a26e907eb45aab33',\n", - " 'dl_image_idx': 2157,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 138,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': 0.81933951,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.81934]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1532\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c067441bed74bbdb4912282e908c839732c738bc',\n", - " 'dl_image_idx': 20,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 49,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': -0.56446892,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.564469]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 203\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ea9abea5ffe81ea1d18f3e04bee818b02aaba5d9',\n", - " 'dl_image_idx': 305,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 196,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.53974569,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.539746]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 218\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e857f8a9cca8d6107429c55ed906e8e5f26e489e',\n", - " 'dl_image_idx': 2313,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 119,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': -1.8097513,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.809751]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1232\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '565abd7e393f66440fe4fbc7e6d2f7d94b3bb3d1',\n", - " 'dl_image_idx': 1067,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 268,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': -0.87431562,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.874316]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 42\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c2551a03d402ab6512767fc126f2c13e128c94dd',\n", - " 'dl_image_idx': 732,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 52,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 2.7840416,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 2.784042]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 545\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_6'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4c4a223b8642ff7c80db97a660ac37ada8cba732',\n", - " 'dl_image_idx': 18,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 252,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 0.4290472,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.429047]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 117\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f902bfd250c81fadb08dfd4f72142be754b0c305',\n", - " 'dl_image_idx': 63,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 227,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -1.0893384,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.089338]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 162\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '651fec065959e808bc89da805081b9b6351247db',\n", - " 'dl_image_idx': 515,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 19,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': 0.56503862,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.565039]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1462\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '20ea298e808bd56a2611f5561e61a22ac677ce5c',\n", - " 'dl_image_idx': 248,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 250,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 0.76431376,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.764314]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1531\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a2a09293b7c8581ce1dfbad6cc70e813abd23c27',\n", - " 'dl_image_idx': 630,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 128,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': -0.37275311,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.372753]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 89\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_4_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '54edd3a27d69e6b7b8715997a65f4fc25f39f1a5',\n", - " 'dl_image_idx': 478,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 59,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': -0.017377187,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.017377]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 557\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'aa03252a9866fbc790516c9378a74f216f9e36f4',\n", - " 'dl_image_idx': 2111,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 192,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 37,\n", - " 'dl_value': -0.57388729,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.573887]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 991\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8794bdf7366c87bd0e40d1fafb967f7637cb320d',\n", - " 'dl_image_idx': 639,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 232,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': -0.67125112,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.671251]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 520\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a19201d2d1d62feb2d6b07d010ccf8ca794b4d07',\n", - " 'dl_image_idx': 47,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 12,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': -1.0862613,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.086261]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 314\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c36eb1b348d78fbd3e5662ead488ac381c8c8f54',\n", - " 'dl_image_idx': 929,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 139,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': -0.75382018,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.75382]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1026\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '402300715f1ce0faa75002a0513ac8c96f90d028',\n", - " 'dl_image_idx': 1305,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 149,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': -0.81670105,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.816701]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1793\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1e2421b2f1c3efae98f063f53cc6b750058e4bdf',\n", - " 'dl_image_idx': 1458,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 193,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 28,\n", - " 'dl_value': -0.95711619,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.957116]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2170\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c5cbc021aa6b07fca48064d531e2328110394e4e',\n", - " 'dl_image_idx': 2090,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 199,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 48,\n", - " 'dl_value': -0.26892024,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.26892]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1481\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_8_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6578a72b283441047e5c8d31b31e5ff88679c399',\n", - " 'dl_image_idx': 108,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 21,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': 0.025297217,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.025297]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 309\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ed89c24397bf420cd7138178e7e90feaa0cf7063',\n", - " 'dl_image_idx': 704,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 219,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': 0.28811979,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.28812]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 727\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fd88e731055dbc0db46841fa8f0018c5ea29a557',\n", - " 'dl_image_idx': 417,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 221,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -0.31373146,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.313731]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 564\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_4_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3f5fce66cc7e7cad6fd2f996cf361d0a3a413dc4',\n", - " 'dl_image_idx': 495,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 109,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': 1.3627962,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.362796]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 231\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '082f0e5cc064bfbdf7a9679e1aca6d74503260d4',\n", - " 'dl_image_idx': 719,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 275,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -1.1336665,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.133667]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1379\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4a482bcb2f940d16f0edbb357e23a11812a304ba',\n", - " 'dl_image_idx': 461,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 55,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': 0.31073838,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.310738]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 578\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_6'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fcdc34eecc7bffa848937a54799c42c33cacfa13',\n", - " 'dl_image_idx': 610,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 234,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 0.36226043,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.36226]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 254\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '47a5b4d3542b88d3a456f92e4c0ff9a652f9450e',\n", - " 'dl_image_idx': 436,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 35,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': -1.4128684,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.412868]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 70\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b0ed269cd258ec72a1a6cf5d1101c565b8b48eb5',\n", - " 'dl_image_idx': 1678,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 287,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -0.13213529,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.132135]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 402\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd49745dce48b39f9ed2eec19cf1f751537969d7c',\n", - " 'dl_image_idx': 1256,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 114,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': -1.4740006,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.474001]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2298\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0dc74f2e6e69d439f70438ac54d26f9b09158579',\n", - " 'dl_image_idx': 316,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 173,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': 0.60991251,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.609913]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 382\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7e5e18f5204bf9e9dd0b9357e963c11ff19aa847',\n", - " 'dl_image_idx': 1187,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 249,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': 0.50764692,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.507647]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1828\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4d495fa4f1a7a51c60355b0affa6ecab5971a1e4',\n", - " 'dl_image_idx': 1276,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 48,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 1.1214902,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.12149]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1868\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_7'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'dd971075ec86530e9cb824b3127a9330bbace66f',\n", - " 'dl_image_idx': 568,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 97,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 45,\n", - " 'dl_value': 0.14353055,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.143531]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1875\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4fefbea44756719f5cded1539b1c092e352beab0',\n", - " 'dl_image_idx': 143,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 163,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -0.78409314,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.784093]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2566\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b76fe48ae5d3b2bedcfbd9895292f362a87dce8d',\n", - " 'dl_image_idx': 1565,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 135,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 37,\n", - " 'dl_value': 0.33433372,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.334334]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2284\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '750a5231f44e225415b228c4fe40bdac38bfb8e3',\n", - " 'dl_image_idx': 2062,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 31,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 0.40305492,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.403055]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2021\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1deab4537aa26a5b31c5124688eff130ac918847',\n", - " 'dl_image_idx': 252,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 232,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 36,\n", - " 'dl_value': 1.0060612,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.006061]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 655\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c42dca8dd7543c4a8908cd30c02adb63eb80e8a7',\n", - " 'dl_image_idx': 900,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 34,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 49,\n", - " 'dl_value': 1.5599227,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.559923]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2067\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c8a269145b3b5ac358986aa128adeb2437ba93a6',\n", - " 'dl_image_idx': 215,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 222,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.2651957,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.265196]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1027\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e67189e5940b05cf56013b7f64d3c72b77f4f5ba',\n", - " 'dl_image_idx': 995,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 257,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 48,\n", - " 'dl_value': -1.3274148,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.327415]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1728\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f78e88d13617e0cd63362559ec9d9f7d88757db8',\n", - " 'dl_image_idx': 229,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 125,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': 0.065707669,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.065708]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 445\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_0_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5d2e6851b50a3254b64d0dce91a4be97d0ae0ff0',\n", - " 'dl_image_idx': 444,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 243,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 1.0610251,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.061025]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 158\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7feef9674b68fd46787ee6fe9a1dcca27a9ac6bf',\n", - " 'dl_image_idx': 289,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 19,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 2.429692,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 2.429692]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2509\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a471b96f7fd251d54334f999408f30f722d710ae',\n", - " 'dl_image_idx': 1623,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 9,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 28,\n", - " 'dl_value': 0.29763263,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.297633]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 497\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_8_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1babceba1f2ad492dad0d692e05f01e2555476a9',\n", - " 'dl_image_idx': 242,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 289,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -1.0780118,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.078012]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1058\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6664c54d38a0edf63a4751754bd3db1d8a46c086',\n", - " 'dl_image_idx': 180,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 178,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': -0.57278746,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.572787]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 265\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7edd311b0ef7a9e61c62c6cbc2b3cfc2b5b6ea9d',\n", - " 'dl_image_idx': 4,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 111,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -0.94326574,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.943266]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 531\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_4_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '44ec1e8ca05787a2aa955016367d038dc924f66b',\n", - " 'dl_image_idx': 809,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 197,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 42,\n", - " 'dl_value': 0.10730832,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.107308]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 783\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '018cbe39c543ff4762667256b1e11300fbd5b968',\n", - " 'dl_image_idx': 1572,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 37,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': -0.39108819,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.391088]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1974\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a82b06560fec76771e760afddde3aa4bd5cc3355',\n", - " 'dl_image_idx': 42,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 113,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -0.43618593,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.436186]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 437\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '54518d1b71a70514c0a5eab07604218bfd77e6ee',\n", - " 'dl_image_idx': 159,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 228,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': 1.6177154,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.617715]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 168\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd0b58f29536c46593d91552effad8c0e92441b8f',\n", - " 'dl_image_idx': 581,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 143,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': 0.36887461,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.368875]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 136\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '383b7f7f11cedc8ab73de426e634f58cec57a8e0',\n", - " 'dl_image_idx': 1580,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 275,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 45,\n", - " 'dl_value': -0.28382063,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.283821]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1700\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bdb301c0c70f435581e10abf0a89beaef1e8d0d8',\n", - " 'dl_image_idx': 56,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 76,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -1.1088793,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.108879]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 128\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f1a2bfe943eff61f0438b521e7dea71cec69aa88',\n", - " 'dl_image_idx': 2283,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 217,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': -0.15727057,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.157271]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1461\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '69a43aff5f1cad38763b1c0fe49f20fcfbff4924',\n", - " 'dl_image_idx': 281,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 137,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 21,\n", - " 'dl_value': -0.10939341,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.109393]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 84\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '525903ca9bf26d5d2b4ca65129b1e022da85832d',\n", - " 'dl_image_idx': 159,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 46,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -1.1427113,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.142711]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1437\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_8'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5e547f381fb842d3bf44bf9a53ba2d22b4a099c2',\n", - " 'dl_image_idx': 291,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 86,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': 0.16959518,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.169595]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 138\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a7eca85563794c674ef389f7dfd8c8c79a6f8a20',\n", - " 'dl_image_idx': 1839,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 127,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': -0.88675117,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.886751]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1254\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_8_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '160ecebc7024ecb064a57b4c7cd9cef15ad62cb1',\n", - " 'dl_image_idx': 133,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 80,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -1.7457397,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.74574]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 329\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2dd4170e8c8585137d0f9d02e077e4edd76d6ae8',\n", - " 'dl_image_idx': 399,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 46,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': 0.24175106,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.241751]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2149\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_8'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '082bd7b95da6f1454422a5ed12b31c82f56905c3',\n", - " 'dl_image_idx': 547,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 92,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -0.4595122,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.459512]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 190\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '95d4eb5e479aea1b122d98f3d38bb3b4c9756613',\n", - " 'dl_image_idx': 1205,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 124,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': -0.31104064,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.311041]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2015\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_0_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a6d8a1975136c1e48e4eb0ac29909972f18c3804',\n", - " 'dl_image_idx': 2525,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 147,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 21,\n", - " 'dl_value': -1.1126322,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.112632]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2437\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '04bd930856c610e92fddfcf7c6daf2a160fd79b7',\n", - " 'dl_image_idx': 2275,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 27,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 28,\n", - " 'dl_value': 0.84672618,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.846726]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 540\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f3f6686df0ce854600b132b8dae1bd6b37975072',\n", - " 'dl_image_idx': 474,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 19,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 37,\n", - " 'dl_value': -0.61375153,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.613752]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2321\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c2c6f4c2c4ed79f6c7d8cfd12af06567b052f468',\n", - " 'dl_image_idx': 160,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 93,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': -0.7370742,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.737074]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 490\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ad6165d7ca6e99b6b207f98c772d7463f38822ef',\n", - " 'dl_image_idx': 1469,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 202,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': -0.44053283,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.440533]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1867\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_2_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c557b2907f91ed986c7dfb6706bfcbfcc860c923',\n", - " 'dl_image_idx': 2505,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 237,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -0.54923248,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.549232]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1975\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2df114247c0eb4a02c9f11567a8f8b87cf357c1d',\n", - " 'dl_image_idx': 2323,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 36,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': 0.59143627,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.591436]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2450\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1529a6b4107ea26a4840457326be4730c28b1e8d',\n", - " 'dl_image_idx': 374,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 118,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 0.39973277,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.399733]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 618\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '73c7a0a01d708bfceb013cd43a1a48b73b966175',\n", - " 'dl_image_idx': 1661,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 283,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': -0.27124131,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.271241]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1606\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_2_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a4e952b860b5dc6f11d84bce09ddef6bbeaccb02',\n", - " 'dl_image_idx': 207,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 140,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': -0.084548168,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.084548]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 86\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '360dea2223a5dc27798b9de1ab234add04263792',\n", - " 'dl_image_idx': 1212,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 149,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 46,\n", - " 'dl_value': 0.79346323,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.793463]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 686\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b5cd64441015236d85125c9bef60190d1bfc55e5',\n", - " 'dl_image_idx': 2123,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 221,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': -1.7106434,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.710643]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1702\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_4_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '74e2f20460d5aae2382b25d96d98b3549b1e4285',\n", - " 'dl_image_idx': 1776,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 88,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': 0.017188342,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.017188]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2313\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '01692e6fcbbfe7d9fa624e03bb176ba5c211db54',\n", - " 'dl_image_idx': 439,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 25,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': 1.6241684,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.624168]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 307\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '04786bf649a119e4674e50836cf11532695a9e68',\n", - " 'dl_image_idx': 125,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 218,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 0.72738272,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.727383]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 213\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c59a097e0994cf281da915811ffead368cd464b6',\n", - " 'dl_image_idx': 765,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 69,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 42,\n", - " 'dl_value': -2.2727139,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-2.272714]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2288\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3267bf2428d4ba35d36514e8dd1cd3917e2ba3c5',\n", - " 'dl_image_idx': 1499,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 107,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.98575449,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.985754]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 195\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e1610a3387478c1fa21345f4a8da0da1a98a7033',\n", - " 'dl_image_idx': 154,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 234,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': -0.14046702,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.140467]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1177\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2f3c97951a38e65ba41b619b80408bf6445a6f28',\n", - " 'dl_image_idx': 202,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 13,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -0.99702299,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.997023]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1579\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4b47465a9513b829b09f5d04c38ffe455ea1c661',\n", - " 'dl_image_idx': 115,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 281,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': 0.71183604,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.711836]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 270\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5e19d7e99d1c2af3adf7c435ad388efc8bb357e0',\n", - " 'dl_image_idx': 2055,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 154,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': 0.32690778,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.326908]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1765\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_4_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '00836081613ed80ce2a16a277e95c87740181429',\n", - " 'dl_image_idx': 1860,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 244,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': -0.45734599,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.457346]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1157\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '76e6024cb64a4a5b48731031d3c7d728d31ad0b5',\n", - " 'dl_image_idx': 550,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 204,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': -0.087095104,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.087095]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 630\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_4_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1e83487175f95370726be101a41ac1cc0fa2a154',\n", - " 'dl_image_idx': 1242,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 138,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': -1.0747532,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.074753]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 694\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '216a40c814de023e751714f13f357b214fcfcf4a',\n", - " 'dl_image_idx': 378,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 118,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 21,\n", - " 'dl_value': 0.74255633,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.742556]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 551\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '55b01bdb9913974a5cac13fad10ff33a99d45551',\n", - " 'dl_image_idx': 112,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 240,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': -0.59385914,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.593859]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 107\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'efe5547b149f6b0fcb68c253cd01b8e086b366d3',\n", - " 'dl_image_idx': 1659,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 40,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': 1.914816,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.914816]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 414\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8e69835da1bd74f0d193d2b8c9dc46a5555bc76a',\n", - " 'dl_image_idx': 110,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 30,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -0.99767965,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.99768]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 250\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5f00a62f84cb27f57ebaf020357891c549ee441f',\n", - " 'dl_image_idx': 462,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 281,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -1.1908904,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.19089]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 289\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c4448d1a342d78641c8a392fa0eb3a65fffd4dfa',\n", - " 'dl_image_idx': 359,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 83,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -0.30972889,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.309729]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 583\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8c48fa205e6380d8646061f86dd889dce69e5d8a',\n", - " 'dl_image_idx': 308,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 100,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': -0.19747013,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.19747]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 501\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_0_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '22f11a838344b3ccce3c04e3773f634253dce241',\n", - " 'dl_image_idx': 2337,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 45,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': 0.32157075,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.321571]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1857\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_8'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3d618da1d4e6151440a5281149d91509820636e3',\n", - " 'dl_image_idx': 1862,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 217,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': -0.41113597,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.411136]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2046\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5b3894f7d1d7d37f83d041646c033a4306f31c30',\n", - " 'dl_image_idx': 1446,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 279,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': -1.1764225,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.176422]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1431\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '56cb65eb4d7def64bce315d9850fde9866d5a662',\n", - " 'dl_image_idx': 2298,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 67,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': 1.2144285,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.214429]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 655\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1c052b170cca635417ca4e0f7aee365cf4025f84',\n", - " 'dl_image_idx': 1726,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 187,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 35,\n", - " 'dl_value': -0.45961618,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.459616]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1255\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_4_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '45b221ea1ed093c991ba6503c57093f6bea2c9d7',\n", - " 'dl_image_idx': 91,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 250,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': 0.32757711,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.327577]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 19\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6ad6def7301955e47fbd174b658f8d2950765975',\n", - " 'dl_image_idx': 118,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 110,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -0.085192323,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.085192]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 311\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '92b7745aee784170c45b7bc655b2eb7b6a734ee2',\n", - " 'dl_image_idx': 440,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 118,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -0.94012356,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.940124]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2539\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e7b046dedb38e342a14b6e9d7efe14c411c4044e',\n", - " 'dl_image_idx': 1016,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 72,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -0.41548219,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.415482]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1442\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_5_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6e6f519eabaf59c2489cfff5e6769b387363091b',\n", - " 'dl_image_idx': 2059,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 255,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 0.14857379,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.148574]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1720\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '64f0b2f6a236bdee0668d53a88a24d3fdb301e0b',\n", - " 'dl_image_idx': 930,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 230,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -1.0435061,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.043506]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1114\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '32cc85793000ef72bf6d745474dd982e1579dc67',\n", - " 'dl_image_idx': 612,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 166,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -0.82550865,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.825509]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 324\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1ec6058f8bb30d29762aeeac4d6c69f9ec722a0d',\n", - " 'dl_image_idx': 318,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 81,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': -1.526432,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.526432]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1619\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f6d3cb63342b710f2f1c7739b94db719b807ff40',\n", - " 'dl_image_idx': 1807,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 2,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 33,\n", - " 'dl_value': -0.59161288,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.591613]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1501\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_5_8'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ad2eb338bb91ffbaf5f20296ef6a0d9e8cabf11c',\n", - " 'dl_image_idx': 2204,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 183,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': -0.60236335,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.602363]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1549\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_5'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '44f390a06d4c0c9ff28227ca57641b5a605758fe',\n", - " 'dl_image_idx': 1793,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 236,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': 0.41407672,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.414077]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2036\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3a2e8aeaae50f81d1d4fc716969bd2c8e169b9d9',\n", - " 'dl_image_idx': 5,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 196,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -0.30343696,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.303437]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 344\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f83ed1c4f7c1ab58fe69fed9a3a75b9ba16c8616',\n", - " 'dl_image_idx': 372,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 11,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': 0.55623239,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.556232]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 423\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ad1569f39b7469916b5519b8f527aa50214799aa',\n", - " 'dl_image_idx': 929,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 232,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 0.067252144,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.067252]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 731\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e752adec08995d6360b2ee41a1ae8bc6ceb9e0ed',\n", - " 'dl_image_idx': 420,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 145,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': -0.38718626,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.387186]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 255\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '45f64203bc821620c3bc9048f4e1e99404ae8ae4',\n", - " 'dl_image_idx': 1733,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 59,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': 0.065476775,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.065477]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 31\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2b5e8686dcd97969e47df077d04a247d38ea7db0',\n", - " 'dl_image_idx': 544,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 7,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -0.53929102,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.539291]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 227\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_7_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '97192b2f3031d07c7117270b318d21c127242393',\n", - " 'dl_image_idx': 2455,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 24,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': 1.0573558,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.057356]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1768\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1c46e0c9b997088db9d83ea840e694bfd5ee959f',\n", - " 'dl_image_idx': 1530,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 92,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -0.88539505,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.885395]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1978\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'db87a1c72d207f783f6ab230143d6b94ee243423',\n", - " 'dl_image_idx': 2438,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 14,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 49,\n", - " 'dl_value': -0.86380905,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.863809]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 71\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f1bd8bbdb3507556301e328028eb4720b49967a9',\n", - " 'dl_image_idx': 470,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 3,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': 0.37121463,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.371215]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 269\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_7_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0dd695994191137b5e4d24096ef272a1e54bf4ea',\n", - " 'dl_image_idx': 498,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 242,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': 0.072236955,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.072237]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 335\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c6ec4e024ed8e5221194805bd79842d6f5fe7c7b',\n", - " 'dl_image_idx': 307,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 177,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': -1.4593884,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.459388]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 478\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_8_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4f5db4cadfd733acc4036c12fb3b052c20270e8d',\n", - " 'dl_image_idx': 154,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 281,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -1.1638592,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.163859]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 220\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'be09d5a099dd2aa6d6db6ee6b3c93f31c7514c7b',\n", - " 'dl_image_idx': 943,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 4,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': 0.2463337,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.246334]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1592\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_6_8'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4a482bcb2f940d16f0edbb357e23a11812a304ba',\n", - " 'dl_image_idx': 461,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 21,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 21,\n", - " 'dl_value': 0.80962038,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.80962]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 578\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9363219a8e746fc0cd17dc7e7a58b4316e8214bf',\n", - " 'dl_image_idx': 2142,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 64,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': -0.02392065,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.023921]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1032\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_6'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5aebe6f460a7336f43a6af1e58eeeab8bd89fcf2',\n", - " 'dl_image_idx': 322,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 285,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': -2.119334,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-2.119334]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 282\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd61fbfe60406925ed887cd46696a38915401fd10',\n", - " 'dl_image_idx': 904,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 232,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -1.2492622,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.249262]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1004\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '11d5993213bd94bf8cda3431997b0e2888202bbe',\n", - " 'dl_image_idx': 2246,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 0,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': 0.87386,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.87386]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2518\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_5_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f83ed1c4f7c1ab58fe69fed9a3a75b9ba16c8616',\n", - " 'dl_image_idx': 372,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 60,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': 0.069568776,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.069569]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 423\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_3_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '93737fae115ff9fbc371295066b3d4a9703488de',\n", - " 'dl_image_idx': 1632,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 16,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 43,\n", - " 'dl_value': 0.30392757,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.303928]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 597\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4e0f26132afeacb96caa5ef4ee30b05e5938c5ea',\n", - " 'dl_image_idx': 2459,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 14,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': 0.92444396,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.924444]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2037\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a3ec24085421617649195d3cdb57cc7219f5f34e',\n", - " 'dl_image_idx': 349,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 94,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': 0.79841816,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.798418]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1421\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c2b2c5b6dddf63afcc74289a0bfef3c1adb3da8b',\n", - " 'dl_image_idx': 2212,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 158,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': -1.5316687,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.531669]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 485\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a925bb4f6fb8d8e73d83fe510796fab3c0e502ff',\n", - " 'dl_image_idx': 2419,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 249,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': -1.2118959,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.211896]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1603\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '27bff527337a1df152eefdcd45325cf5daaf76d5',\n", - " 'dl_image_idx': 102,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 199,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.66306913,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.663069]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 298\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_8_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7c0ef67c1b3daceae91fb756c5e60a070ae404fb',\n", - " 'dl_image_idx': 1411,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 179,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 29,\n", - " 'dl_value': -1.3277276,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.327728]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2281\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_8_8'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8383f632b25a9dfac9bdff362234d0f2df9ad1eb',\n", - " 'dl_image_idx': 360,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 137,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': -0.82434595,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.824346]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1562\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ac2ca681980fce7d038b837b6b3bdf1db72bd440',\n", - " 'dl_image_idx': 14,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 52,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -1.1485646,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.148565]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 157\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_6'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e6b06cc28405b6fc61e6ecfec4a7cf55f9630a5b',\n", - " 'dl_image_idx': 1298,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 280,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': -0.53585327,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.535853]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 426\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_1_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bdfbb741de0faa02976af8a2a723dfd54d073f20',\n", - " 'dl_image_idx': 2193,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 122,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': -0.070912175,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.070912]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2179\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_8_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b54aadf412bc8e90e885c7941951586ce72cfd4e',\n", - " 'dl_image_idx': 72,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 267,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -0.9345963,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.934596]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 631\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '95feaf65efe3fa8b7b5f48b41ee7a81c6557845e',\n", - " 'dl_image_idx': 587,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 258,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 21,\n", - " 'dl_value': -0.81104213,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.811042]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 581\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6c7daecc1e61141c7b75d549ac3d3d05e51056ab',\n", - " 'dl_image_idx': 511,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 104,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': 1.0287476,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.028748]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 165\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_0_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '99a1ae764206313291c073000353a5261ee319bb',\n", - " 'dl_image_idx': 987,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 176,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': 0.3996745,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.399675]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 789\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '612929286e43af83b7c3663adadab6f05655351c',\n", - " 'dl_image_idx': 192,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 23,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -0.49157465,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.491575]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 150\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '337b3f1243beeb5bf43b62aa0800137e687fa7f6',\n", - " 'dl_image_idx': 342,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 49,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -0.46364668,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.463647]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1403\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3e4e0ac420225bb6a3a5dfcca4dd1880be79d150',\n", - " 'dl_image_idx': 310,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 82,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.54400152,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.544002]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 357\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '688906e64f78f25a29c75ca4ae2fc209b5d6c682',\n", - " 'dl_image_idx': 1028,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 93,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': 0.53143477,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.531435]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1024\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bea56cca4a9ebeedc2c4e493dd6ee654cc13d57c',\n", - " 'dl_image_idx': 754,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 192,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 42,\n", - " 'dl_value': 0.48094246,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.480942]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2418\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '91f0f36a648a3cd9c8847260f088ed07fa8dfe8f',\n", - " 'dl_image_idx': 700,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 173,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 37,\n", - " 'dl_value': 0.59664375,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.596644]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 907\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f4a11c226cb9b32c1bdd9f23002ff117790db970',\n", - " 'dl_image_idx': 262,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 100,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 0.97789872,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.977899]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1545\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_0_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bb8310bb5d08a7b1c14ea5aba6ba385c2827e387',\n", - " 'dl_image_idx': 145,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 262,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': 0.4818317,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.481832]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 529\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a7921edee8c274410bfe85f7719bb95ec417442c',\n", - " 'dl_image_idx': 1344,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 100,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': 0.3305136,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.330514]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1355\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_0_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5a7610cf0afe66a841ea369ba7f024fb3dd56534',\n", - " 'dl_image_idx': 1157,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 294,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -1.5852073,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.585207]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2537\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3c8aa87f1aae7949924f80c951d5ae06b6131968',\n", - " 'dl_image_idx': 499,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 110,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 1.4526812,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.452681]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 43\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3d65c96dfdf7fdf8383dd558d7cdad21d650a790',\n", - " 'dl_image_idx': 1621,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 191,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': -0.22880736,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.228807]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1359\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_3_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '51c1fb90225cb91acd7e7fbcf24bdcea91b2b2ad',\n", - " 'dl_image_idx': 532,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 56,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': 0.58511245,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.585112]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 413\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '62d9cdb263ae757415eb7c450147e3c4d0b2259f',\n", - " 'dl_image_idx': 1219,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 289,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': -0.68444037,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.68444]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 971\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0333301170427e10f0c8b75acad1ce5753d85389',\n", - " 'dl_image_idx': 2542,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 152,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': 0.14327452,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.143275]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2158\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '547cdaa9eac5cee7232a826a234b9913202c85f0',\n", - " 'dl_image_idx': 1703,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 288,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': -0.21091823,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.210918]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 399\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6250113f1ac20437c74368757a96a2b6bec1879e',\n", - " 'dl_image_idx': 1000,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 135,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -0.74348789,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.743488]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1593\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5c4ec697afb663d56b6f40720a70af86ab45508d',\n", - " 'dl_image_idx': 2504,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 92,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 43,\n", - " 'dl_value': -2.1311762,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-2.131176]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2214\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4b47465a9513b829b09f5d04c38ffe455ea1c661',\n", - " 'dl_image_idx': 115,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 47,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': 0.56191742,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.561917]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 270\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_6'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6409ec7ea7851feccb78e42f96adc8bcf965b492',\n", - " 'dl_image_idx': 351,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 228,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': -1.9066058,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.906606]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 52\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fc907915066c72f377712f54ed153d0803537de9',\n", - " 'dl_image_idx': 1791,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 195,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -1.1586833,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.158683]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2001\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '42ed0725650c22064160f7899a8416841dca76dd',\n", - " 'dl_image_idx': 524,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 131,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': -0.72462398,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.724624]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1826\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_8'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '66b99499afeffac919bc77613d3a1d797c1e51fc',\n", - " 'dl_image_idx': 376,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 59,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': -0.52270818,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.522708]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 363\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '598e7ecdf43514f1781b6045721d9684a67543f8',\n", - " 'dl_image_idx': 1730,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 151,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': 0.54714817,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.547148]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1928\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '19d190859741b0ea5a6506c6b415cf18cf97c743',\n", - " 'dl_image_idx': 621,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 264,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -0.96955204,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.969552]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2449\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '190777e758aab6d4070eacd8d0aae394a4b5ca7a',\n", - " 'dl_image_idx': 1581,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 98,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 0.89316916,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.893169]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 669\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7c48b85d0fe13918fbe6c94aba6a8294fb3a6185',\n", - " 'dl_image_idx': 1234,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 175,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 0.17286101,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.172861]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1767\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5d5020ab0e7f631a665d8a086745e37373421503',\n", - " 'dl_image_idx': 1658,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 258,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': 0.19597547,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.195975]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1510\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd6204cb41769e3f391aad7ac63b20220849151a6',\n", - " 'dl_image_idx': 1835,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 17,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': -0.36928436,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.369284]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2358\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '277f9ab753347c3c5931e689bc1cf766084c4905',\n", - " 'dl_image_idx': 1532,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 245,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -0.25283709,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.252837]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2500\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '98d9c9a0087cbf9f814011d278f46a9d5f958cca',\n", - " 'dl_image_idx': 1747,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 113,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -0.76000327,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.760003]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2148\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1d65f070d4f51027211d4ba10b0358f86d07ce32',\n", - " 'dl_image_idx': 770,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 271,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': -0.85076797,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.850768]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1446\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '123fe1fc516624215d1e239d21ae9215dd58c943',\n", - " 'dl_image_idx': 261,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 150,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 0.17724423,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.177244]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 302\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b8757484027b448737f42bf853ca4fd31753fd5e',\n", - " 'dl_image_idx': 1088,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 203,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': 2.2982161,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 2.298216]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 502\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_3_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fe4f9a92c45af3487323f2f145d2e49594b61f33',\n", - " 'dl_image_idx': 245,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 56,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': 1.9027324,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.902732]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 440\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7fc06d1d5881b2f49e33d1a6af42808a0d78743e',\n", - " 'dl_image_idx': 262,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 142,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -0.49918073,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.499181]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1264\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c3222cd3db4f0e52d442f225160ac27c0eb5808d',\n", - " 'dl_image_idx': 916,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 117,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': 0.058759347,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.058759]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2487\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ea031b2517f62a70576712cf9050d9384f2bb057',\n", - " 'dl_image_idx': 407,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 59,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 2.4774323,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 2.477432]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 154\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '667b71cd68fea190537411ae4b176d1f77fdac53',\n", - " 'dl_image_idx': 556,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 105,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': -0.57054603,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.570546]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 274\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f2a2413141e1250bc3a1f8b0b22f7dc0555746ad',\n", - " 'dl_image_idx': 550,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 286,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': -0.81410736,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.814107]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 701\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_1_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'dd8ff3ddeac872a646b02b8fdcf286c87cdd7720',\n", - " 'dl_image_idx': 333,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 280,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -0.92971104,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.929711]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 23\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_1_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '31d66e11611d3efc4d9dd724c10f2df3ef352fc3',\n", - " 'dl_image_idx': 912,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 16,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': 0.78765994,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.78766]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2016\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e161dcf47dd167083f9d5ed40d2383960575ab1d',\n", - " 'dl_image_idx': 2173,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 203,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': -0.26984647,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.269846]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1981\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_3_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '222c35287dbbb95d0268ac73b470fa3863cc9f09',\n", - " 'dl_image_idx': 302,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 23,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': 2.3327372,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 2.332737]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 303\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'feded8165bb2cb766100d56bea36ad0607d23b8e',\n", - " 'dl_image_idx': 176,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 26,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': -1.6819777,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.681978]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 552\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7ebbfc9c24e7fc55cc05c6d9ec38f15eecfc19f4',\n", - " 'dl_image_idx': 188,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 92,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': 0.10052554,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.100526]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1632\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6e7725dd9a91a91d57ba87890f1aa4dc1b2ed478',\n", - " 'dl_image_idx': 200,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 142,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 0.34020361,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.340204]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 962\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4001351d97d56f5e7a9eafa596f9080bb410a8dc',\n", - " 'dl_image_idx': 2454,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 155,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': -0.36421967,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.36422]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1333\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '66b35f50362efc5276fd95b130c5580ec0f2b9f3',\n", - " 'dl_image_idx': 94,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 102,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 21,\n", - " 'dl_value': -0.31262943,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.312629]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 207\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3b7101533f9a363f2e6fba91c55bc29764089024',\n", - " 'dl_image_idx': 1241,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 87,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 28,\n", - " 'dl_value': -1.4376856,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.437686]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 80\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c41aca22834baf56232fcfdf9c8faf487f7f2583',\n", - " 'dl_image_idx': 672,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 178,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 29,\n", - " 'dl_value': -1.3179531,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.317953]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 267\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_6'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '62d3ae09a065c28be13758f79c13d96c151ed408',\n", - " 'dl_image_idx': 1288,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 36,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': 0.27805722,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.278057]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 483\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '724a517c82280835c88b1bfd12e2956da0d46f27',\n", - " 'dl_image_idx': 1058,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 54,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 28,\n", - " 'dl_value': 0.1377773,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.137777]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1830\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '688f55ee8763a4de73169b0b29b1a4ac25cf152c',\n", - " 'dl_image_idx': 1571,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 250,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -0.27066746,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.270667]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 540\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bdd8c442ac00dc85196879b871bb6fdfee9bd729',\n", - " 'dl_image_idx': 692,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 91,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -0.11544351,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.115444]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2003\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'cfd1b58c0fd2a1c5e0edc32d0ac826e16494ff8a',\n", - " 'dl_image_idx': 206,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 95,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -0.46137464,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.461375]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 105\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3d24581d1d84c0c3115141342a23063825ed6eba',\n", - " 'dl_image_idx': 813,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 113,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 1.5155416,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.515542]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 606\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9baac3ca82e1a673f0122a547228c4558406e1ad',\n", - " 'dl_image_idx': 2462,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 142,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -0.46929502,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.469295]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1241\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '592f6ccf41a8e229da5d4bc4f217baf440ea13ef',\n", - " 'dl_image_idx': 2226,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 74,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': 0.17433806,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.174338]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 732\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'cf18f8b7230d801d679abaf8f8ed48c7b87d156e',\n", - " 'dl_image_idx': 393,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 12,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 0.44120553,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.441206]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 385\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6f867252361e1d3a074f3c6c74b97c1eb17e4372',\n", - " 'dl_image_idx': 606,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 224,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': 2.8404846,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 2.840485]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 467\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_5_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd8fb7ebe61673cf58c6c7eebdc7c652630b1c72f',\n", - " 'dl_image_idx': 2380,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 172,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -0.28208512,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.282085]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1940\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_4_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a862492af2fd49b74a48935cdafd00fde18780ec',\n", - " 'dl_image_idx': 605,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 172,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': 0.6820479,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.682048]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 230\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_4_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd7db9e33f8daae828031db236550e6e3cfb56e9a',\n", - " 'dl_image_idx': 253,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 122,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -0.0036824408,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.003682]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 30\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_8_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '83baa8b3551ca365149059b778ab2bf6521596f9',\n", - " 'dl_image_idx': 65,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 20,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': -0.11173946,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.111739]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 318\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '19d7f4176405e148612ed64d483313b6fca312b5',\n", - " 'dl_image_idx': 2131,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 194,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': 0.32529482,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.325295]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1238\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8b37939ed477f9fe252c8d67cc232b246b730f60',\n", - " 'dl_image_idx': 254,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 10,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': 0.15031168,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.150312]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 472\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '88604fdf19bee952279d6b14e7fbf1fc1f9b7e7b',\n", - " 'dl_image_idx': 2227,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 163,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 0.27891991,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.27892]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1128\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '320e47f094c3c49bd7e6fa963a88be4bf357be22',\n", - " 'dl_image_idx': 2098,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 273,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': -0.50406569,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.504066]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1279\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0774075705cd11f7f9a116ce27013b1c25c18933',\n", - " 'dl_image_idx': 1291,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 2,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': -0.0087574106,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.008757]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1901\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_5_8'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1ec0cb843cc04e0dd0394a5040b77d8f78f8d885',\n", - " 'dl_image_idx': 98,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 24,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -0.065198451,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.065198]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 504\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8f32b804d7562cbbef68092db240387c251b1afa',\n", - " 'dl_image_idx': 2281,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 75,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 46,\n", - " 'dl_value': 0.47626624,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.476266]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1898\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'cff61621f9b9c6c72f6d16885fa2f79ea0647017',\n", - " 'dl_image_idx': 257,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 208,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': -1.0097229,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.009723]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 498\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b531d7dfc461ab0bc9558773135e6cc1ccbe5b5c',\n", - " 'dl_image_idx': 456,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 167,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -0.5507589,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.550759]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 426\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c2f807acbce4e2cdbf13a6ecf3cb155ffd7951d7',\n", - " 'dl_image_idx': 1531,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 154,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': 0.18459035,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.18459]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 708\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_4_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '190226448aac45de09259bd8b76e01a81ade351e',\n", - " 'dl_image_idx': 1512,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 190,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': -1.4705572,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.470557]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 484\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_5'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '35f5905a47b034ebc23854ff186d728d943cca9a',\n", - " 'dl_image_idx': 88,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 283,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -1.3641075,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.364107]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 270\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_2_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c2e8c2fd41542d979881be1759e7cb6b905b5606',\n", - " 'dl_image_idx': 1756,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 150,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -0.50893676,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.508937]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1920\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4ab131d1e0831ddaf7152ac4243930fcb42ec74c',\n", - " 'dl_image_idx': 1510,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 32,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 48,\n", - " 'dl_value': -1.3718014,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.371801]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1786\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0cd2e0a6937c24a8eb2aea54973ed29a0f9e4efe',\n", - " 'dl_image_idx': 2262,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 237,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 34,\n", - " 'dl_value': 0.67748487,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.677485]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 856\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e6b61bbdde7a71389d52d5dc93ae6a43b990260e',\n", - " 'dl_image_idx': 522,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 0,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': 1.911141,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.911141]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 453\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_5_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'dda1c226d725c81e2b6aab3facc83a4ca9101364',\n", - " 'dl_image_idx': 1109,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 92,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': -1.4278228,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.427823]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 380\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '40c8caefe8e31133985747354d4d2bedd69df540',\n", - " 'dl_image_idx': 561,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 108,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 0.20337312,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.203373]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 645\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8a83606f4c348f9281ab68550a7f00d102421f17',\n", - " 'dl_image_idx': 2339,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 134,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': -0.51777637,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.517776]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1271\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_8'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '53eb298a3c4f8584e2c0719281b95cc5f316c8c2',\n", - " 'dl_image_idx': 930,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 252,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 1.2054137,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.205414]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1477\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '89fdaf99fa4742087d5103a9ccec93fb92c520ae',\n", - " 'dl_image_idx': 1777,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 233,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': 0.81524366,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.815244]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1791\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2b986629976b57c4abf2af847fcbf310dfb31e94',\n", - " 'dl_image_idx': 445,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 237,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': -0.014514616,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.014515]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 599\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '08a0414c720c9a1b89457919e5a16a578df6b04f',\n", - " 'dl_image_idx': 1355,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 108,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': -0.038079973,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.03808]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 474\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7e2a85f71ccc870333e55eb777dd31fd936767dc',\n", - " 'dl_image_idx': 1075,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 146,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': -0.67394847,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.673948]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 391\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '337b3f1243beeb5bf43b62aa0800137e687fa7f6',\n", - " 'dl_image_idx': 342,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 107,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': 0.69314224,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.693142]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1403\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2c2351f2550172235fe804818c690975d02205bf',\n", - " 'dl_image_idx': 407,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 214,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 38,\n", - " 'dl_value': 0.90747875,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.907479]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1986\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ea2d7b24e2ee44785a6ccd58dd8e40ae1186fd4a',\n", - " 'dl_image_idx': 2194,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 260,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': 0.71673846,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.716738]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 516\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8c7f895890532ea38b6dc3c61b7679f664d78668',\n", - " 'dl_image_idx': 1737,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 152,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 0.0,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2255\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6e81ebbc9b32acb498da6ffde642cab88624b603',\n", - " 'dl_image_idx': 2461,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 35,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 33,\n", - " 'dl_value': -0.018125484,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.018125]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1958\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2947a09bf5dcda52e7c25e0cd7f029f679985518',\n", - " 'dl_image_idx': 1074,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 82,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 36,\n", - " 'dl_value': -0.12282998,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.12283]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 558\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7ebda1c46449978ccd4d889fc8e04f27b1fde3b9',\n", - " 'dl_image_idx': 737,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 128,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': -0.20865434,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.208654]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 173\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_4_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '29512876e05dc83520299ab20c25127acc2ce18e',\n", - " 'dl_image_idx': 545,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 145,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': -0.11266422,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.112664]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 517\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f484d3b22ebf2286d0250926d37f34bb6b0482e9',\n", - " 'dl_image_idx': 2389,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 83,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': -1.0232832,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.023283]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1296\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8fa2b77a9d82c993312ac3911840d972667e491d',\n", - " 'dl_image_idx': 1626,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 45,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': -2.1636515,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-2.163651]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1658\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_8'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7f697fe4bfe29bf834b6898561819a06b3c3c9e5',\n", - " 'dl_image_idx': 905,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 115,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 1.2664874,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.266487]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 117\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '90a77bbf4f94ba08e4225ea12ac06e3fcf21ab19',\n", - " 'dl_image_idx': 179,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 116,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': 0.13903774,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.139038]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 77\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9fe57925763e2e5e0d861e6c91ec05725ec5088a',\n", - " 'dl_image_idx': 1832,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 56,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -1.4590976,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.459098]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2233\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5f949da1aacb2d114c11e0bf95a73de2fe3397fc',\n", - " 'dl_image_idx': 103,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 92,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 0.028468233,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.028468]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 499\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4d592416dbe55650c91fafe63bad9fe2affabfc4',\n", - " 'dl_image_idx': 122,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 252,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': -1.3106025,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.310603]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 491\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7ed2ebc4c6f935ab4f8d86efa6be7092dd2bab98',\n", - " 'dl_image_idx': 1115,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 137,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': -0.58500397,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.585004]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 230\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a545781fabe1da2a414efb43e3b4b01bf2698029',\n", - " 'dl_image_idx': 149,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 175,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': -0.2269657,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.226966]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2546\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bdb82d26280db280fe970827f2ec2930d9a01220',\n", - " 'dl_image_idx': 2022,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 272,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': -0.23215795,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.232158]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 800\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '15205daf67926d54a01c7961f84d932033a99db6',\n", - " 'dl_image_idx': 1660,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 205,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 34,\n", - " 'dl_value': -0.27502054,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.275021]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1519\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'eb0e5301ba6c8517e893248aa764f32d358a7993',\n", - " 'dl_image_idx': 1481,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 148,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 36,\n", - " 'dl_value': 1.0454805,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.04548]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 228\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ac20dde3b00e596602621513c561eb1bb146343c',\n", - " 'dl_image_idx': 408,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 152,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': 0.54877859,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.548779]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 407\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '174d2f462de1500e1ded3713a624ca5d8d9b8f65',\n", - " 'dl_image_idx': 744,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 1,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -0.66021663,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.660217]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2069\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_6_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3cd4984f6b3941c471b56e324ac67051a1a9caae',\n", - " 'dl_image_idx': 2357,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 225,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': 1.4425484,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.442548]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 113\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '06245fe2a69d1621a96c26757074067ca411c3cd',\n", - " 'dl_image_idx': 959,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 72,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': -0.64881426,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.648814]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1686\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_5_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '84c368ad5b4c24017bc35dd90c8bc53f4625e64e',\n", - " 'dl_image_idx': 522,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 228,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 36,\n", - " 'dl_value': -0.52456057,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.524561]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1790\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '82144e2092e3e02b4fca39d9270675a1686138b6',\n", - " 'dl_image_idx': 2176,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 47,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -0.9159255,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.915926]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1935\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_6'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a9a0a7d48692934b49d9f4ec303aaccff6bfd7a9',\n", - " 'dl_image_idx': 104,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 168,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': -0.28960192,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.289602]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 164\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '417626cc1b313ef1526259340148bbaddfa16427',\n", - " 'dl_image_idx': 623,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 185,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -0.85646427,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.856464]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 488\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '05954905d976ab1cf247c49fc31e534a8a047ba1',\n", - " 'dl_image_idx': 36,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 100,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -0.82815331,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.828153]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 17\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_0_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0c7724412b8fafd9961cc27874ddeefe95fdcac1',\n", - " 'dl_image_idx': 1122,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 94,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': 0.95612556,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.956126]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2038\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4782261e74e134cef4a998cceac5c40c9956583b',\n", - " 'dl_image_idx': 625,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 94,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 0.4655146,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.465515]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 32\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5f4ea554c5cf965520a54fbb0a18fcee3931c954',\n", - " 'dl_image_idx': 25,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 118,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -0.49132487,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.491325]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 475\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6097b7eee9c1f30bc8249ed75057c4866a07f055',\n", - " 'dl_image_idx': 1351,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 131,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 37,\n", - " 'dl_value': -1.5516807,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.551681]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1409\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_8'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '60638a53945b2481d42b0dc28cb2613238d1665a',\n", - " 'dl_image_idx': 825,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 258,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': -0.55623835,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.556238]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2449\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9536a981237b85e6dae72fb95e6fa13c746f18c4',\n", - " 'dl_image_idx': 2045,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 214,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 0.1851566,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.185157]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1597\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'be5888d2cbe8ce06fa6d35a13eb9a58149c0b784',\n", - " 'dl_image_idx': 27,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 72,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': 0.13097864,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.130979]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 647\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_5_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e752adec08995d6360b2ee41a1ae8bc6ceb9e0ed',\n", - " 'dl_image_idx': 420,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 231,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': 0.29804385,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.298044]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 255\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '892c34bc3c13dce4ddb789cadabfeda83c8e0741',\n", - " 'dl_image_idx': 1090,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 107,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 0.29168656,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.291687]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1480\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '330ddf0430e368e42c099a70b725775d661d32bf',\n", - " 'dl_image_idx': 427,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 239,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 45,\n", - " 'dl_value': -1.2178614,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.217861]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2436\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0190904c58e854f9720e36fe587ad0bf68a71c6d',\n", - " 'dl_image_idx': 2425,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 219,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': 0.61629593,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.616296]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 161\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2fe54f78caa0ad8432346022b90fd203f7a05f83',\n", - " 'dl_image_idx': 104,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 34,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 46,\n", - " 'dl_value': -0.037827183,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.037827]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1450\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '92231c766385b306babcca57f1fe1c9ec6eec681',\n", - " 'dl_image_idx': 203,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 126,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': 0.99393517,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.993935]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 33\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3e6833f21e4815f6d5b148e0df7ad455250cd2f8',\n", - " 'dl_image_idx': 334,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 120,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 49,\n", - " 'dl_value': -0.67474747,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.674747]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 408\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1',\n", - " 'dl_image_idx': 434,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 191,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -1.6860054,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.686005]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 21\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_3_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'af9b99f3f659ea1a99d6f376b34c48c0cfa457c5',\n", - " 'dl_image_idx': 2116,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 201,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -1.0862753,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.086275]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2387\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_0_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '09d2924723bc4677df666072b6e25e988d5f0668',\n", - " 'dl_image_idx': 460,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 266,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': -0.71982992,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.71983]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 8\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1ee44e5cc37f6a7eb354d6f09119a5cbd513fc7e',\n", - " 'dl_image_idx': 433,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 53,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': 0.34623894,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.346239]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 748\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_6'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4434d6d9bfaadd044a9eb8d8bcc7bb7f67d1aca4',\n", - " 'dl_image_idx': 321,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 85,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': -1.5673282,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.567328]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 339\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '57c916fea55a0a1928a6e28cd478b1512f302a37',\n", - " 'dl_image_idx': 609,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 240,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 0.33726114,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.337261]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 646\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_0'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c77b4eece3196755c443a9ac28dca9534a1c1034',\n", - " 'dl_image_idx': 448,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 183,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 34,\n", - " 'dl_value': -1.1803616,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.180362]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1969\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_5'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ae316112871c3af31460036e9bdee3ce4cf1da28',\n", - " 'dl_image_idx': 509,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 60,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 28,\n", - " 'dl_value': -1.1459322,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.145932]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1173\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_3_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7fe922466cebc8c1e963d7d63b1003e9f815ef5b',\n", - " 'dl_image_idx': 1326,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 176,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': 0.81226557,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.812266]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 862\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_7'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b8927c7f0d78d42d528ffaa7f9dcbf0938093612',\n", - " 'dl_image_idx': 1432,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 155,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 43,\n", - " 'dl_value': -0.46343303,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.463433]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 589\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c47cb34756cf933bcdd8d9d837e869283e1af57f',\n", - " 'dl_image_idx': 352,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 264,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 1.0985405,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.098541]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 756\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '214dbf931d8d8c36f9fd67a75bb9b3bd10e538c2',\n", - " 'dl_image_idx': 430,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 222,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': -0.35844973,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.35845]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1674\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_6_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0bd6e1f2f0e974865aadd3014e3984cff19e1a62',\n", - " 'dl_image_idx': 797,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 144,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 29,\n", - " 'dl_value': -0.91956753,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.919568]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2367\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6d2faf36ea57f56282661e07b2f3e38d0735f981',\n", - " 'dl_image_idx': 366,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 18,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 20,\n", - " 'dl_value': 0.23789968,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.2379]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 537\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0754aa1f7300b2093a1111076d776a19789fc072',\n", - " 'dl_image_idx': 1296,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 54,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 13,\n", - " 'dl_value': -0.064674288,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.064674]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1345\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bb05d073c756e040967c2a62737e50517ebfc95e',\n", - " 'dl_image_idx': 619,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 5,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.17624904,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.176249]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 48\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_7_8'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8e27dea5b84e3a1ddeef421bdbc150628bfbedd7',\n", - " 'dl_image_idx': 2470,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 257,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -1.7844201,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.78442]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1679\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f1a49d8df9b3535a95dda95b88c0d8a9cb50f676',\n", - " 'dl_image_idx': 139,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 75,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': -0.0028249593,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.002825]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 34\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8a72e2bfdb8c267b57232bf96f069374d5b21832',\n", - " 'dl_image_idx': 357,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 186,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 3,\n", - " 'dl_value': -0.40355691,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.403557]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 0\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'e63a9ce54ff6aa0f00fb5cac2d5b3d76f2255506',\n", - " 'dl_image_idx': 361,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 126,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -0.225205,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.225205]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 495\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0779043e4b8c04fcfca74eae5124f71d13d4bb71',\n", - " 'dl_image_idx': 385,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 122,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': -0.98339152,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.983392]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 114\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_8_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4486b84bb614557cf49c67df3cf9ecea873be743',\n", - " 'dl_image_idx': 2044,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 287,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': -0.49341586,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.493416]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2012\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7461a903d0d2ef013a1b0a07582eb773199a89f2',\n", - " 'dl_image_idx': 971,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 125,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 1.0299449,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.029945]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1640\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_0_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fb686729bff0586ad284167ecfd21aa1f82e4929',\n", - " 'dl_image_idx': 450,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 79,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': 0.54069585,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.540696]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 199\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '36db24ff49d1e53a12af349c465fc32592168b4b',\n", - " 'dl_image_idx': 570,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 294,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': -0.0091817807,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.009182]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 266\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_4_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2e1852e9055243d0be64948128a136fdc52ede9e',\n", - " 'dl_image_idx': 100,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 165,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': 0.99958831,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.999588]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1722\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5a0da0b97b8aba7f83966d3f53d25a5fdbcaa320',\n", - " 'dl_image_idx': 382,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 51,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': -0.1006712,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.100671]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 451\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_7'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ffefd7bb06be3b40589a3487d14991ca925dc618',\n", - " 'dl_image_idx': 1023,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 281,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 0.41091126,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.410911]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 323\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7fdf8c0fa744394f5e11f36e75dfe00816a3b147',\n", - " 'dl_image_idx': 1072,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 104,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': 1.498915,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.498915]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 195\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_0_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '70dac8fc71a298b7eaad5c1e4f770c44e8f73e05',\n", - " 'dl_image_idx': 2029,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 35,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': 0.36611331,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.366113]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 988\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3f569b6fa3297e650eed0728d437a44ec83c5da6',\n", - " 'dl_image_idx': 519,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 277,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': 1.667484,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.667484]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2209\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'bdfbb741de0faa02976af8a2a723dfd54d073f20',\n", - " 'dl_image_idx': 2193,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 275,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -0.93435115,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.934351]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2179\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0243f4831f338e09801093463e96c8232ce5ec79',\n", - " 'dl_image_idx': 1567,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 230,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 0.57035774,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.570358]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1364\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'dee004c0a5be197214b5ada632dfe386eaf825cb',\n", - " 'dl_image_idx': 215,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 175,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 4,\n", - " 'dl_value': 0.98356527,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.983565]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 155\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '45564088770cdc6e87cdd8bfd161465ba653163a',\n", - " 'dl_image_idx': 848,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 77,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 0.14505969,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.14506]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 66\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_8_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c127a7d977f54bc4129302f734f8cb3f1a6eae88',\n", - " 'dl_image_idx': 546,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 190,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 16,\n", - " 'dl_value': -0.52800494,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.528005]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 291\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_5'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '01dbeb6d2413a4b53851f106e6c55bf8c9139422',\n", - " 'dl_image_idx': 574,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 265,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': 0.70597386,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.705974]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1631\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '69ed5033442a96cded5c76a00eef31941f42f98f',\n", - " 'dl_image_idx': 529,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 15,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': -0.31289163,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.312892]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 277\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '72bf80bea61b15d51de3e65fccc5b315682daed1',\n", - " 'dl_image_idx': 1152,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 105,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -0.36910725,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.369107]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 632\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'fea097c66fe3a3c0be3a418e39852a5c604473fe',\n", - " 'dl_image_idx': 1407,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 180,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': -0.10867728,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.108677]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1266\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_8'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5265e92b005cb96fab76b3c9cc9dde05195e549d',\n", - " 'dl_image_idx': 2531,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 3,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': 0.78927636,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.789276]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1837\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_7_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2fb7ac80b43a60e1f37d2dc06a2567adcbce2836',\n", - " 'dl_image_idx': 1313,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 217,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 44,\n", - " 'dl_value': 0.57873321,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.578733]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 401\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '39c205c7ab87817ef5389c184f1232c356145373',\n", - " 'dl_image_idx': 600,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 186,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 43,\n", - " 'dl_value': -0.87590861,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.875909]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1839\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_9_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2f122220245779a7073d97024f9a5b84aa8cfa51',\n", - " 'dl_image_idx': 165,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 107,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 1.3897685,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.389768]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 608\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0e4a08111aed414e41518c2c7c10bb940e96bd25',\n", - " 'dl_image_idx': 1573,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 107,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': 1.2496203,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.24962]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 188\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a06dd9a01a10fdce27fc7bb263a11aea60d22a8e',\n", - " 'dl_image_idx': 2279,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 173,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -0.30575955,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.30576]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 327\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b944a7185636009cf8efc79eec804cd22b160da1',\n", - " 'dl_image_idx': 2235,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 218,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': -1.4831555,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.483155]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 872\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1ba0c0608fba92cf971a4d8ed753539e73f0c6c3',\n", - " 'dl_image_idx': 209,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 28,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -0.55938363,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.559384]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 634\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c6fb4f35719f0f205f5ad65f4fd2794212c5d139',\n", - " 'dl_image_idx': 594,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 270,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 15,\n", - " 'dl_value': -0.20293291,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.202933]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 5\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_8'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd2c3265d16f85d0e4bab1217127a22160a3d1294',\n", - " 'dl_image_idx': 496,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 150,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -1.2003495,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.200349]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2378\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '526db6e619f9b9752e8fdb2db06c2f5163a5fcad',\n", - " 'dl_image_idx': 201,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 14,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 0.78069317,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.780693]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 513\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f34a7d1d9d5a07d60f8176ebb7286b24fb8e6f89',\n", - " 'dl_image_idx': 1990,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 83,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 36,\n", - " 'dl_value': 0.20280506,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.202805]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1596\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0d71dbf0490f8742844791c75b60808d815a9e40',\n", - " 'dl_image_idx': 1818,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 150,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 0,\n", - " 'dl_value': -1.8342969,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.834297]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2110\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd6864f3d8be7e011618462b813424f4d6e68cc14',\n", - " 'dl_image_idx': 1845,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 60,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -0.84253305,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.842533]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 215\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_3_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '5294e20ff618c9b4ce2eeb8e7984140a9ec4d77c',\n", - " 'dl_image_idx': 847,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 216,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': 1.8256155,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.825616]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 394\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '675e73c8bd562362a4e310b5675dbff17a9cb1b9',\n", - " 'dl_image_idx': 244,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 118,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -0.98520571,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.985206]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 414\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'de494b867faeb13422f13e0d52ef58237b39efa0',\n", - " 'dl_image_idx': 858,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 54,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': 0.83858573,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.838586]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1095\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_9_5'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a79446757c4f7e0b257d6917bc1741e4f34b4bdd',\n", - " 'dl_image_idx': 44,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 64,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 0.88218671,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.882187]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 442\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_6'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b9c3df22ddf34b028a8bee7b4579ecde32ae78a4',\n", - " 'dl_image_idx': 490,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 115,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.31986293,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.319863]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 369\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0bf1b0c17bdbc60c5c927e536209737e13141274',\n", - " 'dl_image_idx': 53,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 141,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': -0.31457552,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.314576]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 321\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_7'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '129b3dcb5eee92cc062a594fe3776bc098c957d7',\n", - " 'dl_image_idx': 441,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 238,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -0.033271275,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.033271]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 417\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_0_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '965c676bd9f6638f282f944652993599d65da881',\n", - " 'dl_image_idx': 109,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 30,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 7,\n", - " 'dl_value': -0.33921519,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.339215]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 532\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '89d1140fe4f71a039ffe51b594cd8dc2df2d221f',\n", - " 'dl_image_idx': 516,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 117,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': -1.0125017,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-1.012502]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 574\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd7efac271ed7805cde8ee483970d3e3881db4033',\n", - " 'dl_image_idx': 506,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 82,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': 0.4896284,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.489628]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 577\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_6_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0344cbfa77e46a9ebacf2403294c7e4a020fff03',\n", - " 'dl_image_idx': 119,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 256,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 5,\n", - " 'dl_value': 0.60076129,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.600761]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 108\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_8_5'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '0ee288ca8612309036984b13e3c1955e264339bf',\n", - " 'dl_image_idx': 755,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 207,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': -1.491902,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.491902]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 565\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_6_0'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f354f0fee3334dcce8fe9cab5c5caaee1c6e9ac2',\n", - " 'dl_image_idx': 2123,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 146,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -1.0285523,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.028552]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1612\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_8_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'be2035689915d4220cab524b4857a380ee99d04c',\n", - " 'dl_image_idx': 2161,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 260,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 43,\n", - " 'dl_value': 1.1163473,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.116347]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2514\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_3'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2c4daec60b7008a719189e8f23ca04b1c3cdefb9',\n", - " 'dl_image_idx': 1912,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 44,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': 0.42456341,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.424563]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1689\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_4_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6014e670908f65417ee739e2e16b9a2b01e94317',\n", - " 'dl_image_idx': 1138,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 234,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': -0.60002542,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.600025]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 376\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_1_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8776682ac4cb6f610cf1cb72bcf3c1bbde533111',\n", - " 'dl_image_idx': 1370,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 10,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 37,\n", - " 'dl_value': 0.32269204,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.322692]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 978\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8625d947e4dd448fa0e168bbada17f0f44b48f0a',\n", - " 'dl_image_idx': 620,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 109,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -0.59267306,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.592673]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 216\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6071bcb8933e099b75ab30401a6dba546cb7ec0b',\n", - " 'dl_image_idx': 552,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 284,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': 1.6125579,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.612558]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1447\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '730cb686e508b0ede02c748887b7f7a26ab05002',\n", - " 'dl_image_idx': 2266,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 263,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': 1.2201008,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.220101]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2490\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '633357e827c5611db482a20220e6f2f67433e1d4',\n", - " 'dl_image_idx': 2406,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 175,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -0.48906764,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.489068]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1178\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_7_9'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '97d1741034ccd4f6bc59af1be8213859190287bc',\n", - " 'dl_image_idx': 1688,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 23,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 1,\n", - " 'dl_value': 1.3786349,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 1.378635]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 455\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_6_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '2f984ad51543b2358b6eef2f7e7a6d549d28f842',\n", - " 'dl_image_idx': 2485,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 4,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': 2.6043468,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 2.604347]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 844\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_6_8'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7b0114d63d3f3de2e25eea4a048a57fd292275e7',\n", - " 'dl_image_idx': 818,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 27,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 29,\n", - " 'dl_value': 1.7553941,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 1.755394]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 172\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_7_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1a561177cff5c817c49a41978db66bc4b59f3dcc',\n", - " 'dl_image_idx': 1332,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 192,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 34,\n", - " 'dl_value': 0.22708568,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.227086]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1988\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '30db3f9fa473fe0da79a18b57a84e6ba726762d8',\n", - " 'dl_image_idx': 597,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 36,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': 0.062256943,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.062257]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 894\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_9_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '912fdd33aa3348f0ce4144a77d7588b1dc7d9a03',\n", - " 'dl_image_idx': 132,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 285,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': 2.542618,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 2.542618]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 292\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '289862651ab020f6a0c48a99f040d7b42cc3de61',\n", - " 'dl_image_idx': 1786,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 96,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 26,\n", - " 'dl_value': 0.55816513,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.558165]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1641\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd4c3b4d4aefd29fd168a2c3c9a9962d99653a715',\n", - " 'dl_image_idx': 615,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 78,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 0.26870787,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.268708]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 4\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd64fd0ae89ab2b882f33064c951054a9737e1527',\n", - " 'dl_image_idx': 2113,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 259,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 0.0018595626,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.00186]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1111\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1cee2d7782d9672c829f5846cdb1b193eab818e2',\n", - " 'dl_image_idx': 1146,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 263,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 31,\n", - " 'dl_value': -1.3759027,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.375903]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 355\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_5_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '49a2966cb7ca798dfcd6a85b4c07cf89b4471bac',\n", - " 'dl_image_idx': 252,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 51,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': -0.082421139,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.082421]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 525\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_7'\n", - " - subregion (neuroid) object u'aIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'f83ed1c4f7c1ab58fe69fed9a3a75b9ba16c8616',\n", - " 'dl_image_idx': 372,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 31,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 14,\n", - " 'dl_value': 0.063480951,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.063481]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 423\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_8_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'cd8c4d602c8c87b580270c27ccb4a74a806bfd31',\n", - " 'dl_image_idx': 1498,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 121,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 29,\n", - " 'dl_value': -1.0294832,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.029483]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1002\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '937b0c65830bfa8fc1245e628ad13fe4981513b8',\n", - " 'dl_image_idx': 554,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 228,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': -0.86594743,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.865947]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 257\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_4'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '190226448aac45de09259bd8b76e01a81ade351e',\n", - " 'dl_image_idx': 1512,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 179,\n", - " 'dl_neuroid_row': 8,\n", - " 'dl_repetition_idx': 41,\n", - " 'dl_value': -0.72559243,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.725592]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 484\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_8_8'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.4\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '17398b630e53ea089a1572906b8e7fc84970aaff',\n", - " 'dl_image_idx': 143,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 38,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 23,\n", - " 'dl_value': 0.075713672,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.075714]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 215\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '3b44bcea9639afedab562cfde6111ee72fb72330',\n", - " 'dl_image_idx': 541,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 150,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': 0.44909814,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[ 0.449098]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1108\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '47d2e1b6ff84cd0cb6261e0e1a32b235f85487a3',\n", - " 'dl_image_idx': 1740,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 205,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 8,\n", - " 'dl_value': -2.008461,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-2.008461]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 547\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_5_1'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6967c72b3def35c348793bf89817ab6ce7135b46',\n", - " 'dl_image_idx': 1632,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 166,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 30,\n", - " 'dl_value': 0.044004578,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.044005]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2299\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '1b9629119db7e9f020ae6fd24052ab4726b13fda',\n", - " 'dl_image_idx': 157,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 266,\n", - " 'dl_neuroid_row': 0,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': 0.26048821,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.260488]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 633\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_0_4'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.8\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '8ea30ba0736418d7282258d1f1c8c7e20c990a0c',\n", - " 'dl_image_idx': 661,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 216,\n", - " 'dl_neuroid_row': 6,\n", - " 'dl_repetition_idx': 17,\n", - " 'dl_value': -0.74258208,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.742582]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2512\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_6_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '121e91559a79f2e14bebe8099bfefbee7aca33f0',\n", - " 'dl_image_idx': 210,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 63,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': 1.2711409,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.271141]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 258\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'c502687e1be45fa82af913e6023c1cd62280326a',\n", - " 'dl_image_idx': 358,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 142,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': -0.019242678,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.019243]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 37\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_7_6'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'a79446757c4f7e0b257d6917bc1741e4f34b4bdd',\n", - " 'dl_image_idx': 44,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 69,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 22,\n", - " 'dl_value': -0.018492032,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.018492]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 442\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_2'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '41e122515c2d9a16faa2f0ad9aee8fa714b7f117',\n", - " 'dl_image_idx': 855,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 5,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 151,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 34,\n", - " 'dl_value': -0.36530972,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-0.36531]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1934\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_5'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '46ff702085afba575fa9c31080b5d284041312b0',\n", - " 'dl_image_idx': 14,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 91,\n", - " 'dl_neuroid_row': 5,\n", - " 'dl_repetition_idx': 25,\n", - " 'dl_value': -0.37741899,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.377419]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 868\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_5_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 0.2\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'db9ccbc86bb0cc56a4258c67749e09b8a338af43',\n", - " 'dl_image_idx': 167,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 159,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 11,\n", - " 'dl_value': -0.34928364,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.349284]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 378\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_3'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '52ca565c513e095a84ac0dc522c8d007c61db383',\n", - " 'dl_image_idx': 2206,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 0,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 109,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 32,\n", - " 'dl_value': -1.4272331,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.427233]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1697\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_3_0'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '4154752c1c6bb11bbed31b5bad1ff27523f240c9',\n", - " 'dl_image_idx': 1210,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 245,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 40,\n", - " 'dl_value': -0.66311425,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.663114]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1882\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_3_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '162470dc4894be2985fbb0d5539a54239f8916b5',\n", - " 'dl_image_idx': 608,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 8,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 136,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': -0.27307898,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-0.273079]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 609\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_9_8'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '88ac8a62d1c908af30886154280a233eae8a645c',\n", - " 'dl_image_idx': 2478,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 76,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 6,\n", - " 'dl_value': -1.9015366,\n", - " 'dl_variation': 'V3',\n", - " 'xr_sample': \n", - " array([[[-1.901537]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 2448\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_7_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '9a91da049405f91de1bfc3dac3f4a9ceb7e561c3',\n", - " 'dl_image_idx': 1404,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 105,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 27,\n", - " 'dl_value': 0.2481574,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.248157]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1459\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_1_2'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'd929d8936220fd002dd0dc75f2f0f517eebb9c63',\n", - " 'dl_image_idx': 1150,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 126,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.65836912,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.658369]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1411\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_M_9_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '898c599bbb0fbea06266b5dfc498fdd735ae5f90',\n", - " 'dl_image_idx': 1845,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 65,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 35,\n", - " 'dl_value': -1.1679057,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[-1.167906]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1825\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_3_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'ca6fd09611a1ed3885c31a77eddb26a244b75f99',\n", - " 'dl_image_idx': 1853,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 7,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 284,\n", - " 'dl_neuroid_row': 9,\n", - " 'dl_repetition_idx': 9,\n", - " 'dl_value': 0.8015725,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.801573]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 1604\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_9_7'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.8\n", - " - x (neuroid) float64 1.0\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '6a8b93cd59587cc2969a126361bce879774e7f4f',\n", - " 'dl_image_idx': 313,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 6,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 269,\n", - " 'dl_neuroid_row': 7,\n", - " 'dl_repetition_idx': 24,\n", - " 'dl_value': -0.14052899,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.140529]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 16\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_7_6'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 1.0\n", - " - x (neuroid) float64 0.6\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '99aa7af1006b861ca7bd888689c298ff8e4d9655',\n", - " 'dl_image_idx': 127,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 4,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 63,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 10,\n", - " 'dl_value': -0.34178901,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.341789]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 259\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_A_4_4'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -0.2\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'eadfb3cf7bf54f7f5102286ece37b64e56733c13',\n", - " 'dl_image_idx': 29,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'A',\n", - " 'dl_neuroid_col': 3,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 200,\n", - " 'dl_neuroid_row': 1,\n", - " 'dl_repetition_idx': 2,\n", - " 'dl_value': 1.322029,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 1.322029]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 602\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_A_1_3'\n", - " - subregion (neuroid) object u'cIT'\n", - " - y (neuroid) float64 -1.4\n", - " - x (neuroid) float64 -0.6\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': 'b363da6dc06f83d682f04ee0592211cd1c41a082',\n", - " 'dl_image_idx': 205,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 2,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 241,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 19,\n", - " 'dl_value': -0.55214262,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.552143]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 308\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_2_2'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.0\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '97757502b8c2e32e47a79a266f2da46bbb7165d1',\n", - " 'dl_image_idx': 591,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'M',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 246,\n", - " 'dl_neuroid_row': 4,\n", - " 'dl_repetition_idx': 18,\n", - " 'dl_value': 0.92151803,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[ 0.921518]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 18\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_M_4_1'\n", - " - subregion (neuroid) object u'pIT'\n", - " - y (neuroid) float64 -0.2\n", - " - x (neuroid) float64 -1.4\n", - " - region (neuroid) object u'IT'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '7d91320e796ed81c7a4e6a20faed16195303b618',\n", - " 'dl_image_idx': 922,\n", - " 'dl_neuroid_animal': 'Tito',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 9,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 292,\n", - " 'dl_neuroid_row': 3,\n", - " 'dl_repetition_idx': 39,\n", - " 'dl_value': 0.37267905,\n", - " 'dl_variation': 'V6',\n", - " 'xr_sample': \n", - " array([[[ 0.372679]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 182\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Tito_L_P_3_9'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -0.6\n", - " - x (neuroid) float64 1.8\n", - " - region (neuroid) object u'V4'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170},\n", - " {'dl_image_id': '650358beac9b9260dea25ff5bc65ff943b7bbd1a',\n", - " 'dl_image_idx': 33,\n", - " 'dl_neuroid_animal': 'Chabo',\n", - " 'dl_neuroid_arr': 'P',\n", - " 'dl_neuroid_col': 1,\n", - " 'dl_neuroid_hemisphere': 'L',\n", - " 'dl_neuroid_idx': 108,\n", - " 'dl_neuroid_row': 2,\n", - " 'dl_repetition_idx': 12,\n", - " 'dl_value': -0.26336458,\n", - " 'dl_variation': 'V0',\n", - " 'xr_sample': \n", - " array([[[-0.263365]]], dtype=float32)\n", - " Coordinates:\n", - " * stimulus (stimulus) int64 642\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object u'Chabo_L_P_2_1'\n", - " - subregion (neuroid) object u'V4'\n", - " - y (neuroid) float64 -1.0\n", - " - x (neuroid) float64 -1.4\n", - 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1000 rows × 12 columns

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" - ], - "text/plain": [ - " dl_image_id dl_image_idx dl_neuroid_animal \\\n", - "0 082f0e5cc064bfbdf7a9679e1aca6d74503260d4 1359 Tito \n", - "1 40c8caefe8e31133985747354d4d2bedd69df540 561 Tito \n", - "2 d74fb40d88d6b4139346e0be3236da980a880c3c 787 Tito \n", - "3 6d15e65826ca09ea0e51c25c8005c1fdcf542cfb 273 Tito \n", - "4 8bac49738ba85900e6395d8d2a75d0e53efa64b5 2360 Chabo \n", - "5 1e9ff75c2c5794b644aeb582a95ee41eda8f8d29 2326 Tito \n", - "6 ad09855a7755701a8d9c11fde725ad76044b0cdd 427 Tito \n", - "7 b66671aed7c38cf4ecf869694d3532ae09802c55 277 Chabo \n", - "8 a27cf585cef0941973572d5265af974c78dcb036 536 Chabo \n", - "9 b60675f2b54af66bb192ef883ea5242adbbfdeae 300 Tito \n", - "10 63889f5983235823d5126fd0903bc8b30ed41a0d 1383 Chabo \n", - "11 106abf0c8b70ac7717b79a0432137c0ee7c2d3c0 430 Chabo \n", - "12 691384450355c07ad084613a08a018ae8e1eebb2 1 Tito \n", - "13 f9c4709bf115b6a5ebfb21d8642760b3947e1587 1064 Tito \n", - "14 d61fbfe60406925ed887cd46696a38915401fd10 1544 Chabo \n", - "15 a22aa0af2fef1f0a648f79aba5fa22bedc6c1c95 1956 Tito \n", - "16 2970df86fb9706eb1ef066a7120049eb7bb8656e 95 Chabo \n", - "17 de32e3f47daaa60cbca95c4cbcfbec1372eb91a0 1399 Tito \n", - "18 1d9e426371ba9061f468491439df06ccebdfa76d 1682 Chabo \n", - "19 cd8c4d602c8c87b580270c27ccb4a74a806bfd31 2138 Tito \n", - "20 28b903af5db5a703437b44715c81ef50f8b3e897 365 Tito \n", - "21 e2821515707ddbd23cc04d085b25c975bbb4bd28 673 Chabo \n", - "22 7859a3f7adbe745dda45c1e1294fe15520480a8c 2411 Tito \n", - "23 3d858b4bc82f5d02fbf800e1c66a1e9be99cf714 404 Chabo \n", - "24 83baa8b3551ca365149059b778ab2bf6521596f9 705 Chabo \n", - "25 4c18e3050e18b581c303cd21e8e24c10cfefbd99 877 Tito \n", - "26 36b1652f43aedf23e39f351fd931ab0cb4c410b5 350 Chabo \n", - "27 fe42df2a3d9a7b285d3e4478a299a4acb6cfa6a7 2350 Chabo \n", - "28 45564088770cdc6e87cdd8bfd161465ba653163a 1488 Chabo \n", - "29 bb1c929420d8566cdbcc2e46e88b8e37b42db522 702 Tito \n", - ".. ... ... ... \n", - "970 612929286e43af83b7c3663adadab6f05655351c 192 Tito \n", - "971 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 92 Chabo \n", - "972 42b1902dafba9ba9def79ea65bf53aa25bf2704c 186 Chabo \n", - "973 2ba5a992fc991d89599ee947da8064c4717d7327 452 Tito \n", - "974 7cd382ea155d67227b21efb511eb457d9a5d186d 514 Chabo \n", - "975 9b35887ca300b07ad46f5572bddf59417f27b55d 354 Tito \n", - "976 d1bfc7f4621499838d037519e743dc1ab0eba912 337 Tito \n", - "977 a7a31e65e2770c7a503988835c24aeab689abf47 130 Chabo \n", - "978 6c7daecc1e61141c7b75d549ac3d3d05e51056ab 511 Tito \n", - "979 64b31e4fc9af784e16ea3d2e2c48dd1eda5a261e 43 Tito \n", - "980 025f6fd6e41e19a963994f40a407b552364e7e20 51 Tito \n", - "981 3e52a117766d348d8bcafa3f6b7bb2fc7b86a69d 380 Tito \n", - "982 28e31e574f90690adc228f38e4fff0a192bf8645 592 Tito \n", - "983 4d1697209942afa1fc5a3d1394af394a8c6bf89b 600 Chabo \n", - "984 9298606f65e0329d01678060669c4b11603854f3 323 Chabo \n", - "985 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 594 Chabo \n", - "986 d7efac271ed7805cde8ee483970d3e3881db4033 506 Chabo \n", - "987 42530b90eb5a8d8c706f0d3da438194e53fe0efe 295 Chabo \n", - "988 9d3e3c2b4f8574d75430ca4e43fbfa408a672893 871 Chabo \n", - "989 675e73c8bd562362a4e310b5675dbff17a9cb1b9 244 Tito \n", - "990 27f69468c9d6019ed0d22b9583c94c5b58198c1c 280 Chabo \n", - "991 ce6f689e9cda8b99876f6c21d4dc7783ff0bfec8 1131 Tito \n", - "992 12f3f525d1b4955bdb16042b1707c51eeb695726 1428 Tito \n", - "993 4191a6bacab5edb094295b9633f81a92b75ea839 1068 Tito \n", - "994 11bfd22097d9c511621ef17b2c13067dc1cd7352 579 Tito \n", - "995 bca29d68c1a3dc15008c46641ec9ab68af61afbe 2399 Tito \n", - "996 9e97efa8d00619f48230a3b85b01e3eec9b8f5fb 1105 Chabo \n", - "997 4e4bcc22e7ca74932ddca31aab916609379d88af 1539 Chabo \n", - "998 c4e54fb54af7d029a5893839f9488ee794d45e35 2402 Chabo \n", - "999 ac20dde3b00e596602621513c561eb1bb146343c 408 Tito \n", - "\n", - " dl_neuroid_arr dl_neuroid_col dl_neuroid_hemisphere dl_neuroid_idx \\\n", - "0 M 0 L 255 \n", - "1 M 3 L 157 \n", - "2 P 8 L 289 \n", - "3 M 5 L 161 \n", - "4 P 0 L 106 \n", - "5 A 5 L 183 \n", - "6 P 5 L 287 \n", - "7 A 3 L 72 \n", - "8 M 1 L 126 \n", - "9 P 7 L 277 \n", - "10 P 8 L 16 \n", - "11 P 5 L 88 \n", - "12 A 3 L 199 \n", - "13 P 3 L 261 \n", - "14 P 8 L 16 \n", - "15 M 1 L 254 \n", - "16 P 9 L 15 \n", - "17 A 2 L 196 \n", - "18 A 5 L 57 \n", - "19 A 0 L 207 \n", - "20 P 6 L 267 \n", - "21 P 0 L 114 \n", - "22 P 2 L 169 \n", - "23 P 0 L 107 \n", - "24 P 5 L 33 \n", - "25 P 0 L 286 \n", - "26 M 5 L 8 \n", - "27 P 5 L 33 \n", - "28 A 5 L 49 \n", - "29 P 2 L 169 \n", - ".. ... ... ... ... \n", - "970 M 7 L 135 \n", - "971 P 9 L 18 \n", - "972 M 9 L 0 \n", - "973 M 2 L 162 \n", - "974 P 7 L 39 \n", - "975 P 0 L 164 \n", - "976 P 4 L 262 \n", - "977 P 6 L 28 \n", - "978 A 9 L 177 \n", - "979 A 3 L 199 \n", - "980 P 4 L 165 \n", - "981 A 1 L 205 \n", - "982 M 1 L 253 \n", - "983 P 3 L 93 \n", - "984 P 7 L 21 \n", - "985 P 6 L 100 \n", - "986 A 1 L 84 \n", - "987 P 7 L 21 \n", - "988 P 3 L 85 \n", - "989 A 3 L 200 \n", - "990 M 6 L 9 \n", - "991 M 7 L 141 \n", - "992 P 0 L 220 \n", - "993 M 0 L 247 \n", - "994 A 2 L 194 \n", - "995 P 3 L 260 \n", - "996 M 9 L 0 \n", - "997 A 7 L 50 \n", - "998 P 9 L 14 \n", - "999 A 5 L 188 \n", - "\n", - " dl_neuroid_row dl_repetition_idx dl_value dl_variation \\\n", - "0 8 3 -0.069820 V6 \n", - "1 2 26 1.029303 V0 \n", - "2 7 42 -0.099910 V3 \n", - "3 5 2 -1.523151 V3 \n", - "4 1 13 -0.041955 V3 \n", - "5 9 48 -0.756154 V3 \n", - "6 4 24 -0.952831 V0 \n", - "7 5 17 0.722394 V6 \n", - "8 9 22 -0.043383 V0 \n", - "9 5 18 -0.351518 V6 \n", - "10 6 35 -0.817322 V3 \n", - "11 5 23 1.356050 V0 \n", - "12 8 45 -1.087423 V3 \n", - "13 6 18 0.559377 V3 \n", - "14 6 3 0.151680 V6 \n", - "15 9 8 -0.739535 V6 \n", - "16 7 19 -0.075857 V0 \n", - "17 7 17 1.437180 V6 \n", - "18 8 2 1.345689 V3 \n", - "19 6 2 0.051768 V6 \n", - "20 4 22 -1.202833 V0 \n", - "21 5 33 -0.618711 V6 \n", - "22 5 14 -0.373665 V6 \n", - "23 2 3 -0.011931 V3 \n", - "24 8 39 0.032283 V6 \n", - "25 1 7 1.596673 V3 \n", - "26 9 7 1.218748 V0 \n", - "27 8 4 0.499515 V3 \n", - "28 7 10 0.491433 V3 \n", - "29 5 29 1.866614 V6 \n", - ".. ... ... ... ... \n", - "970 5 43 -0.246267 V6 \n", - "971 8 28 1.787546 V6 \n", - "972 5 3 0.485160 V0 \n", - "973 3 8 -1.226590 V0 \n", - "974 3 21 2.220902 V0 \n", - "975 9 21 -0.261895 V6 \n", - "976 7 14 -0.734114 V3 \n", - "977 9 14 1.462006 V0 \n", - "978 8 1 -0.423413 V0 \n", - "979 8 4 -1.809458 V6 \n", - "980 8 13 1.471692 V0 \n", - "981 5 7 -0.938347 V3 \n", - "982 7 22 -0.257800 V0 \n", - "983 6 25 0.071052 V0 \n", - "984 5 3 0.549723 V0 \n", - "985 0 26 0.201303 V3 \n", - "986 7 22 3.510830 V0 \n", - "987 5 4 -0.829535 V0 \n", - "988 3 2 -0.000928 V3 \n", - "989 1 17 -0.115193 V0 \n", - "990 8 4 0.562179 V3 \n", - "991 7 13 -2.359118 V3 \n", - "992 8 22 1.063871 V3 \n", - "993 4 3 1.164804 V3 \n", - "994 6 11 1.533773 V0 \n", - "995 9 27 -0.756631 V6 \n", - "996 5 26 -0.636773 V3 \n", - "997 9 46 0.326749 V6 \n", - "998 6 30 -1.779291 V3 \n", - "999 6 13 -1.083155 V0 \n", - "\n", - " xr_sample \n", - "0 [[[\\narray(0.502869963645... \n", - "1 [[[\\narray(1.029302597045... \n", - "2 [[[\\narray(-0.46849203109... \n", - "3 [[[\\narray(-0.11618404090... \n", - "4 [[[\\narray(-0.04195535928... \n", - "5 [[[\\narray(-0.36435416340... \n", - "6 [[[\\narray(-0.95283097028... \n", - "7 [[[\\narray(2.630948543548... \n", - "8 [[[\\narray(-0.04338267818... \n", - "9 [[[\\narray(-0.98455733060... \n", - "10 [[[\\narray(1.720752000808... \n", - "11 [[[\\narray(1.356050491333... \n", - "12 [[]] \n", - "13 [[[\\narray(-0.33065000176... \n", - "14 [[[\\narray(-0.97299230098... \n", - "15 [[[\\narray(-1.51600027084... \n", - "16 [[[\\narray(-0.07585664838... \n", - "17 [[[\\narray(-0.78647679090... \n", - "18 [[[\\narray(-0.65639549493... \n", - "19 [[[\\narray(-0.49092769622... \n", - "20 [[[\\narray(-1.20283293724... \n", - "21 [[[\\narray(1.807505488395... \n", - "22 [[[\\narray(1.180279612541... \n", - "23 [[[\\narray(-0.96471077203... \n", - "24 [[[\\narray(0.585385441780... \n", - "25 [[[\\narray(0.689662396907... \n", - "26 [[[\\narray(1.218748331069... \n", - "27 [[[\\narray(-0.87704384326... \n", - "28 [[[\\narray(0.847694456577... \n", - "29 [[[\\narray(-0.10963396728... \n", - ".. ... \n", - "970 [[]] \n", - "971 [[]] \n", - "972 [[[\\narray(0.485160022974... \n", - "973 [[[\\narray(-1.22659015655... \n", - "974 [[[\\narray(2.220902204513... \n", - "975 [[[\\narray(1.733789205551... \n", - "976 [[[\\narray(0.978021562099... \n", - "977 [[[\\narray(1.462006330490... \n", - "978 [[[\\narray(-0.42341259121... \n", - "979 [[[\\narray(0.591689109802... \n", - "980 [[[\\narray(1.471692204475... \n", - "981 [[[\\narray(0.750478029251... \n", - "982 [[[\\narray(-0.25780022144... \n", - "983 [[[\\narray(0.071051739156... \n", - "984 [[[\\narray(0.549722909927... \n", - "985 [[[\\narray(-0.82815331220... \n", - "986 [[[\\narray(3.510830163955... \n", - "987 [[[\\narray(-0.82953530550... \n", - "988 [[[\\narray(-0.00092755595... \n", - "989 [[[\\narray(-0.11519253253... \n", - "990 [[[\\narray(2.108559846878... \n", - "991 [[[\\narray(1.180749177932... \n", - "992 [[[\\narray(2.580444574356... \n", - "993 [[[\\narray(-0.41362732648... \n", - "994 [[[\\narray(1.533773422241... \n", - "995 [[[\\narray(-0.45911419391... \n", - "996 [[[\\narray(0.796662449836... \n", - "997 [[[\\narray(0.354323506355... \n", - "998 [[[\\narray(0.040671963244... \n", - "999 [[[\\narray(-1.08315479755... \n", - "\n", - "[1000 rows x 12 columns]" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_samples = pd.DataFrame(samples)\n", - "df_samples" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , 0.71044864, 0.28179112, ..., 0.23257068,\n", - " 0.14798986, 0.32170821],\n", - " [ 0.71044864, 1. , 0.09864109, ..., 0.35317389,\n", - " 0.2825441 , 0.56120719],\n", - " [ 0.28179112, 0.09864109, 1. , ..., 0.20944814,\n", - " -0.27039143, -0.21974919],\n", - " ..., \n", - " [ 0.23257068, 0.35317389, 0.20944814, ..., 1. ,\n", - " 0.3326391 , 0.27584533],\n", - " [ 0.14798986, 0.2825441 , -0.27039143, ..., 0.3326391 ,\n", - " 1. , 0.54945685],\n", - " [ 0.32170821, 0.56120719, -0.21974919, ..., 0.27584533,\n", - " 0.54945685, 1. ]])" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "loaded = np.load(\"/Users/jjpr/dev/mkgu/tests/it_rdm.p\", encoding=\"latin1\")\n", - "loaded" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(64, 64)" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "loaded.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dldata", - "language": "python", - "name": "dldata" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/packaging/notebooks/2018-04-27_rdm_test_fix.ipynb b/packaging/notebooks/2018-04-27_rdm_test_fix.ipynb deleted file mode 100644 index f81187688..000000000 --- a/packaging/notebooks/2018-04-27_rdm_test_fix.ipynb +++ /dev/null @@ -1,3566 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import mkgu" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "from pytest import approx" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , 0.71044864, 0.28179112, ..., 0.23257068,\n", - 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" [ 2.449372],\n", - " [ 0.401197]],\n", - "\n", - " ..., \n", - " [[ 1.121319],\n", - " [ 1.719423],\n", - " ..., \n", - " [ 0.800551],\n", - " [-0.019874]],\n", - "\n", - " [[-0.518903],\n", - " [ 0.696196],\n", - " ..., \n", - " [-0.603347],\n", - " [-0.175979]]], dtype=float32)\n", - "Coordinates:\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object 'Chabo_L_M_5_9' 'Chabo_L_M_6_9' ...\n", - " - arr (neuroid) object 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' ...\n", - " - col (neuroid) int64 9 9 8 9 8 8 7 7 5 6 4 9 9 9 9 9 8 7 9 6 ...\n", - " - hemisphere (neuroid) object 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' ...\n", - " - subregion (neuroid) object 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' ...\n", - " - animal (neuroid) object 'Chabo' 'Chabo' 'Chabo' 'Chabo' ...\n", - " - y (neuroid) float64 0.2 0.6 0.2 1.0 0.6 1.0 1.8 1.0 1.8 ...\n", - " - x (neuroid) float64 1.8 1.8 1.4 1.8 1.4 1.4 1.0 1.0 0.2 ...\n", - " - region (neuroid) object 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' ...\n", - 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"metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assy_hvm = mkgu.get_assembly(name=\"dicarlo.Hong2011\")\n", - "assy_hvm" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[[ 0.121288],\n", - " [-1.784021],\n", - " ..., \n", - " [-0.968256],\n", - " [ 0.183887]],\n", - "\n", - " [[ 0.512693],\n", - " [ 0.512693],\n", - " ..., \n", - " [ 2.449372],\n", - " [ 0.401197]],\n", - "\n", - " ..., \n", - " [[-0.384445],\n", - " [-0.384445],\n", - " ..., \n", - " [-0.377575],\n", - " [-0.393541]],\n", - "\n", - " [[-1.358922],\n", - " [ 1.665531],\n", - " ..., \n", - " [ 0.208243],\n", - " [-0.358707]]], dtype=float32)\n", - "Coordinates:\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object 'Chabo_L_M_5_9' 'Chabo_L_M_6_9' ...\n", - " - arr (neuroid) object 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' ...\n", - " - col (neuroid) int64 9 9 8 9 8 8 7 7 5 6 4 8 8 6 7 5 7 7 6 6 ...\n", - 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id (presentation) int64 3201 3202 3203 3204 3205 3206 3207 ...\n", - " - image_file_name (presentation) object 'face0008_rx+22.971_ry+82.398_rz-36.793_tx-00.166_ty+00.243_s+00.571_b7a254d0579b34d7139608df41f8f1df2aed7d9f_256x256.png' ...\n", - " - object_name (presentation) object 'face7' 'table3' 'airplane2' ...\n", - " - rxy (presentation) float64 82.4 26.17 -88.53 70.74 77.61 ...\n", - " - rxy_semantic (presentation) float64 82.4 26.17 1.467 70.74 167.6 ...\n", - " - rxz (presentation) float64 22.97 79.57 -38.11 -86.38 -54.87 ...\n", - " - rxz_semantic (presentation) float64 22.97 79.57 -38.11 -86.38 -54.87 ...\n", - " - ryz (presentation) float64 -36.79 -82.65 -116.8 30.74 ...\n", - " - ryz_semantic (presentation) float64 -36.79 -82.65 -116.8 30.74 ...\n", - " - s (presentation) float64 0.7137 1.257 1.532 0.7187 0.8285 ...\n", - " - size (presentation) float64 256.0 256.0 256.0 256.0 256.0 ...\n", - " - ty (presentation) float64 -0.166 -0.246 0.508 -0.344 0.243 ...\n", - " - tz (presentation) float64 0.243 -0.804 0.665 -0.886 0.927 ...\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170\n", - "Attributes:\n", - " stimulus_set_name: dicarlo.hvm\n", - " stimulus_set: background_id catego..." - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hvm_it_v6 = assy_hvm.sel(variation=6).sel(region=\"IT\")\n", - "hvm_it_v6.load()\n", - "hvm_it_v6" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[ 0.087381, -0.035234, 0.16607 , ..., -0.083298, -0.04096 , 0.009889],\n", - " [ 0.10009 , 0.07415 , 0.136218, ..., 0.049036, -0.040681, -0.049637],\n", - " [ 0.196117, 0.095425, -0.012496, ..., 0.088381, -0.079695, 0.023809],\n", - " ..., \n", - " [ 0.03505 , 0.009078, -0.098099, ..., -0.030416, -0.081772, -0.185693],\n", - " [ 0.065006, 0.072046, -0.032077, ..., -0.127431, -0.025176, -0.034318],\n", - " [ 0.102832, 0.102089, 0.089803, ..., -0.055868, -0.108431, -0.083379]], dtype=float32)\n", - 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"metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hvm_it_v6_obj = hvm_it_v6.multi_groupby([\"category_name\", \"object_name\"]).mean(dim=\"presentation\").squeeze(\"time_bin\").T\n", - "hvm_it_v6_obj" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 1. , 0.53554 , 0.68793594, ..., 0.47855005,\n", - " 0.61849201, 0.55141304],\n", - " [ 0.53554 , 1. , 0.43818664, ..., 0.30374824,\n", - " 0.41313837, 0.35983676],\n", - " [ 0.68793594, 0.43818664, 1. , ..., 0.34846228,\n", - " 0.2325291 , 0.2625039 ],\n", - " ..., \n", - " [ 0.47855005, 0.30374824, 0.34846228, ..., 1. ,\n", - " 0.33263902, 0.2758457 ],\n", - " [ 0.61849201, 0.41313837, 0.2325291 , ..., 0.33263902,\n", - " 1. , 0.54945692],\n", - " [ 0.55141304, 0.35983676, 0.2625039 , ..., 0.2758457 ,\n", - " 0.54945692, 1. ]])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - 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"(array([58, 62]), array([62, 58]))\n", - "(array([58, 63]), array([63, 58]))\n", - "(array([59, 60]), array([60, 59]))\n", - "(array([59, 61]), array([61, 59]))\n", - "(array([59, 62]), array([62, 59]))\n", - "(array([59, 63]), array([63, 59]))\n", - "(array([60, 61]), array([61, 60]))\n", - "(array([60, 62]), array([62, 60]))\n", - "(array([60, 63]), array([63, 60]))\n", - "(array([61, 62]), array([62, 61]))\n", - "(array([61, 63]), array([63, 61]))\n", - "(array([62, 63]), array([63, 62]))\n" - ] - } - ], - "source": [ - "stuff = []\n", - "for i in range(loaded.shape[0]-1):\n", - " for j in range(i+1, loaded.shape[1]):\n", - " print((correlations == approx(loaded[i, j], abs=1e-6)).nonzero())" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['ELEPHANT_M',\n", - " 'GORILLA',\n", - " 'LIONESS',\n", - " 'TURTLE_L',\n", - " 'bear',\n", - " 'blCow',\n", - " 'breed_pug',\n", - " 'hedgehog',\n", - " 'BAHRAIN',\n", - " 'CGTG_L',\n", - " 'DTUG_L',\n", - " 'MQUEEN_L',\n", - " 'SISTER_L',\n", - " 'cruiser',\n", - " 'motoryacht',\n", - " 'support',\n", - " 'Beetle',\n", - " 'alfa155',\n", - " 'astra',\n", - " 'bmw325',\n", - " 'bora_a',\n", - " 'celica',\n", - " 'clio',\n", - " 'z3',\n", - " '_001',\n", - " '_004',\n", - " '_008',\n", - " '_010',\n", - " '_011',\n", - " '_014',\n", - " '_031',\n", - " '_033',\n", - " 'face0001',\n", - " 'face0002',\n", - " 'face0003',\n", - " 'face0004',\n", - " 'face0005',\n", - " 'face0006',\n", - " 'face0007',\n", - " 'face0008',\n", - " 'Apple_Fruit_obj',\n", - " 'Apricot_obj',\n", - " 'Peach_obj',\n", - " 'Pear_obj',\n", - " 'Strawberry_obj',\n", - " 'raspberry_obj',\n", - " 'walnut_obj',\n", - " 'watermelon_obj',\n", - " '_01_Airliner_2jetEngines',\n", - " '_05_future',\n", - " '_19_flyingBoat',\n", - " 'f16',\n", - " 'junkers88',\n", - " 'mig29',\n", - " 'rdbarren',\n", - " 'sopwith',\n", - " '_08',\n", - " '_10',\n", - " '_11',\n", - " '_12',\n", - " '_18',\n", - " '_37',\n", - " '_38',\n", - " '_44']" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "original_objects_by_category = ['ELEPHANT_M', 'GORILLA', 'LIONESS', 'TURTLE_L', 'bear', 'blCow', 'breed_pug', 'hedgehog', 'BAHRAIN', 'CGTG_L', 'DTUG_L', 'MQUEEN_L', 'SISTER_L', 'cruiser', 'motoryacht', 'support', 'Beetle', 'alfa155', 'astra', 'bmw325', 'bora_a', 'celica', 'clio', 'z3', '_001', '_004', '_008', '_010', '_011', '_014', '_031', '_033', 'face0001', 'face0002', 'face0003', 'face0004', 'face0005', 'face0006', 'face0007', 'face0008', 'Apple_Fruit_obj', 'Apricot_obj', 'Peach_obj', 'Pear_obj', 'Strawberry_obj', 'raspberry_obj', 'walnut_obj', 'watermelon_obj', '_01_Airliner_2jetEngines', '_05_future', '_19_flyingBoat', 'f16', 'junkers88', 'mig29', 'rdbarren', 'sopwith', '_08', '_10', '_11', '_12', '_18', '_37', '_38', '_44']\n", - "original_objects_by_category" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array(['bear', 'cow', 'dog', 'elephant', 'gorilla', 'hedgehog', 'lioness',\n", - " 'turtle', 'ship0', 'ship1', 'ship2', 'ship3', 'ship4', 'ship5', 'ship6',\n", - " 'ship7', 'car_alfa', 'car_astra', 'car_beetle', 'car_bmw', 'car_bora',\n", - " 'car_celica', 'car_clio', 'car_z3', 'chair0', 'chair1', 'chair2',\n", - " 'chair3', 'chair4', 'chair5', 'chair6', 'chair7', 'face0', 'face1',\n", - " 'face2', 'face3', 'face4', 'face5', 'face6', 'face7', 'apple', 'apricot',\n", - " 'peach', 'pear', 'raspberry', 'strawberry', 'walnut', 'watermelon',\n", - " 'airplane0', 'airplane1', 'airplane2', 'airplane3', 'airplane4',\n", - " 'airplane5', 'airplane6', 'airplane7', 'table0', 'table1', 'table2',\n", - " 'table3', 'table4', 'table5', 'table6', 'table7'], dtype=object)\n", - "Coordinates:\n", - " time_bin object (70, 170)\n", - " * presentation (presentation) MultiIndex\n", - " - category_name (presentation) object 'Animals' 'Animals' 'Animals' ...\n", - " - object_name (presentation) object 'bear' 'cow' 'dog' 'elephant' ..." - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hvm_it_v6_obj.sortby([hvm_it_v6_obj[\"category_name\"], hvm_it_v6_obj[\"object_name\"]])[\"object_name\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " assert correlations.shape == (64, 64)\n", - " assert correlations == approx(loaded, abs=1e-6)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mkgu", - "language": "python", - "name": "mkgu" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/packaging/notebooks/2018-04-30_knownfiles.ipynb b/packaging/notebooks/2018-04-30_knownfiles.ipynb deleted file mode 100644 index facb0d8db..000000000 --- a/packaging/notebooks/2018-04-30_knownfiles.ipynb +++ /dev/null @@ -1,101 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import mkgu\n", - "import mkgu.knownfile\n", - "from mkgu.knownfile import KnownFile as kf" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "path = \"/Users/jjpr/.mkgu/data/dicarlo.Hong2011/hvm_temporal_neuronal_features.nc\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# mkgu.knownfile._db.drop_tables([mkgu.knownfile.FileRecord, mkgu.knownfile.Sighting])\n", - "# mkgu.knownfile._db.create_tables([mkgu.knownfile.FileRecord, mkgu.knownfile.Sighting])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.1 s, sys: 5.04 s, total: 20.2 s\n", - "Wall time: 21.3 s\n" - ] - } - ], - "source": [ - "%%time\n", - "kf_big = kf(path)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Users/jjpr/.mkgu/data/dicarlo.Hong2011/hvm_temporal_neuronal_features.nc'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "path" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mkgu", - "language": "python", - "name": "mkgu" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/packaging/notebooks/2018-05-01_gallant_data.ipynb b/packaging/notebooks/2018-05-01_gallant_data.ipynb deleted file mode 100644 index 2dd5b80c6..000000000 --- a/packaging/notebooks/2018-05-01_gallant_data.ipynb +++ /dev/null @@ -1,59030 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "import re" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import mkgu" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from mkgu.knownfile import KnownFile as kf" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [], - "source": [ - "from mkgu.knownfile import FileRecord, Sighting" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from mkgu.lookup import pwdb" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from mkgu.stimuli import ImageModel, StimulusSetModel, ImageStoreModel, StimulusSetImageMap, ImageStoreMap" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "[4354015 values with dtype=float64]\n", - "Coordinates:\n", - " * image_file_name (image_file_name) object 'gallant\\\\V1Data\\\\NatRev/stimuli/r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - " * neuroid (neuroid) object 'r0148A' 'r0150B' 'r0154B' 'r0156A' ..." - ] - }, - "execution_count": 7, - 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"189287 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189288 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189289 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189290 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189291 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189292 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189293 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189294 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189295 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189296 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189297 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189298 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189299 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189300 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189301 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189302 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189303 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "189304 stimuli/r0225C/test.natrev.andros.24_pix.imsm/...\n", - "Name: relative_path, Length: 189305, dtype: object" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_massage[\"relative_path\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[ 0., nan, nan, ..., nan, nan, nan],\n", - " [ 0., nan, nan, ..., nan, nan, nan],\n", - " [ 0., nan, nan, ..., nan, nan, nan],\n", - " ..., \n", - " [ nan, nan, nan, ..., nan, nan, 0.],\n", - " [ nan, nan, nan, ..., nan, nan, 0.],\n", - " [ nan, nan, nan, ..., nan, nan, 0.]])\n", - "Coordinates:\n", - " * image_file_name (image_file_name) object 'gallant\\\\V1Data\\\\NatRev/stimuli/r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - " * neuroid (neuroid) object 'r0148A' 'r0150B' 'r0154B' 'r0156A' ...\n", - " basename (image_file_name) object '1.jpg' '2.jpg' '3.jpg' ...\n", - " exists (image_file_name) bool True True True True True True ...\n", - " full_path (image_file_name) object '/Users/jjpr/dev/scratch/gallant_data/gallant-V1/stimuli/r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - " relative_path (image_file_name) object 'stimuli/r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ..." - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "for column in df_massage.columns:\n", - " gd.coords[column] = (\"image_file_name\", df_massage[column])\n", - "gd" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "def get_hash(file_path):\n", - " f = kf(file_path)\n", - " return f.sha1" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'a3673c0959f08bebcd16d128e55cb5e8e79a398b'" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "get_hash(gd[\"full_path\"].values[100000])" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[ 0., nan, nan, ..., nan, nan, nan],\n", - " [ 0., nan, nan, ..., nan, nan, nan],\n", - " [ 0., nan, nan, ..., nan, nan, nan],\n", - " ..., \n", - " [ nan, nan, nan, ..., nan, nan, 0.],\n", - " [ nan, nan, nan, ..., nan, nan, 0.],\n", - " [ nan, nan, nan, ..., nan, nan, 0.]])\n", - "Coordinates:\n", - " * image_file_name (image_file_name) object 'gallant\\\\V1Data\\\\NatRev/stimuli/r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - " * neuroid (neuroid) object 'r0148A' 'r0150B' 'r0154B' 'r0156A' ...\n", - " basename (image_file_name) object '1.jpg' '2.jpg' '3.jpg' ...\n", - " exists (image_file_name) bool True True True True True True ...\n", - " full_path (image_file_name) object '/Users/jjpr/dev/scratch/gallant_data/gallant-V1/stimuli/r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - " relative_path (image_file_name) object 'stimuli/r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - " sha1 (image_file_name) \n", - "array([[ 0., nan, nan, ..., nan, nan, nan],\n", - " [ 0., nan, nan, ..., nan, nan, nan],\n", - " [ 0., nan, nan, ..., nan, nan, nan],\n", - " ..., \n", - " [ nan, nan, nan, ..., nan, nan, 0.],\n", - " [ nan, nan, nan, ..., nan, nan, 0.],\n", - " [ nan, nan, nan, ..., nan, nan, 0.]])\n", - "Coordinates:\n", - " * image_file_name (image_file_name) MultiIndex\n", - " - image_file_name_level_0 (image_file_name) object 'gallant\\\\V1Data\\\\NatRev/stimuli/r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - " - basename (image_file_name) object '1.jpg' '2.jpg' ...\n", - " - exists (image_file_name) object True True True True ...\n", - " - full_path (image_file_name) object '/Users/jjpr/dev/scratch/gallant_data/gallant-V1/stimuli/r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - " - relative_path (image_file_name) object 'stimuli/r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - " - sha1 (image_file_name) object '33613a99bdc22fe498864bf32b798e018a961ed7' ...\n", - " * neuroid (neuroid) object 'r0148A' 'r0150B' 'r0154B' ..." - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gd = mkgu.assemblies.gather_indexes(gd)\n", - "gd" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[ nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n", - " nan, nan, nan, nan, nan, nan, nan, nan, nan, 0., nan]])\n", - "Coordinates:\n", - " * image_file_name (image_file_name) MultiIndex\n", - " - image_file_name_level_0 (image_file_name) object 'gallant\\\\V1Data\\\\NatRev/stimuli/r0223A/test.natrev.andros.24_pix.imsm/5204.jpg' ...\n", - " - basename (image_file_name) object '5204.jpg'\n", - " - exists (image_file_name) object True\n", - " - relative_path (image_file_name) object 'stimuli/r0223A/test.natrev.andros.24_pix.imsm/5204.jpg' ...\n", - " - sha1 (image_file_name) object '9f9c016dfae5d84cbf0645c09b3f3048cef15222' ...\n", - " * neuroid (neuroid) object 'r0148A' 'r0150B' 'r0154B' ..." - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gd.sel(full_path=\"/Users/jjpr/dev/scratch/gallant_data/gallant-V1/stimuli/r0223A/test.natrev.andros.24_pix.imsm/5204.jpg\")" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Users/jjpr/dev/scratch/gallant_data/gallant-V1/stimuli/r0223A/test.natrev.andros.24_pix.imsm/5204.jpg'" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dupe_sample = kf(\"/Users/jjpr/dev/scratch/gallant_data/gallant-V1/stimuli/r0223A/test.natrev.andros.24_pix.imsm/5204.jpg\")\n", - "dupe_sample.path" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['/Users/jjpr/dev/scratch/gallant_data/gallant-V1/stimuli/r0211A/test.natrev.andros.24_pix.imsm/5204.jpg',\n", - " '/Users/jjpr/dev/scratch/gallant_data/gallant-V1/stimuli/r0219B/test.natrev.andros.24_pix.imsm/5204.jpg',\n", - " '/Users/jjpr/dev/scratch/gallant_data/gallant-V1/stimuli/r0220A/test.natrev.andros.24_pix.imsm/5204.jpg',\n", - " '/Users/jjpr/dev/scratch/gallant_data/gallant-V1/stimuli/r0223A/test.natrev.andros.24_pix.imsm/5204.jpg',\n", - " '/Users/jjpr/dev/scratch/gallant_data/gallant-V1/stimuli/r0225C/test.natrev.andros.24_pix.imsm/5204.jpg']" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[f.location for f in dupe_sample.file_record.sightings]" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Users/jjpr/dev/scratch/gallant_data/gallant-V1/stimuli/r0211A/test.natrev.andros.24_pix.imsm/5204.jpg'" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dupe_sample.file_record.sightings[0].location" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fr = FileRecord.get(sha1=\"9f9c016dfae5d84cbf0645c09b3f3048cef15222\")\n", - "fr" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Users/jjpr/dev/scratch/gallant_data/gallant-V1/stimuli/r0211A/test.natrev.andros.24_pix.imsm/5204.jpg'" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fr.sightings[0].location" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [], - "source": [ - "def first_dupe(sha1):\n", - " fr = FileRecord.get(sha1=sha1)\n", - " return fr.sightings[0].location" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[ 0., nan, nan, ..., nan, nan, nan],\n", - " [ 0., nan, nan, ..., nan, nan, nan],\n", - " [ 0., nan, nan, ..., nan, nan, nan],\n", - " ..., \n", - " [ nan, nan, nan, ..., nan, nan, 0.],\n", - " [ nan, nan, nan, ..., nan, nan, 0.],\n", - " [ nan, nan, nan, ..., nan, nan, 0.]])\n", - "Coordinates:\n", - " * image_file_name (image_file_name) MultiIndex\n", - " - image_file_name_level_0 (image_file_name) object 'gallant\\\\V1Data\\\\NatRev/stimuli/r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - " - basename (image_file_name) object '1.jpg' '2.jpg' ...\n", - " - exists (image_file_name) object True True True True ...\n", - " - full_path (image_file_name) object '/Users/jjpr/dev/scratch/gallant_data/gallant-V1/stimuli/r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - " - relative_path (image_file_name) object 'stimuli/r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - " - sha1 (image_file_name) object '33613a99bdc22fe498864bf32b798e018a961ed7' ...\n", - " * neuroid (neuroid) object 'r0148A' 'r0150B' 'r0154B' ...\n", - " first_dupe (image_file_name) \n", - "array([[ 0., nan, nan, ..., nan, nan, nan],\n", - " [ 0., nan, nan, ..., nan, nan, nan],\n", - " [ 0., nan, nan, ..., nan, nan, nan],\n", - " ..., \n", - " [ nan, nan, nan, ..., nan, nan, 0.],\n", - " [ nan, nan, nan, ..., nan, nan, 0.],\n", - " [ nan, nan, nan, ..., nan, nan, 0.]])\n", - "Coordinates:\n", - " * image_file_name (image_file_name) MultiIndex\n", - " - basename (image_file_name) object '1.jpg' '2.jpg' ...\n", - " - exists (image_file_name) object True True True True ...\n", - " - full_path (image_file_name) object '/Users/jjpr/dev/scratch/gallant_data/gallant-V1/stimuli/r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - " - relative_path (image_file_name) object 'stimuli/r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - " - sha1 (image_file_name) object '33613a99bdc22fe498864bf32b798e018a961ed7' ...\n", - " * neuroid (neuroid) object 'r0148A' 'r0150B' 'r0154B' ...\n", - " first_dupe (image_file_name) \u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"image_file_name\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"presentation\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"image_file_name_level_0\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"image_file_name_original\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/anaconda/envs/mkgu_packaging/lib/python3.6/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mrename\u001b[0;34m(self, new_name_or_name_dict)\u001b[0m\n\u001b[1;32m 834\u001b[0m \"\"\"\n\u001b[1;32m 835\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_dict_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_name_or_name_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 836\u001b[0;31m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_temp_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_name_or_name_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 837\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_temp_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 838\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda/envs/mkgu_packaging/lib/python3.6/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36mrename\u001b[0;34m(self, name_dict, inplace)\u001b[0m\n\u001b[1;32m 1528\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1529\u001b[0m raise ValueError(\"cannot rename %r because it is not a \"\n\u001b[0;32m-> 1530\u001b[0;31m \"variable or dimension in this dataset\" % k)\n\u001b[0m\u001b[1;32m 1531\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1532\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'the new name %r already exists'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: cannot rename 'image_file_name_level_0' because it is not a variable or dimension in this dataset" - ] - } - ], - "source": [ - "gd.T.rename({\"image_file_name\": \"presentation\"})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pwdb.connect(reuse_if_open=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gallant_v1_images, created = StimulusSetModel.get_or_create(name=\"gallant.v1\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gallant_v1_image_store, created = ImageStoreModel.get_or_create(location_type=\"S3\", store_type=\"zip\",\n", - " location=\"https://mkgu-gallant.s3.amazonaws.com/gallant-V1.zip\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eav_object_name = AttributeModel(name=\"object_name\", type=\"str\")\n", - "\n", - "eav_object_name.save()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gallant_v1_images.save()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gallant_v1_image_store.save()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mkgu_packaging", - "language": "python", - "name": "mkgu_packaging" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/packaging/notebooks/2018-05-29_alignment_tests.ipynb b/packaging/notebooks/2018-05-29_alignment_tests.ipynb deleted file mode 100644 index 2c5aa3f8b..000000000 --- a/packaging/notebooks/2018-05-29_alignment_tests.ipynb +++ /dev/null @@ -1,2122 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import string\n", - "import numpy as np\n", - "import xarray as xr" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[-0.507435, -1.19325 , -0.144582],\n", - " [ 0.258848, -0.71531 , -0.683058]])\n", - 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] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(\n", - " array([[-0.507435, -1.19325 , -0.144582, nan, nan, nan],\n", - " [ 0.258848, -0.71531 , -0.683058, nan, nan, nan],\n", - " [ nan, nan, nan, nan, nan, nan],\n", - " [ nan, nan, nan, nan, nan, nan]])\n", - " Coordinates:\n", - " * x (x) object 'a' 'b' 'c' 'd'\n", - " * y (y) int64 10 20 30 40 50 60, \n", - " array([[ nan, nan, nan, nan, nan, nan],\n", - " [ nan, nan, nan, nan, nan, nan],\n", - " [ nan, nan, nan, 0.951908, 2.19363 , 0.800204],\n", - " [ nan, nan, nan, 0.060204, -1.334579, 0.421212]])\n", - " Coordinates:\n", - " * x (x) object 'a' 'b' 'c' 'd'\n", - " * y (y) int64 10 20 30 40 50 60, \n", - " array([[ nan, nan, nan, nan, nan, nan],\n", - " [ nan, nan, -0.530381, 0.584825, nan, nan],\n", - " [ nan, nan, -0.220513, 1.550545, nan, nan],\n", - " [ nan, nan, nan, nan, nan, nan]])\n", - " Coordinates:\n", - " * x (x) object 'a' 'b' 'c' 'd'\n", - " * y (y) int64 10 20 30 40 50 60)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.align(da1, da2, da3, join=\"outer\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[-0.507435, -1.19325 , -0.144582, nan, nan, nan,\n", - " nan, nan, nan, nan, nan, nan,\n", - " nan, nan, nan, nan, nan, nan],\n", - " [ 0.258848, -0.71531 , -0.683058, nan, nan, nan,\n", - " nan, nan, nan, nan, nan, nan,\n", - " nan, nan, -0.530381, 0.584825, nan, nan],\n", - " [ nan, nan, nan, nan, nan, nan,\n", - " nan, nan, nan, 0.951908, 2.19363 , 0.800204,\n", - " nan, nan, -0.220513, 1.550545, nan, nan],\n", - " [ nan, nan, nan, nan, nan, nan,\n", - " nan, nan, nan, 0.060204, -1.334579, 0.421212,\n", - " nan, nan, nan, nan, nan, nan]])\n", - "Coordinates:\n", - " * x (x) object 'a' 'b' 'c' 'd'\n", - " * y (y) int64 10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.concat(xr.align(da1, da2, da3, join=\"outer\"), dim=\"y\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "unable to convert unnamed DataArray to a Dataset without providing an explicit name", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mda1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mda2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mda3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/anaconda3/envs/mkgu_packaging/lib/python3.6/site-packages/xarray/core/merge.py\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(objects, compat, join)\u001b[0m\n\u001b[1;32m 508\u001b[0m dict_like_objects = [\n\u001b[1;32m 509\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataArray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 510\u001b[0;31m for obj in objects]\n\u001b[0m\u001b[1;32m 511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoord_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmerge_core\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict_like_objects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/mkgu_packaging/lib/python3.6/site-packages/xarray/core/merge.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 508\u001b[0m dict_like_objects = [\n\u001b[1;32m 509\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataArray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 510\u001b[0;31m for obj in objects]\n\u001b[0m\u001b[1;32m 511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoord_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmerge_core\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict_like_objects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/mkgu_packaging/lib/python3.6/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mto_dataset\u001b[0;34m(self, dim, name)\u001b[0m\n\u001b[1;32m 350\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_dataset_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 351\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_dataset_whole\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/mkgu_packaging/lib/python3.6/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36m_to_dataset_whole\u001b[0;34m(self, name, shallow_copy)\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m raise ValueError('unable to convert unnamed DataArray to a '\n\u001b[0m\u001b[1;32m 307\u001b[0m 'Dataset without providing an explicit name')\n\u001b[1;32m 308\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: unable to convert unnamed DataArray to a Dataset without providing an explicit name" - ] - } - ], - "source": [ - "xr.merge([da1, da2, da3])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "unable to convert unnamed DataArray to a Dataset without providing an explicit name", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mda1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mda2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mda3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"outer\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/anaconda3/envs/mkgu_packaging/lib/python3.6/site-packages/xarray/core/merge.py\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(objects, compat, join)\u001b[0m\n\u001b[1;32m 508\u001b[0m dict_like_objects = [\n\u001b[1;32m 509\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataArray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 510\u001b[0;31m for obj in objects]\n\u001b[0m\u001b[1;32m 511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoord_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmerge_core\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict_like_objects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/mkgu_packaging/lib/python3.6/site-packages/xarray/core/merge.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 508\u001b[0m dict_like_objects = [\n\u001b[1;32m 509\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataArray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 510\u001b[0;31m for obj in objects]\n\u001b[0m\u001b[1;32m 511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoord_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmerge_core\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict_like_objects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/mkgu_packaging/lib/python3.6/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mto_dataset\u001b[0;34m(self, dim, name)\u001b[0m\n\u001b[1;32m 350\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_dataset_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 351\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 352\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_dataset_whole\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/mkgu_packaging/lib/python3.6/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36m_to_dataset_whole\u001b[0;34m(self, name, shallow_copy)\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m raise ValueError('unable to convert unnamed DataArray to a '\n\u001b[0m\u001b[1;32m 307\u001b[0m 'Dataset without providing an explicit name')\n\u001b[1;32m 308\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: unable to convert unnamed DataArray to a Dataset without providing an explicit name" - ] - } - ], - "source": [ - "xr.merge(xr.align(da1, da2, da3, join=\"outer\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[-0.507435, -1.19325 , -0.144582, nan, nan, nan],\n", - " [ 0.258848, -0.71531 , -0.683058, nan, nan, nan],\n", - " [ nan, nan, nan, 0.951908, 2.19363 , 0.800204],\n", - " [ nan, nan, nan, 0.060204, -1.334579, 0.421212]])\n", - "Coordinates:\n", - " * x (x) object 'a' 'b' 'c' 'd'\n", - " * y (y) int64 10 20 30 40 50 60" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da1.combine_first(da2)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[-0.507435, -1.19325 , -0.144582, nan],\n", - " [ 0.258848, -0.71531 , -0.683058, 0.584825],\n", - " [ nan, nan, -0.220513, 1.550545]])\n", - "Coordinates:\n", - " * x (x) object 'a' 'b' 'c'\n", - " * y (y) int64 10 20 30 40" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da1.combine_first(da3)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "da1.name = \"da1\"\n", - "da2.name = \"da2\"\n", - "da3.name = \"da3\"" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Dimensions: (x: 4, y: 6)\n", - "Coordinates:\n", - " * x (x) object 'a' 'b' 'c' 'd'\n", - " * y (y) int64 10 20 30 40 50 60\n", - "Data variables:\n", - " da1 (x, y) float64 -0.5074 -1.193 -0.1446 nan nan nan 0.2588 ...\n", - " da2 (x, y) float64 nan nan nan nan nan nan nan nan nan nan nan nan ...\n", - " da3 (x, y) float64 nan nan nan nan nan nan nan nan -0.5304 0.5848 ..." - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.merge([da1, da2, da3])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([], shape=(0, 0), dtype=bool)\n", - "Coordinates:\n", - " * x (x) object \n", - " * y (y) int64 " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da1 != da2" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[ True]])\n", - "Coordinates:\n", - " * x (x) object 'b'\n", - " * y (y) int64 30" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da1 != da3" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "aligned = xr.align(da1, da2, da3, join=\"outer\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[ True, True, True, True, True, True],\n", - " [ True, True, True, True, True, True],\n", - " [ True, True, True, True, True, True],\n", - " [ True, True, True, True, True, True]])\n", - "Coordinates:\n", - " * x (x) object 'a' 'b' 'c' 'd'\n", - " * y (y) int64 10 20 30 40 50 60" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "aligned[0] != aligned[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[ True, True, True, True, True, True],\n", - " [ True, True, True, True, True, True],\n", - " [ True, True, True, True, True, True],\n", - " [ True, True, True, True, True, True]])\n", - "Coordinates:\n", - " * x (x) object 'a' 'b' 'c' 'd'\n", - " * y (y) int64 10 20 30 40 50 60" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "aligned[0] != aligned[2]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([0, 1]),)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.nonzero(np.array([True, True, False]))" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "array([[ 0., nan, nan],\n", - " [nan, 1., nan],\n", - " [nan, nan, 2.]])\n", - "Coordinates:\n", - " * x (x) \n", - "array([[0.1, nan, nan],\n", - " [nan, 0.2, nan],\n", - " [nan, nan, 0.3]])\n", - "Coordinates:\n", - " * x (x) \n", - " array([[ 0., nan, nan, nan, nan, nan],\n", - " [nan, 1., nan, nan, nan, nan],\n", - " [nan, nan, 2., nan, nan, nan],\n", - " [nan, nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan, nan]])\n", - " Coordinates:\n", - " * x (x) object 'a' 'b' 'c' 'd' 'e'\n", - " * y (y) int64 10 20 30 40 50 60, \n", - " array([[nan, nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, 0.1, nan, nan],\n", - " [nan, nan, nan, nan, 0.2, nan],\n", - " [nan, nan, nan, nan, nan, 0.3]])\n", - " Coordinates:\n", - " * x (x) object 'a' 'b' 'c' 'd' 'e'\n", - " * y (y) int64 10 20 30 40 50 60)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.align(da4, da5, join=\"outer\")" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(0, 0),\n", - " (1, 1),\n", - " (2, 2),\n", - " (3, 3),\n", - " (4, 4),\n", - " (5, 5),\n", - " (6, 6),\n", - " (7, 7),\n", - " (8, 8),\n", - " (9, 9),\n", - " (10, 0),\n", - " (11, 1),\n", - " (12, 2),\n", - " (13, 3),\n", - " (14, 4),\n", - " (15, 5),\n", - " (16, 6),\n", - " (17, 7),\n", - " (18, 8),\n", - " (19, 9),\n", - 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" * y (y) MultiIndex\n", - " - tens (y) int64 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 ...\n", - " - negative (y) int64 0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -13 -14 -15 ...,\n", - " \n", - " array([[ nan, nan, nan, ..., nan, nan, nan],\n", - " [ nan, nan, nan, ..., nan, nan, nan],\n", - " [ nan, nan, nan, ..., nan, nan, nan],\n", - " ...,\n", - " [ nan, nan, nan, ..., -299997., nan, nan],\n", - " [ nan, nan, nan, ..., nan, -299998., nan],\n", - " [ nan, nan, nan, ..., nan, nan, -299999.]])\n", - " Coordinates:\n", - " * x (x) MultiIndex\n", - " - lower (x) object 'a' 'b' 'c' 'd' 'e' 'f' 'g' 'h' 'i' 'j' 'k' 'l' 'm' ...\n", - " - upper (x) object 'A' 'B' 'C' 'D' 'E' 'F' 'G' 'H' 'I' 'J' 'K' 'L' 'M' ...\n", - " * y (y) MultiIndex\n", - " - tens (y) int64 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 ...\n", - " - negative (y) int64 0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -13 -14 -15 ...)" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.align(da6_reloaded, da7_reloaded, join=\"outer\")" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((96000, 10), (2, 1772))" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da_loaded = xr.open_dataarray(\"/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/V2Data1/DGrat/data.nc\")\n", - "non_nan = np.nonzero(~np.isnan(da_loaded))\n", - "(da_loaded.shape, non_nan.values.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((96000, 10), (2, 1772))" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da_loaded.load()\n", - "non_nan_after_load = np.nonzero(~np.isnan(da_loaded))\n", - "(da_loaded.shape, non_nan_after_load.values.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "nonzeros_raw: \n", - "[\n", - "array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n", - " [0, 1, 2, 3, 4, 0, 1, 2, 3, 4]])\n", - "Coordinates:\n", - " * x (x) MultiIndex\n", - " - ints (x) int64 0 1 2 3 4 5 6 7 8 9\n", - " - nans (x) float64 nan nan nan nan nan nan nan nan nan nan\n", - " * y (y) object 'a' 'b' 'c' 'd' 'e', \n", - "array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n", - " [0, 1, 2, 3, 4, 0, 1, 2, 3, 4]])\n", - "Coordinates:\n", - " * x (x) MultiIndex\n", - " - ints (x) int64 10 11 12 13 14 15 16 17 18 19\n", - " - nans (x) float64 nan nan nan nan nan nan nan nan nan nan\n", - " * y (y) object 'a' 'b' 'c' 'd' 'e']\n", - "nonzeros_aligned: \n", - "[\n", - "array([], shape=(2, 0), dtype=int64)\n", - "Coordinates:\n", - " * x (x) MultiIndex\n", - " - ints (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19\n", - " - nans (x) object nan nan nan nan nan nan nan nan nan nan nan nan nan ...\n", - " * y (y) object 'a' 'b' 'c' 'd' 'e', \n", - "array([], shape=(2, 0), dtype=int64)\n", - "Coordinates:\n", - " * x (x) MultiIndex\n", - " - ints (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19\n", - " - nans (x) object nan nan nan nan nan nan nan nan nan nan nan nan nan ...\n", - " * y (y) object 'a' 'b' 'c' 'd' 'e']\n" - ] - }, - { - "ename": "AssertionError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"nonzeros_aligned: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnonzeros_aligned\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mnonzeros_raw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnonzeros_aligned\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m: " - ] - } - ], - "source": [ - "dims = (\"x\", \"y\")\n", - "shape = (10, 5)\n", - "das = []\n", - "for j in (0, 1):\n", - " data = np.full(shape, np.nan, dtype=\"float64\")\n", - " for i in range(shape[0]):\n", - " data[i, i % shape[1]] = float(i)\n", - " coords_d = {\n", - " \"ints\": (\"x\", range(j*shape[0], (j+1)*shape[0])),\n", - " \"nans\": (\"x\", np.array([np.nan] * shape[0], dtype=\"float64\")),\n", - " \"lower\": (\"y\", list(string.ascii_lowercase[:shape[1]]))\n", - " }\n", - " da = xr.DataArray(data=data, dims=dims, coords=coords_d)\n", - " da.set_index(append=True, inplace=True, x=[\"ints\", \"nans\"], y=[\"lower\"])\n", - " das.append(da)\n", - "nonzeros_raw = [np.nonzero(~np.isnan(da)) for da in das]\n", - "print(\"nonzeros_raw: \")\n", - "print(nonzeros_raw)\n", - "aligned = xr.align(*das, join=\"outer\")\n", - "nonzeros_aligned = [np.nonzero(~np.isnan(da)) for da in aligned]\n", - "print(\"nonzeros_aligned: \")\n", - "print(nonzeros_aligned)\n", - "assert nonzeros_raw[0].shape == nonzeros_aligned[0].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[\n", - " array([[ 0., nan, nan, nan, nan],\n", - " [nan, 1., nan, nan, nan],\n", - " [nan, nan, 2., nan, nan],\n", - " [nan, nan, nan, 3., nan],\n", - " [nan, nan, nan, nan, 4.],\n", - " [ 5., nan, nan, nan, nan],\n", - " [nan, 6., nan, nan, nan],\n", - " [nan, nan, 7., nan, nan],\n", - " [nan, nan, nan, 8., nan],\n", - " [nan, nan, nan, nan, 9.]])\n", - " Coordinates:\n", - " * x (x) MultiIndex\n", - " - ints (x) int64 0 1 2 3 4 5 6 7 8 9\n", - " - nans (x) float64 nan nan nan nan nan nan nan nan nan nan\n", - " * y (y) object 'a' 'b' 'c' 'd' 'e', \n", - " array([[ 0., nan, nan, nan, nan],\n", - " [nan, 1., nan, nan, nan],\n", - " [nan, nan, 2., nan, nan],\n", - " [nan, nan, nan, 3., nan],\n", - " [nan, nan, nan, nan, 4.],\n", - " [ 5., nan, nan, nan, nan],\n", - " [nan, 6., nan, nan, nan],\n", - " [nan, nan, 7., nan, nan],\n", - " [nan, nan, nan, 8., nan],\n", - " [nan, nan, nan, nan, 9.]])\n", - " Coordinates:\n", - " * x (x) MultiIndex\n", - " - ints (x) int64 10 11 12 13 14 15 16 17 18 19\n", - " - nans (x) float64 nan nan nan nan nan nan nan nan nan nan\n", - " * y (y) object 'a' 'b' 'c' 'd' 'e']" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "das" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(\n", - " array([[nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan]])\n", - " Coordinates:\n", - " * x (x) MultiIndex\n", - " - ints (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19\n", - " - nans (x) object nan nan nan nan nan nan nan nan nan nan nan nan nan ...\n", - " * y (y) object 'a' 'b' 'c' 'd' 'e', \n", - " array([[nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan]])\n", - " Coordinates:\n", - " * x (x) MultiIndex\n", - " - ints (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19\n", - " - nans (x) object nan nan nan nan nan nan nan nan nan nan nan nan nan ...\n", - " * y (y) object 'a' 'b' 'c' 'd' 'e')" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "aligned" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(\n", - " array([[ 0., nan, nan, nan, nan],\n", - " [nan, 1., nan, nan, nan],\n", - " [nan, nan, 2., nan, nan],\n", - " [nan, nan, nan, 3., nan],\n", - " [nan, nan, nan, nan, 4.],\n", - " [ 5., nan, nan, nan, nan],\n", - " [nan, 6., nan, nan, nan],\n", - " [nan, nan, 7., nan, nan],\n", - " [nan, nan, nan, 8., nan],\n", - " [nan, nan, nan, nan, 9.],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan]])\n", - " Coordinates:\n", - " * x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19\n", - " * y (y) object 'a' 'b' 'c' 'd' 'e', \n", - " array([[nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [nan, nan, nan, nan, nan],\n", - " [ 0., nan, nan, nan, nan],\n", - " [nan, 1., nan, nan, nan],\n", - " [nan, nan, 2., nan, nan],\n", - " [nan, nan, nan, 3., nan],\n", - " [nan, nan, nan, nan, 4.],\n", - " [ 5., nan, nan, nan, nan],\n", - " [nan, 6., nan, nan, nan],\n", - " [nan, nan, 7., nan, nan],\n", - " [nan, nan, nan, 8., nan],\n", - " [nan, nan, nan, nan, 9.]])\n", - " Coordinates:\n", - " * x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19\n", - " * y (y) object 'a' 'b' 'c' 'd' 'e')" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "realigned = xr.align(*[da.reset_index(\"nans\", drop=True) for da in das], join=\"outer\")\n", - "realigned" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mkgu_packaging", - "language": "python", - "name": "mkgu_packaging" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/packaging/notebooks/2018-06-22_behavioral.ipynb b/packaging/notebooks/2018-06-22_behavioral.ipynb deleted file mode 100644 index afe9567c0..000000000 --- a/packaging/notebooks/2018-06-22_behavioral.ipynb +++ /dev/null @@ -1,523 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pymongo" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client = pymongo.MongoClient(\"localhost\", 22334)\n", - "client" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client.database_names()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "db = client.mturk\n", - "db" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "db.collection_names()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "coll_names = ['objectome64', 'objectome_imglvl', 'ko_obj24_basic_2ways', 'monkobjectome', 'ko_obj24_basic_2ways_mod_ver2']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "client.server_info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trial_level = [\n", - " 'ImgData',\n", - " 'RT',\n", - " 'Response',\n", - " 'StimDuration',\n", - " 'StimShown'\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "document_level = [\n", - " 'AcceptTime',\n", - " 'AssignmentID',\n", - " 'Browser',\n", - " 'Comment',\n", - " 'Condition',\n", - " 'CreationTime',\n", - " 'Description',\n", - " 'Duration',\n", - " 'HITTypeID',\n", - " 'HITid',\n", - " 'IPaddress',\n", - " 'Keywords',\n", - " 'OpSys',\n", - " 'Reward',\n", - " 'ScreenHeight',\n", - " 'ScreenWidth',\n", - " 'Timestamp',\n", - " 'Title',\n", - " 'URL',\n", - " 'Version',\n", - " 'WindowHeight',\n", - " 'WindowWidth',\n", - " 'WorkerID',\n", - " 'Zoom'\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "countings_select = {}\n", - "for db_name in ['mturk']:\n", - " db = client[db_name]\n", - " db_counting = {}\n", - " for coll_name in coll_names:\n", - " coll = db[coll_name]\n", - " coll_d = {}\n", - " for doc in coll.find():\n", - " trial_count = len(doc['Response'])\n", - " coll_d[doc['AssignmentID']] = trial_count\n", - " db_counting[coll_name] = coll_d\n", - " countings_select[db_name] = db_counting\n", - "\n", - "countings_select" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for db_k in countings_select:\n", - " db_v = countings_select[db_k]\n", - " for coll_k in db_v:\n", - " coll_v = db_v[coll_k]\n", - " coll_trials_count = 0\n", - " for doc_k in coll_v:\n", - " doc_v = coll_v[doc_k]\n", - " coll_trials_count = coll_trials_count + doc_v\n", - " print(' {}: {} trials'.format(coll_k, coll_trials_count))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mome = db.monkobjectome\n", - "mome" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mome.count()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "momerand_index = random.randrange(mome.count())\n", - "momerand = mome.find()[momerand_index]\n", - "momerand" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "momerand_index" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(momerand[\"ImgData\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "momerand.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(momerand)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "[(k, type(momerand[k])) for k in momerand]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "momerand[\"Qualification\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "momerand[\"RT\"][:5]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "momerand[\"Response\"][:5]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "momerand[\"StimShown\"][:5]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "momerand[\"ImgData\"][:5]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "[len(momerand[k]) for k in trial_level]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sample_index = 123\n", - "[momerand[k][sample_index] for k in trial_level]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def strip_response(response):\n", - " return response.replace(\"_label\", \"\")\n", - "\n", - "strip_response(momerand[\"Response\"][sample_index])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "momerand[\"ImgData\"][sample_index][0][\"obj\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "momerand[\"ImgData\"][sample_index][0][\"obj\"] == strip_response(momerand[\"Response\"][sample_index])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def correct_response(doc, trial_index):\n", - " target = doc[\"ImgData\"][trial_index][0][\"obj\"]\n", - " response = strip_response(doc[\"Response\"][trial_index])\n", - " return target == response\n", - "\n", - "correct_response(momerand, sample_index)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "names_simple_trial_level = [\"RT\", \"Response\", \"StimDuration\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "names_stim_shown = [\"Stimulus\", \"Choice_1\", \"Choice_2\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def df_from_doc(doc):\n", - " df_doc = pd.DataFrame([x[0] for x in doc[\"ImgData\"]])\n", - " for name in names_simple_trial_level:\n", - " df_doc[name] = doc[name]\n", - " for i, name in enumerate(names_stim_shown):\n", - " df_doc[name] = [x[i] for x in doc[\"StimShown\"]]\n", - " for name in document_level:\n", - " df_doc[name] = [doc[name]] * len(df_doc)\n", - " return df_doc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_momerand = df_from_doc(momerand)\n", - "df_momerand" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def s_from_doc(doc):\n", - " l_correct = [correct_response(doc, i) for i in range(len(doc[\"ImgData\"]))]\n", - " df_doc = df_from_doc(doc)\n", - " levels = [df_doc[name] for name in df_doc.columns]\n", - " mi_doc = pd.MultiIndex.from_arrays(arrays=levels, names=df_doc.columns)\n", - " s_doc = pd.Series(data=l_correct, index=mi_doc)\n", - " return s_doc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s_momerand = s_from_doc(momerand)\n", - "s_momerand" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s_mome = pd.concat([s_from_doc(doc) for doc in mome.find()])\n", - "s_mome" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(s_mome)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s_mome.index" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(s_mome.index)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da_mome = xr.DataArray(s_mome)\n", - "da_mome" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "da_mome.to_netcdf(\"monkobjectome_behavior.nc\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mkgu_packaging", - "language": "python", - "name": "mkgu_packaging" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/packaging/notebooks/2018-07-31_temporal.ipynb b/packaging/notebooks/2018-07-31_temporal.ipynb deleted file mode 100644 index 56bf9a469..000000000 --- a/packaging/notebooks/2018-07-31_temporal.ipynb +++ /dev/null @@ -1,422 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "import brainscore" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from brainscore.assemblies import AssemblyModel, AssemblyStoreMap, AssemblyStoreModel" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from brainscore.stimuli import StimulusSetModel" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "from brainscore.lookup import pwdb" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "[3125076240 values with dtype=float32]\n", - "Coordinates:\n", - " neuroid_id (neuroid) object ...\n", - " arr (neuroid) object ...\n", - " time_bin_start (time_bin) int64 ...\n", - " col (neuroid) int64 ...\n", - " hemisphere (neuroid) object ...\n", - " subregion (neuroid) object ...\n", - " animal (neuroid) object ...\n", - " y (neuroid) float64 ...\n", - " x (neuroid) float64 ...\n", - " time_bin_end (time_bin) int64 ...\n", - " region (neuroid) object ...\n", - " row (neuroid) int64 ...\n", - " image_id (presentation) object ...\n", - " repetition (presentation) int64 ...\n", - " stimulus (presentation) int64 ...\n", - "Dimensions without coordinates: neuroid, presentation, time_bin" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hvmt = xr.open_dataarray(\"/braintree/home/jjpr/dev_remote/dldata/hvm_temporal_neuronal_features.nc\")\n", - "hvmt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Make the DataAssembly lookup meta" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "stim = StimulusSetModel.get(StimulusSetModel.name==\"dicarlo.hvm\")\n", - "stim" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assy = AssemblyModel(name=\"dicarlo.Majaj2015.temporal\", assembly_class=\"NeuronRecordingAssembly\",\n", - " stimulus_set=stim)\n", - "assy.save()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "store = AssemblyStoreModel(assembly_type=\"netCDF\",\n", - " location_type=\"S3\",\n", - " location=\"https://mkgu-dicarlolab-hvm.s3.amazonaws.com/hvm_temporal_neuronal_features.nc\")\n", - "store.save()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assy_store_map = AssemblyStoreMap(assembly_model=assy, assembly_store_model=store, role=\"dicarlo.Majaj2015.temporal\")\n", - "assy_store_map.save()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'dicarlo.Majaj2015.temporal'" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assy_model = brainscore.assemblies.lookup_assembly(\"dicarlo.Majaj2015.temporal\")\n", - "assy_model.name" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "asm = assy_model.assembly_store_maps[0]\n", - "asm" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://mkgu-dicarlolab-hvm.s3.amazonaws.com/hvm_temporal_neuronal_features.nc'" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "asm.assembly_store_model.location" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bf = brainscore.fetch.BotoFetcher(asm.assembly_store_model.location, \"dicarlo.Majaj2015.temporal\")\n", - "bf" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/braintree/home/jjpr/.brainscore/data/dicarlo.Majaj2015.temporal/hvm_temporal_neuronal_features.nc'" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bf.output_filename" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "getting HvM_with_discfade.zip\n" - ] - }, - { - "data": { - "text/plain": [ - "\n", - "array([[[ 1.34598 , -0.673589, ..., -0.325305, -1.456868],\n", - " [ 2.2433 , 0.205005, ..., -0.325305, 1.165494],\n", - " ...,\n", - " [-1.872702, -1.347117, ..., 0.46407 , 0.093515],\n", - " [ 3.003558, 3.020907, ..., -0.37123 , -0.180458]],\n", - "\n", - " [[ 0.841973, 1.818767, ..., -1.061501, -0.24362 ],\n", - " [ 0.841973, 1.01637 , ..., -0.265375, -1.055688],\n", - " ...,\n", - " [ 1.784337, 1.089535, ..., -1.370883, -1.295151],\n", - " [ 0.842859, -0.330911, ..., 2.118782, 1.031901]],\n", - "\n", - " ...,\n", - "\n", - " [[-0.089697, -1.042081, ..., -1.008055, 0.97547 ],\n", - " [ 0.807276, 1.637556, ..., 0.882048, 0.97547 ],\n", - " ...,\n", - " [ 0.796412, 0.559612, ..., 0. , 0.330872],\n", - " [ 1.592824, -0.239833, ..., 1.628444, 1.158051]],\n", - "\n", - " [[ 0.582239, -0.420673, ..., 0.841737, 1.731793],\n", - " [ 0.582239, 1.382211, ..., -0.129498, -1.265541],\n", - " ...,\n", - " [ 0.242736, 0.411805, ..., -0.673322, -1.876212],\n", - " [-0.566383, -1.235414, ..., 0.168331, -1.023389]]], dtype=float32)\n", - "Coordinates:\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object 'Chabo_L_M_5_9' 'Chabo_L_M_6_9' ...\n", - " - arr (neuroid) object 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' ...\n", - " - col (neuroid) int64 9 9 8 9 8 8 7 7 5 6 4 9 9 9 9 9 8 7 9 6 ...\n", - " - hemisphere (neuroid) object 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' ...\n", - " - subregion (neuroid) object 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' ...\n", - " - animal (neuroid) object 'Chabo' 'Chabo' 'Chabo' 'Chabo' ...\n", - " - y (neuroid) float64 0.2 0.6 0.2 1.0 0.6 1.0 1.8 1.0 1.8 ...\n", - " - x (neuroid) float64 1.8 1.8 1.4 1.8 1.4 1.4 1.0 1.0 0.2 ...\n", - " - region (neuroid) object 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' ...\n", - " - row (neuroid) int64 5 6 5 7 6 7 9 7 9 8 9 2 1 3 6 7 6 6 8 5 ...\n", - " * presentation (presentation) MultiIndex\n", - " - image_id (presentation) object 'f1ae7c2160c6cbb309b940a0dc127a8aa1060e5d' ...\n", - " - repetition (presentation) int64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...\n", - " - stimulus (presentation) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 ...\n", - " - id (presentation) float64 610.0 224.0 185.0 522.0 523.0 ...\n", - " - image_file_name (presentation) object 'Apple_Fruit_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+00.700_01c0425e982e4edc276fe5373dbf8e5e49bd9a16_256x256.png' ...\n", - " - object_name (presentation) object 'apple' 'apple' 'apple' 'apple' ...\n", - " - category_name (presentation) object 'Fruits' 'Fruits' 'Fruits' ...\n", - " - background_id (presentation) object '01c0425e982e4edc276fe5373dbf8e5e49bd9a16' ...\n", - " - variation (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - ty (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - tz (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - rxy (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " - rxz (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - ryz (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " - rxy_semantic (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " - rxz_semantic (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - ryz_semantic (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " - size (presentation) float64 256.0 256.0 256.0 256.0 256.0 ...\n", - " - s (presentation) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ...\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 -100 -90 -80 -70 -60 -50 -40 -30 -20 ...\n", - " - time_bin_end (time_bin) int64 -80 -70 -60 -50 -40 -30 -20 -10 0 10 ...\n", - "Attributes:\n", - " stimulus_set_name: dicarlo.hvm\n", - " stimulus_set: id image_id ..." - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assy_t = brainscore.get_assembly(\"dicarlo.Majaj2015.temporal\")\n", - "assy_t" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mkgu_packaging", - "language": "python", - "name": "mkgu_packaging" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/packaging/notebooks/2018-08-02_behavioral.ipynb b/packaging/notebooks/2018-08-02_behavioral.ipynb deleted file mode 100644 index 1634cf4b2..000000000 --- a/packaging/notebooks/2018-08-02_behavioral.ipynb +++ /dev/null @@ -1,27618 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "import glob" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [], - "source": [ - "import zipfile" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "import boto3" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import Image" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [], - "source": [ - "from brainscore import knownfile" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "[273852 values with dtype=bool]\n", - "Coordinates:\n", - " bgname (trial) object ...\n", - " bgphi (trial) float64 ...\n", - " bgpsi (trial) float64 ...\n", - " bgscale (trial) float64 ...\n", - " category (trial) object ...\n", - " id (trial) object ...\n", - " internal_canonical (trial) float64 ...\n", - " obj (trial) object ...\n", - " rxy (trial) float64 ...\n", - " rxz (trial) float64 ...\n", - " ryz (trial) float64 ...\n", - " s (trial) float64 ...\n", - " texture (trial) float64 ...\n", - " texture_mode (trial) float64 ...\n", - " tname (trial) object ...\n", - " tx (trial) float64 ...\n", - " ty (trial) float64 ...\n", - " tz (trial) float64 ...\n", - " RT (trial) int64 ...\n", - " Response (trial) object ...\n", - " StimDuration (trial) int64 ...\n", - " Stimulus (trial) object ...\n", - " Choice_1 (trial) object ...\n", - " Choice_2 (trial) object ...\n", - " AcceptTime (trial) object ...\n", - " AssignmentID (trial) object ...\n", - " Browser (trial) object ...\n", - " Comment (trial) object ...\n", - " Condition (trial) object ...\n", - " CreationTime (trial) object ...\n", - " Description (trial) object ...\n", - " Duration (trial) object ...\n", - " HITTypeID (trial) object ...\n", - " HITid (trial) object ...\n", - " IPaddress (trial) object ...\n", - " Keywords (trial) object ...\n", - " OpSys (trial) object ...\n", - " Reward (trial) object ...\n", - " ScreenHeight (trial) int64 ...\n", - " ScreenWidth (trial) int64 ...\n", - " Timestamp (trial) object ...\n", - " Title (trial) object ...\n", - " URL (trial) object ...\n", - " Version (trial) object ...\n", - " WindowHeight (trial) int64 ...\n", - " WindowWidth (trial) int64 ...\n", - " WorkerID (trial) object ...\n", - " Zoom (trial) float64 ...\n", - "Dimensions without coordinates: trial" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "da_mome = xr.open_dataarray(\"monkobjectome_behavior.nc\")\n", - "da_mome" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "273852" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(da_mome[\"Stimulus\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "24796" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(np.unique(da_mome[\"Stimulus\"]))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "https://s3.amazonaws.com/objectome32_final/23ee8cfc91e68912e2c0ba347005cbc7337b0838.png\n", - "objectome32_final\n", - "https://s3.amazonaws.com/objectome32_final/71fe1eaf3c8d104baba4747caa7040a009d4e393.png\n", - "objectome32_final\n", - "https://s3.amazonaws.com/objectome32_final/9a9f92bf308bb3eb7b6f8e87f3e8658731030add.png\n", - "objectome32_final\n", - "https://s3.amazonaws.com/objectome32_final/20d710e5481e0c55a304506e889592365803893d.png\n", - "objectome32_final\n", - "https://s3.amazonaws.com/objectome32_final/76082e4901531cc1ca0a47685301032758d336dd.png\n", - "objectome32_final\n", - "https://s3.amazonaws.com/objectome32_final/15a1c66900c32f1d09084b241099c51706fc365a.png\n", - "objectome32_final\n", - "https://s3.amazonaws.com/objectome32_final/37d2131bb44b6c35bdea7c8600bb577bccf2f169.png\n", - "objectome32_final\n", - "https://s3.amazonaws.com/objectome32_final/221f4843c4b5855ef1fc8b8bbcb9c8c1d91abe8d.png\n", - "objectome32_final\n", - "https://s3.amazonaws.com/objectome32_final/fedbe87dc039129c1523e6f308a5d7cf57b32773.png\n", - "objectome32_final\n", - "https://s3.amazonaws.com/objectome32_final/17faa084316bd5f4cec5492890399c0503deb00b.png\n", - "objectome32_final\n" - ] - } - ], - "source": [ - "for stim in da_mome[\"Stimulus\"].values[:10]:\n", - " print(stim)\n", - " sp = stim.split(\"/\")\n", - " print(sp[3])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - 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" tname (trial) object 'Objectome32' 'Objectome32' ...\n", - " tx (trial) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " ty (trial) float64 -0.9526 -0.5328 0.301 0.1686 -0.2945 ...\n", - " tz (trial) float64 0.02157 0.6214 0.8252 0.116 0.7043 ...\n", - " RT (trial) int64 6396 3291 2368 721 830 778 1305 1093 ...\n", - " Response (trial) object 'lo_poly_animal_TRANTULA_label' ...\n", - " StimDuration (trial) int64 100 100 100 100 100 100 100 100 100 ...\n", - " Stimulus (trial) object 'https://s3.amazonaws.com/objectome32_final/23ee8cfc91e68912e2c0ba347005cbc7337b0838.png' ...\n", - " Choice_1 (trial) object 'https://s3.amazonaws.com/objectome32_final/label_imgs/weimaraner.png' ...\n", - " Choice_2 (trial) object 'https://s3.amazonaws.com/objectome32_final/label_imgs/lo_poly_animal_TRANTULA.png' ...\n", - " AcceptTime (trial) object '2014-01-03T02:47:08Z' ...\n", - " AssignmentID (trial) object '220SV7WGKCC09HICG0UMQAPQH722SC' ...\n", - " Browser (trial) object 'Chrome' 'Chrome' 'Chrome' 'Chrome' ...\n", - 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" 'Size': 124191,\n", - " 'StorageClass': 'STANDARD'},\n", - " {'ETag': '\"e3048d855b36836df7d8afcf8dff6c53\"',\n", - " 'Key': '03e6663945c7f521611243bdf0ab133f2e2b4919.png',\n", - " 'LastModified': datetime.datetime(2014, 8, 13, 16, 31, 23, tzinfo=tzutc()),\n", - " 'Owner': {'DisplayName': 'dlmug',\n", - " 'ID': 'e7effa2018f00aa7ef9361caca0350b409d79587291aee4d0fa0c9bca40efa91'},\n", - " 'Size': 114547,\n", - " 'StorageClass': 'STANDARD'},\n", - " {'ETag': '\"8274e06bfeb4627f5f71c70bb96f3a2a\"',\n", - " 'Key': '03e6b5363dc441c44976311c780144292bde02ba.png',\n", - " 'LastModified': datetime.datetime(2014, 8, 13, 17, 36, 16, tzinfo=tzutc()),\n", - " 'Owner': {'DisplayName': 'dlmug',\n", - " 'ID': 'e7effa2018f00aa7ef9361caca0350b409d79587291aee4d0fa0c9bca40efa91'},\n", - " 'Size': 130024,\n", - " 'StorageClass': 'STANDARD'},\n", - " {'ETag': '\"d6591c3867e3db08eddcbecffc22e3fe\"',\n", - " 'Key': '03e6bf9c20b2016943c65386aa51ce47286166f1.png',\n", - " 'LastModified': datetime.datetime(2014, 8, 13, 17, 22, 31, tzinfo=tzutc()),\n", - " 'Owner': {'DisplayName': 'dlmug',\n", - " 'ID': 'e7effa2018f00aa7ef9361caca0350b409d79587291aee4d0fa0c9bca40efa91'},\n", - " 'Size': 77124,\n", - " 'StorageClass': 'STANDARD'},\n", - " {'ETag': '\"66df9e1091c0c64139092b1643bdc0cc\"',\n", - " 'Key': '03e73b107d539a1af55b2af1bf7dbb1e1b7e2d07.png',\n", - " 'LastModified': datetime.datetime(2014, 8, 13, 17, 14, 2, tzinfo=tzutc()),\n", - " 'Owner': {'DisplayName': 'dlmug',\n", - " 'ID': 'e7effa2018f00aa7ef9361caca0350b409d79587291aee4d0fa0c9bca40efa91'},\n", - " 'Size': 55974,\n", - " 'StorageClass': 'STANDARD'}],\n", - " 'EncodingType': 'url',\n", - " 'IsTruncated': True,\n", - " 'Marker': '',\n", - " 'MaxKeys': 1000,\n", - " 'Name': 'objectome32_final',\n", - " 'Prefix': '',\n", - " 'ResponseMetadata': {'HTTPHeaders': {'content-type': 'application/xml',\n", - " 'date': 'Wed, 15 Aug 2018 17:50:29 GMT',\n", - " 'server': 'AmazonS3',\n", - " 'transfer-encoding': 'chunked',\n", - " 'x-amz-bucket-region': 'us-east-1',\n", - " 'x-amz-id-2': 'qr/fO0eBDjiqBGdcV7HyaRHu5vHWM3qFfVj9RK08kgJXKUx6nx5U/yFjkNzxr0S4/w6lmsupudw=',\n", - " 'x-amz-request-id': '684F01002655BF1E'},\n", - " 'HTTPStatusCode': 200,\n", - " 'HostId': 'qr/fO0eBDjiqBGdcV7HyaRHu5vHWM3qFfVj9RK08kgJXKUx6nx5U/yFjkNzxr0S4/w6lmsupudw=',\n", - " 'RequestId': '684F01002655BF1E',\n", - " 'RetryAttempts': 0}}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "o32f_list = conn.list_objects(Bucket='objectome32_final')\n", - "o32f_list" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['ResponseMetadata', 'IsTruncated', 'Marker', 'Contents', 'Name', 'Prefix', 'MaxKeys', 'EncodingType'])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "o32f_list.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('ResponseMetadata', dict),\n", - " ('IsTruncated', bool),\n", - " ('Marker', str),\n", - " ('Contents', list),\n", - " ('Name', str),\n", - " ('Prefix', str),\n", - " ('MaxKeys', int),\n", - " ('EncodingType', str)]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[(k, type(o32f_list[k])) for k in o32f_list]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1000" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "contents = list(o32f_list[\"Contents\"])\n", - "len(contents)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'ETag': '\"583167d856e11480842ebfb35f0aa047\"',\n", - " 'Key': '00014f75ffc9aba6855a57f2d61165f787c168de.png',\n", - " 'LastModified': datetime.datetime(2014, 8, 13, 17, 58, 46, tzinfo=tzutc()),\n", - " 'Owner': {'DisplayName': 'dlmug',\n", - " 'ID': 'e7effa2018f00aa7ef9361caca0350b409d79587291aee4d0fa0c9bca40efa91'},\n", - 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" 'Owner': {'DisplayName': 'dlmug',\n", - " 'ID': 'e7effa2018f00aa7ef9361caca0350b409d79587291aee4d0fa0c9bca40efa91'},\n", - " 'Size': 88707,\n", - " 'StorageClass': 'STANDARD'},\n", - " {'ETag': '\"dad1ae264a571e86c7677f78023a184b\"',\n", - " 'Key': '000aa307ebc660b33e3180f9126a8b1fd751009f.png',\n", - " 'LastModified': datetime.datetime(2014, 8, 13, 16, 53, 51, tzinfo=tzutc()),\n", - " 'Owner': {'DisplayName': 'dlmug',\n", - " 'ID': 'e7effa2018f00aa7ef9361caca0350b409d79587291aee4d0fa0c9bca40efa91'},\n", - " 'Size': 128584,\n", - " 'StorageClass': 'STANDARD'},\n", - " {'ETag': '\"f789bfadf0214dc71926756ec73ac773\"',\n", - " 'Key': '000bf137451cf6aa1a04c33af5af6d95ffd78886.png',\n", - " 'LastModified': datetime.datetime(2014, 8, 13, 16, 38, 35, tzinfo=tzutc()),\n", - " 'Owner': {'DisplayName': 'dlmug',\n", - " 'ID': 'e7effa2018f00aa7ef9361caca0350b409d79587291aee4d0fa0c9bca40efa91'},\n", - " 'Size': 41366,\n", - " 'StorageClass': 'STANDARD'},\n", - " {'ETag': '\"87940c97b8b8c2058fa181310325d09b\"',\n", - " 'Key': '000cf36a4a962b20ef83cf0fb830148a2f0e4b5a.png',\n", - " 'LastModified': datetime.datetime(2014, 8, 13, 18, 2, 22, tzinfo=tzutc()),\n", - " 'Owner': {'DisplayName': 'dlmug',\n", - " 'ID': 'e7effa2018f00aa7ef9361caca0350b409d79587291aee4d0fa0c9bca40efa91'},\n", - " 'Size': 55031,\n", - " 'StorageClass': 'STANDARD'},\n", - " {'ETag': '\"871b9eb4c81542dece64b9a976290132\"',\n", - " 'Key': '000dc710704f4788fbde57539b6ea10e8ce743ff.png',\n", - " 'LastModified': datetime.datetime(2014, 8, 13, 17, 47, 41, tzinfo=tzutc()),\n", - " 'Owner': {'DisplayName': 'dlmug',\n", - " 'ID': 'e7effa2018f00aa7ef9361caca0350b409d79587291aee4d0fa0c9bca40efa91'},\n", - " 'Size': 84243,\n", - " 'StorageClass': 'STANDARD'},\n", - " {'ETag': '\"03582aa6ec656fc628596f090f610bd6\"',\n", - " 'Key': '000e5e949b3340dcb07b28dcfcfab57f88f71ede.png',\n", - " 'LastModified': datetime.datetime(2014, 8, 13, 16, 16, 49, tzinfo=tzutc()),\n", - " 'Owner': {'DisplayName': 'dlmug',\n", - " 'ID': 'e7effa2018f00aa7ef9361caca0350b409d79587291aee4d0fa0c9bca40efa91'},\n", - " 'Size': 58611,\n", - " 'StorageClass': 'STANDARD'},\n", - " {'ETag': '\"e35f5a8b1ec84a997f02f10af082d417\"',\n", - " 'Key': '000edd315d1296d24c3ac414f58fb8019cedc605.png',\n", - " 'LastModified': datetime.datetime(2014, 8, 13, 17, 4, 43, tzinfo=tzutc()),\n", - " 'Owner': {'DisplayName': 'dlmug',\n", - " 'ID': 'e7effa2018f00aa7ef9361caca0350b409d79587291aee4d0fa0c9bca40efa91'},\n", - " 'Size': 77852,\n", - " 'StorageClass': 'STANDARD'}]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "contents[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "s3 = boto3.resource('s3')" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "s3.Bucket(name='objectome32_final')" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "o32f_bucket = s3.Bucket(\"objectome32_final\")\n", - 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" s3.ObjectSummary(bucket_name='objectome32_final', key='03e5cb9ca5da33565c81f6b2c7f77a4c0f6e6ed6.png'),\n", - " s3.ObjectSummary(bucket_name='objectome32_final', key='03e6663945c7f521611243bdf0ab133f2e2b4919.png'),\n", - " s3.ObjectSummary(bucket_name='objectome32_final', key='03e6b5363dc441c44976311c780144292bde02ba.png'),\n", - " s3.ObjectSummary(bucket_name='objectome32_final', key='03e6bf9c20b2016943c65386aa51ce47286166f1.png'),\n", - " s3.ObjectSummary(bucket_name='objectome32_final', key='03e73b107d539a1af55b2af1bf7dbb1e1b7e2d07.png'),\n", - " ...]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "o32f_list_2 = list(o32f_bucket.objects.all())\n", - "o32f_list_2" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "192131" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(o32f_list_2)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[s3.ObjectSummary(bucket_name='objectome32_final', key='00014f75ffc9aba6855a57f2d61165f787c168de.png'),\n", - " s3.ObjectSummary(bucket_name='objectome32_final', key='00020ad49ddc22a218852832a7996066978cc080.png'),\n", - " s3.ObjectSummary(bucket_name='objectome32_final', key='0006ce0a208b19f20ee78d5a22e1ddeb6280d8dd.png'),\n", - " s3.ObjectSummary(bucket_name='objectome32_final', key='0007bf3b71135f55fe95aee43c4d514873344c04.png'),\n", - " s3.ObjectSummary(bucket_name='objectome32_final', key='000aa307ebc660b33e3180f9126a8b1fd751009f.png'),\n", - " s3.ObjectSummary(bucket_name='objectome32_final', key='000bf137451cf6aa1a04c33af5af6d95ffd78886.png'),\n", - " s3.ObjectSummary(bucket_name='objectome32_final', key='000cf36a4a962b20ef83cf0fb830148a2f0e4b5a.png'),\n", - " s3.ObjectSummary(bucket_name='objectome32_final', key='000dc710704f4788fbde57539b6ea10e8ce743ff.png'),\n", - " s3.ObjectSummary(bucket_name='objectome32_final', key='000e5e949b3340dcb07b28dcfcfab57f88f71ede.png'),\n", - " s3.ObjectSummary(bucket_name='objectome32_final', key='000edd315d1296d24c3ac414f58fb8019cedc605.png')]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "o32f_list_2[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'00014f75ffc9aba6855a57f2d61165f787c168de.png'" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "o32f_list_2[0].key" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2014, 8, 13, 17, 58, 46, tzinfo=tzutc())" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "o32f_list_2[0].last_modified" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Acl',\n", - " 'Bucket',\n", - " 'MultipartUpload',\n", - " 'Object',\n", - " 'Version',\n", - " '__class__',\n", - " '__delattr__',\n", - " '__dict__',\n", - " '__dir__',\n", - " '__doc__',\n", - " '__eq__',\n", - " '__format__',\n", - " '__ge__',\n", - " '__getattribute__',\n", - " '__gt__',\n", - " '__hash__',\n", - " '__init__',\n", - " '__init_subclass__',\n", - " '__le__',\n", - " '__lt__',\n", - " '__module__',\n", - " '__ne__',\n", - " '__new__',\n", - " '__reduce__',\n", - " '__reduce_ex__',\n", - " '__repr__',\n", - " '__setattr__',\n", - " '__sizeof__',\n", - " '__str__',\n", - " '__subclasshook__',\n", - " '__weakref__',\n", - " '_bucket_name',\n", - " '_key',\n", - " 'bucket_name',\n", - " 'copy_from',\n", - " 'delete',\n", - " 'e_tag',\n", - " 'get',\n", - " 'get_available_subresources',\n", - " 'initiate_multipart_upload',\n", - " 'key',\n", - " 'last_modified',\n", - 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" 'd': {'foo.txt': None, 'wah.txt': None},\n", - " 'e': {'bar.txt': None}},\n", - " 'f': {'baz.txt': None}}" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test = [\n", - " \"a.txt\",\n", - " \"b/c.txt\",\n", - " \"b/d/wah.txt\",\n", - " \"b/d/foo.txt\",\n", - " \"b/e/bar.txt\",\n", - " \"f/baz.txt\"\n", - "]\n", - "d = {}\n", - "for k in test:\n", - " sp = k.split(\"/\")\n", - " descend(sp, d) \n", - "d\n", - "# d = {\n", - "# \"a.txt\": None,\n", - "# \"b\": {\n", - "# \"c.txt\": None,\n", - "# \"d\": {\n", - "# \"wah.txt\": None,\n", - "# \"foo.txt\": None\n", - "# },\n", - "# \"e\": {\n", - "# \"bar.txt\": None\n", - "# }\n", - "# },\n", - "# \"f\": {\n", - "# \"baz.txt\": None\n", - "# }\n", - "# }" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "360x360\n", - "label_imgs\n", - "nolabels\n", - "postmask_360x360\n" - 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" '03de332b603a145e89026d5ee57b107bae519780.png': None,\n", - " '03e01b9256156859f3c850bc5f12048d51b18f32.png': None,\n", - " '03e1a2f89823b8b3e4b0f3e807b75ca28b33f6a2.png': None,\n", - " '03e1e10e079c30353c07461da0f1a371b0dff9bf.png': None,\n", - " '03e296d9cf6b593ea8823b81013d640701d30b08.png': None,\n", - " '03e2d1b426e715d1e65f6923b5f2e8b736515c09.png': None,\n", - " '03e469f254194f354a0da42ed3c8ebcaf6827f27.png': None,\n", - " '03e49eb40d0076f48ede972e96e3f0059179d63a.png': None,\n", - " '03e4e2dec2468c67a3518c76542c8e9125d2eaa1.png': None,\n", - " '03e50bb5c9d1a192efff40f662282d88e346e06c.png': None,\n", - " '03e5cb9ca5da33565c81f6b2c7f77a4c0f6e6ed6.png': None,\n", - " '03e6663945c7f521611243bdf0ab133f2e2b4919.png': None,\n", - " '03e6b5363dc441c44976311c780144292bde02ba.png': None,\n", - " '03e6bf9c20b2016943c65386aa51ce47286166f1.png': None,\n", - " '03e73b107d539a1af55b2af1bf7dbb1e1b7e2d07.png': None,\n", - " ...}" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dirs = {}\n", - "\n", - "for summ in o32f_list_2:\n", - " sp = summ.key.split(\"/\")\n", - " descend(sp, dirs)\n", - " \n", - "dirs" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['360x360', 'label_imgs', 'nolabels', 'postmask_360x360']" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[k for k in dirs if dirs[k] is not None]" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "64000" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len([dirs[k] for k in dirs if dirs[k] is None])" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['00014f75ffc9aba6855a57f2d61165f787c168de.png',\n", - " '00020ad49ddc22a218852832a7996066978cc080.png',\n", - 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"metadata": {}, - "outputs": [], - "source": [ - "data_dir = \"/braintree/data2/active/users/jjpr/mkgu_packaging/dicarlo/objectome\"\n", - "!mkdir -p $data_dir" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "image_dir = os.path.join(data_dir, \"stimuli\", \"objectome32_final\")\n", - "!mkdir -p $image_dir" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "o32f_bucket.download_file(o32f_top_level[1234], os.path.join(image_dir, o32f_top_level[1234]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for k in o32f_top_level:\n", - " o32f_bucket.download_file(k, os.path.join(image_dir, k))" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'uq_stim' is not defined", - "output_type": "error", - "traceback": [ - 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], - "source": [ - "df_bucket_map[\"image_id\"] = list(map(lambda f: f.replace(\".png\", \"\"), df_bucket_map[\"file_name\"]))\n", - "df_bucket_map" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "assy_name = \"dicarlo.b.h.Rajalingham2018\"" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "stim_set_name = \"objectome32_final\"" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "zip_name = stim_set_name + \".zip\"\n", - "# I need to rethink how fetching checks if we have a zip file already\n", - "# Right now it's at the stimulusSet level, but it should be at the image store level" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/braintree/home/jjpr/.brainscore/data/dicarlo.b.h.Rajalingham2018/objectome32_final.zip'" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "target_zip_path = os.path.join(\"/braintree/home/jjpr/.brainscore/data\", assy_name, zip_name)\n", - "target_zip_path" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "!mkdir -p {os.path.dirname(target_zip_path)}" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['/braintree/data2/active/users/jjpr/mkgu_packaging/dicarlo/objectome/stimuli/objectome32_final/0093bf74ea59e1d492e832e12d6d2a5feecd0f52.png',\n", - " '/braintree/data2/active/users/jjpr/mkgu_packaging/dicarlo/objectome/stimuli/objectome32_final/02b4775c6ed28fcd307ebf10017a29753c425039.png',\n", - " '/braintree/data2/active/users/jjpr/mkgu_packaging/dicarlo/objectome/stimuli/objectome32_final/02bc6278c1f259f35315248de85187b5bd5111e8.png',\n", - " 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'/braintree/data2/active/users/jjpr/mkgu_packaging/dicarlo/objectome/stimuli/objectome32_final/8707d5dc152df21b0d0d655e7a0ea0d504af5455.png',\n", - " '/braintree/data2/active/users/jjpr/mkgu_packaging/dicarlo/objectome/stimuli/objectome32_final/8931109740f159700cded70b9981a77392410367.png',\n", - " '/braintree/data2/active/users/jjpr/mkgu_packaging/dicarlo/objectome/stimuli/objectome32_final/8c39d500964756d1a360dd8406803bec61eed82b.png',\n", - " '/braintree/data2/active/users/jjpr/mkgu_packaging/dicarlo/objectome/stimuli/objectome32_final/8e6f37594c133690cd07f0a4aa9d91f81cd080a5.png',\n", - " '/braintree/data2/active/users/jjpr/mkgu_packaging/dicarlo/objectome/stimuli/objectome32_final/94e221e98f2fd6bdafa1368fd6ff782973123db4.png',\n", - " '/braintree/data2/active/users/jjpr/mkgu_packaging/dicarlo/objectome/stimuli/objectome32_final/9a1448fd5360a8e25737ecc05783edbc67fa8b08.png',\n", - " '/braintree/data2/active/users/jjpr/mkgu_packaging/dicarlo/objectome/stimuli/objectome32_final/9c4b5fbba3db1723100fc41754a3559880aa3172.png',\n", - " ...]" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "g_images = glob.glob(image_dir + \"/*.png\")\n", - "g_images" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [], - "source": [ - "with zipfile.ZipFile(target_zip_path, 'w') as target_zip:\n", - " for file_path in g_images:\n", - " target_zip.write(file_path, arcname=stim_set_name + \"/\" + os.path.basename(file_path))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "s3.Bucket(name='objectome64s100')" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - 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"image_dir_64 = os.path.join(data_dir, \"stimuli\", \"objectome64s100\")\n", - "!mkdir -p $image_dir_64\n", - "o64_sample_key = o64_top_level[1234]\n", - "o64_sample_path = os.path.join(image_dir_64, o64_sample_key)\n", - "o64_bucket.download_file(o64_sample_key, o64_sample_path)\n", - "o64_kf = knownfile.KnownFile(o64_sample_path)\n", - "o64_kf.sha1" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'3ac64fd3d2201ef99edbcf703f5754e2631db7f2.png'" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "o64_sample_key" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'e9bce8d69f080f65efd8818a5b1f221157842024'" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "o32f_sample_path = os.path.join(image_dir, o64_sample_key)\n", - "o32f_kf = knownfile.KnownFile(o32f_sample_path)\n", - "o32f_kf.sha1" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('/braintree/data2/active/users/jjpr/mkgu_packaging/dicarlo/objectome/stimuli/objectome32_final/3ac64fd3d2201ef99edbcf703f5754e2631db7f2.png',\n", - " '/braintree/data2/active/users/jjpr/mkgu_packaging/dicarlo/objectome/stimuli/objectome64s100/3ac64fd3d2201ef99edbcf703f5754e2631db7f2.png')" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(o32f_sample_path, o64_sample_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('/braintree/data2/active/users/jjpr/mkgu_packaging/dicarlo/objectome/stimuli/objectome32_final/3ac64fd3d2201ef99edbcf703f5754e2631db7f2.png',\n", - " 'e9bce8d69f080f65efd8818a5b1f221157842024'),\n", - " 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\n", 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"63996 c1eddeae9248bea69e4bf8ce8991b37712739d82 NaN NaN \n", - "63997 3e7d0ae2b92802bfdc425caefd9da1442f45d3d2 NaN NaN \n", - "63998 9733a7dc3df0fba5df9faf857c4b8be374f8fafe NaN NaN \n", - "63999 2d3e69127979ce256bc43d223f12459f05df2f96 NaN NaN \n", - "\n", - " internal_canonical \n", - "0 1.0 \n", - "1 1.0 \n", - "2 1.0 \n", - "3 1.0 \n", - "4 1.0 \n", - "5 1.0 \n", - "6 1.0 \n", - "7 1.0 \n", - "8 1.0 \n", - "9 1.0 \n", - "10 1.0 \n", - "11 1.0 \n", - "12 1.0 \n", - "13 1.0 \n", - "14 1.0 \n", - "15 1.0 \n", - "16 1.0 \n", - "17 1.0 \n", - "18 1.0 \n", - "19 1.0 \n", - "20 1.0 \n", - "21 1.0 \n", - "22 1.0 \n", - "23 1.0 \n", - "24 1.0 \n", - "25 1.0 \n", - "26 1.0 \n", - "27 1.0 \n", - "28 1.0 \n", - "29 1.0 \n", - "... ... \n", - "63970 1.0 \n", - "63971 1.0 \n", - "63972 1.0 \n", - "63973 1.0 \n", - "63974 1.0 \n", - "63975 1.0 \n", - "63976 1.0 \n", - "63977 1.0 \n", - "63978 1.0 \n", - "63979 1.0 \n", - "63980 1.0 \n", - "63981 1.0 \n", - "63982 1.0 \n", - "63983 1.0 \n", - "63984 1.0 \n", - "63985 1.0 \n", - "63986 1.0 \n", - "63987 1.0 \n", - "63988 1.0 \n", - "63989 1.0 \n", - "63990 1.0 \n", - "63991 1.0 \n", - "63992 1.0 \n", - "63993 1.0 \n", - "63994 1.0 \n", - "63995 1.0 \n", - "63996 1.0 \n", - "63997 1.0 \n", - "63998 1.0 \n", - "63999 1.0 \n", - "\n", - "[64000 rows x 19 columns]" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "o32f_meta = pd.read_csv(\"/mindhive/dicarlolab/common/shared_stimuli/meta64.csv\")\n", - "o32f_meta" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "containing_dir = os.path.dirname(target_zip_path)\n", - "with zipfile.ZipFile(target_zip_path, 'r') as new_zip:\n", - " new_zip.extractall(containing_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [], - "source": [ - "import peewee\n", - "from playhouse.migrate import *" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": {}, - "outputs": [], - "source": [ - "from brainscore.lookup import pwdb" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [], - "source": [ - "migrator = SqliteMigrator(pwdb)" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [], - "source": [ - "image_unique_name_field = CharField(unique=True, null=True, index=True)\n", - "image_sha1_field = CharField(unique=True, null=True, index=True)\n", - "assy_unique_name_field = CharField(unique=True, null=True, index=True)\n", - "assy_sha1_field = CharField(unique=True, null=True, index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [], - "source": [ - "from brainscore.stimuli import ImageModel, AttributeModel, ImageMetaModel, StimulusSetModel, ImageStoreModel, \\\n", - " StimulusSetImageMap, ImageStoreMap" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['assemblymodel',\n", - " 'assemblystoremap',\n", - " 'assemblystoremodel',\n", - " 'attributemodel',\n", - " 'imagemetamodel',\n", - " 'imagemodel',\n", - " 'imagestoremap',\n", - " 'imagestoremodel',\n", - " 'stimulussetimagemap',\n", - " 'stimulussetmodel']" - ] - }, - "execution_count": 119, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pwdb.get_tables()" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [], - "source": [ - "migrate(\n", - " migrator.add_column('imagestoremodel', 'unique_name', image_unique_name_field),\n", - " migrator.add_column('imagestoremodel', 'sha1', image_sha1_field),\n", - " migrator.add_column('assemblystoremodel', 'unique_name', image_unique_name_field),\n", - " migrator.add_column('assemblystoremodel', 'sha1', image_sha1_field)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['https://mkgu-dicarlolab-hvm.s3.amazonaws.com/HvM_with_discfade.zip',\n", - " 'https://mkgu-gallant-crcns.s3.amazonaws.com/gallant_crcns_v1_stimuli.zip',\n", - " 'https://mkgu-tolias.s3.amazonaws.com/v1_stimuli.zip']" - ] - }, - "execution_count": 125, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "image_sel = ImageStoreModel.select()\n", - "[i.location for i in image_sel]" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [], - "source": [ - "import brainscore" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/braintree/home/jjpr/.brainscore/data'" - ] - }, - "execution_count": 127, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "brainscore.fetch._local_data_path" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 181, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pwdb.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 180, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pwdb.connect()" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 138, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import importlib\n", - "importlib.reload(brainscore.stimuli)" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('https://mkgu-dicarlolab-hvm.s3.amazonaws.com/HvM_with_discfade.zip', None),\n", - " ('https://mkgu-gallant-crcns.s3.amazonaws.com/gallant_crcns_v1_stimuli.zip',\n", - " 'foo'),\n", - " ('https://mkgu-tolias.s3.amazonaws.com/v1_stimuli.zip', None)]" - ] - }, - "execution_count": 177, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[(ism.location, ism.unique_name) for ism in ImageStoreModel.select()]" - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total 280\n", - "drwxr-xr-x 2 jjpr dicarlo 4096 Aug 27 17:06 dicarlo.b.h.Rajalingham2018\n", - "drwxr-xr-x 5 jjpr dicarlo 4096 Jul 31 16:04 dicarlo.hvm\n", - "drwxr-xr-x 2 jjpr dicarlo 4096 Sep 18 15:09 dicarlo.Majaj2015\n", - "drwxr-xr-x 2 jjpr dicarlo 4096 Jul 31 15:38 dicarlo.Majaj2015.temporal\n", - "drwxr-xr-x 18 jjpr dicarlo 4096 Sep 19 15:39 gallant.David2004\n", - "drwxr-xr-x 3 jjpr dicarlo 4096 Jun 11 13:56 gallant.Willmore2010\n", - "drwxr-xr-x 2 jjpr dicarlo 4096 Oct 30 2017 HvM\n", - "drwxr-xr-x 2 jjpr dicarlo 4096 Oct 25 2017 HvM_temporal\n", - "drwxr-xr-x 3 jjpr dicarlo 4096 Aug 15 09:53 tolias.Cadena2017\n", - "drwxr-xr-x 3 jjpr dicarlo 245760 Aug 13 14:47 tolias.Cadena2017.bak\n" - ] - } - ], - "source": [ - "!ls -l $brainscore.fetch._local_data_path" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "HvM_with_discfade.zip Variation03_20110128\n", - "Variation00_20110203 Variation06_20110131\n" - ] - } - ], - "source": [ - "!ls $brainscore.fetch._local_data_path/dicarlo.hvm" - ] - }, - { - "cell_type": "code", - "execution_count": 190, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "gallant_crcns_v1_stimuli.zip\tr0154B\tr0162B\tr0169B\tr0208D\tr0212B\n", - "gallant_v1_single_electrode.nc\tr0156A\tr0164C\tr0170A\tr0210A\tr0215B\n", - "r0148A\t\t\t\tr0158A\tr0168B\tr0206B\tr0211A\tr0217B\n" - ] - } - ], - "source": [ - "!ls $brainscore.fetch._local_data_path/gallant.David2004" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "v1_responses.nc v1_stimuli v1_stimuli.zip\n" - ] - } - ], - "source": [ - "!ls $brainscore.fetch._local_data_path/tolias.Cadena2017" - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "metadata": {}, - "outputs": [], - "source": [ - "stim_paths = [\n", - " \"/braintree/home/jjpr/.brainscore/data/dicarlo.hvm/HvM_with_discfade.zip\",\n", - " \"/braintree/home/jjpr/.brainscore/data/gallant.David2004/gallant_crcns_v1_stimuli.zip\",\n", - " \"/braintree/home/jjpr/.brainscore/data/tolias.Cadena2017/v1_stimuli.zip\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 192, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('8a4b5ace844b99213319706f68c554d285e7827e',\n", - " '/braintree/home/jjpr/.brainscore/data/dicarlo.hvm/HvM_with_discfade.zip'),\n", - " ('c10ce47134b8b7f0421e81f7a3211970958dc402',\n", - " '/braintree/home/jjpr/.brainscore/data/gallant.David2004/gallant_crcns_v1_stimuli.zip'),\n", - " ('27d081dce5c0e21bf00eba1500d0fdee55d01baa',\n", - " '/braintree/home/jjpr/.brainscore/data/tolias.Cadena2017/v1_stimuli.zip')]" - ] - }, - "execution_count": 192, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "stim_kfs = [knownfile.KnownFile(f) for f in stim_paths]\n", - "[(kf.sha1, kf.realpath) for kf in stim_kfs]" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hvm_neuronal_features.nc\n" - ] - } - ], - "source": [ - "!ls $brainscore.fetch._local_data_path/dicarlo.Majaj2015" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "getting hvm_neuronal_features.nc\n" - ] - }, - { - "data": { - "text/plain": [ - "\n", - "array([[[ 0.060929],\n", - " [-0.686162],\n", - " ...,\n", - " [-0.968256],\n", - " [ 0.183887]],\n", - "\n", - " [[-0.725592],\n", - " [ 0.292777],\n", - " ...,\n", - " [ 2.449372],\n", - " [ 0.401197]],\n", - "\n", - " ...,\n", - "\n", - " [[ 1.121319],\n", - " [ 1.719423],\n", - " ...,\n", - " [ 0.800551],\n", - " [-0.019874]],\n", - "\n", - " [[-0.518903],\n", - " [ 0.696196],\n", - " ...,\n", - " [-0.603347],\n", - " [-0.175979]]], dtype=float32)\n", - "Coordinates:\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) object 'Chabo_L_M_5_9' 'Chabo_L_M_6_9' ...\n", - " - arr (neuroid) object 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' ...\n", - " - col (neuroid) int64 9 9 8 9 8 8 7 7 5 6 4 9 9 9 9 9 8 7 9 6 ...\n", - " - hemisphere (neuroid) object 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' 'L' ...\n", - " - subregion (neuroid) object 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' 'cIT' ...\n", - " - animal (neuroid) object 'Chabo' 'Chabo' 'Chabo' 'Chabo' ...\n", - " - y (neuroid) float64 0.2 0.6 0.2 1.0 0.6 1.0 1.8 1.0 1.8 ...\n", - " - x (neuroid) float64 1.8 1.8 1.4 1.8 1.4 1.4 1.0 1.0 0.2 ...\n", - " - region (neuroid) object 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' 'IT' ...\n", - " - row (neuroid) int64 5 6 5 7 6 7 9 7 9 8 9 2 1 3 6 7 6 6 8 5 ...\n", - " * presentation (presentation) MultiIndex\n", - " - image_id (presentation) object '8a72e2bfdb8c267b57232bf96f069374d5b21832' ...\n", - " - repetition (presentation) int64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...\n", - " - stimulus (presentation) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 ...\n", - " - id (presentation) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 ...\n", - " - image_file_name (presentation) object 'astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_ecd40f3f6d7a4d6d88134d648884e0b9b364efc9_256x256.png' ...\n", - " - object_name (presentation) object 'car_astra' 'table3' 'face2' ...\n", - " - category_name (presentation) object 'Cars' 'Tables' 'Faces' 'Fruits' ...\n", - " - background_id (presentation) object 'ecd40f3f6d7a4d6d88134d648884e0b9b364efc9' ...\n", - " - variation (presentation) int64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...\n", - " - ty (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - tz (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - rxy (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " - rxz (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - ryz (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " - rxy_semantic (presentation) float64 90.0 -0.0 -0.0 -0.0 -0.0 90.0 ...\n", - " - rxz_semantic (presentation) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...\n", - " - ryz_semantic (presentation) float64 -0.0 -0.0 -0.0 -0.0 -0.0 -0.0 ...\n", - " - size (presentation) float64 256.0 256.0 256.0 256.0 256.0 ...\n", - " - s (presentation) float64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ...\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170\n", - "Attributes:\n", - " stimulus_set_name: dicarlo.hvm\n", - " stimulus_set: id image_id ..." - ] - }, - "execution_count": 151, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hvm = brainscore.get_assembly(\"dicarlo.Majaj2015\")\n", - "hvm" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "getting gallant_v1_single_electrode.nc\n", - "getting gallant_crcns_v1_stimuli.zip\n" - ] - }, - { - "data": { - "text/plain": [ - "\n", - "array([[ 0., 0., 0., ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " ...,\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., 0., 0., 0.]])\n", - "Coordinates:\n", - " * neuroid (neuroid) object 'r0148A' 'r0150B' 'r0154B' ...\n", - " * presentation (presentation) MultiIndex\n", - " - presentation_id (presentation) int64 0 1 2 3 4 5 6 7 8 9 10 11 ...\n", - " - image_file_path_original (presentation) object 'r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - " - image_id (presentation) object '33613a99bdc22fe498864bf32b798e018a961ed7' ...\n", - " - id (presentation) int64 29882 71211 63228 49398 ...\n", - " - image_file_sha1 (presentation) object '33613a99bdc22fe498864bf32b798e018a961ed7' ...\n", - " - image_file_path_unique (presentation) object 'r0148A/NatRev.MountLake.40.150.imsm/1.jpg' ...\n", - "Attributes:\n", - " stimulus_set_name: gallant.David2004\n", - " stimulus_set: id image..." - ] - }, - "execution_count": 188, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gallant = brainscore.get_assembly(\"gallant.David2004\")\n", - "gallant" - ] - }, - { - "cell_type": "code", - "execution_count": 193, - "metadata": {}, - "outputs": [], - "source": [ - "assy_paths = [\n", - " \"/braintree/home/jjpr/.brainscore/data/dicarlo.Majaj2015/hvm_neuronal_features.nc\",\n", - " \"/braintree/home/jjpr/.brainscore/data/dicarlo.Majaj2015.temporal/hvm_temporal_neuronal_features.nc\",\n", - " \"/braintree/home/jjpr/.brainscore/data/gallant.David2004/gallant_v1_single_electrode.nc\",\n", - " \"/braintree/home/jjpr/.brainscore/data/tolias.Cadena2017/v1_responses.nc\"\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 194, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('b927a6cd3320baffe954313834f73525ab6bc9a1',\n", - " '/braintree/home/jjpr/.brainscore/data/dicarlo.Majaj2015/hvm_neuronal_features.nc'),\n", - " ('4feed57f0cd5e52c41622a8a1080a15184f9d76f',\n", - " '/braintree/home/jjpr/.brainscore/data/dicarlo.Majaj2015.temporal/hvm_temporal_neuronal_features.nc'),\n", - " ('cda1b6fe02fe02462ae804b11f895a4803bd4d4e',\n", - " '/braintree/home/jjpr/.brainscore/data/gallant.David2004/gallant_v1_single_electrode.nc'),\n", - " ('7deae2c6e301aa822f0749bde360bbf966d64a2b',\n", - " '/braintree/home/jjpr/.brainscore/data/tolias.Cadena2017/v1_responses.nc')]" - ] - }, - "execution_count": 194, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "assy_kfs = [knownfile.KnownFile(f) for f in assy_paths]\n", - "[(kf.sha1, kf.realpath) for kf in assy_kfs]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def find_file(image_store_model):\n", - " data_dir = brainscore.fetch._local_data_path" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mkgu_packaging", - "language": "python", - "name": "mkgu_packaging" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/packaging/notebooks/2019-04-30_brainio_tests.ipynb b/packaging/notebooks/2019-04-30_brainio_tests.ipynb deleted file mode 100644 index c7094287b..000000000 --- a/packaging/notebooks/2019-04-30_brainio_tests.ipynb +++ /dev/null @@ -1,6514 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "source": [ - "import os" - ], - "outputs": [], - "execution_count": 1, - "metadata": { - "collapsed": false, - "outputHidden": false, - "inputHidden": false - } - }, - { - "cell_type": "code", - "source": [ - "import shutil" - ], - "outputs": [], - "execution_count": 2, - "metadata": { - "collapsed": false, - "outputHidden": false, - "inputHidden": false - } - }, - { - "cell_type": "code", - "source": [ - "import pandas as pd" - ], - "outputs": [], - "execution_count": 3, - "metadata": { - "collapsed": false, - "outputHidden": false, - "inputHidden": false - } - }, - { - "cell_type": "code", - "source": [ - "import brainio_collection" - ], - "outputs": [], - "execution_count": 4, - "metadata": { - "collapsed": false, - "outputHidden": false, - "inputHidden": false - } - }, - { - "cell_type": "code", - "source": [ - "from brainio_collection.knownfile import KnownFile as kf" - ], - "outputs": [], - "execution_count": 5, - "metadata": { - "collapsed": false, - "outputHidden": false, - "inputHidden": false - } - }, - { - "cell_type": "code", - "source": [ - "target_dir = \"/Users/jjpr/dev/brainio_contrib/tests/images\"" - ], - "outputs": [], - "execution_count": 6, - "metadata": { - "collapsed": false, - "outputHidden": false, - "inputHidden": false - } - }, - { - "cell_type": "code", - "source": [ - "stim = brainio_collection.get_stimulus_set(\"dicarlo.hvm\")\n", - "stim" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 7, - "data": { - "text/plain": [ - " id image_id \\\n", - "0 1 8a72e2bfdb8c267b57232bf96f069374d5b21832 \n", - "1 2 27f69468c9d6019ed0d22b9583c94c5b58198c1c \n", - "2 3 6af1cbb28aacea6c582faa07e92d8325fa7a29d7 \n", - "3 4 d0f7a45b377d4920c3466ec7a20dce9437a150d6 \n", - "4 5 d4c3b4d4aefd29fd168a2c3c9a9962d99653a715 \n", - "5 6 c6fb4f35719f0f205f5ad65f4fd2794212c5d139 \n", - "6 7 ae2b8aac003c867a772647197dcd3c050b75a06e \n", - "7 8 a7a31e65e2770c7a503988835c24aeab689abf47 \n", - "8 9 09d2924723bc4677df666072b6e25e988d5f0668 \n", - "9 10 1df484a50ff7fbd85112990b7f8fe616c49adb28 \n", - "10 11 e134c3d8fa8be11c8eb0b8d55e7f534286f44736 \n", - "11 12 6997bb3d5bef2f6f9d6cfbdcaab1444680a44cb0 \n", - "12 13 fbef152524d2e593206fd976afde5e0506f477a8 \n", - "13 14 a17db26a610f96215d94cd673fd982c5bb3fd524 \n", - "14 15 41e376452bcb4a61646304b4fd397f11e329ebb2 \n", - "15 16 36b1652f43aedf23e39f351fd931ab0cb4c410b5 \n", - "16 17 6a8b93cd59587cc2969a126361bce879774e7f4f \n", - "17 18 05954905d976ab1cf247c49fc31e534a8a047ba1 \n", - "18 19 97757502b8c2e32e47a79a266f2da46bbb7165d1 \n", - "19 20 45b221ea1ed093c991ba6503c57093f6bea2c9d7 \n", - "20 21 f680c591330f26793f4de603e26cc584ae19ddb2 \n", - "21 22 a2de393bdfbaaa69500cc1b0d1bfe0f37c6459e1 \n", - "22 23 f4fe19dc937fb33d9439fc99f4ea17fc76591e36 \n", - "23 24 dd8ff3ddeac872a646b02b8fdcf286c87cdd7720 \n", - "24 25 3d31939c0fbbb695d865f3e010d9860b65c674f2 \n", - "25 26 88429363542db8c918d7112a7335fcca62966a3e \n", - "26 27 1d832e0abdd6266fb3dcacfa05df1725d6b08fb7 \n", - "27 28 b54f9e7fbc9f49bb60f6d24aa9e90b7623a4b42d \n", - 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"5745 5746 5650c755edcbeb39a1d8b848128bd52e97a3c2c2 \n", - "5746 5747 10a6a9b7b8a5a83708417f4feebe7f12eee6ecec \n", - "5747 5748 d15aa059b0180b200fb82fb8039247c3b53f9d66 \n", - "5748 5749 bb2ca2aeb9a7996ef849aeca2388fece5d66dd58 \n", - "5749 5750 af8325d2d77b8475a00eb9a5296689adb4a8f39b \n", - "5750 5751 5cbd24a0ac1881551f356940236e021bd29cd1de \n", - "5751 5752 feef7d8cf74ce5f8a8d0612e015e0de363587e93 \n", - "5752 5753 6f61f7e3130d2fef3deeb310e08a927521cb932d \n", - "5753 5754 43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501 \n", - "5754 5755 3fe99701f03c85d5b2017d4ec33eb227a695386e \n", - "5755 5756 06a3f98dc29129a672ab2af8b5b02d3a2cc82a85 \n", - "5756 5757 2c381c66bf457942bf4818e54b4b5172b3cc0cac \n", - "5757 5758 d6a597127092d38ed37bcb19967075698acb35ee \n", - "5758 5759 cc975ea997317b74a79b9f5568f8008acdec341e \n", - "5759 5760 e35071a43f7a0dafc21b18229ca36f1940c2c567 \n", - "\n", - " image_file_name object_name \\\n", - "0 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... car_astra \n", - "1 _12_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... table3 \n", - "2 face0003_rx+00.000_ry+00.000_rz+00.000_tx+00.0... face2 \n", - "3 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... walnut \n", - "4 walnut_obj_rx-90.000_ry+00.000_rz+00.000_tx+00... walnut \n", - "5 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.00... airplane7 \n", - "6 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0... apricot \n", - "7 Strawberry_obj_rx-90.000_ry+00.000_rz+00.000_t... strawberry \n", - "8 face0002_rx+00.000_ry+00.000_rz+00.000_tx+00.0... face1 \n", - "9 junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.... airplane4 \n", - "10 CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000... ship1 \n", - "11 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... ship3 \n", - "12 clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_t... car_clio \n", - "13 breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.... dog \n", - "14 Peach_obj_rx-90.000_ry+00.000_rz+00.000_tx+00.... peach \n", - "15 astra_rx+00.000_ry+00.000_rz+00.000_tx+00.000_... car_astra \n", - "16 _37_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... table5 \n", - "17 Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000... car_beetle \n", - "18 sopwith_rx+00.000_ry+00.000_rz+00.000_tx+00.00... airplane7 \n", - "19 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... ship3 \n", - "20 watermelon_obj_rx-90.000_ry+00.000_rz+00.000_t... watermelon \n", - "21 cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.00... ship5 \n", - "22 SISTER_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... ship4 \n", - "23 _44_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... table7 \n", - "24 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.0... face0 \n", - "25 MQUEEN_L_rx+00.000_ry+00.000_rz+00.000_tx+00.0... ship3 \n", - "26 _18_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty... table4 \n", - "27 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0... apricot \n", - "28 Apricot_obj_rx-90.000_ry+00.000_rz+00.000_tx+0... apricot \n", - "29 face0001_rx+00.000_ry+00.000_rz+00.000_tx+00.0... face0 \n", - "... ... ... \n", - "5730 TURTLE_L_rx-89.052_ry-84.231_rz+74.978_tx-00.5... turtle \n", - "5731 rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.5... airplane6 \n", - "5732 support_rx-35.196_ry+40.372_rz+72.620_tx-00.22... ship7 \n", - "5733 celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546... car_celica \n", - "5734 GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.04... gorilla \n", - "5735 walnut_obj_rx-03.741_ry+88.416_rz-58.012_tx-00... walnut \n", - "5736 walnut_obj_rx-85.252_ry+61.551_rz+86.382_tx-00... walnut \n", - "5737 _014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_t... chair5 \n", - "5738 _008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_t... chair2 \n", - "5739 _001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_t... chair0 \n", - "5740 _010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_t... chair3 \n", - "5741 face0008_rx+45.258_ry+22.427_rz-17.151_tx-00.5... face7 \n", - "5742 Peach_obj_rx-124.608_ry-73.686_rz-79.329_tx+00... peach \n", - "5743 alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.40... car_alfa \n", - "5744 _033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_t... chair7 \n", - "5745 cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.43... ship5 \n", - "5746 _12_rx-84.440_ry-65.300_rz-13.491_tx+00.149_ty... table3 \n", - "5747 TURTLE_L_rx+69.658_ry+61.236_rz+07.161_tx+00.3... turtle \n", - "5748 _38_rx-55.612_ry+05.591_rz-84.185_tx+00.148_ty... table6 \n", - "5749 _004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_t... chair1 \n", - "5750 _01_Airliner_2jetEngines_rx+49.355_ry+95.933_r... airplane0 \n", - "5751 face0002_rx+42.742_ry+64.386_rz+87.404_tx-00.5... face1 \n", - "5752 walnut_obj_rx-111.241_ry+59.110_rz-35.777_tx+0... walnut \n", - "5753 alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.42... car_alfa \n", - "5754 _05_future_rx+09.073_ry+65.480_rz+74.178_tx-00... airplane1 \n", - "5755 face0004_rx-41.898_ry+48.146_rz+51.346_tx+00.5... face3 \n", - "5756 _08_rx+11.985_ry-70.967_rz-45.818_tx+00.371_ty... table0 \n", - "5757 Strawberry_obj_rx-162.084_ry-14.276_rz-29.169_... strawberry \n", - "5758 face0004_rx+29.466_ry-75.159_rz+12.283_tx+00.3... face3 \n", - "5759 Apricot_obj_rx-159.704_ry+63.053_rz+87.598_tx+... apricot \n", - "\n", - " category_name background_id variation \\\n", - 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5760 rows × 17 columns

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] - }, - "metadata": {} - } - ], - "execution_count": 21, - "metadata": { - "collapsed": false, - "outputHidden": false, - "inputHidden": false - } - }, - { - "cell_type": "code", - "source": [], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": false, - "outputHidden": false, - "inputHidden": false - } - } - ], - "metadata": { - "kernel_info": { - "name": "brainio" - }, - "language_info": { - "name": "python", - "version": "3.7.2", - "mimetype": "text/x-python", - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "pygments_lexer": "ipython3", - "nbconvert_exporter": "python", - "file_extension": ".py" - }, - "kernelspec": { - "name": "brainio", - "language": "python", - "display_name": "brainio" - }, - "nteract": { - "version": "0.14.0" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/packaging/notebooks/2020-04-22_brainio.ipynb b/packaging/notebooks/2020-04-22_brainio.ipynb deleted file mode 100644 index cdc70395e..000000000 --- a/packaging/notebooks/2020-04-22_brainio.ipynb +++ /dev/null @@ -1,2366 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading lookup from /braintree/home/jjpr/dev/brainio_collection/brainio_collection/lookup.csv\n" - ] - } - ], - "source": [ - "from brainio_collection import list_stimulus_sets, list_assemblies, get_stimulus_set, get_assembly" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['dicarlo.Majaj2015',\n", - " 'gallant.David2004',\n", - " 'dicarlo.Majaj2015.temporal',\n", - " 'tolias.Cadena2017',\n", - " 'movshon.FreemanZiemba2013.noaperture',\n", - " 'dicarlo.Rajalingham2018.public',\n", - " 'dicarlo.Rajalingham2018.private',\n", - " 'dicarlo.Kar2019',\n", - " 'dicarlo.Majaj2015.temporal-10ms',\n", - " 'dicarlo.Kar2018hvm',\n", - 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" 'dicarlo.hvm-private',\n", - " 'movshon.FreemanZiemba2013-public',\n", - " 'movshon.FreemanZiemba2013-private',\n", - " 'klab.Zhang2018.search_obj_array',\n", - " 'movshon.FreemanZiemba2013.aperture-public',\n", - " 'movshon.FreemanZiemba2013.aperture-private',\n", - " 'movshon.FreemanZiemba2013.aperture']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list_stimulus_sets()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "brainio-dicarlo/assy_dicarlo_Kar2018hvm.nc: 100%|██████████| 120M/120M [00:02<00:00, 15.2MB/s] \n", - "brainio-dicarlo/HvM_with_discfade.zip: 100%|██████████| 235M/235M [00:03<00:00, 61.1MB/s] \n", - "brainio-dicarlo/image_dicarlo_hvm-public.zip: 100%|██████████| 132M/132M [00:02<00:00, 51.5MB/s] \n", - "brainio-dicarlo/image_dicarlo_hvm-private.zip: 100%|██████████| 104M/104M [00:02<00:00, 44.2MB/s] \n", - 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'Chabo_L_P_7_6'\n", - " - arr (neuroid) object 'M' 'M' 'M' 'M' 'M' ... 'P' 'P' 'P' 'P'\n", - " - col (neuroid) int64 9 9 8 9 8 8 7 7 5 6 ... 8 7 8 6 7 5 7 7 6 6\n", - " - hemisphere (neuroid) object 'L' 'L' 'L' 'L' 'L' ... 'L' 'L' 'L' 'L'\n", - " - subregion (neuroid) object 'cIT' 'cIT' 'cIT' 'cIT' ... 'V4' 'V4' 'V4'\n", - " - animal (neuroid) object 'Chabo' 'Chabo' ... 'Chabo' 'Chabo'\n", - " - y (neuroid) float64 0.2 0.6 0.2 1.0 0.6 ... 1.8 1.0 1.8 1.0\n", - " - x (neuroid) float64 1.8 1.8 1.4 1.8 1.4 ... 1.0 1.0 0.6 0.6\n", - " - region (neuroid) object 'IT' 'IT' 'IT' 'IT' ... 'V4' 'V4' 'V4'\n", - " - row (neuroid) int64 5 6 5 7 6 7 9 7 9 8 ... 8 5 9 6 8 7 9 7 9 7\n", - " * presentation (presentation) MultiIndex\n", - " - image_id (presentation) object 'e42b0ad9a8ec3becec08a916045443a7aa1537af' ... '587782ebef93e47e96f5b7b654cd54c8ede6802e'\n", - " - repetition (presentation) int64 31 31 31 31 31 31 ... 31 31 31 31 31\n", - " - stimulus (presentation) int64 569 232 571 585 ... 267 264 654 655\n", - " - id (presentation) float64 4.708e+03 3.408e+03 ... 4.429e+03\n", - " - image_file_name (presentation) object 'TURTLE_L_rx+48.464_ry+14.803_rz-20.792_tx-00.219_ty-01.139_s+00.513_b0d2bb0fbbc1e6eb1e5b1030a289069c0be998d1_256x256.png' ... '_004_rx-01.728_ry+10.812_rz-28.183_tx-00.209_ty+00.910_s+01.120_7d0eeef47ba7dcc9eea20b1bdc744f0f5052da99_256x256.png'\n", - " - object_name (presentation) object 'turtle' 'ship2' ... 'chair1'\n", - " - category_name (presentation) object 'Animals' 'Boats' ... 'Chairs'\n", - " - background_id (presentation) object 'b0d2bb0fbbc1e6eb1e5b1030a289069c0be998d1' ... '7d0eeef47ba7dcc9eea20b1bdc744f0f5052da99'\n", - " - variation (presentation) float64 6.0 6.0 6.0 6.0 ... 6.0 6.0 6.0 6.0\n", - " - ty (presentation) float64 -0.219 0.153 -0.207 ... 0.005 -0.209\n", - " - tz (presentation) float64 -1.139 1.009 -0.673 ... -0.074 0.91\n", - " - rxy (presentation) float64 14.8 68.45 39.8 ... 20.79 10.81\n", - " - rxz (presentation) float64 48.46 -61.62 53.74 ... 88.16 -1.728\n", - " - ryz (presentation) float64 -20.79 -69.42 ... -25.35 -28.18\n", - " - rxy_semantic (presentation) float64 104.8 158.4 129.8 ... 20.79 10.81\n", - " - rxz_semantic (presentation) float64 48.46 -61.62 53.74 ... 88.16 -1.728\n", - " - ryz_semantic (presentation) float64 -20.79 -69.42 ... -25.35 -28.18\n", - " - size (presentation) float64 256.0 256.0 256.0 ... 256.0 256.0\n", - " - s (presentation) float64 0.6412 1.232 1.246 ... 1.356 1.6\n", - " - degrees (presentation) float64 8.0 8.0 8.0 8.0 ... 8.0 8.0 8.0 8.0\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 -100 -90 -80 -70 -60 ... 160 170 180 190\n", - " - time_bin_end (time_bin) int64 -80 -70 -60 -50 -40 ... 180 190 200 210\n", - " Attributes:\n", - " stimulus_set_name: dicarlo.hvm-private\n", - " stimulus_set: id image_id ...,\n", - " 'movshon.FreemanZiemba2013.public': \n", - " array([[[0., 0., ..., 0., 0.],\n", - " [0., 0., ..., 0., 0.],\n", - " ...,\n", - " [0., 0., ..., 0., 0.],\n", - " [0., 0., ..., 0., 0.]],\n", - " \n", - " [[0., 0., ..., 0., 0.],\n", - " [0., 0., ..., 0., 1.],\n", - " ...,\n", - " [0., 0., ..., 0., 0.],\n", - " [0., 0., ..., 0., 0.]],\n", - " \n", - " ...,\n", - " \n", - " [[0., 0., ..., 0., 0.],\n", - " [0., 0., ..., 0., 0.],\n", - " ...,\n", - " [0., 0., ..., 0., 0.],\n", - " [0., 0., ..., 0., 0.]],\n", - " \n", - " [[0., 0., ..., 0., 0.],\n", - " [0., 0., ..., 0., 0.],\n", - " ...,\n", - " [0., 0., ..., 1., 0.],\n", - " [0., 0., ..., 0., 0.]]])\n", - " Coordinates:\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) int64 1 2 3 4 5 6 ... 25 26 27 28 29 30\n", - " - region (neuroid) object 'V1' 'V1' 'V1' ... 'V1' 'V1'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 0 1 2 3 4 5 ... 25 26 27 28 29\n", - " - time_bin_end (time_bin) int64 1 2 3 4 5 6 ... 26 27 28 29 30\n", - " * presentation (presentation) MultiIndex\n", - " - repetition (presentation) int64 0 1 2 3 4 5 ... 4 5 6 7 8 9\n", - " - image_id (presentation) object 'dfa618e0503a4251582450e88daf0c21a5abed95' ... '4ba2605a32ef862bf6f1fc4032aa25a4381f0114'\n", - " - id (presentation) int64 133388 133388 ... 133453\n", - " - image_file_sha1 (presentation) object 'dfa618e0503a4251582450e88daf0c21a5abed95' ... '4ba2605a32ef862bf6f1fc4032aa25a4381f0114'\n", - " - image_file_name (presentation) object 'noise-320x320-im327-smp2.png' ... 'noise-320x320-im327-smp6.png'\n", - " - texture_type (presentation) object 'noise' 'noise' ... 'noise'\n", - " - texture_family (presentation) int64 327 327 327 ... 327 327 327\n", - " - sample (presentation) int64 2 2 2 2 2 2 ... 6 6 6 6 6 6\n", - " - resolution (presentation) object '320x320' ... '320x320'\n", - " - image_id_without_aperture (presentation) object '0e8609dc2778a848d7ed8b355d9331160ac2974d' ... '888526f132a230245a15ef3aa10ca05cac246574'\n", - " Attributes:\n", - " stimulus_set_name: movshon.FreemanZiemba2013.aperture-public\n", - " stimulus_set: id image_id...,\n", - " 'movshon.FreemanZiemba2013.private': \n", - " array([[[0., 0., ..., 0., 0.],\n", - " [0., 0., ..., 0., 0.],\n", - " ...,\n", - " [0., 0., ..., 0., 0.],\n", - " [0., 0., ..., 0., 0.]],\n", - " \n", - " [[0., 0., ..., 0., 0.],\n", - " [0., 0., ..., 0., 0.],\n", - " ...,\n", - " [0., 0., ..., 0., 0.],\n", - " [0., 0., ..., 0., 0.]],\n", - " \n", - " ...,\n", - " \n", - " [[0., 0., ..., 0., 0.],\n", - " [0., 0., ..., 0., 0.],\n", - " ...,\n", - " [0., 0., ..., 0., 0.],\n", - " [0., 0., ..., 0., 0.]],\n", - " \n", - " [[0., 0., ..., 0., 0.],\n", - " [0., 0., ..., 0., 0.],\n", - " ...,\n", - " [0., 0., ..., 0., 0.],\n", - " [0., 0., ..., 0., 0.]]])\n", - " Coordinates:\n", - " * neuroid (neuroid) MultiIndex\n", - " - neuroid_id (neuroid) int64 1 2 3 4 5 6 ... 25 26 27 28 29 30\n", - " - region (neuroid) object 'V1' 'V1' 'V1' ... 'V1' 'V1'\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 0 1 2 3 4 5 ... 25 26 27 28 29\n", - " - time_bin_end (time_bin) int64 1 2 3 4 5 6 ... 26 27 28 29 30\n", - " * presentation (presentation) MultiIndex\n", - " - repetition (presentation) int64 0 1 2 3 4 5 ... 4 5 6 7 8 9\n", - " - image_id (presentation) object 'bfd26c127f8ba028cc95cdc95f00c45c8884b365' ... '26747eaff286d025cfff822d7c8cdd6b16b7198b'\n", - " - id (presentation) int64 133415 133415 ... 133378\n", - " - image_file_sha1 (presentation) object 'bfd26c127f8ba028cc95cdc95f00c45c8884b365' ... '26747eaff286d025cfff822d7c8cdd6b16b7198b'\n", - " - image_file_name (presentation) object 'noise-320x320-im327-smp1.png' ... 'noise-320x320-im327-smp3.png'\n", - " - texture_type (presentation) object 'noise' 'noise' ... 'noise'\n", - " - texture_family (presentation) int64 327 327 327 ... 327 327 327\n", - " - sample (presentation) int64 1 1 1 1 1 1 ... 3 3 3 3 3 3\n", - " - resolution (presentation) object '320x320' ... '320x320'\n", - " - image_id_without_aperture (presentation) object 'd8a999326b756317c7d5cf513d0cfe8eb2d7c62b' ... 'e6910fe0b53858d294b2890024d355d92606a1f4'\n", - " Attributes:\n", - " stimulus_set_name: movshon.FreemanZiemba2013.aperture-private\n", - " stimulus_set: id image_id...}" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a_split = {name: get_assembly(name) for name in split}\n", - "a_split" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'dicarlo.Majaj2015.public': 3200,\n", - " 'dicarlo.Majaj2015.private': 2560,\n", - " 'dicarlo.Majaj2015.temporal.public': 3200,\n", - " 'dicarlo.Majaj2015.temporal.private': 2560,\n", - " 'movshon.FreemanZiemba2013.public': 135,\n", - " 'movshon.FreemanZiemba2013.private': 315}" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{k: np.unique(a_split[k][\"image_id\"]).size for k in a_split}" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'dicarlo.Majaj2015.public': array(['IT', 'V4'], dtype=object),\n", - " 'dicarlo.Majaj2015.private': array(['IT', 'V4'], dtype=object),\n", - " 'dicarlo.Majaj2015.temporal.public': array(['IT', 'V4'], dtype=object),\n", - " 'dicarlo.Majaj2015.temporal.private': array(['IT', 'V4'], dtype=object),\n", - " 'movshon.FreemanZiemba2013.public': array(['V1', 'V2'], dtype=object),\n", - " 'movshon.FreemanZiemba2013.private': array(['V1', 'V2'], dtype=object)}" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "{k: np.unique(a_split[k][\"region\"]) for k in a_split}" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['dicarlo.MajajHong2015', 'gallant.David2004',\n", - " 'dicarlo.MajajHong2015.temporal', 'tolias.Cadena2017',\n", - " 'movshon.FreemanZiemba2013.noaperture',\n", - " 'dicarlo.Rajalingham2018.public',\n", - " 'dicarlo.Rajalingham2018.private', 'dicarlo.Kar2019',\n", - " 'dicarlo.MajajHong2015.temporal-10ms', 'dicarlo.Kar2018hvm',\n", - " 'dicarlo.Kar2018cocogray', 'dicarlo.MajajHong2015.public',\n", - " 'dicarlo.MajajHong2015.private',\n", - " 'dicarlo.MajajHong2015.temporal.public',\n", - " 'dicarlo.MajajHong2015.temporal.private',\n", - " 'movshon.FreemanZiemba2013.noaperture.public',\n", - " 'movshon.FreemanZiemba2013.noaperture.private',\n", - " 'klab.Zhang2018search_obj_array',\n", - " 'movshon.FreemanZiemba2013.public',\n", - " 'movshon.FreemanZiemba2013.private', 'movshon.FreemanZiemba2013',\n", - " 'dicarlo.SanghaviMurty2020', 'dicarlo.SanghaviJozwik2020',\n", - " 'dicarlo.Sanghavi2020', 'dicarlo.SanghaviMurty2020THINGS1',\n", - " 'dicarlo.SanghaviMurty2020THINGS2', 'dicarlo.Rajalingham2020',\n", - " 'dicarlo.Seibert2019', 'aru.Kuzovkin2018',\n", - " 'dicarlo.Rust2012.single', 'dicarlo.Rust2012.array'], dtype=object)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list_assemblies()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "brainio", - "language": "python", - "name": "brainio" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/packaging/notebooks/2020-08-14_npc_v4_data.h5.ipynb b/packaging/notebooks/2020-08-14_npc_v4_data.h5.ipynb deleted file mode 100644 index 80170d02b..000000000 --- a/packaging/notebooks/2020-08-14_npc_v4_data.h5.ipynb +++ /dev/null @@ -1,3703 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "source": [ - "import tables" - ], - "outputs": [], - "execution_count": 1, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-09-02T19:24:14.431Z", - "iopub.execute_input": "2020-09-02T19:24:14.454Z", - "shell.execute_reply": "2020-09-02T19:24:15.944Z", - "iopub.status.idle": "2020-09-02T19:24:15.924Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "h5 = tables.open_file(\"/Users/jjpr/Downloads/DiCarlo Lab/npc_v4_data.h5\")\n", - "h5.filename" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 5, - "data": { - "text/plain": "'/Users/jjpr/Downloads/DiCarlo Lab/npc_v4_data.h5'" - }, - "metadata": {} - } - ], - "execution_count": 5, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-21T01:42:50.753Z", - "iopub.execute_input": "2020-08-21T01:42:50.760Z", - "iopub.status.idle": "2020-08-21T01:42:50.864Z", - "shell.execute_reply": "2020-08-21T01:42:50.886Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "for node in h5:\n", - " print(f\"{node._v_parent._v_pathname}->{node._v_pathname}\")" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-14T20:01:35.807Z", - "iopub.execute_input": "2020-08-14T20:01:35.812Z", - "iopub.status.idle": "2020-08-14T20:01:35.825Z", - "shell.execute_reply": "2020-08-14T20:01:35.836Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "print(\"graph LR;\")\n", - "for node in h5:\n", - " print(f\" {node._v_pathname}([\\\"{node._v_name}\\\"]);\")\n", - "for node in h5:\n", - " print(f\" {node._v_parent._v_pathname}-->{node._v_pathname};\")" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-17T21:30:06.764Z", - "iopub.execute_input": "2020-08-17T21:30:06.799Z", - "iopub.status.idle": "2020-08-17T21:30:06.948Z", - "shell.execute_reply": "2020-08-17T21:30:06.968Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "h5path = \"/neural/synthetic/monkey_m/stretch/session_1\"\n", - "node = h5.get_node(h5path)\n", - "node" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-17T21:42:38.677Z", - "iopub.execute_input": "2020-08-17T21:42:38.683Z", - "iopub.status.idle": "2020-08-17T21:42:38.698Z", - "shell.execute_reply": "2020-08-17T21:42:38.713Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "[type(node) for node in h5]" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-17T22:21:27.381Z", - "iopub.execute_input": "2020-08-17T22:21:27.387Z", - "iopub.status.idle": "2020-08-17T22:21:27.405Z", - "shell.execute_reply": "2020-08-17T22:21:27.422Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "[node.shape for node in h5 if type(node) == tables.array.Array]" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-17T22:25:59.265Z", - "iopub.execute_input": "2020-08-17T22:25:59.271Z", - "iopub.status.idle": "2020-08-17T22:25:59.289Z", - "shell.execute_reply": "2020-08-17T22:25:59.307Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "type(h5.get_node(\"/neural/synthetic/monkey_m/stretch\"))" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-17T22:14:32.082Z", - "iopub.execute_input": "2020-08-17T22:14:32.087Z", - "iopub.status.idle": "2020-08-17T22:14:32.099Z", - "shell.execute_reply": "2020-08-17T22:14:32.103Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "print(\"graph LR;\")\n", - "for node in h5:\n", - " if type(node) == tables.array.Array:\n", - " disp = f\"[\\\"{node._v_name} {str(node.shape)}\\\"]\"\n", - " else:\n", - " disp = f\"([\\\"{node._v_name}\\\"])\"\n", - " print(f\" {node._v_pathname}{disp};\")\n", - "for node in h5:\n", - " print(f\" {node._v_parent._v_pathname}-->{node._v_pathname};\")" - ], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-17T22:45:33.826Z", - "iopub.execute_input": "2020-08-17T22:45:33.832Z", - "iopub.status.idle": "2020-08-17T22:45:33.863Z", - "shell.execute_reply": "2020-08-17T22:45:33.870Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "[node._v_name for node in h5.root.neural]" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 8, - "data": { - "text/plain": "['curvature', 'naturalistic', 'synthetic']" - }, - "metadata": {} - } - ], - "execution_count": 8, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-21T01:44:28.082Z", - "iopub.execute_input": "2020-08-21T01:44:28.088Z", - "iopub.status.idle": "2020-08-21T01:44:28.103Z", - "shell.execute_reply": "2020-08-21T01:44:28.109Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "# assemblies?\n", - "for monkey in h5.root.neural.synthetic:\n", - " for setting in monkey:\n", - " for session in setting: \n", - " print(f\"synthetic_{monkey._v_name}_{setting._v_name}_{session._v_name}\")" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "synthetic_monkey_m_ohp_session_1\n", - "synthetic_monkey_m_ohp_session_2\n", - "synthetic_monkey_m_stretch_session_1\n", - "synthetic_monkey_m_stretch_session_2\n", - "synthetic_monkey_n_stretch_session_1\n", - "synthetic_monkey_n_stretch_session_2\n", - "synthetic_monkey_s_ohp_session_1\n", - "synthetic_monkey_s_stretch_session_1\n" - ] - } - ], - "execution_count": 14, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-21T02:22:22.480Z", - "iopub.execute_input": "2020-08-21T02:22:22.488Z", - "iopub.status.idle": "2020-08-21T02:22:22.502Z", - "shell.execute_reply": "2020-08-21T02:22:22.509Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "for monkey in h5.root.neural.naturalistic:\n", - " for setting in monkey:\n", - " for session in setting: \n", - " for i in range(session.shape[2]):\n", - " print(f\"{monkey._v_name[-1]}_{setting._v_name}_{session._v_name[-1]}_{str(i)}\")" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "m_ohp_1_0\n", - "m_ohp_1_1\n", - "m_ohp_1_2\n", - "m_ohp_1_3\n", - "m_ohp_1_4\n", - "m_ohp_1_5\n", - "m_ohp_1_6\n", - "m_ohp_1_7\n", - "m_ohp_1_8\n", - "m_ohp_1_9\n", - "m_ohp_1_10\n", - "m_ohp_1_11\n", - "m_ohp_1_12\n", - "m_ohp_1_13\n", - "m_ohp_1_14\n", - "m_ohp_1_15\n", - "m_ohp_1_16\n", - "m_ohp_1_17\n", - "m_ohp_1_18\n", - "m_ohp_1_19\n", - "m_ohp_1_20\n", - "m_ohp_1_21\n", - "m_ohp_1_22\n", - "m_ohp_1_23\n", - "m_ohp_1_24\n", - "m_ohp_1_25\n", - "m_ohp_1_26\n", - "m_ohp_1_27\n", - "m_ohp_1_28\n", - "m_ohp_1_29\n", - "m_ohp_1_30\n", - "m_ohp_1_31\n", - "m_ohp_1_32\n", - "m_ohp_1_33\n", - "m_ohp_1_34\n", - "m_ohp_1_35\n", - "m_ohp_1_36\n", - "m_ohp_1_37\n", - "m_ohp_1_38\n", - "m_ohp_2_0\n", - "m_ohp_2_1\n", - "m_ohp_2_2\n", - "m_ohp_2_3\n", - "m_ohp_2_4\n", - "m_ohp_2_5\n", - "m_ohp_2_6\n", - "m_ohp_2_7\n", - "m_ohp_2_8\n", - "m_ohp_2_9\n", - "m_ohp_2_10\n", - "m_ohp_2_11\n", - "m_ohp_2_12\n", - "m_ohp_2_13\n", - "m_ohp_2_14\n", - "m_ohp_2_15\n", - "m_ohp_2_16\n", - "m_ohp_2_17\n", - "m_ohp_2_18\n", - "m_ohp_2_19\n", - "m_ohp_2_20\n", - "m_ohp_2_21\n", - "m_ohp_2_22\n", - "m_ohp_2_23\n", - "m_ohp_2_24\n", - "m_ohp_2_25\n", - "m_ohp_2_26\n", - "m_ohp_2_27\n", - "m_ohp_2_28\n", - "m_ohp_2_29\n", - "m_ohp_2_30\n", - "m_ohp_2_31\n", - "m_ohp_2_32\n", - "m_ohp_2_33\n", - "m_ohp_2_34\n", - "m_ohp_2_35\n", - "m_ohp_2_36\n", - "m_ohp_2_37\n", - "m_stretch_1_0\n", - "m_stretch_1_1\n", - "m_stretch_1_2\n", - "m_stretch_1_3\n", - "m_stretch_1_4\n", - "m_stretch_1_5\n", - "m_stretch_1_6\n", - "m_stretch_1_7\n", - "m_stretch_1_8\n", - "m_stretch_1_9\n", - "m_stretch_1_10\n", - "m_stretch_1_11\n", - "m_stretch_1_12\n", - "m_stretch_1_13\n", - "m_stretch_1_14\n", - "m_stretch_1_15\n", - "m_stretch_1_16\n", - "m_stretch_1_17\n", - "m_stretch_1_18\n", - "m_stretch_1_19\n", - "m_stretch_1_20\n", - "m_stretch_1_21\n", - "m_stretch_1_22\n", - "m_stretch_1_23\n", - "m_stretch_1_24\n", - "m_stretch_1_25\n", - "m_stretch_1_26\n", - "m_stretch_1_27\n", - "m_stretch_1_28\n", - "m_stretch_1_29\n", - "m_stretch_1_30\n", - "m_stretch_1_31\n", - "m_stretch_1_32\n", - "m_stretch_1_33\n", - "m_stretch_1_34\n", - "m_stretch_1_35\n", - "m_stretch_1_36\n", - "m_stretch_1_37\n", - "m_stretch_1_38\n", - "m_stretch_2_0\n", - "m_stretch_2_1\n", - "m_stretch_2_2\n", - "m_stretch_2_3\n", - "m_stretch_2_4\n", - "m_stretch_2_5\n", - "m_stretch_2_6\n", - "m_stretch_2_7\n", - "m_stretch_2_8\n", - "m_stretch_2_9\n", - "m_stretch_2_10\n", - "m_stretch_2_11\n", - "m_stretch_2_12\n", - "m_stretch_2_13\n", - "m_stretch_2_14\n", - "m_stretch_2_15\n", - "m_stretch_2_16\n", - "m_stretch_2_17\n", - "m_stretch_2_18\n", - "m_stretch_2_19\n", - "m_stretch_2_20\n", - "m_stretch_2_21\n", - "m_stretch_2_22\n", - "m_stretch_2_23\n", - "m_stretch_2_24\n", - "m_stretch_2_25\n", - "m_stretch_2_26\n", - "m_stretch_2_27\n", - "m_stretch_2_28\n", - "m_stretch_2_29\n", - "m_stretch_2_30\n", - "m_stretch_2_31\n", - "m_stretch_2_32\n", - "m_stretch_2_33\n", - "m_stretch_2_34\n", - "m_stretch_2_35\n", - "m_stretch_2_36\n", - "m_stretch_2_37\n", - "m_stretch_2_38\n", - "m_stretch_2_39\n", - "m_stretch_2_40\n", - "m_stretch_2_41\n", - "m_stretch_2_42\n", - "m_stretch_2_43\n", - "m_stretch_2_44\n", - "m_stretch_2_45\n", - "m_stretch_2_46\n", - "m_stretch_2_47\n", - "m_stretch_2_48\n", - "m_stretch_2_49\n", - "m_stretch_2_50\n", - "m_stretch_2_51\n", - "n_stretch_1_0\n", - "n_stretch_1_1\n", - "n_stretch_1_2\n", - "n_stretch_1_3\n", - "n_stretch_1_4\n", - "n_stretch_1_5\n", - "n_stretch_1_6\n", - "n_stretch_1_7\n", - "n_stretch_1_8\n", - "n_stretch_1_9\n", - "n_stretch_1_10\n", - "n_stretch_1_11\n", - "n_stretch_1_12\n", - "n_stretch_1_13\n", - "n_stretch_1_14\n", - "n_stretch_1_15\n", - "n_stretch_1_16\n", - "n_stretch_1_17\n", - "n_stretch_1_18\n", - "n_stretch_1_19\n", - "n_stretch_2_0\n", - "s_ohp_1_0\n", - "s_ohp_1_1\n", - "s_ohp_1_2\n", - "s_ohp_1_3\n", - "s_ohp_1_4\n", - "s_ohp_1_5\n", - "s_ohp_1_6\n", - "s_ohp_1_7\n", - "s_ohp_1_8\n", - "s_ohp_1_9\n", - "s_ohp_1_10\n", - "s_ohp_1_11\n", - "s_ohp_1_12\n", - "s_ohp_1_13\n", - "s_ohp_1_14\n", - "s_ohp_1_15\n", - "s_ohp_1_16\n", - "s_ohp_1_17\n", - "s_ohp_1_18\n", - "s_ohp_1_19\n", - "s_ohp_1_20\n", - "s_ohp_1_21\n", - "s_stretch_1_0\n", - "s_stretch_1_1\n", - "s_stretch_1_2\n", - "s_stretch_1_3\n", - "s_stretch_1_4\n", - "s_stretch_1_5\n", - "s_stretch_1_6\n", - "s_stretch_1_7\n", - "s_stretch_1_8\n", - "s_stretch_1_9\n", - "s_stretch_1_10\n", - "s_stretch_1_11\n", - "s_stretch_1_12\n", - "s_stretch_1_13\n", - "s_stretch_1_14\n", - "s_stretch_1_15\n", - "s_stretch_1_16\n", - "s_stretch_1_17\n", - "s_stretch_1_18\n", - "s_stretch_1_19\n", - "s_stretch_1_20\n", - "s_stretch_1_21\n" - ] - } - ], - "execution_count": 12, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-21T01:52:45.652Z", - "iopub.execute_input": "2020-08-21T01:52:45.658Z", - "iopub.status.idle": "2020-08-21T01:52:45.672Z", - "shell.execute_reply": "2020-08-21T01:52:45.679Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "h5.root.neural.synthetic.monkey_m.stretch.session_1" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 15, - "data": { - "text/plain": "/neural/synthetic/monkey_m/stretch/session_1 (Array(51, 50, 39)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None" - }, - "metadata": {} - } - ], - "execution_count": 15, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-22T02:34:02.332Z", - "iopub.execute_input": "2020-08-22T02:34:02.339Z", - "iopub.status.idle": "2020-08-22T02:34:02.355Z", - "shell.execute_reply": "2020-08-22T02:34:02.361Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "from PIL import Image" - ], - "outputs": [], - "execution_count": 16, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-22T02:59:08.853Z", - "iopub.execute_input": "2020-08-22T02:59:08.861Z", - "shell.execute_reply": "2020-08-22T02:59:09.625Z", - "iopub.status.idle": "2020-08-22T02:59:09.632Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "img_m_s_1_00 = h5.root.images.synthetic.monkey_m.stretch.session_1[0]\n", - "img_m_s_1_00" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 19, - "data": { - "text/plain": "array([[127., 127., 127., ..., 125., 125., 125.],\n [127., 127., 127., ..., 126., 125., 125.],\n [127., 127., 127., ..., 125., 125., 125.],\n ...,\n [127., 127., 127., ..., 127., 126., 126.],\n [127., 127., 127., ..., 127., 126., 126.],\n [127., 127., 127., ..., 127., 126., 126.]])" - }, - "metadata": {} - } - ], - "execution_count": 19, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-22T03:10:17.467Z", - "iopub.execute_input": "2020-08-22T03:10:17.473Z", - "iopub.status.idle": "2020-08-22T03:10:17.487Z", - "shell.execute_reply": "2020-08-22T03:10:17.493Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "h5" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/Users/jjpr/anaconda/envs/brainio_contrib/lib/python3.7/site-packages/tables/group.py:1205: UserWarning: problems loading leaf ``/readme``::\n", - "\n", - " variable length strings are not supported yet\n", - "\n", - "The leaf will become an ``UnImplemented`` node.\n", - " % (self._g_join(childname), exc))\n" - ] - }, - { - "output_type": "execute_result", - "execution_count": 20, - "data": { - "text/plain": "File(filename=/Users/jjpr/Downloads/DiCarlo Lab/npc_v4_data.h5, title='', mode='r', root_uep='/', filters=Filters(complevel=0, shuffle=False, bitshuffle=False, fletcher32=False, least_significant_digit=None))\n/ (RootGroup) ''\n/readme (UnImplemented()) ''\n NOTE: \n\n/images (Group) ''\n/images/curvature (Array(370, 299, 299, 3)) ''\n atom := UInt8Atom(shape=(), dflt=0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'irrelevant'\n chunkshape := None\n/images/naturalistic (Array(640, 299, 299)) ''\n atom := UInt8Atom(shape=(), dflt=0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'irrelevant'\n chunkshape := None\n/neural (Group) ''\n/target_inds (Group) ''\n/target_inds/monkey_m (Group) ''\n/target_inds/monkey_n (Group) ''\n/target_inds/monkey_s (Group) ''\n/target_inds/monkey_s/ohp (Group) ''\n/target_inds/monkey_s/ohp/session_1 (Array(19,)) ''\n atom := Int64Atom(shape=(), dflt=0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/target_inds/monkey_s/stretch (Group) ''\n/target_inds/monkey_s/stretch/session_1 (Array(19,)) ''\n atom := Int64Atom(shape=(), dflt=0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/target_inds/monkey_n/stretch (Group) ''\n/target_inds/monkey_n/stretch/session_1 (Array(18,)) ''\n atom := Int64Atom(shape=(), dflt=0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/target_inds/monkey_n/stretch/session_2 (Array(1,)) ''\n atom := Int64Atom(shape=(), dflt=0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/target_inds/monkey_m/ohp (Group) ''\n/target_inds/monkey_m/ohp/session_1 (Array(5,)) ''\n atom := Int64Atom(shape=(), dflt=0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/target_inds/monkey_m/ohp/session_2 (Array(9,)) ''\n atom := Int64Atom(shape=(), dflt=0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/target_inds/monkey_m/stretch (Group) ''\n/target_inds/monkey_m/stretch/session_1 (Array(5,)) ''\n atom := Int64Atom(shape=(), dflt=0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/target_inds/monkey_m/stretch/session_2 (Array(16,)) ''\n atom := Int64Atom(shape=(), dflt=0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/curvature (Group) ''\n/neural/curvature/monkey_m (Array(28, 370, 52)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/curvature/monkey_s (Array(37, 370, 22)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/naturalistic (Group) ''\n/neural/synthetic (Group) ''\n/neural/synthetic/monkey_m (Group) ''\n/neural/synthetic/monkey_n (Group) ''\n/neural/synthetic/monkey_s (Group) ''\n/neural/synthetic/monkey_s/ohp (Group) ''\n/neural/synthetic/monkey_s/ohp/session_1 (Array(47, 95, 22)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/synthetic/monkey_s/stretch (Group) ''\n/neural/synthetic/monkey_s/stretch/session_1 (Array(47, 95, 22)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/synthetic/monkey_n/stretch (Group) ''\n/neural/synthetic/monkey_n/stretch/session_1 (Array(31, 90, 20)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/synthetic/monkey_n/stretch/session_2 (Array(20, 5, 1)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/synthetic/monkey_m/ohp (Group) ''\n/neural/synthetic/monkey_m/ohp/session_1 (Array(51, 50, 39)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/synthetic/monkey_m/ohp/session_2 (Array(40, 45, 38)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/synthetic/monkey_m/stretch (Group) ''\n/neural/synthetic/monkey_m/stretch/session_1 (Array(51, 50, 39)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/synthetic/monkey_m/stretch/session_2 (Array(33, 80, 52)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/naturalistic/monkey_m (Group) ''\n/neural/naturalistic/monkey_n (Group) ''\n/neural/naturalistic/monkey_s (Group) ''\n/neural/naturalistic/monkey_s/ohp (Group) ''\n/neural/naturalistic/monkey_s/ohp/session_1 (Array(33, 640, 22)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/naturalistic/monkey_s/stretch (Group) ''\n/neural/naturalistic/monkey_s/stretch/session_1 (Array(33, 640, 22)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/naturalistic/monkey_n/stretch (Group) ''\n/neural/naturalistic/monkey_n/stretch/session_1 (Array(34, 640, 20)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/naturalistic/monkey_n/stretch/session_2 (Array(34, 640, 1)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/naturalistic/monkey_m/ohp (Group) ''\n/neural/naturalistic/monkey_m/ohp/session_1 (Array(38, 640, 39)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/naturalistic/monkey_m/ohp/session_2 (Array(36, 640, 38)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/naturalistic/monkey_m/stretch (Group) ''\n/neural/naturalistic/monkey_m/stretch/session_1 (Array(38, 640, 39)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/neural/naturalistic/monkey_m/stretch/session_2 (Array(36, 640, 52)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/images/synthetic (Group) ''\n/images/synthetic/monkey_m (Group) ''\n/images/synthetic/monkey_n (Group) ''\n/images/synthetic/monkey_s (Group) ''\n/images/synthetic/monkey_s/ohp (Group) ''\n/images/synthetic/monkey_s/ohp/session_1 (Array(95, 299, 299)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/images/synthetic/monkey_s/stretch (Group) ''\n/images/synthetic/monkey_s/stretch/session_1 (Array(95, 299, 299)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/images/synthetic/monkey_n/stretch (Group) ''\n/images/synthetic/monkey_n/stretch/session_1 (Array(90, 299, 299)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/images/synthetic/monkey_n/stretch/session_2 (Array(5, 299, 299)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/images/synthetic/monkey_m/ohp (Group) ''\n/images/synthetic/monkey_m/ohp/session_1 (Array(50, 299, 299)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/images/synthetic/monkey_m/ohp/session_2 (Array(45, 299, 299)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/images/synthetic/monkey_m/stretch (Group) ''\n/images/synthetic/monkey_m/stretch/session_1 (Array(50, 299, 299)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None\n/images/synthetic/monkey_m/stretch/session_2 (Array(80, 299, 299)) ''\n atom := Float64Atom(shape=(), dflt=0.0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None" - }, - "metadata": {} - } - ], - "execution_count": 20, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-22T03:13:28.605Z", - "iopub.execute_input": "2020-08-22T03:13:28.612Z", - "iopub.status.idle": "2020-08-22T03:13:28.646Z", - "shell.execute_reply": "2020-08-22T03:13:28.689Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "img = Image.fromarray((img_m_s_1_00).astype('uint8'))\n", - "img" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 21, - "data": { - "text/plain": "", - "image/png": 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\n" - }, - "metadata": {} - } - ], - "execution_count": 21, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-22T03:23:16.783Z", - "iopub.execute_input": "2020-08-22T03:23:16.797Z", - "iopub.status.idle": "2020-08-22T03:23:16.825Z", - "shell.execute_reply": "2020-08-22T03:23:16.839Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "img_nat_00 = h5.root.images.naturalistic[0]\n", - "img_nat_00" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 22, - "data": { - "text/plain": "array([[128, 128, 128, ..., 128, 128, 128],\n [128, 128, 128, ..., 128, 128, 128],\n [128, 128, 128, ..., 128, 128, 128],\n ...,\n [128, 128, 128, ..., 128, 128, 128],\n [128, 128, 128, ..., 128, 128, 128],\n [128, 128, 128, ..., 128, 128, 128]], dtype=uint8)" - }, - "metadata": {} - } - ], - "execution_count": 22, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-22T03:26:53.171Z", - "iopub.execute_input": "2020-08-22T03:26:53.178Z", - "iopub.status.idle": "2020-08-22T03:26:53.195Z", - "shell.execute_reply": "2020-08-22T03:26:53.203Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "img2 = Image.fromarray((img_nat_00).astype('uint8'))\n", - "img2" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 23, - "data": { - "text/plain": "", - "image/png": 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\n" - }, - "metadata": {} - } - ], - "execution_count": 23, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-22T03:27:24.663Z", - "iopub.execute_input": "2020-08-22T03:27:24.673Z", - "iopub.status.idle": "2020-08-22T03:27:24.696Z", - "shell.execute_reply": "2020-08-22T03:27:24.707Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "from pathlib import Path\n", - "data_dir = Path(\"/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019\")" - ], - "outputs": [], - "execution_count": 24, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-22T03:32:47.603Z", - "iopub.execute_input": "2020-08-22T03:32:47.612Z", - "iopub.status.idle": "2020-08-22T03:32:47.627Z", - "shell.execute_reply": "2020-08-22T03:32:47.636Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "target_path = data_dir/\"test\"/\"img_00.png\"\n", - "target_path.parent.mkdir(parents=True, exist_ok=True)" - ], - "outputs": [], - "execution_count": 27, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-22T03:40:22.020Z", - "iopub.execute_input": "2020-08-22T03:40:22.028Z", - "iopub.status.idle": "2020-08-22T03:40:22.044Z", - "shell.execute_reply": "2020-08-22T03:40:22.058Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "img2.save(target_path)" - ], - "outputs": [], - "execution_count": 28, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-22T03:40:22.159Z", - "iopub.execute_input": "2020-08-22T03:40:22.171Z", - "iopub.status.idle": "2020-08-22T03:40:22.188Z", - "shell.execute_reply": "2020-08-22T03:40:22.199Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "from brainio_collection.lookup import sha1_hash" - ], - "outputs": [], - "execution_count": 29, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-22T19:18:07.095Z", - "iopub.execute_input": "2020-08-22T19:18:07.105Z", - "iopub.status.idle": "2020-08-22T19:18:07.122Z", - "shell.execute_reply": "2020-08-22T19:18:07.132Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "sha1_img2 = sha1_hash(target_path)\n", - "sha1_img2" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 30, - "data": { - "text/plain": "'a5ca287ed63a8a1712acd20685744cdb6e96cba5'" - }, - "metadata": {} - } - ], - "execution_count": 30, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-22T19:19:33.832Z", - "iopub.execute_input": "2020-08-22T19:19:33.841Z", - "iopub.status.idle": "2020-08-22T19:19:33.865Z", - "shell.execute_reply": "2020-08-22T19:19:33.874Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "data_dir.parents[6]" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 31, - "data": { - "text/plain": "PosixPath('/')" - }, - "metadata": {} - } - ], - "execution_count": 31, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T01:25:09.052Z", - "iopub.execute_input": "2020-08-23T01:25:09.062Z", - "iopub.status.idle": "2020-08-23T01:25:09.089Z", - "shell.execute_reply": "2020-08-23T01:25:09.099Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "img_temp = data_dir / \"images_temp\" " - ], - "outputs": [], - "execution_count": 32, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T01:49:52.772Z", - "iopub.execute_input": "2020-08-23T01:49:52.785Z", - "iopub.status.idle": "2020-08-23T01:49:52.808Z", - "shell.execute_reply": "2020-08-23T01:49:52.820Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "img_temp_nat = img_temp / \"naturalistic\"\n", - "img_temp_nat.mkdir(parents=True, exist_ok=True)\n", - "img_temp_nat.is_dir()" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 34, - "data": { - "text/plain": "True" - }, - "metadata": {} - } - ], - "execution_count": 34, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T01:52:45.912Z", - "iopub.execute_input": "2020-08-23T01:52:45.923Z", - "iopub.status.idle": "2020-08-23T01:52:45.948Z", - "shell.execute_reply": "2020-08-23T01:52:45.959Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "for i, img_np in enumerate(h5.root.images.naturalistic):\n", - " img_pil = Image.fromarray((img_np).astype('uint8'))\n", - " img_path = img_temp_nat / f\"img{i}.png\"\n", - " img_pil.save(img_path)\n", - " sha1 = sha1_hash(img_path)\n", - " img_path_sha1 = img_temp_nat / f\"{sha1}.png\"\n", - " img_path.rename(img_path_sha1)\n", - " print(f\"{img_path} -> {img_path_sha1}\")\n" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img0.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/a5ca287ed63a8a1712acd20685744cdb6e96cba5.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img1.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/06fc797aecd8c4baa55f7240bd2f81fc99c34676.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img2.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/79791f716c4cdd42f8e82a91f56b579d07b83485.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img3.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/400071b6ede4e8d15a851b9880714454909fda39.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img4.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/e47fd0be98d9177bd9f392d3a5fd6d4325641095.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img5.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/593a2df27c63d8d63e73f445a55ef5df756de32b.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img6.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/d8a8145b2903373aafea5b0a804140f272a2f869.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img7.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/da4c9d27b809c8c351de30deb85ad0f10ee12554.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img8.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/d434ac2f492da1e7e4619574fdcbc3177e089733.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img9.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/cef2b4cfbda057f83abd78bda5e5e2540db815f2.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img10.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/234102cc73abe8155621356aad03b724715e7b27.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img11.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/0071a2dd239e99a6064369d2be464d49247759d0.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img12.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/ceea9c9c19a6508e635e94e820a0c80c521ae9d8.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img13.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/fcd6e20a5b737a0e23b6568bbcfc1002e7d87a3d.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img14.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/3ce1c234fe1a48d0be3473a57cb5cc959a8eab8d.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img15.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/bbaf323b93936ecc16dbc6c4cb2f3cac1391f630.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img16.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/6e5e9da09cfeae0951a74957047974b4fc421b9e.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img17.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/4ee987d07861ffc114c6e3d2cb3a6970f39da8b6.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img18.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/112470323b9879a155237f1d5ae31d83b52645f9.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img19.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/fca55dc1ee741fa7fb68ab135cab98a608cfec42.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img20.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/4fdebf8c1c484ec7ec4049a62f6039f2ee760538.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img21.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/36c1bd15a897851a8c3e45783d4d0e819a06a14d.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img22.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/51cb25f76df110a89770050eae0618c351375666.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img23.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/e7a0b76a47b539e11faaf261e24e03d37503226d.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img24.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/a9f10a6713400a99fd1061b462435eff12e2b1b0.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img25.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/3bf274675e9a272d57e3a40fbf2842c2f6992f00.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img26.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/38de0db2bf2bb7971c2c726975d4b138f1704992.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img27.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/1b81054f8ab642999bbd08b6815fb3fbabcdcbd1.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img28.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/49bcd712ac519f77aed75e6d4b0a9b0035a104bd.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img29.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/3f7190841d0b2c5873387877fea8d2f0f8f410b3.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img30.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/e9fd4f6e57f0826e3d8f671edb42e17378297042.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img31.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/400ec63984741401a24614603b4641db193f1daa.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img32.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/ace33a132666be7f97c110d3f9f53bd02fc6e238.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img33.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/0cd8d566ef8a0c8adf34144b67a9189d61799267.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img34.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/ccc8f0e39c1d779967c4e278e402cca059c69099.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img35.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/2527623079564df245f3bab8916bb6a9411cf6b5.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img36.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/2533d34596b162b925cdbf442e369b4437d160db.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img37.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/7b34f5f2f1aa783f4b3e9624443e5ce9118a8bfb.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img38.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/bc7e72866de82e2acf00eb04daaca5da381bb18a.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img39.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/ecb6cdd6db8f5e42f35112e26014848c6cf2f1b6.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img40.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/edef8d92f81f943c032be87199b886ab3e311b02.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img41.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/5650ac3e5db935763faff648c7e80b256ec3d9ee.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img42.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/02d612a4f12fa84938399f61cbc5610baf591091.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img43.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/75d9290ab2fb5e82efe8be9706f4acb4774b7d62.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img44.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/5df081f02fb6a511284f1840d9b03dae22bf8941.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img45.png -> 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"/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img588.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/d878384a8137ad4bf9407fb286a70faf7c7d350e.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img589.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/84ce8e9e59cbdb5e4f214112d9cc68858a413dc9.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img590.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/c9ec93b701e85677ee86e3aee7b31790b12cc470.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img591.png -> 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"/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img595.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/f967aab30928a1b32a9f6a2317fa40f8779a9b6d.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img596.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/a32688b7b96a7185d5e83a49a48d9bec4a5727c2.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img597.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/450e706b5c67facee3d472929785691969228b8f.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img598.png -> 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"/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img602.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/ad6b040ca392090bed96de9308d454949da97d7c.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img603.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/4f98ca1b26627b42b56840e52f33d08a760fb3a4.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img604.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/132ea7b52c9ac44cdbd57cb4a08611acaa234616.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img605.png -> 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"/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img609.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/72abc42d449d6b9a73c57acf05538934a1349a60.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img610.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/85c9f8ad1621dd7905e88b54603e1fb00f739ebf.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img611.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/739779fabaa41d12a166f68a70a8a27edf0e4f16.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img612.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/6c980467236d2bf2f4e380108ba47bc6e00e9360.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img613.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/5149d903e4b4142d12833bb5466c4bc85de50174.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img614.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/c18b8b4300948a5ee1c449abdee19e61fd5a7f8a.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img615.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/a61cc59842ffef89c5a5d2ce39facd6ba2110aa9.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img616.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/e16942eabd140933c7830d4d75f9c5c3ff7e0216.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img617.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/75b6813975dd62be8e93c4ac60a99207657a34d1.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img618.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/9f27b58c8db24a288233814502292c43deb6155b.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img619.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/cf6fee84026c262bd3dad74f6a160915c16cc12d.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img620.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/a4f036a67072f89d060b44cccafcf42a2041fd15.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img621.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/9ac1add9586a26209dbfd6e43c8ba2efae287a04.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img622.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/4d62fdadf9f7552198fec83a0527b08dc1252fc2.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img623.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/fbffa96ce2d2ff49b94268159ca3e3ffcac7ccf5.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img624.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/0cbde11889f0f8d13cac0fe87c4307a9ab4350f0.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img625.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/557e6d7364f46f45359c02d074bb38bf3f0a61b2.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img626.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/e13d07c4c19922198007d4cbe9a9ac2c2a6ff86a.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img627.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/a0612bbf2fe3114720bf290aa262e24640400d34.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img628.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/0a5a0ab82497fbd859e9f4f0713b4751ba9540b2.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img629.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/81c5ef6edd390395cd3b9a31984e7f8a107dcddb.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img630.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/2f82c033a71aeeb17df1e93cf94f229a0ac26815.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img631.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/75ebaf926e4ac2e9af5a520c4ddbb6c51c50aebd.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img632.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/de54101b8f532d1baf5c35590c5156c1e4c5082f.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img633.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/52cf656a5268fd843c9b7d0bebb8f3dd1929ed64.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img634.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/9a6e63193692bee0c391ad2af4302960df6ffe26.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img635.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/3b6bcb2c4e5462d54b96b2bff622743b450e1ba7.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img636.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/5a17149822d473e51eaad08c3abf140a70ca3b32.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img637.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/24fa29575bd6ec26db1ef6f21f292ae30cfa2ad8.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img638.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/7c88e08733a470f02c6e3d6fae7f5a6c6d91089e.png\n", - "/Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/img639.png -> /Users/jjpr/dev/brainio_contrib/mkgu_packaging/dicarlo/BashivanKar2019/images_temp/naturalistic/1d30891e8c7edd7b2181ffab1c569c9731bc7ceb.png\n" - ] - } - ], - "execution_count": 38, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T03:07:03.593Z", - "iopub.execute_input": "2020-08-23T03:07:03.616Z", - "iopub.status.idle": "2020-08-23T03:07:11.090Z", - "shell.execute_reply": "2020-08-23T03:07:11.218Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "from brainio_collection import get_stimulus_set" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Loading lookup from /Users/jjpr/dev/brainio_collection/brainio_collection/lookup.csv\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/Users/jjpr/anaconda/envs/brainio_contrib/lib/python3.7/site-packages/xarray/core/merge.py:10: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n", - " PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel)\n" - ] - } - ], - "execution_count": 1, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-09-02T19:25:09.801Z", - "iopub.execute_input": "2020-09-02T19:25:09.819Z", - "iopub.status.idle": "2020-09-02T19:25:14.025Z", - "shell.execute_reply": "2020-09-02T19:25:14.042Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "from importlib import reload" - ], - "outputs": [], - "execution_count": 46, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T22:40:38.035Z", - "iopub.execute_input": "2020-08-23T22:40:38.050Z", - "iopub.status.idle": "2020-08-23T22:40:38.073Z", - "shell.execute_reply": "2020-08-23T22:40:38.095Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "reload(get_stimulus_set)" - ], - "outputs": [ - { - "output_type": "error", - "ename": "TypeError", - "evalue": "reload() argument must be a module", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mreload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_stimulus_set\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/anaconda/envs/brainio_contrib/lib/python3.7/importlib/__init__.py\u001b[0m in \u001b[0;36mreload\u001b[0;34m(module)\u001b[0m\n\u001b[1;32m 138\u001b[0m \"\"\"\n\u001b[1;32m 139\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModuleType\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"reload() argument must be a module\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 141\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__spec__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: reload() argument must be a module" - ] - } - ], - "execution_count": 48, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T22:41:48.068Z", - "iopub.execute_input": "2020-08-23T22:41:48.084Z", - "iopub.status.idle": "2020-08-23T22:41:48.114Z", - "shell.execute_reply": "2020-08-23T22:41:48.127Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "import brainio_collection" - ], - "outputs": [], - "execution_count": 49, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T22:42:08.055Z", - "iopub.execute_input": "2020-08-23T22:42:08.074Z", - "iopub.status.idle": "2020-08-23T22:42:08.097Z", - "shell.execute_reply": "2020-08-23T22:42:08.111Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "reload(brainio_collection)" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 60, - "data": { - "text/plain": "" - }, - "metadata": {} - } - ], - "execution_count": 60, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T22:47:21.698Z", - "iopub.execute_input": "2020-08-23T22:47:21.716Z", - "iopub.status.idle": "2020-08-23T22:47:21.750Z", - "shell.execute_reply": "2020-08-23T22:47:21.765Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "from brainio_collection import get_stimulus_set" - ], - "outputs": [], - "execution_count": 61, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T22:47:22.933Z", - "iopub.execute_input": "2020-08-23T22:47:22.950Z", - "iopub.status.idle": "2020-08-23T22:47:22.974Z", - "shell.execute_reply": "2020-08-23T22:47:22.992Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "from brainio_collection import list_stimulus_sets" - ], - "outputs": [], - "execution_count": 63, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T22:47:51.849Z", - "iopub.execute_input": "2020-08-23T22:47:51.862Z", - "iopub.status.idle": "2020-08-23T22:47:51.884Z", - "shell.execute_reply": "2020-08-23T22:47:51.898Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "list_stimulus_sets()" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 64, - "data": { - "text/plain": "array(['dicarlo.hvm', 'dicarlo.hvm', 'gallant.David2004',\n 'gallant.David2004', 'tolias.Cadena2017', 'tolias.Cadena2017',\n 'movshon.FreemanZiemba2013', 'movshon.FreemanZiemba2013',\n 'dicarlo.objectome.public', 'dicarlo.objectome.public',\n 'dicarlo.objectome.private', 'dicarlo.objectome.private',\n 'dicarlo.Kar2019', 'dicarlo.Kar2019', 'dicarlo.Kar2018cocogray',\n 'dicarlo.Kar2018cocogray', 'dicarlo.hvm-public',\n 'dicarlo.hvm-public', 'dicarlo.hvm-private', 'dicarlo.hvm-private',\n 'movshon.FreemanZiemba2013-public',\n 'movshon.FreemanZiemba2013-public',\n 'movshon.FreemanZiemba2013-private',\n 'movshon.FreemanZiemba2013-private',\n 'klab.Zhang2018.search_obj_array',\n 'klab.Zhang2018.search_obj_array',\n 'movshon.FreemanZiemba2013.aperture-public',\n 'movshon.FreemanZiemba2013.aperture-public',\n 'movshon.FreemanZiemba2013.aperture-private',\n 'movshon.FreemanZiemba2013.aperture-private',\n 'movshon.FreemanZiemba2013.aperture',\n 'movshon.FreemanZiemba2013.aperture',\n 'dicarlo.Rajalingham2020orthographic_IT',\n 'dicarlo.Rajalingham2020orthographic_IT', 'aru.Kuzovkin2018',\n 'aru.Kuzovkin2018'], dtype=object)" - }, - "metadata": {} - } - ], - "execution_count": 64, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T22:48:04.941Z", - "iopub.execute_input": "2020-08-23T22:48:04.954Z", - "iopub.status.idle": "2020-08-23T22:48:04.986Z", - "shell.execute_reply": "2020-08-23T22:48:05.003Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "th2 = get_stimulus_set(\"dicarlo.THINGS2\")\n", - "th2" - ], - "outputs": [ - { - "output_type": "error", - "ename": "StimulusSetLookupError", - "evalue": "'stimulus_set dicarlo.THINGS2 not found'", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mStimulusSetLookupError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mth2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_stimulus_set\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"dicarlo.THINGS2\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mth2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/dev/brainio_collection/brainio_collection/fetch.py\u001b[0m in \u001b[0;36mget_stimulus_set\u001b[0;34m(identifier)\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_stimulus_set\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midentifier\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m \u001b[0mcsv_lookup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzip_lookup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlookup_stimulus_set\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midentifier\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 194\u001b[0m csv_path = fetch_file(location_type=csv_lookup['location_type'], location=csv_lookup['location'],\n\u001b[1;32m 195\u001b[0m sha1=csv_lookup['sha1'])\n", - "\u001b[0;32m~/dev/brainio_collection/brainio_collection/lookup.py\u001b[0m in \u001b[0;36mlookup_stimulus_set\u001b[0;34m(identifier)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0mlookup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'identifier'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0midentifier\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lookup_type'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mTYPE_STIMULUS_SET\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlookup\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mStimulusSetLookupError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"stimulus_set {identifier} not found\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 33\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlookup\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m raise RuntimeError(\n", - "\u001b[0;31mStimulusSetLookupError\u001b[0m: 'stimulus_set dicarlo.THINGS2 not found'" - ] - } - ], - "execution_count": 62, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T22:47:23.982Z", - "iopub.execute_input": "2020-08-23T22:47:23.998Z", - "iopub.status.idle": "2020-08-23T22:47:24.027Z", - "shell.execute_reply": "2020-08-23T22:47:24.043Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "brainio_collection.lookup.data" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 65, - "data": { - "text/plain": " identifier ... stimulus_set_identifier\n0 dicarlo.hvm ... NaN\n1 dicarlo.hvm ... NaN\n2 gallant.David2004 ... NaN\n3 gallant.David2004 ... NaN\n4 tolias.Cadena2017 ... NaN\n5 tolias.Cadena2017 ... NaN\n6 movshon.FreemanZiemba2013 ... NaN\n7 movshon.FreemanZiemba2013 ... NaN\n8 dicarlo.objectome.public ... NaN\n9 dicarlo.objectome.public ... NaN\n10 dicarlo.objectome.private ... NaN\n11 dicarlo.objectome.private ... NaN\n12 dicarlo.Kar2019 ... NaN\n13 dicarlo.Kar2019 ... NaN\n14 dicarlo.Kar2018cocogray ... NaN\n15 dicarlo.Kar2018cocogray ... NaN\n16 dicarlo.hvm-public ... NaN\n17 dicarlo.hvm-public ... NaN\n18 dicarlo.hvm-private ... NaN\n19 dicarlo.hvm-private ... NaN\n20 movshon.FreemanZiemba2013-public ... NaN\n21 movshon.FreemanZiemba2013-public ... NaN\n22 movshon.FreemanZiemba2013-private ... NaN\n23 movshon.FreemanZiemba2013-private ... NaN\n24 klab.Zhang2018.search_obj_array ... NaN\n25 klab.Zhang2018.search_obj_array ... NaN\n26 movshon.FreemanZiemba2013.aperture-public ... NaN\n27 movshon.FreemanZiemba2013.aperture-public ... NaN\n28 movshon.FreemanZiemba2013.aperture-private ... NaN\n29 movshon.FreemanZiemba2013.aperture-private ... NaN\n30 movshon.FreemanZiemba2013.aperture ... NaN\n31 movshon.FreemanZiemba2013.aperture ... NaN\n32 dicarlo.Rajalingham2020orthographic_IT ... NaN\n33 dicarlo.Rajalingham2020orthographic_IT ... 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" - }, - "metadata": {} - } - ], - "execution_count": 65, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T22:52:00.793Z", - "iopub.execute_input": "2020-08-23T22:52:00.811Z", - "iopub.status.idle": "2020-08-23T22:52:00.858Z", - "shell.execute_reply": "2020-08-23T22:52:00.875Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "brainio_collection.lookup.path" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 66, - "data": { - "text/plain": "PosixPath('/Users/jjpr/dev/brainio_collection/brainio_collection/lookup.csv')" - }, - "metadata": {} - } - ], - "execution_count": 66, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T22:52:59.216Z", - "iopub.execute_input": "2020-08-23T22:52:59.240Z", - "iopub.status.idle": "2020-08-23T22:52:59.337Z", - "shell.execute_reply": "2020-08-23T22:52:59.355Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "import pandas as pd" - ], - "outputs": [], - "execution_count": 67, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T22:53:23.823Z", - "iopub.execute_input": "2020-08-23T22:53:23.840Z", - "iopub.status.idle": "2020-08-23T22:53:23.872Z", - "shell.execute_reply": "2020-08-23T22:53:23.886Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "brainio_collection.lookup.data = pd.read_csv(brainio_collection.lookup.path)" - ], - "outputs": [], - "execution_count": 68, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T22:54:08.692Z", - "iopub.execute_input": "2020-08-23T22:54:08.709Z", - "iopub.status.idle": "2020-08-23T22:54:08.738Z", - "shell.execute_reply": "2020-08-23T22:54:08.754Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "th2 = get_stimulus_set(\"dicarlo.THINGS2\")\n", - "th2" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "brainio.dicarlo/image_dicarlo_THINGS2.csv: 100%|██████████| 283k/283k [00:00<00:00, 877kB/s]\n", - "brainio.dicarlo/image_dicarlo_THINGS2.zip: 100%|██████████| 230M/230M [01:21<00:00, 2.81MB/s] \n" - ] - }, - { - "output_type": "execute_result", - "execution_count": 69, - "data": { - "text/plain": " image_file_name image_id category filename\n0 aardvark_04s.jpg 1 aardvark 1.jpg\n1 aardvark_06s.jpg 2 aardvark 2.jpg\n2 aardvark_10s.jpg 3 aardvark 3.jpg\n3 aardvark_14s.jpg 4 aardvark 4.jpg\n4 abacus_03s.jpg 5 abacus 5.jpg\n... ... ... ... ...\n7411 zipper_08s.jpg 7412 zipper 7412.jpg\n7412 zucchini_01b.jpg 7413 zucchini 7413.jpg\n7413 zucchini_08n.jpg 7414 zucchini 7414.jpg\n7414 zucchini_11s.jpg 7415 zucchini 7415.jpg\n7415 zucchini_12s.jpg 7416 zucchini 7416.jpg\n\n[7416 rows x 4 columns]", - "text/html": "
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" - }, - "metadata": {} - } - ], - "execution_count": 69, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-23T22:55:09.621Z", - "iopub.execute_input": "2020-08-23T22:55:09.642Z", - "iopub.status.idle": "2020-08-23T22:56:37.410Z", - "shell.execute_reply": "2020-08-23T22:56:37.456Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "h5.root.target_inds[\"monkey_m\"][\"stretch\"][\"session_1\"]" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 71, - "data": { - "text/plain": "/target_inds/monkey_m/stretch/session_1 (Array(5,)) ''\n atom := Int64Atom(shape=(), dflt=0)\n maindim := 0\n flavor := 'numpy'\n byteorder := 'little'\n chunkshape := None" - }, - "metadata": {} - } - ], - "execution_count": 71, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-24T00:43:00.084Z", - "iopub.execute_input": "2020-08-24T00:43:00.131Z", - "iopub.status.idle": "2020-08-24T00:43:00.165Z", - "shell.execute_reply": "2020-08-24T00:43:00.196Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "h5.root.target_inds[\"monkey_m\"][\"stretch\"][\"session_1\"][:]" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 73, - "data": { - "text/plain": "array([53, 31, 17, 20, 16])" - }, - "metadata": {} - } - ], - "execution_count": 73, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-24T00:43:43.476Z", - "iopub.execute_input": "2020-08-24T00:43:43.493Z", - "iopub.status.idle": "2020-08-24T00:43:43.531Z", - "shell.execute_reply": "2020-08-24T00:43:43.550Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "(\n", - " (53 in h5.root.target_inds[\"monkey_m\"][\"stretch\"][\"session_1\"]),\n", - " (54 in h5.root.target_inds[\"monkey_m\"][\"stretch\"][\"session_1\"]) \n", - ")" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 79, - "data": { - "text/plain": "(True, False)" - }, - "metadata": {} - } - ], - "execution_count": 79, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-24T00:46:13.654Z", - "iopub.execute_input": "2020-08-24T00:46:13.672Z", - "iopub.status.idle": "2020-08-24T00:46:13.709Z", - "shell.execute_reply": "2020-08-24T00:46:13.727Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "(int(True), int(False))" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 82, - "data": { - "text/plain": "(1, 0)" - }, - "metadata": {} - } - ], - "execution_count": 82, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-24T04:35:00.350Z", - "iopub.execute_input": "2020-08-24T04:35:00.369Z", - "iopub.status.idle": "2020-08-24T04:35:00.412Z", - "shell.execute_reply": "2020-08-24T04:35:00.432Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "brainio_collection.lookup.data = pd.read_csv(brainio_collection.lookup.path)" - ], - "outputs": [], - "execution_count": 97, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-24T05:50:38.004Z", - "iopub.execute_input": "2020-08-24T05:50:38.027Z", - "iopub.status.idle": "2020-08-24T05:50:38.070Z", - "shell.execute_reply": "2020-08-24T05:50:38.152Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "list_stimulus_sets()[::2]" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 99, - "data": { - "text/plain": "array(['dicarlo.hvm', 'gallant.David2004', 'tolias.Cadena2017',\n 'movshon.FreemanZiemba2013', 'dicarlo.objectome.public',\n 'dicarlo.objectome.private', 'dicarlo.Kar2019',\n 'dicarlo.Kar2018cocogray', 'dicarlo.hvm-public',\n 'dicarlo.hvm-private', 'movshon.FreemanZiemba2013-public',\n 'movshon.FreemanZiemba2013-private',\n 'klab.Zhang2018.search_obj_array',\n 'movshon.FreemanZiemba2013.aperture-public',\n 'movshon.FreemanZiemba2013.aperture-private',\n 'movshon.FreemanZiemba2013.aperture',\n 'dicarlo.Rajalingham2020orthographic_IT', 'aru.Kuzovkin2018',\n 'dicarlo.Rajalingham2020', 'dicarlo.Rust2012', 'dicarlo.BOLD5000',\n 'dicarlo.THINGS1', 'dicarlo.THINGS2',\n 'dicarlo.BashivanKar2019.naturalistic',\n 'dicarlo.BashivanKar2019.synthetic'], dtype=object)" - }, - "metadata": {} - } - ], - "execution_count": 99, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-24T05:52:45.508Z", - "iopub.execute_input": "2020-08-24T05:52:45.530Z", - "iopub.status.idle": "2020-08-24T05:52:45.575Z", - "shell.execute_reply": "2020-08-24T05:52:45.596Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "from brainio_collection import list_assemblies" - ], - "outputs": [], - "execution_count": 5, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-19T22:57:21.819Z", - "iopub.execute_input": "2021-01-19T22:57:21.844Z", - "iopub.status.idle": "2021-01-19T22:57:21.880Z", - "shell.execute_reply": "2021-01-19T22:57:21.903Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "list_assemblies()" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 6, - "data": { - "text/plain": "array(['dicarlo.MajajHong2015', 'gallant.David2004',\n 'dicarlo.MajajHong2015.temporal', 'tolias.Cadena2017',\n 'movshon.FreemanZiemba2013.noaperture',\n 'dicarlo.Rajalingham2018.public',\n 'dicarlo.Rajalingham2018.private', 'dicarlo.Kar2019',\n 'dicarlo.MajajHong2015.temporal-10ms', 'dicarlo.Kar2018hvm',\n 'dicarlo.Kar2018cocogray', 'dicarlo.MajajHong2015.public',\n 'dicarlo.MajajHong2015.private',\n 'dicarlo.MajajHong2015.temporal.public',\n 'dicarlo.MajajHong2015.temporal.private',\n 'movshon.FreemanZiemba2013.noaperture.public',\n 'movshon.FreemanZiemba2013.noaperture.private',\n 'klab.Zhang2018search_obj_array',\n 'movshon.FreemanZiemba2013.public',\n 'movshon.FreemanZiemba2013.private', 'movshon.FreemanZiemba2013',\n 'aru.Kuzovkin2018', 'dicarlo.Rajalingham2020',\n 'dicarlo.SanghaviMurty2020', 'dicarlo.SanghaviJozwik2020',\n 'dicarlo.Sanghavi2020', 'dicarlo.SanghaviMurty2020THINGS1',\n 'dicarlo.SanghaviMurty2020THINGS2',\n 'dicarlo.BashivanKar2019.m_ohp_session_1_nat',\n 'dicarlo.BashivanKar2019.m_ohp_session_2_nat',\n 'dicarlo.BashivanKar2019.m_stretch_session_1_nat',\n 'dicarlo.BashivanKar2019.m_stretch_session_2_nat',\n 'dicarlo.BashivanKar2019.n_stretch_session_1_nat',\n 'dicarlo.BashivanKar2019.n_stretch_session_2_nat',\n 'dicarlo.BashivanKar2019.s_ohp_session_1_nat',\n 'dicarlo.BashivanKar2019.s_stretch_session_1_nat',\n 'dicarlo.BashivanKar2019.m_ohp_session_1_synth',\n 'dicarlo.BashivanKar2019.m_ohp_session_2_synth',\n 'dicarlo.BashivanKar2019.m_stretch_session_1_synth',\n 'dicarlo.BashivanKar2019.m_stretch_session_2_synth',\n 'dicarlo.BashivanKar2019.n_stretch_session_1_synth',\n 'dicarlo.BashivanKar2019.n_stretch_session_2_synth',\n 'dicarlo.BashivanKar2019.s_ohp_session_1_synth',\n 'dicarlo.BashivanKar2019.s_stretch_session_1_synth'], dtype=object)" - }, - "metadata": {} - } - ], - "execution_count": 6, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-19T22:57:23.566Z", - "iopub.execute_input": "2021-01-19T22:57:23.589Z", - "iopub.status.idle": "2021-01-19T22:57:23.637Z", - "shell.execute_reply": "2021-01-19T22:57:23.661Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "for stage in [(\"naturalistic\", \"nat\"), (\"synthetic\", \"synth\")]:\n", - " for monkey in h5.root.neural[stage[0]]:\n", - " for setting in monkey:\n", - " for session in setting: \n", - " print(f\"pytest.param('dicarlo.BashivanKar2019.{monkey._v_name[-1]}_{setting._v_name}_{session._v_name}_{stage[1]}', ({session.shape[0]*session.shape[1]}, {session.shape[2]}, 1), marks=[pytest.mark.private_access]),\")" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "pytest.param('dicarlo.BashivanKar2019.m_ohp_session_1_nat', (24320, 39, 1), marks=[pytest.mark.private_access]),\n", - "pytest.param('dicarlo.BashivanKar2019.m_ohp_session_2_nat', (23040, 38, 1), marks=[pytest.mark.private_access]),\n", - "pytest.param('dicarlo.BashivanKar2019.m_stretch_session_1_nat', (24320, 39, 1), marks=[pytest.mark.private_access]),\n", - "pytest.param('dicarlo.BashivanKar2019.m_stretch_session_2_nat', (23040, 52, 1), marks=[pytest.mark.private_access]),\n", - "pytest.param('dicarlo.BashivanKar2019.n_stretch_session_1_nat', (21760, 20, 1), marks=[pytest.mark.private_access]),\n", - "pytest.param('dicarlo.BashivanKar2019.n_stretch_session_2_nat', (21760, 1, 1), marks=[pytest.mark.private_access]),\n", - "pytest.param('dicarlo.BashivanKar2019.s_ohp_session_1_nat', (21120, 22, 1), marks=[pytest.mark.private_access]),\n", - "pytest.param('dicarlo.BashivanKar2019.s_stretch_session_1_nat', (21120, 22, 1), marks=[pytest.mark.private_access]),\n", - "pytest.param('dicarlo.BashivanKar2019.m_ohp_session_1_synth', (2550, 39, 1), marks=[pytest.mark.private_access]),\n", - "pytest.param('dicarlo.BashivanKar2019.m_ohp_session_2_synth', (1800, 38, 1), marks=[pytest.mark.private_access]),\n", - "pytest.param('dicarlo.BashivanKar2019.m_stretch_session_1_synth', (2550, 39, 1), marks=[pytest.mark.private_access]),\n", - "pytest.param('dicarlo.BashivanKar2019.m_stretch_session_2_synth', (2640, 52, 1), marks=[pytest.mark.private_access]),\n", - "pytest.param('dicarlo.BashivanKar2019.n_stretch_session_1_synth', (2790, 20, 1), marks=[pytest.mark.private_access]),\n", - "pytest.param('dicarlo.BashivanKar2019.n_stretch_session_2_synth', (100, 1, 1), marks=[pytest.mark.private_access]),\n", - "pytest.param('dicarlo.BashivanKar2019.s_ohp_session_1_synth', (4465, 22, 1), marks=[pytest.mark.private_access]),\n", - "pytest.param('dicarlo.BashivanKar2019.s_stretch_session_1_synth', (4465, 22, 1), marks=[pytest.mark.private_access]),\n" - ] - } - ], - "execution_count": 95, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-24T05:45:46.641Z", - "iopub.execute_input": "2020-08-24T05:45:46.662Z", - "iopub.status.idle": "2020-08-24T05:45:46.707Z", - "shell.execute_reply": "2020-08-24T05:45:46.728Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "from brainio_collection import get_assembly" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Loading lookup from /Users/jjpr/dev/brainio_collection/brainio_collection/lookup.csv\n" - ] - } - ], - "execution_count": 1, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-19T20:54:08.374Z", - "iopub.execute_input": "2021-01-19T20:54:08.397Z", - "iopub.status.idle": "2021-01-19T20:54:11.862Z", - "shell.execute_reply": "2021-01-19T20:54:11.896Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "mo1n = get_assembly(\"dicarlo.BashivanKar2019.m_ohp_session_1_nat\")\n", - "mo1n" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/Users/jjpr/anaconda/envs/brainio_contrib/lib/python3.7/site-packages/xarray/core/dataarray.py:217: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n", - " elif isinstance(data, pd.Panel):\n", - "/Users/jjpr/dev/brainio_base/brainio_base/assemblies.py:213: FutureWarning: The inplace argument has been deprecated and will be removed in xarray 0.12.0.\n", - " xr_data.set_index(append=True, inplace=True, **coords_d)\n", - "/Users/jjpr/anaconda/envs/brainio_contrib/lib/python3.7/site-packages/xarray/core/dataset.py:167: FutureWarning: the 'labels' keyword is deprecated, use 'codes' instead\n", - " idx = pd.MultiIndex(labels=labels, levels=levels, names=names)\n" - ] - }, - { - "output_type": "execute_result", - "execution_count": 92, - "data": { - "text/plain": "\narray([[[-0.117862],\n [ 0.049042],\n ...,\n [-0.05465 ],\n [-0.874225]],\n\n [[ 0.855779],\n [-0.43881 ],\n ...,\n [-0.644619],\n [ 0.115542]],\n\n ...,\n\n [[-0.117862],\n [ 0.902783],\n ...,\n [-0.447962],\n [ 0.214519]],\n\n [[ 0.125548],\n [ 1.024746],\n ...,\n [-0.447962],\n [-0.676272]]])\nCoordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 24 25 26 27 28 29\n - image_id (presentation) object 'a5ca287ed63a8a1712acd20685744cdb6e96cba5' ... 'a5ca287ed63a8a1712acd20685744cdb6e96cba5'\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 24 25 26 27 28 29\n - image_index (presentation) int64 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0\n - filename (presentation) object 'a5ca287ed63a8a1712acd20685744cdb6e96cba5.png' ... 'a5ca287ed63a8a1712acd20685744cdb6e96cba5.png'\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 'm_ohp_1_0' 'm_ohp_1_1' ... 'm_ohp_1_29'\n - animal (neuroid) object 'monkey_m' 'monkey_m' ... 'monkey_m'\n - setting (neuroid) object 'ohp' 'ohp' 'ohp' ... 'ohp' 'ohp' 'ohp'\n - session (neuroid) object 'session_1' 'session_1' ... 'session_1'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 23 24 25 26 27 28 29\n - is_target (neuroid) int64 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\nAttributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.naturalistic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.m_ohp_session_1_nat" - }, - "metadata": {} - } - ], - "execution_count": 92, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-08-24T05:38:09.335Z", - "iopub.execute_input": "2020-08-24T05:38:09.354Z", - "iopub.status.idle": "2020-08-24T05:38:10.235Z", - "shell.execute_reply": "2020-08-24T05:38:10.294Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "bk19syn = get_stimulus_set(\"dicarlo.BashivanKar2019.synthetic\")\n", - "bk19syn" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 2, - "data": { - "text/plain": " image_id image_index \\\n0 33ca8ceef160d93d204121f09ea4b49c582a4abe 0 \n1 073f80dd7f30c32888460eaaa287c12284fb30f9 1 \n2 310feef7c4da3e638b3fa8a3bb016bc9f05d2e74 2 \n3 c3f3a5a10ed15fb7b8a7442f060dbcb48e3411e7 3 \n4 f5d83ad68c681be1cd967baa3c01257165f06b43 4 \n.. ... ... \n505 f90a1b30631f80825fb69225ebb27f3b4808de31 90 \n506 200bcdd8934ed30db6cc8b3fa99ad3274285ed7b 91 \n507 eaf63cdbd2b0c5aa82e01e4aca1e104c93077040 92 \n508 365a65f83be86f359021837cbd25502596047da5 93 \n509 041d4bd57e069b74d060d82292002aae967a56ee 94 \n\n filename setting animal \\\n0 33ca8ceef160d93d204121f09ea4b49c582a4abe.png ohp monkey_m \n1 073f80dd7f30c32888460eaaa287c12284fb30f9.png ohp monkey_m \n2 310feef7c4da3e638b3fa8a3bb016bc9f05d2e74.png ohp monkey_m \n3 c3f3a5a10ed15fb7b8a7442f060dbcb48e3411e7.png ohp monkey_m \n4 f5d83ad68c681be1cd967baa3c01257165f06b43.png ohp monkey_m \n.. ... ... ... \n505 f90a1b30631f80825fb69225ebb27f3b4808de31.png stretch monkey_s \n506 200bcdd8934ed30db6cc8b3fa99ad3274285ed7b.png stretch monkey_s \n507 eaf63cdbd2b0c5aa82e01e4aca1e104c93077040.png stretch monkey_s \n508 365a65f83be86f359021837cbd25502596047da5.png stretch monkey_s \n509 041d4bd57e069b74d060d82292002aae967a56ee.png stretch monkey_s \n\n session \n0 session_1 \n1 session_1 \n2 session_1 \n3 session_1 \n4 session_1 \n.. ... \n505 session_1 \n506 session_1 \n507 session_1 \n508 session_1 \n509 session_1 \n\n[510 rows x 6 columns]", - "text/html": "
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" - }, - "metadata": {} - } - ], - "execution_count": 3, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-09-02T19:28:04.731Z", - "iopub.execute_input": "2020-09-02T19:28:04.751Z", - "iopub.status.idle": "2020-09-02T19:28:04.825Z", - "shell.execute_reply": "2020-09-02T19:28:04.872Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "bk19_assy_names = [name for name in list_assemblies() if \"BashivanKar2019\" in name]\n", - "bk19_assy_names" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 3, - "data": { - "text/plain": "['dicarlo.BashivanKar2019.m_ohp_session_1_nat',\n 'dicarlo.BashivanKar2019.m_ohp_session_2_nat',\n 'dicarlo.BashivanKar2019.m_stretch_session_1_nat',\n 'dicarlo.BashivanKar2019.m_stretch_session_2_nat',\n 'dicarlo.BashivanKar2019.n_stretch_session_1_nat',\n 'dicarlo.BashivanKar2019.n_stretch_session_2_nat',\n 'dicarlo.BashivanKar2019.s_ohp_session_1_nat',\n 'dicarlo.BashivanKar2019.s_stretch_session_1_nat',\n 'dicarlo.BashivanKar2019.m_ohp_session_1_synth',\n 'dicarlo.BashivanKar2019.m_ohp_session_2_synth',\n 'dicarlo.BashivanKar2019.m_stretch_session_1_synth',\n 'dicarlo.BashivanKar2019.m_stretch_session_2_synth',\n 'dicarlo.BashivanKar2019.n_stretch_session_1_synth',\n 'dicarlo.BashivanKar2019.n_stretch_session_2_synth',\n 'dicarlo.BashivanKar2019.s_ohp_session_1_synth',\n 'dicarlo.BashivanKar2019.s_stretch_session_1_synth']" - }, - "metadata": {} - } - ], - "execution_count": 3, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-19T20:54:54.037Z", - "iopub.execute_input": "2021-01-19T20:54:54.063Z", - "iopub.status.idle": "2021-01-19T20:54:54.113Z", - "shell.execute_reply": "2021-01-19T20:54:54.136Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "bk19_assys = {name: get_assembly(name) for name in bk19_assy_names}\n", - "bk19_assys" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_m_ohp_session_1_nat.nc: 100%|██████████| 9.76M/9.76M [00:03<00:00, 3.09MB/s]\n", - "brainio.dicarlo/image_dicarlo_BashivanKar2019_naturalistic.csv: 100%|██████████| 57.5k/57.5k [00:00<00:00, 439kB/s]\n", - "brainio.dicarlo/image_dicarlo_BashivanKar2019_naturalistic.zip: 100%|██████████| 20.0M/20.0M [00:10<00:00, 1.91MB/s]\n", - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_m_ohp_session_2_nat.nc: 100%|██████████| 9.07M/9.07M [00:02<00:00, 3.11MB/s]\n", - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_m_stretch_session_1_nat.nc: 100%|██████████| 9.76M/9.76M [00:03<00:00, 3.00MB/s]\n", - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_m_stretch_session_2_nat.nc: 100%|██████████| 11.7M/11.7M [00:03<00:00, 2.99MB/s]\n", - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_n_stretch_session_1_nat.nc: 100%|██████████| 5.44M/5.44M [00:01<00:00, 3.05MB/s]\n", - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_n_stretch_session_2_nat.nc: 100%|██████████| 2.13M/2.13M [00:00<00:00, 2.76MB/s]\n", - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_s_ohp_session_1_nat.nc: 100%|██████████| 5.59M/5.59M [00:01<00:00, 2.94MB/s]\n", - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_s_stretch_session_1_nat.nc: 100%|██████████| 5.59M/5.59M [00:01<00:00, 3.02MB/s]\n", - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_m_ohp_session_1_synth.nc: 100%|██████████| 1.04M/1.04M [00:00<00:00, 2.12MB/s]\n", - "brainio.dicarlo/image_dicarlo_BashivanKar2019_synthetic.csv: 100%|██████████| 58.4k/58.4k [00:00<00:00, 419kB/s]\n", - "brainio.dicarlo/image_dicarlo_BashivanKar2019_synthetic.zip: 100%|██████████| 7.28M/7.28M [00:02<00:00, 2.95MB/s]\n", - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_m_ohp_session_2_synth.nc: 100%|██████████| 724k/724k [00:00<00:00, 1.19MB/s]\n", - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_m_stretch_session_1_synth.nc: 100%|██████████| 1.04M/1.04M [00:00<00:00, 1.43MB/s]\n", - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_m_stretch_session_2_synth.nc: 100%|██████████| 1.35M/1.35M [00:00<00:00, 2.34MB/s]\n", - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_n_stretch_session_1_synth.nc: 100%|██████████| 714k/714k [00:00<00:00, 1.18MB/s]\n", - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_n_stretch_session_2_synth.nc: 100%|██████████| 21.4k/21.4k [00:00<00:00, 184kB/s]\n", - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_s_ohp_session_1_synth.nc: 100%|██████████| 1.21M/1.21M [00:00<00:00, 1.52MB/s]\n", - "brainio.dicarlo/assy_dicarlo_BashivanKar2019_s_stretch_session_1_synth.nc: 100%|██████████| 1.21M/1.21M [00:00<00:00, 1.66MB/s]\n" - ] - }, - { - "output_type": "execute_result", - "execution_count": 4, - "data": { - "text/plain": "{'dicarlo.BashivanKar2019.m_ohp_session_1_nat': \n array([[[-0.11786183],\n [ 0.04904198],\n [ 0.56265136],\n ...,\n [ 0.2156809 ],\n [-0.05464976],\n [-0.87422499]],\n \n [[ 0.85577937],\n [-0.43881017],\n [-0.58192849],\n ...,\n [-0.89875423],\n [-0.6446187 ],\n [ 0.11554231]],\n \n [[ 1.09918967],\n [-0.80469927],\n [-0.12409655],\n ...,\n ...\n ...,\n [-0.89875423],\n [-0.05464976],\n [-0.5772948 ]],\n \n [[-0.11786183],\n [ 0.90278323],\n [-0.12409655],\n ...,\n [-0.42113917],\n [-0.44796239],\n [ 0.21451904]],\n \n [[ 0.12554847],\n [ 1.02474627],\n [-0.35301252],\n ...,\n [-0.89875423],\n [-0.44796239],\n [-0.67627153]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 32 33 34 35 36 37\n - image_id (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 32 33 34 35 36 37\n - image_index (presentation) int64 0 0 0 0 0 0 ... 639 639 639 639 639\n - filename (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 'm_ohp_1_0' 'm_ohp_1_1' ... 'm_ohp_1_38'\n - animal (neuroid) object 'monkey_m' 'monkey_m' ... 'monkey_m'\n - setting (neuroid) object 'ohp' 'ohp' 'ohp' ... 'ohp' 'ohp' 'ohp'\n - session (neuroid) object 'session_1' 'session_1' ... 'session_1'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 32 33 34 35 36 37 38\n - is_target (neuroid) int64 0 0 0 0 0 0 0 0 0 0 ... 0 1 0 0 0 0 0 0 0\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.naturalistic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.m_ohp_session_1_nat,\n 'dicarlo.BashivanKar2019.m_ohp_session_2_nat': \n array([[[-1.01752473],\n [ 0.32806625],\n [ 2.64124085],\n ...,\n [-1.4961274 ],\n [ 2.70566705],\n [-0.35466163]],\n \n [[-1.76668835],\n [-0.51871242],\n [ 0.08506273],\n ...,\n [ 0.48113348],\n [-1.06824941],\n [ 0.22002157]],\n \n [[-1.20481563],\n [-0.68806815],\n [ 1.13760667],\n ...,\n ...\n ...,\n [ 1.28343654],\n [-0.88749534],\n [ 0.39134357]],\n \n [[ 0.75537673],\n [ 0.51212095],\n [-0.31239004],\n ...,\n [-0.74571214],\n [ 1.74341969],\n [ 0.39134357]],\n \n [[ 1.92262555],\n [ 2.00045133],\n [ 0.33171314],\n ...,\n [ 1.11434082],\n [ 1.74341969],\n [-0.68083059]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 30 31 32 33 34 35\n - image_id (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 30 31 32 33 34 35\n - image_index (presentation) int64 0 0 0 0 0 0 ... 639 639 639 639 639\n - filename (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 'm_ohp_2_0' 'm_ohp_2_1' ... 'm_ohp_2_37'\n - animal (neuroid) object 'monkey_m' 'monkey_m' ... 'monkey_m'\n - setting (neuroid) object 'ohp' 'ohp' 'ohp' ... 'ohp' 'ohp' 'ohp'\n - session (neuroid) object 'session_2' 'session_2' ... 'session_2'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 31 32 33 34 35 36 37\n - is_target (neuroid) int64 1 0 1 0 0 1 1 1 0 0 ... 0 0 0 0 0 0 0 0 0\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.naturalistic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.m_ohp_session_2_nat,\n 'dicarlo.BashivanKar2019.m_stretch_session_1_nat': \n array([[[-0.11786183],\n [ 0.04904198],\n [ 0.56265136],\n ...,\n [ 0.2156809 ],\n [-0.05464976],\n [-0.87422499]],\n \n [[ 0.85577937],\n [-0.43881017],\n [-0.58192849],\n ...,\n [-0.89875423],\n [-0.6446187 ],\n [ 0.11554231]],\n \n [[ 1.09918967],\n [-0.80469927],\n [-0.12409655],\n ...,\n ...\n ...,\n [-0.89875423],\n [-0.05464976],\n [-0.5772948 ]],\n \n [[-0.11786183],\n [ 0.90278323],\n [-0.12409655],\n ...,\n [-0.42113917],\n [-0.44796239],\n [ 0.21451904]],\n \n [[ 0.12554847],\n [ 1.02474627],\n [-0.35301252],\n ...,\n [-0.89875423],\n [-0.44796239],\n [-0.67627153]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 32 33 34 35 36 37\n - image_id (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 32 33 34 35 36 37\n - image_index (presentation) int64 0 0 0 0 0 0 ... 639 639 639 639 639\n - filename (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 'm_stretch_1_0' ... 'm_stretch_1_38'\n - animal (neuroid) object 'monkey_m' 'monkey_m' ... 'monkey_m'\n - setting (neuroid) object 'stretch' 'stretch' ... 'stretch'\n - session (neuroid) object 'session_1' 'session_1' ... 'session_1'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 32 33 34 35 36 37 38\n - is_target (neuroid) int64 0 0 0 0 0 0 0 0 0 0 ... 0 1 0 0 0 0 0 0 0\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.naturalistic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.m_stretch_session_1_nat,\n 'dicarlo.BashivanKar2019.m_stretch_session_2_nat': \n array([[[-1.01752473],\n [ 2.81185662],\n [ 0.32806625],\n ...,\n [-1.87369821],\n [-0.39759216],\n [-0.35466163]],\n \n [[-1.76668835],\n [ 1.07134628],\n [-0.51871242],\n ...,\n [-2.56950126],\n [-1.57689092],\n [ 0.22002157]],\n \n [[-1.20481563],\n [-0.45160027],\n [-0.68806815],\n ...,\n ...\n ...,\n [ 4.43664328],\n [-2.45046215],\n [ 0.39134357]],\n \n [[ 0.75537673],\n [ 0.10277588],\n [ 0.51212095],\n ...,\n [-1.59477901],\n [-1.06992009],\n [ 0.39134357]],\n \n [[ 1.92262555],\n [ 1.49972966],\n [ 2.00045133],\n ...,\n [-0.88519992],\n [ 0.31062196],\n [-0.68083059]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 30 31 32 33 34 35\n - image_id (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 30 31 32 33 34 35\n - image_index (presentation) int64 0 0 0 0 0 0 ... 639 639 639 639 639\n - filename (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 'm_stretch_2_0' ... 'm_stretch_2_51'\n - animal (neuroid) object 'monkey_m' 'monkey_m' ... 'monkey_m'\n - setting (neuroid) object 'stretch' 'stretch' ... 'stretch'\n - session (neuroid) object 'session_2' 'session_2' ... 'session_2'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 45 46 47 48 49 50 51\n - is_target (neuroid) int64 1 0 1 0 0 1 1 1 0 0 ... 0 0 0 0 0 1 0 0 0\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.naturalistic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.m_stretch_session_2_nat,\n 'dicarlo.BashivanKar2019.n_stretch_session_1_nat': \n array([[[-1.92662651],\n [-1.77608875],\n [-1.33246072],\n ...,\n [-2.26649039],\n [-1.14696755],\n [-2.56690244]],\n \n [[ 0.24859697],\n [-0.53580331],\n [-1.77484475],\n ...,\n [ 1.85818881],\n [ 0.44781492],\n [-0.05956818]],\n \n [[ 0.55934318],\n [-1.77608875],\n [-2.43842081],\n ...,\n ...\n ...,\n [ 0.33569991],\n [ 0.71580249],\n [ 5.82924744]],\n \n [[ 0.32948304],\n [-1.99391701],\n [-1.87154198],\n ...,\n [-0.91740388],\n [ 3.05076305],\n [-2.65253799]],\n \n [[-3.37476417],\n [-2.42977094],\n [-2.46380209],\n ...,\n [-2.17050767],\n [-1.08032103],\n [-1.83172005]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 28 29 30 31 32 33\n - image_id (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 28 29 30 31 32 33\n - image_index (presentation) int64 0 0 0 0 0 0 ... 639 639 639 639 639\n - filename (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 'n_stretch_1_0' ... 'n_stretch_1_19'\n - animal (neuroid) object 'monkey_n' 'monkey_n' ... 'monkey_n'\n - setting (neuroid) object 'stretch' 'stretch' ... 'stretch'\n - session (neuroid) object 'session_1' 'session_1' ... 'session_1'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 13 14 15 16 17 18 19\n - is_target (neuroid) int64 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.naturalistic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.n_stretch_session_1_nat,\n 'dicarlo.BashivanKar2019.n_stretch_session_2_nat': \n array([[[-1.57223025]],\n \n [[ 0.11833991]],\n \n [[-0.72694517]],\n \n ...,\n \n [[ 1.29984119]],\n \n [[-0.90306113]],\n \n [[-0.29114382]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 28 29 30 31 32 33\n - image_id (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 28 29 30 31 32 33\n - image_index (presentation) int64 0 0 0 0 0 0 ... 639 639 639 639 639\n - filename (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 'n_stretch_2_0'\n - animal (neuroid) object 'monkey_n'\n - setting (neuroid) object 'stretch'\n - session (neuroid) object 'session_2'\n - neuroid_index (neuroid) int64 0\n - is_target (neuroid) int64 0\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.naturalistic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.n_stretch_session_2_nat,\n 'dicarlo.BashivanKar2019.s_ohp_session_1_nat': \n array([[[ 5.42008837e-02],\n [ 5.89201169e-01],\n [-2.11065999e-02],\n ...,\n [ 7.55221741e-01],\n [-3.38734767e-01],\n [ 2.14938436e+00]],\n \n [[ 5.42008837e-02],\n [ 8.33583567e-01],\n [ 1.21231033e+00],\n ...,\n [ 6.73362715e-01],\n [ 1.08880878e+00],\n [ 1.79143438e+00]],\n \n [[ 5.42008837e-02],\n [ 6.70661969e-01],\n [ 5.39537459e-01],\n ...,\n ...\n ...,\n [-8.26469854e-01],\n [-1.23746844e+00],\n [ 7.52028679e-01]],\n \n [[ 2.31266923e-01],\n [ 1.40231997e+00],\n [ 9.60398094e-01],\n ...,\n [-1.91777643e+00],\n [ 3.91672677e-01],\n [-6.42846768e-02]],\n \n [[-1.13775503e+00],\n [ 1.21662463e-01],\n [-2.75171687e-01],\n ...,\n [-1.37212314e+00],\n [-1.14696060e+00],\n [-4.72441355e-01]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 27 28 29 30 31 32\n - image_id (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 27 28 29 30 31 32\n - image_index (presentation) int64 0 0 0 0 0 0 ... 639 639 639 639 639\n - filename (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 's_ohp_1_0' 's_ohp_1_1' ... 's_ohp_1_21'\n - animal (neuroid) object 'monkey_s' 'monkey_s' ... 'monkey_s'\n - setting (neuroid) object 'ohp' 'ohp' 'ohp' ... 'ohp' 'ohp' 'ohp'\n - session (neuroid) object 'session_1' 'session_1' ... 'session_1'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 15 16 17 18 19 20 21\n - is_target (neuroid) int64 1 0 1 1 1 1 0 1 1 0 ... 1 1 1 1 1 1 1 1 1\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.naturalistic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.s_ohp_session_1_nat,\n 'dicarlo.BashivanKar2019.s_stretch_session_1_nat': \n array([[[ 5.42008837e-02],\n [ 5.89201169e-01],\n [-2.11065999e-02],\n ...,\n [ 7.55221741e-01],\n [-3.38734767e-01],\n [ 2.14938436e+00]],\n \n [[ 5.42008837e-02],\n [ 8.33583567e-01],\n [ 1.21231033e+00],\n ...,\n [ 6.73362715e-01],\n [ 1.08880878e+00],\n [ 1.79143438e+00]],\n \n [[ 5.42008837e-02],\n [ 6.70661969e-01],\n [ 5.39537459e-01],\n ...,\n ...\n ...,\n [-8.26469854e-01],\n [-1.23746844e+00],\n [ 7.52028679e-01]],\n \n [[ 2.31266923e-01],\n [ 1.40231997e+00],\n [ 9.60398094e-01],\n ...,\n [-1.91777643e+00],\n [ 3.91672677e-01],\n [-6.42846768e-02]],\n \n [[-1.13775503e+00],\n [ 1.21662463e-01],\n [-2.75171687e-01],\n ...,\n [-1.37212314e+00],\n [-1.14696060e+00],\n [-4.72441355e-01]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 27 28 29 30 31 32\n - image_id (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 27 28 29 30 31 32\n - image_index (presentation) int64 0 0 0 0 0 0 ... 639 639 639 639 639\n - filename (presentation) object 'a5ca287ed63a8a1712acd20685744cdb...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 's_stretch_1_0' ... 's_stretch_1_21'\n - animal (neuroid) object 'monkey_s' 'monkey_s' ... 'monkey_s'\n - setting (neuroid) object 'stretch' 'stretch' ... 'stretch'\n - session (neuroid) object 'session_1' 'session_1' ... 'session_1'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 15 16 17 18 19 20 21\n - is_target (neuroid) int64 1 0 1 1 1 1 0 1 1 0 ... 1 1 1 1 1 1 1 1 1\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.naturalistic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.s_stretch_session_1_nat,\n 'dicarlo.BashivanKar2019.m_ohp_session_1_synth': \n array([[[-1.50660476],\n [-1.16108872],\n [-2.39110662],\n ...,\n [-1.18531338],\n [-1.32623403],\n [-0.68220458]],\n \n [[ 2.33677472],\n [ 0.07708221],\n [19.6892106 ],\n ...,\n [ 1.34698313],\n [-1.60212707],\n [ 1.8731719 ]],\n \n [[-1.50660476],\n [ 0.23185358],\n [-0.81394111],\n ...,\n ...\n ...,\n [-0.84767384],\n [-1.8780201 ],\n [ 1.67660448]],\n \n [[-3.04395655],\n [-0.54200326],\n [ 1.81466809],\n ...,\n [ 1.34698313],\n [-1.60212707],\n [ 2.06973932]],\n \n [[ 1.56809883],\n [-0.54200326],\n [-1.33966294],\n ...,\n [ 1.17816336],\n [-0.49855494],\n [-1.46847427]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 45 46 47 48 49 50\n - image_id (presentation) object '33ca8ceef160d93d204121f09ea4b49c...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 45 46 47 48 49 50\n - animal (presentation) object 'monkey_m' 'monkey_m' ... 'monkey_m'\n - setting (presentation) object 'ohp' 'ohp' 'ohp' ... 'ohp' 'ohp'\n - session (presentation) object 'session_1' ... 'session_1'\n - image_index (presentation) int64 0 0 0 0 0 0 0 ... 49 49 49 49 49 49\n - filename (presentation) object '33ca8ceef160d93d204121f09ea4b49c...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 'm_ohp_1_0' 'm_ohp_1_1' ... 'm_ohp_1_38'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 32 33 34 35 36 37 38\n - is_target (neuroid) int64 0 0 0 0 0 0 0 0 0 0 ... 0 1 0 0 0 0 0 0 0\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.synthetic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.m_ohp_session_1_synth,\n 'dicarlo.BashivanKar2019.m_ohp_session_2_synth': \n array([[[ 2.70631835],\n [-1.34692801],\n [-1.75853424],\n ...,\n [-1.88534211],\n [-0.2667739 ],\n [-3.12331504]],\n \n [[ 1.50007911],\n [ 1.09718418],\n [-0.80410589],\n ...,\n [-1.64535457],\n [ 1.98977225],\n [-2.32634769]],\n \n [[ 4.15380543],\n [ 2.87472032],\n [ 2.53639335],\n ...,\n ...\n ...,\n [ 0.75452082],\n [ 4.74777309],\n [ 3.25242375]],\n \n [[-0.42990366],\n [-0.45815994],\n [ 3.4908217 ],\n ...,\n [ 0.51453328],\n [ 2.49122694],\n [-0.73241299]],\n \n [[-1.39489505],\n [ 0.43060813],\n [ 1.10475082],\n ...,\n [ 1.47448344],\n [ 1.98977225],\n [ 5.64332579]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 34 35 36 37 38 39\n - image_id (presentation) object '91851b3b2ac6181661f24584008a3b07...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 34 35 36 37 38 39\n - animal (presentation) object 'monkey_m' 'monkey_m' ... 'monkey_m'\n - setting (presentation) object 'ohp' 'ohp' 'ohp' ... 'ohp' 'ohp'\n - session (presentation) object 'session_2' ... 'session_2'\n - image_index (presentation) int64 0 0 0 0 0 0 0 ... 44 44 44 44 44 44\n - filename (presentation) object '91851b3b2ac6181661f24584008a3b07...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 'm_ohp_2_0' 'm_ohp_2_1' ... 'm_ohp_2_37'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 31 32 33 34 35 36 37\n - is_target (neuroid) int64 1 0 1 0 0 1 1 1 0 0 ... 0 0 0 0 0 0 0 0 0\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.synthetic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.m_ohp_session_2_synth,\n 'dicarlo.BashivanKar2019.m_stretch_session_1_synth': \n array([[[ 6.1801542 ],\n [ 1.31525314],\n [ 2.34038993],\n ...,\n [ 4.0480994 ],\n [ 0.88091022],\n [ 3.05257643]],\n \n [[ 3.10545062],\n [ 2.86296681],\n [-1.33966294],\n ...,\n [ 4.0480994 ],\n [ 0.88091022],\n [ 2.46287417]],\n \n [[ 0.79942293],\n [ 1.00571041],\n [ 1.28894625],\n ...,\n ...\n ...,\n [ 1.5158029 ],\n [-0.77444797],\n [ 1.48003706]],\n \n [[-0.73792886],\n [-0.23246052],\n [ 3.3918336 ],\n ...,\n [ 1.0093436 ],\n [-0.49855494],\n [ 0.49719995]],\n \n [[-0.73792886],\n [-0.85154599],\n [-1.33966294],\n ...,\n [ 2.19108197],\n [-0.49855494],\n [ 1.28346964]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 45 46 47 48 49 50\n - image_id (presentation) object 'e3fce400c9c72d28460bcd87dccd739d...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 45 46 47 48 49 50\n - animal (presentation) object 'monkey_m' 'monkey_m' ... 'monkey_m'\n - setting (presentation) object 'stretch' 'stretch' ... 'stretch'\n - session (presentation) object 'session_1' ... 'session_1'\n - image_index (presentation) int64 0 0 0 0 0 0 0 ... 49 49 49 49 49 49\n - filename (presentation) object 'e3fce400c9c72d28460bcd87dccd739d...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 'm_stretch_1_0' ... 'm_stretch_1_38'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 32 33 34 35 36 37 38\n - is_target (neuroid) int64 0 0 0 0 0 0 0 0 0 0 ... 0 1 0 0 0 0 0 0 0\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.synthetic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.m_stretch_session_1_synth,\n 'dicarlo.BashivanKar2019.m_stretch_session_2_synth': \n array([[[ 4.84227555],\n [ 0.54491884],\n [ 0.09665379],\n ...,\n [ 0.43138056],\n [ 0.23336831],\n [-1.87534102]],\n \n [[ 1.29726062],\n [ 6.03849985],\n [ 2.97613118],\n ...,\n [ 5.90659541],\n [ 9.3531561 ],\n [14.06298772]],\n \n [[ 2.0062636 ],\n [ 0.54491884],\n [ 1.42564335],\n ...,\n ...\n ...,\n [ 1.80018428],\n [ 1.24667806],\n [-1.87534102]],\n \n [[ 2.3607651 ],\n [ 1.46051568],\n [ 2.97613118],\n ...,\n [ 1.34391637],\n [-0.77994145],\n [-0.73688897]],\n \n [[-0.12074536],\n [ 1.46051568],\n [ 0.31815205],\n ...,\n [-0.48115524],\n [ 1.75333294],\n [-3.01379307]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 27 28 29 30 31 32\n - image_id (presentation) object '6a0f2219aa8d3973a541b86b3f442455...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 27 28 29 30 31 32\n - animal (presentation) object 'monkey_m' 'monkey_m' ... 'monkey_m'\n - setting (presentation) object 'stretch' 'stretch' ... 'stretch'\n - session (presentation) object 'session_2' ... 'session_2'\n - image_index (presentation) int64 0 0 0 0 0 0 0 ... 79 79 79 79 79 79\n - filename (presentation) object '6a0f2219aa8d3973a541b86b3f442455...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 'm_stretch_2_0' ... 'm_stretch_2_51'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 45 46 47 48 49 50 51\n - is_target (neuroid) int64 1 0 1 0 0 1 1 1 0 0 ... 0 0 0 0 0 1 0 0 0\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.synthetic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.m_stretch_session_2_synth,\n 'dicarlo.BashivanKar2019.n_stretch_session_1_synth': \n array([[[ 0.44182522],\n [-1.69101687],\n [ 0.69164297],\n ...,\n [-0.47815931],\n [ 1.26603026],\n [-2.19709344]],\n \n [[ 0.71358279],\n [-0.12936521],\n [-0.01894912],\n ...,\n [ 2.03771485],\n [ 0.68864298],\n [ 0.57724043]],\n \n [[ 2.07237066],\n [-1.37868654],\n [ 1.40223507],\n ...,\n ...\n ...,\n [ 3.66563342],\n [ 3.96050424],\n [ 8.20665858]],\n \n [[ 3.43115853],\n [ 3.30626845],\n [ 2.11282716],\n ...,\n [ 2.33370004],\n [ 9.92683947],\n [ 7.51307512]],\n \n [[ 0.17006764],\n [ 2.36927745],\n [ 2.11282716],\n ...,\n [ 1.88972225],\n [ 2.42080482],\n [ 4.73874124]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 25 26 27 28 29 30\n - image_id (presentation) object '511202f8d5ac2c35a3baea9bba5cda40...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 25 26 27 28 29 30\n - animal (presentation) object 'monkey_n' 'monkey_n' ... 'monkey_n'\n - setting (presentation) object 'stretch' 'stretch' ... 'stretch'\n - session (presentation) object 'session_1' ... 'session_1'\n - image_index (presentation) int64 0 0 0 0 0 0 0 ... 89 89 89 89 89 89\n - filename (presentation) object '511202f8d5ac2c35a3baea9bba5cda40...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 'n_stretch_1_0' ... 'n_stretch_1_19'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 13 14 15 16 17 18 19\n - is_target (neuroid) int64 0 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.synthetic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.n_stretch_session_1_synth,\n 'dicarlo.BashivanKar2019.n_stretch_session_2_synth': \n array([[[ 3.99474644]],\n \n [[-1.53814337]],\n \n [[-4.30458827]],\n \n [[-1.53814337]],\n \n [[-4.30458827]],\n \n [[-4.30458827]],\n \n [[-4.30458827]],\n \n [[-4.30458827]],\n \n [[-4.30458827]],\n \n [[-4.30458827]],\n \n ...\n \n [[-1.53814337]],\n \n [[ 1.22830154]],\n \n [[-1.53814337]],\n \n [[-1.53814337]],\n \n [[-4.30458827]],\n \n [[-1.53814337]],\n \n [[ 1.22830154]],\n \n [[-4.30458827]],\n \n [[-4.30458827]],\n \n [[ 1.22830154]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 14 15 16 17 18 19\n - image_id (presentation) object '8fafb30db6e9c886ca0c6aa0bcfd22fe...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 14 15 16 17 18 19\n - animal (presentation) object 'monkey_n' 'monkey_n' ... 'monkey_n'\n - setting (presentation) object 'stretch' 'stretch' ... 'stretch'\n - session (presentation) object 'session_2' ... 'session_2'\n - image_index (presentation) int64 0 0 0 0 0 0 0 0 0 ... 4 4 4 4 4 4 4 4\n - filename (presentation) object '8fafb30db6e9c886ca0c6aa0bcfd22fe...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 'n_stretch_2_0'\n - neuroid_index (neuroid) int64 0\n - is_target (neuroid) int64 0\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.synthetic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.n_stretch_session_2_synth,\n 'dicarlo.BashivanKar2019.s_ohp_session_1_synth': \n array([[[ 5.44182115],\n [ 3.60232573],\n [ 0.40124912],\n ...,\n [ 1.61945172],\n [ 0.20158402],\n [-0.93319192]],\n \n [[ 4.9820884 ],\n [ 0.87590797],\n [ 1.07926973],\n ...,\n [ 1.86960467],\n [ 2.46071528],\n [ 1.12125261]],\n \n [[ 6.36128664],\n [-2.33164234],\n [-0.72878523],\n ...,\n ...\n ...,\n [ 0.65057007],\n [ 0.53900233],\n [ 0.71915028]],\n \n [[-0.92314502],\n [-2.96424962],\n [ 1.19460171],\n ...,\n [-0.6140015 ],\n [-0.62311769],\n [ 0.29156942]],\n \n [[-0.25649972],\n [-2.66159869],\n [ 2.01264419],\n ...,\n [ 0.30568691],\n [ 1.70112235],\n [ 3.28463544]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 41 42 43 44 45 46\n - image_id (presentation) object '479ebc91a6735fad6f764f522ae4e2cd...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 41 42 43 44 45 46\n - animal (presentation) object 'monkey_s' 'monkey_s' ... 'monkey_s'\n - setting (presentation) object 'ohp' 'ohp' 'ohp' ... 'ohp' 'ohp'\n - session (presentation) object 'session_1' ... 'session_1'\n - image_index (presentation) int64 0 0 0 0 0 0 0 ... 94 94 94 94 94 94\n - filename (presentation) object '479ebc91a6735fad6f764f522ae4e2cd...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 's_ohp_1_0' 's_ohp_1_1' ... 's_ohp_1_21'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 15 16 17 18 19 20 21\n - is_target (neuroid) int64 1 0 1 1 1 1 0 1 1 0 ... 1 1 1 1 1 1 1 1 1\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.synthetic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.s_ohp_session_1_synth,\n 'dicarlo.BashivanKar2019.s_stretch_session_1_synth': \n array([[[ 4.9820884 ],\n [ 1.99855058],\n [-0.27677149],\n ...,\n [ 3.87082828],\n [ 3.13845466],\n [ 1.63486374]],\n \n [[-0.53470455],\n [ 4.40421331],\n [ 2.20930407],\n ...,\n [ 2.36991057],\n [-0.25024223],\n [-1.44680305]],\n \n [[ 1.76395918],\n [11.62120151],\n [-0.05076462],\n ...,\n ...\n ...,\n [-3.2581057 ],\n [-1.97892437],\n [-1.84633487]],\n \n [[-1.14536012],\n [ 0.66756147],\n [-1.94122778],\n ...,\n [-2.22345623],\n [-0.81680436],\n [-0.56359229]],\n \n [[-1.81200542],\n [-2.05629685],\n [-0.16880242],\n ...,\n [-1.99353413],\n [-0.62311769],\n [ 0.71915028]]])\n Coordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 41 42 43 44 45 46\n - image_id (presentation) object '25d81a4f8bfae95cd02c07bc38141052...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 41 42 43 44 45 46\n - animal (presentation) object 'monkey_s' 'monkey_s' ... 'monkey_s'\n - setting (presentation) object 'stretch' 'stretch' ... 'stretch'\n - session (presentation) object 'session_1' ... 'session_1'\n - image_index (presentation) int64 0 0 0 0 0 0 0 ... 94 94 94 94 94 94\n - filename (presentation) object '25d81a4f8bfae95cd02c07bc38141052...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 's_stretch_1_0' ... 's_stretch_1_21'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 15 16 17 18 19 20 21\n - is_target (neuroid) int64 1 0 1 1 1 1 0 1 1 0 ... 1 1 1 1 1 1 1 1 1\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\n Attributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.synthetic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.s_stretch_session_1_synth}" - }, - "metadata": {} - } - ], - "execution_count": 4, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-19T20:56:47.088Z", - "iopub.execute_input": "2021-01-19T20:56:47.114Z", - "iopub.status.idle": "2021-01-19T20:57:30.362Z", - "shell.execute_reply": "2021-01-19T20:57:30.431Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "[assy.shape[1] for assy in bk19_assys.values()]" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 10, - "data": { - "text/plain": "[39, 38, 39, 52, 20, 1, 22, 22, 39, 38, 39, 52, 20, 1, 22, 22]" - }, - "metadata": {} - } - ], - "execution_count": 10, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-09-02T19:43:13.142Z", - "iopub.execute_input": "2020-09-02T19:43:13.164Z", - "iopub.status.idle": "2020-09-02T19:43:13.207Z", - "shell.execute_reply": "2020-09-02T19:43:13.229Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "sum([assy.shape[1] for assy in bk19_assys.values()]) / 2" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 12, - "data": { - "text/plain": "233.0" - }, - "metadata": {} - } - ], - "execution_count": 12, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2020-09-02T19:44:12.269Z", - "iopub.execute_input": "2020-09-02T19:44:12.291Z", - "iopub.status.idle": "2020-09-02T19:44:12.338Z", - "shell.execute_reply": "2020-09-02T19:44:12.360Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "darren = get_assembly(\"\")" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "# packages in environment at /Users/jjpr/anaconda:\r\n", - "#\r\n", - "# Name Version Build Channel\r\n" - ] - } - ], - "execution_count": 2, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-19T19:58:16.019Z", - "iopub.execute_input": "2021-01-19T19:58:16.049Z", - "shell.execute_reply": "2021-01-19T19:58:18.742Z", - "iopub.status.idle": "2021-01-19T19:58:18.765Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "import xarray as xr" - ], - "outputs": [], - "execution_count": 7, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-20T00:59:59.814Z", - "iopub.execute_input": "2021-01-20T00:59:59.838Z", - "iopub.status.idle": "2021-01-20T00:59:59.875Z", - "shell.execute_reply": "2021-01-20T00:59:59.899Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "concatted = xr.concat([bk19_assys[k] for k in bk19_assys if \"synth\" in k], dim=\"presentation\")\n", - "concatted" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 9, - "data": { - "text/plain": "\narray([[[-1.50660476],\n [-1.16108872],\n [-0.19559382],\n ...,\n [ nan],\n [ nan],\n [ nan]],\n\n [[ 2.33677472],\n [ 0.07708221],\n [-0.19559382],\n ...,\n [ nan],\n [ nan],\n [ nan]],\n\n [[-1.50660476],\n [ 0.23185358],\n [ 1.97294636],\n ...,\n...\n ...,\n [-1.43410283],\n [-1.46334066],\n [ 0.40909557]],\n\n [[ nan],\n [ nan],\n [ nan],\n ...,\n [-1.43410283],\n [-1.26413053],\n [ 1.30027885]],\n\n [[ nan],\n [ nan],\n [ nan],\n ...,\n [-1.04455219],\n [-1.06492041],\n [-0.48208772]]])\nCoordinates:\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 'm_ohp_1_0' ... 's_stretch_1_9'\n - neuroid_index (neuroid) int64 0 1 10 11 12 13 14 15 ... 21 3 4 5 6 7 8 9\n - is_target (neuroid) int64 0 0 0 0 0 0 0 0 1 0 ... 1 1 1 1 1 0 1 1 0\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 1 2 3 4 5 6 ... 41 42 43 44 45 46\n - image_id (presentation) object '33ca8ceef160d93d204121f09ea4b49c...\n - repetition (presentation) int64 0 1 2 3 4 5 6 ... 41 42 43 44 45 46\n - animal (presentation) object 'monkey_m' 'monkey_m' ... 'monkey_s'\n - setting (presentation) object 'ohp' 'ohp' ... 'stretch' 'stretch'\n - session (presentation) object 'session_1' ... 'session_1'\n - image_index (presentation) int64 0 0 0 0 0 0 0 ... 94 94 94 94 94 94\n - filename (presentation) object '33ca8ceef160d93d204121f09ea4b49c...\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\nAttributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.synthetic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.m_ohp_session_1_synth", - "text/html": "
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" - }, - "metadata": {} - } - ], - "execution_count": 9, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-20T01:07:53.524Z", - "iopub.execute_input": "2021-01-20T01:07:53.549Z", - "iopub.status.idle": "2021-01-20T01:07:53.985Z", - "shell.execute_reply": "2021-01-20T01:07:54.407Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "synth_names = [k for k in bk19_assys if \"synth\" in k]\n", - "synth_names" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 11, - "data": { - "text/plain": "['dicarlo.BashivanKar2019.m_ohp_session_1_synth',\n 'dicarlo.BashivanKar2019.m_ohp_session_2_synth',\n 'dicarlo.BashivanKar2019.m_stretch_session_1_synth',\n 'dicarlo.BashivanKar2019.m_stretch_session_2_synth',\n 'dicarlo.BashivanKar2019.n_stretch_session_1_synth',\n 'dicarlo.BashivanKar2019.n_stretch_session_2_synth',\n 'dicarlo.BashivanKar2019.s_ohp_session_1_synth',\n 'dicarlo.BashivanKar2019.s_stretch_session_1_synth']" - }, - "metadata": {} - } - ], - "execution_count": 11, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-20T01:09:30.134Z", - "iopub.execute_input": "2021-01-20T01:09:30.161Z", - "iopub.status.idle": "2021-01-20T01:09:30.209Z", - "shell.execute_reply": "2021-01-20T01:09:30.229Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "{k: bk19_assys[k].shape for k in synth_names}" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 12, - "data": { - "text/plain": "{'dicarlo.BashivanKar2019.m_ohp_session_1_synth': (2550, 39, 1),\n 'dicarlo.BashivanKar2019.m_ohp_session_2_synth': (1800, 38, 1),\n 'dicarlo.BashivanKar2019.m_stretch_session_1_synth': (2550, 39, 1),\n 'dicarlo.BashivanKar2019.m_stretch_session_2_synth': (2640, 52, 1),\n 'dicarlo.BashivanKar2019.n_stretch_session_1_synth': (2790, 20, 1),\n 'dicarlo.BashivanKar2019.n_stretch_session_2_synth': (100, 1, 1),\n 'dicarlo.BashivanKar2019.s_ohp_session_1_synth': (4465, 22, 1),\n 'dicarlo.BashivanKar2019.s_stretch_session_1_synth': (4465, 22, 1)}" - }, - "metadata": {} - } - ], - "execution_count": 12, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-20T01:11:19.955Z", - "iopub.execute_input": "2021-01-20T01:11:19.979Z", - "iopub.status.idle": "2021-01-20T01:11:20.033Z", - "shell.execute_reply": "2021-01-20T01:11:20.056Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "[sum(x) for x in zip(*[bk19_assys[k].shape for k in synth_names])]" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 16, - "data": { - "text/plain": "[21360, 233, 8]" - }, - "metadata": {} - } - ], - "execution_count": 16, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-20T01:13:36.093Z", - "iopub.execute_input": "2021-01-20T01:13:36.115Z", - "iopub.status.idle": "2021-01-20T01:13:36.164Z", - "shell.execute_reply": "2021-01-20T01:13:36.187Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "concatted.shape" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 17, - "data": { - "text/plain": "(21360, 233, 1)" - }, - "metadata": {} - } - ], - "execution_count": 17, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-20T01:14:18.569Z", - "iopub.execute_input": "2021-01-20T01:14:18.589Z", - "iopub.status.idle": "2021-01-20T01:14:18.631Z", - "shell.execute_reply": "2021-01-20T01:14:18.651Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "%matplotlib inline\n", - "from matplotlib import pyplot as plt\n", - "fig, ax = plt.subplots()\n", - "fig.set_size_inches(15,25)\n", - "ax.imshow(concatted[concatted[\"repetition\"]==1].squeeze())\n", - "ax.images[-1].set_cmap(plt.get_cmap().set_bad(color=\"black\"))\n", - "fig.show()" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/Users/jjpr/anaconda/envs/brainio_contrib/lib/python3.7/site-packages/ipykernel_launcher.py:7: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - " import sys\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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0.00168447]],\n\n [[ 0.85577937],\n [ 1.63456145],\n [ 0.79156732],\n ...,\n...\n ...,\n [ nan],\n [ nan],\n [ nan]],\n\n [[-1.09150303],\n [ 0.41493109],\n [ 0.56265136],\n ...,\n [ nan],\n [ nan],\n [ nan]],\n\n [[-0.84809273],\n [-1.04862535],\n [ 0.10481942],\n ...,\n [ nan],\n [ nan],\n [ nan]]])\nCoordinates:\n * presentation (presentation) MultiIndex\n - repetition_index (presentation) int64 0 0 0 0 0 0 0 ... 37 37 37 37 37 37\n - image_id (presentation) object '0071a2dd239e99a6064369d2be464d49...\n - repetition (presentation) int64 0 0 0 0 0 0 0 ... 37 37 37 37 37 37\n - image_index (presentation) int64 11 347 55 229 379 ... 555 101 365 411\n - filename (presentation) object '0071a2dd239e99a6064369d2be464d49...\n * neuroid (neuroid) MultiIndex\n - neuroid_id (neuroid) object 'm_ohp_1_0' ... 's_stretch_1_21'\n - animal (neuroid) object 'monkey_m' 'monkey_m' ... 'monkey_s'\n - setting (neuroid) object 'ohp' 'ohp' 'ohp' ... 'stretch' 'stretch'\n - session (neuroid) object 'session_1' 'session_1' ... 'session_1'\n - neuroid_index (neuroid) int64 0 1 2 3 4 5 6 7 ... 15 16 17 18 19 20 21\n - is_target (neuroid) int64 0 0 0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1 1\n * time_bin (time_bin) MultiIndex\n - time_bin_start (time_bin) int64 70\n - time_bin_end (time_bin) int64 170\nAttributes:\n stimulus_set_identifier: dicarlo.BashivanKar2019.naturalistic\n stimulus_set: image_id ...\n identifier: dicarlo.BashivanKar2019.m_ohp_session_1_nat", - "text/html": "
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<xarray.NeuronRecordingAssembly 'dicarlo.BashivanKar2019.m_ohp_session_1_nat' (presentation: 24320, neuroid: 233, time_bin: 1)>\narray([[[ 0.36895877],\n        [ 0.65885716],\n        [ 0.56265136],\n        ...,\n        [-0.47266364],\n        [ 1.4182419 ],\n        [ 0.00168447]],\n\n       [[-0.36127213],\n        [-0.68273624],\n        [-1.2686764 ],\n        ...,\n        [ 1.49195297],\n        [-0.00930164],\n        [ 0.00168447]],\n\n       [[ 0.85577937],\n        [ 1.63456145],\n        [ 0.79156732],\n        ...,\n...\n        ...,\n        [        nan],\n        [        nan],\n        [        nan]],\n\n       [[-1.09150303],\n        [ 0.41493109],\n        [ 0.56265136],\n        ...,\n        [        nan],\n        [        nan],\n        [        nan]],\n\n       [[-0.84809273],\n        [-1.04862535],\n        [ 0.10481942],\n        ...,\n        [        nan],\n        [        nan],\n        [        nan]]])\nCoordinates:\n  * presentation      (presentation) MultiIndex\n  - repetition_index  (presentation) int64 0 0 0 0 0 0 0 ... 37 37 37 37 37 37\n  - image_id          (presentation) object '0071a2dd239e99a6064369d2be464d49...\n  - repetition        (presentation) int64 0 0 0 0 0 0 0 ... 37 37 37 37 37 37\n  - image_index       (presentation) int64 11 347 55 229 379 ... 555 101 365 411\n  - filename          (presentation) object '0071a2dd239e99a6064369d2be464d49...\n  * neuroid           (neuroid) MultiIndex\n  - neuroid_id        (neuroid) object 'm_ohp_1_0' ... 's_stretch_1_21'\n  - animal            (neuroid) object 'monkey_m' 'monkey_m' ... 'monkey_s'\n  - setting           (neuroid) object 'ohp' 'ohp' 'ohp' ... 'stretch' 'stretch'\n  - session           (neuroid) object 'session_1' 'session_1' ... 'session_1'\n  - neuroid_index     (neuroid) int64 0 1 2 3 4 5 6 7 ... 15 16 17 18 19 20 21\n  - is_target         (neuroid) int64 0 0 0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1 1\n  * time_bin          (time_bin) MultiIndex\n  - time_bin_start    (time_bin) int64 70\n  - time_bin_end      (time_bin) int64 170\nAttributes:\n    stimulus_set_identifier:  dicarlo.BashivanKar2019.naturalistic\n    stimulus_set:                                                  image_id  ...\n    identifier:               dicarlo.BashivanKar2019.m_ohp_session_1_nat
" - }, - "metadata": {} - } - ], - "execution_count": 37, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-20T17:06:21.360Z", - "iopub.execute_input": "2021-01-20T17:06:21.394Z", - "iopub.status.idle": "2021-01-20T17:06:22.740Z", - "shell.execute_reply": "2021-01-20T17:06:23.668Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "concatted_nat.shape" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 38, - "data": { - "text/plain": "(24320, 233, 1)" - }, - "metadata": {} - } - ], - "execution_count": 38, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-20T17:06:28.125Z", - "iopub.execute_input": "2021-01-20T17:06:28.162Z", - "iopub.status.idle": "2021-01-20T17:06:28.224Z", - "shell.execute_reply": "2021-01-20T17:06:28.252Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "nat_names = [k for k in bk19_assys if \"_nat\" in k]\n", - "nat_names" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 31, - "data": { - "text/plain": "['dicarlo.BashivanKar2019.m_ohp_session_1_nat',\n 'dicarlo.BashivanKar2019.m_ohp_session_2_nat',\n 'dicarlo.BashivanKar2019.m_stretch_session_1_nat',\n 'dicarlo.BashivanKar2019.m_stretch_session_2_nat',\n 'dicarlo.BashivanKar2019.n_stretch_session_1_nat',\n 'dicarlo.BashivanKar2019.n_stretch_session_2_nat',\n 'dicarlo.BashivanKar2019.s_ohp_session_1_nat',\n 'dicarlo.BashivanKar2019.s_stretch_session_1_nat']" - }, - "metadata": {} - } - ], - "execution_count": 31, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-20T17:00:07.161Z", - "iopub.execute_input": "2021-01-20T17:00:07.191Z", - "iopub.status.idle": "2021-01-20T17:00:07.254Z", - "shell.execute_reply": "2021-01-20T17:00:07.282Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "{k: bk19_assys[k].shape for k in nat_names}" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 32, - "data": { - "text/plain": "{'dicarlo.BashivanKar2019.m_ohp_session_1_nat': (24320, 39, 1),\n 'dicarlo.BashivanKar2019.m_ohp_session_2_nat': (23040, 38, 1),\n 'dicarlo.BashivanKar2019.m_stretch_session_1_nat': (24320, 39, 1),\n 'dicarlo.BashivanKar2019.m_stretch_session_2_nat': (23040, 52, 1),\n 'dicarlo.BashivanKar2019.n_stretch_session_1_nat': (21760, 20, 1),\n 'dicarlo.BashivanKar2019.n_stretch_session_2_nat': (21760, 1, 1),\n 'dicarlo.BashivanKar2019.s_ohp_session_1_nat': (21120, 22, 1),\n 'dicarlo.BashivanKar2019.s_stretch_session_1_nat': (21120, 22, 1)}" - }, - "metadata": {} - } - ], - "execution_count": 32, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-20T17:00:38.977Z", - "iopub.execute_input": "2021-01-20T17:00:39.009Z", - "iopub.status.idle": "2021-01-20T17:00:39.070Z", - "shell.execute_reply": "2021-01-20T17:00:39.117Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "(lambda x: (max(x[0]), sum(x[1]), max(x[2])))(list(zip(*[bk19_assys[k].shape for k in nat_names])))" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 39, - "data": { - "text/plain": "(24320, 233, 1)" - }, - "metadata": {} - } - ], - "execution_count": 39, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-20T17:06:49.101Z", - "iopub.execute_input": "2021-01-20T17:06:49.131Z", - "iopub.status.idle": "2021-01-20T17:06:49.194Z", - "shell.execute_reply": "2021-01-20T17:06:49.224Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "%matplotlib inline\n", - "from matplotlib import pyplot as plt\n", - "fig, ax = plt.subplots()\n", - "fig.set_size_inches(15,50)\n", - "ax.imshow(concatted_nat[concatted_nat[\"repetition\"]>30].squeeze())\n", - "ax.images[-1].set_cmap(plt.get_cmap().set_bad(color=\"black\"))\n", - "fig.show()" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/Users/jjpr/anaconda/envs/brainio_contrib/lib/python3.7/site-packages/ipykernel_launcher.py:7: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - " import sys\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": "
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HB9wzWNSug9uXVvEb0WecjOHvVywGZRmgjgbTNpDplClLgIuiaQeXYzyKsoWS/3fKXlWU5wmyy3o7FKHqGuiSWJmVNIXuSrgQmKcp9H5HaMBkzGd0vwbmM0jfRz3EkJG6WlPnzRbJ1QX1Mp1kMoa/uYObTaHmWsk0R/fxBaTt4W4YhOtsAtmVcJ+9Rfvvfox0t4fereCqmvK9vGRxamfxT9VE0BiLR+mgX1UBTWpgVhkqzest8K6EhuBZFdJn5maVAy7oe9KSz3uHtxUvRc7KaOK4S2525gIMCMQQ7KG1YNSCLTQEX0mWWVmfACPJ87iryHzGHPJ8xkUwGtGwAOBkAT1dREitnCy5S50s6BKdLvj9yZj/hdy6pRkfkj2k3J4iO3f/Y9mj/BnL6jKdxEIOzk8YkNY16w8nC+jd/ZEeKHIa2Xgc9UGaHOl0eKJJmtIgywrSebiyRffpK/jTObqTMU+TN1fIPr9G/8kLujDLOWMdc9dkNBrwT0XOr00/sbSrjMfRZ4fhiAJ0WObz4Z40jejWoIOW5XDPA9fz3uEFQ+XQMCixwNAZxls9RJiuU3EGIzhexwEZKbkVm/p+gORaOVt7T7chTQcQ0mzKXPLZCbTIIDphQNVamrLvWSY33Ife3QNZzgxEmjwuu/dPlz34+nUz4FnSlKeTxRm62Q2puQ3L8Nz991xAP30D2VVHegBgirUsueuuN1w4QSeABl/ZaWqAuOTdPZClSAHoF98g6XvLoJTQpkWy3/M5vr+GFgWfdwjOx2NAW8oFRJckwKF1MooVaLRDFi3IplVN3QJ4sOvo8u34TH63d/iA9S9y7o7hxQM0lpA/n0+5SxT5gF23XV2nFmxNJvHhBvyG9p5HaAgqyyrmd7UyF0EEuL0nHjtgWkYjvkTLDCBNWb4vChotMKAzH5A9yPEk2fN8MIzGqo8h87OcDW5QnjNYtOILYcgEwcnffHWkh7YtocNdP+jzszdHOoXeABZ0WAkNrpze3lEWizVkMqYM0wn8z14P+P/tLkKnA0pUsoxxyGI2NJe09nzeXxMENiriJqF1E4NaWcy4IMyFEVtYSBMu0u9BSz5vgw8dRF13hNw7wkykKXC/GYBVdrSJ5XSlZC46pL5Q5BYMBoN29C0PQFqom+HBCQsb/vaORlYSW4OQS84zwou7DrpaAwD8es0X8Zjsff9k2Zm7p+whXgjBsZQ144o8o4ygkakhLnW7o45Ne6RHuNSwPBgVBNEd6BTx+gHCcdBdJmcWkCpdlfYPXgGXZ/xZ1XERzOdwZ6fMbIX7CvvcLCWE4vxkwDM5R/hD4hiE2mKXIqe76T1hzIsZA9uQTp1Z1idAOx65nrfBqx92t5A3b5qIrwg5aRkVBB/VDXfHwtJWlnrTumF+u64jJkPSlJ+fJgxC65oPNeBQAGZBpgY/nU1533QKmUyGna5peZxmGdyrF4Podf2o7DFH/wTZ6fea7L4fEIbq+ffPTqF1TeNKHHdbf2Cc0zFkOj3SI+6aQNQnLPio02g0PGsAeHkJGY+YHqwbNmCcLKBvLpHe7uBnI0OLbrmbz4kjav/wzWCIPeVH3RDSUDXQxYzVWRG+q9vVkKK1zA5P0JyupO3uujBskAEDdTL6XpN65j68HV19b7t0SwM0KKjWjfVV+qF8H9wGr1BY2jJ18QGya4a7mAADviZNI2w4fJ6+uAD27E5CntEILS2GsqQhXJ0BOyIc0bTQxRSJyfu47LUVYv6WshtGXq1BBePRAK0N/aDeAydzyGY3VJKBIz3kMH06yoGypI/ctFEnzVLiZwAa0/UdA/q+R/OzK+S/ekvocM2YJrlbx/ZJLRjLaNchXRkCdDGL7XjS9YRylDVkb89oXMTTVQzDhNRO0LYDyppoz82OWRoR5t2tUBXxQ49cz3yHN7hsSCGOCqYGG1YgpciZL+56ZlXMJSDE9sCQspSZkKKIJfzoUlhaTKyqK2nCB5ck3GlK9nyi7YCqJmrx1rAll2fA2w80hqaFhpTjcs7d+RHZY4X2byl7qNbKyHLQITUrwoDPglBc3w15/JL4lyM9xmPGC8CgT90c6SRdT73yjIZpMAlc3yL/zXt0H18C17dWPS3R/eSK0ILeA/drLgTn4D7cM9Df7Pi9XRl3+JBahnOsKbTDDo6a0GapW1Z29xZc9z66gWzza9gMc785hlB/x/W8Dd65WOE7yrEmSYSGAszDI2HQIlbSj03BzjGVNiq4qxZsEUMom1vAFXYI9Z7H5L4cqn9ZTr/YHqZkGcFMX73l/zdsSpbbFV/W9S1TlI/IHlr3niT7eBRll9C+V1su3wBdOpvQGLxnxXM5j1VK+vLbIz20LLnTT8eDPlV1pJOuN9Tn9n6ALaQpkaKTEZvfRSC7Ev5+hfSbu6Fq3LTwH6654EKFuKqHvgODS5DJQGJlOixCDTv9waKGo67wbJiJwW5IrXr/vUHr83ZprLkg5NtDSuuo7B6CQMNoSGJNBfdlbOcLDRShJxS+h+733LF2e2YLshTYlUOpP88IFhsVLFmPc8jXH6CnC8hmT1/5J6+h72+4YwWWg7phwelbTdDflj3CYp8gu253UXaZEPaA81MWpOaWYrSKqTgHnY+hqy11mI4h6y36P/wJkrtd1AOnp4BVX9lX6widuMoGncyQYlaobZmvTxzw4Y6LdDFHd7VAMp9Ar++x+U//Hcx+vQb6BST44VUDyew07ToAqeHpLRAvqwgXBhDbLCNVSNsC2Yi4mqpmFs0abGJnWkgSfE/Q+rwNXsDjPQROfQ8sZkT79Ubt0DlmNqyoEf3VoLhzgCOiToNPbS4MAL5sz4wId/cOaMivoiH9db+BrCX6/1AF3l7TSLKMHfRtx7K2BVGSJtC+fVj27KBK+D2yy3hKmHDfc0fLMsYNANOMiaN/GzI3quzydxlx8iJIfvOWPzM9EKrBgYvm7TX97/Vm0Gm9Heg6YjfXmrFOknDTSBK4DzfGU5Ni/r/8BbTv4RZz+O2OoC7n4JaLgV4kYOwBaJGzx7auGUOs1sCLS0KlA01IknCRn50Q15+lgFWuWXnugFdXRIv+Thu8V2YzQpSepjSqYCybrTVbGyuA+iGNF4pSRWFBoBooywK7RGwBeGC3ZxAUmjAc05ha1TFIhPfEjnzzHuocff7FjC9IlSWDurYUYAPkeWww/y7ZIRKDzO+VPRi7AasAQHc7tvl9uKGbtttbNbOA3+0jREKblh1RwLEe2y0/u0YMAPv310hevzjWaV/Gvy1Fzt+dTpnKTVMuiO2OMITNFu5kCb29h3pPSIIF6Op9bNhmX6s1mK/ZMqm95ewzw+4E+hJLKMhkwq6tUQG937DIJgJIQuaE6/uhieaR63kbvFX21Fi+xBoCYqdPUcRdSrt+WBwix4GfE3MjDny8LONOXuTQixO4bTmkwgBo7eEWcx7jl2dkHhjlcAEktt3RJ83yeKxiVMQGh1j8eUB2Gp8+TXYRGl+4N8vo1+9L4JM3kO2eVBYjYvoDz4vu9jTAkj62+/nPoh5S5BFLLilb6Nx4dKwTMKR/iyTi6+PiNLfLdx3ceIzEFnLy5iWgiv7rd3DLeSwQDqhVGdCOlmWSNAFcRhfHCRvVsxSiTDXquAC2O2gj7CPYV8xgBRBZYH/43c7D69DKJW4oRIyKoafRsO2SWJ490HJYd5BW1eC7G6blyIcfF9wd2naIB5zlw3fGPvDNe76U2xW0DH7v6LigFJi4wlFtu+tDsgfMzJNkN0SlBOazveH0swzOmBZgWRAABvO1E8L+hvv0k2M9Qo/oejPo860imXof8Tux+RsgjsjcOSQJ3MJw9LZgtKygowJuOUfzjz6hGxhw/GnCDWA6oaF2PX3y8Le8Zw0gz5i+rSpifJoWcrIw4zb6v1FBUKD3XADO0cd/5HrmO3xC8JMqKRuUnTVwDsiEbW1mECIyvLQiZ8A4m3CnAAbAWMvmDnSGp+n7ARYcUIJ5xt0TsAxCC6T0MVGZzzgZDbiXroPuzcBDsWY8Ggpj3yV7OO6fIHtsjTMmBZlMeMKERVqWhlvvTIZiKLBNJ5Tj/fWRHqHTKuJTJqPjSrAZf6w+Ny1lMoy9Lys4r1F+5Bn8N9eQPEf/YY2kaSHLOYrPb+EDg8LhiRb6kYEhjumHBYYsHaALoQvtIOYI/n7Mdk3YHB5rGQ9cz9vgVZlFGRU0oKqmkWrGB5KmkIkVhMoKCL/bGEtVMKDMsgJlxRe42fJonU+J8jPKOH9+Anl3w+yDLQD/guVyL4Lkdg2cnzDImk1YlAkY9lEBTNj/qb2HmLE8JLuWpXXzfL/s0dg7luxhbha8Zx46xCJpCvSkuZDT5ZACvFjCXa+gF8uoh7x+QfmrmrHIbMK8e8h+BTckTfh8VqTkCIjS5NULylVVrN5aStH/9DXcX/4G7T/8BNntnr2/t4x5MCH+XspqaNQQgWw0EjPpZht7FWIK1xa8jAo2jIjQdbONRQ+acA7hD991PW+DD+AggDtrqCZmKb8OVcaD1FlkGhsReCT2Odo0PEbDblvkfMF1Q7x4wW4dJAkXmXP894smFlzU5NCmBe5WhvewtrzNBlKO+OL2VWTIekj2UF5/iuxIueNJWcW+T9mVxizgSJ46GjFecAK9vbdFnkPXFWS1hroE7u3NoMdub7CEE+hqQ31eXQE3d1EnOBmIWYs8FrH0J68h3kOdg5+dI1mVdKFOFnD7mkxjXpmdWe+B5WLAuYROq9KKbQdFJgADD48IdDFl5sUzNam9h7QtoRjzKQtVIfN2dUE9zpaPmtTz9uGFaSfNAn/LwEmIUcHvuwPsCzAYCADMp/QNzWdE27HgMZ/SUKwIpZ+84u55ecbey8Dce3VO5N+oAC7P4D9+Sbfo5QW7fLIMOF3QuKZTVljzDPrqHKGJ+0HZTYanyA4gyg7r3vKnC2iRE79ycWaErdZFdXUOnC6pw/kpdThdHOmB0yVpSJyL+vhZcaQTnCMGp++5uL1Czk7IVQNAvnoHt22gWQI/HZHnsiWrQvabdzzlrCGc8VZ/XAm1zis1316bNu7SZG62PmMjagoNIwECrUY7IuMxGdGyjPc8cj1vg1ceU3LwoLTrI05DVhsaSOArTANXC31BUiwr/XU3VDsDsZJYdkC2ZWwMBhBzztJ2SD6sjAPS06VJyHceaTZ2JY9UEX7eviLlnvePy77dP1l2NO1QqRUBuh5us4PUDdymhBjEV0Nuvqwj4wH6njrcrY/1+HADdD38uw9Rn+Tt3ZFOxBYJ4cF7q1aXFdqXS2iewv/BG7i7NaRsoEUCv2Rs0FxOLdBljt0H9yVkmKYHfDOGyNR9BZwu+IwWtnsDdirSvYrAtzY8G54U2vVc4L/zaUnb4XRL7nNJUwY9K+OAVKU7krgYjOp2z7RZBYQhCFqWQFlCQ3DY99w9m5YFj92eDF2Wm0aWckcNPPB9D2y2bGbwFthagOebJrLboiyHI7ko2Az+kOxta67OE2QPaVgLbLVpjn3cETE0gGVowvdFyBCcJAM4zvTQhvJKksDf3rM522KboNNh40bsAPOK7C+/IivZzR0bVwCk+wr1z18iX8yQXe+h6w1cH2ICRPQkXRk/sMZ1PaQYKsLaNrG5BmJ8+eMRG1fqlm7sbs9kRm2MBTvb1dvuKLX8Xdfz3uEBBjG2urVuGNBlxGIEmmaIs9YvGbAp56eRci6000Wa6hDJOzFIbcIgKiG1BVxCg51NiV9ZzC1YpCFIQviBLOaMC6YTZlrynIUT++xHZVd9uuyBa966rQDEtj82Q3gGnUGm0ORdFJEpQHe7Iz3ccsGAve+jPu7y/Egn5NlADZINzRX6+mLYga/O0L8+h+5L5N+Qe1Jqxkt+u2PQnNlJdn1vXJo1F+a+HCDOgTL79Qsu5tCq13UxztLtbuCtL6uBE/T1C2a0Qkbpket57/ChwdeIgEJKi+16GYAsBoE6Zr+o5DmrePsKmIyZieg6pvLK0ngdW+s1NYRhngFVA//qikWcdMzd8OUlfMIXLktirxHGt5zQYPyrN3CfvwMA6MUp5O0HZhVSNiI/KLtxrT9J9tCimKaD+zMZ0zDsmEfVDARKVQ1cnLLDaTIlS9n5CXzqoh6+SOkOtYbqPFlAE+a7o05ip6gqAOO1T2BBReoAACAASURBVBJonkKXM6AdoX45Q/HNBt0/+ATp9RbVRwsUX2/hF2MA58DXN4CfWQcTi21wMrAqH/Dc6+tLEkzZ5BWiQZuBms+YgqXIueFYrCUbEmWRkOn/D1kawzlr10d2Lm3Ik47Vln54SMtFvAyp3AYGXh8HI8jU8tpFTt82z6CTEdzNfQQmxdSXddlIWQ9NCIs5Dazv4X79FcLECrlb0yBa5sIl7OLfJTtAsNpTZA/BnWF9IoIwuAXG1SJVw+rjeES/3PLccRyO8U7qvoQ7XbLiWtVRHy1LIM+jTjxBMn5OksS/n3zx3lCLilHbQbMU2Ve30F2J0c2dGScRmvriEu7rD6QDLOuYccGIGS3d7c099JD7NfTyjAxwgaw1TQeezvDO6gbIRkOa+fKMwWqeRbDeQ9fzdmlCysq6XoKfKqOC/1XNgIm3QhErqsKswmZHv84ImyK6rutJ5lPVVtJ26E+nbB0b5eSNKYphbhNANyhx0LMlv2/Zk4BLbz99SbfCejfjvQ/IHqdhPEH2AFsOnOiBtyU2iauS90WVrXkhK1RQR4hwRz7QA01LIwmDGRLHYQ8HOsl4jPbTl/xelg2ETlkGX1bM9VtAi9AEHxZz35Ph4f0NMUaNzWhy5sKF5zceM516AJnWImdwD0RIAoxNDUDsIdbe6gIA052BOuWR6/kbfG9ZCiHpqDYkSQrUeQiBUd/H39XWsiCBueywSSKgGTc7g9JO0V8skdztmJar22EyR+LQn88pyoJ+cT8r0F9yEMEhwVP267c8hlWByXjA6z8ge6TUeILsuq+i7JKmhBCEGVA2cym52w3Znb4n6WvfQy/PqMP72yM9/MUyFpWCPm5THpNWZSmyX7+l+6Yau5F0s2G8YkxtcVOoauJ4xgetdgEP1ZL7PbYRrresFjeNUZQb/Pl+wxTjriSEouusmaaiUVuFmTETK+Jyv4kTQXS/f9SknrfBw1J5XW9dP9bMUJFRNzYWWHsahLtm4GMMlNTadTYbqItNydq26N9coL2aM4tQMZj1c5KM+qtT3P4nb+DWJfzpDOVHc54UiYP0PbrLxcCu1bEi2376EjphPvr7ZA+sBk+TPRtwQwa88icz9G8uaJwdJ430Fwv4+Rg6HaGfZPBXp5C+h1uXkNnkWI8sQffqFN35LOrTXcyPdGpfnsRKs07oggS2L3d5PjTNlCUN8WwJ/+EafjqG7vYcsDAaofsHP2UgPZlEJjGecgki16f16gIAXAJ/vmDyAIgTPzTP4vdiE9DBJePR8PMHrh909Pzf9lpmV/rHZ//l4JcFZcIs1KBwmvKBhSLGQYdMgNrGjp3FPObN43Q8Gy4WWvJkPI6sBlof8NccMBH47Q7uZMnjOsspY98TaRhIRA+n4H1b9pZN20+SHWBwtpgPx7Yqf35QjIm8N4YHCk3RgLkpAe2YppFBzd+vCCne7pBcnNOITKcQLAdfWqZjmwRYcREe0Npp18HNpoQcN81QNRbCsN2SRE+RuhyIiNcwrjJ0mcVJh8Z2ECAaWuQMxFNjKhsVhGkYx41MxkDT4l9++d//b6r6H3yXTT3voBWILWAADNLaD6McgXhkBiSfeE9SoKPRiA7ou6EHNJTxYc3aicZxiIEZK35mcKsiJ2I60OcZLYeEKRbLBXRfQa9vmRJ8TPYDNoPvlR0ABLGVEMAAh3VMM4ZxjzFVaRXPQPkRn9WhHpZbD/r0768jIlPEQTu7J5xYwVgNUBd6c/1+DzeZwK/W/LeskFwyVXk0mcNmZ0XeG2uMCQGwhOmFeca6iY33FGcdUNvdUWVa93s2xCysPfGABPah6/kbvLi4+4bRjxHtFyJ5Y9sNI1JkSrasMApFcsNYdx3TgeIgWUJfs+EEC9nuWYQKWPKqtm75g64ly0nH0+DyjAWqUcE8sipwMgcw51wk/4jswLCDf4/skY67d8NuPptAjH1XDMvfX50iWe0G0JhlQGIPbeKiHv5iaUjOhpmbskT3hx8hvdtHndyuZOl+X9GlEQGyFM56d/1mC7dcwDkW9Jx64MUlkncfCNE4WRLRuCR1H6ESw0kXacRhu7MToD845WLLnzXC21jOcA8ADlILsUT/eIYGeO4+vNjO2BrsNnGR6z2wc0li2HXViNpDy8nR6n18SBJ2w9AIbcGi1g0hCqEY1bSs/okwNx52sXNivtVQj2FMDlZbYtLXbHuT8AKde1T2wKf4FNmZ3UgiiRScjZ/pexp7S9Rm8v5u6AWtm+gfS2jiPtDDXa8gn79lF5Lpk311e6xTVVMv5yC9Z9vd9R0bSnZ7VkC3O3uONfVcD5kS/+E6kmBp1wO1tSGenwxNM3ZpXQ8bwbeos7UsycIW7lE/BMNGPhXp0Pvf6UqrGG5brFLZDTyLjm5JHOnoXBwKoF03NAIY5Qbx8JlVF422uW4iCwCDWTYzw7EoJJMxU4BZCnl3yyxIngELpsJkOhlcgOmERZwio9Eb6vJB2S0gfZLsof3QIArRnakP3AULaJFYE0SRM6VpLF1yenKkh1YVm9ebNuqDrj/SSc+WxmfvOMjNjFxmM1JqAxw3/+KS7svcimCX5/zd2ZSpy5IN6jIeMwCtDVYtZJaT8Rj65srii4RBv9eBXjzPB7boEE+BKU1cnA2NI4HW8JHr2bs0POqsqmZjW7RvmX8tbYR73UDm0zjbFACQpxAnTOkdjEGJGZBDrHcg7heDA4xG3InOTkj60/U2V8imAa63xNLvShpVazR7LgGSOcvfafq47CFd+RTZDVqsAdRWMNcuAH3YUG2tG2hpiMOyGrq8AldNlg96hCnk+4onxHzKQc2G5kTbcTc3yj5kKe9phyaT2LRxy+Flcr1C/+YC7ov3wEcvmVJNkoFqL+zoEVZhqcXNDvL528E/V40zdPWguUUmxiM/yunObfcDhmg2YTr34NT4rut5G3w4zgGgHLjgtaWycAc4agNTDVQcHuj8YDCBrQCIAWpEAwI0wNCllCbA3gMrziRCvbeGCMPKG2uAX28gwRAA6KtzVjWBgRXtIdkPr++TXYQLJzwT44ph43nOcZDB7w0c64D1rHZAPmVWaaJRD/TE8/j1GsnlBZnGgN/SCQCNzTgtQxCvpk9gbnPXKwDgUOaaALHucoH0z37DuKXrIbAcvNq0ccD0thTjyZxsBOk4GrMs5ly8tkhks4OEZnTL+ohz7HcFhh6EB67n7dJY6xiACPBSO3YB8NhL3EA1EY76cB3uKAB3ssK41cMsVGsIkfPTyMfO4ovE1rKADffbHf3ikgUad3pCcqOefO/ubssds+BO+pjskiZ/J9kBcLdNg/wGCT4/5ckh1iitOpxkziH5+M2RHgAIMSiKqI9MJ0c6BQwSWoPvWsZIP3pF1oRRAV3M4C7OUP/8JT97OQFeXGL/6SmSTQ399CMjmTLEpgXQGk40y0iJYYd0Oia8OaREw2h6i1s0TG/03ghylffMDJL8O02XjYMUnHOkbwvkPIDlgpmqi3QaLpAb+UH5UORRjak1XW0gpydw89mApozGSJSiLGY2hJfBpwuuw9kJP2O3j2jFmL4LO1c4mR6QXYN8T5Bdm2aQXYQQ4yThcLHlYsCJG5noIbWFZDbxJIyPCXoUuVFWz6M+qJsjnWQ2jZkl0odYitV7NH/8R5RtnEPTBNk1eej7aQZc3yLbd0AicPdbQgWm5pbMpowpxjZkrfcD30+SkJs+zwiem064GGbTyAgR+TnPT5i0SHmP5hl9/QOs/Xddz9vgFewg6v0A/gr5aSexggrgqBQfy/DWNB252q2RQAMlW8gHg7tdpKwIuI+Q3ms51h1WocX1HQO+PLPCzYgT7DZb+u01Ke8ek50TNZ4ou8GDAyiLxSpP+WtOxtZ9SY4X20kDYE47Nqn727tjPcqKMq83UZ84AzbotLffCTuwZZGkajD6sy/hr2/h3hGy4O7WQJIg/4KdR+mf/iU0NWjG+5vBv7ZilRqGCSFGOJlDnbCGUdVMI3u1RWr3LGexSIUPd/H00utbZofsWT12PW8f3glfYhjrnqZWuDCfNxDsp0mE+4aXieWMwVCaDmPNg8sw5w6n72/ZqGCjEbWuafQGJQZYmcW+JC+LpeCQpiQNHY/gLi9o0GenhurjyHpZLkjV94DsyLPBT/0e2fvLEyTmI+t+D7y8BO43NodpZJXJmg0j4dmZn43ec4xNaFwPeiQHsU3iqA9wpBOUeun1LTBL4s/RcqCCjEfwV6dwmzK6b/7dB+Lquw74q8+5wYTmkTiuk9VcBVilbktgu2VwurT2wutbEizlGWOCKiEmXsmdCfV8R33PZ20Y+whPeOB63gYfq4o+ugaxiGKZg0BdgbY1hKNxNm73JB0NbX15PkAHuo4GlWeQyRm/ty+h87kBt4YCiRYZcHsHXF0A44KgppCHt0BXq5rBkn2PXfbd47KHCdtPkD15exPBUTIeQ6+5u0mgh86zYQx719F9uOeOKwU7/WU65WILenQ9QW6BMDVJGNgmSdQpAMbk4oyBo/eUe1Sg/aOfIP/VW8j7O2Lz6wZ6Moe8foF+OQEul0i+/AD/4gyyr4HVJm48GsZgAvZcbEJL6Oaq6iFoDizLLRd3fDedzcFK0whAi/HQI9ezd2nCLB/Yv9ElMdxJ4HSJnf9iRPkAAvm+iPClh9UfcPZWoIFR4Mn+AOdh9BBS1pDZjAZycw89W9rgYvsbbWduhKX+DFyl5sY8JHtowXuK7ACi7IGciTQlycDcYFzzAAb3oevoL5c2NvNQj9Kmjoc042KG5OPXRzpxoIPVE8YjPo+zE7RvzpB92EJPFzH3rosp5OaeDMZOIH/2K4LXPvuGzzvk3ZOEhmtBvc4mVhSUeGqHOgCAgbq8KIZ7wigg6zKLJ/gTcGHPe4ePgWB/MMfIMNPGxxhfvn1PRgXTibZDBqoLKeuBGz5JeP9sAkkS+oCL+bADGy5F92XEqrvLc+a/398SCNW0wPkJ/KSAe39HklRLk8bBXMDDsofrCbKHcjoJWtlTi90OeGH87LY4QjEnDEtQr8DNPXw35LOjHga5lbOTgQd+tyJTQ9CpZ5wQ4hIYJV/61yX8eg1n9QR1AjlZor+7h4jA3dxSnq/e04254YmkIX++2bGeAQC3K6g39KRREGrbsqbhhM8gt/6Crufzul1RlvCMrCsuYIkeu563wQcsu6EUJctiU3P0e9s2HrMCgxEYbCCu/OBOGK5FgDhqBmVl6TwrVVf8WrwfYL2q9OPtd/zdPdN372/4s4XBai8WSN6vgHfXrACGTp3vkh2g2/ME2YmfkdiiiDCYQJV0G21nCMw2fl+bJo51lyKHuzij+xL0SBIg8VDDnbvTE+jP3kB+89WgU5IYwxqDTZ2MgLsVZJIx05MkQA7+flXDLRZD7SC8P2uAwaiIQ4PFmBtYUxiaONR7SMLR8jpmb4KuN5D5LE7+CLGFnpywYBjgGCGB8T0+/PN2aczoDg0hVjABrvKQ1gvdUcGvC4T7ASMT/EPVobjz4Tbep5vNcCxaIwZ6axQPhEHgLi2vruJu5KaT2LyR3G6Hrpzg8z4gu1bV3012y/ogTYEPt5EGRDcb4tNV6f96HUZ1dh38uw/HethnS5FHfaRqj3UCbOcnpYhs6NpoT7a0OLlvMomFowhz6A2jU+SsMO9KFvraLtZAYvZqs2X111KKejInPKPrmDJtOxJPhXE9RRFH3ke49IuL4W8/cj1vgw8+fKDVAKKrEQwzouhs+Fd8kGGyRWgwSFOmuSaTAZY7nUSYroz5+zIeD4WdFxeQ+Rx4dcXSe9dzCnXdQk+sE2o2iZXQ/pxVQTldDqnFB2QPbMFPkV1nkyh7xO5MRpS/N8aC8TgGtchS+E9fs7PpxRmn6b24PNIDScL8dp5Fffrl+FgngIuu4Smq8wnCqCF3fga9samBFh9J0Dnssss5d+blnKfchF1Uui+HYLTjM4gBZ25kSlaEgrGooe/NbbSJhFZADD3PcrtijDQdP2pSz9ulCb2MYhhvG8JL8BWLMzKbDmCrAKDK0mGqBMBOGNWBQ92MQvOM+VvnBqhwmEwxKsiy2zTM6uz2kLMTJHf2GfsK/uMXkH0Nf3kC9/4Oyds7uhuzycAy9oDsEHm67FXDdJz3Nj2b2QqdsjoplXX2H0zmS242HEV5v2Uq0yWQkLo7O4ljgOKU7H2FdL071mm142J8fQWsd0PRbJRzYPN4xOaRUQG/nJDIaTYxxgKgfDXF9BcfrFDmBihIfTzoWJv+CP8Tm2HuzCUM9CShYwwYsksAsGqHRRbSqw9cz9vg1Q/lfYDGFEY/5hnZg6dWEAn+csaUnwT/Mc/iLiXjMf3cUGAaZYC3ypwqqR4SQkz9rODE6bMTNlRfnQ8TKdIE/mRG3sSm5fGbZeivlhzJvhwhu7UH/4DsutsDF6dPkl1HCeJoepO1/vQS2W1JV6NkoUZUjImAQXF/Pmev65n5u10X9YiFrYwTvf3JDJo6umWmU/PxOfLPrtGejpEFtytLIds9vBGxou/hag4P1oSclMm+gm62mH7Gnd+XJbvDfODOHDEbpRon8jE5AWLnvSed32gUCWQldFON8sgpGZmLbfBEHMz2yPW8DR446m4S42fU1po2ShDRZ610MasBRGJRVDZ97oCeDWlKaMGOXTVuOhkKQi3zuy5NmRG5vSNfiyrz3KsN/L5kYWQcONU5bNdtCDbLblOE3tOHZIfXJ8sulrsO5X1dbZD/6xXRj2E4QNuQGe3mDr7vOYpmtYEGoqMXl/CrddTDr9YxIPdNy0FsqtBQQ2hyZL/YwtcN8oNgGKMRi1MZmRF0X0YYsqxJhhRTjSIxeAYwwC2yjNkoAGowYN1XkJlRYAcIR4gjVIeBaE3De9Ikjv3UFcf6BGbjx65nbvDGox4gvMCwS+6siBR8OpfwuIf5dwH9eMBYgHRwkSILbxhZfkZKCTk/pVF0/ZDuM4y23twzUAtBc2nTNtKEGA/+Icq7KwcY73fJPh3HHPj3yR44baLsAUEZXKb5FDo6BW7uKX9kXTAs/ZwMw+7ibNDDmk9Q5EDoCR0V0LtV1AkAZKacw2Sy+MUE7nbDZvUshWs7BpwV+W5kSYaD7qNzuLKFW+2GYQ82ICI0pcQ4IZxcVc10pHWPkRmhiS2WHL52ALazYhwcq9V6f7BYHrieucEbyMtoLySMTAk7XihVWzN2zKEHny+090Ugl4+5+wjOqmsCmVZbDiK+uT8eaZkmpIyeTqEBF973kYNGq5qw1us74DWZy3B9y+P4Mdlb6y99guwyMn6ZMGomfK/peDI4R//d2Mi0JvZFq4puXMCMH+rR2gxUz5mnOptwet9iPuh0vxnoDI27Xm7ugOUC+sXXXDCLGXd1RxY4f3sHGY+QvL/hKTVi1dYZCzHGI0K084yB7HoXodHcgBARk/DePp8noG73HLi83g09wVlKDH/XsRjY/S7v8AZvDcMHdLuHzIo4f1RHuWGk93G0CzKj33OTwXev6ljBlPWWu1qR25Q6UkrEVKdNmpM0hZ4ugG0J/7OPkLy/o/FPRpCqhp9P4LKUQxA2uzi2RY2bRQ1e/JDs6Hvo+cnTZB/lhAOst9wpl3PugHUzpDPzDH45hfMe+uIMPnVIvro2bD0gpyfoT2ZRD0yX8LMJqpdTjP/vL6lPYZuD6aSABdqETIvxVEY/OTRxjAqmEsuaKcrZmFwxdgLFukHfx9ZEBPKp4NIFtzNNCYsIE1Fu7/n/AF2e2xVdGsP9S2q04r0NXkseTzw+87SkdeHvq+E4PGiekK43DnHhDhRGpfQ9x0ja+HSIsHGgrONIGbHZQgAIJisraEKEntr4F9nsScG32cepHeoctPdIPtwDX70jVV+YmgHQGFdruhWPyA7g6bJ3/SB7x7HtSBPeF7v8KyIXvcLdrJFcr2k8RiPo33040gMA3PUdRv/6N4M+13dHOsnMeB8nI2A5py89HfO/f/gHdCcNnSlVg/58HqHIcc5skUNeG6NZQHlmWYRMQD3rBhODC1hqOOKeJhP66XV9zAwc2JQNXswOrqH976HreRu81yG9BwZzIUetB7ub7vZWwq+GPk+b1CFhkEBok7Ppe3q3ikxaWG352V+8oyHdb1gY2llXVaCju7qA2xsnS6CEuDobaDxuV4izW/fV47IDT5f9gHULLqGffbc2DE3KwLo3KLBNC6GeVdw0ABzpEfE3Lon6xCJO0MliAdnsiKjcV5HJN3nL0fa4X8cxl/JXnzPzVDXor06ANIW/uWNFGuACPaDpCHl0bdqIDo2ZoMANFOj5bCYWWRwMWRqqtYFN4iBt+dD1vA0eypcoYl3xxTCzFBjINIGBRx0wHkO+aN2RkvmwsofljC5M2A1OF8R2XJwyM2KNCQD4Oe9v+RIOOvLFmpjx/tZSitaAcL+BLucsfz8mu8EdniR78NuN3zGwEyMxYqOrC/bFzsLQtZ7BX0+SKXiFfPTqt/XwPmJaZDGHuzw/0knb1sbcNxCbkQoAOFnA39wadYiPY3Pc2YkRwyaQP/sVp4R/8mbgqg81h9YKbVZZhu8Hur39fsjH5/nQ1BJa+dSau8OEdhGrM3yrP+KB63n78EZwBGCguPA9R6Ibk2wcLHZAOKT5iPQTiYuzhPgD2w3CWPZAC7HZwIfBu+KAdkdD80rjMGIjDR1BwTiNrVbb9oitC9e3zDUHVoLvkH1gMPhbyu4E8FZ4CQs2pEo9Ww0ReW8ImZCuI0nSYh71gBKe4K9v4KzjS2sDigWdwiAEr4OBbnYcWzkeQd9cEQtfVsCrS+jX7zhFu2khH7+GOgf8+vNBzkBvvVwMKdjQgbbbDZBhG3oAVTbMjwqOrg8nZlUPNHvhuVUmp/wuN4CEyyguAAw7T+jqn024I5RV7HYJpEjBZwyjHtG2fDBhTKV12kCELoINxJLVhp32ARdjjMNh8EGocuL2HvrxSzYwjwruRutdpHx7THb0/ZBV+R7ZYQUWtd1bipy76q4ELgxrEkZCLmZsJzQZQhcT/vCnwGoX9fAXS/r6oQA1HUN/8wWzIUGnkJLNUo6qn4wAnxP96BLgrz8DTk9o0F++5Zyoz74EbBiCG48gZ6fwK07WxrZDYFGINB3A8C7Cjh+gy7Mp5TH2hgC5VqMriYS1oSodioyPXM/b4IMrUhTxpYfG7vgQwmh04Ci3K8CQDgzpw0PauHAk3q0G43x/Q1dE3ABVDdmFyRgQm9Y3nXB0TNNA/uYrpvlUCTVuWo7KGRWW7/9u2QE8Wfa4UMJQ5boBdtZj+s37uFi0BinqzI0JXJJ+XwIfbuCBqId8+W7g58lSYLNF8voFZz4FnZp2gC+7BNjtmNa070dodu+ZBjWyJwBwRQEUBfzdPTFAFnDHGkJq/44LY2nTAZ4NQ1R6H+n2QhUalhkLxFcArBbTfK+xA8/dhw89j4HbJXSrG6+7dr0xfBUDbDY0WBw0S8Mo7ERkGE+TJjxaT5eEA58u+TcK4263r910QoTkYg6cLuAuzrm7XZ3TxwzV0PkM2HOCtDs9iR08D8keRkM+RXZkaZQdaRrbAmW5ICa+KAhYU+X/h4VzugTUU4ePXh3pAYALqe+jPiSrOtApMCGcLnnvbMbA9qOXvOfVFXSzhb8njELOTpm6nEz4/MYjQgqAIZi0NkcNhEt3K+vvPeg3Diew9zaCyJnPrrzn9n5o4jlgaYso2MdM6u/bRv/er0ANUdX05ZzNEE1TGlLomcyySNZ5iDmX1NoAA7dJ4CEEuLvdraxv0jInxpSrYQcJCMq6YTqw43hIvL/hfKQAab1fMVjte/rXTh6VPdYYniL7vhwqlaHzKezO9+sYxLnlgnHHYk6m3zA2xrq5jvS4YoAqk8mgz25/rNO+hN6vmBfv+9j5hK6n+3e/5mbx+gVdqb4Hzk+BNEF/xc4oHRdcnLMpu6MCNiY0W1+Rsbj9yeXg4oSEwajgswzDLEIB7fUl/w31C2CgQPke5rHnbfAKyMkiVhbjNA873kPfZaSREIk/5+wmOxYXs8HY1MidipwGAVYgAdtR28ZcAbo1/vaOPC6Gq+mvb2gEQOQ616ZhWvCzL6F1A1+br/2I7GIp1SfJHlrk1BrNC2tiDtmW2Pvbs4JaGvbcJn342zvodnukB77mXCqW5A3oVlZHOsHbadO0kQwWXQdZb9GfTmLqUrZ7dJdzdC9O0C/H8IcQXUtpousjBSEALt6yhqzozmSffzhu3khTFusmk9grrA2JX93KmtC9xo1KbCLKb5Fcfet65j48jzDxHrqt7BizHkprgmYmwQ946pC+2pU0jiQhUCnsHuZni9bEaR8gIKVuCBTLMstRC9yLS/jFBPhwD1kumLpbb+lPvr7ixIqAx3n7AbKcE3jWtKyOPiR7b+Xyp8huo+uRJMBsAmz3NpYmYeFnu+cCDgPXTpdsmEg5WM0tzvh7kyLqQe7HCfxiAgldY1kKvLyMOmlVQ15dDVO/VaEGMUjf3gOBMhBA+mHD55dndDleXSF7e2d4/lMatjEkiBykDsOsWu+H/w9gsgnJWmU2hZwsaMwBS2QLMHL7AN/LKwk8d4OHsrCRJPQlq3rgRQwAovEIEngUDUyGQDxaFEN3kE2AC0Gi9qTSkyKnu1CFdrzEvjY4rXq4AN+tmyOiU/2bL6HOZhaZa8Q5rLa4HpFd/zayHwwZlp0FpfUwBEC7LtJ6KwApa1Lm2e6ot3ckbXq3G/RISCeC99ecBJhnwPnpkU6SZ9C3H4gKNa53GRXQizMW2YxfJ6aHvRJLNJ9Bbu45Hr6zNGiaDHNU03RArZYVK91huEKsUxgvzWg0QMTDYtjsmP6dT6O7JVlGoF71ODz4ebs0Lhm6+nsf+1IBcAcNNBPW6RMomDXAVKdjGmEgQBoP81DFpkOHIzuMVYEL7sMATNPGyI4scAptZBIpry2ADP23bUff9jHZT5dPll3rJsrOppehBU9tYJjkObM+YT7seHSsw3Z/pAczPGUwSAAAIABJREFURH4YpQPQJz/Q6ZAvH2ka+02ROOiLcwOwtXQ3ygrtTy4M18LgHO+veZLsDv6WGW6ceeWZ29fdntj88Zj/BphyqFVY8M9sjPB5bJi7l/GYX6+/f1br897hdeiwkQDoCunFwP1o+djA4x67oLyNUG9augzmugQG3tCBwwHABD8hNG0UAyxVzjjwC3ergW5OhyZoqB8mhizmxuxVDLvoQ7LH/tUnyC7KglFHdKUmdhKocuDY9S3lDws7/MwuWc7paqgOeoCnQ7KcD8RMRuoadIpTAFUBAZupqwrYbMlubBktrRtoVSH9q69ozAFqEYabJTbRJOTM1ei/vQfGBndIU+jKeoI3O4LKlrPI0a9NMzRqB+RmmsbGEd2X3OVHv8uVVhidtA0fjpVGMGDUcQFgxIpkwtRVzMnOpzaozNyL1IiTfB93XYSx8gEhGUlaPbMNGXE2uL2PZESh/C3OwX/8gjtPa43UeQapsojce0x2dD10OX+S7Np7SEJUIdKUVBenS3Y6GdNv7KGdjIfJHzbOHV2P7s0Z0n/z66iHhHy4bQRhIqC+uRp02leMQ9puIKa6WELKBs2bJXzmULzdwm0rlP/kpxi93aEfZ0j2DfppjmTXQHYV+uWEnVQB6+NZzArDI0IRLM6lujyB9MpYwrhxJEtZaLO2RH/Oaq00BqYzGEfoPXjoet4Gr55+5HbPAGa3j/Rwsdc08JkHgFiY1rzd01CKPPKaaFXZNL+egd9sYt/L+PuBx7HIo7+vAFAwPSazKQ1zYiSfX7yLeXGoRiSm7hlcSVE8KHts0HiC7FJWUXYkyUAVbkRRSBL6zof+LQCsNkwX3q2Qftgc6aHbne3gVdQHwJFO2vXDwAhv3Da7PfTVBYq/+Io58M0WmE0x+T8+R/eTK2Rf3tBAf/OVzZgq6DeHoDukdkMhrmnZKhlkblrIvqYRW1GLz8dFXh/UDdza7g8AN8PXI/ldpukAK3IS5hWFAVcwfIrtahoCzK4bxtUEBquAvQ6UFQFyPBmxQ2bMdrswZRrAMP8VAdJKg/Q3d/Q9NxsaTDgRjKcxpkmzjDn4R2QPv/ck2YFhvpFRzknODp+Yh/bDYF4NAX3fG8FqCv3ymyM9AIsl2i7qg7Y70kmKsMA4nUSKArrZ0kBHxUCJ1zN9mn7Dz0XXA6+uhllUdXuAjU+HwlKQfbuP+H9/dUpjt2YYwonToU4S4Ad1w/tU4a9O+bODNOVD1zM3eDCYOTDEw9GMUCs9W6+o9lapVI2DxMKD1pbj30PBRrfWmK1GWiQSS/QAjvjGNVD6FXk0GreY29CzULDypHeum9iT+qjsXv9OssfxOG0bG7K1rhGYfQGDLBgrgu5LaNvCvbg81iNQ8fWDPjKfHetkNQsZFTHTMnDPO+pX2mZScrF3r04pm/csNL37QPhweLZlxfRpF9y+eoAnNw3k62vqFE5Euye4MgyIK6uuGk/N19fcpNqDouID1/N2aQS2AxMTHQwCIH4CIwLHAAyV14T+Xhz9HjIMQNyVkBhr2WrDF5k4Bpp5NtA3h2qfgZm043hE/+4DDcACSx+w3UVBlt08i7ABWPHoO2W3aX1Pkj2kVauaf3cy4Qmz2sDv9nHeEosxhEFr07Bkn6axiBPmSsliDv/hBpKmZBwrSy6u3R7ifdQpTiXZbI1y+54Lfb0ZTsAwOmfGHt3kF5/RDQyw49GIs6J2ZaxiYzYZUq1X51ARyDYZdOhtfmvISoXnGPLsV+eQuzW7rPYHLA8hJnnket4GrxhoqwO4KnT0A5H3UQBLF/axIherdl7jqBhWH8lAoKokJbo4hVvZyPjFjL5wyBMbd4rkGXB5Bn17DXdFKml/MoPcrIkVCT2h5wu4uy0bMDa72Jz8nbJbZuTJsocOIMtYyGxKAqRAmFo1Rsx0MOQ4DCDY76Efv4T8TRP16P/Rp0h/8TkD69mUtCPXK0Ak6gRVkjLNJjQsqyE0//5Pka5rtCcjFF/cA2mC6qMFRv/7b9D9408hbY/9mwkgwOJ/3aK9nCJfbQcgXjjRAMZSc04w1NWGfn9Idwb5jWLw8B7MpzxVjLobwDBV8JHreU/izq/0P375T3HYxhZJRf3BRDvrpIFNywOofOSm/LYPCPC4VQVWWyv9t9DTBVvy6oa70t2anx3SdUZVJyKRJz00Eg/dV2GBJkOH/XfJbpMDnyR7KNgYxYZmKbuh0oQ7Z5EPwx1sAh7ShAthvYuIxyM95lPOX11Mqefh3ww6Bdflnu2CccJ4XXMG6+19PBXDoGJtGrhArJrnZBwOsRMQaa5jTFIdD6ZDyyIaRJhlqtvIJQQg9r5KgBQHXL0NbdDE4V/99X/3OzqJOxhn08bJeACGgMoaAaSWSN0WCzihqprY+PPDo873wP0GMhnBVwNEQda7YWe5W/PlNg27bYzzRLc7pjP3Ro0dIAAI6UbjgRyPIlz4u2QPbFxPk70bfODNltVgdzAg2XZ/Dm4ohsyTMY1FqrqmHbhb7lb82lsdYW/wgTSNOslsyqxO4IdcESzmN1sSJSXsqlLDyETezH0JZ3yTgYE5ThtPE8ptDR0C8HknbliQxhSHuxUnkzsrhO0rS7WSvlCs1yAOlduXv+OFJ2FgFEBYEQPftszLzqeM6L0f+kGtOeDIhwYGqgsngKPPrEUOd3bCY7vrCTUIw8gyVhb7yxO4zR7+ZAppe7jbjclS0k9dzpmnVuWOGxgRtvtHZZfJeKD1+z7ZQx+sE0hBdjSdjpmlsu4hLXKbI5Uy3WoIUCly9KdzJPcEk0U9btZcHDbAQfclGY/7ftDJk9mXE03mNO7NFu7lFQtqu9LIkcQIXBm4w5gN9PwF3P3GiliGhw/dXar8G2HHn4zpcnpPjNEoj5if8Hli8AQ1Zga8u2XsFHZ8EU4QuX7YpJ53lkZ1yDTs9gPNdN/zBd1vrH9zUEMSgwI0DfPhAA2kLLmzh4/eV/Gol9WWWBBr0Ah/W6sKye2aH3G7hXQeuphCp2P4P3hDsqX1lu5ECDQByGrDnecR2bkbPVH2qvpt2QMLbyh4GV898gx+OqYxKkmUktt1RBF+Ww8Agz7b/ZFOutsPw9BgMOeXl/TD9xXRidMxcHYyQCFOF3R1ALSXxmY8n9qc2Czi2qWsB8IlI50SGz0EVcKHA3uzkc3q6SLe727WvA8YUJ0hHfvI9bwNHoi9itFfBwbu9Laxbv69jUC0zEKYqBGw42G2qjUNhIkTcrvi4LFRPhh70w6sW9OJDTgweO/X7yHrHWRfwd3vhp5YA1B5y1Q8SXbgybLzRuvKL4mXkaqB3K6GdJ1IZN2VdzeDzxwITK/OfksP3K2hry8GfU7mxzolhgI1fFAknEoS+NmEsAYA/fmMBuzY++v/8CcAgPzPvwRE0L5YRshEvMKO3HEwg3YdmSRaY6MIG0K4EotXRJiONvwOgXN2goQi3iPX8zZ4x5yxFDl3B3v4HKg15sjzkznZqMKAhLDLpimbFPJs2IEuz4aRiNbLKZ01gagOVB57UnSE0fERWnx+AiQO3eszBMZh7T3Qtvj6T17D1bZYLFB7THY5XT5ZdiRJlF1m0wEzYr22AAYDN7pteE8d8nSoSB7qYXOhND3Q54tvjnSS8Qhf/8nrOF5H7G/p9S3w2VfQL76Gjgq4/+uXsbla2g7urz6Pje3atkh/8TkzVJktosC9D+KPIu01AJkN/JC6nA14+INp6oFOL4yplBkHWmhZcdLfI9fz9uEBqyxaU3TgRs8Gt0NueHyGQkokNOo6Yk2sQVps9et2NyD+ipxZmZeXwHYPHRUQkNBHvBKrsyshbY/+xQk0dXDbBtJ7yGaH/qNLuA07qV79z7+E/+gSyWaP+mdnKH71/nHZ2w5iadXvkx3ORvZsd8O0jLEtkjD7te2ISVnv0V3MIV6R3G7hAytClh7rsW8gTtAuR8hWFWSzQ/fvfQLpfdQJeUa9Xp6TT3KUsY3RuprgHOHIn7yBth35fa6mcG3HrqcLngBS95DrO/ryiUMcAuE9YdknC+JqDircKHImDowKEB0OSGY5JFkLctsH+kLJM5JIPXI9b4MXGbjQA62FVfDiALIij0dpJCA1VjFNk4Eb3rDSMhoNu+PdmpmS0MShSizKbMJALaQFmxaJ4ax1t4ebjIGR0WmnCdR89uTdPdB1KH75jkFX4h6WPU0A9zTZUTfEyI9GMUB124qyHgSDUpPQKK0bEpgCSIIOX70FLs6iHgEIN/qLr+gKjAokn72zpvEk5sR1X8ZZrLIyklfvAZcRrvCzj4HrewR+TP3l5/AWhMrtPU+5xTzCsgEY3j8fimn3ay6cgPExzFLsWw3BuzXaBI6akC4NrBTaNBG+8dD1vA2+5wwiOBloKgDjgOFqVvtZ8AfjUKzDXK0qsK84PGtOMJVkGQtPo5y753rDAkvIIlyeAZt9zCb45RRuW9Ed2LCkHowDr6/g1jv616fmigQq7IdkB46zC4/IrqrAfk/ZvYesGQCKUQRGvMuoiO6RZBldpn1l8IkW7lAPcy10MbPTxEbOhBjm9RWD+ZOcozuDu9V28Ne30K4jM8E372nQJaET7sUlh8QZN6SMRnSBMjbXiMGyY6yQOFamA9zh6pynRZFHOnMJ3JTigMqGK4j1DI9HbErf7Jn+DFXnB67nbfBALNRAiF0nZd1kSDFiOOr+H+repMmyJEsT+lTv+CZ7Nrt7DJmRWVlVTXZVSwsCiNAsmiU72AHrXvEn2LFkAYLwG+AfFCK9AWGBINUgdNGQVVmZFR7hs01vHu6gh8V3juqz6DBzKyRBLK5ISVWFu5mf855e1aPnfINNNF1ZkuhQFpyqqoEvul6pgdxp+tMx/KZBOBqi/+IE2boBJrR7kdxDzqeQzMH1AnEOkmdoXoxR5B7dNxfI51qSeI9wOYVrxxCTqTCdmIdiV9zHk2K3+t/MEgCEkzGkzOHaAdxmj+6bS+S3a8hkiPZ8jOJ2Q63MskA4GiB8eQqsm5gHMg/X5bSOrwvms9gdXHY9uq/P4ZsOzUmN8mYHqTL4LTH6frWJHRbJOQNwUy1hJmO+zJNJvBvZn6HtgKpIRmyGFxpSgArGe50taNfzdscNYjxk58YGUzW9ZmWgF9Uih3t5gTAogTcPr6fnveCdi8MJGxw5Bf8DuN+CCj5ddkQondy0EWRGUaI+yT87B//9J7bYsgz5dheJExEv7x35qTv6NrnVBuUVd7fCNGZWa+JRuj79fVukIg/Hfhj/Z2KXuiQuv+s4iMkzuG/fURJDOZ75X13rAMuhuJ4p2pIElWyzg18rlEHzCHYh/3QNeM98dGJsOdkltf49PWtlvyfGRaU2kOeU26hKuMszWvPczSPEQZqG95qPUO1ICq/iZhbFU407C6kIMvM2xCuAd1dpKaghNO2Kinjyo+2oBWqS4z91yxuzg4/H9EaxLkHwQyk+9IootCMPNuIv0/BDd1rpW5ZMqsMe+++tIghN3EgXEZZrRD3IECIkGAbx1XoXJlVnOiwPxO7qOmFAPhe71eomImWowKZFaIjPjy3VHhBp08jdyChNCzfIYh4u83CTCRUMLB+7S1hOgNru6LS3DxRWKgulRm7h9KXG5RnCx2u4PEf37gPyb74G2g7+2/cIOqkWGxLaZw3Evjru5jzd1izdpFH3FOkjEM5ehmigENQyZ7/nEO3AceWh53kveOV7Rm6jTf5Wa9Zqhpc2N+o8hxvqF288UIWU4vwYWKlPaqYqV4MBXMuaV7qeGPlsH+tq2jXmbPNp58GbLqIIx+5lwQVnd4khOZnoe3YQHordFAqeEHsEn+V5WuxOBZ6agmXFehspdf7eMIsmzO7ilJdwzQNHY2C+pN+sYdPLgvW35iTbLSfSa7LBZKKy4yN146sL9IMCbt/Df/8B7utXwJsPyH/2JXvq2kYN37yC/3Cn2vpNnBQjBPbv11vg+IhlXKYa/M4RC7RR+04F8dmdyznHYVpRwB3p5+PpvoJPDy+p573g1auTJQHZNxHX0nZRysKZXJyabEkIugg26ZZ/dcvL3wES0Rj0RAwOuYBVBNWBgydZbxDMB2q9jn8u2x1LmT0no2haSkl4RweQPIfZMv5Y7ABJG0+KXUuniOK0nS4oPn65IuT30zX88ZQjes9xP/HqHeTNe0iWxTywq2gs4CicKtsd/PE08nIjdn+hwrP+YB6hLVW3zuEzT2jG5Rn19F9dssNkUuR9oFmz4XeAaHGJw8v8fEW8TNvCbV260IOIWVdVkGbHuv3g/pZM6nLIdg8Xfsq2ldBpZeajJymUcW9Yb1GdFlGEHZxSwfZaqqj+pFMpu4i2U0ZR7O1CBxqqk0j+awBeXcKbBDWQyo1vviLu+9MtoarrLcJoAD9T63iRR2OHYeKfEruS1a1b4TIfUY+oK/JTAUKX247+skrIcFkGOR/Sx1Qv7gDY+zadl7aF++Yr1sLTccwJziW49K6JJZgslwjbHbKLc74Q6u+EzBPIdnyU1JlN6c27qA4s+wbO/WAiKqpLcyA2G4nbZpAAROkUQDm2hyJUSuZ57Hnek1YjPltdltNzybTe4eiOIadTnWxWSYxHj3XZbLlT1FUysfU6/KhKtrSOj/illTr21wucHE+oe2JGuOMxwtkxf8/Ha4oL2b9lYCjtFXNR/4FiNyKHlgjxS66ony6rg0XX97FsgElMzxaQLy/u5UHpjD7Z2X+8Jh3vICd7adBqq3Iygrw8gzs5hh+owOnlGXvuX72EOznmSeRcVEWj7N8wnk42fIpf8dBQlErSDsKSMMv4Ug6HSobX0hEgXdI+q9NpvG+5oohwh4ee573DO6QPSvEwNPmq4xuNtgOu7mDSePHYBVKvXPmSCIHEDJP4cA6Yk9cqTQO3UVtJNRCTK8XFFHmkurmrWyIZN1uE1RpuMtGLU5NiMneNR2JnfVw/KXYbqMhyrYwmSdbvm52iHvnv07AsSXWjqrgI/2bFF9qk/6z9p6YH2GzZCpwvYk6U9iNBJHIHlmvmn2Vkf+U5Tc7efADyHP6LlwjDipfi8Yj28FbK1RUVF8x0AtBFrmT58+OkxOA9GWkmdR70JK5KlmzGNb66jfHJbk+p80eeZ77gXVTWPcQ5x+6Gc/eUCKLMQ0txoGiTkqmJrV1+dLIIhddKVQJ3NDnGdAy8v+IuCLCjsNkSqXe3SMTrsmCdX1dxYujWW75cigt3pv77I7EDeHLshBfgPrS5rsgWsstsENb2FyeMY9/QQW+75TBI1QwsDxGFW/gsaeZD8SmWE8AyzOiCQ056+7MJsg+BhsctGVeYKgZmq5IkJ1OEYQXvHHB+ytKoU6OF6Zh6812nswp2sTBTYSq7qGqJE0ub4RDS7JNScJ7Te8qm7gBP7Uee573gI0/xwFTAEIWAivB0idCr2iVuNLh/ISoKihTlOellhk3veto2NhxkyM0dP8T8wCvIaserW0rSKYYbmSempGkhM7YNjRcqs548UyNK/0jsAqRL6udi7/cxdgA8nVZriNcyyYjWyyVwMyOIqu8JTchzyHzBgdugjnm4rocsVvFijsxDXp5Fd3GZUXjVDQe8jyxXBLxtd3C/uSHuyDk6i4yGxKfvqc/j9oQz+E93WiYt43TZDWoaw42H3MlXmySBWKnW0DXxUc5kDI19BUAuTglhBi/9uFtAzo8JD5mMgP3BnONHnue94O2IV+5n1E1X8FAcRwPq71PwQqPjeDesowxGJPpa+6/vI9zWTcaQmzvWmlnGmhjg3x2PkrDRmm06lAV3+ps7dQrJE0FjMk5wAZsy/kjsh45zn419bCN/HYKNhjRP1tPGjUpi0L32+4PAlwW7MdDLuKeimOVxSK2zfPDhhi+G5hQ7WFUJWEu1rhF+/Uv4qzlkWKP/+hyy6+C//wC8OOdCzjL4mxldQGyhbre8tLYdOzhAXLRmbIZuS9GrU94FjBPgoGXg0Rj+Zhb1cqJ0+NuP6lKyjZPuh57nfWkVLQFKqvk6U5fVW7/sOGo3iefor7rba0tPUg1vi8t2yQiBVfWAIj+Qs9Y+t2JLxGrytlFCwh791TV3x+WS9b52EwB+uaYh81DsTnfIp8QuPxw4mU6jdU1U+TcOs9qGi93p3aXR/3aQh58e8QULfcwHx0f3coqnk2082hvP3t8ifLqGfPsG+e/f0/t1PIJ8+4aWm+/ZCPfHU4T1hsKuVRVlwSUE/o+6nVNiQy3qdTd3VUXtegPMiXA3N4iyEnRMt8YNh/zO9GL70PO8d3gk4dKImDN36gP7d3hqsGC3P6jVfUQ6whNKivkqwgyQeaAcJPPgFRAmA3jnKNFt4kCDmmXGni+HhAB3PCVwKsvgfvE1uyJug/7lCRlFplz2SOyxA/OE2KPWogQu4kyP+rKgK7gppxnlcTRM1MGy4Euy3cK9OI95wIwPtHPjq4odqaJIOXU94xoPedk8n8Ktd2i/Pkf361fI9j3yuy26cQXfBmRa37vjMTavRqg/beAHFbqzMfybmzSlPhqzowVeWl3uIXqXYGw6Ga6rCGEwT6c4ZLMNQSS2Ld3R+J620I89z3vBe31jlZEvQ8Jl3a7hFG5OhzcOqHTnPp2yZ5xnqU1oNjM6qkeu+uJolajQUEf9051yTou4e8jFKdx37yh7rbuybBXBt94gfLxCdnYCNx4SHhz4ZWE65k79UOwmLvSU2I0llemFzJQFTK7vbg5xnk7dKshK/9eOx/xXr+BuQZcPzUPUcYQKviEunsOcpKMKssGWnU5Pi9UGmQ3GLs9QLDbE1MwW8HmG8N1bDNdfAlc3cKMR8k/XwGSS2ofLdRwAmrqDqxT70x/QHg8gBPBawnR9ghlHfcwunopoH6/hn3dJo+07qMEVRKWV25bSEqqlLrt90lhfkoeJBbVmxJjwJmMBsD1n/2PPPolwxg5A38Nd3SY2fkdDMdbBhAj74ymxOQuVq8sz/q7Z4vHYdbE/KXYTh0Iq8cydT5Tvif2egDCAAK66Tridq1vgy5f38oiDKyDls9vdywnA/bwAljnqZYW+B27uuOPOFpx8L1bIXr0APl4D56cIN8TSy27Pk0+tfuKg6FBWz7RCC3o+xcu+5/3LjJbFsD/aioxeWIb5eeR53gteTycbZrjVJh1lQNQ8j4MMVQWIY2mV3Ihu2FvKzmG+TKhFvfy64YBqvkofQx+AF+dxUuhOpnSoLgvIgH3v5t/7h9zJ64q1+NkxpCwQ/ujrqFz2YOyG/ntK7M6l2FUgyphP1Lofqf4LB1PxZe4DL6BNy9PwIA83GrJFeXYS88HLi/s5jYbM65R5uemE7DDokGdQp88LgPvyJfPd7dnR+XidvFcNmqD6+hSuVbHchvAAc/GTquT9bECCOAVqt0kZQS/TYkJZenmX1YHX6wPP8y5pnEsLx4SU9LIKgH9mO4ENHkyr0X6HXgQjSVqPcfEZoApeUWp539xXrrq+QzQe6zrVKs8g17fwRxNU/+f3hLROaCJmWi/eOS4005X8sdgPOjSfi926USbfIV3P/vd2Gye3TplTYj5O17f8mcxDkLPzZFic5Trhg9REwR9NIF1/LycYYeZ2xkUZAjzOgOEA23/0Ner/5W94X4Bi4O/m8BdnPJlGA+B4DLz9BLy45CTX0KL6vQrYOIAIXFkz7zyjTr+jw7YLIWnwmEuLXd67DhCKYrnOqw7O4x5Pz3uHN9yKjpHjbqhdFJSFtgB93D3kkBkPqLgRzXBNbsINBmnQs1AJOLOGNxiqcVAzH52no8hSUcaeuptM+BJsd3DTo1g2RCe+h2IHnhw78jxZqouohPiKP1fpiP0HlvQkTBO3Hv/tgzxcVabjX/PB9e29nABEp2w3HMANanZn2hbl//hXXGC3d0DbIFzfchGrRIdbrOE+3FCS5O++50k6GABH4zRjCLxwxgu4fd96KY8dtqNxGigptNo5guNiZ6yjw0qc0TzwPPMdngtGjpW2NVvGy5qbKFPdE5MCf6BcZfVxTXcJ5z0JzrdLxXd7DjjchN2NUr2RvOeiuzzlv3MzgxkQoA9wR2NqSn73gTtRqa3K4RGHMJMBnHoTwbk/WOyRqG13lqMRX7Kx6r7MV7zo6u5npmwyoSYkGUHlvTxiB8gmx3lGIabdPuYElR6MPkzqxtH8+TfwTY/VzwaYfLtBttjBAeiOB3BdgN8QhtydnsO3PfxyR6UyQNunlfIJfML1Hw0J0jM1iMzD9XlkM6FpIwxbihwynsJfz0nkDpJQqMXjS/p5L3iFxjodhcfbe3bfgRvaYbDjksOYYTQVQxC4Dzc6tRQKGamwKkZDLrrdnkCu2zmk7fhFT0bEr0yGLBeGFfzNAvLyXCelATg/VYyLwHeKkZkeEWo8Gv5BYofWtui1M/PpBnJ2wpKr7xG+egH/8Vbbhz3/d9dTOvBoxBf35Ij9fM0D3sPfLYHeAZqP27eEWWhOzrnELLpjmYFBjfI9ye8j75D93Yf4MhS7BlLkCJMa+P13yOUV5O2HpLSw2XJCnOfJnkcv1v7jrZ50QeHFbYIBf9wAUU5P25TWqt3tgPGInS/V+Xnsed4LHi7atAOA9NpDVvsa+WH3og8JlHRAFzOjMjG5Nu+AHtq2I25DQqDzXB/Y3tPBjhvUkKtbYkkMIns75+9Tdg68B8ocYTqG7zq+AFn2B4sdQIpdAV9us0viq++v+QLOltHFT1T7xYlwx7xbxEmr29GTSYCIhrR8AMScZH7oFlLGUikUOdy+QXHFiXQ4HsEDkPkK4ecvuKOfnqC9mCAbVpDNnsjLgcR7ihuPUi2vOpWRh7BTfy3rotlcwjnKiZv+/maXysOlxl4+XtI87xpeWS6uKrXmTrBZE9E0FCIJ0EW85JLxTzJF7Febr2eQ5HQxqLk7FAWi0KlBFzy1awCw1CgLKucOatb8DRcOVH3YvXkfL4quLB+NPeLbnxL7ZBRjj+oCh6rPcSqrZgeF1vxOL3LVgcnBATTC6d3A8nHnp/doF+CsAAAgAElEQVRzKovU184y6lnmGVzTcqK65vDMv/5I/IwEdJOSrcrdDtm//B1PEX1Mqz4OvHTazT/Uaa4BzFSqO24MzlGWQ2mZ0ZBYTY+l5URcxj/lSaux9pXn6dR925gx9wwD7AMzGW3FzUDd6KCsmGiDCEQPUFfkceAE5+BOT2LvXKocXltvsjxoe4UA/+oFP+ycKr9uepTMyhar5JH0I7Hfs7/5XOy7JjGo7GKrlzaZjlXOYp9+l4gSTzi1DFUOPx7HXros15T4UL4ttEUou/39nCzGkyljWeigz0ucDAOIMtqurpEvyf7CoIZ7dclBkTmGuwPhKP1+UZWA9fkPB01lHr1guZtvKUxrtjxb1ds0lKf3wLCC3P6USdzQnS9PtSxCH3dplPk9pSp0feo8mBMecF+fPfN8icoC0pFW57pcdVV04GS1Y8/pohwckxIc5OYOfjxK5r+bDSQI2f6jISl+dQ1X+odjL7Inx251qVN0KPwAMp8BVcU7Qkc8jAD8b3mWjBX2e7j5gv129YACwHzXG5K8vedLvd0R4685wVSLW9V+b1veTUzE1HEhhtsZXF0hNHO4uxn6zQb5yRTh/SeYZn305LKTydqyG7qUy1K7TubHqt+B0xc/DryKIikg6L9vk2OocsRjzzNf8GBShXIgW2K+SX4uU49+WEeVMPaj24Sp0PGzmREQfxIi1c1NxqTm3TiVr+buiLaLuHZZrjgAAae0fr5il+fFKXvTd+ovmnlgq31z42w+Frv3T4o9jtLLIo7XcX5KBYKqhLwY89Kn8cvBfEGOxoQC37HjFPNwDjiewC83ERTmvIdcHKecTHfGnFOAOPyBT2JIvq7YAl2uWEr98c+BxYY7/KdrwJGKGXVlRGg7KQI/X8fvOX5mTRPvM/Y5GWklnIzhl5TpTsJRStAZDu6Xej/yPO8Fr3BZp64a8T+3LXmctiMuVsR/5zmP2SAJnLRvKCWh+PeIi/eeJcx2B2/YFJVvJsG7jwQTAIofX5IjGoRDqw9X6dK63ZGM4IkJMQ2Xh2KPE9UnxB7NHMxasiwjPBj7Bu79dZwpxJfW7HNs59fH8sDJFHh/rcYEHMLJ+Qnc79+knFSu2kpBYoBUkkQluV1Vsk06W/Jl2jURk+O8h5yfahdM0ZotjZv9jWpAahtRVpvkjnKw+AEQvVmRoulvVRax76OOUGSEbbZpXvHA87wvrXbsjQb0G0UCG8ULSlUm54xYA/Zpgmfak2BP39V1In/3xMbIcpUuO0FYOrQdwt0sKicA4ELebHmhtH9zvebxWynM1zvu9MqtfCh2nE6fHLsb1jF2ALzMKaXQHAjjBS/oYCpQYJVuIoEKuwd54G7OcqbtUj6KsbGcJIQ08PEOuJnxMwH4uw3KezOD7PfoT0ZAkaMflnEA5HZqd7/epIHSIZVRL53RiAJgI0HLGCgt8p52kOkDdX0kllDW5ABd+8DzvBd8IJPd2cXHPkTrSLRdPPLMzAuAtv8O6nu96UvTRIiq2+wUtKUL7qPaRrRNtHDxJ8eJLLxv4I6IgHR1naaFB7Wo6wNLitWaO/sjseN2/uTYZbNLsTvHYVLbxPuAdB3k4zXrcNXMt0GWn4xTDod5FNSAiTj63Z54n4OcLK946tmCyxTNeTNjSTUZAZdnyH77BpJ5FN9fJwCelmFuOEC0swGiywpUa0YGqsqmZY8b8XOQTsWh2jZi38X4srWKUwXVCBrWSYblged5L3gBouFXCFGqQfQSFd9oG9GrWoCxeKIss+gidC52duJjvezhgD9XVSylDEGo/qiy3xOGoG27sFFanLl7eBcFRc2W5bHYuaM+LXYAMXbb5e8pbKm/Uljw6A8LbQXmOYLmEG7v7udh5BSfxXxkt7ufk3ENdLNwRUHhpr6nuXFZ0P5yuSYPuK6oGb9YRj5A+HgVL6PxRcxz/fOegq2rjbY1RRd6RnDddssXxaDUajFvxnNutVGDaH2ZgiQ5jwee573gnSIiDzAmYkCpEGjHaMwXoQFALCtUe13MQ6gq485NYnCbJpwGB8hY17tC++2jIVymHQzt0VuN7oe8MPnjKReISYFY+eT947E3zZNjBxBjj0bJekFzoyHjDcITyRZlwQ6Nnx5FLM1hHq6uiZjcbGI+8tWL+zkpXVE2W8at3Nao/Gt3D6UBynrDE8qGZW0LfzRBmC+4EZR05oZzsf42Xm+EQO85rXV1BWNsyXgYMT1QVGUcFg4GKiduukGPL+lnvuDdAWtI4hcbqW5e8SNOdWGqigtDPX+c93EnFRPwCfaBkWkUd0zvohkuBjXlLeqSO6RJYOiwC6KQhxNS2OKOrLtz2O8TMvGh2OP84POxu8zH2I0lJapsEH2Q1IkQzqlrds4c9g3Ni19c3Msj3N5pXZ2lfN58vJcT8iypGyjYLho2fPGCepK//AI4P+GiN6nqV5cJq5/n8JfnZHttdjRNUKU2+3NzWZQtd3XczVVgVTHw89RONY0acxUnb2Abv0OKND38PO8uTaCwPttgSlwIfZRpcDrt5LFLooUbDUgbM6OAAyGjaHNulu+7veLYTwgNHlOhK6kAaF8dvCi69ZYT1iX/PFzfwk/GscaVnqrB2ctL/u5HYkevWixPiN3IGDY2dyFApiOO/gvi813TUoVA241Oj3+A/XspdLKrebiXdNQ2wjR737ifk76AyH0caLm6RpjN4T5exUVnZgboOg6pFmtSBQcDXn6XSyJJFfkp+4bQ4MPJt+YsUGjDdgdAYQg6/Ta9fbpz66LPEZGrVKP4Kdfw1hbU6ajJLceOhg11AvEi8KrfOEzEX+n6uCPAaetqMODvM80YkVQbqp+Q1axycsT6ervn5WxL1QB4vTiZuZdXXIhOEp1JfTwUe/b02A2aIJstsfne0xm7KCiNZ/FPKQ1iQyvbDKLh8g/zqCqVBmE+OD66l5MMKuZVUBbb3Ez86Qlx/xdn8EckzVBfhw0BGVQHaswN/MU5+/ldpzqRJeESR2N1eOmjn5Yb0Fw44oRUW8eNRzRVOxqn1qP+nBuwhv8cjgZ47ju87iiRNOFc0idXEy07wuRuDnOTMPc8hD4JigIRkCXbbVIF2+3h1htIVbFuz3PIPrB/3ffAYkkLl6oE1mtIwVG4K9lSDG/e8/8+mrCfvt1zB1TG/YOxH2jbfzZ2n7omsYY1Q+QdL4dSlJDFMpI6zDFctjuWCGZYbHlYObXf0xmlJAbGjYYxJ9rA13wRFWcv8wVNFfoe4fo2Lr5+NofLc4T5EtnlOfrrG/5ZCJCra7iffcmdu+vZvdIcoS91FKxydCgU5xOgbKgwaJMQNBuhoki8YCByGB57nvcOD8XSHFhO8mhLpGM3Gsa2l8vzKJJqBA8AEajlVOsc1vEYj7gwJ2NqL9pkczKK4DI3HCTx0vNTtv1MrQvg7qXTS+rN6FRQrRUfjB14cuwGorvHB704013viCfHlxcJS6MLlTu34+8fD+/nAVClbDSM+Tj975ZTjFO7LFKXcJMx/NEEXul97oAW6YZDZOengHPwkzH8eAT3zVf889sZIRp6GkGlu+OdxrBLc0KPMbXZBXv9UiuDrFX3bdUQQh/4M9aX/8zzvHd4OC6w8TE/dG1pRVKEXRQPSwXo+FvBT1IVcDu9yPUh7Qpdzx7zbsedfUfHvNB1BDMBOv3UfnLTUtWrD0C74dFv+I2ui5dBUztww+HjsWfZ3yt2e3nM9lKubnja7FXe4/sP/PdXZDNhv0fY6Yy1bXkSHOQBCcD7K95JNswnfPjEe8dBTgBUFU3xPC/OIXdzyDdfAr99TdfufYPs5JgDI1NH7gPCy2O4N+8JqvMeWG34u1Zrlk9BzRRUAlFG7DyZ0TIFqrTX/umG/91cT6zt67XJoDr7nyNxP/MFD7pULFdcfH2gCpdNEPUSg90udVh6ArTMH4movsA/t13ENGOcg78444ujYquuVLuZtqV24maXhlPLNdB15G2WBYkOALJ3N+i+vkD+iUAzG2o9Gnvfk+DxlNgPUZC5qqWNxyxRjiYJkJVrO8/T4c4ETdH1xOzsm5SH/d6qjPnI7Qzy85cxJ4ig+9kl8rc3vNhv6BwI5+C+fZuUA/RnvZHPi4INgjfv2SlSXoCrK5ahACEKzvFO4nyUL4TzxPV7FyXDYTRDFcU1odnomBLovhj/jUeeZ7/guTD9gYubjryDkF2/1Vr74CFRm/hsaqojDZxs91yuWSaYt5O1I7e7dNG9uoV8QQKyU+6oH48gsznc9Aj+dsaYxkPkb2+4qDY7XtpsNP5A7FFV7Amxm+A/8oxMLfNB0rYjRFWJjcyy3rBF2PdkLF2eQoqMMnaahyl60bt2xXyOxsDrDzEnbHdc7OZnW+QR9oCLM+6wH68IIDs74WIuC0p9vLxkrT2bwx1PVVFZy7OiiBgal3luEmYdf4h41CaAq0rK8qnQlDNY80QFZc2N/BB6/MDzvGt45xKhQr8oG964slDFL5c0Vsw0qyqT9qDWvmLCRfsm4jpEjb7iKN10Fg0/8vIcuL5TBWNa30jXcxrrvcpYFPySjBRuliyq+PVQ7NI8PfZoXb/n/cFgwrJex1oXAOMqi/sEEe+A6zuEMruXB/Is9b01n/6PvryXk5kTY7vj+F814vHinNPVvocbj9H+2Td8mSecC/R//BVfvn0DNxrF3GWxhMl/y3JNzZvNjgC6QcXujH4uUvAkw+0sYmec/YxJmCyI4oxuJZvdT3zSqkQG6brkxmdPLE9Usi7jOBpty7F0r/AAq8fLMoke6XAn9oTXG8hkmOTtbJhxO0/lTR/SGBvgF2yOcV7LjZs7lajTQddjsavWzFNiF+0mMfY+TYzznPJ4IozfdO9FuLtut1x4TQvf9PfykKrUS3YW88l+8/p+TvZZtC3zWqwIJnv3kVwAFaAq/q/X7Ltv98DlGfI3NxHYB1NqM0OKruf/XZXatdIp9/UdWVQ60XWrTRoMduoxpdY3ACIXFipPKOMhmw0HOjk/9jzvksYl/05R8SFZbTjIUS8kV9GcAEVOPfYsgxsW3E20E2MdFVeVLA9GQ2q+jxUGa+Ao79OEVOthOTtGNx0gv1pwkVclMKzZmrubsQZXeqB89QLueg4MB7x0PRI7nIc7Gj0pduccQVoGH848d95BBbdrEM6ncNsmXjhlWCt4LaeYlO723Z98HfNACMB0THz5ek98fNCOh+VUFhG/DmEZJtd3eiqGqGnDC3WL/voGvj1FWK5Ybhk+31SMnYseq0a7dIOan+t0wp1+NODQrCjYmZkt2F4V4UnkXNSXd5knSd1kCedL/p5Hnue94CHA9Szhube7hJk2DLqnDSXmy3hhkcWStXMIrA2BOFZ3hX6JtjuCkz3ZrFjDG6ZjopPMLEP+6YZY766DU5v5qEimlyfsG7jdkKXCfMEL12OxF+zSPCl29Vtl7as7ctOyQ3N5Bn81U+m9Mok/bbe8aKonqtvu4d6sUh6rDWQ0gP/2A5KPbXcvJ1cWkVGEPGPpNhnxZ9WBJGy2aSD1zdfAzQz+8jzZw4P0QQCIzuKrdeo4ddqavL7llFXlO6Qu+VkDfME2W94P2padHb3g44qu3zInF+BzDiDPu6RxByRum0COh/fFdkKgBMWXLxKyMM95oVUp6nhzz7KIEUHfswtzNCEc1X6nErLtSwpHQx6jc7U8Lwr+/y8vKEy05qXJjUeQgTL7T4/54T8Wu8noPSF2uw/A9BxNcPSUFjFQ2EB0qN7tmZdqLoajIQkWB3lIS5aVvDhL+eT5vZziNPlYzR2qEjKqIdMJHQw3W/jzU+JqLk6JeDw/QTgeo//Fy4P5R8mSxIBvNhlVWyDDM7nxKKq0ObWfh5aETvkFNn8xTq/xCyIV9DMOIM97wWstCiCx1LVNKKpZIyHQO/TDVUxW+sAjXVF12JMRQ2yL6PBCGTUb1XK3zopevsyF2u1bHrOG41ZYrWsJJPNnJ3rhzOCv50miumkfjz3Pnxy7OXEY+CwKkFp/X+OX0ynjN7Wzricbad/SlvMgDzcewS3W6CdVzAevLu/n1LFUCGPtcdcl/PWcokp6wobRADJSZ5EiB67v4GcrZH/7ljHVZRxoAVAgnEQxVZtfwKusiXpYWfvSfkYWS7z9T35172djzz1QbvCQe/zQ87xLGmsVGvVOj9x7QxEgDmMAxDYdMr0wGf0rBLhDpd6qpOyD9oKdCa6GQIEjs4n/dMMvxXkqdmUZMeDWYTGwkhoTSx+A79+TQqfDpR+NXWHET4kdoioBLfFArioptdeq9v1mxzvDegMZ1BzSWB9bidthu2OJonnIbA7kOYpvP5K4rZ91JLID8RTK3uyJZnz9Fu7iLFnzXJ7BrzaM7fgIoa7g1huET3Tkdsq5jd0v7UJZzxzoqZ5gKmu7hpDvQUVrIiWnoCrh2gJf/nd/S29Zc0c5O2aZdXPHO0bDn3nsed4LXrmRmIzgWh0jmzzHyRTiHDEZCkqCCI9yk45zDmjAD0xlpd1gwN18s4sLOZYL1ju36WffA6I94bIgO1+xHBiPogKxITBNshkAa+KwfzB27BrWok+IXfKMXQtFHwLgLtj3qY9f1pCVsCZWGDPdQAgd9qcnfEksD/WilWYT84nWM5aTQo5FLen9ZJyI2m3HluFkjHB5AvzdW84oDHdj9fagJsVxtkhkDuvFCwdGTiek0vc0Lru5g+QUjIp8YSBZ3Gx2fOHmK36eQ8X1i8R7z0PP817w3iuajt6dzvWUkgNIR9NLZrg4JpPd9Brril+IUynlIo9WKqI1LgACvqqC7iBbLkCnwycMhuheTOFXDfxyDRkP4bdDSObhtzQclqblB7/bR2CUK3J2GAYVy4QHYqfryOZpsZsoUV3BGyrzmy/gVzuEYQ2/2kCqkjY2RR5x4gCArkdQRKVT12zLg2oKVPuCcksBxJwwnVBRAUhDqhencLMVmq/PIMVL5Is9spslMBpSqhBA/+U5/LfvAfASLl9c8N8Q4R3jQFkgSgMatkahxOh6lkN73kukyHlfyTzh0dsd4QlNC7mbwR1P2dKtf+oufmYx7zzH7EWRShTtkbuPt7G9aPoprtaaXDmgZhEJ1YEUILXfup6Xx8UqtSOXK+RBmUg5peXkdArxHrilYZf7N/4IeHvFgdSgogRfnhFnrmXLg7GrA/eTYh+o+sFyDQwH3OE+3kL6Ht7YPiXRhrJvUsdH+9buwxVbpvsm5oFPNyxvJiPIfEEDsvEI8vNXMSfMl/y76oUquz3c3y0gRY78f7tRO/iGhmf7Bnj3Cf1qDW80R0Vs4m++hTs7TUoIznGI5RzkaMhcgqjW/TgKqMbPaDxi3AMKasl0RLcWLUndmEKqLs+I13nked4LXhGDhoORDnESF9GShjMx6Ysi52KLmuw+Go7FHaNt1Tg4Q5iwfvarHfpffsHdCkhIvC8ugLZHP6mRf5oDwxpuMkb359+gen1DvPxmBzfTHnDXs/e9UyuZh2IHnh77SnfBfRsHN2E6hl9vyfpXnDtf1oyngnOU3piOIfkRXNtDTo5iHv0vvqD2jCrvdn/+Dcq/+hZ+vk45eeJaMB7xtK1K/r73n1h+lAVLvEFNSeubO06JJ2PuwPMFoNwD6TrmZpRABYphtlQNe5Xb0L9vwDoYEaXvGdOwprBrZEp190uxn7RPK1y83AEATsfx6IMI22qdWlJmHmiRnD2g2IvtPk3vbBhjT9PCvVvxAx0O4N9sEs66oTGBv6XMdX63YAdl3yCs1ihX69gflyzjDiN0qkBVEYtyCNz7YewhpB3wM7FL18fYZbMhpPnNe2A0ihh+ZNk97L1s9UT7eA0/GCDc3sGfHMc8/Gqt/X4Oxkqb6s4WKSeA8eqO7SZjFUWt4U1RQIQsqbMTvRiXvNSPderZq0CSMbwU2ium1yMB0vZJAEsn0OSzKpp0p8Sc0EcB1YgwbTtgoBDuovisIcIzX/BgWVOVXJybXeyhSwjAaJB0x3sVH+1ocBtfFFMEC0JjsdmSqEXnDtgy2rrUvnBsd/U91cfseM4zhLsZ/MU5x/6ZhxuQ2iZeZbBtt2u7x2M/ndLr6QmxuyzjcT5TaT+T1ug6BZDpNNZn0XvJHU0ShbHI4b56BajkhsszYDDipdTzTmLOISiLmBNE6JQiQbViVOX35QWaiyG6QYbBd0vAA7uLEequx/bXr5BtOiBzaIc5Bm95Yvrllt0cxcLH2cR6C2Quqq3Jcgl3cqzugpyiym6vZVoVX2Z4Xbp5zp85nhKEVuTA9cPL6XkveMOfmM2kIurMzNZq2vh3bMTc9YALcI3cq2/dVncRhQNTkGnAXvt2SxFVEV6kvOMAab1ViABNxPCrb9gd+OIS8rvXbBOWBeAD8OULuM0O4d1HdlwUzvqjsd8tnh67SLywyXYLFyrS9hYUIXWuJuJxMFDlsYy9+Kbh6aBSfjKoYh7mgi3qQo4vLsnWevch5hTJ7p4wA9nt6dY9W6FsO5SfboDzE2CxQj4kbr1+PQNmC/S/eInBbMeyybv48jmlUEYJEgPH5ZkqJXAQCIAt3WEd0ZGy3lKUylrCAD/jUlufq3VyRHnged4L3koaHT/HnrQ+hh2Hc7FHHZUC1OcTABeRSHLL3jfav97GLgEuz7m41cQ3iu7rKFu05eVEKP2mBlpGvEbogSuOx/1kHNlTj8a+wdNiB2LsUQ99trzn/4RLGrCZZJ3znm3bkth0mICste5u6F/lqjLmQ4JFysnarQJEWLCfr4lkrAus/+kfY/zXdwhfXSC7JfLU7Ru0v3yF/DffQb58Qbfu6QD5u1tEMzMzLLPZRaNwAfsM9HJr6g1sr7ap5QtElTXT/mdH76Bt+8DzvBe8akvCGz5b1X4PLqIAL4PuaJKw7WrOG1k6tltJgPM6nTUYsHNJMWw8ZDegUCucECBfXnKRbPaQ2QJyOoH86mv49R74wLPTjQZxh5aygMyWQJvI4j8WO3JtXz4lduXuOl9AugbIa8hwBHdHAni8jHc9Y7HLvpK8pZhC/tVv4X79q5iHQSrQdQiWz4HUtEltGwNLyoLgruWKZVWWYfK/v4es1sjqmifIjPIaRUv1Ab/aQGZz5OvxPWM3o+dJ5lmWrrfsPFUqp2eKc86l3X4yopFbWbBxcLegPPZ6k4yYLe5Hnue94J2Lu6TTBSNKEKDOS4jWhaL25oYujOJITp0iVHfGbvP233ByRBJJrbXzHomxVNfAegfMFgjfvEK2a+DsiF6r8lhZ8CRoGuqrn56whMmzx2MHoiT03yt2HcHbqYPFEq6pCSGwaXHmWe71PRfGaAD/8gXk3VXMQxZL4l+2e2RXc+ZzdkIzA8vJyB4nUw6B8hwyGaF//ZYxmcygKa6Bnafu/UdkShkUEfhDcSTvIBubPOu02SAGEpR4vo0cYHdyzBNhteFJZl60zrOE0ZeCjobhPgz7R57nveChtd9kpHzFAzjBvlGnDdUnMSaP4t6jtovuGEYXMxguS5w9x9lVCbfcRMtI530iXRQ5XFUhe08PIqnu4zXk/SdqwQ+HkaUPgCXTY7GPh5HK99nYjZgdAmtyw4zY1Ha7h1tuEE4ncB9vEb6+RLbapLZekUOub+FGo5gHtLMUrm4oaw0QenGYkxJDsGWZIE0LLFfwv/iaM5A8o9nyq3NkH8iM6k+PkH+6g+z38EpuD+Mh/JyS4rLZRh2aaKhw0DkTdR4XNWKTjXbWnNM7ju78lpv+DICknPzI87wXvAMhsFrrucwnUaI842Vrq1J11pILVMqKR6jKXsh2yxrTHaiZAdQ+F6FplpGGrb6vK7jVNkpfo+sgHxcJDjsZwx9P+UWuNuw8qP2KyfU9FLssljpLeELsEGCzjapj6HsuhMzz9DDdxt99D3c0gf/b70nwtkvnaksZj4M8cHwUUZXh/Sfm8+IcWG9STsuVkkT0YikB+PoV9i/GKJYNQpkhGw/gtg36V+fwTYf9iyHyoxJ94SGZw+DNEu3FENXdAhC9f2Se312e8XRtWm4MOjyS0YBQhDyPxHAJgXCQ6Zh3LQPk1TSAgKhYlPspL3iAuuBKAHBFATfII5JOZgu4kynrbp2kijKhIurOdg/nE3HayN9qmehGw7TIDIWooqJGwQvbHVteqkjgTD9xs0k7Zp9IEZ+LHQBx6k+I3S6e1hsXoSKbsamcug26suTP26K2kmG1Br64BL5/n/IQIQ5HJOXz/XtdYJrTwWIX1bvE9+9R37BOzhTgBp/Bv9/DnR5j+BvVxdSevDQNKsXkx1LN1A2cIxxhwYswFkvKixiNUk8EkwN36y2w2kDOjsmQMuySyY2fHScF5AeeZw4P1t3QJN+A+LZLCIntpONqqIyDqIATEXr33fBICwvpAtk0cZFF5tJ6kxg/mQeKHP54Cnc8RfiHvwTaJhJQXFFEryjqwVRxuPRY7ACeHLvs7wPGqAuvnR+j4oU+vcT696OyWuZpPX+QR9TEKcuYjzuZ3s9JiSERlz8ecc6gF27YfwOw/qf/AHI7w+rPXgJ9wOzf/yX2v7pUUVSV6SsKzhnMrWM4YEm5b3jaqFamFDo5tUGcXWa958/o0MqkVyJHwNQnHnme94J3iJchCBVmzdnODQeUfzYNRrCWE8WSR30Ue9TBGaHX3V7r4UFNeWmvyrwqhCq7vUpC7FIJsdsje/2RtWUIPBnsA/eOfFDvKaZqhOQHYoeOxZ8Su5uMY+z0a9UJr7UsV6pk0Pfp5dCBDRwl/GS1updHuGLNbc7iErscBzlpn1ysjWvT2GENGdYII2L0XVlg+N0S/a+/weDDBnI0wsn/9C3Kt3NIVUYDZWlb3md00Rsx3eWq0aMyIm61UUKHfgb7Bm6+Qjg5Sj/jaAUk9jNVFS/Wjz3Pu6QJ8gNJij2wVW6llg7OaQpK5kVdxLc82s4MC2LFVWwJSCKjgGoqrjY8QvVSKN4B2x7uZ1+wTgZI+asr/htePY2UZ+mKAuHlGdz1HNmXr4j5fix2HLCRlQwAACAASURBVFzWPhf7aoOofGzdkEp5oUVSKZOQcWikGHLZqcXmoAKuZwc+sEv4sxNS7WzS7NlxwnqTdHJUMY3/INWW478/p7mZrNbs5zcdsvWOpJOqRP/FGfy2TW3FTk8mEbKujFAD8GXb79lH1wu9mTc7kwisaTwcc7XBlYTYwMC+SS/8A8/z3uEh8ag2MVQAyVVCdQ25SMsow4yM3FGTcEu1eODxXpbsEtjldbZgPdy2ijg8cPd4+5GXt14VhfX3mSOe6BQTANz3H4DM0wRAwqOxR+eQJ8QePVwzz3q74gXUzJPFVBIAxq/KBAAXN95+RLi7u5+HWuHIdhvz6aeDfy0nQEuHzLO23+6i8XH4dA2cn3D6udoC8xWan59xh+4C5Pv3BHrdzbV081E2HLt9EmWqK6BIoC+zzCQ8YhdPNbfVnzEN/rqK1kIAeAk++imTuH3G3cp7TtqsHWWPHcOqDGZDElPldbpzwSmuPmJU9PJpY+6jMdk5trAKyknI+QkHUMM6WbMPa0iVwS933KkmI040AVq29z3cN1+xzmy7B2Nn7dw+KXZrSQIHkF97YQx337TovzxH9uHuQLm346Vws0N2dkJtGctjTCxLN6mQbVv45Q7ZnJ0sy8leSleVrKu/fsmFvdcZRdtBBorUFIGbjFB8WBIO8PYTa3KbOAM8ETKfeuqAlih9VGVDCMzH2sLa+3erTSLY9z0hDTczziNML1TV2x5dUv8vl+L/P49xTUNQk4Iy7nqi4CMUhcJyD7AoqlZlx70BleB9FO6RrotALde0FBHynvLTTQuZDBFqap/svp5SK3FMi8nsekHCNsCf7wO/lPmSX5YSKx6NveueHnvTxthxdgLpOvSvTlm21BVLnckIocg4WR1UEO8gkyEHT0EQpqP7eXR8gcpvr1I+quJgObnhgJ/LcADXdvBXM4Xm0htKfvYF3Hfv+e91HbrLI4TffwfXdgjLVWSOcaini77rk3qylSVAuugXBfv7qm4gF+oCqDKD5LLSKgdAUh/OMkiexZPtocdZ8/85PtPqpfy7R/9hVOC1HdK0BQEkjqrtSEruxr5RZz1t9ZnGil4UpW3Zgx7XcB9vI6rQui3YqtKvyTFbu1IHVgASurIu+dLYZSzL1KBg+XDsBud9SuxW4nRdnEq6okjSgCawulrz0mlYFUU8Qi+Obr6KecjgoA+ui6T/5RfIv7+KOWGxYvljALZdE19MGVTk/ho6U+8UzZ9+geJffRehCyaPYnr4TvH7stvH3ThadvaKpDyaJNyT7ugRdi2q6WPS2JnOCxQHJE2D/+HDf/svROTf+rE19bx3eIALZajOdiafYcfhYS2vmOuoyutUjVYRh2ItvsmIhIkx7dL9pzvIq3P+vp42lths2QFRYJkNhmSn7KPxiDvRekOF3+Wau1Gtu7JhvR+LXeX0nhS7XTYn6rKhrUBRaxqrW03mgvIVxNHLbscuSdvdywN3c/a/l6uYT369vJeTqyu+DItVetmXKpbUdpCfvSQu5vQYYbFEuDxB+fsrGBc5yt6dTPmCTseaKztcOJnyRakr3gVGQ+YiarMzGTHW4UC9Yx1/xmAbdcXy6WiS/nzyuPLY817w6h8qilWBIusAwERDxRbSZhs1SlCTCoYb9RTd7bmT7RseqeutogFLLv71DmE2j2wiDnaoeeJuZnppooS0CwKZL9Hf3gEDVRZTaTz37kr1Dimc+mjsq/WTY8d8FWOnu+AWcjzhZXe1JkRgNueOWLOrQeIFvarczQz4dH0vD5xM40ll+UiR389J/z1XV3HRu5qCseF4DLfRy/3VLdyLc7iWv7v/46/4M5dnXLRVmeT6gpLZjSMA8LvY7qkN2bZ6lwmJsBMo5uTU+SRKtKy37OO3PE0R/nUT6x8+z/vSCppdxeNL3/zY3748g7udq0sE/ZScDi6cETo8AViurnix3O0pdLTaRFNfFxRaYObCQXXnJyNg30YgmKsr9NMRfJ4h2wzZkTkew7cdS4YdJaDD1Q2d8Jr2wdit5HhK7AAvjgY1cJkHFPvjBgPuyIfmbIa+FCHp+m6VSCmaB/oAOT4iqb1pmU/bAXWVcmpaGihbexPaQVlv4d5eQV6cAW8/0mqn6yHXt5A8p1+rfa6Zh7x+C9RqNtzSoAG7/T2WlizXvLju9jD/WpoPd0nFzBoNQSKaVDYbblwqPHXomvhjz/Ne8IGtPbFRu9pKAto6e/dRZTa8KtMqO+dWJe6ahh9G1yVdFwC4a9iF6EMkc1PkSPXTtY6X90T7mccSAODNO9rjmDLYSu1jrm8RDPKgwyN493DsB//9s7EbzQ1a73bdwamwB1ra9YTNRl8q9bH1Dvjd9wgiSaZD8zAuqDOpj7JE+Lims4flFPoogSc2Cd3ueDqs1vA3M4SO1MZwO0vYfK3NZUm2EzUvtW0qLt47AMRZBaZjyuW1icvqioKfhTHPVptoAUTLzR3QK1LSTOMOVel+5HneC96rnJq6PYiZFngP5D7pujun3kgHxr2Adja8WjOqIKjhTeqKt/pRDbfexdYZvIe3YcmJU5y5tvfWW7gvX1C2Q7VuZDyEFBn8zYJWMiEA7z5GqbqHYpfOxQvt52LnfECSe+F4lOQ7RjVLi6MxyTBZBm9KB10PV2zZ8TFNHs0DwxriPfxmx0VflXA/ewk3W8WcAMDfLNC/OIafb8gbUHeP6N5tnIKqJHJzwWFcuJsliUDgvjAskLgM2y3z3jUQCZGxFC/7xvtVEwtTETZCC4zrEITyHvbdPfA87wUv4IKpK04bAd29uLPZ7d7ZMW12kCIRvxKxNOrrGrs6+wZYtXDWirO/EyTpS2q3hLY42gXJVRJuveFC+nAFr8epMzgCOD10wMOxm5joE2J3eQ4xj1eAo3WVL3Hz1T1WEyq2JJ2pnTkq9tKqp4l5YDaHc2rIUFVESZpLiuYkyxVQVcg+zhLHdbNVEvY6IijDh0/wZ6cIbz+wlQmdBJvPVRDI0Thp3Bx6WJ2d8EKdeZ60+z0x8KaRY6dcdUAPPD1Ordo2GbxJ5uEeh9I88wXvHGsydXmIhAnrdmQZF0XXxy/VGV8SuG9jaEJEQsKwBB1bb9QSMejAabdX3mueyAjTIyIzi4I1vf4eMQKK0upkw0/bv7hIuvIPxW6diCfEbncBURU03mskkkIi7PcAbmA7pwwq4OqG2i3DOuYRUaMXp3CLFX+3umlbTnA+QqERaAvvsowX5qMx3G6P/mIK1/bocw+/36P7469QvL+j9N7RANmbKy52s7sX4e8wyqLxUO1lMK6CkkAiK8zKui4H1oJoY1SkqbVTNbnHnufdpQGPNmKl29i1iXxP1VWM3kjmupHn/BnnEiGjLJJe5PkxIbVNq3WnykMs2YmQ7S4aJaCqqJkOBXgNVcJOBYzo1qEuFKHnrnZ9G2vQB2PP8yfH7g5j7zrGbkYL03HCvpi4kXnM7hv2ys2B5CAPHB8xJuuMTFgmHeYEIF7sI90S4Kmil+nszRVc28O//gA3HpHLOpsDn27g/+6d0hMl6WKWWgJZS9k0ZUTnF2ol7waD6Psap9LDYbqb2F1rtz+gerY/fVMzGVSsHYtCBz1Zavltt+y/mgJB2yawk2rHW0nhdKDjqpJO0TrFlPUGKEYEWNkOZDuvgZiAuBglU3DWegv36kWajppQqv3sU2I/mvz9Ywf4M4oXD0PW6jJQEJsiCVFS5cxq3HA+hVutUh7qyUoXbuaDQX0/p6YFXJl6/3rxj4MzI8cYl8Ae08nZbOkbeztj392EWs2qUrHxbruD9IhlHvpe5f+y9PtsITsXdTFjCajQDTcZxe7NQ8/zXvC2A5nYUPzPIUnk2ZjaugPKYLcaWjZbtvGsM5DnCS9e19wdtD+MQc3OzW6v7CeV91CtQxQ5YaqLJWvV9x+Booj29AD4b2+1Z200wh+L3Xbwp8Ru4v86qIoXvT7Aff+RQqiLdUJPmrXNbscNYbejlPdhHvvU8rR8sN7wFPhBTtJ1cH7Az2ezpVbNbscyKfOcEeh8IJoD972WYRKVCGSs3rd2EmWeGCYAuDwllVBfBjHtoPmSF/IuTXjtdAHAtuntPKFHpxPgw8NL6pmXNMS/uLpmLdd1kb9okNHoomF6L7qzRKARwP8mlJSLqD1jBUWsh4K1lE0UnUK8ftAmDQdQtHQygj85JszWuJUibDHqv/lo7Hn+5NgBxNijz1HBXc9VJbE53ifIbF3FnViKnJf16fheHnJyxMHU5VnMB+cn93MyxlVVqQGZnqgSiKFfLCnrURZw0wkvsgoYky8v+fPTiUqfdGQpbehjK8s1cTn6ueHTLXvxg5qff6vk96MxB13Xt9wgFHkpTcvv9G5BUJpijgzK/dDzvBe842DBJm84O2Gd3PexXRmxQPqFy76hjvvpcRzfUz5aMTEAj75BxUFTVarKb6DOpJoZyOmUZYA5SyjOpvnlC4Ky7CQAEL644NGc5xyGTY9YtjwWe1E8OXbozMAeGajfaVXy313TdgbTScTiuFoNEhbs4jglUVseUuY65GpjPs3l6H5OXacMp4xT07Nj/vzlGcLPLqPLSPjmFX1az04VY19Taez4CMg8LSxFZw5dp2Vclr4bLUcgtLkkNGELmQzpONJ1aZBnn4lyhTGo+TPm6Dc5OG1/5HneJQ3AHblVE4HbedJJ2e0TrDfT3q73gBNCBPqQdsogsQfscr2YTcZJXk5blu7NRw46fMZeehAEpdw5JWcXf/M2UvVcSe327HoeyxO3IxYnQlUfiN15r6Cxz8cOxzo11vpdH+EJTk8POB1gRWhwC8ypIylAspUxkvm3b+FGvLjLFcf1pd5XLCdpW3ZUlqs4y5DtDridIZuMicx8/RZusWR7c7U+sJDc0grnds6yzmxuDAw2HnIX186Q2zdpuKekd6y3wMd1msUA3KBUkFWkT2rBSn/8aUMLDlCE0rZxKmcLBiqnzLLjwARgv8ehiKepUxnhmG7WWt8ekrcnIx7TowHQsXvjnOOLYVj6s2OOzIUa5e76FphMWG+eqn4LEC9uD8UuOhl8SuxxBG+/Q13I3WTMz2i5UiUA8lCdGRPUNaAX+/Dpmr/zgBMQu1QATM9FmiblZMYNUyVZaOnV//HXAAD/5oolzPFRRGH6fcMX8ZuviFcvCpKu7xYqr6EwDXNN3FIclZAOSnNEortdcnMtD4Ekf1KVURpcLP78oL35wPO8SxqACXsfR+/Scce0frYbDiNkNCoPODXj0ps9ZZV7JZRUkcAd9WKs/u3pBxqZQMquoVKW6kO++xgnft68i4yAoEKhYrJwj8TuyuLvEXuXYgdSe3PfJBSoCNutoyHxLjZDGKRFfi+PkyljWixjPtju7ud0N4+DKzQt27K7HbK31/DffWLdr6cXPlxBihz9W7Ynw+9eQ+YLbiC3cyI3uy56VmGzVdfzjvCR3QFmSDs4xkDDnsJRsl7zBTFUa+iJpalKRXIu03DuoeX0B1mU/189ApjMMnewAXeuQyc8QMfcu6TO1XWAlGnAVFdwWeA0MnCBWXvM5Rk/yIJHayQkeNpPWkcnWsgrRl4WJCC4yURt1VUiTrR8qutHY4+16xNih0rt3bN/NEmP2VIHREK9ScWfS1Uxh8US0fJmvoh5uLs5SwPnuXAWK+a3WKWc7ETR0ob5jtG9PEa22EGKjJIfXY/25Vco394BX3+B3TdnqNR1xd3Osfq3f47R/30Vdd2jmKqe0gAvwa6uCIRT3gKVE3hyuKLgSTuo+TPeAeISeM65xGd45HnmC15NhD3htubGFxn2pp0O1rA20DBvIqcDpdinNULwmIOdcHKC2398gulvN+gmBZqjDMO3O2SbBt2kwsd/Z4iL/2OP/XGO9csM9W1AtejhW8HVPy5R3Qn2xw5f/vM59pcDlHd7AGN8+5/9HH/0X/3to7GbwNJTYrepaxRsyjy2f/oC1ac1+nGFzasa5bJHX3lU13tI6dENcuQrOnB3kxL/0X/9z/Hf/+f/Qcxj8Y1HsQKWvwj4+V90uPrHJbYvAo5/42JOu5dDuCDYneY4+t0aToC//mcD/Pq/eAcZ1vB3C7LHmhbljpNTWW9QfU8aX1iRNjn+y++UTFPHwZ5JCWI6BhR7H82hvVfLSp2a2neYcxLuxtSSlN2eL/igogr03SL17h94nj3j6Z+8/E8TAEvZQtHc1zioloOpiqm7c5xOAvwQizyiDJGrpPTtHDieALczMm02ipP3DmE6gtt3wPVt3LExUMqdc7G9KJMRMFtCvryAv1sm9pK2HH8s9uh28YTYozLXZhvBYwCU3hei+x2ANGcIAncyjVNVt94e9O8VfWgydsbmKukCbjm5HfUy3VBBbU3LF/bsGOtfHmP029v4XUldwN8u0Z9PIVUG1/YIdYHi/UwxLh0v8EUeyzAXJMZg/fU4PVZbUVks011lt0+zADWMkGHN+0Gpmjci+Ivf/5cPMp6e/Q5vY/J7UtF28TJbFCM4K4EjMou0Bo/1sC2uECC7DpgvElIvQgu4GGWz5UI/mkSnvPDilO22vo+/U4xNn3m4DzdsN64WybDgoditz/6E2COvFYjoSuc9LWWC0KpSkYWmWObqOroOusUKYb6IJ0V4cQr3gdabYkM0nUkc5iRKMpcFWVHxdPp4jeH37/nfDDKgAC///Yd0ge06iIlQVRXzE4mL+YffdaT8rbeRghlfyH3DzcK+w9kytpQFqUTEIX7qR55nvuBDskQ35o1qk8QLjfXWM61FVUMSQBpHm7SeV31HxaK7uqYEXOaB8YAtwc0Osl5z+DKoELIMflmgPz2C3+yBzRbdzy6Rvf5I6/XpCO5mwd1eneTceMR6M8jDsfd9XAyfjd0g0H2IgDe5POMkVyeSyDMqCoTAXW+7p8FwHxCmY7gJjcFiHsf0cQp/eglxYD4jCiZZTs563CpMZbLjrizR/KNvUP32I5z3aH9+gfxuA/Ee3fEAxeurxCEWQX9xTLFVICmFGWJST0y31+/5WIF6IxJS6EgeEqRBhLOHQEaWZJ4ngimY/fBF+sHzvBf8DzskQFLUBckBcUxveA9ztADS+N4w0kUOJ/vEbCoLipQqUdl1pOLh1SWw3MBtdrys7Rv4piPi73TKxaE7tf9ww+PVH7EOH+mLo/5GD8UeFXH/nrHHIZpBl+uK+HwbkLWSWo+6k/rNjpPNzMc8wsUx3CpQKtsYRVpWxJyali1JK9Hajq3b2QLFX/4WQXf8fLlC82/+Cvn//C9RvHpJ/sBoQIxMCPDvrjggKguehnXF4dmBG7qzqfSuIWBPSd6mYOxU2s8M29xixRNnxgZBFHBSvaCHnue94APbg0baMPVdU9AFwHLD+ahrIsYR1SdeFI2TqlqH0rZUFug6YKZfuNXvt5TMsxaXG9TAu08U3r9RbPiYX7yrymieAO3tx8sl8HDszseW5Odij251SseTEABVQfN3at1oI3XnaEkpIbL42ZFqgNE05uHeXalGZB7zwYcr2kdqThBhu7Np01zgbp5INAcT4OJf/Bb4B7+CfPeOu/ZswYWqUirk5i75YjZgTZ9pT300jIoFhydcHKqB7VkEmsrZXcJOgQj9Xm3uyyv+yPO8+/Amb+E9EYc5e9FO33iy87Poq2rtKwD8e6oz7vI8KlSJdUxUFhvK4jEcR9QnX2/ivyOrdZSjQBBS3O7mtHDf7RLSUVuLbnqkevSPxJ5nT47dXhbrSrjyQFxUFcJkuU4t0apUsrd6zBYF264HedhnR49a5gPgXk40QMtZ45sq2nYXFR2i3LeO/v3NDHh1yc/ijNrwIoIwmydsjgpNxXajc4y9admr325ZnhUH2pKBL54cupvod2abYqRi/qS1JUWosa4EhHhBNZC/gbF09zadwyiXZyyn/V67Myqi6jkkcsM6CfMHUdv0jB/6gNBhp8Zj5m7t6orcS4CL/vSYuA9td1K2bh1xMw/GDjCWp8QOAD1iiWSUQFhpY7uiCpNG4NgP7wMHecQO02YL7LWMOj+FvP8Uc3JKJIkXzIL49fZPv0S+3ANtz5drpJqQRQ68/4Twy6/gXr/nKXNK+Q1stsBI9fPXm1S65TllxJsW8AEI0C5anhZ3Rc0guZnx9PCeJZPZ4+CgGfCTruGLArg4I9+yZe3Ji2fPVhUQsdsAWBbUJbEoopbvTcvFPB4QQqv1u4wGhKPmOVCXbJEdCAPB61GceWAyQncyQrbSsX/miZU3rcYRh0rdqxPkr0kAD9MR3LZ5MHapS14ynxJ7XRJlWJVqVXNCzIj3rLX3DduW6hjoqjICyA6NvyTPUh43M/6+F6fw8zXzub6Nrd7kW8VFHi/Dyxzlt1fcVb96QZnrVp0BhzXc+SmwUhpg2yZckXJhUeSxrYquV83KLeO4W3FBW7nk04sWO1Ba9ztTdj4aK5JU70ufabM/7wVvkNIIlfWx1SerDeu22JrUY9pQgSJsWRmbptFpqB7Jzo7oPQcXaBqE4wlrYhtdlwWwF2CzQ952CeNRFJDxAMgUX75v4AY1st+9J67jaExL+aZ9OHZlVT0ldtd13PVsUnu3QJSe22xVQUFRj0puka7nkGY0hGz2rOGLMuUxGjLuLTXWZTwAFmDppzmR+F1H7A7eX6Xuis8gNiByqmT26ZZwg4y+snJ2BHy4AaYT1t5mGGFgMpMPBMniCAGyaRQLtI2AN9lsqZKgIk1GRQTA+Ufo1W7If3aHf941vFfmzrBOljGm8qv4FeIoQkrUdNPt/zc9eEA1Trb8+94TfvrijIhLEbgPV5FE4Y6P4jHpDG8+nSjwbAu33HBn2+0husO78ZAYGTPSfSz2unp67EqikO1Wf95HKKwzBzshtCBezjMPd3yUdF+a9n4eiknhTsl8wmJ1LydyYzkVdWXBf0s/RzcZwe9b4mWU0odTGipIXRBG8Nd/x9r89Rs1EyZbKd4fFM8EoysCSRXhsKQZDuC+eKHgPx+pjrAZhW0qh+TwB57nvcPDRcSckZwluMTXdJL6s9AvVelnKAtO+HSQI3VJWt54nNj81zO4tgHGLI8idqNto0ycGwwIJx6NyEIyds9imXQci4Knh47Cw/UtvPqmPhj7IV3uM7Gn1qV2gAx6fD0joVq/ZE5FB/HPZamUPpMVP8zDRJxuZ7yULpbwRyokqzmZVqXdAWSn4k7OQc6O4T5cxQEbTo/h5ktuQq/fsZ15TIfEWN7YcAxIsOw84981WqPKA+LsJE3B204VxyQOqKIG/nrDsq9vEzn8ked5L/gQiM/Y75VAUCrXUcWE2jYNd1T3HEqkgPI3xWecIG63FBcyVQJbbPmAv8eO3X1DqMDtLH1Bh+JHyqTHQF21JxNdSENedOcL1WTc/OFiBw6mrwkF6bTWx3INORoDdUnIdB/Y6rQptUgUOEoiTh7RMzXPgYGo/MYm5WStSIVRIM+5MKsK/vouso4AIPz+NbKLc7ZM8zyB0dRl0Z8e8yRQNGgE2m12vBMp+C76zM6XvNhudiyDVIMGIopwpVJb5ArvmwgXfux53gseWqfaTmn4ZxPSP+jTAkiO1qGPOyO7FiHtinb89R0Xl/bC8eEKkmWssw+I2wBiN8ONhpAQEG5uY20e1J5GmiYCnEiMrh6Nnb/fPzF2n2LXKbEsFLPz5gO7Tqp0Fkwk6W4GBEHYzeBPjhFWa2TnZ/fykLt5MjZTjsBhThGHH1R9uSMWxko0+1k/GiI7P+PfWaxgbiWuKqNlEByRjSgLLvI80zvSHoCPWCAXyqQwYS/E0TiSeJBn0YgOm+192EGepYvuA8/zXvCex7GYy8PtAbOorlnD7rQ8cH2S3NALXhxbl8rtLHIejWov6RoaHACgku5oAG/EcEfwGLYNuvMxsuUO2HescU+n2L86QvWXv43je+QZwmgAv97GET26/sHY0Wtb8Qmxo+tpPLxR146B4uKblkTs0yncaoP9n7xE9fqWOu1FhvLNLbVouh7uz/4E7aiMeXSnI+S/fQN/dAqpCuxfHaH+9gZyfZtyAiKND14ZVS8vSOwYDaLuvuT0ZYJzSXHNOcjRCH7XRJ1L/kKXuAJBkr6QniZyN4fLSKhBkZMz4BxPKIOEZOw4OSv/bu5oyHY4uHrged5oyfJS/snFf3wfX6IsKBv2xB3eDGlN30QRj4cQU+q/lPESaO0yahMqTFdxL9HZ2U4EnZCy175NvW3789Mp+pMh8o9z1vdaNz8Ue8T3PCV2Q1eqPguA1PE5mCJb7z76HlUl6/TDU8UmvZqvqyuWCB1FXaMRMMAFdn5KRTWrr4c1x/pNw8WnwDPnHOTkCPLdu+jzGrX2DemoZYtstpFpFVUNDj7LmKe1Mw1gZ3eLngSaQ7vPQ439v3j33/xU9eGteyGp9jVOpnIjgdQ3d8OaWud1lcoFI1EDiVhtuu9tS8FPa/GVBdA2UaYD4NDDGfam69C//QB0HcJmA5wdJ6bOaoPsN68hN3f8eVvoD8R+KKP32dhFDkjhOQFjyqyKujm2SFt2PcyAWeYL5Y2u7+VhT1DGU9io6dtBTghC2MRuz7xWa0IGMlr0uJpEeNls0d/NOFcwZOkVwWLhdobtn1wSB6Mu5jYJPhReNSubSMW0F9EQmn2fcD1A7OpI28ZuGr2ufsrKY0L5Y9nt1IKRx6GznaJXbLm2qEjN0zbcas0dyZS1bIc3mOtynewgAQ4vdJd0ZcGLW0PHi+j6PBgge3kJZB7Zy0uKq6qfqnQdNRGrCv7FBVuIj8Uenh67/U+E4y4pT+fMCbzrGL+VCLqY3GQSOyEA7uVhL4h/cRHzAXAvJzcaMq/RkOplZsjW95D1GqL2l64s4P/o54qopBanPz0B8hz+7AT1//pb/u7VhieFvZxOYdOnx9wETCJQT8foc6W4JlmrYbKoR+/pscIdtoQK11WSJ3/ged41PHQHLJOvpzneOa+2J2a1OD3iLd85nTLWislIBAc3HrF2H9RUDr6bR7a7WA1qSM8MyAAAIABJREFUZZLuIOHqhnVkH4ACcfcP17f8fT6VLHJDCxibWD4au00UnxK7oRU73QFHA8pYLxImRlTv3ht4LUhUZbBFCSDmYY/MOWizfCSERO1bLDnSny+0NapdpRfn7Ho1DfwJMTNY/j/UvUmTZEmSHvaZvd13jzWXyurqZQYDDAHiwEXmzh9A4MB/RRHeKMQvwZx4BC8EISQHPWj0Umsusfr+3N9ixsOnaubRkxEZEILCKBdpqarO9AhVd3tmaqrfsqP6LxC7OqLFY0Yii5JnYUKq34ExhgMsIYYYAGqAEHQlqzKgSTmNppgU9HPU3nxZsGx64vWyd/hEygzLC4yKFqHveZRORtLRkOHMXjirVcm+u1wcVaATWqp4HtVqDQPIcKln7Rvcq/M8OmokHOPDxU4PxZrEGHcyDovc9z13nydiD1PG58QufftQZok0hfJhQ/yWk2ffO1E7sAGgFnr4mgcQavzgP7Xe/IOczGQcNgIzFlOFq1v4X7wRsN0+zC6MnALuF69oTTMes0xLU+BmwQnxgbU/9geeVG1L29B6z4YAwAdGdTTTNCpLAHyP99ygFivep8ByMQyknni97AXvVEpPanApHR6oxYocnVcrRN0J902E+O7qUFb43rGzIdBYf2gi9zSxUkeXfBjOTzj0EEk+NyyDrAaAWGsrdLVgTWsGA05vn4gd4iX1nNghGHAv7uEkQgu0V/2TrHhGafxD/hPWEKcjJ0fII0n489RREIB59+ZBTmrJY6oq0OfUUNi8v6EnU1nyxBFxU1NVSO425LQuloKulMnvkK6AZlBSl2Y4iGJUJ9NwefdVQek9QZt6FZc9nvbO1Nfq6L+z9OeOlgRr2aYFWhOmc4EAYY+wNF3HfjQAIxLPRncIY4D9PjCMAEQHvUQWVk2XOL/h5c7vavj7BcuKwwHeGthv34uA65ia5/dLuHqPZDiE35OLicVKyiK9gH0+di/6j8+JPWBZAN5rFC4gJgHeJsD9AjA2lDHe9ZE8Yg3JEbf3IQ/quKTcdTMBz324oiS/5ETcuReIgwkOIF5QoPZw4K5/ew+IYTKylCWdYmTOTrjBiPKb2dbMTzcCnU3cLNhCresgpBou+Nua6EjdxdOU/X6AD3NdB/Pmn7dMR0J9ckyldLmTmlvqNZ9YQLQSgz8qwNaZ/rf3UBF904mH0nINDASxl+cRsajdGmN4oXXcUVVl2ExGJEav1sDbS6B3sABhAPcr4NDCaz84zygn8UjsUGreM2I3CpjK+OUH+lyWBvtIM5+x3ac7/6Ghr5LAk/1mx9glD/a2KTvi25b5XN0Cp/OYk0wwURYss3YkuBhraaJ2ecYFN7+I0n839+yfTycw9T6WIwJGAxAfXoCnw27PBdu0LL3KnEST8QhuNIC9uafbymbLNuuwgll2AZ9EuIgOt45cVD7zetkljaD+TO9INxsNoqOHvpyQqAWiijxjnzixXMg6pDg0XDCiCOAmnNQ5ERlFvef/p3jxwwH9m1PgZMpj/1x2Ku+B0zmBWss1f/96B3c2F8jvDv3JhIvxP1PsfjqKsXvPOD0Z/3QAySOSUuEQVUmJO2tZLswnD/OQE7J/cxryMaPhg5zQtHBnc54Qm13A9+tFV7sudrODGw+Anz7CvbtgCdS0cKczcobXa4EvsJTBTDQwEwuzb+Lnog94zW4P9gfYJS141FEQiTj5qYGE6NwojAHHM4fPLamXPXi69H8z+xdhjO8f1MPR3U9tbzSXgK/QHUxbgEqX00tdI5IXaRLc8/xmy66Mlha6Y/aOgj81x9vKjELXBZ3E/mSC5G4VLprHUtn/IHaV3XhO7DIQ0uGKEqmDEbFOi3d7tujmIvmnYkcyOUaahDz8oIC9ug+18TFcQ3MCwNNGHyIFqaUJmndzFH+8Rn8xQ/L+NlAQ3c0d8Mt3BJaJebJ+tjg0gXIYxG2N4XzhiPEFIFh9QnX3T+eiI6qShXF6G4aJVQm/3uBvr/+Xn6lMBxCmn0jE4l3q39Bh6cXusW2DOQIRdUfimwA/UOmGmJHskIMKbljxy7hfBe9THaWbQRXJGNaFWhLLTRQDqinR7JsWdrsj11XkPZ6MvebF+Tmx+0Tab3LxNlXJy/RsEsghXggiJs/JG61lOtqyhHJXNzBvX4U8zNGAxu8IbQ4oS8kJPf1azXgYLtSKIs3vl/DGwN7LJLSJ+vVmuYHb1UDecfy/3gKDkmoDZRHsM/XBNmXJh07RnYOKeY954qhxhH6Wiv0J+kK6+ewP7I5dP76cXvaCF0CQr+uwE6M1MDYLwC9z7PqmpUHTRiMvnRoetxKPzBGsfvFlEZVoITtMAxkKiaRGcoTfKHPg4w0vuPt9+FL8YsnL73YXIAqfjV1YSM+J/YHGjWL6tVu0WtMTVSXntjVwfc+aX23f14LJub4NeQQrGZ3iljnw0yfmqEZuQPB8DZd1UV32X13SZKF3XJhbSoOrF60piockju0uwiZ6Mp0g5HTVmjQKT9bvYSla+92RLn5ioyRi18E4cTpR6Ia+55HXy17wijWxCRdQVcXSRNtk1sad6OjYDeSB8qiWPcLQmIHYH6oSlkg/QL2W9EOcjClfsd6xdJC/Y3akl/lMR/qdIBGH6N6eIv3h+unYu+7ZsSNNOGzpOpZAgwF3tvWG/w5edn0lwkSnM84ZdBeV14M8AM4LrCVFcLen7Wbfx5yUJD0ZBRkPX+UwiyXsYkNrykpauOMRF2Lv4Dbi91oWEes0m4gMuMxQFFqh5VpZ8qRRiT1rhAhPorna86CasjV5aFgGyt9XNOrTWMmXvuAhcADxB1InPCMLBUkSdSOV0Nu2YTc0QzHI0vePh7xo6Q4n7nEGMlGdjLljKX2sd6Tr1QfWy21L1a73N6EFGXE4eVC9Sm/W0jI9/GeJ3e/2wGzM2AMEWZz7nOMCek9hKGQZ7LaOSgjjEUucT9cwmIU8FIePNCUtUTQe0fchpyCDEfQwHfmvY1paakfE3dB5HE0Lc3YC0zRh41AnbSueVKHu7vpwnwiis8OKP3c6IRFeW4zeIyi3iTO6qj2He4G+ftZtSQCqjAvvWcIMRrElp0Tnfaw3VR8d+oE6x52lLHjpUdBZkQdCAmplDMlIXi+bSQL//pNo3wiG4yPH9P528ZByJuoC/nCAu7lFcn72ZOzwPtpkfil2IMROdlUKrDfBOTzI8DVtcLKjmpln791TZc19ug556Gfrdzu6a9wueJoYE3MSWh42lrCB9YYPx8Up/C2ZV3Y2jYvxdE5Za+2zJwkBaNJJUYUFn6VBl8YIUVzbpKYiTDgQQbQ9e2iCCC66nifGci3wgorfXZY+ONE+93rhC96HnS74KAmTyHjPmltrQYXPenHGUACVTFC9qFv5QcmL0P4AM+QH6KuC8tE6xduYWPNbAzOaBTiv3x9ERkMsGL0TLIwPl10rKMCnYg9aiM+InYEYxn57DwOE6bCCz4w4Y6DIuXtqhydNgf0ezV+/Q/Gn65iHlGVKU0RiYaWXH3KSLormhbaFn4650N6+CoMgk2fw0xE5BW8vYD8maP/qLbw1yG53FH0SyAPNjH1oiwYtTSBCihPLElFzqypifFabQGE02zryYrsumJr5L7QlX3Yf3iNc5oyOztWOUZk3qg4m43WUBT+I4/rR+XhB1GM75dDGi6s1ZhO0b0/Y9ZnI2HpQkfAAoP3qlKXO6YxDLHk4zHDIRZSqcVgH98s3hAA8FbtaND4n9uQodsGIu/MZH9RBRf+l2SRMM7Xu5vsd8Ooc+fX2YR66G+pQThf+cU7+z/K6PIEbFYQXXN/CXd3AzSeM/XbBh+aOXID8dx+Q/1/fsQRSYobge8yh5QlbFUSUKolbVJbRKKRBhkjSpTKzCd8j9qJ8X0NkatcF9YinXi+7D59d+L+Z/YuwKyn4P+isHGOf1T4G4JF9TN5wgpvP8/hlAwKntWEy+oDK1wqqbyRfvuyiD7DikzFrVImJUn2ZkKkHgaHz2djVfucZsetAyW93XGwq7gQI3sQEaZEgJlqVQZsxkEUEGGeKPMwYVNgVzlM1TMgtgWTRdSxtxA1QBaZwfhqs5P1yxQvvcgP3i0vY7YGDqzSB+3hFfI9q0XgPf9xwuFuGr5DmFLvIXhLq5DFP1ZcFoQxaRmk78/KMG8l6h3/9/f/0M+3DG8OjVfQNCbEVC0glYSteRTig3JXycDliPRsHGvRc7USQSS6KCWGqapgV+ryjYVwoby7kojTmoshoUuymMyS3ax7Rry8A59C9miL/9jqQjj8XuwocPSt27doIjBjSh1ebzUCISZKogqCMsP2egLGzKcxP1yEPn6cUTFK4Q5bCL1bw42HIKYisvnvN3XPfRML09S1wzKaSHd78378H3r2RNqSNsF41QQDYSdEd3btQivksjUMye1R8CDzBe0pte6UE6oNYlVQPXqyjotkjrxe/4APiMKW4j9nWQf4OAD+4ufBG1bBWp5/q6CZmvX5I/8/AuRyUwB1loB+0BBXXIZdLg5zuO5MSdtfAbEQtILFIrsRrdDRgW7PIkWzbyFZ6JPbAVHpO7EC0gmn7AEPw0hUye9HInAyB9S701oNrXpqgH+bIiphH+t0VF4eIkfosZVlkTcjJWBvYVUbcCAm028MdDkHMyp6dkAugl+G7Bf8pv58oyjJ6auVZMCgzfS8wbxvlsaUDQ5zNDt4mgZSi70HbsV3aiQhTInTF6ue84L1/uDNo50KtIGV30yPee7pce+/FpCABmoaKuGnCSamOtp2TtqKLyEKtM7VtuCLPE2kKfLpB8om9cbdYUg0MiNqPGwufEilobu6A+Yy77COxB93G58Re5DF2AMGkrWOHw7fCL9Ux/cmM8wIpm0xnkfwfvwNO5iEPZFIKeBFg7Tq2BMejmFMmYLDEwtX7qMTw6hxmUMDUDeEYaYL+n3wDc+jhBhmSzQHduMDhNMfo90v48xnMYhPLxtVG+K3SoqwPAcqhpxYGFdvBQBxmSU9eXwrz4EyDQznztGvlC1/wBtzBtXvS93GnUJ6q4l6aNtZ1Ig9HBj13B3/QvrWPrKbX5zB3S7bKTsp4+1dLmKal/J707tXpzuiIXPvYMpAyw0E0PV5tn47duf+E2Bvp1UfSNKxh/Ls9YIcEnDVtdDTpOpZAkoORkybkAXBg8+pUBJUKdL9+jez7m5CTkZmEqUo6oSQW+CT2l7VBdzJE9j0HXOkPNxyQ/fYa9tUF8qt7ZB95orqTCVuR9YF1ti1iu1FOcT8dyWcvJBnl8sqQzaRVZEIpocd7wIsKm9ALf97akoZycSp/EbifCgabjCLmeyDmwTJNxGxCDEoj5UU1AQ4tScpKINnWsS+/2sCkKWvdj7dBNMkKstCoCFElDuDC3LczAWqVBS9v8yl3pqqAOZLu+PPYkSTx8vyF2FWqA6czKhefTBFMeBWKvN3R+uXVKczuECaOvm2Zgwqsah4AWVKfbrlgVhtkSQJ3v4g5SZmAtuND7D178IsV0HXItjVLsz29mUyeBVCb39XA/YL19vUtzMUZA2pauPmEbWHpKLn5GHaxCYwzMxpwPqENAKfMNwvkLEfdmO1Ju9rBH2q2gHWo98TrZS94R7k4s97AGxuZ/mpysFgFhV9TC6FDdgYs1AiXSEeDSO0L5gQ6pEhT1uv1HvZ2BT8ZBVvKYBqsffO2i9J6ibhmaCvw7QXsZs/Fut6ypHks9rYND9WXYg9Yb5k7+LtldPkDwugdu5r2Owchnwt/1i9X6P7qa2TfXsU8QguzhylER3IpNjeakyw6AGLGzAuu954DrvsFydrqsKelYJLAvHvD0wfEJfk04c80hjry2o83hg7ogQlGso0iQ4NM4HoTTeC8pwHyQUVhDywJR8PY03/k9bIXvEwIATxgNgFAcNEQ4rPXi5pOJhVaqmjEqgqgKzMoWTeLSzXqffQw2mzIfjKGP8sauNUadjjgg3KgdQzH/wMSwQcDEXmiDr27X8AeKSJ8NnYG+bzYtX1oydAKLCF/ZAW53vLn67TYCVtKPsrs/f2DPPzVDWmAMqU0WQb3zWskN6uYkxLIhY4Y2q/vXsFnCQ6nJbJlg/RuC5+lsMsN9n95iWTXwXYO1nv4MkM/LJD9eMvPODlSLRAgWND6AajGkKYsQdVxUSX1rAFgCLUA4vuKghfmrge6pwdPL3vBK8E5S2F8FhdMyQ8liBL5OFjS/u4DM13VeZcFE/rviVyCyoKXr8kwTj/3ByCRiWaeEZG428cOye09/H0De3rC6ehoCIxZGydvX0eGz2Oxq7jQc2KXh8EkdAw3iY0tzkNDnE3XEYIr+vLBoj1NWW4NSphX5yEP1X/3h4YX1dt7mG4O9+k65ARrYNosLDpYA4yHMLsD3MkQg99+jDuqNVSCcx7p736ggNN6C9z3yMZDOmar/c+xdv18GjtU+gADD9SDgdhmBUACyWIt0GKR5BOVhOP3fO71she89qNV2jkVJV6xOTGqGmYsfHOIcABd1H0fQUZtG4jL6A+BAOJkWGSqCvh4Q8FVHRj1PXxDuxZdOO5+IRY2JEyrhrvfHwSbIzqPVRn1Gj8Te1hEz4kdCLGbnBLWkDIIiWUbsKq4MxcFS8FGTwZ2cNxPH4jY1AdAB2hNC6duIP/+D7DTcdTUdzaKUqk0xtUtUBRIVxu49QYmS4MdDpxD/kFgyT+8h2s7uX/wkk07G8Eobbb8HXu2SuF6XlATy+6RKKiFskeGUDgcgH3Oh79pgbaBmU3D5PVLr5e94IGg1+J7B29EZVfBUoo4hEwwRanWP5hUKvnZS4dGRvciPmpenQe4gU5YTTngjjSbs6yS1h2SBObrt4Tg3t7z0noyl352y1aaEq+V8f9I7FEm8Muxqy29SZOg4hvM04yFEcShmYzldMgidOHsHObQwBYFubmqiqakjVfnsJJP8uaSUt+ak8KpkxxBtnucRLOEN5fwRU4l4ckI/scPMNMxL9ptx+GTYmOkPNWH2KjEiiiowYEPxZZlphesTPicqpKnr0CDw4lTFPy7F6fA9d3PvIY3hrQtVezqutCWMjI8CjxOrZHPZsRqOBcvStoBqQ885gGRtBtwd5EOBoCIuRkNySaqD/ziTmYsB5qWF6jhgMjACSeJxvvQJw7u0UX+nyV2dzKGvVsHUSMAHMAUOeG19ysAEO36JLh8myJnR6U6ImFoHquep9n9ihe+4SBMTUNOTQsgDaWCb0lOUcgv1ht+joMB3Hc/wo5HcNe3FINStWRVVtMSzRigqo6GijKT6HtevgXfbhLL716pjY6nqJEuWdj1RTrcS3v5wVDyM6+XDR4zIFHACqm5yHmMaZ9WZJV914cWn9nUrE2zlH3knbjCCYJQp3E0793TqQKI3k9dT87n/kAGkDJrVhse5/sDyxDnqLr18Zpjbb3oikoxxFPpsdh9mT87drvYhNhV0BQA428FqlDkgS+L0zmxQPtDBIGp5qTmAQRcjeZjyuJBTpQIqWMLVYWOTucwwyHsZAz3q7dAmiB5fcmHrCpZ8g0HnJQWBYzI7gVtfUVLai/e+wAL9rWoMhRCTBeMv99sWc7oewSPY0pKe2A6Dmy2p14ve4d3LppXKR1NhkhIErbzAB5tIsnsN9JFkcsRgLBLhE4HwJq7FZWupo3sKmPZupS61H26FhgrrRONz1njd5wCezUdALibCoQYxrDOfCT24Of0jNih2jAK3mpa/hxreGHO2VJU+K3/cBUveH0Pt17LtLINeaB2CDrzWcp8zubADx9iTsq/WG+EyN7DHzrYtoO7ZAlhmo7KCkXOecPpnAjIQQHz01X4KpUr7A8NSyslnqsSmXOxDdsSBYnJiHxdIdt7bR4IDifgbaxsaN6Tbvjh8SX1she8tB2NtcSSbP6M3S7DCCOYFV/mcmSW7IXLjm0qmaLKj/VNQ7vytgOWKwqnrtfBaMvvOen0ovtojIEfDfiFFpRyNhNS2kxBo2OVbcZ0HKxgnozdeHZsnhG7WkgCoGDTeAgsOTjS/rjfECtu0pT9eDUbU5it87BlGfJQrLxbLGGylPkozkVyAmSh5nlwCwwgs/cfecn8/ffUqmk7DgF3HLLZumJvvWmDLU2QKjk0XKDGRIsaPW2s4efVdXQ28Z5tZGGs2cU63F0ARBMEpVh+YfD0sksauaX7rmfyvQuajN6JZN6ex5zf77moUjEZlsEPgEhTS9PQBtQFiNcXggtPA4NJpeX80YdnNjsgz9CfU9rOyxFv1IpFuy73K+D1RfRDeiR2pOmzY1fD3SDlsdmFBR1OppQKaVrbI2NZ8Of4cM0jGrYNQj5ms3uYExAUwHC/4sK9ugWaNjp+WIv+fhF5sssVY12tBZ6c8wFVYFlP1WdkAl9Qm82zeRRCPUZLKhXRWtj7Fd+TZfF9qjMvcuNfer3sHR6Grb4sjzYniqLTHvz+wBv/Pprtwjv4liVA0HQ8alkFmbu2BbZ17Mk3eiFjq9PIBTNopHQdkh/bqImSZxzmTCf889mEEh7LNbyxT8auvNlnxX5sntB1VB4QqUDYhPV5IZNTIXpjvYZa5ZjhAG4tl1fJA0C82G42Aah3nFNwMdfXfAp8uoH/5VvacgKwp3NRc6NgknUOXhxJYAz83QLdr18jTZKoKOBlilvvAbn8GwHCad3uuy7OJ2TeEISiqlIkxbsIleg6lo4/+z68djASURHIMo7e9bKisg0AzHAYNCOVtEGYro8kEJWf8w5mwBZasMNZrLljnEyJEjy7IAhsWAXFXlgL//VrTlVviTtxpxPYj7fAqIJ/fYq+zJD8xx+fjv3A3vuzYvcO6OSC1veA76Pqb5YCOwt/OuOFru1Y4xqKO/nLS5j7Dcwv37GUkDz8lLIi7asZku2Y+BshwIScrhcs36oKbj6CXdfAeASz2oWywjtP68/dntS/sgA+XrMMEuy+3bckeggM26gMts4s9BRpWxK4NzsER3GZ+BqArWLVnzGW5Y++R5CkP29tyUxIDWXJf2+F4zgcRL7lbCICnZSBM9VxW6pgj1tJxNoRyDK2xloBZ21rHskZF6BpO/i+hxvksFd3rLMFPenbFna55ZDn7SV828HsDsRmr3cwWQp7dQ//6gy4Wz4ee5I8P3YRU4L3MC6jZuSw4gITHXU0LS/bUpv76Zj812/fwxcEsqFpYx4VCRfp79/zEnm/RP8X72B3w5ATjGFe2xp2TaCYf3UK8/6GgquvLuiLtd5GIFzvgnYNZhOWX7/9k3Bk+4ChCfOKA72bAmam4aVWhWf9rg7YpaC8dtR+xXzKMlCEqsx49KQQ0wuv4WVAM4hHscKCYQyRiUJgMGURvJH8gIZnvuIxH0oSawNNzosSltb3pmY9HYjeAOyfZDE0LS9Fw4o7r7Dv8fEG5kCVLt1V1cNIzXUfiz1c9J4Rux8NQuzsgJTcqQXohbYNGPrgKnLDkgNVyQWkzt6Sh9nuYZcCkJN8zG//9CAnpAm7ZIdDbIv+x+/Q/eYNzF/9it2o95/4AA1o4qAPt8ky+CG9VM03X8Wa/MhRBeBk2YyGD2Q90LvQtgyblbZcdWl09NuCYIhUlvsYL/+518te8BB2/7bm0acLZ3/gwEGPZ8GP+L0Mlg4NcH0vEhCyCI61JFv5md5zgY0GXGTTEXeYnXQBzk+4EEaDOC3MMsKS+z76MXUdj9m25e+YTwOh4bHYg1rwM2I39SHEbrIssqHkHoGErtwQUrMZVHyAjOWJkiTs4hznIRo3ZjyM+bx78zAnY7hrin6PHw1g3lzCtj3wxx+5gw8FoXi74AN6cwd3NiUT6op+V+ZeHESyTDaCBH42JjkcCF0cACxP2sgY8/t9IKabEb8jkyaR/5qKUnFZBDuhp14vu6TpejnWBB1X5LykqfVj14U/40jdyuha1L2UZOzc0SBlH02w6j3lPnS83TveCWrx/7y+gzeWLczNThQHMpYKkzE/cAFpQfmjaUqmkfNs9T0S+3Fr7Uuxqyye6sQr1NjXe17iRkPGKuN/v1hxxN9sOPy6X8lcwYY8gvOdc8DtioT0VixqNCfdLXVH/ngNpClsnlGD5vYe7qtLlj8pSyHs9/CpRX85g/nd97yAjockXrdCXMly4KePvAMkVri3gv4EAnLUHw78rNbreLdZrDi9Hg353QHx+1MYwxOvl73D6wRyWBHd1/f8slVE83TGp308BM7mbDnq6Fm10wFgOuZkE4g0NRVLFeiumYy5MxvLfy9LUuX80Y4hk0+cnfAD1tbd2RxmUMKen8LoxO/V2ZOxYzZ5duzB5Kssog+qxv/6nLv6RCzojaWUiEKrnQg8aayah0pf3y1CPupNpTkB7PUjz5iXgM/MescOVNPCrnbEInkPXN+i/euvkVwtYeuWHZz1hhiX+RS4POPPGJScvp7NQ00OIH42Utock9BxKtPaM/mnyOt5vdyfzWFmk4ddpc+8XvYO71zsUkhPPNTBXR/E+tH37LCIpri2GI2w4Knu29Dd4qtzJD/dcCqZkS0fEI6p2EkKwdpo/3zB3jKKnPiZJtpm+oydGCL+ZJo6HRMmwIA/H/t6A0zGz4pdyyP31Tnstx8ipt97ljcSv/rQ+uUmqgEsySQy8xknwZIHJ785UJZw7z9FWfB6H3IKU2MhTavOo7u+kX5/wd3W0hDCG4vs776H29WwUkLa+Yw79aGNaMmmBXJwuDYespWrcImulzsGO3BG8fDbmvzWhmJQUCW2AWHOZrnh8E768o+9XvaCP5bGPMK/m0zQgUribdrg1EHihgiU6gBDvlxf10h+uuFFzkkvuCqjwGeecQKoyl3DisdmWcS+b1myEyDS1/7QwFyc8gufj+FF1UwXx6Oxqyz3M2KnNMaBsctASetr4lyy2NER+UE/GlCZrW1hihxuOiQhQ/JQpKmfjGBVBs9aeHMIOaHruXuPhsFex1Qlpfn27Nmr7DYhAxX84QB7MmcXy/LECVAQkcVG2wWZDoiqMHoXiCjm0MQjgx+YAAAgAElEQVROjHPRZt7Jd7NdRD0eAaB53QC/ILX3sksaAwK5SnGmNibCfXWy2QhwShGPOk2Vy1m4xKja1/H/N6i4aNRfabcPgws/KOHGsgi9iC5dnrE1t9mSBPLVJbH0yhkFF7QXO56nYvfD6vmx689VBpK+RHYutASV9mcMW6uDEv7dK/nv/kEexlr4kylzlXz6V/MHOZkmKoSZLON9SjQf3a+/4nv1hMkzqqHVe7i7e/6OmSj9/uU3fBiqIhrMKSBtNonyedKfh7UPmVG7OmjfI0ujs6C8L6g5qOHxE6+XvcPLJcUowi4TPZNjUkTXAVu64dFJjgMNtTQ3It9mRBpaOynwnt2J/QEmSSLMdLniuH3fIFFTAX0dqIoblAU+HOJu7xyNzjYbxnK0Y3029uWGnaNnxK7qBWhbHuvCHfXDClitOXU9GtLxoctpJ6M5/PQRmE5CHn40oI48BApd72H3Hf2iJCdALvwdjYiRCEgrSVgWAkH6293ewZ7NgTznz7tfwouvlvnpOmpeFmKgJl0Yo5KERy/V1lcIMBR1KmpqQeFYJPkAcFPpui8KMb3wHd6wG9L1AUfiG5G5UCzIcMBaU3EmeS67+JFcQ5ryPSI9B2v5wQtRmzIYeVz0TUN9F4DH5XLFL0dBXB0x3GZQwanVvPMEPVUVuz2yEz0WuzpdPyd2tYxX8weolfxqIwM48ZRVHNCAk2G/E+OAvqdZ2HEetTCZpEwwWQb3uz89zEljcbSuNHkeWEW+d6z3K7Knkq/ewH/7I0++5YoP/Pc/sV0KwA9FpezQ8ORTISllKvU93PkssrKEU6yakT5N4NdbvkcGd6pjE7T3J6PAC37s9cJ3eNFFkaFD8CFSeT1wtwu+TepeBwQ2fnAKGY/YMVCiNBCZ+4gjabV0gREtx54IQn+3iCQGsbUEIJ6uFAoiSKzniaF//ljsniYIz4kdRp0ynOxkMrFUK0hxGgm1+XYHtaX0axkuWRJNNA8v6MTgKrKrYU9m5LZqToKFZwkiG0xRwE+G8PkU6Bzsbg//9hX8+yuY1xc8AZeiTT8dwzuP7i/fIvuw4MXeGGC9DSdfwMxUJfE52jIViqPyf03bwZ/PeTlVLJAQabSGN13/UCv+M6+XveANQhvOeM+e8nIjF5kqWiU6D1PaIIxkug7+7IR/D0CQyy7LSCCWOtm/PiVGRLRXgrWl4lFUjntYwn66gx8N4D5ew4pNe1AWEw3KYMFjbYQvfyZ2X++fHXsQj1JqW1WSsC20P6hvlAyi/P2CxIuqhF+ugdMZSSpVEfLA5JSX6yQhS2k05GJTKh0QYcLquDEo4T/dsKTqHfxuB5/nMG7MU+fmjlDrSlSQRW4k/fffkV3VNvA+5Sm6E8iwdlUOTRCuMscnntj2mDQFbhfskBW5tIXlvYlYe8pJ/dTrZS94OVL9ngQOk2WsG7MskKP1sqd+T0F3ZbHicZxn0RNIkXRCFwPkoio+pGa3f+C85xZLdiSE6I2yZI1blkQUTsZwm22cbM7G8KsNd+3QR34kduepffOc2FXsVT+Wbc0uSVkGUBxGA7YgdViz34fdz2x2cKt1kOxGWcK//8RdWNy03Yq2lcbakBMfxoJQ3yQRsriYk3kfTk7Uez7ssykfsBFxRH65ikJROknVjlWaRr5qS2KKcooD6V2VExRsJjMUNW4IHlaiDW+UGfXE62XX8ALNNcNBtD63cUc1w0HUaddhkuozVmK7nqVQNz0uQk4jzUhG8Vv6GJl986CT4w8NAU9CmDaTsfx/Y5Ycry84lXx9wd1cjl2TpmTzDwdfiL16duwmy2LsAnWwoyFLmPk0XLL1Z4dOk1yykaawl+cP8yhyLpQkCfnoxDPk5EXaTksLMVJr3s7RvZph99/8irv+dAz3qzfwwwrdX30NdD3qf/418Jtv4shfCNfaUlVCi8lznk4yZAIQ7yoXp/ynqDr7Jbm7dHBkm9MMhyFvX9fxPY8tqf/Xi/L/09cRQ+iIGRPab2oDqePkTsoS4X6qTIbJM1kIQgvrCNoCECaQqgcP1YkRsSXW8oKOFHCSqSq4H96LatghmKMR7CXwVFnAj8a+Pzw7duJaZLoqbdJwad/WESar8txpGrV3pDTrzid/lkcnkh91zAd4mBMQWp6+ER5A2yG7WiP5wwcM/s3vqTXZtEh+uoErU6T/8UeY+oDq3/wO9nYRRV8BOa1FzSxNeII1DdStMbRc9V6zIOQhnMZVJfJ7or2pAqxARMEuVk+uqJdd0gA8suo+HM+YTwEx3Q0a8UC8UK63rCdl6qiECd4FTMBnYH+AmY7hpkOYJX1EzW4f21ynMu5vWor9FzlwJaN352DfvSEE4GYBZHSlM/sD/OUp7G4YBiKPxY4iJ1DsGbHDmhh7xbuLHw85jW07mgTkGQWNDC18/PRNAKBhW6MfZEiO8ziX/IwhUfzdGxoVuGHIyQwr5qUW8Is1fNuiOx1h+1+eYfp/3sJnKfpBBtOPYHcN2r/+mintTmDvN2hfz5DULZJPi2jsrMKukC6b98TUtB1LkrLgRqCw55RQC6UzmuGAzYzxiOXjcsXyrqCL4VOvF77DCzs9ScJInFIbhIKqZSPxGDLdrKi+pdQ2k+dE2ZVyQZRJp+qs2BXt08PEUBf8zV08zrc1rdkTCzco2RorcmJEWpFxvl2Q8bPZEZPSuydjD/eJZ8TO1qCPx3aSBDAcrOVO2LE/rZLc5tMtcHNHdWTvkf/dDw/yAEDAWV3HfH748CAnv9kGFKQ98rBN77aY/rtr+B/ew7Qd0qslzKGH2dTIPq6R/d338ImB6XokdQv7u+95R+qpNOGnowdtSXQ9F3PCh8uXRYBwq6V80KdXFeUh7ei9NQ8kUYKiwyOvl73DO8dLkE4erUB1DyI/ofaL2hterthvbttgHAaAI3a93FlRDvCe3QZjxKKmEv1yovKMtfBXt3B9DyueT753MNd34feb03lQFDZJQvdrgDu/dCEeiz3k9ZzYdahis9jhwNH0FSBEYVfDzKfs0kiN6xZibqCbg+ax21PEqGmBqxvGWpXRWaMq2f0ZiihVKbwAS0lxNQo2m51wcrfwxsJ9vIIdVkh+/xMwGiK523CRqiqa8omVU6BqEnVs86KS7piWJ0da+GazC6cwh31CBPIdWVFfUB972QtepORMYCelob0IgAtUlXJVRzGxMEbG/KLnjjylYJGUIGY84jGapSwjTmdA72hDr2AuAOZ0HuxZ2m8ukP6HH4DpDLgTqQwxVDCDineAV6dED8qo3a/Wj8euF9lnxK4qxf5mwd65TndHA+7swkXF5Rnbn3lGQwTnYC/PAe9Zul0vYh6Hhlgh5+A9AV3+1Snw/ceQUxBnmk958sj9wc3GwNkU9mYZ1BbccgX88i0S9VodDo528I4dnPWGD+l+T4iwdLKMtSzR2g6+4ISY9p5Z1PsBWOKpxqQoQiAXArrSG7XEfeT1she8Ts26Lhr2yoXS7/ZhV9CpHAYV3ZxVXXd/YM+2rkURmDxI7A9CUE44lRRtGqu9cYEaYFjxqDUG6R0pa2T+UGzIDyv27ddbDle+u4479tFF9bOx6679nNiNJdn82LxMebZFzh27aaN3kuzWXpXBdMEc5eEGBUznROWLADSzrgm5lpxgDbrfvEH6998z37al5fxiAxwadF9fIP3ploOoyxnsH9/Dff0K/mwClydwRYLsegc7HvNEHQuUYzYNTtyQHLDcUKj1SJzpAZmjKtkenU/ls7BBHMoYwwfPudggeOT1she8fE8P3Li7Pn4wlu7OfrMDzsXmRdn93gWAmSlL/gxx/vNwMoks4i7sXHDq9hOyaiD6hv7QBGt4v91xEFMW1FI8P4MZDZB9f0Pq3aER44EEvnWPxu73e3YgnhO7/ryuh++a+JDoZU5xJbX03pMkajQKJ9T8dM05gORhnBctlx3zV0W2P3wXcvK7mo4gciKipzGzF0pd8scOfjYB7paw19R6NPsW/u//iCRJkCq3dSKDKSWw9GL9o12WLXXq4b2A+Y78Z7WUUl9c4QCbVIBrnmWNkcEYdLL8yOtlX1q94Fy0NXXczdA6bkfwVfAw7fsAoApkAMV+i6iPyURsKEtJAp4MCaaSQVC46SshAQCshTsZR6/QMof5+m242FI8qIffbOEuRH3rqdjn02fHHnI4jh2QPj310v1oEOTsfF3zvgAIcTqJ/y15wDn4URUmzL7M4cr0QU6mLOCLDG46pMKZkFXMiDJ7fkdMjjGGeJbpBLi6RXJ2CnsyJ7ZnMKDiskyJY+tUwG9JwjJtwAcs2IpKze6lfaqwAT+suDF4mdImpPp5IbF/CS35shd8mnDAIh9+ADsB3AUKKROKnLvfaBBrZMu6VwcdrOEzfvCyUJToAU87RMxoaemtCQCw+MFaOkp3gsO/vuP/AB7TyjPte9jNHjiZPhm76d2zY8fJNMTuVa0BYPzX91wwIhjLy23CS1/vyBTS+8pRHn6zhblfwZydhHzs5vAgJzQtFQtulgGKbRZr+Wwy4Fdf8zMaVPx81ZsKQH854+YxrGDfvBLtShFr2mxCZyzou4tbCRTOYBN6S+kCbjtOgvU9CtsuC8Io5Pd+yZj4ZS94AW7R51SwJtptcI5Ku8KgMUXOHVomi+j7oL4LyIBGa0K5SHnv4T9es3UnOpYAeGmS3rAXMoe/W0TOq/Bf3a/eMEaFALcdsSTLlbCYHo8dwoN9VuxH8wGjrnUA++hlIVIXLljMmMQGkjrultFL9SgPI31rACEfsz88yMmrXuN6AzMeM/fRAP3HT+ivrmE+XhNZeXdPRbI0RX9zx4f+j++DmoD7KBqTwqHVWQIODWPL0oC110l44BDo59X3hGIcGp4CpZzQehfTYdzPW4gJUQFXXkpghqDoVL7BKJRVhTgB1nNKFFFNdPn/TVmSMDwa8v3Ow2+l/puNufvtQaOwNXUbw+7a0BzN/P23cNbCnsygRAxvDfrXb5H86WOkKH4mdu5o5nmxA3EKqUAx0Z+BEkZchNkaHeHvWOMCCXB1Ewx9vTrleXHFlnxQHx7kZGTIZk5mMlhrROaviO0/54jN2e9hpPwAAH84iGqakNCrktRBnwWD49BRWlKslYMyfoZBt6breO9pxRRuWAHZmJKGSiJRL1zt1jzxetkLHg+xIVrXwUt/u6UeigF4RAp7yZQF3HhI3RUhX0AIIH40iHbnakis4LLLM4LD5MP02x3MjE7gPk1YI3Y9f/ftgkdsnos+5IFdnUOL5H775didJ6XwObGr3s1oEPVmlOkkpApKWfj4kKjWTCZj/EH1MA/V5PkojuG3C8ZzlFPQZE8p3814UtiTOUVYhwPKnoieuwLqUBYwR4vULZYs+4RSGXTmgQiSK/LQYw8bg6N0oOkdS7DJmN2jvqdSQytdLn2wxHnlqdfLLmmM4WRSj2QgXCJVeMc0ooOu0tnSJbE392zp9RHLcSxQ5IUtc/jFCfz5iZiSSQ0q00//7jIsEuq8p7y4joZctPUebj6KR7Cw900tLcUnYgfw/NiltYmbu9i1UN2WIos7HRBVGcBdFm0byq3jPOCIrTeTo3xmkwc5kW5nxJ7Gh9PGnYzp/rE/8HuoWFbZy3NOqpuWgyZ1Cnl9wZ+nHaIjU2IAAdHp93vm3XbMRz87I1h+nfYaG11AWmqB0jYzj+955PWyd3hjgJMZdwph4ATL9slIPiQpeVK5AKYJa/sspQucDni036zsfADYH1B8e8vdLEuDckEgfu+I+wjSzqVcgNME/u0F8PvvYe+p8x7c9Eoq2ppt/WTsWKyfH7uj7Q504DKs4tAsz+CLlO+7OGF5oLv/fs+dXbpOQZunzPlw6qIrR7TcXNecLGtO00lYWHwPe/12sYGawXFgV8LcgJCKJCHzaL2Be3UO/PF79F//BbL1lgtSBGvNUGQFVSOoyKIWj8gI+tEgzEhUmS3AKYQ55rMUqJUmaeGLOFH+3OtlL3jvpFazAegURJQWa6hdOyxrfeNFdNN7iiEB3D0FgGWMEX130RIXlwldbJiM2EcXhKFChsOC0YGHtbC3K3T/9NewP9wA1sCdTWEXG04L1exApCY+FztcD5Pmz4sdvIhC7GD8cs1SC3KhFUMCFSLyw4p8VUGUhmmkXmq3NTAcwA1KIDEwdcN83pwAF5OQk9/uOD2+WzMvAD5N0F1MYA8dXJkhvV4TmPb1K9kMmGN/+Sukiz26//ofIb2vA7Y/WAkdJNatTKbXNHaAkTwU+Tge8bOUsg5A1JtPLEvCqoqIzJ81WtKIPeOMkzqsd0xelQZOTmjSm6asQ1VqrevIlhlVbLkBXCjTEcztgjvT+QkXjiyKMKUEgm6hF1AYvKNwUGLJI00S+KpA+uGel7XBAPZuTSLzfMbFWRYsWR6J3Yykj/2M2H2Wsr6+XXDRn59wAKNox97FBxXgriiyH77eh8urB0IeaFpYYS7Be+bz061AHZiTMQbmbs0LdpqG2j75t/8BsBaJlCn2ux72zSv07z8SZrDfI3/9Cr5tkR2m8D+8h5cWrV+u492po5KDd13oTkGwPL4RnvGujp2sP+vAeCWSKIRauLlPvV74ggcXQdvFkTNk8loUwKYOHxAVZqU2nQz5Z7rDSY2rO1wQTR0PgUPLHUeMAmAMTMUjs7uYIIUsoLajD2nXo381RzfKkf/2Rw6QDJGB5s1lGJX7RJxJHou96wNQ60ux62UPAC9vScKFXxXc2bseZrWBn0/I2lKhpaaFPz+B2R/QXU6Rvb8PeQRhJQBuWKAb5cjua5iPtzEn6YKhGAKbHeHJRQF7fgZf5ujOxjQcHtC1xFYl/FevkXgaQfgshbm+o/ajclVF5VdbuUFt7dBwkbdCDFFJ8SQh6UZNLVRVeEN4MICoeiCQ8adeL3vBO/9AXUCRdeEp9p6JJza0xbz0kzGoovV52wYpCzMZhxu9Ucm+IocfFLC3chwaftHp7ygY6hPLv39HgSP7wxUKoeopydiL/qM6VwdJikdiD6SGZ8RurCUEYiKnxa4OMtb9eIBkW8NdzGG+ew+MRsDAwqwoYW2XG/giQ/b+/mEeSioBYO8MCiVVH+WEqmTJIYKsOJmS0zoawg9L+Nyiv5jCrmp0p0Nk+zk3ivkAyXJPAsiUGjL+bsnBmw7PfBOVmNW/aj4VGmQa7IRUtYBKDTXLvYNc/sWO3pc5P7+ui1PoR14ve8FbIwCo8ohJZCK+Jc+CLorqrpuSil9B6HQ04O4tdjN6gdTj0d0tSKQQ8JIanPn1RmrJlIsnz4+YN2XYff12CzMeE+uSWErK7ffkmN4vH409uE4/J3YgDGsCi19Kq0QRi+9vYq/++h7eO2DPOQPnA5Tn0DzMaBjKGajyWFUCu5gTRE1ML4hmuaHygLXAH35ApgTsJEHqPZWD//JrpL9/D/fVeZTMcy5cwH3Thukq1Oisk/uVKJL5yQiYjoNDt+96GPD7hi1i182ZIMOHNI3iqk+8XvaCdy4g7CAahkFQX1GFQPwzIQz7Vpylm5YTTG2JHQ2ClARhZ9OADPTbHfHqAOiQ3cD89Im2LF28uKrevK9r+N7BFgWwlveVZZTjfir2zZYL/z81dtdEhYQBtV6McxzWJBmFoKqKi6FpOVkF4HY7JOdnUVdmsYxzASD2+o9z6ntgu42QABl6+dU60CaVXN1eTJDe3CG5WQF5DrtiR8ofSYCEWlxpjoJBwppTb5qmCeRby5SORhF+MuTsoyrhi4pdJiA0EZAmvOccEVU+93rZCx4moP9MmhBWK6Nk7xzNCoZHF1MtJ5QgXBJNiVbUALqO+HIhDfiK42lXTGF/vALOZnzfoeXR++4S5poXxeaXZ8ivtujGBZKVxAHA//INsNjC7ERc9WQaVYmfir0sWHM/J3adtuZivJamxK5v90BVsC34/gbegoZqLfVpFD6Meg/77jWwrUMerkyF8VUHHUxf5dSD0Zy6KGXoyxxmvePFvWCZ51cbGh0DSBc18OYSWO/oiJjN+WCUJcxJEQZM6Amr8KMB70vrLTeUImPZo5dVPQl0JdQHnk7ajq3KiDm6uYdBHhXKnlpR/hnHwP9fr2l27v9m9i8BINTHQYEWQPBuMibYqxzXyspkCqwom0TorYzhw8v5ACpTSK7+XZWTMDIIC5aJQyofoCoDNNcPK5ZH4kD4aOwqF/ec2LVffhy7/AwzHPJyJ76ygUWlhmcHufAeDvy7KgWiTC/hhwLgaaMtPsXT6xBLZUAEAOclXzseBfc9d0fpPqtkj+GAkh/G0OjgaCgUypo+ilQFqe6+D58xBVOF13tsDqcDLn2ffiY2wd9e/c//u/f+v/rcmnrZO7yh/ylrS6GAiVmWEUICrICt9kdaiBkFkJBnsRTRRde2xGYcWqllB7zMWQtfZjAfbrlrqNnYaMjj/+wEfrkheUG0T4J2eSeKv+MhYxZpPACPxq6EimfFPpTfoZdegToHGY4RidC4ugFMGo3bZOGaqsThH3+F/N/9IeQRdO/TJKqKKTlackLv4I1wB4wJAlY+TYCvLuGzBL5u4coUydWS4DhrcPiLVyj+dM3TdyJ99M1O+uzi66QbrbXswgA8VesaODsRji4tf1R4KdjKpykgbVtfFcCHK5j5LCoSP/F62QtewGNhQiqmWEZ2htDiqipiwMsyjNLDYtEdqov+P2Zbc8faH/jlrTYyzZQHabGOMh3H5UGakLN5esLaVKWnFZ563PlR+5tHYveTEcyye1bsODTB38gfGmA2BoSGGDoVeqHW3y3ITtR7IE1RfHdL8Jjk4VtBfmpTQD4flcUOOSnQTZWV2w7YbGHul/zZ4xHMZgtcnJH8XVVs15aFEMBF+PVIZcykSQSvKXnFiV7nyZwPpNb5Hal+dCXZRx7vdQ1vBIN0MqexWRkvtI+9XjaWBnhgNutVO1C0BIPKlFgrBnx0kjzERauGi0pTiwquybMACcZ8IuKdPXfUhDopJC5L96PIYd+84tg9ywSDQh8mdB3cag1Yi/5k8sXYAwjsObELJl+dqkPseRYl9uYTyoXIzqgdIYC4Ib/ZPcgj+NLaJOQTYtacBEaN05l0uWyQznb62fd9AJD5pg3lnVcHvuFASqMkQDOQkx6JQcVujKPWD1XIunj6VGWAUwTo9KBiN00EpHRyzs/KfBE89rJ3eKlH/UwGDzKS9kXGf44HbF15QR4qeq6QL3Y2CZ0OnIgmTL2Hn0+42+sUb1Dy1i/1IUTyAtZyNwXih3rc6bGWvqkiC2dfXcAvV0g+SGtts3s89oIqxM+KvcgZi4DMggaO9v4tL39+No4XVQWcVSWxO9e3wPlpzOMgd4TJKKgt+P2eZZfmVNONED9+JGYlFUDXoIL99S/Yb1/vaeNZ5MBvvkY7LpEWopn/7jVwv+JJVeTBScWLsRnSlJ/7yYxdGr3XzCbAvuGurTLhXtTP2g6mFd3Pk1nQ4gmwZO3ePPJ62Qtehh9mUx+NmndAww/f1CxJjLKT6oMcu22UjHZkFpnFOnRHjI75j6zOfdcDJ1OYuyXLBqXkaZ0okz53OoP56RO7LNqmKzIYx86MGY+iFMVTse+bqNXyhdiDvmRZsBffiky09t0dJ5thkqzKXrVAD+oD/MVZQE0S4y4uHbf3UagqTR/khIbisH5Q8nfKPULNj7NtHUunNIH56QrJ6wu+13ng/Sd6xIqGjNIqDQiXgLXAWHTqXc+WLhBPNdXPKQu+Z1DK7xtwNrJYw8vpoLj48J5HXi+7pDGGTHUgdipsIn3oA4/p2wX/+eE64qKN4W6oApuNYC5k+OTrmvX7fs/+bicE5J04YU9HXETDKlrIDCv0X52zR3xOdV/MJrwLrLdcYD2BXf3JKMh0PBp72z4/dmND7MEiUzAvyFLmYA1jSRIuOCM4ldMZF8Of5QHnWKqczkM+7qvzhzmpcYG1xMCkCfpfXALGoH81hxuLvqXzlMg+mXGym1q4b39gm/Jsju4XF2I/2Qf/Vaw23PHFtZBEnI6tzG0d7jxBH3655gO7WAWdGlPkLIVW63DB/RKn9YW3JS/838z/ZSQ1GMudQLUTlSMaZChskL8weR53q0MTdR11R+q1+yADoTbK3oXxujqDlNJHLooogiQuIMRhZ5zWaitNJa2VafW52I9lOr4Qu0ls1IfX2AECwtoOdjziw9u0sf7v+/Cg+aaBfX1JgSbJA20DMxxGoaaK8tu+72NOeR4/I/mZsesl5g9abu1qisjqyTgZcwgmYlCc3Ao25sjOMyg1CFPLS4mp9zNTlqLnU0Rj4uNW5VG72ZQlfF3jb2/+1c+4LVmW0nelPDOsGA27h8g437acU8lCBBC7F6LNqD31BxNG3UkOB35JqzWd53aE9tr57EHnxE7GUY6v75G8fUUYgvNU/bq7Z4dDWo9Pxi6L/YuxJ+RsGi05BlUYFhlRGIDzQUkAWSqqXmRXWVUp1m4MENqrx4QRVAmwXMWcjAF6BD0cM5+ypBoO4K5veUpsthRj3e7Yfxe5cb9aR1h0WQT7S9+0YqBGOIHW835MgFpQfssySoesN5GjADKrwh0AEMlwoTEakd27eXxJvfAFDxlGyLhZW1zKndTb+64mWeHICsfPJzDrHXu4QwFjHe+OWi+LKUFQpZ1OhDdaBHsaRQmi78XpeiBMnksewYMB68vlmiKkQkbgPeCR2AF2HJ4RO3Z77sC9i+CzIoc5yGLQwUuWwUxl3B8eNslhQ1cQzYPaOWl0QfH8DMygijk1DUueD1dRWlu1b8RryRRFpOulCfztNkAxzHjEh9ZY2ZEtHzABxAE8KXzXsYRSnI/CMITjavKc9yRxbFT81DHkwgyriKh84vWya3ggchgBKOUPkL66EAjCFwAEGIFZbYNVixHZOYWg+kYGH0nC2q8QrZf5NMhOAGD7y1r4txeksfWOp8B4ELyLzIB27ooDwWwcypanYvdt++zYfdOE2ANlT+L3g5IX94Ly0chSkSziDzMAACAASURBVMiTkuFszgepbR7kASBMUzUfnbhqTuh75nVxSrOH7Q5495rwiW++ClRINWzwg5J8gFI0Kqdj7u6TET/3WnBIaq7sPd/vfIR+iC5+MHToSGw3m100o1PEpWiAqpocRCv0qdfLXvDeh6c6XDz1Ju88pem0wwLwCZcuB9uKJvTWA5baym4j9ajf76lYsN+zY1Lv+cWmadCasZ/uYA6tYGJqdnmu7+AXK4oZqWKWehHJneGp2E2aPjt2o5gcVeTq2ZFRVQCz2sQFXu8JJBOTgTBsG40e5AEg9OY1HzMcPMgpyIrXhyBO5VNLSQ4g8n9F1QEfruDn1KHHxSm60xHc6SwOxcpSTh4Xv4/xKJZtRxdOUxSR4KHlIcD3BHpgnDWgKoPU+FOvl73gDUkDOIIBI8+i6I72cqcjfrHaahT+ZzABTpMAgKKDtMAVpmN+yYkFLs/gRgQkhRo5z9gBSVMSmxPSAH2Z4/DPvgHOT9DNBmyxDSq40wl8nqH563esP5+IXe8Oz4ndex9jL2k546uCQxtj0L89o5CqjNZN73gCiXSFnwxJ3zvKw48GpMjJ53L4Z9+wtj7KSYdD7mxKYdSTKcyPn3ipX25YfsjDiKrkHcFa9vsBpLcb2PUWh//iXShhqB9DUjZbp/vgyxRKMYCf1dF7fF3z8zxmjeU5W6ZZFnRuwnseW1Ivvksz+e+hPkgA2KITxGHwAlIyg0IDjuu4o26OwmrDz1O36faoW+JcwHH7eh8vVmVBxtNy8/BOcOQ56rOUmo07QmyjNMc/jD1Y1z8ndmOiqZn8PFOW4qaXwt7Tg8mnCSfH0qcPUt2HhgOm+1XMY7WFu1/A/5NfwS53zCfPYndHdV6OZhJefx4A3C/5+RRFuK/4HbHvZjRkqdR2sEPxipqMwz1DVcYiaUZ+riIz1btWOkfB+ujoPb5peK+RzcJvxEd2UOFf//F//Jl2aSAXKd35dDFot0Xt3IVAwbf4cPQrrtyMhsEeB9ZETPbdkrW1E8lrdb/zFA6FtugG9G7F9QH9agXbTEggH4/gN2vgZAZ4Vb0SdYFu/2TsxAjZ58XuPNuh0m0ypYC8ui5i4gcVh0giy21PZmFi7Hc1u0+TccgDRQ6TJLAf7lgTewes1jCn85CTqQ9hMoyM2ux+KGrLgn5U+p0/HIBfvIXd7dHPh0jeywV8NAjamb53QCv3Ft2JrQkGdWY4ECkRmS6rzQ4E/Cd4JQDciHRDKvMIumt+5iWNyaUUkVab17G/1IChTlWT26nwHMdk+pDgK7tkVXH3aVse0brwsoy1+VS0JXf7+EGrTUy9h0ks7G++Cc7Z7sMn1sC7PeECFV0oiBkvnow9lGPPiT1NQuxBwKkoIihtMora89IF8putuI301I8cDh/kgZ6c2v5yFvIx49GDnAAwr92O7hpH9w13fYv+n/2G/y2gMHu/4s/vnHhm1bxrjIfS3WLZY6oKDzywVI5QF+vpLD4UevfJM/SzUXyPyBQG0+fRIBgVP/V68Tu83+2AchaPOTHwAhC+YN91oV1l7pbs/25dhBELGtG3LXCQFlYtpmKTkYDJOtaDx0bFTSvQYUO5ueUaPksI1ALYg05i29K0IrSqJc5+/2js6i36rNgVAXnwfAhqnbQeYCC+SdKiZU3N9qtvW2A65qJoG/hsFPKgCGmO5P0tsUUAXJ4Ci2XISVUjFMaBPINPEjS/vED+rYH99hNzKQvODUQUyi63cK/Oaf8jk1ojk19IfKFE2VMrXzkAqOuoh2OOJPe2O1iVJGkafvciyedv7tgOPsbFP/J62Qveg1O0e04DfQ/hNdqwg0AmooAsUuejArAxJAUPBgEIZYaDoFOOroNTD1aAwqOHQ6TiDQciPtrDKzn4Dz9EyGySiE6jIw80FcWyxZ678ROxo+/hBZH5xdh3+8B9DQSUtuGlXluaQu5mWy+PLb6P12zzNS3MzV3Iw22i5LjZH6Rd21J5QHJCAnZc0pQO41kG3Nwh/e5HOGsDydpfRz6tLQp06zWS2QyuppSIrUr+3rX4var6GAiB9vLgGzF3CA+rXohlIv1Am1PuRxy0cSKsDudPvV72gjeySJRkDLA7sd4GWKzuRIqtQGqjGKe1PI4VyyLoywAV6BKgdnGae2j44SlctW2FalaGnwfHXdQMqXtulYDhHOvqyTjsSkjto7Hron9O7GZA9QDVjkea8mFIBcIsI3zfdoz/IF2MagTvdgEqgCyPedQuGAGr5627u4fv+wc5AYj6lXLHCDj1PIN5dUZR2lrUgJ2jEULXAW8vYf70A0+ioiC8QCHHAP9dpL61JaknlBkMeEcTdxJtUepQMJA9BurILrRK/3MuaZIjjmKRs+20rcMHYLS+C2QBqZGnY3YstnXEpAsFzu8FJ9N10XzYWgAZ25SrDeAtT4TTOczZPNLvtnVsK3YdzDdfEbFX5pTHPp1zd15vgbMZzKZ+PHbtLT8n9oRSfuFe4VmW+eUaZj6luJJg5tG08GeXvHCuN8D5CXE+V3cc72seI7ZXqdzLUsC8e8MHVHLCzSL27IsMuFuyM1IV2P/qFPl1jWS5hZsM4PMU5vs1/NtL9IMMzbxA9Ydb+F++QzevkP/+Axe+TlaVJqnetvbPPHnLAvBp9HSq99wEVHRKulZGfWS3dZy+P/F62QteNR//3LdHWme+KoDlhonq0V7kwHId6WA6dFICsTnifOYZicgQ4sN6E1RpTSJa6k3LUfn+wImhTD5NngOfbngUS7sM3sOsa/S/uIT97hMvn4/FLqXJs2JPYlsTgOzqoiu53gZPU3d7B3txBny8IcS4LOFv76VNaYAmiXnsD6QxWgN3d0+LTJHDCzmBtEN0Hcx6B386g79dwBiD6rctYxhUMN8t4X71BsY72N0e9uYe6SfCBGzbIfvxA/rfvKOiQdvB/fINkltBkipUQLHwIprlNzuY0UBqf4fun/4K2Y+3QSKFglaiciz3ITefwKoxxSOvl73gpZsA/UCELWRS2qSg7djZcNLDFUAVAO500xGlr7XbAdD6RVVsrWV/Ok2kR028it/vI8v+9Rz+0zXr5Szljumo8aLmuAHk5YhmTO42jKvtHo9d9dGfE7vz0t8me0ixM0Z1XSZD6tm/uQQOLUFsW47izYS+p6Zp2XaVPPzdAubyjBvG64voYNK0MSd9pSlUIhxdB/fVJXbvxqh+2vKiPhsDDuj+8TfwuYVppkiXNSfYwwrtN2fI/v4n3gPSFPbbjwhKyk3cEALnF+Ale2dEna1F9u1VfBCl1PF1HSh/Jstgru/wpanSy17wjqq/iqHgZbLlrqOYEpsEwrMBgou13+2IJASk9qapmHqzhkW/buHHQ7iLOezdmjXieBRpcocGEGUAs9nxwx3QBCzWkkI2V312ddTTYcrnYk+SCFr7UuzNIcRuyjKKrOpJdbOAe31K5bS+py7lzgCjYRgedRdTpE0b8jDTCVXL9g1zlVzM6TzkBCA8VDplRp7B1A0GP25YslkLX+WwP3yEeXUOu9vDDUoS00XDMv/2hiWII4dVbeN5z0nDcE/bpnQpFAGp1Zp/Jz2iHTqx0JT7DFpRJP4CvQ946Qte0JJ+s6Mrh6pwZRnpeIdGFAbq8GEomEh5or5p2b2YjNgxSSPSzgD80O8W8TKnnMzehcsfnGet7z0gR36g+Q0qceijB5HfbGN9LkfzZ2NvO9bFz4idf+6AlJrzvNuYMPU0ZQF8uuMFdzQAVDxK7C9928Be3cCnacyj72F/+Ej3jtU64st3dciJFptisKwEDu2y3FtxACxhBbdjFmu2D5uW3Ne2g58MSFYXtlmU0xDQVwvW5XkWy7/piKWaIiqtJdtKGw4HDyTykHT7AG3wm90DLZvPvV724Angl3NxwmmdQHlZcrioQqukZK0D1dJFjk2jYqfajTmZcsIJiIcTd5kHGu6HAy+E8xkRkoXU2kJ8CO+XSaypqtiN0dPhidgDofkZsStORbs6ZjiIagzyO43Ig+gAze8PfGCnI5j5jK56R3n4WgjWdR3z0Z8pOVHstAoyHshyIkovz2hWNhnDf0XTCGQZN6dahJ1u7kk8uV7QSh4In4mvinBZp9NfKe58hGrQ0tKHzyDs6tL9Uh8oM5DumeP3Fjp6T7xe9oI3IgVdi5iQAP/NkKpVXjscx7gVIE4uCxlGVGVsh6UpBzyp6FMWOY/CNGU9f+Sp5FebuIuqsGtOs2BfsjbnRW/Ly14rKgCHA+v9J2IPBO3nxN52D3A5aurrD01ESSqppe9Zlon6AO6XEf9ynMeIF3DYJOSjIkeakw58ADCvtgkuJ7CWZd13H3iS1TW8NVR1AIhTOp3xQrlcs2QCSPTwUqoGQnkTh2dTuowbUWBGmnJSOxkJzPlosKTdnOmID29V8D1PvF72gtc61VMlOIho7g98onsHLMVJQ+WkexendFuRnW6jJk3Y0Xd77mLyMPi6hllvZUeTkiGxAkqyorEuk19jgKtbES7aUg56NGS/viyA1xesYZ+KPU2fHbteIBUGodBgM6hgT+f8s7rmz9X5gWBvTBnpcsd5AOxMHecT8CySE5KEeQ0HzOvshFaS9QHY1Rz1Dyqq+s4mwM0d3Kdr8X3NgPef6NGaSVnqPO8P+tAoYlTqcL/fA+stObW9gPq6juVgYsMmE2Aa+pCvt4GYYtqf9aSVI3ZIjamOH4GMvd6Ei6v+e1DrVQ7rEbNfOZBeuKVGd4xMWDWzMfvp8iUEwdaupxfp/TIQKkzTsuWn7HvX81i+X8G0bRyUPBb7VsFgz4gdIDyg6wjy0uNd9Of15IHMFxRNGKhx0upD04Y8zHQM9I5lyWRElGXTRmNga8Np5zdb4muEeOLmE5iqgJU5gyly7L8+QXG7wPa/+2tk6x59ZZGe/BrJtoX9tGDrtG0jrl0u8r6WEtIQW+O3O1FXyyLvVU5lvaRqGRdmKvWeJ+p9C2+e3sNf9oJXgJEe9yq7bEzYzdC2vPQo40fIEwHT3rQRZ10W/MLFx5QQ0ypqr1/dcjdac9H71YY/bzgI/kW4vmVrL8+g+o2sH3Pg9j6Qlu3ZCdSf9bOxqyHvc2JXBtKgCox9L2pdvmmJupzPola+llaq23g48EKbJiEPd3sv3ZGe0II84y55lFMohdoOZiwdkCyF3e3J5uo6NgOcR/6//Rbm4gyj//XvASCSU8qCk+6i4KJXEo3CBgQyrVRKNYMmhbHhIC9N+D1vtrwvKXRYT0cliuj/98TrZS947+GWq+BoTaBVEwgSvmkCa/1B50RVZBXLHRQICCfV7ozChnFxyp25qrh7FEXYZc1sQtaPICl1HA6AD0eZsy4+NEFb0Z6dAE0Lt9k+HvuREcCXYtc+f1gkkM6MNYDoTmK1YZ9eWo7eGtbvp1OYpoX7dM3SS/IwKFiDK10QYPeoaUNO/l4Mm49I435YceB2aJib4F7s6QkNiP/RL2G+ew97Mg9eTr4mxscL3idApoGoUND3ggGir5YRSx8AYYMIg6Yij6ficWnUO3yJ3/Gya3ggUtUSkbcbj2IbL025MwMRjKRsJpXACD1bkccocn4oVhCFvSOHVLsHnnozZiBffsWF4Sc0MFZHiwACu7ljWSFOd74s4KbDL8YO4NmxR5kQaUuKtF8gX9+vmFO9j5faxYo+TTvaStqT+YM8AEQ8ikJx+/5BTnqJ5YMtPNSuZx5K+hC6na8K2MtzDqLmM16+11siUIcD5i0DuJCf8GBxdsL7T55xszk0US5bKI1hxmGov2PSFDg7kU5XETE4P+u2pCcc1gtf1MvNXpF0Xtg9psijfkvbwi9XsdtxzBn14mShxydAxV+AFyohXJjNjrvSu9e8fLYdzIojfLPZ8djcU6I6aDvKojHbmtJzTfNk7HD982N3LsQOgLELwjN4yzqhBo6GQd7O1zUvuvsG/cdPD/JQmqBfrkI+frN9mNPxg6dGEAv+/YBhArhBrDZ8AD9ck1i+3XGh3i15UnhPUoluAinvFebQ0qKyd+hfyQV8NGAvfrePeHjVnVRHFoDvO2KI+Xof6/5HXi97wYuzg+58RnYZ5XtiOortQcGMm0xYRo3qESaR+KxqwAptVRWB2ZgLb0TosB8N+HOWG7F2tJFgnVHawo8jsMsPK/bnl2vpJhQ8IZ6K/WT2/NgVTtuyfDNFAT+sRM2Lk1uV9PD1PignmIwcWjcokby6fJhHvSc5ezwK+fivLh/mpDIoAHdPVREYD7H7518zzwOVhd3lScTk7/ZhTmHGQ7i//JodqCVBaTxJWUr51ZqX0fM5kp9uGPNmByN3DnQdPyeAdyvvWc+Ph3zfhKQQLQvNF/rwL7uGhwnmVhTvIU/T1xzNm/oQ9VxUWx0IUnGqIR8uvgKE0mGPP50EzyJ/wqFMMhSB1iRB88050hVLiO7rCyTbhu52TUvE3mQENx3C7IV3eUpaHFGRKdn8j8SO1UYmq1+O3Q9K/sxOTqkiR3s6RJpadMMCyVYuag4UXdo30V8JACyw/G+/wvTffgx5bP/JBdJdj+JHWuKYQ4N+NkB6nJOUB+50CLvhbtv+xRvkf7pC+XELsz+gfXuCZNeKVrw0EgC5B3BTSK6XsetUsy3rj7m+zpEr3HXBPt4oJAMI5BMDxBbrYh0eLr/e8D15HjVyHltRL5vEfe7/5ux/iPzOIg8fqvojQVW9pD3JXbGPP+RIjk8vSwGe2sWSgrWq4NibVmrJQ+Cl+v1BRttlwIJDCAdeRDy9GhoLEjPwVz8TuypoPSt2+TPt6oQ/cz6SmY/dMIxoqZclh0VaFkjXRnv46PvoHKJsof+HuzfpkS3J0sM+szv67B7DizflUJlVxW6KaJJCi002JBDUQpC0EAUtCC4IUoIA/gXxJ2grLSSwd9RCgLQhxIXA1riQFhoILSR2S01VVWZl5pticA+fr9/BTIvvHDOP6ox4IXUXEKkLJPK9eOHudq7bNTt2zjfI/fSi+ageS261prte3USYgALsVCa7bmAmI7irGyq2qRCUwBQCPskercIav5pHi15++J1jSUKteh07oTgfOsZ68H3IAeRppzQQbLNiSQBWNbS7qFuY1HBNQX10pdOpjJweGEO+Z2xUB0tTfvlFIWRswc0c5cm+7YDTaXhfJDYewrIc9nQG0+vBPjuDnU7gRJzowbGHFvnHxw5r4titkfJeGogSpiw5/iN8jZ1OgpYjAHTvP9yJIzS/sjTEQ2e/GBNqatd7ldHrWP+nM6CYGwz6vE+jAcx0DD+/5WQH2B+Q+2QyqpqZfp8d534JM+jx5yr5rbvacBDl+dpWyrKsWIXXWPKdaQTHMqwZDvmaB66nndIca49YQ+ElLcd1grazLDO6UyrXGmXmqMnwEccx3FjB2bvZGZLbTTDs6qYDJMrUn03gZqyidL0MtmqRVDXcdAQ3JC4m/XBLj1cApmnRnU9gDg0ShQQ7f//Y1WbmEWMPxAhriGEXK0o37NGEzFrY6yUxQts9c3kAxhpWjKyFeX2B9iiO+scXyN8ugcUK3ZcvAOeRLPeEFktMeHZK/uyzU7h+ASt+tO71OezX74RyeAAuzohz7xfcpVKKO3WnI5qmaSNOv4tUdOZ1pXae96PImcqoSoMjMtWv11wYxkPaD2mpuW5id9h1cMtVdE2553raKU3x3P/u678V8OJ+V8XVT6SfzXZ/V8vExNUwwEXVdVpvrOrQHBPAz07udPmCDb12RLXzulzx5pcF/60oQioSVH6NJWoRuHfsATD2mLE7FzHpR9Y3JFuLMOlkTISh6i4e6kDh09QrrJJ6/8R02FcHxlOLXaTEBMjupmKqos5rsiwA5IxUXBR/DwDmZIrudESShzHshKoEiXaAtfOt2jtSS1fEaYB+q6Ly0WfeUQ7uOgrDbvdQpeMfbkrjPfztKtTMjdRfqbuYhK4c5MsPvMgsCxPGHxEbfMnGBXXUj0LPUqrhykTx8wU/uxK8jeT1frVh7p+LYNLzc7jbJb9EpzV6y1Jn0z48dsXkP2LsXtTOAg0wsdFPVlSJ3dUNUx/VXZT7p3IcfPMYh6qi+bqO8TTtnZi8c+T3piIF7j3dsquKk1T0ZNQATR88NC3sL9+zPLla0wpICTCjYQComSxjqTfPo+JyJw/m6Yz/1u8xHfXk9mqKZ1LCQcxQuAsy2T9miPC0UxpAViZZlY/axv5QB7v20IUEAp4EbctOokBJkYqQUNME8jMA3iypAJjRkN1Y4Y+aPA+iSBgWsQOaigjr+yskp7PAlcWHK+a2u/3R4eqesY8GdzuoD439dhWYPqx/J3HiaF07HHDl/coIDPNtC7fZIjk7jXHsK5YjNdWSA+GdmHSXC8C3Cn5xS1O3Nfm0XgSajJZsrTDDNH15dgp7uxY3PjLPwsEUYCyQtK1tOeaTKS18jipWqgka8DMqEy4AP9XU98Phg9PpiU94TxjrUviPmp+JApcCqqCphEpBq4yFnv6P88WU5UIFRtFMq4s/twk8+LogBedcVLkS/UWz3kaJ7dEo+JsG5k7dPDx2ke941Nh1JU5FVXeziQ+HsTzaCxjOtx18K6VY5znJsowwgCN4BQ3EeED0ecYJpqu9xATVzFf+7WgY+LFmOmGX9PIG3Y9ewn71NqiC4fkZx/L2A00kAOb56iWVJvRp0hgKMRw+GNbVJeUzh4avzUSWUPVohJcAgA/X6YxnlkMNHB4uSz7tCW/khD+bSKfUx2CVBTMZwbQdAWBaby9yoIlO1siVfbS940XqG6H9DelBFHDztdjAHw4IUiCKYen34K/mUQ9GWfPqLyqk8KBUcM/YPUCVhEeMnVIklAbxyxUfpF7BWr4exLVEqdgUMU4I1ZhnZySlaxxZxgf9ULMhpGXZugkxoVcG7R9lHeHFM/g5xZog99PsG/YbwC6w/+YtiTNTxu5Wa1iFVJQFF4uyCChU7PYsJFhLjX3FzqsmvEJB9qL6MOwDt2umPMbA7+V73VXE0z9wPe0cHj5MnMCBlNUzcEnbjoe1YwKENi3qJpIkDkfIyIBFOcKUKHZd2tmBziYCSGbQ53uJ9DWSJIoTASyd3a6YJiyWTBEeGLvJskePXeXpwoPRtpw0ms8LghLSWg8HPKlgoG1hml+JY9iHaq8HvM9kfCemUBLsOsa13RFaIZPKDPqAtVQYa1sqLdcNS6Ii9wcQI+RH9HCC93yQ8owPrdIpnf4nRRTvafy2YnfV90TdrFeEMcSehBeX7l4web7v+hOt8MaYrwGsAXQAWu/9bxtjTgD8ZwA+B/A1gL/hvV8YJqr/AYB/HcAOwL/tvf/fHvwA74OdDEebxnqt+jZJU8J7A2y3khI0wVCYuBQj+bLgqNsuKHchQVjJFbPi5gvWi/sl/GrDVWS7DAq4FGiqg26M3++lnJiT6CBoS7+r7x+7NVyxHjX2KiIakySogaFz0ZrGS+XCO7htJSpeAHbr0ABD14U4/Ipl0G5xCzsU7UfgbkxSHjRK4JCdEtdzmhwbA7dchXOCWSri9ACsN/De0/cpz2GENxzUEKQ6442Byo+EHoWyzaRh5qsDF4e2A+a3fM1aJBGX63BYx/aIOXbP9aeR0vw17/2xq87fA/Dfeu//fWPM35O//3sA/jUAP5H/fgfAfyz/v//yCIcReM+VZb2VvzsYm7FF7ZjvquGAV5WAI32VsJXrATEXLExOIVV/cQI7X8OLV1Igc7ctMD2jDqRiV/IMdixc1DQBihFxNAAJ2/sDD6YPjB3jSXAA/9jYgzy0ITbcDAaxdJqlxP9cnJA/2nZUDtZDeVnQVAyCGdI4xH4nOZnBO8d4dHXXmERz3R9q4GzGFn8jndcNXUrsszMCzw41KX23MulnE5jFMnhqKf4fTcM4+ipoJZBjVXqQ+26KnFgZKdP6QQ+oqqgE3TlgNuaYslRYbclHJ/yvI6X56wD+gfz5HwD4N49+/p94Xv8TgKkx5sWD72SoWMtSFG3TjeSToTqhNWbpZgYQkZEW9nGnL6d2oW6hvm54gCoLmHc3McVJ08BRteORCEJlpLFpZzZJWHK7WQCXN4QXVwdgtWHqkWcPj/12/eixU7FLxq6akOIgqOcS8+4moimFzOG3O07K8YhoyaM4/G4PGItuvojxLJZ3YgrqvInlw6mVlzxD89s/wf6v/lm4yQD+ZMKvS0qKftjjRGxbNvBePwtgMSOwYFUMXv3OJ0GLHwCJJWkqZBdxaDEGZrXhDts5bP6Fz9itFXyRX28DCeTXLabqAfxXxhgP4O97738PwIX3/p38+3sAAsHDKwDfHr32O/nZu6OfwRjzdwH8XQAo0zEnjnqpNm2A2Bqpw5tBn6ucKJSZskB3Nkby4Rb+YgS8vwFmE3TjHpIPtwEv7ctccsOc6L7RgBgRfwjpCYnTIh0t0F+UBbf3yRAGo6h2MOrDJwmVr4xhrgncO3auePmjxm7k9doc8sM+c9UhlYO9NSxzqgqZuHGbzrHMl5ZIPnlFUJzG8fwcpm5gZ2P4xYo70OQFzHITYiKqc8ID857EdCMTtfjlHOlkADtfw52OYZWNBUAthdxPPoX95gNsItieLBNhKhdw8aP/4Rd0NSzLiC7Vzz6WWVQ5wrbF8J98w3OApFBKDFEpv4euP+mE/xe992+MMc8A/NfGmP/r+B+9914ehkdf8tD8HkDwmD8cgPfCmBEQVXCoW9dcHQ9W8CZ0b7ZfvyNga7cLq6u9XpAFJJ6eWK+BouCB61DLaX8fiOOqaovVhunTQIgTWj9eb1nZUI1EZVI5Ub5dLJlr3jd2VT5+xNhDWmQT1tuvbsjdrGkDqQwuv9vRCa+m5mJIkyQGADEOfQ/pomK9lVTsEGICQCfx3Y72kKs1BVstuQh2SyUBu9qh/eQM6dcfiNWZL9F88Rzpz99Ru/7Ne36uc4RGaJc5fAhpj3fOOaqmLLutHljD/W+joJbuDkFB+IHrT5TSeO/fyP8vAfxDAH8JwAdNVeT/l/LrbwB8VR0NcQAAIABJREFUcvTy1/KzB0bHBo/7/CUrFQml8bzYTfqLUz7hZRG6dkhTKtUeAZVQCMHZEHhlej2Yk1mssIyH3C1GQwKpBOJqStqmazXADHpSjx7JpK2oFS8ye75g/mt6vbD93jd2rd0/auzGhrHDJhRP1U7lkFgZJaIjEcixgLu0nIksvxOHmU5id1fiQZHfjUkI4zg7YVxlCbx4BjhPtz5NodZbpO+pTtANKaGdvZc+w2oDOxkz3xaVYowGVFkeDQU4R6aZdl/9gDo5uloHbP16y9fogyGlUO1Om+Hgoyv8/+cJb4wZGGNG+mcA/wqAfwrgHwH4O/JrfwfAfyF//kcA/rbh9ZcBLI9Sn/s+BGbQh11LhSBNeSjKMvh9BStqXgACFe04DwcQa82TYbhBSGzQpVEGfKC7zSZMd5Sql1jhbkoLXVhMJsvIXYWA0uQwHCQ1JN26b+xKanjM2M2wH79cGTtGA45dqzN1A7w4B2YTojdL4azKQ9z+9NWdOLrzCZGFp7MQDw713ZgUAnGzYFwyPveTT+AnQ3SvzkjEOKNjh9Ma+MUZx/+c2jR+2KfT4GbD3Wa9hV9v4Tc7eDlLIctjleZmQZ8saWSpgC0AppOQPsKRJY7fbIXx9OsTU70A8A+l6pEC+E+99//YGPO/AvjPjTH/LoBfAvgb8vv/JViS/BlYlvx3PvoJIk6qNLMgz9BJi71pmT+u98yZtYbeNMS1dB2MmAsEoR9j2L1MJbU5m8XJ3nUw+1gCtLpt5mx2+G/eUni0blgp2O5iDt801K3MMnTnUyRXtx8f+2z4qLEHCqGSuB0Fnrx3UFFWIzqcZkPLTa3qdC/OkFwvkf7BV/RlkjiskL/dWYlE4kGR88AtnxmqQ0XBtGO/hy8zJG9ogJDss+DD1P7kNcw/+UM0//JfQPKLt1j+1R+jd10jvdnTlKHfI+4FkAacqpwd1c2dD0KpEC6vH/b53cnOZVpRTSsL4ADGqiSfY3n1e66njZbML/xfGf31oKAb7Cc325hnCvP/jnqXOuK1HcFPmhMaESY6SO7ftjAjGn0FwjLAL1FEVdXBGkD8/UoqEGXBmz8Zc/LcLmHPT4NWud/t7x/7EfHjo2MXcNTx2E1RRMGkQx06lsFZ3DkKp6qFvbx3iGM4DAA5HYPp9wRGIXJ9m23EFYnArN/tpKfBnU/PF/7FM+Crb2EvzkODjIJQol0jBHbvXPSjOkY+Oh+040NhQe5zqL4clRyDo59i6KWrDAC///4/+qG6+IGkgkS8R/NMVKjk8KcrqMpSJ1a+XIprwpog+G96JfmUjSj1lnSe4OpKCKvKavtBD6ZK4EY92JZqvPoZvhbwmXNEQY5GsUny4lmslBjz8NiFZf+osaOBn0059raFHw5Znxc5P2QsQ7afPkP65oZCSeLqHdhJUkkKnzdfRvSi7kKqbCYxmemYsh+ShuFwIHzidgV/qAkZOJkRrw7AfP4azhiYlfy91wtAMTavNkCSwE7GURNTPJt0DOE6iJaQqCmrDqVafoZmofZVqkN0XnngetoT3oBfGPh/3CxYOTFS1SgLAD7wRSGMHN8vgT1tIb2ygLS0t9mRKLFYsRFzaCj6A/B9CoKWkCZMN+oaeLPiCrLcwL88g7leMpWoKmA6hnv2kmL/zsOfToHtPqyy9409SGw/Yuw0JZOxty3Mdg93PgWaDj5LKPOdZ0i/vSK3880HpmpKC6wONDF+8yHEEdhIxwhQ4E5MdrljtzlLmdZZC3M150R8dip18I4FgMWK77FYstTqHA3Kej345+cw1wvuKtIkCuXHLI3NJU1nAOJpOsfddzySJmAn1j0pjJBR7s4X8wOf8ACxIXmsfgRIrYqMapdNlbsAYH7LMpqwZxQUZbZ7qH47koQ+rQPmiMoNDepdlaQ80mxCJ/S77z4AvR4naMnJYL+hWL9qsaDPFfrBsVeHuMJ/ZOzGez5ESRK05c2bS+4qzvMAK+cJv6Y6F5o2WPSYfg/+8jpCikUSJPy+1saBOzEFbutwwIekqoB+D93LU8BauFkftmIZsB2doPhmzipO16E7GyH56j18kaGblDBXgv1XXHy/5AIRiOYmpHI+z9jE0lVbOQSzMbDZ8zWaDhUis50dKTA/cD35CR+cNQ6xbhu2WOdxzPwPvj+9XkQbTnqhEYNWupm3B6YBkxERhCdTpgpZGtlLZQFf0/QWqzXzedGLwa5iq3t+SxpeWQCNsI9U51yICveN3VglNX987F5EpwKJvOvujmd/iFrqaSJy1AknjZUYpApy53XrLdznz2ldOejBHGwwPGZMDWeI4/uocGtyuUR3eYXs/IxpCoBCav70vN0huV1JF3SD5O2HKM7UCL9AaI4msdxxV9vYXLJWYBNZlEnsOuDdJTAZRxGoyZAVHhGgBRC1K++bTn9a8/LXcnmELxJqEak28tZGrUKxXAzCTSqLAXBVl+YFKx8i2ukcqy3GUrh/NODfy4Jd17YN+BHT68EPemjPaWzQvTihSu3FGdx2x88fDbhSalojlLb7xk4x0ceNXYFfoe4t7CRTNxR1nQxZHixYTjWbHbE9ClXYH1jaPI6jaYFeCbupQjx+2L8TE/o90X8hXsmvObmbT05hP32F9uUJcH4K9+Vrdj5PJvzd5+eS9pzBjIYkjLRtQD5qVctvd1H5rczDecOsNuLI5yOS9EiE1nvhCq/YLDPycPr2h77CqxCQTgDngLLPg+BBpB+858Tc7JjzTYYs8TUtD6R6E05nURIvzwg9TRO0P36JdFXB9TLYrUxQcavrRgVMmdJZOkuQvZnDnU5hmg5u3Id5fwP/009hb9bUgukcH5blhqu0NoG+b+yq6fiYsQPRK6rtYPIc7nQcLGfgHNy4h+RKHFG0YSMPTzcbwFYtfGpDHN35BMnbG+5ery/gxn3Y2w1lPDSmpoVpwIk4GtCNG0D27Q0OP36G/NsFlcOKHG7Qo3HwvmJHFgAur2HKEu2LGdI3N+xMd13gt4behfJz25akkxE1gozKnHfi/pFL3p/StpO83R787VLU0iI5/r7raU9454hP15Uxy6LyrpTPIJqKiuM2y010u67rSGNru1hhqQ5cVTdAprS/V8+o+S6y0/52ibTtAgbENw3coYaZ33JCXZwBowGS9wtObmuYrzvHlaxXRoLy941dmyyPGHvga4pdvd9sYeo6NJvMaguTJfJzEXE9mZJwXhSwqzVwcQZ7uQhxJIc6IhDfXAIXZzxjHMck5xJft6wIrfehUpN/d8vzSprCfLiByekiaL74FHj7AeZELDw3OyS/eEvOqnawGyHgqwitwIZNLmoJ3nPR6EtJtN8XdxPBPB1q7sYqalswhcOwf9cw4Xuupz3hjQk6I77I4RcrTippFJnxKJT9oA0jgdVqux1erOatZcqxr6Kfk/dMRXYVzM0K/nRGZaxCVK4mQ37ebs8Js9lF9YG5OGwPelyJOsfPa7uAPgyVj+8ZO4whMOwxY5cOL/ZVUBhQqARES9HerDhZ0pTqY84Tv1JVrNXv5DMkDiTE6UB5vPMl/NmM/9eYAB7OJbc2Q2rQdCes4e/+7BkGX62YFk0GSKxFV2TY/rXfwOiPFqyEec8qyxHrSzmyKopljAkyfiYXvZvERi5B28a49TXKae26YASHYf+HTvGDGIZ1XA1USto5Bt11wJCUPUipEYCsjPuAH1erFF8d5EaK7IVzTBv043YVJ4i0u8OXbwwNuw4HdF+8RHK7o7WKMnVUOmNfkbZ3qAML596xCzjtMWP31TaqG7gOSImaDI7dDR/iUPMu2BCjXAhTEVwvWBeXOACwg9p2PLwnlv8mqFG0VA0L54Z+Sf370RD2lx/gqwqj6ykhxUkCu90B/R7s128xviQJxKtpRSfKB2I+pgdTDwS4cCDUGMOUE4JoLXOpiIm0iKYsylcY9vlwns5Y+p1NHpxST3vCq0Sy0r+UtC3/5j1RfnAeuJrDi9GvX61jh1B15AVwhCRhBxKIq6bmlFobVkePxgGXOxp5GQMcDiy1bYmUtNMJgvlWnrFst1ixQaXt7nvGjiL7fzd2IMrLqZJCmpAPqpNhNCCaUFGcTiiDiyW3fTWBkwnvd2Js9t07xvP5J6QpSkz60PnDgYrEWmEaDYCmhruex1UWgB8PqY4guvFIqA7hN1tqz3cuSgIKYSZwg42JnVWbBO6r9z72LazlPetIjYR3/B1VZPae1McHrqddpQECAQDGSAfSykE2ouWCmpc1/HdFAYpfk8kyroCayugXIX5PAARfvY58TsGtmPGIuTAQaIWmV1JS7/U5U44kkfxySCAVECUl7hl7tG/8+NgJTeDYVaYjTBABfaFXwr+7DGeFQH1sOyITh/0/FgcSywdM4jGb3a/EtA8cWZxNg9QeduwJmCQJbn9uvYZZb2E/f830AoCb3yKI3aYpdWXyLPQGzKBPdbMkCT5SJs/5QNnYQDK5yHTkWXiNliZhRJ3u+XmAQz90Pe0VHrLtqmqYIhG3gu5TrXfVTNzsWDsWcU/FSZvjP+vEzzPW3oW259sVJ0Aj+HLBbHsgGIOp7qOvDrCzHswf/Bx+NJJVyRDFOF/GFSl9YOxq6fKIsSNNAtnb1w0rJotlVBlrGmDZcIfQbqV3QCJEamuAzeZuHGoikGWECMyo4nAckxmPCBhzhk061wEQjMzrC5ofzJd8SH/yGdzPvoF/fY5kf0D34gTNOEf+YQt7cxvx7Zq+tAIGE4K4Ntn8iIQcFaMND65+L5ZmZ8HgGDwj2dt11BB64HraK7wxsbEg1QvvHLfhthPnN9GcuV3xBmlncbMVD9Be7Ni1LVc/UbT1IuFmhoOI1daDkvgdGc2phZsJgKkMwPz4WNi1i6x7ha/eO3Zh6Dxq7Epl2+05yUU5IRz+5PPUDdyMhzyo7iuu3GVBD6jjOLqO8Oi2i/GUxZ2YqDDmQ3wAG3em14PPpXkkymB2uQV+9AmSN9fwgx7s7RZJ1cF89w7NF8/jIT5NCHtQT9VDLe4iIo0uup9e1MoAML0aDhCMFETdwJcFm4VSvjV5fpdY8j3X017hE+a1CuoKuau13HaBqK6riECRx2DdW4jDJeGtRrRpgkxfOeaNs4aHIyD4GRlZVbqTMRK1egF4qEyli1lx8rvZEObQRtx907Ix4v29Y/d7ISQ/ZuwiXR200bOUq7mkQ6ZX8tBZN7HZ1TkxG07QnYxhv/qOtvISh5+M2G+Q+rrpHA+mohTsZkPYxYZxHNizMHtKbft+CfuLN3wIZhM2rC5vYKsDq16HGn65RtIvAGORfXPNOKoDUyzd2byoER/j3ecLmOkkHHKNOLEHKXF9TZGTjWYtUaODQTijPXQ97QmvFQ0n5OC2g8lF6llFhiRHDjDRVLA1SvBW/clO0ICFsufZVNGmB4ocfrmiPXvaDxJw9rtL4s41xbkR6ejlilv+bgerknjWAssNW+XqpXrP2AFw5X/M2NOEX7qqkwFBr8b3eLgLvM7DAcaUUYt+u4PdVVzh15tIrwut/20Uie2/4HtKTJTokDGJaC2mY4794gzdsEDyz76JB+J+jwfkIVUV7JwNKN8vmbKpiZrwdwM8ORE2lXNRDEvl/4CwuGG/pAjVoYbpZ/DbBsZ7MsCOodEPXE87pVE03DHsU90ymlizVZioMo94mBOCwZY+R4Hg23UI1u8i0OknVCYw00mA7vqqYgvfGn7JkxEhsf/cjzgRRFTUlCyPaZ1ZyeBhNb5v7KoO/Jixl3kYu2/EZU/cAc1eDt5tx4klpg8Eq6kNpUH37Zs7cYSxnU1DPPj23d2Y9AHsugi3uF1RvgNAMt/Av37B9z2Vw/qzU/h9hfbLF8zVZxPueOprK4UC+k+pTqTnvzcNhWybVhhMez741SE8rL6q2JWVCpf3nq8ByKLa//oYT7/+y9i74j2QPBgQSToeXoLdej9ap2O3Z46rK5r38Icj0sXtKipXVQc2baoqohfrBsjBL/lw4CGvyJF89R4my9C9v2Tum6XB3gYdhULdfIHk/CxCc79v7NXh0WM3i1VQ7TJ5TlViBZuVRTyoXi9Y5juS+waaCEW+noc4cDgAWU4jZo1H7eolJix5EESawih9Lkvhtx381Q3s+SkfgDSlx9PbBfxnL2HzHNk31xxL3cDNF+S1Jr1YPuz1wg6uaQqygth6KatqV1oXDCW7qDctvbt0rpiPOvgBT32Fl1KdKQv+d4ST8CKwielY2DNyolceZpLEVvagL9LKgyi3PBywNFYW5F2Kf6gZ9NlokrTDjEbyXmWY/CpghH0VVh8tH/q2gx2PAwbo3rGLPsxjxg7gaOzi1FHkTOtaTg5lV4Xf7/f433jEGIC7cYg4UjdfxHia9m5MRc57IuVNzY/983OYwQC3v/OKO8lkRLmOL1/DrvfoLqZs2Cny88efskegFZSWWCEUOWVApmNRKxa0qConqyM4QOiDpJ44mXCxmI5Dww0AiTrZw2v406b4icdTUPqVLzxISUgtWXEmXjUWlbmu+pBHJ3eF2YYvXVIOP+wz/1SFMpGnJmryBLih+Rc0LeoccC6EZzFY8IslJ2FVRf7pn8bYhRKoJbeQqmiqlCRU3ZVGTQBZqRwIQBWDyTDEoa6CQVakc8DLZwGLYpqW54bRINTdvfQgQt3fCrjv5pZnqV7JB0UVfgHi5k/HxOuI2QKAI6lD3gMG5iKLSeal6fcDjyDsWmmEcR97eOlrfv/6936ghghgd40kYqm3HznemUE/Tpiah09d4dB1sTHlPX9P/25EqqMsqeZV1+zQaTdUIAFhQm8ELvvyGbz8DAlhxbhZsEGzZNrhd/tgevDQ2NV87FFjB6I6b9McyYiU8d/XW6ZobSsWMSbIgIe7eRyHc/EQK/Hg23d3YgIgjicm/n1+S0jzfEEtm0qcRtoWfsGHyZe06nTyd/PNO465V4adzoxGjDd4YRmaIEzGUeFNOtXBGGE2EQFXc4esbSZj7sy6Cz5wPe0Jr5UP51juSyzLgnqYzFLJ5Tox2soCbkMbViptFyxo9AZLDgyAKYUxvMnjEdTR2X/zlumAMbDnpzDzJQ9snjgZd3lNnRjtgH7xmtIcorfy0NhNv/fosYeuY5JwrLorHWp+2aq5U+Qcf1FELqvwbs2wfycOdB0nFxDiQa/8lZgKLgrSP2AXdMjq1Oev+MCEBlmP9+rZKeHPdQN7fsZdYUipEIwGxL4oh7fH5pcZ9rlD7SpWjRTfVJAC6UXO3G93TIVkbphhn363ayGPGBMRlPdcT/vQCghMFbyJstIExwptDmVS61bhIr2qI2mOuuFhJ8+CQ7bK+Jn1ji32QT+QlpEmMK+ec4KWeUAPulEfSUfmvc0yEsMFJWk/zJmr3tYwiX9w7NoEeszYTZ6H8Yaxy6FPjZb9mtqLfrOLTKuc3FPfdfBvP8C8eh7iMMMBc/XdHuZ0FsZmjmO6mhPRud5yclZ1BO1dzwlSm45gNnt0797D9vvqu8jGUZ7d7XwuSSDxADutW547/GoN2ITl3JZuKcYYHlCbBqgSpk/OUxdeLDaVHK4NLJU5eeh64is8ormYlhHzPGioe3Xy0/Kl/l31ZAQzgywNmHUAXHmnPMj5b94yz+2V3O6Db6gDruacILdryjLnGew377h1dzQywHJDetqHax6kPlxzBUuTB8ceCBCPGLsfR2yJPxyA6Sjm5k3DFEbNCzwRjmp5jw9XVDDIsztxoCVM13ddjGezvRsTAHy4jv+XXoEfDyn2NGXjrn11guTiGfznL/kektLc6Y7qeePId9WkUoUaCltMzi3e+6AnqTucMQZo6vAaQDrdo0HERtVHVL97rqc94T3CF2d2VSgjeiUQlEUsoQEMdl/x37UFDdwxCghqvA0d/OxoyJLgeBhvcMZVFKdT2quLtXwwSxAZO//F60DShuilm9lEwFndg2P3w/6jx25019GqVNPKuaAV4NWAKUFZMgVRK3rZCXxZwG22d+IINXAgeq6W5R+LiehS1tPpmNKSgte07O5udkjfzCkN8s071vNXG1reD/uBpUVnPsHWiySJr2s591RMeW5XoRGnFD/Fy7MkmsfXNA2/010VsDa8wT/kxpNKNSsqTmlz03GwVfebXVittGZr8jx09sKNOEIoBi9RZTeVOQ9fKkUHkGpW1Wx762rdSM5/MuHk2kmNXUuFGevfpl/KZLx/7KaqHz92OYwCoEydSnxL403VhL36lqYpH7CDqDGsNkieX9yJQ40ZTL8f4vFtezcmwdaY4UDi6guYLaUz33rL/zcN8Itv2Dy6nvPzVxvgl5QOdfNF7Kl0DmrErHJ+Rl1MjlZpynQn8TWzSegnEKKR3OE0B5nxj0ALnvaENzhiErViu7InJlp+ZvrC4u+VEYqqW6KoD4RtLqh7edGjkWqPepEGdwoX2EIq9UaXOmq5mM2OB6gyi75IAP8ssnxBleBPeeymLIJuji8ydM+EE6sUQyAeYAcDKogN+3Q1OYrDNw3MdExhVY3n+endmPRQD0RrekCYSdxx7KAPd3EC8/KCO83FGXfRVxcwL57xwXn1nCXRXsmYywJ+MmI6olAFIEqUi9gSrGX3uF8Gk2g/oh59iFUh24kVpYgfMqdV1LugLntqy6417dGA27u1gSWj3U2jMFQFbKne4qAfEIymaeDOZqS8KQEE4Op0OgXE+NidjmE6T+F+ORy2n54j/cU7fmkbWiv6IoNRSY40hZkW94496CA+YuyAlCMH/UgHtAZmXyHZkGnEnwnupoyYGwAwuwrV7/4misttjKPI0Y17sF+/hxn20X56juzDkhNfY1LrHp2AZQH34YoPaNdROrtX8rDuRU6wqmmj4z2MlCZNPYj4dVEpNst16DR778mt7fX4YCtgrKRxG4yBvbrlgyJqDCFxkaqVH/b54H+kLPm0J7zQ5MIhZdtF/XMARpViE7pB+LoBzmdcqZ0L8Fqlqhlro4JVmtDqZbunTsv8VryP1mJ3LpO4X8Iut/BqUDabAIcG6S/e8d+XG+bJdc0bXuRR2Mml945dfWYfNfYsC2Pnm8kB1ss2DoRur28aUgobce8YDeD7JXo/v2aTSePYV7DzJSEGZYH0F+9C7VtjOn4PNNSYMaMhJ6KqgKlI0moN/8kL4MM1kvMz5tj7ig4qQKQ1qnZkv8f7sKXFPGwStSJ7AjIzBqjltWoFOugTeqHAMj1rSCVMecT3XU97wgOBf6mXURuY41xVcCawCXC14Pa52cHvBJLbdkze9ECj9D1RPDAouWLI6ukFU+PrJngiGefJ8r+eA7MpsKE6gOn3WSVxHr4oYKxF9+Yd7Hj84Ni5SuWPGntAdAKRzqaqu3nOSQOwSnNEmoAwn0xZMi/e70McfMgOch8TYMMJ6rsuxpSIIFTbAuNRyKkBCM3uEBhhRgjwxyssTd62Qcs+IELbDvCtQKX7ETs0GrNbnaVRkUwFZfMsOKGgyKlaIPo2EBqkKsc9dD3tHB5H3dQsC6QK7UiGiWAsDzFDoYlJBSZ03byQtrOMOA3pVAYko27ZPU4M0xd4sFRuMJsEu0RTUpTVffkKdjxiF3A2ZRNnMoTPUiSzKVOOB8YebNIfMfZg8HWU04cD97AfDnLaJEOvDIAy//qCP8vSO3H48RBmOKDar8Rjzk7uxASbhOaUz6RnkKXwuwpO1NhoeS9k7UPk37rbJc3U+r2AvDRHFEZI6bb6/DQ8SFqIUHsfMxpELy9tKqUJ6s/PuPgkSdTNT2kdZEaDh+fTk8fSTP8tqE1NYL47F7EnacoJrchHVRNQsJaungBXizwPRIJArFC6mL6H1oOFchd+x1quolnO8Qz6URquaSK0VuCvAO4du8mFEfWYsUtvQKsawcZSLGHgOtF/FNPi/Z6TVfTelbhux6MQhxMp7GNiBbxnRUZVBURjXgnpQUL8dsnKiWhSqnGzPT+lR62iNb2jE/duT+kQZWsVOXUvE4vgpi52PmY0DApn4aB+pLgWXBgh55qyFARnfL+H5LKf9gqvJgX9MqYvkEB1RRz2WeoT5KMpS26hZUlStRUDsckwrFZGxE0hq3qAIKec5N5JmtRxImh50A96UA8ogD0CdzNneuI9W/qZKA8o3PeesfvD4dFjN4N+hAEAAQcUPJFGw1hzV7WERojaPSpymdcv7sRB9V7iiEL15ezkTkzBxa9pQ6ri1xtKZr+8YBe2LOB+68d0DqkbjjdNRb2Yu4J5eYHgcihqxUZ6ESFugKu4lErVLzbMA7n8sH9UsSqD6TSsCQ7eD11PO4f3kJO9qNgmNqYF2tjR1aeSkpsxAelogAjOAmLlo8iDjrsb9OAuJkjWB3T9HMkNMR7+UKN9NkayOuDwfIDyzQroHNo/9yMkmwPmf36Gs//mK+DLzyhm2nVsx2/3aH7rC+RfXz04dpNl8aD5sbFLXdrozmETbvddBz/so5sNYXc9NJMS2fsluskAdrMXDcYCcID57h2q3/5xiMO/PGden6cwhwbzPz/D6f9yxdVUY0rYO/CjAWEAs4m4BtogPeIuTmA66k66H71E8t0Vuucz2F+8ZbVlPKCM4TW5sL+ad6uZm5YZQ8qlmcd4SPFa57g7q5lcksAI/8CXOcvM680dsNz3XU97hfcumvu6Lq4yIvZptIzYNOK9VMXVSmvjirvQQ554nMISLGa3e6Tf3cBsdkiullzNDjVM2yFdVrDrLXp/8AbttA9zqGGch71Z4fR/vhSFXeGyConbtx2yDyseEB8Ye8CWP2Ls4SCoAlLWsMyZpjBVTR33zQ7JisbBds0yq2k7mKqBXW+BLL8Th726hf0wB4AYz/X8bkyaNq23TMOqmmcBY1jtkXKhvd0C5yew//c38LMxTOviWeXDNe1C1QdLvK9MLlh7XbG147xYsoLTK4mVn99GBpgoCZvRkJUjze0XS4LKbMLXPHA97RX++LLUkWEZSuhwtWCqBWvhbRLa0UFotW7EKoYoR5+mzANVNwbyMbkxAAAgAElEQVQlqxyJjQfF9ZrW6ko2bmpkX70nFOH/mMNLg8RttqwopGlUx7KGQC2l8N0zdlZSHjl20dD0gt/3dRMt2JMEWDZc9ea3wHAAP1+Qd1tVrKZkOdDU8A1CHAqFsL94Q1kRqea4y+sYk+D2TZHDW3k4mwam7eCurmH6fbi2ZUmyrmHHY5jFCm6+gFMvquEA7vqGB+pjwwMpnaJiioZDHc9HCl0WMzi/r0Ke76uKr9EihDgbQtWS9Tx0z/W0J7ylopfRLVTlKeqajhZlThy71mj7NBLQtAXGBMqebqeab6s/kx/14U/HsDer0OU0lumHHwglbX7L1yjeWrZ6QKoDsg0DAE4mPKAJAO2+sZs9WGt+zNj1MxUaURaiVbPhlg+wiypnku7zC9j3twEOEOx9VusQh89S+OsF8PwMZr2L8QwHMSYxE/NNQ9jvekckpbXwf/HPAKsKdn8A9hW6H38Kc71E/cU5sn9aY/6v/hTTf7aFvV7BffIc5rvLCJpL0wAbCRxeNX7QVdsmkQifS5XqWKFCdwXngj4PBv2gtXnf9bQnPBBFNBUj3VJw1APssmYZLWtURN8SVKWdSxXhD19glrG2XlIOw1/f0tGuEy1FSRuM5PlwR5WL6kDN9O0OprOx5LbcwL06h6l50MNO0JcPjD04dT9i7IEFpKC26QhYLFmSa9oIJRax2Oyt2F0WIjIqnN3jOPzNghM7J6TWdGKGcDoLMXnv4F9fwL6/gRcXcez3wOkMyc/f8fCdJnAvzpAs1vDDHrL3a5heD6f/4xseesdD2G/fhwM53VcIGYCn+BKqI1K5KjTIQ21MP8ZvDV9X5JzY8l5QBKXesweupz3hxdEOhzpIXcAKWXexiquSNUFHnJbrXSBPa2ph1ttQ9fGd42piLdSlQkuRrm5Yvtvt+cUcaZebNA2KX36744QVmTx7s+LksqJdeYz6+56x66r0mLEHIkknzn6KvrQ2OgZut3KQr6LY0mEn6EhxzTvUIY7QuXx7zfi2O1Z0qkOIyZiUqspNQyy79DN8nsFKrt9dTGhKrP2B1SZaTaYpcHkDtZYMD+F2D6g+UNfRERwIUiOAomTZBAMQVSCqQ9C+DDo16jheNyTiP3A98Qkvge33UchIPZqAqDqwq0KLWbEoJB630ZYciO1q1wEd+aVG04c0DXksrRxNROtpbjiQrqZwOQnDpX2kWp6bPIF3gtsZDe8f+5Z+qo8au1QyApml60Kt3wzI+TQjdjzteBTt18VW3qQpMBvT3kfi4GoICjEtV4xnveGBUWMyVh5Y0aa3tJXEV9/CyUOYAnBLKheYQZ8l3dWGY392ClQp/HZLhQMVtBr2AzrUZylwu2b3uxJxqj2rL3pOM3kePVublvLjzvF1qmjWtoQ//6BzeGODMFGwiRFho0BhU3+fIzShen8a1TFR9rxKsonoj9q2e5GAM6IDE5pFZcED3dkUZrMnqOzLT4DdgeSQQR9+1Bd57Tpa6Wgn96GxDwdBiOljY/fawZTSJIyJ+jd7Crb6UZ+pWtMAz06A6wXgTTBTM00Lf34S4jCzCVv66y2lpq/mtG8vyxiTkqS95wQb9IBFRXvOD9ewz87QnQyRZCnchys+YHogV4xQ29LaUp28VX5DcDqmExMEm0Q1uck44mVcF0uX4txtAO5WQKxs9crAanvoetplSXih5eVcgQS556sqGgcALKUd21MCCIZhNmHep2lDR0aR161TJermywhO0kvtam7XhBKnKczXb6jSOxACx7UQlbOMsFVjgrLBg2NXrPtjxg7EsadpsKQHEIjQZr4MiEozX7JsBwQlXzTtnThU5csUeYjHv764E5OiO01ZcufpJKUpC9jpBNvffAa7q3H47BSm34P74hUAwH35movFaACczbD9rZfhHpkiD2VZNshkZxSdIRxqxqmY+VLGeKiDjyyUVwzEVG2zizv2A9eThxb87rO/eQdGYNKj+vWx3IV8GXf+rliV4/Y9ENrxUQ/mSC5DBU6P8kpl7IdDlyjb0qm6iA4XQFTzNdGG8XvHrjXux4xd49QxtG2QvFAlLmR5GL8ptP7vg6SdW60j8EuVeZ2n6liPEtymLOCu5yEmyvtpxagJYzd5jk5Kn2FHynMxFmtDbG6zRSLO3BA9HJNld1Ce+n2QjCJWlkd29Gro5nd7OQOVYcEKtEDhsvqqAoriB+zEbS3b4HtCWJVIAXnSvZSk/LBPBSy1j1RlK8jhRxlSSlfLM8AWsSKgud+xLsxkJE2PDEYXfbF3994HoaNQ9ZB6vRkOWXasDgQz3TN2tbd89NhllcNoQAKIilQJzJk4GxvVvE6mnKh7HoDtxXmEF/d73P4PNckZS0KiUTewz85iRep8Bn+14P3xUgZsWF61w4FIldcwac6dQ9Caqvup1S+AB/CIgPTw8BFjBFDyb76M+bo4lwdLHOlEB5W16Yix6euV6ugezuGfeEoDUtcGvXCj1YoFPdFR7JUwnQvbo6IHfV0HtoxpO3ipV8M76cqJtUpZsGuYJmTgW8sJq02fPlcUJAnr2KMB37/MYyVnPMT+n/+M0hViv/ixsftd9eixB4yISmtbAzel36qfju4w940VGmHbEULbUu3XS4dU46AEdZ/+VhJP93x2JyazqxhXnwhRRT26+QLoOrjlKlhueqEDom7QffdOTBSIzQlZhOT4ZtBjbKKTD4CWP7q6d44QBsU0HVH3VBY89D2sNCCdiw7gD82np53SPPN/ZfRv3MVHaHcyVE9ijRvS8dTqBAJC0UeFKr10K1bR0VZ0z0V7XOEI9mQW8O7m7IRYjt0ugptUScw74PyUDB1VsG3q+8e+3cZ/+9jYjx1DdOyqaCysozAOsfEBENTC7HRCh708i3HMF/zzoM90ZziI49D3Smz8mXZIFZaR5UFJAQdZaTXd0wYShJjSF7Bb28aSp5Rcfa+IZ4YjVKgC3YKD+XQUdOB9nrGz6sTbtqp46M0z+MMBv3/193+4KQ2KggcwwW9YJT6LngwArobL9V1MhrrlSU6s3UY36MEuqXuOxKLrZUjmG6A6oHt5Crtvgoern/ThFxugPIXv5UDnZbW7gNk30XJxyOaIG/dgvYebDpBcr+D35t6xYzqiIe9jxl7XMOMR3JG7nhuVsLsarshgdwfWv08mnDDzJRWRhXbn6wbNX/oN5G9uQxw4PwW8R3M+hm3OYPaNAMNMjEmFTLNU3MVTwIlqwm4PdzbjTrjP4SZDdJMS6eUqFhO6DrYscPjiHMXPLlny1BVYzzCiUU9EJh+aALPWsuSA6ZbCls1qAz+RB0AAZMH18COc1qe9wucX/nc//dssnQGs39YNv4iy4GqjBxjFkKuRwKEWM18XVXaVEaRdOV2Juy7UsyGr4J2DrhdSt8rNHYhu9PsKdiR65cdANgg+JM/+1MduyoJNGF1RBQSnVSs19w3ak4rZ8aq1PoiCUNYCWUr8jOb1XffHY9KDqBFfLSnD+n3F3LxumGePR3+sO+x3O1aIxiMeeEUzJ9jVHB8+Ae5WyrLSSlwoBMiZRfm9eu7q9/gaye//8Xf/4Q8UDw82aEwqJAjdqkOLmoAreCeiQqLLbkz0DQLkkCqdQGujPYxiWzKxptfJfjjwgJiI7WMgOQhC8ah97bVUt9tzkvR6cBvqVD40du/948euD5GV9r8KOgmQjCnSDu52yTxYrHe8ojiHA2KDjuNIxLRMOrUAgBfP7sbUNHzIjw7SyFgWNZMx7MU5TJ4TqPbpS7jlijCO8QDt61N0zyY8PPdiaZUma0OWaI9VwlSGUB5iryoO0IeC3AelCSqtMTTzmub/B6oFeumpfLESIJaL+GjFT7ctb8huH1vsRcGfD/oy+dJAFEaaAIMEViWYnWeaNF8CSS/eVO9hJ2OaFM+X0qEcRGlqJYQMpmhenSD7WnTWtex2z9gBBKb9x8aOmvj4oB6gLfjJEFYhCpMxUPPQGiSztafgPfDTz2GvlzGOPIPJxsTUjIZAnsGVGYkcGlOSA6OEXVPvgdkErl/AzG/hnfBL5YG0zgGTMVGURYbkw3VsmAFMRXo9HohF5QFardpTiCkoNpcDYvG1IqZVNBXKShOYCtwVekS7BtWIHzynVbuQ650QBlwkAztHjDbAiaUu295z61VjMG0ACSTWH2pus7IKBtOCt5exIbUlgMmvCDs1t2v4lvVgr/gbkLup23A6p4cozk4+OnYjWPbHjN0LHifUrpuWOJMwMeVAeTjE8XeO2PumIQPr6zd349juaKyQpSEee/srMSnbaUSBKrM/wC63aH7rC5h+H81f/JIr+MUZ3NkEaFsKqGpV6bNXwNkM7W+KPjwQyC/okZqnoL2Qxh0T0LWc23bwRcYFSHQ8AS4AUOhw17Fc+xHW09Ne4b0LznXBklCrKcd8087FbdnFFRRdd7f5k0sq0x0CZDfkskCEIsvnaEnNAISvdh18UVCVbEhanR30ubLsK06mYR+4nhOrrpzZ7xu7NmEeM3ZrAkzW73bMl6UEGIBaQKhDB713qbJ472GHYq8pcSi60u/2ou/Isu1xTFhvSYdsu6hutq+Q/+wd3GqNrK55L6YTmM7xEPvmA0xdU/r6/RVMWSL9OcF5HgjQCJVfCQ+oI17JTMaEH6tNpd63rSi0VYfYdATg4QIUgQ25h8+kT3vCJwm5kWroJcx5xa2gV7KWrlbvANxsDLurogZL3cDUDdv+h5qNH23i6I1zstJ2HXySsOkiubZJLNx2B6NyF9UGMDao47rNloJOCh6ruXvg4ozvec/Yfduy1v2IsQe9lerA3oD4HiGxJF8Ad50v1ExBu8FZCr/Z8OFSgNxmc4Qp94znxTNguwsxmYzxWQBY13yIvSdZBZDUMKX0HwCr9LvpKWXFez0+rMs9LW90XADr+sJWAhB2YDjHtETOWEZBZk3Dg2+aUEG4yJn69YexmnRsEnHP9bQnvNODnWdlxDlCaMW4y2/37Gh2jm3pQw17vYiM+s0iOk5I3gpADAeIGOyez2CXcasMprhtRzm4Xg57ueCW3Hbwr1/AXi/YgFncwvzoEyrtNi3w/Jxd0R47nKoa/H1jNzltFx8zdn+oYbyM3Vp+2c9O6M806IuFTsrmjXzpoWw7BPzzU7giRfrtVYhDy5LGea6OaQp/PWcFRWNS5KaoA/iqYmVGDu5uvqBaQZZSEcw76trcsiFFttMcQUJQ6YnVgSJUAB9WdQA5FlGS6qEfD/idNGBXPBE+QVVJ9UlW9LKIOPoHrqc94eG5AmTUGSc+5K6UBn9NsOEykUw/jVozUprT0p5K7sEmMGmC5JodPmz3MbXZV9wBlivY2YSTrG7gbhaUopA0xwwHnBhygPXLNevfixWrMw+N3XuZ/B8fOyCl0PTI4O3Ne6DXg69ET92KtqLg5s1qy3PIcABzu4F/8w64eBbiAAR4pRIkAM81RzH521XUABr0mKM/O2NaMRkxNUoSQpz7PbjTMeymQjcbhPtqZ1P2LpZrUUD2EQTnKZiK0YDE8PktJ/F0FA6fpmLn9Y6UtjUwmXR/t3tWcfplwCo9dD3tCW9tlKdwDuZgIv3tiOZl9BCq/kPiB+RHg6g3mKUwngclBVTxRjquLFnJXHxXCVHEAc/PBILacaVvOzaw1Ct1sYX/yWfUVtzuYE6mAh/o4AfDSOD4vrF7z0bMI8ZujFRMshQIpgrSkk8SgS+0AfnpRuOwC6Fp4Gdj2C8+o4qBxNFdTGEvlwKXFjGjsxO4D1chJmilKkth2jJ2TzvqzvuGD4CaNdgbNp3sckcM1E4kNBTKLfm6GQ35uXLvvewAWpGC0jadj8YRAuVG0/IAu94CIsxqqgOlsz/CdgKe+oTvuli/1kbGvgoHOl0xvdRudZvEoZY0gD6e2O2ikFArN0X8WAHwfVdr6kYeaRb6q7ng1tvI6NkfOMFublk6a+RLHPQJjmpqpgvzW+ah941dmzqPGDtswpJjK0hJl8dqRdsS675ak3CxP/AhEonuAL7Tg6LEYX/5nunTZMSu880tJ9ZxTGkCpP3gn2oGA/giR/tyhmR1QDcpYb++RP3nPkGyb+GtQXqzRTfrI7nZwIm9jesXSN5dx3RNwXaKBJXuLvH9+/A5Zn84MmhzvL/e8V5kGXdU53nPM83pj+Dd33M97Ql/TI07bigkghD0ctgCYDIpoykd7ggABWO4mu5N7PJ5H/2C9D179BQyOunKEvUnp8hutnyftqWZQWq5yj8/h09I2/Ol1MazFK6fU8rydn3v2KFNpseMXYguXg+q3jNvTxPAS+1ZCReDHg/p4yHPLQDqT06Rf3XJGrzEgdkEbtij0/i4zwrUdAwMejGmxMYHK0tZZVlukHz7FgCQFgVcXaP4P6UefruifN937+B/9AljO9Sw7y5ZtVEIdZHD9I7EldZbpmzjUTiAmpXY40jq53sFSfudo/1N0wIHL4oK4hAixJKHrqc94bWsd6jh4SIk4GhrNaNhqMUet8MB0JtI83XVNxyLk4e09P1qQ0nuPIdZbgOORWXqsj/cc3UGyAyaL2GcgLveX8H2qWBm9tGqMplHKbr7xo6uo37jY8YuDRwzJonbH2rKSw/67M6WJR+qQ83JrlWsIgcODbI//CXcvoKpqhCH2e5hrxcwkxHw5pLvv9mxHi8xYbWP0iWqNJBJp/niHHh3Sa/XxS3MVsa+2cIkCQ/UXUcnvyzjZG8phOr3VYR1BBlCKURstjDnp/CjPnB9G6RPjBzwyY4iod3IA+kXt6zrf4TeBzz1CQ8IxrsDkERPVUE0GuVhGvkdQfUF7IcXbUZVC1aJZ8XJ1A2pbg0htEYNAHTVSCZMXy5vIslj0DuyT6dFjelT/QqLJQVL392KIOsDYy/Lx4/dMe9XEwJdCFCzVId9Jbh6qpSZEUVgvfJgR32YqwUnl8ThFbJ8qEN5UUnxGpNpW9bf247lwMUSWLfM9cuMB/q2Q5JlaL54jvSPvoXJc7jTKcx6i+5iiqTI0T6fwr5bwB8cYcuapgABQWm0+danfr+pDrGJpC4pJ9O4Q3lCKIw6kzxCZg946hNerVCen/MGrbZSIiviYXNI304jeiSa+0Hb613HWn0lGorO8cY1bXSe7glisU94sFHpizThBDeW2ygAU0uFQc2Bu46yfWmC7otXSN7ekLw97JOg8NDYi9Gjxg5PE4OAs5+OI8MKoNqX2NP7NOVhUDqUZtAjEUV3G4X06sG7RahpQ6smGlNi+bne83AsO4Mvc2rmb6XH0CuR/vwd/ItnTHtkMic3a5Zbd3U42GrlyasKg1RlUAmm3XVAQWAetPmVZTxLrbcRRAbwdZ2jUoGek4oftEyHiBnNl7xBeghUK5mmBfaraJWSZ9zShz1uqZoirDbMMcs+J0NigR1XCfdsRl+j0YC/Nx6yajIawA1LmKaDf3nGqkVZoP78HOn6AFP3gaZFez5G+rO33FKdk44kSdMPjr1uuKI9Yuxm0Of76sre8CxhNsT6mMWK9XU5A/g0gU8S2F3FXN97+MkQPrUhDn864yraOTqc1H20sz7Sy1WIyU9GAQrsh/0gZ22qmofXVydIFzse5Ncb6l0qO6s6RImUTvkFTaQwqg1lIt9PwNywWYYkCVIfVFCLvrNIAMAGdxBKo4j1jes9OKWe9oQHItNFYKIBYCScTQAxBdAKyy0PsqoJQ5nrCqaSVVoaLQBgLxfcOuuGK+t6Gw6Jtm64mq22XLEB5F9fMb+0XP2yvewEuz0ScaxTEvKDYxfpuMeMHbt98D8N3URdofvM382uihBbI/o3dQNsd6QFVlL5kTjMoebD0E8pIGUt0mV1N6bVJqz6ZiOmwIslTRvefkC22nByFzlX+sUSXrTdoeYKZRmQnQGqnAsuKPB4pfvq5EFAQu+npchwyGt82wksWMRUffz+jTFA0b9LlPme62lPeOeD+azvnGijiNJs3cA7sa5RYwTPpkZQ2q2pUmvyPCrw6qSTG+13e8IBbMKtUTUPAe4g24R4a8nj3fUNzGZDf6M8P6Kt9QWSe+DDJQyie8cOPHrsgSVkrXQYDSEFvZLd1IbQXzR1ZCop9t8mwM0t3OEAO5uGOBRaYLKMkoPSdNIyKQRGYIaDqPm+3RIYVzfE7aTywN4sgPMT9gAmAx6CL864OyhdTya9etj69SbKlKjEt4s9Ad914TyjRHJi8PeSFnWBiG/Kggd8/bcHrqc94eVSgBU11QfSoLGAdfHwE7qZ0pJuxWkupURH+CJBABPJ0iX9TSvJMccj5rtrIViIoa5fb4FXF9RH6b8ivkWVsISUgLrh55/OgCIFRJn3vrGbsowr8kfGDhzRExWBOTiq5Q/HrGgMBrHhI2VOX4vH6vtL3gONQ6pG3XyB5MUF4zmb0YBYY1LewcUZ7/d8CbM/oLu8Dto5Jk3ZHNwfCA1e9lgUuF6EQ6dbrviwaYmzOoTmk99VAARFOozsMLNcwxtBjEqFhw9DwSZTmsA3ArxLqHXjt12EKdxzPX14cL8PzMax7S8YbF83zLuzDP7lGbf2XsmD1pESsLKYTJEHCTo/HnJFtha4XnDVmI1ltadDBRLLg2tHNQJczTmJFiu2ycsSzW98whU3z4KRgS9S5qwfG7vY2Dxm7JgMw9gV7x4IEtWBGJrZmI2XujkiR9igZ2NPZnfi8M7Bz8ZIXlyEeMzt+k5Mvi8GDZsdfJHSdA2AnU6oWtAnOMzdLinelOfAs1O47R7+1UWIQY2OvUA2fHdUJVMDBZuQlJ5SRJW7kwnEmpB2CkIWQNgJve6ew+hYft/1xFd4wXvP97FLBwTpaRXHN2+EemdsrDh0DqpNqc0p6tGwe+mFIkfx0pTbctcxHRkNKTWnlRBrYVIAl9esZ4tWSrYYEsi02QbisXmzR7CpeWjsS3GdfszYjSGK03kSRbY7UdlaCmDLwahuzPwWwRZn21EfZj8nYrJzIQ7fOZh9BV9IOtDRbiZb7ENM2BLa4Osa5s0l3z/cE0Mo764CqjXcz3+J5PkzuF9+B/v5a/hffAPnqBPvawej6UrbCRo0YyNJ2F9qK4T1Vmw4bWzYKe7dmABThnaiZ5LrC+VSzZrvu574hMddGlgWzXHvYL6llm2yJJJDjIFvEfDWxwc9/bMp8iA5FzRnVPUrTcl0GvSAq0UAnyWzKd+jyCkwenFGIkmWsrSpSM79/uGx6/WIsQe9S4AHza4DzmZMvQAeaMVqE0USc17lwZ5OCQg74sfaXhbMGuxoyMPnYsnDn8SErqP8hsbVtvCzibhtrGlolojx2VrUGpIEuLoJ0t3Br0nVD7S7LOQXPxKJ68Rysu8rdnxvV0w5FdahhO59QjyTpoDyMHjpywR/rHuuJ57SCKVLxX0q+p9SCcCKyGcabdjVi1VMf01Z0FZd3CaQJGToACRijIehDo9Dje7VGelkujJtdyyzTYboziY8wA37cLLCAiDLpmlhVhu4sxlU2JOHqfvHTkOxx4091MdPprSt7/Wi1k6SsPIih18z6AUtHDOgVbzZ7uEnwztxoMj5fnke49EyosTk+yVb/HLQhPcsFXpRPfjJZ7CnM56D0hT1Z2cwvRL1X/iScavrx08/59fpHFMlRY0aA6NKyk4UB3olm0m5qAxLugggKBlgvQ27pmkJa6A+TRPFnu65nvYKb01kGrVtULBFVQt2OrnDmYwqANLCdypcZKPprUJQRUy//eQcyWILlDns5gA3GcCudvCJhfvRS8pxJAauTGGLHL6Xo331Y8ABXS9BcbmDXW7RnU1gdwf4XoGbf+klzv77b8m9vGfs/nCIxOiPjB3HY1fXul4RaHF+ID8rpEnVOrjnpzCbfTgEdpMSycaGOOoXYxjv4V5PYTvHeAoCuTQmX2Rwz09ha+n+pgmlSDYHuGG00HTDHmz2HPnbW3SfPEfx9TVx+qM+sf77OuKEdvuAsQ/aQPLefldzt2jINiP0Yx34rypHbtKEcA1ReDCDPn9PtG8eup72hHeOOazkxN55VmYAMQOrosOGqu2maaijhzp2msKoc4T3gAFzaOeRbnbSvBAtya1suQ1gFxt2+nolzLYSjPYI2bfvgTRFBgATaskn1YGQ3qrG2X/3Texc3jN27KVz+oixe3HnJpmE5TfTCI5mNgnuJUbfq1fQmUMbPasNsm1POraMo9juxYxAdp40RS67msZkhQbpdcdLLJL3C8AY/hsAVAfY7/aUBpmM+dCvmeq5XkZzs9Mpm1v7mqlMYuOqvd1TSaEnbn57KQJYKSOXpVTk5MyQJuQuOBcBZ3shg4iEx0PX005pjCVOQm9OeuQE7T1b/qpfom1qrcN6H3UIpbFjpCsXVMgEa6Jtbi8gLt9nzmwUjNS0XG0LcYBOU2JNrOVhF5JvSwmTtpDFg2M3g8Hjx65oQD0M65VYrupCYvZ7kQpZbzmOqgpGAl470xKH32wEo9KFeNB1d2Kizo04nq+3gY6HtoVZb1G/nkZCxumMK3rTwv3oJe/3H/yc411uYN7dhPIvdyEeXrW6YvTsVOQ8N1gbDqCB9NIdvUZ5vTt2do3qdv6gD63GRNEhZTdJfqtbXhBWKguYRLqP+yroz6jtI6TxBMgh8Gjr06pLAGbtD0BRUKDo5QX81Q3pdqKjaIyUyzZbqeCIMvBSIAGSe/rb1b1jv/O5Hxm7kZQsXELsOG6/0xBgIKoKYDkxLfggjAaszAAxDgBuvoB59ZxIRCFBq5eqEa0dKhKLxr4e+vs91J/MkH93S9HZNMH+9Ri9P/qA7mSM5LsruIsT4M/8CGZN7A1ubnmuCMQOeRBVxRkI7iBGqzFKThdZEt+2YmEpBPftnmcWhTxoI+yB62lPeO9o/143XIlyyW2taIu3beBBKtdSn36FoQYzAm0/pzaeCyajYIKlHqq+X/KmliVwOoZZblnCHA34RS1X3GYHPSoViEqvz1L4Xg5Tt3BljuT9zcNjNybUjD829oCbSS2ZT6q2laXo+gVtKk4tkyQAACAASURBVJcCL35+JkA5YWdtdiz9vXrOppLEgdkYdsmUDYmA5JZrmKQNMQGyM3h6OZnNnjvK5Q3SqxtqVOYZzHqH3v9Ou8nkmrRG5QH7ImOfwIjygpQmgwu3N8zzAZEuFCJ31QWAnlZrVGgWSCIBvmmDGhmSPFSz7ruedkpjbayGjNiqV/uZY3hrcLpTOWdZ0YPNjOJPjAky1SgLbu/yMLjPLpi/zpesTuwPMLsDXT+MuE9Yg+4nrzmG5TrKygH8/YY7hP3uMjhS3Dt24NFjN1qaE5udINV3u0JyuQDWW44/S0n62FP5wOzJLPKrDXA5vxMHuaId3Ok0xgPciQk19SbNZhexPvtDMHxwl9fAhys+yJmYQGy2PB8lCdpTAvHcZhs7qQ3TxhAjWPP3istxjnG46N2k4wnG0hC+gKZrygFWZYkHrqe9wjtpjx8OPMmXBdMF/TfdYjXnVQMy1TBUjUW92pYO0EDEy0gpMLla0mCgbriSbYlRMWXBLyJN4D9cI7m5FcxJFO33mx1r6WJQ5l0XIAT3jd0L7fBRYxePWQjmh5J81Iv0onWTXC1lonj4kzF5nisq9yJNgm6lxmHOT/kwfbgJWBQ3v4WdjEJMJiF2x2QZD5fbLfDqOcxNx0kvasW+qoJtDl6cA28/oHtxgmQtqNEiZ2fbGCrFqUCr9+Slzm9DU5AOHyLcKg+Z7xUcq6SUGPTivQQCHuhj9D7gqU94uVjPjrDSQKROU8o4LDdkAEmzAzsqEARvVsXVq5pW291dySQlMQArHZJremUoqTX8i2fAzUIQikK2HvSIlZHPhBFhILWkuW/sSKiV85ixyxgDI2g8gvpVAWCODGnmpAkx7066l5Mh/VVlAmscPuXOYYo8xJN88pJNHIlJzyxh1+n1gNs1a+CDQZAzQZrCX8051u/eAwDsz77joVp3WCCYT/im4USXur4/mfLQ2nVAkgbpPWSiS9MSn+8B1vy3ez7EJ1N+h1Yol9bcXeC+53raE9774ADhD45fmLUx+LqG2cmBR7AmUPqYVl68h+nipAmNCVUda4hruSN7l6Ws66q6V9PAHBJgsefqPl/C7UTUdDoCakE45lmQw9ND8X1jD5Dex4zde9FQFBjCZkcpCyVNLDeRHpgkwPUtvGqtL1bxdh7HIYdNVAdA41ERV4mJHlVCqMgzwEW4szsZwXz1BuZkCjcZwlrL842gKf14EIjhfjIEViSUoCGCFKtNbLKpHuQxBVKksdUtPUCC1Xi4E8GmlIC00KW2Dx9an3YOD/BL+D7Ip3TmVPvRty3LcAonBeLBSzt8q3VETBZicd65YM1IuAJLX6bXC/aISiL2nz7nl5oLA+d0SjF/eTBxswDqho52Hxl7cOJ4xNi9ktXVZEzLo0p4TmxYEWlTmbDU1+vBrdaMv6nvxAFrgX1FOp7EYybjOzGpTCAEDUp7+Ap+PIBd71H95Z+ifTaBzxJR7+Vkrb98Rn/Ww4FxWStSKNI1VSy8Kqr1yljWXa2DAhmMYZqSZ8BswkO8HOp1EYDcm3AO+kHDg7XsWFrBVosgPiDaL714WBmPQhrhy5x4j17B1a+UHHAs+Wmeke/54Tq6TYwGwHJFipw2eLZHxr4A8R3iNQSAY1Ii+KBPAsmhpvhQT9g59419POBu8JixWwNY6coapkW4vOGkVD+oXFZN9VQq8iitDVALc7UOcXgRZzUfbiK76ldiIuy2FPO2Muh54v0Vus0WvX1F25uug8tzSnK8fY/ce7ideGpt9zDbHTCd8AOyNADoYG08/8hh05xMQ+MNu32UU1ltgpKD3+642C3JdjMnU95r1Yh/4HraK7zz0flB81VNFVRvRaoWfrXmE54m8efG8IvvFcxra8G9VAeCqSwPc2Y0ZPluOmEzSysSKlA06AdIsZ+M+KBlGUuRTYSrYl9x8uQZx/DA2HF58/ixKyWwaZmKrdhh5QFWJsJmx9VZcDrhek6Ny8OPL+7GcXHCCfnyLMRz+OzkbkzqZDgZM+0bDoK3VvLsnBP39QvY4QB2PIIpCqoHr2iSZocDJGcnsC+fExrQCbSgX5LMUVXBm5UVo3FM20rCpwFw4tfizFJR5SxcaSp6QA17CPqae66nPeHDgfNICCnQ5IQjWcZGSihf/T/UvdmSZEt2Hbbczxxz5FSVdUd0o4GGSBCm0Yx/oCeZif+lL9Bn6EXSm95oMoGEKBIGdKPvVHVryszImE9EnHPc9bD2do8AKrMStDZa3mNGA7tuZdbeEX7ct++9hlZ8j+4XnDxud4GPCUBkOlrgbMwatuVpARwt8tEA7nLCGlQAZQAEqy0x3M256AY9dNdn/HsywGJf+OHYTVk+PfZ/rHku4ks4GwMXZ9z1+hW8DLa6FxPu/qMBzJ61bfHj3UkedraC3+9JcZR8ih9nJzlhPEB3fcYFJ/RFU8nQTuEBworqbu/gLifhNFD1NF/XIu8tGpo6NOz1YHo9krVF3sS8vYlyeQvRpTmwhayL3PSoo+9VQNXT1gdFzvbvZ57nXdIYQ3ZLv4ofpAwm6B5HNSqTCu9R7VTkqDRVFeSjabHYRuUv1WHZ7YGcalYegnOxhsfm/YKlwl5G29JJMYs1/y01H1uskdR7fhFpyiO6qh6PXelpT4gdQzL2CWkuuFtu6ihsBBDzc78Aqgr2p4880bRtC8Dfz+POmKbhtPFtF/LpXr+FrcqQE4CQFzoXTBtMWcbL53YH37bc8X++YVtysY6yedNJPOHEYEI9XQGcMM54FxKdSHVjASLE2Ca8r8jnYapKKH+iYpC4z9bwz3uHB8jS6VxsbW3qUNeajGyg4Fda10Gokx8YF5exNuzggQLWtux1A3GYUdLo12+jAJG69alroL+fk7e62ZJ3qZM9UQxjvZ38UWMPO7wR3M2RwGrguALswe92RBUeyBNViQvTP80jEMwFuek3WySvXvyjnLoAboM1fGmqkpgdZVZJR8nNhYzy3U/8nDZbGSitiPJ0lNVTf13fScPgfnHEikqDc7hfriMEQ/IILoBzsbjc7emUst2yRDo2t3jged47vDX0+ixymG3Ci2ZVUD8xy3jB2x1CP9hYi+5qiuSePqS+VwpBYA/fK2D3TVC/QufCNM9XBX/P/iCAtSoYZ/k04YdaFTDOoxv3OT4vS+5SYhTs1xu2+oCoL/9Y7FURp5afiR1NG5QPgtGXcwAEppBYGJMTM7NY89Is7h8mE9qcmKppHn6zjTV6VQF6OR30Y06LJVGeieV4X8VhpTPk9/KCpqmcaFvY6QTd7B4mSWD7PZaFb294rwEQlJH19LFCZ6wKwFawavEzigYNJ2jWNAG8JTJ1uwPShKbLgef8S+7SOKF8Ldb8wJxj+0qpcHK5U00SAEhmSdwZdnvZTTrS2QB+0CphJ+UGNlsyhkRGwiilLs/o5uEoJ+33e+Dnd/CjUdCS8R9uKXUn5YIXQzOzFyLIA7ErifopsfM/JlEiXIzXVK1YVXnxkSA3dz/nyZHTYM3UNZyYn2keih3vZvewmo8O8yQnVKUgJddBK4bqYGx3AiD52xq4r6+RvCeryRYFmWSHBubdHUui6ZgLviWqM3ABpDeP+ZKwizSNFvSqvKD8AJ1XAGzLAmyVdhRwMoN+5PM+8DzzksaL414Wyw+lcRkbeaTCWg/Yca3TRZYDSRIkMwIM1xqK+Csz3phQvwMIdbvvXERWinVN+PcUKyOIwmArDwSMzEOx+2H/6bEDMXadKEr8PMbjxU9Nf+1oyJciYdfJnk1P8giOIMf5ACc5Bc/UXhXLoyF3Xnt+JobDRGTazY6lzrDHskibAIllF6zeE3u/E9PjhhqYQVEiMNiSWK4o1kbJ9C3NopX44ZWTnCZimHYEwX7ged47vGDF/aAXdkiT20ByRpoQw7HdRX2XJBG7lCgzYQBgOGDpoHQ1QFqCa8r1iaKA0d0yo8CQPSdTH50DLs7QXY5g386C6ZlJ+/EipuXJoB92wwdjT+zTY6/KEDt3xzLIiAR5Pi/66t6ztNk3dMseD2BlqGOvLkIeMIYArD/9GpivuTNWIuqkOU3HUa5cUadZGlUYAPipuAc2LbrffE1MUlXyIuoc/IszdoLSJFAyfcGWLul6W6j7uOn3mE/bUT5cvw9rWeJ9vGPrdbEOOHwgCeUNy8/H8fDPe4cX8JiaFETmPwFN7EmvibnolWFypzuHIiH9aMAvWm3Rj9GAIkrq13Tn9v2Kl8VhP7TwsD+Qr9m0SG9peX74l9/QG3UqwqX9Cm46jLWpqhA/ELtZbZ8eu94JjgZNiiQ12x38oBJhJdGDbztK9qn8HoD2cnSSB6yFu5jCruqQT/vr65OcFJocNp62hZ/Nyc9V8NuHW7YxsxTmb7/jf693LH2slfbnIZKxrYk8YIFGqKuh4vu9+rTKYoc1nJ30Kv6M4SkWOMG1KCovVrG8e+B53js8fAR5aQswFU6jHnmqIAzwcnl0FIb+74JSF6EVJvgLLXVgLXB9FS5qZtDnMGgyIi49y5D9/i3vApYmWsXfv2Vd+pGUN2MNzM2cZYLAX6lX/unYMejBtOnTYk9T9sIBibkL9ux+f2Bdfn1FCY27e55mH+/gpUzzziG9YcmjeWC3h83z0CEp/v4tP4ejnPzhIPgaFwWlALhvX8LUDZpJCdN5pDdLcgGyFLv/4TcoXy+wux4h/7iBzxIkCo2eyER5sSb4zvH79WlC8JhMVo3CDKqCdzjprxv9XBIbfa12CtIr+MJ/RrXgeS94L0Aprat3ewSDAaG0sfZLwp8ZkeDw+73U+dLicy7W8SrRrBY0TQPc1Lyo2oRfimqk1Owza8njPt5GlhMQDb8UimtNkMbDI7EbYd4/Kfb9QaQ8stCv1962UdGomxnLBYUPq9S21r8CoQ2lm7Xh83PLlQCvTLwXlaUYTqyh9vahU/L//R6wlosnSYIdpSlLlG+WcP/wI8r5WYA9e8Hym/WW9bmX3Tvly4vlin8nSYK8YNjtAW5EW3H2EyyOUSXhPA/fLUVwB48uqedd0lhDYJNQ3oLuYNNwAAO5FOmFD4jwULFSUaBSsFhUBKaY3mrby2Ty76imOsCFImQH9ZoyMkZHJ4tpOoZqS/r9PnZQ8vzx2EXQ/0mxi+W99z7suqaiyZhvGpYTIkMSdCr1RTuSuTjOI/w7QMiH+jcxJ83L9HrhIqmfAf7sW77gf/IFLSkvzzjW/3gH+6uvGUuW0fP121c8abacEYQhHMBTb9CnivGXl8ypkJgD1ZA/46cSu+GmYgb9SG8cDSlNbh9f0s97wSvSMxck3LEEg3NcOD3qsBDuymMOSRLGzDpO9yUFVU2v4uUuFfOBiylLjzxjvay1ZJYBk5HYsHi46YC7XlWiu5pGW3cdail8oFfysqf1/wOxK03vSbEPeiF2pTX6UpCM4yECk0sVGqoqaPGYXkXNTGn1aR7eOeCCGj0hH73wSU6KYFRYgCly4KtrmLKkUsLVOexsxbuFlGBK1PACu/bbGj6XVmKesV8vsfB0baXlaamtn8slvIn0TVNV8JMhhWONYKIs+c5eiduHhqXQZ5z8nveCN4jMfsjuWB0Bo1LpC6828aIn3EmIbLPZ1EQm1pFB5BWimiSih1JH14k2UtBwN2ctL1bwfn+ATxMOsACWEh9vg9wGFYM7epPqC/VA7L7t/rNihyVdUf9Mic7BAbBtiUg8SA5tRzBX057kYbKMDKWLScjHZ+lJTlpSKY4G3gPvb/iZOAe8eU+05G4Hv1rD3c3g1xt0b99Hul/bwv7w7gT7gvtlKMlOMPAQKLe+eLppeA8zW8T/fSRCa4Z9doPaNlAmH3uedw0P2dV0QphlAcPhHe1TUOSxZaYAr6pkzSwSHKYmE18dQMx6H4++mxnU/9McGrFfsbH29Q549QL2hqQKJUh3L89h397w353TDdsvV9zFlNjwWOzamXlK7N3RTqucXH3aFphRKzPEDwGq7fbAeQWsVnAfbmC+/TLkoYQJlySw6y3z+cPryAqbrwQLT2wQlEOcpsB0jG46gDfnsLsG2LdoroZI72v4LIHdUkeneTWGaT3gPLJ395zkFkUsu5RNBc4Z/GhABYXlivqeq3XAFCFNhdOayHSX4qwQ+IW2TJXn+tDzvBd8YuPwQ6eAZU65ORmvqxUkDGXm0Ha84EjdboQWpx+uyTN+qfWOyMIz1oV2tQ3aNV5r6/EAzUUP2dsFR+Q/35C9c7SLuG+vkXxcUPyo4Vj88BdXSOc72Pvlg7Gf4Nc/E7uCpIzwR02eyxSUEASfJXQxUe0dIWfb+QroHA5/9Stkt8TXaB6YjOCyFPZuDj8RSMT1FTBfhpyQ9dlBUXZS18GfT2BmCyRth8M357BbokLz13eROuk9sNsj/7EFdnu0374IefBEbSOTyhigKvh5vP0IjIfcMHQQKOQdQq1bwNLCBwAwGnCj0valAtoeeYxqtTzHZ5xf+X89+TcIxl8qs6a7oeq1uKMchNqmLS9jbVDWCo/zXGDWUkD1aKrpmybQ9CBtTzgXjcyqIg6nmlbgAeKSrWKlamQgfkSfjF11cv65sbfsRARuq5YbzoW/GzRlCum4iGKXOTQxDwWIGUucDSDlizvJCUAUUnUutAjdlPrudrOHK3PYP7yGGY/QXk9h9y3MjjOS8OKlQgbRz/OII2CUCRVofdLR6lXBmBjaitYLa5EHUk04JT0/t//9u//lr733/92n1tTz3uGtkBnyLPShWQ5UbFf2S8JENfkk3vwDaTrPYA4CNdU6eVBxwqc6juJk7Xol7OroSOwcXK8EEkMszvlIdi+h181p7Y7JiMpcl2dso7XC3lE8zqdiTyLR+rOxH5ogNeJ1gQtWxcvJYNqOWjlNy1wvz1j6jIdwWYr9yz6Km23Iw+zEz3W5irIYxrC9KjlBQGbGOU4zm5YKw6Mh7O9/gv/yGriZIUlT+CRB93IK8+//HvbFJev6tgV+3JDt1LTRvCzPIvF8zkuvV1hGYmO5Fy7qMm0HSDifryLXAIgkeTGweOx53gu+66InqTpmdLLbAnSaU0KBqPbS+kUsaGqRy/M+EoJHQ2q3pCkhqIcDrPdAkXMnaltCDtb0bLXes0/d7wE/f+QgajKGmy8Clc9sd6GhpNaXAY34QOww5smx+5Y1tBkNga2ISq028tnsI4XxmChS1/LydTAAqk0NdzsLeQSH8aaF2dRw8wXMxRnbq5JT8JHaS4ydC2Rz/80ruCKF++1XSOc7GOeQvL0DXl7R7OzNHRffdsvFPZsHMoyva2CtUGpLgofOLnThHhq2eb0PE11vbLjfBG0hsdWBSqTsfsk1POKFJuiLKztpJSbCjXRBdLA0ohCoX21Y8/4jLXY1RwiwV5GqpvFWwh1N/750VWy/F5GUZQFkKexoyL8rF04jolDE2JQRevtA7MRy+6fFbg3gbBCW9QqJaNsItFIKXduyNCpEZm9b81TJ0pM8/HrDYVG/F/O5XyCYhTknzCSWfoG8UQps+bs3sM4hHQ0B7+E2pEi62T2SxZI1OcD4Xr+FPT8TFz8TNwAtR4TD6rf7yG9NExhD7D3AwRZEco+/l+R7fzYG3t/IifCP6I2feJ73gpfBU7SMaYLzh0rTIUlgDPUTzaHhqD5J+HMCy4X3gacaTH2LnNLYM4G5ZiKzURYEeg0HgTkPR0Nftar3H2cRAKZTzJXYxgzF9rJXRizIJ2L3juXUk2LXe5ZYNpp+jy/Pq0vY+zUvur0qCEf5A0sZul93fLHFxl7z0O6P75WiUuCAr66JjZGcTJ+cWXM25e9NpZu1qam5maVsMRY5LMAWqt5fypyWm8MBS5ijYV9oCWspk0irNM/gWxNVEvKMp5pgiRR64QW1iq6Dma/YoVL7z1+0IYIHe+Rq56Ik6F4VfZLSJLp8qK2hYrsF6uq9FymKHqU2Wi4EuxR2zYGlgarmmoQSFhBFWy9O1OZuzvpRp5gqMiQDFz8dBbw3Oy4Px27+GbEDCLEHISmA8adivLDbR35smrKEEMm9MAs4ysMvV3C3M/if3oZ8/PevT3NKE1Ee2AoEYB3Mgd0Pr4HvXxNQJtIaZlML/qimRF+ew9/dy2fZ0e0jSaKkSJ4BZ+PgRMLdfg837EdJxIB56iIidTIK9Ef23/cI9pfHLdtPPM97wcNHbRet5VLKPhuB9PqURA6jRApVyBLdmTA1lbLFqrue6Dy6xTJ2VtKUCzDLuGMCrFd3Ow5cdLDRdcDZBH6xJBFCyQk3s9iydO7R2P1u9+TYjehU2g+zOJwCAmtLgV7MSyiAnWMOeUb9mdn9aR4ATJ4HWAHOJuz+HOe0l66PAMioa09ZPTsacSoqZZEfsYUJAJiOw2mIqiRRJUkoCqXdFFVhmC/hv7iKO3RRwK63cgcyPL285+9zlOIwooPpv7jiZyNu4L7MP3tpfd4LXgVEdconwwdAQWWWwKINIa5BVEmPRFU96Dq20XQxmohhMWkaJbkFvoq2BbY13P2cNXli+cVJKQQRHzXjEWtf3aU78lC9tP4ei90M+k+O3etODZYzwb1OnLTVcxVAxJYXOVuukoM9m57kESQAj/IBcJJT0M90VCHz+8ORJU0G9+sv4C7GoQeO+ZI9++WaXNXVitCHtgt3AeCU0AHnSQEsC2Fpif2PcolFL9Jsar6Ierm3lj8n0AL6Ph3BNx54nveCd0eIQCC6bytZOqXilU7uQr2v/MaKYCg/GfLD0Q/8+jwcp7i+5C4qdaV/eR46B/j1V2TrlKVI5tEswYxH7NsfGv7ugjgdnE9IuJhOwmJ5MHbtvDwl9izeY/x6w9o2sYzfGEKbpYXnX55TXydNmcP1lfTt1yd5EBTn4GfzmI9MLkNOPWJ4fK8k4XwqVMC2g1uukLy/J6Zmt4fpOAR05xOWGFfnaP7qV3T8Dj1/dmWMJeMLvQpm0Itu3blc8L3nRqCbhOWLaQfyM70ygt0GLAPZVXKnM4tPPM/70goE7DfkkqcSGuw0SBvOGrpoC8UrylELq12hs8LltHdL1teqBZllpJw1Dcxbaqj7zRZ2f+CXfy9///oqahuKdJ/5+T37zCIMCuWufi723S6iIj8Te2h5CnHcz5dcHPMlY3/3kSTsQY/xK6y4IWPKj/qcNajxwfVVNGs4QlUaO2B7UnNqWBcHPkGewacUsHVfnMHc0ZrelwXs7T1f9BuWjGZTIwPQXY7hvjxH9t37MCDyzgUHwuBuWBbEsrcdjEoOemp2qs2l6YQXIMhR07YwtbRtB2yXfm7S+swXvEwcdcqWSasu2BqyMxGmdUUu1oxlJCQrqcKIHrlM41SKzncO8EJ02Gz5d4S04WvyVL14mGLBy5nf7eitNBqiu53BFuw+0GlbxP6VovbHiF0gsFoamX6PGuxpypJLIb8fbrjr6eXVO54IixXUZyrkYS18J1PYxTKQrE9yAuLdIo04FbPdIa336M4GsMuadxCVNalKuMsz4OcPwGaLpCyR6OkpKslGiCeUK0nDPcRoDx4I/Xoj7VVq3cvFP7HA3gO5dmyEFAP8wg0RjIndh64TxlDKBTMakjQsu4Bvu6gMHBwhbJRntpZw3G3N3ykWLyZNwhdgikJIGTbKykmf2u/34bKHNOXUsWmQXF3w8tbrRU3EQmwyH4v9cHh67MaE2E3O2lxVB4zUyH63hxkOo51jWfCeIC+IGQz+SR464Ar5lMVpTmnKsqaQwZa2XesduvMh7B9+Zs0O0PFvPOBL23VUPZOX3Je5dHoEv7/bsw3aiQz2oeFp7cXfdlOz0yWbk3ZjAvFHB4+qOVTX0Xz6M1CZ57/gy4KTOFHbUnF8v9myvrY2yDQEfXA9CkNNLFDdQ8PLU1XGLozWiZNRnH5u6mg7n5IFpYQTrambL89DmH61ChdAv605wNEa+qHYhTD9lNj1Imuk146eYMoBSmgMeozfs6vVXZ9BpbD9QLD/6/VpHjq4OcrneFLrV6soLiUDLGQi2dE0sN/9zL+3P/CC+uEWdlOju52xBNxs4e7ncLM59l9P+XkUJK4Y0foxZclum6ow1Dv+ncTCjIbBidxUVVjYRiRReOcRuHFRyKZTfbaked4LHoC/nwsZQIzDxEnOpGmUlEgsv8wAAeawxq/EdEwmqqoq5oW6B2MCIMksWY+iISrPa+vsiBTs2xbmbg4Yg/RGdpROZPXqHaeLx9juR2L3ogbwlNgVMeq34u5d73hfMKTkhe6I0BXtTx/g11ueIDW1M406iEseND9rOXSSfGK7U3ISNKraC4WYdVFdnQclAv5iDzvox88uy2AHfRR//Q/8uUo6Q9oRO27ZqvcUEIgh6FWhm+QHPaqnadtR0ZTKJGtldnEstPqJ55nX8FL77Q+AF8+fNOWHs2frThGJamxlpJ6Gkx6+Tt5UML9MhYEvgx4dx+cZd96+dCYOB+Cra/jbe3JDty3s5Tl7/6sVBYjOJ1QGK6Qltt1R8kPBT4/FDjw9duCfxm5tiN/IMMdvd1Tmnc0FeuC5SMVp/CQPx/re9Cv4+YL5XJ3zVNOcDtFzClXJPruoPphexYuvTLEVWmGEgeUnI+DtBynNBsEd0K821KofDIRi2Z1c3NG28GcT4PY+ilttd4xFNgWFGyBJwgZlcjLAHu/CP/cd3jvuYNMR0YHyGIHlwvMSZlTHxdgwBArOcEJa9oMeIQAN226YjoAkgZsMuSskonFjuZvAGLRjOT4H/SA7zS9QesS7A3Z/fh1i8RP5stUK/pHYTZ7/58XuHDAdsaRRxlZJ0nPQqnz1gnYwxqD5q19LDr3TPNqOiEog5rPenuYk6mgojgY6H+8Aa3F4NYEpC7S//ZplyfUlfL9CNx3SxXxQwP/2W+B8iu5sFF+INKFejg6gvMC7j2T/TL0PIDYAAVYdzJ4B+bx8lPmTgWAYeD3wPPsd3jdNQAeGD13H7m0cXkB2S6+Gt0DAkxsdcojXqJnTgc8vlrB1Ff4N3hlK6roASH/3JpiGGSeMlefmcAAAIABJREFUmoXU69KWLP82lj9G22s/vIGZTsIX9KnYtd5/Uuy600vvGfsDCdENuzpGOiD+wK6LUXgDgOx3P/Oiemhgx6OYx3rN37PfB6K3SoaEnGrZdXOZ9i6W3Ol7JfK3c+DQIP24hK8KuCKDffsBpk+p76Rp4YZ9tBcDZD/eRCWGshANGnvEQuviqSbEeb+NxBlYCz9fcMfXNibAE6ulEoSRCfSxLPqnnue94L3nMX+8wAF4cFdQ1ozf70/LEyU1i+EW2TruxHkPjpedsNjTlB9aXQeZCGUjBaxNYtH9+VdI393z38oylizTUayhd3vY4SCWKA/FnnRxNvCZ2M1x7G0LGGl32oKdmykHTbbX4yjeUglAa27dPY/zAHh/8C8v2VbsHOvhzsWcyoIvRdsGGUEv/FLfSikyo6uhfXcDfPES+PvvYS4viKtfLJHdieSeqCpAHMF9KveWahJBbmEhi93OqA/Xy5HM1jBfXROakKXA8Jwc10GPvfsN3bgx6Alz6uEl9bxLGkukohcNcqNTymGft/qWpghGse2yq6k1S+hHOxc0C4G4+LDfC+0spXoBiC/xQoYAIJ0Rut2ZQR/p61sujq4LJAY4xzo5ly9V0XuPxI6ue3LsUFKH/nd99nQM9+sN45cWqxlysR/nYETQSvOAtmDf3xzlU5zmdGgix/XIHY+ygaL8VrK08l+/BN7fwP2r31Bm42IKMxwGBpOWHHQpNEHzB/fLSAoHu2C+KoienC2QvJvxZ97JpLzeATf3wmdYsT8/obKE2dR8ER5bUv+c9fdf/uFlKYyf9QsU/IkxJpJ6tfedpuynt12krgE8lktphYl4kZlOeJnt92DWW7iX5ydAL//lC5YR0zHar69im89Tv9FsavgvLrkDb2vySwc9foFi1vVQ7MpJfUrsSh0MWO+EMhpmyjra/eoLthRVoKlp+WKlKUuRroMZDE7yaH91DYwHJ3qUZrc/zalpeIKMR8y7kt7/EdrRVwX87B7m+58B55G+voHZHWg2sVpRyVh0I0P9LbOJQHeUz8f3K2C2IGlFRGDDsEmVxZwTnR7OBfRnFBUaYOAPPM+8pJFd5pjlDsRywdrgHueHPeqWCLvJyMI0ekwrhUwVAkQXBlVJ0vb+QLhtr5KWoKFBsfcw+wPSn+/4pcsHa1YbMqHqGhgMaBTQtJwWKoZbobCfir2WC9gTYg8TYi/akkcyg/Ae9qcPQJrCLVfUSpfhmrdH8oIi2Kp5ZO/uBa+ShHy8dm4kJ0zkPrDZ8qTznpr3swX8x1u+nB9uAQhJZlvLAE88bq8uYFcbuPs5TL3j/aYqeIe6PIunluzKJrHw4yFcmcHWu8DJ9dMRfa2ShPBj56TMk8HTeBhUJwIf+YHneS94desQ9CDbX8kJqUK1w81yw8UoI3oAQS9GAUvBDMFmJHvIBccA3HESC8xXQaUX8ufBIuZ2I8e0SGgM+rHu3B9gPAFZ7n7O1p/2hj8Ru+9XUUz0M7GHjkmR05/qbMzYpW2otbbJUhJMihwm8SegLZ3Mah6+iP1rk2fRvv04J8HqKNzClzl37v2enR1rYHuEM/vlCuZswr69XHi94F7MoC8Nhpq6950L/rHe+xNGGDIHuxZzOFU6U6/ZroPR+YG2cBsLFJmcsjvu/o88z3vBAwF/AUUk6gWu7fhFH7Pf8zwqA8tuGVhLRU5JOnXCq0WmejQIQxKjalcq6dE5+PMpsTSiZNy9nMJu9lHIU2p1AFCtc3tx9vnYgVDmfC52vz/QiLiRcmKx4smkNppeAHDDPhfksMdFYgyRknkGbGqR7vYBfRhI7b2SJYq4k2hOUZ99x4uwfCfm4gyrv7zC4G9vsftqivKHOxz+5TfIbzZovziDTw2SbYtmlKP87hbt5QipSJn4zkUYr5DDiX+vozy2Dp6yjJzY82l8wYucpJFDQxzO/hBVJC4mwPoX3aVx7GGvtyK02Uhrrgjw2mAYIH8WFo+gBQG2+NC0cbqq/NTRAG42hx2PqGTw/lYofkIh2+35Je/2YUiU3MqwRQZV8I59ZRE7NXlG6uDN/NHYw7/xlNitIcZcX5DOsd4WWxg4H+JHKSVDwgXq50uYyYgv6KYOefj1PckfRU4SumjeUN2BORmR79a7g6qn+arA4O9m8L0Cxb//Dv76EsWPd4BzyBRC8f4WxfUl0HZI7kUX6GbGkswaeJmQWlEtMInlfUFPb0FymrLkICxN4sm82sZLrhO1Ziv3il+05Q1ABSy51atpgN9sAxvIy6QtoBUP4iwhLUVYwx6wthkHfWDVAGBdb8cjXmDXNdtyScK+toKRVPcmSXgBlJMBYJvP9HqsuYU7Cmth3t3xC3okdnTs9T8pdn201egdfIvIvhIXO2+PpruOmuxGa+vVmmWI5qG0vttZ7MDkGWt4ycmvtyKkKuXWeMCafb4IXSSfJMAPb4hUFAVkNUP23/3E/951wDdfSuno4ItClIRd7IiJ8BTlAUXkdjwAVIiqLIHbGX+3tJHRtnIpd9H+KHt8ST/vBW8MBZO8j31qIGIoBDtiqjK07gJdT2QoSB4ugwQ0y4UkXohUWOmwD0JMALgw+r0gaGq2O7b/rggvMIeGxOvDgbUqEInFMphSTPcnY/cc+z8l9vAov1UgxFp3+/MJOxVnE8pbX07ptyqCTigLYLVmWSR5UPKjjqTuA43ZrCI5VUtHiSxaP6cp8PISdr7iTn6/AF6+Al6/o6LwRi7jd3P+XI9+r5iLQZo1wGwXTllYA4yGvB+stlQKHo/ouyWYeXri7k5gxfA+WBX51TpSLPPHwWPPe8FbS7UqGdVDhHqCkZeqBuwP8ehNk/jWF5Ss9ust/KsLmLsl62dxtEPbwr+/iQpjYqJFowGxplws5VLHYYh7/Y7/W5n36smk9a6UKr5fATf3f5TYwyW17eIlWg2/8hx4/Q6+KgNm3P/4s9xvHNxMFl6/D8zmIQ+/XEVpvtmc5gll+Y+8bG0UgJJ/z6/WMBdTtF9dwKUWWZKgHZXIhgNihKzF4XqE/G4Oe3mB7moM03kKtnoxcTOWCEe9W+llXkBrqHe8j7QdF7NACAAI+V2UyASWYQ7iujgaxIn2A8/zXvAK7j9SnlVHDj/qU0VgQD8i9AlmMsUwYEBCezGxXOwAP+DVhniVFxew0hJTSTp0jm24wYA6kil/VuWfrTGhT+5Xa3pBKUXPiYS3McCH21CPfyp2vL8htvwJsXtlNhU5pTvShNCFqmArtCpJ4rBWxEeF6NF2FGT68gXw41vmJHlQsm4vE1PaWvqlMJs0p4ITZ79cxTbwqxfwP7yBtRbJqxfA3T2SH9/Af/0F8P4Wzb/6Fvnf/IF/r+2QvL/nSwhE6Y1eFcW0tjvu5mJqplabZlPzc9aXsMh5MqlhQ5bydDEWXowm/PZx8geAZ64tmV36f93/n3gZUdZNaEX66JBnbGi5nZhaqaWNHqWAaK4oz1Rq8c7x4qrmwIo3UZUw6ZgY/bOE+Prk1UuqeYlmSjhm9TNt2odjV6LHU2JPjlqTwaWER7sXvyY7HsHN7vniZTlzSxKWRuIqokq8JhMW1aGB2x9pvST0PNWcAIhuzBHAbS+nSNcRytC2gU+ru2t3O0NyIQ4gWsMnScg72FAaw5dfyy8rxhLLVfhcTE/Ug7W+T+hjhbv7eMKOhlD9T78/4P+Y/a+/UG1JgN0FVdUC4nRtsTp1lB70gbqOwyWRywsXKO16yI7BwU2fH5jIWofdZDKik8V0wsUm5GN/v+DE8OKMyr6zOTsdyrZPU9L2hC/7aOxqVPbU2LXkUDnvFxfk1w56QbbCjmQA0+/BLymMakZD3imUPqd5iDxH0jnOBLxHN+nBfPc25KQWOAxCFlx/Ei+XxoSWoBsPYJzD/nqI4scKm99coHq7hp2vWRYqZVDuXYpIxc0983EtTK/P3Xo0jFJ6Ilei+B+vXN4kkZKo4M8cRDwqy4DZw8vpmS94Q7aSfMkA2H9VHZiuQ1DgPWpReu+BTR0AS4GalyRx9BzESV1k1p9P2TpbrIKJsd9sKeo/GkZyh+4uh4b/lnQWzHjE3y9OHiq/8cnY85wXwifETrhBG9lReRaVxKSnHU4nY+G2d3LZlfbmpgmdquM8/HLNWl/ySd7fxBj0jiLEdx2cKUbHi4ow5jSBs7cUsSpXW/giQ/9vXtOLdika7+ICCIAvIsDP35rAGfC7PbDewJ9P2UlTyZSjE5JYJ7ZOg5fWbhfvVJ95nveCNwaBPKHYbNfBNy7qlnQdkJjIKjKG3Qfp7vg8I2wXR0wbhSNkaZiyAoJzUak94Xfq7q/Wk36zDcMqJzHZizOZLO4YQ5LLBW3/cOz7g0iDPCF2ZfeoMFHGGYHXi52eIH2x2mylZVcWos6WHunW2HBpVZ0dBYJpazHktNvTkRuIJZj38C8v4cqUBO7RIBIxehWn1elRt+sV3QXNanPUIUvDC2WOuMZET1b0rh0PgqlZkGsZDaO7oUmBYRkgHDpV/pwx8fMGjxmIw7KN00/hOgYHZ3HvCC4ZDWtK7PZRHk6O3zBq11G8IhLFeExx4eRZRqlu0ysjs6ZXEf0nu42djONgajwIeJFwgXwodpXne0rs6dEXKmRxxY5Apq8mywKgKzh+Wyll8gy4OjvJg/yAklxbjf/66jSntot1uILXVHF4L9NNQ+mR7qsr+DKHGxMX5L+4QnM9YrtSyxLDS3h4CT0N5vxqzVNjMuS/J0OysAwScS7f1vF+JO1Ir3Ikw0FgSD32PO8FDzmq9wcee8cDoa7jAgOi5Ym6gWR0+QhMd4BfmDhp+PUmGikkvJD6zokWo/AxhcDshYzgjzEuWud3XQBnmTyn7uK2DvXvY7EH6O5TYt/tQ+yq66KXay94dd+p8CsxNKYvA6OmYQ7fvz7NI6WOS9BlnIzQjavTnARuTJhzHcBueH8DfPcTcHsPv1jBrzdIbpfw3/0Es2v57+8aZDcbyuzdilbNaMjNJmhgyu83fAnMSkjkoqQQOLvHqmSi/kDPqKPlG1TlfsltSfEx1SPbK4sfgmlfrOXiKoSGomCtqrVxYuM0U1B8oWvQUUouOHF4B0DcJ5yUOAAvZXkGrzXjZAgsHQ3PDgeCmADWkkoq3+05AMqzh2O3LF+eFHuawshOG04pTwvL0I2BC7uyGQ1ZYjV6AiTAn3xFpV3NQz5XBXzhbg779oOYikVLUO8Jw6aceAozHMC9/yjkF+liOUfR1CyD+ektutUKaZZRqvC4S7ORBa0zDyDyWBcCJmta0X2Xz7LXI8sMgPdCgZT7G91KHH9Gjc4+03V83gteUY4Z22KhtSb1qT8cWI/qRDKxwMHCiM+nKpNp/Qtl06viWErrF1NVod0I2SlNVVHtaneIU9LEwt/ccUGo3B3AboYxhK6uxStK22kPxV7XT46dC9QEu0b9M5NlR8ZjQtJou4gy7PXipW+xjneVRJTPsiN0Zp7B9qq4iMUn1gDh3/CHBqgKmK+/gH/3EfZsGuVAvCfx5N1HJFeXhG28vGJXaLmiWKta82xqNggUybreRk3NjJuB2tzAWhLD5U4DgEMyVRbuiLA0acI7zf4A3D6ypP6Iy/OP/xgI9sTGo+yonQeAR6FyJA8N629l96iEhNTn3nuKE1VlEFwK5OGyIHPGmmh63JAh79uWdjadg5mM4w4DsKwSc61uMuAJ0BJ1+VjsqhL8lNhNr4yx62Lvy/3gbCIu2HyJVN8mzFdEzwXAaR6q+d52IR+vpBPJKUjaZRlhCX0hWNd74NdfscUq5BGcjUn8EPEn5Fk0RZgIBdK5oDBsmjaiJrsOPqMgFTUtK1EL9rTkUV2etgskeBVk9aNBvHzX+yj18cDzvBc8DKefzsPLUMb05dJYFTC9Cm465BeluHPZcVhvZlFM6NDIuFzgv+MBuzR6yQSkF0+HD0CGLqNhNDko8mhCPBnx7wvT32QZx+dpElj+j8UO758ee9uF2GlGPOCUNUujygLY51eJ7nCBk8GM75WneWTcQY0qEkxG8GV2klOw29RRfpayu1VkMK/fw79+S2ix3jWyFPV//Q3c/RzNb17BjeUekWfRezWhuZuSVMyhCf5RJpeT+HhjUArhbs+TQn7G9HuxNWrMkzxagede0ugF0Xt+CEVObE2awM/mQL8H8+6WZY8y3VUhQFp3EHmIoDAmLn1GShu/29O0a7+HadvA7vFeLCONYctsOAgGxt7LZVDl8oocH//nP8OL//NNJEHLuPuh2D0A8655WuwKapM6PnQw8iyahNU1e/YyjFGtHeoy7mCaNPqfbmuYjh0YM+gTFNc0MPeLk5yw3eLDv/lzvPjfvoNZiQzedBymvaZXcW4hv7N7dYne//sa7s++paqBTprffojtTcEJAQimEVpeAoJA7QueXyxxzHYXlOEgMuEmTdmSLuI8wxTJZxf9897hhVmEIpdpYcdetra59od4Y+/EtFdbXqJ8G9pqlh2ME7y1uujpzb7IuYMJqdloaWCjCYHpV7EMynPY8ylMVeHq//oY6/9hP5DBH4rdlOXTY5eOjMpRQPQoqUqcBOKIFR1MBCUEURDzHm52f5IHse0VN45Q1pUnOcEmzEvuQGY8ItR5uYbfbPhyC1XR5DlMQ91H+/6Oc4r5Av52xt1ZO0k6fBKih9pQ+s02nqwqoeeUMEOXEX8sdwJCQvSya3olf8cvesFDBgnSVQAQBfJ7FY9yIQj7/SGgD9VxG0BU8Do0wOU0jrRTkYxILEFiQAB3HUtIY9gnATrP0f72a2qgf3kV/rufU/jfaMtP++qfiV3/3pNiT9MYu5Q2Rl7Ok6NcBlRmPIrdijQBhn3YF5cneYTBXb8X8yny05xKcfNTLIv3gV9r+v1IQBGskV2sw53H73ahK2a+esW4hn25U0inST5nM+jx7+5lp1ad96oMOpv62YXvxhj+3JCKBdgfom7oI8/zXvD6ReruJ/xHZTAF8c/mCHiVprx8ai2uRIV+BXO/jLrkaumibCRjw0Ut9IbPJjDa664KZD98gPv6BYxz3G3LEmY45Oi9Jtuf09Bd5MQ+EHvI7Qmxw7kYu3JdU55wvi8WOcby56Te7q7PAGOp/+KoZHCSx6gXF5zko3LdmlOYJQgAzg0rnhBVCdOv0J2N2KmRPr07H8FeXpCgPR3z8ysL3geU3yubCOT+EdwNvcgmNoLFb9tooZNnxMnrQFBPaYVZKwjw+GceeJ73gjcyLdzWwdwXQBi8uBdnou8uH1YlRI/7JROXnq4b9tlelBLFD/v8kPeUzTZb2tAblbYGW3e4mfHI9NRvUdVcq3DhrqPOZJaxphXMd7BefCR2FPmTYwcQYx/1hd7Y8t6x3op8diEY9w7+7h7Juxk7PfWevk0X45M8zE/veJlOkpCPX61PcvIbijr5FYVmzbs7wac7+PkCdt8EAzczGcEuNpTMlhLLrDa8E6xWwYsWTcsB3Xob5TqKLCqSOZZHKHKBCMtkdzgINbyeNr7IAl6eroO9X7jyGMA3u6piGQIOhHBoYG8XQT/cVIr5kCNwd+Au4hysaI+HCedqEyhikN0ZF2cEUXUOpoS0/no0PVPIQZbBrTdRQAkN8SsiF+ffvGfbUtR1H4vd17vgxfq52H1FShxjp7eV3265WHTaOC5JeFZGECC+UQS/mZ9v+BlIHh6gCBIQyeaZALQ0J+8B30VdnbalbqVSHpcboN+TXEVvX1lQwsQym5o49TRlp2h/CJIhoVS6m8N7R4HVY3OyrXz2AlpT0VQo1PpOBncVvWP9sYv6A8/z3uGBuDC9566w2wcNRGRphPcqcz1LRb4jCY4VMCbgbgK9bksDYTcZksO5Id3NTCiyijHJEvZ8yt3l8iyAyUyvgvv2FetzecygT8CVc6TcHfEuPxm7THufEjsW6xh723Kxn09j2fHinPFnGcxkFBfoeABTFrDnoqp2nIfIcZg0ifmoq7U+03Go9elVK4CuLA3x++0uKIKZjChO+mIV7M4IANDv9oT1Aizh1GEQkCGXeLVWJV9uRVC2sVdvBv3gA2X6vUj1U7EpYXk9upz+eavvv/QjDhm7XSDpwmZi0rWlxDKAYNMowxrfOcFM22gbo8KbR4oAfn+A+XDHD9Uajr5FBg9rAM7DKV9V3e4ODckPr7twQbPnUzTXU4ob7XZw/3CH5Gz6eOxtGzVZPhc7hDqo7CmApYkSIN68iyN8gRP4riNhxUl7NbFwd7uQhxMhJSf4Hvu6g6t3wa7Hnk+B+RLNb75E9uaOG4ReHN995GcmnNnuzTvYqqQQVFmge/cBydmUXZSbO3Zw+iXvEPWO7KSNtGQF2Ic8lT8XtGgn7LE8Czgev9uHn/HCUzCFGFh4cT5RqMcDzzNf8OQ8oip4c693Ua8FgrMQLURTH4mEWsvddDIMgCQzHrE2Xm3i/38htoqCPPS9kuoFuqvkGXEozglUl/h4K61BArTYDcl+umFcZxMkg35EKT4Ue78fXDw+FzusYZwrUd8SnRYSWeTSql0eUdc18m9556gKttrATqqQhwF4t7g84xQ0TWGTRDxZBY5bFMwLoPz2dgc/6MFdn+FwVqJ8s4RPLezlGfavRkh2Hfy2gbueAocWu6uv0P/b96j//AWq//BaoNNJVDJT1YLhIF7QQWiEHw+h3k6hBNNHS7OhdIkOUbjW9x9HTD7vkkYhs6oHD4iMhYtydFu6YfgyD1+UTxOKDq1rXvLkYhQeURwwWQbcLwkh2O6iGtagJ3rke7hBySPcOQ5uehUvvR27Dr7IZPfZccx9aLjYRSX3wdhFCeEpsR/buHiFNR9j5JcrlhZKIhkN6JvkHE3GBiV2f/X1SR4+Y9sPAr1F11GQ9Sgn+inRmNkogrTrkHyYo/zr72CWG9i7Jcx2h+I/vkb29h7JYoP0doXkfoP+37xGdzVB+e++l4u9vNR9aQ0LSQaOrCukCU9iYXGxuyMX4FxhGAJ6u5sjCL46gVfr/eeR53nv8N4RZFVVrM1kuuabhjQzPd5kgqqydEbrdXkhqJ24pbmWE8cMEKdjhE4H73lx6tHeMSy0DzP24vdCkFiz7Om+ukJys6C/kRIsVIWgaWC6PAx3Phm7Mq6eEDthv7IbKoBKVYbbljt407Bk6wvlT50EmxbmwwzVbEUpDMkD8yWcaNBoPvZ3P7GdqDkBjFv9piCnkfheKe4ftzOYLEM3GcC+vYGTz8Y7B5cnsNMxOzMqNwjExd8r2eXZrwkxGA5YnqUp41WdGpm6B/iEcyLNl8Jk0hlS+MMjz/Pe4SGXmd2etbAkbtTXZ7cPEF+0JDMr9PVY89Hv93xxBPkYaklVtFWJ60GfpcdkxH93w06I2dShxsZkCACwP7wj2VmM1Yy1LHmU6WPtZ2LfPTl2NM1R7PtQB5u+KBOr8rBi3AHu3saGyyWA0zwAmOsrDoMkH3x1fZITwBIrEESEvN5djPhznkKz/osruIspktsF/OUZbM3PoP3VNb2whDQeVBCaJs4TRHENWSaKw2vCq+V+5rfbSN1T8rdIifiWuvLUpUlOL9wPPM97hweiQ7WUDcHc4AgiDCE/e4HPhlaf9oNFSFQJBb7t+N+9B1TOcRs5obidQRlKauuuXE5T77kzXl+xNleK2aGB8ftIrKjMo7GHSegTYvf7A0xmolGDgswSy+Pce8bfo0QHvANEcsNv2aZ0F2PY93cxD0v5Dy+XcHN9Jc4oeVQ+bjt44dCGp22RfJjzwn59xY7KBxI8uleXsD+8hf/iBQzAy+5qRWyPtiWP+KywVAozeyviUEQ++iLn39k3VGI2BhhRnAoHElrMVi6/VcFTwxNmYfZH+vmfeJ73gvcuTPGMTBONkJ9NmnIoVBRBojlgs9uWRl3GBiUBlWMzvR5wOPC/S1kRL0xgDSmyy15ReuJ96gF0H29hqxL+3UfCjFWPMUngNnTW820Lu5N+90OxS5fjSbEDoUui/XcABLDVu/By+NVaYqKBmLKq/G4P/O4HuCQJeYSnKNi/f/dRpAZtzEkNl4scbsXWqBkOeDrVO+D713Dy77jtFkYpd7//Hh3AIVKWwpbskxt1RdRTDeCfaQvXeQCe85BCpLX3PA1M0wYb+mBw1jRRkUGdBtXQ4YHneS94yEUviUL3Kv0W9CIdhYQCEyhJuFhE2IdDGPkzdaZORT5jfwAS0UJZb6TFtRHu5oHSc5NxBFrt9kimYy5S0YJElkf9FOGv+nX7+didB/DE2IXz6tcizlQSH6/diUAuV6k/7bSIYZidjIPiQSC7eGn7OUfkpspkdy7mpNia3S6WYqs1faTk3uJL4tC1NvZdB5ulMGfTUBICiKKnRk4xxdqXRdTb1Fp9MoJfrPm/gWBIHH5Pnkfp7zq+vqbrgjzfQ8/zXvDGcmQvkmp0c5bRs7XwpgMuJkFnPVpQCqnCmODjGdSGR5VQ65KTHq9RkzFFViZC2lCNGRBu4ARUhXoX3MADc0rIGcnLq0Ayfyh2DHLuYP/M2P2CmixYrWXC6IOIk8my6AUlswe4Vni2EYJLB408xGwylmFmOhYEZMxJkZBKzvAQl+xWNNp3ogdflTzNkoQt13tOwd1qDdvrBUfzgB1Slw99UY+08Y2ScypeaBWHpKKzfrNl3AIwU3M43zWxVfnA88wXPKd0yMvoJ6SaKQAXyu08UMGguGod5eswRneWQY9twkSQkmrh3hy48yUuKNL6+SIMVkxRUEyoV8F88yVr/NQguOo5B3/oAm3O3c5oiLDfPxx709KM6ymxF3mMXfHyIq0ROkBZRuDZx7tI/VMuqUhR234/5OEWy3Ay8FJpuLiciznt2gjRBvjZDAecYdzOmH+ahnasybI4Qe734Ed92DEv+S7PYLc7whfyDMgEVnB1zpJxUzO3TrovRR66UKZXsW2pSmuTEVDTfojtUk94cFlQnvyR53kveMcvjONr6smYLAuKA0pr886w1aj97v2BWombbfww1htRJ5CLqRKWxm0oAAAgAElEQVSueyXQyW7bEY+h9b0Z90NP3ZxNuCstVnBiAx/sXTpqzbjZHCaTgZgo2j4Uu47AnxI79nTf09i9wAh8JzKD4OzAbHfwNglGD8flGwyJJiGPnigUZOSqmp4gIQ9NyEk9Y71qP4o0BoyBq3eU8+hVwGyObrVG8vKKnk4Ac9JOy1qcU5TRtN7ylDGGpYsncTw4HQrmRi/yaFpuTqGU6Wj+fHcP6I7eOeB2Bm8fR0s+7wXvpS0mZgMwcbcxaRLctZWwoYvGlIXgQvhzfrMN/WSvnEdrTmpfs6MTBqWuZVi1XPP47TrBWRPGaoRJ5L96Afz0DmYyhu+VsFqrVwWdpB+J3btIfvhs7OrWrS+v6uIExKULbUvq0OdigSM7vWGu2O1jHi/OYHYcwEFQku6blzBNF3Oqd4RV60UTCDidpKr4QpWkECYKkvuTr2A3NVUMAGA6JjHl6LIaevEAid+i7hZcDu/o0hf77dExRH8mnF6NUB5lgh5cVB54nveCF/KDDor04UJyEV2YHPVfVX6jV8FAILf9XmgPoj3ScncuWrM3DbsVKrbakIzgVmvZrdhydPf3sIM+d7w/vOYOWe8IU5iO2ZIU71IAD8custhPid1vxfOoJfLQ5Bnctj6dwG638XeoaoIoEDhlJaWROmg+zAIuSMFb9s0NF7HmBNAuUi+sMuwy0zFjuphyA6j3QWXNrrdc3HrvWW24mKdjfi5D4RLL3cVvtkFvBtLF8ZMRL6vaYhQHP2yo+eM3W84h8jxo2wd7zc88z3vBAxH7XBUwO8RBDcCjWLog4f/mGaUllqtoJqBKXaJ8pfWtbxrYF5fyBewDH5VoPqm9qzLU0djWSK5fBnKHnU74960lz9W56AH7udg39ZNjp37inn+vaYDOwg76wS3EjEf8nbs9YBPY0h6hK+WyObsn7FfyYOelDEKtADsw9mwaczoIYvEgmjqGF0l3O+NGcys1vjFw8wXsn53LaJ+nk+rSmF6P5BX1sVqsAllF1RyMWGCaTU1zOEDg28TIGJ0NaLtVwWT6eQ56wHIdySEPPM970moM3PmItae87f4go3trQ6vMpAkHOONBmOCZPI9lwWgQ9V16wklV2Wltd603cTHWNY/ZpomTS5V3Xm8oRaGc0cRGs4Ca9br7+hrI0sdjV6WBJ8SuoqzokWeqXzxsEkSTlHAOQNq13D19JRaZVxcneeDynECwo3zsdHKa0zHexYrOZ1XCfPMFlv/jfwVcnYvvbQH77Vcwqy3m/+0Lfn6jAfy/+DXlt7+8ivRBEXQCEFuLEEz+pobvVyzjlKm2rSO0W50YgYh4PZD1Zep9zP+R53nv8J2DXWyi6sD+EIFCKuCZJDxOvac6l4CI9Fg2ADCbC3hJ1L5cB3SW9e7+QFhw1xE/or1v1/HIrGvuvvJv+rYF5gsutJpdBxKebXC7sz+9g09TWKURfip2gTQ8KXYti5Q51XXhJPLes7zoxDMqTeHmi7AZ4OMdSdF1DXeUhwEXjR2NeEkPNpBtyImtyCxCgQH42RzGjzH+mxa+yOEvJ+h6GbLv3sMP+xj/zQ0v7Xf3SLYVkKWcvobPFvy/ecbfeThwdz/IIl654N0ViO4CNfCHhjOFw0Gm5hn83glOpxNs/ONoyee94K2JR7/qp9ssDi+0hXewYVEp8Vl/DkcXJPasmzhEkfLDVFWcgO52rL87QUf2OZmFMYTIvvsY4QPyUMxIhjdGYi6LR2NHI3E8IXZTFmJhL10L7e13jifDakMLGSlD7GTMwIwBKvBlqOuo+3I2oVRILQMlycf95ivY378++Wz00miGA3E8Sdn+u52RK5tnyIT1ZER2w9c74MUF3Ou3xPuMBlK2iN79ehOUEELrV+9Y0nbVzpWva75AdR2VgZ0P4rcGiLo4x5CNB57nveCBgA400pcOC/IIDWASYcuI7Uy41YtKsI7pjfakDwfCTYv8SJVXvIVswj7x/YKXs/sFv+z9Hma5hrk4owWNYMeDTbyOu/URh5I/RuxEdZYRAQnEC+t6G8RGQ/zazlOZDtW4n/ZDHshooqz/HUkC++aGl2S19BFhWJLaTWBwuUGBZD/A7teXKH66RzeogGkfdtdi+d+8xPAfiJM3f/ot4BxcKtoyApLTFxAAyzKR5KBKMvU1jbVHjilysfcuXs7tEYRZTyAXu0kPPc+7hlfdlSyN4j3SZ1VFrGMcOBSsBfB0UG3CfhUXkuLIZfHjbBI5lEIRM1uZmi45RkeacKq6PxC4NB6GkbguamrHSH18dcZj97HY2/bpsevfEYZPGJzJNNJnaVyQ3rF3L/cWAOFSe5wHqjJABzQfk6anOWn7T5XU5Pclt0v41Rr5v/07mO0OyXwN++N7mMUao/9wA3u3RHK/oZdsvYf54S1fQGtFn7JkuaLQ6UZfBLm/iJyHyegH5RVCMRwGvRwAAgmmyoSiPo87V596nv0OH/u30k4z3cluSs+ghONxnUR6sX0BuOjaLtbNXlw5CoH/yu7mRwNgseSHKdhzpdlxRC/wg493PAGGYj5gbXDyDn1hMSR7NHaFTDwh9mCFo0e+6OxQbVjMHc4m6F6dIfn+PRfn7sAcViuaDOeZDN9EfEpBeTofGPYjNFlyMiLf7cUrVWt8369Q/8VLFLc1/GoHs97CffMSdtfCVRmS3QHrf3EFe3Do/ad3HHYp+lOpkoJbVw1+dB3QSpdMBKMCub3IuSkt1tS39LxXmH6PHafO8R7U6wXm2EPP817wJiplAWC9ltKyMThrqK6Mgo90gCOYcRgT62NZ7ACiapmIH5m2om2K62j6Kx+oWyyFYc/F5usaxhpeBg8HYDgM+OxwangPk5ePxq5GxU+JHeqG0TnANzCtifaY8hKYtkPydz+yO3Rzxx1SEIh+tztSP9M8drzotVREw8c7ADjNSS/5mdhkSlvS7A6o/u3vOBDrkWKYzNaEVPQq+K5D///+IdTlfrmKp4w1hGSLRAnaNjj3Bci3ymp3Rxo8AONUIwwgDg2zTNSYH+ezAs+9pNE22BHUVbEeXllCMmpWwJP2YcOly/vYSwbCMAZJwjafdwQ7iZqA1vZmPAyoPoA1vlOjMeepcKCGXwDw4kIs5NuoGfNI7ErgfkrsQXBKUZYq661tSu8Zf0KDATscwPZ7LFV6lUhw5Cd5aM+dtqCSz8XZSU4my4AXF3FCmljuugKyM0olXG3g7+65e8tidPMFMUAqdqVI0t0+DpIyUXYeDQTbL2JVRRHLvpau3GbDyytEAFfLthCb2moebTKfep73ggci/U5G5MHA1hIQResZCiH5/SFiVUAYbtBcAeIxKP+NpOc8uE4D3DV8XQcRftPvhXakHfSh+ox+uYLvOn6xALBci4yfpSHBZ2IPO9sTYvfyO0gaFwGpPONAbRe7RaZXhd3R7w9059jWLJE2m5M8TI/QAL3QqjvJSU7GcJgjWBwvCsdOBZ+WxMEE5/F+jy/9+RTJ5UVsyYpYUxB7FQ1Or5fU+Sr4Y/m2I7n9SNrPyP3KHw7xZ4zhz+2pasaLq/uFd2mExqYScEGlS1CKpqAdiooaAeAXYeXLD7BRUuso6nmItZ7CgithvFcUBDXbXZSGdh4Y9uCzBHh7AzOdwABwox7MZgfbdkJGEJm3lxdopj1kr+94fD8QO9KUqMwnxK6PbznxNP0qqJOZr1+xI7LdA9sd8MULlmYKjmta+LKAzYhd1zwwHcMcGrjJAHa5ZT7zNcz1Vcyp6yg1OBQqZOeAxQrmT75COy6R3gkgL0uBuzkOX18gWR/gehlSJ0T2zqH7sy9g39wJSIyQCsKlW+aTiOT3Rsgyu0OE/Upni6pojnMJYwK8mIoNstCtPSkjP/U87wXv5ENOo2UhwWJdrMt3IuMg2G5kGT1UV+tY03VdBFblcgGUTkU37iO5XcCdDekp2isDXsXPl8Eu0fZ6wKAPfz/nov/5Y+wULVYsHwQfkq22EaLwQOzw7smxB9sZfTH2B2DTkVy+3QG9Er5XBIMBs91JfSwKvYsVX5zlKuSBek/JEUWD/vyR3ZB+L+YkpYZZbWOXZdgHPs6Qvhe5P+8JDjMG+e/fEoZ8ZPrg1xuk7z6KFn8ZOazOx8Wu/lRHwlU4qusVE28GfTFKlpal82z5qkKDdHYee559SQNA+rQ+0N0AubnXcfIIsPbVMiL8uV58rRzJe7o1K97afv+GtXUtEnBz6iD6wxGZYDqmZIalM5/P0lDqQFW8vIfrk1Pqx4M4VXwg9tBqfELs5jj2HYFkoffv2NYztbRM5ytuEgKvdRfj+G8e5eEuxsD1JV9wyaf77TcnOfnw4sRJKwCq9vZ6wPUV7NkE7uU5AKnbkwTu22v4l5cIGvWAMMz2UF6xKYuoQGbJFfZFJtDfKnbVziaxJaosLJ0ii69W4Cjr5/3I87x3eCBISyik9KTzUdDtAsbEiZu0C41qyAPRWl12WCO2Mn48IGa764DZnBdJqRlNmrD+7Qk8tl5F9r+SiZ07mvY2JDg4B7NYU9LjsdjzPLQZPxd7yKETwzLtUowHwM09EZAiRecdW3Tei837j+9ImGjakzyM4u3TJAC00tvV6QQbiDKAzgEmZctV7kGYL4Esg72jno99cQk4j+T9fRwuffGS0h5dd+KJG2RK1O3be6oWWEONSDWUWK752RS51PmcORhj2DJVHNOmZjfrM2Kqz3uHN2ACNh7nJ3bsOqjRvrn8/3Ug4UvCTqmHwomdUSuZroNZ0CXaXU25k1yds9YXkSf39Qup7aUD0KvgRwO4IXcgk2VRRxII2ijubMjj9ZHY/W7/5NhVLFRJ4eh4sTOLNcygB/fVFXA2YR2uFj4qozEdA4cG+z99cZIHRgPCEoo85NNeDE9yCngcgC/AtgbqHbrzIeq/eIn2T1/BD3tovzgjWKxf0av1QnjAwyHMbAHXK8NFVf1ZtcyEGhaLfIrpUyveVEdWOikltbUL5rd1gAObouDPqCz4LxotaW0EGQFQ2xrT78m0VMLXI0+1ScSY18jRjgseiyjElSPPeCna02/J3q/Z9lL8tUBr7f06yOEhS4MzhakPcOcjNF9fsITQgZDqrOQpKWmPxB4W01Ni39Yx9hEVdo20Pv16CzvfMP7Ox9/pPHdueTny//T6JA+z3rLduT+EfJI//HySk3ZDvIoepbSttMsa5fczpB+XQNvB7mKHxHQO5rs3wBXLHCoS2EDQDoYJxx0V4cQqVxgJde0DkrKu48lwXLKI5F7ANe0Pn720Pu8FD0MysBh7Bblk1XFXdktgxMiHETiYHAaZ+Upk66QEEfNh0+8J4GnLYca25p8v13SoHlQwqw3McsO23WLN4/nmDna+RvbzDO58BDcW0NioDz/q80i39vHY9Yt7QuzaiTAyYIJK7amCrujLmPmK/+39DevhjeSz2lCN4SgPVCVPsiQJ+eDl5UlOSsMzkpsCu9ywlB56Sg2ZQ0sIwb4J2CO8eU/jtfMJXE5te7oBjgTDUwK9it+HdF1828V5yTEmZjKKAle9Kp7SsskQt+9PWrQPPc+7hm9bYj+kNGDveBtobiaJ1pD68QTY7P4Qd1hPknAwIpabPgCiEK+vYN59hFcFBBEbNbcLtj3TBKgKuHEPyft7+K+v4fIUyft7mgn0CYM12x1gDLrLCewPbx+PPU15SX1C7BAjY2OoI2myDGjktOlVvI/UNfyArUZTFtSRT5NA0et+fQ3zYRHysOs926/DHvzZEC5PYb9/C1RVyMkP++T5DoUvK1zT5OdbDuGMgQ0TVIF6yKli+j36R51PkSykxbo/sM2od4COpwZEW0apj+gcIM0DlEUQjDJ5G5sBR42H4Not5PPHnue94A3iRTHPKSUhR38oaa4vecPfEj6qJrbh1h6wK5zk+ekQuF+yRFgs2c+/X8I33KUgCsGmyIMOpEro2dsZXNPCCsTXdx0vzgsqdrm7GUxVws7mQbv+wdide3rsOpE9jn23F9v4ON01i1VUPlDszYKO4OmNiJNKHv7QEFLsfcDGm7IUbybJScui5YoX+joNHkoKNXbzBdlXm22AD7jVGubLa+DDDcymhlssqX/pPUwpisvqTqhSJXkGbNtoIn055UvbdlKj0wDOdI4/06ZB3cCI9c0xBPmh53kveGvZcuqVJ7UwvOdus93xw9JFohomMuELbT0hRpthH/5eXCU6R9cPWQS4vuIHqnaRx0CqxAJlDoyHsIeGXZFDA5xPhHtJ0SQ7nQAAPHbEcO/2D8YOHcU/JfY845BK1I0hl3AvVj5+LHJ0myj17dXka9AD9g07S2kS8jCzOQFhIleCQ4P2mxdIZuuQE6l9lBwMciG3M+rIq2ipMVzswwGhuw21Y8ymhi/ELLlpuDBlruKrAmbrAsgNbRvpir1Kykpx7FP7nyO0ZjBaA4DG8xKuWqGfIXE/8xoeLAG2R2QLrfFm80jFq3esucVOxWs9p/WxqgmsNpxSCtgrOEGrzLVcbAGESWNwyity8jK1u3I+YY0fcC4HuMsJF7MOgB6J3ZTlk2P3u32IPYDAIFPUkXRjDg1fzCLnAEsw8sZ5IEvRXo5O8jAjuo4HGfFzeVmPc9Kd2Dn261cb9t6/+YIufsMB/G+/hTmbBKJ2+5e/CkQav62Bswns5YWgTmWRzlfhFPKHQ7TgVDAdwPzVfRGAH/ZDrU4MPP8f0jS0iKGfwSPP897hRQuFdu5ySxe6lylLCoTO16xhVVcmTfiFZRnrV/nyfL+KIK7zCY/o97c8LoUiZxx7vWio2ht2jDzjjiXS3UTm5cC7W37R2k57d8v252gY2fQPxO5HMiV9QuyYjPh3AcY9HVE/frkG1roTpryELtbA1RlnAYslW637BvY//gG4PI95AKFdqhaTyR9+5u+RnAAARY9T117FLhUAXxZY/+UVBr9fwCcW7csJ0h8+4PByjHS1R/P1JYz3SH+ewecZXJVzkntEwwuT0kGPhBrP2YMHIvpUQGG+Fhhz2xLqMR5wBqDCtGKD43/xC17aYcaY6FyRJtRd6TrY+1VU6FqRNmaACDzS3ThL+QFVJRfVYs2dcDyEunKEDy7PuPPLF+5HfZj5Coc/uULWK4GV1KoiXqR4dAgwyo/6aM77yL//GDscn4jd7A7xFPlM7EYUynya8KXd07TAJMMTZxKzO1B9TGpaxdX7UR/Nr69Q/P59yKPrsy2avLkBJB+8vOSLpRj7WoZyF1P+2/MV89zuMPp/1vDDHpIfP8DkOdzFFOm/+x3a//7Pkf/9W7gXZ3DjAfUk+2X0nfKejQZ92VTKUHH6wOlASTeIwyEKrq423AzzJEIJ0vRUXuSB53kveBna+FQWddME6pt3Dqbt4KZD2P2B00mlisml0OcZd9mmDd0Mo71u70XNy4Wpnl9SwBOC2QAA3N7DbWtkf0eVL6/qwmlKENn9nLW7TkbnK+T3y9jheCj2egf34uxJsbuzIexsxd8nZAd8nLFFJ5hxxr8C6pp3h+mEOeyIrMy+fw2XJCEP+/ZAWPSBorEmTbmgnYs5CQEEt/fCt5WJ8ajPNuSGdvN2d4Dd1MBoiPw7scXcNfA/vgG++ZIYJXEiRJoCryizDUcbTtb7nDO4UQVbE0Xajfuwb2+4caQiL9h2HKAB/HwAdOdDJPcb8nJfXQLvH15Sz3vBe8/aVWGjAC9mcvzBOdgPMy4WAZIpMMn0So6lFVct8hi+7dimVGa8/F9vk2BD79cbHuH7PaEFQhiGc3A3dzTjlVoyaNSrpJ0wi8zuIO3HT8fum+bJsZu3N9z1lKwhi9g3DXfcQZ8v4dkkAtLWay7o3U78lHyg9JGpJYKnXUfStxI1BOIblI7FuRBpIqN9SmL4FWVNbJYGISYaD7e8DDct8OU1vbMWqyhJWCYws0VsY97NA4HFlznsTx9J12taGiy0LU/megdsNrw7LNexJZkksD995P8uCyS3i0eX1PNe8EKEJugo1mbKSQ0tNCBoHvLDbsLPKwgLTUPYrECFI0M+E4SeC1osJs+JH3eeokMAd9Fejypes/sg4cHLoUo9E5uiktSPxt514Uv/bOzqmqGqut4HV2tT5vwZz5eRHZ+WJ9eG0tHudgY7HsEtliEPL4sHXQcn+SBL4e7nMSchy/iug3G8CHtpufpaPvvbGXxRwH31EvbNR77g6zXw4hK4u+clvK4DfxjbXXBC8TpRFjkTszfBoC0Q7XuU6TMVIdVsSRJop6ZygTb4GRdu4Ll3aYQ0YaqKwKAiZ5fFuWDB4s5HXBhnk4CoQ0ZlXaO273kmBsE2thyThBO83V58g0rW5oKrRlEIqymDHY9gX1zyYjfoU3HsIEyeLOciz/MgjQ0IOvGx2Dv39NjT9J/GnqWMX/UWy5KLQy6FpiypNVMU/F1ZepIHAJ50F2cxH+A0p+Eguo6IQXCYL2h50/BUsm9vuIsL8M0s12GnN1XFVqV2rEYDnkSbaEfpmya2VHf7YEfkB9SZceM++b8it6d6NaHzowPCXzRaUrArftgLiLswcr6Ysg+/Ft3F5ZpSbko8sCYgKQGwXaiqtEXOS91yA38x4VG7P5DIYOVL3ZOKdtyr9mdjXkLrGvbqgse24DmUsIFhH92kh2S2ZlnzUOyb+umxVwXQr3gBbDu2Igc9ogsvpmJcTIUDv6Hql9nuuLNOx/z3RWBJ87BAcPv2ux3s1QWx7NMq5pSlHECdT1hO3S/50r28hC9T2Nka7csJku0B5v0dXwq1pllv2fIsCnZV1tswQf7/qXuTJVmTLE3oU/1Hm83NhzvEkJGRJTV0lSBSCCyqEOEdEASBFe/Se1YIO16keAAWSFNAA91VZFVmzHfw0ebhnw6L7xxVs8pwv95QDR62icx7r7kfNdNf9Qzf4IwBBvCktoLWNOKnY/5egN2oumaXx0RlF0seRnVMVSHCZoNhbh55vewNb6Tm+SoSKQwNd30f81EjRuipDxUPNUqbyzJu6JoOdsEpr+vo7jfo8UoFYC7YQaZj0I967zcPkLpSFtKa13q/Tx3KTtgjX66RKsTgqdhF64jnxG65vwCkECrBBGmixSv1L7vVGn48gtw9UNNR1wB1O5HFMq7DO0KjRf2lsAaqOjr79UsWk526Ce4P/P0XZ8CHG36GwwHSv/+edje/+pxplsKQu6szuO/ecR0/vod7fRW8WbE/RFXiYy3OugnDP6o08xBwPT4QslwBBZ3LRcVkA/TbunW/aAKIbvhwbfd6EeLrXWCvo2mDhydpeV0kCRiGQwRRU9IHEojs93wYJiPt67bRkQJgbq0njxtRNdi9fRUpdKUyjZomDH2CQ8YTsQdG0zNiJ4jLK0k7i2yvIgfOxtwAkxH8xUzl8Yrw965UzkBdna5jPCTIyydhPe5scrImso0aFoxZRo2cD6QH+umEff/zKZI3r9DOBuiGfTQXI0i/RDMqsP2rP0b7m8+YKu4P3LCmJbOvtHtW8gYz3P+gz4chzyNuRm9S1+8HBxGXUXw1gOjMRMLe88jrZZ/wtjEnQ1LSDgcV75Goub7bBd4rTcAMQdlqrpeEos8NeoGBZFgQAry24Tp0xqg/wqs7K5Y2O+JdNjvI1TnpcU3LQrivLte7A+SzK2B7YKrxWOx2kzwjdqRJ7NEfTIuxiuQUG1ilSeDukizdqoJwDfnqM7r46Tp4M+bAgbm0XJ1Dihze+7gmLRRDR6RpIZ+/gr9+QHd1RpDZoYLMJkh/YsfJ382BIkf27hppQwv77s0FsKuI9TG9/U41grTLgzRhy3a1oVZQlgHtEXldp7KhPXmo+L48i5280VGd8NiW+ve2Wf85XtaGe1hw3O58UOA13mPAn5iGi21Q1ZuRrov/e7Xml7ajIq08zInByDIy8bXDgarmhzul+q8M+xTs1GJTRn1uki27D7JmN8StNmzz3a+Au/mTsct88ezYZbOLsdd1dBl8mHODdML4FWjmhn2+r6rQjftAnsHfLk7XYQ4hWRrW49bbkzWJsYcsNxaB+3AHmfEmCDbv13cIvlRmufnmEvLHX/JkTz3rj776a+nsA97zd+52XON0xFtoOo5ssFSxPpb6eK+CuNpVM5fEXhlM0556vewT3kGLN+3ZGuHX8lnTUAQiXloo32F5M83JEkjVRlKw/fizKTc92OXobu+p0T4aQpYEXHULArZMdbe9voG7TzmcWm/Q3c/hFUsStOSBo8LzkdiPjBI+Gfsxodnec6gY/3YHWVFPvru+Dc7izqSpf/8jOl2fLJZhHbi544OyPXCqOxwA6w1kfwhrAsB1aQFtrUy5voczdWXpWOdMBuj+j9/C/Yd/Bv/DNbpeBr/gSe1/91NUDzaqny7lWJUBN/esPao0fA7hPao/A+f+iZKDNg6MJJL9ktGSAj0NlAidFqoT2YaBimx3sbgTCd5EAXxk+At9uUE/DE1kvoxuH56niVPRfn9+xoFJmrLY8jz1/Bdvgbs5CyZz8esEmE0gt/eR/Gyeo4/EDp0JPCt2xYcEaTnDzc+XfH/OusCrHg4AtgGdC56vstlweqrrQEEea3J5EVzxBIAf52FNLqUEodw98HfvaKQg+wNclqJbroji7BWUNe+VcFu1+/wR9LxKPGM5ThXN1kelBqG3FC5nLPLVxMGgyNgfVCe/C+9jQe2ik4tP2JFqO+D68S31sje8c6dDIoAPgOLQLb8L6lYq3AMRiHQsMIssVu+1sY26ADjiwCINkhvWGgwufr2SJ6WZkL2/5ilqXRc9hbHZsWDd7pnKpGl0iH4sdnXZ/lTsJsEXWpt5Fgo080s1PyhXUrVLHhY8HTsBetrNOF6HORjmGbBz8ZYB4ppEuLFENehVQ97PzqhQIMJOVZKgu5wBH65pkQMWvn42jYOhroNsVd5wrbIfNv22Dtec2CipVP7QOi6e6sOy3fKmAHRgqMM7R2M4MZeRJ14ve8N7F5RrJU2YIwMBU+H6aqQ1oeTbifmWcyRCtx3ZQapY5Zxjd0KoovQRBDAAACAASURBVOXsenQOXb+Eu7nnRrZOTJETebjZUj573Id7oBSGLFf8WVbUWUdHVREsr/252KlQIM+K3Wnr0Ckq0hlt8GzC360tWueIjXFZBnzxhht2q8ZkhszUdcD8UYGgh9+8nSH5++/CmiRNOOswcFniIXtwYDYbYfvFCPmiRrKtkNytgPMZYcTjHmRXQ0YD+PUW7dkIyYc7xl/kxA6ZGpxi7mXYJ5RZGVuSDcJN7CbjwFvlDazE+rqOFEo9MMIA7ZHXy97wcHEwkfgIA7ApZNex6NztI2pSpTakzIOcRqCAKZOfp1uhhOYj8rHyXMW7QNfrEspouCGxJ4H4oDS27pwsH7dPSJ3bHNAOC6Q3lJR+LPbguvec2IEQOwD+1zilzsFdTIEDi2E36EMSD3GOgK6mhSQO8vGGWpK2Dm19tucjJNdzoG2RfpwDR2vya9L6jDhCRlgDf0vT4fI6hROgKzOKL2Up3HoLnyaQJEH1aoBysYY/1AHF6dbbSILpOhJSoLORQ8WN35Cr0H5+Cf/jDTd7kQNDVVUulBU1HkK8B9I22OV0k7NfMHis62A27DatFBGeTiYh3St5RZpGTKI+pQD/fdexoNnppjU5t07g1lTGDR2BQ8VNNuwHi0d/v0L7ZkZiduIpx6cty265gi/ykFdKQjv65Bsd7DwVu/fPjj0YBW9o1+P6fUjm4daqZGwIUICIwc8vkdwsmPuKsGs0OztdhwrFJjcRbCVatIY1AXBtArnfEsaQJsFsTRZLpG2LbjRA9v6eDLTdAd35FPj99+j+gz9C8cOcMn3Xd6rr7sK8IU62PaHb/R7kbEyVM32gk9vlCWXRVTWlyp3jBjdivA0oNzskv2zGkw6eDEkIhCGNDXRkpca2nWo3pkkgQkdfVH3PehOqfjRkC4lJQqw2QRbE1U0c/uQZ0BC05bIMfrklNCHP+AClCUwn3jX63355ggv52djz7Pmxt22IPei0JwnjL3ISrlebkN/75S6iMEeD+DAcrcNlGeRsDMmzsB6k6cmapK/aMGcT/jcIxjpCMKoa7ob9d7ckBNivt/DnM6TfXfNBVuUEJP5EM0ZMH966WiLA7TzWEk3Lf2Oxa7oDkOnVvlUZEJPd0+/Kfu5jr5d9wqsCmHUwRNrAWrKNAOe5aeoKbjgMKrSQDs4ryUDzWmPTSEq7RHfHlqSpklEQqA2AJBn04DpBojk78gzVZ2fIv71hDlnkPGmGfeD2AT7xnFBu95FM/FjszfNjD44XTUNoRMrUwVj/YtQ/8zq9e4BMx+yt1z7IVh+vw3ijYjlzryCZ+3hNy3Vw6UaaAIM+5P01xBCqZgxd1cTjpERb+umED+x8yVutV0a4hiIjTYUBPZW9rhuFbZexkXCoyZNVKXFpWtZCRY7kes7OzXp74qhyIh71M6+XveFBSY2AQFQTBKcqsS7L4NBAWiA45UkHlH0Wel0sSAFEep6RpOs6qvemypax7sle8SN6ArvJGLLdI/vXv+MpohIbSFNu8lRx4VXNLk2SnzB7/iB277U3/+8Yu/XEFQfjkgS4p/6NzJfxBru+Y5x1A2wcuvUG/nAI60BdRVL5dssefJ5xymlrshqiVRMChQu7IqcSgWpqomvhXl2wnqgHsSuVJGzX/viBD01JaC/qWJvIdsf5hU6gDVrs9gdOYbsyEOld2gV0pcv0Jvae77GH0Di6j7xeeEqDWLQN++wBH4PFjqdqznNo1OvFq09PhqBjaHowJqgKwH32GigKIiF7ZbA9dCNVzi1L4NUFb4nRgBzR8Qjdbz4L3q+mPWM68yau+lTsQQj2GbG7PIuxmySdtuxks6Eaw2wCNx4SijsaAZ+/Jrx3MiZ0OM9P12FYnroJ67FWoK2JEOejlmUSbxqXZ2hfn3MTj4acmPYLyGKJ9tWU0+rtDrhfRCeS9TZCoK0l3EmAMkBFZl1V83NYbSJfdbHi4afvIZw48gWCtIl1wx55vewNr/vZeU/89GgYjLd4DermSHw42aTM9URQkNigF3vVaaJFkFeXjzGpaiaToVxQ0zXHdMwv534OjLRNVlBjxn/7nr/jbBwMtfDmkrbwit1+KnY3GDw7djkcYuzAkRnZOLYMm1ZN0lRW5PqOrdPtVoFfg5N1uEGfwyT1m/Xfvo94++M12VeR02DMjYb8LMsSyf0yFLcyHgL/1zdwv/ocfrGFfPYK+Ppz3jZ5xlt0MoSpE8t4CJmM4u8R4UM2GeswsY2pn7qNAOB7+iVvDH2fm4wVhJdEa8tHXi87pRFwOqkuD27HVqHLMsh8BemO4AIiMa1QLAryjD1fxXyb1blLk6iCZZLYdw/AaBgnoUnC7s6gT51Eo6RtdmjfXACJo6ajttNcnit81wG/+wGYTp6OXbpwYn8q9mMfWUBhBf0yqJDJoMf4zybAvkLzdobUZOsuZhEId7QOSTycmjS3n19xPb/9ngAsXRN6JdlFti7tnsh6E7E//T6w3zM1/OItOy6HA2mQuz1QMk7YLdDSZdApAjKQrk3BYLEELmYsgo9kCOkySIBZwB8Zc2qx5PfYNCfwi597vewN7x1PkF5J4rKNpY0+N54CuwOcU8CS0bxqR+CX6Q8qGtHG0EgSmgmsNlHkJ8/ZNzc3voMKMplNC0As/AZI9ocA0ZWKnFJUNfDuI3A2jdBd2zQ/F/tABYeeE3sX1Y8hAszGwGoTW7PzpcbPTk3y+3cQ51kMW3FbK7pQ14GbOz5keY7k/S3XczEjElTXJPMlN1qeB0EkWa3Vt9UTaTkaQLIUfrEi/S9NWYDfKl+hroNwrSsK9cY9RNhyy5NcvHbc2pbtRwPSmbdto8hPkZOuV3Ah3+x4wh9hlH52S/373bH/L1/G+6wbyHpL1o2J7njPYu1QQVZrnjhGwDZsyv7A/LrIQ7EIaHchoB/bqFBwdc4vrN8LSrkA+9PdlFd599VrnlLjIeM4P4v1wWjETTmb8qR6Mvb5s2OXqo4nl3N8UBXegCKP1/hkFOJ2WlAbw7/581+frMPNzoDLGX++rscUFWxNtjmlVGjAZku2027HoteEkz7ehK/MDfqnoqYqb+0Gfe3mGOzalI69plx9nfDyUGCuzoms7AmvDvxi54NjCro20hjtUHji9bI3PBQh5x07AerMASBaxdd1bO8Z11HH8gSDqYqu2aGYqRhA9S0hjqNbb9gGq2tewYrVlrZjkfbde/68v/+WiMeHBU/ozU59nIqoFqYn3ZOxF8WzYzfCQ2jDAhRmVSw/sgzdegM8LBn/est2oEiwu0z+19+erEM2W+DmHu37j3E9wMmaAGJiDGPu+r2gCelmZ4yjXxJB2TQkaQOQz66CYKqbnZFl1XaRS9tJ0BziG7THbg8DwLX2jnJ1HLVzgSC3bU0Gqepwmzz1etEpjeQcPIie0N2gx3E5gHY6hK8auLWSJMyJr1cSk5EmkKygzslyR8UC5bEiSzlcWa6B8ync/YIfrBFAGv1YVht2P5RfCUcVYNfvRYOwn46geWcTkj4AdG8vkdwvH43d1S38avO82DV3dssNMBuQOD1fERejOBjX74X2XqAKmprubEJ4xLGefllA8gzeeebOZQ4sN5AvXoU1WSsUzgGVZ69d4Qyi2H/ri2O1Bh4WaL94Df+7HyDaewe09TgY8MHv9eINIMIDwTiuIrwRvE50DZ+kk2lJjjTl1eISQBzySRe0ah57vegN76qGX+Z2DzlU8Lu9umjnQRLCBjvBCU+lo91oCLfaINH3Uoq5C71nnnwthy8i3CB6OtgX4oYDDnGcj2yphwXkfAq33KD9/ByJP0It1joYyjIKCGXZo7HjasYU4hmxYzIkoSRVSe6GnY9gvZmmwG7P361uIdRarHiL3T0Q47/exnWYQ8mgx6HTcgNpGnT9PKzJVL+ozEbIruXwUlWUGtkfuLG//hyuoV2PNA2qP/8CcEDxr79VeLWDS7IoN2KvToA0iubaMMqw+2EecXS78UvqAMfv1OWcNmOx/iSJ+2WnNErhY2Gjbb4jGQzz53Rlwd669bDzPKQxrANclGkDgr8pT0cVJrUOzXqjwyO9ZkVYwO4P2isuCIFNPJJ//IkCp0c6NpiO0D7Mgzv0Y7Hj4+3zY1+s/yB2QB9IaIpkXYr1BnI/Z9rWSUzPHuan69DuiGx2YT0ATtbEv99wM01HcaOWBUVSxyPgfAo/GcPVrFFc3QJ//BWKnxbIP24gn79C98Xr6M3kPesFBb85lbkOLttNy+GRT4KCs7nzuX4ZVR0S9WvtRLtgR4bTT7xe9Al/ojHiveK5JeiiuEEvFDihY9G26i80DF0XWkMeouZ7njM/Nok5Iwy3HU/E1YZ5bdeRVKCwWlP5Qq2qtlVN3HyWs2D1Dq6m8e8JvvxnYgcsJ/107AZuCwKkg77Gc4j/RokWSKhBKfdz/llRBLHWUHgeKg7QLDdXeLMrC8jDIqwJANe1O3CjTUaQ+zm669s4WNPbJjw0bQenmJ/u4w38bMquCxCw+Njt4uDLTuROAsuJOvYVp9xpqrzlI/nDYweVw4GDQyXVfMoF5GVveFPjaluelIYvhxZQuz2ChykQJqpOhIRqJU3DnPuUDG0EaDiVlz4CJsnDVonQTKew2vDqBgI7Pjh3nE30Slah030FqQ4hr4YVmj8Xu5EnnhG7qFSFg6ZbXjdypUWaoTxNHzOPD7lUFDgSa7PqOmS+hEGmzZrH5dnpmlZrQpVXm9glylIiNLMc8vYi+Lvi9QVwOwdyFbiaL+EHPXaOFisqIC/UWKGTcOJDWWvWSBBlNbmyVELNlht62KfBwnDIDtV6owMt5QCUfZX6/iW7+ImEIZAYCKtpFT2oE0Q7AZOEqYHpu9w9BE1JUUBY0GHv2Bc32QpAQVoma5GmPK1M9Ws25QY0qp2lEsM+p4brLVxVo319FuAAdOp7PHZR8vjzYpcYu+PkVvql5tQbbV1WoeiOP9fH+iLLT9bhBn12oHb7sJ5uOjxdU6/HIjDLmFbkNFrGq0sAgL9bsreeeDYYZhNU/+JzIiCnY4hqS9ImcxdTGqP2JZ6plaYl9kCaCJUUGWER3hOSfXkO04x3w0FQPgCIoXEPy09iaV72CW9GwKM+T8z7BVORnirZmoqYKgm7pE/GjUlsl2RLuarmF9lpAWxsnt0hKuEOB/y5Cg924xG6YUlA1E/XEFUwwM0DU6mqhnz7I/BVFCDyc0KMJaOtvCkc/FzsAdv9nNgt3zWNeCs8szR4zrrhIP5uVRlzju4c3cWExsv387AOSUkh7PKUfNSqhp+v2evWNYn9/n4RiBtoGnICpprqALS5bCnx3Ra09WnP+kiWml6YyTDAG6k6KlzTFM61sZlgtL4e8fWyJ+NKNlumr8YlsEaE8nuxO/Bz3R3w1MvJcZ78wl6T8rX81Zf/tVqlcAhlPqWuUM/RLI0a7Wr+BRBv7UxHXaep2PNDgVrNQMf9stlqanCIqETr7+4P2hHQy/DqnDgVAG46Rvf+I/1Iy4K/SzVWRCW5H4s96K08J3Zz8jZNSec4BVWwlah6g6w3VB4z0ogV/dbrB+I6zF2jOJoftC3c+VlYE9Q02NSUjWUmmx265RK+KIhjqWuSN+ariG7VU1h6BecSzngCbcQqeRcKTxuUIVWg35JKDKbfD0N+KlUTgNYz2qdXdKfLM/zNj//t34rIf/Rze+plpzRtx6fc1KTMGsVeRpJOPMfSQPh7p9ed2+ziaWKbo6qDD6jUdTDjcoOBPhBFhKH2e7zaJyOYGyDAzS7zJfzrK268uoZ8vAXyDO2Ha2C5fjp24PmxJz7ELusNb6c+rXvYOlS9dC1a3XAY+tqux7mBGw5P1zEexcJR1+NGw9M1TUYkWqvPlM0LXJoQGgxOoaWugZ8+RvFXlRDs5gvI++uABuUaFV2aJkHtwSURsx+gFHbgbLcRUmGf0WgQ4NUGvwgqdJ94veyUBsJia78ns73UEXKeMYdXfAZJDGUk8CppQQ4HmNJYkGDrBNK1xH2bXJ8NLvT0dS5Cfbvlig5093NlTa3ZOZhTr8b60t1qTerdehu6L+xTPxK7dyr49IzY05RtO/vftw/xQXJOYQwuPqg2yNK2prQd5TLKMq5jqUT0qqLUxnwZOki2JrfZcV1JokTpDg4FT/P7BRUCipztxPMyGL11WQK0Ar9YA1mK9mwA//t3jCnxESAnpDBK21HnMkkAaKqqwquYTcnBNVj1agXsUnZ9bApuadDh6IB55PWyN7yjpqIrVTO8aeHKVPXUfdCAPG6rAeDJt93pieeD2pjr9Zj/3i/gemo9c9wNMXWAsDE3vNoTPVHNLcQIJJutblb93XUFZDn8aBhO5MdiR9sCarP+qdjZltThynzJVEg7R7JnW84p1c0eLjHbm5Gygswx0NahE2STsQvpTydxTbk/YRAFKMNqjVZbunwo2ZGSroNLEhLd64Zp3YI2meE2aRoyotQrN1gNtW1sBpi1T5qy62SShgBjaxoEy5tclaALsAP2i5bpSIhbZ47bRKSdumHIZMQidH+AM+J1kUdNc6O8tSQ+I9FqfxQHTtIvYwGonQTsVQfy1QVzci163XpLubhDFTsdO8VwL5YBxot+yS7Kavto7DQXbp4Vu0mVuP0hCjNZz3tAjqmM+oQemBdsYTIfatepkhthHaqjE1KFxNPjabEOawpEbmOAGSOs30cyGuLw6wtk9zu4pkPXz+GXO3T9ApJ5dHkC1wrS+w12X03R+9tv+LPMvEA6SK36PdZataJ0tz/Rl3FmktB2QfOHh5Vi6e2meoYhwsve8KYw1pKF49BEiIB0YbNDOqYJAByqOFbPRjyptG3nnIt9YD1dnPa5ZbsLVuimmotCBY2kg3OjiKGfTpga5FkkM2vfm6dVR0KydI/GLsMRhYeeETusnSnCNEABXMFdxDvIDwvG3zRhNhHEnnQNAoR1hBNUoQxyPyf2/XAIazJmlfXwxYBbO7ZIs+tb+MmYP+NXbyHf/Yjk7Wt0H66RfPU5v5/FCuV3P8K9uuTnYzLlJl/StFyPCOM2ycJ/IpkNJXS7Q8XP13J39bcyHFEoaB95veyi1ZkUchrgAdJ1wXdJtltVAWYXwuV6evZKfmm1bjKzO7c+r8lFqDw0RgNaqE9GCpUd8brWPD2c3Eb5U3fokIpY7vvFqyCG6vLsydjdvnp27PA+Do3KMmwanE2YmvRKjvc99SsNRRpSDoDT4KN1cGqrxbyuR/b70zXp5+SKIqA3AwoU0PqpAgpK7cmf/wbduw80V/j2R8KR15vQMw/yJIEqeIR81B69bLYxfcnMj8qHBkZI8Xpl+Dz/4D1PvF72CQ+JHRZDxzWNGoC14QMhCtBH3ErbRWCZabIboQKIp8b9HJiMSSw2o+I8Y0tMv2TpFWinA+q5mIbNjqAuaVvA3KdHQ7h3N7FbkCZA6x6NHSqQ+qzYvQP0Y7CWo1j8ANCPOb3VH84eQnU+gXNoJ/24jqrmRlG0ohwqvTFcXJNCc4NhmeHOSxb2zdUE6ffXkEOF7pKaN+2f/Qa4maP5j/8ErhGk//a7SOBwlNczEJ0NmAKNMcvY9l1vVeZbYQc1UZMmqwcoAjPVmsjeI0n4+8deL3zDA91iCffmiqwh7wOCzvreWG14MtQ1T5wDfX+seHF2Oh7oixqYNt4xT21b5tsN/4ss5ZdhoqVZqpIQA+a3kyEfjPGQ8F7vgYlOPC9ncA/LIL33VOxIPKetz4nde6BRrMuWY3/0CrYvzdhY/9yGWTgbMxZVVesmg9N1jAd0+TCI8ngIGZVI3t2FNVla0331hn6uPa1N1lu05yOkH+esCRL1XsozSOZJMZzveeq/viBO38SSsgxo9oFEb3IdhBIojkaEqYvdBKmqrJlRM8Ce/qFirbWvAo7G2pmPvV5+SvPmKoj+o65PTmzXaddhNOCQo1+SeH0+5alVUNZOhj3m1ibSoydFUL5q2iiZ17RoJwPIaAC8vQLqBu3lBNXVADIZoutl2kPuKIC033ODXUw5mR0rUTnPnow9dEyeEbuYmRgQT0YVHWrenMFV9Un8SBJ0Zc6fvdnxtN4e/nAdXcd16nqSh83JmmTQAy6mkNRDxkN0ox59a0cDJD/d4vD1JdrZEGhbtFMC3vZXPaTfX8NtD6h+pWJJZUFGk3ZUANC55GLKh7pXcrJcN9HdRLFPYkrKlbqaX0x5yzQkiCBLgyBtoCI+8XrZJ7yA43S96sWueyjDZb/XfFhTlb1eyestjb127GW71Ta2BtMESFQu434BqSueLvdz/swiR7LdqRIYtWr8coViNgUOFRKAeanVA2kKWayA+zmS2VTVD6gM5tonYq9q9safE3uQGKGyrpuMiV8/VEjuFxzE/PgR3V4VGKoaXs0dOjNPuH+AT9OwDn825sn//hqibKt2veFASdfkihyy3SG5TnlyKhHGb/eQ/R75Dw8UW7qcIXl/i267w+BffRtupewn7flvdjyRAR5iZUnurAouwTmiOa1usANpuVL3RacpTBI8q+xnYasSKYcKmE2IQ3ri9cJPeKhfEvM/w8CHfLUs8QfQiDzjqXU/D5NYqWvtqET9Ruz2pM5pX9eVJfygrxPIPHBb3aAffEnbzy/RnU+ZdydMj1xZ8FS2LkG/1H69fzJ2SPf82Os6FpC9XqghXJFDXp3TBaMs4S+pmYPPXoUujB8O4AZ9+FeXJ+vA9R0/g7II6/Fff3m6piRRmh1xPka4RlUDl+ecor66gFtt0d7eAV9/CVksyQ24e+Dkeb6kQrK2ZwNE2h/1+LeU4xM7LOqGfzZTO02RaOVzTKXc7qLGDfRwVJrho1vqRWNpskv5q9l/EaaMrsgJApsv2d3oosR0WHRZBtw4gNOppbptBxOD8yl70mVBFd07pfKZxYt1XIZ9fmEPS/bMtVMgExKfnfbTTbUMRa7Tz+Tx2IGIb/lU7ABMS0fqOk5jzdSr7dCeDWL8Wgxiy7mCtRNP1mF4IsO1iBoIv74Ma0KnvkqeupDSL4GPtwxdUzLs9vwZaYpuvoD/6gvg/TUfljeX/Nkfb9ghOhZ0skPLsOwAv59Bj9Bf5/m5vL4Abu7jvzOpE7M76gRuMlIFOeJ+/of7//5RLM3L3vDla/mrs/+cp42ZCewO/KLnSz7NXXckubbl1dkdQUbN6bntQpelmwzgtgeO6M3SBThlyyiJOJw4vVK9WFl0dusNfFGgM6m+IJ3H3+dfXbIL8kjsJlr6rNgBXvMi7ErMprSs0ZamtF2YMQQbRx3OWO4vuz3/TNdhzoDddgs/HJAE3gl8rwxrogxJHfVzdJP611eMSbH7SBN0eQp4ILleYPMXbzD4334AihzN1QR+X8OvdlEiZbePtpWhePX0eFLP2fBdqNti8Mnd7mnReXUGf/0Q3ERkrPVKkuBvfvffPLrhX3YO3+ngQYnRYswZHbhYnmsnh0t8FAOt6sCvtOmgoSX97YInYJGHSaWpmEl7JO7jPeRsEtGRZRkwOV5Fh3yW8spfrYA3V/DmoariTI/FjkMF1yTPi304iEhPFWyC2sYjTdUzqeEEeb7iZhYBWoSbzPxbbR0upbqZ1zmB/+ItmV5tG9bEg0Ix9kk/DKnaswH899csIu8eIG8v4X+gKHvz9Vv0f/+gEOQEyWLHG3CtMxO9TWAdrl7JB8B4tWXO7tNuT8yOkltEhF0ngDLmS+3YKJTa/k7Onu7SvOwNL0pLU4CXdSikU8FQ2yzax0Wasvi0k09POgEidkMLXbvm0VZk00/GBFQ5H8WDug7y8ZbgsUYZUNLx3+pDEJ2zVRGhVbLzfh9ZST8Tu3RkNz0nduPWBkKJYcHbDmjoEOjUspFUOqZv6KkQ6sdb1hyKsETbodvvo/xHnsX1HK1Jdnw4TX3Z9QmiSw4VH671Ft1nV3A/fuTf7XZIf7jhad00nN6W6oBuqWDi0Z2P4bYHmhkY/mZPwJiz/FwEuJvzcxgN4dIE3bgPd1DPVmundh2x/iLskKki9GOvl73hnbGOEuaem13UTVc1XhPfCXIVqu6FkkZkZktpXRecT3kKFkm0pvFsURqJAkLx/e5yCn+35Gapk5DmyHbH4jBLIVnKgU7Ton0zg59vIP0C/h7sK/9zxG4kB6s52g7d+Rh+tUNzNUb6I5WC3XZH3MxqA1yeMT1I2WGRXqEka11HnnGy/LBkDOMR/0z1eNpJH37T488AAi/WzaZxeptncJt9ZIi9uuBBcvfAB8kGVyafZ/TFD3ec7CrBhB+KY6tyteHGNdW51SbMFvwDNehdVTMl2lf876HibbDeseh/wgHkZefw2ZX89dV/Gf6/NK2O4Jto425/3i8pu6aTQ9fvK/SAxRn1GFkEukGf//aox0tlrCoWms6pkH8SsO92cofxvZkiA3r1K5LPppJHPeF/GrthQJ4Tu00Z5QgOHToVCh026Qz4JNxkoehOU95MZ9OTdSDL0X15Bffb7+HSlO1WBcUB4O+11iEQjNOOVdDgfZDN7jZbTpDzHO5ixhsTCJ8jP4gu4H/gk/CZBG0a+zubTucZ00B7v09CexbWMTK90M0WSJIni9aXfcIj6qQHfcdKnejs5NNBhpgDs2HG+aawsZCrb2nbRVmIq/NAwsDDEvLZFfz9KuaRbctBzPUdb4b1Fm52RsBZv4Sfr1horbaQukb3+px0OVUHkM49GrsDwkn5ydgVtgxtd6JrgYuzIykPT8JJX6G+eRb0XRzAoc67jyx4dR0oChap2wpuNoX0SyTbIfNlW5PCkQF2jKCkcadQ3iANCPBg+PIt8PE2AtYSVVNzPj78SRJtMtsuAuQC2K0ItYOI8mjVdZ1iqW24rYPzoraAQ8r2xOtl9+FNzkIFknj69E67KUq8dnkW/x4IrJkgq9fvUWxzMgoeq265Bm7uWeW3HdzHe+bemx3lKpZruNWWmPL5EjIZBdIxoFh0bWH+3b/8ilfqfMn4puOnYzcs/zNitxOMbCVh7Ost3HINp2ZmMMJMEwAAIABJREFU0nZUQ9vv+d+q4qarG4Up56frMCWF23lkYjXtyZrcdo+/+5dfca268dE0wNU5mtfTqIM57KM7G7ODkiTk8b65VMJ4wjRLZyGB2FGqb60NjbSekMWSE1alNMpWmwCJ3ooDVZvoeGsjS/kesOHwKV2aF5/S/NX0P+N13bSx7eddOPkA9oQDz1VhtGESavY13RE7xgwSTChJJSFQ19H12tplQGxRdhLVuJompjSaRqDI+ZCoQGlINX4m9mAX85zYm5Y/S6XBnUlQa//7OA0CNPUQlR5RfikuZ8CHm7AO1FRvEOMKqA6mbLZhTeF36s9y/R5nCS3d+GQyovTGbAL54X3g2JphtMtzbn5+SXzwDL5shg69Ijgaci6gGH1NcYIN5xEAUPolW5A+8gKkolKCW67xN+/+u18opxXgl2OngVNhUh2EIEkIXhr0OZXTDxNAsFuUrgunh7OrsVBMx/lZ1D9RlV2X5+ztvrkMnQ036JPfORkSRizkmfozTvVM2kOW6/Blhzz0nyF2eBdjN2fAQZ/xW/ekV6jAVB6lwd9QTsP1exw2Ha0DaaruIEVYD4CTNUXzgyL6POkNJpMR3P6A7uoMoilF+0ef8SsbUEtIdjRXa6a9CBHJtFgulcEF8PRWbIwUiqEZUzLE9fsBSiADZax1HZrPKNkhacKZRV+L12H/ye30snN4B35Jx0qzhitXxQK3ZqHiti3pbYMeOZn7ik+9atDY6UqtSh1JL1YUAjKCcVkEcSS33ECmY3Y/lhv+W8XaYDaF2+5p4DU7U3GmLeTL18CuAlZrxvdU7MCzYwfsFusit7VXMqZeGbEnqugVMPlLldXuUzIEm11Yh3zxCu76gR2cfhkMjLsf35+sCdAZwXjITbvbs4jMFKj1ux8AAMmb15B//ImfrzG7dju49Q7pzT1kNuXvtlTKe6BrVG2ArWHUDVyjSNbVJnSxrNEAkQDlTr+/jjeFcwEp+imZjhd+wh8Rrw+HcCIEFo4RCKo6EJXRHgmm5jn7v0DoOTv1+ZQyZ+HXtPyC9vx5zizNmyb6BaU05wpRbXaQ5YpEh/2BQ5Uih79+oHP12STmko/Ebp2QZ8Wut5NThpWomS8uznidFxnXMBgERztn3kjGALq+PVmHO7BukUMV1tMNi9M1qdgTijzK8s2mCG7gYI3iL8+jHAiAIAuYppxpVDWwWAWRKFlv+OCoxHW4mcxONEsRLOh1/ejoKg7TmHcuoiOtw3M//6QT9wvf8FB3O+opOlUECJY0TcNrr8hDQWj28gZ5NVhA6B17HaSst8SFHNmo0NVvp1estj4/3PILuqd7NTcJlYe79x+DR5Ps6EsEkSDc/2TsXff82K3zcHwzHCrG32hReqhCGiF7NUJuVNPmg+JfjtbhjK86GoT1+H/44XRN6w0/jyNdTBsmdZst8f496nuGjplJaOvDJKqs5lQ+m6yuWLyL0vLE4MhFHlxLnLVjVWA1CM4qPkj2hwg9UEfzABN55PWyUxpIBFgFSTwdVHQC8frlZ1lIW5zhMJTwIXVNnqcyp4Lcw5HtiswmcA/LqPmY88REWfCLKnO4+wXx5TWvYTdQIFbTkoyx3qGbsK3nnU6Cn4rd7HWeE7uIQhWOYh8P4bqMmBKFSkjTcoBU1UFH0+x9ug/XcF+9CuuAczSC+HgDfzbleoZ9jvJ1TXaqUs2XUA5TKcZvvoB/WPHfpil2/+mfYvBvryGvZsD2gP1ffA7fCvJ3S3bDjB9rAkzbA8zMLfi3PhxJf7QdJVHsoFptgLMxodid8LPo2GJ2QACTmQ7oY68XfsI78jaPOknEsycRxppQo1CGfX4IxzxIIACmDAxFWCqdJbpXM5p7LdbcBGo94/YHyHiI9V+84tSybpjDGivKuUA9rD+b8csZ9uDnK+aS5lb3VOymTvCc2LVwhOoyun4PUuSMP1fJkNEgFsdFDikLTiyzFLLaMC8/Wofb7OjE/S++juvRFNHWhLalQKkqfnWzEdrXZ9TH3+xZ+2Qp2ldTlB+2LEQ19uLDBtn9Dri9h4wGkKnKnShq1KVHp7H31BjqnxacbjRiMVrkhDIkPs40GoLujIZoxbsbjZ7cUS/8hAedLoCoMgXtihi+pGnCJE6snyxdgKAyZ9wG0JfT/NLlOdxPNwhsfoAtR4DvO1QY/F3HjsZ8xVOtLCjg5Ng7loc5siPEn3QdcPPAAqzX+3Tsh8PzYgcCFc4kKrBY8VaSDijZppPVmgoE+32EJO/3wMUM3buPVFbTdchuRwLN93tgNGT6tVxB2jauyftgcobtHj7xTEHWG27OwwGSJEgU2NYtV/BFzsK/V8b27/trdsNyynrj3TUkPdp6bQtcXdCRxfR+gvLYjgeExiE+4WfUgp+ppUZNy+8ufXpLv/gN78oi6goaRADam1ZRJDH47ZHTdoDIAji2cieZQE/pShGNdqLadbrbR/PjhwW/kDdXdAw0/ZgiBzZbyHyhYk2eHQQgais+EbvLOW19TuxwPk5cFVcT9O37/aBpSVOwXB0IkzjMeVgAf/wV8O1PYR3oOk5az6ZR/wc4WZOpkjmRUEzLxQT+LkPz+Tnfv6+BQwMpM/giJ+UPgP/wQGzR5gC/2cXiWfXkOSVNICp5Tt6rUh+H0c3b5SpTUuTc0IUZRhgcOoGsVSO0auM6Hnm98JQGHKaYYRUQ3TSKHGbD7tJEhY3qMG4OY2eVmbAuidjJBfA0rOvY1pvqJNUGKHVD0FOvxxbfkWlwkI4wHyXjroqg+3jDzfFE7LRweV7s1q0JIDKAD9RIU5ZBGTaIFW8ANFZHr9bffnuyDjfoh5+Lo9nAyZqAU+OxNIH75icg8Ui//Yjku49w727gqpp1RFXD/d03SN7TsrLtpyyooemZqSB7HzpNwXF8ygk4ziahjSqGmVGmlr1cXyW0Le6zCfDqgm3fT3BaX/iGlyiFF/AoFVMLVdHlYEanrooXD3lzytRAegXVeE0oySZ2qzXl46qKBIKHZUBQkqq25SSx0iLy/oECoVtCCNgRUd2YPAudEn8xi4Kgj8QuTfPs2GW9jbHbBLJpaViw2hD/Mx4QK6OydfDUnOxu7ymp9/mbk3WgUre/5SquJ0tP1uSULI6+mVKQRCPbPbrlSmmSBe10AHS3d2xRHg6QzRb5b9/z5z0sCHlYrNRMIgEWa27iTAWe5ivefE1LuITNWIDg1AIgbnxRTfm64c1qHZwjZYOfe73wlEZPLZNZa1qeanUT6WKLFfNCI1UUuTLdE0DNarHaMJc2Y9uqZp4NhH6wu1/wajWP0DTl3yl0t7t/gD+boru5Jfumqqghab3uxTKCl+o6ykU8Frvz7Dg8I3YcxQ7orecNPeiIrKxrDtHMw6lpVHOSOXv3++/gz87iOhZLnq6DPrr1hg/D7zfwk9Fp//72PhwuLlUoQ+iSKBVxMlbBU5XWLggA6x7m8OczwASg2pbrPoJtiKkxFzm7Mm0R1JRdnsNZfZMm6O5XOsmOpz1V1ApgNQ+E8KdeL3vDG+EX4JW3pkAQui4ObibURJFBL0pZZxnz2gPJz66lSoEk2lorVaH2bEwLyK5jR+Nezcg8dVowmwDrHU2+1GbFf8nxuV9vOTUtVFKiyNG+msJvK9pZvr+LCMGfi71uIL3xs2KXWrUtdYoYZPJGJFW45Zrdl7oJ7VQ5Glqh7eDPzk7WgV9/BvewRnf/APf2Ffx6i/btOfDjTVgTTSNyph9ZCmz3QM2UAq6H7vqW+X5ZAJ+9gnzzA9ybK05rzZEcQHdzqzAGbfHuq6DWhpSqBjIeAGUOWayjuwcU6dkrgPWWJmo2pzA9+KEqNg/VA+oXXbT6I+EiK5xM2jpLeVWnaXDtOH65XRTmN6UtlylRQRF7bl+hG5ZA/5xSFb0ruPmaRATtt8uoD8lTSOrhewVbk2nCHr0ZLah1vKtbuH2F6tU5ioe1eg/9fOxBvPUZsTvr95s+S56hGw3gt3u05yO4HtuBrm7hdYDjdgdImtD9o2qA8QBdloR1AEA3G8Ft2XVCWSB52EQobt2gvRwjXW0AcJPK/gB5fU7+6v/+WyRvX0OyFPWbMdI77aYs13BXF/yMVDHMv75ianZ9R0W0suApX9M0wZUFsNqqYbO6Bxqn934e8E2y2bEB0esRwrykc0nQ3xwNolLdI6+XveGNBNBmwHbNtMRAVfMlrzqpQysxCJmCQ54Ar90fCExarSOzSHHx7v6BJ4/5pbYEeMl6GwgiDuCHfz6lEkDTQKZjuH0b8m/Z7+G+fQcM+sh/mkfM/SOxAwjt0H+X2JF4uCSBv32AiAQNHWfElSwDjOoHwJlg65Z4cVuHe0e7eBNSlemYnZDdPqwp+f07dowA7dW3cD9+pEvhWOHPmx3yH1qmMPsD3OyMCgof5xwCaQvXHaylyLSRQzbWPTIaENym2vHO1B1K1bIRUVsfdUzpOri7edDvMYdGtzv8wlMa+9pEoou09whLcvHvZR+xNYYvN8IAPIdJSJIoNGqQ1+Ewpk15BlnXqlPYBQJDt1F99dt7tJsdH4zdngVvTaEjFAWJx/sDqWw2ZX0kdoMMPyd2GMjM1ANs+lrVkMQf5fPKc62r0AMP0tn5EVXw9p41RllA7h64MXWju8k4rkmEDnrG5wUoiZEqHLpuqEGjEtz+zStijPQGwXTM2PVzMDPhMD8xSPCDsr7WG57m6lkV+v1Nw3+vMh3hxnxQ+qXh57ujyfwjr5e94Z3nF1AQ0BX8k9YbXmvDHgkMQm2S0BoE2I3oFQH0BEtndgfm51XN/xoqbzyMvkltCxQ9dhWkhnt9GfJG92VBx+nNLrYGAU5nX19GYVZNtR6L3Zkg1DNiD2Cx3SHa2zgHmYxoHz9fBdis/R4A0Z1jp/XB0TrEWpZZCvf56yBxgd0+rAnm9GFcX9OSWW85PS1yDqPUmrN+e4Xs9wc6gNQ1mWFth+rPv0Dx7S35tMpcckoLpLeVAud8EnPwuglqx9a27b56g+R2GSw6Q4fLOZK/809v5xe+4cGhifIfrVVnI3n3sIR4F4BJBhW1B8HpKRlkKw4HTkB1yBGGPOoyF+we84ztr8tZbHntybrBT9eMZdiPJHCl0+F+zpO+ZWrxVOyyI4f1ObG7nU5lez11EHGB7GBAKvTLKFtSVaoioBPXyZjrOl5HJ5AVT1S5eQCGfTQXI6Qf27imj7fAeMSDoN8Lp2u3WkMe5vCjIU/ey3OgVyL527+HTMbsyWcZusUSfjhA/r/8Y+yve88iO0spIDUbw7+7IU7/ULEDpSYJkiZApUTtLOVmTxK0r8dUfHh/y3+nFj0QiYfDI6+XveHVvgVA7Lua092GYkLOueh5auwfveqMDxsYO8a/vJoR776m4a4rCrKBzs+Yu282pMQZPBgkH2CxjgAo5a26nrYClysE47HtNhZjj8Wu9cKzYp8M4R6OFA+8hyzWkb+ZZ8CHG57a2ja0TY26AnZJgCuEdaQc0UvVKrupoxrw0ZpcllHLxszg1H1D/vQr1NMSybZBsjrAHSrUb6ZI11vI5Yy/J0ng9nt2v2YTKkUAAQJtn63Xz80Uj7FYcfOaczqg5m5q0OxcfA+gXRvlGaTpHzQA/unrZW94w44o5euErW7mVb2SfXEd14f35ZlanvNDgvI8UVXAbUVQ1qAfAUzqOuGKnOCv1SbIWAeYgXSQ7YGk55xdBOkVcHUN6ZXBhSJYMFq++XOx27DqObFv90fOIJkWrh4o+kG4VRRkBueAgdrymNGZFcDhAewgYwW23dF3VtRdw5VlWFO4uaBzAIVQJx/nSK4JiTbl4/T//Iak+H/8lpr7uz0/p+u76CG7jIOxAB8wZ0Bty1LNuQl+rqHmUsFX5BkLazNC0AI/6FIeqVn83Otlb3gg0MSOCzPTmZG248mqcg9oeArQSl4ZQ7meyM0uMIGQZ+GG6Fbs+waeKcCprDpWyF4ZPuZp6jmhRSfAeBicqrFacUO1HSeTVf107Ed/98nYNdZgwuAMXdhTqIT6qa7WhPkuV9xYzjNFKAvGoA4fKArqPQ4GEJ+E9YgiSm1NUlX8PQO2+4yC2N3eAX/0FXD/wM9usYZ89oq275Mx1zgeAvuKRa6ZJduNrJY1Yv+/pgky5GiyvtvHTa+HgKw3hGIDRLFaVyrRFmhaxPrlkdfLhhYo0MuNR2w7WY9WK3KzYgwspv1e8RWO1b3iNFyaMBWwl04K5VDBjxXDYZbtQJTdq2t2PqylV+RMdfSUbS/GwXPUDQYRBWm3w1OxZ9nzYz/6Es16xk3G3OyvL5ijqwY7AG52tfNxWYogInu0DpsEu9Eg3hplcbqmXsl1mfMJQMfxywu4j3fwVxcsgA2ntFph9de/Joygr66EVls07anr4v4Qh215TpK5HUh2s+z2EYKhuPtwE1xMVcIjjV5UFVWhn3q97A0PPeHVMduY9QCI/VZn6PDSPq/zPjpoAzF/bRq48TD8eQBiWVvOoAc19WNclnFzKJBL9nvmxPbr/uEHRS2qOJGKjz4rduDZsYvqTLrxMJyyQb7vlnm3tURhqgf385N1dvcPp+tIEhLXj2KQ/eFkTbTnaVjraOrgZlOqO2xUf2e/p7rvegepGwz/YcHfua+5Ae/m0Ze1yJlq9RQqrBNqKLMsfEbOcf5g6aa5t/TV2CJN2Z3znv/OBpSmE/rE62Vv+DQJKDj7YgIZoib3kWmAsu7PxlEla9CLp7piY2zMTl2TTh02+qp/sqPagLGVVObPetH8oR7Nn33FB+JyFsSdqJt4JBjatSzUnoq9qp4du/M+xO7s59lpmWp/Xm3tDSNOIwH9ekWxL0frQJ4RwLXZxfUAp2uyB9W8az0FXztVFOtu72ECSrLbUSPyhvRGt9nFVNC08LVgFXPwMOftwBXQNXvKc0hdh+9OypxdGwXohQdDDwDTrQ8Yq8e21P+Dbfj/3atTxdiE1idBL8b4pou1bqAast2TnmYV+5EM88lJmhzZoB8q4r9N3q1teUJnOdC1bCsWlLGQht6i/n/+O2DQg3z/jl9wv6cYGYHLqKSAvcIDzF7y52I3EvJzYrf/Jl6dQKK5ADtQzOPpaH2I7U+VCpGqBj57DXy8DevgoKnTLlEK+f4dJb5v78OaqIcjJKXb5np/TXMF59BOBvDzNdwIOPzqHMU3N3wAegW7MiPqb1ZfnCH7N98z386z6IerDC/WM1EtGb4XUjrpiIPCUpUgDEptKg36WZgy86c8nl74hlfD2ZzY91Cg6RcSvvgiZ6GluZ7rlSouqv5LZmSmp5U0ShTolUCZq3SHapYHzckc6IGGv7tDIFS7t5dwiw3tYh7mHM4sN8DrC3RZAndo4PxYrRSbx2MHnh+7pUWVThON0lc3nOqqzHagv40HhBQ4B3F74GIKXN+zM2LEcIDT5i/fUmmh7QiJbtuwJqcGy9juw+9AryQcOEngPlwDZ1PIdoc8U9fsN5eU9Xt1yW7MvkL2b76PgybtWp0IgDkXdTn1e7fDwKw3XZpi/ZefY/CvvtWuWxGEtYys7kxC/InXy05pghTD0XPZKLrxUIWcVaxtp9Be2exOcdG7vRZAbSy+2oihhslo66a061RKnVRudjypPp/B7Spu5lY9Vd9fMwXYV3A/XRMzsliqrMbjsUOHXc+JXdouxi6dEsQlxg+wUNST3hy0paZILJoWqKuTdYiqo7nVNqynW65P1iT7A0FjyzXXZTfUZAw3GpInC/B33M/5QD8suebrO8p04yhVsTU4F9uMJ2pwCpMwTNF4GBWNewUG/9M3keFWN1GTxseevIyHT26pF77hESCk4eWT4C9qqgNOOwyGH3H9khvEXPq0MHKjAWlhqrUiTcOCa07zLFmRXR/s0Fcb4PaBPfTdHvm3N0Tvaeqx/k9+E2X37hSEtlOfIVMgeCT28MU/K3Z3FHvBP1P3EhwqSm/0ekQLml2NPZDeA7cP7C4drcMdKqYRqi4sXQffK0/WBCCYKMhuz2K7btC8OeMDM9YTNUshn13B9fv0wBoN0f7JF5A//Yp/t6drB/TBlqaNkhwm2aECTS5Nogqc6tBgfwAeFoQlK70RSohHp2ZxpoCwfbpL87JTGiAqSnXdiS54METo9zi86ATBUuVIZSzozrh48lGbPYnjcoA9YYMRmyjSZAQpM7j7FTfTck0PpM0OTgTD//F36N5ewrUCdzeHuWWE1OOp2LsuDM0+GftoABy0Vbrfw2VD4m+cQzfqwS+3MQ2yQV2aBpEm+eIV6lkfxT9+DOuQXhEJHWnC1Oji7HRNa4UOG/NJH8TkH34MVjhd08A7B7fdUW57t0O7WCGZLxmjUQdFovveuAgW8kYGCeCxY/K7ks1dvw+MBtTE6fWCg6NJZ4dJ7bGZ8SOvl33CC3h1VXUoAN3uEHmoScJJpWGgdXiEplHb9yToh58Qta1TUNUcYBiTqFdEnMn+QKuWTHmouwPQV06oSW4nCfzDOrYye2XokpiQ0qOxA8+O3cjhLlfXQSu6K/VOOiaPJwSeGWvJtR0kS5C/W5ysQwZlfI+t58PNyZqg5JPggJ0of9R03Q86mVZ2lL+kL6v85Z8AALo7Ugmte0Jl4jpAejmTUKHZbdSz5w/pjnr/TBnho0VQUD8zRxRHa8tjWZSfe73sE94qd1PsPfZaBYLzsnPtidSbeFBOwzYMEHXmVXkM2y0LtCxnEWacSSBAamW+hLu9p9jpwyLqqpiCr7nP3TwAdYPOppfbbZSpeCx2NSl+VuxW8BpC8O5BuQAdXKtma3tl/7vIkqJiwRzu9h7uYsaHQdfhqzpuFB2Sdat10HnBzQNE6wGp17Gl6R3brj6JLVAFzHU35LQm99o923XBmAFAMG0L61VgXZDlU6WHoKy82aqhNDszwSBBOrhUXf1MucCwR5tfNKcVTBPyPA50dMhgmoTxRBBA2vjhZtkf4CpCK0u/PFeW2nMehpwaQJS2yzP6O222nGzuD9EX1IwYdPDhBv0IpzWW0xOxB7/YZ8TuzLN1t4tk77aFK5RcrTiaYC5gWosAcDGD31fcuPt9XEeaxE5VXbNdq90bWxM7H1HuWkYDIiizlIOnr7+EfPdT5OV6j+4DLStF4jQbAGEQZu1phmnWvTKtnUSNDcypZDhgkW8ISq11RGXLwyG127GV7B3fc//4dnrZKY1+6TIaUJpBZZs5AS3UNFdbbMOBXrU2GfUBMy1dxy9TiRrOOeLf1QNVam0LAmF4YxNGiACXM94KdQVZrULXQXZ7Flq9kqP9jfaFZ9OgovVY7G7Qf3bs/EcKBNPYXU89XB03Jkyjsm1Z1yjJwpkmznp9sg40nDzLYhm7KGl6sianVEb7O2d27xccUrm6ITRjMibE4fPXxNYMB8DXXwJXF3BvX4V2KT+DfhwaHU1FXZoShtHrsT1rnGV9QMzFJbwnQCcy3vLK1/3klnr+7vv/4SXg5rAWZF0H+Wj+/ZHmon0QWRonjs7Fnq66QwTczJa+pa5fAtMxpSush5uwEPJntIJxq23In81gV/YHIgUNVaiTX2QKfVWC92OxS5Y+P3al3Fn7zqkflEtTrqXIIfcPQQFAEh+k/ER76e5serKOgAQFYn6txAxbU3ADsdh06OWqGvj6y1CIoxNCk5WphKaBVzc9t9lBvnwbfbV0bmAaPq4sIqDMNHW6Lv69FeF5xpmAvcfmDqbyIDRj+2Xn8EAcuJjrnTndJSzqRCUpXNex0lf/IBaBak+juXPI4+1naycD94uAWTFxJ7Qtuw7LBJ21Mp2ahZljhxWPClhyreo82sAEeDR2t90/P/Y8+4OR+Qle3eSlNxvSDj/cxBvr/bUiO0eU5tB1EAvPnJl/XhCrYw/lLpqsSV3zFjO1s66DW65YRySe3NPvfgK+eIPu/TX87Ix6NN//RMLNxQTYkgMg+z07LCoiBfNsAgAkkOUK7mwSxGbDgZIkVE1QXuuxEze2MU21VPOx18ve8Hr1icFwRVigAeRRth3hog1FgiRLuWH3dLwOY387nXLdBGnCU7Bp0Q378Ns9mVPmfO3UcePVBTshVY3ubARJHJKfbiHjIbpBCV81YUwuiQ9GaPu//DXK7x44tHkq9iJ7VuzduA9/r454da0WkhzI2Nhf0gRe04XgBbXakLGUeBw+m6L4xod1uFpJGnULNxpwPe9ugIuzsKYgGaK0Rdnu0f7mM6S3K9S/ec25hPbN3XIFvPsI96vPKH1SUlMGQLCTdPsq3LDHRhFuNKTKmGH5W95sopihAN0eDSOJezgAgHCqu33F22yxenJLvXCPp0v566v/KmJGOomIwyMXP2maU21GuwotH1fihHUuAAR5arNvdKMBp5omoiSiyrtr4NUF3HwVrWyAIJkduJV2SpnU824XaWc/F7vhQZ4T+2gY5bWV0meFbDjZZ2dsR368jZuqbrQjoqP7yTCsw01GnOoWOf9NTQPggN2xotlO2DwLlj4GvOtu7uCyFG44pN6MedE2TfC/NYqh8Wr5AfgohOWT0IEys2iXJlHO3NOu00yhiRFSa079rKSq4+Apy570eHrZJ7wgOjqEAieSKNC1EPVUDYQKBTnJdhe7B7qRaOKlRWDXQuqKG99pS1F79N2cPeugIPbhBlIr/uNYi3K54rDKWmxA5Jh28nTsVc2233NiV26qGaUBgOzsZ/PfdB9VdqPPrpKsVbHMfg5wuo4dMUPddht/92pNkzNbE4DgL1XVQT1MDGqtm01u7yAikLqBV9KK75XoNjsC025u4acKQLOc3GYfmvKJAr9ks4VMzonzEeGEvNeLcwXtrFFH/wCT2g6mF7/oHN5OGXd0MjZtdNEw8JieGOEatJcIXJYEABoMUrBRqbqLGfxeP7TEM83Y7YE3VxHRJ0QbutkQmC8g0Fblli1CSx/QHBWbswlPUpOV+LnY7fWc2LMsxG7TSTcasvhLPAVLp3Sxdo6u4l4LXFFoL3v5SViHtQEN8SnbHXv1D4u4JjPMBZUvAAAgAElEQVRnyFjrWEHq8gxeCfCy28Ofz3gKv7oA3l1TwjtN4fOMxepmG+DBdnuZMyEN2IooB1Ko2FWrkIE0icoG3h0ZLh9iHdVwwi17HB0uP/962RsewmvWcmvvyes0PmTDFpyst7GDUeQqERdhsraZXJoGJwrkLPiCYFFZQtZrIu7uHphPKk6eXZA5v7yHeXQT0bQhwBM22i/WNODJ2LVN+pzYw2heyRMB+nD3gODqpw9m4Hq2tplSdMtV4NnaOhiwQGZT2s9r/MdrMiiyrNYxjThK5VyRs8PlPfVotnvq5IhT28qME9XPX8F/uAOKLDzAoftiMwNL+4AIHjNIhkGqA6ehjSJPVhd1Et3In3i97A0vKr5flnyiDYFY1TwN2xYwMdmDQkVVoFS0E3Gc+7FvrRAD00JJU1UIU+v4lU4V6yqcSC5NIWBnxF9eMKZBPzxEbFHS1UKs+yI6HHssdiC0QT8Ze9MiWLZvdzFvbTu4gQK97Get1vDTibYUK1U8S8Mo3tYBFZOC3VSDPrrFEhgNw5oAROt3IMAnuuUqpkG68f14REKIrs3Pzrgxx0Pgdz9A+j06eOsBxR/IWyMYKJjIlNYz9gou4BU5va4so80NwO+vPjKHe+L1sjc8XCikQiFpAxXvKXe33mrflkVhoO3ZEMLSjN2e3qJmaAsodLYhoMp0HHslTzhF5HWGQGw7tOcjJB8eyA7a7lUdoI6UPSDKb4gLBd7PxQ49sZ4V+3YHGY9PYzcN9fFAMfwtdXTqhsVrVRHm0CsgZUFTgjSJ6/jxhgOqfo9Moq1uOgWqGTLyeF3SHwH3tOrszqk/I2Wu3kwZ8OvPIHmK5McbdOdjuF1FmPCf/Ar+JzNW2wareQAsnNMICQ5Yp+2WflX6nlAAD/vB0jMMxhQmDh9VFh57vewNbz14ILQWw1DE3KD7JTfqchMZRocDHLRL0TRB0NQdAcXg3MkVLqaUpXIPrsj5c8xZL8vgv3mH7lAB8yNszuwsuNjhfMpB03YXW2iPxT7oB4+kT8Uux7EXuf5cxo/NlmlEkgB3O+o3Psw5am9b4PqOpJLDIZgw+2/e8aTfKfFjT0y+Gw5oT2Nrapogo2HQW0sBJXFoX58BIkjqBt3tPXw9Rnd7h+5Pv4b7/Y/aKOiAVt9lcoF6Q1KhLYE5GAKANFUEw5kzn5I95HIW6hQMSvbwlW7ociXXf4LE/bI3vFXy+z1w4GTRjYanrnjKdoFhyhWUhbqJKUxVn7Qk0XWR9GvT1U4gkwELpgVzWiSUzRaAqlx//z2cyukFSYztPkAH2kkfyaEGsjoWi4/Fvj/wZHpG7GQ56Wex2uhJNoyoSpOO7ve1wORmQNuyJVjmgBtBRMI6cHUOvz9Atnv4ixlTuYsz+LIIa0Jds4A1+mGptjNpQrnBD3fMm8s8pCD+9RVkvSNMoG0hmy38DcF5UvDzd00RapRuNoJfsZtjdYrBqGXQoz792QQoMmpJFjm66Qh+veVaxxdUoBvQfE0mI+D941vqZUML9Ep3ZRkVv+wkPFIWABClNUJF7xWz0oPJfSBNQ5UfTG3LAu3FhOZl5jM6UnWA2QTduAdkKbL3D8CbS0Jb84zF2v6gKQyhrMmHB27G2TRABh6LPZxIz4ndCs79nhId5kulqYAM+2onr3J+hsXXWUI37tGY+GgdYSJ5MY3rSf3Jmlyf7tft55fskPRUFsPW1u+hO6OCr5uM0F1MIIsVuqkOhUYDdH/yK9YlFQ2QsdqEnrzUNVWM64YP/4HEdrdhmzZwe1cbsqds6vv+WllcTez9rzaM/fCLLlojos6KEdqcHA2QdMAjXceRtznXWR/ZbgDdZCiKKGXtHFyvxwmjWrqIQlZdvwTmK/ibmi2/ugHeqwbivWpGViziZLcHljRCc8WIZOksZU/8sdizlMCt58QuCnkoighJXixVOKlktwYIPFEzBzAyiv/uA4dDVR3XoQUsjCHkHSejR2tyaQpZrOGXa+BsQmud3Q7ukMMtRFOttc4nGur1n02oAQkA17dIlgqjLoqovKYMK4BYKekVTE+VighDsR4jRq1wBUJNJP2S4LjDQVlqvVALPPZ62Rve+dABQdPCoeaHnPFDdKNhkLfDVgs6w7kACK7bRR5wG07JGi7VAtHH4rK9GFPX/FBFXLUI5Swupsw3Tdvchi4qWBRsJ9uW/WzjhD4SO6oqipx+Knbr5Yuw6+IVv9J1TCeUDC7LtaZpvPlksz0dxBytQxRq0d0/0LpHO1EA4prWG7XN8TCXb1eWTB9UEYyp0Iyf40qZV4eKhfN4QJjBfk+E5+4Q3U60tSp1TSZZmsKVKQ8cTSUt/TPh2JD6NXRgx3LNFMY+MwOdPfF62RteJFiaBOGhLhZAoi06qFxzYDYZ4lCvd+bRqurVKzUn9ZDNhtPSJKH67e1SN4meEkWhyr4pyckA9WtUG9GNhzTs8klIN2zCCZ88GTuAqLHyqdjdUey7HU/A1RpmD49CEZXSxR68QSA6CXLWAMI6YHbz4xH79MMBMK9P1gQg4s6VpNE9zOHbNiJGs5TmFKs1H+D318S3GLBM++tuX/GBsRvbTJqtW3MEHzATBrGTW4nuQT5EJA7Z7ue8CbVIN8j3Y6+XveETz9NxqJNNg5JaL7pfQuYrbenFQihYS4rA9ShSZO1GWax41Xcd3HAYrN3doB+MxaDSc0x5SooVKSwVowGpcNBBjRWKhwNwfsbCqa8P1aF+PPbxAHI7f1bsGA14Oi5W+uB08Xbo05QYRU6AlqmgOU+R1PUGfjiN0GGD1/Z7cYpqrU5lGdmaxDu46zuuuyziAZNlxPYPW3SjAdpxgeyHOwT/qrJgnn/3wDSy11NIRhLJ90cvafV9Sq3EZAiYb5PJiuc5W5pH77X3yXYfhKJcWQA3j2+pl1206iSOBOvI4yTQqCGmXTeSkXjFTHl1MBImmdYiU0ivjNV9usj5MCgKDzqul8WKJ1XdkFzgHD/YpoWfTdF8/QYA0F5NAjzVaSGFqmZu+VTs693zYwf+IHaT+6CAUxK9rIqcI/7piIOs2ZRrUIKIrUM2W4iqBtt6zAjN1uQOJiDrow5QryQDaauGED99RPbdDaSq0J2PKQu4XEF+eMdbI1VF4ywLKhOBWHOcbhmNUWcFAOJtadwAw8gfM9kMSKcqbuE9j7xe/obvaWFiX7YCh45pcDIaRJmLXhTut5QoeLqmaWTfKImC8szxipbtnnl6WbD3bX9v/WjnIMs1sh9Uc+W33/PPVVZOzKpei7/HYg995+fEri4lAV25VYyOKYtttoz/+3eM/4f3FGkqC+DmnvXAen2yDtcr2ea7ewjrcRpTWFOjBWHbcl1tC1xQiwZJAjkbh+4M2hbup2vI5Qwuy4iJny94eKw3iolpIpvJMC+JNgi0F3+sHeTGI9WuaaO8IMCTPMvIIRj0+B6TbNFh1WOvl53SAByqmFepFSTWOxeJxUsnRNgBUTPdtAvN+c24mu3R2F89QY1VA4Afnmqhmywe1HtJ7h/4wScemI7hFffi+j1OPe/mQL+M/kuPxQ6wL/2M2K14Nz0bI0E7y921kPPTCdOZPItipKJYnkyL9OAhRbiCy3MWjtamPFoT0pSpT79HE7GmocaNTyDDPvmsX30O9+42emF1XUCVutGI3Z7xMALA7IE2AneaUOtnOODD5Xw0ctPGgcvz6GpYFvxcF2uC7tqOQzSd4H6KxP3yT/iHhVrWdNFFQ3vZ0jTA3ZyntilyWUG1XvMLVansAJe1Ez5NIWt1g3aeJ4NhOVRgVHZ7ul1rK1CaBp1ex93tPTBforu754bLMhKctSCVun4ydnj37NiPocfUYiH+R9Zr/lvwNOzmiygnuFds/2jEh7quTtehxsTWyelulfl8tCZZ08ZSzHFDJ6WWQvirC0rylQW6VzN4lfLr5gs0v3mD9mrCW2lzBEE29QEgpn4+YYvWQG1pSq8p76P+ZpGjO5/yPTaJNVdAgJ+xWtI/9XrZJ7zXsfNoALfasEjRLsux3ruoqA9KZf07x7ZdpkVf0/KUUYy6KeM66xIotj2g8Y65kmZcVpFphF+/hbtZRBLxdMxW4V4nqMM++ZZacD0ae+K5mZ4ROyZDmjRYmzLP4No8QpKbhmjFixl/1li/1v2Bve8kQbdaIfnis7AOr0wrFB0HXGcTSnicTeKa1MPKZRnXtd2zIL9fMG0b9tl56fXgvv+ArqExcnJxDv/bHznMappIiOn3o+qDc2FK7AY9yM5FkkyeBTcP2WoRfT+Hc563p4HETHjJO7i8x45NcVoQ/9PXy97whoh8WEQE4bFt+XodNr7JUwQGl8lhqHGCGPHZPFg1VaB1TA7TJwxa5nqS/d/UvUmMrFmWJvSd+082Dz69IeJFRmZlZlWJ6gJBdYNqxQJKgJDYAQKJQkLqBYvewgKpJWDBCtFsWgIJ0WxaLGGBqtVqCbGgEdWCVlV1FVmZlRkZ78UbfDBzm3+zf7gsvnPuNY8K9+dZA3j8Uirf83jmfo75tXvPPecbfOKU6EHVW6ctu8CRNRBTkccJrU4EH4x9W4aB1Edjv12Sjudc5IR6DzM6CCpj6w2/BkTMOUD4cJ7fyQNJQqm8xTJ6yAIswdqo9RJQjJqX7A/wVmbpxRVZhmaxRPryOZoPV3BnU156Nzt4cfC7DU8a1aMXreUNTGdozKDyrKWqbHbUBPI+zEBMbcEcxWU05AlgEibtw4Onp13S2Ii9SwmGYP4VpNuiY5uva+6iCsMVEe2ANFxI05HCalUnJU1pFKCXMEkcp6gnY0popAl3mskI7Wcvgo48np3x+NSWn+TZkQPGmvzVXledNh6I3frkj4ld3J+OfdBnzTrqx7p8OgZOxoxtNGBJpKoM0u/fzUNlq+XsJOTTfv7iTk7B/ypTz6m25S5qLdrTCX2dvEf6CbtW7W/8avz9pSnf9+98SumPbclLOBDlBg3uW+iHwSl+SO8novJ+5n5ur/GmoV+pfn6nE13PH1pSf/bV+P/Fw/GxL1R5ynu2xBIXGPcAd6eAPTETgyThwhsNuYh2+6DTLoMeobH6NBcTNK8uIn7DEHxFjnY6gCsPoW5si4ww3PIASRI0z6fB0gWn0xh63Twce+h9fzx2jAchdogDel20Y+JVZLlBO+igeXXBzsz+oIMf5QAMesS7FPmdPHziWJ60bcgnuV7ezalHjZjAR229Tre7vC/c3NIxu9dFOx7A73ZIFztIVaP69c8JGThUwGzBbhTAP5+M+WHIs+AJ65Xobu4gMlvEUnO2iJo0yqLC1VzfZ5X/zlJuPN9qxpMIa2O9+R8DrgL9y0ksFUy+w4BVAN9EJS5I7SLxQVlPblsC6y2a6ZC/HIALZbeGZCncT95AJrRZZ10vtLwc9YGqgtsqum+3191Hpa973YdjtxgfEbsACK5/qhXjltvoiLfdUxdGzY3laqYkjhJS13BvWYaI7a51DcxV5RiI+bRccCEnw9+fTom5OZ+yO1LXAW4QBnbXM55+lzfAcIDs977gPebAy7Jba4nkHPz1POLhG71DdAb8oF7f8s5i9E0olGK2QHsxhfsw42uG/QCtwEIv7uXDU1bgqS/49ojIa4JE3sMflEShKgNBysF+SXUNII1QXO/5ZlinQCQgI/1mC4hDUjesMWe3MH9YfzMPBBROAIdIvrrmz1ttVT+mJlJvvweKE47bA2Yd98duA5hHxB7wO3aZNnhEngUob3J1qxfK28iy0p8rvS7a8wlkvgp5YHZLg+DRAGI68eOBSmIwJzjCLwRgXlYuZBk/vHXNyawOpNpBFw5AdTFCVu55uinxGs4F9/HQTm3beLdZK6juSCk5wMPtYvr+RqfQLU2V0wTi+RqvM4mA37/neeILvo2XSACmuR6Y/0GpSnvTu10AQMF8O/XG740jCm0Zql2K35WqUbmniGmastCz0kLxGX5XwtnAKKmiFNyHay7GLOcR71u02200Q7g3dhUUfUTsFHdqFIuecqBTFIy3ruHXOuHV+P1mw++d+rDA5KsreLW4cVmG1sSL3l8Rddnvwf/sNeU+NCef0bWQk9EWvlXotNlobndAnqGZ3/IE+9HPIM8v4P7PP4QfD9H+fIbk1UvtgBHKHOpswwx5H7gJ9F1VaXO96AfJwrXyAFRbKFyqbV6SH2n+P/A87QXvJFDZkDjFeXSDZJ30eyoGWkd2uy0UoUM3AO54wT6xVhF+xcqYIkBVh7oXiiMX42wCkMlYwVAtMJ2yvbfZUk+xVK3GXQvpsvMhnSJIUX9T7FB9ycfEbosFxt5SvRaDyKLS6at+DSlrd1Q1d/O1qhnvypCHuzgj9OD5OV9zqBQ3dJSTEdQrF3xb/WqN+oev0PRSFD+fQeoG8vIc7mbJ2NsWyfML5vLrP4Bf6w5vJysQ25KmPNEq1Hd/4GlsRhDeB1SnjIcxHiXwoPXAsAdsvJo7KybqASzN017wivugjYsu3MTF2zxwR2NdzB4lz4izMQ15o6eZuq4qHviyJGZdcdzY7iIEdr1RknSPNSOgWBsadnlFG8r1LRfYbgdcnLHWLQrlZf7FxO63igO3Usk5+Fo/vEC4M5ivFMnte530roFul5LgWR7yoLZkTv+m/YEl0HSM9noWczK9+pNpMAj2VY30aomk3+GUc72BE0F7NoZbbNCcjeBWpbYfW15q+z09MV0sKwH+XkTgQfM6kz8P8iA66AqOKYahV4iFMcKoaODC7/Wh52l3aTyCL2qQyZtTJMm3Kpjf67KW63fjG7Le6oVW4bbGIjqWuAYQ3C8Ue+21t22PdEhOCIpXiqqUXgcyHXPXbLVUGlBSz48HqH/wUnvpD8QOPDp2SZPIxjf5OiOBiwRLGS6OBHh2HnTeJcuChcxxHjY48/tDyMcPendzSlVVbL4glqbIqdlTHiBvPnA+sCuJovzqEvAe7vUlcD2DrLeQn7+NIk7ro2mrebM6x5NIBalkOmG9nmdsgfY6EY7QsvQxG6Awgc7S+JqdDsYeeJ72ggd77eKoG37Hb/VYRcy5yFw/0jjx+33oOYfWmnoJWa3n11vuJkXOfvmRmq+fDLlIj4nf1zNyXpsjeuERJAHXc6Q/eh3wK/fFHkSmHhP7kayH3x8iXgba+x8OyfVcrhj/7TJKgdc1IQ7TyZ08aAWvAzTNB6/f3cnJxF7RNMTQNG2svc9O+H598jyqM9s0eToOzQS/XLEDZBgmgN0YI7A3bSgd/WodYAsiooR4xcakCb1vszjLEN3RJU34exwNPkrxe9oLXoQ9c0eg1p3/ZLuednEMDRkw5VnKXcy6HMNBXECDnkpld4m4s0vUnoKcEJWi/uoDj82qjm3Bbif8Ion06ynrJqcduiH68uzB2AMM+DGx97pxWNShN5PpPEpRwHQVAUQ8iVeNyiRBoAwe5eFvF8TYaB3tlyvmdpxT2zCvi9OA4Rfty/u3H6ImvoLP/HLFzePyRuETJNbg7QfGZifN7erugEjFlSBHtbv9d3s/yz378YB2uzjh9eWerxkPIvjvgedp1/AtVXWhrKEgWqqydWHkDbCVWOSsXe3TX5bwCh8IeihNCzns+PqyDJxRZBnfZDXUgm+17cf2I9YbmN6jFEU4XXy5Z13sPfD+msQHc+s7mdwf+/EI/BeIPTCgcgV1OcX7FJzQ2v0CCQ2Rvbrn+c2GC9Dy0IXELk4RSRhGTQTIempaoNwQfrDcBJlqNxqivbzmJbppgJfP4F+/gwBo93sk+Qn8ZAg3XwK5dnpmt/yeTiJKVf9uOfg0Yd0vLnyog3YNeKKFE/hwIHNrPOBrFDn50PO0FzyEBGXrVdeNHp+NWqkf2bs4qKt0xn6xfhgkU5zL/hCdmxWkJAPe/H234AdLj2ypKNyJYR94fk5x/24H7aADd3XLy2+/h/ZsDPnqCsFgeKQgs5o6jg/FbhJ+j4kduzLGbnQ/EcCw39atWa+5060kuFi7fg/t2RRuswuw23bQgXyYKb59An+tMoKLJfyr5zGnMWG9Zjzg+13InijK5voGrih4Gd9s4L94AykKtMsVXLcDfz0DDge01jQY85QTO81yEm1C16rDiz7Nj1W4SrXgJeGcAvMFT+rjjaHXBTY7yoIvlsTWPGB589EFLyL/HYB/HcCl9/7X9GsnAP5HAJ8D+ALAv+m9nwvBF38LwL8GYAvg3/fe/1/6mt8G8J/ot/3Pvfd/5+ML3sN8koxX6WvtweouZtrh1v6DkSTS9I41jTlYiwi8U3jw7ZL/dnd0pPoWranTKp/VA5x4rjZq9HXg///45+zv5zll4EyDPk1hkN/7YrfF/pjYjVAtIkCuO7N5yqpwKW4X1H28ngGtR2snX+Igb96hVZ2ckEdZMoavPnDHddoK/XpOAMxwzK83LBtcErTfg8BTUcAvlnDPL9C8+wD5wXfhFutwugQb+YDPIfzBD/vRW7UljNoomMGXKs9557Ce/KDHwZhOpn2pMxSbCD/wPKaG/+8B/Ctf+9p/DOAfeO9/AOAf6N8B4F8F8AP9318H8LeB8AH5mwD+eQB/DcDfFJEpPvoIBx+Hw52az6spGF0qNAWTrACCW7Pf7qKNzPFrjeyr5Iggv5yrqGm3Q4Xc0VAhxdF9o7m6gVfMtxsNA0TBxDwBRInqB2L3VuY8IvbAR60qHcbprui1F31k+4IkYbs0cVzgrQ/k8eM87GchS0M+0u3czUm7I1+3c5deB/Vf/WXq5ptR8ngI+aXvoB33kVzwVGxPhryrnJ9GllqRhzwAhVVnadDUEZtJHA8ci/zO32VfhdhR1wqS66rk4J9z8OS9/99E5POvffnfAPAv6p//DoD/FcB/pF//HzxvHP+HiExE5IX+27/vvZ8BgIj8ffBD9Hc/8tOZoOHGxUV/UOfgzahMXHSJsBakCLXjg8epasFsdjAGVNBi2e7Cm+rTNEo8V+R0uqHS23pd7P/a5+j97z+h/1Mnh7OhlXUv7BJa7bgT3hd78HJ6ROydQodXO2Liez3t9ScsxRZrthaHA54ILydUCTAlXZdwIdvlvNclFqWqiWZ8fw2nXRzfLWJOVsqcjCELYotQEU6Q/+htQHmG0zbPKP3XKYDWw12CJY6VXjrZxrBP7A/AtvDNbTSoOBCU5+ua4Lkqixo51qZME+ByBlFWFmcnlOM7BgV+0/Nn7dI8896boNl7AM/0z58AeH30797o1+77+sOP4ciBQPC1+jmoB9d0mgg+nbZjAzC5i2DetdmF72uDJT9fBGyKHw9ZNmx3ERg1HsCXJdqLKfx8gd6PrtWCsaRQqDrn+T13ct/vEiXY6Twcu7YyHxV728bYgcgRbVoe9WPVcKlqtvMOig83+Y+xIhKP8vAJ7wK4vo35AHdzKjmUwodrfs1H2ZDD91/wVLk4hXz+KV87HRMOPRyGjok7PyWLazQIYrVSHqjZacJQwF1zN90I/GbLD0BXJ+Xlga85VJEyGd6TLfx09JdP8dPd/GOX40c/IvLXReQficg/OrS7AB+lfFwa6W7OheMfZ9NYL4NHrm9bXqhGg0g8SBxEbSTNsdoUe/2gR4mLAWUmYIOYvbqAAJDxMNqx2y/7dhXLoV43Er+dezB2/AKxe724ybAfYBE+S9U5PGGrcNDjvx30woQ4sLL2powc84BzaCcD+JdnMR/gbk6TEfM6mbDuz3i3kMQh+4OfsV16u+T093ufApc3aD9/ifZ0BEzH/HDdLuA/fUZY8GoTB2W2cHd7vg9NQ0SrarxzJqKalfYhLkmUl5L4mTil1sHebBH/fM/zZ13wH7RUgf7/pX79KwCvjv7dp/q1+77+px7v/X/jvf8N7/1v5NIJbSgT15ecVudhGOMEcrsK6D0RFfLv91iSzBZRt8XUcA9V6E37pg2tMD/o8RewUuv16zkX1/4Aty65s3YK7p424i/L0B8PhgWlWqs/FHvTPj52cyTf7mJ5ZBrrdiLcLqnxuNsfEcAVl5I4FWiNecj+APdhBjnUIZ/2bHwnJwg1ZNpRN/ToAaB9dgKcn1KhrNcFFmtU0y5kOMD1PzeCrHfY/vAUu5cDyHhEKLNvA0pSTGpPSSBhd2+a4FDue2Ramdu26RPZqU6yTBbBZyox8rEa/s+64P9nAL+tf/5tAP/T0df/PeHzLwBYaOnz9wD8lohM9bL6W/q1hx/DUBRHl0HVVw+uGarzEoBItlPpL9t2TMNmAISbiqrNSpoEPqpsdvwFjIZcQCr9AIDlQkc5qb0e/9v7K35tV3IBK2HZ2wfggdil13187OLif3NCKIJKgPiqYh0PsMyxBZSqIpjyXtvZ/G4ehjm5vAn5uOvFnZxkxbIveXtDUNlmh3Z+C3lLiRIs11yYkyGKP7kE0gRn/3gFzBfo/8F79P/gHTVwZvNgl4P9gQMrNXO2QZ+vm/jh9T6IpNp7TkduClqhU4S8Ajqy3JPt9eel+InI3wXwDwH8soi8EZH/AMB/AeBfFpEfA/iX9O8A8L8A+CmAnwD4bwH8h4zfzwD8ZwB+V//3n9oF9sHHQyG20T/JG1LO+yDFBoCf/KPbvwGkAoYcOFIsIDjJG7zYfFWNp2q8VIMV7Pccv9d1/NmV1uMbYtp9VXGnT9NIBnkg9js6Kx+JPQyVwAs89kQthnbr7Sq4dty529Q1p6WeNvDHebQqCR6ESVXB+E5Oys0NdkF1zYU6HgCzW+x/7RW/d9PCFxnayQDJ9RLVr32H96MDxZX8Zy8Ya4+sLr/ZBZiBbEt+UM8mZELVdaQBnk540nx9gnqoolry6STMCDBb8O8PPE/ftvL5vxPacEExWDVlUNfBAYJCpUf/3cBJNriwN9hsGoXCR76TU8xzNGCdWqsb9YqKuUH/pNejZ+l6ywvY7JZc0X0F8yrFzS0kTVH+8gsUv/cFJ5j3xO7LkrveY2I36byCWuy4XUaOrE1vO3koweC/5ndkOpfLVcxD243eXEdmt+xjN03IKQhLWa29obgqVK7ETcbBy0LK8DoAACAASURBVCmchDpoQqIYnSSBG6rKm9n26GkX/KlMnezYatMpqE4E/nYZdWmAOPFW5Qe7TPv9Ae50it/52X95r23l08bSaAvKyNBBPx2I4KQ0iW+2TSgN9wKEAQ8xKBQskkGfF8os5fQu5f+jyLnIay5wqXSHdMS3yHrLUbwpiG128KsV8S7LDevZNEHnj99z53wgdhn0Hx07Sdwau/qp+raN0nptyzw0frssS6/HFmbbEB58lAcMjThfhnxwPbuTk69rupOs2BWSwQDtp+dA08B9/ooKY6dTohSfn4dLcbtasyQb9CGfPA/vv0llWzeH9wQXy7nySABKuQR+u0PQ8z8/ibOS1LD0OuBLUzhVTn7oedrQgtbDL6mSay5ywSUjIQDLLm/i2MZCt8M3bbONb46RJQx/Y2QKc3PebDgksp+VESRmeA/ixnfcaS9O4a9n4QIqw2Ew7/VZCjlUaMyX1AxzvyH2IIz6mNiBGLvFn7InjrrW0kHVyqoqGH/BUIQAZKAdI81DUHAxpwlRinlOVeLtLuRkmj1Bu2fYg/vxa5Ypr99yt1fYsrtWjfrFktCCzZZwAqv7zW1FL9wBop24uKMPerEWv77Vi26h8oAEqgUT4owIWNMRap+dQN58+PMPnv5/fZxEfErbcjihrBqSLsrg1gzvw7ibmGlt51mP3SSaTV6joAOIpGnQjZGeHqH7Q+RvNlT4kuGQ/errGRdXudfuB+XfguKBc2T8mJXNPbEDiLjwj8Qe+Kz9bmBG+dqYUjog0p6/dLtUJPOU7/PbbSBvH+dBo+GKmjBqEuH3isy0nJRdFHQbl2xN4uwkWGe2QyV9HyqKRf3qL8HNV6ifT5AsS5584xF76Qb8SlPIpAjdF2OYeVOLvl1peXNUwqQJUNbRQ/YQS0O4hHzX6Th2r+55nvaCB4csMugTkai/JKP9BRw2ELULDSvudLSugClRx+fQrUl095uOWJ9avVurEtZixV3JH43WdyXH6csNVbdul0A3i2g+81k9tB+NPdD/HhG732y5y5oljAmrrrdUEqhqYLkO9xqZjIM0uBsNQykV1Np2ZSSCQ0+wQY+97u0u5lSoMZr3lNmw02K5Dvj9xHyqKr7WKSc1+dFr2tvr7h1OpaZhn73VpacXeRHhJpNGfMwdC0oleRgV09xEpNcLp5xUD+/uwFOv4W0CudkeUb6SaINeVSREqJquqc/6ck+E4WrNvy9X/HeHQygbuJuR5+o7Ocfdg15Q8YLjEU5GTQ0/oBirzJf8Xtq2NJUCfzjAF3mU3FPiwr2xA4+OPdjYqyWjV6t1UdFWOMf4O2r5uFqza+G03Zel9F86yoOWPB3IySTk4we9uzntIzyBOjw6MDNHcYA/X2HIMhxEsFzi2JlRAJjf7VTRLL07HDLPKgC+UwRWk6FhDUxnysMAAtPLfLqsdx8EnB54nvYOL3qUex9dIaxHbSg5m2CaVoqxZ46dIFTZgIthEyELdcP6fauYl1rd8EZD7mzvr9EaTuRqRqe+2ZxlUF3DnZ/Q5lEvUKILxK83xNro0OTPGzt/njB2LV38YsmpZ+vhd8vguI00Ja7/3SU7PCW7K65u0O4PIQ//4YqqBOCH09c13LsG/vl5zAmINMLDIez27ecv4NZ7jtebBvXFCNm7W/67xKF6OUb2T74kvBqAjAbRw0nNJoLPVqreuDVtSNHtBD/Z0KbNMgS3E0A7OCq5XbFV6ndlLAsfeJ72ggdiYk0bjLqMaC2DfmgVEpKr/e1cp3BANDroFSrrIUHQ03cLapxfzSJt7GzI11ycAVkKN1vwl/3sDG3qkBQUMfWdHL7DLgWcYydDTQNCl+QvKvY0IVAqESDJwr/zGXv+NFhu4C7OFNYgirRsKX1d12hncy4szcOtOrzATkdwK7We3O3RHue0WAJI4KF9eOOT1roQbzhQyl7fAFVF3P3NLbJ/sgg+tP78BG614S5sd55EldZUWwjGWgJhz9btCaWkNgWC28pOoQmGDl2tESyDvoYI/frztBe84K71vLah+HfVW6mpvyhHENlgzWIdDsXJWM82EDbKA3vngz7hvuMR6+TtLsJR+z1quSw3bNWpTT02Dm5TsB896EcrFrugZvJw7EpRfFTsSmfjsO3AO4YuDH+gMlm7XHF6q7o1MqCSmK8OJJj3+9zpLQ+ztr++ha8OwMbRDeTqNuaUZWF3Rd2ExSWbXcDT+9Wad4w8B36yAF4+5wd7uwM2Ww6lmoYkcRW9CnLX0sbJ6PbIktMaElkW3k+cjAPYLCiYeU/3Q7PxTBzgH97ln3YNrwOUO3hsI3w4dlN8nvECN+gFpKE4x9r7wJG03dy9Omb7tqWlzXodjlc36LO7YW2vtlGF2vXRNFb12hWH085vuRvnqtlihOO9Smg8FHvbPjp2vy1D7HBCPRkRxqaANDfo03DNavq9lgytZ15G8dM8gtGamRAAwK68m5NOSwMCUdR3dtjnPWhH6RM3GVOibzohEnO5ii7agz47SjWRn/ba8PtV/rB5NJkDiLkz8sTs86KsZHdJOX+QNA0wA1/XvNB+ZJD6xBc8Qt0XJpHbbUDSAcS/eO+JCTcR1LoJ1DFvdjY1SRFmEyOdIvSmg/SbEaLBjoavKiDL4fo9eBUFAhAlMGxB68XNFMxkOtZfwv2xi3OPjh3An469YPzS7aiUiNeOjAQsjXQ6vI9orX6ch29058zSkI8//pAe4gcDQFRBUKSjORm6sxMKuaYp/HREtKVqy4h2ntzFmcoVlvxA6KXXb8yATe5AvfkhjchKr/ImcAYZrghPsKGdUh79oYpfu+d52gtehOZcnTxOH/M81LbS7waFsKCTrvozfjQgXEAVakV3LOlTB4UdmSTwWOmUvQ+DjDB0SpWd1DQ8Mo3EYSWMAr/MYBjitGWaPRi7TUofFbtCI+h01ygEmMKpfrOjBr2C1+h9xYXqV2vCCdKEJc1RHm4clRQsn6C4YGA2jVmUS2sf+HY2RzKd8H27XQK/98dAnqH945/CdzI0V9es4Xfqkm29dpEo7a0TVklTRYBKlB0vsij1Adz1yArCTJRhtMEa0pTvU/VtruF9LEcCy96Uu7QvDbOZTNW31VCU+wMXl/Zufbfg8b2lVyjUlQNexX0cbSz9hruwByLTZn8gbmS3o4JAmqKZzeF6PZXtVrHVF+fctXV3EqtPvyl2ex4Tu8W51Ymk4co3O37oj04Cv1GFg6TDHVvVfVEd4Js25mEnR7cL1DWa2RzJ2Wkoi6TThumujIccYpV7YF9DvvsKTSeF1C3cbAV3NkUz6SMRgd/uyZ7KMyoZpA6H0x6Kn11DnApO6aDNd3L4G6oFN+Me3PYQ4BOGrfEl4vDwxSkvzFdzyGhIW8264eaUcx2I+zbv8MfqVHYZNB6o90FXki2uNiAKkSakmZkligiJHEWhZUqm37PhGN3gxt4rHiZlyZDnQKeglYwhBqsD/KDHuvXrO8/bD+yrQxfSQ7GfTR8fe+JC7NLvsuavG9boAAFYiiGXXpf9a+/5msWS3wu4m4fV0arT4yYkvBznZPgXv95QpdgQm8sNkvdzoPHwoz6a0wH/rtaZ7bMTLfPo7Of2TZD5hs0e9PtgNACcg9seVB9TOa3lHkF52QZkrz/wNSdjtbk0zaAkTmXdw0v6ae/wAFtyJReZLFaRM5oTJtu8PEXy5upogup0DL6OO6qh95QnSd3DBu2r55DFRqejSujQHnD1YoL0agXs6J3qR334LEH5YoDeH74HOgWasxF/KeuSMeZjNNMeZN8gmS25G98Tu8wWaD49f1TsZqNDexft6Z9N2NZcaG86ccEU2fcKeDdC28uQ3u6A5SbU8ZaHzxIkszXaYRduxlH+7tUY+aAIObVFBrfYwHcLNKMO0g8Kv12smYuqDSdfvQc6HYLGNlu48ZBt0M4zoKqR/sm7AKEwBWdrK9rgTzZKWncSUaJL+szKjlLjMuVCl20ZyCMA+JqmhSQt/PLbDC1oPXvNTau46TQexXobT97N7ko9KHZFel2IqRqotouorJ1JQbj5Sk3GdDdFExZ+9sWljvmFHYftDk4E3dcfKNEhAmdjdeWMymaHtKrRDjpB1+a+2H1VPTp2USVhKN5dRJlSBssNI3XGLyu67yWhpFJg1lEeVBZr4f+fn8IP+oAIOr//OrzvGA/gFNPS9nKkl2pZfz2ncOt6C3lxwVLkcMDhBy+Rv5kRyDXqwTm2OfHiIn7YgQA2A8CvtS3hyKYbWdeQ8YjlynoLlAe+pvVxV9eLLo2Tcwpq2Z3DpALveZ72ggc7GKYEJlkWsNNe0Xy+rmkfryhC6XWPFMu4OLx6pbLV2AFabSG6BO1nF3BfvAuXzlDzKnvIl3teIEsVcjqbEj+/KwFUwPNz4ugPByqNiUDe3bDceCB2w3A/JnYSwNX9r23Z7el2Q2kA2OW6iW0/dQOhEx7H+pgMQx6Gh0lOprxY7krI2ZDgOMupboDlKphFyP5ACMN8gXa9gWw2kA/k8+ZfXKGdzeFGQyIpz064WVzeoD0cAoAs3LtUfc0fKr5XGrPkOU+u5UZbjVXos/uyZPfM+viHA0njeY5gB/o1SZGvP097wau0xTFrKbKBHJBIJHIDrEfrmq27bXmHWW5AKL/dRWx2XSOZb+DPTqJshuov8vLa4WKvav4SEqclTgr/6gVwPedRnKYQ79F2c8iHWQSIWWfiG2KXXk+hvB+PHWC3JGDDez2WME0S6m6/PyhZpQ00uuByNxoEE7OQR91QK3JbMr9XL5hbt3Mnp4AIPRx4MZyTKJ28GLD801MYh4rkkraF+95n8G2L+myIrPX8/odDvJMtVhyMifB0XKxDxwZ6etGaU2ESxrN1qqps0nouCchTX5Zxs3rgedoL3uSQzczgSBDfJpftxRSy3Qf5BlvI5kJnI/3QHRgPYU7cRiCR9faOzLLvUHULADEd+qY3pz1I3UI2ZViUVhO72Ypx9HvwiQtYkHtjB8nQj4ndd3LIasPYDfM+mtCMoKpZOojAf3IOuV0H1CYSMrFkt4e/nNHjVPNwuyraV3aL6MG62cacrm6jiKu+L2LqYU3L7zdfAquGF+OzCRfrtoQ/HJBe3cCfnQCrFSHIpl+vdET4luQZgxXoBJqyhMp5KMuwEaBRy8xyrxxZPclMUqX1kft7z/O0F7wHF+lsEahjvmkjsMk5yJsPnObpaBopseoEK5GkHcqHTod3gv2WXQw1HwDARaE4cKlqtgszgs2k3wWuZnDzNLQBgypYlsCtlDK43vK4XhE+/FDs/nB4dOxYcgf0WUqDg1RZSwaX3R+IZlR+qN+WLF+yDP7tJXyaAtMRP1SWh6n09jr0q8pSyO0K/ignaklWaKZ9OvzNl9TdeXdJsNmCcibBUvPdCjiZov1wRSCXatC05R6u22UZqnJ5NhH1nZy9fCiWf7EkcaSN6sGS57GkMmi1EXKEU2VJhszlSL/nm56nzWktnvnfvPi3I2HCuhnhdq4X0qa5W0IAkbxgeGtFOAaddtNHAeKuYE7QLuEHxNp3ToJtSzObw41GAYPtDxWS0ymPXvsltU3cge6LHYgMp4/FLhI6NcaeCvVqy1aqr6p4IpgS7/4Q3LTb7VYxP8yjVRKMHCsx5xlbkJaTGqdhp64megcK9pCXN5w9lIdAyrCLqH14MRnxvmBcV+8jpgmIMoF2twGiA6F9SPSSHYSbjjYw4GhCrKXV77z+W99STqvXHrcdXVkWlbmKPAr62AjaSB7WssqofyJ5HrUMsywOf4xPeTolUWMyUuWsPhe7TjqRJBybFzmSs9MgfSdJguT5BX8pKQVUJSV+hKz9B2I/HB4f+7FigbllGKbn4pQCUIM+5OUzSLdLtKPhUDLmkDx/diePyHo6hHwA3M1Jp68yHMSTqaSdDWYLxvr2A0+vmznzWpBw3by/5M67XAcsjq+qOAkVIY7JCDaGqzGteH3sw4ybOe8wRmo3Molq1Hjr83/kedolDdRy3DDxu12YDJo+jFlNSrcbDIDDDmJvoO5e0h7tKkBkzCuENu5CdLr26w3H/MrcR7mnzuJGfaAOFX/+oMu69ewC2CreZr19MHYz8npM7EFRF9qObbSkShxz0g9LezZCst6ieXFCJlKWcUwvwnvEs7OQB0aDwFW1E8xcUkJOxnldb7QfTi+rdtLH/vtn6LxbwzsHnznUv3SB/GaL6jvfR365we63/mk0hWD0R7dBQiToCBmfVbkAXu8E4VTqdaLbonVp0hR+tQoYfDuxA/o0z0g+V1nF+56nveBNVcp7JQqQrUOAVM72HMBFYqq8+30Q44TqIIpKPAQtmbrhcW4XNjvSrfwwqYjTCWv7+SKg/mS24C6p7tDBzLjhZRZZSm6nUdDuiR3uwEX4iNglJ9GZhgURSiwdJUsoc999eclT6PUlT47tFgLeJarvnCP90euIXnQq7mSSfbMFHf0+XMecNK/QYtSd1f3sLYrfp3aN61BHMxn1gasZ8i/JFutfz+kgeDOn72ueQZxCJzpFxMDohgABP8CqYYksg5hKtJV4R4vZex/KIul3o0bltxo8ZmwfI+5ajeccOylT9fwxpS0gtLsky6g+2+uwzu922CffH4CJyjjbhPUIt2K/AMkyXj619PHrDYkUmy3dor/+mNjTcsPvZ5e5e2KXbvfxsTsXY1fyuddLLq5mjDvPoua8DmJErSYhwonrUR64UZWB1TrkI7eruzmp4YQs1+yLD/sqJZ5DfuV7kCQJhgjNuMucLk75fc9PUJ8OGNuLi9AntxmEPaavIzo5D6KxWWw3+6oiWnM8vLMuAhvL8PmDQWzz3vM87R2+1csJ9GKix69v2zvcUcmyILccdgyA3FBrA2YpZBU7MkYCARAs3f2gRzK09fYNKtzj5FSyKKHXnI2QXC0iHa3XZXelaSNX9oHYzZz4MbH7bsHpKRBMgcU+mDpux6Ei0XqxIokbgC9bDnXyHO2P/gTJJy9CHjC9HhEgTWg3+fqSJ6Dm5Jcr9vEV9y7WRcoyuNUO5V/9AdJNBbceIblewa9WEAD7v/I58ss1kj/4KfD8HM20h+SWlETfLegba9LXuzg8Cw0Uy82QqqMB26YLklz8dsdycLm+u14S96dUhb/+PO0d3nuYjeQx8ZedCBULHY/i4hQJGGtvsngm6K9j6dDdKHJ1ftaLoykHNI1eNmOd77d04QhiQeUeyfs52usZ/IdrlbOmqnAQEDpUD8YuIo+OXTQ2Gglo7Ostf6bW+75uAsbEb3bcKbXGReK0BDzKA4hlheYD4E5OaJiXV73KQARpGvjZHPk//COk728hb96hfjaGDIfwwx7y3/+Crc3TKeRQwf3eT/jzzX1EYcDwXsF10QcATuAVPQqAOaw2yhZTOZS+MthaHYzp+/cxj1bgqS/4Y+SbajFKr8t62VHV1ycuYrl1sCRKDKZoaSdyRkXYdbHaT4nPNAcTTu+AKJfRNLy4bhSaYGq13mtZ0oE8O2PvWOU0TPItqNneF/uw//jYy32M3Z5KfVl3ZWRCrbnjsZPShOmtX2/gTqZ38mhv5mxvqouh7+RcSEc5AQrNte+ZKM5eF747P2VJVBTEHjmB/+mXkOEA7naNVssmd6ooTb18EminciSbXSzdAOKPykMU2wL4HpibiZ4KxBypKOywH1u+3+rBk8l0WL/cqa+ovflAdIYzSbzFKmoXtv7oe0XVMSmoMiZFDvfsnJ2MihIW7nqBYAVvff5nZ2gToaGZ1fdVDZ8kaKcDJPuK9ed3P4G8vWYJFGiF98Q+Y938qNid8BKr2i5+uYb/zksakyWUrPZOePTv9myzlvuoe9Nl71zGg5jHDz+DX2wD0CxAlY9yokseTyPvtMNydqKY/7VewhOKtA77aF+/hSsK1F9+heSXvsMPzs1tlNpwqjIwHpG0YuoRNnVNU75HlQpEAaz9l6uw2cAlCMoNy7V2bCYcmB2rPdzzPO0FD8/FYD1zgL9E8wuq69jLhVquWJ9WIcGGc/cHsENibT9H4Jm/ugnUN7ejtmG7WIZxN8o9sFqz/dfvEelojnq7EvL7PwamE/a233zg0Gq+iAq498Qe+sePiB1NAx9ep4Own7/lBNVw9ioPIsMB/NsPtJm0Nl+aon3zFm48Cnm4n7/nJXa/V48ljfc4p7oGhgMltNCOBrdLdeDTD0CjCmBgD19OJkienbG+1n66GCTaOTr0qeZmmApD35eunoCdAr71EezXPRpKdQqWZomDb0xnSC2MlEb40PO0F7w4djuaNvSafZEp+6YM/Wt7E0h+rkLyMGm7hoL53nZubUv6XgdOL7/t2QjJ2xuq3Z5OOcb3nsdvWUKmI6Bp0Dw/5+Uuy8jrdIrNr2nNSBxNnzt+t7g/drVffEzs3nsCwBQ7gyPwF0AERjuYwn35gfH3e0Hfxa23/JpXCqDmgdMp6+B+NygE+PGAbUnNiQJIDZ1ARLiIh32gPKD+5VfIfvYBmIwIv1DgGZoWIjpoGw9RXQyRLku4myU/WFZr2wzAt5DJSIFx8WSlJ5e2GzOqJIeBoRNuMEoA4i9QBZyGA+D6/iX1tGt4eLYGgfAGhe7HoE8y8pAjfEkTHpeF+n+agGqpBI7E8VJnNu9Ny19SuSd++4v3cWQOAFc38O8uw8XPdwl+Sm5WbE/OtTVpWjAGDGuUite2D8aO/eHRsUuahti9lTYt45eS3k7ui/eqQMD4/XYHXN1EL9uLszt5IHHxMm/5qCBqyMmAYvMFcf3Wqm0apD96zV33egbpddFcU9uxnc2Jj9lsgcUKrm7R9NVja7XWHFQ9QsFrMF6BTn69aUk26nxS7uNdYrOLIDG92wSRKOMoP/A8bSxNduF/8zu/zQuURCiwmLyaKk5Bj7+AMznWg0wS1qBmMKB/BxSdZ24S6gZt01df19SjOZkGST/p91i7NkrJCwTuLOzafrUO2ulm9vuNsTsXOzEfiR1AdAoJo3QdTI0GuoCTwA8I9xsrKbpdNO/ek7OqeQDkv8JwK/ZBP85pu4tT2ADPVb0dtbukjv0Broik9mBEps4dMugrwKwKCgP2eJPdMHGpNcVXjzkMAftkTn+KW5I8i3j6gENy+HuXf/teLM3TLmkcW3XS66GdDoB3NyxNVutohTIasG3nfRjM0MS2y129JjgriO4byVeEU1qzjjGTgJaeT1JGsJe3i25ZQk6mwHIVrS1XK8gzaqO3EyoNuOfn7Mk/FLsI0MkfFTuqKths4uCiJotCibFVK8ibWwqYmhBslgVP1OT5syBe5Ut1/R4NWA8X7NB41We3nJBnEduiLUa/KyGfPCc0WTkE7WQA/8dfsAFwu6Q+jXVyphP2zPcHvt9tywu0qSzsSvVbVWmQ8Si4b6PX5Xtom8N0xBNxVwKZYuH7Xb5PuzIO6S7vWU946iWNV+JDJ4ebr/km+CPMc7fDLoPplDgXjnuUex0wcff2ZalEhqOxuWJn2pMhdyaTaz5GOaroqIkKYU9mk58T9iufvqCj3m4H1C2wWMF/8Qambnxv7MDjYzf8/qGKbB/7XqKeT0nCkqhu4F9xcSNN0L48J7F6Nr+bR5JwUU5HIR/p9+7kJI6QBt/vsoME8FK7JVUQAPN88wHu2TlRmZ+/BADUv/oZu0f9LnB5HVCYAGiNuVzx9PM+oEr9aAAzODAkaZAOOVT8PvYarfdFmVNQFbRvN+PJWnRemfR1DdlL0I5BVcf+qw1vDocILDKD3UMVdR4NuGSlgwjczTLsegBCXxtAuGQFB0DfEuvejcq9cqiAPIcoudkD8MMeR/X3xO53u+DB+vHYdVBj7TvvA9EZ13N2TW7m8OIg0sBdqvvgrkTSqDZlvx9r4u0uXEr9zZxYmW4H7ftLxmQ5JQl751q6BB5AXZO8UmnJZMq+eQb39grIMmRfzdTt3EMmY3XlawjU+/R54CG0ox75BNqCbicDEmx2ezKylA2F8z7x8CLhe1lp1E7Y95dBPw617nme9oJvmkBNg3PB7lCSRH2aXNAQF6PSDftK2uiF4xBqG4nWsysARAt0W9xpGjUVE5YNfqnGYdZd8C0FjS5vEHTOL6/ZavOe/XxjShlN7b7Yj2r1j8Zu+BJ/dCHbU/KaI/9U23pa5qXqTj3oH6l2qbu35hFMi6uauPamCcOzkBMQL89AGKC1yxXEumSdAu2CZUz7/jLek0ZDxvHuEr5pqIpW5PGkMnL+uxt44/wq4d13tJ26TQIs45j0g5vbO/cVZ50a7yMh/p7naS94IVvdT0eRlQNwEU7HNORqW/7S5gu+aRu1Ntwf2DK7XUJaHySZw+JtGkUwakuubeFO1AHuSDvFiA+YUJLDv7/iQp6M2J8vCnY+OgXk/RXw7AzOXEnq5t7YTVLjUbEbRzZLwwkl0zG/Nh0HowJ4H8y/DEqAbocf9IsT4P1VyIMGES38Z1PI+6vQGpRBP+Qk6y0VgVcbSo1sdmjHfbg8J1S33NNIodtBOx3BP58iuVxwent5Q4WzyTgaFgA8kRVXw5jbCJ/u97kZFDpjKePwKdToh0q7NIo7UihGgG8YJOSe52kveHheoJxwZ6i1/9pRANJO35Cm0amd4smtRahiPmQK6RDHrCpNUFS9kMQYSSYCmqaBA4q25WClbuC7ndj/VXgxnIO7uaVH0uUs6ks+EDvSlMjKR8QeOJ67kngUk9PWMgkJIcK+U5BwYR60SbyHNAYeszxuFzwhjR9qu+RRTkhTuJtbLuxtCd82cAAv+JXi0FcbtJ8+A376Bs5gFt6j3e/hxqNgrhAUfrUkCibERrxWtWPp0VlFRoN4smTs4vjFEigotW1cZP73yCEITi73PE97wbeePeHNLiIKbWGabIPCRNlGK4EhbSjFak7Dwpd7VeZVV+xG5emaFtgTAiBVFSSa4T385sCux7akrEe3Ey0tbcF/uObl7FDBf/KMuyHAWB6KPfhMfTx2vhdt/MXuD5Hyp52XdrOFbLaUEAy8xgAAIABJREFU9/5wDahFvLVCnXJnLQ8Uhe6WTciHd4ci5mT0QyeES2y27NIcacz4siQc43SK9u0H4oXqmtgdUypWSIWoy570WAIKwIW71ylxVz22tMtlnAc0LdXWTidwazV+/jpSUnvwH7Oef9oL3jnu4kUOdOPOGPifkxGFeozhnyZAeeBYuq/1bK19XOOStkqk7nYgB+1BH6kG+4MyqbY6nCn33FFrZdbbVFJpdqbt7uuaU03zcXIq6XFP7DIdH1H8Ho49iBSZJF+vy9aocj4BUuHMOMyNRypkSvvNUAYd5WGTYsbbhReBGw4Ym+XUesD5IAIrRUE57dGQXa9n5ySO2DT7O5/QVGHc58V5PESbZ8DPv1LNHa9dqCM0KDgplkylRvZ7OCOoOJUzUaU4Wd+yS7VTGZPWc7ef3XLCqneMB5fUX+wK/Qt+DLfSNAQsqd1k+CWXalpgFovGnge4C9a1DiZadgz0omNu1X6z1YnfhvLVOhH1iyXvDrsS7WpNKxkLab3hz08iECxoS5pEtsIJHoodu/LRsaP1MXa77Cp0104AcU7LFOHUVIQ5LJZoVQn5OA8SRnQCavnU9d2cdOjjD4cgcS0vn8FXFdpdqdowQg2cwwH+y7eEZPz45zzJLq8hX76NlLzVKurUG2He3ldrHhSqZ7mlKC2ntmZGoR2to9ajoTWDnLfZ4tzzPO0dXgVBDedsplqwUXKSRGVdcYCPbT2/3/O11gHRKSVaz6Ne5e+cYlQCUVrdoI2NFFphKY2ITTkAQFwY2kbkoKkbfFo/GvuxO/VDsRu97ih2K6PMfhN1Q717M20AWEqk2sWwKa/lYa6CSnkMz1FOADtIXnH+vibMAM/OkIxH8T5kOX72EpgtFIqsJ9r5CWmRvQ5PMYMsONFyVBc/iqChA32vJM+4e08nUZw1z9iGrSqgkyPoySviVZwDZvcvqae9wwuFNW0HtV3SzLIou6xa45o4RLhbdzpxJ7Cxc/o1DfGTMdox3fv8qM8WYE9ZOQB8rwCaFu24T4WtqkL1V77HYc2QJO/6+y956Stydjq6BV2qdZR+X+y+LB8de7inBGQo2OE5GaN6dQZf5IzfetRZGtCa0igArde5k4fvFqzLh/2QDyajOzlJv8eSTPOykwhX82Ar78uSf24ayFrbp2miSggJd/3VGliu2VVTHBOGfM+8tSpVlqQdDwLbqT0b873wXi+uSn4plBZZcQ7SjlUdrtyjPRs/uKSe9g4P3XHrOmLj6wa4XbKzUasTnm+B1sX2XUcNzEy+wR95KyWOlyPvOQbvFLwE6ZAHm20c/qScxLrLeeCIZn/wBRd4mgCDPmls4xFwqIN9YnM+QbLePhh7oBH+orGb5MiWilzZtoyTxu2WJ4jhb1T6AiLs2qw2ketq769+DWlC283jnFRrRmz41rTwL88gP/uKfX3DGCVOpbJVw0ZE/V47wRRZ5Miqx+LynnAFhX5IVVMgVXvuzpSRzWt3RMM5E9L1TUtGldnkiEQ15Xuep73grRMBcAdJXLQcT6O8tK/BXWp/iBIWRjiwi1t9hEbUyamkKUubcs8LHHAEzBLeEZzj0e89F+j5Cdp3l8FBQxIX6s/gcfrHX8D3enFY8g2x+8OBXYjHxA6E2P1aF2gAT/GiZ4QJMaSlfpi8dkX8/BbI8piHEjAkTYKhm6y3imt3YSF77yHlXgFbDRfheIR2PKDk9nwV9Tg7OfzpBLJYQ/rC02PWcGZiascqHhWk9oBQ3h2/L7DSMs+AXRPKJsO7B40eIAjjBhbYA8/TXvDaCw7w3mMZNTP/ShPWfWrwCyCM/H2WRny5uWRvdjymR312UXqdoDXv+x3IbMVux3wROjjSV2JCoYydPsVMUYGE6T0VeSnIqrQ9fc19sUuvx372Y2LPs/g9RGgIoPLRUuRsRTr9gFpZ0zTBoRBtS3iwDpukkIgjup6xO5M4jvXf72NObcsPUkeHV2nKmno8gtuWvKBquSSrDfxsTkyNxiyHCuj3KDK71PyKPMAWQofKOUBUdqOqSWzpFvyg6cQYgHJyu7FFvCvj4LFIaG7xrR48+VYtxSWgCf1Bp5CLNdF63sfd2eT0jDq228GbuJK+ER4ANjtqNGYkZKA8sC5UeICofqFkGdqTIeTdDcsTEbT9Lpwx7Yf90DqUTodTyW1JENpayc/3xA7fkqX0C8YuaQp/ecNdUDVvIMLpJHQotFhx18x5oviX55DGw8+XMY9BhxiW774KOHh584GTW8upqjhMU517eM/3r6rQno4g767ZIp0vaHrc7aB5/RbJpy/QfrgKgDa3P/B1TifnbXRDkcRFZQk74ZpG5wJ6wpnJmdH80jRq6asGqJkwf+x54gvexzHyEZ4cG4Kf/FbFibSUABAWjLnwkVbWYR1oHQivSMZS5TrqGuJGwFtF4nU6Og6v2KUB2Nrc7SCLFbxv1SeVBGjpdHiUv7vka1drVfR198euuPbHxM4hFTUjvUM0ULMOUZpCtKbGbsfSSYFbmI6JV9lsCCCzPNb0uJKbObxTEzetg0NO+wPw8oIfCLuEbza0qHx/g3a9gX92wsFW3aB99wHuhHBgKYoAYPNNowKpbAeLlomhLdn6oBMElTkxIwSTYTkuGYMShV704bRbVx1ih+qe52kveCCw76UgNiRwOoGg+W6/dBM+BZQI3HoOX/w+HOHs0nCsbcoEMhlzAZvw/l4vw00T605A/UGT4AlqIkKoayrvWszGZ30gdsbqHhe7uBh708JDe/PmxzTosV3okgCVMJUw3Nxy6FNlEWhnp4lJXBiIDLiTE1IqCodhjqNygmRZbL1ezmNfXqEZ/os3hDXoz3Nnp9pe1fdWQX9GXZSK7ycXsc4xjnvtxjqzjWE8ILTEFMfahncv97DMHvDUF7xR51RkNCSoHFJrSwU5DxMustpWOwFhtzS/pFTtEvvdaHjruNAkyyLZAmCNWpb8UKgsn19qnV/ugbQJ+vOSZfw3Zr6w2d4f+5FBwEdjN8kQa8PaSTUeclFd3wb1AGpBlvzw5hnBata1+VoeML/W7S7o1RznZEwka9NKKWEXTs5O2cKdL7h7n07h31HISazdmDjIh2vGUVWAGTcnOvMQgb9d8YNtOCJd6GEIZx++to1sr9slTxwj0zQNsTjLNdUbHlpSf0FL8y/pkUBWQJJEMaXVOqDkonN2G4BPxta39papfkHkrqnx/gA/GrBGHfTQfv6CJUSh+JuCBA5k1CfH7ZIXte9+QhloNQEwwdL2bMoYspQX0wditxLhMbH7XifEHp6TMeNPaNsjgx5PFpXCay+mgUHkOzl/1nEevQ4RkGkS8vHPT+/mpMhSMQ6pCJrTIapXp2ieT9FM+2hfPUc7VLBap8PyEQitWBkO0JwMOF8YDsIJYe1J07FHoX62RQE/7CmdkHh8GQ6iTpD3AXps9TuKIhJgbCh4z/O0d/i2DZxVKXJl3vBT7Ud9wm6dKFuIwCzzMA0iPQAXtOlEJglQawvQLk8qpup+tgIGfUgrEVvtPXx1iGjC2xUkpzYLcnZZ/JraLvL+irWrnSTA/bHPl0Ane1zsqqxgk2UBVHmrJZpRrTgDHS9NCejyVOaS3R7y7JytSStd5kt+AId9SOODG8hxTl6lONBGZGLyfk4/1vU6llVCeZFmsUSiSEjp91jq7A9I6oZtRiWFm6it6CXYe09pP9N8L2nZg5SaNDIcsCPV1+Ggk8BjMHK5Xyw5LPtWU/wSxwTGbJsFZTDYL4xtrYAoNGBVmtLrVBWBpTrSkdxqa8+wK0cMqIAh9z7iSvIMMqWKsDGiZLOjctiGSD5znbaJoK8q1qYPxW7lw2Nit2e7i67d4LEfILYKL/ZmXdMquwlQcvT2Th6+qjgcupmHfKSq7+REPFHMCw3NzVDXbAEeyfY1v/wK6eevcPhnvkvhqtOJnoKdWMaZpJ45klc1pT9sypxl/CBpM4HKbNH0AABfs9pwUzAO8noN83f1ycNL+mkveKu3K8WRZBmHLVa71w0vZHnGBWgiPN6zNWc8xyMVMb9XG8Q04XFvpUBPeaH2ywGCX5NxJf3JGM13nzOuxSrYyIeTw6aHgz57zQ/F7v3jYwdi7MbnzDPG/5xqveiRoSS5tiXtadqwCI7zkA5NjIP04GKFdti9k1O4L5i0SZrSwbyq0N7MOFVtGvjtlmKqszny3/85y5urCGhpr274B7vM29Awz9janY7oK1WTlBPkwMtDuJib8TLyjIO0cs+BXZEHEwsA8TS853naJQ2OpCNcZLeEDof2Y5GlHOAY7sKMCIRdBWKuVVx/NAitTt8t4BNBmyRI3l2j+qUXSC+X9DptUrTjPpphB1lVs95db1F9MoTbDyB1D3h7ieqzc2SvqfzjP30GuVnSvvH8FIC/N3ZfN6pt/4jYW8/dXUkRkqU0Tl6UkLrF4dUp8p+8o8Hb7gDfKyCHmp5MRYb62RiZmaxpHuHnVDV76nUvvNeWkz+dAO+vuNtXNfE6Os4HEDtUec5uUJ9cAV/kgPbNgyqyalza6+hfdYgy4noSGGXRNy1LPaUEykBLO5Pcdgklv0192fvQAXpwRT15XZqLf4tH565UK8ZtwJ3YODqQPIb96AOkThNUsEp5FwACtsR2DtOG9x1e5uyX204HHKPv1Ul7X4XWWvAvGg/ZmVCyBUz+z7ihNgP4htgDEvIXjR0IUtcGxDLisuisAW3LC3eR0iVwvcX+Vz5B/mYe8vCqxkvkpXacdiXw/DzmpPIaoctTceG1l9fsreuJ6A8V3HSC5vomUPpwdsIFqeJK7vxMxZ9UgaFThCm5SXEH1hcQLr04OnH9gT6xst4qpETbxqqNYzij33nzX39LdWlE9dQPFNH3622UbTbJ58MhyFnIktNTX9dUwe0UCqhKQg8bHZ1Q1k3wPUVVEcR0qOCbFQc57254OarroOceqH01hydYbSCn06giNlObx+sZd+e6vj92E2B6ROyYDIHbFWNPXOhZex2Khb4+AF9tuRtudsDsQEYTgPwf/wkNAzQPcY6EcZX2EOdIXzzOaVuybDgqkazNKXlOFpVCotvlCq7X45CsU8C/fstyKfPsyVcVALLJzCtXGvIUJMuUrL6NhmZpGuYLNCOu2UFqok5QaEAaR8D70EK973nyC940D2W5gUgdJ5QAbyB5J1yG4IS78awOhO3gD1Rk8UgcDyGmOmbGvl+9Bz55TgDVekOowPkEPksgVQOpGjSdFMl8g/b9JaWi5wsuFNNm1wkttDan+9w9sfc7cVr4kdix2gTGvy9b9vSFCl7t+QTYHSird3HGVmWRA6sZywRXoHk+hVts0faKO3lI4qgPU9cswcp9FD4yCcBtGfHnaivpphPAe4LOFDtjvFgKobZwzy8izsUw73aymImDI3LVHw4BxoxGBVTrmh8IIAL9FktIvx9QqL6lZAp3fB3OfasdQIzK9+GarSvtnx/bGwLQXwYlqmVPRVm/WgN7inIGhKJxYK9npK9ZC+7qhm/ybMF/q50B+fk7iJNgRpYUOT8M/X4QLvKbTZjKotuJE06zcLwndmhr7jGxA4gT2bYhzFYRlW6u7tudDvmzmw2wzWN343CA++lXgEvgtnnMQ9lS0u/xtJsv4L/7CdzlPObkXZTzgLKJuh3eUaYTDt+uZ1yQn39KbfjvfQZZbenterOKJZmqREhVRZdzdeEDwK8V+R0DNxyXcm0boBHhdQkti3zd8OJ7Mw/Q4fuep73g7ZmMaBSmda1vmkBPQ82FFhwjTMohSZTzuoc4FUk9mXC3PJ2GGvHwfIxsRuRkO+qh7V4gfTcH6gb7f/Z7KN6vgW2J5jmtIHEygjSEybYfroAffg63LnXETVRmcz5G8m72cOxt++jYfU8vtIZdH/bRjntwiy3vGydjuCXdQJpf+Q7Sd3OiLXf7QLSoJ11kb+chj/2vPke2PCD78hr+fAK3IPm8nd+GnEKN3cn5wRdB/eoc6ZfCer0oKDN+MoW/vOFuv6L8oNupcXBRcAJcFOE9CDaTAIFyqmfpVVLQr9bcseuanSyAZdBoAL9V6qEOmEwVAcs1gW7bI1TqNzzfikurt6mdTeeA0Evm8XZEUWvbQFkzdlPYXXUnwHxJkNZoyIni9W3EytuFUnHzAPhnE+Y/mVAKY380+p6OCDmw6ajWvyac+o2xG+z1MbEb2GpKk19f16yPRVSRQV3GN1vViCwjpqbcRyzMUR4mkGrlgzc8vi4+TBXvXze8yzjHvMo9f8ZsTkqhnWz7A5rrGyQnU/gTEtSlqnkaJSpjreWNT5Ng4xM++CKhAyO5mtBZOWSX83LP9qOVSkKogzd3cp3g/s6X/9W39NIK7Qzo9BJpSkiw9mlR1/Dbow+sV+aM8UcNdGSSb90usNtHmO1szh0FiOWOqEt1moZxdoDs1jUltIucKMK3l+wSbHY8ut+pucKR+9x9sYch1yNil0wd+nZcvAJEiELrg4qAr2tI04QPlCmtGSla+v2Yx3odhY9EgJcX8D97HTovUGc8DAfAfMVyo6oiGjHLA8gNiUN7MUWSJnGQdj3n4EgnroaFAcD3q8gBNIqziQrQxkazibBXdQYDokG9caWqgwxI8L8Vwceepz14UseOYGViCDq1NyHOos9/Z3Bca9kBUXDJdq26Vk3HnHW1jqrhhHaL+jo3GnL0rQKnhjJEmpLpVNfA9Vy7BnGHduPRkWxG58HYfd08PnYgxA7oKL7f4wVuOODXL04pZ6dxwiWqp6k59Pt38pDJ+I5xGK61Lj/O6QiX4qsqALYMoYjRgHnWDZLbNWX9TOEtzwL0GrvyCAJ9pNmZJpwvpCm7Lkdqx6ii1y2EUALTuIGdcimV2AxaQVOJb/PgyYOYij53YdnsQv/YzHxxqEJbK/SpvbJnFG0p6obhF2vuONsyOMIBYJ292vIo1p8TRJPOpuxUDHrAQlW9FIzWXs+AH3yH/fq6IdDs2PXjgdil33187IYn6mvbr9djR2exJtF5XxFX1Lacot6uAmLzuBOF21XMo8iB52c6kKMyQjsZAD/5MuQkKgci4yEBccoM2/zwDE3hkO5a5LMSTT9DdrVF/ekJNi8LDH+2QfnDE/S+XMGLoJp2kP/hGx3CUe3Y5xnLGoBzhPmSH8jWA92c95eeTn61gwVR7IxOtn2/y4u+8XpVY+eh54kveJWOtlrP+7gAxIW6DyLwq0Oo502VK3gk1U1ocZlchXVHAAQgGaqaC1PF/8VoarlqxxtZ2RHBKN0O5P0NhyjbLbBS2xudKEqT3xu7X60javAjsYfHlLWqKkiJyFdXrLdbEsDllrAE69OLDrS8DtssDw8QVmxc2Z7mcpyT98CWGvfiKxrBdTvo/e4X8JsNT7skQaqlSn49R/5VF342R3/9jO/BfIniWuU5TElNW6o2+cXVjO/p+ZRiqr0ON40gN9jE9iMUULfb83VGpzSy0EdYT097wR9hxdEpAq+Tknk83trTEdwNbSMNZhB2WfVzMllsGfTC1BPex0WnilU2/IFLCPoy82MlTotzFAja7iAbGnvx3yvISbssAe5quJRvih1AezZ+VOwiAgy6QREARQ68v+IuWRSsiYE4pV2uGMN4wOFSlgHzJdWDNQ9RaT30u7Sw3OTMaziIOYmw3anljD0y6EXsj1nXX8+JcclSDrjevAcuTkkxPFRwF2esvQGWOL0OzN5HRgO+bkPxJTEQXrcguG005JxDieIQgXdKIikoFS69Hj80JuFyz/O0Fzygfpx0XxbDz1gpsishWyVPHzjckCaB7BHJI2mio2vwwqM2jMhp705KXw7sKzQnAxr0TobAh2uSGnodiPdoLqZw+4qDqE3JE2O1hh90A/qRPFHPo3ZbPhw7AHl3/ajY4YS7tEFi8wx49YItQyfRuNigwOZxennDGn9Mf1e5XYU82m4G54Rl3rMzfshOp9SZt5yCKFVGsF1dw89u0f7wMzSfnUBqD7ev4VNHyHCWwqdKQO934TsZxJ1h//kU3R9fAVfzKLJU1cCOkuPenLa7Bd9zLeuopqzQDKVpSq0y5Uq2gfeMe68Wm8cn4jc8T3vBt9rNWK3ZljLlLtttbaQMxFKjBkyCGS3NfgHExaJiQH5bRg7keksx/y/ekThtRr+3S2C5gm8aJO9YIjgVLg073vUt8fLiIPs9ycx5Bp+mD8d+pB/zsdjR+mCS4C325Zrc2qZl26/fB7Y7cmkbGipL4lSmrmQplWcxD23BttstpCyPpEF8zMm6MIjwXNH3SRRmLZ0idlZydSmc3VI9eDiA35Xo7mhkhuoAX2vr11y9ndBrt2mAslTpElL9fEtEqVfjNXgtWawkMylD1dg0i86Hnqe94KGDhSwDCq2FC0olozwEplBI0pzf9gdIlgRj3WCkZQ4fO5IdZKgLar+BP5tA5toP7hXwayDIwQGhtehHA0hNqQ4/X/ADVAwC0tEuxR+LXXq94Pjxi8QOi73XZTmQceTuR/0gGOttPA8E+iFOp9wxgxHCAb6q2TfXqa8f9IDX72JOAPMqcl5gAd4z1NFbjiDRfrsLHk7IUgqyApAsRXu7IBR5QOaSX22iMlmfZZBTSEfQie/zXuJXG0IRqlqn3D2eunWDttehSsRszi6U6to/JLX3tBd84igFPRnCXc9Zu6lVOZOOXqbIUpI7el2qXbUtvU03u7CozLhMPIcy3iTpugVkX6H55AzJJR0zJE1o67LYoHoxQbKtIDtl0r88BxoPbLY4/OAl0vmOzKMO9Wo2v/4C/T+6gpSHe2PHgrzYx8Tuu8qZPY5dzRR8kUOyVBW7VOVLOaVBUjrP4HsF6rPBnTzcjvMJfz5hPtpODDmpBxWaJpZpkyFNHvaEIbsPM8Z/fhIu2M2rcySLHXafT9D70SXa6RDy9or/zix8TA15QRtQ7z3vTkZ1VK0eX9fxojvosyRakDAvy1Wg9wXFg331p5bR8fO0+/BgHetWm8CuMTgoLeeFfdjDIZpsmcw0oJNDjqj9SPHU2104Gn2/w8uPmghDhJeimiQJN19RjPRA3cTqfMA3daeUv5MJ8i+u4G5X+PHf+G540wf/91cBRnxf7AYdeEzscqhi7MKcZVvC97XL5BTbX2Tkr/a7aMZd+JMx2tMRc1hs7uahqE8AIR/5Wk6+18GP/8Z3VdhJL9Kv39F8oVfQbFhhvj7P0Iz7aN++RzJbA7dL9P7wPZrTIXzmeNIZcnRXhvarH/RYg0/H2mfX+t57+EG03UFdByKK9Hs8IU6nbGmmaRS9Gjzclnza0IL8mf/NT/5dvlEKY4VIvLQcgch8WYabOhqlfSne5s5Y35j4ppFido1AxIdrr9xvdxHZ1ynYrTFo73REckSvewe+APVwCtPBe2IPDhiPiB1FrhryKQdZNozSyW2Q+DNiOMCh1lbNGHodIg07nZBH0J0xXIrBgHdlzMmUBOyybb5Wgz75A4njRfR8yp85XwaFt+r5GNn7BXyngLuec1hmKg1pEr2ohr1w6Q9k9k7OFmSWxbIvyKq03JQW6ziVBviaHduYD0ELnvgOjwCwgrpSAwg9aslzmLlVEBm1Vl6nE6el1oozy5U846J+dsYj1lpwNhY3fcdXL7jbVxVbgPo9MR4AV7MgAiVd2jP6oR65NqF8IPYgevSI2AP+PuPCk05BUNizM22r6vcyLPjZhKefXUTrBrg4u5MHQLyOjEchHwB3crIaG1nKvLqdgASV/YEdK3O/vrxB8+m5aroXyL+4gn/zDvLVB7TPT4PmJaU42vChlbX6WgERd7SvwubAL6jIlH2YrWxRFTUAVGJQsslDz9Nf8KrNiE4RbMttUAIgsvWtZ69kbX8yhndCuQqAr9c2m9cRvmx2JBvrBap9dhJG674sgbeXvCsMB2j7Skaual6YtpTS86owLOWBQyozIftY7NrDf1TsIrFF6HhRlPKgSgOOCz1JOIXsFJy6rjaKhenD9zrYfv/kTh5+1I+LTvMxLLvl5I38slzxVNvuWIp5D7/Zwd2uCTP+cAWcn8D99CvIoA+32aF5NgG+/zk3mzcf4CdK+hbhh0h17f1ANTjblpqZBhbrFOQPW/7TMTcUu7+MWV5SSIrNAhn04mvuW05/7gX5l/l4TwRgrTuATSAPFfvGZnbV6/KXrJqEyDOK8I+UpneEd4Eou8hsarTU8YeKfXFV5TU9GdkSy51cEv7rDweg9aSynYzZC68btuP6XRVNlWB2cF/svixZGjwi9sB/NX2dqg6ljThH6WwAsAmxYWYS5fK2LXq/+wWQuJCHaElHY+StUvOauzkZHsbgDtqP96NBZKONR0CaoBl04OoJe/DbEk03Q7YqWVrtSmC1DQAwM5mASFBGkG6X7VAnpPHVDdI3N1HNbbmOXrtOgNsNu1HdLvxhH9ho6ZubB5fU097hjUu537OeTlP2aE2If37LFuD1bWT0V8Se++2OC7tuuLOKhH6utwWnztN+0CP0tk9hIt/rAKcT+GcnPCZVPBQFRYFkrOJBX30g1HVAm3c0DQWOOqo+9kDsZPs/MnYXYw9y3tMx409TWlJOR6T2pUkknJySmeQPFaejR3n4HifDfreL+QB3c2oankJ5HiejKry6/3RM1tOYDipuXUIOFapzGjOktyXaMeVDdv/UJ3zfN9uo6GaQj/U21u+K5Az4HtG2sCOl0JvTyXypKE2FiNR1LBs/gph82ju8sv7FOaBQnXaVuqCxWDQmNkhuUOjSutgw8yT5ZsEmxpdltGzp6IVzx/G1mEWMN6GhHNjuQytQDCg2fEZlAN2RfKeIRGq7UP5FxK67rT9UkE7sfWNXonl+imSx4WIQxa13O7zElQf+v/dRflvzuINZ0e6U73fw/1L3Zj2SJEma2KdqZm5+HxGRd9V0dffMcoe7SxAgQGJeCILkM4EF9kcQ+3P4slj+Eg74QPKBTwQWBDjLGTT7rMqszIzDw+/DDhU+fCKq7t0VkTG7PT1RBgx02RE4AAAgAElEQVSmuio8QsRcTU1U5DucikTJaMCH1iiIZqw2HcMdjujc7YHrO0pt5xnw4TPan3+N4u8+QEYDdqfu6vPJsp5HUKSuCvZ1EkV9ccGD57FKNjxXU7jVlm+0zZYKaVWVdDaljUJNqJtkb//A9bx3+MCTvdR1VP0FwN2xpvW6GX25TpGmn8cqgsrO5JkVCiyKhJSgU8r5kr1dtYJB3SD0uwijHv93kXNkXdXkvGaeZUlZcBBV1ex8fL7T+voYdc8fjD3Pnhw7vEuxm4/skXLR/rffQ9ZbdliMxHxkfR/6XSIj7TB7kodstvSJNf36NsDfLs9yAogjEtPeOVbAx2s+VB9ukvKD5/fgd8yZdf8GQUF0vb/7lHR7vKezuO7K0dKz1Z1bO2IAZyFmSoEp32Aw9WDooTvP+RkgqR88cj3vBZ8pmVgFOs0ggO0yVSHQNqEcDrE0kKZNJr82LgeiopXrliqM1GGby0b4Ly6owhsC3KcbQmQPFWSzi1J5KNkyk/WGh7GP11zckxFwNSUE96itu8diN3jzE2J3RZFi9z4C3ozk7VSmQ4qc8XcKIhs/3QCTIRdNkZ/l4Qq+MWS9ifnI4XCWk+wOLHe8BxYkexvC1GmLEEUOWa3hLy/gdgeEe0r/cTpKO5v66yuC7qpKVSao/c62bEaao3XDmhZihsMqiS6VYmSMBK4bHXrd9Bl7I9hD+MD1vBe81XWGpbAbVat7ndaDJFBwgdiOIbsd61OvZGgtEaCw1HgYaxqi7QZ9eo76LMlQG9n5eIwOHuHmDtIGWkG2ISn77g/coQCVsds9HvsJNuWLsR+rFLsNYXSI41Qh2A36ZGP5DOH6NtXK13eQ7Q7toHOWh+z33FlVWz0YseMkJ0iImjgA+Hlts4b7RYI/5IQPyGbLVnGhjiVDKgxkf/Nr3lMju5tAE6CWNid/w/BOOpMw125DggLgppPnnBmY0rOE1CJ95HreCx6qvqVaJPHfmpaiSlGbJsmZ851R0YwBrwvclWXcUUV3ILSBLbcTBj2aRsuEkBZPbWI/LbXRtYXpDBA2VlSiQowfjV1lJZ4UuwLKXFmqrSV9nWK51LZkDpmBWJZFrqothuwX357lAZXmRmhjPijys5zgOLuI/fHZlAYHdc0OSYc0P1lv4L5+Q7zM1QVks4H0OeAKi2XMzfW6vFcma2hamHp/Ir+2303S4d1upFuKscj0M9LqJtY0bFuGkLjGD1zP/NAK0tDMi9MOfAfS++A8vzAg8UDLItLoAFD+4kSAlLtpBkA1yTPCWWW9hbx7Cb/ascYsOjwwjgbA7ZzjfFuIivdArQjG5Zpl0nYP9Lvsvnj3eOzepQPdF2K3hR47EN7B9YeptadOG+ZMbThzFB2eFQY9yPefuXNbHnmeSOta+5twa8xpwE6NxSuZR/iK9jn+O+XSrjd04N4dUH/zEsWvPwEvLkkimYwjT/hs4GRnAivbACQXkAShpo2NEs2rOrVmoZh8634B7PYYOeaR63nv8A46yOnxdaW6gc57Ih4nQ94MWwi2E1xMeMjRNiM6RZzKAhy7u34/CXVuKD7kjqoqNuixVCg7pM/pSB5AVAfDYkWKnw574HUAZGRk9TN6KHbX6Tw5dhkPEgUwyxi7SQzWalTmPaJJcJbFrhTAyaSzCavmES5GMOuYmM90eJ6THTJLneZu9/Dvb+A/z2l3oyoIuL4DDkfkf/ctPW/7eujsdSHbLcL8Pi1sq/3tUoKLWV/aoTdqwZvmZK88k+CQ3T7CLswvCkWOL0Flnv0O78oODzHqfRRxGM6zA2E/aDvpZsfyJAjJESoph6A71clOKas1X9v2UKgbH6WXG8hiCXcxg1nCyFEBXL1u7FvLes1X/d09+aKFPC12c+J+Quxurx0Ok5szky/ntE1Ild34MOmAy5y2AQBF5ywPP1+nvrhKXLRljqzXTTmtNxznHw7Rt9UZKO6br5njoA9ZrYmw/L9/ifqrKTr/73vM//ufYfzrPbLJIPqtSk8d/NQTFgCZXKb4azs/AIQMGPbVGE6/s8spy58R+bz2IDilUErGbg4+PbyenvmCd1pPZgBUeKfIkyKVCWnu93DdftKUaRqaDaj6l8sBCUr8reszdSo37HNx6JTUjMZwrNi12atW+WxMvMp2B+cc+9+LDWMRgZtNSGKw8b9a7DwUO6BgtSfEbq95qdVkLc95PtBFL1rGuPUusp1MvcuAbXZQtjyidIiO8tvXl8h+9YEsMMtpNOQiDJL0LKsK4advIbmHO7bIbg+Qty+Q3++Bn75Dtq0Rvn6J8W+4G7t9hfZqDP/ddYT1RvEl5/ThN4ZXDqmOVEM4HKnBE8J5b13dy8U7SpKoXAcmI+D6lofYR65nvuDBaaCiFB20Vuv3Elm3rnnTTLHL2n2nvdl+l5bre6359KAThZCKnB0Whava9M8pUVpGfXY79FUvszH8ekdG1OU0WjpaByR2IB6JPTpYPyF2UXZQjN0W8aDPh+huoQOuDqSkXbxbqADqsK+MK8WMWx6TETDIIBHGTBbXaU4megpAO0MV8PoF3N/8EhAheaSq4JarWKpkLQdBWQjsw/d78Ddz/j3FBMl2H7tTYkhUNX+IZgmG88lp4yNFDswXyeHwBDjmnOPGNJ0kp5YHrue94CXEmjRCX0MbF7nLkfRJlPiLpkluFSJpWqjCRsiIf8doSDrczVxVx3Tkv9xA6ooYjcVK4bz0YZVWhyDff0bYH5BdzICj0vM2SpY2Gbm6hnO9h2MfDUi2fkrszkdlNJfnLAn2BwKwbu5hYkvh7p45hhbiFcn4+RahbeG++Qr48Cnlcazgah9hDmF/gC9LlkCak3MuvkVcr8cYFmu4Vy8QZiPgfk3UpgiOX8/Q/fUNmpcT5B/uIMM+vB6MRU2Uo2lDfjKJHvZ58B3og7nfxzeLUf3cZMzNZzJW65s+y1R7U6iep+t0vjhpfd4LXlnqLvPRdTpqwush0YVucqEWAfo97ohTsnIMkirdPkkj717C3+hNmS9okKAEZGfeTIWanikmpv75GxQf5mkBvnoB9Etgvopse1cU/OxGJaj73JUeih2iXY4nxM5++B7y7iVNkvMe/958oQ8Azcq8DWpevYTf7vkWfHlJ+tz1HM0/+2nKwxhYoyHr6X4Jd30fmWDxbaV5yWZLdYGmAXokf4TJEH53QPV2ivLbOWS5Rt4SgkC3bX5HMh3C3Syiq+EZbbJCnJaKyZBo7i5j6SKrddLjVEgFSzEfPyOBiMv4dn3get4LHi5in6UNQHOI3Y44hKhq3qzJkF+S0dyUmA2AX/yWnRF895mY8LqJgqxSdiKlzDkH5PzdzddXyNZH5AslRztHSlroILtZ8O80x6R4a0pZ2op7LHYx84UnxB6tGb/7HNGSKPITGT8bwbuogyPrDf+79zRN3h3O8gjjHh8Q1dDMbhaJJtecoCW9J8MpSFIc7nQg6zUwp2hrMb9nF+diCnhPOfHXL7kwD0eeT4Z9Dq6sTSkSZwGuUxA+3LSQ2znPEaoo5kotXdo26sLLsYrGb07fzEk/9Mdc0oQ29WTNxArQUXMTIa4u87RzN0UygDdpf4hMm/jqHA/PNR2PFbszNvzJ2B1w+yPy312zZradtmkQ6ga+7LDDA1A91+T39GpvbuGHg3iA+sHY9RD5lNgj1PgkdtmrFmbZUZt2RVKGwA6GwRM+KeZlNOQ/ax7+M/gA5jnPCas1D96rdcrJJtF2yM8yAHr+GY0I5pIAp7Wz9Eq4/RH+8oLxmyDreqe8VB5Oxc5dbaXlWJZcPXSCKiKJK2A7u0+waxwr5h70wQmcELvwOLTgeS947dJEnAQQ5ZZjRwMg+dk8T52LrKJIK8uJ94BPzhtyaM9O9M57dlmC0D4SSF0EB0o+dwruKKtNtHwBECeWrkdJCT/mF/1Y7NFS/Qmxs1OiMnQSTkSQiigJ6LwnNbDoxPOCK9T47HBE+HxDKQ/NA02j8h49yJJSJDAPJsvJJEPsPphOZpbFqazUdVQIlg5l/2RPJKrcL4iXN2pfXdEtu2kUEnAikGoGDEGAYY9vBpMzgZY9TcO3jj0ApsEfJH3WP17SPPPBk4sHGzfsaw0d4qha1Akj+hC1dtDRXUOlpiFCubhBj7VorwfMJnGyF65m7L/PJpxUamtSBj1izSWo2Kq2GsfDxGEFIhhNumz/uX43HoQfit0NB0+O3SaoYqrGs4kCx4iDkfFAD3sjxh9aeiyZ/eXLCy6U0zzKkuXCsfqDfGJOw4EqF/Ms40ajJDp7rBCWK543ZhP+zIdrliTOpf5/VaN9fck30FCtPMtS6Yc+HU5F4cfHYyp5gAiYM/7x6YKWRqfS+hBHwdZHrme+wyvjaTyMrTp7CKSqgF6ZTMGahjdaCc1QryR47Z3rjhKxIiplFy7HEO/hh0OEHt2g3WoL6ZVoJ1qS9N4AALz3xIiEAHkxQfbhlrQ0hRK4hvW0bM1x5Phw7NYWfULscD7GLiEQe69ll7y5pCHDcEjaHsADs/d8axQ52lGJfDikGYLl0S2AuoVbrBEmA+bzcQ50QsxJVCZFtvSNMoOECHF++4rt3sxzN//6LdueQ7ZxjRTjfvktzQr00B4FT51LXRld5AZnAMAJs8IhzEPL3liuUyTPLcXXuEHvR+7xBMBNx6wPtcY2lSvnPV0fAN6siwlpZIYfb5qTheZj7SyzMQkOWgP7RTLG9asdZLEERhy7Z7/8ENF4ZjbgdgRPee2oGBNflitCZw2K/IXY5XR3/0Ls1IbXmvjzrZYMAPKcE1P1q3KLNWS7ZU29P3Dq2CmQf5hDjhWym2UyTfh4DXcxRbicwn2eMx+VtracnHo+0XBsz1Iwz7mjZhlwp4phb15yEX+8Vvt4bhzedNsPx2jwAGRJSwjQ+UOTlOEGOghsAy2ILiYcQh0qVW+rVWU4pJ57j3KIqBsiXh+5nndJAzD5uuYOqLaQ0dxXx/NS18CnW62lT3AaepiJADOAi72u42tStrRoiYq8VjMrpkQ2W0pc9HrxNe50QNP+p9+wXs4zdhaMD/uE2Kkd/7TYo1vf/Yp1vr3WVSEYR/b9RfvRERJwOwe++xjVjE/zcCUNGfzdIubjuuVZTtK0zMs0dfKc+JhK6/RMzz3zJQFuf/aWcS430a7SVBZMX57mDAe2Wff7pNmvMuSy3aUzCsAFnOfqaFjzc+oUYi7hst0R62/yfY9cz3zBE7vuHLHiYkKayrABkEBJeR7J15EkYT3ZIKmjIUKvJ9OmnE1STXlqTGAtwtmE0m/jAQ+klzNIVSN88xahzOIrt309i0QMZ6oEj8TOhf/E2MtOjD2WZv1eAkq9fqHdlh7fCP0+/znPqbw7myhnN+WBS5Y3jIv5yP3yLCcAsQw6vfzlBfMsCvjRUDH8e7j3HzlbmAzhrvgzzZ+9pG+Uc0nq2zseZmcTPUvp8K1bKhd3pgA+xb2bP+yff8Xzy2TM84fi612nA0xHBOr93lnkD1bUsxZiKl7IX43/5QknVZM5PaAYGOxk4hatXgAugExbe6ZPbnotR6WYOafuEXSBjgJKhjoscoiNvb1jDamqBnHa17bUTrTFrAfRB2M3HPxTYjc5Ch3mxJ8/VWNQi3jXOfGqalvuwEAii1geOp1l+ZA6Tmc5qVGCoT7jPdT7FodIRZ7AbVq2if6z7PeMu9dNB/ZBH2aVI/tDzMVNxqzFFfobhVL3++g2iCJnTW/t0rbloNAEpSTgr2/+7Y/U48kARU173q6r6si8kTyPwwwJQomHQxUHGjZMiQyj8VCVdQtgOuEObMQJ46N21M6yruEN1fduClzPuVMWOSXnumXC2WdezbpU3LVtIa59MPaoOf+U2Nsqxb478AGdjKN4k3TZ0fHrrXJfvarsZlTkNRHY1Sbm4UYDnhOGA2DQYz6zSdoQgNgqlTXnBA5g9yoI/KhUxhMHY+F+AffuNcL3n1W5rYkufm5QJLyMUviiqsObK/j7dZIYnCl8wFxb1ltOg3VYiBAo7PTxlpvJoMfPBupKSlkANw8vqedd0jh2JiLQSFW6pK55iAKI5ThBJrpjHUnSyDMOahRX7byO+TudWA4E0zHf7iG9Tmp76WGyfXPFw2IIyQEwqAOgjrPDbJgWmA2KuuXjsYs8OfY4QdV+/5lMeObpCG73w/sowQFHAaP2zRVwfXueBxDV0CyfMB2e5SS7Aw+c1iItVf67TucN+eoN43/7Cq5p4b5iRwtvXvLwrHU36obT7P2Bh86mgewPcbFbd8Wtt/HtEk2GdWIr2x0/Y1pDagjtLP8i/5EbE4egLnPKbDI+KsABjDjgfsmOh4r5R8Pfqj7RdDnGKSSqmsi95ZqiS9s9X9GdAv7THcxyRvIcmFfwVkYAXBS7HfC7Lf/mz78Gvv2YMN7ffc+aWWva6AD4A7Ejz58ce1RYMImRY8VSxX63lS6NikXdzmlaVlWQ+T28gqqcyemNRwg3d9xltS0qzsGLILy6iDk5he3Kdpcg1SoWBbCGx7ffRxXm8P0H+MsLiHNJ6mQ6htwvzr9X3b2doTh3Kz7kVvqYFWee3pYACBtuQ3LxO1actve6VIZWbcvHrue94I0MIAFRUcsWjdXNJiC0XNPMAIhSD85kIAolECuwy8oJSGCLzoR+bKpndpGnjKRuCVlv4ccj9pk/30J+8VsurDZEYrG0gYwqW5QPxX48oQx+IXZRxj6dqptUcggx9O5ixoWomHlvDt9tGy3lWQM3MQ/3+gUVFooc7tUV82ka4CQnsVmBxoLQRiRjWK1h5mKYTRD+v98gu7pEuLnlA7rbJ+7qaBQNEfg97LgpqGCUmIhsXbM8se9ju4v0RpdljAOA26c3n33GVVoifaFT87xLGsPRdLup3ZfnabGPSJo2fZcoKqqqVmI3paq4A6iHqu3CaBqEOXcfWROqG7s5dnXLaJLrRgP2hauaKMSrS8ZntfbXb1hyZKmL9FDshsh8Suxu0Eux93rUY1F4gCtLyPyerVU7RJqOJoCIQd9szvLAahMPx5aP65bnOYmkfrkeCsP9QimTpDq68RDIyICSwwHylz/XzzPX5s/fxoeVEOMy3ovI8S1O3hi1dbL4/crhEEFw1sGiLEeTrH3qKmoN2Zp46HreO7ze8Ei7k8An/XRn1x636byIyTCb2qyhFxertKMY5W84JH3teIQbjehU0SnIpOkSUGVuGyyLcuDDJwLIxiOE1VqVt1TV924B8Rn13w0s9kDscjgCefm02I9V7PRY7xltYPxA5LnK4QiECtip/YxZsVc13GyqHlfMg3TAA6SlnJ8fjwgVcC7mFLH6VzPV21FJ7uUK7mIWJQxdVZML+7Ov4H7xWxqY3d1Ddnvkv/z+vAtUmEEzyyXX+HRIzzJuBJ2ChhA7teLRpgKKLs8bUrDbtdmxoxMkOZ38qD2evOMOUlFlC6MxTbiM9WIC+4asKztMXhV7BTjTVHdqke5U29yY+bLdAVoK2GE2qpM1DbDdEkdiu4jzCUhmaMrRALhex1amH48S7OEHYkeneHrs3kc5ORMUlc02MaHKTtJYVJ0X58sk5yHCmvf38sDLS/imJfirU8C/uITcL1NOQajSu6Lrt5UoopBsf3VxZhPpfsfFbTRGfzlDuLuHv5hCVht2fQ4KXrMpuVH2DPA2GvJgfDjy33WKqAoBr2cjaxsDzHXQS2pmX1pSf7TF+Q9yuQTlbSm86ToFwsUoTRuBRCho2njQsiddmiaxjrQ7EgWEJLBO7PeSdozpHAqVgv3ljItUXbAjdOC0txyE2BHbxQCtYx+OPb7GnxA7TmI3/D+E/XobMEkbuBObFb0Bsnpd+MsZ2k+fz/KQWmUDrbtT1Qg3d+c5CXXfo17PsWIpMRogrEgCj6WbIiqJdTkwhv0B7tUV+cKTEe+DesjaoI8KcEVUoIjx2K5tZxftHqFTsPTUssruDd8QPlESH7ie9w7vwFeW9W8zD3es4dd7Pu3D/tk43u2P7Devt3SW2B3I3tH+rLMblmUJ06JIu/B6huzDbZqWntTx0gaKeC43kHev4Nfb2CJzb16yc2AqA6MBOxqH6tHY4c49RR+Nvd+lSVinSPotgx7cnhPe7H6L8GpKd70R7Wxcp2ApoLCE8F/9c/hffIh5JD0Yoj9lu+OmsNnGnFDVkEyN2vKMrdumQXsxRPt2imK+o/3lvoa8ugBE0A5KSEb1YGQObl+hmfZQrLYsq5qWPXYrU/ZHlil1w7yqOoLqYIJS3nMG0DQka+8O0RXctDQRQqQJPnY97wUfAknFmY9quzZ1dN6TfaSXKIPG+rjufhV3SGkauLqIpUC0RlS7Sbm7h19vogqZUzElN+hzEng8wm08UYi//C1E7Wxch9Yrscuz2abdObRpmvgDsTOm+mmx2w5r0hz2+w4HuF+sgUEfXs2I7dXu+vqaP3JB5f/+N9w1LY/tDuI8B0SrDfO5Um1NzQn7A3fs+6WeJzJgf4Bf7SH5AOI9srs1XfiWG7QvZ8iXe0gnh7uZQ15Qb9JXvTQRtzeC3aeqil62sNZqU6R5xslGIh+X8HrGcaot6Q4VLTYHE5gL4mPX8y5psoxfRFlSTkOVqmI5k2XR9sSZQJJeMuzzc4pVR09FiHzGFqLiZlzZ4Y0zSEFQ3XYJkc5mTBvZ7hK8F4g/j6YhWtKnLxSzyeOxX82eHrt1XXwWuz7SBsB51t1KZA/bXQTKyW6fujCdDhUETvJwBW1/3Hh0bhNzkhPrZ244sPKq14WrG/h9Td5sG4D7Jdq3l3Bti3bIHb796WsgcwjTEbLP2glT2p5stvy/tTr4nS52fYOZ+ppBNdxqw7y1GyX6OTOokM32i6pjwHPf4duWbbdKaWXO8XCoQw06u9H1AocjMdsVnfCcYVWsfbY/JJBWrZ8zV2jFtDho3axsoIi9ubpgaXE4sgPR4WQ23N4RMlyQQifbPVzho9zFo7F/f81d9O8Zuxx0clwUEFGhJuV9uq/f0H3brDHvl+SZHiv4n/0kjd9PDpqyWsN99YblynYXvVANzekupvx3ZSeayMlqDdzOEUwAqW3hv/2M9uaOB/mmRv76FVB2EG7uEEQV2IoCQAYU0JpcsPjvfo7p//oLPszGQDtWcaYBIPliaTt39V//DOP/45fxrWF6nQTkPS6X/bwXPFR35MT3B0s6ULuyTIMZk4Yz0U/9Z6lqkrWNG6k3SEKIQC3jYdI5TnfZl5fA3T3C7ZzIQ8Nq5BnVwS64e/vZFGGzZUtwf+DOaON6k9Z7IHbkeSxzvhi7gcXsbRCCak36tBBHA0p2AIxVbXBkvgDKEvL+I/zLq5SHQqTdoE9xpMxDLi/hvY85uaJI6sGObhzSVZIMoEYFFfygh/bFBHlPHzSt/WnQVsYWp1hb0zpA3mH6v/06ftvRORA4J2OLun1kGeAdJv/nb1mOSXvWKDDoxmPX817wimLE4XgigXGyOHRaBwlAR71EVXCJLCEVGj2o04d2HqKybr+kzJviWJArT/N2zoPTC+7s2PGwhssZF8yG3QnX71F7RTHvGKnRVl2nQ+NDsecKF3hK7NZblhDrdxiFznvWs0fCG1D20sAu7wND6kO6spNQhurKHe0fNR+/ZoloObF8cXwgtB9OxlOg3VCek2RSN/DbI99w3kPm99F2U6oaXrm+gDYA6oqgMjWMONuVT1u9M/Wbqvgd1P/Zz9D51ackTNsgPSCmhDyb/IjBYwA12keDyOWM4C7t0jjngIspfUOLPHodYTpiN0AHMK7fY+srai0WEa+Cht5MUZOlKFjnrnd8VY6HkLcvuEj6ZZKVNle+psGv/0dOGM2jCc49GjuAJ8eOyfAPYzeYcd0gvJzRW0pt3EVVwOKYXYWezvIweuRsHPOp3s3OcsLhyLwUeoy6gQz7CJMB47qckeLXLXH4agzXLVG/meLwX/4F2p++RvPnbwlbMMlBO7T3erx/Nn3N7Q2m90XtMZ0ZI3gHvH6Bznd3/MygH0nf0au3W/KttHkcWvDM8fAv5a8u/pXaw7QROBV3uhNic1QgK4p4wIrMJru0HZlAXFzMrmkpl/3qkgCrMxHSXMsdSl9bq9DcKNgR6VOFYKhtRhUVAvBg7Pz74Wmx22tbJGmw2Pdmkt/aixYlPYu6jchAVRHqhu1OzSNCphWCDe16mDa+67PlG/+etQHNrDljx0YCZwHNqwny310zzLEqh/VLUhDHA5o423RcSyMXKKpkyg7OyqFBL7oPGt/XPGNd00ZH8qidc8oU8x7/y/v/6ceLh7dFDSCWJs5rHWtDCoBfYJnFU7uJItmiPMWvnC46t97qmD+Hv1tw2mliP0Mq47panantIQB48+sGOCohRKlzsbbWkuah2HE8JhDYl2I/eeUblsRMziBK8SsUDhyEQlJVxVyMkNLS+tLykMORE9Gy5EM3UKBXt4w5ASABQyfRbjTkDKOqIdU2tl1R1ch/+zl9Z9s975vSAZ0Iyz1RczWzuQ+imjmDeLaK0ic9FWC6nUO0tMFyTQ14Y4SNCvbxyw7CqAe/3CZV4geuZ7/gcaw4TKnUnflYURauOXHO0I4G9BB2xgSycfRswmHK/gDknPbJfg9kWu6EEJ0u0C2BzZa72XiU1LZUC1LU4c/kpg1VSWUs/aJUGvvB2E26+wmxR9cQEyjKM8hWNWSKDvVmQoi6NObmLccj5NgkiK5hiaxnX5ZxLG+OhQYBiDicbhmnm9gf4j2RNy+BX38LP5syjl6XWpz7Pfzrl3zwd/soE24PPpwnbMIIIIeK34sqIYjzLOHmC977bpcDv0bZV9o9imcxwwLpPTZTh4eu573gQ+BOozuf7bCyNrUCHwWH0DS8kUCqzY0P2jTEnvssDpXYUcmTInDZgVuuIRB2JkzV12zh7cpzOOiuaXrkdojNs3RQNW+nB2KnqVj1pNhlu0N0+D4c+SZQmT0bQOHqAlHa2ly7nYebmELyeR6xxbjdR0UBJ3KWE+Wrq00IkfgAACAASURBVAijkIq7PJoG7v1HYvlV/xOi6szDAcvMr98qH8BBbu5UyFZl8Xq9s24KD+YnC3WhXlk6/UVoVTSqf3J/TAiWB39CjFug+jG3JbUXfi6NXKabYwoDQBI6tQ6F90AnT71243qawlWny1KkT4qYaTu6UvmgeUYGkIgybIxn6ghpsKmoGetWNaQhNQ0AMB3RPfqh2E+cK74Yu/dAr6OxK/w3y/gg9Htw6AHbfYIEtyosquWgvHsJ97t1nEYCgLx9AX99T9y75iNDYuajXLhKWwOInR6paxoetyP41YbdnMMRuJrCv7iKfXwoQ8kVBeTtKzogmnoDQIhACJBuCb/cxHo9DriCRI6CqTKj0vZr2QE+qxZ8bSyxnJvTF67n3aVRn1ZkKg9tB60TKQzplbF8kKpKvd9el8q1600EaNmXJ4MeuxhvriB5hnAx4v9dTeJil04Bv9zCf55HRo/JvZ0KOgFI+PmrC75iL6dw++PjsStz50mxmy4MkHa1YwV5c8WH5FjRwsb79HvLDrs1F1P4z3Pg5dVZHv5mwYV1NY35uM3uLKeId7maJUmQKbVxwrA8643L+0+qb+8hgx7dDo8VJ7w66JPNJo3+b+bA7Rzu040iP/VMcMJwipPYIw2WpWkpPfL5lv99s+X90I0vTm8fuZ73ghfhYlLvHzQN/7fJXvQVS7HdccdUsgMyYkRc3egEUy0ezSlDe+/+bgXXBrhDzX/eUisddUO56hBovb7ekhwMUMhppbov0zG7MVVN4sJmRyBTVfOLfSz2qnp67N4nszLFtLQvZ/B3K+6kKl8tumDdasPPHCvlevIAfZpH+/aSQ7fFOuYjh8N5TiLMSxGaLvNEffZ68L/9mFqCRUFE5psr4HJGbM3FLBoHy3ffM24VbzVxp8j4Mty/YpLccBDPEdGAbb5g+drtslTTf+96Ks2hVpfxdz5wPe+SxtS5lD0jxwoOxmwZsM7UAxp35pwHLYOWms+R6dEoVc4wLKh1zC4B4VjBbbc8NGUe2GoXoSzZBdntqVX+O1X6DQJsd+zEqHBQdO1brNmleSx2laf4e8eudbF/f50egMWK8TsHaTKWHnOduupCCx8/wff7MQ///oZ/49RFr1OoRB5zciKc1p6IrGK3h7u64N+qKp0KU+cm+3gbMfj1f/4XyG+73GhuEaU+EALfAtZWVY8tGY2p36/+UiaqSqc+1YL/vbrfqJuyXvN78lk0Tn7oet4LHki91qPatVdVXKwmcuTQcFey/nyeRcBThPk6lZyoVeLD6HQAOyuXM/JQbSJalgnAZF2LYZ+AKe/R3t4Rl22ljsptwOthynUej73T4RniKbE3bYwdXr2RQsvFOuoD/S7fEJ9vIuHc2o2mfuBnM+7WmgfW27QANZ9s/JKwAsvJuj0iiYzS66beuXfRSdCtNnQgfP8J7u0r5H/7bezuiN7D+ABb6dYGuC7LMLc/Aqe7s6E1TcakrhNUuGtqxIQUu+FQQW9ybnL8A9fzXvB5RtxKVSfZCTt0ej3Q6hcmgx68TRC3ewqlvpjC1S3ceodwOYafr9nqshvV4+5Kzqv2h0dDYte3e0ILNnuK9e8OLIUASL8L//VbdhOaBhjTvyj0u/C7A7DeILwZsUX2UOzQQcsTYpfxgIZl4xHvSwiJu6nuIWHch+92kyLAhlqVMhhywCNynofyW8PLGdzuCP/1W3aqzJTZ7pO6G1rZJV3Vo385g1/tCHwbjyJ+BxdTwhzGyUe2+ckLFN/eJhNhu2xgZirQTUvegrn6mbyJO4FU9xJJxA16fEudeMxG0v1DS+qPszL/gS4Tx7RdS7EuZPvQ5jECsHZ7DlqsV93llA+qOubn61jCRF0UIwIHgfs8J/eyR41D4tP1y9/tksGA95BPN/BXF5BXV8CHT3TRuFvw9+oY393M02DmB2Kn8ln1pNhxUymu5hjbsPZWMKMztz8kttRqTYGqTgfy3Udt5x2A/T7mwbeQh/vNB3airi4gt/fAaU67GtjuUtzOAdd3CA01Zow8Y+1UNx2z7apdK8s9u51DzGU7KhLsIwlF1pvIgoqiqyY9YlPn8QhyO09rI7T8XBByjS0nOWkh/8D1vBc8hImZGOopgcJQfwrAci4HMu4oJrBJ97xCdWIUY+EcpEU8AIXVmoz6okuMdVUBalZAXiu/NNOrgaj30GJFnmhB82J5+5IkboPuWp/7odhDiMC3L8Zu0NdTwjrAcuMURWkEbyAOu4Bz5GHMAwq+K0u4uiLRxju45Sbl1DTA25eQ7z5yBy8KvlkGPYTfvYfv93mP9NzDQ7NJkSg5W0s4vH6RYjDYtW0Oi/XJTEJNKUZD4ps+3fCt0bTwwwE7bEWOdtIjlKFku5ZSh31+5hHw2PNe8BlVeaVXAjf33EENb2Ksdq3tZL9nX9a+TJV2sENq7HXXdZS7AAD31Wsd59eQy1ccT+8PdJwb9hGmA2Sf7vkqLztc1FcXVCu744AEIcCpTLNzDnJJDLqJBv1g7JXtnk+IvW6irXrUbOyVwP4YQWZU6mKMsj+wV36o4AG0X72AeIf8+3nMAw13UHeoIBcT5tME9sstp26Xb9jJmH+732XHJgRksykfnneveE+mUx54r2YIv30PfzFlzlkG/OwrYKulVF3zPugbQwCiMrVdK8uVanRSux7dbpx5sLyqgSDIf7dm+eYdsFjy0Kpdp8eu592WDAR6uWUa4Iiy3t3ukHrjSpFDTnir6/f4SrQv17ocANuMdjDsFHCbPeGxvRJuX7Em7fdiLem/IyDK7Y88WDlHebdvP6WetZ4nkGVERy43/NnHYgeeHLsME0rSGb3QdCSNKtejWQLMUGF/ZAsyy+C/u0b+/fwsD7faAHcL7oyWj+6UMae25Qxgv2deBvLa7mNnyr3/qG3DJWO7X8L/7M+SzIhzcL/7yDOFqjRIl+4l8A5hosZupsAwHMbJLWEDB6VAZlGkqZ0N4vzB7Y90FumWvK+n0IwfuJ73Dm+XvQKBVPc1DR1AjIdq9bP5/tggxT5rO6YJ9juntDwFTNkww5N0jduF8krJp8RilcT4VWbDWpokVxP7HbVf7ID5QOz0lHpa7M64oPo3KYut8RsobK8Yl81OtXc2wMZzMnk8Iqw3cG9fxTyMzwsg5oPbOUVdNSdpQ8IsLdbAoAdZbxhDt0z1+lI5uIMBwnINuZsjG41UT7NQmRSdcncKyGLNUs85+O0uqSPr5fZHfsb67UA0j5DtEX53gJixmSofoGnZ1s0fX9LPfsHzoKZDI7WpQaXov/0xEZKbwC9ehN0XbZ0ZycD16HDhgiTdQkUCyk5lI47HhEzUxe4GfWC7V2DYSYts0FcPpTZ2I/jmyHSRVY/GbhLQT4rd9GuCMoUOR8WtqDz3fn8Gs7CHQnZ7yPyeWuyOnSfLQ2rApLflcFApvjztpNbt8B644OQ4ShT+9CWOrwYo5lQmQN2iuuyj/LjC6r/9Oab/7hr12wmaboZixfizX32McoMmvwFA25uCaDjnSHzBEWxlGjzZGgn2mQj11s+Y9If/MXdpXBLRl/1BBfJ5UDXbeIPmyn6vLPZjct5QYdIICQDSoggBcjmF79CHCG2gjN5qS6y2Yt/FatrXVxDnIEWW6nz9eWNmNe8ukK0VVhsCJ6wPxe4chypPiV3ZSma8KyEALy4Ryg6kzODqlrjzriIxtatiMniy3vJAt9nFPNy9i0rDzpy9m4aSe5pTJJ+vNpCrGcKLKbLre+D9R3R+xw4RLqeQ7z+j82vKeUz/9yVcr4vOd/coTg2F1fMqPpiGxVcrSwihAa5bJudDs6RUzR6DX5iolqhtZoR7dPyP3InbFqcBihQohDxXfEqb2DgAv1BhHetKOjrHromOxmW7VeSgwN8t0q4gwsWuUABashzgL0jrk5pvB7dUzIv3CN9+T+HRDeWb/ahHDXmA6Mdu+WDsBhx7UuyHIw/DFjsALNbweQaZjli7ilCAtVJ6YacgFqfbpZXNdx+QvXoZ8wBYgjlFdzrvgW73PCdVMUOvC1zP4b1j7IM+nAjLpMWKJdaLC8inW36mDfy54YDiTqK+r3mehmknoDoDu1nZaU7hkQBSdrgJbfcqFBWSG2PmWdro5nJmOfQD1/M+tAL8Inu9OM10HaIMXZ/CnFG5S7metFmkZSS0xy52I5yL1o5Ope6QZ6TYmUhTSRt3l+fRSQ/qnIfdnmXCaAAJAX425c+WHbjJGP76nmXN8Zgeogdih/dPj93kPEYjxZsUhCeIcDBVKHjK4leGk8tzyGTIz/zzf3KWR/v2klDpXhnzQdk5zynPaS/T41sAAAnu0NF+j8588tUryO8+8N8vVwiXYz4oKsfnJ2pQPBpo+3KjYLpWAWB8e0V1t7aF7HbJkU9FrEzVQQ5KBj8co9CUVCqN+GN38TNGjb3WYBLNQBr9G0HbSAW7QzoMerWTt9N70wC1RNFS1++xY6EEB/R7wP0KokbD8fe2IcnZLdZ8BbctGUUicHWNsN7Q82g8Snj5B2KPbKinxG49a4PPFgUfvlpBXUbTW6zpInJaotzeA50O/O3yLI/sbg25X8JpfBDqyqNTxJxMXkQ2VZLIW2/ZLRoMyCm+vgN+/R54+4pds/kS7m6F8NN3cMutvv10M1Fq3inAy1SGow9rEMiwB9wu2D0D4mHe9ftxaBehBqZA0SsJx/4Cp/XZ7/A2hAEQaWDGcnIj1qjOOe5Wmy1OnfQMAwIgDm6kaaM/kbx5yd+lNW+4mnHHuJoCswmJ0aqQG00RDlUinVQ1wutLYEzcvHvzkj9rf/+R2K3D8feNHZ6LQ4Z9xn855bBFVPj0WCWa22zCBXQ4Evp8kkcY96M4asznzcuznGIceuZw3S4wHSNMR9zhtyR84KvXwGLF0qqqES7HcN9+ogVoliH8jO5+nA6nzUbqmg+WakTG3VvBdxFgJsLvoqqiK2I0oSsVk6Tu6V/iaD/vHf4UF+FdPMxIW7NeM72TikrCbjxiByNom6tteTBrQ6S8xVac93CfbogytHLnw467ttLN4qGqblScv8OJrc8ixBW/+i5qu0Tbmd1eqYDtg7FHnZcnxA6n43bjygIEfy3ohI2P1xw+rdaRPifK8ooHv7/5BfD6VcwD336EOI9wv4Afj5lPVZ3npOq9sZ16OESV5VDVCQF5Tey7TXvdr9/zDHFs4L2H/9V7lmN6xakqQIaY/R0zTRhOIw0RJjq7O8CsSpFn5zilugK6iuf5Qh/+ee/wTqeOysUEEL/wSBlrmoS/UBcMZwwjpajFw6N+nowntVR/8zKOzN2gn8oRLT8ovrSBm4wgk1HqrwNAGyiLbdooIcDcL8gQejj25Gzx5ditfjZVAnQKvhnKkgfQNy/5titoU0kCdS++bZxa2Z/mYRj0eI74oZxOD4AZ27jhqxdUDfjZV8TODPu8d2UH9c9eM9dXV4xXzyPhz7+G2fEwj07SfK9VHCvzVJDo9Qje6/f1HKONi2E/cY7trasH29jpUv7CY9fz3uHt7WT0L+jQxiwMFWbKssNHrDgAxZj7cyubkvovst0n+boVRVTd/pAeoC75mxgN2NLrqsX5dg8J9DX1VxeQ9YamCJezuFBZY6ee+IOx60L8+8buVH9dWgWOKd81Yn7MTKGgdiaqmho2plmveQCK1gycZnrlxMp2l3JSp0Q7Q0gbCLMY9iF/92vIaEgszasrIMtQ/OojD86HiqrDHb4Rsw9tnAInyHMTfydCm+TLmybq7kcJE+fgDi4ND4EovMQulvJk1Qf3set57/CG0DM9dTn5ku1qw7l+i5oKR2CZyj07hZqarjo7IbyhmI7ihM6ZUkDmWZea6kGR8+9PxnyN29/WNhyyjBCAU6m4x2I/HJ8cO4LE2KPefeb5AKiUCY2J+/x9JWmCxLLQKib7+t1ZHlbf4+VlzEful2c5yXYP06dHUfCscTjAtQHZyxd8M7664vT2cgppA6p/8RO2YF9coP6zK9WLP5LDqnW5DPux/naDHtxwwBymY5Y+mecGYq1bkTiBhpo0u7ITdYDYvfI803xhwT/vHR6Ku1hvEe3bizy+HqUNcBlZ+mZ0FbHSGXdNAVLXIUi6IYGSHa7XI6bcOTpI74+cbNZ1XBQuI6Hbe094q0verphNkpz09QYYjyKiUUaDh2M3oaUnxJ6ktrWGDYHYljUHRNIrk8CStVJrVVa4mHLxrrfRmtJ7jzDo8vO3hEtLkUcHPcvJVI2NrIGq4T3Z7tUCvgFWa2rJNC3coIfO//M7yOUMfndgB2it09n7FRd1GzjMG/ZTzqaqvNf+/WiohtFWhlJaBEFYkhnJo9ZZxz3BY/72/keOhwfx1+7qgnUjFDttu3HGyZqz1lTbpptXdoC90chchPVGFKBesrcDZkiiRfZwLFaxqvJ3xLM422U/3XCXul9G2Qn52Vdwi00ST3okdrOOeUrssa1pMhxFRssYw+jPl8TTd4pkbKBalbi9Z03c5e4c85jfA6MR5GrIXD7dwF1dQNp1yilCFhS8paKmMughzO+RXcxYw1c15NMNHwaAnk+fb3iu8DpRLjtR2JWHfz3gS6DkRmBr1NSOI74ISJ2yjg4cVYgVCrVwF1Me+E/EWB+6nvmCl8jMidJ1nU48vEh7Mmo3UJUxoExvpQ0QhIRLsc8pBsf1ejz1OxdNzADtD4cQW5YAIPs6DbsGfapobXbKAqrZ6+4U3Nnni0djp3yce1rsqvXogFSmSJ5IHH0e4LFjCSKTEc0iOhxwIc8QPt9QPdjyUIkL91k1Y8zrajqOOVk/XzZbuNmED/rtnIA7K+Hul5DpmAOpvUIj8gz+YpbOE0qsgYlUFVq2mZGwygDi8y3fJKrbKeMhlQ30AC5dPfw36iPV7STZQOf4Zig7wMeHV9TzXvBO69/fE0KC9mOhwkFOcdXRxLdp2KrqDKIqLzxOzgQuaZCf3EQ36MfJHQD+rtWaqmWbXVKq7SmacbsDrBsCcJHsjzxYmsfrQ7FbD/4JsQsk4cdDIOVO25oolCze67LOrmuSOexNIsIcgEj9M4MIWW+B2Zi99o2yuxarlJM9qF3ayri+UuqGfT58TUvrehWikpeXcAXlsGPbNs8hP3sL3CnJQxd8lB3R85TbOojPqNGpzt8UxkI0RTADNTF/XYVExIM9QDzSY0vqeYupvpC/GvwPUfskIhlPZKMjK8n5ZHwrwm5EkSsK76Q3a6/D036tdUkUjhthrWa7Dij2nEoGPA/oQm7b1ApTcoN9qaibB2PHiSXOF2PXkuwsdv3n+PYw/meep5af/RyQ/pvmwRpZH1TNJ+JwNCfTr/SDPt1F9J6Fg1pfqi6N6P8OhyOy4QDtaoVszAdJDkf4XjfdP0BnIrqxWGmi7uSmyBbvTaYmzkrYicpj9l1KSEpmLRUR/vrm3/5YxVTVrrGlUW/qaBgpWvUGowgoD4GyXLM7sNeDnNea37kokx0VcYO6+o2mEVTlTK9wMkaYL0gtCzQ0lvk9/GRCDcbXV5EUIrs9cPRwgwHCYskWYFcejJ2x4UmxuwHlNUxfBvtDLFWidaftusreQtOQ19rrItzdsy05Gcc8rC0rwx7x5asN/KsXJL5oTpHEkWXJgRxANl/yATUq3qsr4G6BLM+BV1fIPAd27sVF9OFyBqX2nlNlW7Q+g+tkUZ0YQLTxhBkQj0ZkQk3GgK/4GcP2+JNz2RM27+e94G3niVNPF8fjsjuknbNt+Qp1XheDS2i/IHGXFJGkRVgHts900uoO1IK01p1UtWqO92Ir0aCnYb7gAvp0y1cyEFW42NJMctcPxg48PfZjlXYwg8g2TRI3qrU8s85JWbK/rvG6Pg+gZ3l0CgrGzu8hWhZ4Q5+e5nSSN7ZbHjr7XSAboHo7ReeXHzlTmI45PCo7aP/yG8ABxXd3rO8BlllKTzyDAEuABNoN2eFdWi3b+j3+e4WRSMXFzu9dzzVWptpi/4J68PPuw0PbiFZPGhbG6rTqRPZByxDn9BV58to3wVLDXcMWvl6u308HVi0HXLfLdpf9jotpEl81JOF4GKHGbJmNEVnzitF+MPYTMvWXYickQmMfD1XPRTtN4cQ13DN+8y9NPyPwpualeVh54roJGclJ7XlOMR4JEZpM+ewGnd/e8IDZBjKhPt/C3cyR//vfIP+Vnhw/Xp8M4Ui+gU6PXaeInR0xep4pF4hQs/9wUDm9NG3GaBCBgxFyrcJVsj4RjfqB65kv+FNEpE8dlBNeahwCOWLdIxhJX5mmZ4Is01o9JBCV1rWyXCn7aJu+4KahLfua/V+ZL86nfOAX71TYFHmO8PkmGRw793jsef7k2J3t5G0gItLOLqpEZpLacmDLThTGjKY511s8zUMZXWGzTfnsD3+Qkx1+zarG9C7lcORiVJi1K0tOZzsduMsZ31aZB15dqUaO1uQqr0Iegk88VFWMkKqKLVWn7VTjrwLgZwwR2XLazp2fjDOXPb6kn3dJ4zVhAxipdYocDmwPqksejbJquJ4i/Oqa8NVGXToUoefynPS7EKIAU5SyblvWzrsDcdunuux6yaBHxVyrtbtdtvbM+eNiQpY/dGzfhgdjx7APWayfFnvZYUfEJPryMkEWJqO4aPx4pJLWA3aKjhV37KZNB1LNA/MFXFkiUxElWa7p7FHVKSej++n/d/0uy6A2oPmLt8hv1kSZDnvcOU3T3RxU2gDXHKlIdnsPBA+X61llu0/NB4M81DXQ7xELNB4RAm2SekXBDtd+nzYP/RlY2XlyDnjoev5dmvG/jCd51y0TxBZARD4C51J2J9J1Zyd6G2ZYzWeuIEHZ+vrzcXHobu/6PbJ7Op3YweHB85A6GrYbag3uTH/yodhP4v9i7D6LpdiphHRUzgVS56VTpDq3aVT0SE3LxqOYh3nIynrDh1LBcsbeQlHwAbIBUYeerfb3RQS+LPkWVPyLlS7heEQ2GiVZPuFQLGpBmq6OwnxNASHmnmVpWGgY+EbFaI3MbffytMMFACL469v/+cfapWHpEUkCp31thcqKtgoBJIft3MONBgm30rZARnQhwEOY65FILEMaYXHn8JB3L6nKC8Jh3WQE2WzhX70gFMH34q4T/UOtvaeScS5IsoZ8KHYTFH1C7Ge0NefiInA9dewraNjrB32EVxeU+G5Tu85dTOljdTjEPKL8n51ZihOAmfX8FVYgNaXzfFGwM6SUOumVnCUs1sxnNAC8R77ZEaPvHHBFzU4sVsmvqaq5MShsI+L+X02p79PrRj8puwdx7qLNBITEF0DeS+4lX7ie94L3nvLN8fDXRg6mGWPB2Pz7A7sFJotR01YyliRN6rNH/6W+Eqe9Zzmz3cFf3yvGuuHfXqo0tllX6q4T5vd87R+PBC8FFQRVF2mZXALoPRg7AKAsnha73QuVtDYIMC6p2+4almOoapociJBm2ClY26+pv+7yPOURlIStMiVhfs+25HYXc3KiAy+tw6VH7ReZjYHvP7Ps6/d4VpmMWbYAZI19vmVMJqKkIDiIlpC5bgJq6+m8p4iS4XkUwg0F0WE2ocXmdKzkdkRzYzcaaEl6grN54HreC16EYkF5nl7zeiNkp1qJtsOd4K2p8a5tvDwnY/9wAA6HqMWI3Z7qXID+PmXvKG5DeiVhrpMhcK9lyHTMTkRP6G2kAK/TB0fyjPiZY51kLX4o9roCdtnTYleCtwk3yf2SC7mlc0nEias6r4HbpFdywU2GbO+FlAfWWrocj4nLejuPZBbp0z/J1Q3cfMkevFIh3XZPHE5VIaw4Qc1MytoGV1nGVuiO7oIoy9hKPaVcRmJMpfAKg2mbe4pNUlV3h16zJ2Vh0yQ1Ywk/ciyNAauAWAaYywVrVR9r33iANc105yDC0zu9R7MooEp4AThGN7pZlfDXcjvnKzrPCR/QssRtdokg0rZE9nVL4kuspi5Lihmph9SDsRsE+Cmxewc0kmT27EHX+J1zCNtdxNyYjiVuVdC1qlNb0Wx6igKyWpOVpfmgyKm1aTl5dsKkabjYG7O23Kc3BliONde3yF9eseMzYJvXT8YQx86R8yo1mOdRQYK5q7pa4FkA+wPc5YxvjqoGhlRIkLaF3C+Sx6s95IMeO09K6Plxy3SYtQyQ2lXadwUAjIds8R2OerjsxvEympYYGKWCmf18fA0OTO5ho0Cqhm3JPOcXZTIdVxeQ+2ViTKmrBzrFWUlDgBWpaagbdjyiHMYPxN62nCI/JfZBP3qiAqAziOlSKmXPGR0RSEMrACjVXXy5Ao7HlIdzXFTrTcrHDt5Wpulgzg7AyHOIdWIAYDKED4Rw57sD/5sJnyp4zPX7kehi36XrltyoTibQkXSjLDEUFF2KEok22FMh2th4UPy/KzupA/bYkvoPXYt/kiuoXon2myPVzfu0mHZ7DnRU7ct6z/Cq8X7GiyUZwnXUydrQkh2CqVy3Gw+irtfjYjcK22wSOwpOCc7YHyJX001GZPtMxoTZbraPxu60A/KU2GmPydghEh26ZatmyOoL5cZD7fNzeGOqCzLsRwc+y8Mu1y1TPnl2lhPKDpzSAd1kfDYow4tZeiDXVA12iiAFkLQn80zvbZk+K+xaUZ+fhHRjr0VNHs1fVvR5wkjJHvoZAPzcsYo6NmYf+tj1vHd4p4MXtY6URndFhRy4XTK0woZeSWfWKpLx8KkHR7NVRFXzFf3igpDWY8W/oZhv2R/49tgfges50XnWTnz3GnJ7z67GxYSjfO9jv13yDG61iVLWD8WeLBm/HLvrFHyDDPXBWG14COxfsBavJcnRmUpZtyT81jnI9ZwH0vUm5aFSda5bsjQzqep3r2JOrml5lmkD8xoNIwfVLTfU0NRzT/vmCv7bj8B9TXpfUaD6J2/Q+e0tp5/eRVeRSIi3Q7kpRzSpro9vAVsKe4pM2TGeVvbaiNAefhwwPnI97wVvvfJTiO2xSj1Yf9JX11ZW/HnDrth/N7sYg9Y6R26nsY/yPImD2DMyQAAAIABJREFUBuGBdbXmDTVa2nIT1cmkqgkyMwZ+lvGgByQ5isdiB1KMX4iduKEsqZE5x4OrdnLkcsoFqLLRsUw4VFHwVfpdqhpoHlQm2yagWVXzDXGi1S67PTEsxyN1Xxy1Hqk+NuBkVuN2qzUwGCQsUK+L4sOCymMmL2iet52CkuJNy8P+dp9ILkVBuZG9qq3pmcZ+h/S7fLPs9vwOrWmhHN8vXc+7pIFEt2Xn3NnwxvrGNo4nAOsEpAVwkduC63Xjq+/slA+kvxEtZVTs9HLGL+pyFnvr9VeXcJMRwjdvo3Kt7PZ8WO6X0W0uma39cOxow5Njd6ooZu1NAEkBrA3RblK+ecf/prueIS7dcAB8+HSWBwC4IetryyfKedtin02SdeZ8EfHortdFWK34hnj9gspk/R4NyQZ9/u9Bn+SNPDmfRHO0qibZ4+6eQ7Gqilgn2e24m6vgVOzRqww47u4jXsa+f2LoC4T15keuWgDocESTsBJBOadpCtdGbDeANPBRpS0Asd0FEYqEHo7UZdwfSYEzzXbvIRdT9umdY1kzX6D+i6+QLzso3t9Buh347z4Bxr0cDdNiVI9VfL59PHbDqzwh9jj5FUEUH21bejYdK4iWH/52ybfekbJzeHmZoL7vXgOfb2MebndAGPYhV2P4xRb+u08IP3mNbL6JOUmn4FljPIgWm240hGz38D//Cf/uegtMxmhnA7hji+qyh+LvdpBOgeYvv0G25UPibxYJ4jHIYmkXUY9a7sRhmbLF4L02L5AI8TbbMFkTNZPws+n5G/UHrme+4E92O12sstslooD5NeV5pIUhyxL2vGnj4CJqsatAqLSB5cnhqDCCoLQxlWzTejiMBvBVjeLbG+5A/W68qXH3M7kNgx1cz1PH5KHYTRDpCbG7fpcYH0VUSkPJO3fUcmSQ6IHolszveISbE4svLy7g3n+EGw5THocDfBuAXR6tK/37G/b2NSe3O3Ax3q/Szr/mNDP0CmS3K/6u9Rbu5g6u7KD4oPCA2xpF3aD9+Bl+NCQDLOe5RKqahG4gSgLyPKBmCP0u5F4teKwMyvMIL6BqsDYt8pxUP3Q4HPtRK495F428oARd16H9eSQtH49MvleeeJ+WrHslsDdrrTUottuk5dRVTswicTTkkAeANJR0c+8/8qCkPqauyBFu5ySFbHf0dd0dUskBQHY7tuUeiZ1qZM3TYrd7cRI7qYCqSBzI9reDsxGg7bPu/Ue2TO8XMQ8AQAgIn28A75nPeEgVM80JpqMTydT6FiwKuL/9DWQ0jD1+V3b4NtjuuDs3LWSxJNupaXj4Vd15V3Yg6mboAFUuyKJCsMzG/JljxXJFVYLDZEqVNkVQSkXIg9vsIqzjFPb9g0vqj7c6/wGuQNCQG48SxctGzUp1cwPVYlHkodXHEaykN9ipWq+zboMSBVzZ4Rf09Rv+Pvv3/R7/VtGhpXyWUfJif4C/nEVcO92ms1iqUAj1ZMd/IHbX6Tw99n43xg5AdVm0p92n5g6+fsOhjGnbFIUqLHeAosPFfpqHtUv7/bN8TnNyk1GCN7R8AJHnkMkI+//mn5Hw3S2Bt69iB0leX7IUAdjezHPg5RU7PnbWqGuStwMJ3K5b8v/2nKK6e54PcDXluaqlY5+/WyjStY04H9e0fOMt0mceu573gncuKXWpSlWUnNY61w5aEHIyDedOwSFJhzwlCaTdTSgTPRtTJWCx5uvZsOHHYwQ5uS1lJnB9y/JIX+tSVdz1FCno+v20yMvO47EDT45dumWMnYq52tocDRL4a7Hm5NQ4vlmWiCPe0XD4NI+yJHIyT/lgsTrPqT6BPBhaUSWv+//Xb2Op4ZYbym4fj3C7I+o/e4Hqmxeov7qEvH4BLAkjxmzMfCcjli5FDhkPIrkDhQ7JxsPYrbJOmRsNlaie8w3gHD+nAy036FNC8At9+Ge/4CGCcDGKmuXRfhJg+1BbgnHXd4496P2BOGxF9QHgDQfS9O72ngvdiNKe5GpnRGfrHph1vGq7wKs2Zce03JVeV6qa7Wode/oPxS7D/tNjz3yM3Ubn0rQEa9mbxb7oYJrrh2SH0zTIXr86ywPTMSG9QMpn0D/Lyax5oNrxslyxLPl8y51dJb9lPAA+fGYs332P4v0d8n/3C+S//ki/WgV0mSCqqxuayS3W6aEKIW4Ozryu7hbRfRstz1jxM4Y+NYBbQ3M23C0eXVLPe8Eb1uTTHXvHTRMlsqWqtccsqScbmLhJcMTkFzqtmy9445Tx48pO8lMdKOy3T6CZ63apt6hfuis7FPscDeNE1F3OiFg0qO71nR6Q2zgpfCh2I18/JXZ3v4qx28NoU0kxU95BTw+4/USV63YTT/RYneUBde4Qdd6WzZb190lO0RvLZEV6xK2YDZG7J1MMzsFpWeR+8hUh3aMhy7WqTnDfU3KGgc3WW3aV4tBvH0nqka0GRHYWgORgqMbOst9zIzglyz9wPe8FDyS7ctuF60aJHCpQZHBXZf/L4XA23BEbrPyeaKmoVJ8NXXB3z4VxoLEZuZRHliJmUny/4uFQgn7pDeTjNWS5xi//9U/ZEblfctGpaP9DsdNM7Wmxsw33e7Hv1P3u0w0X1d39ubDsXrmgWk6hrs7ysIfAjUcxH1lvznKCc/jlv/4pH9L7JT+rJVRYrXl/tjuWNLs93NtXcMcqWvoYx8Dw907fftJTjchOkbBSu0PEv4u9mZT4ISstiX76jgu930tnKCNte5d08h+5njfjqfNK/uryX6U+tcFjj8eobWLaKQCSBuNszLoRiOZXUYm21FpT1XhxOHJSOV9G6K5ps8huH8FQ0iXNTuYLyE/fcedu9RWsNETpFMBynQSbTLz/h2Lf7hIi8AuxR4eMsoxvH2gNK+oc6FYb7vLLDeTVJby+1eRyys7HoMeFaXkUOd98V1NIJ2c+isu3nCJ0QOmQriiScNX+yAfEQGe9Lg+lR4X5KgrVHrK/vv43f6plAwAPMp6e9w5virtt4ABHR+9u0E9iQ4GL5Gx6uN4m07D9Abiga7QzOplTt7dGd7nVliAu43xWyZmCUnNqNGxtvg/XLCmU9SRNgzDVg5aB2k40K38wdsPRPCF2Y/eYhWM64Lasc48Va/p75dN+ppGYiADfX3PHXW7O87hfsuW33sV80Oue5QTnEaZDreNZblBZLaC9veNB8VhxtL8jR1VaguFkteYbTEum53I97wV/Mmo3YU+TujBWPkSA+SJZqKw3sS6PN/qOTBwq8Gb6IGURbSi7HQkZ8wUJEQCilaW9ZhXZ6Abqit1qWaKHQ3+7JIbczgedzqOxR7HQp8QuIcYuVcXYlysuply9oozXafIVu31i9e/2NBI7zaPbhet10d7cxnygqgWWk8s881IugdH+pMgR/ot/ys/95F0kYUA15V3dRCiHm47P0Jn/2NfzieQHL+O0ZkDWiYz1yCXtlokZI/KHwkHmso0y0suI6+4kpB3A/vTdPV/N4xFrwc+3wIsL+LpJLhXjAULm4Fe7JJb04kJVAwJ/j/fx8Plo7KrA9aTYnePvbJpIe3MXMy74ieJ/5gv+fJ8OGridA/DAu1fwh4oO3YYlGg+ALT1ifUWFAtlsCWterWNOEdEJ8N9VNQ+t3iO/voWUJfxyw0NpIKZHTtCKbjhgn/0L0hl/yut5L3gRTh2DmnkBSTfGU8ErdlqsPQWkCa2+9m1CaewgO8y50ZAIw/U6MY02O0BJHFiuAc/ugL+c0as0zxL47NUVwi9+g+zqIgqFyuHIyaxXsvKDse+eHLsRqWPsAClv0wnkZo5oVHZzBzcgKYXO3TVwuyBMoOwgLFcxD7FJbttGRxBZroDXL2JOAJKV5P2KD/DFFO2HT4B38Kq3L2o93x6OcFkGPx6yBDoemcsXAF1/yut5L3g977nhIEpTo1smU1rg7CAK4ORw2omvYBPitx3eqX+RlB2O3DsFX8OmENC0QM/+uCOTH0i1dp9EY/n+GtnLK5i2YbgYwR26CP0S2ac7Wl8+ELvr9SJD54uxOwcnST3NlHOl7ACXJ5qYZcmD7MUEXs2KLYdwNye4yvLocTLqx6NIbof3ZznhggfecDWBO9AiU/IMfjJim1M7KK7DaXVWlml4VpLg4Q5HBFUvfg7X83nX/NDl2cIDQKCUc+xTWx09G7NnDH7Zrig45Ol0uLim+rpXypgddGWotu7aenPHCuJdPLSi7EBGfWqW55yihumQh7GLCcJslEqQQY8disOB6MHtHtnNgn/jsdiBp8ceQjqkq2+Tcy4KlcLitwFbnkHKgjlMhtzhtRSyPEwcqv3qRcyneXd5lhNUOMrP1+z0mNVmrwc/nRDu0Kf9pXMObjJSryXV1dxsSRC/mP3RlsR/7PW8d3i1bIwSG0DqTrQt3B6cbnpP1dsg7EK0LZCViRGlqrWxH2y77W5PKYo2sAYe0CnO6UNgLTp0CvhPd+zJD2iggMzzv9/dA/0+ubDrXTI0W20ejZ399s7TYjd4xCmbKacLiFPyBNabFP+xUheRY8oBOMsDCkvI3t/o0Moj21ZnObljRTSikjawB88G/X6cGsubS6AJCGUB//4aTs0Q2jcXyG6WCJMB3IfrP+26eeR63ju8c+mgZoc2BSah7ECGvcjqcZudYklYv0a62LCfxtYAdzYgYUSUd+ouZ8kNWzmWstuzbHCO/65pIqsJQFqI1llRKWvZUQ7ksdhxOX167HYQ1F0ZQTUmgUgBdJez9HNWM9ugSku3szwKfZhOKXG39+c52dRS83J98nvblxM69/W68Os9W5uBrir1N68Q7hcIhUf97gJuX51ZDP1jX897h5eg8tJ77UVnSU9xvQU2W5qIiSQSAaAioAQ/uQ0J0Hb4ckXBXbBp2e826TbTfilIfTM+avj+M8FgbYBIAN5/4oFwMuahd7WOE1vK+WVsWR6Pj8e+PwCD/pNiNx1JVxQJkFZXiYBe1RHWbMhNVOqacbtnvFcX/J2ah1tteKBsW8hSlY+B38tpz1x06IW6huxq+E6BMOnDt2MO7g5H+DmAukboZiimE8ixgWsCgWrPaME/7x0eKnVtkhNCsjKJCYoLX61jvxnQsXRdK9/yRJbCJp1WUhiZQN3xXJ6pWTClqs2Pyb19FcFNbjhEOB7hp5Mox4HZJMF2T9k20a7nh2OPI/MnxG6fl6aNDiVwnvHv9izTcjVRtt8dFP5QlszB4tM8CFoj+TzmczU7z8m0L21yLEIFsCKHq1scv5oQtDUaQIY8XHfe8/zi39/wM3/2Bsd/+u6PvTD+g69nvuC1+9AtCSEwcL/JXhiJG+CXowcqNE20SYk7pzneQQ+JSrczJ8AIpwVUsKnlLvzxmovwSHlol+cICyp/hfsF5PMt250q/B8F+e3A+kDs9vefErsJNgGEJJsYKkTRomZmZoYH1sLUNwE+XkM2u7M8ZLOJC9rywaebs5wQWhodKFrSeQ9c36Ge9RC6OTq3O4TZGGHcR3MxADoFtv/JFaRb4PAvvkYz7cK1gs73yz/qqviPuZ53SeM8MSB6+HKqI26UOQDR4c25Ji22PE/UMDtwGSzWDo5aX0t0BKFDH4Ie6ERYwtgupy1FP52kt0avS9jCcg1Z7OG+fgNsVvrz3Why9kOxi/kWPSV2Y/lkPo7qzwSHzHHkcqZUQQ7W3LAPWbW0uawroOjEPNzFjIYP3kWd+ojR0Zxct0t213iU6I8XExR/+y03CcP3HI/I8xyhqjDY7hCWa/S+fguZ31OJbbn6EyyWp13Pe8GDchk8qGWxS2JovMhzBNhqWypzyPRZRIAGHOCoiXFE2fW6/D1D1sQy7LFMMhPfQQ/hp2/h7zd8k2z3cGPaIrr9MbYLI3a72yUArd+D07H+Y7G7QT8eoL8Uu0lly+EE9ObUV+piwo7OeEDao3Mkpa/J+wzfvIFf7pJArOUxv6fUxnhAU2MRdmuaJuYk9qCFEC04ScGbQL77Hr4sVahpSrixip56IJo+h9Wa/f/9P8oC+oPreZc0TuWcTWsdSJ0OdZwW3QXdessdN8/SggmS+tcvZtQ0MU0ac7GrG+21S1TodZmnzN63n7kD61BKyg7CoBvZOkZsMKxJ++6KNfZowIfxsdiDPDl2V5YxdjNzsF48giixXK0n7QEsO5D9AdkHVU+4np/l4aYTguSyLOWzWJ3ndDGBSYqboq/MF3BBkL15RakQEZX+IFvJmYqD2e785B3at5d/gsXytOt57/CBHqNO+76u10ttMxMAUogBQpt2QxNTyjykUlODFVucJs0sh2PqdLQtcSJHwgJcr0dl4jbojr/hznp9i6zXi50Uadn/NhpddrsiIrFRM+LQPhx72eHPPSX2PIdbqyVOXStW5xjVvGS/j60/OaiqcodljVQ1Ych1BVmHmIfkOc8yu0N66IHznA40WSOuZkvYwum13gL9HsK4D/nwEdnb13Tg/uoNwm+/Q3YxQ+gVcMdzHaB/zOt57/CAMpCOsSYW1UGUpkms/GE/ul7YF+86RQRruVJF/a2tB3AxXsyiQCeASH0zlSs3HUOWK4Rv3iTNlGGftXSR82dGQwKrOtREdNOJdn7co7FHkaUnxM63hVLwhgN9kHqMvz1RKc4zUvcmY35+OGB+yxXcbHqWB2aT6Oph+ch6c56TUf7yDG5KyiF6XcjtHGHY570SQejm8F+/Bf5/9t6lR5IsSw/77rWnvz08XvmorEc/h8OhOJI4HHEgrUVpIwECCHIh/gLutKC0014CBBGQdiK05F6AwFlJELggIFKQOMOZaXb1dFVlZWXG099ubm5m92rxnXPNozsj0lvTXe0jtAGJ6o4I9zgn/Nq1c8/5HtuSag2dFNH5GaesiwI+e1r+7tu8jnuHD12VPcvxkguX3EyqeRllISVkvATNRmt5SA2Pfxce+WoFExwp0pTKWQ3t2v1yJZ0KD/uFSHWIbaTXRSZE5+CIUZYwVdSycJ6IHXVzcOy+3+VOrIYIamGvaEbZ0RU5qgrJKOSp4SxQ1Q/yMMpvTVMC6OIY5tULljWSkx7kqaFDIrlf0w3FbLbs15+MYMoG/uoW/pOXwOdfwDoHN50BUQR7fnpUO/xxL3iAdfpGBJekJNGvB+UpY4IAvyoHBD14Ue3yglMH0Mrd7SqOzq1IvOV58AJVpg67KcowiuEWC9huF265oseRHCDVac9vina3juzjsetZ4oDYMVsGsaRWNqMJQyFkWYu+VB9XFXyqavi6/Lk8tC3pxOrdLVcwGyGtqOfTnjGx3xSsy7//CfzXV9IF6sNP57CWxmT+q28ID06T1qFwUxBifSTX0Zc0aBwwHvAQJhNJk7eCoQ9alF2WK15lqvWKo1am2oiOunR5TJ6FKSOAFsprBIsv8swmywDvYIckNFghZAMy1MqzQK6GFTuXJ2L3eXp47KL3HuiCyuLXOlz79SoYKzcfjOHi6+QEcO3lAUE2GsUBDfrspuznBNBTVbQx4bzI+UUo/r0fwA97qH/wEae2wx78xy/Q/O734eMI29/9FJvf+04A4h3Lddw7vKhnGV0U+iGIw7XCadHQQMCLuRZEiJOM/V2rGBxHrMFny4ARRyWsKdlpVZ8cnTx4LvnxAJguYJIE7nQMO1tywV7fwX/yEczdggfJi1O6UX/5BsG977HYVTD0gNipjCuxT+dSUw8DAlMxM6bTaXH40iUyAk0AADy/CHkgioKujVlueMYQG5yQ00cjYLFmC1R8nfzFBLi6Reef/xv4qkYyHQindg0sV0gmY/jZHB31Yk1i+Ndvv+2V8+h13Du8FT0WnTQmNCNDJYOXJKFLXJLQjiZLg96LVyEjK3LUqsilssyG8noQAobR91ZrlqrmkKcjOJAuZTDsdIHmcsxzQK8L88UbAMD6r70IHQ3z6Ud8MjwVO3B47EAbe7xXboicBbKUr1Pk6HgQwGq+3+VBUyUyJA+kCTBboB53Qz7+2emDnExRMq804RkiEw13Q8K56ffgLk6Ausbib74Cogir33kGXJ6j+uyS5VVVPzjT/Lqv497h4Xl4Esc5vy250znXaqCrK7M+DbT1qOqySUJ6m6AEUVX8sGRn93UNxF0g6VL+rdxxGDNbhkVldBcVkSS7KIDpHO6jSw5t6ga9P7tuFYp3Irn3VOzqtXRA7PCujb2uYSrCIMJTo3G8WWQgZgAO0QDGXu7Ygl2s2zwWS5hOB8nrW+Lop3OYKIJ7fhZywrZE78+ueRjudhjPjNY5Rg7U9uoeAND7X/4lfJah+7/9ayDPEL1mPe9mc9gP2NB8m9eRL3hwYRgaGqiZldFROsBppMpYRDbUnAAeOGAHg9w4bqWYRSsF66LllcYR8O62JYPkGZpxH9HbW2q1pAmx7gDs7TxI27mzE1jF1h8Su0hlHxS7TVpEpbKelN0knRqzrEVnhgq8ZlvCOwd3MkA0Y/dmPw9lTanYKwDqZu7l5GX6avVJIj9jet3gmueHfbKifv93kHx9x+5QVbH7czKEuSPPAEcCpznuBe98kMZQeKy25YJCbVUF7yOy9nctyz5IyYnGuOJTZDH5tbQ21bZcNRW1lk4TuKsb2NUmUO+o1itSHGsaGpuTMczb69AxwXwB38n5+56Kva4Pi10NvpwSYmSwtRElMXWwVveN1RreRoBrEN3M6CmbJj+Xh99s6LatcItB/0FOaBzzimNgt2tFkwa9dkOILXw3Q/wnX8LVNYVZsy6hBhtCoH38mz78gZdvtRwbdcOQHnSxbRdeI3W4DpHUszMiojJYtD8QTkr46BenagWU6VMgwBrqmuWQp4OFaraYQZ+MKWkXmm6XNav4NPlaLDIfix3gJPmQ2Ou92Ps9mcQKV2DQY3kmGjuwhlPYOIbJxFxAFud+Hogi2IszHr5vaHjmhl2Yd3Wbk5JfioI3mvM8zG+2PPyq9aY+jT5+QaxOWQF5Dnc3hbk8gznAIfvbuo770Kqai+MB+7pV9bB+73fbwY5CBGTMHzinqmmujhtOOjLetwwnEUMNuBcVQpot2GsWErKiCU0cw99NZYglC1MMuEzUHiKfjF0d6w6JXWp+OB/0NBXPj9kSPo6CbiSc3NDa2txVxL8n6cM8SiqHmaIM+Zh3dw9yAvAgL78hUtOdDuFfnsOdjmm2cDIiUnNXoTmT6fTpEPjOR/B5xr/rkVxHv+BNTw5jAHeXOA4HwWAikCRB8DPU6TLUMf0eVNDJJAl9i0Rt1+vCVIHQbUlFAudIqB4PW7FVld8TvUl0ctGIEc6qlidGyOCqxvtI7H5zeOxKdjEno/bvEseMPxI1XjFbMF1ROG6aQOr2VUV48F4e2sPXtic6OQkg+zkBjFGfeJdnFJldbGBev4O5uYe5umPJUjcEwf3pT4GXl7x5AJg3V3AihHUM13EveA9+cKohXmxFEiMLE0OIPbnpdQNJwdc1229yiKR/kGsd50Tq2Yg8s6qCcaK45YJabQgvUEluxaanCW8KQFqHAi8Q6Q9YS3ht456OfdQ/OHbT64TY1THblyU1Ihsn54Em8GkBkOm02vC9nYf/9OXDPGQGQI1L5tP00gc5hYk0wDgXa/jxgE8G8bQ1keXgKaPcnh0OYJYb1N99jqafcff/jfLYgZcBH603U6IY4/ZRyw5FAVNLHbvZtjsXwA9fZZ7lYKrtQUUgql46UrGNd0K1K3c8BDYOgANSIaFkqXRKmhbLXu/aPrOeKfKMdozePxp7sL0/JHYVgxUfKGU4odwBoz78fCVS38KYUsGpqgLWG77PRjykNI9Oh12hvXyiP/uSfxPNSWEMedYO6N7dAKcnnBVc3fKcMyEhHb2uQBEskq9u4VcrQg2OaMEf9w6vXRXXtHxN7XysC6AS28rZ8iE9b7d7YGqrdokqfa2Gx8FEoCypBe88cTQpfwZpwoOh8Ev9/ZS763odnhRBObiqiEuPI7YE1Yn7sdiBw2PXQU8iRg172pVmyx67PZuQqWX3OiLGthCCqn6Qh68q+NkCbjwI+fjPXv58TmLjg1iGVScj4PqWv1ehzNtdiNvnYjoWWZjx6OF54giu47n13nsZsXgUV2vVKNRet1DW0DiCodT3SFlDAlpSQXAjKr5GCBNuMoBViKzgRfygBxQl/OUpTNPQVj2Jqa9S7Liz7w9SXLvbu0GOaLXh4rDR07HL7v0Lx54kYWrrBh2YbSWEa8MS52TIOv3mnjnc3MMkOdyoBwu0eUR8ctrNFl5+t10WD3M6m1CHRp0I0RAbtC/t0TQtqnO3Y0dGFnhzPoL90ZfwH78A7v9CC+GXdh33ghd8C7KUnNA4ZrkhuuPBkxQgXlvqziA+GrRnvBzmpJyRD8xeT/n+AjOgERknkfZOVH29h/E5zNvbAD8wIuLqG9pCmpSHX/vlFWvcb65a7utjsXt/eOxxJDttEyDEfrGELbbwQ/qmBnmOdQErxHR7NyMcuShgvlgBvV7IQ2XDUWzbfLBpB3SisIBEbHx04HY7JaZo1IfZ7WBsxoHcszPYxbqFZuwq2HVJ7ZoP2NB8m9dxlzQAH7XTOQ9ncUuU8HXTmovVNf/osgCp617wEStlRXCp8z6w9ilfzUOed04gxxT/90UhYqiej3x149ByRdSHzfMLvq+CzaZzog6NeTp27f4cErsufk9rnSB9XZOphZJTVX8yZPxSx/uiYCvSeTQ/+PhBHrzpJFfJx8Txg5z8tgx0REznQQocWQo7X7WbztkJzHYHdz5mCSgerEYEqVRN4Riu497hReE26MiUO+lFbwMASluMpisUOieKw7bVU+ShMW51XoxAg6Xr4tVnSQ29mgY+SVu4r7hi+9UaOJ8Ac9dS9lTiI6d0hnrIqVDqo7F7c3DsEF9XQA7UcUztmM1WhF+F77rcUPQoEl3HJA1AuejffMUniebhHS05DXgjdjp8EqRpyMnEMWEK0wVzUdFVIaXg6hZmNIT/5oqDLB1C3c8oUAtOo38DLfhFLlXRzXNZFNLNiGMuUL3iCHC58jRAAAAgAElEQVQRlXSrinS6BVlLZtBnjz2Oufitha8ExajkBC1thHdqep1Wnm9bEjPiPT9Q7Z03DWWrT8Ywyw2aZyckQyxWAXL8WOwenLweEjtLFr4G+4dtYXEFPLwuWKA9eBbcZU2//zCPOBJ9euHC6hOnqkJOcI5nGEPTNFPVLZHmpuDuXwsI7+MXcD/+KaKLc96AvS4FXk/HwFff/IoWxy9+HXlJI4dWMacFEOplaAmiKEJxoUZE3qXRvrrqr2cpe9eyi5selYGV5hYOXzphXNOuxa9FwChPAuPfFwXcbE6FsG0ZnDOiqxlLjDhuPY4eiT0AwQ6I3eRZiD0YGgvx2ouaQDjQCt7elyVvFKEyhq6L5IGyJbBrPqbfe5ATjGH+IkeibijKlQVAgNgnL2F2FSKR48agh+ZUZLijPYjzEVzHveCtAQa98Ef3dQPf77a7kRj4+mG/Nb/dlpz8Cc9UOyEkd2QtMtB5+DSh+m0kB62GCluIIpYCz85YbkzGsFPW/EZKk+j8jOL/pxPWzmnCelimmyZNn4xd4Q0Hxa4iUfuxdzIeJCejYDrgJX5jjPiwCoxB6XZ7eahQq+J/ovMz/uxeTj6Rmt5QijsMkBz1bppPLsMTAJHlbl7TWNkUFXwSwXxz27qUHMF13Ase4E42HgLDPiXqqrauNusimIMFmp2+BmjJ0ECrvJuJfIW60b29a4FiqRA/yh3f651oukzn1E4ER/Mmk3r99AR+PCB+vNhykS03YSz/VOz7xmYfjB0IsXvnWFevZLddF8EozYgIKzo5++Q5ua4wBuUnpw/yIE2w4RND8vF59iAnU9UB46/tWbUAje6WiL684txhvgJu7om37+QwizXboW+uBcj2G7TkYZea9c4WQQwVpXYvPD+ouoZZSc+6Q2OB4NTckMSALOX7dDucUsYxtdWlxvYrMTWTwywA+Nk8HOR8sQW++ob8T7F+8esNd9/VhvX4cADMRXVXTMxMHD8auzoUHhK7L3cwgx5jB4DFir+zEhWDwQB+yW4MsoyLLBLztsEAfrNB9uUd3KYIefhvrh6YM/j1hjdEFIWcIHkpJNoYSob45QpuQaUxI5giv1iGw24zmyEaDMi02q1hru++pQXz4evod3i/3bJvrIdFRe8puAloobi39y2SsK7bHbJuAv/TyyFLR+/UiFHZDduyi5Sl1O9Rf73f403h2Kd/cBD1dIMOuuw6NHoqdq2BD4ndtrEH5bSdOP81NClGHLcmwz1xH4ljmi30e1Qu28vD9ntUZVhv2nw6D3PSvJSUomhJ89FzRGenoVTycUTvqDQBLk4Rv3wh7tslY9knxfyar+Pe4YEW6djv8vGqO7CYfQHSclTG0p50h7KE1CBXe83BmHgyYpkhTn1+u6WCQVXDD7osmZwHXlzy/WLLaWS5Q3NxApunNAJ+fgrz9g7NxQh2w/ob1/dPxy6DokNiVzO0kEuew4zoNqhURVOUcGcj2EWCZtSjG0masMzQbtReHthWQK+D+nyI5KsbNBcngAV1KCUnu9jArwvUn14imhfA/Zy814LKadX3LhHPtmh6KaJFCQy7sKst6o9OEX99Bz8atJzeI5m0HrcTd3Lh/+DZ32sXt+ieA2iNerWtpo7WQloIGukBsBW3TnuNo1bNphBZDREOjWwrdVHtwrjfL1dsbcqkk6SILUkb3sP2ukK0kDZjEDlKHo09YPIPiD28zpgAIfZl2Up1iIs3tXUE058mLG0kB3d3z3pd8jDDAVDu2J1RYVkBiYWcRC4QQJD39sK1bW5uyVW1hlh7kRL3RcEDtxJk5Onwh/N//KtZJO+/HnXiPu4d3ntxzK7hV3UgOMCawCbiiFz60loeAFCdRq/tP+lGeP36muWFmy8C99Rb0axxQqFTmWcRWYV3aOYL2D4l89DJ4e9ncE0D08mptpWwq4EoCn3z98a+d/N8MHYrwkxappRl+16bDc3U9H3kfKAGZNgUbXcHCHm4m7sAVw6KBnLgDTnpgq5qOL0p04RPyCgKzC99+rjl8oGqgsnSgLE5luu4F7xK7QllLlz6YSRxgM5CdnzlhJrugDu6tUBH8OTGAZGUNr0eOz0qJKqa8d6zY1JsYZIhOyLaDfIe8WAAeI/mfAzzk9eInl0wjiyBLfpsdb55x8PcHt3vZ2P3qiVzSOyV9Ogj6f6oZLbK8QHsyvQ7MNMFD+c6zpeywqp0t+RhGmLozaAHs6vQnI9hr6fwy1Wb03rDsm+2JBJzuiCO//qOk9XJmJPUQZ/wYs11PCT8eTSAKXds5R6JXPaRL3jiyNVYWInJMIalgsrNGQdUhYzk01Y3vtk7jFoDgN8nlJUjcowH1EncboFn58R7b7cBVhsGQL2OQHErylC8u4NrGkAOjFh6eInNnoyFSIFHYwdweOwV+BRK01aCD3ulkLYfd3IoX/vApDLLNQFmK9HK1Dzk8rf3MJMxond3LO32c2oa4HbG96wqwpS/uWKf/u4efjpnafSzQ6l1QWDd22s6pcyOBFeAo+/SmOB6Hdg1OmlU9w6xdw/MHDEAo/qAckNL7lDOtwAocKEYff+uHFItlXKNMfCLFbsnxvLnhOTt58sgtWEGfZlwkpIXjMai6MnYARweu+BsFC8PgAdSVRHod1pdShuFDo4xop9/e8+bYz+PPR9ZzcePBw9zApiXEkYA2JMxW6ivXgQsk4LFYA03kwVlQIwM8ezl+S95Xfx/v458wYMCQl1pmYkyLieLsRh5iYqvgMGCQJHU1aqPbuQA5mvKRZt+lz32JIbviohqKb9rWxIXfz6hW1835wRUSOXav/a7Hckc+rs2W6p/ab/8qdjz/PDYsyzEDhux6yNqAiZLKZW3o6WP6eZcaOMhcwCYw2evHuRhVhu4V8/4fcnHrDYPc8pzxlHXbG+mSSv4dHXDG1lnDKfj8LcxE0p8+GfnnAj/xvLm0MuLMq6w9yMrwK8qwAX8dssDm9amGQ2HOfhx7cFJd3XBxZu6hnv1DPb1u3DQ8s7BCKLRqCiTYtN1EuvED2rQZwwXE2CxDjxP7ZwgqPq+P3alER4Uu5YuQlxBsYUf9OBePUN0PUVQIgZ4MO/TVBhVTdqd6nPuqjaPRup60c5Xu0rT6YScADzIy5cl/HQGp8ZqTro2WzFVyKho5gva3pst/xscUI7gOu4FL/1n/aN5b6F2kAGvHkXwRlCLnY7sgob0PyeE5D2BUb/esPvTNIhmK3il+3nfIhNzYf5r/3vQB5ZreIXpNg38bMFd9d0N0OnwbNDNgdmSQkfbEuq0997Ya1pPHhS7d+xISexoqBYWqUt3lsIXFc8eSQosl0Ce8+YSzgCenRPYJnmYQY83Z0nTNT9b8L+rdchJhaNgLeUKB30aEn/xNb8mm4ibF7DjEdx0CpNlcJsNosEAfkXXEntE4LHjXvBRa/4VdlUlMtcNh0TKE1W/p5MhDcLStKXKOQf0ey1H1lru3kLrc6M+7HJNCCzAHbHfI3gqiWnhXtc0Ohv1YO+pPuxHPWBV8JC2547t5fV6o70vduzLXH8odmVI7cXuE5o1uLMTAFKbBhvPDjE/oivpT4bYXQ6QfVGFPHzOUigQr+OIN+zzs5BTkBXRz6PYUu3s+QXPNpdnNDmrG9Q/eIn4R+TgxpcX3Pk/egZ8+QbqhHgM13EveCEUeOkQBDZ/HMtOSAoenCdZOiENzdc1e9iisIW6DviWcO0sMe8AzM09uxOCOoToy5jtDh4Ich2+aWBlIgtjYJYrLgoZZMFxFO+KLazrBa2Y98Vu8ky0HQ+I3UbhgGmSBH4jOjPjkXBWhbInjCg0DdutibQ7qy7if/av4EfDkIcBWoFXAZgpe0pzCgQc1bXpjYD7Gdz9DLbXAe5mIl7VIP6jP+f7qrlCFMG/ftvOHI7kOu4FDyEeFLIj76pWrk5/okOrGMWE+EIYQ1obx9qvFibToEdLxzQRMVMbJqT18xPEb4iedMMu7JxjeZMkQLdD/XRHtpPv5vBv3vEmOD9rhztZBivqB0/F7jdb3nCHxC6tSj/owU8XrOV38qRoGvjxkB2ZjZRrcrB0wy4sAG8M7MkJ4cKSh5/NBSwXhXxQ7uBn25CTOx0GQSVkGcu9QR/RcADf68B//RZmPJS2aMGeft1ILAVsJ+eTZrn+TR/+0MsUJQ9tZSm6jlQd8JstvG+AVd0+MpWk3esGHqUxbJWFIc8ddWKw27Fmny+pMLYpEL+5C7W7KXfw3sE0ojog08jmforoZMwdtN/jbrjbBawOGtfaTT4ROyew68Nid3K4veMBla+TduGgz/ME2AP3qzW7RFlK6HPEVqQHHuSBLGOrVmSxVZ/S7Of09pagt7X05S8m8LdTAthW1Lvxqw2VDHYVy5xOJ+jq+ziCf3tN2uGRXMe94CN6JpluhzWm7opyuDM2aXEmagqmNXLjWtOzfi8o8xog0Pn8fMmD5bbkVDNLATEQ8/MFB1F1A3c+ht0RYBYJlt2ru4a1jM8JUfskB26mMIMeBzOPxd7vtejJXyR2oK25RfbadPLAmcXZhOVeUZJbenFKH1WA01TJo4U1kBPrkxhYLMMh1XQ71M/Mhct6Ogbe3fAsUsrgLk147vAezQ9fIfrRa1IIrUX1yTni2xXViZfHI6Z63Ated7ztFthwyLSPKfe7kh+M4m3UGkb02AMASzs02620My2x6FnKXvWeNJ4uKjMcsEc/XyLaVURPbrZCgmhgnGNf/eaWJY2nkbBZrOE2G9blT8UeTNQOiB14CBYD4RBGiSQqZ6KCS6r1OBywnbgh6jO6mYU8fCdjP11dSZyjAVoch5zMaIhg4LxY83txBGMpBe5u7mB39I2NfjSFr2uecZYrJF8J/HpXhSHaMVzHveANuw5Gesm+IVXOxBE83AMCAyl6ZRAehbXBXyloyEdRW0KIEkEQO00THizrLXfn5ZpMnjTl7n0/Z1uyKISgIfS4H3wKSC/cL2iYEAzPnoo9Slrg2IdizzLZkTmZVQ1LxHHrvJ1y4BUO0Km4h6g5gUr/SR4m5XTYL0XBQBy03Wze5gQIG0ykQ8odD7tNIzW77PZFAbdaI7q8oDHxi0u4N+9gR0P27I9owR9Pv+h9l1o1Nq4lV4z67QIGgq4Mkpi95VE/7LZ66KO8nkxJIytMqSYIH/mq4gcPtDqUedbKxjnZ/eOoNQ/odWGeX8DeLcLgyH/6ApiMuaOq3uQvI3Z1IIz0ANsI46ppubT78dc1D8kKPRCpwf08gubOZhPyAfAgJz/oIejJT8awkzFbk6cnMC+fESQGwH/8HPY7n7SklTTh92NxLTyb/JIWxF/8Ou4dvhGxJek5I44ptt/r8uAnkF9EoNt0nsujuWqRj2HwtJMd1YoxMB0+sFoLt7QBbMJW56bgQKZgC9Gt1oJ6bGCSOPTPjSAQtRwx39wQJts0hBZLa/C9sbvm8Ni3pbQlRYdeHUw6ObDasI6+m8JtNny6lGWrVxkgvju4rWvzkLrfiP4MALjpDNachJwg3SMSSQR8lsRUC95RH8c7TwW3Tg5UFWv2W3k6yJPF9H/Tljz8EhNfxKIk4BxQu+CZpLYxXvidXCRRoLgFASatc/evHeUsINQ574rQM/aNgzEuGPrS/SMGkhRuvQmKwb4UUokKm+3/jidi9xthJB0SO9DGvg81Fmy72WzhlVa4IVJRySlYrVlapYKFkTz8vXB4kzjkYzqcMGtOEBduJDFnA/JU8ZdncJ0ETSdB8mNqzjTjPiJhdTXPTxDNC/irGyJHj+g6/gVvrBw0Aa3AaBsjNizetYtZH9fCyCd7yQamEHYVvI77ZVdXcJZfrniIrWsYQ4kMv9vBZpx6BjSiOodE1Hy3Q6GxiYSfiWOYk3FrE/No7KbVkvxQ7GhjN2kaDMVgDRBnLfqz3+P7NHwy2G4XfrmEyXO4xRL2fBjyCFY1QEA1GiFoa05+t2OXyRhCCQCgaKiU8Pot0n6PZU/doB5lsNshTFkhul/J4VkoidHxOIAc94KPIv7BVT5j0AVuZ6ylT8eiqy5DqIIYmOZyDDvfsAd+cYKmm5JfeTkhVv1WsNmG9jJ+vmA/fTRk52KxYs2tT449O0hEkWiuUHAUHap7odyRdHEyBDSmicT3WOzKNT0gdpx0STC5nXOYtdm28tUA6/tR0vJH87iFQV+ewztHPIu2I6OorefzbqDuubMRjOjKBKEmUVc2/Z7cfEBzNkRU7tC8PIP9yRv4i1PE85LnoSRG+dEI2f/zBdwnz2CLKsCkj+E67kOrYkdUi2Urpl2yiNDILudarcfofkVce5bCNB7RT9/BlzvYJc22Qo2cUJWg+mvfgRv3AefQjLsiHio7dlXB9Yk78cMe3GTAkmFC6xnT7fC1eSbS1TLUqWsOZp6KXbokh8RuyirErpABdz7mAdpaij85R1x6J0fz7KRFYG62cKqps5cHTscwpydoPjpv86ndg5x8VcENOtSE37uib8TO5k9/Clyc0mnkRz/l5/H2Gtm//BxoHKJv7sjA0hv7CK7jXvCC+cau4gLyQuDYVdRe9CI+2jTkdsqB01cVQU6LtWC2WX6YJWUm0O0Eg6/kdgVT7OC3W8TXcy6K7Ra+ruE+e0FWVcIDp11t4T6+hI8t6u+9AAD4iPLW9m4R7BndxUlQ7300drWnOSB2H9kQu19vRBFY/kbFlgO67TbcXHZJ5YCAAm0aFN89e5CH2e7gIwv7xbuQj1Hwm+SEhnkZMXCAMa0OzXbLEur6juecF5f8uTQJTC3vqITmv/z6W102T13HXdJ4cHeR4YY6Z4RHspYlwqRHtxMQhn5XAQPROPeeZUpkKUKUxGEkH8RCVZf9dhZMB+znX7O+1aGVsYhKyunFFTtIUbFlR8MalgMAzJs1x+lPxZ5n7JsfEjvAg6+eI7rkrvpdRUhBUcIbw//OVzCRRbxYAR1Oee31FJ17WcyqC6+WPs/OYDZlyAdp0ubUuCAaa+SJB1DBwOQ5e/EnI8KARUlYMfdutYYd9OlcPh79Bktz0OVFl3zQB1LLLknJ3djIh4ktsd3emrakaBpg1IdpXDAFRmQDZDbAZl9dwmUJorf35F6OejBVAxdFsOsC63//++j/62s0kz5cEiF5O0X1/ATxYgsfW/jPXqDJYyRXCy5UHTZpn/yp2Kuatfchsc+WFC0VzUlsS1SfXSJalXzq9HNY72kRaQ1cEsGnMaK7JZrTAaJ7g/LjCeLVLuRRfnoG4z2StwvuwrGFefUcrpuGnEy54+ZQVjzfbLbB3lN1LZssQRRFvCE6GfztHexkDFsJVXE04Hnn7a9vGe1fR65Lc+7/4OLvBvoaZJwf4Kbqu2rNg/8Gx2xREAPQWkRq3Q+076OkCn1PoCV/dDtBxz1gXGqWISaKWvsba3jIVePhNBHVsV9C7DJ08npAV0KLOobr67OsdedT90IraE8tsXRKLK1Yqh9EbT5Aa78j9vMotq0xQlkieMnuTZz9Dz6F+fwr2NMTuOtb+L/yGaLrOZsC1uAPp//Tr2CFPHo9qktz3DW8LCCT5+0UT2wkTZbBWCs4dBmVa2uvroFBj8RsafNB+KPh5+NYjIY5vaTyrnzQeR5+fl9iQ2ti1DUHNqcn8vOiC6lDnCwjjuaJ2EPMh8QOhNiN4IDUTQRnE4K/GtdS6aIolGJwlO9r3l09yMOIIoMqIZgoag2GJadgpJYKd/VszHPA5ISwAiBMhKPrKW3n50vYkzHMn/6UN4Ji9I/kOu4FDxNQjH63a5W6tHuTZ6KA2xoBs3WYsKzo0l4G5zLalrrfDHrSS7c0/urksOentG7pihJvmvB1JyOUv/sZtv/OZwFPbkYDuB98DH83hX9+Cr8t8frvfEJFYueAyYg99Kdid83BsVPEybXdjiwFJuOgZ6/xmzzjjZLnQRPTv7wETkZwv/87D/LY/e53gdMxlQo0n235ICeTZ3j9dz5p9fhvZ8DZBP5+yl282ApRvcMD92oNv9uhfnsV+Lmk/h2Px9ORlzQX/g9e/ectE0lKD19VYddQffMH9jiNI7N/W7ZqvXXNBXIyAqZz7sCbbXidF3sbv9kGXfWASZHdGqdj4GZK5a7lCtHkhH3xsmoPm5MRcD/nDtzJH43dZwmwXB8W+2RELLza3AjcmEGK+KuWVlLemFxctquK5wW1tdE8vrmiy8e2ZPtxuYLtC0tLcgqIzu0WOJuQzrdYihyHEL9X64ChRxzBT+cBwWn6PbjrW9jJCf7p6//+21w6f0ml9gw1U3SHUxHS1q9UdsgkDtISSBKYSCCxqlDWNPBOalnpbPiFTFYbLmy/KUL967YCie3kACoeQNOEnRHBwFgdu9cNJfAU+zJbwG82xJQ8ETuKolVP+1Ds9/MQO5yDX4jokcRv8pzwgDimzF4YxokjnzEw/X5Ac5rpAm5XwRRb0vHSlDqRwMOcjGnJ7LMF0aIA3Hc/4kDJWmLtraW47KakRAeA5nyE4rKD3v+5/bZXzZPXcS94D5hhP2DV4R18f0Bt9yQhhDZLxTGD/qYBzagSdqpxqPBclYZW0wJxtTbDAXcm7+m1pCVEkkg3iNNWMxwQdhvZ9hC7LYFz6rLgni5+vtw9HbuYsh0Uu69C7L5uYLTdqcOrqobVQ2Rk+d7lDhiQjKHllJmM2zxS2lFaM2DppJALdf9W18NzToX9rgrlV3S7YK47kQ/POoje3nJ2sVrDPr+E/eoK/a/JDQ7alUdwHXcNbw3/8MZQwzxJKG+dJPwjdvKABwm+RXJQ0yltkKXu5vzfupCbpjVHiKOgYhDeT8gYtLhct36liyXLhV0FM+wTBDbqs58P8Heg1b95NHYdQh0Qe3hf56R/Lgt0U3DYUzeMd73hE2y55s4cR/DLJfx2C3c2epCHGY8C1zfkk+cPcxKRp3Cglh3f3d7TgAEkgqPcwa1lqBdFredsRqAdFLl5BNdx7/DSxlPMNlV0i6DVEh79WiMDARbrC7F3NBZAQ80WHS6JAgCMhTFNEEUKIkp6LlDGlfBK/XIt0tD8He72nk+M1UaAYxFZUIsl63fg8dhrt+eX+oHYAfimEmOHPfeRPcUylODfaq9l6hek1pkkAb54E4Zeip1vfwcvX9d0PtGcdOg0HtHsbLVmefTJS5j7OZ9iCh+OYz551hu4+yns+Sn8at3Kbh/JddwL3ojOeMRa0UeWY3qAk8mCTtF+0IMpZKcW0jK85+FrsQ6lSljwAJ8eSQyUspCiiCVKJOwh2c18UcD0ejzUdjpcJFUFDAccJqnqcJLCXd8SfdntBvrdo7EbExTEPhi77OCQ77H/3oiM3UqohCXMpEd+7lBMxMQ/1t/e07ZSHEbCgV8np5EFhgM0VzeIPnoecgrlm+zYtt9jq3W6gJ+MWlCdMTwIVzVMJ291KcdDNIMu7HoLXH9bi+bp68gXPKh4pfV2I+5zS2HmKwtoukBwupaxuMlSmPs5a2HVegwiqjmxKJsty4044i4u4qAmigKjyHsSoIPuuei4m07OUbzX3rqYpAnOhObAj8euBgMHxS7aNYBAgOuGu7qYOGhuSpamQkLNel1scSAGDUFWUPm9lpr54Ym0l5O/m3LRL5bsu++dZXB1SziGkRarGk0IrJoIzgHsqgCUEHIE13EveO+5GDtZuzvOV6F0CPW5/JEpreEfkqE34myh7tQirWfSFDg/4SSxcTAVKX2mKIVaF/P33s+5uCZj6qtoTxwIgxufJTzo5sKTzVKC1Z6IXd1IDok9nAUMczDdLturSdzqTg5bexnVj4H3XKDdDkf897M2j9MT4H5OFbKCnRT/b/8Q+PKqzamT8/denvEmy1P4cR9Yb/kkErdDvxLnEe/hToew8zV2n5wiuSEv2O2TVn7N13EfWsERtlltAv48qIXFccBZh44CIEwdGgCrvosqAwfEYtNwyGItbdWVxT9bskXo6eFqZkvWsP0eaW37XRTniLHnL2XPu0+JO47T7ZOx63VI7EY7KHKj+m1Jo2AZbJmiZHmTpVzsXQK7vEqKNw3MZvswj7tpOExqPvGbu4c5VTWfDFVN7ExVAz/+sv14oohPIGvCodm8uQaqGunraVAes+env6wF8Re+jnzBk0XkVSoCaHejvfE4AD72hbnjxRmbE8KWfEA56XbMbTeks/mqgpmvZNAjzhwL+f+nJ+2EM7L8/eqc/ZPXrOdnS75mVcDoAnbu6djFEfyg2I1pY9dBYd3AbraMu5vDzFeUylOubNOQzGINcHoiDihtHjgZ8f3qps0HeJBTeM22ZF5zSgtivmoltWPq87jbe06qOx24kyE1ecRWNLz3EVxHXtIgAJ9QVfzDC/YDzpP61ri2BQiED89kIiikOA6l02kdH2Q5amLee93wPX8/ZW2aJsD1HVxdt8KnxZYkaTmgBWO1yAbJD78t2UF5KnahAP7CsWcpoQfLFYzrtraSzlE+LyIQzO9p7WBXwYyH7CppHu9ueNgtd8Bqx3zEd1Vz8uUO6OaAV9nwBnh+waeLKI/xwJ6wG3M7ZemUxzDPz/nkXBetOsMRXMe9w0sPOxy8RAJOXasV9eg3XIRBtUt76AInUKHR4MQtuvK+qmDHI5Yc+n3nKTkd2Qe68gAIrgoGBu0O7RvHHQ4Q2lzWums/Ert2Mg6JXduDUL347TagOE2esyceRGVldxesEMTQwF3dPMiDv7uAetSaNOXX93JCxJ07tH/znBPXyALnJ1j+uy8lt4jw5THt6l0WA3czkfcYofnOi1/dGvkFr+Ne8BAW/qAXuiD6NZMmbAFGJFybTicQj+EpOWdEw8XkebuoAfjzCQ95kQ6JqIWOTHrK3Zxj/ZQ7l+7gfjoP7Uu32RAOvC1hRHxJVYV1x34qdmTpwbFjMm5jF0UxL/J7EIiy3wqEN02ClafJMsIGxGZnPw/b7z2gSjrVp9nLycQx7KDPzpAxYU7gxj24ToLBv7oizseLT6zQHJM3cvNfnAJxhGi2+RWukF/sOu6SxrtLULYAACAASURBVCB88D7no1w9Rb1ON41hR8GSWa99bLPZirpYw4WTpiwj+l3Y+SoMtQBwh31+IaZlNfvzmy1J2VVNta3lSro99GCNzs8Y4kfPgXUh9De6BZpulwwl4PHY56uDY4cRq8u++ENJ2eOlnMCwD8TitqfngkbAbNawVhdQWsgD8reV0iU6P5MbyIWc1HvWbzYiH+KBsxPYWWt2bKr6gUet33FizLbmTMqx49lXjyeS910qD5HEZACtC35g0l3xmfS4q5q7qqACAbBrkacsGaxtfZUEB+M7GeG/dQ3T73PIs6vCbmV6HeB+Fmh3ZjiAW63hxoM2vsgSKZmlbM9tt3w6dHMunidi19bmQbELrl7bm76T8W9zPiEk+X7G+PdKM9OVrlDTcGFKO1XzCKXLPs5FIAuak8npTYWLU77foE+XQwBmucbyb7xkb37Yp7LBG3HtOx+zDDsd00+rezzqwce94PcWRRiyWOljVxWJyIMe23+9LmtWJ5M/3SmlZRasc2KRoFgX3NFlt2tenFIjPYm5gL2nlJw+BaoadjKGubpj73rLborP6JeESkx6nWcLVDoyj8Xuq+rg2AMJXISZ1DfWbHdwgy7cp8/RvDgVsFvcGi0oX7ZpaFu5lwe9X2mlGfIRmcGQk/y80hG9bBq4voM7O0F+s4NXQaqPX6D+rY/h5SzjlYOgYq9Hch13SePBg48oeBnFjqg/UrmTQ2ErQurLkvX3dN5O/lSuWfEnOllV+b1dBSuALu+5myoW3lvTmh9HFm6+hFVb+l3F2nVBsSMT2bbjYg1Vvx6JHa4B5svDYjcWqMsgHkVBJKI8o23JUi1JWWMXnMz61TogOhU75O5nIQ+WZnLWmM15DsgTGBGYdYslGUxrOnl754Hlkrzdi1OYt9eIyzH8uxvY0xNgsUL0+QLm/Az4+kpEXg18kv785/prvI57h1e4q/iGeq1LAZImAjUufmB64MWAIACb8jzYQxK85VsTYICLd69fH3a/NCFdT/E1jeNhL4q4oNKEAytd7PMFrWGAsLAejT3PD4/dmjb2fQPjmLu5F0vOUBLtURrhhbqYpA/yABCeTJqPvZ0/yMmv1nxN0zAvWbxGc7m65WcgEtmm30Pz7hrYVXBlGZxL9G9yDNdx7/BA4E768QBmVbRuGzKEMWnCQ2Eprbs4g0HWih3tKg5mxJUDxVYUACpCAYB2ASgst5RHddPANA7ucgKz2ZPVvpmi+r0fIv3jL/nzXYEpX1An3vU7iG5mT8e+LQmMOyB25Cl9Zostmk8vEd0tgX436NH7yQAoBZIbRUCewDee5VQcsR+exNzBJQ/fzWC2Fdwgh/3qmvm8XXACKzm5QQ/mbgb/4hym2LE8uZvB9Trw3+xgPn4ZprzWTuCLAtHZhBKFYs68+b3voPdH3wBHAqc5bopf9sz/rdF/CgCtEwbaHdiodrsOZrKU2BH1XJWRuapwBR2b1Zo7WCcjba3f5a4VWcGsGJhy1/o8CTIRt/cwgwEPvfcztjbrmjfQcgM3GcCutmLAEAWZ6vfFDiCYEH8odi+2lrAmuIH4frc9X6iUhmLxVS5QgWWCF4JIaeD2nhLX0xUdDFcb5nM2ER0dyUm7PipeFVnePD2iPY0+sepG1BmyoFfvu7l0vSqg28E//cl/+20sGb3+kqoWgAyjoEM+GXExJEJ6FnAWgJY9pOoBacrvZSkX+ZBa59hVwcTLFCUPe1IaQXVrAoaEO6bvUnnATE5Qv6RGJS5O0VyOW+1JAHa6YndlsWQb8InYTSc/OPbgdr2ja6Hv5vAdYmioICaH8vmS2jUxgW9QaIDksJ+HKYjxtwKLwMUp3KDzMKdQ8qWhtQqAv3e14exgU/DcIv5WvqrQPJ9w58+IHA34+yO4jrukUTJFlrHTcnXbEq4LtuyMQHix3shh0LU6LduSr/NeWoNiziXeo76uEX1zRzkJG3Fkr2Ao54F7xwOivJ/fVYjmCziBCZh+B5iveOMNCMwyyw314dUK8pHYvRiVHRI7FK6s8hxFSayL1Ozm7TW8ZcfFL0g6N8u1tDvliVKWrMElD1/XwWzMT2fMZ2qAV89CTvAeftgH7qZEg9Z1aPFCuk+UPIngLs5gZivgZES+aycLrWC3Pp7B05Hv8K1oEKwN3ROTZ0FWGklCmG2vG+zdSYdTWxcRExJXDZOl2Ldp14Oh0ZpdpbHjmC53w35wzDDDPqrf/gR2PILpdYMSsZfevG8cB0n6+idiN3F8eOxS2uzHbnqdVudmNGzx+FnK7pVa4kTsk5uT8YM87GgY3FA0H5PnD3PS90wStiqthZvO2PtPaFoM73jQTWP4zRbbzybwxmDxb51j88kI6HZg962Jfs3Xce/wVpy465r141aUwJYrPvqN4Q4mAypITeydAy4mwTjXiBqwiTklDepeJ0M0gxymkZ517YJrh4ks6rMB4rdTfk/wJ8nbGRfibkeg1Nmolel4cUYF3izlDqnqCu+JHRA48CGxd3I+FYRS53cC6koTYlfyGKZ2sPcmDKV2FwOkr+9aR26p4TUPGIPmfIymlyD98pZ6Mt99jvh21eYE8GaLIpY11tL3VdQTgnb8poD96gqm30Xn81vAOQz/2T1BdpfnbVPgCK7jXvD74//5ij1yxYuL+0c4VEk5EEjUdzN+fynu1+uiZeZUFVuA0wWiohRbFtGCUWk8Y0SuWjDjEECVOmVbwwnn1JLwXIqU9R4UWL2c3hc7AO7eB8Ruqjq0Lr3Ke0jcAGCdOPZVFbDZwIyGSP7sa+7Q8yW8d1QT6PdCHiaOYL/4BpEoIcAaxD95y6ec5IRyR3ripiAhZl3AX0xI6pi302I0DdyuImJyW8KOBkH+A0ekHAwcfUlDsBTyjL3rJAndDmPZ4w5YjUEv0N2UxmYGfWJAgFYTRjUX04SL3Bq2NhVbPxlDrefRNNKVaXdrnI6hygE8gHZ4QNtsUL2cAHWD7Q+eicTdE7Gn6cGxA/i52P2wB9Pv0v2633sQv0/icHNzFjBA9Orlgzzc+TigKUM+qq6gOamCwXP61eJiws6NwjTSFPZsAtPvIRLTM/v9TwEAdtCHHY9YLo2G39Zy+eB13AveWu6k+kErMCriiJ5tPFkoa0pPq0tHaEWKmKjvdVrIrU5Rlysuwkx64YKj8XkaJCd8V9CKTkoVAYm5V8/YngOAOMKP/+EPEW3Yjsy/IPzgqdiDJc4BscOaELtOhk1RtvEbA1NWcCdDdlCE3YS6Bi4ImfBJ/CAPUwoUQrpW7tUzDrH2cjLLNX78D3/I8iqOwmJXeLAZ9uGzlDfcs3PAWtSjDluoJyOU37sI/N9juY57wcMHZB+6HX6AeUZzXXWMFrMuiDBp6CAAXDSDXlgQJstaet0eZJeoRHlNXbPVtyb816wLdm+EvO17OXyeIrpf8FcsV4Dz+ME/+gp2vuYoXkFoT8VuDo+d4qhZe+Ps82pjwpVR17Brqqf5LGmtLXcVsTfTxYM8MFvArzbwg+6DfPZzQhThB//oK54tBBEKgPF+syfO2uvAZxH18jcyVbUW6RWnr/Wz4zE2O/IaXnrP4wERhgA/wG0ZetTK6vHbmruqaDJSYTfih61+TDsewIwTFn+WUQ6jbuAH0meu6pY40aPHkSlKLr4shb1hfe1FswZddiB8UcDUCcygB9fPYa7uiS1/JHYTtWXTB2PfI437ckctdsXsA3wiGdreKC7HnY9h6poDqWEPuJGFKHnoe7soIlG9abD5/e+g98fvQk5q1IazCc8R2tu/voMrthSWisRas9jCnJ3C/+inqHcVbJ6xVDQW9u5Ixqw49gUPqeHVBRvgH1ik5Lxqwghmwws2hd0OWr3QDS8C9rsjccv297M5H+11zZ57tNcW3NLw15clTK9HT9QkodPd+WkQDuXE13MHX66pCgBQh/KR2KkusDksdj2cA+y17wCs1wRm7SoeRFV1rJMDyyXpenkON5vD7KQzNZ2FPLzCiFdrunScn6L7L74MnN3g3g2QiL6r6D0LEHHZ7/H7Qj6Jzk7R3N5RYPZ+Cjs5IUIzTQhoO5Lr6Bd8mDSqIJG07oxlD1l74Eb8krh7Ri2uRO1okryl8RkeVBGLjqKM7sN4XBW64oiKZWcTTgtHA5hdBfPyGVw34249GkjbMYOPI+7UtaiZPRG7dw44OzkodiBqb4Y4bumD1lCZYLZk3dwX5+xBj2eSyMI8vwDuZ/CjAZCnbR7LNW+shAdd181g6i41ZCQnn57ALjZwgw7sYm94pO3UiWVnSr/8W9+Bf3cH++oFXJ4B4wHMuxseWo9kzR93DR9HwKAno3R5fNdN4IM252PiOQynhxCLed+jQpg7G7E7Y4kS9N2cpVBCEX+UO7iTPln94knqsxR+vqCGi7UsG+YrdlJUtex+Rnv24MLR8ACtpmBFAZyfPBm7SZKDYzdxHGI31ooECONHHAEnwyBF4vUA7hzxMZstc7i+fZCHryq2HqOozefdzYOczDe3wLak0zZY27vrW/jVBtUzalP6dcFJ62JJY7f5AliuYb76BvZuxsn1sP9trponr+Pe4eua0hApBfmDDo300aPrKfvk25KPerVojyJaoqsnq/etkpiQk02nAz/sEy+jGpZJzMNdkgCdDvy7GxoKvL3mh3t7y3Zc0mJvcH1HYkddA8/OqCQ2GQNXtwLlfX/svqqAbXNY7GXJIY4+rVR5YCg0xzSBb3ggxVRk+eKY3NeOQBt6HS56yQMqt3EypBlb08AtVrCTcchJ8f3+42ewV/eU9qgojxL/6DVLodEQzf0UdtCHm874pBmy+4MsJVT53c3PfbS/ruu4F7zIWGiJYbALACxADorq2xTHnPwJWi+oAZc7Sj33xYB3XQj1zQHXt9RNF2NgLFasuyPawZuTEbAqgH4fWBdwQsqAwm53FTmh6zVvoNsZddWvbltpukdiB+QscUjseS5CR0UYTqHcATM5HygTKdT9CUkbnmpoZiWH37087GRA7Zil0P3iCLaT8wkiOfmaJHO8ueb5Zrlu7XU0/vWGOJwsC4hUs9nCRxbubsrzTfUbXZoDLy9Q2z3vImuDborpCEy1E7VgJiEwoG7YXUniVvQ0khtIetx4cQl/MyX2pao4yhfJOCQxoM560vqzpydchJMR0YIKhz0/RTPswJQNjHN0Bnx3x67QY7Hn4vN0SOxSuqiGjbFS+pwM2effbBl/loYb0g/7Ad8P70kcXxchDx/ZIKuBJIZZbdD81ieIbxYhJ1PVaE4HsIuCP7PcUJk4SeBdw4N2v0ddnzShDWchejVCNDdZJkysX88K+tnruBd8wIZH7e6nlDgRHwpWNtqtqWqgm3P3rWv4nQsSdl7H3fr2tdDlFDMvnR8ju7dfb6irOJ1zkAOwvXl9z50cCP10+/WN+LvGiBbChtqWj8butyXjOiR26QwFIacdSSphMAYE9lMQmNXdFSB2XmIOeQCt6ph8L1p3A6VQmUz26xsOjuqG+jfOw59NYOZL+OcXXNiiM+nrGuaTj4R0zhva73ZHZYhw3Aveg7onMwoNqW0MAC7SLA36h977Vkn36pbQVzUWsyJCOhzw5/f02NUNxMfS91Y9dgFNmc2WN0VV75VGjliZNIXpipioUOEUnIbGPRm7UcDYIbGrBLYYKhC8xpvCOEErZhlwV/A8sd2202jn2xz28oB38OMhuzWSj11tiLORnOA5E1ARWjMe8Ya4vefu/tU3LJuMgRnJwflr6klGzy5adbXoeHojx73gnWMfOzwe01CnmixjR0DhsnkushYyDdTDnrb5jOHC7HUCEykQwdebVufFe/idWkVa9rHzLCiQubdXMHkGezJmLLsqnAnc/ZTeTgthClXV47FXVahtPxh7FIXYFUAGY/lPDuSm3PGACHAmEJsg6+c2G9rwFEXIw5c7WEVE1jW/r2ZvmlMl+PeqgkkiuJs7GpXdz/g3OJvQJ2q9AfodRI4wBiPmyQGZekTaksdz673vUiwJEMx6vZgX+FKkKBoXPnhYQ3x5HAc9w9BXl12UI3IbIKsmjogJGfQoJa0EaREwxWev4F894/tmKeyLZzC9HtzpkGoFPXJr/WYDfP+T0B0J3q2PxS7W7gfF3jQhdr8j1FdhC9STz9h7Hw1h0hTu+684BTYGZjQAPntFDMx+HuLQgSwN+aCTP8ypJCgOhiRylRCMXlyyVLmbwjcOdjQEbu7h12uWf+uCN+HlGf8W8fHsq8cTyfsuL50Y3X3Dbi7a6dme7HQpQLKU2i3QAY3KRUsXA7WIEqUEk7HOjlmzpwlreG2nAbCzJXdGFTNV+/hNAaRpEGryzy9g1q2ysHcO0Anne2JH3D7qPxi7Lpi6DkpifidmaIMe/GzBcqnccQH++DUgLiB+XcDW1KUxi2Wbx3bLv0O5g1utYTYFTdh2VSs+1evCDXqwdzMeWju8Edy4T+jAtoRfrOAuT4EvF8BnL4GfvIZ/dg7z1ZIQh7fvYM8mwPzbWjRPX8e94BVGG8ci6D9vWTtKqyt30hGQHa+TwRT8r49Yl5ucuBiz2VL+TheQDkSqGj6O4LMUVhhD7tUz2OWGrbjTE5hdhfrZGNHtErbXykz7iNNGezuFu5ywHn+7Dvj6x2IPshsHxM4ngGXsSjLvdjgJtpYKZJUoC2+28GdjoKzgzseIpkvCBS5EGlDzuFsBdUOiepbyULpas3MjOTXnI0Rf3wB5BtfPYRckyNjZCvXzE0RTwI96JK4PB/BVA3N+CheJ5adz8D/4FH51JC0aHPuCV7ZNVQF37YfvpTbFfMk+tCXzycRxMCAwBVjvlmIocD+HB9qDo5gENBcnMJVorW+2cOMBzNUdoukS2++eI3vLboUb9xFfL3hjrFdc0I5DLGUVmbVK4zVCmkgejd1EEfymPCz2jLJ7wdWvaei0cTvnLnrShy0ruG4CnyZ8KnUyLvY4ag2Vx4OQR/nxBOmVTEfLHUy/C3f5nJaUkpN9fS1nnzWBYMWWcIg37xDNaV1pu53WmqekCbNdmCArHhXlAwXmX/d13AveyoceR6FLoRgVAByTb6XEEAVeOBd2RC0B1DdJJe6MYs+bhrBY1yoLW+eoNrYukIplix/1qefiHJBkfK/IcqhTSD0eRUJ0ruAnY4mrfDz2OIJBeljswgsIDuTOwS7FzDiOYK7uudCmbNG6yYBQ5c2W9XhRov7sGaJVGfJI38wEd9Ml+CyyMLULag7E8dgQi9lVQK8LD8B97xV8TGnA+HbJ1yYx3KADlDXqSQ9Rr8MO0mKN7V//GHj3a1lBP3cd94LXAZGiI7VzAdmpV5u26yDUN68wAV1sah8jBy5f1y21TkjbflsCCkKrG6oJuJqaNE1DgzHIKF9w4f7tNayUCYFXK717czIKC/bR2LflwbGrOZqv6zBBNYnoTMYx4+1kwO0UZjwM2Be4Jlj9xN/ct4YPwz7pib1uq0789ppDJHVIEV17E/NzUFdx4xyixZqH2apmubfdUbnMObirGySrCYJxWpIg/79++qteKQdfx92lMaZ1ko6jQCsz4m3k6xoYD6lQK2hC0+vyg4sjjsHzlFNAwZ+bNG0NC0Th1/S7nFYGSxgpe3aVoAwzLrIlRZlQVbC6a6vrhnM8GI4GwfD4qdgRxwfHbvIsxI5IkJMikadtS7PZBndxAO05xVr40QB+1H+Qh0mEJLJctfkAD3KCNYF/a0YDqGhXcznm659fwC9X2H3nnGVQr8N2rUyUlS5pjki14LgXvPJCjWl9Sbdl8FE1vS6nfLqTAlykxZZ15XJN5F4c8wCpcns76qYYY1iTbwo+yherMBVVWqBZbdi+E+IEvA828b6q6OE67BH9OBhQmuN0zO8/ETuAw2OvqhC7LyltB2tlUksxKsoJJgLFIEUwIDNXG+DNuwd5eMGomzRtbe/r+udzShLh37birnZRYPfDF3DdDDg9Qfr5Faq/8jHQOJS/9QI+T7H8D77HQ2s3b2mDR3Ad94JX3HoiO+B2b0St+JOStbKXD9qNB0Gpy/Q6XLgCwwUgagcNYQX9bpCLC0pbztGNr3F8VKu4qRMLSGuDw56RiadZCbVO5Lb9F1/z8f9U7AoOOyR2ucL0t24kTvFomq8Yv3o9rUQcSkRPg/LCXh5mcsL6HAj5oKof5qTURxVf0pvJOSQ3a9glB15wDsmffgWzLZFereB7OQY/msKNujDbXcv4OoLryGt4RyQiemK/KFBa/YDk8W7imEZe6w1sIE80gR6n3qhYrlpy9rpoXebKMvTdaTxQAN2cjCYQ4YBeh08ElaFOW7mN4KwHwC/XsJfnnAI3zeOxA4Gn+gvFnqQiYU2fV1Uf89utiCwNyGpSs7fFiu8nh17NA4Bo58gTJE0A/ExOmpe1ZHLloqRc1UBZtTryWUraYRLTqOLiBLibMZ9eNzh+H8N13AtedV1cOxUFop9bFLR2iWASy927k8PPC9mN6GKtiEhvLHHn3tGycr1uJa2LQsb2ewL+alu/XMNtCikvkgeaLCYa8UN9e82bab4gk39Pu+VnY/dVdXDsOojye67XCosI5mWNgyu2QFG0ZsVKVE9iYn+iqM1DBGXdYhFEae14BDebh5yQpbzBK0rq+Y1AF84m8EkMu1yjuRih6adIf/QNlRS2JVwWI1IiifNUNDgSSPxxlzTOQ+0jTTdv3ftUg0XERwGwLy7qwNgUQaqaVL441KwKV2Bf3LVqADrhFJ8mAFT8EkMv08lh4hj13/gtPlU6OXmjvS7jUt3HrpQiKiHyWOz674DYMeiF2AEEczI1OoCYOajVpcnSdjqbJjwI9x7mYbodoCxhJX+V0tjPKXBuAf58LHo0my3M12+BxiG6XSD5k69FVJV9+mjdUiXdbH5Uq+y4d3hjuOB0Ry8EBbgt+QGJrboRgy0AAZaKqg5w2gAJdi4MR3xOYoIZDvi/b6YyAY2Ie/cefjonorBxHC5lKdLP38JfnHLXf3dD4+J1ETxZfV0HTAzU2vI9sSuG/KDYBYODsiQaMpcSQlqriGPu9i8uufhPRsBiFbo7PktpiDZfhTx8XXNaO+xDbXZ8ErN00pxEbBVpKhieBm65gp2c8IBe1zyQJjGwFNxTVdMQDgCahgyq++W3vXIevY5+wQOA6+awt9MWAw9A7WuoWV4Fi5gwRLK2dcgT1QDfyagG0OUuxXKCchOQA6ZfrmhUMF/wjCB9bD6ut5ySApwsRhFwNw3KvUGaWvmvdfN47DK8Oih20XU3WuNvdwEe7KX9adKEc4ktRZh8XcPPeTg15Q5YNw/zAFgiTRcEpm0KmJMRpbclJ6PdnD0sj704a7HxcUzN/G6XZ6CUJZDJ5OlgBfdvj2eLP+4FD3oVWTXqBYIdjN9seYBaVC0sVmltegDVA6qWKNuSB9DpQiQ5pG5OE7YBjSghFFsy7a1tFco6eWj3qS4Nmgbm+QW1X8Su0W8Kuv91u0/HHkd0IzkgdhNZQDDtQRsysvBeFpK1XIAZEZp+vuBENuXgilS8NW8ezUNYSWgaguaaBlitmcdeTohj4vrf3RLznqXw4xT++YRMKE8TNzPoc0DW75GxNRzwxhKF52O5jufWe9+171zX7xEE5hzrY0smUNBnPOEhy8Qx+/P78naQetgaIUFH/JfnQMUpIZGAcsCUncrfTUU+Q6h8AC0fvYc9GXNYM18FISJY26rzypngsdj9oHdw7F40MNXrycQRB0jqLaUOIXrJmcWv1iyDmoZx7+VhZOGbs0nIx4yGD3PSUuz6jnnpxlGLGGxOqRPf66D85BQmS1G9nMCdjbD+6y9Q/dWPWR6lx7Pgj3uH126fthFVcFT7xwLtRRwT1agvk965r6r2ca9mA6BAktHp68UZzGJFnuq9wAfKHXB5xhZbr0N/JRF9sq9e0Pdp2OUIf9SnpksScwd2ngdfbcU9Fvv1rZCzD4h9tQliTko4N7KI3NkpzKZkS3C+YrzqURVZ8lCzBNG+Po6CuQRVaYYDuGGXUt/Fts1JvV1VniSK6BP10zetxk+HA6/0bgZfloj/iPqTvcWwVYrYu3l/3ddxL3h4OsCpuGiewdT1Xq/YwZd1+wECAuSyrbw0IAMjaTWKIq8XTLi/vWft/tU33ImEvoblipDbouBi0wX2esqd+C6CazjAwtUtzGQMdz/johe/VD1Qvi92Iw7YB8Ueixrwfuy7il+7n7F00eFaUTBugHmJ4bHbVUF5GADLnm4HzXLV5lNsqXcpOZHM0ufh3XvmNZ0RU+89y5iNHG6HPeBGLOejiKyvmqbP7vbul7ss/gLXkS94w8ew1rnCGgouF1LysJ71PFBmcStRZ0l2CCYCwfmjAWzcqh70BVffyVnLyyGUPlIcDCn5IZJyw83mfPzLYlSjNLXS9GqC/EjsviqDleQHY1eIAngO8MqQyjoEb9UNDRN2O3ltQ3jAVmTzul12dbT0kXIIxiBSLcrZnMbLyrV1JJyrPacVux7byVuZQwD2bEJbIgDNcgX89vdgv3rLJ6SoHOA3h9YDr8hyEJPE7WheXbhFag7WitTdrgWIJUlg85uVCBWdjlvDMrFzr16cwBY1bFlxahlZ+MtTLv4kxu68h/T1FNXvfAKzc4g2O1STDtLrNbmxSQwfGWAygHl3h+aTS0T3K1TPRkiuFhQgfSR2P+oH58APxa6xmarmgTCOUD8bw1sD4zyi2yVMnsE9O4WpGrguIQR2s2ObsKFsYPXdi5AHjIFZFag/OkW8IIzYd1KgdiGnupsi/vO38JcTYFPCNMTbN8/GiK8XPMBHFv7VM+DLt7Df+xT4yWuYyRj+7Q1jHvRR/c4nwP/+7S+f913HbVsZn/u/NfnPuAi0jtThiwqi6vAFCDt+OAyKkleQj5OdTSl1RlWCpzM+SZScoU+QNGnNwOqa75ek3PEVSiv2LwDY4RgOyPgX+ZBHY9/uoTM/FLvuuCq2JEbEofevOpZB4UDaqsYGWAGAB3n4HRGSflMEvQecrAAAIABJREFUfRnUNbH7mpMoEviqCl0YvyZ0opn0Ed2vWrU2IX3XL08RfXkF9/ElykmG7p+8g88S/OGP/5tf6Vr5metR28rj3uHVBQ+AieTQJnYtgHZebNsSDAs2k0UohyUdsXdydh0mY/bhu7nI0p22NLr9IZCSMaKIk9K6hp+MYGfSndgUMJ2cB74319SojEQ5bLEK0873x06I8CGxq48sCiF3Sz6K8DQ9ofttdzDjIaWrxU82+MNORjDzVcjDjEdCVM+CgbHPh+THSk52seFcQgZ1pqrDwrZfXXPjePWc7z9nXz76MYFz9qsrdG9yNGcjmB/9JcLDG2P+sTHm2hjzx3tf+6+NMW+MMf+3/PuP9773XxljPjfG/MgY8x/uff1vy9c+N8b8lwdFZ+h8sY92DPbqSmaQCaAvKWxkIksIrcBzvZQRvq7DtBPTBXfYm3uY5Yalw3xFtv1qAz9bEBeu6MiFTAq9D2QQulWLo/aba+628yWRgbNlSzZ5JHbv3MGxY75qJ7VVRe/TckelAH1Cz1c8G6z2VH6rWgZAlmJLe3n42RwotnA3dyEfDpHanFy/23aMpgsZdEVw91OeW+qa8On1Gnh+zlkAgOZuSrmP2RzRbBU6SsdwHXKa+J8B/O33fP2/897/rvz7XwHAGPPbAP4ugL8qr/kfjTGRMSYC8D8A+I8A/DaAvyc/+8HLpHKwEwYOAHY8FMaqu6iOwaUONqNB6+skGBmAi20fa+K3pbT65HDsGhnYiMb7puAiqCouKOe4KPWmaFxLNHc+0Ak/FPu+GsEHY9cdva4JNJNePAD+TtHO4f8XIanlkjmkCXPodx/kAX1y5lnIx/R7D3Iy726Yl2hgeu/Zk08TmqSdnwqNsAf/xdewl+fM64ffCU8jd30b2sHHcH2wpPHe/x/GmE8PfL//BMA/8d6XAH5qjPkcwN+U733uvf9zADDG/BP52T958t0MgsKYUc125X6qlIUjACzsoqpG1jgOd6YLLhhrWBqsC5YcqZiEqcZ8UcKdncCuuCN6R8saP+yzTq2lBTjsE0ufp/DvbuFfXQJvroGTEfzpkLLT6uxdN4/Hrlo5B8Tu8xQAXTs8XLCBR92wl+48XQonI/bguznMIm5tLvMUrpfDiG4l7meMr9dF9b3nSN9M+TvKCn6xCjmZVBy4o1bHx4+HMOsC0WCAZjJEpHJ9ImGCyzOWSFEEXJxRIsSa/1/IdPwDY8zfB/AvAPwX3vspgJcA/vnez3wtXwOA1z/z9d//4G9oXGDN03PItkjE/5e9N/m1btvug35zrrX22nV1qq+6le1nPztEGEwiuQPpQA8pSCDo0YMG/A906CLRQwQJmkE0IxohSgsJhEgC2LHj2PF7t/qqU+26WOUcNH5jzrX38z3nbjuv2JHukq7ud77vnH3G2HuuucYc41d4Z2j/uPRQWr+TlgWg6sIQCa8hAFAUilFx8C7ZYg2M9rDNcMBH9nzJP+8yQgbEcbEkCbAg3h1fvgPabX7I7x/YR9f+uul1fy6xY7kJDiWm2yFO309CP9w1EuAAW4RZp5HbK2iRg8WaEArNw4xHkO0eyZ98E/JxuQ7yNCfJcv6MhyfUjlBha5H/+BVatxsAUO9Zi/qTa0T3y2D+YIoS7uPdkZ7nr/r6qzZI/zsAvw7gdwF8APDf/LwCMsb8Z8aYf2yM+ceFZKwfWy3Cbv2jH3pwi2OWAN0OH/GTEaG41sD0++yjtxLuslp2GP9nL4LU7xKOm9BD1QwH2vWg2oCU9JjCxZhdjptLHgCHfb52WRKesPIEjaQpi56LvZWcHrvCj72LICLbDKFqB/PiirF5hQQAuJzye6YjlfdrHeVBfLy+B5qPvbo4yglxzLwUii07dp/cao34//xjTqKzjDfybg/8v3/Cp6NOmUXlwqX4V1xMVUSChZsx5n8A8L/ql+8AfHLwrW/07/DM3//sa/8dAH8HAEatGzFxfERRk2G/2V0BLs684K6VRQ0tzRhIVsFbQ3JQow7ZKkoaDl2DHhdEmtBgAErYSGLIxZClB8AdXodA9XQI8zCDvZgG5CJeXUPe3ykpQxfkU7FXFSe4p8QurtGI3ygBu9OGzIrghm0GvQAzltGA6sFJgnLaQ9SKgfcPkMiEPEyH+Hi5nFB0aTqkwcQ+a3Ly7cw9kaV20OcGMV8ChaUsX4vyJ3ZCS0yZL/leOkdOrj/fnIkW019phzfGvDz48j8A4Ds4fw/Af2KMSY0xXwD4EYD/G8A/AvAjY8wXxpgWeLD9e9/7iw4c7MLjfbbkhxzpNDQ6kKwr1S6y21V9SLWGjGkMJlkG2Wc8pFV1EEcyKz6azWIdrGDEOcjjHPZhCfPhUTmjlp2dzQ72/T11HB9ngfgBgL+3q56wz8TuD7QnxQ6E2Nn9qQkNUCUEKGdUioKvM1vy+5xD8tOPsA9LMrsO8pDNlpTAb96HfNx8cZyT70S9vmFMnXbA1djxiGQRBcaFibLGCoAzDA/aO5Pre3d4Y8zfBfC3AFwaY94C+K8A/C1jzO+C8K6vAPznACAif2yM+V/Aw2gF4L8QkVpf578E8L+BDeb/UUT++HujO3yfREKnQ3Z79qxbVBHwrTkvd3HkOGE4jcVkxFpYWVRe5yXwO72Ge1HowTaGq2vibfYZTHvKjoszcIslx+xpCiQ6CS1KiDEw4rgYD+vW74g9jO5Pid3v9E4gruLwLKuDPg326g3rzwKl1vuDPp8wIrDTCdxsHvJw24yvm6Yhn+j1S8UWaU79Lmv/lUpq+7amx81vts3QbdjnxlHrIfZizKnwessn25kIMZ33pDV9Ib8/+tskFPjBTCtpJqVeeg5QQwCqeMl+TxJE2BGrxhkPaEzOPDmhlaicXCtoqqMsYCZj9qr10OaBVH6AZS4mtILsdHiwu5wGS8pAJnkq9kGP+jWnxH54KdxYVpvm0OuFlXz8RcnvWa8bjP+wT7Eln8d2B4yHx/Bm4DingtAETzmULONNNFvwZlPZkqCSXFX8OU+e6fd4aG618Pe/+W9/sYvl+PpXdNLqdw+vritOLdgjLkpvDADAmJotvlI9kcpKdW0oPRFkOrTbgV0GlAWcYmtsr9uwe4RPAXf3wG5Di9rubrZgTEpzc4MOzIqGB2Y0JPBMFRCMNyx+KvbdvvFK+p7YxdEJRKoKmI4h6hYIPR/gnsYOThGQAMJil6riTZRlfJr5POqa01s1Q7PTCWS1PsoJAPPyKmMAF7snp+wztj1rJcXrDVxvtoim1KNH7cI55hyu817wys7HeMBF5Y2B90p0NtRUr1UlF1VzkDW9jpoL5zzgWUM13gPLF4n7dL2oKshooLIdNbwJgBQFa1QdAGG9po18XUOyDHYGunU4x5r4xSXMahukqCUvno5dBKaqT4od6y3ltfs9li69DgWbBj1S9vKc8We0qJSiBPIcpqtllcqRePQn1msyupK4kfHLMuCTl7CzdcjJKGsLYHnkLoaEG9xz+BSeKDkhzPbmCvI4R/zZm+AqTtz8L3XVPHudD27zuy7DbonJCi4YKOmjTc8hXz7Ybz+SZ6kSGjQbzrhgoL3pnDW6KGMJADsmHq/zMAvSe7LZcuG1Wtwli4J1+WioPfUIGA+bsb1vGX584BNA7eufi11Uo/Kk2LvtJq7dgVnCYtUM3h4Ui27pvGd6FGkyk1E4AxzlEUdkdLk65GP2+VFOovr2ACjjcTdnmdZO2Z7VktD0esD1BWS9gXz6CvXbD3DX9Kl1n75ky/RMrvPe4dXOHUVDdDamCr1fWNs4VKiwKD1Rq8YfSevKwNABGsiusWyzVXUjvwcOd+AcGfl+Stprw9zOGvvL9Za9+Iq2N5iOGueLyYiLQdGU3xn7eEDz41Ni19IDgOLka1Svpoi+zBpgXBGRTlfVnMIa5iJpAiQaWycNeaDTBq4viJff55D1FvnvvEGqE2IpSu78/S5bt7s9cDXl64z6JIjUfBrKsAfXSRCphId89hrlIEWyzWDKuunanMF13jt8FFH7sZMGGxZA23xRpEOZNkuEKDrSnJR+lz139W1CEvOg1VN4gQhwOSYVrqxgBj2Yx0XjBzUZEs/eabFt97hit6GquQOrUrB01OpS3fi8qwZGg2djN9oWPSl2Y5rYu3yt+HZBWIGeQcygFyzsjTdrmAypi99pEX9/mAfAFuXDIuSTzPZHOUlH83KEYJi8gPS7cL2UpJKLCdxkCGnFgANks0E9ZT7xIoMbdWGXG5S/96Nf2RL62evMd3iVllB8CSoFNnXbPAxtd8BIa+R9BhTgQhGhhY0vIQAiAOO40TwH2Da7n7E1129TGk4Xi9llkCyHvVeFs7KEWZYKs3Vs+1lL/Em3TcMBNUmQdkqebK/zZOzIcuJlTok9siF22e0bOxzF6qAoVRNed+ftCljHrPnbKcz9AvXtHaLLiyYPT9lzNUyrDQFosJAkISezyyDLDURckNY2mx2i1YYH5DSFWfE8IO0WpHaIbhcqNmVhvr4H2m0k/9fzkKlf5nXeO7whG0jaKXu77fRIn9EkCTHa6y28fSLyIoieet/UIAgK8NBnTFAGZo1e0rbFC5d6XcXpSB/ZlJqGMSRK+1ZhFHE6u9NuRRKzxn6Y8enzTOxIW6fH7i+vRaNqvGZDgrbkBeP3WBwvqe0xRNYi+uT1cR6ddiiHAtR6vTnKCVbx8v0e82olcJcjnll6eviPY05iv/0AezmFu3+gHs9Pvla7SwM7Hv1i1sdf4TrvBQ+wxFAdSdnuOaEc9LkYjGFt2U6bQY8OTEQFT1FWKnPRpOqmA9rb3EyDyq8sV0dS1rLdBeqdWW3gxn3WvV6AVTVdgpJvqnj4Q2no52LPi9NjPzT2nYyCFry7mbIssQayXHEwpI57VEvYBeCazBdHedRXI84GOp2Qj/vRJ8c5ZTnzspYkk92etj4ikJsLDpdAPJIdsoSzkzFk1Icd9Ak3KEvM/50vfqFL5C9znX1J09DjbKiVZb0hsKsoIFdT1uBZDsAQQKULVVR/BsbwEV2UQJbDztYU758fSMBdTIDVhm1HgJ2PjFNKWs3vCaGtHYnTaqYmcQTj247dDtt8nvwccDx/MXbEMQ9/p8Q+7IfYzWbHFmRZwc7XqKdD2A2N14IUyagP7HM1cKPbCK4v+Zqahyn1fY2jkI/daGdIc0K3zSdQvwus9wdUwRh2uWlarFXdDMHSFg+0ANx8gfr3fozhn/8gtXfipRaL620w+AV46BPVcjH3Mw5pxAU5aRNZyJIfhlSqdWiNqoUpHt0b/uYFe94f7zky9xoxKp1n4pjw4U9ecES/3BBG4OtagAu/LIl89DLbniP7VOzWnB47wO/xMGCPYAQQ3S9483y854G3qnluUD8rxLTEMbuM4kyah9lmDW91u+frb7aAn1MUJUnmZUkQmG+NrreU1Mtyvl8Pc7qe1A5Gnwr1b7xG9OVH2BfXwD/552yDnsl13gs+suHNRK/bjOID6VhLhm4K8aQLY8jtvNBevYqFml1GUsdkwDG6r2UjEp/l81cQB9iaVu4oSoKpJmOCpB5X3OGUB+p1E7HLeFidjoAZYQem2yEC0x9Y/2VjVxlqM1GCtRqI2U1GCO58Bfn8FczjCiZtuLABnxNFqD/eIrq5bvLw2B4lifh251FOVQ28uGJJ5QSu24bZ7VHfjJFfdtCaZYhEKNp0S3ogpbeF73GnDfzm5xBrzwZLc94LXniQ8tQzeFnrKGps0a3aRhYly4lIdVUq7a6sNiorFwGlYf/ctwm9YJETHvrUdBgjlhB2OGiwLYqoxMU44E/8gdCkLchXbyE/+oxtv7JkS/O52LUjc1Ls3tHPP9XiCOY9OyBIqGhgNyoO67XwvZZNFHFn/zd/G7hdNHmICwfPIF0IHOWEVtJQ98qKT6TLCaKPc3S+vg3aOXarOH3l0EZL7S7lBcz77Vl5PJ33gtcPz4+3g0ARwEOV3zXzXFt+HeI2/KFStc5lvQkvadptfu3NwJKEBI4tYQqwkZK5NzD9PkuAd6rmH1nW0LrYTafNLo/XyXl7S0PhJOFify72pdb4p8Tulb60TRmM1PZ7QAVLZbsjaSNpNcKsimGRz1/D/tFPIL6zElmY4YjY9dGAkOnNljCKg5ygXN9D1xJ8vGdLNkmCSK2UJY2cv/0IXE5glmvIy2u4r9/CfPEJZHYm/D6ce5fGmIBngY2IH1eWk19kZAGpU5zH3kQRBzRaKpB11OVi17ak6fWCrLT/fqlUtk/H+m6+gL2d8cbT3yN50QDKPHCt22HHo9/jTaOsqudiR9I6PXYvxqp+TabXa35fr8vX6nYoq+eNyTzsOWnB3s5g3rw8zsNDfQ/z8Tebzwng72mn2rOXRjobhDcYnYPYjJo8mC9RP87hhh3Yq0uWeWd0nfeCh1HRopwj/YQgJynLwPo5ctbwYkkA2336SPdKY5LnwGTI2jaJOSAa9AlpVecN0+uyrIljmE9fNYjMSLsmLZI5AgXPUW7aO4XIsI9gVPxc7BrjSbF7n9fJUOl2MXA5gejuSwnuPUxC9WHUdeNK4hQMN18d5WGShJb1Wj4FYNlhTonm5YSdoj5Lk4AEzXIiJrtdFDcD9u/7PZjf+XVEj2vIoMufPXjC/qqvM1/wisE2hm9aHHFRttOgAEAwmONgRJUMqIeu8NtasS7O6Q60aoY8zlHHpZU0qr77PXf/RIcvTrXYq0pJza2ADwegJmg7tkh1DB8MgJ+NvT45djmMXdUBQmweVmyphCz7fSOylCRcnJ55dZCH7PcUcUoP8hkPj3OqVTvHmlBCemscKUuYYZ9fJzFaf/S1Ov85PhV1am3aafO0OIPrvBe8QTDjhbGc6CnuA04CScLL5oknX9R1Q43zsFvneGCNKWaEKAooRn8AljjiLueNg4uSO3YcQ26mBIs5xxjqRvrOWzqavATmSy7WAyPh74rdtFonx85dctto0URRwMYAZBvBWkDVv8Tj9uOY+jTDPp8Oh3n40qSuQz6unx7nZE2wBsV8yXJptuCQqyghvQ4Vx3KWbu5iCDebo/r8Bu7+EfLhjmcDTyw/g+u8D62e4hZHBHOppoyI0KFCbRO9ZY3xepBZTpRfpgrDkeJnFF9uFOFnVmrUmxdAnBAoNqt0EewbY99eF8hKoiyjSNGMBu5hxrF5WenNQPaPXIzZe9+5J2P3gLaTYvdoyU6bB9Jhj9gdj3PfkztqoHzSF1eUFslUJMm/nYd5eF3KLA9tSbvJeW7RnFDXTV6ADqC2cG+usPm8j+EfPbIsMgaumyJabICrC9hvHyCvXxCYlxcoLwfAT35Vi+j4Ou8dXoQfoLWwizVpdZsdF49zHIz4LgagfWwlaGfFMT9UaPfix+9mu2e3Yp+rSUANu1QNGT0QekI08oLoxrIiU8njXaKoUffabCH9DmvnuxlxNM/Ejro+PXbgIHaraE8OoSjeVAYCCZxwpxfR+tuR1F1WR3k45aPKzUWTT5Yf5URhWO0ytVNi+zttRLMNRv/HV7wJl2vgcQ6bFcxHKZPV1QD1IAUe5ojvfzAmPvkyrRZ3JP+Baj9bipLY9TZ3PYgEm5lgHuYHL62EjKfaM3DqsHN68y0zHLCV2OtytL/ZsnTYbLmzbncNpS5pQRYrTm7FAhvaO3KhVkHp7LnYCcw6MfZdRrK2CBDpDVJz8hoOwXEEWe3CoVUqGi6za1PBzefcuTUP0+0oaG4HASi1Nxkd5zQdNfMDR1kO69+n0RBYEDHp1hvYPd0RUZJSmKiRg4hryClncJ3/Dt/vUu3rwBwMUdQwcRR2C5W+hm8tilAL0jOj1tuAlAQQ2nEA2F3xN8B+TzYSwF1ZR+2m0wZurrhILcWQ7PUlbK/Lg1mrxQOwx7EX5c8tdtNth9hNkgQtG69PKTkPzKZF1z232TYHxZRox+jFzVEefH8dxVQ1H9nujnKijqTS+GJt3a43dA65uw/zBnsx5XuY5VRG8LqdRUETY4/iPIPrzHd4A8yXjbVKsFg5sHiMIuo0emivajbSSeNAaVjtZzxs1vunetYS8pLlQaI6KnkB+N+XpoQT7/NGs70oeZMM+/x7kaCSSzHS5NnYTYdk7VNiDxLbcdx4PPVoisZ6Pw/xEzefEyC2V3k8r0t/mIe1NB/e04oTRQlzOaAOpOZk+orX9+4j1kKuLyCtBPnvfop402wgrW9pfCaDDvJJB+mfvoe8uIBd71H+7m8A//svZcF873XeC94oQ8gzgVbroBcPtXKXFjHx3kUPzgXpDQDctTspy4osVy2XpDkQ5wceUh2Sl4ODnueV5jnMNubvQktd9PQMsdw0bh5xRFTjmJgX456JPcvZ2z4hdvhujMfNWxsO4maXqZ+U3jRW8UEPSsTIlQj+M3lQTq8Mir8iDvIwo0yHz+nQI7Yo4W6msIsN6lEXnX/0E8ibG5i3tySAdNsor3pI/vk7ROkNb9qfvoVzDsls/oteKSdf573goe4T3svJs36yPKh5maJkPe+NCArX1N6TESUw9jkXRRzT//RAJcuP7aWqmn62FM1On6j73WYPsyYBXHz9rBLT4lGEI7bf5P0tzNXFs7FLWWr//oTYI8KEjdeurCrAJnoeKBpBp9U6DM5Mu605UN1AHmbAy+uQh0QRZwQPM/XRcnwKHeQEHZQBANopcUJFAfuVKrV985Hv23IFxBO0/vwWaLUQv3skzkcFY2X/A6f1tEschyH+AOeZ9O1UH/vqi9TrqDydCWWBabfZzSiVF+pNv1oJGUNegKksA/OHB7kiuGS423tIN+WiK0m7QzvlYS6yoS9vCr1RkrjRQs+L52P3i/iU2Ou6iV07P9A2pul0eFY4jP/mKuSAyylzmIyP8vCqBkibfCTLjnKS/Z55+bmHiqLKmxv6Nr28Qv3Xf42/4+0H8luXK06xV+rUfTmFnU5+Jcvnu66z3+EB8A3s6MKrKsiWEFuJSYaGtUBdNB0YPyIvqe8oXWoimqpmx6NL5lJ9McD6i9fofbuHtCaY/0Ybl3+4YZ/cdTH7d7/A4Jsctp/i9m/20L11GP/JCve/N8LoyxFmv52i97FG790eVTdBertBfTPGu//4U3z6d796PvYkbhCf3xe7ipLKjrozSFvIPr9Asua0eP+qh9ZqDDggyiqUwxYgQOtuC0kiVKMU//Dv/8/49/7D/zTkMftrBlf/3w3mP4rQuROMvhzh9m+kSDYIOZW/cQXjgLpj0b7dwy53ePvvv8Dr//4PEIsA3Q7iD9pirWvYj/e8qasaLs9hO23I128bCMUZXOcttZdcy+9f/kecWJZloK/5ncYDtWSXERildDVvA+n/TvKCBz89qCKKGiaQp8Qp71W8ZIaaeQVpOy9Nd/fIzoc6UHv1YagamOl1tB8dhdbkd8WOmraYp8Qe1IN1pzeDfnO+UNWvoIOvTzMkcWOQ7J1Duu0mj4cZAWm7fcin/OIG8d3qL+SEkZpAKKIS1qJ4NUbrJx+5mEUgy7XicSK4fhf2cUFOwC6Dmw7wD/7gv/5lLp0npfbOvKSR8CgV371wLnQ6pCwb6KrquxxeYUxuuXg9nBXGsPUYk1wtA+W8pi1OXv01GgAvriBJDPf5SwofpSkwHsBdT1h2JEnglbo31yr/MQ2OgE/FbpLk5Nh96xFQWO52rzetCkBdTHiz9rtUI7ucQl5e84WGfeDFFUy/e5SHmU7YTbqchnySj8ujnEysN62XNZmOUV2TFdZ6O+N7ut5AFktUv/MZpNehL5Q6gMg37yilPfuB4nfaZUwQ7JSCuouH0Fejtov81jhIT9Bc13tAsS9uBv3QZzdxzJ14n3F4pW1BWa11V2VLjpNVUuPsYgVMx6QCLgC7z5tDadoC2j3YQkVTywriF+MTsQNoxF2/J3Z0uzAJgW3EyXe46L2qgM4NjNekzHIesHsdtkbbbciGtMCQh2+Fqo673ees4w9zMgZIE2CvriV3D4jcFJiO4BI1l9jsIJMR4n/2NW9mXfCSZRxOHejWn8N13gtezb+k3+WHf2COZXodovF6XYK+9jmMc1o6RMRxOBpxGW9XqWz+oP/ilQxaCT/4HCw/rqYQb6N+NSX/1Vou/kSHKoMusFpDjIVxSuLOiiB5bfrdBhfzXbFD+9onxC7bXQCiBXyPt9FMqQ9vuu3GLOGAvWRSfYJtNnwNn8dm05BhbAQZdHl2KcqQkxQFTNncGKav6skXE5gPd0EzHtZCPn0BZPTDFX1aml3WOAmeyXXeC15oBGb8bqo7oJQlF6EKJJE32mmGNnnBYYx2QrzOjJdu9tLQUjtasqsIqVGxUzy4UEaYrGDdHceNCJIFMTYvrgI2RnZ7fr3eciGomOhTsUtZNoJK3xO76Spe35M0vHqv4mIE4M2vmHuaL1MXUqoKJivgFAvv87B6LpEvXsO8f6BCGXCUU4BWJ3Hzu+MY8u4joQ6TEdztA+zlFPhwB9PtEGZwdYH6wy2sPlXNGR1az7yGR1AEoJGB4YesEtKSF9rKU8iuLwuihpnE2l1tZ/TQGOC7PdrFYDIiTnw8aEb/Kn8n3XYYHtl+D7gYw3Tb/BBnC8Jkhz2Y0ZBSdONBAwt4JnbT6Zwcu6w3TexFEZ4EptehdMhkyH64b22q1WSw3enqZPYgD7ckdsbsiyYf4DinvubVoj+UqIKaVU8sPC5gL6fk415MAGNghwO4+0e43/sxBVbzHO5m+itaQH/xOu8FrwoBxisFCLsXwbSglYT6ODD0gSBeSot2WkOSzOAHS44LUplJWKyB0QBmsSYPtcsDo+l2CCFW7RspS7KMum2Cwtpt4HpKtv9QYca1Q/3FC8pJPxe7B8CdEDuNwcqGpFLVLK1qR9Wzfc4DtrVK4K6If69rvsZqi+o3Xx/lYa8vgziVzwfj4VFOACDDXmBxmUGPPfeKjtxmPOShNctoHDFf8GPrtBEv9oFIbj8+/rJXzpPXeS/4g0v2+zAiN1alqGMa9EqHkhviD1vWcBcvJ467AAAgAElEQVRtUaUXNb2gYA2HNTHJDX63NO2U9WY75YFvRTk92e35OJ+vCP0FYG4f2Zp85OheIqIwzWxJCG1ZIf7mrgGpPRE7LedPjN3LaLSUwWQN24RFEWACRq01JSNq0ay3DWAtiZF8fX+UR/75Jaeq622TTxwd5ST7DGa2JDOqrHj4fJhB9hnqD7dc7HnBia0vueA7TI5KbG1a6pzLdeYLXnjg0RJDFIYLoOGaijSquQeEhuB2vcvYrjNk78hmy9dzPLS6UZ+7s0r3mYO2pHtzRTBVOw2KY/LyguYAn7/i76ol4G9ks+UhFGiUBJ6IPdTnJ8QuuyzELuoxK0kcnE1kNOCNORow/iRmHx2gbaU1qD/eHuWRzHaUHFGKn/v8FVw7PsopMLZEmJdKm5huF9Hrl+z69Lo8B0zHMNMx4+2kwSgOxhDicCbXeS94QegCwEnTpgOCBotoaSBFw74PCyNhyYPlhvWzJyoDPNANerDLDUnSrYQfal1Tbu9qClMq6QOAtKkiYGdr1JdD2G8/NmRsa7VO7sLUDvWbK5ZMz8RuOp2TY/cGB0FFLG1x9O8dQFICwljmsNwxeRH03E1Zwfxrv3mUR91LUb6gyKnPx27yo5zqFxOWO9MRuQSR5eKOLGHF6n9lH5aQ5Zrf+/6WnSYPhLsh3PhcrvNe8L4vXVbBnDcs2APIrIkj9TEl6tEMB+q0UQXILqxpBll+V91l7PKk9HAyZdUYCpSVivlzGmvnm9DStAuKC0m3DddVksdqg/LVFChKVH0KtD4XO0uaE2P3JBEdtqGuUV8O9QnQgcmro/hR11QtLisuwiwnm+sgj+SrWySzHaSVhHyMfn/IqaxZ3iWEMEsrgckUhvyJ7vBXF3wadDuQ2MK8uoF8855xxxSNrV9f/lKXzXPXeS94rWcDYs+P/VtJ6ElLffDv/uCnKER4Fzxx4fFvBj2C0jzEFmArcLvnDqna8KS8FZCbKR/pw57aMjZkBrPeUpRU5bHjn34A4gjpn98S8vBc7HF8cuy+A2MG6olqLaL5lgC29Y6LfUvFAO/oEXLYZZAbdlIO85B+F2a5adhYGsNhTvaWednZml0hVT5zY3UAiVRCRA0W5E+/5NOk0w7MMVM72OUPffjTrqrm0ChTlF/tAFcE92ejUFnTTjkdVb0Z43HjXhGsOFhUu4qdDy/Tl3GXlzgG7mfsRbcS4HEOKSuY9QYuL2BmC9IEez0qE5QV3fp6HcBGcLMFLeC1D+9VvZ6K3e/Yp8QuXodypyJL3S5ffzxkbuut0vSWR3qS2O0glrJ8PNBmIQ/ZbIF2G+7hkT35smLtn+UhJ1jLvG4uOVjzqNKs5L9XVcDKS54jurqkYoIO+tx8QTZU/3xKmvNe8MY0JmSqzBUmjpGX22Bfm4MYkqxpaclDqvEgMCV+kP2ku7Q1MIOGqHyogShFSQ+ku0fWzOqh6uUvEMewCcfuIo6L5kGVBKLG+/Wp2Enjy0+L3dgGs2+j4B8luz1piKo0bDzZZKPeq06IxHTCG1VcyMOOe3D3j4imE6XwcSnYSSfkBGM4e/AtytUG6HchX37LjaiugbtHiPJajSd8e36uUgCDytkZXOdd0gBcKMNBEAmCTithjPa4201Xo9cNbT9MR0T2aeljhoNQSpiIAv/UlVRBIdPwOll2WMiHO+DNC+6i3kP1wNw4lCMAW4aeALJR9YNnYieQ7MTYI/sXYkddh7xEbw4zHGhnKOUOHFmYwQB484LDtIM8ZLOFHfQbNxVrg0yfvySj8UJQDotjHoLVutOXZygL2PGItpW1A370GZzXtfxZws2v+DrvHT6iWVfQbNd61iSKfdFHp9SO1vKqpY44pvC/HgzD/w/c8gLdzyvs9rsUG/K7a6fDBb7dQ17fAKstuw+eUJ2prrwubADE50cRUfzBIPi7YxensIZTYu92D74uCT3oqvu3YmeCy8l+38jtOSG3dct5Ag7yMG3Kfrjf+owH2azQw3PU5OS5sCriatJWg70pSuJtRGCvLiCrNWSXwXbawNtbqp8B/HzOCIJ+3ju8c0QwthI+jr3OYZsYkWCXHlnFsERBXAjaogsliMJ1KWFRN6Kl/uZZb7njKqqRLtK1+ip5JS4VRdJd2VP2YE3wQAoDp332fOx6nRI7SykliHhf1X0WRFdNq8X4tyR4S6o6OXulJRYUXzrMQ8oSZjSE/dOvG5Ce6sr4nIy1kF2mbuJGpfPaMGmLdjadNuTHnwN1jeq3PoFkGcp/49cBAPUXL7D9G5+z6/Ti6pe9cp68znvBwwTNFdamWxIjsiJIQcCYIAMhalEDgLu0Lws8jkV9V03Le7zqZLJ2jYlYXTcoyihSqT3FpMwVL76kZY67e+BrKS/VzFdBMDUgG5+KXTsvp8TuIcEBJ2NZ4shuT0k75bVC5whmtW0ET/tdksV32VEest6wrOp1Qz5SVUc5SVWHckoyLnw/GXarNTAewn71EbLLEN+vgc9eo/X1I/DiCvGHOfp/8J435A/w4BMvxbwYED9iDgFRfpdUGQoRCTdBwIurbpJJWzCGGi+hRhUJViwSsX1XX40Qvb2HjAcwqy13+s4IJi/hhl1Y38K7GAN3j0QJ5gXk9Q0kMpSlHlKxQLKMNfxTsetNeFLsQBO7P7xORlyYU8bnXTp4QFaz4zRl96bTCph5n4fpdZsbzOfz4ppPEc3J3D5SqWC25mZQVnC9NgWnfu0N8M1H1D96Q9K2EuVluYbRcpET5D7LwDO5znuHN4YEY6BB8/mDn2vQhQFr7plDIoq10QVWUoGXBr591qc6FHF9mp25iyHsl++pPbki/MB0OzBfvYMkEeyKh1kZ9bmDJgmx6FHEaa0uHo+XN0nybOwATo7d/6wZ9LlzxzElNBS+DI0/CDoZ4m6CNeZX7+Bm8+M8vNtg2mry6aRHORn1kYVzzCuOYD4+slR6e8tW5J99w2HV44w3XUTmVbDAXG+At2fid4Nz3+EB4HGuEm7ay5ZCIbZ1aOeZyDa1s2JmoG1L00pCyw51DdnxyWBi/QC/5ZDIvN/x75QoIqs1S51+H/jqPVlCe3qwiu9O3M/YvwdgZwr+mi0bO/jvi13kpNgDJGGnE9U0ZQszVWx/2oJ5fw+3zyiFV9WK8dc2aL9PZ7/DPLZsO8p6E7ot8uW3fCr4crEoYNWeE7Ml4y4rdoOGyghbbXjTvbpBdT1EoihUMxqS85uXfA/OBD923gveWsDQGFiSGPZhHjDfXrDIeIKHKvr6Ry9irdXrulG+Xawa7cV2SvHQ8VDNBJJgZoZIjYHbUdNS81PR6wtyWwc9vp76Mkm3zZ/zLCYRYFk/Hbtf5CfEjiQmcrHXhaiNpPEk7n6XC74oeSM4x5arv5F823O54VDN61EOB9yR9YmEQY+6N/6p6XUwu23gcc7vV/CY7PcqQ6LlW10DyxXixQrONxKUvC5R9ENb8uTLs2U2FACVyYi7it8JPXPJt/vSFocx42FQy0WsT4HNjiXFas1OQ1E2Bl+RhTi6VhsdGHHBNBVfMC/4cEenjdpx59KFYdZbkipGQ/6Obvf52BM+TU6KXQ/SogdFo5rtgZRuDcRGkIzxY7/XYZUjvv4AxxPymM25mRRFk0+7fdTVEs1LPINru2NZ1WpRv/I2h3z6CvjyW/b0a0cNGnVbMZ1Ooz58Jtd5L3idtJphn7J1m13TL48i7kr7PHRhUG7JJFo1gxIoyyjskl7uznco/O5j2fNHXfP1FkuqbE3GDWYky+g03W4BWQG3WnOx9Pt8avz2F8Djmr14f+M8FbvOBE6K3cuHpClkuQG6bZYs/X7jDAgA4wFvhprtXFjDc4jvQHXbIQ8uziH7/6M+kBVEb262TU56SPewBwz7zGMyhFVpQNw+UgFhswUuxnAf7sjD3e2D92wwczuD67wPrQAXmx+xR+pUDR4KTV7qYjj4YJQbKkqQAMBHcZZzl8zzgGOBf3R32qGvL9sdF5pS2xBZOl6oZF6QyCtLvRnouSTDPqK7JbtGL6+1fHkm9iQ5PXaF2kqeB8gAKYI2YP2lYj3N+Csu9NEwOIADOMrDXl3weyfDkA/22VFOJj5oy9aOpRwAs8+R/fgl3PUEuJpoR4gqB6bTBq6msKNhkBGpfv3lL2u1fO913ju8mnzJZnukzeIPdeEABzSSGHnBdmA7Za3ZTlUslERu0+buGPrztTpQX4w5FfWv4UNYbYJcnuQFS53BAChY/5pKR+8iVBve7gnaMubZ2Gng606L3d8w7TblvKFQgLwgj1aE+pG37KF7cSbULnBkAxdA88BqzdLq4z0wGjIf9WvyOZl+j4f0OFY8Tkdbkx2k31Ag1RSEC0uHrDH34gqmruGuJ2xnliXi5floS573gjeWOxlAh7isITd7yxbEER/VWqN7+Q7uUH3CYickWMN3HHydrHgcAMCWNDyJI5h9DjcZwm73wVFakhhWfZhkuYYZ9oPJlyl1l9fDplvsuFP6Rf9dsavQ6F82dr+zw9qmj97rKMWwTZWwrIC7HAGVY1dlueaiH/SbPIoScjUFHhah9JLN9ignSeKg53MIx8CX3xJSMBo2psZxBHf/COtbrqKwhrJENfqhD3/aZcgHlULx6nnBjsQhZcxJo+pblPxQvCKZc+xpq547ydAFnbSLkh0HXahop2GKCAB2vaVS1yUPYW7YYW0cR5BPX/AA1+2wn+0cD5k+7Ei5oT+n2GWXhdhlv2+gAmXFJ0pZ8WkwHvCGHPZg7xdHOVS/+fooD3dDEwNcjpt8+r3jnBY0LAvllTdc/uw17MWUP5O2gJtLdsxevyBlsihQv5yyZKsdkm/Ph8R93jv8gXhRgMmGzgQPcihKgpr8xNUbAFQVZKe7DYpAOQvGwKLtun3OrkJVNXowaQuyXPH79zkZQ9/mED/syooGg/PxHhgOGghtUVLqbr19NnZa1GxOit2ztZrYJXBGTRwR4KZnEDMcQJSEDgBmuYIZDGAX3OV9Hvizr4Bejy3M9U59ZNXgzOe0WgNxChSFKizQ1xZvP5Lr22nDbXcwGW3vTY+tTdPrInp7D2954x7Px/LmvBe8VRy576b4HrI3DfBuFp444TEbxgCHIqZKt/NcT+N389mSylrtlMYBnjyR6+J6nMNcTsP3SNqCe38L8/KaVjbjASeeFamAxhqIq+H6bdhalYqfiF1aCX/2lNidmhJ4hYDxoHG4VstI1LXqvLPLhG6Xr6GYHOmkMI+LkIdV+07Z7inpMVsE2qLPyehNwINyzTLMg9YOh35Jix0kxecEJpm2R02vB5xJd/K8F7ySnw8htoiioAJgdMAj+33TeVGeaMCcGMsDme9D6+tCPVMlssCadbXs9k3v2hgOVLxzXpYDSrA29zPeNPczII65y7XJHZWihHl3R23JZ2LnzWxOi91G3GX9+zJbHqM/d7uG7rgnZka2Oy58dRX3rKiQh76+FBl35XbKp9RhTt4nqjjwqAIaPfui5E1WVXBvXsJ+xaFdoF+qulr9YgLc/YLXyonXeS94q5NJj0XRKV/44EXI3Pfydl59S4QaMZXu2NLIYsD3lB0HR7Ldcdo6XzRkau0A2d6IgLKPcx6OlwXsp6/ZS28lwHYHmY45tHE15PUL2McVf4+apD0Ze11zGHVC7NLvcnDmvy9tAVDJjUghvFkGXE613IoJPdBDbn0zhvlnP+VQSPOAyvyZdhqeJLLZMk6fk8oZmm5HocI7IInhLoYwuxxoq3rCTmDzktIliYXdlYRdXE0hkRrGncl13gte9Q1DdyOOG1c5gDXvasOduCyDjAWnk80OI1WlqEX9/37PKWCm4/rdnsjJn0FTynaHKC+426Ut1tsPc9bbK1UxuHuAmU64uO6pvBUGZq3Wk7GbNGWtfULsWG7oKQU0mpRKEeSfiV3BZscuSpYxv90OkuWI3t7DAZwE+zyyTEsROv/JahM2mJCTzgAapWE+UezjiucMfxCPLORxAVvXcJ+8oBXmeAhzPwOcIPJ2n2dwnc+t912X4r6hh00YqhhAydOUuEjZizfK4UxbwZDXtFqsgZOET4I+9RX9IznUvgAf26oobOJYe94UQjJpq6Hp+etizPKm12N5VDvU15OAOfd49qdi91rxp8QOa5rYgYYi2O8xr7QhqhuPP1K3P+RUMLY3V0d5kNGVNvOBizFzPshJdlRCkF6H+J5+T/UiiyCuFASfBj2YTgf2/T2fFIsVh1AA6h9/9gtaIH/567wXvFO/0kInqr1O6LQgZ+dFsvxop0OWUxRox5agrNbNdNSba6m2o9/Rje9pqyiSn16aKKJTxqDPn/ddFCdw4x53bE/4iCPY9Y6KvKO+xvXziR113cTuyxrv86Tqx1CpPi/hF9SH05TDI+2p+zxkyBhxOQ75SFUd5RRIJestW6Xa9zeDHssbACaj7ozZ7FjGeWFYL+vd7yL68gd48GmXEpYljoBcF63uwlLVjUY6WN6YXpddi2GfN0uqbBu/+yUJBzCqCmyKkuoHHuPu7Vv80KTuNkoFAPHtixVMksB+e6fDp4wLLC+AGQnUZrkJlpRPxj7QXfSU2Htd3oyHsW+Iazf7HOh0Ggpevxc046XdUuUD3cV/Jg8AhGf0urDf3tFXdbFpckriBpLhySXzJcswr66c5bAb5QcDlC7xZnGbDXE26x9UC067RIICLSIOMYLGujcxANiNsIaP38mQbcJCyREey+KVvbyalwjr1iQm4cP/XZaTHfS4oMTcaKjTzySwhhDp+H++ZEsP4EHy5jK070xWPB+73y1PiN3vrsZzZoUO5bLacFfXc4cUZVBKgzXMIcuZQ+C0Mg/TadNE4WEW8jEfH49ykjVfH3HMQ3mWE2k6nRANqtNhs9GOjgjkNz9nKTXsA2nKpsAP+vAnXt5te7FuTLy0PwygERzybKGqYncgy8KjGaohT83GMhyCsaSFOrJcCdQESHkJCjMacFwPcCcb9oDIov7iBdxk0DCbipIdm9qRSjekjnrgqD4Ru6zWJ8cejAlqEjuw3FAhrNvhAGjQ0xq7y79rc+5gRoPGbWS5OcpDkpgT3BdXIR/35uooJ55x1ElwOKBP1JpTalmvm85OKwH2GeopNSrdeAA8zJvP6GCA+Ku+zrukUQKD8YpXplEcQF2HHdAYc2AFc4Db8IdGgPrplQKnxgOWAt4msmi8WmWzO/pgTTyEbDcwyiyy//QnzYGy1w2iTpLlMFWXT53xoJG6eyJ277B3Uuyq+usNIALcN8spm73dc9H7Ydd8ATG20dIxJJfIusnD19zyOIe5p9a+nS+Pcyro7WSA8BTCy2u4dgKz3+vurcpwnTbs2zvmBZAT6xzyFwOk//SbX8z6+Ctc573gAX2jpVGz8pO8um4oZKXSyDyYSmG2vi4W5wIzygx6XOAiDbWtTV6neKaQl+GejHh4HZCIbPY5D7KXE0g3Bd7dsXXYJtFaHhdcpAfKZk/F7tWCT4q9diF2X97Ibs9SwcMBfMzdNqQ9IpF6s21gymV1lAcix7idQCLLfICjnMJrA3z9OAY+3AFFATMe8ayzpey23D4Qcjxfwvo5hBOktw8/7PAnX0IWEmzUwF09Nhvgwcv3qj0kVn8ONiLzSOHDgZDgJ5TiYGpNf7niQUynkqLjelltAiwB641iQwRmsabr3aCP+u0HRG9ecuHmPEDKas3uSJY9GbtUNRGFp8SeF83ZQH2cEPGgLLUD6hwoD3b/7Q5otyngtFyRmJ4k3IkP80hiyOMcSFO6Eu73MMNByEnENTck0Bg71DU16T/c8f1RyxuZzYGbKx5cs4LGaf0+pf/O5DrvGt5Sj9FoD9h4IkOWceeLY0AZPabfbaTwbNTIUEP5lU5U2qIV3EOk3+VhstWiNbvXllR3EOMxNmmLJUVVUeu8LCiotN4gulBKW1lSkFQ1bUy/+2zsEHd67KN+iB3B+SRuZgTWMH5fEnkFhULPAHkehkQ+D2Mty7eDfFBWRzmZXjfod3p+cZhHqH4mihL141y5uDHk7Qd2pFTyTzYb3vxncp33Du+U/FCzR43IBs8jgDufn6CGlpuXmtaDou8wAAAWK472VTMF6nItLuJu60klgwF3MzUz8MAuE8eQizHw/g7y4Y50v7zgjlpW3DGzjB/0as1y4onYoeKpJ8WeqfmZj0GnpNjtm7H9XuEFAJC0+PtbSThQYjIk5t+/hp/kFiSdyIc7UhPVARDealJvAgA8kH8gKMbsMrZLOwL7yUvUf/4VoqvLcEMB4GaRtM6KxH3eO/x3Xb6tqHVhgAIUpfI7SV4O3wewrPCX/zC8HqQ38z10wfPQhe1O69QG6mf2OXf6oQqnhkXWCC3BS9P9vGL35Y4Sqz1BO+ShpZHp95sY/P+9tuPdw1EeUA8r70Ti8znKSal+h/KBSGLY6QTu7oGDs/0eZrFGdH2leJ4JSSotEnTcm+umBD2D67x3eGsbLXcFYwVicyuGFJSHxj6DRI7j7bxskIh+jO/11QGIl6/Tg6LpczTuJn3U/RTJO2K3DVK4Nzcw336EGQ7gRj1gR4dr88lLoiJrBzsesaVZeS3IiICz+yX/7onYEUeNGOz3xa67tKiCMJSUDt8SBSgodTfn2UPlR6iOkABFgerHnyJ5Pw95yPUEJitRD9qww57a8LQBkSanfQZ0u3DTAeyC1kC4nMK9/aCma0Nq79Q13GQI3D/AXF8ACouW1RrRQvFCZ3L9q7HD64IKj3od3ARNGhFgSGqb7PdNO7PfC+N371saeKyRwgli1Vvf5Ui+vOUjucW2n9nsgemYte6GwqTS69D9AiAUIMuDbaW7HNGd4+P8e2MHcHrsZdXoTNZ1gzcX7bCstzA7rdNrB4wGwc0EIsB0jHixP8rDztYweYHodhHyEW11+pyordmDvZvzJmunMJsdUZdJK5RpMhmqUtkAkkSoX04hD7Om3XpGHk/nvcP7LoY3FvbdgiynFEReAJsdD4TejEDtFoOcXdzUzF501LQSSnYkCXfdbhuyWDW7vhceTWK4dgtWpMHSzJcNvrxWUoS3j1FQlxQlsfDOPRk7EZWnxS5ZTiFWb+iw15vdu4a0U8YvrmFD6W6NJIHrd2C/fEv4sM/D1/B+Cr3eAuMhv9acsC8Jk1Cijez2BJltdpDtFibPSWTZq4pbO4VR4ra8vIZLYiImzwgted4LHlBbmHYDVfXucB5Lozu18UYF2lkICsEF5e1oVemCFrzxu6daQGI6pqjq6qDOnS0ReWIEqC4QfEmVOYVOJ1jSy6jPhe815J+JPaAmT4jdtNOm41LRQ4m7PuOVdgvoqPmxWl2a7Z6T0DRBNFtBkhb775pHwLqPhjz85gWHUUnS5KTKCkYhEqadQu4euci9hqUIb9KihBkNSHDv94APd7S0BJqn2xlcZ7/gvd4JIgsUze7J3SvWDkhE1KGyb0yrFRaZ8ewcVeCS3R7GH0p1NzNlu1EYqBT8tN2FDxKRqieIDpFSul0HJS6/m+oi9OCrZ2MHGtjD98Uu0sTe7TTEap3SBl7ufg9JWsB6Q4k7zU+8Cpla3kAkQJWRJoQSVJX6XVUhpyDVcVgCJjEZYP0OJG2pIfIucA3gwW3WkBfc6RDctv/VrJ+fvc67hvc7iMeieIFR55r6VEFhmIwauMGg25RDfiF7yWZvNBw3P+/htZLpAOfwivkBh8VTVbRY9whHhe7KctUQqwe9UKs/GXunfXLsHmMPaxirFztVeqOo6jCSFuO3pvkepQ4ysCYPb4aMu8cmn9HwKCeTJBRvWhJZKdttsOGUbjucJ6rf+gTV7/4GkOVY/ttfoB51UF+Pkf/eb2D3r39yVuAxc052JD97jVrX8vuDv930o5W0Eepc32L0GHFxHHI4aSaPh655h5dOW5G0mp/1rco8Z1mhu2pwnM4y1MsVotGQP+/l9DqdJobIQryKb5b/fGI3tpkl+N6+F6lS/i2soetgi5j4gK70LVfgKA9P8SP8gQAvt89gR4MmJ08M9y3TouTUttVqXEJ6XchsDrfPEL16wfjKKkAkOCm3+Aer/+kXvVwOr38iIv/Wd/3DeZc0AqL/1BFDdkQQeslp0+00Qxu/i1QV0GkBWfPwCnqShyVQr0O9mN2ej+2SjCdUFUWUQDiCnU64QNopTLeDuN0m83+jFjlpytfNC8jVhLDeiwlksXo29qBQfHLsnaBb4x22AcDUDSANNwP6q04n/JkD+LTokM3nQZlt5frGRJPal9eEDmtOVE2L1K5TYQpVBXc1DerFECFceNAB7uZUHLudIfudN3CJQefLOeNY/RzXxb/Edd4LPqJgp+m2eUiN9PCZFywb8oIL1ztQe8vzvICMBjyEaW88YFhEdJGoXXza4gfqHORyRDNeL7w6HjbAsFbCw1unjfpiQJkL1a2BUVOx2wfIxQSSxoxvu3869qJUZa4TYveDKv+U8pqVelAN5gi+7+5lRQr1he13mNvD8igPUYlu10th4wjl9QDJrYScTL/HA721fN+8u0lMHHz5coL4fgXjBHa2RvXJFewf/gvgxTXSP/4WAAK98Vyu817wdc1F4EWOagesN/wQtzt2QfwOr2AqAA2lzz/KdzQHkKUyb9op8LBgGTAaNF2UmqweWVDhV9YbIB6R/NxuBUSiXWzJZspycjpVisOop6nZ1KTnRdHTsdc1b7wTYgfQlECRZeythGYEqk1vHFuS4dyjcACpa6oLPCzgsqzJI45gSgdkBWxZwZQVkrs1/Z58TuoZayydRXw3yry7ByKL6A//nG59SleM7ywcoPo0NF0O548zuc57wWtLjnVtqUL92sJrqcuG53fWNXfQJGZZIcLF1W7xa30sBxy54sddK4Gdr4j6u5+REdTrsY5vt2lklra4y202rKcPMCxmn1M46VASWm0vgznwd8Qe5OtOiV35qqEk69By0vS6vMGyIvipmk4neK/CC6nOl8B0DDNzIY9moWaUG4kssfVeja3VUiJ4M0/o/QsAACAASURBVDQy6lxuul3IbqfnFH5tumy32qtL5mkM3P0jW6/rM1FhwrkveM+jLJXuVtXhAOjLBBFp7NqNIUAsiprdybff9OeN2laavdbl724hF+PQo/Za8MGk1y8+DYkHT62fe13W5gA7F8MezGLdTG877SdjR1lA0vS02NMWW51e8gMIkAvpxOTZpmk4L5h22rRcfSfo7iGQMwCwZPOlhkdyqkxeyKnXZSx6RqnfXMF+c8uvby7hrAViC2wzCrheDGH2BepRB/G398CvvYHUAhn3gDPhcZ9/W7J2zWJVfqXpdQO/M9SIliQHXF8EriWcipR6hzwlaQPg9xfqwq3eq+x1Z3xdlbEwo2HzM9eX3K1V0diNGtgv4gi4I0WvuhkzrmdiN9PJybEHzcfD2DV+oFn8Vg/HojF6EJcZDRn7QR5GNfHl5XXIx/S6RznJRs2YtR1qv3xPGuBuD7y/hX1cEH5hDOoPH2He3cOsNojfz0juvpvDLjewX9/+ghfK6dd5L/jIBty16RJKEPxZASCOUV+NiWvvcnjkmUvUXNQbI6JMBkptq/U6oTtxiORzF0N+v+qpsC9fhSmtWW0ID/YO1bczPiV6HchiBXl1CcQx4oc1ZNR/PvayOjn2wIzyAy5jIKl6wG73wZFPnOPPXE2bmn/QYw5JfJxHwpvP7rKjcuwwJ9PtstukWHcYC/vVB964A6IrjRNguUZ0eQFcTXiuUWiylCXcpH9WDiDnXdJ4X1StgwHwsW1MULO17++1ZFGR0qLko78oiHEXHmwD7U6t6k1kIZsGP46ygFnzoIndwViwTeZ9mIjOlmHKiaKkonBMV2/zSHaRLFfAOgJ0p/6u2AGcHHsYHO2zQPo225qlVdLiQTPPCXVup8B2S1mNSFGc3TYxMcqU8i4o1MxpxJ3qb9/zKeE1PSu2fmWzJT1wuebXqzVdTu4eWZI5ASJDEFpdw+U5rNInzVfvgx/uOVznveDtQbkQR2wPblQASFt7pp0231dWgehgFN9tDsuAuubuJWzjuS/eIPrwwBug3W6I3FnewI63O8ibG5gPDywLdlmAFKN2nIzOV/R5nW84WHpx1Uh6PBW77wydErtXNvDYoS3dQdyLC9iiIv7HGJh+3NTjmoPrtil/fXMJuRw1ecwW7Md7XA0AfPYC5nYRcsLVBC62sKsDF0F1MpT3t/w5a2iFuSYV0/RaiIYD3eXBDeCMjM3Oe8E7dYX2Oz0QxuumrsPOG5z0uh1CeKtaW4hqKS+NjY0RYYuwqmDzEjLoEU34/h4y7hP3PWB7UcYDjtJzWtSIMQRqqSCRaXsQV8TF3u/A7FQPxvNPn4q9KJqx//fFrvEakYai105hcxo9SIca7ui2YfICbkDdd+yyoPOIsuKTTvPAZBS6J9JKmM+4zzg1J5QV7LoIujVhltFrw45HgW9salVGLktI6SiT/fKaB/6qBpISOBOJ+PNe8NZypxr2deFXMJ1egzbUAyUd7ZT40G0TfehpfFHUeBx5K8Y4BuKUdulZjmifc+e/nXFxAVw8611wwxNjqRNvTFiskimGZqyO275nDgTLxidjb9E0+JTYAUVqqpMf4ohPN0AVybiby90DUZHzJSEB3XZQBEaasjOkeUiWAZMxldEe5+ybf3yE1HXIyXgRVX06yqjPM8O/+Br+5GMiS/GmyKKeLxHfXLEUU5tLt8/OiuJ33gteQOYOEKCxst03TnZqnWjStMGO1AcDGG27efCS1E5Vg3PugAUd8wzAXXK5Yu992Oe/bXeNWXCnwbMHkVOF2AKAWyxhXlDICK0EZpc9H3us+jSnxK5O3qbTYW+83wteVCR1dxWyq4hO772k3SbxUEUnIQ/E1KmBoZNhMGZWbUh/ucWS/fZuG3h/R+J5pw0zGqIe9YCyhl1yyGX/+jXk3R1LMu062fWON90PaMlTLv3wszzAZU2sTtYKeqLFo6pydaiGG6wpvT6KNTw46v89dU6Kklov3kkkTTman6vuYjsNC8GTp73isGSZ+jwR3WjHI9bF+5z1rXPPxt7YU35/7EHEtVS3bcWnS1GyHKl0WKSED3hMPRCkPUy/+xfyMB7Y5hXNtEz0OUmWs3TJMh1GJXCXI8gnLwAAdr2jPrwiUc0uh/vkBZ3+ZnOY+Qr1i0lz5jmD6+x3+IAZVzXbAIIyyg31f5/l/LOXh9srKUPFiEy7DaOa5sFG0ho+jgFqsBgiHT1wyu/MXoICQEPw3mspoiq6ABB8XNtpwMU8GTvQsJi+J3YY2wDAQsdmzz8/zDn19eTu3b6BTud5Y1ycZUdQYY+I9DdAwP532o0ycKtFd5F2uxlkffmO5WGnzdp9TwVkKUoYDBHVTl0IY8igh+jt/fE55ld8nfeCj2jGa4YD1qtVRUx5qMMjPsa9MW7aUq1I9pgliXXnboVSIbhKt1sweQl3NYXVw5sb9WD2BUkNfe5yRp3+6mEH0WwDN1CWUq20v6QXLDBFmU6mrCCthLCEp2IHmhbj98QukQUy7eOrLqQbdRHdLWk84GWz+1QZri4HiNa59s8j5qNamD4PSWK9UWrIdABTK63wMKfa0cQMADopoQezBc8weo4xkxGw3sDlOczjDPbFNdztPV/r7gFyfRl4AudwnfeCF+0lexFUr1ee5/zzoRpB7YDtLtwAqFmbI2a3obGKQXPgiyOY20eiJeOYzhba9zbLNSeRxgJlgWhPrRrrD6DdDl34+j12bh7mcJMBbEUDM1OU7Ok/Ebvs9w3P9XtiN/smdtlsYdKUN2k7heumsIp/McsNUNeIZ4twc0tNPRtcT4F3H0MehERXPHuoXSWMOcrJZEXovZPQUgXlMdPvoboewf7JVzBpC7bThnt1BXl/D3tzFdhSMl+x2/Xhl7pynrzOe8F7ooR+GBAh8OvwgOVH7t6cV3HtptdtbNiHA5WgPuBWdtoETMXq3eqVhEUabDzAGjfxRgNKuctzohBbCsXV17XrHXd3X248E7vxZccpsWc5jDT4c6/4JUkMu8tDfe19qsKNJBJsMPHu43Ee+lSwVxeQhWq+a0njc5LrKV3KrSVsWfFBbtSDuZ0hmlFO23Q6kH4H0YeHZq4wGfHmGfSCK/k5XOe94I+oaq6RuPAGBNorRitRJ+qUO6LHyXiyc1XTBGC9OzYPW2/hdnuWFJ4YosCwgOH2Qkh7PSDWypLKVZDU80TV50haXgk4hfgu5XfF7mM4IfaAozmMvaqA9bbRnHd6Bin9E6UFr2IAqwSRwzyqijfA/SMPqXkOo4vU5yQf79kO3amhWp7z7NJKGjnvywmwy+DaLUTGIPud10j/4CuUP/4EsS8l786kCY9zX/CO5GWobYxf3LLbh6EMANahWm9z19Xvs4aDpiyHWVTHND/VgvcDFWlTQvqIhaTwATgH9HtsK15NiHmpKjKP9jkHTvMVEFm4V1ec3mb587HXKtJ0Suzeqh4KFKtdaHlKEhNaoJY6qGvClbXe9zcy+l3+WfOgM8eW0F7t8UuWH+eUl4AywurpENFCteGNIZZGBLJg6RfdEcuf/j8/hel20Hq/IMHkYUaDtDOxrTzvtqTXeem2m8WqFizB2ABoFlcrCWWJrLcNCvK7Hqm++2Ite/Br7YgcEKSR5ZBuG+5qzJ1NwWbucoTs936NnqbOwcxXbDO+uEJ0N6fRmDp1PxV7IHefEvsBiUK8LB/AJ4LKgWC9DfJ2Mh0RXKa/V7p8Whzm4YZdtl17nZCPvLg4zmm95RMjL5jXas2dfcZB0+pvvmHJ8tlL7P/aK4LWXl6h+OKah/tWArx+gfLF6Oe/Nv6K13nv8ABrxKJsuJfaTgS0bZYm8MuZWonthlmvwxlR7RcZD4grmYxYay9XnNau9kFjPfgobXeQV1cwuxzmwyMf51qu2I+PaO/YvZAs42vMizCAwe0DMOg/H7u6gZ8Ue+2I0vx435xfRIIcNfFEpsHqLNQYTU2CIYLq1ZTmYl48aZspl3UHm+XMxys8aE7GOZqsVRWFVrtdPsU+3gHTEQb/8E84iNrukP6ETwI3XyD+OuLEuJXADAdI9gdnp1/xdf4LXqQ5THkTADUYkKIABl11mC6O8OVSFECqknlAMD3AaEh3bGMgn72Csxb2/T3cuA+TlTzYRhwQmdmav7/bZnmQtjiKb6e0t2klMK0W3KADkyaouy3YdYb69QXi97PnY9/tgMnwLx079lo7t1PIZ69Qjtto/ZTxW68fYy0HT5WCu/Y5oi9VIMpza2vHG2IyVDxOi4ZmBzlBBKaTQpIIyFT5YbaAvbmiJ2ynzYPxxRjY7eEuxrBxTCLMbAm32QYn83O5znvBH9bc2pEJ43nfB1Z63s8KmprSqO9SCpNpT9o5lgtqBGbeP8CMB2yzfXzkr3RCyxs1EsDlRO1hIrKOnOhgKAqW7FZrZYshTFmRAFGWMFH6ZOym0+FE95TYs6KJvUuLe1NWMO8fENlrfv1RmVW54yFc1YHRSoiU/PJbXfDMI9APXc2a3JdNBzlRtSAOFEcSuNnhcat1Y01fVjCqeICq4jDsYgxstjBJwoV/Jtf53HpPXJ5uJuplRMxJ3TCDAO6KHgPiSGCWqmJN/LhQkFbegJg8hlscTccMJTOMthypNkbpCny8547q4QTLFQ+NhxLQ3mzgXrsRavfyXOwhllNiL4rm+/MGuAZxiO4XAVkpuz1Llo26hPv/Pt7TbPggD763O3ULZz5HMN5cb8q01agmHAzNqG7cBa6mkHcf4QbU53QXY2A8hOulMIqYPCfw2HkveEuBIbSSYA7mfZnC4TKmHWMw0a2qQG8Tr+XodRFFiO7rpNzRpmN+v2o8wijR21p2cNRChi4YNAsIQxg9dJpulybDm21wx6BmTfps7OKJHafE7mUyVJ7DxBEBWtMxobuet9ppK1G9RbHTLqEDJkmA6+lRHsjzRkFB8wmuIz6nomBevkPUaQfTYjscEpZx9wiTpjB5Tbrkx3vgcY7ow4xnkNo12jpncJ13SQMTSAwmL+BuxojeP3LK2eEhM4ii+m6Gr0/7HUjSh3lcNRj2dkpxz7ZqsxzivOMIsNrZKKvGRylJwsDJePqdN+W9mbIUGvb5yPcH08spY0uejt3sbCOH/T2xS3cAk5WcmM6WDUVRdedl2Of70KcRGoaN5qPp9xjb3Yw7ts8DnQBlOBpmffYy5GSqmgf1XcZ6XzFJSPXcstObfpdBImpJui/ewG72bJcWJSEcZfUDHv6kyzkgL2C1exL5w+ZuH/ReAlbbucBkQtpibS/CRbJYs3xRjXWT647zsOC/e5K1t3O3tiFqTwcw7+4CjBcAwWVJDHtL0ja8tuPFpMG/qIbLk7F736QTYrcZxVONMZDD90f1acxq0yzcvODre7iwExoarKiz4/Oob8aI7lyAXEgSA932cU5JwkNnkrBjk7NrhLG6heiNUr2+gM0r4NUNJGFekkQob/pIv3oMT8NzuM67pPG48IPHOYBGPto51q0AvEepqKovtRMpGBqUBeqa/77ZcnFdT1na+A9QmT7S7/J3JXFT5viDoIf9el2awYAAKxvB9dr6YcfHyr/fEbusNyfH7s8frKF1Uusc8TGthPECtJ70NjXaDvUEcXd7f5RHdLtQLfsi5GMCBVFzSluAjSjMZC0VFwZ9HnJ3CmdQm07z5TuItYiWe9SjDux6j9a382ZifSbXee/w0J5wmoZF4Bd6IH+MBqw/Ox39N9v0ur3zhCda6KJBpGYFD3PCWv0I3DOJVJcGIjDv7gkd9u7Ugz6no9+858FtMICJ2Aa033wgMOztnD6mz8Quccy++wmxQ1TqW00dEMdsn+4z7r4liIv5+BgQmEG//nEOYyzQU49Xb5Oj7CkyrbqQb94Db15Cbu9DTkjZepXZgnnpe2KzHPV8QfHU1Rrmcsqb8faRN9OHGrWeERDZoHBwDtd57/AKpPJ/5qKMudj9ofUAA2PiuOnc6DRTklglJjw734auA5y+vreb9IfhuiFXG3+YnI5DqxDWwA4HjfqZNweYjDhlffMyiBo9GXtRnhx7Aye2jUGbdk+ko5Q/awNePix2zcnEEaWuD/IAwBZot9vko+rDISfV1YFi282gH2p4+/kn+hq8+eS3vmCIowFwcwn76WuYT14qx+BM6E449wVvjLYBvXJYFdj7XhIaAFlChth531rztozeO0mUjwpruGMaA4z6ZBllGZGKs7nay9C8QLIskBdICLHA/Zwc1bIk2Gq9buREoohlwQd1s34udq8ucErsfuFZQ9KIMfzeUZ+dkM2WCMeWkknEaZelDKwwz4AKefh+vCfKK4TgMKfApzUmlDveSFne3/KGHahhQmyBPEf5cgLptTk0W+8gn75o8ElncJ19SWN6PdarVUVIwC4LMFs/qjdJEkbosE1ZwMUrRwq2Qdvcj74jy87NoAe5nsBuMu6aecFa+O6Rbb7XCjPYZQ2vdLXhbp6wvehbg+h31cnumdhrBY2dErt/L1qtRnVs0OPhuaWeVZEljCGKgDxhDgAxNoqMBBDygNDLSgq9wfo9SDtlm9Hn1O/whp9nQTnYdHhOMS+ugH1OUvd+j7p9jWhAFTfz4QH5X/sE5qaP9M8+kiP8/pe3Zp67znvBC32Rgp/pfElSRV3rZLIGHJQMUgU5ai9rwT5+1CgROJ1muhqyVVPjmmpdcv8YCBOm7AHWwHmz3m4CvL3lTqwc0EDsmC9DmWFilkRmVzYyHU/F7iG+f9nYPUHE68IDjfHbiL1x0+tSb7LXJfzZGMDWLEc0D69gEAwRipLY94OcTK/DgVmShFat17CU7ZYGzFUFE1nE/+RPgekE9o9/CnTaSP/xv4CIKPNr8ctbM99znfeCt7axmPcHP6eiSXUNRK3mkX2IM7cmaDkCCDhyqZo/IyGtzniseveC5UFeNKCq4YCAs6wA2i3ggQQLrxzmW3/y/7P3Zj2SJVea2Gd2d9/dY8ulsopFNtlqjTBPAwxakF71KAwgSH9BP0lvEiDMk36BZgABehA0GGBmMGpMq8khi6zKLTJW3/3uZnr4jtl1JzOinKNmlTfACzS6mBkZcY6HXbNj53xLVVG06fJl1+mQP38ydisa739s7IBYRgYddbGXAhjwcul4tUkCuytIwh5ksN99gJpOfB64uqA8x/694HzGS67EbtcbLvQmgBpF/J5CSbRblka6N+RdQGsSUMSjyn75Cnrvs8CJoAtOe8G7I90NZ1wf3GHJ92QyfFlgpD53F1Wn2AVABW3XUnQXSYdMXG+J/BMMvEpimPMxaX9hQADVq3MgryihV1as+0UESY2GMBEXoQI6HPtTsTs99yNit2FAPI1o4qg0oaFY3QgFUKAC5zOWTZG0Nd0LtMmBL15y8Uoe6tMDT6yrM5ZCJRlhFuhych2pNJb2YyFmxRk7VACHUL0U6u4R9qtX0GsOndTdI8vENKEM+Yk8p73gAXYcAs2BjfSQkWXdJLRpePS7EbkAojx7XwdAJbBdR7tzO9q+d5GT1qsqr3+OXz7A9tmTVw9z8WASaQ1Ft2xcC7Ph1Qjqu2vujJDx/HOxA56N9L2xh6EMyMTDaVd0JVIYUhczy4DHJa2A9j2YXFm1XrMV6vKoKyAifNle33bQ4P2cKsrqYbHiZTdNvE5PcHlBSPLjHGoVcqJbyQm0WMF8eQWV11Cf7rp5wwk8J7/gXR3p3ad14I9sGAOrA2qu5OJPutmxTSe4ECtsezSirZ5SJlrFMWwuJcZwwEXp5LcF56LPz2QQJBfcMIDdlix1yhI66nkCiVqugUEfSg84CLp9fDZ22zQ8wY6I3aEsVZryFEgTTkfdi5NQFqQzL+bk2DqZPteGFdFWFQawNuTd5WHu81FJwhLP5VRUwHJNjI7jC/Qz2PnSd4AAQEWETbe/+i2CQZ9oy1+JFqWcXKfynPaC1wrOKdthwOFESrVm6yuBWLiEwHIj3NC6k8DY7u0ugjJ0RzXOpjDvr6Evzw9ZR1EIFYVkBfUzyktPJlTnFREnuoCIwNI29xNOu97CfLpF8OLy2djdUOao2CdDYLH2C9zL8Z1NWY7IncLu+0pp3WlR9sbepMDloQyZWMrZaMou7O5MztgYNmDHZrHmC5XEsFfnwLtrAukmhERbrREM+pzGymUa4lWrxiPgmz/1YjnuOe0F7xxAmpbH+jbnjliIwFCYePy2iiIuDLkoeVrcfs3sHsHL4OYO+vKcVL5KpqvGQkH67OKSYVsD3D16NKHNacFur2+hhsr3q113RcuFUTkX78/E7g2Qj4l9se5i378P3NzRR0ok+LDdUlvSUmrb78B1jUDkr10e3r6nbcktOJvQZbttu5yMTJhd1whCmq9qQY4SEozhADaOoMcjKg9XNfKfnaH3N++5EajT2eFPfvCE38Ou+HowDHmBcrzWsgJWGw5cWsMyQBaW65AAYF3srOiTBOb+kYvP9bsd019pj0dBzUsp2lYWivK7r5WBD0CcjHIT2jh6NnanLXlM7J6coQPGJcYGSBL+mW+B0u/VCmZ+Pwe13BzkAYCUvLL0+TgjNZ+T6wo1oqZgDU2MFyu2NVcbdmRWa15MrWU3KC/R+/W9Vyszi+Xf88L4T39Oe8Fb41uAdk+Z119W3TApYl3qnePABWet9fbv7hcGrTjdbCl/4XAwiCLBn2uYyyn/fZ53qENhD6l+H04OQ2UpB0qZ2F/2OHZX5zMvP/dU7M7U7JjYbdP62H3f3MrXX0ypfRNF9JdV4ppiTedmEnNId5CHE5UVZw9b17B/9fVBThC2k4qlhTsc8P/2nExcm7b+xWuY+wfS/9oW7Rnx+naXdyKzJ/Cc9oIHd024RTkeCEOn9bY0cJqLaeLxI2pI8U7VI7HDSlmBi5kIi/a9RAai2O9m1tDhWt+JvnuWeatKOxrArjaov7ogD9Q9Aim2reElrygJKXAL8onY1Xh0fOzW+NhpeCa7fNMC13dcxE1D3L8l4cKBzVRPVJK324M82MM30KOhzyeYbw9yUnHcDb72RGFtUbDDkyb+tDCR9s6DAFBcplRDG484yDqR57RreB0Q13F5DoWK7TFLVz1UtR+8KGthNzvujLuqk6OWx/23mq9E5q7wnQ5UtRdLVVp1EtphAPvinBryTUuqX5Ig/NU7lg/ChkJeystTwvQz6KqG+eICwcOaE8anYge40x4TeyL8V1d2GcOYkriT8WgNTQ0c/CCOiKg0ppMYDEOfhzdxmwwpySFesSpNu5wAvkgXU1rmJDEQyazAWurFr7ZQcYT0d/ewl2eMI03Q+80cdtiDjQKoPAHu/uSr5ajntBe81Klqm3N3GfRh88JbN7o+N4BOb11rWLCtaDc7XwJQiJQuFcph1jc7djQAsv6XW9jZmAoBUUisyXYHWAvzszfQ24IxbXbA+ZQqBRtx/YsjqPs57GgAndes15+J3ZNNjondcVNF8ttGIU+F5ZrGysbAjummbXsplb6mI164xQRZX13ArtY+D6sU7PkU+kHG/udT2Ps5zJdXXU7rHd3IlaJxWhgArfTrxwOUL4dIi4qygXnBk+LuEfZiRu2aYZ+maX8mgPxxDyG5LEEcfNUa05UWSnlcCeJILGsaOmo4jfem6TilccQpZRDAjPuAsbS6sZaXO/dzBz0iIAd96G3hHTHcMa/f3nCnXO9gH+ZoX5+Tzvebb783du+0fUTs/hLpGFt14x1CiKsxjL+qoZYb1t2i2OtfdOAP8tDbnARzyUelyUFO0Ar2YQ71sCBH4GFBdeGSCsvRsmQ5lyVAlqKZ9KAGfVRXQ7Rfv4Dtp2guxwSPnchz2ju81rxIllICODyKtMGU00tXihewOOK/ccI/zlHDQQiiECovaNXuuhdFTTEnV6uKvrlvY44GsIs1EIWoX04RfVoQu7LZcXQeBZSSxgjBpzkQhQimE94xivLJ2LFYHR/7eMjpbV748sRJdCOWQZO1sOOMp1PTwJxP6e8kJ5iNI+78kkewJd5e1zJt3uz4gkdhl1NZQUmrUe0Kvnyy0zfDBMG92FPGlPbW/+6XsEmCOImZH4AwS72t5ik8p73gAS/PjJCIPbpWGBn2yAEVBl2bzwoORWsubJGVRkl0JNKEi8qVQnVDcSVXVxsDQFj8Dgs/HQFRiOh6TjxKP6MQ07ageFEUeuOAA7m+52LfI49/b+zGMJZ9c2JHaLmbw3x5yZfNfZ8goHpAGFKZeLmhccF+HqXY1Q97QmyJoX73gTY1kpPH/TuAnRLXlShEPYpRTc6R3O2gVzlMP4P6y69h4hAmChBEIfRyQ6nC+HSW2elE8rlHyAkQy3XoYK8nrQ/obDBCwnCOF8Z6CK1db6SscZYumeestpMBdFH5XroNA74E4yFr6NuHzu4xS2HOx1B1C5OECHINoGWtvzcka19Mob+97miFn4tdLonHxA5NUJrD3KgsJXhM0fc1eFj7Ms3xW1UciaReLoOyKWwv8XnYkRgnNy0QBjBJiHA6htVdTi5GJxRlnVOgMUi/WxBsllAhAUrx5xUNtLyUZka1heBx9QMtmO9/TnvBA1S4cjumhh8gKQB2uZbxt+nEfqLYY9Wt02BXyg9BAPJksSO9L1hsvFkxJqS5ASBrqd+DqWrgm3fAy0vY23uWFU2DMB9yytjLuAOLWx/CkDYv3xf7o4C4jogd1nS4maahScF0zNNmuyNmpaw4WHJ2kmkKPMxhBb+ubu95org8BoOOMG5a5jNfcpFLTrauoUYDj/xUYUigmfjQmsUS+uKM+eclBZ/OZ7DXK5ifvITOKV1ol6ez4E/70iqCSN6YzJpD1a49byM1GPCX5TTlRfzHDZNgbEeoluknqX0CHzZiDFaL215M0VI9GUMP+sByzcUk8tG+xhfeqVMdQBiIMGr2PbEHx8cOdLE725vtjrigfo+9byM2nrVMXTcb5qAVMfFVfZiHM2gDunzcbMPxgJ28oBODFU6sGQqvdTSkalqawCyWMGUJe0/ogn5/BzQtGC/e4AAAIABJREFU7W+EbXUKz2nv8FIzYjbhcb3ZHcAA1HBP8TbQbI85AVEnUbErgIkg/2p2P/b14B2YTGWZFyNSSexFjtzFUDUsr7Qok9ko7O4QTUOcTM3dl26AIY3Hnoq9xwviUbHvCtGzl9iHfUFWhl1rNsuIPd/mHGwFAV+Mfgasd5wQ7+UBELNvvrz0nk9wfFeXkwPU5QVPFSGl62+vYfLCK4qZzRZ6OKD2vPOAVQq4vefPWe9hgX7k57QXvLhaqLz0euoqjjyUFU3bAbEcc9+1+4ylTYuzuonoWuFKDFUq7mjSTbDDHjscYeAvjmbch76dy87XcVZtFAKPHW3NjgbsiQ96Xv4aefF87FpxIR0Rux30gO2KJUXNut17RvVSTmOFC4tAc/LqJrfrndhdykBK8lCDPsygB9UYn4/94grqw22XU91QFfhqBv2w4gXXWCjbh33zAmq5ZTnj0JtgG9SKSrHpJwjeCdb+RCDxp73g/TEf+B0UDrMedz6kmAw5HYyI8UZRAr3EX9ogjnXKWs/Yd96oWGv+3UbAU959WkHNl5SYW2+IPRkPaE+zEHTh+2sunjplGRMErH3FExX386djdyXREbF7ibtAy9CKrUwMJ7CBgrpdeiluD0XYNJ0hnOhP2v085ivg7oFlSVECZxPoe5G4djn1ib1Rnx74stwvgDHLL7UVsnvdyAm2Zcu1lwpv2EDPZW5xQnj4067hnQyHsPLhpC4CThRtoNlm3Dn1XdOZAMgvmvgS68ftqNh3t85IbDqGTRPKYVzNiHkZimnvi3PaTg76sKO+l65Qgz7UNoeeToTZJP5Ioz537NZ0GPUnYgdwfOxCQEFV86TqZ9R+fFxC1S1wPvFAN4wJ7nIerCpNiJAEDvIwVzPo2ZTwBMkHwGFO2xxqOuY9oWmJ8xFczOY/P4fKS5hBxhPi8gx2sUL1agQzyni6lDXa1+fA+HSEmE57h5dHiWSFVxgoCg5a+hnH/L2MtX5ZQRnb7ehF5cnRyklaBwHUYs1LWcRdWA/6sAD045rdjh51VXD3yO5NoIFcBi9adbLSbcuL6eUZsNxwADMeoJ30oZe7Z2P3nNZjYheog5P9dpxZpAndPty8YL4k+6km31YNB7xUipIBLeAlj+tbuuslSacOPB3z0ik5WaX4/aOQO7twaG0aY/ivv4MdD2mFI+rLajRAuCh9fnbY4/3gz6oFRz7GsBaOY+JIAEJuA+kYyPjcbndCbZOLlhA3YLuF5kgYAAjlrSqO8YuQu6MVo4PhgIjHuqZl5GZHIJlha1CNhrCrNTAdU78d8FhyvdzSGGG54b95JnYYke4+Ina7x2ri9xEX8YQipyqwsHXrtSPthi+T90o1FuZxToKGy8O9aADLxukY5uONv4iqbc4LctNAbeXldBfu1RZmtQbWG9II7x+gv/qCl/p3n3iKfvkK6v11x7s9ked0IvnsI0KmAmtVeclfsjD4UddUGhCVYWcf6cVEjaG6mBNlrSrKVQs6kRIUjcB4efFyxr8qDGGF8GzFbQQAzP0jgWIFydq2JXpQOde/MIQKAS8T+ETszhv2mNiV2OCoXuadQux643+mbcVmcrPhZyLmCn5X1zRV2M/jDz7pooK6OKNtpeREDnAi5hFOR0e6U7/4CfTNY6cesSJF0X5aQr+4BN5+JCzBsdNO5DntGl7BM+WVdGSIPSn8jmNXa98Ltj3iV+x6w2M2oDGYKgh2Uo5BNRnyF+ek5RyvU1CVnpn04sIrBQA8GfRkDOiAzB9AdulcFAdY/jhtm+diV3F8dOw2iXzsVji3yIQrG2i2K6OQtLtQFAQcC0vcvfVPvzrIAwBfSNMe5HOQk5xEALjI5aSEsbB/R5KqLWRoF0Vo33/kz1YKuDrv5iTB6SyzE9/hSYvzO3Ag+ix7LUbvLlHVbKM5x4u89BNWFcdcJG7nc55NZQXMxqw5q9rDCdRqywUYyeKxFuYnr4BQQy+2sLMhXfBcSdPPPATWDvvspBgLWzwTexQdHbtqTQc5ELMyezEDPtzQGC0Ooe+X9FVyU08BnCEkwrJ8NUbym8LnEVw/wgz7LOsCxa5LEnOndjnVjSeV20GPfzfswy5WnqitJJ/m4RHhqxdob+7ICShLBLNp1y49kee0F7yTpCgrkiecrqK47AHoJoYO7Wj3lAlc3RvoztKxnwGNYOn7PRIrtExdeynUUrxJt1su2oRepsH1PZqfXBHQdT9nW7MVIwWJzx/tblH3e0/H7mrpY2Jfk3Ct+pkvS/RmB/R7sJ/ueekuCuBT4XU0VVHye6UpbGsQ//tvgMnY52E3G2iHRXL+VxGdEX1OTpobAFYbloPrLZUJBn2iLtcllDEILs9hb+8RnM/QvjqD+rvf8fMeDTux2RN4TnvBO8UCpbijOH13gLt2XXsbFzeQsa6Vucs7XXgRKVVadzYvjtztfI9GQ9avowFPAFd7r7ewiyVweY7weu7peDYvYL5+Af3uvpPsqxvvDqKchuQTsdtdzhfiiNg9wdzVw5qGayqKvJOeGokzdhRycWrdeTaFAZAMDvKwouuDIIAaEyKg+j20Px91OTmEJFhdQimoMIS5J2E7zAv6wbatTL0pBa6/eU8lgzyHeZx3sh0n8Jz2goe4cLSmUwBwu+U+9qNpafl+t4ASBGSw2HAXjSMu4MlIRv8rsoHEac/h0NG0LG92RUcdrGWKOxl7heD2bIjg0xyqlyH87rYTQa1qfxq5FqKn3n0mdpVImXVE7OZsAr2jg6C73BK+62YKKfTjmvV8VROnrzVbsZqnm0qTgzxUFLFse5jTfl5enP2c3Mm372ACrWlRKdAGxA0A3jHan7xil8YR0uMY2lh+Fn/WljziUaCeSy8FDPwI3e3KTj7C1jWCOyuIwRp6l3PntGIfbywhBM4Vb73rfoGuO5N0stRWtBpVL6Nnq1xwYQyCd7es9cOAu54jkgQadpDxorneCpz36djdaXNM7Hqz8z15m+dcoK5MUIp6jokQSJxH1WrjObRq0PP28y6P+s05wju+QLaX8uX8dOdlsb2wa6B5L6kbfg5xBPWwEJXjgi+6EMah+XmoGS3uqasjKhEn8pz4ghfDXH9BknAd08lZ1AioiYuuByU7nzIWVite+mSXVUHgsfA2jaGKCu1sgODTHO2kj2Aj8tJKoZ0NYKMAwcMGZtKHXlNMtHw9RvL2Ubil4nNaN7zsDnpoXk0RfVo+HzvAnf6Y2JViieFODU04BHoh1OMSdjzkjuvar0qx9BL6XTvrQxcNVFX7PIK8RnM5QjjfoXw5QvL2EXhxQW0bl9N6y1PUWuLeqxrLf3yB8b/ZCbBtRDnxqoZNYwQPazT/2VfQuxpaBoMqL4ntOZEd/nT6RZ97HG9zl3OhlBXsNhdEXsmLqDMEc0Th9ZaXrcWK/2a+5KDIWP9v0TS0n29amGEGvREJikamqq4ubi1U3dIsDCC17WcvkVyvsPv5OQCgmfV9+dFeTiloqhV/5nOx5/nRsdv1tvu3eQG73qK5HLFbMxrwxTNG2pk0J7PSejT9FKpukb/qH+RRnWUIbwmcc/k4qW+XE0IaLiAKScquaoz/7TU7WkVBDfrNjr+Hu0fYJIb+29/ywjxfCrjvdC6swD+AHZ7dA0EGOtRhn7Q0r/HipPPqGnDQWXfhc0d0GBAL4hCK/Qx2voIuU74MTQv17pM3G7N1DT1fU0SpLCkm9LhA+KEBkgT9XQG72iC8vafu+maHQKh70d+97TRonopdcDPHxG6r2sfuNDWjb285gZ0MoW4eyHkV9TJnUWPrGuo3b6HSFL23OMgjfVzye21zWGPQ3xVcnKvHLqe6hloSv+OteiZDqEbMFYQYr5IY9v4RzayPaDfl3cKZNRQl3QY//Uhr6Pee017wDvTltFs8/5NycirkDuqc+VQklDYrFDkAKGTK52xurPU9ZhUQAqxaSznpL19SD74oO4uXJO5Ej0ZD2CyBDRRQt8B2BzUYdshHIV2bL19Cv/vkPVE/FzsgZcwRsSsnuuoYULUstLYF8hLm6gzq/TWQDj3/tJ0MECy33T0jCg/ygDGMpaphX54xn8clTwjJyU5HRFU6ppbYDHlaoqA7bV0DWYrgP/wWpq6hvvqCXIDbe768765/oAXz/c9plzRODMhabydj9y1wrPVSGjibdBdRAOZ8KhcpMfAyxhuMeQLEaEANFgBqPCLyMKTNDEsSTTGh8YBligyv9GrH6W0UwTzMPTjMCiQgWG69sfFTsat+dnTsDs7sTRKArpwZ9qB3BdlOAqUGAP3xjuWQVsxhvjjIw4o+pepnXT4y+HI54Z6XUzvpXmqVl56WaNebDoe03pBRBfB02eW8q5zQhRU49R3ejbVdv7yuvQmYLSsuhDUHRGq17VR8raG4p5QUtq47N72a8tTKWnI+dQBsd4TDijSGCgPYRnAzvR6/30AmjK4jUdU0D9C6U9cVuWp7/8hSy1ndfCZ2uukFR8XunET8ywJwAT7s2JMHyGftc8HZpuxMER5og6OmE9ibe58HzqbsnlQ1F7DX3NnLybmXi3qxTWNZ8Ba4OGP3SGvSB68uqGEjsAb15hVLrKaB+dkb4N/9MEvm+57TXvBaeXCXI1dABEZRFPxgjQHSiMMeZxLmsDF1wwXjQFeAALXcQCYk0ykv2UYLtO91qyT2O7MHlYUhAV9VTZL0ak28TV5Spm+zoyjraOgBYk/FrvqUlT4qdjEgRilTUCc74k6RpoEtLduLdw9yBxCvKJkzsBef+jwswLvEixnbtEIwwXzZ5RSG/B1stnzx11s5eUTZYTqiBN/rC6iHFYnlxnZqCcM+lNYI7v4MDz7ucYZmckG1raFOYxCwPq5qXqY2u85e3jniFaUf6dtd3slYD/rsmEiJY5crj1L0dpZpwl3QWUuKvY26uuCdQWAJajqGlVNC9TM5SQyP8ViwMk/FbuzRscNYKNVwd97tPCbIISNVJJ2U1caXP+ZxIapjBLPp8xljlTzsxxvyc8u6Uz4oyoOcAIFiGNuhHi/PiPEvSrqDTEbQqx3a2zsEF+eUznYwi8cF7z2P8x9j9Xz2Oe0FL4x9RGF3SQ2JX/eQgaoWEnPKndBabz6g3C4pAki2ablILs+oJbNccydPU98mVFHERRYQkqunE3Z4WsPF7hXK2q7ESNi3xoy1uGcxPRd7XQsf9IjYQzERdj8vS3kiVabT2NnjutrWQPd7/kKsxyM67M0mPg93GfcSGlKT+48+iVlmtZQgcfoz5t1HqNkUZrvjZ3p7D+doYrc7QoLXa0DxQq6q+s+qBX/M47yXKHXR+QV5EoVou+8DrFRBuQ0A3AkdjsUtwocFj3S5LFqAwxanAel72gr2+gGqEc34t9ewX7yEvueO5UslzYuwuZpA76rjYofU4sfEXtWdWBM4CbYXs+5rg4DxN1J/r9awsylfvgVZXGo6AfLC56GyjDn2UnZiAODnX0Gtdl1O4wHLtCTuyO0//woGgFbKT3c9QjOJOdcIpYTrZ7T5mQyB27/3pfGf9Jx2lwbWS1FAdNM94CkU2lkccTI5HLDezAuO5Z3zhhaGv2PezCasq538xmbHUmC97dSCGypvqXc33IGDAPp2zrbl/dxT7Xw7Uro0wac5Ze0C3Q1unoodODp279s6m/ACqhTUzUPn/Fc34rgtymphCNzNmY9gYczN3UEetq6B9594IZV86ll2mNMg86Zoakl8j3p/A70SzcoFdfDt9S1VHgYpVQ8qlkn23TXajzdevPUUnhPf4QWplyaeXUQ3u4T1Zg66zQm+3V/wRJMFdePrT+Wk9BZrmFfnpOPJHQChjMcHPVLbtOYk9mzCrktGLXl9tyCCsqqBF+fAesfOhcPJZAlLkUQw7E37dOyR2MUfETs0UYpWjMUwHHhmlE058ncue06Gj/cTqhSgbYFf/AR2k/s8MOizDnfa8S/OEa7KTrOnaSnLp1V3t7CW1EZj0U57UMMUwYd73m2shSpFBGrQ473n9SX0h1sPrziF58QXPLwRmBI2EaKI9bI4WagthYkwX/L1yFIuZNGiUU651mFYtOJwKSZeBY0MUFojx3/JC2kY8OIqjH4b7X1USgEfbjh2LytqyltLJ+zxiKXG98SOzc53Mr43doD/TiuoJJOyZSNT26YTYEpidlScsrArQ5IY+n7ZSfgB7AxZyT8KgQ830FnKtqTLqWk7/cn90qVtEfx6xZeyl8E+ztmavDjjyxwE5NAqxXIs37P8+ZGf0y5pFOtgK96hLE20x4uwbaZF+lrKCNd+dBjyphUydcRfhuvF54WnzKmqhh1Tw1z1exwIicKBbw/OV1yQD3MSrUdDKpY5kJbox/hhmFLPxu6myEfFXjc+djRNR/SWu4baFYy/FTCaezmTmJfxmoKzB3ncPXQvu+SDpjnIyb8gTmFBxKJsUVC/BryPqCE/C4AEc8dGM4slEEf+a0/hOe0d3gLeAsZNSo0gBl1nBGBPOgw65YF+r+OYKiX/bb2zntozIVBpwl3YEacbsMsi2vQ2Y73vBIZUJk4YZYXdf/EKvX//FjAB7GRA7HdZedWBZ2N3GJpjYtfax+7Mlf1kVmADarEm5mbfClNrSnUEmrLZu6ITSgI6QSiH4wew+8tLn5O5lItveaiaoKYT6FeJhziYQNEC6D9+S8+oNEH9coJgVRKGYS3w//xQi+b557QXPKSrojVLjTDkEel+oQ15oL4+Voou1+JOZ8vSq5fZHaeSTvZCaVcny+Vyveak0pUBedF9n80G6KUw0wH0t5+gbARrDLL/+1fAeES4wf2S/9YtJi+B8UTsgrs5JnbUdSc3orSfuqqQBHFbUNHYx1/VfHmcVnwjYqj7eay3vJvczfmZyOdwkNODyBDmOduOzgHdbRBxBKsU9OOWn2GvRynCQCO8W5PD6172E3lOu6SxFt7nSNRznXnAPgkEAHdCt6gAT0xwj0rTTgjJKXlJmYMwgBqPYM7HHUNJa5ifvpLJZwzbSxDcLVmXQ06GyzM//LH9zKsN2CgUTfhnYncv7BGxA/Cxwxqxjw+9+6Aa9ImlcXeRQPNy6SACxiD/2flhHo4TfHXm82m/ujrICW0rjh9kcKmM+B/bz2C3W2rprzYskQLNe8/9I+8QN/e+bemoi6fwnPYO7yzaHeOoFftJR5zYQx1Cdk21X+4EZADZfuatZ9RoyC6EE04F2AevamiQ3I0SgGmhf/uRF8yigPruGuj3uAjyvMP5DAd8KQR3Y8uKO7ErTZ6I3W533NWPiN0CUCWFoVCU3oZShSP29Lec7lq3y5uWswbNkkiNBsh++QnWtF0eAKUGa8EEGYvA2Uu6nKxlXmnKvJKY/3s8gmkNdNP6+4oSAVcVCqf3fMYOVz8j7OJEntNe8KYb1vinadjXdlIWreFCEV0ZOGrcaMAFY4xnA1ljoBrNLooQlNVo0Gk2+naitOEiEiDUnkWmzYiNsdudNxJGGAJVRXXdjF5SuJ//wS69H7vSulMO/iNit5VoW/ap/6LStLPHKUouSmv5QmQDljJJDJvF0Ddtl4fgeVyM3hnFfd5h6Ce7SGKgn1EKvG44XCqH7FI9zIHJiJfi8ymBb0nkTzKbTjo64gk8p13SuIuZRxLKBU6sID1KEei6HG0rrb4V++DOACEMWYc6qQvpk7ezkQeIOaqaJ2AXJXBz53dd6xa4UlQPrnlhtMMeUYvh73WQnondlzxHxG6N8bFDB91lvRHp67LySmc2jYXsMvRWNbi54x1jP4/LM5Zeq7XPx5yPD3PKUubl1MZmY3ZxfveBOPr317BlBfPuI//+d+9YKt0/kvEE8AUou+nzj/2c9oIHBHVYsx51u5F334i6jovrJycxsFj7boXXXAe4KHJCZx0hOnCXTdFzwXorTCvBfw+HVOESmpuDx9rrW98zV2vu9vqBCxWON/tM7Go0PD52kbpTeclyxXFycxmKBYzfbjgxtquN3B8M7GbDxQ8c5IHW8DQTjL+9vvXWly4nNzhTG/nvlbQX3V0oCKBHQ+gs5RT3fNaR1F3Z1O+xBDyR57QXvAI7A240HwYsKVyfWkbvVhj9KFjmKLFWVL2Mkz/RK2cdHXDnEqMxu9kKItEQYKU17Gbj5adtnvOCtlpzRL9cwcwXUFlKOOxeJ8XMFyx/9rD3T8Vut7vjY7+YdrGfTVl3L9fsOm12xN+7F8n1yleSgwDW7HhwkIdabbwLucvHoSUPOlvi6KEqmi2b+QJqNMT8v/1HWP03f8WYZ1PYixlr9yhC84s3MGcTmK9eAFWN/J/+/IdeOU8+p73gjeWxLhIWaA17wo7cLb8YFbBVR2isEYhutWdFabkIjCHqcN/0YDTwKgUQSh4uznj52u4orPrV606x+Oqc9i4A2suJ182xu5x661XtidnPxa6cONQRseNh4WNX7qI+6PNFS5MOMXlxxpPg5WXXyRE4svnVNwd52Dxn6/PFhc8HTXOQkxOMohBt7R0Esctx9n/8DsNfLmE3G5hBCnz3Aao1sNsdovcPUB9uoP72G9jtDtm//vUPuWqefU57wQOeNmede8eBE9+ehot7hF1kh33+W0tZat+3t8YvMCW2MNZd4mThO51HTEbcbVfUVUdr2FsOKVwa3C44xRz26F436nMkL/iU52K3TrPxiNgxGXWxO8ntomT8Mgm2RUEQnFLEqVdkdrmTQU+nB3kgSYjQ3BU+HzXoH+QEAOp8xvnAbEzebZryZ0UR9GLNIdSmgD6b8oTs92DORlD9HvSLS8Z4QvDg017wbsIYR4K3zjm6dgOQKKJEnFIdRc55OO0K9sz3WEuYjvk1bvTetGxR9jIOdJKYgxOxpvQKYqJypkYDmNnw8HtEIRcNwFp+saalTD97Nnb1R8SuysrH7vm8k6GX8sN0zMWr6FullMiMN42o+Irtzl4eKqJbiK+vo7C7lEtO/q6UxMwrS9jCvTxH83rG/x0SflG/ngFRjPqnL6DefUL1s0v+mbV+AziF57QXPGSXdGUBwLpVDAQgbCRkKX95IkntEIB2l5M7KkKhdO8Ws18nj71YwwYa5qsr7nxVDczGXHyPS4lBaIFVTYUDsab0ejJBALtccWyfxN4+/rnYbZ4fHbvNCx+7YzepupGuTg+YL2G+ukL7xYVXDbOZ2FzKy2Ru7g7yQNuytx/HPh8lmB2f09WMBJEdgWmYL4mZ+XSH4EFUhqX0Cn/1DjAtwv/3O6g0RfQ33/LPAHaCTuQ57T68a9uJW4VSikz52njJCIwHwHJDSbv9tqHopaCsWHuXFfveqRNX1d0wZbmhTboIrjpEoEoTbxGD1g1i5tx1taagkuukzCYUIFptyBDqpSQ/PBE7hkNOIo+IHcZyaCReVEoYUras2E2JIujvbriLt2330rUtsNywLIujwzyccptAkb2HbFV3BJXVGphNiAKdV54KiSiE/XQHPZt0buJuuhtH7GalCVvAjgd7Is9p7/CCM/G7ltDxVJqyN91L2TI7n/B4d5qItbCbBCujXD9/OoJNY1L9djnsmysOrvoZv5dguWn1IsMSsYr0JUxZCgUwgJqMgLMpB0EFp6JqPOTlcr17NnaEwdGxq2Hfx65SXhoRBLBvrvwEV/VSj9S0y3WXZ1152MJ+Hr40Anw+1ZfnhzlF4lgym0CNh8DFjCWMUtD9HtrZiD/vzVVnlbMr0Hx5yTuGGEX8wQDuR3xOe8ED3N0cOfpx2SEGnd56KEhBp+PiUIgOsQiw1hcst6u3EYb0YAW426fdL4x/Jr34iEwj61yyz2fAq0te3OR72u2O5Ye1nbKAoCWfjF122WNiR910scu/s1lC9tKUF2ubJmheTPg1ZzRDJuGExg56Nj3MYzLqPmPJJ/7tzUFOCNi2xWrDvFZbqhpcnXNAFigRcG1YQkl3R1eyWSQx0DRsX57Ic9oLXoGdCellO3MxB3xqLkbdzuuEisQPyat0yeJSAqSyjbhxOxjtsO+xMLR0EXaOO4ZlsZmJmA48LDqdlkJ8m5KEl8RP99xl3cJ9JnYrpcExsTvWkXUX6ThiC7BpgI833HXvHxG+u+fJIWwpNA27QKJbv5+HUwO229zngzA4yAlt6wdIsGztIgyhVluoooJe7jysWm12NDt7nEPVUn6FAdR0jNHf3P/p1sgf+Zz4gucOpYTU7HZHB2UN3956ZKPzIgXQAbuM8ZBcfgMnfVH4X6Zdb2EfF+ykzJdeHYBCRgXM45wyF//xrTcgsFsRUapq2Jt7thlXa+CcbH4lte5zsavN7ujYrezsSosbiMiLkOwhl0k3Pd4V3JWd1Q8A8zj3JBCXh32cUx47DHw+drM9zMmIAlvbMi9xGWTrtCfkFepYmtWaL5u7FItIlnVzhhN5Tqe4+txjAS/VXFaUfhAbFjSNd+jwpr1SX/pdVFj5KCqv3Y66AcZipmtkEU2GhBScT1mzAsSkrDYsBbQiA99amMmAMhoCOFNNQ32ZLSXn1NU5nfcE5/Jk7IMeF/0RsauesI7qxhsZqKZlGTbo8SJtLWt8YUIhDAjukhzsek0IsfNUddDqLKXJcVVTOWw07HJKYn5GQxlyFWL2EIbAxxuYqoZ6dQUFwPzjv4D+8Eg06uOCn0GWcdGfEJbmtBc8ACfujyyl+KhpuSiCwA9N4EyHgU44FOBCEwirY+cjDHyLr30xhdUZdFHDjHtoBjGieQ692EDVDZb/9dcY/mYNGIPNX4wRLxsk12vsfjZF9mGD7U+GqHsa039zC/PFJbQYLdz9V1e4/D8/Ph97Xh4dOxYrdljCgGSS9Y7S3IGC3paof/EaQS6DqsZAVQ3KVyOk39zBnk9hogC//R/f4Kf/29znsXkVo42BKLdIFi2yDxus/skLBJXtchokQO8SxYseer95gJkM8eF/+Apf/K+/gur3oaYRsM1hNluEN3JHSWPY3iVBY+DJdkqGCOqUwPm//4zjK/vX43/WYWcAkbAIOp0XwY6wjZj6WlOcwjCtAAAgAElEQVRlqW/B2aI4VAbY02pU/R7MMIPKxQLSOVvbThHY+Sc5vIudjQn/1dqL/tssAW4pncGhT+vd+j4Xuy1LTjCPiV1pthzzgm1ORxTZL5PGQ74kMqH1rcDLM+DmnrutczIUF2/abbaU1rsXrm6/1xkZtLJZ5KVnYLnYvBKZVqDt5RzB+Rn7+WfTA1EnKI1/cfs//YlXy8Hzb621/+Rzf3HaO7wiU8l7JQFwViwOGgtFepwVyhyM8U52kCmg61TY1Zp1rXtRLmZcbFUD20to3TgZUty0nyH/eoreL29gV2uYL19CtUJ2+HjPiy9Ak4FATo2eKALX7Lo8F7t32zsm9rbtYt/lQBRx4ttamCxC8GkO00tYlkyGUPMVuaxRyEv2oM97QJ77PFReAdMRzDCFfnsL1UtJqIyjLqe6Ybt11AfqhOhKKb/sdsufscuh0gD6bMY6P4l9aWQ3G49jOpXntBe8oByt03hvW+5UVc1d2LRs5FjrF4ajzjmnD3dxs0XRQQzaFna7hbbWf62OxY6+bmCTBHa1RrZcw+xyqDiC/uadALEoUOos49Xba8rLyW6r8lJ2ZSUepk/EXlfsyR8Rux8UOQ5ssYZar6EGA9gbOoLrm0e2EgE69K03HEylKXUmX14CD3Ofh1Mr0A+8XDt9SVR1l9N8yRr/070HqNmmod19nwYN2vnYrtbeGM22LS/ul+c0Tbs8A04EP3baXRrH2zybsL0nWpMkRMjkMOL/Zx846qQr+j1O/oKA/zvLPBEZaUKMuBBDMB1zUc2IFFRJTKFR0VoBCKJCoGHWG49Bt9sdnfGK0tvD27Lk97mYPRs7BAt0TOwIgi728UDIHaE/MRziUo3EYbzm9FP3e8TpX5yhejU+yMPBm1Ucd/lstoc5jUdif5nAlmJ8cD4l/mYyZPlTi4DVcMDySUSh7GoD3N5DRRHql5MfZ/185jntBS8uFWq+gjICQhLWv5VOh8fEOAUCwP+3hwjkZWeBHoasYY3hL3izIfy2NaxjDfvo7hi2cxEwmi9JieuxpsWwz1p1sfTQWUfTM28/8EL9TOwqS4+PvW197F4JAGAb0VjxSjXeywpKe3cRu17DzpcI/6+/OcjDLpYcmFWVzwfAQU52xy6SdaTusuRnBKD4eua16+31LWN9XPL7RRFfEJkVxL/++EOvnCef0y5pwPacqmpK31mpcduWC6aUPrUxbLOVtHb3C9D9+7L2dDenDU+/UmrPUzM9YBtQXPNsnhMTs93BvrqCuqFpF9IEJgkp1SeW8CoTUdUoprLZl6/ZCn0mdpsX/sL5fbHbpqH0XZbyojkZ+hNFaQ1rI4mfqgu2n5EYUpbA6xfAzT30T7+CTSOfB/V1QrQvplBlDb3cwgrx2uck5srucq1isf/RGtnffuScYDiAmo6kCdBANQ1tPa9mnGQ795ITeU57wVuwrQeRqdhvbzVtZxggztX0RpUBSUPFYFWoQ8nsLOGgRrRf6HghZBLnhSrYFguaCKg7qg07sVUFsMvhRJGkb+10YNq3HxBcnD8bu9KagLEjYvcqwlnCy/J627maDAZEY3oCSQtc38L33j7dUV5jvoIOgy4P+Uz0kg58SBNgs2X/XHIizijmYG4oiM71huVOj3Lc9uYOejrx+dnNlnLcw4HU8kHXdj2B58QXvPW7HQColrxPG0fAci3ScyQ7uzpaJaw3lVPp/b3dReUlrBJpDOlxYzryC0TVDTUXh3200z6CmwXMxYR8VTedzaRGdbtp08KutzwtsgR68Ib2OA4B+bnYs/T42K0BEIhTCY2JMeRl0UYhyxdZVKpu+IJtd16SBKstUFcwL1/6PDAds96uatjzKfN5cQF8vPU5KYCn08tL30lSO7mQD/t0BRRxVTXos4QbDb1IrG1bmPMx9KeHP+Ei+eOeE+/DX9q/Hv4z3+t1nkMARCa68oRpqnYVndKB0r6M8DWo+3cy7UQcdVqTbnGJ7xK04oWu2tOUUZoTS3fcx4Qou06Fs6hsP35CMJt65d/Pxu562EfErqKIcezHLpadbAum/CxEB8cWJV8oaz0zyuYFL8uSx76vky87RB/T5eROAVtWRFEWpWDqM2JmwpAvgjG8BCdJxzUQ8JrZ5lBpgn+5+l9+mEXD5x9oHx6ik+4GHk0rwCzFXQzg7ibKX87ukdBXI/zNrgYF4Be4E0OyID/U5nn3i3fmgdsduw9KScvPQJ/NWLsGmshAGbKoOBZiuOILoPXzsRtxGzkm9rL0uvAAgISCT3AKZQ4BKfcPlSb+BVdB4NueajDwecDZ7AisFw7zsp+TmxWI459r25piAdu2hF2IzB/N2srupc5SvgRVfVI1/Gl3aQB2HLSmWUApJmFRJGZdoh8fx/QTkladGg46ojTQYVSGffa93Q59PiXUdrNlG1GUfFWvx9YiADNf+N49Xl5w9xI3DuWmla69GdCaxk9Xn4vd0fmOiT2KfOzWxaIV4z+Tlp9rgyrdnUizCZAkzAE4yMO5IFLsVRZk3Rzm1OPF1lrLwdWLC6jhAHo2RTDlz1UDabnmhVcQVlHEvOMI+sUl9HgPivwjP6e/4HV3iXRmAgBJC36olOesHffw5L4DEopNZNMS0OWO7jD0LTYAsO8/edcNu15TH6bP/rIVkzLcPpC1VBHjbrc7SkIrYT9t2Y3x5IpnYne73jGxEyPTdjulI1Tcz2Fv7nnBfP8Jzl6HXaMGWKw54BoOuLvv5aEHfZYpSezzUdPxYU6rNWN21pb3c+a/E6z8ag3zMBfIdQZzew+b50ROBmIycf9IzP6JPKe94C01E1VKfqaric1syKPcqeqGoqCllRfut25876Q8hOEPS2dsW1UkMwcaeHFO1N9oyAtYvw817BNFORPi92yC+h99CfvyEnoy9pgZ9Vc/ZW0sOum4mJFoURTPx96ao2M/YEIZy9jDkPF/+YqzgVdXUk9bvixRxIulomKY0793eXgBqKb1+TgBJZ+TqCXYN1fAxYyL+udvAADtPS+iWvDyZrtjiRMEjKXoTBDst+9/uDXzPc9pL3ilCGl1chXyS9PLLXelSPrPAGGtzqbeSVi4v5Pa15yNRJIi8d0R6jjWosbV8hftsCsL0VJ88wKqqhH97VsSJ9IYGFMHXc83Uh9TqtpGAdR6CxXHz8auRoPjY9f6D2PviXDSruBLIYRzFUewVzPielZr2NdXhEv87t1BHta5i8RRlw9wkBPWW6hBn8yqKIANA+hvr6GiCHo04qVZXlolkuP64owguqnMBJx68ok8p73gLUhoEHlpJYMmVDUVulz3xUlpyKVTZQLaEvFTpwujrh+4u0otq1LxZMoS2JdnMBOKj6KXUSB0MoRqWuiyZg98NITNYk5DAw0UJdrLcUfJywsOW3oZbNM+G7styqNjt1niY7dF6TtEKk1gRj0u/pdnXkdHr8RhZDKCXm/ZYvz6zUEeMAbtubQmJR8bhYc5jYkStUMuekg/3qw3RIJqTZLMjkKsZrPl9LptO7ls4KTgwafdpVGK7bmeyDcb6xGINi+6PnfTEi4gamQ2L6Q33OMvqdfj1wf8Baks4+BILBsV4I3JHHxX1a13q1aPS9jRAHbtAGctd6+6hl4XHNoUJezZhKhJ58RRlk/GjqahsdgxsYusti8T+pnXv9EPNFZWzqi413laKTFH8BfkvTxUEiMoqu5e0BoqO+zl5KRAMF9KrA3sekPvWkWHbj0Z8/RZbKB/8VOoQhzCrUX1eoIoCKjWfCKuNye+4AUeXJSsawc9AHLUC+nZO2U3DSegSQxlO4YQZhPYxZoTytbwa5yWYy+F2WyhU1q1OxFS1etxETmMi4P/hkGntisqw2q14QnSNKIDL6rGotL1ZOxZ1nlOfV/s0nZUAF8MweUDAMIA5mFOyQylqCNzNgWW7JpYY4hu3BWHeRQFW55bYV051eS9nFQv8/LcbHlqwLZ+CguAf//+hqVN3XStyapG9MsP8D6zJ/Kc9oI31htkAaAWi7XevwhhwClnVXvhUVXV3laeC0fIEhvastB6RoY0N/dsmeWFRwJiseZQZdDv9BQPBkCEM6htLtbxWz+owmLFvz+fcSG17dOxy98dFbtzBQxD7vJaZAJv7mk54wgXdQMkCdR6S3ZkXhDdeD/nqZPEPg9vUSDqamqbc+4wHXegtIpzBVuUHTmkalH9xUtsX6eY/Pt72CiEXqxZVoUBypcvYUKFaF1D1wYm1Jxc/6sfbtk895z2gleqG1mnrJkBsN4UyIGqam8e7PAyMMYziGAMhzxxLKoA8vVp7OWmVQ6SnpsW9nwCJSR7++YK+lGk8x4WsNsd9Dn79c3rMwS/fk8sj1MbGPa5yy1X/PnCDvpc7GonMh/HxJ4mHFzJywFRH3Ofj91SLk/NJsQKlTW5q+stmVkiWrWfBy7PoIrSfy7N6zOENwsKSbmcnEdsHB8Yn8W/uUb0dzW1a7Y5N4tPxMMnDwu6ptzPgUBDi3X9qTynfWl1Ou1hwF0R8AoEXqclibtLo7Xd18kk0QsqJbG4dxBma9OY8hqi9+7KD7XasvxoGqhvP7KbcfvIskWgxbYsEfyWJgDm4ZGXs6bhrt+0sK8vvUbLU7HbfnZ87MZ2sStF3ujjnCWJDODcS6JWxLTTmbth+SGeTAd5LFacNDetz8cW5WFOErPdiMv3nlZO85dv+O8zSpSo8aibVDv1tRcXpFBO91QZfuTntLE00YX969l/D6/4q4M91wzbtRBbw8ut4D7cSJ96L5qTRAfGquoO+ee6B2YPpOZw8K3xgxzbGug+x/ntcoVgNOBIXqaLdpd3isG7HKYsKUHtMCufid059x0TO3fxHWPvZd20tWn4NdKrt2XFl0K+v6uzlXuB9vKAFffCvICKQkBwMCoMfU5oW/68mjry1spn7v7bibfKZ+tZZYAv05w+5r/M//kPsGL88w8US6OUeJgKcSMK+YsYylEqEz6VF4AKPLpSOaKzq5+VaLZUInmhaOXefnlFG0onwCSYdD/YcdDf1nAxTccI04S74mYL/OwNcP1ACbrbe+DqAipLEeR7QLCnYndKvsfE7nD3vRB22Ce8F4D5+hWC60fG1ksJZXBGbUUJlYQy5U29QoLLw90x9MUZlY03W+jphB0XyUmVlejf9IFRxHItz2F+/gY6r2G1ht7seFeYjYF7A/vqHOr9DdpXZ1CNgV4kjP/dD798Pvecdkkjj5UWoN3sWE8KHpweqjsvVWet6Bg6wJIb3xeduBIA1rNVDf2WAqRW+uMIQ14es4y76mqN9vU56YSDPrHrIuGtBn2om0fgckZI7mRM6IFjCP19xg742NV6620t9dubDixW1V7dDELLU4M+1HCA9vW5dxf0eQAee+/+3JyPD3KCUszLGJZOTQP7xRWHT+sd1NuPQiYhqQbjAfCr3wGXZ9C/fgf13UeBUf8ZPHbkI+24wHU1KAXn6Gi2aaBCOlwgib0LnW+NOYy4MwfL8+7U0KrzIdIadiaGXlXNCeWWljf67W1HsUsTmOmIO3UuC9HV7esNhZyqWtw59LOxQ7Ayx8Ru69rH7oyMnQufq/+tk/iuagqnVhV37qKgKkESH+RhZdHbsvL5KMGt+5y2OfPSmh2kuqY52vkMZjzgpDXhvUZt8k468PqW37s1wIvzbqp7As+JlzToygt3udM0HvMAKWlPUm1ALFkc59PZziQxd8C9f6Os5aVLVAnUwwLo92CbFaCF/jcaEH4gBl5mtYbKC8YUBCJZXfJE6PU4FV0RjqCmY//zPxe7y+uY2BVwgKFn7d965QRbVowf4M+oO4UzlcSw1rLVGkddHq+uYN5fQ4+GnJSKbr5jgqksk8uwmJIVBVQcw8wX0MJkMpst1R5aAxUB+HADfXFOQJyQ0tGKbueJPKe94I1li2vDboIKNH8hDYX7VUBbdzQyljckDVMeg3qMFui0JV2bLQh8x0Gfz9h5kT64iiLZRcFflrHAqyugqNizDwPYl+cwoYb+5gPBYk5mr6iANIV9MYS9faRLxxOxQ6ujY1d7OzuaBjYMiX6say+v4eiOCoA9m7L0UWKDuVizDr+YdnmUFdDrcTP4+VcwoeZ9YJf7nJSbOmfi36oUX8okRvNyiiBLYPopVJ7AZDEwHaA66yF+N0f1YoxwXcJqjfKf/hz433+UFfQHz2kveK041h6JqpbrBDgFryDoSBFumCP/Ww1pOOw6B0opoM/WIxoaANi65q6WJLy4AkCr2NVxrnkAcDf3qgE2L6Dc//V7dJse9aHmK36tXDpVL6XiwVOxG7vnRvh87FDi7leUXOxi5uDU0exqzRNll3OKe/fIOGpx9QCA6egwj8USajrhaXR9z8vz+QzKGJ8THpfw1ppJTLrf+Qz2cYngm2tYORmQJNA1BajivIL9eIMoCGA/3kD3MmTfdMjJH/s57QXvdpeyYtuwcayctnOsEMeMg86G0PcQhVBhv+vnN63vfdswAHTf29MAYDdmNiYfc7OFHfZh+imCTw+s8UMNlVesZ4d9liKbrdeL9J6pQFdCPRV7Fnd/90fErqqaffWUSmNKiBxEesZ8+Zab7sVqDcwXlwjuFjydXB79jI4ncqexwz7qyxGiD22Xk7y8djyQAVoCk0bQAxFs6s06WW7XgWoN8PoFbBoBP/0C6mHFWJY/7NJ56jntS+v+iEBkmv1fNeyte4UBN6QR+T273nb2j8LFhGPuiOa7s2/xXQihudko5CSyrLhQXPlz88gLnSKMAFrD/OSlX8jNxYiLcLnu4nkqdsHYHBW7ICsd+lLlJfTDojNdAw7i94Zsl2ewSYTgbkELnv086qZz9ZN8wv/wu4OcrOjmoGW5h0BDfXqADTSdP7Y7noKuUyP3Gp5YllImAE5p1nPaO7y7tDp8uNbARi5qDTsOKo58Xx1idAaA01JZQLCWOw/Q9dyN7eSzw4Dakustvy6OgKJC83KK8NsblkR5SYWtbe5lq1GU0N8sgPEIQAhdEGxmx0MCsJ6LXanjY9/7eu8WAk5szTCDXufUg/y45tcC3L2LipfoKKKdvXg/2UGP6Eqtuwt9FMGeTSmvLTmpsuq07J389rqBKmuYBeX3uouuEL0B4PqWdwnHtVWns6+e9oK3DjAlUnYOwhsT/GRXm46MPBkBec7jP46A9cYPYpwdOoz1U1dvGhyzm6A+Pfj+NuoGqGuE35WdtMZq4y9sNqfpgB4NOdRRCmhb7qRpTHMz6W8/GXvTArb+o2P3F8i2JQTYwQy2O48IteMhIbkl6X2wFtgVLLlcHl6/PgLEskc7fJLLyem6a2Ll1a6APZ/R+dv11sUVxKzXCEYDOnW/eQX74RM/1/jPYqrHP8r12sNDiGkrwxU3zm8NVXKlr94RlOXC547UgCoItqW7hkrijva3yzl1XAtwW2QsVDCkcNOg3/k46QD68hzm9p5Q4uXak8FVmXqw2rOxhwCsPi52pQBFEovNcy/7ocIQuJtDTcYwD3NpWwZAUXh7GiwrUWjYQlnT5eH0Kt9+hOr1oC/PPYfV5xRo7s4Nea3WGrYnR0Mu+NEAqmVpo89m/JW9ecVy6cUlSSbTIfS7T3/Gwx/7OG0YJXWjkrE+JaBzMf6NYLVgSfoZVCPSHQI9cFNGhzdxD4cuO04llWJXRfA5Kgxh8gLYCGnbOWA3DdAWMKuVh+s6CIKeTYCqRvu4QjCLn499uT46dowHLE/KSl6k1scPa1hWKMJ4+blUchkNYfINX4444ufl7GjEWxYA7HbLfETjx+fkhmDOwtPycm5u72GbBvp+Thhy08BWFXRG2iHcy97LoG7vO4PjE3hOe8FbyESS/WviZ1ovi6d6Wdd21BqIadIFrbxjhmMfebFSWfgINP9uOuEi3+XcpYc9b/KlR0OYswmUQyEWJXQvI1MqDHyNrMIAViuYcR96uUVwIaN8rf9+YnfwX/cy9lKO9NdbIEw4tNrsuOgnQ2hXsrQt1MtL2DSB3uZAL/V5qH4PdjyEbloqhck9Q+0KnxOSocgJCry3pOWNvjyHubnj959N2LPvZYCx0BlRoPb2vjupXlwAv/oxFtAfPqe94N0jxgEA+ItwLhruCG9FizFNiI1xMhhty1ra9dOFg6ni2Hs4qSSG3Ww4/axr4H4B9FLeD+IISpRx0e+zRi7ZZ9ajIQWUpJeutIZa7fgzNlvG9UzsNGU4LnY76HEhCj3RRiHwKIrHKIFce2tO3C840EoS4dBW9GByymOSh3PHtlVNUaXRkLFnic/Jbned2OxoSLwPABgRpHK29UCHRapr/swwpC59HEE9Lv7EC+T453Suz597lGI5EQbeat6K16mTd/M8VIBDJWEV2aYlLzMWmKrTdZlNuODWW9bNvQy4OOOktd/jonSArLxge3IwYJuvrGAHPeiLMxR/9Rpm3GN7bkTxJDMZ8E4A8EV6JnaCvI6M3eFgZpMDNQHi0EWn3UlzhAHU2RQqiZnDbMIc+v2DPFSaejUBn898cZjT2YS79IAzB9XLvLuJeXjs5gOzMfDinC/Y2ZQ7/2iI9r/8R9BXF382Jj76cUAvUeTycnlp0rGJ4phUPycFITW6iiN2V8KAO9aYixJzmYAM+16fHQAhDMJfte54P5vCxvI1WpOkbSzsNkfy9hF6V9HaHeAvuWbnQ41HMjh6OnYVx0fHrucrH7tjVkHTGtPGEf/daOBVEKwDcTlHkjhE+/WLgzzsqC8klMTno968Osxpz47eXeydbqS+PGefvyiAmzvYIKAy22LlP4/olx942T0hF7/TXvCQ9l3dEF4bRWzPuUtkGFCMabujOYLusOcwBmq59u03VdaCBxF1r6Kih2hrgNWGfWwl4qYlvZJsoKEXm64/vtxw2GKN9z/CmJaP5nIKk3KhOW/SZ2NvmqNjh2g7un62KirYMODPAVhK9OSlUcr37e2ENbhebBAsdod5hBpKLspdPuVBTi4vj9wUXXuInId9f01b+skYerkBru88itS1Y9G2tK0/kee0F7xyda8wdvanlXXdAZqShMMRh5xsWtkBow56oB0BJGWZUIrzhZC09eMapp950rJ6XNLtus/LZTsbsC348lyObbkEb3bElH+8h/71Oy7eOJLW49Ox26o6Pvay8rGrNIUtK5hhSpqjDM7041oGXIooziSCEnEm28/8HcDlofLKO3G7fOxme5CT7aVQH+99KQdNq5x2NoDZ7oCv35AkksT8eb0MeHkJNRig/cs35AwPel0//wSe017w0rMmIjATFpLsZEIMtoOeFyZCXdP+JZEuRxh0R7KxHYmilxGnrqmf6C6R6sMNv3Y25qIrSuBhAUQx9Ltb1rN1CzzMeanc5dRNbBqWTFnKXdKdBM/ErrLs+NiV6gzORKNGf3vNGB0W30FxBbeutnlXkjjo8H4eeUmFsLL0+eB8dpjT3fwgL1Q17OMCwQ0vo+0w8epnZkrtyuIriqwG8x3Mz7/0nOFTeU57wSsuaDUacufb13Xf7FgiLNYHjCUVx93fNS13c1dCJLHYslPeGc7Bw43+z6dcfCK7hzThFLQku9+GAVUA+j2v/IXbB/7cLcFTKCvqR2r9bOy2rI6PXfAqJFNL7E5b0xkkuJfXmb053mtrgMmI9vP7eQDs5Ly+6vLZ7A5z6qXMS6a61MAPYc7HsHmB6NsbXrLTGHrOTlfyceN1LIO7JS3s2z/34Y97tBb2DYWNbFGyvpROCIyhYKi17CWXNRfBoMdaNAqhGva4AbA7EceyozYd/NdYHt9O/8X16oXFr86m3FmFXgchSdi84HQ2DNir3+yIV7l+oCTGM7GrMODF+5jYJRYVx94AwiuXjYesp9ME2O26zy5L2VkJArYdry745y6PQY9+TB9uAKeQLNqWPqei6mANSczSLU2hHlawr1+wTVnWxNs7pQcRfWpeTKDzCCovYN5cArc/xIL5/ue0F7y13dTPihNeWfsWn9KaAKX1zhuGQSkuvCDwLTS3M/ldTwYoXisGgOmNEbhaOhLN+KoWL6nKGw6oQZ9y2kqzFx8TmGW1gr2asZY2dOZ7NvY4Pj72opt42pz+SdAywBL4sOklCHo9vrx1zYXupPmqGma77RxARIHNNo10abZwuvK/nxMA/oww5F1AXmL74ZEvkTHUtfx4B7PeQG93aH/xJfTf/MabRwT3qx9h8Xz+OfEFj44BBHiLFdfxsFXNGlTpjrkvlpOOuGBF21y1QrawtoMKmJa1fS9DcL/krrfawBb0csJQd3hwh7EJNNW9ehnsYgU7X3hHPHXzyEVUN95t+6nYAbCFd0TsCAIOfFyfXmxk7G5HB44kYfyZID5FNkP1exwaJRGAPutxycPmOV0Hy5I1ei9j/V6UXU5i12Nty/IPNIhQWUrBVAeSu36AWW8QvLwCyopGacMBL+ZxBHtCg6fTXvCOzTSb+C4DlCYiERDZuLSj8JUV+ZVOpBToFpKcCICUE2ni4bc2SyhznbObYF+eQT2u4QwKmosRImNhtUJ9OUL4sEXbS6AXK+gXlz5O6+4WUesHPE/F7u8PR8S+/9i65tQzDKCiEPXVGMG6hA0UCdYA0ZIVtWTMly+J6b+coRmlPg+I6Zq5OoOqG+bjzBJcTo6La0wX03DATUKMmW3TQL26grbGC8yqXUFEqbVQV+d8yU+EAHLaQkzhhf3r2X/H3XH/4rMvaATAix0BPLKN7XbGfWiu2Ep6VGHTcPcXUrW3inF1a9MceEN5dlIQdCKjTQPV68EsltDiYO1BV47H+rnYHcvpj4z94M+cp6vrjRsiKv3FVgdAWcLZ29ui6PIA4G0uXT5i3uBysrsd5UxGQ542DlqdJiSjXJ0BoYba5Ki+mCL53R3MsA919wj7QhzM6xZ4/wn/Yvk//ymWyFPPP1AhJq15tDolX3HIcAQOgqCILfEXMedcHVEOG5sdobjS1eDlS4SRpDtjIyE5uAVgLTDs0QCgbvh1SnFR3T4S92J7vka3SQR9NjiXlCkAACAASURBVEU7HSJ4XKH6y9eIf3cLZ2z2udjdCXNU7HLSIKEok4dFD/uwA5oXq13RnTCjIUwvhd4VpN25F2oy9HmovORpM19xEdseJ68Pc5+TshmnxK5bo6XES2LYuweoD930OPxXH2GzlN60WgG//g56PKJoVXU6ffjTXvBKybhfS8+ZrtuublchpaOVO0prtv3sroGqa6gNB1NO7NSVNFZEizyepm46WQzpXVvxKXJ660rahGrQ40VtvvKerao1QFkhmK8pE32z2jsNPh+7k9s4Lva6UxTLc54s0ibUhdTJRcmXSGtOiO/FeynLiOfZ7OjgLXkgL/gzMzr82fkKyOmb5XKyZQXVBkCWdOCwQQ/2u/d+puAcBvVkLPeLnefn8nIe4ZRqiBNf8OioZUpRPFU6Fl7tquGFCoJFN/0M2h31jjxhOjy4Mpb+TVqREP045+XNtGwfOs9Wp3++3nTSGClRhlYsbmxRspvjnKsdC2pNArhyXNn/v7EL2EwZ4nSsVoQFuMGWYmmF61vYLIX9eMOWpZueOiLH3UOXh/u+y1WHo19veHK6nECIAD6VdDXZ7YDlil5WNUtA9cVL7zhuV2vGt6Pdp2dWRRGQ//4v98d5TnvBu6PYmIP+NXfKijsawN533XRkY9dHF+gBxUVJJnHqWEoEVBXQ9b61ggqJF1GDPilx7rLm/v5sCmdJgySGXqz5c/oZ7GQALLdA3vqvfzJ2hyA8JvYk5ikShdyZs5QxO5vJXsqLsAzXlMPkZKTq2TCgo9/F2WGemy2BbjO2P9ufvUS4LLqctGZeIdUP1P5J2actjn1csIQaZlAf76lXs+3DpCF0Lu6A/Qw4kc7kaS94qI5s7XrSWrNDkCVeYsPGEQFVdQ216viTtjXsdIjMNIwRckPoAVEcm4to6Iw6LX6XW4rey2oNiOSGlTagOwU8UflxwTrXAboEmPZk7NJvPyp2rVl2OTvItoWq9syFbx89Bl7pRuTxlJfeU+czlhXzpc/Dm0Ost/5SHNUN7Grjc1LW8nu5F8cRzQd92MUS1t1NHuewb99DTacIvrsRfmwMNR6S2XVC8ODTieSJx3cPnNScmAP4S1dNP1ILgd46Lqfgwz0Yq2m9shcA30JzEtR4fcWesrXeoBhnI9jlhrW6Qwh+8ZJfU1bsac/GJI1oEUtyCmHfE7vr4hwVe1F2PXzRmvE+V5EYNL++YtkEEM4glD9kKex6i/brFzQ8cHlcDqEaQwbUeMCX2VEcJSdr266rFEXAoMeX9PrWK7i5F0tPxp3+vYMni/y33Z4IoRWnjqUBeKHa7oi8a8nftD2pgedLHsthILrophPuVIqlh8BtvRwGwN676DLi5YXgSP4/9t4lRrotSw/61j6POPHOyMz/dV91u8tVXe1uWRi3ZBoGCCG1kSdGIBggQYOQkCyEYIAAMYSJ5QECDxhYopGxmCCBBAOLpsVjgCxbuI0fTTdd3VW37r3/K/98RcbzRJxz9mLwrb1PRNXNvNHFvdWRcI9Uun9lZkSulbHPPmuv9T3WhNV62kyKlQzNt1+wvh72oO+do3rSi4bG+uKc2i4nQ+76qtBhj2N+UzK4L3YqCh8We1QWBnhWWK7gB72IY8HJCLJYkygSWrVpYgdslioA9vJwN/NYrkWD5vPTvZykS7y8FB1SH71CC/rTIsu5EXz0gjfhyYikl4+ex9alNwKJGx0PPPi4+/DZU/3V838htgqD1xKHIi6Ki0azg8DEX62i3xEABEk6ALY7pnu9bgAt86hhG00MnqtLWjICLDNoBpa3vk9Bmbg0l+sdKZDwfl8Ye7VtQWB/lNiDUXGABazWdoMO2FVJE05Rk6TVyQzlzo4xgvS7vOFCfz7L2O1Zrtqc6sZ68t1WecE38NZ6dCYJCCfwBi12PVNfqGtoVcMN+tC6xv80+y+/2sXx8PVI+/CBCxpUf3duzjgtDYsoJ0mCuPFe/DpbY90WR9PPuHDMICHyRquKO9cWpmhWc6pYbqI8n6jycX/dWtb7pxO46xl740GQyMRKY1vwi2JPu20n5stiD9PY/g4sAVYy5VnLxwXiTRQXcppCk4Tx2VlGoqrYPooxmKeFnKBmhBbawHkGrQX4U99BPeogv1gAdYPt+2Pkv/uSsOfFCv7pBPLJK+gvfRvy8hLVdz8G/revbln8v7mOe8Grt/61xt0JgMllNK2iwWbTmhQ7mxragglW7XHRhK5LHgBbLpKRta7ZyQm0v4qTWH8zJYJwNifhwVCFACCv3rFNORy2tftd25K4P/bGyBxfHruIcEHXDSfDtrMj4WEWXXAX98rafrON54FgqaOLBS1vLA+U1te3fn5QNIbXmBOShLV7sAEqS2AyRnI1Q/JO40S38/01pTmcQ/P8DO71JfDBcySXd0CSIP/h5Ve9Mn7q67gXPIQ7pDH4aVLcIytpVbZa5sGmpig4pHEuaqHE/nuwbAwDnM2GB9d6y93PzL6o40g6nb+6pvZ7nvBAF8wJgoiTabtItxsJHAgHOZdEE4IvjD3LDo5drTziU63HzgkQSxxdLAw2rNH7lehHDzQGhdhs2M60PGADI+nk8Hez9m+QZ21OAG9G811FnrFZELpA4ljuNQ2ai3c8uP5wjqauINM7k+0ofrZL5kuuoz+0RjqcekMfbuLuypG7i7Vx9BWNXqdBccyxpWZlDMIhUL25bmzZ9jNXjIBnCUJKqOoYQ/BGgi1OZNR7XP7pjwhX2GyicsFDsavqwbEDaEuw1TqiIaGeO7jX+DSKV/x+E0uf3Tzi33e9bplVVb2XEwDm1dDpL/x95PyUwLegz58kkF/6DqTfg/v5j5CcncJ9/CHkT36bZWHA7hzBdeQ7PIwEPOKHFZwlAN6qUXy0z1o7KIGlKR/FnZxQWpOXk44Jmzae3ZJBD85rJF2LI7FZe0Vs8UU/VFVov2Bta4MnXa4IrOoV6P3hNWObjGNf/cHYiw7J3QfEzgltK86kwz5368RBguZjMBguN+ztm4Sg1A2020FzOkByu9zLQ0d9aJZQ5TcoHszmMSdZb5jXaMguTFWjOR8huV3yRgtAul4RkZqy2aKZL5AUBfwPPwOePyVn9kiqmiPv0jzRXz355/h/AhJwV2Q09K2B+HiPnQmDq7KvbTW5kabDbgrrPEQMuklQaNNAspSDFZO2gAj7yxk7KdItYodCG4/5n/uTGP6Nf0juaq/bDmnui333+rLYg5Zkp9MKk4bYN5vYsoxaljtPMG3o6C3DoRE9pO2Tm8EDAEi3gL+bswSxnEQEs1/7RYz+5++3glHdIgo0Ecu/iZxbrameHDeiYOywXn9jW3nYZX/IqgJyc/EInZui7WcTOqCxlcjDYytICnU7Krw+Arf0ySlktuBEMFDa8gYY9Eik6EoLrurk0XxABj1sPzhF/vKGaEdVjP/2K+j7z03+Lo1PjPti1/W6hQN/WeyqAJKoGixpSmxLvwsXsPYnI0iSMJ8gg71rUqDaSvRttizNTkbskati+8Epsu+/4s1qOSFNmNeLJ1RxCFr0HzyD/sGnVFz4+APmdLcgQeR8DABw72xWYe5/32BpDrrsEGYHIwBsIwYH6yyFNA0UHpIXrQLActX21QEb5LDuDV0TVaVcnThyWWtaw0hmo/6ytNrY7N5nC5Yh5QJIU2R//5biQ2ESmeeQytSAVyRTa5jiflHsIrwhDok9dEm8OfaFafBmwxiShISO0D8PjK5qy8O0vYd0u20eqzVEnCmjNcj+/i3PINNZm1NAj15cQV1CkvpsATRKkkjTwGcJ3NUd/PmYOvXOwW1roio/f00a4De2lYdeJm9tzPc9nXEDYQUwU7R7DOq4YZHUO0MgID6uERbPatUqB9Q18dvzORdHnrUHS3svti6zKG8dF3PQdw+LxaAD98YO/FSx8/XKLktw+zCBJz9fxI5Q0KGES9ixef/5Xh6St+3GkI+ftSpq4dL1GsHZGzMiR+XNO6JMGw93M6c15tUd/Nt3cIs1ZFVSM6fo8DNaHcn2jqNf8EBQCgPQHvoCXKAJ9a22JOegmmt2imwDrlpNmjS1tiFbh240ZG09NDt5648HzIoEaG1DJz5nMt1aVcB7T2NphSQhudsre9hfEjvjOCz2+KdIU9t1zWPWJeYjtSEcNzeYcyCJ2BNIEqoC7+YBgK1as+4kXTLdz8nyAtDigQA+nYZDPgWzlAdrAPJzHwJXt/H9tPHw55PWMO4IruMuaQStZIZJXAu27AOfTQiPXa2BXpfQ2romwjC8PCAiAUSDNCDWuJomVBIY9IBVifrbL5B9fk0W0aoEnp+j6eXwnRTZ61tKT6y2kE0NPyzgZmv452dwl1O2N0eU5tAXZ5DLKZC6e2NH0xAxeUjs5YadGxjEOXHwJwOaAXuF/vwHkMsp/MfvIbm6Q/3+KZJZSZnt/gjNoIPkYor6O+/t5aEi5PGKwA8L2lZWdcxJewU1dCyvcBitvvUErvJwyx5QbuEnQ8ayrSCTMbBa80n4gqYI9S9+DPzNr2+Z/FGu417wMEivcTijhSJAsaSwKFYEZ0lo24VHtr1WEheHLwCiP1OUqihyaFkivZzbVJYaLPrZa7iP3kNyw0d9+uk7jvtF4JwQEnzrgF4B1GAJ0+1AfvQaMJeMe2NX68IcEjvQSlMbLsfNHA+lInBmceleX0ItzijL/e4azp3D39wis+5M+uk7qp6pQt+8gxsO4By1KHFxFXMK0GZZrLj4txX09g7Zm4zEd5MHlDdXUYK8GfeRBPW2bQU0Al/snEn+mK/jLmnConBCab1uEZ3rwmNST8dAlvHx3O/yA+qZo3Watk+IoAz8nIJEqsqyQRyk3HKiaoQKzBYU/jcfU+12KF467Js15IBPgCQBzk+sPemo5SgCfPii9Vi9L3Zxh8ceZDueP2kdv4N1TzB363bZkSk6fF871PuP3+NAqd/fywO3d1zI3/s45qOp28/JNDKDv6wWHXJcux1onvLvta2As5NoyuAWazTPTiC3M/hxD0gT5O+OBx583Du8KQuI+YUSiWhTy34XcjenJ6lxRmVtfe8gbZGQRB0AYLKtortHkKPT1Rq6lcj8p2mYtg4aQRjJOXY8Gk88ubMuy9WU5VG5Aaot+9RvL8mMCh2me2LHobFnGaSu92LX6bx1JLSDoeiAC/31OyqS1Q2SN1cAwA5U+H1VRauazRayaaLokrxc8oa2nAADlHnlIEvECCEdtkPXJWObLXkzOwdpPFzJLk3y7i6ysI7lOu4dPpQzJmyk67XBTikWGszJUFX8eoAPqMaaN+ijUEOlaSUs7GeROKPRmYpAVcUuB8CSx8+M8tbrsrbe6bjonOZfMuhxqlpuqBuzWj8Ye2wfHhJ78HSt6/Z/1TbCl1UZv84XjD+lKrCWpcVRQwa9vTwA8Of+4JM2nzDAspx0uWJenTwaNsAUHvy7K97wszl0uULz+i30ZAh/cckb07TstdyQ9XQk13EveCf7nYEsa6eKZWnTx9xqYPNKqm06WteUonOurZ9Du6+quJA2xL1LQax4MCYI/9XlCtLp0KwMYN/5bgY5GcWhUlD3gu2EWlPxQPL84diBg2OXfnf/JlNliRIETsdDxm+tUgmeTQDk9IQT2izdy0MGfXadRqM2n+dn+zkBpi5m7doAixYhRqeq+P7jIZL3ngPvbswJcRE7PTIetnigI7iOJ5Ivuryypg7iQgE1WHRaqltdc5AUvp9Y39ns4AEgmmvlLSgq2OWQLGF67ClvCg398G4B7RV077bLnU2gt1NOKrdBfdh0K6uKiruTcUvvuy92uwEPiT34t4angBjHFM5ktO/MfDj05dM0GqjpYknc0LurvTwiTLqTx3zcdLGXk4SJsSkJB+gFig7BYf0egraOdjumn7mF/86H/P/OsXwLM4sjuI68hmf70A96cI2nDPXbayrU9rrQExItonHA3Rz1x8/Ykiu30CKHpg7u3S27Eui0BIXbhd0EOUfp9QaofctMQsJd0vRigtpBdOrYUvKueXEKd7sAluw9+xfnAIDEnEPuiz3S+A6JfdQHKhNZyrNW0s5uYsz5uzRMhZuGtMNQ81c1YQ/TecxDq4o3i8Gisa0IDGt8m9NihaCN05zye3rehZrRQjy7GLpUn51Dbu6Q3CxY6w/60E8+/4bEffDlG6BuoqiQuyAZBIWJHM3mUJdwV72bAyJIf3TBcsUrZF1SvkKEhGuvUPWQN9eR/aOjPi1uxoNW+WvYB26maD54zt50z5QNFkvgiRnwGk8zeX3dSlpsK7iFkUnssX5f7FqWHBwdEvvlDbs6JssXPFjhDbH47JyqBJstD4ibLbsuvolkcFmV+3k8PeNB1ZQfZDREcrvczykIQa3WcIuCG8GbVntGlyuWLHVDiMLbS/gPnkHKCvr8CXw3Q6JKR8QjuY67pDHpaqRpnFpKmnBXMV4p1Ec6HVQpuxHsZkziOYof2YRS7GvqPXc9NQOCDlUBpKK6gWwq3hDzJfzZiK9drqP8HAD4sxGQprj5Jz+KBzpcXBnR+f7YARwcO05GbeymXKD9bmv4++6GizrkNbTJZ5ZBRwPCgBervTyQOMMCuZhPMEMLOQFgXolrzRK6BcWb1iXLrOkdmssrlk7lhlDji0u46Rzusws0b98dlfX8cS94VUrEZWTmBAdqcY56KiLAZMyF0umQ47ou+V+rgaW0kscOgTDDLzgHfXba1t+A2ccsW2z5j16zldfJOU0FuPOtSwLPqio+LSa/9QNgMuZie/GUu/gDsUtRHBy7BGJG3fCgakYR+uyU/w43kMUfrW7M2kbuFsBktJ/Hu2ta/OzmU2R7OelkhMlv/QD63hPmFc5Mk3FLkUwSJE/OiauZnMBfXtPB3OAV/N434LEDL9kfvqj5mwbFseBVtLbByHLdThjDZRIY6ORc5HUQNHJwsxX89I51etNw4OTogqfbio/9XhFfxwOt9ckbz0FPcAI0EwTZbIE373YGTF8cu9b14bGbPg3x5k1kGrnZik8MCerC1KzEzV2kIup63cqa7OQRO1bm8o3GQ8pqP6frKeAbuMspIcXBYCEcqpOELtuG9NQhD6fJk/OW2bXZHpVMx3HX8OpJWggW7YGUbHS2iIU3sjfNDhCJGXvy2Nuklc9wSewNU31rQXw8uAP7qxtKcSzXlJ8I8htFh5rrpsVI+bsstvD85TWkz3JCy5K/957YUavBfQ+I3X1B7E0T/+vLkkq9szlRjytz+1CjL7695PcD8rHosB3asRJmviArLMB5d3JiLhvedKGDJUIRqE4H+tnrSG7B26tWiBaArmr+3nKnO/THfB33gjfOqvR63FmLTqvT2O22ByuT4AjqXAEGG9hNNAAOPfANa+SAV6kq4HzCyWW/C12uSXHbbFoKX1lCxkP4cR/uOqE30mrND//sBOK71KEsOpSfHvbtg1/cH3tVteXUl8VuN5yWBrn1OwTwuoHr5FzY3YL4m143bgTubEJ4RK+gOrDlIQZnln7B1uhqDff8KUneIafNhsSSIJVt7ie/+fY/5+dzH2JgFw18JJqS4TruBQ+Y7Tk/eF0s7QagC3f0abLBkpg3k3Q67YHXXKQBRK9TKtqmURdeZwuqdK1L05dJOKwKljFFQa34ixu6ca9oLEBMyow7/+0d3HjUHki9Phw7sDPt/ZLYRaDbzV7e0slbA+QsJbQ3mL0FvRuA55H5gjv+Th4w4SYAPEeIoHo+RpomMSdJU+jtHZ9845GRSn6MnvjIruNe8F4jll3XawRddQ2soM2Wj3qRPaOAwLLHGpHAIWEBzBbsOnjfuvalVOVV04yU1KRBipzlRNNQT7HXY0fEdkRJklab3StLGlPv1VDX3xN7UOI9JPYoxxHOGHne9uGDIGqAJQSyRmMtyemcZdDtlDeu5SEpode62cYnVvo7n7DdGnIynRr+3eYs446YA33IddwL3gk/AFVIntFOJThom/oAnLC/PFuyVZdnbDWejtmdCIfGAELrFuRZbqvI7vfDLtXD7JCpHWP9T2dsCTaeH/T5hBjy+RL+xTmSmxmapydI3k25O7/3jFrp3QISyN/3xK7dTtsb/5LYddBlx6ZbtL32fhfqHPUwixxYW6kWeKeJA1Yl9MUJZEXTCABtHhdXJL30qSbsX5xDM4f0YhpzgjG1AnlGA1jtEV/HveC9tSUdBy1uuSZdrOiwu1ATzis1d0URaY10L2/jh6NBjXddsn8/Iy81WNFI1UBHffheDvfZO0hDs2JJSXDQ9Rr6/lO4GyIUtZMTe74ukdgOjdMTPjGKDurzIZJP3jKHe2IXG1QdEjuqNnYA7LmLAOsNR/fbMC9YUeRp2I9TWbcorUVKsaaQh07GdEDRTsxH+j3o7V2bkypwemJnkS4J6keEfPxpriNvSyLatiNNWGM7011PE8ILzDdJ0pSHKjsAksJHFS/p96BBIi8PUhj2aLY2Hi5vkFxRgUtXJdB4+KubWA/L60t2RC6vOfncUmQpOITIfMkd0Hukn5mc9AOxo6oPjl02VRu7yfJFUaQA0728YdnRyYlgtFJIZ3NEq86dPDBbsEW52bb5XN3s5QTvWxtM74mzr45niPTTXMe9w0Oh0zvWngFJGBbgcMDpYeKi5SMWyz2UogIsY9YlyxXnoFe30WVDyxJ6a0q7vS41Ic3+hiTogpDbumbXpNmY7oqPh1IOgmgC7Exxl3Da8uHYV2vu0gfErnVDYsfVbYREYFsRsbguKeGXJAAq3tibbSSK88asdiQHmUfQ4vHrkgde34pahZyCi2Hk0q7KyBp7rNdxL3hxFCkF2l2xqtpyIHQtmqbVRgwqvwAf4UGazlNWTwZ9QzBy+udOJwj2Nej3SJuzKSJx5g6u16Wi7mzFwZIpDUiWtS3GPKO62OmkRRwGAvcXxR6AWwfELsOCN+2gHxeyFB1oVVHPESCQK2jbm32mJA7odolJX9J/KuSBXgFc3kJ2RV231X5OaQfYCnMxwje6xdF4rv4015EveNjgxQG6r8BL5d+01US3Fpt0cgQ1tShFEXb0+dKmlsb6r1jbhmERNtvWtj1ov6QpF94F1QgkTTkM88rd0Rw+pJNT9tqmm9LrPRi7mrzGIbHrjFb3UT14NITOt/v6NQDjsqcSnCPRY7FsZxA7eSAMyxZLuKBl2S3gZ/M2p9GA0nzhjGEy4o/5Ou4Fr+BOVxpyMCw4m7CK2vh6R0Y6Xk3DOtcrxYXyLLKVKJZE+3b0uuxoGLhLsiyiCgM8Gb0C0fdp1G3r/8WS5cyLM+DiGjLssyuTmJv2A7FHae0DYo948rqG1hZ76JgYvkWNqE5CS7EztKLKQdSHD3nkJL4kg34rtxeIH5aTLFatqXKWthCFt1/Hh/2zuY58wRsrKUwdzeCLktJk3MCZZUy6s5itMxLVxsL7hUEPwB1/seRObJ0gZBknnvNlxIvrco2g9SidnAJEAG+4rpkOvGWHw19eswQJO7XXe2MP2vQHxR7MHAJzS4S7eZJwt68qYLbgVLiuOWgaDqhpY3J+ulyaChrz8LP5PozBa6sdaTmpamvPOZ2xTfv07Ge/Dr7C67gXPAxr4g1v0unt6bcHjIkCENTtbhRG9WGKCVMNc2jFlVTZ4w81cZYZadowK4FccTI0t2oL6ek5HUDep95iuCEhAnnvmQlEecDwJPfGDhwee1yYdmAWYRkWDqJFJ5JC0DS8CUy7MthaYmndFstjTxwpSYD3qebw4zlFodTRgO3PN+++no/6Z3Qd94JPEooWrUrICUnK0u1GxGLoauigxwMX0CILB11gbqpdoVe/tXo/1PGqFBcF2jLmbMJFGPiiN3eseZ8YFHe25EHw4gp6MoJzjj3w9QbaSSG1RzPIqFuTJPfH7oyofUjssPo7TaCeE1Ux82J9cQ68uaIOzM1d+/cqipaldXMH+eAFPVUtD80SSmaXGz6RLq54o/S7MSfURIBqYf62682jP7Qedx/eK3fTqopa6kF6TrpU4FXvOWTxhAWgaYyIYcoAm22LATGSteQkVEeX6sZDFmv4YZe/r2miXIYEj9WqhlxPoxy0FG2dH1hD7m4Jma+QXE75tQdiB3B47HUTY4/qwVtzAVxZt2hh+vPXJoq6rew9aSjcnPT28/jB58T59LsxHwD7Od2a5vuqhMxXcWL9mK8j3+EdF0qacPiSJrH7oXnGx26e8euLVcS8o6q5kFXbw1qeQVThRz3IzZwHvrFJ43U7FF6aragmbAdOf3EJ9+ScC26+ZNuv6MRDHsFdnsCqhlBcMUEl7WSQdXpv7EEn56DY7X39qAe5uOFuu9kCT8+iBB55rxwkaeJM6cCs7bMMyXTFEjDkYTALVHXbeel3ucgtJ+mYE3knj+K1j/067h0e4Adf1ZxImpGZeh9xHZo4+EE3qmT5UQ86IC4FgIHAOvz/geSQJrE80VVJ5bHbmTH0E2q6BDe+xAGTEaRbQNKE/qhpyhICYEfkbsYe9XgAvZtHqtxDscu2Ojh2ZDuxN6Hs6cX4AVDpa9Btoc924IedTaSq9/IQZ+SSMOk9Hbfw5ZDTak3dm+WKeQ2OR33gp72Oe4e3XnUYomiA8BYd4O0lP8zZAm46i3hy2ZFm1oAYNJa9+gZYr7mLm22NFJ1Yr/pBB8nNgh98llI5wAjQzZMTJDczaDdDk03477MTbD6YoPPJJR/3XgkpBjiut6HPF8aecpc+KPY8I4liTT4t1iW233sPnd97xY7LyRBS1Uhu54Qqh3Js0Itwh+37J+j84UXMo3l6AtQeblnCT4ZIbmbczXdzCgYSRnTBfAn/wTPg6me5CL7a67gXfDA9CI/4xSqqAwT5u4A5R5Kw/Vd0THqi7VFHSK1ZQCJ0NdYc78v1lNzM5Zpf63apILbjvZTcLYHNFsnn7FIELEt+RWw5Ujr9yaBPFYIsezB2Hfa5oA+IHVkK1HQR0dUakufIf/clb4qiA3d1y/cI1L00jewtTGeQQR/5wwCIYQAAIABJREFU3/sBNE3bPN7c8CbaVkh6Xb7v+Snw7rrNKVj1WFmEohO9nB7rdfwlTVWT5GFmBFGwyPrGCKKonZyLw7yfdLlupfpMzUsCF9OMFMKiJ2DLREObpjUtq6oWk2KYFy1LlgrvP4tepsTNu6gGEHVYHogdppZwSOwRXRmmtDu8WjXJQQSFNbsJAm1QrP6WwWAvj3DJ+Wmbz91sP6eqanmwiaMywfx4JDd+muv4FzzAaWhN6QxdrViTFlS2Yj1OdWAak5mO+rBvoK+8VR0bDVqbxTwHzidUDRZaQkqnQyxMWKC28+KkZTJJt8v+9orgMhkOomNe8+K03Zm/LPaic3Ds0slj7JJlkDxnp+d8wtq7VxDlqBrjD27ikcy9WOzlEWp3vbpp8xn093PqFrxp+j2eIbrdRz94OvoFr6sVAV3enDKc2U6W1C7Xu3lU+9XVmm1AVSIfTf9Q65qHu+msVeYy6CusRShWuuzS46TosJNT1fCDIpYL0sk5zGk8TRUaD12tkXz+DtppeakPxa6L5eGxb7Yx9uBADucI3V2uDZOTkktrTysJyg6O3Shk+X4edsloGPPRcrOfU0YcUdDs0dWKh+9HfB13DR/6wyKQTFrph8Cr9J6tucWK43CTsBOAGBJn93Og9zWeJmLOsWbud1t1ANNkh2okPsBc+1Bu6Uo35mBGVmWLC89SaJJAqoxeqNdz6PMnHOQ8FHvi6GJySOwGO1YEbygbVBWd9lzgfVyMOuwxvs4JB25JQrXhJo95BLSobrbk81atdEfIKYLs1iUZU71e25J9pNdxL3gY7sXKC+nkhkQ0USATKQJAZs7dvD2UWk87Mv0BoFpBil5ULtDFMuJWtCKkV5dr3gi3M/iVHTTzjDfDYkl9liHVyBSgBk1GZQE3XZKEsS6hAT5wX+x13d40Xxa7KrDdMvbA8a1rGpuF36PUycHtjD3+klLVGPahry9amQ7LI3bAfu59YL1t89nJKTqnVBV/f7cbFZAf63Xc0QegFxAJCxJ2v21FKbo7O0TZoTTAe6EkXcguITlNzQbTuKF1TarbbEGkoNlXRhhukrDTErDrgz4VArYVYbQvnrL7ErTeV+tWJXgHq/KFsatS7/KQ2IE29i6lsyXPo9ugbrcc0L274g1wbZaa6qFXN+TU3txSVcHygEEn5N0tBWtnc7jz06jkQLFX+539HnRV8oaZjH8mH/3XdR13DS/CGnY04MGyruPUT0MdW3S4mId97mzdLmvecAhMCTAT56KTXtSmHBAQJWlK3EjGnZwY+5T1bZ6T/KC6L7g06JucRmaDI+Eh0+II8tb3xR506Q+KPc9aF8BAVqkqdl7yjLHUDeRkzDPGoN/W9d0ikrB/PA8eyrcxH0KDd3KytqguV+TJmlvfY76Oe4f3Zg5wfUtjXCCCsABQF13MVfv2jvDezZa7bIDbmnV7eCxTFgOR7BE1GE0EKezmgTsqWWbSHSm0aeB/9BJuPOTiWCypHNCzR33TsBe/ayhwT+xalrypDol9W7VyJN4DG9OYDPGH8iM1iW+TL9FgW5+Z7N9OHoHzGjXeF0su+ldv25x2SB96e9cSVx7xddwLHqSX+ckIbsrFHQFYzkUB0mAUgDRlSWBCRhIMgOcLLtyiw754sHxZKiTP4Z+dsqW32vDDrmrIJsH2TzxD/vktsCFWRbMR5INnkLslJMvQvHcGN6cJL5QSdpEEbvJ698ZudMKDYi8KIOMwLJJSRgPe8JMxZFPBnw6JBRoN+LQpCTzzZyPIogTefw5M5zEPtyjputfvkLyeZWxtfu/jNieDJNMggRIgstnerzj2CK4jX/BmBPbmHfVggraiiRDplCoDscYMnFHv23rcygG1ml2CcFFdQ0YcxsibK/v/Q2ju2BY8PUF2ZRBdEWiWEJlo6sO6XMG99Ox4qLa2LpstD5PB5Ou+2Gdz7vAHxB7EmSRN27PC2QlhEXcL6GQEeXlBq0wACEZrvQKysjNAMCWzPLCtgPkSrh7xL71cAR++gHt5GXPSki4mWm6osyPSSnQ/0uu4a3gYCWLQN7a9MYJCC28yRrBslzCq31HGimpeoSY1xVvpFe00tDFHjiDpfEOlAVzdkgyxXPEAWZqR2p0Zg9nUU9Yb272pAoxelwZpNlW9N3bTeT8kdlVt1XpNVFU2pkE57LcSH9NZxA0hccxhSQAYc93Jo0tfK9zOYj4yne/lBIDgujS1vB63Jg1w7Du8oF0ETRONAlByWqrzJRfv1jTOgx2NCTQhWMCEy0jZwTxA7+Zs5wUTs6kpf5plpL+dwj05Z3fibs7dNU3hb6ZckNsqth+1LAkSU6UVfL/3cOx1A+llh8UebN+thSrdgmZjhqGXXteogEGJ2ENXzIMuHSPo7YwOIpaHdAsqCA/7USJQ63ovJ/imZVvZ9wIX97Fex73gjTGPrWObsCii4q8A/GCNdCxFp2UTZewwBK1IAHYAFESf0rCwwk6/Ay7TsgSy3IyJTXIjz6DNHHACFw56jac363hEZOJsDhkOKLEdBkz3xC6GTTkodicxdklTatEUHfbtw2F0OGzjD1gZcYZ5ryjTcTpp8zC7++bVGySTE0STCBtSyXjEsu38lKXjeMinzXjwqNGSx1/STMZmw8h2oZZl1EmRjDWuJI5DHedahzsgDkmoCJxyMRlWPTCmUAU2lG/J02IEitBLr2v2s7OMgxxVthbDQMsrpTSCZjvAxfZA7PijxA7E2OPufDdj/KGz432UBZc0ob9qn+7cAMiB3cmD0nsObtDmwwNymxOSpFUzC3ld3X5tn/bP4jruHT5xrcW7udVFvfWgCBz9kpQ/Y3qRoSMTXTiGfTrtWdsQzgHPnvDD7nVYE4cx/XhA/qYqiRaNBwY9QggWK/jnZ7R4bDz0oxd0BBwNuTgCaUT14diz7PDYAT5RdmN/7xl/f+I4Ve2YaYOROqJ7dpIwh3LLm93y0GencNckvfizEdx0Af/8DHI9iznhZERr+eA8uIPXf6zXcS/4uiFI62bKP/bTMx7yXMdUsEhj062VDwbikn5vjzQtIqx5fQudjdrodQ1xwn51teVOuN5Y3559bn16RokK4WKUT18D4xH87RRSUE+eCzRnnIE/ez65N3YFIJoeFLvkOSEFRYdxb7bA3SzGrFkOzEFG06qM76mh9q5qktMTF/OQcHYRifm4+Xo/p5s74vyN6I3gyP2Ir+MuaQTAlK7W0iNoi6WDb2Wh7ZCGJxPW3omLQx1Y2aFBfHU44KN7NOBrqlAjE+siRdEiJjcbw750gasbm8bWqF++AsRRUbdbmKECH/d+No92lEjcVxa7drIYO19oashVez6RTs4hV1my3lfP9qijbiaAvTyCWBP/zszHv323n5O3zpUxrnZtcB7rddw7vLg93UVUFSluZhmPLIXUNdGFpdWyNsQJfFcptzTpXZXszjhHDEsYkwfvos2Wi76TszRIU8hH59DLG+D8FNjwsJl8+2PgbkEM+mwBpGBMjYdkKUsaoHX5uC92gK3QA2KPXqrOQest3wcGXw5oSsPlSMDJZBk7N9UWWjvg7ARI2jx01Icszc+q12M+J8+Bz97u5QSAf+PxyPL4ZtL69V3CgYiMBnys5nnbuWgaoCK6UBerKEKqNc2Mg2481NhFgcUUtBudteyCYUIwSavsvbcVeal5BkxnBifuAbemCByQjM7Ui2uO+AFERTNdLO+PPUkOjl2BtuUJ8HeuSx6czd4d6iPYC5sNcTXLFUsk7yF3C+7SlofMlnaI7kSQmpuiRWsG6IXsYOuBozIo+2mu417wQU7C/g3VKKIUwFG6WEVkotY10Xyz1oodMOSiGaBJJCY30adJyi0lKoSWlZSmMAmQxYqHt411c1wSQWv0hurzdc7xvLEqoe8/BS5uHowdVQVs3GGx25RTkoTOfya9LTb5lKqGnoz45BnYobZuuEunFIMCsJ9H0xDzfztr8ylNQXgnJwAt7zbMAh7xddw1fEBoBy8ngAOcMBUNzKLNJuLZZb7krh2kqIPirTGBCIBqeHDcbNmFqCriZ1Ylh0A7l+QZSRRrc+8oCZPVcsMywXrruq2AiyvW3uttG+M9sUfk5gGxB85qeG3wiQpCTboq2ZEZD+LOH3XmBz3u4L1iP4/1Gri8IS/A8vE305/ISQOU2TaJx34d+e0q1mJjj5kHuyQeLrUBx/VuBzqb8fEsQKv5mBLXomvTW08TAAlUmtYEIc9YAgTuaZhumsV6IH8HUgTMbVrFBkudDvxyGamDmiQPxg6XAc4fFDtpfsTSxAMr0O62TijHcXFFUNpmuycgq+sSWC6hgUS+2fIJ5rUVWVqXcN2C5ZvlFGAXulpTS6fbjQC4x3od+YJHy181cFZ8zALsTiRZa+vey6IamG5Igo4w27A4RCL0FyL8EL2HrEr4zRa7vrCSZdFkAAW9kHQ2hzsZc0GdjlukZN1wMhtMv8w2577YtSxbjuqXxb7TiyeAzBw6wgRXlaUUeA6Qk1H0xqI3VAd4eko5EstDgkpZUBoG4D94ArfaxpwCBkcCQK5ueID/ZtL6NV2q8HezyDXdw8WIi8z8gCHRxTIuIACtVTsQW3ER32LqvH6xZP1a13Chc2OPb387pZpYbXIb17dwT8/je+gnn5sfEknYfjLik+T2Dqjrh2MPpJBDYgdi7DxI2g0QbDM3ZmFjZwadzvhas4THeNC+p+Whs3nU1gn5uJeXezmJmUFEYghAePAjvo57hxcx4dMEKEg01s0WkiSR66mAje6tddcruFN2U9apRr6QNIn80mB3KVkGOTFNGefITgrSdZ0OuzLLdSwPZDSMnR3JMrY2xfDrvS7jK3LIZMy62sm9sUfszB8x9nhQtTamZmnbijQlNVSGobEpKZbrKMcR8sC6BIbsEsVWaaD/WU4RmFYSO48g4/eIr+Pe4YGWTxp03NOkxZ6DvWjtd0kwHhhBuugY4tEockWHAycTPZKgpwhEwSI1H9WAbddtBT/u89A3HLC+ns25G4+HrfpAtUU0KEgT3jChf/1A7BI6I4fEXlWtYBPsJjkZxr+BplQyQ9gEBgZu21YsYQY9yORkLw+dmLP2ZtPmE8qxkFNdMy870EtVE0L8iK+j3+G13LCGLDdxYho9RAG20YwHqmXDeju07dKUWPiq4g5cbnYgxWb/OKKCMNZlFEIK43N5fclSIpgMiwNOBsDFJWt/oF3QSUJyBUz6IpgV3Bd7MDc7JHYjbut8yScTwJjrOhoh63zO84apEOsdoc46m7eUyJ089PM3QFGwvrd8gk1PzGkyJjRCrIRLk/ameKTXl+7wIvKhiPyvIvK7IvJ/ici/bV8/FZHfEpE/sP9O7OsiIn9FRP5QRP6BiPyjO+/16/bzfyAiv35IgNLrRslmSUhv07JsrSCTxNS9mojV1ro96GnQRQwYECdxAKSqlN3odmyB8TGObitWpAXlNfRkyAV7dQP9+H34pxMql2VZdOWTlxc8pJ4MWY49EHvo2hwSu+7EHg++wXd2u4UGm5xgX3N9y358UDwrcqI1d/Jwgz6xN/1uzKd+/2w/pwUlQcLwDPPlTxipPbZL4vTuvh8QeQHghar+XREZAvhtAP8sgH8VwI2q/iUR+Q8ATFT13xeRPw/g3wLw5wH8WQD/mar+WRE5BfB3APwK2GD/bQB/RlXvxZuOsyf6jz//l6JHUtwli040/YKn2sCuXmIc7viGLcHdrkzojNQ1aXKN5/AqOF8bBQ+dvBVczWnZGKU8dv8+BqrSVclFZjrq0dv1vtjDTn5I7IFdZbxTDrI8cH5K7fntNk6OI6Wx3EBGA0IHig4xNWnS5hGGb7s3VCC0B6AYjPFkZmaBevibV3/1wTVzBNdvq+qvfNE3vnSHV9U3qvp37d9zAL8H4H0AfwHAX7Mf+2vgTQD7+n+lvP4WgBO7af4cgN9S1Rtb5L8F4J958Jcbx1N6BetQEyqN/qWdnAdJ822Sfpdfc44YFIPgSr9HbPpwwEXWt5r56iaCtEIHRDodLmBj6ePpGV8zHvEDP59Ael003/lgBy/fGHmbXQ8NagAPxB48pg6JnV6tg/amHfRZglzdsOQJ73c24U02GQHnE+jtHdxoyByq7X4eTcP3MFPm5jsf8Am3kxO2FfMKTiDu6I98X3r9kTIQkY8B/GkAfxvAM1V9Y996C+CZ/ft9AJ/vvOylfe2+r//47/g3ROTviMjf2dbsA+tsQViroQgBROsZXa2Bycikog1/0smJQQn1cuOJGZ8vWN6YerAMCOaKagKhY2KDGa0q6Mu30GGfO/14yKFSWSK9mLaTx2CAsNlwR+zkrbTePbET8ntY7Lqt2tgB68M7yICO3kHRV1drDqjmKxI1OoQL6Mu3kMnJfh5Bh8aQkOnFlLHt5BQO5v47H5Jo3u8esEqO+zp4wYvIAMB/C+DfUdXZ7veUddHDtdGBl6r+VVX9FVX9lTzptcJCibXmwuIKbtgiPLiF2tLs2KkcRv+m2I/eFSEKmBSDBQM8bIa6Gk6g65J0vosr7vyxlk5aYjTAm2bYY4mRuKhI8GDsdXN47E7a2JV2mOHn0ekAz87bnJIEOp+z0+NIAXT9HvzF5U/mAZgw64/lYzkBgA57cJ++5VNovXn02pIHLXgRycDF/l+r6n9nX76wUiXU+cHP8BWAD3de/oF97b6vP/CLrW7c/ZA3Vq+GUbtIRBGqHeQk0PmCMZgqBUFHbStP8gzNc5IyyPP00G+9YH07Mxz7oA/0upCzCfzZCDq215+OiX0ZDrgLdnLIbInmdMCuz2TMRf1A7HByeOxmRclPzESVOjma52cRyRgx//0e8OwJ5wh5TmueXhf+l7+9n4dNZP2gF/Opf+HD/ZzSlK6FJyNyCDo55Od2P8LHdx3SpREA/wWA31PV/2TnW/8DgNBp+XUA//3O1/8V69b8YwDurPT5TQC/JiIT6+j8mn3t/isshvUOtSyq6u4IfQIto95oblo3HNDYiF69MYoCra5pkNzOW2YTADddsG1pBzldLAnGmi8gry+Bmzv46V0kWix+6Rl3xzVht8kPXrHksUnrQ7HHA+kBsUNcG7t1YvRuRoubqiL8Gezw6HwJMWUCLTdkLa1LJN//bC8PAFFBOOST/s4nezmhsEP3esO8svT/F5zWfwLAvwzgH4rI37Ov/YcA/hKA/0ZE/nUAnwL4F+17fwPs0PwhgBWAfw0AVPVGRP5jAP+H/dx/pKo3D/9q6jUiS21RuJb5H7RmToYchgT9xCzloCRN+VgPnqyO00d1QrHQokMyw2RMAsbJkHBfMzyD83S2NtVc4vJ7wMQESa9u0f9bP+D3egXtcQBo0SG+PUmiGOsXxq4KPDs/KHZ4mhJoUBRLrZNi4kpSdDjhnS8N289+vFZG+J4vgPefExMf8jAyhzQN/wZXt/Df+XAvJ2wrHrqXK0p7B5mTR3x9afSq+r8DQbf6J65/+gt+XgH8m/e8128A+I2Do1M7mC5WrI2DPHTY2euG0tTaYsTj+NyobNEKBmBZYyq9qOzDm87ZQgSxJuoSSGJPFk/Xi0B60NkiOnP41QpuOGAPf2X0tzQlnr6Tk/xRFPfGLkkCuZ4eFLtqE2OH9+1TI03j16Tc2nvRrTvkrHdz3iyv3hItaXkEf9oAWPM7KMiQE6oqMsGwWPF3/3it/8iu475dk5RUtPmKO9im4n+DQZhZ3shmCwwHLahqtTaFXfMmMom9KF8XVHyX63bHXm+gL55yd76eRmxM8Iqtn46Rvr7B5heeo/jhFRKj2fki486aZ62KQtNE0vS9sScOSPKDYo/TT1M2kOGAU8+7BZC6aNIgQTdmW1Ejc1uh+vkXyF7fAE/PIdsq5oGmgZ50o3RgMhxAX15SVyfklLX2lzIZQ2dzNN/7Fnt0j/Q67gXvPQ9NYTFUFfmVxuVkvZ22mO4gs9Hr8r/h8d438JYqF3RVR40YvZ3xSdLvQV6+sS4NXS/485y+pm9uocsVit99xd/vaQnj3tFBT9KUO+JszgPt0nbhe2InbDc/LPZtxaeCKnf3aMljIqdvLwEDrOHtJXvtswUgguzNLdGcl28go2HMA+qB5Qr+o/fgzEQNdQ1NXMwp0BgVBlEY9JG+uv7Zr4Ov8DruSYK11eIOOR4iulVXdVvHmjV7JGtsCYTSbqclHmepTSPX3I2HA6uTaz7ysxQInQ0n0YgseqQCUdBJFyuWA++uuYCt704nbo70o0b8PbEjTQ+OXXtFG7uj+kKo83W5Zs88SykzYlIhwSVE5wvm+8GLvTykS6Ept1i1+VT1Xk5AO11lXk3bWn2k13FH77kbS20eSeUm0tNIzvAtngZgSWG0NJTeMPOJ8T6tIxJIEQmRizIZs1242fKDDpS5DlGLuloD56fwPbb/ZL2Fng4h724NLrw2iY8OkBFPr2cnPIyKfCWxI5BIzG8WVc0ngfXoZThijX3CXNDr8ukYpsbLFVZ/5lvoeR/z0CxBcrOgcdm4TyeQD1/YwbbtCEmeExrckAHlx4/bFOG4d3iAO4pqxHMEXLgGabkTjtGj+gBgUnP9lm1vstjoFpHuh7qGDnqE1va7hNcmCfR0HF/XnI+It1msqLpbVsDVLdzVXYTnSkGyN7xH9eIEzbhPZtGXxe7cwbFLnrWx2/uouXv7956wvBr2+TQ4HXMRB3K4E+DsBL2/+f29PKRq4Pus4UM+sljt5aTPTvne3RzNuA/tdx+9MfFx7/Cq0NspOadBtda5tmUU6trpLMpRRynqqo6erLB6VlfrdnElCSeP4WBWlkC/DyxX0eUiuZjyZlitqRiw2bQsq7fGH/WesF3fIPvsihYyJtOB5fL+2LsFOyuHxN4t2tjDU2G24CwhTflkurzmqLuu+bNFQQeQ+QJ48ZTQguvbmIczEJ0C/N1vr8hbzbKYk8yXVGv7kYlOBUTpI76OO3rnWFfbIxqN5/Qx9KbNh4gfxH7nVM3gAOuytaMMZU0AWwXShSpkQra+iLS27QBt5Cdj1q+Nh4z7bPUFooYIMOhxd0wTSNZDcOPTtdwfu6n3HhT7j9PqzOYniEnpumT8AY/z3ogl2mYLPT2B3M1Rf/QUqfcxD61pwKbPn0BeXTCfbhElQTDosTXpPXTSgexMix/zddwLHqSsaWI7owhQGcS3tEOeb7sJu2pfQa5Ovad9TJpQy3Gz4UAIiPrwMhjw4KbGTIqHVetSrMr4PZQbIM/Q3Nwiee85hzMAuz4irPsbD2n8g7FrWUa48ZfFrv0ucSwbQhUkSHRsttHMgVQ9xobFAr7xcKcnnIyOBkimq708OF/YQD57DXQLNDe3cIM+b7SY04bc2NUaGA04QTYj58d6HfeC92QGyWLFFqHbMR8Iu9uK6lkREBXKFe85JS06LT69W3AHN0itjAbQnJJ00jTwT87gbqhSpv0uMOihKVK4dRe+k8G9fAc9HQONR2LTzRYQlrCbstlyOlluqCl5X+yNPzj2oPwbSxoAeI+WmdorgJp6kM1HHyK5mMKfDulX1ckhXtGcj6CJQ5ImbR6TEQd38yV0NGA+5WYvJwF4gB12eaMNB/DnY2JjH+l13IdW58jALzqmq9i0bcR1CQ2udcFFGzCrGdcyjgAupLMTtu7MExV5Bu134Qdd/p4Ttg11SOUuWa5RnfcoW3E7gyttIef0aQUAvL3C+ucmvEFenAHeo352EjHmD8XOnfzA2Os6xq7ex/ZlhOuqQk+GSF5fE904X5N/av11t9oima5+Io9whXyq776/lxNUoS/O4Md91M9OoL0C7u5xC6oe9w4PELe9XBMuC7RQVuEkMUg463hIPEvjgUbZmw6sKCdA6JyEXbTcALdTJKcTkrPFQUy/Ro1xlP2fU8j5KX/+7RU0TeGsFg8qYr3ff8fFa0+RtG6gN1OSMR6IHWEqe0Dskufx4A2T2pBw0A6HYq/QassbKDzVqooYmE3FyexOHri8Jl6+MT3LNEH2/Vd7OemqhLOa3dmTyM++IXF/jRc1GTE2JdtIxbP+dABlZVk8YEGEj+RyYy4fZSs4Ol+yRx1cM148Bcotqu99iOxyAT8s4JYsL3y/g3qYI1lWSKYr1N96Ardt4FOHdLoCpnP4X/gWpGogS9bvsuHhs/7ln0P2+fXDsQM2EDsg9k5Gj9leF7ICkKaoPjxDeruCL3IkV3csbawbtD0fIP/kHfwHTyDbGk3RQzXKUXw2jXlI1UBrDzed86k2nUOfn/EJZjnpZAiZslcvFU2Wq198H/hffvYr4au6jnzBgzXr3YK7FgzQ1etCtlUEcu0SGnTQIz4k7OLhNRsjRM+XPKj2e8DVFDoZIf/kHZAmSJbGNS06cO+ukadprKNTADJbsA2Y8AZyn11w961r/P6/+zG++xu3kOkcyWLz5bGvVpz2HhK73RhRErsjyD69JCS5k3OAVhrbKk2QXVKDxn3yGvCKdDRAsuxBZouYB3qsy3XNJ5D2u5CXFzzbWE6YLfD7/97P4xf+05exg5V//v99ePAf36VgPZ07s1rvRew6spSHqjzj7mNQgKj+azWoAO1Br65bJeHNlgt/tmAffG62NXXD8iIoAi+WJGAkCbsvqOHvTGY7SWxXzvDdv/xDhuwV8qPX9Ex9IHZs5PDYTecyKgkHZpb6iLQkBTCF1mjpeyIAjCl1NzPZbcvDzhFaboh5D5gfrzEnqSp89y//kL36EMMj79Ic96EV4AEtz1oMC4y7GahmpvESBIsAtIsiEC52P6Sm1ZkMo3fp5ARcjQeUreh3KSUN0OAgTYltUc9duNclJKFpTP/RyqvzCSel56dfHrvq4bGHwVXTtAypXsH4g3P3eESMTb9LfE1dMwcn5jI42M/D1BDcaNjmY334mBPAFqVIq+rwyCl+R77Dex4oA60t9MLHQz7mtxXbarvy0uED8YaMDMhEs3OUXhH70SICXdJTVZcrYGXeS0Hht9tl/drvET4MsDTqFrxX+h3dAAAgAElEQVQplqZNH1qOQVN9W/H1a703dl2tD46d9vQO0itYDlUVW5XBIBnGrLKbQbIMKIacrOY5cwiGDiGPLOPObje89ruAkbpjTuHJGMgyO/o5j/U67gVvSr5xtwG4I65K43uyFpe6YY0bnP3McU7NNABBurpuWOcmDki4+4qp4kqWtayk1ToiHTGdsZNhmpQ6Z0dHL6/pYZqlNCIYDSBXt1HxV7o0WLgvdgm19yGxB7qgxS6dTtuNCcjRLINfLGlDud1GQolut8whaNkEbc0MFIq9WwDLGnp5HTE8MadwMyyW7Cw9mUB/9PJnuwa+4uv4S5pAuTPRoECTI/bEtS5+YUxvDt3ISMjQujGlLzscqm8F/m1H1W3V/tzuLjZfAueTGIr0e9SB7PfYRVmuIuAq1L17O+ADsetme3DsUdVAfSw3NGjXA9HkQUzFbE+p2Cs1apZt/1z6PcYxnfHwHPKZL/ZzCjADtfbo3YKS4I/4Ou4Fr97guisSsc1Be9eJIlo0WmmgQWN9y754tE1vTLtRXFTgZR+b6sThQCi9orV3B6K0hYjwsW8LHSejtnQB2E8vN8Cwz0NjWT4ce10fHLs418beyVtvp0A9DAdaYA+ZKV06iOPiCtLv7+cBu4GLTszHTU72c1Jli7Qo6HNr5sWP+Tr6kkbrmgdKI18HVa/weNdBj60/r1AHRIcMI4folqRpHfSIAAwY89EAmIEfajACHg25w54MgZI4FT/u0/luTUy67xSQNIHvF3CBCpilNrwBnT/KLRXAHop90Gd//YDYIw82TbgYJ2MOkrznIbTbIR5+PCSDC2ApaHgePxmy7+59zENGQ6gT1N/9EOn1Ar5foHlxgux31jEnzW1GEBxQ7ID8mK/jjl7NcuVuFrVaAIKaJLVFYPrtYkKl/AFT7Kprdi0WS/5cXdPM2Ht6qBrjKZYepq+ut3dEIpYbOAC6WBFXPr2DG5Ap5W6mXAhBkTcoDizXPGhnKRfVfbGv15C6Piz2MGHtdtmKnc6p1iBCpxERng/mS/Oh2kRcD6o6wgF288DpBJjOkN7eQQZ9uJsp3LPzvZxkVTKvdQnZPm4R1XAd94I39Piu4GjcoYGWTLFr6CtiGo4rHhzNxgYgQjAIG0GVj+nVmrtlt4NmWJAFdDYCtjXq9yZIFpsIDRDn4E+GcNM5/IfP4cotmkEB2dZw8zWqj84htcfmT72H3m9/+mDskqYHx651E2MPwrBi/NfmdMDJ6LamgfFiDXEUeNJeQSufYRebp30UP7qJeaz/xDk6rwu4xQqoG/gPn0O8h5yOY06yLCGeevPNpI9kXt4rX/FYriNf8IgHtGjuVYO1rXNt667ocOEGHqqVEmFAE32RvGddu60gqbXdel1I44GqRGq7pUwXnK4GNKExgwDAXU+BJEEyXUDnCyTrfjQ3zt7wMJx8Un5p7FqWnGgeGHvQmIm7fYhntiZILE2oKZkmUeEM8yV0vYbbbNEJ+H57XfFyBrm5i9iiZLowTJHEnLQwfqwIUhN2CuSYx3od94IPpgGdDj/QYPdij+1Irig3ezsknBDDnWVEHwbceGXAq17B1tt8yX64IROl16WOTN+eBHc0BQ7wAO13+e/1Gn5dwvV60OkdseWqnA2YCFJcePfFnptz4AGxIxgd2ARYBz0KtNZJhAxrXRMPn+U87M/mVko5/t5Xl/ybBteR0B7t96CLBfy6RHJ+xsGY5RShzVkWPZ/887NHbWp23AveZOdk0GkpbrA6OMhahJF4ngGl4ci7BReMKQwjSeLrqdRLgzMF4E5PyP8UaZlMwV7GFmYUPnKUtUaVwXmFnIzitBSdDFrVkRmkS1pf3ht7EE06IPYooREGUKVZy3vPsiWoDt+hVV1Yrvg1VeYwn+/YXDpgs45uf9Lvw5mFpZjobHD7xmQErDetWcQjv467LQmre+eLFuNhrb1QC9NRo+HjOOjDlBtawARD4mDoG+QmpJXnAAApqVog/R4/WFPYokGYdUqylIfCoMeYJNi+P+FTY7OlEFK5hc6X7Jx4fTD24AJyUOxeY+zR4My5aCGPxMSoTKFYgyiTGbChqrD9R769n0cnJ4l72I/5aJbu5aR1w4Prhnmh8ZDa/8Rn9Jiu497h7TEvO6KpwUMVAGvyNOWh0Hiq2iuA2YK0veCQ7RuO+AvCCiTPSNq4vWspb4MBd8VtxUXvEkrrhV233MTBTzA3cLWPtbgfjiAX11yQ11PuriL3xz4Z83xwQOxSFEZCL1vrnMWSpY8JQElmmJ2rG5YhT86inaY2DfLXd4zD8sB6DWfeU6gMLn11AzfpxZwkcfDDPuTNOz4lNlvg8zc/8TE9puvIFzz4wZ5PqDAWpO9MGBWrNcFc3oBY2yo+7tE03LHDDlnkbOeNB8CmMldsc7grt3F8T9J4QeWusxO4TdChRIQKBEm95Pc/j9rt8vINOynOUaV3PHo49nJ7cOxRcGk8IF6n8a2ktvFd4892OnCJ481g3q2SZdHeM+YBtOcNu4ma89FeTgD28pJOwrbmI1bqOO6SxlNPUVZl7ITofMFyJogpTWd83FZ16wpS0fcI28qUvdh3hxPg9o4/M5vzMGdepjoaEJ8ORM14vLtuiRlAxLv42bz1UzIzhU//4i9F3DuenfP3PxR7GO0fEnuAO9zekcxt3RT13txFEsYfpMAB7sZ2NoH3wO1sL49dE4aQT3I128tJG49P/+IvtW1g1XZjeKTXcS/4HVxKaIlJnkff1VDS8KDZmCs1yc9xB9xW3DGD2Kk3cdCOvcZcNmRV7pEu9uALVcUyoSigp2O4s1MuyG0Ff30DZBk+/uufUkNHhMjEsPjviT2aHxwQO+q6jb2kKlqMURxLnVUZd2U9Hbc68lZC6XK5n8diwXJnuYr5NK8v9nKCenz81z9ttXzShJIlj/g68pKGxgHIUtbGm22UnpPcWn/lBjroQhbspEggNhujBwA/vDQBJGcLMtTpJ0OO5MOfoZNDCuHCN+pgHFKdT4Dlmu289ZpCSr0u6/oiJ2z3W+9Rsg6GV/d6f+zWcjw8diN2G5JTRKDnE8jdoi1RnCPRZLaEmpQ2Yb8ruGdP+GSwPKKOZhBo7RZY/VPfw/AfXLQ5VRXLqdEYcjvj19b7LoaP7fpS28o/zmucPtFfPf3nY/tOOnmrpw4guNqxdWcmAJstD6m2+/FA6CNcNr42tAQT1zpwiGtx6+KMDNEQl1LX7RR00OfUdTbnIrP3jcjGIAMYNNi/KHZ7v4NiB6J1JZKE8dq/dUs7ezhpD9QhfiDm4CYnhtAMQq52UN0S3y+O/XpdLNuc7PXS68IvV+Y22Mf/+PKvfF0f+Vd13WtbeeQ7vEnPpRphqpKmkcAAkdjX1qaKWJjYznMGPit6rVVj4JF2XHSV1gl3Ud/vIHl5GVn/SBI6ZCQJ/Isz2sV851uQ6xmCcllEVYrAnw7gFhu+79UtD6hfQexRV2c4oGx1z1xHthVH/x22G5El8QkTlQv6XcphGyUy5BFhFtbdgUGm93IKArN1A3nvGYkr3xgifI2XYcKDoS/WZaTERcWC8YA7ZDANAPgYDv6lQLSMYeeiaQcrzqF6foJktYXUHvUwhxv22S4c9uMN0Qy5AJpf/hbyl7fssyd8vXrPMudmimRX+zHLWjmPL4o9SQ6OHbAOyy5RJRBaMgftdSCNQjY1rSl7HfbLszRq7pTvj5DNtjGPbFpCNhV8L4ebl+wgrUqiSi0ndHLo9S2teUrTujwZPWohpuNe8Orj8AfBX2iXENE0++Nv60jo1gY8Jd3ydLWyEiPZIYJsIZUg/b8/A0BYQf720rA5BSXqDIOevNpAxiOkgSBi+jVRhs94ruoEsjHtxvWapdA9sasZJBwUe+iahB66KnDJHrykKTCd8SC7WnPhXtokOehApikK54DP37R5GA836fdYw6cpTSEWq5iTLlbM62YK7M4wHvF13F0acT+mzJXs2MUkUTVAuoUtJtv5zKhL8oxlxWTMD1iEzJ1Bjztnj47cMhqwnz0ccKH0CmLfv/WcC6xjHZduAYwHXMgAth+e8aBZbnjg9QrtGlalKB6MXbrFwbHzb2Gxd7tW83d4uE0TLvZ+127CgvEGncu65iE0S/byEOeoxrbZxnx+PKcw5MKzcz7VToaPHg9/3AsehA7EEgTGcNrSbCsi96qai9Hs0iGG5Q6GxHeL2D+WLONOGPAmIO5FZouojCvLNaTTgbszvfQ8ayek767jeSBZc3IZJqmRHvfm3ZfGHiEKh8RuF6W0q9iu1eUqdpRktohfl25hg6IOnGlwyqvLvTy0qjhnmLRW9Ho3288pPEmvp8xrOme8j/g67gUvAJz90UNLb6e/LWEkv63ioYsfdG5AK2GXxAl3+S7lMGKv24BkEWAWaX8p+93LdXTxI9li3e6S2wru07ekCAZTstEAOuyzfZgmD8YeDrKHxK7bKsYeYcQBm2OHXyRJS/urQllUGl2xC397u5dHYFJ5O5+EsmsvJ2c6l2nKvPKMO/0jvo57wQOEvAYIbNBL2bVqV7LxQztNNzZmD/oz9m9ZrqnPaJY2oaUZSc9pQgmNomg9mIItzqAfnyZSdHgDPT2LtTXqhnCC9QaYLVpdyIdit/r9kNjhJMYOoD3ghpbseBBLplhK9bqtakHikDx/tpeHeg8ZDeA+e9fmE0oiywlVRfBb0zCvzePuwQPHvuAD1DaYcRUUHiLrx9QHQlmTJNwtQ2+624Wu161kBRCNwCRtf5bGBux4yGzRkq0Nw4LahJGenLJrUeQsGaaztqfuPReKTVPx7Dweju+Lne2+A2P3uhN7GsnfketqrKXwFEBKWl60vA9mZDt57P59Qz5alns5cfJLuLIE8+Rwwz7S67hPICI8YFkvOMpFAxEaoJMxd1bvgTxte8veAycj7uy7RO2+kZs7OYWPshSaOOJURkPCbFOq/OrtHeRswg9+U7HEECF55MkpBz2zBTst5gQo5Yb4nDxj1+ae2HW15uH0gNjD00H6PGxLYXj6iotV8wy4mQJmxYkgqV1VkLzbzhsa3+ZxMqJQ06Af84FX5hJyGg14U3UMEzQZ8+/ziK/j3uFDWzIc8Dp5nGoiJ1dUFit2KKLYkj2yux3i003oKMIFwrTROK06m/PDFGdS1naYTRJKcixWgG+gVzfserx5x59/dQH97JX5vdqgKk1osLAq6TTyQOySpgfHHpXAdmPfbMnJdY6LPUxWQwkV+uZKJ3N/cbmfx+sLauwbXAKvLoCrm/2cAA6vMuYlJgX+mK/j3uGd2dkYeEm7HOAEoSEJveL5EsGYTLpddjISo+3ZTodOzvq9rvl0qFmXRhLIsM8yoGno0BEAX8H8t/HQ2QLuyRmhvP0enz7h0LjZ8FBnpY6cTfZM1H48dnjD1v8RY0eSRPdsXE/5JOj3uLgrHsJ12GeZo4RFi/2toiLCbGH0SI0IS+n3bKCVxpxIIlnCmais5tk3bcmv9QoED8N/yHwVrWMQ9BmDAfCgx4VckfYns4XR7IyuFxg8gz50Q78kGQ1i3xxN0zpjGxhLhgPoeEBtmsRZSeFp7x5sHZOE4kvDIf2cJmPgwxeM7YHYkSSHx27KaDLocxFuNnzNaMByzO8oMYT403Zh6piw5908pN8DztmHj/nMF3s5aVlChkMEP1vpdVsI9SO9jvt2DVaOYUpp0hZoGvqXlo4+RqGuDF0L42UGxj9RixlQGypx0I9lgV7dkIkUVL6CrEa1Zeel14NUW2iQ+ig6wPSOWo1XN6zbgwFaUASeL6kA1vh7Y8fNHTAZHRa7dXMAcII76JPMkriW2K3sCklRAMs1VI0Hu+Sk1i+XkNss5oGSfXq/WkFebSnzPRxAf/B5m5PJaQfWVfDJeszXce/wkBbluMv2TxIOWkSoFOC5yLA7iGo8D1s2rNGtqYFZ3aoZd183HPADzXN+4FVFVGBR8MZwQiUAgDtquYm7pxsN+f+3FXAy5G4ZWneBqH1f7MDBsQOIsUuW8ZAJRAkQmIlBoAZGHZ2iiI7a7tmTvTyosraAfPRezMefj/dzOj2h4JM9VQB8I7X3tV42jNFyQ9Siej5eg5lw0EE3ky/0e+3BTxVYryl9Z15JutkYYtEWWePhNyQ662oVF07E2wAR9iu9LnS2QHM3g7PSBmFSW5Y0IQb4u8yjKbzXF8WOesN6/5DYbbeWNOX35sso3hQXYJZCyw3caAg/m7N7E1CWIlCv0OUy5qF1DXcyhr67Nu6sh7y8gNokVcuSpsbOsEXbCtLP6A/1iK/jjl7QAr+KDluJy3V005Cc2i3a+FYiDlwwkdyw2fLn1xRORZ5Rns4udzKm4NDlNb+Q59zxbNcPPx/wJemzJ4aGNIUBNQ344aD1U50vOfV8IHaS0w+M3SanyDMSTbxn3KbnjrMJcHFJ7qzlhE5Omb+iYHkVhJgCTmY8pDTgcGCS3Wk0dgg5oZNQ7u9uDjk/ZXn0yEua417wQCwDsK1atd0wGs9SoOT4XZyLgxh4jbUuqtZyXs2qXob9SIxAZSa8T874wasC20XbnbG2onY7dM7rkzElVQ2tKrjRkDX09M4UzGgyQFrhA7Hb+eSg2PM8xh6lRgAbjNWM5ckZ/x6rkiVPoPKZSpme9IDFKuYBAH4yglusjN1UWwyrmFPIC51OVCD7BkvztV4tux7mySpp0lrEGAQgStYZ1Fb63XbAZFQ3dkWo6BtAY9LpcNffvaqah9jNZs8FQ0yVtxkVwN2CfqwnoxgfIQkVd2zTjXkodgkSJIfEbq3UWLOHv06nQ8v5MNBarhn/fMnJ6Yby3WgaoiV38vCjHkutumnzAfZyQlXxCeH4JFLVR9+WPO7oBTxsBudqQ/BFsy979IduhW63cTfEsB+nmOo9X2PUNW0838/MCJCYTqWJJIkIOyHzReS/qtIILbHpa3o5gx9SVlpmS+iZ2bz7JrYG91y3fyx2FQdJD4sdXiG5xR7IIQY8E5HWUSTgc4Ie/smYu/pqDfnRK+7glofczTisKjqQQQ/pJVUNtNeJOSFN2bJ8S5k+SfNHv8Mf94JXhYyGrL/TlN2CummHIqUNe1YlooEvwA87cFaduYR4Svah9GQhJUZ4tq6LDnvc7W5nQJ8eqUgSyLjLA68p8WKzZb99uYZc33LHG/RJ6TPyiHY7kMXqwdhDfofELomLcnyRK5uYiUNVU68mz4BL0vtacrYJKo3tb9Ur2jyShOhPIOaj3kOubtqcthXzGpi46ngAXDxiYUkc/YJHnHii8YQaBGAVwIX4/7D3Zj2WJcua0Oe+xj3GnFlZ0x3OvcCVkFpAS6h55R+AxI/jkX8BUku8IVrqBwTogs49Y405xLTHtdfgxsNn5r73PRVRu7srq3egdKlUlVkxmEX48mVu9g1KcEAb+EsHeJKt9riX3gG7HWSp3Y7RiBcwZfu42Qx4d8tN6R0vl5MRN71Bcp3jBqgrTjjrKrY6I+nZame9WLrnYjcy9RGxS9+zY2KMKXtb1BVPd9WdFBWNle2WuPqzGV3BxyO9NG9iHjItCE8YBj70SkPcz0ls8Ldac+i13X3q0nzcJUQMOseORh/S6a6ALFeVbN9ZCw9Q5SyWLXHiqB+zT30Tfa3Las0J6bbR+jsjZiTLENqWcIKu50Zq20ge8a9vOHJXp0EosSSaiwFPx+4cy5FjYneeb4G6ZskzDNycO307bXa0ylxvolBsWG+IsSkLhOUK7ovP+GazPLZboCjjQ2/3moOcAA7YnOPfmVrCC16nveHVxU/aNnFCvUuw3j2bR0iAeSBBBURRKUrQ1IINnw4cdGHiJgIOLpLyuIC/vIidCnc25/cKARgp3sTIH/0AmZ4Tjfj1G6oHeP9k7KLlxHGxV3pqExjm6pq9cesijQiNcM7FE9hfXfDusFrDX14gfPsDyyLLQwkoaHkxxYhdrIOctCsjLYVmnZFVkj/ai1sn3qVB/GEjz1LHwIjRWaZGwTV/aaJMfeskqA0lp5FqLjCfxhre1AdI+2sQbs61o+NZ616cQeYT4OaKm0l136NqgZUDJkXX0VnPt8pEeiZ28506JnZp273Y8xhfZB+FwLuA6ctcnXOaPOa0WOZkKx3ksWuJ6pxNUj562bWcRGHF8XAxye4XvE57wzskYJQhBK3dFwI3krLsxRR09dQCwBPV9BV1oOLMtn0IwPs7/vKLnOTsLfUYZb0l3xOgstfDkjXu/YKb+mERT0aZak+8yFN8pn7wTOzSNEfH7hQr43Zt7DC5Xcv4DU15No3+svj+HYdNeznIX39xkIfZ7VgLEy3bvPs5ucUqTnChrcn4Vn2h67RLGtOAX28S3htgDR30l+605g0eAl4UDYhlHZADArcJMmlrz20aYuLP5rSYNA6nqKP2VnmtdsJdzOH6PhJK8OGOnRkDnFUV7wCbDf/9VOyjkWLbfz52Z76taojGk9jF3r0s16pZKbyYTidUXlhvGMcwUP8+z1IefU88zWTEjV2r2fEwxJzMN9blWbLRfOHrtDe82q1HFV0jNPR9gtqKpEtdUfDkNLy8KXl5HzEr0fomEqDVqS43nmwAJmUigVRaP6uSMW7vuRmaHaX2VBbP1RXC6zdwP7wj1GA0ShTBn4rdJKmPid1+HOZoaHifbI8ymGeUz3MKaeg6hF2rUhsV5M018MfvUh67gXeapkn53D1Ev9oYV1kmxYY8f/GDp9MuaUR4GVTNlQMz3xAgF3NlEqnXUd8T4aejfWSeOBlVGZOLOU0QYrngo5EA+iF5LDlHeQvTaS/KhAP/7IbOHV0Hl2UJW14WPEXrmi3Qrns+drWkOSr2LIuxu7pWSeuKm3I+TYSWokzdnF3LHLQT4/743UEevJh6fXiYj788P8jJGfbI8Wu+dBwNcOonPMAe93qrHRafOhZZFsfkBwoEu5av/sk4+ixFrLsIMJ3wtV9RfcBsIEXLHPGqSGwMpM2Wp6RJU//uTxzEZBnwig8cbh+AHnGI5cYjljjPxA6AdfMxsQeB22SMvW2J/THN+LsHIhzLgoOm8ZhoyTxPb6bMc7Pv5eG8h7QkwkTjYQB4dRVzinceU0l44XxW4AVseBu5I9s7CYHU8bBflIjWtwr2Wix5GhoTKM95GTW367LgZq7K6KMavwfAj1csCUQQHh7hb65UtYybOYwruB9u2W8PQ6zLgxJHnosdRREFnH4udoH8ZexNA/QqwDqmWKy/ueKmHI+SGVuWqeQHL+uWh+jl012ex9JJygJS5Qc5if4soiTKC1+nveHNBGDvVBSTxBgGAqr09ETfExJrbTPnI/oQ3pHXmWcAtJY2emBRAM2SiMAiPRTmlyqqx+JfXbMfHyTG4jZbSNMoaWKArFaxz06Vr+7J2GML8IjYsRc7/z4keIJNXr1DuL1XEFtLJKgSvkXhyOH+IeVRVekQsAdxPo1yHZYTgLTZtbR5yeu0a3goudpqzSEkrZp+iCQIl3kK/QPJc3XKGnS/RpY+qR2YMx7yDG6q/W0FRtE3VbXdM6/+rWsObLwaE5QFSdllEWWmkefKklLNy+diVxDYUbHr5xj9zx46gxNEB0BbRcn4nU/eUlV5mMduF/+J+fTDQU603ZwwzilzeemX1tOOXgkJbN2p2L91VnSZdynuHzlxNB/UbZMMCoDEJtpjM0VwV11hOJ8ie/8QsfKYT0n8mM8geUYJ6sUGKEq2/Gxim+exxIm1bhh4T8D46diHAeLccbGrYvB+7DJRfExOvP1wMaNcd1kgTGv4xzX5vl2fpqrj85TH6xvOAdou5bNWZxPNSb56DbfcUgWh7ZKWzwtep73hVdsQl+ckJg8hyjW70Yi8zyDEsLgssfwBQmBnU55wQqXgqA0/DIQOLJbsZlQl3D/9Gbi6UHyNIysqzyDLFtjt4K8vIXf3vFSeTSk9t1rrwMkTm1JVvGz6DG5UPhs7ACVs/HzsEgRuVGk9b+3JJS/aI4Uo/9OfCSvYOrgPdwSjqT5O1MPcy8MUhmWziflAgWKWk3t7Fz9fhoHgtBcul33aG950F7fbyLaXhgpYslzTg8g+1DokwxBPX1muYq0b9R5tLVckUGhN6s/PeHksyzjWlxCImjybEqZ7ecGSxsSdphN+fLSR6bQLtE0dkidix3zKKecRscO7dLoH6lji4oxDs5o0RH9+ppS+MS/EInxb1HO2OB8e2YGxPACdrFYxHzmbwplcYBSrmkUGlqvr5Er+Qtdpb3g4tuhsStirwXDTHjCKrMtiF0WEIW10cxCxC+Bux7dDDxIo1DDMNGEkBHaAFI2J8YiS1aa1rrVzlNXoOt4lJpN0CQQSBv6J2OXugZvs3yt2NYLoB5LHVbkAozpJgfQ9pChZTo1HVDTYz8NKQe/4ADkHubvXN4vmZLJ9ACHTNpx7weu0N/x+Tdzq63sYUjsvCBlGzlFCQ52qRSgEyglmFt00XF5CipIbRaeSoWnUnJeb18oNN9ESqNmh//vPUXxzmxwAVYaOztlrMosA9uHPqMeId7eqE/lE7MqxPSp2pfpJUXJDdy2kg14iS/1RSdKjzHPIqyu+AUYV8LCEXJ6hP6sP81COrmnjuKoiWE5zoixIDhcCB2jbHQ0aXjAH5LQ3vJ0oVxekvGmnJcrGeU/lLef4b3W8JrJwEhlKhnWX81li+4ADKK+4E7cm/DZaM5qhV1mg+P6eJ91mC5lPWTPfPXJAFYRqAc2OxgZZRkOE19fcIE/F7hwvnMfE3naQ2Zh/13bA5UU0OsNsAjxq+bNYcWM6R4qesadGZDkV623Ko2ngLs4RzsbwG+bTf3WD7E9vU05X5xSMGo/Smy0bP/srO/V12hvehka39wR1aasOXZ8sGI39r9ISYjo1P7znlNJn1KARRUcakGu/7n63BqYTWjPuWn6ec9GoONpVquS09D1C28L9w2/gvnsfN3t49yHZx9xrr/up2IH4dvrZ2AGgaRh7UPhuWfAN96DTZtO4VB8ocT03R2QAACAASURBVGqv03XAKsCdz3m3sDxUUtv/fglpO4S2RXZ5BnlznXJ6XFJlWKe+rioh6m7yUtdpb3jnKB40nRLvoeN0V1fsT2eKYrTLnncRp249bbsERrk7RRPC12lTlwVkuaTAUlWmYRfAi57KStsD6CdjhBDgfv8th1UAXD/AvXnF09ZO5Wdij0Zix8SuDwUhEdS/jHmov5Q8Llg2mbCTAtXQ98DrGwy/+yP8+VnKo+/ZOVI3lBAC+asmIQ7E3rybjHnR/jRp/cjLpOr0gsUaV2LHxZVFsn8Zj3jpmqjLhbGNgsSLqewU7962EWOOHR36wmoNuDbxRk0EtdmpDTv1KWU6hjws4a8vo3WmNA0vh6sNy6AhHDKdfiJ2ZNnxsXvPC21JOIKBdO1hp5pBDzdC5Ory/tHATelLm331hZ7+qrOp0AZpW8BnzKcfqIAWcwIPifvHpMf5afD0kdcQSHcbQsK8KCUutg6VimY0vgjNBZQEPSRB0B1100WJ1ZHTqtJ0bnBAFngZ7Hv25YG0SesKoWkQFouo/uVySutF3ZhBlbyeid2wQMfE7nLt4ux2xLpvGmAP/yOrFeO3VuiIsnooSuXb7uHY96a0Tq03w2qNsFgg++wVpOljTpGKWBZsD0uaFr/UdeIb3sXXvssUcKV1tbX69v/eVSXrzGaXMDQqWe3yMmJQ7FRz2jO3lqCfjHlB03G8OWu4muWPrNbUo6/r6I6HuqYK8HINefUZsG6id6wxnv5DYzddHRqxqTDU2QwAGOO9bsKqikhLd3EeRaCk62gu/LiKefibKw7e+iHmY87glpPo5VzOVZZvu+OD/PirboJfdJ02lsb8mKbj5JLhtTd/MefUT8fgbC36iCtxVRldLFye85RStw03GlHtd7li16QskhzGhp0MWa6AH98rFFgvgHlOfEulJO8QIG9Uos97uO8/UPZus+Vg6bnY6+ro2F1dJacQgLGvt4x/10ajNTeqiQC1KfJyxYdlOkH4f393kAe2DXvuYYj5OJX0tpzc7YNSFvfy+iTE9BFXoGKXa7LIFHKmJmwXs73SBECyfVTsRxwmhUC4gLYxXdPyJOsHtiWbHYYvb5B9r2q6PoO7oKWMLJbA5TncrkV4dQH/9o7ty7qCu13wktd2kNfsfbsLVfx6JnYxK5xjYvc+Dr+Q6fCqLAAUCLMJ3DDAPfR8AEpeunF5Tg5rYPvWKw/A8jhYfc98TA7QcjJVg7MZH+izWSR2v9R12ie8wVEVNShbVcfa92pVe3inSmKx5ecdywXbMGYO3HXslwOQ3Q7hYgr3uASyDH5BEzFi3geyo4YBuLnkA5Zl8B8eeTFdriHNjg4i6y1xKvcL4m02TXLFfir289nxsZse/L6ciMbvH5YkwtibSzUhTfaPxgzKhtrLA1nG2YSJMC3XfPj3cnKO0100O+Jvup5vsxe8Tv+EXyhWXUISURoCT0gJEWIroiAyCcAAno77WPR9hOV6ww5LWcD9+QeKITWqMrbbRUEllAXChzu4TM3Kdi0HNyJpWqliSHbKm3iSbDZ8/T8V+/tboK6Pi325Zv8dig7dtfzYqkK4f2Dvv1mxU1PkUaMeAx9a0Xaq2+1SHkjlEucHIwx3D/CjOuYkfYha9yiTg/lLXqe94Z1nnanY84hLUUlq2bU89a4ueHIZCMq6H9bHrivS4rqO1L4846m2ayF5zhp/NFLdRTp0m0epMZ7ienVF2MDNJaeQqipgrczw6gr+3T37+bU8HbvGc1Ts2wYigbE77dcHVfe9vED0djKFBRVkxe0DUOXwVclOzmSSfrTzGfNvGj68N5fIVKE45mRMMddAPr+BazqS2F/wOvmSBjlfvQadZVehUuLGGO6M+BHZEzFyNuJXM2AjaEeTMeufq8wFAHZTNk1U+LLhj9ipD24Sd/dIpOLDknW5yd8BQFHAPxqYK3s2dnh3dOzWQoyMKENzikSnbTp2Z3Fg5po2aUp2HQdge3nIHZXR8Ooq5iPT8UFO0X18NGJet/efTM0+6jJ4MEBciXVGVFvmQO/FVAHM6rHtoqhRdNoIA6eoWVL1Cg+PqWOiNbLpWbL86COdMLy/Zevy/S0kzwmu0s+V7ZY81k0T3UOivMUTsYvBBn4mdpPJjiytLo8akrLZsmTJMqoDP1IsKhiFEeCbcFBfKc3DjUcId/dwqltjEn1hsUw/DyBNnH3Gfz51aT7iEkQVAJOTo0xelhhG+t+yZ+OCYaCRbq5qYANPVpOsM6CW9D3dLgxq7BzH7UWqc/18xiEOkPrxQ4ALHaWoi0LhCJThY0+f/NXnYo/l1RGxx6+V5+l0N8/ZyTiWXKIGy/ABLi+iIKufz/hAdW3KY7fj20Q7XS50wA/vDnKS9SaR0TWeT3j4j7kUeyJFTj121WwxCQ2pClLYTNio66nN3vVs/c0nwN1j9FfFfEog197/Q1lE68twMYdfrmHydgD4mg8BMp9gOBshW+7gLlSM9Pt33Cw29q9LuG0TB0rw7snY3Ui7J0fE7jq2DcU5lkDzicpsqAqyQnzZjVINeEN85nlCg+ZZzMNvWvhbZXe9vua/v/mBX09zcqrTk6bHL5vtBADulKUXzoob+Vfn/10aZ+tr1hy4oTCBKAS69zEmtprAVNrvjmP/vbq+aYDXN7yMekeDMi1X5JEtP6griBkiA8D2v/471P/r/wl/c62n+JaoxNv7Q8L1T8WuePyjYs+UUD4MbKkaEaTr+DV2O+DNKz7AWra5qox9dPN43c9Dtg0/bjZh+xUAhgHb/+Y/TTmpDIrsqCgsiyVk2+B/Wf9PH+13/gutfysi//Kn/sdpn/C2yfohqnYZxY36LNP06q1rbhwFbyHPE0fUtFy2TWQEucsLyHqT7OUfl5DrC2qqm39q27F/3bFjIw+PCNsmYmjG/8c3CEWR6HBG6K4qli+KNvyp2AUlN9sRsZvYarS/ubxgu9Zkw8uC8e909O8dv5aSQQAdaq1WMQ9rl8rjImJl/Oubw5yyLPX9VWKExse/4h74hddpb/igljDK2jeHDoKaGiC08XIn6038uIij30cqGhz36hx4d6twV6WvDbTBccs1xGdJucs7thJ3EnvQzsodnVxSv6XhhQ6gjk3b0n9KZwU/FbvL0qX152JHEAjCYey6GQl8GyWIsWf3Krz7wKlrlAvM9eepvXQTlDVE53SC8OM7OKv5rWwc1MpySJPel7xOe8NnKqaaZcl5L88TZW9KmQ0ZyNu0i6wEARCIW1mt+XWsnXankhjq4mF/b/gXiMDXFT/v+gKhyIBwDtd0hA1MJtSR33Vwm4onrsFtV2u4+RTh5gz+7QPMAOGnYperc7jbh+Niz2g+hrtHVfX1xNa8u2U3aFQBC/bZZVRBvIf7qy+AH94Db14hjEq4zQ7y15/HPMLlDG65xXA5hRsEbtdBvryB/7CIOVF5rYJcntG+Z7Wmy9+P/xH2wi+0TrsPL4gnmVOScuxX53m0YHdGTLaTazJinVrk5GCaWCrAEmg6SuWSkiuc9vxd0wJ5hvB3XwK7Fv5hBTcIpNayqCzguiFBFaqCCMsQKKiUZfDLJikGPxF7FH06JnYjhFRJEydUBT9uMoIbCJGWUQXXtAgTXlLD330JAPAP2pLcy2OYVJBRheztQ8zHr3aHOV3REtM8XN14TCntF7xO+4QXScZbGUVO40brFa4KJTCb8YBd/Doa9iLPkp6KGgdjvQFmUzVMG9J4/4d37Ed3Gfzvl9yUkzENxZodMJ9xlG+SFabFvt1qrEq4VhBYtNf8qdjN4v2Y2PWCja4jR7XI4b/5kXeTuw0fjjwHPtxB8hzZh0fIdgv/+3Ukj+PhMcUxnyH/x/vYZsXbD8xnsYRkWcppofGpjLd0HfDZzYsmcZ/2Ce/ATWJtMehmibqKPrln3Fzy7/LsQOVWipzaMuNaVXQL1qYbBYplXjEuQwRV7UtFSwi0Z3/ziiF9/QVf68NAQFizo75NWVJzxhw6DLD2ROyy2R4fez/E2KXrkpu3CNzFWdzU7mx+oEzs9PJJiPD0IA+LAUDMB1+9OcxpMo59eZZeY7iH5S/5G/7V18mf8LJaH1zGDHZLckXgON2sG/U0jP9fW36ich7iswP9F5zN+fr2PtrJAwAUBuy6AWFcwjd6GlfaC292rGsnI75lTENdiSOA4mS0Xv+p2KNjxzGxA5GoAoCYnS9ewa8a/oymY2BUMTZTQ3h1hTCu4JdbhMsp3D/+Ae5vvop5uG2NYJ6tAOTyDMOkRPblm5RTr21QteBE32P4/Ar49lfcA7/wOu0ND5fE/4Eo9Ck7NfcNajJszho2dTTVLeWCusyzPDC2kBEhup4KwGcz4P6Rr3M1IPDvafmYNYqQdI4QgTznQ9L1kB/eEbylok0y1SHRTnvsZfZk7JxeHhk7kCiCeqF0d8vUmQkBMLOEYQCKMQkc72m9md2tOGdYbWIeEgLcvUKdJ2R5ZX+658XbctK4bSosTQP/229+zQ3wi68T3/DQlpjCW1UGL/akjehsmJHVKgkT9YqHcT7ZsWdZOt2DxHahW6yoPBDBZS2iPaXiXNzrayAfkai9WvNBLO378s/y/VtqtW84kn829iCQdndc7FpuWexifX8g4tUBbW+WBbE+/8xXys1ntLSxPIJA2ke2UE32w2T2LCfvEqbGZgkvfJ32hveek8taWTyPS33VAzibpV/6EPhL7XriUPTfANLfNTuCwcY1R/ejCqEukT2uIVkFmdRw90tgTsfpcDZBGBXIv7sDXo3ovxoCwmdXcBvVbCkLyHQEuZrDP64x/NVrZKsd/KJMpI0nYndtx8vmv2vsCjMAgFCXQOaQvX1AuJqTiXU+4yHQ7BDOp3Drhi3HcQ335WcxDwBsoZ6re+HjEuGv38B/+z7mNIwKFD8+cHg24wOM+xdMaMWpb/gQePosVzpqL+Olzi3X5JUuqXobaQnNjpoqaw5YBFALeNWF2apg0XoL37XA2Zz4lRU9l3D3yK/37VtkQbg5H5ZRWderUpjznifqO7Wln02Rv32gDWUYgI7yeE/GLsI3xTGxm9X8lmq/WGWABPi+pwwHAP/2jqfxnZo2TMdw377lhVYFoNxynRSCo8mb1ujeI/uwIORAcyr0c2WzSfeRT1J7H3HpiNxpnWvtPzef8RewbZIEHIDoPOcckLk98dMh/T3AksbTCsbs55PBQQlZLpWiV3L6OBlTu30yJgOqrjEsFvB/9SVP6odHyMMiyXTk6uadZ0/HDhwfu/MxduuYGARBVuyxo6KNptOhmdw/0PXjccF2Zl0ps0s9pIyI8uEBAJhPVQGf3aScDJptF+iWF+yXvE57wxuepVLIakdzYVGpa3jPX0BOep2rSl4CxzUvbQahNUeLaA+TKdZFp7iZhxsCcfLbHSeWux2nmVfnCU+y3AC/+QruYQUvgWVKnvPjmwbyd1+T82oW7mXxdOymp3NM7PagKtHE5RmlrbuerUsdDMFarWczQnmXK/jrS4SJcl3NMXy5oYTeMESOqpdA1OSH+5gTgGTuBsDlc36dTyf8x1p72A/FqkQ042bLhyHPATFUn/qS6iUUQBqYGHunLHnx27aUkTO0YlEAbRtNvAiJDXTVjk57PfxAorObTyGFqnKNx8AwIHsgjsbVFbBaQzb9LxO7tlVdqTJ7D4+sq/VriiqUya7lQ6H4GFeQh+qWK27md7cxD3lcUDJQlQ7cfMq3iaIn3XjMk309qPfVBmjxyYn7oy7nE9XO6Gm+4Gs5z7lJhwEQz/pb8d/pJMyTFJ1tCjvhyzKRH1RezunQJlzO4N4/cLMYBqcfIMsluxo3l9R3zzI1EaPMdv/6DPlb8KK5x+7/qdijNv0xsauQlHQd0LYcnNkbImreeNbtW3XzVuSmvL6EX2xI8J7PYh7+jKe1v7kisfvuAc7ohJqTtMLvtVjy57BtIpzjpa7Tjl6UBN0Qjx1r37Lgpmq7RHo2Vw778159TJ4mLWOcntbRm1QvdAghlhNGwg6vzuHvV3BDwHB9Bj8bq45NBRfmGK5ncIPAP6yBhyXyP76NuBdpOw6bnogdE9rWHBM7ZhOgaRm7fg3UFeO2YVkIcE0yPpOigtu18B8eIaMKzdfnGP3uQ8xD9Ofbn4+RbTu4MIdUJYZZFXNyQyAy83yecP2fLq0fcXkXBUBlRcQhwsCN0RYRTisAx/llkbxEW1o5Ou+TZuMwpPF+EEiz2YMAZGTkz6Yc4a838P+04htkPEK2ZzTm7h7gigLZck0yxsW5KhCvqBq22XJDPhO74fKPiZ3lT5a+f1AJccXDy2qt5BQli/QD6/um4dvr3S3qD3dAXcc8THm42BIF6YoCYVIj+8OPMad439gxnuh68oLXiWNpHCeO0zHcuOZmssHIbJIc585Vds/TItI5B1yeRQUAuySKXVyBKHtH0oWyhGbTZFlzccbh0mQMGQKGN9dULVBWPzK650XfUlMA6AeWKEX+fOzA0bG7zKfYB+04KdYlwpODSmpY/Jkntmejm/7V9UEeYipnKkeCLIP7w3eHOV2eJwqiyglGPfoXuk77hB8CTzj1VJK+5+bNMnZI1NHarbeJDL2HHY+27TlbkE4J2BKGVAuvNzTkbTvIcstpZ6ZWkkNgH1528N/8CHGeLCUlj8h2S/m51YYxbj0nnDaVVIPjn4y974Hx+KjYZQhJeQFIpI+242V3XPO/t4xfug7oAKw3SY34HRGRB3kA6mWlolazKcLtXcrJO5Ud1It3GF68TMdpn/B2ktnGLgqSHYbhwHhMQtDX9FjhrOxHuzw/tFlUlTKAWitiF0XDlqjxcLyYlQVkXCfjMdWZMXyMvzhnDa0qZdZLx+UZv/8zscNnR8duorFOiSwWu3lBoVdlg5sr/vn6gt8DiHcTFOVf5mH0P/OsaprDnMyRxMjcZfniZTpOe8NDBzCK2BNFJ8aS4mwaW4jsdSsWRNV0Yfbv/ZBc+kRPzbrkRrJBynwaQWAQ4WkMUHjpXKWpJyM1FdPXvn5959TZw3TdP9whuuU9EbtpvxwVuxk6iBCzXpess1XOQ3Y7qhWrl5R7WCYZkGFQ8aj5X+ZRFDRv6/bKmL2coFJ8tmS7TYOyF7pOfMMLa9ltwxp310VhJBnXBEBNFesOJGiuten6gZtiOubGswtrnrMFdz6DfPE66k7Kbsc6WrEnAPixXU9pjdUGOJtBrs75/7wHzqaE5wYhzmZCNTQM4dnYpW2Pjl0M2FUQsOZ6lhZyPkO4mMeHkw8x7wmurpUDwHIu/P5PB3k4fRDD2STmwzfcXk5nxOVgPmVesxk7Ri94nX4Nv1qzH2+qAFCRI3OiznO+qtVMwDlHw2KTmlYt9GgeBiSh0m0D7z2kqkjghta08ykvh9uGaEdAcScjdjLul3xIZpdwq60CxALcipNOWSwTpv2J2LHbwd0vjovdWqkAv76VIx/u4WvqTTrV05F+gCvVsqYu+W/nkH32mj9LzcNcPvyjmSMrsXxcx5yctmuN9ih1GfHzL3Wd9gmv2HRczAkU27WRgeO8T2XHZKRipXkauoBTVel7bqiu4+bSlpwb1bzE9kOqZfOcHZX1lvLRZcEuSl3DXV9y0vr9e5Y+WQb5A7HhrutpZbnaQFYbbl61enwydnXDPiZ2N6pj7NJ1/LOVHfr3AGJHyKDFWGq78nzO+PbysCks7NKaZbwL7OfUqOxH3/MSe/fwyePpoy5R2bvbe/arnePo3kbugAoU6WYwUJjV4iVPSVlv+As1htHe13HjGlJXVPRSE1+cq7LutiEbqSiTC/XVOeT2AWg7veB1AApi4EcjuEplQUzm4qnYt1sVhvr52K2+ll2rjnqrNCmuS7ien+c2TXLzruuEYf9wh/DXX8B99z7lkWV8KE3Kr+0gwEFOMOzPeKz3nYET5he8TvuEd7px5jP+go3UoOKmUMIxJTsmPEFNmbeutJPTH/beTU23rokp2TRRAg8f7qlP0yiM175nGNQxL7BLoVABe+ikbYk96fukVDwaPRu7G42Ojp1wCMYeRVStu9Oo5nxjjn6qj2N6PYsVy6rv3h/kIU2TOK2aj7GbLCfjscqWbwIZVZ9O+I+6FH8tAE+wuoqTRAC8QD4uWZOat5FJ3DXKGmrbCNVFp0yh2YSXSrOENE6rwWYjFKFM36/IqWCwaciFnY4hf/yWvfTMR3Sk0fIiz/Sp2Msilj4/G7tRDEX49ccjbW2yVy9VATwuOHjqOl4+g0GIx0nMta5jHnJ3z4OgYrdKpmO41Qbhw13KCULuwPkc8riiOfIn8NhHXMPAAZBdNv+ZCx4aPa3sYtkPdNLWjWFKWaLu1JHC1zQEk41HgBeKoV6cQaoSuF/wl/+45MTUucReun8EXl1R82VUIJvPyDBabuByxuumY3ZajBn0ROwuzxIF8Gdit/aptQTl/hG4Omcb0nvW2xdnvPOsNiShX8zhP6h5QeaBV5ckltjn31wlIwaA+Wwzqo5pThhavhGXa3a67Pv89qP9xj/6Ou0Nb2N8gJu/KiFGKjZNFSVjw7k41XTO6XjfNoqWHGr/COd4Ajcc3IjCe10/AGPqTBqswSSsSd6YIvzpW7jPXsH/8QfqTNqpavLdItSOMb+lJ2KXXZsksn8mdriS5ZvFfjHWkb+HzMZwTcvJ7HLNSysAv2F3xiwxAahVDvPg3UFdwPMM/o8/8GFrdimnfE9ISnj/wWT0K2+CX3addg0vSO4W2kVw3hMboiWBKBiKov86elc9dWl28b9hPXC1yXE6OsdAB22nJ6M0OzpjhxAJE7Ld6tBnBTcaIby/BYDEjjqfQ0JAuL44eECei93q6aNiB2LsKAvGu2lY1y/WPIEV3iDNjnW7YYfUG8rcTSwPbBv+DBfLmM9P5SQhxAffjeu/dAB8Yeu0T3jnEi5F/yzGcw2S8CVqGmY1ro3FnXmS2uduNkkQVE9cV5aQM52yilDBwEghXgdDdYkwG8N/+44gsHPWyHL/yE2nuB7J1SK+7TjB3N8c/yx24tXluNihF+RMXcNnU8qEtJT3iMbBK3XzuLnk9+57DqhGFfDDO+D6MuYhIcBtG6Cq4Kda59f6cZqTAEmHPgh9sppPNfzHW0GFiURSGVAWSojI+EvLPKTXLoV1Y4aBIC4JkdJnuBCzfJfHJevT7ZbDniwjs2cIPD1VncxI1JmygcygzNCWYbXmxfNxAX+/BMY1L7RvFTT+VOzWojwmdlEHEMUPWezOtHK8g9vukorxj+8ho1pNI0pCDYoSeFjEPEwqHM5HoJgDgL2c3GbLONV1XMqC96YXvE57w++bENipZ7zMPIdT4JTTPwPgCb5rD0kUZQn0WYLY6hRTbDNavdrp96hK8kMVUei2Xk/ULrpk9zdzZL//npY5W9VerEq6fTyuWINX5ZOxo9mluvxnYo9eUVEoiqhK2TZwIVB6I9cW7a4F5jPVq1Q3ERVcRdvFPMwgwqmjYX8zR/a77wDnYk6A0gzXG7aBtYR8yev0a/h/7mLnfaqRgUjqgHM8ebOMqEPbXEgdEgQVZLKSQjfFwRqPgM2WPfT1lvhvdQ1xeUbg1bhG/s17PiyTEXvWXcdLb9irxZ+LXdWCj4pd8fqs0ZuI8Ixfy75noUrDmdc7TsOO0cMi3S00D2QZXOYRrs4P8vnnOUX9Sz0k3OevP9qv+9dYp73hHSKrybAkEa03DBwaZRlH5wqxjbBXeyAMU26bYjrmv9UkGM5xVL9aEy+yXLOMqswxe85/K3vJrbeEEFyecUPVhWpF1ghv30PalgrD1rV5InYAR8duQyuZjoGri3h5ZUuWl0pZ8eIqiqlBEDqRNG3KYS8PzKd8GP/8fcwHwGFORaET25b/7HYvXojptDe8INHUgIh5MSc8l7PuxnKdLoSGSdEVzYBLhQM/LIgrn4wizxXm3LciscONRtEq0jUtlX77noyhH9/y1FUDMPzhO+rCa/nl6hr+9Y1aVD4Te1keHTux6Br7YkX8zWLF+C/mhPcaNkYC5HGZyrbtljnsDvPA/WN6kPbz2c+pSBIhf2HQ/ELXadfw3qkERogucg5Qp2xiyzkqH5Klo0gcjxveBP2QGEXncw5gOrbwkOdwBYDxiMpePWWw3XQCl49TV0RP5ezmmh0e/bOfs0Mi6422Exu4QSU1yvLp2Eejo2OnUvJwELurK8bfdcB0ouWSmZeB94dh4N1lu4VsldwRpbv1Y4tcwWlDRFbGnNo2mqqJasSjrj6RuD/asrrVjMhMZaAH1WzVNRvQdpmVIdruizBg9U+y4YwAUZLDSNCGR6ey7wC5fyT8uMijzAb0dIwSHIZh6VVtLM8gnZYZQhvJJ2PX6e9RsZuoqkrlGRAtxrTeRqAdAMi2ZWmzU3k8ICoX2+e46VTr/yzyYN10Qo8qy0lFruxzpe+B7cs+5U+7pAFi7Q2w7rYugdjl00BZ2razEyxCftU4ADARJrWSNKkOoVZ7/DizdhyPeCJeX0YbSTedwivgKxKq1fQYfQ+ZTXixq/h9novdGSXwiNhdXaXY7U6g8Zu2JFucydQBYVBTZGEOr64P8kDOB1DOZzEfuX88yMmNRvGi7PI8uQ2+4HXaJ7wiBwHwgnp5phBYPd2uzjlscXxtYzpJwqTec+N5DzeUB5NTZxtdN43c0c4xGnlNxsSjfLjlL31fCdh74sIVw+N8zoHNpoFbrFjubJT3qqfrT8buHEusI2KH4m4AsJVqD7A6daNrgaxOcwub7tq6feCbYj8PUx8wl0Sb9O7ndKuye7MJzCHchKJe6jrtDW8OIEYiflxi30jZ7XQTdB3c+Rn70oqTcdsdN15ZHLbWdNwvA8fuMr6A303S5UytIOEzuLM5XJ4jXJ/B3ymLabfjUGo6grt75GmsQDPZ0nE7qgi07ZOxy3abHrCfiR0KNjMvKjNckK5j792s6c1f9fwszQ2uLjSWDvL5dcxD+p56OnpplymdBN2H+5iTOD54cv8IN5/C0bXpYAAAIABJREFULdcIy9Wvuwd+4XXiJQ1f3a6ukvKVabp4DnJk10YStPXXjT/qVB/GFWprA8S+twkL+YclwvUZIILhzSVkPuVGH9cIX96g+/oGrhtoBR8CpC4RLmcYZnwjmDu2jKooOiob8lifj12Ojj1i3I2T6x1wfc48hpC6TXkGmasN57jmgyeC7usbft+9PPD6JrKdYj4Py4OcXOaZl01au+7Fy3Sc9gnv0qQPQwDqCU91uwyaZrlKUiMMh1gPfeXbECjax1gnpCL1zi85aMq+v+XlTCew/rffILu+5NRxVNNnKfNwbQc/pPocIcC1AWYR7zIiGZ+NPUvoyp+LPfb0gdjbd/cLQOOP5sfDQD366SRxT4scxdtHXtTvFzEP4/nKEOAfVvBDODSMUww+IcSqeDYaof/88pNP68dbCtG9POcpo0SLSNK4OIumwM7+mYyp3VJVVCrwnq9oHayYAKqbz5JM9TAQ523TxPGYrcO5Uv36IcIO5PJMOaD6ozPVr13LuteWqQ88Fft0cnzstqyzNJ9FCxzOE2rG7xw/vtmp5eWOE9md4tr38nCjWmMfYj7hy5uDnCSECCmOab37NHj6iEsZPst1dANxeZZ8kjbbpPmuBGezVzfUIgD+4h8WajGf81S0TbDTXvPDIkrgIYRI0QuLZcTBhFcXcHePnEY2O8i4Rnj/gZs/z2lNk2f8OiE8H7tCCY6KXZUZItFbYQXOVIdFGH/Ya3MGKv+G1RphsUxy3JqHrNbqCp7FfLIPi4OcnNpuSk/PWJTFixdiOu2SRpCUw7QVF1/xiip0QMSruKLQi6NqSfY9T1MFigHg5guDkioUlZgrh/RxGV21pSqJQjQZkM0W3i6HAEuDQiW77S0glN52W4UhPBd7ELhMjovdOjshEL2odwV6t9KR3AF8UKZKPOk6yHwKH2bpnrHaxDzCroW/vuQGNsGotgOmKSd4n+4ZIizPPunSfMQle4YI6m2KIBAo6nG3g4i22qyWdT459TmnnNI+kkBECRGy2mtFti1xLJMxNWWGgfWxkiQgwg3TD1HPBSKQdx9Y/oTA7o0Oj8L9QyJ1/xKxR1k+/fvVirxVldtz4xFkueL0Nwz8GtZp0RyoMTPEPPzZHLJcISxX8GNaZro8h/zx25RTPzBOX/Mh3zYvvg9/8iUN23pZJCjHpUMnl+fsHOR5lIbjpTGLJ2e0XzetxrqmTY1NEusqvQH0e8UhTaVDqW2jA6UQp5r+mg7akVDd93wgdPDzbOwms3dE7K4qY+wAaEejljhRkk+ED5l97ngUyyJXVRh+fHuYh6on+Okk5iPT8WFO+sA651gCfcLSfORlAxiDwRaVSmIoOWM84qtfZemQZ3sQ4GQOwMmhkjCMuQ/AhaB1LCer8b+LAgKyiYLqsPjJmJ8/5RAm3PPvXZEDswut+2v2v9Fw8xla8qdiL+oIKf652AFERKb0FH2Sh0feM8ywzdTEdOBFUgmx82GxhL+4QFhvUh52qQYoxXf/ANd1JH9YTvq1zRXEHoqXjKU57RPeeKFaRxuuXZarNGbv+wRdBdSqpU29b7DGN9c6BNWW0V92FEA1pdxhiBABWW8iVMCQiFZXu1EN9/XnVOV9XKiN5RayXkO+eKWtvGdi1+95TOwAUuwADwKr43vVyel7+JsrPtwjdm72FdVwMT/IY/+NY/nIcnWQE4IqHQ+BeXUv36f19De8KXHtCxFNxsSPq+BR3CDmg1rX0RvKZOfiGN66DiFAZixrnHOxTnajESEAZzP1V9VSYqQnXLOD7No0cexaliWjmv89ncI/ruOE9MnYgaNjl+k4xg6NnYoFE7Y3p1PGr2KpsmWnR86oFoyySH15zYOxdxFfH5YrYof2cnJnc+ZlmJ1XV7/4r/jXXqe94Q1cVaq+uuozSrNLqlkicQNFxtC+jY0IsSCGJGwoLS1bvvLdfJb0HIsi9rejAlies4yZTQi51fLBVxXwo77bVcgJeR5peqZ7+WTsfX907E5bgXQPnDH2gt9LpqOkPKzcWGcy2N5RGzOnn+tBHibCtGlSPlV5mFPXk5CuebnF+sVPWk97w0OSglbmk5NHUaiLn/alVR0LIVD5V5GA8asUOQFTVUmsiwmo2kbqOj4UdqJfn8MtNxRmMsUy1XYJf/M54BzCb74ieG0yiRfi8NVn/F5X59r+fC724ujYzUbHVWVkRYnyZd2KMnhuNmFsZUF4QdfDLTfJuO2Lzw7ycJMxbW9UfDX85iu+ZfZyChfTZApR5MQL7cX2EtdpX1pNDMlOPhFe/OwUjJ0VTypblnHQA/C0Vb6na3YRd4I8Yz/65oKveeO7KhYFXZ8MwfTCKU3DPveogv/D9whtB28ajEFRl84he3dPwvRqwwenH56O3dYxsRd5jB19T1mR9/dkPlXKhCoL+H/6BpjNIO9vgbpmq3O94SZ/XBBeoHm49TZCMywfub4APtzHnPy7ezqhb7aQ+ZR6NdcXv8Zv/qOtE9/wPp5IaDvWrKYPaRu+LPhL73oOfFQRN8wm8OstOyKDQmPzjCfVRIdLZQGZj+F2PSTPIEUGv9hwc352g35eIb9dw1UljQOqAt57DFdT+G5AdqtuHE1LG03vKVS0awlCy4YnYxftjR8Tu4wqODdwwGQP0c1FdAdxec6Turzh1/rshrlmGfxyDakrbP7LrzH5v39MeWQZMBkhXEwRcg/fDfBLljcxJ1MZ2zbMazyiuvILXu6Ue6tn1Wv5V+f/PetclXSOp59huzPVQTdShKIoYy1s9pCGfzdXDsOLm7qXfdxswgfEPqZk79rUwNyUw6loFmaWNEEoe7fkAxNl7p6KPQQ+fEfEbkoKdDEZ0t8rWYWkbYUVjOt0QbWuzqiG3N0zZlM1axq419fA44rdmFG6C8ScLGYdniHPIIsl/ucP/+OvuQ3+fda/FZF/+VP/47Rr+CCRzW9mAbLZsA+92rDF97iK+G4AcYRvG87ElSI2xer+EIhbmdHf1DArWKzYkssUY7Jcx/aoG9eQ+wfGMa7Rv7ngxjPFsO/fQrZbhMuZChg9E/t6c3TsVjdLkbOzotADUTkRgJgb2WwZv3JT7aGW5ZruhHt5+OmEpZDeLcweZz8n6/EjCDs/iyVBai94nfaGdw7ysCBx2HQffcZXvb7KYx8d4OjfILaq/RKFgzISJvD+jr1uFTRyy7VeILVEUnkKk/FwdcUHxv55fcMT+f0dsj/8qBJ4qh2pmu9upw4bv1Ts94sUe0eIMc3RPOPuOrZeFb7gZlNu0F2rhgrKGtvLI5qeATEfKfLDnEyC0CdFhpfehz/tGl6ECruKLDQKnHQdX9PbRq1vyvQ5znEDVGUkibBsqSCzMfBeJaStgzJRK/i21SkiHfhcWQDTMUQ7LFLklMjQTod1icLZBO5+iWjcu2uB5Qris18u9iwjceP9fWRToa6AdQCuz4DtDhB+HtoVa/6BnytnU2JyRhXvDJpHLNdM8TjPEeoCfjxKOSlpXFbrpICWn/aW+bl12ic8aLkChfSK+pEa59Ssa6RpWCKIRGUBubtPr/62JRLx7pHOeCan9+YGYTbhhRFIOvGzSerYDPQ5destT8iuhyxWGG7YevTvH1LNa9Dg60tOM5+LXUFrx8QOIMYum4ax9wPwRi+n9mZQ/Xa33CBcznihXeul/X5xkIdNVuXuIebjvn17kJO8vuTXGI95t/n/AYn7xDe8TkABXqx0Wom2i7WugaUARF9TCjSVijzUS7ld8JT1L+stByk/vufmAHiaPq648duOnqiKAZdRxe8vAleVyH68TdJ5Ra4S1VQfZhvUPxu7qFDqUbEbLVEx8FFWY7Em//VzJW54z3tBWbClqLqSYtade3lEd8TL85iPq+uDnPxiw7z6nm+FPdO1l7pOPHp1z9huuXmiwFFBYU9T69J1QOFruzTl7ELEoJteOoBoPIbv3iX5izDw0grw4nl7z1/4uUrrrdaEB5QlgsKD8bhUckTHi6NaT8L5J2PHbseL5BGxixKtEYaoGhC1dIYAt1orLoeXZ1lvGKddcgH25e8fYh7IMjjnEN7fRhkQ6XuC0iynPI/QCtEukRvVH+l3/eus0z/hw3Cg32KepvZnSl2oWVnTsOzIMuoqVlXUanGjEaltRUFXutEoijm56YSnqr2u8xwoSmJJnALA7h7UUExx7etNilLl9MKri7hRTRvmqdjdbHZ87JpnNFUejRSfUyHqyxdlcs1uO/63PVB7McQ8Ckp3m77Pfj6WkxFYpNmlB/CFqwef9gmvPeYIZRVJqrlZxte0AbTGo9RVsf63iMpRm7u1oiS7LmG7L895kWs7yHTGcsb7KGcdrubwDytgqpjwz2+odrDYALecdmIIwNkMbrPjRlWurJir3k/EDiDa2vxs7NZn954yIl1HTM1mywf1bEp7+bJQgSeFLDgHOT+HlDmwVYqf5TGqgPkEWVEgvLpg+XJ7D5dlKae+Z152yX3h9Ttw8hseRPOFwI2oSgHxBNUhTHTAmI75gBgEWL2VrC6VyShq2Tjv45BGipz4Eysd1K4dfQ8psmgP77yHuydKUrZqEDaq4FZbara8uuAk9HwGLNbPxm6u1kfFvtlSr8Z04K22P58rRHrgZNdMy2ZjXlQB/r9JBX/bsEtlud8TTz/c3SNT0VUUearzV1vqbS7XEBWRQlUSqvCC12lPWvMb+Vc3/0MiQThlFg1aKigtDqKnUVFGsSLYIMlOJueSHJ1NDrs2bmyStncJguv3Lmi7Xexziw6szDpSetLqXF3vEb4ltf1+idjz5P4RY7fP06+xHz9NklXtt1O+quW7l4fFIg0hBeh7reWHSDOkDmaT4vbu06T1o609kFXSS9HNZDWsLduogEpRKwRWmUI0JUjsIXjHj3Ge6MC6Stj3soj95whDBodDblTDT8ZwdUWWUJbIGP5sTtx4VcW6/qnYD9bPxM5prMZuD6M9JHXFj6mTYFJUXFZYgGy2B/3++LXUdNnycXX9Fznx4xzzKj/V8B93OWXrlCU10OsqIQ4V/+LG9YHTBrIM4WIKf6uvdJPLmI5VRiNPeJUQONBZbSGrDWtXFVSV+0f4V9fEzUwnVDHoBzrmqRmBO5urfAYIDz6bwD+ugVEFNO2zsUvbqgjrEbEbJNfwPGV52B40L1WFDaAfqJ/z7pZyeibTcTZPeZgQVJGz/LKSay8nVxR8+OcT5lPkREv+X7/eFvil12mf8EACUQGQiljyKIxqk80QUs/ae7hOe+OTEWtz73hJMyCU0eWGgYSHEIDrc57oK4XoZh5ye0+WTz+wTdcSNy/jPV6saqfLwwKuJb4lnI35fZ6L/Xx+fOyzSdKozLM9rD0HXrJcA9fnGL68iU4mvGjynoJXVzoHSHng/S3cdkfDM81nePfhICd4T3jE3SNxO5vtoXHCC1ynv+EN8VfQnzS6UbddQjqKABdzehK1LSenzY4XrQfyTd1yo/xPFV6q0ikpeio6UxnYbOHmM7bmNg2/F8BN6N2ePvxAsofWxW5JDmz27fufj12VeY+JHSGk2OsqXWaNqaXxZz/eM/4ghCfMZ9HTVcb1QR4AFE+UxXyyz18f5ISySKwvJa/gzauP9qv+NdZpb3gBfyk2pczzqPHiyiLS3FAUHKGPRumyVlfEdVvdDBDnbu0224w3lzzp+oF1cF3RmNisaJzjhnxzzU26Z0mDIQDv7rjhrMSoythmfC52V1VHx27CTjIoXMHebgCnqXs4eVdXkIq1dvR7co4/q708wt9/za+zJ/8xXM8PcxJJJWClwlUms/1C12nX8Erxk8UyYswJ290lpbCySPIWeQ7X+8TIt0ubmvRGvXabWPoM0IFPrKO9S9S/YeCFryzg3mvZsVgRBtC21J+RAFkoUbvUkb3+N7x/OvbtlsJNx8SuYqnxp7JcKV2vYkembRHajh2iLIPc7fjgdj2C4mzcestukubhf/tnereqLJ+0Ldzb9/rW0ZzyPOnr3LPPLy9cLvvENzxlKtxsSkNg62TYq7gqAVMO040v8VRSqG9ZKOLQR30V02UhTDiDq/Kk5mtvBAnAoHo0Rr4wffiqoo/TZJwuv23L0bsIwnIVR/VPxj6dIlrX/Ezs0qmSr4G3nIOokpgba2+9cmxNrtbws6m6+w1wVU2QnQLfYh7aInWjEQdZw8BY9y70EIGEgfDhqoSIUNlg/R9tO/wHr9MvadRM1/roAGCu2VBddKfS1M4EQM3uEUmThptYnTNs+mlT3FztXC7Po1rBYRekog6NWUjuOIWV9SYiFc1wQWxiaiynJ2IXFUE9Jva/cNDzPhHYz2dUNqhrEky8bm51745t1a49zGMYEk9W8wnXZwc5ibkbGopzs42X9Ze6TnvDA0qG7nlhNEc7QKeRylUV0utERCG4I56iZfIbRZ6mpwCiDAZdRlYsPd7fRnaPy3MCxBZLBZQtEdTWUoYAZ8YDinIM6w3C7T3fHIb1eSb2eAc5JvY9TZqo5BsYOx6WkMcF4zdjhTBEeQ2nymMoysM89Ocw/PA25uP+8N1BTvAOYb3Rr0Ww2Uvvw5/2hheJtjDxsmQO1l1HbqZ1P97d6ikrvLAtlqTtqe/RPjhK2pY+p7MpT9qi5KRRVcZcrYjAYYCfz1iPDzqgubzgaapYGz8ntHj93/4DOaIA3xBt9zOxN8fHvq9LmWWMfTxSLflSAXGjVOYZdxeAm0yYw3R8mIfyW/3ffp3y+erNQU4xL/OavTj7pB78UZeBx4pCfUdp5yjWIisLiJIcrO6MOo2jmm28Mk05pdlBPJI73ZpISZlPyBoaVcST55kOYsh5dasN3OW5wnpbclvXG4XnDnCTESb/+h8hf/slN916y036XOx1xSHQEbHLRi+WHqme3u3SXGCmOJuZ5jGbJOqiugviPQ3aYh6jGqhL4Nsf4S60lPnDN3xwNCc0O0z+9T8C0wlnAkpCecnrtDe8d6kl5hwHKoYSHAagBR3thtQXj9rmujEiEEthuADiNDVOGtsOw8UE2e2Sl9s8owHwV2/gHmhYhmbHjbZYATeX/Lsip9bLdgc/nUC+/8DvkXk4Vz4fe56RLnhM7ECMXZqGisajmsMg7+AWawyfXcD/+R0wrtnGrCs+0BNKB8IMzDQPk96QL16z5q9K/ixfX8ecUBQke+c5H8yqpOXNt7/uNvgl12lveOVlokdiKhkrCODGMO/Tcc2R+WYb+91o1K9JZS+sPenmUzKZPtxDdhwA+fvHQ8nsMEB+9ydIsQe53bWJFKLCTmG1RvbqWvXU+6gaFsnTT8Rurn3HxO6yFLspgeH9LS/QaqDs/7RTNpW6jhtx+3HJbpG2LC0PQh064Lu38QLs6hry/buUk913Hhcsa+oK2Z/e/iq/+o+1TnvDq/quGRKgH+jVtNpE2Y04bVT4quFTIMLXsPXqlRQhK9VLHFTaLlC6QoqcX0PdMtB2bC1OOW2Fp6MdRiXEObgfPgBZhuyLz4irKUt2jUxaT4FcT8Vu7cl/19id98B2B7m+JCl7XMPdq11PkZP7qmYJgMKXnYvDq5jHpgEmI7gwQZiP4X74QKhy36ecbJqba0fIptEveJ129NYvbhWj3e1BA7Is4UmKItbTaKkKACG/NNlVDnAAT/dNE+UnoP10t6Tgvys4XHF1za6J+Z3OJsDdA2S5QvbqRsVYs9Tbbxpu+lFNiqAaHDwZO6CIxyNiDwHO3kzqIYuuIwHycQGcnzH+pgGmU05YoWXQ/SMv56sVL+eWR5YB6w0kz+G7nm+c7U47NMwJZcHv1/Vw15e8t7xwEshpb3god9M5wEjE91uInTY2WWzbA8w7Nlu+jrVOdyaUJELDXyuJsiwC0KRXr6PJOBqNoe1oI9P3kAXx62zNKb9zT/fS1RQljW+eIM/HnmfHxx4EcKL+r1kcmtEQoef0Uzm0KAtgtUotWDNaLkqga2MezJ/wYMvHLOfjm6DZ6cbXmYIRTl6wIcJpb3hP5n/sDKg4P9RszKhzrjQ6nb5+i5zMIeconFpk3JzOwY0LnoDns6jL4rY7uLZD+PI1sNqy29H1wHyK4XyM/Ps70ui2O+DVJdxije7LK2TbjkbCFYdOw6xG/m6B3X/yGeo/fCDk+BeIHZMR4bkau0zHkEovoXXJt0c/8O+HAbi5Ysk2DHBFgf7NBbJ1C/e4inkMFxO4PgCDwD+u0H15heLtI8LrWczJ3y6oIAzAbduo5vCS12lveEjsetDAa5cmlrbMUibLOECpHHUY1bHamdiQclplqdzOTcN/bJQ+DPAfHlnPGz7n7gHZ+zuISWu3HUuc5RLF91QHgPPw5h01EDNT/u//D3B9+YvF7tTtRJZqVbNpqDgcqJljbihOp7eyWrPTMh5DNhtkv10DN5e8+Goe/nffMsZ+SPlMx9Sm0ZxkXMPfLlLd3/XpIvtC14lveMfReq5DFBuxa7tP8gxuQ5FP1+wgYK/djep4yYqO1uMabitsvRWqOAwQuw4g+/4W4XIGv2RffHh1Bt+oo3XXoz8fIytySO4h12fI3j+QAF4XQDdEnXbkGfxMcTLPxG5T3qNiX1LB2NnDUZcJ75JnGF6fI/vuA8J8zPgvzzFcTeG6Aa6b8u/0YhzzuJwBAeiuRsguZswHOMgpTGtkTat0R0IiYjn4Qtdpb3hrEw6Bp55tGIASeIsVa09t4cE7li4gotBVFS+MTZss1FUqw3kKlmY6opfpGP6OlpWuH5Ctt7EHLm2L/MOSp+8Q4CTwBM08y5wQyGfdbtOgKWLmfzp26LT0mNijMkNVclill18JxLpku46n89s7fZMNyO4feZleLIHJmAyoH9/HPMy/qvhBPWQBZXINMadsQVNjl6uqQwgvvktz4tACtvtku03ioG3LFtz9Iy9si2UCatnmtd582wGPK36uSUjv2nihdHnOel5122XEC5vUKphUqmv2lZoAOAfcXPDSpyN+ACRKjEe81GWZiqc2z8aOYTg69rh2LdxipZ0aH61orDSS15eRUBLpea85I3Cb5iAPP50An12z46P5YNce5ESZvRFZX1kWXRNf8jrtDW+4caek4nP6MbnlmhqK6r/knKrbmgV8WaqAUcbet3U2hsA61lOiT/pemfwOLgi/bp7HMgZm917kyW3bOj7XF7SNmY5UObim+GjmMby5juZiT8VOf6cjY5+MUuwAN2mpKMZmx+8fhPRAfQhEL96u69OpvJ8HAHz3lvcMzaf929cHOaHtmNftPR8s+/MLXqe94cHa15V6iTNLmCyLlDnZNBEmjDyPVpZGwHCLlXqXeo7ZJ2NuMuuevHmFaFgwYj+dwk0uSVPfPcaeuGm/4ANViOUP3yQjM8X++NVWYcZPx+7K8vjYuz7GLoNibsx0eDyK8Ai3YC/e3S+0j08NTXiP8Pb9QR4ACS4R2vDhHuV3hzlJPySDNs3tE1ryYy+R6DonOv0zfLsbjWInI6IKbaiT06VOzM1OR+pJ6UsxKrsuThgRAkkQZkgwm3AzmxFYniHcnKfavmm4IRXoJaoYBsW1Pxe7dN3xsQ9DjN2MmmWkEiVdjzAfH8QvQ2CLUj2corPHXh4sUcax/w6AglT7OUlI2joa50tHS572hjejMQWIGT6EknR60phkhta00nWJnK2WMsZDjTBbI0NfX/Dyu90lhGLX80Q1CxqAhsPzCSDCjsf5HPLZFfmw52dKtGiIWPSeciCGq3kidldVx8euD0hcdcWH4/oCMhnBrxq+nVTFDJdnhCo4B7lRK/m/+eogDxlV5K2qgoJ8dkVt/L2ckGV8CxWFDuh2UVr8pa7T3vBAepVOFNJrtWWlpcC2IbZcpSucESXyDM4wK7uW+JY858YwJhLA0qgueYG8OucmrNTictfy1HtzhVDmCOsNT9aq5CBmSYy9VAVr9I6EarFy6bnYFfh2VOx+L/aOsYvJhOxahNmI+jmLdRqmLdaMc1AztMfVQR4oaHMjioJ02zZZXWpOriyZV5Ezr6/foPurm19/D/yC67R7TM6zZgwCt68AFgKFRo0fuq/9rv8fdw+UiDY6oBI2qOkyQFoAbz8Qj/L+nq/3u8ckpVeVLG/uHojByXPW+291rh6Eb4fRiMOojW0clfGejJ6PXXvqR8VuBmv6UEjbwd8+sBTKMrjVmkOmxwXQk/cqXnm594/UnHlcwCuTC29ewTUtW5bOAz++ZwlmJY3l1DTAak0I8ph4I/dd+/F/7x9xnfYJH6lq2Z6yAGECoqpc0IuXNI3S6BTlp35K0tHYAGDNbXjzfWZTlJlWqxl/xdaj1GXEuLhcrWLynNzXYYhu3gBBaeE1ywdcnrF1+FzsinM5KnbnYuwsh0o+OHXNS7gxkmbTiHB0dZXajc7BX1wc5BHe3/Lh2WxiPlAQWcypKEj9MxfzfqC7yQtep33Cq/EYsiwJ+//EEmsfZlkyNlCLeAA0QshzTirN3U6ZQK4skox1MYJZRrq6gnz3Fnh1BfGs82VUccy/aSCjOpr8oiqBt+8hN2es2394F1lET8XudBh1TOzSExNjjifoe2A2IRCtpMgTlDboJpM4wHJ1BdmqytnjigrDmocfU1bQXczZ3TFK4F5Oru0g9w/U3swzsqteuC7NaZ/wpvg7olBolKoD23FuXFPS2ToHw8DTtOu4YVTmwk3HHPoogcI5pw7Te2JFI51KViXEU0Mdoxq4fQDu9Zf8sIA5c4eHR26sDw9wG5YB/mHN0km5oc/FDuDo2J1KjPDvVDBKFcXcYs163uQ31FwBfZ9kA28fFCqc8gjvbwlue1imfOrqICc0O84QQmBen/rwH3mp8phpnRuEV8xapudr2Ex7acVYpl+S+p7KcpVOUcOo6Kg/fP2KqgHLFfD6mqfeTh00ri+ANzdwZYFwoa/2fgBeXcFXqh5cV5Hl5Bq9YK42EZT2VOzStkfHDiDGbm8G1BU/rutYey9XnIiKJK0a6+a8uUG4PjvIw331hg/SehPzQT8c5mTwnY4GAAAgAElEQVSXb4MLdx0dRF7wOvENT9NeUcdrVxScPM5UImNExww4xy6FqgTIjM4g4p3qsHCoY6962zwyGcEv+ODIF684VFKeq5iDturK+NsF+r96xW7PpuGb4usvINMRZFQhXJ3zwTmbovviMqoCPxV7VAo4JnbVhwfAe0LPzotMRjRhWG0P4/d8g8l0zLdF1xMJupeH03aj6WeiLNB/cXWQE9qOeZ3NCCuejGP8L3Wd9oaHdkcWqzhWp3WjArvu+RpmGTBOmurrLTfrwyKd6EYVdC4SI+wEdss1/O2CpYEpCHS9Cpou2bacjJD/+MBL3a5F9w9fKyVQy5RdizDmYCd/T3m952K3Ac9Rsfd9jN2ZAZuKSfk7fi9/uyBAzHu2QUXYack8czibHuQRIb9VGfPxu/4gJ5lN4HZttLKXzH8SYvqoSyekriy5WdR23RUUIzXIrTOWkl74ZLWOil2ifWyY3Jy5bNQVBUhbTjnF2P4A++jmYm3lihEfdhxSlX94x/+/3mr/eo1hVMA1LcLZmDj152Ivy+Njr8oYO5xn7A9Lxm9T0HEdeaxx0+5aIkXzDMOkPMwjqHfrqIr5+OX2MKftDm65xupffM685qNP2pIfdQ2qb6jWj9HFQ7seriiU72lCpWrHOJ1QB6Zp1EZeLWv0NKNGZJsueEAav7cdN4XBjt0AfLiDvLrmMOj8jJ2Kz66BpkFYLOEVPlC8I/TWL9cwy5ynYjf8zFGxtyl2p7gdU/WVfgCkA+aT2DY1AVY3n7E37zP49jAPksUD8O6WXazProF3t4AqlfmKztsuzzH53/4JKApkbUcyzAtepx195tW9Y8KTuB94WvY9uwX9EEkR5kFkGHPUFVDPifNeI766AUSDYTS7iJ2Bmp1F8SPv4M7nrJe7NvXgRfgxOvTyVxep3OiHNKGdTSAPy6djNzm+Y2LfkwgUEbiMRmvucakXUw/sutThGdcsZ7zyZIsC7sMjv57lkROLJI8LPtze0/1jvYk5xbfQbM/w7XwO/PhrboJfdp12SSNg6zFTNS7RAY75hZqcRVkQDgvEel2KnB0SR+qa6cLs2y9K33O41HbkinYduybbhpADVRSWr9+kjVmouu/37zhEmnCsHz+3H1j79sPzsWfZ8bHnWfJs3UNtSt9H5hZdQC6BzRZhoqXeahNVEKDQAMvDoAnh77+K+YTZ6DCntmW5t6XKgzQUb3rJ67RPeAh1FKeT2PoDwI6LaqXYiWWnq1g5sVwCkwndPNRMmFDbTqecetl8UBKGdwRb7VrK7202fN17B7dYkvr2uExuHBnbdO5WYQCm6Nv13KwhPBu7NA2ciqT+XOyyaVLspmuvWvdkRKn2/Q/vIFkG/82PlOHote3pHdzFOdwfvz/IQ4YB/o+bqM2D3/4JMh7FnMRc/eoSaFryYt/d/pob4Bdfp33CB2FZoe3B6Cy3p7wbHTk8x++mcuvOz9g6NBGhPINsNlF7Zp+b6cYjYmPUfMz64CgLbpQ9R76oXLblxVBMchpQLItKYzS752M3Z45jYgdi7Mjz+HFxaYcHo5rDIxVmhXNsJV6cK6Y+5SGbbSSGWD5uVB/kFNu4ixXzunskeeUFr9Pe8EDSNy9yuuGVat9uUtnmh+q1g6FajpJnsRduqMqIMdHPcXUd63I3rvm125YdDxNlun/gcEY3jBuNIIsV/PVlhP1KSTEldzaP+HcEeTb2OAg7InanNpr2prJ7Bi3ndcYwroHdjvGvN+wmCS3l5f6BFMO9PPwZ6YhuNk35AAc5iRpJuJLoUVyd82L7gtdplzSOGi6y20U2kbRtasVtG26cVpV67cIZAokd2y03hYK3ALDdaKyhXPHeYUjfryyB5Zo4FYACRhIYg2HE+54bzTZeT911ADzV2y7F8lTsXUek5DGx73YxdgDssGQZy6pM9W5Wa9WVL4kAtTeDCFxRQdZrclI1j8itVaUxFKRLmqy40xmAPC5ZEs1n3OyvroDHj/T7/hXWaW94aMlibtHqJmcqAFGLcRjSpdI+s6XrtFgrzy5rKiQKICkATEZJc0U5oa7LEK7mcLt+r40nkPkE3tEHSsBSwxz/0PfxTUKbnezJ2KPDyBGxs6XZs91o437F3Vv9H0nou46dq/FIkaGehJD1mt0WMz/b0rpH2o6nfddHpTTLCYqulPmEUtl1jeFsr7x7geu0SxpB3Niuqlhb6ms2CiipukAUQfWeteh8CpxNY6fEMCjufM7NoaemzKcsYYYBOJvpfwfI+QzDVB+MmkOb5X/1RVLe8p4byGpalctDXSWyxzOxO3P+PiJ2BsHYrTdvXRapSwyfXSSF4nHNnKqCDKgbKhWE//w3B3mEyxknsvZ2s+7PXk6086RwExRf45uXrVpw2ie84sDdfAZsk5s1FHbrVK+F5gOeXY1cXa4H1uNQjyLzUsKWrn1uOuHr2oy9hsD+uarq4psfkH8Dpf7tIFWF2b/5M4a7e2Q31wjvPrADE0hDlF1Lh4yW4DA3mz4fexCg3R0Xe1GQNLJtiLDMM8iH+2iG5iVAnCepw/r7Bp8oiMXJ11ta+2geqMpYrgCI+ZhkN/IMeFiwd2+QapFPxsQfdRn52l7bmdeWICeXyT80I4zWOh9B0iXR1ATskgiwxDFih6mQqXO2GQ+4qwvgi9eU45hOWRYA8F99ThDWq2t0/9kX6P/u81iehKlCZ19fR2jBk7F33dGxy65NJ/224cddnTP+2UTdBB1kPiE55PNruLMZ3NUFwt98TtQncJCHrNbA1Tma/+JvYj7hy5uDnOSL18zr5pzfu8jR/YvffPRf+8dcp33C70MLnI+OewA4gTUJC+8JAxgCXOXjqSVtm6wf25btxxb882bLzTTnFDFuOL0EynYL1/1/7L1JrGxrlh70rd1Hf9rbvCbzZWUVFsayysJYAiZMkBETZI9sS0xhgCWQmAAjJp6BkZAAyQjPQBYSDCxkY4HEBA/ANjKGsstVWZXNa253uuhjt4vBt9a/I5x5zotCeZM4xdvS07v33BMn1jrx73+vf62vKbh773b0Mx2PQo8duwbZ77zhwhjkrJsbaj3KZhdc+h6LHV0L8UHSt8Qu5jaCCgR9eUvR/KIkNYz/w5Jlx/v7AGuIlpwF6LAgddDy0NUa0bbE4Le+ZjmzaxCtUsTf3IacovkKGA0h8zV0WKAbFUjfPG948GkveJOS0EEeCNWyJaQWaQItMj7Gk5h8y6HtsNMx1XYTUuVwNuUiDM7dCV/n3qwi6F5eQHaGo3HdRwDt9Qw4n9DH9G6J7mzCg+Sugs4M6rtY98Rpl+YwMNpjsfv7HhX7rgyxi9laOnwXdoDuLqeMf71F/cVLJB8WwGYD/fQFujyF1C109CrkIa+uaexQ1YxhVxFr8+r6ICeosuvUtIifuXIwcOolTdcGqC5aa9c1Ri6uapKubdEDMIscjtTVGTppSuUsAPhwx3H73Zw4nNWG5wCAu6KLL1nfmzF0iJYb7qwuTQ070H3zngvDyhP1KS5A4aWnYq+b42Nv2hA7TQlaOn2sNlzwu5LxW7szXpYBpiANn0jRfH2Yx2rDG9CdQvyG28/J9CwBg2E4g+oZX6e9w0vEbsqw4KEuo72kmmmApClpa6FGj3pTMCV0IEhSNw3NyBYr7qKNGQGYhrtORrSPzLK+H6/KRzzAxQXwcW/IRsDkLNIkQH/doga52dA/FnuWQY6Mvbuc0lXPdR8TKhio68TEMXQ06F1D3n5gtyXL0H35BvF4FPylQh5iNMZIuLjjmGSXOO5zMlaYVhWHatvdd8pjH/XqiCLEYsUatWY/PIy8AU4QaytDbKjkHqpoW8PJUGkL9wvuWIsVy4WLM2pLZill7SbjIC+nFbVsAvjKfFybzy65G88mvdqB2dnU37uCJjHa6zNz5Xg89iCwekTs8u4uxC5WwyOKiIvJqaMjixURmg0tfHS1gVYVorMZc6irgzyQZz2l0PJBmhzk5LwAmYw5/fV/e8bXae/wfnUKxEbgaEtgNmbLrKp7K0ofsecZD5pGmEan5G4avtwVCiQxZa/zM9aw1lGRYWH1Nnh4TRPgYsY6uG0R//ZPUf+xHyB9vyRZwswUMBwg+d1vWBadDb81drTd8bH708xuEJfNkOU6nDUgwvLIShOZjEwuTwmAG4+h01HIQxrDy3SK+ovvI32/RPfFa0S/+2Wf013ZS+uZGyCuLp41PPjEF7wEzfLwoScJPVTznHVlY+q4flgEWBas1sB0TNmJ1Zo9c9tFcXkOvZuTJBHHvfZ5FBGZGEfcbTcb8krHIwofbbZAUSD93W9MZWBvx54vuECnY8S//VMgz5+Ofbc7PvaiCKoGkmU21OphCuLIRlXoxvrwNR3GAbAzU7B75HnIeBQmvp5PJOcHOaHtoPMFRZiKHCgF+ub9r+zT/xjXaZc00CBIFIy4ANaZZdkvNpty+mFPV4Yb2dgBMUlInXMY7e19P2GMI+JMXOyoa20qarbzaQp9mJt1pE0eOy6yaDqBLmktKYNBsKok+rJ9MvaQ4RGx+03jLUse5rmzy2RMVQYxC56OuHWkGb+vrpjDanWQRyipFsuQDx4WBzmha4NrOQD+Xp65TMdp7/BRzINmHNFSBgi/fMlz1pyOLdmVQaxICvBx7R6nTsKoTeDUyBVU1o2Bqwt0w5ydDmP5667scStNy1F/p70Hkw+RzmYBz6PjIZAmqH5wjfwnNz2K8hfFDvQ4+W+L3VhOSGKWUUNTKXZ/2Yszeq3+7A1kdg7ZbKHnU+rL+E0Ycxoc8sCI9f/X7wyT1HBANV+FnAJb62wS4NI8FH/UT/2jXqe94JXGYm7HqIZCDBxVG7wEGQvfLauK5YDtrrKv82ilAbTjwr++6DXfAX7A600wAECRc5fsFHp3DxmNArhKN1voza1NOmP2+qsambnqYbV+NHZEwlLjmNgtPjEqoG63xOcsVnzvJIZsSiqE2VNBlhuodj30YU4RJs8jtF3RA+Dw9gM0SfqcDLiHtzehO3WAw3+G14mXNOiJE0AvNe26ND5ldIK0CSwFsSJbMNiVvYW7Khe7RHx6AL2qF8BB0XTM2rrIeyFTAHI2Q/vCbGO6DjIcIDqbcSc0eWzJM+jAuKBPxA7g+NgnoxC7JHFfInn8W+rK6GzMgZThaILzx9ZKnL08ZDxifDZogmtV7uXkeclsAskzYua/K2k+7hV6wkF3MQ6LALXVtY4Td0Uu1Z4Xaj1xRLSj9F61jgZh6KRn1FdEkgTHbG0a4PYecj7jYs1TSFkjWm4CCUSKnHqNpiKg9t5S2m79VOxlxZLmiNh1uQYuZow9pm+rwnD401Hv/rElaM1tL3W94Y6cZ8B4SB6s5cF6X4H5gk+rxQruSh5y8rxWa7Zw7xf92eeZXqe94E32Tgf5z0tOb3ckS28IppIJOaBqZYS4Tou7Wbj3UWcTzaalQoAIsGyhnamDOT69rKyfbdPF7Y4djIL4mmBwEHReWg6uRkO012eI7uY8SD4We11DpDgudiD05UUV0sT83iQBFkvGud2ybCqrQ/KItEBVQQwf5Hng3Ozq8zzkE7QvLSc1aXE6hO+gHV/3nJ24T7ykIZZDNru+BZkZwrFpWXs3DXdsE0J1CWz2v2PWvzYxlDVBVxJHUIP0AuDPLIowuNlXLZYsY+kxGiJIaNcNpez2HEVkMubTpmnoedq1T8c+nRwdu+RZiF1rw76kaW9yVlbsGp3N+LMnkx4IF3Fq3N3dH+ThLogAQj4ADl1SOkp0qE+qRb5zAPmolx3Qgm6L4cPJ3DdLGt+VAR5C/UOMJCyywB0FgvudNDbosb8Hf1IgYMp1vemxJN5RWa6hhnl35QMaGnTsnhgU2Ft/j8bedUfHHhb2HqXQocwyGPBnti26B3LvdLvtF67hYQKF0PPwej6JQz5EYO7l1LXMa1D0XlXj7xhPH/WS2dRQex2COFCQujDSsn9I+wtmw44CqXElyxeD4oaJa5KQ5pdlxNLseTEhtrLFWPx+M1AAyiDJcQRdrqANVQrCf/fzb40dTXt87ECIHdFe3e+qA+MRy7ui6EutPfw/2g54cXWQhy5X9HaajPp8gIOcEMXMZ7UOm8B3ujQf87JSBIOCRmGqbNsVJi89HhrgyU0TeIiVsgIy+qsSp75HY2t7L1OIUF9xNiYwyxe7adS4zbuY4JEY5sWfDAJwwbiiMMD3Op9RpsN0239R7OhYqhwTu1rpg7YleSVLeVOMBsTfL9bE7HgJY08XVx0LKM8kDnlInkHnBKOFJ91q3d/8cQR54ABKd2Xg1bo31nO9Tn6H16RH8QEIOA9JEuhqYzeF9lzTbs/zdFdyYiksf3S15gKoa0pBT4fQ15f9e3nJYB+6ToahkyNlzQVzblIc7pO6hxFvr6aU8N4vHx6JXRyLf0Ts4sjG1Mqzqia+Zzq0vrvbXdr+FUfA1QV0MiLOv2n5u9rPo66BqzPoJ1chH5lND3IKTKerc7Y7L896Lc5nep32Di9iknJtwLeEIVJtbniOVRnZbu8iSsMBJfN88Tn60aABSCLIm1vubiLQzpj8hlmXbsSvD3Lu5FvW7I5vQUKzMYkiYL2Fdi2i1ZotPoMc46nY0/T42E3pDE0TnhLE9fM8oGUJpBl0teqffgCfNvdz6PmM2JpkEPJATKePKIn5u0kSYJADN3chp6jIOZRqWu70726+2+E/6mVTPRmbfrp2PdqxKIK9JGYTAHbYTOLeH9V3PJetHu+Z6qYJF4sNamTIhaJtx8VTljy0PlgHJc/587yzE0VhaBPgDVHMxW4lwlOxq3Ndj4k9SXqIQEa6n2NrJE0JVBsWjLslLl/nC94IbcccgMM8YiqxhV+1O5fs5aTVXkt0tbGb4nkPnk57wavJQe+RDtSJFm3LcXlZsZ+8WLKrUFa9FQ4Q1LvcYsZ3XDQtD6CO82477rSzMSebaQr9/CW/d0eqXvvD19DtFu0PPzXn7Ajtq3N2Ls5nZkZWYP3HP+FrnogdwNGxOw5I7aCLpiWactArpKE16Q+nHZ6fBUKKxBEe/vQ/fZBH82uv0fyJ30D9xcuQT3s1PchJBgVwPkP76VVPQXzmTtynXdIAfYsOCI9rdRNfJ3bYsOhgMXnfOCIWHpfn/cHOW327jo9rVQ5tkhhYE9+iTUt3DYA/p1MkX90CRYHka/6/+9k3iM3qnbb0F0BVY/Rbb7899q7qNWm+JXZpTDjWD6wm0KTrTejda1mZzSe/N8htWFvy/G9/eZiHg+jiyPrwBaKvPkBX65CTRowhAtjNaru+AfBMr9Pe4SUyjPnkQOYas0kQM9LxkDvgZMQPfWB6i6kJEdmwR8xxGmnaM3v2FL9kPDS7GzWceRcw6AB6QJnbSa43lLaw3jfiGNHtA0kZrcmHPBX7dHJ07KF0s5siKK65VkyWUuqjoJOfUwihxPto26G7mh3m4bAKA6bpegN9dXmYkyNCm5Z5Aehm3+3wH/Gy1p1jUYqcBIr5kouzriErUyBbGnJwSyt3yTLaqOueKnDj/FbzSJpN2PbzFqWJqnLRd9yBRwNgW0JfXqCLBdFyB2ljyHRM8vTlBQnQyzV0NmEfO4kQzddPx74r2Q05JvYkgU+dQ0u1Iy2vG2SQDSESQZO+rMxGJ+JN1bbA7/yE7C7LI75dcrFfnQGbHRWEgYOcpG5YBhUZMJzSAmf3vJULTnvBK3rCtO/E7grdGbgqjoJvkvs10YG6t61HHPccU7sEGWlrScLdvGm4QABOQtsW3bslx/JNA9lsIdqZ+94OWjfEqEfC940EePeBse5K4GzGQ+Njse997Vtj3+2N8yNhp2i5Ijnc1cXaFl1teu6BSdVBv3pDVleW0pLS8lCfqr6/hbYt89mVfF/PyX1tVxuWYyKHB/9neJ32ghcElpGiXzAACJhql1ykTQug7TExdd1PIwGze9fe9MBr5CLvh01N2+vFANAiQ7TZ9bgaALh94M9OM0SDAV3zzDgMaUJRp4dlkJ4WeSJ28146JnYMMioli3FjRYKsX4BN1A2k41NJHBpxNkG02rLvPiz6KentA8FjVY3u5g4yHCIa0IVcP3/d5+QCUI1BDPaNHZ7pddo1vCprW98Zm5b/deZQNxj0H4LjU2CtOyNWiLf5XNHLUJB8jVk3Ojiqbft++A0VtnRYsFQojW736ho4n/aW73aT6GqD9nJMDLsNh56K3Y0NjordF/D+WD+K+F8Sk2hueHzUDev/OAZuHqDTkflBrQ7ycE2a6GWfjzbNYU51zXZkZzMEJ5c/4+u0d3glcSKQKczyRZIsqN3SdMses7bIxBWADQp8ABUA+Ci3nrdsyyB1h6oiAMx+hi5WwHwBvbrg4363g3zznosDtmvfPLCbkaVIfvo+9Km1aZ+MXaLo+NiThGWM/15ik9sb0osJ24RPsF3JkuPtDTSO+CT58g27RQCwlwchCKZett5QqHm5OszJpQGbhkC1JEH3/hljg3HqO/zezudAK8my0PVwQy9stvxPpJe+AG8WGQ97IJUdCB3diO2Oi12VuO/JuDfyup/zYJrnZucYsy3oo//tDt39Q28v6TV43aB796G3znkk9jDAOSZ2N0vOMnsS2c1gwqq0uGm5sMOwqGMOsylzSLPDPMoS3c0dnxqWT/TqxWFOZUkdTMPQ6GbL3vwzvk57wSv6ndgVCuwD1aYF5qsebTgcBDy67soAIXCsOSKr9e17YFxY2klmXBg+RZ2MaeFoyEqZTrj7FcWe2kFMYrQBwzZ/6gvoJ9dA07BM6LonY9f15ujYtapC7DIesTvlN58ZJchs2uPps5Q7smNj0pS83P08koRPs718dL44yEmKAps/9QVf3zTA6+uP+GH/aq7TXvAAD0xNS/fpJOawqaVsHDqqCYR+tIjxPvtKzSXzkMSsr838C4MiGAT4xDZo0zixxBxDdLHsf95k3LcsBwXiF1fQqsbwb/8OZEN4cPv1m2+NXbLs6NhJ3DDjtdWa8ScUk0WeB5qjrtes/aMokDZcjgPAQR4BOmzal4EvsJcT2hbDv/07fO2upJ7lHif3OV6nXcODQxRMRr3oqB8+jXSMsjb5apoLuARGIDw3dgjtrI/tkOO6MQnrGFoL9GxCu8YoAq4m5G9a+SEXhhosK+h8CZlNgjW9pglkMkY3uwJaRTQcIPrkBYVeu+7x2G0Ce1Tsrlfpffiq5kG0qoPHq+aZvabl1NhogwBYrp2fhQ4UyooQAVUgmnIeUFbQqwtaX1pOut5Cv/8J5N0dzwu7Xf8zn+l12gtewR3Rp6RJzOGMow3dUbshRU0cIuAGAz4Gb1qiHrdGAikrljjzRTA4k7t53/NvGtbkVcW+9modmECBFGI4fZ1TEDXalZTtMPkNjQgYezR267AcE/s+nj8QNT7cmnlZxPcxhQStSM0LJJQiNxLHBkGaBGBJV9V8Qjif9ebuICeoInp3x5+7NsmRQfGdqdlHu0T4CPWJ3z7EFuBhy00T0gRSJ0BbhRIBkYQyRdZGezO7G9RU4BJwR6UrnuHWRaDWGQkmxdb1QKfkhwbrS94AGGW9m8j7W8ikCP3xXxi7PWWOiR1dF2KHmZzJYNDv+F4SbW33zVIeMIuCpch4RDUxc/6T0RDdh9ugkBby8d+5g+kc0lDV7PBsSw68nvF12jW8if3Tqyjt7WiGA34QbQctsuDP5P12XW+5gzkOxieGacrOiwGqtKx6QoN5IfHPKXfDeK870nbEoVj9LdMJF2+aQc6mJH2o6bv79POp2IHjYzdwmUzHCH6uQOiwUFov6UVWi5zvo7Zouw4yHh/m4ZdNZ2U6YRmzn1NZ0a+1rnuZ7Rc9YeY5Xqe94I0L6i1F7mJsJcLFi4ym5qPwMNG0DgY62tnoxssVKw86V901Uoh3R4CeKNLSGicsEDGFAbedHxjMIEtZT3uXx2Q+now9jo+OnTEx9nB49fajo0ZdcgSGrW/N8E2pz9ldzQ7yQBz3QlGWD4CDnBDRISQcpFsSSp7zddoljV9uMykRiQmN4Vjc/8h2QTRtrwoQ/E/pl+rmYOJ1v4/hzZBAt9sgHKoJF4NORqz7VdFdTBB9eADiLJA0NE0QrTbUbKkqdN97gfj9HDJNeqjvI7GHWI6IHU1jHlBZMGP23djZV2pDM/GSyHv2Rc4/15QG8Tzk+hLtxRixdbCgSqBZmYecYO8hURRAa1Lkz1qX5rQXvIHHZDiklmJVs12mHSl5hg33tlwYxTs60THudgALve6m4SO/LGn3GGvQTtSy4gESAD7cEUlZ1RCT7NC1Hf5c4/FsBuemxl/fENrrMfki/EWxu2bkMbEP6CKCpuF/oyHjaNv+jGHOgN16Q6PhpuFB9GHBhars6+/nEbu6sUmJYDwk2Mxy0vnSbqYSMja4wTPf4U+7pIki4smTuB+mJDFkNOynkpFZOOYZdMRdGXnW66+YMoBORuy4zCa9TMfZNODeaUiQU1cxyzhRdE0bv0QMtEYsvgysVWfvpW1n02GyqZ6M3W/AY2I3yxw1OiBvkITxW1wOQ47OZsFBkIwoPiV0tTrIw+MNHrVFDnlYHuQkhv2HlTe6K599H/60FzzAg59j1lvTctmVfb08KOiVBADLdWDcS90LpIbBih0qEUXsY682oZ71xRM6HQ7J7ZSL1B2325aLah9I5a2+yASZvDZ+KnajGB4Te3ARt4OjDgu2ME21QYqCGjNjSu7hfBpykPGY55UXVwd5OIBOt9s+n300pJ1fAASBJklTun4/4+u0F7y34+KI9jT24RJ45UwkytHpdhsEjrwViB3baFpWwPu7oBKgTcPFlZIp5F0cgqSoOw9Yz90UzvR+zvG+YWRciMlbfUENwXTU1dugj8SuTXN87H7DuL68W/1Y+aTbbdBulzwnzt+Vx6zFKXVzmEdHyXCZjPfyiQ9yktkklID+VIh+8ryduE+7hgd3F533rHttGj5+YSSINKEOetv2hAsbiweAljH8te56BNcYiwMAACAASURBVOBq3YshlSXw4Z480c02jODFOzXmv6RlBb1/IFliUPB9IoHeGZS4qgymmwSU46Oxm5nZMbHrZsfFmSRENVrsAHpJvg/3QF1BXXu+btCZJj06RbdcQYaDkIeXRXr3wPxs4CQWu949BGIJQJEmL4+e83XaC15g/M8xSwGvkVvu/HTda9hpcV11gCWEMaEIB6Z1SyhXYAMla+VJUZg2TWegqwn0Yc56fb1huePtzaZFNJvy6wPSAb37EolAx0N0X36D+OL8ydj3h1zfFvvP/Vo8doAmyQmJ20j6YZSCbCiZjKko9oPPe4FUk9DW9RbatoiuLsjRvb7kz7WcEEW8iR3CvFzzRn/GXZrTLmkk4tjbnPSC7iPQS1DYIdKtGAFwMV2cQWfjXjF3D5ilU6trJyPWvZOR9aULIhebFt1vfM+kL4zbWTdovv/COj3WNryYcWGYvoyqQuoG0a9/wa89FfvF7OjYpchD7DJgH16ShDid8SgInSIiyVwHOfE9v/E9tliLAnh/d5CHPswhRY5oOgn56K48yEmHBe05VxtITf3J5z5pPfkdXjfbQJZwOQyAJYFEUdBd0ari3/OMO9i25H+wQcxkBFlxEONUN3fJDhS4qmb7L88Q/fgb6Msrft/dHMgzxP/4S+DFpYG/WsCcreFKwmc0FpC2I5NqsXo8drOhPyb2AC9WBXY7W/R0+dbU7DdnExombLa8kdME0Y+/CTeP/vhLvr/nkRNjg3MKvkrTspzay0nATo76ANDLrmd8nfaCb+lWpy1LgECJs0c1XOjIcC9BkQAIqgau8Y71lovd3O6886FtR32aYDWz5yhSVlQjsOGL5PZztltoTRKHeN/cyBiYjChSut2Ss/pY7IY5PyZ23W7D8Ax5HnTlUVbA/Zztw+WaN1KWQZZrdGsqoMlixRsFCL6ykmf8XYyHJv9dMR+DV3hOulrb+yZBmOm57/CnX9JEYtxVK0d8pG5S2RAhQ2nXi6YSmLXXpXAFLxc92i8hfNLpXQqAPfskAW4feNA7pwKBmouGVjWxJ8NBOHxKlrF7sjVPpsHg6dhHg+Njl6ifvnbUvAktw9HQLG4iik1Z/NF0wkOpKn2bXl4f5AEguHV7PvrZy8OckoQ3nFJ3U9fb7/rwH/2a9IZhDu4K1izRnoWNqwLXNRlFvkunHLsjGIg1wU5eXXlrOg6dklCuDAeQIkc36o3J3PM0Gg25UG/ve+mNsR0Ys5SeTKpPxo77+dGxy2jQx+5c2Dyz+OMejnC/YPy1tRKHA8p37OfgeURR0MT0fKTpDnIKeVnbFl377Hf40y5phHW2Tm1XaVugOCNQa1eyVBnzkIkd5ecAcIxu/khqHQ/EEZAUkK7j11R5kKxr6GKF9vMXkLpFdLvgYt3uoKqINju2Ba8vKI7kvfAoAkZDa0FSyas7nyB6WAEXM4obPRE7Ih56j4m9mw4RiVCGo6oBSXhD1g0PqE2L9rNrxO8erNauWJZsaG8pmx30/oG8VstDdzsalfmwK4qA1eYgJ7Uyy31hZTrhz3/GXZrTXvAdoaziBGmAO3OSBAcQ1sJdOISx79wR/BSzFlaAzCXXfdkYHCCOw1g9fnvfg6jWW0A7dD/4BNE/Nhv5+wXr2FXDgc92S7zLZAzs2MGQpdHv7uY86AGPx16WlAI5InZ5c8syxhGNjsR0FGPdMP7W/KmylBPesmR5tFxBLs+pbOx5ANC7B3TLFeIX18zH0JSeE9o2eFSJSXiHXJ7pddoljXByqB3BWpKm3G0A1p1xFASMgtioS+ZlaVAKcHUvreuwQ+uOctjkxnYcNhnrCE0DVDXiN3dWqxcElfmwp6rsqRGzFk8Svk4E7o0kef5k7DA1sGNi59CsDuAxnx/oegN8uAvxd/OF+TXteOP47j0lBmc/j86GTOJIS/Og2s9JBoPAbQ2c2+J5qxac9g5vSiyEp0bQsiQLKYp4qxrMV5u274L4B+NUODcVWG8Mopv1mjEODtvuuIPFMbUWmxZSN2ivZ4h+8pa0uzxDOxuh+f4F8n/0NcWOPtTcwccDoDLaYJ6i+8EnRE46Je8Xxe5MpiNil7bjQnWZD7O5lyyF5pnp6AzYgTqb0KpmUBj8uO0nq3t5RGlCCMIf/TVEX77n66ZjetN6Tg0tL3F2Tp0aHQR3k+d6nfaCl4g7zPmUg480CeVGaNkZV1V3ZS8k6i8f2vjf7SK7jgvFPVrNgFimE37fdkej3skYUEX0o6+C04dOx4jvFogfOAyThyVjqxtgteZEM44gizXi+wW5oavNo7FjTGv3Y2JnqcIprizXfJ0Dx+IYWuSUshah9sxgEDyjurMJ5N0t++2LVchD1xuoRIi/+hDyQZYe5ITYEJNu5tw0z57EfdoljUFo6cdqDCeXkbORvWxL0tvM6Mvd97Sqeu3FyBapEESlqw1750XOll9pWu1Z2st62KNcbBIrpq+oZpCAzPRqBpyCfvlnP+UgC0C3XPUmZY/ELqvN0bHraNDH3nbskY+HPW7e4tec8WuRESNf1YjmK+bw1ZuDPAD0kuGWj+7Kg5wkTfHln/004HpkOPhOiOmjXoJgBOZyFrIzpayHpZkbRNyBy9KmlnXvs+osIljPue0oRGTkal2tgbMJusspmqsJOoMYqC+6QcHyxnQYnf+q51Og7ajGaxPTz/7mDbrZCFrViCZjuKHBY7E71v2Y2F1wKrQE64bOIau11fUtmqsJd/k45v8vz60dSxSmfPb6IA+xg7dutyEffXV5kBOaBp/9zRvogk8zdfOEZ3yddkkD9J2IPU0Z2qqb/eSu5EgeYM2cpr05mFB6TuPoUCavo6a8JAn05h6SpYj9MGs+qMFIeLXuyQ+DAtJ26N69ZVvQx+yqkPuFDXo66KaEzNInYw86jsfEnuw5nVgf3r2f/Hvj3/ua+pJmRx/KOGcoLZdQw8uEXVoiQFvoV8wnelhB93ICwK6OKoAOsqu+68N/1Mu6FjoZssb0fvq++0VsNDiDtQKA5lbXFxmQm775eAgpaTnjRmKyLaEvzoEOiOYr4mmsA9JNh5C6hWYJZL1D8+kZ0Cq6LEb0vQvE6xpSNaguhsh/dsdyYkdl4t0ff4H8y3v+/MdivzynQcIRsbsHFQdVjF+zlDFPhnB3wfLXXyD//Q9ov/cCXZEiXu4g7+6gry4hDVWLPQ+IILnfQvMY7ThDvK4Rf5hDv/ikzykhLr4b5Yg/2I3z+sV3ujQf7fJac1f1Voxx1hv7jgYGMbD227AwrqcJ/jspI46oEDAeQmrpMThxTGK21697BsBRWbHut2FT9qMy+EBpw9oeuxLZ1+/5XktlJ6PIkf+fP+YiTtPHYzda3lGxu1RgbW3P5To8XWSz42t2JfLfobdU9LP3iL3zMxxAbhfB0tLzwMWMG8FyhSincKvuSkRVHXISa7fGD3Ho7ePm7le4AH7512kveICLYrsNuBOJyJzXzY7kirg/hqi7YWcpYQfmaQqAQ5zliotn0fS0PGcazcbQ2wf2n4uc6lvrDSG8m63BETromgfN7v1NAHZJkXN4tLE/v7oGbu6fjn257Eueb4k9yGu4VF7X44I8XszGwVkbdQXtqBUPV2MADvLA7b1ZdCYUY3p/E3D2npO7oYShmNuFPuPrtBe8yT+7fAa/FvERnqW9PqJT1gBOS02HMahzuV1kkRP5aJ0Y3e1MXzJB9+4G0XQSJPZcz1E7LorgvmHeqE52jl694I1XlqbXvuJunrFT8ljsOhv3rnjfErtj9XW746IbDXq1AROM0nc3PKekWX+j2MFTuw54ccUeu+dxdc6nz44H5kCQiaM+J4CQ4DynVr5ryj/j67QXPEA8tyqkFASLxSxFUCWra3Y8ajvEumhqHBN8FUfsjjhWJU1ZH6sCRQaNOOGMVNFdzRDdzK2ujgOqUZqWRAoRYmOc4B3HHMRcngHzFbpPrhG/v2dffLN7OvYsBQxn/22xB9WylK/VIqNrtuPYo4gsrMmQJZhZUsqgYB3uB9DJOOSB1YYxnO/JhBuTK+QEBCqhDAeB6/ucr9NuS7o7tE/3fKHVHP2j64hrMenq0A3Js742rpseN2OkabrRmY/Rh7tg8xJ9eOgVewGWF2VNilynbBk+LFiSLFbkgG5L+7cd4ntz7d6V0Ono6djf3x4du253IXYAQY1YdhUXdFXzKbSido0uV0EKHB9uaXNzvzjMY1CwjLq9D/l0k8FBTtJQxk8yakzqdtdrYz7T67QXvKuDLTd83JpigO52/UJSZV+72sOQxzGHPHUTRuHqqlm+mNUsIc+mHO7c3tuo34gQW1vUDvvdbE1rkihLH9roZkP3DDMfgFLDXRbrJ2OX8ejo2DGbhNjVxGNlY0rEdUMBqdt7DpyahkjM+zkhE+MxdDzotXA8j+2O09woDvnI29uDnNTz6uw90+cNKwBOvaTplHUv3LKdU0Y449/VuYDQwgTAD9XUw1wzXeomAM6CRDVg2vGUvg69cHfKm46DQ4jDY8MBssgRuaO3ITeliYKcBaXvBo/GHggpR8RO/1Y1wraVO+stzwJZCmly9s8flgHU5j9bF0tgkPdYHM9jsWQf/3xKOmGRA8MCkSunJQmh1ObwLV33nanZR79cSNT0YDSm7DUALpCrM7N9JN8zKNwWeS8zvd6y5ZcmgONT9haY5hmQKSeXDrzqut5oIEvRzYaQpqMBcFlygac5d0r/mXUFxHbTDPYIF4/FbgoHx8Qu6SCULrrZQTINO77zZ2XVQs/GhBanCUshM1bWPEP3g1dWpjCP5jc+Q3K7IjYniqCp4YMGeZ+TquVl8IzxkGeTZ3yd9oL3DkzbApqwFoUdPLckWCC1aatPD22M78MaSWxyWtW9Kq8fDLuOpcF2CwyHkNuHoOOOOUsNiSProVOyGoMB249dh+76jDY3a9NiN0Pg7v6BmpIugfcLYgcAQXFU7I7ZR9MwNitjJMtYmkQRe/6bHet/L1tM50biiJNkM3eTNEUyX4aOi+eDQQEd5n1OowHz8smuve9zvk57wUN7zmnbP07dRUN3O6AxuMBiyT6yw4CN5uYOGKF8ieNA9EBMoBUK6qZ3szGi5Zpv0jTA9QUHPpsddJCH+tkZTdGHB+jFjPYyZm4AVUQvr1lKPBV704Y247fG7sSQ2HrrCaU80NJUQacjyGaH7maOyL/ubdc8Y4yLNXd9y0OalB2bqgY+ecEnQ9se5mQ+s+KbxK5E+8NPnzXj6bQPrcFlGz35wfAo2rS9RkuaEiy12VHHxUgdkvU2kIHo7D3xtusdp0VIgYtNKOmBrUlZrnuwVN3Q9ymKIJsd2stJbwm/NcKFGS0ACDE+GnuRHx+7uRDSsa/k7m++TDDnbr0n4dzPHwzCRJ7qhhoze3mEEiuOQz4HNvdbe1p0HQkheRYwO8/5Ou0F74MOl43wer2mhFx7fQYZFuhenPd6i50G3LvmNjy6viBWxXRZoAqkCdoX5yRsjAd0yGjMUmY04iKs6yAwKqsNcH0OHdACJvryvRGbO+jZhE+b81n/nkX2ZOwAjo8d6GOXiLFfTBn/xYxtxPG417sEeD64PO9hCVZ7ex7SdtDZOAy7oi/fs37fywkXM+aVZ4xnUHCK/Iyvk17wmsS0Th9ReGj7z34BfX0FGQ6w+WOfINpVYVijpguz+s1P0H12DdlVaK6nuPuXvkC0XKP8tWvUr88AEbTXM2iaIH5zA7lfIPrZG3Yv7uYcrW+27JNXNXvdqzXr2Pe3kJ++CbQ3GQ6gX73lYopiynos1tCyQns1eTJ2b3EeE3v1yVkf+w9fU2Tpy7eI39wAH+7ZilTl0GhnPNabO+D9Lbsxuwrd3f1BHtju+Nq7h57G9/bDYU5v3rNteTdnXvtWOc/0EhfpOcVrll7rPz/9M2bc1fWSGN4e65Rere5GDZgyrqnwpglLiLLshzZG8QuvM+2WoKplOBUZDdEtloheXLEed4scw5ZE52emJ2MT3NzOA0vDqIu5fT8We9v2MN9vix2Aa9+olU6Bg9o0nJDOFyF+uowYXKAyQ+L7hz4/oGdSVTX1Jc/PAu7Hc+Ih2SS4k8ToiRH+x6/+04/1kf+yrr+nqn/yF/3DaR9aJWJd6gJFacK6237xDsryUkBcGjrJgIItQt2VvSCRWbc40lCLDChrSNNAry9JZQMgGV3+ovMz1sbnZ9z1z6bAuw+ILkiukMmYC+nqggvp9oEDpXRvKPZY7K5Fc0zs4yHrbVcpS1PutpdnRHq+vwWuLznESiQAvYjFUcYIkrlDHrf3PAONRyxvqoqxR1HIKdAGR4NQEunmee/yJ77ghZ0GJ3y0rZmJDUJHRJIkTAL3L21MOsPgvKzhaQvjfkkA0M3GkLJiJ2Y6JjbFFb/iiNJ2u5Jgq7s5MBrZQoxJ4hiNiMdZraCfvmQs5swXbN5/Uexte3TsUtYhdgD889V5eELgbMoFmWe8sUz7Hi7zbepj+3l0n70kbPjugTIkSRysLPucTLYDCGeFfYTnc7xOPHoNEhPY2kKM9sQ8fUK63VFla7PtVYV9wBRJ0Ej3gZIs12zl3c2BhwV3NhHg7gEaUT7DlcBkPEJ3e49oULAenxM1iCIH1hsuTsP0SNuyPo5iYFg8HTvABXpE7JomRiYZ8SnksQNUJVit+VRoTaHAb1agR1quKL7kecibG77Hq2vOHIocUXRGHI7nZAdppAnw7gb66vo7EvdHvVRJuLZdV2uDEphOSvvqknzL8bDvU4uwpTaxTkfb9TrnTp8bFDyg+hPCF4lID1VQ7VuXecYP+vah//ddST/WOA6QAVlte7x4VT8Zu5qWzDGxy7bsYwdC7EEZrCiCHJ4kMeP1mUHbsm25Xh/kAdeYdMudXYn2/c1hTpXJ/80XPE/87JtnT+I+7R3eF2DTcMc9m0LnKz7qywrxhwe68C3XPf4jjvpOhPWhdU0nbbgjt5solCVQ2I63Zb0MF0SqYsggI4UuioDFmoOgPAPaFu1kiMhKFF1tIJMRuskA0ft7+7lT1tuPxM78jotdrAfusSNNw3kiiMDGMaX+7pekKt4vWLqNh9AiQ/1HPkH2YR3y6KZDKpp1LTCboZ0MIfMFebuWEwAOoO65Wchw8B2n9aNebsBrdDOstwfGW3Sr64CB8Tp9wQAHtaaMR2TnNy0Q9VNPmewNj9xivaxs54/Z0nNH7Szl3wFK29UN7Wc2m7Azd1cTRKrQLz4BfDE9Ert7VB0TO1TNCnNvl58vgcmknxpHEeTLtyytyopgMZPxFgD5T28P88gzSByh/d4LxG/vmc/ERGUtJ8nt9dMxn4LTcTjYP9frtBe8mB2NY77jOBgIC9BjY0xfUluw7nXEomNmDNqqDd2pZTAw4zETR12uyfhvGrYQJ5NQIuz7PXn5E5yuAR5quxbdao34d0sowF3eB1yPxK6TKSe5R8Qengj7sd/dMy/tuz0wXD7SrFdc2+1YbhmjKeRhCzd+98DOTdtSTvt+HnKCCLrVunfi9hLwGV+nXcOLfWDO2G8ajtyt40Ft8ypMMcWGOb5YJKKlupb8Pp9walnxkChkIkmW8SAaR6xVl3sueC6FUeRAnqH9jJNGmU5oBmavj8Yj6OcvuRtaCfJU7Lh7ODr2cPAG2BPfbCGzKRlYdd27DapZ9MRR+DekGdGds/FBHjomVzfYf9oBez8nqCIaj/j+ZhP03MFjp73gFUGiWYqcH5wf8Eyly4c24f/J3nDF1LMkidkhETF/pJidEbN1h9vnvLwiY+ni3H4+/U8D/qasqCZs4qah5+4gr9K0bYo84M4fjT1Nj47dqX3BFtN8qIK5A8A25mAQVIZ1t+vtac6mtO3cy0NaqhajLHux1jg+yAkAn2b+Hnn27A0RTv/55C26hpIWASabZcH/SEYDsoxMiRddx3G5ab0A3EHVVQi222Adqe2e48bXb4HRKLjd6WJJUSO3cczSYOUoWdabGOxKHmhv7iixcT8HXK/ysdjN1+mY2OGlmE89tzuWY1l6KAMO2GDLFrpPot+85xOprvo87u75OqDPZ74wrrApoJUlHUZcmny766HMz/Q67R0e6InDIoTl7lH0MB0HryMx7yMtTB57PAzW6WJajDKbsGyZTbmD+S5+PjM3vJw7Y5HbYZi9dBnY00Ai/n085s2yWvPnGrBLUgK+ZDgIALXHYpfR8OjYJYn72DO6A8pkzPj9aTQe2cLcWYkTw42HZTohVGE/D7ecHw1DPjIeHeQkEyvPIuHNm+ffdWk+7sU+M3vYCVn++33yhyV3KO94xDHFheoako5JbEiSgDNHErOsCFNOw4x3LfR8xrr5YUEwlzOiVivqRU4nQJGju7kLsn3y2WvKY1yec+d8/QJ4WKBbroId5KOxVzV38iNjF1SEIKQpd3j3fqpq4m0GU+jdAwkoD3OeT1IaD1PuhE8Az0PzDLibo/NOVJLwSbOf02oNmPmzS/chEmD9q1sBv+zrtHd4tUNrp2FxBIy5TSL3F4yjDyVJQufBF4bkVkZ4uw0AIkE0m/KgV5pMtAHH2IuvIUVBInNKO/loNuUToqXWopjshkwnkDUFjKIJubBPxa7b7dGxQ7sQezg0ureTQyzK2sgfLQ3NXND14oz5ZelBHnj3gU+A4TDko5+9PMjJn2jslh0aSjzX67QXvLvgDYvecRoIJGUe5vh1EetkuMSdmfl6aQDblYODnU0htawI4x3kfOxPxlTnTRKCsxI3RUuIhy8r8mOzFNUX14yhbvoBU9MGBbInY8+yo2OXJPm52MWwMjrIqX8z4K4tkzHFpIYFbx4TmpLx+CAPGY/pUjIahHzk7e1hTpFhikwwSoaD79SDP+qlGg53vkAhUQ8oc8Vfp9B5jzlNTfU3BlZma9PSMQ9NA9XuAAKLmwce7lJazaBrw/cG973FErjv0K3WiGZjYLtD+g9+H50po8l4xGGVadwEhtMjsWvwcT0idkM90p+V3ZowPX5/y0W+I0VP04ydl5smeFt5bY84Dnmo8vehbQt5/SIcSPdzQpIABdWNgxXQMxdjOu3ovRWXJSav0YZyQOuaO+N4GBz3JMlt1G4tupg1rkOJ2SWJQy2sux11aZq2RxlqDSmGoUuhl+dUFHYU5Q8/hby9h37+GvjyDaJXLzg8iiPqp/vlUt6PxA7fwY+IXcvSJPtibgC5CSPtSAtUs7JB2wZTA8QRaYNFTnrfbseDrcXRXc0gX39ANBoAu4r5vP0AACEntioj6NbKwtHwwKXkOV6nveDVduRK+v40wF+8q2IBQfNRTXE3GIS5NstuFxymD1ypR0N2HV5cEpA1mwC3NpmMI8pc3M1NjoOep/Hdiq8FqAXTmqRHFBHlODRFL+exPhZ7yTbfUbFHMVuFqnwqrDeE8RYFOzsbxoyBISZnE+LdHZd/N+cAqqxCHtG7O3ajmjbkg6KAdtuQk2x2PNyu1pQ6SWJg/t3g6SNe/SRUhgUlKiLhjtXxwIUh62SXoKagqNnIm3GZDAY2VY2DYZjMeBjD+Qya2NdWmx7Sm6Zs2bkrSBKzT22v1SKz6aMdXlcbNNemPtYZ4vCJ2AEcH7tJBbqZsMwm6M6ndnCPQpmhSczD87bsB0iR0Bl8PDzIQ2sal7WvL0I+AA5yonxf/zuRze479eCPetmYPQC0JOJiupsT+xJETY243NqAaWs6jIZCDBiZwSDQ6LyfLJFAqhrdywsKLS1W3IWrGrpYh46NNC3BYnEE2WyB8yn09oE3TkuJvvh+zcOds5meir3tiFs5JvYkgSRGAdQa2O0QtR3VicFpqH5yBVlzICVpCjhpxP8PHOQhRQH9cAcMX/H3eD7lE+v9bcgJQE8jNLKKVt+VNB/1khFr9DDt8ymiMZi8BNDG2D2DnMx891dK2IFwojO0Cz9DRgMKDiUx5Kt33L28To0i7nYibOPVbFFiY9PGD3es/1XDYKabDhDXTZhWPhl7lvKpc0TsAHppbMCGSsqOkmnby1fv2PkZDYHNNujfuBJydzaGfP0h5KGrFZAkiDZVn890QhiC57Qr2Zo1x5TvxFQ/9uUTPtvpxARRZTjo613XmzkzhKPZLwImVZES/CWOGwcxK5Kl1vemjJwUBcFUy7XV/DEBVtsd5eZCX1yCArAuV9wJbboa/f437Cwtllx8T8WepkfH7urHAOCGxkGf0hTBiCpNGL+ZGiNhva/bHeQnXx/mYcbD8rDs80mTg5wAEAM/X9pTp4R88vJXvw5+iddp7/DG99Sy6he36bZLlgY1AvEDY5JAij3M+XJtB7OmH0A5iGtHnIieWUlSpKy1m5atRS8rLmaU6jCt9vb6DPEDrdyxK9GdjxHdLbkwfQfOc5YbRfF47NYmPCZ2NDyYS5JQDu9hwY5Ty8GTDofQnHLfbsXj5mcynVA33iS2g16+XbreQC7PQz5I05CTu5YEuuAzhwYDp77gAeh6zZ2r6YIupGRZUOBCHLNerqq+VvaBiVBSo58Odvw5e50aGIRXrDevSULLmHnDetrNyGz4I7/zEziFRAYF5Ov3dPtYb9hF2ezYB0+Tp2P3GI6J3XAx2nQc9QNGWbTuz3oNAdC1HUnoas7d5lpICY+9ReuDrPkCXVlC3pt2npG9PSdUNZ9wriWvZsDwjK/TXvAiVAHbl8gDDNbbAtdnkMWK6lx3cxvcGEnD4LU6yLmDAnaDWNtvV4app6KBFEM+CS5mkHtqvMiUAC2dG9F7Z4/0+7n18c3mpmK9Hizj/T1ckuMXxC6pkTuOid1nAHEv3QGzlQcQ2FqiSiud+0WAU6BtyUOtaqJELQ9kKeR8hmho9buVV7rZhpxwfUEKYl0TjiHZs5+0nnYN7wCoPGVNG8dByEjyjIuhyPmIvphxcdUmYDodk6vqMtQ+ZfWFsueJqp+9BLIU3avLXhS1baEmriTj0YG+Yvf5q9B2rL93zTLCiRWDvD9sPhE7BsXxsbuuZFARSO3PJssXR72wUt2whz4e8u+WckZlYgAAIABJREFUg87GB3l049502fNpPr08yElM6hvnM0IX6jpIfD/X67R3eK+j17aTWV+bClkRpIl6gNVmy3YfYB0GI0D4TpxlgaWv9j08OCYcwsQx5M1N7+xRcnqKNx8I/XVMe1khen/D0ieOkX5128MITG0smBkAj8fuALIjYg+ly/2iB5I5meRuzlmB0Qilqulc4opnScIcjKAe8jC7TylyRD/+CpokiH/7p4QXWE5wZQSDRUgSQx8Wv6IP/+Ncp73Dg71nl55Q8yMNMFWgB2W5+dY+UbozPmjbsh3n3Rl7ndY1R+hnNoSaTaBD6rwDQDfM2HqMY7QvzrkTDwqSv7c74GKG8ocvTIDUFuVsjO6zF98au1bV8bFr9/OxD430MRtT0kM41NJhwXLFa/amgZxNsfnnvjjM4/KMwlF7+cj57DCnNO07SrNxLwD1jK9vXfAi8rmI/C8i8g9F5LdE5N+2r/+HIvK1iPx9++9f3XvNvy8iPxKRfywif3rv6/+Kfe1HIvLvfWt0ar3yrus5phE1XcJiGQ44fTRUolvKy2jIVqChBpFnvZOFdVNc+Ag3D9DlkqTqmwdOPZME0e9+ydH7coXomw8c0Vc1PVYB4O0Nst/6ku+5J3wUf3hgjE/ELklyfOxGCwy28wCnu2lKzZq7OWP6cBviD7qZ2y3QtBj9g28O87h9gLy7Rfv1mz6fpj3ISVdrDsP8aRIJ25jP+DqmpGkA/Luq+n+IyATA3xOR/8n+7T9R1f9o/5tF5I8C+HMA/hkAnwD4n0Xkn7J//s8A/MsAvgLwd0Tkr6vqP3z0nb3nPR2b43Rr7TlqqKsqXUAMHMYhjk0Fo4h1ct30/qzeqrN/950R4yGVvAYFy5jlmn8eD6n6FRGWgM0Wuz/yGvnXc0RV3euuW++7+94LYm0WSw5xnogdgEFwvz12rLdBK1LGo/48Ykpo4vrxqtCJyeQBlOEeGN5mueEObnnobAJEgngyohqx0RHFnhRYrulxtdnxyWJ9+j/0XRpVfQPgjf15KSL/CMCnT7zkXwPw11S1BPBjEfkRgD9l//YjVf19ABCRv2bf+8SCBz8Es3LhkEj6IYyP96MIuuVkMUwvTRKOAqokQtPYKyVWfLWB1KbBuNrwZ5qGi6pCtjvukPb9+EASdPG777hT+wgfHBJJVSP6qenCXF2YnmT3aOy63RKzckzsedbHvve+stoQ5mulUHc1C+eR0Blyb9eyAq7OQh4iwpsjiVnPrzfs9IyGfU6rDUFj6w3PDOPhs6f4/YFqeBH5AsCfAPC/2Zf+ooj8AxH5qyLixd2nAL7ce9lX9rXHvv74peCH6YwbIzOHnvIHOksjz8Kj3/2I0LZ9m9CZTDFH9t6m1Mszs7wxg7IsDV0SxDF0MkQ3SNlJub6AXJxTBi9NoJ++oMfTtuQO3rTsZrhmTds9GbsUxdGxh+GVk14APqEuz4LZMOII0S3bqezKmKPHdIRuYDfIXh7IMz4dgZCPnM8OcqK8SBtasDrMgReXR62VU72OXvAiMgbw3wH4d1R1AeC/APBDAL8JPgH+419GQCLyb4jI3xWRv1u1G+h8abITZeCS6q402KzBfeuGWou7Xei8eBtQHYfu2oxJwiEKcOjo17Tozg0FaR5LABB99SGUHsHsF4BUDWRnqgVmfKB5Bry4RPmDa3qnPhG7Ns3RsYvdONSaZw3fXc1YZgwLLlx3GCkyiKsXNw1kvUX01Qe2Kffy0CFLHR1kIZ/2anqQk7dHoUoI9abkmeEZX0e1JUUkBRf7f62q/z0AqOq7vX//LwH8D/bXrwF8vvfyz+xreOLr4VLVvwLgrwDALHup7jSHxIgQudnIuAru7QNbiBLx39oWer/hrmq1sh8aqdDV8dCa5BwgGaVPlytEacJDqhsDvL3pZT2ahljy7Y5TToMbd/MFxCl3aQJZrJDPl6be1T0auyM9j4ndRZngBPTBAPLTbwyOQLiuLleUvXYOQVlxUHX3AJlO0L37wMmw5aF390CaIRqZXqQIIiu3PCcAwfdVFiveLGcTPpuf6fWtC16o/fZfAfhHqvqX977+2up7APgzAP5v+/NfB/DfiMhfBg+tvwHgfwcr8t8QkR+AC/3PAfgL3/LmvS66Pc5dOx0ASRttB7y8gsxXAUcePEyNyaRZyg6MC5RWVAvWT64hS2o+ytUFJ5sPSzrkAdBRAbldEI9iJmA6GQH3RDfW37tC+lPTeDeLyYButJ78Y7EDODp2nS97+l+Rkfo3GqG7nAJNB1msGf9yDaiiuzpHtNqQq3pzz6fEZ6/55LI85OK8B6BFPCd0l1O0oyzk1F2fQRabfggWx/+/YDz9iwD+dQD/l4j8ffvafwDgz4vIb4KV9k8A/JsAoKq/JSL/LXgYbQD8W6raAoCI/EUAfwtADOCvqupvPf3Waqq/fOw6ZS1YyhQ5D1OrTe94sesCitBLgOBe0XYQsHOBuqEf6cMiUOZwc0+Opwi6swnkp29shG8L1bXbDfyV/vgdd/8JJTmQZ8CuQncxQXS/ejp2EZI/jok9SXjwBeEDiDhwk03JDpGhMXExg97eI7qh75O+umaHpbbX+dkgS2l+UJCY4mA2eVsjjaI+p8ioiWbyQOnuP+QLXlX/V3B3/ievv/HEa/4SgL/0C77+N5563c9dHQ2/ZDQMGI5g6W71bwBa7Ws07v27bkourDgKXQ+I4dF9kSZWylxfILp94Pd+/a6X66hrMwegXryOBiaJTYocYcwNd8WLCeTr95x0Ao/GrhXr7KNi9w/AnyKGjRGHSHgcrluTUVt+PwddLln+WB7RjIyp9nKCaL4h9NchEZaTvLnhIi/rcBPrPi7oGV6nPWkV9Iq9eUqaHMC2nS8Wl4oeUntFTeSI2osRD2smraHjIdtvXcc+vmu1G9ArWm36xTQaBlK0brbQyMBgeUaHvDiG3t6z1RdF7PUPc1pfRgb7fSL2oGZwROyo6j52dxAf0Y5ejSKIKCKeflCgu3sgKWQ0pOnCrgw+VSEPt+P87Z/0+SxXhzl1LLN0vmBeqs++LXniWBoe6iQxy5eyDDsYtQ634bBHlw9T8nJgWMNOh2QprW3S1LDqNkzJM+j3XgFVQ8z7sDALSspp6Gcv6erRDFC/nCGquBNHeYZuUiC+XaJzp46uY1swTUgF7DqDM/zi2CUxV7wjYteq5qExz620AdoX54jWO76fwXb19SVkuYX++vcgZU0VtV/7DLLaovzsHLnqQR7x+wfygi9m6CYFqrMcg9+76XM6nwYzZWxLHmzPz5618thpL3iAj3cHgKVp0HpxuTxtmn7IE0fEjLttS0LXO20a3hhen/pj//YeUZb1Mh53c5YHSQyUYI0cx9D5CunvvWGdOx4DqjQ+WK4IAdhVvYaNW09GyZOx4w8QOwB+v/NdsyHi+yXffzBgvE2LaEH+bPzmJqAa5at3wOU58p/eQtfbwzyEBG+sNohUMfgw55BpnyRi7VMxV8EDkddneJ32gt/r0shkZFS0imWB40uGBR/lszEtZVJT8NrHsAC9daRPaSuaBHSXVBqI7pZoPr9GvCohc7rvbX5wjnRZIy5Ybkir6ETQjQnT7V5M0IwSDH7/DkBO7Pj5FN3LM8Rv7ojKfCz2tuWA64jYJc94AHbVhapG8+klkqaFFhnaizGSN/fsyqQJfyeqZFyNBmguR7j7o0Nc/52HkEd5PQIApPdbdC+mQKeIygzN55chJ6lq6GCEbjZEtC6BqkL32TXw9lfz8X+M67QXPKwP7LWt2O7StrRVd1/VJOYj140Bogia5uZtGvctQt9pdztInkMnQ0TLLWvkukY83/I1Zmk5+L3bMNTR0QCypBuGfL2GnE2hDwuk1xdcGHmG+oevkbybI1oa1/Sp2N1D6YjYdbujc4epIUgUIblZ0n+q7RD/9F0PSbbSQzcbYDaFlBXSr0tcb2pEN/OQR3bDKbVutojjmPkslkg7PciJQRLTJFmG+O39r/Lj/6VfJ7/g3c4ltM5M0hkPC2LIfSDik0q7pE5YNyMPZYEMB8RzOzThbh4ofIKU9o05WT1aVbbjEhvv7TzNUrZCTRMe6y0PjnGE9M2DtRcrHvBcXOkXxd4a1ODI2J3L2vvNNoDjavy1dtiFGTDrekMydxxBfvI1mVSWB+YLoCggF2f8WbuStXrXhZyk5Lkmfv8AN1jWQf4r+dw/1nXaXZrEHuHOCAIoq+HgqSLnvwGcmDqUVowX+uKSHY0sY8cijog2jGM++icj6GSI9nrGxTyb9LJ7l2ecVI4GFCsdD6AmM627XTh8qrUH5X7BBZGSaNFdTp+OfTA4OnYAIXYpCsbXdcBkhO5iwhgz4946mnEyYg5pAh0NUP3mDw/ywCcvg2pCyKdpDnJCFFleyi7XbEyHwWd8nfYOb0wi3NwRhegfULmH53h/CxWxHnkJpFmvEvDhnjv0jvVn0HoBONo3A7DorcFw5xU0ikmUjgQqEUnZ2gVGEZoG6hqOdrjU2/sAu4W1F6OqfjJ2rSrg/ea42LUDHhZ8r7qGtLaj39xxdx4UPBxvt5TUW66CO0jXssuSff2WubvBWtlb2ewv9GhQ9Dml1NBHXQPb7R8KMdXT3uHdKcOkooO+unkk+aDF9d7l/IztRitFJEuNVBHbo519d++BS1GQTXQ+4+PfDMHg+vGBbbQnXf3yCtHFeb/z7nbEsIxHwOevw6BJXYjpsdiB42M3PIv7uaoq0Y5FQYODsyl79HbDBIlwAPL5a6MF1gd5RC+ueHO2bcgnckiF5QQgOIu4U8hzv057wQOUmOj0oB0WpOw6Stxp05hrdENt9KrqnTE6uujphiN8GQ7C4iNfs+NiMUFTra0f3vWmZI5ulDzjE8XxJHt+R7rZ8KDpZmGqT8YuRvg4JnaY3IYMBz3OvWmJstxuSViJhGrHbmgG8L3ffGAb9eX1YR4AIBG/7k5+pntz8PvfbAJX97mbIQAnv+Dl58U7veaUqLeA6ZQfoiH8UNU9PgUI7ndhSOVfA0f/kiS0gbQP23fUEEVRcIdtOx7yjEjtDiEA6/Du9t6s3imr92TsjrA8InbdlX3sLiHuk9KBPXncmQSgtIlEBzno/cNBHqHWd3eSqoJ7yoa4RXqfJ1NAwNXFH+wjPLHrtAsy76acmf1jaRDZOCZ1LbNhjk06NbVhzWxsygQRsN5CxgbLrYiJkU57Pfg4ghY5na0HBR/hVU06XNui+95rxPeUwKMsdQNcnCFaroN7H+KIZ4Lvf4oOgFzMiBsvq0djx65ki/CI2LuRSeK5cVqaBNw7vPe+IMRXxqNA1cNkBI0jdOMBp7IGfHMwnZNKPJ/2aoposepzslauXp5BRRBtdsT/POPrtBe8YV6CwKg5a5AWtyPXE2AtWtX9gW+5DvxVFHnPGkriXmS0rHosuknd6WLZK/aOBtBvPhAjX1bsx7cEa0lVszOy3vSl0mAA2ZWQTqE3dxQ1BR6PHQDmq6Nil3e3PMCax5IUBWS7499LE1xKErYMFyt7jy1wdQHZVYhUecAdDEIe3XpDSPByhTjPoKMByS5uPd+29qQQ4OYe0YhYHvknSp7ndp12SWNMJfUOi7XKfPgiRU6+p7P+hwPeIIOiLwlM6kJfXrIeno7p0+TaL9r13Y+ME1QZDtkViWPW5YWRM8Z0BtHlinVzbNaOacYnzWpjh0eTsnsidlT10bG72gGmY05Pd+Yy6PGLGSUv1yyBmpbdmocFlQfWW2J19vLwOj5+/bLPp2kOc9rT23HO63fqwR/7cgMwIBya3KMUNSekeFjy8Ge0NuxKHkgNv4IoQmTyErJcB7odmsasZAy+uyupK2majhLHbO9t6d0qqw3QkczhTCx9mBNK68SOjoQKRPJ07Fl6dOx+7hDDvoeevascOAxBhArBZxPeCE3DEidLIT/4/CAPGRNa4LMBKfKePWU5ATjM6w/BddoljSoPeFXFQ2Ac9QoAPskU04SROKgABCkOm3R6t0Pc3MuES2U45E0zHrLmNm11ynpwEetoAGx29HnyxZVnBHyZfjwAKgm/uoRsrfzZ7p6MPah5HRF7UD3eiz04oDgf1sBxUhipZDCAVlWYAuM9sT2eh1vZ6GZjKgum1bOfU1n1hJSLGW+Cu/n/N2vhl3Sd+A4vwPmUXqJJbKN6ehJpZdIWfrD1kXkc89EtrkxmILPJqH9NUUCKHN05J5yyLUlarurAN5WBKXgt1uE96s+vgFfXcGUBzTOWA6rQ8ymJ3asNa/g4fjJ2xPHRsdNjtu75pWaH052PSfm7PGf8qoQgTEahPJL5ErIgnnc/DykrHlZf9vnU378+zGk0gJ5PmdvKnEwmo1/h5//Lv058h++Atx9oxQIEyTqXpAtwVWMlqbfU1o3VtiY/3Smw12sPPNNdGQ6DsrFD4XZLa5zVGrg4CxQ7NA3SH78liSLn2UHKCiiMMFJWxNeXJXEy2y2wWD4eu5uUHRN7HPVPtEiIsxmNIG9ueTOtTWRJhCJQwyHzWq37JwZwmIf5r7a/9gmin72H5BnSt3M+efZzAvrJ7G3z7KX2TnvBxzE/SK8jt7t+iFPk3G32NMvFal4d5JD5itaM6x1LDBECv7amue5UtiwlF7SqoWdjSN1SzWsyBsoK9fevkX5zj+qLa2Q/ekNG0SfXiB9W6GYjyGrL73ebyNxqYVfzfSR2Say1+AeMXQc5QV1Wi2tVo/7jv4b03YLvNRkaLLmBRGM0L2fE4dQtkg+LPo/zGTTPEH9zi/azF4gfVjQ7+/xVyAmA4fTJJ3ju9D4AELdxPMVrlr3Uf+HVn+8Xji8K9y014zDkGXS5DvxP120P5GOg/xlGZJDMmEW+8K2MUNv9gz1OkXMa6+1MZyg5XsXrcK+7c7OHdCDYY7EbUvKY2NWYU4xfestOg0sE8ok9BcIkGuD3RIL26zeIX70MeXTzhaEjW3Z+nLq3n5PBm3W96f2utjv8rbf/+a9qCfy/vf6eqv7JX/QPJ17DIwyfqOrb9ruMjdy7K0Mcmtu27koq4zpMFuAHdT7t9SSvzuxnx6E7ok6aGA0pnTEogKvz3vF6wm6Hj/nx6poIzN2OdW6RQadjIhH37e4fiz2Oj45dBkUfu100H6aymMffvjo3/A7V02RQoJuNoesNovHoII9oPIJkKbovXvf5nM8OczKxJgBEim53zx5ecPILXr2GXqzCoUuKAm6WEC02FAc1YSMZFMCbD5xsultFXUPuF+yYLNeQ5ab3Nm07OtfFEb1Pq5paLiW9V7vPX1FurlPo3T26hznqH7wK8cnLK/qXbqnKJVXNTo/Ik7FrXR8dO5o2xB76/C7y9OGehJI4QvzuoTcj2+yAskK0XDOHNDvIw1lX8Te3IR9drQ9z2pnSmMuTAMD57CN8yr+667RreID8UBcdde9TNwTz/nnTUOnWWf0GjQ2lBNAbFpgxAOomuFzLaoPuYY5oOgmqYlpRck5u7njw3MTWHoyQ/PbPGNp2y5KnoFa7zKbAZotuuUJ0Nns69iShitkRsbv4aeDBGtCM3NkYstmhu7vvZfoc8mCHdbm5g7y4IuTX8vBLmzbkI8MBFcosJwB9KbelAvJ3ePiPfQVnDOtFt52peLXQxuU0DN04GdOBD4CiCwtGXNB0RfMvLa1b8vKKO2Sa0LvJFQ4MA66bLaIXVyRL70vnGTwXbQs5m/FR71bxbQf54jMe+p6IPWDzj4hd2461NsChVtfRoCBLOQmOY8gnL3nA1R7l6a58wWghS0MeDmKT0YDfZ5NXybKQkxQ5sNtBL87IvT2fEbPzjFvxp73gxfDY1r5zixYXP5XxiFQ881NCWVGPpbTHr3s1BUpewZ+RUbVA7uZs3213wEPJnbrtOK1MM/5s58G6+phLaixW7HkvVwGwpfMFuzu3D1xoT8ROIdX6qNgptZ32Uh7FsDdW80N5XbE7k2bc+RMjb5TroF78c3J/szH0/S3hzAs7hO/nZK1SvZ/z75vds+/UnHYN71hyNwPYs4ShCcDeSB8gcWJXWfcmCl0TMVEhuu2ZQ533wH3qeX1JM7BhAbk4p/fR65fsUpxN+CTxP/sNE0W9fHSesaTx0sNnBI/ELll2fOxxHGIPN9HFmUGejSV1fUkiyrAwCb0h4QL+/XV1mMdnL/kU+vRln49zXj0nd04B4PKAeHX9sT7tX8l12js8YDqKLbURk4Q1Nlhv6orDJFG6XwPo+8UrDlYkN2BXngVXaoAHNCkK1sdpGg573WQEefM+tPO0roEbyu+Rb8o2Y/cwRzQcQlZJT/h2KIABvp6M3U0Pjom9yInM3CudHNuOJCbMeEflg24ygry7NdJGy/KpMyOzvTyid3dQ7aA/+wZycc58zF0k5GQ3W1BeiKJnL6Z62js8WIf6iN3peQDYN46EbTTD3AS9l9gOmMMBP6hOzWvU0JCJHRpjI2QYth5Vjeje+tMugzcZ99PR0cB+foRoOOQCdkqdY2QAtiXb9unY2/bo2AGE2AFjJpkJglrHRk1PMrpf9Dh35RNSJmPS+PbycGxRdH0Z8mHJt5cTwCePE0L2dX6e6XXaC15M7WqfuVNV3PGENpD7O44fDPlnMwGL476cSBITNTLMSllxsZUVD2U2UKJlpWlHOh81tQMiYH6qEUkoD2Z0tjZVsK5jLV8UT8c+Hh0fe930sYvpWM6XbJ3uKpqZPSwJe16uegqiD6XaDrpaHeThuHbN0pCP7naHOTUt83ItTlXCiJ/xddoLXrvgOYSoJzqg7Vh3Nk3QjBHDeuNs8vMkBafT2a7ou5+qEhtir9fpiHXwYMDSwqQvJImhVYXumnUzspS9e4CH2/Waw5qBIRYHAy6ep2JXPTp2krfjngGmylamEmcjoxHr9WHB+M1qM0j5xREJ3vt5AEGTx/Ppvnh9kJMUOfOq/h/23mzHtSzJElt7n5Ez6e53iBuRlVVZgxqQoBc1BLQe9BMCBH1C/4ve9KBvUX+D0FA/qFCqzipkZGYMd3B3OqdDntH0sMz2IbPCPaiuuJGkEAcI3LjudLrZ5T772DZbQ4V/4Sh+pddl1/AC1qFa+4ZDZk5BVIgq2jYsHxDH3Om80vl8RTPgmj31AK2VTq3nVXE3z3Q4VUO0dw7rdyuM1sUx8N2nAMNFEgOvbtiXTlL4SQZ5eCJUQMWjXox9t9NFeUbsccyhkJkDr7c9aK7tAmNKyopf1/6+lViSpZCP97TJ1DyQ06wMRQEMh2zN/sPvgF9/GXJCXXOeoHALqergC3Wt12Uv+EjFT8fjfhEJIbYAgPmU4KtDSQsboBcsnY6DI4c70k+HjvGNBE1cSgn54hb+cUPSh15uNOTiG4/Ylza7y/1eSw0ACjILGJhWh0HDjJrsz8V+d3N27BJHitpUZORswp8zCmCWUpf+w2MvMGvljFEYU7ZJQx5GZr9d0BYnVgHXQxVyYouSigjOOeb3C+Ppc14utPoCxNW+Y9oxVierfYtzDjIdc1Hr1FHiqEdIAiw9dLHJkNh410rfiel62lz4faMhd1Cl1kkSo72b8r3UXBhxRCvIL3gQfCl2ty/Pjz2Oeiud+VQl9aIQP9IErhWeaZTA7eI4tC0BoP27vzjJQ7T9KYrDNyHW45xIMsn5pJmMflDG49quy97hu35HdFmmrtBE96FtCY9tu14S20b86+1pq3BJDcmAFdfSQb64g/vmA+T1Lcfz8zElsgEStYc5lcfKCu1Xr4DZCH6758Am9nTOMHWuOEI3HcIVJQ7vJhh8+7EncvxA7BA5P3anPzscqjerqMNeDle3aPOYhshVRdDYwbG7EkehVeor3uAhj7JGN6Q4qtwtILGHA/4kJ24A1a8WhB+nCeRu9ot68Ge9RAin1QUCH/Ulgnc9zFdxKuaU59SQ140G7F8bPc+mmwDw/T3fe7cnotDG8QD75LsiTF+j7x/RrTfAuzdwTxsiDT/eK2amC0oHrqox+N0y1OzPxe5MXeyc2LsjokitRJHNjqA05+AnI05H45hf17mCbLaQ33wFf7+CX+1O8nDjEfw3BWl/ooKvdQP59n3IybT003+i15UcSjidH1zrddkljfFOpZ+S2nQ1gKNc724damw76HVdj/P2FHUSUREjnXYCOmzKMsgg488OB4FyJ7MxMKdgqZ9NeXgb5r3QaRKzlhbdEVVnxmXZy7EDZ8du09PAXci0dEkJScaaWB8Rzhu62+kR1r+j1k0Sn+YRRYQUZFmfz2R8klOYRyiH9/8P18Xv8FIUBEw1DXvCAG9TQ0qGA6HaxejfA449jlkbr7ccwycJOx6mLFCW8MY5fVyxdrdec1UD7z+FbkjXNHBbHfXbJHOz5cIrK5ZHzgPrDTAZvxh78F09J/biQM1IlcaWzS4gMaG/Q5oGUuyZ4++/D5h89833jHd/0EO15pGlQFWjK0v4roWoVryL45BTaInq4Rdd28OWr/S67B1eLxd5HgK1J47oSJTIGPYiZOmL8OuGATHHa5ODti5J3asJy8Oyt6IBd1Roj5vO3XugbeFvF8FnSdSgWAwnrlgUdC1RmD8Wux5Sz4od6GNvGsZuwlD6O47jl6oKRGzE1MhxaXKax/EhXvPxi/lJTjaLCHnF8dWrB1929M6xLFD9GMkSmnVVFeG0hkeJI7b27JFvlpEANRzTNHReMB2w5WZjfpOsA/rpo8reBemKxRzd0wrY7oA3d/APrm/9TUaQLCXyMoqAmxlbewYu+ylir2sAnrHvD3CTCWQyhNsUbEeudtTeUcpggAJEvpfHvlvAP21CHm48BpIYkeKJpGnIo22aPqfiECQBYZzayAP3n+0T/+zXZS94fWxLQ0PdoM8IBPsaANx1DuXpz0YRd8dOuKgPunPpYMdFnno0jaqCae2MiH1ryVPIYgD3zQdiTuxweqTLIqs1XCd9TS4CfFpyeAUCvH6K2MP3DR1a13CbApIl8B+X6G7nNCOuqXsj+z3ccMgbt1Xsu1rRhzwA/tths/HuAAAgAElEQVSWVc/Gev9wklO3GMMfSt6oqy0Prh+u2/Lmsksac9eLSH420f5ApC6rgB8JO2SahO8FePF6y0NZ5KmvAnA33uwg24KYlzjm4c18W5/WdMBrGvJhDdjlfY95+eqLAHEIgkhxRHmP4fDl2Ov67NhdnvWx20E38uzIDHK49594s97Mia85ttbRMsiNh6d5tNryvZuHfGSzPcnJfXhUaHEvIOXevfm5V8FPel32gtdFhCQ5oaVx52zg0uS04yGUljZb9wAaa9sTRhKAnlGk7cFuteZun2fqukFIrJvPgoWj7AriUER/tqYjhjSq1zgZEzejWuovxq4srHNiN86rtGwVnigaqJFDt1rzxtapcnArB+DmM05kj/IwtCQ+PvT5DPKTnFysZs51HST/TFbkWq8LX/Dodxg7yHXqUq29agDsdauRF7seEnDrAe+ekM0DvVFMGwYAF+lwQNfq3Z43UpJwKrnbE6+uu6r8/T8pNW4IWa6ILOxa1reJHnAVafhTxe4i38ee51zUumCDPJ+aJchuj2696Y3SpGMOZXWSB7Hyjd5kvncrOcoJ3pOYPh5RgKrtfhFT/ayXc2zrWWcD5FzKoVR4wFGL7Ig9RFm7TrEoedBsMX10vkjYn25atHczGoDdzOmp6hzrVSVfmD+TG4/gf/0ly5TlirIWec56ebMFnjbUb7Gb6qXYjzA7Pxq7EVRSo/kpDHk27ondt0RAusmIZYeylYy9hcX0JA+XKMNpOOzzybOTnBCp/o5zcNMx3Hh49cpjl73gAeVj0iM0HK6810dvEnTXAYRa1aUKxTWBJZHQTnOmSFCqg8dkRNZR7NHNhtz5BhkkTVD/7TseGrMUMh2hm40geYb2i7tAHpeWVo7yF+8I4pqPUf3NFz8au9T1+bFbaeJ0smxQB4U/EGDmIb96y7Jjt4e8uqFhRJZA8hTVu9lJHvVv3rKmv5uHfLr5+CQneA/5i3eQOEI3H1PBTFWYr/W67C4NwBLAJKeTJDj7OVP30gUhVcW+tqIfjaeJQxlodjLSunbNjgPSBNgUcN/fA7MJ/EdCApyah6Vff+Lh0mpuoW+TA4DxmDV60xAI9v4T5DURl8nvvgWmkxdjD6bFZ8QOAFhM+XolVPuv3wOvFnDrnXahauBhCSik2H3qldKk65A1LWmNmof/uAwwZ//9R2A81sGZ63NqGkp8DAfsMnl39X34y97hjUMaR9xpjxW51PFCVIvFpakq5w6VCKELLYr4KPYebr2Fq5W17xzcasv3nU8JHnt7G1zyeOCM+b3pBO1iAikOkF+/Yx98Rkc7N5mwDbiYsQWovXrE0Yuxw7mzY3dZGmI3CT83HjL+siKIrDiQfB3HwQ/K/Fcxn7JFeZzHmOQU2WxDPr1fFXOSjg6HADnAuPIDK3DpO7zhXgB+oMcGAV0XvIoCOSHycEbSPkYgNk04JMpmy0FQHBEnfr+k7Xqt1uzbbfjAcSipDFaWiPYHSF3B/f47qgc/Nuj2B94Qpg7WlHBZivYP3yK6u+3LqB+IXcqSAqpnxC4tW6qy2fLrcQQ80qTMZSnw7UeKRT2u+L7jccDFy3LF1mWSQLbbkAc+PRAG4R2fMI+c5ErbhpzkSFLEqSGzS67buvKyF7z3xKC8vuEwqGnh8riH2JpwqQktHTHt3YCSds6odKMhuskI/uGJCrwNf757u4Df6sKejTmCX63hhkPqr6cxovs1ob+NtgO9gzyuOIpfbSHzSUAuymyCaMgbCsv187EvZjQuOCf2LEO3mMI/PIXWoLy5o8W9+ktRnyelqUPXQaYjDqeiCN2vXsOvSVqxPA5fTTH4xw+cPQCQxxXkr76Ef1iHnMxxxA3yfvJ65ddlL3jRKeZ6xw828mF3BHgAlMmI3kZxBBwESI7UA+wJ0RAX7pdr1sRPG5jLdVSzKyKTEetwH8FNJ8TOr9aQv/4Va980AT7ekxs6GujNaOyjKngouYKlkDNPpedi35dnxy6qpsDeOIkfWG8gZnlpEIfVllNc85M1Lm8rwHINkS7kkX98oHBTljJW7+Getqc52TyhbVmuFcW/tOK8suvCF3ynFu9HMhbHnQ0gLCwpSMzAvuSuZBNFG7CYXowSw82M2Kak2Oxg1uzytGbvebWG/8P3/EWrTcC5yKrvVMjTir10UxZOYspfx+SrPhe7uQqeFbv5TEkHqXVIpJLYRiIJagINFQjsbBBysPc8ygMAbwTznzJ8keUkXa9zn8Rss175deGHVg1vkPeiqDsi/pyaB6Bu1Dt1FswDws8k/WAJXac6i1zUbjoJC1Va7ber9QwtZhQAZq+zeGZj7oDWZpxQx7H4t78OAx2Xpf3B85nYg/zGObE718ce6ROorEJcYk/Cru3nDFBZkNsFtSa/fHuah8p3YDEN+bRvFyc5IYqYl6qzOdPxueLr4nf4gME2FKFruMsNctay4yFcVbM9p+CwMFZv1YSgOmoDDnIVOOogkyG6YQq/SQPO2w0HrPE3BcueQl3uDEkJhMXgRkMe6iYjDP/xno4gez1TrLcvxg71ej0rdkN6DujPiq4DXt+Spng7hb9fqVlCFH7eqZCqqbNBa/UgA1KqNo8NsqII/uvvdXDGnABg+I/3RHemCQnuq+sWY7rwBa+jd+dUp1FFkfRxD8V9UD9dxUq960kKIuHQBYDvs1cnvEMJVzeInjZkFwGKNa95mMx6lKLR72RHH9TOLGDaFlJW8Low/V5fX9UQ716O3cji58SuKmjHscMgENo9QdOw3laqn4j01L2mYQvzOA8A2BXoyhIuivj1klgey8nmBojj/gkUX/aS+bHrwksaBToVRbB5AbTW1MOm25fsg0eEzcJEixTjjaYhWWN/4JjcpLbNidt23+EgIBENYivzCQc1Y3JGEcfAr76An07g5lO2BU39txPiTcbspTs91D4XuxTF2bEbPxVWt5ureFmyx28SGlryiE5sZb/nAdo5dI/L0zwAuNsF/HQa8mn+u//qNKeIh30pyx5EduXXZS94q02P6GeBgO3YPzZZOTk6DJrVfNBnNwUuQxsCQQXMXC/kYckPW3drACpBFwV6nksSuoPEimFXFKasN2G3xf0j3y/I6v1w7NCnwzmxU09eY3e+F2ZtO0pzO09ZQOgNtVyFHGQ04E381ReneWQZZFv0imiHEsl//vYkJ9nrU2Q0BO4fA6Hlmq/LXvCmDRNFPUiq6yDzCYcywyGRinHc67CYbJ3XUsJHAcQVQFMGy7VrT1k8+10BtLXZUXLOqHBKKwQAOZRk/89nfRcGIL+0OPSqYc/FPh6fH7t0IfYA5TUCSUcpbDca9h0hqOpCHNOQeLeDfPP9aR56dbfTkA/2h9OczJStpLkyzOfpiq/LXvBQdpIuUKdsfjyuArTXpSmhAHbI0/rYDYf6/UQx7+y8BC6s7mxmzW7TSABhl3N5Rt34zRb4lQLCZtPeRj5NCaXdE/cubccFeaNWmy/Ezt3zzNhV1dfsZ5yqFpjAqptNCU0wkNl0wtfYTfjqFm40OskjtGi/u+937UF+mlPLzpaIEs2rGn4x/3wf989wXfaCV7lqyVLCduOYQCaFucog44fkHD8QUyWYT/qOhwmXToaUsDP13WFOhTCvDKL9od/BtQUoyxWBZt7BPTzxvZcr7pxpQsjsaNSP50fa9ntcsQPzQuxuPDo7dqjw0knsTQv54pblT9txgZrL3l79YuOY2Jz1lsTvozwAEHNj55g0Qff4dJJTyMsOqs790pb8rFfb9ujFtuvrYWMlHUpOD+OYi6HYszuhwxSnjH1pGrb+bNhzKDm2z3XxqRaL00OjqKuGUxt6DHo9ddwtgIKSFxJ5HmpvFvzdsfbIBxlr/SR5NnYanRXnxR5FxNrHEc8Lk3HgzWI25lkjTTiAUviwTEc8FJuMyOs7QgUsj/2e3k3alnTDAaI4JshMc5IsgVuu2RyYToBif/WT1sve4R14UFNtFDmU4U8yfY7EgXzfMjPwFQAVPo3D7k0Zigqy2xM3si1I2F6tOSUtDlwY+wPkac3uhJYfiCPulnrTyPcfiZlROQy3LdiOXG25oF+IPRyMz4gdbRtiRxyTlZUk8A/rnra3WvNJpkhIt1wHxTUZD4nBP84jy6gTPxqEfACc5vTxIRyQxdNczVCW13pd9oIXkE2kkhih7k4S4lCUhoc45m6sO6MNmTiB5AHUFL/C39OEA6ZhTjyLp0kx5hPulgMFea1VuSzRhTZQo4M842vuH9l23LF37+qGsOWmfTn2ND07dpukBk3NlBRBGbALFA6VUcT4NzsEQ+OmAdbb4BBoeQBguzFNQj7I0pOccHfDvBacBLvRAHha/5kWw09zXfaC94474nTSk4gV7+GKQz+RVAUw0dKBP+vpOp3EfbdEuykc8pCvibIiOcM4nFUdlIWtxnaLObpxzlocAGYT6rNUNfBaRZfShPh2EBgWSpfnYvf+/Ng76WPXMwCiiPFbx6Uj9t5VdT+tPZSUz3OOEIKjPNxoSC5rcQj5yGp9mtPDkjf/MQ7+ytuSl13D25Ty+PJqzTIZ80O1kbtaSsKrAsB4wNpWRC3YR/Abkpmdqv3KulCGUQq3mPEDBwJLSbY71vZxDP9xqaSQFNhsqQIwGkIelhCT1Hh4IrJwOKDU3XHZ8qex2yI6I/bws9q3d3kWjNDCP9Vs2sevAyqXJCxrypI3V5b2ecQx3GiI9v1HmpnVTd+lsZzUTVBWmwA+c7PJVQsxXfYOr6AlumaXnDKakpap2IpqMZoSrzpvoO2C56ns9vBPWyVhuECCdpMxXBLzQyypCObynBrvTcNOShxzoQ0HhAZnKSEDb18p4jKiEwjA13/1Voda7cuxR/7s2IPPkt38XW9m7GZTtjF1WGXEkXDgNY7s65vTPKZkPvnpNOTD39fn1HdzUuaVJlffh7/sHd4+wDwFhjncaqOOeGUQRbKbIshFdx3r682OB6z1lt2NNIFru94bVbHeiGPIasOuy2oTlMlQ1cBAhziHg5ZXh74tt3xSFYIDfD2kokhVAQ8V63YfvRi77A895/RHYgfAcst7SKNdH7XtscUdnD8Ohx6FCZ3ixjHw7Xvt9/d5WCeoW62Zj5Z9ISctl6SuOdHthE+LK74ue8FHnh2EYPkS9/robQvJdAcySWe1ieFizck0ArhY6oYlyu0isKXcekczgCRC9LhG99dfwq8Kkh26FphNWL/GrJfdiDupDHOqgOUZ0YtN20NvWzrlYTgIFjs/FDsicl3PiR0pzRxwu2AnaDYG8hQoa2A8DG4g/mZORw/neNNUFfDmjp2cquaNp3m0ixHc5gA8LOHevuL7Tsc8A2hOiKJeQ6dpeLO9vv3Fev6zXZ2wJ75csythjB+AH8ah6hlCVR34rwD6BQSouL8aDpQVP8x9ybLmwwOcd5DJCNG39yqKpMTqukF3/0hEIgDsEVCJ8D4QRSAC2W6BuxuCrr6lHqWMBs/GLvs9F/sZsaNp+9i9SmWYUJPQ2Fhe3/IJVuhTaDJWYkhBAdW2hTsMQh5+vWGMXlut3lOar6pDTs455hVFXOjv73+xrfzs1/1jP0ZX4y20bd9Xt76wYT72h2AYYO7a9sg3iTqCs+gA6MbDfhdLk15fUl/vhgMuNqtpdYJplpZyKHmwHY+pR7ndszPzI7G7JDk7dnjXy+sFXXl1MKz5RIBznBsMc54btmRwUVh1APzdX57k4cYjRUO2fT53i5OcqI7Acw3zGkPe3v68n/9PfF32Di9d8DyF7pBSFL0sXRoDD8tAynDDIT8s01GPY3Y0jHwsHTsnsbptNA2wLYJvKpwj0hDoZe6cIwZFuZ0BKNZ1XLRNS1jC4cCafbvjE6GuX459lJ8du4s01krJJRml9gJnFejdQZYrXaw6+LKdGjjJw4GAMbeYAYUeit9/4rnEclK7SjcY8KCdJsBvH37mRfDTXpe9wzstL6YTLpi6ZgehbYNbtcsV9HUz52sVEhAEkBpS7lwc9V0H04s3Oh4QXLbN5iWAvZzj70/VBVuxJ1KTsQQorNckri30PH859rI6O3Zpuz52ffLYYgytSqMP5nlwFbFDKZyD+/rbkzxkTzSlKJeXXrbNaU42zLL5wXAAbz36K70ufIcn00fWG7bq7HEfKcvHduu25SHNuhebXnUrfJhA+HvQgu+svNGBjrJ6uGg6lhlZqnaVLCnczSIo9srDEwFZaiUpE4ojGazZZenzsZs7xxmxu+jI+0mfHNjv6dEEdlGcc+Hv4XcB7NrkeS8vaHmYcprz/WE6y05zslapgdmA3jPrSq/L3uFFiE50tI0JOuZaA4d+N/SR3rGHbTYvAHpNdgNkmWpuSXk7F3keNk0zvazCgnSjIWEA1nsH+Pi/naN9NWfXZjHrUYoqwoqmIUvppdiB82MHTsyEpawo4+2OPFmNqVXSOkcmevAcDPj/f/eXJ3nQPEH3O80HwGlOneghnV0tKfa/eDx91ssGLbMxP6jZWB3l2h6DMp8eQQZywlud+pROxtydJmMOWnQRu5zjdZlN9FDJ1qfczlnPv33FtmLkibPR6af8xdsgh+3/+L5fgNMxD44lhZHar171JJPnYp9Nz45dJqMQO4AAcZDZBHI7J6YmjkhJTBO0v34DfHoEhoMgqNSl0UkebjRka3Y0CPlUv3lzmhPAvAwaMRz0ePorvS58wfueaxqzDSlWiycxUX1OrSDHKnPdcngjJtE3yLlbmc2MLkrRm6l7NUeXKhc0Vhuc4oDg1DEZse/ddhQg/eI1e/PGLBpm4fWubtDNRoi++cRe+kux74qzY3fFoY/dOYh3aO/IVDIPJ0QR3Jac2mhzAN7eMaanDWQygq/akzwkp9JxNxuFfOL14TQnIBzQqZyc/owf/ue5LruG1/LDNS0/WPVKssexA/o+vPkgxTFxLk3bD3KMrgbduVYb9vAjDzws4XOVtvv2Y3jMhw7ItgDKkjuyCPC96ji2LXvf334EhkN2UyYj+K0umtWaO/kzsaOlicI5sUul54Gj2KPvSz6ZoogzBecgmw1cXaPbFfDTCTs7ZQk0Lfy+JJZe83DrHXv4m23IB7/9PeTdmz4n5dC6PGNeVX31RO7L3uGjKGjIEMex5245Gva2NHqgEwVHIY76Lsb+wEd9WfZs//W216gpK3ZITCJaOxow0+PIo/vqNXdqbQ26mzn5pYsZ++4qg42moQPIZsc4FrOXY7+Znx2704MztW56vfjwtDIv1UQVDLKU1jydAFmG7qvX/PmjPKBdLrx9FfJxg/wkJ2cOf1XFvIwyeMXXZe/wqokSdhbniR8JSl408HXmnbrdnRwIBeCoXHfw4JVk9u+GhTcnbQWVoay4yHYFIjUmCO9dNyQ6DwdBEgOrrWrPWL+d5mbyQux4WEK0p/5jsVvrEQA7NlVN6p61Zicj3lxNf8B1n5540NzvEX1cUjE4Hpz+HgDy7XuIlY6KmLSc6GLiWO+rL+21X5e9w6tbtowG3BFvqFVuZI7uL9/xMDobswMxZFeFCryDvpU2GXJ3zVLS7pKENuzrLf2QlNQgKolh8h9uNKTWzK6ggu9215cX2x1JGTpU+vrf/xvia0S4KCejF2MPB+kzYnejQYhdRELsosQOM1uWw6E3UDiCE0uxB5L0NI/tLthbhnwm45OcZFfg63//bwKbyqVHHrBXernjYcmlXbP0jfwPb/4X9TXqhy9iQ6MkCZaTqFVrUdlCABRdqOx8A5wZGaRtw2FR1ALSDfKexLEtIDcz7va7PbpXc/gVmUSy29MGslDlgTxl3W2ElCQOXNHnYg/l2hmxw/kwvDJUpA22IEI4weOKPzMe8sbTA68NknD/CDcYhDwkTXiQVut6Nx4SePa0DjlBhI6Eegaxr/2H3/6vP/NK+P98/UcR+bc/9I3LLmls3G//r3935nrnPdxY8SJ53sMFAC5uuymavv/tTBYDUN3HiB+y6kS6AKttIV9/Q6x5HMGvC94Mmx3caEBIb0Icu+3sZnQsqiQWSocfiN3w9ufEDoA7//HmJML4s5SHX+3+GJvK1BXcnv6wbjEnotLyWK6IC0rVw2pDkJhT4Vm3LUI70m4w13aBZHKt14WXNMoQinw/jFHMOADFnEQIKsNtyz420FP6jEkEsF+tVyBKD3MeQFu1pzHNmdEA/u6GtbJh6B+eWObsD8CbO5YSdpBLEiIJ1f3aDQcvx66/75zYXZ71scdxPwkGCFceqIBTw8WJhOoKpmPvhjmfAsd52DQXCPm4PDvJScqyVy1OYv6uKyeAXPaCByeFyFIuBiVOS7HnByvCPrUtVFsoIF7GqWmXMYlc3bDdGPmgquUOFXdko9l1LEPCB6t0QHhPZlTdoPvqFVxxQP3f/gbtl3e9fkzHeAyM9VLshAecGXtdh9gJB3BBt90MFshz1fjN1WN/CF9r5oOTPNp3t9zZJ6OQj+TZSU4u08N8nrE8ahq4v/rVz74KfsrrsksagG2x0YDovt0+7NYupVGYxBHr59GA0N/IA37All1EYzFTKMC2CIuPuBa930WoYLDbBxyK7PccyrRCYJbCcRFHiN4vIYMM6R8f2BlJtK8+VGsY66q8FDsQSqAfix0JtR+dc6zx44h5OwekYxJZBhncQbtLVQ3XCdGdLe1v4r//HTCfhTz8Xrs6xRrRbg8ZZGhng9OcAFUoE5JD1lvI/fJn/PB/+uvCd3goq0htYmKtMe1D2B9Yc4uQvLDekAv6sFQ1gjhYtbttwR1yrPxPu/aHoO2CKOo/6Js5/OOGMtNK2pbiACnodO12e9bJ5ppRVegmo4C6/LHY0TRnx46262PXyxle3qARgPbitT5vW+BmTpXiFQFfx3ng4Yk3xmgQ8onvN6c5TUfsWOlmQP/YX7A0n+/SSWDQXhHpzQ2OuzTWa/eRspEiDnKqugd9GftfP/xAsIDW9qtt8EaFdzQkS2L2+c0eXsiJ9aN+4dG63fFA/P4TF4lOSV+KXbru/NgNU3Qcu/Xci4Na2WzZgTqUvcbOUhlZ5lZylEcv/nqUj9XumhMeV8yrrCHT0U/4wf75rste8NAP2jigpnve9chDdhFatgYjr255JE4c17ey3iiysNenkSeSM526ZMh6wx23UjaViQ4lKk/tfJCfRhTxsNu1kOUTzwbTCSl9H+57a/lnYjel4LNiLw597MeoyaeVbgo7Hm5TbSWqjQ+ahjcfwCnsUR6mRNwN85BPIIRoTmj03LDfU8lseN09eODia3jpuxXO8bGaZ7SAidRnNU2Jb9eyxPyZgnqu9bV1MZh0HDsPKQ9i0wlbfubaPRnxoDgaQtZbvo8+Idyh6tuHNfqOSaa9a+96KO8Lscuu4I57RuzhimOgKHp8e6Kvm5Ns3t0/Mp5ScUKR7/1Y45ga8oPTReuLQ9+ZimMuesspIooUqqbmyorT4yu+LnuHV5FP6rmojkuxZ02raENYh0NVuqSsgBlNuYyxhKTHmOMY/PT6hv13RQQiS9mGNPpelgJv7wLOxI3o/4SZclYnI1S/eRPOBWKdnjThwn0p9tnk/NhNQ7Ouw4HX5Rm1ZiYj3vhdB/fuDeES0zHl8Ywz8PZOlcf6PLrtjkOyqu7ziaPTnLQV2b677VuaV96WvOzbVbEobjToe8xOsdmjIWG0XRIk8tx4FDRl2LXQA51KbaBhO885x59tWpoVb3TyuC24AFU6w212vXpA23HhPj4RGNa2wNMa6bZgDx6Av19yOtu0ZPmvd8/GjkKnoGfEHgZYaaJKDpySomm56xrhWo2OZToKpA2MBr2031Ee/tVtr45g09+mBXbFSU4AEH33QGhxCeL0f5Hp+ExXmDzqB+J8L4K628OZ3IWaERDs5E9FjswPSSeast6SBKKQBBMmxXrLdmUUofv0wLLk1Q13zkOF7m4GiRwiAO1sCJ/E6MYsD6KPT8Fv1a223B0/PgB5/mzsUlXAmbEjTXjTrLdUUdgWPBPs9uHmxGoT4nebAjIZQj49ws0mvCE8dSQtD1e3kNkIXR7DVw18EgPFAd27dyEnOZS9LPiKqsnt2wXwzZ9pPfwE12UveBu+NC3r32OogQ5agrCRXUkM19JEjOrCg36XbBVn3jRcoOpCLUkMd3ABX+JNZqM4BDEo980HOOeB0YD6NVkK/7jiwe52QcDVaMih0+OKkITIPxu7S9Mwuv+x2MNNkyaEC49V+13JKk7x8OgE6FSTcrkm40ldwbtPD3Cv70IebjSgBGHJRU1qYgv/hw99Tirw6kwCME3gv/7+5/nsP9N12TW8SmRIWQXysGwLTh2rOozBxSxsoNPFPOuJHMY3zbNAdiYxomI5sNkCj0/saqy3CHqWsXZyVmt2Q6IoTGilrvWAmHFhml3lcsXfbcq/L8WuZcRZsZuWjXOqnKCCTGVF3cqigLQdBZdMetDKuKcNZLXm0+U4D5tFZFnIh5iZ05wAsHtl1j1Xjpa87B2+JcqRHQIeNl0c9XV9WUEWU4KoDpXi0eMA5IJT4VR99BtIK7D664bQ3SSGe1zR/CBNOLlME8hiQoRk3aC7m8EtN+gmA3iA0F5DScYRQVW3c2LLdSG+GLuhD8+IXUaDoDDsMsebTuX/MBvz/ZsGbj6j88dqC0zH7PVPRvC7PWT5BDcehzzgHDDPqGKWD1VcdU9skeU0zKlVY8bExeGXkuZzX1LV7Ma3LQAVPvKOi9g7uEeeoMwmRqqaC+vhiY/qrmO9bJITJnKUJjzAFgd2XqIIMsrhlypDvd7CN21QInNlzf74qxm7LbMJ8OkR3Vdv4NcFXJKwtl8VkDxji/OF2F0cwakC8o/Fbt/jYEplRAYZN4SDal0W2n7dFgHqgM0OPkl44EzuaHejebiY6EtpWpZhswlJ5cc5Pa54eG27YKZ87SXNZS94h95J2mh4o4HqRFLfRYCAOBR1yAbQ97IzCpYG3H+a9GpkJrlteunffOAj3kqQ/QEo8r4Mch7Rt/fUTn//idvntB8AACAASURBVO/19beB2RQ9cHAly5XKVPvnYzeNmDNix1HsADcBvL9n7pHna9o2HH6lOgTbe9kf2IqdTXjY1Txkv+fXq5p6Nu8/9f8+lpNBkKMKzo/5+iuX6bjsBQ9qKjrza/URnG96TIrKP7skgjToF5JyTF2iI/7jwYqhGruW/eqyYn3qPYdCByVs1zVfO6FZgFMyNQY5XNsCChTDcs0FOhiwRs4ypcoNAuT3B2P3DmjkrNhdHHMSa+cLk+wwlKe2IJ09BeYT3emTPock5muMFD6fMU4VTsUwZydoNAg5yeHAfwuFQbg45uDql7bkZ7qOpaRj2j6iaQPX1Q19GMG7NOndNao62LQ72yUBdhyAwJKSLb2dnHN6sOPkNQynbEIK8mfdcMCDcicsCaoa3eHQW9V/9QXViKdqPmxaNT8QO4oidG1+LHZrTzqAu3qsMh9ezyFZxvczZbX7JSEFVr7t9hBtK1oenTqPY7NVgdgI3WoN33UhJwBUQjD5PueufvB04V0aqDKBQmGt85Bnar3e9tY3duhTtS7JiSmX7Y71uVm4G8bFQFsAd/A8Dy7cbjLuJ7NRxJ97+4o3H8BpLMAppxI83M0Cfrnue/5m1vBM7C5Jzo4ds3GI3WWZUu6cijfRhNjl+dF/PVkk5LDbneThZ1PG/vZVyMcv5qc55TktORMaJKNpgC9e/xyf/Ge7LnuHB3vLoSXXNKG37XSEj0MZnC2CZEaSsHd8ZPtukhOcdJKhL+p9JGUJEYEfDVkHNxRNDSoHZQW/9gGQ1W136v2qOu37PbDtWCJZiWtkkudiB86PfdvyZ012b0g13wAwMxudtqWpsLl+1Eri1q/L4dDnoc7aLs9CS9KPRyzLLKe2A3YIJnByOLBUuuLrsnd4gPVqkoSdVkzGGdAa90+0UqKIu7/KYzhPZKAM8+CUJztlTKk2jMnvyXTcK/1GEW3WW54hTLsRIBAtulnARRH8dBLY/mLEEh+FtuJzsct+f37sbXfi8idNQ/aVaeSYfKBa1UN0cqtPLIqnDk/ycOMRB1lqXBzdLND95RenOQ0HzOtIRfnar8te8HZIc44HrYj6KWGHFAnAKqeLTaojXyIoj1SEIkZAzxN1jl9rO77GO+DjvQ5s4iMCtvoiNQ0BXioz3T4uCaXVAZOpkoVFqdDl52KnSsF5sYfL7CoVY+/WW3QPyx4ePBiE+KVS04WmAaRD+8dvT/N4WgXJDssnul+f5GQ0SqeIT9kW6JZPn+ez/pmuy17wQG8Po9xPKfYcqOgu7IJDhjnzjYKibrgxjPAsXVDkFRHlkuoCjGN2LjoqDYf3H6lWTEpVYHYtWvjJmLowZjJgGjBqiGDeqs/FHiT3/otj55TWTydESCq0F53Kj9TVaQ76b2l5IMsgZYnoZtHns92d5IT9Qdunogd819toXul14TW8KES1vy+dVxLGanuq76K+S9gryVmVw6hvDhI04lhLA0UtTieQxyVrVTUsc4NBv4CTmBPK+Uw9W/Uw2bb0RdVDLHSgE6T/Cu2DvxC7GTqcE7uLPADW3jIZkcI3n7LWh3aQRkO+Z6ueUPMZXQkByG6H6Fdf9gs3+RNRVMvnUPZQ4a6jJInyDZyqCcv2lz78Z7wc++IqGRfsWPQRHsBYgIoW1X1rr6yUVcQdNXxw1s9GFBS7kCbK9dwH6T5ENC1DlpGAXdUBTgAVSJX7R/q8jseBtudaSt/JZvdi7Gw7ZufFntPQzKlglETU0pGgI6MDorruFRCMBAKE2YCUVZ/HdstzS9PyBqhq4G5BLR7Nyax/gueU6QFd8XX5JY1JYPgo0NdI2NDdzepjBT2FQZPtdq12RXQhhCGUfe3dG/4eEeqzD45Elm4pSOqU1ieHA58swrrfHu/mpSSbLXdJQ0C+ELtT1tJZsZuKcNOwi+S9unlT6NQ8qkTjx3zKMsfOIR1dBk/yePeG76WHXOk64I/fn+a0pzGEHEr977p78MAVLHhZrVkyGG490f5wlgYgmNj4XetyZ8KpuiCCEJJp2QBcjHEMfFr2sni7giz/mznMxhGGrMzSHrJrnksKbrN+vnv3hk8eq+dfir1tz4/dzhtC+xnrrph/U+gIFQfGv972O7KVTMBJHnhQzuqu6PNJ4pOcrCvjhoNezOrKLW8u/Pmkli0ddzWpa6N1c0fqVFTJYLZpyu+ruYBAd1LdgW1xOO8VzaglxTCH1A26twtEDxuiEx9XJIDUtHzp5jYkEnhDWt7OATMKBkj+uF2w5FlvXo7d4APnxO5UacA6QIeSFL4jqQ5RcnrwggV44ymJBastkKUhD/+w5hBsmJM9VfNALt6FnNxqC7mZEX15uyAvoKqB337mj/0zXpe94KOIi2M24e7kekHUMKUU4U6ZJsC+5JRyXwKzAQkMNUnaJ6AnI2gbxLaj2FJUN3097D3coUJ7N0X0aQX/tAUOJdov7vhzhxIyHnDXNiOz6YgQ2pspIqDnrv5Q7HWj54UzYlf8OwCV53PAQfViCh50XVnRMKHt+D5K63Nlhfb1HJESxS0P5KzrXVmHfLrJtH8Srbb8c73jQi8ryKrsfaGu9LrskqYTum/oLtvroY/591pNCHYFyRtqLYMkZgdDBzuy2XFxquQEspS0tapmbZooMlDBW64h6wiHEn5XsjZPEyDP4CsVW+06uK3ibAbcKTu1hIm+v+d7vRQ7cHbsdhaQ/Z6tzYhMMFdRLyYo/RZKGFlTY4dOhzX8rux765oHAJKzTfKk6+Cftic5AXz6dTNta07HvWLDlV6Xfbs6d9oVOBAC4LqOchqRD4pYoiAtWuQ0fWcGbOvJp0d2K7SN6Tq2PF3k4R7IeAqqwGkSSNVuU0B8xLo4z4D7JbXelcRtuunwHv7TI19jBJMXYod0RF2eE7uZJ0fEs7hDxcPqaEiySq5erqjDE0S2BdWJk5g5qECT5SFPK0S20xu7a7kKN5dLU0KFd3uS2QFuBPMp8P5nWwE/+XXZOzyE9ev+EKaQznvuriZC6n3Ao4TBjGJmwru0XZg6Wj0tB3oewcbtecZd1avKgXPcfWPVTlfdGltkuKHNo3z5OgyBXEahJQnO3s/Hbpo158RuE1IbRhlUV9YblkUm4LSYMv62C9Nb13bBDOI4D2SZYoPakI/t3pYTOun/jbuOknzHMoVXeF34gnc8rJlejIKYAGiHJuu/ZywmQzwOVCEsTfj9gUJcDQWoeBJZTNHNJ0Bdk/5mSMjdHm4xQ/3FnO3CNzdqYkzPJutx+3UR2ooyHnL3/3CvnZMXYtcB0zmxO/OcUsiDiyOeF8YjdJMRS466VvJ4zv8X4fwAYA7ASR7yxR1cmjB3zcdNxic5hYN11wE3M8KKP97/fB//Z7guu6QBVBZPx+u286kHEgDWrErogKPcHOUlcu1fa2suVy6rmoi5yHPHLiugqli2fPOBpYWw9y7bHeJ/3KGravgkhkSeOPI0ATaE5xqu3EUeuKepALIsPDlejN2mqT8WOxBiN5lA3D9xkap+pTjP9ykdB2hZxhwelojXG2A6QXf/GPJw35O55T48QLoW2LgwlAo5GaYeAD4+8EYc5MDuM3/mn/G67B3eeKjWUhThLjmnR6nTRcIxvYqQ1nWAvgJQVhRhucExD+DBta65iw4GXCxR1CsbrGgZjzim2Kj6vLp3b+jBlOfUq1TjLwBwsynfqywh2+LF2NG258cOhNilLJW55AOTClHE+PV1eHPH/1+pTWVMiMRxHlLsaXdTliEft5id5mQalqLt4Su3rAQufYdX94pAIjZ/07YjrzTXRZKlQFXxdc5RGWyzoy7LrgCmY+q3pGkv/VzVrMO3BfvuNtxZzICDWjwa2lEf+ZLGcPuKgkSLCbxZ1Cf9BFWGOfvp6r/0XOwuz8+PXTEtMqDhGPYHdQqnPk3Qf2waYDKiFMhszIN5pcwr1aixPPwgh3jH9wPQLiaobnPk/6noczJd+kQFpPZ/chC/wuuyd3hIT1VTHXTputBJQV0HSTg31tZZHJOkEMeBjuaUWidFAaw2gf0UtNvtz+GAr1XsiFi3ZUkVYf+0pX67CPzX3/FnkoQ1cdehe3gErMZtuxdjl8Ph/NiBEHvgAJRVEGAyI7bw+kPJ96lqtiydY51/nEdVA6stv6755P/X705zUnQnAEKn9/sT26BrvC57wXcSrF44Bt8HDA2gHQzrn5s1jY7gZb8PeBdZb/rDqmnLm+2NOmqYq0bQkDTDArWlcfuSvNfJWLHrvYmCU0t5N8gJxIo824YvxB7sZM6IXWzcn9Dg2BkozCnJXXd5l6Yw+3oTmrUc/Gp3kocMMrg04cTV8jmiBJqBQ8DjJClclp0INF3jddkL3oHTQGUNucgHLqc0Df9uH35dh963aB/ZHWs1Wv0ZeU4LYzpsdJst25BRRBmLogjkh5OOxKEkgvDxqbeKNyWDLKVAURyHs4Dz/sXYTyx3fix2u8wgLY65++51SLTVw7I+xURNhE1h7eTSPMzdT9abPp8/yUmqinl1HTXjm+bqd/gLL8gUfWK+p3nGA2VxRJJW23Y56FSzbQM3NLhkx8p/9X3XI/Tf373ptRvzDE7Vg6XYw9/dEL8uQry7djHgHAnPhwNM+s7//gNVwNqOejem6/Jc7OoSck7sNCWOT2J3Y7WjGQ0oylQ31Jo8OiDLoQSmAw6m0hguT0MeMhrwtWqy4BdzvtfDMuSEOGZeeca8pZ8AX+t12QteMe+SxPxwin3YPW3Sah+uS4hIlJEqds0mPJyqbJ3kKQcsXUfMiXOqQ3O0A5YKfx3kcElMESNVGz7WopddwQX75g7yh+84jBoPIfePwGjExW62N8/FbvS9M2JHR1xN0K03I+I0oX39oUKQy25bng26jsrBS1Vm22zg377pS531lhvAIA8T1m48h4+PcnIOGA96/H9Vh27QtV6XXdIIOP5v2pMPEkBvxa5IRQAsV7ba2dgWgdiBYs9HeK6Q4s2O4K9jnyctDwxUJWZCphLXsu2bz27IHj07P1pyOAe8uuWitUnrS7EfyrNjlyQOsRNaULLHXnHYFOr+3U7jryGTEXMYMh6/WJzkIV2HbjE9ySd6vzzNSW9E1HwiyBe3V9+lufDo5cjMoA0jbpdnAe9txgCm2IVEjQ3yjFBXdcSWYs9xvLUlmxZ4fQNYv3w+hWx2bBsOVJZiNCBuBSC5GaAnkgfcIEM3zKg/OR0BXYd2NoAvKnRRjuhxfXQY/Zexi8jZsbu2CwbEblvwZ9KEDiDOUSZvPmUdXxz4hCgOhD2rjrxxbi0PBwREZHc7ZT6jnB0by6mqGX+WQJII7lD/wmn93BdxIxyzS3EIXwMQHrUmrREIynHMA2Ojnqxl1etDbpq+J/60gex27IDUdb+DtjH9SNME0gmJHFnK0uafvuaftwu4D4/s/jyuILsCEd5QjVeET58XYndxzBvsjNgBhNilE55s9i2dQ+KIN6TacsJ7OOWy4uMu5IC6grz/FPKgZZBOm1dr5vP+gT+nOSHPIB/u4W7mcE9V0Ka55uuyS5qu61W3jNMJ9Ix+QClqxJkElbCuO/E4RRSxhWnUQGX+o1M57rqGTMf9JNEOnZMRIB1VupRd5CYTfu/9J/bKTRxpNKTTnclkFMWLsYvJ2Z0RO4A+dk9z4oDr0cmxTMenVpw6Jea0l685zgOqBQ8g5COb7UlO2B94c2x3PUDuyvXhL3vBa8nihnngcx67WVNZuO0VCRSPLlUd4LZS1+xwzMbc0fQg7OKYP6s7ltvrRFEHTy7y7Jw4NSQrK/WFaoJagrtdhIUlCl0w+0s3Hr0YO3x0fuxqmWMOgSbvQfk9tlndvuRubt5OonihsgSc5/se56FtWTcZ9flMxic5ASSY2+9z3l89xe+yF7wOloK6gBoDGy48YNcdcR9uW3Ch6uAIQN/PLolTcbE65qlAEZKUOJH9vhdAqmv2uddboK7QrTdcFA9PpPWpeq88qbOGlS6A2mHG/NkXYgdwduwmOEXtGbWZ1J07aGfu9yfcVbQdQWRpAtQV/JtXp3k0Df999f0p8xGf5ETlYvWYVQFXp7Doa70uu4ZXpr6xhCRLIKste+VR1MtaB130Gm48DJqJ5vLh1G/UZUp2qLgrCkAceZrAJTG66RD+wyMAT7z4dEQGUFHADYdc2IcKGA65w/76Czgb0yvAyhzCMch5UH4mdpip8jmxt20fuz2dbmZsTQ7UZeSQEAWph16n/36II2J8lk+8wTQP+eIWfrnl9Hg25dMniiCv5iEnNx6xxleYMd7c8Ua+4uuyd3iAO6od9NQ5I8jYAfxwhzk/2Bn768Fq0jo8pl9jGolxpOeDCW+cfclB04qyc8gzthff32utm6N7XPKJEFqABVxNmqHTfnn7Zn6q3fJS7FF0fuxx3Mc+GfPsURyCaJJb73hzHapw6A19fdOcf313kof77pOKMsUhH5TVaU5mQa9DO9e0v5Q0n/syB2sAMMNelyTobo8erSo85A5V8DkNZAuzmbHrUIaBCz49Qh6XvcIvwMOgc5w4AtypyxL+ZqF1NIknbjSEfP0Ny4SaAynXKDLSDqg/VexWqhx4Y8J05z89suQxQkkcsUa3+v9hycP5IAeW69M8ACWepH0+m+1pTlkKs6S34ZeRRK71uuwF3yk23Nzs7CBV1/BLar7IZkuStkpZAyDjSLsd3FE7grAUsyJVrbvYiFhwOySq67UUCvRKE/bddYG40aAXWqrYSrRpr+z3cIXKaxcH4uFfil3PIGfFrlqUYdqrO3gwMzZo86Hk7p2mPEt0xOV0yuI6zoMGzB1vBs0HSXyaU3FQf9v+pr3267IXvOksmnhoWRHB57Xr4L2qeqnYUZYGAnKYjGob0g2HvYtfHAU1LQB9F+TxSbUZ9UDbtvAPTyR0rEjY7tZrAsvW2uuOIpIyOqHzRtehW69DPM/F7ibjs2MPstdaLsmhpGiU3nx8Knl0KqokqzVvEOH/+4cnasUf5WGXqDZ8p4jSk5wsr6ZVheXLXi7nXJedgXUh2lbr5y48ZuFVocs7mDiqFOwhH0N34Z3uVgVbkPZ9XWiiagLBHS+oefler13fS1Zrqu22XXABl13RqwDfLeDyDNHb1zRZeCn2zfbs2NFJiD28X5oEJ0J4B5MARxz3N5NTaERVwS3mJ3mEy9TGoohPnKOcTHHM/FuhLdRrvi57wXvFmet/AAh9PRy4W2pbz3A1hgFxgwF7yiY7bR+S89wNPWW2paT6mDs+1IrKzqmimZtNuShe3QAJkY1SVQSYzaZcCCqdJyPa5shyxXLphdjRydmx01fWh53cKZlbtMcOVfZFloUnE5UR+DNuNkX34dNJHifKCJbPn+QkZdUjOLVbdO368Jd9uzpwsSZxsFAPks+DnLVsmkDiCJKl8E8bjvQnw8Ayck3Ltl0cscaNUshsEsyKDQaA/Z6PbD2Y2oSzG+a9/s1khG6cwz9u+v66HeqU0CFxBFEnb9c+H3sApp0RO6EGDRDpE0LRn9iwXse+pIR3HENanBA63NtX6LIU7qsvuENrHhJ54GkDl0ScQ+wK4PUt8PEh5GTuHy7PCEQb5PBZCvzuz7Yi/tXXZS94U8Xa7Hrl3SjqpSO6DpKo3NzTBiLKDNrtezZT03Jian6vo2EPjZ2OIU8bQGgyJhV3fzMBdqMh5Hd/ZJvSOaAs4YFwkDNxo6As8O1H7tB3N7yhDC/zA7FL0wCjwVmxS1nx+6phH8gq0zE5uQYbNjqgtjBR1VQh0/Yjoijk4cYjiHSQSqEReUZ4MxBykjjm7j7ItXV73f5OwKUveHObjDwXiU5eXd7DXJ0eplwcQTqt148YPIYdsR2ZLTYB0MKZZMb+QHSifcAAF6uZljnaycArrr1t0T4u4bOM5UasXYw8A7oO3f0jD8lx/HzsTQO38WfFHv45TICpqrlo7RudQMp96MJQnDXpubF22DzKQ55W4fwA72h5c3fL6bXmZNr0sj+osVmvjnCt12UveKDfVTrRWtYFeWfZFVxIRgQBWE6IdjfimAg/deszlGQ4CIouhCPIK03AFBczyMn874hZMUeN0Mc2YJfIEY9U7WP0oPxs7E3DxX1O7PY7TZlMZQLNVdtlKW/YUmmIq3XoAIWcgJM86DElJy3RYKZgOQG9G7jNBLqf6IP9M12XfWiF9J6p9p99p2kpNjTuPZjCQTE3MnXUt/tGQ+5WX77moVUdqG2BA2DbTk3LAGiPu2ONq+ZfAAkc0au7AA0gEOsAWa5IstBF9FPFDuf/RewuJ3Y96NMAcJMJZL+H11wBKBlFXfyO8zDYRpaGfGSzPcnJjUfMa1fwLHE0T7jW68IXfP84D6Aqw2RrKw5q7ygGldVDrhsN+IMmk63tOf+gLTkb5ABcXHcLuPmMO59+jdQ+z5p9Ounl+UzMydw0VHMet/Pex9T3Ou0/GHsn58cOhNhdHJ04hQfHwbsFkZODAaHOq3V/ZnAklB/ngVgN4WyBAyR3HOUkadK7Dm6LIPB6zddl366eJYIbDrjbmtNFngfxIaSJmv26fmgUpaw7R0PiTrz+GVOIyCk2pPvVa0T3a8jttCdcDzJqpt9M0A4S+EODZpIBHkg+7SCxR33zGr5mTV7NUgx/e09RKBEKHo1z+OX25dgbNT07I3bJEsJ/TaWhE8ggQ/fuFmg6+EMFiRxkOoSrW9S3IyTOQfIEnfcUmQLQTF6HPOrbEdrMI6o7iHPwdUdhprYLOUmakFerJSDzy69aPfiyFzxwAv4SM/sy+TpAe+d05oAIZD6BW++40EyiQmvn0LmI2KP2u5IL7lDD1Q3RkutCaYAHRA1BWumHR+7O8wkkipD980eqj91NMfq/v4eMB3CbgovxeBr5U8Vuf6rKASISu/2uPDIs8CSqlzWSD2t+bXcAhhn8Zo/63QLpP30f8kg+bpC0LTUjZ1NFkdZo3932OdUNbzRVdJBiD9iE+UqvCy9pwA++YI/cRb431zJ2kKl7RZ4f8tOGeivmjTod9Z0XYxdZ2+/jA7qnFfmfuwL+fkWMzG5P7fcP9+iUlC0FIbLRE80LJPLw//h77sJPGyBN0N5M1cKy+dHYZTY+O3bXtH3sh5L/v9mxZy4S6myTEHRNS6Tn0xp+uUU3GiD++9+d5IGHJcuUm3nIR5rmNKfHVe+FZSZuyom91uvCd3jXIwMNHqsyzlKRp+o2PITJdsdDoH04dQOZjHTnVThwEpMMPRmxF68YFakqrWWJqXEuCy1Cv9qGbovb7SnElCboNluWSIcD6+i6QfRxya7KdgdMxkDyQuxrsBY/J/b2KPam4cmmVI13tdoEwN27OHBIZaJQAPxqCyzmbLNaHtaN8R6y3qDbbBHd3aJr25ATALXzLBh/lgG/wIM/56XtuqbpW2YGfe1YGkjb6UFQ62I70OnjWMpKwV1dP7Qxap0dwjoTRNVBl00pB3kgaiBNelCXPjH86zvg7Sv9fTXjiPRsIT8Su9L+zopdFztMJEoEbjTq47fDqepR0j2bh+DuacXYlk8neTgzYzA0J4Bu+XSaU9f2h3uT6b7yLs1lR2+KAT4i0Kusgo4joBPPmwUHR0rLQ+TDRFJ0B0UL3f30cX7wRBUGWeoWiFRPRhcj2pZ98tEw9KaDRN/hAJ9lkKc1um++R3R3w2nneMRyJYl7N+7nYq9VxeyM2GW9CTejTXhDP14PsXASdOGpetaxL+9ogixVL7HhlM8qdYOuaeAHObyZJB/n5EgYd4mqttmZ44qvy17wkMC8l2IfsB3hcj6o6bo8V2CY69n7nfRQYsOE1DV/ziTs7FC53/cakLqovDpsw4STrJOSqePHbAz3z78ncrGsSJeztqZ0L8cOnB07AWZOSeIKKDPvV2HHRp7WcLe8+ZEQQWnSIKZB70aDPo93b+AfV715QpIE0amQE9CfHVRxGa9vrrpLc9kljfPUO1dbGMOKOO/7x6tNCU05DOCCbXXHNfisUvc46EkojafiRqFdOJ8SKgz06ltedRaHA+DVglNNq5s/3MN99QVEhzxmPmAGAy/F7gaD82N3LsRO4ofKB5ZVAJm5PIc8rUP8KNWhu1SGV12d5GEsLWnbkI8MstOcrM7XM4Eb5pQnueLrshe8qBR023EiGPlek8amj/o9u5y2z1wcQTJ1y7bFdeAj3phULqLsnqEmnUpaS1FQti6YHrMjhO8+hMOqLFfcFZcrdnQAlkmHEu2ne8KAX4hddKc9J3YxzXctT1DVNF6IPDojrSim38pAEWEOKvPhFvOTPIImfp71+TxtTnOyGzJYz5dXr0tz2SWNQYOHhiI8ksSYT3qGv1dQlGkw6kAHUcTetDliGCTYBFrbDvXbGXzVwu9rdMMUrm7h3z8ASYJuMVHCdInir2+R3U+ADnAlW5eyK1D893+N0f/zke9XHIDFFH4+YV97Xz4fu3NcdOfE/sUt3FqdOTqB5CmqdzNkTQs3HqIbD0i+rnLIKCfVMPJwyzUPrjcT7L+aYPif70MehzdD5N8XcF0HV7dwuz32f/sa+X/6+iQnADxADwiMu+4KHnBywYeQWfJK/t3d/xw0WNx4RHe9LOUOanJ2BtW1w5patRvk18Bax+q96OixZN0HGQ97WEBKYBrajkOgRFuH2vXwd7dcjDtqtLvxOBj/uiTpOaZR9GzsNr08K3Yrs45jVz6rlUqYT/qn1CCnCGvTqH9VR0nB+SzkYQJQ3d0M/mHNm+3jAyHRmpNBESwvUQDZf3j/v/+ZVsTZ138UkX/7Q9+47B0eUPZODEQ8pAVmUJ5rb1jtHIdW12uJEMb2vpeIiyMuujgCvHZLDgcuiuUKYu29sgpQXjvUuixD+3aBSA+EeHji4Ahg6XNQjPkwpy7jj8QelBLOiB0gzsVsKF2eB4FWN1F0p7KsEEXE4Jt9pc0AxuPTPD7QBdCvtHR7eKJCgx2szXzBPJ4WUwrLGmb+Sq8LX/CO/9i1EjJME9K+a/1hVRJzcdzvONhSEAAAIABJREFUrruih9561YOpWbdL08KlntDXLGXfezTsf61E4b2lrChcOhwi+lZr89mUr29bSJJyF89UittEi5zvdWh+IHY0DaDGZj8WO6QjviYgI5VQMhyENqFThz4cnwnSNLRe5ctXcH98H/Iw+PHxz4uB2SynToBBygPwbk/Pq0EOrD7Pp/1zXJd9aHWE0tKMNw7mwvZ3s3EUEVXZ0pozSXhQNJPfTt9jOAjtP6lqGgEou0kOJUuEglJ2sitY1ohicGxhjkbB1a9bb9jGtOFMsefBtTWn8OdjZ5flzNjzvI/9dtEv4uUK8rQOh2WKuB708Ktwg/GQJdK3n07ykNokRNrepXCzPc2pLHu6YNvREO7KJ62XvcMLAj1ORKjlDh3aWEsypsOGiDH7u169yyafusM7w62YuVgccyA0HrLmHQ95QNvvg0aLjAb80A3GcETkABActmW5gvzqDVzJ0T8lsF+IHVAYw4/H3hOp2VUSoIcGa289vHdG2W2yqFSxbK7lyCDr84jMVrMN+fjFnJNZzcklCW+oNKFW/prdnms2Jr7sBQ9w8GItPadjdZv6ear8mjIvd/2qZxipwJJTPiuAfmpqB8CmYSvQOZoJVHXAsktdA091uLGkaUj2jiK02x2nk2ZFk6XAd5/gJuNgPvBi7M6fHXsgqESRUgLR81dV3joIreqk2GWqUHwogV1Bp+7tts/jmORSVSEfZ9CIjlLdpqqAiibOZqFzrddllzRAoKkFvZbjy3Yn6fp6Pk2Aru3H6SZnAeV6qnqXSVu7PGPv+nYeXueUJRSgxOBhz90siGGZTuBNWno04A7f8vWyLdgx8e7l2LvzY5e2C7HL4dDr2HvXl3e38/7JpVAGiGHsj8golkeehydNyGcxO8kpUAC7LjiLy199+a/+SP+c14UveNLbuAse7cymgpXpuN55ehqV7CiEeli1FgMHFWB5oRQ3dBLKAPnDdypI5DmwUREjZ1ga7YTIeEj9d5XLxnLda8xPxsEL1dTMno0d+C+LHegltzuV444ixq9PPCNmG9PJjYactB7l0T0uw1Mv5JNnJzkZ9kjKMjCt3DcfPteH/bNcl13SRBHcMEe3mNA92gzCgCC5AYCMoKcN5M0duthT1NT0IzNwpxoPexSi+hlF398DgyE1V/I7DnnSJFitd7Mhh0zeQbxHcztG8v4JGI8gWapDLw+pdEq638MlCcq/eYP0myVJJc/Ejps5/aTOiB0RTZPdvgSGA7TzMXxZs/05nnLoNciU2E3svGu7MAzrxjnK3/wN8q8fQx7xao/61QjpP3/kgTVLg9R2n5OCxgzjE8csea74uuzBU/pG/t3ifzr9opYhUhwC0Atl1TONjKOpSEX7mXCpbUyoi53iTJI0lBlSVsHZDgBcQhfvblew/EhidDrAMfkKl2ckkwwJY3CDQajlfyh2ADxwnhG7HWwpjqolmtbUMGcRTy6qy9K+FFMsjdQNnE1NNQ//6pY9df16pzW8tG0vyQFoyaclVZoCXYf/45v/7V/5yX7260oHT84BiymCB2mn+oxlBdzMemSh9rCRxJA4p+vd/kDKnJmJJTElN8qKPqdxFHxbMZxy0SmbyGUZ2T/TCb+ecADT/dd/hS6LEG0r+N/+kfWuWdjvS7ihKhGrjAaG+bOxu/U2dFp+LHYA3FnLiofqVzeMHaAk3mpNVQLr2ByrEizmVD1TNTPLw79/CooJ7btb+N/+Ec1/81dIvn8KOblDpRPh7nROcMXXZS/4TpW3Nlt2GQA4ECvu1FIGgIocdXCSBUMy6Trg/ol6LebKoSJGruvoYg1wx9/v2V+3w+XhwK+Phv3rywrRP30Lt9lSd6alubATgaw2RB1WFaAuINK1YSz/g7FnKRf2ObHn7OFLxVanfHzoMS3WVapqPmFMnkN1IaVpVNqDgynLoysr+OkEUtXwv38PaVv4//MfgHdv+py864nyZgr3i23lZ7y89opvF3BVzaFHrK0zLS3EO04BFwt1p1YA1nhIib39AbCvFXuO3DshGaLYc2T+pNS36YQ7pOlWiuigBnDqthHleaDUyXoLN5v0yrrDQa8QbGSJ52LXc8VZsRt0YZhzmDSb8k97Qhj8YDzq260eQXtHDgdS9Zo25HHSudGzkMuz05zimAMoI5/rU+aar8te8B3tV7Dbc2Gb/iPAHbgoAgDMPa4CPxRxzC6HakW6I4loZ8OmRh0ubCdtlMvpI2BFlKEJMUlL+0y3JbczWM9XdRjny5Z+r6hqyDQD7h/59+diDyrFZ8SuNDxOaD3tJOOYJQoASBTk90SZS6gr5mUMriTmv4HlUZZ8XzNgM0J52/YQBXUmdHoDS1f+Ipf9WS9T8jXtc/tyngUWj1HuEEUqYqSGY6OB4kVcL41x9B6IIh4e66o3QFADXwDBjEBaipxis1MHPbb8uicy+qWlhr0ZmgGAfPN9b2DwTOxuMDg79hMQ21DFkoo94zeLHtHDq+Xj/Al3tv3jtyd5uMGApPW66fMx0VnNSTZbPXwfmyn/Ai34fJcdmLQ7EA5PBos9GqyIAbSahofOhyeWGNZhOLKmtM4DZmOKLg0yuPUWcjPmQTGOeMhthuzFH8rANkKSsJX41RvgD+/R/eYtouWO5ceQi97f3QTh0+diD7fAObHbP0ddA7Vya9/ecYEe1FI+jiGLWe/Xaj5TZlM/SOGWmz6PQQ6vtbnkKdxXb+CetpD1ts9JhKWUOQgClPW44mHrZS94rzu8TUZvWetiMeX3l+teKtrkMsajgEmR8RCSJ5TXiyNgMSOOxHxam4ZmBAoYw2qD4KO023NnM1hClrGEaTsech9oDOA3h77eL/ZUHrt/5MJQDuwPxS6PT0Ft4Edjd66PXdWH3VoVEaxV2TTA/ZLllWnXxzzjYLOFPK0g41HIwzk1Ni5L4JAwH9WcsZzkUHIQV5Zw2aR3O7ni67Kj73TBjIfcea0eLg6h7SeJarBnfOzL3txBUgK27g8Qr3DfbUGd9cGAffbVui8VDMHYdX0JkiZcaNuCB9mkRrdaK81NVYsflkBOT1Z8+QauOMAtZgF3/1zsAtCY4JzY4wh4Khl7E/NwbXaUXn9H20KkZr/e6nGAZc1iBq9wZcuD2pJd7yKCTqe2fU4kgu/hFrOjTtd1D54ue8Erh9NVNbrFlKXFXq0gq5rGwesdd9ViTxVe72kUcP/IxQqw736oCCEYZMBedWrubtjOM4m9PFXanYfrOnTTYYgDSQyZDOAGGV9jH3xMap6LI2CjMn37smdlPRM75tMwOf2x2EWkRzs+bXgjzSdAM4RkOlAa5Cx98oz5th2VFLIUksVwbsqbTPPwG9Idnf4bII7QvrtF9N1Dn1NV9//W4yHw+PQLp/WzXgq+QlXDfXjg4xUkFbuEI3mIAHudmtbc4VxxAMYj7uhGepaOZYXtyLs9FcIGA7hvP/KQuHwi+aGmL5Jf7Vg+pKkylLhLdlsOrNwXr4HVlvrpIhQyWsyD+u6LsZcq8HRG7PAOMp8y9oisLVr2dIxJIcHSdvy6ToHZhXrkjaBPCstDjrTju9VatSoLyOvbPieAbc26ZtnnozCZvdbr8rs0its2dr9L06AvI1aLAyxRzGzgQJFUEdrBu4jYbjwseSi0XrIRP0TQvV6wvtUrECjUOABVzUmmmamlKaeihnrsiCc3EJjL85djr6rzY1ejZJdlPSTCDuz6+7vXi/6fbTBAtysIv1AvWv7MUR6Wp8p5uDQl+O0oJ1lv+CQ0os1udyLhfY3Xxd+uhvM2uYvwgZsOpIqAYq0SEy1JzlJoi00pduFKtL/etSxpnAM8uZ0ym5Cil6qHFHrrd8PWuOmkl6b7/gP87Q3fV1RwaTzisEZJ0s/FHlTMzondxF8T1bIUneKapmQn5KaqgYLkKfxItWsOZS/Jd5SHwQpsBwcAZCm6796HnEKuekMg4nnhmq8LX/D8kF084t+SGFiu4IxsnSboZiP4dcHBkfXbFzPiZrStKVu6bkueAQo6k/kEeNpQlEhE25Btv7jsvbyDpClcQZtJ2RXAdKziRxm6xyf4+Yz2OMavvZ1zmJSlz8YuTQv54va82POcvflCB2PzCQ+1ZkMz4OHd+U776h7d3QL+fglMRoq85PnF8jBlAwwH1MRvWjR3E8Rt2+dkUIJiz5u4aXoNniu9LrykcQRM6a7sdnt++KZu27Rs29XNkWmvcCxvtvUGnDLCctcBwwEX7HhI7RY1J5AsUeK0PjXShHzPpw26V/NQEgBAezvhGP7NHWQ84G6bpVzk24LxvRC7i6PzY0/iPnbpKAY7HhIfM5vwdyQxsewicNsCfknHcLcvIWmC+tevTvMwh0SdQLe3E/iyOc2pqiCRdo2ylJvMZPRnWw4/xXXZC14JGljpdE8IoDIfUSlL/l1NyAzqLIeSfeSy7EkW24J80KomhqbYc6HHMdyhohGBDZ1y1rPdYgKnhGz/6YmsojhGNx3A/cPvsP0f/5aL29w89pyatq9mXCwvxa6spnNiR9f1sTuKwrq6oZTH/8vem/RYtmXnYd/ap799NBmZrymyiixSlGXDgiAYpocyDGtGw9CAMw8EaGLoB3howBNPLMCAPRCggeCJLBgwLHggTyzAI1oQYckQRdFVxWLV67KJ5vbdOWcvD76197mXfBHvlv3y1Q0xD/DwMjMi7l0r7j77rL3W11jMMl8Z/sVgBnlGumGvNFeP7CgPXa5Q/9or/potn7afH+UUBaIKQiBU9UgV4Tle573gxShsQbKiKvmhmtO0jIaRFypV1VHWqjJOV8VcrqVnWjABzuuV7Ts7QEZzBa/0NlWri9ebTuzoy7eQPIP7GdVEh//8KxMudSRxmD2M++mX0S37sdiDcOspsWuWxtilMAmNpmXLcrePMGeKNNHoDGaELMs1sN6g9+P7ozxwMUb22R0kz2M+2Rf3RzlpmVN+L5BKglb/M77Ou4ZX0uF0Zzh12Hj9YszH93YXxUZ1vY4aLjCbSm1bdmVsV4/fuzey9JYyFFKV0DSBH1VIlmv+fbG00sNUEfoV23rWl3YXk6jpyEmtjfDX2whjoCrwI7E3DU0JTondFm+YS8CAb7K1eUCRs/RYE2Smwz5v2kA9LINMdtLlMV/yrPEwB8Sxw/Qw40E15LTdQQGI7iIE4rlf573gD02BM3PHyDL2mkML0cgZ2vooIa27PaRfxnahpAm1JbcEWnWipsLDq3OQ+xmSdWfiK6Mh1XhHgyg8qv0K8jCH3lxGOxjN6P4hpiUZShTp9WxXfCT2LDuS4X4qdgBAQnkQaZqI2UGacFDVtgfugfY7S1MOtbL0mAtreUTrHIAS2PczTlSBLqfAyDI5jyAR/kEu+31dgYWfOANKcUc7JEIjyFAPSNYIagRQQwqGXWlBhpBuNlEvHk0LWW4i4CrI0ulmy4PmZMRpo2k2ynYP/4LWlPX3X3Z6lCakxMNjFnEoT8aeJKfHHhbqZtMdOO3QLrV1TvIsPgVkX3NR72tgtmAOD/OjPABAlibdF/IxknrMKYg7GeYIbfuhpHnfVywNgjHAdscdOogIwUjRZjoAVQ6WTAApfG+Umk4SIhHNYCwY/OpqHY0JqBiwiwykKNmxTiA29k9/9Dkf814BM0KQmv5N2tKK8snYLf5TYo/irolNSvO8A8BtNiSEzBc84LZ7CHKqhNnAjUO0FaBVzEPNIyqQP9IffU4M0O1DzCk+KYDog4XF87biPu8dXtAJBgVjAADqfYcjDyP8hu28YPwbxUqd69R3rR6Pj/MkgQ57sUUX5S2CjEfbQnoltVyubRgzHnFn7ffMEHgSBztq2HcpCy7Wp2LP0pNjDyjFCGpT5Y6bJEc4fNxcxp+POYAYevfyxVEe7vICeHXNG8zyiZr3ltORSfMhl+AZX+e9wyvipJFiRQ669+aGZzXs1phAWdYxhnoV+aJ2WARgh9W6k6yzLorPU9TX18i/eMD2B1co/+g1dFABNXvOvl9CVlu0lwMkZU51slEfslzzXHDHNp/cTdFeXwGXA6Rvpp3H02Oxb3dRsvobYzc+K9GR9q9ZClFF+3IC2bVwScJhVJbBX5qO/XbHGj5PIZs9kLiYh89TuNkK9Q8/RjrfMp8ffAI0vstpteFTqd9Dcz1Ecr9kx+YZX+e94INZQJ4RRnv4y3auM0TY7Vk+BD6p9cPFsPQAOm14oPN2UoX87EvkZQlkGYp/+RnLkbZlvSwOMmNJk7y9o/ATAExn0DSlhF3TcIGoIvmjz2xh1wap9Y/H7hwduE+JHWB7Mvwly6KfVPJmGnH9xAV5yDrnmcWgv5KmPCcsljEPqffQNEW6XJMOmKaQ5YobQ8gpyAButkj+2AyJBx8GT+/3sp6yBMmLA6drHfTiQETKkmPwQGzOUgRnbMkNx5KlHMsnzkwCurodam51YiTooDlTU/1LhgNIv4J/eODrtx7+V19RzyYsUCfAC8PWBIe9R2KPTK1TYp8MY+zSr7rD9WodzyVQtU6Po2VN8MBKqLujD9OjPLQlKAy+jfkAOM6pMMhybdIgWfaB4vdeL6U58JEAUegetG3nMgfQwWK25NDFiM+Spl19HtqNoXXoPXBzBaw20bUDeQYx1z5drtj5GA2gX74BxgMgSeAuLqCfvIB7cw/3+btOFiO1Rb+rSaRuGr7HI7EjSdg9OSF2Wa7Zdw/KxmkapfFkuSZC0wwfxHtAKd+HhxlnAj07mH/1NuYhH7+Mejn+5SXcm3vUP3iJ9CdfdTkF4FhVAjub3rrz3yOfus47emeP44RiSFJV/OW3LfDyOgKaAr5cBj2O/03qGs7xKZAmJH5EwrPptAfzAiedTLVJdEhVEVqwr4Ff+5Q77N0Dd73P33Q4+yJjzR+k9AJGZdB7MnbJstNjvxh1sV+MudPmjN9fjA5UxvYEktV1fE8tMsi+ZvvyIA8/KEgomQxjPslqf5yTKRcENWE1W8vnfJ33Du99ZMzDUIOo7f+mJROGNXACrBtaox9aypjgkgRcTsPRvq7WbMkFwrMINM/gDCIg2x3cm3vooBcRjaGGlqpC+3KC5N2MpI48hTgHf0W+qnOuI3A8EnvAzZ8UOxBjRzAlLtkZctMFF3maRJI4ri9pWx+sea4vgOmCu5vl4ebWg9/sgIN8MBp0OW33kVPsLyZwy20Emz3X67x3eHE8tIb/A9xBzdkPiY3KyyKSLgLDPrQFo5x0kXFsDnS9eAOKIUv5wSfspshuz/p4wu/X5RrNRxf8eQNTuU1NPRfnICu2E+XLd3DLLfv3Rf5k7FIUJ8eO1sfYJbXYkwRIE/gJ0ZKyIUwCiYPMFnyf1boTXrq5PM7DOWifXlEhHz8ZHOfkg26l42J30tnSP9Pr7Hd4pAlk2Gf3wvTKpW7Y9fAemM658wZWf8/G4QGnst+ztDARpSikmmfwRn72/QKJTR+1Z4ZhkxH8oOROeD2B1B7NzQhuvcfu1QDlZzOsf/uHyB/2SDY1ml6G9F2G+sUQq09eYvx//JRak4/Erk1DYvYJsWvFWv2o9HLC+L2Pk2LfKyFtCxFB/WqMNAgxOYf9iz5y77s8tg18mWL/m6+gqaD8bIblr0+QfDKIOWG9Q3M9wOZVier1Fum7Bea/NQH+5S9pPXwL13mrBxcv9bcHv0Pygmqn8SISuxARCJZ0bn3BnCsK+quJKQWciw1tQlmjdc3yJkxWze0Pap2MMG29nNiInq/XvpzAzdbkpzZNB/BqOLDS+fLx2G1qelLsmTkO1jV3Z8PJ63LFn0sc5PKiE5e1ej5MeGOewSThckLIgfFedb7ge2828N//KOakpuwWrEEDNPgf//S/+WUtiVOvR9WDz7ukMWlrbRrTTCcGRuuaHZDg6AEcOeHxH9T+zXAoYee0hXbIaxXnoBGkZSYCwz5LikGfC2o05M5sCESta0irXOx1zcVgznh+Pmd36anYDe5wSuxR1zGhaKru9l0Hp8iBLGf8NlTT+YJdrGGf8IlBn0OygzykIuRBi+5mQt0c5SSDvmllBgU4Rz2dZ3yd94JPEqIOJ2YTGZR1A3LPyMgy6HEHN0UxSVMePCt2GmTQZzsu5YictXHCXa/10bgMMAJGygGQf5h2bU0jL8vLaxNMauHe3PPrNj3V3R7IM3o/jYdPxx7k606IXVsfYydX1rye+j3oqxeIToOF7camc4ONyW17DwlEFMtDN1vCJja7mA+q8jgnw93HLtC+/qBa8H4vZR0bUJPmZscFlLCtmBGiC5Huw2yaiAikbDRrZ+z2xKYYdl1H3L11ULGfPRrwtZuWZUlVsXV3NUH7csKSqG/80Sw7tq9ZraMaQHwaPBV7lp0ee5p0sQc1tILxu/UW/vsfdSylfg86MoPiwxyWy6M8MDEFtABxzjLoq6vjnAqC1mLvPUs7fNAzvc47+mANudkaz7TthICMfKH7fQe9bVr2vYE4oAEAsWlksIxUz8WP1RqoKrjXd3zN+ynduJ25ghj9D8s13C7wOzN+nyEjZTKGbohx0cWyk7EIOuqPxQ6cHHus5VPDtu/2vKGmc2iSILEnh5oamawl1uvqhBozgwFwN415YLWOFjY6m9Ml5HN2p2JOtqMHJ0BUZffUeKbXee/wgugtFCwgdbtlJ8Uw4WLUOfW+mxDagZbtvsIwIa6rjfOcj+zJCLpYsK4e9OLhEEFCI3RXQj0rjupbQ7NxH49Y+7amFnB1wV0wY1/+ydidOz12IMYOEaqLbbcsl+zpoINe53kVbCnDbqwKfz0+ykOyjAt4X8d8kKZHOUmvYl3fq7qB2AdDhPd4JQlr2GGPOpJty/H6/Qzo99Be9NlREIF4O/SVBVARwqtmBS/DPg9ngwoS5PBa+pDqpx+huaiQf34PfzWBW1u/uVdCnKAtcyARtP0MybqB1C28CFxVdHCFNR2t0bTAaAhdsocvy/TR2LXMo6jpN8Ue242LdWxL6kc3NE27uQA84FYb+MkQbr1FO+7DrW1gp4q2X0JU4X/wcczDzTdRmluzBK4q0A5LTlstJy3zuAnIaAhNE+jl8Fkzns57wQe03nobJTLUJqXwHsnrh07GIlxq5l+bHRdVak7WtbB3nzjW8wChAuKQ/XxHwVFTG8NkRFuaPEOyWANpguRz804KZIxhn+4d+z3drYuiYwNNhlycgWf6NbHLYo0orvQNsbuVMZ2sAyODPtwtbSdlMiJYbNCHmy2h+z1ccAcx/9dkvYXu9nD9qssDYPdlvSVqc9iHpqQ6xpz2B08d7wmzeHP7/j/393idd0kDLgBsdzy0mfiR1jXBXcbrRBjkqPJRPFuQubTasJ9ufw6HQN1sifMuS3Zr7JGuNuUMujJ+3Oei2e6sE3NAYjaann5y0/kfGXfVD4yU8VTsqifHDu+7A2xZRuqdlCVkue5KMXP9lrLoaIc1e/aBcRXyCObDEnRmnEP6k6+OctLgCBIcQHY7tL/1q9/xGvh2r/Ne8KbJclg36n7PD9radqyfaxvBcwdHVVKkSDUygMgXdd1gJwx+ciqYRfRiECENPfC6JlalCp5Hdga4n0K3W7iHbrik0xngHJLZKrYZH40dODl22BmEf24jrEDrGv7lJRemeblGqG/IwWYA2iuP85jOu16+5QPgKCfpVTwkt20s39K3z9gNAede0ph4kXhvlDvCbbXMIyk7+CXF8scrsDePImMbaZqa2VfTCfpnKbBa0VCgyEnCCFqLa9qsy3QOfXUNvLsHpp4wgOsL4PYhHjZ1sYAMBlQTGI8Y3+09RZCeiD2aj50Qe4AzBw37OAmu93B3c6i4DtQVprJRP16gFyP4H/2U8nkhD/XkrgLs7Fg+SJKYky7XPGwbv1aqCv7th5LmPV6mvBuY80qbGu5O2sFvxbyUKor4R7c55zoMd9hFYV2MpoX0+/zgKwK4dNinI8hoiGAiLDvCDvR7L1kybPeQ0YA7eNsCn7wyYVchnW6/Bz66Yc37ROwA/r/FvqFimgz7jF/oZStZxkGX9felZ6/36gVkV8P9hV8/zmMw4A1xPenyAY5zCl2hYMlZ14zzGV/nvcMHVmeaAFtlO83grTLosSecpoDao94sZvgzllpo6zmJctNSFlF7BSuKj0YJudrcMYYD1tdZCszmcCKktznHBTCd87XmRhRxDn7Yg/QK8kfDsOax2MPB+ZTY67qTyhZh98Q4vfAm2xGw621LXuuwD2c8Xs1S6E8/4yQ15NG0kNEA+sWbKA5F1YL7mJNYXmKitdjsY+nzXK/z3uHVPEa9Jw4kcRzuJAlbf6G+tN0+6LaoGQ8j1O6m5whjM6GxFuF8CRlSIlu3O8huz68HTfRhr2MlpUl32AwqY4kRS0zt1y03rH831Lx8MnaT4zgp9nB4DDrz5lKi8yVLs8WKKmEGS9bZHPLmjofcxYo5/Ob3j/LQUR/NR6aJHybPWXqcU1A8a9qYl//+R7+s1fCtXOe9wwvYh99sY60uoyEHLYFxlGcsS1RijSuwGrYsuHCDpAdgHZOma/MtFkBmRgWzOQnOuVH8ZnP25AGeGcSAWUGyom1JmytLYLdjr3q1howGbD8W1eOxNw3Uy2mxm5N3gA7rdkuCduKgixVfu6rsJttFXXfqwDvgYQq8eceb1PLAl2+QvKXva8T1bHd0FA85+ZYT1uDEnWdws/V3ugS+7evMF7whF/s9fthZSuVcm1IiQHqrypS6DJbbK6mZaKNztG3nJr3bszwIh0mAC2c2p+ZMQDYC7JgEeGxlZInlkvVv2/JQGuAChomRPonlKI3A/UjsmiZmlnZC7KHcCmTzMCk290BtGt644qLaQZjQho6Q3t3zxg55hMPyiwlnBUHbBp0WZYQ3RNO1TZQdfK7XeS/4IE60o+epmnOHpmk3PCryqAEj2YE7dllEHLfWNXevgKUxyG60Vw9qAc5FG0wEWG+vYi0b6ukkYZ88lBcmUY0th0Wy3nLXBbhDPhb7asMJ7Cmx7/Yxdkm7GycqL4zGhsn8AAAgAElEQVSGnOQaIpMqDfYEWK4oGRhq8oM8IA54/Q5aFOxULVfHOXkP7F2cF0hVscv0jK/zXvBOupE2rHYNA5t9DR32IfMlOaoNDnakPCIH48J2Dhj06OYH8Omw20dScvByCtaMMhzAj/to8wTp2xn8ZAA3XZLi1rTsedc12lcXSD5/xx3ayM7Ssx3ScPZfF7sM+5TYOyX23Z4eqlvTsDHRU+z2wMtrPi2C/aZNb/XKTNmyFM2kxw9aNeax/+FLFD95yw2jX8V8kKYxJ5TFnzFRfu7XeS94BXezIOSpampdhi6cLzlMCfQ3G8AEkdKI7Gsalie2YKDKLowIwWBVxR21zwlp8GVy2x3dqvc15GdfceFud3HgpQDcj34OffmCB17n4k3Iw+bu8djr5vTYy6KLPQzjWmLgpWkpfW0281IWwKaFW2/4JCoLpDalRdvGPPI/3AJ5zu9JHL/e70FX65gT7h54hlosCYybLbpy75le592lAaILSBiRA+ACLHKSN+aLTn4ukDh2+46Z5D2/b72JHNFgR4PWzMGqAjAyNPKMupHjIfvyNpWVXkWRJRsiBQt76ffZBambOPyJCMcnYofq6bFvtl3sAGM3nI0f2tR2Muw4r2FwNR7yJgkt0sM8gIOb3Mcp7mFONGhrokO3mGzKc77OO3o1ZSwYTS9IbBhdThIH9e0BFbAFYN/j7e+u66hIVRnVTgGhUq8ANAwOwqxFwadKvwd9d8ddPRwQVyuKLJmlDIDorSQlzQvkYQ7sdowLeDz2sAhPid2rxVBHRxFdb3jzfPGWsTVN5wxi3FxJ6HTo395CrOUY82g9XJ/ye/CmUPxgGvHBL8q8qDClaULgvz7n67wXfJKwY1DkEUSl/Qqy4u4XCQzjAV3zep3IqiQJh0sBweicdXQIlYVIN+jJch4cC8OZJ47dmCThMOZhxp0RYHdEBKoKf3vP6abxUSXPeIMWBTA2I4XHYjeJvV80dl1vGMtiyYmpnT24kwsx900DUQ+tSXB315cst+4eujyaJmriBCwQsvRP5UQVMwA8cG8YA54xnOa8S5pg+W7aKAA4YQyYcKu58faOEhdNG0VWAXDBqHZEjmAmsNuz2xAUCYKctTc0YjBEBvhaRWEqYDQhQFlAX12ZEkHLhQ6whZrZos+zp2M3546TYt/XXeyAWd2bvuV0xoUrAjhuEO76MuYgl5NOzeAwD4Cwh9AVenXFDeQwp0Aq39fdWeSZ296c9w7fmktH62PfXCMxWSk4BEQ2f2if8eseutoY/pyPejFZaslp1qVhsV9N+HOGG5eB8VqDvmTTcprplVj3uoGbr+E3G8jQhI7SlIurblgmzZasox+LPbQuT4nduke6XHFRTxdG7E5pQAywf7+iu4mIdGbCcqBlA3R5mCxIEHhy8zVQFvCz+VFOgQUF76M2/nO+znuH975D/YUr2LXnRoIOThrbLXvaQSjVauCISGyaKH2nYTcFTGVrwx01cVxkttsH9V8AHNiojzeA3j1AxqOo5qtl18cG2F16Mvb8F4g91P4AgtFbfKrY/8WGWzH+8PuzWl8fpsd5vLjiTRMGUXcPPMQe5ATwaYLAtd3uPjhxv9fLuWjxGB2yL8YkVqdJh013Al1TdiKAwqLlepDZCAMlq8Fhiym0BXW96ToQGxNZtUEO7WJY7vg/+Rzo2YfettDFku+zWHU4dDv0Ruz618SuWUo48Cmx7/Zd7M4dAM889MHM1WYbM15IALdnqZIZdCDPqF1zkAc2O6h6dpbcQX/9MKd9zSdC0/BG8HQXfM7XeS94gPXlYskpYpCsCM7VgZi9NUWtwOa3joVa9yWo8UZp7dWGgK5gvJs4dizShL3m0YDGYL2Sxl8fv6QS17AHKflaEo3KUirtfvUWeHF1oFSmT8Yu5vN0Uuxh4QNdR8a0K/3lEG665A0WlBTyHFHf3kgtm7/wEuWf3Mc80HrG4D1nB7VNlg2OrIMKuLVJ8ZiSHlEM6hlf513SWPtNQukCLtboYRQe9TbYCcjCaAhs2JJoPFzXXFiHKmObbfywZb1lu3C64A75+pY1fpaynfnVO1IFLRY/t0FM0xJ1ueBkU+cL4lueiF2Xq5Nj16aNsYcefTBckNd3fILsu3anFpmVSjuj8gnKf/Hz4zzM8l6H/W4jaJrjnNKE9fxuH+3t8dXb73YNfMvXee/wJgaqox4kDJkCctB7+hEFzyTTbYwmwrs9P9TQ3jOzgCiJYQ4Y2rQQZyJIW+LrpV91SMN9zfe2FqF6D8yXhmunKpgYll1HA2J2wN69rrePxx524BNi18kAMl8y9oJljW420cFbVxuaLuzN9nK2RHQ5nM551lmt4Iq8yyP00+/oaKIAb/6DnNR7iJ19ZLvjOeNDW/L9XjIgJj1YMEqaRDRfsFLXvek1inSP5+CCt1iaBytVdOPAJ4Cj7ApaLlGWoij4PnnGNuCgzwXqzUwhyyinF3xYnRE8sjQSnZ+KPQqknhC7rDZd7Llhfaoq6ttAventZN0TwitvDoCv8YPvHeUhoyG/bq3UqFNzmFP4nfV63OFvLp+9ENN57/Cq5FWmCbBccaeqa+Dqgm099VG0NFhO0gbeDnWeUFpdb1knCxdHsMoJZQe06gY4uz1QSze5dMLXMHUvv1jCDQdcILOFkTP41NDVGrreIHmYEacSZDm+LnagMyL+ptiD7LV6QoabTpZPd3u4oPcenAATF4dQaNle1Xd3cONRzAOmbqZtC2y3nY7+QU6hI6T7PXQ644Dqmffhz3uHV6IHNQyEgnGALRgJymOBqb/bk8xgtXC4Yk1c5PR1sppZBn36lw76xid1FBgV4ffZwVaNyylpCleZX1JdA5fjuFjDDeImY/5M0z4Z++FrflPsEmQ2bq6iCrK2nvFf0iYn4GiCgRoA4OYqqiRIv3/0nnJ5AQC8WSwfXSyPc7qcMJ40jXm5i8l39OG/n+u8d/hgMtM0VBdIXNRZpFaKGkvHA1cT2kOGxRI8TZ3r7GW2W1LnANbzmy3LFHPEix0WzwmoVBXr+MmIHqiGtQEA6VXwP/8S7vLCWoBmi7ndcdEFLM1jsSs1a06JHeKAhrETX2Rnhs2WN2eWQTfW9zcVBgCc4g4HfK96D1xfxjz0/gHS78NPZyxzeiyRjnK6ewCqir+bNGXrdP2hpHl/l0t4cMuGHIAsN0C9jm03JI6CSkVOsBMAhDaec0CRQWfLWIsTq84xvpY5e96VHRyvLjjRtEUDwBCKJaT18DcXkC07OWo6L+7mmjXt5dhMgVOgX1HNy+DHj8ZuSgQnxR7YUr2KfNhBzywq2d/XsqA0YIh/XwOXE/b8A27o5jpK88m2JjdgsYJ7cUU0Zq+ETBdHOcnabG8+vmFNv1h1bKhnep33gvc+9rFpRmADmdALbj1xKG3LssQs5XVfU7rC5PIAEHwVXnO/7wzDjKSsVsdGpS7DjuhiSbm7zZZoQZue+h//Cdz3PuYBcM0Fo1++4e44X/BJYIq/Xxe7ro3IcULsETPvPbsx5sOk3sN/7wbJ6wf27WHPRCOiwDnuzplp2wAxD38/hUzGvNn3NfyP/wTJ9RUP2JYTapJscPvA80TTAjdj4PPvbAV869d5L3jAiBYmb504soMCvDZNOzpdPNjxsR8PgIHbmWfAdgvsJO7g8C3g89j/Dv8P0nJRqqPMOdEM6MU8g3vxKfTnX8D/pV+nX2viKH9htjQkmT8RO3By7PGMsDMlMpcAqQP2e7iffBG7QBFJGTivZhsfBmFaFTEPd3VBMaeLEVDXcC8+5Tni869iTmiarmtUGwfh9bvv5nN/T9d5L3gn/AANyqvmHC29PsfxSRI11QFAldh5GQ7YU04NA952vW6WEsbyCaoFRcFDJUjeEFib0nseGJ2wlq1s50scdLagUfFPPodcTDi+D1qR/YraLoEv+3Wx25PppNhDNyVNO7pdgEqkabSeR2W+UPU+qg0EDoC25A+EPLBY8e8LygLqbAGdjI5yomdrHt9TN1vopy+BZyw+dvZdmtB64999VBGImpG7PVlBByTnaPpli1bNPCGKHYUDZZYZFt6MFUK5ocoORzjo7vb8+sOci8UMxdzFhLEETZu3pgVzN+3ano/FbuCyk2IPu31QAgPPFzAHcQ3GEKFd2Rqy8eaKLcntlgjQgzzC9+l+H/PBV2+PcopDu9ref7cDfvSz7+7zfw/Xee/w4IDGT4bUd5Tu/mSPek256MDxNGUuqBLVpwpsdpCe2boYc0mvLwgxMMkJKQuWLbYbiwgJ02UObJIoViqJA4yMLQMahrnJuBNOfXlNiHHrIenTsaNv8Z0Se5FHGiFE+KTZm8iqmS9oSdogFiu4yZgwaMMJQc004TCPIueEWXIOnwZ99uEPczLYsi6WfPJ8/BLy+TMWh8e5L3gbj7vpgq03k52ONe3F2BQBtnws90qO4tfbTh/de+jWfF0dJSfcbBmhAO3Hl3DbGm6xgR9WcDu+ju8XcKsd2t/4FPUoR/F2DdQt5H4G/2ICaalx0457cOs9MeoXQ0AE+994hfwPP4c492jsul4TPXlK7CLWMrVW7JLYdS2LTu+yadFej5DUDdrrEdywh7aXwzlBez1E8pOvsP0rP4h5NKMSyXoff8/SmvdVVcSctF/B3U1Jd+xXvAE+unnW0ILzXvDSsYUAG0IFuWvnaK9ouBQEJYCcI/wwNZWctTlhtUamNlc62ddIP1tG6Wy3r9mz9wr39g6oGyTvMqTmrRSlML6643uUBZK3s6jtKOsd4D3yd9ZTP/DA/TOx5zmfFqfGvt3hyAN2OodMRpQBmfM8kbylTk0iAt1s4d40kKpC8uMvAHGo/u/PYh7ZPZ8mfrGE9Hrk0u73kNEw5iSmualF3qFTb++/0yXwbV/nXcOHq2k4tt92Q48gK6drIhCDjqMu1/HrwSdVrS0Ya/mGnZN2MrCd8sDaJU3596qKKmKhxkavYt3sW+hkyP6593wvVfqfrjbA2/tusT8W+35/euyLVRd7gBL3e4zf3EcoHkUxpoiPsSlxPJsc5KHBW/YH34v5oG6OczLCuARDCdWOHvhMr7Nf8Lrbse4M00PYB2dKWtLvmS6jqYblGXfBPOsYRskBQ0jssNt6JK/vuJPtukWHsiAacrXuVMhaUwLYbFk/l9S4iXJ7Q3ZeNMhk2KH46djz02MPnahQ49viT17fsZbPWHvryjouDZGWwXIyTnAP8sDNFVuhP/0s5hPmAjGnxZJ5GdVRNrtnz2k98wWv0e0iCP1LbosnTBpNv1zSA7KEYdglt53bvi8uPJdwcU2GiCYFVxdRwBRXk25gFRzxPr5hjRvk/4Jo6XgAPyghRQ4/6bM9+epF5LE+GntgQp0Se5Ef3TRBxlonw469dXVBvfhDq8886xzLA3nb8pD5ClqYU6DlgyQ5zqlkXri6YF7/Blzn/XwS6Vp1AHfCvR207AOKaMm6pryFp9MGdnvumkAsYwieqthn39ckM1QVMN/H91NV4K0RN7Y763J0igGyXEO9yVrPF5BPXsHdL7jj3s65A99PgV6P7h/fQuy6NVBZVTGuvWFqFgtoAMVN53yimK4OivwoBzHh1chVtXZraJWq95C2hX+Yxpx0z6cRbu+jW7d+gAe/z0vMa8m6Ewa60vGQYqRNEwFNQYpa0pR+Rss1d6+l4T+ci76jejHiAAkVP3RDQEIEWG344arGdqXMV9CgyvviBR/tYUfc19SW2exY8lxaS880Xx6NPSdu/ZTYJUsjl1Vr+qqqkTXaiyHcassOz7APzJaU6TAKIQZ0/sZiTR0cy0P6PTtXJGztbneEPxzmNF91bc8BtS6dc8Dq6U/tnK8zX/CA5hkZRW3dTSybhnjzqoBsdlw4aRJbdDCsSYAAx9JnvSEryeSowwQSmxnLgZT1tqYJ8S/rDbBcQ30L2di4frFmTfvq2uAKOyI2t1vIZAyZLrjAnGPv/5HYYeoEJ8UeNGpGAz41whBIBMm9Ei9TFBFIB+v4wHvglowmDHp8IoU80qR76mx3wKtr4N6UxywnVCX0/oGa+VMr5YKJ8TO9zruGVwWmi1iPoixM8pk80MDLRK+ifaNBX8W52DfWoAu/pxufWLmg9oFK4sjbDKyjuubrmBCp9EoOlEyyWhcL4KMX7Ls/zDrdyCwn3ADggtzXT8YeQGknxa4aY4fFLoMBGUqtj3r3UpWMsV+xVbnbG/TXRFsP8gj5oW5iPmiao5x0ueYkOpRlAFuUz/g67wUPHtA06KQE79AkITrRsNlaZMds+iLvFtB2FyXsdL1hCRF0XWqy/9XYPxABxkO2H9OUqMSGwybClA2bEmh5ecbx/aCHP/7bPwQ+esGS5EB/5rHYI8XvhNh10IuxUx3Mevx1E58mSFMeLMdD4H52rJ7QtGh+85OjPILJgv8Lv9rlAxzlhPEAf/y3f8ipcq989tBgAJDAqD/Ha5zd6G9f/KfRbiUMbSBy1NcGQEZTnnVmB72qo6M1Tae90jTsL7fExvv5ggOlIFya5d332PdrSxAZigJ+PifuZdBnzb7fU7HA7B3RtlGeL1pSfl3sre/e45tiFzESSMOSKygfDwcsuxLXURJNH1MS1wHLYIf0JIl56HbLmn7N3r8MzKD4ICeAbdH4FDM9zH/8+X/7rX/W3/L1+6r6V7/uC+ddwzuj5vVL00/U2O6Tsjx29PDm6WoGvFG8COCHVZU89HkbzhQJdLcjz9MmoKFLImYEAPUsHUruuto0SK6vABDUJhdj6Jt3UXKa3Z89UDgqH6w2j8YeJpenxK6DHvXhrb8fkJa623Vo0vB6ZsMTGFZyQfk/7PYUeLU83KDfqQNbHLpYkhBiOelyBWRULUaSsGT60KV5j9eBm4YMejxcgS21OF0Uiepj1G0xQFh4nNswKforZZlBZZPOf8kOkmJUOSR5/JDRNMBsDzUyRyBGw2snxxGMy15cAO92lMt28mTsUO0gBt8QO2ZLHqjNgzVo7LBV6DtTZRFE+xyAJPdAoCly6GzZ5eF9jAn1hjf6cNBZ7WxopqYGaYDnueO5lzXnXcMHILbXzv0u2MYHS0XYrupt7G0yGrF88AEa3EZ9RhlaTzkgG/O8G/GLxBIh3lA9Ep91tze1styc7YzJ1Ks4Xb2bUpqjVx3rv39N7PqLxA50uphmvym9Hmv6xMXDdkRcArwxmiaWRNorj/MIch5AzEeMdhhz2u2YlypvWk9J7ed8nXf0Ch4yzTEDQOdZKi6SKwDEWjS6VucZsKsjUyga+wLsnCQJuxQGx5X7GQ9yYUcLr1PkkRkkpsAlTQMU7O0H8zPJc+hoENGb6j25rY/EHmXwTogdTiBiH5V6IMlYZ/crPiVCiVTkpCwCxO1vTC+zMLO13T7mwXNGHkWkUJRc6MNBl5MpE0OVB+J/A6at573DO4l+pPB8FFNiLmHtad2KMIanOJFNQbO00zs/kL0IRl3IUg5bZvMOGBUAZq3v+K01CSMRO78l8UKLnK3FLOdr5RmVDurQocmfjv3AgvIbYw83alnwdbK0c/wLTYfD+NOUhsRGW0TdwN8/HOURLOwRVJgL86r90zkZDELqhofpDzX8e7xUORFcrREd9mB183YHmKdSZAQFGQwkwMOMhA5xnaa5esiW/9cVHTNgUnV4ccGSpF91HYkkofBoAJY1lOZD0wDLFWRkxmiHSMa2hY6HdBd8KnYDlp0Uu/BQKVtjY9mkVlU5FW0a4GoCma8gZcLDcq80o7IKmqVwecbefJAovLxgqZXy/bBccSbQ73U5BQ1PI4HH3/Uzvs58waPjcdpOJmb7CK+seUPHAoQZYF8THFV2JUfUkwwwXuOLalAycAq8e+CCswWtDZ2sZbNhDWzfK0UOBe1g2psJ3I9+zh0w9NXXW2C5oh9rnj8au1QsR06KXQSQpHudnFqREEc8e5oC7x6g9Z5dJd/yRgOgkyHkq7fQq4ujPOA9S7t1Gu1tdLuF/7VPYk4Aj1G00zlGfT7X67wXPMDJpqlzSb/XHc4AkqkP+tA6m/NxrL4TMDLTMw39bFX25Hd77pJr6qpru7MFaDqNiYsk6tCHV1X4xYIgLhEkt3P41kMyOgrCDM9kNOCB+InYtQHQ7E6LPfothYOu3fCVHUyLgucAVWC1Zn1f2pnlZ1/wPd7eHuVxaNOjaq4hWXqUU5xBBIBZ0JZ/xtd5L3gn1E4xsdLowBfsF5uWENem7SQ0VKGjEXHi2x2FkdQQiKqdMldKdKKbjKHbHVzPDm3OQaoxtMzhHubQpoWrbJrZr+DWWy6EQQ96N6VUXZrEn9WbK7S9AolXvvcjsat1eU6Jne3KAEJzHZHbe8jLa0ITrmlFiWA3qQod9eEW69juPMxD1lsqog37kPWGk9Ukgb6963LKsw7bc3NFS50PTtzv8fIKnc7iLgnTX0GSEOdhg5cAypJexd3v7iHKWmBFeYw4dfTKAU7b2o6qsQ8e4a+jAfD2DpokcSfU3Q5i9TnuZxwEJRz0BMdsLTLgfgZ3S4t57HaPx17vOTw7JfbwZPLKcqo2hQWX0KEDYAfJQGUyMn/W7Zbw4XD4DBTG9uD3sbHW7NaeeAc5wcSjdL02+EHbuaM80+u8F7wT1qRZeiTTzAlrApSjTofxakJpuiLncMdqZDXZCzGsTNzlg6ZLaG32qBQgvZQIySSJ8tS6XEfChizXtIqpTTXAmcrvcgXssw6uHDosj8SusEHRqbEDsXcvZdkRSEQ6yY52d6x3E84WWWruIHX8uWjPGax/6hr+zTtOYENO3kedfLEnIIb9Z61Lc94L3mvnkJFlhPMGS5fQ4TB7SNiCib5KpYmeGtM/TBADTADOcRizWHHCWOQcx682PGgO+0QG3j5E5z7tlcCbW+7MkyHw5VvusknCm2fYJ56+yBlnsNn5mthF9eTYo95kmXMnNslrHQ3oWpJljK0qoA9zDr9ySpNgQ94vxkOgKro8AMhizfq+bqCTIdz1xVFOJKUoO1eGrvRfvvklLYZv5zrvBQ+bLJrmuQYQWBIcrc2h2pyi1exsqCuz6xCAB68lNaeckqbUbxRqtoh96GJDFgkmZXkOrLfd8MZMArjwDSJg7n8aLCGj6NE3xO7cSbGLCDEyNVuQUtFcWOZLfkOa8skz6NkTZkNoc0Bd5jkHZtNFl0eWducfyyeQXkJOut4eYG1Ib5TvfQz86+/s4//Wr7Nf8NLvwU+GkNkyUtYAmLak69qMgU2fpR17Kc+6RZFxUKXzBeGuaQIsFiyZrIMj9zO2BQMuPk1JMqkbkkcCiylNgMkIeje1nv4abtAnuGq24AJy8nTsoqfHXuQ8EM8XjKkgoVq3W3aG7GbRLCU0eDxgWVbXwMWYuc2WfH3LQ8oCSFO0N2Mk90tgMuJNP1/GnKLTyoMJ0eQZb9BnfJ33pBUAEsfOCMBOR3TMMPsXMwiIWJl4wPOddXvAj683neVM03aj/CBZN+yztAgaMIZ/0cWS9bP6Tg7DZOqkJE5cg5nwoN8Zfz0Ve2AynRK7Wc9LWXKHD4M0u1ECfEHe3UMCnj7U+IsVfz5Lj/KIv94v77p8yuIoJziJT5po+fPMr/Ne8MEQN4Ccgv6iCSlF2Yo+CRtRlbfIWVIUOb8GRE1IqBpJw/A4ABdjeNRvWb4EwBnuZywnqhJyeUGQ2XJFKO3FhGWEeSrR93VFZtGw/2TsQU7vlNijEKwq+a+Bfij2vqZsABCN6Vdr/owqD8z3s3hoDXnE163KmA9u749zChj7xOx0mvaDx9P7vUxG2jmO+OuG5GsDWGlGrmZwoQ5yddIAqEr4XoHk3ZQlSJGz87LddcbGvRFbemHELgL0Stb445GN3efEzNsNIxhxoSaOP7tcU8z0oyvI2l7bVHefij3i3E+IXdMEKOgQCIBQ4cmoMzK2UkjAG0ZECFyrSmrlzJZoPr5Eiu7GD9xY3yvhLB/Z7Ehat5xk0Icf9+GWW7Yps/SD1N57vaxHLrMFe8VlwV0qTVhjmk2LrtYd5nvFx7k4B7ew0f1uz4UVdsE0gaz2NCEORImAcXHSga7MRwneUxC1aYDJmNib3Q7athyMrVaQz94cKRDL1cXTsZtT90mx96uIBwrfr3sjhtsUNkr7LVf8uaKIPXOpKiT/z8+Bfj/moQBxRG/fQUcjzgyuL6FfvY05adPQLvOgzfncEZPnv+DDqL8sWXoAXBQAd53wAQQ8eVHYFPQBsjEWk3qg7EV0oZjjdHSlO/gQg1hShMYaPl5sWqp5Rv320RCY84AqVcWp7cU4DrCCPfyjsQc+6gmx62oTNSflsG43Gp/OF9bzz7i7B23K0SCWJLi55oAs5GEgsOTyghPb0RD+9Vu+fsipaYFUgNYxtmcOHAOewYKPpIsgMWdue8hSA3q1Hb6jMb/U1YYLIGiw9Lh4D6XzkCbspe9raK+AvLlH+xufUkl4vqZI6aCCisAtN0CqlJ8uM8z+yq9h8s/eAHOgfXUBNyeepx5XSOdbNOMS6Y++BIDHYw/t0BNi1/0+xq4bHoz9ZABprE7vlZQHHFTQLAE8iKL0HtqnFo1mDsnDqstj1IPsG/gswewvjjH5Z2/g/+1fR/Kvf9blpMryabuPZhDaK4FnrJh93gtehGNxG7xEQJeRF6IMno3NY9fGnPkQjIDNmSO8ZnhyYLfn0MeRgJ1M1yx9Aqd0sYIzqevgquc2W1z8HmU8MBrSX2lQQR7mSN89QHol0rf3HfHjsdiDlcwJsYtPO/CbQRXcdMkafkcEqKpCHuZ8as2XwItLyK42KRPPhTpddHlYTS5rxcXvrYDdHsliRfXgkNN6ByxqtmhDWXP78MtbD9/Cdd4LHkZuPmQNmbIubWlsGBIUgZHyIBgW997ktEMJk2VstY0GkH0N8Qr/kqRsfHQNN1/DD/tw5vFU/9YnSKek/WnqiCQc9+GLlJLam5o666sd8TdAJ4bUttGr9etij858J8SuVQHZSuztw3v4yamO3VIAACAASURBVIBPooqaNHo9hsxW8P0KMujBVxmSvWnUpA7y5S30kxcxjyCBrf3yKB8FupyCno7p/2iW8ob8sMO/xyv0jINyFzritPQrTiUD7LalPqLWdXeIM+I1D6ItwVg2OUVuffLdnjtoVcKtt4TqiiD/uaES5wvgkxsaBNzO4IIWzHaHbLWJWpCxXu5VwKZ9MnaMBnHA9Y2xi5gHq6kWVBVkxfagrDbs8Hx1C6QpnNBzNsmptyNim8PFCPLZ65iHzJY8myw2cNO2g1rcz2JO4cmBAJFYbznUesbXeS/4Q+cLAJIikowxGvADAKLWSsCiiGpXNmQpJOkZR5MUNTEnEH0gEjPiWJoWujXhxKbhzTQZQt9suZsuVyZFnXOE733EzogqdNwnF9XETLHdPRp7pAKeELuutzF2ACxz6saM1ZwRWwxHb1j6IMCKzRZ6MQJupzRJCHkkCTDfQi4nZrJWmlBsl5PuaP6m+z3PQbrvnkDP9DrvBW9sfg3CqYN+hzExHAz11vOo74LWdkKzdxTvo2Q0HUActNl3NoxGzxNz2EbrgesL4PaBIk1NS7HVOgiQ7tgWnC/ZCw++T1UJ8RqHWEEl7LHYo3b8KbEDHPwYKUSXK2rLFzlr99LMlYcDOoOUmcEozN3vdko48mEeOTs6ej8leG6+hIwGhClbThFWXNcdnr9+3p2a817wgojUCySLoBAWJpPBMFfShPiULKPZWRAiBYDFkju5CEncoc3nHCeim103kLFBj0xG3GHDdNE5Er4vJhwEVQX0bgr93kviXrZ7tBd9uNJMwsLh9JHYNU1jmfVNsZM+6Bj7ctV5yZqSmJqpmqy3kPGQT5bWxxtSshS7X/keij/6MuaBly9Y5hheCFVBVOnDLOaEuoGYI6CWOcunMPx6ptd5L3iYQFG/YgkwsN74fMnF1PqoIEypZ9dBcFsTR93uOhDUvmYPPYzsRdi5KHLg3Z0NVnJgtSHZwSsd/1ZryHBAaMH9A2XowvDmp1+wZq9KeiPZWQBJ8mTsAhC78wvGDit5wuBNl2v+7GwOv685FTYVMYrB9qBljvwPPiP5I+Rx98AnRmGQY+PP4jAnoBNsNWmTDyTu93mFHTFQ4+qmo8y1HjowvfdBxVG5CDDqG4NnRyzKsAdtWvhRBTddsfYt8g6cNRlCrf2pacLyZDTghNEcQqKLXZYCL67IMjL9SXlxQ+mLsCMHsobtiI/GPuQE95TYqR4cuiXGwProml2ZT2+QPCyIj8lSQgpUgV/5mHDggKW/GEEWqy6PxBGvkwjcm3to08D/6iu4n73ucgoqwr2yM2R+5sbE573gw45jCrhxFzJnbZn7SLJWpWy0rM2+Jcso6K9sbSabXTThFWvXYbEiJc5QiBFhON1zR329i2hFWW0I2Aqip16tC2KYmr45XpuWzaGI0dfGbu3IU2KPQq2BsVXmcK/vODtQ5WG2bbvujkgEvUUVNduxQx5SFrwBarYi4elg4permBMAxnl7H6EI7kMN/x4vk+nAvo44FACdK5316CUFuyHbXZSODpPLo+EV0JE/lmuCtF5cdibFBkiTuoGOkw6tKQJfZEgeUtQfXSDZ1JC6hZst4a/HbAkC0KsJZLZE+ysTuC/fsTx5LHZT7z0pdueAPUnoIkKs++U4EjnE9WK/PmDxo2NIlUP2DY0cLkYxD7XziaQpc6hbtGWKpPVdTpdjPvHAVqiO6DDynK/zXvAiERoraWKaLUWn15IYU2jAYQx6VdRjgVdIbv1j5zqPpuAGYrY0rmnNuW7PgdBi1S3A0MloGrgXl9A8Q/bZLfxsHtGXbrrk7td66M+/BMoS7iefUbLuidgBnB673XhRYqMq+QRo7UBdFnzahH6/CaqqCcVK3wzN3t7FPHA/hQJ8Olg+7orw55jT7TTSDXW3hzwYlv8ZX+cdve1UOqLsBMWCrEvgla3APCOxYbWONo0AgMmw+3DMvEB6FT/AIHWhSuhumhLzkmcUWnUOeHEJXI6JLbdLzIrdjUc8TG62XfmiCtfvUfaiV/Eg/ETsUp4eu6ZJF3uAGASub9MQT1RVPCsMKYONIie8oCyPcgh56KcvIUUB9+Kqy2c6P8op0v3SlDaWgx6aT67e16f9nVznveDVDn9r7mKBy6nGHtKgZHsxZqkQDogHzKGIQYnS0E20pPE3F/C9HP5yCGQp2ssBQWOTIaRpUd8M4mLQrMPj6JDQXtxcAc7Bj3p0JBkPoU7gby54Mz0Re+x2nBC7bPdd7ACwr9F+fMX3uZzAX40ipl7TBLgcQ8uCLdWLEdx4hN0PXx7loYlAxwN2kiwfKYujnOJrvrqCOoFWOZLV/jv56N/Xdd4LXiQOmGDdCkkTlgvhalsSroPFYkAmGodTQk0c+s15xm5DVcAtt3DzDWvcpoXb1Kxr53y9/E9uuSgSB83Z2WivR9Aii4deLTK42YrkjTX79bIzUsYTset4eHrsQBe7/dkttnDLbRRHDTejNC1ksWYsJvOh4wGy6fYoj+TNFLJY0+k75JMkRzlpmjCvmqWMbPYfwGPv9TIR0uCqR/x3EndrqRvWtFnGAVJYcKlJXZiFupjlomzNsc4AUZqlXDBZGiWisd50fFbA5Pi2SN5Q6sPNc+DdPTExbQvMljQ/iPLaGfAw48J+KnZTQjspdu+72EXYnnSOQ6U0oU9s2wL3a05WQ7nWNBSCvRjD9/m0CXkEfD18C/nZV8wndJFCTuY2KIs1ieOtj92b53qd94LHgS6Np9MFxBGUlSQ2RDqgzwUZaoCLN026Do33XMxZFjsiEvilmQm2GkxYgEiulgVt13VPyxg8zA90Ij1kbO7X3kehJLjEFIGfiL1pT489QJmNQI6mhfZsAYbYs5R4ntDJsRalVCWwWCENXaiQhxkgi0gnHTIZUfI75GRS31oYQcZgFc/5Ou/ow9BkZl0DI10Hf6fYhQiuFoYl17C48yw6eUTFMXRsHzQN0YSFiSPNlwYOM5LIZAS9vef3fvIqareoKQqTpkdihxYFICXJEgAhAmX5eOzLZSR2/yKxw7fQne+QlkE1zeDGyFJo3URTNd1sCSgLl+WhpnHp397G95btDjjMabPl02c6j4fi564ted41PKxfHWSaxwO2+AxSK5MRF1JZsIsR8NtpStuZyuQmejQbkDQxsBWtX6QsIkQBbQt/c8Hvt/eT1gPXl/wPoJ3jpy+pShCsJoMX1GIF7VPaQnolgpzdY7FT5u/E2IEYO1zS6eUcHIbRttBf+5Q/89ENuzUAF+z1JYdQB3lI36iEn7yK+fh3d0c5iXNs0wI8aIt0Z4Znep33Du8oGEodRKOaBXe9omALbTwgJDcz0VITStWm4RS1KNjfNnGiQNaWNCUCMcj5FQV703lmdu8ZF8SBhaQY20jXa8rQAdyFNwaB+OqWZdFqTdjtavN47LCD6Cmxm3eTlPb3JAPeUewVLqG4apbDgRsE9jW14qsK/s0tXL9HAreJKEXnwySheGuwnDdj45hTcBqPw7/n3aEBzn3BA0cUv9A606BgmyRRTg5II7OI8nZJVPISEQLCVLlrB/mMT27gHpasU/sVZGaydBuKHsloyJ004MTTFNDaDMXWcM4BQdipKuEHJWS2Aprim2N37uTYFYixB9MzGQzgL4dwiw2wcdDRgDh2E4XFwM4HvYq4nXd3kWxO2PKasthZyk1lvYYLw7IAWMsz3tirNSfaZUHQ2TO+zrykUe5YFbmdgdIHo+CFelubNgKd+GPKWnazJS491Oxt26kNew95cw/d7uAvRjw4TkaE346GrNXrOvbAiWI0vIpv4a4v+bN3VuOvN2zfiXBw1PonY4f3J8euLy672ONBtmX88wWpgest3QGXho9Zb6KYlGz3VC04yMNdXbDMMSaVu74kyvIgJ1lveZ64pFxfdA95xteZ7/AmB52l7AenSXTCU9NCxNq8mgJwCohTSqlMYaxt2errV/RHupqwHKkbjtmByNl0F2MuqCKHf3UBt6S0R3M1QDrbsMc9GsafwW9+H7qtiUVfrKGjPjmkTfutxe6Wa+hkxNj3NfRyDE0Esq2Npmdlz8UI0rQUV7p9IPTX0I5a8SwR8vD9Au2nl5BWkb2e8r2vL4DPvupymi+jpLj2Sr7W9cUHIab3dqkC4jjSN1RkhAzPl9FhDrudGQonnXkwrCOy27MV6H2Ur5bpgnoxGTElUvGx7x7mUTEA3pPsbPJ72YqdGWQpdDrn2H40hDwsO7itEUFgrcwnY7fO0CmxS8IBUERC3j7AlQWfIiX1abRXUuJDBC5g173na+z2kBuKLIU8XOuRzBYRd48XlyyxDnJSpeOILNbROVDMavO5Xme+4E2xa77g4SxcoW/ctKb10hETdLczmeusG+PDEIpmEKbBH3W75fc2DfB2wdo10PLqoIfj4S4nph2pNEcI4KvWRy14mFa7D055WvJGeiz20N05Jfamif11DfAGMyjGllRCzOaEKQ9skrve8PfX70N3O+hPP4N7cdXl8eUb3jxF0eUT2p8hJ6/R9kanM7jRMN6Qz/U67xpebEceDtiCC6KlAPHjZQEM+9bKMwfsquK/T0adiliPJmTRuh08UMbSJE2BLDdlXTP8CgOWJCElLmH3JXRBjsPkAVQnQ8hHN3DDQbSXfzR2m7CeEjsOYpdeaYdhCqtKYa3VLO8Wc7hZUuvNm3XPYR5uQJBbdBYMv4fDnBI7WO9ryEc3kTH1nK/z3uFDedF66JBu2RHBF74WREs3ZsMeFu1uH8sYLXPWzE1DWh0MlLalHJ0OesCIdDuocgFcTvhEaNqorajfe8m6GQYNMC1IxsBpqLTe+v5GXnks9qD4e0rsQUdy2O+MD0zZIOrX31wSA2R82ziFrRtAFf6ja8jnb2IeaA7INW2f+WzNVTDkVDemirAnFONiDH3m8OAzX/DobN/XW37QK+5YUUf9btrxL8NiqspO7VYkOl9orwQe5nSxMzgA4CB3U7OdNKvGuuZNsVgCVxdmW+8hn73pPJREDALA2jZg1oOymGYp+bKPxd62J8euFyOWbw9zQg5C7OGG3O46rZ3Ae82yqKGJNIH8yReQfj/moU0D1++R/WRPkSiFHfruu709UUhkkbrp2qrP9DrvBR92yfsp3egCsCooYZlokc4XHS48z6BrG5AYFU+CvjnAGrluOpmMIG0HHBwuExr/Gl4nUPukLGKnhX6sQ/jZHG5iIqrbHdCraA42GrIT9EjsUWjphNijabLRAREM1YygEn9dszknrbsd3zvkmGUs2RbLLo+ltWozG0IF6b2q6nJS35k2mKftB5mO93kFy8UiJ079fmqTUqPHFXnUVwm9aTgq/eqExgHskvBwGXHxWcrFZVIUKATiGu6Y/QrBsj3uulcT7rTjATRPIXVLKbsvbiEvryPQyl+P4FY7yKcfsbxxzaOxIzOa3QmxY5Czzbnd8fDaq7h4JyP4LIVbrpnH5Zjl0NBIJ6Y4gMRFA7WQhzNcjPZLSOPhqwzus7d88oScJnyyaN9KtMRx6vqhLfmeLu+5q1j3Qgb9jsihyj8vzac0y9g1STzpdHNTFnAudhwopLRFlJ2eGlXPxFJ1X0P2e3ZVmlC/1pwu9irgq3e8Afc1nOnK68MU8uIaqGu4n3zBxdgrowDTo7FvtsCiOS32uuaQymAKCD6qb+8g6k0j3kjhTdPtwkkScwhPo5AHuy8GK85zOKvddbvrcgoy3Isl26TbLYFpz/g67wUPjb5CKm2H4wYiZj2gCbWurdY2KbiwWzZt9z2bTSdrV5VRWSCUBTIadjovSKPxsQiphn69gRsPo3qALldwV5dc2FnGocybO+Oe+qdjDyjPE2JHknSxe05iUZXmN9VEfi6cM3dtA6/ZjSy9FMhS82BlHnxyFnDh3BJumM0m5kSfJ0p86O0UeEUxVvzz7+bTfx/XeS94EwoK9Xc0A+hTbxGe+vGwHU5NPxKrDSEChWlALlextRel+ZYrW6gd3FX2NVDvaRqs5n8UX99c/fqVuWzYrt228IslJEtpHQPQbS/Ytz8WO3By7JJlXexZZibIdNUm+M3OOjUPlJwi22tvamiSMKZ+P+YRNCgRJ8AGVzjMadDn9wUs/+t36E4Mz/M67wUf3S94sNLWRyt4qIG5fGu6LaYEsOLAJcBZ1VhC1JSkx6maxiReXFH5a7WmWwbQOX8H4oVZr0uvR9bP3RQY9LkQyxJoGriba9bXuz1NjBeO5Ye4R2PnU2d/WuxAF/tmQ4jwcHBgMLxih2bPQZafzTksA6DNnA58kxEXveUR7SeLnGeh5Qr4+IaqwpaTmvOJXoyil21zM/4gxPTertDzNWm6+OGv1p11fIDuBjHUNOFB1sja4klLC9gU3e2JFd/XNOcVoa5iUPgC+GfTg5Q876wu6wZ6NWH9/OISej/jQXFJtWBJPXS6sPeruGCfiD3slt8UO+o6Qg2iC3lqiEoYktI5+JeXlPOeGIk7YOB3NUu1wzyShOTs1vr+Ly7hyxTutu5ysjkHxZvY9kx+9ryduM97imCkDRj/kw7WnmXBgVephk6CEabplue7tibAA2PTcJcNveo7uuSFaWKUtza0oWQZ/Jg7vhaZoRdbYL5kb79tIQ9zLuy64ROgV5KVtN8/HXtdnx67SBd7IHvnGXA3pZyf9c3lZ1/F+GVviMnpHH48YJv1MI/JCO5+wQ6P5eOWu+Oc7qfMa75kXk1DgNkzvr5xwYtIKSL/VET+hYj8gYj8l/bvPxCR/1NEfiwi/6OI5Pbvhf39x/b17x+81n9h//5HIvIff2N0YRCTZRx1pyl9i7a7bmrpxABOq7jjBTm7MHQJh0p2IbamzmUY9fnKSiMrkczqHp5+rm666Loel0RShsOolCVx6AY3xsOMWutjqvI+FbsaTPmU2NVgxbrdcrrrXHTlgHPRBUT6ZtxWmWaOV0hVwU0XcJcXfyYPAOaRZU+Y6fw4J/u9yHhIEF3TPHvVglN2+B2Av6aq/y6Avwzgr4vIvw/gvwbwd1T1hwAeAPxN+/6/CeDB/v3v2PdBRP4tAL8L4C8B+OsA/nsRSfDUFexqbPoJoANabc16vTFtyaaFLtfEhKsSMLXfd9PQID8H8HvM0U9X6+71TaBJV2se1t7dG/G5hizXRDlOiTD08wW/72HOm2O7A664+/lByd31idgpwXFi7MG92y7d7tiHNzCbeD5BdEVCiX97ywHajsrEyKlCdpTHfHlALmc+AI5ygv2edLawN9aYw3O9vnHBK68gKJjZfwrgrwH4n+zf/z6A/8T+/Dv2d9jX/0Nh3+93APwDVd2p6k8B/BjAv/d0dK6bSgbX7IA8VG8E6KDmxUllaP1JIEiHoY4tPriE35sG8SPrdZtrRpTEq4z1ExZ/0HI32hsAGgj0K77u1YQlQNPAvX3oIAiPxE4S+omxi+tiT5Kuvem1A4WJsydHa8wla70G/9d6f5RH+P1KnnfzgT+dE8C8gis38OcDPGY78e8D+CGA/w7ATwBMVTXc7p8D+MT+/AmAzwBAVRsRmQG4sn//vYOXPfyZR94YEVsiVUpo7ngQlQFQlVS3FeEOOepDlmYUZgMiyTJS3BrzTp30WA6IkEoHdAZjPYPzJjkXXCBCt9SVkV7Jerbfg7uwLshiGf1Q9eU1D3hmGYMkfzR2HlyT02JfrHjzT0yRONyUdUNwmQ2oAq4+dF6i51SZA+Mhb8iQh9nT62QIWayZjx3aY04i1vfPgIFhkf48iKmqagvgL4vIBMD/DOC33ldAIvK3APwtACiTgSEJkwhsigAsgIvISQe7NQJHwJuLM3Pf4MKx39M4LDhjr9aQizHacR+uV5LkXVVxEKP3D9C/+AMkwfpxsUDzF7+P7LPbTuA04yFQipwLs8hJ8VuuAdSPxh5LnRNil4RtzhA7ysJai+vIaMKoz9dvWkrn3dv8f9iHn/SRvH6IEhy6WESd+6hCkLhOvSzkZMbHlAxpKe/x50mXRlWnIvJPAPw2gImIpLbLfwrgC/u2LwB8D8DnIpICGAO4O/j3cB3+zOF7/F0AfxcAxvmNal1TYCjgYcLhzjApsT6eDM2ogBLQSDhQEeegbQ2kRXSgJi/Vs7R4mMHd3gNFQdcP8DAqYRr7szcse9acdAb1YDFdmiC7oU1j/ewlBzV5jqdiR1UBQQXgm2JPsg42ADD22RJRoNVKEg2alUGvp8iB5RputqS9zXwZ88DDjE/JfEB1BmsQiPedlEhSdorG6uNc4Tlfp3RpXtjODhGpAPxHAP4QwD8B8Dfs2/4zAP+L/fkf2d9hX//flZJc/wjA71oX5wcAfgPAP/2GN+eHP+zzMbyzg1xwsM4z9skTBzzM+Cgvi7iLitXhXETOyBimO1Owv66fvIRcXwI3l4QCD/oRQSj9HnDN0kWuLzliL3PIqxeQqwv4FxPePB+/pNowAOn3IeNRpB8+FruO+qfH7n2MXTKC6TDm3/WjK77GzSVZV4M+h1/jIW+QXhVzOMxDyhL+e68gi1XMB01znFOakvrXqzilzTP46/E3LZmzvk65XT8C8PetjncA/qGq/q8i8q8A/AMR+a8A/F8A/p59/98D8D+IyI8B3IOdGajqH4jIPwTwrwA0AP5zK5Uev+xxjzDMSdyRUbGu1qy1W2vbGc81yE5zRF90XqdhwrqhXb2qQl6/o77LbN4NdfKMj/6HGfR+iqDViPGw21lbH3cLWbC00trgB7N5xx56JHa8fgeU5UmxwyU0ZhPW34Qib6FpCvf2ga/x+h2/r8j5no6Wn0FcSRLibGIeeYbkq1u2II2uiDyj7IflpJtNV05Vhin66Z95KD+rS6Ie4hle4+yF/gc3vxvLFklT/tn65KF+l2GfCgBAdPhA23Z/Dou9bbtF5LvDIZx0tjebLR/5TlhS1DV3zbqJuHgYMRtA9715HgVH2y9fk+Zn+Jevjd23BhE4IfZA5i6KDrse++R67Pa9WLI0CWJLowGfIE0DmYxjHlIW0WUkij1lqcGlmRNa6s7rckVowpoI0P/t9u9+xyvhF75+X1X/6td94cwLMn6ooRMTgFaSBUyKdos0lC2GGoQIpGniEEeco0T1ZseFVuXcuQZ94sKnC9bggTBiKMRgWiDjETHy7+75M1URMTeyJSUP0zlUHPDv/Abw2dtvL/bc5Kw3O/JdV+Y1NV90wzQxq5uiiPLgaFvg9oE0xiJnrR7yMAk93WxiPtK03RArqBU7YXm0ZC0vw8EHLM17vZyYebA3xd0qquyqMf9DKYHdnh2MxZKP90Ol3sRx4ALEPjhaG7zsO1t3eNOw2W7hb+8hWUoIwMOMNb1NeGVtU8805XtaL1/SBO6nX5pM4BOxG9z3lNjhpIv9dk2P14BwNL1JPjEIpiOK1Jy4i4Il1s01d/OQBwA/nUWlMllv4w0UcwpwCLPtAcAS7xlf542lgbK2NXobQEShet91NAAOjazjoatN3EXj15X6LGpy0mGnjRCBPDda3zqKk8p4RJRhWXL3ND8pTkVpUxMJJaF74qQjP/v2ydgRAGMnxB5KnCimGr6nLPgUyTMu+s02WvfADIVFJDKsjvLYbAg38F0+4aAecjpyLSlyU3c4/z3yqeuso9c8o2JvSUeK9noEaTxkvUPzYoj0wUBfpjGpVQHtl1CADha9EuocRBXtsED6Zgbfr6BZArfasnQIoLHrCZlGAeK72bEGD8JIoyHNDVYbmgX0KujUuJ9mC9leDpDcLYCX1/SFXe4ej326iXzRb4pdReBWm+PYH+ZmbZNBpktg1Adup0RUJi4eWtVk/Pwf/QRuMo55QAR6T+MG3WzZidkTnhByogtJgublGOnrqbn4zX+pa+L/73Xeh9b8Rn/74m+YTsyBfkqedfzUqOC1R5S/CJIXre8OYlbzRh0XQzditqTLhpUOmmd8vO92ttvtjB7YlSNIUw6xsoyKX0HeI7QBg4tIgCJ8XewAf+aU2A1fI0XB/1c0F1Z34P8k0mm3z5ed0jDA9xgPzcyhijeajvoRnUl054GsnvnaRu/XImdpBDzrQ+t5lzQKdiCKPOKxo/LWckXg1XIFrDemQuCMprbt5DD2NRdMSbgAPU9Nk3FDy/eIgz8QWJLMnEGcIw6lNkv5NIWu11xAAPAw56E33AxtBwh7MnZjNp0SOwxtCefMmMCQnjYl9ROLJdTXJpIqmUEUArryKI8EMlsyDssnqopZTpIzLyq0mVPgv+mDp1/upawv54vIfoI9quOIPvBDi6IbjZuOCkCmkO5rMnlApGGYRrK7Icfk6tuHKAGCt3dcuBcT+OkMev8Q3xOv33Xwgn3N4VKQ/9vtutr4sdgDq+mE2IPdJXH3XMy6XBEuvNtDPv+KcaUpuzIXHA5pXZtzeEti9kEeOp3xBgpY/Nfv/l/23mRHsiTZEjuid7TZp4jIyKpXherX3QBBNAgC5KK/gHvyK/hLXPA/ektuuGmAaDbYePX6DVU5xuDuNpvdSZWLI6LX/DHd0zIyLdM84AokMtLThyPhevWKihw5h+XXg5jCehPNHaIorWnXPNN13hveOcglO39kECYPZKttZE6KgnqR1t2s6/4UdA64mPAUNXsbbd/LZMxNNGDnk98rZyVGOMUvBdmE8QJrA9faCbWqCDrPOVbDNB49if1wUuvHsMtkFLFbo0qyjN3XIoeMx8Q/GRHvlhNYEEccRQ65vHgYh16a/XgY4wn3i4cxAYwr4+BJuJo9+0vreW94UULToOzLZeZcoY0fvmaTfmyvzOlkUeTwX1yze9l20Q81lDn8xQRhNKAXaUp9GLzT4nKS9JLVNmSSc9NiyHnYMChY19+Qw04HD3LaqfueIhT509i/eHU09pClEbuMh9Fi0k9ZXgxlDrz7yHw+SbgxrWOrbn0mAmtx4GJKXn7dxyOXswcxRYqzSgk+d7sb4MyrNEaOEuOFWzkySaj7qJdHEp3IbxezmN9XrF0nCUK97wdApXFd6AAAIABJREFUbueUpkgSSM0LZVBNFv/mirbrRQbZUhApZAnccovmD6+QvdO6tal+AQhfvoIHIGWO9mKA9P0S/g9vkXxcPI1dL4XHYBdzDr+dc7BaL95utetnW5ME3ZsLJB+XzNNFooCrv57ybTlfxzj8dABMB+iGKfK/3sU3jGRZjMkB/UW4buhA8tJ4OuEKYNoSKxiqed40PH3qhvwOTQtkPOxzTOPEqOlvqBsgdT1BKwQ2eUzmbjyEvL/nxdLG2T7cwWUZ/HqDbF9RH7LVqknTQMqCXBZVA0v+8g1QFkiia8jj2MP9Qi+1R2BvW2rE64AHQCkQAGwiffMOcn2J5BuddFquqYQgjgJRuz38YgmfJDGO5OsPQAhIhgNWrJoG7d++RfZNF2MKXQcJpGjzPpA/mLx6juvMN3zoZy9t+gmI6YOYgKn3/PNuH826BOhnOL1np7DIWZJT2xjpOlIBfMcN5MOBTLZjuU46io7q0HfIM6BWpV3jtABs249HvdbloIxEsB/Cbrn+Udid67Gji0bKojwYmYxpfVNT3YC6+soNalu+WUYjlQRhHN7owx/vmILtK6R/9xUJZhoTmpaqa6o5iTThIfGM13lv+APVn1DpHKgNTNipHXpZastRg5byLGeORgjLVdyQkqYcsJYWoSWbMMpfmM76fs9TOkvZxdRSZNjt4aY5/P0c7vqKdet9jfbtJZLVXnUl3ZPYzdbyGOzwAVDsCJ6XT9OPV4MHXuJzPkiVIy15V6m0R0bVgtEsxhEdRbIMwXu4aR6npiwm3C+40b1Kfu9r3oWecUpz3u8nYwDuq15dy/WtcrFLGMATuaoeDEkEs3xp21gnD00TU5I4EncxiQ2XyHVR/ZgwLFm2tHRD50D960uEf/dv+FBocyh9N+eY3rAfTHkMe1ivj8ZuD3VUHrPvvdvpA6Hy3Sq5Fy6npAhvtpDJiDFk+YM4wm53YA6RR4/afxlTlPZe78iWNN/WZ7rO+4QHeDqbjESaciKnzFkxONRuAYcvIhXYe+W3O9bA0UUOSrzwVqoWZi56H++pCQn0nUqj9AJ9h3KzhdQ1ZMfLcNhQqzGsN/0l9MewG5fmCOxxaBu66cuyj92UDULgeN9+D1mlCF5tMlebmOJEzo891NZ/2FeQmrO27u/+EmMiic1ox6u+9/GM13mf8C6hZPPBHCic46tVRYmCUW71JELX8TVvk/dav2ZuqyeW77uXMh5yMh+gM8aXbyJTUC5nbMaUBcK/+j3CZKSlwVEUJAp/eBt1ZmQwgIzHJG1Nxk9iFxVkPQY7jO9u/zhHq800pRv5a3XY9l4nlbTH0PDSK+MRJ5UO4vD/5g/RHdzisTeOxQSgN2Uej5nOXH7+E0+/3QoBeH8LjEcPP9w0nDk1SoB5l6rKL7wnZxx4cPmDOt8h1P3FbLeH7JlOxOkhq11vtty09wvIYhWbVmG/Bwp1EPynbxBK+i6FHQ0DQteRgLVYPY5dG1pHYe967FQyFtpSAvy693e9UULd6OXX9Zz7QQH5528QkiTG4d5/JI04S/l1hV6iD2OyB805hMUSgglLnM94nfeGNyH/lsYCAOImCNMxBUEnIwQRboR9xV/OaMAL26CImyeMBjxdbYjbud4f1RpCIiop1/GErmqqD0wnCNsdB0hC4AaqG+z+x7/F4L98H20uaVQ2ZlOqUNyPYJf19mjssbVv38u5vkEWQuTOS5qykrJRHck0YUq03qL9t39A+mEZ4xAluZmdjqhVz/6//TLGFBLl4FQ15OqC87ib500tOO8Nb7/kXEtyOj0kmVopti2lJkwqT2dPZcu6sRzm+CsaJ3DML++ZllXNh0elrjFf8RJ7v+Cg8/UU7t0dwtsbuO9vo6IAnEP5f/0Z4YtX9HotCgqtflQFAFU0eww7yuJ47GnCC6O+1cSkAk1a42IK85wK2z1r80qskyyDf32Jbpgh3e1jHEFpCLLaquufQ7gaPoxJKRgoclKIbVrrGa/z3vBApOICpBGEqmaHs+3Y9VSCVVQiOyj1hU6Htc2IS+dYTS8eRn/9cKcXv95TSrKMJ6UqhLnvb6OJgYhQwCjPEO6Xferw7XsAoOufMSsfw76vVEDpCOwH/q1oWyoeG6XBB0iTK0OzVUmRfS/TEQLk+1vkf6Hvk8WB+wW/pq7hJmOEiwn8n/8JyZdv+pjs7adztKGqgObhZfu5rfO+tArI8cgybemr8VdDSeew2/EXmiaxhi1pSjmLsogTO3AJvVBNjQvovVLBioyMhz0t9+aqr8zUB1LVWhJkJWQbfVtloF3fyxkVu5RW/CR274/GDnE9dpeQWpDTJEGmY53NHfJeEjgg/uC+UTexKRXjqEhriL5Tqy3c1cWDmKIqsXOU4BscSPA903XmJ7z0pKl9HS9QeHXVv8ptoHlYchNOR6QCD0rmoLsKMhogFDmkSZlGWIOmkSg/jb1OOPlAwaLxMHZyw3hI1a2WmpQhTckx2WyBP/0OoWr7C+OUPrBxrO8R7FIWPKWPwB4HRVYblijzjClPkvBr1fDMtCtlWLL23nacyfUe0g0RZuMYhxuPWJZ0SRweEXUisZjMHzZafu6rqDH/XNeZb3hEUhNCYFltsyOfxdr1Nj2kaYksNLevdAPXav2y6a0njYtOkVUwNTBab6qXwfsF9WquL5hXv7mhn+u+0jtADV/XkO9uyWAE4lBFxPQU9gO7yR/FDujpz2FxUwnDbs+xvqsZZF9xg6cJUxrVxrEY8OH+QRwm5REv7fsDOUKNKfLyV2teXp85jwY49w0vYGVjtWHZTTcCkoSv5jR5OMxd5Czd2ekZqI+OrkMAc+CwWkdaQWh0yMGs5u11fXMVqQj47gNP9PcfY84MkLxFd5CiVybuPPPcEJjGPIVdiWBHYW+7iB1JwtLnesOprbJEuFtw3K/I+/tKpoKqbYvw3Qfqyqy3fRymevDFDQ0QlivtXvcxGRcHQBwtDNMRZXCf6TrvDR8OuoEA2X+m76K1cklcTxGwET1jM5qokc6r2sB3CAGi/98k+6xaI9t9rKqIcegTB/cvLe+tB6D1bplNORIIpdQqj+VR7G17NPZ42loTq2nYKFptdBRP8VtaMhrQZEEv03I549dfTGIcYTQA7hZ0MhloU+5iylKvxSQCzKbR7yrUNfD99uS/9lOu897wIlFiGnnGunFd94MJWUbu+q6KpUI4x19810VuiZkbw0p74oBEFb2qGtixXCjrLR8OG/1rVfnXSZTFC9tt1KqJJ7o4oKImTBBhOnF9CVj+/gPYg/ecUDoGu3HqVTkNXaeD5orfvue7j0BZRhk+qLRg+HjHjZ9nvbzfWr1XN1uKv6Yp8N17fQNVUdIDqw2rRHmmdOOXS+vplvdMCXzgL8/Gzsy4QP9MhTC67cElpO8qb1001zenj2gupvOekmW9w4YIv2de9vJzlisnCUSEtFrj1HeeHBjboFbbL32U8n4MO4IqAB+DvW56gpjO24rJWE9GCFbP75jD+/W+N1Jzqgp8cwl89V2MA6OZXqz1DZMkwNtLmh1rTNSpGTBdKos4DvmcnbjP/xbStlEVjBdKk7jwUYSIUho+WtuINnjiwIRSZ6Use+aiXgIBIFzNqAMzGig1t6NEx06n9S9nkRTmplNuwNkkzp5C5S2QZb137I9gt5ncY7BHlmXnWRs3ab6ygHS+96OaTgDn4GZsRMloGOdo8c33D+IIuz1l9kbDGI//x78+iEmyrL/cexVhfSlLnnIdaOZ0HU9L31Fbve3Up0nLf9a0cQmC15O8bWFWNvGEdBLz+WB5c1UzVSkLBJdQziJJ4G60/FnVffpgKcb3H/hzg4qZ6lgdnPB7DcqnsZuu/THYnTDNUIFTdGpnWTfc1MMS/tt3FG4qS+L3ASGQvSmjEd23D+KgXuSGebnGk7y+QVit+5jalnF19I4V61Y/43XeG14ccKVlQePA6GAGkkRdMvTUNItLAAguWuXAq8jQYNBTXWsqAofdLooPiVrYoOuAfBR9WlFVwOsbUnozbcRczLjxjf9iztpbqnZJwuZQGJaPYsd0RorBMdjL4UMqtA6IhITqxqhCtOBB1wHji0hMC/cLDn9AOTwWh83PmmZ+nlFVeDTsY3KilGilMKQpew/PeJ33hge5JSjynltixCloiz5xvWBpzo0SOs+6MaAkq6S3eNS8lK9qj9DuIaMRu4tFEUVObfhaLi/gv3uvxDJVLagbtuSLgrx2TVvcZNy/Nbx/ErtseTE+CruJxhpVdzjgxzqdWwV6BeGmji7h0fj48gL+q294KdU4vKmwVTW8nvSuKHiCW0wmzgr094hnnsOf94a3MpzmlGFYspRW5AizMT1WnWgpsCChbMwTCust/OUUbqOi/uMhX+V7iXm8TMexrs0TM+GFLUujsGlIE8gXr3gaFzkwGcHtqBQWipzqAiazYV1P7XTKcvModtwtokv3j2EHwJLnXvrTtiyYdijLMcwXfFBHI/YQVPhVri4Q0gTJF28oha1xyGSsF+KaOb/9nMOYmpaVsAHvCiFR0tr3v8Fe+IXWeW/4tu1ThxAgiwYYD9k8ipaQPMfEBIrW2zibKkYK877XnSnLvrbvhA2hkkQvsSrIgQ6kbPfwyxXciA9M0KFnFKQIh6YBlm3PelRnPXy4AybjR7EH79m6PwJ78J6phGG3+0CqUoBmpqxiU8HSEHEI8wXfYKMBwnfvYxyoKn68buhLVRScDTDfqiXvEJI4yFZpEcsDU7Znus57w1vd19rz3pMma2W0A1UD67xa4wb7LqYBNlDNTqeLVRIMSnR/eIPk4xLSduhez5DcrXlCFhkCgJAlkNEA7cUQEgCpryDLrZ5+KZXB1juEfQV/qerBIDntSex1TerwMdh96LE3rPj4N6/gllvy7p2j8vFAf+bVjPV9HSf00yGkC5C3r2Mcbr5ByNKou4OmRff7V0ju1jEmirjW0S1EMED79vLlhD/ZCgeb2ZiG4jTnzXs7GZWzs/lTyVLKadzec6OURS+AUKkJsVpDJl9/iLSC5ONSm0k1ZOviiJ2/GCP5yztWXlp9sxj3ZrmJubVb98MRQd8cj2GXPOcpfwx2vcCi0upL18B9UOFUJxBPPozsQGzbHb/ufknBpuUWIc8QhkUfR6U8HZu6yjMk95v/X0z8GVq9DgHJPzxvj6fzrsNr40WGqnab0a3aKh4yKPuLpHULVftQ1mpYoGmGiYpCRFUKHGvvOqcapmP+PJW+C1avb1pI00XH6jAbo/visvd8AhBeXZFAZhSB+UqHMB7HHgWZjsAeBkXELqoZiYz3Aj8bIqQJwnQMfzkm7eFqxvSr8+iupoz/fvkwDq+qbpNRjAdd9zCm4YC0BnMyB569evB5ozdhJOORBPU8TbXq0rbcmE6VvYzo1DTUaNRqRWgaiGj60HaUlXYJ/63VGilU8tqkP5oaoVX+y/cf4LUEic0WTq0vbYBDPtxxM86XcaoqAD0R7IewA72x8o9gh8lytB09owCSx0KA+3jPhwDg28kJMF8gFAXQ1HD/8FX882EcYsSzj3dwZqgMPIgp+l1pI02K/Nn7tJ43etcTtaIzx3bP13tU4iJ1NlrNAH27XbXUpevIuUn0TuCVZamep2E8BBZrSs19fcu0pSjYWTSiWJbALTbwsxFQtahfT5D9l7/2Dn96wRaRyG2xmdMfwi6R8XkEdu9ZSrXmVqd9BWte6VvD/47aku0f3yCZb1k+nNBfVZYbOnRrHGE0oJzezSWkYTz5Xz8+dC3c70krGI9o6jwd93O2z3Sd94YPIf7FQ5Sjbm51O3qvwndAPmTeGi961F23Mbm49hU3hw1FDwfUbdlz/C39u6+40dXqBdt9z10BmGNXBXB7j3yz46V2tYZMJ9ScuZpRAClLqVlz6LD3L7DbfOox2CXLInbj3ITdjvY194vYUEu+vQWcQ/KXd9phHVLDMlcJ8A93MY6oXjYogLs547E3icVkVavdjpfw5Lwz4GPWeW94UdWCYdlbKdoGHI/6oWjbyNrRDPuK+ikitHoEtD4+ICnsakbGow50+5sZ3McF/JsriF0GKyVxvbmBbHao/3iN7MOGp+zra2CzQ/jyFWTfUHz1knaSYTxEKJSnI/I4drtPHIGd6g2K3SV6X9C31fUF/x52FSem7pfc6E6ouHA5Y219S4tOi0PulwiTIfwgg0sZT/1vfo/sdtvHNBn2w+FJAtlVqP/Vmxc+/MmW92zlA6TXdp7CQmXJKXqTqQCUtntgNKBjdVZTl1DFi2jscqoXknz3kaf3Xi1mdLMzx70HnEP+1T1Py9kE4cMtrSOVgRmyHLIEH873t2Q86gztY9jJbEyPwg7TlwTi3Krsaw6ZDwdRqCl6TQH0jAV4Ef54z4eq62IcIc+AD/dIUr4tQpoi/4//tR8Uz3JOZ6Vpb2c5GiL78/Ou0pz3hk/IyQ5O1EUD/S8U6M0FjNmno3sP1HfVURvgpcuGPWB/BrUV2RlV+QzpvwaTEcJ82RsGJA54dc3N1XUIVxckZeUZpKGbX+zgdt3j2N/csNt6BPboF5Vnys93QKvlROeYll3NIIuVjhJuexHU97d8Y+zrWIo1S5zYuBsP+YZ4cwO5W/Qx7Stq8JhuT5o8kP17juu8k7IQWK1QNbBgaQbAzaROGKHIeIFT7fS48gOarZ2mhaoBVzVkUKL74hrdmwtI01Kq4mLMTVDk8FcTtv2vLoAsRXc5ImNQXbwjtTdJ+NbYM60Ilvs/gV3uFkdjtypMKLS0WtVMv8yq52JC/k2awl+M4b98BT8bM4Y/vEUoC16cD+LwU02L6ibGg+8/PIwpY6fZHP+6S7W8fMbrvE/4EHqTrzSJQ8zRJ2mz44VKy5NRYMlk63bqxWrDz84BH+fRwiWsN0hMhHSzAYoCTr1ZJcsg9wtIWcCvN5DpBMm3t6TOvruLshzhfsEmmCmPfbjr5S2ewp4kn4Yd4J3g63dULatqVRquILMp3MeFasKTB5OohSaKHP679zEODMr+QvquiTo3Mh3HmEyACTpY7p45rQA4+xMefaPDbNYtl03TXknXPscugjaQUaona9v2nc0HdeWi7yK+uuZMp+XutQ5a75R8NlDLeR3yiCrCB2pcYUaqbff2hk2jp7DvdsdjVw13SVwcIpHpBFIU8NcXvCC/uoa/m6uhsObhNZtPYbfrDYs1DnjPn9m0MR4Zjx7G1Bg7c0j2ZZa9uPiddOkFLVRVVMDqeeNBfUR1WNqJqngFnqTqeRpWa+a5pjJmv0zLp426u1wzB9aTDzdX7MJ2unHnS3XGs1Jo1tOUxzQXQ9MiTEdIPsx5uj+BHV13NPb4oNk4Yghx47nFminUck1Wpw1kpykFpYLnpt9sHsQRdjRfDn/4MsZDJYQ+pu6Ly/h3ZC6HOBxseYbrvFMaHZcT1VIP5nlUqUyd1bgB1oxzbc3r6Spti+CS3oHDqiAla8phvnggzxHSlKU8APjuPekB1oFU7njYK3++qoCrC57kH+9ZCvz+A78mju6FR7HzJD8Su4m+AvyakiSxsNnQ6CB4oGoha15w0fBkl+/e8y5gdITgYxzG08F375Xzo4zL2STG5CyupoHcL+HXG7jryxP/0k+7zvuEF6GlY5r0PBLv+Utoaegrw5KvatOYFNEhaaW0aus+GA1BhKd6CNQ8v7qATCbUVb++jAMPRqiCOP68QcFNdDGLJUvczakOdjHlppiQjxPWHKp+EnuWHY0dgzJij1aaItSiT5V9+eWb6AYilxfkuBtNQhzCZvMgjtgJfn0d45E0fRATnKjGPBWL3cXsRbXgpCv4Xm3AHVyYbAIoCiB1kY9ip36o6sgZN88mSZnaQAQwuZuKzhfQgWbkvUU86gaYjVmWHI/YWV2Q/hvUvj4MClZIPNMXGY+AOnmQyvwgdnX6OAZ72NDAjemOKh50ntQDuwO8v40zrlFoFeDmno05aLLZxjhQ5EyZ1i4K0srljNY7FpOj3xVHJMueQvGM13lveHF97mrSdN73o2522jkddrYTzS6M3usAhU4ddR4yHPZydabNuNogXF4gfLiDCZeGklUNB7Cln2fAesOTT5mFfrmCM3PhYclcfql27ca2fAS7lf2OwW7ipz32FBhkOvNK+8wwHlB2r+0gwyxqb4oPrGaBFAWLA6Z6Vha9rXxV07BNYwLAuJZrVm0O5QGf6TrvDW80WhGKm252UdPFbOChBDCMBpwYMr1J1XaJaUDTAGWOsFgyVRkMeApmKbq3N0jma7T/6ktywjtSAcLf/g1C0yFkCdrLAfKWjaTu1RWS+RYuSeCvp5CGmwVtx3Z9kcF9S1WDR7HnGR+cI7DHitFiScxDpiaiWpJ+MoLb7IDRACFL0U0HcIstO7YADZYLVnosjubtBVzVwm0qdJMSyXyLMMjhRGJMEgKpx3/4AmgO3novAyAnWtYlDSFuGBkNIksSAFl8hVoqikSLykMGYhiWwLsVN7IOWoe6BlYryGAAt9kiAEiW/G+/WJIW8JVeYNsW2T8LhyiqGsk9XbYDABdCzyHXXNhpTdw27g9hD4slT9xjsO8r5a6zmWRqYUFb/7LeIFgJcTqBfP+Bnwdwg262EK3WWBypCU3VDRL1aZUkge+6GFNwwov3vWh60zfwnus670urXd4KraEfUILRdRzaAHorRVPrUj4M1MUaH+94wdOyogxL5aBoSjQouVHGI9VM70tvUuScA1W1XckzXnTzHO7yAn654qabTVSwqCNtwOTzHsGOq4ujsWPWYw+rDekLWUb8Ql67TCf8745S2HG9vo6d2gdxqBSI3FzFeOTm6kFMABiXlWIPBWef6TrvDS+cQAqZNXD6Copd+qziEEwvRcVS47coi7iRoQxJtF3/Pa2RZK4azvGBMLMBlecTzV/9n76MepGhaahDo5x59+qam79umBs/gV3W26Oxh/Ggxw4qGMSG2cUkVoIoraFvxMHgQC9TN+lBHGG3J5aPdzGe7nL0ICbrJQDoOT/PvEpz3hteqzRiMtLWUOk8bd47dh9j7TsEbozrC5KoBgU3h21AgI0TEW7Km0vm9sHzc61y4hzC5TSqG4SGX9/87Vu4r96T/9J1HK7wnm+aJOHJPJtwrG5fPYkdaXo0djdfR+ySZ8TedVQQblq6dgyK3r81oYhUuJzGB0guLx7EAXX0CH/6XYzH7duHMQ1KxvX6OsrsyWT8W+yEX2ydd0IWwLG11ZpVjzSFZOjNhk16Tr2XQtuSj7Ld8aRbrSmuVNf8eNLTieEDbSWLPDr4ATpGVzf82sm4n+XcV0j//A358+8+Qn7/BVXF9ntKhmSZGo95JMu1nt5PYPf9BfVHsVv5MtDyBtsdH6Jv33N6qigQRPjz0xSotnx43tOETYoCfjaCq5s+DlVWk33FGH7/BfD9B75RNKZgD+s9G3TBSsDPeJ33CQ8tPaq2uqhDhb3ORYx6qyXHNGXzBVA1YNfL3FkTKcs4RDEdc/PtdfpovtSOppb6bKJfvVOD6bqLCpt+uOMputn2lRVjOJqawhPYARyPHeix+46pVl1H/KFuSBlwwhq9HgSSpnwInIP88zcP4gjeqwHaNsbD6bKDmIBojiZl0d8rnvE67xPekTkoY33Fv7/lCbTdxVq5tB0JUabkZXOkacIppKrmCTvQgeqGWi82qxmyFH48QMgcpPNwyx1VtkzjRdW2QiLAXm3hncr0lTlcnscSYfunN0g/rtH88Qb5Xz7ycx/BjozmCMdgFyBOOEmaUk/mb76Am6/RXU8gTQdfZki/uWVVB+AFN0uBm0uErsP2373F8C/LGIefDVmSnA2QfXvHeN7cAFUdY0KaAPsa3dUYbrmD/+ISUl8D//dvuit+1jrvDe99dKGTtqPYv7rPQS9kSFO6WFiHsdJBh4xGCQDYGLpbIGgdPdrWANSjabTunSQIqpAr2x1Pw0GJcDenbuRmE9W6UFVAed07Z3cdsm89wmaHfLnu1YEfwc4UKzkKexRrVTltLGo4vfS6LOWDutjQjv7dLbuy5uChSsGj//QtwnIV40hWA4R9hexe5wWqil9nJmg2rN11cJrSuLt5n+I903XeGz5JOKg8LGkeoPRZgYozGUvRdFPsQdAuKnJ1CNnuaQNj0tcXY5qAmXxGniEMcshm/+D0FRH42YimAGCJsn09hds1kF0NWW3gX13wz7sK3eWED0+RA27IN8UvgF3Gw370sK7jm0l8oLma2sF3oxzZPEf1r98gm+8h8zX82xtICPB5gmQ2jnFQBXlKTZ48hexURnuz62NSlQQZsn/gL6dw6+2LmOrJVtfx0rda8zQ2dmGaMKWZL/l5iaN0HRBtXrDhxU0Gg6i/Hkz56/a+n28tC5oErzVX14fCLpXOeOMg3z39fn7g9FFA/vodGYvjIeTP/8xZ1q5jQ0zkl8Eupvzl+bHlmg+E8nnka6oKyDcNAoD8//ln1ZrvkJhuz4fbKAkeZmNtRgXGHgL7BTroYjEFp4rJmy3gg1KRn7dc9nlfWkUHlsuSpbzg+0uhCI2AR8PoVCF5zv8ucnY6rXliNWnlsMdLLMBLWtMclCW7XnHAbBvrhpe/b9/xARyQX+LfU6BVxkOE9Rby9jXgBH61jjXsx7Ajz47HPiwjdurX94MYJtUXBgUwGyt+sjAhEjdoFE7VOB6Ixmo8AB7EJCIUi2rbOJvw3Ovw533CAyzDaW5p7tSxcmHja0nC4WenM5oXE8hqG5ssSDmTiY/3sXUvKgUN03I0WxndKDIY8BK32bE6M+LP9rMR5OOCGxZA+ONb/jfAO8VoyMmp5fpJ7IeqZD+KPaFCsOj4nzhHdz279CqVWO6XwHikl3RKd0d2aKPiTRbHdAhpOnT/+FckN9d8Y3xxDfnr931M6mOFNCW1eLMF3r5+1inNeZ/wENJz9xUFjvak3AZlEpLnzjp8bCwlCUuGJcVWY4nwbsETq/OqBZPGigYVwQJH4fKMdfK25QbfQpUsAAAgAElEQVS2y+x6Q32XjaZHX1wzHVKV3WhdD7DqkiRPYg9mbnYM9vU2YuesqlZ41GFEVGQVQp4Muo4PSdv2nq+XFw/icB/mkH0NN53GeNx8/SAmZClPelU5lizjz3rG67w3fKCLn5RFb0B2aP1irXbbxJ4KATIYQPZ15MSELV24zSDMmkKxEpE4yJI2jrhfRN5IbPkrZ10WKyDP4OcLyD99E2UyqHGzgr+Zxan+sNs9iT22+I/BboZpTnpDYSWlIc+o0bNc8zS/X3BeVlUMLHXz7z48jAOI9wiLJ6w3D2JCVVOTpmlYKq1qhLv5L/or/rXXeW94gK9v00iBNmtsk6m5L5stWsNerDi83DQqWU1CVVDGIeomdhkBMBc2QwFtZMFpkwjgz1EvqNDRxkbyvDf7vV/qZNYY7uMCUtU9Se0p7EqLOAq7vhFgU1EHZs1huyMRLFUX7VTNlo0RqjHIaPQgjvjw+S7GY19nMQEgu7SqGddoADHS2zNd573hRfhLy7J+c0C5LSZ3IcIqhpl0DQeU0bbN23bKKCx48bOpfzXxCp3n98tSdi1tI9okktF5V+v486nGpfyWhkPY2Ffw8wU3+KAg9fcp7Eb4Oga7SI89SYjdLqCeMiPI0l6loG3jz4n1fTM2O4hDipwPz2E8BzHRnEEpGeZi+HJpPeEKiCq68Em8tCFha9zMeGkATC65jf1JpoxKdaAOew5PS54/tG1Ri0p0XeTOhIPmSpQCASJPXRIHpEVvDHy/JP/kYKI/tN0vh31QQDY7qjSYnMehXy3wQO8mYqgq0hA2W3T//b9F9g/f9XFYzNMvqLiWFoxhPIoxofPwlcr5Af3d6RmvMz/hcdBwafpSnW7cqBtp8hUJpeBEbR3tc0kHcEqA6j1S5WLKQW6AG3254iZIHAlZOoxt6RMAvgVMz3Kz5WaZjeFmU1XYpeioUYYfxV5Vx2PXjnJou37I3Afin4zj5TRYJ9fp0LYpFKQpsm/vH8ax2fLE/v5Dz6hUK3qLCW9fMS59UA+VH57rOu8T3k5FAFLSFibWsU2YCGA1AWWsqEBf6Xaas9u5YqPF6tMNZTOQpfCv3iKZrxFeXQC7mvzwLOVpZzJ3b66ANdmIUip1d066rfgAv1gi/Os/wK138LMhkg+LSGH4IexxHYP9bs7cPNH5gKYhD77z8JMBH9COD3UYljqwTlXhUOacuBqWlPTWOMS8p5qmj6fIgXdVjCl0ntNff/M2pjLd766Bj7/eFvil13mf8DoxH9mIpsxlp42eWGG1jv6pVrmJql4hsAKjD044PD23O/jRgFow+wqwfdi2rPHfXFKXcbtHSB3EB3R/fBM1I/0fv2AZsGnhxiMkiw1kV8H9+a+8JD+F/erieOzTSY9dDZml7YDtDu79PcKo7Cs9YBkz5BlpxHcLhLKAqGCTxUE1CHMiYTyyqx7EhI93nJ56f8u31q5C+v4ZF+Fx7hs+cf3Ym56AYspgKhGNJIlDCVJQPyXqz5jGor4pgpn56gpVDfnmHdyCFzp3Owfmq/5zP94Dt/dAUyO5WyMsV0j+/ms2pW7v4f7+K5K8Gk5KxUZYlnLA4insi9Xx2M0eU6U5AHDApCLd2L27YzxW89/uWKZsaHSM23taUx7EYbqSaNoYj58MHsbkQ7x3sDqkamjPeJ13SqP2MCFNenlq1XVBVVMNYL2lG4h2Lq3cZpaN0dZyWPLksw1TkP/S3kyQfT/n92pawGvLfjLmRjX7m61pQSY0LZ5OooKwjIcI93OmO0XOy+p6+yT2qAd/BHbkGQcyrFqiymO4uWT+bQ/WaMAZ2UFJstzhMLg4pnAah6lBYDyK8ch8zcabxoTJCOHDLd82KvsdZpOXlOZkS1W84kRSQbtHqxpIp15PWUZZax1OjmpcmQ4r5xnzcqfCRYOSf15vkX19y1f4fIXuZgpMqQyAfQV/OUFoW3RfXqN7e8Om03gIGQ/JUOw8wu9ese1/dcmhDRVAhRqTPYYdStg6BrvNsEqa6uZz8WGXtuODMV/x72M65s8QdReBpkKX0wdxhDdX6H7/ivo0Gk8oi4cx7TQudRFhWva8FYTP+4RXBYEwX/a58KAkN0WEKYRVDu4omxGneebLaAUZFXFD4C9/tWENuq4hADuPwxLJ16xYBB9I9vrHr+lf+tV7dhkTB2f6kmXO9OIfvqICwXZHTrn6n4bFkqnCY9i7jinKEdixsSqNirICwN09bTJNpnt4kMeXBfDuniYNVgV6/xHJsoxxyN09c3mAD1eZUyHhIKawr2iGYDY9SfLsTc3O+4QHy4BWuQDATulq07f9TV891/E8VdSSNI16jfF7HZgDh/2eHdWmYVrQ9apg0U9Vf17Y79kUGg35dWkKfP+xvw/4gDCb6IbU3Fhz3sewh/Xm07Db3wlAcpriO7ybWCMqNr+M9XkQB6VFppTpsHhurh7EFFMYa4Il7tkbIpz3hndO5TImkW0YNNcV56IujSSOv+DRkClBqidfWei/y54WbJWOooBZ0PN7jUir1fSDFz42o2QwYBqx3UOGQ17mDisqIUAWK8iXb/ix8ShSfx/DbnqTR2G3eVZ7IItCNXX0EttSpjsYpdcoCA2NDmQypqnDQRxIEl5Md/s+HuXWWEyhbRmXPXh1Q67RM17nveHNxc97+oq2LevPAD1Dm5YnkBnziqisXF/DDsptb39/zfrydNw3rS6mdN0bDXhJTBytJ0OAjEfo/rt/Hb8nEgf/+9d0u76YULoiz6gmZiVHEf5MZSQ+hR2+Oxq7pEnEjjc3xH5zSRry62ue1Du6h4SrGZ3Ef/eaacjNJSACd3n5IA5cTFnBKfIYj//9qwcx2Z0hTEas7ztHv6hnvM4+h4cPzCUtNzZzgOWa+e5yxckiq0LoYESYjpmbq1R28vdf959XFvyl2iYx091dC1ltOVq32SH989cIr69Zy65qJCAXRVTZKwCscNjGVCGncHNFinBZPIod6oJ9DHb4ADQbfl7bsRpzt+DPmi/5vfKMbMuVDnx8/Y4CqLs9wvUFIGPI7byPI0sp+nrg7i3/5Z+AybiPKaV6sOzr/rL6zE/4897wADef8UY8rRlp4Oti88maNPFzhmVPchLhSZgmkOUmasOj7fhLNHrCYY3bud6zabXl6ZrRISOmJxuVtzD2opP4Of5PX8LNl09ih90TjsAum13s0IbRgPx9/R6oVWms88BuoyODDcum212MIUxHD+KItfXtjl5Vmy3c5YUS2kS1Jp3eb6iJI5Mxu8LPeJ13SoPAE80mb5qm10VRnglU1Stavyeao+qpym8T2EQyBqGehnZBk7Lo1X/3FfD+Y0/gcsKBkIR5dNhs+f3rBv5+jrBakd9SVfHhc3/+K6spT2HvjscemiZiJ9FLS52d7210qpoDLDpYEpYrlRpnDb0b5Q/iEDVZ4IOxjw/+YUyhUXUGVU0Lq/WLLs1Jlw4hIASeNMZN0RlSBE9OiCpz2ccgrt8kSQLZqNfSaPSgLAlV34WnyVdQv1I4NSW4mKo0nqoGDErW6edLyPUl1cFsY9rl1Tn6na63T2N36sl6BHaO8B0IM2klSLIMYTJiKlU3/RsjSymqVNUsKw5KpN/eAQdxhM2WaVRO20y5Zq4vKrYKQHU3fWRrhutLdoif8TrvDQ+JhCrJ8zgiF7Y6Z+qyA5UvJWBZt9Ir791ZFUSJYMOSOWmiG2t/MAy93j4oXcpea+6TMdOZ0ZB6NT6wMTMast5+MWPem6uP6nylSmfyKPYHrM0fwW5pBtScQAZldCXEfNXX8K1kukdPQ8gK0gHEQbK0j2M0pEP4YsmK0b5iGncQE/bqXr6lUzcWq/5heKbrvFMaYZsd0JG5EHqpaICveMvlLf82vrmWG+GcTjQlbOOvNjw9dSg6TggZK3M84hTQAW0X+4qv/lJ9noZlv1lHIyrxOuGQRfDA1Ywn8lPYVfv+KOx1E7GLiTgBsVwoJQe55WLGkmjBscL4s5KEb6mDOLDbI1xqlUfjkdHwQUxB1QpkNokl3Ojs90zXeT+uPrAqkFEXMl6yhqrXYqlCmpLxaNUGq1nbGGCmrnpdpwKl6qinp69dOlE37Lxm5nitHy9LJVx54PoC4XZOy5s1XftEKxvIcvLIv3uPUBTAdvsk9nAsdsvfi6L3d1WdSARPKY40BTa8VIfdrldI26lZgyc12uIIiQO+/8jGVJ4DXYf2ZgL3n/6+j2mxis0uXEyBqkFy+5LSnH7ZiJueyGG17m0XwRJb8D6mKmg73Qz2QCQUR9KmjAwpeyfDAdmC6swREp1Iqmgxg/GwZwcmCX/5K57iMp0g3M+5MdIk6sHAB8p7zFdPYg/ec/Mfg13vGqFumH6oGYQ0beyAoqopNJUkTN+seaVWmXFpHDIaRK8qiyd9N4dXBmdIE8hsEtWPxd5YL0PcJ14u6ct2bRvlOQAgCjWtNuwSmhJAVZGolef9KJw7UPFVmb2wXEXR1NB2vR+q7xC2W27O0RBhwrq7TEYkZ+nIn7x9zZP9/W2/Ee/uicfJk9hFDYSPwR60SsIpKv0et/e9okCm01A3l5GUFvbVgQjTEP7+/kEc4cBR+4HjyUFMYbEi/WK7ZVz75z/Tet4b3jbBgYMFfZQKXthMGRiIdvFhu+/z4qaJygG0Wm/7+VFr0+/rOD0VU4fJWDcwZfZkfSDX8eGWmPZ7nrCX0zhYbUPZofMxf34Ue9sdj1018CN9WL2k4vefrzj1pMZoYbmKD5zxe9zl5YM4bCAGZpxsVOeDmJCrWluqJsqfgSHCeW94aF5bFP1ruW0f6sXYjOZup4PSQsbjjgoESBL+wrTTacJHYm3ywOmf0HkyCTvPU8yz9Bfs59UN/51oNSRJEO4XlLTIs75CkmfM/Y1r8wh2G9I+BruUZY8dWr3pOuI3QplKb4Suvw/Y/GtoW7pzH8Zhf39VFePBbv8gJna598SmPPnnXoc/8w0v5HnkGS9jTcNfcnLAZgyBJ59LYk5PHcmcuboNXBtr8OoiSlVjsY6O22IW7VCSV93AvXnF03A0jAYDkue8nO725NP4TmU0clZj2panZpL8Ytij3IZpwhjlYLHmpvYd8RvJy7ql6uFqOpKHcSBJyNG/uerjAR7EZJo18WFz0n/eM11nf2mVnUrUWRVDTXbDfk/WoFcVsUODXR1kQE0nvVBzwigAzJmNn34x4WSSiZReXURJbVEjBNQNUBS8YHYeeH1Fq5lByXa+XZ6rmq97xfOj2GNp9cexwwc+OKtN3HwyGHAyaq+6OBXlRmQ44KZUo+N4qqv7SIwjNBDJgXofufoAHsRkTT2bjELdIOB5r/M+4QVsvBQ5cH3B6kGrtffxqHe7CL5X2TLxpDRBMCajbRg9rcJEeSXbPbovrpVrk1J8dLPlRi8LCh0NSrbUL6fkyd/OIbMJN9a+4sRSmvRWNiazYbOtj2E3ffgjsItOS9nlFUYd3u7hZ+M+bfM0gYtNr7Ig92dQIvnizYM4oldTkvTxXEwfxBSFWk09IoQXtuRJlw/khAyHwO1cN0CH4IXSb00bRY1CCOSJiFIGmpYXMCDOcsKaSbf3rD07h+Tjgg9O1yEkRewkxirG1YzGvLsqTixFAtq+AsIOWHFmFLMxZLHmfWFfMSV4DHtVH4+9U49U26RV7xXl5iulN+9ZqlRKQ9jvmVrdXAJ+gHZSIm3bGIeo0x+qir6yIQDzJUufGpOUpZo36Om+3bFp94zXeZ/wCJElKMYlKXVzWgowm+jUUM6Gym7HvLjTWU+rbKgGSyRC6YC4GYDFqSQVUIpVliThRs8zhDJHNyN3noKnBZBRz11mEwTrjgLKi3kce2ia47F732O3vB+IXHrZ7tkp1bcDBiXTsCyDzFcISQL3n//hQRxIE3Zjb64exHMYEwYlu6wA43pz05Panuk68w3fVzls/C2YproNOy9WvVh/CLE9HlTX3C54ommK5DQPExGENIG/GMPPhsD1JdrXU9a09cTv/vQFrXEA+FEJuV+iuVCG47BEuJjA/+E1T/2mhbP6+zHYgaOxw4eIHUKfJ//qAiFN0L26QPd6BlxfEnuWsmmkl1VkKWRfwV1dPozjd1f9Q6Xx+A+3D2Py/sDYTTg0/sw3/Nmjt9lTBA9kBdD1ZsFWNbHyoZTqZOGUbGWyGF2nrXVSg8N2z1Thwx3k5pKUWx+QrbfxQhmqGsnffcWf5QRutUYQh+L/+M/A9VU8VZN7iZNDYb0h1qrqS5WPYMf1Jb/HEdgB8FKbZxzWSBK4d7TNdHeqG+MPKilCee2wr6JcYNCGlcWR/eUDU5RXV5CmJcc+TYG7RYwpHHD8bUjluZclz3vDO8c0oMh5IUsTysVVVRzEAKDlNn/w5/5CZjRfaWk5b5dApAm7qAAwm0CWa/ibGWRbAts9ZDZB/fsrZB/WvQ79qIRsxkw/VJmsuRgg++tHVQxO46ie1I1q2jyCXZ35fip29hQStF9cIP36lumJc70g6njISy8A2eV8k+UZtn97ieGfP8Y42nGB7Nt7dJMSSdMynrevgTTpYyoypmnTEatBIVBq7/uT/+ZPtsRKZue4Ztmr8O8v/xe+8qO0tKrnJglP9+2Wp6iaGEAlNuKyr3EJX8sifV3ZkycvaQqZTqijKMLhbXPS25J8JUXBJtRuR9MCbcZE3yMnfcpi/w94FHsUJf2p2DvPy6+ZJ6sCsYzHET+ylCYJdgE/OJUtDgCaZmm5UgR+t4ebTfqYEn07+dCXVkXwHxb/+4l+47/Y+o8hhP/hh/7HeZ/wAMt5iWPVwkp21nZXKm0IAVIONQ0QIM3jJBF/qdCNk0aPVJlOmA9b6TFN4EDfJMx4isMJ3HqH9u0lktUe7n4JXF+S4DUbQ+4W6L68RvItLd4xGrDWPqBd+5PYh8R7DHYZlqyoZBkEDUltsxFkuaVX004rPsPX0XbSLTZxztUtNwiDAt3FsI/DypamTDYbI9lVwG7fx2QKZyrWCgD+7v433A0/f533hncJuSzGm8kySubpALdJYIudvGXZN2OMLdj53rX7UL6upssG2haSJLycOseT824Ra/BIU6RffWCVosi5iRYrYL/niVjXHI7YV6TiliV/znj0JHZoM+wY7ABPY3iaN0iSQP7xawot6UVZnON9IMshX7/TqpSe7JMxcL9EutrEONB5hPmSTbk8B9TCPnRdH9Ptfc+pUSEo9/oG+KffaD/8Auu8qzTBc75Su4hRAQDox/IOZkXDbsdBaSvhVTrZtN/T+0hVBeC7XpN9POYvWpV0ZTLixXU6UXUwzamzNBqERT2bPIdXZWIpC57+igNV/SR2UcHWo7C3XY8dID1AaQrwWgFSDUyZjHhH0DTNyq5o6gdxUOfSsU+g8UAnqWJMk7F2gTUFyrIXMdWTLhGOmpkkHcBRujSJ5T8RAQa0UZeMQkZi1IGUlpTiB8oeJN/chFJF9RdlMoKfDoFtxTG2ywvKWAAUaFpu4C/GkG940svlBd8AiUNyfUnNyYsxumGObLOLMh6iJ+kPYk8SVlyOwZ6ojEfb9YKu+woyKNFdTuDWO4RXF5D1DmFFRTNcX/JCXvKtJDuqIcQ4LiZkWY4GgAzjXeUwJiQJFYXVljMMS0qEPGMx1fPe8N6zJHY4DSTS02RtUkk57AguTvyH7Y416BB6aTpR/9XVOlY8wpbajWIbJajkXprCrzfAQpmDfz9nxeLbPdMpNRLu3n1A8voG8u1HZHoiS1n2F8VHse9j/f3HsHMCS/kzdcOusZZa3WLFE3qek+tf6Rzqd+/Jm9GN7Hc7uNm0j+ObTW8pv1xF/r0MBjGmoH8vwd6Iasr8nNd5b/iAnjGo0nRwjhUUG2wG+lE9TUsQmkiSihqNWr+O43Bat3aTMfPdqoLMpnzAfOAM6XikXVJ1upuOH2i1h/0e7mKmndGOpcDD7xGHT34Ae9v2l9Mfww5E7CTBpb2IrDmSrFZAOmCtfUwbHBspFOeAmyu6hR/GYWncoGQ8v38DfPO+jykEIOhgzHjUWwA943Xe6FNaSEaf033FDQM8ECI176EovaFzoTIakBPjHSsgPjC/7TqeWm9fwTsHqRvIynG4Oc+iIXEYDxFWG56cszGZlcMBKyrXFxz3m03IfRmSvYilzpUOS+B++Sj2aJJwFHYKRhl2ABwUT6hvE0Lg5dd7VTLek/oLsAJlb4yDOLq/eQ2EgOQbzU+uLyDfa7VJY8J03Mtxtx3C29dxgPy5rvPe8Ag6ccNqDWZj5qWrDaUqEnJSouZ64oBAj9VIuZ2MqNyV0mfJmIwyGiC8+9h7nxYF5aK7jn/e7aMJcEgcMG8QxMF/9S05J/Mla91tC+Q504LhkA9S0/aaL49gR0eW5FHYnSNd2U70Ikf47j0voCB/H2XJi+mHSmW0VTHMLsr3S3ZbNQ7313c85bV/IPMlexHvPsSYsFzB76s+3bEB8me8znvDB53xVJKXVA3l64xLLsLupMnB2ZqMIr9c2o50YK8qXU0b0wTJMuDNDeSeqrnIqTIcVtRugV4UAW3eXEzhXl9RxexqBjdfxc6nG5TUeLmcIXz3vu+qPobd/QTsiYPsiZ2UioTCSTs9yY1WMJz2FGO7K6hQbNhsqGSgcUjTAkO9Q+z2wNUM3TBHstvHmCQEpmgaFwBeip/xOu8Nb8JAG+aepicDk5U+ZBC2Xa/WtZcoi42mZeVjt9cpoJqpgbb9ZbPrc1kAodULrqYRSFNu1HWHcHvfq3vNV7y8AhyNq2rIxRRhvuRJv9s/jV29m47CvjzArspjSFzk78hgwO9hjuJA1LrEYs0G03jMOr/GgbLs33Yaj7vzvIdoTAAQ7he8I8yXsXT5nNd51+EFqp+YPpinDE3DyXqAc6jmeaoCRNFLSVfQqabQsk0f9vs4o+knI6oRXEzIJ08TDnSbl5JqRHZvb7Su7eKAhgwH5JtoVzast5CiQPXv/qBzp09gb7ujsQd7KFzCn5VnemmueCFOHHAx4SBHmpDDrwQ4SXsaw4M4jPowKPuBk7Z9GJN6Oxk+Ppgv5LHTLVGpOe/ZTi8zXqyQUONxteHIXaAyL9TJQwZlP7lTZJFfIlatSROevGmK4MCae91QfXdQ8uQdD+GHBVzdwE+HcKst/GSAcDWG29ZMExIHLNYqnQfyTroOPndxRvVR7Ovt8dgNd+DkVthseaEMgarAK/V6AvhzU3pEocwZ23JDP9rJIMYhTcdGWN2w17Cr0P7t75DcrWNMCAHmkiJpykMhz4Cvf6P98Aus897wxscOAbi9hxhFtaqoA9l54N1HPhhJP2QRp5XqOpoXh6Xm21XFAQcdc3OLDcuI4xHLk+stmzG3c7gNue9usSEVd03p7DBfcMIJ4CYvJ9FFJLQdyv/z/0UYj3qdmx/Cvq+Oxg6vvYGqgngtM2olSSy1MuVhgNNP1vBS2Y+QDR7GYTLjbQt8+w5BHFLvSVHWmMK+ipjDZMQ7xRmTDY9Z573hRfi6VjNeSw0ky/qSnnmRDjnmFlUGADZoWtU2V+WAaPqbJtxcS7bKw3bfq4epEGnYbHlSqgRHENfbsKtTtl8sAVNEWK35JhkOe32Zx7CbVPYR2EkrkN5ucjyi0GkIzLfbFmEbIv5Q0cNVilzHCjty79frGAcVCHbEnelcwf0iskZR1ypIpepkd3Pm9y9VmhMv5ziIYKQvHxDAkTe7QMow4+yr6Ubu9v1mCk1ME6L4UNvytT8ZseY+UK1GYweGwF9sSdvIyKHJUgQTONpXwKsrOK2dh7aFTGckiKkLx5PYBwP+nCOwx68H+kZckgCDAqgaxqGS2ShyvgnyjDG0LTAcsPI0Hsc4AKhGvTr77XVmd1j2MenflcwuyADtPC+8z3id96UVYHVgvY3qYDa6JlnWKwTEDaWXRzv1nSoW7A+sbcykoKpZoem8OuFVZEzqxgm7XZTOQ9fRf2m90WEMGhjg+w/wJmrkEkpZ3y/h7+fctE9hb9vjsSdJjx2ItXXZ7Fi52VXcsADxq4pBFJK6u1deTx+HfY9Q1X08wMOYPHN4NC1Lt4vVs1ceO+8TPqGkhTnbwU4ilZXGZMR6spkBJAlF/7OU2u5pQo6MlRbbPNbVkabw11PIZh+tGH2ewq123KAoeaq13CTttITLv6Aj9s0Ubk7p6nAzAz4uIMOSFZ/7JcJ/8yeETRWtbX4Iu5hK2RHY0bb8e2i7qHDmZ2MgdSSMWQNKhPTiCZ0GZU3JjrDecmPfXMQ43FZtetoW3R/fwM03LNMu1jEmgFx8PxvB3S6Zzy9f1INPtzrPNCJxQHCsKjhhc2axjpvdNBvhHDfLcg1zrg4tp5fESpOjAVv+kxFZg03Ni+OXryF//Z5VDuXFh/mCY3sf58jul0wX2i6WEv3v38B9f8vN5ARO+SlusWXlJc8exW4iT8dgh3NsOo0GZFhmKeSbd33X9eMd8LsvgK++A8y0oWkQnKomNzXr8AdxhMWScZcF3D9/x7/vJIF/exNjEqvz7ys1gdg/e9WCMx/xex3+/eX/3A8z68ib5HkslcWLoDELx+Moj4GuY3oAxFf6oWbMf/j4v50M+//0+n9FVPr9Aey2jsFuAx6R3QmcFPtnsJ7piJ/SeQH0mwCqWKAbJV7MgHgRjF3YLIOkqi1Z5mzv73acwJ+MT8rrljzrBzZ+CDvwYLb1Sez7PaDqDBzuSJ41J/23XGd/aQUQB6TjZe5COSPaJYz6LdYFtVq21bFDAFabXtLOPnbKZbTex7AnydHYTU4b0EvmGb+Vz32d9wkfQvQLNWkNNC1nMXViKLTkm8hg0Fc8ADZIbHLf1LxE7SJTx0rFKaFbJeUR7KgbUpGPwB6rM50HXDg59s95nfeGB3gqDspI1413DrWwgQ8IDqyza2ntcKMcSlIAAIqhWtqcuIFiwx+PYJeS3JSjsNtpX5gA68sJ/6nrzDd84MV02fslsUupGo01R+LEOYSuUd3GtjdKSFOIo0FZ8Lp5RFI5TwkAACAASURBVPoxvlMuMyF7DPtux6HrY7CHrp/BVZ3Kl/Vp6/xzeLOTNF8lnfYH0E//G2ddzbdgTD9AZSiyOD0V9krU8r/CKfkUdqUpHIU9SXrs3v862D/TdeYnPJc1XWxiX9oO/mICpxRhUTe64EmdNZEm48xg1xsQBFCK4zBnPsmy4e3HsN/OeyGkH8OuGjW2zWU0ePznvqwn13mf8DrsHE0KUhqNhabhhrGhh0NlAIAbxmTwPMlZyKxjOeTl8cT6KtF9+zHswPHYtf0vaqLw3LVhfst19id8ZDgCfN2rvF4UFGo7SAo1Cet6HrfOdPLri2hMjPmSjhq/AuvvKezRDv4Y7J1nE2u+jKzJl/Vp67w3vA4+mMucCaGK2bB4boTQeJYcgweQxHE4K/0x96XJQZDuR3/sL7LyDKjrx7GHAOA47JI4QBIE6XjK18/bheO3XOe94e1yJu7Bx0LXcLPrzKnYAEWa9tRagDmxjrqZ3QydORJyzE+4bBjjUeyqVXMU9kQFnIKPb4aX9WnrvP/mRC99QYe5OyoPSJZxxG2s86g2iKxTPJIm/edlFCqSjL6jMhk/4LKcDHqaPI09cUdjp0R2iBKBL+vT15lveJ3sv77UIYze88jEkrDbk+FY5CpudFD6S9nRDIOC3+vVJfPo8Qi4uTgt9jR9GnuWHY1dRoMee5adHvtnvM57wwNUtFU+d6xnW4lxv2cde7cn31yHOZAkbOw0OmCxWHFjWXWk88Dd4jfFjiw9HntVR+yhbX8d7J/pOu8cPoBlvcmIJT67AALkjE/GnO00vydzs04ca9Ydh47NoTqa9qYpx/5Oucwz9RHs2FJJ+CjsVp9XrZtITHtZP3md+YZXkdNBiWDkLyAKIIW1jqKZQKk5ahjHJvBrgidnPuyrOFp36BF1Euh1w8GLR7AjSaJFzo9hNw8o06SUPD8p9s95nfeGz1KqDei/YYKhAOvv4yEtZdba3HGOuohlwcFnOwn3ldbtlcE4nZy80xreXMPdzh/FDudpZHwM9gOZ7UOb+Jf109d55/Btx42w3vIXXeSRPyN6qZOq6bknOg1kg9ixk5mmTGV8oLLWahOVy0613P3yaezA0diD8mdMePXU2D/ndd4nPBCNc5Ek0UHDKhnRxQ7oR/3+BZtQtJ0vucpHd53W4k/8rIv0Gus/gB1AtHH/MezwIQpHvayft857w6tgKHPcViXtuijCFAYFJSqmI56WqlQmg/JgNpQGYWE8hMxXvAACJ69nh8mQg9yPYR/SD/YY7KhoLWkDI2Y5/7J++jrvDd91CHdzoCjiADTtYYSsx716o36cq5Lump+rSl1mXIZ9B6w3dLPb7X8VuTiZr1ileQQ77lWu7xjsopo2u71a0Z+Y6fkZr/Pe8OIgVxfqfVREcc84xd+q8YC67clETXW7DmE6pqajdjHNzQPQ8uSpN32Rc7TvMew2gXUEdogczLompy+pfsbrvC+tmr8Hs11RLyXR7mS4nPaioEapDYENn80uctCRZZAdy5siQguY9rT5cDDJvEewI0uPxh68j9hFnQhf1qet897wAEKRkzQ1KKKEHDrmuyFROm1OngwS9/CymKXcHGnCr9eOpgwHv8oF8CnsURX5COziXN+NBV4urz9jnXlKA15KwX/Dueg3FPYV5N0t8+GqUn0a31d1lE0Z2palwBD49UMVPjpxHszO6BPYByXLi0dghw52yzDrNSRf1iet897wB2k2L2s6HaQ1bf4Plhijfbs6zsFJtJEJux07lFnGdOZXkIuLHdVHsIfN7mjsMhkDu+ZXw/45r/P+20uoqx4lNhLha90sGPOMgqMqZ2c66nDCXN1KgNCafZb20/+nXpMRwmL9OPY0oUPfEdjRsANr01BRtuNl/eR13n9z3pMaa5WWtmVKoDOd0rScDtK2vVg+DETDX34fCjaFjJIeKIvekuZESza7p7FXzfHYB0WPPfiTY/+c13mf8BAdgNb/CiEa94bNjhWPNKUdi04wiXNMB1SeTpyjJ+tqDdzNgeHw1zHXVX7MY9gR/PHY5yvW8IfDfnD9ZX3SOu8TPujlbbOjS0XbxiEJmYz6C9ycnPGw3TMt0GqHiLAsaLJ3WUaTgF9h4ilU1ZPYIwPyJ2J/SWd+3jrvE96x7CiTEclfwbG+fTEFqobc8RD0pE/UJl0JVyWniKj74lkJ0RNUxqOTc8plMia57THso2FUJfhR7INSS5kUZX3Rpfn0dd4bXkDPJKPQ2ojbvu5r0Xo57A3J9ON1zUtf3agbtnZmZ1P6HZ36pBT5xbCH1YZzrrNpz6R8WZ+0zvv96D1d9qw2ndA+JjQ9rVbWWzXj5ZxnCEF15elmF3kpusJi2acOp1zmEPgY9jQ9HrvNwy6Wqm//MvH0qeu8T3gfojNdaBrWtXUcjh6iOXnkLgE+3tNeRihOKsaVaVuENCV5LE2ZC1sb/4Qr3i8ewR5JYkdgl4yiqpKpQ/aLcsEnr/Pe8GZq5pUmq7yUUPdlOxkOmdu6TOXoBnzl2+Yy/9NS2/uJY76cJj/yw3/ekvGIf3gMu3VQj8COLKPXU6JeUS/04E9e553SADwFzRkjsxa7XtrM+nFId2rJMvUe1Tq1c9SAKQqamQG92fBvjd1csI/Abid6MDvJl/XJ67w3vA88GduOG6VpI68E3rMVPyi5IWyjtD0pK2y2kK2ekqsNqyGlcVjqp3/2z1xsKj2BXY2Aj8Ee6iZih8jJsX/O67w3vFY6QttSjqOqI90g1BzbMzGmsFozZQn6kGx3wGzSfytz2TYO/ak7rTrV9Bh2ODkee5FH7KGuXzqtP2Oddw4fAk85NQ6Iv/hoA+mBnXYpSz0tRfr0YcmNFDoPbPexFBnq5qGd+ymg103UoPlh7Kqadgz20PTaOoPBywn/M9Z5n/BQDkxds8m05+bnwLOW7AY8RVnuS8gvr2qmO1kWFYYl1c2jGyeceojCd09j9z8Bu3M99q47PfbPeJ33CW8CqLMp2YV104+6TcdA08JPBnBKqTWeObT7GkYDdjv3SRQtDXUNXExo0X7CFd6+hry7fRS7/ATsYTSAbPfRulJeUppPXue94XXaHyGQSmueSVkG7Ghw4BaBl70kYZrQqFvevqI7nkl5KHELdUMS2WR8Uujy4e5J7GG/hwvHYZelOn5Y7v8y0/rJ67xTGiNQmfqWNXO6jhdC62DqxyRxcbJIbMLogJoLaH3cJSfPg61Z9Bj2aE58BHabzpLxiJWelxz+k9d5n/AhcCZ0r5aT2rXEYADcL5VLvmHrvW2BLAe6ivkxEKX1AKUUJAk3oJP4PU8Gvaqfxt7UCD4chZ18/jo2pUL9wqX51HXeG95O5eEgOtuJ0WfLAmjVul3H4USEqQsQ+eRhNAAWa1Y31NtVZlOeknengy7DkrX0R7CH4PuH4MewF3nvS+uE3/OE2D/ndeYb3gFXF5wIatrIq0GrG3c0BCr993bH/1c3sUuJtoPsa4SLCfPjQQGkE1Y8ktPmwWFYkkfzGPbxiHeUI7CH5ZofSyeQ5Ybf+2V90jrvDd91nFkNIRqChdUauJxBOjUTUK31ONw8KPsuZar8ExMotSoJ0M+anmjJXB24H8NupcUjsAeAzEsAoevwwpX89HXel9ag9fIi5wSQiqICVAVAWUTjANaoPbnjWXbALW+i+RlFSXU29NTQvX8Se2ia47H70GN/US34Weu8N7zQHCwKKKmJgWx2rFkfVD6i0UGeRc55JFuFwPG4IieZ61fQZoyy2I9gj3J5R2AH0GN/oQb/rHXeGx6IfHIA3Li1diI7/0CUKNSqzOV9bzGj4kuhafi1tonaFmGz+W2xz3ufph/FDvz62D/Tdd7vR2UGymAQbWHM3xRty1MPAC6mbO7kpNjKZBzH6tC0gEv6zzX7x7YD7k+IXevlj2HH776gUdkx2IelOnmH3qrnlNg/43XeG950WnY75r9pSiLVkG33mOvOlwhCOb3QeaCh6Ggs+SWuH41LEm44f9rUgAJSyePY333kx47Bvt2zXm+O3i+Np09e573hHTeolCUNDbZ7uuKZ5UuRU31sX3PUrywgpYqSth0veCpSKoMSIWedW5ZrnqgnrsObWsIPYZfxiOJKx2Bv2n6A5W4BmY5f6vCfuM48hxeEzZbT/xVVusQ2+4CyG1I3/PhwyLy3aYG2g5+N1Tpyh7Ba0/xsu6fHqbmKnBT6j2Cv6uOxl3mPXb/3y/q0dd4bPtDIS9ZbTikVByVJq6tXNULT8AIr6gjSNHCrTSRsQdRcDGCKsDm9Pjya9mnsSXI0dpiRQ+KAy+mLPvzPWOe94QH+4oMKiLYdO5dlobyUki37olBVXs/PU69TgGVN0VQm7LjxjMV40iXyJHYrPx6F3YeIHXfzF9WCn7HOe8NrKQ9Jom35VqWmlaPSdlTpMoc78zc13ruZojnHu8BkzK/3/uSqBaGqn8Quzh2NXYZlxE5jhdNi/5zXeW/4xPV2jts9O6tOOBKnS6qGG19PUAAqSZ3GE5afyMsgmoad1uTEm+Zi8iT24P3x2JOkxz4cnB77Z7zOu0rjA0twqptuOotRWz0EtuyVZRjdMra7mCPbCvs9ZDjsbWMWy5NCl131NHZz+DgCOxpSjEPnIW3Xmxi/rJ+8znvDQ8t7ddObfjUtJ/0Bmnwp7VfSJAothTr000HAw0rIxUT1HrPTAg/hSexwx2M3xTQoc/LwDfeyfto67w0fQt90MecO52JKEjZbdjBN1AhQP9TAQQqPXl5PJfnEhqbdiXN4Gyd8DHvd8CJ6DPa2RfAB0rZAnr/4PP2Mdd4bXoTdygnb76FugJmSsHYVZDpmR7MsgDbt50YBoGBLP1iKMB5ycDtxlK07sfqYqOjSo9iHA6Y0x2CfjGiMljheek3G72X95HXeGx56uTuoQ4fVhpvEe4RiAGy2vaWj75i6JJS3Ezshq5obyfs+r65/BVOEp7AbqewY7F3HB6CqIdNJNFh+WT99nXeVRp337M/IMqYEOhkk2z0bMwBTB6c8GeEYXBiqwJFp2JSF1rbzk+vDh7p5EjuA47EPyoj9V9G2/4zXef/NGVEqz6I0RfBezXzVEc9YkOI4I+o98/q64Wid0BUPgzI6a1Dk9MQpTZ49iV0Gg+Oxe99j992LdeXPWOf/N9e26t7RsRljmvG2EgckOSWwAeBqRpcNoE8dfIBAa/bWAbXy5KmWyNPYM9XLOQZ7khxgf6nB/5x13hs+qOfRcsX0oGG7PXj1PdJaNpUASg5OmGjpYfs9eF4AjRb8K3BRQlU/iT0sV9Gj9Uexb7YRu4yGL/Tgn7HOO6Vx0ue3YOOFrfYBmYSmD3/gtg1HA2AR6WdXLVd2Cevfievr3CdaUhZPYhcrRx6Dvesi9rDbnRz757zOe8N78sklz0j6UpJVWK0hOS+BMpvy1BbpL4l20VMpO9N1kcTFbuuph6HNTv4x7EEN1o7BLmkasQOnV1z4nNd5b3inp5uRpdKk16PxnuSqqubmVxk6EWEDx+zanaYN00k0JJCUBmOnXNZFfQy7TCdHY0eW9WYKeol9WZ+2zvuoCOBU0+bAkNclOj5H6KGqyIT0avY1HLA5k2U6EyrMm7c7Pdk1jz61VEcIT2Pfakp2BHbsdqzkvLAkf/Y67xMegadgWfAil6axps78vetl6gDmw6sNL7Rti1BVvZaL8uCD+SydesN33dPY0/Ro7FKWETvEnR77Z7zO/oQPbQdRLkqoap5yRu/tOPsZKuWWJEmvnV4WwC4wpagbAAk32Y5jc/EieyroWgl6DLvx4o/DjojdeDgv69PWeR8VTiBjXtRCR/FR02u0pk4w4lWaMj8OAWGt7fxBSU+ltiPhqm3pipf0U1CnWlIWT2KXsjgauyktSMEH4tTYP+d13ie8CKsaoyHEh9h6R9fxl992TAEqnSyqGyDP+lzXynsD61Ly1BS7/J1yZVqDfwR7qCpeVo/BLuoc/mth/4zXeW94a8Co+3QwG0ilzMKHeOGLzRidaArtAc/cfJMAYE9Ndjls7pxi6dDGY9ilKLjZj8BugqzYB368Pa22/ee8zjulgfA0bBrmuqGvslBclKU+ZBnLezYSaMMXNgqXKGFMaCYmRRG12E+20uRJ7KFujsZuzSj4jnSDU2P/jNd5n/BAPzSR5/3J1nWsc6cJT+rsYDQuS6njovbyIU1IxHIOoch6M7NT17JVG/JR7ObvdAR2yTOEQRH5/C8yHZ++zvuEF/B1rqpbAPrUJHHRAiaagWlTRnYVgg14rDZxPtSGKP4/9t6mR7Jt2w4ac+3P+MqsqnPOe/eKZyQESDRoGPSwaVvCGDrwC7DoICTaCNFCBtFGooNEw3w0ES0LgZAl6NDgw0gPCyQkG1lg4+t77zlVlRmf+2tNGmOutSMvFVG7Iu/Os19oT6mUlZmRWaMyZ6yYa84xx0CejW4qoFl6HbspFHwrdt3NhgiviWmf8MGb9PMzk8Il5rfKelibBqhrTifrhh9PEp6qHz9zLc42i3A48tJ6MC7K2Be/5x1P9kvYTSF4EPbQhgzmCZ+erv/bc1yMaZ/wMF0acUyYxLFHnSS86NnHsSh5agJGuU2s5EmjsJEISWOyWvJrRu7DR0+mC9gp4TEMO4CIXZbl+NjvOKb9kwtcGgvtPFt9zkX3O7E9TwRCmPWs9USreWrD9/W6nk5vMqKPXJoL2LXtBmOPymmn05usJt5zTLuk8UpJirbtfZJanI3YrV3n9eWovutoOpAEESaH4PkqRsSK20Yjhdb1VeyBMTkIu/fs1Jj3q5zdC+b4tph2wpv6ltrEEvueCy6rBbsYXvuOS9uxm9G0vO8+rCFBqNSs3HE4GQ993IQPOvCXsEf3wSHY04TlzeEEKWYC2Wti2gnvO/JGTqe+qwGwPj6e+FZcJGVJmkK21vWoKp6MAE/1X/9oJ6fjqVnvRoWuz1vW6Jew15TZG4R9z20tTRy3o7bjYr/nmHbCQ8g5KUyj8dNz1GeMkhVOaCrwuOHpbVo2yHMuRIuwGxLUusKIf7ECfhwReppC8vwiduQZ9WYGYAfAV4O25f/78WFc7HccE094QA8HSJOxDg6Tyq7jxc/0IdXZCD7PegEj46ZQ4q7hpdA6H9p2NCkYM7xexY5WgU9Pg7DHroyYTuW803pzTDvhnW0AFTmnpV3HU/N4pHy0CNQJsN33Fz7TdQnTTfXeppgZp5fha0fm0kRdyUvYVVmbD8HuOU3W49Eu3XMdf2tMO+GD+bBRAyg9fYAsFtDDkTutifHcrYetTUMNxmAGXBgN13vowU7Gtht98KS7Pdf4LmAHEDtGX8UOxK9RVV7U57gppp3w4tiJAXq1seWSAx3ARvNNb3oQpLMTF9/XoMQbFqXLgt2Tzbj6jLJYXMUuoU4fgv3dhtSCsuCTYebD3xzTTniAxl/O9Vs+QTZPLClUeeFzptKlymUP8zSNSruJydwF9eBP4xOwrmHXw8H04L+OXTrfO3YDvYvhHN8c0560whyo244UWhu4RPkLoJewCJTZLKW1+2rBkuhxbRow1JbUjooBMjYBKzh/X8Aujw/DsddNxA5gfOx3HNNOeAFP8izln3Badx10WfZliS0+Rw75qWKC5xlLgcCObEhFwPEEHXnSCueuYo8xBPvp1GNP0/Gx33FMO+EhPMmbFpqlPJ2dY/ciSyGHU7yQIiNnHM5B3z/wy73npNPMfbl5ZNIYY3NSggPfBez6vB2OPU177G07PvY7jmknvGm7IEsh2z3pBMaHkU/P/LgIa93EsXuhypMxLEjXTSRooes4wHmLS1/TXsUu69Vw7FnWY5/jVTHtS6uA5sNlYS/9HSevdc3T8mkLeVgzWZq2VwFwRsXtPNAGmYuEXRETOR29ly1yHfv+SDvKIdjPzBPmBe7XxbRPeO9tB5R/2MHwwGbFsqTIWe+a12kUIc0zaJGzBRiEl+omshIB9EvWI4UeDr8/7EDErqvF6NjvOaad8Ca2FNbkJPSsPz/3TtxZ2pcPIixXqhq6yNnrDvrsNtbXMLYf+YSXx4fr2BM3GLuulxG7nOp50vqKmHZJE/RcfvspytNF+Yqug342r9U0pTOGrc/Bd5D/+1ekHbiE3JXOs1YuCnJwxi4Nqvo69t2+58J/Dfupgqon9uNxLmteEdNOeAAoCvapV2Y1X9XcAirMHCxI2uV5vzEUODgw8tny7LLnbOJa5MBvR8QtchW7VlU8qb+KPU16KjGMOTkm9juOaZc0Ata8JlMB2PJEkrCXnSSUs3v/EI19kWVRYSx4KSFLe0mMc276qNjl94f9VEXsMmvSvCqmfcJ7T5t2IMpd42isw+cdoo/SQaFBWNWouC92Pw8sA3RPy3ayE0cmjx2PJIJdwg6wizMAe+zXA/GCO8dtMe0TPqgEn4Xkea/vYie1iNgpaTIXaW/0q4FvA0RNeFUdvRcvq+VV7KFsGYJdNuuIXdL0beYIdxrTTnhVXtLOP9R2/OV3pksjEnvYcNz017omOctz8TnYvQffVzmTvxgtgrX8Jex1PRi7Ho4RO02MZ3rwrTH5hI9rbmn6Up9RaAOpZxtBeqB7hmQZ1/+CrLZyiqk2bdXO9yzKsaBbQv9esAMRO3R87Pcc0054CGS9YoJa/Q2wNOHgpmXC6FmJE0SabMsIYAmje9q+a2Mc87EVeF1yHbv6wdgpV1L0dvWzevDNMfGEBzf8nbAmFgddL/nS3zRMnMc1T8Xlwuphx762Kuvi1Gpja2Piu/fR73TUcHIde1kOx/7hMWKXspw5Na+IaXdp1BKzqqnGlWXA7gCslmZK3HEROk2B5y2TKAxzzr9NXdNQrKogXQcFxjf3rRvI+8fL2JuG9jtDsP/0iZIdVo7NcXtMO+Gdi652MfE7H/dYxWwrERQJANJn85z0XDs1JVg+hkXpMPEcMeTMke9L2HGqDOcA7G3b32e8yW3PcVNMvqSR1BYonIsyFXo6cZhjL/0sBXzkyWhpFjdn7Tu1oQ/yrB/+/JzYPzwOxo4i77G7t8F+rzHtEx5g266qqMcYVHaTBPj4mXW5iNFr7ZJ3OAGHE9R3FD06nXiBbBpazUjJduHYJY0pD1zEDgBlOQx74iJ2+A749Dwu9juOaSe8DRTPrdbDSz4ak7Oo615mOjzO8dSEM2m6msxKMSe/oNU+aiQOaC5jD5fXIdiDkGoseea4Oaad8KHuLsu+TXc4xIllGCJpUObqul7Gzt6PtS/AO4E5d49t7htbkZewL0qyIIdgNzNirSpebsc2ZLvjmHbCA3E1LyYrEBNGjye2GE1LXdOU5r7GK0fgp4iQrVg3UcnrTcx9r2Hf7WOp81XsaconQ+jTz3FzTDzhbSkirOTVDfDdI+RUMdkB1sZFHh3wAPTaLmUR7Sm1yPrOSJaOb2pm/JhL2GWxiEJMX8MeXqEkmKPNqgU3x6QTXgsq7GqRk1sC9MJFS+OYB2c+2xjSxAGLAtJ28A9LuM879reTBLJZ8cStG3ZDRoz2H/0l0t88X8ZuqgRDsOuyhGSU5NPNavwn6x3HpBNeqgb46TMvgElCo69w2QykqlPK7aCq4sKFCKkEdQPZHXgaqnK1rm17U+PTaVTs6d/6+6Y3cwH7fg9k+TDsH5/Yucky4IymMMe3x7T78AKeiOsVOShGvtJgv56wBqbCcNGf5GnKz5UFe95dx/F9Wcax/tiLFJIm17EXxXDsm1XEDnHzEsgrYtInPGCipHbBi6t9wgGOLBZcizMarpgSb/RDOlXshDiBHE+9H1TogPyc2APFYAB2bPckvHnlETV3aW6OaSd8SI5gDtaa7EVitpP2dwnUX1Pogiq7IaHFZzuhIkI/1DChHTNS03O/gF1PFduqA7Dr4WRceDr/jY79jmPaJY0ituLoVu2gwZDYCYcywak61LtAv+ARkqaixktM9CJ/YSk5Dna9ih3AYOzx86HzMzb2O45pJzxIwpL1Mq64AYg1rGxWPZ88cTzJ04T96zxjF6elqpd6z0T3yhbhcnwC1lXsthQyBHuUHAzl2Btgv9eYfMLjVJFGcFa3auiwhM1+oLeGCetv4UQHIGuqDOt2Ty6Kc+NzaVSvYtdlORy7SMSuQS5wjpti2gmvGvdWAcS2nmRZzz0Jn69tH/R47Om0wfj3bJgDgKfk6MbEzXXsn7eDsbOzZNhF5sHTK2LaCS+IE8lg8Ct5xvLEBEmxKOP+p+Q5pChoGhbqXcfRvJRnql1B6HRM6GlyFTucDMaOh3XELpv16NjvOabdpVFlWy9xwNMOKPK416mnigvN213sWkQbmaoijyXPeDKGJRHp++GjQ28avrJcwC55Nhz7c9Njf96+jZDUnca0f3LORT13TRxw4kkI1X4JwnuI12hQjGDT7j3H+k0LfdzALwskn7bQIo/iTmOGrJYxcb+EXRMHNMUg7HI4AYkj9pM9Iea4Kaad8KEvvd2zo2HGwgAg230vgZE4SGOPPZ6o4tW2NB+2etf93X/AvdBT1Xc+xoxgaHYBu9Y1S54B2MOrmZg8R1QvmOObY9o1PGzwlCbROl695y88TanFmDhuChl3XkzcSJa20lc3wMenvnToKI8hm/W40JPkKnb5BuzBxEw7H5XI5rgtJn7Ce+5w1nW/0e8E+PAO+umZp16WRR9W7Xwc6uB4imQxSl9I7Oxo2/LCOyb04/E6dsBsLb+OXQ+nF5tTY2O/55j8CS9FTrLXcsEEd46eSXlmpyS1W+LUUqQ/KdO0J2tZi1Mf1jzhQ5tvrDBVhUvYAQzH7iRif9GinOObY9onfEILGKgCR+PUZOmL7ga8p9/pKj9biZNoCiz2Mf3wCPz0mR9/C075w5qXywvYZVmyWzMAO5KE94+6gYafyxw3xbQTPpj72hof0oStvncPwHZvrMKTLXPzIve7bUcx3Rf5+MTkCbqMI7f25FTHcuZL2CNNYAB2rWp+j1PF/0M7c2lujWkfFWHDLXGm0Xgkk3B/nnG/rgAAIABJREFUZMKcE6qaOgosiXP9mD5I2AVZ6vi9R+7SBFnsS9ibZjD2F8JR1rma47aY9gkfnO3SFNjkPDUPB6gIueVpAv3uAfLbz5D1ui8L0hQC0OR3f2RpUZpwk3PQkt9r1MhSiPeXsRc5dF0Ow27fD86UyGZDhJtj2gkfXt69J/cktaXorjOdyBTy64ovBHXdGyQsSmjtYxmj3vNE7WxN7nl8v1PdH3kyX8COqoLsD4Owiwh0ZyVcXc8bT6+IaZc0Tnrbx2VJhS7zOUWS9DIWHa1kooGAcU1UlSYEec7HiYsJhLFX/MriKnYkyWDsWJQRO4DRsd9zTDvhRSIPBXXDIc7+SAkLk5nWznPfs+2o2xi6NsK+uwQHbhG2A72+zeZ/ll7H7txw7MdTxA5gVi14RUw74dUYhkVGhqH1n3V/YIuvzOmMF8zAxAGPG/Jssgz6uCaXpW0BO3ElcZCjueKNil2vYkeaDsbe/fL7HvtyOT72O45p/+QE7LMfrR0XhEeTBHo4Qs4dtjvbAw1Eq1PFPnbXcYnCe/JvworcyC4amjjIFeyoKshqNQh78qsf+378qeqFWef45pj8T06bhszBLCU3Jc+YsF0H3SyhD9xmksKESrsO6DxraPscXbE7+B/eRWFV3ax+Vuz+H/szw7EvFz12J2+C/V5j4glvui51A00T4A+/7zf/v3vP2vhY8VIHsD7/8NhPIpsW/rt3PBnfbSBVy+FOmvDVYcxw7ir25Nefh2NfFBE7HtbjY7/jmHbCe88FEAByOEGe99RidA7yvIO0HYlUpyouQcux4gL0egHpPNxPn9nj/vFTXIaWdnxdGtkfr2LXMh+OPbj5hdW+efB0c0y7hg+TVHPHwPEE+IQEKhMgldLMypo2kq7gPSSYBthCBYCzGr4efetJ2/YqdtnueZEdgF12h35SvD9SnGmOm2LaCQ/YqN1W+cTFyaO2HWCyeVH6Wn1gI/Q8G1XAJfycV8AGNzIylwZBS/ICdkkcuUIDsNMBpOOAyiXjY7/jmPZPzmgFWrIMibW30WZ1WfYdjqbl45oWau8LzIxgUfKkTZNILxh70iqrBdD5y9itPBmCXZ3EMkzzbF7xe0VMO+G9p83LCUYN8Exaay1KcNTOMyZwGOEfT0zyjOt/UUuyadnOXC1Hr4MDteAi9qaNRmZfwy7HpldLTsxzdo6bYtqXVvNw0jJ/4U+qTcN1uaBcYKKkwU0DsK0g45VT96UhhyXPmExjn/B5dhV7tJEfgD06iJgD4Uweuz2mfcI7gTw+kDMTSgQzMZOi6H2aEuOgFDl72Ha66qIgK9IEV5FkcQj0whZyjChySJFfxK4PK8juOAg7AsMyOAOOjf2OY9oJr2CvHTBXvIarb0A8pTVxkMq6NlY2yLJkkntlmeAc/HcPcE97JvtbjOathLqEXaqG7iUDsAOI3ZyxJ8T3HtMuaWDL0GFNr6PktDjHrZ/jiS4h4XEAEydoMQZdx8MRbnvW827eQKZD9Sp2fXoejB1N22O37z3HbTH5hI9tR1XIesmyAOC4XrWfTH54Zzo1TGixjoxmaa9DUxa9s94byNVdwx70dIZg1yKL2GWxmKX2XhHTTnjv+fJv2oykyZoNe9hNbVqWAts9k6tp+XVW58r+CHnYQA9H1soixi8ffwHkGnbJssHYpfM9dtuDneO2mHbCO4lJoAdbjAhiRs5xE2hRQLe7XlcdILmqNYJZ6Ig8brhwkTjK3I188ZOyuIodWTocu8l3IHF8lZgvrTfH9C+tIkyYtoWqi1o00bk61LOmAqCqwE9HTlVNkFSfd2QZigBPwfR35OFNGDBdwE4J77DC+BXsofR5YrLr7OR3c0w74QHjhC/7vjTAvc7VEpIkvfzFcsGOTuKAlgvPaBponrE9+LgBPj+zU6IKycqfFTuatpfe+Br29ZKyJGnKJ8y84ndzTLukAXp5Cuei76kEBYIQZv4bvI+0bnrj38MJyDKyK42KG3QdR41zWZAvYQ+t0SHY2y5ij7Ijc9wU0z7hLTEC+UqKHKiM6Rj2O48dyWEAB1VG2tK2hew6awfyMYKaHZvwsTGhNw13bi9hD//+EOwivaHyG2C/55j8UaHbXf93W3TmO/zFB2VeOGGtXNdkFlaVUW7NasYufOTR+D7Rxgqv17Gn6XDsQZ5jteTldmzsdxzTPuGdY1KYoCjqhtTaNGHdezSpafUAEi44Nw2QOqizpem2i99DwiUS6G3gRwpZs06/hF23O5Y6Q7B7D8lzqggHJbI5boppJzzAWjestJUFsDtQzuJUReowsg2nlt4DDTiaD9tBbUfeyvbABHtckKPyFq29K9ilLAdjR9Oynn9cQD8/Qx4242O/05h2wquSzpumgAcgXHCGk7jZLydAn3bkjJcFL4qt8cuPNpz66TN0syJvpWlZX7+BA0i/xfQF7ElC+b0B2IPcoDQtW5ezbeXNMe2EB3jBC5PF4H90tEllnvVO1V1HurBt9Ev4uiyFZGk/BKpbdjzCpHasCI7aF7Dr8cjNqyHYzUlc67Yvfea4KaZ9aRUAj2dCowDr+s2avemw6R+Ws53rN/pD669uWBqcMySb9qWS8CjY5Sp2edgMxv7/wzo29juOaSe8KiSQvdKErELvufkj7GzIgVzykMRqKrt6OEITF93+pPMUYF2UbyNV17ZXsevhOBi7VnWPfY5XxbRLmmDfbtv/kqbQuqE46ZP5lQZnOxMwksOJjwm2j4cTa+TQyzZ/JCnGPSW1ro0LfwF7oBcMwA4nEEfs7P7M9OBbY9onvLi48aM1Ha0lTaD7A9t0LeUttPOxj80WXsb3DycmT8vFCW0anryJG32RgpaUl7EjSYZj9xqxz0sgr4tpn/BAv4CtHnjakU9Wmj9SkXODqOv4985DO3PW8BrNwF7QaZ282DH9ubDDSb/Q/TXsToAO9rZ7O/x3GNNO+MBotHIEqvxlB95J01gnxKSn7URUOwHFmXJvcBGxsT7H9yNTC45HnuQXsJP264Zhh7n/eB0d973HxEsaieN4bRomxnpJowEnTKigwuVoYhwSBokZhHlPe5mwQL1ZMWlGVu+SPL+OvSwHY0dQEt6sjBYxL4DcGhM/4cEBTtJ7G+nztneybvrLIID+MYF9mHAJWo8nLlCUpdXQWX/SjgW9pST3RezBi3UAdnhlKWQ8eJk3nm6OaZ/wzlzsNmu75DFJJcvYqgsnX+I4jTSeuJQFZLHo6cC5jerFXhW8fxPLm98XdimLHru4mQ//ipj2Ce+4JaSLonfFgzEIVwtABH61gARd9cOJ01fnoKWJrdreqC6KOASS2r0cRI0QullC2vYydlX4zWoQdgCU3BaBeMr3zXFbTDvhu45Jvz+S5RhYgl3HC+uHR8hvP/KkLMvePSPLSC6zZW193tIFxBiLWC0obTdiyKfnq9jFueHYl0vIqSL2shgd+z3HtEsacbYBZEvOoa71HrJcQHZHlgM/fODjvfblS2r8GQB4/8jkMdVe3R95yo4ZZXEVO/97A7F73ysOt+342O84pn3Cex+XI8gv6S+bujtEryP5vO3tKNOUp3+Q4/Me2O7ZA88zjuizLJoVjBW6O1zFjjD9HYLdnjRaBWmSmTx2a0w74QGWAABPQAk9aschDRyVC4LWOsg3kyyjE97pxC2hzrNk2B/6RBo7wpDoIvZkOPZgxFwUtuY39+JvjYknvHFGbLyO1aLfC7WBTXCwhnZnSl+NKQVIf5I7s4YMyS4jV3NtGwluX8KOth2O3UyVg1Sf+plLc2t89bcuIqWI/M8i8r+JyP8hIn/FPv6fisjfEZE/sT9/1j4uIvIfisjfFpG/KSL/9Nn3+ssi8rfsz18ehDAktUlaaNBndNLbN6qdgieToOs6djzStO97O6E8ddO83bTyCvZIdxiCXVyPfS5nXhVDTvgKwF9Q1Z2IZAD+BxH5b+xz/6aq/pe/8/h/AcA/bn/+PID/CMCfF5EPAP4dAH8MHt3/q4j8NVX9dPFfdq43L0jT3l/1YcNOhmrkmciH98DhSM2ZtGDShCWPKJNBu3cN3lAjhqxXTPhL2PMw/R2AXRXA22G/5/jqCa+MsH6f2Z9rr6n/EoD/3L7ufwTwTkR+CeCfB/DXVfWjJflfB/CXrv/j5IKzS9HGU08Px4CNA5rEFIHDLmgwQQg6jYHPAvSydWOf8m13HXtdD8YeI5RIM5/m5hhUyIpIIiJ/AuA3YNL+T/apf9/Klv9ARMKx8w8B+LtnX/737GOXPn45gi27c/GiJoFM1XZMjP2RpKrdnrUvQDYi7ALYdtDjsafXRhXhkakFdXMde9cNxi7hFer81WKOm2JQwqtqp6p/FsAfAfhzIvJPAvi3AfwTAP4ZAB8A/Fu/D0Ai8q+JyN8Qkb9R64m/4CJndwWI7EEpC8j7R/49TWxB2vQbPzy+MC6TsoSkKTnqAI3BinGnlbIsr2IPa3pDsCNJInbJxsd+z/FNrQpV/Qzgvwfwl1T1V1a2VAD+EwB/zh72/wL4M2df9kf2sUsf/91/4z9W1T9W1T/OpeBqXN30L+dZGpeg9XkbVXfJI/ds55n2TNwn7TqTzGDpINv+RB0rdLe/il2cG45dNWIHMDr2e44hXZofROSd/X0B4J8D8H9aXQ4hde9fBvC/25f8NQD/inVr/lkAT6r6KwD/LYC/KCLvReQ9gL9oH/sKAMcOhlFndX/szX1TMwxoO+j+EMsBPRz4Z3+I1NygAqbPO8pW128gdXEFO7tFA7GHcud5RxrxW2C/0xjSpfklgP9MRBLwCfJfqOp/JSL/nYj8AM5L/gTAv26P/68B/IsA/jaAA4B/FQBU9aOI/HsA/hd73L+rqh+v/stJSsnoZQntOgoWdVVcqECaQKoaCuUCRZaRs9JycRoiVCfLEmpUZinw0yfWxI8PwI/f+NP6lvjuPeRYXcQu6xVblUOwJ46+rj99AsRBHlbjYr/j+GrCq+rfBPBPfeHjf+HC4xXAv3Hhc38VwF8djK7r2M1omjikEbOd1O0OAquN1UM7QBIP1D6WAWp8+fN9VgTJ6bHFjD49c3n8Evbo1zQAe+WB3Z59eedmIaZXxPQnrbYAEtynJejBi2O7r27ipU/rxiwirSUIk5+2MT9H/MLPjb01pEzgi9j3B9KCB2Cn9XxL7E0z69K8Iqad8EI9dfWWGFXN5GjbqNiFx03vleRclPWQsuCm06JkwrUtsM6hT0YVHpkPL2UZbWu+iD3ovA/ArnUNedwQ+3Ixd2leEdNOeLVf9mLBnnTQR1cfF5zjCpx1QqJj35occrR9iYDWFALqGjLyCa+nE2v1S9iTZDB2eCWlucjZyZnVg2+OaSf8uVy2CPDde5K/Pj3x80FcaVmaepeL70uQ0wtSdikFkZBnVCEbWVtSVksm7CXsqrdhFxkd+z3HtBPeFH6D4gCOx16cyVkdnGfAxxPpteFyuPO9SZhXoKri53R/GJ8pCURfp4vYw93iW7EDb4P/TmPaPzm10uBMwyUOa4LWjH1OypJJAiA6X5vIEUDeDclZqY38Ry5pbJB0EXu4SA/B3rY99sD8nOOmmPYJ74TeTGsrD4I/q3PsVb9/gNQNF5y9UvO961geBPe8NGF5EaaTllhxnW6koC6NXsZerCBtNww7wPeNSzOn++0x7YQ3Oxvd7iK3XDLEJWg6anS2r9r2XJnw0q8KQdlzzQGWCWcirGOFHo0VeQl7VfFUH4LdJTQy80YxmKX2bo5plzR2lklYhjiza9S2ZYkQat/FomcalqWVEKZBk6bUd8lzW6nzb2Bb6a5iR5IMxh5V1hJn94CJ/9omHJP/ybFnnvFlHuCam/FokCZ0zRDHrf7w8t+27GXbdn/o5aMsorlvdNAbK5xcx951g7EDiNjJrpx14m+NaSe8k6jnronrd0Dzs2WOnUnnvdtwQrkoIy8l+iQlCQc2ZoQmi5ICSSOGlMVV7FKWg7F3/8gvInZtu9Gx33NMO+GVvHI9VbSD6Tpqre8PRrntomQdPm95knoPXdPSUtuWHZEja/io97jb9wk1VnT+KnY9nQZjT/6vX0XsAMbHfscx7Uur+apKbs4dypJAvXB6GvglYbfVez4+cFfsVNe2o317YtLUq2Xv5D1WZCm1cy5gl82aiT0Eu4m/RlntWUz15pj4CU8hJl2WrF0XCyZAlkFXC7b+zvvTVQ38wQdb+OiXKyRxvdRdkKQemUsDMz24hB2najD24PXERXQ3PvY7jsn/5MQ56KcnqDj2qm1gI9t9ZA5Sz8XzMvibj/RFqmugRn+SP295MubZm01br2I/HoGiGIRdtnuWOGbTOTuA3B7TTnivHM3LmQZN20JDO88SJr7Mty13STvP99OEbnkiwHrJlUAR6Bske7S1uYS9LF+WKFewq8l9xL/PcXNMO+GTBPjwjmSq0GExMzD6IDm+5Fc1ueRJbnzzvsaV5SL6tEpJy0pJE+jDmhoMI4W8e4CmyUXsCh2MXZa9OTHUk4g2IvZ7jmknvO+AHz8BywXbemGpGYhSdLJaAl1NqYswercFCa1rSGu27c87lkAu4Un68WlU6HHCegF71MkZgF23e/4snDEoR8Z+zzHtS6s4nnLGMWGZ0E8ww+ckcdBf/MCECeZlIlzy9p4KB0rbGCmLXsJ6TOhleRW7VvVg7LJZ9dhFRsd+zzHtEx625ta29HNaLKKctAbpulAT/+o3/ZaRPSZoMwbFXQ2SF8CbiBl9DbvqMOz6bAYISg153Y8r9X3PMe2EF/BSZxYxwdEaZcFd1z/8njX5p2euvYUe9sJ2Qzcrul67hHW7jfjFuOq4rGr5+kjcVewoS9riDMG+XpJVCUp1yPt342K/45h4wtu2kMnV6XrJS2BVk1vytOslLUKNbNwTLXNOJNOUX5+lkFPNrk+WQccWJI0Gw1/GjqYlvgHY9UDP17BMonNJc3NMO+GtD63bPbsTe0DNvTrqvJc5RGhyoN4D+yNl+NreS0nbFhL2Rb2N838a+YgMS9sXsItzTOwh2JekG0T7yrGx33FMO+FVI68cQGQ6Kmyo4z1Nw4LRcNsyIQ6HyCEPfW+taybMamkXxnH72WGgdBH7qYIAw7Bbx0dWS/48ZkOEm2PaCS9CIdI869UHzjRlZLUEqhqyKYHDETgXHE0SwBVs74WyJkl4ifzwSFWA67pnr4Meyo5L2B/W7LEPwb5ccNHbLulSFqNiv+eYdsIDkYQVTksSsEIJcGCJUFW2IQROL1OjG3izkrFFaD2d+AT66dP4E8swLb2E/VQNxo5APygKPu55d/WfnuNyTDzhqZ0uaQoUJXUaf/zc67KUBXRRQD4+cXJ6OEFSkE+OGkgLlg6LBfRhBXnasW4GeOKPeUqajc1F7IuSWAZgj+oNZQ58fuZiyXzC3xTTTnhVbgQ1DS+BJiWtTUPy1+dn4FmgIsBvfmTNmySUxPAKoDIVryNwONAM7Hjk4wNrcizodQ245DL2j59Y0w/BHkzQ4p7sbEx8a0x70hp2WvMc8rCGJI4nZMK+uhQ5T0CTugBg2/7cA+1d8hTysOnNxMRB1utxobvkKnYEg4Yh2IOsiAQVh5Gx33FM+4QHYpLAa6xxAeq+ACDt9nENVDbYUY0yfLA2oDw+0KM1M8+kJBl/icIJ1QkuYJeiINYh2J3w/2lu4vMCyO0x7RPe2ndRSyZxrHc3K56cmTlrV80LkwA9VS8FR+vmBc02PmbEkOXiKnY9nYZjP9U9dt/NUnuviGknPAD88gcmQJbylAwdiizlJe7dA3SzpDdSSyVefP+OjzEvJ10tbOs/4ZR1WfJkHTNUr2LHL34Yjr1tI3aIjI/9jmPaJY0q5HkfSVVU4m15WS0K4HDkMGa755IHwL3QYBgQjMOa1rjk5pgXljPGjKa9jn27pxrwAOzaeb5C7PZc8dvO5LFbY/InPJW33EsnvHcPZiCw7BmIVuNK4vqTsKNMdfBI4iArf8lHHytMmeAi9jwbjF2C12yRR9XhOW6LySd8/OUGR2rnIKb3EkyG5WzpOXY5nIvL0JKbJ1S0ikxHb0u+SMwvYA+KC0Owa1W9dP0YG/sdx8RLGs+uhPKEfnGyKV09dL2EVA3E2/uqQIPILpRF2SsEmPWjHk4veC6jQK9obXMJu2Q2eBqAXYqit9xcLnp+/BzfHBM/4XttydjGgwmS2vsSbNhNn0bKgjWx91FXHUHezrjokib958aKs1eTL2LvusHYtW0jdr8sx8d+xzHthLd7Jc0BfM9/SWhDyV61j2txAC9+vW680FZGlV2PhlIX6v3oxmDRZv4S9rIYjF2ci9jl1z/NpmaviGkfFQpeMM3cSwAmynYHNP1lVNKEgkfeR9cNWS44ufTKzkZNI2AJJmNjO3Eb7fcSdniWa4Owi/Dj3vdmbnPcFBM/4SW62+nhYPqKVO+C95A8M1WxlK3HlBbuIibFF2xl7Huh616s040KvcivYtemGY49cRH7C637Ob45pp3woKkXCmq7k2bg+rIkSM41DXvU5pYXHfuCTHVlYk6lCR0lLspRjxbhyXUJeyhLhmBv2ohd0nR87Hcc0y5pAAqROull51CTi5Ln0fYxSNVpMClO097xOrjpAdHlGkA/4PnZsB+pgDYAu1a9ZX2QCJ/jtpj4CS89CSuEo1nxOUdFypL1uem/aN1AD8feMe9w7CetoeMx9sXPjMsuYZcsG4w9EMu0bakhP19ab45pn/BheGPiQ7o/MAnixDGNm0OSkXsiVU1lYavTNXFcp3MOqE32o2lHd7PWtr2KHc87PhmGYPfs9EiRcx1wOZc0t8a0Ex5KWq+91McJ6cdj7GUDYAJ4z9IlTfmSb+09MYkOHE+8KJZmFDb2tDLQAi5g17blNHUIdqUFZhBgxX4uaW6N6Zc0v/iep/NZv/oFn70seLGz+jZoNDJRlN0P82gNe6YQaxeOGS65il0Wi8HYQ18+aNyPjv2OY+IJD8jWTrM8o7+Rabogz1jPhstna1o1duprlvYDnTzrjQdAdS89jtzec3IVe1z9G4AdzvXYV2+A/Y5j2iWNoD/pmjayBbWqYv0bNoNiBCmOj5+h7x5IwzXNda3rqPMoI9fBEghjF7BHesAA7LoogE9PNDcOBm1z3BQTP+Fp2gsgvrRrba4fVgNr3bBcSRIbKtmTQBzkVHMrqjEXa5ew9nfSi6qOFaYadhG7yXEMwS6nusd+Lgg7xzfHtBM+dGkCDaAsOF11Dnjc8Bf/4ZF/d+5Fvz2cqEhc74HqjKNuw5xRoYcuzQXs+rwbjj1xPfYkGR37Pce0E17Adl6exU4LYEvQ2z01XHYHlgC7fV8enC9Etx306ZmPPTcncOP+18W569gTNxx75yN2eQPs9xzT/smJcKtpt+8vauHiB04y9WC6LWZJSZbhsX+bJiwNThVH9mXxwvN0tEjTq9ihOhx7VUfseIvllTuOaV9aAZY1S5YkcVtfjYuinoOZqoYqR/jqfTwlpSxZUgRTs6cdv1bc+KekE6D1F7FLnveufF/D7sQUEHz/uTluimknvLEGKURkCeoEQBIvbxqGMFbva2ferocjl6RDH/spLFu80Yta3UT675ewK9APkL6CXdIUCrVXg1mT5jUx7YS30w3Wu5bgRp0mUK+QhS1Hty30YQ39+ETKbXjpD3RbR3Zk0ICRt6iBg1/TBezYrCBVMwg7bBtK6/qFzc8c3x7TruFhiruLApo4Dpqc9HJ1ALSwwczTlu58WdbbP5ZFn3DHijVxmGi+gZv1NexyrAZjR5pG7FKWsxP3K2LaPzlvXJpzWq9z5JUYXVZ2B/a1k4TJ4s3x+niEKH1ZUds20XbXS9g14zIOta65P3sBu7YtZNcNwq5tB4SWZN1gLmpuj2knPJTS0EAcvWtdk09ui876sGab73d5KE3br8blVP+SIudFMMvG3xzquuvYnYNuVoOwy3rJU782HZtqltq7Naad8M7RpzRISWcZ0Pm4RIHERT8kdKx5tWl6v1PnmHSVmf/a6F4WC8hmDfx2POjk61zBbooLQ7DjeOIFtm05cR0Z+z3HtGt4Rc+M/J3FZTraeVreLBbsedd1rJvPlXhVFbJZk8NifqjRFG007Hode+IGY8dy0WMHxsd+xzHthBdT4fXerB4pOydlwRq+NdmNRcFy4Lv3HFadal4SvWd58O6hH8cH76U3sLy5ir1uBmOPrdc84+X2DZbQ7zWmXdKoQneHqK1IUdGEVjChtXiquPXfdZTACKKl5mYdBEwD3UqOJ14Yx8betsR5Abu2HWQgdsAoCWGJJR93W+ueY9on/Hm0Rqpyjsl+ttup5ZnIaNPwRF2U/HuecWFis+Ilt2n4uLfsZX8BuyzLwdhFpMe+WMx9+FfEtH9ySkWC4HkqCzMZcEL7+KDH+LTDC8Ls/gh4W5kL7UhVIM/7Ef6Z/+so0BuyJC9iB0/tQdjFmQSfRsXhOW6LaSd8kkTfUi1zJoNjbQxVthbfPdAho6F8NuqGIqX7I4WLElu2EGFZYOSx0SWn3z+SDXkBO04VX3WGYF+UpBsEtYZZLvvmmHZJo3aiBRpwCH/WndkfoZUZ+FY1Bzv7Y6/hcqzivqgsSuvfJ6OXBVI3V7EjTYdjP54idu38XNK8Iiaf8LJYkAYcOCT+jDAGxAQO20WyWXMymdAUmCUFTcXiNDP0tMeMurmKXZtmOPa27bH7N6A233FMPuHhvVk+prG1B4BJEazZAyOy68gxV8/d0cTFx8Jk7CRwz8eG3jRXsQcW5CDs9v0kcdEOc47bYtoJ75LeZrIsmKiBONV1HNOvFv2yhU0sWbM77omaIKmsluaLatYyY288bdbXsa9Xg7EjSXrs1u2Z47aY9k8uaLmIQPdHcko6zwQxaQvZ7qFV3fe8bYwvy5Jfb55KQF9G6HrZD6BGCg3JfQG75tlg7LIoI/YwbJvjtpj27Uc9+ShBffds8CJlQdnpoPfeknBFMzGSqyRNONj8HVsBAAAgAElEQVRJjJOTZaQZBIbliCFVfRW7fvwMTZJh2LMUcqJLiHaeQ6o5boppn/CK2E4MfW2kKd8/HskabFtyU8Jk8syOUk2CmvqOeeyIjJ3sAKhCcAU72nYwdnze9t2ckb2p7j0mfsLbSOZhTTGl44kDmCThECmcfocT/16wVJFgz951rHft70HASBYLLl+MyDiMVpmXsAdDtgHYo9Ja+Jm8f5zZkjfGtBPeCaeMbce2XJKwTFivaOKbOJYO3kf9F0lcr0YWxEjtMXEBY7MmSWvEkLJgbX4Ju/Xnh2APl9TA4R8b+z3HtBMerGUhQmbh4dQ7YazY48bjmssd20NfC2cpqbVOmHBpStU+o+AiWMyMGU6uYodJYw/BrjUTPHKAkmlXolOOif/khEmhGl00tG76k261oCTd7shTMRgdNC2HVqf6TJU3ebG8PbptjFzHjiQZjv1x02vxNM1sefOKmHbCq/aj9rY1yQu72LUdBY4SR5+kU8VkMcv2kDg4VXx7PLF8AFjPH0dek2var2A/DsYupjzMr23Hx37HMe2ET5No/YJAjw3S0eFi56n9LssFT1Dvez+kIFNto31xvcLX2LaV/v3DdewuGYxdt/see5qOjv2eY9oJ33aUmeu6vuYOjtq2tofAbxeBrJe9jHaeme66OWnYk0eW5UtDhZHCWSvxIvY8G479w2PELtavn+O2mHbCA1FmTncHjtxD8qhCP33mXxvWxvH9toN+fo6ydFrX0O2OCXj2+FFD9Tr28+nvV7AHVmX81rNX680x7S6NE05HlyWkackg3B2iCJO8e4SmCbsZoX+dJuzIpL0tveQbcstPNbTIoe/WkP1p3D78sqTY0iXsm1Xfifka9iC9V+Ss6989zH34G2PaCQ/03kahVx1G8cFRe3/kxlAQKfX8L0nbGaXgxMXpwIl/3gG6Gr+Xfe4p9QXsfllCfv3Tt2NPU2Duw98c0y5prJ2Hnz7zl1/kvQpvw9E83j8AsB611zi00SX9nyTLem2XsBL4vBudUx47KRewy69+Mxi7dj5i12U58+FfEZM/4fVUcUmirqFdR1pA8GoqCuBpSx/UA1fo1AmkaYCfePLTJkp73rmpB2s7cqfDiGAXsec5dCh2oMf+8TPFWOe4KaZ9wkNZ8yYJFbeCsYF5IemJq2/wnipkD5ueryKOTwiAPHMRvv+45lJ1WYwLveuuYgcwGDuAiF0Wi/Gx33H86TjhV0tShRuaGQT1MAVILwDIJDxVL5mHJlENgO9XlZkMUBfmZ8Ve19HXaQh2Nezz2f66mHbCi0DePVDXZbNmX954JerprqFF1i9J56boZTbuCHzzIHtno33JsvGXKExl7BJ2ADyxB2AnH96WvNOUF/Y5boqJlzSA2vIGmjYuVGiZ97yY7Z4JkGW9HuPhBCwXvOjtj2xF7g/9EkjTvFgEHyW8/t6w42n3ttjvOKZ9wiuApx3LgMRxr7NtIWYsQAMwo9Si44AH4BTTe2Cz4tcfKybUctn3upuRL60mEHUJe1AxGIQ9TYC04Nu2m5dAXhHTPuETx6VlYx7qqeJoHmC5U5bA45pCR0kCKXLIZgVkKS3bAeD7d+xpP6z5vomqRu32kUIf19exOzcYe4yGJLSxsd9zTPuEb7venuY3P/FjddMbAqj2zhirJTsgNfj2aPXuqeap/ryj4lhhhgRjX1p/87H3aL2E3azkv4YdZcEpbVAseIML973GtBPeuhOSZ9F2XduWnY8ge9G0lJwOZYsNbyRMYoPSV1lAjieWFYGxOGJIWVjdfgG7lSdDsKvp20RDs5k8dnNMu6Qx5TEA/CUvzqaMquxqtC0np+cCTW0bRZzUBJhwqvj1Jnkx+saTOY1cxF7Vg7FL+PpF2dMV5rgppp3wQOxyQJVKui5hlyIsNie2yRTYh2XOcqAz25iwSZSmLAWOp37N7+fEniQ3YeekdqYW3BrTTnjhEjescyFpSjKV0WbDogSAKCMtTUu6bVXHVwEJA52zC6AuRp5WBgWxS9jTZDB2PZ0i9miLM8dNMe0aPkjLhRNNhHVtmkAk75ed0xQwfRfN0ihLhzTpL71hZS7L2P0ZW18ySyHBke9L2DvPodkQ7KsFS58si/eCOW6LaSe8137g0vVKu2Iud7o/RIqBhGRAf2LSBDiNDn4i0vfux44gf30R+x44poOwBy37gF23+/Hx32lMu6QxTrl2nh2b1nZYN+xDiwh70uKiHaRW5m2aZUxwe/kXZ7buluxjn5JBNewSdiTJYOzadj12380n/Cti2gkP8AJnRl9xW2i7Zwej89CnLSePXqNRgJ4qUnKD6gGoSYMTpfmgnpTcnxE7vA7GDvU9dpe8DfY7jWmXNCJUGwiKXN738tfB9mW9jAMd3W4hKztBO8cnBUit1bphabFeURojGZd3KGkK9XoZe2ZDqSHY7ckt6xUntvMJf3NMPuEldDO8xm5HWJvT1k7OrmP3I4qWUh6Dm0S2G7pesq232/dMxjEjS1mvX8AexKGGYMdmRVm+3Z4X7nI5LvY7jmmXNHbBQ93wdCx52gULdwA99VeVA5qui5NIresoqY1wwj+sLQHHPeF1f/y9YZf9MWKP64Bz3BTTTngRJsmiJG32eOLlLpyGvmM3pG56O3aRXrkLxpsP30c9NR6X5Qtp6lGgF/l17GFfdQB2PimIXdtudOz3HNMuaVSBqur13FPWwkGdS4M0nbj+tEQvVR3qZ62quDrHzad6dI14rWqgqS9jPxz6wdrXsIf/Y9iEmvnwN8e0Ez5xvMiFhYmmPw11s4o9bbiQNI7qvOslR/GOknpSFFy0aFp+fZFDum5cbZd3G2q/X8IehFKHYPcmsed9HJzNujS3xbQT3hS6JE1IojIHDQDA07ZnDrat6ap7c8qwroi3sgCgPmOa8OtPp/F9Wk/1Vexql9Uh2MP6n9rFN8p9zPHNMe0aXsGTzrl+5H4eIYHOIuyRIuhJevJtJM9iTSx5Pr59u0ntXcL+JSfwS9hjsov0NOI5boppn/BQJiesa9F10YoeXRelqLXz1HqxE1O975NCXG8UJkJL98Mp1syjhdlqXsTu3GDsQWYEtps7OvY7jmknfLjQZSkEOetg59jpeFjT5U5Mn9FJ3BGVRUmq7fZgFz3PMf3hxBM01NdjRlnwznANe/B0+gp2APbktjJI57bkrTHthIf2DtQA25SLAtIkwO7ApDoycbVtbbCTsl4PUnzW58bnLVmHp4qL0WNvDbXdVeyxRBmAXduWok0nwz2f8DfH5Gt4yXMOb7znZPVYxZo27nYaEUuDtc1qQfpAcNpIEybeqTIR1m78U1L1KvYo1TEEe0j2Iudlez7hb45pJ3zi4pTynD8SPY5Cm66j5HS8CAanjMd1zz8JRgJVzVp4WY4KXR9W17EDw7EDPfamGR37Pce0E16VCRC2f1YLDm/2R5YAIkyA8BgRYyBa3R8kObKM+6Bpyse23egLIHKqr2N3bjB23R977Gk6/vLKHce0a/jOXKsBXtaCJ2tQ0m1qyPt3rOeLHPrxE5AFXnnGjkZY9rDvQ1XfEzDytFK3e1IfLmH3HeRhMww7ECezWtWjY7/nmHbCQ6m8a36n2nZU/93ueUomzjRcEl72yrLXXTxjIYpRcfVwJK+m85ByZIqt+qvYtcZg7FpxBTCqF8yDp5tj2iUNpJe2sLdSWRLUFDFS2/AHqKWuJ1MI7jxbgCZ7obsDly7MQnJ09eAkuY7dyWDsACJ23e7eRPn4XmPaJ7wI9B/+BXCsIVXdS1p4zzU3EZ6ax4rCR4X1tU2cKWwZaZpAsiBJnXOK+a4AfjMi9CyD/8V3l7GnCTRLB2FHbe3NNOdw6v3DqNjvOaad8ADk7/8YOTA0FTCabdtS9sI4K1BPodKOdbKc6tjLlsY8oRYlh0FVjdEJtqrXsXsP6fww7JuVSfOZMNNsTHxzTLyk0ainKDlPZlkuoU0D/0c/cDqpCtmsIKulUQYce9pt24uXpkkvl/FW63HeX8cevWYHYLfhGjlBszb8a2LiCS+xf67HI/vQdlq6/+c35KiIQJ+3PLWXJVuOuYmOWldEd/uemru3Ce1b1PBXsAerykHYLXR/eOEcMse3x7RLmsAZz1JIsmSCFDnkVEGKHP5xBXk+8HOdh+YZqcCnip0Zk+OLLMSWztaaOIiMfNKb0Osl7IEHPwg7ELEjz+cl7lfEtBNefd9OTFP2sPdHqJGtXFigMHVgfN5SJXhRQo8nEraAmDRa1/z7dj++V5LpwV/CLonjnWII9kBHKAv28+c+/M0x7ZJGQTbkcsFLp0h05wbQ27on5ozxw3uWAk1LQaM0fbEwIc4x2ZwjP2VM6N5fxQ6Rwdh1tYjYtWlGx37PMe0THuhr790BwbGafHIucuipijur8rTrVQpMB16VCgXnSf8mdbBJc1zCHrafhmCX/ZHks9Cz389CTLfGtBNeAN3ueJFT35+OSQJJaPgriYN2sJd9jYvOcElMmGjt7hwTLPgrjQk9Ta5iZ9tRBmLPWd9XczvytTHthFdEUwGx1bawIYQksWRSSGKrcKbtot4MjVWBgh8PlpGSppZM457w6v117MsFy5cB2EOQTkFD5jlui2nX8LA2oi1zI017d2uAJUBVm9Fv0y9NOEr0xQUKI46Fuvpcy3G08Hode9MOx940EbsG25s5boqJJ7zSmDjPIMuSCeLOIDtHZqEp9MbTL/BngsiRSWVLcA1RP/4AR/117MBg7Nr5iF3CIGuOm2LyR4V++mynY0r1rbZlLW46L3o62andJ5OUlmC+g7bgW0f1LlHlcvRufI31a9gVMIGlr2NHmrI7o0ols8/Po2O/15h2wjtHvjvAmnZPaWntmujEJw8bXvqWZT+WB2xVru03ndoWcFnk4MBnwMfxoMti0ZuPfQk7AHwL9jRlOXM4sv4fEfs9x7QTHsIBzhnVVnImLa0cE6uFK6qQJUk/ljcOeajzpSzixTUqfo0ZYYf2EnbTkRyEvTjX03wD7HccE//JKbnf1uGQxLG27Tp2W5qGp3wYRAUpjNDjrk39K+E6XRzmBMvIMaPzvzfseoZdinx87Hcc0z7hbXeTyeF6ryRvil5nnk1S5DZU6uXtIC62+LRtX/ikqh+XIKy7fay9v4Q9+FcNwQ6vfQ9+luh4VUw74c3qUdfWs94fKYTadqTSPrC+l+c9/OMa7vOWCyGdhyxK+HUJ+bilKGuWwX/YwP30HE0HRo0fPkCO1UXs+P49uy5DsDsH/7Ak9rYFlouv//tzfDGmnfCmHyOnGpo4lgjtsRcoDQStUwXXtjz1z5YjxEyA9XSCFAXcr+2m17aUyBgznrZAll3G3pBNOQQ7xMEdz4ZNszHxzTHthIey3g6KAy6JPBQJMtmnyhamrRY2HkooX7Tt6JNk7UtZrwAzDhs12pZ0gUvYu86e0AOw1w0/t16ZicLI2O84Jn5pxQu3C0mTXqC07XqjsiwzZzxz+2gadjeMoRjqX1lQak+7M8HSscL+7UvYg6XlN2O3i/Act8W0T3gxc6+cYqTaNJw0Wg8eTng5VHvehrq8bdmrBnrTBIDfQzVuF40KvTQK7yXsAB24h2A3DU1VksmkmK3nb41pJ7yqTVQVslpAKiE1IPS3XQJ5fOAGU0cueaDWBl31SMxyjkMboxSLjNylOeubfwm7Ns1w7IZV8pyCTPMJf3NMO+EFrGfNxFeKnBtEpgCAo1F8XcJRvdnEIE0hrW34ByMxk7OjfF/H03JM6IF3fwl727KUGYJdHGt91b7VOcdNMe0aXhWyWEAeN2xPWvKHkIdNz0IEeJq+f2RyAMD7Rz4uyyBlybfOQYqiLznGgl7X17GvVoOxx/+Dcxw+jYz9nmPaJ7xLgMTBv1tDjjXkecePd10cwMjCtv1t95Mf6/vUulqwZGg7lgjHE/dklyXw98aDLg8bwPvL2LOU4lBDsJvhGY4nvjp9eBwV+z3HtBMeAJyDe9qzxi0L1rynmuJEla3qZSk/HjRc8gxaFnT5CKUMEKUx0JrY0ZhhfJ2L2I+VyX1/Hbt4Es8g1tYcG/sdx7RLGtg+aKi9TxWwJa1X6oZSdV0H3R85tKlq0mdPFSQ4YJ+xEAH0bb03MBW4hp2n9kDsVgpp0JGfDRFujmmf8L4jL/zp2body54L3zSk1jrpJbB90JP3FDjy2rMkTbQJbQvVtB8IjRRsl+pV7BiIXc64QOo7YDd3aW6NaSc8rJ8dHO9OJ55woQ520ltC2ng+CBxpmVN/pq57BQAgTi5lswE+/YzY86zvyX8Fe/B1kqLo7SxHxn6vMe2EFwc9HM3XiEvReqriVpA8bKIERpShDmEmv+qSaP8ui4W56cno5r5SFNexpwlpEwOwS2aakk1jtpvjb2vda0w84QF53PCEO1UczJRF/9Z7yGZFEtaGknVyOEGPR8hmTZnpnCa/al+vD2vIdg8sRt4LTZMB2MtB2OE1YsdPn/j5OW6KaSc8QN+j797Z4kPHX3zTRLlr9Wbc++m5n2D+rvN120VfJdkfo/T0z459fxiEHUIlA9kfOaSa2ZI3x7QT3oYx+rSDOqHuYmV1bZGzw5H0yxWhe0EnDdtqcmGvlJPKXqZvXG0XPZyuY9/uWMoMwB63nqy0mXVpbo+JtyXNAS9NoD98gO72/WSyabkj+v6BJUKQpwaouS4CWS6NpZgBf/AdZLnk14qMXxaov459uRyMnYS3pX1bP5c0r4hpJ7yz9bz1Eu5w4sXPdj5hPXg0bWzvoeuitUzoyqgNpuTjk6l5mbnvyBtPsl5dxU6BpWHYg/EDbBVwXuK+PaZd0oRJ48FG6m1LIlWWcBGi88BPn6F5Fu0gJWEbMEhbAGf6lEBvBzm2E57V3pewI0kGYw+lje4P7PJs5y7NrTHxhD9zs3MJgr9p4Jb/bkjiePmrKrIUbbgjScbT0vszbclxp5V6OvUSHV/C/jvKA9ewA4jKBWHlb47bYuI/OQHOFHMlTfr6NWzv20kYmJPc/E9JFHOU1wsyGXHoM3KyE5C7jt06L0OwA4jYZexd3DuPiSc8+FK/XvFyl6YsBwDWvHkGSUxr0T4uRc4keVjHeleWJbAo44BHipz9+zHDyXXsi8Vw7Jbo3G3txsd+xzHthHfmoBEMDcIIHrZRlFmdezz2k0o1uenPW+hqYfqTZvi7KNmx6bpoVjBWSJ5fx564wdj1HLuT0bHfc0w74b35mDYNFXgtCQBE9QGAlAFZL61MSEwz3pGPsl6yTPBGyrJF6bEvrTGRL2APLh6DsAMRO9p2tp5/RUz70gphH7uumTi5tRTrml2aqo57onEAlTh2RFRJ3tod4oUPsC6Nk8ivGQ+6u4qdisYyCDutckKXpphX/F4R0z7hgUi4kuXSdjqtRdc0kOXC+OE+sie1qnu1gzPFAlXlat+DXRzfoNNxDXvUlRyAHb7HTm/ayf/aJhvT/8nl9stvqEETVuEgLta+slryMpplUWxUz1wz4qXvcGQ9LcLL4M+IXdar4diXZY/dJW+D/U5j2gnvBNiseCIuyjiaF+cofREMetuOdXCe8bT87h3kYRP9WcU5aJlzSdpbuTC2Ls2yvIo91uEDsEMkYseHx9mJ+xUx7YRXQI4VFx/MrxTeMzmalm9NtUt3B37Me8juwMeZPDXyjOt1hyNLByspRo22u449TYdjb9qIXfbH8bHfcUz70ioSW49BOpsCo2bPXje9ekGZRvdqLC250jS2/wBQ3DTU0mN3OrKzH+0XsJ9fpL+KXSRi14pWnXPcFtNOeJxZywOQ6ne6EyaoJEa+wlknRoLjXdBfz7Kox44KsWQYLToqElzCLo4dmSHYtWki3wZOxlc+vuOYdsJ7pXRF2NL33roUlM4LbUk1NbFwkkrYcEo4qYwu3JlxatSPvuKnpwpiLcYvYm/NkXsAdrETPqgWjI39nmPaCW+LD/jwLtauUX4uTSHvS9a2WRaHNpGl+M7EjYTLFxCh1F2eGbV45Gnluw3009Nl7KmVMUOwnyry4HPK9J2LNc3xbTHthFcF3j0An545pAkEqtWSJ19YhDZTX3FkHCIyLF2/WhesY+qmNxEbMaRuoNewLxeU8hiAPfBwIrls1qW5OaZ9+/EK/PQZAF5qKgZz3uMJUhbx8ge71GFRRqptHPIIp7ZhXU6rcdW79FRdxa7P28HYg8pBmMiOjf2eY9oJD7CWDZ5MZuwbHDyiDAY4wIENb/Rw5PZ/UfTbQlkWN47IrXmDXvYV7HDJcOzmHAKAk9m3wH6nMe2ETyhMhLLgL73jxS8m7vtHdi2WZuSrGk3A5GHDLkia8OsTc/1bczd07L1QWS2vYy/ywdglyyJ2ybJ5p/UVMe2E9xp5KIE4FVUIAJOpq+iOcd65KHKT2i7j4yKzsmlH14YHQDGlK9i1aYZjD0JSTTsTx14Z0760CqjNaBYv5JJIHNycD3SgTRzKaFX3Q5skgZgDXrzgSt/bHy2cu4797HFfxR6e2KHNOdfwN8e0Ex6AfP+BctKHE/z7B7ifPnNjaLVk96PIgMq6FwUXtWW54OcsNEkgVc2PZynkafsmm//XsMN7aJoMwg5P71bNUi6FzwsgN8e0E165+CCbNZClcD9+Im+8sA6Nc9AFa2QuTJusxYHqYpKzPAjUXH16plxG22J0NorqVezaNGy5DsHuHPRwIPbjcd5rfUVMO+GdsJ13Ji0XBkda1ZBVDtkdouCoeg/pOlJxzdUadQOtKggKyOMDT9iHzejGYFrmfFJdwr5YcMFjAHa8e4Bkhj3PSSib46aYdsJ75YlWlkyM4EzdmurY8RTd+OIwp23ZunvaUdIuDGuc47aTV7b/Rpark2PFqegF7C9MGr6CXbd7akp6tiixP4yK/Z5j2gmv3qwauf+pacqxfLj41Q0TIU0BbaH7PZDlPB0TW+cLrndNwy6J70giG/uEr6rr2KuKl+ch2EWirg08ZtvKV8S0Ez5J2Kl498D3jyf2swO1Ns+hRQZpWqCzmtc8TrVuIOsFTQXKZe+nlKXknCfJqKYC8rChtuQl7M7RtGwAdv+4htsdiP1U87GzIcJNMe2E7zo7DXvjgChICnBieeS0NVwI9XDsfZF8xu7GnssTqCqu1iXJ+Cf885b/ziXsbQd8fh6EXU4VVD2xez93aV4R0x48QeiNlCbRbl2D/WS0cXe9ZksQNoJNOgFuHm1W1IkpCpYMXRfbgONBd9exl8Vw7OtlxN798sP42O84pn3CQ/sWXljkCJdPYdJI4siZD6cp8MKtOlz6KGBEoaPzbaPxoPvr2It8OHb1UfbD/Z2/DwTZ7zm+OSZ/wqv3lLoIU0jveQI6Chdp27KVV+TsbHQc0uBhzQ5OGszPyrj0/SZO1uKuY8+zb8B+trA+77O+KiZ+wlOyTnf73rGv63hKJgmXnzsPpI5DHluM1u2OJyUAHE7szJjsdEy+sYWYvoIdTTscu4m/SuL4RNrNctm3xrQTXkBzsOCSYYscYVWPhKyS5UBmhmEPa/a18/xsWdsEkNZL6I+felrCmNDT5Cp2JI6iUAOw68bMFd4I+z3HtEsaEZ54ZvgVQhsqACNxvTGC6atHeYu27Xvg3rO82O5t4+gNPJLS9Cp23e4GY8fHzxH7vO30uph2wnul8GhVR0330MmAKnS355jetFv0ZAvfZ0mmrY3jvUaqLaXsRjZEqOur2OMm0xDs4iL26No9x00x+ZJGt7uzi5rrExZUIUCq5JsAfBKEz6cpR/VOXhCuNBKyRi5pnLuKHUnCLswA7PYFLImMqjDHbTHthHfUYNSy4OZ/eta+E4G+20BONaQ903uP3Q0XT084G+F7TyEm+1r8g/Gg63oJqbOL2EN5MgS7rhbAsaIQU90A378bFfs9x+RLGtQNE6ZtmTA2dNGmgTzv+1p3vTTxIuHjDse+Ji5y2yN1/bbTyO09OVZXscP7wdhlf+yx+25uTb4ipp3w4TJnoasF1LaXyBy0aasqZHeIlu66WtguqWO/O02AzSrKVWNRMolGDN0sr2MPJsUDsOu7TcQui8Xo2O85pp3wnX+pL/m0AxAc7RIKNVU1B1BVBXSedfD+yORqWzpitx3FVE2WOooejRiyPVzFrt4Pxi6ftz12YFYPfkVMO+FV+2GMmQfIomRrseUwSRLzXE0oyiSOSgeBJx9580G7MRgCj1wWhOHWJezi3GDs8Xsej9zwmkuam2Pal1ZBX7aErkrn41II2pYv/52HOOErQuh2iNESgtVj21L7JUw8xzY1C5fPS9i7bjB2rWpImXLF7+NncuvnuCmmfcJbh0JXC768r1c2vSyhTQP//Xu+Cpzbw+QZ8N27l56mZ87WaFvoZtUzEkeKiPsCdthpPgj79+/i3yXPR8d+zzHthFeFHE6QMGg5VcYzd5A0hXva9baOQYW37SCn2pywz2T1VGk907ZcuqjH1XeRpr2KHaYSPAj7j58j9rDpNcdtMe3XRgWT4XDWlUhTarQY1VdEgER6clg4NYVlQhjkwNulME2jGu+oES6WF7DjdCK2odjPnQhnMaabY9onPGxI1Hl2NmwbKL7cq/b6jKX5KNnyM2CJcV42WI2sdT3+eN4S9yJ2Pxw7Ne21P+FnasHNMe0TXrjpLw8bljeBQts0vYOHtScBAC34cm8TWADUc8lz9rfrpl+zG1vqoizYRbqE3ZtO/QDssllBt3tiV503nl4Rkz/hpSy5FXQ8QYuwwFH0/W2rxynBwZNc6obtRyX5DKo0GAumvua1NGq03XXsXTcYe5AF16aBblazA8grYtoJr2A9a/wXqRq+7JsIKdq2v5ACrJGrisn2sOHpKWYrY+N82az5duzWXqjFL2FP08HY/Q/veuwi42O/45j2Ty7U31nWu9yZgKp6T5OD33G0kyyLchcQgdYVBzoAyyNTQohU3bGghwvyJexJMhi7/PYz6cZdBznNQ6fXxLRPeOfYvqvrntab0h5G0rTnquoFnskAACAASURBVJh+jRYZuj/6gaWClTxijhqAmZp9946CTCMvUsiivI49HY49mpl9Z/34eQnk5pj2CY9ehg7HEyeOYUraNFH+AgA7IMcKybHi6Ro4461E9zzJMuA3H3myjm1b+TXs5+rFX8FOORIBfvMR6rvZEOEVMe0TXpUJUwfZioRqXc71v3QRUmhD5yLwTBa9MgFr4sRKCdseegsn7ivYo4XlAOza+YhdynLm0rwipn/Cmw1MXI542rFUOBz7y9sZwYpW7ejbdyLQQL8NE03TXB8XuH4du/ffjD1a4cxxU0z/hN8fY0sPbQdZLfopaRgolYUpASS9TeXRdj+blifouR1k142/0xqovhewa1UNx564HjswD55eEdM+4cVq2Jwcdq2q3tPUBjnBv0nSJHqeIk1tcdvHSaUktjEUpKsX49bwkqbsCl3AHkuaAdhRgYzLtuUT4nEzKvZ7jmknvCo7Ks5B1wUklCN5xtNwUUKdQDrax0jd9EnVttCHNeRU0ZigoEGBpnaSjlzSaN1cxS5lQTXgIdgXZW+R8/l5FlN9RUw74WGTyVNF92yAL+sNB04CUD24KCCnisl0OFrNnEKed3FVLixQi2YkZo3d2nO0mr+IPU1obDAEu31cNIOKI9tzjpti4gmv0blODyeKitrlTbKMU0tzrUaSUJqubZkkp6oXIe26XvVrt7flkZHX5ELNfQm794OxQ8SkBfe07Zld/G6OaV9aTUw1WMHIYsGedViAUB/X4QDQVSNx0dYGsDW5MOGs6ICtwTtpzHDJdexdNxx7oCG4BLo/jI/9jmPaJ3zgjZzto0rGkqRfn+NbXS8gn6RPjjzjyW5tPPWBptvZ+yPLdAQ25gXsEBc3m76GHeH/0HZ9W3WOm2LaJ3zg0qTWNxfHS2DgpqQpSwDvSam13nWk0eaZWePQuh2qHDy9QcKQ/34Fu/rB2AMf/ne5N3N8e0z7J2hL3H6zYpdlvYy9aG3bOMTRID7adj1Rq+2iTIYe6GbNnjfr5LH3QsW5q9hlvRqMXYo8Ypc0nXdaXxETT3hnJ2DFi9yp4onZ2dj+RI675Dm7OV0XT32t68hOjMsiVc3++PN2/ItfkV/HbriGYA8+V3GyPF9ab45pJ7wqa2878bTteBomPNmR2umdpX0SGP88TiMb6/KsFnzb+aggMGo07VXsmiaDsQfxKO08h2czteDmmPalFbDVuC5eNgFESWnd7U1rfd8TsBLH0bwTlgim8YLjEeoSXiarmnXxz4gdn554YR2CfbMCnmziLMInyhw3xbQTXoQXvmBe1vnolYSuA37xPRCkMIw2gJTliyys154mkM5zWtnZuP79A2TspAkn9yXsacrafgB2BXrtyZNp3MxxU0w74aHk0gBnG/5FVCvQHz+bsVkHWZbQY9MvR9j6nDYN/U4DK7FuesWyMaPzV7Gj67jPOgC7GE8edWPO3TN57NaYdg0P6V/Ww/5qaxtB3kdiluRGF1gsuPYXtqGyjN2S4I7XmbeSc+OXBWEn9QJ2LBeDscdXhYR6lHNJc3tMPOHNKmZRcsAU6mBjEeqigAYmYkpBUsCIWwk7POp50up2Z8sTZCvKcmTrSv0K9jQZjj0oIIjjpXts7HccE0944WaTc9BlyURpO5567x/o/hG2n8wvSTcr1szW85ay4Kn5h99Dy5zlQ11Dx9alkevYAQzGLmkSsUuajI/9jmPaNbwqN37SpF+K9nS9k+2+t7Ix2YugP4M0ZUsPoKdpmlKv3Vz/pCxI1hozmhZqwk9fxB4oE0Owi7C9WdWs6w8TP6cmHNNOeAFfvq0EkCRhGzI1XrmpGsjuAF2yCyMHs3v//r2JmXroesmux6kiz7yqeyGnscK5q9ilabnjOgC7pgnk85bYD6e5D/+KmHbCq3Lz3/jh1GlcAJ+fXwqN1g0kIZMQywWFSvfH2APH8w7q7YL46ZkJM7ZcXWuDpwvYtWmY9AOwA+D/9dMzW5TpYlzsdxwTf23k1FGriglTFny5NwUCtG20cNTt3vxXzVhgy0ThKdtaL7yLzMnR7dudu4qdjtzDsMNrxC7JbD3/mph4woObQ0F/5owtGOxhAJB56IStOyDuhWpVc42uLNm3D3IXzuQufk7sIYZgByJ2AG+D/U5j2gkfxJOcsyXmLspJA6CMHehsF7eF2o5dGfM6lWPV1/q5OXAvyvGHN1l6FbvW9XDsQMQeVczmuCmmnfCAjeGTyCiU3AYyi5IKXjZyl8Wil+Y756IHTZjOQ6uKiWPajT8rdpHB2CVxPfag1DDHTfGn4tJ6rvarJ+O2tx1JV2b4q887yHJhagGm2lvVnFo2ZiSQMIm0afoSYqxou+vYV8vB2AFE7PH7zHFTTDvhQak9rSouQGRFr+SlSiLVakmjg/ePvdJXxv62Ng2fFG0HbFZsAa4WkOcOOraYkfHeL2LfH4ZjzzJI0xA7EKeyc3x7TDvhFdBFEbkvUuScXFr9C+EgCSJMoCBf1zRUKTB6LrqOBKzO03DAhJlGhd52wOPiInYpy8HYAUTsgU8zx20x7Z9cMBKIdawRsLoujtwB9Nz2rovydWKCqfqw4kUP4PfY7iCrxeh7rZJnV7GHrs0g7Cakqtsd+/XzEvfNMe0TPli3Ny30wyPk0zPbdoBt/gv8uw21W4K9vAjbeaYhKc97IM+hpS1JH07Q/XF8AlaW9sJLX8CuhxP0D78bhB1N009ZgRctzjm+Laad8EFfcbfnxpJYnZsk9EFKEshvP77QckHXsVZvGo7rlwuagwUHvDQFfAcNE8yRQvdHiqdewq46GDtcAnz8zFmCKrCfbStvjWknvPmV4rt3JE91ngkgAnx4xz51lvZeqCZ1gc73F740gaBkXzwIHKUpd1w/jgjdeO4XsYcuzBDsQO8KUtXQP/huVOz3HJNPeK1rajPm5pXkEtbu232vW5NQDYBtO/M2rete2csWKYINJJKEfJUxI0uhx9Nl7Iuybz1+DXvTmBJxHiXE57gt/lQUg5HtCFDfxXyRsFxwWGPS11pk7GM7B1ku4L9/DyQO/nENfVgBf/g9ky/PeML/nNhtKDUEO4zPD5Peeyvs9xgTP+Htzf5oQxvXlwBeIdaLD/aOQfwIbUuW5XYHTRJSbcMO63nbb8wIlIBL2BNTIRuAXQHI1pQMPOv8OW6LaZ/wXRcXn6UoyBbM8/+PvXfnsWzrsoTGXPt9zolHZn7fV9XdhdQtgQM+Dh4YIEA0BkgtYWC0hIOB1AbQP6ANrAYHo0UbeIBwaGFgNe2CGmEBQpRAgqrqqu/efETEee3nxBhzrX2iOs/JnSe/FXf30Z7SVUaeeOS4ETPWnmvOMccw8tcoXiRFQaHRrqPURccxPhJbvnCmftB1rJWtPRgz9On5MvYin4w9UAqKnBtSsafENxzzPuH9EreXlB4UaBqyCZsmnJToaRUjq4rlgxOeqo6jefFfQ5y9PwmKwtEiTS9jB1inT8HenEjwuROhpiW+O+Z9wgPQL098wye3Oxm6mBqA14v0yeOd86DUcdF+GJeqi5xlQeytoUEvYx90OnYnI/b+DbDfcMz7hHdChwzvU9pyRxR5BkkSYE0LG+l66MsOSMyO8sMjOxle26UseDn89IUJU1XRhzeyKqFHOY+9aSkINQE7ug7at6NU9rLEfXXMO+GVMhe63dENo8iZBE0LHWqznk9ebTJBlW2/suCjX5WDnv3e6t+Bl8LYnY6uv4zdOSb2FOziqDbWUzJwoQdfHzNPeA22MRCM3Y7MjMnuVsD+EJYiQu/aU2m7lkMmgKfjsQ68Gj3EpdhqZ5aTZ7Ar6DM1Bbs4N2IXFx37Lcf8a/jnF6pyFbltAxmf3QlLAfDyJ44iRfC7ov0QiFlBzct/bJG/XrOLEYNexC5+W2sC9vC2CN24Y2O/4Zj3d87vhHqxUQv/A5d1Fdb8gkPGyYVO8iyQuII/antSC0cMWZUXsaPvp2PPsxE7sLAlfyDmnfCeOgBz/BAJySNpao7VRi0oC9OLP7B2PtY0L9sdOGwyfUbZrMd6OWY07UXsACZjR5aO2Jed1h+KeSe8d/FrWgQTsLK0ssBkMGwHVLc79rMzs30crAzwpKxBWRpY7/vVMniMcO4idm3bydj1cByxA/Gx33DMPOGVZgBOwiN/HMJ0I60WCLbtXqtGypJ6jXUTxEzDDqw4ymPEDG9kcBb7iVz3N7ADGLGbDeYS18XMEx6QQdmLLgtyzFNaPaIsKDBaFhzHWz2smxVLBi+l5zipVCsNtG25jXS3+WWxd91k7PjVuxPs+Ztgv9WYd1sS4ICmbsKppk0Pub+jX1Lfj8a9dhmVruNrW6MPn0w39cszGYfH40jk+qWwdx30uZ2G/c9+5uumh69fnuNjv9GY9wl/6q0qJi3tkpDsALjUnGd0sj5VIusHnoa50W4T6sOIyOicFzO8IvAZ7LJaTcYuVRWwI03jY7/hmHfCm2Od3q3NvGzLBChJN5D7O5YAfQ9keZDUg5BTHqxmjGOOqgwnZXROufeRPYNdi2wydm2aEbt3JFziqph3SSOONIBBzSTMMUka36UZgrep+AthZovS3jjg8zMXtoeBS9y2Phd9OF8WbDOew/7lGSjLSdgloZu3FDkt7Bfy2NUx7xMeJ4OaY80ftF3etOuoCLDbvzbqbWgOpmli2owy7sb2w+izFJse7Jc6zmF//zgZu77sRuwuiY/9hmPeJ7wOvNC17GVL8ppHokcKHHmnDT3WLAeGAXjZBR6KvmxZ+xrFQHeHV9PPKGFuHeew49OX78I+7u4O/EVZ4qqY+QkvoxO1j44a61yJS8KYXduW0nXAmNxA6HGfjvQlTSAPd3GhG+ntHHYpisnYQ/Q9oEN87DccMz/hzdPUOUBbaA/IahWc7aDDK6qsHo+QqjIRU7N8HzQIlKr3aHUSnYCl3lP1HPahIS9/CvbhBHviRoHVJb475n3CC9jLNmqtJHaJ9TaQ4gL7EP3AvzfNP6a9KCKsm7sunJixFXglTS5jByZjhw4jdr+4vsRVMe+Eh7BXnWXhNJc04cVvGMZ+9Kl616DwRgpSFIGXAjMVk5JL06/W7WJEklzEHp5MU7DbAokYySw69huOeZc0gmDvIlk2Smxk2ZhQVclLqLUctW2pCNDTVEBseIVVCZi8hSQuvqmZyEXs2veTsYd+PcBfkMWY+OqY+QmPwG70LTqvz4g0oZt1kUPu1qOrxprCR1rm458A5NhQFCnhsoUe618Uu2zWk7FrPWKXIn8b7Dca8z7hVUfZuboh7+RwoORc30OOyuQF+PhPU4hq0IMPO6IiXOL+/DQ66sXmlHvbyjPYdRiA3WESdkkTqMde5OM64BLfHTM/4TmKVzMO4GmZBOtHrRteAHUI/BhfI+tuT00YbwT2sgsb/9q28V00THDpHHbU9WTs6nvwb4X9hmPeJ7x1M+Txnn8/mrFXT0qt3q05oBlKWsZ0PU9QJ+Se5Bk11fse+ng3npxJQsv3iDF8uId72p3FDudoYTMBO6oyeLiizxblsR+IeSd821lfWnkSnpj50kqeSUtVgiFITuswcExv9vTatsBvP5K70vf8GpFLGvlHH6FeJexr2IeeF9kJ2MX/knr7m3bZeLo2Zl7SIDz6pSjG7X2/2JwkkPs7/rlZj0QyEeBX78Zl7zQdlyz8tDOP3KX5FnZgMvYgLWK7sW+C/UZj9gkv63XQahETQtVh4MZQntMFzzno4cDa1uQuZLsPe6WoytHnqaooUhq7Lfkt7Fk2GbvXkqRiWvIm2G81Zp/wYT3PdCS9eq6kKYlZXRfW5GRlJYuN7/V4ZCnx5Tn4JgUP1DfY/L+I3VQMpmAHELDrG2G/1Zh3wouYXHROqeg0AXzieL+khzsOkhJTI1N9VTcjcWEiqyaTHerkmNCL/HeGXfIsYMfQR8d+yzHvhLfQY/2aN256L+h7Gg70wyi/0Q9hWVpVofsjdV8AJpbqm9bAvwvs/t6xnOw/HvPu0pz8gH2HQ4qCpCwTVoLyRPRud8FZAwj8Gd3uTzolPdDuw9g/GvS6CTLXX8UOWKvx29hlVfLzOlM+XhL/6ph3wicUWyLha9xc0rrmI78quR3kTb68kvB6xY8rc8DkMZAW3CPd7scTP2Y8bICn7XnsztFlfAr2rmfPfrsPNOIlrot5J7w3A/Cnno3UJct4Sh6Or1V3AV7s9sfRLc+IV9o0EO+p5IdAMeNldxG7AKNX0zextzQwFuEdYKnhr46Z1/DklqjJSmvHxMUJLVjblnwafyG1XxI/sFLTegl1cduSruvLiljR9xexa9tOxg4gYNfnl/jYbzjmfcI78sn1fkMj32MNHXqqAVgXAwDr27ZlD9tzTh4pT42OZCwtc6Aqxh53ZIqtVBXbjuewexrBFOxtC9xvWNIMA5mTS1wVMz/hbd/T02FFbGu/hz5sTmgHymml32oaBsjuADnU5M/YL4bsj4BfnH6Dzf9L2McW5LexS1UG7CLyJthvNeZ9wiuCuoDa5U7WFdt8X15Gym1RQJ9eRvEigFYxRinQY80Ttu8hfc8eeWypvRNd+K9h92XMJOy+N+917d9CJvBGY94nvAkt6c7aija8IUW2HtuSgJmFURVYd4dRLcDYiSIS1AD0DXySdLe/jP2UwPYN7MCoKU/57YUPf23M+4S3FT9t27EbU5jmou9LZxlbfbYtBBvehJIhS0JPXETouuE55jHDyWXsthwyFTsA1vmpLmzJH4iZJ7y18nx7ruuB7hCGSGIU2rBVBAQjMT0eecL6JRGA9fvhyN5+5Lakn+aew06eezUJu3Y9sC6J3fTjl7gu5l3SAONj35+Sfnpp7hq63zNJjGkY+O6nJUzT2CbRYTQXe4te9gXskqaTscPJiF3eCPuNxrwTXghPNyuaBSQuqHfh4Y5TzPePVANYVyGxpSyCaYDkGR01VhUTx4/579ZxsRuF9xx2VOVk7PjwOGJPkvjYbzjmnfDepxWgwm5VMgFs0VlXJaRpKXeRmoRdWfAUzDMubpshmPr2ntiebGwF3q6/jL3MJ2OXQz1iT9P42G845p3wTighvTuQZnCs2dEoS/av/WjeG345h+FxQ/mLva3/VZZE63KUqBs0uvU8zHbyHHbYBtQU7GjaEXuaxMd+wzHvS2s/8EJnI3oMCmz34+Wu6yADTXy9xIX7s0/h0yVNgMMO2vVwn16Y+P0A9DXQxN38191+vLB+BTvnCMkk7ABnB9oPfFosujRXx7wT3upWWVk343AkLeB4DHW5rsrxpBc+EaDKBfA8gwwDJLPLXsKOzvDhHu7LNip0yXMS1s5ghxPo+4dJ2IfHDbA9jEpky4rf1THvhFdr73W9PcrZl2bvncKj8rwLSlyyrkiltdNbni2py4KnYtdDhx6u61hyRMWuF7FDh8nY3Z9+JMls6I03v7Qlr43ZF4N6ONC892XLhClNV92zCj37UYeQMJKmPA296JEf9BT5uAQSHbhexI7H++nY+xH70qH5sZh3wqtSyqLnpo8UOekGp0JEaQq8ezCpC8pvaNdB393bkKYgIctbv/ukeQN9+N8Vdvz6XcAePKKWuCpm/53T55fxLyKhFBHnqEQgAnnZcQrpy4M0ZVdDBLo/cuhkl1zqsffRlcfg5DJ2wzcFO376PGJXjY/9hmPeCZ848srTFFqYkJKvvb27RpaOSxL9wFaeUWxRFpB1ZeJHORV790eu0MXmlN9vLmOvm+nYN6sRe54tfPgfiMkJLyKJiPyvIvLf29//ioj8TyLyhyLyX4tIbq8X9vc/tPf/5ZOv8Tft9f9TRP7lb/6jfQ99eeGp9vmJNfunJzIOfWtub/wUL+nxvDWnPMpZ6O4Qto3Io+l5aXzZfdc36rvj05ffGXYcjiP255f42G84vueE/w8A/B8nf/9PAPxtVf2nAXwG8Nft9b8O4LO9/rft4yAi/yyAvwbgnwPwrwD4z0Xk8sjQOchmwwHOZh3acZ48pRUlLCRNyUR0jq0+c65GmrD78f4hTCq9k7VUkTeeNuuL2GFak1Owq2rADiA69luOSQkvIn8A4F8D8F/Y3wXAvwjgv7UP+S8B/Jv29l+1v8Pe/y/Zx/9VAP+Vqtaq+v8A+EMA//zFf3jgQAbHmuVLmkA2K1Juixyy3Qf3O93u+DEfv/CCZyepHo7ApyeWMX4ANAyvLSQjhB7ri9j1cJiO/eEuYJeyjI79lmPqCf+fAvgPAXgS+QcAX1TV28n9EYC/ZG//JQD/HwDY+5/s48PrX/mcr0fieNFbVYFxqCfbPrpZWTKlkBWTR5wjYUuVoqRWCwMAHu/Jo1lX0cWYZL26iF026+uwV8UipvoD8c2EF5F/HcBvVfV/eQM8EJF/T0T+oYj8w6bbc2Ooabkt5DVceg5zZHdgrVs3Y8svz4DnLSecT9uRx5ImputSA5+fx6WKWFE3F7EDmI7983PALrtDfOw3HFMmrf8CgH9DRP5VACWAewD/GYBHEUntFP8DAH9sH//HAP4pAH8kIimABwAfT173cfo5IVT17wD4OwDwUPyeSlmOrtk6QPIqTCO16yF3pYmSdtZrp1kYjvWrAZM0re2Q2un4Jg4gF7Bvd+zTT8GeuIBd64bcmyWuim+e8Kr6N1X1D1T1L4OXzr+vqv8OgP8RwL9lH/bvAvjv7O2/Z3+Hvf/vK7Wq/x6Av2ZdnL8C4J8B8D9/89/3znbetbrhxFIPR24ttd2oDAxYZ2cbLGfgzCPJFivwFrSCKdjXq8nYIRKwy9KS/KH4kT78fwTgb4jIH4I1+t+11/8ugA/2+t8A8B8DgKr+bwD+GwD/O4D/AcC/r6rfnPFzHc4ER4X9dN3tzR7G4JcFt//rxhYkNkF5mP+XjgsgSTKKkmbxk/4Sdj3hynwLu65PsHf9m2C/1fgu8piq/gMA/8De/r/xlS6Lqh4B/NtnPv9vAfhbk//BgfudkqahhadNYz/4brSfB8I2kTYt62azafdWMwJQLUCcjecjqxY0TdCC/xp26ECuzQTs+FLTGkdccPxb4rqY93fOSFR00CjCLmdYynYCXhMQ1AgAytJ5/nhQGc4yc8V+m/pXssyS9Ax2cWFv9VvY/aYUVKFefWGJq2Le1AJbjECejQnjrRu7fqx9PbvQ29rk+ai17hwTzn9Omo6a7DGh2+LKWew+2adgr5vxc8yefonrYt4JDwSNFjGxIu+PJHnGMqGnWFNQ+eqpty6bFZPk9FLohBdJT8KKGf7fOIMdQz8Zu6TJiH3o3+wpdYsx+4SX1WrsU3v7dh8JjX3Rnqhx+dr8dBppXZJALxjeqKy5gF3yfDJ2tXblUsr8eMy7hgfGix3AS5tPCs9xBwLjkJJ2KdS/NvTQhq9DHIc4qwp42Y2eqbFi0IvYfUzCDgTs0qQUalriqpj3CW/1rRTUVBeT2vChvblT68D3Fdzyl4wlAwWNvNmA0XBfdsDQjxfGWNC9j+wZ7DzhJ2L3+jwvu9C1WuK6mHfC40RstKpMccDEipxjouT5mACnei1pSj66c0yY948AEEzDEJtx6E/xM9gBTMduEXQnF7bk1THvoyJJRsvJpgnqXbqu+Ih/vIc8vXBJu+3IWXn/ANkf2XfPM0jdULTUjATEMzDbuCXNqSLBV7FXBeS5nYQdWUrFhUGBzQo4LDId18a8T/hhINlLBLhb888iB15245ocwITxzEMvfATQUMCfpl1PI4G6wXC3fn2iRoigSHAOu0/aCdjRdgG7bPfRsd9yzDvhfTOl7Ua3O8B4Jo6P97KwwZK195qWq3IAhY68FWTbmSR1D/fxS0isaOFLmjPYtcgmY9e7VcCud+v42G845l3S6MB6N3HA4EIp4ikC3hFD+wHY7c0x4wh8/MzppTEQATAB82x08IusDx/sKc9gR9MSzwTs8vmZFp2e2vxG5LdbjHmf8OI4XveKAzD5C0+essubp89q3fCy9+5hXIfzdGDb9hevyxg54aUoLmL3OCZhL4s3xX7LMe8T3gn0994Ddccf+v4IkY4lyvtHJtT9GkOZI3naAaoYHtaQ3RG6KqF5BvflBbJZYbir4J73PGXXb+BmXeTQD/dnseNQQzfVJOyhdGuNBLdQhK+OeSd8P0D+5Ce27jyb0Flv/sWERrsOLnHkn6wquJ++sJxIUyZHbybA/+gjVAdOXZ9eoveydbeHPL+cxz4MwJ/uJmH30nxIElIV9svg6dqYeUlDHjnSlOXAidqA3m+o23K/IT/F7CiDe0aRk3loCxRSFsFqxrf6okJP08vYnZuOPc9GmxwdomO/5Zh3wgPsXa9Kbi15da5VxVPOr+k5I2Z5lw0vSQeM5mXePaMoxnF+zLANpbPYfUk1AbtWxTi1TZaW5I/E7BNeNmuIp8aaraPuD0Bim0D9MNpXGnceaRL0ZzAoCVreXWOzgkR24QYAbzh8DjtEpmMHAvYlfizmnfBOgvEX2jYYkklZcAjT9RRjUjUOip3cTcuhVZayBelZhl3Hth8QfYlbq+IidpjkxhTsUrcj9kHjL6DfcMy7GPTGxPvDSOl1ZgGTJJS+MCakKlV69VgDjsxDvOz4+cMAvGyNoGU98Njksd0Begn7/mC04W9jl2F4zatZyGNXx7y/c/4SN9AMQKyjAUdFAP4dpggMnqS2aKG2Guddt8NgZ1Ao8JpXHyPyjOXMGew+2adg10HJoBzUtp6WPvy1Me+SRmEJQwotrCxAkvAXIctYJqwq4NfvOeCxNqBUVXDhkPVqdOQARsnsmNEPl7GbMcIk7MCIfejjY7/hmPcJbxbxUhbsa4tw5/NwYMI0ptO4P0AOR/4CdB27IydlhHSmGJym/K+Pv/kf7O7PYBfjAk3BDmDcaV1Ux34o5p3wUBKq/N86Lm5IRst5PXXISx1fq+sxqQGIy/iaE56OAOCSN9h46oFOz2LHMEzHDoxtzAHQZdXv6ph3wosbmZHO2cSyoehoXYdtIvQ9SWHGnacPa2JaMMZf4msIoAAAIABJREFUSRLW8OIoUBq5Fy9VxcQ9gx2qQX7jW9jV773apTV87BLfHfOu4YUnI+1eHMsEgBfCLCNr0BiT+NU7nqJFwZr5bh3kMZAkvOSmKXdFd4f4F7/EXcbuT/IJ2CVNA3b+t1xar415n/ADdz7RKXTQ0OkI2uttC7nbUI/ReyD5UuHzE79Gmo5clKoiN6VtoXVkevD+eBG7ZOTDT8IOnuriX1uWuK+OeSe8CB/7WUpuuCpEel5k/Sl5rAMRTFWDEKlu9yxdvM27l6kebLd0VQI/RYTuB17nsAPTsafJWPfXDTs3EbHfcsw74VWpse6Ehrxm1Q7HHVX4jod4gSWyEv2Ct253ptzFel+3eyaiDnTdiAndNpjOYReRYGXzLezoklHQKU2iY7/lmHcNDyWj0Hs6WcL4kDSFrFdMrvePo8JX04yajmb1ro2ZAJvAafRpZd9fxK5tOxm7GBcHfc9fjmXSenXM+zsnjnV8lnKdz9QHvCiqNi1kf2BCbPejD6qdmpKmoRNCuTorJU41X2JBL8uL2FGVwETser+B/1URvwy+xFUx7xPeqxYca47cbeEj9Kn9uP5UFdg6IqQS1KyhvVR1b2Sz43G0oIkUuttfxK7b3WTs+Ph5xN710bHfcsz8hOfyA980Wb3Bal6APfWEDnloW6DreeqnCT2dOke3DTMmhnOUufDDn5hhYq/nsEtRjBfpb2H3lAMv0bFYz18d8/7O+ZpX/pzarzllSOJoKb/dm+HvkV0Ob/m4qsx8QEMJBBtCRV/i9tyXM9iRJpOx62YVsGu39OF/JOad8MPA8qNtg4ouBiUPxbgl3podIpxuti0XLLqOHR5/wSttqGNKArGlLnS/v4hdt7vJ2GW7H7EP/SLT8QMx74SHAO8eSLpq2lGmwuvKeKuY7Z4XPC99Udj+6qAcw4tQrUBpJuD3R6OGSy5il6qajF29qbKcPPGWuCrmXcMDwMcvJGA5CUmhWQrsj0z4uuEYfzd2OnAyiQxTWXGsj22q+SYXv0vYlQOwydid7bzKG2G/0Zj3Ca/KqaLxydWSVXYHqgBv1qNYUZ7xctj3PF3TlH92HRMmz4KGI7ouXIajRdddxl6W07EX+YjdevZLXBfzPuGdC7Vs2AX1j/PEuhsr8mNQj655wRu1bgCQZKaJA57IW5GyGGm3kULWq4vYtW4gd+tJ2INRsT3RVJdL67Ux7xNewIQpcmhmfk6eGisCfbjj2007arWkKRPNuCkA6HC9O0A2K8hmzU9fR1YAELmIHb96nIwdwIjdSXzsNxwzT3gBfv2O08q2M/Zhz8TpOuq77A+jvXtVQssc+PDOFsAT6P2aQ6eqBA7HUZ33xNo9RmiR/e6wp+mbYr/lmHdJ0/fAP/oJ6lfbxJnL3Zqy0R8/0zisbUmz9W28NB1LlmfzP93RnED2RyZR7Pj8xIXxc9h/+sQTewr2QXmy74+kFC/ksatj3ic8JJQgGCg9Jytban7ZcQTfNKPHqXOjnJ5PoDxnn9vXz3YxDBTdWJGmF7HDyXTs/pLa99zjXaznr455n/DCpYhXP/CuIwnLOaqIbfcUHd2sod4uxsSNBACKHJLQG0k8B+cNHDS8vMg57D5pJ2H3epin35clrop5J7wjM1CdQI5jGRJMwY5U8EJVQD89BaMDWZs/akI+Ch7v+Yl9H76OlpEZh9ZyPIedCgrJNOz+F/XYmLzgUsNfG/NO+H6gxIVPjrQgoSrL+PeuZ/vOv5Y4oO6hzy/kq5hqMJ5emGCexNW2kMj68OHO8DvArqeaOl+eF9vKH4j5f+dMo0W3O/7QzYUvLEUPfah99XAIYkb6sh31X2A6MYMNdoZ+1H75pbArOT1TsIvjhfdNsd9ozDvhE8fd036A3N9BzQbSh9ytx83//YG1cJZSfNR7Opndoxh/RsucDnmx6/j7Df/Nc9h9L30CdnWUHNQyB768cN91iati3gnf2+6pCCRZkYCVUERJNitOHf3eKEBzMBvjS1FYRyNlLWxkLHnh4oV0kRtUz1vSCc5g1+cXntgTsIu3tXzZccp6XHxar415tyVVmRSq0OctX3NmArY/WBeHigRS8UmgdcOe9/FIOQs/kteBlF3/dmzJ6aa9iJ0rgNOwa9eP2IH42G845n3C2yVTzMZRux5o96yH05S6NGXJROp6CpMCJq2XMLEPB358WfBk9DyWVQF8iog9N4rvGey63RHvFOyqLMGShK+tqrjYbzjmfcKf9pudsQbTlPVt05An3rTQw8FORwkiR7IqR82XtoU+v3AZQ/VN2IavOilfwY7ETcYOL7enyg7OElfHzBN+fNPzSKQq2ap05LfLugr0X60b9r+9XUyachVws+aTwNR6g0BSzDi5FH8Ve1FMxg4nI3a/3L3EVTHv75yJD2ldA1+eWQp4kwBfm9tStNdiPF2aAADU4Mf5EzcxZYDIl1bv+H0Ou6QInJhvYQ+8eb81tVxar47Zn/C6I2dGqoqnn2Mti8c7OuJV1Zggg43gvcpuYltOJkQqlbXznHCZOyZ05y5ih3PTsRd5wK7e6W+Jq2LeCe/d7XJOIjnAydim+/IyDmzcyeXWFi58GxCw5Lm/Y+cjScaLYEzotrx9FrvIZOx6rAP28H1Z4qqYd0kjLjAM0XamRDaQGlCWrJO3HWtes5NB17HPblv+2vUcXpkcteepxC4LpCjGJY6vYA/aOhOwiy2hYxigRkJb4rqYd8KrsqNhAxwItRclz6nj4g0RdIBkJWvzxIUujNruqh/texFT1M2YcLGg+4HYGezjaf1t7H5AJWbyFhv7Lce8E97Ztn6WcWPpWANpwfacPz2Nc0IFXnnVygwUXdNil6YNiSh3G+DneNBls7KOytexq3cGn4JdhPu9JggbG/stx7xreCi7MT5McQB9D33YMCHSFLquRhsYrxDg1bly05E3Nw5eBlOWCDGj6y9il6qcjF1PsRdFfOw3HPM+4e0Hr6rAy3bsaDgBfvpMzRdtgbomhbYs2Q5MxvIBzTBeIM29z/u1xgy2IN157AMVhSdhFzc6D54yRZf47ph3wttwCc7RTFgkELB8TQs4ykkbX0XyjIOZ3jxSq3FqSfcPS5zICS/rlfFpvo4dg+2vfid2VV304X8g5l3SDENw35M8p+z00diDg0Jr7oTK85b0ga7juH67Y6fjVJnsWEPrmnIZXTdaWEYK3e0vYgcwHfvhMGLv++jYbznmfVSIwPs86eHApW0rcyRNWNIMCtWe7UtTCQ5OGWZCAFP70kFIu30D20d/2TyHHaDA6hTswaK+aalStshlXx3zTnhws1+ybNRTHxTIvA1lYkMYk8Dwa3BlwXG8V/DqaSqGuzU11v1AKGZ4X9Zz2EVMYHUC9iyDFpnpw6eLevAPxLwTXtX8jZS1r+eZeBcNX5OLC312ADwV/eqf73dnGWRnxDEvPR0TuveFPYf94Y7OHlOwmyYlRFgWvYHqwq3GvJ+N/tE9DMDdGt5F20td4OGOJ2ieBccNcXz80+4yC5J3WmTGZ3FMnMiMQ89vP4dd9sfJ2FE3AbtkWXTstxzz/s5544Jh4K5nmow9aOd46gUWZBISOeyvpgll7nr7/LYNknvR62Crt89hp5zeNOwQAQ5HyvE9v0D6Mi72G455n/DAyHk5oQHTMGAY9VnalgmhSlWvrmfCmI6671urKvTLc1D7+iWx+3W9SdhVA3a4pZz5kZh3wquSI54kIyXWSfAq1bs123ZqE1njlaPvqUyQJDzd/TDHOaMUl/HZkl13EbvYXWISdqMjyKoK8h5LXBfzTngBtdyfXyg62rajtsuxNoqwEcJ0GC9+WTb6KxnFODheDwP0ZUvtl5jQvY38GezatpOxY9CAXTbr6NhvOeZfw5fFK5UwMblpAJSd23KbX9YrbvmLcERvpr5IE56exjT0SggS2dxX25Yremeww2X8RZiCXYex4/OyHRdZlvjumPcJD2EiWFkgVcWT3Xu3eq+jdw+k/FbGRkwTTia7jp9flTxBk4S/QGkyrsvFiiS5iF29l9ME7BAXsHNrauY/thnHvE94KIcyJkcHYJw4urGXLts961z7EzCeij9dD0eWEn0P8Ykf21Sg7y9i96f4FOyysuXvYTCO/UItuDbmfVSIcAJZ2H+e9OWXo/NsNOsFwmVQqpKbQ5n5O/nyYrPmBLRto2/+s8d+Abvx/Kdg1ywN2Dl5nfk5NeOY/XdOu551cJYFy0mYzAWadrzsDWoqXR2k5the2zZowtPa3Wi1d2voU/yL3yXsr1xIvoX9ZYdwExC32Fb+QMw74QfldPJlC9RNuGiGLoU4MgeFq3QKe7/vfyfOOPMnpRHAZI+8F6rHGvJwdx67bTxNwY5BR2PjIgtS3Et8f8w74R1PM9msWbeabbzvamjT0rf0ZQdNEnZBmhaaWLvPVuNeLU7owPo5jytXJ6vqMnZxlBr5TuzI8kVq7wdi3jW8vjYkQ5KMGoxNSytHUwPG/Yano993XVVhdB9UfNcrs3lPxklnLOheXOkMdjiZjF37YcTu/3+WuCrmfcIDYbLoE0gy73aXcKiTpjy1X3Y0CkjII5f9wRKdZY+a+CoS623HThongZb8VexdD3WYhD2sJSYUWUWzGBNfG/M+4aEkTwHjls+xhu72vKx6e3awry2J44nZtKHP/cpUzHc5gPhrcn1/GfvQT8YuzgXstNCZ/zk115j3d87btvvLnclGC8D6vusgRcXXrVaWwgUKMNqOtOCuCxrsaFuWCLGXKH79HvLl5Tz2vg8Cr9/CrsPAFqcpCUuynPDXxrxPeL9orToqGByMZ+I1a2wXNBgf1A0/tiEdWI4NuSiHI0WNEnPdiLwAEpz7zmFfryZjlywL2LUqomO/5Zh3wsP4Mmny2pPJfFehysvo3ZoL3febkZloKgFITJv9bs2OibeGjL3XqnoRu+7207EDI3a/DLLEVTHvhFcNFu6eAAZgVOuyRWd92ZEf8/mZbEjreAAIVjKyO5CD4k/c2BTbtruIXdJ0MnZdVwG77o8LPfgHYvYJL3luU0pKbaDrRm3FPAsO1dr1rMu9a3WejfurOoyrcl7OLrKYEbUhL2AXmYxd6mbEPvTRsd9yzPvSCmWy1Jw+SpZBO1gNbsshpr57as2uqmzt9QOVg8WNnHQRjvtj27d7lbMz2OHArswE7Ho4jqKsS4fmh2LeJzxswLRe8Qff9aG/LUU+aqkPGtp2qsplD9UTdqIEOTuvw/4WiXMJuw7DZOxi+jbiOTlL0l8d8/7OucQe9UMY1ohzQFlA90duFPl61pai/VkpfmnaS2T7j/HT29j68Js1//0z2AFw8DQFu1cfNpm92NhvOeZ9wg8DOxwZFXT9yQiTs/CDG7lb2zqc/e/0RiizYZPeb/jnMPDt9MQQOFLo7nARu9eWmYRdNWCXNI2O/ZZj3ic8YMQrrrlp3fC0NDVefVgDn76QYpAkwH6cWErb8fSvG+B4JEHLOeCFRmISu4b3ixrnsPfGtZmAHUUxYncuPvYbjnmf8CLQ+zW7Md7tw8SIAACfvnCxwxiFslmFwZIeDjaKT2gRaTrr/qRH5J1WybKL2CXLJmNXc+MOSmSRsd9yzDvhnUCebTpa5DwhvZyFd/EQgb5/YFIpuzrDH/wa8nAP/Pq9bf0P0Ps17wNdB3x4hMYe3uTZRewo8snYxbkRe5HHx37DMe+SpqfzBer6laGAti1Pvi/PfM2sHfVlC8kyuD/6iezD52HcE/3yMuo9/vR5XKiIFPr0bL3/M9gPR3ZhJmCnFb0Nq1520bHfcsw74b20xqoimep4hKxodeP3Q8keTMbBTFlAjjVQcHijRQbYOl9oSSpt66NCz3NeUM9hh+lPTsAe5D3SFJLq4gDyAzHvo0Ic7dY9nzxNoftDmFrKqmI3wxsjpCkJWqph0TlIeVj9q4cDS4TNKi72sriM3XdiJmD3+696OPAXJzb2G455J7wq5PGe00pPFPMiqM5RfsNCysIMCAob6feh5y6JGzXiE/bETz83SrTdReyn1pPfxG7fC1KNXXzsNxzzLmlU2bbzuuh5HiaW6mtjP3l83ga3Pugwvm6Gv+h7tveqEro7RC8LtG2Bw+E89jQlhinYRdhWrUrKBC4rflfHvE94r0vz/jGM0yVNWIv/5gM7NavKrNppgMb/UkhVsYsDawFWFclbVibIw11c6Gl6EXsQdp2APbh07w6QPI+O/ZZj3ic8TET08zNdqc2fSQ8HSN9DB+6Lysp4Jt4jSZULFCZxgcS6I2Zjr8ca+uUpLnSzmT+LvWlInZiAHYO+wo5DZOw3HPNOeG8Kdr+GtrzoBR6Jc5BNAU0T4Ei3O3Q9Ox2HI1BV/PzCjNHA3rXuDtS6ye+BzxGx5xkkSS5gX0PzbBr2fqBOze5AisK7yNhvOOZd0ih48jUtnTEy7rjK2roUqqal7mwFjhc67Qd2OszzlNyUIdTIuq749WJGP1zErnk2GTs7O4a9KuJjv+GYd8ILV/xkUIoXvWyhTcMEcI797TKnY0ZRMCmKnCXE8wvf37Z8CvgvuV4Bv/0Unw+fuIvYZX+cjN13amS9Aj59iY/9hmPeCa+AHGomAACIC9wTLn+QEyO7A5ebvdlA10Hu7yh35xzk2LBm9rukwxu0Jbv+Inat6+nYbX9Xd2ZbubQlr4551/CeBvubD7z0eRcPgHV4VTBh1lUwOBPjpACA/t4HyOfncVUQ4Gkq8nqxOkYUOTntZ7Cj64GJ2HG3BmpSEpBny07rD8TMT/hR4sKLjsqqop5L20HzFMOjcd3XVSBgIUv5y9B2rIWtBBg+3LOHnaXxrR/7/iJ29P1k7HKoR+xNu9hW/kDMPOHBy97zDtIPNDE71kwE5+Ce93B/+pHJZVxxJKzt5dMT5OmFpcCKmozuec86+Fiz8xEz7PJ5DrveradjH4YROxAf+w3HvI8K71jnxVD3LevZveeL2zTyaUv6bGb1bZ6xdffpiYsVXQ+ta0iXhTF/7DpYj8cgtvQ17DgcgKKYhB2qwdryLbDfcsw74ZNklIy2fU7pe9ax/QAteemTVTnqvzguRsvzjuP7quCpCS5SBF2YzTou9ncPrNPPYPfl2hTsWtPGUpzj8ne1GBNfG/MuaQaFbvfhZNPKFh96MyV+YqsPdQPdU5Zau46vpQmHP2036lDaxdEzE2OG7I8XsWtdT8YuWTZiF4mO/ZZj3gkPsFOT0+IRn594wet7ruk9bNh98bwVGPMQMElsU/A9GdRIlrEduHoD68dL2N8/TsbufaAky0yvZrGtvDbmXdIAQF1DzfJF0pR2NX7jv6beux6P7HOrQmuKG2nTjIJLJryqXW8y1f3bbA1dwI6Pn4GqmoQdTjiB9bLbywl/dcz7hNeBFztPlxXhJLJtedq9vBjTMB1fzzOe4mJJUuRcxABemSAEn9RY0Ov6InbKcEzEbqZnAEZzhCWuinmf8LYLKmVBn9Ii5yK0bS9JWVKQKTE5PquNARswDQMvrEVOYhbAVp+pkEWFbmoDZ7Hf3U3Grv3AckfJHo2N/ZZj3gkPo8iWWXjEewEjGg1T1ctvA0macrVuS+2ZoNKlA3dFfbIl7lVdHyX87uo57MN3YPeceID3gYVLc3XMO+G9uq71qWV3sC6GLVRsVqxnM0sqVZ6K2ckSdJlD9sdx4no4AmkRX58xz0gaO4fdmXT3FOzOsfY/HMNFd4nrYvbfOckz6KcvlKpI2JP3u6H6+Qs3mbxeZNexj12yJAh27r4t6P1Z32JSOehl7L6Wn4A9kOW8ufFiPX91zDvhFfRF2qxHaQvTWJeqgrx/x49rOxLE7HQk6czu46sS2O45jl9XlMRQfRMHkEvY0bYUSZ2AXbuehLOarE9ZL23Ja2PeCY8Tn9Yk4Qno90K7ju7aJbkmXrCULhmjuq6kCUuAYz0OncziPWpYF+YcdvRD8HT6Jvauh253Yd91MUS4PmbellT+oMsSUpW8bJYFBzdJQkk6c8jzDhroOut7cynaW8joMIy6jnebQMONBr1tL2M3aY4p2PGwCdi1bqJjv+WY+XdOzCY+Cxc8NO0oKV0349teiho2oh8GoCrHyyHA91uieQZlPOjuMnbnrsM+9PGx33DMvqTRpqVktAj78o4Od9rQ0hFDz0vh0Jv2S8cWX5ZBn575mvW3ySc3N+vYF1cdLmMH7BI6AXtd8wLb8KmmPy8b3NfGvBM+4RQSxXqsd01NF852Rp+3TJCmscvdiq9/emLJ4xcxHu/JMRcBCts4+hQR+/tHbjKdwQ6AWCZgD6bE9lTjbmtE7Dcc8y5pvE18a2N11WDDrk3LhLJui6wqUm37AfK8Gw0FGrb/5Hk7lhJdj+ExspjR5+eL2EOJMgG7HusRu0h87Dcc8054xx+yFllwxQPA5Y8747NbW09Ts3hPHJcvRLgu1zQjn8WfrF0HF7kskFV5EbseDpOxS569KfZbjnknvFI5NygA2y6nDgPbfbl1LtqWffa2I29lbW4aR9Nw9AsX/olhFOGo0Q8XsXs+zCTsXmkYsJJscQC5NuZdwzuhW0fXM3FMuUvKAlqYyq43J64bvu0cRY76AfpwZ4oACYb7FaTuAqcm9iK0ljlwvz6P3dy5p2DXNAGSCnKsoUW1cGl+IOad8P0A/NnP7GSkCRO1bsh63Nv+p3VgICSEkRjWsOPhR/PHGk6EhmJZBmgDSSLLdDxtxw7S17AX+Ujz/QZ2Kc3tJMsA6+0vcV3MO+FFOFnMUmjiuL+aZ2wvOmrLSE9SmG5W/POnTywLVjwJdbfn0KbtxpF+mkY/JSXPAMnPYzcu/BTsejgG7FItJ/yPxLwTHubYISuIjeDh2D8X50aPJBFK0DlOMP0yBQ5H64ycfGyWQTcVF6Vjhshl7F4bfgJ2yayNai1MWdiSV8e8L61eZbcwSoBf7ctMygIIF1e8e+D7E/JV1NiJQSUAsImmjJTbiKGr8iJ27brJ2HW7H7FnWXTstxzzPir6frRp77j9I3nOll7dQFvufmo/kBzmhLUvAHxhLUzylf05NMEMLfoCyOdneEG8r2GnGfFhMnY9Hondv7bEVTHvhAeAtqFVzf2GSXpCAsPm0Ray7QT3rbsk4WZ/bZIX3g9qXb12AIk5rXRCy8pz2P0iyBTsbcduze7AJ8G6WiatV8b8Ez5NmSRNO+ovGuMQbRdOu8BD8VNYx11RkXLUWT8cT9bsInNp7DQ+i91JoPl+C7vWDdCKmRsvNfyPxLyLQV+zJo5lSFWOAqUZH/O6WZFZWJYsA/p+7ILcbTjw8eVB348ufpFDyuIidjTtZOxytw7Yl/W+H4t5Jzxsqtj1tFs/HEnCWlfQ/YHLHJsqGB6whWlmxXXDzkZLu/bQC3/YkGJ7rL/xb/9gJO4idgDTsVvNrw8bLr7Exn7DMe/jYhhYt25WFC7ybTmAb7ct3OcXJlVdcyDT0yuJ6l3klksn5mKdciB0qgIQKcJd4Qx2ZOl07N6R/GmL4S/8Bs4T05b47pj3CS8SXKelsB1UR91FbVvg4W7UmRQ3qhw8v7CrURZB/gIAS4re+tn3ccVUpcgvYtfETceeZwG7+/glOvZbjnmf8DBVXc97SRIIaN0oVQn9+BnI8/C6r5GlqnjROxzHy63p2EAHCEB3jZjhnb/PYQ+a8N/G7hUNvHx4dOw3HPM+4UExUl2VrMG7joKkAE//+zu+vyrGU9wUumRdhZE9nOPlMM9GB8DYktNJchl7lk3GLlk2YndJfOw3HDM/4cE++7EJ7EK87FjK7A6BNitegtpvFHnSlVcCaFsIcrYmtzuO8YfIbclvYU8TEsYmYAdsQuuJY2+B/UZj3id8mpBma3J02raUu3jZ2Z6okGIgwhPck6pSnoK6WVH1K8+hG1rPy/0dlzMiX1qHx7uL2PVYT8aOu3XAHvr4S1wV8z7hu54KXHb6SVmyvgVC0uBABzw9HKgUIB2gRsVtWptWCi1kRKDPL6+1GiOF+/z8ao/1z2NH3wO//TgN+9OW21DPLzzhF12aq2PeCQ/wUmeWMWhbqm6JsA+fOCArOKAS4UApoUCplAV74d4N29vFPNyTwxKbjyJyETtUIavVJOwAqHjssS+2lVfHvBPeWnGswS3qhrotg0KdKQF7oaVVGQx9g1aj2cfozuxn6iZ+sgO2xlecxQ4gJPW3sGMYOC2uG5ZGCx/+6ph3De9PPpeEEkS7Hrhbs89tEnpoWutgOBKvVKn2lSbcId0fKVN3t2EyiRt747EiTS9ih0smYwcwYk/T+NhvOOZ9wgsHOPA8kzSFJAocmyBTFzgydTPWxqbHiCThKp9ZR6LteIH1shcxoRuV4Rx2b3AwBbt6Ke08pwGaZ1wu8d0x74S3hFDPbOwbXuL6npc3v1WUZeaVJCZcOlhd3FG9N7jjpeMAJ3JZ40uZs9j7nlSDCdgx6Dh8csnCh/+BmHdJA7Nq3Ky49gawtj31SCpLuvLdbVi2pCl9TNOU7zOLG/zq/ShYWlXxL37WijyPPZmMXRI3Ys+z5dL6AzHvEx4KCNtxcMYq9IvQAGSgua/ubXtJB0iS0MfUG4E5K11+/mT1NFgW9JEpwsaROYcdhwN/8SZgV69NmdjXrRe25LUx8xPeamERykib1WTYGhqGcRKZmIzdKe8kzzhVXdlI3pkKgkuALL6Y0UXswHTsQMCubfsm2G815n3CJ+xU4DcfgEMNTRMuYAPmdZRAspQ+Svuj9byTse2XJmQlHmrg3f34uZ6n8tuI2B/vSRA7g3349Xu47X4a9l+/h7zs7fWUXZ2Y2G845p3wfU9m4U+foTrug+rdmiP6YeCpuDvYiWgn5X4HPN5B9kdyZpKEH58kwRkv+orf5ye2Es9glz/7CHUyCTt+/mLCTTWXVz4/xcV+wzHzkka4AOKdrNuWQ5q2Y087TdjXTlwwFvADH/EJ7dzY1TDfVMmy0eY9VqTpReyyrqZjt66Sxxwd+w3HvE94KI3WFHZkAAAgAElEQVR5D7Y9lLFDwdG8eSQ9b/norxsOo2yzCJ6fkibsdHjOig2ego17rPDdoDPYdUcsU7ADVu/7hfVm4dJcG/NOeM8t8fJyqhCjGgAYBzgAkMGYiY5rcWXJBDFx0jD88c55fdyHmzzcM5HPYT85sb+JXYS/tFUZRFmXuC7mnfBdz6GMM5saAME7qe9fqQ9IahRcVQ5uDoegzqtty5rdS2Mk47g/VujnJ9O3P4MdbcD/TezAyAOyp9US18W8v3MJpfb4gzZp6Swb6bS+rz0op5bepNjLW1TGgc8yYG2Se6qjam/EkPsN3ziHvSimY09Tvk+VSmtLH/7qmPelVXWUpBjMUXtV8tK5rkZWob/4KWX09HC0y56OT4GmJR+979+ED4+mvYy9aSdj12MdsAcKwhJXxcwT3i57dRMWmPHlmW087500GNdGzPZxf7AEc+NJbhRcwGwhuz4+l8Zk9c5i12Eydj+0Us8JWrg0V8e8SxqAJ/K7B27xA8HkS1b2yF+teJI6029xDkgd9Rf7IZyWsqp44SuoLxkmmL8QdgHoATUBO3VpEroPHuuFLfkDMe8TXoQnmu2FegUxcQ663VvHpX3lZA2bWKqtx2ln4kZ+o8gnWBq3LAgtxjPYkaWTsesp9jSNjv2WY94JDy4+oLN2nrdi99GalLS/xPle97GmWoCvgbdmfuBJZ1kW3ePpm9iBq7Dr8fgm2G815v2dExmNy6w9p748uFsHSbrQyTAueTgFRUyOw/ssudFdbx8Ze5byF/Ic9v04kPomdoBPiH7gU2Efv8t0qzHzE14tSXtgVfGCN2iwu9EV+eT9X/wwylF7SYy24y+ECSLhWAeddXx45NeNGV1/ETvSdDp2PyxLE7YrY2O/4Zj3Ca+w1bcBGDoOaLpRtEh2B2hVIPmTjyYlnQCtlQ3+hE2t9hVhK7DrgO2exK2o2NltOYcdADRLJ2EPW1NdxzuAH0Yt8d0x+6NCUyaDbmzBOU14ag4DzQMOJmiUJqFG9u54ADj4uVuHz5Wy4H+x5eq8w/bvALvkWcCuwxAf+w3HvBNeQHk6ALI9vF68dg54vINuVhQX7Wwo45PqWLPtNwyQ7X6UyE4SWlfGNkVw7iJ22aymY7dyhzuvGh/7Dce8Sxpf84qMbbqhDzJ7QbZus2JnQ5kMw6/ewe0O9EcdBpYIPc0I5Hn7Ji4a2jQXset2zzJnCva248c9b6PjvvWYd8I7W+J2jlPINIXc30H3B9v0N75410MHUga0HyA/fYL2PfkqTRuGPPj4mc5664pTz4ghZUF++znsqtDjROx5NmI/ncIu8d0x74SH7XEWGRPnWFNv0jkmcJoCXcLhjOe99wMvfFnGrsYwvDITk7LgllFVxYXu3GXs3lp+CnYbXklZUFQqNvYbjnknvA7QlxegKcJ43S8/+E4HbKlCnOP7soyT1bYb1/i8YleWUcnLSeiNR4tjTWbkGeyqSt2aCdj12HMRZn9kWbR0aa6OeSe8neKhK+G1F4eBiSHCE/TAS554SbvDkafh2izmrY5Gzw0qJC7+xc/THs5gF4B1+hTsnjdf5GxJLmzJq2PeCd8b43B3eL1EkSasi01CT09ft8uh7npgt7dNoYr8FZeMF8fIoS+7y9hNF2cKdqh5tfpaf+HDXx3zTniYua+t+gVDsoYW8p4pKd6xehggieNQ6f0D5GX/2kGjH4KHUvRFaB2AtDiLnaDcNOymLe93ZN+E6XmjMe8+vDt5dBu/xEtoS1nwtQfbLPJSFwDVd19OyDKJowdqkY9U28i2MXK3uYhdsuw7sKcj9jxfLG9+IOZ9wqsGIVTJrX71e6HDAHSAdD2JVX4hJE2ts9GNtNshp/3M85aTTOdIM4gZTWsT1a9jDxbyU7DvtuzS2KpfdOw3HPM+4QX8QVfVq2FR6EMnbjw9PX12ZZYww0DvpKpiV8a3BPue/z1svvIP/g7DW1Kew/6bD9OxF0XArv0QH/sNx7xP+GEYB0w6jDyTJOclzmuv24qclAWnr+CKHbw9/W7PGv7UfeMp7tTS69Gcxf7bj6NI6jewQxy7TIPp9ETGfssx74QXRx1Fn6Qnu5ziCVebNe0c16ux1ejNfpXqwl5LXgD2sD07MSb0onitFfnnsTvaz0/C7o0S2paX1s3ixH1tzDvhAdbC1sWghczJ5a/ijqekafBHCr6sfpkCo2qXlOW4bhc7RC5jz7LJ2IN4lCe+LX34q2P2NbxurAWXpqO2YpaFUgBVOTpTqwKrKrQgtcxHLs5f+A1lPfoeWhVU9o0ZibuMXXU6dq+/0/e8AMfGfsMx74QHOIn0ui0dfY4AQPIM3V96z5NPhKdgkpBZ2LZU4O0HTi03K/qmAiwZPj2xzfcLYg/dmQnYfW3PUix9E+y3GvMuaQZlzWptPcnMwlEEWtdI/q8/4jDnSJe+sBnk5JWhMY41/Pp0OCkjQ/e8l3PYUcPKmW9jR54DjdltnvziLPH9Me+Ed46ipLBTsW7YtUlTXmjvViZqZBNZdDQDc1z2CJ6nAHC/geyPJGutV/EvretqHCZ9FbstoU/B7tWP96Ylv5zwV8e8SxodKEqqCt0fmRB3G6PY2qWwt/66l6wD+Oj3Hk5dBxEZFYSBcVE6ZtTNRey620/GHqgJsDtAbOw3HPNOeEjwLeVeJ00GtG3Z1+77sArnT0vPlfnHNGD2tmaXZ2OrMGZYvX0Ou+fFT8GuTTtij23GduMx84QHZFVyzW0Ya1wvX6F2avPxb+pcTnipM30X2az5ccY1F5G3ufSpXsTuT/Ip2GVdBexLOfNjMe+EF1Pe8rotPvqetXiaQO83ocetTTvWxQCHOc4zEoXrdW0bngxRw7mL2HVdTcauRinWtoW+bONjv+GY93fOO0837ejN6i+CRryS5y1Pzq7jiQqwC+Lt5ht6K6GnspfkOf8e+dJKkdTz2GV/nIxdtvuAHS7+lPiWY94JLwiJIGka5KKlLK1FZxc5EeD9I1uBeRb8lABY58PEi9KUHZAs42kaE3qaXMSu+/1k7Cxx0nHCurAlr455J7xzTASv1yKOJ5yvfU/sa2S7Z/vvz6l7eXsYb/mubTuWNRFD19VF7JTMm4bdS/bRFmdYLq4/EPNO+IG+TOr1FXUI6ls62GUOgFaF2UTueEraTqh31QiXXOuISFmMI/1IIbvDRey+Vp+EHRixr1fRsd9yzHvwBDvljIRF1YE9gu2LTSdla68VRVi8EBFe7kzFC3Uzdjl8b/uXxO7X+qZg7/qAXY/1IrX3AzHvhFeM3QsYrRYYPZq8RqPf5h80UGn1ZfvaEMxb3vQDsDuErxUN+jDwjnEOuxPocRp2tQGU9gOCP+0SV8W8Sxp/aX28o2OfXTyDem6WmvxGz+WIPHulrw5vRgyw/SdCZ+wij97PFucuY/c2mhOwe8k+GqC5pRf/AzHvhIewvWeaiiRgKf8EWKZ4ZS6fPEK7+rBJlOd8CqQJT90s48fHHs8nyWXswGTssNofWcakX6gFV8e8E97oJVIWYXE5nJb+8W8MxNC5sIuueDMCgOVDqI1tyBO702HcmHPYtW2nY7c+PJzwwrp0aa6Oedfw4CmHfoBsVuESF05Jb8fe9eNWkHfsa7txrJ+lkIFO1qibcT3wF8Qu+aiA9k3sNS+zqJvFwe8HY94Jr+APXpVTx9YueY11Z9I0OGRIngUXPByOPAkPR9a+nldflq/r5KjYra14DjswGXsQXk1T6H4Pub+Li/2GY94lja+1zdFa0pTai1k21r8iY+ei64JxGI416+hhsFZeRT5633OzyEzFokH3FOQz2PVYT8auZR6wy2YdHfstx7xPeCAo7qIzLRkvUeGXn9PklU1MaN85qhRIVfIXozbhozwLmpVRY9CL2P3zZRL27Z5PppyUiOjYbzjmnfCO+55qj3xpO2CzIlMyz3khvF+HroXsj0GaDjBPtJ1tCa2q4IynRQ6J3MuWsuAi9xnsyDMueU/Aru/uKb+XJIA2i4vfD8S8E34w2/XdIUhdkJRVjVPIP/uZfBRfk0vGS55JXiCl3U2wgdwf+Pc87impxxqyWZ3HnqaTscvnZ769P7DmXzyero55J3zioM8vwId3pNN6ASWvD9+0wOM9T88kocKY9al1XQFip7oINKuYfEXOdb/ICS93a+jT83nsx3o69lXJJ4AfmC0Jf3XMO+GVfHjx/qrWsw4qAObVypOwBe7WwKHmxfTphWVFz163qLJcONbsf8d20Wi7y9iTZDr2w3H8JTlRYFji+2P2xaAUOetgG6eHdTi77Klfn+t7ntwi5JM/3puDSAItTpy41aadD/Fbe78r7Nr1ozoD8CbYbzXmfcIDHLy8cFXO96y162wq6djZOHAcL9YCxKA8PWGipmnKS6CZgwF2SfwnBDseNuzUJAlwPL4N9huN2Z/wONZcplClbIenBeQmRmqaLQBGA1+zmNHj6HgnZRF8oZAm0Wv4b2L3zn0TsAMI2KWq3gb7jca8E947wxgffEzekiVKb2N5r0TgE/nxnnXz4z2Q2EmapbSCTxyXLmIPb7yV/DnsnhYxAbt0fcAOID72G475lzSez26Pf5p8KetfP8X0pgKSQY9mFyPCFl/HLogcbZqpCvjVu18Su21ETcGuuwP9n/6cRuUS3x/zTng1HokJMkmRj6544kjK8oxDwCaad8CnL8D93Qnj0NS+jt2YdGXcpAndmDPY4YRl1gTsklB2T6oKCu7LLnFdzDvhBcGuEc7q2ySBrDbkkxxOLm9emNTc8nA4hi6IDAPQDTQp6Dp2PXb7M//o7wi6XzI/g11s7XAKdlUN2MU56McvUbHfcsw74R1FR4P58LEOYkRSlhzIHEiukq6H9FQX0/2BtIPHDWRPy3eo8mRsO9bEWQp8igddNytIP5zFjsSRbzMBuzgXKBSyP9IfKiL2W455J7wqJ5B74aM/y3gBHQaWDDvugYq5auj9BrI7BNKV/OnHV6xKcS5oTEob939dmpb0gnPYJadT9xTsAH9p9mxTLm3J62P+XZq7NXvXvp73mi3Oje05b1FvJYJ6LrlnGFYly5g9qQUc5ESetHb9Zex+iDQFez+M2Ns2PvYbjtmf8NIPQFkGFw3tB24seS6N3x3VcSlE8nwc5XuZOy9J7evmyFIX2nVWpnwdu75sIUUxDfuq5P+rlxpZZDqujnknvCdT3a34GBeBiGm8pwkvdGURZDjk2AB5hv7dGslvn6AfHlkDH2tSgm3x4y2cuHG3Bo7NWez48I5jhinY1xXreE8tXpy4r455J7yXl/vpE0/HIuefmxXk2FCrsW7C0oSn0CZ1Q1HSY81xftOOpKtBoX0bevjR4sszNEkuYz9Z+LiIfX+Emk8ruo5PhSWuinnX8M5x0SPLOI30m/sAW3QmUgonY72cJEwSLzwqEgwJJM8pz5ck0TXiZb26jN1b2kzADiBg97uxS1wX8z7hFSMX3FTGAFAjXRwAipRymbvhKZmlLFnqBiiy0ffUS9Xt9rw0xk6armdr8gx2L6o0CbsT8oZ2e1tEX+Syr415JzwAfPzMkbof3ohjD97LbdgFMNiz12b2q8puxqBQKKeVO+q7aNdD9A3KggvYIQLZrL4bO/r+lav3Et8X805434MurSvht/1BUSPK1PlEKlgXZ1QJ5pIFdV8oWT2Ek1OkD07X0aD7DssZ7NpYD34K9qYl/iL7x1mUS3xXzDvhgSAmir5nHe6Tw3radNqQUUPSDMu0bSFqigBdT5kLl/DPE3HVXwq7JG469rYFxI2ers0ipnptzPvSeiqWZEsU3hzAC5MGN78kId8EoBa7UFJPD4fRBW+wmt/r2sSEfvoE+Rr2fpiOPUkCdngHwCWuitmf8LJaQauCk8imJVXWBFGlqsappCWPrKuTaWUCFKYSAMckS+zPVeThjchl7OuKigRTsKcpdLBa/nCAbNZxsd9wzDzhKZGHpoF6uqx3xDgeR8lpl0AKs4XZHYChZ3ng62CAEhnGKQ/lRMzwM4Qz2CXLWJpMwI6+GbFn2dKH/4GYd8IreBqaQ4ZaFwMiJF493I0DpLqxjohA0tctx2AxU5UkdTXt2BOPBd0Ymuew67Ee++nfwj6cYK/rsU+/xHfHvItBS0o16/VQF/c9yxnPizGhUXHulY+Sdv1IMMsyep3WdXT3DwCjDeUZ7FIWk7FL4gJ2rx2/xHUx7++e32nNc04hbeNf23H9jYvP3uaxZxngE0zpzwqvHizCy2GSvL5UxgjvP3UOe5FPxo6qDNilLOJjv+GYd8KfXPyQZ1xxO+lXw7oZgNXFRT4+8s0aUtuW1IK7DT+uKtgZySInTZZexK5pMhm794nSquCdJjb2G47Zf+f0WFNpN004fXSUkZaqpPz0ZgUAJlnnIPd30MRRh+Z4ohhsEtR42bHjkUbuw6texv6ym44dABoALzuWNP3Clrw25p3wOrD/7MuBruMJua6gT9vRyeNYs0SoG/75sguTWO0HkrVExhrYSfwlCr/PegY7//++A3vXcYaQZ8sCyA/EvEsaCHvOJ495OMek8MleG/HKXPpQN1TqsoEOHk2WripHtQCrq6OGc5exA9OxA+MvKrB0aX4gZn7CW1lgZr6+5oWzv3sCmZGrII5PhZcdT8SuozRdnlPhYOghSRZG+lGh279/DjuaBsA07Nr1I/aFOPZDMfMTHqF3rV03noy99bittae7PYL9I2xn9KSToW3L8sD483K3HtuCvxR2c/GYgl3W1Yg9y94E+63GvE94IHQuRCSQqAC8PjEBTixFoOKCRaWKg2R2UXQuDKD0+WVcoo4Vg17G3nUnS+iXsYfhmmogmi1xXcw+4SXLxgGMCGm2fnjke9i2UKFqjtbDYDJ3HExJVbKzcaz59Yqc/e2Y4QTizmNXe20S9roZfansTrDEdTH7hNeuh8CP6oeRj9KSIy5FzlNeBGIDHjHbdk+lFWcsRR2gPZj4b5A0F7H7iekU7F59uIcRzpZL67XxT8RRoQMTW9I0bPtIlo21rueR+0viMHDQ5M0HgLGzMfQsN97INuYsdpPmmIJdffk19OTPL5Y3V8fsT3g51UL3Jl9FzhP7pNMSliQcOOzxNX7XsZQYBrYGu2503vgFsUtRBMHUb2EPasFeFzNyh+mWY/YJr0077oMmjvWvV+b1Tneed+IUcCNJK3RmGu6QBtEjb2gcMwa9iF0Ph/Gk/xZ2XwL5p8JywF8d8y5pVIHWm/t27EHbsgS55tZuBMgfd0l4n9aNTWr9ErUGaY/w95jRdZexu2Q6dvsa4vXmY2O/4Zj3CS8CZLyUSlGQNFUbAavr6XlqtbHerSk4anukQbMG1g1xtvGfZUBfx3ezzjNL2K9jhw5c8J6CvQeQgIKscVHffMw74RM+xiWv2K7zBDAxM4HWetm+POl7+rA6N2q8NC3r4GEA8jzY1Uev4+/WwJfn89i9uOoU7NaK9EsjunBpro55J/xwMpFclRA/dgdoHuDbjYBZtyfQLV3z4IVJgeDPirZlz3sYIOtVVOhyqPk0OYc9SYBEJ2HXw5F8ePN4io39lmPeNTyshz0MwDMZhj4RwgndjYzE0P3wda99jO4ObO2Z9LSc/KL8k4AdQMCuh8ObYL/VmHfCO3O4cy5s+ai59wEYvY6c49u+THDCJ0OW8jRflSwx2o5fL0mic8p1VV7G7suSKdgHHbEDCx/+B2LeCQ+YjoudelVJ9YEs42npBZWco+tdkXORwuwg1T4W/UCd+TQZZeoidzqkHy5iD7LXU7DfrQN2L/OxxHUx74RXsLZNE+iqJMXXnD+0pLfp8OGR3Y37NU++lgk9PGwgg46Pf7Nv180qfG5c7HoZu+pk7P27dcAejJWXuCrmfWn12pINe+6wzowea56gAKTr2K47bevZ5+huD9yt+bFtNxoPdF38wZOpnZ3F3vdwP3+ehD3545/5tY4NF1kOi8fTtTHvE/40Kc3mUU1b3dvCAKyXySik67VX+xLnmCTey9UkM2DmYlHjdNH6a9i9NuYE7OqHTX0PfX55M2rELcbME96WN/KMj3HrU2vbQpsG+nhHvvnTC9+fpvyYrmeC+ctg0/L9nnCmyktsxNAsvYz9fj0Zu5gLINKUNXxk7Lcc8y5p+gGyqqCfnyhRI46MQS8ouj2M5l/+Qmc9bm1byHbP09wk7/znaT8AfpE6Vnx5of77Gez66SlsQ30Tu60EBiOH2NhvOOad8E44MHq85/TxywvgbK/T28hk5pxxrHnxM01Gub9jKdO0wO/9CvK848f2PbnnVQH8Nh50KYuL2KUfpmP/+AVYVaP+5ONdVOy3HPNO+EF52h0TSlt48SIdOH0sC6iZ9KrfCT3RVYdJ1smnJ/a0h4FOeEUO2cYd3nhm5lnsWQZ92U3CDoBljjcm3u6jYr/lmHfCi9AipirC+puk1k8XAQ5HjtvNMUNftkYOGx0+/OBGrDMTFqAjL0KLcXzOYdc0CR2mb2HXIqMwk2lPxsZ+yzH/S+uqDG1FLWxYMwzQLA2KwHBCHsqHdxg+3LNfrYrhVw+8/CWOF9zeBEqLHJpE/l937iJ2OdSTsftSDHnGO01s7Dcc8z7hBwWeXkbDgKeWLbquA3Z7vt6O1u7ysgM+PwPgzEr+9CNLh7aFdH1YppCqAra7qNC1acJF+WvYNUkgmn03dtIiFi7NtTHvo8L32kWgd+vAPuRrDnq35tQSdAoBQOPfsuAYv6LFo6xXXKpIUxK0ui66i0awszmDXapyMnbftvS2OIsDyPUx84THyCE/NpxSGuNQ1hXr2t0B/e+/G/VamnZ0yPDydX0P/OY9nxhpQhZj7Emrcxexo/4O7HUTsL/JlPiGY94J76eLjVm1pwl0b+a8VsroMMD9v7+FFIVJd/Sjf1PXjw4aP3/ma41x4mN3Osy97yx2YDp2r7zgZbOXLs3VMe8aXgR4vCeRyngowb3P02rXFWR3INPQVeOlcFCWDMeaJYxnVyaOSZ+/wYrfujqLHc4B95tJ2L3UB7E38bHfcMw74YcBcqhpYDaMm0DBp/TjFwBg16LruP+qA7A/0Ehsbzz0pg9ueHQOia+xrtsdW6pnsOtuR5xTsIvQ6Myw636R27s25l3SeJJVmnLqqkMYw6uXpPOP+yxnjz1JeBrmGTUb/apc0HEUeL/TqNDz/DL2UwzfwB4sbpzYJXbe59ScY97fOa/ElWXAqoLmGfTLM19bVaxpi5w1cdex5bheGQ2Yn6/3GxK0Erpwh1+A2BrraXoZu3Psw0/BDlAuO89JPfCbT0t8d8w74W0jSA8HYEeuSfBIetmyf33qlLGqXisDD8oxfZHzaww6XgQjq/Dq4UCfpnPYuw7iHbq/hd3KIh0G9uIXjfirY94JnyS0X//9X8Nt98GmUpxD/xd/jeTzCzRNSMDa06hY1iugH0gOU4X0AznnZc4LYllAy5xrdZ8jYn+8h355/p1jR9MC93dxsd9wzDvh+2H0Yz21d3QC90e/BcqCU8iXHX1Y2xYCk6fuB36eCLsy2z0VCw5HyFtI7T1tL2NPE0iXTMN+bEgkOxy5RLJ0aa6OeSc8OKIXL4garNhPRutdN/qwliV738MA6TomeJ5RJiPLIClok1M38TXWnVzGnuTTsZeF2Vqas1+zCDFdG/Pu0gBjgnjBpTQZL5wt+SleDcAb/4pIsHIPMtSm2+jFjKIn/OkC+Vew6+EwHbunSSeOfPrFEOHqmPd3TthC1GEYW3bAKFrkxUm7PgiTSpbxJDQqsGQZBYyGgZqN9nbsix/pweexy2o1GTtUA3YvCrvEdTHvhFcloco5ulCXdLTztjV6PAK/+UCn7XS0gpGy4AXPc1GccGJZ1+HrIXJrT5v2Mvb9fjJ2rZuAHUB07Lcc86/hn19YHngh0dPXm5bbTF03rsh5P9NPX0xo1SaZXmV4u+duaWxzMCeXsff9dOw2GVa/5/rlKS72G455J7wTai2+29CefVCIdND3D5CnLfTxnhJ05oMaamS/5S882WFSGPrwyAXo3/sVJTAitvYkz0kHOIf9bgXsj5Ow62bFE/9pSzsckaUteWXMv6QpciZM1/OS2vXAn/0MgAq9fqcVD5uwTEGiVgI91hz07PY8SZ+2rKU/fom/NdR1l7G/7Cdjx28/Bey63S8bTz8Q8z7h05Q//HUF2R+hW7bjgplBnkF/9QDsa+q52MdLmnAF8IGnoRxq9L//Du5g7hplTkWwiDF8eITb7s9jV4X+xV9Nwq6rkuKq3nl8aUteHfNO+LaD7g48Jfs+mAHo3ZorcaqQP/mZ7brelHa9pF7d8HLX0Acq+eOfmWR1A7nfjKdrpJCfPkEHPY89zyZj9/+vWjfk5SwJf3XM/NloHk99D/XJ6hxH8QC7GAPFiyTPuAzt3T2KfNweGtiKVCszdLuL74TX9xex63Z3FXYvqb3EdTHvE945yOODSUo3wRbG81KwqoBkDVgSyWplmi7KFbrUdF4SB1QFlQL6niP/yCGVKQ6cwS55zsvoBOwiMs4c9vs3wX+rMe8TXsEk8DrrIlBTztW2Hbf9YaKkXUcdmLLgL4MPEZKvfP+9616LncaIYbiMHZiMHd7epzBd+djYbzjmnfDA+MP2gxlbk4M49rPTlEOZA7saYnWwvuzIYWlblguW6BQ3Td/GVOASdltgmYJdzRpH84xfazFEuDrmfVToQALWn/8BJwmT59hzqcOP5K1dp007kreco3vf4ciT/UtPabvITnhaU/P9LHZb5ZuG/cBVP499oRZcHfM+4cUBH96NVN4sI53g4Y7vNj0XJAkTpsi5BJ24V5LSWubkp2RZ0HyJ7dMqRXERO3wvfQr2sgzYg9LwElfF7E94fKRLBjeDDpy8Pr2w5eenk13Hlp2RsBQAXnbB4h3PA9W8zOsUXT+yJmNBr2vgeDyP3e+2TsCOnErJeqwDL2iJ62LeCS8yymvoMHq2+jrYEkBFAhvRe6Gi7gJ3BVCjTXUAAAKsSURBVL50sH1QfkzchD8dMH0NOxwlN6Zg9y1K8RLhSw1/dcy7pDErR+06JnhHOi3r4IQEK28J2XXmlGcXUnGjEq8JNkGH8ZcltkxH217G7lUXJmCHnmAHomO/5RCd8WkhIj8B2AH4+ZfG8obxK/z/7Z2/axRRFIW/g5hKQSMSQgz+AJt0LkFSiLVsE0srU/gHaLngX6CFhWAjKEQQbRRMY6ESsDIoojEaNImNymoKQe20uBZzhUHEYpnMm513PxjmznlT3PM4zLw3W2xefqF6z/vNbO+/BhodeABJz8xsOnUfdZGbX6jXc7OXNEFQMRH4ICuGIfBXUzdQM7n5hRo9N34NHwRVMgxP+CCojMYGXtIJSW8lrUvqpe6nKiRdl7QpaaWkjUp6IGnNz7tdl6TLPgfLkjrpOh8MSZOSFiW9kfRa0lnX03g2s8YdwDZgAzgEjAAvganUfVXk7TjQAVZK2kWg53UPuOB1F7hP8ec/M8BS6v4H8DsOdLzeCbwDplJ5buoT/iiwbmbvzewncBuYTdxTJZjZY+DrX/IsMO/1PHCypN+wgifALknj9XRaDWbWN7PnXv8AVoEJEnluauAngA+l64+utZUxM+t7/RkY87pV8yDpAHAEWCKR56YGPluseK+37tOZpB3AHeCcmX0vj9XpuamB/wRMlq73udZWvvx5bft50/VWzIOk7RRhv2lmd11O4rmpgX8KHJZ0UNIIcApYSNzTVrIAzHk9B9wr6af9y8UM8K20DBgKJAm4Bqya2aXSUBrPqXfx/9nddyl29BvA+dT9VOjrFtAHflGsT88Ae4BHwBrwEBj1ewVc8Tl4BUyn7n8Av8colivLwAs/uqk8xy+tQVY0dUkTBFtCBD7Iigh8kBUR+CArIvBBVkTgg6yIwAdZEYEPsuI35mdSnJuwkUgAAAAASUVORK5CYII=\n" - }, - "metadata": { - "needs_background": "light" - } - } - ], - "execution_count": 43, - "metadata": { - "collapsed": false, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-20T17:14:51.274Z", - "iopub.execute_input": "2021-01-20T17:14:51.307Z", - "iopub.status.idle": "2021-01-20T17:14:52.930Z", - "shell.execute_reply": "2021-01-20T17:14:52.998Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "concatted_nat.size" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 44, - "data": { - "text/plain": "5666560" - }, - "metadata": {} - } - ], - "execution_count": 44, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-21T20:22:01.073Z", - "iopub.execute_input": "2021-01-21T20:22:01.102Z", - "iopub.status.idle": "2021-01-21T20:22:01.250Z", - "shell.execute_reply": "2021-01-21T20:22:01.304Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "import numpy as np" - ], - "outputs": [], - "execution_count": 45, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-21T20:24:10.930Z", - "iopub.execute_input": "2021-01-21T20:24:10.958Z", - "iopub.status.idle": "2021-01-21T20:24:10.997Z", - "shell.execute_reply": "2021-01-21T20:24:11.023Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "np.prod(concatted_nat.shape)" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 46, - "data": { - "text/plain": "5666560" - }, - "metadata": {} - } - ], - "execution_count": 46, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-21T20:24:33.260Z", - "iopub.execute_input": "2021-01-21T20:24:33.293Z", - "iopub.status.idle": "2021-01-21T20:24:33.369Z", - "shell.execute_reply": "2021-01-21T20:24:33.402Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "concatted.size" - ], - "outputs": [ - { - "output_type": "execute_result", - "execution_count": 47, - "data": { - "text/plain": "4976880" - }, - "metadata": {} - } - ], - "execution_count": 47, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-21T20:24:48.298Z", - "iopub.execute_input": "2021-01-21T20:24:48.337Z", - "iopub.status.idle": "2021-01-21T20:24:48.405Z", - "shell.execute_reply": "2021-01-21T20:24:48.437Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "# I expect the number of NaNs in concatted synth to be \n", - "# size of concatted - sum(sizes of all the sessions)\n", - "print(np.count_nonzero(np.isnan(concatted)))\n", - "print(concatted.size)\n", - "print(sum([bk19_assys[k].size for k in synth_names]))\n", - "print(concatted.size - sum([bk19_assys[k].size for k in synth_names]))\n", - "assert np.count_nonzero(np.isnan(concatted)) == concatted.size - sum([bk19_assys[k].size for k in synth_names])\n", - "# or\n", - "# sum(shape[0] of all synth assys) * sum(shape[1] of all synth assys) - sum(sizes of all synth assys)\n", - "assert np.count_nonzero(np.isnan(concatted)) == (lambda x: sum(x[0]) * sum(x[1]))(list(zip(*[bk19_assys[k].shape for k in synth_names]))) - sum([bk19_assys[k].size for k in synth_names])" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "4319940\n", - "4976880\n", - "656940\n", - "4319940\n" - ] - } - ], - "execution_count": 61, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-21T21:34:10.324Z", - "iopub.execute_input": "2021-01-21T21:34:10.354Z", - "shell.execute_reply": "2021-01-21T21:34:10.462Z", - "iopub.status.idle": "2021-01-21T21:34:10.422Z" - } - } - }, - { - "cell_type": "code", - "source": [ - "# I expect the number of NaNs in concatted naturalistic to be \n", - "# max(shape[0] of all nat assys) * sum(shape[1] of all nat assys) - sum(size of all nat assys)\n", - "print(max([bk19_assys[k].shape[0] for k in nat_names]) * sum(bk19_assys[k].shape[1] for k in nat_names))\n", - "print(sum(bk19_assys[k].size for k in nat_names))\n", - "print(max([bk19_assys[k].shape[0] for k in nat_names]) * sum(bk19_assys[k].shape[1] for k in nat_names) - sum(bk19_assys[k].size for k in nat_names))\n", - "print(np.count_nonzero(np.isnan(concatted_nat)))\n", - "assert np.count_nonzero(np.isnan(concatted_nat)) == max([bk19_assys[k].shape[0] for k in nat_names]) * sum(bk19_assys[k].shape[1] for k in nat_names) - sum(bk19_assys[k].size for k in nat_names)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "5666560\n", - "5356800\n", - "309760\n", - "309760\n" - ] - } - ], - "execution_count": 56, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - }, - "execution": { - "iopub.status.busy": "2021-01-21T20:49:48.984Z", - "iopub.execute_input": "2021-01-21T20:49:49.018Z", - "iopub.status.idle": "2021-01-21T20:49:49.082Z", - "shell.execute_reply": "2021-01-21T20:49:49.113Z" - } - } - }, - { - "cell_type": "code", - "source": [], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - } - }, - { - "cell_type": "code", - "source": [], - "outputs": [], - "execution_count": null, - "metadata": { - "collapsed": true, - "jupyter": { - "source_hidden": false, - "outputs_hidden": false - }, - "nteract": { - "transient": { - "deleting": false - } - } - } - } - ], - "metadata": { - "kernel_info": { - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.7.2", - "mimetype": "text/x-python", - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "pygments_lexer": "ipython3", - "nbconvert_exporter": "python", - "file_extension": ".py" - }, - "kernelspec": { - "argv": [ - "/Users/jjpr/anaconda/bin/python", - "-m", - "ipykernel_launcher", - "-f", - "{connection_file}" - ], - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "nteract": { - "version": "0.27.0" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/packaging/notebooks/2020-11-22_dldata.ipynb b/packaging/notebooks/2020-11-22_dldata.ipynb deleted file mode 100644 index c2c66bb9d..000000000 --- a/packaging/notebooks/2020-11-22_dldata.ipynb +++ /dev/null @@ -1,1829 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from dldata.stimulus_sets.hvm import HvMWithDiscfade" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hvm = HvMWithDiscfade()\n", - "hvm" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tabarray([ ('Apple_Fruit_obj', 0. , -0. , -0. , 0. , 0. , 1. , '01c0425e982e4edc276fe5373dbf8e5e49bd9a16', 256, 'V0', '01c0425e982e4edc276fe5373dbf8e5e49bd9a16_V0', 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\n", - "15 -0.000 -0.000 \n", - "16 -0.000 -0.000 \n", - "17 -0.000 -0.000 \n", - "18 -0.000 -0.000 \n", - "19 -0.000 -0.000 \n", - "20 90.000 -0.000 \n", - "21 90.000 -0.000 \n", - "22 90.000 -0.000 \n", - "23 90.000 -0.000 \n", - "24 90.000 -0.000 \n", - "25 90.000 -0.000 \n", - "26 90.000 -0.000 \n", - "27 90.000 -0.000 \n", - "28 90.000 -0.000 \n", - "29 90.000 -0.000 \n", - "... ... ... \n", - "5730 108.965 -2.787 \n", - "5731 39.683 -43.622 \n", - "5732 175.425 -72.142 \n", - "5733 81.842 -8.823 \n", - "5734 51.514 75.404 \n", - "5735 165.191 -82.655 \n", - "5736 178.226 77.849 \n", - "5737 153.350 77.975 \n", - "5738 100.359 -67.726 \n", - "5739 20.711 30.773 \n", - "5740 164.496 -24.282 \n", - "5741 123.535 -2.610 \n", - "5742 106.022 27.868 \n", - "5743 168.461 -87.580 \n", - "5744 53.356 18.413 \n", - "5745 20.940 57.500 \n", - "5746 44.236 65.502 \n", - "5747 101.280 -0.264 \n", - "5748 146.828 -0.592 \n", - "5749 84.323 11.103 \n", - "5750 176.269 84.744 \n", - "5751 63.561 -73.545 \n", - "5752 64.282 75.781 \n", - "5753 94.012 72.695 \n", - "5754 13.512 -31.938 \n", - "5755 139.824 66.625 \n", - "5756 120.229 -87.242 \n", - "5757 154.361 38.988 \n", - "5758 38.006 51.389 \n", - "5759 101.540 79.689 \n", - "\n", - "[5760 rows x 17 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_meta = pd.DataFrame(meta)\n", - "df_meta" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all(meta[\"id\"]==df_meta[\"id\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dldata", - "language": "python", - "name": "dldata" - }, - "language_info": { - "codemirror_mode": { - "name": 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-30,Beetle,0.0,-0.0,-0.0,0.0,0.0,1.0,0a94e5e3111d74320cd08361e2c9d8c87aaac12f,256,V0,0a94e5e3111d74320cd08361e2c9d8c87aaac12f_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_0a94e5e3111d74320cd08361e2c9d8c87aaac12f_256x256.png,207af40ae3cdf86f513e8e3e705c6cea1d53f648,Cars,0.0,90.0,-0.0,30 -31,Beetle,0.0,-0.0,-0.0,0.0,0.0,1.0,10cae423ac465104444c7dc4826ebe38b4b7bc48,256,V0,10cae423ac465104444c7dc4826ebe38b4b7bc48_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_10cae423ac465104444c7dc4826ebe38b4b7bc48_256x256.png,177833c929c4cd0fbebfc7291b37af6e183c5982,Cars,0.0,90.0,-0.0,31 -32,Beetle,0.0,-0.0,-0.0,0.0,0.0,1.0,11b63b2c5269c46021a8f2be4cae8e51e3f70a3a,256,V0,11b63b2c5269c46021a8f2be4cae8e51e3f70a3a_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_11b63b2c5269c46021a8f2be4cae8e51e3f70a3a_256x256.png,4e321648eb945616c7454148db3fcd861da48472,Cars,0.0,90.0,-0.0,32 -33,Beetle,0.0,-0.0,-0.0,0.0,0.0,1.0,1b5c48a9f7f467865ff32d84e8e29dc03aa751b6,256,V0,1b5c48a9f7f467865ff32d84e8e29dc03aa751b6_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_1b5c48a9f7f467865ff32d84e8e29dc03aa751b6_256x256.png,650358beac9b9260dea25ff5bc65ff943b7bbd1a,Cars,0.0,90.0,-0.0,33 -34,Beetle,0.0,-0.0,-0.0,0.0,0.0,1.0,73fa52b75fc56362bfa856785dfb051c2851bb76,256,V0,73fa52b75fc56362bfa856785dfb051c2851bb76_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_73fa52b75fc56362bfa856785dfb051c2851bb76_256x256.png,929596b60d1901a7137261510d2760ba7fd48795,Cars,0.0,90.0,-0.0,34 -35,Beetle,0.0,-0.0,-0.0,0.0,0.0,1.0,7bda99c1885fe0b07f9acfd12018cf94a164c626,256,V0,7bda99c1885fe0b07f9acfd12018cf94a164c626_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_7bda99c1885fe0b07f9acfd12018cf94a164c626_256x256.png,1c2c7701c93207cc8166966ba530573c7d8e2526,Cars,0.0,90.0,-0.0,35 -36,Beetle,0.0,-0.0,-0.0,0.0,0.0,1.0,90f464d4725214ddae0e9d2dd2e759db0561cb94,256,V0,90f464d4725214ddae0e9d2dd2e759db0561cb94_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_90f464d4725214ddae0e9d2dd2e759db0561cb94_256x256.png,05954905d976ab1cf247c49fc31e534a8a047ba1,Cars,0.0,90.0,-0.0,36 -37,Beetle,0.0,-0.0,-0.0,0.0,0.0,1.0,a8c6688324da3a7fca5ae52a94b76caed0d64503,256,V0,a8c6688324da3a7fca5ae52a94b76caed0d64503_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_a8c6688324da3a7fca5ae52a94b76caed0d64503_256x256.png,4cab9e81ffb339bad86cbf9a38f611438ef4a189,Cars,0.0,90.0,-0.0,37 -38,Beetle,0.0,-0.0,-0.0,0.0,0.0,1.0,b125a656add83a448a3dc04fc7f1110516328f2d,256,V0,b125a656add83a448a3dc04fc7f1110516328f2d_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_b125a656add83a448a3dc04fc7f1110516328f2d_256x256.png,42a9123430c3818c8642254cb15fb67cce594b2e,Cars,0.0,90.0,-0.0,38 -39,Beetle,0.0,-0.0,-0.0,0.0,0.0,1.0,ee4f3b944cbc347eb1018a12ad762a232e23d06a,256,V0,ee4f3b944cbc347eb1018a12ad762a232e23d06a_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/Beetle_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_ee4f3b944cbc347eb1018a12ad762a232e23d06a_256x256.png,044b86ce88cd8bbb047c076d4427a59050c5897a,Cars,0.0,90.0,-0.0,39 -40,CGTG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,1279014ce3fe19b50fc8dcb72f9736382ac7a892,256,V0,1279014ce3fe19b50fc8dcb72f9736382ac7a892_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_1279014ce3fe19b50fc8dcb72f9736382ac7a892_256x256.png,c43cefc1318a495618474f27b29bbebaf1638aab,Boats,0.0,90.0,-0.0,40 -41,CGTG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,1827371389795949398e266c2ddac7a670852760,256,V0,1827371389795949398e266c2ddac7a670852760_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_1827371389795949398e266c2ddac7a670852760_256x256.png,1f2b884951df98f2f0b26cb8d420bbca8b74ae50,Boats,0.0,90.0,-0.0,41 -42,CGTG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,2da20b81d55b817f1019f791d9776723cc86d503,256,V0,2da20b81d55b817f1019f791d9776723cc86d503_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_2da20b81d55b817f1019f791d9776723cc86d503_256x256.png,a82b06560fec76771e760afddde3aa4bd5cc3355,Boats,0.0,90.0,-0.0,42 -43,CGTG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,5f6accc3e7c90a71cf4bcd324142b06a4fcfb778,256,V0,5f6accc3e7c90a71cf4bcd324142b06a4fcfb778_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_5f6accc3e7c90a71cf4bcd324142b06a4fcfb778_256x256.png,64b31e4fc9af784e16ea3d2e2c48dd1eda5a261e,Boats,0.0,90.0,-0.0,43 -44,CGTG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,80fa1c6e6259b7a1eabe49f06c2d15916565b15c,256,V0,80fa1c6e6259b7a1eabe49f06c2d15916565b15c_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_80fa1c6e6259b7a1eabe49f06c2d15916565b15c_256x256.png,a79446757c4f7e0b257d6917bc1741e4f34b4bdd,Boats,0.0,90.0,-0.0,44 -45,CGTG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,9770675a55c7f0a643d555d1c5615f814698d4f5,256,V0,9770675a55c7f0a643d555d1c5615f814698d4f5_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_9770675a55c7f0a643d555d1c5615f814698d4f5_256x256.png,f7da8f132bb70207da9462d491503cc742070bd1,Boats,0.0,90.0,-0.0,45 -46,CGTG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9,256,V0,a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9_256x256.png,e134c3d8fa8be11c8eb0b8d55e7f534286f44736,Boats,0.0,90.0,-0.0,46 -47,CGTG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,c6dfaa2e1991caf236d96fea30f655efe9777118,256,V0,c6dfaa2e1991caf236d96fea30f655efe9777118_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_c6dfaa2e1991caf236d96fea30f655efe9777118_256x256.png,a19201d2d1d62feb2d6b07d010ccf8ca794b4d07,Boats,0.0,90.0,-0.0,47 -48,CGTG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,d4c45585948d33966a451cb6e618505693091c4a,256,V0,d4c45585948d33966a451cb6e618505693091c4a_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_d4c45585948d33966a451cb6e618505693091c4a_256x256.png,2f95ff072713b48ca5a841a0e40b6776d50ce299,Boats,0.0,90.0,-0.0,48 -49,CGTG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,e4dfdcce5a2597e632e6d87378c58e4f99621199,256,V0,e4dfdcce5a2597e632e6d87378c58e4f99621199_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/CGTG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_e4dfdcce5a2597e632e6d87378c58e4f99621199_256x256.png,7e7ba5aeb4b0cda8d21464334cc7e885c338c46c,Boats,0.0,90.0,-0.0,49 -50,DTUG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,0392f019836b4ff006d8360fa98b7ce556de9848,256,V0,0392f019836b4ff006d8360fa98b7ce556de9848_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/DTUG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_0392f019836b4ff006d8360fa98b7ce556de9848_256x256.png,2e10c8194b3537804fbe6d587f2b4e1362e5ad59,Boats,0.0,90.0,-0.0,50 -51,DTUG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,06e48c385078bdf9b730624c34ea62267d6c01ef,256,V0,06e48c385078bdf9b730624c34ea62267d6c01ef_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/DTUG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_06e48c385078bdf9b730624c34ea62267d6c01ef_256x256.png,025f6fd6e41e19a963994f40a407b552364e7e20,Boats,0.0,90.0,-0.0,51 -52,DTUG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,3270ec6eb7918f7e565da242721ade80f9895a32,256,V0,3270ec6eb7918f7e565da242721ade80f9895a32_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/DTUG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_3270ec6eb7918f7e565da242721ade80f9895a32_256x256.png,b713c093a9135539f1fdd0b92c44f59688267561,Boats,0.0,90.0,-0.0,52 -53,DTUG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,33d0e5d4be161f0fda147f577cb3ced00cba7696,256,V0,33d0e5d4be161f0fda147f577cb3ced00cba7696_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/DTUG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_33d0e5d4be161f0fda147f577cb3ced00cba7696_256x256.png,0bf1b0c17bdbc60c5c927e536209737e13141274,Boats,0.0,90.0,-0.0,53 -54,DTUG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,561c84a23fcc11df50a1c441a23d19cb48d35220,256,V0,561c84a23fcc11df50a1c441a23d19cb48d35220_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/DTUG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_561c84a23fcc11df50a1c441a23d19cb48d35220_256x256.png,0015b49a190e9bce70b108b28dc1a0674d3c9e66,Boats,0.0,90.0,-0.0,54 -55,DTUG_L,0.0,-0.0,-0.0,0.0,0.0,1.0,72f9342dcd5fbfd4da495874b74f5751b89182b1,256,V0,72f9342dcd5fbfd4da495874b74f5751b89182b1_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/DTUG_L_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_72f9342dcd5fbfd4da495874b74f5751b89182b1_256x256.png,30e28e1a41f9aaee0024af7236d1bd31ac77bfc4,Boats,0.0,90.0,-0.0,55 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-64,ELEPHANT_M,0.0,-0.0,-0.0,0.0,0.0,1.0,396df21791761d666ddf50a145b3a3ecaa71b402,256,V0,396df21791761d666ddf50a145b3a3ecaa71b402_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/ELEPHANT_M_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_396df21791761d666ddf50a145b3a3ecaa71b402_256x256.png,3d5a8e86dbf72f2b5f355eac6f3b58b7b74a4f86,Animals,0.0,90.0,-0.0,64 -65,ELEPHANT_M,0.0,-0.0,-0.0,0.0,0.0,1.0,39e1ee0316caddc003e8876d2bed4bebb58cc7bd,256,V0,39e1ee0316caddc003e8876d2bed4bebb58cc7bd_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/ELEPHANT_M_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_39e1ee0316caddc003e8876d2bed4bebb58cc7bd_256x256.png,127cc3c176ac940eb3b9a0c681a5cf3717002fcc,Animals,0.0,90.0,-0.0,65 -66,ELEPHANT_M,0.0,-0.0,-0.0,0.0,0.0,1.0,453b1827b6444f117df2d36b071a9bfa76819773,256,V0,453b1827b6444f117df2d36b071a9bfa76819773_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/ELEPHANT_M_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_453b1827b6444f117df2d36b071a9bfa76819773_256x256.png,f2aa920dc7e7f758bc41d8df683f1e2f8386cd24,Animals,0.0,90.0,-0.0,66 -67,ELEPHANT_M,0.0,-0.0,-0.0,0.0,0.0,1.0,5b8bdcb3d62ab99558019aba3d1c71ff2f64056b,256,V0,5b8bdcb3d62ab99558019aba3d1c71ff2f64056b_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/ELEPHANT_M_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_5b8bdcb3d62ab99558019aba3d1c71ff2f64056b_256x256.png,c4fac169f2c9ce65d4647435e1d720ea25554dfe,Animals,0.0,90.0,-0.0,67 -68,ELEPHANT_M,0.0,-0.0,-0.0,0.0,0.0,1.0,5c5642f6e26bbf0f69c20ffa979006699f6b46ed,256,V0,5c5642f6e26bbf0f69c20ffa979006699f6b46ed_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/ELEPHANT_M_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_5c5642f6e26bbf0f69c20ffa979006699f6b46ed_256x256.png,c0faaf46dcecef224e90f0a3e9751ca447543f2a,Animals,0.0,90.0,-0.0,68 -69,ELEPHANT_M,0.0,-0.0,-0.0,0.0,0.0,1.0,dd22af46339b5d82d9f6bce3e35ef0a1befc6aeb,256,V0,dd22af46339b5d82d9f6bce3e35ef0a1befc6aeb_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/ELEPHANT_M_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_dd22af46339b5d82d9f6bce3e35ef0a1befc6aeb_256x256.png,2aaa58fffd1f4186dbfb3ac75fcd0a97384845e6,Animals,0.0,90.0,-0.0,69 -70,GORILLA,0.0,-0.0,-0.0,0.0,0.0,1.0,0a236f86457c43fa178a0d1f15f7f43a08f5266e,256,V0,0a236f86457c43fa178a0d1f15f7f43a08f5266e_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/GORILLA_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_0a236f86457c43fa178a0d1f15f7f43a08f5266e_256x256.png,733e5e2a5ec68f5320b25e1ae349ad7400ce584c,Animals,0.0,90.0,-0.0,70 -71,GORILLA,0.0,-0.0,-0.0,0.0,0.0,1.0,165ab309c6c8350ab399fee902530d9d10627586,256,V0,165ab309c6c8350ab399fee902530d9d10627586_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/GORILLA_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_165ab309c6c8350ab399fee902530d9d10627586_256x256.png,3bef4df9345cc7cb1fb353f649d58a14f63622a1,Animals,0.0,90.0,-0.0,71 -72,GORILLA,0.0,-0.0,-0.0,0.0,0.0,1.0,1baeac5f77c5e79b1c6c55bd136432955e8ce750,256,V0,1baeac5f77c5e79b1c6c55bd136432955e8ce750_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/GORILLA_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_1baeac5f77c5e79b1c6c55bd136432955e8ce750_256x256.png,b54aadf412bc8e90e885c7941951586ce72cfd4e,Animals,0.0,90.0,-0.0,72 -73,GORILLA,0.0,-0.0,-0.0,0.0,0.0,1.0,1d4220ca6c0446d3f93abcbcbeef7d8bcd3b1836,256,V0,1d4220ca6c0446d3f93abcbcbeef7d8bcd3b1836_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/GORILLA_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_1d4220ca6c0446d3f93abcbcbeef7d8bcd3b1836_256x256.png,2dd74575485af2f1fe6350320dfb8b32ce87e522,Animals,0.0,90.0,-0.0,73 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-382,bmw325,0.0,-0.0,-0.0,0.0,0.0,1.0,79dbecd8b42989a4ea53d6665adc9c4a0af228bc,256,V0,79dbecd8b42989a4ea53d6665adc9c4a0af228bc_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/bmw325_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_79dbecd8b42989a4ea53d6665adc9c4a0af228bc_256x256.png,5a0da0b97b8aba7f83966d3f53d25a5fdbcaa320,Cars,0.0,90.0,-0.0,382 -383,bmw325,0.0,-0.0,-0.0,0.0,0.0,1.0,7e757bc5d882e1c7b12d271a9339d0b9ece2e776,256,V0,7e757bc5d882e1c7b12d271a9339d0b9ece2e776_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/bmw325_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_7e757bc5d882e1c7b12d271a9339d0b9ece2e776_256x256.png,8a90447df66c39778d7f762e7d0bf699aa5d816c,Cars,0.0,90.0,-0.0,383 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-388,bmw325,0.0,-0.0,-0.0,0.0,0.0,1.0,d78e909711717af8f43a610bcced7da513896e59,256,V0,d78e909711717af8f43a610bcced7da513896e59_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/bmw325_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_d78e909711717af8f43a610bcced7da513896e59_256x256.png,ce9058e582a54276120e9838d8db2c3d2127c161,Cars,0.0,90.0,-0.0,388 -389,bmw325,0.0,-0.0,-0.0,0.0,0.0,1.0,eb7652b3e1be20916e0323dfdf605cd36e7b5117,256,V0,eb7652b3e1be20916e0323dfdf605cd36e7b5117_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/bmw325_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_eb7652b3e1be20916e0323dfdf605cd36e7b5117_256x256.png,ce12da9ae8fbe6780e0d49d2125aec82db4a6a6e,Cars,0.0,90.0,-0.0,389 -390,bora_a,0.0,-0.0,-0.0,0.0,0.0,1.0,091d3594feedc3f07219b80efad4d151ce8afc91,256,V0,091d3594feedc3f07219b80efad4d151ce8afc91_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/bora_a_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_091d3594feedc3f07219b80efad4d151ce8afc91_256x256.png,dd6b6e8bf42c043c5d589c76b458de2ce7f53ea9,Cars,0.0,90.0,-0.0,390 -391,bora_a,0.0,-0.0,-0.0,0.0,0.0,1.0,2043d43e2439e21449f77e26a5f5a1ed9f74cbf6,256,V0,2043d43e2439e21449f77e26a5f5a1ed9f74cbf6_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/bora_a_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_2043d43e2439e21449f77e26a5f5a1ed9f74cbf6_256x256.png,ba225dde71ce6163faf33e3e99689135e54ffca9,Cars,0.0,90.0,-0.0,391 -392,bora_a,0.0,-0.0,-0.0,0.0,0.0,1.0,4186ef7a8ec117ae6a898e7f055061ce6cd49f29,256,V0,4186ef7a8ec117ae6a898e7f055061ce6cd49f29_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/bora_a_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_4186ef7a8ec117ae6a898e7f055061ce6cd49f29_256x256.png,48ea9f3cd844b785015c9db2cb7ca3afb8a372aa,Cars,0.0,90.0,-0.0,392 -393,bora_a,0.0,-0.0,-0.0,0.0,0.0,1.0,49c0d7fc28a319a6e7445824b7124f044a7276b5,256,V0,49c0d7fc28a319a6e7445824b7124f044a7276b5_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/bora_a_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_49c0d7fc28a319a6e7445824b7124f044a7276b5_256x256.png,cf18f8b7230d801d679abaf8f8ed48c7b87d156e,Cars,0.0,90.0,-0.0,393 -394,bora_a,0.0,-0.0,-0.0,0.0,0.0,1.0,4f112237c3286cb48808829df82eb3c565f04a03,256,V0,4f112237c3286cb48808829df82eb3c565f04a03_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/bora_a_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_4f112237c3286cb48808829df82eb3c565f04a03_256x256.png,a48861cea5d6e7711670a221f9e6b7e6c3214da9,Cars,0.0,90.0,-0.0,394 -395,bora_a,0.0,-0.0,-0.0,0.0,0.0,1.0,52d88d1b0c95e82a642dacb1854b1c06f4f7b954,256,V0,52d88d1b0c95e82a642dacb1854b1c06f4f7b954_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/bora_a_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_52d88d1b0c95e82a642dacb1854b1c06f4f7b954_256x256.png,0e33235307b35c29342667a1b40e76d9fec3b54f,Cars,0.0,90.0,-0.0,395 -396,bora_a,0.0,-0.0,-0.0,0.0,0.0,1.0,53991aafc814d9db3b4bb72b1127a00dd068c54c,256,V0,53991aafc814d9db3b4bb72b1127a00dd068c54c_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/bora_a_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_53991aafc814d9db3b4bb72b1127a00dd068c54c_256x256.png,7be3dd2c7b6297210b4fcc1f550b633085739871,Cars,0.0,90.0,-0.0,396 -397,bora_a,0.0,-0.0,-0.0,0.0,0.0,1.0,85bc69d8900affd2edb409623b8a1a799d7aad62,256,V0,85bc69d8900affd2edb409623b8a1a799d7aad62_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/bora_a_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_85bc69d8900affd2edb409623b8a1a799d7aad62_256x256.png,7bb961860a3805d69880714a146e205ab52397f1,Cars,0.0,90.0,-0.0,397 -398,bora_a,0.0,-0.0,-0.0,0.0,0.0,1.0,95a0cbf72ee41ab168797ed2fbacf3528039ea0d,256,V0,95a0cbf72ee41ab168797ed2fbacf3528039ea0d_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/bora_a_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_95a0cbf72ee41ab168797ed2fbacf3528039ea0d_256x256.png,1da370b3fb652c829e637a819cef6cb369917292,Cars,0.0,90.0,-0.0,398 -399,bora_a,0.0,-0.0,-0.0,0.0,0.0,1.0,d721b5f8d4c57c7814feeac9b4a4286d5240b978,256,V0,d721b5f8d4c57c7814feeac9b4a4286d5240b978_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/bora_a_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_d721b5f8d4c57c7814feeac9b4a4286d5240b978_256x256.png,91fb0d5dd7b207aee5639af55bfdbb5e28d6159f,Cars,0.0,90.0,-0.0,399 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-402,breed_pug,0.0,-0.0,-0.0,0.0,0.0,1.0,748be7fe8771ab1fd229d5d99ab04c83263a1751,256,V0,748be7fe8771ab1fd229d5d99ab04c83263a1751_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_748be7fe8771ab1fd229d5d99ab04c83263a1751_256x256.png,9da86664ae7c62860094b5fdeace63870c5dbdbe,Animals,0.0,90.0,-0.0,402 -403,breed_pug,0.0,-0.0,-0.0,0.0,0.0,1.0,7fd69a9d0382aad15b67b3b55ee4c315eedba1be,256,V0,7fd69a9d0382aad15b67b3b55ee4c315eedba1be_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_7fd69a9d0382aad15b67b3b55ee4c315eedba1be_256x256.png,b64a713676ad771e355fe0cad98121f1cb329d92,Animals,0.0,90.0,-0.0,403 -404,breed_pug,0.0,-0.0,-0.0,0.0,0.0,1.0,857be447ed335aab92d8e8fb6c6e4c073efb8d4a,256,V0,857be447ed335aab92d8e8fb6c6e4c073efb8d4a_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_857be447ed335aab92d8e8fb6c6e4c073efb8d4a_256x256.png,3d858b4bc82f5d02fbf800e1c66a1e9be99cf714,Animals,0.0,90.0,-0.0,404 -405,breed_pug,0.0,-0.0,-0.0,0.0,0.0,1.0,8f388437a015e5358e27dad6f0d60e023db40e60,256,V0,8f388437a015e5358e27dad6f0d60e023db40e60_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_8f388437a015e5358e27dad6f0d60e023db40e60_256x256.png,a17db26a610f96215d94cd673fd982c5bb3fd524,Animals,0.0,90.0,-0.0,405 -406,breed_pug,0.0,-0.0,-0.0,0.0,0.0,1.0,b16a365e902a934fa6fc882b082d10d8478fd8e3,256,V0,b16a365e902a934fa6fc882b082d10d8478fd8e3_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_b16a365e902a934fa6fc882b082d10d8478fd8e3_256x256.png,5daa617603c155b73c7f3fcc9194d168055ce031,Animals,0.0,90.0,-0.0,406 -407,breed_pug,0.0,-0.0,-0.0,0.0,0.0,1.0,c998a82b119b6e22d725ff3c7abd178c823f4093,256,V0,c998a82b119b6e22d725ff3c7abd178c823f4093_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_c998a82b119b6e22d725ff3c7abd178c823f4093_256x256.png,ea031b2517f62a70576712cf9050d9384f2bb057,Animals,0.0,90.0,-0.0,407 -408,breed_pug,0.0,-0.0,-0.0,0.0,0.0,1.0,eb9bc453fed0ce8b963fa6ee91febc1fc51da518,256,V0,eb9bc453fed0ce8b963fa6ee91febc1fc51da518_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_eb9bc453fed0ce8b963fa6ee91febc1fc51da518_256x256.png,ac20dde3b00e596602621513c561eb1bb146343c,Animals,0.0,90.0,-0.0,408 -409,breed_pug,0.0,-0.0,-0.0,0.0,0.0,1.0,f182547a8387d1db3957884660fc98f76a20ba09,256,V0,f182547a8387d1db3957884660fc98f76a20ba09_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/breed_pug_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+00.800_f182547a8387d1db3957884660fc98f76a20ba09_256x256.png,6422e3188db17a3352df0a87ae60146f02e261d8,Animals,0.0,90.0,-0.0,409 -410,celica,0.0,-0.0,-0.0,0.0,0.0,1.0,145a6e3292048ff41b3b98a3ed7c2b4986831b44,256,V0,145a6e3292048ff41b3b98a3ed7c2b4986831b44_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/celica_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_145a6e3292048ff41b3b98a3ed7c2b4986831b44_256x256.png,b3a2b55bd1706daa85b2121912da5b319503c107,Cars,0.0,90.0,-0.0,410 -411,celica,0.0,-0.0,-0.0,0.0,0.0,1.0,43ee5593426fb8507236387fbed1e5ad8fe0b9c9,256,V0,43ee5593426fb8507236387fbed1e5ad8fe0b9c9_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/celica_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_43ee5593426fb8507236387fbed1e5ad8fe0b9c9_256x256.png,d5969faed9a76f3acadb83b3410ccfa067202071,Cars,0.0,90.0,-0.0,411 -412,celica,0.0,-0.0,-0.0,0.0,0.0,1.0,696d067653516065070bba1171ba31138761d59e,256,V0,696d067653516065070bba1171ba31138761d59e_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/celica_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_696d067653516065070bba1171ba31138761d59e_256x256.png,11bdad2c347f801ed2f05e7c951793bcf52295fc,Cars,0.0,90.0,-0.0,412 -413,celica,0.0,-0.0,-0.0,0.0,0.0,1.0,72c8349c52d0bef543d62b0ad98e7152cd3195c3,256,V0,72c8349c52d0bef543d62b0ad98e7152cd3195c3_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/celica_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_72c8349c52d0bef543d62b0ad98e7152cd3195c3_256x256.png,2f8939cfd56352d6d0053426267b388bf9d53d02,Cars,0.0,90.0,-0.0,413 -414,celica,0.0,-0.0,-0.0,0.0,0.0,1.0,757303652079b872705ce7156b1d0bf3a6d182e2,256,V0,757303652079b872705ce7156b1d0bf3a6d182e2_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/celica_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_757303652079b872705ce7156b1d0bf3a6d182e2_256x256.png,a451516b0b675844033433a080e78e8dbe8ba424,Cars,0.0,90.0,-0.0,414 -415,celica,0.0,-0.0,-0.0,0.0,0.0,1.0,9421c29f959ef4cea9c94d7b3db30ca32a9cab41,256,V0,9421c29f959ef4cea9c94d7b3db30ca32a9cab41_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/celica_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_9421c29f959ef4cea9c94d7b3db30ca32a9cab41_256x256.png,729ac4cf87f60e9c4bd5d3567481cf758db40541,Cars,0.0,90.0,-0.0,415 -416,celica,0.0,-0.0,-0.0,0.0,0.0,1.0,9704d9bd5ba8d9d0cf645b33dac345fe02bdb6d1,256,V0,9704d9bd5ba8d9d0cf645b33dac345fe02bdb6d1_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/celica_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_9704d9bd5ba8d9d0cf645b33dac345fe02bdb6d1_256x256.png,84b637c9fb1b8a75479285ba04a42222c9a4e8eb,Cars,0.0,90.0,-0.0,416 -417,celica,0.0,-0.0,-0.0,0.0,0.0,1.0,cb78a57effaacd6f2e9f4cf7f0975c38a64a075c,256,V0,cb78a57effaacd6f2e9f4cf7f0975c38a64a075c_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/celica_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_cb78a57effaacd6f2e9f4cf7f0975c38a64a075c_256x256.png,fd88e731055dbc0db46841fa8f0018c5ea29a557,Cars,0.0,90.0,-0.0,417 -418,celica,0.0,-0.0,-0.0,0.0,0.0,1.0,d056d2d66bf1abed8c6b536fa485285061213e2b,256,V0,d056d2d66bf1abed8c6b536fa485285061213e2b_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/celica_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_d056d2d66bf1abed8c6b536fa485285061213e2b_256x256.png,7373253fee3a7e019bf22869987bb41ee1007208,Cars,0.0,90.0,-0.0,418 -419,celica,0.0,-0.0,-0.0,0.0,0.0,1.0,eda16292bd0a5fce946fadf6adf785f8177c4157,256,V0,eda16292bd0a5fce946fadf6adf785f8177c4157_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/celica_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_eda16292bd0a5fce946fadf6adf785f8177c4157_256x256.png,2b4dda24459acfbcb83cab8af5010de1278f2e9c,Cars,0.0,90.0,-0.0,419 -420,clio,0.0,-0.0,-0.0,0.0,0.0,1.0,09a811c55f85c083a16fa2fd09eed4b4fd197827,256,V0,09a811c55f85c083a16fa2fd09eed4b4fd197827_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_09a811c55f85c083a16fa2fd09eed4b4fd197827_256x256.png,e752adec08995d6360b2ee41a1ae8bc6ceb9e0ed,Cars,0.0,90.0,-0.0,420 -421,clio,0.0,-0.0,-0.0,0.0,0.0,1.0,2ad12a1d5aab242ff082f0984974554b158a7db1,256,V0,2ad12a1d5aab242ff082f0984974554b158a7db1_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_2ad12a1d5aab242ff082f0984974554b158a7db1_256x256.png,5765288803df4381660e30a0a56d3ce8b605e5ec,Cars,0.0,90.0,-0.0,421 -422,clio,0.0,-0.0,-0.0,0.0,0.0,1.0,53f68a4f0862fc51262ae776eaa2a41e1e5ae3c0,256,V0,53f68a4f0862fc51262ae776eaa2a41e1e5ae3c0_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_53f68a4f0862fc51262ae776eaa2a41e1e5ae3c0_256x256.png,5d9ac75f2efcd4abe6146315eb2ef69a7eca1dd6,Cars,0.0,90.0,-0.0,422 -423,clio,0.0,-0.0,-0.0,0.0,0.0,1.0,55a014c8cf793919be2b31f0d1fda39076ce23e3,256,V0,55a014c8cf793919be2b31f0d1fda39076ce23e3_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_55a014c8cf793919be2b31f0d1fda39076ce23e3_256x256.png,cf1f1948b5e55815a11b72c52831275bababaeea,Cars,0.0,90.0,-0.0,423 -424,clio,0.0,-0.0,-0.0,0.0,0.0,1.0,5f495b09814e3a411ce90c4ce49661b5655c3635,256,V0,5f495b09814e3a411ce90c4ce49661b5655c3635_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_5f495b09814e3a411ce90c4ce49661b5655c3635_256x256.png,a30fd787865feeec3b54d86a9168e5032eaffd7e,Cars,0.0,90.0,-0.0,424 -425,clio,0.0,-0.0,-0.0,0.0,0.0,1.0,ab335090d2f2aede6e50f7a2036bd5a0ffad2ab5,256,V0,ab335090d2f2aede6e50f7a2036bd5a0ffad2ab5_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_ab335090d2f2aede6e50f7a2036bd5a0ffad2ab5_256x256.png,0dec7aeb27a8eaffde1e9dc4e1c68aca208ea349,Cars,0.0,90.0,-0.0,425 -426,clio,0.0,-0.0,-0.0,0.0,0.0,1.0,bdd43e6965f7ffa00201796daf22d40cf53ba44e,256,V0,bdd43e6965f7ffa00201796daf22d40cf53ba44e_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_bdd43e6965f7ffa00201796daf22d40cf53ba44e_256x256.png,de6005b6c4eae85070ca2674466739b213404481,Cars,0.0,90.0,-0.0,426 -427,clio,0.0,-0.0,-0.0,0.0,0.0,1.0,e64995bef8927ca5d993ae9d119bad38f5a73d53,256,V0,e64995bef8927ca5d993ae9d119bad38f5a73d53_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_e64995bef8927ca5d993ae9d119bad38f5a73d53_256x256.png,ad09855a7755701a8d9c11fde725ad76044b0cdd,Cars,0.0,90.0,-0.0,427 -428,clio,0.0,-0.0,-0.0,0.0,0.0,1.0,f87a34a883bc0edca419e9e06ca4ab154e072561,256,V0,f87a34a883bc0edca419e9e06ca4ab154e072561_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_f87a34a883bc0edca419e9e06ca4ab154e072561_256x256.png,4e05f442f98dceb778e7de1f2d0f700241461756,Cars,0.0,90.0,-0.0,428 -429,clio,0.0,-0.0,-0.0,0.0,0.0,1.0,fa3aff9af6b31c0527d07d84a9f49b95f265f1fb,256,V0,fa3aff9af6b31c0527d07d84a9f49b95f265f1fb_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/clio_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.000_fa3aff9af6b31c0527d07d84a9f49b95f265f1fb_256x256.png,fbef152524d2e593206fd976afde5e0506f477a8,Cars,0.0,90.0,-0.0,429 -430,cruiser,0.0,-0.0,-0.0,0.0,0.0,1.0,265ff902c72de4537465507ec9759dfb78aa7d38,256,V0,265ff902c72de4537465507ec9759dfb78aa7d38_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_265ff902c72de4537465507ec9759dfb78aa7d38_256x256.png,106abf0c8b70ac7717b79a0432137c0ee7c2d3c0,Boats,0.0,90.0,-0.0,430 -431,cruiser,0.0,-0.0,-0.0,0.0,0.0,1.0,2b6cecbb41225444591d526c630a14f55242b461,256,V0,2b6cecbb41225444591d526c630a14f55242b461_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_2b6cecbb41225444591d526c630a14f55242b461_256x256.png,cbc3449dccccb56dd73022253e3d276ed3076b62,Boats,0.0,90.0,-0.0,431 -432,cruiser,0.0,-0.0,-0.0,0.0,0.0,1.0,53bdbd64d21df2fc954b8109443523f9425536cf,256,V0,53bdbd64d21df2fc954b8109443523f9425536cf_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_53bdbd64d21df2fc954b8109443523f9425536cf_256x256.png,faed79b890348a6f419c54d6681608d1cd0c13a5,Boats,0.0,90.0,-0.0,432 -433,cruiser,0.0,-0.0,-0.0,0.0,0.0,1.0,72824c435d21e931932d65d4f53fd1208c7ce979,256,V0,72824c435d21e931932d65d4f53fd1208c7ce979_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/cruiser_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_72824c435d21e931932d65d4f53fd1208c7ce979_256x256.png,79d8e812eae9ecf647eb1eff72d613dff34b1062,Boats,0.0,90.0,-0.0,433 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-546,junkers88,0.0,-0.0,-0.0,0.0,0.0,1.0,72e9aed6437e87e2178587a4cbaf860f23db315f,256,V0,72e9aed6437e87e2178587a4cbaf860f23db315f_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.300_72e9aed6437e87e2178587a4cbaf860f23db315f_256x256.png,c127a7d977f54bc4129302f734f8cb3f1a6eae88,Planes,0.0,90.0,-0.0,546 -547,junkers88,0.0,-0.0,-0.0,0.0,0.0,1.0,79e75348080854b7305d4f7a397a5552e5858f3d,256,V0,79e75348080854b7305d4f7a397a5552e5858f3d_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.300_79e75348080854b7305d4f7a397a5552e5858f3d_256x256.png,082bd7b95da6f1454422a5ed12b31c82f56905c3,Planes,0.0,90.0,-0.0,547 -548,junkers88,0.0,-0.0,-0.0,0.0,0.0,1.0,a761405e11a29fcb3b1cea4d35b2a690dc1af993,256,V0,a761405e11a29fcb3b1cea4d35b2a690dc1af993_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.300_a761405e11a29fcb3b1cea4d35b2a690dc1af993_256x256.png,a27cf5cde1fc5af661dcc7a6eb30512d69609ecf,Planes,0.0,90.0,-0.0,548 -549,junkers88,0.0,-0.0,-0.0,0.0,0.0,1.0,c3b58200001926336739cebf2e48d652860c1198,256,V0,c3b58200001926336739cebf2e48d652860c1198_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/junkers88_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.300_c3b58200001926336739cebf2e48d652860c1198_256x256.png,bffb5a5531aabefe3a9f0eee48963e1dbfc2f5f2,Planes,0.0,90.0,-0.0,549 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-552,mig29,0.0,-0.0,-0.0,0.0,0.0,1.0,297bf446c63103efe4b63aa65d0f0fb770808661,256,V0,297bf446c63103efe4b63aa65d0f0fb770808661_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/mig29_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.300_297bf446c63103efe4b63aa65d0f0fb770808661_256x256.png,a47e4cc4b9bec21fb978253358e0d29d177e0f51,Planes,0.0,90.0,-0.0,552 -553,mig29,0.0,-0.0,-0.0,0.0,0.0,1.0,36a006940d6c48beb636ed916fe01e701bfe8443,256,V0,36a006940d6c48beb636ed916fe01e701bfe8443_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/mig29_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.300_36a006940d6c48beb636ed916fe01e701bfe8443_256x256.png,8a63355f30050fda7ab28c2da5c882c9b2cbb672,Planes,0.0,90.0,-0.0,553 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-556,mig29,0.0,-0.0,-0.0,0.0,0.0,1.0,a8d40a194f1dbbd86033f0faf38896d21edd4b64,256,V0,a8d40a194f1dbbd86033f0faf38896d21edd4b64_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/mig29_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.300_a8d40a194f1dbbd86033f0faf38896d21edd4b64_256x256.png,667b71cd68fea190537411ae4b176d1f77fdac53,Planes,0.0,90.0,-0.0,556 -557,mig29,0.0,-0.0,-0.0,0.0,0.0,1.0,aa581bba4aaaa69adc21107d13f49f329932a5cb,256,V0,aa581bba4aaaa69adc21107d13f49f329932a5cb_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/mig29_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.300_aa581bba4aaaa69adc21107d13f49f329932a5cb_256x256.png,b6a2d2da4cc340163110a950058996a1388781f1,Planes,0.0,90.0,-0.0,557 -558,mig29,0.0,-0.0,-0.0,0.0,0.0,1.0,ca6e5cd2f794a4ad4e63187d17dd1faaafc362b9,256,V0,ca6e5cd2f794a4ad4e63187d17dd1faaafc362b9_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/mig29_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.300_ca6e5cd2f794a4ad4e63187d17dd1faaafc362b9_256x256.png,47a4eec1362fe99e764337d8369e047ac06cec1a,Planes,0.0,90.0,-0.0,558 -559,mig29,0.0,-0.0,-0.0,0.0,0.0,1.0,d06782c538b8680e0ee431aea236d170763c9a18,256,V0,d06782c538b8680e0ee431aea236d170763c9a18_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/mig29_rx+00.000_ry+00.000_rz+00.000_tx+00.000_ty+00.000_s+01.300_d06782c538b8680e0ee431aea236d170763c9a18_256x256.png,b6a11feda6ca6b522626600da2de5606d2e78e73,Planes,0.0,90.0,-0.0,559 -560,motoryacht,0.0,-0.0,-0.0,0.0,0.0,1.0,01e5dbe59764bb4833500988d33ecb08f3bf07ea,256,V0,01e5dbe59764bb4833500988d33ecb08f3bf07ea_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/motoryacht_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_01e5dbe59764bb4833500988d33ecb08f3bf07ea_256x256.png,d792f0ad021b3baeed06535f6077cc36f76762ac,Boats,0.0,-90.0,-0.0,560 -561,motoryacht,0.0,-0.0,-0.0,0.0,0.0,1.0,1083a73d528ad6a9414bc5f51db5c855f5386bf1,256,V0,1083a73d528ad6a9414bc5f51db5c855f5386bf1_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/motoryacht_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_1083a73d528ad6a9414bc5f51db5c855f5386bf1_256x256.png,40c8caefe8e31133985747354d4d2bedd69df540,Boats,0.0,-90.0,-0.0,561 -562,motoryacht,0.0,-0.0,-0.0,0.0,0.0,1.0,4888b0a4271b4ef87c076c49459282bb84b4ffe1,256,V0,4888b0a4271b4ef87c076c49459282bb84b4ffe1_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/motoryacht_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_4888b0a4271b4ef87c076c49459282bb84b4ffe1_256x256.png,5e19aacfee6fbb965504d5a94a19a6dc34fa38a9,Boats,0.0,-90.0,-0.0,562 -563,motoryacht,0.0,-0.0,-0.0,0.0,0.0,1.0,536c972be730dd5a59218f913bbbc287188824f4,256,V0,536c972be730dd5a59218f913bbbc287188824f4_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/motoryacht_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_536c972be730dd5a59218f913bbbc287188824f4_256x256.png,3fb3583feab4d130313e132179e9b19e1c8c5401,Boats,0.0,-90.0,-0.0,563 -564,motoryacht,0.0,-0.0,-0.0,0.0,0.0,1.0,56255976818909328fe203df1eab4f863a4ed952,256,V0,56255976818909328fe203df1eab4f863a4ed952_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/motoryacht_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_56255976818909328fe203df1eab4f863a4ed952_256x256.png,29e9c527755122641bf6f9853d4ccf6bb88caa37,Boats,0.0,-90.0,-0.0,564 -565,motoryacht,0.0,-0.0,-0.0,0.0,0.0,1.0,78a747cce8b4eb74fc5aeb9be8ced864bf7ccce8,256,V0,78a747cce8b4eb74fc5aeb9be8ced864bf7ccce8_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/motoryacht_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_78a747cce8b4eb74fc5aeb9be8ced864bf7ccce8_256x256.png,ba75a486df6b3979b579e6591580180ec2115101,Boats,0.0,-90.0,-0.0,565 -566,motoryacht,0.0,-0.0,-0.0,0.0,0.0,1.0,855118380729aa372588f16c3d7e084945c3cab1,256,V0,855118380729aa372588f16c3d7e084945c3cab1_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/motoryacht_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_855118380729aa372588f16c3d7e084945c3cab1_256x256.png,fdb96ddafd13a3b793f4e9837c2c063d8c172d67,Boats,0.0,-90.0,-0.0,566 -567,motoryacht,0.0,-0.0,-0.0,0.0,0.0,1.0,bae993df45b2433bceb66d5aa7e7d1526418dac2,256,V0,bae993df45b2433bceb66d5aa7e7d1526418dac2_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/motoryacht_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_bae993df45b2433bceb66d5aa7e7d1526418dac2_256x256.png,341e8449d194399f36babd0bb9025368eb4d6717,Boats,0.0,-90.0,-0.0,567 -568,motoryacht,0.0,-0.0,-0.0,0.0,0.0,1.0,c280fbdeca914db53621256842575a5baa0b56a5,256,V0,c280fbdeca914db53621256842575a5baa0b56a5_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/motoryacht_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_c280fbdeca914db53621256842575a5baa0b56a5_256x256.png,3fc60cd117aaa9fd40dc765ad0318384141fc7e9,Boats,0.0,-90.0,-0.0,568 -569,motoryacht,0.0,-0.0,-0.0,0.0,0.0,1.0,e44c296c0ccb602e6defded43704d7f12bf1ab9a,256,V0,e44c296c0ccb602e6defded43704d7f12bf1ab9a_V0,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation00_20110203/motoryacht_rx+00.000_ry+90.000_rz+00.000_tx+00.000_ty+00.000_s+01.200_e44c296c0ccb602e6defded43704d7f12bf1ab9a_256x256.png,115d2b285cd3edecb3d47e911cb310bbec14f096,Boats,0.0,-90.0,-0.0,569 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-727,BAHRAIN,22.371,-42.336,0.661,-0.202,-0.05,1.14083333333,7da4f59e278c97b5e802c2e2defbbb3148e9aa58,256,V3,7da4f59e278c97b5e802c2e2defbbb3148e9aa58_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx+22.371_ry-42.336_rz+00.661_tx-00.202_ty-00.050_s+01.369_7da4f59e278c97b5e802c2e2defbbb3148e9aa58_256x256.png,616f0b944da69ee3331afe3a1e1b67f8a7ee58b6,Boats,22.371,47.664,0.661,727 -728,BAHRAIN,26.69,-27.892,27.773,0.207,0.32,1.25583333333,ee596b3ccb6a15b98bd3f22e673b99c0c25cbe34,256,V3,ee596b3ccb6a15b98bd3f22e673b99c0c25cbe34_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx+26.690_ry-27.892_rz+27.773_tx+00.207_ty+00.320_s+01.507_ee596b3ccb6a15b98bd3f22e673b99c0c25cbe34_256x256.png,35f5905a47b034ebc23854ff186d728d943cca9a,Boats,26.69,62.108,27.773,728 -729,BAHRAIN,30.28,41.72,-22.041,-0.246,0.28,1.11083333333,c6dfaa2e1991caf236d96fea30f655efe9777118,256,V3,c6dfaa2e1991caf236d96fea30f655efe9777118_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx+30.280_ry+41.720_rz-22.041_tx-00.246_ty+00.280_s+01.333_c6dfaa2e1991caf236d96fea30f655efe9777118_256x256.png,e1a3bf2ba29622eea62bafb2ddb2161382c855c4,Boats,30.28,131.72,-22.041,729 -730,BAHRAIN,31.127,-40.397,22.497,0.295,-0.215,0.885833333333,903897856fc38f9d6e9dd44b61426f0e76d735d1,256,V3,903897856fc38f9d6e9dd44b61426f0e76d735d1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx+31.127_ry-40.397_rz+22.497_tx+00.295_ty-00.215_s+01.063_903897856fc38f9d6e9dd44b61426f0e76d735d1_256x256.png,061b761052f2c40646713c107a35979691930749,Boats,31.127,49.603,22.497,730 -731,BAHRAIN,36.863,5.999,-0.429,-0.255,-0.577,0.993333333333,cb06be64afd1e6ea94aa0a3a115c2f6723af374f,256,V3,cb06be64afd1e6ea94aa0a3a115c2f6723af374f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx+36.863_ry+05.999_rz-00.429_tx-00.255_ty-00.577_s+01.192_cb06be64afd1e6ea94aa0a3a115c2f6723af374f_256x256.png,564078cbc2c11d859e419abe7274a04f6b17e6a7,Boats,36.863,95.999,-0.429,731 -732,BAHRAIN,38.148,-9.645,-4.035,0.127,0.02,1.17833333333,56ae9ed8ec43c5bd0b3e14c737f949bb2ccc8e30,256,V3,56ae9ed8ec43c5bd0b3e14c737f949bb2ccc8e30_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx+38.148_ry-09.645_rz-04.035_tx+00.127_ty+00.020_s+01.414_56ae9ed8ec43c5bd0b3e14c737f949bb2ccc8e30_256x256.png,4906390f62af63a0e181662257b1430fb0390902,Boats,38.148,80.355,-4.035,732 -733,BAHRAIN,40.698,40.282,-3.235,0.118,0.007,0.903333333333,50303909e122277ca5a1423e0f4a5d00763105fb,256,V3,50303909e122277ca5a1423e0f4a5d00763105fb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx+40.698_ry+40.282_rz-03.235_tx+00.118_ty+00.007_s+01.084_50303909e122277ca5a1423e0f4a5d00763105fb_256x256.png,7ebead0a56b23fae1c644eecee576d4cb6d2b898,Boats,40.698,130.282,-3.235,733 -734,BAHRAIN,42.189,-6.257,-40.412,0.174,0.387,0.883333333333,67aa255a8029b67e1f2315e446df3a035d24834e,256,V3,67aa255a8029b67e1f2315e446df3a035d24834e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx+42.189_ry-06.257_rz-40.412_tx+00.174_ty+00.387_s+01.060_67aa255a8029b67e1f2315e446df3a035d24834e_256x256.png,0bd1e0cd5b58f8ec8feaaf45ac67bffc74c155fb,Boats,42.189,83.743,-40.412,734 -735,BAHRAIN,42.676,-5.118,-11.334,-0.079,-0.309,1.19166666667,2ad9964a2ec16b0ef3703728d01106f1f7c61942,256,V3,2ad9964a2ec16b0ef3703728d01106f1f7c61942_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx+42.676_ry-05.118_rz-11.334_tx-00.079_ty-00.309_s+01.430_2ad9964a2ec16b0ef3703728d01106f1f7c61942_256x256.png,2c85b195f4379ea7b4b909c8194b848d2bfef9b6,Boats,42.676,84.882,-11.334,735 -736,BAHRAIN,43.53,-10.989,-21.088,-0.19,0.39,0.93,c94a847016b6ae9eee346a25be12593d02b74ea4,256,V3,c94a847016b6ae9eee346a25be12593d02b74ea4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx+43.530_ry-10.989_rz-21.088_tx-00.190_ty+00.390_s+01.116_c94a847016b6ae9eee346a25be12593d02b74ea4_256x256.png,e98a672e39e54626ff665db7d983408ae5c2e264,Boats,43.53,79.011,-21.088,736 -737,BAHRAIN,-0.132,13.606,-8.602,-0.258,0.049,0.935,d9140f3623a074f59a49c30cc95184cee3e88bc9,256,V3,d9140f3623a074f59a49c30cc95184cee3e88bc9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-00.132_ry+13.606_rz-08.602_tx-00.258_ty+00.049_s+01.122_d9140f3623a074f59a49c30cc95184cee3e88bc9_256x256.png,09eb030d343db7ac267dd6bdf46946d9053c69b2,Boats,-0.132,103.606,-8.602,737 -738,BAHRAIN,-0.282,-4.138,-23.27,-0.277,-0.382,1.1575,29a4d701d32eaa1e53866dac7fe1cccf98f2d22d,256,V3,29a4d701d32eaa1e53866dac7fe1cccf98f2d22d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-00.282_ry-04.138_rz-23.270_tx-00.277_ty-00.382_s+01.389_29a4d701d32eaa1e53866dac7fe1cccf98f2d22d_256x256.png,fbb36da3a9dfe5fcb57f81140d90f40690a3a115,Boats,-0.282,85.862,-23.27,738 -739,BAHRAIN,-1.096,44.654,2.702,0.286,0.386,1.0925,adc87c9c400bf905678aa8d7684dcf2f18eb2ed9,256,V3,adc87c9c400bf905678aa8d7684dcf2f18eb2ed9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-01.096_ry+44.654_rz+02.702_tx+00.286_ty+00.386_s+01.311_adc87c9c400bf905678aa8d7684dcf2f18eb2ed9_256x256.png,3235676f49bfb50624d8060f9a59529519ec11be,Boats,-1.096,134.654,2.702,739 -740,BAHRAIN,-1.859,13.142,27.225,-0.133,-0.018,1.18666666667,70296c9099b444abf8e38312a778d719b3c74788,256,V3,70296c9099b444abf8e38312a778d719b3c74788_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-01.859_ry+13.142_rz+27.225_tx-00.133_ty-00.018_s+01.424_70296c9099b444abf8e38312a778d719b3c74788_256x256.png,6995fc4fb6e11c023f84bb0ad87074b894f1fda6,Boats,-1.859,103.142,27.225,740 -741,BAHRAIN,-3.163,-27.727,35.925,0.167,0.011,1.2125,4880acb3e77f281b7f95fdf158a49e757ae3dea6,256,V3,4880acb3e77f281b7f95fdf158a49e757ae3dea6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-03.163_ry-27.727_rz+35.925_tx+00.167_ty+00.011_s+01.455_4880acb3e77f281b7f95fdf158a49e757ae3dea6_256x256.png,295f4b2ca438240bed496f65b6ce734210de3d21,Boats,-3.163,62.273,35.925,741 -742,BAHRAIN,-5.506,35.281,-9.935,-0.111,-0.291,0.8525,15076205c332fc687e33bb7bd059f7625081a8f1,256,V3,15076205c332fc687e33bb7bd059f7625081a8f1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-05.506_ry+35.281_rz-09.935_tx-00.111_ty-00.291_s+01.023_15076205c332fc687e33bb7bd059f7625081a8f1_256x256.png,635d332a373d812605eacd37d28ff6363c8be7f7,Boats,-5.506,125.281,-9.935,742 -743,BAHRAIN,-6.002,10.983,-4.562,0.234,0.139,1.1575,1ba991f859921e090a9cb79fe2c4728dfaaa5e90,256,V3,1ba991f859921e090a9cb79fe2c4728dfaaa5e90_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-06.002_ry+10.983_rz-04.562_tx+00.234_ty+00.139_s+01.389_1ba991f859921e090a9cb79fe2c4728dfaaa5e90_256x256.png,8a51bfa517e41252e0b19fe0b92fd2f00cf55fc8,Boats,-6.002,100.983,-4.562,743 -744,BAHRAIN,-6.722,-13.488,-6.899,0.183,-0.252,0.951666666667,c78093437e223f4d8fb3c46f821820b6b8673947,256,V3,c78093437e223f4d8fb3c46f821820b6b8673947_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-06.722_ry-13.488_rz-06.899_tx+00.183_ty-00.252_s+01.142_c78093437e223f4d8fb3c46f821820b6b8673947_256x256.png,31e69cf3fb3e702a3c6e810a0ef90a16e9ea59cf,Boats,-6.722,76.512,-6.899,744 -745,BAHRAIN,-8.349,-30.448,33.107,-0.1,0.383,0.783333333333,edd88c69386cc59e77a8bc1b3b77e5d374f3228d,256,V3,edd88c69386cc59e77a8bc1b3b77e5d374f3228d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-08.349_ry-30.448_rz+33.107_tx-00.100_ty+00.383_s+00.940_edd88c69386cc59e77a8bc1b3b77e5d374f3228d_256x256.png,c574efdbf0f57b47a7adf3d20feafcc3c974e689,Boats,-8.349,59.552,33.107,745 -746,BAHRAIN,-11.376,19.681,33.288,0.3,-0.188,1.2525,3270ec6eb7918f7e565da242721ade80f9895a32,256,V3,3270ec6eb7918f7e565da242721ade80f9895a32_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-11.376_ry+19.681_rz+33.288_tx+00.300_ty-00.188_s+01.503_3270ec6eb7918f7e565da242721ade80f9895a32_256x256.png,085c15d201d08946d3a81e78ca408653c0b7b7ac,Boats,-11.376,109.681,33.288,746 -747,BAHRAIN,-16.408,-4.713,32.025,0.071,0.296,0.831666666667,f0c0a4538c6334f1d24838f20c151033de5747bc,256,V3,f0c0a4538c6334f1d24838f20c151033de5747bc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-16.408_ry-04.713_rz+32.025_tx+00.071_ty+00.296_s+00.998_f0c0a4538c6334f1d24838f20c151033de5747bc_256x256.png,2752a3f4fbd4028e75f83b91b4ae07c34c1b8fa3,Boats,-16.408,85.287,32.025,747 -748,BAHRAIN,-18.564,13.179,22.426,0.186,-0.35,1.22166666667,c012c9ee55b0d3f84080c2fe046a947a91c8bbce,256,V3,c012c9ee55b0d3f84080c2fe046a947a91c8bbce_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-18.564_ry+13.179_rz+22.426_tx+00.186_ty-00.350_s+01.466_c012c9ee55b0d3f84080c2fe046a947a91c8bbce_256x256.png,c8b74bd1cc5c15b6607c02cfe07a08edb701dd77,Boats,-18.564,103.179,22.426,748 -749,BAHRAIN,-19.114,-40.672,18.483,0.084,-0.478,1.12833333333,1dcc93eaa9cfafa81d370cb23c1037415d8ac845,256,V3,1dcc93eaa9cfafa81d370cb23c1037415d8ac845_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-19.114_ry-40.672_rz+18.483_tx+00.084_ty-00.478_s+01.354_1dcc93eaa9cfafa81d370cb23c1037415d8ac845_256x256.png,5be846da7302444dea33bde954edc9360b6cf9c8,Boats,-19.114,49.328,18.483,749 -750,BAHRAIN,-19.144,-43.369,-41.465,0.005,-0.067,1.27,871a59b6008f25264ba06c36b82abfaea34a6d49,256,V3,871a59b6008f25264ba06c36b82abfaea34a6d49_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-19.144_ry-43.369_rz-41.465_tx+00.005_ty-00.067_s+01.524_871a59b6008f25264ba06c36b82abfaea34a6d49_256x256.png,ff2a6b90fb81b39238ec98c415cd05dee2089f5c,Boats,-19.144,46.631,-41.465,750 -751,BAHRAIN,-19.327,-19.096,-26.654,-0.202,-0.571,0.845,31acd16e06baef8541a5eedbf158a7ebff7ae43f,256,V3,31acd16e06baef8541a5eedbf158a7ebff7ae43f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-19.327_ry-19.096_rz-26.654_tx-00.202_ty-00.571_s+01.014_31acd16e06baef8541a5eedbf158a7ebff7ae43f_256x256.png,059450f27cae59ebed39bea48ee8d415e78b779a,Boats,-19.327,70.904,-26.654,751 -752,BAHRAIN,-22.986,12.262,-10.139,0.097,0.195,1.23,d3196c6410ce9303231509db26af0b376af4aaaa,256,V3,d3196c6410ce9303231509db26af0b376af4aaaa_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-22.986_ry+12.262_rz-10.139_tx+00.097_ty+00.195_s+01.476_d3196c6410ce9303231509db26af0b376af4aaaa_256x256.png,6455dd7b2fc8bc0b204bdf0be61405e16d547700,Boats,-22.986,102.262,-10.139,752 -753,BAHRAIN,-24.454,1.656,22.792,0.102,-0.344,0.833333333333,aa8298441a903c62d75f02af052b9a92c5242296,256,V3,aa8298441a903c62d75f02af052b9a92c5242296_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-24.454_ry+01.656_rz+22.792_tx+00.102_ty-00.344_s+01.000_aa8298441a903c62d75f02af052b9a92c5242296_256x256.png,52775986f0def2857c83515c74e6648d590c5796,Boats,-24.454,91.656,22.792,753 -754,BAHRAIN,-33.929,-2.348,-29.856,-0.065,-0.419,0.828333333333,6ddd9cfe2ac524b83ca3f3894ef6f21f4cfb80a3,256,V3,6ddd9cfe2ac524b83ca3f3894ef6f21f4cfb80a3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-33.929_ry-02.348_rz-29.856_tx-00.065_ty-00.419_s+00.994_6ddd9cfe2ac524b83ca3f3894ef6f21f4cfb80a3_256x256.png,2459c5ea6f1bcb0fb641178ea35d453cb1c8ac19,Boats,-33.929,87.652,-29.856,754 -755,BAHRAIN,-38.719,21.475,36.438,0.101,0.505,0.799166666667,ed2460bf9a0ded0ea20104cbc8b0cef3f3d92afb,256,V3,ed2460bf9a0ded0ea20104cbc8b0cef3f3d92afb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-38.719_ry+21.475_rz+36.438_tx+00.101_ty+00.505_s+00.959_ed2460bf9a0ded0ea20104cbc8b0cef3f3d92afb_256x256.png,74fa357beb36fcea67153563a8bd249b342038d0,Boats,-38.719,111.475,36.438,755 -756,BAHRAIN,-40.464,-12.709,20.914,-0.21,0.113,1.28,6a8c1e0b1065da58ab3a3912b0e57d2765172a5d,256,V3,6a8c1e0b1065da58ab3a3912b0e57d2765172a5d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-40.464_ry-12.709_rz+20.914_tx-00.210_ty+00.113_s+01.536_6a8c1e0b1065da58ab3a3912b0e57d2765172a5d_256x256.png,6e62893991175c9469f8573b34c09ae9145a4199,Boats,-40.464,77.291,20.914,756 -757,BAHRAIN,-42.438,-29.744,40.338,-0.273,0.001,1.125,8dd8bb7572486bc82ab7eca8fbbef0ec0704f598,256,V3,8dd8bb7572486bc82ab7eca8fbbef0ec0704f598_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-42.438_ry-29.744_rz+40.338_tx-00.273_ty+00.001_s+01.350_8dd8bb7572486bc82ab7eca8fbbef0ec0704f598_256x256.png,1ee27075d74daa37d250bf2ccbb59154db4671a6,Boats,-42.438,60.256,40.338,757 -758,BAHRAIN,-42.495,-24.922,-14.693,0.264,-0.22,1.1775,994fd3d9abc894cf28a6d853caa7ee24243ae213,256,V3,994fd3d9abc894cf28a6d853caa7ee24243ae213_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-42.495_ry-24.922_rz-14.693_tx+00.264_ty-00.220_s+01.413_994fd3d9abc894cf28a6d853caa7ee24243ae213_256x256.png,7c3322049581ee7d8063494c020cc85acbb4f9a7,Boats,-42.495,65.078,-14.693,758 -759,BAHRAIN,-43.468,-32.005,2.08,0.157,-0.001,0.921666666667,33d0e5d4be161f0fda147f577cb3ced00cba7696,256,V3,33d0e5d4be161f0fda147f577cb3ced00cba7696_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/BAHRAIN_rx-43.468_ry-32.005_rz+02.080_tx+00.157_ty-00.001_s+01.106_33d0e5d4be161f0fda147f577cb3ced00cba7696_256x256.png,479cd110b314c3f32fc3c355b9ca48fcece542e2,Boats,-43.468,57.995,2.08,759 -760,Beetle,8.071,25.851,-40.503,-0.248,-0.478,1.253,485cf5cdd83cc299e227744abac75f74156dc7ea,256,V3,485cf5cdd83cc299e227744abac75f74156dc7ea_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+08.071_ry+25.851_rz-40.503_tx-00.248_ty-00.478_s+01.253_485cf5cdd83cc299e227744abac75f74156dc7ea_256x256.png,f840a7fbfe0a0e45ef031dce7db647c4ffff37ec,Cars,8.071,115.851,-40.503,760 -761,Beetle,12.433,25.963,-43.835,0.181,-0.077,1.035,d14b8a030598f44875eb523685f6c102bd727192,256,V3,d14b8a030598f44875eb523685f6c102bd727192_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+12.433_ry+25.963_rz-43.835_tx+00.181_ty-00.077_s+01.035_d14b8a030598f44875eb523685f6c102bd727192_256x256.png,e05b493a11db293c5c04b9e9c2f6045a88c1b999,Cars,12.433,115.963,-43.835,761 -762,Beetle,12.437,38.326,25.543,0.151,0.273,1.06,dad6f748c7df3d8cbc4a4895278e880172227eaa,256,V3,dad6f748c7df3d8cbc4a4895278e880172227eaa_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+12.437_ry+38.326_rz+25.543_tx+00.151_ty+00.273_s+01.060_dad6f748c7df3d8cbc4a4895278e880172227eaa_256x256.png,c50fe820a7de4c291b73b7fbbf82d46933c0ac52,Cars,12.437,128.326,25.543,762 -763,Beetle,14.667,-33.084,6.647,-0.104,-0.3,1.146,7719da100d1bb7d418cc00a9f9852c149344ebeb,256,V3,7719da100d1bb7d418cc00a9f9852c149344ebeb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+14.667_ry-33.084_rz+06.647_tx-00.104_ty-00.300_s+01.146_7719da100d1bb7d418cc00a9f9852c149344ebeb_256x256.png,1669a5b77cf71a4708647d426284e62842bf85ce,Cars,14.667,56.916,6.647,763 -764,Beetle,15.489,-13.429,-40.428,-0.258,-0.406,1.156,3cc9901a64b4098f8b2fed4d53848097d24ffabc,256,V3,3cc9901a64b4098f8b2fed4d53848097d24ffabc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+15.489_ry-13.429_rz-40.428_tx-00.258_ty-00.406_s+01.156_3cc9901a64b4098f8b2fed4d53848097d24ffabc_256x256.png,222fa7648e11e177a49fe88698cf948211578016,Cars,15.489,76.571,-40.428,764 -765,Beetle,16.164,-5.058,21.533,0.067,0.166,0.856,649b6644656e7d09ae5432a3b8f9430063931aa6,256,V3,649b6644656e7d09ae5432a3b8f9430063931aa6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+16.164_ry-05.058_rz+21.533_tx+00.067_ty+00.166_s+00.856_649b6644656e7d09ae5432a3b8f9430063931aa6_256x256.png,cf975c6d6676e97778cd605b08003d49b6324280,Cars,16.164,84.942,21.533,765 -766,Beetle,20.417,10.643,37.848,0.049,0.377,0.905,0c166b03e25ef1071384e0ba04e2241062aa6dc4,256,V3,0c166b03e25ef1071384e0ba04e2241062aa6dc4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+20.417_ry+10.643_rz+37.848_tx+00.049_ty+00.377_s+00.905_0c166b03e25ef1071384e0ba04e2241062aa6dc4_256x256.png,70687e5ed9ce7f8ff617836ab3e407b289527059,Cars,20.417,100.643,37.848,766 -767,Beetle,20.738,3.554,-43.827,-0.162,0.0,0.79,7e757bc5d882e1c7b12d271a9339d0b9ece2e776,256,V3,7e757bc5d882e1c7b12d271a9339d0b9ece2e776_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+20.738_ry+03.554_rz-43.827_tx-00.162_ty+00.000_s+00.790_7e757bc5d882e1c7b12d271a9339d0b9ece2e776_256x256.png,265749922499fe2afa9c2625c60ea7320e9af26a,Cars,20.738,93.554,-43.827,767 -768,Beetle,20.994,-23.991,38.976,-0.093,0.227,1.143,fb8bb50ea0ba032af1b84b1e991692635e20fa1f,256,V3,fb8bb50ea0ba032af1b84b1e991692635e20fa1f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+20.994_ry-23.991_rz+38.976_tx-00.093_ty+00.227_s+01.143_fb8bb50ea0ba032af1b84b1e991692635e20fa1f_256x256.png,9b56b66e8e46e7e7b51684db87a9c563f540502a,Cars,20.994,66.009,38.976,768 -769,Beetle,24.744,-16.666,-5.861,-0.241,-0.314,0.914,466ca0c51ee0d49dd4f377111c2ed982dd061fee,256,V3,466ca0c51ee0d49dd4f377111c2ed982dd061fee_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+24.744_ry-16.666_rz-05.861_tx-00.241_ty-00.314_s+00.914_466ca0c51ee0d49dd4f377111c2ed982dd061fee_256x256.png,d88a72783d3b92ddccd8f162c44ff754531c6ff0,Cars,24.744,73.334,-5.861,769 -770,Beetle,27.991,-0.103,-15.163,-0.156,-0.341,0.998,5b48d5fdadde701bbdf3c598fe22cca24220679e,256,V3,5b48d5fdadde701bbdf3c598fe22cca24220679e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+27.991_ry-00.103_rz-15.163_tx-00.156_ty-00.341_s+00.998_5b48d5fdadde701bbdf3c598fe22cca24220679e_256x256.png,4599ce2e4534d348e09d24229403a6de1ff435c6,Cars,27.991,89.897,-15.163,770 -771,Beetle,28.834,-1.376,23.189,0.185,-0.41,0.983,757303652079b872705ce7156b1d0bf3a6d182e2,256,V3,757303652079b872705ce7156b1d0bf3a6d182e2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+28.834_ry-01.376_rz+23.189_tx+00.185_ty-00.410_s+00.983_757303652079b872705ce7156b1d0bf3a6d182e2_256x256.png,a0f46a8df622846f0c5bfbb237419158c58a5962,Cars,28.834,88.624,23.189,771 -772,Beetle,34.904,-32.555,-38.814,0.094,0.286,0.823,4e7ef2780706b385a50241db47c81e23f535e7b1,256,V3,4e7ef2780706b385a50241db47c81e23f535e7b1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+34.904_ry-32.555_rz-38.814_tx+00.094_ty+00.286_s+00.823_4e7ef2780706b385a50241db47c81e23f535e7b1_256x256.png,c5fd0a4695c25ee3a60ef74f0f3ab5f141efe644,Cars,34.904,57.445,-38.814,772 -773,Beetle,39.459,34.103,-4.51,-0.262,0.247,0.798,e91ce8e221b8f9a3d9f4a8743e8fb8871308ab95,256,V3,e91ce8e221b8f9a3d9f4a8743e8fb8871308ab95_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+39.459_ry+34.103_rz-04.510_tx-00.262_ty+00.247_s+00.798_e91ce8e221b8f9a3d9f4a8743e8fb8871308ab95_256x256.png,d012851976f423aff8f546f0d974da4a235bf6fc,Cars,39.459,124.103,-4.51,773 -774,Beetle,39.692,-44.847,30.908,0.003,-0.387,1.212,b9add36dd5768d3d585186712eb74782f31a3d78,256,V3,b9add36dd5768d3d585186712eb74782f31a3d78_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+39.692_ry-44.847_rz+30.908_tx+00.003_ty-00.387_s+01.212_b9add36dd5768d3d585186712eb74782f31a3d78_256x256.png,09f39d0ac06332e0410ad5f68077411f447bb2ad,Cars,39.692,45.153,30.908,774 -775,Beetle,39.748,-33.048,34.358,-0.213,0.519,0.791,383b539b6597a2a5c521051e384b8a46a30843c0,256,V3,383b539b6597a2a5c521051e384b8a46a30843c0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+39.748_ry-33.048_rz+34.358_tx-00.213_ty+00.519_s+00.791_383b539b6597a2a5c521051e384b8a46a30843c0_256x256.png,0404de511933b3917b60d2529214f4cec8444da3,Cars,39.748,56.952,34.358,775 -776,Beetle,39.916,-39.761,34.815,-0.23,0.082,1.144,f52d93843d1cb4cbec654721fe5ac3ca3a862170,256,V3,f52d93843d1cb4cbec654721fe5ac3ca3a862170_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+39.916_ry-39.761_rz+34.815_tx-00.230_ty+00.082_s+01.144_f52d93843d1cb4cbec654721fe5ac3ca3a862170_256x256.png,542f1d0ceea7d8f962f95156b011d1b712b82875,Cars,39.916,50.239,34.815,776 -777,Beetle,40.644,-25.123,24.102,-0.248,0.555,1.133,9704d9bd5ba8d9d0cf645b33dac345fe02bdb6d1,256,V3,9704d9bd5ba8d9d0cf645b33dac345fe02bdb6d1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+40.644_ry-25.123_rz+24.102_tx-00.248_ty+00.555_s+01.133_9704d9bd5ba8d9d0cf645b33dac345fe02bdb6d1_256x256.png,1d98abfe8b89c356da0e65b6f18aaa67fc3ea3de,Cars,40.644,64.877,24.102,777 -778,Beetle,41.057,-16.968,42.687,0.261,0.346,1.035,83a4f8c692d043d0c7b11ec2a8380ced6e617c52,256,V3,83a4f8c692d043d0c7b11ec2a8380ced6e617c52_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+41.057_ry-16.968_rz+42.687_tx+00.261_ty+00.346_s+01.035_83a4f8c692d043d0c7b11ec2a8380ced6e617c52_256x256.png,a975861d469bbc32b334d0313a2bf08e7a40a83e,Cars,41.057,73.032,42.687,778 -779,Beetle,41.624,12.137,20.524,-0.204,0.592,0.871,172b485bdc1110ab85d82828903f92c0758019dd,256,V3,172b485bdc1110ab85d82828903f92c0758019dd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx+41.624_ry+12.137_rz+20.524_tx-00.204_ty+00.592_s+00.871_172b485bdc1110ab85d82828903f92c0758019dd_256x256.png,bcfbcc5bf06402f21d9475a6825432ae7c0478cc,Cars,41.624,102.137,20.524,779 -780,Beetle,-0.496,3.613,28.624,0.112,-0.18,0.989,a75a4ea1798e8d0a3462ed4164b9a148150ff778,256,V3,a75a4ea1798e8d0a3462ed4164b9a148150ff778_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-00.496_ry+03.613_rz+28.624_tx+00.112_ty-00.180_s+00.989_a75a4ea1798e8d0a3462ed4164b9a148150ff778_256x256.png,f9765e92045018cd34671b45b88a84079bf09108,Cars,-0.496,93.613,28.624,780 -781,Beetle,-1.037,35.588,42.675,0.006,-0.119,0.923,b626ee5405859c7fadf99a5af834ec63084140d9,256,V3,b626ee5405859c7fadf99a5af834ec63084140d9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-01.037_ry+35.588_rz+42.675_tx+00.006_ty-00.119_s+00.923_b626ee5405859c7fadf99a5af834ec63084140d9_256x256.png,d9654e43850d2366b8108b33e0c14d888271a61e,Cars,-1.037,125.588,42.675,781 -782,Beetle,-4.623,-29.242,-28.209,0.019,-0.496,1.059,f8c6379f0cf97ffbadc01b9bf26b4ef2bbdcd118,256,V3,f8c6379f0cf97ffbadc01b9bf26b4ef2bbdcd118_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-04.623_ry-29.242_rz-28.209_tx+00.019_ty-00.496_s+01.059_f8c6379f0cf97ffbadc01b9bf26b4ef2bbdcd118_256x256.png,dcb09b82bdd7a0a4e64abe6b79e68ed58afc34fa,Cars,-4.623,60.758,-28.209,782 -783,Beetle,-6.302,-39.833,22.244,-0.197,-0.042,1.234,f144e3aabccefb4228b8257506a2aa26ba5b4a6d,256,V3,f144e3aabccefb4228b8257506a2aa26ba5b4a6d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-06.302_ry-39.833_rz+22.244_tx-00.197_ty-00.042_s+01.234_f144e3aabccefb4228b8257506a2aa26ba5b4a6d_256x256.png,4fefbea44756719f5cded1539b1c092e352beab0,Cars,-6.302,50.167,22.244,783 -784,Beetle,-6.311,-12.969,-10.266,-0.256,0.389,1.145,f7d374efbc50d8b7333ec0669dee95ca76c39f42,256,V3,f7d374efbc50d8b7333ec0669dee95ca76c39f42_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-06.311_ry-12.969_rz-10.266_tx-00.256_ty+00.389_s+01.145_f7d374efbc50d8b7333ec0669dee95ca76c39f42_256x256.png,cfdb30100fa9fcfccb5e9b5bcca7d53a444456f3,Cars,-6.311,77.031,-10.266,784 -785,Beetle,-9.203,-13.978,-19.852,-0.279,0.396,0.814,1b5c48a9f7f467865ff32d84e8e29dc03aa751b6,256,V3,1b5c48a9f7f467865ff32d84e8e29dc03aa751b6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-09.203_ry-13.978_rz-19.852_tx-00.279_ty+00.396_s+00.814_1b5c48a9f7f467865ff32d84e8e29dc03aa751b6_256x256.png,71812987325e8904d71be4fad139e179adedfe67,Cars,-9.203,76.022,-19.852,785 -786,Beetle,-9.994,-14.893,34.453,0.272,0.215,1.237,2c8dc25cdd2a6bfe4a104b5618ba0fd115c53948,256,V3,2c8dc25cdd2a6bfe4a104b5618ba0fd115c53948_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-09.994_ry-14.893_rz+34.453_tx+00.272_ty+00.215_s+01.237_2c8dc25cdd2a6bfe4a104b5618ba0fd115c53948_256x256.png,6ba031eeee7a0aae3efc7bd3158d7d49a4d1d290,Cars,-9.994,75.107,34.453,786 -787,Beetle,-12.162,-43.589,13.544,0.21,-0.43,1.069,d3f1888b9aaa393c83a1efe6890ea06b46b41270,256,V3,d3f1888b9aaa393c83a1efe6890ea06b46b41270_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-12.162_ry-43.589_rz+13.544_tx+00.210_ty-00.430_s+01.069_d3f1888b9aaa393c83a1efe6890ea06b46b41270_256x256.png,d74fb40d88d6b4139346e0be3236da980a880c3c,Cars,-12.162,46.411,13.544,787 -788,Beetle,-15.441,-8.922,-23.857,0.019,-0.477,0.926,d8c51a01039bb4807b1d46c112baddf993a599a2,256,V3,d8c51a01039bb4807b1d46c112baddf993a599a2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-15.441_ry-08.922_rz-23.857_tx+00.019_ty-00.477_s+00.926_d8c51a01039bb4807b1d46c112baddf993a599a2_256x256.png,4ab0e1f078c95b4638738a893f13ab3d84425101,Cars,-15.441,81.078,-23.857,788 -789,Beetle,-16.907,-40.045,-14.821,0.252,0.473,1.052,2ad12a1d5aab242ff082f0984974554b158a7db1,256,V3,2ad12a1d5aab242ff082f0984974554b158a7db1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-16.907_ry-40.045_rz-14.821_tx+00.252_ty+00.473_s+01.052_2ad12a1d5aab242ff082f0984974554b158a7db1_256x256.png,a545781fabe1da2a414efb43e3b4b01bf2698029,Cars,-16.907,49.955,-14.821,789 -790,Beetle,-18.486,-23.773,-36.021,0.229,-0.184,0.857,238d7aa277a364aba2e419276e3dd188f585969c,256,V3,238d7aa277a364aba2e419276e3dd188f585969c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-18.486_ry-23.773_rz-36.021_tx+00.229_ty-00.184_s+00.857_238d7aa277a364aba2e419276e3dd188f585969c_256x256.png,b0e33e0dc6388209f3ea6dc77526f9fa27c66d53,Cars,-18.486,66.227,-36.021,790 -791,Beetle,-19.407,6.577,9.679,0.044,0.209,0.915,04b0f600a1c2f30809e4ccdd40a7eb427735f876,256,V3,04b0f600a1c2f30809e4ccdd40a7eb427735f876_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-19.407_ry+06.577_rz+09.679_tx+00.044_ty+00.209_s+00.915_04b0f600a1c2f30809e4ccdd40a7eb427735f876_256x256.png,f93763e22164060962997c98102e884367bfece4,Cars,-19.407,96.577,9.679,791 -792,Beetle,-28.802,34.166,-12.027,-0.19,0.029,0.782,2f7b5ae717dc0fedc5ce69acc3c304ebff8fd0e4,256,V3,2f7b5ae717dc0fedc5ce69acc3c304ebff8fd0e4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-28.802_ry+34.166_rz-12.027_tx-00.190_ty+00.029_s+00.782_2f7b5ae717dc0fedc5ce69acc3c304ebff8fd0e4_256x256.png,6c879989e7c793e61bfb1845a44de256f9d1b60f,Cars,-28.802,124.166,-12.027,792 -793,Beetle,-30.276,-43.983,-0.001,-0.036,-0.148,1.077,26dafa135e1dff533a4fb78bd37d5cf3f3df3298,256,V3,26dafa135e1dff533a4fb78bd37d5cf3f3df3298_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-30.276_ry-43.983_rz-00.001_tx-00.036_ty-00.148_s+01.077_26dafa135e1dff533a4fb78bd37d5cf3f3df3298_256x256.png,42ae643382fc10d28ac2001ba4280d766af20a96,Cars,-30.276,46.017,-0.001,793 -794,Beetle,-32.748,-15.965,13.419,-0.138,0.362,0.832,074e9d8e271baea0638336d96f0577edc2a5cc77,256,V3,074e9d8e271baea0638336d96f0577edc2a5cc77_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-32.748_ry-15.965_rz+13.419_tx-00.138_ty+00.362_s+00.832_074e9d8e271baea0638336d96f0577edc2a5cc77_256x256.png,e1610a3387478c1fa21345f4a8da0da1a98a7033,Cars,-32.748,74.035,13.419,794 -795,Beetle,-33.548,-43.889,-2.597,0.044,-0.228,1.276,acc3ac7ad765333096be9909ff1d809ce3a0014b,256,V3,acc3ac7ad765333096be9909ff1d809ce3a0014b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-33.548_ry-43.889_rz-02.597_tx+00.044_ty-00.228_s+01.276_acc3ac7ad765333096be9909ff1d809ce3a0014b_256x256.png,2e5fbf01afda8dbc923826fdda6f85d1c6ec1c4a,Cars,-33.548,46.111,-2.597,795 -796,Beetle,-34.278,-25.796,32.455,0.14,-0.577,1.104,01c4a004939adfc9e0b21445ec5cfd12e818b549,256,V3,01c4a004939adfc9e0b21445ec5cfd12e818b549_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-34.278_ry-25.796_rz+32.455_tx+00.140_ty-00.577_s+01.104_01c4a004939adfc9e0b21445ec5cfd12e818b549_256x256.png,356db2b823ac9a70e43f1b67abf9b0d5145198c1,Cars,-34.278,64.204,32.455,796 -797,Beetle,-36.801,-39.959,-2.124,-0.12,0.127,1.088,4583a1153e9c146c4493dbf3322f6cfb73abdb8e,256,V3,4583a1153e9c146c4493dbf3322f6cfb73abdb8e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-36.801_ry-39.959_rz-02.124_tx-00.120_ty+00.127_s+01.088_4583a1153e9c146c4493dbf3322f6cfb73abdb8e_256x256.png,a036bb492354179874b8040c80d7defea35f11ce,Cars,-36.801,50.041,-2.124,797 -798,Beetle,-38.62,-42.572,30.73,0.193,0.514,1.081,426a5eadfa574e3a2aa6c8152fb33c61cb72031b,256,V3,426a5eadfa574e3a2aa6c8152fb33c61cb72031b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-38.620_ry-42.572_rz+30.730_tx+00.193_ty+00.514_s+01.081_426a5eadfa574e3a2aa6c8152fb33c61cb72031b_256x256.png,c492ea48033ab65ff6780fd1ff650359ef649f0a,Cars,-38.62,47.428,30.73,798 -799,Beetle,-44.507,-17.354,25.558,-0.124,-0.255,1.058,8d2d322420f66650f8f95c19750c9e9b72330ac6,256,V3,8d2d322420f66650f8f95c19750c9e9b72330ac6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Beetle_rx-44.507_ry-17.354_rz+25.558_tx-00.124_ty-00.255_s+01.058_8d2d322420f66650f8f95c19750c9e9b72330ac6_256x256.png,05c3ad26eb51f1024af644e4d7ecd4c54d49991c,Cars,-44.507,72.646,25.558,799 -800,CGTG_L,7.095,-29.491,-41.727,0.279,-0.265,1.10583333333,536c3ee2159da43bef7aee20348ae8e7a09bfe0d,256,V3,536c3ee2159da43bef7aee20348ae8e7a09bfe0d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+07.095_ry-29.491_rz-41.727_tx+00.279_ty-00.265_s+01.327_536c3ee2159da43bef7aee20348ae8e7a09bfe0d_256x256.png,9f3815a89bcb70abd6545b4885f4af7fcf04576f,Boats,7.095,60.509,-41.727,800 -801,CGTG_L,10.603,-5.829,-20.929,0.065,0.494,1.075,fb616b1e25cfb3d6f3c341bd930ea89bf8f5036b,256,V3,fb616b1e25cfb3d6f3c341bd930ea89bf8f5036b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+10.603_ry-05.829_rz-20.929_tx+00.065_ty+00.494_s+01.290_fb616b1e25cfb3d6f3c341bd930ea89bf8f5036b_256x256.png,4435fb27ee33a3855e0c5b28a70e635565aa3a28,Boats,10.603,84.171,-20.929,801 -802,CGTG_L,10.935,-16.812,36.367,-0.052,-0.555,0.885833333333,2c336fabe41662a39f600c01ed83381e7d17985c,256,V3,2c336fabe41662a39f600c01ed83381e7d17985c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+10.935_ry-16.812_rz+36.367_tx-00.052_ty-00.555_s+01.063_2c336fabe41662a39f600c01ed83381e7d17985c_256x256.png,23b8d74d0ee130c7251e231117b25ca5e22ff9f5,Boats,10.935,73.188,36.367,802 -803,CGTG_L,11.02,35.578,-27.471,0.278,0.232,0.9875,6f69f2c2bf34893a87db16e5b8ff662100200ded,256,V3,6f69f2c2bf34893a87db16e5b8ff662100200ded_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+11.020_ry+35.578_rz-27.471_tx+00.278_ty+00.232_s+01.185_6f69f2c2bf34893a87db16e5b8ff662100200ded_256x256.png,f23e0f8d55d41bf0bd0b3471acf7e09e4868c4a7,Boats,11.02,125.578,-27.471,803 -804,CGTG_L,13.759,11.351,13.401,-0.109,-0.169,0.97,a18b32ee5064d5dc99fbf9a08b4c2177e06d6870,256,V3,a18b32ee5064d5dc99fbf9a08b4c2177e06d6870_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+13.759_ry+11.351_rz+13.401_tx-00.109_ty-00.169_s+01.164_a18b32ee5064d5dc99fbf9a08b4c2177e06d6870_256x256.png,c524ffa1a8fabfa8e11088a4919d2133979c5aa6,Boats,13.759,101.351,13.401,804 -805,CGTG_L,17.987,17.797,-22.139,-0.085,-0.514,1.15916666667,1827371389795949398e266c2ddac7a670852760,256,V3,1827371389795949398e266c2ddac7a670852760_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+17.987_ry+17.797_rz-22.139_tx-00.085_ty-00.514_s+01.391_1827371389795949398e266c2ddac7a670852760_256x256.png,2baae4971777d6d93be62f3bee2c8ad1289fcdee,Boats,17.987,107.797,-22.139,805 -806,CGTG_L,20.518,36.224,3.824,-0.16,-0.536,0.846666666667,239eeb2c453058c64041976909d4d3b609d03685,256,V3,239eeb2c453058c64041976909d4d3b609d03685_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+20.518_ry+36.224_rz+03.824_tx-00.160_ty-00.536_s+01.016_239eeb2c453058c64041976909d4d3b609d03685_256x256.png,c733c53720019eecaa5e67ebcd98cd215b3a327a,Boats,20.518,126.224,3.824,806 -807,CGTG_L,21.299,-8.429,0.099,-0.266,0.403,1.2675,6b5f18637d06bcc293c35864ca4ef9b14818f979,256,V3,6b5f18637d06bcc293c35864ca4ef9b14818f979_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+21.299_ry-08.429_rz+00.099_tx-00.266_ty+00.403_s+01.521_6b5f18637d06bcc293c35864ca4ef9b14818f979_256x256.png,cd084caf71685bf7732cc5dbc745537aeccff718,Boats,21.299,81.571,0.099,807 -808,CGTG_L,21.652,2.075,26.782,0.298,0.01,1.2075,a6da4ac51c383eb9702f414e908e80a3fa8b91bf,256,V3,a6da4ac51c383eb9702f414e908e80a3fa8b91bf_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+21.652_ry+02.075_rz+26.782_tx+00.298_ty+00.010_s+01.449_a6da4ac51c383eb9702f414e908e80a3fa8b91bf_256x256.png,cfab47c02202068a5ff99e24108d404e65ef64ea,Boats,21.652,92.075,26.782,808 -809,CGTG_L,23.36,-0.543,42.434,0.272,0.124,1.09,7f920f6d466f9485a410853e96d4a115bc631a72,256,V3,7f920f6d466f9485a410853e96d4a115bc631a72_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+23.360_ry-00.543_rz+42.434_tx+00.272_ty+00.124_s+01.308_7f920f6d466f9485a410853e96d4a115bc631a72_256x256.png,01960ca4402c62dcf784bf42a5e6906fb756ace9,Boats,23.36,89.457,42.434,809 -810,CGTG_L,25.342,22.842,29.699,0.271,0.207,1.14833333333,8c301dfd5070ce97f797f2e7b5c7856459a61e91,256,V3,8c301dfd5070ce97f797f2e7b5c7856459a61e91_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+25.342_ry+22.842_rz+29.699_tx+00.271_ty+00.207_s+01.378_8c301dfd5070ce97f797f2e7b5c7856459a61e91_256x256.png,54d03ff0b89551b21ce80c6cd54f8014d2fe10a9,Boats,25.342,112.842,29.699,810 -811,CGTG_L,26.669,-1.949,1.563,-0.229,-0.361,0.818333333333,3ab4437cb99398cfeba153a475fcf0e62263e271,256,V3,3ab4437cb99398cfeba153a475fcf0e62263e271_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+26.669_ry-01.949_rz+01.563_tx-00.229_ty-00.361_s+00.982_3ab4437cb99398cfeba153a475fcf0e62263e271_256x256.png,4d5387d617c86773c441857c7f7daea91fc166f8,Boats,26.669,88.051,1.563,811 -812,CGTG_L,27.781,-43.753,12.435,0.082,-0.39,1.12083333333,987d03ad579a8d41a74b0a7e0bdaf1f8ce77b9d6,256,V3,987d03ad579a8d41a74b0a7e0bdaf1f8ce77b9d6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+27.781_ry-43.753_rz+12.435_tx+00.082_ty-00.390_s+01.345_987d03ad579a8d41a74b0a7e0bdaf1f8ce77b9d6_256x256.png,7cbb5d7c5800aa0643706a1f02a0054a8c4b2734,Boats,27.781,46.247,12.435,812 -813,CGTG_L,28.194,32.511,-39.622,-0.125,0.308,1.21333333333,77e577e9600e9eb87689ee6afb0952ece7db78d0,256,V3,77e577e9600e9eb87689ee6afb0952ece7db78d0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+28.194_ry+32.511_rz-39.622_tx-00.125_ty+00.308_s+01.456_77e577e9600e9eb87689ee6afb0952ece7db78d0_256x256.png,feb2281e5fd2d7c5046a8e8ebb2d6b72b0558ddc,Boats,28.194,122.511,-39.622,813 -814,CGTG_L,30.127,4.781,-30.2,0.249,0.304,1.28583333333,08471b83c1a13ac18437bcbb1081be3c69542b1d,256,V3,08471b83c1a13ac18437bcbb1081be3c69542b1d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+30.127_ry+04.781_rz-30.200_tx+00.249_ty+00.304_s+01.543_08471b83c1a13ac18437bcbb1081be3c69542b1d_256x256.png,182d0bfc1e8c4301cc69ea812a6ba924bf9221b9,Boats,30.127,94.781,-30.2,814 -815,CGTG_L,33.288,15.776,-8.254,0.236,-0.007,0.963333333333,39d1300c4fb81210175cb571fca06fc31bbe482a,256,V3,39d1300c4fb81210175cb571fca06fc31bbe482a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+33.288_ry+15.776_rz-08.254_tx+00.236_ty-00.007_s+01.156_39d1300c4fb81210175cb571fca06fc31bbe482a_256x256.png,a0e13f665b3a8e50f56604f762b5c4d7f36594f4,Boats,33.288,105.776,-8.254,815 -816,CGTG_L,33.526,-32.747,0.826,0.083,-0.244,1.20166666667,ebfd7c9fd55b14dddce95f6844d89bc538d2af41,256,V3,ebfd7c9fd55b14dddce95f6844d89bc538d2af41_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+33.526_ry-32.747_rz+00.826_tx+00.083_ty-00.244_s+01.442_ebfd7c9fd55b14dddce95f6844d89bc538d2af41_256x256.png,506c441b4fb13c09d9d42875815b0ec52cc9c8d0,Boats,33.526,57.253,0.826,816 -817,CGTG_L,35.277,21.258,15.682,0.052,0.326,0.796666666667,bf4561809db4724ef76c98b319ab1607d47a9331,256,V3,bf4561809db4724ef76c98b319ab1607d47a9331_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+35.277_ry+21.258_rz+15.682_tx+00.052_ty+00.326_s+00.956_bf4561809db4724ef76c98b319ab1607d47a9331_256x256.png,4b05ff2a54d3565cb6ccce7ded2326b1cdc159b3,Boats,35.277,111.258,15.682,817 -818,CGTG_L,35.3,-27.733,-31.023,-0.109,0.079,1.1875,85e25b1e14e37f1c4bd2eb200fc77c398614cdb5,256,V3,85e25b1e14e37f1c4bd2eb200fc77c398614cdb5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+35.300_ry-27.733_rz-31.023_tx-00.109_ty+00.079_s+01.425_85e25b1e14e37f1c4bd2eb200fc77c398614cdb5_256x256.png,152eff2604450efbf1411f1bdb12403ab34892af,Boats,35.3,62.267,-31.023,818 -819,CGTG_L,40.802,-33.152,-30.562,-0.247,-0.298,1.28333333333,ca8601f7fa06d0d7ea5742820ea741bab28e079d,256,V3,ca8601f7fa06d0d7ea5742820ea741bab28e079d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+40.802_ry-33.152_rz-30.562_tx-00.247_ty-00.298_s+01.540_ca8601f7fa06d0d7ea5742820ea741bab28e079d_256x256.png,e8f0363d68a3d9301bd15eda1b00cca7b2d96320,Boats,40.802,56.848,-30.562,819 -820,CGTG_L,41.029,15.354,-23.511,0.231,0.434,0.775,4ec679efc872f3cd04a36c4ce4a6c22acaf2e3b4,256,V3,4ec679efc872f3cd04a36c4ce4a6c22acaf2e3b4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+41.029_ry+15.354_rz-23.511_tx+00.231_ty+00.434_s+00.930_4ec679efc872f3cd04a36c4ce4a6c22acaf2e3b4_256x256.png,1ff4e5715e9ec44a28b7d375105bd7cde90bb40b,Boats,41.029,105.354,-23.511,820 -821,CGTG_L,41.246,38.467,24.809,0.02,0.052,0.906666666667,71ddd06b2c38cae221f8a3559d002ff13680593b,256,V3,71ddd06b2c38cae221f8a3559d002ff13680593b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+41.246_ry+38.467_rz+24.809_tx+00.020_ty+00.052_s+01.088_71ddd06b2c38cae221f8a3559d002ff13680593b_256x256.png,0cc6d5a5b6837d133a6b4a50d15641c87ae91ae1,Boats,41.246,128.467,24.809,821 -822,CGTG_L,42.531,-39.959,-23.687,-0.127,0.31,1.1875,655bdb9f63e11e8f519809e17a450b6a2a269496,256,V3,655bdb9f63e11e8f519809e17a450b6a2a269496_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx+42.531_ry-39.959_rz-23.687_tx-00.127_ty+00.310_s+01.425_655bdb9f63e11e8f519809e17a450b6a2a269496_256x256.png,5f4df4bb284d796e81fb1ce561545f8f08b95375,Boats,42.531,50.041,-23.687,822 -823,CGTG_L,-1.278,-41.295,14.712,0.271,-0.38,0.918333333333,1ae1f9a9e8fe735f0934e468b5674d12f0afaa31,256,V3,1ae1f9a9e8fe735f0934e468b5674d12f0afaa31_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-01.278_ry-41.295_rz+14.712_tx+00.271_ty-00.380_s+01.102_1ae1f9a9e8fe735f0934e468b5674d12f0afaa31_256x256.png,9f5c60c90495221f9f201dc3fba1ad47e0f7b092,Boats,-1.278,48.705,14.712,823 -824,CGTG_L,-10.418,-7.14,-18.045,0.288,-0.354,1.21333333333,73821cf7ffd15127ebbb209c5e9bb94f35a088fd,256,V3,73821cf7ffd15127ebbb209c5e9bb94f35a088fd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-10.418_ry-07.140_rz-18.045_tx+00.288_ty-00.354_s+01.456_73821cf7ffd15127ebbb209c5e9bb94f35a088fd_256x256.png,197e0d26736cd690dd515fb5385c98a24732e419,Boats,-10.418,82.86,-18.045,824 -825,CGTG_L,-13.716,-0.675,-19.316,0.076,-0.132,0.805833333333,060f0eceac361392b19c8d89879c2939fe1c3a35,256,V3,060f0eceac361392b19c8d89879c2939fe1c3a35_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-13.716_ry-00.675_rz-19.316_tx+00.076_ty-00.132_s+00.967_060f0eceac361392b19c8d89879c2939fe1c3a35_256x256.png,0497ad698bedcdb240d2ab1f35ffbfcc6beb936d,Boats,-13.716,89.325,-19.316,825 -826,CGTG_L,-13.847,6.611,-29.297,0.138,0.128,1.05,cd1bf83d814c864893c3d54f0f69ea570fe3e680,256,V3,cd1bf83d814c864893c3d54f0f69ea570fe3e680_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-13.847_ry+06.611_rz-29.297_tx+00.138_ty+00.128_s+01.260_cd1bf83d814c864893c3d54f0f69ea570fe3e680_256x256.png,95061fbbf610d46bcba58c7a279a1aa9f6893b2f,Boats,-13.847,96.611,-29.297,826 -827,CGTG_L,-14.023,-22.738,-40.002,-0.229,0.535,1.2925,f2f5ac6d81984ce598fa60e3142a5ba2ab399b83,256,V3,f2f5ac6d81984ce598fa60e3142a5ba2ab399b83_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-14.023_ry-22.738_rz-40.002_tx-00.229_ty+00.535_s+01.551_f2f5ac6d81984ce598fa60e3142a5ba2ab399b83_256x256.png,3af68e32656a4be1d00e8d6cef1dbf43b5b8ba04,Boats,-14.023,67.262,-40.002,827 -828,CGTG_L,-14.701,22.676,32.706,-0.223,0.471,1.12833333333,3be6deaada039dd8c403ebaa016a656cbdbc03a8,256,V3,3be6deaada039dd8c403ebaa016a656cbdbc03a8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-14.701_ry+22.676_rz+32.706_tx-00.223_ty+00.471_s+01.354_3be6deaada039dd8c403ebaa016a656cbdbc03a8_256x256.png,52ffb314f341b366e8efcf7a766de8ee1a4e073d,Boats,-14.701,112.676,32.706,828 -829,CGTG_L,-18.111,36.796,32.223,0.066,-0.011,1.01,b83ad31a7679b3890b433648c84fc5291feece15,256,V3,b83ad31a7679b3890b433648c84fc5291feece15_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-18.111_ry+36.796_rz+32.223_tx+00.066_ty-00.011_s+01.212_b83ad31a7679b3890b433648c84fc5291feece15_256x256.png,b29fcefdb165e4322dae1bf70b1c108363c81567,Boats,-18.111,126.796,32.223,829 -830,CGTG_L,-21.761,41.124,-37.304,0.113,-0.031,1.0525,69a36a40f71554c6860a980216f9e0772c2d024f,256,V3,69a36a40f71554c6860a980216f9e0772c2d024f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-21.761_ry+41.124_rz-37.304_tx+00.113_ty-00.031_s+01.263_69a36a40f71554c6860a980216f9e0772c2d024f_256x256.png,164870e9b69fa6114efe787ef48ca7e799187ceb,Boats,-21.761,131.124,-37.304,830 -831,CGTG_L,-23.819,27.301,33.099,0.114,0.402,0.875,e1089275f5cc104f982f7839ab5ed4c4a97c77f8,256,V3,e1089275f5cc104f982f7839ab5ed4c4a97c77f8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-23.819_ry+27.301_rz+33.099_tx+00.114_ty+00.402_s+01.050_e1089275f5cc104f982f7839ab5ed4c4a97c77f8_256x256.png,db3132736957ec82039b5251699e088fa5c1ffa5,Boats,-23.819,117.301,33.099,831 -832,CGTG_L,-27.881,-5.999,-21.792,-0.038,-0.25,0.92,7519a172b3e731f3015c6959d866793bebafd8ff,256,V3,7519a172b3e731f3015c6959d866793bebafd8ff_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-27.881_ry-05.999_rz-21.792_tx-00.038_ty-00.250_s+01.104_7519a172b3e731f3015c6959d866793bebafd8ff_256x256.png,ffdf4f231454c92cdb2af94414885fa5137a2974,Boats,-27.881,84.001,-21.792,832 -833,CGTG_L,-32.5,-35.569,-21.48,-0.187,0.042,1.29916666667,00b602c77f28f4d19414291beb3820480b1d2598,256,V3,00b602c77f28f4d19414291beb3820480b1d2598_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-32.500_ry-35.569_rz-21.480_tx-00.187_ty+00.042_s+01.559_00b602c77f28f4d19414291beb3820480b1d2598_256x256.png,3db11c676a57460b38090ef5dc81b16321bd1baf,Boats,-32.5,54.431,-21.48,833 -834,CGTG_L,-34.903,-4.984,-44.055,0.028,0.43,1.0575,4b0de3148f44f41099715341a8dc6620bb2fa388,256,V3,4b0de3148f44f41099715341a8dc6620bb2fa388_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-34.903_ry-04.984_rz-44.055_tx+00.028_ty+00.430_s+01.269_4b0de3148f44f41099715341a8dc6620bb2fa388_256x256.png,edf6a588ad8b6ce3b5aecd5f863470ae212f61e8,Boats,-34.903,85.016,-44.055,834 -835,CGTG_L,-38.974,11.632,-23.713,-0.021,0.18,0.843333333333,b5c9ee0fb2ab4326db4815e57686799c78f652c4,256,V3,b5c9ee0fb2ab4326db4815e57686799c78f652c4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-38.974_ry+11.632_rz-23.713_tx-00.021_ty+00.180_s+01.012_b5c9ee0fb2ab4326db4815e57686799c78f652c4_256x256.png,044d85d39502fcde397e263dad77b9da966f497d,Boats,-38.974,101.632,-23.713,835 -836,CGTG_L,-41.852,23.62,40.343,-0.157,-0.156,1.00333333333,b1517dd6fa029ec4f4330ef88eed7e8b67d8269f,256,V3,b1517dd6fa029ec4f4330ef88eed7e8b67d8269f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-41.852_ry+23.620_rz+40.343_tx-00.157_ty-00.156_s+01.204_b1517dd6fa029ec4f4330ef88eed7e8b67d8269f_256x256.png,0edf9727bd9ffa04a13d038f0de5f72042153343,Boats,-41.852,113.62,40.343,836 -837,CGTG_L,-42.837,-16.185,-28.52,0.098,0.598,1.2375,7c169cd77290d4290b60440e3694e55839f1f14b,256,V3,7c169cd77290d4290b60440e3694e55839f1f14b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-42.837_ry-16.185_rz-28.520_tx+00.098_ty+00.598_s+01.485_7c169cd77290d4290b60440e3694e55839f1f14b_256x256.png,17bdaaa93078fd7ff0c7d2a29a0515c3a8abd7fb,Boats,-42.837,73.815,-28.52,837 -838,CGTG_L,-42.947,-29.2,-19.857,0.078,0.336,1.08833333333,65f9db9506401153ee8b7185f53783779fea6418,256,V3,65f9db9506401153ee8b7185f53783779fea6418_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-42.947_ry-29.200_rz-19.857_tx+00.078_ty+00.336_s+01.306_65f9db9506401153ee8b7185f53783779fea6418_256x256.png,e0020b48f853aa97892149d94c49bb3563458293,Boats,-42.947,60.8,-19.857,838 -839,CGTG_L,-44.24,-19.328,-5.164,-0.227,-0.126,0.895,fd73d24b99c154e1947a86087e8d119792474db4,256,V3,fd73d24b99c154e1947a86087e8d119792474db4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/CGTG_L_rx-44.240_ry-19.328_rz-05.164_tx-00.227_ty-00.126_s+01.074_fd73d24b99c154e1947a86087e8d119792474db4_256x256.png,b5b7cd3e6e4938095979038151b0ff0c6950bdbb,Boats,-44.24,70.672,-5.164,839 -840,DTUG_L,10.325,-1.227,-44.328,0.291,-0.593,1.1775,85373b59851a67787252bdb555683a100fe95a3e,256,V3,85373b59851a67787252bdb555683a100fe95a3e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx+10.325_ry-01.227_rz-44.328_tx+00.291_ty-00.593_s+01.413_85373b59851a67787252bdb555683a100fe95a3e_256x256.png,428305cc1a08867799e92f054e91076c87531271,Boats,10.325,88.773,-44.328,840 -841,DTUG_L,11.021,-11.6,-25.105,-0.122,0.2,0.889166666667,f59ccef5039125f4da8fa2bbf4a2b771287a086e,256,V3,f59ccef5039125f4da8fa2bbf4a2b771287a086e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx+11.021_ry-11.600_rz-25.105_tx-00.122_ty+00.200_s+01.067_f59ccef5039125f4da8fa2bbf4a2b771287a086e_256x256.png,526db6e619f9b9752e8fdb2db06c2f5163a5fcad,Boats,11.021,78.4,-25.105,841 -842,DTUG_L,16.194,-33.643,16.219,0.266,-0.311,0.8325,2e73b45239867451c9aa68282fb3479f58b43176,256,V3,2e73b45239867451c9aa68282fb3479f58b43176_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx+16.194_ry-33.643_rz+16.219_tx+00.266_ty-00.311_s+00.999_2e73b45239867451c9aa68282fb3479f58b43176_256x256.png,2f3c97951a38e65ba41b619b80408bf6445a6f28,Boats,16.194,56.357,16.219,842 -843,DTUG_L,17.522,15.475,10.85,-0.145,-0.505,1.2,5992272eafc269c7d260cc2a1d8f8450adfbb36f,256,V3,5992272eafc269c7d260cc2a1d8f8450adfbb36f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx+17.522_ry+15.475_rz+10.850_tx-00.145_ty-00.505_s+01.440_5992272eafc269c7d260cc2a1d8f8450adfbb36f_256x256.png,254aec677443fc78fd1aa0a63e37bd2dc4c85ec3,Boats,17.522,105.475,10.85,843 -844,DTUG_L,18.911,-5.157,39.988,-0.179,0.567,0.974166666667,0415122e8e9c0c2c42e072ea56995670fd035792,256,V3,0415122e8e9c0c2c42e072ea56995670fd035792_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx+18.911_ry-05.157_rz+39.988_tx-00.179_ty+00.567_s+01.169_0415122e8e9c0c2c42e072ea56995670fd035792_256x256.png,9f3434d0a88e6963e3c8cd51d7066b31fd5aebcd,Boats,18.911,84.843,39.988,844 -845,DTUG_L,25.635,44.586,22.625,-0.057,-0.523,1.03666666667,5f6accc3e7c90a71cf4bcd324142b06a4fcfb778,256,V3,5f6accc3e7c90a71cf4bcd324142b06a4fcfb778_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx+25.635_ry+44.586_rz+22.625_tx-00.057_ty-00.523_s+01.244_5f6accc3e7c90a71cf4bcd324142b06a4fcfb778_256x256.png,051e68178d3bd935badb85d44c160d09df189c02,Boats,25.635,134.586,22.625,845 -846,DTUG_L,26.855,-17.869,25.929,0.057,-0.251,0.799166666667,9a09e2b6a283da6b9c5716ef3af38fea5b075db3,256,V3,9a09e2b6a283da6b9c5716ef3af38fea5b075db3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx+26.855_ry-17.869_rz+25.929_tx+00.057_ty-00.251_s+00.959_9a09e2b6a283da6b9c5716ef3af38fea5b075db3_256x256.png,7d0b9779c59abe5b5b79f34b3256d8db7f236634,Boats,26.855,72.131,25.929,846 -847,DTUG_L,28.436,27.677,-16.882,-0.138,-0.502,0.8625,cc293fa2ef4dc0635084a31502dcb46a412f1f21,256,V3,cc293fa2ef4dc0635084a31502dcb46a412f1f21_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx+28.436_ry+27.677_rz-16.882_tx-00.138_ty-00.502_s+01.035_cc293fa2ef4dc0635084a31502dcb46a412f1f21_256x256.png,90530c200d2a68d723b572a6d8ec6b9db05b3aa0,Boats,28.436,117.677,-16.882,847 -848,DTUG_L,28.861,-41.471,-10.769,0.292,-0.144,0.874166666667,18bcf15bdda5957455c59c5ffd43c2f525b84193,256,V3,18bcf15bdda5957455c59c5ffd43c2f525b84193_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx+28.861_ry-41.471_rz-10.769_tx+00.292_ty-00.144_s+01.049_18bcf15bdda5957455c59c5ffd43c2f525b84193_256x256.png,69984148f3fed0f4d7287741f6e6e521254b2b6c,Boats,28.861,48.529,-10.769,848 -849,DTUG_L,34.137,1.979,-15.731,0.103,0.034,0.841666666667,a30b89db224c29df24834a79f929b4bbb14d1e18,256,V3,a30b89db224c29df24834a79f929b4bbb14d1e18_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx+34.137_ry+01.979_rz-15.731_tx+00.103_ty+00.034_s+01.010_a30b89db224c29df24834a79f929b4bbb14d1e18_256x256.png,ab4cb060dc269486711dfb620bbfa65026686aa8,Boats,34.137,91.979,-15.731,849 -850,DTUG_L,34.458,25.948,-31.908,-0.111,0.576,1.18083333333,ab71b535b89f1aaa1784edbe7d85b1070e9c8c36,256,V3,ab71b535b89f1aaa1784edbe7d85b1070e9c8c36_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx+34.458_ry+25.948_rz-31.908_tx-00.111_ty+00.576_s+01.417_ab71b535b89f1aaa1784edbe7d85b1070e9c8c36_256x256.png,d64301b087243c5ad68b8d0b79212846502c8529,Boats,34.458,115.948,-31.908,850 -851,DTUG_L,36.423,-7.874,-1.663,0.011,0.181,0.875833333333,259008287af6650fb0164da7adb3375f3c7923c0,256,V3,259008287af6650fb0164da7adb3375f3c7923c0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx+36.423_ry-07.874_rz-01.663_tx+00.011_ty+00.181_s+01.051_259008287af6650fb0164da7adb3375f3c7923c0_256x256.png,1d228f62093da1334fe58def8acf9a5bc2ac1180,Boats,36.423,82.126,-1.663,851 -852,DTUG_L,37.668,-23.182,-39.519,-0.106,0.449,1.0925,2ce92540bb99e863e36b102752fa69ecf84cc417,256,V3,2ce92540bb99e863e36b102752fa69ecf84cc417_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx+37.668_ry-23.182_rz-39.519_tx-00.106_ty+00.449_s+01.311_2ce92540bb99e863e36b102752fa69ecf84cc417_256x256.png,71e38af8bc6efaf6a5b93ccdcaaec2c132fc2481,Boats,37.668,66.818,-39.519,852 -853,DTUG_L,-3.546,23.454,-30.601,-0.15,0.529,0.936666666667,218623261bdc38f2d9abfbcb4c6da4c570bca5cf,256,V3,218623261bdc38f2d9abfbcb4c6da4c570bca5cf_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-03.546_ry+23.454_rz-30.601_tx-00.150_ty+00.529_s+01.124_218623261bdc38f2d9abfbcb4c6da4c570bca5cf_256x256.png,4ba0455300dcbdc9c4dc12512bc9604bb64f1c60,Boats,-3.546,113.454,-30.601,853 -854,DTUG_L,-5.142,-34.273,-36.829,-0.178,0.36,0.8125,265e398ff7cc0366e5d5d0127cd6e0a2a7478c77,256,V3,265e398ff7cc0366e5d5d0127cd6e0a2a7478c77_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-05.142_ry-34.273_rz-36.829_tx-00.178_ty+00.360_s+00.975_265e398ff7cc0366e5d5d0127cd6e0a2a7478c77_256x256.png,af4ca6e3454aa3f29adab9667d0c2e053b307c0b,Boats,-5.142,55.727,-36.829,854 -855,DTUG_L,-5.986,33.242,-34.999,-0.087,-0.48,0.825,112ad048b1023af980e79407e3114eab3216b7aa,256,V3,112ad048b1023af980e79407e3114eab3216b7aa_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-05.986_ry+33.242_rz-34.999_tx-00.087_ty-00.480_s+00.990_112ad048b1023af980e79407e3114eab3216b7aa_256x256.png,c8a269145b3b5ac358986aa128adeb2437ba93a6,Boats,-5.986,123.242,-34.999,855 -856,DTUG_L,-6.649,-11.707,41.556,-0.289,-0.553,1.12416666667,08b1edd9ec85640ab325daf8b51f8b2e4fc96edb,256,V3,08b1edd9ec85640ab325daf8b51f8b2e4fc96edb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-06.649_ry-11.707_rz+41.556_tx-00.289_ty-00.553_s+01.349_08b1edd9ec85640ab325daf8b51f8b2e4fc96edb_256x256.png,724190b00db37fe4bbcacb5c3defd2dd0406c0dd,Boats,-6.649,78.293,41.556,856 -857,DTUG_L,-6.756,-21.404,22.683,0.063,-0.289,1.13666666667,9770675a55c7f0a643d555d1c5615f814698d4f5,256,V3,9770675a55c7f0a643d555d1c5615f814698d4f5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-06.756_ry-21.404_rz+22.683_tx+00.063_ty-00.289_s+01.364_9770675a55c7f0a643d555d1c5615f814698d4f5_256x256.png,080edf6a8fc5166409a078d4e532e0d2fabdf2ec,Boats,-6.756,68.596,22.683,857 -858,DTUG_L,-7.027,-8.998,5.141,-0.094,0.331,1.0025,610d246da7483e921d03e3bc002a2f25b3312c0c,256,V3,610d246da7483e921d03e3bc002a2f25b3312c0c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-07.027_ry-08.998_rz+05.141_tx-00.094_ty+00.331_s+01.203_610d246da7483e921d03e3bc002a2f25b3312c0c_256x256.png,c064dfe8e06821f923f59acd8027d539da2e65fd,Boats,-7.027,81.002,5.141,858 -859,DTUG_L,-7.839,-35.572,34.431,0.099,0.387,1.09166666667,6ce7115209f73928e383c1b876f7de3eea1f24d4,256,V3,6ce7115209f73928e383c1b876f7de3eea1f24d4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-07.839_ry-35.572_rz+34.431_tx+00.099_ty+00.387_s+01.310_6ce7115209f73928e383c1b876f7de3eea1f24d4_256x256.png,b7cf2a369158d3827a7e3e06f1bcf0f48302d9fc,Boats,-7.839,54.428,34.431,859 -860,DTUG_L,-9.247,12.159,31.618,-0.076,-0.196,1.205,0cb6eadf4d23b96f6c4247d96807625f723ac9a9,256,V3,0cb6eadf4d23b96f6c4247d96807625f723ac9a9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-09.247_ry+12.159_rz+31.618_tx-00.076_ty-00.196_s+01.446_0cb6eadf4d23b96f6c4247d96807625f723ac9a9_256x256.png,7d1c97a32cfdd1c488d3d7d5efba5894121138f5,Boats,-9.247,102.159,31.618,860 -861,DTUG_L,-10.693,32.842,-8.466,0.152,-0.215,0.900833333333,f8ecb27283eb7a58f7963c3f5f4c00296a3ee953,256,V3,f8ecb27283eb7a58f7963c3f5f4c00296a3ee953_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-10.693_ry+32.842_rz-08.466_tx+00.152_ty-00.215_s+01.081_f8ecb27283eb7a58f7963c3f5f4c00296a3ee953_256x256.png,3e94a0c19c047007f7fb36123a6ba831ecb6c07e,Boats,-10.693,122.842,-8.466,861 -862,DTUG_L,-10.802,-1.081,-38.789,-0.207,-0.315,0.938333333333,1617c34e3ef2a08c1ff6e5f2025de71b1d71d01b,256,V3,1617c34e3ef2a08c1ff6e5f2025de71b1d71d01b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-10.802_ry-01.081_rz-38.789_tx-00.207_ty-00.315_s+01.126_1617c34e3ef2a08c1ff6e5f2025de71b1d71d01b_256x256.png,c8bb7b5123c392a4982d2df472135e547f5ba26b,Boats,-10.802,88.919,-38.789,862 -863,DTUG_L,-14.725,3.058,41.114,-0.226,-0.136,1.1975,f614d5a36d4061285b7da56ebda32eda3b609f3b,256,V3,f614d5a36d4061285b7da56ebda32eda3b609f3b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-14.725_ry+03.058_rz+41.114_tx-00.226_ty-00.136_s+01.437_f614d5a36d4061285b7da56ebda32eda3b609f3b_256x256.png,7c8fe96161fa68a6884c92007ca98a10f39a7295,Boats,-14.725,93.058,41.114,863 -864,DTUG_L,-15.963,-24.19,-25.647,0.176,-0.497,1.00083333333,d4c45585948d33966a451cb6e618505693091c4a,256,V3,d4c45585948d33966a451cb6e618505693091c4a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-15.963_ry-24.190_rz-25.647_tx+00.176_ty-00.497_s+01.201_d4c45585948d33966a451cb6e618505693091c4a_256x256.png,e68520c686a2a63f6ff2d48e8832005c635703cb,Boats,-15.963,65.81,-25.647,864 -865,DTUG_L,-16.859,11.073,30.337,-0.3,-0.484,0.9925,64d1051dcf8fc4e01736c7f687a6c41bbc83fc79,256,V3,64d1051dcf8fc4e01736c7f687a6c41bbc83fc79_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-16.859_ry+11.073_rz+30.337_tx-00.300_ty-00.484_s+01.191_64d1051dcf8fc4e01736c7f687a6c41bbc83fc79_256x256.png,c1cc7eff40976745511b9c692a64846aa37b4108,Boats,-16.859,101.073,30.337,865 -866,DTUG_L,-17.675,-41.24,-12.702,0.024,0.515,0.981666666667,fcdac39d34292ab7744412d35414c608915479f6,256,V3,fcdac39d34292ab7744412d35414c608915479f6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-17.675_ry-41.240_rz-12.702_tx+00.024_ty+00.515_s+01.178_fcdac39d34292ab7744412d35414c608915479f6_256x256.png,53d322cc4e7eb2645536e1bd62c7f42095a33ce8,Boats,-17.675,48.76,-12.702,866 -867,DTUG_L,-17.702,19.301,4.649,-0.248,-0.538,1.055,24ce1e27cfd7d381bedbfdc4ae01489fe53a1b1b,256,V3,24ce1e27cfd7d381bedbfdc4ae01489fe53a1b1b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-17.702_ry+19.301_rz+04.649_tx-00.248_ty-00.538_s+01.266_24ce1e27cfd7d381bedbfdc4ae01489fe53a1b1b_256x256.png,996b979b1ebfcb3ce4a75db8019c92e5f9d8d2ee,Boats,-17.702,109.301,4.649,867 -868,DTUG_L,-17.941,-16.509,9.934,-0.178,0.21,0.8925,e6e35b52067f0ea85e1741f401e5c76bc8f2cf31,256,V3,e6e35b52067f0ea85e1741f401e5c76bc8f2cf31_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-17.941_ry-16.509_rz+09.934_tx-00.178_ty+00.210_s+01.071_e6e35b52067f0ea85e1741f401e5c76bc8f2cf31_256x256.png,b369ba9d54ac4c2c3dddf577fdf3eccbcf8f3b94,Boats,-17.941,73.491,9.934,868 -869,DTUG_L,-18.282,7.522,-30.866,0.03,-0.056,1.15916666667,04d5eba225ec9ba7c6ea7aaf2fdd94cd93102496,256,V3,04d5eba225ec9ba7c6ea7aaf2fdd94cd93102496_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-18.282_ry+07.522_rz-30.866_tx+00.030_ty-00.056_s+01.391_04d5eba225ec9ba7c6ea7aaf2fdd94cd93102496_256x256.png,e839f791b2f221dc8545f32e10f3722c6c4a1279,Boats,-18.282,97.522,-30.866,869 -870,DTUG_L,-21.516,21.173,44.671,-0.059,0.573,0.781666666667,dee909737ef57861eb9983348b1d413fc98c8b0d,256,V3,dee909737ef57861eb9983348b1d413fc98c8b0d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-21.516_ry+21.173_rz+44.671_tx-00.059_ty+00.573_s+00.938_dee909737ef57861eb9983348b1d413fc98c8b0d_256x256.png,7ff9e5b9e8e076975ad08c8e98f7331a8297c458,Boats,-21.516,111.173,44.671,870 -871,DTUG_L,-25.335,18.985,33.286,0.078,-0.356,1.245,7cce9edf08e4b1e886010cd03518df2f90e0eed8,256,V3,7cce9edf08e4b1e886010cd03518df2f90e0eed8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-25.335_ry+18.985_rz+33.286_tx+00.078_ty-00.356_s+01.494_7cce9edf08e4b1e886010cd03518df2f90e0eed8_256x256.png,9d3e3c2b4f8574d75430ca4e43fbfa408a672893,Boats,-25.335,108.985,33.286,871 -872,DTUG_L,-26.808,5.687,-25.692,-0.188,0.545,1.21583333333,6c0c57ffaefb4de5bfab4cbce2aacaa22166583c,256,V3,6c0c57ffaefb4de5bfab4cbce2aacaa22166583c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-26.808_ry+05.687_rz-25.692_tx-00.188_ty+00.545_s+01.459_6c0c57ffaefb4de5bfab4cbce2aacaa22166583c_256x256.png,7e5ffd54186dab09c2ad9b9a8e16cd9a943e98d2,Boats,-26.808,95.687,-25.692,872 -873,DTUG_L,-28.096,38.736,-5.307,0.193,0.42,1.24666666667,4aae431de2d4c4b5b28a53075fd9a0a49fb0b12f,256,V3,4aae431de2d4c4b5b28a53075fd9a0a49fb0b12f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-28.096_ry+38.736_rz-05.307_tx+00.193_ty+00.420_s+01.496_4aae431de2d4c4b5b28a53075fd9a0a49fb0b12f_256x256.png,4c7a4d0924d54d2fa04cebe79f50b13ef337a49e,Boats,-28.096,128.736,-5.307,873 -874,DTUG_L,-28.596,-28.485,-31.784,0.055,-0.393,1.14916666667,efa3084c53bf8247a41107e9fd7a98587cef4456,256,V3,efa3084c53bf8247a41107e9fd7a98587cef4456_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-28.596_ry-28.485_rz-31.784_tx+00.055_ty-00.393_s+01.379_efa3084c53bf8247a41107e9fd7a98587cef4456_256x256.png,7777db2691a56d6a6505559bdc3fd67ef081152b,Boats,-28.596,61.515,-31.784,874 -875,DTUG_L,-31.049,-15.676,39.812,0.156,-0.351,0.8475,63ffca95fd045098fc4e6cb36c7cc44fd0495fdd,256,V3,63ffca95fd045098fc4e6cb36c7cc44fd0495fdd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-31.049_ry-15.676_rz+39.812_tx+00.156_ty-00.351_s+01.017_63ffca95fd045098fc4e6cb36c7cc44fd0495fdd_256x256.png,af42c7a1d020e634702344e8b6164744c746ac0f,Boats,-31.049,74.324,39.812,875 -876,DTUG_L,-43.02,24.797,21.633,-0.161,0.142,0.778333333333,c60952cd750901e6e0b7e6ad8e01768637a90f18,256,V3,c60952cd750901e6e0b7e6ad8e01768637a90f18_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-43.020_ry+24.797_rz+21.633_tx-00.161_ty+00.142_s+00.934_c60952cd750901e6e0b7e6ad8e01768637a90f18_256x256.png,79c9cf26b38b0ebbf7bb43f6a16e70a4065a1ef4,Boats,-43.02,114.797,21.633,876 -877,DTUG_L,-43.134,-38.566,24.738,-0.089,-0.358,1.19166666667,0eed3012be7378f0cdf8f88a7891f0969182d4a3,256,V3,0eed3012be7378f0cdf8f88a7891f0969182d4a3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-43.134_ry-38.566_rz+24.738_tx-00.089_ty-00.358_s+01.430_0eed3012be7378f0cdf8f88a7891f0969182d4a3_256x256.png,4c18e3050e18b581c303cd21e8e24c10cfefbd99,Boats,-43.134,51.434,24.738,877 -878,DTUG_L,-44.097,41.685,-30.792,0.166,0.04,0.955833333333,e7592078b5e45cd89ec96455a5286b2ed296a48b,256,V3,e7592078b5e45cd89ec96455a5286b2ed296a48b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-44.097_ry+41.685_rz-30.792_tx+00.166_ty+00.040_s+01.147_e7592078b5e45cd89ec96455a5286b2ed296a48b_256x256.png,e854cce3e97780c5e00c66f27b82e6ab50388eb7,Boats,-44.097,131.685,-30.792,878 -879,DTUG_L,-44.51,36.547,5.218,-0.188,-0.482,0.800833333333,f48bd7b7c3e04d2c6fad984ab7489fc247bc8836,256,V3,f48bd7b7c3e04d2c6fad984ab7489fc247bc8836_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/DTUG_L_rx-44.510_ry+36.547_rz+05.218_tx-00.188_ty-00.482_s+00.961_f48bd7b7c3e04d2c6fad984ab7489fc247bc8836_256x256.png,86119ee3f13b9f80aeb45e2c83b6a08919ced561,Boats,-44.51,126.547,5.218,879 -880,ELEPHANT_M,0.757,-7.395,-13.172,-0.017,0.241,0.9325,c51b7bb29cbabb98b365838fb0db60e5515307ad,256,V3,c51b7bb29cbabb98b365838fb0db60e5515307ad_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+00.757_ry-07.395_rz-13.172_tx-00.017_ty+00.241_s+00.746_c51b7bb29cbabb98b365838fb0db60e5515307ad_256x256.png,44628f0be12ac7d718d3651ae568b8a6af1dae76,Animals,0.757,82.605,-13.172,880 -881,ELEPHANT_M,5.567,-35.721,-25.514,0.279,0.303,1.08625,3d044ab5519bfbb501b371743ddb8783ac9d5717,256,V3,3d044ab5519bfbb501b371743ddb8783ac9d5717_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+05.567_ry-35.721_rz-25.514_tx+00.279_ty+00.303_s+00.869_3d044ab5519bfbb501b371743ddb8783ac9d5717_256x256.png,240e06a9f5c462d8d42cb87f06f5c96ef65a7144,Animals,5.567,54.279,-25.514,881 -882,ELEPHANT_M,7.174,25.738,27.66,-0.059,-0.265,1.2925,a859cdd85dab1a1bb46f9efa01a8d41afc7ea840,256,V3,a859cdd85dab1a1bb46f9efa01a8d41afc7ea840_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+07.174_ry+25.738_rz+27.660_tx-00.059_ty-00.265_s+01.034_a859cdd85dab1a1bb46f9efa01a8d41afc7ea840_256x256.png,1babceba1f2ad492dad0d692e05f01e2555476a9,Animals,7.174,115.738,27.66,882 -883,ELEPHANT_M,7.783,-43.224,14.689,-0.022,-0.494,0.84125,eee209523cbcce2da2fd13760fa6601959dbb293,256,V3,eee209523cbcce2da2fd13760fa6601959dbb293_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+07.783_ry-43.224_rz+14.689_tx-00.022_ty-00.494_s+00.673_eee209523cbcce2da2fd13760fa6601959dbb293_256x256.png,f21151cbd2867408892bcdfa3a30f814088268da,Animals,7.783,46.776,14.689,883 -884,ELEPHANT_M,8.636,40.173,15.092,0.205,-0.246,0.86375,df2393e84809d856c0feb2588032688a0516ff5f,256,V3,df2393e84809d856c0feb2588032688a0516ff5f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+08.636_ry+40.173_rz+15.092_tx+00.205_ty-00.246_s+00.691_df2393e84809d856c0feb2588032688a0516ff5f_256x256.png,399d3de5c80a2accb1650bec7fe099e959fd16ba,Animals,8.636,130.173,15.092,884 -885,ELEPHANT_M,8.811,-15.685,2.0,0.094,-0.251,1.23625,8ef9deea1dd0d6f974fa9cc1417e16a465d986d0,256,V3,8ef9deea1dd0d6f974fa9cc1417e16a465d986d0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+08.811_ry-15.685_rz+02.000_tx+00.094_ty-00.251_s+00.989_8ef9deea1dd0d6f974fa9cc1417e16a465d986d0_256x256.png,2b4ab2b5c3f7169602cc8357e9ed1228b8005b8f,Animals,8.811,74.315,2.0,885 -886,ELEPHANT_M,9.131,12.258,-5.935,0.108,0.58,0.91125,70499ae51d379138356236e3dcd894ee48fd576e,256,V3,70499ae51d379138356236e3dcd894ee48fd576e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+09.131_ry+12.258_rz-05.935_tx+00.108_ty+00.580_s+00.729_70499ae51d379138356236e3dcd894ee48fd576e_256x256.png,1af2671262dda07583183063cf1c8b40d0cb951a,Animals,9.131,102.258,-5.935,886 -887,ELEPHANT_M,11.638,2.114,-40.102,0.098,-0.169,0.87125,d41e8c6c3b184d7a2d3ed83607c12f00acb1520b,256,V3,d41e8c6c3b184d7a2d3ed83607c12f00acb1520b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+11.638_ry+02.114_rz-40.102_tx+00.098_ty-00.169_s+00.697_d41e8c6c3b184d7a2d3ed83607c12f00acb1520b_256x256.png,721c887c59e8f929a965f7f5c5351a3aa35e47bc,Animals,11.638,92.114,-40.102,887 -888,ELEPHANT_M,13.371,10.284,-31.168,-0.14,-0.405,0.95125,cab037a42132b950ee6e9b317ae2980f16e5883a,256,V3,cab037a42132b950ee6e9b317ae2980f16e5883a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+13.371_ry+10.284_rz-31.168_tx-00.140_ty-00.405_s+00.761_cab037a42132b950ee6e9b317ae2980f16e5883a_256x256.png,eb5de25682e7f39ff10bffc345c8994bb0d223fc,Animals,13.371,100.284,-31.168,888 -889,ELEPHANT_M,14.878,-7.278,18.924,-0.196,-0.554,0.85375,32944aa228b960b1c82febf687355f55965d8be0,256,V3,32944aa228b960b1c82febf687355f55965d8be0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+14.878_ry-07.278_rz+18.924_tx-00.196_ty-00.554_s+00.683_32944aa228b960b1c82febf687355f55965d8be0_256x256.png,1a89f0ce0ac770d614518f0073e255858eada145,Animals,14.878,82.722,18.924,889 -890,ELEPHANT_M,15.66,24.764,-22.772,0.175,0.082,0.91375,91c50df1fd32b43a2ef22a35f8d426eb1ed8221e,256,V3,91c50df1fd32b43a2ef22a35f8d426eb1ed8221e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+15.660_ry+24.764_rz-22.772_tx+00.175_ty+00.082_s+00.731_91c50df1fd32b43a2ef22a35f8d426eb1ed8221e_256x256.png,958bb19a9963a4e5e0d80ccdce0001c5471b175a,Animals,15.66,114.764,-22.772,890 -891,ELEPHANT_M,18.371,-20.294,-24.94,0.226,0.218,0.8975,4cf43987ed88f23e77f9f37e178e71a992904f14,256,V3,4cf43987ed88f23e77f9f37e178e71a992904f14_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+18.371_ry-20.294_rz-24.940_tx+00.226_ty+00.218_s+00.718_4cf43987ed88f23e77f9f37e178e71a992904f14_256x256.png,9c41c6454d303c248926c525782ec1b7df858eec,Animals,18.371,69.706,-24.94,891 -892,ELEPHANT_M,20.443,-35.871,-3.179,0.096,0.374,0.87375,9e891eff816d5cf4b9035784c733e4fda684ba9b,256,V3,9e891eff816d5cf4b9035784c733e4fda684ba9b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+20.443_ry-35.871_rz-03.179_tx+00.096_ty+00.374_s+00.699_9e891eff816d5cf4b9035784c733e4fda684ba9b_256x256.png,969091e901af829c1728be436d30a7aedd3be8c2,Animals,20.443,54.129,-3.179,892 -893,ELEPHANT_M,23.41,30.573,-14.941,-0.284,0.22,1.08875,754a344cd59eb06c5f84c2e5267e60792502371e,256,V3,754a344cd59eb06c5f84c2e5267e60792502371e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+23.410_ry+30.573_rz-14.941_tx-00.284_ty+00.220_s+00.871_754a344cd59eb06c5f84c2e5267e60792502371e_256x256.png,5a453a173a9c261669f1f17cdcf1126f1fb98766,Animals,23.41,120.573,-14.941,893 -894,ELEPHANT_M,23.422,41.576,41.852,0.005,-0.406,1.0725,91318c3c1f254748856df272cafb19062aedc8b2,256,V3,91318c3c1f254748856df272cafb19062aedc8b2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+23.422_ry+41.576_rz+41.852_tx+00.005_ty-00.406_s+00.858_91318c3c1f254748856df272cafb19062aedc8b2_256x256.png,71175b9556bcd844b72095294ec0e031c19c5c21,Animals,23.422,131.576,41.852,894 -895,ELEPHANT_M,28.493,11.855,34.722,-0.237,0.57,0.85125,279032c50a5107ac14f43c4c716bff096300f1a1,256,V3,279032c50a5107ac14f43c4c716bff096300f1a1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+28.493_ry+11.855_rz+34.722_tx-00.237_ty+00.570_s+00.681_279032c50a5107ac14f43c4c716bff096300f1a1_256x256.png,49f39aa20fc9f561381698ab2a6828c5a2033a73,Animals,28.493,101.855,34.722,895 -896,ELEPHANT_M,33.833,14.593,9.86,-0.248,-0.266,0.83375,a52710e26503a1e3ef16be0885199842ba39d304,256,V3,a52710e26503a1e3ef16be0885199842ba39d304_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+33.833_ry+14.593_rz+09.860_tx-00.248_ty-00.266_s+00.667_a52710e26503a1e3ef16be0885199842ba39d304_256x256.png,18ed00f42e0ad80d45efce716489357a2c8abaf6,Animals,33.833,104.593,9.86,896 -897,ELEPHANT_M,37.023,-10.644,26.25,-0.085,-0.55,0.88875,75e3160ddae2a64a2c200fb8ef1613fec85b6bcd,256,V3,75e3160ddae2a64a2c200fb8ef1613fec85b6bcd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+37.023_ry-10.644_rz+26.250_tx-00.085_ty-00.550_s+00.711_75e3160ddae2a64a2c200fb8ef1613fec85b6bcd_256x256.png,b8b9d09eb65cdc665b63e30d41d346330c89bf64,Animals,37.023,79.356,26.25,897 -898,ELEPHANT_M,39.66,-22.376,40.414,0.227,0.024,1.23,e966b027d2ab3faa9e0d331aa15be65b230477eb,256,V3,e966b027d2ab3faa9e0d331aa15be65b230477eb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+39.660_ry-22.376_rz+40.414_tx+00.227_ty+00.024_s+00.984_e966b027d2ab3faa9e0d331aa15be65b230477eb_256x256.png,9e92401a560f28f4337733e8408a039d9b014e72,Animals,39.66,67.624,40.414,898 -899,ELEPHANT_M,40.939,-10.281,-41.32,-0.03,-0.319,0.785,f802f622b75e2ffe3aff6f84c33295d3b19bab8c,256,V3,f802f622b75e2ffe3aff6f84c33295d3b19bab8c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+40.939_ry-10.281_rz-41.320_tx-00.030_ty-00.319_s+00.628_f802f622b75e2ffe3aff6f84c33295d3b19bab8c_256x256.png,372a7173a4cb77736c4982326461918775d72383,Animals,40.939,79.719,-41.32,899 -900,ELEPHANT_M,41.002,31.827,17.52,0.204,-0.284,0.9125,710930fa3b53c88079df00e51c2708cdaa36cab5,256,V3,710930fa3b53c88079df00e51c2708cdaa36cab5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx+41.002_ry+31.827_rz+17.520_tx+00.204_ty-00.284_s+00.730_710930fa3b53c88079df00e51c2708cdaa36cab5_256x256.png,131bcd86b6b34ede0e8aea864717133977f7603b,Animals,41.002,121.827,17.52,900 -901,ELEPHANT_M,-9.031,26.63,-37.772,0.064,-0.594,1.2675,8fab1dff8f7731328bb060d0468290367ec68fd8,256,V3,8fab1dff8f7731328bb060d0468290367ec68fd8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-09.031_ry+26.630_rz-37.772_tx+00.064_ty-00.594_s+01.014_8fab1dff8f7731328bb060d0468290367ec68fd8_256x256.png,14dd1acbcd46f7f66400ceea3ac4d29abae4fe6f,Animals,-9.031,116.63,-37.772,901 -902,ELEPHANT_M,-14.217,-20.57,34.209,-0.233,-0.063,0.945,2e87f1dc8d7712a69465436094f4b3d02f9f226e,256,V3,2e87f1dc8d7712a69465436094f4b3d02f9f226e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-14.217_ry-20.570_rz+34.209_tx-00.233_ty-00.063_s+00.756_2e87f1dc8d7712a69465436094f4b3d02f9f226e_256x256.png,f4a11c226cb9b32c1bdd9f23002ff117790db970,Animals,-14.217,69.43,34.209,902 -903,ELEPHANT_M,-14.768,-6.153,13.525,0.102,-0.072,1.135,59d8829fcdc8fcf561f512c4726861e188b52351,256,V3,59d8829fcdc8fcf561f512c4726861e188b52351_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-14.768_ry-06.153_rz+13.525_tx+00.102_ty-00.072_s+00.908_59d8829fcdc8fcf561f512c4726861e188b52351_256x256.png,3c1f0b9c9df42848fa3de6321d9119d9d3dfee7c,Animals,-14.768,83.847,13.525,903 -904,ELEPHANT_M,-15.55,-18.268,36.712,0.209,-0.384,0.98875,16eb070f661b33b2e3dcd08d6d4e37d5c3595ae1,256,V3,16eb070f661b33b2e3dcd08d6d4e37d5c3595ae1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-15.550_ry-18.268_rz+36.712_tx+00.209_ty-00.384_s+00.791_16eb070f661b33b2e3dcd08d6d4e37d5c3595ae1_256x256.png,01029fbfd12c28ee82360690ce2ef95ed797f6ee,Animals,-15.55,71.732,36.712,904 -905,ELEPHANT_M,-16.241,-25.404,-7.406,-0.097,-0.511,0.92875,223ba9b4c3264839b5d119a9ee3ff4f7add72469,256,V3,223ba9b4c3264839b5d119a9ee3ff4f7add72469_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-16.241_ry-25.404_rz-07.406_tx-00.097_ty-00.511_s+00.743_223ba9b4c3264839b5d119a9ee3ff4f7add72469_256x256.png,52685de8b595efddb46898d3f4b833338297acf4,Animals,-16.241,64.596,-7.406,905 -906,ELEPHANT_M,-18.799,-19.132,-17.076,0.086,-0.526,0.89875,a6ef75e4e9871cc152c483067323a65c7bec7dbc,256,V3,a6ef75e4e9871cc152c483067323a65c7bec7dbc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-18.799_ry-19.132_rz-17.076_tx+00.086_ty-00.526_s+00.719_a6ef75e4e9871cc152c483067323a65c7bec7dbc_256x256.png,5a67e52bb67a8cdeeaa51218f8619af23a041544,Animals,-18.799,70.868,-17.076,906 -907,ELEPHANT_M,-20.292,39.178,34.087,0.299,-0.548,1.1075,e86a453dba20ae2c2804c6be6f3781e811b92e3a,256,V3,e86a453dba20ae2c2804c6be6f3781e811b92e3a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-20.292_ry+39.178_rz+34.087_tx+00.299_ty-00.548_s+00.886_e86a453dba20ae2c2804c6be6f3781e811b92e3a_256x256.png,857413489e88fa74b5b8a3bca7eb55ddfc374c08,Animals,-20.292,129.178,34.087,907 -908,ELEPHANT_M,-23.536,15.802,44.077,-0.154,0.073,0.91125,0e33ad953e9bf75af6912369e4b376d7b176d6ac,256,V3,0e33ad953e9bf75af6912369e4b376d7b176d6ac_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-23.536_ry+15.802_rz+44.077_tx-00.154_ty+00.073_s+00.729_0e33ad953e9bf75af6912369e4b376d7b176d6ac_256x256.png,bea8f8a25f4df9abc87a699e5de7699c722b244a,Animals,-23.536,105.802,44.077,908 -909,ELEPHANT_M,-23.609,-41.236,27.075,0.16,-0.156,1.15,420238f7cadc02858223d9893530a8c47e5a102b,256,V3,420238f7cadc02858223d9893530a8c47e5a102b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-23.609_ry-41.236_rz+27.075_tx+00.160_ty-00.156_s+00.920_420238f7cadc02858223d9893530a8c47e5a102b_256x256.png,102ad9d68eb9bbddda01c779a825fe26f7c2cbe8,Animals,-23.609,48.764,27.075,909 -910,ELEPHANT_M,-24.561,26.85,41.649,0.265,-0.451,0.81,a1fa39acbc0ae640f664b3edd01d1256db4cb3e3,256,V3,a1fa39acbc0ae640f664b3edd01d1256db4cb3e3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-24.561_ry+26.850_rz+41.649_tx+00.265_ty-00.451_s+00.648_a1fa39acbc0ae640f664b3edd01d1256db4cb3e3_256x256.png,d90f6284625588209bab2c1b7eee7640b1e7c859,Animals,-24.561,116.85,41.649,910 -911,ELEPHANT_M,-30.394,40.035,39.49,0.147,0.204,1.11125,905989ef67d7ce5310d04e117dd2ddbb3c027ba9,256,V3,905989ef67d7ce5310d04e117dd2ddbb3c027ba9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-30.394_ry+40.035_rz+39.490_tx+00.147_ty+00.204_s+00.889_905989ef67d7ce5310d04e117dd2ddbb3c027ba9_256x256.png,f3215890da6bac05d934412b96aab9d9c35ac8d1,Animals,-30.394,130.035,39.49,911 -912,ELEPHANT_M,-31.25,2.964,-23.937,-0.2,0.198,1.01,e117c7a521f7c1f27be4481775dbc08731e66e25,256,V3,e117c7a521f7c1f27be4481775dbc08731e66e25_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-31.250_ry+02.964_rz-23.937_tx-00.200_ty+00.198_s+00.808_e117c7a521f7c1f27be4481775dbc08731e66e25_256x256.png,0b238951eeebd06f8d5048c5c71905dd7960a1ea,Animals,-31.25,92.964,-23.937,912 -913,ELEPHANT_M,-32.406,39.534,-27.89,-0.179,0.378,0.99875,22d00b957ff43a6afb09354a7cf81c04bc7c5f7b,256,V3,22d00b957ff43a6afb09354a7cf81c04bc7c5f7b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-32.406_ry+39.534_rz-27.890_tx-00.179_ty+00.378_s+00.799_22d00b957ff43a6afb09354a7cf81c04bc7c5f7b_256x256.png,ec7172237c22ae4f28e2d1427b31808a714545af,Animals,-32.406,129.534,-27.89,913 -914,ELEPHANT_M,-37.651,22.053,44.785,0.072,-0.107,1.28625,033843880077ec51da238b22764903ee134e5863,256,V3,033843880077ec51da238b22764903ee134e5863_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-37.651_ry+22.053_rz+44.785_tx+00.072_ty-00.107_s+01.029_033843880077ec51da238b22764903ee134e5863_256x256.png,3601e17b4b82adb1e335265f0b373aa531f76b5a,Animals,-37.651,112.053,44.785,914 -915,ELEPHANT_M,-39.529,11.359,38.633,0.287,0.595,1.24625,66791367350558bd50030aac8e26e2a9b1fa8ce7,256,V3,66791367350558bd50030aac8e26e2a9b1fa8ce7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-39.529_ry+11.359_rz+38.633_tx+00.287_ty+00.595_s+00.997_66791367350558bd50030aac8e26e2a9b1fa8ce7_256x256.png,4f2033c0acb1f8f706d56f85c6721d917fc91273,Animals,-39.529,101.359,38.633,915 -916,ELEPHANT_M,-40.052,-2.335,3.746,-0.151,0.55,0.9175,683aa2f44ab404ca479bc883f5ff0c0d543085e2,256,V3,683aa2f44ab404ca479bc883f5ff0c0d543085e2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-40.052_ry-02.335_rz+03.746_tx-00.151_ty+00.550_s+00.734_683aa2f44ab404ca479bc883f5ff0c0d543085e2_256x256.png,3c76a15a399599301834d68f901ca79ba86f3486,Animals,-40.052,87.665,3.746,916 -917,ELEPHANT_M,-41.489,-15.178,40.655,0.282,0.039,1.275,9be7de3c97b5e29697eb41879f0a0585fbc21895,256,V3,9be7de3c97b5e29697eb41879f0a0585fbc21895_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-41.489_ry-15.178_rz+40.655_tx+00.282_ty+00.039_s+01.020_9be7de3c97b5e29697eb41879f0a0585fbc21895_256x256.png,500d163c6dfeb1397033320965b2f139c307bbe8,Animals,-41.489,74.822,40.655,917 -918,ELEPHANT_M,-42.446,22.936,-43.435,-0.153,0.315,1.13625,62c9626e99ed9eb49adc16e33a5a228919d54723,256,V3,62c9626e99ed9eb49adc16e33a5a228919d54723_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-42.446_ry+22.936_rz-43.435_tx-00.153_ty+00.315_s+00.909_62c9626e99ed9eb49adc16e33a5a228919d54723_256x256.png,40df6569fe38b63932bc35c49910151c8d353a5f,Animals,-42.446,112.936,-43.435,918 -919,ELEPHANT_M,-43.907,-4.25,19.612,-0.183,0.49,1.17125,0cf274f52cce316d020075b391997541e67c8353,256,V3,0cf274f52cce316d020075b391997541e67c8353_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/ELEPHANT_M_rx-43.907_ry-04.250_rz+19.612_tx-00.183_ty+00.490_s+00.937_0cf274f52cce316d020075b391997541e67c8353_256x256.png,c6528ff8ea2f48d4bcc2fa0fb39ce9f096bfe274,Animals,-43.907,85.75,19.612,919 -920,GORILLA,1.138,9.687,-8.742,0.001,-0.436,0.7975,0f0fc67c95e43f98ad49d42c20e185ae8bd57e29,256,V3,0f0fc67c95e43f98ad49d42c20e185ae8bd57e29_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+01.138_ry+09.687_rz-08.742_tx+00.001_ty-00.436_s+00.638_0f0fc67c95e43f98ad49d42c20e185ae8bd57e29_256x256.png,620118b3a5de4af7521622603c94417f3e6c6893,Animals,1.138,99.687,-8.742,920 -921,GORILLA,1.433,-36.831,35.331,0.131,0.241,0.785,c1e55da1c4a3213eb143ac84687f832a8b589ce0,256,V3,c1e55da1c4a3213eb143ac84687f832a8b589ce0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+01.433_ry-36.831_rz+35.331_tx+00.131_ty+00.241_s+00.628_c1e55da1c4a3213eb143ac84687f832a8b589ce0_256x256.png,0bafd3ea1fbbab639863067c0aaaa898572b18a7,Animals,1.433,53.169,35.331,921 -922,GORILLA,3.922,15.228,25.98,-0.093,0.399,1.08625,23bd019e8b44bbe72e388da427cfd4e9689fa4bc,256,V3,23bd019e8b44bbe72e388da427cfd4e9689fa4bc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+03.922_ry+15.228_rz+25.980_tx-00.093_ty+00.399_s+00.869_23bd019e8b44bbe72e388da427cfd4e9689fa4bc_256x256.png,c27942474fc870bc57162c922e40dd54231d40fb,Animals,3.922,105.228,25.98,922 -923,GORILLA,6.258,34.516,26.546,-0.145,-0.397,1.0375,3b1fe23c52aeb611512a890b75b562a4f4aadbb4,256,V3,3b1fe23c52aeb611512a890b75b562a4f4aadbb4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+06.258_ry+34.516_rz+26.546_tx-00.145_ty-00.397_s+00.830_3b1fe23c52aeb611512a890b75b562a4f4aadbb4_256x256.png,cb4fd691a83ef44e0eb5a9454100d40d3af53728,Animals,6.258,124.516,26.546,923 -924,GORILLA,9.516,32.512,-2.949,-0.185,0.348,0.93625,1c62ad5fdfe17bffc8b424c5dc71605c1e38adc5,256,V3,1c62ad5fdfe17bffc8b424c5dc71605c1e38adc5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+09.516_ry+32.512_rz-02.949_tx-00.185_ty+00.348_s+00.749_1c62ad5fdfe17bffc8b424c5dc71605c1e38adc5_256x256.png,378fd7fcc442d1ee134ed33cd4363a79694cd5d2,Animals,9.516,122.512,-2.949,924 -925,GORILLA,9.769,31.985,24.934,0.09,0.28,0.78125,15b2f9913e5f373cf90c62f0093ee03db300a696,256,V3,15b2f9913e5f373cf90c62f0093ee03db300a696_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+09.769_ry+31.985_rz+24.934_tx+00.090_ty+00.280_s+00.625_15b2f9913e5f373cf90c62f0093ee03db300a696_256x256.png,2bcd140d15caa85c261346ae1dc2b02172c0aa5b,Animals,9.769,121.985,24.934,925 -926,GORILLA,11.829,6.559,42.26,-0.077,-0.031,1.0325,3489581237a6ecd6dcf98d71b1d7393a4cd39e33,256,V3,3489581237a6ecd6dcf98d71b1d7393a4cd39e33_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+11.829_ry+06.559_rz+42.260_tx-00.077_ty-00.031_s+00.826_3489581237a6ecd6dcf98d71b1d7393a4cd39e33_256x256.png,16b254b48b37615544e938bf69ff9539eb5125f6,Animals,11.829,96.559,42.26,926 -927,GORILLA,12.218,-3.239,36.279,0.189,0.313,0.8975,774ee4e1a66bc666f3bf70bac487e4e62a4559a1,256,V3,774ee4e1a66bc666f3bf70bac487e4e62a4559a1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+12.218_ry-03.239_rz+36.279_tx+00.189_ty+00.313_s+00.718_774ee4e1a66bc666f3bf70bac487e4e62a4559a1_256x256.png,df80a7cfe2f4d3321fa27d7a21afdd8c6427695a,Animals,12.218,86.761,36.279,927 -928,GORILLA,13.888,-13.621,13.767,-0.078,-0.107,0.85125,2704edbd6e5eb04b3a99f2a7eb00c9472ebb1e7d,256,V3,2704edbd6e5eb04b3a99f2a7eb00c9472ebb1e7d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+13.888_ry-13.621_rz+13.767_tx-00.078_ty-00.107_s+00.681_2704edbd6e5eb04b3a99f2a7eb00c9472ebb1e7d_256x256.png,76abade877135982d14be2848999a2d84cf55fa8,Animals,13.888,76.379,13.767,928 -929,GORILLA,13.987,-43.246,-3.315,0.044,-0.263,0.89125,402dde17d0731f990db11cdb13a52a6ac0a22b7d,256,V3,402dde17d0731f990db11cdb13a52a6ac0a22b7d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+13.987_ry-43.246_rz-03.315_tx+00.044_ty-00.263_s+00.713_402dde17d0731f990db11cdb13a52a6ac0a22b7d_256x256.png,7feef9674b68fd46787ee6fe9a1dcca27a9ac6bf,Animals,13.987,46.754,-3.315,929 -930,GORILLA,17.782,30.676,23.523,0.16,0.237,0.91375,562dbb2032ab46702743ba2fb2d61c63843c5d92,256,V3,562dbb2032ab46702743ba2fb2d61c63843c5d92_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+17.782_ry+30.676_rz+23.523_tx+00.160_ty+00.237_s+00.731_562dbb2032ab46702743ba2fb2d61c63843c5d92_256x256.png,eeba9f00f285806e14e464b1965b4e4b36d54e65,Animals,17.782,120.676,23.523,930 -931,GORILLA,18.134,-38.619,-30.642,-0.048,0.532,1.1175,31bcff8179667aacf0a305c1d2697382c23cb137,256,V3,31bcff8179667aacf0a305c1d2697382c23cb137_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+18.134_ry-38.619_rz-30.642_tx-00.048_ty+00.532_s+00.894_31bcff8179667aacf0a305c1d2697382c23cb137_256x256.png,8ad10d4f7b8f7cb66d866032fafbfa9b58708781,Animals,18.134,51.381,-30.642,931 -932,GORILLA,19.165,43.709,-34.032,-0.176,0.001,0.8125,a47a2f7d0341a156c4df0e98638dd61d17bd41b1,256,V3,a47a2f7d0341a156c4df0e98638dd61d17bd41b1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+19.165_ry+43.709_rz-34.032_tx-00.176_ty+00.001_s+00.650_a47a2f7d0341a156c4df0e98638dd61d17bd41b1_256x256.png,49e528665e2f778362431c7793b6353928c0fdb6,Animals,19.165,133.709,-34.032,932 -933,GORILLA,22.176,0.815,-15.68,0.153,0.442,0.99125,8860546de8f15c9bfbc272ddad3b4606cc7c95b5,256,V3,8860546de8f15c9bfbc272ddad3b4606cc7c95b5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+22.176_ry+00.815_rz-15.680_tx+00.153_ty+00.442_s+00.793_8860546de8f15c9bfbc272ddad3b4606cc7c95b5_256x256.png,84f11e25872de13221610b00b37ba785a75c6901,Animals,22.176,90.815,-15.68,933 -934,GORILLA,23.301,44.305,-5.226,0.141,-0.417,0.995,b2c128d8c36947ea1c9963c17728a87a3134e27b,256,V3,b2c128d8c36947ea1c9963c17728a87a3134e27b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+23.301_ry+44.305_rz-05.226_tx+00.141_ty-00.417_s+00.796_b2c128d8c36947ea1c9963c17728a87a3134e27b_256x256.png,3c97e513df3c08dac4e0d4e6828677201dfe9dd3,Animals,23.301,134.305,-5.226,934 -935,GORILLA,23.35,9.186,-8.929,-0.017,0.578,1.29625,61343354f22127a49e6ebdf52bbe3c2d2436c7be,256,V3,61343354f22127a49e6ebdf52bbe3c2d2436c7be_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+23.350_ry+09.186_rz-08.929_tx-00.017_ty+00.578_s+01.037_61343354f22127a49e6ebdf52bbe3c2d2436c7be_256x256.png,909ba4aa47d5525a769aa08c3ac778a3054d27f7,Animals,23.35,99.186,-8.929,935 -936,GORILLA,27.99,-21.499,-6.666,0.236,-0.47,1.10125,81e25381b364bb07def6f38d81fc16ef04bc1745,256,V3,81e25381b364bb07def6f38d81fc16ef04bc1745_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+27.990_ry-21.499_rz-06.666_tx+00.236_ty-00.470_s+00.881_81e25381b364bb07def6f38d81fc16ef04bc1745_256x256.png,08eb11bbe625b190d872670664cf9df1a7a008c2,Animals,27.99,68.501,-6.666,936 -937,GORILLA,28.503,0.015,-44.201,-0.207,-0.293,1.17875,fa00cdadf0c3a2b21a483e8bf0a9fd2eaad6d2ba,256,V3,fa00cdadf0c3a2b21a483e8bf0a9fd2eaad6d2ba_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+28.503_ry+00.015_rz-44.201_tx-00.207_ty-00.293_s+00.943_fa00cdadf0c3a2b21a483e8bf0a9fd2eaad6d2ba_256x256.png,c77f683efa21e619a644c4e14f785ef4b35a6495,Animals,28.503,90.015,-44.201,937 -938,GORILLA,29.043,39.484,-14.216,0.143,0.533,1.22,55d59e534798bdbd10bffd8d12f6a0c62355cc05,256,V3,55d59e534798bdbd10bffd8d12f6a0c62355cc05_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+29.043_ry+39.484_rz-14.216_tx+00.143_ty+00.533_s+00.976_55d59e534798bdbd10bffd8d12f6a0c62355cc05_256x256.png,d35f85cd270b5f75f9db992db048b90fca56ea86,Animals,29.043,129.484,-14.216,938 -939,GORILLA,32.669,-25.135,-22.24,-0.223,-0.386,1.25,5de224ae81d4886a190042d6d365d0a3b6e5fa73,256,V3,5de224ae81d4886a190042d6d365d0a3b6e5fa73_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+32.669_ry-25.135_rz-22.240_tx-00.223_ty-00.386_s+01.000_5de224ae81d4886a190042d6d365d0a3b6e5fa73_256x256.png,e32c73ee1f0e7f97eea5a1801a0327850837090f,Animals,32.669,64.865,-22.24,939 -940,GORILLA,34.232,-11.173,-6.222,0.076,-0.172,1.23,85eebf555bb9513080a6eaeb6292feff23a8a675,256,V3,85eebf555bb9513080a6eaeb6292feff23a8a675_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+34.232_ry-11.173_rz-06.222_tx+00.076_ty-00.172_s+00.984_85eebf555bb9513080a6eaeb6292feff23a8a675_256x256.png,6601556d3a1a4c0e0d32835e04142ed088260bff,Animals,34.232,78.827,-6.222,940 -941,GORILLA,35.715,37.261,40.013,0.117,-0.332,1.1875,fc7239ce7f3d8fd000d6a68663faa6463ecce0e0,256,V3,fc7239ce7f3d8fd000d6a68663faa6463ecce0e0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+35.715_ry+37.261_rz+40.013_tx+00.117_ty-00.332_s+00.950_fc7239ce7f3d8fd000d6a68663faa6463ecce0e0_256x256.png,bf2000f6368e6166d6b3793187a87707cc0ea5fe,Animals,35.715,127.261,40.013,941 -942,GORILLA,37.025,18.034,-18.676,0.178,-0.259,1.0725,6aa21753991dadafcd5ca778e1aa0b2bbf99fb86,256,V3,6aa21753991dadafcd5ca778e1aa0b2bbf99fb86_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+37.025_ry+18.034_rz-18.676_tx+00.178_ty-00.259_s+00.858_6aa21753991dadafcd5ca778e1aa0b2bbf99fb86_256x256.png,ba195ea73a2c861d8e716435e839abf21afe5f41,Animals,37.025,108.034,-18.676,942 -943,GORILLA,40.275,-5.417,25.91,-0.241,-0.406,0.86375,437b2f297d9a3f1ae1f208be52b65acbe570cf86,256,V3,437b2f297d9a3f1ae1f208be52b65acbe570cf86_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+40.275_ry-05.417_rz+25.910_tx-00.241_ty-00.406_s+00.691_437b2f297d9a3f1ae1f208be52b65acbe570cf86_256x256.png,37444fbf7472989b18eefc1b772d6f286cde3760,Animals,40.275,84.583,25.91,943 -944,GORILLA,41.142,-20.281,-5.187,-0.128,-0.436,0.8425,b6ceb16fcb47f9c55386ef32202d7aacf4761bad,256,V3,b6ceb16fcb47f9c55386ef32202d7aacf4761bad_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx+41.142_ry-20.281_rz-05.187_tx-00.128_ty-00.436_s+00.674_b6ceb16fcb47f9c55386ef32202d7aacf4761bad_256x256.png,32ad147e326b5fe88804e91102679df9d6aaf685,Animals,41.142,69.719,-5.187,944 -945,GORILLA,-2.427,-29.171,3.251,0.113,0.109,0.815,f17a0c55fbf6a6dfdc7188ac928bafb2862c21bf,256,V3,f17a0c55fbf6a6dfdc7188ac928bafb2862c21bf_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx-02.427_ry-29.171_rz+03.251_tx+00.113_ty+00.109_s+00.652_f17a0c55fbf6a6dfdc7188ac928bafb2862c21bf_256x256.png,9c3feac6d7cdd50ed390a1072f2b166aaedc5057,Animals,-2.427,60.829,3.251,945 -946,GORILLA,-3.693,-35.506,-23.743,0.244,-0.028,1.29375,5ef1c7a6c4942e7f115026f04f630f0d45420c36,256,V3,5ef1c7a6c4942e7f115026f04f630f0d45420c36_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx-03.693_ry-35.506_rz-23.743_tx+00.244_ty-00.028_s+01.035_5ef1c7a6c4942e7f115026f04f630f0d45420c36_256x256.png,061913258a6a6d51ad2f1634521c06e4e06fccde,Animals,-3.693,54.494,-23.743,946 -947,GORILLA,-6.141,36.4,21.907,-0.26,0.062,1.05,02a5b8211203908bf510849af43e1f7e89e95110,256,V3,02a5b8211203908bf510849af43e1f7e89e95110_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx-06.141_ry+36.400_rz+21.907_tx-00.260_ty+00.062_s+00.840_02a5b8211203908bf510849af43e1f7e89e95110_256x256.png,cf287de97f91a71b65eeed2e534a24ce32072112,Animals,-6.141,126.4,21.907,947 -948,GORILLA,-11.096,12.256,13.966,-0.257,-0.424,0.795,833f8ca08ce67bf67e0d80d5a40d47d2e08ce8ed,256,V3,833f8ca08ce67bf67e0d80d5a40d47d2e08ce8ed_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx-11.096_ry+12.256_rz+13.966_tx-00.257_ty-00.424_s+00.636_833f8ca08ce67bf67e0d80d5a40d47d2e08ce8ed_256x256.png,d1cc03e172b5bc2c0f5ecbf7965490720b31af5d,Animals,-11.096,102.256,13.966,948 -949,GORILLA,-14.244,-32.233,14.026,0.123,0.249,1.17875,8a5b22423db75d5fb6957d28714c991c63c11715,256,V3,8a5b22423db75d5fb6957d28714c991c63c11715_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx-14.244_ry-32.233_rz+14.026_tx+00.123_ty+00.249_s+00.943_8a5b22423db75d5fb6957d28714c991c63c11715_256x256.png,220ae15b36bafaa9b2f617793d64b290816fd922,Animals,-14.244,57.767,14.026,949 -950,GORILLA,-16.47,-31.203,22.955,-0.12,0.252,1.12,3349857dbefd9f4194c68810f7b588194d093ce9,256,V3,3349857dbefd9f4194c68810f7b588194d093ce9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx-16.470_ry-31.203_rz+22.955_tx-00.120_ty+00.252_s+00.896_3349857dbefd9f4194c68810f7b588194d093ce9_256x256.png,5377186a8784e5d872885c6f86f803d255f7c3fd,Animals,-16.47,58.797,22.955,950 -951,GORILLA,-22.441,-28.817,38.184,0.194,0.397,0.7975,477674f59a30d02366d0838fbaedbefbfcc44264,256,V3,477674f59a30d02366d0838fbaedbefbfcc44264_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx-22.441_ry-28.817_rz+38.184_tx+00.194_ty+00.397_s+00.638_477674f59a30d02366d0838fbaedbefbfcc44264_256x256.png,491ba7b3c8a2ef66b40418a2b2ef9dfe92f37511,Animals,-22.441,61.183,38.184,951 -952,GORILLA,-22.469,5.629,44.721,0.171,-0.069,1.20875,2de46236398ccab49c790275d2cfdce8365dee01,256,V3,2de46236398ccab49c790275d2cfdce8365dee01_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx-22.469_ry+05.629_rz+44.721_tx+00.171_ty-00.069_s+00.967_2de46236398ccab49c790275d2cfdce8365dee01_256x256.png,32b78a830d9fdd6bf0dfb1c3f72e996aecfa41b4,Animals,-22.469,95.629,44.721,952 -953,GORILLA,-25.023,-40.715,-23.307,-0.245,-0.333,1.2975,b1650890cea344815194f8ad6b6027a4b0064d5e,256,V3,b1650890cea344815194f8ad6b6027a4b0064d5e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx-25.023_ry-40.715_rz-23.307_tx-00.245_ty-00.333_s+01.038_b1650890cea344815194f8ad6b6027a4b0064d5e_256x256.png,25ad2e0bbf6dc98b5644b2f7e994d844dc82ac8e,Animals,-25.023,49.285,-23.307,953 -954,GORILLA,-25.568,-32.632,-30.803,0.049,-0.324,1.15625,7cf028c765798e26aab915dcebfc87a55e0292f2,256,V3,7cf028c765798e26aab915dcebfc87a55e0292f2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx-25.568_ry-32.632_rz-30.803_tx+00.049_ty-00.324_s+00.925_7cf028c765798e26aab915dcebfc87a55e0292f2_256x256.png,e46866ddc9347daeefa8313027253dda72c954a4,Animals,-25.568,57.368,-30.803,954 -955,GORILLA,-25.784,-2.848,-31.5,0.019,-0.222,0.9675,8ce23f8a99744f6b67348bd695e50071bc54e758,256,V3,8ce23f8a99744f6b67348bd695e50071bc54e758_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx-25.784_ry-02.848_rz-31.500_tx+00.019_ty-00.222_s+00.774_8ce23f8a99744f6b67348bd695e50071bc54e758_256x256.png,a21d8e07e4f2164f61bf62d32c7dbd28b4fd80c9,Animals,-25.784,87.152,-31.5,955 -956,GORILLA,-27.863,-8.117,-22.408,-0.149,-0.098,1.16875,95c9617559c961936bd77600471827cab71b5eac,256,V3,95c9617559c961936bd77600471827cab71b5eac_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx-27.863_ry-08.117_rz-22.408_tx-00.149_ty-00.098_s+00.935_95c9617559c961936bd77600471827cab71b5eac_256x256.png,a258ec8067d9401676747a9e5ea9fc396e360e65,Animals,-27.863,81.883,-22.408,956 -957,GORILLA,-37.875,6.581,-37.03,0.266,-0.429,1.285,4eddad92e733d8aadcad044bca211c7c7a52aac0,256,V3,4eddad92e733d8aadcad044bca211c7c7a52aac0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx-37.875_ry+06.581_rz-37.030_tx+00.266_ty-00.429_s+01.028_4eddad92e733d8aadcad044bca211c7c7a52aac0_256x256.png,1a51c276e2a0b2a76b27098334b941700fe3cee9,Animals,-37.875,96.581,-37.03,957 -958,GORILLA,-40.085,-40.479,8.749,0.29,-0.58,1.26625,949d675cdbacbba8b5613a81c4e49e0d3b558664,256,V3,949d675cdbacbba8b5613a81c4e49e0d3b558664_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx-40.085_ry-40.479_rz+08.749_tx+00.290_ty-00.580_s+01.013_949d675cdbacbba8b5613a81c4e49e0d3b558664_256x256.png,9134a4d684293fdd4db7a500e77935e23326c1e1,Animals,-40.085,49.521,8.749,958 -959,GORILLA,-41.45,-44.587,-19.33,0.241,-0.38,1.27375,37ec27a83b5456ec7d50f15108e6f8b7e9c3365d,256,V3,37ec27a83b5456ec7d50f15108e6f8b7e9c3365d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/GORILLA_rx-41.450_ry-44.587_rz-19.330_tx+00.241_ty-00.380_s+01.019_37ec27a83b5456ec7d50f15108e6f8b7e9c3365d_256x256.png,10180965eed1ccb833a839529f255385435e4c29,Animals,-41.45,45.413,-19.33,959 -960,LIONESS,5.22,-40.834,-22.112,-0.024,0.469,1.2125,812c9b2f069a8c38356d530bd916b9e75dbae788,256,V3,812c9b2f069a8c38356d530bd916b9e75dbae788_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+05.220_ry-40.834_rz-22.112_tx-00.024_ty+00.469_s+00.970_812c9b2f069a8c38356d530bd916b9e75dbae788_256x256.png,ff2bdaaa444d375ab41471e019dcb8f177f8d9c5,Animals,5.22,49.166,-22.112,960 -961,LIONESS,5.901,42.981,-29.565,-0.085,-0.053,1.01375,08c08be1135b5dfab52baec5bf93b5640c59eb45,256,V3,08c08be1135b5dfab52baec5bf93b5640c59eb45_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+05.901_ry+42.981_rz-29.565_tx-00.085_ty-00.053_s+00.811_08c08be1135b5dfab52baec5bf93b5640c59eb45_256x256.png,05d72aaf376e27388c6833eacd4b840a4f9b54a2,Animals,5.901,132.981,-29.565,961 -962,LIONESS,5.962,-18.651,-44.352,0.02,-0.221,1.2,1300a32ad619d41cef4cd963e1a396d413203066,256,V3,1300a32ad619d41cef4cd963e1a396d413203066_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+05.962_ry-18.651_rz-44.352_tx+00.020_ty-00.221_s+00.960_1300a32ad619d41cef4cd963e1a396d413203066_256x256.png,36ed4e34ef9656deb8dfd083b686139fcf321884,Animals,5.962,71.349,-44.352,962 -963,LIONESS,8.118,25.684,31.693,0.226,-0.118,1.01875,e76b57d16def6cc06cb990828988da12168b3701,256,V3,e76b57d16def6cc06cb990828988da12168b3701_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+08.118_ry+25.684_rz+31.693_tx+00.226_ty-00.118_s+00.815_e76b57d16def6cc06cb990828988da12168b3701_256x256.png,9cb4e1ae93b92c48c67705c1802cf328257796cb,Animals,8.118,115.684,31.693,963 -964,LIONESS,10.903,35.963,-36.582,-0.297,-0.423,1.0075,3c0d7a2d5e94c46815fcd742b4bb98260240fd82,256,V3,3c0d7a2d5e94c46815fcd742b4bb98260240fd82_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+10.903_ry+35.963_rz-36.582_tx-00.297_ty-00.423_s+00.806_3c0d7a2d5e94c46815fcd742b4bb98260240fd82_256x256.png,6af7ee7d8a3ae7b8f80549248f2cc4dc5893fcbf,Animals,10.903,125.963,-36.582,964 -965,LIONESS,11.031,-1.231,-3.908,0.012,-0.195,1.23875,0d46e1b9721be8727b8216648f7071b172f3e9a5,256,V3,0d46e1b9721be8727b8216648f7071b172f3e9a5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+11.031_ry-01.231_rz-03.908_tx+00.012_ty-00.195_s+00.991_0d46e1b9721be8727b8216648f7071b172f3e9a5_256x256.png,f0085bacd7a0cbc5ee4c04f1eaf24c5015597c01,Animals,11.031,88.769,-3.908,965 -966,LIONESS,14.23,-17.948,-9.033,-0.043,-0.559,1.2825,fce197ea55a1765cb55d7d3f6b6e03345562751f,256,V3,fce197ea55a1765cb55d7d3f6b6e03345562751f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+14.230_ry-17.948_rz-09.033_tx-00.043_ty-00.559_s+01.026_fce197ea55a1765cb55d7d3f6b6e03345562751f_256x256.png,c97af9d95d54aaa102a0f99491e996c9519e2f6d,Animals,14.23,72.052,-9.033,966 -967,LIONESS,14.283,31.008,-13.288,-0.132,0.308,1.27125,cd4d35c07425fd5fb03b94f8006800feb0973775,256,V3,cd4d35c07425fd5fb03b94f8006800feb0973775_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+14.283_ry+31.008_rz-13.288_tx-00.132_ty+00.308_s+01.017_cd4d35c07425fd5fb03b94f8006800feb0973775_256x256.png,eb025656d86a55ec888f4b3a1a7152fdc73e0970,Animals,14.283,121.008,-13.288,967 -968,LIONESS,14.801,-2.885,-21.196,-0.11,-0.388,1.13625,3e49538bed84d10a5b9fe424b8ba059f17c019de,256,V3,3e49538bed84d10a5b9fe424b8ba059f17c019de_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+14.801_ry-02.885_rz-21.196_tx-00.110_ty-00.388_s+00.909_3e49538bed84d10a5b9fe424b8ba059f17c019de_256x256.png,27ae4fa2b9e55a07f92b7dc4462d7cef984a4225,Animals,14.801,87.115,-21.196,968 -969,LIONESS,18.398,5.946,-31.62,0.241,0.386,1.09625,49761457340538c3fb7a28886a04c6203f70a558,256,V3,49761457340538c3fb7a28886a04c6203f70a558_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+18.398_ry+05.946_rz-31.620_tx+00.241_ty+00.386_s+00.877_49761457340538c3fb7a28886a04c6203f70a558_256x256.png,5bb5423ae134aa4a254ca753fedcf08720105087,Animals,18.398,95.946,-31.62,969 -970,LIONESS,19.049,44.595,-26.452,-0.013,0.363,1.12,b34f8b43bc5e2c466a8f4de59153b7e3b4bf367f,256,V3,b34f8b43bc5e2c466a8f4de59153b7e3b4bf367f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+19.049_ry+44.595_rz-26.452_tx-00.013_ty+00.363_s+00.896_b34f8b43bc5e2c466a8f4de59153b7e3b4bf367f_256x256.png,773160ab7195db12f58b7d819b1688678e49be62,Animals,19.049,134.595,-26.452,970 -971,LIONESS,19.328,-17.234,-37.736,0.038,0.295,1.15875,b97834501fcadfcc3bf8b794e13af0ea900bf4fa,256,V3,b97834501fcadfcc3bf8b794e13af0ea900bf4fa_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+19.328_ry-17.234_rz-37.736_tx+00.038_ty+00.295_s+00.927_b97834501fcadfcc3bf8b794e13af0ea900bf4fa_256x256.png,43ad32f4f2967425d5711284bc685b5b93273784,Animals,19.328,72.766,-37.736,971 -972,LIONESS,21.123,34.348,-44.749,0.059,0.304,0.79875,e2ad94823df8613db98380f36ec772fdbebc61a2,256,V3,e2ad94823df8613db98380f36ec772fdbebc61a2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+21.123_ry+34.348_rz-44.749_tx+00.059_ty+00.304_s+00.639_e2ad94823df8613db98380f36ec772fdbebc61a2_256x256.png,b66a63fff3c8497f63a2b0b98fd1a6703e0350ab,Animals,21.123,124.348,-44.749,972 -973,LIONESS,25.726,6.108,1.148,0.14,-0.24,1.04625,7b10c64a27639ffc6c76d871cc2640aae35c4c2a,256,V3,7b10c64a27639ffc6c76d871cc2640aae35c4c2a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+25.726_ry+06.108_rz+01.148_tx+00.140_ty-00.240_s+00.837_7b10c64a27639ffc6c76d871cc2640aae35c4c2a_256x256.png,07d52a769e9bf0c7809a7e96330a08b5aa9d2d50,Animals,25.726,96.108,1.148,973 -974,LIONESS,28.2,-37.311,-28.396,0.156,-0.106,1.17375,d91c18a70363e441706e5346fddeae1c1bdd5f17,256,V3,d91c18a70363e441706e5346fddeae1c1bdd5f17_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+28.200_ry-37.311_rz-28.396_tx+00.156_ty-00.106_s+00.939_d91c18a70363e441706e5346fddeae1c1bdd5f17_256x256.png,3e6833f21e4815f6d5b148e0df7ad455250cd2f8,Animals,28.2,52.689,-28.396,974 -975,LIONESS,30.001,-3.932,-12.291,0.199,-0.126,0.8575,baf2b41e46788afd5de84d7b92b08a080ee4e3ff,256,V3,baf2b41e46788afd5de84d7b92b08a080ee4e3ff_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+30.001_ry-03.932_rz-12.291_tx+00.199_ty-00.126_s+00.686_baf2b41e46788afd5de84d7b92b08a080ee4e3ff_256x256.png,25c82adb362b138ba43c71c3b539b2e7b3047481,Animals,30.001,86.068,-12.291,975 -976,LIONESS,30.096,-3.653,33.204,0.238,-0.075,0.915,00518e1654f4d403817a0fa9d819906890a98d89,256,V3,00518e1654f4d403817a0fa9d819906890a98d89_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+30.096_ry-03.653_rz+33.204_tx+00.238_ty-00.075_s+00.732_00518e1654f4d403817a0fa9d819906890a98d89_256x256.png,d284832060795c458fa32528d531109965e45709,Animals,30.096,86.347,33.204,976 -977,LIONESS,31.788,-16.82,38.499,0.11,0.413,0.8875,9ac19c5c39c07b06247010145e3047bf71db4e9f,256,V3,9ac19c5c39c07b06247010145e3047bf71db4e9f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+31.788_ry-16.820_rz+38.499_tx+00.110_ty+00.413_s+00.710_9ac19c5c39c07b06247010145e3047bf71db4e9f_256x256.png,a340770f41a3bae0df419f52d1bba823a5f0c327,Animals,31.788,73.18,38.499,977 -978,LIONESS,42.289,13.972,11.846,-0.068,0.479,0.915,a79513834753159896095cbb47a064b9eb19d57c,256,V3,a79513834753159896095cbb47a064b9eb19d57c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx+42.289_ry+13.972_rz+11.846_tx-00.068_ty+00.479_s+00.732_a79513834753159896095cbb47a064b9eb19d57c_256x256.png,e2ce999f4043ea46e470a535f15f81bfa835bdda,Animals,42.289,103.972,11.846,978 -979,LIONESS,-0.543,17.633,13.14,-0.165,-0.251,1.15125,0fa675f344d8903171a28074956b9fed140fa3ff,256,V3,0fa675f344d8903171a28074956b9fed140fa3ff_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-00.543_ry+17.633_rz+13.140_tx-00.165_ty-00.251_s+00.921_0fa675f344d8903171a28074956b9fed140fa3ff_256x256.png,4e37fd4176f24e086f567e9804f9b47793182f5c,Animals,-0.543,107.633,13.14,979 -980,LIONESS,-3.444,-39.276,-33.793,0.191,-0.493,0.8725,5b8bdcb3d62ab99558019aba3d1c71ff2f64056b,256,V3,5b8bdcb3d62ab99558019aba3d1c71ff2f64056b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-03.444_ry-39.276_rz-33.793_tx+00.191_ty-00.493_s+00.698_5b8bdcb3d62ab99558019aba3d1c71ff2f64056b_256x256.png,4e4bbd0ed72e4571892a4d9230c06bd10586011e,Animals,-3.444,50.724,-33.793,980 -981,LIONESS,-5.919,25.446,7.867,0.102,-0.412,1.1075,48023c8e9b5668827baaeead7d411ea5f0ae86b4,256,V3,48023c8e9b5668827baaeead7d411ea5f0ae86b4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-05.919_ry+25.446_rz+07.867_tx+00.102_ty-00.412_s+00.886_48023c8e9b5668827baaeead7d411ea5f0ae86b4_256x256.png,99eb4018b9a365135c9179e6becf8dbb52c3ba3b,Animals,-5.919,115.446,7.867,981 -982,LIONESS,-7.659,-39.549,-22.634,0.253,0.344,1.09,e1f6946e3892b324c98027ec0b8b3ed2b62af9e8,256,V3,e1f6946e3892b324c98027ec0b8b3ed2b62af9e8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-07.659_ry-39.549_rz-22.634_tx+00.253_ty+00.344_s+00.872_e1f6946e3892b324c98027ec0b8b3ed2b62af9e8_256x256.png,61fb699963224ce272db234726f08a9d35e95405,Animals,-7.659,50.451,-22.634,982 -983,LIONESS,-12.911,-19.384,37.697,-0.039,-0.206,1.2125,e07203089cad08022fef776a87b6de73e4c41027,256,V3,e07203089cad08022fef776a87b6de73e4c41027_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-12.911_ry-19.384_rz+37.697_tx-00.039_ty-00.206_s+00.970_e07203089cad08022fef776a87b6de73e4c41027_256x256.png,ab669e35b02b037c4ba9cbb52d046d5c1d7b6949,Animals,-12.911,70.616,37.697,983 -984,LIONESS,-13.388,-40.817,20.349,-0.244,-0.357,1.07875,94f6baf6e952fca9524e6a7211011e30308e3eba,256,V3,94f6baf6e952fca9524e6a7211011e30308e3eba_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-13.388_ry-40.817_rz+20.349_tx-00.244_ty-00.357_s+00.863_94f6baf6e952fca9524e6a7211011e30308e3eba_256x256.png,d1a312b9c3f4fd9276380889015bced6ca41077a,Animals,-13.388,49.183,20.349,984 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-987,LIONESS,-29.123,-44.6,31.154,-0.238,-0.098,1.15875,7632bf6e47a56ce11afb6f31c6e4a93d7a849ac4,256,V3,7632bf6e47a56ce11afb6f31c6e4a93d7a849ac4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-29.123_ry-44.600_rz+31.154_tx-00.238_ty-00.098_s+00.927_7632bf6e47a56ce11afb6f31c6e4a93d7a849ac4_256x256.png,40519c77e0981339932623698e4a21a1f10c6600,Animals,-29.123,45.4,31.154,987 -988,LIONESS,-29.169,5.753,-39.293,-0.045,0.211,1.185,25055cf0fbb05fa117aabfb1b08cad4e33220860,256,V3,25055cf0fbb05fa117aabfb1b08cad4e33220860_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-29.169_ry+05.753_rz-39.293_tx-00.045_ty+00.211_s+00.948_25055cf0fbb05fa117aabfb1b08cad4e33220860_256x256.png,e069ce0ec9c0ad8445fb1f92dadd0146585e6a13,Animals,-29.169,95.753,-39.293,988 -989,LIONESS,-30.625,21.013,30.661,-0.172,-0.498,0.89625,c6996af0e24b892c7f89c844c54a564fb837943b,256,V3,c6996af0e24b892c7f89c844c54a564fb837943b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-30.625_ry+21.013_rz+30.661_tx-00.172_ty-00.498_s+00.717_c6996af0e24b892c7f89c844c54a564fb837943b_256x256.png,a3ec24085421617649195d3cdb57cc7219f5f34e,Animals,-30.625,111.013,30.661,989 -990,LIONESS,-31.169,-44.533,5.065,-0.292,0.511,1.05375,93c911cd037efab1727e88299981933107bddbdc,256,V3,93c911cd037efab1727e88299981933107bddbdc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-31.169_ry-44.533_rz+05.065_tx-00.292_ty+00.511_s+00.843_93c911cd037efab1727e88299981933107bddbdc_256x256.png,b3299152e84fc0a409bd7ae8b0b5c45f03cfc1f0,Animals,-31.169,45.467,5.065,990 -991,LIONESS,-32.284,-7.558,16.463,0.013,-0.525,0.89125,2a4f86fbf4153e5e7d0380f86a9f4edc323ba10a,256,V3,2a4f86fbf4153e5e7d0380f86a9f4edc323ba10a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-32.284_ry-07.558_rz+16.463_tx+00.013_ty-00.525_s+00.713_2a4f86fbf4153e5e7d0380f86a9f4edc323ba10a_256x256.png,8fbb5de3fb95a5f607ea9904114c8e4ffdc1f7c2,Animals,-32.284,82.442,16.463,991 -992,LIONESS,-32.315,17.286,-35.774,-0.126,-0.465,1.01375,2f404df3e6c88e26cabf746b573a13ffe2e7b51f,256,V3,2f404df3e6c88e26cabf746b573a13ffe2e7b51f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-32.315_ry+17.286_rz-35.774_tx-00.126_ty-00.465_s+00.811_2f404df3e6c88e26cabf746b573a13ffe2e7b51f_256x256.png,c47cb34756cf933bcdd8d9d837e869283e1af57f,Animals,-32.315,107.286,-35.774,992 -993,LIONESS,-36.562,31.378,-39.527,-0.165,-0.519,0.935,731d43ecb3ddc257ef84ec35cd51111808615a73,256,V3,731d43ecb3ddc257ef84ec35cd51111808615a73_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-36.562_ry+31.378_rz-39.527_tx-00.165_ty-00.519_s+00.748_731d43ecb3ddc257ef84ec35cd51111808615a73_256x256.png,1cd65036817cbba9f514e382df74981bb1b7f3c2,Animals,-36.562,121.378,-39.527,993 -994,LIONESS,-37.761,11.219,-39.881,-0.128,0.144,1.00875,1771a8b56b4b063bb8ff256f9d7adc870348cca4,256,V3,1771a8b56b4b063bb8ff256f9d7adc870348cca4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-37.761_ry+11.219_rz-39.881_tx-00.128_ty+00.144_s+00.807_1771a8b56b4b063bb8ff256f9d7adc870348cca4_256x256.png,53fc3383464467e0d17e6803cb6830b48973c383,Animals,-37.761,101.219,-39.881,994 -995,LIONESS,-40.61,-28.557,3.55,-0.224,0.387,1.18625,05b5ffb46892603b4825075ed267b9736220e9b1,256,V3,05b5ffb46892603b4825075ed267b9736220e9b1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-40.610_ry-28.557_rz+03.550_tx-00.224_ty+00.387_s+00.949_05b5ffb46892603b4825075ed267b9736220e9b1_256x256.png,e38967317ff267b7df6e7596e5486ab95c3824f4,Animals,-40.61,61.443,3.55,995 -996,LIONESS,-41.652,-0.317,-36.856,0.291,-0.303,1.10125,c82ccb76229ad183b50c35f93c1d5d2558196560,256,V3,c82ccb76229ad183b50c35f93c1d5d2558196560_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-41.652_ry-00.317_rz-36.856_tx+00.291_ty-00.303_s+00.881_c82ccb76229ad183b50c35f93c1d5d2558196560_256x256.png,9db28a44862669b42805c5c50eca7eb40cd0d41d,Animals,-41.652,89.683,-36.856,996 -997,LIONESS,-42.932,-38.55,-10.851,-0.073,0.477,1.05875,449ec2a5914755944e5117d710734f42482cf02b,256,V3,449ec2a5914755944e5117d710734f42482cf02b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-42.932_ry-38.550_rz-10.851_tx-00.073_ty+00.477_s+00.847_449ec2a5914755944e5117d710734f42482cf02b_256x256.png,bdb057fddd2d265674082a5ad535de4355c76ca5,Animals,-42.932,51.45,-10.851,997 -998,LIONESS,-44.208,-42.954,36.489,-0.022,-0.106,0.86375,c460d2f97714ef9d00248a7717dc25e9372d8b47,256,V3,c460d2f97714ef9d00248a7717dc25e9372d8b47_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-44.208_ry-42.954_rz+36.489_tx-00.022_ty-00.106_s+00.691_c460d2f97714ef9d00248a7717dc25e9372d8b47_256x256.png,18f90885c4c507d6a0cf8220d42a7a84a4e0fad0,Animals,-44.208,47.046,36.489,998 -999,LIONESS,-44.414,-37.128,31.622,0.006,-0.444,0.8575,5d6b03fdad7cf67af709b7ccb661cc0960f5890f,256,V3,5d6b03fdad7cf67af709b7ccb661cc0960f5890f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/LIONESS_rx-44.414_ry-37.128_rz+31.622_tx+00.006_ty-00.444_s+00.686_5d6b03fdad7cf67af709b7ccb661cc0960f5890f_256x256.png,f8d4964b8fcdfdb85d109aeba6be7cf9e4ffe00d,Animals,-44.414,52.872,31.622,999 -1000,MQUEEN_L,2.695,-6.991,8.989,0.21,-0.041,1.27833333333,c45575932e11b10fcd5c8137474ea5d40f71ac89,256,V3,c45575932e11b10fcd5c8137474ea5d40f71ac89_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+02.695_ry-06.991_rz+08.989_tx+00.210_ty-00.041_s+01.534_c45575932e11b10fcd5c8137474ea5d40f71ac89_256x256.png,8383f632b25a9dfac9bdff362234d0f2df9ad1eb,Boats,2.695,-96.991,8.989,1000 -1001,MQUEEN_L,2.717,-40.876,-40.508,-0.08,0.036,0.888333333333,82e6932e1378155ac305372172c349bd63efb8c8,256,V3,82e6932e1378155ac305372172c349bd63efb8c8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+02.717_ry-40.876_rz-40.508_tx-00.080_ty+00.036_s+01.066_82e6932e1378155ac305372172c349bd63efb8c8_256x256.png,7c0b89df72d9bdb23574e3f2748dad5c9ba75753,Boats,2.717,-130.876,-40.508,1001 -1002,MQUEEN_L,3.915,-26.322,31.617,-0.268,-0.51,0.926666666667,5ddeb88eca6dea112166343f91389abc9ebbc29f,256,V3,5ddeb88eca6dea112166343f91389abc9ebbc29f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+03.915_ry-26.322_rz+31.617_tx-00.268_ty-00.510_s+01.112_5ddeb88eca6dea112166343f91389abc9ebbc29f_256x256.png,662d717d565949994af7addb082139d42d74847d,Boats,3.915,-116.322,31.617,1002 -1003,MQUEEN_L,4.296,-8.085,-35.545,0.136,-0.164,1.125,0f9d1587369a546e1e53704016e7ea5ce6e08bce,256,V3,0f9d1587369a546e1e53704016e7ea5ce6e08bce_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+04.296_ry-08.085_rz-35.545_tx+00.136_ty-00.164_s+01.350_0f9d1587369a546e1e53704016e7ea5ce6e08bce_256x256.png,1ca5263462b5b02c76cb18f40be7002a5d0b58c7,Boats,4.296,-98.085,-35.545,1003 -1004,MQUEEN_L,6.209,-30.74,35.529,-0.067,0.082,1.16583333333,93a2bd2f6e0db9e456c2bbb872dcc1a67109b4c0,256,V3,93a2bd2f6e0db9e456c2bbb872dcc1a67109b4c0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+06.209_ry-30.740_rz+35.529_tx-00.067_ty+00.082_s+01.399_93a2bd2f6e0db9e456c2bbb872dcc1a67109b4c0_256x256.png,0359febe06601360e9e1941affacd36d737a7168,Boats,6.209,-120.74,35.529,1004 -1005,MQUEEN_L,7.26,-29.826,-29.479,0.013,0.567,0.785833333333,5472cbde719d09004a64195d70b21993a220eb0f,256,V3,5472cbde719d09004a64195d70b21993a220eb0f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+07.260_ry-29.826_rz-29.479_tx+00.013_ty+00.567_s+00.943_5472cbde719d09004a64195d70b21993a220eb0f_256x256.png,186acbb7c1e2b21f7d3b19761cbec4a53506f36d,Boats,7.26,-119.826,-29.479,1005 -1006,MQUEEN_L,9.172,-42.709,-14.108,-0.102,-0.445,1.23166666667,f16d545a70267f9f230a1056536a32285cc665a1,256,V3,f16d545a70267f9f230a1056536a32285cc665a1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+09.172_ry-42.709_rz-14.108_tx-00.102_ty-00.445_s+01.478_f16d545a70267f9f230a1056536a32285cc665a1_256x256.png,477ad0ffec28ae34d584c914de28a6f1035d7beb,Boats,9.172,-132.709,-14.108,1006 -1007,MQUEEN_L,9.919,5.903,-5.19,0.258,-0.134,0.975,ccb3a534fd11d08a1ac93f85c8865bb70d90fe42,256,V3,ccb3a534fd11d08a1ac93f85c8865bb70d90fe42_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+09.919_ry+05.903_rz-05.190_tx+00.258_ty-00.134_s+01.170_ccb3a534fd11d08a1ac93f85c8865bb70d90fe42_256x256.png,56162d94234b7b81bccc93b4707147d5bf2bdd77,Boats,9.919,-84.097,-5.19,1007 -1008,MQUEEN_L,12.494,-25.976,2.613,-0.267,-0.302,1.1825,27ff4c8ca44e4ffac7f23419aa9f9e574fe3cb37,256,V3,27ff4c8ca44e4ffac7f23419aa9f9e574fe3cb37_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+12.494_ry-25.976_rz+02.613_tx-00.267_ty-00.302_s+01.419_27ff4c8ca44e4ffac7f23419aa9f9e574fe3cb37_256x256.png,4327d82deeff64610239ee3fc9648d6cf5c48324,Boats,12.494,-115.976,2.613,1008 -1009,MQUEEN_L,13.671,-41.895,23.853,-0.263,0.549,0.961666666667,bb78d80f3a0a3de1bdd88f9488c7a73dff136dac,256,V3,bb78d80f3a0a3de1bdd88f9488c7a73dff136dac_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+13.671_ry-41.895_rz+23.853_tx-00.263_ty+00.549_s+01.154_bb78d80f3a0a3de1bdd88f9488c7a73dff136dac_256x256.png,0088124f2856fcb7adc5c47a6b5d426f58d7b96a,Boats,13.671,-131.895,23.853,1009 -1010,MQUEEN_L,17.049,-1.482,-39.341,-0.247,-0.586,1.2925,4e19da2f44d548c751bd2b2b0b12ad0b17d78b96,256,V3,4e19da2f44d548c751bd2b2b0b12ad0b17d78b96_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+17.049_ry-01.482_rz-39.341_tx-00.247_ty-00.586_s+01.551_4e19da2f44d548c751bd2b2b0b12ad0b17d78b96_256x256.png,8c91ceca2486ed929630b5d3e7e341199bf7026a,Boats,17.049,-91.482,-39.341,1010 -1011,MQUEEN_L,17.13,35.845,28.609,-0.259,0.403,0.848333333333,a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9,256,V3,a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+17.130_ry+35.845_rz+28.609_tx-00.259_ty+00.403_s+01.018_a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9_256x256.png,c8134d18433f4048f4cf0adf6b1c56df2f9c069a,Boats,17.13,-54.155,28.609,1011 -1012,MQUEEN_L,20.63,-31.003,-23.973,0.231,0.29,0.968333333333,5830b489a9ec65c488913c796e132145bb1e0206,256,V3,5830b489a9ec65c488913c796e132145bb1e0206_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+20.630_ry-31.003_rz-23.973_tx+00.231_ty+00.290_s+01.162_5830b489a9ec65c488913c796e132145bb1e0206_256x256.png,7340d57df8cde2f918862456619e3b9250a74ab4,Boats,20.63,-121.003,-23.973,1012 -1013,MQUEEN_L,22.082,-26.503,-16.784,-0.299,-0.365,0.975,434850dba61b6ffccb75ce49bec670a647faa8fe,256,V3,434850dba61b6ffccb75ce49bec670a647faa8fe_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+22.082_ry-26.503_rz-16.784_tx-00.299_ty-00.365_s+01.170_434850dba61b6ffccb75ce49bec670a647faa8fe_256x256.png,6ab55018b2dd8ba58e171715362f92d7d5812403,Boats,22.082,-116.503,-16.784,1013 -1014,MQUEEN_L,33.475,-31.223,2.71,0.068,-0.487,1.20333333333,b6cac69324041c7e91721ec7c0e97adecb1401fe,256,V3,b6cac69324041c7e91721ec7c0e97adecb1401fe_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+33.475_ry-31.223_rz+02.710_tx+00.068_ty-00.487_s+01.444_b6cac69324041c7e91721ec7c0e97adecb1401fe_256x256.png,1529a6b4107ea26a4840457326be4730c28b1e8d,Boats,33.475,-121.223,2.71,1014 -1015,MQUEEN_L,34.176,39.385,-41.276,-0.113,-0.005,0.848333333333,1f01bc204ff8cab9a91a17131fbe316723cd9f07,256,V3,1f01bc204ff8cab9a91a17131fbe316723cd9f07_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+34.176_ry+39.385_rz-41.276_tx-00.113_ty-00.005_s+01.018_1f01bc204ff8cab9a91a17131fbe316723cd9f07_256x256.png,c410a934d41cdc492e872a78865b594e0c899d6e,Boats,34.176,-50.615,-41.276,1015 -1016,MQUEEN_L,37.855,20.164,30.081,0.03,-0.214,1.28333333333,91badb38d2039c5c95f8a438a7d3b8fae67ad7b9,256,V3,91badb38d2039c5c95f8a438a7d3b8fae67ad7b9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+37.855_ry+20.164_rz+30.081_tx+00.030_ty-00.214_s+01.540_91badb38d2039c5c95f8a438a7d3b8fae67ad7b9_256x256.png,f49cd17d14d54472289c4f2585c015e28ac3a051,Boats,37.855,-69.836,30.081,1016 -1017,MQUEEN_L,40.877,17.904,42.342,0.145,0.546,1.21583333333,8f971f0650fcd283d1af228e4ab66022beb71ebe,256,V3,8f971f0650fcd283d1af228e4ab66022beb71ebe_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+40.877_ry+17.904_rz+42.342_tx+00.145_ty+00.546_s+01.459_8f971f0650fcd283d1af228e4ab66022beb71ebe_256x256.png,dbf14bc146fb37ddf864b1818e39ff5da5780d63,Boats,40.877,-72.096,42.342,1017 -1018,MQUEEN_L,41.456,-18.956,29.236,0.295,0.242,1.10166666667,5eeb41e4b0166da9c6a7f13f8740808528ba64ee,256,V3,5eeb41e4b0166da9c6a7f13f8740808528ba64ee_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+41.456_ry-18.956_rz+29.236_tx+00.295_ty+00.242_s+01.322_5eeb41e4b0166da9c6a7f13f8740808528ba64ee_256x256.png,eb3ae4e58d52634c72aceb48fab95a95cdc4285d,Boats,41.456,-108.956,29.236,1018 -1019,MQUEEN_L,43.865,18.383,1.141,0.077,-0.218,0.855,5d4da6ff7835bfad109c7c27b7c65b9025abf3d6,256,V3,5d4da6ff7835bfad109c7c27b7c65b9025abf3d6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx+43.865_ry+18.383_rz+01.141_tx+00.077_ty-00.218_s+01.026_5d4da6ff7835bfad109c7c27b7c65b9025abf3d6_256x256.png,daa118846c841353804ed9a170607b88b83e3157,Boats,43.865,-71.617,1.141,1019 -1020,MQUEEN_L,-3.34,-34.588,0.91,0.099,0.013,1.1975,36ab5e37719f1a683ccd5571ce54e305d94af47f,256,V3,36ab5e37719f1a683ccd5571ce54e305d94af47f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-03.340_ry-34.588_rz+00.910_tx+00.099_ty+00.013_s+01.437_36ab5e37719f1a683ccd5571ce54e305d94af47f_256x256.png,4da0dfd08b326d4594481925e431cd13806773ef,Boats,-3.34,-124.588,0.91,1020 -1021,MQUEEN_L,-7.992,34.181,22.525,0.249,0.241,0.891666666667,ac6b8f62555a0037ebe4fa9ced489abaa67d4a49,256,V3,ac6b8f62555a0037ebe4fa9ced489abaa67d4a49_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-07.992_ry+34.181_rz+22.525_tx+00.249_ty+00.241_s+01.070_ac6b8f62555a0037ebe4fa9ced489abaa67d4a49_256x256.png,908131c800002a974a8be76bf36f4601cd9df5f2,Boats,-7.992,-55.819,22.525,1021 -1022,MQUEEN_L,-8.416,37.411,-1.337,0.102,0.048,1.11333333333,ba27f552bf554e19666823ebca847e609363c05b,256,V3,ba27f552bf554e19666823ebca847e609363c05b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-08.416_ry+37.411_rz-01.337_tx+00.102_ty+00.048_s+01.336_ba27f552bf554e19666823ebca847e609363c05b_256x256.png,b307d74b62a8703a98e3ae1cc65556b521dff875,Boats,-8.416,-52.589,-1.337,1022 -1023,MQUEEN_L,-15.941,20.869,-30.191,-0.175,-0.139,1.12416666667,4d735bb50d5671038bc0e65942a8718ab8dd4605,256,V3,4d735bb50d5671038bc0e65942a8718ab8dd4605_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-15.941_ry+20.869_rz-30.191_tx-00.175_ty-00.139_s+01.349_4d735bb50d5671038bc0e65942a8718ab8dd4605_256x256.png,b68086277cc20b3db2de2964339d45e8b1df33c1,Boats,-15.941,-69.131,-30.191,1023 -1024,MQUEEN_L,-17.688,-18.078,-3.671,0.151,-0.123,1.05666666667,36375bf4b975dc187a84598302336f5d163f52e8,256,V3,36375bf4b975dc187a84598302336f5d163f52e8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-17.688_ry-18.078_rz-03.671_tx+00.151_ty-00.123_s+01.268_36375bf4b975dc187a84598302336f5d163f52e8_256x256.png,819953859a8a3fc4d111f159976a725e089e0c1c,Boats,-17.688,-108.078,-3.671,1024 -1025,MQUEEN_L,-19.834,14.424,-6.983,-0.217,0.167,1.1125,bcbfb7f6f104bdba7d62908b5eb5e1ba232da6d3,256,V3,bcbfb7f6f104bdba7d62908b5eb5e1ba232da6d3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-19.834_ry+14.424_rz-06.983_tx-00.217_ty+00.167_s+01.335_bcbfb7f6f104bdba7d62908b5eb5e1ba232da6d3_256x256.png,e57724f945724a75a62af7a453a5e8db4dd1aff8,Boats,-19.834,-75.576,-6.983,1025 -1026,MQUEEN_L,-23.942,-44.556,-43.989,0.27,-0.114,1.2475,41c152a34df114d531f527d456ee2e3076fbe058,256,V3,41c152a34df114d531f527d456ee2e3076fbe058_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-23.942_ry-44.556_rz-43.989_tx+00.270_ty-00.114_s+01.497_41c152a34df114d531f527d456ee2e3076fbe058_256x256.png,dfb64beedf54f2fc7c88c733b74b8e9ec2c09e40,Boats,-23.942,-134.556,-43.989,1026 -1027,MQUEEN_L,-25.319,35.92,27.749,-0.168,-0.069,0.980833333333,c727b46c2a7f7af5dc2bed1fc7ce2196fbdd4e9d,256,V3,c727b46c2a7f7af5dc2bed1fc7ce2196fbdd4e9d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-25.319_ry+35.920_rz+27.749_tx-00.168_ty-00.069_s+01.177_c727b46c2a7f7af5dc2bed1fc7ce2196fbdd4e9d_256x256.png,afa68ff258aad51759d6c35f2c9cbdeebb7608c8,Boats,-25.319,-54.08,27.749,1027 -1028,MQUEEN_L,-25.48,1.788,-33.449,0.272,0.517,1.2525,a2608db10b9588d20ede7859dfbe577ef72d84c2,256,V3,a2608db10b9588d20ede7859dfbe577ef72d84c2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-25.480_ry+01.788_rz-33.449_tx+00.272_ty+00.517_s+01.503_a2608db10b9588d20ede7859dfbe577ef72d84c2_256x256.png,98a4dcbd22d93449719d168c95bff883154309b5,Boats,-25.48,-88.212,-33.449,1028 -1029,MQUEEN_L,-26.953,31.602,-42.612,-0.223,0.536,1.09416666667,3c869495bf570779cde046b2ad820591549eddeb,256,V3,3c869495bf570779cde046b2ad820591549eddeb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-26.953_ry+31.602_rz-42.612_tx-00.223_ty+00.536_s+01.313_3c869495bf570779cde046b2ad820591549eddeb_256x256.png,15be26764d25255ef487fbb8fd9ace2c4e4ee0e3,Boats,-26.953,-58.398,-42.612,1029 -1030,MQUEEN_L,-29.272,-33.98,35.749,0.177,-0.315,1.29333333333,8a3d9b3bf408c5a9369502586672f79e4cc3bb85,256,V3,8a3d9b3bf408c5a9369502586672f79e4cc3bb85_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-29.272_ry-33.980_rz+35.749_tx+00.177_ty-00.315_s+01.552_8a3d9b3bf408c5a9369502586672f79e4cc3bb85_256x256.png,080cc4be8bf8ab2994ddc52d235f785a785ef655,Boats,-29.272,-123.98,35.749,1030 -1031,MQUEEN_L,-31.035,5.983,21.612,0.248,0.54,1.05333333333,922bbea99958ebf8ffee43ff2dc8a11b5bab38cb,256,V3,922bbea99958ebf8ffee43ff2dc8a11b5bab38cb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-31.035_ry+05.983_rz+21.612_tx+00.248_ty+00.540_s+01.264_922bbea99958ebf8ffee43ff2dc8a11b5bab38cb_256x256.png,b684a2bb27cea626d5d81718c2432877152e7a95,Boats,-31.035,-84.017,21.612,1031 -1032,MQUEEN_L,-31.404,40.3,-5.451,-0.005,0.056,1.10416666667,2061c1cd9bd54068d5f7afa38fb57f6d151e4cfd,256,V3,2061c1cd9bd54068d5f7afa38fb57f6d151e4cfd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-31.404_ry+40.300_rz-05.451_tx-00.005_ty+00.056_s+01.325_2061c1cd9bd54068d5f7afa38fb57f6d151e4cfd_256x256.png,91efba645657195537fcd45e7c17ef0a1568b576,Boats,-31.404,-49.7,-5.451,1032 -1033,MQUEEN_L,-33.782,7.98,-16.507,0.024,0.56,1.10416666667,7ea2e2e4298d7555f6ad06af48eb8ff58beed4c8,256,V3,7ea2e2e4298d7555f6ad06af48eb8ff58beed4c8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-33.782_ry+07.980_rz-16.507_tx+00.024_ty+00.560_s+01.325_7ea2e2e4298d7555f6ad06af48eb8ff58beed4c8_256x256.png,88ffc45068230d62a07e622d6b4913a71fb42a02,Boats,-33.782,-82.02,-16.507,1033 -1034,MQUEEN_L,-34.234,-21.25,24.356,0.09,-0.2,0.998333333333,f892fe5fce155fd5c95aa2706d35dfb64590269f,256,V3,f892fe5fce155fd5c95aa2706d35dfb64590269f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-34.234_ry-21.250_rz+24.356_tx+00.090_ty-00.200_s+01.198_f892fe5fce155fd5c95aa2706d35dfb64590269f_256x256.png,e1ef8532e13df4d08750f82cb6537406cc4d079e,Boats,-34.234,-111.25,24.356,1034 -1035,MQUEEN_L,-34.6,10.93,42.265,0.073,-0.027,0.8075,cb949ea7af7ffc2ba704bb4f64ef625b47844a92,256,V3,cb949ea7af7ffc2ba704bb4f64ef625b47844a92_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-34.600_ry+10.930_rz+42.265_tx+00.073_ty-00.027_s+00.969_cb949ea7af7ffc2ba704bb4f64ef625b47844a92_256x256.png,ae2f579813fa7bb643f9af5b3015d88a49e650bf,Boats,-34.6,-79.07,42.265,1035 -1036,MQUEEN_L,-36.362,-39.96,44.34,-0.121,-0.082,0.873333333333,e197ad3ef734ec7c61eafe0322b0d4aae0380403,256,V3,e197ad3ef734ec7c61eafe0322b0d4aae0380403_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-36.362_ry-39.960_rz+44.340_tx-00.121_ty-00.082_s+01.048_e197ad3ef734ec7c61eafe0322b0d4aae0380403_256x256.png,e57471cd70314702182867ec05c091b6fac8d9ac,Boats,-36.362,-129.96,44.34,1036 -1037,MQUEEN_L,-36.926,-6.53,17.813,-0.033,0.137,0.945833333333,0c4fcac8572e5d81d1f62ccce8593ca19532e5d0,256,V3,0c4fcac8572e5d81d1f62ccce8593ca19532e5d0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-36.926_ry-06.530_rz+17.813_tx-00.033_ty+00.137_s+01.135_0c4fcac8572e5d81d1f62ccce8593ca19532e5d0_256x256.png,99f7c02343e29e697dddb74f2a7bc5dc072af7e9,Boats,-36.926,-96.53,17.813,1037 -1038,MQUEEN_L,-39.899,21.822,5.489,0.174,-0.202,1.225,75816eecb3d714e876cab93d0fcc9387a757d2a8,256,V3,75816eecb3d714e876cab93d0fcc9387a757d2a8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/MQUEEN_L_rx-39.899_ry+21.822_rz+05.489_tx+00.174_ty-00.202_s+01.470_75816eecb3d714e876cab93d0fcc9387a757d2a8_256x256.png,4b31d4fd768b8d564093d92586e12545f31a3941,Boats,-39.899,-68.178,5.489,1038 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-1123,SISTER_L,13.375,11.012,32.615,-0.008,-0.53,0.855,4940c07aef46d914f4c17a3cec16a295a1219640,256,V3,4940c07aef46d914f4c17a3cec16a295a1219640_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx+13.375_ry+11.012_rz+32.615_tx-00.008_ty-00.530_s+01.026_4940c07aef46d914f4c17a3cec16a295a1219640_256x256.png,d8462c6a7d77b426439e4d8404fec128a88bb14e,Boats,13.375,101.012,32.615,1123 -1124,SISTER_L,15.401,13.72,8.536,-0.21,0.099,0.854166666667,9a8fe4bba0c06a1d0490b744de6ccbbc8feb989a,256,V3,9a8fe4bba0c06a1d0490b744de6ccbbc8feb989a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx+15.401_ry+13.720_rz+08.536_tx-00.210_ty+00.099_s+01.025_9a8fe4bba0c06a1d0490b744de6ccbbc8feb989a_256x256.png,43e6095502450f79c72ea281e566966ae0a2df91,Boats,15.401,103.72,8.536,1124 -1125,SISTER_L,15.828,32.614,5.022,0.252,-0.153,0.96,307a9d6ad42365ad6aea9f66d4f74d843f378d6d,256,V3,307a9d6ad42365ad6aea9f66d4f74d843f378d6d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx+15.828_ry+32.614_rz+05.022_tx+00.252_ty-00.153_s+01.152_307a9d6ad42365ad6aea9f66d4f74d843f378d6d_256x256.png,d3b99133a57df24627435ca730cdd23265d5b82b,Boats,15.828,122.614,5.022,1125 -1126,SISTER_L,17.063,-36.335,27.416,-0.058,-0.568,1.25666666667,3e058598889926b353d3cd8409ca5bdc31a5bc4d,256,V3,3e058598889926b353d3cd8409ca5bdc31a5bc4d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx+17.063_ry-36.335_rz+27.416_tx-00.058_ty-00.568_s+01.508_3e058598889926b353d3cd8409ca5bdc31a5bc4d_256x256.png,a83f3f0b43931793f8496e07714bbe7e12d6b168,Boats,17.063,53.665,27.416,1126 -1127,SISTER_L,19.462,-32.131,-10.944,-0.288,-0.548,0.97,dd8a48ef86e287bb9620b7fcacd7d7dd9a222472,256,V3,dd8a48ef86e287bb9620b7fcacd7d7dd9a222472_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx+19.462_ry-32.131_rz-10.944_tx-00.288_ty-00.548_s+01.164_dd8a48ef86e287bb9620b7fcacd7d7dd9a222472_256x256.png,b583666f2018ad86eefd1f221cd52aea262ca6c1,Boats,19.462,57.869,-10.944,1127 -1128,SISTER_L,20.479,-21.704,-27.606,0.277,-0.014,1.20083333333,4be25984e445478b31e931c62d88add12130a0b8,256,V3,4be25984e445478b31e931c62d88add12130a0b8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx+20.479_ry-21.704_rz-27.606_tx+00.277_ty-00.014_s+01.441_4be25984e445478b31e931c62d88add12130a0b8_256x256.png,29d672f1ae7888c2027b88a98b7b8ff26a9b58fe,Boats,20.479,68.296,-27.606,1128 -1129,SISTER_L,20.67,-24.201,-3.778,0.218,-0.14,1.10583333333,eb8790c289a509973c7d4128950fa13cfcbc18f7,256,V3,eb8790c289a509973c7d4128950fa13cfcbc18f7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx+20.670_ry-24.201_rz-03.778_tx+00.218_ty-00.140_s+01.327_eb8790c289a509973c7d4128950fa13cfcbc18f7_256x256.png,985ec5b20ae524ddb12db945ec45022d7d5f1eca,Boats,20.67,65.799,-3.778,1129 -1130,SISTER_L,21.303,21.567,19.905,0.299,-0.031,1.15,8083394c9b920d881069fbf6f27fe9802b7bd1ce,256,V3,8083394c9b920d881069fbf6f27fe9802b7bd1ce_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx+21.303_ry+21.567_rz+19.905_tx+00.299_ty-00.031_s+01.380_8083394c9b920d881069fbf6f27fe9802b7bd1ce_256x256.png,234f153bee6ebfa69f8397d5d474cdc124d29d14,Boats,21.303,111.567,19.905,1130 -1131,SISTER_L,22.639,3.643,12.35,0.269,-0.35,0.806666666667,0331e370878c06dbe26cce37c9c006fbd868b983,256,V3,0331e370878c06dbe26cce37c9c006fbd868b983_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx+22.639_ry+03.643_rz+12.350_tx+00.269_ty-00.350_s+00.968_0331e370878c06dbe26cce37c9c006fbd868b983_256x256.png,ce6f689e9cda8b99876f6c21d4dc7783ff0bfec8,Boats,22.639,93.643,12.35,1131 -1132,SISTER_L,30.113,25.392,35.734,0.165,-0.502,1.10333333333,a8331742399e19c079fb30d913dde0d6e0353d9f,256,V3,a8331742399e19c079fb30d913dde0d6e0353d9f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx+30.113_ry+25.392_rz+35.734_tx+00.165_ty-00.502_s+01.324_a8331742399e19c079fb30d913dde0d6e0353d9f_256x256.png,366602d291f87d2793d1789fcb76cdc380b4dce3,Boats,30.113,115.392,35.734,1132 -1133,SISTER_L,33.981,-30.033,24.93,-0.18,0.135,0.9825,9305ef4033cf6d398569f9cd24558469a826eb90,256,V3,9305ef4033cf6d398569f9cd24558469a826eb90_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx+33.981_ry-30.033_rz+24.930_tx-00.180_ty+00.135_s+01.179_9305ef4033cf6d398569f9cd24558469a826eb90_256x256.png,4707b2936a36aeccacc26dbd9bb10571bf7ce328,Boats,33.981,59.967,24.93,1133 -1134,SISTER_L,35.516,14.547,-22.777,-0.185,0.477,1.25,57249b2e6df9c731424eea1421d05f1a56a7dd03,256,V3,57249b2e6df9c731424eea1421d05f1a56a7dd03_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx+35.516_ry+14.547_rz-22.777_tx-00.185_ty+00.477_s+01.500_57249b2e6df9c731424eea1421d05f1a56a7dd03_256x256.png,3ab38b07b4b49834e8e06bf88403bdc77564449c,Boats,35.516,104.547,-22.777,1134 -1135,SISTER_L,36.474,-37.801,-41.212,-0.067,-0.495,0.791666666667,dae970c8d8a615e08a567e64ff452643ac4ed401,256,V3,dae970c8d8a615e08a567e64ff452643ac4ed401_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx+36.474_ry-37.801_rz-41.212_tx-00.067_ty-00.495_s+00.950_dae970c8d8a615e08a567e64ff452643ac4ed401_256x256.png,06d9c7364ca07993ed0e55e090466e86cc4e8c9e,Boats,36.474,52.199,-41.212,1135 -1136,SISTER_L,37.931,0.418,23.071,-0.29,0.335,1.045,e7df341a53fdd0b9a04b02430f1b85d97ae17f0e,256,V3,e7df341a53fdd0b9a04b02430f1b85d97ae17f0e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx+37.931_ry+00.418_rz+23.071_tx-00.290_ty+00.335_s+01.254_e7df341a53fdd0b9a04b02430f1b85d97ae17f0e_256x256.png,d0ed513340933dab737aeb1ebf759380bd1341f2,Boats,37.931,90.418,23.071,1136 -1137,SISTER_L,39.433,21.407,-14.642,-0.112,0.513,0.975833333333,b501d923aa9aa3144bd64e2cb4cbf2c0f93f748c,256,V3,b501d923aa9aa3144bd64e2cb4cbf2c0f93f748c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx+39.433_ry+21.407_rz-14.642_tx-00.112_ty+00.513_s+01.171_b501d923aa9aa3144bd64e2cb4cbf2c0f93f748c_256x256.png,01c742f3cb64a77a0f76e10052392fa13afe11d1,Boats,39.433,111.407,-14.642,1137 -1138,SISTER_L,-0.067,27.895,-6.716,-0.255,0.201,0.935833333333,11c95b7e2e9113d428f1f2c37030820353d432f8,256,V3,11c95b7e2e9113d428f1f2c37030820353d432f8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-00.067_ry+27.895_rz-06.716_tx-00.255_ty+00.201_s+01.123_11c95b7e2e9113d428f1f2c37030820353d432f8_256x256.png,989af7f719d7a527169ab1b9328dde884e7d319b,Boats,-0.067,117.895,-6.716,1138 -1139,SISTER_L,-0.471,-6.117,-37.294,-0.255,0.412,1.03666666667,a39cc5ae5eab3af9ca093180ae041b4a83054faa,256,V3,a39cc5ae5eab3af9ca093180ae041b4a83054faa_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-00.471_ry-06.117_rz-37.294_tx-00.255_ty+00.412_s+01.244_a39cc5ae5eab3af9ca093180ae041b4a83054faa_256x256.png,ecfe40f2837099fd3727b64c06e1d1557bd2be76,Boats,-0.471,83.883,-37.294,1139 -1140,SISTER_L,-1.018,-3.278,-1.872,0.287,-0.106,0.82,1279014ce3fe19b50fc8dcb72f9736382ac7a892,256,V3,1279014ce3fe19b50fc8dcb72f9736382ac7a892_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-01.018_ry-03.278_rz-01.872_tx+00.287_ty-00.106_s+00.984_1279014ce3fe19b50fc8dcb72f9736382ac7a892_256x256.png,db6a0dec76b597e86d8d2c382946e3d46060f6d3,Boats,-1.018,86.722,-1.872,1140 -1141,SISTER_L,-2.623,3.26,17.634,0.144,0.369,0.980833333333,eb2d5ab5ffd4154f07b002623e776dc20b7741f0,256,V3,eb2d5ab5ffd4154f07b002623e776dc20b7741f0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-02.623_ry+03.260_rz+17.634_tx+00.144_ty+00.369_s+01.177_eb2d5ab5ffd4154f07b002623e776dc20b7741f0_256x256.png,67c27d3b6ab01c2736a59f51b6f07f550cf615f5,Boats,-2.623,93.26,17.634,1141 -1142,SISTER_L,-5.828,-21.056,32.457,0.288,-0.208,1.01666666667,666efb300871cfb6b75ec6750cfc956c25fcefc7,256,V3,666efb300871cfb6b75ec6750cfc956c25fcefc7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-05.828_ry-21.056_rz+32.457_tx+00.288_ty-00.208_s+01.220_666efb300871cfb6b75ec6750cfc956c25fcefc7_256x256.png,b53ce20448de6527760fb420f2eefacaf25ae0f6,Boats,-5.828,68.944,32.457,1142 -1143,SISTER_L,-8.066,-13.018,28.042,0.134,-0.202,0.9425,4499b1c34b28305264fb818734e543f8d2ec2137,256,V3,4499b1c34b28305264fb818734e543f8d2ec2137_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-08.066_ry-13.018_rz+28.042_tx+00.134_ty-00.202_s+01.131_4499b1c34b28305264fb818734e543f8d2ec2137_256x256.png,473709a38de8fdfcc0687142929f4727a5e87038,Boats,-8.066,76.982,28.042,1143 -1144,SISTER_L,-11.303,8.333,14.46,0.101,0.561,1.145,3b0644f72c284c67e51b97c595ca986a3e2303c6,256,V3,3b0644f72c284c67e51b97c595ca986a3e2303c6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-11.303_ry+08.333_rz+14.460_tx+00.101_ty+00.561_s+01.374_3b0644f72c284c67e51b97c595ca986a3e2303c6_256x256.png,9b33f2cd9bb3afeeaf754c86d275d2767d1ab044,Boats,-11.303,98.333,14.46,1144 -1145,SISTER_L,-14.714,29.916,-2.219,-0.173,0.318,0.856666666667,61e71ce138629eab7c9b765162d98bf9728e8e4f,256,V3,61e71ce138629eab7c9b765162d98bf9728e8e4f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-14.714_ry+29.916_rz-02.219_tx-00.173_ty+00.318_s+01.028_61e71ce138629eab7c9b765162d98bf9728e8e4f_256x256.png,170d1bbcaaafc9510bc7f582167b9462744c1223,Boats,-14.714,119.916,-2.219,1145 -1146,SISTER_L,-20.544,19.323,27.726,-0.19,-0.023,1.13833333333,5e57fe63fff464626b01590862c24ed8ace07826,256,V3,5e57fe63fff464626b01590862c24ed8ace07826_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-20.544_ry+19.323_rz+27.726_tx-00.190_ty-00.023_s+01.366_5e57fe63fff464626b01590862c24ed8ace07826_256x256.png,517e3a330de0531762ad55e18f446b283599a65e,Boats,-20.544,109.323,27.726,1146 -1147,SISTER_L,-23.796,10.474,-11.312,-0.107,0.076,1.0525,1fe8830d0d0347244362e51c45ec5367429c003f,256,V3,1fe8830d0d0347244362e51c45ec5367429c003f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-23.796_ry+10.474_rz-11.312_tx-00.107_ty+00.076_s+01.263_1fe8830d0d0347244362e51c45ec5367429c003f_256x256.png,ce0e1c9ee1b9eb2221b692ee04a6b95adf4f8e6d,Boats,-23.796,100.474,-11.312,1147 -1148,SISTER_L,-24.055,16.878,44.751,0.107,0.044,1.145,eece9268ba876b2a666156189b9cfb84e8613679,256,V3,eece9268ba876b2a666156189b9cfb84e8613679_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-24.055_ry+16.878_rz+44.751_tx+00.107_ty+00.044_s+01.374_eece9268ba876b2a666156189b9cfb84e8613679_256x256.png,4fa12dd04dd222346c0947431d9999b3455612af,Boats,-24.055,106.878,44.751,1148 -1149,SISTER_L,-24.23,23.074,14.179,-0.183,0.306,1.1,b28ae9be054238b39874073b2a9d0f084641f570,256,V3,b28ae9be054238b39874073b2a9d0f084641f570_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-24.230_ry+23.074_rz+14.179_tx-00.183_ty+00.306_s+01.320_b28ae9be054238b39874073b2a9d0f084641f570_256x256.png,f5d83f1cf21346ed251836b24161935a61476daa,Boats,-24.23,113.074,14.179,1149 -1150,SISTER_L,-30.322,14.288,22.916,-0.159,-0.02,0.9775,f3edd2aedeb9abaff5c393292f7a98de65808e4b,256,V3,f3edd2aedeb9abaff5c393292f7a98de65808e4b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-30.322_ry+14.288_rz+22.916_tx-00.159_ty-00.020_s+01.173_f3edd2aedeb9abaff5c393292f7a98de65808e4b_256x256.png,1ea787e692a6c1713d20c2c464c32f50025cec3e,Boats,-30.322,104.288,22.916,1150 -1151,SISTER_L,-32.313,8.688,3.938,0.167,-0.201,0.78,3f63d146616a268256f718813e2de109918dd9d4,256,V3,3f63d146616a268256f718813e2de109918dd9d4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-32.313_ry+08.688_rz+03.938_tx+00.167_ty-00.201_s+00.936_3f63d146616a268256f718813e2de109918dd9d4_256x256.png,633b5511dc21d2188a70545663fba9b2d21437d5,Boats,-32.313,98.688,3.938,1151 -1152,SISTER_L,-32.828,-1.667,-42.738,-0.129,-0.254,1.18916666667,d7b276076e85546e440aac297f78d3c4d85dfab8,256,V3,d7b276076e85546e440aac297f78d3c4d85dfab8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-32.828_ry-01.667_rz-42.738_tx-00.129_ty-00.254_s+01.427_d7b276076e85546e440aac297f78d3c4d85dfab8_256x256.png,b05f0801c62f840645be96db7111172dfbcad5df,Boats,-32.828,88.333,-42.738,1152 -1153,SISTER_L,-32.963,-5.978,36.252,-0.222,-0.121,1.215,2fc4c54f96ba1fcdede5ca8ffe4866466ffcc95c,256,V3,2fc4c54f96ba1fcdede5ca8ffe4866466ffcc95c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-32.963_ry-05.978_rz+36.252_tx-00.222_ty-00.121_s+01.458_2fc4c54f96ba1fcdede5ca8ffe4866466ffcc95c_256x256.png,1a3b2f22aa39b069cc16b5a026716c5328a466a9,Boats,-32.963,84.022,36.252,1153 -1154,SISTER_L,-33.48,42.703,18.083,-0.228,-0.438,0.889166666667,57b817b30fd40e5e8f4eaaf85f12da8bcc8c1a15,256,V3,57b817b30fd40e5e8f4eaaf85f12da8bcc8c1a15_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-33.480_ry+42.703_rz+18.083_tx-00.228_ty-00.438_s+01.067_57b817b30fd40e5e8f4eaaf85f12da8bcc8c1a15_256x256.png,0d16dec055e7e48c379b57e27b0c79a90193403e,Boats,-33.48,132.703,18.083,1154 -1155,SISTER_L,-38.132,30.305,-5.646,0.233,-0.573,1.18416666667,a51c72d8b4a890cffa5d970d91f94c9ba307f26b,256,V3,a51c72d8b4a890cffa5d970d91f94c9ba307f26b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-38.132_ry+30.305_rz-05.646_tx+00.233_ty-00.573_s+01.421_a51c72d8b4a890cffa5d970d91f94c9ba307f26b_256x256.png,0130b9fee42f0c11530b99fe9b3d60ca0b5674bf,Boats,-38.132,120.305,-5.646,1155 -1156,SISTER_L,-39.671,2.602,0.349,-0.22,-0.382,0.790833333333,9f2054a459bcd0e3c0c85895041717fd56c457f8,256,V3,9f2054a459bcd0e3c0c85895041717fd56c457f8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-39.671_ry+02.602_rz+00.349_tx-00.220_ty-00.382_s+00.949_9f2054a459bcd0e3c0c85895041717fd56c457f8_256x256.png,d1a76bea7787d1a95a7fddccf72c3269378c9a7b,Boats,-39.671,92.602,0.349,1156 -1157,SISTER_L,-40.117,18.635,7.102,0.114,0.578,1.0025,912aa6f72f5e97f1924c3abd06216a29a561e5bb,256,V3,912aa6f72f5e97f1924c3abd06216a29a561e5bb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-40.117_ry+18.635_rz+07.102_tx+00.114_ty+00.578_s+01.203_912aa6f72f5e97f1924c3abd06216a29a561e5bb_256x256.png,ab422b969b2c9f20063653335f99746e0852cd26,Boats,-40.117,108.635,7.102,1157 -1158,SISTER_L,-42.095,6.875,-10.087,-0.077,-0.561,0.780833333333,dc1c63d08cb660f4c7519679fe629f05f8d74578,256,V3,dc1c63d08cb660f4c7519679fe629f05f8d74578_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-42.095_ry+06.875_rz-10.087_tx-00.077_ty-00.561_s+00.937_dc1c63d08cb660f4c7519679fe629f05f8d74578_256x256.png,fc3c19f62a8d1916aaabab453b14b8d3ab8a3fc6,Boats,-42.095,96.875,-10.087,1158 -1159,SISTER_L,-43.999,20.267,-11.892,-0.13,-0.582,1.16916666667,c3bf046d9fa29946909cb4a299574725b9911a4b,256,V3,c3bf046d9fa29946909cb4a299574725b9911a4b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/SISTER_L_rx-43.999_ry+20.267_rz-11.892_tx-00.130_ty-00.582_s+01.403_c3bf046d9fa29946909cb4a299574725b9911a4b_256x256.png,f6513c037079ef9d0332b5f29834c9c1b9334db6,Boats,-43.999,110.267,-11.892,1159 -1160,Strawberry_obj,-10.3339098653,0.0433977094503,3.95892426574,0.144,0.321,1.01714285714,eafaed806b969f52189f4b58300595fbea958a2e,256,V3,eafaed806b969f52189f4b58300595fbea958a2e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Strawberry_obj_rx-100.334_ry-00.238_rz+03.717_tx+00.144_ty+00.321_s+00.712_eafaed806b969f52189f4b58300595fbea958a2e_256x256.png,3a0fb2ea632335285f9efeffc2627b45a8f458c9,Fruits,-10.3339098653,0.0433977094503,3.95892426574,1160 -1161,Strawberry_obj,-10.4184432336,0.689765895867,36.2668863045,-0.091,-0.545,1.28,ac4b0926c53484b24f5e76e53c42ff2f22043e4f,256,V3,ac4b0926c53484b24f5e76e53c42ff2f22043e4f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Strawberry_obj_rx-100.441_ry-03.746_rz+32.458_tx-00.091_ty-00.545_s+00.896_ac4b0926c53484b24f5e76e53c42ff2f22043e4f_256x256.png,93b22676013a1d064885c7504b4acbfe9d8d356c,Fruits,-10.4184432336,0.689765895867,36.2668863045,1161 -1162,Strawberry_obj,-13.8225984533,-3.4814371433,-15.7201248028,0.252,0.22,0.781428571429,09a750fa1657ea63f48427b05d95804aee85db35,256,V3,09a750fa1657ea63f48427b05d95804aee85db35_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Strawberry_obj_rx-104.246_ry+13.864_rz-01.434_tx+00.252_ty+00.220_s+00.547_09a750fa1657ea63f48427b05d95804aee85db35_256x256.png,84c368ad5b4c24017bc35dd90c8bc53f4625e64e,Fruits,-13.8225984533,-3.4814371433,-15.7201248028,1162 -1163,Strawberry_obj,-28.2388707498,-0.362003670658,-27.5580598753,0.185,-0.235,0.821428571429,b11974f722063a8e1b0da3b76f2b4813cd6115af,256,V3,b11974f722063a8e1b0da3b76f2b4813cd6115af_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Strawberry_obj_rx-118.241_ry+00.674_rz-26.793_tx+00.185_ty-00.235_s+00.575_b11974f722063a8e1b0da3b76f2b4813cd6115af_256x256.png,b6580dfd2bf3b6fc65244d2d8741f93eddc975b3,Fruits,-28.2388707498,-0.362003670658,-27.5580598753,1163 -1164,Strawberry_obj,-28.1694760706,-5.60034837405,18.59382631,0.224,0.023,0.928571428571,e7c8352f3163523025a88c59126e2ac670bb72c1,256,V3,e7c8352f3163523025a88c59126e2ac670bb72c1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/Strawberry_obj_rx-118.676_ry+10.328_rz+30.328_tx+00.224_ty+00.023_s+00.650_e7c8352f3163523025a88c59126e2ac670bb72c1_256x256.png,e0bc45237a6188115fa24629e06c7e3bcc7db306,Fruits,-28.1694760706,-5.60034837405,18.59382631,1164 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-1247,_001,19.839,-22.363,-41.804,-0.203,0.211,1.10714285714,7329e2fcadc3863a3c1f2c059b0d123eb37f9a80,256,V3,7329e2fcadc3863a3c1f2c059b0d123eb37f9a80_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx+19.839_ry-22.363_rz-41.804_tx-00.203_ty+00.211_s+00.775_7329e2fcadc3863a3c1f2c059b0d123eb37f9a80_256x256.png,cd803a996810d194b81c1a022ef381e704cf5e81,Chairs,19.839,-22.363,-41.804,1247 -1248,_001,22.695,0.818,29.032,-0.127,-0.558,0.814285714286,a961044778179051bfd4b47ac1236fd9d8964964,256,V3,a961044778179051bfd4b47ac1236fd9d8964964_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx+22.695_ry+00.818_rz+29.032_tx-00.127_ty-00.558_s+00.570_a961044778179051bfd4b47ac1236fd9d8964964_256x256.png,162470dc4894be2985fbb0d5539a54239f8916b5,Chairs,22.695,0.818,29.032,1248 -1249,_001,25.555,7.188,-4.902,0.198,0.582,0.858571428571,bcfddc4e84b790e519292f2dc2418aac9eae5dbd,256,V3,bcfddc4e84b790e519292f2dc2418aac9eae5dbd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx+25.555_ry+07.188_rz-04.902_tx+00.198_ty+00.582_s+00.601_bcfddc4e84b790e519292f2dc2418aac9eae5dbd_256x256.png,4afc4daf6497eb65727b7b9d41c1ff22712cce1e,Chairs,25.555,7.188,-4.902,1249 -1250,_001,26.018,8.58,-26.838,0.008,0.095,0.785714285714,6eecadfecbc396968c5ccddcaffaf45bf3e7f15f,256,V3,6eecadfecbc396968c5ccddcaffaf45bf3e7f15f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx+26.018_ry+08.580_rz-26.838_tx+00.008_ty+00.095_s+00.550_6eecadfecbc396968c5ccddcaffaf45bf3e7f15f_256x256.png,46a0e0a126ca2518604ed05b443685739dd1cf2d,Chairs,26.018,8.58,-26.838,1250 -1251,_001,26.784,10.189,-16.165,-0.017,-0.083,0.875714285714,508a48a7b1a8609d45395bd999f34e57849d051f,256,V3,508a48a7b1a8609d45395bd999f34e57849d051f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx+26.784_ry+10.189_rz-16.165_tx-00.017_ty-00.083_s+00.613_508a48a7b1a8609d45395bd999f34e57849d051f_256x256.png,c2db7320051c12ac18a63c8eb477c6455fc31fd8,Chairs,26.784,10.189,-16.165,1251 -1252,_001,26.812,9.433,-32.0,0.272,-0.537,0.885714285714,e00dd5f509c6c13c1992376c3c94d3adc7427086,256,V3,e00dd5f509c6c13c1992376c3c94d3adc7427086_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx+26.812_ry+09.433_rz-32.000_tx+00.272_ty-00.537_s+00.620_e00dd5f509c6c13c1992376c3c94d3adc7427086_256x256.png,e9366e44aede36657f96f4aeeb9330f528fb6ea7,Chairs,26.812,9.433,-32.0,1252 -1253,_001,27.105,-43.102,-7.23,0.151,0.308,1.26714285714,b99d354629bec3043d5959cc4cf68b7564d785b2,256,V3,b99d354629bec3043d5959cc4cf68b7564d785b2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx+27.105_ry-43.102_rz-07.230_tx+00.151_ty+00.308_s+00.887_b99d354629bec3043d5959cc4cf68b7564d785b2_256x256.png,de1354ceddda7253be12c2a61e4c2871a60bc693,Chairs,27.105,-43.102,-7.23,1253 -1254,_001,29.397,-23.862,-10.184,0.026,0.473,0.844285714286,c54cb7f24890bbc2916c14fcd8c44d48324a4997,256,V3,c54cb7f24890bbc2916c14fcd8c44d48324a4997_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx+29.397_ry-23.862_rz-10.184_tx+00.026_ty+00.473_s+00.591_c54cb7f24890bbc2916c14fcd8c44d48324a4997_256x256.png,b754ea25305ec04f06b1bae873f41c8a3bad3424,Chairs,29.397,-23.862,-10.184,1254 -1255,_001,30.126,-4.138,41.581,0.198,-0.485,1.23142857143,d1d6490c93034408ee748ab8105becf89c420f55,256,V3,d1d6490c93034408ee748ab8105becf89c420f55_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx+30.126_ry-04.138_rz+41.581_tx+00.198_ty-00.485_s+00.862_d1d6490c93034408ee748ab8105becf89c420f55_256x256.png,6483c410ac79a9aa04c224904fc2d77b032c7215,Chairs,30.126,-4.138,41.581,1255 -1256,_001,41.365,32.391,33.668,-0.106,-0.108,1.18142857143,4445ebd92fd78a798f3bc97ea4492acee7328536,256,V3,4445ebd92fd78a798f3bc97ea4492acee7328536_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx+41.365_ry+32.391_rz+33.668_tx-00.106_ty-00.108_s+00.827_4445ebd92fd78a798f3bc97ea4492acee7328536_256x256.png,e613c4c1ac61f70e051706b15cae911b8b1d895b,Chairs,41.365,32.391,33.668,1256 -1257,_001,-2.529,5.939,26.355,0.001,-0.451,0.942857142857,a21f66a63e652764adc36430667bf8fb19545c90,256,V3,a21f66a63e652764adc36430667bf8fb19545c90_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-02.529_ry+05.939_rz+26.355_tx+00.001_ty-00.451_s+00.660_a21f66a63e652764adc36430667bf8fb19545c90_256x256.png,13bfc3a22c337baeceec6d680ee7e104a2e43ed0,Chairs,-2.529,5.939,26.355,1257 -1258,_001,-2.727,20.083,34.155,-0.117,-0.118,0.784285714286,950c37f3f239f19cb5f8eab52f182d3d9273786f,256,V3,950c37f3f239f19cb5f8eab52f182d3d9273786f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-02.727_ry+20.083_rz+34.155_tx-00.117_ty-00.118_s+00.549_950c37f3f239f19cb5f8eab52f182d3d9273786f_256x256.png,7876be8b31a98aee164eb203cffbb6a1bd0da881,Chairs,-2.727,20.083,34.155,1258 -1259,_001,-3.327,-8.41,-41.658,0.291,0.434,1.17714285714,0305e44e2372d98c664f9aef754d004b2bbde8d6,256,V3,0305e44e2372d98c664f9aef754d004b2bbde8d6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-03.327_ry-08.410_rz-41.658_tx+00.291_ty+00.434_s+00.824_0305e44e2372d98c664f9aef754d004b2bbde8d6_256x256.png,cf083e2a93d8750ece7c360c497f2a07b545f7b7,Chairs,-3.327,-8.41,-41.658,1259 -1260,_001,-5.184,-3.894,7.011,-0.273,0.585,1.28571428571,3203f579fbf0b4b6f8093042d476b8a1febe5440,256,V3,3203f579fbf0b4b6f8093042d476b8a1febe5440_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-05.184_ry-03.894_rz+07.011_tx-00.273_ty+00.585_s+00.900_3203f579fbf0b4b6f8093042d476b8a1febe5440_256x256.png,8625d947e4dd448fa0e168bbada17f0f44b48f0a,Chairs,-5.184,-3.894,7.011,1260 -1261,_001,-5.522,42.861,-1.992,-0.104,-0.17,1.17857142857,7781aea936c53219abd7577c19eb27d5f2244aa3,256,V3,7781aea936c53219abd7577c19eb27d5f2244aa3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-05.522_ry+42.861_rz-01.992_tx-00.104_ty-00.170_s+00.825_7781aea936c53219abd7577c19eb27d5f2244aa3_256x256.png,441197fddded222582c467944ff0a52b86506477,Chairs,-5.522,42.861,-1.992,1261 -1262,_001,-8.448,-4.59,-9.973,0.233,-0.177,1.04285714286,5fce7ce47c2ab83d1b6e321964bb76eb0f974d77,256,V3,5fce7ce47c2ab83d1b6e321964bb76eb0f974d77_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-08.448_ry-04.590_rz-09.973_tx+00.233_ty-00.177_s+00.730_5fce7ce47c2ab83d1b6e321964bb76eb0f974d77_256x256.png,3f3c1cfe4f69a9c8eca455268b8446e97f130419,Chairs,-8.448,-4.59,-9.973,1262 -1263,_001,-10.979,41.023,20.67,0.188,-0.002,0.985714285714,dc8504ff994142a717db0a38737c94a328d09ee9,256,V3,dc8504ff994142a717db0a38737c94a328d09ee9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-10.979_ry+41.023_rz+20.670_tx+00.188_ty-00.002_s+00.690_dc8504ff994142a717db0a38737c94a328d09ee9_256x256.png,9509857f5d9af7d2c76261acebb586cce3dd8a97,Chairs,-10.979,41.023,20.67,1263 -1264,_001,-11.206,37.021,-14.723,0.098,0.374,0.92,f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6,256,V3,f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-11.206_ry+37.021_rz-14.723_tx+00.098_ty+00.374_s+00.644_f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6_256x256.png,3c5f35116be0b2867846c66152a2c6a9cdf3f6a4,Chairs,-11.206,37.021,-14.723,1264 -1265,_001,-13.14,-26.381,9.423,0.218,-0.0,0.895714285714,9b87c8be1440ce9626bcfe656700ff3ac8f909fe,256,V3,9b87c8be1440ce9626bcfe656700ff3ac8f909fe_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-13.140_ry-26.381_rz+09.423_tx+00.218_ty-00.000_s+00.627_9b87c8be1440ce9626bcfe656700ff3ac8f909fe_256x256.png,b1dbd1fc08edf4dc0814c5578e2c5544ea4d255d,Chairs,-13.14,-26.381,9.423,1265 -1266,_001,-13.565,24.042,-41.771,-0.278,-0.581,1.23142857143,87e86e7f8320576290bdabf4d576565dd09b972f,256,V3,87e86e7f8320576290bdabf4d576565dd09b972f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-13.565_ry+24.042_rz-41.771_tx-00.278_ty-00.581_s+00.862_87e86e7f8320576290bdabf4d576565dd09b972f_256x256.png,87865a5780ed2d51066cac1e2ef01c2339ceb36d,Chairs,-13.565,24.042,-41.771,1266 -1267,_001,-17.079,28.448,-12.119,0.096,0.433,0.861428571429,be6d08da6fae9c920f0691503ee8c62b4493bdef,256,V3,be6d08da6fae9c920f0691503ee8c62b4493bdef_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-17.079_ry+28.448_rz-12.119_tx+00.096_ty+00.433_s+00.603_be6d08da6fae9c920f0691503ee8c62b4493bdef_256x256.png,5888b4f1f65d414875dd61035276202188c08308,Chairs,-17.079,28.448,-12.119,1267 -1268,_001,-19.823,37.424,42.938,-0.132,-0.598,0.801428571429,820976938964f69b4e571c7fb1808dee19568125,256,V3,820976938964f69b4e571c7fb1808dee19568125_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-19.823_ry+37.424_rz+42.938_tx-00.132_ty-00.598_s+00.561_820976938964f69b4e571c7fb1808dee19568125_256x256.png,b60c3781385c97be07672da14b6c5813069d437f,Chairs,-19.823,37.424,42.938,1268 -1269,_001,-21.263,-16.534,7.128,-0.165,0.095,1.03285714286,10b0bff2f9b7f0a853edac7ed9b5e2d737da033a,256,V3,10b0bff2f9b7f0a853edac7ed9b5e2d737da033a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-21.263_ry-16.534_rz+07.128_tx-00.165_ty+00.095_s+00.723_10b0bff2f9b7f0a853edac7ed9b5e2d737da033a_256x256.png,4dc5fbaba903a63e202938b0efcc415a63b860c6,Chairs,-21.263,-16.534,7.128,1269 -1270,_001,-24.48,-5.441,-28.255,0.247,0.305,1.05,8a7df0f2f3ac5c6fcedbf4d015c1d59b183e8892,256,V3,8a7df0f2f3ac5c6fcedbf4d015c1d59b183e8892_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-24.480_ry-05.441_rz-28.255_tx+00.247_ty+00.305_s+00.735_8a7df0f2f3ac5c6fcedbf4d015c1d59b183e8892_256x256.png,888d5308d748c704757fe8ec32ff2bedaddde257,Chairs,-24.48,-5.441,-28.255,1270 -1271,_001,-25.097,12.343,4.59,0.017,-0.481,1.29571428571,b88b90a6f92c2235f4dd7ee79e853df1391691e3,256,V3,b88b90a6f92c2235f4dd7ee79e853df1391691e3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-25.097_ry+12.343_rz+04.590_tx+00.017_ty-00.481_s+00.907_b88b90a6f92c2235f4dd7ee79e853df1391691e3_256x256.png,532b98e5518927658dc8e404090f34a1e249a9fb,Chairs,-25.097,12.343,4.59,1271 -1272,_001,-28.494,-15.471,5.078,0.274,0.351,1.12,eadc9d71c69955c40ea9855ff14339f05de7ccbc,256,V3,eadc9d71c69955c40ea9855ff14339f05de7ccbc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-28.494_ry-15.471_rz+05.078_tx+00.274_ty+00.351_s+00.784_eadc9d71c69955c40ea9855ff14339f05de7ccbc_256x256.png,63254cd014553a3cfa56cbf4eedc54d2a0589760,Chairs,-28.494,-15.471,5.078,1272 -1273,_001,-29.436,0.152,4.329,-0.194,-0.032,1.00285714286,fb7dc3e628898c9b3a2ae09fa612035d89bfa658,256,V3,fb7dc3e628898c9b3a2ae09fa612035d89bfa658_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-29.436_ry+00.152_rz+04.329_tx-00.194_ty-00.032_s+00.702_fb7dc3e628898c9b3a2ae09fa612035d89bfa658_256x256.png,9337d81a81fb1c7401de15816ee50f0be2eb3815,Chairs,-29.436,0.152,4.329,1273 -1274,_001,-34.25,21.332,-24.163,0.059,0.314,0.875714285714,314a3c37d9002be0f5e605e508bc7bbee70587af,256,V3,314a3c37d9002be0f5e605e508bc7bbee70587af_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-34.250_ry+21.332_rz-24.163_tx+00.059_ty+00.314_s+00.613_314a3c37d9002be0f5e605e508bc7bbee70587af_256x256.png,e9b66a2b2c2a320df881b78f9649c133c9803604,Chairs,-34.25,21.332,-24.163,1274 -1275,_001,-36.663,15.587,44.632,-0.049,0.042,1.00142857143,25d483de97c94f25d20cd935751ad0175285f22c,256,V3,25d483de97c94f25d20cd935751ad0175285f22c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-36.663_ry+15.587_rz+44.632_tx-00.049_ty+00.042_s+00.701_25d483de97c94f25d20cd935751ad0175285f22c_256x256.png,69d41272a156bd80cea6c2e875d91c32c06d1e40,Chairs,-36.663,15.587,44.632,1275 -1276,_001,-37.669,34.189,15.181,0.171,-0.445,1.11,f18957057474d802552e6f0a6a0580f41b61e6dc,256,V3,f18957057474d802552e6f0a6a0580f41b61e6dc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-37.669_ry+34.189_rz+15.181_tx+00.171_ty-00.445_s+00.777_f18957057474d802552e6f0a6a0580f41b61e6dc_256x256.png,4ad75873b5dc3ae41edd9f696912f04ca6805236,Chairs,-37.669,34.189,15.181,1276 -1277,_001,-37.951,4.51,-18.378,0.229,-0.175,1.26285714286,7601f7e89ce32d86c1aa648ed8f5f55c28b1b461,256,V3,7601f7e89ce32d86c1aa648ed8f5f55c28b1b461_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-37.951_ry+04.510_rz-18.378_tx+00.229_ty-00.175_s+00.884_7601f7e89ce32d86c1aa648ed8f5f55c28b1b461_256x256.png,101e37f5dc9cde94bad6e66e5a70569d7bc754f3,Chairs,-37.951,4.51,-18.378,1277 -1278,_001,-38.745,-30.852,19.587,0.131,-0.008,0.771428571429,aa2f3cb1876be06a7c4de609cc2f9f91e995972a,256,V3,aa2f3cb1876be06a7c4de609cc2f9f91e995972a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-38.745_ry-30.852_rz+19.587_tx+00.131_ty-00.008_s+00.540_aa2f3cb1876be06a7c4de609cc2f9f91e995972a_256x256.png,fba14224b8c2059975d93909731331d9bde1acea,Chairs,-38.745,-30.852,19.587,1278 -1279,_001,-41.567,19.749,32.43,-0.28,-0.233,1.25285714286,5556b40175798dcc04a639dd5ca815c7c51aaaee,256,V3,5556b40175798dcc04a639dd5ca815c7c51aaaee_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_001_rx-41.567_ry+19.749_rz+32.430_tx-00.280_ty-00.233_s+00.877_5556b40175798dcc04a639dd5ca815c7c51aaaee_256x256.png,f128987c9d5739e0d1173f56718b572c996c83c6,Chairs,-41.567,19.749,32.43,1279 -1280,_004,1.249,27.153,19.74,-0.249,-0.056,1.08285714286,bdb7fd771c6e6ff2e8d3e28fa3bc940143635ed8,256,V3,bdb7fd771c6e6ff2e8d3e28fa3bc940143635ed8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+01.249_ry+27.153_rz+19.740_tx-00.249_ty-00.056_s+00.758_bdb7fd771c6e6ff2e8d3e28fa3bc940143635ed8_256x256.png,91ea68b973227dd3a741206f6a2cd6983ccc3d6a,Chairs,1.249,27.153,19.74,1280 -1281,_004,2.514,8.221,-24.349,-0.055,-0.558,0.971428571429,3a171566076eefefb90a106e9313818790990976,256,V3,3a171566076eefefb90a106e9313818790990976_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+02.514_ry+08.221_rz-24.349_tx-00.055_ty-00.558_s+00.680_3a171566076eefefb90a106e9313818790990976_256x256.png,eb549c3a9457c70eaddfcb2a28df2449b9459c1e,Chairs,2.514,8.221,-24.349,1281 -1282,_004,3.954,-3.589,39.376,0.011,-0.319,1.27428571429,6e12708c273f976d805c614f215965a075669e09,256,V3,6e12708c273f976d805c614f215965a075669e09_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+03.954_ry-03.589_rz+39.376_tx+00.011_ty-00.319_s+00.892_6e12708c273f976d805c614f215965a075669e09_256x256.png,3ebfd523494e0c193fed6e22bdc4288edc1bfa5d,Chairs,3.954,-3.589,39.376,1282 -1283,_004,8.267,11.615,7.602,0.087,0.062,1.06,62a5e71d75d31031913a2693e7f9121e74e6332c,256,V3,62a5e71d75d31031913a2693e7f9121e74e6332c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+08.267_ry+11.615_rz+07.602_tx+00.087_ty+00.062_s+00.742_62a5e71d75d31031913a2693e7f9121e74e6332c_256x256.png,2dfaf099e44ad9cd82ccef7f538655a3fd6b7f25,Chairs,8.267,11.615,7.602,1283 -1284,_004,9.217,12.072,9.441,0.165,0.221,0.95,2e0c72492bf42b72d46532bc18eaf758f4310411,256,V3,2e0c72492bf42b72d46532bc18eaf758f4310411_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+09.217_ry+12.072_rz+09.441_tx+00.165_ty+00.221_s+00.665_2e0c72492bf42b72d46532bc18eaf758f4310411_256x256.png,4143239bcdd84fafd6f10336719b06fb429b6284,Chairs,9.217,12.072,9.441,1284 -1285,_004,9.77,20.382,25.639,-0.058,0.212,0.93,b17746284e158d6fc1f5a246465c7eed7bc212b0,256,V3,b17746284e158d6fc1f5a246465c7eed7bc212b0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+09.770_ry+20.382_rz+25.639_tx-00.058_ty+00.212_s+00.651_b17746284e158d6fc1f5a246465c7eed7bc212b0_256x256.png,381ed7f5a88a4ac5ed067fa8a2de27c88288445c,Chairs,9.77,20.382,25.639,1285 -1286,_004,11.926,-44.019,38.652,-0.103,0.521,1.18714285714,c3edce2a8fce8c088605bd27fe1f2e7958939f54,256,V3,c3edce2a8fce8c088605bd27fe1f2e7958939f54_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+11.926_ry-44.019_rz+38.652_tx-00.103_ty+00.521_s+00.831_c3edce2a8fce8c088605bd27fe1f2e7958939f54_256x256.png,7c5174365cf0ed5a3319250d5913bcd8217f1454,Chairs,11.926,-44.019,38.652,1286 -1287,_004,12.395,-40.607,32.322,0.257,0.546,1.02857142857,6d4edcf292c1a9122661f1979806bff6e61ea996,256,V3,6d4edcf292c1a9122661f1979806bff6e61ea996_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+12.395_ry-40.607_rz+32.322_tx+00.257_ty+00.546_s+00.720_6d4edcf292c1a9122661f1979806bff6e61ea996_256x256.png,5712442d08b9caa389e6b25eceaa32fbcd0aedcf,Chairs,12.395,-40.607,32.322,1287 -1288,_004,12.774,-28.825,36.718,0.261,-0.513,0.798571428571,dbc0de757481c2949e6b05dc902ed0c39bb4c283,256,V3,dbc0de757481c2949e6b05dc902ed0c39bb4c283_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+12.774_ry-28.825_rz+36.718_tx+00.261_ty-00.513_s+00.559_dbc0de757481c2949e6b05dc902ed0c39bb4c283_256x256.png,c95a41e70991e0ce352bf24d09886fe76043bf15,Chairs,12.774,-28.825,36.718,1288 -1289,_004,19.38,-28.754,-2.021,-0.278,0.333,0.897142857143,200da3ca81185cc0aa5da69fba5a1350a9e8ccee,256,V3,200da3ca81185cc0aa5da69fba5a1350a9e8ccee_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+19.380_ry-28.754_rz-02.021_tx-00.278_ty+00.333_s+00.628_200da3ca81185cc0aa5da69fba5a1350a9e8ccee_256x256.png,bc4eb19ff06fe7e36e254a6d460419f819344d86,Chairs,19.38,-28.754,-2.021,1289 -1290,_004,26.234,19.705,42.155,0.016,0.482,1.09571428571,c381f84930961c0cc2cffed2d93ea70cde064cda,256,V3,c381f84930961c0cc2cffed2d93ea70cde064cda_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+26.234_ry+19.705_rz+42.155_tx+00.016_ty+00.482_s+00.767_c381f84930961c0cc2cffed2d93ea70cde064cda_256x256.png,37bc47f86eb17402dac6c4f374c84e423dbec433,Chairs,26.234,19.705,42.155,1290 -1291,_004,26.684,32.023,35.614,0.111,-0.447,0.797142857143,8b37078b5606c07c2647f33aa62c55bd27a3ff2b,256,V3,8b37078b5606c07c2647f33aa62c55bd27a3ff2b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+26.684_ry+32.023_rz+35.614_tx+00.111_ty-00.447_s+00.558_8b37078b5606c07c2647f33aa62c55bd27a3ff2b_256x256.png,5b968b904a872db4133da505a328660190aa24c8,Chairs,26.684,32.023,35.614,1291 -1292,_004,27.335,27.011,-0.622,-0.085,0.145,1.09857142857,de97a2b3adba24d3a1db9fa4b4fe962f69ddb9e3,256,V3,de97a2b3adba24d3a1db9fa4b4fe962f69ddb9e3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+27.335_ry+27.011_rz-00.622_tx-00.085_ty+00.145_s+00.769_de97a2b3adba24d3a1db9fa4b4fe962f69ddb9e3_256x256.png,f6170f63dac565740407f419a33bb89bcdf15b29,Chairs,27.335,27.011,-0.622,1292 -1293,_004,30.856,15.425,-36.501,0.157,0.469,1.22571428571,099b63552d20e5129bf6236c651f66708979f86f,256,V3,099b63552d20e5129bf6236c651f66708979f86f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+30.856_ry+15.425_rz-36.501_tx+00.157_ty+00.469_s+00.858_099b63552d20e5129bf6236c651f66708979f86f_256x256.png,6b4fe8c01bcb454a48349ce5402834781d6c18ed,Chairs,30.856,15.425,-36.501,1293 -1294,_004,36.212,10.289,23.21,-0.18,0.221,0.784285714286,5ea86b75bb6c7bf3dbfcd6a218e16a1b65b35836,256,V3,5ea86b75bb6c7bf3dbfcd6a218e16a1b65b35836_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+36.212_ry+10.289_rz+23.210_tx-00.180_ty+00.221_s+00.549_5ea86b75bb6c7bf3dbfcd6a218e16a1b65b35836_256x256.png,820ea4e4a0872d9330eb237b40a9f8080531e4db,Chairs,36.212,10.289,23.21,1294 -1295,_004,36.959,7.896,-38.41,-0.102,0.186,0.822857142857,7e01a01a162a55fb7d4ae78b93122b830ce64d82,256,V3,7e01a01a162a55fb7d4ae78b93122b830ce64d82_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+36.959_ry+07.896_rz-38.410_tx-00.102_ty+00.186_s+00.576_7e01a01a162a55fb7d4ae78b93122b830ce64d82_256x256.png,12396212017e175831c1d4b2b79eea74484e65fa,Chairs,36.959,7.896,-38.41,1295 -1296,_004,39.456,43.204,37.802,0.172,0.214,0.908571428571,14a8feac1534487fc2a47b8ee5892212e8e7e1da,256,V3,14a8feac1534487fc2a47b8ee5892212e8e7e1da_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+39.456_ry+43.204_rz+37.802_tx+00.172_ty+00.214_s+00.636_14a8feac1534487fc2a47b8ee5892212e8e7e1da_256x256.png,a04838ffd998070284c743be4aaf5bc834929ece,Chairs,39.456,43.204,37.802,1296 -1297,_004,39.951,-12.912,10.861,-0.038,-0.342,0.898571428571,3ca80677db32092afc6afe54005f93024b23c5ed,256,V3,3ca80677db32092afc6afe54005f93024b23c5ed_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+39.951_ry-12.912_rz+10.861_tx-00.038_ty-00.342_s+00.629_3ca80677db32092afc6afe54005f93024b23c5ed_256x256.png,23dcd4a076e244486ba5718f638ee95647ed5c34,Chairs,39.951,-12.912,10.861,1297 -1298,_004,40.426,42.36,-25.155,0.119,-0.457,0.822857142857,4e61984e708996ee131c3174455228e082b7f0cd,256,V3,4e61984e708996ee131c3174455228e082b7f0cd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+40.426_ry+42.360_rz-25.155_tx+00.119_ty-00.457_s+00.576_4e61984e708996ee131c3174455228e082b7f0cd_256x256.png,6a69e054380055c601a8ceff8f3498b951cbe13f,Chairs,40.426,42.36,-25.155,1298 -1299,_004,41.076,-19.056,18.097,-0.183,0.178,1.10285714286,1eb88ea68d56ff47747f281c5a4ccadc6f15b91e,256,V3,1eb88ea68d56ff47747f281c5a4ccadc6f15b91e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+41.076_ry-19.056_rz+18.097_tx-00.183_ty+00.178_s+00.772_1eb88ea68d56ff47747f281c5a4ccadc6f15b91e_256x256.png,e4f8597df4670d5b9fc13e59675364cf8b6e44e2,Chairs,41.076,-19.056,18.097,1299 -1300,_004,41.203,19.75,-3.365,0.022,0.021,1.22857142857,c63843de17ef88e6fca7c8660e3d8e806f61ca04,256,V3,c63843de17ef88e6fca7c8660e3d8e806f61ca04_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+41.203_ry+19.750_rz-03.365_tx+00.022_ty+00.021_s+00.860_c63843de17ef88e6fca7c8660e3d8e806f61ca04_256x256.png,a4151bd5169c85e631f71467ada8074b130a57f4,Chairs,41.203,19.75,-3.365,1300 -1301,_004,43.155,-44.972,39.218,-0.273,-0.566,1.27,8feaa7a857b0b6adcc119ba5eb74269f54f1e7fc,256,V3,8feaa7a857b0b6adcc119ba5eb74269f54f1e7fc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+43.155_ry-44.972_rz+39.218_tx-00.273_ty-00.566_s+00.889_8feaa7a857b0b6adcc119ba5eb74269f54f1e7fc_256x256.png,5a2d6009e3ddc79a5c372261965fa927a0248534,Chairs,43.155,-44.972,39.218,1301 -1302,_004,43.811,-30.85,-15.942,-0.292,0.122,0.777142857143,1aa56b9b9931cb0cd7d5d0ff7c474a14b02fe9ce,256,V3,1aa56b9b9931cb0cd7d5d0ff7c474a14b02fe9ce_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+43.811_ry-30.850_rz-15.942_tx-00.292_ty+00.122_s+00.544_1aa56b9b9931cb0cd7d5d0ff7c474a14b02fe9ce_256x256.png,ac1d0fe9ffee70472beaded5e99cdcb4599185c9,Chairs,43.811,-30.85,-15.942,1302 -1303,_004,43.948,6.485,0.738,0.173,0.358,1.27571428571,e88547462b496e694c494477d9b3a5ab6d2e9b14,256,V3,e88547462b496e694c494477d9b3a5ab6d2e9b14_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+43.948_ry+06.485_rz+00.738_tx+00.173_ty+00.358_s+00.893_e88547462b496e694c494477d9b3a5ab6d2e9b14_256x256.png,287a5a1bb3c8e948e12c86a3b4628dee89eba7c5,Chairs,43.948,6.485,0.738,1303 -1304,_004,44.795,14.818,37.137,0.217,-0.052,1.11714285714,b5f48e480933912d0d283e3db2004122a99d2216,256,V3,b5f48e480933912d0d283e3db2004122a99d2216_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx+44.795_ry+14.818_rz+37.137_tx+00.217_ty-00.052_s+00.782_b5f48e480933912d0d283e3db2004122a99d2216_256x256.png,1c7ca24872d3ce858bf303ffed02d0ce075ef8e2,Chairs,44.795,14.818,37.137,1304 -1305,_004,-2.165,18.707,-34.447,-0.101,0.581,1.17714285714,c7d0863311389f3bfe651364f90c85791ff38723,256,V3,c7d0863311389f3bfe651364f90c85791ff38723_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx-02.165_ry+18.707_rz-34.447_tx-00.101_ty+00.581_s+00.824_c7d0863311389f3bfe651364f90c85791ff38723_256x256.png,1957700193b8a8e7188e2bfecb9b83f8bf556362,Chairs,-2.165,18.707,-34.447,1305 -1306,_004,-4.06,36.039,-40.239,-0.172,0.167,0.877142857143,7a225c98f6829139426a64942f834595af2e776b,256,V3,7a225c98f6829139426a64942f834595af2e776b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx-04.060_ry+36.039_rz-40.239_tx-00.172_ty+00.167_s+00.614_7a225c98f6829139426a64942f834595af2e776b_256x256.png,5cdd474194b556e3fe0d0fa96c09da4b15fff511,Chairs,-4.06,36.039,-40.239,1306 -1307,_004,-10.084,-20.908,-35.267,-0.096,0.185,1.13857142857,d041c29ce9ce315e8e89d067db33953bcb0d6193,256,V3,d041c29ce9ce315e8e89d067db33953bcb0d6193_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx-10.084_ry-20.908_rz-35.267_tx-00.096_ty+00.185_s+00.797_d041c29ce9ce315e8e89d067db33953bcb0d6193_256x256.png,9c4f051c00ad23c1388fc27055ea530047e66cf7,Chairs,-10.084,-20.908,-35.267,1307 -1308,_004,-13.304,2.154,42.414,-0.035,-0.048,0.882857142857,a2a0c06737c71115bd7e2fe25bc5280309f6cd72,256,V3,a2a0c06737c71115bd7e2fe25bc5280309f6cd72_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx-13.304_ry+02.154_rz+42.414_tx-00.035_ty-00.048_s+00.618_a2a0c06737c71115bd7e2fe25bc5280309f6cd72_256x256.png,f7d2c9932f70d9e5f5d36194be00204b32fb03a5,Chairs,-13.304,2.154,42.414,1308 -1309,_004,-16.678,-28.643,4.874,-0.271,-0.5,0.992857142857,7b1a3ba0745f647b5e7f89a357b17ae78f8dd03a,256,V3,7b1a3ba0745f647b5e7f89a357b17ae78f8dd03a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx-16.678_ry-28.643_rz+04.874_tx-00.271_ty-00.500_s+00.695_7b1a3ba0745f647b5e7f89a357b17ae78f8dd03a_256x256.png,86f5c08e82e6375cbd0f7b8fcc792322bd209b94,Chairs,-16.678,-28.643,4.874,1309 -1310,_004,-18.583,2.671,-0.353,0.001,-0.278,1.00285714286,ebd0901e30c6486fc9a9af344d2bad9755a2c042,256,V3,ebd0901e30c6486fc9a9af344d2bad9755a2c042_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx-18.583_ry+02.671_rz-00.353_tx+00.001_ty-00.278_s+00.702_ebd0901e30c6486fc9a9af344d2bad9755a2c042_256x256.png,7b87a9c36b42b0f8fa4689db307bd0c6c6a17c10,Chairs,-18.583,2.671,-0.353,1310 -1311,_004,-21.988,4.514,-32.221,-0.137,0.388,0.844285714286,a8b2a5e00fffd1a1ec99a9f0291b199a932545ab,256,V3,a8b2a5e00fffd1a1ec99a9f0291b199a932545ab_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx-21.988_ry+04.514_rz-32.221_tx-00.137_ty+00.388_s+00.591_a8b2a5e00fffd1a1ec99a9f0291b199a932545ab_256x256.png,0c08168139da91dfc0a3b919599951a009c9da32,Chairs,-21.988,4.514,-32.221,1311 -1312,_004,-33.917,-4.257,-44.133,0.011,-0.38,0.987142857143,97c7e5fab3616fd83d1d4915032cfaa402cb442e,256,V3,97c7e5fab3616fd83d1d4915032cfaa402cb442e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx-33.917_ry-04.257_rz-44.133_tx+00.011_ty-00.380_s+00.691_97c7e5fab3616fd83d1d4915032cfaa402cb442e_256x256.png,8b00837cb4707860a868f237b4a0131e454f34a6,Chairs,-33.917,-4.257,-44.133,1312 -1313,_004,-36.603,10.321,16.609,0.024,0.249,0.795714285714,91e68c65ff92e5a77f0fb13693bfe01bc429da18,256,V3,91e68c65ff92e5a77f0fb13693bfe01bc429da18_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx-36.603_ry+10.321_rz+16.609_tx+00.024_ty+00.249_s+00.557_91e68c65ff92e5a77f0fb13693bfe01bc429da18_256x256.png,3ade751b115c7813365f97174e75106af2b88310,Chairs,-36.603,10.321,16.609,1313 -1314,_004,-38.01,-11.892,-26.32,0.299,-0.217,1.26285714286,4247e308df753502e3a2ccb7d1cd07346beb74d0,256,V3,4247e308df753502e3a2ccb7d1cd07346beb74d0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx-38.010_ry-11.892_rz-26.320_tx+00.299_ty-00.217_s+00.884_4247e308df753502e3a2ccb7d1cd07346beb74d0_256x256.png,32f0b2e1e56b61cd5b0dc2c1fdc769edf57e9c6d,Chairs,-38.01,-11.892,-26.32,1314 -1315,_004,-38.458,32.42,-38.527,0.215,0.06,0.944285714286,c20bfce0dadd4a79a499529a481827339257f231,256,V3,c20bfce0dadd4a79a499529a481827339257f231_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx-38.458_ry+32.420_rz-38.527_tx+00.215_ty+00.060_s+00.661_c20bfce0dadd4a79a499529a481827339257f231_256x256.png,80a3b8ea9592eb5149f900c21aec3a2acb039427,Chairs,-38.458,32.42,-38.527,1315 -1316,_004,-39.33,8.417,-20.904,0.189,-0.119,1.09,4c381cd8ab9f43ee9dbc89de210bc4f5a28eb8f0,256,V3,4c381cd8ab9f43ee9dbc89de210bc4f5a28eb8f0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx-39.330_ry+08.417_rz-20.904_tx+00.189_ty-00.119_s+00.763_4c381cd8ab9f43ee9dbc89de210bc4f5a28eb8f0_256x256.png,ab1890b92e1b1cf2ea500eff9537b3111f620ddb,Chairs,-39.33,8.417,-20.904,1316 -1317,_004,-41.743,-27.045,-10.753,-0.141,-0.253,1.01857142857,a030c963a1e7d301b38c28db5492155862735133,256,V3,a030c963a1e7d301b38c28db5492155862735133_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx-41.743_ry-27.045_rz-10.753_tx-00.141_ty-00.253_s+00.713_a030c963a1e7d301b38c28db5492155862735133_256x256.png,21b241436923f482ded0113a8bc2a9ea424cca3e,Chairs,-41.743,-27.045,-10.753,1317 -1318,_004,-41.785,-11.968,29.595,-0.203,0.208,1.11714285714,6480c32626969bfc8d45ba531e885e483d45a857,256,V3,6480c32626969bfc8d45ba531e885e483d45a857_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx-41.785_ry-11.968_rz+29.595_tx-00.203_ty+00.208_s+00.782_6480c32626969bfc8d45ba531e885e483d45a857_256x256.png,b3e62e3ef908fc4ce13f3db0ade952299317f49d,Chairs,-41.785,-11.968,29.595,1318 -1319,_004,-44.659,-11.069,27.736,0.24,0.384,1.18,143176a572871d3144176d44b50d0e307f2cf148,256,V3,143176a572871d3144176d44b50d0e307f2cf148_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_004_rx-44.659_ry-11.069_rz+27.736_tx+00.240_ty+00.384_s+00.826_143176a572871d3144176d44b50d0e307f2cf148_256x256.png,77ea535b5aa44ae632d4a544dbab428fc1e01f94,Chairs,-44.659,-11.069,27.736,1319 -1320,_008,0.445,-18.961,22.624,0.226,-0.302,0.812857142857,12a1bc89263468dbb57dc3faad823d70406a7321,256,V3,12a1bc89263468dbb57dc3faad823d70406a7321_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+00.445_ry-18.961_rz+22.624_tx+00.226_ty-00.302_s+00.569_12a1bc89263468dbb57dc3faad823d70406a7321_256x256.png,a69409037f26e2510fe6c8deb6790b4e8c6e309d,Chairs,0.445,-18.961,22.624,1320 -1321,_008,0.511,23.676,35.058,-0.099,-0.506,1.28571428571,d9ede3319cb2aa9f1c7b572e11fa4ec993f1f944,256,V3,d9ede3319cb2aa9f1c7b572e11fa4ec993f1f944_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+00.511_ry+23.676_rz+35.058_tx-00.099_ty-00.506_s+00.900_d9ede3319cb2aa9f1c7b572e11fa4ec993f1f944_256x256.png,0f9306f469108e5794d53f73d767e3648b8dfa1b,Chairs,0.511,23.676,35.058,1321 -1322,_008,2.9,-43.772,-25.926,0.288,-0.183,1.05428571429,171db4c33cc0be705f8f39c4aa8b66d54a3ba3ab,256,V3,171db4c33cc0be705f8f39c4aa8b66d54a3ba3ab_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+02.900_ry-43.772_rz-25.926_tx+00.288_ty-00.183_s+00.738_171db4c33cc0be705f8f39c4aa8b66d54a3ba3ab_256x256.png,c24c32c346eb5da24f45486c348093a90c030bce,Chairs,2.9,-43.772,-25.926,1322 -1323,_008,4.202,-29.811,-30.561,0.283,-0.129,1.17857142857,f00fcc99fc3d87e882af7edc6be3740a0d005b1d,256,V3,f00fcc99fc3d87e882af7edc6be3740a0d005b1d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+04.202_ry-29.811_rz-30.561_tx+00.283_ty-00.129_s+00.825_f00fcc99fc3d87e882af7edc6be3740a0d005b1d_256x256.png,8c64caca51b1fd96498bf82f2631e6ab87bbddc8,Chairs,4.202,-29.811,-30.561,1323 -1324,_008,9.219,21.772,-12.845,-0.263,-0.582,0.95,0fb23fba83c2463dfaf9fc958fc5fc501d4cad7b,256,V3,0fb23fba83c2463dfaf9fc958fc5fc501d4cad7b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+09.219_ry+21.772_rz-12.845_tx-00.263_ty-00.582_s+00.665_0fb23fba83c2463dfaf9fc958fc5fc501d4cad7b_256x256.png,628ddac4dd76f64026f584c06403482f7e61d16a,Chairs,9.219,21.772,-12.845,1324 -1325,_008,9.578,19.153,-10.809,-0.019,0.382,1.08142857143,c918f1fd3ebecb930752a5fdd2bc0148a0c5cc2e,256,V3,c918f1fd3ebecb930752a5fdd2bc0148a0c5cc2e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+09.578_ry+19.153_rz-10.809_tx-00.019_ty+00.382_s+00.757_c918f1fd3ebecb930752a5fdd2bc0148a0c5cc2e_256x256.png,963f24714e1c336db71aeb3a83ef3e1050286cf0,Chairs,9.578,19.153,-10.809,1325 -1326,_008,13.836,21.134,-31.437,-0.23,0.456,0.974285714286,e1cde1b716dad49d7d1ff5101c6fc9fc2f3735b3,256,V3,e1cde1b716dad49d7d1ff5101c6fc9fc2f3735b3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+13.836_ry+21.134_rz-31.437_tx-00.230_ty+00.456_s+00.682_e1cde1b716dad49d7d1ff5101c6fc9fc2f3735b3_256x256.png,eb7ca00e1487a5ae231bad64e9a511fd12c82832,Chairs,13.836,21.134,-31.437,1326 -1327,_008,20.812,44.738,12.049,0.299,-0.266,1.12857142857,59f6debfb1184b73f28cec213a542bb4323623f8,256,V3,59f6debfb1184b73f28cec213a542bb4323623f8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+20.812_ry+44.738_rz+12.049_tx+00.299_ty-00.266_s+00.790_59f6debfb1184b73f28cec213a542bb4323623f8_256x256.png,103ae7cee985ededb07f9f4b92c49eb324522f7e,Chairs,20.812,44.738,12.049,1327 -1328,_008,21.452,-5.025,-15.558,-0.228,0.191,0.825714285714,d47691b862f9293007d166c163be5367dbbe67a5,256,V3,d47691b862f9293007d166c163be5367dbbe67a5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+21.452_ry-05.025_rz-15.558_tx-00.228_ty+00.191_s+00.578_d47691b862f9293007d166c163be5367dbbe67a5_256x256.png,aa16bd4c38119ddaae35c010af55a158d8b4878d,Chairs,21.452,-5.025,-15.558,1328 -1329,_008,21.491,10.304,27.61,0.046,-0.069,1.03142857143,26a70f8f406687e48ee4d025bb64201b81959f89,256,V3,26a70f8f406687e48ee4d025bb64201b81959f89_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+21.491_ry+10.304_rz+27.610_tx+00.046_ty-00.069_s+00.722_26a70f8f406687e48ee4d025bb64201b81959f89_256x256.png,f961dd1e6f78ce1ddf03d3ad4e0c7d81d451d04c,Chairs,21.491,10.304,27.61,1329 -1330,_008,26.501,-5.188,-8.714,0.288,0.134,1.24,cf94b87edd6cc4b84144906efd279e06218c7581,256,V3,cf94b87edd6cc4b84144906efd279e06218c7581_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+26.501_ry-05.188_rz-08.714_tx+00.288_ty+00.134_s+00.868_cf94b87edd6cc4b84144906efd279e06218c7581_256x256.png,76ac6124e24b0beee77452a3bd28e1fa54a712e5,Chairs,26.501,-5.188,-8.714,1330 -1331,_008,28.952,34.713,-19.84,-0.265,-0.459,0.855714285714,9618171c30dcb379ca4f3a97dd5da2a410d42744,256,V3,9618171c30dcb379ca4f3a97dd5da2a410d42744_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+28.952_ry+34.713_rz-19.840_tx-00.265_ty-00.459_s+00.599_9618171c30dcb379ca4f3a97dd5da2a410d42744_256x256.png,c23fda37757be485a95d9aaad73304d5cef442cb,Chairs,28.952,34.713,-19.84,1331 -1332,_008,36.114,-13.931,34.758,0.254,-0.265,1.15142857143,563ab585dd0de7c5d09590f04549e32d7b7c096f,256,V3,563ab585dd0de7c5d09590f04549e32d7b7c096f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+36.114_ry-13.931_rz+34.758_tx+00.254_ty-00.265_s+00.806_563ab585dd0de7c5d09590f04549e32d7b7c096f_256x256.png,1dea736811812c59074a8353bdf5130750beba76,Chairs,36.114,-13.931,34.758,1332 -1333,_008,36.936,8.015,14.716,0.19,0.57,0.954285714286,4850d4321c26797dcbc9785beedfa01d46dc7cc6,256,V3,4850d4321c26797dcbc9785beedfa01d46dc7cc6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+36.936_ry+08.015_rz+14.716_tx+00.190_ty+00.570_s+00.668_4850d4321c26797dcbc9785beedfa01d46dc7cc6_256x256.png,6287925c4415ae87727674fd2940296e3e27dc46,Chairs,36.936,8.015,14.716,1333 -1334,_008,37.847,-11.21,-5.956,0.034,0.545,1.14857142857,f4e6250cd8956947601b528757a2371400ff8aa4,256,V3,f4e6250cd8956947601b528757a2371400ff8aa4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+37.847_ry-11.210_rz-05.956_tx+00.034_ty+00.545_s+00.804_f4e6250cd8956947601b528757a2371400ff8aa4_256x256.png,e8956cd4f6209c24d0d00e998388d31f36d34630,Chairs,37.847,-11.21,-5.956,1334 -1335,_008,44.941,-6.45,36.992,0.272,0.542,0.798571428571,4efe811bef69005e8d0bc86d662abe34aaacb4f3,256,V3,4efe811bef69005e8d0bc86d662abe34aaacb4f3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx+44.941_ry-06.450_rz+36.992_tx+00.272_ty+00.542_s+00.559_4efe811bef69005e8d0bc86d662abe34aaacb4f3_256x256.png,46877c65edc57e66163c8cd7d1fb88b5a5c99ba4,Chairs,44.941,-6.45,36.992,1335 -1336,_008,-1.525,35.714,-28.162,-0.252,-0.388,0.941428571429,93fbae3137e57bba69db955d5788c80042ee66b3,256,V3,93fbae3137e57bba69db955d5788c80042ee66b3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-01.525_ry+35.714_rz-28.162_tx-00.252_ty-00.388_s+00.659_93fbae3137e57bba69db955d5788c80042ee66b3_256x256.png,5c2fb0a5b679e168b823c5120fe276dc0b84cbec,Chairs,-1.525,35.714,-28.162,1336 -1337,_008,-6.338,28.735,16.188,-0.079,0.09,0.852857142857,072d52a261dc325d88850043ac435da6408284cf,256,V3,072d52a261dc325d88850043ac435da6408284cf_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-06.338_ry+28.735_rz+16.188_tx-00.079_ty+00.090_s+00.597_072d52a261dc325d88850043ac435da6408284cf_256x256.png,3c8372a649d1da1825b26cd830717fb97c2d4ca0,Chairs,-6.338,28.735,16.188,1337 -1338,_008,-8.978,18.065,18.709,-0.134,0.422,0.931428571429,035039166d14f49079f6d7885cbcb3c32a878e0a,256,V3,035039166d14f49079f6d7885cbcb3c32a878e0a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-08.978_ry+18.065_rz+18.709_tx-00.134_ty+00.422_s+00.652_035039166d14f49079f6d7885cbcb3c32a878e0a_256x256.png,7611705b9efe33efb0a0cb1150cd8dff337c1b2f,Chairs,-8.978,18.065,18.709,1338 -1339,_008,-9.168,40.226,34.685,-0.207,-0.594,1.06857142857,bd3ac7240c79c2d9fc42f136b0cda0c086521934,256,V3,bd3ac7240c79c2d9fc42f136b0cda0c086521934_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-09.168_ry+40.226_rz+34.685_tx-00.207_ty-00.594_s+00.748_bd3ac7240c79c2d9fc42f136b0cda0c086521934_256x256.png,8141348935924f2ce0cd6136ec5e1b81a784052f,Chairs,-9.168,40.226,34.685,1339 -1340,_008,-11.562,-20.589,10.684,0.137,-0.277,0.907142857143,366fb365218d0f26defbffba6e16f35e345a286a,256,V3,366fb365218d0f26defbffba6e16f35e345a286a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-11.562_ry-20.589_rz+10.684_tx+00.137_ty-00.277_s+00.635_366fb365218d0f26defbffba6e16f35e345a286a_256x256.png,02e2ff60de7f6e49863da4b86cf3b55daf57a300,Chairs,-11.562,-20.589,10.684,1340 -1341,_008,-14.251,32.414,12.85,-0.09,0.17,0.77,f90fd6ff11d298700b3ba7f3fb86042f22d5e5e9,256,V3,f90fd6ff11d298700b3ba7f3fb86042f22d5e5e9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-14.251_ry+32.414_rz+12.850_tx-00.090_ty+00.170_s+00.539_f90fd6ff11d298700b3ba7f3fb86042f22d5e5e9_256x256.png,bfc20589a243db70e0373bee8620578b40ee771b,Chairs,-14.251,32.414,12.85,1341 -1342,_008,-15.559,-32.882,-1.227,-0.149,-0.282,0.904285714286,a1458856b6110a854e61701e199f18d9c2972ca3,256,V3,a1458856b6110a854e61701e199f18d9c2972ca3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-15.559_ry-32.882_rz-01.227_tx-00.149_ty-00.282_s+00.633_a1458856b6110a854e61701e199f18d9c2972ca3_256x256.png,35f2392ca376e233ea6e52030804c474d120078b,Chairs,-15.559,-32.882,-1.227,1342 -1343,_008,-16.391,-28.45,14.948,-0.17,-0.171,0.994285714286,676270114cdd5cd76c077d7a6a3b207a40b8ea33,256,V3,676270114cdd5cd76c077d7a6a3b207a40b8ea33_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-16.391_ry-28.450_rz+14.948_tx-00.170_ty-00.171_s+00.696_676270114cdd5cd76c077d7a6a3b207a40b8ea33_256x256.png,6fd17b5c5091ea6d16d7f52cf8cfd93edb262f37,Chairs,-16.391,-28.45,14.948,1343 -1344,_008,-16.888,-44.36,-10.206,0.269,0.04,1.10142857143,34c165b5b2bdf4147b863f0197bed81d2af9a91f,256,V3,34c165b5b2bdf4147b863f0197bed81d2af9a91f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-16.888_ry-44.360_rz-10.206_tx+00.269_ty+00.040_s+00.771_34c165b5b2bdf4147b863f0197bed81d2af9a91f_256x256.png,ed89c24397bf420cd7138178e7e90feaa0cf7063,Chairs,-16.888,-44.36,-10.206,1344 -1345,_008,-21.851,16.975,-8.762,-0.008,-0.518,0.877142857143,3824c685d55d08b9cd4575386da2a0a232fbda79,256,V3,3824c685d55d08b9cd4575386da2a0a232fbda79_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-21.851_ry+16.975_rz-08.762_tx-00.008_ty-00.518_s+00.614_3824c685d55d08b9cd4575386da2a0a232fbda79_256x256.png,829d1e9b2d07451e6979f4a693cc4c9b356e43dc,Chairs,-21.851,16.975,-8.762,1345 -1346,_008,-21.876,-44.864,17.455,-0.253,-0.015,0.945714285714,ce30c9b81be31a2ec4f51a04b30ee139a5e1d0b2,256,V3,ce30c9b81be31a2ec4f51a04b30ee139a5e1d0b2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-21.876_ry-44.864_rz+17.455_tx-00.253_ty-00.015_s+00.662_ce30c9b81be31a2ec4f51a04b30ee139a5e1d0b2_256x256.png,dea0cf2ab84c4f7aaa34f51745d79dbcbd8b75c1,Chairs,-21.876,-44.864,17.455,1346 -1347,_008,-23.15,-39.692,39.719,-0.079,0.375,1.03285714286,c6b784915aab9ad77c3f0bf7430ed20425e2b865,256,V3,c6b784915aab9ad77c3f0bf7430ed20425e2b865_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-23.150_ry-39.692_rz+39.719_tx-00.079_ty+00.375_s+00.723_c6b784915aab9ad77c3f0bf7430ed20425e2b865_256x256.png,e694f853b9ac008fe4d7d983dcecc0b042de783a,Chairs,-23.15,-39.692,39.719,1347 -1348,_008,-23.28,-27.923,8.66,-0.258,-0.222,1.04571428571,fe0ce4bf750dff9855467d9d91093196d7456f68,256,V3,fe0ce4bf750dff9855467d9d91093196d7456f68_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-23.280_ry-27.923_rz+08.660_tx-00.258_ty-00.222_s+00.732_fe0ce4bf750dff9855467d9d91093196d7456f68_256x256.png,b71ef1ffb641ce938f32f32b83f9eb9f1fea8bad,Chairs,-23.28,-27.923,8.66,1348 -1349,_008,-23.361,-34.05,-14.195,0.015,0.157,0.811428571429,591c6a080a3571919fae6c05b6a4f2df9a43bcbb,256,V3,591c6a080a3571919fae6c05b6a4f2df9a43bcbb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-23.361_ry-34.050_rz-14.195_tx+00.015_ty+00.157_s+00.568_591c6a080a3571919fae6c05b6a4f2df9a43bcbb_256x256.png,dbb26d33f997dc71b267bbb7a73f80c4a01f8fd4,Chairs,-23.361,-34.05,-14.195,1349 -1350,_008,-26.465,17.05,29.554,0.217,0.216,0.978571428571,ba4949b8ecd181e3a259bb32d59bfe60f2a54861,256,V3,ba4949b8ecd181e3a259bb32d59bfe60f2a54861_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-26.465_ry+17.050_rz+29.554_tx+00.217_ty+00.216_s+00.685_ba4949b8ecd181e3a259bb32d59bfe60f2a54861_256x256.png,ebebc840593dda9cbedb5f56375fc197c19b13a8,Chairs,-26.465,17.05,29.554,1350 -1351,_008,-28.409,41.084,13.343,-0.133,0.42,1.27,209eccace5961458326adc3f0c181b2302e53036,256,V3,209eccace5961458326adc3f0c181b2302e53036_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-28.409_ry+41.084_rz+13.343_tx-00.133_ty+00.420_s+00.889_209eccace5961458326adc3f0c181b2302e53036_256x256.png,0e9c6addb42a24a316c36cf6c76536ca6fe55471,Chairs,-28.409,41.084,13.343,1351 -1352,_008,-30.541,8.357,15.745,0.242,-0.572,1.10714285714,cd88ca779b06d8829d75f3e19f5298b88dc3ff2d,256,V3,cd88ca779b06d8829d75f3e19f5298b88dc3ff2d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-30.541_ry+08.357_rz+15.745_tx+00.242_ty-00.572_s+00.775_cd88ca779b06d8829d75f3e19f5298b88dc3ff2d_256x256.png,a127edf8bdaedd4eab109475114f556a9dd7fc7a,Chairs,-30.541,8.357,15.745,1352 -1353,_008,-33.716,22.443,-11.135,0.244,0.371,1.24,acaba78f4f970c5db0de9998a3b60545f1206a8c,256,V3,acaba78f4f970c5db0de9998a3b60545f1206a8c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-33.716_ry+22.443_rz-11.135_tx+00.244_ty+00.371_s+00.868_acaba78f4f970c5db0de9998a3b60545f1206a8c_256x256.png,bf72a3ebfc89b4e0b35418c6ce9d34555cd7364c,Chairs,-33.716,22.443,-11.135,1353 -1354,_008,-34.102,-42.538,-11.77,0.237,-0.115,1.09,b11081fee9665c1fb83251e3c80867360887bf8e,256,V3,b11081fee9665c1fb83251e3c80867360887bf8e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-34.102_ry-42.538_rz-11.770_tx+00.237_ty-00.115_s+00.763_b11081fee9665c1fb83251e3c80867360887bf8e_256x256.png,d4adb78144259e5776a7dd353b8aefed31d2df34,Chairs,-34.102,-42.538,-11.77,1354 -1355,_008,-35.207,0.782,-38.338,0.001,-0.007,1.24428571429,813335a9d4f550fd9a8e6edb357bdb9ef1da6630,256,V3,813335a9d4f550fd9a8e6edb357bdb9ef1da6630_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-35.207_ry+00.782_rz-38.338_tx+00.001_ty-00.007_s+00.871_813335a9d4f550fd9a8e6edb357bdb9ef1da6630_256x256.png,becd4dd66a0ea7e008c5bcd8440e68a64bb60af0,Chairs,-35.207,0.782,-38.338,1355 -1356,_008,-35.341,1.919,-44.671,0.198,0.598,1.21714285714,1661fa8d2e3c24f6e7f82e9395c803aa2e130b2f,256,V3,1661fa8d2e3c24f6e7f82e9395c803aa2e130b2f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-35.341_ry+01.919_rz-44.671_tx+00.198_ty+00.598_s+00.852_1661fa8d2e3c24f6e7f82e9395c803aa2e130b2f_256x256.png,047443fcb1b3bcf9c1f1d54182e77c878971f4c1,Chairs,-35.341,1.919,-44.671,1356 -1357,_008,-37.144,-16.73,-8.439,-0.018,0.122,1.24857142857,9aeb73e8f23eaa0e424a7efbca825b26b029ae98,256,V3,9aeb73e8f23eaa0e424a7efbca825b26b029ae98_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-37.144_ry-16.730_rz-08.439_tx-00.018_ty+00.122_s+00.874_9aeb73e8f23eaa0e424a7efbca825b26b029ae98_256x256.png,74c58df2d19667c55ece5142b5a89972ca14d869,Chairs,-37.144,-16.73,-8.439,1357 -1358,_008,-37.639,-31.01,-6.073,-0.079,-0.271,0.808571428571,bb254bee596e7cf779009c6d5338aa1986c56b59,256,V3,bb254bee596e7cf779009c6d5338aa1986c56b59_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-37.639_ry-31.010_rz-06.073_tx-00.079_ty-00.271_s+00.566_bb254bee596e7cf779009c6d5338aa1986c56b59_256x256.png,fb6e29ccc071bc03d5cf6e5244a58b789a0d20c3,Chairs,-37.639,-31.01,-6.073,1358 -1359,_008,-44.394,9.804,8.972,0.138,-0.238,0.88,01fd68c7194189fa61aad77894b6f931708c7d18,256,V3,01fd68c7194189fa61aad77894b6f931708c7d18_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_008_rx-44.394_ry+09.804_rz+08.972_tx+00.138_ty-00.238_s+00.616_01fd68c7194189fa61aad77894b6f931708c7d18_256x256.png,082f0e5cc064bfbdf7a9679e1aca6d74503260d4,Chairs,-44.394,9.804,8.972,1359 -1360,_010,0.146,25.394,33.789,0.228,-0.568,0.965714285714,0bfac703a6d95f21dc1fde68d25255003de843fd,256,V3,0bfac703a6d95f21dc1fde68d25255003de843fd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+00.146_ry+25.394_rz+33.789_tx+00.228_ty-00.568_s+00.676_0bfac703a6d95f21dc1fde68d25255003de843fd_256x256.png,b3d9d65043266dc19a9a1d8da9bda45a4d782a55,Chairs,0.146,25.394,33.789,1360 -1361,_010,3.124,7.057,-17.074,-0.124,0.19,1.24714285714,1d7988aa4ff591d8792740008856cd12c371aa64,256,V3,1d7988aa4ff591d8792740008856cd12c371aa64_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+03.124_ry+07.057_rz-17.074_tx-00.124_ty+00.190_s+00.873_1d7988aa4ff591d8792740008856cd12c371aa64_256x256.png,d71437f6e7a012516ce4ae58085886d4d2c6fad9,Chairs,3.124,7.057,-17.074,1361 -1362,_010,8.559,4.826,2.241,0.074,-0.073,0.882857142857,ad2ea806c2a1906d9fc951ec2e3342674d61b0a6,256,V3,ad2ea806c2a1906d9fc951ec2e3342674d61b0a6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+08.559_ry+04.826_rz+02.241_tx+00.074_ty-00.073_s+00.618_ad2ea806c2a1906d9fc951ec2e3342674d61b0a6_256x256.png,1aefb65b9ce3ff494fbe77dd2cb60768c7bd12f3,Chairs,8.559,4.826,2.241,1362 -1363,_010,12.566,-3.273,-17.17,-0.239,0.102,1.08285714286,4dcef6082ea29ba141baf859705d7853b08c6a09,256,V3,4dcef6082ea29ba141baf859705d7853b08c6a09_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+12.566_ry-03.273_rz-17.170_tx-00.239_ty+00.102_s+00.758_4dcef6082ea29ba141baf859705d7853b08c6a09_256x256.png,a76515cc5c3d1c0be72edfa2f416a923a7ecaed1,Chairs,12.566,-3.273,-17.17,1363 -1364,_010,12.699,-37.872,-0.571,-0.098,0.232,0.994285714286,153e365271cf31dbd9c507ab8705614bec31881d,256,V3,153e365271cf31dbd9c507ab8705614bec31881d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+12.699_ry-37.872_rz-00.571_tx-00.098_ty+00.232_s+00.696_153e365271cf31dbd9c507ab8705614bec31881d_256x256.png,50fa190035610b400f1e2aa1251f68758814b150,Chairs,12.699,-37.872,-0.571,1364 -1365,_010,13.394,-0.449,-30.478,0.072,-0.327,0.981428571429,0868dabb44f2ccfdff1f192f5b13629349d0ceef,256,V3,0868dabb44f2ccfdff1f192f5b13629349d0ceef_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+13.394_ry-00.449_rz-30.478_tx+00.072_ty-00.327_s+00.687_0868dabb44f2ccfdff1f192f5b13629349d0ceef_256x256.png,1bac8d5cd7394e4ad430478690c4cda39882da6c,Chairs,13.394,-0.449,-30.478,1365 -1366,_010,15.37,-10.141,0.492,-0.274,-0.495,1.18,225bd86afb65f34d18d08558d6a23c7ba38d37e4,256,V3,225bd86afb65f34d18d08558d6a23c7ba38d37e4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+15.370_ry-10.141_rz+00.492_tx-00.274_ty-00.495_s+00.826_225bd86afb65f34d18d08558d6a23c7ba38d37e4_256x256.png,6c4b680118204c1864ac5194c7f74b46b9c32617,Chairs,15.37,-10.141,0.492,1366 -1367,_010,17.309,-24.835,19.375,-0.262,0.194,1.00714285714,3fae329dcc0d28ecd2e48e08abde50a3752e0233,256,V3,3fae329dcc0d28ecd2e48e08abde50a3752e0233_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+17.309_ry-24.835_rz+19.375_tx-00.262_ty+00.194_s+00.705_3fae329dcc0d28ecd2e48e08abde50a3752e0233_256x256.png,4373bdd0b949a54a2efcd14073f022966ab17d2d,Chairs,17.309,-24.835,19.375,1367 -1368,_010,18.036,1.492,40.263,-0.269,-0.534,0.884285714286,2a88b63ad08a895d3e467520fd3c094a2c1cee63,256,V3,2a88b63ad08a895d3e467520fd3c094a2c1cee63_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+18.036_ry+01.492_rz+40.263_tx-00.269_ty-00.534_s+00.619_2a88b63ad08a895d3e467520fd3c094a2c1cee63_256x256.png,537311d6adf9c317e7b35918be08192a62da4086,Chairs,18.036,1.492,40.263,1368 -1369,_010,26.981,7.588,-21.043,0.183,-0.188,1.07285714286,2b35c554dd1f06562db7a1ec145ae6ca6f71f099,256,V3,2b35c554dd1f06562db7a1ec145ae6ca6f71f099_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+26.981_ry+07.588_rz-21.043_tx+00.183_ty-00.188_s+00.751_2b35c554dd1f06562db7a1ec145ae6ca6f71f099_256x256.png,358340ebabd9494baa6e8ca70a8096a34b12c873,Chairs,26.981,7.588,-21.043,1369 -1370,_010,27.11,29.492,-11.565,-0.038,-0.343,1.01142857143,d8a7baac6273fb898d2def52818d062b9f130234,256,V3,d8a7baac6273fb898d2def52818d062b9f130234_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+27.110_ry+29.492_rz-11.565_tx-00.038_ty-00.343_s+00.708_d8a7baac6273fb898d2def52818d062b9f130234_256x256.png,f86dc56f5d8f5a934cd5a85351b38942abbd488e,Chairs,27.11,29.492,-11.565,1370 -1371,_010,29.656,-33.447,-31.179,0.164,0.147,0.794285714286,4f9ba51336b3a50f872299b43ab4607e49aa92a8,256,V3,4f9ba51336b3a50f872299b43ab4607e49aa92a8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+29.656_ry-33.447_rz-31.179_tx+00.164_ty+00.147_s+00.556_4f9ba51336b3a50f872299b43ab4607e49aa92a8_256x256.png,831ea8dde76beddfe16fdb2af9143469d7ff3a1a,Chairs,29.656,-33.447,-31.179,1371 -1372,_010,32.053,-19.245,-10.816,0.241,0.013,0.81,d7f7f8e0c1edf3227db47bbb22ed5b729ad46220,256,V3,d7f7f8e0c1edf3227db47bbb22ed5b729ad46220_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+32.053_ry-19.245_rz-10.816_tx+00.241_ty+00.013_s+00.567_d7f7f8e0c1edf3227db47bbb22ed5b729ad46220_256x256.png,c2551a03d402ab6512767fc126f2c13e128c94dd,Chairs,32.053,-19.245,-10.816,1372 -1373,_010,32.159,17.937,18.847,0.002,-0.109,0.858571428571,455f9dcb945380e2704e87aef10cebf3fc7c3723,256,V3,455f9dcb945380e2704e87aef10cebf3fc7c3723_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+32.159_ry+17.937_rz+18.847_tx+00.002_ty-00.109_s+00.601_455f9dcb945380e2704e87aef10cebf3fc7c3723_256x256.png,1fabb868084cd8d31fed74a3bb0f185a28753ca6,Chairs,32.159,17.937,18.847,1373 -1374,_010,34.177,-43.973,26.686,0.259,-0.059,0.944285714286,d52caad6335b8a1d67e980581d92b41298ec57dd,256,V3,d52caad6335b8a1d67e980581d92b41298ec57dd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+34.177_ry-43.973_rz+26.686_tx+00.259_ty-00.059_s+00.661_d52caad6335b8a1d67e980581d92b41298ec57dd_256x256.png,f7dce583f2fe9bf90b232ab51e1c827c322d3d3e,Chairs,34.177,-43.973,26.686,1374 -1375,_010,34.736,8.147,-35.923,-0.202,0.532,0.887142857143,70744d31c5db05b8263cea1f6e8e9a61ea0d00d5,256,V3,70744d31c5db05b8263cea1f6e8e9a61ea0d00d5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+34.736_ry+08.147_rz-35.923_tx-00.202_ty+00.532_s+00.621_70744d31c5db05b8263cea1f6e8e9a61ea0d00d5_256x256.png,dae6d22f770bd86dafdce9d0ed721b654234e909,Chairs,34.736,8.147,-35.923,1375 -1376,_010,34.901,22.264,-23.57,0.239,0.577,1.04,621157c64ff5d43cceeda40bf3cb014f60b76a22,256,V3,621157c64ff5d43cceeda40bf3cb014f60b76a22_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+34.901_ry+22.264_rz-23.570_tx+00.239_ty+00.577_s+00.728_621157c64ff5d43cceeda40bf3cb014f60b76a22_256x256.png,6b7a7b554830525654b4024cb9d2acc75a50eb19,Chairs,34.901,22.264,-23.57,1376 -1377,_010,37.192,-23.215,6.62,0.135,0.27,1.14714285714,4997f6f140c2ab47a584542e090951fe1d89b595,256,V3,4997f6f140c2ab47a584542e090951fe1d89b595_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+37.192_ry-23.215_rz+06.620_tx+00.135_ty+00.270_s+00.803_4997f6f140c2ab47a584542e090951fe1d89b595_256x256.png,ff5636d96d736401911d6ffc1c120a57908c1ffa,Chairs,37.192,-23.215,6.62,1377 -1378,_010,39.873,-30.175,43.538,0.048,0.327,0.941428571429,6c39d6d2b9f08ff2e71ff30c99442b3ba1771294,256,V3,6c39d6d2b9f08ff2e71ff30c99442b3ba1771294_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+39.873_ry-30.175_rz+43.538_tx+00.048_ty+00.327_s+00.659_6c39d6d2b9f08ff2e71ff30c99442b3ba1771294_256x256.png,a7b1c5360da3df4b760a40e80df9aac25d5ae29d,Chairs,39.873,-30.175,43.538,1378 -1379,_010,40.787,-32.427,-25.951,-0.055,0.239,1.14285714286,46db653aed2c80ea0345d5b92c20fc1fdf3585ad,256,V3,46db653aed2c80ea0345d5b92c20fc1fdf3585ad_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+40.787_ry-32.427_rz-25.951_tx-00.055_ty+00.239_s+00.800_46db653aed2c80ea0345d5b92c20fc1fdf3585ad_256x256.png,5c7df1bbb1e389db8a53af26415ffce086d206d3,Chairs,40.787,-32.427,-25.951,1379 -1380,_010,41.205,-39.381,-36.175,0.149,0.044,1.28714285714,067f7640506019ffad1b673ba046ce36cec9b838,256,V3,067f7640506019ffad1b673ba046ce36cec9b838_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx+41.205_ry-39.381_rz-36.175_tx+00.149_ty+00.044_s+00.901_067f7640506019ffad1b673ba046ce36cec9b838_256x256.png,8088ad85f2a5edf81582a91fb9a767b63b19eabb,Chairs,41.205,-39.381,-36.175,1380 -1381,_010,-0.664,-26.502,20.193,-0.207,0.555,1.01714285714,fda2cb6e877bb688c1f67d198e85a6300828083a,256,V3,fda2cb6e877bb688c1f67d198e85a6300828083a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-00.664_ry-26.502_rz+20.193_tx-00.207_ty+00.555_s+00.712_fda2cb6e877bb688c1f67d198e85a6300828083a_256x256.png,f078c5f93fe07de6f9be51cf5f819be51ab4d9f5,Chairs,-0.664,-26.502,20.193,1381 -1382,_010,-1.159,-22.305,1.598,0.185,-0.321,1.23,f994a73fe3cfa7681fe1f95b5d555c562bade695,256,V3,f994a73fe3cfa7681fe1f95b5d555c562bade695_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-01.159_ry-22.305_rz+01.598_tx+00.185_ty-00.321_s+00.861_f994a73fe3cfa7681fe1f95b5d555c562bade695_256x256.png,f7434064cc5251d1f314869f877fd74554b226be,Chairs,-1.159,-22.305,1.598,1382 -1383,_010,-2.535,2.415,15.17,-0.116,-0.193,0.828571428571,35b834eabf174dbcb8984efcf819ab5726834409,256,V3,35b834eabf174dbcb8984efcf819ab5726834409_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-02.535_ry+02.415_rz+15.170_tx-00.116_ty-00.193_s+00.580_35b834eabf174dbcb8984efcf819ab5726834409_256x256.png,63889f5983235823d5126fd0903bc8b30ed41a0d,Chairs,-2.535,2.415,15.17,1383 -1384,_010,-3.583,-0.041,-5.598,-0.011,0.107,1.02,723ee61af2f0c5ea4383641ee58a7e12c82624f0,256,V3,723ee61af2f0c5ea4383641ee58a7e12c82624f0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-03.583_ry-00.041_rz-05.598_tx-00.011_ty+00.107_s+00.714_723ee61af2f0c5ea4383641ee58a7e12c82624f0_256x256.png,174d2f462de1500e1ded3713a624ca5d8d9b8f65,Chairs,-3.583,-0.041,-5.598,1384 -1385,_010,-7.988,42.994,-17.689,-0.224,0.271,1.00571428571,1fc55405696701b7673cee0b3649c7cc974ffb53,256,V3,1fc55405696701b7673cee0b3649c7cc974ffb53_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-07.988_ry+42.994_rz-17.689_tx-00.224_ty+00.271_s+00.704_1fc55405696701b7673cee0b3649c7cc974ffb53_256x256.png,a2608f7d073dcc49a38ff2b2c5f9d61f36878c93,Chairs,-7.988,42.994,-17.689,1385 -1386,_010,-14.848,23.938,2.716,0.256,0.26,0.877142857143,fa1abc1dd83079194392c5a06c9fa74914c07896,256,V3,fa1abc1dd83079194392c5a06c9fa74914c07896_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-14.848_ry+23.938_rz+02.716_tx+00.256_ty+00.260_s+00.614_fa1abc1dd83079194392c5a06c9fa74914c07896_256x256.png,f9c7ffdfa1a433256f815c896a12314b67178e89,Chairs,-14.848,23.938,2.716,1386 -1387,_010,-17.56,-1.82,24.446,0.112,-0.195,1.04142857143,9dd9fda0a86507ca5d518c25c03a69a983ec646b,256,V3,9dd9fda0a86507ca5d518c25c03a69a983ec646b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-17.560_ry-01.820_rz+24.446_tx+00.112_ty-00.195_s+00.729_9dd9fda0a86507ca5d518c25c03a69a983ec646b_256x256.png,5a29092de692ce3230ac7717bf6e1809e687fc60,Chairs,-17.56,-1.82,24.446,1387 -1388,_010,-22.228,-16.281,43.259,-0.132,0.327,0.845714285714,08063d12255795f6c3c274d9be9c6292529484c8,256,V3,08063d12255795f6c3c274d9be9c6292529484c8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-22.228_ry-16.281_rz+43.259_tx-00.132_ty+00.327_s+00.592_08063d12255795f6c3c274d9be9c6292529484c8_256x256.png,5a18c8cd1f01924c6b821d888033068f2d10f526,Chairs,-22.228,-16.281,43.259,1388 -1389,_010,-24.266,15.45,-6.018,0.13,0.38,1.20285714286,88a4b3a09911de79a1ed9b25f6b3e878f16965b7,256,V3,88a4b3a09911de79a1ed9b25f6b3e878f16965b7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-24.266_ry+15.450_rz-06.018_tx+00.130_ty+00.380_s+00.842_88a4b3a09911de79a1ed9b25f6b3e878f16965b7_256x256.png,e30ee18271a2329806f08835bbc480c78442f6a4,Chairs,-24.266,15.45,-6.018,1389 -1390,_010,-26.043,28.141,-9.256,-0.242,0.227,0.882857142857,cac49056c511781d08ee84eba0e3f5532b4702af,256,V3,cac49056c511781d08ee84eba0e3f5532b4702af_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-26.043_ry+28.141_rz-09.256_tx-00.242_ty+00.227_s+00.618_cac49056c511781d08ee84eba0e3f5532b4702af_256x256.png,503fa08bca20bdf265cf38bbf867fcfc222af40d,Chairs,-26.043,28.141,-9.256,1390 -1391,_010,-31.84,-1.457,28.867,-0.145,0.353,0.811428571429,ee7ef89b9fe77128d92af961ec9fcad8d372fbf4,256,V3,ee7ef89b9fe77128d92af961ec9fcad8d372fbf4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-31.840_ry-01.457_rz+28.867_tx-00.145_ty+00.353_s+00.568_ee7ef89b9fe77128d92af961ec9fcad8d372fbf4_256x256.png,d20fdfd9fcbb80cd0ccd810659fffdfe12fd29c2,Chairs,-31.84,-1.457,28.867,1391 -1392,_010,-31.89,-20.532,43.715,0.268,0.069,0.941428571429,e141454d2547126e980f5a7e3991de6711e8a0db,256,V3,e141454d2547126e980f5a7e3991de6711e8a0db_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-31.890_ry-20.532_rz+43.715_tx+00.268_ty+00.069_s+00.659_e141454d2547126e980f5a7e3991de6711e8a0db_256x256.png,47a898eec4d4a29f821e914b15ddcf310c4047fc,Chairs,-31.89,-20.532,43.715,1392 -1393,_010,-33.844,0.139,-13.849,0.276,0.308,0.921428571429,29283f07a4d9534bca401ff9b5faaa00e959145d,256,V3,29283f07a4d9534bca401ff9b5faaa00e959145d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-33.844_ry+00.139_rz-13.849_tx+00.276_ty+00.308_s+00.645_29283f07a4d9534bca401ff9b5faaa00e959145d_256x256.png,63fc0920158f6b093d1850ff99a56b036ba16f11,Chairs,-33.844,0.139,-13.849,1393 -1394,_010,-34.815,19.116,30.945,0.068,0.191,0.817142857143,e52bb3edf25bb8483dbd78acc4c324dd263e9aa5,256,V3,e52bb3edf25bb8483dbd78acc4c324dd263e9aa5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-34.815_ry+19.116_rz+30.945_tx+00.068_ty+00.191_s+00.572_e52bb3edf25bb8483dbd78acc4c324dd263e9aa5_256x256.png,bea56cca4a9ebeedc2c4e493dd6ee654cc13d57c,Chairs,-34.815,19.116,30.945,1394 -1395,_010,-39.017,44.571,16.825,0.101,-0.52,0.86,d6d6aa8603d4a5abebc21e26969f043b19b6c54c,256,V3,d6d6aa8603d4a5abebc21e26969f043b19b6c54c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-39.017_ry+44.571_rz+16.825_tx+00.101_ty-00.520_s+00.602_d6d6aa8603d4a5abebc21e26969f043b19b6c54c_256x256.png,e17c25f9d4b9d311a451a9be3570547f07e44bb2,Chairs,-39.017,44.571,16.825,1395 -1396,_010,-39.712,-13.595,-5.471,-0.17,0.417,1.13428571429,5baffb7f688834c1b6af3b27fb5528e0ec2172e3,256,V3,5baffb7f688834c1b6af3b27fb5528e0ec2172e3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-39.712_ry-13.595_rz-05.471_tx-00.170_ty+00.417_s+00.794_5baffb7f688834c1b6af3b27fb5528e0ec2172e3_256x256.png,98ef9956c9e395b198d59071e06c0a8f469603de,Chairs,-39.712,-13.595,-5.471,1396 -1397,_010,-41.14,11.692,-23.765,0.111,-0.208,1.14857142857,6b7510e171421e33edfff6be5813a7ce9678707c,256,V3,6b7510e171421e33edfff6be5813a7ce9678707c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-41.140_ry+11.692_rz-23.765_tx+00.111_ty-00.208_s+00.804_6b7510e171421e33edfff6be5813a7ce9678707c_256x256.png,0998039c000f7ed358185ab030dae8a319eb8dea,Chairs,-41.14,11.692,-23.765,1397 -1398,_010,-42.101,-41.418,18.99,-0.204,-0.159,1.22714285714,cec4adba55aa783a9b1614f2bdf1e5e1e60060fa,256,V3,cec4adba55aa783a9b1614f2bdf1e5e1e60060fa_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-42.101_ry-41.418_rz+18.990_tx-00.204_ty-00.159_s+00.859_cec4adba55aa783a9b1614f2bdf1e5e1e60060fa_256x256.png,eee9e203f6b4cf64226f56f1a14692e92d815b70,Chairs,-42.101,-41.418,18.99,1398 -1399,_010,-44.26,14.16,19.873,0.068,0.267,1.11428571429,8c8b4fabaa9df1c223dddf898e06e5556a5d5925,256,V3,8c8b4fabaa9df1c223dddf898e06e5556a5d5925_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_010_rx-44.260_ry+14.160_rz+19.873_tx+00.068_ty+00.267_s+00.780_8c8b4fabaa9df1c223dddf898e06e5556a5d5925_256x256.png,de32e3f47daaa60cbca95c4cbcfbec1372eb91a0,Chairs,-44.26,14.16,19.873,1399 -1400,_011,0.112,35.027,-41.989,0.234,-0.49,1.03714285714,03dc408a622a65b9ad984771217fffc830dfbea6,256,V3,03dc408a622a65b9ad984771217fffc830dfbea6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+00.112_ry+35.027_rz-41.989_tx+00.234_ty-00.490_s+00.726_03dc408a622a65b9ad984771217fffc830dfbea6_256x256.png,13d9f968ca63cc8f65e80b6abad4b5aacce5b8ee,Chairs,0.112,35.027,-41.989,1400 -1401,_011,0.339,-15.883,-14.345,0.194,0.341,0.931428571429,9a3cfaeb73e9a445c62c8861079336143eac49b6,256,V3,9a3cfaeb73e9a445c62c8861079336143eac49b6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+00.339_ry-15.883_rz-14.345_tx+00.194_ty+00.341_s+00.652_9a3cfaeb73e9a445c62c8861079336143eac49b6_256x256.png,9b02152efad566794cd2be7080375a16b7d54e19,Chairs,0.339,-15.883,-14.345,1401 -1402,_011,0.466,36.644,26.679,-0.264,0.213,1.16571428571,8f5686781231fff360eda0267eac78848d144e7e,256,V3,8f5686781231fff360eda0267eac78848d144e7e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+00.466_ry+36.644_rz+26.679_tx-00.264_ty+00.213_s+00.816_8f5686781231fff360eda0267eac78848d144e7e_256x256.png,4864a88f6c89b493846a2d15eee399d3ac455103,Chairs,0.466,36.644,26.679,1402 -1403,_011,3.069,-11.971,40.085,0.261,0.477,1.17428571429,21aecb18b64c47d33c714073000096a3490674c4,256,V3,21aecb18b64c47d33c714073000096a3490674c4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+03.069_ry-11.971_rz+40.085_tx+00.261_ty+00.477_s+00.822_21aecb18b64c47d33c714073000096a3490674c4_256x256.png,5b520f9a8eff5dc197183e34455876d74cbfbee1,Chairs,3.069,-11.971,40.085,1403 -1404,_011,6.997,13.717,-8.046,-0.238,-0.21,0.787142857143,893ef5508b3d0115afed7fb9b54a0b3928cb5d78,256,V3,893ef5508b3d0115afed7fb9b54a0b3928cb5d78_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+06.997_ry+13.717_rz-08.046_tx-00.238_ty-00.210_s+00.551_893ef5508b3d0115afed7fb9b54a0b3928cb5d78_256x256.png,3bea0d77f056b62b95da8681a6674cb250cc1d09,Chairs,6.997,13.717,-8.046,1404 -1405,_011,10.037,-2.493,34.375,0.241,0.535,0.812857142857,9f84970bc3da0b9eb8ecc38b4f32a9df6ec31ffc,256,V3,9f84970bc3da0b9eb8ecc38b4f32a9df6ec31ffc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+10.037_ry-02.493_rz+34.375_tx+00.241_ty+00.535_s+00.569_9f84970bc3da0b9eb8ecc38b4f32a9df6ec31ffc_256x256.png,c59a097e0994cf281da915811ffead368cd464b6,Chairs,10.037,-2.493,34.375,1405 -1406,_011,16.523,-4.469,6.482,-0.288,0.583,1.07142857143,79b6edf21a20d3719534e84e89e2017f7b30246a,256,V3,79b6edf21a20d3719534e84e89e2017f7b30246a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+16.523_ry-04.469_rz+06.482_tx-00.288_ty+00.583_s+00.750_79b6edf21a20d3719534e84e89e2017f7b30246a_256x256.png,7c832a5cffbde1f30c6b49cf813b0ee37e63efae,Chairs,16.523,-4.469,6.482,1406 -1407,_011,18.23,-11.939,22.941,-0.042,0.461,0.874285714286,190b7bae335a1a5c3f606418bceb4100d5fd27da,256,V3,190b7bae335a1a5c3f606418bceb4100d5fd27da_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+18.230_ry-11.939_rz+22.941_tx-00.042_ty+00.461_s+00.612_190b7bae335a1a5c3f606418bceb4100d5fd27da_256x256.png,fa6ec5406409575da10fd7a572422f737eb0b4e4,Chairs,18.23,-11.939,22.941,1407 -1408,_011,18.86,-42.846,21.484,-0.198,0.288,1.26571428571,8e1c5ed84a1e7b20178424d5d82c8b517f53ae37,256,V3,8e1c5ed84a1e7b20178424d5d82c8b517f53ae37_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+18.860_ry-42.846_rz+21.484_tx-00.198_ty+00.288_s+00.886_8e1c5ed84a1e7b20178424d5d82c8b517f53ae37_256x256.png,3a96e824087b244f903b18a626de827bebe5228f,Chairs,18.86,-42.846,21.484,1408 -1409,_011,26.385,-25.795,5.438,0.154,-0.372,1.14142857143,828850cf3e0695ec1d4d32219904cf5e199210e2,256,V3,828850cf3e0695ec1d4d32219904cf5e199210e2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+26.385_ry-25.795_rz+05.438_tx+00.154_ty-00.372_s+00.799_828850cf3e0695ec1d4d32219904cf5e199210e2_256x256.png,588df44780c5ce3493a9830926f0fc34937276fe,Chairs,26.385,-25.795,5.438,1409 -1410,_011,28.088,-0.115,26.485,-0.299,0.583,0.881428571429,3760302b482c688550a3a3e87ec4d50b9c99855d,256,V3,3760302b482c688550a3a3e87ec4d50b9c99855d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+28.088_ry-00.115_rz+26.485_tx-00.299_ty+00.583_s+00.617_3760302b482c688550a3a3e87ec4d50b9c99855d_256x256.png,ff1e25f3baa3cbdefe9edc473ac263f578ef8d40,Chairs,28.088,-0.115,26.485,1410 -1411,_011,31.005,11.748,6.924,-0.145,-0.544,1.26285714286,cb40eea4d91e603380e46852251ad642dfd0ba75,256,V3,cb40eea4d91e603380e46852251ad642dfd0ba75_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+31.005_ry+11.748_rz+06.924_tx-00.145_ty-00.544_s+00.884_cb40eea4d91e603380e46852251ad642dfd0ba75_256x256.png,165ba58153e50f39949e9b5acbd4b599e83d0cf4,Chairs,31.005,11.748,6.924,1411 -1412,_011,36.235,39.332,11.44,0.183,0.565,1.08285714286,d97fbc54b35e756a570b29b864fab21b936b9e1a,256,V3,d97fbc54b35e756a570b29b864fab21b936b9e1a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+36.235_ry+39.332_rz+11.440_tx+00.183_ty+00.565_s+00.758_d97fbc54b35e756a570b29b864fab21b936b9e1a_256x256.png,36aa2e70aebc44d56a066743fca74205e82ee1dc,Chairs,36.235,39.332,11.44,1412 -1413,_011,37.766,-18.51,15.517,0.123,0.225,0.831428571429,f2121217dc351171e3cd06e4da5166f56d1b74c8,256,V3,f2121217dc351171e3cd06e4da5166f56d1b74c8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+37.766_ry-18.510_rz+15.517_tx+00.123_ty+00.225_s+00.582_f2121217dc351171e3cd06e4da5166f56d1b74c8_256x256.png,e8835ba8f009f1ec62210ed65223e126ed5fe566,Chairs,37.766,-18.51,15.517,1413 -1414,_011,39.309,-18.173,-1.459,0.204,0.253,0.987142857143,4d1f2d68c420c00ff90fd1483aa35d798d6a2188,256,V3,4d1f2d68c420c00ff90fd1483aa35d798d6a2188_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+39.309_ry-18.173_rz-01.459_tx+00.204_ty+00.253_s+00.691_4d1f2d68c420c00ff90fd1483aa35d798d6a2188_256x256.png,11b5afff22b702d300e74a27064872ac3c45fd9c,Chairs,39.309,-18.173,-1.459,1414 -1415,_011,39.913,-6.104,-31.278,-0.185,0.303,1.21,d3db7f335817bafd13b4cf5260b64eb4efa10c37,256,V3,d3db7f335817bafd13b4cf5260b64eb4efa10c37_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+39.913_ry-06.104_rz-31.278_tx-00.185_ty+00.303_s+00.847_d3db7f335817bafd13b4cf5260b64eb4efa10c37_256x256.png,d61523843123b0b692aad574513e536f4097d6d6,Chairs,39.913,-6.104,-31.278,1415 -1416,_011,43.481,-20.529,-43.074,0.138,0.544,0.827142857143,f4bf116aa76038d252a7faf3e976950802aeddb0,256,V3,f4bf116aa76038d252a7faf3e976950802aeddb0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+43.481_ry-20.529_rz-43.074_tx+00.138_ty+00.544_s+00.579_f4bf116aa76038d252a7faf3e976950802aeddb0_256x256.png,2f2532531e5f0079e3af120bc87095a7487bc0b0,Chairs,43.481,-20.529,-43.074,1416 -1417,_011,44.07,27.542,40.533,0.249,-0.515,0.867142857143,998862e6e8937e797dfd7c44c84018f8f194b6c8,256,V3,998862e6e8937e797dfd7c44c84018f8f194b6c8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx+44.070_ry+27.542_rz+40.533_tx+00.249_ty-00.515_s+00.607_998862e6e8937e797dfd7c44c84018f8f194b6c8_256x256.png,7399464458db96f5dbab8b7a17be07844cbd1a3c,Chairs,44.07,27.542,40.533,1417 -1418,_011,-0.334,-12.109,-10.294,-0.233,0.104,0.961428571429,3c3af88971c4063e1cdae89a846b49abaaff1ded,256,V3,3c3af88971c4063e1cdae89a846b49abaaff1ded_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-00.334_ry-12.109_rz-10.294_tx-00.233_ty+00.104_s+00.673_3c3af88971c4063e1cdae89a846b49abaaff1ded_256x256.png,9bd28652b282108ed652e4bde9cfd4593184ddf6,Chairs,-0.334,-12.109,-10.294,1418 -1419,_011,-0.906,-22.863,1.231,0.206,0.576,0.935714285714,984a5130b11c59c9c1ac9a37fdb9fed22bc10dd7,256,V3,984a5130b11c59c9c1ac9a37fdb9fed22bc10dd7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-00.906_ry-22.863_rz+01.231_tx+00.206_ty+00.576_s+00.655_984a5130b11c59c9c1ac9a37fdb9fed22bc10dd7_256x256.png,5eaef9fb2fa650f6cb8a3f34fd02e76f9901d691,Chairs,-0.906,-22.863,1.231,1419 -1420,_011,-2.998,7.757,-21.584,0.165,0.292,0.925714285714,fc00f5489216dc7805530851d13f2209efe4ca21,256,V3,fc00f5489216dc7805530851d13f2209efe4ca21_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-02.998_ry+07.757_rz-21.584_tx+00.165_ty+00.292_s+00.648_fc00f5489216dc7805530851d13f2209efe4ca21_256x256.png,8d57a417ed6901a5b327040b279cfc005a1a2e2d,Chairs,-2.998,7.757,-21.584,1420 -1421,_011,-4.233,15.221,-22.951,0.173,0.456,1.15,e820d78fb3e87e550e774e755800efbc2460baea,256,V3,e820d78fb3e87e550e774e755800efbc2460baea_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-04.233_ry+15.221_rz-22.951_tx+00.173_ty+00.456_s+00.805_e820d78fb3e87e550e774e755800efbc2460baea_256x256.png,1adb089ad446b99d64c088e242c3d1a7e638322a,Chairs,-4.233,15.221,-22.951,1421 -1422,_011,-5.349,9.828,-37.44,-0.125,0.206,1.26285714286,76500afa627165a856198bacaa673ef403cfa614,256,V3,76500afa627165a856198bacaa673ef403cfa614_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-05.349_ry+09.828_rz-37.440_tx-00.125_ty+00.206_s+00.884_76500afa627165a856198bacaa673ef403cfa614_256x256.png,9a087481affe197df0c7c44019fae3498b60c1b8,Chairs,-5.349,9.828,-37.44,1422 -1423,_011,-5.381,-31.016,22.612,-0.295,-0.447,0.787142857143,b2f1bc07bbbfcc512417602baf81212a7ee6a83d,256,V3,b2f1bc07bbbfcc512417602baf81212a7ee6a83d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-05.381_ry-31.016_rz+22.612_tx-00.295_ty-00.447_s+00.551_b2f1bc07bbbfcc512417602baf81212a7ee6a83d_256x256.png,1baeb092108331c68b0ca97bd8d1c82255b19986,Chairs,-5.381,-31.016,22.612,1423 -1424,_011,-5.917,-11.06,-25.538,0.162,-0.231,0.938571428571,7ee14e601158be8296ba23f915b2e8d558d3447a,256,V3,7ee14e601158be8296ba23f915b2e8d558d3447a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-05.917_ry-11.060_rz-25.538_tx+00.162_ty-00.231_s+00.657_7ee14e601158be8296ba23f915b2e8d558d3447a_256x256.png,3dcb46cf6f3716faa671f4f5467c0a69d29a948a,Chairs,-5.917,-11.06,-25.538,1424 -1425,_011,-5.959,32.874,-20.784,0.146,-0.297,1.29857142857,00daa196fbb4420fca26f5694f7968b1471cb1f8,256,V3,00daa196fbb4420fca26f5694f7968b1471cb1f8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-05.959_ry+32.874_rz-20.784_tx+00.146_ty-00.297_s+00.909_00daa196fbb4420fca26f5694f7968b1471cb1f8_256x256.png,0aded53579b14f307e81709b2c66e359d954681a,Chairs,-5.959,32.874,-20.784,1425 -1426,_011,-7.529,28.235,34.521,0.09,-0.098,0.964285714286,a72f188cceb202fbf93ec2e9d1831c0b785a0db0,256,V3,a72f188cceb202fbf93ec2e9d1831c0b785a0db0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-07.529_ry+28.235_rz+34.521_tx+00.090_ty-00.098_s+00.675_a72f188cceb202fbf93ec2e9d1831c0b785a0db0_256x256.png,8817940ab2bc8dba9faf6dfc01d71afbd9832c47,Chairs,-7.529,28.235,34.521,1426 -1427,_011,-7.629,-18.002,10.134,0.045,0.256,1.08428571429,3e7df394d2c5ce2471f40c0317f5ea48549938a7,256,V3,3e7df394d2c5ce2471f40c0317f5ea48549938a7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-07.629_ry-18.002_rz+10.134_tx+00.045_ty+00.256_s+00.759_3e7df394d2c5ce2471f40c0317f5ea48549938a7_256x256.png,10fc308639db7492ba92eaaab8d3fb0447b4054e,Chairs,-7.629,-18.002,10.134,1427 -1428,_011,-11.44,-43.708,-23.023,0.031,0.136,1.13714285714,5add875a8f9d93fe4088773ab9d1d2056c0f167c,256,V3,5add875a8f9d93fe4088773ab9d1d2056c0f167c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-11.440_ry-43.708_rz-23.023_tx+00.031_ty+00.136_s+00.796_5add875a8f9d93fe4088773ab9d1d2056c0f167c_256x256.png,12f3f525d1b4955bdb16042b1707c51eeb695726,Chairs,-11.44,-43.708,-23.023,1428 -1429,_011,-12.081,35.193,-22.145,-0.274,-0.274,0.945714285714,5139391218ee07bf361c4b0303d60775d7f5017f,256,V3,5139391218ee07bf361c4b0303d60775d7f5017f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-12.081_ry+35.193_rz-22.145_tx-00.274_ty-00.274_s+00.662_5139391218ee07bf361c4b0303d60775d7f5017f_256x256.png,ed61a85435344a86782c7e538a848225bfdf6175,Chairs,-12.081,35.193,-22.145,1429 -1430,_011,-15.243,-14.667,28.982,-0.218,0.255,0.862857142857,fc966ce1db2b12b82d29ae9cad9c40f89ec50cfc,256,V3,fc966ce1db2b12b82d29ae9cad9c40f89ec50cfc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-15.243_ry-14.667_rz+28.982_tx-00.218_ty+00.255_s+00.604_fc966ce1db2b12b82d29ae9cad9c40f89ec50cfc_256x256.png,af840ebc9fe0434c7a11e8f8fed5b8000e2f2695,Chairs,-15.243,-14.667,28.982,1430 -1431,_011,-16.617,-14.32,12.188,0.059,-0.563,1.20142857143,84e8bad860da27775d5c9d7a5e262c5fda3ce435,256,V3,84e8bad860da27775d5c9d7a5e262c5fda3ce435_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-16.617_ry-14.320_rz+12.188_tx+00.059_ty-00.563_s+00.841_84e8bad860da27775d5c9d7a5e262c5fda3ce435_256x256.png,c0bf6bb321a20919fe75f3a397eb51696be1575e,Chairs,-16.617,-14.32,12.188,1431 -1432,_011,-16.705,-22.893,-12.961,-0.204,-0.496,1.09428571429,08e669ddc7070ad3d5c166646873dfaf5bea3e60,256,V3,08e669ddc7070ad3d5c166646873dfaf5bea3e60_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-16.705_ry-22.893_rz-12.961_tx-00.204_ty-00.496_s+00.766_08e669ddc7070ad3d5c166646873dfaf5bea3e60_256x256.png,d1e7d4ae7597949b6ab1a03eea0d23116ed4d9c6,Chairs,-16.705,-22.893,-12.961,1432 -1433,_011,-21.728,25.698,-30.709,-0.246,0.164,1.24,73c7832732b2a635cefc10e965e6443d557e0d64,256,V3,73c7832732b2a635cefc10e965e6443d557e0d64_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-21.728_ry+25.698_rz-30.709_tx-00.246_ty+00.164_s+00.868_73c7832732b2a635cefc10e965e6443d557e0d64_256x256.png,f88c8ff93b6943b5a76c053e0822cdf488cf6edc,Chairs,-21.728,25.698,-30.709,1433 -1434,_011,-22.085,40.744,-15.593,-0.238,-0.337,0.975714285714,6917a1092e7361ec77df87c8c065d8fd21f942bc,256,V3,6917a1092e7361ec77df87c8c065d8fd21f942bc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-22.085_ry+40.744_rz-15.593_tx-00.238_ty-00.337_s+00.683_6917a1092e7361ec77df87c8c065d8fd21f942bc_256x256.png,523714e70dbedffbca3bc11e98a3f2d4dfd675ea,Chairs,-22.085,40.744,-15.593,1434 -1435,_011,-22.303,-19.693,39.873,0.184,0.463,0.841428571429,c2938c1df3fa8542979de8de900ee47633f23c44,256,V3,c2938c1df3fa8542979de8de900ee47633f23c44_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-22.303_ry-19.693_rz+39.873_tx+00.184_ty+00.463_s+00.589_c2938c1df3fa8542979de8de900ee47633f23c44_256x256.png,216f5eec6dd0c9e2249a69e85ffdf5a687387ef5,Chairs,-22.303,-19.693,39.873,1435 -1436,_011,-24.23,-12.151,25.733,-0.209,-0.559,0.808571428571,2c5f6f26083a52e6e156bdaead57b94830239ec9,256,V3,2c5f6f26083a52e6e156bdaead57b94830239ec9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-24.230_ry-12.151_rz+25.733_tx-00.209_ty-00.559_s+00.566_2c5f6f26083a52e6e156bdaead57b94830239ec9_256x256.png,8230172541acd5dc1b90f9c6fcfc290a58d7a704,Chairs,-24.23,-12.151,25.733,1436 -1437,_011,-28.16,-14.084,36.103,-0.206,0.595,1.19142857143,119094b11cf4f1208e7b95168955bb7089b64c44,256,V3,119094b11cf4f1208e7b95168955bb7089b64c44_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-28.160_ry-14.084_rz+36.103_tx-00.206_ty+00.595_s+00.834_119094b11cf4f1208e7b95168955bb7089b64c44_256x256.png,0bd6e1f2f0e974865aadd3014e3984cff19e1a62,Chairs,-28.16,-14.084,36.103,1437 -1438,_011,-30.127,-29.213,44.17,0.008,0.073,1.11,b90b0d95ce4488673ecc01645a329d4040eafc8b,256,V3,b90b0d95ce4488673ecc01645a329d4040eafc8b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-30.127_ry-29.213_rz+44.170_tx+00.008_ty+00.073_s+00.777_b90b0d95ce4488673ecc01645a329d4040eafc8b_256x256.png,9a919cb846f828cce1535616dbd9bc122f9c3c90,Chairs,-30.127,-29.213,44.17,1438 -1439,_011,-36.013,-19.505,26.278,0.084,0.574,0.777142857143,63ca45197b3adbdc1b857636d7f3e191ce3ca3ff,256,V3,63ca45197b3adbdc1b857636d7f3e191ce3ca3ff_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_011_rx-36.013_ry-19.505_rz+26.278_tx+00.084_ty+00.574_s+00.544_63ca45197b3adbdc1b857636d7f3e191ce3ca3ff_256x256.png,49ce2adf4885ff72c6f01f80b9413be89b87790f,Chairs,-36.013,-19.505,26.278,1439 -1440,_014,1.625,20.037,29.748,0.243,0.459,0.951428571429,d76b18ca4843c9646198c7fbe7b525652b0c0945,256,V3,d76b18ca4843c9646198c7fbe7b525652b0c0945_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+01.625_ry+20.037_rz+29.748_tx+00.243_ty+00.459_s+00.666_d76b18ca4843c9646198c7fbe7b525652b0c0945_256x256.png,6b9a242494970c3557ced36d4fe364195a179b2a,Chairs,1.625,20.037,29.748,1440 -1441,_014,3.569,-4.665,12.029,-0.142,0.594,0.952857142857,b3a1f0879f658a970e33a2d75eb9045f6261ac69,256,V3,b3a1f0879f658a970e33a2d75eb9045f6261ac69_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+03.569_ry-04.665_rz+12.029_tx-00.142_ty+00.594_s+00.667_b3a1f0879f658a970e33a2d75eb9045f6261ac69_256x256.png,fbee4a5798026344eaf3189b3815b0ea217457f0,Chairs,3.569,-4.665,12.029,1441 -1442,_014,4.511,26.356,-38.241,-0.151,-0.419,0.877142857143,76f15caec77df24766399b0430cd4c37f3293297,256,V3,76f15caec77df24766399b0430cd4c37f3293297_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+04.511_ry+26.356_rz-38.241_tx-00.151_ty-00.419_s+00.614_76f15caec77df24766399b0430cd4c37f3293297_256x256.png,97641ae4d0f5f95d3ae0c59e46d4dabfde4243e8,Chairs,4.511,26.356,-38.241,1442 -1443,_014,6.961,-9.33,-31.158,0.216,-0.54,0.841428571429,303a8fd44426f2064105e91b6e790f606d60080d,256,V3,303a8fd44426f2064105e91b6e790f606d60080d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+06.961_ry-09.330_rz-31.158_tx+00.216_ty-00.540_s+00.589_303a8fd44426f2064105e91b6e790f606d60080d_256x256.png,9211b35a979d5a8979b4b72bfcf93090fda03cc5,Chairs,6.961,-9.33,-31.158,1443 -1444,_014,6.994,-4.341,4.006,0.137,-0.583,1.22,c9e5b15a5b48d8ecb64c6ee38c831a5971976227,256,V3,c9e5b15a5b48d8ecb64c6ee38c831a5971976227_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+06.994_ry-04.341_rz+04.006_tx+00.137_ty-00.583_s+00.854_c9e5b15a5b48d8ecb64c6ee38c831a5971976227_256x256.png,f65ac63eb6a63ef8c611254b9050f6079c93ab5c,Chairs,6.994,-4.341,4.006,1444 -1445,_014,8.288,22.133,29.312,-0.253,-0.272,0.862857142857,38e5121e132969904299e4402683b15670b1d1c5,256,V3,38e5121e132969904299e4402683b15670b1d1c5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+08.288_ry+22.133_rz+29.312_tx-00.253_ty-00.272_s+00.604_38e5121e132969904299e4402683b15670b1d1c5_256x256.png,7a519cdf92b3a017ae09aa87274cc50a46fad00c,Chairs,8.288,22.133,29.312,1445 -1446,_014,9.259,-6.218,-3.827,-0.272,-0.163,1.12142857143,78c379dfaeb34fc15dde9c80e81dddc27c8bfb63,256,V3,78c379dfaeb34fc15dde9c80e81dddc27c8bfb63_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+09.259_ry-06.218_rz-03.827_tx-00.272_ty-00.163_s+00.785_78c379dfaeb34fc15dde9c80e81dddc27c8bfb63_256x256.png,c6c83005b2d6826e5361871a9b1965c3da0e8eb3,Chairs,9.259,-6.218,-3.827,1446 -1447,_014,9.895,15.82,-27.045,0.031,-0.303,0.961428571429,0d944a5c935dbb8a926303060563f571bd96d13d,256,V3,0d944a5c935dbb8a926303060563f571bd96d13d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+09.895_ry+15.820_rz-27.045_tx+00.031_ty-00.303_s+00.673_0d944a5c935dbb8a926303060563f571bd96d13d_256x256.png,c9c7c9158627ed437cf8dd8bff0d2852a9eaf3d4,Chairs,9.895,15.82,-27.045,1447 -1448,_014,10.189,-2.017,14.463,0.106,0.401,0.93,a7cb8a7270f6898f91ac3c08676fdf0eb9a587ec,256,V3,a7cb8a7270f6898f91ac3c08676fdf0eb9a587ec_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+10.189_ry-02.017_rz+14.463_tx+00.106_ty+00.401_s+00.651_a7cb8a7270f6898f91ac3c08676fdf0eb9a587ec_256x256.png,eb8253505ec3056e18c6f893a2e2bf8cdb6727f7,Chairs,10.189,-2.017,14.463,1448 -1449,_014,19.027,19.001,-6.152,0.012,0.257,1.29285714286,db3cbafc9be2ec451702dbe598390bf19936c09c,256,V3,db3cbafc9be2ec451702dbe598390bf19936c09c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+19.027_ry+19.001_rz-06.152_tx+00.012_ty+00.257_s+00.905_db3cbafc9be2ec451702dbe598390bf19936c09c_256x256.png,44ec1e8ca05787a2aa955016367d038dc924f66b,Chairs,19.027,19.001,-6.152,1449 -1450,_014,19.851,43.254,20.887,-0.26,0.567,1.06857142857,6ace5a71630b9531e891b4e8d425251fbc668bba,256,V3,6ace5a71630b9531e891b4e8d425251fbc668bba_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+19.851_ry+43.254_rz+20.887_tx-00.260_ty+00.567_s+00.748_6ace5a71630b9531e891b4e8d425251fbc668bba_256x256.png,83f286b0a691d1870aad4251dcf3e8166c7b407d,Chairs,19.851,43.254,20.887,1450 -1451,_014,22.143,-27.381,-36.641,-0.201,0.034,0.981428571429,0cfb3a5851f73d3832c809e7464186c93f45d7fa,256,V3,0cfb3a5851f73d3832c809e7464186c93f45d7fa_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+22.143_ry-27.381_rz-36.641_tx-00.201_ty+00.034_s+00.687_0cfb3a5851f73d3832c809e7464186c93f45d7fa_256x256.png,67a529a890994764632eca29355962aa510fd097,Chairs,22.143,-27.381,-36.641,1451 -1452,_014,27.034,-13.718,5.33,0.154,-0.492,1.15857142857,8d6ebd2ea133a76d991a1ac27458402ec83c2410,256,V3,8d6ebd2ea133a76d991a1ac27458402ec83c2410_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+27.034_ry-13.718_rz+05.330_tx+00.154_ty-00.492_s+00.811_8d6ebd2ea133a76d991a1ac27458402ec83c2410_256x256.png,f01d1db9dcbba74c376f97cb7e467545574c0833,Chairs,27.034,-13.718,5.33,1452 -1453,_014,27.332,-14.255,28.802,-0.293,-0.018,1.06285714286,7d0eeef47ba7dcc9eea20b1bdc744f0f5052da99,256,V3,7d0eeef47ba7dcc9eea20b1bdc744f0f5052da99_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+27.332_ry-14.255_rz+28.802_tx-00.293_ty-00.018_s+00.744_7d0eeef47ba7dcc9eea20b1bdc744f0f5052da99_256x256.png,3d24581d1d84c0c3115141342a23063825ed6eba,Chairs,27.332,-14.255,28.802,1453 -1454,_014,27.775,17.81,-28.551,-0.022,-0.186,1.18428571429,f2be17b6bc73f38f6c59c2c14e552fd752445d67,256,V3,f2be17b6bc73f38f6c59c2c14e552fd752445d67_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+27.775_ry+17.810_rz-28.551_tx-00.022_ty-00.186_s+00.829_f2be17b6bc73f38f6c59c2c14e552fd752445d67_256x256.png,a7c52d731672bc9d1b7770c4e06dc4cf4520c0ed,Chairs,27.775,17.81,-28.551,1454 -1455,_014,28.183,3.432,34.803,0.203,0.191,0.88,52314c5abb6879f841345919f47d38f2b0eeea16,256,V3,52314c5abb6879f841345919f47d38f2b0eeea16_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+28.183_ry+03.432_rz+34.803_tx+00.203_ty+00.191_s+00.616_52314c5abb6879f841345919f47d38f2b0eeea16_256x256.png,7e4a17e5cd9a0ca57009739ef992deb9d5a8be15,Chairs,28.183,3.432,34.803,1455 -1456,_014,30.71,-40.004,-37.141,0.106,-0.321,0.907142857143,462e95337f49869a363056a5df4b1cf9c438cf3a,256,V3,462e95337f49869a363056a5df4b1cf9c438cf3a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+30.710_ry-40.004_rz-37.141_tx+00.106_ty-00.321_s+00.635_462e95337f49869a363056a5df4b1cf9c438cf3a_256x256.png,4b1fdb3fe0d8bba55d1e99de2e17625522b33f29,Chairs,30.71,-40.004,-37.141,1456 -1457,_014,33.726,8.704,-34.747,-0.221,0.441,1.29571428571,74cbfbacb518c74d5defeb0d921697f47b82aa09,256,V3,74cbfbacb518c74d5defeb0d921697f47b82aa09_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+33.726_ry+08.704_rz-34.747_tx-00.221_ty+00.441_s+00.907_74cbfbacb518c74d5defeb0d921697f47b82aa09_256x256.png,4b39bbd1f4765c553c81c15896291a985c8c43b3,Chairs,33.726,8.704,-34.747,1457 -1458,_014,35.98,-42.083,-37.558,0.014,0.502,0.905714285714,2bc3f4df16ae23ed9ad589d58ba9ae8b1eaf0c2f,256,V3,2bc3f4df16ae23ed9ad589d58ba9ae8b1eaf0c2f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+35.980_ry-42.083_rz-37.558_tx+00.014_ty+00.502_s+00.634_2bc3f4df16ae23ed9ad589d58ba9ae8b1eaf0c2f_256x256.png,7b0114d63d3f3de2e25eea4a048a57fd292275e7,Chairs,35.98,-42.083,-37.558,1458 -1459,_014,39.111,-10.42,40.714,-0.261,0.559,0.854285714286,1be5f5ebeb87759a024d1614fb880e237e81a324,256,V3,1be5f5ebeb87759a024d1614fb880e237e81a324_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+39.111_ry-10.420_rz+40.714_tx-00.261_ty+00.559_s+00.598_1be5f5ebeb87759a024d1614fb880e237e81a324_256x256.png,75b1eff996f33911d8513dcb9845b543ac38a0e0,Chairs,39.111,-10.42,40.714,1459 -1460,_014,41.874,-3.02,-23.008,-0.042,0.118,1.01428571429,928c78663e63a045a50b9a0fb7106b4ba73505af,256,V3,928c78663e63a045a50b9a0fb7106b4ba73505af_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+41.874_ry-03.020_rz-23.008_tx-00.042_ty+00.118_s+00.710_928c78663e63a045a50b9a0fb7106b4ba73505af_256x256.png,8beda5a5b9680769825e1a4aef4583818833f27b,Chairs,41.874,-3.02,-23.008,1460 -1461,_014,43.788,20.788,-17.133,-0.046,-0.385,1.02857142857,e09ddf452c4cf4f28d45ff712a7ce2bc75edb8ab,256,V3,e09ddf452c4cf4f28d45ff712a7ce2bc75edb8ab_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+43.788_ry+20.788_rz-17.133_tx-00.046_ty-00.385_s+00.720_e09ddf452c4cf4f28d45ff712a7ce2bc75edb8ab_256x256.png,bfed84a2aee44fab1ee7d39e8d1bed49ec54aa3e,Chairs,43.788,20.788,-17.133,1461 -1462,_014,44.862,-11.783,8.534,-0.122,-0.074,1.29428571429,ddc82cd6b7752b5b4ef901ae89db0f442220fc79,256,V3,ddc82cd6b7752b5b4ef901ae89db0f442220fc79_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx+44.862_ry-11.783_rz+08.534_tx-00.122_ty-00.074_s+00.906_ddc82cd6b7752b5b4ef901ae89db0f442220fc79_256x256.png,e2d135389ed27f61053ccfc01e12630357a8aa51,Chairs,44.862,-11.783,8.534,1462 -1463,_014,-14.071,10.266,5.335,0.051,0.354,1.17142857143,2ec278ce2a58bb226eebd35a824f79d3da78153a,256,V3,2ec278ce2a58bb226eebd35a824f79d3da78153a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-14.071_ry+10.266_rz+05.335_tx+00.051_ty+00.354_s+00.820_2ec278ce2a58bb226eebd35a824f79d3da78153a_256x256.png,3fea0808bc5ad42f4f90aca6d9d01d6b1661c8ba,Chairs,-14.071,10.266,5.335,1463 -1464,_014,-15.724,-42.059,41.737,0.041,-0.437,1.27428571429,9341a2292908ff174b67368d8374b276a87ce802,256,V3,9341a2292908ff174b67368d8374b276a87ce802_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-15.724_ry-42.059_rz+41.737_tx+00.041_ty-00.437_s+00.892_9341a2292908ff174b67368d8374b276a87ce802_256x256.png,0fc114076e7d539572ac0834d25861dea623d3b9,Chairs,-15.724,-42.059,41.737,1464 -1465,_014,-16.043,36.682,22.817,-0.165,-0.237,1.25857142857,53858c4ec7fdb09883b48c1f2b69a294519519b3,256,V3,53858c4ec7fdb09883b48c1f2b69a294519519b3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-16.043_ry+36.682_rz+22.817_tx-00.165_ty-00.237_s+00.881_53858c4ec7fdb09883b48c1f2b69a294519519b3_256x256.png,60638a53945b2481d42b0dc28cb2613238d1665a,Chairs,-16.043,36.682,22.817,1465 -1466,_014,-17.316,-20.52,-44.265,0.067,-0.372,1.24285714286,c47e12bd69ed6dd40a3b8153ec4e41ba2f6bed46,256,V3,c47e12bd69ed6dd40a3b8153ec4e41ba2f6bed46_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-17.316_ry-20.520_rz-44.265_tx+00.067_ty-00.372_s+00.870_c47e12bd69ed6dd40a3b8153ec4e41ba2f6bed46_256x256.png,7a2c01f1f41421d0593b283408949affc3e2bacc,Chairs,-17.316,-20.52,-44.265,1466 -1467,_014,-17.747,-39.912,13.637,-0.054,-0.285,0.877142857143,3ddf0000247914568e3ea1507399720294042f8f,256,V3,3ddf0000247914568e3ea1507399720294042f8f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-17.747_ry-39.912_rz+13.637_tx-00.054_ty-00.285_s+00.614_3ddf0000247914568e3ea1507399720294042f8f_256x256.png,3165715fefb79f56f0db25fa509fe4673ad10c9d,Chairs,-17.747,-39.912,13.637,1467 -1468,_014,-19.032,-40.961,26.511,0.234,-0.064,1.08428571429,96d61701b1e675c1e44b33f9d1f12b6a9a1a6dbb,256,V3,96d61701b1e675c1e44b33f9d1f12b6a9a1a6dbb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-19.032_ry-40.961_rz+26.511_tx+00.234_ty-00.064_s+00.759_96d61701b1e675c1e44b33f9d1f12b6a9a1a6dbb_256x256.png,3ca722d16116e5bcc00515e1440e6f4aec40cb6f,Chairs,-19.032,-40.961,26.511,1468 -1469,_014,-24.265,26.675,-41.382,0.159,0.258,1.21428571429,2d3f73d7ee9961ba84507c77a494a0ad5ac81fd7,256,V3,2d3f73d7ee9961ba84507c77a494a0ad5ac81fd7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-24.265_ry+26.675_rz-41.382_tx+00.159_ty+00.258_s+00.850_2d3f73d7ee9961ba84507c77a494a0ad5ac81fd7_256x256.png,1f4dcbaea06ff7c9ae3a34a8a744842b38fce0d7,Chairs,-24.265,26.675,-41.382,1469 -1470,_014,-27.309,32.24,22.853,-0.222,0.548,0.911428571429,c6f2f3a8427bedd10e72aac323da8af9becf4dfb,256,V3,c6f2f3a8427bedd10e72aac323da8af9becf4dfb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-27.309_ry+32.240_rz+22.853_tx-00.222_ty+00.548_s+00.638_c6f2f3a8427bedd10e72aac323da8af9becf4dfb_256x256.png,5c6991ab1e435498f0b5b43cb82c6743342c91d2,Chairs,-27.309,32.24,22.853,1470 -1471,_014,-30.084,12.011,8.387,-0.293,-0.313,1.15,30badb926233fd0ff4d00a274c77543ab98ac0d3,256,V3,30badb926233fd0ff4d00a274c77543ab98ac0d3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-30.084_ry+12.011_rz+08.387_tx-00.293_ty-00.313_s+00.805_30badb926233fd0ff4d00a274c77543ab98ac0d3_256x256.png,55aa30ad77130e2a15d3c8305fc9532b6c66ba62,Chairs,-30.084,12.011,8.387,1471 -1472,_014,-32.323,-30.006,-44.77,0.258,0.219,1.28857142857,c871914ff0033664b448480c823058ffc61cf5d3,256,V3,c871914ff0033664b448480c823058ffc61cf5d3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-32.323_ry-30.006_rz-44.770_tx+00.258_ty+00.219_s+00.902_c871914ff0033664b448480c823058ffc61cf5d3_256x256.png,1926027cf7e65769ef416f30357300486677d81f,Chairs,-32.323,-30.006,-44.77,1472 -1473,_014,-34.734,-7.388,28.177,-0.295,0.095,1.29857142857,8ea3eedcc172ab450eae312da0a103919332f169,256,V3,8ea3eedcc172ab450eae312da0a103919332f169_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-34.734_ry-07.388_rz+28.177_tx-00.295_ty+00.095_s+00.909_8ea3eedcc172ab450eae312da0a103919332f169_256x256.png,54533a8365358248e4123d753c85bbead1e99cbf,Chairs,-34.734,-7.388,28.177,1473 -1474,_014,-36.906,31.699,-28.015,-0.142,-0.19,1.28571428571,0a602ec80fabb4c8ddb82c2898259d6e86f21075,256,V3,0a602ec80fabb4c8ddb82c2898259d6e86f21075_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-36.906_ry+31.699_rz-28.015_tx-00.142_ty-00.190_s+00.900_0a602ec80fabb4c8ddb82c2898259d6e86f21075_256x256.png,f4603b58318b9e573e3b2c00b8371b482db89cf5,Chairs,-36.906,31.699,-28.015,1474 -1475,_014,-37.206,-9.167,2.295,-0.021,-0.299,0.817142857143,05e047d5c97f390ffcded5988c1226c4c75c8efa,256,V3,05e047d5c97f390ffcded5988c1226c4c75c8efa_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-37.206_ry-09.167_rz+02.295_tx-00.021_ty-00.299_s+00.572_05e047d5c97f390ffcded5988c1226c4c75c8efa_256x256.png,e9214a8fe4ec47a1c49084c12d3a36f31ab9f4c7,Chairs,-37.206,-9.167,2.295,1475 -1476,_014,-40.53,30.249,5.07,-0.217,0.135,1.20142857143,05391aefea027a1980dd2489b0497cd8fc51f823,256,V3,05391aefea027a1980dd2489b0497cd8fc51f823_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-40.530_ry+30.249_rz+05.070_tx-00.217_ty+00.135_s+00.841_05391aefea027a1980dd2489b0497cd8fc51f823_256x256.png,dbbc8844cb8516b6ca76efa0c7ff7484a5a5fa7b,Chairs,-40.53,30.249,5.07,1476 -1477,_014,-41.592,38.922,1.078,-0.125,0.274,0.782857142857,ca464997730d4746acd22f22c50f26b43f84418b,256,V3,ca464997730d4746acd22f22c50f26b43f84418b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-41.592_ry+38.922_rz+01.078_tx-00.125_ty+00.274_s+00.548_ca464997730d4746acd22f22c50f26b43f84418b_256x256.png,cf3bedef0f08ae15f8273371c8e6679cd2dad9a4,Chairs,-41.592,38.922,1.078,1477 -1478,_014,-44.44,43.043,-41.456,0.188,-0.107,1.21142857143,1c9ea65b274bb6b62ac78efecf11c6f7dc6969b9,256,V3,1c9ea65b274bb6b62ac78efecf11c6f7dc6969b9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-44.440_ry+43.043_rz-41.456_tx+00.188_ty-00.107_s+00.848_1c9ea65b274bb6b62ac78efecf11c6f7dc6969b9_256x256.png,48d197fcd70ad80fdf4e20ee898b450c3c477069,Chairs,-44.44,43.043,-41.456,1478 -1479,_014,-44.675,-39.959,13.669,-0.18,-0.587,0.91,c14d9a53ac45a386b8d13eefb8a5540775d5e90e,256,V3,c14d9a53ac45a386b8d13eefb8a5540775d5e90e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_014_rx-44.675_ry-39.959_rz+13.669_tx-00.180_ty-00.587_s+00.637_c14d9a53ac45a386b8d13eefb8a5540775d5e90e_256x256.png,4d534c5e88653f8c6307e9ade1dbece631dcfbfb,Chairs,-44.675,-39.959,13.669,1479 -1480,_01_Airliner_2jetEngines,32.2329760312,0.078024674396,29.3823844911,-0.21,0.119,0.928461538462,0aa7473f621b1cd46092e246e57c171172884e1e,256,V3,0aa7473f621b1cd46092e246e57c171172884e1e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx+00.066_ry+57.767_rz+119.424_tx-00.210_ty+00.119_s+01.207_0aa7473f621b1cd46092e246e57c171172884e1e_256x256.png,dce8a10a20a9d07d373d2f4ca7fabd0ce77eec61,Planes,32.2329760312,90.0780246744,29.3823844911,1480 -1481,_01_Airliner_2jetEngines,23.0697444577,1.29236803449,-18.0154888571,-0.159,0.484,1.06692307692,0bc73a4870694c5b36952a17a779125081cc3e06,256,V3,0bc73a4870694c5b36952a17a779125081cc3e06_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx+01.189_ry+66.925_rz+72.491_tx-00.159_ty+00.484_s+01.387_0bc73a4870694c5b36952a17a779125081cc3e06_256x256.png,d85330faec596b5f0deebade2ce5c5a3515f2960,Planes,23.0697444577,91.2923680345,-18.0154888571,1481 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-1486,_01_Airliner_2jetEngines,27.958947083,23.0975329909,-49.596334135,0.245,-0.149,1.24,f3de8a2c5b3c46c6dc3c640986be70bfcc7f649f,256,V3,f3de8a2c5b3c46c6dc3c640986be70bfcc7f649f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx+20.274_ry+60.013_rz+51.711_tx+00.245_ty-00.149_s+01.612_f3de8a2c5b3c46c6dc3c640986be70bfcc7f649f_256x256.png,a36f529a142295cd89fb0332d64d9affd173ee30,Planes,27.958947083,113.097532991,-49.596334135,1486 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-1504,_01_Airliner_2jetEngines,-15.211138981,-13.8088614982,1.30316485716,0.204,0.202,1.08923076923,39cd057e00979b8706148c959d4a045363150882,256,V3,39cd057e00979b8706148c959d4a045363150882_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx-13.316_ry+105.642_rz+94.993_tx+00.204_ty+00.202_s+01.416_39cd057e00979b8706148c959d4a045363150882_256x256.png,2a87dc33df52267816eddb76da2a1cd43dd2d748,Planes,-15.211138981,76.1911385018,1.30316485716,1504 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-1506,_01_Airliner_2jetEngines,33.1440393565,-21.2279328644,33.9220679178,-0.149,-0.303,1.15769230769,8cc97eab29fae3376bdd179b9c114dc3a09a4bb2,256,V3,8cc97eab29fae3376bdd179b9c114dc3a09a4bb2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx-17.648_ry+54.988_rz+111.932_tx-00.149_ty-00.303_s+01.505_8cc97eab29fae3376bdd179b9c114dc3a09a4bb2_256x256.png,f6eea1db1081a4ca4321a5f18d25cc82f442be18,Planes,33.1440393565,68.7720671356,33.9220679178,1506 -1507,_01_Airliner_2jetEngines,27.4535299432,-22.1699696285,-14.9243841976,0.234,-0.25,0.851538461538,cafd8d40345ae6a7f4452554e2225f93e19c5cf1,256,V3,cafd8d40345ae6a7f4452554e2225f93e19c5cf1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx-19.564_ry+60.707_rz+64.436_tx+00.234_ty-00.250_s+01.107_cafd8d40345ae6a7f4452554e2225f93e19c5cf1_256x256.png,8bb92f8e7639aa63ce908473d0304c42257aaf17,Planes,27.4535299432,67.8300303715,-14.9243841976,1507 -1508,_01_Airliner_2jetEngines,-14.4148121809,-21.4499975713,-49.5252916932,-0.233,0.35,1.01153846154,64bc58228089c7d2e7009c1958896d7956f4850b,256,V3,64bc58228089c7d2e7009c1958896d7956f4850b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx-20.743_ry+105.438_rz+46.061_tx-00.233_ty+00.350_s+01.315_64bc58228089c7d2e7009c1958896d7956f4850b_256x256.png,2def9fae302ce19ba4773b393f55093a25523402,Planes,-14.4148121809,68.5500024287,-49.5252916932,1508 -1509,_01_Airliner_2jetEngines,34.844271372,-28.9128999729,-25.8479276508,-0.098,-0.068,0.821538461538,7b5a9f30006a8b7fcb3d933a4362cee8b7b1483d,256,V3,7b5a9f30006a8b7fcb3d933a4362cee8b7b1483d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx-23.378_ry+51.505_rz+46.638_tx-00.098_ty-00.068_s+01.068_7b5a9f30006a8b7fcb3d933a4362cee8b7b1483d_256x256.png,9a9c3d55be9769cc11cf19517891ef5c221c83a2,Planes,34.844271372,61.0871000271,-25.8479276508,1509 -1510,_01_Airliner_2jetEngines,25.1034217516,-27.098908109,10.854358522,0.294,-0.022,1.12923076923,c6d970dd34be0f6e5d71e2b35f394e26a503d823,256,V3,c6d970dd34be0f6e5d71e2b35f394e26a503d823_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx-24.362_ry+62.243_rz+88.606_tx+00.294_ty-00.022_s+01.468_c6d970dd34be0f6e5d71e2b35f394e26a503d823_256x256.png,7d2ef68436087322166581dc8c50942a0f617a6f,Planes,25.1034217516,62.901091891,10.854358522,1510 -1511,_01_Airliner_2jetEngines,14.5572170739,-26.1433930839,-22.927979512,-0.155,0.317,0.925384615385,71d050e6469090c5ec4a7c5146ab48b8fe1e328c,256,V3,71d050e6469090c5ec4a7c5146ab48b8fe1e328c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx-25.244_ry+73.866_rz+60.039_tx-00.155_ty+00.317_s+01.203_71d050e6469090c5ec4a7c5146ab48b8fe1e328c_256x256.png,e39497e90649aab6a196b20b9659de49d2ab8bdc,Planes,14.5572170739,63.8566069161,-22.927979512,1511 -1512,_01_Airliner_2jetEngines,-6.32176592727,-26.3040956394,14.7694374396,0.177,0.426,0.915384615385,787eb7be7ae2366f7dbb59ff5e00c84a24c10bd8,256,V3,787eb7be7ae2366f7dbb59ff5e00c84a24c10bd8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx-26.132_ry+97.045_rz+107.885_tx+00.177_ty+00.426_s+01.190_787eb7be7ae2366f7dbb59ff5e00c84a24c10bd8_256x256.png,98d615740b3b99d688aa29b61b2104c273e2ac38,Planes,-6.32176592727,63.6959043606,14.7694374396,1512 -1513,_01_Airliner_2jetEngines,-6.70651621592,-27.0752316966,3.36263730569,0.081,-0.11,1.17692307692,0245641b416a9e9c518519bf04c1c653ba49b13d,256,V3,0245641b416a9e9c518519bf04c1c653ba49b13d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx-26.875_ry+97.523_rz+96.779_tx+00.081_ty-00.110_s+01.530_0245641b416a9e9c518519bf04c1c653ba49b13d_256x256.png,f4ca4bc4e6f1772a50fc075709934ad0afb05508,Planes,-6.70651621592,62.9247683034,3.36263730569,1513 -1514,_01_Airliner_2jetEngines,-28.6308558001,-31.4638395674,-4.02101510921,-0.168,0.354,0.838461538462,16b2ad286a9a050c8a1eccecd4bbbdb76a822960,256,V3,16b2ad286a9a050c8a1eccecd4bbbdb76a822960_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx-27.267_ry+122.620_rz+102.321_tx-00.168_ty+00.354_s+01.090_16b2ad286a9a050c8a1eccecd4bbbdb76a822960_256x256.png,62c69a6794125076d07d0d740c4d6ac2ae13a1c2,Planes,-28.6308558001,58.5361604326,-4.02101510921,1514 -1515,_01_Airliner_2jetEngines,10.5160758515,-31.544922618,-7.76523805115,0.058,0.353,0.987692307692,3b037a6280f342fe8a4dde9ccee8a618a4dddf2f,256,V3,3b037a6280f342fe8a4dde9ccee8a618a4dddf2f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx-30.956_ry+77.712_rz+75.842_tx+00.058_ty+00.353_s+01.284_3b037a6280f342fe8a4dde9ccee8a618a4dddf2f_256x256.png,e195773a699dabff002d9487ad6cca76e6f4aca6,Planes,10.5160758515,58.455077382,-7.76523805115,1515 -1516,_01_Airliner_2jetEngines,-29.4157437496,-39.3724987822,-50.4691534143,-0.023,0.07,1.15153846154,04bc95e4d792e2dcd5c40a13d84fea3ba5fd0927,256,V3,04bc95e4d792e2dcd5c40a13d84fea3ba5fd0927_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx-33.544_ry+126.106_rz+61.482_tx-00.023_ty+00.070_s+01.497_04bc95e4d792e2dcd5c40a13d84fea3ba5fd0927_256x256.png,5a91ce8295901b5db047fc4fb67e2c173fd63087,Planes,-29.4157437496,50.6275012178,-50.4691534143,1516 -1517,_01_Airliner_2jetEngines,-2.57966711521,-36.4048036485,4.65911187058,-0.221,0.562,0.822307692308,adb1c0b4433df6619626334cf3ffcd16ffe2b8b8,256,V3,adb1c0b4433df6619626334cf3ffcd16ffe2b8b8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx-36.362_ry+93.204_rz+96.560_tx-00.221_ty+00.562_s+01.069_adb1c0b4433df6619626334cf3ffcd16ffe2b8b8_256x256.png,948f7342df3c633f201b3f512604539c48d1ed30,Planes,-2.57966711521,53.5951963515,4.65911187058,1517 -1518,_01_Airliner_2jetEngines,6.10265376523,-39.0337613504,-35.5877365251,-0.035,0.006,0.883076923077,05823d190543365d67ee28e40dbbee02c47da244,256,V3,05823d190543365d67ee28e40dbbee02c47da244_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx-38.771_ry+82.163_rz+49.486_tx-00.035_ty+00.006_s+01.148_05823d190543365d67ee28e40dbbee02c47da244_256x256.png,ff75bbe6b85a18ff12bf39dd79d60e50523939c7,Planes,6.10265376523,50.9662386496,-35.5877365251,1518 -1519,_01_Airliner_2jetEngines,-30.0897612334,-46.3863057897,-64.0204173988,0.266,-0.324,0.910769230769,dce413556e505555fdcfa2269f7fb40adae4257e,256,V3,dce413556e505555fdcfa2269f7fb40adae4257e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_01_Airliner_2jetEngines_rx-38.788_ry+130.031_rz+53.734_tx+00.266_ty-00.324_s+01.184_dce413556e505555fdcfa2269f7fb40adae4257e_256x256.png,70a77e7c8fb2a8bd732a2e84c711c8470c50de18,Planes,-30.0897612334,43.6136942103,-64.0204173988,1519 -1520,_031,0.666,-12.034,-17.231,-0.238,-0.204,1.18571428571,19fd0bc486eca84622f435ffbf0fa91f48f6ea01,256,V3,19fd0bc486eca84622f435ffbf0fa91f48f6ea01_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+00.666_ry-12.034_rz-17.231_tx-00.238_ty-00.204_s+00.830_19fd0bc486eca84622f435ffbf0fa91f48f6ea01_256x256.png,11c5882a80cd06fda42dc5a1853659ef96ee81a8,Chairs,0.666,-12.034,-17.231,1520 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-1529,_031,25.635,-1.502,-29.101,-0.18,-0.543,0.787142857143,57686e39359c38773f96c12594bca0301ae48475,256,V3,57686e39359c38773f96c12594bca0301ae48475_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+25.635_ry-01.502_rz-29.101_tx-00.180_ty-00.543_s+00.551_57686e39359c38773f96c12594bca0301ae48475_256x256.png,bb752b6a66a83df70bb3cbb4360cdba3d7e6f826,Chairs,25.635,-1.502,-29.101,1529 -1530,_031,25.997,-0.715,16.182,-0.094,-0.028,1.22142857143,a3f86d2e3c9eea1373c750634dc5b52caf3821c7,256,V3,a3f86d2e3c9eea1373c750634dc5b52caf3821c7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+25.997_ry-00.715_rz+16.182_tx-00.094_ty-00.028_s+00.855_a3f86d2e3c9eea1373c750634dc5b52caf3821c7_256x256.png,494a9a482234973ab6305cd354e54494720eaba5,Chairs,25.997,-0.715,16.182,1530 -1531,_031,27.052,15.498,35.953,0.058,0.485,0.862857142857,4078307a9e2fd7ccb3846fdbcf0cea8badf811b6,256,V3,4078307a9e2fd7ccb3846fdbcf0cea8badf811b6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+27.052_ry+15.498_rz+35.953_tx+00.058_ty+00.485_s+00.604_4078307a9e2fd7ccb3846fdbcf0cea8badf811b6_256x256.png,4283de4522a0377034e0b3cc2037e4522aa7a68e,Chairs,27.052,15.498,35.953,1531 -1532,_031,27.813,17.607,25.301,-0.088,0.369,1.12571428571,27c82de347064b827dc3826e3218753cf476a8c7,256,V3,27c82de347064b827dc3826e3218753cf476a8c7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+27.813_ry+17.607_rz+25.301_tx-00.088_ty+00.369_s+00.788_27c82de347064b827dc3826e3218753cf476a8c7_256x256.png,6040f8b28daaf56c421480cf8f18f979f413d503,Chairs,27.813,17.607,25.301,1532 -1533,_031,29.712,-10.837,21.755,-0.074,-0.496,1.06857142857,1329951701950f2e781c194fe84274a35cf396fd,256,V3,1329951701950f2e781c194fe84274a35cf396fd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+29.712_ry-10.837_rz+21.755_tx-00.074_ty-00.496_s+00.748_1329951701950f2e781c194fe84274a35cf396fd_256x256.png,9a0e5864d43d9705843be9df8d2d664f10d7e9ed,Chairs,29.712,-10.837,21.755,1533 -1534,_031,32.691,-0.297,-31.758,0.209,0.57,0.865714285714,faa8871298c63faeb4093c7a5a6bc94906b3a5cc,256,V3,faa8871298c63faeb4093c7a5a6bc94906b3a5cc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+32.691_ry-00.297_rz-31.758_tx+00.209_ty+00.570_s+00.606_faa8871298c63faeb4093c7a5a6bc94906b3a5cc_256x256.png,5db47a1ca0a1273604e4e25e17bd9bf8617177d5,Chairs,32.691,-0.297,-31.758,1534 -1535,_031,33.538,-31.96,-29.163,-0.008,-0.226,0.918571428571,aada038fd72a4c5a689105e2c6fa0f48bd3256d7,256,V3,aada038fd72a4c5a689105e2c6fa0f48bd3256d7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+33.538_ry-31.960_rz-29.163_tx-00.008_ty-00.226_s+00.643_aada038fd72a4c5a689105e2c6fa0f48bd3256d7_256x256.png,fc895b7f657f231ee86a536b4665ddd6eca864d2,Chairs,33.538,-31.96,-29.163,1535 -1536,_031,34.083,12.215,-15.49,-0.178,-0.242,1.11428571429,9c2250763f6c5fdf880efd0ae631871d6ad0aa25,256,V3,9c2250763f6c5fdf880efd0ae631871d6ad0aa25_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+34.083_ry+12.215_rz-15.490_tx-00.178_ty-00.242_s+00.780_9c2250763f6c5fdf880efd0ae631871d6ad0aa25_256x256.png,bfb985c523e718c36fd165b80250558c7442e6e3,Chairs,34.083,12.215,-15.49,1536 -1537,_031,34.384,9.794,-8.045,-0.011,-0.596,0.777142857143,11efe87a7dd321124df31ab0a0c2790706e48cd8,256,V3,11efe87a7dd321124df31ab0a0c2790706e48cd8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+34.384_ry+09.794_rz-08.045_tx-00.011_ty-00.596_s+00.544_11efe87a7dd321124df31ab0a0c2790706e48cd8_256x256.png,dbb1cb7ade266043922d7d325b93e26585d76e52,Chairs,34.384,9.794,-8.045,1537 -1538,_031,34.725,-26.957,27.695,-0.041,0.203,0.792857142857,3993b69acec08df621cb811f39ccdda4414f0525,256,V3,3993b69acec08df621cb811f39ccdda4414f0525_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+34.725_ry-26.957_rz+27.695_tx-00.041_ty+00.203_s+00.555_3993b69acec08df621cb811f39ccdda4414f0525_256x256.png,14dca46740ec8237fd95bad8f796f7cc160f95ed,Chairs,34.725,-26.957,27.695,1538 -1539,_031,39.838,-22.347,43.72,-0.031,0.081,0.837142857143,f7ab730628b083dbc6fddcecba6074539bd26d9b,256,V3,f7ab730628b083dbc6fddcecba6074539bd26d9b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+39.838_ry-22.347_rz+43.720_tx-00.031_ty+00.081_s+00.586_f7ab730628b083dbc6fddcecba6074539bd26d9b_256x256.png,4e4bcc22e7ca74932ddca31aab916609379d88af,Chairs,39.838,-22.347,43.72,1539 -1540,_031,39.867,4.817,19.456,0.132,0.077,0.84,837caa1a434e13de04554c3e750ff42761258c1e,256,V3,837caa1a434e13de04554c3e750ff42761258c1e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+39.867_ry+04.817_rz+19.456_tx+00.132_ty+00.077_s+00.588_837caa1a434e13de04554c3e750ff42761258c1e_256x256.png,c42dca8dd7543c4a8908cd30c02adb63eb80e8a7,Chairs,39.867,4.817,19.456,1540 -1541,_031,40.269,-26.914,29.995,0.293,-0.247,1.14142857143,5d2941435564b889572131651b9c50a09d9df31a,256,V3,5d2941435564b889572131651b9c50a09d9df31a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+40.269_ry-26.914_rz+29.995_tx+00.293_ty-00.247_s+00.799_5d2941435564b889572131651b9c50a09d9df31a_256x256.png,25cfbc48993800905b5d8e6c002f659d31faf453,Chairs,40.269,-26.914,29.995,1541 -1542,_031,40.523,-29.595,32.925,-0.188,-0.406,1.06571428571,afc436570fdabf0b88c298d254d5bc8d1237b464,256,V3,afc436570fdabf0b88c298d254d5bc8d1237b464_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+40.523_ry-29.595_rz+32.925_tx-00.188_ty-00.406_s+00.746_afc436570fdabf0b88c298d254d5bc8d1237b464_256x256.png,3191ee64ec40c85a35b3d36082040825e23bf0d7,Chairs,40.523,-29.595,32.925,1542 -1543,_031,42.198,7.024,34.792,0.117,0.559,1.14142857143,b70fd2906724c3574568604add8c87ebbc0471e7,256,V3,b70fd2906724c3574568604add8c87ebbc0471e7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx+42.198_ry+07.024_rz+34.792_tx+00.117_ty+00.559_s+00.799_b70fd2906724c3574568604add8c87ebbc0471e7_256x256.png,25fdca87c97ace0754e55186ed1458d0a1e50717,Chairs,42.198,7.024,34.792,1543 -1544,_031,-0.796,19.423,8.928,0.116,0.289,0.908571428571,cc63b6ab4954ee694a431a868a7a2b9a9c6c1ee3,256,V3,cc63b6ab4954ee694a431a868a7a2b9a9c6c1ee3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-00.796_ry+19.423_rz+08.928_tx+00.116_ty+00.289_s+00.636_cc63b6ab4954ee694a431a868a7a2b9a9c6c1ee3_256x256.png,d61fbfe60406925ed887cd46696a38915401fd10,Chairs,-0.796,19.423,8.928,1544 -1545,_031,-1.898,-28.849,-5.404,-0.242,0.013,0.867142857143,9cb0c5d55fb231df8e5e9d495a79fb16f3e9879d,256,V3,9cb0c5d55fb231df8e5e9d495a79fb16f3e9879d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-01.898_ry-28.849_rz-05.404_tx-00.242_ty+00.013_s+00.607_9cb0c5d55fb231df8e5e9d495a79fb16f3e9879d_256x256.png,c92e5c4e9bb03accd42496dd9098c87bc6de7811,Chairs,-1.898,-28.849,-5.404,1545 -1546,_031,-4.031,2.864,14.305,-0.093,0.041,0.96,3369eec41e2d0479836373fafacc1be874641e47,256,V3,3369eec41e2d0479836373fafacc1be874641e47_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-04.031_ry+02.864_rz+14.305_tx-00.093_ty+00.041_s+00.672_3369eec41e2d0479836373fafacc1be874641e47_256x256.png,a979ca71c5ebc9acbfbae51714d6bbfc6429045c,Chairs,-4.031,2.864,14.305,1546 -1547,_031,-11.469,31.551,-21.88,-0.175,0.164,0.797142857143,af08d01e2773ef804926b5b1cf44fd19853f4fe7,256,V3,af08d01e2773ef804926b5b1cf44fd19853f4fe7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-11.469_ry+31.551_rz-21.880_tx-00.175_ty+00.164_s+00.558_af08d01e2773ef804926b5b1cf44fd19853f4fe7_256x256.png,015282073827c7400796d7f8ef3fc41ca5c0cf5b,Chairs,-11.469,31.551,-21.88,1547 -1548,_031,-11.571,12.575,35.91,0.038,0.452,0.951428571429,3ae0cba4a53088c32be968960a80062d47231b45,256,V3,3ae0cba4a53088c32be968960a80062d47231b45_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-11.571_ry+12.575_rz+35.910_tx+00.038_ty+00.452_s+00.666_3ae0cba4a53088c32be968960a80062d47231b45_256x256.png,35425af53ea6841b9119152a498185e84f2d8113,Chairs,-11.571,12.575,35.91,1548 -1549,_031,-15.145,-1.351,33.333,0.206,0.321,0.928571428571,4389a1a8b3fd270676ffa3faef7f18aa70db0d38,256,V3,4389a1a8b3fd270676ffa3faef7f18aa70db0d38_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-15.145_ry-01.351_rz+33.333_tx+00.206_ty+00.321_s+00.650_4389a1a8b3fd270676ffa3faef7f18aa70db0d38_256x256.png,1ece73c0e03500d16baa4b46b71b87c14c34b6e9,Chairs,-15.145,-1.351,33.333,1549 -1550,_031,-15.388,-18.581,-26.229,-0.278,-0.418,1.27857142857,413be77def23c10d3b8c77de814c4a0b692598f1,256,V3,413be77def23c10d3b8c77de814c4a0b692598f1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-15.388_ry-18.581_rz-26.229_tx-00.278_ty-00.418_s+00.895_413be77def23c10d3b8c77de814c4a0b692598f1_256x256.png,81563cb310442d664f27bb47e7387d5bd60d21bc,Chairs,-15.388,-18.581,-26.229,1550 -1551,_031,-15.892,-41.94,-38.021,0.146,-0.229,1.29714285714,0f3924ca00742be360748f6a041758003fe0ecdd,256,V3,0f3924ca00742be360748f6a041758003fe0ecdd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-15.892_ry-41.940_rz-38.021_tx+00.146_ty-00.229_s+00.908_0f3924ca00742be360748f6a041758003fe0ecdd_256x256.png,ae8321bcb28458f76a75b9753f9a998acfe26071,Chairs,-15.892,-41.94,-38.021,1551 -1552,_031,-20.767,-12.264,11.453,0.172,0.595,0.86,57d76749e0080bcfb2cd3e4f6a34efe0f1e12434,256,V3,57d76749e0080bcfb2cd3e4f6a34efe0f1e12434_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-20.767_ry-12.264_rz+11.453_tx+00.172_ty+00.595_s+00.602_57d76749e0080bcfb2cd3e4f6a34efe0f1e12434_256x256.png,d4ff55acfcfedd11b40c84d4f5a9024a44da6560,Chairs,-20.767,-12.264,11.453,1552 -1553,_031,-24.381,28.251,15.022,0.117,0.332,1.04,f42b6ca9edb4b8012ab6ab852552a338ff5f4fdb,256,V3,f42b6ca9edb4b8012ab6ab852552a338ff5f4fdb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-24.381_ry+28.251_rz+15.022_tx+00.117_ty+00.332_s+00.728_f42b6ca9edb4b8012ab6ab852552a338ff5f4fdb_256x256.png,988c1317ae6eb7eff4ea7049242533ebee06c6f4,Chairs,-24.381,28.251,15.022,1553 -1554,_031,-26.26,0.321,-44.643,0.159,0.001,0.888571428571,8a0448c07c7c6a23612aa99d3dc3311af4115a15,256,V3,8a0448c07c7c6a23612aa99d3dc3311af4115a15_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-26.260_ry+00.321_rz-44.643_tx+00.159_ty+00.001_s+00.622_8a0448c07c7c6a23612aa99d3dc3311af4115a15_256x256.png,fbe376f087f7842edc1e2f105a4cee41158d1df8,Chairs,-26.26,0.321,-44.643,1554 -1555,_031,-30.793,43.297,16.087,0.165,-0.219,0.837142857143,1f283bd7e99f267055802b2efeed64f002e4dd47,256,V3,1f283bd7e99f267055802b2efeed64f002e4dd47_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-30.793_ry+43.297_rz+16.087_tx+00.165_ty-00.219_s+00.586_1f283bd7e99f267055802b2efeed64f002e4dd47_256x256.png,d7167a9b9196f8b1077c706adb7efaa14fa2d15d,Chairs,-30.793,43.297,16.087,1555 -1556,_031,-31.233,3.639,16.956,-0.262,-0.077,0.848571428571,98feb010e212ddfa87bf30308786ff32bbcbc499,256,V3,98feb010e212ddfa87bf30308786ff32bbcbc499_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-31.233_ry+03.639_rz+16.956_tx-00.262_ty-00.077_s+00.594_98feb010e212ddfa87bf30308786ff32bbcbc499_256x256.png,e9b2923564682e1142b00cae42df9fae0a0b1f43,Chairs,-31.233,3.639,16.956,1556 -1557,_031,-31.7,-35.511,-16.223,-0.045,0.088,1.12,b545f06e8ded3411e2402f7c05d533fe173ec81f,256,V3,b545f06e8ded3411e2402f7c05d533fe173ec81f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-31.700_ry-35.511_rz-16.223_tx-00.045_ty+00.088_s+00.784_b545f06e8ded3411e2402f7c05d533fe173ec81f_256x256.png,b951b44ec0bc148da2ef7a1104c50aa62ae5eb04,Chairs,-31.7,-35.511,-16.223,1557 -1558,_031,-33.822,12.082,25.594,-0.218,-0.06,1.18285714286,7140c8f6f54e06e53be5fe1f29255ffd1ddd461c,256,V3,7140c8f6f54e06e53be5fe1f29255ffd1ddd461c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-33.822_ry+12.082_rz+25.594_tx-00.218_ty-00.060_s+00.828_7140c8f6f54e06e53be5fe1f29255ffd1ddd461c_256x256.png,9cf781174c75d5a9a8264b3c6fb6ac750b41c3ef,Chairs,-33.822,12.082,25.594,1558 -1559,_031,-39.701,20.25,38.603,-0.237,-0.123,1.2,42bc31e2b31052ddbf437c0feb19d1595db1f03f,256,V3,42bc31e2b31052ddbf437c0feb19d1595db1f03f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_031_rx-39.701_ry+20.250_rz+38.603_tx-00.237_ty-00.123_s+00.840_42bc31e2b31052ddbf437c0feb19d1595db1f03f_256x256.png,53c6c916d31a482deb0f215913e3ec72f120f548,Chairs,-39.701,20.25,38.603,1559 -1560,_033,0.319,-26.488,-26.253,0.004,-0.344,0.942857142857,80aa6bb8b0c2277b5479a6f13111083d804d3460,256,V3,80aa6bb8b0c2277b5479a6f13111083d804d3460_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+00.319_ry-26.488_rz-26.253_tx+00.004_ty-00.344_s+00.660_80aa6bb8b0c2277b5479a6f13111083d804d3460_256x256.png,8052c2878f8daa5ce8bcb22b5b46a21bf4ecd881,Chairs,0.319,-26.488,-26.253,1560 -1561,_033,15.326,-32.714,-43.337,0.189,-0.163,0.887142857143,b4c0c91eb8faaaab57c121792c9d7c16db5f7b76,256,V3,b4c0c91eb8faaaab57c121792c9d7c16db5f7b76_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+15.326_ry-32.714_rz-43.337_tx+00.189_ty-00.163_s+00.621_b4c0c91eb8faaaab57c121792c9d7c16db5f7b76_256x256.png,330a096dd85dcfd024ecf3825008657ca03d9219,Chairs,15.326,-32.714,-43.337,1561 -1562,_033,18.872,-38.932,26.895,-0.041,0.491,1.21857142857,8776a054289e0cea2e4907f50b060adbf9f535c3,256,V3,8776a054289e0cea2e4907f50b060adbf9f535c3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+18.872_ry-38.932_rz+26.895_tx-00.041_ty+00.491_s+00.853_8776a054289e0cea2e4907f50b060adbf9f535c3_256x256.png,43c4a1c6b2f38289ec006e0005153b77a6e2f0a5,Chairs,18.872,-38.932,26.895,1562 -1563,_033,19.427,19.982,2.575,-0.047,0.578,0.885714285714,2f5bf24e94e5528c62faf46494cd035421d93cd1,256,V3,2f5bf24e94e5528c62faf46494cd035421d93cd1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+19.427_ry+19.982_rz+02.575_tx-00.047_ty+00.578_s+00.620_2f5bf24e94e5528c62faf46494cd035421d93cd1_256x256.png,52ea5dab58cf4432c3bdc84b9886a4099d444840,Chairs,19.427,19.982,2.575,1563 -1564,_033,21.701,-2.596,34.186,-0.067,0.447,1.13857142857,669bc5e0c209058f8db624db685101b115e28e88,256,V3,669bc5e0c209058f8db624db685101b115e28e88_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+21.701_ry-02.596_rz+34.186_tx-00.067_ty+00.447_s+00.797_669bc5e0c209058f8db624db685101b115e28e88_256x256.png,b07b1db5f5f6fba3ec59f427d757304eda598b74,Chairs,21.701,-2.596,34.186,1564 -1565,_033,22.79,1.376,34.726,-0.028,-0.158,1.02714285714,6fa763e2769f7d945f6a32b14ded5dfb2b366d31,256,V3,6fa763e2769f7d945f6a32b14ded5dfb2b366d31_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+22.790_ry+01.376_rz+34.726_tx-00.028_ty-00.158_s+00.719_6fa763e2769f7d945f6a32b14ded5dfb2b366d31_256x256.png,7904ecd908855a0a3862199955e82463d9ec6ec7,Chairs,22.79,1.376,34.726,1565 -1566,_033,23.592,-42.268,-44.473,0.068,-0.061,1.1,6621033f66f04ab759ce84ba596b5e28402e340b,256,V3,6621033f66f04ab759ce84ba596b5e28402e340b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+23.592_ry-42.268_rz-44.473_tx+00.068_ty-00.061_s+00.770_6621033f66f04ab759ce84ba596b5e28402e340b_256x256.png,cf078a6a72791a00a968527275744d442f14541f,Chairs,23.592,-42.268,-44.473,1566 -1567,_033,27.68,-40.021,37.094,-0.06,-0.344,1.06857142857,2a07a8f00f7ab01b54fbfbb595f8e0e845f7435d,256,V3,2a07a8f00f7ab01b54fbfbb595f8e0e845f7435d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+27.680_ry-40.021_rz+37.094_tx-00.060_ty-00.344_s+00.748_2a07a8f00f7ab01b54fbfbb595f8e0e845f7435d_256x256.png,3eef6d57c88522c21e789cf207e176d969cf0ebb,Chairs,27.68,-40.021,37.094,1567 -1568,_033,29.131,33.889,-13.422,0.129,-0.234,1.00285714286,3f1e6a14b1d6fad615df7941515a50a8feb1bb41,256,V3,3f1e6a14b1d6fad615df7941515a50a8feb1bb41_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+29.131_ry+33.889_rz-13.422_tx+00.129_ty-00.234_s+00.702_3f1e6a14b1d6fad615df7941515a50a8feb1bb41_256x256.png,380298e08baec53951e558dbec3e43d93fc3c844,Chairs,29.131,33.889,-13.422,1568 -1569,_033,31.521,1.419,-42.236,-0.199,0.066,0.945714285714,273e8a4d03fa725139c356ad59095b7c049b53e3,256,V3,273e8a4d03fa725139c356ad59095b7c049b53e3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+31.521_ry+01.419_rz-42.236_tx-00.199_ty+00.066_s+00.662_273e8a4d03fa725139c356ad59095b7c049b53e3_256x256.png,ad1569f39b7469916b5519b8f527aa50214799aa,Chairs,31.521,1.419,-42.236,1569 -1570,_033,32.906,-3.361,-36.042,-0.121,-0.251,0.845714285714,36cdabcae39b3ddcd583f6f7e4bce3b8f1bb6d2f,256,V3,36cdabcae39b3ddcd583f6f7e4bce3b8f1bb6d2f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+32.906_ry-03.361_rz-36.042_tx-00.121_ty-00.251_s+00.592_36cdabcae39b3ddcd583f6f7e4bce3b8f1bb6d2f_256x256.png,64f0b2f6a236bdee0668d53a88a24d3fdb301e0b,Chairs,32.906,-3.361,-36.042,1570 -1571,_033,35.762,33.948,-27.809,-0.03,0.255,0.87,fcf0ba510fbe030768fa4aff073e36f8fb2dfdb1,256,V3,fcf0ba510fbe030768fa4aff073e36f8fb2dfdb1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+35.762_ry+33.948_rz-27.809_tx-00.030_ty+00.255_s+00.609_fcf0ba510fbe030768fa4aff073e36f8fb2dfdb1_256x256.png,dd046276922046af66d09fe73139b1c87aaf1677,Chairs,35.762,33.948,-27.809,1571 -1572,_033,37.107,24.343,4.197,-0.147,0.447,1.23428571429,858b26d3678ec08631705de185074dec53651818,256,V3,858b26d3678ec08631705de185074dec53651818_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+37.107_ry+24.343_rz+04.197_tx-00.147_ty+00.447_s+00.864_858b26d3678ec08631705de185074dec53651818_256x256.png,0d6d266c5940ec5ebb6211694f2281a9e84f1b37,Chairs,37.107,24.343,4.197,1572 -1573,_033,37.421,-31.282,-43.264,0.206,-0.052,1.10571428571,d5de7de1725da1e5e04b39e52bced08929b46278,256,V3,d5de7de1725da1e5e04b39e52bced08929b46278_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+37.421_ry-31.282_rz-43.264_tx+00.206_ty-00.052_s+00.774_d5de7de1725da1e5e04b39e52bced08929b46278_256x256.png,cefb0b520fa308d633198f6caee79ce005c337bb,Chairs,37.421,-31.282,-43.264,1573 -1574,_033,37.44,14.731,-18.048,0.104,-0.546,1.04714285714,94b21d737d4d6cb1450575bc23e7551b4db9125e,256,V3,94b21d737d4d6cb1450575bc23e7551b4db9125e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+37.440_ry+14.731_rz-18.048_tx+00.104_ty-00.546_s+00.733_94b21d737d4d6cb1450575bc23e7551b4db9125e_256x256.png,f8d3291ac178fd0fea6915be0d75961f50f94499,Chairs,37.44,14.731,-18.048,1574 -1575,_033,38.613,43.324,10.179,0.179,0.594,1.04428571429,28395c765b7061a901bad3ce2b96abb1a27c10cf,256,V3,28395c765b7061a901bad3ce2b96abb1a27c10cf_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+38.613_ry+43.324_rz+10.179_tx+00.179_ty+00.594_s+00.731_28395c765b7061a901bad3ce2b96abb1a27c10cf_256x256.png,10cf24ede84094cdfb5579d53dcb8b3263435468,Chairs,38.613,43.324,10.179,1575 -1576,_033,40.025,43.647,-40.44,0.179,0.331,0.814285714286,0c8291cec8e24b72b2a80f1bbb9e81cd2f5eddd7,256,V3,0c8291cec8e24b72b2a80f1bbb9e81cd2f5eddd7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+40.025_ry+43.647_rz-40.440_tx+00.179_ty+00.331_s+00.570_0c8291cec8e24b72b2a80f1bbb9e81cd2f5eddd7_256x256.png,30ec651883838be38a1840db028eb94926ef530a,Chairs,40.025,43.647,-40.44,1576 -1577,_033,42.628,27.836,15.801,0.176,-0.324,0.84,a5556c7b65c5a2df6012b955b25d64212574f56d,256,V3,a5556c7b65c5a2df6012b955b25d64212574f56d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+42.628_ry+27.836_rz+15.801_tx+00.176_ty-00.324_s+00.588_a5556c7b65c5a2df6012b955b25d64212574f56d_256x256.png,beb87d0dd43c7bcf1de4caba4ecadfabc437422c,Chairs,42.628,27.836,15.801,1577 -1578,_033,42.812,38.027,25.006,-0.098,0.497,1.18714285714,755dcba5f54dcd8ed1be76a6929cd172d4b77028,256,V3,755dcba5f54dcd8ed1be76a6929cd172d4b77028_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+42.812_ry+38.027_rz+25.006_tx-00.098_ty+00.497_s+00.831_755dcba5f54dcd8ed1be76a6929cd172d4b77028_256x256.png,1e7cc87e7e35fb3c71a2ac7c6babd0af008906ec,Chairs,42.812,38.027,25.006,1578 -1579,_033,44.876,21.355,41.75,-0.198,0.19,0.94,b68f8bfb20445c167e032f5497abd1cdba339bcf,256,V3,b68f8bfb20445c167e032f5497abd1cdba339bcf_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx+44.876_ry+21.355_rz+41.750_tx-00.198_ty+00.190_s+00.658_b68f8bfb20445c167e032f5497abd1cdba339bcf_256x256.png,5ed14e1d4d5903a63dce041560bbe2a36c01218c,Chairs,44.876,21.355,41.75,1579 -1580,_033,-3.329,-20.302,-14.359,-0.048,-0.32,1.10714285714,e39c2d7241ceb77f929f412074c33ddc74b57ec0,256,V3,e39c2d7241ceb77f929f412074c33ddc74b57ec0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-03.329_ry-20.302_rz-14.359_tx-00.048_ty-00.320_s+00.775_e39c2d7241ceb77f929f412074c33ddc74b57ec0_256x256.png,08445bd60393f8e411e0ae9477c69427769c2d72,Chairs,-3.329,-20.302,-14.359,1580 -1581,_033,-4.484,17.513,5.859,0.032,-0.332,1.27714285714,c75bef4048e308c32cba568752fb4ea1708aa3ed,256,V3,c75bef4048e308c32cba568752fb4ea1708aa3ed_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-04.484_ry+17.513_rz+05.859_tx+00.032_ty-00.332_s+00.894_c75bef4048e308c32cba568752fb4ea1708aa3ed_256x256.png,112adce8d5bbf2752e5941c0d25383c3653a12a7,Chairs,-4.484,17.513,5.859,1581 -1582,_033,-4.533,10.579,-32.335,-0.065,0.231,0.891428571429,d2a384b031c61463edf27e0c1754f6fb63cd62c9,256,V3,d2a384b031c61463edf27e0c1754f6fb63cd62c9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-04.533_ry+10.579_rz-32.335_tx-00.065_ty+00.231_s+00.624_d2a384b031c61463edf27e0c1754f6fb63cd62c9_256x256.png,cd74796a94bd517a5c4ee153108891584de1b162,Chairs,-4.533,10.579,-32.335,1582 -1583,_033,-5.964,-11.197,25.888,0.078,0.454,0.874285714286,3d5a9dc7c49c5503515d6279808dcc8830754525,256,V3,3d5a9dc7c49c5503515d6279808dcc8830754525_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-05.964_ry-11.197_rz+25.888_tx+00.078_ty+00.454_s+00.612_3d5a9dc7c49c5503515d6279808dcc8830754525_256x256.png,be09d5a099dd2aa6d6db6ee6b3c93f31c7514c7b,Chairs,-5.964,-11.197,25.888,1583 -1584,_033,-7.379,-0.391,0.434,0.118,0.219,0.812857142857,44b47517b90fa5af0e6e8ed08c59145d60fbb569,256,V3,44b47517b90fa5af0e6e8ed08c59145d60fbb569_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-07.379_ry-00.391_rz+00.434_tx+00.118_ty+00.219_s+00.569_44b47517b90fa5af0e6e8ed08c59145d60fbb569_256x256.png,e1ae712c7353aebc5f691fc406b058c5d98890f6,Chairs,-7.379,-0.391,0.434,1584 -1585,_033,-8.603,30.556,-6.966,-0.169,-0.534,0.93,3b4fc840e691900af3e372c6a7326942d59a5243,256,V3,3b4fc840e691900af3e372c6a7326942d59a5243_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-08.603_ry+30.556_rz-06.966_tx-00.169_ty-00.534_s+00.651_3b4fc840e691900af3e372c6a7326942d59a5243_256x256.png,07fc44ffd3de7c502c3f0b733d670cbeff61094c,Chairs,-8.603,30.556,-6.966,1585 -1586,_033,-10.44,39.749,-9.201,0.068,-0.113,0.838571428571,479d6ca9a605dd54c7427c3e2a8011f316cca670,256,V3,479d6ca9a605dd54c7427c3e2a8011f316cca670_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-10.440_ry+39.749_rz-09.201_tx+00.068_ty-00.113_s+00.587_479d6ca9a605dd54c7427c3e2a8011f316cca670_256x256.png,ddbd34db122aaddd44cc9adb76594ea7774a1186,Chairs,-10.44,39.749,-9.201,1586 -1587,_033,-15.846,41.736,36.86,0.12,-0.52,1.15571428571,e73349ec3a990631d88db27cf0348c94d914a7d5,256,V3,e73349ec3a990631d88db27cf0348c94d914a7d5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-15.846_ry+41.736_rz+36.860_tx+00.120_ty-00.520_s+00.809_e73349ec3a990631d88db27cf0348c94d914a7d5_256x256.png,0cc3ea05a423e22afb2c585ee6e3390e33641689,Chairs,-15.846,41.736,36.86,1587 -1588,_033,-17.555,5.785,-44.309,0.173,0.225,1.07714285714,55f0face2a2effcc9a40d63470c4e91c13821bb5,256,V3,55f0face2a2effcc9a40d63470c4e91c13821bb5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-17.555_ry+05.785_rz-44.309_tx+00.173_ty+00.225_s+00.754_55f0face2a2effcc9a40d63470c4e91c13821bb5_256x256.png,27816c162acd7cdaa80ff7077c53cf63ff78fe60,Chairs,-17.555,5.785,-44.309,1588 -1589,_033,-19.688,5.444,34.167,0.284,-0.088,1.01571428571,17f06a52bda8d950e2764a26b18c78255d348cd9,256,V3,17f06a52bda8d950e2764a26b18c78255d348cd9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-19.688_ry+05.444_rz+34.167_tx+00.284_ty-00.088_s+00.711_17f06a52bda8d950e2764a26b18c78255d348cd9_256x256.png,1348603ea10ddfdd058fe327d53706eebf999b06,Chairs,-19.688,5.444,34.167,1589 -1590,_033,-20.384,42.749,25.095,-0.15,0.501,1.29714285714,4a51cf4af93feb23b252669d4ca48cd9eaecd1e5,256,V3,4a51cf4af93feb23b252669d4ca48cd9eaecd1e5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-20.384_ry+42.749_rz+25.095_tx-00.150_ty+00.501_s+00.908_4a51cf4af93feb23b252669d4ca48cd9eaecd1e5_256x256.png,3f214d63a5faa857e87b646182ba2ecfbbedda59,Chairs,-20.384,42.749,25.095,1590 -1591,_033,-23.187,-7.918,26.979,-0.205,0.166,1.04428571429,4ad1e9bf87a0a54433fefcdf2630623a31172dde,256,V3,4ad1e9bf87a0a54433fefcdf2630623a31172dde_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-23.187_ry-07.918_rz+26.979_tx-00.205_ty+00.166_s+00.731_4ad1e9bf87a0a54433fefcdf2630623a31172dde_256x256.png,e15f00c16a6c372c92af88d320961ce6c914e5d5,Chairs,-23.187,-7.918,26.979,1591 -1592,_033,-26.639,-18.749,-8.015,-0.284,0.263,0.822857142857,a677b19c0ed6c5d7aee4808d72ac51e8807f0bd7,256,V3,a677b19c0ed6c5d7aee4808d72ac51e8807f0bd7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-26.639_ry-18.749_rz-08.015_tx-00.284_ty+00.263_s+00.576_a677b19c0ed6c5d7aee4808d72ac51e8807f0bd7_256x256.png,4216db731813a0b991bc1a1c0197642501c1f1d4,Chairs,-26.639,-18.749,-8.015,1592 -1593,_033,-28.183,-16.394,-4.958,-0.024,0.487,1.24857142857,ea1542cb038b8da0274bd83986e96537587371fb,256,V3,ea1542cb038b8da0274bd83986e96537587371fb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-28.183_ry-16.394_rz-04.958_tx-00.024_ty+00.487_s+00.874_ea1542cb038b8da0274bd83986e96537587371fb_256x256.png,ab40fa89b5624e33e308001c9237ae5b6061a49d,Chairs,-28.183,-16.394,-4.958,1593 -1594,_033,-29.926,0.491,-26.015,-0.236,0.534,1.28714285714,7ff1f88083a8a4e1e3c83af91eaa5d40185ef334,256,V3,7ff1f88083a8a4e1e3c83af91eaa5d40185ef334_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-29.926_ry+00.491_rz-26.015_tx-00.236_ty+00.534_s+00.901_7ff1f88083a8a4e1e3c83af91eaa5d40185ef334_256x256.png,28bf70f4562ca371132cc9f0b708092f3eefa01e,Chairs,-29.926,0.491,-26.015,1594 -1595,_033,-32.418,-39.833,-5.567,-0.144,0.482,0.907142857143,86b3399964c93339c5f7531317f6fbd590b908de,256,V3,86b3399964c93339c5f7531317f6fbd590b908de_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-32.418_ry-39.833_rz-05.567_tx-00.144_ty+00.482_s+00.635_86b3399964c93339c5f7531317f6fbd590b908de_256x256.png,e6bff17a684239b8a49fbe3fa09fe4fc027c9b46,Chairs,-32.418,-39.833,-5.567,1595 -1596,_033,-34.363,-24.428,-10.821,0.22,0.446,1.04142857143,ac272b9ddbe6e1bc2c3ba20b6b3d4bd85674d871,256,V3,ac272b9ddbe6e1bc2c3ba20b6b3d4bd85674d871_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-34.363_ry-24.428_rz-10.821_tx+00.220_ty+00.446_s+00.729_ac272b9ddbe6e1bc2c3ba20b6b3d4bd85674d871_256x256.png,19b2c5f7f0f5d3cf0218892a716bbcfa39fb1aa4,Chairs,-34.363,-24.428,-10.821,1596 -1597,_033,-36.464,36.803,20.59,-0.155,-0.142,1.21,7c7368b846c0f9c02efa392d49e181dc1771b9ff,256,V3,7c7368b846c0f9c02efa392d49e181dc1771b9ff_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-36.464_ry+36.803_rz+20.590_tx-00.155_ty-00.142_s+00.847_7c7368b846c0f9c02efa392d49e181dc1771b9ff_256x256.png,774c422bc095da9f676381e1aa7b1cbc4c3f1ca4,Chairs,-36.464,36.803,20.59,1597 -1598,_033,-43.305,31.628,-6.698,-0.096,0.559,1.08,48c5378bb411809094cd0ca5aac622ec429aacef,256,V3,48c5378bb411809094cd0ca5aac622ec429aacef_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-43.305_ry+31.628_rz-06.698_tx-00.096_ty+00.559_s+00.756_48c5378bb411809094cd0ca5aac622ec429aacef_256x256.png,70097b22e557f2016f4f2a6345a98ce4591a11eb,Chairs,-43.305,31.628,-6.698,1598 -1599,_033,-43.755,21.416,-23.826,-0.094,0.282,1.27142857143,658db4a5da65431e9ba9f82cf2edccf54ad0447a,256,V3,658db4a5da65431e9ba9f82cf2edccf54ad0447a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_033_rx-43.755_ry+21.416_rz-23.826_tx-00.094_ty+00.282_s+00.890_658db4a5da65431e9ba9f82cf2edccf54ad0447a_256x256.png,06245fe2a69d1621a96c26757074067ca411c3cd,Chairs,-43.755,21.416,-23.826,1599 -1600,_05_future,-3.97994127449,0.311751824905,-35.0793619564,0.206,-0.456,1.11230769231,eec926aa19d77c2afa3e0fe4967ceef395343a95,256,V3,eec926aa19d77c2afa3e0fe4967ceef395343a95_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+00.311_ry+93.980_rz+54.899_tx+00.206_ty-00.456_s+01.446_eec926aa19d77c2afa3e0fe4967ceef395343a95_256x256.png,b04487414fe29eb8508bf13ec167e9dbd9d8e96c,Planes,-3.97994127449,90.3117518249,-35.0793619564,1600 -1601,_05_future,-15.3075689457,0.802471401512,3.73886571395,0.087,-0.433,1.13384615385,4ebb334e84124c0ed685511e06fdfaf1aa8b55ef,256,V3,4ebb334e84124c0ed685511e06fdfaf1aa8b55ef_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+00.774_ry+105.309_rz+93.527_tx+00.087_ty-00.433_s+01.474_4ebb334e84124c0ed685511e06fdfaf1aa8b55ef_256x256.png,859fae29adacdc0543f9e4cd1736e6e735f6dcd6,Planes,-15.3075689457,90.8024714015,3.73886571395,1601 -1602,_05_future,43.0599926825,4.06515660497,1.1909706359,-0.16,-0.344,0.834615384615,91971a7b6e3dc769ecce495f245ae8b32d409f26,256,V3,91971a7b6e3dc769ecce495f245ae8b32d409f26_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+02.969_ry+46.868_rz+93.969_tx-00.160_ty-00.344_s+01.085_91971a7b6e3dc769ecce495f245ae8b32d409f26_256x256.png,615fd60fdcbfd810338308a11f94181f739d4a51,Planes,43.0599926825,94.065156605,1.1909706359,1602 -1603,_05_future,-42.0007786051,8.45100013149,-4.45638014057,-0.242,-0.081,0.791538461538,0c34f628c38a36f301499c8a38563da14de86c99,256,V3,0c34f628c38a36f301499c8a38563da14de86c99_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+06.270_ry+132.312_rz+79.866_tx-00.242_ty-00.081_s+01.029_0c34f628c38a36f301499c8a38563da14de86c99_256x256.png,6216d2a1591ede5e78dd026201f3bd8eb6fb7060,Planes,-42.0007786051,98.4510001315,-4.45638014057,1603 -1604,_05_future,43.449146688,9.13011953436,-27.6509721421,-0.295,0.586,0.916153846154,5cee94352b675144b214c4196c67206082af684c,256,V3,5cee94352b675144b214c4196c67206082af684c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+06.615_ry+46.186_rz+68.656_tx-00.295_ty+00.586_s+01.191_5cee94352b675144b214c4196c67206082af684c_256x256.png,a0100dd138b68b9810e8bc09f72d3e5c16a513d9,Planes,43.449146688,99.1301195344,-27.6509721421,1604 -1605,_05_future,41.0464269989,11.7773407269,4.53584613201,0.273,-0.407,1.10923076923,400995a7167e2c851519319b6c8e79a4ac01c1b8,256,V3,400995a7167e2c851519319b6c8e79a4ac01c1b8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+08.855_ry+48.349_rz+102.332_tx+00.273_ty-00.407_s+01.442_400995a7167e2c851519319b6c8e79a4ac01c1b8_256x256.png,e35054dfdcc7a3aa7561b3cd0dcbefc4e206bd43,Planes,41.0464269989,101.777340727,4.53584613201,1605 -1606,_05_future,20.8247546089,14.4258117791,-10.0861879133,-0.257,0.453,1.00307692308,e34c02ff3e09d7cb630d21ee1b8a2591bbcd56f2,256,V3,e34c02ff3e09d7cb630d21ee1b8a2591bbcd56f2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+13.465_ry+68.558_rz+85.139_tx-00.257_ty+00.453_s+01.304_e34c02ff3e09d7cb630d21ee1b8a2591bbcd56f2_256x256.png,1ff37f1f2ba560f48587a0a3818d2e002d8c4d6a,Planes,20.8247546089,104.425811779,-10.0861879133,1606 -1607,_05_future,-4.90774353172,14.3949081121,-35.8791124148,-0.052,0.398,1.09538461538,d9ac79bd5506a76708444a04176b1f7b3efddc12,256,V3,d9ac79bd5506a76708444a04176b1f7b3efddc12_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+14.341_ry+95.066_rz+52.863_tx-00.052_ty+00.398_s+01.424_d9ac79bd5506a76708444a04176b1f7b3efddc12_256x256.png,6fc0d4c6ee14f068eef852a7393a2f83bb5d8095,Planes,-4.90774353172,104.394908112,-35.8791124148,1607 -1608,_05_future,5.91511381704,16.4922977851,21.5353797545,0.26,-0.532,1.27153846154,08ac7c2f9dbb769ff020bbf20a97759f2c3972c3,256,V3,08ac7c2f9dbb769ff020bbf20a97759f2c3972c3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+16.402_ry+83.833_rz+113.283_tx+00.260_ty-00.532_s+01.653_08ac7c2f9dbb769ff020bbf20a97759f2c3972c3_256x256.png,bb7eb3357271976de68a7aa3bfa73682a3afe656,Planes,5.91511381704,106.492297785,21.5353797545,1608 -1609,_05_future,-11.5672195276,16.8143268385,-36.643430664,-0.166,-0.316,1.07461538462,ba1bd5019208455f5d4a651fca3db3c6987ee6fa,256,V3,ba1bd5019208455f5d4a651fca3db3c6987ee6fa_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+16.463_ry+102.069_rz+49.889_tx-00.166_ty-00.316_s+01.397_ba1bd5019208455f5d4a651fca3db3c6987ee6fa_256x256.png,7246e3787d489d138902ff447802d643e67eb7cc,Planes,-11.5672195276,106.814326839,-36.643430664,1609 -1610,_05_future,3.99200716231,17.7565101803,4.21189391817,0.232,-0.576,1.08076923077,dd874f7a6e57ddc4780d29d1798f8c77b9ddbabc,256,V3,dd874f7a6e57ddc4780d29d1798f8c77b9ddbabc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+17.712_ry+85.809_rz+95.489_tx+00.232_ty-00.576_s+01.405_dd874f7a6e57ddc4780d29d1798f8c77b9ddbabc_256x256.png,0984818a9214b47ae01365b94112fa5153b0a603,Planes,3.99200716231,107.75651018,4.21189391817,1610 -1611,_05_future,-31.3572064881,26.8688779372,0.528632435766,-0.055,0.457,1.24153846154,6255d018352facaa072dd81e00b8bf0dce85f696,256,V3,6255d018352facaa072dd81e00b8bf0dce85f696_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+22.702_ry+124.338_rz+75.759_tx-00.055_ty+00.457_s+01.614_6255d018352facaa072dd81e00b8bf0dce85f696_256x256.png,7461a903d0d2ef013a1b0a07582eb773199a89f2,Planes,-31.3572064881,116.868877937,0.528632435766,1611 -1612,_05_future,-21.8719612564,26.4787524388,20.0615702649,0.279,-0.191,1.06692307692,c4bd44f73d8f0ff2e0351cdc357495b52a97967a,256,V3,c4bd44f73d8f0ff2e0351cdc357495b52a97967a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+24.442_ry+114.155_rz+99.549_tx+00.279_ty-00.191_s+01.387_c4bd44f73d8f0ff2e0351cdc357495b52a97967a_256x256.png,cfcc95ab829f0cfc3e2ca8830cd004c89b6f3943,Planes,-21.8719612564,116.478752439,20.0615702649,1612 -1613,_05_future,-8.24614009347,26.9082398387,46.9762216502,-0.144,-0.566,1.02,92247150e0823cd70f61437df14fd1e7cf230195,256,V3,92247150e0823cd70f61437df14fd1e7cf230195_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+26.608_ry+99.231_rz+132.813_tx-00.144_ty-00.566_s+01.326_92247150e0823cd70f61437df14fd1e7cf230195_256x256.png,b0b6ffcef2ac56dc95cc528a9267f004b77d1189,Planes,-8.24614009347,116.908239839,46.9762216502,1613 -1614,_05_future,3.47178331744,27.6761365498,6.45984275396,0.048,0.574,1.19769230769,67a111d612a91cabbf1306207a98872af6d4ee6d,256,V3,67a111d612a91cabbf1306207a98872af6d4ee6d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+27.621_ry+86.081_rz+98.279_tx+00.048_ty+00.574_s+01.557_67a111d612a91cabbf1306207a98872af6d4ee6d_256x256.png,564f974a681635108bf80532f54edd71f37baac9,Planes,3.47178331744,117.67613655,6.45984275396,1614 -1615,_05_future,19.6678116575,32.2734155848,12.3723667253,-0.142,0.03,1.01769230769,7621f9c4bf13db6a09363239b8d1ef5828368bc4,256,V3,7621f9c4bf13db6a09363239b8d1ef5828368bc4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+30.186_ry+67.085_rz+114.372_tx-00.142_ty+00.030_s+01.323_7621f9c4bf13db6a09363239b8d1ef5828368bc4_256x256.png,2929057f08b508f4457a7c7685f118e331b01d7c,Planes,19.6678116575,122.273415585,12.3723667253,1615 -1616,_05_future,32.6624567572,38.6631297243,-47.5368004677,-0.046,0.011,1.09769230769,9814d436398e47d3475e723929df2408ab6dcbad,256,V3,9814d436398e47d3475e723929df2408ab6dcbad_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+31.732_ry+50.614_rz+65.818_tx-00.046_ty+00.011_s+01.427_9814d436398e47d3475e723929df2408ab6dcbad_256x256.png,426bcf9e3fa5f68c494b77e7252e59dd1c882b9c,Planes,32.6624567572,128.663129724,-47.5368004677,1616 -1617,_05_future,-13.8879680417,33.0324803146,27.8954265776,-0.242,-0.374,0.88,f4745bd37cbe2e4fff592defa97b28322e26058a,256,V3,f4745bd37cbe2e4fff592defa97b28322e26058a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+31.950_ry+106.432_rz+109.025_tx-00.242_ty-00.374_s+01.144_f4745bd37cbe2e4fff592defa97b28322e26058a_256x256.png,111516df4ffb4654f8df8af2de7e2f6fe1cbe488,Planes,-13.8879680417,123.032480315,27.8954265776,1617 -1618,_05_future,-13.5762786886,33.3545617719,-17.6053861243,0.215,0.416,1.04461538462,198337893910f74e20f251a36ae6bb7ca7bed099,256,V3,198337893910f74e20f251a36ae6bb7ca7bed099_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+32.307_ry+106.125_rz+63.611_tx+00.215_ty+00.416_s+01.358_198337893910f74e20f251a36ae6bb7ca7bed099_256x256.png,a4c1310120df854a09030c17cb19a939e436ce42,Planes,-13.5762786886,123.354561772,-17.6053861243,1618 -1619,_05_future,22.6283950078,37.7167179503,3.23161978922,0.07,0.476,0.836923076923,6030698657c41c0c48408fd33ae2745cede97b38,256,V3,6030698657c41c0c48408fd33ae2745cede97b38_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+34.379_ry+62.213_rz+109.802_tx+00.070_ty+00.476_s+01.088_6030698657c41c0c48408fd33ae2745cede97b38_256x256.png,44ab15143ae9b83a1e0c60937d77090e1e93b36d,Planes,22.6283950078,127.71671795,3.23161978922,1619 -1620,_05_future,27.4374634972,52.1681638701,-5.44390959642,-0.264,0.106,0.965384615385,1251d3832e7de1442c6452fa93fe75a9c02867ee,256,V3,1251d3832e7de1442c6452fa93fe75a9c02867ee_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx+44.505_ry+49.753_rz+115.239_tx-00.264_ty+00.106_s+01.255_1251d3832e7de1442c6452fa93fe75a9c02867ee_256x256.png,4505e77c3712f2fb6ea4ecad98f2004a6f13fa88,Planes,27.4374634972,142.16816387,-5.44390959642,1620 -1621,_05_future,-9.51074448278,-5.31121002987,-11.4390379538,0.24,-0.551,1.12615384615,954e2dd1e73ac01adea6066ef515bd46e84a7064,256,V3,954e2dd1e73ac01adea6066ef515bd46e84a7064_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-05.238_ry+99.551_rz+79.441_tx+00.240_ty-00.551_s+01.464_954e2dd1e73ac01adea6066ef515bd46e84a7064_256x256.png,1e706d8749e0679a5b9c46a3c2c447eb64687c13,Planes,-9.51074448278,84.6887899701,-11.4390379538,1621 -1622,_05_future,6.02215198793,-6.99680398871,8.27368546755,0.178,-0.452,1.12307692308,ebf8f294a1f78f597d0032456d6546df5648507d,256,V3,ebf8f294a1f78f597d0032456d6546df5648507d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-06.958_ry+83.933_rz+97.536_tx+00.178_ty-00.452_s+01.460_ebf8f294a1f78f597d0032456d6546df5648507d_256x256.png,e3fc67a8cf9c0b21fa3a24c84c3460603e5fce85,Planes,6.02215198793,83.0031960113,8.27368546755,1622 -1623,_05_future,4.92085567227,-12.7688546673,-41.2413324056,0.109,0.302,0.812307692308,581efed2e64d0745988ac6a18355647cd1bb036d,256,V3,581efed2e64d0745988ac6a18355647cd1bb036d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-12.721_ry+84.955_rz+47.645_tx+00.109_ty+00.302_s+01.056_581efed2e64d0745988ac6a18355647cd1bb036d_256x256.png,1abb3fc8c85a3537090b1d1976ca840d2472f194,Planes,4.92085567227,77.2311453327,-41.2413324056,1623 -1624,_05_future,2.86628992574,-16.9358257017,-1.87562792554,-0.275,0.216,1.02230769231,5a38b191f3aaa9592b8909777441e9dab3913997,256,V3,5a38b191f3aaa9592b8909777441e9dab3913997_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-16.914_ry+87.004_rz+87.252_tx-00.275_ty+00.216_s+01.329_5a38b191f3aaa9592b8909777441e9dab3913997_256x256.png,fb1e43bbf6b28f45492b9c9639b8f53f6cc92395,Planes,2.86628992574,73.0641742983,-1.87562792554,1624 -1625,_05_future,-35.7694662427,-23.9863047721,0.686487021423,-0.174,-0.091,0.824615384615,42802465e7fada6c9127d50602891f858eb13be4,256,V3,42802465e7fada6c9127d50602891f858eb13be4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-19.259_ry+128.256_rz+105.265_tx-00.174_ty-00.091_s+01.072_42802465e7fada6c9127d50602891f858eb13be4_256x256.png,5f7bc8a85dd7222dfe4ef4a39f01c70d6a766118,Planes,-35.7694662427,66.0136952279,0.686487021423,1625 -1626,_05_future,-18.8606694992,-20.8193610968,-34.418812524,-0.04,-0.344,0.888461538462,3c689288f630d7460d5211d9de0012cf87f7434f,256,V3,3c689288f630d7460d5211d9de0012cf87f7434f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-19.654_ry+110.076_rz+62.589_tx-00.040_ty-00.344_s+01.155_3c689288f630d7460d5211d9de0012cf87f7434f_256x256.png,f22d80284c4f74007815cf58ea54d708b98fe16a,Planes,-18.8606694992,69.1806389032,-34.418812524,1626 -1627,_05_future,39.7987257224,-26.5132027537,-24.5105428779,-0.126,0.314,0.812307692308,0ee542cbaee5a359be7c596e53f926f75e49f89b,256,V3,0ee542cbaee5a359be7c596e53f926f75e49f89b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-20.058_ry+47.045_rz+47.780_tx-00.126_ty+00.314_s+01.056_0ee542cbaee5a359be7c596e53f926f75e49f89b_256x256.png,866fdfab39a49db4daad9bbce54a1bf4081e2050,Planes,39.7987257224,63.4867972463,-24.5105428779,1627 -1628,_05_future,36.2469574622,-28.9178756312,15.5339817718,0.079,0.147,1.04769230769,370675e703ffd89d0e770ae29bd10f8b3e9a1fa7,256,V3,370675e703ffd89d0e770ae29bd10f8b3e9a1fa7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-22.953_ry+50.051_rz+87.445_tx+00.079_ty+00.147_s+01.362_370675e703ffd89d0e770ae29bd10f8b3e9a1fa7_256x256.png,6677f4e1d8666422606bb1f2f08b41eb08d8520b,Planes,36.2469574622,61.0821243688,15.5339817718,1628 -1629,_05_future,-19.1090614752,-25.5479181781,9.68634191923,-0.251,-0.539,1.26230769231,50052875ede95febad733224d9ec007fe2f5d95f,256,V3,50052875ede95febad733224d9ec007fe2f5d95f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-24.048_ry+111.007_rz+108.580_tx-00.251_ty-00.539_s+01.641_50052875ede95febad733224d9ec007fe2f5d95f_256x256.png,508c3c93243be35c8974025beafbb864529b0296,Planes,-19.1090614752,64.4520818219,9.68634191923,1629 -1630,_05_future,-34.3814124662,-30.8741089003,19.2323369362,-0.016,-0.535,0.936923076923,dc14a5ebbfec532d8067ef7a6b89d1514e639932,256,V3,dc14a5ebbfec532d8067ef7a6b89d1514e639932_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-25.056_ry+128.562_rz+127.888_tx-00.016_ty-00.535_s+01.218_dc14a5ebbfec532d8067ef7a6b89d1514e639932_256x256.png,29f9863d54569bd7db07d71d10561036d3117398,Planes,-34.3814124662,59.1258910997,19.2323369362,1630 -1631,_05_future,21.9778391462,-27.6330363345,-21.6526352582,-0.255,0.412,1.16615384615,bac9a67537f0ebdf9b480415a8da49f8c265c570,256,V3,bac9a67537f0ebdf9b480415a8da49f8c265c570_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-25.474_ry+65.509_rz+57.262_tx-00.255_ty+00.412_s+01.516_bac9a67537f0ebdf9b480415a8da49f8c265c570_256x256.png,ead4e349e5367e1faf6a2bf86e52df65b015fcf5,Planes,21.9778391462,62.3669636655,-21.6526352582,1631 -1632,_05_future,-3.28130082782,-32.3164005023,25.7823731584,0.1,-0.155,0.947692307692,7f76fe25a54dc2d9011737a152a1912a2a39342b,256,V3,7f76fe25a54dc2d9011737a152a1912a2a39342b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-32.257_ry+93.881_rz+117.856_tx+00.100_ty-00.155_s+01.232_7f76fe25a54dc2d9011737a152a1912a2a39342b_256x256.png,91060c440eb9b9436779fcb46cf77df32bdec6f1,Planes,-3.28130082782,57.6835994977,25.7823731584,1632 -1633,_05_future,-5.178966186,-33.0842569691,33.3203709455,0.09,-0.431,1.13615384615,5aa00e37f27454a387745c49c2fe03b0135f30fc,256,V3,5aa00e37f27454a387745c49c2fe03b0135f30fc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-32.932_ry+96.174_rz+126.686_tx+00.090_ty-00.431_s+01.477_5aa00e37f27454a387745c49c2fe03b0135f30fc_256x256.png,7e7b1a59339659ab910509bb894aab1e0ea4b0f4,Planes,-5.178966186,56.9157430309,33.3203709455,1633 -1634,_05_future,14.6363320397,-34.7815092825,-30.4130541212,-0.28,0.096,0.916923076923,8f84afe9ca0336d31a221ac47e778d02cc2e6a52,256,V3,8f84afe9ca0336d31a221ac47e778d02cc2e6a52_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-33.500_ry+72.361_rz+49.633_tx-00.280_ty+00.096_s+01.192_8f84afe9ca0336d31a221ac47e778d02cc2e6a52_256x256.png,eeda8e883e2c3c004cafbaeaec7e7ca43ae96fdd,Planes,14.6363320397,55.2184907175,-30.4130541212,1634 -1635,_05_future,25.3946123009,-38.3708142241,-5.22423896352,0.275,0.377,1.25461538462,d7a9d333436972c250cea5868afe543cab5b17d7,256,V3,d7a9d333436972c250cea5868afe543cab5b17d7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-34.109_ry+58.805_rz+66.021_tx+00.275_ty+00.377_s+01.631_d7a9d333436972c250cea5868afe543cab5b17d7_256x256.png,e67189e5940b05cf56013b7f64d3c72b77f4f5ba,Planes,25.3946123009,51.6291857759,-5.22423896352,1635 -1636,_05_future,7.7384527963,-37.5896064088,7.05694497654,0.213,-0.56,1.18153846154,e17e08b0f4a81fbb484de4bcfa5cdf428c0ff3b3,256,V3,e17e08b0f4a81fbb484de4bcfa5cdf428c0ff3b3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-37.189_ry+80.269_rz+91.139_tx+00.213_ty-00.560_s+01.536_e17e08b0f4a81fbb484de4bcfa5cdf428c0ff3b3_256x256.png,045c069406a532e51060d1af08d3f8cd573a0645,Planes,7.7384527963,52.4103935912,7.05694497654,1636 -1637,_05_future,-33.1098521104,-46.1897048835,-69.7298447028,0.156,-0.01,0.810769230769,f25cf233df13a5addd332cfbac13c57e8c480347,256,V3,f25cf233df13a5addd332cfbac13c57e8c480347_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-37.190_ry+133.290_rz+49.928_tx+00.156_ty-00.010_s+01.054_f25cf233df13a5addd332cfbac13c57e8c480347_256x256.png,0cf1996d142c45f437e3c17152e7893ca1528b41,Planes,-33.1098521104,43.8102951165,-69.7298447028,1637 -1638,_05_future,1.60731781421,-43.9497278935,-9.50913499069,-0.283,0.381,0.883076923077,7a9dc9ce7d61409febb4e5417199845c76e3ec79,256,V3,7a9dc9ce7d61409febb4e5417199845c76e3ec79_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-43.928_ry+87.768_rz+78.942_tx-00.283_ty+00.381_s+01.148_7a9dc9ce7d61409febb4e5417199845c76e3ec79_256x256.png,35cd6525e02162d0bedc87a5262b3606ce980642,Planes,1.60731781421,46.0502721065,-9.50913499069,1638 -1639,_05_future,-13.4350925776,-46.428174713,1.74858299222,0.174,-0.301,1.04615384615,d6704f6497c1425331170fd5d837af93ebfbb465,256,V3,d6704f6497c1425331170fd5d837af93ebfbb465_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_05_future_rx-44.804_ry+109.115_rz+105.473_tx+00.174_ty-00.301_s+01.360_d6704f6497c1425331170fd5d837af93ebfbb465_256x256.png,1d7917754397e8f847e8f2a886f5efaa10411744,Planes,-13.4350925776,43.571825287,1.74858299222,1639 -1640,_08,0.571,-41.586,-17.448,0.091,0.298,1.22285714286,8eaf28c227531ad8df4ac0f21ebc312ae75b1bd3,256,V3,8eaf28c227531ad8df4ac0f21ebc312ae75b1bd3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_08_rx+00.571_ry-41.586_rz-17.448_tx+00.091_ty+00.298_s+00.856_8eaf28c227531ad8df4ac0f21ebc312ae75b1bd3_256x256.png,98d9bd95347407a111b783dacd591f4cacafa969,Tables,0.571,-41.586,-17.448,1640 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-1655,_08,32.443,-29.095,-16.869,0.251,-0.319,1.22428571429,b60a7a4bfe609ecd1ceb41b266c263a3e9234ebb,256,V3,b60a7a4bfe609ecd1ceb41b266c263a3e9234ebb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_08_rx+32.443_ry-29.095_rz-16.869_tx+00.251_ty-00.319_s+00.857_b60a7a4bfe609ecd1ceb41b266c263a3e9234ebb_256x256.png,cdfb323ea4c60f5240a6d1b3f1e6514bca998585,Tables,32.443,-29.095,-16.869,1655 -1656,_08,32.93,43.751,-2.138,0.101,0.346,0.875714285714,bb47bb39b8e4314b3f5bedea59e040eab7e4c73b,256,V3,bb47bb39b8e4314b3f5bedea59e040eab7e4c73b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_08_rx+32.930_ry+43.751_rz-02.138_tx+00.101_ty+00.346_s+00.613_bb47bb39b8e4314b3f5bedea59e040eab7e4c73b_256x256.png,7a5899836694355a8de20c4e55c5ac30504b14db,Tables,32.93,43.751,-2.138,1656 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-1841,_19_flyingBoat,-27.0105077132,1.80380832059,-25.4445785353,-0.263,-0.165,1.00615384615,55b5cf8c80acce511efa4f06c86d2b0db7d1737d,256,V3,55b5cf8c80acce511efa4f06c86d2b0db7d1737d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+01.607_ry+117.022_rz+63.736_tx-00.263_ty-00.165_s+01.308_55b5cf8c80acce511efa4f06c86d2b0db7d1737d_256x256.png,274399907339003b05b0ee3caf9087fc533fbc82,Planes,-27.0105077132,91.8038083206,-25.4445785353,1841 -1842,_19_flyingBoat,-20.8217029274,6.61046196253,46.0859098448,-0.134,0.13,0.892307692308,b654b947a96e87958d10f9fb1520e516d837d64c,256,V3,b654b947a96e87958d10f9fb1520e516d837d64c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+06.177_ry+110.949_rz+133.727_tx-00.134_ty+00.130_s+01.160_b654b947a96e87958d10f9fb1520e516d837d64c_256x256.png,76a14f3c71198b71e0183198b180a05eca07a334,Planes,-20.8217029274,96.6104619625,46.0859098448,1842 -1843,_19_flyingBoat,-30.0787784604,8.78679223659,35.0288591506,-0.139,-0.122,1.14230769231,8e77c46a8f1ad71f80d4431834a535bd901311b1,256,V3,8e77c46a8f1ad71f80d4431834a535bd901311b1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+07.596_ry+120.373_rz+120.599_tx-00.139_ty-00.122_s+01.485_8e77c46a8f1ad71f80d4431834a535bd901311b1_256x256.png,ec5c1049997ba1cc8c2b619097cfeec2b9e66e1d,Planes,-30.0787784604,98.7867922366,35.0288591506,1843 -1844,_19_flyingBoat,-21.1424129646,10.2442705948,-1.09841412562,0.001,0.431,0.994615384615,69816752a31f5d96db7f4743012b084ab77c4243,256,V3,69816752a31f5d96db7f4743012b084ab77c4243_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+09.548_ry+111.454_rz+85.172_tx+00.001_ty+00.431_s+01.293_69816752a31f5d96db7f4743012b084ab77c4243_256x256.png,d9e0902f65b12c5a6d9feb43871631a50989c61d,Planes,-21.1424129646,100.244270595,-1.09841412562,1844 -1845,_19_flyingBoat,36.6265066222,13.4293239887,-7.05735088354,-0.139,0.145,0.827692307692,bf32590ad4b070c754c7c864aee1db249e4c0be0,256,V3,bf32590ad4b070c754c7c864aee1db249e4c0be0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+10.742_ry+52.610_rz+91.050_tx-00.139_ty+00.145_s+01.076_bf32590ad4b070c754c7c864aee1db249e4c0be0_256x256.png,a960f3091ec9c6f61c2e816b532e2717499216c4,Planes,36.6265066222,103.429323989,-7.05735088354,1845 -1846,_19_flyingBoat,-11.5752517481,10.9769350639,-13.2872007363,-0.075,0.586,0.882307692308,da55a80fb6fbb0f0b677c426d4d7eba8ab14b6ab,256,V3,da55a80fb6fbb0f0b677c426d4d7eba8ab14b6ab_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+10.751_ry+101.785_rz+74.484_tx-00.075_ty+00.586_s+01.147_da55a80fb6fbb0f0b677c426d4d7eba8ab14b6ab_256x256.png,28ca0bdf69e90394d5ac801e4788bf5dea88ed95,Planes,-11.5752517481,100.976935064,-13.2872007363,1846 -1847,_19_flyingBoat,42.4723580813,15.3836655902,3.56038450697,-0.242,-0.514,1.20461538462,534ab7dcad031e64694be36f76210b0be0b7dc62,256,V3,534ab7dcad031e64694be36f76210b0be0b7dc62_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+11.284_ry+46.485_rz+104.085_tx-00.242_ty-00.514_s+01.566_534ab7dcad031e64694be36f76210b0be0b7dc62_256x256.png,4a9c296e3f543d7db5a644faa369a443e1968e82,Planes,42.4723580813,105.38366559,3.56038450697,1847 -1848,_19_flyingBoat,21.9984903857,14.6498334759,21.7794645473,-0.21,-0.576,0.968461538462,298b3d0bf94974e40d331cdf117aed453e6fb24d,256,V3,298b3d0bf94974e40d331cdf117aed453e6fb24d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+13.562_ry+67.336_rz+117.372_tx-00.210_ty-00.576_s+01.259_298b3d0bf94974e40d331cdf117aed453e6fb24d_256x256.png,f434aae4ba1b6bbb48504801b2da7b0743c32ebc,Planes,21.9984903857,104.649833476,21.7794645473,1848 -1849,_19_flyingBoat,36.1307630761,21.0778397233,-45.5934283424,0.035,-0.051,0.942307692308,c5f274ecdc3975bf847c819a5ae951ea4be6e89c,256,V3,c5f274ecdc3975bf847c819a5ae951ea4be6e89c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+16.886_ry+51.961_rz+57.210_tx+00.035_ty-00.051_s+01.225_c5f274ecdc3975bf847c819a5ae951ea4be6e89c_256x256.png,41ede018c9bbd606a6c61d33ae02f5f70fd20496,Planes,36.1307630761,111.077839723,-45.5934283424,1849 -1850,_19_flyingBoat,14.6431146,18.6271404885,31.3856436602,0.232,0.193,0.781538461538,63efb1658c66037487e3aff4357bd19195db5e6a,256,V3,63efb1658c66037487e3aff4357bd19195db5e6a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+18.001_ry+74.585_rz+126.256_tx+00.232_ty+00.193_s+01.016_63efb1658c66037487e3aff4357bd19195db5e6a_256x256.png,4154752c1c6bb11bbed31b5bad1ff27523f240c9,Planes,14.6431146,108.627140489,31.3856436602,1850 -1851,_19_flyingBoat,24.1897351383,21.4409309577,-53.3517113284,0.299,-0.559,0.854615384615,2212fa2dc1f5057314a2d248de91db43e5d9dfc0,256,V3,2212fa2dc1f5057314a2d248de91db43e5d9dfc0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+19.478_ry+64.238_rz+45.790_tx+00.299_ty-00.559_s+01.111_2212fa2dc1f5057314a2d248de91db43e5d9dfc0_256x256.png,80f74030188e17eaebc175fdb1171d00e1c8ac1d,Planes,24.1897351383,111.440930958,-53.3517113284,1851 -1852,_19_flyingBoat,-23.3528129802,21.9549476679,15.7935604386,0.12,0.449,1.04,b6c8c4ee1d09a7f8cc59e0eb1b133dd7e77c579c,256,V3,b6c8c4ee1d09a7f8cc59e0eb1b133dd7e77c579c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+20.075_ry+114.963_rz+96.715_tx+00.120_ty+00.449_s+01.352_b6c8c4ee1d09a7f8cc59e0eb1b133dd7e77c579c_256x256.png,22d77cd89350e32bcf72df78031f31bcd0bf7717,Planes,-23.3528129802,111.954947668,15.7935604386,1852 -1853,_19_flyingBoat,-1.28127440083,22.7179976953,-4.72361872693,0.153,-0.248,0.86,637c1479d888b313f3279241ed6a2f3c483402c9,256,V3,637c1479d888b313f3279241ed6a2f3c483402c9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+22.712_ry+91.389_rz+84.740_tx+00.153_ty-00.248_s+01.118_637c1479d888b313f3279241ed6a2f3c483402c9_256x256.png,d150670a19442dac7aa4c00c9ce88271ff614387,Planes,-1.28127440083,112.717997695,-4.72361872693,1853 -1854,_19_flyingBoat,-37.9985474468,31.0605503951,27.4658206734,0.208,-0.347,0.867692307692,a6cb1b1dcfa5ef49c693d9aafbcc700c5c013bf1,256,V3,a6cb1b1dcfa5ef49c693d9aafbcc700c5c013bf1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+23.990_ry+132.365_rz+97.121_tx+00.208_ty-00.347_s+01.128_a6cb1b1dcfa5ef49c693d9aafbcc700c5c013bf1_256x256.png,58f3a1316d99b2eabdd0366f1a485d57fefa079a,Planes,-37.9985474468,121.060550395,27.4658206734,1854 -1855,_19_flyingBoat,-12.9054960931,27.7839252369,17.5854534007,0.096,0.28,1.28615384615,70222407751181c4b7723cb09dbda63e7f9c7333,256,V3,70222407751181c4b7723cb09dbda63e7f9c7333_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+27.024_ry+104.520_rz+100.874_tx+00.096_ty+00.280_s+01.672_70222407751181c4b7723cb09dbda63e7f9c7333_256x256.png,caf392d996023d49d9be520da255f865a014f646,Planes,-12.9054960931,117.783925237,17.5854534007,1855 -1856,_19_flyingBoat,1.24532105087,27.9731872439,-11.222320158,0.08,-0.121,0.900769230769,6c042254d9a861e8b91e0118ff22504a7e95e7af,256,V3,6c042254d9a861e8b91e0118ff22504a7e95e7af_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+27.966_ry+88.590_rz+79.439_tx+00.080_ty-00.121_s+01.171_6c042254d9a861e8b91e0118ff22504a7e95e7af_256x256.png,ff6341b2b5c5342eb876d1e9c56f440f1052eca6,Planes,1.24532105087,117.973187244,-11.222320158,1856 -1857,_19_flyingBoat,8.1862145689,30.7223314392,-20.5218769506,-0.026,0.225,0.783846153846,571e9ab908ff9f27b0240f9ec49be83b7173ed59,256,V3,571e9ab908ff9f27b0240f9ec49be83b7173ed59_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+30.376_ry+80.500_rz+74.315_tx-00.026_ty+00.225_s+01.019_571e9ab908ff9f27b0240f9ec49be83b7173ed59_256x256.png,9f9c8577238bceddd796339dde1e1056dbebfc20,Planes,8.1862145689,120.722331439,-20.5218769506,1857 -1858,_19_flyingBoat,-21.166958502,37.5575633391,59.3932345012,0.044,0.005,0.786923076923,77e172aa740f20d9b0237604c52ed87e8bb63be3,256,V3,77e172aa740f20d9b0237604c52ed87e8bb63be3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+34.641_ry+116.033_rz+133.876_tx+00.044_ty+00.005_s+01.023_77e172aa740f20d9b0237604c52ed87e8bb63be3_256x256.png,75b88c8f51a68a5f98004256ab42df314837c322,Planes,-21.166958502,127.557563339,59.3932345012,1858 -1859,_19_flyingBoat,27.4656012481,40.8418288044,-25.9461166776,-0.106,-0.493,1.11153846154,fcb25ffde5a5f1da54e6c7f308a66fea075eb452,256,V3,fcb25ffde5a5f1da54e6c7f308a66fea075eb452_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+35.469_ry+55.507_rz+85.791_tx-00.106_ty-00.493_s+01.445_fcb25ffde5a5f1da54e6c7f308a66fea075eb452_256x256.png,a09980ba2d544ff987ac5b46b97f8dd17a4e36a0,Planes,27.4656012481,130.841828804,-25.9461166776,1859 -1860,_19_flyingBoat,32.0854817126,43.4209270683,-59.6919120649,0.111,0.525,0.895384615385,9d57a9b3c05559bca697c11b99e31248d943dc87,256,V3,9d57a9b3c05559bca697c11b99e31248d943dc87_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+35.617_ry+49.200_rz+56.996_tx+00.111_ty+00.525_s+01.164_9d57a9b3c05559bca697c11b99e31248d943dc87_256x256.png,b40b2add027cff1f9695fe928ca21729a3c52a2a,Planes,32.0854817126,133.420927068,-59.6919120649,1860 -1861,_19_flyingBoat,-0.548859593486,39.7631873844,-42.5053212003,0.158,0.135,0.825384615385,0d3cb3026e62005195614249c4a1380f962efcdf,256,V3,0d3cb3026e62005195614249c4a1380f962efcdf_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+39.761_ry+90.714_rz+47.038_tx+00.158_ty+00.135_s+01.073_0d3cb3026e62005195614249c4a1380f962efcdf_256x256.png,6a7b8ba7a8c1ca80a6290ee68cd25cd5b7587c22,Planes,-0.548859593486,129.763187384,-42.5053212003,1861 -1862,_19_flyingBoat,0.462528944163,40.2495804121,-40.7535434919,-0.013,-0.289,0.912307692308,3608373b14de6918efe6fe30ebee68770ab20a25,256,V3,3608373b14de6918efe6fe30ebee68770ab20a25_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+40.248_ry+89.394_rz+49.638_tx-00.013_ty-00.289_s+01.186_3608373b14de6918efe6fe30ebee68770ab20a25_256x256.png,9a97ac7c7c4c85ef455070f08f93e4c59958010c,Planes,0.462528944163,130.249580412,-40.7535434919,1862 -1863,_19_flyingBoat,24.4800194704,46.4416870274,-29.6812232284,0.206,-0.474,1.24923076923,88f988f12ea682ac815c2f86b5beb326969db1be,256,V3,88f988f12ea682ac815c2f86b5beb326969db1be_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+41.264_ry+56.546_rz+83.865_tx+00.206_ty-00.474_s+01.624_88f988f12ea682ac815c2f86b5beb326969db1be_256x256.png,ec1374ca330fa00e70eae4f8d621e396c334a8d4,Planes,24.4800194704,136.441687027,-29.6812232284,1863 -1864,_19_flyingBoat,-18.15935145,46.8597350975,36.3890573118,0.27,0.501,1.04615384615,2c2679e7746dd0132c6997e9afe3b73ac07fa850,256,V3,2c2679e7746dd0132c6997e9afe3b73ac07fa850_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx+43.895_ry+115.626_rz+107.993_tx+00.270_ty+00.501_s+01.360_2c2679e7746dd0132c6997e9afe3b73ac07fa850_256x256.png,1112238bc68bf35cb4aa73c2f2fb163374250c41,Planes,-18.15935145,136.859735098,36.3890573118,1864 -1865,_19_flyingBoat,-1.27144722311,-2.83069751006,-13.4988618127,-0.019,0.246,0.794615384615,823cfa01b8b724894d1509d7f28e3959c7226fec,256,V3,823cfa01b8b724894d1509d7f28e3959c7226fec_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx-02.830_ry+91.273_rz+76.564_tx-00.019_ty+00.246_s+01.033_823cfa01b8b724894d1509d7f28e3959c7226fec_256x256.png,67dc1d933b827522ce3f45f32f0164594ba0de1a,Planes,-1.27144722311,87.1693024899,-13.4988618127,1865 -1866,_19_flyingBoat,-19.3997636076,-14.877503057,-4.94275096362,-0.213,-0.262,0.901538461538,94dd44ed4c8ace41e9c5d5f2abbf131263b9c217,256,V3,94dd44ed4c8ace41e9c5d5f2abbf131263b9c217_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx-14.015_ry+110.020_rz+90.100_tx-00.213_ty-00.262_s+01.172_94dd44ed4c8ace41e9c5d5f2abbf131263b9c217_256x256.png,35c3a16591ffdfcfc61ae3da8dbd74c8925dd683,Planes,-19.3997636076,75.122496943,-4.94275096362,1866 -1867,_19_flyingBoat,39.7024821017,-20.5776097877,1.46723633472,0.217,0.23,0.851538461538,d4bcb98985d714d35c61e23868a3bc3a2259e3ee,256,V3,d4bcb98985d714d35c61e23868a3bc3a2259e3ee_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx-15.689_ry+48.431_rz+77.981_tx+00.217_ty+00.230_s+01.107_d4bcb98985d714d35c61e23868a3bc3a2259e3ee_256x256.png,0ee3260e66c528927b72091fd28c992caf2ed1b3,Planes,39.7024821017,69.4223902123,1.46723633472,1867 -1868,_19_flyingBoat,-19.0342567639,-19.854269585,-50.5824636227,0.282,0.392,1.25615384615,654e61bf5a38dc54cd45666d124e7ef947436f3a,256,V3,654e61bf5a38dc54cd45666d124e7ef947436f3a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx-18.727_ry+110.143_rz+46.134_tx+00.282_ty+00.392_s+01.633_654e61bf5a38dc54cd45666d124e7ef947436f3a_256x256.png,06ae0fde681de2877c5925cf88ae6be4e34372ad,Planes,-19.0342567639,70.145730415,-50.5824636227,1868 -1869,_19_flyingBoat,20.9181192699,-25.5664332019,42.2588467941,0.099,0.382,0.890769230769,4d6d53ee92b297ec2c52406ff2c6ea3c5cd8ee5e,256,V3,4d6d53ee92b297ec2c52406ff2c6ea3c5cd8ee5e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx-23.773_ry+67.037_rz+122.566_tx+00.099_ty+00.382_s+01.158_4d6d53ee92b297ec2c52406ff2c6ea3c5cd8ee5e_256x256.png,21a677cf98ff9ac36967fdbff84560cad7ee42e5,Planes,20.9181192699,64.4335667981,42.2588467941,1869 -1870,_19_flyingBoat,10.0843493491,-25.7114600406,21.0433248969,0.173,0.129,1.18846153846,e7de00d6f91278bf8e88ad544f4ff995a70cee42,256,V3,e7de00d6f91278bf8e88ad544f4ff995a70cee42_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx-25.286_ry+78.834_rz+106.224_tx+00.173_ty+00.129_s+01.545_e7de00d6f91278bf8e88ad544f4ff995a70cee42_256x256.png,cfb4f0a7cbef258e8759ce0378f8550c90f7911d,Planes,10.0843493491,64.2885399594,21.0433248969,1870 -1871,_19_flyingBoat,-35.9618129756,-36.7757633325,-64.4670523108,-0.033,-0.101,1.00923076923,2294e29de7e5e2317ddd3ad47320850b79eb7ab9,256,V3,2294e29de7e5e2317ddd3ad47320850b79eb7ab9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx-28.985_ry+132.170_rz+49.231_tx-00.033_ty-00.101_s+01.312_2294e29de7e5e2317ddd3ad47320850b79eb7ab9_256x256.png,b8c9b6f2c5f9973498a70f9f1e6bfc1034fb4f12,Planes,-35.9618129756,53.2242366675,-64.4670523108,1871 -1872,_19_flyingBoat,-17.4046145553,-31.6269316341,-8.01757205476,-0.055,0.466,1.05384615385,a24f018d0e99fe56f283535543d3c54d93782520,256,V3,a24f018d0e99fe56f283535543d3c54d93782520_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx-30.025_ry+110.211_rz+92.420_tx-00.055_ty+00.466_s+01.370_a24f018d0e99fe56f283535543d3c54d93782520_256x256.png,b3b2673223e9ce4597cd5dce0666887b2c5263e0,Planes,-17.4046145553,58.3730683659,-8.01757205476,1872 -1873,_19_flyingBoat,14.4909532517,-31.6352774006,30.7323481605,-0.184,-0.54,0.961538461538,46b50c52ab392f80d7f73d1cfb388839d96ea600,256,V3,46b50c52ab392f80d7f73d1cfb388839d96ea600_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx-30.519_ry+73.114_rz+111.969_tx-00.184_ty-00.540_s+01.250_46b50c52ab392f80d7f73d1cfb388839d96ea600_256x256.png,e86092ac49c393abf7a51b8bdd3b37d6ce9b8ee8,Planes,14.4909532517,58.3647225994,30.7323481605,1873 -1874,_19_flyingBoat,31.9452345247,-39.81990501,-5.0684206644,-0.243,-0.392,1.03076923077,cfe97b6bbb10d2981b633c30c94c5c63be0a0150,256,V3,cfe97b6bbb10d2981b633c30c94c5c63be0a0150_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx-32.915_ry+50.929_rz+61.127_tx-00.243_ty-00.392_s+01.340_cfe97b6bbb10d2981b633c30c94c5c63be0a0150_256x256.png,1954141f0fce3eb6cc5fee616684272019a4d722,Planes,31.9452345247,50.18009499,-5.0684206644,1874 -1875,_19_flyingBoat,6.94204998798,-34.3566524222,18.5292792123,0.213,0.08,1.02461538462,b5c89163a00b2fbe7489d11dcd92a1086dfdb6c8,256,V3,b5c89163a00b2fbe7489d11dcd92a1086dfdb6c8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx-34.070_ry+81.610_rz+103.806_tx+00.213_ty+00.080_s+01.332_b5c89163a00b2fbe7489d11dcd92a1086dfdb6c8_256x256.png,05e461a8d79a2c2b9b05e37f2edef16c79d79bc4,Planes,6.94204998798,55.6433475778,18.5292792123,1875 -1876,_19_flyingBoat,-14.1974441583,-41.1785643393,8.93073887895,0.235,-0.591,1.08153846154,ca7016db772aeb7332f8bb91ba7125feed66d4a4,256,V3,ca7016db772aeb7332f8bb91ba7125feed66d4a4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx-39.665_ry+108.579_rz+111.040_tx+00.235_ty-00.591_s+01.406_ca7016db772aeb7332f8bb91ba7125feed66d4a4_256x256.png,706dd4ce8766cd8b8dc3f262e0bb4e2676deeb78,Planes,-14.1974441583,48.8214356607,8.93073887895,1876 -1877,_19_flyingBoat,-7.36105051895,-40.6837004375,-3.40113437172,-0.034,-0.534,1.25307692308,486e2d0cc6cbb9878012e8eac4782906874a64b2,256,V3,486e2d0cc6cbb9878012e8eac4782906874a64b2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx-40.279_ry+99.668_rz+92.884_tx-00.034_ty-00.534_s+01.629_486e2d0cc6cbb9878012e8eac4782906874a64b2_256x256.png,67a8633b333322d99617607149f10cb159612219,Planes,-7.36105051895,49.3162995625,-3.40113437172,1877 -1878,_19_flyingBoat,12.4909406353,-41.5980484512,-23.956709252,0.11,0.194,1.03230769231,146ea4e74791fa1834c8da80892487c533dabf32,256,V3,146ea4e74791fa1834c8da80892487c533dabf32_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx-40.405_ry+73.499_rz+55.174_tx+00.110_ty+00.194_s+01.342_146ea4e74791fa1834c8da80892487c533dabf32_256x256.png,dcc4e4d1efaf691c2c5cd3e680309b7abf19150e,Planes,12.4909406353,48.4019515488,-23.956709252,1878 -1879,_19_flyingBoat,-17.7479167559,-45.3347811836,6.63156210627,-0.271,0.224,0.826153846154,11bd607c9e86c8e15298d224281442f2a7afcec9,256,V3,11bd607c9e86c8e15298d224281442f2a7afcec9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_19_flyingBoat_rx-42.639_ry+114.480_rz+113.772_tx-00.271_ty+00.224_s+01.074_11bd607c9e86c8e15298d224281442f2a7afcec9_256x256.png,818e7fbf51420fac045ae4e3c7965eb39403d6b2,Planes,-17.7479167559,44.6652188164,6.63156210627,1879 -1880,_37,0.26,40.084,12.136,-0.144,0.407,0.834285714286,43bac700065a82fb171cc70b326dcd4e31d516cd,256,V3,43bac700065a82fb171cc70b326dcd4e31d516cd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_37_rx+00.260_ry+40.084_rz+12.136_tx-00.144_ty+00.407_s+00.584_43bac700065a82fb171cc70b326dcd4e31d516cd_256x256.png,c72ce0b8de2534dbc11decdb809fd4e2479b5ebc,Tables,0.26,40.084,12.136,1880 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-1887,_37,13.72,35.595,-8.264,0.175,0.521,0.935714285714,1d905ca431950023d2f9b00ea6adc1c2c60c1d06,256,V3,1d905ca431950023d2f9b00ea6adc1c2c60c1d06_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_37_rx+13.720_ry+35.595_rz-08.264_tx+00.175_ty+00.521_s+00.655_1d905ca431950023d2f9b00ea6adc1c2c60c1d06_256x256.png,47f67935e08dc1108835c2c52e1e299afc77d74c,Tables,13.72,35.595,-8.264,1887 -1888,_37,14.655,7.887,24.78,-0.028,0.115,0.945714285714,0cffa3008fa41e71bc8ad60fd836c6bf11c5ebca,256,V3,0cffa3008fa41e71bc8ad60fd836c6bf11c5ebca_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_37_rx+14.655_ry+07.887_rz+24.780_tx-00.028_ty+00.115_s+00.662_0cffa3008fa41e71bc8ad60fd836c6bf11c5ebca_256x256.png,e3bcbc5022197a8d1af5d739b48eb8d4590fe2d8,Tables,14.655,7.887,24.78,1888 -1889,_37,19.496,-30.887,29.121,0.3,-0.391,1.02857142857,006d66c207c6417574f62f0560c6b2b40a9ec5a1,256,V3,006d66c207c6417574f62f0560c6b2b40a9ec5a1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_37_rx+19.496_ry-30.887_rz+29.121_tx+00.300_ty-00.391_s+00.720_006d66c207c6417574f62f0560c6b2b40a9ec5a1_256x256.png,0c105cd3f71f189643486eaef9cf2919c1cc0e8b,Tables,19.496,-30.887,29.121,1889 -1890,_37,21.467,44.871,32.031,0.274,0.521,0.877142857143,e6227fd849db5f7b5fe0e4e475e56a0c7834c7b3,256,V3,e6227fd849db5f7b5fe0e4e475e56a0c7834c7b3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_37_rx+21.467_ry+44.871_rz+32.031_tx+00.274_ty+00.521_s+00.614_e6227fd849db5f7b5fe0e4e475e56a0c7834c7b3_256x256.png,4af2b13518b7a0a1bf59561d14f994260d4c9b76,Tables,21.467,44.871,32.031,1890 -1891,_37,22.101,-30.334,25.322,-0.084,0.041,1.13714285714,9af1c4a7e007dfb35dd1d08a88c6e5c2470b0992,256,V3,9af1c4a7e007dfb35dd1d08a88c6e5c2470b0992_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_37_rx+22.101_ry-30.334_rz+25.322_tx-00.084_ty+00.041_s+00.796_9af1c4a7e007dfb35dd1d08a88c6e5c2470b0992_256x256.png,3356a9026d7f4dd26d9164be2e20b9b4e25e773f,Tables,22.101,-30.334,25.322,1891 -1892,_37,23.992,32.81,-14.297,-0.188,0.51,1.10428571429,c17deb7bbae9cc7ddb58fe280f960b5a2ffdeed3,256,V3,c17deb7bbae9cc7ddb58fe280f960b5a2ffdeed3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_37_rx+23.992_ry+32.810_rz-14.297_tx-00.188_ty+00.510_s+00.773_c17deb7bbae9cc7ddb58fe280f960b5a2ffdeed3_256x256.png,d680fd2932b6b6adf5155211a6a161843ae148ab,Tables,23.992,32.81,-14.297,1892 -1893,_37,26.388,5.838,32.514,-0.284,-0.171,0.887142857143,12c3519b7c780de90991c5d5f52953d047b6278e,256,V3,12c3519b7c780de90991c5d5f52953d047b6278e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_37_rx+26.388_ry+05.838_rz+32.514_tx-00.284_ty-00.171_s+00.621_12c3519b7c780de90991c5d5f52953d047b6278e_256x256.png,9adc7b3478756a702c2963c2ff5224ed2f7e2a87,Tables,26.388,5.838,32.514,1893 -1894,_37,28.318,29.796,-0.208,0.098,0.41,0.93,120a5bdad65d80a273a9e1d8fc5b653657f565a8,256,V3,120a5bdad65d80a273a9e1d8fc5b653657f565a8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_37_rx+28.318_ry+29.796_rz-00.208_tx+00.098_ty+00.410_s+00.651_120a5bdad65d80a273a9e1d8fc5b653657f565a8_256x256.png,e71e4d1a058d7b06c2ae3464fbb952a7c59b4712,Tables,28.318,29.796,-0.208,1894 -1895,_37,29.206,44.592,5.668,0.196,-0.007,0.892857142857,fda6155b53e755a04498be92dad024b6841f31bb,256,V3,fda6155b53e755a04498be92dad024b6841f31bb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_37_rx+29.206_ry+44.592_rz+05.668_tx+00.196_ty-00.007_s+00.625_fda6155b53e755a04498be92dad024b6841f31bb_256x256.png,06ed27229114347ed00e1b8766f59dc713f3e39e,Tables,29.206,44.592,5.668,1895 -1896,_37,31.745,-3.35,13.647,0.009,-0.228,1.19,508f614b1159f80868d26983923cdb2cc6308f01,256,V3,508f614b1159f80868d26983923cdb2cc6308f01_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_37_rx+31.745_ry-03.350_rz+13.647_tx+00.009_ty-00.228_s+00.833_508f614b1159f80868d26983923cdb2cc6308f01_256x256.png,7f983a4a5f0655ddf9cb8b57908a4d484d34f37e,Tables,31.745,-3.35,13.647,1896 -1897,_37,32.618,-9.771,-20.902,0.117,-0.544,0.865714285714,399aea92e84e4ddda5fa2bdc5c28a4045373a773,256,V3,399aea92e84e4ddda5fa2bdc5c28a4045373a773_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_37_rx+32.618_ry-09.771_rz-20.902_tx+00.117_ty-00.544_s+00.606_399aea92e84e4ddda5fa2bdc5c28a4045373a773_256x256.png,611425c4cf86396e6f40611b13c97104eccc796e,Tables,32.618,-9.771,-20.902,1897 -1898,_37,34.426,8.136,4.344,0.059,0.306,1.00571428571,6fded474826cd02ca4c6f4461271a61fde1081f1,256,V3,6fded474826cd02ca4c6f4461271a61fde1081f1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_37_rx+34.426_ry+08.136_rz+04.344_tx+00.059_ty+00.306_s+00.704_6fded474826cd02ca4c6f4461271a61fde1081f1_256x256.png,24e6ecc1d5e8ac9b0d9a2381d2fd1c70547880a5,Tables,34.426,8.136,4.344,1898 -1899,_37,35.939,29.226,-41.057,0.098,0.062,1.08,66a7175f5e1a709a832bbd8347e7a45949cc4441,256,V3,66a7175f5e1a709a832bbd8347e7a45949cc4441_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_37_rx+35.939_ry+29.226_rz-41.057_tx+00.098_ty+00.062_s+00.756_66a7175f5e1a709a832bbd8347e7a45949cc4441_256x256.png,f2bf46d43f2301e81e73141452178c177cf31de7,Tables,35.939,29.226,-41.057,1899 -1900,_37,36.611,-43.196,33.597,0.039,0.391,1.18428571429,e20e128f8061923fc3ab3fec07fb737b8a787921,256,V3,e20e128f8061923fc3ab3fec07fb737b8a787921_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_37_rx+36.611_ry-43.196_rz+33.597_tx+00.039_ty+00.391_s+00.829_e20e128f8061923fc3ab3fec07fb737b8a787921_256x256.png,e4412b873652cc47bfdb8de0bbeaa5e8c992af75,Tables,36.611,-43.196,33.597,1900 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-1927,_38,28.748,15.987,34.998,-0.23,0.167,0.875714285714,35d464debab62986b651adcb49838fdebe0f59f2,256,V3,35d464debab62986b651adcb49838fdebe0f59f2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx+28.748_ry+15.987_rz+34.998_tx-00.230_ty+00.167_s+00.613_35d464debab62986b651adcb49838fdebe0f59f2_256x256.png,dff503b575b6bc558967c8390f73be1e08c44026,Tables,28.748,15.987,34.998,1927 -1928,_38,31.413,-44.41,-5.234,0.126,-0.17,1.14428571429,ba7982316aee96ced8075b3ae311afd5fd2044ca,256,V3,ba7982316aee96ced8075b3ae311afd5fd2044ca_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx+31.413_ry-44.410_rz-05.234_tx+00.126_ty-00.170_s+00.801_ba7982316aee96ced8075b3ae311afd5fd2044ca_256x256.png,62d3ae09a065c28be13758f79c13d96c151ed408,Tables,31.413,-44.41,-5.234,1928 -1929,_38,39.921,23.302,39.44,-0.108,-0.069,1.27857142857,3a416d624849f068639994d32955824063ce7624,256,V3,3a416d624849f068639994d32955824063ce7624_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx+39.921_ry+23.302_rz+39.440_tx-00.108_ty-00.069_s+00.895_3a416d624849f068639994d32955824063ce7624_256x256.png,1aef535390cd2f0f2912ca93b7b8a68e0655ac68,Tables,39.921,23.302,39.44,1929 -1930,_38,42.458,28.059,27.91,0.005,-0.38,0.79,f8a964ed8b68b63a21201dd169f0a6600b2b31a3,256,V3,f8a964ed8b68b63a21201dd169f0a6600b2b31a3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx+42.458_ry+28.059_rz+27.910_tx+00.005_ty-00.380_s+00.553_f8a964ed8b68b63a21201dd169f0a6600b2b31a3_256x256.png,c5bc78fde52258f3afd728a6e9e74ee220cb24f5,Tables,42.458,28.059,27.91,1930 -1931,_38,42.983,39.669,-13.893,0.175,0.194,1.21285714286,cd8cb8b852666f43863735bc47fe44d580d0ed8d,256,V3,cd8cb8b852666f43863735bc47fe44d580d0ed8d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx+42.983_ry+39.669_rz-13.893_tx+00.175_ty+00.194_s+00.849_cd8cb8b852666f43863735bc47fe44d580d0ed8d_256x256.png,0774075705cd11f7f9a116ce27013b1c25c18933,Tables,42.983,39.669,-13.893,1931 -1932,_38,43.123,28.729,-19.764,0.201,-0.287,0.911428571429,fcf80d7fcd7017b863f5785fb705b0c9a36b7c4e,256,V3,fcf80d7fcd7017b863f5785fb705b0c9a36b7c4e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx+43.123_ry+28.729_rz-19.764_tx+00.201_ty-00.287_s+00.638_fcf80d7fcd7017b863f5785fb705b0c9a36b7c4e_256x256.png,88fa4df8bffbe7244c83575b5e203f77b24fabd2,Tables,43.123,28.729,-19.764,1932 -1933,_38,44.462,1.656,-26.877,-0.052,-0.046,0.988571428571,226044b26fec26630d447d33b0ed9f72cdc617b1,256,V3,226044b26fec26630d447d33b0ed9f72cdc617b1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx+44.462_ry+01.656_rz-26.877_tx-00.052_ty-00.046_s+00.692_226044b26fec26630d447d33b0ed9f72cdc617b1_256x256.png,5b72bbc6f2a3ce84a18190bbc3ae556865873862,Tables,44.462,1.656,-26.877,1933 -1934,_38,-0.602,-24.208,5.011,-0.166,0.437,1.01857142857,5386b79dbc1bddd20df673412b950e717e4eb6b4,256,V3,5386b79dbc1bddd20df673412b950e717e4eb6b4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-00.602_ry-24.208_rz+05.011_tx-00.166_ty+00.437_s+00.713_5386b79dbc1bddd20df673412b950e717e4eb6b4_256x256.png,369af033bd311b96ce399cf6bd1dcadb8daca03c,Tables,-0.602,-24.208,5.011,1934 -1935,_38,-0.787,-41.56,24.308,0.025,0.243,1.13142857143,485606a0d62f63809b568c2ab7159ccae308f685,256,V3,485606a0d62f63809b568c2ab7159ccae308f685_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-00.787_ry-41.560_rz+24.308_tx+00.025_ty+00.243_s+00.792_485606a0d62f63809b568c2ab7159ccae308f685_256x256.png,bd839f6bf5919a4fec9f898b83563a7a1f7e6494,Tables,-0.787,-41.56,24.308,1935 -1936,_38,-1.551,18.347,29.634,0.066,0.447,0.972857142857,843ee973380b8f296eb0f89f39c4e09e5633a71e,256,V3,843ee973380b8f296eb0f89f39c4e09e5633a71e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-01.551_ry+18.347_rz+29.634_tx+00.066_ty+00.447_s+00.681_843ee973380b8f296eb0f89f39c4e09e5633a71e_256x256.png,0754aa1f7300b2093a1111076d776a19789fc072,Tables,-1.551,18.347,29.634,1936 -1937,_38,-2.83,39.717,22.827,-0.018,-0.169,1.21,b1188ce0d9f37b1f937e9ba24ab75ff26101052f,256,V3,b1188ce0d9f37b1f937e9ba24ab75ff26101052f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-02.830_ry+39.717_rz+22.827_tx-00.018_ty-00.169_s+00.847_b1188ce0d9f37b1f937e9ba24ab75ff26101052f_256x256.png,680692437d22a3439112774822a65c9f807420c8,Tables,-2.83,39.717,22.827,1937 -1938,_38,-3.172,18.082,11.946,-0.258,-0.381,1.18857142857,0c04a361705461916339257186311d8be9c7ba46,256,V3,0c04a361705461916339257186311d8be9c7ba46_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-03.172_ry+18.082_rz+11.946_tx-00.258_ty-00.381_s+00.832_0c04a361705461916339257186311d8be9c7ba46_256x256.png,e6b06cc28405b6fc61e6ecfec4a7cf55f9630a5b,Tables,-3.172,18.082,11.946,1938 -1939,_38,-4.749,37.643,-5.919,0.195,-0.54,1.20428571429,a3feb7d612715bce0678e7dbfd5060d8607410c5,256,V3,a3feb7d612715bce0678e7dbfd5060d8607410c5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-04.749_ry+37.643_rz-05.919_tx+00.195_ty-00.540_s+00.843_a3feb7d612715bce0678e7dbfd5060d8607410c5_256x256.png,d0c87e4e7ef1299bfa0440c14c03384f2647e570,Tables,-4.749,37.643,-5.919,1939 -1940,_38,-10.371,22.186,-39.639,-0.012,0.594,1.21285714286,eb6853bb9b055593db3579d7fdfbdd19d4ccce27,256,V3,eb6853bb9b055593db3579d7fdfbdd19d4ccce27_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-10.371_ry+22.186_rz-39.639_tx-00.012_ty+00.594_s+00.849_eb6853bb9b055593db3579d7fdfbdd19d4ccce27_256x256.png,7541253b9821e3a03d079393f493d6b5287f6609,Tables,-10.371,22.186,-39.639,1940 -1941,_38,-11.558,-14.593,-40.585,-0.255,-0.243,0.807142857143,78d168e0045c2dcb20cac42da15ba5bba545f3c2,256,V3,78d168e0045c2dcb20cac42da15ba5bba545f3c2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-11.558_ry-14.593_rz-40.585_tx-00.255_ty-00.243_s+00.565_78d168e0045c2dcb20cac42da15ba5bba545f3c2_256x256.png,1b2e4e459160642f800c6665fd00de83fa6cb7fd,Tables,-11.558,-14.593,-40.585,1941 -1942,_38,-12.885,24.929,28.759,-0.003,-0.499,1.18,630405fa3f075d766d099e11171a5f7aaa23fab6,256,V3,630405fa3f075d766d099e11171a5f7aaa23fab6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-12.885_ry+24.929_rz+28.759_tx-00.003_ty-00.499_s+00.826_630405fa3f075d766d099e11171a5f7aaa23fab6_256x256.png,67f9a0a4066e3ab8f5042916b08c3daad26332d1,Tables,-12.885,24.929,28.759,1942 -1943,_38,-15.128,43.251,-34.46,0.285,0.152,1.24428571429,fa1c4ed63b1b49e9aa5e2d82aaf289b4f05c0ae8,256,V3,fa1c4ed63b1b49e9aa5e2d82aaf289b4f05c0ae8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-15.128_ry+43.251_rz-34.460_tx+00.285_ty+00.152_s+00.871_fa1c4ed63b1b49e9aa5e2d82aaf289b4f05c0ae8_256x256.png,d0bbb603bc20b3580ced64bd8c3c12b86949c765,Tables,-15.128,43.251,-34.46,1943 -1944,_38,-15.964,-44.432,-13.267,0.133,0.162,1.04857142857,eaf7fea7cf539870a738b5bd12ed7c0ff6b97e7f,256,V3,eaf7fea7cf539870a738b5bd12ed7c0ff6b97e7f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-15.964_ry-44.432_rz-13.267_tx+00.133_ty+00.162_s+00.734_eaf7fea7cf539870a738b5bd12ed7c0ff6b97e7f_256x256.png,fc95945b071bb8c68d1c0551cf6f00160e14ef8c,Tables,-15.964,-44.432,-13.267,1944 -1945,_38,-15.986,-25.003,-26.743,-0.229,-0.205,1.03285714286,7ef14a2f921da14bfb54dd29d05c190bf3ed21b5,256,V3,7ef14a2f921da14bfb54dd29d05c190bf3ed21b5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-15.986_ry-25.003_rz-26.743_tx-00.229_ty-00.205_s+00.723_7ef14a2f921da14bfb54dd29d05c190bf3ed21b5_256x256.png,21889729d72abbcc8fff7a281ea0282d51a41425,Tables,-15.986,-25.003,-26.743,1945 -1946,_38,-16.455,35.477,43.31,0.082,0.111,0.84,8aa22ce8ce00b742c703ef41d47332f5b0ea06fc,256,V3,8aa22ce8ce00b742c703ef41d47332f5b0ea06fc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-16.455_ry+35.477_rz+43.310_tx+00.082_ty+00.111_s+00.588_8aa22ce8ce00b742c703ef41d47332f5b0ea06fc_256x256.png,cb567d72a996f58457faa7bdf825ccf87411f277,Tables,-16.455,35.477,43.31,1946 -1947,_38,-16.489,8.129,41.899,0.025,0.53,1.11285714286,cacbaeb11a883e598d68bcbd178c806f91d7090a,256,V3,cacbaeb11a883e598d68bcbd178c806f91d7090a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-16.489_ry+08.129_rz+41.899_tx+00.025_ty+00.530_s+00.779_cacbaeb11a883e598d68bcbd178c806f91d7090a_256x256.png,b603537657c5b78d209e82427cee81bdf7d405ea,Tables,-16.489,8.129,41.899,1947 -1948,_38,-20.392,42.134,30.0,-0.27,-0.33,0.945714285714,e538f8965d92b00222516d74e25b63d6c9ff56a1,256,V3,e538f8965d92b00222516d74e25b63d6c9ff56a1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-20.392_ry+42.134_rz+30.000_tx-00.270_ty-00.330_s+00.662_e538f8965d92b00222516d74e25b63d6c9ff56a1_256x256.png,ad1c1f1c804c90df5dd7fc12b17b9217efd577f3,Tables,-20.392,42.134,30.0,1948 -1949,_38,-28.494,-3.317,15.247,0.145,0.341,0.82,950db7ef697ee51859eed722df8b8e57e05987cd,256,V3,950db7ef697ee51859eed722df8b8e57e05987cd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-28.494_ry-03.317_rz+15.247_tx+00.145_ty+00.341_s+00.574_950db7ef697ee51859eed722df8b8e57e05987cd_256x256.png,95b74ca62d5e3d808d87fb48894dde5b0c20e2a1,Tables,-28.494,-3.317,15.247,1949 -1950,_38,-29.057,8.351,29.272,-0.145,-0.569,1.03714285714,74cd1415ae5e25d49ac3fcbcc7d59f4a0a44d0ad,256,V3,74cd1415ae5e25d49ac3fcbcc7d59f4a0a44d0ad_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-29.057_ry+08.351_rz+29.272_tx-00.145_ty-00.569_s+00.726_74cd1415ae5e25d49ac3fcbcc7d59f4a0a44d0ad_256x256.png,c4faebe54c3f433ea41f33a6623bdd38d37c72e1,Tables,-29.057,8.351,29.272,1950 -1951,_38,-29.136,-40.553,-18.908,-0.144,0.481,0.89,deb261431cca8df5f23ffcefa5699db9e041333d,256,V3,deb261431cca8df5f23ffcefa5699db9e041333d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-29.136_ry-40.553_rz-18.908_tx-00.144_ty+00.481_s+00.623_deb261431cca8df5f23ffcefa5699db9e041333d_256x256.png,228066b9c5ac98845e69ae505de1189b47c91dad,Tables,-29.136,-40.553,-18.908,1951 -1952,_38,-30.14,-6.206,-20.833,0.215,0.072,1.09142857143,ed88610a578383289999477e9cc7d1ac00539404,256,V3,ed88610a578383289999477e9cc7d1ac00539404_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-30.140_ry-06.206_rz-20.833_tx+00.215_ty+00.072_s+00.764_ed88610a578383289999477e9cc7d1ac00539404_256x256.png,3e76afa54871f5d043083c7838643b4d28022598,Tables,-30.14,-6.206,-20.833,1952 -1953,_38,-31.579,-4.679,-20.223,0.178,0.36,0.931428571429,c63ebb1d8d3f5ebfaf64bcbd0224bf094daacb4b,256,V3,c63ebb1d8d3f5ebfaf64bcbd0224bf094daacb4b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-31.579_ry-04.679_rz-20.223_tx+00.178_ty+00.360_s+00.652_c63ebb1d8d3f5ebfaf64bcbd0224bf094daacb4b_256x256.png,9f2e0eb07db94a256bef8fbd78447d8d9dbea814,Tables,-31.579,-4.679,-20.223,1953 -1954,_38,-36.12,-9.628,23.593,-0.26,0.254,1.24857142857,570ced2f447c7d685043142ed9308da97fee1b12,256,V3,570ced2f447c7d685043142ed9308da97fee1b12_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-36.120_ry-09.628_rz+23.593_tx-00.260_ty+00.254_s+00.874_570ced2f447c7d685043142ed9308da97fee1b12_256x256.png,7f416b196e7ac1c1dc0959fd8a0da6748e26f334,Tables,-36.12,-9.628,23.593,1954 -1955,_38,-37.125,-41.816,11.94,-0.094,-0.502,0.887142857143,3b9b91683804878c97728c1323e3f9efb13a83ef,256,V3,3b9b91683804878c97728c1323e3f9efb13a83ef_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-37.125_ry-41.816_rz+11.940_tx-00.094_ty-00.502_s+00.621_3b9b91683804878c97728c1323e3f9efb13a83ef_256x256.png,7e1ecf6faade1bd5e8220838545e10769424347d,Tables,-37.125,-41.816,11.94,1955 -1956,_38,-38.805,-40.917,-26.154,0.2,-0.493,0.937142857143,9ec8f5bd70327d88df1bd0ed1ee453b55ee6bdfb,256,V3,9ec8f5bd70327d88df1bd0ed1ee453b55ee6bdfb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-38.805_ry-40.917_rz-26.154_tx+00.200_ty-00.493_s+00.656_9ec8f5bd70327d88df1bd0ed1ee453b55ee6bdfb_256x256.png,a22aa0af2fef1f0a648f79aba5fa22bedc6c1c95,Tables,-38.805,-40.917,-26.154,1956 -1957,_38,-39.888,-18.367,-33.128,-0.299,0.22,1.00714285714,4f0152395882d0e5a9a5e8e19984b256389c65cd,256,V3,4f0152395882d0e5a9a5e8e19984b256389c65cd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-39.888_ry-18.367_rz-33.128_tx-00.299_ty+00.220_s+00.705_4f0152395882d0e5a9a5e8e19984b256389c65cd_256x256.png,f680cc84b4881df2b9bd779467f55617d3dc41b9,Tables,-39.888,-18.367,-33.128,1957 -1958,_38,-41.533,-43.751,-40.871,-0.095,0.379,1.14,d77b1ec8aa926d49c9b20663b1f5802f167e76ab,256,V3,d77b1ec8aa926d49c9b20663b1f5802f167e76ab_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-41.533_ry-43.751_rz-40.871_tx-00.095_ty+00.379_s+00.798_d77b1ec8aa926d49c9b20663b1f5802f167e76ab_256x256.png,36e98035edd4d4ad7c20836ad5bb92b3a7379cc5,Tables,-41.533,-43.751,-40.871,1958 -1959,_38,-43.247,-24.276,-42.796,0.1,-0.365,1.13428571429,6a257025dc0cb9f3a0438f49396605035a9c44f1,256,V3,6a257025dc0cb9f3a0438f49396605035a9c44f1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_38_rx-43.247_ry-24.276_rz-42.796_tx+00.100_ty-00.365_s+00.794_6a257025dc0cb9f3a0438f49396605035a9c44f1_256x256.png,c43e5d57a7a0b83c617dcf3bcba3959b6e89034c,Tables,-43.247,-24.276,-42.796,1959 -1960,_44,0.093,41.943,-13.927,-0.122,-0.176,1.07285714286,b9111ca6f05df220f2002ca6f0228cad34a2bbd4,256,V3,b9111ca6f05df220f2002ca6f0228cad34a2bbd4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+00.093_ry+41.943_rz-13.927_tx-00.122_ty-00.176_s+00.751_b9111ca6f05df220f2002ca6f0228cad34a2bbd4_256x256.png,1a8b7bb637303979dcfd0486ba830e72fc87e463,Tables,0.093,41.943,-13.927,1960 -1961,_44,2.505,5.58,33.08,0.156,-0.16,0.955714285714,23744aef384b6d7b9deaa9ebe72c1cc1da73afa8,256,V3,23744aef384b6d7b9deaa9ebe72c1cc1da73afa8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+02.505_ry+05.580_rz+33.080_tx+00.156_ty-00.160_s+00.669_23744aef384b6d7b9deaa9ebe72c1cc1da73afa8_256x256.png,5bdae948d3959146a154546a4aecc6830850669c,Tables,2.505,5.58,33.08,1961 -1962,_44,7.955,5.032,14.803,-0.08,0.163,0.882857142857,17f5e969b4c1b7a63d61db649537979874441afe,256,V3,17f5e969b4c1b7a63d61db649537979874441afe_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+07.955_ry+05.032_rz+14.803_tx-00.080_ty+00.163_s+00.618_17f5e969b4c1b7a63d61db649537979874441afe_256x256.png,e1f43c2ba3b55b846e807efa057da737e86913a2,Tables,7.955,5.032,14.803,1962 -1963,_44,9.899,-42.474,-13.747,-0.255,-0.344,1.16142857143,adf900a2d943d51e478e4829e4615ca34a13ba5e,256,V3,adf900a2d943d51e478e4829e4615ca34a13ba5e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+09.899_ry-42.474_rz-13.747_tx-00.255_ty-00.344_s+00.813_adf900a2d943d51e478e4829e4615ca34a13ba5e_256x256.png,9e4b27c7898b667de741db10cd4078b1e752d93e,Tables,9.899,-42.474,-13.747,1963 -1964,_44,9.988,44.419,6.486,0.015,-0.175,0.985714285714,a4e5d98f84c01a3e89998314f018ce4048f9bff5,256,V3,a4e5d98f84c01a3e89998314f018ce4048f9bff5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+09.988_ry+44.419_rz+06.486_tx+00.015_ty-00.175_s+00.690_a4e5d98f84c01a3e89998314f018ce4048f9bff5_256x256.png,199f30ca4e44cf169bc2398cc945e328788af843,Tables,9.988,44.419,6.486,1964 -1965,_44,10.876,2.569,-21.176,0.016,-0.247,1.20285714286,1cab168dffe8b816753f4d3e1700dfb7133c497d,256,V3,1cab168dffe8b816753f4d3e1700dfb7133c497d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+10.876_ry+02.569_rz-21.176_tx+00.016_ty-00.247_s+00.842_1cab168dffe8b816753f4d3e1700dfb7133c497d_256x256.png,714959b07debc1613c0e3f2c738ee1a25bafc203,Tables,10.876,2.569,-21.176,1965 -1966,_44,12.127,-34.626,14.737,0.238,-0.382,1.26428571429,409201b3c5032fde94247b5c6941b65039b46c0d,256,V3,409201b3c5032fde94247b5c6941b65039b46c0d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+12.127_ry-34.626_rz+14.737_tx+00.238_ty-00.382_s+00.885_409201b3c5032fde94247b5c6941b65039b46c0d_256x256.png,4c4f02293191cd85aae4184ae3d9013185699671,Tables,12.127,-34.626,14.737,1966 -1967,_44,16.06,-1.903,-26.578,0.282,0.341,1.28571428571,72a91b2857cb05fdb529d1e220d905ac1d70b282,256,V3,72a91b2857cb05fdb529d1e220d905ac1d70b282_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+16.060_ry-01.903_rz-26.578_tx+00.282_ty+00.341_s+00.900_72a91b2857cb05fdb529d1e220d905ac1d70b282_256x256.png,0732c3ae2b18016897aeafbcfa7f4d9ce39cf51e,Tables,16.06,-1.903,-26.578,1967 -1968,_44,16.738,-10.085,1.128,-0.2,0.575,1.22428571429,c6b89af169b54a1a2598b61d71f94c24d4a32cea,256,V3,c6b89af169b54a1a2598b61d71f94c24d4a32cea_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+16.738_ry-10.085_rz+01.128_tx-00.200_ty+00.575_s+00.857_c6b89af169b54a1a2598b61d71f94c24d4a32cea_256x256.png,51085a88efbc80daff01d9b4e73bdecf88ecf7d3,Tables,16.738,-10.085,1.128,1968 -1969,_44,17.793,40.495,20.508,-0.149,0.228,1.05142857143,91ed26f2ce9fc45a010902c9e6e705e6ae565681,256,V3,91ed26f2ce9fc45a010902c9e6e705e6ae565681_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+17.793_ry+40.495_rz+20.508_tx-00.149_ty+00.228_s+00.736_91ed26f2ce9fc45a010902c9e6e705e6ae565681_256x256.png,45671fbfea65425d719a5f100580cf3e6378167f,Tables,17.793,40.495,20.508,1969 -1970,_44,19.121,21.162,44.744,0.068,-0.054,0.791428571429,96269b7a93815bb892a46250b192aba2ea012555,256,V3,96269b7a93815bb892a46250b192aba2ea012555_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+19.121_ry+21.162_rz+44.744_tx+00.068_ty-00.054_s+00.554_96269b7a93815bb892a46250b192aba2ea012555_256x256.png,ed96f7884d130065bc2cfef386aeb7030d08dea0,Tables,19.121,21.162,44.744,1970 -1971,_44,20.265,24.22,-13.465,-0.017,-0.543,0.784285714286,c56f960703cf2a40bd463b0bd37a592d4e81b606,256,V3,c56f960703cf2a40bd463b0bd37a592d4e81b606_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+20.265_ry+24.220_rz-13.465_tx-00.017_ty-00.543_s+00.549_c56f960703cf2a40bd463b0bd37a592d4e81b606_256x256.png,abf9433dba4d11d47375c6a80634036e426673d2,Tables,20.265,24.22,-13.465,1971 -1972,_44,21.38,42.335,26.571,-0.148,0.041,1.02571428571,be822332fd7c1c143e157c4e09c7a26c1d9407f1,256,V3,be822332fd7c1c143e157c4e09c7a26c1d9407f1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+21.380_ry+42.335_rz+26.571_tx-00.148_ty+00.041_s+00.718_be822332fd7c1c143e157c4e09c7a26c1d9407f1_256x256.png,2e3a51b285abeaba0f7ed2562aa1149cea6065bc,Tables,21.38,42.335,26.571,1972 -1973,_44,22.49,43.854,16.852,-0.061,0.142,1.03571428571,134bd470ee49ce9558a5838b0391e64706a501c6,256,V3,134bd470ee49ce9558a5838b0391e64706a501c6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+22.490_ry+43.854_rz+16.852_tx-00.061_ty+00.142_s+00.725_134bd470ee49ce9558a5838b0391e64706a501c6_256x256.png,865f993242f56007d64a901e45bffa1fc7a153de,Tables,22.49,43.854,16.852,1973 -1974,_44,25.897,11.461,14.655,0.208,0.15,0.855714285714,01415cc6088f18d0d503bae282b43021e3d81533,256,V3,01415cc6088f18d0d503bae282b43021e3d81533_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+25.897_ry+11.461_rz+14.655_tx+00.208_ty+00.150_s+00.599_01415cc6088f18d0d503bae282b43021e3d81533_256x256.png,7596dbfd6b159c531993ebf54cfd3bb679b5b606,Tables,25.897,11.461,14.655,1974 -1975,_44,26.425,-40.583,-12.437,-0.081,0.513,1.02714285714,bff1d30f5b2fa13edb737c83ad4b4bb3d6b82ec6,256,V3,bff1d30f5b2fa13edb737c83ad4b4bb3d6b82ec6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+26.425_ry-40.583_rz-12.437_tx-00.081_ty+00.513_s+00.719_bff1d30f5b2fa13edb737c83ad4b4bb3d6b82ec6_256x256.png,2058c0d4203fd20a4766a84f84323546e6499b45,Tables,26.425,-40.583,-12.437,1975 -1976,_44,29.967,-34.528,42.437,-0.008,-0.53,0.897142857143,d5ea90a7b782ceb2a8232f505132e50968970c00,256,V3,d5ea90a7b782ceb2a8232f505132e50968970c00_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+29.967_ry-34.528_rz+42.437_tx-00.008_ty-00.530_s+00.628_d5ea90a7b782ceb2a8232f505132e50968970c00_256x256.png,2a7e2d466c2a39de14fa1b71c42ac5a587da95fc,Tables,29.967,-34.528,42.437,1976 -1977,_44,30.609,-39.625,-42.514,-0.185,0.035,1.15428571429,c7d495d2cc6385b772588034fbbb901d9aabfc07,256,V3,c7d495d2cc6385b772588034fbbb901d9aabfc07_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+30.609_ry-39.625_rz-42.514_tx-00.185_ty+00.035_s+00.808_c7d495d2cc6385b772588034fbbb901d9aabfc07_256x256.png,99efc8026c98d28f1340ba7e65ad556a447a7181,Tables,30.609,-39.625,-42.514,1977 -1978,_44,37.764,3.983,11.804,-0.207,-0.231,1.14285714286,67995d2f8a660cd90dc27507553716d2253965de,256,V3,67995d2f8a660cd90dc27507553716d2253965de_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+37.764_ry+03.983_rz+11.804_tx-00.207_ty-00.231_s+00.800_67995d2f8a660cd90dc27507553716d2253965de_256x256.png,63afa58e765b7821a175fa172ba1700ba1f92529,Tables,37.764,3.983,11.804,1978 -1979,_44,40.228,23.343,31.23,-0.245,-0.433,0.917142857143,7655bcc428077db2b47939cf3570cffd9ce9accd,256,V3,7655bcc428077db2b47939cf3570cffd9ce9accd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+40.228_ry+23.343_rz+31.230_tx-00.245_ty-00.433_s+00.642_7655bcc428077db2b47939cf3570cffd9ce9accd_256x256.png,91e97cb5a031e4a38f4dd56cd9a96dfe3d03433f,Tables,40.228,23.343,31.23,1979 -1980,_44,41.216,-6.044,-2.624,-0.07,0.122,0.908571428571,0310c4ae939fca78258ec5e724fd17e480d5f314,256,V3,0310c4ae939fca78258ec5e724fd17e480d5f314_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+41.216_ry-06.044_rz-02.624_tx-00.070_ty+00.122_s+00.636_0310c4ae939fca78258ec5e724fd17e480d5f314_256x256.png,84ebe88bfd64ab6a9bc643f1c4d4bef403b6440c,Tables,41.216,-6.044,-2.624,1980 -1981,_44,42.483,-6.008,13.861,0.271,-0.029,1.15714285714,d7ffca9e4bf61da59179132f3eafc16ebec92f17,256,V3,d7ffca9e4bf61da59179132f3eafc16ebec92f17_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+42.483_ry-06.008_rz+13.861_tx+00.271_ty-00.029_s+00.810_d7ffca9e4bf61da59179132f3eafc16ebec92f17_256x256.png,0bfbaead4202bbacf952c90d84a972f636b907eb,Tables,42.483,-6.008,13.861,1981 -1982,_44,42.984,14.48,38.381,0.177,-0.586,0.997142857143,c87747a89dfcd9e19b31fa0a72571d49f5271ffa,256,V3,c87747a89dfcd9e19b31fa0a72571d49f5271ffa_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx+42.984_ry+14.480_rz+38.381_tx+00.177_ty-00.586_s+00.698_c87747a89dfcd9e19b31fa0a72571d49f5271ffa_256x256.png,89f117427acad761d0f4dd75e781606dec53a010,Tables,42.984,14.48,38.381,1982 -1983,_44,-1.26,-41.098,-37.073,-0.189,-0.496,1.12857142857,8c9f83d1494d66433ef69af9f2640a81f6c5d1f2,256,V3,8c9f83d1494d66433ef69af9f2640a81f6c5d1f2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-01.260_ry-41.098_rz-37.073_tx-00.189_ty-00.496_s+00.790_8c9f83d1494d66433ef69af9f2640a81f6c5d1f2_256x256.png,152482c8ca35355451bc4c486d154d4fd2e6dcc7,Tables,-1.26,-41.098,-37.073,1983 -1984,_44,-2.173,39.047,-35.462,-0.173,-0.534,1.27857142857,b99e7d1b34d8e857599d5a741402839653b2d144,256,V3,b99e7d1b34d8e857599d5a741402839653b2d144_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-02.173_ry+39.047_rz-35.462_tx-00.173_ty-00.534_s+00.895_b99e7d1b34d8e857599d5a741402839653b2d144_256x256.png,a7921edee8c274410bfe85f7719bb95ec417442c,Tables,-2.173,39.047,-35.462,1984 -1985,_44,-5.845,-37.026,-27.56,0.012,-0.322,0.894285714286,15d861d5e5d09bb3f9eed561c76e7deffb658371,256,V3,15d861d5e5d09bb3f9eed561c76e7deffb658371_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-05.845_ry-37.026_rz-27.560_tx+00.012_ty-00.322_s+00.626_15d861d5e5d09bb3f9eed561c76e7deffb658371_256x256.png,d4b9017eb9a1f81c5d232862ed6295c9ead62e35,Tables,-5.845,-37.026,-27.56,1985 -1986,_44,-7.7,-41.382,-36.372,0.003,-0.52,0.892857142857,6d577ed964068318c5a634546ad45166781f485f,256,V3,6d577ed964068318c5a634546ad45166781f485f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-07.700_ry-41.382_rz-36.372_tx+00.003_ty-00.520_s+00.625_6d577ed964068318c5a634546ad45166781f485f_256x256.png,8be3310b627ef8c8ad3f831fe0291b8ae375664e,Tables,-7.7,-41.382,-36.372,1986 -1987,_44,-16.419,-25.463,-30.615,0.094,0.503,0.781428571429,04804921e976eaecb417d5a1db99709a746482fe,256,V3,04804921e976eaecb417d5a1db99709a746482fe_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-16.419_ry-25.463_rz-30.615_tx+00.094_ty+00.503_s+00.547_04804921e976eaecb417d5a1db99709a746482fe_256x256.png,d24717071e86c049adc836b0f1b819516e369638,Tables,-16.419,-25.463,-30.615,1987 -1988,_44,-18.727,-9.916,40.029,-0.282,-0.27,0.977142857143,708c279bc31d8fea2d33c43ffeff859a7a4a31f0,256,V3,708c279bc31d8fea2d33c43ffeff859a7a4a31f0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-18.727_ry-09.916_rz+40.029_tx-00.282_ty-00.270_s+00.684_708c279bc31d8fea2d33c43ffeff859a7a4a31f0_256x256.png,e8a0d9dbe58f0c5a9f189002f5a65fd735373752,Tables,-18.727,-9.916,40.029,1988 -1989,_44,-19.268,-12.86,-31.515,0.288,-0.163,1.04428571429,f2c70149b14ed5a41d8750fc45a3350583be45b2,256,V3,f2c70149b14ed5a41d8750fc45a3350583be45b2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-19.268_ry-12.860_rz-31.515_tx+00.288_ty-00.163_s+00.731_f2c70149b14ed5a41d8750fc45a3350583be45b2_256x256.png,0f666dbee037490716e4d43a124ab70895d4cf3f,Tables,-19.268,-12.86,-31.515,1989 -1990,_44,-20.564,28.296,-12.9,-0.054,-0.073,1.01285714286,284cc8ef4e15393ab4f661f8f31d7bcbc909bb18,256,V3,284cc8ef4e15393ab4f661f8f31d7bcbc909bb18_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-20.564_ry+28.296_rz-12.900_tx-00.054_ty-00.073_s+00.709_284cc8ef4e15393ab4f661f8f31d7bcbc909bb18_256x256.png,ffd5542719aa9fe98c47235c58849843176557af,Tables,-20.564,28.296,-12.9,1990 -1991,_44,-27.22,17.51,16.977,0.19,0.596,0.83,5caead713b6a82262f055996e3d53a3c8a3156f0,256,V3,5caead713b6a82262f055996e3d53a3c8a3156f0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-27.220_ry+17.510_rz+16.977_tx+00.190_ty+00.596_s+00.581_5caead713b6a82262f055996e3d53a3c8a3156f0_256x256.png,6097b7eee9c1f30bc8249ed75057c4866a07f055,Tables,-27.22,17.51,16.977,1991 -1992,_44,-33.178,-6.106,43.418,0.009,0.261,0.89,b18247325dfb98484d77f174da0f3852817073be,256,V3,b18247325dfb98484d77f174da0f3852817073be_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-33.178_ry-06.106_rz+43.418_tx+00.009_ty+00.261_s+00.623_b18247325dfb98484d77f174da0f3852817073be_256x256.png,5b75ad773bb4b551b45aa915ecfbfb90c50d985b,Tables,-33.178,-6.106,43.418,1992 -1993,_44,-34.215,-38.984,18.562,-0.027,0.28,1.22714285714,5da8a9b77e8a1dfc06de35868611f3d5f96c3365,256,V3,5da8a9b77e8a1dfc06de35868611f3d5f96c3365_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-34.215_ry-38.984_rz+18.562_tx-00.027_ty+00.280_s+00.859_5da8a9b77e8a1dfc06de35868611f3d5f96c3365_256x256.png,a77dde1bfb08347e7cc627710d38c17eee2605a0,Tables,-34.215,-38.984,18.562,1993 -1994,_44,-36.534,-19.09,23.314,0.018,-0.07,0.852857142857,c38259b607dc0f517d12e29a1ce356bce05af057,256,V3,c38259b607dc0f517d12e29a1ce356bce05af057_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-36.534_ry-19.090_rz+23.314_tx+00.018_ty-00.070_s+00.597_c38259b607dc0f517d12e29a1ce356bce05af057_256x256.png,da3cbe4b8d642190a95c21264486ff8c0b2c73a6,Tables,-36.534,-19.09,23.314,1994 -1995,_44,-39.293,-9.09,-20.165,-0.227,-0.425,1.14285714286,afef464944c33023b8cf3531c9cd5912516f05bc,256,V3,afef464944c33023b8cf3531c9cd5912516f05bc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-39.293_ry-09.090_rz-20.165_tx-00.227_ty-00.425_s+00.800_afef464944c33023b8cf3531c9cd5912516f05bc_256x256.png,08a0414c720c9a1b89457919e5a16a578df6b04f,Tables,-39.293,-9.09,-20.165,1995 -1996,_44,-41.441,-35.718,8.97,-0.177,0.49,0.891428571429,0e8285f0b9b44b997086c9a03cdde13aa11b54f1,256,V3,0e8285f0b9b44b997086c9a03cdde13aa11b54f1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-41.441_ry-35.718_rz+08.970_tx-00.177_ty+00.490_s+00.624_0e8285f0b9b44b997086c9a03cdde13aa11b54f1_256x256.png,fc215615b70874626e3a5aeeae4316c765ec235f,Tables,-41.441,-35.718,8.97,1996 -1997,_44,-41.843,-29.597,32.128,0.139,0.568,0.928571428571,e0d26a51bdb2c2a592369a44a9470d064f26d8db,256,V3,e0d26a51bdb2c2a592369a44a9470d064f26d8db_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-41.843_ry-29.597_rz+32.128_tx+00.139_ty+00.568_s+00.650_e0d26a51bdb2c2a592369a44a9470d064f26d8db_256x256.png,a134913a248607bc2d56b5311ad7955f1ed1ac13,Tables,-41.843,-29.597,32.128,1997 -1998,_44,-42.356,34.717,-35.696,0.206,0.299,1.01571428571,9cde0d2aaafc16fbd23107d8e594f0c0d12269dc,256,V3,9cde0d2aaafc16fbd23107d8e594f0c0d12269dc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-42.356_ry+34.717_rz-35.696_tx+00.206_ty+00.299_s+00.711_9cde0d2aaafc16fbd23107d8e594f0c0d12269dc_256x256.png,8b5586860d462defe9684f0427663b716f06b045,Tables,-42.356,34.717,-35.696,1998 -1999,_44,-43.856,-27.418,34.917,0.095,-0.453,0.837142857143,aafa2cc28a4f071a357a192e8e110d5331d7f3e2,256,V3,aafa2cc28a4f071a357a192e8e110d5331d7f3e2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/_44_rx-43.856_ry-27.418_rz+34.917_tx+00.095_ty-00.453_s+00.586_aafa2cc28a4f071a357a192e8e110d5331d7f3e2_256x256.png,48832ba34c77e703f45a4b64f781530e46c2320a,Tables,-43.856,-27.418,34.917,1999 -2000,alfa155,0.484,40.494,6.627,-0.189,0.349,1.062,eda16292bd0a5fce946fadf6adf785f8177c4157,256,V3,eda16292bd0a5fce946fadf6adf785f8177c4157_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+00.484_ry+40.494_rz+06.627_tx-00.189_ty+00.349_s+01.062_eda16292bd0a5fce946fadf6adf785f8177c4157_256x256.png,cc98a0fb8636da7024753f23d0308b1e801ed8ac,Cars,0.484,130.494,6.627,2000 -2001,alfa155,5.91,11.5,-26.857,-0.117,-0.323,0.89,6a47b35572f881fd7e539d2f30cc93891f6563e4,256,V3,6a47b35572f881fd7e539d2f30cc93891f6563e4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+05.910_ry+11.500_rz-26.857_tx-00.117_ty-00.323_s+00.890_6a47b35572f881fd7e539d2f30cc93891f6563e4_256x256.png,7d6e5dae558c012fc9bced44a3af03d3c6ec5069,Cars,5.91,101.5,-26.857,2001 -2002,alfa155,6.3,6.68,34.98,-0.208,-0.558,1.166,c390f35bbb8c079fe128ec10a1f81fce85df0e10,256,V3,c390f35bbb8c079fe128ec10a1f81fce85df0e10_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+06.300_ry+06.680_rz+34.980_tx-00.208_ty-00.558_s+01.166_c390f35bbb8c079fe128ec10a1f81fce85df0e10_256x256.png,637a36d60474d61569c22a170fda07ebf422eaca,Cars,6.3,96.68,34.98,2002 -2003,alfa155,6.721,29.997,-17.071,0.26,-0.436,1.25,5e40e8fbd3fe0a1090b5c9268077ce34542b968d,256,V3,5e40e8fbd3fe0a1090b5c9268077ce34542b968d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+06.721_ry+29.997_rz-17.071_tx+00.260_ty-00.436_s+01.250_5e40e8fbd3fe0a1090b5c9268077ce34542b968d_256x256.png,843415151405d02f4a50660bed9515b9df5d448e,Cars,6.721,119.997,-17.071,2003 -2004,alfa155,8.631,15.937,-15.911,-0.145,-0.069,0.803,02327540cc110dd98c4d193775d4cfb8354693d8,256,V3,02327540cc110dd98c4d193775d4cfb8354693d8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+08.631_ry+15.937_rz-15.911_tx-00.145_ty-00.069_s+00.803_02327540cc110dd98c4d193775d4cfb8354693d8_256x256.png,ed4d79d8fffd68a3d52ba40321c2d2ff750ca81b,Cars,8.631,105.937,-15.911,2004 -2005,alfa155,10.155,-5.479,-26.873,0.15,-0.382,1.15,6dad99b8723154e6526910f614d0d2452fc1ce2d,256,V3,6dad99b8723154e6526910f614d0d2452fc1ce2d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+10.155_ry-05.479_rz-26.873_tx+00.150_ty-00.382_s+01.150_6dad99b8723154e6526910f614d0d2452fc1ce2d_256x256.png,c9234bb0f070ec7dd222f7b39a968aba0fb0ee18,Cars,10.155,84.521,-26.873,2005 -2006,alfa155,10.804,16.454,33.561,0.256,0.316,1.283,b275799f2145ac011383911cb2b8dd885461e53d,256,V3,b275799f2145ac011383911cb2b8dd885461e53d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+10.804_ry+16.454_rz+33.561_tx+00.256_ty+00.316_s+01.283_b275799f2145ac011383911cb2b8dd885461e53d_256x256.png,80bf04a7715f7e23636f56543cdef1b1dbf2fcf0,Cars,10.804,106.454,33.561,2006 -2007,alfa155,11.831,-17.098,35.267,0.121,0.564,0.921,fa3aff9af6b31c0527d07d84a9f49b95f265f1fb,256,V3,fa3aff9af6b31c0527d07d84a9f49b95f265f1fb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+11.831_ry-17.098_rz+35.267_tx+00.121_ty+00.564_s+00.921_fa3aff9af6b31c0527d07d84a9f49b95f265f1fb_256x256.png,44532fd8d5f338d498f2f540686e9f4974419141,Cars,11.831,72.902,35.267,2007 -2008,alfa155,12.742,-8.002,18.475,-0.249,0.489,0.967,b8a7f00821be7928015d3ad35fd4cd7bc563223d,256,V3,b8a7f00821be7928015d3ad35fd4cd7bc563223d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+12.742_ry-08.002_rz+18.475_tx-00.249_ty+00.489_s+00.967_b8a7f00821be7928015d3ad35fd4cd7bc563223d_256x256.png,1f4c1df61c36737e63a8dd267575ee2aad3c92ed,Cars,12.742,81.998,18.475,2008 -2009,alfa155,13.221,-23.953,24.33,-0.191,-0.269,1.246,2857db2f8a7383ff5329a742d7f617fa39cbd18f,256,V3,2857db2f8a7383ff5329a742d7f617fa39cbd18f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+13.221_ry-23.953_rz+24.330_tx-00.191_ty-00.269_s+01.246_2857db2f8a7383ff5329a742d7f617fa39cbd18f_256x256.png,c93558c6047ec758e8fa57f7283f5c61402ff783,Cars,13.221,66.047,24.33,2009 -2010,alfa155,14.297,-19.825,7.593,0.175,0.242,1.088,846a6a3289ddc1db110a93821a547c6db7485fec,256,V3,846a6a3289ddc1db110a93821a547c6db7485fec_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+14.297_ry-19.825_rz+07.593_tx+00.175_ty+00.242_s+01.088_846a6a3289ddc1db110a93821a547c6db7485fec_256x256.png,8776682ac4cb6f610cf1cb72bcf3c1bbde533111,Cars,14.297,70.175,7.593,2010 -2011,alfa155,21.064,-12.684,-20.857,0.0,0.175,0.815,10cae423ac465104444c7dc4826ebe38b4b7bc48,256,V3,10cae423ac465104444c7dc4826ebe38b4b7bc48_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+21.064_ry-12.684_rz-20.857_tx+00.000_ty+00.175_s+00.815_10cae423ac465104444c7dc4826ebe38b4b7bc48_256x256.png,fbbad524a7281631d75bf7a4d2425156a4a22d52,Cars,21.064,77.316,-20.857,2011 -2012,alfa155,22.292,-29.674,-34.612,0.291,0.57,0.852,c2c28fe2fe6342a6095ff24cc82e63b74dd6b93d,256,V3,c2c28fe2fe6342a6095ff24cc82e63b74dd6b93d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+22.292_ry-29.674_rz-34.612_tx+00.291_ty+00.570_s+00.852_c2c28fe2fe6342a6095ff24cc82e63b74dd6b93d_256x256.png,fe749bcc71e962223a8e5656adc7eba27387970a,Cars,22.292,60.326,-34.612,2012 -2013,alfa155,22.321,34.542,-7.934,0.138,-0.055,1.073,b6a782eeaa073f0217be0828ba0b9c52dec1cffd,256,V3,b6a782eeaa073f0217be0828ba0b9c52dec1cffd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+22.321_ry+34.542_rz-07.934_tx+00.138_ty-00.055_s+01.073_b6a782eeaa073f0217be0828ba0b9c52dec1cffd_256x256.png,5a83f0a3c3d36aa4696d2765e5e9da6e0e8c6a66,Cars,22.321,124.542,-7.934,2013 -2014,alfa155,23.761,21.941,14.259,-0.165,-0.02,0.883,36d91eeaccc5f4d8c2981aa74065cf77723dec0a,256,V3,36d91eeaccc5f4d8c2981aa74065cf77723dec0a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+23.761_ry+21.941_rz+14.259_tx-00.165_ty-00.020_s+00.883_36d91eeaccc5f4d8c2981aa74065cf77723dec0a_256x256.png,38e7a4f4a2da52ad7015677e79a1da0803903639,Cars,23.761,111.941,14.259,2014 -2015,alfa155,25.26,-5.996,38.949,-0.156,-0.144,1.097,324a7138f0e1aaec956e3ebe17683ddcece3f710,256,V3,324a7138f0e1aaec956e3ebe17683ddcece3f710_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+25.260_ry-05.996_rz+38.949_tx-00.156_ty-00.144_s+01.097_324a7138f0e1aaec956e3ebe17683ddcece3f710_256x256.png,dc83a76725508c2f96a8b99c3d68194c8d57732d,Cars,25.26,84.004,38.949,2015 -2016,alfa155,27.145,-15.996,-8.989,-0.102,-0.226,1.028,895f2d731002e62392a46787d4527e0080128d65,256,V3,895f2d731002e62392a46787d4527e0080128d65_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+27.145_ry-15.996_rz-08.989_tx-00.102_ty-00.226_s+01.028_895f2d731002e62392a46787d4527e0080128d65_256x256.png,3c76c952d12eac0a07ee4d933549522c595dece2,Cars,27.145,74.004,-8.989,2016 -2017,alfa155,29.867,39.963,-15.924,-0.11,0.47,1.244,4adddb965bb344cb97092ecf9fd4d32be62ddae8,256,V3,4adddb965bb344cb97092ecf9fd4d32be62ddae8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+29.867_ry+39.963_rz-15.924_tx-00.110_ty+00.470_s+01.244_4adddb965bb344cb97092ecf9fd4d32be62ddae8_256x256.png,1c2ba70361a49cc3153599d19c888dc80338e1db,Cars,29.867,129.963,-15.924,2017 -2018,alfa155,33.619,-5.937,28.805,-0.224,-0.368,0.779,37916384895ceda0c9513b9f056d167313e57e2f,256,V3,37916384895ceda0c9513b9f056d167313e57e2f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+33.619_ry-05.937_rz+28.805_tx-00.224_ty-00.368_s+00.779_37916384895ceda0c9513b9f056d167313e57e2f_256x256.png,328450dec6f82e78fa8fec2c31c66b784b2202b2,Cars,33.619,84.063,28.805,2018 -2019,alfa155,34.445,-42.102,-22.342,-0.081,-0.215,1.066,4c520473a9e43a98f1785cfe134f7a5dd9759d68,256,V3,4c520473a9e43a98f1785cfe134f7a5dd9759d68_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+34.445_ry-42.102_rz-22.342_tx-00.081_ty-00.215_s+01.066_4c520473a9e43a98f1785cfe134f7a5dd9759d68_256x256.png,1120f72be0ad2db786fe8c617b086f8c957aac83,Cars,34.445,47.898,-22.342,2019 -2020,alfa155,34.8,-13.696,-29.097,-0.269,0.287,0.8,4d6372d44944ec4398fbbc10c81e1eb5ec2e357a,256,V3,4d6372d44944ec4398fbbc10c81e1eb5ec2e357a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+34.800_ry-13.696_rz-29.097_tx-00.269_ty+00.287_s+00.800_4d6372d44944ec4398fbbc10c81e1eb5ec2e357a_256x256.png,78bb835f9fc9b9d43cb4269d84d36e59eb9c0051,Cars,34.8,76.304,-29.097,2020 -2021,alfa155,36.421,35.539,6.701,-0.12,0.297,0.916,862be8345396fbc96a4f1c41f8d30808f7070ea4,256,V3,862be8345396fbc96a4f1c41f8d30808f7070ea4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+36.421_ry+35.539_rz+06.701_tx-00.120_ty+00.297_s+00.916_862be8345396fbc96a4f1c41f8d30808f7070ea4_256x256.png,ffe49726e966a565466b749cf66ef26c21227033,Cars,36.421,125.539,6.701,2021 -2022,alfa155,39.396,16.279,-39.313,-0.236,-0.084,1.282,80cf99eba7b3a93f8a5d6210b77a5c9ce5eccc50,256,V3,80cf99eba7b3a93f8a5d6210b77a5c9ce5eccc50_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+39.396_ry+16.279_rz-39.313_tx-00.236_ty-00.084_s+01.282_80cf99eba7b3a93f8a5d6210b77a5c9ce5eccc50_256x256.png,13486005929eeb09595e7101a48a41f848691edd,Cars,39.396,106.279,-39.313,2022 -2023,alfa155,43.76,-41.618,-12.071,0.299,-0.572,0.784,6f460e617774e517e0b4f2077012754e3079975b,256,V3,6f460e617774e517e0b4f2077012754e3079975b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx+43.760_ry-41.618_rz-12.071_tx+00.299_ty-00.572_s+00.784_6f460e617774e517e0b4f2077012754e3079975b_256x256.png,0d93c2c295619c79250ee5be788b2d1f39de52a8,Cars,43.76,48.382,-12.071,2023 -2024,alfa155,-1.56,24.769,15.035,0.148,-0.313,0.819,ccee689aa597aa9e08a77716cefab6b6ea5cd1dc,256,V3,ccee689aa597aa9e08a77716cefab6b6ea5cd1dc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-01.560_ry+24.769_rz+15.035_tx+00.148_ty-00.313_s+00.819_ccee689aa597aa9e08a77716cefab6b6ea5cd1dc_256x256.png,96b746f8b37ff5d635e88cad2fd3c65643ded59d,Cars,-1.56,114.769,15.035,2024 -2025,alfa155,-9.82,-21.473,17.56,-0.1,-0.564,1.295,eb7652b3e1be20916e0323dfdf605cd36e7b5117,256,V3,eb7652b3e1be20916e0323dfdf605cd36e7b5117_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-09.820_ry-21.473_rz+17.560_tx-00.100_ty-00.564_s+01.295_eb7652b3e1be20916e0323dfdf605cd36e7b5117_256x256.png,175529ba8ab1e623f045b2069c84efdad861756c,Cars,-9.82,68.527,17.56,2025 -2026,alfa155,-9.893,-36.655,25.006,-0.265,-0.236,1.184,571584fb758d40d98341474b33509f17aa95a5c9,256,V3,571584fb758d40d98341474b33509f17aa95a5c9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-09.893_ry-36.655_rz+25.006_tx-00.265_ty-00.236_s+01.184_571584fb758d40d98341474b33509f17aa95a5c9_256x256.png,81c28fed6d5f000181d298b4c7e5e69d98da3954,Cars,-9.893,53.345,25.006,2026 -2027,alfa155,-13.797,-32.281,-23.986,0.164,-0.023,1.164,3ffb25549935477cda8178c1ef27e8acbf89c859,256,V3,3ffb25549935477cda8178c1ef27e8acbf89c859_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-13.797_ry-32.281_rz-23.986_tx+00.164_ty-00.023_s+01.164_3ffb25549935477cda8178c1ef27e8acbf89c859_256x256.png,63f309cf1a79cb68999c111a9210a7b49817525e,Cars,-13.797,57.719,-23.986,2027 -2028,alfa155,-21.786,44.104,-31.979,0.029,0.136,1.202,21ef61f3404deb40719dd6ef1dbc10bfbc79c504,256,V3,21ef61f3404deb40719dd6ef1dbc10bfbc79c504_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-21.786_ry+44.104_rz-31.979_tx+00.029_ty+00.136_s+01.202_21ef61f3404deb40719dd6ef1dbc10bfbc79c504_256x256.png,064620af69c8714fc1bbf1da89efcd4964221ed7,Cars,-21.786,134.104,-31.979,2028 -2029,alfa155,-25.148,12.64,14.755,0.214,-0.517,0.936,917fca16e80ed1cbfc64316055173a421320119a,256,V3,917fca16e80ed1cbfc64316055173a421320119a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-25.148_ry+12.640_rz+14.755_tx+00.214_ty-00.517_s+00.936_917fca16e80ed1cbfc64316055173a421320119a_256x256.png,4ef09bbd94046c8c17c2c1207854ee9e75b1ee7e,Cars,-25.148,102.64,14.755,2029 -2030,alfa155,-26.268,-43.084,34.661,0.129,-0.479,0.815,72c8349c52d0bef543d62b0ad98e7152cd3195c3,256,V3,72c8349c52d0bef543d62b0ad98e7152cd3195c3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-26.268_ry-43.084_rz+34.661_tx+00.129_ty-00.479_s+00.815_72c8349c52d0bef543d62b0ad98e7152cd3195c3_256x256.png,cd2895cba3a7fc030feb7f4d10422f18a7086aba,Cars,-26.268,46.916,34.661,2030 -2031,alfa155,-26.35,-22.48,29.785,-0.226,0.536,1.063,9faa6b4d4c8e11354e67223c3f894444c82eefdf,256,V3,9faa6b4d4c8e11354e67223c3f894444c82eefdf_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-26.350_ry-22.480_rz+29.785_tx-00.226_ty+00.536_s+01.063_9faa6b4d4c8e11354e67223c3f894444c82eefdf_256x256.png,7748389efe0e03c45385d978d6c6cf945665ad79,Cars,-26.35,67.52,29.785,2031 -2032,alfa155,-26.59,-31.212,-31.52,-0.096,0.548,0.946,1610c00b393f69705a559565779750b4d129875b,256,V3,1610c00b393f69705a559565779750b4d129875b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-26.590_ry-31.212_rz-31.520_tx-00.096_ty+00.548_s+00.946_1610c00b393f69705a559565779750b4d129875b_256x256.png,d348679a0c5ec4b334553a57f81ae20aaba0765f,Cars,-26.59,58.788,-31.52,2032 -2033,alfa155,-26.976,33.246,36.744,0.099,-0.23,1.028,4307472a346401b92ac5ff3de1ea3ed837d76708,256,V3,4307472a346401b92ac5ff3de1ea3ed837d76708_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-26.976_ry+33.246_rz+36.744_tx+00.099_ty-00.230_s+01.028_4307472a346401b92ac5ff3de1ea3ed837d76708_256x256.png,82afea585451aaf2111f038aa7e80a656fe56e90,Cars,-26.976,123.246,36.744,2033 -2034,alfa155,-27.835,22.572,-30.24,-0.145,-0.343,0.859,0a94e5e3111d74320cd08361e2c9d8c87aaac12f,256,V3,0a94e5e3111d74320cd08361e2c9d8c87aaac12f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-27.835_ry+22.572_rz-30.240_tx-00.145_ty-00.343_s+00.859_0a94e5e3111d74320cd08361e2c9d8c87aaac12f_256x256.png,8b27bf4cb92cce27e832288e74c1a21ca92b3d58,Cars,-27.835,112.572,-30.24,2034 -2035,alfa155,-28.192,42.216,-39.26,-0.166,-0.407,1.211,ea7a8faaf35aee041abb79ecd539195a91a2f884,256,V3,ea7a8faaf35aee041abb79ecd539195a91a2f884_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-28.192_ry+42.216_rz-39.260_tx-00.166_ty-00.407_s+01.211_ea7a8faaf35aee041abb79ecd539195a91a2f884_256x256.png,81855faffd0a09de00e0a8f09b559624945b9c56,Cars,-28.192,132.216,-39.26,2035 -2036,alfa155,-33.751,37.245,-30.357,0.117,0.385,0.864,4186ef7a8ec117ae6a898e7f055061ce6cd49f29,256,V3,4186ef7a8ec117ae6a898e7f055061ce6cd49f29_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-33.751_ry+37.245_rz-30.357_tx+00.117_ty+00.385_s+00.864_4186ef7a8ec117ae6a898e7f055061ce6cd49f29_256x256.png,e4308c9ef3a8ad51b6a871a3c9c81e1eda1dd1cb,Cars,-33.751,127.245,-30.357,2036 -2037,alfa155,-35.711,-10.339,-30.453,-0.167,0.214,1.21,696d067653516065070bba1171ba31138761d59e,256,V3,696d067653516065070bba1171ba31138761d59e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-35.711_ry-10.339_rz-30.453_tx-00.167_ty+00.214_s+01.210_696d067653516065070bba1171ba31138761d59e_256x256.png,b7c9911e0bf7021449d05e4b39e3c01280b49dec,Cars,-35.711,79.661,-30.453,2037 -2038,alfa155,-39.625,10.066,-20.376,-0.129,0.175,1.181,c64f55d43600acfdb6c218aae596089e5109ae61,256,V3,c64f55d43600acfdb6c218aae596089e5109ae61_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-39.625_ry+10.066_rz-20.376_tx-00.129_ty+00.175_s+01.181_c64f55d43600acfdb6c218aae596089e5109ae61_256x256.png,e69ed90e0ca5ea004a81cc5bb8aacac6add89206,Cars,-39.625,100.066,-20.376,2038 -2039,alfa155,-44.028,7.517,-22.874,0.081,0.57,1.023,eb0221427fa0e08b3fa79fc10212f0187d28cc46,256,V3,eb0221427fa0e08b3fa79fc10212f0187d28cc46_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/alfa155_rx-44.028_ry+07.517_rz-22.874_tx+00.081_ty+00.570_s+01.023_eb0221427fa0e08b3fa79fc10212f0187d28cc46_256x256.png,7b856adcff02cc1009f28f138aaa5da0c2f3633f,Cars,-44.028,97.517,-22.874,2039 -2040,astra,1.022,-12.755,4.769,0.02,0.39,1.065,310434abb5a81715e25c30b23f0e8284c63d9be8,256,V3,310434abb5a81715e25c30b23f0e8284c63d9be8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+01.022_ry-12.755_rz+04.769_tx+00.020_ty+00.390_s+01.065_310434abb5a81715e25c30b23f0e8284c63d9be8_256x256.png,e883ed1e78e2707ed5cc3497a9c846cbbaf0d589,Cars,1.022,77.245,4.769,2040 -2041,astra,2.554,23.744,-2.736,0.272,0.419,1.275,af42d44a9aeabc5634e3b7d3433ce0bd788131bc,256,V3,af42d44a9aeabc5634e3b7d3433ce0bd788131bc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+02.554_ry+23.744_rz-02.736_tx+00.272_ty+00.419_s+01.275_af42d44a9aeabc5634e3b7d3433ce0bd788131bc_256x256.png,ba42937cd1628a6e2d5dad688a6c7f040cc85855,Cars,2.554,113.744,-2.736,2041 -2042,astra,8.699,40.97,-8.946,-0.268,-0.054,0.849,16814f8a9afd961f57874a0c23a8a8886188d103,256,V3,16814f8a9afd961f57874a0c23a8a8886188d103_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+08.699_ry+40.970_rz-08.946_tx-00.268_ty-00.054_s+00.849_16814f8a9afd961f57874a0c23a8a8886188d103_256x256.png,dd90edce90cea974a0c66be1a341dedbed970075,Cars,8.699,130.97,-8.946,2042 -2043,astra,11.003,7.152,38.446,-0.135,0.14,1.288,f081f02789dfdd610beb3dbc653102cb38a9b32c,256,V3,f081f02789dfdd610beb3dbc653102cb38a9b32c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+11.003_ry+07.152_rz+38.446_tx-00.135_ty+00.140_s+01.288_f081f02789dfdd610beb3dbc653102cb38a9b32c_256x256.png,2ea95280608424818f01c367b216492ef0c76226,Cars,11.003,97.152,38.446,2043 -2044,astra,12.402,-33.433,22.956,-0.186,-0.336,1.201,ce3f9b7e2d9bf695da3885a356a82165016cb083,256,V3,ce3f9b7e2d9bf695da3885a356a82165016cb083_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+12.402_ry-33.433_rz+22.956_tx-00.186_ty-00.336_s+01.201_ce3f9b7e2d9bf695da3885a356a82165016cb083_256x256.png,b597bb16db49f1bda570606209b6934b28f0baa9,Cars,12.402,56.567,22.956,2044 -2045,astra,13.542,-6.409,-34.098,0.012,0.561,1.009,cae7747d4866ab71939b528ac5b4184b8a01f3c0,256,V3,cae7747d4866ab71939b528ac5b4184b8a01f3c0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+13.542_ry-06.409_rz-34.098_tx+00.012_ty+00.561_s+01.009_cae7747d4866ab71939b528ac5b4184b8a01f3c0_256x256.png,995882357699481c9dab065fd784f1ba771cf362,Cars,13.542,83.591,-34.098,2045 -2046,astra,19.645,-13.827,-8.055,0.128,-0.497,0.791,26341322744949ff23d863a11f14ff9b4fe24a44,256,V3,26341322744949ff23d863a11f14ff9b4fe24a44_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+19.645_ry-13.827_rz-08.055_tx+00.128_ty-00.497_s+00.791_26341322744949ff23d863a11f14ff9b4fe24a44_256x256.png,7a1f8322e58af4bdf5987f1ab1051ebfe64045a0,Cars,19.645,76.173,-8.055,2046 -2047,astra,25.89,12.669,11.871,0.28,-0.578,0.924,a2f08956fe5593c09b41495ab4d3b18af7f74ee6,256,V3,a2f08956fe5593c09b41495ab4d3b18af7f74ee6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+25.890_ry+12.669_rz+11.871_tx+00.280_ty-00.578_s+00.924_a2f08956fe5593c09b41495ab4d3b18af7f74ee6_256x256.png,fea097c66fe3a3c0be3a418e39852a5c604473fe,Cars,25.89,102.669,11.871,2047 -2048,astra,32.182,-41.343,24.987,0.043,0.291,0.873,aff6c85da08a346320f31f5c78bf4ea926ce5174,256,V3,aff6c85da08a346320f31f5c78bf4ea926ce5174_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+32.182_ry-41.343_rz+24.987_tx+00.043_ty+00.291_s+00.873_aff6c85da08a346320f31f5c78bf4ea926ce5174_256x256.png,2c4a5edadfa76efee8ccbf811bc99ace424e45c9,Cars,32.182,48.657,24.987,2048 -2049,astra,32.534,-0.689,22.464,0.265,0.18,1.188,bb495b0e9469f0176e0c3b8a99cb278a68a89f01,256,V3,bb495b0e9469f0176e0c3b8a99cb278a68a89f01_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+32.534_ry-00.689_rz+22.464_tx+00.265_ty+00.180_s+01.188_bb495b0e9469f0176e0c3b8a99cb278a68a89f01_256x256.png,d480dad46fb7320e0bfb866e2d9ee01e481885cf,Cars,32.534,89.311,22.464,2049 -2050,astra,32.94,5.296,3.606,0.11,-0.545,0.795,06c63836651d7ed54d938f632cde845421a911cd,256,V3,06c63836651d7ed54d938f632cde845421a911cd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+32.940_ry+05.296_rz+03.606_tx+00.110_ty-00.545_s+00.795_06c63836651d7ed54d938f632cde845421a911cd_256x256.png,5cff57e13ef474904e4626726e1b34eaab00fc3c,Cars,32.94,95.296,3.606,2050 -2051,astra,35.876,-13.043,-18.32,0.198,-0.535,1.182,bd08e2ea99d10226c2f2763b3c11295a8e2c8ec7,256,V3,bd08e2ea99d10226c2f2763b3c11295a8e2c8ec7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+35.876_ry-13.043_rz-18.320_tx+00.198_ty-00.535_s+01.182_bd08e2ea99d10226c2f2763b3c11295a8e2c8ec7_256x256.png,7c0ef67c1b3daceae91fb756c5e60a070ae404fb,Cars,35.876,76.957,-18.32,2051 -2052,astra,36.99,15.208,-13.513,0.067,-0.144,0.924,cc7ac8bcc837719782574b71d3313d7a912a31c0,256,V3,cc7ac8bcc837719782574b71d3313d7a912a31c0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+36.990_ry+15.208_rz-13.513_tx+00.067_ty-00.144_s+00.924_cc7ac8bcc837719782574b71d3313d7a912a31c0_256x256.png,5eef092b82ff2edf6a4bd8c12d12ae25c7db4407,Cars,36.99,105.208,-13.513,2052 -2053,astra,37.638,10.347,3.56,0.131,-0.371,1.078,20f5352c5cdbaf4a93ef54838903867dd97dbf37,256,V3,20f5352c5cdbaf4a93ef54838903867dd97dbf37_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+37.638_ry+10.347_rz+03.560_tx+00.131_ty-00.371_s+01.078_20f5352c5cdbaf4a93ef54838903867dd97dbf37_256x256.png,a5589dfff150895735432e01b1bfda75652d24b8,Cars,37.638,100.347,3.56,2053 -2054,astra,44.564,30.366,-28.418,0.037,-0.064,1.283,ebdfcc684bbd58dab756aae3a1c1aa97cc0c17d8,256,V3,ebdfcc684bbd58dab756aae3a1c1aa97cc0c17d8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+44.564_ry+30.366_rz-28.418_tx+00.037_ty-00.064_s+01.283_ebdfcc684bbd58dab756aae3a1c1aa97cc0c17d8_256x256.png,91499915906bd4802b46f13deeb6ac3b74839f2d,Cars,44.564,120.366,-28.418,2054 -2055,astra,44.796,-24.099,-32.179,0.101,0.137,1.141,27dae97a6ce4649ec91e3baee1fef3160cbd1881,256,V3,27dae97a6ce4649ec91e3baee1fef3160cbd1881_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx+44.796_ry-24.099_rz-32.179_tx+00.101_ty+00.137_s+01.141_27dae97a6ce4649ec91e3baee1fef3160cbd1881_256x256.png,8d93253efe62eef92717eb6e5d1ecae8149b1c82,Cars,44.796,65.901,-32.179,2055 -2056,astra,-3.87,17.604,14.409,-0.237,-0.476,1.116,a41f8d6e808d181ecfc97925fb6c18eedaa2d5ea,256,V3,a41f8d6e808d181ecfc97925fb6c18eedaa2d5ea_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-03.870_ry+17.604_rz+14.409_tx-00.237_ty-00.476_s+01.116_a41f8d6e808d181ecfc97925fb6c18eedaa2d5ea_256x256.png,583eda06fe31757df8f047f77c4fb28d48f65c67,Cars,-3.87,107.604,14.409,2056 -2057,astra,-4.623,-5.045,-34.426,0.08,0.528,0.907,c7091a4caf6c2d2b96932d67d8c4b873dac572a7,256,V3,c7091a4caf6c2d2b96932d67d8c4b873dac572a7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-04.623_ry-05.045_rz-34.426_tx+00.080_ty+00.528_s+00.907_c7091a4caf6c2d2b96932d67d8c4b873dac572a7_256x256.png,3d4dab8bf1504b3012e0e8b5a9b0068e12b6d646,Cars,-4.623,84.955,-34.426,2057 -2058,astra,-6.8,-11.674,-8.848,-0.204,-0.022,1.207,a17beb47186d13aa2191f43e4da68649eebeeba3,256,V3,a17beb47186d13aa2191f43e4da68649eebeeba3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-06.800_ry-11.674_rz-08.848_tx-00.204_ty-00.022_s+01.207_a17beb47186d13aa2191f43e4da68649eebeeba3_256x256.png,3506d62506cbf58293cf9dfa8bb9c99869138212,Cars,-6.8,78.326,-8.848,2058 -2059,astra,-9.145,0.063,-14.365,-0.177,0.541,1.229,39aded50724bff36e904a7d3b4e86ac6b8598ddb,256,V3,39aded50724bff36e904a7d3b4e86ac6b8598ddb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-09.145_ry+00.063_rz-14.365_tx-00.177_ty+00.541_s+01.229_39aded50724bff36e904a7d3b4e86ac6b8598ddb_256x256.png,1104a2647fa1b3ad0b5597a24661ec2c66961893,Cars,-9.145,90.063,-14.365,2059 -2060,astra,-11.09,17.265,35.988,-0.094,0.284,1.231,d5601b8fd723cb5850691144a78f4ee8f4c9df98,256,V3,d5601b8fd723cb5850691144a78f4ee8f4c9df98_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-11.090_ry+17.265_rz+35.988_tx-00.094_ty+00.284_s+01.231_d5601b8fd723cb5850691144a78f4ee8f4c9df98_256x256.png,730b346559043756024216910cda581f7ba94fe5,Cars,-11.09,107.265,35.988,2060 -2061,astra,-11.827,-14.288,38.083,-0.03,-0.483,0.886,8159b43dd4b5c7d582ead0522c92f76ab6cd7a24,256,V3,8159b43dd4b5c7d582ead0522c92f76ab6cd7a24_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-11.827_ry-14.288_rz+38.083_tx-00.030_ty-00.483_s+00.886_8159b43dd4b5c7d582ead0522c92f76ab6cd7a24_256x256.png,9d4874c8b2b3cfc5c283044f0662a2fd27d5808e,Cars,-11.827,75.712,38.083,2061 -2062,astra,-12.168,32.362,-6.838,0.227,0.437,0.944,ff3f20b489e78ee5d0db356052166caf801408fc,256,V3,ff3f20b489e78ee5d0db356052166caf801408fc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-12.168_ry+32.362_rz-06.838_tx+00.227_ty+00.437_s+00.944_ff3f20b489e78ee5d0db356052166caf801408fc_256x256.png,6da50ca364336f1a832bcd302fd00b9c1a3e2d15,Cars,-12.168,122.362,-6.838,2062 -2063,astra,-16.42,10.331,-9.631,0.259,-0.173,1.262,0370362c70b146979616f5c9e87ac3e08fa4d1b1,256,V3,0370362c70b146979616f5c9e87ac3e08fa4d1b1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-16.420_ry+10.331_rz-09.631_tx+00.259_ty-00.173_s+01.262_0370362c70b146979616f5c9e87ac3e08fa4d1b1_256x256.png,196b91a6b1b7d1ca52bb9aba78cdc7e4f7493482,Cars,-16.42,100.331,-9.631,2063 -2064,astra,-16.74,41.059,-43.652,0.127,0.159,0.819,50ade3d8a01b52e3e6d2d0172a0bd48bce8e6846,256,V3,50ade3d8a01b52e3e6d2d0172a0bd48bce8e6846_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-16.740_ry+41.059_rz-43.652_tx+00.127_ty+00.159_s+00.819_50ade3d8a01b52e3e6d2d0172a0bd48bce8e6846_256x256.png,1ee876e1bab3a5e3950f060f39b3a591b577f9c3,Cars,-16.74,131.059,-43.652,2064 -2065,astra,-16.758,8.186,17.698,-0.043,0.051,1.292,a241a3da27e1114677c580c57f42775825188730,256,V3,a241a3da27e1114677c580c57f42775825188730_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-16.758_ry+08.186_rz+17.698_tx-00.043_ty+00.051_s+01.292_a241a3da27e1114677c580c57f42775825188730_256x256.png,dfac6c19f6855ccbda3136ac7aa475fe2caeaf45,Cars,-16.758,98.186,17.698,2065 -2066,astra,-19.364,-27.008,-9.482,-0.083,0.585,1.053,e82df21de83e6681faa697f4c5f44bad05c9bbb4,256,V3,e82df21de83e6681faa697f4c5f44bad05c9bbb4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-19.364_ry-27.008_rz-09.482_tx-00.083_ty+00.585_s+01.053_e82df21de83e6681faa697f4c5f44bad05c9bbb4_256x256.png,192d47b6be7778702ec50796a800cab8622adfb5,Cars,-19.364,62.992,-9.482,2066 -2067,astra,-23.362,-31.573,41.651,0.143,-0.401,1.088,5a93409f4f5c7c3b3261090530ee4c7cdfebf57a,256,V3,5a93409f4f5c7c3b3261090530ee4c7cdfebf57a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-23.362_ry-31.573_rz+41.651_tx+00.143_ty-00.401_s+01.088_5a93409f4f5c7c3b3261090530ee4c7cdfebf57a_256x256.png,74cf51230d174274bd70ecd4d11e5ac98658c09b,Cars,-23.362,58.427,41.651,2067 -2068,astra,-25.956,-31.741,24.397,0.141,-0.354,0.839,7257560f4fbfeba6a70cea0da94ea73180a5c7ab,256,V3,7257560f4fbfeba6a70cea0da94ea73180a5c7ab_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-25.956_ry-31.741_rz+24.397_tx+00.141_ty-00.354_s+00.839_7257560f4fbfeba6a70cea0da94ea73180a5c7ab_256x256.png,09782e3b7c9ac346bb7323013f5887a36ee39bb4,Cars,-25.956,58.259,24.397,2068 -2069,astra,-26.724,-28.573,-20.155,-0.121,-0.271,1.162,fcaf5863624ad62032319732b35098feb380ab5d,256,V3,fcaf5863624ad62032319732b35098feb380ab5d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-26.724_ry-28.573_rz-20.155_tx-00.121_ty-00.271_s+01.162_fcaf5863624ad62032319732b35098feb380ab5d_256x256.png,0ed89dae23eaf7bb7a03cf952c9210d86a4fca0e,Cars,-26.724,61.427,-20.155,2069 -2070,astra,-28.646,-2.606,5.609,0.042,-0.388,1.088,34eb36940d320c8e4b3d446bc366dc4620058c2a,256,V3,34eb36940d320c8e4b3d446bc366dc4620058c2a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-28.646_ry-02.606_rz+05.609_tx+00.042_ty-00.388_s+01.088_34eb36940d320c8e4b3d446bc366dc4620058c2a_256x256.png,f901256567ce5ea953d22e565be8262e7662e95a,Cars,-28.646,87.394,5.609,2070 -2071,astra,-30.763,-19.119,-23.08,0.193,-0.266,0.806,56028ff5be6025ee5f2963e6d7107937365ce980,256,V3,56028ff5be6025ee5f2963e6d7107937365ce980_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-30.763_ry-19.119_rz-23.080_tx+00.193_ty-00.266_s+00.806_56028ff5be6025ee5f2963e6d7107937365ce980_256x256.png,85a15692c2cd314d3ba2a7bc187b32e896b2c220,Cars,-30.763,70.881,-23.08,2071 -2072,astra,-35.791,-35.567,-24.992,-0.106,-0.058,1.276,c9ed180444dcc05e0b8ffc26b473187d10495d0d,256,V3,c9ed180444dcc05e0b8ffc26b473187d10495d0d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-35.791_ry-35.567_rz-24.992_tx-00.106_ty-00.058_s+01.276_c9ed180444dcc05e0b8ffc26b473187d10495d0d_256x256.png,01485bf3e645c2e08f210d4f2bb4f03a6194bc14,Cars,-35.791,54.433,-24.992,2072 -2073,astra,-36.18,16.57,-3.35,0.161,-0.391,1.23,00ee8b302fec4189a70cb5bf997ebbde9db7eb56,256,V3,00ee8b302fec4189a70cb5bf997ebbde9db7eb56_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-36.180_ry+16.570_rz-03.350_tx+00.161_ty-00.391_s+01.230_00ee8b302fec4189a70cb5bf997ebbde9db7eb56_256x256.png,203a449675275e5a05dc9795f7bc920d10224696,Cars,-36.18,106.57,-3.35,2073 -2074,astra,-37.25,26.181,-12.642,0.074,0.152,0.816,1ceb599d23a380aa296079e6c0b4606e942e75f8,256,V3,1ceb599d23a380aa296079e6c0b4606e942e75f8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-37.250_ry+26.181_rz-12.642_tx+00.074_ty+00.152_s+00.816_1ceb599d23a380aa296079e6c0b4606e942e75f8_256x256.png,4a2e29d7c093592b57fb747e1341cf1eec645165,Cars,-37.25,116.181,-12.642,2074 -2075,astra,-37.729,10.169,16.218,-0.216,-0.349,0.888,2043d43e2439e21449f77e26a5f5a1ed9f74cbf6,256,V3,2043d43e2439e21449f77e26a5f5a1ed9f74cbf6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-37.729_ry+10.169_rz+16.218_tx-00.216_ty-00.349_s+00.888_2043d43e2439e21449f77e26a5f5a1ed9f74cbf6_256x256.png,dbfe3c4a279e8df14d211560f63472b119d5fc81,Cars,-37.729,100.169,16.218,2075 -2076,astra,-38.728,38.301,-7.533,-0.115,-0.227,0.924,3afb2e98886742dc5370ef22d93bb29b2f5c91de,256,V3,3afb2e98886742dc5370ef22d93bb29b2f5c91de_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-38.728_ry+38.301_rz-07.533_tx-00.115_ty-00.227_s+00.924_3afb2e98886742dc5370ef22d93bb29b2f5c91de_256x256.png,290251bd59cdd788d8f827429481deba6da327f1,Cars,-38.728,128.301,-7.533,2076 -2077,astra,-39.33,16.372,-14.782,0.071,-0.035,1.163,14cb02d6e972affbc0062bd53de69f271fb888e1,256,V3,14cb02d6e972affbc0062bd53de69f271fb888e1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-39.330_ry+16.372_rz-14.782_tx+00.071_ty-00.035_s+01.163_14cb02d6e972affbc0062bd53de69f271fb888e1_256x256.png,6152ff6bb752038381e8b01dd957c30decb3d929,Cars,-39.33,106.372,-14.782,2077 -2078,astra,-40.491,-19.966,-39.832,-0.029,-0.492,1.201,87f2147dc52cda0848553476a964e62d7657b1ae,256,V3,87f2147dc52cda0848553476a964e62d7657b1ae_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-40.491_ry-19.966_rz-39.832_tx-00.029_ty-00.492_s+01.201_87f2147dc52cda0848553476a964e62d7657b1ae_256x256.png,78513a7d794694a17baa5a785fe62d72aa74127f,Cars,-40.491,70.034,-39.832,2078 -2079,astra,-41.843,-32.903,5.658,-0.185,0.304,1.241,9e34ebcea81f94e1b1040614f0e0250ed79d7da3,256,V3,9e34ebcea81f94e1b1040614f0e0250ed79d7da3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/astra_rx-41.843_ry-32.903_rz+05.658_tx-00.185_ty+00.304_s+01.241_9e34ebcea81f94e1b1040614f0e0250ed79d7da3_256x256.png,10c81c07ae79ce7ffdb59d29cc2bedda40b196af,Cars,-41.843,57.097,5.658,2079 -2080,bear,0.88,36.522,-41.666,-0.039,0.32,1.115,7dce5d3d65540ecede95fd1a708f956770886927,256,V3,7dce5d3d65540ecede95fd1a708f956770886927_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx+00.880_ry+126.522_rz-41.666_tx-00.039_ty+00.320_s+00.892_7dce5d3d65540ecede95fd1a708f956770886927_256x256.png,dac2edcbfeb9b4cdea73126788017a06952e51ed,Animals,0.88,126.522,-41.666,2080 -2081,bear,5.165,-40.52,0.566,0.044,-0.386,1.12875,079b4bf370d0b5e5f9e41109d7d88abe9a447f1c,256,V3,079b4bf370d0b5e5f9e41109d7d88abe9a447f1c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx+05.165_ry+49.480_rz+00.566_tx+00.044_ty-00.386_s+00.903_079b4bf370d0b5e5f9e41109d7d88abe9a447f1c_256x256.png,1ab356f76618484855c18fb5c4fd13ae24d517c0,Animals,5.165,49.48,0.566,2081 -2082,bear,6.116,-19.279,-5.0,0.157,0.334,1.235,8b2e9047db00b3a03037bfa63b9dddf4220f1494,256,V3,8b2e9047db00b3a03037bfa63b9dddf4220f1494_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx+06.116_ry+70.721_rz-05.000_tx+00.157_ty+00.334_s+00.988_8b2e9047db00b3a03037bfa63b9dddf4220f1494_256x256.png,092a52b95af8241400770e637966905b3afec7e2,Animals,6.116,70.721,-5.0,2082 -2083,bear,13.423,36.959,14.181,-0.271,-0.213,1.23125,a9e245e371f45b3919860c8a17f533876821ad1f,256,V3,a9e245e371f45b3919860c8a17f533876821ad1f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx+13.423_ry+126.959_rz+14.181_tx-00.271_ty-00.213_s+00.985_a9e245e371f45b3919860c8a17f533876821ad1f_256x256.png,a24969a3d030ac7db074d75ac0519fdac95da9e2,Animals,13.423,126.959,14.181,2083 -2084,bear,14.933,3.329,21.228,-0.051,0.244,1.1325,8d60d431c4ddfa1224e2924a9204e1c0f397b4fc,256,V3,8d60d431c4ddfa1224e2924a9204e1c0f397b4fc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx+14.933_ry+93.329_rz+21.228_tx-00.051_ty+00.244_s+00.906_8d60d431c4ddfa1224e2924a9204e1c0f397b4fc_256x256.png,2cfc76b59cb12d02b58554b90439aa7970a3784f,Animals,14.933,93.329,21.228,2084 -2085,bear,15.528,20.106,-10.536,0.162,0.5,0.85375,d424fd30a6a1f449487794c6ed7dfabdb3f62c58,256,V3,d424fd30a6a1f449487794c6ed7dfabdb3f62c58_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx+15.528_ry+110.106_rz-10.536_tx+00.162_ty+00.500_s+00.683_d424fd30a6a1f449487794c6ed7dfabdb3f62c58_256x256.png,12b40e3a282485d4ece760ff646bf88ad16c62d1,Animals,15.528,110.106,-10.536,2085 -2086,bear,17.846,-27.924,37.432,0.085,0.066,1.28375,2c437418d0d1d00784edfcecec7d67fe89ce8848,256,V3,2c437418d0d1d00784edfcecec7d67fe89ce8848_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx+17.846_ry+62.076_rz+37.432_tx+00.085_ty+00.066_s+01.027_2c437418d0d1d00784edfcecec7d67fe89ce8848_256x256.png,5b3894f7d1d7d37f83d041646c033a4306f31c30,Animals,17.846,62.076,37.432,2086 -2087,bear,20.302,16.241,-38.903,-0.045,-0.212,1.2425,54abb553c0ab1a9b2ff9d1b197a7efa785a81517,256,V3,54abb553c0ab1a9b2ff9d1b197a7efa785a81517_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx+20.302_ry+106.241_rz-38.903_tx-00.045_ty-00.212_s+00.994_54abb553c0ab1a9b2ff9d1b197a7efa785a81517_256x256.png,19cad7de0f427f39d3dfaa714bb061be107c5d23,Animals,20.302,106.241,-38.903,2087 -2088,bear,21.305,-12.308,33.647,-0.288,0.188,1.17375,334f4cd87373ee6009e7b8de3346f8dde210a9d8,256,V3,334f4cd87373ee6009e7b8de3346f8dde210a9d8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx+21.305_ry+77.692_rz+33.647_tx-00.288_ty+00.188_s+00.939_334f4cd87373ee6009e7b8de3346f8dde210a9d8_256x256.png,dd7e2efd15146a1800109f4d7b7a62e49d405bce,Animals,21.305,77.692,33.647,2088 -2089,bear,29.88,-27.476,-24.174,-0.102,-0.26,0.83625,fbf48bcd9220bc0ed6d3ea34a0a5cb9b2948a852,256,V3,fbf48bcd9220bc0ed6d3ea34a0a5cb9b2948a852_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx+29.880_ry+62.524_rz-24.174_tx-00.102_ty-00.260_s+00.669_fbf48bcd9220bc0ed6d3ea34a0a5cb9b2948a852_256x256.png,1303602bbc2f8a67901449573b9180659f3cbd6e,Animals,29.88,62.524,-24.174,2089 -2090,bear,31.522,36.538,-10.212,0.222,-0.082,1.08625,36bfbbb25a81a0454f1a17abf91bb4d82d440a68,256,V3,36bfbbb25a81a0454f1a17abf91bb4d82d440a68_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx+31.522_ry+126.538_rz-10.212_tx+00.222_ty-00.082_s+00.869_36bfbbb25a81a0454f1a17abf91bb4d82d440a68_256x256.png,34a4e379f5e9ef97509757b7558be4ac8ec03e45,Animals,31.522,126.538,-10.212,2090 -2091,bear,32.861,15.299,-35.287,-0.092,-0.306,0.9775,05bda3367b9da1481685544cc198c6ed8b8428a8,256,V3,05bda3367b9da1481685544cc198c6ed8b8428a8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx+32.861_ry+105.299_rz-35.287_tx-00.092_ty-00.306_s+00.782_05bda3367b9da1481685544cc198c6ed8b8428a8_256x256.png,e3988744cebf6118aeec637f5860989ef7a0b8f8,Animals,32.861,105.299,-35.287,2091 -2092,bear,37.317,33.82,17.61,0.095,-0.515,0.975,c6f0ee8cf6be83e0791e0e5ca3a5777a151a60d1,256,V3,c6f0ee8cf6be83e0791e0e5ca3a5777a151a60d1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx+37.317_ry+123.820_rz+17.610_tx+00.095_ty-00.515_s+00.780_c6f0ee8cf6be83e0791e0e5ca3a5777a151a60d1_256x256.png,b1051a585dcbb4d4fcc2724c4168c031cc06a04c,Animals,37.317,123.82,17.61,2092 -2093,bear,43.35,19.925,38.458,0.068,0.009,1.1325,fb6ed66438deea7ed2b5a84f12994895ece657bb,256,V3,fb6ed66438deea7ed2b5a84f12994895ece657bb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx+43.350_ry+109.925_rz+38.458_tx+00.068_ty+00.009_s+00.906_fb6ed66438deea7ed2b5a84f12994895ece657bb_256x256.png,00487445258f44c4de1c0a681a21e618c9e148f8,Animals,43.35,109.925,38.458,2093 -2094,bear,-0.967,-18.727,-9.614,-0.019,0.437,0.9025,f4e125f779f4cce7d200653335040b2f39c36f3e,256,V3,f4e125f779f4cce7d200653335040b2f39c36f3e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-00.967_ry+71.273_rz-09.614_tx-00.019_ty+00.437_s+00.722_f4e125f779f4cce7d200653335040b2f39c36f3e_256x256.png,8795ffe469864d654d09ffb9f6776f3924f4f303,Animals,-0.967,71.273,-9.614,2094 -2095,bear,-2.406,-13.995,-32.815,-0.243,0.094,1.0025,1d61905eaecffa23dd100487ea7017151cfb5398,256,V3,1d61905eaecffa23dd100487ea7017151cfb5398_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-02.406_ry+76.005_rz-32.815_tx-00.243_ty+00.094_s+00.802_1d61905eaecffa23dd100487ea7017151cfb5398_256x256.png,0ffccc28b72bc5b7fece0a0447e6efc847494050,Animals,-2.406,76.005,-32.815,2095 -2096,bear,-4.023,14.757,-28.434,-0.0,0.334,0.85875,c28d0a2dd7b6b84b4ee796e244ba2ef642342f44,256,V3,c28d0a2dd7b6b84b4ee796e244ba2ef642342f44_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-04.023_ry+104.757_rz-28.434_tx-00.000_ty+00.334_s+00.687_c28d0a2dd7b6b84b4ee796e244ba2ef642342f44_256x256.png,0a88717566717c21d88d73ace9fd6c765fdf1a45,Animals,-4.023,104.757,-28.434,2096 -2097,bear,-4.44,-26.79,-34.848,-0.243,0.397,1.03625,016d17ca05a7a52737c53034c1553c88a3708d94,256,V3,016d17ca05a7a52737c53034c1553c88a3708d94_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-04.440_ry+63.210_rz-34.848_tx-00.243_ty+00.397_s+00.829_016d17ca05a7a52737c53034c1553c88a3708d94_256x256.png,3cda2a1b8b0b2790d9fbc595c0064de7657b288e,Animals,-4.44,63.21,-34.848,2097 -2098,bear,-5.031,-39.826,28.926,0.061,-0.316,0.85875,b67ac0ddcf89c0c4d6fe56bd2a2efb30e4f5008c,256,V3,b67ac0ddcf89c0c4d6fe56bd2a2efb30e4f5008c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-05.031_ry+50.174_rz+28.926_tx+00.061_ty-00.316_s+00.687_b67ac0ddcf89c0c4d6fe56bd2a2efb30e4f5008c_256x256.png,b6c4da0c3da29cca75c39f62d6f55359f24fedd2,Animals,-5.031,50.174,28.926,2098 -2099,bear,-8.451,12.391,29.679,-0.207,-0.162,1.185,55dd1164c5aed8e6d81be6e8406e3dd77a8b0f11,256,V3,55dd1164c5aed8e6d81be6e8406e3dd77a8b0f11_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-08.451_ry+102.391_rz+29.679_tx-00.207_ty-00.162_s+00.948_55dd1164c5aed8e6d81be6e8406e3dd77a8b0f11_256x256.png,60a7c347506540ab21941dbf4b021e0b45f8b499,Animals,-8.451,102.391,29.679,2099 -2100,bear,-8.608,40.792,44.942,-0.077,0.285,1.19875,6ab6735d846e5b8ddb5243138f09879dba7345d2,256,V3,6ab6735d846e5b8ddb5243138f09879dba7345d2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-08.608_ry+130.792_rz+44.942_tx-00.077_ty+00.285_s+00.959_6ab6735d846e5b8ddb5243138f09879dba7345d2_256x256.png,2caa873be61d738027c9589b484683268cf5fccd,Animals,-8.608,130.792,44.942,2100 -2101,bear,-8.68,38.152,23.214,-0.162,0.155,0.99375,3748dbb0f0b379f23973034fea66430210bf2e71,256,V3,3748dbb0f0b379f23973034fea66430210bf2e71_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-08.680_ry+128.152_rz+23.214_tx-00.162_ty+00.155_s+00.795_3748dbb0f0b379f23973034fea66430210bf2e71_256x256.png,833505d17c16efc58889b15de9071d652833e783,Animals,-8.68,128.152,23.214,2101 -2102,bear,-11.585,7.788,-2.944,0.199,-0.347,1.07625,ad19e0c8556630f1d72e5abce46906fe58a2b62b,256,V3,ad19e0c8556630f1d72e5abce46906fe58a2b62b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-11.585_ry+97.788_rz-02.944_tx+00.199_ty-00.347_s+00.861_ad19e0c8556630f1d72e5abce46906fe58a2b62b_256x256.png,56a89007d57a07695ee3f7329d13a01005ec98f7,Animals,-11.585,97.788,-2.944,2102 -2103,bear,-16.769,-26.927,8.667,0.098,-0.354,1.0625,57ceea567f8c316bfdc4085ef7c55ee38bcc7041,256,V3,57ceea567f8c316bfdc4085ef7c55ee38bcc7041_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-16.769_ry+63.073_rz+08.667_tx+00.098_ty-00.354_s+00.850_57ceea567f8c316bfdc4085ef7c55ee38bcc7041_256x256.png,923afb01370321a71b3429ff783862d005260a6d,Animals,-16.769,63.073,8.667,2103 -2104,bear,-18.636,19.577,-11.266,0.151,-0.272,1.25125,edf6c4638ff490015a7eee422f48c67dfef14b6b,256,V3,edf6c4638ff490015a7eee422f48c67dfef14b6b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-18.636_ry+109.577_rz-11.266_tx+00.151_ty-00.272_s+01.001_edf6c4638ff490015a7eee422f48c67dfef14b6b_256x256.png,2e7329a589c41e4785704bc759887fff96f1d901,Animals,-18.636,109.577,-11.266,2104 -2105,bear,-19.685,-11.854,44.248,-0.083,-0.286,1.2475,148d48e96fe3eb25619574a83ce1b7a311ba4ce8,256,V3,148d48e96fe3eb25619574a83ce1b7a311ba4ce8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-19.685_ry+78.146_rz+44.248_tx-00.083_ty-00.286_s+00.998_148d48e96fe3eb25619574a83ce1b7a311ba4ce8_256x256.png,77dc82982e57f65a80c0a3ca3551e902512e513d,Animals,-19.685,78.146,44.248,2105 -2106,bear,-21.866,38.97,14.057,0.221,-0.543,0.99625,9830ec6f53c89fa31e31846116f412c53753fec6,256,V3,9830ec6f53c89fa31e31846116f412c53753fec6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-21.866_ry+128.970_rz+14.057_tx+00.221_ty-00.543_s+00.797_9830ec6f53c89fa31e31846116f412c53753fec6_256x256.png,42fdcd99feed3f444514e91c073b491cd150ba95,Animals,-21.866,128.97,14.057,2106 -2107,bear,-22.915,-19.234,16.045,0.282,-0.121,1.19375,08c7c12ca67bbe4d78779505bee8d5407866e310,256,V3,08c7c12ca67bbe4d78779505bee8d5407866e310_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-22.915_ry+70.766_rz+16.045_tx+00.282_ty-00.121_s+00.955_08c7c12ca67bbe4d78779505bee8d5407866e310_256x256.png,d0b66caf765f99ae81b2f400e686c70d750abc0f,Animals,-22.915,70.766,16.045,2107 -2108,bear,-25.543,32.932,-28.406,0.087,-0.406,0.82375,8754aaca4549d2f26821497ba032b79c40b79595,256,V3,8754aaca4549d2f26821497ba032b79c40b79595_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-25.543_ry+122.932_rz-28.406_tx+00.087_ty-00.406_s+00.659_8754aaca4549d2f26821497ba032b79c40b79595_256x256.png,e106f322ec54d1f0ecbab2ccfc20e9e75801442d,Animals,-25.543,122.932,-28.406,2108 -2109,bear,-25.777,18.87,44.358,0.065,0.21,1.22875,f182547a8387d1db3957884660fc98f76a20ba09,256,V3,f182547a8387d1db3957884660fc98f76a20ba09_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-25.777_ry+108.870_rz+44.358_tx+00.065_ty+00.210_s+00.983_f182547a8387d1db3957884660fc98f76a20ba09_256x256.png,ad6165d7ca6e99b6b207f98c772d7463f38822ef,Animals,-25.777,108.87,44.358,2109 -2110,bear,-26.462,-14.094,25.579,0.105,0.269,1.16125,ca2897dfc7bb4217dcd4fb55622801004f19d342,256,V3,ca2897dfc7bb4217dcd4fb55622801004f19d342_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-26.462_ry+75.906_rz+25.579_tx+00.105_ty+00.269_s+00.929_ca2897dfc7bb4217dcd4fb55622801004f19d342_256x256.png,420b02fafff4b18c9eb597eae7406cbe5b6f3e0d,Animals,-26.462,75.906,25.579,2110 -2111,bear,-27.576,-6.889,-36.944,-0.149,0.216,1.255,c69ea1a43f219f155215451bab8f90693b6339f2,256,V3,c69ea1a43f219f155215451bab8f90693b6339f2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-27.576_ry+83.111_rz-36.944_tx-00.149_ty+00.216_s+01.004_c69ea1a43f219f155215451bab8f90693b6339f2_256x256.png,3ff5c84ce4aa301a3789cc0971769a75ef6970d0,Animals,-27.576,83.111,-36.944,2111 -2112,bear,-27.641,-22.916,-2.965,-0.192,-0.302,1.2475,420355964e0582b6bd270ee61769b12dd4bee130,256,V3,420355964e0582b6bd270ee61769b12dd4bee130_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-27.641_ry+67.084_rz-02.965_tx-00.192_ty-00.302_s+00.998_420355964e0582b6bd270ee61769b12dd4bee130_256x256.png,d6071f8046059c90d1aef6f113c9e2c5f76b674f,Animals,-27.641,67.084,-2.965,2112 -2113,bear,-33.338,12.473,-35.611,0.23,-0.019,0.9175,573f668986acd75dd9725258f3ebff5220844eff,256,V3,573f668986acd75dd9725258f3ebff5220844eff_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-33.338_ry+102.473_rz-35.611_tx+00.230_ty-00.019_s+00.734_573f668986acd75dd9725258f3ebff5220844eff_256x256.png,5c1b040ebc0e1327ca7ca2920348e22ea6e4729c,Animals,-33.338,102.473,-35.611,2113 -2114,bear,-33.373,33.337,-34.678,-0.208,0.274,0.97875,bd8ce92f0280af9f84d3b99b4379082214650fd8,256,V3,bd8ce92f0280af9f84d3b99b4379082214650fd8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-33.373_ry+123.337_rz-34.678_tx-00.208_ty+00.274_s+00.783_bd8ce92f0280af9f84d3b99b4379082214650fd8_256x256.png,5cf9cfcee30e143a8bfdf05bf3c73a2e61459d60,Animals,-33.373,123.337,-34.678,2114 -2115,bear,-34.682,29.124,-24.44,-0.062,-0.545,1.1175,4b357c5597e59c5d3976cbb8d1c616a7963f494b,256,V3,4b357c5597e59c5d3976cbb8d1c616a7963f494b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-34.682_ry+119.124_rz-24.440_tx-00.062_ty-00.545_s+00.894_4b357c5597e59c5d3976cbb8d1c616a7963f494b_256x256.png,d2d97c669829b02abdf4436c5e400f89df25a561,Animals,-34.682,119.124,-24.44,2115 -2116,bear,-38.679,40.653,-21.734,0.12,-0.119,1.215,9f75049a44924fcbccbc93cfd701069fbea81609,256,V3,9f75049a44924fcbccbc93cfd701069fbea81609_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-38.679_ry+130.653_rz-21.734_tx+00.120_ty-00.119_s+00.972_9f75049a44924fcbccbc93cfd701069fbea81609_256x256.png,2e252d189cb329d64a7090907ecfcbb4b8d7983e,Animals,-38.679,130.653,-21.734,2116 -2117,bear,-40.941,28.911,25.787,0.198,-0.191,1.23,22fb2af815a4dafda4e10da433e9db586a4753e6,256,V3,22fb2af815a4dafda4e10da433e9db586a4753e6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-40.941_ry+118.911_rz+25.787_tx+00.198_ty-00.191_s+00.984_22fb2af815a4dafda4e10da433e9db586a4753e6_256x256.png,c0f9f1388636765b1cbd6ac69cfa3263165c3a39,Animals,-40.941,118.911,25.787,2117 -2118,bear,-42.745,-40.598,15.977,-0.087,0.379,1.075,5d1682cbaae7e450f7bdd2507b026730e20b4ab0,256,V3,5d1682cbaae7e450f7bdd2507b026730e20b4ab0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-42.745_ry+49.402_rz+15.977_tx-00.087_ty+00.379_s+00.860_5d1682cbaae7e450f7bdd2507b026730e20b4ab0_256x256.png,019dcae93081e4d8c3584c63d731835da84966cf,Animals,-42.745,49.402,15.977,2118 -2119,bear,-43.749,-15.418,32.568,-0.288,-0.1,0.92625,0645f6423f8a05135892ec867169672b83628e56,256,V3,0645f6423f8a05135892ec867169672b83628e56_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bear_rx-43.749_ry+74.582_rz+32.568_tx-00.288_ty-00.100_s+00.741_0645f6423f8a05135892ec867169672b83628e56_256x256.png,1045cadfd4b3fafc15cad9de36534e6c62d0695c,Animals,-43.749,74.582,32.568,2119 -2120,blCow,0.46,20.025,-0.666,0.14,-0.501,1.14375,eb98076248ee73948b2413b559b01efccd6f2b93,256,V3,eb98076248ee73948b2413b559b01efccd6f2b93_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+00.460_ry+20.025_rz-00.666_tx+00.140_ty-00.501_s+00.915_eb98076248ee73948b2413b559b01efccd6f2b93_256x256.png,6abe8f1cce54d7fb3fc3d05ab004e54316806001,Animals,0.46,110.025,-0.666,2120 -2121,blCow,1.584,-14.027,20.915,-0.206,-0.018,1.22375,8c44bc9e5ad306f567ee61825392e4cadbf5cc54,256,V3,8c44bc9e5ad306f567ee61825392e4cadbf5cc54_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+01.584_ry-14.027_rz+20.915_tx-00.206_ty-00.018_s+00.979_8c44bc9e5ad306f567ee61825392e4cadbf5cc54_256x256.png,1b05f6250257c7daf98f0b5091a59c3f10563055,Animals,1.584,75.973,20.915,2121 -2122,blCow,2.937,33.248,1.689,0.058,-0.027,1.03625,5d0312fec75ae8898c85981791539024065bac88,256,V3,5d0312fec75ae8898c85981791539024065bac88_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+02.937_ry+33.248_rz+01.689_tx+00.058_ty-00.027_s+00.829_5d0312fec75ae8898c85981791539024065bac88_256x256.png,a97208c1c9808f0a1da8ce05f89bad31cc2da79c,Animals,2.937,123.248,1.689,2122 -2123,blCow,3.435,20.351,-22.614,0.199,0.507,0.83875,3124a9e2bc7e87cdbaf77a3280a8746370aba970,256,V3,3124a9e2bc7e87cdbaf77a3280a8746370aba970_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+03.435_ry+20.351_rz-22.614_tx+00.199_ty+00.507_s+00.671_3124a9e2bc7e87cdbaf77a3280a8746370aba970_256x256.png,728095502caca51e4c967738137ed06ba9f53a0b,Animals,3.435,110.351,-22.614,2123 -2124,blCow,3.762,-21.757,-35.819,-0.237,-0.539,1.05,57c8fa29cd079ca8668f876ded59d1bdc29bb344,256,V3,57c8fa29cd079ca8668f876ded59d1bdc29bb344_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+03.762_ry-21.757_rz-35.819_tx-00.237_ty-00.539_s+00.840_57c8fa29cd079ca8668f876ded59d1bdc29bb344_256x256.png,2a6acd91300861ed9594114068f052bf7150befa,Animals,3.762,68.243,-35.819,2124 -2125,blCow,5.547,-29.913,-6.895,-0.261,-0.298,0.8175,db995f6360596063ca268b6a7d6a49e9f43cc72d,256,V3,db995f6360596063ca268b6a7d6a49e9f43cc72d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+05.547_ry-29.913_rz-06.895_tx-00.261_ty-00.298_s+00.654_db995f6360596063ca268b6a7d6a49e9f43cc72d_256x256.png,12546e3c6ce1f244ccc95ea4dd10e7faea2ca5b3,Animals,5.547,60.087,-6.895,2125 -2126,blCow,6.178,15.372,35.661,0.018,0.196,0.96,0979996edaa8a2622cbb7fd62aacc9c13acd5478,256,V3,0979996edaa8a2622cbb7fd62aacc9c13acd5478_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+06.178_ry+15.372_rz+35.661_tx+00.018_ty+00.196_s+00.768_0979996edaa8a2622cbb7fd62aacc9c13acd5478_256x256.png,4188accb98bf308376da3597b57589e22ddf3140,Animals,6.178,105.372,35.661,2126 -2127,blCow,9.274,-10.843,27.19,0.021,-0.597,0.9875,e7ebc8825894c231f296c8912affa224d72daa1d,256,V3,e7ebc8825894c231f296c8912affa224d72daa1d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+09.274_ry-10.843_rz+27.190_tx+00.021_ty-00.597_s+00.790_e7ebc8825894c231f296c8912affa224d72daa1d_256x256.png,0bfc378cab9ba4963a3784ef0e5c454152a6ca92,Animals,9.274,79.157,27.19,2127 -2128,blCow,11.943,0.585,27.092,-0.202,-0.348,1.22,4c252c0dc5bc5dbd8bbfd262f2559db4c3dc44e6,256,V3,4c252c0dc5bc5dbd8bbfd262f2559db4c3dc44e6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+11.943_ry+00.585_rz+27.092_tx-00.202_ty-00.348_s+00.976_4c252c0dc5bc5dbd8bbfd262f2559db4c3dc44e6_256x256.png,895d52a26919ed7e158a1123d566e6314ef9ffe1,Animals,11.943,90.585,27.092,2128 -2129,blCow,12.894,-41.432,3.442,0.26,-0.56,0.92625,9fe0e70a82ad32374c1fc6225c1fa8f90665cd98,256,V3,9fe0e70a82ad32374c1fc6225c1fa8f90665cd98_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+12.894_ry-41.432_rz+03.442_tx+00.260_ty-00.560_s+00.741_9fe0e70a82ad32374c1fc6225c1fa8f90665cd98_256x256.png,9c7f24d349c9ecc87aa93aba94a43545e4005eb4,Animals,12.894,48.568,3.442,2129 -2130,blCow,15.474,-30.834,-30.65,-0.051,0.401,1.0875,5291b941f8b1792a8b587177894e9109371a747b,256,V3,5291b941f8b1792a8b587177894e9109371a747b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+15.474_ry-30.834_rz-30.650_tx-00.051_ty+00.401_s+00.870_5291b941f8b1792a8b587177894e9109371a747b_256x256.png,1365a1bbc546d60533bb4085837fb10c46c6ba40,Animals,15.474,59.166,-30.65,2130 -2131,blCow,17.105,11.424,44.886,-0.158,0.551,0.905,2b0ca4fda34ec1db972756409921d603901640ae,256,V3,2b0ca4fda34ec1db972756409921d603901640ae_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+17.105_ry+11.424_rz+44.886_tx-00.158_ty+00.551_s+00.724_2b0ca4fda34ec1db972756409921d603901640ae_256x256.png,bce5b3123d8d95174ac36ac697cf598f324ee5bd,Animals,17.105,101.424,44.886,2131 -2132,blCow,17.585,31.394,14.105,-0.246,-0.497,1.15,bba0f2764c50286c155c07384215225bf628a5e9,256,V3,bba0f2764c50286c155c07384215225bf628a5e9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+17.585_ry+31.394_rz+14.105_tx-00.246_ty-00.497_s+00.920_bba0f2764c50286c155c07384215225bf628a5e9_256x256.png,0137908e2619cf7fc3ffb24a2e86fbba17a801f0,Animals,17.585,121.394,14.105,2132 -2133,blCow,19.757,-14.039,-1.679,0.007,0.212,0.77625,9c804f03b866e815faa014ee742f70f5ac7d3947,256,V3,9c804f03b866e815faa014ee742f70f5ac7d3947_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+19.757_ry-14.039_rz-01.679_tx+00.007_ty+00.212_s+00.621_9c804f03b866e815faa014ee742f70f5ac7d3947_256x256.png,88c1f7c850410abc2d433cbe5b2b0440ea21f7fc,Animals,19.757,75.961,-1.679,2133 -2134,blCow,19.838,-15.23,-28.661,0.169,0.417,0.8575,36ad8befabc96d72768a99f3148b0961d73a313a,256,V3,36ad8befabc96d72768a99f3148b0961d73a313a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+19.838_ry-15.230_rz-28.661_tx+00.169_ty+00.417_s+00.686_36ad8befabc96d72768a99f3148b0961d73a313a_256x256.png,1c766000020732c4dd8f3b8e9e93090410ad0023,Animals,19.838,74.77,-28.661,2134 -2135,blCow,24.041,-21.106,-2.572,-0.198,-0.361,1.29125,141ad45f842b076c3fadb323f87267624728e885,256,V3,141ad45f842b076c3fadb323f87267624728e885_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+24.041_ry-21.106_rz-02.572_tx-00.198_ty-00.361_s+01.033_141ad45f842b076c3fadb323f87267624728e885_256x256.png,68e5c98c6f24273fd3dc272253468d4c4e1e035b,Animals,24.041,68.894,-2.572,2135 -2136,blCow,24.998,37.597,-41.561,0.146,0.532,0.85,1d4220ca6c0446d3f93abcbcbeef7d8bcd3b1836,256,V3,1d4220ca6c0446d3f93abcbcbeef7d8bcd3b1836_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+24.998_ry+37.597_rz-41.561_tx+00.146_ty+00.532_s+00.680_1d4220ca6c0446d3f93abcbcbeef7d8bcd3b1836_256x256.png,2ff7acd185eeb20a81fc9b82b654294f83994062,Animals,24.998,127.597,-41.561,2136 -2137,blCow,27.748,3.024,11.833,0.236,0.135,1.255,da87d5227bdfeabc1f7258982cb4629664ef4281,256,V3,da87d5227bdfeabc1f7258982cb4629664ef4281_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+27.748_ry+03.024_rz+11.833_tx+00.236_ty+00.135_s+01.004_da87d5227bdfeabc1f7258982cb4629664ef4281_256x256.png,86aeab68a91ac10d18e99e7e87c58145b196250f,Animals,27.748,93.024,11.833,2137 -2138,blCow,28.371,-6.904,11.585,0.262,-0.027,1.29,69c6bf4049144062b6286d7f4dc59da877fcba26,256,V3,69c6bf4049144062b6286d7f4dc59da877fcba26_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+28.371_ry-06.904_rz+11.585_tx+00.262_ty-00.027_s+01.032_69c6bf4049144062b6286d7f4dc59da877fcba26_256x256.png,cd8c4d602c8c87b580270c27ccb4a74a806bfd31,Animals,28.371,83.096,11.585,2138 -2139,blCow,29.194,-39.054,1.542,-0.122,-0.237,0.97,979980530af643bb078e513ca08e12efc80c53dd,256,V3,979980530af643bb078e513ca08e12efc80c53dd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+29.194_ry-39.054_rz+01.542_tx-00.122_ty-00.237_s+00.776_979980530af643bb078e513ca08e12efc80c53dd_256x256.png,3267bf2428d4ba35d36514e8dd1cd3917e2ba3c5,Animals,29.194,50.946,1.542,2139 -2140,blCow,30.235,-34.093,-13.064,0.004,-0.33,1.05125,c79889e59dfc5a89b4d383da3e14ba7e9327603b,256,V3,c79889e59dfc5a89b4d383da3e14ba7e9327603b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+30.235_ry-34.093_rz-13.064_tx+00.004_ty-00.330_s+00.841_c79889e59dfc5a89b4d383da3e14ba7e9327603b_256x256.png,808d987998976b4c4c46377e56dcb379e1cd347a,Animals,30.235,55.907,-13.064,2140 -2141,blCow,31.132,12.503,-38.45,0.067,0.43,1.055,3ac4358538216f2d9127f4651d2a1d23c4304edc,256,V3,3ac4358538216f2d9127f4651d2a1d23c4304edc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+31.132_ry+12.503_rz-38.450_tx+00.067_ty+00.430_s+00.844_3ac4358538216f2d9127f4651d2a1d23c4304edc_256x256.png,d764f0d96f54ef0e4718b70e82601299412eb601,Animals,31.132,102.503,-38.45,2141 -2142,blCow,37.234,18.708,-13.189,0.298,0.244,1.145,1aea8942376bbdcf8e0178af2c48484046561e80,256,V3,1aea8942376bbdcf8e0178af2c48484046561e80_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+37.234_ry+18.708_rz-13.189_tx+00.298_ty+00.244_s+00.916_1aea8942376bbdcf8e0178af2c48484046561e80_256x256.png,d6e1ecb7bd346bc513feba15221ce7a166ad6930,Animals,37.234,108.708,-13.189,2142 -2143,blCow,42.346,17.39,-41.025,0.233,-0.375,0.99875,30abff76a0a93f96ec19ae48c187b0fa71bf4c4f,256,V3,30abff76a0a93f96ec19ae48c187b0fa71bf4c4f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+42.346_ry+17.390_rz-41.025_tx+00.233_ty-00.375_s+00.799_30abff76a0a93f96ec19ae48c187b0fa71bf4c4f_256x256.png,c08ed64e40fb30f6b7417f5a1c7979ddefb5555a,Animals,42.346,107.39,-41.025,2143 -2144,blCow,42.736,-3.37,-33.894,0.0,-0.175,0.81625,1f16a492b514b24d623bc75b28b838b375668eac,256,V3,1f16a492b514b24d623bc75b28b838b375668eac_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+42.736_ry-03.370_rz-33.894_tx+00.000_ty-00.175_s+00.653_1f16a492b514b24d623bc75b28b838b375668eac_256x256.png,4793532ca1946b302842f63f0422d423f816e027,Animals,42.736,86.63,-33.894,2144 -2145,blCow,43.385,-35.583,-19.79,-0.222,0.446,1.195,fffc99a7758a62fa52fa202432c83f5d93268d57,256,V3,fffc99a7758a62fa52fa202432c83f5d93268d57_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+43.385_ry-35.583_rz-19.790_tx-00.222_ty+00.446_s+00.956_fffc99a7758a62fa52fa202432c83f5d93268d57_256x256.png,bb514efa5ab8c4698b5cf521867c0be0dc504d86,Animals,43.385,54.417,-19.79,2145 -2146,blCow,44.376,25.729,-23.104,-0.07,0.218,0.82375,0b2ad6d1669352a8ad6560389af44d8e93001efc,256,V3,0b2ad6d1669352a8ad6560389af44d8e93001efc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx+44.376_ry+25.729_rz-23.104_tx-00.070_ty+00.218_s+00.659_0b2ad6d1669352a8ad6560389af44d8e93001efc_256x256.png,321b03c158cf137618bdd22acd976923ad1096ae,Animals,44.376,115.729,-23.104,2146 -2147,blCow,-6.103,37.927,-13.436,0.22,-0.268,1.0925,396df21791761d666ddf50a145b3a3ecaa71b402,256,V3,396df21791761d666ddf50a145b3a3ecaa71b402_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx-06.103_ry+37.927_rz-13.436_tx+00.220_ty-00.268_s+00.874_396df21791761d666ddf50a145b3a3ecaa71b402_256x256.png,94b5554006abe3f92404f0a9cddb96aeb19a1752,Animals,-6.103,127.927,-13.436,2147 -2148,blCow,-7.996,-35.079,3.371,-0.169,0.039,0.8,9678bf879addf048906122d6919b4cfec710f531,256,V3,9678bf879addf048906122d6919b4cfec710f531_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx-07.996_ry-35.079_rz+03.371_tx-00.169_ty+00.039_s+00.640_9678bf879addf048906122d6919b4cfec710f531_256x256.png,70f48a42252aa7b64b6b6934156a825aa7fa961c,Animals,-7.996,54.921,3.371,2148 -2149,blCow,-9.705,5.095,-0.187,0.182,0.18,0.9625,429d7e785d86b8b0f7045bae6369b9f731991850,256,V3,429d7e785d86b8b0f7045bae6369b9f731991850_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx-09.705_ry+05.095_rz-00.187_tx+00.182_ty+00.180_s+00.770_429d7e785d86b8b0f7045bae6369b9f731991850_256x256.png,c012bf855ecbb61dc172b9cce5e3c3e24ee9a574,Animals,-9.705,95.095,-0.187,2149 -2150,blCow,-10.843,-6.656,-39.169,-0.179,-0.51,1.08125,536fb6a35123e756a981f66d74229b3ebf90c562,256,V3,536fb6a35123e756a981f66d74229b3ebf90c562_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx-10.843_ry-06.656_rz-39.169_tx-00.179_ty-00.510_s+00.865_536fb6a35123e756a981f66d74229b3ebf90c562_256x256.png,4ab131d1e0831ddaf7152ac4243930fcb42ec74c,Animals,-10.843,83.344,-39.169,2150 -2151,blCow,-11.36,-32.768,32.687,-0.253,-0.004,0.77375,d26adb68200a2bd06c647505e013ebac16337b32,256,V3,d26adb68200a2bd06c647505e013ebac16337b32_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx-11.360_ry-32.768_rz+32.687_tx-00.253_ty-00.004_s+00.619_d26adb68200a2bd06c647505e013ebac16337b32_256x256.png,04e7ea0970db2257d51ff6e5d7be32776e8a251e,Animals,-11.36,57.232,32.687,2151 -2152,blCow,-12.258,8.339,23.42,-0.051,0.589,0.7875,de902fad908bbd641a70b7fe7d60a519ea515e1f,256,V3,de902fad908bbd641a70b7fe7d60a519ea515e1f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx-12.258_ry+08.339_rz+23.420_tx-00.051_ty+00.589_s+00.630_de902fad908bbd641a70b7fe7d60a519ea515e1f_256x256.png,190226448aac45de09259bd8b76e01a81ade351e,Animals,-12.258,98.339,23.42,2152 -2153,blCow,-22.389,2.512,-14.822,0.073,-0.21,1.0825,7dca3825303934d93414b42f013c6312e9cee477,256,V3,7dca3825303934d93414b42f013c6312e9cee477_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx-22.389_ry+02.512_rz-14.822_tx+00.073_ty-00.210_s+00.866_7dca3825303934d93414b42f013c6312e9cee477_256x256.png,75a6e874758927cb07e0168939ba2612279b3879,Animals,-22.389,92.512,-14.822,2153 -2154,blCow,-26.485,-10.217,41.163,0.189,0.04,1.00875,4f72901eee68de343e05bed715c54ee718524eca,256,V3,4f72901eee68de343e05bed715c54ee718524eca_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx-26.485_ry-10.217_rz+41.163_tx+00.189_ty+00.040_s+00.807_4f72901eee68de343e05bed715c54ee718524eca_256x256.png,a642180952f469d53d0f1e35ed04c10681bb6a82,Animals,-26.485,79.783,41.163,2154 -2155,blCow,-26.609,-5.122,-21.089,0.196,0.25,0.78625,2b8cb535630d6b6faf5ea58ab106e442ffe9df65,256,V3,2b8cb535630d6b6faf5ea58ab106e442ffe9df65_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx-26.609_ry-05.122_rz-21.089_tx+00.196_ty+00.250_s+00.629_2b8cb535630d6b6faf5ea58ab106e442ffe9df65_256x256.png,e6b63a3e3dc238ca8d92d2adc4ce4e6292d8b6e2,Animals,-26.609,84.878,-21.089,2155 -2156,blCow,-27.204,-19.114,-21.334,0.016,-0.364,1.2475,b495962c4ed6a10ce7b9dc42c48ea27557c94d95,256,V3,b495962c4ed6a10ce7b9dc42c48ea27557c94d95_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx-27.204_ry-19.114_rz-21.334_tx+00.016_ty-00.364_s+00.998_b495962c4ed6a10ce7b9dc42c48ea27557c94d95_256x256.png,431312658e18dea985288a9808f37a5e539f82b6,Animals,-27.204,70.886,-21.334,2156 -2157,blCow,-27.415,-5.494,3.006,0.087,0.501,0.78875,c956007dcb0f906b8054e2c195f89935e5cfb286,256,V3,c956007dcb0f906b8054e2c195f89935e5cfb286_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx-27.415_ry-05.494_rz+03.006_tx+00.087_ty+00.501_s+00.631_c956007dcb0f906b8054e2c195f89935e5cfb286_256x256.png,30c637fbe437adc789edff3f8c7666813c9a0c2e,Animals,-27.415,84.506,3.006,2157 -2158,blCow,-35.611,-16.804,2.68,0.126,-0.585,0.92,a31faa9fa60c15e1fb4103b2e8d5ff6e29e4fb8b,256,V3,a31faa9fa60c15e1fb4103b2e8d5ff6e29e4fb8b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx-35.611_ry-16.804_rz+02.680_tx+00.126_ty-00.585_s+00.736_a31faa9fa60c15e1fb4103b2e8d5ff6e29e4fb8b_256x256.png,f129b4e21142547ca1ce998f40875cc649771578,Animals,-35.611,73.196,2.68,2158 -2159,blCow,-43.966,-1.311,29.566,0.041,0.454,1.02,e664016eaa17eeef5e8d481024052636e1a96361,256,V3,e664016eaa17eeef5e8d481024052636e1a96361_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/blCow_rx-43.966_ry-01.311_rz+29.566_tx+00.041_ty+00.454_s+00.816_e664016eaa17eeef5e8d481024052636e1a96361_256x256.png,63154b9c54bbbfb9e697633fce925bd71a66c7c4,Animals,-43.966,88.689,29.566,2159 -2160,bmw325,1.764,1.829,25.207,0.066,-0.565,1.16,eea26fc77129885b531fd6052ceb9abf5e63943a,256,V3,eea26fc77129885b531fd6052ceb9abf5e63943a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+01.764_ry+01.829_rz+25.207_tx+00.066_ty-00.565_s+01.160_eea26fc77129885b531fd6052ceb9abf5e63943a_256x256.png,c51cfe1d8dbd70905a21c22bb71d19c3eca3a8f8,Cars,1.764,91.829,25.207,2160 -2161,bmw325,2.082,23.806,28.241,0.136,0.252,0.99,f3b5b8b347bc93e33b776761c0a6a3f624b675de,256,V3,f3b5b8b347bc93e33b776761c0a6a3f624b675de_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+02.082_ry+23.806_rz+28.241_tx+00.136_ty+00.252_s+00.990_f3b5b8b347bc93e33b776761c0a6a3f624b675de_256x256.png,e6afdfa9547f6f971bc529d14e6037617ac84dd4,Cars,2.082,113.806,28.241,2161 -2162,bmw325,4.008,38.396,2.26,-0.061,-0.468,1.08,e0d8adc3d6c520088fa43ea894b5ca0defb6a2e0,256,V3,e0d8adc3d6c520088fa43ea894b5ca0defb6a2e0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+04.008_ry+38.396_rz+02.260_tx-00.061_ty-00.468_s+01.080_e0d8adc3d6c520088fa43ea894b5ca0defb6a2e0_256x256.png,f2dfe7e26ec059ce78d28f65f4f6081d182898a8,Cars,4.008,128.396,2.26,2162 -2163,bmw325,6.248,-23.694,-28.175,0.159,0.114,1.285,9ef5a65aeccd739b350d4e60902164d63400cde3,256,V3,9ef5a65aeccd739b350d4e60902164d63400cde3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+06.248_ry-23.694_rz-28.175_tx+00.159_ty+00.114_s+01.285_9ef5a65aeccd739b350d4e60902164d63400cde3_256x256.png,db6542c5f604aa9532cb157d072f3bc2a0af43fe,Cars,6.248,66.306,-28.175,2163 -2164,bmw325,9.523,31.772,28.009,-0.182,-0.025,1.02,1c2e6d110abe33120dd4ed04e5476f804b644c41,256,V3,1c2e6d110abe33120dd4ed04e5476f804b644c41_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+09.523_ry+31.772_rz+28.009_tx-00.182_ty-00.025_s+01.020_1c2e6d110abe33120dd4ed04e5476f804b644c41_256x256.png,70aa261341bba20781282f7d97539b9cbe99fca0,Cars,9.523,121.772,28.009,2164 -2165,bmw325,9.795,-2.236,-24.316,0.208,-0.052,1.171,985f16893c91680d57e71acc68dad790e247d8b6,256,V3,985f16893c91680d57e71acc68dad790e247d8b6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+09.795_ry-02.236_rz-24.316_tx+00.208_ty-00.052_s+01.171_985f16893c91680d57e71acc68dad790e247d8b6_256x256.png,1ad857e8432a593316a31f6c7e227f1abb6f371b,Cars,9.795,87.764,-24.316,2165 -2166,bmw325,10.942,-17.579,9.909,-0.249,-0.296,0.78,ae3c6f470550061a18bd7e27af48ac8adf2c9812,256,V3,ae3c6f470550061a18bd7e27af48ac8adf2c9812_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+10.942_ry-17.579_rz+09.909_tx-00.249_ty-00.296_s+00.780_ae3c6f470550061a18bd7e27af48ac8adf2c9812_256x256.png,251aaa66eadc55d0bfb79e12d2dcae513d6a99f1,Cars,10.942,72.421,9.909,2166 -2167,bmw325,13.038,24.701,-26.825,-0.113,0.321,1.036,3c5e03605ca43209c5e3468b1a588b43ac064978,256,V3,3c5e03605ca43209c5e3468b1a588b43ac064978_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+13.038_ry+24.701_rz-26.825_tx-00.113_ty+00.321_s+01.036_3c5e03605ca43209c5e3468b1a588b43ac064978_256x256.png,730f2054d563b719d2a866e818dbec1a1e43ecf8,Cars,13.038,114.701,-26.825,2167 -2168,bmw325,20.753,31.814,2.423,-0.094,-0.159,0.844,c1b4750c4d2a5c60a3bc90f96c1b25d38e4310ea,256,V3,c1b4750c4d2a5c60a3bc90f96c1b25d38e4310ea_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+20.753_ry+31.814_rz+02.423_tx-00.094_ty-00.159_s+00.844_c1b4750c4d2a5c60a3bc90f96c1b25d38e4310ea_256x256.png,a5f2a46b4b7e117fce511164907692f4b309fc8d,Cars,20.753,121.814,2.423,2168 -2169,bmw325,23.774,24.801,-20.799,0.157,-0.38,1.025,5790e5dd9a10fb64c32f39e057d13c78bb501162,256,V3,5790e5dd9a10fb64c32f39e057d13c78bb501162_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+23.774_ry+24.801_rz-20.799_tx+00.157_ty-00.380_s+01.025_5790e5dd9a10fb64c32f39e057d13c78bb501162_256x256.png,4b5ddd262c14611e37645482a826420b9389a867,Cars,23.774,114.801,-20.799,2169 -2170,bmw325,24.527,-9.937,-10.716,0.197,-0.065,1.182,f934a940602d18a719e8a3026c201301958142af,256,V3,f934a940602d18a719e8a3026c201301958142af_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+24.527_ry-09.937_rz-10.716_tx+00.197_ty-00.065_s+01.182_f934a940602d18a719e8a3026c201301958142af_256x256.png,147abddcabdf0891b00ae6d09de7b761bf25c279,Cars,24.527,80.063,-10.716,2170 -2171,bmw325,25.812,15.948,24.998,0.06,-0.514,1.176,ca63f7d930e6f2fc0d2606dc7ca68b8ce11e67bb,256,V3,ca63f7d930e6f2fc0d2606dc7ca68b8ce11e67bb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+25.812_ry+15.948_rz+24.998_tx+00.060_ty-00.514_s+01.176_ca63f7d930e6f2fc0d2606dc7ca68b8ce11e67bb_256x256.png,1301f6f89c65f41ee98031c9639308cb3d31dd79,Cars,25.812,105.948,24.998,2171 -2172,bmw325,27.344,0.59,20.661,0.099,0.151,0.808,c883bcad0954fce8f83a52721a7ac741d31e470d,256,V3,c883bcad0954fce8f83a52721a7ac741d31e470d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+27.344_ry+00.590_rz+20.661_tx+00.099_ty+00.151_s+00.808_c883bcad0954fce8f83a52721a7ac741d31e470d_256x256.png,277f9ab753347c3c5931e689bc1cf766084c4905,Cars,27.344,90.59,20.661,2172 -2173,bmw325,27.812,-24.861,28.799,-0.134,-0.087,0.931,fc0c35af01b63b712700752dd7005fbbbfca2430,256,V3,fc0c35af01b63b712700752dd7005fbbbfca2430_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+27.812_ry-24.861_rz+28.799_tx-00.134_ty-00.087_s+00.931_fc0c35af01b63b712700752dd7005fbbbfca2430_256x256.png,03a68a232bce378994adc8aeb781f5ddae81990d,Cars,27.812,65.139,28.799,2173 -2174,bmw325,30.138,-20.105,-16.059,-0.298,-0.265,1.013,087d94504c098c5ebf0ad52164ffd70020644e6e,256,V3,087d94504c098c5ebf0ad52164ffd70020644e6e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+30.138_ry-20.105_rz-16.059_tx-00.298_ty-00.265_s+01.013_087d94504c098c5ebf0ad52164ffd70020644e6e_256x256.png,5ebbc4f128751646b9b06ba1f2d2719e29bd3dc0,Cars,30.138,69.895,-16.059,2174 -2175,bmw325,33.026,-17.475,17.984,0.026,0.409,1.125,193afd101d3a76867ef1fd3d877f71409f538ff0,256,V3,193afd101d3a76867ef1fd3d877f71409f538ff0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+33.026_ry-17.475_rz+17.984_tx+00.026_ty+00.409_s+01.125_193afd101d3a76867ef1fd3d877f71409f538ff0_256x256.png,9d5b7f7041af35840cb6f42a8c95c138cd402191,Cars,33.026,72.525,17.984,2175 -2176,bmw325,35.727,41.832,9.361,-0.167,-0.312,0.803,35f91dddb15173ba4d82bc11b044e512a57477da,256,V3,35f91dddb15173ba4d82bc11b044e512a57477da_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+35.727_ry+41.832_rz+09.361_tx-00.167_ty-00.312_s+00.803_35f91dddb15173ba4d82bc11b044e512a57477da_256x256.png,43d0e28059daa7a6491f9e6c640bdf0385b45e5c,Cars,35.727,131.832,9.361,2176 -2177,bmw325,40.375,19.109,-22.39,0.116,-0.338,1.075,9207ce084d6633f91bbf4f342fc90375c0ea453f,256,V3,9207ce084d6633f91bbf4f342fc90375c0ea453f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+40.375_ry+19.109_rz-22.390_tx+00.116_ty-00.338_s+01.075_9207ce084d6633f91bbf4f342fc90375c0ea453f_256x256.png,7ded18f65048fc1578ed7bd35e9de8522aedef51,Cars,40.375,109.109,-22.39,2177 -2178,bmw325,41.148,-38.859,-36.232,0.219,0.34,0.91,3eaa6f1fe020d5828184bcb88839891548d39c65,256,V3,3eaa6f1fe020d5828184bcb88839891548d39c65_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+41.148_ry-38.859_rz-36.232_tx+00.219_ty+00.340_s+00.910_3eaa6f1fe020d5828184bcb88839891548d39c65_256x256.png,ac30358bf936611aa0aae10942373dd8e7b53fa7,Cars,41.148,51.141,-36.232,2178 -2179,bmw325,41.77,-15.847,32.413,-0.208,0.58,1.12,d99f674edc1bdd4f917e7b1a15e1626a37820a9f,256,V3,d99f674edc1bdd4f917e7b1a15e1626a37820a9f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+41.770_ry-15.847_rz+32.413_tx-00.208_ty+00.580_s+01.120_d99f674edc1bdd4f917e7b1a15e1626a37820a9f_256x256.png,7e3a09c53c63f450a73254141bb8f1d8e13e85e9,Cars,41.77,74.153,32.413,2179 -2180,bmw325,41.834,42.805,33.38,0.061,-0.032,1.295,4df8355b7536806c90d3e3091765e4d2441e616e,256,V3,4df8355b7536806c90d3e3091765e4d2441e616e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx+41.834_ry+42.805_rz+33.380_tx+00.061_ty-00.032_s+01.295_4df8355b7536806c90d3e3091765e4d2441e616e_256x256.png,56055c2c439112b5484ae031cf4902f80174db36,Cars,41.834,132.805,33.38,2180 -2181,bmw325,-0.372,1.519,10.752,-0.228,-0.149,1.087,e64995bef8927ca5d993ae9d119bad38f5a73d53,256,V3,e64995bef8927ca5d993ae9d119bad38f5a73d53_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-00.372_ry+01.519_rz+10.752_tx-00.228_ty-00.149_s+01.087_e64995bef8927ca5d993ae9d119bad38f5a73d53_256x256.png,ab4d3b714a4d2390096318101bf6d6315edb771a,Cars,-0.372,91.519,10.752,2181 -2182,bmw325,-6.167,-12.671,6.454,0.189,0.02,1.227,6c6ed48425ecd8afd34cee10f18e7697555d9915,256,V3,6c6ed48425ecd8afd34cee10f18e7697555d9915_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-06.167_ry-12.671_rz+06.454_tx+00.189_ty+00.020_s+01.227_6c6ed48425ecd8afd34cee10f18e7697555d9915_256x256.png,d8b3afe05fc6d85e28b9b0d75cdeba5c782580c0,Cars,-6.167,77.329,6.454,2182 -2183,bmw325,-8.67,-5.112,22.418,-0.106,-0.513,0.942,524d919984d2052ca26fbb4ef7f15d3fb00bb201,256,V3,524d919984d2052ca26fbb4ef7f15d3fb00bb201_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-08.670_ry-05.112_rz+22.418_tx-00.106_ty-00.513_s+00.942_524d919984d2052ca26fbb4ef7f15d3fb00bb201_256x256.png,47fe5debc222948c397a74072a139afdb94a32ef,Cars,-8.67,84.888,22.418,2183 -2184,bmw325,-12.323,9.377,42.454,0.228,0.166,1.047,a800842e7d9e4570584497667475bb072a96951f,256,V3,a800842e7d9e4570584497667475bb072a96951f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-12.323_ry+09.377_rz+42.454_tx+00.228_ty+00.166_s+01.047_a800842e7d9e4570584497667475bb072a96951f_256x256.png,25cfb3f056122b7c1af02a03d5a31c87461223b0,Cars,-12.323,99.377,42.454,2184 -2185,bmw325,-15.737,24.833,-5.385,-0.031,0.098,1.185,be93d5fbaff234329865c28154643d157629c05b,256,V3,be93d5fbaff234329865c28154643d157629c05b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-15.737_ry+24.833_rz-05.385_tx-00.031_ty+00.098_s+01.185_be93d5fbaff234329865c28154643d157629c05b_256x256.png,e252a07eb6962f6bd15c8f22093e30dad35329ff,Cars,-15.737,114.833,-5.385,2185 -2186,bmw325,-16.402,-17.87,-41.775,0.274,-0.218,1.016,f6c2ea97cd8d60de5faaa5181cd5ec0e1e0692ac,256,V3,f6c2ea97cd8d60de5faaa5181cd5ec0e1e0692ac_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-16.402_ry-17.870_rz-41.775_tx+00.274_ty-00.218_s+01.016_f6c2ea97cd8d60de5faaa5181cd5ec0e1e0692ac_256x256.png,0546c98307af4d7d978f555051db055715e22b59,Cars,-16.402,72.13,-41.775,2186 -2187,bmw325,-17.278,-18.816,-34.011,0.049,0.538,1.089,aa53b89225af4cea3c7fb71213e58cebb2b16d7c,256,V3,aa53b89225af4cea3c7fb71213e58cebb2b16d7c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-17.278_ry-18.816_rz-34.011_tx+00.049_ty+00.538_s+01.089_aa53b89225af4cea3c7fb71213e58cebb2b16d7c_256x256.png,84672758209e23ef19b161c6f203aa568c0b1f12,Cars,-17.278,71.184,-34.011,2187 -2188,bmw325,-17.789,3.048,-24.256,-0.093,-0.337,1.278,24134453878dd9756b3d2c40dbbf391f291f636e,256,V3,24134453878dd9756b3d2c40dbbf391f291f636e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-17.789_ry+03.048_rz-24.256_tx-00.093_ty-00.337_s+01.278_24134453878dd9756b3d2c40dbbf391f291f636e_256x256.png,e7f12fae298176a351695725713dcc38870fa770,Cars,-17.789,93.048,-24.256,2188 -2189,bmw325,-20.248,5.762,-19.844,0.243,0.448,1.257,e571a60810c0a67eae4f36ce3f70073549d8e9f6,256,V3,e571a60810c0a67eae4f36ce3f70073549d8e9f6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-20.248_ry+05.762_rz-19.844_tx+00.243_ty+00.448_s+01.257_e571a60810c0a67eae4f36ce3f70073549d8e9f6_256x256.png,4743bc3842995f5981ea20017b81a62085c3eb41,Cars,-20.248,95.762,-19.844,2189 -2190,bmw325,-21.212,-7.344,12.929,0.183,-0.06,0.811,215dee8e07e2a319510d7cce744a690727857764,256,V3,215dee8e07e2a319510d7cce744a690727857764_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-21.212_ry-07.344_rz+12.929_tx+00.183_ty-00.060_s+00.811_215dee8e07e2a319510d7cce744a690727857764_256x256.png,83378ca5a5e5410e5dc2bf119ce9a1b1f440fefe,Cars,-21.212,82.656,12.929,2190 -2191,bmw325,-23.266,33.999,-9.236,-0.049,-0.412,0.833,74e54989899247ca4ba65ac2991ff6501523f316,256,V3,74e54989899247ca4ba65ac2991ff6501523f316_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-23.266_ry+33.999_rz-09.236_tx-00.049_ty-00.412_s+00.833_74e54989899247ca4ba65ac2991ff6501523f316_256x256.png,88c14dcbacaa351fed2d660d226b9620e9fc322d,Cars,-23.266,123.999,-9.236,2191 -2192,bmw325,-30.129,6.135,-42.725,0.097,0.38,1.041,1ed78938296e02732cde8c27b4a3fa718bc27844,256,V3,1ed78938296e02732cde8c27b4a3fa718bc27844_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-30.129_ry+06.135_rz-42.725_tx+00.097_ty+00.380_s+01.041_1ed78938296e02732cde8c27b4a3fa718bc27844_256x256.png,2424c6e2efda11255d9fbab2ebc89e43a34d3e12,Cars,-30.129,96.135,-42.725,2192 -2193,bmw325,-30.257,16.032,23.232,0.045,-0.465,0.907,5d421dfd9ba93c4cc8c762aa3532e26475decc5c,256,V3,5d421dfd9ba93c4cc8c762aa3532e26475decc5c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-30.257_ry+16.032_rz+23.232_tx+00.045_ty-00.465_s+00.907_5d421dfd9ba93c4cc8c762aa3532e26475decc5c_256x256.png,fdbba2847a90668b5b777a69ae41e8d98846898f,Cars,-30.257,106.032,23.232,2193 -2194,bmw325,-34.41,-15.166,-28.104,0.138,-0.235,0.933,2b40b1163249aa2174a782738fb62900f7c70367,256,V3,2b40b1163249aa2174a782738fb62900f7c70367_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-34.410_ry-15.166_rz-28.104_tx+00.138_ty-00.235_s+00.933_2b40b1163249aa2174a782738fb62900f7c70367_256x256.png,7848901939f79753c519e7d827f706614a0e5403,Cars,-34.41,74.834,-28.104,2194 -2195,bmw325,-38.789,19.439,35.676,-0.217,-0.293,1.261,cdbf1c42cccadc18022b3a6556458102d727e079,256,V3,cdbf1c42cccadc18022b3a6556458102d727e079_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-38.789_ry+19.439_rz+35.676_tx-00.217_ty-00.293_s+01.261_cdbf1c42cccadc18022b3a6556458102d727e079_256x256.png,0e2cf197edd4d469ec335e57d9c6aef9816d1684,Cars,-38.789,109.439,35.676,2195 -2196,bmw325,-41.321,27.316,10.625,0.286,-0.115,1.162,b5542915cafa390db9147a5f941a0524e26b277e,256,V3,b5542915cafa390db9147a5f941a0524e26b277e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-41.321_ry+27.316_rz+10.625_tx+00.286_ty-00.115_s+01.162_b5542915cafa390db9147a5f941a0524e26b277e_256x256.png,8eb36eccf2169e59bedb1d2c905a37304b306488,Cars,-41.321,117.316,10.625,2196 -2197,bmw325,-41.89,18.729,39.707,0.056,0.321,0.886,bdd43e6965f7ffa00201796daf22d40cf53ba44e,256,V3,bdd43e6965f7ffa00201796daf22d40cf53ba44e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-41.890_ry+18.729_rz+39.707_tx+00.056_ty+00.321_s+00.886_bdd43e6965f7ffa00201796daf22d40cf53ba44e_256x256.png,986d6dfb7fd0b2192c074905d4ed7cef24c59d8c,Cars,-41.89,108.729,39.707,2197 -2198,bmw325,-43.275,44.537,31.099,-0.064,0.34,1.037,446805408e5edce5126c9e357281126abd142451,256,V3,446805408e5edce5126c9e357281126abd142451_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-43.275_ry+44.537_rz+31.099_tx-00.064_ty+00.340_s+01.037_446805408e5edce5126c9e357281126abd142451_256x256.png,533a60f820ec3403ef68e1f2ee79ab656a41ede9,Cars,-43.275,134.537,31.099,2198 -2199,bmw325,-44.634,-9.422,33.135,-0.254,-0.021,0.855,7bda99c1885fe0b07f9acfd12018cf94a164c626,256,V3,7bda99c1885fe0b07f9acfd12018cf94a164c626_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bmw325_rx-44.634_ry-09.422_rz+33.135_tx-00.254_ty-00.021_s+00.855_7bda99c1885fe0b07f9acfd12018cf94a164c626_256x256.png,a767d5e8e64f7366e95a79cfa9fea1d12727c422,Cars,-44.634,80.578,33.135,2199 -2200,bora_a,0.5,-44.051,39.479,-0.031,-0.542,1.066,014a280a69a21e296dd0f7a90fd8ec923a7bb5d8,256,V3,014a280a69a21e296dd0f7a90fd8ec923a7bb5d8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+00.500_ry-44.051_rz+39.479_tx-00.031_ty-00.542_s+01.066_014a280a69a21e296dd0f7a90fd8ec923a7bb5d8_256x256.png,763fb26ca5f70c8ec565abfb7900d8de5ff776fc,Cars,0.5,45.949,39.479,2200 -2201,bora_a,3.246,29.771,-5.153,0.085,-0.514,1.005,62383a2a8a133772768b927cc6e31c796b5f2b21,256,V3,62383a2a8a133772768b927cc6e31c796b5f2b21_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+03.246_ry+29.771_rz-05.153_tx+00.085_ty-00.514_s+01.005_62383a2a8a133772768b927cc6e31c796b5f2b21_256x256.png,3a642471153c78312d12721669761c2d80e91767,Cars,3.246,119.771,-5.153,2201 -2202,bora_a,4.285,-30.434,24.829,-0.182,0.087,1.208,dc08c002ce47e9f26406350af9a9c1c6b8dabd1e,256,V3,dc08c002ce47e9f26406350af9a9c1c6b8dabd1e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+04.285_ry-30.434_rz+24.829_tx-00.182_ty+00.087_s+01.208_dc08c002ce47e9f26406350af9a9c1c6b8dabd1e_256x256.png,3dab2dcd361ace332cff7b6e30fca2ca953c7741,Cars,4.285,59.566,24.829,2202 -2203,bora_a,4.564,-12.543,-9.489,0.126,0.576,0.88,275315df97c51740c26c0262ec467dab4a6c1724,256,V3,275315df97c51740c26c0262ec467dab4a6c1724_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+04.564_ry-12.543_rz-09.489_tx+00.126_ty+00.576_s+00.880_275315df97c51740c26c0262ec467dab4a6c1724_256x256.png,f0c8dbe8c1fbc2edbd2c9d02d7264f865b3c9cdf,Cars,4.564,77.457,-9.489,2203 -2204,bora_a,9.683,34.293,14.975,-0.014,-0.529,1.117,0ca062ecee4c5efd479d4814b44ca987cbd5bb98,256,V3,0ca062ecee4c5efd479d4814b44ca987cbd5bb98_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+09.683_ry+34.293_rz+14.975_tx-00.014_ty-00.529_s+01.117_0ca062ecee4c5efd479d4814b44ca987cbd5bb98_256x256.png,a15021ed66376d3118f9e6f833ffe174fce9b195,Cars,9.683,124.293,14.975,2204 -2205,bora_a,10.102,-42.601,-36.657,-0.281,-0.237,0.834,faf3ac047f49d45210d3066a5c9d9cc89c657f49,256,V3,faf3ac047f49d45210d3066a5c9d9cc89c657f49_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+10.102_ry-42.601_rz-36.657_tx-00.281_ty-00.237_s+00.834_faf3ac047f49d45210d3066a5c9d9cc89c657f49_256x256.png,b76fe48ae5d3b2bedcfbd9895292f362a87dce8d,Cars,10.102,47.399,-36.657,2205 -2206,bora_a,15.834,-8.49,-15.199,0.176,-0.212,1.135,ab335090d2f2aede6e50f7a2036bd5a0ffad2ab5,256,V3,ab335090d2f2aede6e50f7a2036bd5a0ffad2ab5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+15.834_ry-08.490_rz-15.199_tx+00.176_ty-00.212_s+01.135_ab335090d2f2aede6e50f7a2036bd5a0ffad2ab5_256x256.png,a75549b1ebb617dd13a0ce4d082a4a2b0f3737b3,Cars,15.834,81.51,-15.199,2206 -2207,bora_a,21.214,15.07,-30.939,-0.194,-0.468,1.267,dd6a8601cd560aa66446e6b7418164c1e2bbc0c1,256,V3,dd6a8601cd560aa66446e6b7418164c1e2bbc0c1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+21.214_ry+15.070_rz-30.939_tx-00.194_ty-00.468_s+01.267_dd6a8601cd560aa66446e6b7418164c1e2bbc0c1_256x256.png,271b037ccdc5548269ff0ddf6586ff9a054b0f68,Cars,21.214,105.07,-30.939,2207 -2208,bora_a,22.676,22.554,0.896,-0.143,0.062,0.772,472a5016df3a8cf90ac364396c0aea5f257e0524,256,V3,472a5016df3a8cf90ac364396c0aea5f257e0524_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+22.676_ry+22.554_rz+00.896_tx-00.143_ty+00.062_s+00.772_472a5016df3a8cf90ac364396c0aea5f257e0524_256x256.png,a11a3c8d43b354e72b52e13aba7d2f229cf3f258,Cars,22.676,112.554,0.896,2208 -2209,bora_a,22.785,38.836,5.687,-0.141,0.434,0.962,b33a8f256ec75b41c4a149f6601f50bea54d34ba,256,V3,b33a8f256ec75b41c4a149f6601f50bea54d34ba_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+22.785_ry+38.836_rz+05.687_tx-00.141_ty+00.434_s+00.962_b33a8f256ec75b41c4a149f6601f50bea54d34ba_256x256.png,4dc5679523831d08a7ad3173a9b4bd9e3e2be087,Cars,22.785,128.836,5.687,2209 -2210,bora_a,24.797,-42.196,36.517,0.008,0.378,1.022,60f44a4d82f0c32eb1cee24ceca6827498ef4c1f,256,V3,60f44a4d82f0c32eb1cee24ceca6827498ef4c1f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+24.797_ry-42.196_rz+36.517_tx+00.008_ty+00.378_s+01.022_60f44a4d82f0c32eb1cee24ceca6827498ef4c1f_256x256.png,3cfb122924a4ab526c8f73e3426812e83c867ca2,Cars,24.797,47.804,36.517,2210 -2211,bora_a,27.599,-33.74,-32.775,-0.218,-0.156,1.005,e74cc8768703a9af05d0d90ba1e1182fffc5c00e,256,V3,e74cc8768703a9af05d0d90ba1e1182fffc5c00e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+27.599_ry-33.740_rz-32.775_tx-00.218_ty-00.156_s+01.005_e74cc8768703a9af05d0d90ba1e1182fffc5c00e_256x256.png,6cea874649c30eaa4ba3a1d832b5301b9555b7fe,Cars,27.599,56.26,-32.775,2211 -2212,bora_a,28.537,28.01,18.75,0.236,0.258,1.091,0001ae2f0933707760f7edae0908f27c790543ec,256,V3,0001ae2f0933707760f7edae0908f27c790543ec_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+28.537_ry+28.010_rz+18.750_tx+00.236_ty+00.258_s+01.091_0001ae2f0933707760f7edae0908f27c790543ec_256x256.png,17088e342c9cc18af7d0e6ce0c4cdce51570c318,Cars,28.537,118.01,18.75,2212 -2213,bora_a,29.967,0.364,-38.614,0.132,0.319,1.275,e7d4f1b627d13486c94307d48f77e0cd6bea4ed1,256,V3,e7d4f1b627d13486c94307d48f77e0cd6bea4ed1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+29.967_ry+00.364_rz-38.614_tx+00.132_ty+00.319_s+01.275_e7d4f1b627d13486c94307d48f77e0cd6bea4ed1_256x256.png,6ec7f79d4f6148cb3bfe1fe2dc1f00bc33e8aa2e,Cars,29.967,90.364,-38.614,2213 -2214,bora_a,39.858,37.294,-22.609,0.146,0.266,0.913,57f665fbfda7d75d729d656499740a36de621a5f,256,V3,57f665fbfda7d75d729d656499740a36de621a5f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+39.858_ry+37.294_rz-22.609_tx+00.146_ty+00.266_s+00.913_57f665fbfda7d75d729d656499740a36de621a5f_256x256.png,c8d163cf151f21c58b4353a37d037540bb8f5341,Cars,39.858,127.294,-22.609,2214 -2215,bora_a,40.36,-44.961,-24.108,-0.096,-0.597,1.086,e3b86a8d38ff9f4969d5efee8b8b2c258f3a010b,256,V3,e3b86a8d38ff9f4969d5efee8b8b2c258f3a010b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+40.360_ry-44.961_rz-24.108_tx-00.096_ty-00.597_s+01.086_e3b86a8d38ff9f4969d5efee8b8b2c258f3a010b_256x256.png,7cf5209c13346426b90a0d3c6404dbf38d3dbfd7,Cars,40.36,45.039,-24.108,2215 -2216,bora_a,40.822,-14.004,27.076,0.13,-0.44,0.842,091d3594feedc3f07219b80efad4d151ce8afc91,256,V3,091d3594feedc3f07219b80efad4d151ce8afc91_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx+40.822_ry-14.004_rz+27.076_tx+00.130_ty-00.440_s+00.842_091d3594feedc3f07219b80efad4d151ce8afc91_256x256.png,647033dde44dc5e6cb8a2769702c3bf4e3ad62fb,Cars,40.822,75.996,27.076,2216 -2217,bora_a,-4.841,-19.617,-13.394,0.01,0.396,1.11,e6a5352fec4c35c04f7e86f56cc0dd093ef45ca0,256,V3,e6a5352fec4c35c04f7e86f56cc0dd093ef45ca0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-04.841_ry-19.617_rz-13.394_tx+00.010_ty+00.396_s+01.110_e6a5352fec4c35c04f7e86f56cc0dd093ef45ca0_256x256.png,3b6e19b04afb55a94f505acbdbdacc583357b375,Cars,-4.841,70.383,-13.394,2217 -2218,bora_a,-7.88,5.071,9.307,0.271,-0.207,0.821,681eca30d9a0a9fb449e400cea414d0f19a17ac0,256,V3,681eca30d9a0a9fb449e400cea414d0f19a17ac0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-07.880_ry+05.071_rz+09.307_tx+00.271_ty-00.207_s+00.821_681eca30d9a0a9fb449e400cea414d0f19a17ac0_256x256.png,4d169648f88280a79910c7b743b416b3b1a979ea,Cars,-7.88,95.071,9.307,2218 -2219,bora_a,-13.452,16.45,-23.013,0.175,0.37,1.246,bc22911d212e289e83b9950051aa736c858152fb,256,V3,bc22911d212e289e83b9950051aa736c858152fb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-13.452_ry+16.450_rz-23.013_tx+00.175_ty+00.370_s+01.246_bc22911d212e289e83b9950051aa736c858152fb_256x256.png,e4a660e966c8ad79e28c1dbce42d1ddc510d4188,Cars,-13.452,106.45,-23.013,2219 -2220,bora_a,-14.527,16.061,5.075,0.292,0.587,1.226,90f464d4725214ddae0e9d2dd2e759db0561cb94,256,V3,90f464d4725214ddae0e9d2dd2e759db0561cb94_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-14.527_ry+16.061_rz+05.075_tx+00.292_ty+00.587_s+01.226_90f464d4725214ddae0e9d2dd2e759db0561cb94_256x256.png,383b7f7f11cedc8ab73de426e634f58cec57a8e0,Cars,-14.527,106.061,5.075,2220 -2221,bora_a,-16.98,16.104,-1.291,-0.168,-0.329,0.788,8222418653374ab0b41b8c36221772416b4904dd,256,V3,8222418653374ab0b41b8c36221772416b4904dd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-16.980_ry+16.104_rz-01.291_tx-00.168_ty-00.329_s+00.788_8222418653374ab0b41b8c36221772416b4904dd_256x256.png,190777e758aab6d4070eacd8d0aae394a4b5ca7a,Cars,-16.98,106.104,-1.291,2221 -2222,bora_a,-18.66,25.585,-5.549,-0.122,-0.557,0.999,d223175b253451d2358a6a0da6672584dfcd7071,256,V3,d223175b253451d2358a6a0da6672584dfcd7071_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-18.660_ry+25.585_rz-05.549_tx-00.122_ty-00.557_s+00.999_d223175b253451d2358a6a0da6672584dfcd7071_256x256.png,ddb83f1fc5c82303f087468f70c47ecf0d1f4431,Cars,-18.66,115.585,-5.549,2222 -2223,bora_a,-22.872,18.657,42.802,-0.015,-0.362,1.226,765ee8ba4601150a2f591db2ec99af1d59048185,256,V3,765ee8ba4601150a2f591db2ec99af1d59048185_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-22.872_ry+18.657_rz+42.802_tx-00.015_ty-00.362_s+01.226_765ee8ba4601150a2f591db2ec99af1d59048185_256x256.png,ce23f8615cb3cb23c7541b43c40ca58099d3a167,Cars,-22.872,108.657,42.802,2223 -2224,bora_a,-23.446,-28.859,-11.251,0.18,0.297,1.255,e3272edc621b301e88d3536a169bda635e49039a,256,V3,e3272edc621b301e88d3536a169bda635e49039a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-23.446_ry-28.859_rz-11.251_tx+00.180_ty+00.297_s+01.255_e3272edc621b301e88d3536a169bda635e49039a_256x256.png,a6f521fb98f25fb829f014e8ca7a4be1e2c94be9,Cars,-23.446,61.141,-11.251,2224 -2225,bora_a,-25.405,-31.942,-28.802,-0.056,0.126,1.086,70a3fae8c61d92be0849c6036baec1aae10dd620,256,V3,70a3fae8c61d92be0849c6036baec1aae10dd620_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-25.405_ry-31.942_rz-28.802_tx-00.056_ty+00.126_s+01.086_70a3fae8c61d92be0849c6036baec1aae10dd620_256x256.png,c2d48b5d365ee78857d0a78f06f5efd72cee9817,Cars,-25.405,58.058,-28.802,2225 -2226,bora_a,-25.603,9.628,28.378,0.005,-0.079,1.216,0eab2b85e32029d5ab43433c097a4538ff8a4f51,256,V3,0eab2b85e32029d5ab43433c097a4538ff8a4f51_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-25.603_ry+09.628_rz+28.378_tx+00.005_ty-00.079_s+01.216_0eab2b85e32029d5ab43433c097a4538ff8a4f51_256x256.png,66146c2b9e0103fb9c849bf5d770c3178722015a,Cars,-25.603,99.628,28.378,2226 -2227,bora_a,-26.581,26.867,35.579,-0.012,0.484,0.954,cfd7cbb5bb7b1f507cf6e54dd01e90e5d8b9c574,256,V3,cfd7cbb5bb7b1f507cf6e54dd01e90e5d8b9c574_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-26.581_ry+26.867_rz+35.579_tx-00.012_ty+00.484_s+00.954_cfd7cbb5bb7b1f507cf6e54dd01e90e5d8b9c574_256x256.png,fcedfd45463f9434f6d819cb370849e97841c68f,Cars,-26.581,116.867,35.579,2227 -2228,bora_a,-27.192,-14.244,28.665,0.158,-0.428,0.946,b1a4dbc50920949d6cc4d34d5f6580a3534acad9,256,V3,b1a4dbc50920949d6cc4d34d5f6580a3534acad9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-27.192_ry-14.244_rz+28.665_tx+00.158_ty-00.428_s+00.946_b1a4dbc50920949d6cc4d34d5f6580a3534acad9_256x256.png,9faca23d77ff3eae69f0e95cd05281c13e5607a1,Cars,-27.192,75.756,28.665,2228 -2229,bora_a,-29.523,42.382,38.094,-0.124,-0.495,1.183,4f112237c3286cb48808829df82eb3c565f04a03,256,V3,4f112237c3286cb48808829df82eb3c565f04a03_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-29.523_ry+42.382_rz+38.094_tx-00.124_ty-00.495_s+01.183_4f112237c3286cb48808829df82eb3c565f04a03_256x256.png,3684ea8a1d699425748bf541d2fd3f8bb81ddfc9,Cars,-29.523,132.382,38.094,2229 -2230,bora_a,-30.919,-0.128,28.01,0.203,-0.08,0.962,51799a9a13f8d22309ef653538aee0cf3d1a34a5,256,V3,51799a9a13f8d22309ef653538aee0cf3d1a34a5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-30.919_ry-00.128_rz+28.010_tx+00.203_ty-00.080_s+00.962_51799a9a13f8d22309ef653538aee0cf3d1a34a5_256x256.png,80d81b188430a8e205c4dde2d1012496c387bb5f,Cars,-30.919,89.872,28.01,2230 -2231,bora_a,-32.234,-26.28,27.906,-0.061,0.43,0.92,0323f252d73a0df4cde14e2456d914d5aaa41032,256,V3,0323f252d73a0df4cde14e2456d914d5aaa41032_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-32.234_ry-26.280_rz+27.906_tx-00.061_ty+00.430_s+00.920_0323f252d73a0df4cde14e2456d914d5aaa41032_256x256.png,dbb61004f53f12aad242ac7748d1da42825d351b,Cars,-32.234,63.72,27.906,2231 -2232,bora_a,-32.474,17.537,-37.727,-0.059,0.295,0.896,b93151e988b2c1284684b1d99332c7bb0d8d0c0f,256,V3,b93151e988b2c1284684b1d99332c7bb0d8d0c0f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-32.474_ry+17.537_rz-37.727_tx-00.059_ty+00.295_s+00.896_b93151e988b2c1284684b1d99332c7bb0d8d0c0f_256x256.png,37163430caf21b9eb5bbc95fb8a5fd496de3c97c,Cars,-32.474,107.537,-37.727,2232 -2233,bora_a,-32.941,-8.183,29.752,-0.029,-0.125,1.002,6ae08af16e8ef2fef7943d808b92b6c6df35561f,256,V3,6ae08af16e8ef2fef7943d808b92b6c6df35561f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-32.941_ry-08.183_rz+29.752_tx-00.029_ty-00.125_s+01.002_6ae08af16e8ef2fef7943d808b92b6c6df35561f_256x256.png,1efd59a3ca1f0db8fcd29f6eb37dc0ed47bd1abf,Cars,-32.941,81.817,29.752,2233 -2234,bora_a,-34.667,-38.64,-43.156,0.241,-0.135,0.892,136c759a63ebebbb3cb69f0c48bfe08619b8cda1,256,V3,136c759a63ebebbb3cb69f0c48bfe08619b8cda1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-34.667_ry-38.640_rz-43.156_tx+00.241_ty-00.135_s+00.892_136c759a63ebebbb3cb69f0c48bfe08619b8cda1_256x256.png,5bc66fb9cc65afd2ad0c47995b10d1fec27ff42d,Cars,-34.667,51.36,-43.156,2234 -2235,bora_a,-34.957,-28.113,4.158,0.002,-0.081,1.025,f87a34a883bc0edca419e9e06ca4ab154e072561,256,V3,f87a34a883bc0edca419e9e06ca4ab154e072561_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-34.957_ry-28.113_rz+04.158_tx+00.002_ty-00.081_s+01.025_f87a34a883bc0edca419e9e06ca4ab154e072561_256x256.png,762f0e6162dd9cb7f1f79345d08e0b24797f2b25,Cars,-34.957,61.887,4.158,2235 -2236,bora_a,-36.058,-13.654,-26.539,0.24,0.309,1.242,24bd7936eced102fb45caa579f966f796a5d0897,256,V3,24bd7936eced102fb45caa579f966f796a5d0897_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-36.058_ry-13.654_rz-26.539_tx+00.240_ty+00.309_s+01.242_24bd7936eced102fb45caa579f966f796a5d0897_256x256.png,2b9be7d5e4149ac47123ee8aedb5d192325f59eb,Cars,-36.058,76.346,-26.539,2236 -2237,bora_a,-37.279,2.502,-36.917,-0.054,0.441,0.886,b7ec60081a1264c0e2270c8c48b3b9cc5bd0bfe9,256,V3,b7ec60081a1264c0e2270c8c48b3b9cc5bd0bfe9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-37.279_ry+02.502_rz-36.917_tx-00.054_ty+00.441_s+00.886_b7ec60081a1264c0e2270c8c48b3b9cc5bd0bfe9_256x256.png,d7c010c168f1790c4bf7ac5fca2d5d7e8c770909,Cars,-37.279,92.502,-36.917,2237 -2238,bora_a,-37.764,-4.073,23.842,-0.138,0.367,1.239,934eb1d2127498eb4ea021c17edf5009e970f307,256,V3,934eb1d2127498eb4ea021c17edf5009e970f307_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-37.764_ry-04.073_rz+23.842_tx-00.138_ty+00.367_s+01.239_934eb1d2127498eb4ea021c17edf5009e970f307_256x256.png,d2ff1580983d6106354e2270e6ecdab516c242b4,Cars,-37.764,85.927,23.842,2238 -2239,bora_a,-43.739,-3.603,-3.69,0.074,0.551,1.167,48edb33f128148fa0ec1b36eea018552a8087019,256,V3,48edb33f128148fa0ec1b36eea018552a8087019_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/bora_a_rx-43.739_ry-03.603_rz-03.690_tx+00.074_ty+00.551_s+01.167_48edb33f128148fa0ec1b36eea018552a8087019_256x256.png,61ef03078374dbb5b9b1f080431dc977334e3b7a,Cars,-43.739,86.397,-3.69,2239 -2240,breed_pug,0.017,7.865,-3.211,0.067,0.423,1.07875,4049c12d01ae947fbcaa2762ca88acef0a3c681c,256,V3,4049c12d01ae947fbcaa2762ca88acef0a3c681c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+00.017_ry+97.865_rz-03.211_tx+00.067_ty+00.423_s+00.863_4049c12d01ae947fbcaa2762ca88acef0a3c681c_256x256.png,11240ebc8bcb4ede5a690bf4354ebfb9daf7b170,Animals,0.017,97.865,-3.211,2240 -2241,breed_pug,2.169,-8.282,-42.751,0.22,-0.37,0.78375,5e6c58ef8be7e455673400403068a9a2e91f96ea,256,V3,5e6c58ef8be7e455673400403068a9a2e91f96ea_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+02.169_ry+81.718_rz-42.751_tx+00.220_ty-00.370_s+00.627_5e6c58ef8be7e455673400403068a9a2e91f96ea_256x256.png,d416434bec1cc3bbcc9765ec9f4ef71829e51931,Animals,2.169,81.718,-42.751,2241 -2242,breed_pug,6.027,-3.262,33.84,0.112,0.554,1.0225,96a320299f31492cde578e021d068fdd6367b0e9,256,V3,96a320299f31492cde578e021d068fdd6367b0e9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+06.027_ry+86.738_rz+33.840_tx+00.112_ty+00.554_s+00.818_96a320299f31492cde578e021d068fdd6367b0e9_256x256.png,628bb8a73ed7284f00124ad1cd65c8a9a265d7bd,Animals,6.027,86.738,33.84,2242 -2243,breed_pug,7.219,8.02,29.976,0.092,-0.508,0.87875,f89532af08baa725c7ff1a9c6c4034a188be0582,256,V3,f89532af08baa725c7ff1a9c6c4034a188be0582_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+07.219_ry+98.020_rz+29.976_tx+00.092_ty-00.508_s+00.703_f89532af08baa725c7ff1a9c6c4034a188be0582_256x256.png,128dcebce0aff19edbac6f8a09684a7e12676350,Animals,7.219,98.02,29.976,2243 -2244,breed_pug,10.862,-29.603,32.095,-0.077,-0.127,1.2625,b8f07351ca03ed9fbeb7ef292d20120fecbd530c,256,V3,b8f07351ca03ed9fbeb7ef292d20120fecbd530c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+10.862_ry+60.397_rz+32.095_tx-00.077_ty-00.127_s+01.010_b8f07351ca03ed9fbeb7ef292d20120fecbd530c_256x256.png,941b2ca534d9bf6c8bb0547ce627865f4dea5636,Animals,10.862,60.397,32.095,2244 -2245,breed_pug,11.873,-27.11,26.733,0.069,-0.389,1.04,0b742365645ed524da938d5765daa4c7587c5fe3,256,V3,0b742365645ed524da938d5765daa4c7587c5fe3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+11.873_ry+62.890_rz+26.733_tx+00.069_ty-00.389_s+00.832_0b742365645ed524da938d5765daa4c7587c5fe3_256x256.png,a30eb0718bdb67e7137786379faec101665dba56,Animals,11.873,62.89,26.733,2245 -2246,breed_pug,13.397,7.269,38.309,-0.113,0.24,0.96125,e7fb78fd9417bcf1534506f3374212384ead5d01,256,V3,e7fb78fd9417bcf1534506f3374212384ead5d01_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+13.397_ry+97.269_rz+38.309_tx-00.113_ty+00.240_s+00.769_e7fb78fd9417bcf1534506f3374212384ead5d01_256x256.png,f6e29f6b6946d72c4511acf8c5531cca6b0e8966,Animals,13.397,97.269,38.309,2246 -2247,breed_pug,19.428,0.025,15.295,0.065,-0.479,0.88375,224110fc2ef79db654249d902dd927e66a2a6e33,256,V3,224110fc2ef79db654249d902dd927e66a2a6e33_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+19.428_ry+90.025_rz+15.295_tx+00.065_ty-00.479_s+00.707_224110fc2ef79db654249d902dd927e66a2a6e33_256x256.png,4ecede2b31a82221e7420d6e502454b1d652f212,Animals,19.428,90.025,15.295,2247 -2248,breed_pug,28.314,-22.839,-20.787,-0.057,0.354,0.9425,437eece8d9969c5cf2553d938e108c41b3c96bf8,256,V3,437eece8d9969c5cf2553d938e108c41b3c96bf8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+28.314_ry+67.161_rz-20.787_tx-00.057_ty+00.354_s+00.754_437eece8d9969c5cf2553d938e108c41b3c96bf8_256x256.png,6be85fd67bfcb526629bbfb241b53063910bbfb4,Animals,28.314,67.161,-20.787,2248 -2249,breed_pug,30.914,5.571,-5.949,0.109,0.543,0.9425,89c931be335604da622f18bac95c37c45ed3aaa0,256,V3,89c931be335604da622f18bac95c37c45ed3aaa0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+30.914_ry+95.571_rz-05.949_tx+00.109_ty+00.543_s+00.754_89c931be335604da622f18bac95c37c45ed3aaa0_256x256.png,f0f3ecf1a6ff13445385c16d511c324cf804ef3d,Animals,30.914,95.571,-5.949,2249 -2250,breed_pug,32.117,8.718,2.385,0.056,0.016,0.94625,775b12e05519a7b25b2506a6318aab9944ee1cec,256,V3,775b12e05519a7b25b2506a6318aab9944ee1cec_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+32.117_ry+98.718_rz+02.385_tx+00.056_ty+00.016_s+00.757_775b12e05519a7b25b2506a6318aab9944ee1cec_256x256.png,cc6d32bd8c99bda4c401c20fe34e6fe7c6014472,Animals,32.117,98.718,2.385,2250 -2251,breed_pug,32.192,-37.853,2.443,-0.092,-0.053,1.2325,94a36fd5fefaf482d3404032f9146029e7ad1d5d,256,V3,94a36fd5fefaf482d3404032f9146029e7ad1d5d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+32.192_ry+52.147_rz+02.443_tx-00.092_ty-00.053_s+00.986_94a36fd5fefaf482d3404032f9146029e7ad1d5d_256x256.png,4d48b0d827b589860c0d7f579e630aa76e862986,Animals,32.192,52.147,2.443,2251 -2252,breed_pug,35.241,25.365,-17.828,-0.029,0.199,1.275,fa01da95e082fd2817fd09c4c55f639b4af5269d,256,V3,fa01da95e082fd2817fd09c4c55f639b4af5269d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+35.241_ry+115.365_rz-17.828_tx-00.029_ty+00.199_s+01.020_fa01da95e082fd2817fd09c4c55f639b4af5269d_256x256.png,6a9b1feaec922a9b7fbf79f8f2eb1ad45b748ac8,Animals,35.241,115.365,-17.828,2252 -2253,breed_pug,35.257,44.504,2.64,0.066,-0.59,1.19375,6b6254f9e8dd06a76a1dd0cbb0be5e80be048de7,256,V3,6b6254f9e8dd06a76a1dd0cbb0be5e80be048de7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+35.257_ry+134.504_rz+02.640_tx+00.066_ty-00.590_s+00.955_6b6254f9e8dd06a76a1dd0cbb0be5e80be048de7_256x256.png,55260d7918f27e508ec529f0c132eef1c1dc411f,Animals,35.257,134.504,2.64,2253 -2254,breed_pug,36.721,43.987,-29.02,0.102,0.591,0.8125,8a62281e1e4d07daedaa0ecb07507f2bd9a84d48,256,V3,8a62281e1e4d07daedaa0ecb07507f2bd9a84d48_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+36.721_ry+133.987_rz-29.020_tx+00.102_ty+00.591_s+00.650_8a62281e1e4d07daedaa0ecb07507f2bd9a84d48_256x256.png,3d066f477d2517966a0d4ff62b3da849dc654520,Animals,36.721,133.987,-29.02,2254 -2255,breed_pug,36.896,31.312,19.474,0.25,-0.576,0.94375,905bec7353cf2ce0e72b2278fdb90562c61c9b8c,256,V3,905bec7353cf2ce0e72b2278fdb90562c61c9b8c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+36.896_ry+121.312_rz+19.474_tx+00.250_ty-00.576_s+00.755_905bec7353cf2ce0e72b2278fdb90562c61c9b8c_256x256.png,e6f235581faeec4d054e69152e75ef41464f0908,Animals,36.896,121.312,19.474,2255 -2256,breed_pug,37.266,38.045,-9.676,0.164,-0.473,0.895,eb491da6c9248dd94f43bad3411c98ec05d17bd1,256,V3,eb491da6c9248dd94f43bad3411c98ec05d17bd1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+37.266_ry+128.045_rz-09.676_tx+00.164_ty-00.473_s+00.716_eb491da6c9248dd94f43bad3411c98ec05d17bd1_256x256.png,cb1315852c4ec4f9120d751ae296510bec3a2562,Animals,37.266,128.045,-9.676,2256 -2257,breed_pug,37.375,29.333,-32.56,0.076,0.311,1.035,32989a507b63017dec6ab5a02c23f694262297fe,256,V3,32989a507b63017dec6ab5a02c23f694262297fe_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+37.375_ry+119.333_rz-32.560_tx+00.076_ty+00.311_s+00.828_32989a507b63017dec6ab5a02c23f694262297fe_256x256.png,f2ab56e7529abc344c8b9da0b8deb6c2c5f03a66,Animals,37.375,119.333,-32.56,2257 -2258,breed_pug,37.907,-1.421,34.869,0.259,0.095,0.8525,ccce4a9a26e588d314f8bccdba5bf2a619a72b7b,256,V3,ccce4a9a26e588d314f8bccdba5bf2a619a72b7b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+37.907_ry+88.579_rz+34.869_tx+00.259_ty+00.095_s+00.682_ccce4a9a26e588d314f8bccdba5bf2a619a72b7b_256x256.png,3a091a1a0c343439f86e02eabefbadec0f6e42fd,Animals,37.907,88.579,34.869,2258 -2259,breed_pug,41.296,36.322,-4.542,-0.171,-0.052,1.215,6df46dbc67f82cb923fcae0f6f2b8b26bdf52cf6,256,V3,6df46dbc67f82cb923fcae0f6f2b8b26bdf52cf6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+41.296_ry+126.322_rz-04.542_tx-00.171_ty-00.052_s+00.972_6df46dbc67f82cb923fcae0f6f2b8b26bdf52cf6_256x256.png,822039da9449e0cfe60714a217300d3bba1779db,Animals,41.296,126.322,-4.542,2259 -2260,breed_pug,43.298,-10.361,-7.407,-0.056,0.216,1.0475,bfc2b4895f4751b34613bd28d2c3c34f20e447d8,256,V3,bfc2b4895f4751b34613bd28d2c3c34f20e447d8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+43.298_ry+79.639_rz-07.407_tx-00.056_ty+00.216_s+00.838_bfc2b4895f4751b34613bd28d2c3c34f20e447d8_256x256.png,bf507ab4a664996707bd7a9b131440f9da40e7f0,Animals,43.298,79.639,-7.407,2260 -2261,breed_pug,43.462,-5.147,12.773,-0.204,-0.031,0.84625,58d51e38b219d7afa8989599107222bec3ca42dd,256,V3,58d51e38b219d7afa8989599107222bec3ca42dd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx+43.462_ry+84.853_rz+12.773_tx-00.204_ty-00.031_s+00.677_58d51e38b219d7afa8989599107222bec3ca42dd_256x256.png,491e45d1aea08f74a2f90b988470f792c65c2e07,Animals,43.462,84.853,12.773,2261 -2262,breed_pug,-4.595,42.923,32.34,-0.275,0.119,1.21125,4c3636dce584477637efcbc6c32e5540627cdfbe,256,V3,4c3636dce584477637efcbc6c32e5540627cdfbe_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-04.595_ry+132.923_rz+32.340_tx-00.275_ty+00.119_s+00.969_4c3636dce584477637efcbc6c32e5540627cdfbe_256x256.png,2234b02aac8402d5d54c5aa5606c3d1e3043c228,Animals,-4.595,132.923,32.34,2262 -2263,breed_pug,-4.764,-17.652,42.331,0.153,0.54,0.8375,39f5345246ea4a0f1a9314656d8ac71792860d70,256,V3,39f5345246ea4a0f1a9314656d8ac71792860d70_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-04.764_ry+72.348_rz+42.331_tx+00.153_ty+00.540_s+00.670_39f5345246ea4a0f1a9314656d8ac71792860d70_256x256.png,a471b96f7fd251d54334f999408f30f722d710ae,Animals,-4.764,72.348,42.331,2263 -2264,breed_pug,-6.103,24.52,7.68,-0.202,0.557,1.2725,d27b7ae32c8ad250d178b1f0269ccb2e9f65586e,256,V3,d27b7ae32c8ad250d178b1f0269ccb2e9f65586e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-06.103_ry+114.520_rz+07.680_tx-00.202_ty+00.557_s+01.018_d27b7ae32c8ad250d178b1f0269ccb2e9f65586e_256x256.png,e9f1d3ede3f5ccef14286c1eb30d46f052866bee,Animals,-6.103,114.52,7.68,2264 -2265,breed_pug,-7.058,16.155,26.579,-0.262,0.082,1.1775,748be7fe8771ab1fd229d5d99ab04c83263a1751,256,V3,748be7fe8771ab1fd229d5d99ab04c83263a1751_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-07.058_ry+106.155_rz+26.579_tx-00.262_ty+00.082_s+00.942_748be7fe8771ab1fd229d5d99ab04c83263a1751_256x256.png,526e5921851abbb13957c5de2dd691cd0be5df9d,Animals,-7.058,106.155,26.579,2265 -2266,breed_pug,-9.598,33.677,-31.293,0.001,-0.229,0.9525,13b480706c6d3facfea44b186d5ed77ca3a099a7,256,V3,13b480706c6d3facfea44b186d5ed77ca3a099a7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-09.598_ry+123.677_rz-31.293_tx+00.001_ty-00.229_s+00.762_13b480706c6d3facfea44b186d5ed77ca3a099a7_256x256.png,d6d6a96bc43bf3975ad936816c6b6fda2fc09e0f,Animals,-9.598,123.677,-31.293,2266 -2267,breed_pug,-9.984,-42.564,25.751,-0.19,-0.394,1.17375,98d5039e9e617fdcaf92f1561ca56acfab1dae7f,256,V3,98d5039e9e617fdcaf92f1561ca56acfab1dae7f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-09.984_ry+47.436_rz+25.751_tx-00.190_ty-00.394_s+00.939_98d5039e9e617fdcaf92f1561ca56acfab1dae7f_256x256.png,95a253a1007a62fb09ed176422ecff084363fdf0,Animals,-9.984,47.436,25.751,2267 -2268,breed_pug,-10.04,19.478,-39.503,-0.154,0.325,0.9075,7d08faa049aaf51d800560dbf350a7ea6256d282,256,V3,7d08faa049aaf51d800560dbf350a7ea6256d282_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-10.040_ry+109.478_rz-39.503_tx-00.154_ty+00.325_s+00.726_7d08faa049aaf51d800560dbf350a7ea6256d282_256x256.png,01b3450f9bf5cf2b57f807428bacf5a783de41c0,Animals,-10.04,109.478,-39.503,2268 -2269,breed_pug,-10.142,38.115,-15.498,0.15,-0.275,0.77875,e2d84d5e149e654e35bdcdebdd56444e770e36f1,256,V3,e2d84d5e149e654e35bdcdebdd56444e770e36f1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-10.142_ry+128.115_rz-15.498_tx+00.150_ty-00.275_s+00.623_e2d84d5e149e654e35bdcdebdd56444e770e36f1_256x256.png,01e2dbe661d7ab815359c6f111169afeae19d652,Animals,-10.142,128.115,-15.498,2269 -2270,breed_pug,-11.236,32.331,-32.697,-0.297,0.068,1.0475,7965598a4a39604d84ef93ad9991203034921ee5,256,V3,7965598a4a39604d84ef93ad9991203034921ee5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-11.236_ry+122.331_rz-32.697_tx-00.297_ty+00.068_s+00.838_7965598a4a39604d84ef93ad9991203034921ee5_256x256.png,174f70981c19e8cda246e3223e53513c6e1f5952,Animals,-11.236,122.331,-32.697,2270 -2271,breed_pug,-11.393,31.087,10.197,-0.103,0.308,1.0575,2b99651129618ab00e511f49027825f614719bf5,256,V3,2b99651129618ab00e511f49027825f614719bf5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-11.393_ry+121.087_rz+10.197_tx-00.103_ty+00.308_s+00.846_2b99651129618ab00e511f49027825f614719bf5_256x256.png,7ca0aea8ab692638b8fdd224d6af278d8fc87a45,Animals,-11.393,121.087,10.197,2271 -2272,breed_pug,-13.673,2.604,-22.901,-0.11,0.44,1.07625,3e4eb09831529620d1f7e60c434c0ecf577453b9,256,V3,3e4eb09831529620d1f7e60c434c0ecf577453b9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-13.673_ry+92.604_rz-22.901_tx-00.110_ty+00.440_s+00.861_3e4eb09831529620d1f7e60c434c0ecf577453b9_256x256.png,93737fae115ff9fbc371295066b3d4a9703488de,Animals,-13.673,92.604,-22.901,2272 -2273,breed_pug,-16.384,-6.995,23.57,0.252,0.35,0.885,8cea5db00d731bdbd94f50655d46afcab0d7c988,256,V3,8cea5db00d731bdbd94f50655d46afcab0d7c988_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-16.384_ry+83.005_rz+23.570_tx+00.252_ty+00.350_s+00.708_8cea5db00d731bdbd94f50655d46afcab0d7c988_256x256.png,505298d7f06ce1ef01e7fe89e47847739765b251,Animals,-16.384,83.005,23.57,2273 -2274,breed_pug,-24.287,41.73,-20.03,-0.194,0.438,1.01125,348ed77df3ce7c3c7872ea6875b1288056f6dc87,256,V3,348ed77df3ce7c3c7872ea6875b1288056f6dc87_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-24.287_ry+131.730_rz-20.030_tx-00.194_ty+00.438_s+00.809_348ed77df3ce7c3c7872ea6875b1288056f6dc87_256x256.png,bca3547642ab7d45151cd75cc758ccf9d25219e9,Animals,-24.287,131.73,-20.03,2274 -2275,breed_pug,-25.017,44.317,-21.098,0.216,-0.337,0.8425,53cab4dd709ce18050cb3be653ac9226519b2376,256,V3,53cab4dd709ce18050cb3be653ac9226519b2376_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-25.017_ry+134.317_rz-21.098_tx+00.216_ty-00.337_s+00.674_53cab4dd709ce18050cb3be653ac9226519b2376_256x256.png,d7feaf73e64522af3d45bb71a8e53f0cc2714223,Animals,-25.017,134.317,-21.098,2275 -2276,breed_pug,-31.978,-21.408,-7.49,-0.285,-0.587,1.0775,44aa99c90dee62e39bb0431267eb2233d820f0ee,256,V3,44aa99c90dee62e39bb0431267eb2233d820f0ee_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-31.978_ry+68.592_rz-07.490_tx-00.285_ty-00.587_s+00.862_44aa99c90dee62e39bb0431267eb2233d820f0ee_256x256.png,52c4986b4bae46cb88ed8f775eac3eaf8cfa8718,Animals,-31.978,68.592,-7.49,2276 -2277,breed_pug,-38.708,-16.805,-24.97,0.022,-0.546,0.85,f0da0d2428524375744729e7ce83bb96ab888963,256,V3,f0da0d2428524375744729e7ce83bb96ab888963_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-38.708_ry+73.195_rz-24.970_tx+00.022_ty-00.546_s+00.680_f0da0d2428524375744729e7ce83bb96ab888963_256x256.png,645a4df10a75d7b90b6381c8f5e2f020012a80f1,Animals,-38.708,73.195,-24.97,2277 -2278,breed_pug,-39.715,35.09,-31.65,-0.028,-0.477,1.2975,f8fee9d5d9a4a3f024028a9eb4f5f9ffe484802a,256,V3,f8fee9d5d9a4a3f024028a9eb4f5f9ffe484802a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-39.715_ry+125.090_rz-31.650_tx-00.028_ty-00.477_s+01.038_f8fee9d5d9a4a3f024028a9eb4f5f9ffe484802a_256x256.png,71063d350cca64a4f48ff63aea998d4e82649c83,Animals,-39.715,125.09,-31.65,2278 -2279,breed_pug,-41.132,35.104,-24.957,-0.002,-0.138,0.7825,f75cdc51441909f2560ecc33487a8add70986864,256,V3,f75cdc51441909f2560ecc33487a8add70986864_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/breed_pug_rx-41.132_ry+125.104_rz-24.957_tx-00.002_ty-00.138_s+00.626_f75cdc51441909f2560ecc33487a8add70986864_256x256.png,740ee971ea2dbe981a672b05a60f21ccb3269a03,Animals,-41.132,125.104,-24.957,2279 -2280,celica,2.139,3.613,21.596,-0.13,0.233,1.225,4fdde4ed9a8379d448077f22dd89acbce39454f7,256,V3,4fdde4ed9a8379d448077f22dd89acbce39454f7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+02.139_ry+03.613_rz+21.596_tx-00.130_ty+00.233_s+01.225_4fdde4ed9a8379d448077f22dd89acbce39454f7_256x256.png,b94a210a5a7a0a708e701aa2744732a537aa5568,Cars,2.139,93.613,21.596,2280 -2281,celica,2.655,-10.156,16.11,0.222,0.539,0.918,b4efb9782d0616d6b055623d64e6b01a0a0cf751,256,V3,b4efb9782d0616d6b055623d64e6b01a0a0cf751_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+02.655_ry-10.156_rz+16.110_tx+00.222_ty+00.539_s+00.918_b4efb9782d0616d6b055623d64e6b01a0a0cf751_256x256.png,05563af806ba2e296345567e8b5b5dc28fa7d7a5,Cars,2.655,79.844,16.11,2281 -2282,celica,2.7,32.34,19.231,0.069,-0.528,1.046,5340e08938cbce5c353d96431d865b6ed44f477c,256,V3,5340e08938cbce5c353d96431d865b6ed44f477c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+02.700_ry+32.340_rz+19.231_tx+00.069_ty-00.528_s+01.046_5340e08938cbce5c353d96431d865b6ed44f477c_256x256.png,127f97a7ad5cf794a4e8530efdd36c1d225f5271,Cars,2.7,122.34,19.231,2282 -2283,celica,7.497,-2.301,-9.331,-0.016,0.3,1.082,8a23f322c24bef2c98332da813a1e7f72ca52bac,256,V3,8a23f322c24bef2c98332da813a1e7f72ca52bac_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+07.497_ry-02.301_rz-09.331_tx-00.016_ty+00.300_s+01.082_8a23f322c24bef2c98332da813a1e7f72ca52bac_256x256.png,5dd44a25068340e36a9765eca23085b565eeb5ed,Cars,7.497,87.699,-9.331,2283 -2284,celica,7.498,-4.029,-11.364,0.14,0.536,0.776,1a27896603fd9fa8d37db3e59c2038169906f5d0,256,V3,1a27896603fd9fa8d37db3e59c2038169906f5d0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+07.498_ry-04.029_rz-11.364_tx+00.140_ty+00.536_s+00.776_1a27896603fd9fa8d37db3e59c2038169906f5d0_256x256.png,dd2ba0808f0a1f1bb40d5f1b11dbcb7146400d5d,Cars,7.498,85.971,-11.364,2284 -2285,celica,9.029,-34.767,-38.034,0.039,-0.306,0.888,6e56d33c1d7626870760f023466b7b6454a27e86,256,V3,6e56d33c1d7626870760f023466b7b6454a27e86_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+09.029_ry-34.767_rz-38.034_tx+00.039_ty-00.306_s+00.888_6e56d33c1d7626870760f023466b7b6454a27e86_256x256.png,0f1a6cb7321551af67e4cbc6618e5f48282fc482,Cars,9.029,55.233,-38.034,2285 -2286,celica,10.581,3.222,2.222,0.103,-0.486,1.04,54dedbf44f818e734266b4929da6bef526f10961,256,V3,54dedbf44f818e734266b4929da6bef526f10961_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+10.581_ry+03.222_rz+02.222_tx+00.103_ty-00.486_s+01.040_54dedbf44f818e734266b4929da6bef526f10961_256x256.png,f898e5b7a346cceb541de0206d0d4c573922c560,Cars,10.581,93.222,2.222,2286 -2287,celica,12.92,10.036,34.184,-0.17,-0.503,1.123,c7e71c68165291c25134e79efa191a8d1af89cae,256,V3,c7e71c68165291c25134e79efa191a8d1af89cae_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+12.920_ry+10.036_rz+34.184_tx-00.170_ty-00.503_s+01.123_c7e71c68165291c25134e79efa191a8d1af89cae_256x256.png,fd9a157549320429e854f40ded3135744c407976,Cars,12.92,100.036,34.184,2287 -2288,celica,15.127,-3.203,28.886,-0.146,0.41,1.046,53f68a4f0862fc51262ae776eaa2a41e1e5ae3c0,256,V3,53f68a4f0862fc51262ae776eaa2a41e1e5ae3c0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+15.127_ry-03.203_rz+28.886_tx-00.146_ty+00.410_s+01.046_53f68a4f0862fc51262ae776eaa2a41e1e5ae3c0_256x256.png,94f83c08c77bcbfc727551852697f8122fc06d18,Cars,15.127,86.797,28.886,2288 -2289,celica,15.603,26.399,35.253,0.214,-0.067,0.788,a05b9e6e36d4a74b8446dc8675115228e8864990,256,V3,a05b9e6e36d4a74b8446dc8675115228e8864990_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+15.603_ry+26.399_rz+35.253_tx+00.214_ty-00.067_s+00.788_a05b9e6e36d4a74b8446dc8675115228e8864990_256x256.png,2fde050f6537b769b2c786ee692c839275050bd7,Cars,15.603,116.399,35.253,2289 -2290,celica,16.585,4.39,-14.99,-0.006,-0.519,0.875,fdc52b9a9c4f0de89a0495f6527cc3df680a5b9e,256,V3,fdc52b9a9c4f0de89a0495f6527cc3df680a5b9e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+16.585_ry+04.390_rz-14.990_tx-00.006_ty-00.519_s+00.875_fdc52b9a9c4f0de89a0495f6527cc3df680a5b9e_256x256.png,257ce3464407ad6933cf7f88f3eccd93163a5df2,Cars,16.585,94.39,-14.99,2290 -2291,celica,17.421,-1.984,19.126,-0.012,-0.34,0.925,47c1e591e12cd17888f5695678a85d7943501cb3,256,V3,47c1e591e12cd17888f5695678a85d7943501cb3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+17.421_ry-01.984_rz+19.126_tx-00.012_ty-00.340_s+00.925_47c1e591e12cd17888f5695678a85d7943501cb3_256x256.png,529d0c4fe20f908ca3a96f5d0769241bfc8652d4,Cars,17.421,88.016,19.126,2291 -2292,celica,17.931,36.025,40.172,0.213,0.169,1.112,18993b1245ee07c3d246df42bf82f3a9d4d1623b,256,V3,18993b1245ee07c3d246df42bf82f3a9d4d1623b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+17.931_ry+36.025_rz+40.172_tx+00.213_ty+00.169_s+01.112_18993b1245ee07c3d246df42bf82f3a9d4d1623b_256x256.png,30a03d5e02cf51a9274672341281163b9adb40e6,Cars,17.931,126.025,40.172,2292 -2293,celica,19.471,21.544,18.712,0.152,-0.408,1.18,88e6448e2561395c5e22bf3be2edae21d881c44a,256,V3,88e6448e2561395c5e22bf3be2edae21d881c44a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+19.471_ry+21.544_rz+18.712_tx+00.152_ty-00.408_s+01.180_88e6448e2561395c5e22bf3be2edae21d881c44a_256x256.png,0873f6c5e2ac26a99012dd2f9e4281584f580793,Cars,19.471,111.544,18.712,2293 -2294,celica,22.0,-0.558,-35.971,-0.287,0.385,0.936,dedd97cd07efe0cb6a09b36f0164e8d7708f2062,256,V3,dedd97cd07efe0cb6a09b36f0164e8d7708f2062_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+22.000_ry-00.558_rz-35.971_tx-00.287_ty+00.385_s+00.936_dedd97cd07efe0cb6a09b36f0164e8d7708f2062_256x256.png,c500f211ece8710ff048fdf474585d2daa549f83,Cars,22.0,89.442,-35.971,2294 -2295,celica,23.76,9.043,-14.549,0.154,0.5,1.141,b75275ed411e99ebe0a7f8d57ef0bf2362a2a2a5,256,V3,b75275ed411e99ebe0a7f8d57ef0bf2362a2a2a5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+23.760_ry+09.043_rz-14.549_tx+00.154_ty+00.500_s+01.141_b75275ed411e99ebe0a7f8d57ef0bf2362a2a2a5_256x256.png,9e4a7327994f7f8bf0c4d4616c673255b7464016,Cars,23.76,99.043,-14.549,2295 -2296,celica,27.467,24.004,-18.364,0.183,-0.402,1.021,735c75b5a1bcb3638f01a6d6d0ba4d490a4018d4,256,V3,735c75b5a1bcb3638f01a6d6d0ba4d490a4018d4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+27.467_ry+24.004_rz-18.364_tx+00.183_ty-00.402_s+01.021_735c75b5a1bcb3638f01a6d6d0ba4d490a4018d4_256x256.png,2c25e64085ef6650eabcb906edd1791f999d6e4a,Cars,27.467,114.004,-18.364,2296 -2297,celica,28.037,42.431,-29.771,-0.157,0.551,1.122,5a2bbfdc57de4e0d72b54fdda928e69f0fda5b33,256,V3,5a2bbfdc57de4e0d72b54fdda928e69f0fda5b33_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+28.037_ry+42.431_rz-29.771_tx-00.157_ty+00.551_s+01.122_5a2bbfdc57de4e0d72b54fdda928e69f0fda5b33_256x256.png,5314268bd4a7f812a1a6ae243d5a3f01579e886d,Cars,28.037,132.431,-29.771,2297 -2298,celica,31.612,-21.487,-5.169,0.162,0.006,0.79,9638814e3f9b548584413e06e65f2516a6704ca3,256,V3,9638814e3f9b548584413e06e65f2516a6704ca3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+31.612_ry-21.487_rz-05.169_tx+00.162_ty+00.006_s+00.790_9638814e3f9b548584413e06e65f2516a6704ca3_256x256.png,5d5020ab0e7f631a665d8a086745e37373421503,Cars,31.612,68.513,-5.169,2298 -2299,celica,37.739,25.226,-5.3,-0.017,-0.5,1.212,0feb8f22c1d22abefb6acbb5ef93695f2094f7bd,256,V3,0feb8f22c1d22abefb6acbb5ef93695f2094f7bd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+37.739_ry+25.226_rz-05.300_tx-00.017_ty-00.500_s+01.212_0feb8f22c1d22abefb6acbb5ef93695f2094f7bd_256x256.png,efe5547b149f6b0fcb68c253cd01b8e086b366d3,Cars,37.739,115.226,-5.3,2299 -2300,celica,39.919,-39.741,9.095,-0.102,0.324,1.019,008c5e0c085829ca9eb824250a29841b95fce1fb,256,V3,008c5e0c085829ca9eb824250a29841b95fce1fb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+39.919_ry-39.741_rz+09.095_tx-00.102_ty+00.324_s+01.019_008c5e0c085829ca9eb824250a29841b95fce1fb_256x256.png,61d10f1d0e08960cb59f004ae3e77202db1e4b4f,Cars,39.919,50.259,9.095,2300 -2301,celica,42.672,-11.614,-25.555,0.022,0.229,1.206,66d77fe2e2f51c754d375e5a998b475463f4659d,256,V3,66d77fe2e2f51c754d375e5a998b475463f4659d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx+42.672_ry-11.614_rz-25.555_tx+00.022_ty+00.229_s+01.206_66d77fe2e2f51c754d375e5a998b475463f4659d_256x256.png,73c7a0a01d708bfceb013cd43a1a48b73b966175,Cars,42.672,78.386,-25.555,2301 -2302,celica,-3.531,-31.923,-13.776,0.059,-0.207,1.108,8c1f554847486da583ba47f996c62309d3ac718b,256,V3,8c1f554847486da583ba47f996c62309d3ac718b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-03.531_ry-31.923_rz-13.776_tx+00.059_ty-00.207_s+01.108_8c1f554847486da583ba47f996c62309d3ac718b_256x256.png,6d9c14edcba8c8984c2dfe473c658cea652f738b,Cars,-3.531,58.077,-13.776,2302 -2303,celica,-6.103,3.949,-35.35,-0.002,-0.28,0.904,52d88d1b0c95e82a642dacb1854b1c06f4f7b954,256,V3,52d88d1b0c95e82a642dacb1854b1c06f4f7b954_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-06.103_ry+03.949_rz-35.350_tx-00.002_ty-00.280_s+00.904_52d88d1b0c95e82a642dacb1854b1c06f4f7b954_256x256.png,1035549fa7e21f6c406c479905571382ff65fd21,Cars,-6.103,93.949,-35.35,2303 -2304,celica,-9.257,14.673,18.117,0.043,0.042,1.114,2e53ba09f7737f394794c76bf78bc36a29bf497a,256,V3,2e53ba09f7737f394794c76bf78bc36a29bf497a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-09.257_ry+14.673_rz+18.117_tx+00.043_ty+00.042_s+01.114_2e53ba09f7737f394794c76bf78bc36a29bf497a_256x256.png,6ced6395e4d3029120d6050c831ae2f505f55a0e,Cars,-9.257,104.673,18.117,2304 -2305,celica,-21.112,8.878,-33.362,0.178,-0.312,1.254,18155d790854f631ef578f5d709b055d300b610a,256,V3,18155d790854f631ef578f5d709b055d300b610a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-21.112_ry+08.878_rz-33.362_tx+00.178_ty-00.312_s+01.254_18155d790854f631ef578f5d709b055d300b610a_256x256.png,4fbfc471cbe3f1fd5c2f9ffd122f88ba87509f5c,Cars,-21.112,98.878,-33.362,2305 -2306,celica,-21.881,38.392,32.508,-0.189,0.592,0.858,78552903abd1cfa4a145d12c4bc7ec0f9ee30aae,256,V3,78552903abd1cfa4a145d12c4bc7ec0f9ee30aae_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-21.881_ry+38.392_rz+32.508_tx-00.189_ty+00.592_s+00.858_78552903abd1cfa4a145d12c4bc7ec0f9ee30aae_256x256.png,b9888d1cb9ba73f3bb5bfd0f30b0f7c487538a47,Cars,-21.881,128.392,32.508,2306 -2307,celica,-22.935,-29.658,-17.257,0.28,0.485,1.171,62b972600d9049e0a7837d6f06688b2339d3f392,256,V3,62b972600d9049e0a7837d6f06688b2339d3f392_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-22.935_ry-29.658_rz-17.257_tx+00.280_ty+00.485_s+01.171_62b972600d9049e0a7837d6f06688b2339d3f392_256x256.png,1c4c3d1c52cf05f827dfc440de76d05f074ae8f4,Cars,-22.935,60.342,-17.257,2307 -2308,celica,-25.419,-22.094,24.962,0.103,0.313,0.825,8ace3a1e66da372409b25a554485b804a1d41670,256,V3,8ace3a1e66da372409b25a554485b804a1d41670_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-25.419_ry-22.094_rz+24.962_tx+00.103_ty+00.313_s+00.825_8ace3a1e66da372409b25a554485b804a1d41670_256x256.png,442b5b5f7774e87d6fccb7c17d32737e48e202a8,Cars,-25.419,67.906,24.962,2308 -2309,celica,-26.826,10.503,-41.13,0.119,0.524,1.219,2ecc9808a5dbe4d849393c5e20b72310e3fd79bc,256,V3,2ecc9808a5dbe4d849393c5e20b72310e3fd79bc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-26.826_ry+10.503_rz-41.130_tx+00.119_ty+00.524_s+01.219_2ecc9808a5dbe4d849393c5e20b72310e3fd79bc_256x256.png,70b5e20c3c0381f3d99a410358f838b4b1771d9e,Cars,-26.826,100.503,-41.13,2309 -2310,celica,-29.036,7.6,-15.617,0.288,0.024,0.969,e01de2d1b3ce15c38cc1f54b2107a94fd45309c2,256,V3,e01de2d1b3ce15c38cc1f54b2107a94fd45309c2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-29.036_ry+07.600_rz-15.617_tx+00.288_ty+00.024_s+00.969_e01de2d1b3ce15c38cc1f54b2107a94fd45309c2_256x256.png,8f1df31051a1dce57c8b7d04593db86e246b5f33,Cars,-29.036,97.6,-15.617,2310 -2311,celica,-30.9,23.553,-26.445,-0.153,0.598,1.102,df8649ff5714596a0846a448315940785eebef90,256,V3,df8649ff5714596a0846a448315940785eebef90_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-30.900_ry+23.553_rz-26.445_tx-00.153_ty+00.598_s+01.102_df8649ff5714596a0846a448315940785eebef90_256x256.png,e9ee031cb77f5a8ab886177069b2a20eba1d4374,Cars,-30.9,113.553,-26.445,2311 -2312,celica,-32.332,12.366,44.557,-0.126,-0.373,0.795,679d15faec83532c20e025dfb45d965e88e15d56,256,V3,679d15faec83532c20e025dfb45d965e88e15d56_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-32.332_ry+12.366_rz+44.557_tx-00.126_ty-00.373_s+00.795_679d15faec83532c20e025dfb45d965e88e15d56_256x256.png,666c2c9339d4cbdc43adae456e35bc17a98a254b,Cars,-32.332,102.366,44.557,2312 -2313,celica,-33.713,12.018,36.234,0.164,0.143,0.823,8b60f18e65412dae65e18ee35076defdfdd6b197,256,V3,8b60f18e65412dae65e18ee35076defdfdd6b197_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-33.713_ry+12.018_rz+36.234_tx+00.164_ty+00.143_s+00.823_8b60f18e65412dae65e18ee35076defdfdd6b197_256x256.png,49f4bc4c6ac5f2ec6c4733e8155aa6da8083839d,Cars,-33.713,102.018,36.234,2313 -2314,celica,-33.924,34.337,30.162,-0.156,0.463,1.293,6be665021bdf28ddf94128b3b551f493684d139e,256,V3,6be665021bdf28ddf94128b3b551f493684d139e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-33.924_ry+34.337_rz+30.162_tx-00.156_ty+00.463_s+01.293_6be665021bdf28ddf94128b3b551f493684d139e_256x256.png,d678c6df0e13d0c92a3bad4732a66c7ad5d1c471,Cars,-33.924,124.337,30.162,2314 -2315,celica,-36.43,-28.253,15.413,0.017,-0.084,1.083,94cb4ead3d39a4aa92928bcffeb798368eca7c91,256,V3,94cb4ead3d39a4aa92928bcffeb798368eca7c91_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-36.430_ry-28.253_rz+15.413_tx+00.017_ty-00.084_s+01.083_94cb4ead3d39a4aa92928bcffeb798368eca7c91_256x256.png,5236c0722c06f07323a43949c2137066492a1a95,Cars,-36.43,61.747,15.413,2315 -2316,celica,-38.161,9.253,42.792,0.145,-0.553,1.263,d2fdcf62f91ec9b749f46e339ccb952c2321f836,256,V3,d2fdcf62f91ec9b749f46e339ccb952c2321f836_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-38.161_ry+09.253_rz+42.792_tx+00.145_ty-00.553_s+01.263_d2fdcf62f91ec9b749f46e339ccb952c2321f836_256x256.png,f0f805e663a04971f942466369c381a8c765dc7f,Cars,-38.161,99.253,42.792,2316 -2317,celica,-38.875,-41.826,33.096,-0.085,-0.256,1.228,d78e909711717af8f43a610bcced7da513896e59,256,V3,d78e909711717af8f43a610bcced7da513896e59_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-38.875_ry-41.826_rz+33.096_tx-00.085_ty-00.256_s+01.228_d78e909711717af8f43a610bcced7da513896e59_256x256.png,1a343e913d7a118a1b0bc2e09995a20cfb106c19,Cars,-38.875,48.174,33.096,2317 -2318,celica,-39.067,36.388,-41.856,-0.062,0.161,1.088,68816672b3c4ca7ae25d31aae549bf1fba5e82f4,256,V3,68816672b3c4ca7ae25d31aae549bf1fba5e82f4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-39.067_ry+36.388_rz-41.856_tx-00.062_ty+00.161_s+01.088_68816672b3c4ca7ae25d31aae549bf1fba5e82f4_256x256.png,b0ed269cd258ec72a1a6cf5d1101c565b8b48eb5,Cars,-39.067,126.388,-41.856,2318 -2319,celica,-44.936,-17.711,-42.886,-0.293,-0.037,1.078,293b6d53a6c846985344f0451829f19a9b42bf6b,256,V3,293b6d53a6c846985344f0451829f19a9b42bf6b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/celica_rx-44.936_ry-17.711_rz-42.886_tx-00.293_ty-00.037_s+01.078_293b6d53a6c846985344f0451829f19a9b42bf6b_256x256.png,8c653ef1223cc030bcc1c7ee326562837003fe49,Cars,-44.936,72.289,-42.886,2319 -2320,clio,0.442,43.794,1.964,-0.219,-0.517,1.063,32efbfa3dd00b34ef801020ddda7296082dc73cc,256,V3,32efbfa3dd00b34ef801020ddda7296082dc73cc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+00.442_ry+43.794_rz+01.964_tx-00.219_ty-00.517_s+01.063_32efbfa3dd00b34ef801020ddda7296082dc73cc_256x256.png,b297789308c2e790d82abb5657c97b617161931a,Cars,0.442,133.794,1.964,2320 -2321,clio,0.85,26.227,33.319,-0.145,-0.309,0.771,0f646f5c705e1064da468e5cddde08f8aa9e38ab,256,V3,0f646f5c705e1064da468e5cddde08f8aa9e38ab_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+00.850_ry+26.227_rz+33.319_tx-00.145_ty-00.309_s+00.771_0f646f5c705e1064da468e5cddde08f8aa9e38ab_256x256.png,9ff008b31e8c6d08103da1d2153a8af012c48733,Cars,0.85,116.227,33.319,2321 -2322,clio,5.579,10.673,4.127,-0.033,0.471,1.072,8f6ea62ded1e6c1dac6bc25cedd9365eb0757790,256,V3,8f6ea62ded1e6c1dac6bc25cedd9365eb0757790_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+05.579_ry+10.673_rz+04.127_tx-00.033_ty+00.471_s+01.072_8f6ea62ded1e6c1dac6bc25cedd9365eb0757790_256x256.png,415fe5933391f212e3dfc3560cf379bcfacb233a,Cars,5.579,100.673,4.127,2322 -2323,clio,6.185,-41.386,11.54,0.295,-0.055,1.004,9421c29f959ef4cea9c94d7b3db30ca32a9cab41,256,V3,9421c29f959ef4cea9c94d7b3db30ca32a9cab41_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+06.185_ry-41.386_rz+11.540_tx+00.295_ty-00.055_s+01.004_9421c29f959ef4cea9c94d7b3db30ca32a9cab41_256x256.png,0d5231ce4bc4a188c468b3f7bebaf6def26814c4,Cars,6.185,48.614,11.54,2323 -2324,clio,12.545,15.159,33.008,-0.219,0.212,1.241,5f495b09814e3a411ce90c4ce49661b5655c3635,256,V3,5f495b09814e3a411ce90c4ce49661b5655c3635_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+12.545_ry+15.159_rz+33.008_tx-00.219_ty+00.212_s+01.241_5f495b09814e3a411ce90c4ce49661b5655c3635_256x256.png,209dfa1ae2dd83ec7dc65ef2551c1c2ce4ca1005,Cars,12.545,105.159,33.008,2324 -2325,clio,12.823,11.198,-24.449,0.031,-0.566,0.78,6345664db9d6a88f8daeec1f71fdce607deabde7,256,V3,6345664db9d6a88f8daeec1f71fdce607deabde7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+12.823_ry+11.198_rz-24.449_tx+00.031_ty-00.566_s+00.780_6345664db9d6a88f8daeec1f71fdce607deabde7_256x256.png,2e5cf7269edaf95f6625a600722d43e640af55b5,Cars,12.823,101.198,-24.449,2325 -2326,clio,17.188,12.52,-19.247,-0.19,-0.367,0.784,f23a97bc6e0317c8d4b32819074edd62fc6c5d26,256,V3,f23a97bc6e0317c8d4b32819074edd62fc6c5d26_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+17.188_ry+12.520_rz-19.247_tx-00.190_ty-00.367_s+00.784_f23a97bc6e0317c8d4b32819074edd62fc6c5d26_256x256.png,1e9ff75c2c5794b644aeb582a95ee41eda8f8d29,Cars,17.188,102.52,-19.247,2326 -2327,clio,18.305,-14.153,26.793,-0.137,0.008,0.843,7c9dda86a484dc8320e04b3034687e49eed9b6e6,256,V3,7c9dda86a484dc8320e04b3034687e49eed9b6e6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+18.305_ry-14.153_rz+26.793_tx-00.137_ty+00.008_s+00.843_7c9dda86a484dc8320e04b3034687e49eed9b6e6_256x256.png,72b6ca5a2a90492f94e8cdcfcca7ad25688812ba,Cars,18.305,75.847,26.793,2327 -2328,clio,21.97,-39.503,-30.975,0.137,-0.357,0.79,8cbf2e60e7a88ab20b9bce6bd33a2931fb41c7b8,256,V3,8cbf2e60e7a88ab20b9bce6bd33a2931fb41c7b8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+21.970_ry-39.503_rz-30.975_tx+00.137_ty-00.357_s+00.790_8cbf2e60e7a88ab20b9bce6bd33a2931fb41c7b8_256x256.png,2bf2e96498272eb4e55cbba580daef5cb2c6d2e2,Cars,21.97,50.497,-30.975,2328 -2329,clio,28.65,-14.854,-28.045,0.243,-0.006,0.987,5ec8a1f16f380ca80a9997242a9ff211728441a3,256,V3,5ec8a1f16f380ca80a9997242a9ff211728441a3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+28.650_ry-14.854_rz-28.045_tx+00.243_ty-00.006_s+00.987_5ec8a1f16f380ca80a9997242a9ff211728441a3_256x256.png,813ec46402721aa380c6c34f9b50b4155ff6b021,Cars,28.65,75.146,-28.045,2329 -2330,clio,30.576,-44.798,34.236,0.183,-0.175,1.139,09a811c55f85c083a16fa2fd09eed4b4fd197827,256,V3,09a811c55f85c083a16fa2fd09eed4b4fd197827_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+30.576_ry-44.798_rz+34.236_tx+00.183_ty-00.175_s+01.139_09a811c55f85c083a16fa2fd09eed4b4fd197827_256x256.png,1c97cf00ed3630a93e2b8ba785e87e33bd25b734,Cars,30.576,45.202,34.236,2330 -2331,clio,32.747,-41.457,-28.618,0.225,0.252,1.181,05778fa8da2421326a1550af4ef6630d03ce261e,256,V3,05778fa8da2421326a1550af4ef6630d03ce261e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+32.747_ry-41.457_rz-28.618_tx+00.225_ty+00.252_s+01.181_05778fa8da2421326a1550af4ef6630d03ce261e_256x256.png,d184f8711bf63e8bec163ed7c05568f4665b016d,Cars,32.747,48.543,-28.618,2331 -2332,clio,35.081,-8.132,-5.924,-0.149,-0.241,0.987,f673170c58cf8b50821c6039b4d9efa615778711,256,V3,f673170c58cf8b50821c6039b4d9efa615778711_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+35.081_ry-08.132_rz-05.924_tx-00.149_ty-00.241_s+00.987_f673170c58cf8b50821c6039b4d9efa615778711_256x256.png,53ef8cbdf52bf10d3fe16e32b4e604334c285df7,Cars,35.081,81.868,-5.924,2332 -2333,clio,35.38,23.094,-33.667,0.079,0.005,1.091,1fddaa2c83db0971b382e410fe1b18a8ae64c303,256,V3,1fddaa2c83db0971b382e410fe1b18a8ae64c303_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+35.380_ry+23.094_rz-33.667_tx+00.079_ty+00.005_s+01.091_1fddaa2c83db0971b382e410fe1b18a8ae64c303_256x256.png,7cafb89cc1f83a0237380b70be88e68425a5b85a,Cars,35.38,113.094,-33.667,2333 -2334,clio,36.665,8.083,30.178,0.177,0.058,1.112,1552fbc8a54a5b46df60e0c437748a06dd4ff464,256,V3,1552fbc8a54a5b46df60e0c437748a06dd4ff464_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+36.665_ry+08.083_rz+30.178_tx+00.177_ty+00.058_s+01.112_1552fbc8a54a5b46df60e0c437748a06dd4ff464_256x256.png,e5299009d6f51172a23387c10b66c2eef72ff01a,Cars,36.665,98.083,30.178,2334 -2335,clio,37.129,-23.12,44.246,0.295,-0.361,1.081,6ff2a4ef49c733012f8af268fdc02aca0d829a82,256,V3,6ff2a4ef49c733012f8af268fdc02aca0d829a82_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+37.129_ry-23.120_rz+44.246_tx+00.295_ty-00.361_s+01.081_6ff2a4ef49c733012f8af268fdc02aca0d829a82_256x256.png,030e18bd50b6dc822219262dcd709b4e17872ff1,Cars,37.129,66.88,44.246,2335 -2336,clio,38.897,12.54,32.256,0.171,0.361,0.88,82c15addd813d8bd04f528b8fb260774998aa08a,256,V3,82c15addd813d8bd04f528b8fb260774998aa08a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+38.897_ry+12.540_rz+32.256_tx+00.171_ty+00.361_s+00.880_82c15addd813d8bd04f528b8fb260774998aa08a_256x256.png,3188e64a16c4d96b12870aba4e97dd22dbb28b1b,Cars,38.897,102.54,32.256,2336 -2337,clio,41.103,-28.358,-40.228,0.181,-0.444,0.973,9ceb982585d9f2700f43e0ac130d56775f9b5a02,256,V3,9ceb982585d9f2700f43e0ac130d56775f9b5a02_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+41.103_ry-28.358_rz-40.228_tx+00.181_ty-00.444_s+00.973_9ceb982585d9f2700f43e0ac130d56775f9b5a02_256x256.png,762c57310f520644c62a7765e0d470247528f78c,Cars,41.103,61.642,-40.228,2337 -2338,clio,42.866,10.572,43.946,-0.226,0.005,0.968,07ae5d2c092d7de8043f427d08e372f83e320a25,256,V3,07ae5d2c092d7de8043f427d08e372f83e320a25_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx+42.866_ry+10.572_rz+43.946_tx-00.226_ty+00.005_s+00.968_07ae5d2c092d7de8043f427d08e372f83e320a25_256x256.png,717d2ee6ce39d5212ff4c3b31cfccd93acd45bb9,Cars,42.866,100.572,43.946,2338 -2339,clio,-1.747,-39.623,-42.049,-0.253,0.209,1.026,79dbecd8b42989a4ea53d6665adc9c4a0af228bc,256,V3,79dbecd8b42989a4ea53d6665adc9c4a0af228bc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-01.747_ry-39.623_rz-42.049_tx-00.253_ty+00.209_s+01.026_79dbecd8b42989a4ea53d6665adc9c4a0af228bc_256x256.png,53d36c74613ded565dbf4a6550495cf0afaca446,Cars,-1.747,50.377,-42.049,2339 -2340,clio,-4.286,-27.374,-27.747,0.119,-0.013,1.167,1f609457d2b8cc02ec03d7c1d506eeb4f7442707,256,V3,1f609457d2b8cc02ec03d7c1d506eeb4f7442707_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-04.286_ry-27.374_rz-27.747_tx+00.119_ty-00.013_s+01.167_1f609457d2b8cc02ec03d7c1d506eeb4f7442707_256x256.png,dec4bf45090a672ef879c186d3528e06e3bad636,Cars,-4.286,62.626,-27.747,2340 -2341,clio,-13.073,-35.329,37.23,-0.0,0.137,0.785,7b4a3aa00b831dfe1a0294d64ae9848533456dbc,256,V3,7b4a3aa00b831dfe1a0294d64ae9848533456dbc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-13.073_ry-35.329_rz+37.230_tx-00.000_ty+00.137_s+00.785_7b4a3aa00b831dfe1a0294d64ae9848533456dbc_256x256.png,4e19e37d648d9cbfeef18376e57d7607b4044f13,Cars,-13.073,54.671,37.23,2341 -2342,clio,-15.995,-0.79,10.572,0.116,0.416,0.981,3a63bb94b31c4beb4cb027b805bf5d0dc7edb20d,256,V3,3a63bb94b31c4beb4cb027b805bf5d0dc7edb20d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-15.995_ry-00.790_rz+10.572_tx+00.116_ty+00.416_s+00.981_3a63bb94b31c4beb4cb027b805bf5d0dc7edb20d_256x256.png,7a7aaa478c557723a14558d9aa0e43064e051d89,Cars,-15.995,89.21,10.572,2342 -2343,clio,-17.064,9.255,18.228,0.089,-0.535,1.149,7a5eda7f4742ac2796019438c09eaa69d89bd3c5,256,V3,7a5eda7f4742ac2796019438c09eaa69d89bd3c5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-17.064_ry+09.255_rz+18.228_tx+00.089_ty-00.535_s+01.149_7a5eda7f4742ac2796019438c09eaa69d89bd3c5_256x256.png,4a11d5a208deb80a168176a6638d4da737f4cfed,Cars,-17.064,99.255,18.228,2343 -2344,clio,-17.615,15.759,2.708,0.172,-0.474,1.102,c07db629a1ad8b262d20caa1f27542a739be24ee,256,V3,c07db629a1ad8b262d20caa1f27542a739be24ee_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-17.615_ry+15.759_rz+02.708_tx+00.172_ty-00.474_s+01.102_c07db629a1ad8b262d20caa1f27542a739be24ee_256x256.png,108cfd27251f2489042e4cdcd270f724b594d14b,Cars,-17.615,105.759,2.708,2344 -2345,clio,-17.794,-20.801,-39.83,0.202,0.527,1.077,85bc69d8900affd2edb409623b8a1a799d7aad62,256,V3,85bc69d8900affd2edb409623b8a1a799d7aad62_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-17.794_ry-20.801_rz-39.830_tx+00.202_ty+00.527_s+01.077_85bc69d8900affd2edb409623b8a1a799d7aad62_256x256.png,c2a3792e11f53dc609b44dfc4723f16b434ade3d,Cars,-17.794,69.199,-39.83,2345 -2346,clio,-18.229,22.428,2.678,0.255,0.509,1.22,d056d2d66bf1abed8c6b536fa485285061213e2b,256,V3,d056d2d66bf1abed8c6b536fa485285061213e2b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-18.229_ry+22.428_rz+02.678_tx+00.255_ty+00.509_s+01.220_d056d2d66bf1abed8c6b536fa485285061213e2b_256x256.png,038e5b7bfbeeba3c5fb7ace4d5cae1dfd46a9f35,Cars,-18.229,112.428,2.678,2346 -2347,clio,-19.199,35.317,-28.889,0.248,0.291,1.087,b0617ea2896b9ecbc5d136cdeb58bc2074d719ae,256,V3,b0617ea2896b9ecbc5d136cdeb58bc2074d719ae_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-19.199_ry+35.317_rz-28.889_tx+00.248_ty+00.291_s+01.087_b0617ea2896b9ecbc5d136cdeb58bc2074d719ae_256x256.png,77bc2ab05816caaf4db2af83689099b30d7141c7,Cars,-19.199,125.317,-28.889,2347 -2348,clio,-20.571,7.605,-16.058,-0.051,-0.188,1.268,850f89de7285acbbef50b773fadd7fc23945caab,256,V3,850f89de7285acbbef50b773fadd7fc23945caab_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-20.571_ry+07.605_rz-16.058_tx-00.051_ty-00.188_s+01.268_850f89de7285acbbef50b773fadd7fc23945caab_256x256.png,cba62e7524ee39168fda6569c32d3a4baf5fe856,Cars,-20.571,97.605,-16.058,2348 -2349,clio,-20.657,41.395,29.173,-0.058,0.449,1.015,c24f3e646217c331c47861761c328937cfb01526,256,V3,c24f3e646217c331c47861761c328937cfb01526_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-20.657_ry+41.395_rz+29.173_tx-00.058_ty+00.449_s+01.015_c24f3e646217c331c47861761c328937cfb01526_256x256.png,c8579a9a723f1b7f3f426df282d2374f6ce1cd17,Cars,-20.657,131.395,29.173,2349 -2350,clio,-23.222,-19.17,-17.456,-0.035,0.165,0.99,ecd40f3f6d7a4d6d88134d648884e0b9b364efc9,256,V3,ecd40f3f6d7a4d6d88134d648884e0b9b364efc9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-23.222_ry-19.170_rz-17.456_tx-00.035_ty+00.165_s+00.990_ecd40f3f6d7a4d6d88134d648884e0b9b364efc9_256x256.png,fe42df2a3d9a7b285d3e4478a299a4acb6cfa6a7,Cars,-23.222,70.83,-17.456,2350 -2351,clio,-23.943,-17.395,-8.423,0.129,-0.48,0.844,f1bed88aa0fa49ce67ca15a82eb37c003e95cb56,256,V3,f1bed88aa0fa49ce67ca15a82eb37c003e95cb56_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-23.943_ry-17.395_rz-08.423_tx+00.129_ty-00.480_s+00.844_f1bed88aa0fa49ce67ca15a82eb37c003e95cb56_256x256.png,30ab1466244363cca30cba2b3dd1f718f6140cfa,Cars,-23.943,72.605,-8.423,2351 -2352,clio,-24.323,35.407,-27.66,0.155,-0.439,1.053,a9790817ee8fd12f6c243c79fd43b592d4fe5c91,256,V3,a9790817ee8fd12f6c243c79fd43b592d4fe5c91_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-24.323_ry+35.407_rz-27.660_tx+00.155_ty-00.439_s+01.053_a9790817ee8fd12f6c243c79fd43b592d4fe5c91_256x256.png,ee97d6e3fb4b450ea2ae6493c77652c4786c02a8,Cars,-24.323,125.407,-27.66,2352 -2353,clio,-28.195,37.453,1.717,-0.11,-0.15,1.088,7f4ad2cd7c887496c1e7223c2c33146b2bf1cdeb,256,V3,7f4ad2cd7c887496c1e7223c2c33146b2bf1cdeb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-28.195_ry+37.453_rz+01.717_tx-00.110_ty-00.150_s+01.088_7f4ad2cd7c887496c1e7223c2c33146b2bf1cdeb_256x256.png,811dcbd407ea7ca09fcaeee92e8031f85d73fbf6,Cars,-28.195,127.453,1.717,2353 -2354,clio,-29.385,2.527,19.636,-0.266,-0.065,1.065,5c13f04ac2fe1799566c6d73f92e79f484b6dbd7,256,V3,5c13f04ac2fe1799566c6d73f92e79f484b6dbd7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-29.385_ry+02.527_rz+19.636_tx-00.266_ty-00.065_s+01.065_5c13f04ac2fe1799566c6d73f92e79f484b6dbd7_256x256.png,909a1005d90d656b63547f4daf24ab1951ca9fae,Cars,-29.385,92.527,19.636,2354 -2355,clio,-30.684,-40.409,6.061,-0.258,-0.35,0.996,11b63b2c5269c46021a8f2be4cae8e51e3f70a3a,256,V3,11b63b2c5269c46021a8f2be4cae8e51e3f70a3a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-30.684_ry-40.409_rz+06.061_tx-00.258_ty-00.350_s+00.996_11b63b2c5269c46021a8f2be4cae8e51e3f70a3a_256x256.png,fa6b2d55e9dc0537bea2afb20b7f2daf0655e799,Cars,-30.684,49.591,6.061,2355 -2356,clio,-34.616,-29.808,20.264,-0.198,0.026,1.187,390f7bc8272394dbcf015a0ca404b00d6ab45326,256,V3,390f7bc8272394dbcf015a0ca404b00d6ab45326_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-34.616_ry-29.808_rz+20.264_tx-00.198_ty+00.026_s+01.187_390f7bc8272394dbcf015a0ca404b00d6ab45326_256x256.png,43b3966a0c6b9a67b2ee1ceceb4d8f39fe812697,Cars,-34.616,60.192,20.264,2356 -2357,clio,-39.289,35.985,-0.586,0.214,-0.263,1.063,92bb268bd82bc9638d2b3095cc60b1d6884307fa,256,V3,92bb268bd82bc9638d2b3095cc60b1d6884307fa_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-39.289_ry+35.985_rz-00.586_tx+00.214_ty-00.263_s+01.063_92bb268bd82bc9638d2b3095cc60b1d6884307fa_256x256.png,e40b071e620c393235fc95127ce2dd066c53e380,Cars,-39.289,125.985,-0.586,2357 -2358,clio,-40.651,-26.716,17.903,0.006,-0.309,1.161,5ff1d617004bba6a49f404d601bc90cc4e2d119f,256,V3,5ff1d617004bba6a49f404d601bc90cc4e2d119f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-40.651_ry-26.716_rz+17.903_tx+00.006_ty-00.309_s+01.161_5ff1d617004bba6a49f404d601bc90cc4e2d119f_256x256.png,03e7ba347a2965cde0d31febac99774570f6cddd,Cars,-40.651,63.284,17.903,2358 -2359,clio,-41.233,25.555,-12.519,0.077,0.587,0.846,368ff9ae8d1fb9822fe4cfbcbca5e36cb7f02a4c,256,V3,368ff9ae8d1fb9822fe4cfbcbca5e36cb7f02a4c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/clio_rx-41.233_ry+25.555_rz-12.519_tx+00.077_ty+00.587_s+00.846_368ff9ae8d1fb9822fe4cfbcbca5e36cb7f02a4c_256x256.png,45488561c55d1f7682eeefe8c348a1a55d0203d1,Cars,-41.233,115.555,-12.519,2359 -2360,cruiser,1.802,-10.572,32.801,0.131,0.349,1.22916666667,9ab1d37c852202692c62b76bf1344e8e9122a8c5,256,V3,9ab1d37c852202692c62b76bf1344e8e9122a8c5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+01.802_ry+79.428_rz+32.801_tx+00.131_ty+00.349_s+01.475_9ab1d37c852202692c62b76bf1344e8e9122a8c5_256x256.png,8bac49738ba85900e6395d8d2a75d0e53efa64b5,Boats,1.802,79.428,32.801,2360 -2361,cruiser,6.763,13.037,-34.934,0.173,0.091,1.11666666667,85e99bdd29b122d564fa9b9c1e64fd486f761918,256,V3,85e99bdd29b122d564fa9b9c1e64fd486f761918_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+06.763_ry+103.037_rz-34.934_tx+00.173_ty+00.091_s+01.340_85e99bdd29b122d564fa9b9c1e64fd486f761918_256x256.png,9db8ca418b7f61bc3f363fa379248123475115ee,Boats,6.763,103.037,-34.934,2361 -2362,cruiser,7.244,-8.212,-13.58,-0.044,-0.003,0.820833333333,80fb5e7469b9c2d9a1dfd53268622d2ed20c0434,256,V3,80fb5e7469b9c2d9a1dfd53268622d2ed20c0434_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+07.244_ry+81.788_rz-13.580_tx-00.044_ty-00.003_s+00.985_80fb5e7469b9c2d9a1dfd53268622d2ed20c0434_256x256.png,13f44f044f7f86bf9eb58e401e76c24a23693200,Boats,7.244,81.788,-13.58,2362 -2363,cruiser,9.549,12.582,39.297,0.22,-0.595,1.24416666667,1f7a582d273b2b1d5b0c830dbc3e612728785b29,256,V3,1f7a582d273b2b1d5b0c830dbc3e612728785b29_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+09.549_ry+102.582_rz+39.297_tx+00.220_ty-00.595_s+01.493_1f7a582d273b2b1d5b0c830dbc3e612728785b29_256x256.png,c7cf2aa4db19b5bcf6fd7647c1391a4a5a5c1667,Boats,9.549,102.582,39.297,2363 -2364,cruiser,12.244,-25.516,-13.868,0.047,0.164,1.05083333333,f5a76d8a019ae8bd9f058ba58d80f550738fa339,256,V3,f5a76d8a019ae8bd9f058ba58d80f550738fa339_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+12.244_ry+64.484_rz-13.868_tx+00.047_ty+00.164_s+01.261_f5a76d8a019ae8bd9f058ba58d80f550738fa339_256x256.png,c79d5d1124fc24a6f7cca8b426dd1c92b28cf953,Boats,12.244,64.484,-13.868,2364 -2365,cruiser,16.234,-12.785,-11.037,-0.142,0.232,1.28666666667,11d76408d1a960f055ab6f8b345d970bf57e15ec,256,V3,11d76408d1a960f055ab6f8b345d970bf57e15ec_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+16.234_ry+77.215_rz-11.037_tx-00.142_ty+00.232_s+01.544_11d76408d1a960f055ab6f8b345d970bf57e15ec_256x256.png,2672d13ac8c36e5194a41ae389b147e61c11d8d2,Boats,16.234,77.215,-11.037,2365 -2366,cruiser,20.411,-19.187,35.359,0.053,-0.46,1.16916666667,b8d5096889d4210b5df304df4d3f2570be4db437,256,V3,b8d5096889d4210b5df304df4d3f2570be4db437_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+20.411_ry+70.813_rz+35.359_tx+00.053_ty-00.460_s+01.403_b8d5096889d4210b5df304df4d3f2570be4db437_256x256.png,1c052b170cca635417ca4e0f7aee365cf4025f84,Boats,20.411,70.813,35.359,2366 -2367,cruiser,22.082,-33.298,10.86,0.074,0.102,1.14,363bc035497c80b1b4c72cdb5dec8ee3dcb99f24,256,V3,363bc035497c80b1b4c72cdb5dec8ee3dcb99f24_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+22.082_ry+56.702_rz+10.860_tx+00.074_ty+00.102_s+01.368_363bc035497c80b1b4c72cdb5dec8ee3dcb99f24_256x256.png,587bc45ec19693d8ffe2e7edd402c6363ea29bf6,Boats,22.082,56.702,10.86,2367 -2368,cruiser,25.82,9.266,-11.535,-0.223,0.354,1.21916666667,95c7718f141d27c0a104aa25463e2dca496bbf75,256,V3,95c7718f141d27c0a104aa25463e2dca496bbf75_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+25.820_ry+99.266_rz-11.535_tx-00.223_ty+00.354_s+01.463_95c7718f141d27c0a104aa25463e2dca496bbf75_256x256.png,f084b6289475421dadb02e8231709d83c513aa5e,Boats,25.82,99.266,-11.535,2368 -2369,cruiser,27.74,-23.726,-25.702,0.135,-0.432,1.26333333333,eb8ac17ea71f6171cfa453993508de3601b54119,256,V3,eb8ac17ea71f6171cfa453993508de3601b54119_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+27.740_ry+66.274_rz-25.702_tx+00.135_ty-00.432_s+01.516_eb8ac17ea71f6171cfa453993508de3601b54119_256x256.png,8a869c49a1884a68414b3ebdcfc3771be1fcd8b6,Boats,27.74,66.274,-25.702,2369 -2370,cruiser,27.796,28.115,11.748,0.287,0.403,1.17666666667,f94f453328fe2f42cd6edcc4fb2b5929fd70e10d,256,V3,f94f453328fe2f42cd6edcc4fb2b5929fd70e10d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+27.796_ry+118.115_rz+11.748_tx+00.287_ty+00.403_s+01.412_f94f453328fe2f42cd6edcc4fb2b5929fd70e10d_256x256.png,598e7ecdf43514f1781b6045721d9684a67543f8,Boats,27.796,118.115,11.748,2370 -2371,cruiser,29.643,7.636,-1.418,-0.142,0.101,1.29416666667,2f330aeef919d7e9dacf39c34a39fbe9b0c9aeeb,256,V3,2f330aeef919d7e9dacf39c34a39fbe9b0c9aeeb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+29.643_ry+97.636_rz-01.418_tx-00.142_ty+00.101_s+01.553_2f330aeef919d7e9dacf39c34a39fbe9b0c9aeeb_256x256.png,a424583f18b3717934594fb5940f0e05803b27c2,Boats,29.643,97.636,-1.418,2371 -2372,cruiser,29.834,13.953,23.093,0.195,0.547,1.035,08bc68c27dc40f5d8e03c704f5c7bb7f15d2973c,256,V3,08bc68c27dc40f5d8e03c704f5c7bb7f15d2973c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+29.834_ry+103.953_rz+23.093_tx+00.195_ty+00.547_s+01.242_08bc68c27dc40f5d8e03c704f5c7bb7f15d2973c_256x256.png,8fdb067c8cd7d96a28459e43cd1ac96afe9cba42,Boats,29.834,103.953,23.093,2372 -2373,cruiser,31.563,15.623,16.556,-0.064,0.492,1.18416666667,536c972be730dd5a59218f913bbbc287188824f4,256,V3,536c972be730dd5a59218f913bbbc287188824f4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+31.563_ry+105.623_rz+16.556_tx-00.064_ty+00.492_s+01.421_536c972be730dd5a59218f913bbbc287188824f4_256x256.png,45f64203bc821620c3bc9048f4e1e99404ae8ae4,Boats,31.563,105.623,16.556,2373 -2374,cruiser,32.258,-17.787,-20.62,-0.072,0.542,0.86,91bf0922196b0d7eba9d380598bd7427a90e1d03,256,V3,91bf0922196b0d7eba9d380598bd7427a90e1d03_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+32.258_ry+72.213_rz-20.620_tx-00.072_ty+00.542_s+01.032_91bf0922196b0d7eba9d380598bd7427a90e1d03_256x256.png,3ba92b9853712753a80b7147bb846894ed73bdcb,Boats,32.258,72.213,-20.62,2374 -2375,cruiser,35.331,22.062,-21.932,-0.138,0.142,0.880833333333,c30f1394195e88021a665fc371fdee4f44822d08,256,V3,c30f1394195e88021a665fc371fdee4f44822d08_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+35.331_ry+112.062_rz-21.932_tx-00.138_ty+00.142_s+01.057_c30f1394195e88021a665fc371fdee4f44822d08_256x256.png,a66635a89308c2d48c1781db9289cee0a2a00cf9,Boats,35.331,112.062,-21.932,2375 -2376,cruiser,35.536,-30.139,-1.376,-0.013,-0.481,0.8225,6eb1f7799cf8b7b82358eec99ef8cde8755131f2,256,V3,6eb1f7799cf8b7b82358eec99ef8cde8755131f2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+35.536_ry+59.861_rz-01.376_tx-00.013_ty-00.481_s+00.987_6eb1f7799cf8b7b82358eec99ef8cde8755131f2_256x256.png,e6850ad324735b8d985ccea4db0885edb78ae943,Boats,35.536,59.861,-1.376,2376 -2377,cruiser,37.012,-30.809,-12.939,0.156,0.543,1.0375,070626a4910e2f45fc14e3fd894265013f93e8e7,256,V3,070626a4910e2f45fc14e3fd894265013f93e8e7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx+37.012_ry+59.191_rz-12.939_tx+00.156_ty+00.543_s+01.245_070626a4910e2f45fc14e3fd894265013f93e8e7_256x256.png,02a4deef758a824d0b48aa78f978da2b88ee62a6,Boats,37.012,59.191,-12.939,2377 -2378,cruiser,-4.798,-7.374,-44.829,0.187,-0.577,1.07916666667,df151ce8251f9a93ba98d5716a532c93574d2d00,256,V3,df151ce8251f9a93ba98d5716a532c93574d2d00_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-04.798_ry+82.626_rz-44.829_tx+00.187_ty-00.577_s+01.295_df151ce8251f9a93ba98d5716a532c93574d2d00_256x256.png,5f1856a7d5469ea36ab139ab2710661eb0687730,Boats,-4.798,82.626,-44.829,2378 -2379,cruiser,-5.107,42.418,37.356,0.055,0.303,1.08833333333,c45de65c7ae95f60915e021565e6f26b40077b76,256,V3,c45de65c7ae95f60915e021565e6f26b40077b76_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-05.107_ry+132.418_rz+37.356_tx+00.055_ty+00.303_s+01.306_c45de65c7ae95f60915e021565e6f26b40077b76_256x256.png,4dd258fe76d583a4d654443e726abd354e87acd1,Boats,-5.107,132.418,37.356,2379 -2380,cruiser,-7.415,43.618,4.335,-0.064,-0.528,0.885833333333,e7e59f50e5663ab90fec033b7d9f745cbedcac97,256,V3,e7e59f50e5663ab90fec033b7d9f745cbedcac97_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-07.415_ry+133.618_rz+04.335_tx-00.064_ty-00.528_s+01.063_e7e59f50e5663ab90fec033b7d9f745cbedcac97_256x256.png,47d2e1b6ff84cd0cb6261e0e1a32b235f85487a3,Boats,-7.415,133.618,4.335,2380 -2381,cruiser,-7.438,-22.348,-17.005,0.254,-0.042,1.14333333333,a30f46f6973ca392be08d1df55b0d69705978c08,256,V3,a30f46f6973ca392be08d1df55b0d69705978c08_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-07.438_ry+67.652_rz-17.005_tx+00.254_ty-00.042_s+01.372_a30f46f6973ca392be08d1df55b0d69705978c08_256x256.png,3f96324d77f768281de908ea6b64e120b1bfe5ae,Boats,-7.438,67.652,-17.005,2381 -2382,cruiser,-10.691,-41.462,19.288,-0.004,-0.585,1.0225,eac56e71344f6b35fd55ab1e3e8e3e9ed952306e,256,V3,eac56e71344f6b35fd55ab1e3e8e3e9ed952306e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-10.691_ry+48.538_rz+19.288_tx-00.004_ty-00.585_s+01.227_eac56e71344f6b35fd55ab1e3e8e3e9ed952306e_256x256.png,196849ea3b76b5f1a8cdcdfeb53c6224bf86bce2,Boats,-10.691,48.538,19.288,2382 -2383,cruiser,-12.632,18.009,-23.28,-0.078,0.058,1.27,b5cf91779233cbc3c6a3fa33c10dfd8ba404fccf,256,V3,b5cf91779233cbc3c6a3fa33c10dfd8ba404fccf_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-12.632_ry+108.009_rz-23.280_tx-00.078_ty+00.058_s+01.524_b5cf91779233cbc3c6a3fa33c10dfd8ba404fccf_256x256.png,12126654c877c606c8c63e409b1d0c3b4ef58e90,Boats,-12.632,108.009,-23.28,2383 -2384,cruiser,-14.808,35.441,11.671,0.102,-0.551,1.23833333333,8ef5db7bbc54b612d1cece038dcf85e061018b2f,256,V3,8ef5db7bbc54b612d1cece038dcf85e061018b2f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-14.808_ry+125.441_rz+11.671_tx+00.102_ty-00.551_s+01.486_8ef5db7bbc54b612d1cece038dcf85e061018b2f_256x256.png,cd0e4fc57bc58b96f245535af7412e21051df3d8,Boats,-14.808,125.441,11.671,2384 -2385,cruiser,-15.907,30.851,18.04,0.149,-0.365,1.28833333333,d260982d381978b4fc64f770dfe7ee3876379178,256,V3,d260982d381978b4fc64f770dfe7ee3876379178_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-15.907_ry+120.851_rz+18.040_tx+00.149_ty-00.365_s+01.546_d260982d381978b4fc64f770dfe7ee3876379178_256x256.png,33abd1468f1a101e0d209cd2b1a012884734a9e7,Boats,-15.907,120.851,18.04,2385 -2386,cruiser,-16.75,32.761,-14.091,-0.163,0.264,1.22083333333,5b85555d681a4e1c05cf39052c1610b7292d1038,256,V3,5b85555d681a4e1c05cf39052c1610b7292d1038_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-16.750_ry+122.761_rz-14.091_tx-00.163_ty+00.264_s+01.465_5b85555d681a4e1c05cf39052c1610b7292d1038_256x256.png,42b48a1e3f60b21cc0dbd7314ce4eb434627fdcb,Boats,-16.75,122.761,-14.091,2386 -2387,cruiser,-19.891,25.695,-5.262,0.109,0.416,0.786666666667,808bfe16b1126cdd0c4e72f04108804cff95e542,256,V3,808bfe16b1126cdd0c4e72f04108804cff95e542_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-19.891_ry+115.695_rz-05.262_tx+00.109_ty+00.416_s+00.944_808bfe16b1126cdd0c4e72f04108804cff95e542_256x256.png,607d97c93a7676406d2e17fc633f630f46023b3b,Boats,-19.891,115.695,-5.262,2387 -2388,cruiser,-20.985,0.864,-38.372,-0.282,-0.142,1.07083333333,f3ef1102aaf4180790b4c086a871c5543d2fff87,256,V3,f3ef1102aaf4180790b4c086a871c5543d2fff87_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-20.985_ry+90.864_rz-38.372_tx-00.282_ty-00.142_s+01.285_f3ef1102aaf4180790b4c086a871c5543d2fff87_256x256.png,d0bf99a1d8fd07ded12e0af8ffe0417499917bca,Boats,-20.985,90.864,-38.372,2388 -2389,cruiser,-22.706,-38.315,13.848,-0.069,-0.321,1.17083333333,2a48f5e31e03965ee3ec040fab130ab17a853fac,256,V3,2a48f5e31e03965ee3ec040fab130ab17a853fac_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-22.706_ry+51.685_rz+13.848_tx-00.069_ty-00.321_s+01.405_2a48f5e31e03965ee3ec040fab130ab17a853fac_256x256.png,2689918b11b03c32603124f026856b04afe939e2,Boats,-22.706,51.685,13.848,2389 -2390,cruiser,-25.388,-16.094,7.027,-0.091,0.209,0.831666666667,3ef5fe59b1d20c6b27b8264ef37c072bb64563c9,256,V3,3ef5fe59b1d20c6b27b8264ef37c072bb64563c9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-25.388_ry+73.906_rz+07.027_tx-00.091_ty+00.209_s+00.998_3ef5fe59b1d20c6b27b8264ef37c072bb64563c9_256x256.png,61f6da9b3ebcdcde57fbcd953a39d3b823940979,Boats,-25.388,73.906,7.027,2390 -2391,cruiser,-25.85,-9.422,-10.442,-0.056,-0.546,1.11833333333,0cbc59f1c6d91413bfd4023300efdf33cdfdf710,256,V3,0cbc59f1c6d91413bfd4023300efdf33cdfdf710_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-25.850_ry+80.578_rz-10.442_tx-00.056_ty-00.546_s+01.342_0cbc59f1c6d91413bfd4023300efdf33cdfdf710_256x256.png,ed69325087722a30425529665e9f916ede7de814,Boats,-25.85,80.578,-10.442,2391 -2392,cruiser,-29.447,21.224,3.615,-0.267,-0.024,0.864166666667,c828ad7c1c2300a14a07869597df22bc9fc6bedc,256,V3,c828ad7c1c2300a14a07869597df22bc9fc6bedc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-29.447_ry+111.224_rz+03.615_tx-00.267_ty-00.024_s+01.037_c828ad7c1c2300a14a07869597df22bc9fc6bedc_256x256.png,7e37b74cd9ac7f72662d9bfaa8c102cf09f5c6bd,Boats,-29.447,111.224,3.615,2392 -2393,cruiser,-30.814,-39.153,-28.816,-0.14,-0.201,1.17083333333,45fb30ab0f302ca0c5c381681dee2fb7aa293178,256,V3,45fb30ab0f302ca0c5c381681dee2fb7aa293178_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-30.814_ry+50.847_rz-28.816_tx-00.140_ty-00.201_s+01.405_45fb30ab0f302ca0c5c381681dee2fb7aa293178_256x256.png,1b63cbb292171e4013d970a0e637819606132de6,Boats,-30.814,50.847,-28.816,2393 -2394,cruiser,-36.756,-12.833,19.214,-0.05,0.516,1.23,dcf8b9b9a7c4312f87b6717000a6ef32b1a3d996,256,V3,dcf8b9b9a7c4312f87b6717000a6ef32b1a3d996_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-36.756_ry+77.167_rz+19.214_tx-00.050_ty+00.516_s+01.476_dcf8b9b9a7c4312f87b6717000a6ef32b1a3d996_256x256.png,213f5bf3d0a7e44a2d6f3f4b27f83b14f54d37b7,Boats,-36.756,77.167,19.214,2394 -2395,cruiser,-36.986,-21.725,6.812,-0.199,-0.39,1.21083333333,47e912f7d929aa10746aa57b7e52d2f3ba570fad,256,V3,47e912f7d929aa10746aa57b7e52d2f3ba570fad_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-36.986_ry+68.275_rz+06.812_tx-00.199_ty-00.390_s+01.453_47e912f7d929aa10746aa57b7e52d2f3ba570fad_256x256.png,a0176e29d27e8b82acf14108a375ac958956195a,Boats,-36.986,68.275,6.812,2395 -2396,cruiser,-38.673,21.386,-34.318,0.115,-0.289,0.9425,24416e6e628d9b7d54f1fade30870e39feae48ee,256,V3,24416e6e628d9b7d54f1fade30870e39feae48ee_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-38.673_ry+111.386_rz-34.318_tx+00.115_ty-00.289_s+01.131_24416e6e628d9b7d54f1fade30870e39feae48ee_256x256.png,c2e8c2fd41542d979881be1759e7cb6b905b5606,Boats,-38.673,111.386,-34.318,2396 -2397,cruiser,-40.226,29.296,37.127,0.101,-0.059,0.835833333333,ccbd9b817580ecfa34e4e9e575975d3344fa93b8,256,V3,ccbd9b817580ecfa34e4e9e575975d3344fa93b8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-40.226_ry+119.296_rz+37.127_tx+00.101_ty-00.059_s+01.003_ccbd9b817580ecfa34e4e9e575975d3344fa93b8_256x256.png,22eb706c9f2ab239f0c392a8fceff62a061e8c2a,Boats,-40.226,119.296,37.127,2397 -2398,cruiser,-41.547,-39.82,-7.832,-0.136,0.053,0.780833333333,39df3d276d9710f25667c66e85af1096d74af4f6,256,V3,39df3d276d9710f25667c66e85af1096d74af4f6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-41.547_ry+50.180_rz-07.832_tx-00.136_ty+00.053_s+00.937_39df3d276d9710f25667c66e85af1096d74af4f6_256x256.png,492857eb6f049218caaaece0bc0683db5c3a5723,Boats,-41.547,50.18,-7.832,2398 -2399,cruiser,-43.871,7.479,31.896,-0.031,-0.042,1.2375,c68a04989e992c91e18f4e2bbf6c0a098764886f,256,V3,c68a04989e992c91e18f4e2bbf6c0a098764886f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/cruiser_rx-43.871_ry+97.479_rz+31.896_tx-00.031_ty-00.042_s+01.485_c68a04989e992c91e18f4e2bbf6c0a098764886f_256x256.png,bca29d68c1a3dc15008c46641ec9ab68af61afbe,Boats,-43.871,97.479,31.896,2399 -2400,f16,5.665,41.802,-39.188,0.294,-0.423,1.12384615385,50b0af3dc7eb9af4f6f07edf71061c0a1abe9531,256,V3,50b0af3dc7eb9af4f6f07edf71061c0a1abe9531_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+05.665_ry+41.802_rz-39.188_tx+00.294_ty-00.423_s+01.461_50b0af3dc7eb9af4f6f07edf71061c0a1abe9531_256x256.png,f939b3600159d36a08149283a2f534f7a9ec5470,Planes,5.665,131.802,-39.188,2400 -2401,f16,10.684,0.746,-23.951,0.098,0.474,0.796153846154,942e9da22daa04cf02af7f9ca7e293eca7eb601f,256,V3,942e9da22daa04cf02af7f9ca7e293eca7eb601f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+10.684_ry+00.746_rz-23.951_tx+00.098_ty+00.474_s+01.035_942e9da22daa04cf02af7f9ca7e293eca7eb601f_256x256.png,c74f9c78c6eb9c34c41057d1134a83de712114db,Planes,10.684,90.746,-23.951,2401 -2402,f16,10.792,-25.798,7.484,0.168,0.59,0.791538461538,702101dc2fb6cf87eba0b07025c1ea9e3fe156e6,256,V3,702101dc2fb6cf87eba0b07025c1ea9e3fe156e6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+10.792_ry-25.798_rz+07.484_tx+00.168_ty+00.590_s+01.029_702101dc2fb6cf87eba0b07025c1ea9e3fe156e6_256x256.png,c4e54fb54af7d029a5893839f9488ee794d45e35,Planes,10.792,64.202,7.484,2402 -2403,f16,10.993,12.715,-20.706,0.266,0.435,1.24307692308,f1e31633e0e7d4f2117284f60a9959d16aec5320,256,V3,f1e31633e0e7d4f2117284f60a9959d16aec5320_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+10.993_ry+12.715_rz-20.706_tx+00.266_ty+00.435_s+01.616_f1e31633e0e7d4f2117284f60a9959d16aec5320_256x256.png,3580b6d7efe4d7405e95568551bdb6a107194091,Planes,10.993,102.715,-20.706,2403 -2404,f16,12.905,32.695,-24.902,0.092,-0.027,1.00692307692,9ef9b08af22543a9aa4858b95022acf5c1508016,256,V3,9ef9b08af22543a9aa4858b95022acf5c1508016_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+12.905_ry+32.695_rz-24.902_tx+00.092_ty-00.027_s+01.309_9ef9b08af22543a9aa4858b95022acf5c1508016_256x256.png,e035d2b7f6ad241bc1bc88e2ef61d49e29dbb525,Planes,12.905,122.695,-24.902,2404 -2405,f16,18.243,24.726,-2.215,-0.278,0.558,1.12538461538,40ac1431f1a3264b41c1eac38af2770d4247b6c3,256,V3,40ac1431f1a3264b41c1eac38af2770d4247b6c3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+18.243_ry+24.726_rz-02.215_tx-00.278_ty+00.558_s+01.463_40ac1431f1a3264b41c1eac38af2770d4247b6c3_256x256.png,6d5ed81cc975befc2134c8192344cf49710e44bf,Planes,18.243,114.726,-2.215,2405 -2406,f16,22.873,16.647,24.235,0.299,0.533,1.16307692308,91860792afc325889700bb9d06e871b034f10611,256,V3,91860792afc325889700bb9d06e871b034f10611_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+22.873_ry+16.647_rz+24.235_tx+00.299_ty+00.533_s+01.512_91860792afc325889700bb9d06e871b034f10611_256x256.png,a691330d4b66ae34aab6c9cc346ddd0152bc7fc0,Planes,22.873,106.647,24.235,2406 -2407,f16,23.43,-14.955,-9.42,0.286,0.322,1.18846153846,52e93c414a9293bab787c7d924800b56562616d5,256,V3,52e93c414a9293bab787c7d924800b56562616d5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+23.430_ry-14.955_rz-09.420_tx+00.286_ty+00.322_s+01.545_52e93c414a9293bab787c7d924800b56562616d5_256x256.png,9c53e93a353602e83ff8b26855eabd236df7d70f,Planes,23.43,75.045,-9.42,2407 -2408,f16,27.981,34.784,-43.345,0.258,0.123,1.15230769231,b758746e3ee3fbd5e88df26f6e063c84702c9aab,256,V3,b758746e3ee3fbd5e88df26f6e063c84702c9aab_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+27.981_ry+34.784_rz-43.345_tx+00.258_ty+00.123_s+01.498_b758746e3ee3fbd5e88df26f6e063c84702c9aab_256x256.png,6de09264627324462279b59fdfb39161df680d86,Planes,27.981,124.784,-43.345,2408 -2409,f16,27.981,-26.039,6.481,0.03,0.247,1.22923076923,92129516ffec31eadaf052618132f5c08cc1dee5,256,V3,92129516ffec31eadaf052618132f5c08cc1dee5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+27.981_ry-26.039_rz+06.481_tx+00.030_ty+00.247_s+01.598_92129516ffec31eadaf052618132f5c08cc1dee5_256x256.png,db05433b8af5fb773a5bdb49cd28729eef69b4e5,Planes,27.981,63.961,6.481,2409 -2410,f16,33.832,-38.617,10.202,-0.299,0.07,0.943846153846,2f7c4448f01901a7d0d4b9da09633a8a23dfde69,256,V3,2f7c4448f01901a7d0d4b9da09633a8a23dfde69_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+33.832_ry-38.617_rz+10.202_tx-00.299_ty+00.070_s+01.227_2f7c4448f01901a7d0d4b9da09633a8a23dfde69_256x256.png,1f23350f7ad2b34eca21126f669f461f3aec7981,Planes,33.832,51.383,10.202,2410 -2411,f16,34.313,-22.411,-26.977,0.294,0.488,1.12846153846,e64f7b5609959f96fe8096d6ec7eb87a85d0c4c9,256,V3,e64f7b5609959f96fe8096d6ec7eb87a85d0c4c9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+34.313_ry-22.411_rz-26.977_tx+00.294_ty+00.488_s+01.467_e64f7b5609959f96fe8096d6ec7eb87a85d0c4c9_256x256.png,7859a3f7adbe745dda45c1e1294fe15520480a8c,Planes,34.313,67.589,-26.977,2411 -2412,f16,39.394,22.577,0.757,0.29,-0.53,1.01846153846,a761405e11a29fcb3b1cea4d35b2a690dc1af993,256,V3,a761405e11a29fcb3b1cea4d35b2a690dc1af993_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+39.394_ry+22.577_rz+00.757_tx+00.290_ty-00.530_s+01.324_a761405e11a29fcb3b1cea4d35b2a690dc1af993_256x256.png,ba21a086db4cad7ed508999ff6b9a22437e95d81,Planes,39.394,112.577,0.757,2412 -2413,f16,41.297,-28.561,0.806,0.188,0.181,1.07,4ffc90d6e48de24297195daa7ceaf5a4d0578649,256,V3,4ffc90d6e48de24297195daa7ceaf5a4d0578649_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+41.297_ry-28.561_rz+00.806_tx+00.188_ty+00.181_s+01.391_4ffc90d6e48de24297195daa7ceaf5a4d0578649_256x256.png,96d73cb743240435e34dccad3039bec6fb70ea2f,Planes,41.297,61.439,0.806,2413 -2414,f16,43.1,3.839,3.918,-0.026,0.551,1.27538461538,8c2735a7d3e1cce9d170b5280722795e785949d4,256,V3,8c2735a7d3e1cce9d170b5280722795e785949d4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+43.100_ry+03.839_rz+03.918_tx-00.026_ty+00.551_s+01.658_8c2735a7d3e1cce9d170b5280722795e785949d4_256x256.png,1f1e123b7901fdca62f814f67f020fc267237427,Planes,43.1,93.839,3.918,2414 -2415,f16,43.304,-26.71,-8.693,0.176,-0.456,1.05384615385,5cee8579b212dd22f3c48e47e8568d3e1f793328,256,V3,5cee8579b212dd22f3c48e47e8568d3e1f793328_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx+43.304_ry-26.710_rz-08.693_tx+00.176_ty-00.456_s+01.370_5cee8579b212dd22f3c48e47e8568d3e1f793328_256x256.png,c6cc4e49aa5c2cb62133882d93e769cf24a9c7ff,Planes,43.304,63.29,-8.693,2415 -2416,f16,-2.011,9.01,-44.156,0.02,0.048,1.22461538462,863817582510964277983a0bee124e90f70a703c,256,V3,863817582510964277983a0bee124e90f70a703c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-02.011_ry+09.010_rz-44.156_tx+00.020_ty+00.048_s+01.592_863817582510964277983a0bee124e90f70a703c_256x256.png,fdddc1140268aaa40e6dea8cfbc808cc9f49e3a6,Planes,-2.011,99.01,-44.156,2416 -2417,f16,-2.193,-32.553,40.672,-0.259,0.192,0.875384615385,7a946d53ea0e9606491ad78c0955fa0020da2045,256,V3,7a946d53ea0e9606491ad78c0955fa0020da2045_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-02.193_ry-32.553_rz+40.672_tx-00.259_ty+00.192_s+01.138_7a946d53ea0e9606491ad78c0955fa0020da2045_256x256.png,ec2c345588df6830530548c8fc407a6481c3ec07,Planes,-2.193,57.447,40.672,2417 -2418,f16,-2.545,-7.658,-40.766,-0.004,-0.314,1.19076923077,5dcfc731d7fd492f66d99096c3e0b5fb87d36fd0,256,V3,5dcfc731d7fd492f66d99096c3e0b5fb87d36fd0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-02.545_ry-07.658_rz-40.766_tx-00.004_ty-00.314_s+01.548_5dcfc731d7fd492f66d99096c3e0b5fb87d36fd0_256x256.png,9a930cfdb87342aa6abc07fa3204134da0c9d2c6,Planes,-2.545,82.342,-40.766,2418 -2419,f16,-3.849,-9.458,28.508,-0.219,-0.193,1.17538461538,bd191c499831d0752b337c3001bd41eb66d014b2,256,V3,bd191c499831d0752b337c3001bd41eb66d014b2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-03.849_ry-09.458_rz+28.508_tx-00.219_ty-00.193_s+01.528_bd191c499831d0752b337c3001bd41eb66d014b2_256x256.png,b5975f37868e33e718b59e9f2242f0373c4f79f8,Planes,-3.849,80.542,28.508,2419 -2420,f16,-5.314,-4.172,29.497,0.258,0.512,1.12461538462,09223e16fe591c76fdec1ceabbc7e2d36d9678d2,256,V3,09223e16fe591c76fdec1ceabbc7e2d36d9678d2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-05.314_ry-04.172_rz+29.497_tx+00.258_ty+00.512_s+01.462_09223e16fe591c76fdec1ceabbc7e2d36d9678d2_256x256.png,ec75c83f63c43c9afc27bd891c0d17dcb26ac958,Planes,-5.314,85.828,29.497,2420 -2421,f16,-6.734,-39.237,33.767,0.158,0.176,1.14769230769,771e5ab1f204905bf03d06936b01bb5f4de92788,256,V3,771e5ab1f204905bf03d06936b01bb5f4de92788_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-06.734_ry-39.237_rz+33.767_tx+00.158_ty+00.176_s+01.492_771e5ab1f204905bf03d06936b01bb5f4de92788_256x256.png,fea8f5812b0b5cfd9b9053419064ccaa670c34b2,Planes,-6.734,50.763,33.767,2421 -2422,f16,-11.382,-0.711,31.254,-0.165,-0.498,1.19923076923,aa581bba4aaaa69adc21107d13f49f329932a5cb,256,V3,aa581bba4aaaa69adc21107d13f49f329932a5cb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-11.382_ry-00.711_rz+31.254_tx-00.165_ty-00.498_s+01.559_aa581bba4aaaa69adc21107d13f49f329932a5cb_256x256.png,256c8638d0d8a190bb1ec2228aee9fe05c3ec503,Planes,-11.382,89.289,31.254,2422 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-2429,f16,-23.609,-43.204,31.389,0.067,-0.259,1.29923076923,dedeb63aba0d5e4dd43baf615c8fbf23b775c933,256,V3,dedeb63aba0d5e4dd43baf615c8fbf23b775c933_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-23.609_ry-43.204_rz+31.389_tx+00.067_ty-00.259_s+01.689_dedeb63aba0d5e4dd43baf615c8fbf23b775c933_256x256.png,0f2f181bd4c79913d6daadf7897b1a47da97260b,Planes,-23.609,46.796,31.389,2429 -2430,f16,-24.186,-11.391,-26.475,0.067,-0.491,0.865384615385,9e44cdecc8dc4e13de70a527d9940bfdd48a0a7d,256,V3,9e44cdecc8dc4e13de70a527d9940bfdd48a0a7d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-24.186_ry-11.391_rz-26.475_tx+00.067_ty-00.491_s+01.125_9e44cdecc8dc4e13de70a527d9940bfdd48a0a7d_256x256.png,9ee68e859bb4f6def4bc2391f6f1cf5b1648c67d,Planes,-24.186,78.609,-26.475,2430 -2431,f16,-26.974,8.367,-28.384,0.262,0.346,1.15076923077,1db322309bb86883e1fd1ddf7d800644dd4fe344,256,V3,1db322309bb86883e1fd1ddf7d800644dd4fe344_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-26.974_ry+08.367_rz-28.384_tx+00.262_ty+00.346_s+01.496_1db322309bb86883e1fd1ddf7d800644dd4fe344_256x256.png,fc907915066c72f377712f54ed153d0803537de9,Planes,-26.974,98.367,-28.384,2431 -2432,f16,-26.984,-15.783,-14.403,0.219,0.011,1.04307692308,581c1859b2f10b28b21e3a29cd003fbd66e7c3b4,256,V3,581c1859b2f10b28b21e3a29cd003fbd66e7c3b4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-26.984_ry-15.783_rz-14.403_tx+00.219_ty+00.011_s+01.356_581c1859b2f10b28b21e3a29cd003fbd66e7c3b4_256x256.png,01857e4c218fcfde7871dec239a131c545f66bce,Planes,-26.984,74.217,-14.403,2432 -2433,f16,-27.04,5.676,-8.855,-0.236,0.379,0.819230769231,426d6a42d816ee9b2b426d0ac5f33c71adcccd7c,256,V3,426d6a42d816ee9b2b426d0ac5f33c71adcccd7c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-27.040_ry+05.676_rz-08.855_tx-00.236_ty+00.379_s+01.065_426d6a42d816ee9b2b426d0ac5f33c71adcccd7c_256x256.png,44f390a06d4c0c9ff28227ca57641b5a605758fe,Planes,-27.04,95.676,-8.855,2433 -2434,f16,-27.435,-4.073,-41.611,0.246,0.181,0.869230769231,901f88e73d35a774b583f57e90a508e5b1e32b66,256,V3,901f88e73d35a774b583f57e90a508e5b1e32b66_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-27.435_ry-04.073_rz-41.611_tx+00.246_ty+00.181_s+01.130_901f88e73d35a774b583f57e90a508e5b1e32b66_256x256.png,bb779bbb06b54461cf03676a6338e5cf2a9aa503,Planes,-27.435,85.927,-41.611,2434 -2435,f16,-39.169,41.672,27.127,-0.201,0.556,1.08384615385,c4baa26183f6df8b340d3210df71fa53456be604,256,V3,c4baa26183f6df8b340d3210df71fa53456be604_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-39.169_ry+41.672_rz+27.127_tx-00.201_ty+00.556_s+01.409_c4baa26183f6df8b340d3210df71fa53456be604_256x256.png,0bf6ba975054c9d0387fcbf03bc8f2675f2fc1d1,Planes,-39.169,131.672,27.127,2435 -2436,f16,-39.941,-31.945,-0.75,0.003,0.392,1.22307692308,d9a49c31fcabfd120e65dda216e2ca974780e3b9,256,V3,d9a49c31fcabfd120e65dda216e2ca974780e3b9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-39.941_ry-31.945_rz-00.750_tx+00.003_ty+00.392_s+01.590_d9a49c31fcabfd120e65dda216e2ca974780e3b9_256x256.png,ba7db1e0b8a303d219e20ab7bb093c6cd583ad95,Planes,-39.941,58.055,-0.75,2436 -2437,f16,-41.763,30.238,-29.677,0.145,0.279,1.28230769231,de421076bacce760a4a2594e7a49a808efac80b7,256,V3,de421076bacce760a4a2594e7a49a808efac80b7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-41.763_ry+30.238_rz-29.677_tx+00.145_ty+00.279_s+01.667_de421076bacce760a4a2594e7a49a808efac80b7_256x256.png,766b3a30ab85d5ac0cf0f7d6b510868047fb225a,Planes,-41.763,120.238,-29.677,2437 -2438,f16,-42.748,-2.564,32.178,-0.114,0.597,1.26,4a76fe3bf71a6273b6584b4f0c71b4de466be12f,256,V3,4a76fe3bf71a6273b6584b4f0c71b4de466be12f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-42.748_ry-02.564_rz+32.178_tx-00.114_ty+00.597_s+01.638_4a76fe3bf71a6273b6584b4f0c71b4de466be12f_256x256.png,f854e0690f0ddd205f4544f1ff550528f4bef3f0,Planes,-42.748,87.436,32.178,2438 -2439,f16,-43.673,1.299,21.593,0.138,0.305,1.15,089c43bf56a20fd538474941811b78cabcb111ce,256,V3,089c43bf56a20fd538474941811b78cabcb111ce_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/f16_rx-43.673_ry+01.299_rz+21.593_tx+00.138_ty+00.305_s+01.495_089c43bf56a20fd538474941811b78cabcb111ce_256x256.png,8ee2a7321ef7956b0eacd0556089b81275804e65,Planes,-43.673,91.299,21.593,2439 -2440,face0001,4.572,33.129,-6.834,-0.298,0.332,1.0975,149daae52720250e24c2a8009f4d4635189d0108,256,V3,149daae52720250e24c2a8009f4d4635189d0108_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/face0001_rx+04.572_ry+33.129_rz-06.834_tx-00.298_ty+00.332_s+00.878_149daae52720250e24c2a8009f4d4635189d0108_256x256.png,fa2791ad22a47ddb9458fb2f7094fe529caecbe8,Faces,4.572,33.129,-6.834,2440 -2441,face0001,12.954,26.275,-36.699,0.177,-0.095,0.87375,57d332a8440128afba2d9b2733954782c0cab7af,256,V3,57d332a8440128afba2d9b2733954782c0cab7af_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/face0001_rx+12.954_ry+26.275_rz-36.699_tx+00.177_ty-00.095_s+00.699_57d332a8440128afba2d9b2733954782c0cab7af_256x256.png,764abf1d574ebfa0c53374dd4fc1ba3183d8b2b3,Faces,12.954,26.275,-36.699,2441 -2442,face0001,14.808,6.467,6.987,-0.086,0.505,1.21375,9bf0bceb6e0e4d5a10573044d029931d3bcc8ae3,256,V3,9bf0bceb6e0e4d5a10573044d029931d3bcc8ae3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/face0001_rx+14.808_ry+06.467_rz+06.987_tx-00.086_ty+00.505_s+00.971_9bf0bceb6e0e4d5a10573044d029931d3bcc8ae3_256x256.png,443b4e061541080b13a3dd2329cbe33f5ccada73,Faces,14.808,6.467,6.987,2442 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-2761,hedgehog,8.52,-0.508,-37.58,-0.133,-0.037,1.14875,291d0e12860279c4034f5b82f9a0f0f5d211165f,256,V3,291d0e12860279c4034f5b82f9a0f0f5d211165f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+08.520_ry+89.492_rz-37.580_tx-00.133_ty-00.037_s+00.919_291d0e12860279c4034f5b82f9a0f0f5d211165f_256x256.png,a93d08e9bdd0c5d299d2c413ff78be92a22907d1,Animals,8.52,89.492,-37.58,2761 -2762,hedgehog,10.351,15.176,19.698,0.112,-0.408,1.2825,1bd425daf9ee31d25ce76573690d2b26f24b5bb3,256,V3,1bd425daf9ee31d25ce76573690d2b26f24b5bb3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+10.351_ry+105.176_rz+19.698_tx+00.112_ty-00.408_s+01.026_1bd425daf9ee31d25ce76573690d2b26f24b5bb3_256x256.png,848fff2ba949cf9ff110acff238f184e950029ae,Animals,10.351,105.176,19.698,2762 -2763,hedgehog,10.425,-27.489,-30.845,-0.258,0.001,0.84625,712868893a0119f79684839d5d186f916a6d224b,256,V3,712868893a0119f79684839d5d186f916a6d224b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+10.425_ry+62.511_rz-30.845_tx-00.258_ty+00.001_s+00.677_712868893a0119f79684839d5d186f916a6d224b_256x256.png,f354f0fee3334dcce8fe9cab5c5caaee1c6e9ac2,Animals,10.425,62.511,-30.845,2763 -2764,hedgehog,13.786,28.894,-1.173,-0.259,0.272,0.8675,045ea9658f3e482e3a05a67b2541d7491876047d,256,V3,045ea9658f3e482e3a05a67b2541d7491876047d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+13.786_ry+118.894_rz-01.173_tx-00.259_ty+00.272_s+00.694_045ea9658f3e482e3a05a67b2541d7491876047d_256x256.png,e86a06aeda62d8c8321500693777db836183c3ae,Animals,13.786,118.894,-1.173,2764 -2765,hedgehog,14.155,41.543,12.594,0.124,-0.386,0.82375,d75ce2982a4bac1308844aecf2146fd2c8ee6271,256,V3,d75ce2982a4bac1308844aecf2146fd2c8ee6271_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+14.155_ry+131.543_rz+12.594_tx+00.124_ty-00.386_s+00.659_d75ce2982a4bac1308844aecf2146fd2c8ee6271_256x256.png,989a182ade8b56de336a70d7117222f397d75f42,Animals,14.155,131.543,12.594,2765 -2766,hedgehog,14.646,35.372,-35.614,-0.15,-0.244,0.9325,365e2ef9ce9f46aa3157d12045991ef9988aab9b,256,V3,365e2ef9ce9f46aa3157d12045991ef9988aab9b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+14.646_ry+125.372_rz-35.614_tx-00.150_ty-00.244_s+00.746_365e2ef9ce9f46aa3157d12045991ef9988aab9b_256x256.png,3750394480b74d5fd53a9b5843b30c106f015dea,Animals,14.646,125.372,-35.614,2766 -2767,hedgehog,16.578,3.415,27.089,-0.173,-0.227,1.04375,609313990c44eaf07ed7fe8b958a60344af6e940,256,V3,609313990c44eaf07ed7fe8b958a60344af6e940_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+16.578_ry+93.415_rz+27.089_tx-00.173_ty-00.227_s+00.835_609313990c44eaf07ed7fe8b958a60344af6e940_256x256.png,3620951d542387881cc208195240b19a7b18993d,Animals,16.578,93.415,27.089,2767 -2768,hedgehog,19.668,-10.947,44.628,-0.093,0.054,0.85375,e669c50b36e318274596e63aca57633f86992d0b,256,V3,e669c50b36e318274596e63aca57633f86992d0b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+19.668_ry+79.053_rz+44.628_tx-00.093_ty+00.054_s+00.683_e669c50b36e318274596e63aca57633f86992d0b_256x256.png,1a7f242964f95c05a51aff07307971eef960dc81,Animals,19.668,79.053,44.628,2768 -2769,hedgehog,22.957,-23.472,-26.138,-0.114,-0.474,1.06625,cc582e985e6019441baefba6c228b92a26ddbed9,256,V3,cc582e985e6019441baefba6c228b92a26ddbed9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+22.957_ry+66.528_rz-26.138_tx-00.114_ty-00.474_s+00.853_cc582e985e6019441baefba6c228b92a26ddbed9_256x256.png,0db5e4b7224e6078ed496adadd00b813eef2cf55,Animals,22.957,66.528,-26.138,2769 -2770,hedgehog,26.415,-38.053,-6.495,0.022,-0.172,1.16125,8c507e2e93d79888f54192f9a302aa4a383bbab2,256,V3,8c507e2e93d79888f54192f9a302aa4a383bbab2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+26.415_ry+51.947_rz-06.495_tx+00.022_ty-00.172_s+00.929_8c507e2e93d79888f54192f9a302aa4a383bbab2_256x256.png,23fd3d01b06c58d5c73f9a4c4714f42382dbb607,Animals,26.415,51.947,-6.495,2770 -2771,hedgehog,27.863,1.475,42.846,-0.053,-0.304,1.0775,8e90b31aa9e580794fe915678d37f8958ce08c4f,256,V3,8e90b31aa9e580794fe915678d37f8958ce08c4f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+27.863_ry+91.475_rz+42.846_tx-00.053_ty-00.304_s+00.862_8e90b31aa9e580794fe915678d37f8958ce08c4f_256x256.png,ab7f260f1892fa63d43ea7c2167c354263eda974,Animals,27.863,91.475,42.846,2771 -2772,hedgehog,28.271,-41.814,21.028,0.163,-0.306,1.19125,30ad9d8f940f71c83f12631d7300ff42d783a26b,256,V3,30ad9d8f940f71c83f12631d7300ff42d783a26b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+28.271_ry+48.186_rz+21.028_tx+00.163_ty-00.306_s+00.953_30ad9d8f940f71c83f12631d7300ff42d783a26b_256x256.png,8f39e66d41ad56cf3ae739a0bc7c5fb43f229631,Animals,28.271,48.186,21.028,2772 -2773,hedgehog,31.931,27.97,14.777,0.033,0.062,0.92625,6714fcf1a70dc7a65023146765eb535ea0ba835d,256,V3,6714fcf1a70dc7a65023146765eb535ea0ba835d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+31.931_ry+117.970_rz+14.777_tx+00.033_ty+00.062_s+00.741_6714fcf1a70dc7a65023146765eb535ea0ba835d_256x256.png,abb5ce53a2de056dcbaf0c5706c5a16fe872b9b5,Animals,31.931,117.97,14.777,2773 -2774,hedgehog,35.951,9.228,30.432,0.256,-0.514,0.84125,3895b87b94463bc60b2fb7fc8c9cbade6e319594,256,V3,3895b87b94463bc60b2fb7fc8c9cbade6e319594_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+35.951_ry+99.228_rz+30.432_tx+00.256_ty-00.514_s+00.673_3895b87b94463bc60b2fb7fc8c9cbade6e319594_256x256.png,1b8051bd1a4efd816e8b56160a36e297f6d1fb4e,Animals,35.951,99.228,30.432,2774 -2775,hedgehog,39.09,16.342,-13.514,-0.029,-0.015,0.86,3d11432f61b65864a049fab76920c71eb3751b03,256,V3,3d11432f61b65864a049fab76920c71eb3751b03_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+39.090_ry+106.342_rz-13.514_tx-00.029_ty-00.015_s+00.688_3d11432f61b65864a049fab76920c71eb3751b03_256x256.png,bffd6700961f9fea49da847b9cb88dddaa20cee2,Animals,39.09,106.342,-13.514,2775 -2776,hedgehog,39.41,-23.516,-29.324,-0.247,-0.263,0.87625,14210a653254bf43a379c00f630d5fb5ed14f732,256,V3,14210a653254bf43a379c00f630d5fb5ed14f732_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+39.410_ry+66.484_rz-29.324_tx-00.247_ty-00.263_s+00.701_14210a653254bf43a379c00f630d5fb5ed14f732_256x256.png,a335a03c5eaf697cd61b3265888b6f732113ddd7,Animals,39.41,66.484,-29.324,2776 -2777,hedgehog,44.474,-40.826,30.695,-0.19,-0.274,1.11125,78ba01c06f6adc1aee048eb7a35769e690f5eaa0,256,V3,78ba01c06f6adc1aee048eb7a35769e690f5eaa0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx+44.474_ry+49.174_rz+30.695_tx-00.190_ty-00.274_s+00.889_78ba01c06f6adc1aee048eb7a35769e690f5eaa0_256x256.png,c91c3ab8ba1f86ed47a7a607efe6f891b6a03683,Animals,44.474,49.174,30.695,2777 -2778,hedgehog,-8.334,-31.793,19.571,-0.234,-0.184,0.91125,55227b4208820e2a5f2d5fed991283f0f466c906,256,V3,55227b4208820e2a5f2d5fed991283f0f466c906_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-08.334_ry+58.207_rz+19.571_tx-00.234_ty-00.184_s+00.729_55227b4208820e2a5f2d5fed991283f0f466c906_256x256.png,2b3f85f8e63eea51ff0b7896fd1eeaf66e983381,Animals,-8.334,58.207,19.571,2778 -2779,hedgehog,-8.396,-10.187,36.28,-0.155,0.392,1.0725,5bab103e9746016525f9996b59d2c583f452409f,256,V3,5bab103e9746016525f9996b59d2c583f452409f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-08.396_ry+79.813_rz+36.280_tx-00.155_ty+00.392_s+00.858_5bab103e9746016525f9996b59d2c583f452409f_256x256.png,3a6b55ee2008f8e28f77ef447afa3d1b72229b88,Animals,-8.396,79.813,36.28,2779 -2780,hedgehog,-10.82,-25.648,39.178,0.004,-0.164,0.8625,61fe31f3fc91f424b8b283d6e1f53c626eac5f8b,256,V3,61fe31f3fc91f424b8b283d6e1f53c626eac5f8b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-10.820_ry+64.352_rz+39.178_tx+00.004_ty-00.164_s+00.690_61fe31f3fc91f424b8b283d6e1f53c626eac5f8b_256x256.png,f12e95758095b7bd7a0053c529a2fb456adc0cba,Animals,-10.82,64.352,39.178,2780 -2781,hedgehog,-11.95,12.321,37.273,-0.202,0.385,0.93125,271820144f9c755a36030dc159c424a21a04b620,256,V3,271820144f9c755a36030dc159c424a21a04b620_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-11.950_ry+102.321_rz+37.273_tx-00.202_ty+00.385_s+00.745_271820144f9c755a36030dc159c424a21a04b620_256x256.png,f04bff6b9b21d316a49e2d66939c04aaa6b6beb3,Animals,-11.95,102.321,37.273,2781 -2782,hedgehog,-12.355,31.743,-18.868,-0.239,-0.367,1.2275,fce297fbb8e9e329f5199e84c4417960a30e43d6,256,V3,fce297fbb8e9e329f5199e84c4417960a30e43d6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-12.355_ry+121.743_rz-18.868_tx-00.239_ty-00.367_s+00.982_fce297fbb8e9e329f5199e84c4417960a30e43d6_256x256.png,5cb8ee66055492327e7c790d9594d7db39222c5f,Animals,-12.355,121.743,-18.868,2782 -2783,hedgehog,-15.046,27.319,30.93,0.117,-0.589,1.02625,825b99366145007cc06ce0ac4c734ecac4b6b4ba,256,V3,825b99366145007cc06ce0ac4c734ecac4b6b4ba_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-15.046_ry+117.319_rz+30.930_tx+00.117_ty-00.589_s+00.821_825b99366145007cc06ce0ac4c734ecac4b6b4ba_256x256.png,f25ebdc213a2ca7d6b696137b2f3992626a16d48,Animals,-15.046,117.319,30.93,2783 -2784,hedgehog,-18.031,17.496,-43.135,-0.062,0.444,0.9775,312eeaaa04ebedc88d031d9b5148851f7b5f0726,256,V3,312eeaaa04ebedc88d031d9b5148851f7b5f0726_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-18.031_ry+107.496_rz-43.135_tx-00.062_ty+00.444_s+00.782_312eeaaa04ebedc88d031d9b5148851f7b5f0726_256x256.png,f3e9289dd2153c34273dcae972a68174a4fe764d,Animals,-18.031,107.496,-43.135,2784 -2785,hedgehog,-19.501,42.219,4.019,0.07,-0.562,1.01875,b70e72c66eee126739245c9ec4a1891e91ded4bb,256,V3,b70e72c66eee126739245c9ec4a1891e91ded4bb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-19.501_ry+132.219_rz+04.019_tx+00.070_ty-00.562_s+00.815_b70e72c66eee126739245c9ec4a1891e91ded4bb_256x256.png,f5d64d24f309513d4dd4306906db21cc18913c9a,Animals,-19.501,132.219,4.019,2785 -2786,hedgehog,-21.863,-26.345,4.835,-0.003,-0.042,0.95375,be53aa0c5757b3a8837147446259c4dce8464795,256,V3,be53aa0c5757b3a8837147446259c4dce8464795_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-21.863_ry+63.655_rz+04.835_tx-00.003_ty-00.042_s+00.763_be53aa0c5757b3a8837147446259c4dce8464795_256x256.png,9d618f5aac6b5394afd5e5c3057be2f655d25cb9,Animals,-21.863,63.655,4.835,2786 -2787,hedgehog,-26.067,-11.613,-40.906,-0.187,0.288,1.23125,85f0a1e318e43b4fa6e3b82eee72770eec3db706,256,V3,85f0a1e318e43b4fa6e3b82eee72770eec3db706_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-26.067_ry+78.387_rz-40.906_tx-00.187_ty+00.288_s+00.985_85f0a1e318e43b4fa6e3b82eee72770eec3db706_256x256.png,cec5adfbbd945074e37e95f0ce061ce437a57b1d,Animals,-26.067,78.387,-40.906,2787 -2788,hedgehog,-26.262,-22.812,11.33,-0.299,-0.26,1.2,4f7c13a88c32ab55e5125946ddce39cc797b45d3,256,V3,4f7c13a88c32ab55e5125946ddce39cc797b45d3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-26.262_ry+67.188_rz+11.330_tx-00.299_ty-00.260_s+00.960_4f7c13a88c32ab55e5125946ddce39cc797b45d3_256x256.png,6b897dd8f700b0c8c0a71570cd687f75a9d69aa0,Animals,-26.262,67.188,11.33,2788 -2789,hedgehog,-31.252,-22.061,-7.198,0.03,-0.526,1.2225,6150a638904812e18ad6fe4e6367add83d8ec35e,256,V3,6150a638904812e18ad6fe4e6367add83d8ec35e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-31.252_ry+67.939_rz-07.198_tx+00.030_ty-00.526_s+00.978_6150a638904812e18ad6fe4e6367add83d8ec35e_256x256.png,32fcba02a27f9f633dce44a1535dfd5706ab8a5d,Animals,-31.252,67.939,-7.198,2789 -2790,hedgehog,-31.725,-12.781,12.724,0.282,0.25,1.27375,6a021c7f291c075d9e5caf661282a72da2898d63,256,V3,6a021c7f291c075d9e5caf661282a72da2898d63_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-31.725_ry+77.219_rz+12.724_tx+00.282_ty+00.250_s+01.019_6a021c7f291c075d9e5caf661282a72da2898d63_256x256.png,0af5067081455fb4af8c2504cfaf4748dcac6c9e,Animals,-31.725,77.219,12.724,2790 -2791,hedgehog,-32.386,-43.734,28.485,-0.054,-0.331,0.98625,68d7d13aa47012ff45717bc5f93c1d4d8ade6d5a,256,V3,68d7d13aa47012ff45717bc5f93c1d4d8ade6d5a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-32.386_ry+46.266_rz+28.485_tx-00.054_ty-00.331_s+00.789_68d7d13aa47012ff45717bc5f93c1d4d8ade6d5a_256x256.png,7c31b0be2a71686933dff6c875cd42da3fe55a6d,Animals,-32.386,46.266,28.485,2791 -2792,hedgehog,-33.289,-4.869,-29.011,0.139,-0.138,0.9675,a545b9071da0d54c33d0f412d30ee2f68fc02fc5,256,V3,a545b9071da0d54c33d0f412d30ee2f68fc02fc5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-33.289_ry+85.131_rz-29.011_tx+00.139_ty-00.138_s+00.774_a545b9071da0d54c33d0f412d30ee2f68fc02fc5_256x256.png,d639a20e8533b1864f715fb5f49e6588b526f688,Animals,-33.289,85.131,-29.011,2792 -2793,hedgehog,-34.29,42.53,-41.532,-0.259,0.494,0.93375,7c40207e3d2cb4219ad8ec17a9d90998dc4df666,256,V3,7c40207e3d2cb4219ad8ec17a9d90998dc4df666_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-34.290_ry+132.530_rz-41.532_tx-00.259_ty+00.494_s+00.747_7c40207e3d2cb4219ad8ec17a9d90998dc4df666_256x256.png,b6c6a22dc147ca1f38c84475419de1c593a2baf8,Animals,-34.29,132.53,-41.532,2793 -2794,hedgehog,-35.283,-20.539,-22.632,0.292,0.287,1.105,914d54489ea85e79ce24a0f14e299d6154890aa0,256,V3,914d54489ea85e79ce24a0f14e299d6154890aa0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-35.283_ry+69.461_rz-22.632_tx+00.292_ty+00.287_s+00.884_914d54489ea85e79ce24a0f14e299d6154890aa0_256x256.png,8d17c5b79a3bcd83d0a5603fbe9aee5aedf8ec32,Animals,-35.283,69.461,-22.632,2794 -2795,hedgehog,-38.469,-33.964,-12.062,-0.044,-0.296,1.1175,d9291961bbed14b5bb75bfe805a86e288e2964da,256,V3,d9291961bbed14b5bb75bfe805a86e288e2964da_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-38.469_ry+56.036_rz-12.062_tx-00.044_ty-00.296_s+00.894_d9291961bbed14b5bb75bfe805a86e288e2964da_256x256.png,f67250e8556741aa7fbb75fedaddcb3404e6f7d2,Animals,-38.469,56.036,-12.062,2795 -2796,hedgehog,-42.561,20.575,-34.238,0.126,-0.363,1.1925,9c85c818cb990e4bc86ff48970eeb2b88e97c4ed,256,V3,9c85c818cb990e4bc86ff48970eeb2b88e97c4ed_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-42.561_ry+110.575_rz-34.238_tx+00.126_ty-00.363_s+00.954_9c85c818cb990e4bc86ff48970eeb2b88e97c4ed_256x256.png,515e2e94aaf2c1b92d036c7cc202a91bd4fa5cde,Animals,-42.561,110.575,-34.238,2796 -2797,hedgehog,-43.18,-5.839,36.01,-0.259,-0.178,1.29,857be447ed335aab92d8e8fb6c6e4c073efb8d4a,256,V3,857be447ed335aab92d8e8fb6c6e4c073efb8d4a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-43.180_ry+84.161_rz+36.010_tx-00.259_ty-00.178_s+01.032_857be447ed335aab92d8e8fb6c6e4c073efb8d4a_256x256.png,5ebf9654bac5e64c4daeb19a02349e57b18ab057,Animals,-43.18,84.161,36.01,2797 -2798,hedgehog,-43.201,29.91,-22.615,0.185,-0.214,1.26875,b7618bfc762b49e21a2dafb953b88f528b2df0ff,256,V3,b7618bfc762b49e21a2dafb953b88f528b2df0ff_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-43.201_ry+119.910_rz-22.615_tx+00.185_ty-00.214_s+01.015_b7618bfc762b49e21a2dafb953b88f528b2df0ff_256x256.png,a0301f9198011feac63c0dd68fceadb2dd4155c4,Animals,-43.201,119.91,-22.615,2798 -2799,hedgehog,-43.459,29.306,-26.429,0.082,-0.248,1.04875,e376079650213537d3c16f11bdcebb812505459f,256,V3,e376079650213537d3c16f11bdcebb812505459f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/hedgehog_rx-43.459_ry+119.306_rz-26.429_tx+00.082_ty-00.248_s+00.839_e376079650213537d3c16f11bdcebb812505459f_256x256.png,2405b51cf8ed1bd4359c74fe811b69b3b973cb8f,Animals,-43.459,119.306,-26.429,2799 -2800,junkers88,1.742,20.586,-22.902,0.124,-0.199,1.09461538462,6f5486ad7ecaa1605ac5fbcad183dcd8b02d17c6,256,V3,6f5486ad7ecaa1605ac5fbcad183dcd8b02d17c6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx+01.742_ry+20.586_rz-22.902_tx+00.124_ty-00.199_s+01.423_6f5486ad7ecaa1605ac5fbcad183dcd8b02d17c6_256x256.png,4ee9b89e38cc96e22d4e5a7bf54fabc63f36b446,Planes,1.742,110.586,-22.902,2800 -2801,junkers88,2.886,43.541,-34.108,-0.099,-0.445,1.24461538462,92becd13af285032ed3cbc249e24c29d2d4f7de8,256,V3,92becd13af285032ed3cbc249e24c29d2d4f7de8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx+02.886_ry+43.541_rz-34.108_tx-00.099_ty-00.445_s+01.618_92becd13af285032ed3cbc249e24c29d2d4f7de8_256x256.png,a746e3ca5dc1145c5cb9a4a6cdd06ba8ef4dd3f9,Planes,2.886,133.541,-34.108,2801 -2802,junkers88,5.642,-32.465,-38.653,-0.149,-0.02,1.16384615385,3d6881be95a926fe0de338708ad5427e524d27d2,256,V3,3d6881be95a926fe0de338708ad5427e524d27d2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx+05.642_ry-32.465_rz-38.653_tx-00.149_ty-00.020_s+01.513_3d6881be95a926fe0de338708ad5427e524d27d2_256x256.png,26f22da13a37faf02e4c94227e2e645b50175376,Planes,5.642,57.535,-38.653,2802 -2803,junkers88,13.686,10.103,24.777,0.227,0.0,1.20153846154,ad75706cc836a59daf170cdf7b6a723656def1a4,256,V3,ad75706cc836a59daf170cdf7b6a723656def1a4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx+13.686_ry+10.103_rz+24.777_tx+00.227_ty+00.000_s+01.562_ad75706cc836a59daf170cdf7b6a723656def1a4_256x256.png,03e76dfb080a4fff3fb31d8a9adca8b422eb43d8,Planes,13.686,100.103,24.777,2803 -2804,junkers88,13.876,23.39,11.451,0.266,0.174,1.07307692308,74011a9f09213fd4cccb9acff5a4866918120dda,256,V3,74011a9f09213fd4cccb9acff5a4866918120dda_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx+13.876_ry+23.390_rz+11.451_tx+00.266_ty+00.174_s+01.395_74011a9f09213fd4cccb9acff5a4866918120dda_256x256.png,51f66cbab8fad414e18d2e8e8932c0ccc1a64a86,Planes,13.876,113.39,11.451,2804 -2805,junkers88,17.769,-12.525,26.761,0.093,0.188,1.01923076923,72e9aed6437e87e2178587a4cbaf860f23db315f,256,V3,72e9aed6437e87e2178587a4cbaf860f23db315f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx+17.769_ry-12.525_rz+26.761_tx+00.093_ty+00.188_s+01.325_72e9aed6437e87e2178587a4cbaf860f23db315f_256x256.png,1578565ee5fa8b64e1345c8621e9c2e2df834f21,Planes,17.769,77.475,26.761,2805 -2806,junkers88,26.084,-31.756,-21.092,0.259,0.021,0.885384615385,d06782c538b8680e0ee431aea236d170763c9a18,256,V3,d06782c538b8680e0ee431aea236d170763c9a18_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx+26.084_ry-31.756_rz-21.092_tx+00.259_ty+00.021_s+01.151_d06782c538b8680e0ee431aea236d170763c9a18_256x256.png,50370a82444109b5a20fec8d67d8b84fca72edcb,Planes,26.084,58.244,-21.092,2806 -2807,junkers88,27.315,28.458,-14.053,0.124,-0.469,1.06307692308,79e75348080854b7305d4f7a397a5552e5858f3d,256,V3,79e75348080854b7305d4f7a397a5552e5858f3d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx+27.315_ry+28.458_rz-14.053_tx+00.124_ty-00.469_s+01.382_79e75348080854b7305d4f7a397a5552e5858f3d_256x256.png,fa5c492df83afb2b6dc174339f1446603cbcbeb1,Planes,27.315,118.458,-14.053,2807 -2808,junkers88,30.661,15.854,13.492,0.287,-0.063,1.27615384615,379531cc6a4346febd64593374721e9d3614e060,256,V3,379531cc6a4346febd64593374721e9d3614e060_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx+30.661_ry+15.854_rz+13.492_tx+00.287_ty-00.063_s+01.659_379531cc6a4346febd64593374721e9d3614e060_256x256.png,cc59a5b51457dc158b25d1e22dda5c233d461a92,Planes,30.661,105.854,13.492,2808 -2809,junkers88,32.599,21.838,30.027,-0.103,0.223,1.18230769231,dd6d00551e6a44d6a9645b09914c61d5a09462f5,256,V3,dd6d00551e6a44d6a9645b09914c61d5a09462f5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx+32.599_ry+21.838_rz+30.027_tx-00.103_ty+00.223_s+01.537_dd6d00551e6a44d6a9645b09914c61d5a09462f5_256x256.png,b2dcc260f6762a854a68a559d81599dd4d18cf24,Planes,32.599,111.838,30.027,2809 -2810,junkers88,34.629,-15.92,-0.495,-0.056,-0.529,1.18,f7d39ccece0be75b36fd9aab9cb3eb7a4c89b7d3,256,V3,f7d39ccece0be75b36fd9aab9cb3eb7a4c89b7d3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx+34.629_ry-15.920_rz-00.495_tx-00.056_ty-00.529_s+01.534_f7d39ccece0be75b36fd9aab9cb3eb7a4c89b7d3_256x256.png,b62860ce5727de706f892a7579e13de73af6545f,Planes,34.629,74.08,-0.495,2810 -2811,junkers88,35.355,-6.525,-43.871,0.027,-0.531,1.28,963c585da474191ed65b269ccf037dc548cfea73,256,V3,963c585da474191ed65b269ccf037dc548cfea73_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx+35.355_ry-06.525_rz-43.871_tx+00.027_ty-00.531_s+01.664_963c585da474191ed65b269ccf037dc548cfea73_256x256.png,7b5f8de6ea81b77ae240ee1b7c025a9c33b2b618,Planes,35.355,83.475,-43.871,2811 -2812,junkers88,38.312,-32.725,-25.638,-0.163,-0.074,1.10076923077,e4b13c05f8cb7bcf5dc6049230cf5d005a8d067e,256,V3,e4b13c05f8cb7bcf5dc6049230cf5d005a8d067e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx+38.312_ry-32.725_rz-25.638_tx-00.163_ty-00.074_s+01.431_e4b13c05f8cb7bcf5dc6049230cf5d005a8d067e_256x256.png,fd3ac01c3251eba6d1e899a2a9bf31ad319e2d71,Planes,38.312,57.275,-25.638,2812 -2813,junkers88,41.261,-21.462,11.317,-0.208,0.496,0.912307692308,3f3e1e4633d3f1d0db3c7810e7356c1e279ef5f2,256,V3,3f3e1e4633d3f1d0db3c7810e7356c1e279ef5f2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx+41.261_ry-21.462_rz+11.317_tx-00.208_ty+00.496_s+01.186_3f3e1e4633d3f1d0db3c7810e7356c1e279ef5f2_256x256.png,e161dcf47dd167083f9d5ed40d2383960575ab1d,Planes,41.261,68.538,11.317,2813 -2814,junkers88,-3.478,22.143,41.32,-0.248,0.399,1.28846153846,66e36f5fb585a8017f82440cd59a4ed82044b264,256,V3,66e36f5fb585a8017f82440cd59a4ed82044b264_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-03.478_ry+22.143_rz+41.320_tx-00.248_ty+00.399_s+01.675_66e36f5fb585a8017f82440cd59a4ed82044b264_256x256.png,e3ecc8610c76b5ac2de727d45ca780d5d554b3a6,Planes,-3.478,112.143,41.32,2814 -2815,junkers88,-11.179,24.505,-20.289,0.251,-0.092,0.800769230769,c9eec65af6ace7b5576c478f9308d25187538131,256,V3,c9eec65af6ace7b5576c478f9308d25187538131_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-11.179_ry+24.505_rz-20.289_tx+00.251_ty-00.092_s+01.041_c9eec65af6ace7b5576c478f9308d25187538131_256x256.png,7ec08dc0b1e679e715344bf81bb4043b0708263d,Planes,-11.179,114.505,-20.289,2815 -2816,junkers88,-12.03,40.604,22.236,0.141,-0.078,1.15461538462,a4331947d567cd0048e2b68ae2f770b84cb73d56,256,V3,a4331947d567cd0048e2b68ae2f770b84cb73d56_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-12.030_ry+40.604_rz+22.236_tx+00.141_ty-00.078_s+01.501_a4331947d567cd0048e2b68ae2f770b84cb73d56_256x256.png,f03fa5d649672bae135f079584c9d9c6785bd703,Planes,-12.03,130.604,22.236,2816 -2817,junkers88,-12.338,39.099,33.982,-0.165,0.066,0.796153846154,2d75ed97a6127aa9497461e7975ae2a826d911d2,256,V3,2d75ed97a6127aa9497461e7975ae2a826d911d2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-12.338_ry+39.099_rz+33.982_tx-00.165_ty+00.066_s+01.035_2d75ed97a6127aa9497461e7975ae2a826d911d2_256x256.png,55cc47bffb793377a74b19c8784882315e80c84b,Planes,-12.338,129.099,33.982,2817 -2818,junkers88,-12.468,28.309,-40.814,0.024,0.456,1.05538461538,0c20a0452c59d93e2be867d4d3e4cd8f2bb0a7e7,256,V3,0c20a0452c59d93e2be867d4d3e4cd8f2bb0a7e7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-12.468_ry+28.309_rz-40.814_tx+00.024_ty+00.456_s+01.372_0c20a0452c59d93e2be867d4d3e4cd8f2bb0a7e7_256x256.png,0fdaf6c56554a01dbdf7e624a9b76f68dc4a8ddf,Planes,-12.468,118.309,-40.814,2818 -2819,junkers88,-12.695,-3.127,-7.799,-0.079,0.485,0.960769230769,181792533fbf70328e4945ae67d6b466d76e84c7,256,V3,181792533fbf70328e4945ae67d6b466d76e84c7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-12.695_ry-03.127_rz-07.799_tx-00.079_ty+00.485_s+01.249_181792533fbf70328e4945ae67d6b466d76e84c7_256x256.png,8db3982131251e408758cec8d986dedef2d35da7,Planes,-12.695,86.873,-7.799,2819 -2820,junkers88,-14.658,-14.951,-17.095,0.262,0.019,0.833076923077,a5e9876e0d248e89c8047853938f1c6ecdc29ad8,256,V3,a5e9876e0d248e89c8047853938f1c6ecdc29ad8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-14.658_ry-14.951_rz-17.095_tx+00.262_ty+00.019_s+01.083_a5e9876e0d248e89c8047853938f1c6ecdc29ad8_256x256.png,d59d71a95c54129fed80660e449cd125e4d05caf,Planes,-14.658,75.049,-17.095,2820 -2821,junkers88,-15.292,-35.583,20.896,-0.234,0.519,1.29615384615,1ac0d1eb0e67d6b5b1cef6b6e041f0efd7ad00b2,256,V3,1ac0d1eb0e67d6b5b1cef6b6e041f0efd7ad00b2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-15.292_ry-35.583_rz+20.896_tx-00.234_ty+00.519_s+01.685_1ac0d1eb0e67d6b5b1cef6b6e041f0efd7ad00b2_256x256.png,d30254a0cfa74cc73dbb9f297390e4ecdc84c300,Planes,-15.292,54.417,20.896,2821 -2822,junkers88,-15.521,37.409,31.383,0.183,-0.518,0.872307692308,d72cf875a00e16982888a03e0e20fede41ac77b2,256,V3,d72cf875a00e16982888a03e0e20fede41ac77b2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-15.521_ry+37.409_rz+31.383_tx+00.183_ty-00.518_s+01.134_d72cf875a00e16982888a03e0e20fede41ac77b2_256x256.png,3230f0b151a79d074289b12e92db709c05b725dc,Planes,-15.521,127.409,31.383,2822 -2823,junkers88,-16.878,-13.692,25.835,-0.285,0.531,1.16,9944b2cc57b0d3f71bd88cdd673675e8be839583,256,V3,9944b2cc57b0d3f71bd88cdd673675e8be839583_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-16.878_ry-13.692_rz+25.835_tx-00.285_ty+00.531_s+01.508_9944b2cc57b0d3f71bd88cdd673675e8be839583_256x256.png,63c36e7372cd687a83ba2e30f99ec129710d019f,Planes,-16.878,76.308,25.835,2823 -2824,junkers88,-18.125,-38.56,-36.677,-0.103,-0.372,1.00076923077,6e67264e9ceef2eb8b4a3a8435bd2dffa634e0e1,256,V3,6e67264e9ceef2eb8b4a3a8435bd2dffa634e0e1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-18.125_ry-38.560_rz-36.677_tx-00.103_ty-00.372_s+01.301_6e67264e9ceef2eb8b4a3a8435bd2dffa634e0e1_256x256.png,6540a46a99183401ec1714583ffec0daf54dc4e6,Planes,-18.125,51.44,-36.677,2824 -2825,junkers88,-18.133,25.635,15.52,0.118,-0.502,1.00538461538,20fd4031a308a8dea9ade32dc2ae24a4168d9b36,256,V3,20fd4031a308a8dea9ade32dc2ae24a4168d9b36_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-18.133_ry+25.635_rz+15.520_tx+00.118_ty-00.502_s+01.307_20fd4031a308a8dea9ade32dc2ae24a4168d9b36_256x256.png,3db92ee2ea336fb9f887dd7e62b205297df04f2b,Planes,-18.133,115.635,15.52,2825 -2826,junkers88,-18.571,-21.092,22.239,-0.145,0.156,1.28230769231,04b41c9b44e6bf2955dfa915595e6460e0d344b6,256,V3,04b41c9b44e6bf2955dfa915595e6460e0d344b6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-18.571_ry-21.092_rz+22.239_tx-00.145_ty+00.156_s+01.667_04b41c9b44e6bf2955dfa915595e6460e0d344b6_256x256.png,ad77bb15fa5483062f8afa0f08201e0da33383b0,Planes,-18.571,68.908,22.239,2826 -2827,junkers88,-19.269,-14.898,27.912,-0.048,0.17,1.09307692308,16a78814a4853fda41bbf66569729e25b2f4fba3,256,V3,16a78814a4853fda41bbf66569729e25b2f4fba3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-19.269_ry-14.898_rz+27.912_tx-00.048_ty+00.170_s+01.421_16a78814a4853fda41bbf66569729e25b2f4fba3_256x256.png,b8e857bc8bf17e1972df95a80b537e70fcd280e3,Planes,-19.269,75.102,27.912,2827 -2828,junkers88,-22.535,-25.807,-41.096,0.299,-0.044,0.796153846154,1d094a828ddfc5fb7b291ffc37da0ebc7edc4ac5,256,V3,1d094a828ddfc5fb7b291ffc37da0ebc7edc4ac5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-22.535_ry-25.807_rz-41.096_tx+00.299_ty-00.044_s+01.035_1d094a828ddfc5fb7b291ffc37da0ebc7edc4ac5_256x256.png,dc89a0ef0db53caa0108617d9348761dd9ac7060,Planes,-22.535,64.193,-41.096,2828 -2829,junkers88,-22.585,44.276,16.156,0.002,0.303,0.949230769231,b006c7b5052980abe431446174e48a8bd3fb4f2e,256,V3,b006c7b5052980abe431446174e48a8bd3fb4f2e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-22.585_ry+44.276_rz+16.156_tx+00.002_ty+00.303_s+01.234_b006c7b5052980abe431446174e48a8bd3fb4f2e_256x256.png,24a8787ba780b016e62a643c9a4a74664e891e83,Planes,-22.585,134.276,16.156,2829 -2830,junkers88,-23.379,31.489,-27.74,0.018,0.025,0.918461538462,d3011fbc86028328f0a5c138008de70591031203,256,V3,d3011fbc86028328f0a5c138008de70591031203_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-23.379_ry+31.489_rz-27.740_tx+00.018_ty+00.025_s+01.194_d3011fbc86028328f0a5c138008de70591031203_256x256.png,28a9074bdcf059148eba2e6ac2a5724e72123c49,Planes,-23.379,121.489,-27.74,2830 -2831,junkers88,-27.86,-1.887,10.423,-0.145,-0.289,0.862307692308,2bfd426e1125be68718a41abf4c08f6423ef1905,256,V3,2bfd426e1125be68718a41abf4c08f6423ef1905_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-27.860_ry-01.887_rz+10.423_tx-00.145_ty-00.289_s+01.121_2bfd426e1125be68718a41abf4c08f6423ef1905_256x256.png,27081e7a16c981800d7c55cfe9220303ad600c13,Planes,-27.86,88.113,10.423,2831 -2832,junkers88,-28.348,-16.304,-35.048,-0.137,-0.126,0.81,e16b5f0a6555b26e6dfaedecd311b1c848e22ec0,256,V3,e16b5f0a6555b26e6dfaedecd311b1c848e22ec0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-28.348_ry-16.304_rz-35.048_tx-00.137_ty-00.126_s+01.053_e16b5f0a6555b26e6dfaedecd311b1c848e22ec0_256x256.png,684dc1456221596290af5c692430d7d06f025c62,Planes,-28.348,73.696,-35.048,2832 -2833,junkers88,-34.755,30.9,-44.59,-0.282,-0.199,1.14384615385,3315edffd84b8d72d432db6307dec84185d336e5,256,V3,3315edffd84b8d72d432db6307dec84185d336e5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-34.755_ry+30.900_rz-44.590_tx-00.282_ty-00.199_s+01.487_3315edffd84b8d72d432db6307dec84185d336e5_256x256.png,203c039a57cdfce1eb58f219ee20ae82808242ed,Planes,-34.755,120.9,-44.59,2833 -2834,junkers88,-39.123,-36.048,33.504,-0.133,-0.478,1.19307692308,49dbfe8feb954106ede2551266b53774546dd6ec,256,V3,49dbfe8feb954106ede2551266b53774546dd6ec_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-39.123_ry-36.048_rz+33.504_tx-00.133_ty-00.478_s+01.551_49dbfe8feb954106ede2551266b53774546dd6ec_256x256.png,fad6325b20f53571665e6e81c6c4885a8d3ae3c3,Planes,-39.123,53.952,33.504,2834 -2835,junkers88,-39.324,37.846,16.344,0.163,0.106,0.890769230769,f5fd8b01019e2e416f4eb78e64741630718e66c5,256,V3,f5fd8b01019e2e416f4eb78e64741630718e66c5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-39.324_ry+37.846_rz+16.344_tx+00.163_ty+00.106_s+01.158_f5fd8b01019e2e416f4eb78e64741630718e66c5_256x256.png,8d188de1ca04a2d94cecb39c8b57dffb14d5e83c,Planes,-39.324,127.846,16.344,2835 -2836,junkers88,-40.321,15.239,-30.748,0.238,-0.103,0.967692307692,241cd9a75f4ff802ca2ad675cfa0d1bd914f6e68,256,V3,241cd9a75f4ff802ca2ad675cfa0d1bd914f6e68_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-40.321_ry+15.239_rz-30.748_tx+00.238_ty-00.103_s+01.258_241cd9a75f4ff802ca2ad675cfa0d1bd914f6e68_256x256.png,38af96944a5a5aea008e2ab47de7ca3274487f9f,Planes,-40.321,105.239,-30.748,2836 -2837,junkers88,-41.752,40.088,14.513,0.187,-0.33,1.16538461538,4d65a917165b3823510d4f1223de25cfd7311959,256,V3,4d65a917165b3823510d4f1223de25cfd7311959_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-41.752_ry+40.088_rz+14.513_tx+00.187_ty-00.330_s+01.515_4d65a917165b3823510d4f1223de25cfd7311959_256x256.png,58a76f21cbe00f8cff190a36fa6095d6437cc9ed,Planes,-41.752,130.088,14.513,2837 -2838,junkers88,-44.56,6.247,-8.543,-0.207,-0.368,1.26076923077,4911ab5340d5d29186212e8265c26fbc810b629d,256,V3,4911ab5340d5d29186212e8265c26fbc810b629d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-44.560_ry+06.247_rz-08.543_tx-00.207_ty-00.368_s+01.639_4911ab5340d5d29186212e8265c26fbc810b629d_256x256.png,4a9533ff4e2561158463dadc6cdf89adabf9aeb1,Planes,-44.56,96.247,-8.543,2838 -2839,junkers88,-44.884,-27.838,32.419,-0.042,-0.202,1.26384615385,2164801f3646e5e82eaa4373f8d249ce17edf1ac,256,V3,2164801f3646e5e82eaa4373f8d249ce17edf1ac_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/junkers88_rx-44.884_ry-27.838_rz+32.419_tx-00.042_ty-00.202_s+01.643_2164801f3646e5e82eaa4373f8d249ce17edf1ac_256x256.png,bfd90a653374decfd2888222fb35f6b8de01ed00,Planes,-44.884,62.162,32.419,2839 -2840,mig29,1.429,3.818,-38.429,0.207,0.569,0.81,e84b0aee769e245510c46fbb20aadc0dca9b51ca,256,V3,e84b0aee769e245510c46fbb20aadc0dca9b51ca_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+01.429_ry+03.818_rz-38.429_tx+00.207_ty+00.569_s+01.053_e84b0aee769e245510c46fbb20aadc0dca9b51ca_256x256.png,b60384819711fc95782f46796b012916b33e1107,Planes,1.429,93.818,-38.429,2840 -2841,mig29,4.787,-5.76,32.739,0.037,-0.261,1.15692307692,9c333349a14005929a5e69729f05c7215db22c6b,256,V3,9c333349a14005929a5e69729f05c7215db22c6b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+04.787_ry-05.760_rz+32.739_tx+00.037_ty-00.261_s+01.504_9c333349a14005929a5e69729f05c7215db22c6b_256x256.png,080f31dd470f495ee5b94299be85b387c3a95fbd,Planes,4.787,84.24,32.739,2841 -2842,mig29,9.766,14.039,20.807,-0.299,0.235,0.786923076923,fcb1c762b474922fc0218c45473ea392ca9a0cf7,256,V3,fcb1c762b474922fc0218c45473ea392ca9a0cf7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+09.766_ry+14.039_rz+20.807_tx-00.299_ty+00.235_s+01.023_fcb1c762b474922fc0218c45473ea392ca9a0cf7_256x256.png,6f5cb581363577c06559df8554d5795ebd15d7ef,Planes,9.766,104.039,20.807,2842 -2843,mig29,17.58,-9.502,-19.117,-0.178,0.19,0.998461538462,ba047351164e68e42e65934b851bb1a52d0727ae,256,V3,ba047351164e68e42e65934b851bb1a52d0727ae_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+17.580_ry-09.502_rz-19.117_tx-00.178_ty+00.190_s+01.298_ba047351164e68e42e65934b851bb1a52d0727ae_256x256.png,b96335167612e2825c34d55cd6159e85487e0e44,Planes,17.58,80.498,-19.117,2843 -2844,mig29,20.918,-3.614,20.658,0.177,0.127,1.25923076923,fc3230a3de3dc270b922579dea2409c5a4929ab6,256,V3,fc3230a3de3dc270b922579dea2409c5a4929ab6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+20.918_ry-03.614_rz+20.658_tx+00.177_ty+00.127_s+01.637_fc3230a3de3dc270b922579dea2409c5a4929ab6_256x256.png,b51e3e29010330d5d241bfcac19926985f393a2f,Planes,20.918,86.386,20.658,2844 -2845,mig29,21.511,24.343,38.435,-0.238,0.488,1.16,e7d670b90763c0fe2cffe82e512ce835be0b1af6,256,V3,e7d670b90763c0fe2cffe82e512ce835be0b1af6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+21.511_ry+24.343_rz+38.435_tx-00.238_ty+00.488_s+01.508_e7d670b90763c0fe2cffe82e512ce835be0b1af6_256x256.png,a1118257e67728206b775a1c782ae34403bae17e,Planes,21.511,114.343,38.435,2845 -2846,mig29,26.675,-37.809,-27.329,0.217,0.294,0.956923076923,53a2e9ff708ff61757df0ccb5a69644239028c7d,256,V3,53a2e9ff708ff61757df0ccb5a69644239028c7d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+26.675_ry-37.809_rz-27.329_tx+00.217_ty+00.294_s+01.244_53a2e9ff708ff61757df0ccb5a69644239028c7d_256x256.png,1f7d6285c5b64ac9d36cc9760c50fc76cc2d8331,Planes,26.675,52.191,-27.329,2846 -2847,mig29,30.779,-4.247,3.665,-0.216,0.331,0.896153846154,a97c3ab821ca6486546ce1785507eced5e392f2b,256,V3,a97c3ab821ca6486546ce1785507eced5e392f2b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+30.779_ry-04.247_rz+03.665_tx-00.216_ty+00.331_s+01.165_a97c3ab821ca6486546ce1785507eced5e392f2b_256x256.png,17b36ee2af2a3d135c1d8fead6d35e566f68f004,Planes,30.779,85.753,3.665,2847 -2848,mig29,30.983,9.88,-5.547,0.205,-0.236,1.24384615385,3bd19b4a8722d8355a8d11741911963bd259fa1b,256,V3,3bd19b4a8722d8355a8d11741911963bd259fa1b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+30.983_ry+09.880_rz-05.547_tx+00.205_ty-00.236_s+01.617_3bd19b4a8722d8355a8d11741911963bd259fa1b_256x256.png,f96eb7198c3b2267cb4a359688d212124a253244,Planes,30.983,99.88,-5.547,2848 -2849,mig29,32.597,-13.797,-24.973,-0.168,-0.108,0.879230769231,346cf3df24a46543d04c4be352fabcb54ca200d9,256,V3,346cf3df24a46543d04c4be352fabcb54ca200d9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+32.597_ry-13.797_rz-24.973_tx-00.168_ty-00.108_s+01.143_346cf3df24a46543d04c4be352fabcb54ca200d9_256x256.png,d81111d64f87151917f42ca5b8f8cabcc6eae7e7,Planes,32.597,76.203,-24.973,2849 -2850,mig29,32.813,29.716,1.873,0.043,0.487,0.776923076923,8e5f4e242c626080735f6f94a92cb926956eb604,256,V3,8e5f4e242c626080735f6f94a92cb926956eb604_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+32.813_ry+29.716_rz+01.873_tx+00.043_ty+00.487_s+01.010_8e5f4e242c626080735f6f94a92cb926956eb604_256x256.png,9afdf281cb3e1e35dd45915569685a57fd2ae107,Planes,32.813,119.716,1.873,2850 -2851,mig29,36.226,32.854,-43.92,-0.06,0.207,1.14846153846,8970a466317e8dc2143fa5d46f4782fff4293263,256,V3,8970a466317e8dc2143fa5d46f4782fff4293263_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+36.226_ry+32.854_rz-43.920_tx-00.060_ty+00.207_s+01.493_8970a466317e8dc2143fa5d46f4782fff4293263_256x256.png,9b6588451abb55f9d7e0791f7b4ca9a7cdabc133,Planes,36.226,122.854,-43.92,2851 -2852,mig29,37.416,-4.883,-3.976,0.138,-0.226,1.05153846154,9876a3e62055da2f043d74d9abc4b4f5259a4ad4,256,V3,9876a3e62055da2f043d74d9abc4b4f5259a4ad4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+37.416_ry-04.883_rz-03.976_tx+00.138_ty-00.226_s+01.367_9876a3e62055da2f043d74d9abc4b4f5259a4ad4_256x256.png,c2b2c5b6dddf63afcc74289a0bfef3c1adb3da8b,Planes,37.416,85.117,-3.976,2852 -2853,mig29,38.637,39.641,-41.375,-0.147,0.021,0.913076923077,68817bb8e148d74ef043f15424e206835cf8c485,256,V3,68817bb8e148d74ef043f15424e206835cf8c485_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+38.637_ry+39.641_rz-41.375_tx-00.147_ty+00.021_s+01.187_68817bb8e148d74ef043f15424e206835cf8c485_256x256.png,2bab220d2215ee01da4cdd42e63f490a2b7c1e94,Planes,38.637,129.641,-41.375,2853 -2854,mig29,41.427,31.406,0.717,-0.249,0.098,1.21076923077,5a2e660e2c73570b1239e960e9dfd0f048a9acf3,256,V3,5a2e660e2c73570b1239e960e9dfd0f048a9acf3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+41.427_ry+31.406_rz+00.717_tx-00.249_ty+00.098_s+01.574_5a2e660e2c73570b1239e960e9dfd0f048a9acf3_256x256.png,0e7da7c5f412b52f70c9412efaf5bc35f245d3e0,Planes,41.427,121.406,0.717,2854 -2855,mig29,42.037,30.891,-18.198,-0.044,0.366,0.873846153846,9545a74dc3b7615efafe988945e596cc1573e177,256,V3,9545a74dc3b7615efafe988945e596cc1573e177_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+42.037_ry+30.891_rz-18.198_tx-00.044_ty+00.366_s+01.136_9545a74dc3b7615efafe988945e596cc1573e177_256x256.png,acc66adba767f7d4281b71f2b0dcc26bcb3f8ff0,Planes,42.037,120.891,-18.198,2855 -2856,mig29,42.824,33.153,19.296,-0.278,-0.485,0.915384615385,01c67050e492c4f4dcd06ba558d942c6909f51cd,256,V3,01c67050e492c4f4dcd06ba558d942c6909f51cd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+42.824_ry+33.153_rz+19.296_tx-00.278_ty-00.485_s+01.190_01c67050e492c4f4dcd06ba558d942c6909f51cd_256x256.png,13a28bca77424e278254b608cce114960da59e4c,Planes,42.824,123.153,19.296,2856 -2857,mig29,42.986,7.333,1.472,-0.161,-0.075,0.887692307692,bddcab5746f12e580376b7c1ad52b3d4d02bed4c,256,V3,bddcab5746f12e580376b7c1ad52b3d4d02bed4c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+42.986_ry+07.333_rz+01.472_tx-00.161_ty-00.075_s+01.154_bddcab5746f12e580376b7c1ad52b3d4d02bed4c_256x256.png,24415e921104fe24905d983ad499a4042a3ae1ab,Planes,42.986,97.333,1.472,2857 -2858,mig29,43.533,20.221,-14.294,0.183,0.318,0.956153846154,8029c0ba7606748aefd9d7fc432185465ac5ea83,256,V3,8029c0ba7606748aefd9d7fc432185465ac5ea83_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx+43.533_ry+20.221_rz-14.294_tx+00.183_ty+00.318_s+01.243_8029c0ba7606748aefd9d7fc432185465ac5ea83_256x256.png,85714fd88de81c6d2d158538c1f24261ded0044e,Planes,43.533,110.221,-14.294,2858 -2859,mig29,-8.261,-9.632,-3.259,0.257,-0.173,1.18923076923,86ce40d6d3395c814cbd3787bf5ecf5e8f64c886,256,V3,86ce40d6d3395c814cbd3787bf5ecf5e8f64c886_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-08.261_ry-09.632_rz-03.259_tx+00.257_ty-00.173_s+01.546_86ce40d6d3395c814cbd3787bf5ecf5e8f64c886_256x256.png,bb7cd55df19411f32b5682910af24fd1b594f2ff,Planes,-8.261,80.368,-3.259,2859 -2860,mig29,-10.329,14.748,-7.358,-0.161,0.345,0.913846153846,71ad745dc9b3cf3d28f517035f25be19380a15d3,256,V3,71ad745dc9b3cf3d28f517035f25be19380a15d3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-10.329_ry+14.748_rz-07.358_tx-00.161_ty+00.345_s+01.188_71ad745dc9b3cf3d28f517035f25be19380a15d3_256x256.png,7d4aba53b7acaa043de966c692d739313c5167f0,Planes,-10.329,104.748,-7.358,2860 -2861,mig29,-10.834,7.764,-32.801,0.001,0.586,1.18230769231,cafa18236218eb0f1cf1eb3789c0722624a278af,256,V3,cafa18236218eb0f1cf1eb3789c0722624a278af_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-10.834_ry+07.764_rz-32.801_tx+00.001_ty+00.586_s+01.537_cafa18236218eb0f1cf1eb3789c0722624a278af_256x256.png,4fb82cd21ea83b8c9b68f8c2559c91a8312c39de,Planes,-10.834,97.764,-32.801,2861 -2862,mig29,-10.849,8.98,-27.87,-0.288,-0.47,0.784615384615,70b490f7cd76c487eb89e73e5e1b1a765d083e66,256,V3,70b490f7cd76c487eb89e73e5e1b1a765d083e66_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-10.849_ry+08.980_rz-27.870_tx-00.288_ty-00.470_s+01.020_70b490f7cd76c487eb89e73e5e1b1a765d083e66_256x256.png,a1edf6639a15cf0b5781caf59f89b6f16a98f3de,Planes,-10.849,98.98,-27.87,2862 -2863,mig29,-12.748,-13.907,27.851,-0.072,-0.278,1.02615384615,318f68900037f22ae013eb9e64fde74940ab2586,256,V3,318f68900037f22ae013eb9e64fde74940ab2586_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-12.748_ry-13.907_rz+27.851_tx-00.072_ty-00.278_s+01.334_318f68900037f22ae013eb9e64fde74940ab2586_256x256.png,44883e10d955eac17b3240c2ea2fe492fdfa6da7,Planes,-12.748,76.093,27.851,2863 -2864,mig29,-13.556,-8.035,-15.14,0.062,0.532,0.953076923077,e923c85ef2b183c902d1a1fb9a7f483bb61169ff,256,V3,e923c85ef2b183c902d1a1fb9a7f483bb61169ff_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-13.556_ry-08.035_rz-15.140_tx+00.062_ty+00.532_s+01.239_e923c85ef2b183c902d1a1fb9a7f483bb61169ff_256x256.png,8c3b5b1007d356fe655908088f737cf54652ccb6,Planes,-13.556,81.965,-15.14,2864 -2865,mig29,-19.794,-6.478,40.04,0.175,-0.382,0.957692307692,cb86f9b682de3a3ec1dec0a8aceec5ef559693d9,256,V3,cb86f9b682de3a3ec1dec0a8aceec5ef559693d9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-19.794_ry-06.478_rz+40.040_tx+00.175_ty-00.382_s+01.245_cb86f9b682de3a3ec1dec0a8aceec5ef559693d9_256x256.png,367e85df65317363ce146a1545928ba1d328932e,Planes,-19.794,83.522,40.04,2865 -2866,mig29,-20.548,-24.246,-15.006,0.184,0.451,1.13384615385,27f4c815ac306641a02560077ccd29d540dbf256,256,V3,27f4c815ac306641a02560077ccd29d540dbf256_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-20.548_ry-24.246_rz-15.006_tx+00.184_ty+00.451_s+01.474_27f4c815ac306641a02560077ccd29d540dbf256_256x256.png,592f6ccf41a8e229da5d4bc4f217baf440ea13ef,Planes,-20.548,65.754,-15.006,2866 -2867,mig29,-23.86,-5.93,3.601,-0.206,-0.432,1.03923076923,ca6e5cd2f794a4ad4e63187d17dd1faaafc362b9,256,V3,ca6e5cd2f794a4ad4e63187d17dd1faaafc362b9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-23.860_ry-05.930_rz+03.601_tx-00.206_ty-00.432_s+01.351_ca6e5cd2f794a4ad4e63187d17dd1faaafc362b9_256x256.png,1a85c9f82c8b9d43f4c0652fb03dc7bad07515eb,Planes,-23.86,84.07,3.601,2867 -2868,mig29,-26.309,-42.097,-38.341,-0.135,-0.559,0.777692307692,b1d4ce4494281bd034f8a64902f8875f4641acf9,256,V3,b1d4ce4494281bd034f8a64902f8875f4641acf9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-26.309_ry-42.097_rz-38.341_tx-00.135_ty-00.559_s+01.011_b1d4ce4494281bd034f8a64902f8875f4641acf9_256x256.png,5894fec3feb138300f600f8f79e20deb9b732925,Planes,-26.309,47.903,-38.341,2868 -2869,mig29,-31.38,13.637,-26.366,-0.236,-0.515,1.25076923077,1fe0cf33d1204fcc078aafe3cc2df58880ae69ca,256,V3,1fe0cf33d1204fcc078aafe3cc2df58880ae69ca_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-31.380_ry+13.637_rz-26.366_tx-00.236_ty-00.515_s+01.626_1fe0cf33d1204fcc078aafe3cc2df58880ae69ca_256x256.png,5bbfc3e2ab2e787e0bd77e3d1da7c39caba76b95,Planes,-31.38,103.637,-26.366,2869 -2870,mig29,-32.926,11.461,-7.951,0.214,0.298,1.25307692308,e0f9a7d0381148955f6c0fb0a4ebef470e8ea6c8,256,V3,e0f9a7d0381148955f6c0fb0a4ebef470e8ea6c8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-32.926_ry+11.461_rz-07.951_tx+00.214_ty+00.298_s+01.629_e0f9a7d0381148955f6c0fb0a4ebef470e8ea6c8_256x256.png,f33d654b7a5a7eccb761e76fde64ce6afb2b0727,Planes,-32.926,101.461,-7.951,2870 -2871,mig29,-32.983,-0.248,-29.295,0.115,-0.157,0.956923076923,25844e51da33e2631a040cd9e2757255278bf609,256,V3,25844e51da33e2631a040cd9e2757255278bf609_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-32.983_ry-00.248_rz-29.295_tx+00.115_ty-00.157_s+01.244_25844e51da33e2631a040cd9e2757255278bf609_256x256.png,08d6e36ece11480e214450ba01efa50c8c015607,Planes,-32.983,89.752,-29.295,2871 -2872,mig29,-34.006,44.521,-26.76,0.08,-0.106,0.827692307692,7e876511f9f8ab380dc694fd5da3461425ea70e0,256,V3,7e876511f9f8ab380dc694fd5da3461425ea70e0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-34.006_ry+44.521_rz-26.760_tx+00.080_ty-00.106_s+01.076_7e876511f9f8ab380dc694fd5da3461425ea70e0_256x256.png,f371c0064ea5cc620697526d09bcff0db21a995a,Planes,-34.006,134.521,-26.76,2872 -2873,mig29,-34.186,4.418,10.75,0.12,0.189,0.771538461538,467e9f9d5eca25c0197c941bb746d022030174c5,256,V3,467e9f9d5eca25c0197c941bb746d022030174c5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-34.186_ry+04.418_rz+10.750_tx+00.120_ty+00.189_s+01.003_467e9f9d5eca25c0197c941bb746d022030174c5_256x256.png,a294c92c2e674b2271d93014d0e982eafad9207c,Planes,-34.186,94.418,10.75,2873 -2874,mig29,-34.725,25.693,-4.665,0.243,0.457,1.13538461538,980deed5f85673eb0766be064d23cd14eb6abe07,256,V3,980deed5f85673eb0766be064d23cd14eb6abe07_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-34.725_ry+25.693_rz-04.665_tx+00.243_ty+00.457_s+01.476_980deed5f85673eb0766be064d23cd14eb6abe07_256x256.png,5362705ed08462713c6377e1727ffbe54ec6e70c,Planes,-34.725,115.693,-4.665,2874 -2875,mig29,-40.094,15.672,28.705,0.27,-0.384,0.780769230769,e03c67e518eb43bb5d644f2f2df220053a925faf,256,V3,e03c67e518eb43bb5d644f2f2df220053a925faf_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-40.094_ry+15.672_rz+28.705_tx+00.270_ty-00.384_s+01.015_e03c67e518eb43bb5d644f2f2df220053a925faf_256x256.png,5e6c9aba4abcf1abd00073cef476786036160e5b,Planes,-40.094,105.672,28.705,2875 -2876,mig29,-41.322,-2.894,-17.76,-0.179,0.24,1.18461538462,4860b973f9d474559e16792cfe6e012ddccc5df3,256,V3,4860b973f9d474559e16792cfe6e012ddccc5df3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-41.322_ry-02.894_rz-17.760_tx-00.179_ty+00.240_s+01.540_4860b973f9d474559e16792cfe6e012ddccc5df3_256x256.png,6646337206b8129c64735d656d75cfd4c7bbc687,Planes,-41.322,87.106,-17.76,2876 -2877,mig29,-41.866,33.392,28.289,0.286,-0.586,1.02153846154,cbfabaadb3c4850574f703ed4f4bc9edb6352849,256,V3,cbfabaadb3c4850574f703ed4f4bc9edb6352849_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-41.866_ry+33.392_rz+28.289_tx+00.286_ty-00.586_s+01.328_cbfabaadb3c4850574f703ed4f4bc9edb6352849_256x256.png,ca999564073fe8a25a316f80e3ca3f388f742df7,Planes,-41.866,123.392,28.289,2877 -2878,mig29,-42.072,-40.849,-3.524,-0.086,-0.309,0.786153846154,6afa09b82a7f12a6a386e7e2ed8587bc1a22a0f2,256,V3,6afa09b82a7f12a6a386e7e2ed8587bc1a22a0f2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-42.072_ry-40.849_rz-03.524_tx-00.086_ty-00.309_s+01.022_6afa09b82a7f12a6a386e7e2ed8587bc1a22a0f2_256x256.png,26ae12475ebee51cf92bc06dc6b9062825023867,Planes,-42.072,49.151,-3.524,2878 -2879,mig29,-43.136,-40.096,-22.911,-0.066,-0.081,0.953076923077,d4a2c78f7adaf6d358d39bae17c0e6a7973bcc10,256,V3,d4a2c78f7adaf6d358d39bae17c0e6a7973bcc10_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/mig29_rx-43.136_ry-40.096_rz-22.911_tx-00.066_ty-00.081_s+01.239_d4a2c78f7adaf6d358d39bae17c0e6a7973bcc10_256x256.png,4df1ad4a9a332de7aea65a43c6ebfd79adfaade0,Planes,-43.136,49.904,-22.911,2879 -2880,motoryacht,4.339,34.186,-25.18,-0.11,0.597,0.865833333333,333ffaa8f1ed166ae3423fec9eae3d1a3a1af56f,256,V3,333ffaa8f1ed166ae3423fec9eae3d1a3a1af56f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx+04.339_ry+124.186_rz-25.180_tx-00.110_ty+00.597_s+01.039_333ffaa8f1ed166ae3423fec9eae3d1a3a1af56f_256x256.png,8966c279b81f417c11cc503480f08a6b72d1d2a5,Boats,4.339,-55.814,-25.18,2880 -2881,motoryacht,6.833,-18.598,9.957,0.243,0.403,0.916666666667,aea82313dd5188ca078ac52b0ff0e63f9bd33858,256,V3,aea82313dd5188ca078ac52b0ff0e63f9bd33858_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx+06.833_ry+71.402_rz+09.957_tx+00.243_ty+00.403_s+01.100_aea82313dd5188ca078ac52b0ff0e63f9bd33858_256x256.png,9778527cb4cf33f5ff207ddae68f75e6e382abf9,Boats,6.833,-108.598,9.957,2881 -2882,motoryacht,7.57,32.99,-6.148,-0.125,0.541,1.02833333333,f16ea5be400497d4ee0122141bb3cf29b56532b6,256,V3,f16ea5be400497d4ee0122141bb3cf29b56532b6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx+07.570_ry+122.990_rz-06.148_tx-00.125_ty+00.541_s+01.234_f16ea5be400497d4ee0122141bb3cf29b56532b6_256x256.png,5af8b956214169bf84c4aea3236b0c0c1a720816,Boats,7.57,-57.01,-6.148,2882 -2883,motoryacht,9.826,-8.49,-20.42,-0.272,0.229,1.08,b665e472c05f196f86ac75f38d3be79f420f420f,256,V3,b665e472c05f196f86ac75f38d3be79f420f420f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx+09.826_ry+81.510_rz-20.420_tx-00.272_ty+00.229_s+01.296_b665e472c05f196f86ac75f38d3be79f420f420f_256x256.png,90eace00f91cc876d7e5d3490bd20b9d80915001,Boats,9.826,-98.49,-20.42,2883 -2884,motoryacht,16.375,3.048,-27.749,0.002,-0.534,1.0875,caa24c596990a462d335ed31f15a4b39a39f4a4d,256,V3,caa24c596990a462d335ed31f15a4b39a39f4a4d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx+16.375_ry+93.048_rz-27.749_tx+00.002_ty-00.534_s+01.305_caa24c596990a462d335ed31f15a4b39a39f4a4d_256x256.png,575f70dcc49c0bb670c4498dc4da91c28f8b35d0,Boats,16.375,-86.952,-27.749,2884 -2885,motoryacht,23.532,-30.784,-35.282,-0.034,0.536,0.984166666667,583d01d2e4af572039bcc075ad617057d4cd36e4,256,V3,583d01d2e4af572039bcc075ad617057d4cd36e4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx+23.532_ry+59.216_rz-35.282_tx-00.034_ty+00.536_s+01.181_583d01d2e4af572039bcc075ad617057d4cd36e4_256x256.png,e849ceb1f27798fbfb4f14ad9984fdd58bafa372,Boats,23.532,-120.784,-35.282,2885 -2886,motoryacht,25.82,28.338,-6.212,0.286,0.006,0.963333333333,51d342775a2bd6ee578b4f19dcb1238d6274f81a,256,V3,51d342775a2bd6ee578b4f19dcb1238d6274f81a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx+25.820_ry+118.338_rz-06.212_tx+00.286_ty+00.006_s+01.156_51d342775a2bd6ee578b4f19dcb1238d6274f81a_256x256.png,ae928dfdb96780de88c56389b447e393046a8783,Boats,25.82,-61.662,-6.212,2886 -2887,motoryacht,27.514,-13.107,6.313,-0.017,-0.486,1.11333333333,a3b9f704bf21b49b05cbcd79a59accf11a480408,256,V3,a3b9f704bf21b49b05cbcd79a59accf11a480408_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx+27.514_ry+76.893_rz+06.313_tx-00.017_ty-00.486_s+01.336_a3b9f704bf21b49b05cbcd79a59accf11a480408_256x256.png,bbe10a13f0e808e778d51dd555d9faa02b870fd8,Boats,27.514,-103.107,6.313,2887 -2888,motoryacht,28.329,-20.253,-29.496,-0.194,-0.006,1.29916666667,8c415f459d2b5e0beb7491a5739e32cf6e2cda13,256,V3,8c415f459d2b5e0beb7491a5739e32cf6e2cda13_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx+28.329_ry+69.747_rz-29.496_tx-00.194_ty-00.006_s+01.559_8c415f459d2b5e0beb7491a5739e32cf6e2cda13_256x256.png,9c1c71828bca60b1051ef5540119858a73b73787,Boats,28.329,-110.253,-29.496,2888 -2889,motoryacht,29.016,7.016,-4.223,-0.117,0.57,1.14333333333,69c1dd41ed5ad96af76463555f7349b980a9c984,256,V3,69c1dd41ed5ad96af76463555f7349b980a9c984_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx+29.016_ry+97.016_rz-04.223_tx-00.117_ty+00.570_s+01.372_69c1dd41ed5ad96af76463555f7349b980a9c984_256x256.png,e9de64804567ed6ecbbc021e14e3cbe039bc9d1a,Boats,29.016,-82.984,-4.223,2889 -2890,motoryacht,30.889,-3.79,12.646,-0.249,-0.5,1.04,9898451b9be00f8506ebe7b865cc46ac243aeff1,256,V3,9898451b9be00f8506ebe7b865cc46ac243aeff1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx+30.889_ry+86.210_rz+12.646_tx-00.249_ty-00.500_s+01.248_9898451b9be00f8506ebe7b865cc46ac243aeff1_256x256.png,939bd9b093deb66cbfa6f37d0d550670fba30639,Boats,30.889,-93.79,12.646,2890 -2891,motoryacht,31.724,-22.662,33.544,0.052,-0.009,1.07166666667,a1117cf220c542e1a3a11c28c1022b5895d1d5fd,256,V3,a1117cf220c542e1a3a11c28c1022b5895d1d5fd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx+31.724_ry+67.338_rz+33.544_tx+00.052_ty-00.009_s+01.286_a1117cf220c542e1a3a11c28c1022b5895d1d5fd_256x256.png,775186bcdfc79e095454e12be37ad5b984bc2919,Boats,31.724,-112.662,33.544,2891 -2892,motoryacht,34.344,3.705,1.313,-0.057,-0.126,1.09833333333,bb99e3f4e04b387bc07442e5b8806a74ce663fe2,256,V3,bb99e3f4e04b387bc07442e5b8806a74ce663fe2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx+34.344_ry+93.705_rz+01.313_tx-00.057_ty-00.126_s+01.318_bb99e3f4e04b387bc07442e5b8806a74ce663fe2_256x256.png,6019cb52a18700e4d66f7f36fd927b20fa954813,Boats,34.344,-86.295,1.313,2892 -2893,motoryacht,38.673,22.881,-43.709,0.073,-0.482,0.965,113166cf5a0ac843722121ca0ccded617686e0cd,256,V3,113166cf5a0ac843722121ca0ccded617686e0cd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx+38.673_ry+112.881_rz-43.709_tx+00.073_ty-00.482_s+01.158_113166cf5a0ac843722121ca0ccded617686e0cd_256x256.png,348b8a9bb7a9e917dc4efc68057328efed9e5d67,Boats,38.673,-67.119,-43.709,2893 -2894,motoryacht,44.569,41.321,24.463,0.187,0.129,0.944166666667,46c96be9f91515a8ea7d301f84268b0f0de807e0,256,V3,46c96be9f91515a8ea7d301f84268b0f0de807e0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx+44.569_ry+131.321_rz+24.463_tx+00.187_ty+00.129_s+01.133_46c96be9f91515a8ea7d301f84268b0f0de807e0_256x256.png,5faf88ac7aa9ad8b6da8b2a53fcebbaf9cf7f304,Boats,44.569,-48.679,24.463,2894 -2895,motoryacht,-2.965,17.83,-1.524,0.198,0.532,0.931666666667,a5236054fa1cd2fece18c927a9e75226b7c92556,256,V3,a5236054fa1cd2fece18c927a9e75226b7c92556_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-02.965_ry+107.830_rz-01.524_tx+00.198_ty+00.532_s+01.118_a5236054fa1cd2fece18c927a9e75226b7c92556_256x256.png,03d63cf9d8aa80767770787c0952898e02f71440,Boats,-2.965,-72.17,-1.524,2895 -2896,motoryacht,-6.142,-24.071,25.398,0.275,0.256,1.1775,41c4e4101680aac36db789b3978288bbe2cf3ddd,256,V3,41c4e4101680aac36db789b3978288bbe2cf3ddd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-06.142_ry+65.929_rz+25.398_tx+00.275_ty+00.256_s+01.413_41c4e4101680aac36db789b3978288bbe2cf3ddd_256x256.png,d5d6f5bb1cca7fea0b45ad039b79d3a7766a4c11,Boats,-6.142,-114.071,25.398,2896 -2897,motoryacht,-6.646,-18.421,-9.764,-0.275,-0.1,1.14333333333,550dfc268e16060404678d7ec93cc3f1626698d6,256,V3,550dfc268e16060404678d7ec93cc3f1626698d6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-06.646_ry+71.579_rz-09.764_tx-00.275_ty-00.100_s+01.372_550dfc268e16060404678d7ec93cc3f1626698d6_256x256.png,197fb367273d1b43a97a56c72744efb55fb326f9,Boats,-6.646,-108.421,-9.764,2897 -2898,motoryacht,-7.337,24.715,7.413,0.157,0.319,1.29166666667,c9521a642d5bb415e23d3b78c73e1fbf69adbb88,256,V3,c9521a642d5bb415e23d3b78c73e1fbf69adbb88_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-07.337_ry+114.715_rz+07.413_tx+00.157_ty+00.319_s+01.550_c9521a642d5bb415e23d3b78c73e1fbf69adbb88_256x256.png,bd4c82a76a1e594fd7076013585d1897616c855f,Boats,-7.337,-65.285,7.413,2898 -2899,motoryacht,-10.406,-18.955,-2.497,0.214,0.048,1.00416666667,34880ba841ddb4225afff71c6cd421fd2c6950e4,256,V3,34880ba841ddb4225afff71c6cd421fd2c6950e4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-10.406_ry+71.045_rz-02.497_tx+00.214_ty+00.048_s+01.205_34880ba841ddb4225afff71c6cd421fd2c6950e4_256x256.png,3517359b503bf6675d30a7baa99323e6007a91ba,Boats,-10.406,-108.955,-2.497,2899 -2900,motoryacht,-11.052,-43.998,41.671,0.164,0.467,1.16333333333,4774e979e2b285ec1ca3d258b63d656015e1d9b5,256,V3,4774e979e2b285ec1ca3d258b63d656015e1d9b5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-11.052_ry+46.002_rz+41.671_tx+00.164_ty+00.467_s+01.396_4774e979e2b285ec1ca3d258b63d656015e1d9b5_256x256.png,1a5c4a49c70709b8bac3a68be5ae13c2842480d3,Boats,-11.052,-133.998,41.671,2900 -2901,motoryacht,-15.342,-14.613,-12.08,-0.174,-0.585,1.22666666667,dc40fe995072341522fa4f9f0c259039c1f73fbc,256,V3,dc40fe995072341522fa4f9f0c259039c1f73fbc_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-15.342_ry+75.387_rz-12.080_tx-00.174_ty-00.585_s+01.472_dc40fe995072341522fa4f9f0c259039c1f73fbc_256x256.png,cf454e2e542e705bd68ea78071e2d957de8f2666,Boats,-15.342,-104.613,-12.08,2901 -2902,motoryacht,-15.7,12.673,39.502,-0.237,0.363,0.9625,62c717e396a5d0fab406ab5c28b0c024e4c43a22,256,V3,62c717e396a5d0fab406ab5c28b0c024e4c43a22_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-15.700_ry+102.673_rz+39.502_tx-00.237_ty+00.363_s+01.155_62c717e396a5d0fab406ab5c28b0c024e4c43a22_256x256.png,de4e903025aa6fc8cd4bf070cdbfe40590183a9d,Boats,-15.7,-77.327,39.502,2902 -2903,motoryacht,-15.702,15.738,-35.923,0.098,-0.232,1.1975,923d4e1897e952fbadfbad54e5b4b58f27f02030,256,V3,923d4e1897e952fbadfbad54e5b4b58f27f02030_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-15.702_ry+105.738_rz-35.923_tx+00.098_ty-00.232_s+01.437_923d4e1897e952fbadfbad54e5b4b58f27f02030_256x256.png,b74a99da46e524e89bfc6ec2e6ca02e213ae7836,Boats,-15.702,-74.262,-35.923,2903 -2904,motoryacht,-19.752,-32.998,20.681,-0.005,-0.191,1.08666666667,6263c3e55204329b4c0eb3fcefe1a9821bb9e3e7,256,V3,6263c3e55204329b4c0eb3fcefe1a9821bb9e3e7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-19.752_ry+57.002_rz+20.681_tx-00.005_ty-00.191_s+01.304_6263c3e55204329b4c0eb3fcefe1a9821bb9e3e7_256x256.png,c835b887a6b812b85c11c2aa14383292968d3076,Boats,-19.752,-122.998,20.681,2904 -2905,motoryacht,-20.189,-41.351,-14.538,0.262,-0.306,0.893333333333,21939c362b2c0a86a519c208f8c7b9f4ab1997ef,256,V3,21939c362b2c0a86a519c208f8c7b9f4ab1997ef_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-20.189_ry+48.649_rz-14.538_tx+00.262_ty-00.306_s+01.072_21939c362b2c0a86a519c208f8c7b9f4ab1997ef_256x256.png,b05b3d86127c08cde4361cc61a4bed63ad1499e1,Boats,-20.189,-131.351,-14.538,2905 -2906,motoryacht,-20.483,36.293,22.229,0.175,-0.043,0.785,33d8809229eef0df411fe6a827d54caa653d7ab6,256,V3,33d8809229eef0df411fe6a827d54caa653d7ab6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-20.483_ry+126.293_rz+22.229_tx+00.175_ty-00.043_s+00.942_33d8809229eef0df411fe6a827d54caa653d7ab6_256x256.png,730cb686e508b0ede02c748887b7f7a26ab05002,Boats,-20.483,-53.707,22.229,2906 -2907,motoryacht,-21.28,33.9,15.279,-0.185,-0.306,1.10583333333,09d6c8c2d7540e10b1314fe465599281ab986819,256,V3,09d6c8c2d7540e10b1314fe465599281ab986819_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-21.280_ry+123.900_rz+15.279_tx-00.185_ty-00.306_s+01.327_09d6c8c2d7540e10b1314fe465599281ab986819_256x256.png,9a4db0d65f2739df76a78fa26ad35f36613a8ff4,Boats,-21.28,-56.1,15.279,2907 -2908,motoryacht,-21.695,-17.932,15.569,0.092,0.098,0.821666666667,638b2d56f24048db9826a353eaab2e9c8947977c,256,V3,638b2d56f24048db9826a353eaab2e9c8947977c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-21.695_ry+72.068_rz+15.569_tx+00.092_ty+00.098_s+00.986_638b2d56f24048db9826a353eaab2e9c8947977c_256x256.png,8ea11b77467f0e45eb5b80faac0978c6f622d6f7,Boats,-21.695,-107.932,15.569,2908 -2909,motoryacht,-24.083,-21.514,4.691,0.01,0.579,1.26833333333,5f861bd26ff043420d97fa12705891e0cb159a9e,256,V3,5f861bd26ff043420d97fa12705891e0cb159a9e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-24.083_ry+68.486_rz+04.691_tx+00.010_ty+00.579_s+01.522_5f861bd26ff043420d97fa12705891e0cb159a9e_256x256.png,e4773a61666c66090600b1831cfb4811d086a7b6,Boats,-24.083,-111.514,4.691,2909 -2910,motoryacht,-25.645,-23.718,-38.999,-0.27,-0.155,0.814166666667,e1f52b7b3340b0fdccb4e2fc79f82c3b6296dcb8,256,V3,e1f52b7b3340b0fdccb4e2fc79f82c3b6296dcb8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-25.645_ry+66.282_rz-38.999_tx-00.270_ty-00.155_s+00.977_e1f52b7b3340b0fdccb4e2fc79f82c3b6296dcb8_256x256.png,530fe04e2f860648ffb5f17896c3f0462c7681cb,Boats,-25.645,-113.718,-38.999,2910 -2911,motoryacht,-30.287,21.068,-15.139,0.267,-0.32,0.788333333333,f7f92c3c507393188fc7d104defc2eac46a64e5f,256,V3,f7f92c3c507393188fc7d104defc2eac46a64e5f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-30.287_ry+111.068_rz-15.139_tx+00.267_ty-00.320_s+00.946_f7f92c3c507393188fc7d104defc2eac46a64e5f_256x256.png,0f7140bfbf6c02b68cb633dbebea650acf0d7f9d,Boats,-30.287,-68.932,-15.139,2911 -2912,motoryacht,-30.965,26.676,43.107,0.192,0.142,0.7875,0045d5078c7a1f82fe336ce75f65226ebbc2433c,256,V3,0045d5078c7a1f82fe336ce75f65226ebbc2433c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-30.965_ry+116.676_rz+43.107_tx+00.192_ty+00.142_s+00.945_0045d5078c7a1f82fe336ce75f65226ebbc2433c_256x256.png,5448f47ff3680c3fa24ea4e19fc76df9d2ea8d7b,Boats,-30.965,-63.324,43.107,2912 -2913,motoryacht,-31.866,-44.289,5.24,-0.173,-0.048,1.18416666667,40f22f32a24680ec0ded7a67b20bfceac33ea787,256,V3,40f22f32a24680ec0ded7a67b20bfceac33ea787_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-31.866_ry+45.711_rz+05.240_tx-00.173_ty-00.048_s+01.421_40f22f32a24680ec0ded7a67b20bfceac33ea787_256x256.png,0443cd54069646ac8c22ca002b186b426d7a8e99,Boats,-31.866,-134.289,5.24,2913 -2914,motoryacht,-32.831,-35.915,-36.545,-0.188,-0.328,0.773333333333,12f6546a01b263e4f580581846acbf539535ca94,256,V3,12f6546a01b263e4f580581846acbf539535ca94_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-32.831_ry+54.085_rz-36.545_tx-00.188_ty-00.328_s+00.928_12f6546a01b263e4f580581846acbf539535ca94_256x256.png,3686557695da7f8df64a2c1186a88501c6fa9cd6,Boats,-32.831,-125.915,-36.545,2914 -2915,motoryacht,-34.494,10.228,0.783,0.014,0.175,1.06083333333,70443c534d217d7e5147e2a3163555224bffff32,256,V3,70443c534d217d7e5147e2a3163555224bffff32_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-34.494_ry+100.228_rz+00.783_tx+00.014_ty+00.175_s+01.273_70443c534d217d7e5147e2a3163555224bffff32_256x256.png,04bd930856c610e92fddfcf7c6daf2a160fd79b7,Boats,-34.494,-79.772,0.783,2915 -2916,motoryacht,-35.54,-29.465,-25.039,-0.08,0.336,0.958333333333,824c7a6bd9bd2d9db1a239a90330ee4547249439,256,V3,824c7a6bd9bd2d9db1a239a90330ee4547249439_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-35.540_ry+60.535_rz-25.039_tx-00.080_ty+00.336_s+01.150_824c7a6bd9bd2d9db1a239a90330ee4547249439_256x256.png,abcc8900e9ce5c2d567d8682f5005ffe694ecdda,Boats,-35.54,-119.465,-25.039,2916 -2917,motoryacht,-38.939,27.697,-32.112,0.031,0.44,0.771666666667,4888b0a4271b4ef87c076c49459282bb84b4ffe1,256,V3,4888b0a4271b4ef87c076c49459282bb84b4ffe1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-38.939_ry+117.697_rz-32.112_tx+00.031_ty+00.440_s+00.926_4888b0a4271b4ef87c076c49459282bb84b4ffe1_256x256.png,84937b15b845eed8803f9c51b0ad6ddcf78e241a,Boats,-38.939,-62.303,-32.112,2917 -2918,motoryacht,-40.024,14.552,-33.46,0.079,0.578,1.15166666667,c785156546bc45145c74283f4a3a6306007db8c6,256,V3,c785156546bc45145c74283f4a3a6306007db8c6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/motoryacht_rx-40.024_ry+104.552_rz-33.460_tx+00.079_ty+00.578_s+01.382_c785156546bc45145c74283f4a3a6306007db8c6_256x256.png,85b796fcd9d3a76be6a36d0e6d3eee158c0b06fe,Boats,-40.024,-75.448,-33.46,2918 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-2958,raspberry_obj,-7.01537930254,-3.30174164649,-7.7914437726,0.156,0.224,0.815714285714,e0d6dad90bd3c188c5c1aac0758c85a17de2aca8,256,V3,e0d6dad90bd3c188c5c1aac0758c85a17de2aca8_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/raspberry_obj_rx-97.750_ry+25.081_rz+17.492_tx+00.156_ty+00.224_s+00.571_e0d6dad90bd3c188c5c1aac0758c85a17de2aca8_256x256.png,2e55a54760c244829d7d9a10353a139c9da09be6,Fruits,3.27699574913,-7.02692720998,-97.3877427609,2958 -2959,raspberry_obj,-7.32939839051,6.77138743201,48.6442001703,0.244,-0.433,1.00857142857,457dc3e9c0ad893fe3c06f1f397120fcfd6e959b,256,V3,457dc3e9c0ad893fe3c06f1f397120fcfd6e959b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/raspberry_obj_rx-99.966_ry-42.511_rz+05.699_tx+00.244_ty-00.433_s+00.706_457dc3e9c0ad893fe3c06f1f397120fcfd6e959b_256x256.png,98f60f1b06b3fe48d7ad69c97b03917335833fea,Fruits,-6.7158033577,-7.38031797474,-42.2236277263,2959 -2960,rdbarren,0.621,20.313,-17.811,0.24,-0.095,0.983076923077,9a73cd0f2c8c4a0254026731109d15444b9a5ae0,256,V3,9a73cd0f2c8c4a0254026731109d15444b9a5ae0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+00.621_ry+20.313_rz-17.811_tx+00.240_ty-00.095_s+01.278_9a73cd0f2c8c4a0254026731109d15444b9a5ae0_256x256.png,935c9abd0ad616ed8123efa242380ab08ea45d8b,Planes,0.621,110.313,-17.811,2960 -2961,rdbarren,0.775,30.933,30.471,0.279,0.277,1.20153846154,370531f4c57ebcaa5a2dbf5fd4cde49a7332880e,256,V3,370531f4c57ebcaa5a2dbf5fd4cde49a7332880e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+00.775_ry+30.933_rz+30.471_tx+00.279_ty+00.277_s+01.562_370531f4c57ebcaa5a2dbf5fd4cde49a7332880e_256x256.png,a17709b4bb68518fd7ab50ea906ab537ec962b77,Planes,0.775,120.933,30.471,2961 -2962,rdbarren,2.613,33.484,-42.596,-0.007,-0.017,1.03153846154,26e291a635d65dcbd7b54ba611bc89e146980bfd,256,V3,26e291a635d65dcbd7b54ba611bc89e146980bfd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+02.613_ry+33.484_rz-42.596_tx-00.007_ty-00.017_s+01.341_26e291a635d65dcbd7b54ba611bc89e146980bfd_256x256.png,a49e1350ac0ec5d2f535651e03d340d73c9d2e6e,Planes,2.613,123.484,-42.596,2962 -2963,rdbarren,3.626,33.518,-7.207,0.168,-0.194,1.23692307692,306b5cab3a33bfb46de6e517e4e8c4c776cb9b64,256,V3,306b5cab3a33bfb46de6e517e4e8c4c776cb9b64_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+03.626_ry+33.518_rz-07.207_tx+00.168_ty-00.194_s+01.608_306b5cab3a33bfb46de6e517e4e8c4c776cb9b64_256x256.png,4ba6a448d58852cc16f25f4f281aef0347e72a71,Planes,3.626,123.518,-7.207,2963 -2964,rdbarren,10.989,-40.275,19.679,0.094,-0.376,0.979230769231,c3b58200001926336739cebf2e48d652860c1198,256,V3,c3b58200001926336739cebf2e48d652860c1198_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+10.989_ry-40.275_rz+19.679_tx+00.094_ty-00.376_s+01.273_c3b58200001926336739cebf2e48d652860c1198_256x256.png,fcbd2d5856fe8545ee8b7b189244c5f8122e7a45,Planes,10.989,49.725,19.679,2964 -2965,rdbarren,15.857,23.053,-2.951,-0.233,0.554,0.902307692308,55b1e56fd00ddd596d60e10b99c2aa216c51b801,256,V3,55b1e56fd00ddd596d60e10b99c2aa216c51b801_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+15.857_ry+23.053_rz-02.951_tx-00.233_ty+00.554_s+01.173_55b1e56fd00ddd596d60e10b99c2aa216c51b801_256x256.png,5db70d6cbfe769977860328321ce490e05cafd8c,Planes,15.857,113.053,-2.951,2965 -2966,rdbarren,16.723,-32.261,-26.294,-0.027,-0.556,1.14153846154,4ae5b44e3ef04267fbef2e9a48bf9f2e8d6c5616,256,V3,4ae5b44e3ef04267fbef2e9a48bf9f2e8d6c5616_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+16.723_ry-32.261_rz-26.294_tx-00.027_ty-00.556_s+01.484_4ae5b44e3ef04267fbef2e9a48bf9f2e8d6c5616_256x256.png,adc12ae31d810d38ffb47208e8a0005f2949a662,Planes,16.723,57.739,-26.294,2966 -2967,rdbarren,17.823,21.752,-29.339,0.188,0.233,1.09307692308,b6c14f77f99aa6533b4022c6406683c9b28390e9,256,V3,b6c14f77f99aa6533b4022c6406683c9b28390e9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+17.823_ry+21.752_rz-29.339_tx+00.188_ty+00.233_s+01.421_b6c14f77f99aa6533b4022c6406683c9b28390e9_256x256.png,3143c24e202ac118eb275e25483f5c2169731878,Planes,17.823,111.752,-29.339,2967 -2968,rdbarren,21.616,30.261,26.581,-0.038,-0.506,1.27230769231,e57befebdd156982268893b74ffe2e826a0953e2,256,V3,e57befebdd156982268893b74ffe2e826a0953e2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+21.616_ry+30.261_rz+26.581_tx-00.038_ty-00.506_s+01.654_e57befebdd156982268893b74ffe2e826a0953e2_256x256.png,3a5b3b63b855a73c6b9aab93b09600212465cfeb,Planes,21.616,120.261,26.581,2968 -2969,rdbarren,23.08,-18.92,-9.205,0.038,0.545,1.01538461538,9d0fca56cb2037678bf3785b66872d755c5c5d49,256,V3,9d0fca56cb2037678bf3785b66872d755c5c5d49_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+23.080_ry-18.920_rz-09.205_tx+00.038_ty+00.545_s+01.320_9d0fca56cb2037678bf3785b66872d755c5c5d49_256x256.png,1763d8563babc5589b7b5ab1efc66c634fe9200b,Planes,23.08,71.08,-9.205,2969 -2970,rdbarren,26.174,27.846,34.862,0.228,-0.303,1.04692307692,2755cd45fdc77af858f1e8149d1b7a811276303d,256,V3,2755cd45fdc77af858f1e8149d1b7a811276303d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+26.174_ry+27.846_rz+34.862_tx+00.228_ty-00.303_s+01.361_2755cd45fdc77af858f1e8149d1b7a811276303d_256x256.png,4b7e4c52d2a6fb102af7d8da9d0d9a0fe8e3ac7f,Planes,26.174,117.846,34.862,2970 -2971,rdbarren,28.404,-7.887,-29.101,0.282,0.587,1.00769230769,18aadfefc26781c7ea330afa1cbb0ed2e4c7cb85,256,V3,18aadfefc26781c7ea330afa1cbb0ed2e4c7cb85_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+28.404_ry-07.887_rz-29.101_tx+00.282_ty+00.587_s+01.310_18aadfefc26781c7ea330afa1cbb0ed2e4c7cb85_256x256.png,c71d0b7e9b2f3ba04d3e06b8e5e9e1c4fa5b55d4,Planes,28.404,82.113,-29.101,2971 -2972,rdbarren,28.428,-39.99,40.779,0.211,0.43,0.896153846154,10cfca5ea068f08b4e70b811446d11a6b7a94150,256,V3,10cfca5ea068f08b4e70b811446d11a6b7a94150_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+28.428_ry-39.990_rz+40.779_tx+00.211_ty+00.430_s+01.165_10cfca5ea068f08b4e70b811446d11a6b7a94150_256x256.png,00c965cf116927c5b407e98c05d8a44779c6b694,Planes,28.428,50.01,40.779,2972 -2973,rdbarren,30.49,16.618,32.468,0.087,-0.052,1.17461538462,e3280dc4030c752d05066b56bd501bbf8a5adcdd,256,V3,e3280dc4030c752d05066b56bd501bbf8a5adcdd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+30.490_ry+16.618_rz+32.468_tx+00.087_ty-00.052_s+01.527_e3280dc4030c752d05066b56bd501bbf8a5adcdd_256x256.png,9d415ca65e4c7dc746e2f16d71921ed68dd2a928,Planes,30.49,106.618,32.468,2973 -2974,rdbarren,32.387,-1.547,28.051,0.089,-0.204,1.04769230769,63e36fc21bcb979b16981de9b228dbe45ff3b8fd,256,V3,63e36fc21bcb979b16981de9b228dbe45ff3b8fd_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+32.387_ry-01.547_rz+28.051_tx+00.089_ty-00.204_s+01.362_63e36fc21bcb979b16981de9b228dbe45ff3b8fd_256x256.png,1536f06c4bb0586d3cba1a9e9e78c55d394822aa,Planes,32.387,88.453,28.051,2974 -2975,rdbarren,34.702,10.153,-18.268,0.26,0.026,0.800769230769,2ad8167b319ea8fd33038befe4de840910397681,256,V3,2ad8167b319ea8fd33038befe4de840910397681_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+34.702_ry+10.153_rz-18.268_tx+00.260_ty+00.026_s+01.041_2ad8167b319ea8fd33038befe4de840910397681_256x256.png,19e5b0d23c34ce9baf3f0d511f4d86d625377d4d,Planes,34.702,100.153,-18.268,2975 -2976,rdbarren,35.339,32.23,-30.774,0.117,-0.463,0.782307692308,350cabe5662ca6b35dee3c87a9ab9673d66d72f1,256,V3,350cabe5662ca6b35dee3c87a9ab9673d66d72f1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+35.339_ry+32.230_rz-30.774_tx+00.117_ty-00.463_s+01.017_350cabe5662ca6b35dee3c87a9ab9673d66d72f1_256x256.png,05b9b23f30ddf16f4c5387200134acab3b54e2c8,Planes,35.339,122.23,-30.774,2976 -2977,rdbarren,38.811,-41.242,18.621,-0.274,-0.514,0.933076923077,c1bbb0869e3fde6808b948e1a08b01c6dab63d2a,256,V3,c1bbb0869e3fde6808b948e1a08b01c6dab63d2a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+38.811_ry-41.242_rz+18.621_tx-00.274_ty-00.514_s+01.213_c1bbb0869e3fde6808b948e1a08b01c6dab63d2a_256x256.png,22f11a838344b3ccce3c04e3773f634253dce241,Planes,38.811,48.758,18.621,2977 -2978,rdbarren,43.506,-7.339,41.123,0.11,0.035,1.06769230769,5c1eefb123a473ef1b9fec13b0c1e8c1b546557e,256,V3,5c1eefb123a473ef1b9fec13b0c1e8c1b546557e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+43.506_ry-07.339_rz+41.123_tx+00.110_ty+00.035_s+01.388_5c1eefb123a473ef1b9fec13b0c1e8c1b546557e_256x256.png,45fe7c7a98334ba13aaf691dbb95b519eb838530,Planes,43.506,82.661,41.123,2978 -2979,rdbarren,43.867,-13.671,-0.751,-0.234,0.253,1.25076923077,df93b58b225578fb3b8ee892d765eb8abd022e8e,256,V3,df93b58b225578fb3b8ee892d765eb8abd022e8e_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx+43.867_ry-13.671_rz-00.751_tx-00.234_ty+00.253_s+01.626_df93b58b225578fb3b8ee892d765eb8abd022e8e_256x256.png,8a83606f4c348f9281ab68550a7f00d102421f17,Planes,43.867,76.329,-0.751,2979 -2980,rdbarren,-0.49,7.6,-33.31,0.091,-0.38,1.11307692308,852a2843cefed7a606e0fe2f81f56bbb8ddedddf,256,V3,852a2843cefed7a606e0fe2f81f56bbb8ddedddf_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-00.490_ry+07.600_rz-33.310_tx+00.091_ty-00.380_s+01.447_852a2843cefed7a606e0fe2f81f56bbb8ddedddf_256x256.png,e6ce78229bfeb13cb5a2f23b812273fcff2ec21f,Planes,-0.49,97.6,-33.31,2980 -2981,rdbarren,-2.825,-23.117,2.27,0.05,0.166,1.04307692308,6bf568b1d914afb26baa8ff463abecf61cd4c5a4,256,V3,6bf568b1d914afb26baa8ff463abecf61cd4c5a4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-02.825_ry-23.117_rz+02.270_tx+00.050_ty+00.166_s+01.356_6bf568b1d914afb26baa8ff463abecf61cd4c5a4_256x256.png,dd476db03f1c27607e1d924bcbb87cc748909925,Planes,-2.825,66.883,2.27,2981 -2982,rdbarren,-5.743,-44.765,-31.13,-0.261,0.398,0.997692307692,d57c2ddc040f235ff379a47ac06ec04ff14f6fc0,256,V3,d57c2ddc040f235ff379a47ac06ec04ff14f6fc0_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-05.743_ry-44.765_rz-31.130_tx-00.261_ty+00.398_s+01.297_d57c2ddc040f235ff379a47ac06ec04ff14f6fc0_256x256.png,6361ad61edb0562e1b9c084ece70058ddb52ec2f,Planes,-5.743,45.235,-31.13,2982 -2983,rdbarren,-8.24,31.262,-41.785,0.154,-0.546,1.00769230769,bb4cadf4fdcb9779677e8c95ef8159d4c13019ff,256,V3,bb4cadf4fdcb9779677e8c95ef8159d4c13019ff_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-08.240_ry+31.262_rz-41.785_tx+00.154_ty-00.546_s+01.310_bb4cadf4fdcb9779677e8c95ef8159d4c13019ff_256x256.png,455df01d0841ed61241f513da1895d4daf520bca,Planes,-8.24,121.262,-41.785,2983 -2984,rdbarren,-9.187,-17.59,-15.747,-0.055,0.473,1.21307692308,67a041abdd8fa2e65f7da965b839a4e1d2ae9a93,256,V3,67a041abdd8fa2e65f7da965b839a4e1d2ae9a93_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-09.187_ry-17.590_rz-15.747_tx-00.055_ty+00.473_s+01.577_67a041abdd8fa2e65f7da965b839a4e1d2ae9a93_256x256.png,08fc35002962a831bc199ec939a7a22ddcd2b6ec,Planes,-9.187,72.41,-15.747,2984 -2985,rdbarren,-11.263,-13.17,12.12,0.137,0.018,0.974615384615,3108374cbe7186b23b58f0deb7eb2474ff3a4fb5,256,V3,3108374cbe7186b23b58f0deb7eb2474ff3a4fb5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-11.263_ry-13.170_rz+12.120_tx+00.137_ty+00.018_s+01.267_3108374cbe7186b23b58f0deb7eb2474ff3a4fb5_256x256.png,2a1d21580400026ef29229f3a5dc6418f232ff2a,Planes,-11.263,76.83,12.12,2985 -2986,rdbarren,-11.28,1.138,22.499,0.133,-0.437,0.918461538462,4484364425ecc03b96eceb537fa80c332b2bf225,256,V3,4484364425ecc03b96eceb537fa80c332b2bf225_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-11.280_ry+01.138_rz+22.499_tx+00.133_ty-00.437_s+01.194_4484364425ecc03b96eceb537fa80c332b2bf225_256x256.png,b7d7ea7158f656f3cfc5158bc87bffef696b4b7e,Planes,-11.28,91.138,22.499,2986 -2987,rdbarren,-13.094,30.748,-18.593,0.005,-0.491,0.983846153846,c66efe56fbb2bcee29caa5588e1ab3541d327291,256,V3,c66efe56fbb2bcee29caa5588e1ab3541d327291_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-13.094_ry+30.748_rz-18.593_tx+00.005_ty-00.491_s+01.279_c66efe56fbb2bcee29caa5588e1ab3541d327291_256x256.png,0f13c3f698baf15cae8432b27960190b0006ac56,Planes,-13.094,120.748,-18.593,2987 -2988,rdbarren,-17.198,11.447,-16.559,0.229,-0.145,1.22538461538,0febcff6532b75aaa14e12364b412bc08624fbb6,256,V3,0febcff6532b75aaa14e12364b412bc08624fbb6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-17.198_ry+11.447_rz-16.559_tx+00.229_ty-00.145_s+01.593_0febcff6532b75aaa14e12364b412bc08624fbb6_256x256.png,298a2500baa41be7c555b6afab3a94ae216baf0e,Planes,-17.198,101.447,-16.559,2988 -2989,rdbarren,-19.034,-3.802,33.031,-0.297,0.284,1.09846153846,36a006940d6c48beb636ed916fe01e701bfe8443,256,V3,36a006940d6c48beb636ed916fe01e701bfe8443_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-19.034_ry-03.802_rz+33.031_tx-00.297_ty+00.284_s+01.428_36a006940d6c48beb636ed916fe01e701bfe8443_256x256.png,d7db75b44ea98f0d00201963e60c8aa01899d137,Planes,-19.034,86.198,33.031,2989 -2990,rdbarren,-23.48,-34.173,33.605,0.17,0.12,0.904615384615,0609351f587aefe86d4254a529d31d1bfbf3f83c,256,V3,0609351f587aefe86d4254a529d31d1bfbf3f83c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-23.480_ry-34.173_rz+33.605_tx+00.170_ty+00.120_s+01.176_0609351f587aefe86d4254a529d31d1bfbf3f83c_256x256.png,1aec0fa372179853c51a4a6b5de3af5f1542f853,Planes,-23.48,55.827,33.605,2990 -2991,rdbarren,-31.898,7.495,-20.328,-0.186,0.249,1.17307692308,dada8e2a57f80498a8cd29465e4a8322d32c856a,256,V3,dada8e2a57f80498a8cd29465e4a8322d32c856a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-31.898_ry+07.495_rz-20.328_tx-00.186_ty+00.249_s+01.525_dada8e2a57f80498a8cd29465e4a8322d32c856a_256x256.png,0a7cbcd3ba4adefa4d29a8b03cd9b4202e6336b9,Planes,-31.898,97.495,-20.328,2991 -2992,rdbarren,-34.2,-0.044,-29.132,-0.189,-0.175,0.878461538462,76208e5d08b39fd577558999835ccfd0e55e47a3,256,V3,76208e5d08b39fd577558999835ccfd0e55e47a3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-34.200_ry-00.044_rz-29.132_tx-00.189_ty-00.175_s+01.142_76208e5d08b39fd577558999835ccfd0e55e47a3_256x256.png,fe31191838a28fab133a4925608cdb79fc89cd3b,Planes,-34.2,89.956,-29.132,2992 -2993,rdbarren,-38.165,-13.816,-39.34,0.157,0.583,1.20538461538,fa4007daf77213cb09552becb05ecce37184c31a,256,V3,fa4007daf77213cb09552becb05ecce37184c31a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-38.165_ry-13.816_rz-39.340_tx+00.157_ty+00.583_s+01.567_fa4007daf77213cb09552becb05ecce37184c31a_256x256.png,ad0a5109df843739b92acae9d3899de57892ed95,Planes,-38.165,76.184,-39.34,2993 -2994,rdbarren,-38.899,44.393,-23.123,0.119,-0.393,0.786923076923,b4f2875b413499eb3d616c12c731a573ab08ae28,256,V3,b4f2875b413499eb3d616c12c731a573ab08ae28_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-38.899_ry+44.393_rz-23.123_tx+00.119_ty-00.393_s+01.023_b4f2875b413499eb3d616c12c731a573ab08ae28_256x256.png,656428c216e8feb4d706f2f56dd5fccc903cfe2a,Planes,-38.899,134.393,-23.123,2994 -2995,rdbarren,-39.95,30.391,2.575,0.06,-0.518,0.904615384615,6350e2d760bc76221aae3dd5c2f921944f4a3f63,256,V3,6350e2d760bc76221aae3dd5c2f921944f4a3f63_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-39.950_ry+30.391_rz+02.575_tx+00.060_ty-00.518_s+01.176_6350e2d760bc76221aae3dd5c2f921944f4a3f63_256x256.png,3c111a8be3bc2902f62f554df72de9e3fe93a571,Planes,-39.95,120.391,2.575,2995 -2996,rdbarren,-40.227,25.316,22.407,-0.024,-0.172,0.953846153846,430857269e75ffb177da221c6e9766991d4e98c1,256,V3,430857269e75ffb177da221c6e9766991d4e98c1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-40.227_ry+25.316_rz+22.407_tx-00.024_ty-00.172_s+01.240_430857269e75ffb177da221c6e9766991d4e98c1_256x256.png,48c01b184d2a67d285195bee5d069828a4406dec,Planes,-40.227,115.316,22.407,2996 -2997,rdbarren,-40.558,32.76,-28.836,-0.17,0.073,1.28461538462,3deac1130a159a93c8cb304e089fee12e673ce1b,256,V3,3deac1130a159a93c8cb304e089fee12e673ce1b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-40.558_ry+32.760_rz-28.836_tx-00.170_ty+00.073_s+01.670_3deac1130a159a93c8cb304e089fee12e673ce1b_256x256.png,3cd4984f6b3941c471b56e324ac67051a1a9caae,Planes,-40.558,122.76,-28.836,2997 -2998,rdbarren,-41.14,-20.705,20.714,0.071,-0.022,1.03692307692,0d29889bfb7f64d4f58f1c219c4ac77aae9bacb1,256,V3,0d29889bfb7f64d4f58f1c219c4ac77aae9bacb1_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-41.140_ry-20.705_rz+20.714_tx+00.071_ty-00.022_s+01.348_0d29889bfb7f64d4f58f1c219c4ac77aae9bacb1_256x256.png,ff2275cade2f43415b673d335842be112c574004,Planes,-41.14,69.295,20.714,2998 -2999,rdbarren,-42.448,-1.999,-42.892,0.132,0.328,0.948461538462,fdd22d651dca790319abf8f62da58f7df5880aa4,256,V3,fdd22d651dca790319abf8f62da58f7df5880aa4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/rdbarren_rx-42.448_ry-01.999_rz-42.892_tx+00.132_ty+00.328_s+01.233_fdd22d651dca790319abf8f62da58f7df5880aa4_256x256.png,bb765198e1e65d1fc0a93255ebbd4a3001dda564,Planes,-42.448,88.001,-42.892,2999 -3000,sopwith,0.309,38.167,3.349,0.095,-0.372,1.09538461538,220a945c6bd4fd5fd69d0e4c10153f36e705c32c,256,V3,220a945c6bd4fd5fd69d0e4c10153f36e705c32c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+00.309_ry+38.167_rz+03.349_tx+00.095_ty-00.372_s+01.424_220a945c6bd4fd5fd69d0e4c10153f36e705c32c_256x256.png,ba784500412bb7681ac89ffdf95d59a0923ff252,Planes,0.309,128.167,3.349,3000 -3001,sopwith,0.715,-28.873,-17.176,-0.061,-0.487,1.23,d8cfe186b70e50d1dc8cd06617bb7f852330fe87,256,V3,d8cfe186b70e50d1dc8cd06617bb7f852330fe87_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+00.715_ry-28.873_rz-17.176_tx-00.061_ty-00.487_s+01.599_d8cfe186b70e50d1dc8cd06617bb7f852330fe87_256x256.png,3767864a1f4f34fdfc3499dca07d2184a3652b1b,Planes,0.715,61.127,-17.176,3001 -3002,sopwith,1.428,-41.289,42.557,0.057,0.379,1.10076923077,00f3161e76a3332c7c3abb1204238f3ebb076dd4,256,V3,00f3161e76a3332c7c3abb1204238f3ebb076dd4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+01.428_ry-41.289_rz+42.557_tx+00.057_ty+00.379_s+01.431_00f3161e76a3332c7c3abb1204238f3ebb076dd4_256x256.png,b264cee708df62d0822f06f465869c06e8306a5b,Planes,1.428,48.711,42.557,3002 -3003,sopwith,3.168,19.279,-9.175,0.27,-0.496,0.786923076923,e1ec1d9bef07caf6e24b81e1275eea5ae9229157,256,V3,e1ec1d9bef07caf6e24b81e1275eea5ae9229157_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+03.168_ry+19.279_rz-09.175_tx+00.270_ty-00.496_s+01.023_e1ec1d9bef07caf6e24b81e1275eea5ae9229157_256x256.png,a24935caa88a6919bdc89283e0ceeba7a93aa747,Planes,3.168,109.279,-9.175,3003 -3004,sopwith,4.804,37.807,37.784,0.209,-0.185,0.966923076923,c2c705881a93a33765d92968d97c9c850b65b09d,256,V3,c2c705881a93a33765d92968d97c9c850b65b09d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+04.804_ry+37.807_rz+37.784_tx+00.209_ty-00.185_s+01.257_c2c705881a93a33765d92968d97c9c850b65b09d_256x256.png,a199eca97125ba1c18b4b8167a935fcaca62727d,Planes,4.804,127.807,37.784,3004 -3005,sopwith,4.846,-9.607,1.509,-0.173,-0.203,1.00230769231,eb7e19da7fe8c5d0accc88a7c4d2aba8f89930be,256,V3,eb7e19da7fe8c5d0accc88a7c4d2aba8f89930be_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+04.846_ry-09.607_rz+01.509_tx-00.173_ty-00.203_s+01.303_eb7e19da7fe8c5d0accc88a7c4d2aba8f89930be_256x256.png,e06ed9ca0d742798a05eed9454ecea6b7fb9deca,Planes,4.846,80.393,1.509,3005 -3006,sopwith,8.233,21.122,41.535,0.093,0.197,0.813076923077,f08e8d56c2a289a3bc33c7ee90b4e7adbc90bf2f,256,V3,f08e8d56c2a289a3bc33c7ee90b4e7adbc90bf2f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+08.233_ry+21.122_rz+41.535_tx+00.093_ty+00.197_s+01.057_f08e8d56c2a289a3bc33c7ee90b4e7adbc90bf2f_256x256.png,4c33ecb7f2ce7084a2f5a06236ad20c439a396d7,Planes,8.233,111.122,41.535,3006 -3007,sopwith,11.382,-31.581,39.616,-0.034,-0.024,0.837692307692,44eaeec3ba6a285c60df789f36eea586d57e3528,256,V3,44eaeec3ba6a285c60df789f36eea586d57e3528_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+11.382_ry-31.581_rz+39.616_tx-00.034_ty-00.024_s+01.089_44eaeec3ba6a285c60df789f36eea586d57e3528_256x256.png,ac23ba2b1ccf975f46d108e4a0251d9854d5612b,Planes,11.382,58.419,39.616,3007 -3008,sopwith,13.034,-3.984,-14.206,-0.069,0.041,0.936923076923,f55379501d8015ff862c71ac4a40b2ef4dc6fee7,256,V3,f55379501d8015ff862c71ac4a40b2ef4dc6fee7_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+13.034_ry-03.984_rz-14.206_tx-00.069_ty+00.041_s+01.218_f55379501d8015ff862c71ac4a40b2ef4dc6fee7_256x256.png,6c79deec56b93376d981e543e05970cde179a362,Planes,13.034,86.016,-14.206,3008 -3009,sopwith,15.923,28.228,-17.609,-0.029,0.319,1.07153846154,392ba23662b421079444617ae3b2e91ad12ef6aa,256,V3,392ba23662b421079444617ae3b2e91ad12ef6aa_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+15.923_ry+28.228_rz-17.609_tx-00.029_ty+00.319_s+01.393_392ba23662b421079444617ae3b2e91ad12ef6aa_256x256.png,39b25e393c55938c32af33a1a0c77ee3a05962af,Planes,15.923,118.228,-17.609,3009 -3010,sopwith,17.653,29.621,3.509,0.004,-0.041,0.866923076923,74ecdf770320ccfca606714b18e7f3fe907b473f,256,V3,74ecdf770320ccfca606714b18e7f3fe907b473f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+17.653_ry+29.621_rz+03.509_tx+00.004_ty-00.041_s+01.127_74ecdf770320ccfca606714b18e7f3fe907b473f_256x256.png,a96eebcc0a422f97310179f824ea444dfa1c053c,Planes,17.653,119.621,3.509,3010 -3011,sopwith,19.867,-31.22,12.67,-0.095,-0.359,1.19076923077,1b80e8a94b784d7e9a99577a1c128474c7f51c77,256,V3,1b80e8a94b784d7e9a99577a1c128474c7f51c77_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+19.867_ry-31.220_rz+12.670_tx-00.095_ty-00.359_s+01.548_1b80e8a94b784d7e9a99577a1c128474c7f51c77_256x256.png,ffdca9f6a398af6c8cdfcba6d741a02384140e7f,Planes,19.867,58.78,12.67,3011 -3012,sopwith,26.111,5.297,43.649,0.252,-0.376,0.956923076923,c256e470856b684d4764a1cb768755d5cf339931,256,V3,c256e470856b684d4764a1cb768755d5cf339931_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+26.111_ry+05.297_rz+43.649_tx+00.252_ty-00.376_s+01.244_c256e470856b684d4764a1cb768755d5cf339931_256x256.png,4ba916e5aebd463303ced992702f5f40b66ab1dd,Planes,26.111,95.297,43.649,3012 -3013,sopwith,27.941,30.829,41.37,-0.005,0.002,1.19923076923,6c8d483d820f610b8f8d32c1eefa9209578e4883,256,V3,6c8d483d820f610b8f8d32c1eefa9209578e4883_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+27.941_ry+30.829_rz+41.370_tx-00.005_ty+00.002_s+01.559_6c8d483d820f610b8f8d32c1eefa9209578e4883_256x256.png,c7313a47c2d3098ab75063dedf489e43919ea5aa,Planes,27.941,120.829,41.37,3013 -3014,sopwith,31.556,-30.362,23.962,-0.059,0.468,1.23615384615,d6399fdb5cfe58a1c1908762cfe82406e2510231,256,V3,d6399fdb5cfe58a1c1908762cfe82406e2510231_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+31.556_ry-30.362_rz+23.962_tx-00.059_ty+00.468_s+01.607_d6399fdb5cfe58a1c1908762cfe82406e2510231_256x256.png,8a89842c864f106b5ba6e37e481e24a87a2dec56,Planes,31.556,59.638,23.962,3014 -3015,sopwith,33.928,-13.764,23.42,0.08,-0.311,1.02461538462,c8a0a46484b613600b2af5f4040d1020c2cc0252,256,V3,c8a0a46484b613600b2af5f4040d1020c2cc0252_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+33.928_ry-13.764_rz+23.420_tx+00.080_ty-00.311_s+01.332_c8a0a46484b613600b2af5f4040d1020c2cc0252_256x256.png,0d2611101bb3885f6b23434bee68473a5e854faf,Planes,33.928,76.236,23.42,3015 -3016,sopwith,36.714,31.961,11.738,0.167,-0.204,0.930769230769,757881d8e9c1520186b6f19ce58a936f8cd933d9,256,V3,757881d8e9c1520186b6f19ce58a936f8cd933d9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+36.714_ry+31.961_rz+11.738_tx+00.167_ty-00.204_s+01.210_757881d8e9c1520186b6f19ce58a936f8cd933d9_256x256.png,a8ca28d649ce57eb072d883576e4183bcdb412ec,Planes,36.714,121.961,11.738,3016 -3017,sopwith,37.324,25.334,-30.246,-0.057,0.034,1.08846153846,f2e35bfcb5daaf1fe7a2f6a16ef9b91e5e1f1b29,256,V3,f2e35bfcb5daaf1fe7a2f6a16ef9b91e5e1f1b29_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+37.324_ry+25.334_rz-30.246_tx-00.057_ty+00.034_s+01.415_f2e35bfcb5daaf1fe7a2f6a16ef9b91e5e1f1b29_256x256.png,4488ad0b9e5b444718361e714c234fc2a40f7efb,Planes,37.324,115.334,-30.246,3017 -3018,sopwith,38.385,21.871,-2.972,0.15,-0.563,1.06153846154,0f6a307d0c0b4d89fe5d7dff5a96d1fa108abeac,256,V3,0f6a307d0c0b4d89fe5d7dff5a96d1fa108abeac_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+38.385_ry+21.871_rz-02.972_tx+00.150_ty-00.563_s+01.380_0f6a307d0c0b4d89fe5d7dff5a96d1fa108abeac_256x256.png,ba0520eb5c060c5d9777afcf1b97a0686dc6fe2c,Planes,38.385,111.871,-2.972,3018 -3019,sopwith,39.729,-12.945,-22.843,0.181,-0.414,0.792307692308,297bf446c63103efe4b63aa65d0f0fb770808661,256,V3,297bf446c63103efe4b63aa65d0f0fb770808661_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+39.729_ry-12.945_rz-22.843_tx+00.181_ty-00.414_s+01.030_297bf446c63103efe4b63aa65d0f0fb770808661_256x256.png,8e520fc91aa9cb710dd182bd1bb7292f807ab9cc,Planes,39.729,77.055,-22.843,3019 -3020,sopwith,40.803,21.183,20.176,0.121,-0.121,1.22384615385,c770a3d1543e5f240db05c8b3f7cbed2e3fba6fa,256,V3,c770a3d1543e5f240db05c8b3f7cbed2e3fba6fa_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx+40.803_ry+21.183_rz+20.176_tx+00.121_ty-00.121_s+01.591_c770a3d1543e5f240db05c8b3f7cbed2e3fba6fa_256x256.png,d8fb7ebe61673cf58c6c7eebdc7c652630b1c72f,Planes,40.803,111.183,20.176,3020 -3021,sopwith,-0.318,-17.104,-27.544,-0.023,0.357,1.23846153846,edc5eab64cbc19131e1f28fc296a6ee3d0e88100,256,V3,edc5eab64cbc19131e1f28fc296a6ee3d0e88100_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-00.318_ry-17.104_rz-27.544_tx-00.023_ty+00.357_s+01.610_edc5eab64cbc19131e1f28fc296a6ee3d0e88100_256x256.png,a7450dc73c309c19ac751a3d3504f0fc84460094,Planes,-0.318,72.896,-27.544,3021 -3022,sopwith,-5.918,-8.8,43.153,0.135,0.195,0.870769230769,c5e0800019045c901b8622c215a871a62cd2d2a9,256,V3,c5e0800019045c901b8622c215a871a62cd2d2a9_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-05.918_ry-08.800_rz+43.153_tx+00.135_ty+00.195_s+01.132_c5e0800019045c901b8622c215a871a62cd2d2a9_256x256.png,900799592d36a0a6104ebb8e2ec48e2c6095d471,Planes,-5.918,81.2,43.153,3022 -3023,sopwith,-8.693,-3.598,42.117,-0.211,-0.271,1.09846153846,b8035d42c216f0b58a96743c9c198d67585829ab,256,V3,b8035d42c216f0b58a96743c9c198d67585829ab_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-08.693_ry-03.598_rz+42.117_tx-00.211_ty-00.271_s+01.428_b8035d42c216f0b58a96743c9c198d67585829ab_256x256.png,88164965de9288630d7c74be94f3847b2be84663,Planes,-8.693,86.402,42.117,3023 -3024,sopwith,-11.033,33.076,-13.265,0.266,0.044,1.20692307692,52629fa75490d61a681ce591faaca2b10afe52eb,256,V3,52629fa75490d61a681ce591faaca2b10afe52eb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-11.033_ry+33.076_rz-13.265_tx+00.266_ty+00.044_s+01.569_52629fa75490d61a681ce591faaca2b10afe52eb_256x256.png,07ec309a28ae7d9fca7d5a170fa8dd3d00ed4992,Planes,-11.033,123.076,-13.265,3024 -3025,sopwith,-13.476,-23.828,-6.053,-0.098,-0.305,0.834615384615,54e6262aad94a3171277d3a33018355832c52f53,256,V3,54e6262aad94a3171277d3a33018355832c52f53_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-13.476_ry-23.828_rz-06.053_tx-00.098_ty-00.305_s+01.085_54e6262aad94a3171277d3a33018355832c52f53_256x256.png,2c8adc53f83ddd7bd1c0dfdb8401009056d2d296,Planes,-13.476,66.172,-6.053,3025 -3026,sopwith,-18.333,12.643,-2.488,-0.194,-0.14,1.07307692308,23957035c1e23cd4e8480b9aae74eca53837d835,256,V3,23957035c1e23cd4e8480b9aae74eca53837d835_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-18.333_ry+12.643_rz-02.488_tx-00.194_ty-00.140_s+01.395_23957035c1e23cd4e8480b9aae74eca53837d835_256x256.png,ebd7a52df8df50b52c5896006319750d4a90b31d,Planes,-18.333,102.643,-2.488,3026 -3027,sopwith,-19.516,14.705,28.96,-0.167,0.504,0.959230769231,823b00e8444166b141eaed90931f3d0199e655c2,256,V3,823b00e8444166b141eaed90931f3d0199e655c2_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-19.516_ry+14.705_rz+28.960_tx-00.167_ty+00.504_s+01.247_823b00e8444166b141eaed90931f3d0199e655c2_256x256.png,2cf156d7833894c4127181bd7cfba3fffedde5dc,Planes,-19.516,104.705,28.96,3027 -3028,sopwith,-20.202,20.427,-23.237,0.144,-0.393,0.936923076923,e74d0b42f12cb39df4234023b3705ab9ed1d8b89,256,V3,e74d0b42f12cb39df4234023b3705ab9ed1d8b89_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-20.202_ry+20.427_rz-23.237_tx+00.144_ty-00.393_s+01.218_e74d0b42f12cb39df4234023b3705ab9ed1d8b89_256x256.png,d9367e8c5d63ebb375b403251d123ea7fdfea7ff,Planes,-20.202,110.427,-23.237,3028 -3029,sopwith,-22.836,-5.325,7.4,0.193,-0.101,1.06384615385,95a1acd06dd4bb1b590627f3d21a6792b8478186,256,V3,95a1acd06dd4bb1b590627f3d21a6792b8478186_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-22.836_ry-05.325_rz+07.400_tx+00.193_ty-00.101_s+01.383_95a1acd06dd4bb1b590627f3d21a6792b8478186_256x256.png,649f44d1355797aa0d9413a53446cc19728929f1,Planes,-22.836,84.675,7.4,3029 -3030,sopwith,-23.316,-8.445,-43.96,-0.069,-0.227,0.986923076923,94d5e38d6e8eb1d2c92cf0434578ed1842c705ed,256,V3,94d5e38d6e8eb1d2c92cf0434578ed1842c705ed_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-23.316_ry-08.445_rz-43.960_tx-00.069_ty-00.227_s+01.283_94d5e38d6e8eb1d2c92cf0434578ed1842c705ed_256x256.png,fcf2d2b8904ecbb88ba57f55682818e9315cbd69,Planes,-23.316,81.555,-43.96,3030 -3031,sopwith,-31.478,5.628,-42.83,0.088,-0.415,1.24846153846,c08111d840b7673f4b5c01b8127fa661cc844021,256,V3,c08111d840b7673f4b5c01b8127fa661cc844021_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-31.478_ry+05.628_rz-42.830_tx+00.088_ty-00.415_s+01.623_c08111d840b7673f4b5c01b8127fa661cc844021_256x256.png,cf0ee008cac7128229898d5496a7bb7c7b1c43a5,Planes,-31.478,95.628,-42.83,3031 -3032,sopwith,-31.922,27.847,-1.208,0.233,0.136,0.832307692308,a076dac94d633ee15f5b150d47ec78f9fde5b49f,256,V3,a076dac94d633ee15f5b150d47ec78f9fde5b49f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-31.922_ry+27.847_rz-01.208_tx+00.233_ty+00.136_s+01.082_a076dac94d633ee15f5b150d47ec78f9fde5b49f_256x256.png,90090bffba917fa61957f776f5761e273b33738d,Planes,-31.922,117.847,-1.208,3032 -3033,sopwith,-36.918,-0.644,-11.162,0.18,-0.32,1.28846153846,8f74fcca99443ceb3047317c0f34b21a8e383072,256,V3,8f74fcca99443ceb3047317c0f34b21a8e383072_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-36.918_ry-00.644_rz-11.162_tx+00.180_ty-00.320_s+01.675_8f74fcca99443ceb3047317c0f34b21a8e383072_256x256.png,0dbbfeca96eebba5ed91bf49cf556e4eb644da9c,Planes,-36.918,89.356,-11.162,3033 -3034,sopwith,-38.154,-1.19,-2.674,0.298,-0.133,0.990769230769,fafb5ced51c03c5bc576c7a28641702f8a658349,256,V3,fafb5ced51c03c5bc576c7a28641702f8a658349_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-38.154_ry-01.190_rz-02.674_tx+00.298_ty-00.133_s+01.288_fafb5ced51c03c5bc576c7a28641702f8a658349_256x256.png,8cefbd3bc87d3648092663267de9c5808082c9f2,Planes,-38.154,88.81,-2.674,3034 -3035,sopwith,-42.056,26.141,-40.37,-0.078,-0.057,0.981538461538,ea9df6de7990a706fc3ea795ce28404d79289546,256,V3,ea9df6de7990a706fc3ea795ce28404d79289546_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-42.056_ry+26.141_rz-40.370_tx-00.078_ty-00.057_s+01.276_ea9df6de7990a706fc3ea795ce28404d79289546_256x256.png,13fff8a47518c2265cf50cbf2b841b54117d1df0,Planes,-42.056,116.141,-40.37,3035 -3036,sopwith,-42.5,-30.474,8.147,0.212,0.417,0.931538461538,6823134952b8f8d4ae75558813b6239fedf111aa,256,V3,6823134952b8f8d4ae75558813b6239fedf111aa_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-42.500_ry-30.474_rz+08.147_tx+00.212_ty+00.417_s+01.211_6823134952b8f8d4ae75558813b6239fedf111aa_256x256.png,ba7c9a8ac12491e8b207d918b2e91080f53a29df,Planes,-42.5,59.526,8.147,3036 -3037,sopwith,-42.805,-28.242,25.232,0.05,-0.246,0.876923076923,0577f5535b5d3d943346a640123845e15db36d5c,256,V3,0577f5535b5d3d943346a640123845e15db36d5c_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-42.805_ry-28.242_rz+25.232_tx+00.050_ty-00.246_s+01.140_0577f5535b5d3d943346a640123845e15db36d5c_256x256.png,3c47f8d32216fd199f9e6f4936a90acbd25fca36,Planes,-42.805,61.758,25.232,3037 -3038,sopwith,-43.522,-23.719,40.757,0.048,0.527,1.17307692308,7ca5718f6144bec31cb312b6ea41465f0c694cd5,256,V3,7ca5718f6144bec31cb312b6ea41465f0c694cd5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-43.522_ry-23.719_rz+40.757_tx+00.048_ty+00.527_s+01.525_7ca5718f6144bec31cb312b6ea41465f0c694cd5_256x256.png,8b43ec02d255aa07aa3cb612daa8bc56f5ad86fd,Planes,-43.522,66.281,40.757,3038 -3039,sopwith,-43.573,23.045,19.954,-0.066,-0.091,0.769230769231,697ffcb723fa1c9dc2974681bf2dd10159d56796,256,V3,697ffcb723fa1c9dc2974681bf2dd10159d56796_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/sopwith_rx-43.573_ry+23.045_rz+19.954_tx-00.066_ty-00.091_s+01.000_697ffcb723fa1c9dc2974681bf2dd10159d56796_256x256.png,bfa955961c2e74138a3fe8d7d0127f4f90e5db5c,Planes,-43.573,113.045,19.954,3039 -3040,support,0.393,12.944,44.442,0.222,0.093,1.04666666667,d37b77d02f9550e1510195d1034bf88ac2008ad6,256,V3,d37b77d02f9550e1510195d1034bf88ac2008ad6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+00.393_ry+102.944_rz+44.442_tx+00.222_ty+00.093_s+01.256_d37b77d02f9550e1510195d1034bf88ac2008ad6_256x256.png,7deb43c76471d94d349d98ca11b28bb4a887f36f,Boats,0.393,-77.056,44.442,3040 -3041,support,0.547,-12.537,-21.63,0.024,-0.064,1.12333333333,b976381b024ad3407b44ecf0bccb634a0febaf9f,256,V3,b976381b024ad3407b44ecf0bccb634a0febaf9f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+00.547_ry+77.463_rz-21.630_tx+00.024_ty-00.064_s+01.348_b976381b024ad3407b44ecf0bccb634a0febaf9f_256x256.png,a4488f4f249cb10ddbdeb679ee8be7804b93e5f7,Boats,0.547,-102.537,-21.63,3041 -3042,support,1.124,-37.145,-30.202,-0.224,0.059,1.00833333333,90468c32039b8d6a49a1254c075761300b15d1f3,256,V3,90468c32039b8d6a49a1254c075761300b15d1f3_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+01.124_ry+52.855_rz-30.202_tx-00.224_ty+00.059_s+01.210_90468c32039b8d6a49a1254c075761300b15d1f3_256x256.png,3facfb974768e9de688e570190ca65f80fc22733,Boats,1.124,-127.145,-30.202,3042 -3043,support,3.332,-18.611,-11.431,0.264,0.13,0.849166666667,3969412d521d04a6f7ea0bd09bdc503ba969477f,256,V3,3969412d521d04a6f7ea0bd09bdc503ba969477f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+03.332_ry+71.389_rz-11.431_tx+00.264_ty+00.130_s+01.019_3969412d521d04a6f7ea0bd09bdc503ba969477f_256x256.png,2392f0f6673576100fb6b90cdbe3c2ae9a5904e7,Boats,3.332,-108.611,-11.431,3043 -3044,support,3.771,-23.77,-13.323,-0.193,0.196,1.02,4520badac513a35f705b3dcfd74ad42fd06f0234,256,V3,4520badac513a35f705b3dcfd74ad42fd06f0234_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+03.771_ry+66.230_rz-13.323_tx-00.193_ty+00.196_s+01.224_4520badac513a35f705b3dcfd74ad42fd06f0234_256x256.png,f7d08264012457a6239f17e47a9ddd49a70e232c,Boats,3.771,-113.77,-13.323,3044 -3045,support,4.697,4.219,44.525,0.02,0.391,0.835,3bf3a81c19fc140d1f75530e68a90fe3e67b10bf,256,V3,3bf3a81c19fc140d1f75530e68a90fe3e67b10bf_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+04.697_ry+94.219_rz+44.525_tx+00.020_ty+00.391_s+01.002_3bf3a81c19fc140d1f75530e68a90fe3e67b10bf_256x256.png,070c86fd3b23f6d1e81b826f8d059fb973915801,Boats,4.697,-85.781,44.525,3045 -3046,support,6.781,-10.771,26.939,0.27,0.063,1.24916666667,5993816ffb9fd288574ec8ee75ef5a970c7ca540,256,V3,5993816ffb9fd288574ec8ee75ef5a970c7ca540_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+06.781_ry+79.229_rz+26.939_tx+00.270_ty+00.063_s+01.499_5993816ffb9fd288574ec8ee75ef5a970c7ca540_256x256.png,7fba644d265d2aa0b064a7c71ba8ce7fadfb7faa,Boats,6.781,-100.771,26.939,3046 -3047,support,6.937,-2.117,-33.585,0.265,0.546,0.849166666667,1ae21d65f8003abdcf258437a6a52761e1d99eab,256,V3,1ae21d65f8003abdcf258437a6a52761e1d99eab_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+06.937_ry+87.883_rz-33.585_tx+00.265_ty+00.546_s+01.019_1ae21d65f8003abdcf258437a6a52761e1d99eab_256x256.png,7594aa1551539ef0546ab11db98a90ef8f42c12e,Boats,6.937,-92.117,-33.585,3047 -3048,support,9.223,8.69,11.194,-0.163,0.389,1.13666666667,e066758a9d11c876f0c556c09f7d656b11e226de,256,V3,e066758a9d11c876f0c556c09f7d656b11e226de_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+09.223_ry+98.690_rz+11.194_tx-00.163_ty+00.389_s+01.364_e066758a9d11c876f0c556c09f7d656b11e226de_256x256.png,4c03bc776be206b918d5b31ea279eb764880753a,Boats,9.223,-81.31,11.194,3048 -3049,support,10.245,-31.834,29.861,-0.287,0.341,1.29,cffe4f8911ca6c41c2666c82d54549bdc839421b,256,V3,cffe4f8911ca6c41c2666c82d54549bdc839421b_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+10.245_ry+58.166_rz+29.861_tx-00.287_ty+00.341_s+01.548_cffe4f8911ca6c41c2666c82d54549bdc839421b_256x256.png,41888ae06361de4bf00e034a54dd2f5d12923279,Boats,10.245,-121.834,29.861,3049 -3050,support,13.25,-14.989,29.188,-0.01,0.12,1.12083333333,9336675e87b9e02d6bb81eb69ea22ad015b5649d,256,V3,9336675e87b9e02d6bb81eb69ea22ad015b5649d_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+13.250_ry+75.011_rz+29.188_tx-00.010_ty+00.120_s+01.345_9336675e87b9e02d6bb81eb69ea22ad015b5649d_256x256.png,e38cea4a9a3f1f654b046353ba3dbfb00eb39689,Boats,13.25,-104.989,29.188,3050 -3051,support,13.287,19.877,-6.031,0.052,-0.078,1.20666666667,5a3b50c108216ec2f195edf6bf2debbacc36e639,256,V3,5a3b50c108216ec2f195edf6bf2debbacc36e639_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+13.287_ry+109.877_rz-06.031_tx+00.052_ty-00.078_s+01.448_5a3b50c108216ec2f195edf6bf2debbacc36e639_256x256.png,1b79181499f09e262314fd21990fa92a549f2535,Boats,13.287,-70.123,-6.031,3051 -3052,support,13.527,42.498,32.675,0.123,0.148,0.886666666667,a261c8734ebb32d404593b967c8860966def7762,256,V3,a261c8734ebb32d404593b967c8860966def7762_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+13.527_ry+132.498_rz+32.675_tx+00.123_ty+00.148_s+01.064_a261c8734ebb32d404593b967c8860966def7762_256x256.png,c9dcc46730a11e6adccc8b088d6f27ab37d890f3,Boats,13.527,-47.502,32.675,3052 -3053,support,14.193,-23.678,-12.023,-0.093,-0.339,1.05166666667,a83e45fbf8752c617b0e6c370337b86d160fe3d5,256,V3,a83e45fbf8752c617b0e6c370337b86d160fe3d5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+14.193_ry+66.322_rz-12.023_tx-00.093_ty-00.339_s+01.262_a83e45fbf8752c617b0e6c370337b86d160fe3d5_256x256.png,451aff502a7dc8d45ac3c2c9ff17248ec50b8403,Boats,14.193,-113.678,-12.023,3053 -3054,support,18.241,13.444,-31.052,0.056,0.213,1.23333333333,8e88371a3c73ec9cc5b7e962604ea6552bb37a23,256,V3,8e88371a3c73ec9cc5b7e962604ea6552bb37a23_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+18.241_ry+103.444_rz-31.052_tx+00.056_ty+00.213_s+01.480_8e88371a3c73ec9cc5b7e962604ea6552bb37a23_256x256.png,98df6d5fdb258461105e364dc245c4d5ed52c1a9,Boats,18.241,-76.556,-31.052,3054 -3055,support,22.902,-43.832,22.971,0.006,-0.379,1.20333333333,57c54936251abc0ee1602dae4488fbc347053c62,256,V3,57c54936251abc0ee1602dae4488fbc347053c62_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+22.902_ry+46.168_rz+22.971_tx+00.006_ty-00.379_s+01.444_57c54936251abc0ee1602dae4488fbc347053c62_256x256.png,aea392bf3d5d8080ed5333eb6c561cdad7a2a1a5,Boats,22.902,-133.832,22.971,3055 -3056,support,24.538,-14.758,-8.121,0.258,-0.172,1.24166666667,9e7ead1073a1b385f64a5fa7fd5fda8e3c4ffb48,256,V3,9e7ead1073a1b385f64a5fa7fd5fda8e3c4ffb48_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+24.538_ry+75.242_rz-08.121_tx+00.258_ty-00.172_s+01.490_9e7ead1073a1b385f64a5fa7fd5fda8e3c4ffb48_256x256.png,f4d1fcd59b3be48361986028f3f7f4c0a2bf6a36,Boats,24.538,-104.758,-8.121,3056 -3057,support,24.936,2.145,24.474,0.011,-0.487,1.02416666667,a6ee64417e4680e214741a1ef6de0a62d0c36537,256,V3,a6ee64417e4680e214741a1ef6de0a62d0c36537_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+24.936_ry+92.145_rz+24.474_tx+00.011_ty-00.487_s+01.229_a6ee64417e4680e214741a1ef6de0a62d0c36537_256x256.png,14b6a5b3d52a6b55d548f37eb0fabf161f882708,Boats,24.936,-87.855,24.474,3057 -3058,support,25.788,-5.212,20.597,-0.136,0.076,1.24333333333,53bdbd64d21df2fc954b8109443523f9425536cf,256,V3,53bdbd64d21df2fc954b8109443523f9425536cf_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+25.788_ry+84.788_rz+20.597_tx-00.136_ty+00.076_s+01.492_53bdbd64d21df2fc954b8109443523f9425536cf_256x256.png,9214196c8dbd835a5875f4019a627a64645959d5,Boats,25.788,-95.212,20.597,3058 -3059,support,26.89,16.45,-38.898,-0.274,0.449,0.943333333333,af7dbded92986ea2c4732a6f19c35e8595bcc55f,256,V3,af7dbded92986ea2c4732a6f19c35e8595bcc55f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+26.890_ry+106.450_rz-38.898_tx-00.274_ty+00.449_s+01.132_af7dbded92986ea2c4732a6f19c35e8595bcc55f_256x256.png,a925bb4f6fb8d8e73d83fe510796fab3c0e502ff,Boats,26.89,-73.55,-38.898,3059 -3060,support,32.002,-41.777,13.853,-0.012,-0.503,0.885833333333,00f94524154d4c4c8efaaa20a390deec4ac75d66,256,V3,00f94524154d4c4c8efaaa20a390deec4ac75d66_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+32.002_ry+48.223_rz+13.853_tx-00.012_ty-00.503_s+01.063_00f94524154d4c4c8efaaa20a390deec4ac75d66_256x256.png,d7da821f42fcda9b55f5246745b2858ae3233b0e,Boats,32.002,-131.777,13.853,3060 -3061,support,33.737,7.404,3.109,0.12,-0.43,0.985833333333,55ce654fd5c7bb0852f31c69930a57d3b9d4add5,256,V3,55ce654fd5c7bb0852f31c69930a57d3b9d4add5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+33.737_ry+97.404_rz+03.109_tx+00.120_ty-00.430_s+01.183_55ce654fd5c7bb0852f31c69930a57d3b9d4add5_256x256.png,31e423a71ddefa779ce113867cb3a37edc3b2b6b,Boats,33.737,-82.596,3.109,3061 -3062,support,34.182,-40.866,14.406,-0.066,-0.379,0.925,fc650f5ce0442d7ef6ca664fc6fda0ea142e52b5,256,V3,fc650f5ce0442d7ef6ca664fc6fda0ea142e52b5_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+34.182_ry+49.134_rz+14.406_tx-00.066_ty-00.379_s+01.110_fc650f5ce0442d7ef6ca664fc6fda0ea142e52b5_256x256.png,a3db00968b0e7a5c9673d1f95db7119dc5f23f98,Boats,34.182,-130.866,14.406,3062 -3063,support,34.583,-10.924,-21.355,0.093,-0.31,1.29583333333,dd9d6e222f9a7685c927656dff626f8b61ae29cb,256,V3,dd9d6e222f9a7685c927656dff626f8b61ae29cb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+34.583_ry+79.076_rz-21.355_tx+00.093_ty-00.310_s+01.555_dd9d6e222f9a7685c927656dff626f8b61ae29cb_256x256.png,b28aa229f7e6354833d1a23d0dce1631cda255c9,Boats,34.583,-100.924,-21.355,3063 -3064,support,35.249,33.321,22.391,0.183,-0.259,0.8,cf545c0247df8a461134e7980df6572f810c5daa,256,V3,cf545c0247df8a461134e7980df6572f810c5daa_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+35.249_ry+123.321_rz+22.391_tx+00.183_ty-00.259_s+00.960_cf545c0247df8a461134e7980df6572f810c5daa_256x256.png,9ca7999cc9c2c77495db341c13b6d13cdfb44e44,Boats,35.249,-56.679,22.391,3064 -3065,support,42.108,4.301,-43.162,0.163,-0.526,0.8525,d0a64f09c81a38d0f38056c149f3623d90b2d6b6,256,V3,d0a64f09c81a38d0f38056c149f3623d90b2d6b6_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+42.108_ry+94.301_rz-43.162_tx+00.163_ty-00.526_s+01.023_d0a64f09c81a38d0f38056c149f3623d90b2d6b6_256x256.png,93e17d12b458132139b7b05949ea861e73a9ca10,Boats,42.108,-85.699,-43.162,3065 -3066,support,43.384,15.797,-19.073,0.227,-0.04,0.970833333333,65fc8fabbbe05697074937247c1f2d4c185434e4,256,V3,65fc8fabbbe05697074937247c1f2d4c185434e4_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx+43.384_ry+105.797_rz-19.073_tx+00.227_ty-00.040_s+01.165_65fc8fabbbe05697074937247c1f2d4c185434e4_256x256.png,2e329b1d6e6c633dbfbd07d680904e4cab73c12a,Boats,43.384,-74.203,-19.073,3066 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-3069,support,-1.914,38.659,-3.849,-0.229,-0.511,1.08333333333,288a9057b36474324777d80975bbf971054bec6f,256,V3,288a9057b36474324777d80975bbf971054bec6f_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx-01.914_ry+128.659_rz-03.849_tx-00.229_ty-00.511_s+01.300_288a9057b36474324777d80975bbf971054bec6f_256x256.png,3094f209ce35151cee831ab1e11b269b3e4b92d7,Boats,-1.914,-51.341,-3.849,3069 -3070,support,-2.916,-8.823,27.51,0.254,-0.413,0.813333333333,5042efa09c93a2377c48a22cd9a88b7822f2321a,256,V3,5042efa09c93a2377c48a22cd9a88b7822f2321a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx-02.916_ry+81.177_rz+27.510_tx+00.254_ty-00.413_s+00.976_5042efa09c93a2377c48a22cd9a88b7822f2321a_256x256.png,f8925daf88aaad59d2f8dbccfa52f87dda5856a8,Boats,-2.916,-98.823,27.51,3070 -3071,support,-8.252,27.987,35.62,0.167,-0.395,1.18666666667,56d2fa0c3dd23540d42b16f2f8a9b5da3c66b4bb,256,V3,56d2fa0c3dd23540d42b16f2f8a9b5da3c66b4bb_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx-08.252_ry+117.987_rz+35.620_tx+00.167_ty-00.395_s+01.424_56d2fa0c3dd23540d42b16f2f8a9b5da3c66b4bb_256x256.png,5195545124123680bcd7099a1c37d4ff6fd7a41b,Boats,-8.252,-62.013,35.62,3071 -3072,support,-17.095,-21.256,-37.628,0.213,0.106,1.2725,7a02c166ec927929995becc3d93b0f7f7195cd9a,256,V3,7a02c166ec927929995becc3d93b0f7f7195cd9a_V3,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation03_20110128/support_rx-17.095_ry+68.744_rz-37.628_tx+00.213_ty+00.106_s+01.527_7a02c166ec927929995becc3d93b0f7f7195cd9a_256x256.png,b4c43e9e96cdc69700694d74e84ca5f37d6ce056,Boats,-17.095,-111.256,-37.628,3072 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-3281,BAHRAIN,6.144,-7.71,4.972,0.139,0.379,1.24916666667,c682e8d311941dc98b2d88b3953f32435dab0388,256,V6,c682e8d311941dc98b2d88b3953f32435dab0388_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+06.144_ry-07.710_rz+04.972_tx+00.139_ty+00.379_s+01.499_c682e8d311941dc98b2d88b3953f32435dab0388_256x256.png,2bb1fee17ec8e59288420ccf0c0387696653e46d,Boats,6.144,82.29,4.972,3281 -3282,BAHRAIN,8.585,68.922,-79.354,0.022,-0.428,1.27666666667,bd1b2877fcdfcbf9a9a6e717b26db556513827b8,256,V6,bd1b2877fcdfcbf9a9a6e717b26db556513827b8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+08.585_ry+68.922_rz-79.354_tx+00.022_ty-00.428_s+01.532_bd1b2877fcdfcbf9a9a6e717b26db556513827b8_256x256.png,3916a2ad525217c711859b64a8781182693871e1,Boats,8.585,158.922,-79.354,3282 -3283,BAHRAIN,11.564,7.913,44.776,-0.425,-0.363,0.9575,ff8dd29c946ae69a20b58bb4afd630bc886f781c,256,V6,ff8dd29c946ae69a20b58bb4afd630bc886f781c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+11.564_ry+07.913_rz+44.776_tx-00.425_ty-00.363_s+01.149_ff8dd29c946ae69a20b58bb4afd630bc886f781c_256x256.png,9467368376518201fb7a037b5dfb26a8fd6e97d3,Boats,11.564,97.913,44.776,3283 -3284,BAHRAIN,15.093,-64.195,71.221,0.524,-1.16,1.24416666667,16c5ee559a7f033a288a55a1d25f76d745e3e5b2,256,V6,16c5ee559a7f033a288a55a1d25f76d745e3e5b2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+15.093_ry-64.195_rz+71.221_tx+00.524_ty-01.160_s+01.493_16c5ee559a7f033a288a55a1d25f76d745e3e5b2_256x256.png,1765362534caaa403a432823e1f66359fbd68d70,Boats,15.093,25.805,71.221,3284 -3285,BAHRAIN,18.38,-65.537,33.757,-0.425,-0.68,1.06333333333,7c169cd77290d4290b60440e3694e55839f1f14b,256,V6,7c169cd77290d4290b60440e3694e55839f1f14b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+18.380_ry-65.537_rz+33.757_tx-00.425_ty-00.680_s+01.276_7c169cd77290d4290b60440e3694e55839f1f14b_256x256.png,d13277351c461734c155906869873518ee0b25b0,Boats,18.38,24.463,33.757,3285 -3286,BAHRAIN,19.462,-72.625,20.869,-0.291,0.23,1.045,a45ad3588f88c1beed8a52d37ccfa7f1cdf6aeb3,256,V6,a45ad3588f88c1beed8a52d37ccfa7f1cdf6aeb3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+19.462_ry-72.625_rz+20.869_tx-00.291_ty+00.230_s+01.254_a45ad3588f88c1beed8a52d37ccfa7f1cdf6aeb3_256x256.png,32d9b3b85fe54371f5c2f018505354bcf977f7d3,Boats,19.462,17.375,20.869,3286 -3287,BAHRAIN,30.783,-65.885,-55.065,0.372,-0.106,1.46166666667,edd88c69386cc59e77a8bc1b3b77e5d374f3228d,256,V6,edd88c69386cc59e77a8bc1b3b77e5d374f3228d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+30.783_ry-65.885_rz-55.065_tx+00.372_ty-00.106_s+01.754_edd88c69386cc59e77a8bc1b3b77e5d374f3228d_256x256.png,a43df506a8f456e50fe7e5762e238baa9dff0132,Boats,30.783,24.115,-55.065,3287 -3288,BAHRAIN,36.148,-46.482,-22.325,-0.536,0.786,1.3275,e8534a863343c8598430fbf8f7a182237248b3cb,256,V6,e8534a863343c8598430fbf8f7a182237248b3cb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+36.148_ry-46.482_rz-22.325_tx-00.536_ty+00.786_s+01.593_e8534a863343c8598430fbf8f7a182237248b3cb_256x256.png,9f756c473ba5a12d4f47dd8fa23ede3c908265a8,Boats,36.148,43.518,-22.325,3288 -3289,BAHRAIN,36.331,15.183,-11.784,-0.495,0.889,0.735,9770675a55c7f0a643d555d1c5615f814698d4f5,256,V6,9770675a55c7f0a643d555d1c5615f814698d4f5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+36.331_ry+15.183_rz-11.784_tx-00.495_ty+00.889_s+00.882_9770675a55c7f0a643d555d1c5615f814698d4f5_256x256.png,092a1e2dce407be41bb27b1078098c881a29d871,Boats,36.331,105.183,-11.784,3289 -3290,BAHRAIN,40.428,68.771,70.106,0.124,1.175,1.18833333333,8ee047aecfab0a7ba73ed051176d0c4d1ed2d94d,256,V6,8ee047aecfab0a7ba73ed051176d0c4d1ed2d94d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+40.428_ry+68.771_rz+70.106_tx+00.124_ty+01.175_s+01.426_8ee047aecfab0a7ba73ed051176d0c4d1ed2d94d_256x256.png,9f8d06eed4be00272af8d1d6d901c931c75ad172,Boats,40.428,158.771,70.106,3290 -3291,BAHRAIN,42.021,16.116,84.766,0.03,1.055,0.628333333333,9e57e01ecf519cb03a85240d007208deb8c383b5,256,V6,9e57e01ecf519cb03a85240d007208deb8c383b5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+42.021_ry+16.116_rz+84.766_tx+00.030_ty+01.055_s+00.754_9e57e01ecf519cb03a85240d007208deb8c383b5_256x256.png,98e879585a0d4e2ad125d00ac1719ccbe99215bd,Boats,42.021,106.116,84.766,3291 -3292,BAHRAIN,42.477,-48.512,87.746,0.09,-0.72,1.4225,f2f5ac6d81984ce598fa60e3142a5ba2ab399b83,256,V6,f2f5ac6d81984ce598fa60e3142a5ba2ab399b83_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+42.477_ry-48.512_rz+87.746_tx+00.090_ty-00.720_s+01.707_f2f5ac6d81984ce598fa60e3142a5ba2ab399b83_256x256.png,30c075cb3ab373d25651b4613e41a8f93b8cef54,Boats,42.477,41.488,87.746,3292 -3293,BAHRAIN,42.52,29.121,-51.522,0.263,0.412,1.42583333333,cd1bf83d814c864893c3d54f0f69ea570fe3e680,256,V6,cd1bf83d814c864893c3d54f0f69ea570fe3e680_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+42.520_ry+29.121_rz-51.522_tx+00.263_ty+00.412_s+01.711_cd1bf83d814c864893c3d54f0f69ea570fe3e680_256x256.png,760bccbb074a5ef75d6ce2d969ea027e4b07d78d,Boats,42.52,119.121,-51.522,3293 -3294,BAHRAIN,42.845,-26.658,83.208,-0.339,-0.962,0.643333333333,1ba991f859921e090a9cb79fe2c4728dfaaa5e90,256,V6,1ba991f859921e090a9cb79fe2c4728dfaaa5e90_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+42.845_ry-26.658_rz+83.208_tx-00.339_ty-00.962_s+00.772_1ba991f859921e090a9cb79fe2c4728dfaaa5e90_256x256.png,f7ad8c3333e4d74d78be705e5b469d354ca2e639,Boats,42.845,63.342,83.208,3294 -3295,BAHRAIN,47.305,-45.781,56.901,-0.554,0.077,1.295,68b4a1bdc580122cabea518dd74e0fe1a01043a1,256,V6,68b4a1bdc580122cabea518dd74e0fe1a01043a1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+47.305_ry-45.781_rz+56.901_tx-00.554_ty+00.077_s+01.554_68b4a1bdc580122cabea518dd74e0fe1a01043a1_256x256.png,853317c07af3053c49689119437486f2ec06413f,Boats,47.305,44.219,56.901,3295 -3296,BAHRAIN,49.94,-18.879,78.745,0.124,1.199,1.1175,307a9d6ad42365ad6aea9f66d4f74d843f378d6d,256,V6,307a9d6ad42365ad6aea9f66d4f74d843f378d6d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+49.940_ry-18.879_rz+78.745_tx+00.124_ty+01.199_s+01.341_307a9d6ad42365ad6aea9f66d4f74d843f378d6d_256x256.png,bb3a615fa3c42ba2a346e5b55ccf4305cd468aa2,Boats,49.94,71.121,78.745,3296 -3297,BAHRAIN,78.079,-23.27,-19.057,0.028,-0.011,1.16916666667,83c9731c1123fe4abe94405ed5ff759daa955dc0,256,V6,83c9731c1123fe4abe94405ed5ff759daa955dc0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+78.079_ry-23.270_rz-19.057_tx+00.028_ty-00.011_s+01.403_83c9731c1123fe4abe94405ed5ff759daa955dc0_256x256.png,d9f9c85ee56a77e18719e7274b668be1df21f9a7,Boats,78.079,66.73,-19.057,3297 -3298,BAHRAIN,79.8,-24.2,86.561,-0.324,-0.187,1.36833333333,23340194c546aaa37b4d509ffe479a9141b25eff,256,V6,23340194c546aaa37b4d509ffe479a9141b25eff_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+79.800_ry-24.200_rz+86.561_tx-00.324_ty-00.187_s+01.642_23340194c546aaa37b4d509ffe479a9141b25eff_256x256.png,1f78750ea7773fa64d18c007f3d0568449cfd346,Boats,79.8,65.8,86.561,3298 -3299,BAHRAIN,85.621,53.638,-85.326,0.512,1.115,1.08,44ed1b1722929add51ffb4c2baed57415c4f5ed0,256,V6,44ed1b1722929add51ffb4c2baed57415c4f5ed0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+85.621_ry+53.638_rz-85.326_tx+00.512_ty+01.115_s+01.296_44ed1b1722929add51ffb4c2baed57415c4f5ed0_256x256.png,8568b2410502fdc3607a472e030b315acbbf522a,Boats,85.621,143.638,-85.326,3299 -3300,BAHRAIN,86.453,-56.977,-38.946,0.191,1.143,0.676666666667,73821cf7ffd15127ebbb209c5e9bb94f35a088fd,256,V6,73821cf7ffd15127ebbb209c5e9bb94f35a088fd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+86.453_ry-56.977_rz-38.946_tx+00.191_ty+01.143_s+00.812_73821cf7ffd15127ebbb209c5e9bb94f35a088fd_256x256.png,2e1852e9055243d0be64948128a136fdc52ede9e,Boats,86.453,33.023,-38.946,3300 -3301,BAHRAIN,89.365,-39.602,55.687,-0.025,0.115,1.09916666667,922bbea99958ebf8ffee43ff2dc8a11b5bab38cb,256,V6,922bbea99958ebf8ffee43ff2dc8a11b5bab38cb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx+89.365_ry-39.602_rz+55.687_tx-00.025_ty+00.115_s+01.319_922bbea99958ebf8ffee43ff2dc8a11b5bab38cb_256x256.png,ad45277dfaa58f13b4439f8dd3f472bcd82814d7,Boats,89.365,50.398,55.687,3301 -3302,BAHRAIN,-2.561,58.683,52.373,0.144,0.797,1.25416666667,e1089275f5cc104f982f7839ab5ed4c4a97c77f8,256,V6,e1089275f5cc104f982f7839ab5ed4c4a97c77f8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-02.561_ry+58.683_rz+52.373_tx+00.144_ty+00.797_s+01.505_e1089275f5cc104f982f7839ab5ed4c4a97c77f8_256x256.png,a8ef1f83cc8ae5a14bbe11229d371df03830d084,Boats,-2.561,148.683,52.373,3302 -3303,BAHRAIN,-22.567,-84.865,-76.934,0.37,0.979,0.945833333333,57b817b30fd40e5e8f4eaaf85f12da8bcc8c1a15,256,V6,57b817b30fd40e5e8f4eaaf85f12da8bcc8c1a15_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-22.567_ry-84.865_rz-76.934_tx+00.370_ty+00.979_s+01.135_57b817b30fd40e5e8f4eaaf85f12da8bcc8c1a15_256x256.png,1bd099d62daf94b4b9307231035717ff3b545fb7,Boats,-22.567,5.135,-76.934,3303 -3304,BAHRAIN,-24.527,28.494,-40.414,0.017,0.359,1.17,d3196c6410ce9303231509db26af0b376af4aaaa,256,V6,d3196c6410ce9303231509db26af0b376af4aaaa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-24.527_ry+28.494_rz-40.414_tx+00.017_ty+00.359_s+01.404_d3196c6410ce9303231509db26af0b376af4aaaa_256x256.png,2fe54f78caa0ad8432346022b90fd203f7a05f83,Boats,-24.527,118.494,-40.414,3304 -3305,BAHRAIN,-32.148,30.015,70.014,-0.597,-0.741,1.46916666667,47d379ef17e3ef108785d124c24b80beeb47887d,256,V6,47d379ef17e3ef108785d124c24b80beeb47887d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-32.148_ry+30.015_rz+70.014_tx-00.597_ty-00.741_s+01.763_47d379ef17e3ef108785d124c24b80beeb47887d_256x256.png,6119c3552955b19059220dccc2475922996c7ce1,Boats,-32.148,120.015,70.014,3305 -3306,BAHRAIN,-38.097,-42.238,60.806,0.111,-0.136,1.44,01634a8fb5e5857b80a088811110213bbd8a142b,256,V6,01634a8fb5e5857b80a088811110213bbd8a142b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-38.097_ry-42.238_rz+60.806_tx+00.111_ty-00.136_s+01.728_01634a8fb5e5857b80a088811110213bbd8a142b_256x256.png,889b6328802242f844fd967921514c709ab1f7c8,Boats,-38.097,47.762,60.806,3306 -3307,BAHRAIN,-39.299,-85.385,-17.607,-0.6,0.781,0.7075,85373b59851a67787252bdb555683a100fe95a3e,256,V6,85373b59851a67787252bdb555683a100fe95a3e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-39.299_ry-85.385_rz-17.607_tx-00.600_ty+00.781_s+00.849_85373b59851a67787252bdb555683a100fe95a3e_256x256.png,a9209230691bbefb8ab55570356d55b5c386ba0a,Boats,-39.299,4.615,-17.607,3307 -3308,BAHRAIN,-40.458,-14.863,55.456,-0.025,0.842,0.8775,8f971f0650fcd283d1af228e4ab66022beb71ebe,256,V6,8f971f0650fcd283d1af228e4ab66022beb71ebe_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-40.458_ry-14.863_rz+55.456_tx-00.025_ty+00.842_s+01.053_8f971f0650fcd283d1af228e4ab66022beb71ebe_256x256.png,09bee0bdea54c139e9644e5209b53886f32b4617,Boats,-40.458,75.137,55.456,3308 -3309,BAHRAIN,-43.299,-12.328,-42.491,0.456,-0.285,1.03333333333,b83ad31a7679b3890b433648c84fc5291feece15,256,V6,b83ad31a7679b3890b433648c84fc5291feece15_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-43.299_ry-12.328_rz-42.491_tx+00.456_ty-00.285_s+01.240_b83ad31a7679b3890b433648c84fc5291feece15_256x256.png,091665c50eee5c9aeffedc1ff3f3f4069737c2a8,Boats,-43.299,77.672,-42.491,3309 -3310,BAHRAIN,-45.18,57.308,36.563,0.071,0.788,1.4925,e6e35b52067f0ea85e1741f401e5c76bc8f2cf31,256,V6,e6e35b52067f0ea85e1741f401e5c76bc8f2cf31_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-45.180_ry+57.308_rz+36.563_tx+00.071_ty+00.788_s+01.791_e6e35b52067f0ea85e1741f401e5c76bc8f2cf31_256x256.png,bd47aa0af9c93e23207b5256394400a1a745cda6,Boats,-45.18,147.308,36.563,3310 -3311,BAHRAIN,-53.414,-32.726,26.802,-0.477,0.776,0.808333333333,5eeb41e4b0166da9c6a7f13f8740808528ba64ee,256,V6,5eeb41e4b0166da9c6a7f13f8740808528ba64ee_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-53.414_ry-32.726_rz+26.802_tx-00.477_ty+00.776_s+00.970_5eeb41e4b0166da9c6a7f13f8740808528ba64ee_256x256.png,80ce3c53e995f039c109d28dd42f1fa654d59fff,Boats,-53.414,57.274,26.802,3311 -3312,BAHRAIN,-61.265,-21.769,12.857,-0.143,0.6,1.51,f4ccac53b7b0126069e75210168ca5b3b0a5caa0,256,V6,f4ccac53b7b0126069e75210168ca5b3b0a5caa0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-61.265_ry-21.769_rz+12.857_tx-00.143_ty+00.600_s+01.812_f4ccac53b7b0126069e75210168ca5b3b0a5caa0_256x256.png,5f7b81c3563a755f92c3cb65585b258f7f03a526,Boats,-61.265,68.231,12.857,3312 -3313,BAHRAIN,-64.439,1.132,-29.263,0.221,-0.434,1.1575,2fc4c54f96ba1fcdede5ca8ffe4866466ffcc95c,256,V6,2fc4c54f96ba1fcdede5ca8ffe4866466ffcc95c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-64.439_ry+01.132_rz-29.263_tx+00.221_ty-00.434_s+01.389_2fc4c54f96ba1fcdede5ca8ffe4866466ffcc95c_256x256.png,1d3f80afca7b043e00b4e5ec8e93e999f80fd2f0,Boats,-64.439,91.132,-29.263,3313 -3314,BAHRAIN,-68.819,-66.03,-68.866,0.185,-1.025,0.654166666667,428141cec44fe3ef7e6f2927080244d39b5a70a1,256,V6,428141cec44fe3ef7e6f2927080244d39b5a70a1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-68.819_ry-66.030_rz-68.866_tx+00.185_ty-01.025_s+00.785_428141cec44fe3ef7e6f2927080244d39b5a70a1_256x256.png,0c77bbb0ebd9ae9233d7528bc358c7d5ae943d3c,Boats,-68.819,23.97,-68.866,3314 -3315,BAHRAIN,-69.064,20.449,15.563,-0.516,-0.123,1.215,35188a6639ef55c9c3659bb994308dc5827d0228,256,V6,35188a6639ef55c9c3659bb994308dc5827d0228_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-69.064_ry+20.449_rz+15.563_tx-00.516_ty-00.123_s+01.458_35188a6639ef55c9c3659bb994308dc5827d0228_256x256.png,aaae0b8c1d671222cff1fbfc17fd7fda3015932b,Boats,-69.064,110.449,15.563,3315 -3316,BAHRAIN,-69.201,-58.015,-25.759,0.43,-0.532,1.42083333333,9f2054a459bcd0e3c0c85895041717fd56c457f8,256,V6,9f2054a459bcd0e3c0c85895041717fd56c457f8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-69.201_ry-58.015_rz-25.759_tx+00.430_ty-00.532_s+01.705_9f2054a459bcd0e3c0c85895041717fd56c457f8_256x256.png,dd95c545a2459b05276eec748d33187d9fa0ff7b,Boats,-69.201,31.985,-25.759,3316 -3317,BAHRAIN,-71.394,87.939,-11.494,-0.123,-0.123,1.45666666667,d4c45585948d33966a451cb6e618505693091c4a,256,V6,d4c45585948d33966a451cb6e618505693091c4a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-71.394_ry+87.939_rz-11.494_tx-00.123_ty-00.123_s+01.748_d4c45585948d33966a451cb6e618505693091c4a_256x256.png,0b0930b242112187588a72080d4fd52b351bb6e3,Boats,-71.394,177.939,-11.494,3317 -3318,BAHRAIN,-75.864,-14.049,-21.982,-0.376,-0.06,1.08333333333,d16e6a6ceffe6cb296bbfb2246016ac229292cdb,256,V6,d16e6a6ceffe6cb296bbfb2246016ac229292cdb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-75.864_ry-14.049_rz-21.982_tx-00.376_ty-00.060_s+01.300_d16e6a6ceffe6cb296bbfb2246016ac229292cdb_256x256.png,6babb55ca6a1abd7428c1b81e6711b7a58a6e114,Boats,-75.864,75.951,-21.982,3318 -3319,BAHRAIN,-79.919,-67.273,-51.939,-0.418,0.28,0.906666666667,1f78df407961264e010731240022d4072364888d,256,V6,1f78df407961264e010731240022d4072364888d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/BAHRAIN_rx-79.919_ry-67.273_rz-51.939_tx-00.418_ty+00.280_s+01.088_1f78df407961264e010731240022d4072364888d_256x256.png,6476bbb3cc1c2c9f5f84f1e8595ce2c966ec804e,Boats,-79.919,22.727,-51.939,3319 -3320,Beetle,4.396,83.635,-73.961,-0.537,1.084,1.43,0dbfd23d38f18bed0a528a3e12fc48c83c3d5910,256,V6,0dbfd23d38f18bed0a528a3e12fc48c83c3d5910_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+04.396_ry+83.635_rz-73.961_tx-00.537_ty+01.084_s+01.430_0dbfd23d38f18bed0a528a3e12fc48c83c3d5910_256x256.png,2ee2fbc1d0f4313440916af69cf042871af396a8,Cars,4.396,173.635,-73.961,3320 -3321,Beetle,9.347,60.716,17.807,0.099,-0.099,1.461,fc0c35af01b63b712700752dd7005fbbbfca2430,256,V6,fc0c35af01b63b712700752dd7005fbbbfca2430_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+09.347_ry+60.716_rz+17.807_tx+00.099_ty-00.099_s+01.461_fc0c35af01b63b712700752dd7005fbbbfca2430_256x256.png,8a3003ffc0692c46b9f01228cb33ec08f4dbedf0,Cars,9.347,150.716,17.807,3321 -3322,Beetle,9.699,-0.44,-47.458,-0.005,0.296,1.325,9af261cf2c60457dd3aefbe5afc5ef45b85406bf,256,V6,9af261cf2c60457dd3aefbe5afc5ef45b85406bf_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+09.699_ry-00.440_rz-47.458_tx-00.005_ty+00.296_s+01.325_9af261cf2c60457dd3aefbe5afc5ef45b85406bf_256x256.png,57b016e53c0a6f286f222290bb65c788d84d2ed5,Cars,9.699,89.56,-47.458,3322 -3323,Beetle,16.75,-41.85,-31.16,-0.489,0.557,1.317,c4b1aa9a453675590ff93c0f7ea2cff0b7aea9cc,256,V6,c4b1aa9a453675590ff93c0f7ea2cff0b7aea9cc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+16.750_ry-41.850_rz-31.160_tx-00.489_ty+00.557_s+01.317_c4b1aa9a453675590ff93c0f7ea2cff0b7aea9cc_256x256.png,4620b05116e2334d176cff01384451061b0e12b4,Cars,16.75,48.15,-31.16,3323 -3324,Beetle,18.468,13.861,-0.641,-0.246,-1.038,1.448,62383a2a8a133772768b927cc6e31c796b5f2b21,256,V6,62383a2a8a133772768b927cc6e31c796b5f2b21_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+18.468_ry+13.861_rz-00.641_tx-00.246_ty-01.038_s+01.448_62383a2a8a133772768b927cc6e31c796b5f2b21_256x256.png,e4c30a43b712767c6bcc1d2a861fe12e558fde3b,Cars,18.468,103.861,-0.641,3324 -3325,Beetle,22.484,-56.616,40.587,0.374,0.138,0.699,dc08c002ce47e9f26406350af9a9c1c6b8dabd1e,256,V6,dc08c002ce47e9f26406350af9a9c1c6b8dabd1e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+22.484_ry-56.616_rz+40.587_tx+00.374_ty+00.138_s+00.699_dc08c002ce47e9f26406350af9a9c1c6b8dabd1e_256x256.png,445afa2d5b85ee596eb8364c8760a45e11be7228,Cars,22.484,33.384,40.587,3325 -3326,Beetle,26.111,87.983,-4.8,-0.362,-0.346,0.82,1abc06de56081bf45b8db2ff9c814ad8425f1806,256,V6,1abc06de56081bf45b8db2ff9c814ad8425f1806_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+26.111_ry+87.983_rz-04.800_tx-00.362_ty-00.346_s+00.820_1abc06de56081bf45b8db2ff9c814ad8425f1806_256x256.png,9a21e4dd644a0eba154d26fd49d4a7f57bd26576,Cars,26.111,177.983,-4.8,3326 -3327,Beetle,27.243,-31.917,83.893,-0.243,-0.604,1.082,97edc6ac3fa927242b0b74011302d3d23b945d42,256,V6,97edc6ac3fa927242b0b74011302d3d23b945d42_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+27.243_ry-31.917_rz+83.893_tx-00.243_ty-00.604_s+01.082_97edc6ac3fa927242b0b74011302d3d23b945d42_256x256.png,4f9bf312d52954582b7d7244f5ccf6b31f749a9b,Cars,27.243,58.083,83.893,3327 -3328,Beetle,37.489,-58.395,50.374,0.085,0.652,0.923,122aa62d7722553f036199f505093b064a76573f,256,V6,122aa62d7722553f036199f505093b064a76573f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+37.489_ry-58.395_rz+50.374_tx+00.085_ty+00.652_s+00.923_122aa62d7722553f036199f505093b064a76573f_256x256.png,ea63aea68a20dcaef8059ad7913b0a8771ce716a,Cars,37.489,31.605,50.374,3328 -3329,Beetle,38.591,-67.026,-72.298,-0.479,0.055,0.665,01c4a004939adfc9e0b21445ec5cfd12e818b549,256,V6,01c4a004939adfc9e0b21445ec5cfd12e818b549_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+38.591_ry-67.026_rz-72.298_tx-00.479_ty+00.055_s+00.665_01c4a004939adfc9e0b21445ec5cfd12e818b549_256x256.png,3beb7add64e3f57765972a3a93293a031c081aca,Cars,38.591,22.974,-72.298,3329 -3330,Beetle,40.949,74.828,37.431,-0.413,0.115,1.564,9542d8a47cf367a5340670aed3ec99bdf38f68bb,256,V6,9542d8a47cf367a5340670aed3ec99bdf38f68bb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+40.949_ry+74.828_rz+37.431_tx-00.413_ty+00.115_s+01.564_9542d8a47cf367a5340670aed3ec99bdf38f68bb_256x256.png,fd868ee46942f63d391ceec817c694eeb8c12e7f,Cars,40.949,164.828,37.431,3330 -3331,Beetle,44.903,-24.507,35.832,0.359,-0.203,0.971,0eab2b85e32029d5ab43433c097a4538ff8a4f51,256,V6,0eab2b85e32029d5ab43433c097a4538ff8a4f51_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+44.903_ry-24.507_rz+35.832_tx+00.359_ty-00.203_s+00.971_0eab2b85e32029d5ab43433c097a4538ff8a4f51_256x256.png,fc80c1a24d8d0063fa351c798e22d26e25021fef,Cars,44.903,65.493,35.832,3331 -3332,Beetle,49.365,-88.841,30.815,0.229,-0.016,1.447,d78e909711717af8f43a610bcced7da513896e59,256,V6,d78e909711717af8f43a610bcced7da513896e59_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+49.365_ry-88.841_rz+30.815_tx+00.229_ty-00.016_s+01.447_d78e909711717af8f43a610bcced7da513896e59_256x256.png,a2922567424353443387e9be616537526b5a3041,Cars,49.365,1.159,30.815,3332 -3333,Beetle,54.632,-49.588,-32.621,0.435,-0.98,1.518,472a5016df3a8cf90ac364396c0aea5f257e0524,256,V6,472a5016df3a8cf90ac364396c0aea5f257e0524_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+54.632_ry-49.588_rz-32.621_tx+00.435_ty-00.980_s+01.518_472a5016df3a8cf90ac364396c0aea5f257e0524_256x256.png,94304a565ae3d667ed6d175a25a8091c8c092319,Cars,54.632,40.412,-32.621,3333 -3334,Beetle,57.867,-39.816,-8.845,-0.368,0.651,1.218,6a47b35572f881fd7e539d2f30cc93891f6563e4,256,V6,6a47b35572f881fd7e539d2f30cc93891f6563e4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+57.867_ry-39.816_rz-08.845_tx-00.368_ty+00.651_s+01.218_6a47b35572f881fd7e539d2f30cc93891f6563e4_256x256.png,5448fe76906f6e6bba37f1100a67816f544095c8,Cars,57.867,50.184,-8.845,3334 -3335,Beetle,62.318,-80.706,81.738,-0.271,1.176,1.522,77c0bdb6c6e1e5980a2fcb2f0a0ab95de833131a,256,V6,77c0bdb6c6e1e5980a2fcb2f0a0ab95de833131a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+62.318_ry-80.706_rz+81.738_tx-00.271_ty+01.176_s+01.522_77c0bdb6c6e1e5980a2fcb2f0a0ab95de833131a_256x256.png,e637cfc0c0cb1cbb0f8559e6080b9a01a427d5b6,Cars,62.318,9.294,81.738,3335 -3336,Beetle,65.041,33.157,80.796,-0.153,0.521,0.887,f8c6379f0cf97ffbadc01b9bf26b4ef2bbdcd118,256,V6,f8c6379f0cf97ffbadc01b9bf26b4ef2bbdcd118_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+65.041_ry+33.157_rz+80.796_tx-00.153_ty+00.521_s+00.887_f8c6379f0cf97ffbadc01b9bf26b4ef2bbdcd118_256x256.png,fac28cae480a37221b3d76073ed06f897eee7564,Cars,65.041,123.157,80.796,3336 -3337,Beetle,66.691,2.215,-70.438,0.137,0.981,1.323,53991aafc814d9db3b4bb72b1127a00dd068c54c,256,V6,53991aafc814d9db3b4bb72b1127a00dd068c54c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+66.691_ry+02.215_rz-70.438_tx+00.137_ty+00.981_s+01.323_53991aafc814d9db3b4bb72b1127a00dd068c54c_256x256.png,4069cf701dee7c47ba55d9af9b561197710aee47,Cars,66.691,92.215,-70.438,3337 -3338,Beetle,77.56,-25.624,0.533,-0.298,-0.4,1.46,649b6644656e7d09ae5432a3b8f9430063931aa6,256,V6,649b6644656e7d09ae5432a3b8f9430063931aa6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+77.560_ry-25.624_rz+00.533_tx-00.298_ty-00.400_s+01.460_649b6644656e7d09ae5432a3b8f9430063931aa6_256x256.png,e2b30287849fd115edaac2ea24be6ffd307f261a,Cars,77.56,64.376,0.533,3338 -3339,Beetle,82.417,19.477,-14.612,-0.239,0.779,0.687,696d067653516065070bba1171ba31138761d59e,256,V6,696d067653516065070bba1171ba31138761d59e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+82.417_ry+19.477_rz-14.612_tx-00.239_ty+00.779_s+00.687_696d067653516065070bba1171ba31138761d59e_256x256.png,c9b69d0419c50dd6d0f36e0a91493c538f367764,Cars,82.417,109.477,-14.612,3339 -3340,Beetle,82.968,-31.535,-15.193,0.215,0.068,1.216,145a6e3292048ff41b3b98a3ed7c2b4986831b44,256,V6,145a6e3292048ff41b3b98a3ed7c2b4986831b44_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+82.968_ry-31.535_rz-15.193_tx+00.215_ty+00.068_s+01.216_145a6e3292048ff41b3b98a3ed7c2b4986831b44_256x256.png,f73d0a8b54b888fcb5824ba3060e9b6ea54fbe32,Cars,82.968,58.465,-15.193,3340 -3341,Beetle,86.738,-38.935,25.21,0.277,-1.103,0.922,136c759a63ebebbb3cb69f0c48bfe08619b8cda1,256,V6,136c759a63ebebbb3cb69f0c48bfe08619b8cda1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx+86.738_ry-38.935_rz+25.210_tx+00.277_ty-01.103_s+00.922_136c759a63ebebbb3cb69f0c48bfe08619b8cda1_256x256.png,376179af66e183c41f945c24e6a50153dcfb51c9,Cars,86.738,51.065,25.21,3341 -3342,Beetle,-4.393,75.432,20.88,0.59,-0.007,0.842,f7d374efbc50d8b7333ec0669dee95ca76c39f42,256,V6,f7d374efbc50d8b7333ec0669dee95ca76c39f42_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-04.393_ry+75.432_rz+20.880_tx+00.590_ty-00.007_s+00.842_f7d374efbc50d8b7333ec0669dee95ca76c39f42_256x256.png,6048e86328c69af3cbda338f96056e22488587cc,Cars,-4.393,165.432,20.88,3342 -3343,Beetle,-9.245,-22.267,25.833,-0.261,-0.537,0.788,62b972600d9049e0a7837d6f06688b2339d3f392,256,V6,62b972600d9049e0a7837d6f06688b2339d3f392_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-09.245_ry-22.267_rz+25.833_tx-00.261_ty-00.537_s+00.788_62b972600d9049e0a7837d6f06688b2339d3f392_256x256.png,b9d57ed1b0e8ae86572d815ed0c6d13af844ebee,Cars,-9.245,67.733,25.833,3343 -3344,Beetle,-18.732,16.617,-83.128,-0.427,0.5,0.723,7d6aabd19445ba994c9eea23305ba9e7c5370c4f,256,V6,7d6aabd19445ba994c9eea23305ba9e7c5370c4f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-18.732_ry+16.617_rz-83.128_tx-00.427_ty+00.500_s+00.723_7d6aabd19445ba994c9eea23305ba9e7c5370c4f_256x256.png,eeea21e3fb5c35fa4fc15995881f18abb239a69a,Cars,-18.732,106.617,-83.128,3344 -3345,Beetle,-23.878,89.935,-37.115,-0.12,0.364,0.719,8c1f554847486da583ba47f996c62309d3ac718b,256,V6,8c1f554847486da583ba47f996c62309d3ac718b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-23.878_ry+89.935_rz-37.115_tx-00.120_ty+00.364_s+00.719_8c1f554847486da583ba47f996c62309d3ac718b_256x256.png,cef2e0da5cbd40eda20c3b4e2c044042cf1dfef1,Cars,-23.878,179.935,-37.115,3345 -3346,Beetle,-26.415,16.807,-19.273,0.431,0.857,0.883,3f3cd9786734d8b2de652cefe6f7289fb60cf0ab,256,V6,3f3cd9786734d8b2de652cefe6f7289fb60cf0ab_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-26.415_ry+16.807_rz-19.273_tx+00.431_ty+00.857_s+00.883_3f3cd9786734d8b2de652cefe6f7289fb60cf0ab_256x256.png,4ed10aa6fc3efdcb0cb92ccb35ea49d8b6f5da43,Cars,-26.415,106.807,-19.273,3346 -3347,Beetle,-31.693,25.944,45.289,-0.245,-0.9,1.056,35f91dddb15173ba4d82bc11b044e512a57477da,256,V6,35f91dddb15173ba4d82bc11b044e512a57477da_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-31.693_ry+25.944_rz+45.289_tx-00.245_ty-00.900_s+01.056_35f91dddb15173ba4d82bc11b044e512a57477da_256x256.png,cdcee29c52ece33df85e54067cf9df67f8ff450b,Cars,-31.693,115.944,45.289,3347 -3348,Beetle,-32.432,64.523,-86.202,-0.007,-0.035,1.07,3bd11a358f4e3c57c91fdeb39cb0df539c55051f,256,V6,3bd11a358f4e3c57c91fdeb39cb0df539c55051f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-32.432_ry+64.523_rz-86.202_tx-00.007_ty-00.035_s+01.070_3bd11a358f4e3c57c91fdeb39cb0df539c55051f_256x256.png,15820b7442df80d6b8b535a4715746d14496b9c0,Cars,-32.432,154.523,-86.202,3348 -3349,Beetle,-44.009,-47.929,-38.539,0.486,0.227,1.326,b7ec60081a1264c0e2270c8c48b3b9cc5bd0bfe9,256,V6,b7ec60081a1264c0e2270c8c48b3b9cc5bd0bfe9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-44.009_ry-47.929_rz-38.539_tx+00.486_ty+00.227_s+01.326_b7ec60081a1264c0e2270c8c48b3b9cc5bd0bfe9_256x256.png,291ad5b8d2ad96ff6ef2a607dca72c25610d67bb,Cars,-44.009,42.071,-38.539,3349 -3350,Beetle,-45.376,-41.945,48.697,-0.523,-0.38,0.789,d494481d2e4cec4fd57e45ac78a18f61943fa6f2,256,V6,d494481d2e4cec4fd57e45ac78a18f61943fa6f2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-45.376_ry-41.945_rz+48.697_tx-00.523_ty-00.380_s+00.789_d494481d2e4cec4fd57e45ac78a18f61943fa6f2_256x256.png,15e993fc54f3c7c972b9d9cd6b1796f8706406d3,Cars,-45.376,48.055,48.697,3350 -3351,Beetle,-56.131,83.646,-35.479,-0.213,-1.024,0.697,d8c51a01039bb4807b1d46c112baddf993a599a2,256,V6,d8c51a01039bb4807b1d46c112baddf993a599a2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-56.131_ry+83.646_rz-35.479_tx-00.213_ty-01.024_s+00.697_d8c51a01039bb4807b1d46c112baddf993a599a2_256x256.png,f51d072e6ddf97633618193b83a24f2a55d56665,Cars,-56.131,173.646,-35.479,3351 -3352,Beetle,-57.488,-79.02,4.694,0.378,-1.167,0.718,9704d9bd5ba8d9d0cf645b33dac345fe02bdb6d1,256,V6,9704d9bd5ba8d9d0cf645b33dac345fe02bdb6d1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-57.488_ry-79.020_rz+04.694_tx+00.378_ty-01.167_s+00.718_9704d9bd5ba8d9d0cf645b33dac345fe02bdb6d1_256x256.png,d741c8b7300831d5523a32d81fb74896191ab10d,Cars,-57.488,10.98,4.694,3352 -3353,Beetle,-69.174,63.92,-30.308,0.501,-1.004,0.702,a37508567534839c48323722e51305668731ce52,256,V6,a37508567534839c48323722e51305668731ce52_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-69.174_ry+63.920_rz-30.308_tx+00.501_ty-01.004_s+00.702_a37508567534839c48323722e51305668731ce52_256x256.png,9f798f67fbac949dc6037add9ac26bf43a2d032e,Cars,-69.174,153.92,-30.308,3353 -3354,Beetle,-70.304,47.106,12.051,0.519,0.764,1.559,1ed78938296e02732cde8c27b4a3fa718bc27844,256,V6,1ed78938296e02732cde8c27b4a3fa718bc27844_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-70.304_ry+47.106_rz+12.051_tx+00.519_ty+00.764_s+01.559_1ed78938296e02732cde8c27b4a3fa718bc27844_256x256.png,4f5db4cadfd733acc4036c12fb3b052c20270e8d,Cars,-70.304,137.106,12.051,3354 -3355,Beetle,-70.657,-9.803,-88.277,-0.012,-1.118,1.522,20f5352c5cdbaf4a93ef54838903867dd97dbf37,256,V6,20f5352c5cdbaf4a93ef54838903867dd97dbf37_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-70.657_ry-09.803_rz-88.277_tx-00.012_ty-01.118_s+01.522_20f5352c5cdbaf4a93ef54838903867dd97dbf37_256x256.png,6d2de643e0b2c7c7cc573cd92f57b96aedb5938b,Cars,-70.657,80.197,-88.277,3355 -3356,Beetle,-70.859,49.78,-40.859,0.367,0.367,1.52,e216af060ba527da4f13642813c7f3e90e1b9764,256,V6,e216af060ba527da4f13642813c7f3e90e1b9764_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-70.859_ry+49.780_rz-40.859_tx+00.367_ty+00.367_s+01.520_e216af060ba527da4f13642813c7f3e90e1b9764_256x256.png,3005daecce79849e293c0ed43b61454fdaa4b377,Cars,-70.859,139.78,-40.859,3356 -3357,Beetle,-75.319,-16.455,-33.697,0.436,-0.557,1.491,63e0c27781e439aed5c9d74a6b82b73a16164055,256,V6,63e0c27781e439aed5c9d74a6b82b73a16164055_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-75.319_ry-16.455_rz-33.697_tx+00.436_ty-00.557_s+01.491_63e0c27781e439aed5c9d74a6b82b73a16164055_256x256.png,d88c492194c28b1e8a60bd6d3967b84ee654265b,Cars,-75.319,73.545,-33.697,3357 -3358,Beetle,-75.969,23.184,-83.666,0.422,0.746,1.435,293b6d53a6c846985344f0451829f19a9b42bf6b,256,V6,293b6d53a6c846985344f0451829f19a9b42bf6b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-75.969_ry+23.184_rz-83.666_tx+00.422_ty+00.746_s+01.435_293b6d53a6c846985344f0451829f19a9b42bf6b_256x256.png,88b2f37e96f4706e20a1fff8d57a688053033f37,Cars,-75.969,113.184,-83.666,3358 -3359,Beetle,-79.661,80.847,17.417,0.166,0.917,1.374,1610c00b393f69705a559565779750b4d129875b,256,V6,1610c00b393f69705a559565779750b4d129875b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Beetle_rx-79.661_ry+80.847_rz+17.417_tx+00.166_ty+00.917_s+01.374_1610c00b393f69705a559565779750b4d129875b_256x256.png,525903ca9bf26d5d2b4ca65129b1e022da85832d,Cars,-79.661,170.847,17.417,3359 -3360,CGTG_L,0.521,42.09,46.568,0.064,0.864,1.14916666667,aa8298441a903c62d75f02af052b9a92c5242296,256,V6,aa8298441a903c62d75f02af052b9a92c5242296_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+00.521_ry+42.090_rz+46.568_tx+00.064_ty+00.864_s+01.379_aa8298441a903c62d75f02af052b9a92c5242296_256x256.png,d2057cd9ca0e5ee83b90be1e8455ba8d54876101,Boats,0.521,132.09,46.568,3360 -3361,CGTG_L,5.056,35.29,-5.66,-0.587,0.667,0.825833333333,bd7198a5d461541b5fb3a10788a6a50f3bc44f4e,256,V6,bd7198a5d461541b5fb3a10788a6a50f3bc44f4e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+05.056_ry+35.290_rz-05.660_tx-00.587_ty+00.667_s+00.991_bd7198a5d461541b5fb3a10788a6a50f3bc44f4e_256x256.png,892b6807dff9cc974e5f9e4e7997866f421db38e,Boats,5.056,125.29,-5.66,3361 -3362,CGTG_L,5.933,-69.409,-72.144,-0.28,-0.943,0.895833333333,9a09e2b6a283da6b9c5716ef3af38fea5b075db3,256,V6,9a09e2b6a283da6b9c5716ef3af38fea5b075db3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+05.933_ry-69.409_rz-72.144_tx-00.280_ty-00.943_s+01.075_9a09e2b6a283da6b9c5716ef3af38fea5b075db3_256x256.png,715a911bd15a206b15fa2d681dbebb2a87e2d073,Boats,5.933,20.591,-72.144,3362 -3363,CGTG_L,15.855,44.991,-61.163,0.512,-0.91,0.911666666667,70296c9099b444abf8e38312a778d719b3c74788,256,V6,70296c9099b444abf8e38312a778d719b3c74788_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+15.855_ry+44.991_rz-61.163_tx+00.512_ty-00.910_s+01.094_70296c9099b444abf8e38312a778d719b3c74788_256x256.png,fede9cf58793a484009e4268c1267e247a2bf7f0,Boats,15.855,134.991,-61.163,3363 -3364,CGTG_L,17.101,-5.846,83.18,0.351,-0.117,1.08416666667,04d5eba225ec9ba7c6ea7aaf2fdd94cd93102496,256,V6,04d5eba225ec9ba7c6ea7aaf2fdd94cd93102496_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+17.101_ry-05.846_rz+83.180_tx+00.351_ty-00.117_s+01.301_04d5eba225ec9ba7c6ea7aaf2fdd94cd93102496_256x256.png,c9d39dc94296fc611644fc82578597b9fe4d54ff,Boats,17.101,84.154,83.18,3364 -3365,CGTG_L,28.542,-19.817,37.915,0.433,0.675,1.195,d735379622220b2b4a9805eed3b8644c126fb064,256,V6,d735379622220b2b4a9805eed3b8644c126fb064_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+28.542_ry-19.817_rz+37.915_tx+00.433_ty+00.675_s+01.434_d735379622220b2b4a9805eed3b8644c126fb064_256x256.png,f15129853efa4daeb9d438bd7542258543cae0a1,Boats,28.542,70.183,37.915,3365 -3366,CGTG_L,36.51,-65.202,26.402,-0.539,0.415,1.59416666667,f695e6e80d1b5bd4a5f5d914bf17a4dc930f354f,256,V6,f695e6e80d1b5bd4a5f5d914bf17a4dc930f354f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+36.510_ry-65.202_rz+26.402_tx-00.539_ty+00.415_s+01.913_f695e6e80d1b5bd4a5f5d914bf17a4dc930f354f_256x256.png,8b87cb11ffba3bf9c7eb8a4d33869e2f8254ca99,Boats,36.51,24.798,26.402,3366 -3367,CGTG_L,42.135,-45.754,-48.117,-0.447,-0.539,1.40666666667,00b602c77f28f4d19414291beb3820480b1d2598,256,V6,00b602c77f28f4d19414291beb3820480b1d2598_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+42.135_ry-45.754_rz-48.117_tx-00.447_ty-00.539_s+01.688_00b602c77f28f4d19414291beb3820480b1d2598_256x256.png,84e7b64f37264eacc9b729839824023ab0143d28,Boats,42.135,44.246,-48.117,3367 -3368,CGTG_L,42.278,-45.702,24.396,0.347,-1.189,1.29333333333,bae37cad93495013180b0fbc58835383e0bbbc9b,256,V6,bae37cad93495013180b0fbc58835383e0bbbc9b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+42.278_ry-45.702_rz+24.396_tx+00.347_ty-01.189_s+01.552_bae37cad93495013180b0fbc58835383e0bbbc9b_256x256.png,87716038179038ed44c4c7fd18719866a099fd8e,Boats,42.278,44.298,24.396,3368 -3369,CGTG_L,47.182,79.274,16.663,0.199,-0.619,1.245,2b58292e7d49d2bbd850434f45a4abdbeb1e14b5,256,V6,2b58292e7d49d2bbd850434f45a4abdbeb1e14b5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+47.182_ry+79.274_rz+16.663_tx+00.199_ty-00.619_s+01.494_2b58292e7d49d2bbd850434f45a4abdbeb1e14b5_256x256.png,72e79dbee63d702b78da2ec1e03fc69387633dd1,Boats,47.182,169.274,16.663,3369 -3370,CGTG_L,47.188,85.774,-11.27,-0.525,-1.158,0.695833333333,7f920f6d466f9485a410853e96d4a115bc631a72,256,V6,7f920f6d466f9485a410853e96d4a115bc631a72_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+47.188_ry+85.774_rz-11.270_tx-00.525_ty-01.158_s+00.835_7f920f6d466f9485a410853e96d4a115bc631a72_256x256.png,e53dee3c9450ede4134e80dbc655b813ac7d9acf,Boats,47.188,175.774,-11.27,3370 -3371,CGTG_L,49.021,23.252,80.977,-0.066,1.127,0.898333333333,0415122e8e9c0c2c42e072ea56995670fd035792,256,V6,0415122e8e9c0c2c42e072ea56995670fd035792_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+49.021_ry+23.252_rz+80.977_tx-00.066_ty+01.127_s+01.078_0415122e8e9c0c2c42e072ea56995670fd035792_256x256.png,ab179c2481c643d284e85c70fe975a4eb70135cc,Boats,49.021,113.252,80.977,3371 -3372,CGTG_L,52.677,71.189,25.06,0.105,-0.333,0.7825,a6da4ac51c383eb9702f414e908e80a3fa8b91bf,256,V6,a6da4ac51c383eb9702f414e908e80a3fa8b91bf_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+52.677_ry+71.189_rz+25.060_tx+00.105_ty-00.333_s+00.939_a6da4ac51c383eb9702f414e908e80a3fa8b91bf_256x256.png,c22036d9bb146dba19759e2773aed47ac0f54e59,Boats,52.677,161.189,25.06,3372 -3373,CGTG_L,55.078,68.609,80.615,0.568,-0.166,1.27333333333,a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9,256,V6,a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+55.078_ry+68.609_rz+80.615_tx+00.568_ty-00.166_s+01.528_a7822b52b3d9b276234e9ef3d78e9e1a2aa0eeb9_256x256.png,910d7bc05871a136bdee1b1bb6a7d4bafeac97b0,Boats,55.078,158.609,80.615,3373 -3374,CGTG_L,57.512,71.698,39.447,-0.479,-0.079,1.1325,e3ee11ad15c438b87466fd5cbe2cd78dd3369f4f,256,V6,e3ee11ad15c438b87466fd5cbe2cd78dd3369f4f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+57.512_ry+71.698_rz+39.447_tx-00.479_ty-00.079_s+01.359_e3ee11ad15c438b87466fd5cbe2cd78dd3369f4f_256x256.png,057261e66adb7ad8046354ada79f73f0e5eefdde,Boats,57.512,161.698,39.447,3374 -3375,CGTG_L,58.131,-48.417,41.665,0.443,1.132,1.50583333333,0331e370878c06dbe26cce37c9c006fbd868b983,256,V6,0331e370878c06dbe26cce37c9c006fbd868b983_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+58.131_ry-48.417_rz+41.665_tx+00.443_ty+01.132_s+01.807_0331e370878c06dbe26cce37c9c006fbd868b983_256x256.png,ff207131d0c3d2b293f69c89ec1ebe9d351e73ad,Boats,58.131,41.583,41.665,3375 -3376,CGTG_L,60.087,59.773,53.244,-0.433,-0.275,0.899166666667,37dc012143cd2d1852d1338e8592ee5ab6f00da2,256,V6,37dc012143cd2d1852d1338e8592ee5ab6f00da2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+60.087_ry+59.773_rz+53.244_tx-00.433_ty-00.275_s+01.079_37dc012143cd2d1852d1338e8592ee5ab6f00da2_256x256.png,927350a9d47d5b66514d6ce5d44fc7666233b017,Boats,60.087,149.773,53.244,3376 -3377,CGTG_L,64.213,-32.828,-2.094,0.012,0.108,1.42416666667,e0647ce2d11a10710e52d3f708676fcbb4249c0c,256,V6,e0647ce2d11a10710e52d3f708676fcbb4249c0c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+64.213_ry-32.828_rz-02.094_tx+00.012_ty+00.108_s+01.709_e0647ce2d11a10710e52d3f708676fcbb4249c0c_256x256.png,dad4750747e2aa8ebecdeae985cb9751adb8151b,Boats,64.213,57.172,-2.094,3377 -3378,CGTG_L,65.861,70.029,35.336,0.163,-0.57,0.726666666667,276d2830e714bc4ece0dd0d6e5b215f80f45825b,256,V6,276d2830e714bc4ece0dd0d6e5b215f80f45825b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+65.861_ry+70.029_rz+35.336_tx+00.163_ty-00.570_s+00.872_276d2830e714bc4ece0dd0d6e5b215f80f45825b_256x256.png,053fcc56876ea33298686014f3fc463b1e9d82f5,Boats,65.861,160.029,35.336,3378 -3379,CGTG_L,73.751,54.844,63.264,-0.425,-0.17,0.724166666667,1827371389795949398e266c2ddac7a670852760,256,V6,1827371389795949398e266c2ddac7a670852760_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+73.751_ry+54.844_rz+63.264_tx-00.425_ty-00.170_s+00.869_1827371389795949398e266c2ddac7a670852760_256x256.png,c8e2b23fc58e52f1ff2bc3b79853e59f4972d77a,Boats,73.751,144.844,63.264,3379 -3380,CGTG_L,75.166,10.179,-86.173,-0.48,0.813,0.814166666667,66fd4612bedfb101f375cdc44303cf90b209faca,256,V6,66fd4612bedfb101f375cdc44303cf90b209faca_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+75.166_ry+10.179_rz-86.173_tx-00.480_ty+00.813_s+00.977_66fd4612bedfb101f375cdc44303cf90b209faca_256x256.png,ebf1fe8e3b804d99aacee1dc6a4aec5fbc8f7872,Boats,75.166,100.179,-86.173,3380 -3381,CGTG_L,77.402,42.192,2.863,0.334,0.858,1.11583333333,3e058598889926b353d3cd8409ca5bdc31a5bc4d,256,V6,3e058598889926b353d3cd8409ca5bdc31a5bc4d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+77.402_ry+42.192_rz+02.863_tx+00.334_ty+00.858_s+01.339_3e058598889926b353d3cd8409ca5bdc31a5bc4d_256x256.png,df19dc1d441374f6f652d75c0869ca202acab898,Boats,77.402,132.192,2.863,3381 -3382,CGTG_L,80.55,-37.654,75.599,-0.441,0.915,1.23333333333,0c4fcac8572e5d81d1f62ccce8593ca19532e5d0,256,V6,0c4fcac8572e5d81d1f62ccce8593ca19532e5d0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+80.550_ry-37.654_rz+75.599_tx-00.441_ty+00.915_s+01.480_0c4fcac8572e5d81d1f62ccce8593ca19532e5d0_256x256.png,a531a256db7d6e51fcff7f669fcc679a9a8a416f,Boats,80.55,52.346,75.599,3382 -3383,CGTG_L,85.882,39.834,-70.481,0.447,0.491,0.684166666667,0392f019836b4ff006d8360fa98b7ce556de9848,256,V6,0392f019836b4ff006d8360fa98b7ce556de9848_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+85.882_ry+39.834_rz-70.481_tx+00.447_ty+00.491_s+00.821_0392f019836b4ff006d8360fa98b7ce556de9848_256x256.png,fdcb5529303f9dabd3374596195f0c01c77009bd,Boats,85.882,129.834,-70.481,3383 -3384,CGTG_L,87.744,-28.649,59.949,-0.049,-0.712,1.37083333333,94605095afc8964ec1f66e2b86174212b0a79f91,256,V6,94605095afc8964ec1f66e2b86174212b0a79f91_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx+87.744_ry-28.649_rz+59.949_tx-00.049_ty-00.712_s+01.645_94605095afc8964ec1f66e2b86174212b0a79f91_256x256.png,88b967b7cbe90e278894043a6155becc16586a9a,Boats,87.744,61.351,59.949,3384 -3385,CGTG_L,-0.713,-51.788,77.635,-0.246,-0.66,1.31333333333,a39cc5ae5eab3af9ca093180ae041b4a83054faa,256,V6,a39cc5ae5eab3af9ca093180ae041b4a83054faa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx-00.713_ry-51.788_rz+77.635_tx-00.246_ty-00.660_s+01.576_a39cc5ae5eab3af9ca093180ae041b4a83054faa_256x256.png,87715dcb057f13a26e8d20b055e8cde90cbef79d,Boats,-0.713,38.212,77.635,3385 -3386,CGTG_L,-1.935,-75.93,-77.844,-0.37,0.311,1.40666666667,de7e13dfab41d7fec5558b2d1e3a4ec1a563f165,256,V6,de7e13dfab41d7fec5558b2d1e3a4ec1a563f165_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx-01.935_ry-75.930_rz-77.844_tx-00.370_ty+00.311_s+01.688_de7e13dfab41d7fec5558b2d1e3a4ec1a563f165_256x256.png,b77d30b85b3af202f386587e64487076cfeb0c02,Boats,-1.935,14.07,-77.844,3386 -3387,CGTG_L,-8.576,-32.569,3.733,0.054,0.274,0.768333333333,8646e060601d5b1e29c9f8d8d64056bef77f337d,256,V6,8646e060601d5b1e29c9f8d8d64056bef77f337d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx-08.576_ry-32.569_rz+03.733_tx+00.054_ty+00.274_s+00.922_8646e060601d5b1e29c9f8d8d64056bef77f337d_256x256.png,d5742162d87bbe7a87e05f51c3b48a546cd09977,Boats,-8.576,57.431,3.733,3387 -3388,CGTG_L,-9.078,-69.777,28.012,-0.427,0.446,0.879166666667,b974ecac7aca17d4d29e99deec56b7f8ae4e2f2f,256,V6,b974ecac7aca17d4d29e99deec56b7f8ae4e2f2f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx-09.078_ry-69.777_rz+28.012_tx-00.427_ty+00.446_s+01.055_b974ecac7aca17d4d29e99deec56b7f8ae4e2f2f_256x256.png,7ebbfc9c24e7fc55cc05c6d9ec38f15eecfc19f4,Boats,-9.078,20.223,28.012,3388 -3389,CGTG_L,-34.306,35.059,-13.479,0.533,0.915,1.435,31acd16e06baef8541a5eedbf158a7ebff7ae43f,256,V6,31acd16e06baef8541a5eedbf158a7ebff7ae43f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx-34.306_ry+35.059_rz-13.479_tx+00.533_ty+00.915_s+01.722_31acd16e06baef8541a5eedbf158a7ebff7ae43f_256x256.png,8b7103ac03633a058e1c66b8191533dc600eadb8,Boats,-34.306,125.059,-13.479,3389 -3390,CGTG_L,-37.329,-82.951,57.816,0.116,-0.782,0.826666666667,333d7cbe236d378aad896caaf193471927b1c136,256,V6,333d7cbe236d378aad896caaf193471927b1c136_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx-37.329_ry-82.951_rz+57.816_tx+00.116_ty-00.782_s+00.992_333d7cbe236d378aad896caaf193471927b1c136_256x256.png,490e56fafc861b0d505d2afbc1fcbe3f7c16bd9e,Boats,-37.329,7.049,57.816,3390 -3391,CGTG_L,-38.163,-0.293,-86.41,0.566,0.294,0.875833333333,a18b32ee5064d5dc99fbf9a08b4c2177e06d6870,256,V6,a18b32ee5064d5dc99fbf9a08b4c2177e06d6870_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx-38.163_ry-00.293_rz-86.410_tx+00.566_ty+00.294_s+01.051_a18b32ee5064d5dc99fbf9a08b4c2177e06d6870_256x256.png,d83fd2a877d160608adbb5042ffb2fef95b8493c,Boats,-38.163,89.707,-86.41,3391 -3392,CGTG_L,-41.282,-66.098,-32.261,0.557,0.166,1.54833333333,a51c72d8b4a890cffa5d970d91f94c9ba307f26b,256,V6,a51c72d8b4a890cffa5d970d91f94c9ba307f26b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx-41.282_ry-66.098_rz-32.261_tx+00.557_ty+00.166_s+01.858_a51c72d8b4a890cffa5d970d91f94c9ba307f26b_256x256.png,668ab8c2f97099d7a11923085f3142acb7632b2d,Boats,-41.282,23.902,-32.261,3392 -3393,CGTG_L,-45.358,41.227,74.268,0.388,-0.233,1.21,b6cac69324041c7e91721ec7c0e97adecb1401fe,256,V6,b6cac69324041c7e91721ec7c0e97adecb1401fe_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx-45.358_ry+41.227_rz+74.268_tx+00.388_ty-00.233_s+01.452_b6cac69324041c7e91721ec7c0e97adecb1401fe_256x256.png,ef869bd3b06b196c0e19d94506498deb4654ea07,Boats,-45.358,131.227,74.268,3393 -3394,CGTG_L,-50.629,-44.395,-27.711,0.248,0.736,1.18666666667,5cfc12d96d2a20e4ab4b04efda5b5207fe7d5d8b,256,V6,5cfc12d96d2a20e4ab4b04efda5b5207fe7d5d8b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx-50.629_ry-44.395_rz-27.711_tx+00.248_ty+00.736_s+01.424_5cfc12d96d2a20e4ab4b04efda5b5207fe7d5d8b_256x256.png,6d476d0d62c4e6d6da95540379151cac67866e38,Boats,-50.629,45.605,-27.711,3394 -3395,CGTG_L,-54.983,56.787,-74.165,-0.573,-0.525,1.39833333333,b28ae9be054238b39874073b2a9d0f084641f570,256,V6,b28ae9be054238b39874073b2a9d0f084641f570_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx-54.983_ry+56.787_rz-74.165_tx-00.573_ty-00.525_s+01.678_b28ae9be054238b39874073b2a9d0f084641f570_256x256.png,8c3fb3b79c3c2d4a1aa08fa1da213b78e7919179,Boats,-54.983,146.787,-74.165,3395 -3396,CGTG_L,-56.997,48.277,-55.864,-0.34,1.009,1.24,ac6b8f62555a0037ebe4fa9ced489abaa67d4a49,256,V6,ac6b8f62555a0037ebe4fa9ced489abaa67d4a49_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx-56.997_ry+48.277_rz-55.864_tx-00.340_ty+01.009_s+01.488_ac6b8f62555a0037ebe4fa9ced489abaa67d4a49_256x256.png,4b45d6ea2b8b61485b5c8100873cd768b299056e,Boats,-56.997,138.277,-55.864,3396 -3397,CGTG_L,-71.367,27.589,-86.233,0.385,-0.79,0.783333333333,adc87c9c400bf905678aa8d7684dcf2f18eb2ed9,256,V6,adc87c9c400bf905678aa8d7684dcf2f18eb2ed9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx-71.367_ry+27.589_rz-86.233_tx+00.385_ty-00.790_s+00.940_adc87c9c400bf905678aa8d7684dcf2f18eb2ed9_256x256.png,3b100678d4b27b37d4eaf694846295b0fa84ad24,Boats,-71.367,117.589,-86.233,3397 -3398,CGTG_L,-74.911,73.072,-45.838,-0.453,0.142,0.7125,e6585e9a86e89c4527769d25b20da7ccb0111f4a,256,V6,e6585e9a86e89c4527769d25b20da7ccb0111f4a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx-74.911_ry+73.072_rz-45.838_tx-00.453_ty+00.142_s+00.855_e6585e9a86e89c4527769d25b20da7ccb0111f4a_256x256.png,59b782a0085cb0efdc48d05fe45169f5ec16e3a1,Boats,-74.911,163.072,-45.838,3398 -3399,CGTG_L,-77.959,-0.285,14.177,0.112,-0.75,1.26416666667,8aead812f847a6846a2d3494ef3dba128ee9107e,256,V6,8aead812f847a6846a2d3494ef3dba128ee9107e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/CGTG_L_rx-77.959_ry-00.285_rz+14.177_tx+00.112_ty-00.750_s+01.517_8aead812f847a6846a2d3494ef3dba128ee9107e_256x256.png,1b5664eaf77aec87cac2942fe3c9581f5fefa6d4,Boats,-77.959,89.715,14.177,3399 -3400,DTUG_L,14.002,-26.695,37.147,0.454,-0.738,1.12416666667,871a59b6008f25264ba06c36b82abfaea34a6d49,256,V6,871a59b6008f25264ba06c36b82abfaea34a6d49_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx+14.002_ry-26.695_rz+37.147_tx+00.454_ty-00.738_s+01.349_871a59b6008f25264ba06c36b82abfaea34a6d49_256x256.png,6e7725dd9a91a91d57ba87890f1aa4dc1b2ed478,Boats,14.002,63.305,37.147,3400 -3401,DTUG_L,14.51,-74.937,-13.606,-0.253,0.381,1.25583333333,666efb300871cfb6b75ec6750cfc956c25fcefc7,256,V6,666efb300871cfb6b75ec6750cfc956c25fcefc7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx+14.510_ry-74.937_rz-13.606_tx-00.253_ty+00.381_s+01.507_666efb300871cfb6b75ec6750cfc956c25fcefc7_256x256.png,203859cab3885d3196a503a653b2f1df4b682af3,Boats,14.51,15.063,-13.606,3401 -3402,DTUG_L,18.635,-24.566,18.404,0.53,-0.307,1.01666666667,cb06be64afd1e6ea94aa0a3a115c2f6723af374f,256,V6,cb06be64afd1e6ea94aa0a3a115c2f6723af374f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx+18.635_ry-24.566_rz+18.404_tx+00.530_ty-00.307_s+01.220_cb06be64afd1e6ea94aa0a3a115c2f6723af374f_256x256.png,79ba0d6b9008d2548ba470708a300bb8184ed6c9,Boats,18.635,65.434,18.404,3402 -3403,DTUG_L,26.362,14.42,-78.617,-0.212,-1.147,0.721666666667,c3bf046d9fa29946909cb4a299574725b9911a4b,256,V6,c3bf046d9fa29946909cb4a299574725b9911a4b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx+26.362_ry+14.420_rz-78.617_tx-00.212_ty-01.147_s+00.866_c3bf046d9fa29946909cb4a299574725b9911a4b_256x256.png,4026f7b7510f26c1403f0c62c09a0d18f9388916,Boats,26.362,104.42,-78.617,3403 -3404,DTUG_L,27.349,71.182,-58.861,-0.158,-0.852,1.4775,987d03ad579a8d41a74b0a7e0bdaf1f8ce77b9d6,256,V6,987d03ad579a8d41a74b0a7e0bdaf1f8ce77b9d6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx+27.349_ry+71.182_rz-58.861_tx-00.158_ty-00.852_s+01.773_987d03ad579a8d41a74b0a7e0bdaf1f8ce77b9d6_256x256.png,851b2659d2f021d75090c959b851bff4f9016f9b,Boats,27.349,161.182,-58.861,3404 -3405,DTUG_L,32.504,4.371,-24.508,0.153,0.823,0.705833333333,c8c00e101ecb2ee1271d320cd4c23f3dc3636b61,256,V6,c8c00e101ecb2ee1271d320cd4c23f3dc3636b61_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx+32.504_ry+04.371_rz-24.508_tx+00.153_ty+00.823_s+00.847_c8c00e101ecb2ee1271d320cd4c23f3dc3636b61_256x256.png,342217cd897d38513259944c5005729733f569c7,Boats,32.504,94.371,-24.508,3405 -3406,DTUG_L,39.566,83.142,-27.158,-0.249,-0.455,1.56083333333,3270ec6eb7918f7e565da242721ade80f9895a32,256,V6,3270ec6eb7918f7e565da242721ade80f9895a32_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx+39.566_ry+83.142_rz-27.158_tx-00.249_ty-00.455_s+01.873_3270ec6eb7918f7e565da242721ade80f9895a32_256x256.png,896c03ceb2f3b868b438c7cc0f4e06c459d259c6,Boats,39.566,173.142,-27.158,3406 -3407,DTUG_L,42.153,-78.328,35.951,-0.366,-0.57,0.8125,265e398ff7cc0366e5d5d0127cd6e0a2a7478c77,256,V6,265e398ff7cc0366e5d5d0127cd6e0a2a7478c77_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx+42.153_ry-78.328_rz+35.951_tx-00.366_ty-00.570_s+00.975_265e398ff7cc0366e5d5d0127cd6e0a2a7478c77_256x256.png,d86dd2e24e4f57ce352fcd017f38003d5d74c4cd,Boats,42.153,11.672,35.951,3407 -3408,DTUG_L,42.393,18.752,18.122,-0.351,-0.532,1.09083333333,bf4561809db4724ef76c98b319ab1607d47a9331,256,V6,bf4561809db4724ef76c98b319ab1607d47a9331_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx+42.393_ry+18.752_rz+18.122_tx-00.351_ty-00.532_s+01.309_bf4561809db4724ef76c98b319ab1607d47a9331_256x256.png,4fb2dffc2e91d0b60bb71bd08e16afc0d26d0925,Boats,42.393,108.752,18.122,3408 -3409,DTUG_L,58.835,-45.438,-49.247,0.422,-1.109,1.235,f94d9cb36b5310fa4ba6b13b377e32cdf14fb877,256,V6,f94d9cb36b5310fa4ba6b13b377e32cdf14fb877_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx+58.835_ry-45.438_rz-49.247_tx+00.422_ty-01.109_s+01.482_f94d9cb36b5310fa4ba6b13b377e32cdf14fb877_256x256.png,1a8f65bba37c41c9bbe5d2317b843dd9bebb18db,Boats,58.835,44.562,-49.247,3409 -3410,DTUG_L,66.278,-73.814,28.395,0.498,-0.904,0.884166666667,b1f88880336e2687ea5df15043b82cd7bdbe453e,256,V6,b1f88880336e2687ea5df15043b82cd7bdbe453e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx+66.278_ry-73.814_rz+28.395_tx+00.498_ty-00.904_s+01.061_b1f88880336e2687ea5df15043b82cd7bdbe453e_256x256.png,ac1521064a8493dfca9e9b6d47e68997f4d199c8,Boats,66.278,16.186,28.395,3410 -3411,DTUG_L,67.155,17.11,7.214,0.467,-0.942,1.04,06e48c385078bdf9b730624c34ea62267d6c01ef,256,V6,06e48c385078bdf9b730624c34ea62267d6c01ef_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx+67.155_ry+17.110_rz+07.214_tx+00.467_ty-00.942_s+01.248_06e48c385078bdf9b730624c34ea62267d6c01ef_256x256.png,fcd151bde7bd4bc122eac4024756534ccb87f17a,Boats,67.155,107.11,7.214,3411 -3412,DTUG_L,70.266,-46.965,24.94,-0.262,0.123,0.894166666667,36ab5e37719f1a683ccd5571ce54e305d94af47f,256,V6,36ab5e37719f1a683ccd5571ce54e305d94af47f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx+70.266_ry-46.965_rz+24.940_tx-00.262_ty+00.123_s+01.073_36ab5e37719f1a683ccd5571ce54e305d94af47f_256x256.png,a66070df392b8a1c2ef01c155882e50758ae9ac6,Boats,70.266,43.035,24.94,3412 -3413,DTUG_L,72.317,23.26,-56.865,0.112,-1.105,0.729166666667,62c59ba7f751e33040748082cfffeadb51bc938a,256,V6,62c59ba7f751e33040748082cfffeadb51bc938a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx+72.317_ry+23.260_rz-56.865_tx+00.112_ty-01.105_s+00.875_62c59ba7f751e33040748082cfffeadb51bc938a_256x256.png,f0dd60153a30efc4575b3f949fcece0395ee36e0,Boats,72.317,113.26,-56.865,3413 -3414,DTUG_L,76.074,-13.132,34.794,0.369,-0.614,0.725833333333,2ad9964a2ec16b0ef3703728d01106f1f7c61942,256,V6,2ad9964a2ec16b0ef3703728d01106f1f7c61942_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx+76.074_ry-13.132_rz+34.794_tx+00.369_ty-00.614_s+00.871_2ad9964a2ec16b0ef3703728d01106f1f7c61942_256x256.png,5380a066c3716819bfe2d881ed477b912afb1db8,Boats,76.074,76.868,34.794,3414 -3415,DTUG_L,-0.306,-23.096,23.103,0.104,-0.719,1.54083333333,8ccbcf6066bb27c0f264b30b6c6636184de6346a,256,V6,8ccbcf6066bb27c0f264b30b6c6636184de6346a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-00.306_ry-23.096_rz+23.103_tx+00.104_ty-00.719_s+01.849_8ccbcf6066bb27c0f264b30b6c6636184de6346a_256x256.png,406bc590ba62bd1e31f10851f09fcdeb28af35ff,Boats,-0.306,66.904,23.103,3415 -3416,DTUG_L,-4.127,-49.259,0.285,-0.161,0.909,1.44916666667,0eed3012be7378f0cdf8f88a7891f0969182d4a3,256,V6,0eed3012be7378f0cdf8f88a7891f0969182d4a3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-04.127_ry-49.259_rz+00.285_tx-00.161_ty+00.909_s+01.739_0eed3012be7378f0cdf8f88a7891f0969182d4a3_256x256.png,8a8b871850623f7bfefb39597dda54f04c28be30,Boats,-4.127,40.741,0.285,3416 -3417,DTUG_L,-6.68,-71.434,-2.05,-0.238,0.986,1.04583333333,24ce1e27cfd7d381bedbfdc4ae01489fe53a1b1b,256,V6,24ce1e27cfd7d381bedbfdc4ae01489fe53a1b1b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-06.680_ry-71.434_rz-02.050_tx-00.238_ty+00.986_s+01.255_24ce1e27cfd7d381bedbfdc4ae01489fe53a1b1b_256x256.png,32ea8bc89ac8fc324e0ac52f31bcf88d01fc56d4,Boats,-6.68,18.566,-2.05,3417 -3418,DTUG_L,-8.964,-83.885,-23.154,-0.382,0.934,1.02666666667,1401e57522d9d717427f671c4f3602ae9049e7d4,256,V6,1401e57522d9d717427f671c4f3602ae9049e7d4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-08.964_ry-83.885_rz-23.154_tx-00.382_ty+00.934_s+01.232_1401e57522d9d717427f671c4f3602ae9049e7d4_256x256.png,1b2fffaca78d160bedc948fd1c0026f3cdad4d6e,Boats,-8.964,6.115,-23.154,3418 -3419,DTUG_L,-10.858,63.747,46.926,-0.134,0.117,1.05916666667,69a36a40f71554c6860a980216f9e0772c2d024f,256,V6,69a36a40f71554c6860a980216f9e0772c2d024f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-10.858_ry+63.747_rz+46.926_tx-00.134_ty+00.117_s+01.271_69a36a40f71554c6860a980216f9e0772c2d024f_256x256.png,436e6111baec152993484be6cc08dbf99d600660,Boats,-10.858,153.747,46.926,3419 -3420,DTUG_L,-12.698,87.264,-27.686,0.296,0.298,1.45666666667,e7df341a53fdd0b9a04b02430f1b85d97ae17f0e,256,V6,e7df341a53fdd0b9a04b02430f1b85d97ae17f0e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-12.698_ry+87.264_rz-27.686_tx+00.296_ty+00.298_s+01.748_e7df341a53fdd0b9a04b02430f1b85d97ae17f0e_256x256.png,ecc0ec1785b4f83315b4f4b31de98f43b1e908c2,Boats,-12.698,177.264,-27.686,3420 -3421,DTUG_L,-24.976,-21.404,-69.424,0.005,-1.085,1.26666666667,ebfd7c9fd55b14dddce95f6844d89bc538d2af41,256,V6,ebfd7c9fd55b14dddce95f6844d89bc538d2af41_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-24.976_ry-21.404_rz-69.424_tx+00.005_ty-01.085_s+01.520_ebfd7c9fd55b14dddce95f6844d89bc538d2af41_256x256.png,a125d7b698fea7aca7f573cc6d8617cc67ee88ab,Boats,-24.976,68.596,-69.424,3421 -3422,DTUG_L,-25.67,31.506,33.828,0.179,-1.03,1.0825,cdff81d5c035c27cd4b0a57c124fa54d78b64d70,256,V6,cdff81d5c035c27cd4b0a57c124fa54d78b64d70_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-25.670_ry+31.506_rz+33.828_tx+00.179_ty-01.030_s+01.299_cdff81d5c035c27cd4b0a57c124fa54d78b64d70_256x256.png,7866f0e760df03f7f8a7bc19a8e5df3a75cc8e58,Boats,-25.67,121.506,33.828,3422 -3423,DTUG_L,-26.965,31.576,-53.29,-0.543,1.072,1.26416666667,682d4d5c560fbb629954608452e942f20500e965,256,V6,682d4d5c560fbb629954608452e942f20500e965_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-26.965_ry+31.576_rz-53.290_tx-00.543_ty+01.072_s+01.517_682d4d5c560fbb629954608452e942f20500e965_256x256.png,25d514a55e6b53acfb21bf25545ee480e46b5b30,Boats,-26.965,121.576,-53.29,3423 -3424,DTUG_L,-29.061,5.864,47.848,0.505,-0.523,0.783333333333,a99f45bc612542855e525bd36bfa6fa736763542,256,V6,a99f45bc612542855e525bd36bfa6fa736763542_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-29.061_ry+05.864_rz+47.848_tx+00.505_ty-00.523_s+00.940_a99f45bc612542855e525bd36bfa6fa736763542_256x256.png,faf3a3090bb4546a941e6d08373baec302c846f4,Boats,-29.061,95.864,47.848,3424 -3425,DTUG_L,-35.372,-53.529,-76.35,-0.183,0.133,1.59166666667,f614d5a36d4061285b7da56ebda32eda3b609f3b,256,V6,f614d5a36d4061285b7da56ebda32eda3b609f3b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-35.372_ry-53.529_rz-76.350_tx-00.183_ty+00.133_s+01.910_f614d5a36d4061285b7da56ebda32eda3b609f3b_256x256.png,bd8ac2288c771230892b0d7ce623f5616e84bbf3,Boats,-35.372,36.471,-76.35,3425 -3426,DTUG_L,-39.522,74.672,-9.667,-0.057,-0.733,1.51666666667,b346173397d000afc0766704f9a8d01148df76e5,256,V6,b346173397d000afc0766704f9a8d01148df76e5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-39.522_ry+74.672_rz-09.667_tx-00.057_ty-00.733_s+01.820_b346173397d000afc0766704f9a8d01148df76e5_256x256.png,4c492409bac585318a3ca5b1494320244760122a,Boats,-39.522,164.672,-9.667,3426 -3427,DTUG_L,-41.826,-79.973,45.535,-0.425,0.665,0.791666666667,3c869495bf570779cde046b2ad820591549eddeb,256,V6,3c869495bf570779cde046b2ad820591549eddeb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-41.826_ry-79.973_rz+45.535_tx-00.425_ty+00.665_s+00.950_3c869495bf570779cde046b2ad820591549eddeb_256x256.png,0d8e0cf8909c50fbef3ec38ace2f380915ef8fb0,Boats,-41.826,10.027,45.535,3427 -3428,DTUG_L,-50.334,53.542,-6.182,-0.45,-0.954,1.08916666667,7a01d1accafa901e9724b36d434330f05e3c5b29,256,V6,7a01d1accafa901e9724b36d434330f05e3c5b29_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-50.334_ry+53.542_rz-06.182_tx-00.450_ty-00.954_s+01.307_7a01d1accafa901e9724b36d434330f05e3c5b29_256x256.png,fa93a1244c09625249c287dc98e8435aea61e027,Boats,-50.334,143.542,-6.182,3428 -3429,DTUG_L,-52.164,-8.499,3.973,-0.09,-0.707,0.906666666667,d9afd39be08e050fdfae46fe15981a7e6c858e0f,256,V6,d9afd39be08e050fdfae46fe15981a7e6c858e0f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-52.164_ry-08.499_rz+03.973_tx-00.090_ty-00.707_s+01.088_d9afd39be08e050fdfae46fe15981a7e6c858e0f_256x256.png,5def84b245f77afd3cb525f9ce4bc4708474f63d,Boats,-52.164,81.501,3.973,3429 -3430,DTUG_L,-54.626,55.891,-73.095,0.193,1.197,1.4775,dc1c63d08cb660f4c7519679fe629f05f8d74578,256,V6,dc1c63d08cb660f4c7519679fe629f05f8d74578_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-54.626_ry+55.891_rz-73.095_tx+00.193_ty+01.197_s+01.773_dc1c63d08cb660f4c7519679fe629f05f8d74578_256x256.png,b4db8c3fc3e5a364312dbfcd0c4d44cf1b5e50f3,Boats,-54.626,145.891,-73.095,3430 -3431,DTUG_L,-59.484,-65.314,-18.708,0.306,-0.334,1.22666666667,53bcaaf027073770dd5d6ddb5370968f379d0244,256,V6,53bcaaf027073770dd5d6ddb5370968f379d0244_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-59.484_ry-65.314_rz-18.708_tx+00.306_ty-00.334_s+01.472_53bcaaf027073770dd5d6ddb5370968f379d0244_256x256.png,c3b934f8c7755191efa92f128acf50758c2cef32,Boats,-59.484,24.686,-18.708,3431 -3432,DTUG_L,-61.621,68.449,-69.425,0.153,1.009,1.2325,b8b857bde96bcb18534da78fb93e3386570cc2d9,256,V6,b8b857bde96bcb18534da78fb93e3386570cc2d9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-61.621_ry+68.449_rz-69.425_tx+00.153_ty+01.009_s+01.479_b8b857bde96bcb18534da78fb93e3386570cc2d9_256x256.png,ca4191dd9b70f90f687930ff26ce8830e74cc97b,Boats,-61.621,158.449,-69.425,3432 -3433,DTUG_L,-66.088,-60.832,6.542,-0.592,-0.882,0.8525,91badb38d2039c5c95f8a438a7d3b8fae67ad7b9,256,V6,91badb38d2039c5c95f8a438a7d3b8fae67ad7b9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-66.088_ry-60.832_rz+06.542_tx-00.592_ty-00.882_s+01.023_91badb38d2039c5c95f8a438a7d3b8fae67ad7b9_256x256.png,d5916c588053fd4f0e6ac399559e9fddd13a94b0,Boats,-66.088,29.168,6.542,3433 -3434,DTUG_L,-69.344,65.601,-55.663,0.541,0.161,0.9825,acfae223f47ed25da0257f34836c3ffc1778b7ef,256,V6,acfae223f47ed25da0257f34836c3ffc1778b7ef_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-69.344_ry+65.601_rz-55.663_tx+00.541_ty+00.161_s+01.179_acfae223f47ed25da0257f34836c3ffc1778b7ef_256x256.png,881136d4eae9b75c0dff16ebbd2a97796a02c0b6,Boats,-69.344,155.601,-55.663,3434 -3435,DTUG_L,-75.456,-87.212,1.016,0.502,1.196,1.3075,4e19da2f44d548c751bd2b2b0b12ad0b17d78b96,256,V6,4e19da2f44d548c751bd2b2b0b12ad0b17d78b96_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-75.456_ry-87.212_rz+01.016_tx+00.502_ty+01.196_s+01.569_4e19da2f44d548c751bd2b2b0b12ad0b17d78b96_256x256.png,9260269ddf65e1ac447773d761805433c73f2d6d,Boats,-75.456,2.788,1.016,3435 -3436,DTUG_L,-77.874,-53.941,40.145,-0.13,0.398,0.8225,88f9a6af643af9bb0a0dbc3c6db4646cbd163223,256,V6,88f9a6af643af9bb0a0dbc3c6db4646cbd163223_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-77.874_ry-53.941_rz+40.145_tx-00.130_ty+00.398_s+00.987_88f9a6af643af9bb0a0dbc3c6db4646cbd163223_256x256.png,5faef92e90dca0e3b5f9563f900c5eaf2704853e,Boats,-77.874,36.059,40.145,3436 -3437,DTUG_L,-80.176,-53.305,-27.891,0.038,0.294,0.7075,221f8c9baee7024ebb96f321c8b70247495f6d8a,256,V6,221f8c9baee7024ebb96f321c8b70247495f6d8a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-80.176_ry-53.305_rz-27.891_tx+00.038_ty+00.294_s+00.849_221f8c9baee7024ebb96f321c8b70247495f6d8a_256x256.png,cbef273d35c1e91a60975ef7374051ea8bf787b0,Boats,-80.176,36.695,-27.891,3437 -3438,DTUG_L,-85.607,39.807,64.827,-0.165,0.627,1.46833333333,56ae9ed8ec43c5bd0b3e14c737f949bb2ccc8e30,256,V6,56ae9ed8ec43c5bd0b3e14c737f949bb2ccc8e30_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-85.607_ry+39.807_rz+64.827_tx-00.165_ty+00.627_s+01.762_56ae9ed8ec43c5bd0b3e14c737f949bb2ccc8e30_256x256.png,4e97bb50e5ce863646928fb43be2dc5352658731,Boats,-85.607,129.807,64.827,3438 -3439,DTUG_L,-86.559,16.189,13.617,-0.51,-0.54,1.50333333333,8dd8bb7572486bc82ab7eca8fbbef0ec0704f598,256,V6,8dd8bb7572486bc82ab7eca8fbbef0ec0704f598_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/DTUG_L_rx-86.559_ry+16.189_rz+13.617_tx-00.510_ty-00.540_s+01.804_8dd8bb7572486bc82ab7eca8fbbef0ec0704f598_256x256.png,b162a1a5c2eb376e3253895f4b0e0bae854a88dc,Boats,-86.559,106.189,13.617,3439 -3440,ELEPHANT_M,0.319,-85.714,-53.667,0.22,-0.965,1.5825,780ad32f99b9bcc6b7914dea7f05ca35bfc19cdb,256,V6,780ad32f99b9bcc6b7914dea7f05ca35bfc19cdb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+00.319_ry-85.714_rz-53.667_tx+00.220_ty-00.965_s+01.266_780ad32f99b9bcc6b7914dea7f05ca35bfc19cdb_256x256.png,b8875087af509af40af85589496eb962755305e4,Animals,0.319,4.286,-53.667,3440 -3441,ELEPHANT_M,2.149,74.265,-18.072,-0.566,-1.172,1.1925,3c0d7a2d5e94c46815fcd742b4bb98260240fd82,256,V6,3c0d7a2d5e94c46815fcd742b4bb98260240fd82_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+02.149_ry+74.265_rz-18.072_tx-00.566_ty-01.172_s+00.954_3c0d7a2d5e94c46815fcd742b4bb98260240fd82_256x256.png,88bd97270d3d086d7161e578eebd8c95fc42c968,Animals,2.149,164.265,-18.072,3441 -3442,ELEPHANT_M,4.845,-21.831,83.899,0.196,-0.344,0.78875,453b1827b6444f117df2d36b071a9bfa76819773,256,V6,453b1827b6444f117df2d36b071a9bfa76819773_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+04.845_ry-21.831_rz+83.899_tx+00.196_ty-00.344_s+00.631_453b1827b6444f117df2d36b071a9bfa76819773_256x256.png,ad3d07e66fcc064aec0f3237169b172b236be200,Animals,4.845,68.169,83.899,3442 -3443,ELEPHANT_M,5.423,-18.976,64.114,0.036,-1.172,1.59125,f5b31650b2c59212d2dff97eb641eb4fea386c8c,256,V6,f5b31650b2c59212d2dff97eb641eb4fea386c8c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+05.423_ry-18.976_rz+64.114_tx+00.036_ty-01.172_s+01.273_f5b31650b2c59212d2dff97eb641eb4fea386c8c_256x256.png,84ebd2829efe81cd07948969652916d5f6616afb,Animals,5.423,71.024,64.114,3443 -3444,ELEPHANT_M,7.095,8.78,-44.725,-0.294,-0.068,1.32875,3b1fe23c52aeb611512a890b75b562a4f4aadbb4,256,V6,3b1fe23c52aeb611512a890b75b562a4f4aadbb4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+07.095_ry+08.780_rz-44.725_tx-00.294_ty-00.068_s+01.063_3b1fe23c52aeb611512a890b75b562a4f4aadbb4_256x256.png,f6d56e33b16ea64da4f525b1350d092d9c273e0e,Animals,7.095,98.78,-44.725,3444 -3445,ELEPHANT_M,8.097,-81.771,-65.062,-0.281,0.054,1.43,91c50df1fd32b43a2ef22a35f8d426eb1ed8221e,256,V6,91c50df1fd32b43a2ef22a35f8d426eb1ed8221e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+08.097_ry-81.771_rz-65.062_tx-00.281_ty+00.054_s+01.144_91c50df1fd32b43a2ef22a35f8d426eb1ed8221e_256x256.png,3e9b57ddf852e2a319ab3b57d1f99d1767899087,Animals,8.097,8.229,-65.062,3445 -3446,ELEPHANT_M,11.008,-58.5,59.782,-0.502,0.515,1.36375,14866dd618014e8528e37928eab3dbd24db6dfcc,256,V6,14866dd618014e8528e37928eab3dbd24db6dfcc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+11.008_ry-58.500_rz+59.782_tx-00.502_ty+00.515_s+01.091_14866dd618014e8528e37928eab3dbd24db6dfcc_256x256.png,4cc43002aef35b029e173b52e9fa468b1b52c56c,Animals,11.008,31.5,59.782,3446 -3447,ELEPHANT_M,12.355,-36.062,-36.682,0.224,0.997,1.57875,55d59e534798bdbd10bffd8d12f6a0c62355cc05,256,V6,55d59e534798bdbd10bffd8d12f6a0c62355cc05_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+12.355_ry-36.062_rz-36.682_tx+00.224_ty+00.997_s+01.263_55d59e534798bdbd10bffd8d12f6a0c62355cc05_256x256.png,1c26088ab53ebc0c046554ff22fecc4d3ba56bbd,Animals,12.355,53.938,-36.682,3447 -3448,ELEPHANT_M,16.302,8.172,-79.695,0.422,-0.519,0.82875,429d7e785d86b8b0f7045bae6369b9f731991850,256,V6,429d7e785d86b8b0f7045bae6369b9f731991850_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+16.302_ry+08.172_rz-79.695_tx+00.422_ty-00.519_s+00.663_429d7e785d86b8b0f7045bae6369b9f731991850_256x256.png,20ea298e808bd56a2611f5561e61a22ac677ce5c,Animals,16.302,98.172,-79.695,3448 -3449,ELEPHANT_M,20.711,-44.862,-58.229,0.521,0.484,1.22625,cb0d5cb55c6a7254261825125f1d827b093bc8dc,256,V6,cb0d5cb55c6a7254261825125f1d827b093bc8dc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+20.711_ry-44.862_rz-58.229_tx+00.521_ty+00.484_s+00.981_cb0d5cb55c6a7254261825125f1d827b093bc8dc_256x256.png,90df60f20897f7cd843759dc91e359b4d9a03352,Animals,20.711,45.138,-58.229,3449 -3450,ELEPHANT_M,53.651,-39.243,72.062,-0.129,0.327,0.665,6606503e9f608e31e3329ac50f85a5f7365075f9,256,V6,6606503e9f608e31e3329ac50f85a5f7365075f9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+53.651_ry-39.243_rz+72.062_tx-00.129_ty+00.327_s+00.532_6606503e9f608e31e3329ac50f85a5f7365075f9_256x256.png,86b01ce334731450a39839c9452370186d44a902,Animals,53.651,50.757,72.062,3450 -3451,ELEPHANT_M,58.043,60.356,10.446,-0.589,0.621,0.665,00bf3b7d35ed5786dca907d369888edbdd5923a1,256,V6,00bf3b7d35ed5786dca907d369888edbdd5923a1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+58.043_ry+60.356_rz+10.446_tx-00.589_ty+00.621_s+00.532_00bf3b7d35ed5786dca907d369888edbdd5923a1_256x256.png,f3f1621d700f3c595eef6c6f2fba7d62fc7d8b0e,Animals,58.043,150.356,10.446,3451 -3452,ELEPHANT_M,65.12,88.545,-57.932,-0.179,-0.506,1.3175,710930fa3b53c88079df00e51c2708cdaa36cab5,256,V6,710930fa3b53c88079df00e51c2708cdaa36cab5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+65.120_ry+88.545_rz-57.932_tx-00.179_ty-00.506_s+01.054_710930fa3b53c88079df00e51c2708cdaa36cab5_256x256.png,1deab4537aa26a5b31c5124688eff130ac918847,Animals,65.12,178.545,-57.932,3452 -3453,ELEPHANT_M,69.288,6.276,78.243,-0.062,0.61,1.12125,995d0335038da905bc13f7de865ab24e4c776404,256,V6,995d0335038da905bc13f7de865ab24e4c776404_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+69.288_ry+06.276_rz+78.243_tx-00.062_ty+00.610_s+00.897_995d0335038da905bc13f7de865ab24e4c776404_256x256.png,6dff2bc0253ce07df50fcd0fd6c96419c7155791,Animals,69.288,96.276,78.243,3453 -3454,ELEPHANT_M,69.525,83.855,80.491,0.567,-0.687,1.5375,a79513834753159896095cbb47a064b9eb19d57c,256,V6,a79513834753159896095cbb47a064b9eb19d57c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+69.525_ry+83.855_rz+80.491_tx+00.567_ty-00.687_s+01.230_a79513834753159896095cbb47a064b9eb19d57c_256x256.png,189fb4d2f144a91193359fef73c36832e2e34287,Animals,69.525,173.855,80.491,3454 -3455,ELEPHANT_M,73.654,-86.158,-1.792,0.358,-0.151,0.7375,1ff79a84e6d398f7b8c9c7d2fd798c6dc8fd329e,256,V6,1ff79a84e6d398f7b8c9c7d2fd798c6dc8fd329e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+73.654_ry-86.158_rz-01.792_tx+00.358_ty-00.151_s+00.590_1ff79a84e6d398f7b8c9c7d2fd798c6dc8fd329e_256x256.png,bc3d64ca982fc7b65dfd453f1aa667f6e26a89d0,Animals,73.654,3.842,-1.792,3455 -3456,ELEPHANT_M,79.145,-46.577,7.313,-0.022,1.148,1.36875,9ac19c5c39c07b06247010145e3047bf71db4e9f,256,V6,9ac19c5c39c07b06247010145e3047bf71db4e9f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+79.145_ry-46.577_rz+07.313_tx-00.022_ty+01.148_s+01.095_9ac19c5c39c07b06247010145e3047bf71db4e9f_256x256.png,02d127de9bf8d09e4892040c69a56da328f67d9c,Animals,79.145,43.423,7.313,3456 -3457,ELEPHANT_M,88.331,-33.292,78.852,0.194,0.535,1.31375,396df21791761d666ddf50a145b3a3ecaa71b402,256,V6,396df21791761d666ddf50a145b3a3ecaa71b402_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx+88.331_ry-33.292_rz+78.852_tx+00.194_ty+00.535_s+01.051_396df21791761d666ddf50a145b3a3ecaa71b402_256x256.png,9c022c2a82f34e904a9572a88e2f9b05381bd26d,Animals,88.331,56.708,78.852,3457 -3458,ELEPHANT_M,-1.408,-33.836,88.372,0.186,-0.817,1.19625,70499ae51d379138356236e3dcd894ee48fd576e,256,V6,70499ae51d379138356236e3dcd894ee48fd576e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-01.408_ry-33.836_rz+88.372_tx+00.186_ty-00.817_s+00.957_70499ae51d379138356236e3dcd894ee48fd576e_256x256.png,7db4bfe86f214192d6b723c74bc1c91ffaf6ca10,Animals,-1.408,56.164,88.372,3458 -3459,ELEPHANT_M,-3.153,-77.196,66.147,-0.542,-0.156,1.1275,fec58a7089c779e6313fa8c713fd7115d6f919ad,256,V6,fec58a7089c779e6313fa8c713fd7115d6f919ad_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-03.153_ry-77.196_rz+66.147_tx-00.542_ty-00.156_s+00.902_fec58a7089c779e6313fa8c713fd7115d6f919ad_256x256.png,c19a5685625c2d591a349d9a9a2efa5689857102,Animals,-3.153,12.804,66.147,3459 -3460,ELEPHANT_M,-5.191,-41.328,22.749,-0.494,-0.292,1.415,a660359cf4c1b0bd3c021b24aeaef507e2de165d,256,V6,a660359cf4c1b0bd3c021b24aeaef507e2de165d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-05.191_ry-41.328_rz+22.749_tx-00.494_ty-00.292_s+01.132_a660359cf4c1b0bd3c021b24aeaef507e2de165d_256x256.png,9231ab223a8bd6d919cbab488a1295fbe3eba194,Animals,-5.191,48.672,22.749,3460 -3461,ELEPHANT_M,-9.633,-2.056,85.874,0.596,-1.135,1.49,6f821d25c064085bad800e447ec98c314fb81268,256,V6,6f821d25c064085bad800e447ec98c314fb81268_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-09.633_ry-02.056_rz+85.874_tx+00.596_ty-01.135_s+01.192_6f821d25c064085bad800e447ec98c314fb81268_256x256.png,0d2501e8d6937b89c394795ae6d6cf937f844838,Animals,-9.633,87.944,85.874,3461 -3462,ELEPHANT_M,-18.566,-25.481,34.781,-0.509,-0.751,1.08,48023c8e9b5668827baaeead7d411ea5f0ae86b4,256,V6,48023c8e9b5668827baaeead7d411ea5f0ae86b4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-18.566_ry-25.481_rz+34.781_tx-00.509_ty-00.751_s+00.864_48023c8e9b5668827baaeead7d411ea5f0ae86b4_256x256.png,7fc06d1d5881b2f49e33d1a6af42808a0d78743e,Animals,-18.566,64.519,34.781,3462 -3463,ELEPHANT_M,-22.505,-29.608,9.755,0.486,-0.803,1.28,4d9d774064cf6a1bd1ec36b2e84d8e2ad7a93ffb,256,V6,4d9d774064cf6a1bd1ec36b2e84d8e2ad7a93ffb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-22.505_ry-29.608_rz+09.755_tx+00.486_ty-00.803_s+01.024_4d9d774064cf6a1bd1ec36b2e84d8e2ad7a93ffb_256x256.png,71676a68fc5e4420d59f4c63cb45070f53b17296,Animals,-22.505,60.392,9.755,3463 -3464,ELEPHANT_M,-25.727,-13.907,39.342,-0.291,0.426,1.20375,5e62756b6513ab7f850c2d577d1ca57bbfd12e46,256,V6,5e62756b6513ab7f850c2d577d1ca57bbfd12e46_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-25.727_ry-13.907_rz+39.342_tx-00.291_ty+00.426_s+00.963_5e62756b6513ab7f850c2d577d1ca57bbfd12e46_256x256.png,573d5cd1f30fc368cdf7f6049b032c30cfa5a8d9,Animals,-25.727,76.093,39.342,3464 -3465,ELEPHANT_M,-33.086,-22.535,-7.14,-0.531,-0.195,0.905,979980530af643bb078e513ca08e12efc80c53dd,256,V6,979980530af643bb078e513ca08e12efc80c53dd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-33.086_ry-22.535_rz-07.140_tx-00.531_ty-00.195_s+00.724_979980530af643bb078e513ca08e12efc80c53dd_256x256.png,c2e297489afb62088aab092db4bfb676c7c9e2d8,Animals,-33.086,67.465,-7.14,3465 -3466,ELEPHANT_M,-47.161,15.647,24.098,-0.098,-0.893,1.2575,8c44bc9e5ad306f567ee61825392e4cadbf5cc54,256,V6,8c44bc9e5ad306f567ee61825392e4cadbf5cc54_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-47.161_ry+15.647_rz+24.098_tx-00.098_ty-00.893_s+01.006_8c44bc9e5ad306f567ee61825392e4cadbf5cc54_256x256.png,4c9ab7a51fd39c54115213911e248358f0cd3cc3,Animals,-47.161,105.647,24.098,3466 -3467,ELEPHANT_M,-49.782,-75.655,-44.893,0.445,1.086,1.43875,bcc0d6abca51a81cc899c36c453a699c8add5f92,256,V6,bcc0d6abca51a81cc899c36c453a699c8add5f92_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-49.782_ry-75.655_rz-44.893_tx+00.445_ty+01.086_s+01.151_bcc0d6abca51a81cc899c36c453a699c8add5f92_256x256.png,dcff52bde1610192ac4f2bf66120f79847b078ea,Animals,-49.782,14.345,-44.893,3467 -3468,ELEPHANT_M,-50.004,-33.448,-44.149,-0.462,-0.346,0.8825,dd22af46339b5d82d9f6bce3e35ef0a1befc6aeb,256,V6,dd22af46339b5d82d9f6bce3e35ef0a1befc6aeb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-50.004_ry-33.448_rz-44.149_tx-00.462_ty-00.346_s+00.706_dd22af46339b5d82d9f6bce3e35ef0a1befc6aeb_256x256.png,d8cb945a58fa51feb684abcd05ae268412610844,Animals,-50.004,56.552,-44.149,3468 -3469,ELEPHANT_M,-50.081,80.235,74.314,-0.022,0.856,0.8175,83f75b65178939fe02ecb0506645890918678830,256,V6,83f75b65178939fe02ecb0506645890918678830_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-50.081_ry+80.235_rz+74.314_tx-00.022_ty+00.856_s+00.654_83f75b65178939fe02ecb0506645890918678830_256x256.png,2e3e2f5f5d9c607df618ea471ed50d2c8de34cba,Animals,-50.081,170.235,74.314,3469 -3470,ELEPHANT_M,-50.29,-6.362,4.528,0.598,-0.356,1.0175,12879e2e899facfc9f73fc91829227a1e9ababeb,256,V6,12879e2e899facfc9f73fc91829227a1e9ababeb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-50.290_ry-06.362_rz+04.528_tx+00.598_ty-00.356_s+00.814_12879e2e899facfc9f73fc91829227a1e9ababeb_256x256.png,942cf8b2f0d0f77c51899854b37bacebf5325930,Animals,-50.29,83.638,4.528,3470 -3471,ELEPHANT_M,-53.358,-10.897,-72.15,-0.55,0.214,0.74,731d43ecb3ddc257ef84ec35cd51111808615a73,256,V6,731d43ecb3ddc257ef84ec35cd51111808615a73_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-53.358_ry-10.897_rz-72.150_tx-00.550_ty+00.214_s+00.592_731d43ecb3ddc257ef84ec35cd51111808615a73_256x256.png,689a824d4249c04f09e1f38c61ffb12413f39eb2,Animals,-53.358,79.103,-72.15,3471 -3472,ELEPHANT_M,-55.216,21.622,1.093,0.569,-0.669,0.65875,e4f9dfcf51be0b69a1d0284b295d77edf22882db,256,V6,e4f9dfcf51be0b69a1d0284b295d77edf22882db_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-55.216_ry+21.622_rz+01.093_tx+00.569_ty-00.669_s+00.527_e4f9dfcf51be0b69a1d0284b295d77edf22882db_256x256.png,fcb4764e4c454e0820317aa0d826ef3e1d31204f,Animals,-55.216,111.622,1.093,3472 -3473,ELEPHANT_M,-55.955,52.883,-67.523,-0.596,-1.104,1.1175,437b2f297d9a3f1ae1f208be52b65acbe570cf86,256,V6,437b2f297d9a3f1ae1f208be52b65acbe570cf86_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-55.955_ry+52.883_rz-67.523_tx-00.596_ty-01.104_s+00.894_437b2f297d9a3f1ae1f208be52b65acbe570cf86_256x256.png,b72666d39072e39ed553824c4159cbedf67ce67c,Animals,-55.955,142.883,-67.523,3473 -3474,ELEPHANT_M,-58.625,-65.499,-51.978,0.574,0.055,0.7825,b2c128d8c36947ea1c9963c17728a87a3134e27b,256,V6,b2c128d8c36947ea1c9963c17728a87a3134e27b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-58.625_ry-65.499_rz-51.978_tx+00.574_ty+00.055_s+00.626_b2c128d8c36947ea1c9963c17728a87a3134e27b_256x256.png,f4e5c406ac08f68a4a394afb34ec12623b647048,Animals,-58.625,24.501,-51.978,3474 -3475,ELEPHANT_M,-60.935,-27.788,46.336,-0.147,-0.005,1.5075,9678bf879addf048906122d6919b4cfec710f531,256,V6,9678bf879addf048906122d6919b4cfec710f531_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-60.935_ry-27.788_rz+46.336_tx-00.147_ty-00.005_s+01.206_9678bf879addf048906122d6919b4cfec710f531_256x256.png,a3194e42b8fda36fa5680d66f3b97d208477e0d2,Animals,-60.935,62.212,46.336,3475 -3476,ELEPHANT_M,-63.876,-66.817,-72.766,0.37,-0.407,1.4025,32944aa228b960b1c82febf687355f55965d8be0,256,V6,32944aa228b960b1c82febf687355f55965d8be0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-63.876_ry-66.817_rz-72.766_tx+00.370_ty-00.407_s+01.122_32944aa228b960b1c82febf687355f55965d8be0_256x256.png,5e430caef5e8c60876c30d08fc23b4d246a3317f,Animals,-63.876,23.183,-72.766,3476 -3477,ELEPHANT_M,-73.205,87.378,-48.464,0.361,1.169,1.02375,2b0ca4fda34ec1db972756409921d603901640ae,256,V6,2b0ca4fda34ec1db972756409921d603901640ae_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-73.205_ry+87.378_rz-48.464_tx+00.361_ty+01.169_s+00.819_2b0ca4fda34ec1db972756409921d603901640ae_256x256.png,a0278bb80f69834ef9552df4fc417e699742d393,Animals,-73.205,177.378,-48.464,3477 -3478,ELEPHANT_M,-83.053,-88.548,-46.539,0.098,-0.166,1.08625,a6ef75e4e9871cc152c483067323a65c7bec7dbc,256,V6,a6ef75e4e9871cc152c483067323a65c7bec7dbc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-83.053_ry-88.548_rz-46.539_tx+00.098_ty-00.166_s+00.869_a6ef75e4e9871cc152c483067323a65c7bec7dbc_256x256.png,56aa2d99153424e3b3db6f2b2059aca9e29407dd,Animals,-83.053,1.452,-46.539,3478 -3479,ELEPHANT_M,-84.283,-12.65,-46.389,-0.239,0.272,1.3525,8d58d7a470b39d1ab3264c38e31f434c4910fced,256,V6,8d58d7a470b39d1ab3264c38e31f434c4910fced_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/ELEPHANT_M_rx-84.283_ry-12.650_rz-46.389_tx-00.239_ty+00.272_s+01.082_8d58d7a470b39d1ab3264c38e31f434c4910fced_256x256.png,7e0715e7d00cff79a527a6e145681e28bd9a4bc7,Animals,-84.283,77.35,-46.389,3479 -3480,GORILLA,2.312,-63.23,83.983,-0.113,0.394,1.5675,7cf028c765798e26aab915dcebfc87a55e0292f2,256,V6,7cf028c765798e26aab915dcebfc87a55e0292f2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+02.312_ry-63.230_rz+83.983_tx-00.113_ty+00.394_s+01.254_7cf028c765798e26aab915dcebfc87a55e0292f2_256x256.png,24e9a2a9b930eb93be68ab8743b3b02c488c25b0,Animals,2.312,26.77,83.983,3480 -3481,GORILLA,3.223,53.266,-75.966,-0.558,0.938,1.45625,39e1ee0316caddc003e8876d2bed4bebb58cc7bd,256,V6,39e1ee0316caddc003e8876d2bed4bebb58cc7bd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+03.223_ry+53.266_rz-75.966_tx-00.558_ty+00.938_s+01.165_39e1ee0316caddc003e8876d2bed4bebb58cc7bd_256x256.png,303b92da1ae2de0741169545b18885557cc4e0a7,Animals,3.223,143.266,-75.966,3481 -3482,GORILLA,5.135,-26.039,62.764,-0.567,0.392,1.3775,c60ce00f6c0be8beddce9325c7afd37b57466681,256,V6,c60ce00f6c0be8beddce9325c7afd37b57466681_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+05.135_ry-26.039_rz+62.764_tx-00.567_ty+00.392_s+01.102_c60ce00f6c0be8beddce9325c7afd37b57466681_256x256.png,4cd854c149cacfe7e6efbb526e6cf9c64a042835,Animals,5.135,63.961,62.764,3482 -3483,GORILLA,9.39,47.046,59.343,-0.069,-0.489,1.0025,f289f7725b8b95ff2da6ced7fcdcde8c7bc9ad43,256,V6,f289f7725b8b95ff2da6ced7fcdcde8c7bc9ad43_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+09.390_ry+47.046_rz+59.343_tx-00.069_ty-00.489_s+00.802_f289f7725b8b95ff2da6ced7fcdcde8c7bc9ad43_256x256.png,f9e8ebced0462993a23191719c1a30313a061810,Animals,9.39,137.046,59.343,3483 -3484,GORILLA,13.366,-78.608,-56.271,0.148,-0.229,1.0225,e664016eaa17eeef5e8d481024052636e1a96361,256,V6,e664016eaa17eeef5e8d481024052636e1a96361_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+13.366_ry-78.608_rz-56.271_tx+00.148_ty-00.229_s+00.818_e664016eaa17eeef5e8d481024052636e1a96361_256x256.png,9d31d2f0a7774da7118ebc2a41eb379a8bc9f0e3,Animals,13.366,11.392,-56.271,3484 -3485,GORILLA,14.762,-83.961,-64.772,-0.378,1.007,0.655,e07203089cad08022fef776a87b6de73e4c41027,256,V6,e07203089cad08022fef776a87b6de73e4c41027_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+14.762_ry-83.961_rz-64.772_tx-00.378_ty+01.007_s+00.524_e07203089cad08022fef776a87b6de73e4c41027_256x256.png,428e013cee244c268ba124ae2cd9b10b6e9f9316,Animals,14.762,6.039,-64.772,3485 -3486,GORILLA,19.114,4.513,17.922,-0.509,-0.882,1.35125,78b172711d9d385bd3cb4bd5ff20188f839d329f,256,V6,78b172711d9d385bd3cb4bd5ff20188f839d329f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+19.114_ry+04.513_rz+17.922_tx-00.509_ty-00.882_s+01.081_78b172711d9d385bd3cb4bd5ff20188f839d329f_256x256.png,86fade31f0f39547958ce6a955bb1c99ef70bc7b,Animals,19.114,94.513,17.922,3486 -3487,GORILLA,26.789,-14.338,4.24,0.023,0.437,1.48875,0d46e1b9721be8727b8216648f7071b172f3e9a5,256,V6,0d46e1b9721be8727b8216648f7071b172f3e9a5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+26.789_ry-14.338_rz+04.240_tx+00.023_ty+00.437_s+01.191_0d46e1b9721be8727b8216648f7071b172f3e9a5_256x256.png,873438eb48eacc5e849ccdc0bab10d849ca7e6f1,Animals,26.789,75.662,4.24,3487 -3488,GORILLA,27.371,17.172,-21.115,-0.334,-0.224,0.94375,c51b7bb29cbabb98b365838fb0db60e5515307ad,256,V6,c51b7bb29cbabb98b365838fb0db60e5515307ad_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+27.371_ry+17.172_rz-21.115_tx-00.334_ty-00.224_s+00.755_c51b7bb29cbabb98b365838fb0db60e5515307ad_256x256.png,3f67ba1c61a832514d807a09e88b2d63882d6963,Animals,27.371,107.172,-21.115,3488 -3489,GORILLA,34.465,36.147,-28.182,0.383,0.756,0.82125,1a65e61958f5e40cbb8dbc2a7a953af67bc940a2,256,V6,1a65e61958f5e40cbb8dbc2a7a953af67bc940a2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+34.465_ry+36.147_rz-28.182_tx+00.383_ty+00.756_s+00.657_1a65e61958f5e40cbb8dbc2a7a953af67bc940a2_256x256.png,a6f4deb64b68772017843af7262bc67ebb24ae50,Animals,34.465,126.147,-28.182,3489 -3490,GORILLA,37.332,-55.05,-22.776,0.305,-0.057,1.2375,2fedfd6fffd108aee255b18e8dfc9faf77629358,256,V6,2fedfd6fffd108aee255b18e8dfc9faf77629358_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+37.332_ry-55.050_rz-22.776_tx+00.305_ty-00.057_s+00.990_2fedfd6fffd108aee255b18e8dfc9faf77629358_256x256.png,1ea595e8241d87fdc9713af22833fd8f1b9e916c,Animals,37.332,34.95,-22.776,3490 -3491,GORILLA,38.7,-63.141,-70.784,0.323,0.287,1.595,1d4220ca6c0446d3f93abcbcbeef7d8bcd3b1836,256,V6,1d4220ca6c0446d3f93abcbcbeef7d8bcd3b1836_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+38.700_ry-63.141_rz-70.784_tx+00.323_ty+00.287_s+01.276_1d4220ca6c0446d3f93abcbcbeef7d8bcd3b1836_256x256.png,7380464f1a1e795341b6aa9918ab0f84c167ae2b,Animals,38.7,26.859,-70.784,3491 -3492,GORILLA,42.88,10.186,-73.059,-0.182,-0.047,1.52,f6aee9b3386b453b8c3a6666df180f8e49d381bd,256,V6,f6aee9b3386b453b8c3a6666df180f8e49d381bd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+42.880_ry+10.186_rz-73.059_tx-00.182_ty-00.047_s+01.216_f6aee9b3386b453b8c3a6666df180f8e49d381bd_256x256.png,40c2e14f7a5195bb66d36f623e8ed6313a386da3,Animals,42.88,100.186,-73.059,3492 -3493,GORILLA,60.944,-59.491,13.924,0.038,0.4,1.12,51e1182097ab9a2412caa8573fa5f7ca95a81c64,256,V6,51e1182097ab9a2412caa8573fa5f7ca95a81c64_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+60.944_ry-59.491_rz+13.924_tx+00.038_ty+00.400_s+00.896_51e1182097ab9a2412caa8573fa5f7ca95a81c64_256x256.png,432e3960a6210b0e730f85d19f6de4b43a2d73c7,Animals,60.944,30.509,13.924,3493 -3494,GORILLA,62.872,-35.341,79.596,-0.285,-0.494,1.33,fc7239ce7f3d8fd000d6a68663faa6463ecce0e0,256,V6,fc7239ce7f3d8fd000d6a68663faa6463ecce0e0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+62.872_ry-35.341_rz+79.596_tx-00.285_ty-00.494_s+01.064_fc7239ce7f3d8fd000d6a68663faa6463ecce0e0_256x256.png,8eed25c5862f18ee3ababd9564a7e960472a899a,Animals,62.872,54.659,79.596,3494 -3495,GORILLA,64.597,70.016,31.113,0.071,-0.985,1.46375,c460d2f97714ef9d00248a7717dc25e9372d8b47,256,V6,c460d2f97714ef9d00248a7717dc25e9372d8b47_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+64.597_ry+70.016_rz+31.113_tx+00.071_ty-00.985_s+01.171_c460d2f97714ef9d00248a7717dc25e9372d8b47_256x256.png,69d0b9ca1da056b21071552827e788acc3f156b7,Animals,64.597,160.016,31.113,3495 -3496,GORILLA,65.839,-69.713,19.268,-0.22,0.228,1.005,f6212a6b9ca5b7b37ef6e7e444a9d80784034841,256,V6,f6212a6b9ca5b7b37ef6e7e444a9d80784034841_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+65.839_ry-69.713_rz+19.268_tx-00.220_ty+00.228_s+00.804_f6212a6b9ca5b7b37ef6e7e444a9d80784034841_256x256.png,86fb54f0ce4f8b63410e7526381c47a52c5baf34,Animals,65.839,20.287,19.268,3496 -3497,GORILLA,72.334,30.56,26.51,0.59,-0.901,0.9775,66791367350558bd50030aac8e26e2a9b1fa8ce7,256,V6,66791367350558bd50030aac8e26e2a9b1fa8ce7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+72.334_ry+30.560_rz+26.510_tx+00.590_ty-00.901_s+00.782_66791367350558bd50030aac8e26e2a9b1fa8ce7_256x256.png,ed198358b168d8eb38d53bb7398653d78208a222,Animals,72.334,120.56,26.51,3497 -3498,GORILLA,74.579,-71.298,-60.971,-0.562,0.374,0.65625,00fe4356fc62a82d108fda7cdb334c30cba27704,256,V6,00fe4356fc62a82d108fda7cdb334c30cba27704_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+74.579_ry-71.298_rz-60.971_tx-00.562_ty+00.374_s+00.525_00fe4356fc62a82d108fda7cdb334c30cba27704_256x256.png,3f476b8d38d91c13d82970415d4595ea472dd50a,Animals,74.579,18.702,-60.971,3498 -3499,GORILLA,78.43,48.438,14.375,0.318,1.171,1.05375,108510b1ee3ba2bd8403bec5cc1c5482b9692bd4,256,V6,108510b1ee3ba2bd8403bec5cc1c5482b9692bd4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+78.430_ry+48.438_rz+14.375_tx+00.318_ty+01.171_s+00.843_108510b1ee3ba2bd8403bec5cc1c5482b9692bd4_256x256.png,a81b04795eedf0533fd8872231cb66855b0603ff,Animals,78.43,138.438,14.375,3499 -3500,GORILLA,79.815,-2.147,60.129,0.591,0.969,1.4225,eb49011e071c087f14c191c4e29a8c00b2d191df,256,V6,eb49011e071c087f14c191c4e29a8c00b2d191df_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+79.815_ry-02.147_rz+60.129_tx+00.591_ty+00.969_s+01.138_eb49011e071c087f14c191c4e29a8c00b2d191df_256x256.png,c3344b170ad339988c59f927796613956edaf5fe,Animals,79.815,87.853,60.129,3500 -3501,GORILLA,86.152,-51.324,-72.548,-0.136,0.16,0.66875,7632bf6e47a56ce11afb6f31c6e4a93d7a849ac4,256,V6,7632bf6e47a56ce11afb6f31c6e4a93d7a849ac4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx+86.152_ry-51.324_rz-72.548_tx-00.136_ty+00.160_s+00.535_7632bf6e47a56ce11afb6f31c6e4a93d7a849ac4_256x256.png,264d5d6b8b4adf5414dc8312eac30ddc62abe736,Animals,86.152,38.676,-72.548,3501 -3502,GORILLA,-0.614,-88.415,42.676,-0.507,0.495,0.745,64d1e45bb14b9981fb88fc6880f9a10439481f49,256,V6,64d1e45bb14b9981fb88fc6880f9a10439481f49_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-00.614_ry-88.415_rz+42.676_tx-00.507_ty+00.495_s+00.596_64d1e45bb14b9981fb88fc6880f9a10439481f49_256x256.png,954f813125bb6ca0750196583f4ca9d6a0169a32,Animals,-0.614,1.585,42.676,3502 -3503,GORILLA,-0.719,-88.302,-45.316,-0.27,-0.943,1.0275,a11e6927fc1aac01675d9bd2014cf791de353007,256,V6,a11e6927fc1aac01675d9bd2014cf791de353007_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-00.719_ry-88.302_rz-45.316_tx-00.270_ty-00.943_s+00.822_a11e6927fc1aac01675d9bd2014cf791de353007_256x256.png,9814111bc0045e58aa92be80a8411513956a2afa,Animals,-0.719,1.698,-45.316,3503 -3504,GORILLA,-17.7,-3.814,-73.361,0.467,1.062,1.13125,2704edbd6e5eb04b3a99f2a7eb00c9472ebb1e7d,256,V6,2704edbd6e5eb04b3a99f2a7eb00c9472ebb1e7d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-17.700_ry-03.814_rz-73.361_tx+00.467_ty+01.062_s+00.905_2704edbd6e5eb04b3a99f2a7eb00c9472ebb1e7d_256x256.png,95d9e616dc8820630419b71e89cb21816aa1cb57,Animals,-17.7,86.186,-73.361,3504 -3505,GORILLA,-24.681,-51.371,67.494,-0.257,0.906,1.21,ab9d8b8337227a1934137535c9371f03ff0e67f3,256,V6,ab9d8b8337227a1934137535c9371f03ff0e67f3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-24.681_ry-51.371_rz+67.494_tx-00.257_ty+00.906_s+00.968_ab9d8b8337227a1934137535c9371f03ff0e67f3_256x256.png,155c42211445f9b8e4a29c545bce84392568bb09,Animals,-24.681,38.629,67.494,3505 -3506,GORILLA,-25.445,53.468,-8.65,0.215,0.405,1.31375,0513d6b6f464db4419e66c985580d2f34c34087f,256,V6,0513d6b6f464db4419e66c985580d2f34c34087f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-25.445_ry+53.468_rz-08.650_tx+00.215_ty+00.405_s+01.051_0513d6b6f464db4419e66c985580d2f34c34087f_256x256.png,d74b7cec0689e358f20a4989560a9aa66d45d7ea,Animals,-25.445,143.468,-8.65,3506 -3507,GORILLA,-40.201,63.869,-31.724,0.243,-1.05,1.33875,fce197ea55a1765cb55d7d3f6b6e03345562751f,256,V6,fce197ea55a1765cb55d7d3f6b6e03345562751f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-40.201_ry+63.869_rz-31.724_tx+00.243_ty-01.050_s+01.071_fce197ea55a1765cb55d7d3f6b6e03345562751f_256x256.png,342b7ecef87954d7bfa38e3cc47313002713cf35,Animals,-40.201,153.869,-31.724,3507 -3508,GORILLA,-43.431,-45.399,5.793,-0.446,-0.347,1.58875,0be9c1b67f80feb55783c50f84a9b549d4c73d8c,256,V6,0be9c1b67f80feb55783c50f84a9b549d4c73d8c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-43.431_ry-45.399_rz+05.793_tx-00.446_ty-00.347_s+01.271_0be9c1b67f80feb55783c50f84a9b549d4c73d8c_256x256.png,e0631a763a540163bd8775379a251174377e2922,Animals,-43.431,44.601,5.793,3508 -3509,GORILLA,-43.832,-80.726,51.613,-0.027,-1.176,0.73875,2cfe4cc16926d9abd253510b6269a817e545359a,256,V6,2cfe4cc16926d9abd253510b6269a817e545359a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-43.832_ry-80.726_rz+51.613_tx-00.027_ty-01.176_s+00.591_2cfe4cc16926d9abd253510b6269a817e545359a_256x256.png,049aec8e5218c081c991195b579541f020c8c72c,Animals,-43.832,9.274,51.613,3509 -3510,GORILLA,-52.42,-71.423,2.138,-0.488,1.131,1.19375,477674f59a30d02366d0838fbaedbefbfcc44264,256,V6,477674f59a30d02366d0838fbaedbefbfcc44264_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-52.420_ry-71.423_rz+02.138_tx-00.488_ty+01.131_s+00.955_477674f59a30d02366d0838fbaedbefbfcc44264_256x256.png,c077dbfe18898a27b3f3049082083c25d187573c,Animals,-52.42,18.577,2.138,3510 -3511,GORILLA,-52.944,68.06,84.305,-0.03,-0.159,1.07125,9be7de3c97b5e29697eb41879f0a0585fbc21895,256,V6,9be7de3c97b5e29697eb41879f0a0585fbc21895_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-52.944_ry+68.060_rz+84.305_tx-00.030_ty-00.159_s+00.857_9be7de3c97b5e29697eb41879f0a0585fbc21895_256x256.png,0518faeff8fa12123279428b01de32915b40cc18,Animals,-52.944,158.06,84.305,3511 -3512,GORILLA,-53.536,-74.023,15.374,0.477,-0.101,1.47625,b7614205c80f163e49cccbce163593d13f3a6255,256,V6,b7614205c80f163e49cccbce163593d13f3a6255_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-53.536_ry-74.023_rz+15.374_tx+00.477_ty-00.101_s+01.181_b7614205c80f163e49cccbce163593d13f3a6255_256x256.png,35245db1760e807e9c28a4da77161c8f5221da3a,Animals,-53.536,15.977,15.374,3512 -3513,GORILLA,-71.914,60.569,-48.774,0.161,0.974,0.71375,5c5642f6e26bbf0f69c20ffa979006699f6b46ed,256,V6,5c5642f6e26bbf0f69c20ffa979006699f6b46ed_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-71.914_ry+60.569_rz-48.774_tx+00.161_ty+00.974_s+00.571_5c5642f6e26bbf0f69c20ffa979006699f6b46ed_256x256.png,2ed81d4c9a82feab084e17a631910a2ae9e015c9,Animals,-71.914,150.569,-48.774,3513 -3514,GORILLA,-73.304,85.828,-50.242,0.044,0.943,0.75375,6dec52041309894f4d4da45448fc6c9a4cd4ba87,256,V6,6dec52041309894f4d4da45448fc6c9a4cd4ba87_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-73.304_ry+85.828_rz-50.242_tx+00.044_ty+00.943_s+00.603_6dec52041309894f4d4da45448fc6c9a4cd4ba87_256x256.png,241a33fd5221263ac3d350bd82ddeab2d0104eab,Animals,-73.304,175.828,-50.242,3514 -3515,GORILLA,-80.65,-18.151,-1.103,0.058,1.024,0.67875,d0a7ea56c8ccd97ce0f3ed2d0265009453270257,256,V6,d0a7ea56c8ccd97ce0f3ed2d0265009453270257_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-80.650_ry-18.151_rz-01.103_tx+00.058_ty+01.024_s+00.543_d0a7ea56c8ccd97ce0f3ed2d0265009453270257_256x256.png,dd4b918c0494832d8165060170001580208f8f91,Animals,-80.65,71.849,-1.103,3515 -3516,GORILLA,-80.941,9.75,-30.746,-0.454,-0.312,0.8575,5728f283a6a199cfebfc4555a57ae08c0205ed65,256,V6,5728f283a6a199cfebfc4555a57ae08c0205ed65_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-80.941_ry+09.750_rz-30.746_tx-00.454_ty-00.312_s+00.686_5728f283a6a199cfebfc4555a57ae08c0205ed65_256x256.png,b36e0b37edc1de3ad456252e561ed09cd697cab2,Animals,-80.941,99.75,-30.746,3516 -3517,GORILLA,-82.855,35.97,77.971,-0.351,0.092,0.99375,2078bfc220cc2c1b0bc4793088ee3e05edc6509a,256,V6,2078bfc220cc2c1b0bc4793088ee3e05edc6509a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-82.855_ry+35.970_rz+77.971_tx-00.351_ty+00.092_s+00.795_2078bfc220cc2c1b0bc4793088ee3e05edc6509a_256x256.png,ac5c0ce0ccd09be4976c15077f6ea87ef9c6661e,Animals,-82.855,125.97,77.971,3517 -3518,GORILLA,-85.276,-74.287,-28.004,-0.12,0.157,1.02125,26667f9e3dad7575fb01344e19045ea4784e558a,256,V6,26667f9e3dad7575fb01344e19045ea4784e558a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-85.276_ry-74.287_rz-28.004_tx-00.120_ty+00.157_s+00.817_26667f9e3dad7575fb01344e19045ea4784e558a_256x256.png,1ec6058f8bb30d29762aeeac4d6c69f9ec722a0d,Animals,-85.276,15.713,-28.004,3518 -3519,GORILLA,-88.682,-35.962,-38.912,-0.16,0.414,1.565,67c89abcaafdbb2831004e311d93f841bd754bdd,256,V6,67c89abcaafdbb2831004e311d93f841bd754bdd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/GORILLA_rx-88.682_ry-35.962_rz-38.912_tx-00.160_ty+00.414_s+01.252_67c89abcaafdbb2831004e311d93f841bd754bdd_256x256.png,5d5ff8ca0f85987ec72955960ce1d30a603fa0f5,Animals,-88.682,54.038,-38.912,3519 -3520,LIONESS,7.104,63.573,-88.092,-0.273,-0.852,1.41125,5de224ae81d4886a190042d6d365d0a3b6e5fa73,256,V6,5de224ae81d4886a190042d6d365d0a3b6e5fa73_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+07.104_ry+63.573_rz-88.092_tx-00.273_ty-00.852_s+01.129_5de224ae81d4886a190042d6d365d0a3b6e5fa73_256x256.png,ee9e3e8f5d15d78bae6fb1741be0e4a9015faedd,Animals,7.104,153.573,-88.092,3520 -3521,LIONESS,9.268,-29.929,46.528,-0.579,0.587,0.6275,db995f6360596063ca268b6a7d6a49e9f43cc72d,256,V6,db995f6360596063ca268b6a7d6a49e9f43cc72d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+09.268_ry-29.929_rz+46.528_tx-00.579_ty+00.587_s+00.502_db995f6360596063ca268b6a7d6a49e9f43cc72d_256x256.png,945897b436052b4a3263cd6fb3632dd447f5e2ff,Animals,9.268,60.071,46.528,3521 -3522,LIONESS,10.351,25.372,-82.221,0.43,-1.163,1.46875,fb6c9fa81f77ea3da443f72811a00ae6ac13fb1d,256,V6,fb6c9fa81f77ea3da443f72811a00ae6ac13fb1d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+10.351_ry+25.372_rz-82.221_tx+00.430_ty-01.163_s+01.175_fb6c9fa81f77ea3da443f72811a00ae6ac13fb1d_256x256.png,55dab740899a496cba90819aa90d81340942842d,Animals,10.351,115.372,-82.221,3522 -3523,LIONESS,17.662,-44.795,52.343,0.532,0.511,0.74875,33eac2f9fdfd763498f28e1e744cb2636d54e983,256,V6,33eac2f9fdfd763498f28e1e744cb2636d54e983_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+17.662_ry-44.795_rz+52.343_tx+00.532_ty+00.511_s+00.599_33eac2f9fdfd763498f28e1e744cb2636d54e983_256x256.png,ef0d64ca792b9068b7fab6b1457bc3d42fc709a8,Animals,17.662,45.205,52.343,3523 -3524,LIONESS,18.212,21.191,58.979,0.505,0.758,1.35875,769f0d7cd4429433d59a130662747b3fc0375c71,256,V6,769f0d7cd4429433d59a130662747b3fc0375c71_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+18.212_ry+21.191_rz+58.979_tx+00.505_ty+00.758_s+01.087_769f0d7cd4429433d59a130662747b3fc0375c71_256x256.png,248f0811647ea61b395311949f66b6b6d28ac2dc,Animals,18.212,111.191,58.979,3524 -3525,LIONESS,19.682,45.658,69.662,0.303,-1.116,1.13375,79564b94fe5dd5605a2bac04989baac47b0900e2,256,V6,79564b94fe5dd5605a2bac04989baac47b0900e2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+19.682_ry+45.658_rz+69.662_tx+00.303_ty-01.116_s+00.907_79564b94fe5dd5605a2bac04989baac47b0900e2_256x256.png,aa35a6c3d87e3c6907f00b758ae476c83e9f4cb1,Animals,19.682,135.658,69.662,3525 -3526,LIONESS,33.253,39.172,-1.617,-0.278,-0.412,1.03625,cf6846a1c3555e3becae9c3d74e1fb84dd60ffc2,256,V6,cf6846a1c3555e3becae9c3d74e1fb84dd60ffc2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+33.253_ry+39.172_rz-01.617_tx-00.278_ty-00.412_s+00.829_cf6846a1c3555e3becae9c3d74e1fb84dd60ffc2_256x256.png,9324d77eb1cb82b3a57a49e8d21613d4d22e5231,Animals,33.253,129.172,-1.617,3526 -3527,LIONESS,35.55,19.503,-71.791,-0.391,-0.956,0.74625,ee731743ae5395f40d626a0ca875f9b9d49ffcbd,256,V6,ee731743ae5395f40d626a0ca875f9b9d49ffcbd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+35.550_ry+19.503_rz-71.791_tx-00.391_ty-00.956_s+00.597_ee731743ae5395f40d626a0ca875f9b9d49ffcbd_256x256.png,f6e1f8d8f5a00306afb6b81daefe75c65471d51d,Animals,35.55,109.503,-71.791,3527 -3528,LIONESS,39.457,-7.879,-82.102,0.474,-0.788,0.77875,0fa675f344d8903171a28074956b9fed140fa3ff,256,V6,0fa675f344d8903171a28074956b9fed140fa3ff_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+39.457_ry-07.879_rz-82.102_tx+00.474_ty-00.788_s+00.623_0fa675f344d8903171a28074956b9fed140fa3ff_256x256.png,9c119f2363228b00df2463fa1ea41eb5eab834a5,Animals,39.457,82.121,-82.102,3528 -3529,LIONESS,41.607,-51.465,-9.744,-0.162,-0.928,0.85625,00518e1654f4d403817a0fa9d819906890a98d89,256,V6,00518e1654f4d403817a0fa9d819906890a98d89_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+41.607_ry-51.465_rz-09.744_tx-00.162_ty-00.928_s+00.685_00518e1654f4d403817a0fa9d819906890a98d89_256x256.png,8c5ea009fae15e277b8b5b4df02ab7a655b21c76,Animals,41.607,38.535,-9.744,3529 -3530,LIONESS,42.959,-49.659,-76.879,0.049,-1.138,0.66125,638b8a400cfbbc7e85fdbe00d9f23bf96a1f507b,256,V6,638b8a400cfbbc7e85fdbe00d9f23bf96a1f507b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+42.959_ry-49.659_rz-76.879_tx+00.049_ty-01.138_s+00.529_638b8a400cfbbc7e85fdbe00d9f23bf96a1f507b_256x256.png,331e8408d562b7adfffa3741ffee410d25a60d8b,Animals,42.959,40.341,-76.879,3530 -3531,LIONESS,47.21,-31.119,78.374,-0.22,0.99,0.77,165ab309c6c8350ab399fee902530d9d10627586,256,V6,165ab309c6c8350ab399fee902530d9d10627586_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+47.210_ry-31.119_rz+78.374_tx-00.220_ty+00.990_s+00.616_165ab309c6c8350ab399fee902530d9d10627586_256x256.png,0dd3fee01f15bf15879b9b5c8898ea24e6f18861,Animals,47.21,58.881,78.374,3531 -3532,LIONESS,51.187,-42.577,-65.491,-0.201,-0.436,0.79125,3f78ffad00ed33a0cbe8e504f34359f4e9b8755a,256,V6,3f78ffad00ed33a0cbe8e504f34359f4e9b8755a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+51.187_ry-42.577_rz-65.491_tx-00.201_ty-00.436_s+00.633_3f78ffad00ed33a0cbe8e504f34359f4e9b8755a_256x256.png,e308c2f44baa0ca73adce6cb2f3e87ff989827b5,Animals,51.187,47.423,-65.491,3532 -3533,LIONESS,63.357,-35.673,53.342,-0.18,0.594,0.6925,bba0f2764c50286c155c07384215225bf628a5e9,256,V6,bba0f2764c50286c155c07384215225bf628a5e9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+63.357_ry-35.673_rz+53.342_tx-00.180_ty+00.594_s+00.554_bba0f2764c50286c155c07384215225bf628a5e9_256x256.png,7f59a40cfa75089bfe6304a4d1bfbf55b60d6c78,Animals,63.357,54.327,53.342,3533 -3534,LIONESS,79.993,-89.243,38.096,-0.352,-0.802,0.7125,e59986f79a74f96cf0f712851ff22f4b5f6168a5,256,V6,e59986f79a74f96cf0f712851ff22f4b5f6168a5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+79.993_ry-89.243_rz+38.096_tx-00.352_ty-00.802_s+00.570_e59986f79a74f96cf0f712851ff22f4b5f6168a5_256x256.png,e62609a3c439e282c358d62f7db5c73c80d76ec6,Animals,79.993,0.757,38.096,3534 -3535,LIONESS,86.712,-40.137,2.43,-0.341,-0.392,1.04,8bcb615d18adc0faff992ee0b7d53113f9d492e9,256,V6,8bcb615d18adc0faff992ee0b7d53113f9d492e9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+86.712_ry-40.137_rz+02.430_tx-00.341_ty-00.392_s+00.832_8bcb615d18adc0faff992ee0b7d53113f9d492e9_256x256.png,8768cb2c78ba0fbac98d89a9263465fe88acd28c,Animals,86.712,49.863,2.43,3535 -3536,LIONESS,89.311,-40.582,-61.348,-0.293,0.192,0.9375,d4f99b6015226fd9e0317b4bf3b53ec1e719a574,256,V6,d4f99b6015226fd9e0317b4bf3b53ec1e719a574_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+89.311_ry-40.582_rz-61.348_tx-00.293_ty+00.192_s+00.750_d4f99b6015226fd9e0317b4bf3b53ec1e719a574_256x256.png,6a165e212718df97546c7f035584c25abe414ef0,Animals,89.311,49.418,-61.348,3536 -3537,LIONESS,89.354,-73.889,-48.105,-0.063,-0.943,0.94125,4e1d520c3e61d98fff37e3745cf3fc058c96979d,256,V6,4e1d520c3e61d98fff37e3745cf3fc058c96979d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+89.354_ry-73.889_rz-48.105_tx-00.063_ty-00.943_s+00.753_4e1d520c3e61d98fff37e3745cf3fc058c96979d_256x256.png,6671f50eeb320d7a2241dbc9546ff4a891573096,Animals,89.354,16.111,-48.105,3537 -3538,LIONESS,89.901,70.751,38.109,0.584,0.678,1.5275,a52710e26503a1e3ef16be0885199842ba39d304,256,V6,a52710e26503a1e3ef16be0885199842ba39d304_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx+89.901_ry+70.751_rz+38.109_tx+00.584_ty+00.678_s+01.222_a52710e26503a1e3ef16be0885199842ba39d304_256x256.png,d9a754ac383ef26bd69ebc2b6506fd3e74207e9a,Animals,89.901,160.751,38.109,3538 -3539,LIONESS,-9.202,-49.882,28.36,0.35,0.519,0.65375,19e8e43f1d6db80257276c7e8a2c34ed5ef5e7ff,256,V6,19e8e43f1d6db80257276c7e8a2c34ed5ef5e7ff_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-09.202_ry-49.882_rz+28.360_tx+00.350_ty+00.519_s+00.523_19e8e43f1d6db80257276c7e8a2c34ed5ef5e7ff_256x256.png,b8c58047a99211a9410b5ff56693b5907c9f6eea,Animals,-9.202,40.118,28.36,3539 -3540,LIONESS,-14.509,-59.627,15.147,0.051,-0.112,1.12875,93c911cd037efab1727e88299981933107bddbdc,256,V6,93c911cd037efab1727e88299981933107bddbdc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-14.509_ry-59.627_rz+15.147_tx+00.051_ty-00.112_s+00.903_93c911cd037efab1727e88299981933107bddbdc_256x256.png,6fe91e827dc3d892eafa19c768b5a143a38ff026,Animals,-14.509,30.373,15.147,3540 -3541,LIONESS,-17.735,43.568,-83.391,0.235,0.443,1.4825,d5a0602a293265c0c587647bc9953ff2ab15c2b6,256,V6,d5a0602a293265c0c587647bc9953ff2ab15c2b6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-17.735_ry+43.568_rz-83.391_tx+00.235_ty+00.443_s+01.186_d5a0602a293265c0c587647bc9953ff2ab15c2b6_256x256.png,f03a6bddecc03c157f69aa5f62519dc71c0e8059,Animals,-17.735,133.568,-83.391,3541 -3542,LIONESS,-21.0,53.1,58.435,-0.455,-0.097,1.39625,b34f8b43bc5e2c466a8f4de59153b7e3b4bf367f,256,V6,b34f8b43bc5e2c466a8f4de59153b7e3b4bf367f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-21.000_ry+53.100_rz+58.435_tx-00.455_ty-00.097_s+01.117_b34f8b43bc5e2c466a8f4de59153b7e3b4bf367f_256x256.png,337b3f1243beeb5bf43b62aa0800137e687fa7f6,Animals,-21.0,143.1,58.435,3542 -3543,LIONESS,-26.573,-65.496,41.629,0.422,-0.148,0.8125,a3d0b2fd7b77e86406438785378d7f80b95b3587,256,V6,a3d0b2fd7b77e86406438785378d7f80b95b3587_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-26.573_ry-65.496_rz+41.629_tx+00.422_ty-00.148_s+00.650_a3d0b2fd7b77e86406438785378d7f80b95b3587_256x256.png,048b30897f353c02246ecaf9fa0c283230f283e5,Animals,-26.573,24.504,41.629,3543 -3544,LIONESS,-30.734,15.765,-32.314,0.272,0.694,1.12625,fbea390471adbf3dea1ed5b42b4766d6a91454c5,256,V6,fbea390471adbf3dea1ed5b42b4766d6a91454c5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-30.734_ry+15.765_rz-32.314_tx+00.272_ty+00.694_s+00.901_fbea390471adbf3dea1ed5b42b4766d6a91454c5_256x256.png,0440deec7566bcebd19def477af4e9ab37157cb5,Animals,-30.734,105.765,-32.314,3544 -3545,LIONESS,-31.162,5.717,5.667,0.548,-0.741,1.10375,aa9f669ddc8a1f97d78d5e6be74641d0a9c84e07,256,V6,aa9f669ddc8a1f97d78d5e6be74641d0a9c84e07_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-31.162_ry+05.717_rz+05.667_tx+00.548_ty-00.741_s+00.883_aa9f669ddc8a1f97d78d5e6be74641d0a9c84e07_256x256.png,d3b80370e224f6752a4e143727ec06693302ca94,Animals,-31.162,95.717,5.667,3545 -3546,LIONESS,-32.233,-64.907,42.098,0.526,0.881,0.79625,562dbb2032ab46702743ba2fb2d61c63843c5d92,256,V6,562dbb2032ab46702743ba2fb2d61c63843c5d92_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-32.233_ry-64.907_rz+42.098_tx+00.526_ty+00.881_s+00.637_562dbb2032ab46702743ba2fb2d61c63843c5d92_256x256.png,b617eda6c1a4352084f64040c92686b3f0e688d8,Animals,-32.233,25.093,42.098,3546 -3547,LIONESS,-38.699,57.335,-17.705,-0.321,-0.49,1.1425,5f99bf24b5279ecb5772f56a32efe6c3c83b6510,256,V6,5f99bf24b5279ecb5772f56a32efe6c3c83b6510_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-38.699_ry+57.335_rz-17.705_tx-00.321_ty-00.490_s+00.914_5f99bf24b5279ecb5772f56a32efe6c3c83b6510_256x256.png,602a5539a65e56618461eb97464254eaf2bca25b,Animals,-38.699,147.335,-17.705,3547 -3548,LIONESS,-41.091,-12.033,74.145,0.54,-0.49,1.1625,11dd53f0429c268c5263f44cc5d70b1699149d05,256,V6,11dd53f0429c268c5263f44cc5d70b1699149d05_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-41.091_ry-12.033_rz+74.145_tx+00.540_ty-00.490_s+00.930_11dd53f0429c268c5263f44cc5d70b1699149d05_256x256.png,c507fa81c5c89e21d93ab82569c8453205112dda,Animals,-41.091,77.967,74.145,3548 -3549,LIONESS,-54.197,64.964,50.114,-0.016,0.721,0.88875,8735aa5c9e9d9c124b7913037c12c217b62a5473,256,V6,8735aa5c9e9d9c124b7913037c12c217b62a5473_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-54.197_ry+64.964_rz+50.114_tx-00.016_ty+00.721_s+00.711_8735aa5c9e9d9c124b7913037c12c217b62a5473_256x256.png,fe2d1dba9e1fcb37cf0aea154c85857c93a34e4e,Animals,-54.197,154.964,50.114,3549 -3550,LIONESS,-62.074,85.861,-13.855,0.095,0.505,1.0575,b8675c4e59f6ae7bdd7431c030893869d78a2eda,256,V6,b8675c4e59f6ae7bdd7431c030893869d78a2eda_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-62.074_ry+85.861_rz-13.855_tx+00.095_ty+00.505_s+00.846_b8675c4e59f6ae7bdd7431c030893869d78a2eda_256x256.png,a4b6976ff9306574fd7188c81ba819add37be515,Animals,-62.074,175.861,-13.855,3550 -3551,LIONESS,-62.381,57.208,31.524,-0.265,0.423,0.7325,039f319d6a4326337d82744ed16724cc94782ca3,256,V6,039f319d6a4326337d82744ed16724cc94782ca3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-62.381_ry+57.208_rz+31.524_tx-00.265_ty+00.423_s+00.586_039f319d6a4326337d82744ed16724cc94782ca3_256x256.png,00fc95bc2f487d5dc8f900747166843346153c6f,Animals,-62.381,147.208,31.524,3551 -3552,LIONESS,-62.649,75.214,74.015,0.452,0.857,1.2875,13b184c4846ff9317cb782bf2aa87af62866151f,256,V6,13b184c4846ff9317cb782bf2aa87af62866151f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-62.649_ry+75.214_rz+74.015_tx+00.452_ty+00.857_s+01.030_13b184c4846ff9317cb782bf2aa87af62866151f_256x256.png,bbc2acab98bd7150e5e8cc8b31ae731ea9661d0e,Animals,-62.649,165.214,74.015,3552 -3553,LIONESS,-66.866,-41.83,-66.528,-0.577,0.032,1.5075,a47a2f7d0341a156c4df0e98638dd61d17bd41b1,256,V6,a47a2f7d0341a156c4df0e98638dd61d17bd41b1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-66.866_ry-41.830_rz-66.528_tx-00.577_ty+00.032_s+01.206_a47a2f7d0341a156c4df0e98638dd61d17bd41b1_256x256.png,efe722969dc2a201c997acd68e15a056b7a27a63,Animals,-66.866,48.17,-66.528,3553 -3554,LIONESS,-69.09,-63.701,-42.404,-0.216,0.353,1.47625,606c68162a3a6e755789a5df016e9f245ef691eb,256,V6,606c68162a3a6e755789a5df016e9f245ef691eb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-69.090_ry-63.701_rz-42.404_tx-00.216_ty+00.353_s+01.181_606c68162a3a6e755789a5df016e9f245ef691eb_256x256.png,b797666cd30d053c80b3a21618b302b960ee4833,Animals,-69.09,26.299,-42.404,3554 -3555,LIONESS,-71.088,34.016,-84.446,-0.127,0.875,1.2375,d00ae4d6781f80f005922132840d2faccf329e51,256,V6,d00ae4d6781f80f005922132840d2faccf329e51_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-71.088_ry+34.016_rz-84.446_tx-00.127_ty+00.875_s+00.990_d00ae4d6781f80f005922132840d2faccf329e51_256x256.png,7b8db97ebc1d03c5ae40081a040c595a101e8952,Animals,-71.088,124.016,-84.446,3555 -3556,LIONESS,-71.511,-82.008,-21.292,-0.261,0.725,1.38625,1f89ca11672953b5342526ff7534cf621eb203a9,256,V6,1f89ca11672953b5342526ff7534cf621eb203a9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-71.511_ry-82.008_rz-21.292_tx-00.261_ty+00.725_s+01.109_1f89ca11672953b5342526ff7534cf621eb203a9_256x256.png,e35a4604160aa3d461a8ae0016a39c768add1240,Animals,-71.511,7.992,-21.292,3556 -3557,LIONESS,-73.428,-44.412,-24.431,0.149,0.079,0.8325,b5e6ff5349f73bca4173ca8ed8c78a7761528f0a,256,V6,b5e6ff5349f73bca4173ca8ed8c78a7761528f0a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-73.428_ry-44.412_rz-24.431_tx+00.149_ty+00.079_s+00.666_b5e6ff5349f73bca4173ca8ed8c78a7761528f0a_256x256.png,11584843e00699762b8a588fc4977cc56be4ada2,Animals,-73.428,45.588,-24.431,3557 -3558,LIONESS,-84.389,2.011,25.594,-0.262,0.087,1.15875,f8f64cc661dc616ac915053b753291c4bcaa886a,256,V6,f8f64cc661dc616ac915053b753291c4bcaa886a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-84.389_ry+02.011_rz+25.594_tx-00.262_ty+00.087_s+00.927_f8f64cc661dc616ac915053b753291c4bcaa886a_256x256.png,32c11449c5f303008dee8783edbcca236341d97b,Animals,-84.389,92.011,25.594,3558 -3559,LIONESS,-86.265,47.94,-56.433,-0.025,0.819,0.905,644033b4b97e6a0e078db60ed726709a42608283,256,V6,644033b4b97e6a0e078db60ed726709a42608283_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/LIONESS_rx-86.265_ry+47.940_rz-56.433_tx-00.025_ty+00.819_s+00.724_644033b4b97e6a0e078db60ed726709a42608283_256x256.png,b3ea8da7c2a98d9abe331610421da7cfdc08000d,Animals,-86.265,137.94,-56.433,3559 -3560,MQUEEN_L,6.472,-45.895,29.633,-0.306,-0.027,1.3675,6255f3a6925a549ce31e8fab23bf7d6063a990a0,256,V6,6255f3a6925a549ce31e8fab23bf7d6063a990a0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+06.472_ry-45.895_rz+29.633_tx-00.306_ty-00.027_s+01.641_6255f3a6925a549ce31e8fab23bf7d6063a990a0_256x256.png,0d51a613255f289464e7d6c7b0b0afbf991135ec,Boats,6.472,-135.895,29.633,3560 -3561,MQUEEN_L,14.061,-41.461,-12.026,-0.353,0.408,1.215,61e71ce138629eab7c9b765162d98bf9728e8e4f,256,V6,61e71ce138629eab7c9b765162d98bf9728e8e4f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+14.061_ry-41.461_rz-12.026_tx-00.353_ty+00.408_s+01.458_61e71ce138629eab7c9b765162d98bf9728e8e4f_256x256.png,2cd708c193f957dd860a8fa82a43a9f4c81cc37a,Boats,14.061,-131.461,-12.026,3561 -3562,MQUEEN_L,17.562,86.158,18.479,-0.319,0.305,0.984166666667,0ab2c4be3b78a75e6e4b9cceac1b982e14546335,256,V6,0ab2c4be3b78a75e6e4b9cceac1b982e14546335_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+17.562_ry+86.158_rz+18.479_tx-00.319_ty+00.305_s+01.181_0ab2c4be3b78a75e6e4b9cceac1b982e14546335_256x256.png,9981e3fbb4915157fc49ee540b2c7c73a4552c76,Boats,17.562,-3.842,18.479,3562 -3563,MQUEEN_L,24.085,86.425,44.731,-0.376,-0.949,0.836666666667,239eeb2c453058c64041976909d4d3b609d03685,256,V6,239eeb2c453058c64041976909d4d3b609d03685_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+24.085_ry+86.425_rz+44.731_tx-00.376_ty-00.949_s+01.004_239eeb2c453058c64041976909d4d3b609d03685_256x256.png,c01550586f63cd131e23398ed1b89cb11b9ac291,Boats,24.085,-3.575,44.731,3563 -3564,MQUEEN_L,27.563,49.012,36.582,-0.007,0.725,1.4225,fbc700e8a2b2d088f513a68ac721804ea3291f2c,256,V6,fbc700e8a2b2d088f513a68ac721804ea3291f2c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+27.563_ry+49.012_rz+36.582_tx-00.007_ty+00.725_s+01.707_fbc700e8a2b2d088f513a68ac721804ea3291f2c_256x256.png,23da9d9e1a7bdaf02c5b7413afd56094aebc85b2,Boats,27.563,-40.988,36.582,3564 -3565,MQUEEN_L,33.851,-77.967,-50.017,0.24,-0.452,1.24583333333,610d246da7483e921d03e3bc002a2f25b3312c0c,256,V6,610d246da7483e921d03e3bc002a2f25b3312c0c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+33.851_ry-77.967_rz-50.017_tx+00.240_ty-00.452_s+01.495_610d246da7483e921d03e3bc002a2f25b3312c0c_256x256.png,fa2790be3c62636fa94cc97f2ce5ed540fc7e5a9,Boats,33.851,-167.967,-50.017,3565 -3566,MQUEEN_L,36.727,13.111,-52.687,0.489,0.472,0.685833333333,4d735bb50d5671038bc0e65942a8718ab8dd4605,256,V6,4d735bb50d5671038bc0e65942a8718ab8dd4605_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+36.727_ry+13.111_rz-52.687_tx+00.489_ty+00.472_s+00.823_4d735bb50d5671038bc0e65942a8718ab8dd4605_256x256.png,e5b9314306f3b9aa535887293e90ca432a7dbf86,Boats,36.727,-76.889,-52.687,3566 -3567,MQUEEN_L,38.746,59.259,3.82,0.162,0.628,1.45916666667,e580f780ba814b6e8945e0d6b6722ecf4bf847b0,256,V6,e580f780ba814b6e8945e0d6b6722ecf4bf847b0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+38.746_ry+59.259_rz+03.820_tx+00.162_ty+00.628_s+01.751_e580f780ba814b6e8945e0d6b6722ecf4bf847b0_256x256.png,6c7923d1ef8f52b1152367c4cd016eabeb6b0ba3,Boats,38.746,-30.741,3.82,3567 -3568,MQUEEN_L,39.674,-5.232,-84.967,-0.184,0.25,1.41666666667,743235d974defeabee6e549a3ef566cdf04f870d,256,V6,743235d974defeabee6e549a3ef566cdf04f870d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+39.674_ry-05.232_rz-84.967_tx-00.184_ty+00.250_s+01.700_743235d974defeabee6e549a3ef566cdf04f870d_256x256.png,fd016dd9374e4d56769b514edb01f8c3f324ec25,Boats,39.674,-95.232,-84.967,3568 -3569,MQUEEN_L,40.449,31.145,-62.804,0.356,-0.708,1.41666666667,64d1051dcf8fc4e01736c7f687a6c41bbc83fc79,256,V6,64d1051dcf8fc4e01736c7f687a6c41bbc83fc79_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+40.449_ry+31.145_rz-62.804_tx+00.356_ty-00.708_s+01.700_64d1051dcf8fc4e01736c7f687a6c41bbc83fc79_256x256.png,62f3f17d4c75e32922b94458925a795a49b787f0,Boats,40.449,-58.855,-62.804,3569 -3570,MQUEEN_L,41.834,-70.771,60.631,-0.369,-0.175,0.709166666667,e197ad3ef734ec7c61eafe0322b0d4aae0380403,256,V6,e197ad3ef734ec7c61eafe0322b0d4aae0380403_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+41.834_ry-70.771_rz+60.631_tx-00.369_ty-00.175_s+00.851_e197ad3ef734ec7c61eafe0322b0d4aae0380403_256x256.png,f1e8ddb0cd346f97235d73a7cf6cdf45f31abdb5,Boats,41.834,-160.771,60.631,3570 -3571,MQUEEN_L,44.548,84.529,6.93,-0.462,-0.617,0.854166666667,c6dfaa2e1991caf236d96fea30f655efe9777118,256,V6,c6dfaa2e1991caf236d96fea30f655efe9777118_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+44.548_ry+84.529_rz+06.930_tx-00.462_ty-00.617_s+01.025_c6dfaa2e1991caf236d96fea30f655efe9777118_256x256.png,bb2938db36e0627cca941df84faabc43665cb26f,Boats,44.548,-5.471,6.93,3571 -3572,MQUEEN_L,44.566,21.131,43.46,-0.442,0.594,1.23,1dcc93eaa9cfafa81d370cb23c1037415d8ac845,256,V6,1dcc93eaa9cfafa81d370cb23c1037415d8ac845_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+44.566_ry+21.131_rz+43.460_tx-00.442_ty+00.594_s+01.476_1dcc93eaa9cfafa81d370cb23c1037415d8ac845_256x256.png,bdb5c686fc2b72713babf9d4efe7a65a3ded5ba4,Boats,44.566,-68.869,43.46,3572 -3573,MQUEEN_L,50.138,-77.56,-80.126,0.509,0.28,1.12666666667,01df62d43d28d2f52bb054f53bd60ec71e93eaf6,256,V6,01df62d43d28d2f52bb054f53bd60ec71e93eaf6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+50.138_ry-77.560_rz-80.126_tx+00.509_ty+00.280_s+01.352_01df62d43d28d2f52bb054f53bd60ec71e93eaf6_256x256.png,264bbb373415825b9f01100a41bd1a69a228e245,Boats,50.138,-167.56,-80.126,3573 -3574,MQUEEN_L,59.598,63.78,-85.73,-0.296,-0.323,1.11416666667,5d4da6ff7835bfad109c7c27b7c65b9025abf3d6,256,V6,5d4da6ff7835bfad109c7c27b7c65b9025abf3d6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+59.598_ry+63.780_rz-85.730_tx-00.296_ty-00.323_s+01.337_5d4da6ff7835bfad109c7c27b7c65b9025abf3d6_256x256.png,ae05544801f380af19ac49f6c62b94628320afb1,Boats,59.598,-26.22,-85.73,3574 -3575,MQUEEN_L,60.013,-16.553,-9.694,-0.49,0.741,0.974166666667,70c81309495fa1bdad7c62e2a442685b85401d84,256,V6,70c81309495fa1bdad7c62e2a442685b85401d84_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+60.013_ry-16.553_rz-09.694_tx-00.490_ty+00.741_s+01.169_70c81309495fa1bdad7c62e2a442685b85401d84_256x256.png,0ca478362093a2f464406a8b9e11404a64f61762,Boats,60.013,-106.553,-9.694,3575 -3576,MQUEEN_L,64.389,58.702,33.225,0.349,-0.624,1.19333333333,45e09ad79d90007693e690fd538d8c93f05f74db,256,V6,45e09ad79d90007693e690fd538d8c93f05f74db_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+64.389_ry+58.702_rz+33.225_tx+00.349_ty-00.624_s+01.432_45e09ad79d90007693e690fd538d8c93f05f74db_256x256.png,5aa3afdf7b72a99db76e6d266db1f8a310d98893,Boats,64.389,-31.298,33.225,3576 -3577,MQUEEN_L,71.838,-56.051,-31.025,0.552,0.054,1.44583333333,81b38ed8f32499def3fc132c82928d0075d8bc0a,256,V6,81b38ed8f32499def3fc132c82928d0075d8bc0a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx+71.838_ry-56.051_rz-31.025_tx+00.552_ty+00.054_s+01.735_81b38ed8f32499def3fc132c82928d0075d8bc0a_256x256.png,1f8811382abd2ff7e7baf27d386fe2424657ca8d,Boats,71.838,-146.051,-31.025,3577 -3578,MQUEEN_L,-1.576,-49.224,-54.86,-0.472,-1.027,1.4275,147020972ae0bb87c398ab4aedeeb3e18c7b07dc,256,V6,147020972ae0bb87c398ab4aedeeb3e18c7b07dc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-01.576_ry-49.224_rz-54.860_tx-00.472_ty-01.027_s+01.713_147020972ae0bb87c398ab4aedeeb3e18c7b07dc_256x256.png,08bb3bcfa73c2b00ebf1007ca2a63f77eb7485a1,Boats,-1.576,-139.224,-54.86,3578 -3579,MQUEEN_L,-3.316,89.882,69.111,-0.31,-0.022,1.0475,29a4d701d32eaa1e53866dac7fe1cccf98f2d22d,256,V6,29a4d701d32eaa1e53866dac7fe1cccf98f2d22d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-03.316_ry+89.882_rz+69.111_tx-00.310_ty-00.022_s+01.257_29a4d701d32eaa1e53866dac7fe1cccf98f2d22d_256x256.png,044be344192f87ebe453cf75fb983e50ddc8f74c,Boats,-3.316,-0.118,69.111,3579 -3580,MQUEEN_L,-5.273,15.419,-37.086,-0.244,-0.922,1.4825,5472cbde719d09004a64195d70b21993a220eb0f,256,V6,5472cbde719d09004a64195d70b21993a220eb0f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-05.273_ry+15.419_rz-37.086_tx-00.244_ty-00.922_s+01.779_5472cbde719d09004a64195d70b21993a220eb0f_256x256.png,c7099a829db079a3e75f6cd49daead9a0dd6e938,Boats,-5.273,-74.581,-37.086,3580 -3581,MQUEEN_L,-11.868,10.851,-67.111,-0.451,-0.227,1.54166666667,d848c9639bcd814d18398ab8f63fb841b6c1e823,256,V6,d848c9639bcd814d18398ab8f63fb841b6c1e823_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-11.868_ry+10.851_rz-67.111_tx-00.451_ty-00.227_s+01.850_d848c9639bcd814d18398ab8f63fb841b6c1e823_256x256.png,9978d99d786635c7257eb9b2e6922be033db82df,Boats,-11.868,-79.149,-67.111,3581 -3582,MQUEEN_L,-12.88,19.957,3.063,0.287,1.172,1.04833333333,4be25984e445478b31e931c62d88add12130a0b8,256,V6,4be25984e445478b31e931c62d88add12130a0b8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-12.880_ry+19.957_rz+03.063_tx+00.287_ty+01.172_s+01.258_4be25984e445478b31e931c62d88add12130a0b8_256x256.png,e7266f982cc210a92941290068b15fcccd1937c9,Boats,-12.88,-70.043,3.063,3582 -3583,MQUEEN_L,-16.381,-51.899,-89.43,0.104,-0.076,0.649166666667,6e8039bd749b25d2e9348f98a05744b78f899de6,256,V6,6e8039bd749b25d2e9348f98a05744b78f899de6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-16.381_ry-51.899_rz-89.430_tx+00.104_ty-00.076_s+00.779_6e8039bd749b25d2e9348f98a05744b78f899de6_256x256.png,915b17978b31d30e46ded15d277c8172fb3d8c83,Boats,-16.381,-141.899,-89.43,3583 -3584,MQUEEN_L,-16.821,-77.293,-47.116,0.145,-0.547,0.929166666667,3ee3ddffe80ca80fd52c2f0247033fd650eaab77,256,V6,3ee3ddffe80ca80fd52c2f0247033fd650eaab77_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-16.821_ry-77.293_rz-47.116_tx+00.145_ty-00.547_s+01.115_3ee3ddffe80ca80fd52c2f0247033fd650eaab77_256x256.png,c7ff04c7eb5ad1382876225bc2b7f6e81ff512b6,Boats,-16.821,-167.293,-47.116,3584 -3585,MQUEEN_L,-21.294,81.6,33.499,-0.193,0.999,0.970833333333,4b0de3148f44f41099715341a8dc6620bb2fa388,256,V6,4b0de3148f44f41099715341a8dc6620bb2fa388_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-21.294_ry+81.600_rz+33.499_tx-00.193_ty+00.999_s+01.165_4b0de3148f44f41099715341a8dc6620bb2fa388_256x256.png,3e1fd162c6f65170df82552b90a64946ce5b02bb,Boats,-21.294,-8.4,33.499,3585 -3586,MQUEEN_L,-21.505,2.217,-40.725,-0.151,-0.172,1.305,259008287af6650fb0164da7adb3375f3c7923c0,256,V6,259008287af6650fb0164da7adb3375f3c7923c0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-21.505_ry+02.217_rz-40.725_tx-00.151_ty-00.172_s+01.566_259008287af6650fb0164da7adb3375f3c7923c0_256x256.png,dab7770808454dbc395147743c90ba46406f58db,Boats,-21.505,-87.783,-40.725,3586 -3587,MQUEEN_L,-31.311,78.881,75.794,0.514,-0.135,1.19666666667,3d7a2434da57d6462fbb5e18723f22184ce40519,256,V6,3d7a2434da57d6462fbb5e18723f22184ce40519_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-31.311_ry+78.881_rz+75.794_tx+00.514_ty-00.135_s+01.436_3d7a2434da57d6462fbb5e18723f22184ce40519_256x256.png,341abf0c3851a7d0154176a5bef0a776bc96dde9,Boats,-31.311,-11.119,75.794,3587 -3588,MQUEEN_L,-37.09,-40.209,69.367,-0.499,-0.364,0.810833333333,2061c1cd9bd54068d5f7afa38fb57f6d151e4cfd,256,V6,2061c1cd9bd54068d5f7afa38fb57f6d151e4cfd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-37.090_ry-40.209_rz+69.367_tx-00.499_ty-00.364_s+00.973_2061c1cd9bd54068d5f7afa38fb57f6d151e4cfd_256x256.png,367d8b4018093dca26205683c624a860f3da50d3,Boats,-37.09,-130.209,69.367,3588 -3589,MQUEEN_L,-41.85,36.716,-38.468,0.309,1.167,1.37666666667,9559f903a93ae5cc7d7849ba88cc3e5da8bdfd90,256,V6,9559f903a93ae5cc7d7849ba88cc3e5da8bdfd90_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-41.850_ry+36.716_rz-38.468_tx+00.309_ty+01.167_s+01.652_9559f903a93ae5cc7d7849ba88cc3e5da8bdfd90_256x256.png,81dd08d389cd8a4788df2171492d498bc78aeb66,Boats,-41.85,-53.284,-38.468,3589 -3590,MQUEEN_L,-42.506,79.254,-80.216,-0.565,-0.717,1.095,94e9323c3e49d32cac0a18ca091537164904574a,256,V6,94e9323c3e49d32cac0a18ca091537164904574a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-42.506_ry+79.254_rz-80.216_tx-00.565_ty-00.717_s+01.314_94e9323c3e49d32cac0a18ca091537164904574a_256x256.png,9fc9d293ca69376f39bfc554e9dc804ec49ea146,Boats,-42.506,-10.746,-80.216,3590 -3591,MQUEEN_L,-45.778,89.334,59.193,0.29,-1.096,1.2625,4940c07aef46d914f4c17a3cec16a295a1219640,256,V6,4940c07aef46d914f4c17a3cec16a295a1219640_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-45.778_ry+89.334_rz+59.193_tx+00.290_ty-01.096_s+01.515_4940c07aef46d914f4c17a3cec16a295a1219640_256x256.png,c57fb178f2f5b0e67ed5efa3717d1a2a2d83fd96,Boats,-45.778,-0.666,59.193,3591 -3592,MQUEEN_L,-61.686,-37.105,-26.675,0.395,0.033,1.21083333333,3ceea466823e64fb59ad99c9c033980e0c4a1b8d,256,V6,3ceea466823e64fb59ad99c9c033980e0c4a1b8d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-61.686_ry-37.105_rz-26.675_tx+00.395_ty+00.033_s+01.453_3ceea466823e64fb59ad99c9c033980e0c4a1b8d_256x256.png,7dc89a04c9709fbafe61956265d858ae24f67b6e,Boats,-61.686,-127.105,-26.675,3592 -3593,MQUEEN_L,-62.333,-53.415,-54.914,-0.225,1.105,1.13,9c44f6b550115bec0b87f1b84fba8f3f4a107bba,256,V6,9c44f6b550115bec0b87f1b84fba8f3f4a107bba_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-62.333_ry-53.415_rz-54.914_tx-00.225_ty+01.105_s+01.356_9c44f6b550115bec0b87f1b84fba8f3f4a107bba_256x256.png,d5cb33169b4d4cb07dbfd32ee2df591af88dcc6d,Boats,-62.333,-143.415,-54.914,3593 -3594,MQUEEN_L,-62.656,77.975,48.208,0.356,-0.832,0.8275,dee909737ef57861eb9983348b1d413fc98c8b0d,256,V6,dee909737ef57861eb9983348b1d413fc98c8b0d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-62.656_ry+77.975_rz+48.208_tx+00.356_ty-00.832_s+00.993_dee909737ef57861eb9983348b1d413fc98c8b0d_256x256.png,28c4556b335ca65c4470491e9eb819c17acce5e0,Boats,-62.656,-12.025,48.208,3594 -3595,MQUEEN_L,-65.225,23.726,-68.524,-0.019,0.815,1.45583333333,f7f2e9e9755c2f77eea2226136f79c09d98c4ebc,256,V6,f7f2e9e9755c2f77eea2226136f79c09d98c4ebc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-65.225_ry+23.726_rz-68.524_tx-00.019_ty+00.815_s+01.747_f7f2e9e9755c2f77eea2226136f79c09d98c4ebc_256x256.png,6bc94a19365c74d79e7d31f4e04f6fd2837706a2,Boats,-65.225,-66.274,-68.524,3595 -3596,MQUEEN_L,-67.577,-3.427,-79.081,0.213,-0.764,1.17916666667,f27f7336e919d50ef88f096b7f4ee0ba02b5e1b5,256,V6,f27f7336e919d50ef88f096b7f4ee0ba02b5e1b5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/MQUEEN_L_rx-67.577_ry-03.427_rz-79.081_tx+00.213_ty-00.764_s+01.415_f27f7336e919d50ef88f096b7f4ee0ba02b5e1b5_256x256.png,42a29687f787fca675f9be3fc9e44ac065f34ddc,Boats,-67.577,-93.427,-79.081,3596 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-3676,Pear_obj,2.21929626081,0.272868736366,-3.53028532329,-0.468,-0.141,1.32285714286,160678ce5350301512c9be5b1ab7d8e80c151d3c,256,V6,160678ce5350301512c9be5b1ab7d8e80c151d3c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Pear_obj_rx-87.764_ry+07.006_rz+03.481_tx-00.468_ty-00.141_s+00.926_160678ce5350301512c9be5b1ab7d8e80c151d3c_256x256.png,051adf5a2576aee570b6e8683d75df661847ca7c,Fruits,2.21929626081,0.272868736366,-3.53028532329,3676 -3677,Pear_obj,-3.04790402082,0.392116233183,28.4584320007,-0.38,-0.073,0.952857142857,5cb4cc3f1933b2f32603b337aec7302ac414337d,256,V6,5cb4cc3f1933b2f32603b337aec7302ac414337d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Pear_obj_rx-93.073_ry-07.324_rz+21.124_tx-00.380_ty-00.073_s+00.667_5cb4cc3f1933b2f32603b337aec7302ac414337d_256x256.png,be520c6ce9af0fbb901a0738ad11643fcf0f6f7a,Fruits,-3.04790402082,0.392116233183,28.4584320007,3677 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-3681,SISTER_L,6.092,-72.666,-46.666,0.45,1.127,0.904166666667,31132b05e38126aedd1b8015aae58397f3d5764f,256,V6,31132b05e38126aedd1b8015aae58397f3d5764f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+06.092_ry-72.666_rz-46.666_tx+00.450_ty+01.127_s+01.085_31132b05e38126aedd1b8015aae58397f3d5764f_256x256.png,8e538f19b408bb0e36a94ad760e153b99ae427d1,Boats,6.092,17.334,-46.666,3681 -3682,SISTER_L,18.991,6.249,-76.41,-0.316,0.304,0.89,025b5cf1db483f0e122cb94c8c14f47839b8e4d2,256,V6,025b5cf1db483f0e122cb94c8c14f47839b8e4d2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+18.991_ry+06.249_rz-76.410_tx-00.316_ty+00.304_s+01.068_025b5cf1db483f0e122cb94c8c14f47839b8e4d2_256x256.png,3dd3e2877a5946b9db544fae513ebb04c9089ed2,Boats,18.991,96.249,-76.41,3682 -3683,SISTER_L,19.609,-25.568,73.991,0.267,-0.225,1.355,1769e7139bd3e5106f9088c98a0c61c6cdd1acdf,256,V6,1769e7139bd3e5106f9088c98a0c61c6cdd1acdf_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+19.609_ry-25.568_rz+73.991_tx+00.267_ty-00.225_s+01.626_1769e7139bd3e5106f9088c98a0c61c6cdd1acdf_256x256.png,478a4f73bfd4234747876a2a13f590605fbb3cb0,Boats,19.609,64.432,73.991,3683 -3684,SISTER_L,21.356,-87.875,-86.203,-0.421,-0.466,0.938333333333,50303909e122277ca5a1423e0f4a5d00763105fb,256,V6,50303909e122277ca5a1423e0f4a5d00763105fb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+21.356_ry-87.875_rz-86.203_tx-00.421_ty-00.466_s+01.126_50303909e122277ca5a1423e0f4a5d00763105fb_256x256.png,dabe8207604a7841beb8b4c5062d3e1e217675a4,Boats,21.356,2.125,-86.203,3684 -3685,SISTER_L,21.645,18.967,36.685,-0.136,-0.136,1.2325,c9ef58ab4aecc2bc86dfa519757dade8e079c407,256,V6,c9ef58ab4aecc2bc86dfa519757dade8e079c407_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+21.645_ry+18.967_rz+36.685_tx-00.136_ty-00.136_s+01.479_c9ef58ab4aecc2bc86dfa519757dade8e079c407_256x256.png,3a14380868321ba50a2e90b8ddfa69327008e7e7,Boats,21.645,108.967,36.685,3685 -3686,SISTER_L,23.171,57.709,86.245,-0.584,-0.559,1.32666666667,da73db27c5af9c98fbf6cd43b22bbd162e862105,256,V6,da73db27c5af9c98fbf6cd43b22bbd162e862105_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+23.171_ry+57.709_rz+86.245_tx-00.584_ty-00.559_s+01.592_da73db27c5af9c98fbf6cd43b22bbd162e862105_256x256.png,bb414b2856fa31e038e5b1214c10b9e23078698a,Boats,23.171,147.709,86.245,3686 -3687,SISTER_L,32.777,-39.569,-39.929,-0.161,-0.557,0.7275,d7b276076e85546e440aac297f78d3c4d85dfab8,256,V6,d7b276076e85546e440aac297f78d3c4d85dfab8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+32.777_ry-39.569_rz-39.929_tx-00.161_ty-00.557_s+00.873_d7b276076e85546e440aac297f78d3c4d85dfab8_256x256.png,6d480c9384cf3a2aa417e8083e42480ab04cb627,Boats,32.777,50.431,-39.929,3687 -3688,SISTER_L,35.431,14.895,36.114,0.518,0.733,1.55666666667,c727b46c2a7f7af5dc2bed1fc7ce2196fbdd4e9d,256,V6,c727b46c2a7f7af5dc2bed1fc7ce2196fbdd4e9d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+35.431_ry+14.895_rz+36.114_tx+00.518_ty+00.733_s+01.868_c727b46c2a7f7af5dc2bed1fc7ce2196fbdd4e9d_256x256.png,92571c51a87b4d56e81a88f18ea5ecaf6f94ec49,Boats,35.431,104.895,36.114,3688 -3689,SISTER_L,43.736,-50.111,-42.213,-0.294,-0.49,0.686666666667,08471b83c1a13ac18437bcbb1081be3c69542b1d,256,V6,08471b83c1a13ac18437bcbb1081be3c69542b1d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+43.736_ry-50.111_rz-42.213_tx-00.294_ty-00.490_s+00.824_08471b83c1a13ac18437bcbb1081be3c69542b1d_256x256.png,0fc775a33852f5600c563bc2601f4731ceed5d14,Boats,43.736,39.889,-42.213,3689 -3690,SISTER_L,51.059,9.612,-4.71,0.541,0.097,0.981666666667,fc5f198c0763c50687e9c3dadb114bc67bce9d20,256,V6,fc5f198c0763c50687e9c3dadb114bc67bce9d20_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+51.059_ry+09.612_rz-04.710_tx+00.541_ty+00.097_s+01.178_fc5f198c0763c50687e9c3dadb114bc67bce9d20_256x256.png,cee0fd4432df707da07aa56341252bda359f7c20,Boats,51.059,99.612,-4.71,3690 -3691,SISTER_L,56.607,-19.308,27.824,-0.501,0.757,1.24583333333,77e577e9600e9eb87689ee6afb0952ece7db78d0,256,V6,77e577e9600e9eb87689ee6afb0952ece7db78d0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+56.607_ry-19.308_rz+27.824_tx-00.501_ty+00.757_s+01.495_77e577e9600e9eb87689ee6afb0952ece7db78d0_256x256.png,3cd6e50fe47b4bd056861b3988546cb1c2da4682,Boats,56.607,70.692,27.824,3691 -3692,SISTER_L,57.748,4.15,-64.215,0.405,0.45,1.04666666667,f0c0a4538c6334f1d24838f20c151033de5747bc,256,V6,f0c0a4538c6334f1d24838f20c151033de5747bc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+57.748_ry+04.150_rz-64.215_tx+00.405_ty+00.450_s+01.256_f0c0a4538c6334f1d24838f20c151033de5747bc_256x256.png,e92eb9ae374b8ae9a2da876797022d5964ef8a70,Boats,57.748,94.15,-64.215,3692 -3693,SISTER_L,58.249,-35.172,48.012,-0.528,1.114,1.08416666667,5b7c4c3b3a392871ddd90cb092f1d6ca0a6e5038,256,V6,5b7c4c3b3a392871ddd90cb092f1d6ca0a6e5038_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+58.249_ry-35.172_rz+48.012_tx-00.528_ty+01.114_s+01.301_5b7c4c3b3a392871ddd90cb092f1d6ca0a6e5038_256x256.png,d87a7704b27e1d2655e1aac71d9160296e61f56b,Boats,58.249,54.828,48.012,3693 -3694,SISTER_L,59.061,79.993,-28.591,0.42,1.051,1.01916666667,4017ba170ef171ec2adff4536e75c9b1bd9c290d,256,V6,4017ba170ef171ec2adff4536e75c9b1bd9c290d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+59.061_ry+79.993_rz-28.591_tx+00.420_ty+01.051_s+01.223_4017ba170ef171ec2adff4536e75c9b1bd9c290d_256x256.png,d46392975de729a52aaf6ed06dbf4baa5a39ff0b,Boats,59.061,169.993,-28.591,3694 -3695,SISTER_L,60.829,-1.861,59.956,0.134,-0.426,1.53083333333,058770ba98782878bd2dd0e51cd78aa2a3a2c0ec,256,V6,058770ba98782878bd2dd0e51cd78aa2a3a2c0ec_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+60.829_ry-01.861_rz+59.956_tx+00.134_ty-00.426_s+01.837_058770ba98782878bd2dd0e51cd78aa2a3a2c0ec_256x256.png,441d9905412ab9c0fcd1cffbe407496b2cbc6330,Boats,60.829,88.139,59.956,3695 -3696,SISTER_L,68.913,11.112,68.141,-0.201,1.071,1.59833333333,c2e2de7b4b3fbd15b54bd7580f88a591abfee39b,256,V6,c2e2de7b4b3fbd15b54bd7580f88a591abfee39b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+68.913_ry+11.112_rz+68.141_tx-00.201_ty+01.071_s+01.918_c2e2de7b4b3fbd15b54bd7580f88a591abfee39b_256x256.png,d2c3265d16f85d0e4bab1217127a22160a3d1294,Boats,68.913,101.112,68.141,3696 -3697,SISTER_L,71.481,75.948,54.492,0.323,0.665,1.55916666667,c4e000ec758b809f0a1132d9e4f6c06b6763a941,256,V6,c4e000ec758b809f0a1132d9e4f6c06b6763a941_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+71.481_ry+75.948_rz+54.492_tx+00.323_ty+00.665_s+01.871_c4e000ec758b809f0a1132d9e4f6c06b6763a941_256x256.png,9e8dca00442f156436da598bd74c28790584b08d,Boats,71.481,165.948,54.492,3697 -3698,SISTER_L,74.822,-15.097,29.858,0.501,0.486,1.48583333333,5e57fe63fff464626b01590862c24ed8ace07826,256,V6,5e57fe63fff464626b01590862c24ed8ace07826_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+74.822_ry-15.097_rz+29.858_tx+00.501_ty+00.486_s+01.783_5e57fe63fff464626b01590862c24ed8ace07826_256x256.png,bdc31f47aae153f8a0be77eb9f736c086db16cc8,Boats,74.822,74.903,29.858,3698 -3699,SISTER_L,79.493,-81.081,84.165,0.363,-0.26,1.45916666667,e9318dad05900bb50acd0c95cba8da3633adb304,256,V6,e9318dad05900bb50acd0c95cba8da3633adb304_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+79.493_ry-81.081_rz+84.165_tx+00.363_ty-00.260_s+01.751_e9318dad05900bb50acd0c95cba8da3633adb304_256x256.png,e0b9d8029f92c946a2f79abf571039167cd63de6,Boats,79.493,8.919,84.165,3699 -3700,SISTER_L,81.221,-25.207,79.145,-0.055,-0.276,1.33166666667,655bdb9f63e11e8f519809e17a450b6a2a269496,256,V6,655bdb9f63e11e8f519809e17a450b6a2a269496_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+81.221_ry-25.207_rz+79.145_tx-00.055_ty-00.276_s+01.598_655bdb9f63e11e8f519809e17a450b6a2a269496_256x256.png,fa5ab85db45baf14a1c008c9eddd68cd6aaf3695,Boats,81.221,64.793,79.145,3700 -3701,SISTER_L,83.451,-32.393,-2.719,0.373,1.078,0.750833333333,22b888aeb07e46824006963abaa25f8daf1fd3f2,256,V6,22b888aeb07e46824006963abaa25f8daf1fd3f2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+83.451_ry-32.393_rz-02.719_tx+00.373_ty+01.078_s+00.901_22b888aeb07e46824006963abaa25f8daf1fd3f2_256x256.png,63cbaabb6af36af5590053725653c1ded3aefbeb,Boats,83.451,57.607,-2.719,3701 -3702,SISTER_L,84.538,-23.15,-41.448,-0.429,0.814,0.6775,f9da7567c3cc618691049ebff726e72c243edf10,256,V6,f9da7567c3cc618691049ebff726e72c243edf10_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx+84.538_ry-23.150_rz-41.448_tx-00.429_ty+00.814_s+00.813_f9da7567c3cc618691049ebff726e72c243edf10_256x256.png,50886a4b3e5247483fb03dbff304ab7a047c6820,Boats,84.538,66.85,-41.448,3702 -3703,SISTER_L,-3.262,19.352,29.679,-0.168,-0.535,1.26666666667,f3edd2aedeb9abaff5c393292f7a98de65808e4b,256,V6,f3edd2aedeb9abaff5c393292f7a98de65808e4b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-03.262_ry+19.352_rz+29.679_tx-00.168_ty-00.535_s+01.520_f3edd2aedeb9abaff5c393292f7a98de65808e4b_256x256.png,691aa8eb2ab9e0bc88a0c1abaa3c19e50f4865e7,Boats,-3.262,109.352,29.679,3703 -3704,SISTER_L,-7.208,67.274,-85.043,0.001,-0.255,0.834166666667,db8eb5ee3610ba9a175f6b62f903cabdc8d8d3d2,256,V6,db8eb5ee3610ba9a175f6b62f903cabdc8d8d3d2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-07.208_ry+67.274_rz-85.043_tx+00.001_ty-00.255_s+01.001_db8eb5ee3610ba9a175f6b62f903cabdc8d8d3d2_256x256.png,27220c2a7712274eddb023d1db7b16e1997905f2,Boats,-7.208,157.274,-85.043,3704 -3705,SISTER_L,-10.7,83.688,19.105,-0.555,-0.195,1.33833333333,fa565ea242d3e09e58991f67620353bec2955c83,256,V6,fa565ea242d3e09e58991f67620353bec2955c83_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-10.700_ry+83.688_rz+19.105_tx-00.555_ty-00.195_s+01.606_fa565ea242d3e09e58991f67620353bec2955c83_256x256.png,87e7c1d986d3302834ab611cff4a34bcb4fc4dd1,Boats,-10.7,173.688,19.105,3705 -3706,SISTER_L,-18.279,24.596,15.625,0.092,0.464,1.39916666667,b097d3177cdf68113c9b9f570febf81147c66bbf,256,V6,b097d3177cdf68113c9b9f570febf81147c66bbf_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-18.279_ry+24.596_rz+15.625_tx+00.092_ty+00.464_s+01.679_b097d3177cdf68113c9b9f570febf81147c66bbf_256x256.png,7231b620f0937ba7526b9b1c34048015dfd98d3e,Boats,-18.279,114.596,15.625,3706 -3707,SISTER_L,-24.116,-22.366,26.773,0.164,-0.445,1.235,d09dcde72372751b9210e3b4497f92a0cf80d0d3,256,V6,d09dcde72372751b9210e3b4497f92a0cf80d0d3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-24.116_ry-22.366_rz+26.773_tx+00.164_ty-00.445_s+01.482_d09dcde72372751b9210e3b4497f92a0cf80d0d3_256x256.png,1b0293eb38f2fd60f9f9afac0a13f24cb82cd81f,Boats,-24.116,67.634,26.773,3707 -3708,SISTER_L,-24.957,82.129,11.78,0.084,0.169,1.05916666667,638b25ece129ca78abba3bf00bc4baf0e72d5edc,256,V6,638b25ece129ca78abba3bf00bc4baf0e72d5edc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-24.957_ry+82.129_rz+11.780_tx+00.084_ty+00.169_s+01.271_638b25ece129ca78abba3bf00bc4baf0e72d5edc_256x256.png,a2fd5793909a5906942278ce428bb8a4b154eb76,Boats,-24.957,172.129,11.78,3708 -3709,SISTER_L,-27.294,-75.565,66.84,0.345,0.132,1.505,c812ce799bcb6dedb12872a76f3a5011c1a77f88,256,V6,c812ce799bcb6dedb12872a76f3a5011c1a77f88_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-27.294_ry-75.565_rz+66.840_tx+00.345_ty+00.132_s+01.806_c812ce799bcb6dedb12872a76f3a5011c1a77f88_256x256.png,ae316112871c3af31460036e9bdee3ce4cf1da28,Boats,-27.294,14.435,66.84,3709 -3710,SISTER_L,-37.156,-0.518,28.798,0.453,0.873,1.4875,1279014ce3fe19b50fc8dcb72f9736382ac7a892,256,V6,1279014ce3fe19b50fc8dcb72f9736382ac7a892_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-37.156_ry-00.518_rz+28.798_tx+00.453_ty+00.873_s+01.785_1279014ce3fe19b50fc8dcb72f9736382ac7a892_256x256.png,f9ff781e494c767875d776736cd22e6528e323dd,Boats,-37.156,89.482,28.798,3710 -3711,SISTER_L,-37.264,43.629,87.073,-0.352,0.102,1.3525,54fca1a886a474033725a8a06636786c0fa40224,256,V6,54fca1a886a474033725a8a06636786c0fa40224_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-37.264_ry+43.629_rz+87.073_tx-00.352_ty+00.102_s+01.623_54fca1a886a474033725a8a06636786c0fa40224_256x256.png,9eafb91e21bf4d354aaf635cb8f50480aee5ec83,Boats,-37.264,133.629,87.073,3711 -3712,SISTER_L,-44.232,53.87,-87.812,-0.404,-0.689,1.43333333333,12ee0154b089bf182c94ff94213035208f9102be,256,V6,12ee0154b089bf182c94ff94213035208f9102be_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-44.232_ry+53.870_rz-87.812_tx-00.404_ty-00.689_s+01.720_12ee0154b089bf182c94ff94213035208f9102be_256x256.png,419275323ad95d7a6f625b09bd49bee01fa6929a,Boats,-44.232,143.87,-87.812,3712 -3713,SISTER_L,-47.134,30.934,-65.503,-0.184,0.886,1.4325,9a8fe4bba0c06a1d0490b744de6ccbbc8feb989a,256,V6,9a8fe4bba0c06a1d0490b744de6ccbbc8feb989a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-47.134_ry+30.934_rz-65.503_tx-00.184_ty+00.886_s+01.719_9a8fe4bba0c06a1d0490b744de6ccbbc8feb989a_256x256.png,b5255e513bddf6bc7636243217bc60b82d5848d1,Boats,-47.134,120.934,-65.503,3713 -3714,SISTER_L,-47.932,35.162,81.385,0.591,-0.038,1.415,912aa6f72f5e97f1924c3abd06216a29a561e5bb,256,V6,912aa6f72f5e97f1924c3abd06216a29a561e5bb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-47.932_ry+35.162_rz+81.385_tx+00.591_ty-00.038_s+01.698_912aa6f72f5e97f1924c3abd06216a29a561e5bb_256x256.png,31ac19298a1257cd837921d2eb1c9be702eaa80d,Boats,-47.932,125.162,81.385,3714 -3715,SISTER_L,-52.622,-50.431,66.617,-0.134,0.249,1.15,19b697b2e57e4bf2099be0a96cf3f5d68ba7540d,256,V6,19b697b2e57e4bf2099be0a96cf3f5d68ba7540d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-52.622_ry-50.431_rz+66.617_tx-00.134_ty+00.249_s+01.380_19b697b2e57e4bf2099be0a96cf3f5d68ba7540d_256x256.png,651fec065959e808bc89da805081b9b6351247db,Boats,-52.622,39.569,66.617,3715 -3716,SISTER_L,-70.05,57.141,-58.737,-0.189,-0.951,0.861666666667,6f69f2c2bf34893a87db16e5b8ff662100200ded,256,V6,6f69f2c2bf34893a87db16e5b8ff662100200ded_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-70.050_ry+57.141_rz-58.737_tx-00.189_ty-00.951_s+01.034_6f69f2c2bf34893a87db16e5b8ff662100200ded_256x256.png,9d8a394f6937694d5fa6c207a24848417569bbb6,Boats,-70.05,147.141,-58.737,3716 -3717,SISTER_L,-82.33,-2.284,85.533,-0.243,-0.735,0.875,ae71e1aba3222258fc4aa179f92eca06a3a0f2bc,256,V6,ae71e1aba3222258fc4aa179f92eca06a3a0f2bc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-82.330_ry-02.284_rz+85.533_tx-00.243_ty-00.735_s+01.050_ae71e1aba3222258fc4aa179f92eca06a3a0f2bc_256x256.png,1c8c9cafbbf8218a31d5e7d6008f8738fc6a3f68,Boats,-82.33,87.716,85.533,3717 -3718,SISTER_L,-86.641,-14.345,13.136,0.492,0.14,1.59916666667,f16d545a70267f9f230a1056536a32285cc665a1,256,V6,f16d545a70267f9f230a1056536a32285cc665a1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-86.641_ry-14.345_rz+13.136_tx+00.492_ty+00.140_s+01.919_f16d545a70267f9f230a1056536a32285cc665a1_256x256.png,0ac91abc46d544dad605f92f88df2c6f1907afe4,Boats,-86.641,75.655,13.136,3718 -3719,SISTER_L,-89.13,65.423,-81.213,-0.196,-0.01,1.04,4880acb3e77f281b7f95fdf158a49e757ae3dea6,256,V6,4880acb3e77f281b7f95fdf158a49e757ae3dea6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/SISTER_L_rx-89.130_ry+65.423_rz-81.213_tx-00.196_ty-00.010_s+01.248_4880acb3e77f281b7f95fdf158a49e757ae3dea6_256x256.png,3f569b6fa3297e650eed0728d437a44ec83c5da6,Boats,-89.13,155.423,-81.213,3719 -3720,Strawberry_obj,60.929387756,-86.1615242556,37.1029048109,0.015,-1.156,1.08571428571,f2357126d85b44ff400f50610da8ecb5c6312cb2,256,V6,f2357126d85b44ff400f50610da8ecb5c6312cb2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Strawberry_obj_rx-01.864_ry-29.016_rz-49.541_tx+00.015_ty-01.156_s+00.760_f2357126d85b44ff400f50610da8ecb5c6312cb2_256x256.png,5acc4ebcc8fb76e5d5295549b4f958f403a5cce5,Fruits,60.929387756,-86.1615242556,37.1029048109,3720 -3721,Strawberry_obj,70.0783892508,-64.3459945466,59.4009617207,-0.586,0.805,1.19571428571,679cd32511443ce47f4ac5efd8cde9c152f45fdb,256,V6,679cd32511443ce47f4ac5efd8cde9c152f45fdb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Strawberry_obj_rx-08.483_ry-18.092_rz-06.298_tx-00.586_ty+00.805_s+00.837_679cd32511443ce47f4ac5efd8cde9c152f45fdb_256x256.png,1fa75f70bb5caf2ade290e972cb1fdfe3b472375,Fruits,70.0783892508,-64.3459945466,59.4009617207,3721 -3722,Strawberry_obj,-10.1528177974,4.23205546964,56.3321142223,-0.002,-0.808,1.3,813820d67a6fe96bc9167070d85dd6258e9b0b47,256,V6,813820d67a6fe96bc9167070d85dd6258e9b0b47_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/Strawberry_obj_rx-100.991_ry-22.396_rz+33.560_tx-00.002_ty-00.808_s+00.910_813820d67a6fe96bc9167070d85dd6258e9b0b47_256x256.png,f07515cff2cafec3ddc8b4c3381e7d5bc69ecf57,Fruits,-10.1528177974,4.23205546964,56.3321142223,3722 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-3803,_001,18.056,6.754,64.753,0.441,-0.027,1.57857142857,1c9ea65b274bb6b62ac78efecf11c6f7dc6969b9,256,V6,1c9ea65b274bb6b62ac78efecf11c6f7dc6969b9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+18.056_ry+06.754_rz+64.753_tx+00.441_ty-00.027_s+01.105_1c9ea65b274bb6b62ac78efecf11c6f7dc6969b9_256x256.png,61721697c327cd6855ec532591aa8fd77021a886,Chairs,18.056,6.754,64.753,3803 -3804,_001,25.049,46.547,-35.212,0.36,-0.036,1.38142857143,db3cbafc9be2ec451702dbe598390bf19936c09c,256,V6,db3cbafc9be2ec451702dbe598390bf19936c09c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+25.049_ry+46.547_rz-35.212_tx+00.360_ty-00.036_s+00.967_db3cbafc9be2ec451702dbe598390bf19936c09c_256x256.png,488fbd8e68d736db757cfaa9a8a72af673c06971,Chairs,25.049,46.547,-35.212,3804 -3805,_001,25.949,-11.238,49.199,-0.428,0.113,1.30571428571,f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6,256,V6,f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+25.949_ry-11.238_rz+49.199_tx-00.428_ty+00.113_s+00.914_f5cd0d63fecaea3648437bb85522e5b7ec5ccaf6_256x256.png,d4dd9a086994a89bf6e2d32efab254bdd4648888,Chairs,25.949,-11.238,49.199,3805 -3806,_001,31.628,68.236,-40.923,-0.344,0.911,0.804285714286,62a5e71d75d31031913a2693e7f9121e74e6332c,256,V6,62a5e71d75d31031913a2693e7f9121e74e6332c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+31.628_ry+68.236_rz-40.923_tx-00.344_ty+00.911_s+00.563_62a5e71d75d31031913a2693e7f9121e74e6332c_256x256.png,38859f06611301763ed3963053c0064e7481890f,Chairs,31.628,68.236,-40.923,3806 -3807,_001,33.709,-35.899,-27.74,-0.217,1.139,1.14,3b4fc840e691900af3e372c6a7326942d59a5243,256,V6,3b4fc840e691900af3e372c6a7326942d59a5243_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+33.709_ry-35.899_rz-27.740_tx-00.217_ty+01.139_s+00.798_3b4fc840e691900af3e372c6a7326942d59a5243_256x256.png,5f2f434ad137be89c9ff5a56add24aa0ccc161f4,Chairs,33.709,-35.899,-27.74,3807 -3808,_001,34.23,-25.797,-64.075,-0.492,0.076,1.28,76f15caec77df24766399b0430cd4c37f3293297,256,V6,76f15caec77df24766399b0430cd4c37f3293297_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+34.230_ry-25.797_rz-64.075_tx-00.492_ty+00.076_s+00.896_76f15caec77df24766399b0430cd4c37f3293297_256x256.png,a0a396889f1c84193eb9bbddc8e0f1b6f9b4c603,Chairs,34.23,-25.797,-64.075,3808 -3809,_001,35.915,-67.867,-49.282,0.125,-0.579,0.761428571429,00daa196fbb4420fca26f5694f7968b1471cb1f8,256,V6,00daa196fbb4420fca26f5694f7968b1471cb1f8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+35.915_ry-67.867_rz-49.282_tx+00.125_ty-00.579_s+00.533_00daa196fbb4420fca26f5694f7968b1471cb1f8_256x256.png,88f968f535ee02f335b141240d17042b423288ab,Chairs,35.915,-67.867,-49.282,3809 -3810,_001,38.389,26.502,-61.01,-0.08,0.854,1.58285714286,de97a2b3adba24d3a1db9fa4b4fe962f69ddb9e3,256,V6,de97a2b3adba24d3a1db9fa4b4fe962f69ddb9e3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+38.389_ry+26.502_rz-61.010_tx-00.080_ty+00.854_s+01.108_de97a2b3adba24d3a1db9fa4b4fe962f69ddb9e3_256x256.png,22d9590484a4fb3ee6dda4728217abf12443f4b5,Chairs,38.389,26.502,-61.01,3810 -3811,_001,55.377,-27.609,-35.377,0.545,0.909,0.704285714286,3f1e6a14b1d6fad615df7941515a50a8feb1bb41,256,V6,3f1e6a14b1d6fad615df7941515a50a8feb1bb41_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+55.377_ry-27.609_rz-35.377_tx+00.545_ty+00.909_s+00.493_3f1e6a14b1d6fad615df7941515a50a8feb1bb41_256x256.png,da5be52408b03ead3d2bf0c59e9edd4d8d71956c,Chairs,55.377,-27.609,-35.377,3811 -3812,_001,57.057,-61.922,53.311,-0.376,-0.492,1.19428571429,4247e308df753502e3a2ccb7d1cd07346beb74d0,256,V6,4247e308df753502e3a2ccb7d1cd07346beb74d0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+57.057_ry-61.922_rz+53.311_tx-00.376_ty-00.492_s+00.836_4247e308df753502e3a2ccb7d1cd07346beb74d0_256x256.png,11c54aac721b624b5e56a37b70825dc85628b8c7,Chairs,57.057,-61.922,53.311,3812 -3813,_001,62.276,-83.949,85.697,-0.336,1.154,1.14142857143,e52bb3edf25bb8483dbd78acc4c324dd263e9aa5,256,V6,e52bb3edf25bb8483dbd78acc4c324dd263e9aa5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+62.276_ry-83.949_rz+85.697_tx-00.336_ty+01.154_s+00.799_e52bb3edf25bb8483dbd78acc4c324dd263e9aa5_256x256.png,37d2e054c584f7489b1d37cc0a9d361aa75309b2,Chairs,62.276,-83.949,85.697,3813 -3814,_001,64.913,22.782,-48.622,-0.399,-0.494,1.08714285714,78c379dfaeb34fc15dde9c80e81dddc27c8bfb63,256,V6,78c379dfaeb34fc15dde9c80e81dddc27c8bfb63_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+64.913_ry+22.782_rz-48.622_tx-00.399_ty-00.494_s+00.761_78c379dfaeb34fc15dde9c80e81dddc27c8bfb63_256x256.png,0e25b516f0ea35f18daf3f0baf7339d76ca938ec,Chairs,64.913,22.782,-48.622,3814 -3815,_001,68.509,-41.243,-38.333,0.305,-0.777,1.37428571429,e2097817b024350ec7d49f9e381b61759e0860b6,256,V6,e2097817b024350ec7d49f9e381b61759e0860b6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+68.509_ry-41.243_rz-38.333_tx+00.305_ty-00.777_s+00.962_e2097817b024350ec7d49f9e381b61759e0860b6_256x256.png,ab9c5ca5dc004d07cb62e0501d2ebb5fcd99e361,Chairs,68.509,-41.243,-38.333,3815 -3816,_001,88.333,-7.255,63.583,0.008,0.172,1.01,c9e5b15a5b48d8ecb64c6ee38c831a5971976227,256,V6,c9e5b15a5b48d8ecb64c6ee38c831a5971976227_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+88.333_ry-07.255_rz+63.583_tx+00.008_ty+00.172_s+00.707_c9e5b15a5b48d8ecb64c6ee38c831a5971976227_256x256.png,630f8dee2999e4de0d47f37b3eeb43d8b82ce93b,Chairs,88.333,-7.255,63.583,3816 -3817,_001,88.488,84.776,-49.97,0.384,0.768,1.52285714286,c20bfce0dadd4a79a499529a481827339257f231,256,V6,c20bfce0dadd4a79a499529a481827339257f231_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+88.488_ry+84.776_rz-49.970_tx+00.384_ty+00.768_s+01.066_c20bfce0dadd4a79a499529a481827339257f231_256x256.png,f98f24a8381cbe22a8a314a7751fa88fa8f5da6d,Chairs,88.488,84.776,-49.97,3817 -3818,_001,89.236,-28.603,-84.761,0.587,1.19,0.681428571429,8c02058996eb9de0e82e2e1561bcabe96a9c5e9c,256,V6,8c02058996eb9de0e82e2e1561bcabe96a9c5e9c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx+89.236_ry-28.603_rz-84.761_tx+00.587_ty+01.190_s+00.477_8c02058996eb9de0e82e2e1561bcabe96a9c5e9c_256x256.png,b7daaf122773986901928ac2d921f2589aa10ea5,Chairs,89.236,-28.603,-84.761,3818 -3819,_001,-4.868,82.836,5.481,0.361,0.925,1.01428571429,455f9dcb945380e2704e87aef10cebf3fc7c3723,256,V6,455f9dcb945380e2704e87aef10cebf3fc7c3723_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-04.868_ry+82.836_rz+05.481_tx+00.361_ty+00.925_s+00.710_455f9dcb945380e2704e87aef10cebf3fc7c3723_256x256.png,f5cabb2eae01ddc7a258553df931de2d2b540d6a,Chairs,-4.868,82.836,5.481,3819 -3820,_001,-13.306,15.617,-24.382,0.265,0.676,1.38,858b26d3678ec08631705de185074dec53651818,256,V6,858b26d3678ec08631705de185074dec53651818_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-13.306_ry+15.617_rz-24.382_tx+00.265_ty+00.676_s+00.966_858b26d3678ec08631705de185074dec53651818_256x256.png,426a265c5170f974cd5d4e464627fed4ed8b66be,Chairs,-13.306,15.617,-24.382,3820 -3821,_001,-24.687,-68.444,61.812,0.126,-1.101,1.02714285714,74cbfbacb518c74d5defeb0d921697f47b82aa09,256,V6,74cbfbacb518c74d5defeb0d921697f47b82aa09_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-24.687_ry-68.444_rz+61.812_tx+00.126_ty-01.101_s+00.719_74cbfbacb518c74d5defeb0d921697f47b82aa09_256x256.png,19d190859741b0ea5a6506c6b415cf18cf97c743,Chairs,-24.687,-68.444,61.812,3821 -3822,_001,-26.161,60.666,83.252,-0.403,0.638,1.29571428571,1661fa8d2e3c24f6e7f82e9395c803aa2e130b2f,256,V6,1661fa8d2e3c24f6e7f82e9395c803aa2e130b2f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-26.161_ry+60.666_rz+83.252_tx-00.403_ty+00.638_s+00.907_1661fa8d2e3c24f6e7f82e9395c803aa2e130b2f_256x256.png,b4493e4ddfdf5f3ef6c8d17db91391a692063646,Chairs,-26.161,60.666,83.252,3822 -3823,_001,-28.724,-78.607,7.69,0.54,0.658,0.802857142857,8ea3eedcc172ab450eae312da0a103919332f169,256,V6,8ea3eedcc172ab450eae312da0a103919332f169_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-28.724_ry-78.607_rz+07.690_tx+00.540_ty+00.658_s+00.562_8ea3eedcc172ab450eae312da0a103919332f169_256x256.png,5fe07191f80abd5db2fbdcdc7b79de2aa76e7a03,Chairs,-28.724,-78.607,7.69,3823 -3824,_001,-34.8,-23.285,6.846,0.161,0.506,1.42428571429,a8b2a5e00fffd1a1ec99a9f0291b199a932545ab,256,V6,a8b2a5e00fffd1a1ec99a9f0291b199a932545ab_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-34.800_ry-23.285_rz+06.846_tx+00.161_ty+00.506_s+00.997_a8b2a5e00fffd1a1ec99a9f0291b199a932545ab_256x256.png,693a69da4e861bf5320d965239302f56ec3107d0,Chairs,-34.8,-23.285,6.846,3824 -3825,_001,-35.775,-89.963,-37.652,0.085,-0.85,1.42285714286,01aad623ac6013e2eb31d1e2528d7c4bc038c251,256,V6,01aad623ac6013e2eb31d1e2528d7c4bc038c251_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-35.775_ry-89.963_rz-37.652_tx+00.085_ty-00.850_s+00.996_01aad623ac6013e2eb31d1e2528d7c4bc038c251_256x256.png,aed3bfc826e57b7f41675d0a8332cb58bb91464b,Chairs,-35.775,-89.963,-37.652,3825 -3826,_001,-40.937,-16.127,-19.319,-0.328,-0.156,1.42857142857,4f9ba51336b3a50f872299b43ab4607e49aa92a8,256,V6,4f9ba51336b3a50f872299b43ab4607e49aa92a8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-40.937_ry-16.127_rz-19.319_tx-00.328_ty-00.156_s+01.000_4f9ba51336b3a50f872299b43ab4607e49aa92a8_256x256.png,668ba58c08418854ec25ab845e10d5fde309e0b0,Chairs,-40.937,-16.127,-19.319,3826 -3827,_001,-48.925,78.121,-18.072,-0.427,0.234,1.52,6917a1092e7361ec77df87c8c065d8fd21f942bc,256,V6,6917a1092e7361ec77df87c8c065d8fd21f942bc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-48.925_ry+78.121_rz-18.072_tx-00.427_ty+00.234_s+01.064_6917a1092e7361ec77df87c8c065d8fd21f942bc_256x256.png,24205f9bc3f9121b0676c0ddc404bccb7937d7aa,Chairs,-48.925,78.121,-18.072,3827 -3828,_001,-51.29,-33.318,26.194,0.049,0.403,0.897142857143,099b63552d20e5129bf6236c651f66708979f86f,256,V6,099b63552d20e5129bf6236c651f66708979f86f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-51.290_ry-33.318_rz+26.194_tx+00.049_ty+00.403_s+00.628_099b63552d20e5129bf6236c651f66708979f86f_256x256.png,792da73dbc192caca2f8b9fd971b43e947b635a3,Chairs,-51.29,-33.318,26.194,3828 -3829,_001,-54.163,-60.819,-66.753,-0.17,-0.198,1.35714285714,4078307a9e2fd7ccb3846fdbcf0cea8badf811b6,256,V6,4078307a9e2fd7ccb3846fdbcf0cea8badf811b6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-54.163_ry-60.819_rz-66.753_tx-00.170_ty-00.198_s+00.950_4078307a9e2fd7ccb3846fdbcf0cea8badf811b6_256x256.png,cb35c260b6ff78ed6929e59618ba8a1fa77a8c02,Chairs,-54.163,-60.819,-66.753,3829 -3830,_001,-55.917,62.643,-68.919,-0.423,-0.602,1.1,6c39d6d2b9f08ff2e71ff30c99442b3ba1771294,256,V6,6c39d6d2b9f08ff2e71ff30c99442b3ba1771294_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-55.917_ry+62.643_rz-68.919_tx-00.423_ty-00.602_s+00.770_6c39d6d2b9f08ff2e71ff30c99442b3ba1771294_256x256.png,193ea312c801ee4ddf77c01ddd873fb8573242dc,Chairs,-55.917,62.643,-68.919,3830 -3831,_001,-57.555,0.665,-16.431,-0.104,0.868,1.08428571429,d7f7f8e0c1edf3227db47bbb22ed5b729ad46220,256,V6,d7f7f8e0c1edf3227db47bbb22ed5b729ad46220_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-57.555_ry+00.665_rz-16.431_tx-00.104_ty+00.868_s+00.759_d7f7f8e0c1edf3227db47bbb22ed5b729ad46220_256x256.png,a8451896e5f30a71542252a0b72bfab55cebd939,Chairs,-57.555,0.665,-16.431,3831 -3832,_001,-60.634,57.466,73.914,-0.325,1.178,0.872857142857,c75bef4048e308c32cba568752fb4ea1708aa3ed,256,V6,c75bef4048e308c32cba568752fb4ea1708aa3ed_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-60.634_ry+57.466_rz+73.914_tx-00.325_ty+01.178_s+00.611_c75bef4048e308c32cba568752fb4ea1708aa3ed_256x256.png,b3ac0607ddfd484f0419f8ba77f0a248940535f8,Chairs,-60.634,57.466,73.914,3832 -3833,_001,-62.868,-54.498,-35.126,-0.274,-0.108,1.42285714286,87e86e7f8320576290bdabf4d576565dd09b972f,256,V6,87e86e7f8320576290bdabf4d576565dd09b972f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-62.868_ry-54.498_rz-35.126_tx-00.274_ty-00.108_s+00.996_87e86e7f8320576290bdabf4d576565dd09b972f_256x256.png,daa8a82299bd7df36e2a15c405f795f9c83001b6,Chairs,-62.868,-54.498,-35.126,3833 -3834,_001,-64.624,-16.724,21.601,-0.55,0.51,0.745714285714,214d958f65b09d20203079a675a1e692f9737604,256,V6,214d958f65b09d20203079a675a1e692f9737604_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-64.624_ry-16.724_rz+21.601_tx-00.550_ty+00.510_s+00.522_214d958f65b09d20203079a675a1e692f9737604_256x256.png,838f5deca59a62ad9239d6cc94113a22fb09f39c,Chairs,-64.624,-16.724,21.601,3834 -3835,_001,-66.376,-64.811,-16.224,-0.534,1.022,1.31428571429,11efe87a7dd321124df31ab0a0c2790706e48cd8,256,V6,11efe87a7dd321124df31ab0a0c2790706e48cd8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-66.376_ry-64.811_rz-16.224_tx-00.534_ty+01.022_s+00.920_11efe87a7dd321124df31ab0a0c2790706e48cd8_256x256.png,e481e8eb84d5a38199ed850668d8d7ce751292de,Chairs,-66.376,-64.811,-16.224,3835 -3836,_001,-74.098,-7.066,-28.404,0.382,1.093,0.78,7bcaf2c259209e9af1b8fdfce6c6e1d815b44754,256,V6,7bcaf2c259209e9af1b8fdfce6c6e1d815b44754_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-74.098_ry-07.066_rz-28.404_tx+00.382_ty+01.093_s+00.546_7bcaf2c259209e9af1b8fdfce6c6e1d815b44754_256x256.png,419989ecd48113913860c592d265ab01d684887c,Chairs,-74.098,-7.066,-28.404,3836 -3837,_001,-74.151,-21.652,61.977,-0.197,-0.042,0.785714285714,0c8291cec8e24b72b2a80f1bbb9e81cd2f5eddd7,256,V6,0c8291cec8e24b72b2a80f1bbb9e81cd2f5eddd7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-74.151_ry-21.652_rz+61.977_tx-00.197_ty-00.042_s+00.550_0c8291cec8e24b72b2a80f1bbb9e81cd2f5eddd7_256x256.png,cf88f652df62a13ca1d94a689d3ca0d609747fa9,Chairs,-74.151,-21.652,61.977,3837 -3838,_001,-83.173,71.234,-11.011,0.367,1.197,1.00857142857,25d483de97c94f25d20cd935751ad0175285f22c,256,V6,25d483de97c94f25d20cd935751ad0175285f22c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-83.173_ry+71.234_rz-11.011_tx+00.367_ty+01.197_s+00.706_25d483de97c94f25d20cd935751ad0175285f22c_256x256.png,68bd1b91a33f5fd2759769445ddb04ee146849f2,Chairs,-83.173,71.234,-11.011,3838 -3839,_001,-83.738,82.863,36.863,-0.583,0.004,0.794285714286,23f6b254f5c8dadac1d161bc40f347c0ced0d280,256,V6,23f6b254f5c8dadac1d161bc40f347c0ced0d280_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_001_rx-83.738_ry+82.863_rz+36.863_tx-00.583_ty+00.004_s+00.556_23f6b254f5c8dadac1d161bc40f347c0ced0d280_256x256.png,20e007fd72b028cfc0a9433f036d6a810b9e0daa,Chairs,-83.738,82.863,36.863,3839 -3840,_004,5.855,71.879,-72.037,0.315,-0.165,1.41285714286,9cb0c5d55fb231df8e5e9d495a79fb16f3e9879d,256,V6,9cb0c5d55fb231df8e5e9d495a79fb16f3e9879d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx+05.855_ry+71.879_rz-72.037_tx+00.315_ty-00.165_s+00.989_9cb0c5d55fb231df8e5e9d495a79fb16f3e9879d_256x256.png,00ba123ee2f9cefc25f7cf2709b08af6796177eb,Chairs,5.855,71.879,-72.037,3840 -3841,_004,8.514,-56.577,79.694,-0.546,-0.194,1.56142857143,eb67aa0f5cab3eeca99de058a0b999a7e3496529,256,V6,eb67aa0f5cab3eeca99de058a0b999a7e3496529_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx+08.514_ry-56.577_rz+79.694_tx-00.546_ty-00.194_s+01.093_eb67aa0f5cab3eeca99de058a0b999a7e3496529_256x256.png,51df8286e7ee0dcd04791ccc9fd38fc17589ec82,Chairs,8.514,-56.577,79.694,3841 -3842,_004,8.944,-73.892,-3.318,-0.017,-0.753,0.941428571429,cce423beb1651e3cf6d22ad04177ae9c3a03acf8,256,V6,cce423beb1651e3cf6d22ad04177ae9c3a03acf8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx+08.944_ry-73.892_rz-03.318_tx-00.017_ty-00.753_s+00.659_cce423beb1651e3cf6d22ad04177ae9c3a03acf8_256x256.png,c196c217a5b14f641f215b523b22b561f36c388b,Chairs,8.944,-73.892,-3.318,3842 -3843,_004,19.193,-16.65,-54.193,0.463,-0.499,0.738571428571,ba4949b8ecd181e3a259bb32d59bfe60f2a54861,256,V6,ba4949b8ecd181e3a259bb32d59bfe60f2a54861_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx+19.193_ry-16.650_rz-54.193_tx+00.463_ty-00.499_s+00.517_ba4949b8ecd181e3a259bb32d59bfe60f2a54861_256x256.png,ddfd3561a1069a31d220455ef00faba26431d34f,Chairs,19.193,-16.65,-54.193,3843 -3844,_004,26.622,-54.423,-80.866,-0.48,-0.465,0.747142857143,e73349ec3a990631d88db27cf0348c94d914a7d5,256,V6,e73349ec3a990631d88db27cf0348c94d914a7d5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx+26.622_ry-54.423_rz-80.866_tx-00.480_ty-00.465_s+00.523_e73349ec3a990631d88db27cf0348c94d914a7d5_256x256.png,3711359e0be611b77882060a722b750b6b88834c,Chairs,26.622,-54.423,-80.866,3844 -3845,_004,38.443,19.108,3.98,0.426,0.766,0.817142857143,c0367d9855182098734ce38f22399f924a74aa7e,256,V6,c0367d9855182098734ce38f22399f924a74aa7e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx+38.443_ry+19.108_rz+03.980_tx+00.426_ty+00.766_s+00.572_c0367d9855182098734ce38f22399f924a74aa7e_256x256.png,4ab00f49c5d8f0f71ea9873df977a17b5da9fa17,Chairs,38.443,19.108,3.98,3845 -3846,_004,45.091,22.687,-51.153,0.092,-0.987,1.01571428571,669bc5e0c209058f8db624db685101b115e28e88,256,V6,669bc5e0c209058f8db624db685101b115e28e88_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx+45.091_ry+22.687_rz-51.153_tx+00.092_ty-00.987_s+00.711_669bc5e0c209058f8db624db685101b115e28e88_256x256.png,f3c1b68ef3ba07c230d51b1b7d904f2b1e0ebdb8,Chairs,45.091,22.687,-51.153,3846 -3847,_004,55.285,-39.223,-40.89,0.044,-0.523,0.842857142857,d041c29ce9ce315e8e89d067db33953bcb0d6193,256,V6,d041c29ce9ce315e8e89d067db33953bcb0d6193_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx+55.285_ry-39.223_rz-40.890_tx+00.044_ty-00.523_s+00.590_d041c29ce9ce315e8e89d067db33953bcb0d6193_256x256.png,a036578b905a76301d6f71ddc4c5b0ff6780d902,Chairs,55.285,-39.223,-40.89,3847 -3848,_004,59.301,50.949,37.197,0.553,0.901,0.838571428571,950c37f3f239f19cb5f8eab52f182d3d9273786f,256,V6,950c37f3f239f19cb5f8eab52f182d3d9273786f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx+59.301_ry+50.949_rz+37.197_tx+00.553_ty+00.901_s+00.587_950c37f3f239f19cb5f8eab52f182d3d9273786f_256x256.png,4ef4aeddf2c39009efcf64a0ced878b2f60a39c9,Chairs,59.301,50.949,37.197,3848 -3849,_004,73.351,-37.878,-25.457,-0.309,-0.494,0.692857142857,d3db7f335817bafd13b4cf5260b64eb4efa10c37,256,V6,d3db7f335817bafd13b4cf5260b64eb4efa10c37_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx+73.351_ry-37.878_rz-25.457_tx-00.309_ty-00.494_s+00.485_d3db7f335817bafd13b4cf5260b64eb4efa10c37_256x256.png,0a2fcd4fd8f213fa25e88bde3d819df5e760d09c,Chairs,73.351,-37.878,-25.457,3849 -3850,_004,75.297,72.438,-75.009,-0.08,0.15,1.36857142857,200da3ca81185cc0aa5da69fba5a1350a9e8ccee,256,V6,200da3ca81185cc0aa5da69fba5a1350a9e8ccee_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx+75.297_ry+72.438_rz-75.009_tx-00.080_ty+00.150_s+00.958_200da3ca81185cc0aa5da69fba5a1350a9e8ccee_256x256.png,d3b2554286ca800519f87f14cb7535fa762ea5cc,Chairs,75.297,72.438,-75.009,3850 -3851,_004,76.706,64.276,61.287,0.276,0.819,1.13142857143,314a3c37d9002be0f5e605e508bc7bbee70587af,256,V6,314a3c37d9002be0f5e605e508bc7bbee70587af_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx+76.706_ry+64.276_rz+61.287_tx+00.276_ty+00.819_s+00.792_314a3c37d9002be0f5e605e508bc7bbee70587af_256x256.png,cfcc2a5fc8bfbcf2f0412cfd70f754a670c38da4,Chairs,76.706,64.276,61.287,3851 -3852,_004,81.688,-21.043,-73.872,0.003,-0.971,1.08714285714,35b834eabf174dbcb8984efcf819ab5726834409,256,V6,35b834eabf174dbcb8984efcf819ab5726834409_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx+81.688_ry-21.043_rz-73.872_tx+00.003_ty-00.971_s+00.761_35b834eabf174dbcb8984efcf819ab5726834409_256x256.png,0cc24ce95116405f95750196250319f357883955,Chairs,81.688,-21.043,-73.872,3852 -3853,_004,86.864,-16.072,30.443,0.597,-0.56,0.657142857143,52b8677d036765ba6c05d8035b641e3c55993f9a,256,V6,52b8677d036765ba6c05d8035b641e3c55993f9a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx+86.864_ry-16.072_rz+30.443_tx+00.597_ty-00.560_s+00.460_52b8677d036765ba6c05d8035b641e3c55993f9a_256x256.png,af8325d2d77b8475a00eb9a5296689adb4a8f39b,Chairs,86.864,-16.072,30.443,3853 -3854,_004,88.164,20.787,-25.352,0.005,-0.074,1.35571428571,072d52a261dc325d88850043ac435da6408284cf,256,V6,072d52a261dc325d88850043ac435da6408284cf_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx+88.164_ry+20.787_rz-25.352_tx+00.005_ty-00.074_s+00.949_072d52a261dc325d88850043ac435da6408284cf_256x256.png,f211be3fd737206ac77d6a3c9c96203d54fdec1d,Chairs,88.164,20.787,-25.352,3854 -3855,_004,-1.728,10.812,-28.183,-0.209,0.91,1.6,7d0eeef47ba7dcc9eea20b1bdc744f0f5052da99,256,V6,7d0eeef47ba7dcc9eea20b1bdc744f0f5052da99_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-01.728_ry+10.812_rz-28.183_tx-00.209_ty+00.910_s+01.120_7d0eeef47ba7dcc9eea20b1bdc744f0f5052da99_256x256.png,587782ebef93e47e96f5b7b654cd54c8ede6802e,Chairs,-1.728,10.812,-28.183,3855 -3856,_004,-6.839,-32.116,61.008,-0.46,0.664,0.797142857143,b68f8bfb20445c167e032f5497abd1cdba339bcf,256,V6,b68f8bfb20445c167e032f5497abd1cdba339bcf_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-06.839_ry-32.116_rz+61.008_tx-00.460_ty+00.664_s+00.558_b68f8bfb20445c167e032f5497abd1cdba339bcf_256x256.png,c0d964e51197c5d6eefd4931b830f13e13a39b12,Chairs,-6.839,-32.116,61.008,3856 -3857,_004,-6.971,-84.829,43.96,0.44,-0.259,0.722857142857,e2e8c24438e6f5d6d52d2b8d262066500997b828,256,V6,e2e8c24438e6f5d6d52d2b8d262066500997b828_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-06.971_ry-84.829_rz+43.960_tx+00.440_ty-00.259_s+00.506_e2e8c24438e6f5d6d52d2b8d262066500997b828_256x256.png,b533568a5577e2455f00c3779a8b77f84d280649,Chairs,-6.971,-84.829,43.96,3857 -3858,_004,-8.933,31.857,22.364,-0.158,-0.34,1.20142857143,8c8b4fabaa9df1c223dddf898e06e5556a5d5925,256,V6,8c8b4fabaa9df1c223dddf898e06e5556a5d5925_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-08.933_ry+31.857_rz+22.364_tx-00.158_ty-00.340_s+00.841_8c8b4fabaa9df1c223dddf898e06e5556a5d5925_256x256.png,82d0d6f79b1dddc400a4cc422351c62388a5f6ea,Chairs,-8.933,31.857,22.364,3858 -3859,_004,-14.58,11.207,-78.362,0.339,-0.659,1.39428571429,171db4c33cc0be705f8f39c4aa8b66d54a3ba3ab,256,V6,171db4c33cc0be705f8f39c4aa8b66d54a3ba3ab_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-14.580_ry+11.207_rz-78.362_tx+00.339_ty-00.659_s+00.976_171db4c33cc0be705f8f39c4aa8b66d54a3ba3ab_256x256.png,85383d3eb45d818e326828b1f94056991a0da481,Chairs,-14.58,11.207,-78.362,3859 -3860,_004,-17.701,-61.028,-39.334,-0.258,-0.884,0.875714285714,13c170cc24df903579baab7297042583ef6b72fc,256,V6,13c170cc24df903579baab7297042583ef6b72fc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-17.701_ry-61.028_rz-39.334_tx-00.258_ty-00.884_s+00.613_13c170cc24df903579baab7297042583ef6b72fc_256x256.png,a2d6c93079507a38c1d5709ba2fa86c52b2d63fd,Chairs,-17.701,-61.028,-39.334,3860 -3861,_004,-20.908,6.209,25.711,0.416,-0.264,0.838571428571,a7cb8a7270f6898f91ac3c08676fdf0eb9a587ec,256,V6,a7cb8a7270f6898f91ac3c08676fdf0eb9a587ec_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-20.908_ry+06.209_rz+25.711_tx+00.416_ty-00.264_s+00.587_a7cb8a7270f6898f91ac3c08676fdf0eb9a587ec_256x256.png,8ea30ba0736418d7282258d1f1c8c7e20c990a0c,Chairs,-20.908,6.209,25.711,3861 -3862,_004,-21.96,-37.289,20.831,-0.236,-0.626,1.53571428571,dd32c9bdc3fb5693e0f669bedf00e0d82796b4ef,256,V6,dd32c9bdc3fb5693e0f669bedf00e0d82796b4ef_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-21.960_ry-37.289_rz+20.831_tx-00.236_ty-00.626_s+01.075_dd32c9bdc3fb5693e0f669bedf00e0d82796b4ef_256x256.png,7b3a31f6b59af7ec8664aad184a79caddd45563f,Chairs,-21.96,-37.289,20.831,3862 -3863,_004,-24.756,63.195,49.054,0.574,0.044,1.49,a5556c7b65c5a2df6012b955b25d64212574f56d,256,V6,a5556c7b65c5a2df6012b955b25d64212574f56d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-24.756_ry+63.195_rz+49.054_tx+00.574_ty+00.044_s+01.043_a5556c7b65c5a2df6012b955b25d64212574f56d_256x256.png,877fff64f05a4e43049e594f2e0c33f943cc9f62,Chairs,-24.756,63.195,49.054,3863 -3864,_004,-26.466,43.296,-68.408,-0.141,-0.836,0.818571428571,3c3af88971c4063e1cdae89a846b49abaaff1ded,256,V6,3c3af88971c4063e1cdae89a846b49abaaff1ded_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-26.466_ry+43.296_rz-68.408_tx-00.141_ty-00.836_s+00.573_3c3af88971c4063e1cdae89a846b49abaaff1ded_256x256.png,bd2b243124d41bc0a9d17d0caeb396e05b98a732,Chairs,-26.466,43.296,-68.408,3864 -3865,_004,-33.725,62.095,-24.377,-0.346,0.736,0.657142857143,f84ae3688e16e2a41725ab38eede21d93d7070fc,256,V6,f84ae3688e16e2a41725ab38eede21d93d7070fc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-33.725_ry+62.095_rz-24.377_tx-00.346_ty+00.736_s+00.460_f84ae3688e16e2a41725ab38eede21d93d7070fc_256x256.png,70c23c23d3cc585dda6698651292e88c1f6c23c1,Chairs,-33.725,62.095,-24.377,3865 -3866,_004,-35.422,-81.017,-13.533,0.462,-0.521,1.45857142857,0fb23fba83c2463dfaf9fc958fc5fc501d4cad7b,256,V6,0fb23fba83c2463dfaf9fc958fc5fc501d4cad7b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-35.422_ry-81.017_rz-13.533_tx+00.462_ty-00.521_s+01.021_0fb23fba83c2463dfaf9fc958fc5fc501d4cad7b_256x256.png,235776b34884c718baca8586881745495b9f72dc,Chairs,-35.422,-81.017,-13.533,3866 -3867,_004,-40.355,-72.507,70.588,0.127,-0.49,1.18285714286,6621033f66f04ab759ce84ba596b5e28402e340b,256,V6,6621033f66f04ab759ce84ba596b5e28402e340b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-40.355_ry-72.507_rz+70.588_tx+00.127_ty-00.490_s+00.828_6621033f66f04ab759ce84ba596b5e28402e340b_256x256.png,b800303fbcd7e3eb0d1d21f92ff6c06bd6c6f619,Chairs,-40.355,-72.507,70.588,3867 -3868,_004,-43.185,-56.117,-27.414,0.438,0.1,0.837142857143,62f522369093a9fc9a836b5994ab188e67dcfa29,256,V6,62f522369093a9fc9a836b5994ab188e67dcfa29_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-43.185_ry-56.117_rz-27.414_tx+00.438_ty+00.100_s+00.586_62f522369093a9fc9a836b5994ab188e67dcfa29_256x256.png,f5ab8d97915ced8e507e5818609c8d96bf107b83,Chairs,-43.185,-56.117,-27.414,3868 -3869,_004,-43.274,36.228,64.769,-0.009,-0.7,1.36,8b37078b5606c07c2647f33aa62c55bd27a3ff2b,256,V6,8b37078b5606c07c2647f33aa62c55bd27a3ff2b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-43.274_ry+36.228_rz+64.769_tx-00.009_ty-00.700_s+00.952_8b37078b5606c07c2647f33aa62c55bd27a3ff2b_256x256.png,90e89b3f1ebba126f8a4b9c37bba8e4f3a1c56ef,Chairs,-43.274,36.228,64.769,3869 -3870,_004,-43.614,-35.776,-71.852,-0.378,-0.409,1.3,dc8504ff994142a717db0a38737c94a328d09ee9,256,V6,dc8504ff994142a717db0a38737c94a328d09ee9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-43.614_ry-35.776_rz-71.852_tx-00.378_ty-00.409_s+00.910_dc8504ff994142a717db0a38737c94a328d09ee9_256x256.png,3a26d3cdf7ae3c7c4fbc965105bda944f39d086d,Chairs,-43.614,-35.776,-71.852,3870 -3871,_004,-43.879,11.232,-34.94,0.249,0.128,0.63,658db4a5da65431e9ba9f82cf2edccf54ad0447a,256,V6,658db4a5da65431e9ba9f82cf2edccf54ad0447a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-43.879_ry+11.232_rz-34.940_tx+00.249_ty+00.128_s+00.441_658db4a5da65431e9ba9f82cf2edccf54ad0447a_256x256.png,61d072cd0d91d3185b48c550144dd82ecbc73bab,Chairs,-43.879,11.232,-34.94,3871 -3872,_004,-44.591,-1.177,31.997,0.364,0.136,1.19714285714,4dcef6082ea29ba141baf859705d7853b08c6a09,256,V6,4dcef6082ea29ba141baf859705d7853b08c6a09_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-44.591_ry-01.177_rz+31.997_tx+00.364_ty+00.136_s+00.838_4dcef6082ea29ba141baf859705d7853b08c6a09_256x256.png,c41aca22834baf56232fcfdf9c8faf487f7f2583,Chairs,-44.591,-1.177,31.997,3872 -3873,_004,-48.374,-85.877,-8.901,0.261,-0.931,1.21857142857,72a514432473886d830d5d32ac464e32306fd955,256,V6,72a514432473886d830d5d32ac464e32306fd955_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-48.374_ry-85.877_rz-08.901_tx+00.261_ty-00.931_s+00.853_72a514432473886d830d5d32ac464e32306fd955_256x256.png,e6ca1530c4ee03190ed600a1eade791b23c889d8,Chairs,-48.374,-85.877,-8.901,3873 -3874,_004,-59.609,-66.99,79.552,-0.178,-0.039,1.07857142857,4997f6f140c2ab47a584542e090951fe1d89b595,256,V6,4997f6f140c2ab47a584542e090951fe1d89b595_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-59.609_ry-66.990_rz+79.552_tx-00.178_ty-00.039_s+00.755_4997f6f140c2ab47a584542e090951fe1d89b595_256x256.png,11c6ce055853b8afe8c50bf4c8ba5e3458a490c3,Chairs,-59.609,-66.99,79.552,3874 -3875,_004,-61.58,35.807,45.315,-0.199,0.905,1.31428571429,0f3924ca00742be360748f6a041758003fe0ecdd,256,V6,0f3924ca00742be360748f6a041758003fe0ecdd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-61.580_ry+35.807_rz+45.315_tx-00.199_ty+00.905_s+00.920_0f3924ca00742be360748f6a041758003fe0ecdd_256x256.png,4fd105d5e375640bed22145e57da3ffcf99e3e9b,Chairs,-61.58,35.807,45.315,3875 -3876,_004,-61.786,-15.292,36.499,0.099,0.934,1.22571428571,3a171566076eefefb90a106e9313818790990976,256,V6,3a171566076eefefb90a106e9313818790990976_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-61.786_ry-15.292_rz+36.499_tx+00.099_ty+00.934_s+00.858_3a171566076eefefb90a106e9313818790990976_256x256.png,6b3f5681803e44ed7acca1f24898eb83a3899946,Chairs,-61.786,-15.292,36.499,3876 -3877,_004,-63.914,1.057,20.778,0.01,-0.567,1.25428571429,143176a572871d3144176d44b50d0e307f2cf148,256,V6,143176a572871d3144176d44b50d0e307f2cf148_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-63.914_ry+01.057_rz+20.778_tx+00.010_ty-00.567_s+00.878_143176a572871d3144176d44b50d0e307f2cf148_256x256.png,ae947cdbcee090e884786a3fdb8799c811621a40,Chairs,-63.914,1.057,20.778,3877 -3878,_004,-70.732,27.552,-58.973,-0.363,-0.965,1.25571428571,e141454d2547126e980f5a7e3991de6711e8a0db,256,V6,e141454d2547126e980f5a7e3991de6711e8a0db_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-70.732_ry+27.552_rz-58.973_tx-00.363_ty-00.965_s+00.879_e141454d2547126e980f5a7e3991de6711e8a0db_256x256.png,31a38cbc77737266e98dc7ff585a5b01efff02d3,Chairs,-70.732,27.552,-58.973,3878 -3879,_004,-78.093,81.521,63.172,0.008,-0.479,0.647142857143,8cfc4aad870c77c1377ec54b745c8aebf0fda3d6,256,V6,8cfc4aad870c77c1377ec54b745c8aebf0fda3d6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_004_rx-78.093_ry+81.521_rz+63.172_tx+00.008_ty-00.479_s+00.453_8cfc4aad870c77c1377ec54b745c8aebf0fda3d6_256x256.png,9b5b1ebfe89375abd92c69092bc636969cb15290,Chairs,-78.093,81.521,63.172,3879 -3880,_008,8.97,-17.299,47.207,-0.154,0.173,1.47714285714,ce30c9b81be31a2ec4f51a04b30ee139a5e1d0b2,256,V6,ce30c9b81be31a2ec4f51a04b30ee139a5e1d0b2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+08.970_ry-17.299_rz+47.207_tx-00.154_ty+00.173_s+01.034_ce30c9b81be31a2ec4f51a04b30ee139a5e1d0b2_256x256.png,54b3cf73f551f2a1181dff1070315f3b6540108a,Chairs,8.97,-17.299,47.207,3880 -3881,_008,10.899,22.311,89.96,0.523,0.081,1.20571428571,c2938c1df3fa8542979de8de900ee47633f23c44,256,V6,c2938c1df3fa8542979de8de900ee47633f23c44_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+10.899_ry+22.311_rz+89.960_tx+00.523_ty+00.081_s+00.844_c2938c1df3fa8542979de8de900ee47633f23c44_256x256.png,56b8037a454afb0ca09ed5aff485c9daffe025f7,Chairs,10.899,22.311,89.96,3881 -3882,_008,16.676,-29.643,-59.439,0.179,0.778,0.682857142857,f33ec67ff6e04d56b0593ba32c395ea3e04109fa,256,V6,f33ec67ff6e04d56b0593ba32c395ea3e04109fa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+16.676_ry-29.643_rz-59.439_tx+00.179_ty+00.778_s+00.478_f33ec67ff6e04d56b0593ba32c395ea3e04109fa_256x256.png,4d59ee461c8448bea595fb95b2662e7d1abbca60,Chairs,16.676,-29.643,-59.439,3882 -3883,_008,17.167,37.916,51.096,-0.426,0.626,1.52142857143,d11233e04f472a4bebead220711dfedb2353889d,256,V6,d11233e04f472a4bebead220711dfedb2353889d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+17.167_ry+37.916_rz+51.096_tx-00.426_ty+00.626_s+01.065_d11233e04f472a4bebead220711dfedb2353889d_256x256.png,9ca849f71cc9925cda661e71370a9bd90af451fa,Chairs,17.167,37.916,51.096,3883 -3884,_008,25.69,-80.487,-72.192,0.092,0.617,1.41571428571,1afa650493d6f8a447a819ac937bcb1fe54c7ded,256,V6,1afa650493d6f8a447a819ac937bcb1fe54c7ded_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+25.690_ry-80.487_rz-72.192_tx+00.092_ty+00.617_s+00.991_1afa650493d6f8a447a819ac937bcb1fe54c7ded_256x256.png,e3190d96a931d3e146d201dca7155d87100f4870,Chairs,25.69,-80.487,-72.192,3884 -3885,_008,26.243,-21.742,42.695,-0.175,-0.509,1.26,6eecadfecbc396968c5ccddcaffaf45bf3e7f15f,256,V6,6eecadfecbc396968c5ccddcaffaf45bf3e7f15f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+26.243_ry-21.742_rz+42.695_tx-00.175_ty-00.509_s+00.882_6eecadfecbc396968c5ccddcaffaf45bf3e7f15f_256x256.png,d5a468c15703e96cf275869990be0391a775db35,Chairs,26.243,-21.742,42.695,3885 -3886,_008,28.688,-53.725,6.069,-0.001,0.831,1.53571428571,ebd0901e30c6486fc9a9af344d2bad9755a2c042,256,V6,ebd0901e30c6486fc9a9af344d2bad9755a2c042_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+28.688_ry-53.725_rz+06.069_tx-00.001_ty+00.831_s+01.075_ebd0901e30c6486fc9a9af344d2bad9755a2c042_256x256.png,b8b66fc19cd9eebc716fff0a96fd351cc79b8a88,Chairs,28.688,-53.725,6.069,3886 -3887,_008,37.122,45.285,21.814,0.06,1.199,1.41857142857,cb40eea4d91e603380e46852251ad642dfd0ba75,256,V6,cb40eea4d91e603380e46852251ad642dfd0ba75_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+37.122_ry+45.285_rz+21.814_tx+00.060_ty+01.199_s+00.993_cb40eea4d91e603380e46852251ad642dfd0ba75_256x256.png,d2af56088e557fbafc9815e9a7486f3efe61823e,Chairs,37.122,45.285,21.814,3887 -3888,_008,39.478,-20.944,14.567,0.145,-0.23,1.08571428571,36cdabcae39b3ddcd583f6f7e4bce3b8f1bb6d2f,256,V6,36cdabcae39b3ddcd583f6f7e4bce3b8f1bb6d2f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+39.478_ry-20.944_rz+14.567_tx+00.145_ty-00.230_s+00.760_36cdabcae39b3ddcd583f6f7e4bce3b8f1bb6d2f_256x256.png,48af3132be3e5f102ddd8a17e6a9866aa1c45ee1,Chairs,39.478,-20.944,14.567,3888 -3889,_008,40.184,11.246,-63.477,0.071,-1.093,1.31142857143,2ec278ce2a58bb226eebd35a824f79d3da78153a,256,V6,2ec278ce2a58bb226eebd35a824f79d3da78153a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+40.184_ry+11.246_rz-63.477_tx+00.071_ty-01.093_s+00.918_2ec278ce2a58bb226eebd35a824f79d3da78153a_256x256.png,2b24f5e5253efe4b49b7306dca3985d04da35145,Chairs,40.184,11.246,-63.477,3889 -3890,_008,48.33,82.972,18.758,0.356,-0.799,1.52714285714,2475216bdffebc48ab20251408b4f0c3cadcc2af,256,V6,2475216bdffebc48ab20251408b4f0c3cadcc2af_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+48.330_ry+82.972_rz+18.758_tx+00.356_ty-00.799_s+01.069_2475216bdffebc48ab20251408b4f0c3cadcc2af_256x256.png,490fe507cc08895368cb50af0a47635a8b308d41,Chairs,48.33,82.972,18.758,3890 -3891,_008,51.46,20.068,-33.327,-0.595,-0.704,0.712857142857,1f283bd7e99f267055802b2efeed64f002e4dd47,256,V6,1f283bd7e99f267055802b2efeed64f002e4dd47_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+51.460_ry+20.068_rz-33.327_tx-00.595_ty-00.704_s+00.499_1f283bd7e99f267055802b2efeed64f002e4dd47_256x256.png,97f456e89f7a29e81a886b7c7cce97bbb58370d8,Chairs,51.46,20.068,-33.327,3891 -3892,_008,54.25,-11.473,-11.452,0.349,-1.062,1.27285714286,fc966ce1db2b12b82d29ae9cad9c40f89ec50cfc,256,V6,fc966ce1db2b12b82d29ae9cad9c40f89ec50cfc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+54.250_ry-11.473_rz-11.452_tx+00.349_ty-01.062_s+00.891_fc966ce1db2b12b82d29ae9cad9c40f89ec50cfc_256x256.png,bdd8c442ac00dc85196879b871bb6fdfee9bd729,Chairs,54.25,-11.473,-11.452,3892 -3893,_008,60.926,-84.874,-30.602,0.309,-0.512,1.37714285714,958d70eb611e3d74ae3e8e40850ca3a07b254acd,256,V6,958d70eb611e3d74ae3e8e40850ca3a07b254acd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+60.926_ry-84.874_rz-30.602_tx+00.309_ty-00.512_s+00.964_958d70eb611e3d74ae3e8e40850ca3a07b254acd_256x256.png,453a15fb45373d8df4586388aaa8a3d097c8310f,Chairs,60.926,-84.874,-30.602,3893 -3894,_008,62.942,44.77,4.731,0.508,-0.924,0.767142857143,6b7510e171421e33edfff6be5813a7ce9678707c,256,V6,6b7510e171421e33edfff6be5813a7ce9678707c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+62.942_ry+44.770_rz+04.731_tx+00.508_ty-00.924_s+00.537_6b7510e171421e33edfff6be5813a7ce9678707c_256x256.png,b19e9e57091a975293e116bbff536b8c9e38e3b3,Chairs,62.942,44.77,4.731,3894 -3895,_008,64.368,89.328,-39.893,-0.166,-0.158,1.16857142857,cac49056c511781d08ee84eba0e3f5532b4702af,256,V6,cac49056c511781d08ee84eba0e3f5532b4702af_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+64.368_ry+89.328_rz-39.893_tx-00.166_ty-00.158_s+00.818_cac49056c511781d08ee84eba0e3f5532b4702af_256x256.png,e16ab6522d45ee4f454aa8506462532770398c14,Chairs,64.368,89.328,-39.893,3895 -3896,_008,69.226,72.335,6.532,0.035,0.505,1.15142857143,067f7640506019ffad1b673ba046ce36cec9b838,256,V6,067f7640506019ffad1b673ba046ce36cec9b838_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+69.226_ry+72.335_rz+06.532_tx+00.035_ty+00.505_s+00.806_067f7640506019ffad1b673ba046ce36cec9b838_256x256.png,a727ebf5c906bac9a3aa326caa94d4af668ddf87,Chairs,69.226,72.335,6.532,3896 -3897,_008,75.068,12.223,11.499,-0.015,0.615,0.701428571429,bdb7fd771c6e6ff2e8d3e28fa3bc940143635ed8,256,V6,bdb7fd771c6e6ff2e8d3e28fa3bc940143635ed8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+75.068_ry+12.223_rz+11.499_tx-00.015_ty+00.615_s+00.491_bdb7fd771c6e6ff2e8d3e28fa3bc940143635ed8_256x256.png,a74b009a732329436483917f07b9244b1dfd9986,Chairs,75.068,12.223,11.499,3897 -3898,_008,87.01,21.054,85.842,-0.41,0.466,0.831428571429,d2a384b031c61463edf27e0c1754f6fb63cd62c9,256,V6,d2a384b031c61463edf27e0c1754f6fb63cd62c9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+87.010_ry+21.054_rz+85.842_tx-00.410_ty+00.466_s+00.582_d2a384b031c61463edf27e0c1754f6fb63cd62c9_256x256.png,6b031b9cf625d1b3bda933911918a057992a92ab,Chairs,87.01,21.054,85.842,3898 -3899,_008,87.233,-72.463,-4.817,-0.296,-0.34,1.51857142857,915f5a4f5e69845a49a954441380fdddb202ae68,256,V6,915f5a4f5e69845a49a954441380fdddb202ae68_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx+87.233_ry-72.463_rz-04.817_tx-00.296_ty-00.340_s+01.063_915f5a4f5e69845a49a954441380fdddb202ae68_256x256.png,48d4f758cc0c6f62baa8d11775c160d9cf79f194,Chairs,87.233,-72.463,-4.817,3899 -3900,_008,-6.516,-23.022,-19.1,-0.035,1.107,0.712857142857,f2121217dc351171e3cd06e4da5166f56d1b74c8,256,V6,f2121217dc351171e3cd06e4da5166f56d1b74c8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-06.516_ry-23.022_rz-19.100_tx-00.035_ty+01.107_s+00.499_f2121217dc351171e3cd06e4da5166f56d1b74c8_256x256.png,91f0f36a648a3cd9c8847260f088ed07fa8dfe8f,Chairs,-6.516,-23.022,-19.1,3900 -3901,_008,-8.093,3.708,50.449,-0.424,-0.719,0.78,563ab585dd0de7c5d09590f04549e32d7b7c096f,256,V6,563ab585dd0de7c5d09590f04549e32d7b7c096f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-08.093_ry+03.708_rz+50.449_tx-00.424_ty-00.719_s+00.546_563ab585dd0de7c5d09590f04549e32d7b7c096f_256x256.png,bd6d42151ab7846a36f51836495c306669939a3d,Chairs,-8.093,3.708,50.449,3901 -3902,_008,-17.875,-77.268,70.627,-0.349,0.655,0.95,254c356d24e8ba9859e250b0e9a6576bef2a81ad,256,V6,254c356d24e8ba9859e250b0e9a6576bef2a81ad_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-17.875_ry-77.268_rz+70.627_tx-00.349_ty+00.655_s+00.665_254c356d24e8ba9859e250b0e9a6576bef2a81ad_256x256.png,0f3ed65f14e926728999de272b915a4f49da719f,Chairs,-17.875,-77.268,70.627,3902 -3903,_008,-18.107,25.791,-2.575,0.424,-1.115,0.661428571429,1be5f5ebeb87759a024d1614fb880e237e81a324,256,V6,1be5f5ebeb87759a024d1614fb880e237e81a324_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-18.107_ry+25.791_rz-02.575_tx+00.424_ty-01.115_s+00.463_1be5f5ebeb87759a024d1614fb880e237e81a324_256x256.png,ba08b323f7f250c8007792a0b561f4e79be6e72e,Chairs,-18.107,25.791,-2.575,3903 -3904,_008,-23.258,-46.857,31.096,0.014,-0.241,1.47,c3edce2a8fce8c088605bd27fe1f2e7958939f54,256,V6,c3edce2a8fce8c088605bd27fe1f2e7958939f54_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-23.258_ry-46.857_rz+31.096_tx+00.014_ty-00.241_s+01.029_c3edce2a8fce8c088605bd27fe1f2e7958939f54_256x256.png,266b92c65c2b165b63fd66157c1dc225abbdef25,Chairs,-23.258,-46.857,31.096,3904 -3905,_008,-30.083,26.311,22.999,-0.016,0.233,1.17285714286,f90fd6ff11d298700b3ba7f3fb86042f22d5e5e9,256,V6,f90fd6ff11d298700b3ba7f3fb86042f22d5e5e9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-30.083_ry+26.311_rz+22.999_tx-00.016_ty+00.233_s+00.821_f90fd6ff11d298700b3ba7f3fb86042f22d5e5e9_256x256.png,8f44a4c6a2e8d6b1e8f2ec494b55992d5e3129b5,Chairs,-30.083,26.311,22.999,3905 -3906,_008,-38.588,-89.683,-12.768,0.002,0.441,0.831428571429,b17746284e158d6fc1f5a246465c7eed7bc212b0,256,V6,b17746284e158d6fc1f5a246465c7eed7bc212b0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-38.588_ry-89.683_rz-12.768_tx+00.002_ty+00.441_s+00.582_b17746284e158d6fc1f5a246465c7eed7bc212b0_256x256.png,09b4445c45424745cf4d2e547e9e2a923e962023,Chairs,-38.588,-89.683,-12.768,3906 -3907,_008,-39.159,-21.143,20.745,-0.118,0.151,1.13857142857,9618171c30dcb379ca4f3a97dd5da2a410d42744,256,V6,9618171c30dcb379ca4f3a97dd5da2a410d42744_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-39.159_ry-21.143_rz+20.745_tx-00.118_ty+00.151_s+00.797_9618171c30dcb379ca4f3a97dd5da2a410d42744_256x256.png,49acbbad7a9518934b9ccd5bbf2f60e3b6609f9d,Chairs,-39.159,-21.143,20.745,3907 -3908,_008,-42.078,-3.07,-81.967,0.135,0.385,1.42428571429,68542d21a0595c375cb36df9eca56435c9360566,256,V6,68542d21a0595c375cb36df9eca56435c9360566_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-42.078_ry-03.070_rz-81.967_tx+00.135_ty+00.385_s+00.997_68542d21a0595c375cb36df9eca56435c9360566_256x256.png,54d60562fddc03b581fbf1836efc9850320ee372,Chairs,-42.078,-3.07,-81.967,3908 -3909,_008,-43.397,52.75,38.415,-0.561,-0.425,0.831428571429,5add875a8f9d93fe4088773ab9d1d2056c0f167c,256,V6,5add875a8f9d93fe4088773ab9d1d2056c0f167c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-43.397_ry+52.750_rz+38.415_tx-00.561_ty-00.425_s+00.582_5add875a8f9d93fe4088773ab9d1d2056c0f167c_256x256.png,1ecae184dd77b9d5be0367b7e6acce0ac8a44b37,Chairs,-43.397,52.75,38.415,3909 -3910,_008,-53.715,-68.012,48.9,0.191,-0.572,1.04285714286,0cfb3a5851f73d3832c809e7464186c93f45d7fa,256,V6,0cfb3a5851f73d3832c809e7464186c93f45d7fa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-53.715_ry-68.012_rz+48.900_tx+00.191_ty-00.572_s+00.730_0cfb3a5851f73d3832c809e7464186c93f45d7fa_256x256.png,f2bebe7cf017a9b9c6c736a8c9a819011a320d82,Chairs,-53.715,-68.012,48.9,3910 -3911,_008,-54.317,38.104,45.419,0.413,-0.674,1.15285714286,1329951701950f2e781c194fe84274a35cf396fd,256,V6,1329951701950f2e781c194fe84274a35cf396fd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-54.317_ry+38.104_rz+45.419_tx+00.413_ty-00.674_s+00.807_1329951701950f2e781c194fe84274a35cf396fd_256x256.png,c7ddf948bfc6a5920b06190ac6caef49c5aa9977,Chairs,-54.317,38.104,45.419,3911 -3912,_008,-54.978,-33.399,-5.384,0.557,-1.076,1.25285714286,e00dd5f509c6c13c1992376c3c94d3adc7427086,256,V6,e00dd5f509c6c13c1992376c3c94d3adc7427086_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-54.978_ry-33.399_rz-05.384_tx+00.557_ty-01.076_s+00.877_e00dd5f509c6c13c1992376c3c94d3adc7427086_256x256.png,ff123fb8cbf6716aa36a759a518bedc0f403dd14,Chairs,-54.978,-33.399,-5.384,3912 -3913,_008,-58.625,-58.19,-3.575,0.296,-0.696,1.36857142857,230aea1ffbf5af5ffb90660bafecaa9f4acaee40,256,V6,230aea1ffbf5af5ffb90660bafecaa9f4acaee40_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-58.625_ry-58.190_rz-03.575_tx+00.296_ty-00.696_s+00.958_230aea1ffbf5af5ffb90660bafecaa9f4acaee40_256x256.png,bd7da1d9b4e25282ce251c54d99a900138a50514,Chairs,-58.625,-58.19,-3.575,3913 -3914,_008,-67.412,-53.699,-38.483,0.208,0.612,0.764285714286,fa1abc1dd83079194392c5a06c9fa74914c07896,256,V6,fa1abc1dd83079194392c5a06c9fa74914c07896_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-67.412_ry-53.699_rz-38.483_tx+00.208_ty+00.612_s+00.535_fa1abc1dd83079194392c5a06c9fa74914c07896_256x256.png,0871ab6e45c2fcedf90180bf8447c30230c31b50,Chairs,-67.412,-53.699,-38.483,3914 -3915,_008,-67.971,-13.62,-46.84,-0.584,0.873,1.31142857143,2f5bf24e94e5528c62faf46494cd035421d93cd1,256,V6,2f5bf24e94e5528c62faf46494cd035421d93cd1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-67.971_ry-13.620_rz-46.840_tx-00.584_ty+00.873_s+00.918_2f5bf24e94e5528c62faf46494cd035421d93cd1_256x256.png,7e99c8100657e572d055e26f31e9fd5e3af4faed,Chairs,-67.971,-13.62,-46.84,3915 -3916,_008,-69.262,73.482,-67.623,0.406,-0.014,0.947142857143,08e669ddc7070ad3d5c166646873dfaf5bea3e60,256,V6,08e669ddc7070ad3d5c166646873dfaf5bea3e60_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-69.262_ry+73.482_rz-67.623_tx+00.406_ty-00.014_s+00.663_08e669ddc7070ad3d5c166646873dfaf5bea3e60_256x256.png,26c328b8e685d105726707e039a44c9619a72cf3,Chairs,-69.262,73.482,-67.623,3916 -3917,_008,-80.58,-58.761,0.987,0.303,0.794,0.952857142857,91e68c65ff92e5a77f0fb13693bfe01bc429da18,256,V6,91e68c65ff92e5a77f0fb13693bfe01bc429da18_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-80.580_ry-58.761_rz+00.987_tx+00.303_ty+00.794_s+00.667_91e68c65ff92e5a77f0fb13693bfe01bc429da18_256x256.png,aeab8d81b24bec5489fda75c13f201072c2eba31,Chairs,-80.58,-58.761,0.987,3917 -3918,_008,-89.524,89.059,85.434,-0.403,-0.392,1.27571428571,c14d9a53ac45a386b8d13eefb8a5540775d5e90e,256,V6,c14d9a53ac45a386b8d13eefb8a5540775d5e90e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-89.524_ry+89.059_rz+85.434_tx-00.403_ty-00.392_s+00.893_c14d9a53ac45a386b8d13eefb8a5540775d5e90e_256x256.png,cfbd9d0d6e7e75ccf27a51e1d1fd23cf00b61157,Chairs,-89.524,89.059,85.434,3918 -3919,_008,-89.816,2.913,28.731,0.429,1.181,1.0,7c7368b846c0f9c02efa392d49e181dc1771b9ff,256,V6,7c7368b846c0f9c02efa392d49e181dc1771b9ff_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_008_rx-89.816_ry+02.913_rz+28.731_tx+00.429_ty+01.181_s+00.700_7c7368b846c0f9c02efa392d49e181dc1771b9ff_256x256.png,86b13198dab64971420055a092ac20c110f92ea8,Chairs,-89.816,2.913,28.731,3919 -3920,_010,4.493,-34.576,-30.644,-0.149,1.026,1.26,9341a2292908ff174b67368d8374b276a87ce802,256,V6,9341a2292908ff174b67368d8374b276a87ce802_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+04.493_ry-34.576_rz-30.644_tx-00.149_ty+01.026_s+00.882_9341a2292908ff174b67368d8374b276a87ce802_256x256.png,b7153b385d82cea333a824dce814683da11e1c9f,Chairs,4.493,-34.576,-30.644,3920 -3921,_010,6.27,-33.145,-56.456,-0.063,0.024,0.881428571429,8a7df0f2f3ac5c6fcedbf4d015c1d59b183e8892,256,V6,8a7df0f2f3ac5c6fcedbf4d015c1d59b183e8892_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+06.270_ry-33.145_rz-56.456_tx-00.063_ty+00.024_s+00.617_8a7df0f2f3ac5c6fcedbf4d015c1d59b183e8892_256x256.png,94634b542b53cb5b333bd179efad2626dda9fcf1,Chairs,6.27,-33.145,-56.456,3921 -3922,_010,11.209,29.175,-2.336,0.555,0.203,0.981428571429,a677b19c0ed6c5d7aee4808d72ac51e8807f0bd7,256,V6,a677b19c0ed6c5d7aee4808d72ac51e8807f0bd7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+11.209_ry+29.175_rz-02.336_tx+00.555_ty+00.203_s+00.687_a677b19c0ed6c5d7aee4808d72ac51e8807f0bd7_256x256.png,3f3d28ffc95b4de627d5e27af448db3c86d94074,Chairs,11.209,29.175,-2.336,3922 -3923,_010,15.983,-45.684,52.596,0.387,0.949,0.838571428571,6e12708c273f976d805c614f215965a075669e09,256,V6,6e12708c273f976d805c614f215965a075669e09_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+15.983_ry-45.684_rz+52.596_tx+00.387_ty+00.949_s+00.587_6e12708c273f976d805c614f215965a075669e09_256x256.png,75de59ad4af04d0a003feac4a2a1b47be835a4b9,Chairs,15.983,-45.684,52.596,3923 -3924,_010,23.923,75.716,-22.37,-0.344,0.971,1.40857142857,5ea86b75bb6c7bf3dbfcd6a218e16a1b65b35836,256,V6,5ea86b75bb6c7bf3dbfcd6a218e16a1b65b35836_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+23.923_ry+75.716_rz-22.370_tx-00.344_ty+00.971_s+00.986_5ea86b75bb6c7bf3dbfcd6a218e16a1b65b35836_256x256.png,a57c662a46a1e89af911f1f899e87543a970e922,Chairs,23.923,75.716,-22.37,3924 -3925,_010,37.452,14.359,-69.07,-0.152,0.475,1.43,843e41e4a2d840432f5cee8ef3b911623289b6c4,256,V6,843e41e4a2d840432f5cee8ef3b911623289b6c4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+37.452_ry+14.359_rz-69.070_tx-00.152_ty+00.475_s+01.001_843e41e4a2d840432f5cee8ef3b911623289b6c4_256x256.png,782edb0ce84be65648104ae689a42b746de88966,Chairs,37.452,14.359,-69.07,3925 -3926,_010,39.752,-53.662,55.643,0.486,0.562,0.894285714286,9a3cfaeb73e9a445c62c8861079336143eac49b6,256,V6,9a3cfaeb73e9a445c62c8861079336143eac49b6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+39.752_ry-53.662_rz+55.643_tx+00.486_ty+00.562_s+00.626_9a3cfaeb73e9a445c62c8861079336143eac49b6_256x256.png,ac701808dcb937cc8d89d484a6c62561ce6f8f15,Chairs,39.752,-53.662,55.643,3926 -3927,_010,45.415,53.036,87.408,0.015,0.382,0.821428571429,3e7df394d2c5ce2471f40c0317f5ea48549938a7,256,V6,3e7df394d2c5ce2471f40c0317f5ea48549938a7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+45.415_ry+53.036_rz+87.408_tx+00.015_ty+00.382_s+00.575_3e7df394d2c5ce2471f40c0317f5ea48549938a7_256x256.png,e4b28c0f2c9915d38877bc30d70bd57587b0a418,Chairs,45.415,53.036,87.408,3927 -3928,_010,46.239,69.52,-76.159,-0.53,-0.677,1.12142857143,bfe3fe5a5473ce909be804068020921c09fc7303,256,V6,bfe3fe5a5473ce909be804068020921c09fc7303_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+46.239_ry+69.520_rz-76.159_tx-00.530_ty-00.677_s+00.785_bfe3fe5a5473ce909be804068020921c09fc7303_256x256.png,a9193944fc08f656b5140449504baba05aa252f7,Chairs,46.239,69.52,-76.159,3928 -3929,_010,46.587,-88.626,-85.525,-0.392,-0.933,1.44857142857,3ae0cba4a53088c32be968960a80062d47231b45,256,V6,3ae0cba4a53088c32be968960a80062d47231b45_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+46.587_ry-88.626_rz-85.525_tx-00.392_ty-00.933_s+01.014_3ae0cba4a53088c32be968960a80062d47231b45_256x256.png,2fd8921fe9de6a7b3b3d2f018b44fda1177264cf,Chairs,46.587,-88.626,-85.525,3929 -3930,_010,49.021,-2.935,-52.625,0.51,-0.423,1.41,9b87c8be1440ce9626bcfe656700ff3ac8f909fe,256,V6,9b87c8be1440ce9626bcfe656700ff3ac8f909fe_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+49.021_ry-02.935_rz-52.625_tx+00.510_ty-00.423_s+00.987_9b87c8be1440ce9626bcfe656700ff3ac8f909fe_256x256.png,30912859b0dfac9c6531061a796fb96f16510fa8,Chairs,49.021,-2.935,-52.625,3930 -3931,_010,52.571,-45.784,-47.037,0.592,1.032,0.718571428571,c6b784915aab9ad77c3f0bf7430ed20425e2b865,256,V6,c6b784915aab9ad77c3f0bf7430ed20425e2b865_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+52.571_ry-45.784_rz-47.037_tx+00.592_ty+01.032_s+00.503_c6b784915aab9ad77c3f0bf7430ed20425e2b865_256x256.png,548d346a6924415effafaf19d6f144bfc9d5c253,Chairs,52.571,-45.784,-47.037,3931 -3932,_010,58.222,75.892,77.285,0.367,0.381,1.51142857143,73c7832732b2a635cefc10e965e6443d557e0d64,256,V6,73c7832732b2a635cefc10e965e6443d557e0d64_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+58.222_ry+75.892_rz+77.285_tx+00.367_ty+00.381_s+01.058_73c7832732b2a635cefc10e965e6443d557e0d64_256x256.png,827e4a21b1082d404a84035a6fa6a29ede1ddc1d,Chairs,58.222,75.892,77.285,3932 -3933,_010,61.407,68.105,24.61,0.332,-0.942,1.27571428571,2bc3f4df16ae23ed9ad589d58ba9ae8b1eaf0c2f,256,V6,2bc3f4df16ae23ed9ad589d58ba9ae8b1eaf0c2f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+61.407_ry+68.105_rz+24.610_tx+00.332_ty-00.942_s+00.893_2bc3f4df16ae23ed9ad589d58ba9ae8b1eaf0c2f_256x256.png,a2d785cf7ddb01dcdc8b829af51e1d95d300838b,Chairs,61.407,68.105,24.61,3933 -3934,_010,71.984,-62.926,80.174,0.532,-1.058,1.08285714286,591c6a080a3571919fae6c05b6a4f2df9a43bcbb,256,V6,591c6a080a3571919fae6c05b6a4f2df9a43bcbb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+71.984_ry-62.926_rz+80.174_tx+00.532_ty-01.058_s+00.758_591c6a080a3571919fae6c05b6a4f2df9a43bcbb_256x256.png,9bb2c8a846f3fda52c07dc033a93a60d5dce583e,Chairs,71.984,-62.926,80.174,3934 -3935,_010,74.952,-44.27,-27.016,0.118,-0.737,0.822857142857,17f06a52bda8d950e2764a26b18c78255d348cd9,256,V6,17f06a52bda8d950e2764a26b18c78255d348cd9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+74.952_ry-44.270_rz-27.016_tx+00.118_ty-00.737_s+00.576_17f06a52bda8d950e2764a26b18c78255d348cd9_256x256.png,1e9862623b6199042e8c15278b5540e85048f382,Chairs,74.952,-44.27,-27.016,3935 -3936,_010,87.809,-70.426,-60.343,-0.029,0.702,1.01428571429,153e365271cf31dbd9c507ab8705614bec31881d,256,V6,153e365271cf31dbd9c507ab8705614bec31881d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+87.809_ry-70.426_rz-60.343_tx-00.029_ty+00.702_s+00.710_153e365271cf31dbd9c507ab8705614bec31881d_256x256.png,cada42a17e29779fb9e24e718f83100a750d4500,Chairs,87.809,-70.426,-60.343,3936 -3937,_010,88.52,2.028,83.867,0.063,0.703,1.12714285714,5a7e143b331fcc11f5690e085fbad3c053418418,256,V6,5a7e143b331fcc11f5690e085fbad3c053418418_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx+88.520_ry+02.028_rz+83.867_tx+00.063_ty+00.703_s+00.789_5a7e143b331fcc11f5690e085fbad3c053418418_256x256.png,7ebda1c46449978ccd4d889fc8e04f27b1fde3b9,Chairs,88.52,2.028,83.867,3937 -3938,_010,-2.684,36.939,74.01,-0.237,-1.068,1.20428571429,d9ede3319cb2aa9f1c7b572e11fa4ec993f1f944,256,V6,d9ede3319cb2aa9f1c7b572e11fa4ec993f1f944_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-02.684_ry+36.939_rz+74.010_tx-00.237_ty-01.068_s+00.843_d9ede3319cb2aa9f1c7b572e11fa4ec993f1f944_256x256.png,87ab60174fbd6fc9114719965f637cffc844cfd3,Chairs,-2.684,36.939,74.01,3938 -3939,_010,-7.728,37.631,-82.944,-0.504,-0.606,1.28,bb254bee596e7cf779009c6d5338aa1986c56b59,256,V6,bb254bee596e7cf779009c6d5338aa1986c56b59_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-07.728_ry+37.631_rz-82.944_tx-00.504_ty-00.606_s+00.896_bb254bee596e7cf779009c6d5338aa1986c56b59_256x256.png,f833a6f0eaca769eae82177d759b3b757d4d0a00,Chairs,-7.728,37.631,-82.944,3939 -3940,_010,-7.941,-13.397,-89.096,0.19,-0.39,0.677142857143,c871914ff0033664b448480c823058ffc61cf5d3,256,V6,c871914ff0033664b448480c823058ffc61cf5d3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-07.941_ry-13.397_rz-89.096_tx+00.190_ty-00.390_s+00.474_c871914ff0033664b448480c823058ffc61cf5d3_256x256.png,f2bb8b95215cdf1d6463ce2eb61f4920eb9bfa80,Chairs,-7.941,-13.397,-89.096,3940 -3941,_010,-8.068,69.315,-53.057,0.413,0.963,1.2,28395c765b7061a901bad3ce2b96abb1a27c10cf,256,V6,28395c765b7061a901bad3ce2b96abb1a27c10cf_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-08.068_ry+69.315_rz-53.057_tx+00.413_ty+00.963_s+00.840_28395c765b7061a901bad3ce2b96abb1a27c10cf_256x256.png,249a8d4110ba6b46226428341ea4d056f0b8aea9,Chairs,-8.068,69.315,-53.057,3941 -3942,_010,-11.77,-26.578,-13.828,-0.133,-0.636,0.757142857143,f18957057474d802552e6f0a6a0580f41b61e6dc,256,V6,f18957057474d802552e6f0a6a0580f41b61e6dc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-11.770_ry-26.578_rz-13.828_tx-00.133_ty-00.636_s+00.530_f18957057474d802552e6f0a6a0580f41b61e6dc_256x256.png,f6080ce3ce18ced41ad468647ed4131b681dce7f,Chairs,-11.77,-26.578,-13.828,3942 -3943,_010,-20.198,89.419,-37.885,0.518,-0.241,0.955714285714,01fd68c7194189fa61aad77894b6f931708c7d18,256,V6,01fd68c7194189fa61aad77894b6f931708c7d18_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-20.198_ry+89.419_rz-37.885_tx+00.518_ty-00.241_s+00.669_01fd68c7194189fa61aad77894b6f931708c7d18_256x256.png,551610cf9e53ae5641d7fda3c18e7df4e97f0f5e,Chairs,-20.198,89.419,-37.885,3943 -3944,_010,-27.614,63.724,-57.569,-0.025,0.819,0.977142857143,d97fbc54b35e756a570b29b864fab21b936b9e1a,256,V6,d97fbc54b35e756a570b29b864fab21b936b9e1a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-27.614_ry+63.724_rz-57.569_tx-00.025_ty+00.819_s+00.684_d97fbc54b35e756a570b29b864fab21b936b9e1a_256x256.png,2b307f42c9aa493c3b0b140758aac4fe75664715,Chairs,-27.614,63.724,-57.569,3944 -3945,_010,-31.403,77.817,31.09,0.494,-0.462,1.51,48c5378bb411809094cd0ca5aac622ec429aacef,256,V6,48c5378bb411809094cd0ca5aac622ec429aacef_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-31.403_ry+77.817_rz+31.090_tx+00.494_ty-00.462_s+01.057_48c5378bb411809094cd0ca5aac622ec429aacef_256x256.png,cbc2797871602ed0d87f0d1e14880f57bcf5b655,Chairs,-31.403,77.817,31.09,3945 -3946,_010,-38.761,-22.017,-14.381,-0.071,0.368,0.897142857143,3824c685d55d08b9cd4575386da2a0a232fbda79,256,V6,3824c685d55d08b9cd4575386da2a0a232fbda79_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-38.761_ry-22.017_rz-14.381_tx-00.071_ty+00.368_s+00.628_3824c685d55d08b9cd4575386da2a0a232fbda79_256x256.png,1a06b5a191c5e4b30244293aaec97533debae71b,Chairs,-38.761,-22.017,-14.381,3946 -3947,_010,-44.697,38.514,24.048,0.158,-0.343,1.37285714286,ac272b9ddbe6e1bc2c3ba20b6b3d4bd85674d871,256,V6,ac272b9ddbe6e1bc2c3ba20b6b3d4bd85674d871_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-44.697_ry+38.514_rz+24.048_tx+00.158_ty-00.343_s+00.961_ac272b9ddbe6e1bc2c3ba20b6b3d4bd85674d871_256x256.png,8cc447c60e18e2c21fb3db25734f217e56e02119,Chairs,-44.697,38.514,24.048,3947 -3948,_010,-46.646,-0.319,-62.338,0.408,-0.222,1.38285714286,b7b735fab626b70e53af606a70d75b0fd4cd5e7c,256,V6,b7b735fab626b70e53af606a70d75b0fd4cd5e7c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-46.646_ry-00.319_rz-62.338_tx+00.408_ty-00.222_s+00.968_b7b735fab626b70e53af606a70d75b0fd4cd5e7c_256x256.png,7ea3b85116738af3c46491091a21eac0addece88,Chairs,-46.646,-0.319,-62.338,3948 -3949,_010,-46.762,-43.336,-29.097,-0.447,0.074,1.58571428571,c54cb7f24890bbc2916c14fcd8c44d48324a4997,256,V6,c54cb7f24890bbc2916c14fcd8c44d48324a4997_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-46.762_ry-43.336_rz-29.097_tx-00.447_ty+00.074_s+01.110_c54cb7f24890bbc2916c14fcd8c44d48324a4997_256x256.png,ab95a960460df15a0deea8afb130519e408fd6c1,Chairs,-46.762,-43.336,-29.097,3949 -3950,_010,-52.65,-58.908,-84.631,-0.436,-0.043,0.951428571429,85f62e5e2cbd7e89760e8e0fc8d032ac749936d8,256,V6,85f62e5e2cbd7e89760e8e0fc8d032ac749936d8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-52.650_ry-58.908_rz-84.631_tx-00.436_ty-00.043_s+00.666_85f62e5e2cbd7e89760e8e0fc8d032ac749936d8_256x256.png,67ee1cb44a66ff08d7cf1697fa40d2fbf95ca1d0,Chairs,-52.65,-58.908,-84.631,3950 -3951,_010,-55.867,-75.986,23.826,0.292,0.911,0.727142857143,a030c963a1e7d301b38c28db5492155862735133,256,V6,a030c963a1e7d301b38c28db5492155862735133_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-55.867_ry-75.986_rz+23.826_tx+00.292_ty+00.911_s+00.509_a030c963a1e7d301b38c28db5492155862735133_256x256.png,de2d280383bee3b945ce3f05fb5f5594900ef424,Chairs,-55.867,-75.986,23.826,3951 -3952,_010,-60.885,-35.383,24.871,-0.089,0.632,0.721428571429,5139391218ee07bf361c4b0303d60775d7f5017f,256,V6,5139391218ee07bf361c4b0303d60775d7f5017f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-60.885_ry-35.383_rz+24.871_tx-00.089_ty+00.632_s+00.505_5139391218ee07bf361c4b0303d60775d7f5017f_256x256.png,2fa2539a9a05382c7ab4842412f5ba4e02aa89fa,Chairs,-60.885,-35.383,24.871,3952 -3953,_010,-61.517,-86.973,28.396,-0.378,-0.446,0.678571428571,55f0face2a2effcc9a40d63470c4e91c13821bb5,256,V6,55f0face2a2effcc9a40d63470c4e91c13821bb5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-61.517_ry-86.973_rz+28.396_tx-00.378_ty-00.446_s+00.475_55f0face2a2effcc9a40d63470c4e91c13821bb5_256x256.png,8199af178ccd0c14d6753988d366240f02aabc78,Chairs,-61.517,-86.973,28.396,3953 -3954,_010,-64.344,-0.493,32.388,0.463,0.576,1.06571428571,52314c5abb6879f841345919f47d38f2b0eeea16,256,V6,52314c5abb6879f841345919f47d38f2b0eeea16_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-64.344_ry-00.493_rz+32.388_tx+00.463_ty+00.576_s+00.746_52314c5abb6879f841345919f47d38f2b0eeea16_256x256.png,9785c466bfe200ea0f5d479db9f3377366635901,Chairs,-64.344,-0.493,32.388,3954 -3955,_010,-65.628,-32.624,61.157,-0.255,0.588,0.874285714286,984a5130b11c59c9c1ac9a37fdb9fed22bc10dd7,256,V6,984a5130b11c59c9c1ac9a37fdb9fed22bc10dd7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-65.628_ry-32.624_rz+61.157_tx-00.255_ty+00.588_s+00.612_984a5130b11c59c9c1ac9a37fdb9fed22bc10dd7_256x256.png,0ee288ca8612309036984b13e3c1955e264339bf,Chairs,-65.628,-32.624,61.157,3955 -3956,_010,-69.617,68.291,55.363,-0.167,-0.467,1.39857142857,ec907051436911435741000a7a11550708227f81,256,V6,ec907051436911435741000a7a11550708227f81_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-69.617_ry+68.291_rz+55.363_tx-00.167_ty-00.467_s+00.979_ec907051436911435741000a7a11550708227f81_256x256.png,e3246c71f8ae6a338e912beb4f5263315a9eb373,Chairs,-69.617,68.291,55.363,3956 -3957,_010,-72.175,85.709,-40.704,-0.591,0.156,0.722857142857,4c381cd8ab9f43ee9dbc89de210bc4f5a28eb8f0,256,V6,4c381cd8ab9f43ee9dbc89de210bc4f5a28eb8f0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-72.175_ry+85.709_rz-40.704_tx-00.591_ty+00.156_s+00.506_4c381cd8ab9f43ee9dbc89de210bc4f5a28eb8f0_256x256.png,62afaf616a2d1b597a38ae0b8cae24c8532a43ee,Chairs,-72.175,85.709,-40.704,3957 -3958,_010,-74.892,-60.234,10.704,0.306,-0.271,0.632857142857,f994a73fe3cfa7681fe1f95b5d555c562bade695,256,V6,f994a73fe3cfa7681fe1f95b5d555c562bade695_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-74.892_ry-60.234_rz+10.704_tx+00.306_ty-00.271_s+00.443_f994a73fe3cfa7681fe1f95b5d555c562bade695_256x256.png,fb62ae6554166571d202d381749e32907b0eb36e,Chairs,-74.892,-60.234,10.704,3958 -3959,_010,-78.988,49.052,60.08,0.513,-0.773,0.815714285714,1d7988aa4ff591d8792740008856cd12c371aa64,256,V6,1d7988aa4ff591d8792740008856cd12c371aa64_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_010_rx-78.988_ry+49.052_rz+60.080_tx+00.513_ty-00.773_s+00.571_1d7988aa4ff591d8792740008856cd12c371aa64_256x256.png,f85fc5166bd620c1a213d2f170e99064fe92c51b,Chairs,-78.988,49.052,60.08,3959 -3960,_011,2.892,-48.1,-82.209,0.459,-0.625,1.36142857143,5556b40175798dcc04a639dd5ca815c7c51aaaee,256,V6,5556b40175798dcc04a639dd5ca815c7c51aaaee_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+02.892_ry-48.100_rz-82.209_tx+00.459_ty-00.625_s+00.953_5556b40175798dcc04a639dd5ca815c7c51aaaee_256x256.png,1347a68b4d6bb72bf1f25116bba9a9df07ddd7f2,Chairs,2.892,-48.1,-82.209,3960 -3961,_011,11.643,-34.349,-85.565,-0.416,0.002,1.17428571429,af08d01e2773ef804926b5b1cf44fd19853f4fe7,256,V6,af08d01e2773ef804926b5b1cf44fd19853f4fe7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+11.643_ry-34.349_rz-85.565_tx-00.416_ty+00.002_s+00.822_af08d01e2773ef804926b5b1cf44fd19853f4fe7_256x256.png,e8c6925adfbb004d0424117bbd92531fc152d617,Chairs,11.643,-34.349,-85.565,3961 -3962,_011,15.89,-48.538,78.951,-0.426,1.077,1.52571428571,dbc0de757481c2949e6b05dc902ed0c39bb4c283,256,V6,dbc0de757481c2949e6b05dc902ed0c39bb4c283_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+15.890_ry-48.538_rz+78.951_tx-00.426_ty+01.077_s+01.068_dbc0de757481c2949e6b05dc902ed0c39bb4c283_256x256.png,1a6eb5da147b4eb5defc15efd958f68dee97226b,Chairs,15.89,-48.538,78.951,3962 -3963,_011,20.998,43.039,-50.164,-0.365,0.306,1.00714285714,d6d6aa8603d4a5abebc21e26969f043b19b6c54c,256,V6,d6d6aa8603d4a5abebc21e26969f043b19b6c54c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+20.998_ry+43.039_rz-50.164_tx-00.365_ty+00.306_s+00.705_d6d6aa8603d4a5abebc21e26969f043b19b6c54c_256x256.png,b7422f089a0534079a2073a4049ef823bc214c4e,Chairs,20.998,43.039,-50.164,3963 -3964,_011,24.177,89.291,40.635,0.398,0.689,1.08428571429,014077247eba147ee5cf6057124e019ce17f35eb,256,V6,014077247eba147ee5cf6057124e019ce17f35eb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+24.177_ry+89.291_rz+40.635_tx+00.398_ty+00.689_s+00.759_014077247eba147ee5cf6057124e019ce17f35eb_256x256.png,bd69b11285f01bbfb118afeaf5d2b1d228d7812d,Chairs,24.177,89.291,40.635,3964 -3965,_011,27.81,-57.737,76.742,-0.375,-0.821,0.92,5fce7ce47c2ab83d1b6e321964bb76eb0f974d77,256,V6,5fce7ce47c2ab83d1b6e321964bb76eb0f974d77_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+27.810_ry-57.737_rz+76.742_tx-00.375_ty-00.821_s+00.644_5fce7ce47c2ab83d1b6e321964bb76eb0f974d77_256x256.png,a55891c274d814c8bf604a794e44415caa1098d3,Chairs,27.81,-57.737,76.742,3965 -3966,_011,40.531,-15.208,40.407,0.466,0.87,1.14,783887c1bcb3cad36484715c2472689cc8939597,256,V6,783887c1bcb3cad36484715c2472689cc8939597_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+40.531_ry-15.208_rz+40.407_tx+00.466_ty+00.870_s+00.798_783887c1bcb3cad36484715c2472689cc8939597_256x256.png,7e5436c282b1c8aa04275b49806c3d44a43ede3e,Chairs,40.531,-15.208,40.407,3966 -3967,_011,43.03,8.328,-81.935,-0.488,-0.385,0.907142857143,9f84970bc3da0b9eb8ecc38b4f32a9df6ec31ffc,256,V6,9f84970bc3da0b9eb8ecc38b4f32a9df6ec31ffc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+43.030_ry+08.328_rz-81.935_tx-00.488_ty-00.385_s+00.635_9f84970bc3da0b9eb8ecc38b4f32a9df6ec31ffc_256x256.png,4ed9499607de2b608044cef32ac388dec6adcb8b,Chairs,43.03,8.328,-81.935,3967 -3968,_011,55.127,81.834,-28.387,0.402,-0.347,1.51285714286,0e4c35a81b1760d98f626c1aba6685d1630cdf0b,256,V6,0e4c35a81b1760d98f626c1aba6685d1630cdf0b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+55.127_ry+81.834_rz-28.387_tx+00.402_ty-00.347_s+01.059_0e4c35a81b1760d98f626c1aba6685d1630cdf0b_256x256.png,33431f07fcc821bb7752fb91a2771178dc046063,Chairs,55.127,81.834,-28.387,3968 -3969,_011,61.179,-82.414,88.217,-0.475,0.581,1.1,d1d6490c93034408ee748ab8105becf89c420f55,256,V6,d1d6490c93034408ee748ab8105becf89c420f55_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+61.179_ry-82.414_rz+88.217_tx-00.475_ty+00.581_s+00.770_d1d6490c93034408ee748ab8105becf89c420f55_256x256.png,53830ff234deec1d589cbd145f59970b982af530,Chairs,61.179,-82.414,88.217,3969 -3970,_011,64.069,3.808,70.428,-0.015,0.904,1.59571428571,aada038fd72a4c5a689105e2c6fa0f48bd3256d7,256,V6,aada038fd72a4c5a689105e2c6fa0f48bd3256d7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+64.069_ry+03.808_rz+70.428_tx-00.015_ty+00.904_s+01.117_aada038fd72a4c5a689105e2c6fa0f48bd3256d7_256x256.png,1d65f070d4f51027211d4ba10b0358f86d07ce32,Chairs,64.069,3.808,70.428,3970 -3971,_011,70.416,28.561,-16.931,-0.287,-0.214,1.11714285714,ef3a3c3263ee30ca957d7d963b613e620335d260,256,V6,ef3a3c3263ee30ca957d7d963b613e620335d260_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+70.416_ry+28.561_rz-16.931_tx-00.287_ty-00.214_s+00.782_ef3a3c3263ee30ca957d7d963b613e620335d260_256x256.png,fa4cedaf9585c9d9c2ac872ad6e31cd1db86be18,Chairs,70.416,28.561,-16.931,3971 -3972,_011,73.46,-74.805,-32.284,0.479,0.88,0.985714285714,5d9c857dc0064c15eaf8acf10da8fd5000443143,256,V6,5d9c857dc0064c15eaf8acf10da8fd5000443143_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+73.460_ry-74.805_rz-32.284_tx+00.479_ty+00.880_s+00.690_5d9c857dc0064c15eaf8acf10da8fd5000443143_256x256.png,8708c5ff2a40d824948eccc86ca9ab60534ff933,Chairs,73.46,-74.805,-32.284,3972 -3973,_011,75.872,64.248,-59.74,0.369,-0.433,1.14571428571,08063d12255795f6c3c274d9be9c6292529484c8,256,V6,08063d12255795f6c3c274d9be9c6292529484c8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+75.872_ry+64.248_rz-59.740_tx+00.369_ty-00.433_s+00.802_08063d12255795f6c3c274d9be9c6292529484c8_256x256.png,396e6d51d53460719adbd2c47cdaff2df89875e6,Chairs,75.872,64.248,-59.74,3973 -3974,_011,77.726,54.947,-6.385,-0.586,-0.558,1.39,34264eb6b645834e00c2dd39e6877820a42f83da,256,V6,34264eb6b645834e00c2dd39e6877820a42f83da_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+77.726_ry+54.947_rz-06.385_tx-00.586_ty-00.558_s+00.973_34264eb6b645834e00c2dd39e6877820a42f83da_256x256.png,0e26d7680c30e678c7037d60d5f436c643b9c2b1,Chairs,77.726,54.947,-6.385,3974 -3975,_011,86.345,36.11,-48.546,-0.162,0.191,1.11428571429,9ebe76d6e35a63bf87ce066fb6fe641974bb21cb,256,V6,9ebe76d6e35a63bf87ce066fb6fe641974bb21cb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+86.345_ry+36.110_rz-48.546_tx-00.162_ty+00.191_s+00.780_9ebe76d6e35a63bf87ce066fb6fe641974bb21cb_256x256.png,e3f971cb43eb6fd123cb1ea2c841b39df737993d,Chairs,86.345,36.11,-48.546,3975 -3976,_011,89.13,38.058,-25.863,0.378,-0.603,1.25,4d1f2d68c420c00ff90fd1483aa35d798d6a2188,256,V6,4d1f2d68c420c00ff90fd1483aa35d798d6a2188_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx+89.130_ry+38.058_rz-25.863_tx+00.378_ty-00.603_s+00.875_4d1f2d68c420c00ff90fd1483aa35d798d6a2188_256x256.png,f05429f36fece0c237456c00cc81242f352924dc,Chairs,89.13,38.058,-25.863,3976 -3977,_011,-0.059,-48.737,-80.511,-0.463,-1.199,1.32,e09ddf452c4cf4f28d45ff712a7ce2bc75edb8ab,256,V6,e09ddf452c4cf4f28d45ff712a7ce2bc75edb8ab_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-00.059_ry-48.737_rz-80.511_tx-00.463_ty-01.199_s+00.924_e09ddf452c4cf4f28d45ff712a7ce2bc75edb8ab_256x256.png,6ce3f474118d9ac09ee06bece54a19ddbe82cf3d,Chairs,-0.059,-48.737,-80.511,3977 -3978,_011,-5.86,38.881,-81.823,-0.419,-0.555,1.19142857143,b22cf753200d4cafd2ca783d9ac5a0305f0ef09d,256,V6,b22cf753200d4cafd2ca783d9ac5a0305f0ef09d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-05.860_ry+38.881_rz-81.823_tx-00.419_ty-00.555_s+00.834_b22cf753200d4cafd2ca783d9ac5a0305f0ef09d_256x256.png,fd8742b84d00073b5f26ab06d17ca475b4f67a25,Chairs,-5.86,38.881,-81.823,3978 -3979,_011,-6.688,-13.098,10.292,-0.249,0.98,1.50142857143,6a15beab557d65b763ecaa578f2ff6a58787014f,256,V6,6a15beab557d65b763ecaa578f2ff6a58787014f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-06.688_ry-13.098_rz+10.292_tx-00.249_ty+00.980_s+01.051_6a15beab557d65b763ecaa578f2ff6a58787014f_256x256.png,180ee8042003d789209740195912af7ada382733,Chairs,-6.688,-13.098,10.292,3979 -3980,_011,-6.943,6.973,74.025,-0.257,-0.951,1.59857142857,fcf0ba510fbe030768fa4aff073e36f8fb2dfdb1,256,V6,fcf0ba510fbe030768fa4aff073e36f8fb2dfdb1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-06.943_ry+06.973_rz+74.025_tx-00.257_ty-00.951_s+01.119_fcf0ba510fbe030768fa4aff073e36f8fb2dfdb1_256x256.png,f4c61e731ffe5adc47da8c764bdabdd4c7e85c61,Chairs,-6.943,6.973,74.025,3980 -3981,_011,-8.243,-34.091,69.451,-0.211,-1.101,1.08714285714,479d6ca9a605dd54c7427c3e2a8011f316cca670,256,V6,479d6ca9a605dd54c7427c3e2a8011f316cca670_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-08.243_ry-34.091_rz+69.451_tx-00.211_ty-01.101_s+00.761_479d6ca9a605dd54c7427c3e2a8011f316cca670_256x256.png,7bac5a6c0eec792e08d1577a9648020d243bd6db,Chairs,-8.243,-34.091,69.451,3981 -3982,_011,-8.809,-79.949,4.611,0.238,0.977,1.27142857143,a961044778179051bfd4b47ac1236fd9d8964964,256,V6,a961044778179051bfd4b47ac1236fd9d8964964_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-08.809_ry-79.949_rz+04.611_tx+00.238_ty+00.977_s+00.890_a961044778179051bfd4b47ac1236fd9d8964964_256x256.png,0ff16d865bd72c3e37188e121d91d39a53a57e4d,Chairs,-8.809,-79.949,4.611,3982 -3983,_011,-18.359,68.059,-55.729,0.383,0.048,1.21857142857,6480c32626969bfc8d45ba531e885e483d45a857,256,V6,6480c32626969bfc8d45ba531e885e483d45a857_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-18.359_ry+68.059_rz-55.729_tx+00.383_ty+00.048_s+00.853_6480c32626969bfc8d45ba531e885e483d45a857_256x256.png,f39bb0c54396f6a203827c872460445c6705b1ab,Chairs,-18.359,68.059,-55.729,3983 -3984,_011,-18.378,65.898,37.147,0.193,0.7,0.91,a3484ba1b9df0b1aaa31e734698a4bd9b5e3d2b2,256,V6,a3484ba1b9df0b1aaa31e734698a4bd9b5e3d2b2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-18.378_ry+65.898_rz+37.147_tx+00.193_ty+00.700_s+00.637_a3484ba1b9df0b1aaa31e734698a4bd9b5e3d2b2_256x256.png,dbc0b1f72c73548a2279792599fc8913c7728725,Chairs,-18.378,65.898,37.147,3984 -3985,_011,-18.405,-50.873,-49.786,-0.08,0.314,1.43714285714,a3f86d2e3c9eea1373c750634dc5b52caf3821c7,256,V6,a3f86d2e3c9eea1373c750634dc5b52caf3821c7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-18.405_ry-50.873_rz-49.786_tx-00.080_ty+00.314_s+01.006_a3f86d2e3c9eea1373c750634dc5b52caf3821c7_256x256.png,85702b3bc102abf6d6bf6bd135225f3c3f4b08ba,Chairs,-18.405,-50.873,-49.786,3985 -3986,_011,-24.543,60.202,61.555,-0.143,0.784,1.36714285714,aa2f3cb1876be06a7c4de609cc2f9f91e995972a,256,V6,aa2f3cb1876be06a7c4de609cc2f9f91e995972a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-24.543_ry+60.202_rz+61.555_tx-00.143_ty+00.784_s+00.957_aa2f3cb1876be06a7c4de609cc2f9f91e995972a_256x256.png,4094ab3df3e21e0a85511e6f4fde833a8618d70f,Chairs,-24.543,60.202,61.555,3986 -3987,_011,-26.188,25.896,-16.019,0.421,-0.295,1.46714285714,998862e6e8937e797dfd7c44c84018f8f194b6c8,256,V6,998862e6e8937e797dfd7c44c84018f8f194b6c8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-26.188_ry+25.896_rz-16.019_tx+00.421_ty-00.295_s+01.027_998862e6e8937e797dfd7c44c84018f8f194b6c8_256x256.png,82c85f4fe5174d3bc81c67d70782aaae74e50e58,Chairs,-26.188,25.896,-16.019,3987 -3988,_011,-34.114,-69.741,55.391,0.295,-0.019,0.842857142857,10b0bff2f9b7f0a853edac7ed9b5e2d737da033a,256,V6,10b0bff2f9b7f0a853edac7ed9b5e2d737da033a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-34.114_ry-69.741_rz+55.391_tx+00.295_ty-00.019_s+00.590_10b0bff2f9b7f0a853edac7ed9b5e2d737da033a_256x256.png,c56ec4890705f275d84accbbb86d01c3daf0b43f,Chairs,-34.114,-69.741,55.391,3988 -3989,_011,-35.972,-16.341,-26.538,0.558,0.226,1.03571428571,7e01a01a162a55fb7d4ae78b93122b830ce64d82,256,V6,7e01a01a162a55fb7d4ae78b93122b830ce64d82_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-35.972_ry-16.341_rz-26.538_tx+00.558_ty+00.226_s+00.725_7e01a01a162a55fb7d4ae78b93122b830ce64d82_256x256.png,3fed57bfe650f262170a3878dcc4c69d535d39be,Chairs,-35.972,-16.341,-26.538,3989 -3990,_011,-37.432,77.337,-42.844,0.437,0.186,0.892857142857,57d76749e0080bcfb2cd3e4f6a34efe0f1e12434,256,V6,57d76749e0080bcfb2cd3e4f6a34efe0f1e12434_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-37.432_ry+77.337_rz-42.844_tx+00.437_ty+00.186_s+00.625_57d76749e0080bcfb2cd3e4f6a34efe0f1e12434_256x256.png,639bd4880e40ca653c2cc545dfe8dc775442d5ad,Chairs,-37.432,77.337,-42.844,3990 -3991,_011,-44.415,59.99,8.12,0.295,-0.531,0.838571428571,f42b6ca9edb4b8012ab6ab852552a338ff5f4fdb,256,V6,f42b6ca9edb4b8012ab6ab852552a338ff5f4fdb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-44.415_ry+59.990_rz+08.120_tx+00.295_ty-00.531_s+00.587_f42b6ca9edb4b8012ab6ab852552a338ff5f4fdb_256x256.png,e7d1ce66ed45edb00d703256c67de63a63c20a7d,Chairs,-44.415,59.99,8.12,3991 -3992,_011,-47.086,-2.786,-52.173,-0.383,0.466,1.06,faa8871298c63faeb4093c7a5a6bc94906b3a5cc,256,V6,faa8871298c63faeb4093c7a5a6bc94906b3a5cc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-47.086_ry-02.786_rz-52.173_tx-00.383_ty+00.466_s+00.742_faa8871298c63faeb4093c7a5a6bc94906b3a5cc_256x256.png,0f3d0abc5d2ccf0495b88e8e79a31d206e21864f,Chairs,-47.086,-2.786,-52.173,3992 -3993,_011,-52.723,16.367,32.907,-0.471,-0.449,1.13428571429,4389a1a8b3fd270676ffa3faef7f18aa70db0d38,256,V6,4389a1a8b3fd270676ffa3faef7f18aa70db0d38_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-52.723_ry+16.367_rz+32.907_tx-00.471_ty-00.449_s+00.794_4389a1a8b3fd270676ffa3faef7f18aa70db0d38_256x256.png,74e224ad537e1a574ce733204e09409c719afa8f,Chairs,-52.723,16.367,32.907,3993 -3994,_011,-62.274,-68.197,-55.853,-0.098,-0.695,0.755714285714,d52caad6335b8a1d67e980581d92b41298ec57dd,256,V6,d52caad6335b8a1d67e980581d92b41298ec57dd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-62.274_ry-68.197_rz-55.853_tx-00.098_ty-00.695_s+00.529_d52caad6335b8a1d67e980581d92b41298ec57dd_256x256.png,eb410de373053fd05d846aa7efa132d4a6d5ca68,Chairs,-62.274,-68.197,-55.853,3994 -3995,_011,-65.533,58.952,-62.979,-0.241,-0.813,1.53571428571,a4dbb88bbdcb9603e76f0def6d536e735616416d,256,V6,a4dbb88bbdcb9603e76f0def6d536e735616416d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-65.533_ry+58.952_rz-62.979_tx-00.241_ty-00.813_s+01.075_a4dbb88bbdcb9603e76f0def6d536e735616416d_256x256.png,3c966d59f59938d51b570c19c0268b37757bdf15,Chairs,-65.533,58.952,-62.979,3995 -3996,_011,-72.436,-27.063,-30.574,0.352,1.09,1.35428571429,d47691b862f9293007d166c163be5367dbbe67a5,256,V6,d47691b862f9293007d166c163be5367dbbe67a5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-72.436_ry-27.063_rz-30.574_tx+00.352_ty+01.090_s+00.948_d47691b862f9293007d166c163be5367dbbe67a5_256x256.png,bc8cae033c34f104099009b59c65cc108c7c4bab,Chairs,-72.436,-27.063,-30.574,3996 -3997,_011,-82.419,-86.027,32.099,0.093,0.627,0.625714285714,1fc55405696701b7673cee0b3649c7cc974ffb53,256,V6,1fc55405696701b7673cee0b3649c7cc974ffb53_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-82.419_ry-86.027_rz+32.099_tx+00.093_ty+00.627_s+00.438_1fc55405696701b7673cee0b3649c7cc974ffb53_256x256.png,7cb4cdce1ce4645654a18f7b1076d395d390ce0d,Chairs,-82.419,-86.027,32.099,3997 -3998,_011,-84.658,-46.598,-89.286,-0.502,-0.867,1.15,38e5121e132969904299e4402683b15670b1d1c5,256,V6,38e5121e132969904299e4402683b15670b1d1c5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-84.658_ry-46.598_rz-89.286_tx-00.502_ty-00.867_s+00.805_38e5121e132969904299e4402683b15670b1d1c5_256x256.png,941cc915eadb7a136604a6d838930d112b296cf1,Chairs,-84.658,-46.598,-89.286,3998 -3999,_011,-86.937,-32.869,70.373,-0.429,0.255,0.702857142857,b90b0d95ce4488673ecc01645a329d4040eafc8b,256,V6,b90b0d95ce4488673ecc01645a329d4040eafc8b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_011_rx-86.937_ry-32.869_rz+70.373_tx-00.429_ty+00.255_s+00.492_b90b0d95ce4488673ecc01645a329d4040eafc8b_256x256.png,f6857c4d2555e0d018906f0c372d9b709d337b4c,Chairs,-86.937,-32.869,70.373,3999 -4000,_014,1.271,-66.334,-5.459,0.164,-0.589,1.11857142857,f4e6250cd8956947601b528757a2371400ff8aa4,256,V6,f4e6250cd8956947601b528757a2371400ff8aa4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+01.271_ry-66.334_rz-05.459_tx+00.164_ty-00.589_s+00.783_f4e6250cd8956947601b528757a2371400ff8aa4_256x256.png,d5f39247c1ef32785b395adca559ae0b1b352699,Chairs,1.271,-66.334,-5.459,4000 -4001,_014,10.216,-56.954,-13.442,-0.13,1.166,1.29857142857,d5de7de1725da1e5e04b39e52bced08929b46278,256,V6,d5de7de1725da1e5e04b39e52bced08929b46278_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+10.216_ry-56.954_rz-13.442_tx-00.130_ty+01.166_s+00.909_d5de7de1725da1e5e04b39e52bced08929b46278_256x256.png,88090445137dc1a2333aaf227c96d4814267f237,Chairs,10.216,-56.954,-13.442,4001 -4002,_014,12.357,86.512,58.212,0.448,0.318,0.911428571429,57090e5c70627cf1e59291c21cf4ed9997f55cbc,256,V6,57090e5c70627cf1e59291c21cf4ed9997f55cbc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+12.357_ry+86.512_rz+58.212_tx+00.448_ty+00.318_s+00.638_57090e5c70627cf1e59291c21cf4ed9997f55cbc_256x256.png,de3409e02120e54b8fe8da816c354f507dc84202,Chairs,12.357,86.512,58.212,4002 -4003,_014,14.745,19.484,-50.458,0.217,-0.409,1.15,cec4adba55aa783a9b1614f2bdf1e5e1e60060fa,256,V6,cec4adba55aa783a9b1614f2bdf1e5e1e60060fa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+14.745_ry+19.484_rz-50.458_tx+00.217_ty-00.409_s+00.805_cec4adba55aa783a9b1614f2bdf1e5e1e60060fa_256x256.png,d5e3b8804c75d595ad596f9dc3374ed636e69733,Chairs,14.745,19.484,-50.458,4003 -4004,_014,16.09,-46.84,24.07,-0.16,-0.693,0.752857142857,828850cf3e0695ec1d4d32219904cf5e199210e2,256,V6,828850cf3e0695ec1d4d32219904cf5e199210e2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+16.090_ry-46.840_rz+24.070_tx-00.160_ty-00.693_s+00.527_828850cf3e0695ec1d4d32219904cf5e199210e2_256x256.png,88f954276bb53915429cc2d117e57c08bc8aaddd,Chairs,16.09,-46.84,24.07,4004 -4005,_014,17.728,69.732,49.317,0.251,0.602,1.28857142857,b88b90a6f92c2235f4dd7ee79e853df1391691e3,256,V6,b88b90a6f92c2235f4dd7ee79e853df1391691e3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+17.728_ry+69.732_rz+49.317_tx+00.251_ty+00.602_s+00.902_b88b90a6f92c2235f4dd7ee79e853df1391691e3_256x256.png,565fbf41a0293ec36b650609a96e05a5c1384aba,Chairs,17.728,69.732,49.317,4005 -4006,_014,21.459,15.925,-36.074,-0.063,-1.159,0.8,07a427f0c07a0370862346cc7b8260a790575512,256,V6,07a427f0c07a0370862346cc7b8260a790575512_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+21.459_ry+15.925_rz-36.074_tx-00.063_ty-01.159_s+00.560_07a427f0c07a0370862346cc7b8260a790575512_256x256.png,ca009db8462a1cb2bf69e100b3825ba0c76cfaed,Chairs,21.459,15.925,-36.074,4006 -4007,_014,29.624,87.904,-54.037,0.125,0.291,0.818571428571,94b21d737d4d6cb1450575bc23e7551b4db9125e,256,V6,94b21d737d4d6cb1450575bc23e7551b4db9125e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+29.624_ry+87.904_rz-54.037_tx+00.125_ty+00.291_s+00.573_94b21d737d4d6cb1450575bc23e7551b4db9125e_256x256.png,929b380c347dc621c9d4842e7901d96fc0783397,Chairs,29.624,87.904,-54.037,4007 -4008,_014,31.124,85.007,29.648,0.033,-0.482,0.85,209eccace5961458326adc3f0c181b2302e53036,256,V6,209eccace5961458326adc3f0c181b2302e53036_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+31.124_ry+85.007_rz+29.648_tx+00.033_ty-00.482_s+00.595_209eccace5961458326adc3f0c181b2302e53036_256x256.png,6f403175d220798e685245c8358e8dc465dfa4d3,Chairs,31.124,85.007,29.648,4008 -4009,_014,36.747,25.56,-50.478,0.534,-0.451,1.59428571429,508a48a7b1a8609d45395bd999f34e57849d051f,256,V6,508a48a7b1a8609d45395bd999f34e57849d051f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+36.747_ry+25.560_rz-50.478_tx+00.534_ty-00.451_s+01.116_508a48a7b1a8609d45395bd999f34e57849d051f_256x256.png,56153a6b2644c13840fb09a3546a727c29c227b7,Chairs,36.747,25.56,-50.478,4009 -4010,_014,45.486,-15.896,87.574,-0.288,-0.012,1.21857142857,617596208742e78eddef69666420f950e969dba3,256,V6,617596208742e78eddef69666420f950e969dba3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+45.486_ry-15.896_rz+87.574_tx-00.288_ty-00.012_s+00.853_617596208742e78eddef69666420f950e969dba3_256x256.png,342890d336cd3445c5d1cc25d42bf2b66fc83b9d,Chairs,45.486,-15.896,87.574,4010 -4011,_014,50.943,53.013,-42.832,-0.066,-0.851,1.27571428571,9dd9fda0a86507ca5d518c25c03a69a983ec646b,256,V6,9dd9fda0a86507ca5d518c25c03a69a983ec646b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+50.943_ry+53.013_rz-42.832_tx-00.066_ty-00.851_s+00.893_9dd9fda0a86507ca5d518c25c03a69a983ec646b_256x256.png,942c9f3ae8005ce3db80f8c1601be5c215f8128f,Chairs,50.943,53.013,-42.832,4011 -4012,_014,58.44,24.028,6.408,0.555,-1.065,1.31285714286,2364179623282c3b7452fb956059e6ebd72c58ae,256,V6,2364179623282c3b7452fb956059e6ebd72c58ae_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+58.440_ry+24.028_rz+06.408_tx+00.555_ty-01.065_s+00.919_2364179623282c3b7452fb956059e6ebd72c58ae_256x256.png,7fba51bf636520f983a0c151726c7123a51d695d,Chairs,58.44,24.028,6.408,4012 -4013,_014,58.9,17.125,22.66,0.524,1.1,1.00571428571,7140c8f6f54e06e53be5fe1f29255ffd1ddd461c,256,V6,7140c8f6f54e06e53be5fe1f29255ffd1ddd461c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+58.900_ry+17.125_rz+22.660_tx+00.524_ty+01.100_s+00.704_7140c8f6f54e06e53be5fe1f29255ffd1ddd461c_256x256.png,ecf3cbdc6cbb4e3dce7449a2740bb1c5b90318d8,Chairs,58.9,17.125,22.66,4013 -4014,_014,62.98,80.791,-6.888,-0.475,-0.373,1.24285714286,adf17c198e188df39cd901c3072172f3efb3ad1e,256,V6,adf17c198e188df39cd901c3072172f3efb3ad1e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+62.980_ry+80.791_rz-06.888_tx-00.475_ty-00.373_s+00.870_adf17c198e188df39cd901c3072172f3efb3ad1e_256x256.png,55008769bc6adce60ce025445d090fa69ee879f0,Chairs,62.98,80.791,-6.888,4014 -4015,_014,65.202,70.383,17.603,-0.121,-0.59,1.05142857143,46db653aed2c80ea0345d5b92c20fc1fdf3585ad,256,V6,46db653aed2c80ea0345d5b92c20fc1fdf3585ad_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+65.202_ry+70.383_rz+17.603_tx-00.121_ty-00.590_s+00.736_46db653aed2c80ea0345d5b92c20fc1fdf3585ad_256x256.png,72100d40e140a93f39b1a3e3d922eb5b3e9371d5,Chairs,65.202,70.383,17.603,4015 -4016,_014,71.089,-52.317,-30.101,0.475,0.893,1.43428571429,2d3f73d7ee9961ba84507c77a494a0ad5ac81fd7,256,V6,2d3f73d7ee9961ba84507c77a494a0ad5ac81fd7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+71.089_ry-52.317_rz-30.101_tx+00.475_ty+00.893_s+01.004_2d3f73d7ee9961ba84507c77a494a0ad5ac81fd7_256x256.png,b62f38b4109be68c0eec95bad0db576ec24fa798,Chairs,71.089,-52.317,-30.101,4016 -4017,_014,76.444,-6.49,9.374,0.596,-0.174,1.33142857143,8a0448c07c7c6a23612aa99d3dc3311af4115a15,256,V6,8a0448c07c7c6a23612aa99d3dc3311af4115a15_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+76.444_ry-06.490_rz+09.374_tx+00.596_ty-00.174_s+00.932_8a0448c07c7c6a23612aa99d3dc3311af4115a15_256x256.png,af7dae2326fa20fe414952e0596e4d49f7343cee,Chairs,76.444,-6.49,9.374,4017 -4018,_014,83.948,-14.6,84.417,0.098,-0.229,1.23,7329e2fcadc3863a3c1f2c059b0d123eb37f9a80,256,V6,7329e2fcadc3863a3c1f2c059b0d123eb37f9a80_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+83.948_ry-14.600_rz+84.417_tx+00.098_ty-00.229_s+00.861_7329e2fcadc3863a3c1f2c059b0d123eb37f9a80_256x256.png,8799691753baae72dfd027a74e15534a5dd72315,Chairs,83.948,-14.6,84.417,4018 -4019,_014,88.989,5.186,89.047,0.408,0.95,1.50428571429,0b8589430de8ab38eb5cccf88dd46654495d215d,256,V6,0b8589430de8ab38eb5cccf88dd46654495d215d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx+88.989_ry+05.186_rz+89.047_tx+00.408_ty+00.950_s+01.053_0b8589430de8ab38eb5cccf88dd46654495d215d_256x256.png,a2a241732931bd68079423f9c42d197b1c87535e,Chairs,88.989,5.186,89.047,4019 -4020,_014,-0.967,72.972,28.791,0.016,-0.303,1.27,3ddf0000247914568e3ea1507399720294042f8f,256,V6,3ddf0000247914568e3ea1507399720294042f8f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-00.967_ry+72.972_rz+28.791_tx+00.016_ty-00.303_s+00.889_3ddf0000247914568e3ea1507399720294042f8f_256x256.png,14ba193967a4c03ee8e6cc04e103d0c6558fe9a1,Chairs,-0.967,72.972,28.791,4020 -4021,_014,-5.114,-13.987,-79.641,0.123,-1.116,0.718571428571,44b47517b90fa5af0e6e8ed08c59145d60fbb569,256,V6,44b47517b90fa5af0e6e8ed08c59145d60fbb569_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-05.114_ry-13.987_rz-79.641_tx+00.123_ty-01.116_s+00.503_44b47517b90fa5af0e6e8ed08c59145d60fbb569_256x256.png,7cc4b79ceb897d149e9edfd04d0d916d357dcb39,Chairs,-5.114,-13.987,-79.641,4021 -4022,_014,-7.912,25.491,4.886,0.23,-0.777,0.81,53166050a15303e71e1a7a3280a451c2d2f8b432,256,V6,53166050a15303e71e1a7a3280a451c2d2f8b432_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-07.912_ry+25.491_rz+04.886_tx+00.230_ty-00.777_s+00.567_53166050a15303e71e1a7a3280a451c2d2f8b432_256x256.png,c88a08d6c4db593edf1e21113f2b94be84503c06,Chairs,-7.912,25.491,4.886,4022 -4023,_014,-10.068,44.131,48.127,0.142,0.886,1.37142857143,bd3ac7240c79c2d9fc42f136b0cda0c086521934,256,V6,bd3ac7240c79c2d9fc42f136b0cda0c086521934_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-10.068_ry+44.131_rz+48.127_tx+00.142_ty+00.886_s+00.960_bd3ac7240c79c2d9fc42f136b0cda0c086521934_256x256.png,b95b6e80442a88998cfddd0443fb664889a77a8e,Chairs,-10.068,44.131,48.127,4023 -4024,_014,-14.681,-34.358,-71.138,-0.234,0.26,0.701428571429,3760302b482c688550a3a3e87ec4d50b9c99855d,256,V6,3760302b482c688550a3a3e87ec4d50b9c99855d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-14.681_ry-34.358_rz-71.138_tx-00.234_ty+00.260_s+00.491_3760302b482c688550a3a3e87ec4d50b9c99855d_256x256.png,9c5d697a54c9068749ee32fd98a5096877716fdd,Chairs,-14.681,-34.358,-71.138,4024 -4025,_014,-28.3,83.489,-55.316,-0.379,-0.151,0.681428571429,b70fd2906724c3574568604add8c87ebbc0471e7,256,V6,b70fd2906724c3574568604add8c87ebbc0471e7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-28.300_ry+83.489_rz-55.316_tx-00.379_ty-00.151_s+00.477_b70fd2906724c3574568604add8c87ebbc0471e7_256x256.png,f472417252541839261d31844c7add1bc1120a4c,Chairs,-28.3,83.489,-55.316,4025 -4026,_014,-29.062,-0.861,89.909,0.262,-0.308,0.935714285714,19fd0bc486eca84622f435ffbf0fa91f48f6ea01,256,V6,19fd0bc486eca84622f435ffbf0fa91f48f6ea01_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-29.062_ry-00.861_rz+89.909_tx+00.262_ty-00.308_s+00.655_19fd0bc486eca84622f435ffbf0fa91f48f6ea01_256x256.png,1d98268faaeb41e3d59c3455050d7e8ab8361267,Chairs,-29.062,-0.861,89.909,4026 -4027,_014,-34.494,7.134,-64.979,0.277,-0.225,1.02142857143,59f6debfb1184b73f28cec213a542bb4323623f8,256,V6,59f6debfb1184b73f28cec213a542bb4323623f8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-34.494_ry+07.134_rz-64.979_tx+00.277_ty-00.225_s+00.715_59f6debfb1184b73f28cec213a542bb4323623f8_256x256.png,c910ef8f1ad9c365a47100b7fa4cf8e86540c4e8,Chairs,-34.494,7.134,-64.979,4027 -4028,_014,-36.938,-88.87,41.951,0.435,0.246,0.898571428571,e491cb04a27389fb36b2431f392dcd815bbcbbe8,256,V6,e491cb04a27389fb36b2431f392dcd815bbcbbe8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-36.938_ry-88.870_rz+41.951_tx+00.435_ty+00.246_s+00.629_e491cb04a27389fb36b2431f392dcd815bbcbbe8_256x256.png,38728472c1e549f86ae2e4c3e121b539b3e391c0,Chairs,-36.938,-88.87,41.951,4028 -4029,_014,-38.903,-17.364,-7.443,0.373,1.019,1.58571428571,90dafc4a303b2f572493e5b4bf1e733ee7f8ae81,256,V6,90dafc4a303b2f572493e5b4bf1e733ee7f8ae81_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-38.903_ry-17.364_rz-07.443_tx+00.373_ty+01.019_s+01.110_90dafc4a303b2f572493e5b4bf1e733ee7f8ae81_256x256.png,ab0266ff99772ac7e2683eea19ef6e90285e741c,Chairs,-38.903,-17.364,-7.443,4029 -4030,_014,-43.782,-3.319,28.375,-0.46,-0.712,1.02142857143,7b1a3ba0745f647b5e7f89a357b17ae78f8dd03a,256,V6,7b1a3ba0745f647b5e7f89a357b17ae78f8dd03a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-43.782_ry-03.319_rz+28.375_tx-00.460_ty-00.712_s+00.715_7b1a3ba0745f647b5e7f89a357b17ae78f8dd03a_256x256.png,4500f4c2400dc434458c77bc449a31794be577fb,Chairs,-43.782,-3.319,28.375,4030 -4031,_014,-51.915,71.897,-19.396,-0.193,-0.499,1.58,7ff1f88083a8a4e1e3c83af91eaa5d40185ef334,256,V6,7ff1f88083a8a4e1e3c83af91eaa5d40185ef334_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-51.915_ry+71.897_rz-19.396_tx-00.193_ty-00.499_s+01.106_7ff1f88083a8a4e1e3c83af91eaa5d40185ef334_256x256.png,404fccc6f01c221472c7e2cecd878dac901dc887,Chairs,-51.915,71.897,-19.396,4031 -4032,_014,-53.746,32.205,-57.061,0.362,-0.48,1.56285714286,93fbae3137e57bba69db955d5788c80042ee66b3,256,V6,93fbae3137e57bba69db955d5788c80042ee66b3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-53.746_ry+32.205_rz-57.061_tx+00.362_ty-00.480_s+01.094_93fbae3137e57bba69db955d5788c80042ee66b3_256x256.png,06be67528b17e5e57588833d598819f4d3b5269b,Chairs,-53.746,32.205,-57.061,4032 -4033,_014,-54.956,-41.394,58.566,-0.104,-0.536,1.12714285714,005d0b8f14a40581365d2237d6317dbe47ea2318,256,V6,005d0b8f14a40581365d2237d6317dbe47ea2318_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-54.956_ry-41.394_rz+58.566_tx-00.104_ty-00.536_s+00.789_005d0b8f14a40581365d2237d6317dbe47ea2318_256x256.png,f3addaa1de264dcd5b58afe2f3be80e06be785ad,Chairs,-54.956,-41.394,58.566,4033 -4034,_014,-59.473,-51.043,-0.946,-0.038,1.108,0.971428571429,190b7bae335a1a5c3f606418bceb4100d5fd27da,256,V6,190b7bae335a1a5c3f606418bceb4100d5fd27da_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-59.473_ry-51.043_rz-00.946_tx-00.038_ty+01.108_s+00.680_190b7bae335a1a5c3f606418bceb4100d5fd27da_256x256.png,f1453b1f7b823b1a64bd54c1fe6dc85942fba2e1,Chairs,-59.473,-51.043,-0.946,4034 -4035,_014,-60.972,-40.131,-38.522,0.398,-0.055,0.805714285714,1eb88ea68d56ff47747f281c5a4ccadc6f15b91e,256,V6,1eb88ea68d56ff47747f281c5a4ccadc6f15b91e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-60.972_ry-40.131_rz-38.522_tx+00.398_ty-00.055_s+00.564_1eb88ea68d56ff47747f281c5a4ccadc6f15b91e_256x256.png,09fd0162e22b8d3f6ff093f02706bf3bca19dc35,Chairs,-60.972,-40.131,-38.522,4035 -4036,_014,-68.26,13.414,42.674,-0.518,-0.916,1.56714285714,98feb010e212ddfa87bf30308786ff32bbcbc499,256,V6,98feb010e212ddfa87bf30308786ff32bbcbc499_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-68.260_ry+13.414_rz+42.674_tx-00.518_ty-00.916_s+01.097_98feb010e212ddfa87bf30308786ff32bbcbc499_256x256.png,8395675b3b373e3bf88312bea07db925b754d4b6,Chairs,-68.26,13.414,42.674,4036 -4037,_014,-78.781,-68.277,26.907,-0.17,-1.099,0.907142857143,a2a0c06737c71115bd7e2fe25bc5280309f6cd72,256,V6,a2a0c06737c71115bd7e2fe25bc5280309f6cd72_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-78.781_ry-68.277_rz+26.907_tx-00.170_ty-01.099_s+00.635_a2a0c06737c71115bd7e2fe25bc5280309f6cd72_256x256.png,abbea54b5bec5f213b129547cc797f22ecd7e9aa,Chairs,-78.781,-68.277,26.907,4037 -4038,_014,-80.724,0.452,-6.539,-0.345,0.636,0.821428571429,c7d0863311389f3bfe651364f90c85791ff38723,256,V6,c7d0863311389f3bfe651364f90c85791ff38723_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-80.724_ry+00.452_rz-06.539_tx-00.345_ty+00.636_s+00.575_c7d0863311389f3bfe651364f90c85791ff38723_256x256.png,1ee2d2a5f34d5bbcefb4cce0a27576eef5d38088,Chairs,-80.724,0.452,-6.539,4038 -4039,_014,-89.141,39.752,-33.362,0.394,-0.686,1.09,ee049b077a033480ee52187c6bb6865741749d0f,256,V6,ee049b077a033480ee52187c6bb6865741749d0f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_014_rx-89.141_ry+39.752_rz-33.362_tx+00.394_ty-00.686_s+00.763_ee049b077a033480ee52187c6bb6865741749d0f_256x256.png,e060eb82513fc6a6a5cb861930232cf2d9d4b6eb,Chairs,-89.141,39.752,-33.362,4039 -4040,_01_Airliner_2jetEngines,87.6953999243,84.2504186231,-127.171795644,0.215,1.055,0.946153846154,13fe29c7453060e8b04d84e88823145542e0e9f6,256,V6,13fe29c7453060e8b04d84e88823145542e0e9f6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+02.293_ry+00.231_rz+47.074_tx+00.215_ty+01.055_s+01.230_13fe29c7453060e8b04d84e88823145542e0e9f6_256x256.png,4c248ecf906f9f5b380b44f5d20ee86484ccbfd0,Planes,87.6953999243,174.250418623,-127.171795644,4040 -4041,_01_Airliner_2jetEngines,-61.294791717,11.8302996353,-22.1896878571,-0.359,0.218,0.86,d6704f6497c1425331170fd5d837af93ebfbb465,256,V6,d6704f6497c1425331170fd5d837af93ebfbb465_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+05.651_ry+151.810_rz+57.400_tx-00.359_ty+00.218_s+01.118_d6704f6497c1425331170fd5d837af93ebfbb465_256x256.png,d7759747a5472711d3729395ebd9bde7d599b496,Planes,-61.294791717,101.830299635,-22.1896878571,4041 -4042,_01_Airliner_2jetEngines,-52.3596075103,12.2513497824,-5.20845448626,-0.376,0.307,1.29307692308,2a2f3aff2e634fdfa8936f8c7e97dc9875e39f93,256,V6,2a2f3aff2e634fdfa8936f8c7e97dc9875e39f93_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+07.446_ry+142.996_rz+75.035_tx-00.376_ty+00.307_s+01.681_2a2f3aff2e634fdfa8936f8c7e97dc9875e39f93_256x256.png,18615dec903788b06aac869cecfbfa7c18a43363,Planes,-52.3596075103,102.251349782,-5.20845448626,4042 -4043,_01_Airliner_2jetEngines,38.7060975683,17.6063598191,-12.3630401221,0.388,0.403,0.669230769231,2060476af66d0d9797ee6ede49f3841d0ab6cd51,256,V6,2060476af66d0d9797ee6ede49f3841d0ab6cd51_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+13.653_ry+49.946_rz+88.861_tx+00.388_ty+00.403_s+00.870_2060476af66d0d9797ee6ede49f3841d0ab6cd51_256x256.png,1d727b35d697985004b4815bcf07246a617502d3,Planes,38.7060975683,107.606359819,-12.3630401221,4043 -4044,_01_Airliner_2jetEngines,-73.7970699121,79.1039150889,88.8756602232,0.372,1.075,1.24692307692,9a898777f383ed71a5b2a2e1589c98bd73e5b478,256,V6,9a898777f383ed71a5b2a2e1589c98bd73e5b478_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+15.903_ry+176.856_rz+100.211_tx+00.372_ty+01.075_s+01.621_9a898777f383ed71a5b2a2e1589c98bd73e5b478_256x256.png,678cc210f2f9ef5f2f08629a52a9cb534bb06e05,Planes,-73.7970699121,169.103915089,88.8756602232,4044 -4045,_01_Airliner_2jetEngines,-38.5215870972,32.5565432248,15.8036487747,0.483,-0.348,0.684615384615,146ea4e74791fa1834c8da80892487c533dabf32,256,V6,146ea4e74791fa1834c8da80892487c533dabf32_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+24.899_ry+133.364_rz+84.119_tx+00.483_ty-00.348_s+00.890_146ea4e74791fa1834c8da80892487c533dabf32_256x256.png,d9491babfbcc6ff1d442e13c5df82222e3242cae,Planes,-38.5215870972,122.556543225,15.8036487747,4045 -4046,_01_Airliner_2jetEngines,-64.129825074,82.5204054898,54.9873939679,0.382,-0.347,0.718461538462,486e2d0cc6cbb9878012e8eac4782906874a64b2,256,V6,486e2d0cc6cbb9878012e8eac4782906874a64b2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+25.634_ry+176.388_rz+63.289_tx+00.382_ty-00.347_s+00.934_486e2d0cc6cbb9878012e8eac4782906874a64b2_256x256.png,666033fdfe744879276af6e7bf3739ca317faa25,Planes,-64.129825074,172.52040549,54.9873939679,4046 -4047,_01_Airliner_2jetEngines,-6.18516322475,28.9936629093,89.630887908,0.023,-0.719,1.27615384615,c3b9662c21cee7572c9bb99f1fd4d3b4b947337a,256,V6,c3b9662c21cee7572c9bb99f1fd4d3b4b947337a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+28.809_ry+97.063_rz+176.214_tx+00.023_ty-00.719_s+01.659_c3b9662c21cee7572c9bb99f1fd4d3b4b947337a_256x256.png,5294e20ff618c9b4ce2eeb8e7984140a9ec4d77c,Planes,-6.18516322475,118.993662909,89.630887908,4047 -4048,_01_Airliner_2jetEngines,46.0945186572,47.188533822,-76.5206532989,-0.003,-0.35,1.37538461538,8cc97eab29fae3376bdd179b9c114dc3a09a4bb2,256,V6,8cc97eab29fae3376bdd179b9c114dc3a09a4bb2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+30.579_ry+33.189_rz+51.353_tx-00.003_ty-00.350_s+01.788_8cc97eab29fae3376bdd179b9c114dc3a09a4bb2_256x256.png,3252d075080995a4df5eceb53af79041c645813d,Planes,46.0945186572,137.188533822,-76.5206532989,4048 -4049,_01_Airliner_2jetEngines,5.01268184635,37.429532746,-11.5069910324,0.462,-0.05,1.29692307692,386b2b0f6c4bfc51042723680027e5ff442f4618,256,V6,386b2b0f6c4bfc51042723680027e5ff442f4618_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+37.262_ry+83.697_rz+82.319_tx+00.462_ty-00.050_s+01.686_386b2b0f6c4bfc51042723680027e5ff442f4618_256x256.png,93d76f81e8c81c1a74d6f56d2dfdbac89029a393,Planes,5.01268184635,127.429532746,-11.5069910324,4049 -4050,_01_Airliner_2jetEngines,-3.86059810208,49.5070293634,18.5315535914,0.009,-0.564,0.641538461538,5a38b191f3aaa9592b8909777441e9dab3913997,256,V6,5a38b191f3aaa9592b8909777441e9dab3913997_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+49.355_ry+95.933_rz+104.023_tx+00.009_ty-00.564_s+00.834_5a38b191f3aaa9592b8909777441e9dab3913997_256x256.png,5cbd24a0ac1881551f356940236e021bd29cd1de,Planes,-3.86059810208,139.507029363,18.5315535914,4050 -4051,_01_Airliner_2jetEngines,-38.9713497181,78.1627054041,80.8884462637,0.058,-0.945,1.34692307692,2212fa2dc1f5057314a2d248de91db43e5d9dfc0,256,V6,2212fa2dc1f5057314a2d248de91db43e5d9dfc0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+49.546_ry+165.771_rz+99.319_tx+00.058_ty-00.945_s+01.751_2212fa2dc1f5057314a2d248de91db43e5d9dfc0_256x256.png,3af855b189938ea3db4ce94b21b32985982f3c19,Planes,-38.9713497181,168.162705404,80.8884462637,4051 -4052,_01_Airliner_2jetEngines,-38.7240652382,85.3070621832,55.4910417881,-0.456,0.858,1.16769230769,80599584dc82abc64653dc6b0fa5bce03d752773,256,V6,80599584dc82abc64653dc6b0fa5bce03d752773_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+51.037_ry+174.174_rz+62.967_tx-00.456_ty+00.858_s+01.518_80599584dc82abc64653dc6b0fa5bce03d752773_256x256.png,d49843717fbcbeea804a489c0fe5c63d4aeb8ae7,Planes,-38.7240652382,175.307062183,55.4910417881,4052 -4053,_01_Airliner_2jetEngines,26.5745945265,66.755350475,-56.8244588201,0.513,-0.075,1.52076923077,f4745bd37cbe2e4fff592defa97b28322e26058a,256,V6,f4745bd37cbe2e4fff592defa97b28322e26058a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+55.261_ry+38.273_rz+79.341_tx+00.513_ty-00.075_s+01.977_f4745bd37cbe2e4fff592defa97b28322e26058a_256x256.png,3b8525941978948a85ccdafecf93e35acc831107,Planes,26.5745945265,156.755350475,-56.8244588201,4053 -4054,_01_Airliner_2jetEngines,26.934287386,70.8143653191,33.4343154919,-0.455,-0.769,1.12076923077,8c2c85cb66e55c51cbeca3a165da3f7bdf525d14,256,V6,8c2c85cb66e55c51cbeca3a165da3f7bdf525d14_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+57.353_ry+32.895_rz+175.904_tx-00.455_ty-00.769_s+01.457_8c2c85cb66e55c51cbeca3a165da3f7bdf525d14_256x256.png,1f7c7e4a743a185d9a843abea79b11dbbd0f338e,Planes,26.934287386,160.814365319,33.4343154919,4054 -4055,_01_Airliner_2jetEngines,-31.2934877625,82.0542798716,-1.27076670479,-0.327,-0.639,1.55307692308,8641ab193959b85bf24675427eb4122501feefe1,256,V6,8641ab193959b85bf24675427eb4122501feefe1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+57.813_ry+167.188_rz+13.770_tx-00.327_ty-00.639_s+02.019_8641ab193959b85bf24675427eb4122501feefe1_256x256.png,41e122515c2d9a16faa2f0ad9aee8fa714b7f117,Planes,-31.2934877625,172.054279872,-1.27076670479,4055 -4056,_01_Airliner_2jetEngines,4.66994893316,70.8009638692,25.4792473454,-0.373,1.008,0.885384615385,8ede55e43e19893d0d40cf7b45275407972dac24,256,V6,8ede55e43e19893d0d40cf7b45275407972dac24_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+70.262_ry+76.050_rz+128.639_tx-00.373_ty+01.008_s+01.151_8ede55e43e19893d0d40cf7b45275407972dac24_256x256.png,cabfe78151ff9d60a4e8ad41c5371d959b042257,Planes,4.66994893316,160.800963869,25.4792473454,4056 -4057,_01_Airliner_2jetEngines,-10.9076617895,80.3756132437,32.9949566793,0.309,-0.706,1.43,adb1c0b4433df6619626334cf3ffcd16ffe2b8b8,256,V6,adb1c0b4433df6619626334cf3ffcd16ffe2b8b8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+75.492_ry+139.056_rz+74.860_tx+00.309_ty-00.706_s+01.859_adb1c0b4433df6619626334cf3ffcd16ffe2b8b8_256x256.png,084a75d5597eea15c0c8dad2fbf781cdb7a3ac63,Planes,-10.9076617895,170.375613244,32.9949566793,4057 -4058,_01_Airliner_2jetEngines,10.5843397086,83.5686956699,30.6173484919,-0.372,0.684,1.16230769231,d8e5b94b720023a6bdc29d639ff6badb540d5538,256,V6,d8e5b94b720023a6bdc29d639ff6badb540d5538_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+77.634_ry+30.940_rz+179.081_tx-00.372_ty+00.684_s+01.511_d8e5b94b720023a6bdc29d639ff6badb540d5538_256x256.png,de494b867faeb13422f13e0d52ef58237b39efa0,Planes,10.5843397086,173.56869567,30.6173484919,4058 -4059,_01_Airliner_2jetEngines,10.5807963249,87.7927621617,-111.552588747,-0.21,0.296,1.24538461538,b6c8c4ee1d09a7f8cc59e0eb1b133dd7e77c579c,256,V6,b6c8c4ee1d09a7f8cc59e0eb1b133dd7e77c579c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+79.194_ry+11.650_rz+56.593_tx-00.210_ty+00.296_s+01.619_b6c8c4ee1d09a7f8cc59e0eb1b133dd7e77c579c_256x256.png,80ae38c9efe4bfbbbcff5e6b64840dca949bf497,Planes,10.5807963249,177.792762162,-111.552588747,4059 -4060,_01_Airliner_2jetEngines,5.5168415397,89.5272213827,-85.0262210404,0.547,-1.094,0.891538461538,79fe02df10dcfbc3054c1ce1573f96e588a4673d,256,V6,79fe02df10dcfbc3054c1ce1573f96e588a4673d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+84.463_ry+04.883_rz+90.068_tx+00.547_ty-01.094_s+01.159_79fe02df10dcfbc3054c1ce1573f96e588a4673d_256x256.png,44be8a22b75734388641865b372aa7b550bcb892,Planes,5.5168415397,179.527221383,-85.0262210404,4060 -4061,_01_Airliner_2jetEngines,-4.91284469513,88.6071791721,16.9232465192,-0.166,0.531,1.37307692308,198337893910f74e20f251a36ae6bb7ca7bed099,256,V6,198337893910f74e20f251a36ae6bb7ca7bed099_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+84.894_ry+164.210_rz+32.773_tx-00.166_ty+00.531_s+01.785_198337893910f74e20f251a36ae6bb7ca7bed099_256x256.png,6f4850f9f667d2b6d23654f0e05918c95cfc1394,Planes,-4.91284469513,178.607179172,16.9232465192,4061 -4062,_01_Airliner_2jetEngines,2.59088013009,85.9092748245,-5.437454895,0.325,0.857,1.53153846154,2cbb2711d6c66b03dea66f6b9c4729d9fb8122c4,256,V6,2cbb2711d6c66b03dea66f6b9c4729d9fb8122c4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx+85.159_ry+57.612_rz+116.858_tx+00.325_ty+00.857_s+01.991_2cbb2711d6c66b03dea66f6b9c4729d9fb8122c4_256x256.png,1e46c45989a3374fb467ffe71d918220342dd822,Planes,2.59088013009,175.909274825,-5.437454895,4062 -4063,_01_Airliner_2jetEngines,-33.6421261783,-18.0871468158,-46.4810666974,-0.026,-1.003,1.34076923077,987cbd5f20914fe67b786813107a9bf1f7872b93,256,V6,987cbd5f20914fe67b786813107a9bf1f7872b93_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-14.979_ry+124.994_rz+53.775_tx-00.026_ty-01.003_s+01.743_987cbd5f20914fe67b786813107a9bf1f7872b93_256x256.png,d94ac391056872e35dfedada61968043883eec4f,Planes,-33.6421261783,71.9128531842,-46.4810666974,4063 -4064,_01_Airliner_2jetEngines,37.0220176232,-23.0226609358,-0.535832897356,-0.581,0.435,1.34615384615,56ac555179d3870e63bbef15d52e86f7a13f2ef0,256,V6,56ac555179d3870e63bbef15d52e86f7a13f2ef0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-18.195_ry+50.668_rz+75.112_tx-00.581_ty+00.435_s+01.750_56ac555179d3870e63bbef15d52e86f7a13f2ef0_256x256.png,b5b88d5dcf9eaa99660f7f4f1b576a726eaa7ac9,Planes,37.0220176232,66.9773390642,-0.535832897356,4064 -4065,_01_Airliner_2jetEngines,20.2985487091,-27.336699804,66.8231233599,-0.441,0.445,1.51153846154,82e68f170721208ecb2a401afa1d9b83474b9ddf,256,V6,82e68f170721208ecb2a401afa1d9b83474b9ddf_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-25.512_ry+67.394_rz+146.656_tx-00.441_ty+00.445_s+01.965_82e68f170721208ecb2a401afa1d9b83474b9ddf_256x256.png,33d75ebcdf6db8e59921814613a5fd6a05f29ebd,Planes,20.2985487091,62.663300196,66.8231233599,4065 -4066,_01_Airliner_2jetEngines,-26.003383581,-37.2210929493,-82.1856441256,0.535,1.079,0.843846153846,571e9ab908ff9f27b0240f9ec49be83b7173ed59,256,V6,571e9ab908ff9f27b0240f9ec49be83b7173ed59_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-32.933_ry+121.491_rz+26.234_tx+00.535_ty+01.079_s+01.097_571e9ab908ff9f27b0240f9ec49be83b7173ed59_256x256.png,9f91c1d481693a9cf44787e0327c473f2a304d3f,Planes,-26.003383581,52.7789070507,-82.1856441256,4066 -4067,_01_Airliner_2jetEngines,-20.1602911036,-46.2015069318,-95.4381584001,-0.212,-0.367,0.661538461538,7366e9df2d5c61b12740e0fc4525c10ee1be5d23,256,V6,7366e9df2d5c61b12740e0fc4525c10ee1be5d23_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-42.653_ry+117.944_rz+14.331_tx-00.212_ty-00.367_s+00.860_7366e9df2d5c61b12740e0fc4525c10ee1be5d23_256x256.png,8d522d871e23b9dc8d2e5976641f9359b3836f69,Planes,-20.1602911036,43.7984930682,-95.4381584001,4067 -4068,_01_Airliner_2jetEngines,-38.5237896532,-67.7180769564,-106.737387814,0.04,-0.572,0.82,c77d47615657c3dc3fc109d9774085128fbb6a78,256,V6,c77d47615657c3dc3fc109d9774085128fbb6a78_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-46.380_ry+154.533_rz+39.922_tx+00.040_ty-00.572_s+01.066_c77d47615657c3dc3fc109d9774085128fbb6a78_256x256.png,8bf32ffdfdc23c26b415923e867c8c8b9f674b0f,Planes,-38.5237896532,22.2819230436,-106.737387814,4068 -4069,_01_Airliner_2jetEngines,3.98371495569,-48.3654980022,15.6238337954,-0.136,0.545,1.33538461538,c4bd44f73d8f0ff2e0351cdc357495b52a97967a,256,V6,c4bd44f73d8f0ff2e0351cdc357495b52a97967a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-48.210_ry+84.016_rz+101.155_tx-00.136_ty+00.545_s+01.736_c4bd44f73d8f0ff2e0351cdc357495b52a97967a_256x256.png,cd91f9841685f57459f9a9885fee89212d9b8af8,Planes,3.98371495569,41.6345019978,15.6238337954,4069 -4070,_01_Airliner_2jetEngines,-22.9530751133,-56.0857194693,43.9067738895,-0.347,1.184,1.56538461538,077327646362dd27e99eb2b9ea276366a157351c,256,V6,077327646362dd27e99eb2b9ea276366a157351c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-49.833_ry+127.200_rz+164.022_tx-00.347_ty+01.184_s+02.035_077327646362dd27e99eb2b9ea276366a157351c_256x256.png,4c400fa0972eaff398cb84f0253922736634f8df,Planes,-22.9530751133,33.9142805307,43.9067738895,4070 -4071,_01_Airliner_2jetEngines,16.477049941,-60.4853261895,70.5056538458,0.404,1.057,0.862307692308,b010c7e836b1dabe9ddbd79b9c805726071ab5ec,256,V6,b010c7e836b1dabe9ddbd79b9c805726071ab5ec_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-56.563_ry+59.020_rz+133.894_tx+00.404_ty+01.057_s+01.121_b010c7e836b1dabe9ddbd79b9c805726071ab5ec_256x256.png,e27200a0b9b1218fc80ccbe71af5b9f538a08ddf,Planes,16.477049941,29.5146738105,70.5056538458,4071 -4072,_01_Airliner_2jetEngines,15.2472199912,-60.3463708633,12.4045849334,0.578,-0.092,0.791538461538,9747c8c0870dae81f4938d244454304de417da2f,256,V6,9747c8c0870dae81f4938d244454304de417da2f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-56.976_ry+61.148_rz+77.611_tx+00.578_ty-00.092_s+01.029_9747c8c0870dae81f4938d244454304de417da2f_256x256.png,7779b0d4eaf9d0aa929277fece80a8f20a16ea65,Planes,15.2472199912,29.6536291367,12.4045849334,4072 -4073,_01_Airliner_2jetEngines,13.1134675358,-60.0161442619,37.4979398809,0.273,0.783,0.638461538462,fcb25ffde5a5f1da54e6c7f308a66fea075eb452,256,V6,fcb25ffde5a5f1da54e6c7f308a66fea075eb452_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-57.520_ry+65.008_rz+106.032_tx+00.273_ty+00.783_s+00.830_fcb25ffde5a5f1da54e6c7f308a66fea075eb452_256x256.png,f3fbeab3ad871fdd3db3053c186e11d5bd18bfd8,Planes,13.1134675358,29.9838557381,37.4979398809,4073 -4074,_01_Airliner_2jetEngines,-16.323688196,-69.9902735017,-112.20724493,-0.422,-1.112,1.36384615385,ab59127a99b7189f7e0fde984d6da9ed2f2489bf,256,V6,ab59127a99b7189f7e0fde984d6da9ed2f2489bf_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-64.390_ry+130.560_rz+15.454_tx-00.422_ty-01.112_s+01.773_ab59127a99b7189f7e0fde984d6da9ed2f2489bf_256x256.png,38d8496f6c466a530b4cae367e05621a659ddcff,Planes,-16.323688196,20.0097264983,-112.20724493,4074 -4075,_01_Airliner_2jetEngines,21.7478888486,-78.8445619431,-20.9324812333,0.016,0.12,0.755384615385,a82d8f37130cf2a50a5253a24273b65f65906e18,256,V6,a82d8f37130cf2a50a5253a24273b65f65906e18_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-65.682_ry+25.873_rz+07.090_tx+00.016_ty+00.120_s+00.982_a82d8f37130cf2a50a5253a24273b65f65906e18_256x256.png,98215ea9b1d576edb00279cc346324d3c2a5171a,Planes,21.7478888486,11.1554380569,-20.9324812333,4075 -4076,_01_Airliner_2jetEngines,-17.80743841,-84.5991785411,15.1227168935,0.079,0.202,0.823846153846,7621f9c4bf13db6a09363239b8d1ef5828368bc4,256,V6,7621f9c4bf13db6a09363239b8d1ef5828368bc4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-71.417_ry+163.668_rz+177.944_tx+00.079_ty+00.202_s+01.071_7621f9c4bf13db6a09363239b8d1ef5828368bc4_256x256.png,45873440c5f3e2fff48302ec8d33409170d99019,Planes,-17.80743841,5.4008214589,15.1227168935,4076 -4077,_01_Airliner_2jetEngines,-0.735309234175,-82.8877148314,-54.7243026873,0.233,1.1,0.722307692308,e17e08b0f4a81fbb484de4bcfa5cdf428c0ff3b3,256,V6,e17e08b0f4a81fbb484de4bcfa5cdf428c0ff3b3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-82.850_ry+95.918_rz+41.148_tx+00.233_ty+01.100_s+00.939_e17e08b0f4a81fbb484de4bcfa5cdf428c0ff3b3_256x256.png,d63dfe021f2361f4894a486c43aa29bd2ee661ad,Planes,-0.735309234175,7.11228516861,-54.7243026873,4077 -4078,_01_Airliner_2jetEngines,2.06326446585,-83.5832348213,-56.5246696626,-0.032,-0.381,1.40538461538,b079ee9293969300d4052d2541fb4cd6ba2de869,256,V6,b079ee9293969300d4052d2541fb4cd6ba2de869_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-83.261_ry+72.133_rz+15.724_tx-00.032_ty-00.381_s+01.827_b079ee9293969300d4052d2541fb4cd6ba2de869_256x256.png,15f9dd14ddd2b1f8b89da843263b0f9470d688ff,Planes,2.06326446585,6.41676517868,-56.5246696626,4078 -4079,_01_Airliner_2jetEngines,0.567308995582,-88.3953041848,76.9050550403,-0.121,0.468,1.40461538462,f25cf233df13a5addd332cfbac13c57e8c480347,256,V6,f25cf233df13a5addd332cfbac13c57e8c480347_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_01_Airliner_2jetEngines_rx-88.298_ry+70.527_rz+147.440_tx-00.121_ty+00.468_s+01.826_f25cf233df13a5addd332cfbac13c57e8c480347_256x256.png,597413379482008d9dc489be118ba9475da036d3,Planes,0.567308995582,1.60469581519,76.9050550403,4079 -4080,_031,6.599,-42.917,-81.305,-0.143,-0.707,0.857142857143,b99d354629bec3043d5959cc4cf68b7564d785b2,256,V6,b99d354629bec3043d5959cc4cf68b7564d785b2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+06.599_ry-42.917_rz-81.305_tx-00.143_ty-00.707_s+00.600_b99d354629bec3043d5959cc4cf68b7564d785b2_256x256.png,b9b3e0fe3222ccafd3684ed0a5c9090b2287f3dc,Chairs,6.599,-42.917,-81.305,4080 -4081,_031,15.661,-85.722,43.318,-0.168,0.119,1.22285714286,676270114cdd5cd76c077d7a6a3b207a40b8ea33,256,V6,676270114cdd5cd76c077d7a6a3b207a40b8ea33_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+15.661_ry-85.722_rz+43.318_tx-00.168_ty+00.119_s+00.856_676270114cdd5cd76c077d7a6a3b207a40b8ea33_256x256.png,7551ba48562dc698529968701428dbd8c2267169,Chairs,15.661,-85.722,43.318,4081 -4082,_031,16.851,-77.982,22.283,-0.394,-1.082,1.45285714286,7ee14e601158be8296ba23f915b2e8d558d3447a,256,V6,7ee14e601158be8296ba23f915b2e8d558d3447a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+16.851_ry-77.982_rz+22.283_tx-00.394_ty-01.082_s+01.017_7ee14e601158be8296ba23f915b2e8d558d3447a_256x256.png,4a4808302316db80140a1beb037010b98e6250e1,Chairs,16.851,-77.982,22.283,4082 -4083,_031,19.899,-67.37,-7.421,-0.173,0.091,0.65,5d2941435564b889572131651b9c50a09d9df31a,256,V6,5d2941435564b889572131651b9c50a09d9df31a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+19.899_ry-67.370_rz-07.421_tx-00.173_ty+00.091_s+00.455_5d2941435564b889572131651b9c50a09d9df31a_256x256.png,a437801e8bebbc8c66e1ee43fa626b93b9d5c14c,Chairs,19.899,-67.37,-7.421,4083 -4084,_031,20.265,-52.897,35.888,-0.517,-0.394,1.57428571429,bcfddc4e84b790e519292f2dc2418aac9eae5dbd,256,V6,bcfddc4e84b790e519292f2dc2418aac9eae5dbd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+20.265_ry-52.897_rz+35.888_tx-00.517_ty-00.394_s+01.102_bcfddc4e84b790e519292f2dc2418aac9eae5dbd_256x256.png,67dca6828c13d87527a5cf115a86f4a642d18fcc,Chairs,20.265,-52.897,35.888,4084 -4085,_031,20.944,-9.104,-74.054,0.119,-1.037,0.7,cbb3ff688d3341d30e99e8c078cdc6cdfa26b38c,256,V6,cbb3ff688d3341d30e99e8c078cdc6cdfa26b38c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+20.944_ry-09.104_rz-74.054_tx+00.119_ty-01.037_s+00.490_cbb3ff688d3341d30e99e8c078cdc6cdfa26b38c_256x256.png,1bcf59a9e5d4f5d71cb64ac8cf060e4e8458421e,Chairs,20.944,-9.104,-74.054,4085 -4086,_031,32.536,-79.103,-45.752,-0.522,0.471,1.51714285714,0bfac703a6d95f21dc1fde68d25255003de843fd,256,V6,0bfac703a6d95f21dc1fde68d25255003de843fd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+32.536_ry-79.103_rz-45.752_tx-00.522_ty+00.471_s+01.062_0bfac703a6d95f21dc1fde68d25255003de843fd_256x256.png,b66cb4a60faadfc4a75f9b03ebd271b61890f66c,Chairs,32.536,-79.103,-45.752,4086 -4087,_031,36.563,-30.952,37.499,0.022,-0.807,0.978571428571,86b3399964c93339c5f7531317f6fbd590b908de,256,V6,86b3399964c93339c5f7531317f6fbd590b908de_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+36.563_ry-30.952_rz+37.499_tx+00.022_ty-00.807_s+00.685_86b3399964c93339c5f7531317f6fbd590b908de_256x256.png,f010c2e129e40a8a3b2efa6438de726f00e4fb34,Chairs,36.563,-30.952,37.499,4087 -4088,_031,39.168,64.686,-13.439,-0.599,0.222,1.02285714286,21aecb18b64c47d33c714073000096a3490674c4,256,V6,21aecb18b64c47d33c714073000096a3490674c4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+39.168_ry+64.686_rz-13.439_tx-00.599_ty+00.222_s+00.716_21aecb18b64c47d33c714073000096a3490674c4_256x256.png,f09f38dab27874b853165e5441c91963cd6c0c0c,Chairs,39.168,64.686,-13.439,4088 -4089,_031,40.141,-76.791,54.422,-0.07,-0.025,1.44571428571,6d4edcf292c1a9122661f1979806bff6e61ea996,256,V6,6d4edcf292c1a9122661f1979806bff6e61ea996_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+40.141_ry-76.791_rz+54.422_tx-00.070_ty-00.025_s+01.012_6d4edcf292c1a9122661f1979806bff6e61ea996_256x256.png,da6d7d2555fb0731c10ea1e74ccb8468fef98ee3,Chairs,40.141,-76.791,54.422,4089 -4090,_031,40.765,-76.477,-17.999,-0.029,0.821,0.824285714286,57686e39359c38773f96c12594bca0301ae48475,256,V6,57686e39359c38773f96c12594bca0301ae48475_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+40.765_ry-76.477_rz-17.999_tx-00.029_ty+00.821_s+00.577_57686e39359c38773f96c12594bca0301ae48475_256x256.png,3f8d8a157ab8d73fabf1a1f4bc882a39bd7b8390,Chairs,40.765,-76.477,-17.999,4090 -4091,_031,41.489,49.556,-38.759,-0.475,0.592,0.632857142857,b2f1bc07bbbfcc512417602baf81212a7ee6a83d,256,V6,b2f1bc07bbbfcc512417602baf81212a7ee6a83d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+41.489_ry+49.556_rz-38.759_tx-00.475_ty+00.592_s+00.443_b2f1bc07bbbfcc512417602baf81212a7ee6a83d_256x256.png,5430832f6295870805b2199e6b4c14bde57fd4f2,Chairs,41.489,49.556,-38.759,4091 -4092,_031,42.312,-9.691,12.131,0.422,1.034,0.938571428571,76500afa627165a856198bacaa673ef403cfa614,256,V6,76500afa627165a856198bacaa673ef403cfa614_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+42.312_ry-09.691_rz+12.131_tx+00.422_ty+01.034_s+00.657_76500afa627165a856198bacaa673ef403cfa614_256x256.png,36a5d82af264254b3b47af4ad8eaf9c29681c314,Chairs,42.312,-9.691,12.131,4092 -4093,_031,43.472,2.027,-1.573,0.111,0.147,1.54142857143,6fa763e2769f7d945f6a32b14ded5dfb2b366d31,256,V6,6fa763e2769f7d945f6a32b14ded5dfb2b366d31_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+43.472_ry+02.027_rz-01.573_tx+00.111_ty+00.147_s+01.079_6fa763e2769f7d945f6a32b14ded5dfb2b366d31_256x256.png,f50e812699b1ace31ba903467a6797205a061cc5,Chairs,43.472,2.027,-1.573,4093 -4094,_031,60.443,87.373,-54.824,-0.584,0.976,1.58857142857,7601f7e89ce32d86c1aa648ed8f5f55c28b1b461,256,V6,7601f7e89ce32d86c1aa648ed8f5f55c28b1b461_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+60.443_ry+87.373_rz-54.824_tx-00.584_ty+00.976_s+01.112_7601f7e89ce32d86c1aa648ed8f5f55c28b1b461_256x256.png,ce8a75706168e6f4a41c685ef1ed1235f4bb4923,Chairs,60.443,87.373,-54.824,4094 -4095,_031,61.493,-68.398,-75.093,-0.492,-1.187,0.71,05391aefea027a1980dd2489b0497cd8fc51f823,256,V6,05391aefea027a1980dd2489b0497cd8fc51f823_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+61.493_ry-68.398_rz-75.093_tx-00.492_ty-01.187_s+00.497_05391aefea027a1980dd2489b0497cd8fc51f823_256x256.png,966ae816b5c97d7ab6b217c25e932ae7f407cd41,Chairs,61.493,-68.398,-75.093,4095 -4096,_031,71.722,40.198,15.604,-0.581,-0.34,1.57285714286,afc436570fdabf0b88c298d254d5bc8d1237b464,256,V6,afc436570fdabf0b88c298d254d5bc8d1237b464_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+71.722_ry+40.198_rz+15.604_tx-00.581_ty-00.340_s+01.101_afc436570fdabf0b88c298d254d5bc8d1237b464_256x256.png,1530e0668c9263d294848acd88703dfd6a9a7397,Chairs,71.722,40.198,15.604,4096 -4097,_031,75.502,10.938,31.777,-0.098,0.497,1.54142857143,f0e64dfdef193a576b16315f038ca9566dd777b0,256,V6,f0e64dfdef193a576b16315f038ca9566dd777b0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+75.502_ry+10.938_rz+31.777_tx-00.098_ty+00.497_s+01.079_f0e64dfdef193a576b16315f038ca9566dd777b0_256x256.png,b9d7e623e2b842dbc37d124699425102f01793c0,Chairs,75.502,10.938,31.777,4097 -4098,_031,78.19,73.488,60.552,0.078,0.626,1.36428571429,4e61984e708996ee131c3174455228e082b7f0cd,256,V6,4e61984e708996ee131c3174455228e082b7f0cd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+78.190_ry+73.488_rz+60.552_tx+00.078_ty+00.626_s+00.955_4e61984e708996ee131c3174455228e082b7f0cd_256x256.png,cfa29907ff51963e9fcee33215d3f90aa59e5ba2,Chairs,78.19,73.488,60.552,4098 -4099,_031,81.103,-4.495,65.968,0.578,0.65,1.24428571429,c47e12bd69ed6dd40a3b8153ec4e41ba2f6bed46,256,V6,c47e12bd69ed6dd40a3b8153ec4e41ba2f6bed46_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+81.103_ry-04.495_rz+65.968_tx+00.578_ty+00.650_s+00.871_c47e12bd69ed6dd40a3b8153ec4e41ba2f6bed46_256x256.png,b882b044d79eecfb9e905077b97912bc213117b8,Chairs,81.103,-4.495,65.968,4099 -4100,_031,84.149,34.221,36.853,-0.424,-0.634,1.47142857143,0d944a5c935dbb8a926303060563f571bd96d13d,256,V6,0d944a5c935dbb8a926303060563f571bd96d13d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx+84.149_ry+34.221_rz+36.853_tx-00.424_ty-00.634_s+01.030_0d944a5c935dbb8a926303060563f571bd96d13d_256x256.png,c6567b843549b33903b1fef48f1e326f34a8504d,Chairs,84.149,34.221,36.853,4100 -4101,_031,-0.822,-21.664,-61.32,0.573,0.971,0.662857142857,8d6ebd2ea133a76d991a1ac27458402ec83c2410,256,V6,8d6ebd2ea133a76d991a1ac27458402ec83c2410_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-00.822_ry-21.664_rz-61.320_tx+00.573_ty+00.971_s+00.464_8d6ebd2ea133a76d991a1ac27458402ec83c2410_256x256.png,e744b3b329b45db6e37f11d250b834b32674814e,Chairs,-0.822,-21.664,-61.32,4101 -4102,_031,-2.402,44.664,-45.617,0.504,-0.745,1.46857142857,ad2ea806c2a1906d9fc951ec2e3342674d61b0a6,256,V6,ad2ea806c2a1906d9fc951ec2e3342674d61b0a6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-02.402_ry+44.664_rz-45.617_tx+00.504_ty-00.745_s+01.028_ad2ea806c2a1906d9fc951ec2e3342674d61b0a6_256x256.png,0790480485b0c6c3d34f1afa4d802359fce1107f,Chairs,-2.402,44.664,-45.617,4102 -4103,_031,-7.123,-3.92,-87.732,-0.477,-0.97,0.747142857143,f6ba02d30214f3a97c9da6f80b82ec45c1cc7af9,256,V6,f6ba02d30214f3a97c9da6f80b82ec45c1cc7af9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-07.123_ry-03.920_rz-87.732_tx-00.477_ty-00.970_s+00.523_f6ba02d30214f3a97c9da6f80b82ec45c1cc7af9_256x256.png,348d0958ede5c19b6ff027191cbf026f9c7434c6,Chairs,-7.123,-3.92,-87.732,4103 -4104,_031,-7.784,63.597,9.211,-0.194,-0.887,0.711428571429,4b6875d91025b6c4c6b542b1c71b77561c94e1df,256,V6,4b6875d91025b6c4c6b542b1c71b77561c94e1df_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-07.784_ry+63.597_rz+09.211_tx-00.194_ty-00.887_s+00.498_4b6875d91025b6c4c6b542b1c71b77561c94e1df_256x256.png,27dc1d7bd42da4feb6f63a7f9dbae26e17dc2924,Chairs,-7.784,63.597,9.211,4104 -4105,_031,-8.49,-62.638,-40.899,0.046,0.473,0.984285714286,acaba78f4f970c5db0de9998a3b60545f1206a8c,256,V6,acaba78f4f970c5db0de9998a3b60545f1206a8c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-08.490_ry-62.638_rz-40.899_tx+00.046_ty+00.473_s+00.689_acaba78f4f970c5db0de9998a3b60545f1206a8c_256x256.png,7f697fe4bfe29bf834b6898561819a06b3c3c9e5,Chairs,-8.49,-62.638,-40.899,4105 -4106,_031,-14.173,-54.287,82.513,-0.47,0.64,1.01142857143,366fb365218d0f26defbffba6e16f35e345a286a,256,V6,366fb365218d0f26defbffba6e16f35e345a286a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-14.173_ry-54.287_rz+82.513_tx-00.470_ty+00.640_s+00.708_366fb365218d0f26defbffba6e16f35e345a286a_256x256.png,59c2e56f21f3e6f665179e0e0d377773d19d78a9,Chairs,-14.173,-54.287,82.513,4106 -4107,_031,-28.434,-2.425,76.407,0.184,-1.174,1.52142857143,f65c8e5847f703d64e6958a8494fce45b40b36a7,256,V6,f65c8e5847f703d64e6958a8494fce45b40b36a7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-28.434_ry-02.425_rz+76.407_tx+00.184_ty-01.174_s+01.065_f65c8e5847f703d64e6958a8494fce45b40b36a7_256x256.png,fe7aadee241f1587000586bb2ae8ea99ec583519,Chairs,-28.434,-2.425,76.407,4107 -4108,_031,-29.776,-48.678,-75.942,0.306,-0.798,1.28857142857,d76b18ca4843c9646198c7fbe7b525652b0c0945,256,V6,d76b18ca4843c9646198c7fbe7b525652b0c0945_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-29.776_ry-48.678_rz-75.942_tx+00.306_ty-00.798_s+00.902_d76b18ca4843c9646198c7fbe7b525652b0c0945_256x256.png,3333a6568407f6c64e65d8871a3e7d8a955f71bd,Chairs,-29.776,-48.678,-75.942,4108 -4109,_031,-38.769,-18.188,-5.024,-0.42,0.149,1.28571428571,ddc82cd6b7752b5b4ef901ae89db0f442220fc79,256,V6,ddc82cd6b7752b5b4ef901ae89db0f442220fc79_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-38.769_ry-18.188_rz-05.024_tx-00.420_ty+00.149_s+00.900_ddc82cd6b7752b5b4ef901ae89db0f442220fc79_256x256.png,f2fb87bee2a255885c3731b60dd0768c294e0617,Chairs,-38.769,-18.188,-5.024,4109 -4110,_031,-40.485,81.557,44.267,-0.316,-0.51,1.30142857143,3369eec41e2d0479836373fafacc1be874641e47,256,V6,3369eec41e2d0479836373fafacc1be874641e47_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-40.485_ry+81.557_rz+44.267_tx-00.316_ty-00.510_s+00.911_3369eec41e2d0479836373fafacc1be874641e47_256x256.png,361bd74e3822e65b65b2379eda822a386a2c82e4,Chairs,-40.485,81.557,44.267,4110 -4111,_031,-43.069,34.144,-72.921,-0.098,-0.318,0.797142857143,650814f3f93ce38034a8e7ab155a88265efa5232,256,V6,650814f3f93ce38034a8e7ab155a88265efa5232_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-43.069_ry+34.144_rz-72.921_tx-00.098_ty-00.318_s+00.558_650814f3f93ce38034a8e7ab155a88265efa5232_256x256.png,511d83ba48cf51ce4e2a0da45dfad7cfbfe921c7,Chairs,-43.069,34.144,-72.921,4111 -4112,_031,-53.961,30.136,24.183,0.032,-0.417,1.18571428571,27c82de347064b827dc3826e3218753cf476a8c7,256,V6,27c82de347064b827dc3826e3218753cf476a8c7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-53.961_ry+30.136_rz+24.183_tx+00.032_ty-00.417_s+00.830_27c82de347064b827dc3826e3218753cf476a8c7_256x256.png,31d66e11611d3efc4d9dd724c10f2df3ef352fc3,Chairs,-53.961,30.136,24.183,4112 -4113,_031,-58.301,36.917,25.656,0.336,0.475,1.03428571429,413be77def23c10d3b8c77de814c4a0b692598f1,256,V6,413be77def23c10d3b8c77de814c4a0b692598f1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-58.301_ry+36.917_rz+25.656_tx+00.336_ty+00.475_s+00.724_413be77def23c10d3b8c77de814c4a0b692598f1_256x256.png,4187ee2372fb018a6f3f349e6e925d078a5e6b5b,Chairs,-58.301,36.917,25.656,4113 -4114,_031,-59.965,69.925,63.271,-0.493,-0.284,1.41142857143,187b0f5d74e17a3aa59379a0cab56face2e3e3e9,256,V6,187b0f5d74e17a3aa59379a0cab56face2e3e3e9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-59.965_ry+69.925_rz+63.271_tx-00.493_ty-00.284_s+00.988_187b0f5d74e17a3aa59379a0cab56face2e3e3e9_256x256.png,12b90efa878ddbf5cab9becb76d154d7cda151a8,Chairs,-59.965,69.925,63.271,4114 -4115,_031,-71.24,79.17,-89.181,-0.531,1.104,1.39428571429,7a225c98f6829139426a64942f834595af2e776b,256,V6,7a225c98f6829139426a64942f834595af2e776b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-71.240_ry+79.170_rz-89.181_tx-00.531_ty+01.104_s+00.976_7a225c98f6829139426a64942f834595af2e776b_256x256.png,991a2bed6d91bc5e820e903af697ce4c16b4adda,Chairs,-71.24,79.17,-89.181,4115 -4116,_031,-71.912,42.264,-76.346,0.14,-0.0,0.682857142857,9c2250763f6c5fdf880efd0ae631871d6ad0aa25,256,V6,9c2250763f6c5fdf880efd0ae631871d6ad0aa25_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-71.912_ry+42.264_rz-76.346_tx+00.140_ty-00.000_s+00.478_9c2250763f6c5fdf880efd0ae631871d6ad0aa25_256x256.png,c3222cd3db4f0e52d442f225160ac27c0eb5808d,Chairs,-71.912,42.264,-76.346,4116 -4117,_031,-72.882,45.353,78.788,0.202,0.087,1.58857142857,be6d08da6fae9c920f0691503ee8c62b4493bdef,256,V6,be6d08da6fae9c920f0691503ee8c62b4493bdef_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-72.882_ry+45.353_rz+78.788_tx+00.202_ty+00.087_s+01.112_be6d08da6fae9c920f0691503ee8c62b4493bdef_256x256.png,0afe9be0120625dd8b92d509b60e2d0f89943fa6,Chairs,-72.882,45.353,78.788,4117 -4118,_031,-87.243,-56.428,-38.987,-0.255,0.54,1.34714285714,4850d4321c26797dcbc9785beedfa01d46dc7cc6,256,V6,4850d4321c26797dcbc9785beedfa01d46dc7cc6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-87.243_ry-56.428_rz-38.987_tx-00.255_ty+00.540_s+00.943_4850d4321c26797dcbc9785beedfa01d46dc7cc6_256x256.png,2fbd8fa0d1cc3ecb31daea831a063713ba584bdb,Chairs,-87.243,-56.428,-38.987,4118 -4119,_031,-89.701,10.279,-0.734,0.351,-0.488,1.48285714286,54bb69c481088cc1b1f3617a5c9cd55277fcf8fa,256,V6,54bb69c481088cc1b1f3617a5c9cd55277fcf8fa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_031_rx-89.701_ry+10.279_rz-00.734_tx+00.351_ty-00.488_s+01.038_54bb69c481088cc1b1f3617a5c9cd55277fcf8fa_256x256.png,edeaa6e2b624b75207c63b7165098050edb6b5ec,Chairs,-89.701,10.279,-0.734,4119 -4120,_033,0.166,52.476,0.098,0.118,0.448,1.16285714286,f8a17eb650f96d227fba8ca78e69def98b3016f2,256,V6,f8a17eb650f96d227fba8ca78e69def98b3016f2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+00.166_ry+52.476_rz+00.098_tx+00.118_ty+00.448_s+00.814_f8a17eb650f96d227fba8ca78e69def98b3016f2_256x256.png,437ecc1fd77417296d6036e8a5e0d1ed845cf0ad,Chairs,0.166,52.476,0.098,4120 -4121,_033,7.388,-9.532,68.546,-0.045,0.378,1.58857142857,928c78663e63a045a50b9a0fb7106b4ba73505af,256,V6,928c78663e63a045a50b9a0fb7106b4ba73505af_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+07.388_ry-09.532_rz+68.546_tx-00.045_ty+00.378_s+01.112_928c78663e63a045a50b9a0fb7106b4ba73505af_256x256.png,ddf1ce48d896bff73158485f7807ab2b3d39bdc8,Chairs,7.388,-9.532,68.546,4121 -4122,_033,8.66,-83.758,-38.508,-0.244,0.55,1.50142857143,6ace5a71630b9531e891b4e8d425251fbc668bba,256,V6,6ace5a71630b9531e891b4e8d425251fbc668bba_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+08.660_ry-83.758_rz-38.508_tx-00.244_ty+00.550_s+01.051_6ace5a71630b9531e891b4e8d425251fbc668bba_256x256.png,7d91320e796ed81c7a4e6a20faed16195303b618,Chairs,8.66,-83.758,-38.508,4122 -4123,_033,12.593,-40.138,86.151,0.556,-0.32,0.867142857143,32bc66bbe0b7d0f0eeddbec1d5fb218b7a4c11f9,256,V6,32bc66bbe0b7d0f0eeddbec1d5fb218b7a4c11f9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+12.593_ry-40.138_rz+86.151_tx+00.556_ty-00.320_s+00.607_32bc66bbe0b7d0f0eeddbec1d5fb218b7a4c11f9_256x256.png,4f1e8a213f0f90fd91edc82cb524e48446059b34,Chairs,12.593,-40.138,86.151,4123 -4124,_033,13.256,68.62,-13.531,0.242,0.061,0.712857142857,4efe811bef69005e8d0bc86d662abe34aaacb4f3,256,V6,4efe811bef69005e8d0bc86d662abe34aaacb4f3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+13.256_ry+68.620_rz-13.531_tx+00.242_ty+00.061_s+00.499_4efe811bef69005e8d0bc86d662abe34aaacb4f3_256x256.png,d01830bac829b4f63bee6f9f76b3f0b2dc6699b2,Chairs,13.256,68.62,-13.531,4124 -4125,_033,19.297,-24.3,8.353,-0.054,0.191,0.734285714286,8e1c5ed84a1e7b20178424d5d82c8b517f53ae37,256,V6,8e1c5ed84a1e7b20178424d5d82c8b517f53ae37_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+19.297_ry-24.300_rz+08.353_tx-00.054_ty+00.191_s+00.514_8e1c5ed84a1e7b20178424d5d82c8b517f53ae37_256x256.png,344627fc6d4640c5eed4a739791f25a9e5a1430a,Chairs,19.297,-24.3,8.353,4125 -4126,_033,21.764,-84.393,-11.055,-0.222,0.791,1.42,303a8fd44426f2064105e91b6e790f606d60080d,256,V6,303a8fd44426f2064105e91b6e790f606d60080d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+21.764_ry-84.393_rz-11.055_tx-00.222_ty+00.791_s+00.994_303a8fd44426f2064105e91b6e790f606d60080d_256x256.png,957ee33fa29952673f1acf48d60eabf7a1fbb4a4,Chairs,21.764,-84.393,-11.055,4126 -4127,_033,23.488,79.732,-72.729,-0.129,0.037,1.46571428571,30badb926233fd0ff4d00a274c77543ab98ac0d3,256,V6,30badb926233fd0ff4d00a274c77543ab98ac0d3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+23.488_ry+79.732_rz-72.729_tx-00.129_ty+00.037_s+01.026_30badb926233fd0ff4d00a274c77543ab98ac0d3_256x256.png,faa363780f58877b3c917beda3f9d4dd160fb061,Chairs,23.488,79.732,-72.729,4127 -4128,_033,28.622,1.328,-29.865,0.245,0.161,1.06714285714,04750f8411face7277b3eebe847c134529896112,256,V6,04750f8411face7277b3eebe847c134529896112_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+28.622_ry+01.328_rz-29.865_tx+00.245_ty+00.161_s+00.747_04750f8411face7277b3eebe847c134529896112_256x256.png,72d6c920d4a554c0161d982a0b6e40ba9f4d5abd,Chairs,28.622,1.328,-29.865,4128 -4129,_033,30.215,65.457,-85.958,0.508,-0.36,1.49,2c5f6f26083a52e6e156bdaead57b94830239ec9,256,V6,2c5f6f26083a52e6e156bdaead57b94830239ec9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+30.215_ry+65.457_rz-85.958_tx+00.508_ty-00.360_s+01.043_2c5f6f26083a52e6e156bdaead57b94830239ec9_256x256.png,c36eb1b348d78fbd3e5662ead488ac381c8c8f54,Chairs,30.215,65.457,-85.958,4129 -4130,_033,39.719,-73.28,-47.007,-0.255,0.427,1.49714285714,ee7ef89b9fe77128d92af961ec9fcad8d372fbf4,256,V6,ee7ef89b9fe77128d92af961ec9fcad8d372fbf4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+39.719_ry-73.280_rz-47.007_tx-00.255_ty+00.427_s+01.048_ee7ef89b9fe77128d92af961ec9fcad8d372fbf4_256x256.png,53eb298a3c4f8584e2c0719281b95cc5f316c8c2,Chairs,39.719,-73.28,-47.007,4130 -4131,_033,40.884,-47.81,56.329,0.043,0.477,1.09571428571,ca464997730d4746acd22f22c50f26b43f84418b,256,V6,ca464997730d4746acd22f22c50f26b43f84418b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+40.884_ry-47.810_rz+56.329_tx+00.043_ty+00.477_s+00.767_ca464997730d4746acd22f22c50f26b43f84418b_256x256.png,19041d957b9a6e1b6e24e5206af11d8abb4b22c5,Chairs,40.884,-47.81,56.329,4131 -4132,_033,54.215,44.797,-37.175,0.558,0.311,1.17857142857,813335a9d4f550fd9a8e6edb357bdb9ef1da6630,256,V6,813335a9d4f550fd9a8e6edb357bdb9ef1da6630_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+54.215_ry+44.797_rz-37.175_tx+00.558_ty+00.311_s+00.825_813335a9d4f550fd9a8e6edb357bdb9ef1da6630_256x256.png,ca8ab081188cff74433df2970d817a991660f6d3,Chairs,54.215,44.797,-37.175,4132 -4133,_033,57.705,36.717,50.457,0.421,0.232,0.721428571429,63ca45197b3adbdc1b857636d7f3e191ce3ca3ff,256,V6,63ca45197b3adbdc1b857636d7f3e191ce3ca3ff_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+57.705_ry+36.717_rz+50.457_tx+00.421_ty+00.232_s+00.505_63ca45197b3adbdc1b857636d7f3e191ce3ca3ff_256x256.png,06a1374cf6c4d5e7a026f549d2ad6891f4a4da17,Chairs,57.705,36.717,50.457,4133 -4134,_033,79.048,71.871,-62.147,-0.499,1.075,1.23428571429,53d13f8aa12a404cd607551c7a36c782c1c97d76,256,V6,53d13f8aa12a404cd607551c7a36c782c1c97d76_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+79.048_ry+71.871_rz-62.147_tx-00.499_ty+01.075_s+00.864_53d13f8aa12a404cd607551c7a36c782c1c97d76_256x256.png,349c447da986769b35f804ef5aa9cc634fac0d6e,Chairs,79.048,71.871,-62.147,4134 -4135,_033,85.035,70.805,70.081,-0.344,1.103,0.86,0868dabb44f2ccfdff1f192f5b13629349d0ceef,256,V6,0868dabb44f2ccfdff1f192f5b13629349d0ceef_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+85.035_ry+70.805_rz+70.081_tx-00.344_ty+01.103_s+00.602_0868dabb44f2ccfdff1f192f5b13629349d0ceef_256x256.png,ff3714c9a91d2ce7f912ceef93f92b23b0e48d45,Chairs,85.035,70.805,70.081,4135 -4136,_033,85.74,75.366,-68.38,-0.431,1.167,1.57428571429,fe0ce4bf750dff9855467d9d91093196d7456f68,256,V6,fe0ce4bf750dff9855467d9d91093196d7456f68_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+85.740_ry+75.366_rz-68.380_tx-00.431_ty+01.167_s+01.102_fe0ce4bf750dff9855467d9d91093196d7456f68_256x256.png,f3da82d0da255abdb67fbf44536cfd5f304928d3,Chairs,85.74,75.366,-68.38,4136 -4137,_033,86.191,-49.929,-81.4,0.408,-0.313,1.10571428571,97c7e5fab3616fd83d1d4915032cfaa402cb442e,256,V6,97c7e5fab3616fd83d1d4915032cfaa402cb442e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx+86.191_ry-49.929_rz-81.400_tx+00.408_ty-00.313_s+00.774_97c7e5fab3616fd83d1d4915032cfaa402cb442e_256x256.png,f9323e7ac601d988f8b0ffb0dc6d04031fddf3f2,Chairs,86.191,-49.929,-81.4,4137 -4138,_033,-2.752,42.781,7.138,-0.574,0.439,1.19285714286,4ad1e9bf87a0a54433fefcdf2630623a31172dde,256,V6,4ad1e9bf87a0a54433fefcdf2630623a31172dde_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-02.752_ry+42.781_rz+07.138_tx-00.574_ty+00.439_s+00.835_4ad1e9bf87a0a54433fefcdf2630623a31172dde_256x256.png,ff23905004ce4198d939c512b110b40dbef33cf6,Chairs,-2.752,42.781,7.138,4138 -4139,_033,-14.512,-89.562,-56.282,-0.406,0.684,1.33714285714,a1458856b6110a854e61701e199f18d9c2972ca3,256,V6,a1458856b6110a854e61701e199f18d9c2972ca3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-14.512_ry-89.562_rz-56.282_tx-00.406_ty+00.684_s+00.936_a1458856b6110a854e61701e199f18d9c2972ca3_256x256.png,09f3f539d07b8982691a81e1843a9ae0bdca757b,Chairs,-14.512,-89.562,-56.282,4139 -4140,_033,-14.557,-36.08,75.252,0.575,0.599,1.49714285714,f00fcc99fc3d87e882af7edc6be3740a0d005b1d,256,V6,f00fcc99fc3d87e882af7edc6be3740a0d005b1d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-14.557_ry-36.080_rz+75.252_tx+00.575_ty+00.599_s+01.048_f00fcc99fc3d87e882af7edc6be3740a0d005b1d_256x256.png,a5bbc95629508cd843b0a692132985e638efbe72,Chairs,-14.557,-36.08,75.252,4140 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-4143,_033,-20.707,-10.63,54.256,-0.024,-0.583,1.36714285714,0305e44e2372d98c664f9aef754d004b2bbde8d6,256,V6,0305e44e2372d98c664f9aef754d004b2bbde8d6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-20.707_ry-10.630_rz+54.256_tx-00.024_ty-00.583_s+00.957_0305e44e2372d98c664f9aef754d004b2bbde8d6_256x256.png,5d24273b04709db5e67c0d698e7894c36d8efc62,Chairs,-20.707,-10.63,54.256,4143 -4144,_033,-25.353,20.873,72.656,0.092,-0.157,1.53571428571,ea1542cb038b8da0274bd83986e96537587371fb,256,V6,ea1542cb038b8da0274bd83986e96537587371fb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-25.353_ry+20.873_rz+72.656_tx+00.092_ty-00.157_s+01.075_ea1542cb038b8da0274bd83986e96537587371fb_256x256.png,a98a7623b1194565c2ac627af252881350cf6e03,Chairs,-25.353,20.873,72.656,4144 -4145,_033,-28.47,67.563,-2.898,0.208,0.598,0.827142857143,29283f07a4d9534bca401ff9b5faaa00e959145d,256,V6,29283f07a4d9534bca401ff9b5faaa00e959145d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-28.470_ry+67.563_rz-02.898_tx+00.208_ty+00.598_s+00.579_29283f07a4d9534bca401ff9b5faaa00e959145d_256x256.png,64b5042af98831633f8b31444978028fde74712e,Chairs,-28.47,67.563,-2.898,4145 -4146,_033,-30.903,89.51,-11.726,0.31,-0.059,0.762857142857,ed7b2a8868c12f924385f86f85e33c8e95f572be,256,V6,ed7b2a8868c12f924385f86f85e33c8e95f572be_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-30.903_ry+89.510_rz-11.726_tx+00.310_ty-00.059_s+00.534_ed7b2a8868c12f924385f86f85e33c8e95f572be_256x256.png,323f2b820db9c4d2f2bdc7f8aaa7f60161f04902,Chairs,-30.903,89.51,-11.726,4146 -4147,_033,-31.922,-76.34,84.794,-0.371,-0.581,0.918571428571,42bc31e2b31052ddbf437c0feb19d1595db1f03f,256,V6,42bc31e2b31052ddbf437c0feb19d1595db1f03f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-31.922_ry-76.340_rz+84.794_tx-00.371_ty-00.581_s+00.643_42bc31e2b31052ddbf437c0feb19d1595db1f03f_256x256.png,2b0d5b7268a1f3a2e11a974d859970b914b20ee9,Chairs,-31.922,-76.34,84.794,4147 -4148,_033,-35.254,-5.535,-63.472,0.381,1.118,1.49714285714,2e0c72492bf42b72d46532bc18eaf758f4310411,256,V6,2e0c72492bf42b72d46532bc18eaf758f4310411_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-35.254_ry-05.535_rz-63.472_tx+00.381_ty+01.118_s+01.048_2e0c72492bf42b72d46532bc18eaf758f4310411_256x256.png,8adb45af423816253f464b1697a6d1a421251ffe,Chairs,-35.254,-5.535,-63.472,4148 -4149,_033,-46.996,-81.254,48.608,-0.205,-0.945,0.848571428571,1aa56b9b9931cb0cd7d5d0ff7c474a14b02fe9ce,256,V6,1aa56b9b9931cb0cd7d5d0ff7c474a14b02fe9ce_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-46.996_ry-81.254_rz+48.608_tx-00.205_ty-00.945_s+00.594_1aa56b9b9931cb0cd7d5d0ff7c474a14b02fe9ce_256x256.png,1bc8f1ba25bacd3ffa3c82f007cc08f8501fddf4,Chairs,-46.996,-81.254,48.608,4149 -4150,_033,-48.276,66.042,-38.785,-0.031,-1.134,1.03285714286,df4d3fe1ee83c8bd8e56447250311d79317eb599,256,V6,df4d3fe1ee83c8bd8e56447250311d79317eb599_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-48.276_ry+66.042_rz-38.785_tx-00.031_ty-01.134_s+00.723_df4d3fe1ee83c8bd8e56447250311d79317eb599_256x256.png,200fd01cc43503000436a0b1971f64161d86fd4a,Chairs,-48.276,66.042,-38.785,4150 -4151,_033,-54.05,2.114,-16.668,-0.024,-1.008,1.08714285714,bbd70f70bf997883fe4a6175a9dca306f4a3963f,256,V6,bbd70f70bf997883fe4a6175a9dca306f4a3963f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-54.050_ry+02.114_rz-16.668_tx-00.024_ty-01.008_s+00.761_bbd70f70bf997883fe4a6175a9dca306f4a3963f_256x256.png,573d8817cb924388dccd124d796d278b1d84c85a,Chairs,-54.05,2.114,-16.668,4151 -4152,_033,-56.466,81.378,-31.438,-0.452,0.792,1.21571428571,cf94b87edd6cc4b84144906efd279e06218c7581,256,V6,cf94b87edd6cc4b84144906efd279e06218c7581_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-56.466_ry+81.378_rz-31.438_tx-00.452_ty+00.792_s+00.851_cf94b87edd6cc4b84144906efd279e06218c7581_256x256.png,8863be95c2d0790b6c6904bcff11142470308514,Chairs,-56.466,81.378,-31.438,4152 -4153,_033,-63.524,-5.46,-82.619,0.165,-0.691,0.868571428571,e5f5a6e365d532944383857cc075ab4ac7b0373b,256,V6,e5f5a6e365d532944383857cc075ab4ac7b0373b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-63.524_ry-05.460_rz-82.619_tx+00.165_ty-00.691_s+00.608_e5f5a6e365d532944383857cc075ab4ac7b0373b_256x256.png,5617fe77afc9258f3c132d01e706ea5b375884c9,Chairs,-63.524,-5.46,-82.619,4153 -4154,_033,-64.531,79.609,-85.89,-0.259,0.611,1.54285714286,88a4b3a09911de79a1ed9b25f6b3e878f16965b7,256,V6,88a4b3a09911de79a1ed9b25f6b3e878f16965b7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-64.531_ry+79.609_rz-85.890_tx-00.259_ty+00.611_s+01.080_88a4b3a09911de79a1ed9b25f6b3e878f16965b7_256x256.png,c7a8979f364cbbcc2857448a9ae7e4716a935a1b,Chairs,-64.531,79.609,-85.89,4154 -4155,_033,-67.932,55.2,-1.643,-0.215,1.179,1.25142857143,5e13ca12b89cd9631656c619a3a0c88b69b75b8e,256,V6,5e13ca12b89cd9631656c619a3a0c88b69b75b8e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-67.932_ry+55.200_rz-01.643_tx-00.215_ty+01.179_s+00.876_5e13ca12b89cd9631656c619a3a0c88b69b75b8e_256x256.png,fd18b1f8ca6d961c163aa56772d813f6cba28336,Chairs,-67.932,55.2,-1.643,4155 -4156,_033,-78.234,19.109,60.521,-0.538,-0.483,0.917142857143,f4bf116aa76038d252a7faf3e976950802aeddb0,256,V6,f4bf116aa76038d252a7faf3e976950802aeddb0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-78.234_ry+19.109_rz+60.521_tx-00.538_ty-00.483_s+00.642_f4bf116aa76038d252a7faf3e976950802aeddb0_256x256.png,9b174deb67217b102c734bd3680d0b581c2838fa,Chairs,-78.234,19.109,60.521,4156 -4157,_033,-80.979,46.199,-0.897,-0.169,1.017,1.50571428571,119094b11cf4f1208e7b95168955bb7089b64c44,256,V6,119094b11cf4f1208e7b95168955bb7089b64c44_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-80.979_ry+46.199_rz-00.897_tx-00.169_ty+01.017_s+01.054_119094b11cf4f1208e7b95168955bb7089b64c44_256x256.png,bb44b210ec5456e3b9266cc8338bc52bca72489e,Chairs,-80.979,46.199,-0.897,4157 -4158,_033,-88.839,44.779,-18.663,0.038,0.04,1.43428571429,84e8bad860da27775d5c9d7a5e262c5fda3ce435,256,V6,84e8bad860da27775d5c9d7a5e262c5fda3ce435_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-88.839_ry+44.779_rz-18.663_tx+00.038_ty+00.040_s+01.004_84e8bad860da27775d5c9d7a5e262c5fda3ce435_256x256.png,2dba629ec381f7064edd9608e2218b84b5f9015c,Chairs,-88.839,44.779,-18.663,4158 -4159,_033,-89.374,-73.386,-53.408,0.454,-0.947,1.31142857143,67f25fbca7fc6db2dbb9a9161a8777c57acd1ee2,256,V6,67f25fbca7fc6db2dbb9a9161a8777c57acd1ee2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_033_rx-89.374_ry-73.386_rz-53.408_tx+00.454_ty-00.947_s+00.918_67f25fbca7fc6db2dbb9a9161a8777c57acd1ee2_256x256.png,029d93aee30d0640f5fc7ea4f8c2c37e9723ebd5,Chairs,-89.374,-73.386,-53.408,4159 -4160,_05_future,24.1934232352,9.95513746071,-19.9362686723,-0.284,-0.537,0.856153846154,0405dd6bf7747d33a030f46c86eabda0bd95e00a,256,V6,0405dd6bf7747d33a030f46c86eabda0bd95e00a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+09.073_ry+65.480_rz+74.178_tx-00.284_ty-00.537_s+01.113_0405dd6bf7747d33a030f46c86eabda0bd95e00a_256x256.png,3fe99701f03c85d5b2017d4ec33eb227a695386e,Planes,24.1934232352,99.9551374607,-19.9362686723,4160 -4161,_05_future,-71.7722306199,44.7301802418,75.7483974874,-0.538,-0.764,1.42461538462,3f4642e7f7762131351cfa09f367323bfc799fe7,256,V6,3f4642e7f7762131351cfa09f367323bfc799fe7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+12.717_ry+166.832_rz+122.492_tx-00.538_ty-00.764_s+01.852_3f4642e7f7762131351cfa09f367323bfc799fe7_256x256.png,e3846aa28ed4a3f459f671f672e4e882c0724b39,Planes,-71.7722306199,134.730180242,75.7483974874,4161 -4162,_05_future,-59.7866495874,26.0406736642,6.07929257337,-0.016,-0.409,1.46538461538,2396f12f624d58fabfbb46a30629caa0b024f3aa,256,V6,2396f12f624d58fabfbb46a30629caa0b024f3aa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+12.763_ry+152.381_rz+73.188_tx-00.016_ty-00.409_s+01.905_2396f12f624d58fabfbb46a30629caa0b024f3aa_256x256.png,5c982c7d7c130d689ab32df664b62ded4fffc7da,Planes,-59.7866495874,116.040673664,6.07929257337,4162 -4163,_05_future,74.331557734,61.082619077,-120.451143756,0.527,0.537,0.783846153846,0931a89d6d9b58a502da478916d98ec9fa6b8995,256,V6,0931a89d6d9b58a502da478916d98ec9fa6b8995_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+13.674_ry+07.724_rz+29.704_tx+00.527_ty+00.537_s+01.019_0931a89d6d9b58a502da478916d98ec9fa6b8995_256x256.png,d1ece39eddcf6362e2b6d920bc3dd23e53363a47,Planes,74.331557734,151.082619077,-120.451143756,4163 -4164,_05_future,-58.2790712649,28.0989967031,-41.3909157968,-0.072,-0.618,1.51692307692,448a5332198dede6d484517e2d035eed420f1d51,256,V6,448a5332198dede6d484517e2d035eed420f1d51_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+14.338_ry+151.398_rz+24.183_tx-00.072_ty-00.618_s+01.972_448a5332198dede6d484517e2d035eed420f1d51_256x256.png,e1edd2dc5ab4aafeb9ddfef8806c88695f746ea8,Planes,-58.2790712649,118.098996703,-41.3909157968,4164 -4165,_05_future,-33.4625702404,21.6601947304,33.7197700655,-0.124,-0.055,0.851538461538,69816752a31f5d96db7f4743012b084ab77c4243,256,V6,69816752a31f5d96db7f4743012b084ab77c4243_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+17.934_ry+125.419_rz+111.368_tx-00.124_ty-00.055_s+01.107_69816752a31f5d96db7f4743012b084ab77c4243_256x256.png,7657523c5adb765442dc4f70425b2f7eceaaa499,Planes,-33.4625702404,111.66019473,33.7197700655,4165 -4166,_05_future,-10.1468905539,20.2482243301,86.4492647694,0.108,0.117,1.05384615385,62c47873da95054ec0b1b84e46ecebdb152a78c7,256,V6,62c47873da95054ec0b1b84e46ecebdb152a78c7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+19.918_ry+100.800_rz+172.731_tx+00.108_ty+00.117_s+01.370_62c47873da95054ec0b1b84e46ecebdb152a78c7_256x256.png,270171c36a1f150a052e2c6e430cc8b7ad926957,Planes,-10.1468905539,110.24822433,86.4492647694,4166 -4167,_05_future,58.2935841923,48.4305365394,-28.9756266404,-0.357,-0.84,0.740769230769,cd1bc26a8810c3a15758ea4c1f4786e6ba5379de,256,V6,cd1bc26a8810c3a15758ea4c1f4786e6ba5379de_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+23.154_ry+22.289_rz+104.833_tx-00.357_ty-00.840_s+00.963_cd1bc26a8810c3a15758ea4c1f4786e6ba5379de_256x256.png,748fa7762811f17d80ff114f38533a5d1cbff871,Planes,58.2935841923,138.430536539,-28.9756266404,4167 -4168,_05_future,-46.234064876,44.1810556427,75.2518216781,-0.241,1.044,1.01307692308,b7596de35497478d95ca18321518a52a0cae821e,256,V6,b7596de35497478d95ca18321518a52a0cae821e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+28.821_ry+145.515_rz+130.190_tx-00.241_ty+01.044_s+01.317_b7596de35497478d95ca18321518a52a0cae821e_256x256.png,bb767c5e9d3c870e1b1bbb372d1f9541aa6989b9,Planes,-46.234064876,134.181055643,75.2518216781,4168 -4169,_05_future,-56.2977581456,61.0698032066,67.1018158393,-0.263,-1.197,1.53615384615,c8ad821cfe1a293c6ac89f0e7214c89e9b62d00b,256,V6,c8ad821cfe1a293c6ac89f0e7214c89e9b62d00b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+29.054_ry+162.118_rz+100.701_tx-00.263_ty-01.197_s+01.997_c8ad821cfe1a293c6ac89f0e7214c89e9b62d00b_256x256.png,7f29c50e22d26a74c979df024dd484a374ba1445,Planes,-56.2977581456,151.069803207,67.1018158393,4169 -4170,_05_future,-32.5321662294,36.7266478556,46.3102779863,-0.289,0.139,0.69,060bd93fe2d4539453753bfda91ea803c1d6ce18,256,V6,060bd93fe2d4539453753bfda91ea803c1d6ce18_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+30.276_ry+128.514_rz+114.448_tx-00.289_ty+00.139_s+00.897_060bd93fe2d4539453753bfda91ea803c1d6ce18_256x256.png,cf7a10f082707b0c0cc50331105afc08f6517a63,Planes,-32.5321662294,126.726647856,46.3102779863,4170 -4171,_05_future,-20.7348370221,36.4215380833,84.57614896,0.343,-0.437,0.826153846154,f677ad3f87da96bbe2368e0693d02971983b1c14,256,V6,f677ad3f87da96bbe2368e0693d02971983b1c14_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+33.729_ry+115.195_rz+159.936_tx+00.343_ty-00.437_s+01.074_f677ad3f87da96bbe2368e0693d02971983b1c14_256x256.png,c9bd8a919d22f035937eda9075b91686182332e5,Planes,-20.7348370221,126.421538083,84.57614896,4171 -4172,_05_future,30.5526501956,51.2428102454,22.0198800326,0.225,0.897,0.807692307692,1dc24c43530afa3ca21470b68a79bdb784fcf61f,256,V6,1dc24c43530afa3ca21470b68a79bdb784fcf61f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+42.186_ry+46.683_rz+144.362_tx+00.225_ty+00.897_s+01.050_1dc24c43530afa3ca21470b68a79bdb784fcf61f_256x256.png,3cda5edd419e73c6b766233e91b20a3ae2eaffe9,Planes,30.5526501956,141.242810245,22.0198800326,4172 -4173,_05_future,39.1439396513,78.8039934654,-159.557352734,-0.457,0.592,1.43,4c625a9945aaf9ce5d9ae7f90688c9979b1e42b6,256,V6,4c625a9945aaf9ce5d9ae7f90688c9979b1e42b6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+49.535_ry+13.417_rz+03.034_tx-00.457_ty+00.592_s+01.859_4c625a9945aaf9ce5d9ae7f90688c9979b1e42b6_256x256.png,24f3a9aac6c894f89254f8b2276544667543e049,Planes,39.1439396513,168.803993465,-159.557352734,4173 -4174,_05_future,2.54251316115,51.7555044659,85.8946571877,-0.082,1.044,1.04076923077,4a85acd9d5cd281433ef82333395dc6872e7edde,256,V6,4a85acd9d5cd281433ef82333395dc6872e7edde_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+51.684_ry+85.897_rz+179.116_tx-00.082_ty+01.044_s+01.353_4a85acd9d5cd281433ef82333395dc6872e7edde_256x256.png,fd0691eaf1617632688351fe96c3aed2c5fa681e,Planes,2.54251316115,141.755504466,85.8946571877,4174 -4175,_05_future,30.8813744084,67.4358378033,4.59088937594,0.539,0.274,0.630769230769,1615e74d0fa919fc6b0e75e857787602f86c6cbe,256,V6,1615e74d0fa919fc6b0e75e857787602f86c6cbe_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+52.423_ry+32.685_rz+145.598_tx+00.539_ty+00.274_s+00.820_1615e74d0fa919fc6b0e75e857787602f86c6cbe_256x256.png,e9585a6a0da189dafc1e2e53e821637da3e4f58b,Planes,30.8813744084,157.435837803,4.59088937594,4175 -4176,_05_future,23.3859165647,66.5239155254,40.2077548199,0.375,-1.162,0.998461538462,596d9088cfe8c548a2ec18650fad36a851781a7e,256,V6,596d9088cfe8c548a2ec18650fad36a851781a7e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+57.339_ry+42.651_rz+172.632_tx+00.375_ty-01.162_s+01.298_596d9088cfe8c548a2ec18650fad36a851781a7e_256x256.png,ab93c2ee1c18e4d9e41328f69bbe71e969f9c9f4,Planes,23.3859165647,156.523915525,40.2077548199,4176 -4177,_05_future,20.7655068562,69.7012526302,-129.083749671,0.15,-0.609,0.804615384615,c1d02ef46a3e44ee0aa1f91ae5c740337b8670e6,256,V6,c1d02ef46a3e44ee0aa1f91ae5c740337b8670e6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+61.279_ry+42.456_rz+04.703_tx+00.150_ty-00.609_s+01.046_c1d02ef46a3e44ee0aa1f91ae5c740337b8670e6_256x256.png,706d56e197f6eb3a2e3d5ecbd3fe4ac9ac4f554e,Planes,20.7655068562,159.70125263,-129.083749671,4177 -4178,_05_future,-14.4481856758,76.8253528135,62.8147490177,0.126,0.657,1.2,ce56947cdc2d9a42e498addc1c5930506d1d7dab,256,V6,ce56947cdc2d9a42e498addc1c5930506d1d7dab_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+70.542_ry+138.504_rz+105.988_tx+00.126_ty+00.657_s+01.560_ce56947cdc2d9a42e498addc1c5930506d1d7dab_256x256.png,2c68f9d97ad65016297b0e16056f4708fcefef60,Planes,-14.4481856758,166.825352814,62.8147490177,4178 -4179,_05_future,18.6166816932,89.7008418188,-122.797966142,0.35,-0.449,0.676153846154,3270bebfdf955125dd749b17171262562dd58476,256,V6,3270bebfdf955125dd749b17171262562dd58476_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+71.381_ry+00.888_rz+56.265_tx+00.350_ty-00.449_s+00.879_3270bebfdf955125dd749b17171262562dd58476_256x256.png,0ddc17d4f00b88adb842ff5145fdef1e5a2cdd08,Planes,18.6166816932,179.700841819,-122.797966142,4179 -4180,_05_future,3.61402913683,82.7438910011,48.1842277866,-0.513,0.782,1.02384615385,09b4928e1e19f74a0f4d31b3f33c6785b5ae1a38,256,V6,09b4928e1e19f74a0f4d31b3f33c6785b5ae1a38_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx+81.898_ry+63.432_rz+164.523_tx-00.513_ty+00.782_s+01.331_09b4928e1e19f74a0f4d31b3f33c6785b5ae1a38_256x256.png,76b1548ba076728c0864c8b8e5a75d6bed6e6656,Planes,3.61402913683,172.743891001,48.1842277866,4180 -4181,_05_future,10.9554574224,-7.1407988869,-12.7541084707,0.02,-0.393,1.12769230769,7916a2cc000aa3959796fa2923460298fbbc6147,256,V6,7916a2cc000aa3959796fa2923460298fbbc6147_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-07.010_ry+78.961_rz+75.882_tx+00.020_ty-00.393_s+01.466_7916a2cc000aa3959796fa2923460298fbbc6147_256x256.png,be27f60fafb615957486d1974d2ebb7cfc8fe823,Planes,10.9554574224,82.8592011131,-12.7541084707,4181 -4182,_05_future,-0.00297052664356,-8.03800001087,13.0015805105,-0.014,-1.189,1.12461538462,72f6ff3a53d37d6b9fb5751c7a4f23b5b3918891,256,V6,72f6ff3a53d37d6b9fb5751c7a4f23b5b3918891_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-08.038_ry+90.003_rz+103.002_tx-00.014_ty-01.189_s+01.462_72f6ff3a53d37d6b9fb5751c7a4f23b5b3918891_256x256.png,49e81959bbfb7c543268c6e662d011daab6f6440,Planes,-0.00297052664356,81.9619999891,13.0015805105,4182 -4183,_05_future,-34.1459683843,-16.2785743802,-80.450505359,-0.281,-0.731,0.953846153846,870ab45ff4aabae5334b0864fc8ea40b48d0de9d,256,V6,870ab45ff4aabae5334b0864fc8ea40b48d0de9d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-13.414_ry+125.243_rz+18.858_tx-00.281_ty-00.731_s+01.240_870ab45ff4aabae5334b0864fc8ea40b48d0de9d_256x256.png,acba8ad651414aa4b1b738d62a7aee99428fdaa5,Planes,-34.1459683843,73.7214256198,-80.450505359,4183 -4184,_05_future,-39.0721323702,-21.1245938964,-102.678426174,0.099,-0.515,1.21846153846,8b7647def26b64ebc23434e943829b2b7750545e,256,V6,8b7647def26b64ebc23434e943829b2b7750545e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-16.248_ry+131.035_rz+01.008_tx+00.099_ty-00.515_s+01.584_8b7647def26b64ebc23434e943829b2b7750545e_256x256.png,c8de4bde122b07035b0dde1ea6c5edf75976ffa5,Planes,-39.0721323702,68.8754061036,-102.678426174,4184 -4185,_05_future,-71.3579676169,-71.4807279192,-118.363635736,-0.571,-0.738,1.38461538462,637c1479d888b313f3279241ed6a2f3c483402c9,256,V6,637c1479d888b313f3279241ed6a2f3c483402c9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-17.644_ry+173.884_rz+42.167_tx-00.571_ty-00.738_s+01.800_637c1479d888b313f3279241ed6a2f3c483402c9_256x256.png,0a8f6f02b80e7d69adcbeb6c5af9f80a08a79be1,Planes,-71.3579676169,18.5192720808,-118.363635736,4185 -4186,_05_future,-57.958250933,-41.6797062445,-38.4958790674,0.543,1.067,1.44846153846,37bf32d0208eb9613ac25ba672fd6e3686fb4dab,256,V6,37bf32d0208eb9613ac25ba672fd6e3686fb4dab_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-20.658_ry+154.946_rz+88.546_tx+00.543_ty+01.067_s+01.883_37bf32d0208eb9613ac25ba672fd6e3686fb4dab_256x256.png,63660ea677d7679eddcc880ee72ea6310f0d049e,Planes,-57.958250933,48.3202937555,-38.4958790674,4186 -4187,_05_future,15.2162326419,-36.4772986123,8.70482893122,-0.107,-0.86,1.32384615385,b8b7d1347674b8e5016acb1cbe226fa2d3972624,256,V6,b8b7d1347674b8e5016acb1cbe226fa2d3972624_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-35.006_ry+71.311_rz+87.723_tx-00.107_ty-00.860_s+01.721_b8b7d1347674b8e5016acb1cbe226fa2d3972624_256x256.png,99a1ae764206313291c073000353a5261ee319bb,Planes,15.2162326419,53.5227013877,8.70482893122,4187 -4188,_05_future,23.1710922043,-52.4248549759,-25.4487739384,0.106,0.066,0.925384615385,9e4672f5e20d28ac98e788b57d39ed9d9bdd80e9,256,V6,9e4672f5e20d28ac98e788b57d39ed9d9bdd80e9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-46.771_ry+54.936_rz+37.466_tx+00.106_ty+00.066_s+01.203_9e4672f5e20d28ac98e788b57d39ed9d9bdd80e9_256x256.png,f721d21fff21060bc6529c4c53e53872322d95b8,Planes,23.1710922043,37.5751450241,-25.4487739384,4188 -4189,_05_future,-38.5674042304,-69.9220761051,-94.1694975485,0.382,0.373,0.74,efa0ce6b61084e566e22b2f61f6eccb111176db6,256,V6,efa0ce6b61084e566e22b2f61f6eccb111176db6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-47.253_ry+156.706_rz+55.448_tx+00.382_ty+00.373_s+00.962_efa0ce6b61084e566e22b2f61f6eccb111176db6_256x256.png,dfedac8e6fd85df2f662730949f77775e704ffac,Planes,-38.5674042304,20.0779238949,-94.1694975485,4189 -4190,_05_future,33.0750914295,-72.5784064586,32.4944332333,-0.471,0.774,1.19538461538,66696fa4a926f8100b8c742a9b56e967f0b97c09,256,V6,66696fa4a926f8100b8c742a9b56e967f0b97c09_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-53.084_ry+24.689_rz+62.393_tx-00.471_ty+00.774_s+01.554_66696fa4a926f8100b8c742a9b56e967f0b97c09_256x256.png,c2dbb68ee069a80738e97d28fcf631b65c41a371,Planes,33.0750914295,17.4215935414,32.4944332333,4190 -4191,_05_future,-29.3508507726,-86.4753963983,-148.150681936,0.459,-0.885,0.992307692308,70222407751181c4b7723cb09dbda63e7f9c7333,256,V6,70222407751181c4b7723cb09dbda63e7f9c7333_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-60.457_ry+173.761_rz+24.687_tx+00.459_ty-00.885_s+01.290_70222407751181c4b7723cb09dbda63e7f9c7333_256x256.png,60596fe88664f617de50bd07a2a2774c1317932b,Planes,-29.3508507726,3.52460360166,-148.150681936,4191 -4192,_05_future,13.0311966178,-63.3471875176,-48.0994056074,0.504,-1.029,1.03692307692,1f00bdd9e2d0caefc99716330feb8c7d750fcaa9,256,V6,1f00bdd9e2d0caefc99716330feb8c7d750fcaa9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-60.543_ry+62.709_rz+17.709_tx+00.504_ty-01.029_s+01.348_1f00bdd9e2d0caefc99716330feb8c7d750fcaa9_256x256.png,a323dff18e64566263ac9277b1dba3950c237ac7,Planes,13.0311966178,26.6528124824,-48.0994056074,4192 -4193,_05_future,-16.8625181358,-81.024570026,-91.7489527478,0.061,0.362,0.662307692308,4bc1f90f00c27ffc8309c26420804eada2621dc2,256,V6,4bc1f90f00c27ffc8309c26420804eada2621dc2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-70.959_ry+152.765_rz+59.683_tx+00.061_ty+00.362_s+00.861_4bc1f90f00c27ffc8309c26420804eada2621dc2_256x256.png,c7ccd6e2fec7d182227bcb1269583cc6f6828666,Planes,-16.8625181358,8.97542997402,-91.7489527478,4193 -4194,_05_future,-11.6566805755,-83.7329907122,-37.9646565207,-0.452,0.219,1.53076923077,a38f7efa470fafdf195bf2b215b97bfddfb4cdc6,256,V6,a38f7efa470fafdf195bf2b215b97bfddfb4cdc6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-76.786_ry+152.115_rz+113.510_tx-00.452_ty+00.219_s+01.990_a38f7efa470fafdf195bf2b215b97bfddfb4cdc6_256x256.png,3a1f41da69d9a6f48354a71984ce366c21de0993,Planes,-11.6566805755,6.26700928782,-37.9646565207,4194 -4195,_05_future,8.81600233354,-83.6055536331,47.281566685,0.358,-0.803,1.42538461538,61e67cd73d4a6e600a99b37c49dfef62d509ced8,256,V6,61e67cd73d4a6e600a99b37c49dfef62d509ced8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-79.124_ry+35.682_rz+83.457_tx+00.358_ty-00.803_s+01.853_61e67cd73d4a6e600a99b37c49dfef62d509ced8_256x256.png,3ec1b956fa73d8aae4ada49636b7517c80727bb9,Planes,8.81600233354,6.39444636693,47.281566685,4195 -4196,_05_future,0.559679248781,-81.5042802418,-60.0165706608,-0.203,-1.197,1.56769230769,eb82f7ad2210ffaf8b9a5a2019ad45e80d723608,256,V6,eb82f7ad2210ffaf8b9a5a2019ad45e80d723608_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-81.486_ry+86.217_rz+26.242_tx-00.203_ty-01.197_s+02.038_eb82f7ad2210ffaf8b9a5a2019ad45e80d723608_256x256.png,9314d3a1da85272afbcdb8471b3a2c1854384be3,Planes,0.559679248781,8.49571975819,-60.0165706608,4196 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-4198,_05_future,3.3750153212,-89.5801470355,89.5826306212,-0.565,-0.591,1.43076923077,3b037a6280f342fe8a4dde9ccee8a618a4dddf2f,256,V6,3b037a6280f342fe8a4dde9ccee8a618a4dddf2f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-86.599_ry+07.083_rz+96.678_tx-00.565_ty-00.591_s+01.860_3b037a6280f342fe8a4dde9ccee8a618a4dddf2f_256x256.png,4e53b429a30fbdc20426307121f63acf93e63885,Planes,3.3750153212,0.419852964493,89.5826306212,4198 -4199,_05_future,2.41737944512,-88.4599869789,-28.1324942978,0.428,-0.756,1.12615384615,94dd44ed4c8ace41e9c5d5f2abbf131263b9c217,256,V6,94dd44ed4c8ace41e9c5d5f2abbf131263b9c217_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_05_future_rx-87.134_ry+32.481_rz+04.381_tx+00.428_ty-00.756_s+01.464_94dd44ed4c8ace41e9c5d5f2abbf131263b9c217_256x256.png,0993b748055eb86cefdadc2759cfec404a83e00d,Planes,2.41737944512,1.54001302112,-28.1324942978,4199 -4200,_08,0.623,46.014,78.227,-0.265,-0.1,1.16428571429,38f4a33c7ea931c5ca9188a1a9cf505e87d5e352,256,V6,38f4a33c7ea931c5ca9188a1a9cf505e87d5e352_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_08_rx+00.623_ry+46.014_rz+78.227_tx-00.265_ty-00.100_s+00.815_38f4a33c7ea931c5ca9188a1a9cf505e87d5e352_256x256.png,6250113f1ac20437c74368757a96a2b6bec1879e,Tables,0.623,46.014,78.227,4200 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-4399,_18,-89.69,58.516,-18.505,0.558,1.045,0.945714285714,38280660d8ac3eafb80f0c44610b9d28f9195f30,256,V6,38280660d8ac3eafb80f0c44610b9d28f9195f30_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_18_rx-89.690_ry+58.516_rz-18.505_tx+00.558_ty+01.045_s+00.662_38280660d8ac3eafb80f0c44610b9d28f9195f30_256x256.png,42d6741eb86a0b341666c70c9686c811b6296a92,Tables,-89.69,58.516,-18.505,4399 -4400,_19_flyingBoat,-14.5494007968,3.03749521923,-35.4992662541,-0.249,-1.061,1.01692307692,3ed848baa6dde6d6da640d75413ae5c1ec9c1a8d,256,V6,3ed848baa6dde6d6da640d75413ae5c1ec9c1a8d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+02.940_ry+104.569_rz+53.737_tx-00.249_ty-01.061_s+01.322_3ed848baa6dde6d6da640d75413ae5c1ec9c1a8d_256x256.png,4e8ede7428dc67e11ca781b0d3494845770df3ca,Planes,-14.5494007968,93.0374952192,-35.4992662541,4400 -4401,_19_flyingBoat,-55.124715623,11.071529499,-55.6039772348,0.143,0.94,1.27769230769,54f2bb814b44cb0587820cd0e1c4aee9671946a8,256,V6,54f2bb814b44cb0587820cd0e1c4aee9671946a8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+06.304_ry+145.628_rz+25.276_tx+00.143_ty+00.940_s+01.661_54f2bb814b44cb0587820cd0e1c4aee9671946a8_256x256.png,c48f2d87146df62f12a7dc6720df990b2a84c549,Planes,-55.124715623,101.071529499,-55.6039772348,4401 -4402,_19_flyingBoat,-69.2619307459,20.3043810577,38.3519298769,-0.449,0.416,1.34230769231,534ab7dcad031e64694be36f76210b0be0b7dc62,256,V6,534ab7dcad031e64694be36f76210b0be0b7dc62_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+07.058_ry+160.450_rz+109.265_tx-00.449_ty+00.416_s+01.745_534ab7dcad031e64694be36f76210b0be0b7dc62_256x256.png,1cd1e9051657903cec85cebb38b13c381b42d83f,Planes,-69.2619307459,110.304381058,38.3519298769,4402 -4403,_19_flyingBoat,53.0041908617,13.7107819677,45.0557220812,-0.372,0.097,0.69,e6dc8444ec90cb401f7ac792a8abd1afa17ff8c8,256,V6,e6dc8444ec90cb401f7ac792a8abd1afa17ff8c8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+08.200_ry+36.203_rz+146.082_tx-00.372_ty+00.097_s+00.897_e6dc8444ec90cb401f7ac792a8abd1afa17ff8c8_256x256.png,bbc7c1da1c0ec4cdaffffd7859dc5438d793ecd3,Planes,53.0041908617,103.710781968,45.0557220812,4403 -4404,_19_flyingBoat,5.80405421984,9.89020726657,-7.10711294539,0.209,0.666,0.629230769231,fa491c159c75aed1c551c66ddc53bb2cf2809c27,256,V6,fa491c159c75aed1c551c66ddc53bb2cf2809c27_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+09.839_ry+84.109_rz+83.903_tx+00.209_ty+00.666_s+00.818_fa491c159c75aed1c551c66ddc53bb2cf2809c27_256x256.png,8033e05bbfaf92b400e965690a59bf81e3f1cd1d,Planes,5.80405421984,99.8902072666,-7.10711294539,4404 -4405,_19_flyingBoat,-22.6022286816,14.0137642294,68.6361969285,0.083,-1.167,1.30461538462,51931715e9279399c460085f7fbaa68dccf114eb,256,V6,51931715e9279399c460085f7fbaa68dccf114eb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+12.918_ry+113.223_rz+153.157_tx+00.083_ty-01.167_s+01.696_51931715e9279399c460085f7fbaa68dccf114eb_256x256.png,95d4eb5e479aea1b122d98f3d38bb3b4c9756613,Planes,-22.6022286816,104.013764229,68.6361969285,4405 -4406,_19_flyingBoat,-68.1588907221,46.5219376489,32.8067925226,0.011,-0.402,0.63,4768be9d0383b8ff296eaee778c928b1460ec5d6,256,V6,4768be9d0383b8ff296eaee778c928b1460ec5d6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+15.662_ry+164.582_rz+78.418_tx+00.011_ty-00.402_s+00.819_4768be9d0383b8ff296eaee778c928b1460ec5d6_256x256.png,8d2cf282b60f33768ac5531e95aed58ac8ed98e8,Planes,-68.1588907221,136.521937649,32.8067925226,4406 -4407,_19_flyingBoat,29.9382602748,20.3655342332,51.843405228,0.057,-0.839,0.814615384615,76717ffd63831564df86dfd8f47b115b3d854ffb,256,V6,76717ffd63831564df86dfd8f47b115b3d854ffb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+17.552_ry+58.437_rz+152.339_tx+00.057_ty-00.839_s+01.059_76717ffd63831564df86dfd8f47b115b3d854ffb_256x256.png,80e2f986a2f79a98ea204bc3429903597eb28f03,Planes,29.9382602748,110.365534233,51.843405228,4407 -4408,_19_flyingBoat,-38.1950631638,35.4919560519,35.4859850259,-0.103,0.201,1.37923076923,c6d970dd34be0f6e5d71e2b35f394e26a503d823,256,V6,c6d970dd34be0f6e5d71e2b35f394e26a503d823_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+27.148_ry+134.019_rz+101.692_tx-00.103_ty+00.201_s+01.793_c6d970dd34be0f6e5d71e2b35f394e26a503d823_256x256.png,aef86616e0b0ad1602a43cd346f935c80e23b9df,Planes,-38.1950631638,125.491956052,35.4859850259,4408 -4409,_19_flyingBoat,-52.7518983773,70.371877716,151.127465913,-0.406,0.927,1.03846153846,63efb1658c66037487e3aff4357bd19195db5e6a,256,V6,63efb1658c66037487e3aff4357bd19195db5e6a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+34.757_ry+165.672_rz+175.261_tx-00.406_ty+00.927_s+01.350_63efb1658c66037487e3aff4357bd19195db5e6a_256x256.png,867f066da0c9a01ffaaf1f5e3553100b613ccf4f,Planes,-52.7518983773,160.371877716,151.127465913,4409 -4410,_19_flyingBoat,-8.32092244002,36.3394466515,-53.2692327058,-0.386,-0.216,0.906923076923,ba1bd5019208455f5d4a651fca3db3c6987ee6fa,256,V6,ba1bd5019208455f5d4a651fca3db3c6987ee6fa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+35.897_ry+100.291_rz+30.654_tx-00.386_ty-00.216_s+01.179_ba1bd5019208455f5d4a651fca3db3c6987ee6fa_256x256.png,30841c1e4621df6c1f024b9e5414d9f3be3e6ec8,Planes,-8.32092244002,126.339446651,-53.2692327058,4410 -4411,_19_flyingBoat,-49.0124234575,87.7439792921,102.662471461,0.052,-0.335,1.05538461538,954e2dd1e73ac01adea6066ef515bd46e84a7064,256,V6,954e2dd1e73ac01adea6066ef515bd46e84a7064_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+40.949_ry+178.041_rz+105.650_tx+00.052_ty-00.335_s+01.372_954e2dd1e73ac01adea6066ef515bd46e84a7064_256x256.png,9d9ef43cc4ffb4353c4af5ade6af39226774ff70,Planes,-49.0124234575,177.743979292,102.662471461,4411 -4412,_19_flyingBoat,20.8865070395,46.2750157795,50.4130997173,-0.068,-0.321,1.33923076923,e57ffdef95b8558db5ed0ee6b529fbb21e5f56ff,256,V6,e57ffdef95b8558db5ed0ee6b529fbb21e5f56ff_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+42.468_ry+61.098_rz+160.856_tx-00.068_ty-00.321_s+01.741_e57ffdef95b8558db5ed0ee6b529fbb21e5f56ff_256x256.png,360dea2223a5dc27798b9de1ab234add04263792,Planes,20.8865070395,136.275015779,50.4130997173,4412 -4413,_19_flyingBoat,21.9986392339,56.7075613766,-77.1370642141,0.535,0.717,0.830769230769,67a111d612a91cabbf1306207a98872af6d4ee6d,256,V6,67a111d612a91cabbf1306207a98872af6d4ee6d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+50.807_ry+53.647_rz+42.564_tx+00.535_ty+00.717_s+01.080_67a111d612a91cabbf1306207a98872af6d4ee6d_256x256.png,384622d1eae0a17076d5652fb8f7c85c1841d65e,Planes,21.9986392339,146.707561377,-77.1370642141,4413 -4414,_19_flyingBoat,-15.9184617947,55.0728169394,41.8568299377,-0.05,0.481,1.42384615385,2f044ae294a23fee6bbe2b7bc0be610726abe88b,256,V6,2f044ae294a23fee6bbe2b7bc0be610726abe88b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+52.040_ry+116.480_rz+110.414_tx-00.050_ty+00.481_s+01.851_2f044ae294a23fee6bbe2b7bc0be610726abe88b_256x256.png,64880e4d12446acaf0b11d42c650b24a5cf9dda3,Planes,-15.9184617947,145.072816939,41.8568299377,4414 -4415,_19_flyingBoat,-2.76082504299,53.157668039,-23.8896953598,0.493,0.527,0.96,231c407ec9aead579ad8422ef862451301d4be9d,256,V6,231c407ec9aead579ad8422ef862451301d4be9d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+53.069_ry+94.598_rz+62.432_tx+00.493_ty+00.527_s+01.248_231c407ec9aead579ad8422ef862451301d4be9d_256x256.png,6fab5e9b3a1446e40a4fa1de71ed2f1f3ee1e158,Planes,-2.76082504299,143.157668039,-23.8896953598,4415 -4416,_19_flyingBoat,-6.87743350716,68.0976122874,68.8167010024,0.47,-0.197,1.46538461538,a24f018d0e99fe56f283535543d3c54d93782520,256,V6,a24f018d0e99fe56f283535543d3c54d93782520_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+67.094_ry+107.918_rz+142.231_tx+00.470_ty-00.197_s+01.905_a24f018d0e99fe56f283535543d3c54d93782520_256x256.png,f9f5c04fa9de57b72d53b8ff0fba3deabff75e38,Planes,-6.87743350716,158.097612287,68.8167010024,4416 -4417,_19_flyingBoat,-2.90094069367,84.0524777286,94.6542681419,0.093,-0.334,0.986153846154,5dd391960cb28d92601caee335ed6d1df157227a,256,V6,5dd391960cb28d92601caee335ed6d1df157227a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+83.385_ry+116.061_rz+158.744_tx+00.093_ty-00.334_s+01.282_5dd391960cb28d92601caee335ed6d1df157227a_256x256.png,12bf4b4fd094ff7d6ab0b70c603d8f0e9d5d2961,Planes,-2.90094069367,174.052477729,94.6542681419,4417 -4418,_19_flyingBoat,-3.78945791671,88.213275013,-13.3171120037,-0.454,0.591,0.886923076923,fd59b6074392fd6100f6c82139f29b9bf52ffbb6,256,V6,fd59b6074392fd6100f6c82139f29b9bf52ffbb6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+85.811_ry+154.792_rz+11.950_tx-00.454_ty+00.591_s+01.153_fd59b6074392fd6100f6c82139f29b9bf52ffbb6_256x256.png,ae614d2a8febbc16b2f6428161b168b00e5bb8b2,Planes,-3.78945791671,178.213275013,-13.3171120037,4418 -4419,_19_flyingBoat,-3.18933919441,88.0532779318,101.916799169,0.441,-0.939,1.31230769231,2294e29de7e5e2317ddd3ad47320850b79eb7ab9,256,V6,2294e29de7e5e2317ddd3ad47320850b79eb7ab9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+86.264_ry+148.632_rz+133.339_tx+00.441_ty-00.939_s+01.706_2294e29de7e5e2317ddd3ad47320850b79eb7ab9_256x256.png,62d9cdb263ae757415eb7c450147e3c4d0b2259f,Planes,-3.18933919441,178.053277932,101.916799169,4419 -4420,_19_flyingBoat,-0.601951998359,86.8316174134,33.1703520205,-0.551,0.484,0.707692307692,3512537861a4a01264ddecfa7a5ef56881372a2e,256,V6,3512537861a4a01264ddecfa7a5ef56881372a2e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+86.775_ry+100.763_rz+112.424_tx-00.551_ty+00.484_s+00.920_3512537861a4a01264ddecfa7a5ef56881372a2e_256x256.png,c7c70d84a5e3511a41f9b14035d5c5b7923173b0,Planes,-0.601951998359,176.831617413,33.1703520205,4420 -4421,_19_flyingBoat,-0.205932408224,88.3870898828,65.9101012458,-0.159,1.057,1.08846153846,0ff090f9be19a6fd1aaaabd649bf11391c4b8d7e,256,V6,0ff090f9be19a6fd1aaaabd649bf11391c4b8d7e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx+88.374_ry+97.277_rz+148.636_tx-00.159_ty+01.057_s+01.415_0ff090f9be19a6fd1aaaabd649bf11391c4b8d7e_256x256.png,71d30a00cf0097f67076aade207a5e138abce18c,Planes,-0.205932408224,178.387089883,65.9101012458,4421 -4422,_19_flyingBoat,-14.9022685924,-5.06166662763,-26.585885488,0.254,0.512,0.79,6255d018352facaa072dd81e00b8bf0dce85f696,256,V6,6255d018352facaa072dd81e00b8bf0dce85f696_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-04.891_ry+104.958_rz+64.719_tx+00.254_ty+00.512_s+01.027_6255d018352facaa072dd81e00b8bf0dce85f696_256x256.png,dd7fd0961f0d2f02f18b4eb365768b869a20174c,Planes,-14.9022685924,84.9383333724,-26.585885488,4422 -4423,_19_flyingBoat,0.949251337242,-13.9279500108,-34.1426046546,-0.458,0.875,0.848461538462,99f2ee9bfa0dceb4d860468c0851fa3787191995,256,V6,99f2ee9bfa0dceb4d860468c0851fa3787191995_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-13.926_ry+89.022_rz+55.622_tx-00.458_ty+00.875_s+01.103_99f2ee9bfa0dceb4d860468c0851fa3787191995_256x256.png,e59ea6bc6c9c9f5fd82043d39546c0b61a8194fa,Planes,0.949251337242,76.0720499892,-34.1426046546,4423 -4424,_19_flyingBoat,72.4065985798,-74.561374941,59.4144840192,-0.405,-0.558,0.943846153846,5269394f6f1a414b24c15dd184959ba9dc4e7bfd,256,V6,5269394f6f1a414b24c15dd184959ba9dc4e7bfd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-16.939_ry+04.825_rz+75.572_tx-00.405_ty-00.558_s+01.227_5269394f6f1a414b24c15dd184959ba9dc4e7bfd_256x256.png,8a814898a8ed29981ed6b2329dfbea87cf231bfb,Planes,72.4065985798,15.438625059,59.4144840192,4424 -4425,_19_flyingBoat,5.30571828507,-25.5915144833,-78.1441790353,-0.51,0.016,0.784615384615,7f76fe25a54dc2d9011737a152a1912a2a39342b,256,V6,7f76fe25a54dc2d9011737a152a1912a2a39342b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-25.474_ry+84.121_rz+09.320_tx-00.510_ty+00.016_s+01.020_7f76fe25a54dc2d9011737a152a1912a2a39342b_256x256.png,a4b9381a28c4dde219d2ac3c170140d726e2e258,Planes,5.30571828507,64.4084855167,-78.1441790353,4425 -4426,_19_flyingBoat,-46.8564189557,-56.6468477135,-39.4795699163,-0.316,0.083,1.26230769231,f61186d1ddd0bce9d9cb2caf94696c2effd152b1,256,V6,f61186d1ddd0bce9d9cb2caf94696c2effd152b1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-34.834_ry+152.739_rz+98.467_tx-00.316_ty+00.083_s+01.641_f61186d1ddd0bce9d9cb2caf94696c2effd152b1_256x256.png,9b933aba7fa2e9f7158588331928129c5ff5e48d,Planes,-46.8564189557,33.3531522865,-39.4795699163,4426 -4427,_19_flyingBoat,-36.6026355583,-55.9401680168,-119.976497001,0.112,0.04,1.52846153846,05823d190543365d67ee28e40dbbee02c47da244,256,V6,05823d190543365d67ee28e40dbbee02c47da244_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-41.688_ry+142.982_rz+11.436_tx+00.112_ty+00.040_s+01.987_05823d190543365d67ee28e40dbbee02c47da244_256x256.png,73dc4f25adf4b3fa9ea9056e787bc185a746d88a,Planes,-36.6026355583,34.0598319832,-119.976497001,4427 -4428,_19_flyingBoat,36.5998343473,-59.7694148336,89.2607555989,0.324,-0.028,1.46846153846,8dcbac40ed56cdf90d751ace9a19f6f7fc537cfa,256,V6,8dcbac40ed56cdf90d751ace9a19f6f7fc537cfa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-43.919_ry+34.135_rz+133.605_tx+00.324_ty-00.028_s+01.909_8dcbac40ed56cdf90d751ace9a19f6f7fc537cfa_256x256.png,87275f29cfdc88f7c1534451ade975fa351c51bd,Planes,36.5998343473,30.2305851664,89.2607555989,4428 -4429,_19_flyingBoat,-11.0492428702,-47.8796691066,64.6689879219,-0.103,0.323,1.44,93650581bf46be8dbd089c6732b99dc07ec69ccd,256,V6,93650581bf46be8dbd089c6732b99dc07ec69ccd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-46.718_ry+106.233_rz+166.636_tx-00.103_ty+00.323_s+01.872_93650581bf46be8dbd089c6732b99dc07ec69ccd_256x256.png,f799d0b6d59776a9f46316c599d073e913aa2074,Planes,-11.0492428702,42.1203308934,64.6689879219,4429 -4430,_19_flyingBoat,39.3410429194,-87.6259476658,18.6622380628,0.041,-0.35,1.20769230769,af6c999cd7b290c602c31a4c0e7670ef8ee242bc,256,V6,af6c999cd7b290c602c31a4c0e7670ef8ee242bc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-50.599_ry+02.893_rz+22.404_tx+00.041_ty-00.350_s+01.570_af6c999cd7b290c602c31a4c0e7670ef8ee242bc_256x256.png,5ca98941d064cfaa3b0416e2166c92a22a44475b,Planes,39.3410429194,2.3740523342,18.6622380628,4430 -4431,_19_flyingBoat,-35.578477262,-72.4705907275,7.97111529179,0.18,-0.326,0.843846153846,21814c5289a2635f95b86a2b71847729d9188b4d,256,V6,21814c5289a2635f95b86a2b71847729d9188b4d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-50.855_ry+157.167_rz+159.474_tx+00.180_ty-00.326_s+01.097_21814c5289a2635f95b86a2b71847729d9188b4d_256x256.png,cee016920ed28ce9ceffd22242be9dea193c62f6,Planes,-35.578477262,17.5294092725,7.97111529179,4431 -4432,_19_flyingBoat,-38.1140712003,-88.5330949244,-116.806245766,0.508,0.665,1.53230769231,9fd6d6b6024da60d70ea60e705fe1e69134c2108,256,V6,9fd6d6b6024da60d70ea60e705fe1e69134c2108_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-51.862_ry+178.131_rz+60.818_tx+00.508_ty+00.665_s+01.992_9fd6d6b6024da60d70ea60e705fe1e69134c2108_256x256.png,80224fb634cb5d859a5ed31ea4aacfbb7aab5ee3,Planes,-38.1140712003,1.4669050756,-116.806245766,4432 -4433,_19_flyingBoat,5.53492552379,-55.3608026494,-27.9402272142,0.493,1.088,1.53153846154,d9ac79bd5506a76708444a04176b1f7b3efddc12,256,V6,d9ac79bd5506a76708444a04176b1f7b3efddc12_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-54.976_ry+80.325_rz+54.112_tx+00.493_ty+01.088_s+01.991_d9ac79bd5506a76708444a04176b1f7b3efddc12_256x256.png,b3ccc2f90f582089f40b96d715f5d1453766504f,Planes,5.53492552379,34.6391973506,-27.9402272142,4433 -4434,_19_flyingBoat,33.3607270926,-81.4083960947,49.8552051871,0.24,1.125,0.978461538462,00f68711543bc698c6be714cb258d6dcff4798af,256,V6,00f68711543bc698c6be714cb258d6dcff4798af_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-55.675_ry+12.784_rz+65.218_tx+00.240_ty+01.125_s+01.272_00f68711543bc698c6be714cb258d6dcff4798af_256x256.png,7c48b85d0fe13918fbe6c94aba6a8294fb3a6185,Planes,33.3607270926,8.59160390526,49.8552051871,4434 -4435,_19_flyingBoat,-5.28897899974,-64.4148416389,22.4057156237,0.123,-0.524,1.45,91971a7b6e3dc769ecce495f245ae8b32d409f26,256,V6,91971a7b6e3dc769ecce495f245ae8b32d409f26_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-63.910_ry+102.099_rz+123.303_tx+00.123_ty-00.524_s+01.885_91971a7b6e3dc769ecce495f245ae8b32d409f26_256x256.png,6decb7f8787edc3c7d204c3f221e9de117371c16,Planes,-5.28897899974,25.5851583611,22.4057156237,4435 -4436,_19_flyingBoat,21.0768325965,-81.3165161526,-6.7564030866,-0.369,-0.753,1.54769230769,16b2ad286a9a050c8a1eccecd4bbbdb76a822960,256,V6,16b2ad286a9a050c8a1eccecd4bbbdb76a822960_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-67.280_ry+21.392_rz+16.254_tx-00.369_ty-00.753_s+02.012_16b2ad286a9a050c8a1eccecd4bbbdb76a822960_256x256.png,153489b0836d9cd1e6dd55f83f2a3b9562576024,Planes,21.0768325965,8.68348384744,-6.7564030866,4436 -4437,_19_flyingBoat,7.86828073904,-83.2840584697,52.734605469,-0.538,1.14,0.948461538462,bf32590ad4b070c754c7c864aee1db249e4c0be0,256,V6,bf32590ad4b070c754c7c864aee1db249e4c0be0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-79.669_ry+40.239_rz+93.436_tx-00.538_ty+01.140_s+01.233_bf32590ad4b070c754c7c864aee1db249e4c0be0_256x256.png,ea05353ce655648f5d42ba28abb9ede34cd56a5a,Planes,7.86828073904,6.71594153033,52.734605469,4437 -4438,_19_flyingBoat,-1.65664039202,-80.1763545101,-5.28562161329,0.202,-0.954,0.669230769231,c0121d44b475fbf98f022b6308f2b82db2f30f97,256,V6,c0121d44b475fbf98f022b6308f2b82db2f30f97_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-80.039_ry+99.621_rz+94.193_tx+00.202_ty-00.954_s+00.870_c0121d44b475fbf98f022b6308f2b82db2f30f97_256x256.png,7b437e5265da952bc6408bded46d6a438655a25a,Planes,-1.65664039202,9.82364548992,-5.28562161329,4438 -4439,_19_flyingBoat,-0.898684616613,-89.679858836,-13.158489234,0.314,1.129,1.14076923077,b14c53fd4b0ec1547a3ef5a65298cc6cdb96227d,256,V6,b14c53fd4b0ec1547a3ef5a65298cc6cdb96227d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_19_flyingBoat_rx-89.046_ry+160.394_rz+147.233_tx+00.314_ty+01.129_s+01.483_b14c53fd4b0ec1547a3ef5a65298cc6cdb96227d_256x256.png,05e311be37df4b8335abde674a9b25cbe77dd3f5,Planes,-0.898684616613,0.320141164005,-13.158489234,4439 -4440,_37,2.234,-65.129,-84.174,-0.26,0.256,1.03,f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2,256,V6,f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_37_rx+02.234_ry-65.129_rz-84.174_tx-00.260_ty+00.256_s+00.721_f0ec7e27681c3c7e8ea8e00ae387a8ab346c1ea2_256x256.png,b24198d15dc6f51636a02c241ae0f1727efc8e82,Tables,2.234,-65.129,-84.174,4440 -4441,_37,3.129,55.509,59.142,0.544,0.74,1.58714285714,ed88610a578383289999477e9cc7d1ac00539404,256,V6,ed88610a578383289999477e9cc7d1ac00539404_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_37_rx+03.129_ry+55.509_rz+59.142_tx+00.544_ty+00.740_s+01.111_ed88610a578383289999477e9cc7d1ac00539404_256x256.png,3b7101533f9a363f2e6fba91c55bc29764089024,Tables,3.129,55.509,59.142,4441 -4442,_37,4.577,61.791,3.609,0.538,0.338,1.12857142857,c7d495d2cc6385b772588034fbbb901d9aabfc07,256,V6,c7d495d2cc6385b772588034fbbb901d9aabfc07_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_37_rx+04.577_ry+61.791_rz+03.609_tx+00.538_ty+00.338_s+00.790_c7d495d2cc6385b772588034fbbb901d9aabfc07_256x256.png,4910d5cd34bff51bf0ce9c5566c97c86a21320c4,Tables,4.577,61.791,3.609,4442 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-4445,_37,24.776,45.775,57.876,0.375,0.747,0.745714285714,342950c856540dba71765ee41b24accaf9e07b5a,256,V6,342950c856540dba71765ee41b24accaf9e07b5a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_37_rx+24.776_ry+45.775_rz+57.876_tx+00.375_ty+00.747_s+00.522_342950c856540dba71765ee41b24accaf9e07b5a_256x256.png,184c0e5b820398374c21dd950511605bcd631be4,Tables,24.776,45.775,57.876,4445 -4446,_37,32.817,-9.824,-62.843,0.139,-1.122,1.59142857143,9dfd8a31693faf49a82f0d5bc66ec75f00fcec3e,256,V6,9dfd8a31693faf49a82f0d5bc66ec75f00fcec3e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_37_rx+32.817_ry-09.824_rz-62.843_tx+00.139_ty-01.122_s+01.114_9dfd8a31693faf49a82f0d5bc66ec75f00fcec3e_256x256.png,797fb30707846be84daed3cd4e023cd977cbdb4e,Tables,32.817,-9.824,-62.843,4446 -4447,_37,34.762,-21.886,79.605,0.068,0.196,0.985714285714,bff1d30f5b2fa13edb737c83ad4b4bb3d6b82ec6,256,V6,bff1d30f5b2fa13edb737c83ad4b4bb3d6b82ec6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_37_rx+34.762_ry-21.886_rz+79.605_tx+00.068_ty+00.196_s+00.690_bff1d30f5b2fa13edb737c83ad4b4bb3d6b82ec6_256x256.png,0c00d62349c50e4ba613463cf423cbc5a55888d3,Tables,34.762,-21.886,79.605,4447 -4448,_37,36.429,82.978,9.088,-0.321,0.805,1.49571428571,1912cd078f1b723b3a34d9d35b7ff1ddeda1d0a4,256,V6,1912cd078f1b723b3a34d9d35b7ff1ddeda1d0a4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_37_rx+36.429_ry+82.978_rz+09.088_tx-00.321_ty+00.805_s+01.047_1912cd078f1b723b3a34d9d35b7ff1ddeda1d0a4_256x256.png,f61572e81b4d36d9f7cf2f3367e1c6a2525b5cc9,Tables,36.429,82.978,9.088,4448 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-4479,_37,-85.16,78.825,76.012,-0.463,1.117,0.967142857143,5b09298fe36ae3c0d1e5faf07919dce6224e5dd8,256,V6,5b09298fe36ae3c0d1e5faf07919dce6224e5dd8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_37_rx-85.160_ry+78.825_rz+76.012_tx-00.463_ty+01.117_s+00.677_5b09298fe36ae3c0d1e5faf07919dce6224e5dd8_256x256.png,bec4f066290450d8ea876dd91a009461079d3349,Tables,-85.16,78.825,76.012,4479 -4480,_38,6.118,-70.797,-30.275,-0.23,-0.12,1.58857142857,409201b3c5032fde94247b5c6941b65039b46c0d,256,V6,409201b3c5032fde94247b5c6941b65039b46c0d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_38_rx+06.118_ry-70.797_rz-30.275_tx-00.230_ty-00.120_s+01.112_409201b3c5032fde94247b5c6941b65039b46c0d_256x256.png,01d2f1335b3232b166339c3b8135b8087b875fe8,Tables,6.118,-70.797,-30.275,4480 -4481,_38,8.391,-2.274,40.067,-0.411,-0.963,1.03571428571,367e6495c9b3436b4eaa9fe2d90c8a35c26b55bb,256,V6,367e6495c9b3436b4eaa9fe2d90c8a35c26b55bb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_38_rx+08.391_ry-02.274_rz+40.067_tx-00.411_ty-00.963_s+00.725_367e6495c9b3436b4eaa9fe2d90c8a35c26b55bb_256x256.png,712a26562c282039b1ba9bdbe301569f270d8d77,Tables,8.391,-2.274,40.067,4481 -4482,_38,8.439,-77.498,88.917,0.191,0.795,1.06857142857,ce34c19289becff56d0d3151cb2fc4f7c7664266,256,V6,ce34c19289becff56d0d3151cb2fc4f7c7664266_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_38_rx+08.439_ry-77.498_rz+88.917_tx+00.191_ty+00.795_s+00.748_ce34c19289becff56d0d3151cb2fc4f7c7664266_256x256.png,043f0bd288679609e3e4e0fe9dd142f23b35b8a5,Tables,8.439,-77.498,88.917,4482 -4483,_38,9.31,-54.26,-75.716,0.411,0.827,1.21,aa2f8f34b136930f41360e46bfc45bda13676ba8,256,V6,aa2f8f34b136930f41360e46bfc45bda13676ba8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_38_rx+09.310_ry-54.260_rz-75.716_tx+00.411_ty+00.827_s+00.847_aa2f8f34b136930f41360e46bfc45bda13676ba8_256x256.png,8a3076651e1c679ff1c06e1b25b2ff3ad447a14a,Tables,9.31,-54.26,-75.716,4483 -4484,_38,22.478,68.175,-19.665,0.228,0.312,1.23285714286,9912645b6f863c3b22d230368a83ce94312c4b9d,256,V6,9912645b6f863c3b22d230368a83ce94312c4b9d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_38_rx+22.478_ry+68.175_rz-19.665_tx+00.228_ty+00.312_s+00.863_9912645b6f863c3b22d230368a83ce94312c4b9d_256x256.png,75f609bc3c6b8b55ee5220dfb2f847f912c60bd5,Tables,22.478,68.175,-19.665,4484 -4485,_38,24.214,-54.914,59.912,0.546,-0.489,0.824285714286,6865e6cc36ff594779aa6d32f02bc287ea105553,256,V6,6865e6cc36ff594779aa6d32f02bc287ea105553_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_38_rx+24.214_ry-54.914_rz+59.912_tx+00.546_ty-00.489_s+00.577_6865e6cc36ff594779aa6d32f02bc287ea105553_256x256.png,92d234244ab8ed4e60c7429814250f6355bddd02,Tables,24.214,-54.914,59.912,4485 -4486,_38,24.736,-87.146,-86.248,-0.298,0.496,1.18571428571,fe2369a91ce76f9787a8ba834bb52ea38f3377bc,256,V6,fe2369a91ce76f9787a8ba834bb52ea38f3377bc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_38_rx+24.736_ry-87.146_rz-86.248_tx-00.298_ty+00.496_s+00.830_fe2369a91ce76f9787a8ba834bb52ea38f3377bc_256x256.png,b94efc87b74aa03128f23c855600451039c59938,Tables,24.736,-87.146,-86.248,4486 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-4489,_38,31.397,27.063,8.11,0.446,-0.41,0.945714285714,3cc5f1ca8a4ac74f6ce3d730b827a55bc2ce2ad9,256,V6,3cc5f1ca8a4ac74f6ce3d730b827a55bc2ce2ad9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_38_rx+31.397_ry+27.063_rz+08.110_tx+00.446_ty-00.410_s+00.662_3cc5f1ca8a4ac74f6ce3d730b827a55bc2ce2ad9_256x256.png,0418a412fe419395375d42fa2d84fc36b3124a2e,Tables,31.397,27.063,8.11,4489 -4490,_38,42.645,10.03,-43.998,0.067,-0.94,1.08571428571,4fa71b42ec10287bde983af1a67c1225b752f946,256,V6,4fa71b42ec10287bde983af1a67c1225b752f946_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_38_rx+42.645_ry+10.030_rz-43.998_tx+00.067_ty-00.940_s+00.760_4fa71b42ec10287bde983af1a67c1225b752f946_256x256.png,1de9ebd60a7d3dd3bfc3e846bf55910294aa3540,Tables,42.645,10.03,-43.998,4490 -4491,_38,47.074,-35.518,69.032,0.136,-0.509,1.37571428571,99b27237fee7e3ae68845f00b7e74928db3c2c0f,256,V6,99b27237fee7e3ae68845f00b7e74928db3c2c0f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_38_rx+47.074_ry-35.518_rz+69.032_tx+00.136_ty-00.509_s+00.963_99b27237fee7e3ae68845f00b7e74928db3c2c0f_256x256.png,358ea673e6c43b5bcaf2cdce953913d7738f39f3,Tables,47.074,-35.518,69.032,4491 -4492,_38,47.728,55.521,-24.858,-0.213,0.034,0.641428571429,80b2bd837d0db85f1c3cdf6a281effba92c70f8d,256,V6,80b2bd837d0db85f1c3cdf6a281effba92c70f8d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_38_rx+47.728_ry+55.521_rz-24.858_tx-00.213_ty+00.034_s+00.449_80b2bd837d0db85f1c3cdf6a281effba92c70f8d_256x256.png,26606e8a9e103408a0563b75a84099a19ef06abf,Tables,47.728,55.521,-24.858,4492 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-4497,_38,77.101,44.7,-59.109,-0.047,0.534,1.14571428571,6489cc15247e5780c56eda736cb53a1f74fdea82,256,V6,6489cc15247e5780c56eda736cb53a1f74fdea82_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_38_rx+77.101_ry+44.700_rz-59.109_tx-00.047_ty+00.534_s+00.802_6489cc15247e5780c56eda736cb53a1f74fdea82_256x256.png,c9cedeb8bf8f004e9b584af225ad5e7b92c85f14,Tables,77.101,44.7,-59.109,4497 -4498,_38,83.015,-40.162,-85.99,-0.53,-0.291,1.45714285714,f9a4c0f530ccfb9eefd33f6984ec5dc7f51b243b,256,V6,f9a4c0f530ccfb9eefd33f6984ec5dc7f51b243b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_38_rx+83.015_ry-40.162_rz-85.990_tx-00.530_ty-00.291_s+01.020_f9a4c0f530ccfb9eefd33f6984ec5dc7f51b243b_256x256.png,cf92f2c485a856f07cd3666a4fe05aa131871922,Tables,83.015,-40.162,-85.99,4498 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-4527,_44,45.331,20.408,-46.327,0.416,-0.023,0.837142857143,7c878017a3236dbb243655a3ae3a3f43b6e279fa,256,V6,7c878017a3236dbb243655a3ae3a3f43b6e279fa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx+45.331_ry+20.408_rz-46.327_tx+00.416_ty-00.023_s+00.586_7c878017a3236dbb243655a3ae3a3f43b6e279fa_256x256.png,e0d73daaa407e59a027dfc7f1cddd296bb3e755a,Tables,45.331,20.408,-46.327,4527 -4528,_44,49.578,-3.095,-15.05,-0.446,0.051,1.01571428571,55f5d40e80068cf06c536a88fe258df0feb75b6a,256,V6,55f5d40e80068cf06c536a88fe258df0feb75b6a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx+49.578_ry-03.095_rz-15.050_tx-00.446_ty+00.051_s+00.711_55f5d40e80068cf06c536a88fe258df0feb75b6a_256x256.png,844d9c963e55461db6bbfd896e56db3cadced844,Tables,49.578,-3.095,-15.05,4528 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-4531,_44,68.775,79.606,87.667,-0.537,-0.844,1.10571428571,d9fb9c1ece872fce419534609316a7f19c704a32,256,V6,d9fb9c1ece872fce419534609316a7f19c704a32_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx+68.775_ry+79.606_rz+87.667_tx-00.537_ty-00.844_s+00.774_d9fb9c1ece872fce419534609316a7f19c704a32_256x256.png,71e1415f587348e33222457fa5085dd3d2518fad,Tables,68.775,79.606,87.667,4531 -4532,_44,71.463,-10.85,-88.235,0.295,0.233,0.874285714286,708c279bc31d8fea2d33c43ffeff859a7a4a31f0,256,V6,708c279bc31d8fea2d33c43ffeff859a7a4a31f0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx+71.463_ry-10.850_rz-88.235_tx+00.295_ty+00.233_s+00.612_708c279bc31d8fea2d33c43ffeff859a7a4a31f0_256x256.png,1a561177cff5c817c49a41978db66bc4b59f3dcc,Tables,71.463,-10.85,-88.235,4532 -4533,_44,77.383,-40.986,-12.959,0.387,0.046,0.901428571429,46f706e4076345f54a321f44e5debeec90770216,256,V6,46f706e4076345f54a321f44e5debeec90770216_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx+77.383_ry-40.986_rz-12.959_tx+00.387_ty+00.046_s+00.631_46f706e4076345f54a321f44e5debeec90770216_256x256.png,9df07298abcf9a218b338363055466faafb5415b,Tables,77.383,-40.986,-12.959,4533 -4534,_44,80.424,-28.306,77.417,-0.204,-0.604,1.59142857143,7a273f4ae59f421e5c07f5a7275fb21ca33fc7c9,256,V6,7a273f4ae59f421e5c07f5a7275fb21ca33fc7c9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx+80.424_ry-28.306_rz+77.417_tx-00.204_ty-00.604_s+01.114_7a273f4ae59f421e5c07f5a7275fb21ca33fc7c9_256x256.png,6cbca2da7e69cbd0f9c91a38c8c035a46b274584,Tables,80.424,-28.306,77.417,4534 -4535,_44,84.771,-83.281,-1.722,0.186,-0.983,1.30714285714,0b9d4f50e36778cd276b7b29a7ef6b2b96aaa66a,256,V6,0b9d4f50e36778cd276b7b29a7ef6b2b96aaa66a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx+84.771_ry-83.281_rz-01.722_tx+00.186_ty-00.983_s+00.915_0b9d4f50e36778cd276b7b29a7ef6b2b96aaa66a_256x256.png,6f2429efed9f10d94254355dd1d71a4a2d57131c,Tables,84.771,-83.281,-1.722,4535 -4536,_44,86.124,67.038,-38.536,-0.567,-0.817,1.33857142857,9006aed7250573ae05626601ee1b7ec5d64ac4c1,256,V6,9006aed7250573ae05626601ee1b7ec5d64ac4c1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx+86.124_ry+67.038_rz-38.536_tx-00.567_ty-00.817_s+00.937_9006aed7250573ae05626601ee1b7ec5d64ac4c1_256x256.png,65b527b851b0449137ffcdfbda32074f61ab1ee2,Tables,86.124,67.038,-38.536,4536 -4537,_44,87.096,-59.16,-3.413,0.325,-0.464,1.00714285714,96269b7a93815bb892a46250b192aba2ea012555,256,V6,96269b7a93815bb892a46250b192aba2ea012555_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx+87.096_ry-59.160_rz-03.413_tx+00.325_ty-00.464_s+00.705_96269b7a93815bb892a46250b192aba2ea012555_256x256.png,5b429c59bca8ba1be815deb22d076a3d1200e070,Tables,87.096,-59.16,-3.413,4537 -4538,_44,87.558,67.089,78.78,-0.302,-0.787,0.988571428571,62a63ffc95ce87b510bf72f42b99b3633067fa83,256,V6,62a63ffc95ce87b510bf72f42b99b3633067fa83_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx+87.558_ry+67.089_rz+78.780_tx-00.302_ty-00.787_s+00.692_62a63ffc95ce87b510bf72f42b99b3633067fa83_256x256.png,723f49c9c92c7c60d16085a5a448c71c1c8c4e29,Tables,87.558,67.089,78.78,4538 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-4541,_44,-1.415,7.574,44.962,0.254,0.512,1.02571428571,0c04a361705461916339257186311d8be9c7ba46,256,V6,0c04a361705461916339257186311d8be9c7ba46_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx-01.415_ry+07.574_rz+44.962_tx+00.254_ty+00.512_s+00.718_0c04a361705461916339257186311d8be9c7ba46_256x256.png,4fbd2595d8f72c1c17a6468415f9e3b759f94a98,Tables,-1.415,7.574,44.962,4541 -4542,_44,-4.978,33.803,27.261,-0.219,-0.844,0.72,d2bad66482ed4ebd1a58553de5952c3961985567,256,V6,d2bad66482ed4ebd1a58553de5952c3961985567_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx-04.978_ry+33.803_rz+27.261_tx-00.219_ty-00.844_s+00.504_d2bad66482ed4ebd1a58553de5952c3961985567_256x256.png,c31a657833aac30df76ba8d06421d0e492857897,Tables,-4.978,33.803,27.261,4542 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-4545,_44,-9.945,-8.834,-39.809,-0.294,-0.793,1.10857142857,202993ce6d68eff9b6d7904ca94a00d1aeff9dd8,256,V6,202993ce6d68eff9b6d7904ca94a00d1aeff9dd8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx-09.945_ry-08.834_rz-39.809_tx-00.294_ty-00.793_s+00.776_202993ce6d68eff9b6d7904ca94a00d1aeff9dd8_256x256.png,d5326095e31c532dc463faf96153bc9f7b7b09fe,Tables,-9.945,-8.834,-39.809,4545 -4546,_44,-11.671,-21.1,-5.718,-0.346,0.861,1.50285714286,adf900a2d943d51e478e4829e4615ca34a13ba5e,256,V6,adf900a2d943d51e478e4829e4615ca34a13ba5e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx-11.671_ry-21.100_rz-05.718_tx-00.346_ty+00.861_s+01.052_adf900a2d943d51e478e4829e4615ca34a13ba5e_256x256.png,83b13d38a01f6372349f745cd3ec791b9ed64022,Tables,-11.671,-21.1,-5.718,4546 -4547,_44,-14.07,-31.831,74.694,-0.385,0.111,1.42,e8282b2907d3785bb1a42d48f359f0450e524f3b,256,V6,e8282b2907d3785bb1a42d48f359f0450e524f3b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx-14.070_ry-31.831_rz+74.694_tx-00.385_ty+00.111_s+00.994_e8282b2907d3785bb1a42d48f359f0450e524f3b_256x256.png,2f57127f1982c97a4259c86c8e7b5a3a107eff81,Tables,-14.07,-31.831,74.694,4547 -4548,_44,-15.507,36.193,-56.74,0.224,-0.361,0.891428571429,f2c70149b14ed5a41d8750fc45a3350583be45b2,256,V6,f2c70149b14ed5a41d8750fc45a3350583be45b2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx-15.507_ry+36.193_rz-56.740_tx+00.224_ty-00.361_s+00.624_f2c70149b14ed5a41d8750fc45a3350583be45b2_256x256.png,89ef4a6ed7af948878df2b5ca0779f055c45cc98,Tables,-15.507,36.193,-56.74,4548 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-4553,_44,-70.61,51.676,77.498,-0.324,-1.111,0.691428571429,ebd74e9c6a0e2ccfbfdb6a468e660e3f32a50e7e,256,V6,ebd74e9c6a0e2ccfbfdb6a468e660e3f32a50e7e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx-70.610_ry+51.676_rz+77.498_tx-00.324_ty-01.111_s+00.484_ebd74e9c6a0e2ccfbfdb6a468e660e3f32a50e7e_256x256.png,bdc0ef3711127d3d4fbf2ca3210547c6eef85860,Tables,-70.61,51.676,77.498,4553 -4554,_44,-73.848,42.053,62.683,0.086,0.014,1.56571428571,afef464944c33023b8cf3531c9cd5912516f05bc,256,V6,afef464944c33023b8cf3531c9cd5912516f05bc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx-73.848_ry+42.053_rz+62.683_tx+00.086_ty+00.014_s+01.096_afef464944c33023b8cf3531c9cd5912516f05bc_256x256.png,766fb4cd4c630e2c49054fe8976114db1d9b2af4,Tables,-73.848,42.053,62.683,4554 -4555,_44,-79.345,-68.826,-83.063,-0.542,0.622,1.49,f192c2ff792ca24329bee7ee9d51e8cfe5431198,256,V6,f192c2ff792ca24329bee7ee9d51e8cfe5431198_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx-79.345_ry-68.826_rz-83.063_tx-00.542_ty+00.622_s+01.043_f192c2ff792ca24329bee7ee9d51e8cfe5431198_256x256.png,9bef5f9960516db6d8044b59b03f4885f5ebb30c,Tables,-79.345,-68.826,-83.063,4555 -4556,_44,-84.073,-38.018,-52.282,-0.072,-0.029,0.918571428571,0807ad7533d14131d01fb26ee19f67524340ab11,256,V6,0807ad7533d14131d01fb26ee19f67524340ab11_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx-84.073_ry-38.018_rz-52.282_tx-00.072_ty-00.029_s+00.643_0807ad7533d14131d01fb26ee19f67524340ab11_256x256.png,5195f9326fda60fae9b92074c86bac85f0bc10db,Tables,-84.073,-38.018,-52.282,4556 -4557,_44,-86.298,35.071,42.669,-0.57,-0.415,0.787142857143,b0553ac5a31c1880d0e04b51b0423ac5212368b7,256,V6,b0553ac5a31c1880d0e04b51b0423ac5212368b7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx-86.298_ry+35.071_rz+42.669_tx-00.570_ty-00.415_s+00.551_b0553ac5a31c1880d0e04b51b0423ac5212368b7_256x256.png,49e8387816e06f5e84337043893a765aba2e889c,Tables,-86.298,35.071,42.669,4557 -4558,_44,-86.982,-46.751,-88.925,-0.142,1.15,1.19428571429,87865e860dd4c459a02e616627dd8996eb377677,256,V6,87865e860dd4c459a02e616627dd8996eb377677_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx-86.982_ry-46.751_rz-88.925_tx-00.142_ty+01.150_s+00.836_87865e860dd4c459a02e616627dd8996eb377677_256x256.png,0035c888464ec1148e7a909a96b28f27edf63780,Tables,-86.982,-46.751,-88.925,4558 -4559,_44,-89.762,51.346,23.132,0.161,0.006,1.16142857143,7655bcc428077db2b47939cf3570cffd9ce9accd,256,V6,7655bcc428077db2b47939cf3570cffd9ce9accd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/_44_rx-89.762_ry+51.346_rz+23.132_tx+00.161_ty+00.006_s+00.813_7655bcc428077db2b47939cf3570cffd9ce9accd_256x256.png,dc13c3ed2d94dbe0abe7832d920cea1af570c20b,Tables,-89.762,51.346,23.132,4559 -4560,alfa155,13.749,88.77,-77.106,-0.333,0.287,1.185,06c63836651d7ed54d938f632cde845421a911cd,256,V6,06c63836651d7ed54d938f632cde845421a911cd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+13.749_ry+88.770_rz-77.106_tx-00.333_ty+00.287_s+01.185_06c63836651d7ed54d938f632cde845421a911cd_256x256.png,bee3cadc52ef812d3cad808ee095d1d92e768b1c,Cars,13.749,178.77,-77.106,4560 -4561,alfa155,14.494,-18.596,-48.696,-0.206,1.044,1.211,c7091a4caf6c2d2b96932d67d8c4b873dac572a7,256,V6,c7091a4caf6c2d2b96932d67d8c4b873dac572a7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+14.494_ry-18.596_rz-48.696_tx-00.206_ty+01.044_s+01.211_c7091a4caf6c2d2b96932d67d8c4b873dac572a7_256x256.png,cbaf03c0455b7756585b694dad2f1e82830104ed,Cars,14.494,71.404,-48.696,4561 -4562,alfa155,19.342,36.191,31.548,-0.018,0.05,1.574,d14b8a030598f44875eb523685f6c102bd727192,256,V6,d14b8a030598f44875eb523685f6c102bd727192_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+19.342_ry+36.191_rz+31.548_tx-00.018_ty+00.050_s+01.574_d14b8a030598f44875eb523685f6c102bd727192_256x256.png,98f231fa9812e487891bf8ebba2c9f34114bb8db,Cars,19.342,126.191,31.548,4562 -4563,alfa155,22.344,25.965,-65.0,0.535,-0.266,1.355,3cc9901a64b4098f8b2fed4d53848097d24ffabc,256,V6,3cc9901a64b4098f8b2fed4d53848097d24ffabc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+22.344_ry+25.965_rz-65.000_tx+00.535_ty-00.266_s+01.355_3cc9901a64b4098f8b2fed4d53848097d24ffabc_256x256.png,a9d92386017708513a75d78bf5c20e8bd696bf3f,Cars,22.344,115.965,-65.0,4563 -4564,alfa155,26.386,25.489,33.18,0.159,0.672,0.67,16814f8a9afd961f57874a0c23a8a8886188d103,256,V6,16814f8a9afd961f57874a0c23a8a8886188d103_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+26.386_ry+25.489_rz+33.180_tx+00.159_ty+00.672_s+00.670_16814f8a9afd961f57874a0c23a8a8886188d103_256x256.png,f21ba7ae039040375f2537f12f49d881242b5c26,Cars,26.386,115.489,33.18,4564 -4565,alfa155,32.377,61.124,39.686,0.074,0.128,1.453,d056d2d66bf1abed8c6b536fa485285061213e2b,256,V6,d056d2d66bf1abed8c6b536fa485285061213e2b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+32.377_ry+61.124_rz+39.686_tx+00.074_ty+00.128_s+01.453_d056d2d66bf1abed8c6b536fa485285061213e2b_256x256.png,f7a953cef9c5708657c41dfc0f937401a883cb58,Cars,32.377,151.124,39.686,4565 -4566,alfa155,39.962,-38.215,32.705,-0.261,-0.566,1.528,74e54989899247ca4ba65ac2991ff6501523f316,256,V6,74e54989899247ca4ba65ac2991ff6501523f316_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+39.962_ry-38.215_rz+32.705_tx-00.261_ty-00.566_s+01.528_74e54989899247ca4ba65ac2991ff6501523f316_256x256.png,0db6fd3ea89d42269a0910243e3a782c8aaf7e6d,Cars,39.962,51.785,32.705,4566 -4567,alfa155,42.953,-38.652,41.635,0.288,-0.834,0.881,c9233b2c1b8a6b2be1721a630c13f28d8c141dca,256,V6,c9233b2c1b8a6b2be1721a630c13f28d8c141dca_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+42.953_ry-38.652_rz+41.635_tx+00.288_ty-00.834_s+00.881_c9233b2c1b8a6b2be1721a630c13f28d8c141dca_256x256.png,85f19973baecf51a3943211f636b0683f7e6c568,Cars,42.953,51.348,41.635,4567 -4568,alfa155,50.246,82.953,79.807,0.591,-1.196,0.894,5c13f04ac2fe1799566c6d73f92e79f484b6dbd7,256,V6,5c13f04ac2fe1799566c6d73f92e79f484b6dbd7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+50.246_ry+82.953_rz+79.807_tx+00.591_ty-01.196_s+00.894_5c13f04ac2fe1799566c6d73f92e79f484b6dbd7_256x256.png,ab64d098de5ab907c31e5c8b20e27eddea2b92c9,Cars,50.246,172.953,79.807,4568 -4569,alfa155,52.107,33.444,-53.227,0.042,-0.652,0.857,014a280a69a21e296dd0f7a90fd8ec923a7bb5d8,256,V6,014a280a69a21e296dd0f7a90fd8ec923a7bb5d8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+52.107_ry+33.444_rz-53.227_tx+00.042_ty-00.652_s+00.857_014a280a69a21e296dd0f7a90fd8ec923a7bb5d8_256x256.png,163463871575f1545f18702c61d124fb867f54b2,Cars,52.107,123.444,-53.227,4569 -4570,alfa155,57.683,58.675,-45.064,0.55,-0.774,0.732,57f665fbfda7d75d729d656499740a36de621a5f,256,V6,57f665fbfda7d75d729d656499740a36de621a5f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+57.683_ry+58.675_rz-45.064_tx+00.550_ty-00.774_s+00.732_57f665fbfda7d75d729d656499740a36de621a5f_256x256.png,fc21b882fd1d2d6b6977729292edf8377da7f909,Cars,57.683,148.675,-45.064,4570 -4571,alfa155,58.336,19.956,-80.427,0.57,-1.017,0.77,8e3cda0a078d50435a992abd3c5333164f2dd08c,256,V6,8e3cda0a078d50435a992abd3c5333164f2dd08c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+58.336_ry+19.956_rz-80.427_tx+00.570_ty-01.017_s+00.770_8e3cda0a078d50435a992abd3c5333164f2dd08c_256x256.png,cf796517ad5bd7ae34b4a2c993009741750a8ed6,Cars,58.336,109.956,-80.427,4571 -4572,alfa155,64.789,-64.497,-68.649,0.512,1.029,1.504,6be665021bdf28ddf94128b3b551f493684d139e,256,V6,6be665021bdf28ddf94128b3b551f493684d139e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+64.789_ry-64.497_rz-68.649_tx+00.512_ty+01.029_s+01.504_6be665021bdf28ddf94128b3b551f493684d139e_256x256.png,fae308eb66997cbeea62ec9bd0df240d32badcee,Cars,64.789,25.503,-68.649,4572 -4573,alfa155,67.332,7.902,-28.473,0.025,-0.493,0.919,eb0221427fa0e08b3fa79fc10212f0187d28cc46,256,V6,eb0221427fa0e08b3fa79fc10212f0187d28cc46_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+67.332_ry+07.902_rz-28.473_tx+00.025_ty-00.493_s+00.919_eb0221427fa0e08b3fa79fc10212f0187d28cc46_256x256.png,c8d3841abf8a64dbbedbc8285b9b65698ec5b29c,Cars,67.332,97.902,-28.473,4573 -4574,alfa155,68.441,-77.508,38.909,0.057,0.395,1.598,318b62afad1f8111cc860c933f7633f124fe76f8,256,V6,318b62afad1f8111cc860c933f7633f124fe76f8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+68.441_ry-77.508_rz+38.909_tx+00.057_ty+00.395_s+01.598_318b62afad1f8111cc860c933f7633f124fe76f8_256x256.png,5f97dc55dc6980dd379cacae0566ce47cc2bde9d,Cars,68.441,12.492,38.909,4574 -4575,alfa155,70.245,-66.964,35.172,-0.144,-0.823,0.98,2e53ba09f7737f394794c76bf78bc36a29bf497a,256,V6,2e53ba09f7737f394794c76bf78bc36a29bf497a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+70.245_ry-66.964_rz+35.172_tx-00.144_ty-00.823_s+00.980_2e53ba09f7737f394794c76bf78bc36a29bf497a_256x256.png,5f5752251e81a8de642d1788006d965386bc3de3,Cars,70.245,23.036,35.172,4575 -4576,alfa155,70.381,-7.176,4.339,0.188,0.271,1.212,4a6ef881e1e17866b64a4fa1e1a6f2916c0214ed,256,V6,4a6ef881e1e17866b64a4fa1e1a6f2916c0214ed_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+70.381_ry-07.176_rz+04.339_tx+00.188_ty+00.271_s+01.212_4a6ef881e1e17866b64a4fa1e1a6f2916c0214ed_256x256.png,05d9a7f7ebde0e34998db4362eda392a568e270b,Cars,70.381,82.824,4.339,4576 -4577,alfa155,71.927,75.34,41.634,0.468,0.207,1.058,ce3f9b7e2d9bf695da3885a356a82165016cb083,256,V6,ce3f9b7e2d9bf695da3885a356a82165016cb083_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+71.927_ry+75.340_rz+41.634_tx+00.468_ty+00.207_s+01.058_ce3f9b7e2d9bf695da3885a356a82165016cb083_256x256.png,316ada27c324c0c32f4c07c2fd01ff90d0578c25,Cars,71.927,165.34,41.634,4577 -4578,alfa155,76.66,35.898,34.971,-0.055,0.92,1.597,cc7ac8bcc837719782574b71d3313d7a912a31c0,256,V6,cc7ac8bcc837719782574b71d3313d7a912a31c0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+76.660_ry+35.898_rz+34.971_tx-00.055_ty+00.920_s+01.597_cc7ac8bcc837719782574b71d3313d7a912a31c0_256x256.png,0a30454f0fc673b8dd45226c1d6a1efc386fc612,Cars,76.66,125.898,34.971,4578 -4579,alfa155,80.699,-56.276,64.37,0.176,0.18,0.683,78fa06720d6f643b05f571356adac79ddd2bbc58,256,V6,78fa06720d6f643b05f571356adac79ddd2bbc58_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+80.699_ry-56.276_rz+64.370_tx+00.176_ty+00.180_s+00.683_78fa06720d6f643b05f571356adac79ddd2bbc58_256x256.png,de5e1b2009f72acfffc9fcc8c1f5fc49241cca26,Cars,80.699,33.724,64.37,4579 -4580,alfa155,86.598,32.215,-83.211,0.075,0.637,1.053,d99f674edc1bdd4f917e7b1a15e1626a37820a9f,256,V6,d99f674edc1bdd4f917e7b1a15e1626a37820a9f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx+86.598_ry+32.215_rz-83.211_tx+00.075_ty+00.637_s+01.053_d99f674edc1bdd4f917e7b1a15e1626a37820a9f_256x256.png,7b56fa5978246b24f6f7a264f59f6b61d43166b0,Cars,86.598,122.215,-83.211,4580 -4581,alfa155,-0.708,-18.418,-77.286,0.582,0.495,0.834,36d91eeaccc5f4d8c2981aa74065cf77723dec0a,256,V6,36d91eeaccc5f4d8c2981aa74065cf77723dec0a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-00.708_ry-18.418_rz-77.286_tx+00.582_ty+00.495_s+00.834_36d91eeaccc5f4d8c2981aa74065cf77723dec0a_256x256.png,f1f27ffd62f20320ff875626d15a16d869d2e4ef,Cars,-0.708,71.582,-77.286,4581 -4582,alfa155,-2.199,-85.914,64.137,-0.028,0.098,1.287,2ad12a1d5aab242ff082f0984974554b158a7db1,256,V6,2ad12a1d5aab242ff082f0984974554b158a7db1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-02.199_ry-85.914_rz+64.137_tx-00.028_ty+00.098_s+01.287_2ad12a1d5aab242ff082f0984974554b158a7db1_256x256.png,eb6755fe172b6796ba1b7abf19b29a7e34510eed,Cars,-2.199,4.086,64.137,4582 -4583,alfa155,-3.829,-28.605,62.157,0.409,0.252,1.273,f934a940602d18a719e8a3026c201301958142af,256,V6,f934a940602d18a719e8a3026c201301958142af_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-03.829_ry-28.605_rz+62.157_tx+00.409_ty+00.252_s+01.273_f934a940602d18a719e8a3026c201301958142af_256x256.png,98cc0d8f063fae1b0c22645f384875ec0f73212c,Cars,-3.829,61.395,62.157,4583 -4584,alfa155,-4.376,-6.228,-60.608,0.243,-0.259,0.704,3de615f395efe1f0aca444529deba8a4a231e900,256,V6,3de615f395efe1f0aca444529deba8a4a231e900_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-04.376_ry-06.228_rz-60.608_tx+00.243_ty-00.259_s+00.704_3de615f395efe1f0aca444529deba8a4a231e900_256x256.png,0103da3916673da0eb88d3d9c17df2fe6ae9605a,Cars,-4.376,83.772,-60.608,4584 -4585,alfa155,-10.055,-21.185,-39.389,-0.254,-1.106,1.091,e91ce8e221b8f9a3d9f4a8743e8fb8871308ab95,256,V6,e91ce8e221b8f9a3d9f4a8743e8fb8871308ab95_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-10.055_ry-21.185_rz-39.389_tx-00.254_ty-01.106_s+01.091_e91ce8e221b8f9a3d9f4a8743e8fb8871308ab95_256x256.png,d642b115481942c1f08c95ea8296f4e2613173aa,Cars,-10.055,68.815,-39.389,4585 -4586,alfa155,-14.768,40.158,-65.718,0.34,-0.927,1.37,24bd7936eced102fb45caa579f966f796a5d0897,256,V6,24bd7936eced102fb45caa579f966f796a5d0897_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-14.768_ry+40.158_rz-65.718_tx+00.340_ty-00.927_s+01.370_24bd7936eced102fb45caa579f966f796a5d0897_256x256.png,ebdb74b6a9700f62d79f5ad3b77a7b8b3687ee6e,Cars,-14.768,130.158,-65.718,4586 -4587,alfa155,-19.491,-30.812,-56.416,0.08,0.903,0.8,5b48d5fdadde701bbdf3c598fe22cca24220679e,256,V6,5b48d5fdadde701bbdf3c598fe22cca24220679e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-19.491_ry-30.812_rz-56.416_tx+00.080_ty+00.903_s+00.800_5b48d5fdadde701bbdf3c598fe22cca24220679e_256x256.png,eda63f684385c0d90c55c8c5f60f799bc7cd89cf,Cars,-19.491,59.188,-56.416,4587 -4588,alfa155,-25.686,-12.178,-34.626,-0.201,1.105,0.823,9c3253dabf258bae765cc0b9a3d21c73a54aea57,256,V6,9c3253dabf258bae765cc0b9a3d21c73a54aea57_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-25.686_ry-12.178_rz-34.626_tx-00.201_ty+01.105_s+00.823_9c3253dabf258bae765cc0b9a3d21c73a54aea57_256x256.png,7972a5fa2f82a868765a9799c44cef879cce8127,Cars,-25.686,77.822,-34.626,4588 -4589,alfa155,-27.865,36.397,-33.673,0.003,-0.093,1.077,c4379b1c2450755e7dd515c81e652a6fef94cf9d,256,V6,c4379b1c2450755e7dd515c81e652a6fef94cf9d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-27.865_ry+36.397_rz-33.673_tx+00.003_ty-00.093_s+01.077_c4379b1c2450755e7dd515c81e652a6fef94cf9d_256x256.png,9b71e02b8656c52529c8aeb962a9cbe4d0d19351,Cars,-27.865,126.397,-33.673,4589 -4590,alfa155,-29.54,36.535,-27.831,0.361,-0.058,1.459,e1e5401cbfcb8ba600d202d77f4e60421b98cfde,256,V6,e1e5401cbfcb8ba600d202d77f4e60421b98cfde_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-29.540_ry+36.535_rz-27.831_tx+00.361_ty-00.058_s+01.459_e1e5401cbfcb8ba600d202d77f4e60421b98cfde_256x256.png,536556f64033c029eed585379dd8b9ede372b59e,Cars,-29.54,126.535,-27.831,4590 -4591,alfa155,-34.426,-70.863,42.943,-0.421,0.009,1.265,c1b4750c4d2a5c60a3bc90f96c1b25d38e4310ea,256,V6,c1b4750c4d2a5c60a3bc90f96c1b25d38e4310ea_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-34.426_ry-70.863_rz+42.943_tx-00.421_ty+00.009_s+01.265_c1b4750c4d2a5c60a3bc90f96c1b25d38e4310ea_256x256.png,2c06a5aa9db4803b06988fecebb763eb4ee01ab9,Cars,-34.426,19.137,42.943,4591 -4592,alfa155,-36.83,-69.77,30.234,0.493,-0.638,1.233,db75a682bd47cec5e8cb0e44a349c9b3ec398133,256,V6,db75a682bd47cec5e8cb0e44a349c9b3ec398133_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-36.830_ry-69.770_rz+30.234_tx+00.493_ty-00.638_s+01.233_db75a682bd47cec5e8cb0e44a349c9b3ec398133_256x256.png,bc54c592c187e22fdca420ddcd4d4a5272ac22f7,Cars,-36.83,20.23,30.234,4592 -4593,alfa155,-40.14,56.251,74.09,0.316,0.081,1.071,71887be74aef2db292882953aa62d98af56cfb32,256,V6,71887be74aef2db292882953aa62d98af56cfb32_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-40.140_ry+56.251_rz+74.090_tx+00.316_ty+00.081_s+01.071_71887be74aef2db292882953aa62d98af56cfb32_256x256.png,13cd7b26eb7949af977103b8bf65816f7b6b7205,Cars,-40.14,146.251,74.09,4593 -4594,alfa155,-43.867,45.261,-21.184,-0.423,1.095,1.297,f1bed88aa0fa49ce67ca15a82eb37c003e95cb56,256,V6,f1bed88aa0fa49ce67ca15a82eb37c003e95cb56_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-43.867_ry+45.261_rz-21.184_tx-00.423_ty+01.095_s+01.297_f1bed88aa0fa49ce67ca15a82eb37c003e95cb56_256x256.png,43fe5ff454d4b4c615f8b44f625c6f8a9cd7d501,Cars,-43.867,135.261,-21.184,4594 -4595,alfa155,-45.966,64.443,8.309,0.154,-0.194,0.922,3afb2e98886742dc5370ef22d93bb29b2f5c91de,256,V6,3afb2e98886742dc5370ef22d93bb29b2f5c91de_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-45.966_ry+64.443_rz+08.309_tx+00.154_ty-00.194_s+00.922_3afb2e98886742dc5370ef22d93bb29b2f5c91de_256x256.png,ecf059617319c26f03183e4be80c664e54120b7c,Cars,-45.966,154.443,8.309,4595 -4596,alfa155,-51.435,3.669,52.036,0.146,-0.534,1.026,bd57f65527d644ecb64bfe21450898e7d3459b52,256,V6,bd57f65527d644ecb64bfe21450898e7d3459b52_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-51.435_ry+03.669_rz+52.036_tx+00.146_ty-00.534_s+01.026_bd57f65527d644ecb64bfe21450898e7d3459b52_256x256.png,3c824906209183e356936a379736cfdfce8366c2,Cars,-51.435,93.669,52.036,4596 -4597,alfa155,-63.749,-22.772,50.919,0.549,-1.049,1.24,a241a3da27e1114677c580c57f42775825188730,256,V6,a241a3da27e1114677c580c57f42775825188730_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-63.749_ry-22.772_rz+50.919_tx+00.549_ty-01.049_s+01.240_a241a3da27e1114677c580c57f42775825188730_256x256.png,dd9e165a89c3df56020b5cffb13f277034e21847,Cars,-63.749,67.228,50.919,4597 -4598,alfa155,-80.269,-18.581,-14.731,-0.357,-0.647,1.237,2857db2f8a7383ff5329a742d7f617fa39cbd18f,256,V6,2857db2f8a7383ff5329a742d7f617fa39cbd18f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-80.269_ry-18.581_rz-14.731_tx-00.357_ty-00.647_s+01.237_2857db2f8a7383ff5329a742d7f617fa39cbd18f_256x256.png,02fbe6adb6360d95c35ac970c51b97857f5960f7,Cars,-80.269,71.419,-14.731,4598 -4599,alfa155,-89.81,-64.382,28.673,0.318,1.151,0.794,3067cc86eb6a5ccdc619b09a9391c254b1d9a453,256,V6,3067cc86eb6a5ccdc619b09a9391c254b1d9a453_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/alfa155_rx-89.810_ry-64.382_rz+28.673_tx+00.318_ty+01.151_s+00.794_3067cc86eb6a5ccdc619b09a9391c254b1d9a453_256x256.png,398bec9144c4e90d474f3f5d05e387c03004e5eb,Cars,-89.81,25.618,28.673,4599 -4600,astra,1.579,-78.008,20.706,0.561,-1.007,1.404,48edb33f128148fa0ec1b36eea018552a8087019,256,V6,48edb33f128148fa0ec1b36eea018552a8087019_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+01.579_ry-78.008_rz+20.706_tx+00.561_ty-01.007_s+01.404_48edb33f128148fa0ec1b36eea018552a8087019_256x256.png,91c36dfe30bddb4d56d59b708a0ead53c66ad76b,Cars,1.579,11.992,20.706,4600 -4601,astra,8.368,-0.168,65.737,0.031,0.354,1.561,e1613c7901838b6547d20e9023adcde3d67c3b21,256,V6,e1613c7901838b6547d20e9023adcde3d67c3b21_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+08.368_ry-00.168_rz+65.737_tx+00.031_ty+00.354_s+01.561_e1613c7901838b6547d20e9023adcde3d67c3b21_256x256.png,31b0389a7f8d42e601eb07993736fdbd920040fb,Cars,8.368,89.832,65.737,4601 -4602,astra,9.295,5.992,-74.509,-0.179,-0.741,1.436,324a7138f0e1aaec956e3ebe17683ddcece3f710,256,V6,324a7138f0e1aaec956e3ebe17683ddcece3f710_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+09.295_ry+05.992_rz-74.509_tx-00.179_ty-00.741_s+01.436_324a7138f0e1aaec956e3ebe17683ddcece3f710_256x256.png,700bbee6851a10520ae9a1be635f2060d45f3696,Cars,9.295,95.992,-74.509,4602 -4603,astra,13.301,-47.55,23.035,-0.552,-0.564,0.963,44e7dae2e90b8c43cf5611d39dbb64687c6bd8eb,256,V6,44e7dae2e90b8c43cf5611d39dbb64687c6bd8eb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+13.301_ry-47.550_rz+23.035_tx-00.552_ty-00.564_s+00.963_44e7dae2e90b8c43cf5611d39dbb64687c6bd8eb_256x256.png,395f530107da06b0369bed2091c061c0ec897def,Cars,13.301,42.45,23.035,4603 -4604,astra,17.686,85.824,15.507,0.327,-0.05,0.96,087d94504c098c5ebf0ad52164ffd70020644e6e,256,V6,087d94504c098c5ebf0ad52164ffd70020644e6e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+17.686_ry+85.824_rz+15.507_tx+00.327_ty-00.050_s+00.960_087d94504c098c5ebf0ad52164ffd70020644e6e_256x256.png,9a91da049405f91de1bfc3dac3f4a9ceb7e561c3,Cars,17.686,175.824,15.507,4604 -4605,astra,19.609,54.809,-1.939,0.409,-0.593,1.398,51799a9a13f8d22309ef653538aee0cf3d1a34a5,256,V6,51799a9a13f8d22309ef653538aee0cf3d1a34a5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+19.609_ry+54.809_rz-01.939_tx+00.409_ty-00.593_s+01.398_51799a9a13f8d22309ef653538aee0cf3d1a34a5_256x256.png,b816c7790f244cfcb15eec047ad02bcfd692acea,Cars,19.609,144.809,-1.939,4605 -4606,astra,20.547,-3.233,78.75,0.324,-0.307,1.368,39aded50724bff36e904a7d3b4e86ac6b8598ddb,256,V6,39aded50724bff36e904a7d3b4e86ac6b8598ddb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+20.547_ry-03.233_rz+78.750_tx+00.324_ty-00.307_s+01.368_39aded50724bff36e904a7d3b4e86ac6b8598ddb_256x256.png,f06a621a17f40c39e1615776b1f067435fecfa59,Cars,20.547,86.767,78.75,4606 -4607,astra,21.437,87.13,11.805,-0.421,-1.149,1.008,b9add36dd5768d3d585186712eb74782f31a3d78,256,V6,b9add36dd5768d3d585186712eb74782f31a3d78_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+21.437_ry+87.130_rz+11.805_tx-00.421_ty-01.149_s+01.008_b9add36dd5768d3d585186712eb74782f31a3d78_256x256.png,ab8af1ed063cf39ebcb0b7e233a0933fdba8cc44,Cars,21.437,177.13,11.805,4607 -4608,astra,22.878,30.017,-33.95,0.338,0.231,1.57,a9790817ee8fd12f6c243c79fd43b592d4fe5c91,256,V6,a9790817ee8fd12f6c243c79fd43b592d4fe5c91_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+22.878_ry+30.017_rz-33.950_tx+00.338_ty+00.231_s+01.570_a9790817ee8fd12f6c243c79fd43b592d4fe5c91_256x256.png,86ab8e2a2e24ee9083bbec184e23122e3a910f2c,Cars,22.878,120.017,-33.95,4608 -4609,astra,42.865,18.093,0.757,-0.445,-0.502,0.842,80cf99eba7b3a93f8a5d6210b77a5c9ce5eccc50,256,V6,80cf99eba7b3a93f8a5d6210b77a5c9ce5eccc50_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+42.865_ry+18.093_rz+00.757_tx-00.445_ty-00.502_s+00.842_80cf99eba7b3a93f8a5d6210b77a5c9ce5eccc50_256x256.png,a891dbd286b25115a7c249b4d72ec2cb51512656,Cars,42.865,108.093,0.757,4609 -4610,astra,48.104,71.037,-29.47,-0.218,-0.758,0.715,0ca062ecee4c5efd479d4814b44ca987cbd5bb98,256,V6,0ca062ecee4c5efd479d4814b44ca987cbd5bb98_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+48.104_ry+71.037_rz-29.470_tx-00.218_ty-00.758_s+00.715_0ca062ecee4c5efd479d4814b44ca987cbd5bb98_256x256.png,388b8f42d7a0401caa60e8b2b9c0d1de6f6ee0e2,Cars,48.104,161.037,-29.47,4610 -4611,astra,50.153,-89.344,57.864,-0.003,0.576,1.317,0a94e5e3111d74320cd08361e2c9d8c87aaac12f,256,V6,0a94e5e3111d74320cd08361e2c9d8c87aaac12f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+50.153_ry-89.344_rz+57.864_tx-00.003_ty+00.576_s+01.317_0a94e5e3111d74320cd08361e2c9d8c87aaac12f_256x256.png,cb17b4ef7b31126640d42b03cc1eb46c019ccda3,Cars,50.153,0.656,57.864,4611 -4612,astra,51.508,73.885,-46.598,0.171,-0.919,1.133,b8a7f00821be7928015d3ad35fd4cd7bc563223d,256,V6,b8a7f00821be7928015d3ad35fd4cd7bc563223d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+51.508_ry+73.885_rz-46.598_tx+00.171_ty-00.919_s+01.133_b8a7f00821be7928015d3ad35fd4cd7bc563223d_256x256.png,106757de1bda158d6e84640d0ad8d648c445fea5,Cars,51.508,163.885,-46.598,4612 -4613,astra,56.141,84.879,-51.979,-0.183,0.42,1.552,e7d4f1b627d13486c94307d48f77e0cd6bea4ed1,256,V6,e7d4f1b627d13486c94307d48f77e0cd6bea4ed1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+56.141_ry+84.879_rz-51.979_tx-00.183_ty+00.420_s+01.552_e7d4f1b627d13486c94307d48f77e0cd6bea4ed1_256x256.png,97bd63eab14f1dc852c1bdcc69ec5a54b4ae9315,Cars,56.141,174.879,-51.979,4613 -4614,astra,62.234,4.78,35.936,-0.347,1.19,1.514,3445b5f27d1390cfb88254052c1f9e7a8a8dc237,256,V6,3445b5f27d1390cfb88254052c1f9e7a8a8dc237_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+62.234_ry+04.780_rz+35.936_tx-00.347_ty+01.190_s+01.514_3445b5f27d1390cfb88254052c1f9e7a8a8dc237_256x256.png,b704c05153e8192e5dd6f9c7f9e3d8166ab24b7f,Cars,62.234,94.78,35.936,4614 -4615,astra,64.772,-86.618,-78.471,0.394,-0.521,0.654,af42d44a9aeabc5634e3b7d3433ce0bd788131bc,256,V6,af42d44a9aeabc5634e3b7d3433ce0bd788131bc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+64.772_ry-86.618_rz-78.471_tx+00.394_ty-00.521_s+00.654_af42d44a9aeabc5634e3b7d3433ce0bd788131bc_256x256.png,5e34980cee4a22a4a91f008cb9b493ef5b43bf81,Cars,64.772,3.382,-78.471,4615 -4616,astra,65.815,-89.259,-16.201,-0.202,0.242,0.943,801fd37d33111f0ed6891871c2b9af74b7d6b236,256,V6,801fd37d33111f0ed6891871c2b9af74b7d6b236_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+65.815_ry-89.259_rz-16.201_tx-00.202_ty+00.242_s+00.943_801fd37d33111f0ed6891871c2b9af74b7d6b236_256x256.png,25272f90708487b5ee349dc661c1e117407d01b8,Cars,65.815,0.741,-16.201,4616 -4617,astra,73.675,25.861,-6.254,0.44,-1.195,1.271,074e9d8e271baea0638336d96f0577edc2a5cc77,256,V6,074e9d8e271baea0638336d96f0577edc2a5cc77_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+73.675_ry+25.861_rz-06.254_tx+00.440_ty-01.195_s+01.271_074e9d8e271baea0638336d96f0577edc2a5cc77_256x256.png,9746c2990730b859965cd8468e41ca9981a0ba36,Cars,73.675,115.861,-6.254,4617 -4618,astra,75.037,66.834,-82.072,0.464,0.626,1.108,47c1e591e12cd17888f5695678a85d7943501cb3,256,V6,47c1e591e12cd17888f5695678a85d7943501cb3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+75.037_ry+66.834_rz-82.072_tx+00.464_ty+00.626_s+01.108_47c1e591e12cd17888f5695678a85d7943501cb3_256x256.png,41d73bdeaa8cf45b06678f9af5e958d7ec69df6e,Cars,75.037,156.834,-82.072,4618 -4619,astra,80.636,-5.648,81.739,-0.539,0.027,0.964,b626ee5405859c7fadf99a5af834ec63084140d9,256,V6,b626ee5405859c7fadf99a5af834ec63084140d9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx+80.636_ry-05.648_rz+81.739_tx-00.539_ty+00.027_s+00.964_b626ee5405859c7fadf99a5af834ec63084140d9_256x256.png,57d73690d7da831d7b91c9b4d737d0a5d3cf2610,Cars,80.636,84.352,81.739,4619 -4620,astra,-0.149,79.836,39.661,-0.194,-1.141,0.766,368ff9ae8d1fb9822fe4cfbcbca5e36cb7f02a4c,256,V6,368ff9ae8d1fb9822fe4cfbcbca5e36cb7f02a4c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-00.149_ry+79.836_rz+39.661_tx-00.194_ty-01.141_s+00.766_368ff9ae8d1fb9822fe4cfbcbca5e36cb7f02a4c_256x256.png,0ea11e8ae70aa5e94a9610eeb39865526834c413,Cars,-0.149,169.836,39.661,4620 -4621,astra,-0.251,65.221,-6.876,-0.166,-0.835,1.267,5d421dfd9ba93c4cc8c762aa3532e26475decc5c,256,V6,5d421dfd9ba93c4cc8c762aa3532e26475decc5c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-00.251_ry+65.221_rz-06.876_tx-00.166_ty-00.835_s+01.267_5d421dfd9ba93c4cc8c762aa3532e26475decc5c_256x256.png,ed1f8498ee8ea84c2e2d1f62627175aeb6e07378,Cars,-0.251,155.221,-6.876,4621 -4622,astra,-8.638,4.135,-37.224,-0.335,0.031,0.878,fdc52b9a9c4f0de89a0495f6527cc3df680a5b9e,256,V6,fdc52b9a9c4f0de89a0495f6527cc3df680a5b9e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-08.638_ry+04.135_rz-37.224_tx-00.335_ty+00.031_s+00.878_fdc52b9a9c4f0de89a0495f6527cc3df680a5b9e_256x256.png,1bbd2b56223d7fbcba98a93eea8161c2d19bc014,Cars,-8.638,94.135,-37.224,4622 -4623,astra,-17.784,71.17,-40.147,-0.245,0.044,1.517,8ace3a1e66da372409b25a554485b804a1d41670,256,V6,8ace3a1e66da372409b25a554485b804a1d41670_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-17.784_ry+71.170_rz-40.147_tx-00.245_ty+00.044_s+01.517_8ace3a1e66da372409b25a554485b804a1d41670_256x256.png,a3523cf285028d464b4ee4f8000ca907eee4903b,Cars,-17.784,161.17,-40.147,4623 -4624,astra,-30.521,-33.775,-82.008,-0.396,-0.598,0.93,92bb268bd82bc9638d2b3095cc60b1d6884307fa,256,V6,92bb268bd82bc9638d2b3095cc60b1d6884307fa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-30.521_ry-33.775_rz-82.008_tx-00.396_ty-00.598_s+00.930_92bb268bd82bc9638d2b3095cc60b1d6884307fa_256x256.png,d22c3806bbdf3114ce0eea9d7a9506a460a36c13,Cars,-30.521,56.225,-82.008,4624 -4625,astra,-33.565,-16.679,-69.952,0.218,0.822,0.946,10cae423ac465104444c7dc4826ebe38b4b7bc48,256,V6,10cae423ac465104444c7dc4826ebe38b4b7bc48_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-33.565_ry-16.679_rz-69.952_tx+00.218_ty+00.822_s+00.946_10cae423ac465104444c7dc4826ebe38b4b7bc48_256x256.png,aca5e57ee53b555b6fb1f9376c22a8101fb8313d,Cars,-33.565,73.321,-69.952,4625 -4626,astra,-35.023,-44.504,-81.914,0.543,-0.054,0.723,ff3f20b489e78ee5d0db356052166caf801408fc,256,V6,ff3f20b489e78ee5d0db356052166caf801408fc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-35.023_ry-44.504_rz-81.914_tx+00.543_ty-00.054_s+00.723_ff3f20b489e78ee5d0db356052166caf801408fc_256x256.png,a0077540b5ee64559a4d9096d5ea6b66217b0bda,Cars,-35.023,45.496,-81.914,4626 -4627,astra,-41.456,73.412,-6.976,0.469,-0.586,0.797,99d6fc931185f148ad118ac9d15ddf55961196da,256,V6,99d6fc931185f148ad118ac9d15ddf55961196da_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-41.456_ry+73.412_rz-06.976_tx+00.469_ty-00.586_s+00.797_99d6fc931185f148ad118ac9d15ddf55961196da_256x256.png,2cdae6a9186eea5914aa5da245756413a8c9ac80,Cars,-41.456,163.412,-6.976,4627 -4628,astra,-44.832,25.274,71.753,0.188,0.254,0.994,d2fdcf62f91ec9b749f46e339ccb952c2321f836,256,V6,d2fdcf62f91ec9b749f46e339ccb952c2321f836_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-44.832_ry+25.274_rz+71.753_tx+00.188_ty+00.254_s+00.994_d2fdcf62f91ec9b749f46e339ccb952c2321f836_256x256.png,ae5b6cbc612d8c748b030138d58bc0d8a5922327,Cars,-44.832,115.274,71.753,4628 -4629,astra,-45.223,-13.526,-70.298,-0.34,-0.386,0.927,d5601b8fd723cb5850691144a78f4ee8f4c9df98,256,V6,d5601b8fd723cb5850691144a78f4ee8f4c9df98_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-45.223_ry-13.526_rz-70.298_tx-00.340_ty-00.386_s+00.927_d5601b8fd723cb5850691144a78f4ee8f4c9df98_256x256.png,52b4ba15728325823d57c84506fdff6613cb5386,Cars,-45.223,76.474,-70.298,4629 -4630,astra,-46.577,-79.652,8.113,0.318,-0.353,1.225,fcaf5863624ad62032319732b35098feb380ab5d,256,V6,fcaf5863624ad62032319732b35098feb380ab5d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-46.577_ry-79.652_rz+08.113_tx+00.318_ty-00.353_s+01.225_fcaf5863624ad62032319732b35098feb380ab5d_256x256.png,7dfb48564b62d0bf96dc5a442e3017c171902f75,Cars,-46.577,10.348,8.113,4630 -4631,astra,-49.736,-52.644,-27.695,0.376,0.871,1.553,1552fbc8a54a5b46df60e0c437748a06dd4ff464,256,V6,1552fbc8a54a5b46df60e0c437748a06dd4ff464_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-49.736_ry-52.644_rz-27.695_tx+00.376_ty+00.871_s+01.553_1552fbc8a54a5b46df60e0c437748a06dd4ff464_256x256.png,d62a29e24268f243e31e4163f36e09a2f9c7167f,Cars,-49.736,37.356,-27.695,4631 -4632,astra,-52.875,-11.918,-27.843,0.224,-0.866,1.294,846a6a3289ddc1db110a93821a547c6db7485fec,256,V6,846a6a3289ddc1db110a93821a547c6db7485fec_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-52.875_ry-11.918_rz-27.843_tx+00.224_ty-00.866_s+01.294_846a6a3289ddc1db110a93821a547c6db7485fec_256x256.png,b8927c7f0d78d42d528ffaa7f9dcbf0938093612,Cars,-52.875,78.082,-27.843,4632 -4633,astra,-52.911,-0.538,81.207,-0.37,-0.882,1.079,b75275ed411e99ebe0a7f8d57ef0bf2362a2a2a5,256,V6,b75275ed411e99ebe0a7f8d57ef0bf2362a2a2a5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-52.911_ry-00.538_rz+81.207_tx-00.370_ty-00.882_s+01.079_b75275ed411e99ebe0a7f8d57ef0bf2362a2a2a5_256x256.png,15160acf5eda5543bd386439f324056e8a5e84e2,Cars,-52.911,89.462,81.207,4633 -4634,astra,-55.43,-21.011,-72.278,-0.231,-0.234,0.677,2264d8f8b3a90c61a939eadedd58b7ab5897dcff,256,V6,2264d8f8b3a90c61a939eadedd58b7ab5897dcff_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-55.430_ry-21.011_rz-72.278_tx-00.231_ty-00.234_s+00.677_2264d8f8b3a90c61a939eadedd58b7ab5897dcff_256x256.png,37af4b46581d85c2ca2a3293ecf922473dbcba24,Cars,-55.43,68.989,-72.278,4634 -4635,astra,-63.395,-60.582,3.555,-0.13,-0.379,0.939,83a4f8c692d043d0c7b11ec2a8380ced6e617c52,256,V6,83a4f8c692d043d0c7b11ec2a8380ced6e617c52_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-63.395_ry-60.582_rz+03.555_tx-00.130_ty-00.379_s+00.939_83a4f8c692d043d0c7b11ec2a8380ced6e617c52_256x256.png,c5dd1544ad3f3973a2a3dd8a8408e3cd004cd169,Cars,-63.395,29.418,3.555,4635 -4636,astra,-76.411,-68.741,76.943,-0.551,0.405,1.597,b4efb9782d0616d6b055623d64e6b01a0a0cf751,256,V6,b4efb9782d0616d6b055623d64e6b01a0a0cf751_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-76.411_ry-68.741_rz+76.943_tx-00.551_ty+00.405_s+01.597_b4efb9782d0616d6b055623d64e6b01a0a0cf751_256x256.png,aa8a2ef0035641c26d9e40921aa306fe707a8ef6,Cars,-76.411,21.259,76.943,4636 -4637,astra,-77.418,-8.359,7.507,-0.278,1.137,0.759,3eaa6f1fe020d5828184bcb88839891548d39c65,256,V6,3eaa6f1fe020d5828184bcb88839891548d39c65_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-77.418_ry-08.359_rz+07.507_tx-00.278_ty+01.137_s+00.759_3eaa6f1fe020d5828184bcb88839891548d39c65_256x256.png,bd4f49a0fd4ad5ba31355b186e5e7b0ff1cb5fbf,Cars,-77.418,81.641,7.507,4637 -4638,astra,-80.808,1.569,11.922,-0.139,-0.601,0.906,c64f55d43600acfdb6c218aae596089e5109ae61,256,V6,c64f55d43600acfdb6c218aae596089e5109ae61_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-80.808_ry+01.569_rz+11.922_tx-00.139_ty-00.601_s+00.906_c64f55d43600acfdb6c218aae596089e5109ae61_256x256.png,e4397bda1ed4b196246b51fa5e8e60f45c7d66ba,Cars,-80.808,91.569,11.922,4638 -4639,astra,-80.953,10.944,-66.773,-0.035,0.361,1.031,a2f08956fe5593c09b41495ab4d3b18af7f74ee6,256,V6,a2f08956fe5593c09b41495ab4d3b18af7f74ee6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/astra_rx-80.953_ry+10.944_rz-66.773_tx-00.035_ty+00.361_s+01.031_a2f08956fe5593c09b41495ab4d3b18af7f74ee6_256x256.png,a539803b909bd5d76079e28313d07f423a50c983,Cars,-80.953,100.944,-66.773,4639 -4640,bear,8.431,-72.464,56.132,-0.297,0.237,1.4875,44aa99c90dee62e39bb0431267eb2233d820f0ee,256,V6,44aa99c90dee62e39bb0431267eb2233d820f0ee_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+08.431_ry+17.536_rz+56.132_tx-00.297_ty+00.237_s+01.190_44aa99c90dee62e39bb0431267eb2233d820f0ee_256x256.png,c67d7efde5072bc2f69be892288a1cc7047bdb68,Animals,8.431,17.536,56.132,4640 -4641,bear,18.383,25.748,42.098,-0.282,0.655,0.91,c37515652dd472f57d80fe6cb98e861fe7d92b51,256,V6,c37515652dd472f57d80fe6cb98e861fe7d92b51_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+18.383_ry+115.748_rz+42.098_tx-00.282_ty+00.655_s+00.728_c37515652dd472f57d80fe6cb98e861fe7d92b51_256x256.png,4e35300da5f56fdba162ca4f843311f208a1b1f4,Animals,18.383,115.748,42.098,4641 -4642,bear,20.143,82.957,22.663,-0.439,0.393,1.1225,56f2198f66c9d9b259d3d8a9909f65311b819f1e,256,V6,56f2198f66c9d9b259d3d8a9909f65311b819f1e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+20.143_ry+172.957_rz+22.663_tx-00.439_ty+00.393_s+00.898_56f2198f66c9d9b259d3d8a9909f65311b819f1e_256x256.png,f660d1fd4ab9d7cabe5d6e3235e5cc4aaa3f27ac,Animals,20.143,172.957,22.663,4642 -4643,bear,20.872,-44.643,-16.319,0.263,-0.279,1.46,c461b579c493247a51297293cb5912321e6e4362,256,V6,c461b579c493247a51297293cb5912321e6e4362_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+20.872_ry+45.357_rz-16.319_tx+00.263_ty-00.279_s+01.168_c461b579c493247a51297293cb5912321e6e4362_256x256.png,964694390f3768969049c245d0a66959041cfab0,Animals,20.872,45.357,-16.319,4643 -4644,bear,21.3,-87.221,-30.851,0.574,-0.78,0.975,a206f7e5868da6b94cc8417071ef2b5f3dda8297,256,V6,a206f7e5868da6b94cc8417071ef2b5f3dda8297_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+21.300_ry+02.779_rz-30.851_tx+00.574_ty-00.780_s+00.780_a206f7e5868da6b94cc8417071ef2b5f3dda8297_256x256.png,7d314d2e634aedfb9a79edec09531696d06294f8,Animals,21.3,2.779,-30.851,4644 -4645,bear,24.9,-29.249,83.997,-0.526,-0.253,0.94875,cc582e985e6019441baefba6c228b92a26ddbed9,256,V6,cc582e985e6019441baefba6c228b92a26ddbed9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+24.900_ry+60.751_rz+83.997_tx-00.526_ty-00.253_s+00.759_cc582e985e6019441baefba6c228b92a26ddbed9_256x256.png,6cb2d6398cc64a29af1fd0891022b3816a8791dd,Animals,24.9,60.751,83.997,4645 -4646,bear,45.408,87.099,-22.369,0.15,0.283,1.43,62d53df641e7cb7e6bbeb1fd3abb9debe904a575,256,V6,62d53df641e7cb7e6bbeb1fd3abb9debe904a575_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+45.408_ry+177.099_rz-22.369_tx+00.150_ty+00.283_s+01.144_62d53df641e7cb7e6bbeb1fd3abb9debe904a575_256x256.png,df51ce9e2af21b9f0c421987dd977fad0707108a,Animals,45.408,177.099,-22.369,4646 -4647,bear,53.018,-72.396,82.389,-0.597,-0.019,1.39875,cb18142e63873891a4f5ac3ff379fd54bcedf411,256,V6,cb18142e63873891a4f5ac3ff379fd54bcedf411_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+53.018_ry+17.604_rz+82.389_tx-00.597_ty-00.019_s+01.119_cb18142e63873891a4f5ac3ff379fd54bcedf411_256x256.png,03a4175db67808ae54ed8a9004697bdc7dd3fe62,Animals,53.018,17.604,82.389,4647 -4648,bear,57.307,30.186,75.965,-0.361,1.165,1.2975,53cab4dd709ce18050cb3be653ac9226519b2376,256,V6,53cab4dd709ce18050cb3be653ac9226519b2376_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+57.307_ry+120.186_rz+75.965_tx-00.361_ty+01.165_s+01.038_53cab4dd709ce18050cb3be653ac9226519b2376_256x256.png,f62d6b98ad590c4f0b48457a811bd69fef0a9bd7,Animals,57.307,120.186,75.965,4648 -4649,bear,58.596,-45.684,21.845,0.165,0.865,0.78375,5a49a0bcd9e633b56f1bdecc9b0e3260327cf4c9,256,V6,5a49a0bcd9e633b56f1bdecc9b0e3260327cf4c9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+58.596_ry+44.316_rz+21.845_tx+00.165_ty+00.865_s+00.627_5a49a0bcd9e633b56f1bdecc9b0e3260327cf4c9_256x256.png,a613c390fa7a724e4e7837842fc5841fb0b08ac4,Animals,58.596,44.316,21.845,4649 -4650,bear,61.943,9.819,85.101,0.068,-0.897,0.82375,23d1117cdc8f8882cc7dc5356ae88a677e142ba1,256,V6,23d1117cdc8f8882cc7dc5356ae88a677e142ba1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+61.943_ry+99.819_rz+85.101_tx+00.068_ty-00.897_s+00.659_23d1117cdc8f8882cc7dc5356ae88a677e142ba1_256x256.png,5abf0e081f6893e607edc5609a1ec599e9982286,Animals,61.943,99.819,85.101,4650 -4651,bear,63.02,-29.846,44.248,-0.029,-1.048,1.06125,1aea9fa4699125aba8541cc4e84eb7ab325e5ba4,256,V6,1aea9fa4699125aba8541cc4e84eb7ab325e5ba4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+63.020_ry+60.154_rz+44.248_tx-00.029_ty-01.048_s+00.849_1aea9fa4699125aba8541cc4e84eb7ab325e5ba4_256x256.png,96b5b1ea921c8be3768a5b04e437446ee3333028,Animals,63.02,60.154,44.248,4651 -4652,bear,66.947,14.465,-12.252,0.387,0.256,0.82375,3e4eb09831529620d1f7e60c434c0ecf577453b9,256,V6,3e4eb09831529620d1f7e60c434c0ecf577453b9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+66.947_ry+104.465_rz-12.252_tx+00.387_ty+00.256_s+00.659_3e4eb09831529620d1f7e60c434c0ecf577453b9_256x256.png,4657b9722f2c98980e017565c3cb164722a10f9a,Animals,66.947,104.465,-12.252,4652 -4653,bear,74.919,-37.981,42.473,-0.481,0.874,1.1275,1bd425daf9ee31d25ce76573690d2b26f24b5bb3,256,V6,1bd425daf9ee31d25ce76573690d2b26f24b5bb3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+74.919_ry+52.019_rz+42.473_tx-00.481_ty+00.874_s+00.902_1bd425daf9ee31d25ce76573690d2b26f24b5bb3_256x256.png,f7c429647d075a1318975dd15cf05404b59f7f53,Animals,74.919,52.019,42.473,4653 -4654,bear,75.268,-75.766,-56.304,0.529,-0.721,1.25875,0bf4edcd65ddf607a37f280e3899badaaf7a0af6,256,V6,0bf4edcd65ddf607a37f280e3899badaaf7a0af6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+75.268_ry+14.234_rz-56.304_tx+00.529_ty-00.721_s+01.007_0bf4edcd65ddf607a37f280e3899badaaf7a0af6_256x256.png,62dc3465053d989d4dd3d2d8f9e7aeaf76ea8788,Animals,75.268,14.234,-56.304,4654 -4655,bear,76.728,8.368,30.44,-0.124,0.368,1.11125,b21408f98d1f40e4f1e7b7d3d9f104b5e91dd0a8,256,V6,b21408f98d1f40e4f1e7b7d3d9f104b5e91dd0a8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+76.728_ry+98.368_rz+30.440_tx-00.124_ty+00.368_s+00.889_b21408f98d1f40e4f1e7b7d3d9f104b5e91dd0a8_256x256.png,195c364ff616d4ef1d991efc28c4913c59021b46,Animals,76.728,98.368,30.44,4655 -4656,bear,80.974,-10.724,82.835,0.55,0.998,0.72125,ce79415a5c6d421484eaaa66b92261c00ca69f7b,256,V6,ce79415a5c6d421484eaaa66b92261c00ca69f7b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+80.974_ry+79.276_rz+82.835_tx+00.550_ty+00.998_s+00.577_ce79415a5c6d421484eaaa66b92261c00ca69f7b_256x256.png,be0dfe407e34fde38fa8f8f4c3c16987ea224987,Animals,80.974,79.276,82.835,4656 -4657,bear,84.998,78.311,-49.458,0.503,0.715,1.56375,079b4bf370d0b5e5f9e41109d7d88abe9a447f1c,256,V6,079b4bf370d0b5e5f9e41109d7d88abe9a447f1c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+84.998_ry+168.311_rz-49.458_tx+00.503_ty+00.715_s+01.251_079b4bf370d0b5e5f9e41109d7d88abe9a447f1c_256x256.png,3abc88d72122f1d6145d863b4ed4521d853e452e,Animals,84.998,168.311,-49.458,4657 -4658,bear,86.936,-71.027,-55.228,0.452,0.973,1.3675,d6cb0fa147fc4becada5ee7850e4b47fee54ff3e,256,V6,d6cb0fa147fc4becada5ee7850e4b47fee54ff3e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+86.936_ry+18.973_rz-55.228_tx+00.452_ty+00.973_s+01.094_d6cb0fa147fc4becada5ee7850e4b47fee54ff3e_256x256.png,1e2421b2f1c3efae98f063f53cc6b750058e4bdf,Animals,86.936,18.973,-55.228,4658 -4659,bear,88.802,-42.176,-62.955,0.329,-0.146,1.27875,a545b9071da0d54c33d0f412d30ee2f68fc02fc5,256,V6,a545b9071da0d54c33d0f412d30ee2f68fc02fc5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx+88.802_ry+47.824_rz-62.955_tx+00.329_ty-00.146_s+01.023_a545b9071da0d54c33d0f412d30ee2f68fc02fc5_256x256.png,ea0b70587e8ecc7557918cd99ec30ec2c9541607,Animals,88.802,47.824,-62.955,4659 -4660,bear,-2.813,87.258,-75.165,0.288,-0.594,0.70875,7059e9c922b1b22e1e3787f0c9cc69f7b5aad44d,256,V6,7059e9c922b1b22e1e3787f0c9cc69f7b5aad44d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-02.813_ry+177.258_rz-75.165_tx+00.288_ty-00.594_s+00.567_7059e9c922b1b22e1e3787f0c9cc69f7b5aad44d_256x256.png,3a3b482d2d604a651483637467c2e067edc26838,Animals,-2.813,177.258,-75.165,4660 -4661,bear,-9.213,-18.867,-37.233,0.271,-0.36,1.2125,9271dcb877a3bbb741bebd8a99443bec9fbdaa7d,256,V6,9271dcb877a3bbb741bebd8a99443bec9fbdaa7d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-09.213_ry+71.133_rz-37.233_tx+00.271_ty-00.360_s+00.970_9271dcb877a3bbb741bebd8a99443bec9fbdaa7d_256x256.png,84fd8d827dd49f7a26a2f70aaf79411853187437,Animals,-9.213,71.133,-37.233,4661 -4662,bear,-10.91,5.423,-23.92,0.396,0.956,0.62625,1791e4d18362857259398bac2e204fae638f495b,256,V6,1791e4d18362857259398bac2e204fae638f495b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-10.910_ry+95.423_rz-23.920_tx+00.396_ty+00.956_s+00.501_1791e4d18362857259398bac2e204fae638f495b_256x256.png,7fa90d12cd62831496ed43ce14d7abc31164a427,Animals,-10.91,95.423,-23.92,4662 -4663,bear,-16.953,77.649,41.616,-0.151,-0.63,1.06125,7bb306897bf034ef14cf62f980f680186f8856b3,256,V6,7bb306897bf034ef14cf62f980f680186f8856b3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-16.953_ry+167.649_rz+41.616_tx-00.151_ty-00.630_s+00.849_7bb306897bf034ef14cf62f980f680186f8856b3_256x256.png,4c98b5c8cc16f00a7dfa94a2a91503be70b38472,Animals,-16.953,167.649,41.616,4663 -4664,bear,-19.148,87.49,-60.468,-0.004,0.609,1.22,442941df2fa7aade057a11104e16045cd8f7f5ab,256,V6,442941df2fa7aade057a11104e16045cd8f7f5ab_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-19.148_ry+177.490_rz-60.468_tx-00.004_ty+00.609_s+00.976_442941df2fa7aade057a11104e16045cd8f7f5ab_256x256.png,5179c3f05b59a78e6b61a0f3c015cd655679197d,Animals,-19.148,177.49,-60.468,4664 -4665,bear,-25.499,-82.173,-3.109,-0.397,0.926,1.34875,47924589d3556f671e3bf31f2eef6fab9a621633,256,V6,47924589d3556f671e3bf31f2eef6fab9a621633_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-25.499_ry+07.827_rz-03.109_tx-00.397_ty+00.926_s+01.079_47924589d3556f671e3bf31f2eef6fab9a621633_256x256.png,94435041ce317c74c86664a2bc6162bc397a45c8,Animals,-25.499,7.827,-3.109,4665 -4666,bear,-35.86,-55.478,80.446,-0.418,0.503,0.63,05256fec8529e9f241a3cf50d45e217c15492176,256,V6,05256fec8529e9f241a3cf50d45e217c15492176_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-35.860_ry+34.522_rz+80.446_tx-00.418_ty+00.503_s+00.504_05256fec8529e9f241a3cf50d45e217c15492176_256x256.png,28293b526d9f5ffc9eadb5e2473fbe6193433e04,Animals,-35.86,34.522,80.446,4666 -4667,bear,-36.039,-72.675,-63.862,0.44,0.062,0.95125,bfc2b4895f4751b34613bd28d2c3c34f20e447d8,256,V6,bfc2b4895f4751b34613bd28d2c3c34f20e447d8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-36.039_ry+17.325_rz-63.862_tx+00.440_ty+00.062_s+00.761_bfc2b4895f4751b34613bd28d2c3c34f20e447d8_256x256.png,67a92409339e1b187fd28ef4a394ee21d9bd292b,Animals,-36.039,17.325,-63.862,4667 -4668,bear,-38.659,1.32,-3.984,0.471,0.268,1.3625,29c27f649e35aeaaab7cfe3261bc1868417bc01d,256,V6,29c27f649e35aeaaab7cfe3261bc1868417bc01d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-38.659_ry+91.320_rz-03.984_tx+00.471_ty+00.268_s+01.090_29c27f649e35aeaaab7cfe3261bc1868417bc01d_256x256.png,f0b17b568e86cdc91fff67c0c5adf28c825ef3b4,Animals,-38.659,91.32,-3.984,4668 -4669,bear,-48.847,26.234,46.588,-0.103,0.647,0.73,73231e7f35212a0e3e6e5c0a700960e175e27632,256,V6,73231e7f35212a0e3e6e5c0a700960e175e27632_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-48.847_ry+116.234_rz+46.588_tx-00.103_ty+00.647_s+00.584_73231e7f35212a0e3e6e5c0a700960e175e27632_256x256.png,02c9a0822fff87b6dbd6018319986d75b7308901,Animals,-48.847,116.234,46.588,4669 -4670,bear,-50.485,75.283,-58.851,-0.569,1.013,1.025,fb6ed66438deea7ed2b5a84f12994895ece657bb,256,V6,fb6ed66438deea7ed2b5a84f12994895ece657bb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-50.485_ry+165.283_rz-58.851_tx-00.569_ty+01.013_s+00.820_fb6ed66438deea7ed2b5a84f12994895ece657bb_256x256.png,457905cb796f6e59fe1ef7b092a34f03683a2e03,Animals,-50.485,165.283,-58.851,4670 -4671,bear,-52.367,-85.563,30.443,-0.24,0.725,1.245,2f4ab9ac069a539c8f1a60c21379efeb7243ec02,256,V6,2f4ab9ac069a539c8f1a60c21379efeb7243ec02_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-52.367_ry+04.437_rz+30.443_tx-00.240_ty+00.725_s+00.996_2f4ab9ac069a539c8f1a60c21379efeb7243ec02_256x256.png,6149efc8d08e8f814a3462af3bc15e406fe5aff7,Animals,-52.367,4.437,30.443,4671 -4672,bear,-56.603,-9.433,24.238,-0.564,0.085,0.69375,32989a507b63017dec6ab5a02c23f694262297fe,256,V6,32989a507b63017dec6ab5a02c23f694262297fe_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-56.603_ry+80.567_rz+24.238_tx-00.564_ty+00.085_s+00.555_32989a507b63017dec6ab5a02c23f694262297fe_256x256.png,07d6b44c3dd482cb64b6d7a617690dd406531aa0,Animals,-56.603,80.567,24.238,4672 -4673,bear,-61.059,40.178,-77.122,-0.563,1.176,0.625,f28d94ee39bf3210dbd2de8b6d7f5ad94200df78,256,V6,f28d94ee39bf3210dbd2de8b6d7f5ad94200df78_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-61.059_ry+130.178_rz-77.122_tx-00.563_ty+01.176_s+00.500_f28d94ee39bf3210dbd2de8b6d7f5ad94200df78_256x256.png,37b3c0d84ddb96d0f477009d916deeef1e5b9992,Animals,-61.059,130.178,-77.122,4673 -4674,bear,-64.654,-22.95,15.998,-0.29,1.076,1.04375,3c22231f0ad784950b36e5621a350d56ccadc94f,256,V6,3c22231f0ad784950b36e5621a350d56ccadc94f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-64.654_ry+67.050_rz+15.998_tx-00.290_ty+01.076_s+00.835_3c22231f0ad784950b36e5621a350d56ccadc94f_256x256.png,802661f3dbd415fbf8f864c64e21f4279322b0da,Animals,-64.654,67.05,15.998,4674 -4675,bear,-68.037,-76.62,79.002,0.226,0.816,1.425,d8a45d6a629cc00255a56ae2d174feaa3ed3bae1,256,V6,d8a45d6a629cc00255a56ae2d174feaa3ed3bae1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-68.037_ry+13.380_rz+79.002_tx+00.226_ty+00.816_s+01.140_d8a45d6a629cc00255a56ae2d174feaa3ed3bae1_256x256.png,1712facb1868d433679bb46b275db57e49b9bf16,Animals,-68.037,13.38,79.002,4675 -4676,bear,-72.706,-87.089,-58.38,0.473,1.02,0.82125,c0ecd3df61ba13a39f3f9af109343254aa0adbaa,256,V6,c0ecd3df61ba13a39f3f9af109343254aa0adbaa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-72.706_ry+02.911_rz-58.380_tx+00.473_ty+01.020_s+00.657_c0ecd3df61ba13a39f3f9af109343254aa0adbaa_256x256.png,3f8a42c8ba189d22e17a1e514b7590b40e58cde8,Animals,-72.706,2.911,-58.38,4676 -4677,bear,-83.181,-61.905,32.325,-0.458,-0.08,1.0125,8d60d431c4ddfa1224e2924a9204e1c0f397b4fc,256,V6,8d60d431c4ddfa1224e2924a9204e1c0f397b4fc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-83.181_ry+28.095_rz+32.325_tx-00.458_ty-00.080_s+00.810_8d60d431c4ddfa1224e2924a9204e1c0f397b4fc_256x256.png,a1007c95cf173f19009cf24cd65c6ef146735eaf,Animals,-83.181,28.095,32.325,4677 -4678,bear,-85.439,76.141,30.256,0.033,-0.462,0.71625,a8702f6c5da8a428c503d52b9fca28427e772364,256,V6,a8702f6c5da8a428c503d52b9fca28427e772364_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-85.439_ry+166.141_rz+30.256_tx+00.033_ty-00.462_s+00.573_a8702f6c5da8a428c503d52b9fca28427e772364_256x256.png,1b8f4f92239490876d1728b49f89acb99faf012a,Animals,-85.439,166.141,30.256,4678 -4679,bear,-89.051,44.54,-10.237,0.244,0.017,0.91875,b70e72c66eee126739245c9ec4a1891e91ded4bb,256,V6,b70e72c66eee126739245c9ec4a1891e91ded4bb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bear_rx-89.051_ry+134.540_rz-10.237_tx+00.244_ty+00.017_s+00.735_b70e72c66eee126739245c9ec4a1891e91ded4bb_256x256.png,f298fbd7034cc30a182bbb52d2bba28bf4a2be66,Animals,-89.051,134.54,-10.237,4679 -4680,blCow,4.035,27.109,25.415,-0.414,0.37,1.24875,c0e20bd1ce04e4323d0d4cead344caf53678c644,256,V6,c0e20bd1ce04e4323d0d4cead344caf53678c644_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+04.035_ry+27.109_rz+25.415_tx-00.414_ty+00.370_s+00.999_c0e20bd1ce04e4323d0d4cead344caf53678c644_256x256.png,4798c1ef6455bcb10d1be0bf984e1239693f1716,Animals,4.035,117.109,25.415,4680 -4681,blCow,4.264,28.764,-60.337,0.101,-0.061,1.34625,d6c5b325aa30e72859131aee6d8f65988cc12903,256,V6,d6c5b325aa30e72859131aee6d8f65988cc12903_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+04.264_ry+28.764_rz-60.337_tx+00.101_ty-00.061_s+01.077_d6c5b325aa30e72859131aee6d8f65988cc12903_256x256.png,eb0e5301ba6c8517e893248aa764f32d358a7993,Animals,4.264,118.764,-60.337,4681 -4682,blCow,10.498,-46.85,-20.768,0.349,-0.996,1.48375,0979996edaa8a2622cbb7fd62aacc9c13acd5478,256,V6,0979996edaa8a2622cbb7fd62aacc9c13acd5478_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+10.498_ry-46.850_rz-20.768_tx+00.349_ty-00.996_s+01.187_0979996edaa8a2622cbb7fd62aacc9c13acd5478_256x256.png,e83610f21f73eb0747fa743e86a9677318090bba,Animals,10.498,43.15,-20.768,4682 -4683,blCow,10.766,72.077,-81.067,0.072,0.677,1.0625,61343354f22127a49e6ebdf52bbe3c2d2436c7be,256,V6,61343354f22127a49e6ebdf52bbe3c2d2436c7be_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+10.766_ry+72.077_rz-81.067_tx+00.072_ty+00.677_s+00.850_61343354f22127a49e6ebdf52bbe3c2d2436c7be_256x256.png,2a66d23c45ef4a61a51f2e5e61c57bc233f6592a,Animals,10.766,162.077,-81.067,4683 -4684,blCow,14.879,-78.831,64.685,-0.274,-0.3,1.00125,113a07840407da90382719c25685b2b2650022c1,256,V6,113a07840407da90382719c25685b2b2650022c1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+14.879_ry-78.831_rz+64.685_tx-00.274_ty-00.300_s+00.801_113a07840407da90382719c25685b2b2650022c1_256x256.png,ee2774f937d1d4547ad2c9aa8b2e9466e637c7b9,Animals,14.879,11.169,64.685,4684 -4685,blCow,21.968,-55.778,-13.963,-0.314,-0.51,1.01875,1771a8b56b4b063bb8ff256f9d7adc870348cca4,256,V6,1771a8b56b4b063bb8ff256f9d7adc870348cca4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+21.968_ry-55.778_rz-13.963_tx-00.314_ty-00.510_s+00.815_1771a8b56b4b063bb8ff256f9d7adc870348cca4_256x256.png,bef07007766538f91c591348e76af1e2c4323220,Animals,21.968,34.222,-13.963,4685 -4686,blCow,24.309,17.713,41.131,-0.077,-0.414,0.92375,07e3b36fb57f251b3e70f1d8774dc65842f4ec40,256,V6,07e3b36fb57f251b3e70f1d8774dc65842f4ec40_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+24.309_ry+17.713_rz+41.131_tx-00.077_ty-00.414_s+00.739_07e3b36fb57f251b3e70f1d8774dc65842f4ec40_256x256.png,bb9f46a19cde5eae1c6aeccbab338679aa68ce80,Animals,24.309,107.713,41.131,4686 -4687,blCow,24.319,20.701,87.67,-0.342,0.39,1.09625,8860546de8f15c9bfbc272ddad3b4606cc7c95b5,256,V6,8860546de8f15c9bfbc272ddad3b4606cc7c95b5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+24.319_ry+20.701_rz+87.670_tx-00.342_ty+00.390_s+00.877_8860546de8f15c9bfbc272ddad3b4606cc7c95b5_256x256.png,9a9bbb23826d33a9639b22c74de8cf65f687868b,Animals,24.319,110.701,87.67,4687 -4688,blCow,30.908,69.764,-86.06,0.31,-0.244,1.06375,234628189fa8e4024cc26c35fd844d33e159514b,256,V6,234628189fa8e4024cc26c35fd844d33e159514b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+30.908_ry+69.764_rz-86.060_tx+00.310_ty-00.244_s+00.851_234628189fa8e4024cc26c35fd844d33e159514b_256x256.png,f01f303fe754705bdca797f6c6966c51283b5556,Animals,30.908,159.764,-86.06,4688 -4689,blCow,41.418,72.634,53.068,-0.356,1.138,1.235,75e3160ddae2a64a2c200fb8ef1613fec85b6bcd,256,V6,75e3160ddae2a64a2c200fb8ef1613fec85b6bcd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+41.418_ry+72.634_rz+53.068_tx-00.356_ty+01.138_s+00.988_75e3160ddae2a64a2c200fb8ef1613fec85b6bcd_256x256.png,b4ee5e93029567176497be9a7fc943a769cd52ef,Animals,41.418,162.634,53.068,4689 -4690,blCow,46.406,31.269,79.545,0.164,0.074,1.51375,352b811b694730d6f78982b4a99f60f16818c8bc,256,V6,352b811b694730d6f78982b4a99f60f16818c8bc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+46.406_ry+31.269_rz+79.545_tx+00.164_ty+00.074_s+01.211_352b811b694730d6f78982b4a99f60f16818c8bc_256x256.png,733dfcadb61a557903e71a47b0c2bb279488710b,Animals,46.406,121.269,79.545,4690 -4691,blCow,47.976,74.102,-80.21,0.303,0.536,0.88,89148236aa212a498a4c086d65bf46fbbf09ddf7,256,V6,89148236aa212a498a4c086d65bf46fbbf09ddf7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+47.976_ry+74.102_rz-80.210_tx+00.303_ty+00.536_s+00.704_89148236aa212a498a4c086d65bf46fbbf09ddf7_256x256.png,c0a560eafbe4d4e7509e640aeb587268caa4aa07,Animals,47.976,164.102,-80.21,4691 -4692,blCow,52.144,19.313,16.418,-0.399,-1.091,0.925,ccc8de44d7f74c534364c331049c052f77f68965,256,V6,ccc8de44d7f74c534364c331049c052f77f68965_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+52.144_ry+19.313_rz+16.418_tx-00.399_ty-01.091_s+00.740_ccc8de44d7f74c534364c331049c052f77f68965_256x256.png,31263b8dcb0dc7627d25694deaae42836f06c3ec,Animals,52.144,109.313,16.418,4692 -4693,blCow,56.65,-31.014,-11.315,-0.377,0.143,1.29875,28d9efc21ad8704088f8bfb06995c6ac08d3286e,256,V6,28d9efc21ad8704088f8bfb06995c6ac08d3286e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+56.650_ry-31.014_rz-11.315_tx-00.377_ty+00.143_s+01.039_28d9efc21ad8704088f8bfb06995c6ac08d3286e_256x256.png,c42ec8925e2eb8b28c0ff5239d043e8429fb267f,Animals,56.65,58.986,-11.315,4693 -4694,blCow,61.607,51.411,62.958,0.084,-0.776,1.3025,b2106d3f3a6a912b5cbcecf76c97d6187736e21d,256,V6,b2106d3f3a6a912b5cbcecf76c97d6187736e21d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+61.607_ry+51.411_rz+62.958_tx+00.084_ty-00.776_s+01.042_b2106d3f3a6a912b5cbcecf76c97d6187736e21d_256x256.png,29a51f688977219d41ec3b2b3592ed040df42108,Animals,61.607,141.411,62.958,4694 -4695,blCow,75.632,-11.49,75.307,-0.544,-0.838,1.36875,a267e3e224622cb5561f868a16a7b884d784c383,256,V6,a267e3e224622cb5561f868a16a7b884d784c383_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+75.632_ry-11.490_rz+75.307_tx-00.544_ty-00.838_s+01.095_a267e3e224622cb5561f868a16a7b884d784c383_256x256.png,5f4aa528f21ecb5cd2f67bda29ab1596f7398892,Animals,75.632,78.51,75.307,4695 -4696,blCow,82.665,-22.032,15.712,-0.086,-0.752,1.3525,ce25b9c1f5c3aacb85975810582b68bda6fbdc48,256,V6,ce25b9c1f5c3aacb85975810582b68bda6fbdc48_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+82.665_ry-22.032_rz+15.712_tx-00.086_ty-00.752_s+01.082_ce25b9c1f5c3aacb85975810582b68bda6fbdc48_256x256.png,e83678182959f8be94f018201a62fe4594b8d350,Animals,82.665,67.968,15.712,4696 -4697,blCow,83.392,-10.411,23.528,-0.135,0.385,1.5375,d91c18a70363e441706e5346fddeae1c1bdd5f17,256,V6,d91c18a70363e441706e5346fddeae1c1bdd5f17_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+83.392_ry-10.411_rz+23.528_tx-00.135_ty+00.385_s+01.230_d91c18a70363e441706e5346fddeae1c1bdd5f17_256x256.png,a83e8966183bb4c97ec34baf54f944cc9e6e7c1c,Animals,83.392,79.589,23.528,4697 -4698,blCow,85.676,-37.746,45.533,-0.439,0.309,1.44625,87c3542455c9b79fd3085b4cdba09dda617a1f37,256,V6,87c3542455c9b79fd3085b4cdba09dda617a1f37_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+85.676_ry-37.746_rz+45.533_tx-00.439_ty+00.309_s+01.157_87c3542455c9b79fd3085b4cdba09dda617a1f37_256x256.png,a9c81fef6575c7faf12967ad19eadb26e3cc7494,Animals,85.676,52.254,45.533,4698 -4699,blCow,87.996,-27.62,52.15,0.006,-1.094,1.33,81e25381b364bb07def6f38d81fc16ef04bc1745,256,V6,81e25381b364bb07def6f38d81fc16ef04bc1745_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx+87.996_ry-27.620_rz+52.150_tx+00.006_ty-01.094_s+01.064_81e25381b364bb07def6f38d81fc16ef04bc1745_256x256.png,065e9eb752eebd54fb0c75d62bb1c203bb200c2c,Animals,87.996,62.38,52.15,4699 -4700,blCow,-2.268,-75.336,2.023,-0.409,0.679,1.49,0b2ad6d1669352a8ad6560389af44d8e93001efc,256,V6,0b2ad6d1669352a8ad6560389af44d8e93001efc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-02.268_ry-75.336_rz+02.023_tx-00.409_ty+00.679_s+01.192_0b2ad6d1669352a8ad6560389af44d8e93001efc_256x256.png,3ad0657035c84833f5ccf3ec73dde76b798ef3d0,Animals,-2.268,14.664,2.023,4700 -4701,blCow,-5.388,14.341,35.283,-0.059,-0.247,1.57125,b495962c4ed6a10ce7b9dc42c48ea27557c94d95,256,V6,b495962c4ed6a10ce7b9dc42c48ea27557c94d95_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-05.388_ry+14.341_rz+35.283_tx-00.059_ty-00.247_s+01.257_b495962c4ed6a10ce7b9dc42c48ea27557c94d95_256x256.png,c96e16aba91d42ce7d25b3efcfa41c70e3fc6b47,Animals,-5.388,104.341,35.283,4701 -4702,blCow,-9.065,-46.895,18.645,0.454,0.751,1.51625,52fea5e6ec9790a7df8582c3d0ebbf794985b2ff,256,V6,52fea5e6ec9790a7df8582c3d0ebbf794985b2ff_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-09.065_ry-46.895_rz+18.645_tx+00.454_ty+00.751_s+01.213_52fea5e6ec9790a7df8582c3d0ebbf794985b2ff_256x256.png,39d4afb003ba0c2afd54b899383fac9346a14854,Animals,-9.065,43.105,18.645,4702 -4703,blCow,-9.616,-13.925,-15.012,0.194,-0.664,1.3025,8ce23f8a99744f6b67348bd695e50071bc54e758,256,V6,8ce23f8a99744f6b67348bd695e50071bc54e758_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-09.616_ry-13.925_rz-15.012_tx+00.194_ty-00.664_s+01.042_8ce23f8a99744f6b67348bd695e50071bc54e758_256x256.png,4e680e85225dbcf771a0a1b2212bfe63bf8c1941,Animals,-9.616,76.075,-15.012,4703 -4704,blCow,-11.369,-83.748,-41.133,-0.104,0.674,1.565,0797112b721bf8c84ff688097cc9c558699e32e7,256,V6,0797112b721bf8c84ff688097cc9c558699e32e7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-11.369_ry-83.748_rz-41.133_tx-00.104_ty+00.674_s+01.252_0797112b721bf8c84ff688097cc9c558699e32e7_256x256.png,04a8e163f51f5daf5789ad1d228b7bc81f07da60,Animals,-11.369,6.252,-41.133,4704 -4705,blCow,-12.813,-24.459,-48.79,-0.563,-0.73,1.455,e76b57d16def6cc06cb990828988da12168b3701,256,V6,e76b57d16def6cc06cb990828988da12168b3701_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-12.813_ry-24.459_rz-48.790_tx-00.563_ty-00.730_s+01.164_e76b57d16def6cc06cb990828988da12168b3701_256x256.png,912c2384532db063e4db3faad52bcc29be8774ef,Animals,-12.813,65.541,-48.79,4705 -4706,blCow,-15.697,-68.533,-6.809,-0.512,-0.328,1.0125,1e61273b439e4db58c76fe35b11c90daa786f3ea,256,V6,1e61273b439e4db58c76fe35b11c90daa786f3ea_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-15.697_ry-68.533_rz-06.809_tx-00.512_ty-00.328_s+00.810_1e61273b439e4db58c76fe35b11c90daa786f3ea_256x256.png,b65f889bd1d42b3e441c1ed7525e5d93d7528acb,Animals,-15.697,21.467,-6.809,4706 -4707,blCow,-23.055,-14.689,8.719,-0.554,0.546,1.11625,6270eadaa2c2e315b7b3c8d1db2ee36a052a88e6,256,V6,6270eadaa2c2e315b7b3c8d1db2ee36a052a88e6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-23.055_ry-14.689_rz+08.719_tx-00.554_ty+00.546_s+00.893_6270eadaa2c2e315b7b3c8d1db2ee36a052a88e6_256x256.png,a9322b421c467e983b3b75bb63f331e72cff4742,Animals,-23.055,75.311,8.719,4707 -4708,blCow,-37.002,54.009,-66.323,0.289,0.606,1.22875,808e00c54367dfd40699e6195edd6643c5f80cd3,256,V6,808e00c54367dfd40699e6195edd6643c5f80cd3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-37.002_ry+54.009_rz-66.323_tx+00.289_ty+00.606_s+00.983_808e00c54367dfd40699e6195edd6643c5f80cd3_256x256.png,16943bf2c196946cb2434933df8b8192007ac23f,Animals,-37.002,144.009,-66.323,4708 -4709,blCow,-37.182,76.423,21.042,0.274,-0.776,0.7175,69c6bf4049144062b6286d7f4dc59da877fcba26,256,V6,69c6bf4049144062b6286d7f4dc59da877fcba26_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-37.182_ry+76.423_rz+21.042_tx+00.274_ty-00.776_s+00.574_69c6bf4049144062b6286d7f4dc59da877fcba26_256x256.png,7f22a1a115bdaa7645a2f8d6ca76d290d772833c,Animals,-37.182,166.423,21.042,4709 -4710,blCow,-40.302,39.922,61.037,-0.45,1.018,1.165,3ac4358538216f2d9127f4651d2a1d23c4304edc,256,V6,3ac4358538216f2d9127f4651d2a1d23c4304edc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-40.302_ry+39.922_rz+61.037_tx-00.450_ty+01.018_s+00.932_3ac4358538216f2d9127f4651d2a1d23c4304edc_256x256.png,dc46e9ceb5a7cd9e57108842f2d17d8b1565793c,Animals,-40.302,129.922,61.037,4710 -4711,blCow,-41.688,62.432,20.406,0.34,-0.808,0.995,812c9b2f069a8c38356d530bd916b9e75dbae788,256,V6,812c9b2f069a8c38356d530bd916b9e75dbae788_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-41.688_ry+62.432_rz+20.406_tx+00.340_ty-00.808_s+00.796_812c9b2f069a8c38356d530bd916b9e75dbae788_256x256.png,e5994a01efe89af7200e8d5281eb2880903875c1,Animals,-41.688,152.432,20.406,4711 -4712,blCow,-41.735,-72.201,-72.305,0.567,-0.357,1.42125,95c9617559c961936bd77600471827cab71b5eac,256,V6,95c9617559c961936bd77600471827cab71b5eac_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-41.735_ry-72.201_rz-72.305_tx+00.567_ty-00.357_s+01.137_95c9617559c961936bd77600471827cab71b5eac_256x256.png,c09575c91b671cec233c5f86196548e3cb22794f,Animals,-41.735,17.799,-72.305,4712 -4713,blCow,-43.396,4.439,38.827,0.052,1.008,1.2225,f0d2ac9b0d0b2feefce5ffe3877b9907a5267189,256,V6,f0d2ac9b0d0b2feefce5ffe3877b9907a5267189_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-43.396_ry+04.439_rz+38.827_tx+00.052_ty+01.008_s+00.978_f0d2ac9b0d0b2feefce5ffe3877b9907a5267189_256x256.png,5317d7335f328414699462c251c02aac777e5ac6,Animals,-43.396,94.439,38.827,4713 -4714,blCow,-43.593,-56.313,-79.086,-0.484,-0.593,1.2525,a31faa9fa60c15e1fb4103b2e8d5ff6e29e4fb8b,256,V6,a31faa9fa60c15e1fb4103b2e8d5ff6e29e4fb8b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-43.593_ry-56.313_rz-79.086_tx-00.484_ty-00.593_s+01.002_a31faa9fa60c15e1fb4103b2e8d5ff6e29e4fb8b_256x256.png,04fa8c43d85c1da1278325a38aaa751b81a453f2,Animals,-43.593,33.687,-79.086,4714 -4715,blCow,-46.768,21.262,-27.149,0.4,0.019,1.19375,e86a453dba20ae2c2804c6be6f3781e811b92e3a,256,V6,e86a453dba20ae2c2804c6be6f3781e811b92e3a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-46.768_ry+21.262_rz-27.149_tx+00.400_ty+00.019_s+00.955_e86a453dba20ae2c2804c6be6f3781e811b92e3a_256x256.png,085c37dc1e292661d1ec0316c9197a42e1079294,Animals,-46.768,111.262,-27.149,4715 -4716,blCow,-75.926,39.322,3.487,-0.529,-0.474,1.23375,d39eb3574486e44fa684767227eae25d06e6f1e9,256,V6,d39eb3574486e44fa684767227eae25d06e6f1e9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-75.926_ry+39.322_rz+03.487_tx-00.529_ty-00.474_s+00.987_d39eb3574486e44fa684767227eae25d06e6f1e9_256x256.png,1437fabf83fa67653eaf4232711bc89c704e186b,Animals,-75.926,129.322,3.487,4716 -4717,blCow,-85.444,44.808,75.34,0.457,-0.266,1.1775,37ec27a83b5456ec7d50f15108e6f8b7e9c3365d,256,V6,37ec27a83b5456ec7d50f15108e6f8b7e9c3365d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-85.444_ry+44.808_rz+75.340_tx+00.457_ty-00.266_s+00.942_37ec27a83b5456ec7d50f15108e6f8b7e9c3365d_256x256.png,43f86dd695a69c6da0208ec2014ad935b27e6493,Animals,-85.444,134.808,75.34,4717 -4718,blCow,-86.552,-47.01,-75.953,0.23,-0.381,1.55375,ec43dd9879c549bd877786fe071c662f11df51cd,256,V6,ec43dd9879c549bd877786fe071c662f11df51cd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-86.552_ry-47.010_rz-75.953_tx+00.230_ty-00.381_s+01.243_ec43dd9879c549bd877786fe071c662f11df51cd_256x256.png,a809d620f372087798a72d8ed4825c0ad3823252,Animals,-86.552,42.99,-75.953,4718 -4719,blCow,-87.223,31.17,3.214,0.542,1.06,0.635,461e80d4a5c38f19d7c2c2b0e2976b0308d68203,256,V6,461e80d4a5c38f19d7c2c2b0e2976b0308d68203_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/blCow_rx-87.223_ry+31.170_rz+03.214_tx+00.542_ty+01.060_s+00.508_461e80d4a5c38f19d7c2c2b0e2976b0308d68203_256x256.png,8abab004326d3ec5ed561c99ccb0436ad4d6c0f5,Animals,-87.223,121.17,3.214,4719 -4720,bmw325,0.671,83.828,-27.915,0.014,-0.741,0.867,fd4376303731b6bd708c4399e23fb3b4a725f30c,256,V6,fd4376303731b6bd708c4399e23fb3b4a725f30c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+00.671_ry+83.828_rz-27.915_tx+00.014_ty-00.741_s+00.867_fd4376303731b6bd708c4399e23fb3b4a725f30c_256x256.png,53a4f98305cba9f909a6d92702ea3580a21670e5,Cars,0.671,173.828,-27.915,4720 -4721,bmw325,9.63,54.378,-11.81,-0.281,-0.318,1.35,a41f8d6e808d181ecfc97925fb6c18eedaa2d5ea,256,V6,a41f8d6e808d181ecfc97925fb6c18eedaa2d5ea_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+09.630_ry+54.378_rz-11.810_tx-00.281_ty-00.318_s+01.350_a41f8d6e808d181ecfc97925fb6c18eedaa2d5ea_256x256.png,4ced92195c5f7e865bbc4f9a30ede4fc952696e4,Cars,9.63,144.378,-11.81,4721 -4722,bmw325,12.548,72.902,-55.171,-0.15,-0.712,1.42,33bf754f1d4a8e8179922a65556b86c2b4b4bdc2,256,V6,33bf754f1d4a8e8179922a65556b86c2b4b4bdc2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+12.548_ry+72.902_rz-55.171_tx-00.150_ty-00.712_s+01.420_33bf754f1d4a8e8179922a65556b86c2b4b4bdc2_256x256.png,090f15e9c452afe7e88233a1655500b9253adb61,Cars,12.548,162.902,-55.171,4722 -4723,bmw325,16.766,1.395,48.562,0.389,0.16,0.896,fb8bb50ea0ba032af1b84b1e991692635e20fa1f,256,V6,fb8bb50ea0ba032af1b84b1e991692635e20fa1f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+16.766_ry+01.395_rz+48.562_tx+00.389_ty+00.160_s+00.896_fb8bb50ea0ba032af1b84b1e991692635e20fa1f_256x256.png,1d09fa0aa1737e6a58fc156778afd9faf3f45006,Cars,16.766,91.395,48.562,4723 -4724,bmw325,21.36,23.908,-46.278,-0.554,1.127,1.181,862be8345396fbc96a4f1c41f8d30808f7070ea4,256,V6,862be8345396fbc96a4f1c41f8d30808f7070ea4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+21.360_ry+23.908_rz-46.278_tx-00.554_ty+01.127_s+01.181_862be8345396fbc96a4f1c41f8d30808f7070ea4_256x256.png,8178d021fffdf505c498b12df1c1aa822e1242bf,Cars,21.36,113.908,-46.278,4724 -4725,bmw325,29.416,25.651,-83.777,0.096,0.654,1.407,a6a28efca9fe4378d8ab241c6c288603ffcb6240,256,V6,a6a28efca9fe4378d8ab241c6c288603ffcb6240_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+29.416_ry+25.651_rz-83.777_tx+00.096_ty+00.654_s+01.407_a6a28efca9fe4378d8ab241c6c288603ffcb6240_256x256.png,54268206e124185b9caebc1d23f2a9a328b78097,Cars,29.416,115.651,-83.777,4725 -4726,bmw325,35.95,0.026,-23.155,0.599,-0.743,1.099,275315df97c51740c26c0262ec467dab4a6c1724,256,V6,275315df97c51740c26c0262ec467dab4a6c1724_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+35.950_ry+00.026_rz-23.155_tx+00.599_ty-00.743_s+01.099_275315df97c51740c26c0262ec467dab4a6c1724_256x256.png,531f22209cdbb260f27c3456f41d54eddf1724bd,Cars,35.95,90.026,-23.155,4726 -4727,bmw325,37.454,-70.92,43.622,-0.014,0.11,0.844,fe38da31edacbcb506287d9252d1de8729d3f178,256,V6,fe38da31edacbcb506287d9252d1de8729d3f178_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+37.454_ry-70.920_rz+43.622_tx-00.014_ty+00.110_s+00.844_fe38da31edacbcb506287d9252d1de8729d3f178_256x256.png,a5cfe49c1b31af034f7668f4a353ff2a5535ea18,Cars,37.454,19.08,43.622,4727 -4728,bmw325,37.675,-45.465,-28.34,0.309,0.044,0.84,c24f3e646217c331c47861761c328937cfb01526,256,V6,c24f3e646217c331c47861761c328937cfb01526_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+37.675_ry-45.465_rz-28.340_tx+00.309_ty+00.044_s+00.840_c24f3e646217c331c47861761c328937cfb01526_256x256.png,d852ad104730c65609f93ee2df5826f0a21b16bb,Cars,37.675,44.535,-28.34,4728 -4729,bmw325,41.56,-29.976,-82.609,-0.278,-0.814,0.666,b0617ea2896b9ecbc5d136cdeb58bc2074d719ae,256,V6,b0617ea2896b9ecbc5d136cdeb58bc2074d719ae_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+41.560_ry-29.976_rz-82.609_tx-00.278_ty-00.814_s+00.666_b0617ea2896b9ecbc5d136cdeb58bc2074d719ae_256x256.png,f0cf8cf6870f21aca24e43be8a945889e4c176a7,Cars,41.56,60.024,-82.609,4729 -4730,bmw325,49.873,-6.766,-57.564,-0.523,-0.859,1.379,23151778bdd5b30afb5773669042cb47dcf09166,256,V6,23151778bdd5b30afb5773669042cb47dcf09166_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+49.873_ry-06.766_rz-57.564_tx-00.523_ty-00.859_s+01.379_23151778bdd5b30afb5773669042cb47dcf09166_256x256.png,1c46e0c9b997088db9d83ea840e694bfd5ee959f,Cars,49.873,83.234,-57.564,4730 -4731,bmw325,54.189,66.184,38.468,-0.118,0.984,1.331,4df8355b7536806c90d3e3091765e4d2441e616e,256,V6,4df8355b7536806c90d3e3091765e4d2441e616e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+54.189_ry+66.184_rz+38.468_tx-00.118_ty+00.984_s+01.331_4df8355b7536806c90d3e3091765e4d2441e616e_256x256.png,c2f807acbce4e2cdbf13a6ecf3cb155ffd7951d7,Cars,54.189,156.184,38.468,4731 -4732,bmw325,59.599,67.794,-75.253,0.031,-0.425,1.121,f5f44b9521417e041b88cff618157faf8e5aaee4,256,V6,f5f44b9521417e041b88cff618157faf8e5aaee4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+59.599_ry+67.794_rz-75.253_tx+00.031_ty-00.425_s+01.121_f5f44b9521417e041b88cff618157faf8e5aaee4_256x256.png,0fc03fc677e42dd26904868a02faea62bc5a20d0,Cars,59.599,157.794,-75.253,4732 -4733,bmw325,66.83,9.411,65.127,-0.513,-0.888,1.266,ba8b51fe175cf3e3de587c80fa2c944ef21da07b,256,V6,ba8b51fe175cf3e3de587c80fa2c944ef21da07b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+66.830_ry+09.411_rz+65.127_tx-00.513_ty-00.888_s+01.266_ba8b51fe175cf3e3de587c80fa2c944ef21da07b_256x256.png,f9f5f36fd078bcd7ca331f3bb242ec62fc743965,Cars,66.83,99.411,65.127,4733 -4734,bmw325,67.826,48.097,-43.543,0.534,-0.552,0.719,09a811c55f85c083a16fa2fd09eed4b4fd197827,256,V6,09a811c55f85c083a16fa2fd09eed4b4fd197827_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+67.826_ry+48.097_rz-43.543_tx+00.534_ty-00.552_s+00.719_09a811c55f85c083a16fa2fd09eed4b4fd197827_256x256.png,ad59245e2b94efa1805442578d9ecb1bdb48bde1,Cars,67.826,138.097,-43.543,4734 -4735,bmw325,69.737,-69.635,36.358,-0.26,1.098,0.838,2d6231bb0270f337526b0d5d3936647bdf6fa421,256,V6,2d6231bb0270f337526b0d5d3936647bdf6fa421_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+69.737_ry-69.635_rz+36.358_tx-00.260_ty+01.098_s+00.838_2d6231bb0270f337526b0d5d3936647bdf6fa421_256x256.png,b174c45c90310c5ca0ac9c80160eee1daa90f4ba,Cars,69.737,20.365,36.358,4735 -4736,bmw325,71.124,2.244,76.103,-0.057,0.307,0.851,ae3c6f470550061a18bd7e27af48ac8adf2c9812,256,V6,ae3c6f470550061a18bd7e27af48ac8adf2c9812_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+71.124_ry+02.244_rz+76.103_tx-00.057_ty+00.307_s+00.851_ae3c6f470550061a18bd7e27af48ac8adf2c9812_256x256.png,8d7bfee621beb9a36a0a04bb924ade69abfb82f4,Cars,71.124,92.244,76.103,4736 -4737,bmw325,71.531,-18.78,20.381,0.056,0.188,1.519,faf3ac047f49d45210d3066a5c9d9cc89c657f49,256,V6,faf3ac047f49d45210d3066a5c9d9cc89c657f49_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+71.531_ry-18.780_rz+20.381_tx+00.056_ty+00.188_s+01.519_faf3ac047f49d45210d3066a5c9d9cc89c657f49_256x256.png,1bd558009e90c47452068d4489dda0be1df3d5ca,Cars,71.531,71.22,20.381,4737 -4738,bmw325,74.97,-42.765,62.5,-0.136,-0.845,1.1,446805408e5edce5126c9e357281126abd142451,256,V6,446805408e5edce5126c9e357281126abd142451_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+74.970_ry-42.765_rz+62.500_tx-00.136_ty-00.845_s+01.100_446805408e5edce5126c9e357281126abd142451_256x256.png,a11606db4392abf39d04fe4d9b9e4c97e9e94868,Cars,74.97,47.235,62.5,4738 -4739,bmw325,75.694,-4.623,41.637,-0.346,-1.09,0.697,90f464d4725214ddae0e9d2dd2e759db0561cb94,256,V6,90f464d4725214ddae0e9d2dd2e759db0561cb94_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+75.694_ry-04.623_rz+41.637_tx-00.346_ty-01.090_s+00.697_90f464d4725214ddae0e9d2dd2e759db0561cb94_256x256.png,eb8cdd0d10e482b8e4afefb9a3ba5e0181923579,Cars,75.694,85.377,41.637,4739 -4740,bmw325,77.735,-31.05,-45.107,0.141,0.325,1.179,679d15faec83532c20e025dfb45d965e88e15d56,256,V6,679d15faec83532c20e025dfb45d965e88e15d56_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+77.735_ry-31.050_rz-45.107_tx+00.141_ty+00.325_s+01.179_679d15faec83532c20e025dfb45d965e88e15d56_256x256.png,4af995149ebeba649706d509b18ac0c5d2f3b4ab,Cars,77.735,58.95,-45.107,4740 -4741,bmw325,84.496,-0.932,-87.591,-0.369,-0.284,0.943,5a93409f4f5c7c3b3261090530ee4c7cdfebf57a,256,V6,5a93409f4f5c7c3b3261090530ee4c7cdfebf57a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx+84.496_ry-00.932_rz-87.591_tx-00.369_ty-00.284_s+00.943_5a93409f4f5c7c3b3261090530ee4c7cdfebf57a_256x256.png,f409a7dab858374df4076c3fd033f23df1f0aad0,Cars,84.496,89.068,-87.591,4741 -4742,bmw325,-8.51,-75.537,-13.713,-0.129,-0.323,0.84,895f2d731002e62392a46787d4527e0080128d65,256,V6,895f2d731002e62392a46787d4527e0080128d65_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-08.510_ry-75.537_rz-13.713_tx-00.129_ty-00.323_s+00.840_895f2d731002e62392a46787d4527e0080128d65_256x256.png,5794dcd316983f4548edb0b8fc0740e30e0006d1,Cars,-8.51,14.463,-13.713,4742 -4743,bmw325,-17.441,-6.027,-51.226,-0.539,-0.709,0.91,5ec8a1f16f380ca80a9997242a9ff211728441a3,256,V6,5ec8a1f16f380ca80a9997242a9ff211728441a3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-17.441_ry-06.027_rz-51.226_tx-00.539_ty-00.709_s+00.910_5ec8a1f16f380ca80a9997242a9ff211728441a3_256x256.png,3ca92f423367c2a93d46212d48d1151b3a45acef,Cars,-17.441,83.973,-51.226,4743 -4744,bmw325,-18.972,51.396,-6.156,-0.494,-0.762,0.799,dad6f748c7df3d8cbc4a4895278e880172227eaa,256,V6,dad6f748c7df3d8cbc4a4895278e880172227eaa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-18.972_ry+51.396_rz-06.156_tx-00.494_ty-00.762_s+00.799_dad6f748c7df3d8cbc4a4895278e880172227eaa_256x256.png,c9ce3187656f0836a54413c31e9207b017a5797f,Cars,-18.972,141.396,-6.156,4744 -4745,bmw325,-19.793,-81.969,85.919,-0.269,1.178,1.227,02327540cc110dd98c4d193775d4cfb8354693d8,256,V6,02327540cc110dd98c4d193775d4cfb8354693d8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-19.793_ry-81.969_rz+85.919_tx-00.269_ty+01.178_s+01.227_02327540cc110dd98c4d193775d4cfb8354693d8_256x256.png,67ae4497ce0c01e705bf22e130f248dc88e44515,Cars,-19.793,8.031,85.919,4745 -4746,bmw325,-24.617,39.521,-54.447,0.241,0.74,1.267,55a014c8cf793919be2b31f0d1fda39076ce23e3,256,V6,55a014c8cf793919be2b31f0d1fda39076ce23e3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-24.617_ry+39.521_rz-54.447_tx+00.241_ty+00.740_s+01.267_55a014c8cf793919be2b31f0d1fda39076ce23e3_256x256.png,31036f7f164253719b3598677dc1ba1d753b2383,Cars,-24.617,129.521,-54.447,4746 -4747,bmw325,-26.004,-37.385,-65.952,-0.007,-0.37,1.489,f4cab1150c8126546586db5648bc70a5d220a201,256,V6,f4cab1150c8126546586db5648bc70a5d220a201_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-26.004_ry-37.385_rz-65.952_tx-00.007_ty-00.370_s+01.489_f4cab1150c8126546586db5648bc70a5d220a201_256x256.png,d1b158f5c4ac9d3d53d650bfe83f6aab2206036e,Cars,-26.004,52.615,-65.952,4747 -4748,bmw325,-26.392,-36.874,71.497,0.537,-0.836,1.013,761df4c90819aa29496f27748565cbec5487e3f8,256,V6,761df4c90819aa29496f27748565cbec5487e3f8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-26.392_ry-36.874_rz+71.497_tx+00.537_ty-00.836_s+01.013_761df4c90819aa29496f27748565cbec5487e3f8_256x256.png,501cefeb87e80c74864504ce4dd49afdae2a4824,Cars,-26.392,53.126,71.497,4748 -4749,bmw325,-34.689,-24.018,-20.683,0.29,-1.05,1.07,5340e08938cbce5c353d96431d865b6ed44f477c,256,V6,5340e08938cbce5c353d96431d865b6ed44f477c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-34.689_ry-24.018_rz-20.683_tx+00.290_ty-01.050_s+01.070_5340e08938cbce5c353d96431d865b6ed44f477c_256x256.png,1cb823cf1177ae55d2c3ffea552b55e89ef3fc08,Cars,-34.689,65.982,-20.683,4749 -4750,bmw325,-36.813,-85.58,-79.619,0.379,-0.276,0.957,1f609457d2b8cc02ec03d7c1d506eeb4f7442707,256,V6,1f609457d2b8cc02ec03d7c1d506eeb4f7442707_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-36.813_ry-85.580_rz-79.619_tx+00.379_ty-00.276_s+00.957_1f609457d2b8cc02ec03d7c1d506eeb4f7442707_256x256.png,bb36bbaafcf20488be00afac72fcf25202b3f60c,Cars,-36.813,4.42,-79.619,4750 -4751,bmw325,-45.015,49.91,59.273,-0.594,1.067,0.777,c390f35bbb8c079fe128ec10a1f81fce85df0e10,256,V6,c390f35bbb8c079fe128ec10a1f81fce85df0e10_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-45.015_ry+49.910_rz+59.273_tx-00.594_ty+01.067_s+00.777_c390f35bbb8c079fe128ec10a1f81fce85df0e10_256x256.png,4ee51df2213bf90f536fab004ade7544ced026d4,Cars,-45.015,139.91,59.273,4751 -4752,bmw325,-51.159,1.379,14.694,0.398,0.141,0.633,735c75b5a1bcb3638f01a6d6d0ba4d490a4018d4,256,V6,735c75b5a1bcb3638f01a6d6d0ba4d490a4018d4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-51.159_ry+01.379_rz+14.694_tx+00.398_ty+00.141_s+00.633_735c75b5a1bcb3638f01a6d6d0ba4d490a4018d4_256x256.png,b9ae3b7be180db48ac522bbf8e50bbe5f944de76,Cars,-51.159,91.379,14.694,4752 -4753,bmw325,-59.298,-11.755,84.725,0.24,1.177,0.936,e571a60810c0a67eae4f36ce3f70073549d8e9f6,256,V6,e571a60810c0a67eae4f36ce3f70073549d8e9f6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-59.298_ry-11.755_rz+84.725_tx+00.240_ty+01.177_s+00.936_e571a60810c0a67eae4f36ce3f70073549d8e9f6_256x256.png,fe6e0c7fab7a83f2173a9da27a1ba8770e8fcc23,Cars,-59.298,78.245,84.725,4753 -4754,bmw325,-62.329,75.619,-20.144,0.106,-0.287,0.697,e82df21de83e6681faa697f4c5f44bad05c9bbb4,256,V6,e82df21de83e6681faa697f4c5f44bad05c9bbb4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-62.329_ry+75.619_rz-20.144_tx+00.106_ty-00.287_s+00.697_e82df21de83e6681faa697f4c5f44bad05c9bbb4_256x256.png,a03b1e78c0e1ffd7bb8d8f420420e3d8fc446f72,Cars,-62.329,165.619,-20.144,4754 -4755,bmw325,-69.527,-53.362,-27.971,-0.573,0.773,1.193,70a3fae8c61d92be0849c6036baec1aae10dd620,256,V6,70a3fae8c61d92be0849c6036baec1aae10dd620_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-69.527_ry-53.362_rz-27.971_tx-00.573_ty+00.773_s+01.193_70a3fae8c61d92be0849c6036baec1aae10dd620_256x256.png,1bd1865cb8c9f7e2fb6eef5299342ab7a5cb5d53,Cars,-69.527,36.638,-27.971,4755 -4756,bmw325,-76.014,-20.759,-10.234,-0.206,0.38,0.715,2f7b5ae717dc0fedc5ce69acc3c304ebff8fd0e4,256,V6,2f7b5ae717dc0fedc5ce69acc3c304ebff8fd0e4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-76.014_ry-20.759_rz-10.234_tx-00.206_ty+00.380_s+00.715_2f7b5ae717dc0fedc5ce69acc3c304ebff8fd0e4_256x256.png,36b24454cd5c5df25a2ff3fb3c00e59637abf49e,Cars,-76.014,69.241,-10.234,4756 -4757,bmw325,-76.705,-18.796,-30.656,-0.338,0.801,0.92,091d3594feedc3f07219b80efad4d151ce8afc91,256,V6,091d3594feedc3f07219b80efad4d151ce8afc91_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-76.705_ry-18.796_rz-30.656_tx-00.338_ty+00.801_s+00.920_091d3594feedc3f07219b80efad4d151ce8afc91_256x256.png,fe3becc5f8e8fdcb3509a9c8a72baedd98259a70,Cars,-76.705,71.204,-30.656,4757 -4758,bmw325,-84.027,33.405,-17.055,0.019,0.129,0.919,a05b9e6e36d4a74b8446dc8675115228e8864990,256,V6,a05b9e6e36d4a74b8446dc8675115228e8864990_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-84.027_ry+33.405_rz-17.055_tx+00.019_ty+00.129_s+00.919_a05b9e6e36d4a74b8446dc8675115228e8864990_256x256.png,f0cbee2047df757949e9c0c19b8c0f43f31b33ff,Cars,-84.027,123.405,-17.055,4758 -4759,bmw325,-86.9,-84.082,35.481,0.509,-0.792,0.703,6ae08af16e8ef2fef7943d808b92b6c6df35561f,256,V6,6ae08af16e8ef2fef7943d808b92b6c6df35561f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bmw325_rx-86.900_ry-84.082_rz+35.481_tx+00.509_ty-00.792_s+00.703_6ae08af16e8ef2fef7943d808b92b6c6df35561f_256x256.png,36b74d1753c4fe9878d037883e9afe904bd78489,Cars,-86.9,5.918,35.481,4759 -4760,bora_a,12.052,47.788,73.574,-0.494,0.869,0.714,9faa6b4d4c8e11354e67223c3f894444c82eefdf,256,V6,9faa6b4d4c8e11354e67223c3f894444c82eefdf_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx+12.052_ry+47.788_rz+73.574_tx-00.494_ty+00.869_s+00.714_9faa6b4d4c8e11354e67223c3f894444c82eefdf_256x256.png,8d1101f118b17c54fbe4cbe9a222c9192a1a00ac,Cars,12.052,137.788,73.574,4760 -4761,bora_a,16.086,-55.744,-25.352,-0.468,-0.87,1.498,56028ff5be6025ee5f2963e6d7107937365ce980,256,V6,56028ff5be6025ee5f2963e6d7107937365ce980_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx+16.086_ry-55.744_rz-25.352_tx-00.468_ty-00.870_s+01.498_56028ff5be6025ee5f2963e6d7107937365ce980_256x256.png,60c97a4893055a480c71dcae9dd582bbac8a7fee,Cars,16.086,34.256,-25.352,4761 -4762,bora_a,18.635,-6.024,-4.345,0.434,0.546,1.518,be93d5fbaff234329865c28154643d157629c05b,256,V6,be93d5fbaff234329865c28154643d157629c05b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx+18.635_ry-06.024_rz-04.345_tx+00.434_ty+00.546_s+01.518_be93d5fbaff234329865c28154643d157629c05b_256x256.png,69487f5d71897b750f2392d8bfb38bf6918be2b9,Cars,18.635,83.976,-4.345,4762 -4763,bora_a,25.597,-0.992,-86.653,-0.561,0.538,1.355,73fa52b75fc56362bfa856785dfb051c2851bb76,256,V6,73fa52b75fc56362bfa856785dfb051c2851bb76_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx+25.597_ry-00.992_rz-86.653_tx-00.561_ty+00.538_s+01.355_73fa52b75fc56362bfa856785dfb051c2851bb76_256x256.png,75f71b227fc29eaa640428f8e11fc7956551ac01,Cars,25.597,89.008,-86.653,4763 -4764,bora_a,26.369,-40.19,-83.036,0.166,-1.152,1.596,06025532e5d0ffc1e95d4e956294da17584d5d3f,256,V6,06025532e5d0ffc1e95d4e956294da17584d5d3f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx+26.369_ry-40.190_rz-83.036_tx+00.166_ty-01.152_s+01.596_06025532e5d0ffc1e95d4e956294da17584d5d3f_256x256.png,3f52f04775ec8c604ebec48481e7a1164e0b50ae,Cars,26.369,49.81,-83.036,4764 -4765,bora_a,33.714,19.003,77.185,-0.37,-0.479,0.948,c76c9b17f936b479b26ec97669a507caf9c06aba,256,V6,c76c9b17f936b479b26ec97669a507caf9c06aba_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx+33.714_ry+19.003_rz+77.185_tx-00.370_ty-00.479_s+00.948_c76c9b17f936b479b26ec97669a507caf9c06aba_256x256.png,0c6d3dd8f3a44df552c48d2f9a1ce27ea5a90f93,Cars,33.714,109.003,77.185,4765 -4766,bora_a,42.658,-77.93,41.449,-0.539,-0.371,0.841,524d919984d2052ca26fbb4ef7f15d3fb00bb201,256,V6,524d919984d2052ca26fbb4ef7f15d3fb00bb201_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx+42.658_ry-77.930_rz+41.449_tx-00.539_ty-00.371_s+00.841_524d919984d2052ca26fbb4ef7f15d3fb00bb201_256x256.png,22f9a3f16b26e5d95c8a6fe9f637200dff8e6d36,Cars,42.658,12.07,41.449,4766 -4767,bora_a,43.599,-40.987,-37.763,0.035,-0.983,0.903,0feb8f22c1d22abefb6acbb5ef93695f2094f7bd,256,V6,0feb8f22c1d22abefb6acbb5ef93695f2094f7bd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx+43.599_ry-40.987_rz-37.763_tx+00.035_ty-00.983_s+00.903_0feb8f22c1d22abefb6acbb5ef93695f2094f7bd_256x256.png,0243f4831f338e09801093463e96c8232ce5ec79,Cars,43.599,49.013,-37.763,4767 -4768,bora_a,44.483,-4.072,-65.414,0.29,0.814,1.332,e0d8adc3d6c520088fa43ea894b5ca0defb6a2e0,256,V6,e0d8adc3d6c520088fa43ea894b5ca0defb6a2e0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx+44.483_ry-04.072_rz-65.414_tx+00.290_ty+00.814_s+01.332_e0d8adc3d6c520088fa43ea894b5ca0defb6a2e0_256x256.png,801a6da19658b034e97bfa730d54dbcbf089853a,Cars,44.483,85.928,-65.414,4768 -4769,bora_a,45.593,3.642,-35.75,-0.449,0.043,1.458,2ecc9808a5dbe4d849393c5e20b72310e3fd79bc,256,V6,2ecc9808a5dbe4d849393c5e20b72310e3fd79bc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx+45.593_ry+03.642_rz-35.750_tx-00.449_ty+00.043_s+01.458_2ecc9808a5dbe4d849393c5e20b72310e3fd79bc_256x256.png,85690e741fc87872b41e3d946698b624e1c2cfea,Cars,45.593,93.642,-35.75,4769 -4770,bora_a,58.82,26.042,-16.343,-0.486,-0.888,1.597,6c6ed48425ecd8afd34cee10f18e7697555d9915,256,V6,6c6ed48425ecd8afd34cee10f18e7697555d9915_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx+58.820_ry+26.042_rz-16.343_tx-00.486_ty-00.888_s+01.597_6c6ed48425ecd8afd34cee10f18e7697555d9915_256x256.png,d05a1431bc15369877078952103ae7a7fd7b063e,Cars,58.82,116.042,-16.343,4770 -4771,bora_a,73.661,-23.107,-83.664,-0.557,-1.007,1.286,78552903abd1cfa4a145d12c4bc7ec0f9ee30aae,256,V6,78552903abd1cfa4a145d12c4bc7ec0f9ee30aae_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx+73.661_ry-23.107_rz-83.664_tx-00.557_ty-01.007_s+01.286_78552903abd1cfa4a145d12c4bc7ec0f9ee30aae_256x256.png,688f55ee8763a4de73169b0b29b1a4ac25cf152c,Cars,73.661,66.893,-83.664,4771 -4772,bora_a,77.45,57.155,-64.942,-0.162,-0.87,0.908,3ffb25549935477cda8178c1ef27e8acbf89c859,256,V6,3ffb25549935477cda8178c1ef27e8acbf89c859_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx+77.450_ry+57.155_rz-64.942_tx-00.162_ty-00.870_s+00.908_3ffb25549935477cda8178c1ef27e8acbf89c859_256x256.png,018cbe39c543ff4762667256b1e11300fbd5b968,Cars,77.45,147.155,-64.942,4772 -4773,bora_a,84.861,80.86,-53.329,0.333,0.326,0.747,86c84b3cf4219b6e76d30356883b6283d5d419db,256,V6,86c84b3cf4219b6e76d30356883b6283d5d419db_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx+84.861_ry+80.860_rz-53.329_tx+00.333_ty+00.326_s+00.747_86c84b3cf4219b6e76d30356883b6283d5d419db_256x256.png,0e4a08111aed414e41518c2c7c10bb940e96bd25,Cars,84.861,170.86,-53.329,4773 -4774,bora_a,87.343,-88.571,77.016,-0.132,0.082,0.812,dedd97cd07efe0cb6a09b36f0164e8d7708f2062,256,V6,dedd97cd07efe0cb6a09b36f0164e8d7708f2062_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx+87.343_ry-88.571_rz+77.016_tx-00.132_ty+00.082_s+00.812_dedd97cd07efe0cb6a09b36f0164e8d7708f2062_256x256.png,9bdc8daf548c1474a84025f2ad43a092c0b2703f,Cars,87.343,1.429,77.016,4774 -4775,bora_a,-3.036,18.824,-69.473,-0.493,-0.391,1.328,d3f1888b9aaa393c83a1efe6890ea06b46b41270,256,V6,d3f1888b9aaa393c83a1efe6890ea06b46b41270_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-03.036_ry+18.824_rz-69.473_tx-00.493_ty-00.391_s+01.328_d3f1888b9aaa393c83a1efe6890ea06b46b41270_256x256.png,c8203d50f72f9245d41dc55c69a57096969f472a,Cars,-3.036,108.824,-69.473,4775 -4776,bora_a,-3.346,87.568,-67.129,0.226,-0.781,1.574,8f6ea62ded1e6c1dac6bc25cedd9365eb0757790,256,V6,8f6ea62ded1e6c1dac6bc25cedd9365eb0757790_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-03.346_ry+87.568_rz-67.129_tx+00.226_ty-00.781_s+01.574_8f6ea62ded1e6c1dac6bc25cedd9365eb0757790_256x256.png,0ae2898b626e16fb3e9eb15c551399027a91da4d,Cars,-3.346,177.568,-67.129,4776 -4777,bora_a,-6.173,33.24,47.893,-0.429,0.22,1.487,6e56d33c1d7626870760f023466b7b6454a27e86,256,V6,6e56d33c1d7626870760f023466b7b6454a27e86_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-06.173_ry+33.240_rz+47.893_tx-00.429_ty+00.220_s+01.487_6e56d33c1d7626870760f023466b7b6454a27e86_256x256.png,05991718e324d33ff6263b367327d7c159a214e1,Cars,-6.173,123.24,47.893,4777 -4778,bora_a,-17.851,-58.289,-58.062,0.385,-0.231,0.726,b33a8f256ec75b41c4a149f6601f50bea54d34ba,256,V6,b33a8f256ec75b41c4a149f6601f50bea54d34ba_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-17.851_ry-58.289_rz-58.062_tx+00.385_ty-00.231_s+00.726_b33a8f256ec75b41c4a149f6601f50bea54d34ba_256x256.png,0494bebbaf05baea0a128bbf473ed2ad997dc9a8,Cars,-17.851,31.711,-58.062,4778 -4779,bora_a,-21.745,-2.099,22.601,-0.35,-1.146,1.388,f23a97bc6e0317c8d4b32819074edd62fc6c5d26,256,V6,f23a97bc6e0317c8d4b32819074edd62fc6c5d26_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-21.745_ry-02.099_rz+22.601_tx-00.350_ty-01.146_s+01.388_f23a97bc6e0317c8d4b32819074edd62fc6c5d26_256x256.png,9264d1335f26516d8a9265cd5cedbb2949e15d34,Cars,-21.745,87.901,22.601,4779 -4780,bora_a,-24.905,29.109,-47.125,0.592,-1.178,1.266,7257560f4fbfeba6a70cea0da94ea73180a5c7ab,256,V6,7257560f4fbfeba6a70cea0da94ea73180a5c7ab_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-24.905_ry+29.109_rz-47.125_tx+00.592_ty-01.178_s+01.266_7257560f4fbfeba6a70cea0da94ea73180a5c7ab_256x256.png,277e47516f9ea60b24dab5f9b5fd8f58ec7f4340,Cars,-24.905,119.109,-47.125,4780 -4781,bora_a,-29.357,14.228,76.581,0.062,0.651,0.939,87b3ad501dabd4a607ef2bff57ce8aa2523abfdf,256,V6,87b3ad501dabd4a607ef2bff57ce8aa2523abfdf_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-29.357_ry+14.228_rz+76.581_tx+00.062_ty+00.651_s+00.939_87b3ad501dabd4a607ef2bff57ce8aa2523abfdf_256x256.png,c03b7349f7c3a23a4a51b7c789ab61288cb0764d,Cars,-29.357,104.228,76.581,4781 -4782,bora_a,-35.846,-77.401,-85.565,0.562,-0.805,0.923,82c15addd813d8bd04f528b8fb260774998aa08a,256,V6,82c15addd813d8bd04f528b8fb260774998aa08a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-35.846_ry-77.401_rz-85.565_tx+00.562_ty-00.805_s+00.923_82c15addd813d8bd04f528b8fb260774998aa08a_256x256.png,3517f253d49fecbc200d1e461789bf1ee9f49f63,Cars,-35.846,12.599,-85.565,4782 -4783,bora_a,-36.021,-58.844,-11.088,-0.041,0.363,1.09,cdbf1c42cccadc18022b3a6556458102d727e079,256,V6,cdbf1c42cccadc18022b3a6556458102d727e079_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-36.021_ry-58.844_rz-11.088_tx-00.041_ty+00.363_s+01.090_cdbf1c42cccadc18022b3a6556458102d727e079_256x256.png,63fb08a2683a797f4463143678d71c375121a77c,Cars,-36.021,31.156,-11.088,4783 -4784,bora_a,-40.019,77.247,-0.449,-0.375,0.664,1.551,e3272edc621b301e88d3536a169bda635e49039a,256,V6,e3272edc621b301e88d3536a169bda635e49039a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-40.019_ry+77.247_rz-00.449_tx-00.375_ty+00.664_s+01.551_e3272edc621b301e88d3536a169bda635e49039a_256x256.png,e0d2b4778e99f0c863c3c08e30c9ce11ea472288,Cars,-40.019,167.247,-0.449,4784 -4785,bora_a,-45.236,-71.311,-26.177,-0.454,-0.393,1.565,255c6c6249a96d9eb2b6e4aca676931470b7b4ef,256,V6,255c6c6249a96d9eb2b6e4aca676931470b7b4ef_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-45.236_ry-71.311_rz-26.177_tx-00.454_ty-00.393_s+01.565_255c6c6249a96d9eb2b6e4aca676931470b7b4ef_256x256.png,9273be72a9fb0045edf3b59c7ed41a4bc53d3994,Cars,-45.236,18.689,-26.177,4785 -4786,bora_a,-48.74,40.521,-49.449,0.553,-0.057,0.73,e01de2d1b3ce15c38cc1f54b2107a94fd45309c2,256,V6,e01de2d1b3ce15c38cc1f54b2107a94fd45309c2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-48.740_ry+40.521_rz-49.449_tx+00.553_ty-00.057_s+00.730_e01de2d1b3ce15c38cc1f54b2107a94fd45309c2_256x256.png,bac9615f51bc9aecc468f552617fb6acaf6369df,Cars,-48.74,130.521,-49.449,4786 -4787,bora_a,-54.225,-30.237,4.144,0.376,-0.101,1.507,1fddaa2c83db0971b382e410fe1b18a8ae64c303,256,V6,1fddaa2c83db0971b382e410fe1b18a8ae64c303_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-54.225_ry-30.237_rz+04.144_tx+00.376_ty-00.101_s+01.507_1fddaa2c83db0971b382e410fe1b18a8ae64c303_256x256.png,08e205100cd81d68b0f7e9fcc4ce611c9327633d,Cars,-54.225,59.763,4.144,4787 -4788,bora_a,-57.844,72.623,82.211,0.156,-0.626,1.346,b275799f2145ac011383911cb2b8dd885461e53d,256,V6,b275799f2145ac011383911cb2b8dd885461e53d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-57.844_ry+72.623_rz+82.211_tx+00.156_ty-00.626_s+01.346_b275799f2145ac011383911cb2b8dd885461e53d_256x256.png,94eb77a01cf45e8b78c845b23b236b02386e56b2,Cars,-57.844,162.623,82.211,4788 -4789,bora_a,-59.207,17.68,40.356,-0.153,-1.109,0.934,2043d43e2439e21449f77e26a5f5a1ed9f74cbf6,256,V6,2043d43e2439e21449f77e26a5f5a1ed9f74cbf6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-59.207_ry+17.680_rz+40.356_tx-00.153_ty-01.109_s+00.934_2043d43e2439e21449f77e26a5f5a1ed9f74cbf6_256x256.png,0dca0ec02d514650d1c32b696426cdf9e6914608,Cars,-59.207,107.68,40.356,4789 -4790,bora_a,-61.702,26.304,82.371,0.479,1.079,1.29,b40fb74edc7d43e866b1a9b847801eed6d4c2c47,256,V6,b40fb74edc7d43e866b1a9b847801eed6d4c2c47_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-61.702_ry+26.304_rz+82.371_tx+00.479_ty+01.079_s+01.290_b40fb74edc7d43e866b1a9b847801eed6d4c2c47_256x256.png,2b228bee9ed0fcc28d57969371c124d1f00abfd0,Cars,-61.702,116.304,82.371,4790 -4791,bora_a,-71.058,-18.944,31.519,0.089,0.386,1.238,172b485bdc1110ab85d82828903f92c0758019dd,256,V6,172b485bdc1110ab85d82828903f92c0758019dd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-71.058_ry-18.944_rz+31.519_tx+00.089_ty+00.386_s+01.238_172b485bdc1110ab85d82828903f92c0758019dd_256x256.png,d1923d223bf8f59c3d4961f6a61d5843a71609eb,Cars,-71.058,71.056,31.519,4791 -4792,bora_a,-73.995,30.039,-27.7,-0.479,-0.805,0.779,66d77fe2e2f51c754d375e5a998b475463f4659d,256,V6,66d77fe2e2f51c754d375e5a998b475463f4659d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-73.995_ry+30.039_rz-27.700_tx-00.479_ty-00.805_s+00.779_66d77fe2e2f51c754d375e5a998b475463f4659d_256x256.png,967e415ae02ba26072111af8b1af3b1994a1fe2d,Cars,-73.995,120.039,-27.7,4792 -4793,bora_a,-75.027,59.07,47.535,-0.11,1.191,0.858,5f495b09814e3a411ce90c4ce49661b5655c3635,256,V6,5f495b09814e3a411ce90c4ce49661b5655c3635_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-75.027_ry+59.070_rz+47.535_tx-00.110_ty+01.191_s+00.858_5f495b09814e3a411ce90c4ce49661b5655c3635_256x256.png,0be41b4f41df3ef5b946027da30688fc138c0fa6,Cars,-75.027,149.07,47.535,4793 -4794,bora_a,-76.699,-42.805,-82.285,-0.43,0.164,1.495,681eca30d9a0a9fb449e400cea414d0f19a17ac0,256,V6,681eca30d9a0a9fb449e400cea414d0f19a17ac0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-76.699_ry-42.805_rz-82.285_tx-00.430_ty+00.164_s+01.495_681eca30d9a0a9fb449e400cea414d0f19a17ac0_256x256.png,78a4866c2727e47f05a94de3dc013e51938c95f9,Cars,-76.699,47.195,-82.285,4794 -4795,bora_a,-80.672,-82.705,38.464,-0.444,0.144,1.083,f87a34a883bc0edca419e9e06ca4ab154e072561,256,V6,f87a34a883bc0edca419e9e06ca4ab154e072561_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-80.672_ry-82.705_rz+38.464_tx-00.444_ty+00.144_s+01.083_f87a34a883bc0edca419e9e06ca4ab154e072561_256x256.png,54f8b258d55f211d173b7b10ae9ea2829c15f2e5,Cars,-80.672,7.295,38.464,4795 -4796,bora_a,-81.013,46.922,28.069,-0.291,1.115,0.947,5db4afe1d831a21185777732932b270b34bfb8ec,256,V6,5db4afe1d831a21185777732932b270b34bfb8ec_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-81.013_ry+46.922_rz+28.069_tx-00.291_ty+01.115_s+00.947_5db4afe1d831a21185777732932b270b34bfb8ec_256x256.png,b11222fe699b452c9096780f02d13ebc43994a3f,Cars,-81.013,136.922,28.069,4796 -4797,bora_a,-82.069,-74.08,45.517,0.324,0.376,0.939,18155d790854f631ef578f5d709b055d300b610a,256,V6,18155d790854f631ef578f5d709b055d300b610a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-82.069_ry-74.080_rz+45.517_tx+00.324_ty+00.376_s+00.939_18155d790854f631ef578f5d709b055d300b610a_256x256.png,e2333eee66bf46ed58d30026dd6ea4498cb53404,Cars,-82.069,15.92,45.517,4797 -4798,bora_a,-82.411,36.34,-51.551,-0.31,1.112,0.637,7c9dda86a484dc8320e04b3034687e49eed9b6e6,256,V6,7c9dda86a484dc8320e04b3034687e49eed9b6e6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-82.411_ry+36.340_rz-51.551_tx-00.310_ty+01.112_s+00.637_7c9dda86a484dc8320e04b3034687e49eed9b6e6_256x256.png,6fba2b1ac57ac61c14dca9f91392b7b41409933b,Cars,-82.411,126.34,-51.551,4798 -4799,bora_a,-88.027,31.508,24.888,-0.322,0.978,1.519,7e757bc5d882e1c7b12d271a9339d0b9ece2e776,256,V6,7e757bc5d882e1c7b12d271a9339d0b9ece2e776_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/bora_a_rx-88.027_ry+31.508_rz+24.888_tx-00.322_ty+00.978_s+01.519_7e757bc5d882e1c7b12d271a9339d0b9ece2e776_256x256.png,d15269a8c62b4aedb4af74de377d38c5d5e95f12,Cars,-88.027,121.508,24.888,4799 -4800,breed_pug,6.894,63.221,-80.537,-0.249,0.14,1.02875,6b6254f9e8dd06a76a1dd0cbb0be5e80be048de7,256,V6,6b6254f9e8dd06a76a1dd0cbb0be5e80be048de7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+06.894_ry+153.221_rz-80.537_tx-00.249_ty+00.140_s+00.823_6b6254f9e8dd06a76a1dd0cbb0be5e80be048de7_256x256.png,d5206f6d239dfbbbaabeb26555d8d5d71404c46e,Animals,6.894,153.221,-80.537,4800 -4801,breed_pug,10.107,83.912,-26.139,0.042,-0.516,1.08125,9fe993257f5dcef576fc412f34d6f4c970343571,256,V6,9fe993257f5dcef576fc412f34d6f4c970343571_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+10.107_ry+173.912_rz-26.139_tx+00.042_ty-00.516_s+00.865_9fe993257f5dcef576fc412f34d6f4c970343571_256x256.png,a57372b3a40b179fbe11a1f5aa9c8eaefd55f7c0,Animals,10.107,173.912,-26.139,4801 -4802,breed_pug,20.794,-40.511,-70.987,0.213,0.644,1.19375,f335eca7430e138449f84397648802ee3222e024,256,V6,f335eca7430e138449f84397648802ee3222e024_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+20.794_ry+49.489_rz-70.987_tx+00.213_ty+00.644_s+00.955_f335eca7430e138449f84397648802ee3222e024_256x256.png,9d4e4141566aa969ea2c0bfbd73139470f710ffa,Animals,20.794,49.489,-70.987,4802 -4803,breed_pug,22.503,-45.358,-41.962,0.487,0.524,1.285,3d11432f61b65864a049fab76920c71eb3751b03,256,V6,3d11432f61b65864a049fab76920c71eb3751b03_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+22.503_ry+44.642_rz-41.962_tx+00.487_ty+00.524_s+01.028_3d11432f61b65864a049fab76920c71eb3751b03_256x256.png,5d289ff82dcc81e70ac382b3fd0c9270e2009096,Animals,22.503,44.642,-41.962,4803 -4804,breed_pug,22.701,77.531,-38.751,-0.354,0.617,1.16,36bfbbb25a81a0454f1a17abf91bb4d82d440a68,256,V6,36bfbbb25a81a0454f1a17abf91bb4d82d440a68_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+22.701_ry+167.531_rz-38.751_tx-00.354_ty+00.617_s+00.928_36bfbbb25a81a0454f1a17abf91bb4d82d440a68_256x256.png,7e61f14993118e5465c3763cb4c941a0884bc70b,Animals,22.701,167.531,-38.751,4804 -4805,breed_pug,24.629,9.96,-30.706,0.576,-0.237,0.87875,00c98e15d4439ebef91780a4aafe90460ef62fb4,256,V6,00c98e15d4439ebef91780a4aafe90460ef62fb4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+24.629_ry+99.960_rz-30.706_tx+00.576_ty-00.237_s+00.703_00c98e15d4439ebef91780a4aafe90460ef62fb4_256x256.png,e616a367b21e8333a380f5dd7aace101779d3659,Animals,24.629,99.96,-30.706,4805 -4806,breed_pug,30.118,21.308,-82.646,0.169,-0.991,1.095,0108526043a056bd4c6a374d260da05f3f6cfa62,256,V6,0108526043a056bd4c6a374d260da05f3f6cfa62_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+30.118_ry+111.308_rz-82.646_tx+00.169_ty-00.991_s+00.876_0108526043a056bd4c6a374d260da05f3f6cfa62_256x256.png,536ffb9cdaadead69f34c854dedaee89180e041d,Animals,30.118,111.308,-82.646,4806 -4807,breed_pug,32.511,37.022,-40.491,0.092,-0.177,0.69125,3f080be31b464d683e2378038f3484d14da43260,256,V6,3f080be31b464d683e2378038f3484d14da43260_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+32.511_ry+127.022_rz-40.491_tx+00.092_ty-00.177_s+00.553_3f080be31b464d683e2378038f3484d14da43260_256x256.png,71eefa76e2c61fbd507f56c60735ad5a3e9e6a21,Animals,32.511,127.022,-40.491,4807 -4808,breed_pug,42.918,49.237,-9.381,-0.501,-0.641,1.0275,41096ec820dfc75e99c70e94fd136dd37f419f31,256,V6,41096ec820dfc75e99c70e94fd136dd37f419f31_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+42.918_ry+139.237_rz-09.381_tx-00.501_ty-00.641_s+00.822_41096ec820dfc75e99c70e94fd136dd37f419f31_256x256.png,7582cd8480985efdf876de878253177271ec7f8a,Animals,42.918,139.237,-9.381,4808 -4809,breed_pug,46.63,-38.689,-44.875,-0.465,-0.16,1.16125,1ff9fb9fca70886eff13e981850e18cbddd9f7db,256,V6,1ff9fb9fca70886eff13e981850e18cbddd9f7db_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+46.630_ry+51.311_rz-44.875_tx-00.465_ty-00.160_s+00.929_1ff9fb9fca70886eff13e981850e18cbddd9f7db_256x256.png,5bfa86158c3d263842548966e4ac3cf4bf6b19e1,Animals,46.63,51.311,-44.875,4809 -4810,breed_pug,48.256,-18.479,36.51,-0.481,-0.4,0.93125,684288202207dda7197b27454818056d93fc6e2a,256,V6,684288202207dda7197b27454818056d93fc6e2a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+48.256_ry+71.521_rz+36.510_tx-00.481_ty-00.400_s+00.745_684288202207dda7197b27454818056d93fc6e2a_256x256.png,2161012d8943120406dbdaf226f170fb775800c4,Animals,48.256,71.521,36.51,4810 -4811,breed_pug,62.446,55.163,-24.071,-0.491,-0.608,0.94875,5d1682cbaae7e450f7bdd2507b026730e20b4ab0,256,V6,5d1682cbaae7e450f7bdd2507b026730e20b4ab0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+62.446_ry+145.163_rz-24.071_tx-00.491_ty-00.608_s+00.759_5d1682cbaae7e450f7bdd2507b026730e20b4ab0_256x256.png,d2930f56f43c5930a2762aa03d1389549f6b856b,Animals,62.446,145.163,-24.071,4811 -4812,breed_pug,64.726,43.071,-85.171,0.147,-0.579,1.2375,9a3281299d1c75c056cfa0495ea5f02ccc5af6c9,256,V6,9a3281299d1c75c056cfa0495ea5f02ccc5af6c9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+64.726_ry+133.071_rz-85.171_tx+00.147_ty-00.579_s+00.990_9a3281299d1c75c056cfa0495ea5f02ccc5af6c9_256x256.png,c58db5f9cadf34dfee2c0772af46ab0e9b61078f,Animals,64.726,133.071,-85.171,4812 -4813,breed_pug,68.747,70.646,-66.065,-0.518,-1.054,1.02625,6edcdc67f699f3d2bac99fa46f4b8590b2c92263,256,V6,6edcdc67f699f3d2bac99fa46f4b8590b2c92263_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+68.747_ry+160.646_rz-66.065_tx-00.518_ty-01.054_s+00.821_6edcdc67f699f3d2bac99fa46f4b8590b2c92263_256x256.png,45d11e553147990f2531081073adb2e331c94736,Animals,68.747,160.646,-66.065,4813 -4814,breed_pug,80.263,-53.26,-2.541,-0.096,1.194,1.245,f6b1bffe36f84a4b26ec0e26fc63b74d3b4a7c7b,256,V6,f6b1bffe36f84a4b26ec0e26fc63b74d3b4a7c7b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+80.263_ry+36.740_rz-02.541_tx-00.096_ty+01.194_s+00.996_f6b1bffe36f84a4b26ec0e26fc63b74d3b4a7c7b_256x256.png,03edb370b7a532cc4764854474186a712f647c5e,Animals,80.263,36.74,-2.541,4814 -4815,breed_pug,86.021,3.063,-51.791,0.445,0.022,1.36,08c7c12ca67bbe4d78779505bee8d5407866e310,256,V6,08c7c12ca67bbe4d78779505bee8d5407866e310_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+86.021_ry+93.063_rz-51.791_tx+00.445_ty+00.022_s+01.088_08c7c12ca67bbe4d78779505bee8d5407866e310_256x256.png,1229b19e6c28487d866a1b71d0ec698200dd189a,Animals,86.021,93.063,-51.791,4815 -4816,breed_pug,87.507,-62.482,-78.684,0.114,-0.566,1.0925,39f5345246ea4a0f1a9314656d8ac71792860d70,256,V6,39f5345246ea4a0f1a9314656d8ac71792860d70_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+87.507_ry+27.518_rz-78.684_tx+00.114_ty-00.566_s+00.874_39f5345246ea4a0f1a9314656d8ac71792860d70_256x256.png,88da2a0163cd4e53756b046db1ff906e58ac8b5f,Animals,87.507,27.518,-78.684,4816 -4817,breed_pug,88.167,76.528,49.585,-0.59,0.344,0.99,d9291961bbed14b5bb75bfe805a86e288e2964da,256,V6,d9291961bbed14b5bb75bfe805a86e288e2964da_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+88.167_ry+166.528_rz+49.585_tx-00.590_ty+00.344_s+00.792_d9291961bbed14b5bb75bfe805a86e288e2964da_256x256.png,37c38e5c45456f56e396adb39a01cd02fb9ab5b8,Animals,88.167,166.528,49.585,4817 -4818,breed_pug,88.292,-36.167,-8.854,-0.146,-0.539,0.775,f008be3ca891e8f6d4704767da7b3fcac0d55dad,256,V6,f008be3ca891e8f6d4704767da7b3fcac0d55dad_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+88.292_ry+53.833_rz-08.854_tx-00.146_ty-00.539_s+00.620_f008be3ca891e8f6d4704767da7b3fcac0d55dad_256x256.png,6725d8e0b704a0a97f923e264be9b0bb6395218d,Animals,88.292,53.833,-8.854,4818 -4819,breed_pug,89.353,88.605,34.572,-0.064,-0.199,1.275,609313990c44eaf07ed7fe8b958a60344af6e940,256,V6,609313990c44eaf07ed7fe8b958a60344af6e940_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+89.353_ry+178.605_rz+34.572_tx-00.064_ty-00.199_s+01.020_609313990c44eaf07ed7fe8b958a60344af6e940_256x256.png,cd2ae2cdf256957cab855f56ed71ca6868700911,Animals,89.353,178.605,34.572,4819 -4820,breed_pug,89.687,-33.79,56.573,0.188,-0.442,1.35375,3b26442c1728f196d3eaebd84a790bb35efb0a50,256,V6,3b26442c1728f196d3eaebd84a790bb35efb0a50_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx+89.687_ry+56.210_rz+56.573_tx+00.188_ty-00.442_s+01.083_3b26442c1728f196d3eaebd84a790bb35efb0a50_256x256.png,10492b5e19dae3ef02a63be796027639ae3c0b19,Animals,89.687,56.21,56.573,4820 -4821,breed_pug,-2.158,60.019,-51.966,-0.38,0.79,1.2775,c8f473f41c76257bcfca1137800ee2190b4a9cd7,256,V6,c8f473f41c76257bcfca1137800ee2190b4a9cd7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-02.158_ry+150.019_rz-51.966_tx-00.380_ty+00.790_s+01.022_c8f473f41c76257bcfca1137800ee2190b4a9cd7_256x256.png,3d65c96dfdf7fdf8383dd558d7cdad21d650a790,Animals,-2.158,150.019,-51.966,4821 -4822,breed_pug,-7.231,29.892,-84.625,0.398,0.811,0.77625,645d9c67b8984e512d85cb52f55e6843cfc8279b,256,V6,645d9c67b8984e512d85cb52f55e6843cfc8279b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-07.231_ry+119.892_rz-84.625_tx+00.398_ty+00.811_s+00.621_645d9c67b8984e512d85cb52f55e6843cfc8279b_256x256.png,29a9d1593b3d6cedfc863726987ffc02f3b9adbc,Animals,-7.231,119.892,-84.625,4822 -4823,breed_pug,-14.224,-41.185,-40.246,-0.033,-0.374,0.63875,c1e6458daaf5542684714ba79cb2fb0b9373232d,256,V6,c1e6458daaf5542684714ba79cb2fb0b9373232d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-14.224_ry+48.815_rz-40.246_tx-00.033_ty-00.374_s+00.511_c1e6458daaf5542684714ba79cb2fb0b9373232d_256x256.png,4d64de1d591525f8babb9b7b61e1748d1548e162,Animals,-14.224,48.815,-40.246,4823 -4824,breed_pug,-17.348,-85.589,89.562,-0.501,-0.599,0.65125,dc6c3fc797f1756e446c62dbe03f773fb63cd104,256,V6,dc6c3fc797f1756e446c62dbe03f773fb63cd104_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-17.348_ry+04.411_rz+89.562_tx-00.501_ty-00.599_s+00.521_dc6c3fc797f1756e446c62dbe03f773fb63cd104_256x256.png,265d9fa6af391c688ac47e3863d2e27d4884a4d5,Animals,-17.348,4.411,89.562,4824 -4825,breed_pug,-18.623,29.478,1.401,0.336,-1.062,1.2975,e4697429a08307303fe8a9efcddb467cb424f88d,256,V6,e4697429a08307303fe8a9efcddb467cb424f88d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-18.623_ry+119.478_rz+01.401_tx+00.336_ty-01.062_s+01.038_e4697429a08307303fe8a9efcddb467cb424f88d_256x256.png,3846f32a375d74451c03c51bf0180d8fd3afddb1,Animals,-18.623,119.478,1.401,4825 -4826,breed_pug,-20.755,-45.875,-23.565,0.085,-1.06,0.69,224110fc2ef79db654249d902dd927e66a2a6e33,256,V6,224110fc2ef79db654249d902dd927e66a2a6e33_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-20.755_ry+44.125_rz-23.565_tx+00.085_ty-01.060_s+00.552_224110fc2ef79db654249d902dd927e66a2a6e33_256x256.png,8fa2b77a9d82c993312ac3911840d972667e491d,Animals,-20.755,44.125,-23.565,4826 -4827,breed_pug,-23.299,28.241,47.041,0.357,1.137,1.45,ec61ce3f2159072e54604e8158871dff9869a2a2,256,V6,ec61ce3f2159072e54604e8158871dff9869a2a2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-23.299_ry+118.241_rz+47.041_tx+00.357_ty+01.137_s+01.160_ec61ce3f2159072e54604e8158871dff9869a2a2_256x256.png,5f0d7c78f8628e06b3b3d3367bf56625b3a0668d,Animals,-23.299,118.241,47.041,4827 -4828,breed_pug,-28.055,-65.112,84.547,-0.448,1.125,1.095,b67ac0ddcf89c0c4d6fe56bd2a2efb30e4f5008c,256,V6,b67ac0ddcf89c0c4d6fe56bd2a2efb30e4f5008c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-28.055_ry+24.888_rz+84.547_tx-00.448_ty+01.125_s+00.876_b67ac0ddcf89c0c4d6fe56bd2a2efb30e4f5008c_256x256.png,81a2adc20a8e98686acf93ba9fb231866319128b,Animals,-28.055,24.888,84.547,4828 -4829,breed_pug,-38.637,-43.022,45.202,0.474,0.742,0.6475,51fd1e7d49aac8a8c1862e4b2d5f668bee8c3207,256,V6,51fd1e7d49aac8a8c1862e4b2d5f668bee8c3207_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-38.637_ry+46.978_rz+45.202_tx+00.474_ty+00.742_s+00.518_51fd1e7d49aac8a8c1862e4b2d5f668bee8c3207_256x256.png,d0877d886434d99c6ef0b20f29924c003b75a428,Animals,-38.637,46.978,45.202,4829 -4830,breed_pug,-51.315,17.799,21.664,-0.428,1.184,1.34125,cd515122279a68f731b3cb87a1415da537ee8bb5,256,V6,cd515122279a68f731b3cb87a1415da537ee8bb5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-51.315_ry+107.799_rz+21.664_tx-00.428_ty+01.184_s+01.073_cd515122279a68f731b3cb87a1415da537ee8bb5_256x256.png,7f4432cfcbead53a6a09b6b0921097537eee55e8,Animals,-51.315,107.799,21.664,4830 -4831,breed_pug,-52.246,79.164,63.447,-0.231,1.18,0.65125,573f668986acd75dd9725258f3ebff5220844eff,256,V6,573f668986acd75dd9725258f3ebff5220844eff_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-52.246_ry+169.164_rz+63.447_tx-00.231_ty+01.180_s+00.521_573f668986acd75dd9725258f3ebff5220844eff_256x256.png,e4b8cceb327705b56e28bce4484f80b328013b1b,Animals,-52.246,169.164,63.447,4831 -4832,breed_pug,-56.486,63.945,28.334,-0.542,1.104,1.16,edf6c4638ff490015a7eee422f48c67dfef14b6b,256,V6,edf6c4638ff490015a7eee422f48c67dfef14b6b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-56.486_ry+153.945_rz+28.334_tx-00.542_ty+01.104_s+00.928_edf6c4638ff490015a7eee422f48c67dfef14b6b_256x256.png,6967c72b3def35c348793bf89817ab6ce7135b46,Animals,-56.486,153.945,28.334,4832 -4833,breed_pug,-57.052,44.378,-45.388,-0.245,0.86,1.49125,94a36fd5fefaf482d3404032f9146029e7ad1d5d,256,V6,94a36fd5fefaf482d3404032f9146029e7ad1d5d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-57.052_ry+134.378_rz-45.388_tx-00.245_ty+00.860_s+01.193_94a36fd5fefaf482d3404032f9146029e7ad1d5d_256x256.png,4944fbfa39250423a640e9a97678907d0a0d03ed,Animals,-57.052,134.378,-45.388,4833 -4834,breed_pug,-71.096,41.263,-88.541,0.379,-0.427,0.7425,db1cac191c2ca12a6cc94f7a548b3d3f56e75bd0,256,V6,db1cac191c2ca12a6cc94f7a548b3d3f56e75bd0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-71.096_ry+131.263_rz-88.541_tx+00.379_ty-00.427_s+00.594_db1cac191c2ca12a6cc94f7a548b3d3f56e75bd0_256x256.png,96e45135eba4f6fa4b1a334f42a343c7124850f6,Animals,-71.096,131.263,-88.541,4834 -4835,breed_pug,-72.263,-2.168,-84.47,-0.194,-0.061,1.01375,f0da0d2428524375744729e7ce83bb96ab888963,256,V6,f0da0d2428524375744729e7ce83bb96ab888963_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-72.263_ry+87.832_rz-84.470_tx-00.194_ty-00.061_s+00.811_f0da0d2428524375744729e7ce83bb96ab888963_256x256.png,4de2465fd32d084fd2784f09d4d20a948dd87fda,Animals,-72.263,87.832,-84.47,4835 -4836,breed_pug,-73.241,72.714,-18.407,-0.089,-0.285,0.95875,291d0e12860279c4034f5b82f9a0f0f5d211165f,256,V6,291d0e12860279c4034f5b82f9a0f0f5d211165f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-73.241_ry+162.714_rz-18.407_tx-00.089_ty-00.285_s+00.767_291d0e12860279c4034f5b82f9a0f0f5d211165f_256x256.png,d256db4f97886fc0cc6296a2ba84f9ef40baa9ec,Animals,-73.241,162.714,-18.407,4836 -4837,breed_pug,-75.61,52.454,-64.997,0.068,1.107,0.68375,c998a82b119b6e22d725ff3c7abd178c823f4093,256,V6,c998a82b119b6e22d725ff3c7abd178c823f4093_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-75.610_ry+142.454_rz-64.997_tx+00.068_ty+01.107_s+00.547_c998a82b119b6e22d725ff3c7abd178c823f4093_256x256.png,9a0fb5bcae71a8bb699b62a3f62db89501718efb,Animals,-75.61,142.454,-64.997,4837 -4838,breed_pug,-82.705,76.451,-82.523,0.593,-0.417,0.67125,3c971ba1db394ba27bc59b7e068200d91ce05ea3,256,V6,3c971ba1db394ba27bc59b7e068200d91ce05ea3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-82.705_ry+166.451_rz-82.523_tx+00.593_ty-00.417_s+00.537_3c971ba1db394ba27bc59b7e068200d91ce05ea3_256x256.png,ad4ea57cd54049986e6a5b326ef1f01991966a5d,Animals,-82.705,166.451,-82.523,4838 -4839,breed_pug,-84.189,4.873,-29.676,-0.211,1.103,1.09625,b2c16589f3ee7d6d52a44d6c86dfb9886a44a5e3,256,V6,b2c16589f3ee7d6d52a44d6c86dfb9886a44a5e3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/breed_pug_rx-84.189_ry+94.873_rz-29.676_tx-00.211_ty+01.103_s+00.877_b2c16589f3ee7d6d52a44d6c86dfb9886a44a5e3_256x256.png,482d37f67f4088f1a99e47928a97f5790298498e,Animals,-84.189,94.873,-29.676,4839 -4840,celica,2.741,-36.27,-68.866,-0.329,0.327,1.489,e6a5352fec4c35c04f7e86f56cc0dd093ef45ca0,256,V6,e6a5352fec4c35c04f7e86f56cc0dd093ef45ca0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+02.741_ry-36.270_rz-68.866_tx-00.329_ty+00.327_s+01.489_e6a5352fec4c35c04f7e86f56cc0dd093ef45ca0_256x256.png,dceea68b09b331e7e3323aeeae81f7fdd3862739,Cars,2.741,53.73,-68.866,4840 -4841,celica,7.203,39.776,-21.905,0.155,-0.209,1.085,f144e3aabccefb4228b8257506a2aa26ba5b4a6d,256,V6,f144e3aabccefb4228b8257506a2aa26ba5b4a6d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+07.203_ry+39.776_rz-21.905_tx+00.155_ty-00.209_s+01.085_f144e3aabccefb4228b8257506a2aa26ba5b4a6d_256x256.png,195977c9fe3498f61d78fddb1548a8dfb0bd105b,Cars,7.203,129.776,-21.905,4841 -4842,celica,17.556,-51.207,13.124,0.267,-1.092,1.411,43ee5593426fb8507236387fbed1e5ad8fe0b9c9,256,V6,43ee5593426fb8507236387fbed1e5ad8fe0b9c9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+17.556_ry-51.207_rz+13.124_tx+00.267_ty-01.092_s+01.411_43ee5593426fb8507236387fbed1e5ad8fe0b9c9_256x256.png,96f59c20b68c5db19eb3b0eb753b455ee25d8351,Cars,17.556,38.793,13.124,4842 -4843,celica,19.162,-21.895,0.721,0.27,-0.66,1.298,310434abb5a81715e25c30b23f0e8284c63d9be8,256,V6,310434abb5a81715e25c30b23f0e8284c63d9be8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+19.162_ry-21.895_rz+00.721_tx+00.270_ty-00.660_s+01.298_310434abb5a81715e25c30b23f0e8284c63d9be8_256x256.png,7e7a03b4fd643af562543d1b105493e5d76da6f4,Cars,19.162,68.105,0.721,4843 -4844,celica,24.572,64.447,82.23,0.058,0.182,0.849,6f460e617774e517e0b4f2077012754e3079975b,256,V6,6f460e617774e517e0b4f2077012754e3079975b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+24.572_ry+64.447_rz+82.230_tx+00.058_ty+00.182_s+00.849_6f460e617774e517e0b4f2077012754e3079975b_256x256.png,fb68860cf490574e5583729972f27d9e3261f830,Cars,24.572,154.447,82.23,4844 -4845,celica,29.098,12.273,-17.355,-0.242,-0.326,1.515,0d27a9180617da4f40823b5818db490950323456,256,V6,0d27a9180617da4f40823b5818db490950323456_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+29.098_ry+12.273_rz-17.355_tx-00.242_ty-00.326_s+01.515_0d27a9180617da4f40823b5818db490950323456_256x256.png,63476e75459f3ad15acbc575215d58ac1c47b7ab,Cars,29.098,102.273,-17.355,4845 -4846,celica,38.799,69.674,76.633,0.301,0.069,0.71,8cbf2e60e7a88ab20b9bce6bd33a2931fb41c7b8,256,V6,8cbf2e60e7a88ab20b9bce6bd33a2931fb41c7b8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+38.799_ry+69.674_rz+76.633_tx+00.301_ty+00.069_s+00.710_8cbf2e60e7a88ab20b9bce6bd33a2931fb41c7b8_256x256.png,a93de3306356a899743a7b58f2d0d2322fb0f832,Cars,38.799,159.674,76.633,4846 -4847,celica,45.477,-72.389,77.306,-0.489,1.042,0.683,65ead48a2fcf73e0cb8909eb84fe7c1a86304da1,256,V6,65ead48a2fcf73e0cb8909eb84fe7c1a86304da1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+45.477_ry-72.389_rz+77.306_tx-00.489_ty+01.042_s+00.683_65ead48a2fcf73e0cb8909eb84fe7c1a86304da1_256x256.png,84148b4be47f349f7ff0f5486ab66135e4f08763,Cars,45.477,17.611,77.306,4847 -4848,celica,52.424,74.169,-55.586,0.133,-0.582,1.548,e3b86a8d38ff9f4969d5efee8b8b2c258f3a010b,256,V6,e3b86a8d38ff9f4969d5efee8b8b2c258f3a010b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+52.424_ry+74.169_rz-55.586_tx+00.133_ty-00.582_s+01.548_e3b86a8d38ff9f4969d5efee8b8b2c258f3a010b_256x256.png,02875e1f3e0e7995a499fd82e2a3b1a2f5ea5935,Cars,52.424,164.169,-55.586,4848 -4849,celica,56.712,-41.782,-72.698,0.192,0.724,0.66,6dad99b8723154e6526910f614d0d2452fc1ce2d,256,V6,6dad99b8723154e6526910f614d0d2452fc1ce2d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+56.712_ry-41.782_rz-72.698_tx+00.192_ty+00.724_s+00.660_6dad99b8723154e6526910f614d0d2452fc1ce2d_256x256.png,6b6afbdc4a7e4568c5ac50e4d7ddf68bf11d602a,Cars,56.712,48.218,-72.698,4849 -4850,celica,62.79,28.429,21.252,-0.272,-0.031,1.144,95a0cbf72ee41ab168797ed2fbacf3528039ea0d,256,V6,95a0cbf72ee41ab168797ed2fbacf3528039ea0d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+62.790_ry+28.429_rz+21.252_tx-00.272_ty-00.031_s+01.144_95a0cbf72ee41ab168797ed2fbacf3528039ea0d_256x256.png,4c2c9a5728c8fd68c6adacd0633411035fa7913f,Cars,62.79,118.429,21.252,4850 -4851,celica,62.867,67.802,15.346,-0.486,0.135,1.489,f9b231ecf3ddfcd464c5eba35a981896af752565,256,V6,f9b231ecf3ddfcd464c5eba35a981896af752565_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+62.867_ry+67.802_rz+15.346_tx-00.486_ty+00.135_s+01.489_f9b231ecf3ddfcd464c5eba35a981896af752565_256x256.png,39db66b656a53da74a3d5e1df54b015404fba44d,Cars,62.867,157.802,15.346,4851 -4852,celica,72.859,12.006,-74.762,0.121,0.267,0.911,485cf5cdd83cc299e227744abac75f74156dc7ea,256,V6,485cf5cdd83cc299e227744abac75f74156dc7ea_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+72.859_ry+12.006_rz-74.762_tx+00.121_ty+00.267_s+00.911_485cf5cdd83cc299e227744abac75f74156dc7ea_256x256.png,70e8be51c875e5ee5b6862ba47e878fc34ec249b,Cars,72.859,102.006,-74.762,4852 -4853,celica,76.093,-87.436,-45.075,0.338,0.14,1.481,da28634d1caa688dd6a5ea5cd8f2f89ee00019bd,256,V6,da28634d1caa688dd6a5ea5cd8f2f89ee00019bd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+76.093_ry-87.436_rz-45.075_tx+00.338_ty+00.140_s+01.481_da28634d1caa688dd6a5ea5cd8f2f89ee00019bd_256x256.png,78d2f3ccdf2aec71868089b84ba423f3c542237e,Cars,76.093,2.564,-45.075,4853 -4854,celica,78.515,-82.367,45.426,-0.222,0.877,0.987,de274c89507cb4101f258abee14de1b9fe41c6df,256,V6,de274c89507cb4101f258abee14de1b9fe41c6df_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+78.515_ry-82.367_rz+45.426_tx-00.222_ty+00.877_s+00.987_de274c89507cb4101f258abee14de1b9fe41c6df_256x256.png,ba28fbd20255c292cc33314e0ed95b6d1af162db,Cars,78.515,7.633,45.426,4854 -4855,celica,82.496,-23.775,57.769,-0.389,-0.828,0.792,4583a1153e9c146c4493dbf3322f6cfb73abdb8e,256,V6,4583a1153e9c146c4493dbf3322f6cfb73abdb8e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+82.496_ry-23.775_rz+57.769_tx-00.389_ty-00.828_s+00.792_4583a1153e9c146c4493dbf3322f6cfb73abdb8e_256x256.png,adb6bf9d439a7941efd7e465a9f82d56c8552dfa,Cars,82.496,66.225,57.769,4855 -4856,celica,84.69,6.995,77.563,-0.514,0.38,1.391,426a5eadfa574e3a2aa6c8152fb33c61cb72031b,256,V6,426a5eadfa574e3a2aa6c8152fb33c61cb72031b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+84.690_ry+06.995_rz+77.563_tx-00.514_ty+00.380_s+01.391_426a5eadfa574e3a2aa6c8152fb33c61cb72031b_256x256.png,8e045443842439bcc0f0504ce971c699dd9f4267,Cars,84.69,96.995,77.563,4856 -4857,celica,86.136,-15.759,78.493,-0.107,0.546,0.869,34eb36940d320c8e4b3d446bc366dc4620058c2a,256,V6,34eb36940d320c8e4b3d446bc366dc4620058c2a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+86.136_ry-15.759_rz+78.493_tx-00.107_ty+00.546_s+00.869_34eb36940d320c8e4b3d446bc366dc4620058c2a_256x256.png,dd5feb02fbb4eb5255c150a1a01d5f7cc7da4848,Cars,86.136,74.241,78.493,4857 -4858,celica,86.192,71.831,77.609,0.351,-0.363,1.573,24134453878dd9756b3d2c40dbbf391f291f636e,256,V6,24134453878dd9756b3d2c40dbbf391f291f636e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx+86.192_ry+71.831_rz+77.609_tx+00.351_ty-00.363_s+01.573_24134453878dd9756b3d2c40dbbf391f291f636e_256x256.png,6f5cc10dd310881d68ee86793e21a99b2b928428,Cars,86.192,161.831,77.609,4858 -4859,celica,-4.025,82.592,71.704,0.339,-0.155,1.324,08139d7090390d7f7a05753a9b676fa19685a0ab,256,V6,08139d7090390d7f7a05753a9b676fa19685a0ab_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-04.025_ry+82.592_rz+71.704_tx+00.339_ty-00.155_s+01.324_08139d7090390d7f7a05753a9b676fa19685a0ab_256x256.png,02107d0fea7035078633f7e2957ecf76c636e530,Cars,-4.025,172.592,71.704,4859 -4860,celica,-8.231,-23.779,43.703,0.074,-0.017,1.147,9a5cf722ca97648e1fbad0a83d4d3d17db2b55f4,256,V6,9a5cf722ca97648e1fbad0a83d4d3d17db2b55f4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-08.231_ry-23.779_rz+43.703_tx+00.074_ty-00.017_s+01.147_9a5cf722ca97648e1fbad0a83d4d3d17db2b55f4_256x256.png,15205daf67926d54a01c7961f84d932033a99db6,Cars,-8.231,66.221,43.703,4860 -4861,celica,-9.101,-89.723,51.237,0.208,1.081,0.76,3d655bf1c7cfa94e816c8cfa800dfc3b6732ac90,256,V6,3d655bf1c7cfa94e816c8cfa800dfc3b6732ac90_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-09.101_ry-89.723_rz+51.237_tx+00.208_ty+01.081_s+00.760_3d655bf1c7cfa94e816c8cfa800dfc3b6732ac90_256x256.png,72bea146fcfb8b6c99f337e3a1aa3ce45057aef9,Cars,-9.101,0.277,51.237,4861 -4862,celica,-14.642,33.659,-20.398,0.211,-0.255,1.501,50ade3d8a01b52e3e6d2d0172a0bd48bce8e6846,256,V6,50ade3d8a01b52e3e6d2d0172a0bd48bce8e6846_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-14.642_ry+33.659_rz-20.398_tx+00.211_ty-00.255_s+01.501_50ade3d8a01b52e3e6d2d0172a0bd48bce8e6846_256x256.png,c57a95d04000ed17adce98d0bf12b5177b598bee,Cars,-14.642,123.659,-20.398,4862 -4863,celica,-25.5,27.715,-15.546,-0.092,0.838,1.249,c6cc950265cd77a58a337f5931bd66d67e50f865,256,V6,c6cc950265cd77a58a337f5931bd66d67e50f865_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-25.500_ry+27.715_rz-15.546_tx-00.092_ty+00.838_s+01.249_c6cc950265cd77a58a337f5931bd66d67e50f865_256x256.png,f2c8ca59439b7f930738659170dd61c716449fb7,Cars,-25.5,117.715,-15.546,4863 -4864,celica,-29.776,63.97,-16.097,0.33,0.402,1.495,c7e71c68165291c25134e79efa191a8d1af89cae,256,V6,c7e71c68165291c25134e79efa191a8d1af89cae_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-29.776_ry+63.970_rz-16.097_tx+00.330_ty+00.402_s+01.495_c7e71c68165291c25134e79efa191a8d1af89cae_256x256.png,5319cda19c50ee48ef9b01695d2a64e149f590d3,Cars,-29.776,153.97,-16.097,4864 -4865,celica,-32.83,59.301,-21.422,-0.286,-0.281,1.368,e4af0fa7f6ccda4cb85635cc028f5463e6a78d66,256,V6,e4af0fa7f6ccda4cb85635cc028f5463e6a78d66_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-32.830_ry+59.301_rz-21.422_tx-00.286_ty-00.281_s+01.368_e4af0fa7f6ccda4cb85635cc028f5463e6a78d66_256x256.png,25267954ebb670336e5bb0c741671f35b7b53c47,Cars,-32.83,149.301,-21.422,4865 -4866,celica,-35.079,-64.619,87.87,0.499,0.792,1.559,ebdfcc684bbd58dab756aae3a1c1aa97cc0c17d8,256,V6,ebdfcc684bbd58dab756aae3a1c1aa97cc0c17d8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-35.079_ry-64.619_rz+87.870_tx+00.499_ty+00.792_s+01.559_ebdfcc684bbd58dab756aae3a1c1aa97cc0c17d8_256x256.png,994ccf3218b3813cf1438c0790d6347a7c3637fa,Cars,-35.079,25.381,87.87,4866 -4867,celica,-37.214,28.414,47.05,0.082,0.636,1.112,6345664db9d6a88f8daeec1f71fdce607deabde7,256,V6,6345664db9d6a88f8daeec1f71fdce607deabde7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-37.214_ry+28.414_rz+47.050_tx+00.082_ty+00.636_s+01.112_6345664db9d6a88f8daeec1f71fdce607deabde7_256x256.png,e2b47c08b0cffa6b0f25ddf041796ee7fe144a45,Cars,-37.214,118.414,47.05,4867 -4868,celica,-37.331,-85.311,-84.648,0.014,-0.233,1.589,68816672b3c4ca7ae25d31aae549bf1fba5e82f4,256,V6,68816672b3c4ca7ae25d31aae549bf1fba5e82f4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-37.331_ry-85.311_rz-84.648_tx+00.014_ty-00.233_s+01.589_68816672b3c4ca7ae25d31aae549bf1fba5e82f4_256x256.png,f8c05f174874e9dc0bb6d3a288e5822472cd1cf6,Cars,-37.331,4.689,-84.648,4868 -4869,celica,-37.506,-51.705,-7.591,-0.119,-0.591,1.308,0f646f5c705e1064da468e5cddde08f8aa9e38ab,256,V6,0f646f5c705e1064da468e5cddde08f8aa9e38ab_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-37.506_ry-51.705_rz-07.591_tx-00.119_ty-00.591_s+01.308_0f646f5c705e1064da468e5cddde08f8aa9e38ab_256x256.png,f92ee96d4257dda39112d2223f4c30d85ab96ca1,Cars,-37.506,38.295,-7.591,4869 -4870,celica,-37.525,-23.307,4.517,0.487,-0.64,1.404,5ff1d617004bba6a49f404d601bc90cc4e2d119f,256,V6,5ff1d617004bba6a49f404d601bc90cc4e2d119f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-37.525_ry-23.307_rz+04.517_tx+00.487_ty-00.640_s+01.404_5ff1d617004bba6a49f404d601bc90cc4e2d119f_256x256.png,493fe6630754b9d5e6f06401d28af14b43bb5722,Cars,-37.525,66.693,4.517,4870 -4871,celica,-42.745,-75.522,-13.524,-0.38,-0.586,1.412,18993b1245ee07c3d246df42bf82f3a9d4d1623b,256,V6,18993b1245ee07c3d246df42bf82f3a9d4d1623b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-42.745_ry-75.522_rz-13.524_tx-00.380_ty-00.586_s+01.412_18993b1245ee07c3d246df42bf82f3a9d4d1623b_256x256.png,4cbc2ea8fade6896faf5aae88dcfc7bdb9da17e2,Cars,-42.745,14.478,-13.524,4871 -4872,celica,-45.724,-85.047,49.668,0.163,-0.93,1.223,4fdde4ed9a8379d448077f22dd89acbce39454f7,256,V6,4fdde4ed9a8379d448077f22dd89acbce39454f7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-45.724_ry-85.047_rz+49.668_tx+00.163_ty-00.930_s+01.223_4fdde4ed9a8379d448077f22dd89acbce39454f7_256x256.png,46a458a5ef6d8638de3fc1ea33ce3dae32d12744,Cars,-45.724,4.953,49.668,4872 -4873,celica,-47.623,-28.674,55.597,-0.278,0.33,0.683,ef819f6f2c09e47c9a1064f780e0e22d9ff75bf1,256,V6,ef819f6f2c09e47c9a1064f780e0e22d9ff75bf1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-47.623_ry-28.674_rz+55.597_tx-00.278_ty+00.330_s+00.683_ef819f6f2c09e47c9a1064f780e0e22d9ff75bf1_256x256.png,05fb530002deabc19705ca0b46c51fc2d3efab0e,Cars,-47.623,61.326,55.597,4873 -4874,celica,-47.739,-41.327,18.99,0.302,0.708,0.931,3c5e03605ca43209c5e3468b1a588b43ac064978,256,V6,3c5e03605ca43209c5e3468b1a588b43ac064978_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-47.739_ry-41.327_rz+18.990_tx+00.302_ty+00.708_s+00.931_3c5e03605ca43209c5e3468b1a588b43ac064978_256x256.png,ed0bdf5e2955ec27fc6fd71886feb20254288706,Cars,-47.739,48.673,18.99,4874 -4875,celica,-52.288,50.441,62.151,0.576,0.279,1.265,0323f252d73a0df4cde14e2456d914d5aaa41032,256,V6,0323f252d73a0df4cde14e2456d914d5aaa41032_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-52.288_ry+50.441_rz+62.151_tx+00.576_ty+00.279_s+01.265_0323f252d73a0df4cde14e2456d914d5aaa41032_256x256.png,24b3aaeaaaf7a5092a3cdf6a1b29c7914fa533b8,Cars,-52.288,140.441,62.151,4875 -4876,celica,-67.605,45.797,-43.566,-0.371,0.008,0.796,e64995bef8927ca5d993ae9d119bad38f5a73d53,256,V6,e64995bef8927ca5d993ae9d119bad38f5a73d53_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-67.605_ry+45.797_rz-43.566_tx-00.371_ty+00.008_s+00.796_e64995bef8927ca5d993ae9d119bad38f5a73d53_256x256.png,35ca2319628ce30fcfeee76b270b30704fb45b94,Cars,-67.605,135.797,-43.566,4876 -4877,celica,-71.878,-7.294,-3.863,0.546,0.724,0.86,f2e18d3ca4f6e9899a519b5d74d2be7c4577a401,256,V6,f2e18d3ca4f6e9899a519b5d74d2be7c4577a401_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-71.878_ry-07.294_rz-03.863_tx+00.546_ty+00.724_s+00.860_f2e18d3ca4f6e9899a519b5d74d2be7c4577a401_256x256.png,a8648457a08c568c907c68d058e2d33bb276a71e,Cars,-71.878,82.706,-3.863,4877 -4878,celica,-87.473,-10.402,-6.81,0.443,-0.101,0.875,f6c2ea97cd8d60de5faaa5181cd5ec0e1e0692ac,256,V6,f6c2ea97cd8d60de5faaa5181cd5ec0e1e0692ac_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-87.473_ry-10.402_rz-06.810_tx+00.443_ty-00.101_s+00.875_f6c2ea97cd8d60de5faaa5181cd5ec0e1e0692ac_256x256.png,ac53a2cb83c4c97c92f90032f3a294553697713e,Cars,-87.473,79.598,-6.81,4878 -4879,celica,-87.697,-62.716,-10.417,0.111,1.012,0.986,b1a4dbc50920949d6cc4d34d5f6580a3534acad9,256,V6,b1a4dbc50920949d6cc4d34d5f6580a3534acad9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/celica_rx-87.697_ry-62.716_rz-10.417_tx+00.111_ty+01.012_s+00.986_b1a4dbc50920949d6cc4d34d5f6580a3534acad9_256x256.png,1de3bd170f5347a287348ee5b7b4f286a03ea6e7,Cars,-87.697,27.284,-10.417,4879 -4880,clio,3.492,8.77,-61.363,-0.051,0.354,1.592,5e40e8fbd3fe0a1090b5c9268077ce34542b968d,256,V6,5e40e8fbd3fe0a1090b5c9268077ce34542b968d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+03.492_ry+08.770_rz-61.363_tx-00.051_ty+00.354_s+01.592_5e40e8fbd3fe0a1090b5c9268077ce34542b968d_256x256.png,ed5edef29c01dd6c6836336cf2b7da06cebc7c2b,Cars,3.492,98.77,-61.363,4880 -4881,clio,5.462,-60.341,-14.817,0.083,0.704,0.799,f3b5b8b347bc93e33b776761c0a6a3f624b675de,256,V6,f3b5b8b347bc93e33b776761c0a6a3f624b675de_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+05.462_ry-60.341_rz-14.817_tx+00.083_ty+00.704_s+00.799_f3b5b8b347bc93e33b776761c0a6a3f624b675de_256x256.png,5d3a03591abf2926e143b8cc1f907b6597ee38a4,Cars,5.462,29.659,-14.817,4881 -4882,clio,9.296,10.288,69.519,0.508,-0.335,1.425,00ee8b302fec4189a70cb5bf997ebbde9db7eb56,256,V6,00ee8b302fec4189a70cb5bf997ebbde9db7eb56_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+09.296_ry+10.288_rz+69.519_tx+00.508_ty-00.335_s+01.425_00ee8b302fec4189a70cb5bf997ebbde9db7eb56_256x256.png,d7fa5925f28bf3c55c65c8be3105ec4e00fbfc21,Cars,9.296,100.288,69.519,4882 -4883,clio,10.259,-54.351,37.497,0.295,-0.381,1.5,8b60f18e65412dae65e18ee35076defdfdd6b197,256,V6,8b60f18e65412dae65e18ee35076defdfdd6b197_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+10.259_ry-54.351_rz+37.497_tx+00.295_ty-00.381_s+01.500_8b60f18e65412dae65e18ee35076defdfdd6b197_256x256.png,78e258f97cc5dc3100bb339556f65b27cffd42fd,Cars,10.259,35.649,37.497,4883 -4884,clio,21.719,-17.714,40.264,0.39,1.151,1.171,765ee8ba4601150a2f591db2ec99af1d59048185,256,V6,765ee8ba4601150a2f591db2ec99af1d59048185_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+21.719_ry-17.714_rz+40.264_tx+00.390_ty+01.151_s+01.171_765ee8ba4601150a2f591db2ec99af1d59048185_256x256.png,e7510b737576a2f8196e7d1a8d6b464480d05694,Cars,21.719,72.286,40.264,4884 -4885,clio,27.206,-18.496,-56.103,-0.214,-0.481,1.506,9b9dc66764aebeb0a67d3689b480462e9fedcdab,256,V6,9b9dc66764aebeb0a67d3689b480462e9fedcdab_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+27.206_ry-18.496_rz-56.103_tx-00.214_ty-00.481_s+01.506_9b9dc66764aebeb0a67d3689b480462e9fedcdab_256x256.png,e57a3268b371cba9ba5373c39a8601fb90d62c37,Cars,27.206,71.504,-56.103,4885 -4886,clio,29.415,83.11,-69.396,-0.247,0.613,0.845,aff6c85da08a346320f31f5c78bf4ea926ce5174,256,V6,aff6c85da08a346320f31f5c78bf4ea926ce5174_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+29.415_ry+83.110_rz-69.396_tx-00.247_ty+00.613_s+00.845_aff6c85da08a346320f31f5c78bf4ea926ce5174_256x256.png,f770c1c237a64e72ed8ddca1218b56445a5de163,Cars,29.415,173.11,-69.396,4886 -4887,clio,30.39,-43.971,13.492,0.048,-1.071,0.82,cbe4693c56a141b1ee56bc63cf0dc0c596d6ce0c,256,V6,cbe4693c56a141b1ee56bc63cf0dc0c596d6ce0c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+30.390_ry-43.971_rz+13.492_tx+00.048_ty-01.071_s+00.820_cbe4693c56a141b1ee56bc63cf0dc0c596d6ce0c_256x256.png,4f7cfce4e815056b7f9216126c826421561855f7,Cars,30.39,46.029,13.492,4887 -4888,clio,30.749,42.923,12.202,-0.504,-0.213,0.917,5944c364079c27a08d65eb9c4eecde9ce5a2d0db,256,V6,5944c364079c27a08d65eb9c4eecde9ce5a2d0db_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+30.749_ry+42.923_rz+12.202_tx-00.504_ty-00.213_s+00.917_5944c364079c27a08d65eb9c4eecde9ce5a2d0db_256x256.png,97d1741034ccd4f6bc59af1be8213859190287bc,Cars,30.749,132.923,12.202,4888 -4889,clio,34.819,-36.527,46.982,-0.064,-0.692,1.26,2c8dc25cdd2a6bfe4a104b5618ba0fd115c53948,256,V6,2c8dc25cdd2a6bfe4a104b5618ba0fd115c53948_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+34.819_ry-36.527_rz+46.982_tx-00.064_ty-00.692_s+01.260_2c8dc25cdd2a6bfe4a104b5618ba0fd115c53948_256x256.png,fa86552976c9b09e2e5f85bf6b0e1f52483c2ef1,Cars,34.819,53.473,46.982,4889 -4890,clio,36.607,38.965,18.051,0.174,0.3,1.442,04b0f600a1c2f30809e4ccdd40a7eb427735f876,256,V6,04b0f600a1c2f30809e4ccdd40a7eb427735f876_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+36.607_ry+38.965_rz+18.051_tx+00.174_ty+00.300_s+01.442_04b0f600a1c2f30809e4ccdd40a7eb427735f876_256x256.png,fa9342a5d4e9d24a38325a595ff045c85918f474,Cars,36.607,128.965,18.051,4890 -4891,clio,38.209,-42.029,10.081,-0.509,-1.076,0.939,9421c29f959ef4cea9c94d7b3db30ca32a9cab41,256,V6,9421c29f959ef4cea9c94d7b3db30ca32a9cab41_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+38.209_ry-42.029_rz+10.081_tx-00.509_ty-01.076_s+00.939_9421c29f959ef4cea9c94d7b3db30ca32a9cab41_256x256.png,9d53a254da1cfe7ed98d8311ef5dec017ae41eb3,Cars,38.209,47.971,10.081,4891 -4892,clio,38.47,69.221,-76.168,0.591,-0.477,1.26,d6288d2ff384590e70fd5aa1d6768ce47a163a62,256,V6,d6288d2ff384590e70fd5aa1d6768ce47a163a62_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+38.470_ry+69.221_rz-76.168_tx+00.591_ty-00.477_s+01.260_d6288d2ff384590e70fd5aa1d6768ce47a163a62_256x256.png,287900f6b85bc275c7de6c7d9ea22cd09893c3d2,Cars,38.47,159.221,-76.168,4892 -4893,clio,38.983,84.327,-38.542,0.229,0.162,1.483,c07db629a1ad8b262d20caa1f27542a739be24ee,256,V6,c07db629a1ad8b262d20caa1f27542a739be24ee_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+38.983_ry+84.327_rz-38.542_tx+00.229_ty+00.162_s+01.483_c07db629a1ad8b262d20caa1f27542a739be24ee_256x256.png,c267f1a826cb435f001c2e08a499baec947d28c7,Cars,38.983,174.327,-38.542,4893 -4894,clio,59.496,86.657,-32.96,0.216,0.821,1.472,54dedbf44f818e734266b4929da6bef526f10961,256,V6,54dedbf44f818e734266b4929da6bef526f10961_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+59.496_ry+86.657_rz-32.960_tx+00.216_ty+00.821_s+01.472_54dedbf44f818e734266b4929da6bef526f10961_256x256.png,c3922ea43a7a94019cdb5afa34479fa7fdf7adf8,Cars,59.496,176.657,-32.96,4894 -4895,clio,64.634,-50.092,-32.742,-0.442,0.112,1.011,1b5c48a9f7f467865ff32d84e8e29dc03aa751b6,256,V6,1b5c48a9f7f467865ff32d84e8e29dc03aa751b6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+64.634_ry-50.092_rz-32.742_tx-00.442_ty+00.112_s+01.011_1b5c48a9f7f467865ff32d84e8e29dc03aa751b6_256x256.png,60712eb43c8fda25b54de3aaab2d883c40fb4c1d,Cars,64.634,39.908,-32.742,4895 -4896,clio,73.097,-37.887,66.648,-0.506,1.143,1.599,37916384895ceda0c9513b9f056d167313e57e2f,256,V6,37916384895ceda0c9513b9f056d167313e57e2f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+73.097_ry-37.887_rz+66.648_tx-00.506_ty+01.143_s+01.599_37916384895ceda0c9513b9f056d167313e57e2f_256x256.png,fc00c1ef8c40ccaf3b18ad5aed5fe1d51a522b9c,Cars,73.097,52.113,66.648,4896 -4897,clio,81.137,-16.967,57.756,0.294,0.146,1.045,7a5eda7f4742ac2796019438c09eaa69d89bd3c5,256,V6,7a5eda7f4742ac2796019438c09eaa69d89bd3c5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+81.137_ry-16.967_rz+57.756_tx+00.294_ty+00.146_s+01.045_7a5eda7f4742ac2796019438c09eaa69d89bd3c5_256x256.png,6b7e1439b68356dbaa0bd41ef36a16d599e023c3,Cars,81.137,73.033,57.756,4897 -4898,clio,82.584,29.283,-86.908,-0.244,-0.011,1.113,664cc28563bd597498a3b90b62f26abf87bc46c6,256,V6,664cc28563bd597498a3b90b62f26abf87bc46c6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+82.584_ry+29.283_rz-86.908_tx-00.244_ty-00.011_s+01.113_664cc28563bd597498a3b90b62f26abf87bc46c6_256x256.png,df09a28415cc4f030ef7cbb6333b3781d0734eec,Cars,82.584,119.283,-86.908,4898 -4899,clio,86.777,-34.132,50.475,0.397,0.422,1.294,215dee8e07e2a319510d7cce744a690727857764,256,V6,215dee8e07e2a319510d7cce744a690727857764_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx+86.777_ry-34.132_rz+50.475_tx+00.397_ty+00.422_s+01.294_215dee8e07e2a319510d7cce744a690727857764_256x256.png,cef1e607d9dc145d5e30a62b20776d4cdc8a34bf,Cars,86.777,55.868,50.475,4899 -4900,clio,-0.727,32.417,26.156,-0.344,-1.127,0.626,6ff2a4ef49c733012f8af268fdc02aca0d829a82,256,V6,6ff2a4ef49c733012f8af268fdc02aca0d829a82_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-00.727_ry+32.417_rz+26.156_tx-00.344_ty-01.127_s+00.626_6ff2a4ef49c733012f8af268fdc02aca0d829a82_256x256.png,c43fa5ab69d76b68348aee8d8a3fcd4aae87b7ed,Cars,-0.727,122.417,26.156,4900 -4901,clio,-2.054,-79.028,85.795,0.547,-0.085,1.363,5a2bbfdc57de4e0d72b54fdda928e69f0fda5b33,256,V6,5a2bbfdc57de4e0d72b54fdda928e69f0fda5b33_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-02.054_ry-79.028_rz+85.795_tx+00.547_ty-00.085_s+01.363_5a2bbfdc57de4e0d72b54fdda928e69f0fda5b33_256x256.png,a5e003bd10d2142c225cf915f10ba80b05f45bfd,Cars,-2.054,10.972,85.795,4901 -4902,clio,-2.604,87.139,53.667,0.101,0.25,1.078,8222418653374ab0b41b8c36221772416b4904dd,256,V6,8222418653374ab0b41b8c36221772416b4904dd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-02.604_ry+87.139_rz+53.667_tx+00.101_ty+00.250_s+01.078_8222418653374ab0b41b8c36221772416b4904dd_256x256.png,51e0715b1ca0d386c9affa88f4ea80298c7b693f,Cars,-2.604,177.139,53.667,4902 -4903,clio,-15.873,-61.946,-51.579,0.38,-0.367,1.564,d81a166ef429b766530d7e7ae5f8af1226c58ac1,256,V6,d81a166ef429b766530d7e7ae5f8af1226c58ac1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-15.873_ry-61.946_rz-51.579_tx+00.380_ty-00.367_s+01.564_d81a166ef429b766530d7e7ae5f8af1226c58ac1_256x256.png,547cdaa9eac5cee7232a826a234b9913202c85f0,Cars,-15.873,28.054,-51.579,4903 -4904,clio,-21.436,46.336,8.593,-0.152,1.117,1.495,3a63bb94b31c4beb4cb027b805bf5d0dc7edb20d,256,V6,3a63bb94b31c4beb4cb027b805bf5d0dc7edb20d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-21.436_ry+46.336_rz+08.593_tx-00.152_ty+01.117_s+01.495_3a63bb94b31c4beb4cb027b805bf5d0dc7edb20d_256x256.png,296bbca80e8d77af147427ad693f04c85dccdda6,Cars,-21.436,136.336,8.593,4904 -4905,clio,-25.892,13.969,67.024,-0.287,-0.704,1.157,ca63f7d930e6f2fc0d2606dc7ca68b8ce11e67bb,256,V6,ca63f7d930e6f2fc0d2606dc7ca68b8ce11e67bb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-25.892_ry+13.969_rz+67.024_tx-00.287_ty-00.704_s+01.157_ca63f7d930e6f2fc0d2606dc7ca68b8ce11e67bb_256x256.png,e7e53fc1707a692a6880d0673028808f48bd89d5,Cars,-25.892,103.969,67.024,4905 -4906,clio,-26.318,-45.62,46.023,0.026,0.612,1.513,b5542915cafa390db9147a5f941a0524e26b277e,256,V6,b5542915cafa390db9147a5f941a0524e26b277e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-26.318_ry-45.620_rz+46.023_tx+00.026_ty+00.612_s+01.513_b5542915cafa390db9147a5f941a0524e26b277e_256x256.png,376833502515f6614c41219a7b51324873940d4a,Cars,-26.318,44.38,46.023,4906 -4907,clio,-41.364,19.65,64.774,0.118,-1.129,0.94,991e13b6fc487fd03e5f742f5d174cd2b42df8e0,256,V6,991e13b6fc487fd03e5f742f5d174cd2b42df8e0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-41.364_ry+19.650_rz+64.774_tx+00.118_ty-01.129_s+00.940_991e13b6fc487fd03e5f742f5d174cd2b42df8e0_256x256.png,082596e3e8b20e4d5b3370a520d29d76797271c1,Cars,-41.364,109.65,64.774,4907 -4908,clio,-41.921,82.77,44.907,-0.318,0.07,0.773,dd6a8601cd560aa66446e6b7418164c1e2bbc0c1,256,V6,dd6a8601cd560aa66446e6b7418164c1e2bbc0c1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-41.921_ry+82.770_rz+44.907_tx-00.318_ty+00.070_s+00.773_dd6a8601cd560aa66446e6b7418164c1e2bbc0c1_256x256.png,7ec8991c8c1536bdd58b68618ebba7d2024f7176,Cars,-41.921,172.77,44.907,4908 -4909,clio,-42.179,-69.461,41.748,0.133,-0.456,0.659,4d6372d44944ec4398fbbc10c81e1eb5ec2e357a,256,V6,4d6372d44944ec4398fbbc10c81e1eb5ec2e357a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-42.179_ry-69.461_rz+41.748_tx+00.133_ty-00.456_s+00.659_4d6372d44944ec4398fbbc10c81e1eb5ec2e357a_256x256.png,f32bcbbd053be688387d91c197281085ca2fdcca,Cars,-42.179,20.539,41.748,4909 -4910,clio,-44.382,-85.582,-57.673,-0.54,0.517,1.341,ecd40f3f6d7a4d6d88134d648884e0b9b364efc9,256,V6,ecd40f3f6d7a4d6d88134d648884e0b9b364efc9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-44.382_ry-85.582_rz-57.673_tx-00.540_ty+00.517_s+01.341_ecd40f3f6d7a4d6d88134d648884e0b9b364efc9_256x256.png,22f09b1312f5e2954d0a8d7ac6066a70d8a44d40,Cars,-44.382,4.418,-57.673,4910 -4911,clio,-49.737,42.633,49.606,-0.291,-1.048,0.972,65113fba9bd7579f6e0b12a9537060864e59c597,256,V6,65113fba9bd7579f6e0b12a9537060864e59c597_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-49.737_ry+42.633_rz+49.606_tx-00.291_ty-01.048_s+00.972_65113fba9bd7579f6e0b12a9537060864e59c597_256x256.png,c34a14c1655466e4d8ed4192c8bd84809df3fd23,Cars,-49.737,132.633,49.606,4911 -4912,clio,-50.22,79.948,20.609,0.58,0.188,1.371,ac54dbd05e2939d6a54bef44de8f73324a4dfb69,256,V6,ac54dbd05e2939d6a54bef44de8f73324a4dfb69_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-50.220_ry+79.948_rz+20.609_tx+00.580_ty+00.188_s+01.371_ac54dbd05e2939d6a54bef44de8f73324a4dfb69_256x256.png,48f045664b5c5245b9a570c99c80d06b9cab26a9,Cars,-50.22,169.948,20.609,4912 -4913,clio,-53.896,-0.947,-72.661,-0.048,1.175,0.673,0370362c70b146979616f5c9e87ac3e08fa4d1b1,256,V6,0370362c70b146979616f5c9e87ac3e08fa4d1b1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-53.896_ry-00.947_rz-72.661_tx-00.048_ty+01.175_s+00.673_0370362c70b146979616f5c9e87ac3e08fa4d1b1_256x256.png,a73f569d036d55bd9c3c0782e462a465e208a9f3,Cars,-53.896,89.053,-72.661,4913 -4914,clio,-54.935,8.426,27.914,0.577,0.648,1.142,27dae97a6ce4649ec91e3baee1fef3160cbd1881,256,V6,27dae97a6ce4649ec91e3baee1fef3160cbd1881_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-54.935_ry+08.426_rz+27.914_tx+00.577_ty+00.648_s+01.142_27dae97a6ce4649ec91e3baee1fef3160cbd1881_256x256.png,17372f63c621daceb5446ab687c7786929955d8d,Cars,-54.935,98.426,27.914,4914 -4915,clio,-61.927,-33.897,84.172,0.238,0.516,1.597,1c2e6d110abe33120dd4ed04e5476f804b644c41,256,V6,1c2e6d110abe33120dd4ed04e5476f804b644c41_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-61.927_ry-33.897_rz+84.172_tx+00.238_ty+00.516_s+01.597_1c2e6d110abe33120dd4ed04e5476f804b644c41_256x256.png,8db9226d72e6b1fd503364c768b5b2ccab39e32a,Cars,-61.927,56.103,84.172,4915 -4916,clio,-73.314,-47.647,-79.414,-0.331,0.637,0.826,11b63b2c5269c46021a8f2be4cae8e51e3f70a3a,256,V6,11b63b2c5269c46021a8f2be4cae8e51e3f70a3a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-73.314_ry-47.647_rz-79.414_tx-00.331_ty+00.637_s+00.826_11b63b2c5269c46021a8f2be4cae8e51e3f70a3a_256x256.png,845344194a14b2f70e5f907569d79faa9ec4a161,Cars,-73.314,42.353,-79.414,4916 -4917,clio,-74.235,55.236,34.696,0.068,-0.489,1.053,a8c6688324da3a7fca5ae52a94b76caed0d64503,256,V6,a8c6688324da3a7fca5ae52a94b76caed0d64503_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-74.235_ry+55.236_rz+34.696_tx+00.068_ty-00.489_s+01.053_a8c6688324da3a7fca5ae52a94b76caed0d64503_256x256.png,2124d8d88b32af66678572ea2b36689d113701d2,Cars,-74.235,145.236,34.696,4917 -4918,clio,-82.924,87.818,-54.966,-0.417,-0.743,1.396,60f44a4d82f0c32eb1cee24ceca6827498ef4c1f,256,V6,60f44a4d82f0c32eb1cee24ceca6827498ef4c1f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-82.924_ry+87.818_rz-54.966_tx-00.417_ty-00.743_s+01.396_60f44a4d82f0c32eb1cee24ceca6827498ef4c1f_256x256.png,f88b66fb1616ba336e84cba09ebede25a72480e8,Cars,-82.924,177.818,-54.966,4918 -4919,clio,-88.824,1.319,49.17,0.42,0.572,1.431,49c0d7fc28a319a6e7445824b7124f044a7276b5,256,V6,49c0d7fc28a319a6e7445824b7124f044a7276b5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/clio_rx-88.824_ry+01.319_rz+49.170_tx+00.420_ty+00.572_s+01.431_49c0d7fc28a319a6e7445824b7124f044a7276b5_256x256.png,1fb035b599be5c2ec37fa679b34bf139dc983a66,Cars,-88.824,91.319,49.17,4919 -4920,cruiser,0.698,-12.86,44.725,0.441,0.059,0.7375,7631daa2acec60b8ddfdc51fda3fbaf41178af77,256,V6,7631daa2acec60b8ddfdc51fda3fbaf41178af77_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+00.698_ry+77.140_rz+44.725_tx+00.441_ty+00.059_s+00.885_7631daa2acec60b8ddfdc51fda3fbaf41178af77_256x256.png,a48db86162bf5c2ac384ab280d24b96e3682e5fc,Boats,0.698,77.14,44.725,4920 -4921,cruiser,11.022,-77.411,-8.181,0.381,0.875,0.636666666667,c30f1394195e88021a665fc371fdee4f44822d08,256,V6,c30f1394195e88021a665fc371fdee4f44822d08_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+11.022_ry+12.589_rz-08.181_tx+00.381_ty+00.875_s+00.764_c30f1394195e88021a665fc371fdee4f44822d08_256x256.png,467544feb988ef480ba4ad8dbf29954114fa8872,Boats,11.022,12.589,-8.181,4921 -4922,cruiser,18.228,62.099,69.757,-0.149,0.665,0.7775,d594bf201ef7142eafd2e1c0d874d31ec782e85e,256,V6,d594bf201ef7142eafd2e1c0d874d31ec782e85e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+18.228_ry+152.099_rz+69.757_tx-00.149_ty+00.665_s+00.933_d594bf201ef7142eafd2e1c0d874d31ec782e85e_256x256.png,37a0a39b6a71bb6491a192396162ec454ad1c61f,Boats,18.228,152.099,69.757,4922 -4923,cruiser,18.433,26.519,69.191,0.367,0.543,1.39416666667,e6edd60558cc424d590498a21557e4b391cf7011,256,V6,e6edd60558cc424d590498a21557e4b391cf7011_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+18.433_ry+116.519_rz+69.191_tx+00.367_ty+00.543_s+01.673_e6edd60558cc424d590498a21557e4b391cf7011_256x256.png,4ef3b6e777c3b6abcbdac71c09bf8c96ab688dac,Boats,18.433,116.519,69.191,4923 -4924,cruiser,20.939,-26.786,23.684,0.348,-0.372,1.21583333333,e581111e0dafea50427cc79ba57c0c7898b8bc9c,256,V6,e581111e0dafea50427cc79ba57c0c7898b8bc9c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+20.939_ry+63.214_rz+23.684_tx+00.348_ty-00.372_s+01.459_e581111e0dafea50427cc79ba57c0c7898b8bc9c_256x256.png,33b22ddb82cd1e4175b9c2fdef23e51b2b700876,Boats,20.939,63.214,23.684,4924 -4925,cruiser,26.553,-7.875,5.786,0.066,-0.399,0.905833333333,e1f52b7b3340b0fdccb4e2fc79f82c3b6296dcb8,256,V6,e1f52b7b3340b0fdccb4e2fc79f82c3b6296dcb8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+26.553_ry+82.125_rz+05.786_tx+00.066_ty-00.399_s+01.087_e1f52b7b3340b0fdccb4e2fc79f82c3b6296dcb8_256x256.png,d3875474c8436ff58fb1ae6ac2d3e36b514c436c,Boats,26.553,82.125,5.786,4925 -4926,cruiser,28.383,81.877,78.714,0.097,-0.48,0.854166666667,57284c07c28e8b122251477d4cd7b3ae46a05543,256,V6,57284c07c28e8b122251477d4cd7b3ae46a05543_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+28.383_ry+171.877_rz+78.714_tx+00.097_ty-00.480_s+01.025_57284c07c28e8b122251477d4cd7b3ae46a05543_256x256.png,55d2ecef32b4194bc64b8470a2d2898fd3e341cc,Boats,28.383,171.877,78.714,4926 -4927,cruiser,28.672,-8.714,-63.923,0.103,-0.697,1.04833333333,150c936a3a5e5bc1c0a89cfe1fe637c2b049aba1,256,V6,150c936a3a5e5bc1c0a89cfe1fe637c2b049aba1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+28.672_ry+81.286_rz-63.923_tx+00.103_ty-00.697_s+01.258_150c936a3a5e5bc1c0a89cfe1fe637c2b049aba1_256x256.png,75aee49c5b52935325655731a543de05df5a8af1,Boats,28.672,81.286,-63.923,4927 -4928,cruiser,34.502,21.388,76.862,-0.261,-0.841,1.32583333333,f30398d25c89c8d7cc54fe74a083d1f38f925545,256,V6,f30398d25c89c8d7cc54fe74a083d1f38f925545_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+34.502_ry+111.388_rz+76.862_tx-00.261_ty-00.841_s+01.591_f30398d25c89c8d7cc54fe74a083d1f38f925545_256x256.png,5dd0fd75ed1ad1a320b14b752c700e4c6394b14a,Boats,34.502,111.388,76.862,4928 -4929,cruiser,35.875,74.715,-62.161,-0.478,-1.147,1.59,4ab1c6f5baa69e23620e787d33efc60cb34b49da,256,V6,4ab1c6f5baa69e23620e787d33efc60cb34b49da_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+35.875_ry+164.715_rz-62.161_tx-00.478_ty-01.147_s+01.908_4ab1c6f5baa69e23620e787d33efc60cb34b49da_256x256.png,f4b3e7ef829479a61baf375ddb1eccb264fb241d,Boats,35.875,164.715,-62.161,4929 -4930,cruiser,36.182,13.104,6.828,0.447,-0.577,1.48833333333,333ffaa8f1ed166ae3423fec9eae3d1a3a1af56f,256,V6,333ffaa8f1ed166ae3423fec9eae3d1a3a1af56f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+36.182_ry+103.104_rz+06.828_tx+00.447_ty-00.577_s+01.786_333ffaa8f1ed166ae3423fec9eae3d1a3a1af56f_256x256.png,7017010be550fff37ecd423a7867d36a3b5a5da0,Boats,36.182,103.104,6.828,4930 -4931,cruiser,44.365,-46.244,79.951,0.399,-0.748,0.859166666667,b5cf91779233cbc3c6a3fa33c10dfd8ba404fccf,256,V6,b5cf91779233cbc3c6a3fa33c10dfd8ba404fccf_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+44.365_ry+43.756_rz+79.951_tx+00.399_ty-00.748_s+01.031_b5cf91779233cbc3c6a3fa33c10dfd8ba404fccf_256x256.png,baab32e4c7764a158b46b29d757af3115d96c060,Boats,44.365,43.756,79.951,4931 -4932,cruiser,45.068,-85.507,-88.145,0.151,-0.622,0.931666666667,1416c2ddd03d543326596ed7b15d0a8279d17029,256,V6,1416c2ddd03d543326596ed7b15d0a8279d17029_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+45.068_ry+04.493_rz-88.145_tx+00.151_ty-00.622_s+01.118_1416c2ddd03d543326596ed7b15d0a8279d17029_256x256.png,d0cc68c3d4c35b2e04cf4cca06991e696bbb27ce,Boats,45.068,4.493,-88.145,4932 -4933,cruiser,45.937,-22.277,-85.862,-0.557,-1.1,1.33,8d558d6216746ba2276b4fbe2875b1e7b7f67460,256,V6,8d558d6216746ba2276b4fbe2875b1e7b7f67460_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+45.937_ry+67.723_rz-85.862_tx-00.557_ty-01.100_s+01.596_8d558d6216746ba2276b4fbe2875b1e7b7f67460_256x256.png,921a1f1bce4b17208e250269215af922800609d9,Boats,45.937,67.723,-85.862,4933 -4934,cruiser,54.187,-67.142,63.988,-0.198,-0.123,1.53083333333,016aef2320cd3c98e191199214a36114872e7f0d,256,V6,016aef2320cd3c98e191199214a36114872e7f0d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+54.187_ry+22.858_rz+63.988_tx-00.198_ty-00.123_s+01.837_016aef2320cd3c98e191199214a36114872e7f0d_256x256.png,17b3f8a058ad9b79a3ca440036fe43b70fb6abd5,Boats,54.187,22.858,63.988,4934 -4935,cruiser,64.549,-5.193,29.556,0.397,-0.959,1.05916666667,a83e45fbf8752c617b0e6c370337b86d160fe3d5,256,V6,a83e45fbf8752c617b0e6c370337b86d160fe3d5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+64.549_ry+84.807_rz+29.556_tx+00.397_ty-00.959_s+01.271_a83e45fbf8752c617b0e6c370337b86d160fe3d5_256x256.png,db18bd60325ad73f6ad3b8afd17e9bc97661730c,Boats,64.549,84.807,29.556,4935 -4936,cruiser,64.578,-65.876,-37.979,-0.398,0.212,1.19166666667,352210296bf46eaa3e4597ae345da92895a10ce1,256,V6,352210296bf46eaa3e4597ae345da92895a10ce1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+64.578_ry+24.124_rz-37.979_tx-00.398_ty+00.212_s+01.430_352210296bf46eaa3e4597ae345da92895a10ce1_256x256.png,4489885673ba8d42156a4607319951a16a1ac575,Boats,64.578,24.124,-37.979,4936 -4937,cruiser,67.075,74.402,1.733,0.071,0.592,0.645833333333,6dea12fd00df64c80af68771e7c3c059ecf9a757,256,V6,6dea12fd00df64c80af68771e7c3c059ecf9a757_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+67.075_ry+164.402_rz+01.733_tx+00.071_ty+00.592_s+00.775_6dea12fd00df64c80af68771e7c3c059ecf9a757_256x256.png,8c7f895890532ea38b6dc3c61b7679f664d78668,Boats,67.075,164.402,1.733,4937 -4938,cruiser,72.205,70.779,88.314,-0.288,-0.834,0.819166666667,dd9d6e222f9a7685c927656dff626f8b61ae29cb,256,V6,dd9d6e222f9a7685c927656dff626f8b61ae29cb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+72.205_ry+160.779_rz+88.314_tx-00.288_ty-00.834_s+00.983_dd9d6e222f9a7685c927656dff626f8b61ae29cb_256x256.png,8ff6bb41ed13056caccc72142a5123b5870175f4,Boats,72.205,160.779,88.314,4938 -4939,cruiser,77.237,-23.149,87.951,0.302,-0.364,1.38916666667,1ae21d65f8003abdcf258437a6a52761e1d99eab,256,V6,1ae21d65f8003abdcf258437a6a52761e1d99eab_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+77.237_ry+66.851_rz+87.951_tx+00.302_ty-00.364_s+01.667_1ae21d65f8003abdcf258437a6a52761e1d99eab_256x256.png,1be6873d0139de6d233993d6b4978063738b2adb,Boats,77.237,66.851,87.951,4939 -4940,cruiser,86.684,-68.225,11.964,0.239,-0.386,1.3925,4774e979e2b285ec1ca3d258b63d656015e1d9b5,256,V6,4774e979e2b285ec1ca3d258b63d656015e1d9b5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+86.684_ry+21.775_rz+11.964_tx+00.239_ty-00.386_s+01.671_4774e979e2b285ec1ca3d258b63d656015e1d9b5_256x256.png,187b86395f5e9fa5531cb48b18dbf367db3d1f18,Boats,86.684,21.775,11.964,4940 -4941,cruiser,87.086,39.999,50.526,0.339,0.988,1.45666666667,2c35e256605a34069beefa139c6f8484e41851fd,256,V6,2c35e256605a34069beefa139c6f8484e41851fd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+87.086_ry+129.999_rz+50.526_tx+00.339_ty+00.988_s+01.748_2c35e256605a34069beefa139c6f8484e41851fd_256x256.png,b3d83abf6c8c06de4f39e4c8ada668128700a73a,Boats,87.086,129.999,50.526,4941 -4942,cruiser,89.769,-1.135,81.033,0.196,1.08,1.37083333333,f48ffbb4fa54fa1b7e000f2eeeb6e3d2eb7bda46,256,V6,f48ffbb4fa54fa1b7e000f2eeeb6e3d2eb7bda46_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx+89.769_ry+88.865_rz+81.033_tx+00.196_ty+01.080_s+01.645_f48ffbb4fa54fa1b7e000f2eeeb6e3d2eb7bda46_256x256.png,c3efaa9d1728b428d3cc1a52f06d0c3f9cc30967,Boats,89.769,88.865,81.033,4942 -4943,cruiser,-0.389,-5.583,-71.536,-0.055,-0.886,0.775,3c8e15bef13d4f784cfca068c1db2d51523f0855,256,V6,3c8e15bef13d4f784cfca068c1db2d51523f0855_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-00.389_ry+84.417_rz-71.536_tx-00.055_ty-00.886_s+00.930_3c8e15bef13d4f784cfca068c1db2d51523f0855_256x256.png,561c6fdb8e5c481813a57df09ab540ddbda6256f,Boats,-0.389,84.417,-71.536,4943 -4944,cruiser,-2.803,1.561,-33.13,-0.492,1.084,0.7975,2e73e928158e7181ceecbd92ae6eba601eafb229,256,V6,2e73e928158e7181ceecbd92ae6eba601eafb229_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-02.803_ry+91.561_rz-33.130_tx-00.492_ty+01.084_s+00.957_2e73e928158e7181ceecbd92ae6eba601eafb229_256x256.png,e231aea066923bdd8be9c47f89e07dbecb677350,Boats,-2.803,91.561,-33.13,4944 -4945,cruiser,-8.975,27.838,-46.189,-0.313,0.863,1.41,4cea70da30b48a1ab6771b7bfa43d773cb1f3652,256,V6,4cea70da30b48a1ab6771b7bfa43d773cb1f3652_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-08.975_ry+117.838_rz-46.189_tx-00.313_ty+00.863_s+01.692_4cea70da30b48a1ab6771b7bfa43d773cb1f3652_256x256.png,51239ff6d6f7d83b05f60a87152c8b0339b660dc,Boats,-8.975,117.838,-46.189,4945 -4946,cruiser,-10.373,17.611,-23.291,-0.292,-0.728,0.815,90468c32039b8d6a49a1254c075761300b15d1f3,256,V6,90468c32039b8d6a49a1254c075761300b15d1f3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-10.373_ry+107.611_rz-23.291_tx-00.292_ty-00.728_s+00.978_90468c32039b8d6a49a1254c075761300b15d1f3_256x256.png,2f4a002fdc91540d701a69e9215751ea2a6ff4ee,Boats,-10.373,107.611,-23.291,4946 -4947,cruiser,-14.678,-20.726,47.688,0.512,-0.097,0.708333333333,1bae161e1ca8f23d7406b2174ea4f394a6ddc113,256,V6,1bae161e1ca8f23d7406b2174ea4f394a6ddc113_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-14.678_ry+69.274_rz+47.688_tx+00.512_ty-00.097_s+00.850_1bae161e1ca8f23d7406b2174ea4f394a6ddc113_256x256.png,98d9c9a0087cbf9f814011d278f46a9d5f958cca,Boats,-14.678,69.274,47.688,4947 -4948,cruiser,-17.601,47.861,89.553,0.005,0.516,1.30583333333,06402ab2a24b46ff1e8b129a88d8886421352a9b,256,V6,06402ab2a24b46ff1e8b129a88d8886421352a9b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-17.601_ry+137.861_rz+89.553_tx+00.005_ty+00.516_s+01.567_06402ab2a24b46ff1e8b129a88d8886421352a9b_256x256.png,89cea2716d0b56bdf25959de68f4ebbf99f4ff13,Boats,-17.601,137.861,89.553,4948 -4949,cruiser,-20.348,-17.281,32.969,0.509,0.609,1.47,2daee7746ad01c98bc7f2e18a3539d068fc4bf99,256,V6,2daee7746ad01c98bc7f2e18a3539d068fc4bf99_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-20.348_ry+72.719_rz+32.969_tx+00.509_ty+00.609_s+01.764_2daee7746ad01c98bc7f2e18a3539d068fc4bf99_256x256.png,02425dd6392d030616cb8d6d01f4fbd87820203f,Boats,-20.348,72.719,32.969,4949 -4950,cruiser,-20.721,55.228,-40.974,-0.466,-0.618,1.34916666667,210ee594a707b0fa8405f40fa493991f0b93e5ca,256,V6,210ee594a707b0fa8405f40fa493991f0b93e5ca_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-20.721_ry+145.228_rz-40.974_tx-00.466_ty-00.618_s+01.619_210ee594a707b0fa8405f40fa493991f0b93e5ca_256x256.png,93c0343eee1d80b45a99f6b508fa83ac0b8c7304,Boats,-20.721,145.228,-40.974,4950 -4951,cruiser,-27.665,-83.39,82.81,-0.221,-1.066,0.723333333333,00f94524154d4c4c8efaaa20a390deec4ac75d66,256,V6,00f94524154d4c4c8efaaa20a390deec4ac75d66_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-27.665_ry+06.610_rz+82.810_tx-00.221_ty-01.066_s+00.868_00f94524154d4c4c8efaaa20a390deec4ac75d66_256x256.png,03b6d184977d081c891a6bcd1389c815fa50c1db,Boats,-27.665,6.61,82.81,4951 -4952,cruiser,-40.926,27.747,-16.848,-0.009,0.985,0.826666666667,1c57833d6449ff5bf0e9177f9a4b171cf16698d8,256,V6,1c57833d6449ff5bf0e9177f9a4b171cf16698d8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-40.926_ry+117.747_rz-16.848_tx-00.009_ty+00.985_s+00.992_1c57833d6449ff5bf0e9177f9a4b171cf16698d8_256x256.png,eec2d15ca74cfe69c90defdc60c449f6d2d449e5,Boats,-40.926,117.747,-16.848,4952 -4953,cruiser,-55.798,35.681,29.47,0.298,1.168,1.17333333333,1083a73d528ad6a9414bc5f51db5c855f5386bf1,256,V6,1083a73d528ad6a9414bc5f51db5c855f5386bf1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-55.798_ry+125.681_rz+29.470_tx+00.298_ty+01.168_s+01.408_1083a73d528ad6a9414bc5f51db5c855f5386bf1_256x256.png,bab2c81c74eaf9fe059efc49a1ab8c7171535a49,Boats,-55.798,125.681,29.47,4953 -4954,cruiser,-59.848,1.07,-14.708,-0.382,0.326,1.185,00b9334afb5268a64ab035fcbe0b4f85fa929564,256,V6,00b9334afb5268a64ab035fcbe0b4f85fa929564_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-59.848_ry+91.070_rz-14.708_tx-00.382_ty+00.326_s+01.422_00b9334afb5268a64ab035fcbe0b4f85fa929564_256x256.png,5d336eaa8f8b8a00af744adb7dba34775fac0889,Boats,-59.848,91.07,-14.708,4954 -4955,cruiser,-68.231,-47.244,-26.996,0.106,0.453,1.1475,85e99bdd29b122d564fa9b9c1e64fd486f761918,256,V6,85e99bdd29b122d564fa9b9c1e64fd486f761918_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-68.231_ry+42.756_rz-26.996_tx+00.106_ty+00.453_s+01.377_85e99bdd29b122d564fa9b9c1e64fd486f761918_256x256.png,0f6591ba94cb410677357db091d1503ed57a8e6f,Boats,-68.231,42.756,-26.996,4955 -4956,cruiser,-71.145,-0.809,-30.805,0.43,0.209,1.07333333333,4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b,256,V6,4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-71.145_ry+89.191_rz-30.805_tx+00.430_ty+00.209_s+01.288_4a257824a10bdae247bd9d6e18ccdf0cd9c3fe1b_256x256.png,5650c755edcbeb39a1d8b848128bd52e97a3c2c2,Boats,-71.145,89.191,-30.805,4956 -4957,cruiser,-77.671,-26.663,-5.289,-0.28,-0.645,1.1325,93ac31ef0bd9256985a1253b6f1f133e824c9d78,256,V6,93ac31ef0bd9256985a1253b6f1f133e824c9d78_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-77.671_ry+63.337_rz-05.289_tx-00.280_ty-00.645_s+01.359_93ac31ef0bd9256985a1253b6f1f133e824c9d78_256x256.png,1b6608fce8a8d7014e0fb6a99ae4fa34b4497145,Boats,-77.671,63.337,-5.289,4957 -4958,cruiser,-78.122,57.427,-61.343,0.041,0.794,1.00416666667,ccbd9b817580ecfa34e4e9e575975d3344fa93b8,256,V6,ccbd9b817580ecfa34e4e9e575975d3344fa93b8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-78.122_ry+147.427_rz-61.343_tx+00.041_ty+00.794_s+01.205_ccbd9b817580ecfa34e4e9e575975d3344fa93b8_256x256.png,b8f274dd0bbb0872f09cac290083ad2647708eec,Boats,-78.122,147.427,-61.343,4958 -4959,cruiser,-80.601,-41.288,80.282,-0.591,-0.886,0.65,5cfc772167e1e8d7c9068acd5429a01e900d44b5,256,V6,5cfc772167e1e8d7c9068acd5429a01e900d44b5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/cruiser_rx-80.601_ry+48.712_rz+80.282_tx-00.591_ty-00.886_s+00.780_5cfc772167e1e8d7c9068acd5429a01e900d44b5_256x256.png,c95bae5f7ea1550413d42673ada593935961162b,Boats,-80.601,48.712,80.282,4959 -4960,f16,10.034,89.804,6.808,-0.351,0.248,1.16,4e8a19cf1a37e17ababec672969cd1182a5afd1a,256,V6,4e8a19cf1a37e17ababec672969cd1182a5afd1a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+10.034_ry+89.804_rz+06.808_tx-00.351_ty+00.248_s+01.508_4e8a19cf1a37e17ababec672969cd1182a5afd1a_256x256.png,302bc3f59b5f8950f5129f9169261399636d26d0,Planes,10.034,179.804,6.808,4960 -4961,f16,12.553,12.949,16.776,-0.566,0.886,1.39846153846,52629fa75490d61a681ce591faaca2b10afe52eb,256,V6,52629fa75490d61a681ce591faaca2b10afe52eb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+12.553_ry+12.949_rz+16.776_tx-00.566_ty+00.886_s+01.818_52629fa75490d61a681ce591faaca2b10afe52eb_256x256.png,f9316bbb265133a71099700c81ff1822bb4b6a44,Planes,12.553,102.949,16.776,4961 -4962,f16,23.885,-23.285,1.846,-0.02,0.379,0.673076923077,0f6a307d0c0b4d89fe5d7dff5a96d1fa108abeac,256,V6,0f6a307d0c0b4d89fe5d7dff5a96d1fa108abeac_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+23.885_ry-23.285_rz+01.846_tx-00.020_ty+00.379_s+00.875_0f6a307d0c0b4d89fe5d7dff5a96d1fa108abeac_256x256.png,e98ebb0ccba23965aaae45d1b758d5cbd1b48678,Planes,23.885,66.715,1.846,4962 -4963,f16,26.964,-34.279,-64.117,-0.511,0.091,1.54461538462,4c61c31f72914c3f1948af80800219ac7655ccca,256,V6,4c61c31f72914c3f1948af80800219ac7655ccca_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+26.964_ry-34.279_rz-64.117_tx-00.511_ty+00.091_s+02.008_4c61c31f72914c3f1948af80800219ac7655ccca_256x256.png,2b75c4efd20520d4fd57a0933e8e303a960aa0da,Planes,26.964,55.721,-64.117,4963 -4964,f16,27.907,-40.005,-26.916,-0.051,1.109,1.36307692308,14cfdfd9ea093d19ce66ccb29db6c748120dee82,256,V6,14cfdfd9ea093d19ce66ccb29db6c748120dee82_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+27.907_ry-40.005_rz-26.916_tx-00.051_ty+01.109_s+01.772_14cfdfd9ea093d19ce66ccb29db6c748120dee82_256x256.png,c3fd5e7e06a51183e02f53448be00f269f512055,Planes,27.907,49.995,-26.916,4964 -4965,f16,40.381,81.101,87.861,0.577,1.099,0.924615384615,12e561afcdf3da806379b55913fd48f9c66faf39,256,V6,12e561afcdf3da806379b55913fd48f9c66faf39_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+40.381_ry+81.101_rz+87.861_tx+00.577_ty+01.099_s+01.202_12e561afcdf3da806379b55913fd48f9c66faf39_256x256.png,96edce90bf29bebca2215efbc5b9aa0d955a8834,Planes,40.381,171.101,87.861,4965 -4966,f16,46.35,-40.376,-18.453,0.198,-0.923,1.46538461538,0caa0b80bcc320e49a990d01174ac90df8cdea5c,256,V6,0caa0b80bcc320e49a990d01174ac90df8cdea5c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+46.350_ry-40.376_rz-18.453_tx+00.198_ty-00.923_s+01.905_0caa0b80bcc320e49a990d01174ac90df8cdea5c_256x256.png,7b12be8e18f8c2237980bc318467340d22941de9,Planes,46.35,49.624,-18.453,4966 -4967,f16,46.405,74.811,26.453,-0.295,0.741,1.53538461538,b8035d42c216f0b58a96743c9c198d67585829ab,256,V6,b8035d42c216f0b58a96743c9c198d67585829ab_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+46.405_ry+74.811_rz+26.453_tx-00.295_ty+00.741_s+01.996_b8035d42c216f0b58a96743c9c198d67585829ab_256x256.png,1b6d846f22be28b33daba4d7501b012cc18d38db,Planes,46.405,164.811,26.453,4967 -4968,f16,52.236,-87.023,10.677,0.207,1.162,1.27615384615,8a27da1ec537a1fdfbd1f26b57330dc0fd2a0066,256,V6,8a27da1ec537a1fdfbd1f26b57330dc0fd2a0066_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+52.236_ry-87.023_rz+10.677_tx+00.207_ty+01.162_s+01.659_8a27da1ec537a1fdfbd1f26b57330dc0fd2a0066_256x256.png,a41665d8628641eeb08d350e64ca8c62f369c5b8,Planes,52.236,2.977,10.677,4968 -4969,f16,52.935,-54.055,-1.978,-0.103,-0.425,0.699230769231,13fb94f9aa84e3b4c71293d1bf214009f05c0b44,256,V6,13fb94f9aa84e3b4c71293d1bf214009f05c0b44_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+52.935_ry-54.055_rz-01.978_tx-00.103_ty-00.425_s+00.909_13fb94f9aa84e3b4c71293d1bf214009f05c0b44_256x256.png,2395e119b957921bae00e37f8b8586bb633de602,Planes,52.935,35.945,-1.978,4969 -4970,f16,57.372,4.418,12.906,0.224,-0.607,0.955384615385,e3ba7c7c4d79965067671bdba3d4945e791bc961,256,V6,e3ba7c7c4d79965067671bdba3d4945e791bc961_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+57.372_ry+04.418_rz+12.906_tx+00.224_ty-00.607_s+01.242_e3ba7c7c4d79965067671bdba3d4945e791bc961_256x256.png,d20dce9cd299d9caf4634bbb6c227c94ade3b53b,Planes,57.372,94.418,12.906,4970 -4971,f16,59.649,44.163,14.039,0.391,-0.42,1.57846153846,f71e221b101daf574f0bd572b169063b42b89dd7,256,V6,f71e221b101daf574f0bd572b169063b42b89dd7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+59.649_ry+44.163_rz+14.039_tx+00.391_ty-00.420_s+02.052_f71e221b101daf574f0bd572b169063b42b89dd7_256x256.png,72cdfdd6cf5d5f62fd35e971e3bf2000ffd21e8c,Planes,59.649,134.163,14.039,4971 -4972,f16,60.248,69.368,-54.291,0.487,-0.622,1.24769230769,d225fbe3a34e1a1316952d599761bf53520ce110,256,V6,d225fbe3a34e1a1316952d599761bf53520ce110_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+60.248_ry+69.368_rz-54.291_tx+00.487_ty-00.622_s+01.622_d225fbe3a34e1a1316952d599761bf53520ce110_256x256.png,3e4d0a94ff0dcc86d4a0ee9942baa2996ff9485a,Planes,60.248,159.368,-54.291,4972 -4973,f16,63.592,-59.835,-32.755,0.367,-0.67,1.12923076923,dada8e2a57f80498a8cd29465e4a8322d32c856a,256,V6,dada8e2a57f80498a8cd29465e4a8322d32c856a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+63.592_ry-59.835_rz-32.755_tx+00.367_ty-00.670_s+01.468_dada8e2a57f80498a8cd29465e4a8322d32c856a_256x256.png,51f6bebab8cc27e37c928d75c7ed2ed6ed6e969b,Planes,63.592,30.165,-32.755,4973 -4974,f16,64.578,27.642,-23.893,0.155,0.932,1.37384615385,346cf3df24a46543d04c4be352fabcb54ca200d9,256,V6,346cf3df24a46543d04c4be352fabcb54ca200d9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+64.578_ry+27.642_rz-23.893_tx+00.155_ty+00.932_s+01.786_346cf3df24a46543d04c4be352fabcb54ca200d9_256x256.png,d0ee034cd151c8344914447c682465f2c0756e8f,Planes,64.578,117.642,-23.893,4974 -4975,f16,68.321,3.784,-51.195,-0.414,-0.463,1.01769230769,901f88e73d35a774b583f57e90a508e5b1e32b66,256,V6,901f88e73d35a774b583f57e90a508e5b1e32b66_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+68.321_ry+03.784_rz-51.195_tx-00.414_ty-00.463_s+01.323_901f88e73d35a774b583f57e90a508e5b1e32b66_256x256.png,ec73456af00208bb30ea5a8531ee0bc50371f30c,Planes,68.321,93.784,-51.195,4975 -4976,f16,69.849,-27.32,-88.373,0.516,0.197,1.01,5461dbbb682b73fbaf4f096c3f2567bf428a53e0,256,V6,5461dbbb682b73fbaf4f096c3f2567bf428a53e0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+69.849_ry-27.320_rz-88.373_tx+00.516_ty+00.197_s+01.313_5461dbbb682b73fbaf4f096c3f2567bf428a53e0_256x256.png,74e2f20460d5aae2382b25d96d98b3549b1e4285,Planes,69.849,62.68,-88.373,4976 -4977,f16,72.389,49.462,34.844,-0.437,-0.177,0.805384615385,602bc12d1a86645be44574d013c88d450288ade8,256,V6,602bc12d1a86645be44574d013c88d450288ade8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+72.389_ry+49.462_rz+34.844_tx-00.437_ty-00.177_s+01.047_602bc12d1a86645be44574d013c88d450288ade8_256x256.png,89fdaf99fa4742087d5103a9ccec93fb92c520ae,Planes,72.389,139.462,34.844,4977 -4978,f16,73.344,25.09,48.242,0.134,-0.219,0.673076923077,6dc6ba1c1c04250c00ae0c0094d0b2aa621c3edc,256,V6,6dc6ba1c1c04250c00ae0c0094d0b2aa621c3edc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+73.344_ry+25.090_rz+48.242_tx+00.134_ty-00.219_s+00.875_6dc6ba1c1c04250c00ae0c0094d0b2aa621c3edc_256x256.png,0836a9f058d4f66597fefe6b68903c86c0d8ab6c,Planes,73.344,115.09,48.242,4978 -4979,f16,75.267,-50.466,47.335,-0.441,-0.413,1.22461538462,67a041abdd8fa2e65f7da965b839a4e1d2ae9a93,256,V6,67a041abdd8fa2e65f7da965b839a4e1d2ae9a93_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+75.267_ry-50.466_rz+47.335_tx-00.441_ty-00.413_s+01.592_67a041abdd8fa2e65f7da965b839a4e1d2ae9a93_256x256.png,d8c259747c5c1c6e6cf470d0ade11446b361c32c,Planes,75.267,39.534,47.335,4979 -4980,f16,77.765,47.306,89.601,-0.3,-0.845,0.734615384615,467e9f9d5eca25c0197c941bb746d022030174c5,256,V6,467e9f9d5eca25c0197c941bb746d022030174c5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+77.765_ry+47.306_rz+89.601_tx-00.300_ty-00.845_s+00.955_467e9f9d5eca25c0197c941bb746d022030174c5_256x256.png,474ee87a87d6114fc351e752f8b2eb35ea4cf832,Planes,77.765,137.306,89.601,4980 -4981,f16,87.462,-42.547,-29.999,0.563,1.178,1.50461538462,787999b0f3db6f0003ec7fc6987228643736d294,256,V6,787999b0f3db6f0003ec7fc6987228643736d294_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+87.462_ry-42.547_rz-29.999_tx+00.563_ty+01.178_s+01.956_787999b0f3db6f0003ec7fc6987228643736d294_256x256.png,ce68302ebc3238ae1fd00816b738dbcc4f08a7c7,Planes,87.462,47.453,-29.999,4981 -4982,f16,88.909,-51.952,68.235,0.089,-0.763,1.56384615385,032f34672d7ab1a53abe50ec973c1e9c0fd125c5,256,V6,032f34672d7ab1a53abe50ec973c1e9c0fd125c5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx+88.909_ry-51.952_rz+68.235_tx+00.089_ty-00.763_s+02.033_032f34672d7ab1a53abe50ec973c1e9c0fd125c5_256x256.png,a0da683ec5036001adf4f0d43afcb54d7bbd9b03,Planes,88.909,38.048,68.235,4982 -4983,f16,-8.483,83.941,-13.373,-0.359,-0.991,1.32,c43bda53064ea7ab02851d82cb93f462c2e58eca,256,V6,c43bda53064ea7ab02851d82cb93f462c2e58eca_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx-08.483_ry+83.941_rz-13.373_tx-00.359_ty-00.991_s+01.716_c43bda53064ea7ab02851d82cb93f462c2e58eca_256x256.png,7e129dc2171448aaf0ea376657b56d6f60f3e1cb,Planes,-8.483,173.941,-13.373,4983 -4984,f16,-9.377,72.591,-74.813,0.42,-0.722,1.48538461538,2f7c4448f01901a7d0d4b9da09633a8a23dfde69,256,V6,2f7c4448f01901a7d0d4b9da09633a8a23dfde69_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx-09.377_ry+72.591_rz-74.813_tx+00.420_ty-00.722_s+01.931_2f7c4448f01901a7d0d4b9da09633a8a23dfde69_256x256.png,cf3e50a1328140e57153a12fb11558993c81c657,Planes,-9.377,162.591,-74.813,4984 -4985,f16,-22.057,-89.937,2.446,-0.233,-0.095,1.53230769231,a8d40a194f1dbbd86033f0faf38896d21edd4b64,256,V6,a8d40a194f1dbbd86033f0faf38896d21edd4b64_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx-22.057_ry-89.937_rz+02.446_tx-00.233_ty-00.095_s+01.992_a8d40a194f1dbbd86033f0faf38896d21edd4b64_256x256.png,52497d9923ae3840f004b04a28a1991e942b4191,Planes,-22.057,0.063,2.446,4985 -4986,f16,-37.068,-89.505,17.39,-0.424,-0.604,0.627692307692,92becd13af285032ed3cbc249e24c29d2d4f7de8,256,V6,92becd13af285032ed3cbc249e24c29d2d4f7de8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx-37.068_ry-89.505_rz+17.390_tx-00.424_ty-00.604_s+00.816_92becd13af285032ed3cbc249e24c29d2d4f7de8_256x256.png,289862651ab020f6a0c48a99f040d7b42cc3de61,Planes,-37.068,0.495,17.39,4986 -4987,f16,-39.142,35.843,3.018,-0.103,0.68,0.675384615385,a384a345f190959f828743f728fe6998d3d2b2e4,256,V6,a384a345f190959f828743f728fe6998d3d2b2e4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx-39.142_ry+35.843_rz+03.018_tx-00.103_ty+00.680_s+00.878_a384a345f190959f828743f728fe6998d3d2b2e4_256x256.png,d895e0064508619619c288a415f518f91cba2481,Planes,-39.142,125.843,3.018,4987 -4988,f16,-42.384,-26.99,-34.048,0.03,-0.997,0.633076923077,c5e0800019045c901b8622c215a871a62cd2d2a9,256,V6,c5e0800019045c901b8622c215a871a62cd2d2a9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx-42.384_ry-26.990_rz-34.048_tx+00.030_ty-00.997_s+00.823_c5e0800019045c901b8622c215a871a62cd2d2a9_256x256.png,de8345186905bf6fb9804dd600fde4a46500c456,Planes,-42.384,63.01,-34.048,4988 -4989,f16,-45.794,-46.765,-56.403,-0.409,0.237,1.04615384615,a2f42d6de829abcd33e6180633c92156f81b8f5c,256,V6,a2f42d6de829abcd33e6180633c92156f81b8f5c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx-45.794_ry-46.765_rz-56.403_tx-00.409_ty+00.237_s+01.360_a2f42d6de829abcd33e6180633c92156f81b8f5c_256x256.png,958be4ebc61d752e0b743700777c58afe2c228db,Planes,-45.794,43.235,-56.403,4989 -4990,f16,-46.534,64.247,28.383,-0.288,1.013,0.826923076923,fd4c3ce4003336817a163cd7c6f6d4ce10a82c76,256,V6,fd4c3ce4003336817a163cd7c6f6d4ce10a82c76_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx-46.534_ry+64.247_rz+28.383_tx-00.288_ty+01.013_s+01.075_fd4c3ce4003336817a163cd7c6f6d4ce10a82c76_256x256.png,2744dfe248293bced9591756bf5fbc7273c7a60b,Planes,-46.534,154.247,28.383,4990 -4991,f16,-47.81,-34.001,39.346,0.359,-0.48,1.09076923077,ba047351164e68e42e65934b851bb1a52d0727ae,256,V6,ba047351164e68e42e65934b851bb1a52d0727ae_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx-47.810_ry-34.001_rz+39.346_tx+00.359_ty-00.480_s+01.418_ba047351164e68e42e65934b851bb1a52d0727ae_256x256.png,457942f5d2420f409b777e6ebc92debf609dfd5d,Planes,-47.81,55.999,39.346,4991 -4992,f16,-56.929,9.503,-16.266,0.228,0.429,1.11307692308,5c1eefb123a473ef1b9fec13b0c1e8c1b546557e,256,V6,5c1eefb123a473ef1b9fec13b0c1e8c1b546557e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx-56.929_ry+09.503_rz-16.266_tx+00.228_ty+00.429_s+01.447_5c1eefb123a473ef1b9fec13b0c1e8c1b546557e_256x256.png,1eafa05d4eb9879fd867956c6545ae794cc9bb24,Planes,-56.929,99.503,-16.266,4992 -4993,f16,-57.494,43.481,84.198,0.377,-0.887,1.59230769231,a6bfbe5d9a49d781948092c8fc654861c6287c2c,256,V6,a6bfbe5d9a49d781948092c8fc654861c6287c2c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx-57.494_ry+43.481_rz+84.198_tx+00.377_ty-00.887_s+02.070_a6bfbe5d9a49d781948092c8fc654861c6287c2c_256x256.png,21c7cbe90003e1171805ed3e90a746dee542b141,Planes,-57.494,133.481,84.198,4993 -4994,f16,-66.007,28.037,-14.753,-0.502,-0.69,0.917692307692,cc9fa97e718a585b6e7cb4d99afd17872c445eb6,256,V6,cc9fa97e718a585b6e7cb4d99afd17872c445eb6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/f16_rx-66.007_ry+28.037_rz-14.753_tx-00.502_ty-00.690_s+01.193_cc9fa97e718a585b6e7cb4d99afd17872c445eb6_256x256.png,01a4f2015bed02d4fd49b66c4bbd92681d371197,Planes,-66.007,118.037,-14.753,4994 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-5077,face0002,-79.274,28.83,-33.518,0.343,0.047,1.33,208bdebf615fcd01cf8c9451885b7da250eae088,256,V6,208bdebf615fcd01cf8c9451885b7da250eae088_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/face0002_rx-79.274_ry+28.830_rz-33.518_tx+00.343_ty+00.047_s+01.064_208bdebf615fcd01cf8c9451885b7da250eae088_256x256.png,cb9b343486a25c8086665a77f45f5c01c3bc28b2,Faces,-79.274,28.83,-33.518,5077 -5078,face0002,-84.551,6.772,5.424,0.023,0.737,0.8375,6e02cf3db148d784bca3052ce2ed8b55aebd052a,256,V6,6e02cf3db148d784bca3052ce2ed8b55aebd052a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/face0002_rx-84.551_ry+06.772_rz+05.424_tx+00.023_ty+00.737_s+00.670_6e02cf3db148d784bca3052ce2ed8b55aebd052a_256x256.png,12291906130b768026d8c5a7c2c2e1f765d4b72a,Faces,-84.551,6.772,5.424,5078 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-5089,face0003,43.577,-47.144,-54.972,0.096,0.705,1.37125,3757671b36d32fc0944c32da9776831a90609ead,256,V6,3757671b36d32fc0944c32da9776831a90609ead_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/face0003_rx+43.577_ry-47.144_rz-54.972_tx+00.096_ty+00.705_s+01.097_3757671b36d32fc0944c32da9776831a90609ead_256x256.png,f0626b7c150c8c2c8a9531acb0e0b5e3b9e6d8c3,Faces,43.577,-47.144,-54.972,5089 -5090,face0003,44.98,55.477,8.879,0.315,0.9,1.0775,3dc7335ce388f516317fdfdbec69b85180d75e2f,256,V6,3dc7335ce388f516317fdfdbec69b85180d75e2f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/face0003_rx+44.980_ry+55.477_rz+08.879_tx+00.315_ty+00.900_s+00.862_3dc7335ce388f516317fdfdbec69b85180d75e2f_256x256.png,8472c7e3203a9768e8a3cc83f9ca71d37f229109,Faces,44.98,55.477,8.879,5090 -5091,face0003,46.845,84.77,-84.533,-0.309,-0.239,1.2575,187973546f42d59426315de537b85f5c9dbf4d1d,256,V6,187973546f42d59426315de537b85f5c9dbf4d1d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/face0003_rx+46.845_ry+84.770_rz-84.533_tx-00.309_ty-00.239_s+01.006_187973546f42d59426315de537b85f5c9dbf4d1d_256x256.png,6d72c71bae63fd2a5fb1811d115f99eaa8a55c75,Faces,46.845,84.77,-84.533,5091 -5092,face0003,51.439,8.881,-21.977,-0.518,-0.301,1.1375,46dade614b365e49698e799a364392d4335bf1ea,256,V6,46dade614b365e49698e799a364392d4335bf1ea_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/face0003_rx+51.439_ry+08.881_rz-21.977_tx-00.518_ty-00.301_s+00.910_46dade614b365e49698e799a364392d4335bf1ea_256x256.png,bbb7018e2be2e7b2a5504eafab7960169f6c3efd,Faces,51.439,8.881,-21.977,5092 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-5321,hedgehog,22.452,-4.108,-45.598,0.079,-0.049,0.7325,165c8257bbd9a909888f188d6a3de4fe01df2084,256,V6,165c8257bbd9a909888f188d6a3de4fe01df2084_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+22.452_ry+85.892_rz-45.598_tx+00.079_ty-00.049_s+00.586_165c8257bbd9a909888f188d6a3de4fe01df2084_256x256.png,e45c1de74cc789e324a7cad1131eb49d15fbd2ff,Animals,22.452,85.892,-45.598,5321 -5322,hedgehog,25.181,27.204,-61.833,0.187,0.402,1.11875,ddbc6c7feb4b78813f845fcb876f8c18f24a1117,256,V6,ddbc6c7feb4b78813f845fcb876f8c18f24a1117_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+25.181_ry+117.204_rz-61.833_tx+00.187_ty+00.402_s+00.895_ddbc6c7feb4b78813f845fcb876f8c18f24a1117_256x256.png,ad2959f1c88c314d86e179fdcd4a516cdc23fcc2,Animals,25.181,117.204,-61.833,5322 -5323,hedgehog,25.667,-11.499,3.958,0.279,-0.442,1.11,8be76a68e76ff35cbaa0fb77159fdc5d0a8bd3ba,256,V6,8be76a68e76ff35cbaa0fb77159fdc5d0a8bd3ba_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+25.667_ry+78.501_rz+03.958_tx+00.279_ty-00.442_s+00.888_8be76a68e76ff35cbaa0fb77159fdc5d0a8bd3ba_256x256.png,b5cd64441015236d85125c9bef60190d1bfc55e5,Animals,25.667,78.501,3.958,5323 -5324,hedgehog,25.683,67.27,-64.175,0.591,0.755,1.15125,2b250d6cd4936486c3541567461e3f2ee4e9847e,256,V6,2b250d6cd4936486c3541567461e3f2ee4e9847e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+25.683_ry+157.270_rz-64.175_tx+00.591_ty+00.755_s+00.921_2b250d6cd4936486c3541567461e3f2ee4e9847e_256x256.png,0e0211aef33aff02efb0c48711292862933cea0a,Animals,25.683,157.27,-64.175,5324 -5325,hedgehog,26.549,72.18,-1.098,-0.058,1.094,1.0275,68d7d13aa47012ff45717bc5f93c1d4d8ade6d5a,256,V6,68d7d13aa47012ff45717bc5f93c1d4d8ade6d5a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+26.549_ry+162.180_rz-01.098_tx-00.058_ty+01.094_s+00.822_68d7d13aa47012ff45717bc5f93c1d4d8ade6d5a_256x256.png,0af12c14d24d62054fa43130d15887e30ec31605,Animals,26.549,162.18,-1.098,5325 -5326,hedgehog,33.57,59.647,70.104,-0.223,0.532,1.295,905bec7353cf2ce0e72b2278fdb90562c61c9b8c,256,V6,905bec7353cf2ce0e72b2278fdb90562c61c9b8c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+33.570_ry+149.647_rz+70.104_tx-00.223_ty+00.532_s+01.036_905bec7353cf2ce0e72b2278fdb90562c61c9b8c_256x256.png,25a95d22d517ff3a798b9717f2178311775911f6,Animals,33.57,149.647,70.104,5326 -5327,hedgehog,44.084,-43.357,-44.372,0.407,-0.374,1.49625,aec89b484ffc241168b63cbe27e94d6f94dcaa8a,256,V6,aec89b484ffc241168b63cbe27e94d6f94dcaa8a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+44.084_ry+46.643_rz-44.372_tx+00.407_ty-00.374_s+01.197_aec89b484ffc241168b63cbe27e94d6f94dcaa8a_256x256.png,5c1305096ff0a9588a34502960e18189ed6313a5,Animals,44.084,46.643,-44.372,5327 -5328,hedgehog,50.449,-23.946,37.423,0.53,0.081,0.97625,e5b8121e1439e4f92a94450690526f65f0c960cd,256,V6,e5b8121e1439e4f92a94450690526f65f0c960cd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+50.449_ry+66.054_rz+37.423_tx+00.530_ty+00.081_s+00.781_e5b8121e1439e4f92a94450690526f65f0c960cd_256x256.png,ad7764ab86bce5543542db0e5dae3cb380e150f4,Animals,50.449,66.054,37.423,5328 -5329,hedgehog,51.379,-61.484,15.74,-0.059,0.373,1.0975,5dee87095621ea39381fdf8dfb82edb17cfb51ba,256,V6,5dee87095621ea39381fdf8dfb82edb17cfb51ba_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+51.379_ry+28.516_rz+15.740_tx-00.059_ty+00.373_s+00.878_5dee87095621ea39381fdf8dfb82edb17cfb51ba_256x256.png,6578abff1e126056bec6ce52a9bd17b4ebd22b29,Animals,51.379,28.516,15.74,5329 -5330,hedgehog,53.675,30.853,-72.397,0.101,-0.915,1.3575,00595e310a394f41e2b0bc57775e90c6931501ff,256,V6,00595e310a394f41e2b0bc57775e90c6931501ff_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+53.675_ry+120.853_rz-72.397_tx+00.101_ty-00.915_s+01.086_00595e310a394f41e2b0bc57775e90c6931501ff_256x256.png,4eb04ab772f95be0cc91b93d988c91ca8cdb4b87,Animals,53.675,120.853,-72.397,5330 -5331,hedgehog,58.687,-13.865,80.883,0.374,-0.578,1.57375,0c6f9eb9f02ae8e256ebeffe6b9b7319a28fbadf,256,V6,0c6f9eb9f02ae8e256ebeffe6b9b7319a28fbadf_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+58.687_ry+76.135_rz+80.883_tx+00.374_ty-00.578_s+01.259_0c6f9eb9f02ae8e256ebeffe6b9b7319a28fbadf_256x256.png,19d7f4176405e148612ed64d483313b6fca312b5,Animals,58.687,76.135,80.883,5331 -5332,hedgehog,69.448,-45.215,-14.188,0.546,0.102,1.305,ef162920c6c63f9b29c4b80b3f257927571c2c5b,256,V6,ef162920c6c63f9b29c4b80b3f257927571c2c5b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+69.448_ry+44.785_rz-14.188_tx+00.546_ty+00.102_s+01.044_ef162920c6c63f9b29c4b80b3f257927571c2c5b_256x256.png,a0334dc9e7380f309fa75d55dd9dab9093cf9cc7,Animals,69.448,44.785,-14.188,5332 -5333,hedgehog,72.668,50.078,-10.169,0.072,0.028,1.3775,562f6a4281c0a9c8c55100fb1df214ea046103d1,256,V6,562f6a4281c0a9c8c55100fb1df214ea046103d1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+72.668_ry+140.078_rz-10.169_tx+00.072_ty+00.028_s+01.102_562f6a4281c0a9c8c55100fb1df214ea046103d1_256x256.png,f8f90c29413d9e595c381c426777685f90c4ef8a,Animals,72.668,140.078,-10.169,5333 -5334,hedgehog,76.648,-77.187,-87.729,0.026,0.605,0.935,d75ce2982a4bac1308844aecf2146fd2c8ee6271,256,V6,d75ce2982a4bac1308844aecf2146fd2c8ee6271_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+76.648_ry+12.813_rz-87.729_tx+00.026_ty+00.605_s+00.748_d75ce2982a4bac1308844aecf2146fd2c8ee6271_256x256.png,4a66393e5fa6ed0fe9cee7000e13c99add04ae0c,Animals,76.648,12.813,-87.729,5334 -5335,hedgehog,79.982,-54.356,28.555,0.578,0.328,0.73625,b7618bfc762b49e21a2dafb953b88f528b2df0ff,256,V6,b7618bfc762b49e21a2dafb953b88f528b2df0ff_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+79.982_ry+35.644_rz+28.555_tx+00.578_ty+00.328_s+00.589_b7618bfc762b49e21a2dafb953b88f528b2df0ff_256x256.png,f94a3e634b8d66807b3ef92759c31c34a6a8c7f7,Animals,79.982,35.644,28.555,5335 -5336,hedgehog,82.13,89.359,-18.907,0.514,0.453,1.1475,4a3edcef7a125a6afe60061606fa8156956b6938,256,V6,4a3edcef7a125a6afe60061606fa8156956b6938_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+82.130_ry+179.359_rz-18.907_tx+00.514_ty+00.453_s+00.918_4a3edcef7a125a6afe60061606fa8156956b6938_256x256.png,de5ac1b606c24b978bdda1a5f5bf758f8c659ffa,Animals,82.13,179.359,-18.907,5336 -5337,hedgehog,83.451,-49.03,46.922,-0.308,-1.075,1.49625,fc785724ee3b3a75af34291cfabf0861900c97a6,256,V6,fc785724ee3b3a75af34291cfabf0861900c97a6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+83.451_ry+40.970_rz+46.922_tx-00.308_ty-01.075_s+01.197_fc785724ee3b3a75af34291cfabf0861900c97a6_256x256.png,fee28165aac4525692af5c3582519f8505c56fc2,Animals,83.451,40.97,46.922,5337 -5338,hedgehog,84.421,5.758,82.408,0.568,-0.406,0.6325,46d57bf4b0b9f227e6f1f8af5037d0da47dce0bf,256,V6,46d57bf4b0b9f227e6f1f8af5037d0da47dce0bf_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+84.421_ry+95.758_rz+82.408_tx+00.568_ty-00.406_s+00.506_46d57bf4b0b9f227e6f1f8af5037d0da47dce0bf_256x256.png,8b5e8cb5f377c42e169ebfa2f1480a5c2fd96037,Animals,84.421,95.758,82.408,5338 -5339,hedgehog,85.512,-40.299,83.432,0.192,-0.139,0.6925,20889705a7edafdc0d5bc8973efeaa864bb83849,256,V6,20889705a7edafdc0d5bc8973efeaa864bb83849_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+85.512_ry+49.701_rz+83.432_tx+00.192_ty-00.139_s+00.554_20889705a7edafdc0d5bc8973efeaa864bb83849_256x256.png,e473a505f753e28bafa17f73ed78a76500624158,Animals,85.512,49.701,83.432,5339 -5340,hedgehog,86.349,61.817,28.913,0.016,0.732,1.51,a9e245e371f45b3919860c8a17f533876821ad1f,256,V6,a9e245e371f45b3919860c8a17f533876821ad1f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx+86.349_ry+151.817_rz+28.913_tx+00.016_ty+00.732_s+01.208_a9e245e371f45b3919860c8a17f533876821ad1f_256x256.png,c5f2244e702ebaa64cfe4632923d654854c2a784,Animals,86.349,151.817,28.913,5340 -5341,hedgehog,-0.14,10.15,-11.347,0.262,0.99,1.1425,4c3636dce584477637efcbc6c32e5540627cdfbe,256,V6,4c3636dce584477637efcbc6c32e5540627cdfbe_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-00.140_ry+100.150_rz-11.347_tx+00.262_ty+00.990_s+00.914_4c3636dce584477637efcbc6c32e5540627cdfbe_256x256.png,a271da3b62d98bea42d7fbd06caef3d0bd6aced7,Animals,-0.14,100.15,-11.347,5341 -5342,hedgehog,-3.607,-40.112,39.15,0.596,0.192,1.2,6d4510106f502c5e6675ff5cd8755a701cc02393,256,V6,6d4510106f502c5e6675ff5cd8755a701cc02393_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-03.607_ry+49.888_rz+39.150_tx+00.596_ty+00.192_s+00.960_6d4510106f502c5e6675ff5cd8755a701cc02393_256x256.png,9363219a8e746fc0cd17dc7e7a58b4316e8214bf,Animals,-3.607,49.888,39.15,5342 -5343,hedgehog,-4.123,81.643,-0.95,-0.121,-0.882,1.3675,0d5984f03583f8982b2c7c4b5b4cac1b678f5abc,256,V6,0d5984f03583f8982b2c7c4b5b4cac1b678f5abc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-04.123_ry+171.643_rz-00.950_tx-00.121_ty-00.882_s+01.094_0d5984f03583f8982b2c7c4b5b4cac1b678f5abc_256x256.png,96edccb623178b8129ded3863d484cfbf8ea1949,Animals,-4.123,171.643,-0.95,5343 -5344,hedgehog,-5.836,-48.061,-39.258,0.22,-0.115,1.26125,dd2be8146a11b9d854f1fed30afe396842ff7436,256,V6,dd2be8146a11b9d854f1fed30afe396842ff7436_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-05.836_ry+41.939_rz-39.258_tx+00.220_ty-00.115_s+01.009_dd2be8146a11b9d854f1fed30afe396842ff7436_256x256.png,5f65f0fee1c601ec32e7f89b54827b8401657277,Animals,-5.836,41.939,-39.258,5344 -5345,hedgehog,-6.027,77.94,18.14,0.341,0.78,0.72625,cf7fb1b59578c91dbafcc44ce867c518c4ddc3e8,256,V6,cf7fb1b59578c91dbafcc44ce867c518c4ddc3e8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-06.027_ry+167.940_rz+18.140_tx+00.341_ty+00.780_s+00.581_cf7fb1b59578c91dbafcc44ce867c518c4ddc3e8_256x256.png,56accc7785282e68fb2d006074a85957e72d4069,Animals,-6.027,167.94,18.14,5345 -5346,hedgehog,-6.388,-62.43,36.915,0.038,-0.342,0.62875,63bd2e4139afdb7cf3828b0149a2dfde1c39ad08,256,V6,63bd2e4139afdb7cf3828b0149a2dfde1c39ad08_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-06.388_ry+27.570_rz+36.915_tx+00.038_ty-00.342_s+00.503_63bd2e4139afdb7cf3828b0149a2dfde1c39ad08_256x256.png,aa72d6e40bfd74cbaaab109da9ece75c264464e8,Animals,-6.388,27.57,36.915,5346 -5347,hedgehog,-10.328,-38.312,-69.184,-0.069,1.112,0.66375,e2028f5799698294de17ff2e2600d883aa72e7f0,256,V6,e2028f5799698294de17ff2e2600d883aa72e7f0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-10.328_ry+51.688_rz-69.184_tx-00.069_ty+01.112_s+00.531_e2028f5799698294de17ff2e2600d883aa72e7f0_256x256.png,2968920db44a7d11c181279764272c99466eeee5,Animals,-10.328,51.688,-69.184,5347 -5348,hedgehog,-10.499,-20.621,38.189,-0.504,-0.796,1.03125,775b12e05519a7b25b2506a6318aab9944ee1cec,256,V6,775b12e05519a7b25b2506a6318aab9944ee1cec_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-10.499_ry+69.379_rz+38.189_tx-00.504_ty-00.796_s+00.825_775b12e05519a7b25b2506a6318aab9944ee1cec_256x256.png,be08fa3a3ff80e6fc50a60d48c51a3b5ecb29abd,Animals,-10.499,69.379,38.189,5348 -5349,hedgehog,-12.005,88.049,-67.364,0.356,0.039,0.7175,c7458f4811c6e0180b718a76dc037e52ac0cd6a7,256,V6,c7458f4811c6e0180b718a76dc037e52ac0cd6a7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-12.005_ry+178.049_rz-67.364_tx+00.356_ty+00.039_s+00.574_c7458f4811c6e0180b718a76dc037e52ac0cd6a7_256x256.png,8ca8e510c1d7a93d2c44e925fbc0b4c4e1ce4229,Animals,-12.005,178.049,-67.364,5349 -5350,hedgehog,-16.668,-57.483,37.355,0.197,0.008,1.435,aaa75625eedd144b703525075121f3b64e3a0aba,256,V6,aaa75625eedd144b703525075121f3b64e3a0aba_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-16.668_ry+32.517_rz+37.355_tx+00.197_ty+00.008_s+01.148_aaa75625eedd144b703525075121f3b64e3a0aba_256x256.png,b158b04b49b77344808c5647e69381766c40a01f,Animals,-16.668,32.517,37.355,5350 -5351,hedgehog,-21.025,-75.6,42.558,-0.554,1.154,0.67875,eadb83b105e098c01d75751264b23b4a4546596d,256,V6,eadb83b105e098c01d75751264b23b4a4546596d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-21.025_ry+14.400_rz+42.558_tx-00.554_ty+01.154_s+00.543_eadb83b105e098c01d75751264b23b4a4546596d_256x256.png,590c83f7a461b358ec1c1a63121ad7733fa3243b,Animals,-21.025,14.4,42.558,5351 -5352,hedgehog,-28.86,88.664,-46.998,-0.049,-0.385,1.545,748be7fe8771ab1fd229d5d99ab04c83263a1751,256,V6,748be7fe8771ab1fd229d5d99ab04c83263a1751_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-28.860_ry+178.664_rz-46.998_tx-00.049_ty-00.385_s+01.236_748be7fe8771ab1fd229d5d99ab04c83263a1751_256x256.png,1b9692fde06e390fabd92f042a2eccd75f1f17fd,Animals,-28.86,178.664,-46.998,5352 -5353,hedgehog,-47.266,2.936,87.879,-0.226,-0.858,1.33625,b16a365e902a934fa6fc882b082d10d8478fd8e3,256,V6,b16a365e902a934fa6fc882b082d10d8478fd8e3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-47.266_ry+92.936_rz+87.879_tx-00.226_ty-00.858_s+01.069_b16a365e902a934fa6fc882b082d10d8478fd8e3_256x256.png,c96c0da93121df03a6fb791c1939ecb1776433cc,Animals,-47.266,92.936,87.879,5353 -5354,hedgehog,-63.179,-49.473,66.859,0.533,1.133,0.8975,0e38d23153e6fbe1a592e4f2392a7c2c27d553db,256,V6,0e38d23153e6fbe1a592e4f2392a7c2c27d553db_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-63.179_ry+40.527_rz+66.859_tx+00.533_ty+01.133_s+00.718_0e38d23153e6fbe1a592e4f2392a7c2c27d553db_256x256.png,e5b4e6ac692a69e903dea325f8f36a07bfdb6058,Animals,-63.179,40.527,66.859,5354 -5355,hedgehog,-64.161,-22.388,-64.795,-0.451,-0.943,1.475,d02e1ac03b09bd77fcf1e57e8192f9393c33d0a7,256,V6,d02e1ac03b09bd77fcf1e57e8192f9393c33d0a7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-64.161_ry+67.612_rz-64.795_tx-00.451_ty-00.943_s+01.180_d02e1ac03b09bd77fcf1e57e8192f9393c33d0a7_256x256.png,426eee44d1b33081d6bbe82caa5236ae8cc50179,Animals,-64.161,67.612,-64.795,5355 -5356,hedgehog,-64.18,-25.543,71.968,0.417,-0.271,1.5525,78ba01c06f6adc1aee048eb7a35769e690f5eaa0,256,V6,78ba01c06f6adc1aee048eb7a35769e690f5eaa0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-64.180_ry+64.457_rz+71.968_tx+00.417_ty-00.271_s+01.242_78ba01c06f6adc1aee048eb7a35769e690f5eaa0_256x256.png,8f93515349f310e42df5a1931b45cd252aeb5d3f,Animals,-64.18,64.457,71.968,5356 -5357,hedgehog,-64.606,30.522,-50.609,-0.599,-0.209,0.81625,4838a10143f80ec74132289d0649fc452d4d4a0f,256,V6,4838a10143f80ec74132289d0649fc452d4d4a0f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-64.606_ry+120.522_rz-50.609_tx-00.599_ty-00.209_s+00.653_4838a10143f80ec74132289d0649fc452d4d4a0f_256x256.png,1b7b2f8ad699a70dd4cbb651a26e907eb45aab33,Animals,-64.606,120.522,-50.609,5357 -5358,hedgehog,-77.364,75.031,24.474,-0.051,-1.112,1.48125,13b480706c6d3facfea44b186d5ed77ca3a099a7,256,V6,13b480706c6d3facfea44b186d5ed77ca3a099a7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-77.364_ry+165.031_rz+24.474_tx-00.051_ty-01.112_s+01.185_13b480706c6d3facfea44b186d5ed77ca3a099a7_256x256.png,cbc3084487d0f54ba286d40005af80faf0e9f5dd,Animals,-77.364,165.031,24.474,5358 -5359,hedgehog,-82.222,54.251,-72.32,-0.566,0.327,1.5425,6714fcf1a70dc7a65023146765eb535ea0ba835d,256,V6,6714fcf1a70dc7a65023146765eb535ea0ba835d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/hedgehog_rx-82.222_ry+144.251_rz-72.320_tx-00.566_ty+00.327_s+01.234_6714fcf1a70dc7a65023146765eb535ea0ba835d_256x256.png,37be48c5fe9e1a331ea45b859dd82a1e532447b8,Animals,-82.222,144.251,-72.32,5359 -5360,junkers88,1.236,83.871,16.62,0.395,-0.671,1.40384615385,b1d4ce4494281bd034f8a64902f8875f4641acf9,256,V6,b1d4ce4494281bd034f8a64902f8875f4641acf9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+01.236_ry+83.871_rz+16.620_tx+00.395_ty-00.671_s+01.825_b1d4ce4494281bd034f8a64902f8875f4641acf9_256x256.png,8892931834c46d8e010017be384695b5f27f9194,Planes,1.236,173.871,16.62,5360 -5361,junkers88,10.378,43.943,-2.061,-0.163,0.375,1.37153846154,1250ba8efc92aa029feafda4f14bcff562aa0708,256,V6,1250ba8efc92aa029feafda4f14bcff562aa0708_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+10.378_ry+43.943_rz-02.061_tx-00.163_ty+00.375_s+01.783_1250ba8efc92aa029feafda4f14bcff562aa0708_256x256.png,be2035689915d4220cab524b4857a380ee99d04c,Planes,10.378,133.943,-2.061,5361 -5362,junkers88,13.477,6.794,-13.538,0.032,0.211,1.20230769231,ac5a8c8856b56090caa530d5876c34341ed75d4c,256,V6,ac5a8c8856b56090caa530d5876c34341ed75d4c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+13.477_ry+06.794_rz-13.538_tx+00.032_ty+00.211_s+01.563_ac5a8c8856b56090caa530d5876c34341ed75d4c_256x256.png,0380c201e52465cc99e632791c4dd2c4cddc7eb0,Planes,13.477,96.794,-13.538,5362 -5363,junkers88,15.639,38.255,-3.792,-0.54,0.345,0.828461538462,8f74fcca99443ceb3047317c0f34b21a8e383072,256,V6,8f74fcca99443ceb3047317c0f34b21a8e383072_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+15.639_ry+38.255_rz-03.792_tx-00.540_ty+00.345_s+01.077_8f74fcca99443ceb3047317c0f34b21a8e383072_256x256.png,2a58e3c7db29eea47533b810271e5450f1b7cfdd,Planes,15.639,128.255,-3.792,5363 -5364,junkers88,19.262,-46.82,45.497,0.157,-0.997,1.02153846154,1a298012e4ada69b207e874dcf7139091afc5924,256,V6,1a298012e4ada69b207e874dcf7139091afc5924_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+19.262_ry-46.820_rz+45.497_tx+00.157_ty-00.997_s+01.328_1a298012e4ada69b207e874dcf7139091afc5924_256x256.png,b819beb9c32e4c9779bda792548530666bb3d4e4,Planes,19.262,43.18,45.497,5364 -5365,junkers88,26.214,-82.802,22.688,-0.149,0.827,1.27230769231,6e67264e9ceef2eb8b4a3a8435bd2dffa634e0e1,256,V6,6e67264e9ceef2eb8b4a3a8435bd2dffa634e0e1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+26.214_ry-82.802_rz+22.688_tx-00.149_ty+00.827_s+01.654_6e67264e9ceef2eb8b4a3a8435bd2dffa634e0e1_256x256.png,afdfd7b5f64729d46183750dbbeec8d59b3ef24d,Planes,26.214,7.198,22.688,5365 -5366,junkers88,32.669,-68.855,52.285,0.218,0.801,1.56384615385,4d65a917165b3823510d4f1223de25cfd7311959,256,V6,4d65a917165b3823510d4f1223de25cfd7311959_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+32.669_ry-68.855_rz+52.285_tx+00.218_ty+00.801_s+02.033_4d65a917165b3823510d4f1223de25cfd7311959_256x256.png,3684eaa8a51c2da894050362debb7a26140a1fc4,Planes,32.669,21.145,52.285,5366 -5367,junkers88,33.301,-40.392,31.792,0.218,1.114,0.66,fa4007daf77213cb09552becb05ecce37184c31a,256,V6,fa4007daf77213cb09552becb05ecce37184c31a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+33.301_ry-40.392_rz+31.792_tx+00.218_ty+01.114_s+00.858_fa4007daf77213cb09552becb05ecce37184c31a_256x256.png,105256c2f24ceb7b2f1a6a45033f83db51e77675,Planes,33.301,49.608,31.792,5367 -5368,junkers88,36.307,21.953,-48.724,-0.032,0.19,1.45461538462,cddcacd9bef1adebac6786638fedfe0ad5242e61,256,V6,cddcacd9bef1adebac6786638fedfe0ad5242e61_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+36.307_ry+21.953_rz-48.724_tx-00.032_ty+00.190_s+01.891_cddcacd9bef1adebac6786638fedfe0ad5242e61_256x256.png,6686e061066b54983314dcb015d875ce2e85c55a,Planes,36.307,111.953,-48.724,5368 -5369,junkers88,37.952,-82.873,0.428,0.287,0.12,1.00769230769,b8a916729338d242d515cf26222e073a75edc8a5,256,V6,b8a916729338d242d515cf26222e073a75edc8a5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+37.952_ry-82.873_rz+00.428_tx+00.287_ty+00.120_s+01.310_b8a916729338d242d515cf26222e073a75edc8a5_256x256.png,c37bde0ccad2077b1484729bd298d049f873e9ca,Planes,37.952,7.127,0.428,5369 -5370,junkers88,46.672,-25.408,17.344,-0.084,-0.003,0.921538461538,edc5eab64cbc19131e1f28fc296a6ee3d0e88100,256,V6,edc5eab64cbc19131e1f28fc296a6ee3d0e88100_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+46.672_ry-25.408_rz+17.344_tx-00.084_ty-00.003_s+01.198_edc5eab64cbc19131e1f28fc296a6ee3d0e88100_256x256.png,bdb93858d67124de93a3631d80a991f23e9153b6,Planes,46.672,64.592,17.344,5370 -5371,junkers88,49.058,70.556,-37.942,-0.305,1.152,1.02692307692,231ada0da55ca977ef2110b4f463d3b0c79089a6,256,V6,231ada0da55ca977ef2110b4f463d3b0c79089a6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+49.058_ry+70.556_rz-37.942_tx-00.305_ty+01.152_s+01.335_231ada0da55ca977ef2110b4f463d3b0c79089a6_256x256.png,2a30e2121cbd58388ff24aa6ee38f9d62041962f,Planes,49.058,160.556,-37.942,5371 -5372,junkers88,49.896,-67.941,9.773,0.552,0.742,0.856923076923,8970a466317e8dc2143fa5d46f4782fff4293263,256,V6,8970a466317e8dc2143fa5d46f4782fff4293263_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+49.896_ry-67.941_rz+09.773_tx+00.552_ty+00.742_s+01.114_8970a466317e8dc2143fa5d46f4782fff4293263_256x256.png,e9dfa14af31957de519ce0144bf579f805cd49dd,Planes,49.896,22.059,9.773,5372 -5373,junkers88,52.221,-57.273,47.893,-0.463,-1.094,0.899230769231,4ffc90d6e48de24297195daa7ceaf5a4d0578649,256,V6,4ffc90d6e48de24297195daa7ceaf5a4d0578649_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+52.221_ry-57.273_rz+47.893_tx-00.463_ty-01.094_s+01.169_4ffc90d6e48de24297195daa7ceaf5a4d0578649_256x256.png,81e2c38b3bf591a4c04a39fa270a063f390cb34f,Planes,52.221,32.727,47.893,5373 -5374,junkers88,56.02,-39.3,71.734,0.413,1.05,0.910769230769,392ba23662b421079444617ae3b2e91ad12ef6aa,256,V6,392ba23662b421079444617ae3b2e91ad12ef6aa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+56.020_ry-39.300_rz+71.734_tx+00.413_ty+01.050_s+01.184_392ba23662b421079444617ae3b2e91ad12ef6aa_256x256.png,3a4dd9314f0c27616545ffea9f624ef1432adeee,Planes,56.02,50.7,71.734,5374 -5375,junkers88,63.046,56.695,27.187,0.222,-0.353,1.25,b5667fcf25b9b97186230fdb814a901a5b1d4ce2,256,V6,b5667fcf25b9b97186230fdb814a901a5b1d4ce2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+63.046_ry+56.695_rz+27.187_tx+00.222_ty-00.353_s+01.625_b5667fcf25b9b97186230fdb814a901a5b1d4ce2_256x256.png,07540063b99050b90116cc2d450bd0ce50b99764,Planes,63.046,146.695,27.187,5375 -5376,junkers88,64.669,-17.909,-45.248,-0.577,1.009,0.93,f55379501d8015ff862c71ac4a40b2ef4dc6fee7,256,V6,f55379501d8015ff862c71ac4a40b2ef4dc6fee7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+64.669_ry-17.909_rz-45.248_tx-00.577_ty+01.009_s+01.209_f55379501d8015ff862c71ac4a40b2ef4dc6fee7_256x256.png,82144e2092e3e02b4fca39d9270675a1686138b6,Planes,64.669,72.091,-45.248,5376 -5377,junkers88,70.716,-6.172,17.653,0.528,0.555,1.53153846154,00165483ee546315b190e813fc6fd4e718c48b9b,256,V6,00165483ee546315b190e813fc6fd4e718c48b9b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+70.716_ry-06.172_rz+17.653_tx+00.528_ty+00.555_s+01.991_00165483ee546315b190e813fc6fd4e718c48b9b_256x256.png,fbb0d138fde761cfceb1b0cbd2c15d779a5a6cb6,Planes,70.716,83.828,17.653,5377 -5378,junkers88,84.485,-76.655,-9.379,-0.41,-0.786,1.16615384615,7f69e4e98c004b2d57a9fa6b1c4fa18546b21571,256,V6,7f69e4e98c004b2d57a9fa6b1c4fa18546b21571_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+84.485_ry-76.655_rz-09.379_tx-00.410_ty-00.786_s+01.516_7f69e4e98c004b2d57a9fa6b1c4fa18546b21571_256x256.png,7a1e30a76b6937a3d89f9eaf877c93cefcca02c0,Planes,84.485,13.345,-9.379,5378 -5379,junkers88,84.764,66.23,-77.311,0.278,-1.006,1.14076923077,eb0f55427837364aed6c417d2a13f184341b8545,256,V6,eb0f55427837364aed6c417d2a13f184341b8545_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx+84.764_ry+66.230_rz-77.311_tx+00.278_ty-01.006_s+01.483_eb0f55427837364aed6c417d2a13f184341b8545_256x256.png,c944f3daab7affe14b30e8c95bea8ef5a25597ae,Planes,84.764,156.23,-77.311,5379 -5380,junkers88,-8.035,73.255,76.373,0.591,0.7,1.08692307692,83bab58e117b2c15a63dbc98cb57be1bddef1fb5,256,V6,83bab58e117b2c15a63dbc98cb57be1bddef1fb5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-08.035_ry+73.255_rz+76.373_tx+00.591_ty+00.700_s+01.413_83bab58e117b2c15a63dbc98cb57be1bddef1fb5_256x256.png,45973d430264f2a30bb9c5b419befc1bae820cec,Planes,-8.035,163.255,76.373,5380 -5381,junkers88,-8.291,43.373,-70.561,0.075,0.261,0.782307692308,e1ec1d9bef07caf6e24b81e1275eea5ae9229157,256,V6,e1ec1d9bef07caf6e24b81e1275eea5ae9229157_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-08.291_ry+43.373_rz-70.561_tx+00.075_ty+00.261_s+01.017_e1ec1d9bef07caf6e24b81e1275eea5ae9229157_256x256.png,71f56315f1802e809649019ad438c868d0dabf58,Planes,-8.291,133.373,-70.561,5381 -5382,junkers88,-12.213,79.097,6.047,0.261,1.182,0.688461538462,5a9811184df9698a34352b7837be8190e6ce0ff3,256,V6,5a9811184df9698a34352b7837be8190e6ce0ff3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-12.213_ry+79.097_rz+06.047_tx+00.261_ty+01.182_s+00.895_5a9811184df9698a34352b7837be8190e6ce0ff3_256x256.png,29c19e7ae6e2fd2a27a2a253472e3fa67b06b0bf,Planes,-12.213,169.097,6.047,5382 -5383,junkers88,-23.361,-2.717,-31.934,-0.545,1.179,1.23692307692,df93b58b225578fb3b8ee892d765eb8abd022e8e,256,V6,df93b58b225578fb3b8ee892d765eb8abd022e8e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-23.361_ry-02.717_rz-31.934_tx-00.545_ty+01.179_s+01.608_df93b58b225578fb3b8ee892d765eb8abd022e8e_256x256.png,26eff8f23b1611cec9cb15f9d79bb03400ab5fcc,Planes,-23.361,87.283,-31.934,5383 -5384,junkers88,-24.776,-82.894,61.693,-0.019,-0.185,1.17692307692,1373b7f6e2e78f94376e2ca4fafa9a802292ac50,256,V6,1373b7f6e2e78f94376e2ca4fafa9a802292ac50_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-24.776_ry-82.894_rz+61.693_tx-00.019_ty-00.185_s+01.530_1373b7f6e2e78f94376e2ca4fafa9a802292ac50_256x256.png,6a6f0df046e94c9c53e93cec5f69810b8a2cb7fc,Planes,-24.776,7.106,61.693,5384 -5385,junkers88,-26.984,42.193,75.682,0.177,-0.278,1.04769230769,7be266230522eda62ff469abb397aad5b6e9f2e0,256,V6,7be266230522eda62ff469abb397aad5b6e9f2e0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-26.984_ry+42.193_rz+75.682_tx+00.177_ty-00.278_s+01.362_7be266230522eda62ff469abb397aad5b6e9f2e0_256x256.png,471b48e8a48ee4a06fbb5f9c50913cae101e355a,Planes,-26.984,132.193,75.682,5385 -5386,junkers88,-33.792,-45.312,68.763,-0.488,-0.698,1.54076923077,b6c14f77f99aa6533b4022c6406683c9b28390e9,256,V6,b6c14f77f99aa6533b4022c6406683c9b28390e9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-33.792_ry-45.312_rz+68.763_tx-00.488_ty-00.698_s+02.003_b6c14f77f99aa6533b4022c6406683c9b28390e9_256x256.png,199cb4a010c654ef4a1da0baa306c075a9ddd95f,Planes,-33.792,44.688,68.763,5386 -5387,junkers88,-34.33,-65.397,-10.046,0.299,0.784,0.820769230769,af68e845d5ef79f2563b8da8a50329ef2021a8bb,256,V6,af68e845d5ef79f2563b8da8a50329ef2021a8bb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-34.330_ry-65.397_rz-10.046_tx+00.299_ty+00.784_s+01.067_af68e845d5ef79f2563b8da8a50329ef2021a8bb_256x256.png,9ae87bd93bb793b122660aa25d555112cdd06311,Planes,-34.33,24.603,-10.046,5387 -5388,junkers88,-42.478,52.441,-61.755,0.164,-0.068,0.890769230769,d81d0d332bc33ca6fbf9f2c4b9d82c1032a42fed,256,V6,d81d0d332bc33ca6fbf9f2c4b9d82c1032a42fed_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-42.478_ry+52.441_rz-61.755_tx+00.164_ty-00.068_s+01.158_d81d0d332bc33ca6fbf9f2c4b9d82c1032a42fed_256x256.png,29549d27066f8ebf15bbaaadab38db2d3f22e0d0,Planes,-42.478,142.441,-61.755,5388 -5389,junkers88,-43.368,37.15,65.933,-0.474,1.029,1.30461538462,9711a7f06787ae6fed62f5d0f6cbb1072a277611,256,V6,9711a7f06787ae6fed62f5d0f6cbb1072a277611_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-43.368_ry+37.150_rz+65.933_tx-00.474_ty+01.029_s+01.696_9711a7f06787ae6fed62f5d0f6cbb1072a277611_256x256.png,1efa69915ddd4c41d395441f99186586a432298b,Planes,-43.368,127.15,65.933,5389 -5390,junkers88,-48.341,-16.676,80.042,-0.496,-0.691,1.39076923077,569b47846513e0b640c70870c93b26a65f06f6d7,256,V6,569b47846513e0b640c70870c93b26a65f06f6d7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-48.341_ry-16.676_rz+80.042_tx-00.496_ty-00.691_s+01.808_569b47846513e0b640c70870c93b26a65f06f6d7_256x256.png,a0d9bcbfba90b850fdea78906fd46e7b3f753576,Planes,-48.341,73.324,80.042,5390 -5391,junkers88,-52.541,-37.456,-52.522,-0.441,-1.098,0.662307692308,a4331947d567cd0048e2b68ae2f770b84cb73d56,256,V6,a4331947d567cd0048e2b68ae2f770b84cb73d56_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-52.541_ry-37.456_rz-52.522_tx-00.441_ty-01.098_s+00.861_a4331947d567cd0048e2b68ae2f770b84cb73d56_256x256.png,0dc55eecf24aac0311f6f3404790cf0595df4635,Planes,-52.541,52.544,-52.522,5391 -5392,junkers88,-55.278,50.36,-43.762,0.164,-0.972,1.56,bddcab5746f12e580376b7c1ad52b3d4d02bed4c,256,V6,bddcab5746f12e580376b7c1ad52b3d4d02bed4c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-55.278_ry+50.360_rz-43.762_tx+00.164_ty-00.972_s+02.028_bddcab5746f12e580376b7c1ad52b3d4d02bed4c_256x256.png,f004da91bd7cc6e9fb24d8b1776440cce8ce5421,Planes,-55.278,140.36,-43.762,5392 -5393,junkers88,-60.43,-85.979,76.586,0.213,-1.185,1.04923076923,8c2735a7d3e1cce9d170b5280722795e785949d4,256,V6,8c2735a7d3e1cce9d170b5280722795e785949d4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-60.430_ry-85.979_rz+76.586_tx+00.213_ty-01.185_s+01.364_8c2735a7d3e1cce9d170b5280722795e785949d4_256x256.png,bdfbb741de0faa02976af8a2a723dfd54d073f20,Planes,-60.43,4.021,76.586,5393 -5394,junkers88,-64.288,35.916,-24.942,0.595,0.293,0.833846153846,8029c0ba7606748aefd9d7fc432185465ac5ea83,256,V6,8029c0ba7606748aefd9d7fc432185465ac5ea83_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-64.288_ry+35.916_rz-24.942_tx+00.595_ty+00.293_s+01.084_8029c0ba7606748aefd9d7fc432185465ac5ea83_256x256.png,ea2d7b24e2ee44785a6ccd58dd8e40ae1186fd4a,Planes,-64.288,125.916,-24.942,5394 -5395,junkers88,-67.133,-10.134,40.313,0.583,0.355,1.07307692308,dedeb63aba0d5e4dd43baf615c8fbf23b775c933,256,V6,dedeb63aba0d5e4dd43baf615c8fbf23b775c933_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-67.133_ry-10.134_rz+40.313_tx+00.583_ty+00.355_s+01.395_dedeb63aba0d5e4dd43baf615c8fbf23b775c933_256x256.png,6fb09aaff3f434501c099c1a4cdcf2f821ff65a3,Planes,-67.133,79.866,40.313,5395 -5396,junkers88,-69.659,77.839,-81.205,0.347,0.669,0.708461538462,980deed5f85673eb0766be064d23cd14eb6abe07,256,V6,980deed5f85673eb0766be064d23cd14eb6abe07_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-69.659_ry+77.839_rz-81.205_tx+00.347_ty+00.669_s+00.921_980deed5f85673eb0766be064d23cd14eb6abe07_256x256.png,0ac76c4a832327636d2ad70d1eb15b381d7885e4,Planes,-69.659,167.839,-81.205,5396 -5397,junkers88,-71.501,85.718,-31.279,0.346,-0.021,0.832307692308,d3011fbc86028328f0a5c138008de70591031203,256,V6,d3011fbc86028328f0a5c138008de70591031203_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-71.501_ry+85.718_rz-31.279_tx+00.346_ty-00.021_s+01.082_d3011fbc86028328f0a5c138008de70591031203_256x256.png,847ac6d5e68e47b4232d379a9e58c0b2249ad0af,Planes,-71.501,175.718,-31.279,5397 -5398,junkers88,-72.452,89.81,-30.033,0.576,0.3,1.29846153846,d9a49c31fcabfd120e65dda216e2ca974780e3b9,256,V6,d9a49c31fcabfd120e65dda216e2ca974780e3b9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-72.452_ry+89.810_rz-30.033_tx+00.576_ty+00.300_s+01.688_d9a49c31fcabfd120e65dda216e2ca974780e3b9_256x256.png,8d5df7e60f5d4a53fe8987e76ed700c798729500,Planes,-72.452,179.81,-30.033,5398 -5399,junkers88,-88.431,17.123,-28.236,-0.266,-0.251,0.777692307692,8182ae8080d2453b9513483e1e39aa90cbd627c3,256,V6,8182ae8080d2453b9513483e1e39aa90cbd627c3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/junkers88_rx-88.431_ry+17.123_rz-28.236_tx-00.266_ty-00.251_s+01.011_8182ae8080d2453b9513483e1e39aa90cbd627c3_256x256.png,2890a3a879cbc66783b023b4d8c7a420d7c263b8,Planes,-88.431,107.123,-28.236,5399 -5400,mig29,6.912,68.04,29.028,0.491,-0.938,0.684615384615,f7d39ccece0be75b36fd9aab9cb3eb7a4c89b7d3,256,V6,f7d39ccece0be75b36fd9aab9cb3eb7a4c89b7d3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+06.912_ry+68.040_rz+29.028_tx+00.491_ty-00.938_s+00.890_f7d39ccece0be75b36fd9aab9cb3eb7a4c89b7d3_256x256.png,5f7dfb67450eaee6beb7d08fdfeb086bbe75c9e7,Planes,6.912,158.04,29.028,5400 -5401,mig29,8.024,58.493,5.269,0.161,0.645,1.16538461538,ecf7d584291379984973b37d3edfcdde7bf6e772,256,V6,ecf7d584291379984973b37d3edfcdde7bf6e772_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+08.024_ry+58.493_rz+05.269_tx+00.161_ty+00.645_s+01.515_ecf7d584291379984973b37d3edfcdde7bf6e772_256x256.png,dd8b8b3fa09ccf5a907337163491e4a1e25c141d,Planes,8.024,148.493,5.269,5401 -5402,mig29,11.334,-43.332,-87.226,-0.592,-1.127,1.33230769231,5cee8579b212dd22f3c48e47e8568d3e1f793328,256,V6,5cee8579b212dd22f3c48e47e8568d3e1f793328_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+11.334_ry-43.332_rz-87.226_tx-00.592_ty-01.127_s+01.732_5cee8579b212dd22f3c48e47e8568d3e1f793328_256x256.png,f16528382689e54691578cf16df78e8f6cc6157f,Planes,11.334,46.668,-87.226,5402 -5403,mig29,14.158,21.693,-63.943,-0.39,-0.101,0.643076923077,a8242b56aef5d1f8d30f3cf44ea37f499f631ab1,256,V6,a8242b56aef5d1f8d30f3cf44ea37f499f631ab1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+14.158_ry+21.693_rz-63.943_tx-00.390_ty-00.101_s+00.836_a8242b56aef5d1f8d30f3cf44ea37f499f631ab1_256x256.png,7971fdba501c2d2a869dfb216c6037f265ec0273,Planes,14.158,111.693,-63.943,5403 -5404,mig29,18.347,30.947,78.99,-0.461,-0.85,1.11230769231,e0f9a7d0381148955f6c0fb0a4ebef470e8ea6c8,256,V6,e0f9a7d0381148955f6c0fb0a4ebef470e8ea6c8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+18.347_ry+30.947_rz+78.990_tx-00.461_ty-00.850_s+01.446_e0f9a7d0381148955f6c0fb0a4ebef470e8ea6c8_256x256.png,ad2eb338bb91ffbaf5f20296ef6a0d9e8cabf11c,Planes,18.347,120.947,78.99,5404 -5405,mig29,19.633,10.689,-76.191,0.114,1.059,1.39,ce47a6e93e5ab116bb0f4c168ce83ad0389272a4,256,V6,ce47a6e93e5ab116bb0f4c168ce83ad0389272a4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+19.633_ry+10.689_rz-76.191_tx+00.114_ty+01.059_s+01.807_ce47a6e93e5ab116bb0f4c168ce83ad0389272a4_256x256.png,c7f25178fe57e60928dcb5d5c257e62a2be19c7f,Planes,19.633,100.689,-76.191,5405 -5406,mig29,20.964,33.732,69.887,-0.528,-0.398,1.56,0febcff6532b75aaa14e12364b412bc08624fbb6,256,V6,0febcff6532b75aaa14e12364b412bc08624fbb6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+20.964_ry+33.732_rz+69.887_tx-00.528_ty-00.398_s+02.028_0febcff6532b75aaa14e12364b412bc08624fbb6_256x256.png,52ca565c513e095a84ac0dc522c8d007c61db383,Planes,20.964,123.732,69.887,5406 -5407,mig29,24.103,26.917,-0.355,0.308,-0.076,1.02538461538,f3be7680cc08ad747dc583f2434cf8c1a82e2b8c,256,V6,f3be7680cc08ad747dc583f2434cf8c1a82e2b8c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+24.103_ry+26.917_rz-00.355_tx+00.308_ty-00.076_s+01.333_f3be7680cc08ad747dc583f2434cf8c1a82e2b8c_256x256.png,50b57c2d28a36ce8389bbce1304320ab73d6d8a7,Planes,24.103,116.917,-0.355,5407 -5408,mig29,32.134,14.477,-70.221,0.319,0.047,1.35461538462,5f5aa765582a32beb0e9e6103d7e872ada69d7d8,256,V6,5f5aa765582a32beb0e9e6103d7e872ada69d7d8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+32.134_ry+14.477_rz-70.221_tx+00.319_ty+00.047_s+01.761_5f5aa765582a32beb0e9e6103d7e872ada69d7d8_256x256.png,bb2b3fee3398ac6ec854fc5e76f47a88aaea1ba4,Planes,32.134,104.477,-70.221,5408 -5409,mig29,33.663,-4.909,-76.178,-0.479,-0.695,0.793076923077,297bf446c63103efe4b63aa65d0f0fb770808661,256,V6,297bf446c63103efe4b63aa65d0f0fb770808661_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+33.663_ry-04.909_rz-76.178_tx-00.479_ty-00.695_s+01.031_297bf446c63103efe4b63aa65d0f0fb770808661_256x256.png,10cac93b6f5683d3adc5f989f522a1b4e6dc77b8,Planes,33.663,85.091,-76.178,5409 -5410,mig29,43.169,24.234,-80.583,0.521,1.114,1.50692307692,1ac0d1eb0e67d6b5b1cef6b6e041f0efd7ad00b2,256,V6,1ac0d1eb0e67d6b5b1cef6b6e041f0efd7ad00b2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+43.169_ry+24.234_rz-80.583_tx+00.521_ty+01.114_s+01.959_1ac0d1eb0e67d6b5b1cef6b6e041f0efd7ad00b2_256x256.png,b95193d111a4bb24725363f11f21f81118e7c954,Planes,43.169,114.234,-80.583,5410 -5411,mig29,45.2,74.902,-64.942,0.269,-0.535,0.854615384615,9d0fca56cb2037678bf3785b66872d755c5c5d49,256,V6,9d0fca56cb2037678bf3785b66872d755c5c5d49_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+45.200_ry+74.902_rz-64.942_tx+00.269_ty-00.535_s+01.111_9d0fca56cb2037678bf3785b66872d755c5c5d49_256x256.png,452e98cd43bb45e4ba5b5b2d74e24e1b65dc3a0f,Planes,45.2,164.902,-64.942,5411 -5412,mig29,46.314,81.501,-67.465,-0.138,0.939,1.09153846154,0d29889bfb7f64d4f58f1c219c4ac77aae9bacb1,256,V6,0d29889bfb7f64d4f58f1c219c4ac77aae9bacb1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+46.314_ry+81.501_rz-67.465_tx-00.138_ty+00.939_s+01.419_0d29889bfb7f64d4f58f1c219c4ac77aae9bacb1_256x256.png,869c3410bdcd88105c745d395d68067a65a28c52,Planes,46.314,171.501,-67.465,5412 -5413,mig29,49.855,36.88,-1.321,0.188,1.171,1.56846153846,07b007dac70b420825dfb43acf1b41965a05fc8d,256,V6,07b007dac70b420825dfb43acf1b41965a05fc8d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+49.855_ry+36.880_rz-01.321_tx+00.188_ty+01.171_s+02.039_07b007dac70b420825dfb43acf1b41965a05fc8d_256x256.png,f20f68a59d6fe86ca3ad993d139b711f716ea5a7,Planes,49.855,126.88,-1.321,5413 -5414,mig29,54.899,-86.609,44.531,-0.398,-0.748,1.08461538462,4bbb91b810d068dc6edb4bb5a365cacaf5f27fcd,256,V6,4bbb91b810d068dc6edb4bb5a365cacaf5f27fcd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+54.899_ry-86.609_rz+44.531_tx-00.398_ty-00.748_s+01.410_4bbb91b810d068dc6edb4bb5a365cacaf5f27fcd_256x256.png,f68077fadc16fd40e28d855d1980d53146874347,Planes,54.899,3.391,44.531,5414 -5415,mig29,56.99,25.806,14.842,-0.396,1.036,1.00769230769,4911ab5340d5d29186212e8265c26fbc810b629d,256,V6,4911ab5340d5d29186212e8265c26fbc810b629d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+56.990_ry+25.806_rz+14.842_tx-00.396_ty+01.036_s+01.310_4911ab5340d5d29186212e8265c26fbc810b629d_256x256.png,725fddf87f3c5b85433281b856af047f067ee91e,Planes,56.99,115.806,14.842,5415 -5416,mig29,58.141,-86.576,22.793,-0.419,0.552,1.30769230769,24c7f849bf1454f9bd0640300ecde335978803bd,256,V6,24c7f849bf1454f9bd0640300ecde335978803bd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+58.141_ry-86.576_rz+22.793_tx-00.419_ty+00.552_s+01.700_24c7f849bf1454f9bd0640300ecde335978803bd_256x256.png,25a477f05f200da1552e9374e420c0bf6dffa0e8,Planes,58.141,3.424,22.793,5416 -5417,mig29,64.176,-80.763,-86.413,-0.25,0.845,1.02230769231,74011a9f09213fd4cccb9acff5a4866918120dda,256,V6,74011a9f09213fd4cccb9acff5a4866918120dda_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+64.176_ry-80.763_rz-86.413_tx-00.250_ty+00.845_s+01.329_74011a9f09213fd4cccb9acff5a4866918120dda_256x256.png,0d47731a424a39f5f96bbdc6cc5ebdd0c68e9ed3,Planes,64.176,9.237,-86.413,5417 -5418,mig29,73.829,27.575,47.633,-0.25,1.053,0.729230769231,90f7b14c2606d8d9532cdc20ac2247a27019d734,256,V6,90f7b14c2606d8d9532cdc20ac2247a27019d734_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+73.829_ry+27.575_rz+47.633_tx-00.250_ty+01.053_s+00.948_90f7b14c2606d8d9532cdc20ac2247a27019d734_256x256.png,ca2ca320a35992c3810e0b2a79ffe0335efe9376,Planes,73.829,117.575,47.633,5418 -5419,mig29,78.618,-50.484,1.332,-0.229,-0.413,1.40615384615,bd191c499831d0752b337c3001bd41eb66d014b2,256,V6,bd191c499831d0752b337c3001bd41eb66d014b2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx+78.618_ry-50.484_rz+01.332_tx-00.229_ty-00.413_s+01.828_bd191c499831d0752b337c3001bd41eb66d014b2_256x256.png,690ba64560649110a8873e274423ff818693775b,Planes,78.618,39.516,1.332,5419 -5420,mig29,-7.008,68.235,24.384,-0.175,-0.565,1.57307692308,db7c4caa151c3fdc1019919151e9bf67e12f4e7d,256,V6,db7c4caa151c3fdc1019919151e9bf67e12f4e7d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-07.008_ry+68.235_rz+24.384_tx-00.175_ty-00.565_s+02.045_db7c4caa151c3fdc1019919151e9bf67e12f4e7d_256x256.png,97eb6a89f974e82128aa7754ca8b300634c33157,Planes,-7.008,158.235,24.384,5420 -5421,mig29,-11.518,-78.962,-52.42,-0.426,0.358,1.25384615385,dc0f75fd6a0bb39d7410038e58b490d9d0f56c40,256,V6,dc0f75fd6a0bb39d7410038e58b490d9d0f56c40_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-11.518_ry-78.962_rz-52.420_tx-00.426_ty+00.358_s+01.630_dc0f75fd6a0bb39d7410038e58b490d9d0f56c40_256x256.png,602af624b77e944ebc5e950b5f971f8bca0f6e5e,Planes,-11.518,11.038,-52.42,5421 -5422,mig29,-15.071,-1.69,-42.75,-0.413,1.089,0.72,18aadfefc26781c7ea330afa1cbb0ed2e4c7cb85,256,V6,18aadfefc26781c7ea330afa1cbb0ed2e4c7cb85_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-15.071_ry-01.690_rz-42.750_tx-00.413_ty+01.089_s+00.936_18aadfefc26781c7ea330afa1cbb0ed2e4c7cb85_256x256.png,b5fbbc7f6d3a7fa5e25dbe6b06fb79f37ae48ec2,Planes,-15.071,88.31,-42.75,5422 -5423,mig29,-18.067,-31.07,77.29,-0.539,-0.378,1.49538461538,cbfabaadb3c4850574f703ed4f4bc9edb6352849,256,V6,cbfabaadb3c4850574f703ed4f4bc9edb6352849_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-18.067_ry-31.070_rz+77.290_tx-00.539_ty-00.378_s+01.944_cbfabaadb3c4850574f703ed4f4bc9edb6352849_256x256.png,426dfbe25df4e5b3f44107321a351e8ab89d70d2,Planes,-18.067,58.93,77.29,5423 -5424,mig29,-28.884,-7.1,85.986,-0.062,-0.406,1.49615384615,f8c74e74233495977a053b09fa7b7bbde563e644,256,V6,f8c74e74233495977a053b09fa7b7bbde563e644_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-28.884_ry-07.100_rz+85.986_tx-00.062_ty-00.406_s+01.945_f8c74e74233495977a053b09fa7b7bbde563e644_256x256.png,5dcc967ccc4935a8457e8b491f20f97da6fbecd1,Planes,-28.884,82.9,85.986,5424 -5425,mig29,-28.992,81.211,3.018,-0.213,-0.188,1.19846153846,91860792afc325889700bb9d06e871b034f10611,256,V6,91860792afc325889700bb9d06e871b034f10611_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-28.992_ry+81.211_rz+03.018_tx-00.213_ty-00.188_s+01.558_91860792afc325889700bb9d06e871b034f10611_256x256.png,c79f658f23348dbad21268a647ea2f409e6500ea,Planes,-28.992,171.211,3.018,5425 -5426,mig29,-31.632,-49.035,12.831,0.213,-0.659,1.09692307692,6a5afeb2c3dedbf5b332410e7b009df81e5dd254,256,V6,6a5afeb2c3dedbf5b332410e7b009df81e5dd254_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-31.632_ry-49.035_rz+12.831_tx+00.213_ty-00.659_s+01.426_6a5afeb2c3dedbf5b332410e7b009df81e5dd254_256x256.png,b427f1b9592ac408fe0180fb41913a8942ed0f35,Planes,-31.632,40.965,12.831,5426 -5427,mig29,-42.235,-83.502,-27.317,-0.556,0.936,1.35538461538,9944b2cc57b0d3f71bd88cdd673675e8be839583,256,V6,9944b2cc57b0d3f71bd88cdd673675e8be839583_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-42.235_ry-83.502_rz-27.317_tx-00.556_ty+00.936_s+01.762_9944b2cc57b0d3f71bd88cdd673675e8be839583_256x256.png,88604fdf19bee952279d6b14e7fbf1fc1f9b7e7b,Planes,-42.235,6.498,-27.317,5427 -5428,mig29,-44.945,-48.318,83.652,-0.251,0.456,0.768461538462,36a006940d6c48beb636ed916fe01e701bfe8443,256,V6,36a006940d6c48beb636ed916fe01e701bfe8443_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-44.945_ry-48.318_rz+83.652_tx-00.251_ty+00.456_s+00.999_36a006940d6c48beb636ed916fe01e701bfe8443_256x256.png,11948a2cb793279754a593fc84eccaec2e78b017,Planes,-44.945,41.682,83.652,5428 -5429,mig29,-45.789,44.384,-15.849,0.458,-0.457,1.45307692308,43f6f8ef2bd61a095cc6eb5ba6eeea7501093aa4,256,V6,43f6f8ef2bd61a095cc6eb5ba6eeea7501093aa4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-45.789_ry+44.384_rz-15.849_tx+00.458_ty-00.457_s+01.889_43f6f8ef2bd61a095cc6eb5ba6eeea7501093aa4_256x256.png,2b425f3aa80fd819a49b209d6d832ff4f05434a0,Planes,-45.789,134.384,-15.849,5429 -5430,mig29,-48.027,-22.227,39.261,-0.292,0.248,1.05384615385,9a73cd0f2c8c4a0254026731109d15444b9a5ae0,256,V6,9a73cd0f2c8c4a0254026731109d15444b9a5ae0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-48.027_ry-22.227_rz+39.261_tx-00.292_ty+00.248_s+01.370_9a73cd0f2c8c4a0254026731109d15444b9a5ae0_256x256.png,5c85d15402b42ab058ff9c6a811b5b205602a38c,Planes,-48.027,67.773,39.261,5430 -5431,mig29,-54.872,77.61,-61.506,0.243,0.927,0.828461538462,2047480945c709a441ed1a351ba68807989f5c66,256,V6,2047480945c709a441ed1a351ba68807989f5c66_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-54.872_ry+77.610_rz-61.506_tx+00.243_ty+00.927_s+01.077_2047480945c709a441ed1a351ba68807989f5c66_256x256.png,75487a7072695b8d4ccb8b44ed579b7578ae0516,Planes,-54.872,167.61,-61.506,5431 -5432,mig29,-55.896,-22.751,29.079,-0.286,0.056,1.10230769231,e7d670b90763c0fe2cffe82e512ce835be0b1af6,256,V6,e7d670b90763c0fe2cffe82e512ce835be0b1af6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-55.896_ry-22.751_rz+29.079_tx-00.286_ty+00.056_s+01.433_e7d670b90763c0fe2cffe82e512ce835be0b1af6_256x256.png,ac41a16dff74132a1332812cf034acb59871221e,Planes,-55.896,67.249,29.079,5432 -5433,mig29,-59.039,10.06,80.246,0.46,-0.236,1.06384615385,9bac40fcb5242cabfbf61205e443b0bd63ef68e8,256,V6,9bac40fcb5242cabfbf61205e443b0bd63ef68e8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-59.039_ry+10.060_rz+80.246_tx+00.460_ty-00.236_s+01.383_9bac40fcb5242cabfbf61205e443b0bd63ef68e8_256x256.png,612d68e6fedeafee37998b40460f5e397527aa55,Planes,-59.039,100.06,80.246,5433 -5434,mig29,-62.485,12.815,40.738,0.562,-0.394,0.786153846154,0ee09cfb58391bd41aaec7134917008feca87cf9,256,V6,0ee09cfb58391bd41aaec7134917008feca87cf9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-62.485_ry+12.815_rz+40.738_tx+00.562_ty-00.394_s+01.022_0ee09cfb58391bd41aaec7134917008feca87cf9_256x256.png,a2685a6284bdca2d1e87f1a7064a8a6451cd3f57,Planes,-62.485,102.815,40.738,5434 -5435,mig29,-62.885,-62.433,17.947,-0.112,-0.12,1.37846153846,17369ebdd7d57c36e22fe5a62426bb85b1badae6,256,V6,17369ebdd7d57c36e22fe5a62426bb85b1badae6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-62.885_ry-62.433_rz+17.947_tx-00.112_ty-00.120_s+01.792_17369ebdd7d57c36e22fe5a62426bb85b1badae6_256x256.png,b944a7185636009cf8efc79eec804cd22b160da1,Planes,-62.885,27.567,17.947,5435 -5436,mig29,-62.987,21.492,70.371,-0.231,-0.175,1.00307692308,a248054c63a83ec09eeedc672d912f867711a620,256,V6,a248054c63a83ec09eeedc672d912f867711a620_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-62.987_ry+21.492_rz+70.371_tx-00.231_ty-00.175_s+01.304_a248054c63a83ec09eeedc672d912f867711a620_256x256.png,8b43bc6170127d84082503d8f9b8d3319050c950,Planes,-62.987,111.492,70.371,5436 -5437,mig29,-75.691,40.649,85.048,-0.358,0.582,0.766923076923,823b00e8444166b141eaed90931f3d0199e655c2,256,V6,823b00e8444166b141eaed90931f3d0199e655c2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-75.691_ry+40.649_rz+85.048_tx-00.358_ty+00.582_s+00.997_823b00e8444166b141eaed90931f3d0199e655c2_256x256.png,73beab9ecfedaea036111e75c11c2d923fe4b6c4,Planes,-75.691,130.649,85.048,5437 -5438,mig29,-86.942,-16.635,-19.252,0.132,-0.097,0.767692307692,c70f165cdbfc1b88954ba15aaad2c9b91ef5af19,256,V6,c70f165cdbfc1b88954ba15aaad2c9b91ef5af19_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-86.942_ry-16.635_rz-19.252_tx+00.132_ty-00.097_s+00.998_c70f165cdbfc1b88954ba15aaad2c9b91ef5af19_256x256.png,69e14b66fcc11443c67a4222bf96958732b75ade,Planes,-86.942,73.365,-19.252,5438 -5439,mig29,-87.302,82.424,10.804,-0.586,0.374,1.39307692308,a181ffa36b3f4bd2e5af8f1529d65bd4ab2cf19c,256,V6,a181ffa36b3f4bd2e5af8f1529d65bd4ab2cf19c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/mig29_rx-87.302_ry+82.424_rz+10.804_tx-00.586_ty+00.374_s+01.811_a181ffa36b3f4bd2e5af8f1529d65bd4ab2cf19c_256x256.png,f7b6fe21f367eb27e9c522861301166c89fccfb0,Planes,-87.302,172.424,10.804,5439 -5440,motoryacht,2.531,-66.746,-35.456,0.105,-1.006,1.39583333333,872a4db6fac22487f5431c6373e697fab0d7a0c2,256,V6,872a4db6fac22487f5431c6373e697fab0d7a0c2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+02.531_ry+23.254_rz-35.456_tx+00.105_ty-01.006_s+01.675_872a4db6fac22487f5431c6373e697fab0d7a0c2_256x256.png,2a9003e86725b2b01a30b2e7fb9c64d3d325097d,Boats,2.531,-156.746,-35.456,5440 -5441,motoryacht,15.369,84.15,29.693,-0.073,-0.444,0.971666666667,096777ca2725eaca0155380cbdf2e6f3be002e2b,256,V6,096777ca2725eaca0155380cbdf2e6f3be002e2b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+15.369_ry+174.150_rz+29.693_tx-00.073_ty-00.444_s+01.166_096777ca2725eaca0155380cbdf2e6f3be002e2b_256x256.png,578a8a408d1988e2c73f2ea4e97fe580b24267bc,Boats,15.369,-5.85,29.693,5441 -5442,motoryacht,20.215,-30.011,-87.025,0.548,1.132,1.13916666667,cb08e43219cfaa432fb37bcdabe44505bad18cda,256,V6,cb08e43219cfaa432fb37bcdabe44505bad18cda_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+20.215_ry+59.989_rz-87.025_tx+00.548_ty+01.132_s+01.367_cb08e43219cfaa432fb37bcdabe44505bad18cda_256x256.png,8575c4b1f30c882ddef6cc8c064c983cb6f1fb6d,Boats,20.215,-120.011,-87.025,5442 -5443,motoryacht,24.865,59.056,2.526,0.241,-0.434,1.1725,e44c296c0ccb602e6defded43704d7f12bf1ab9a,256,V6,e44c296c0ccb602e6defded43704d7f12bf1ab9a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+24.865_ry+149.056_rz+02.526_tx+00.241_ty-00.434_s+01.407_e44c296c0ccb602e6defded43704d7f12bf1ab9a_256x256.png,1048a89ea9c0ed917c93288f0de2c5463547f434,Boats,24.865,-30.944,2.526,5443 -5444,motoryacht,30.902,11.341,9.516,-0.36,-0.362,1.3425,f73a5e8845efd1cb8bac1366935954162c6c2bb5,256,V6,f73a5e8845efd1cb8bac1366935954162c6c2bb5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+30.902_ry+101.341_rz+09.516_tx-00.360_ty-00.362_s+01.611_f73a5e8845efd1cb8bac1366935954162c6c2bb5_256x256.png,8edef354b7044f0d0a5faf725afb727dd2fb48ea,Boats,30.902,-78.659,9.516,5444 -5445,motoryacht,33.252,-34.059,39.025,-0.52,-0.201,1.38583333333,36ad6ba106f67a4c4e1bbbd82e5780d1bf2a5396,256,V6,36ad6ba106f67a4c4e1bbbd82e5780d1bf2a5396_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+33.252_ry+55.941_rz+39.025_tx-00.520_ty-00.201_s+01.663_36ad6ba106f67a4c4e1bbbd82e5780d1bf2a5396_256x256.png,9b980135b75b6402ea0b3c5a556265b520bc872b,Boats,33.252,-124.059,39.025,5445 -5446,motoryacht,40.276,8.475,-56.483,0.243,0.166,1.03083333333,cffe4f8911ca6c41c2666c82d54549bdc839421b,256,V6,cffe4f8911ca6c41c2666c82d54549bdc839421b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+40.276_ry+98.475_rz-56.483_tx+00.243_ty+00.166_s+01.237_cffe4f8911ca6c41c2666c82d54549bdc839421b_256x256.png,11d5993213bd94bf8cda3431997b0e2888202bbe,Boats,40.276,-81.525,-56.483,5446 -5447,motoryacht,47.171,87.125,86.083,-0.329,-0.278,1.02583333333,1d3dcdf1d5dcc85477c4060c4752208079f7f3f7,256,V6,1d3dcdf1d5dcc85477c4060c4752208079f7f3f7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+47.171_ry+177.125_rz+86.083_tx-00.329_ty-00.278_s+01.231_1d3dcdf1d5dcc85477c4060c4752208079f7f3f7_256x256.png,49b51d21bc2ecb69b2e069e8a5dcc0fb9edff8a5,Boats,47.171,-2.875,86.083,5447 -5448,motoryacht,50.827,-27.623,69.048,0.224,0.58,1.25666666667,7a02c166ec927929995becc3d93b0f7f7195cd9a,256,V6,7a02c166ec927929995becc3d93b0f7f7195cd9a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+50.827_ry+62.377_rz+69.048_tx+00.224_ty+00.580_s+01.508_7a02c166ec927929995becc3d93b0f7f7195cd9a_256x256.png,e502a4ae608a11a069590ef0c10b3c34f29341e9,Boats,50.827,-117.623,69.048,5448 -5449,motoryacht,57.682,-16.932,-40.255,0.148,0.694,0.85,3d01973e739be315ea82874b8618537443cac488,256,V6,3d01973e739be315ea82874b8618537443cac488_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+57.682_ry+73.068_rz-40.255_tx+00.148_ty+00.694_s+01.020_3d01973e739be315ea82874b8618537443cac488_256x256.png,efde2653df95dd1773fc53d85b6b352a84aeff2e,Boats,57.682,-106.932,-40.255,5449 -5450,motoryacht,60.858,-26.105,25.335,-0.554,-0.823,0.825833333333,12f6546a01b263e4f580581846acbf539535ca94,256,V6,12f6546a01b263e4f580581846acbf539535ca94_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+60.858_ry+63.895_rz+25.335_tx-00.554_ty-00.823_s+00.991_12f6546a01b263e4f580581846acbf539535ca94_256x256.png,6baf67ba49d6f8fff2570742030893706f60bb28,Boats,60.858,-116.105,25.335,5450 -5451,motoryacht,61.496,39.293,57.587,0.255,-0.299,0.8475,730bb6cdb30de2aec4188d849538a039417d9cfc,256,V6,730bb6cdb30de2aec4188d849538a039417d9cfc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+61.496_ry+129.293_rz+57.587_tx+00.255_ty-00.299_s+01.017_730bb6cdb30de2aec4188d849538a039417d9cfc_256x256.png,75092e42b2a44ffd21c12574959b179399d7011d,Boats,61.496,-50.707,57.587,5451 -5452,motoryacht,63.436,18.648,-22.72,0.374,-0.994,1.185,a99f4ebf9d96eec5c13c4bc51010e9c417c2d1a4,256,V6,a99f4ebf9d96eec5c13c4bc51010e9c417c2d1a4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+63.436_ry+108.648_rz-22.720_tx+00.374_ty-00.994_s+01.422_a99f4ebf9d96eec5c13c4bc51010e9c417c2d1a4_256x256.png,62f1a2b9b22cd4a3586e273b9ac26d730745cb61,Boats,63.436,-71.352,-22.72,5452 -5453,motoryacht,67.716,13.378,43.717,-0.397,-0.024,1.2075,4d9bbefa8ca27a844848b94b37df478e8f573400,256,V6,4d9bbefa8ca27a844848b94b37df478e8f573400_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+67.716_ry+103.378_rz+43.717_tx-00.397_ty-00.024_s+01.449_4d9bbefa8ca27a844848b94b37df478e8f573400_256x256.png,8921d377446a656549bed360bf732286f83a7ef3,Boats,67.716,-76.622,43.717,5453 -5454,motoryacht,71.111,-67.803,-35.909,-0.549,-0.989,0.958333333333,df151ce8251f9a93ba98d5716a532c93574d2d00,256,V6,df151ce8251f9a93ba98d5716a532c93574d2d00_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+71.111_ry+22.197_rz-35.909_tx-00.549_ty-00.989_s+01.150_df151ce8251f9a93ba98d5716a532c93574d2d00_256x256.png,13d6027b4ed65adb4e56680d143831b2221a851b,Boats,71.111,-157.803,-35.909,5454 -5455,motoryacht,73.462,35.283,78.08,-0.423,-0.427,0.988333333333,3abdd32105c2614f2640cfc0348ac370c9bf86a5,256,V6,3abdd32105c2614f2640cfc0348ac370c9bf86a5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+73.462_ry+125.283_rz+78.080_tx-00.423_ty-00.427_s+01.186_3abdd32105c2614f2640cfc0348ac370c9bf86a5_256x256.png,435482f02bfc2fd4890349d4070c5c77db07a766,Boats,73.462,-54.717,78.08,5455 -5456,motoryacht,73.64,63.626,3.277,0.393,0.848,1.25083333333,fc650f5ce0442d7ef6ca664fc6fda0ea142e52b5,256,V6,fc650f5ce0442d7ef6ca664fc6fda0ea142e52b5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+73.640_ry+153.626_rz+03.277_tx+00.393_ty+00.848_s+01.501_fc650f5ce0442d7ef6ca664fc6fda0ea142e52b5_256x256.png,0d2c6c6078a1fcbff66ee4387ea87e3f43556a9d,Boats,73.64,-26.374,3.277,5456 -5457,motoryacht,75.901,35.694,77.614,0.194,-0.948,1.12833333333,fbe4722ab029833a6ef65b039dc9323b35c14b95,256,V6,fbe4722ab029833a6ef65b039dc9323b35c14b95_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+75.901_ry+125.694_rz+77.614_tx+00.194_ty-00.948_s+01.354_fbe4722ab029833a6ef65b039dc9323b35c14b95_256x256.png,fa429185af49b995f7baa6fd8db3d5c4b71f2e91,Boats,75.901,-54.306,77.614,5457 -5458,motoryacht,83.542,89.394,82.928,-0.437,0.83,0.851666666667,c280fbdeca914db53621256842575a5baa0b56a5,256,V6,c280fbdeca914db53621256842575a5baa0b56a5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+83.542_ry+179.394_rz+82.928_tx-00.437_ty+00.830_s+01.022_c280fbdeca914db53621256842575a5baa0b56a5_256x256.png,40f0e9ed40eebff3ce9eeec32b365b65668a673b,Boats,83.542,-0.606,82.928,5458 -5459,motoryacht,84.531,-68.708,39.71,-0.15,-1.02,1.43583333333,070626a4910e2f45fc14e3fd894265013f93e8e7,256,V6,070626a4910e2f45fc14e3fd894265013f93e8e7_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx+84.531_ry+21.292_rz+39.710_tx-00.150_ty-01.020_s+01.723_070626a4910e2f45fc14e3fd894265013f93e8e7_256x256.png,1573529414e00169a116c39751f81595c540efdc,Boats,84.531,-158.708,39.71,5459 -5460,motoryacht,-17.422,-67.193,5.809,0.597,-1.194,1.385,eddeaf2320b1e866b296ad8927066d16f52a91e2,256,V6,eddeaf2320b1e866b296ad8927066d16f52a91e2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-17.422_ry+22.807_rz+05.809_tx+00.597_ty-01.194_s+01.662_eddeaf2320b1e866b296ad8927066d16f52a91e2_256x256.png,e944033a39b2f70c24cd0fec780174b0b4fe0d08,Boats,-17.422,-157.193,5.809,5460 -5461,motoryacht,-20.135,10.282,-38.454,-0.431,0.293,1.34083333333,66fdc79c2fe3161e77f0014ca77c53f6ac52960a,256,V6,66fdc79c2fe3161e77f0014ca77c53f6ac52960a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-20.135_ry+100.282_rz-38.454_tx-00.431_ty+00.293_s+01.609_66fdc79c2fe3161e77f0014ca77c53f6ac52960a_256x256.png,f14a929f735f584eec9db5b893e6846f73d50d5a,Boats,-20.135,-79.718,-38.454,5461 -5462,motoryacht,-22.674,83.86,-64.866,-0.047,-0.977,0.711666666667,80fb5e7469b9c2d9a1dfd53268622d2ed20c0434,256,V6,80fb5e7469b9c2d9a1dfd53268622d2ed20c0434_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-22.674_ry+173.860_rz-64.866_tx-00.047_ty-00.977_s+00.854_80fb5e7469b9c2d9a1dfd53268622d2ed20c0434_256x256.png,0cd2e0a6937c24a8eb2aea54973ed29a0f9e4efe,Boats,-22.674,-6.14,-64.866,5462 -5463,motoryacht,-26.045,47.197,26.013,0.138,0.47,1.30833333333,47e912f7d929aa10746aa57b7e52d2f3ba570fad,256,V6,47e912f7d929aa10746aa57b7e52d2f3ba570fad_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-26.045_ry+137.197_rz+26.013_tx+00.138_ty+00.470_s+01.570_47e912f7d929aa10746aa57b7e52d2f3ba570fad_256x256.png,2d34227eb515fd07bea485b8797a0157fa663941,Boats,-26.045,-42.803,26.013,5463 -5464,motoryacht,-26.937,-36.08,-88.846,0.173,1.147,1.27833333333,23ac2b45f4fafb4c4ac79ca33f8f7362984c06cd,256,V6,23ac2b45f4fafb4c4ac79ca33f8f7362984c06cd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-26.937_ry+53.920_rz-88.846_tx+00.173_ty+01.147_s+01.534_23ac2b45f4fafb4c4ac79ca33f8f7362984c06cd_256x256.png,ab0bf57addd9314b6c8efc97d43043aa7b7ad999,Boats,-26.937,-126.08,-88.846,5464 -5465,motoryacht,-28.909,-85.749,67.386,-0.349,-0.94,0.7875,01e5dbe59764bb4833500988d33ecb08f3bf07ea,256,V6,01e5dbe59764bb4833500988d33ecb08f3bf07ea_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-28.909_ry+04.251_rz+67.386_tx-00.349_ty-00.940_s+00.945_01e5dbe59764bb4833500988d33ecb08f3bf07ea_256x256.png,a2a97864348cff1be7719400b7df7720ce736036,Boats,-28.909,-175.749,67.386,5465 -5466,motoryacht,-31.789,-32.843,84.422,0.057,-0.715,1.5025,1a64b0801962781d4664b479d12ea534c9d97e7f,256,V6,1a64b0801962781d4664b479d12ea534c9d97e7f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-31.789_ry+57.157_rz+84.422_tx+00.057_ty-00.715_s+01.803_1a64b0801962781d4664b479d12ea534c9d97e7f_256x256.png,4feae480ca0f025f22471725dbf2c9efaca430db,Boats,-31.789,-122.843,84.422,5466 -5467,motoryacht,-31.811,4.317,-14.55,-0.568,0.788,1.57333333333,41c4e4101680aac36db789b3978288bbe2cf3ddd,256,V6,41c4e4101680aac36db789b3978288bbe2cf3ddd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-31.811_ry+94.317_rz-14.550_tx-00.568_ty+00.788_s+01.888_41c4e4101680aac36db789b3978288bbe2cf3ddd_256x256.png,bc927b12feebb1649f9f501254ab0d26edcaca02,Boats,-31.811,-85.683,-14.55,5467 -5468,motoryacht,-34.002,18.119,32.48,0.599,0.885,0.745833333333,2803207159619e1c65acc18fa3785f5222a1c77a,256,V6,2803207159619e1c65acc18fa3785f5222a1c77a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-34.002_ry+108.119_rz+32.480_tx+00.599_ty+00.885_s+00.895_2803207159619e1c65acc18fa3785f5222a1c77a_256x256.png,f83c06fd872642b2b78c6621b02563e06ed41021,Boats,-34.002,-71.881,32.48,5468 -5469,motoryacht,-36.905,58.763,-31.381,0.252,-0.742,1.12166666667,029fa4764ba50f0afada0f720e504250a88140a2,256,V6,029fa4764ba50f0afada0f720e504250a88140a2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-36.905_ry+148.763_rz-31.381_tx+00.252_ty-00.742_s+01.346_029fa4764ba50f0afada0f720e504250a88140a2_256x256.png,0c231262e3e7511db6f23b565a9ae83857a79caa,Boats,-36.905,-31.237,-31.381,5469 -5470,motoryacht,-37.137,-47.623,-51.923,0.553,-0.787,1.18583333333,16e6f726e2d06f6afdc50bd597e3a58da9536549,256,V6,16e6f726e2d06f6afdc50bd597e3a58da9536549_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-37.137_ry+42.377_rz-51.923_tx+00.553_ty-00.787_s+01.423_16e6f726e2d06f6afdc50bd597e3a58da9536549_256x256.png,2005c1e009e32fd18b8b616554b9431a8b3adf0c,Boats,-37.137,-137.623,-51.923,5470 -5471,motoryacht,-49.308,44.81,-27.166,0.106,-0.684,1.48,638b2d56f24048db9826a353eaab2e9c8947977c,256,V6,638b2d56f24048db9826a353eaab2e9c8947977c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-49.308_ry+134.810_rz-27.166_tx+00.106_ty-00.684_s+01.776_638b2d56f24048db9826a353eaab2e9c8947977c_256x256.png,099920e1d3d39dd5f685984fa593e227770869cf,Boats,-49.308,-45.19,-27.166,5471 -5472,motoryacht,-52.651,-63.651,-25.637,0.096,0.836,1.52,dc40fe995072341522fa4f9f0c259039c1f73fbc,256,V6,dc40fe995072341522fa4f9f0c259039c1f73fbc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-52.651_ry+26.349_rz-25.637_tx+00.096_ty+00.836_s+01.824_dc40fe995072341522fa4f9f0c259039c1f73fbc_256x256.png,d683dff1c69dd7827fd576e5ddd769064b7b2dc0,Boats,-52.651,-153.651,-25.637,5472 -5473,motoryacht,-60.849,5.306,28.682,-0.599,-1.1,0.841666666667,b83cb34ae04b170aaaa4137921be957610f72c55,256,V6,b83cb34ae04b170aaaa4137921be957610f72c55_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-60.849_ry+95.306_rz+28.682_tx-00.599_ty-01.100_s+01.010_b83cb34ae04b170aaaa4137921be957610f72c55_256x256.png,ba74f50056c8fc98613dd2854b496dc18632af7f,Boats,-60.849,-84.694,28.682,5473 -5474,motoryacht,-72.194,-10.696,-52.241,0.24,0.7,1.26583333333,9c79b41ac1a2d5ae0bbb49b75b3a9eaafcb7647d,256,V6,9c79b41ac1a2d5ae0bbb49b75b3a9eaafcb7647d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-72.194_ry+79.304_rz-52.241_tx+00.240_ty+00.700_s+01.519_9c79b41ac1a2d5ae0bbb49b75b3a9eaafcb7647d_256x256.png,d09739215941ffee7b0778d95a393742ba851937,Boats,-72.194,-100.696,-52.241,5474 -5475,motoryacht,-72.52,26.126,81.427,0.302,1.196,0.975,b29f5666df4936408a3398fb31b834a116d2623f,256,V6,b29f5666df4936408a3398fb31b834a116d2623f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-72.520_ry+116.126_rz+81.427_tx+00.302_ty+01.196_s+01.170_b29f5666df4936408a3398fb31b834a116d2623f_256x256.png,1ddf988070dfdb17c44ef7b2b882110e598575ff,Boats,-72.52,-63.874,81.427,5475 -5476,motoryacht,-76.162,-36.333,-25.89,-0.13,0.066,0.883333333333,f9df60b330d28e2d9be63017cff757b480e63101,256,V6,f9df60b330d28e2d9be63017cff757b480e63101_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-76.162_ry+53.667_rz-25.890_tx-00.130_ty+00.066_s+01.060_f9df60b330d28e2d9be63017cff757b480e63101_256x256.png,43db5e53fdec7a0a36ab64838d7a6838af228ba2,Boats,-76.162,-126.333,-25.89,5476 -5477,motoryacht,-80.976,57.944,1.056,-0.31,0.688,0.981666666667,b7613340d4d94a0fbdcaf03fd19b65dce705dc5b,256,V6,b7613340d4d94a0fbdcaf03fd19b65dce705dc5b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-80.976_ry+147.944_rz+01.056_tx-00.310_ty+00.688_s+01.178_b7613340d4d94a0fbdcaf03fd19b65dce705dc5b_256x256.png,55dc1d517d75479822224409445e67c7c039921c,Boats,-80.976,-32.056,1.056,5477 -5478,motoryacht,-82.467,-65.074,-37.801,0.552,-1.127,1.17083333333,40f22f32a24680ec0ded7a67b20bfceac33ea787,256,V6,40f22f32a24680ec0ded7a67b20bfceac33ea787_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-82.467_ry+24.926_rz-37.801_tx+00.552_ty-01.127_s+01.405_40f22f32a24680ec0ded7a67b20bfceac33ea787_256x256.png,275616573032c2c7e366cb2fac18f2aaa05740e8,Boats,-82.467,-155.074,-37.801,5478 -5479,motoryacht,-87.373,-35.827,2.577,0.259,-0.442,0.815,9336675e87b9e02d6bb81eb69ea22ad015b5649d,256,V6,9336675e87b9e02d6bb81eb69ea22ad015b5649d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/motoryacht_rx-87.373_ry+54.173_rz+02.577_tx+00.259_ty-00.442_s+00.978_9336675e87b9e02d6bb81eb69ea22ad015b5649d_256x256.png,a06dd9a01a10fdce27fc7bb263a11aea60d22a8e,Boats,-87.373,-125.827,2.577,5479 -5480,raspberry_obj,76.8724669139,77.0341071014,-39.2329231036,0.306,-0.53,1.58142857143,383513932e3c0d08fb4199a4a6fae2159dea57f8,256,V6,383513932e3c0d08fb4199a4a6fae2159dea57f8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/raspberry_obj_rx-02.921_ry+12.804_rz+38.129_tx+00.306_ty-00.530_s+01.107_383513932e3c0d08fb4199a4a6fae2159dea57f8_256x256.png,ed607cf11034a1e2ae41c9f5212a3c0957035876,Fruits,-12.7870823574,87.0046478591,-52.5345321788,5480 -5481,raspberry_obj,82.0931692545,-14.570202551,41.4360168831,0.102,-0.858,1.27428571429,255ace85d6a586bfc59273fa2404c4e55fef4c4b,256,V6,255ace85d6a586bfc59273fa2404c4e55fef4c4b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/raspberry_obj_rx-07.651_ry-02.001_rz+26.732_tx+00.102_ty-00.858_s+00.892_255ace85d6a586bfc59273fa2404c4e55fef4c4b_256x256.png,8f32b804d7562cbbef68092db240387c251b1afa,Fruits,1.98317879552,82.3443870572,-63.0014831453,5481 -5482,raspberry_obj,38.3915415478,-80.0608323551,158.463229202,0.514,0.595,0.78,43c75f32cdfcdf280d0288843f2d23bfc5929403,256,V6,43c75f32cdfcdf280d0288843f2d23bfc5929403_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/raspberry_obj_rx-07.775_ry-51.186_rz+74.674_tx+00.514_ty+00.595_s+00.546_43c75f32cdfcdf280d0288843f2d23bfc5929403_256x256.png,302ed82a6dccd530badb5691573604f75295bbaf,Fruits,50.5357897707,77.7108885156,-5.77965302344,5482 -5483,raspberry_obj,22.6365539084,-80.4665161289,164.960615696,-0.416,0.855,0.73,44e3fb41d12083746b965915dd9c97f218732187,256,V6,44e3fb41d12083746b965915dd9c97f218732187_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/raspberry_obj_rx-08.793_ry-67.079_rz+78.659_tx-00.416_ty+00.855_s+00.511_44e3fb41d12083746b965915dd9c97f218732187_256x256.png,f1a2bfe943eff61f0438b521e7dea71cec69aa88,Fruits,65.535462664,68.3384281257,8.53456540787,5483 -5484,raspberry_obj,-5.33034397977,-10.2251753132,-145.380242435,-0.019,0.624,1.07428571429,3441341005705c81ff117ebeec8607c0b5f3ac4c,256,V6,3441341005705c81ff117ebeec8607c0b5f3ac4c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/raspberry_obj_rx-101.518_ry+62.274_rz-82.629_tx-00.019_ty+00.624_s+00.752_3441341005705c81ff117ebeec8607c0b5f3ac4c_256x256.png,b237115b36253b346bf6f5b5ab4b54f2dff0bd67,Fruits,10.1804856167,-5.41586087476,125.579779744,5484 -5485,raspberry_obj,-7.56472684593,10.6094880102,61.482403507,0.382,-0.88,1.36428571429,e157aaa0af432e9937171f7b205edbc13a9114de,256,V6,e157aaa0af432e9937171f7b205edbc13a9114de_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/raspberry_obj_rx-103.005_ry-54.197_rz+06.582_tx+00.382_ty-00.880_s+00.955_e157aaa0af432e9937171f7b205edbc13a9114de_256x256.png,1b53d60b30535e8124107127bcb27baf76f6ab28,Fruits,-10.5160954227,-7.69473292045,-29.9301937653,5485 -5486,raspberry_obj,-1.44453948904,16.4895979834,60.7653252306,0.248,1.025,0.96,d54cd49ea4dfae5fcbdaa8776bd19324c4ff191e,256,V6,d54cd49ea4dfae5fcbdaa8776bd19324c4ff191e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/raspberry_obj_rx-106.551_ry-84.923_rz-24.367_tx+00.248_ty+01.025_s+00.672_d54cd49ea4dfae5fcbdaa8776bd19324c4ff191e_256x256.png,f30a56b763d9f5074a119db64073cb7a320847ad,Fruits,-16.4842079385,-1.50647193323,-29.6622283735,5486 -5487,raspberry_obj,-3.17633501149,17.1571541015,103.245280737,0.397,-0.654,0.958571428571,d80d662a7cf321c5f0d58f656632fe2b2000c41d,256,V6,d80d662a7cf321c5f0d58f656632fe2b2000c41d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/raspberry_obj_rx-107.440_ry-79.346_rz+23.420_tx+00.397_ty-00.654_s+00.671_d80d662a7cf321c5f0d58f656632fe2b2000c41d_256x256.png,db30a94ef488d557c4af2c08920c1ffbb4ae2ce4,Fruits,-17.1299809985,-3.32394314528,12.2652408905,5487 -5488,raspberry_obj,-13.8801885375,-11.3836019381,-23.3961585166,-0.038,0.492,1.16857142857,a8be833bace4a00a0b535b5ef9a062603cdfe6ba,256,V6,a8be833bace4a00a0b535b5ef9a062603cdfe6ba_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/raspberry_obj_rx-107.880_ry+38.616_rz+16.610_tx-00.038_ty+00.492_s+00.818_a8be833bace4a00a0b535b5ef9a062603cdfe6ba_256x256.png,1b05c02b45b2288398cbbfbb4022496562d06b6f,Fruits,11.0469481072,-14.1476418588,-110.63095887,5488 -5489,raspberry_obj,-12.1037232436,-15.1482014659,-24.5683182359,-0.224,0.289,1.43285714286,0210ca7c4fcdebadcba001916e3d695dccf8a889,256,V6,0210ca7c4fcdebadcba001916e3d695dccf8a889_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/raspberry_obj_rx-109.301_ry+50.626_rz+27.673_tx-00.224_ty+00.289_s+01.003_0210ca7c4fcdebadcba001916e3d695dccf8a889_256x256.png,6b344faa66a6e92b31e24529f15ee403fc26fd46,Fruits,14.8036574153,-12.5259057974,-111.319360889,5489 -5490,raspberry_obj,-18.6098205455,-10.7995861926,3.34843403688,-0.574,-0.315,0.725714285714,0fd1c94105770abd45f0ffbdbd83bc478446231d,256,V6,0fd1c94105770abd45f0ffbdbd83bc478446231d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/raspberry_obj_rx-111.420_ry+29.094_rz+34.217_tx-00.574_ty-00.315_s+00.508_0fd1c94105770abd45f0ffbdbd83bc478446231d_256x256.png,fee25bd8ecaf03bc76175db2986c0650351b7ebd,Fruits,10.2286656155,-18.9216871543,-83.1680789301,5490 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-5519,raspberry_obj,1.86884287311,3.87555917134,-15.7632351606,0.184,0.053,0.627142857143,c8821beb9ee16a9d96e7f347aa88301073b761de,256,V6,c8821beb9ee16a9d96e7f347aa88301073b761de_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/raspberry_obj_rx-85.698_ry+64.231_rz+48.531_tx+00.184_ty+00.053_s+00.439_c8821beb9ee16a9d96e7f347aa88301073b761de_256x256.png,c3eeddec8ecef080a146864c55f7897a81b7f3dc,Fruits,-3.87349460563,1.87312328761,-105.636653768,5519 -5520,rdbarren,3.978,67.267,48.65,0.065,-0.888,1.40846153846,6350e2d760bc76221aae3dd5c2f921944f4a3f63,256,V6,6350e2d760bc76221aae3dd5c2f921944f4a3f63_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+03.978_ry+67.267_rz+48.650_tx+00.065_ty-00.888_s+01.831_6350e2d760bc76221aae3dd5c2f921944f4a3f63_256x256.png,a1fbd84a41d98426696abfde7c7bb61cf308b861,Planes,3.978,157.267,48.65,5520 -5521,rdbarren,9.907,-30.06,-12.034,-0.553,0.951,1.23846153846,40ac1431f1a3264b41c1eac38af2770d4247b6c3,256,V6,40ac1431f1a3264b41c1eac38af2770d4247b6c3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+09.907_ry-30.060_rz-12.034_tx-00.553_ty+00.951_s+01.610_40ac1431f1a3264b41c1eac38af2770d4247b6c3_256x256.png,d881fe82cd698631a9e25785e77ec507dad4eae9,Planes,9.907,59.94,-12.034,5521 -5522,rdbarren,16.879,9.529,59.916,0.339,0.004,0.676923076923,55b1e56fd00ddd596d60e10b99c2aa216c51b801,256,V6,55b1e56fd00ddd596d60e10b99c2aa216c51b801_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+16.879_ry+09.529_rz+59.916_tx+00.339_ty+00.004_s+00.880_55b1e56fd00ddd596d60e10b99c2aa216c51b801_256x256.png,9ede16fb1d6844a96ab9c215e0006674088b30f4,Planes,16.879,99.529,59.916,5522 -5523,rdbarren,26.758,-29.7,-39.783,0.274,0.783,1.40538461538,72e9aed6437e87e2178587a4cbaf860f23db315f,256,V6,72e9aed6437e87e2178587a4cbaf860f23db315f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+26.758_ry-29.700_rz-39.783_tx+00.274_ty+00.783_s+01.827_72e9aed6437e87e2178587a4cbaf860f23db315f_256x256.png,2df114247c0eb4a02c9f11567a8f8b87cf357c1d,Planes,26.758,60.3,-39.783,5523 -5524,rdbarren,26.764,85.854,42.809,0.362,-0.735,1.37538461538,49dbfe8feb954106ede2551266b53774546dd6ec,256,V6,49dbfe8feb954106ede2551266b53774546dd6ec_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+26.764_ry+85.854_rz+42.809_tx+00.362_ty-00.735_s+01.788_49dbfe8feb954106ede2551266b53774546dd6ec_256x256.png,df6da4def80ad3759b0d51980904e12019ecc2cf,Planes,26.764,175.854,42.809,5524 -5525,rdbarren,28.074,-25.126,-36.243,0.341,0.587,0.910769230769,2755cd45fdc77af858f1e8149d1b7a811276303d,256,V6,2755cd45fdc77af858f1e8149d1b7a811276303d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+28.074_ry-25.126_rz-36.243_tx+00.341_ty+00.587_s+01.184_2755cd45fdc77af858f1e8149d1b7a811276303d_256x256.png,98ecd4743dfafcafbb64d4b068a99d91c1a68ed2,Planes,28.074,64.874,-36.243,5525 -5526,rdbarren,28.884,45.096,-75.933,0.111,1.077,1.09076923077,f942bd422b0f833a62edb22d044d2763b5eafa47,256,V6,f942bd422b0f833a62edb22d044d2763b5eafa47_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+28.884_ry+45.096_rz-75.933_tx+00.111_ty+01.077_s+01.418_f942bd422b0f833a62edb22d044d2763b5eafa47_256x256.png,58d2d5d2da454cbfdbd16b4945f02fd34a25d40c,Planes,28.884,135.096,-75.933,5526 -5527,rdbarren,37.239,-1.081,-63.88,0.469,-1.083,1.47615384615,3108374cbe7186b23b58f0deb7eb2474ff3a4fb5,256,V6,3108374cbe7186b23b58f0deb7eb2474ff3a4fb5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+37.239_ry-01.081_rz-63.880_tx+00.469_ty-01.083_s+01.919_3108374cbe7186b23b58f0deb7eb2474ff3a4fb5_256x256.png,a583e30455282e8f289da91bc1e8147ea49535d0,Planes,37.239,88.919,-63.88,5527 -5528,rdbarren,39.047,-46.77,2.355,0.238,0.129,0.821538461538,a97c3ab821ca6486546ce1785507eced5e392f2b,256,V6,a97c3ab821ca6486546ce1785507eced5e392f2b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+39.047_ry-46.770_rz+02.355_tx+00.238_ty+00.129_s+01.068_a97c3ab821ca6486546ce1785507eced5e392f2b_256x256.png,190b8b051124eff06af2a62eafa471d7a275e566,Planes,39.047,43.23,2.355,5528 -5529,rdbarren,44.618,-75.048,-20.888,-0.053,1.13,1.46,fbbcf261575a03918acc1cdfee766d7b08d466f8,256,V6,fbbcf261575a03918acc1cdfee766d7b08d466f8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+44.618_ry-75.048_rz-20.888_tx-00.053_ty+01.130_s+01.898_fbbcf261575a03918acc1cdfee766d7b08d466f8_256x256.png,1ae7e7480dbec2bf7d9060af877f95f8ab06c6d2,Planes,44.618,14.952,-20.888,5529 -5530,rdbarren,57.862,-33.725,68.742,0.256,0.329,1.08692307692,1465cd2a6af17c2dbbb9de0c087df424bf964107,256,V6,1465cd2a6af17c2dbbb9de0c087df424bf964107_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+57.862_ry-33.725_rz+68.742_tx+00.256_ty+00.329_s+01.413_1465cd2a6af17c2dbbb9de0c087df424bf964107_256x256.png,c587d2f8e315b4a68b1b4ee7d9b6d463b2f0cdff,Planes,57.862,56.275,68.742,5530 -5531,rdbarren,59.036,-61.252,-78.911,0.49,0.111,0.935384615385,acf24e399895029287f44cb399344c2102c85acc,256,V6,acf24e399895029287f44cb399344c2102c85acc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+59.036_ry-61.252_rz-78.911_tx+00.490_ty+00.111_s+01.216_acf24e399895029287f44cb399344c2102c85acc_256x256.png,cca0495271bb69c7d30d6f79f3a0cd3796e98dc5,Planes,59.036,28.748,-78.911,5531 -5532,rdbarren,63.57,-69.61,-82.017,-0.434,-0.325,1.53923076923,571a187178631c7d9449a939285ce0e350c0f2e8,256,V6,571a187178631c7d9449a939285ce0e350c0f2e8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+63.570_ry-69.610_rz-82.017_tx-00.434_ty-00.325_s+02.001_571a187178631c7d9449a939285ce0e350c0f2e8_256x256.png,0b90cec2d1804dd5d1c567a8d579422cd25a5992,Planes,63.57,20.39,-82.017,5532 -5533,rdbarren,65.689,-67.713,43.026,-0.342,-0.295,1.50461538462,da424163208e6254176f58c49f290935d2c37264,256,V6,da424163208e6254176f58c49f290935d2c37264_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+65.689_ry-67.713_rz+43.026_tx-00.342_ty-00.295_s+01.956_da424163208e6254176f58c49f290935d2c37264_256x256.png,82bcb6476dce33b618e9d1202a66f95386e2899f,Planes,65.689,22.287,43.026,5533 -5534,rdbarren,67.96,-62.297,70.463,-0.494,-0.025,0.730769230769,a076dac94d633ee15f5b150d47ec78f9fde5b49f,256,V6,a076dac94d633ee15f5b150d47ec78f9fde5b49f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+67.960_ry-62.297_rz+70.463_tx-00.494_ty-00.025_s+00.950_a076dac94d633ee15f5b150d47ec78f9fde5b49f_256x256.png,5228e6a908aab8e892e68af4751c890b5b4ba664,Planes,67.96,27.703,70.463,5534 -5535,rdbarren,70.53,20.867,79.376,0.266,0.953,1.30307692308,20fd4031a308a8dea9ade32dc2ae24a4168d9b36,256,V6,20fd4031a308a8dea9ade32dc2ae24a4168d9b36_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+70.530_ry+20.867_rz+79.376_tx+00.266_ty+00.953_s+01.694_20fd4031a308a8dea9ade32dc2ae24a4168d9b36_256x256.png,4186ea36635a6d41b0c16f2c3307a95a1a40999d,Planes,70.53,110.867,79.376,5535 -5536,rdbarren,74.287,-16.098,45.247,-0.112,-0.76,0.884615384615,863817582510964277983a0bee124e90f70a703c,256,V6,863817582510964277983a0bee124e90f70a703c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+74.287_ry-16.098_rz+45.247_tx-00.112_ty-00.760_s+01.150_863817582510964277983a0bee124e90f70a703c_256x256.png,cf75baacd1efcc201da9ec827c6d97c5e5023b5c,Planes,74.287,73.902,45.247,5536 -5537,rdbarren,76.166,79.263,6.355,-0.018,1.184,0.837692307692,325fb6faadb61f7e8b7b1150c888925c2ec24eb3,256,V6,325fb6faadb61f7e8b7b1150c888925c2ec24eb3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+76.166_ry+79.263_rz+06.355_tx-00.018_ty+01.184_s+01.089_325fb6faadb61f7e8b7b1150c888925c2ec24eb3_256x256.png,f9179aa049aa536889d84505b2e7f1286561dd26,Planes,76.166,169.263,6.355,5537 -5538,rdbarren,77.173,-27.462,-86.365,-0.091,0.924,0.773076923077,d7a2d0e383ac09c7fcbba8a80c14c9d72bf13597,256,V6,d7a2d0e383ac09c7fcbba8a80c14c9d72bf13597_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+77.173_ry-27.462_rz-86.365_tx-00.091_ty+00.924_s+01.005_d7a2d0e383ac09c7fcbba8a80c14c9d72bf13597_256x256.png,6f4db169691f5cfa68a43920fa2bae2cb4673af6,Planes,77.173,62.538,-86.365,5538 -5539,rdbarren,79.591,-67.58,-33.503,0.438,0.426,1.27076923077,0609351f587aefe86d4254a529d31d1bfbf3f83c,256,V6,0609351f587aefe86d4254a529d31d1bfbf3f83c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+79.591_ry-67.580_rz-33.503_tx+00.438_ty+00.426_s+01.652_0609351f587aefe86d4254a529d31d1bfbf3f83c_256x256.png,c2b40c504f16cb464f9a73eaef321322229dae99,Planes,79.591,22.42,-33.503,5539 -5540,rdbarren,79.672,-10.999,-64.489,-0.23,0.307,1.13076923077,5ec9d9627ffd61c904916af9c197117f8200df89,256,V6,5ec9d9627ffd61c904916af9c197117f8200df89_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+79.672_ry-10.999_rz-64.489_tx-00.230_ty+00.307_s+01.470_5ec9d9627ffd61c904916af9c197117f8200df89_256x256.png,9d1086bb37cd2ce96d0c70a21914202c7af4a480,Planes,79.672,79.001,-64.489,5540 -5541,rdbarren,85.729,-20.512,-72.036,0.359,0.825,1.21307692308,ea9df6de7990a706fc3ea795ce28404d79289546,256,V6,ea9df6de7990a706fc3ea795ce28404d79289546_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+85.729_ry-20.512_rz-72.036_tx+00.359_ty+00.825_s+01.577_ea9df6de7990a706fc3ea795ce28404d79289546_256x256.png,308f2f4c631c40426936a115bf0f4c57d22b4ac8,Planes,85.729,69.488,-72.036,5541 -5542,rdbarren,85.793,-89.693,89.845,-0.393,-0.539,1.47692307692,f23b6d063069f8ab1bf3b712c6bc7d8d74ccef6c,256,V6,f23b6d063069f8ab1bf3b712c6bc7d8d74ccef6c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+85.793_ry-89.693_rz+89.845_tx-00.393_ty-00.539_s+01.920_f23b6d063069f8ab1bf3b712c6bc7d8d74ccef6c_256x256.png,d988e7a7ecaa21b6975ed376c5f06a5f4b2432a5,Planes,85.793,0.307,89.845,5542 -5543,rdbarren,87.179,-6.885,83.515,-0.506,0.545,0.865384615385,b12bea23c904ba70f8c3e963f07928c5e9bedd25,256,V6,b12bea23c904ba70f8c3e963f07928c5e9bedd25_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx+87.179_ry-06.885_rz+83.515_tx-00.506_ty+00.545_s+01.125_b12bea23c904ba70f8c3e963f07928c5e9bedd25_256x256.png,b5975c2ddcc838547c0bc2e9ed45de9eb692af54,Planes,87.179,83.115,83.515,5543 -5544,rdbarren,-12.871,63.138,42.946,0.349,-0.762,0.703846153846,26e291a635d65dcbd7b54ba611bc89e146980bfd,256,V6,26e291a635d65dcbd7b54ba611bc89e146980bfd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-12.871_ry+63.138_rz+42.946_tx+00.349_ty-00.762_s+00.915_26e291a635d65dcbd7b54ba611bc89e146980bfd_256x256.png,74de6d695d44969b6e086e9f590508d43ba54e43,Planes,-12.871,153.138,42.946,5544 -5545,rdbarren,-19.002,-6.722,-89.573,0.053,-0.956,1.17769230769,2f02008c21529d2fab150c014fb0d1c2f0f09783,256,V6,2f02008c21529d2fab150c014fb0d1c2f0f09783_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-19.002_ry-06.722_rz-89.573_tx+00.053_ty-00.956_s+01.531_2f02008c21529d2fab150c014fb0d1c2f0f09783_256x256.png,6a7ffd73836ad2ea94ee149ebd2313525487509a,Planes,-19.002,83.278,-89.573,5545 -5546,rdbarren,-20.168,33.688,-53.047,-0.528,0.132,0.753846153846,bb4cadf4fdcb9779677e8c95ef8159d4c13019ff,256,V6,bb4cadf4fdcb9779677e8c95ef8159d4c13019ff_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-20.168_ry+33.688_rz-53.047_tx-00.528_ty+00.132_s+00.980_bb4cadf4fdcb9779677e8c95ef8159d4c13019ff_256x256.png,45bbfcf88207c7f55657e19e9bf2c06abbe89ee5,Planes,-20.168,123.688,-53.047,5546 -5547,rdbarren,-23.44,-56.188,66.09,0.419,-0.282,1.20307692308,594e165534bcd8e1ac5ac484b79e53b79d6dfe32,256,V6,594e165534bcd8e1ac5ac484b79e53b79d6dfe32_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-23.440_ry-56.188_rz+66.090_tx+00.419_ty-00.282_s+01.564_594e165534bcd8e1ac5ac484b79e53b79d6dfe32_256x256.png,63c774e37136365e94a038d35eb2c48b01096837,Planes,-23.44,33.812,66.09,5547 -5548,rdbarren,-25.766,2.81,-47.388,-0.066,-1.061,1.34923076923,f2e35bfcb5daaf1fe7a2f6a16ef9b91e5e1f1b29,256,V6,f2e35bfcb5daaf1fe7a2f6a16ef9b91e5e1f1b29_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-25.766_ry+02.810_rz-47.388_tx-00.066_ty-01.061_s+01.754_f2e35bfcb5daaf1fe7a2f6a16ef9b91e5e1f1b29_256x256.png,3e13e419409352ea280fb6bb91f3afa5cfadb8d9,Planes,-25.766,92.81,-47.388,5548 -5549,rdbarren,-46.037,58.271,-27.432,-0.502,-0.927,0.758461538462,014df87dd18bf4dd75dd8b2b7430b7d0a7231cd4,256,V6,014df87dd18bf4dd75dd8b2b7430b7d0a7231cd4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-46.037_ry+58.271_rz-27.432_tx-00.502_ty-00.927_s+00.986_014df87dd18bf4dd75dd8b2b7430b7d0a7231cd4_256x256.png,cf85cd8eb635c34fcfab8ddaf78741280e17c13d,Planes,-46.037,148.271,-27.432,5549 -5550,rdbarren,-46.311,83.92,-81.398,0.191,-1.13,0.863846153846,0577f5535b5d3d943346a640123845e15db36d5c,256,V6,0577f5535b5d3d943346a640123845e15db36d5c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-46.311_ry+83.920_rz-81.398_tx+00.191_ty-01.130_s+01.123_0577f5535b5d3d943346a640123845e15db36d5c_256x256.png,07db4261e1332bc1f77136af62513a0c0b442621,Planes,-46.311,173.92,-81.398,5550 -5551,rdbarren,-46.436,12.696,89.027,0.218,0.137,0.674615384615,92129516ffec31eadaf052618132f5c08cc1dee5,256,V6,92129516ffec31eadaf052618132f5c08cc1dee5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-46.436_ry+12.696_rz+89.027_tx+00.218_ty+00.137_s+00.877_92129516ffec31eadaf052618132f5c08cc1dee5_256x256.png,9473cb92c8fb252a373c6c1c8a183bc51bb2ea59,Planes,-46.436,102.696,89.027,5551 -5552,rdbarren,-57.865,57.067,-84.067,-0.558,-0.036,0.692307692308,8cc441a843715832f7690ee7f95935a5cba9d8e1,256,V6,8cc441a843715832f7690ee7f95935a5cba9d8e1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-57.865_ry+57.067_rz-84.067_tx-00.558_ty-00.036_s+00.900_8cc441a843715832f7690ee7f95935a5cba9d8e1_256x256.png,f74772345686c7b6be093667e350a1977ec810f0,Planes,-57.865,147.067,-84.067,5552 -5553,rdbarren,-67.801,-66.132,-6.454,0.542,-0.87,0.820769230769,771e5ab1f204905bf03d06936b01bb5f4de92788,256,V6,771e5ab1f204905bf03d06936b01bb5f4de92788_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-67.801_ry-66.132_rz-06.454_tx+00.542_ty-00.870_s+01.067_771e5ab1f204905bf03d06936b01bb5f4de92788_256x256.png,6b87003f02400681dc545c7cd57cde747c1cb98a,Planes,-67.801,23.868,-6.454,5553 -5554,rdbarren,-67.954,42.772,-37.096,-0.133,0.749,1.16307692308,86ce40d6d3395c814cbd3787bf5ecf5e8f64c886,256,V6,86ce40d6d3395c814cbd3787bf5ecf5e8f64c886_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-67.954_ry+42.772_rz-37.096_tx-00.133_ty+00.749_s+01.512_86ce40d6d3395c814cbd3787bf5ecf5e8f64c886_256x256.png,6f30a6e34ade7dcbd370ed3c39ec400217f31dca,Planes,-67.954,132.772,-37.096,5554 -5555,rdbarren,-71.72,33.039,-86.594,0.041,-0.324,1.20615384615,b006c7b5052980abe431446174e48a8bd3fb4f2e,256,V6,b006c7b5052980abe431446174e48a8bd3fb4f2e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-71.720_ry+33.039_rz-86.594_tx+00.041_ty-00.324_s+01.568_b006c7b5052980abe431446174e48a8bd3fb4f2e_256x256.png,cafa1b9da6b4429bc894c61258fb08addcc7e161,Planes,-71.72,123.039,-86.594,5555 -5556,rdbarren,-76.327,71.536,44.714,-0.02,-0.129,0.636153846154,1fe0cf33d1204fcc078aafe3cc2df58880ae69ca,256,V6,1fe0cf33d1204fcc078aafe3cc2df58880ae69ca_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-76.327_ry+71.536_rz+44.714_tx-00.020_ty-00.129_s+00.827_1fe0cf33d1204fcc078aafe3cc2df58880ae69ca_256x256.png,eecace769a6f29ba6518b50f21c0a4987fbdd7ca,Planes,-76.327,161.536,44.714,5556 -5557,rdbarren,-78.512,28.101,-79.662,0.002,-0.033,1.56538461538,39bf8a03a687a8b90ce90c0cd72a60432103f378,256,V6,39bf8a03a687a8b90ce90c0cd72a60432103f378_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-78.512_ry+28.101_rz-79.662_tx+00.002_ty-00.033_s+02.035_39bf8a03a687a8b90ce90c0cd72a60432103f378_256x256.png,168bb46bc6d2559ce8c771ef3e3064f6f8288b5f,Planes,-78.512,118.101,-79.662,5557 -5558,rdbarren,-78.787,-13.274,-12.143,0.584,-0.887,1.55769230769,4f53508d71c635ac0fa735608a32ca61c8cf0d84,256,V6,4f53508d71c635ac0fa735608a32ca61c8cf0d84_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-78.787_ry-13.274_rz-12.143_tx+00.584_ty-00.887_s+02.025_4f53508d71c635ac0fa735608a32ca61c8cf0d84_256x256.png,e0f0716adfccca50d42e97937d53f308f645b48a,Planes,-78.787,76.726,-12.143,5558 -5559,rdbarren,-87.713,37.835,15.199,-0.226,-0.342,0.703846153846,5dcfc731d7fd492f66d99096c3e0b5fb87d36fd0,256,V6,5dcfc731d7fd492f66d99096c3e0b5fb87d36fd0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/rdbarren_rx-87.713_ry+37.835_rz+15.199_tx-00.226_ty-00.342_s+00.915_5dcfc731d7fd492f66d99096c3e0b5fb87d36fd0_256x256.png,4d177e65a6386cd48a917bb7c1f7c526c1c2f61f,Planes,-87.713,127.835,15.199,5559 -5560,sopwith,6.14,-3.225,14.315,0.35,0.603,1.01076923077,8e5f4e242c626080735f6f94a92cb926956eb604,256,V6,8e5f4e242c626080735f6f94a92cb926956eb604_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+06.140_ry-03.225_rz+14.315_tx+00.350_ty+00.603_s+01.314_8e5f4e242c626080735f6f94a92cb926956eb604_256x256.png,2737dee68fba0acc4091aee1990221be5b868081,Planes,6.14,86.775,14.315,5560 -5561,sopwith,9.25,-41.564,87.88,0.07,-0.561,1.12846153846,c4baa26183f6df8b340d3210df71fa53456be604,256,V6,c4baa26183f6df8b340d3210df71fa53456be604_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+09.250_ry-41.564_rz+87.880_tx+00.070_ty-00.561_s+01.467_c4baa26183f6df8b340d3210df71fa53456be604_256x256.png,ba5b62f33b749090f0d9133182937e2038532308,Planes,9.25,48.436,87.88,5561 -5562,sopwith,19.394,-38.579,85.767,-0.522,0.567,1.52230769231,6bf568b1d914afb26baa8ff463abecf61cd4c5a4,256,V6,6bf568b1d914afb26baa8ff463abecf61cd4c5a4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+19.394_ry-38.579_rz+85.767_tx-00.522_ty+00.567_s+01.979_6bf568b1d914afb26baa8ff463abecf61cd4c5a4_256x256.png,4dcb78070fedd1e8bb7010f53a9b7cd38e25d107,Planes,19.394,51.421,85.767,5562 -5563,sopwith,26.506,-75.979,64.856,-0.248,0.816,1.17307692308,64a66a1048a8073573d0c843b8c00c5a04e9601d,256,V6,64a66a1048a8073573d0c843b8c00c5a04e9601d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+26.506_ry-75.979_rz+64.856_tx-00.248_ty+00.816_s+01.525_64a66a1048a8073573d0c843b8c00c5a04e9601d_256x256.png,447b7cddf4f35c63407ab0be4e8977ecc19c2686,Planes,26.506,14.021,64.856,5563 -5564,sopwith,32.018,89.449,47.76,0.035,-0.815,0.774615384615,d57c2ddc040f235ff379a47ac06ec04ff14f6fc0,256,V6,d57c2ddc040f235ff379a47ac06ec04ff14f6fc0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+32.018_ry+89.449_rz+47.760_tx+00.035_ty-00.815_s+01.007_d57c2ddc040f235ff379a47ac06ec04ff14f6fc0_256x256.png,4e8a3f67f138478556c009e0cc9fa98a619e59cc,Planes,32.018,179.449,47.76,5564 -5565,sopwith,43.038,-33.609,43.584,0.166,0.925,1.23076923077,2d75ed97a6127aa9497461e7975ae2a826d911d2,256,V6,2d75ed97a6127aa9497461e7975ae2a826d911d2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+43.038_ry-33.609_rz+43.584_tx+00.166_ty+00.925_s+01.600_2d75ed97a6127aa9497461e7975ae2a826d911d2_256x256.png,db4a0f6c1cc2baffe03f819b7e1d835bbf15e9b5,Planes,43.038,56.391,43.584,5565 -5566,sopwith,44.451,81.593,77.19,0.282,-0.414,1.49461538462,7259e3de7e3f6b899a2dc257b1cb3439b51de4ca,256,V6,7259e3de7e3f6b899a2dc257b1cb3439b51de4ca_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+44.451_ry+81.593_rz+77.190_tx+00.282_ty-00.414_s+01.943_7259e3de7e3f6b899a2dc257b1cb3439b51de4ca_256x256.png,158a7717f2a30b9051a87904c849730f0ae7fbe6,Planes,44.451,171.593,77.19,5566 -5567,sopwith,46.594,14.276,51.967,0.351,-1.062,1.34076923077,66e36f5fb585a8017f82440cd59a4ed82044b264,256,V6,66e36f5fb585a8017f82440cd59a4ed82044b264_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+46.594_ry+14.276_rz+51.967_tx+00.351_ty-01.062_s+01.743_66e36f5fb585a8017f82440cd59a4ed82044b264_256x256.png,4541a2a9484eb3da4627a27e5f36c12b38c82eae,Planes,46.594,104.276,51.967,5567 -5568,sopwith,57.97,21.282,68.634,0.352,0.563,1.21692307692,79e75348080854b7305d4f7a397a5552e5858f3d,256,V6,79e75348080854b7305d4f7a397a5552e5858f3d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+57.970_ry+21.282_rz+68.634_tx+00.352_ty+00.563_s+01.582_79e75348080854b7305d4f7a397a5552e5858f3d_256x256.png,e32901eea8eca23422d6485d6ca420dc020cc66c,Planes,57.97,111.282,68.634,5568 -5569,sopwith,59.843,-82.576,45.57,0.23,0.846,1.09076923077,5b505b59ff24589e0c70470f09124a875546ec1b,256,V6,5b505b59ff24589e0c70470f09124a875546ec1b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+59.843_ry-82.576_rz+45.570_tx+00.230_ty+00.846_s+01.418_5b505b59ff24589e0c70470f09124a875546ec1b_256x256.png,93c0130c3257249f500171d131f6f800cb0eb775,Planes,59.843,7.424,45.57,5569 -5570,sopwith,69.214,10.954,17.869,-0.269,0.805,0.820769230769,0bbd4a41e38190a43ec8849a0ef1ea9e0116b59f,256,V6,0bbd4a41e38190a43ec8849a0ef1ea9e0116b59f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+69.214_ry+10.954_rz+17.869_tx-00.269_ty+00.805_s+01.067_0bbd4a41e38190a43ec8849a0ef1ea9e0116b59f_256x256.png,aad5b0e4f9e58b287f9a16666dd20e19feaff3ed,Planes,69.214,100.954,17.869,5570 -5571,sopwith,75.12,-60.521,39.911,0.117,0.743,1.48615384615,c7f51cd095fd5cb1da9ad59f9ae26fcb179dd69c,256,V6,c7f51cd095fd5cb1da9ad59f9ae26fcb179dd69c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+75.120_ry-60.521_rz+39.911_tx+00.117_ty+00.743_s+01.932_c7f51cd095fd5cb1da9ad59f9ae26fcb179dd69c_256x256.png,6cb0c1357d8453ece2af20c9782ddb5395393628,Planes,75.12,29.479,39.911,5571 -5572,sopwith,79.784,62.151,-48.91,0.332,0.129,0.925384615385,e3280dc4030c752d05066b56bd501bbf8a5adcdd,256,V6,e3280dc4030c752d05066b56bd501bbf8a5adcdd_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+79.784_ry+62.151_rz-48.910_tx+00.332_ty+00.129_s+01.203_e3280dc4030c752d05066b56bd501bbf8a5adcdd_256x256.png,21f2931aad6d36da70fd3bd457a528ff8f17db39,Planes,79.784,152.151,-48.91,5572 -5573,sopwith,80.218,42.067,4.571,-0.062,0.323,1.08230769231,9b1e70938b185af809bdce8fadf70cf6a4b85528,256,V6,9b1e70938b185af809bdce8fadf70cf6a4b85528_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+80.218_ry+42.067_rz+04.571_tx-00.062_ty+00.323_s+01.407_9b1e70938b185af809bdce8fadf70cf6a4b85528_256x256.png,cd71b9017b0f1e4db6bd92a64d3968fe369d3877,Planes,80.218,132.067,4.571,5573 -5574,sopwith,85.02,-6.796,54.129,-0.564,-0.103,1.07307692308,62baa34834889847978d4972a6f7e5a39f26644b,256,V6,62baa34834889847978d4972a6f7e5a39f26644b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+85.020_ry-06.796_rz+54.129_tx-00.564_ty-00.103_s+01.395_62baa34834889847978d4972a6f7e5a39f26644b_256x256.png,06642a59a5c01045395eb4b19aa091e6f8bfa024,Planes,85.02,83.204,54.129,5574 -5575,sopwith,88.599,33.995,24.635,-0.517,-0.986,1.34307692308,33c55c325147f80a20c58a7c95db65fe7845eefb,256,V6,33c55c325147f80a20c58a7c95db65fe7845eefb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+88.599_ry+33.995_rz+24.635_tx-00.517_ty-00.986_s+01.746_33c55c325147f80a20c58a7c95db65fe7845eefb_256x256.png,55b1ea834966afb062cd7bcf654b7920411459ad,Planes,88.599,123.995,24.635,5575 -5576,sopwith,89.463,24.406,-14.573,0.453,-0.449,1.57923076923,27f4c815ac306641a02560077ccd29d540dbf256,256,V6,27f4c815ac306641a02560077ccd29d540dbf256_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx+89.463_ry+24.406_rz-14.573_tx+00.453_ty-00.449_s+02.053_27f4c815ac306641a02560077ccd29d540dbf256_256x256.png,5ef815dc39036c120279a57f282c186a4cd5ed1a,Planes,89.463,114.406,-14.573,5576 -5577,sopwith,-1.479,-79.39,-89.38,0.095,0.859,1.35538461538,57914f12988e9ca7a7e65b9129383cad2f34e0fa,256,V6,57914f12988e9ca7a7e65b9129383cad2f34e0fa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-01.479_ry-79.390_rz-89.380_tx+00.095_ty+00.859_s+01.762_57914f12988e9ca7a7e65b9129383cad2f34e0fa_256x256.png,1a5133c2cf1dd4661c2da0d58e58dd0cb60bf7f9,Planes,-1.479,10.61,-89.38,5577 -5578,sopwith,-7.061,-17.742,79.858,0.577,0.244,0.853846153846,b95eaacb68d2dcf6390b44ac7fe8488ab57dbab4,256,V6,b95eaacb68d2dcf6390b44ac7fe8488ab57dbab4_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-07.061_ry-17.742_rz+79.858_tx+00.577_ty+00.244_s+01.110_b95eaacb68d2dcf6390b44ac7fe8488ab57dbab4_256x256.png,f0888e6a852fe4d7a9615c470c790ac7df53b5b6,Planes,-7.061,72.258,79.858,5578 -5579,sopwith,-10.919,-44.41,-9.368,0.198,0.844,0.980769230769,10cfca5ea068f08b4e70b811446d11a6b7a94150,256,V6,10cfca5ea068f08b4e70b811446d11a6b7a94150_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-10.919_ry-44.410_rz-09.368_tx+00.198_ty+00.844_s+01.275_10cfca5ea068f08b4e70b811446d11a6b7a94150_256x256.png,1dfd9352d51614af9f4a8e9ef5f323a0887af348,Planes,-10.919,45.59,-9.368,5579 -5580,sopwith,-14.162,-29.298,81.559,-0.585,-1.148,0.639230769231,9fbdb2c5aa54f3c5e44a96eefa339ca36d95ddc1,256,V6,9fbdb2c5aa54f3c5e44a96eefa339ca36d95ddc1_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-14.162_ry-29.298_rz+81.559_tx-00.585_ty-01.148_s+00.831_9fbdb2c5aa54f3c5e44a96eefa339ca36d95ddc1_256x256.png,1adf779306b8d991b907fda86efe0c9ed93ae55f,Planes,-14.162,60.702,81.559,5580 -5581,sopwith,-15.635,-14.82,3.401,-0.362,1.049,1.37461538462,4a76fe3bf71a6273b6584b4f0c71b4de466be12f,256,V6,4a76fe3bf71a6273b6584b4f0c71b4de466be12f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-15.635_ry-14.820_rz+03.401_tx-00.362_ty+01.049_s+01.787_4a76fe3bf71a6273b6584b4f0c71b4de466be12f_256x256.png,6b7fb0ca4383832a8816733a7f153986092104da,Planes,-15.635,75.18,3.401,5581 -5582,sopwith,-22.613,-49.565,23.547,-0.566,0.931,1.09769230769,a761405e11a29fcb3b1cea4d35b2a690dc1af993,256,V6,a761405e11a29fcb3b1cea4d35b2a690dc1af993_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-22.613_ry-49.565_rz+23.547_tx-00.566_ty+00.931_s+01.427_a761405e11a29fcb3b1cea4d35b2a690dc1af993_256x256.png,5ec50c3ec2bed443bb9644e1e0665bfb190943c2,Planes,-22.613,40.435,23.547,5582 -5583,sopwith,-22.989,-34.871,54.109,-0.278,0.166,1.46384615385,1c3e3b3932d40107ae365097b7360d6cd1cf0086,256,V6,1c3e3b3932d40107ae365097b7360d6cd1cf0086_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-22.989_ry-34.871_rz+54.109_tx-00.278_ty+00.166_s+01.903_1c3e3b3932d40107ae365097b7360d6cd1cf0086_256x256.png,4b76252b2e3f1bc403a87c200ef71c95c921f5f2,Planes,-22.989,55.129,54.109,5583 -5584,sopwith,-23.292,3.321,67.379,0.154,1.196,1.58692307692,a5e9876e0d248e89c8047853938f1c6ecdc29ad8,256,V6,a5e9876e0d248e89c8047853938f1c6ecdc29ad8_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-23.292_ry+03.321_rz+67.379_tx+00.154_ty+01.196_s+02.063_a5e9876e0d248e89c8047853938f1c6ecdc29ad8_256x256.png,5d96d1f2a15612282e8f429ca28c91b2005494fb,Planes,-23.292,93.321,67.379,5584 -5585,sopwith,-26.135,77.273,-15.41,0.289,0.346,1.45923076923,3d6881be95a926fe0de338708ad5427e524d27d2,256,V6,3d6881be95a926fe0de338708ad5427e524d27d2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-26.135_ry+77.273_rz-15.410_tx+00.289_ty+00.346_s+01.897_3d6881be95a926fe0de338708ad5427e524d27d2_256x256.png,f65f689b5046e12f30ce509e5a44e654fe29b9bd,Planes,-26.135,167.273,-15.41,5585 -5586,sopwith,-32.38,72.517,-13.403,-0.443,0.712,0.901538461538,45c1ef51b083426382f67a5b501e2794c9265c56,256,V6,45c1ef51b083426382f67a5b501e2794c9265c56_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-32.380_ry+72.517_rz-13.403_tx-00.443_ty+00.712_s+01.172_45c1ef51b083426382f67a5b501e2794c9265c56_256x256.png,7f9838212f38d45426a0245ad4fb89356bb9a3c6,Planes,-32.38,162.517,-13.403,5586 -5587,sopwith,-34.0,-87.186,-9.937,0.398,-0.664,1.03,0401a6d648226a3cb5177efd3d744b4f3143520d,256,V6,0401a6d648226a3cb5177efd3d744b4f3143520d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-34.000_ry-87.186_rz-09.937_tx+00.398_ty-00.664_s+01.339_0401a6d648226a3cb5177efd3d744b4f3143520d_256x256.png,1c87e716d4ffceb5c5d030ca4a607bfbe1abb7e8,Planes,-34.0,2.814,-9.937,5587 -5588,sopwith,-38.515,-19.624,44.96,-0.49,-0.577,0.653076923077,9545a74dc3b7615efafe988945e596cc1573e177,256,V6,9545a74dc3b7615efafe988945e596cc1573e177_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-38.515_ry-19.624_rz+44.960_tx-00.490_ty-00.577_s+00.849_9545a74dc3b7615efafe988945e596cc1573e177_256x256.png,fe817a17c9f9ac42cb31e10eae29c5b8329f9636,Planes,-38.515,70.376,44.96,5588 -5589,sopwith,-53.821,-22.344,-74.777,-0.175,0.882,1.10846153846,c2c705881a93a33765d92968d97c9c850b65b09d,256,V6,c2c705881a93a33765d92968d97c9c850b65b09d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-53.821_ry-22.344_rz-74.777_tx-00.175_ty+00.882_s+01.441_c2c705881a93a33765d92968d97c9c850b65b09d_256x256.png,f484d3b22ebf2286d0250926d37f34bb6b0482e9,Planes,-53.821,67.656,-74.777,5589 -5590,sopwith,-54.555,33.616,-14.825,0.418,0.779,1.53692307692,f9b595e5ed0ab41ca8f693860c013ee38eeb5347,256,V6,f9b595e5ed0ab41ca8f693860c013ee38eeb5347_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-54.555_ry+33.616_rz-14.825_tx+00.418_ty+00.779_s+01.998_f9b595e5ed0ab41ca8f693860c013ee38eeb5347_256x256.png,1051b3f06d801cc12053171a4d682f4c96c99432,Planes,-54.555,123.616,-14.825,5590 -5591,sopwith,-57.748,-21.861,15.9,-0.518,-0.72,1.10153846154,cb86f9b682de3a3ec1dec0a8aceec5ef559693d9,256,V6,cb86f9b682de3a3ec1dec0a8aceec5ef559693d9_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-57.748_ry-21.861_rz+15.900_tx-00.518_ty-00.720_s+01.432_cb86f9b682de3a3ec1dec0a8aceec5ef559693d9_256x256.png,16d93fde98c6c3f300a1ac2c6bb5ce81b40694fa,Planes,-57.748,68.139,15.9,5591 -5592,sopwith,-68.324,-49.922,12.405,0.028,1.039,0.770769230769,286c13ad298485e20738362b52fa8dd4aa2db87b,256,V6,286c13ad298485e20738362b52fa8dd4aa2db87b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-68.324_ry-49.922_rz+12.405_tx+00.028_ty+01.039_s+01.002_286c13ad298485e20738362b52fa8dd4aa2db87b_256x256.png,d3632cfc351f4aa65b37a4bc7c828385e2238eee,Planes,-68.324,40.078,12.405,5592 -5593,sopwith,-75.245,-67.011,82.701,0.386,1.02,0.872307692308,dd6d00551e6a44d6a9645b09914c61d5a09462f5,256,V6,dd6d00551e6a44d6a9645b09914c61d5a09462f5_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-75.245_ry-67.011_rz+82.701_tx+00.386_ty+01.020_s+01.134_dd6d00551e6a44d6a9645b09914c61d5a09462f5_256x256.png,f89a0567306d0a500822d2910c853f60bf7f055a,Planes,-75.245,22.989,82.701,5593 -5594,sopwith,-76.421,-88.249,16.907,-0.476,-0.829,1.03307692308,82d0828c7a23d1886e6e08aa431a21b832c416fa,256,V6,82d0828c7a23d1886e6e08aa431a21b832c416fa_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-76.421_ry-88.249_rz+16.907_tx-00.476_ty-00.829_s+01.343_82d0828c7a23d1886e6e08aa431a21b832c416fa_256x256.png,da8bcf7d9c7ee503428dbcbe7fd32b64e2d9a5db,Planes,-76.421,1.751,16.907,5594 -5595,sopwith,-76.52,-12.371,44.052,0.296,0.762,0.716153846154,1db322309bb86883e1fd1ddf7d800644dd4fe344,256,V6,1db322309bb86883e1fd1ddf7d800644dd4fe344_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-76.520_ry-12.371_rz+44.052_tx+00.296_ty+00.762_s+00.931_1db322309bb86883e1fd1ddf7d800644dd4fe344_256x256.png,c756e13322a694b5ddc70ac31ce7e0c0d36841ad,Planes,-76.52,77.629,44.052,5595 -5596,sopwith,-76.566,27.105,49.915,-0.291,0.236,0.726923076923,6515541bc6a400e3d7afe97556b905e79b9d0c3a,256,V6,6515541bc6a400e3d7afe97556b905e79b9d0c3a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-76.566_ry+27.105_rz+49.915_tx-00.291_ty+00.236_s+00.945_6515541bc6a400e3d7afe97556b905e79b9d0c3a_256x256.png,584f856c0d3e6e8649e0289de9b22bdf9b06c5ad,Planes,-76.566,117.105,49.915,5596 -5597,sopwith,-77.313,-0.898,44.562,0.258,-0.206,0.656923076923,4860b973f9d474559e16792cfe6e012ddccc5df3,256,V6,4860b973f9d474559e16792cfe6e012ddccc5df3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-77.313_ry-00.898_rz+44.562_tx+00.258_ty-00.206_s+00.854_4860b973f9d474559e16792cfe6e012ddccc5df3_256x256.png,8fbc69320b16f22cf74416f59bf499fcd66682d0,Planes,-77.313,89.102,44.562,5597 -5598,sopwith,-78.854,34.453,61.465,-0.0,-0.958,0.696923076923,c66efe56fbb2bcee29caa5588e1ab3541d327291,256,V6,c66efe56fbb2bcee29caa5588e1ab3541d327291_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-78.854_ry+34.453_rz+61.465_tx-00.000_ty-00.958_s+00.906_c66efe56fbb2bcee29caa5588e1ab3541d327291_256x256.png,aaf2e49949bb15e60956f04ca7ac9a17ece0a821,Planes,-78.854,124.453,61.465,5598 -5599,sopwith,-85.173,-61.788,-12.769,0.386,0.266,1.05,44eaeec3ba6a285c60df789f36eea586d57e3528,256,V6,44eaeec3ba6a285c60df789f36eea586d57e3528_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/sopwith_rx-85.173_ry-61.788_rz-12.769_tx+00.386_ty+00.266_s+01.365_44eaeec3ba6a285c60df789f36eea586d57e3528_256x256.png,11068a501b1757281a00cfc1dbc75fbbd8908723,Planes,-85.173,28.212,-12.769,5599 -5600,support,0.103,23.298,-54.263,0.165,-0.906,0.84,d9b32bf07c4609b4acb9696ec80bf7257b428c6b,256,V6,d9b32bf07c4609b4acb9696ec80bf7257b428c6b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+00.103_ry+113.298_rz-54.263_tx+00.165_ty-00.906_s+01.008_d9b32bf07c4609b4acb9696ec80bf7257b428c6b_256x256.png,d42ca3c35be4a09c2290a7d4d662270fa1f99ef1,Boats,0.103,-66.702,-54.263,5600 -5601,support,10.945,51.917,-33.914,-0.554,-1.179,1.30916666667,da7e9999f58a85ec33cb873182928a1375054791,256,V6,da7e9999f58a85ec33cb873182928a1375054791_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+10.945_ry+141.917_rz-33.914_tx-00.554_ty-01.179_s+01.571_da7e9999f58a85ec33cb873182928a1375054791_256x256.png,d4fa377bc1c539c00dec2c56ddea42d00f833567,Boats,10.945,-38.083,-33.914,5601 -5602,support,13.435,-51.424,75.474,0.445,0.86,1.40416666667,68b52498f3f57a1678a92ea45da5ff36ec67e6be,256,V6,68b52498f3f57a1678a92ea45da5ff36ec67e6be_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+13.435_ry+38.576_rz+75.474_tx+00.445_ty+00.860_s+01.685_68b52498f3f57a1678a92ea45da5ff36ec67e6be_256x256.png,78aa6cc84efdcb92527fea56ed327161a044450f,Boats,13.435,-141.424,75.474,5602 -5603,support,13.619,77.39,7.504,0.453,0.996,1.4425,683110e3e453817e6bee7e604b25f200f3c67e5e,256,V6,683110e3e453817e6bee7e604b25f200f3c67e5e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+13.619_ry+167.390_rz+07.504_tx+00.453_ty+00.996_s+01.731_683110e3e453817e6bee7e604b25f200f3c67e5e_256x256.png,67575bc912103e1244d92ea7298aa9a29038d03a,Boats,13.619,-12.61,7.504,5603 -5604,support,14.177,7.757,-71.085,-0.403,-0.125,1.56,3114dff53337f44fbb41ab2f0d7258e9740a983b,256,V6,3114dff53337f44fbb41ab2f0d7258e9740a983b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+14.177_ry+97.757_rz-71.085_tx-00.403_ty-00.125_s+01.872_3114dff53337f44fbb41ab2f0d7258e9740a983b_256x256.png,3a6041f53af06eb028055b36774d8e25f181aa3d,Boats,14.177,-82.243,-71.085,5604 -5605,support,21.772,-22.743,-42.693,-0.278,0.31,1.18,cc4ffbc12a9cbb9b9e9d19dad11f57c9cbb7a595,256,V6,cc4ffbc12a9cbb9b9e9d19dad11f57c9cbb7a595_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+21.772_ry+67.257_rz-42.693_tx-00.278_ty+00.310_s+01.416_cc4ffbc12a9cbb9b9e9d19dad11f57c9cbb7a595_256x256.png,a878fbbaaf697e714353d4c4f0962c2f2231ce4a,Boats,21.772,-112.743,-42.693,5605 -5606,support,22.806,-33.721,1.901,-0.269,-0.082,1.49916666667,b665e472c05f196f86ac75f38d3be79f420f420f,256,V6,b665e472c05f196f86ac75f38d3be79f420f420f_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+22.806_ry+56.279_rz+01.901_tx-00.269_ty-00.082_s+01.799_b665e472c05f196f86ac75f38d3be79f420f420f_256x256.png,633357e827c5611db482a20220e6f2f67433e1d4,Boats,22.806,-123.721,1.901,5606 -5607,support,26.889,71.427,-2.884,-0.471,-0.318,1.12416666667,c0dabe48b2816c02247915ecc60541789013ae61,256,V6,c0dabe48b2816c02247915ecc60541789013ae61_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+26.889_ry+161.427_rz-02.884_tx-00.471_ty-00.318_s+01.349_c0dabe48b2816c02247915ecc60541789013ae61_256x256.png,e7ac7f23afced5207ca1d3778da1344f65d81f93,Boats,26.889,-18.573,-2.884,5607 -5608,support,29.939,14.856,64.44,-0.471,0.969,0.830833333333,29bbc94867083f413e41225ebaa879b9cda7bafb,256,V6,29bbc94867083f413e41225ebaa879b9cda7bafb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+29.939_ry+104.856_rz+64.440_tx-00.471_ty+00.969_s+00.997_29bbc94867083f413e41225ebaa879b9cda7bafb_256x256.png,07e15a7787b892af6f59ec4e42355ae09e47e04e,Boats,29.939,-75.144,64.44,5608 -5609,support,32.722,6.056,-28.236,-0.095,0.943,0.933333333333,5f861bd26ff043420d97fa12705891e0cb159a9e,256,V6,5f861bd26ff043420d97fa12705891e0cb159a9e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+32.722_ry+96.056_rz-28.236_tx-00.095_ty+00.943_s+01.120_5f861bd26ff043420d97fa12705891e0cb159a9e_256x256.png,34d7f457ecc146e288c6851a7577f2ca61efc39c,Boats,32.722,-83.944,-28.236,5609 -5610,support,38.035,-55.898,7.287,0.537,0.422,0.783333333333,e11714c058fb7c07f4fd8c1831685760388ca44b,256,V6,e11714c058fb7c07f4fd8c1831685760388ca44b_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+38.035_ry+34.102_rz+07.287_tx+00.537_ty+00.422_s+00.940_e11714c058fb7c07f4fd8c1831685760388ca44b_256x256.png,d9ec7f65a52bcf0240860e06a03294b4e83ff9fa,Boats,38.035,-145.898,7.287,5610 -5611,support,41.369,41.615,-71.49,0.598,-0.95,1.56166666667,64d1db24ae2fc94ac26101da6bb89e6b7ca7c05d,256,V6,64d1db24ae2fc94ac26101da6bb89e6b7ca7c05d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+41.369_ry+131.615_rz-71.490_tx+00.598_ty-00.950_s+01.874_64d1db24ae2fc94ac26101da6bb89e6b7ca7c05d_256x256.png,11ce9840cccfc4f53a11c9993760bd44c0eec0fe,Boats,41.369,-48.385,-71.49,5611 -5612,support,42.536,-62.247,87.345,-0.014,-0.661,1.17583333333,a8e6fe45a79059d6890e840fb67b8d7fcca2a9df,256,V6,a8e6fe45a79059d6890e840fb67b8d7fcca2a9df_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+42.536_ry+27.753_rz+87.345_tx-00.014_ty-00.661_s+01.411_a8e6fe45a79059d6890e840fb67b8d7fcca2a9df_256x256.png,2e2b1fba76743c9a26189176a672ec88b2f4737c,Boats,42.536,-152.247,87.345,5612 -5613,support,44.543,-67.971,54.984,0.08,0.098,1.23666666667,69c1dd41ed5ad96af76463555f7349b980a9c984,256,V6,69c1dd41ed5ad96af76463555f7349b980a9c984_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+44.543_ry+22.029_rz+54.984_tx+00.080_ty+00.098_s+01.484_69c1dd41ed5ad96af76463555f7349b980a9c984_256x256.png,c2c36b4d4489367784321634834573fa205b678e,Boats,44.543,-157.971,54.984,5613 -5614,support,45.796,26.966,-0.44,-0.289,0.031,1.47416666667,feb82189fafb7bd98e0af9ce928ddc06b637b682,256,V6,feb82189fafb7bd98e0af9ce928ddc06b637b682_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+45.796_ry+116.966_rz-00.440_tx-00.289_ty+00.031_s+01.769_feb82189fafb7bd98e0af9ce928ddc06b637b682_256x256.png,ee3646205114049df20e0a9c61c305ff0866363c,Boats,45.796,-63.034,-0.44,5614 -5615,support,51.799,-49.662,-43.958,-0.365,0.236,1.01666666667,c50bcb7e456cd1030b394f7a4e8e1eb3705ef030,256,V6,c50bcb7e456cd1030b394f7a4e8e1eb3705ef030_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+51.799_ry+40.338_rz-43.958_tx-00.365_ty+00.236_s+01.220_c50bcb7e456cd1030b394f7a4e8e1eb3705ef030_256x256.png,fa7fe737cf8415ba6f1f3b9d259daeeee1761b51,Boats,51.799,-139.662,-43.958,5615 -5616,support,51.973,-25.146,-47.388,-0.585,-0.625,1.2025,eb8ac17ea71f6171cfa453993508de3601b54119,256,V6,eb8ac17ea71f6171cfa453993508de3601b54119_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+51.973_ry+64.854_rz-47.388_tx-00.585_ty-00.625_s+01.443_eb8ac17ea71f6171cfa453993508de3601b54119_256x256.png,a1d88bb80d9d1e2d5b647d09b13ba110860e2bb5,Boats,51.973,-115.146,-47.388,5616 -5617,support,53.275,78.017,-85.839,-0.548,-0.554,1.22833333333,1e2d669d469ad02401f5b69fce097b4d20f86449,256,V6,1e2d669d469ad02401f5b69fce097b4d20f86449_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+53.275_ry+168.017_rz-85.839_tx-00.548_ty-00.554_s+01.474_1e2d669d469ad02401f5b69fce097b4d20f86449_256x256.png,7fe759fe5aae9f7440293a2e2fd6a1be4820f86f,Boats,53.275,-11.983,-85.839,5617 -5618,support,62.605,57.267,47.69,-0.42,0.903,1.44333333333,f893f94914da7421b2920a00839e4d48679ca2c2,256,V6,f893f94914da7421b2920a00839e4d48679ca2c2_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+62.605_ry+147.267_rz+47.690_tx-00.420_ty+00.903_s+01.732_f893f94914da7421b2920a00839e4d48679ca2c2_256x256.png,654bba7be488410f6600763fd15e433361234905,Boats,62.605,-32.733,47.69,5618 -5619,support,68.688,-57.065,-34.236,0.441,0.286,0.719166666667,bdf68b6861fea7ffe0da3bdb6f214430b28543cb,256,V6,bdf68b6861fea7ffe0da3bdb6f214430b28543cb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+68.688_ry+32.935_rz-34.236_tx+00.441_ty+00.286_s+00.863_bdf68b6861fea7ffe0da3bdb6f214430b28543cb_256x256.png,aab043255bbd91a6219cd174d57b16886669bb1a,Boats,68.688,-147.065,-34.236,5619 -5620,support,81.923,-10.04,51.753,-0.581,-0.35,1.52583333333,efd5f624b2de83392f65432e251fcf60346659df,256,V6,efd5f624b2de83392f65432e251fcf60346659df_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+81.923_ry+79.960_rz+51.753_tx-00.581_ty-00.350_s+01.831_efd5f624b2de83392f65432e251fcf60346659df_256x256.png,01d1945d2653fdf012a56b8809a2042ad857df6d,Boats,81.923,-100.04,51.753,5620 -5621,support,85.834,-63.524,-86.003,0.251,1.11,1.51583333333,22b8b3c44ffd99b2dfd03b1ea3ecb9c5926d28ea,256,V6,22b8b3c44ffd99b2dfd03b1ea3ecb9c5926d28ea_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx+85.834_ry+26.476_rz-86.003_tx+00.251_ty+01.110_s+01.819_22b8b3c44ffd99b2dfd03b1ea3ecb9c5926d28ea_256x256.png,c9986e97ea527ba64db3434a5913b9d0818dc713,Boats,85.834,-153.524,-86.003,5621 -5622,support,-0.14,-37.495,-6.026,-0.16,0.805,1.0175,981a93a63fc27ea86682ba14998d2bf4302cad07,256,V6,981a93a63fc27ea86682ba14998d2bf4302cad07_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/support_rx-00.140_ry+52.505_rz-06.026_tx-00.160_ty+00.805_s+01.221_981a93a63fc27ea86682ba14998d2bf4302cad07_256x256.png,4d9152583436bc1182185de96266bd0031515d7f,Boats,-0.14,-127.495,-6.026,5622 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-5729,z3,63.566,-40.296,-61.601,-0.595,-0.195,1.48,fa3aff9af6b31c0527d07d84a9f49b95f265f1fb,256,V6,fa3aff9af6b31c0527d07d84a9f49b95f265f1fb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx+63.566_ry-40.296_rz-61.601_tx-00.595_ty-00.195_s+01.480_fa3aff9af6b31c0527d07d84a9f49b95f265f1fb_256x256.png,834f248ef88b8eb6dc7bc0a14ced52de64191a33,Cars,63.566,49.704,-61.601,5729 -5730,z3,64.885,18.965,-2.787,-0.553,0.113,1.562,409eb800ce69d346b771fc09119896d441ee28da,256,V6,409eb800ce69d346b771fc09119896d441ee28da_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx+64.885_ry+18.965_rz-02.787_tx-00.553_ty+00.113_s+01.562_409eb800ce69d346b771fc09119896d441ee28da_256x256.png,7acf9f121ed2c04c859c69fd07fddbf26bf296e2,Cars,64.885,108.965,-2.787,5730 -5731,z3,65.932,-50.317,-43.622,-0.356,0.595,1.2,383b539b6597a2a5c521051e384b8a46a30843c0,256,V6,383b539b6597a2a5c521051e384b8a46a30843c0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx+65.932_ry-50.317_rz-43.622_tx-00.356_ty+00.595_s+01.200_383b539b6597a2a5c521051e384b8a46a30843c0_256x256.png,5265e92b005cb96fab76b3c9cc9dde05195e549d,Cars,65.932,39.683,-43.622,5731 -5732,z3,68.439,85.425,-72.142,-0.165,0.45,1.419,52d88d1b0c95e82a642dacb1854b1c06f4f7b954,256,V6,52d88d1b0c95e82a642dacb1854b1c06f4f7b954_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx+68.439_ry+85.425_rz-72.142_tx-00.165_ty+00.450_s+01.419_52d88d1b0c95e82a642dacb1854b1c06f4f7b954_256x256.png,d8eb454f67f1a167d7fd85b3d0f5044e7fe9deec,Cars,68.439,175.425,-72.142,5732 -5733,z3,69.157,-8.158,-8.823,-0.568,-0.86,1.23,b125a656add83a448a3dc04fc7f1110516328f2d,256,V6,b125a656add83a448a3dc04fc7f1110516328f2d_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx+69.157_ry-08.158_rz-08.823_tx-00.568_ty-00.860_s+01.230_b125a656add83a448a3dc04fc7f1110516328f2d_256x256.png,088198f46559d2e34fa4b6f2e82f6d5b308edc12,Cars,69.157,81.842,-8.823,5733 -5734,z3,73.886,-38.486,75.404,0.022,1.033,0.671,61c2c2c399c44f9c0dfc16a820d4289e80ae7e32,256,V6,61c2c2c399c44f9c0dfc16a820d4289e80ae7e32_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx+73.886_ry-38.486_rz+75.404_tx+00.022_ty+01.033_s+00.671_61c2c2c399c44f9c0dfc16a820d4289e80ae7e32_256x256.png,99208bb6b09c2d519fd5b612d3c6811de2d5fdaa,Cars,73.886,51.514,75.404,5734 -5735,z3,77.115,75.191,-82.655,0.049,-0.871,1.438,eea26fc77129885b531fd6052ceb9abf5e63943a,256,V6,eea26fc77129885b531fd6052ceb9abf5e63943a_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx+77.115_ry+75.191_rz-82.655_tx+00.049_ty-00.871_s+01.438_eea26fc77129885b531fd6052ceb9abf5e63943a_256x256.png,1e9c7f4d6e538056b78df86ca950e625af1362f3,Cars,77.115,165.191,-82.655,5735 -5736,z3,80.452,88.226,77.849,0.58,-1.192,0.747,a17beb47186d13aa2191f43e4da68649eebeeba3,256,V6,a17beb47186d13aa2191f43e4da68649eebeeba3_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx+80.452_ry+88.226_rz+77.849_tx+00.580_ty-01.192_s+00.747_a17beb47186d13aa2191f43e4da68649eebeeba3_256x256.png,852413b76463b469c08d85190190ab4d98d5287a,Cars,80.452,178.226,77.849,5736 -5737,z3,89.56,63.35,77.975,0.546,-0.52,0.633,85bc69d8900affd2edb409623b8a1a799d7aad62,256,V6,85bc69d8900affd2edb409623b8a1a799d7aad62_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx+89.560_ry+63.350_rz+77.975_tx+00.546_ty-00.520_s+00.633_85bc69d8900affd2edb409623b8a1a799d7aad62_256x256.png,6bebebc4a7fd838b015451789a8e346c215f2333,Cars,89.56,153.35,77.975,5737 -5738,z3,-3.998,10.359,-67.726,-0.411,-0.907,1.184,03fc246032eff7f0be22f744a830666894606ade,256,V6,03fc246032eff7f0be22f744a830666894606ade_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx-03.998_ry+10.359_rz-67.726_tx-00.411_ty-00.907_s+01.184_03fc246032eff7f0be22f744a830666894606ade_256x256.png,3459aca59a6d9ba51ac702caea98794314509538,Cars,-3.998,100.359,-67.726,5738 -5739,z3,-9.929,-69.289,30.773,-0.483,0.457,1.025,193afd101d3a76867ef1fd3d877f71409f538ff0,256,V6,193afd101d3a76867ef1fd3d877f71409f538ff0_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx-09.929_ry-69.289_rz+30.773_tx-00.483_ty+00.457_s+01.025_193afd101d3a76867ef1fd3d877f71409f538ff0_256x256.png,775570462ec9b39f637c75cf48be4a0ffee4dba5,Cars,-9.929,20.711,30.773,5739 -5740,z3,-13.186,74.496,-24.282,0.551,-0.574,0.689,cfd7cbb5bb7b1f507cf6e54dd01e90e5d8b9c574,256,V6,cfd7cbb5bb7b1f507cf6e54dd01e90e5d8b9c574_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx-13.186_ry+74.496_rz-24.282_tx+00.551_ty-00.574_s+00.689_cfd7cbb5bb7b1f507cf6e54dd01e90e5d8b9c574_256x256.png,28cff77bb8639846c2bc3e98f45a64df157b3ccd,Cars,-13.186,164.496,-24.282,5740 -5741,z3,-16.08,33.535,-2.61,-0.138,-1.084,0.782,f081f02789dfdd610beb3dbc653102cb38a9b32c,256,V6,f081f02789dfdd610beb3dbc653102cb38a9b32c_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx-16.080_ry+33.535_rz-02.610_tx-00.138_ty-01.084_s+00.782_f081f02789dfdd610beb3dbc653102cb38a9b32c_256x256.png,14e0c51e4ffc5550a6eb700ddf60b83f0bf077b1,Cars,-16.08,123.535,-2.61,5741 -5742,z3,-19.952,16.022,27.868,0.225,0.028,1.181,934eb1d2127498eb4ea021c17edf5009e970f307,256,V6,934eb1d2127498eb4ea021c17edf5009e970f307_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx-19.952_ry+16.022_rz+27.868_tx+00.225_ty+00.028_s+01.181_934eb1d2127498eb4ea021c17edf5009e970f307_256x256.png,0333301170427e10f0c8b75acad1ce5753d85389,Cars,-19.952,106.022,27.868,5742 -5743,z3,-20.279,78.461,-87.58,-0.522,-0.422,0.889,e74cc8768703a9af05d0d90ba1e1182fffc5c00e,256,V6,e74cc8768703a9af05d0d90ba1e1182fffc5c00e_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx-20.279_ry+78.461_rz-87.580_tx-00.522_ty-00.422_s+00.889_e74cc8768703a9af05d0d90ba1e1182fffc5c00e_256x256.png,3bdf67ef326747837836cd069f5ccf865250e55e,Cars,-20.279,168.461,-87.58,5743 -5744,z3,-23.153,-36.644,18.413,-0.408,0.835,0.842,ea7a8faaf35aee041abb79ecd539195a91a2f884,256,V6,ea7a8faaf35aee041abb79ecd539195a91a2f884_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx-23.153_ry-36.644_rz+18.413_tx-00.408_ty+00.835_s+00.842_ea7a8faaf35aee041abb79ecd539195a91a2f884_256x256.png,8068a3ce81c3f27604e9aaaceb3712aaf066f50c,Cars,-23.153,53.356,18.413,5744 -5745,z3,-28.337,-69.06,57.5,-0.464,-0.972,1.364,985f16893c91680d57e71acc68dad790e247d8b6,256,V6,985f16893c91680d57e71acc68dad790e247d8b6_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx-28.337_ry-69.060_rz+57.500_tx-00.464_ty-00.972_s+01.364_985f16893c91680d57e71acc68dad790e247d8b6_256x256.png,d8987a9d6b3c3bfa0739af673ab5911975d4992b,Cars,-28.337,20.94,57.5,5745 -5746,z3,-38.736,-45.764,65.502,0.312,0.042,1.428,ccee689aa597aa9e08a77716cefab6b6ea5cd1dc,256,V6,ccee689aa597aa9e08a77716cefab6b6ea5cd1dc_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx-38.736_ry-45.764_rz+65.502_tx+00.312_ty+00.042_s+01.428_ccee689aa597aa9e08a77716cefab6b6ea5cd1dc_256x256.png,fae996892a974ea3d950f5762e80c54d8655ab72,Cars,-38.736,44.236,65.502,5746 -5747,z3,-49.16,11.28,-0.264,-0.277,-0.83,1.555,eda16292bd0a5fce946fadf6adf785f8177c4157,256,V6,eda16292bd0a5fce946fadf6adf785f8177c4157_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx-49.160_ry+11.280_rz-00.264_tx-00.277_ty-00.830_s+01.555_eda16292bd0a5fce946fadf6adf785f8177c4157_256x256.png,06d285d078dbbc488e55c75e481d826d2a0428c0,Cars,-49.16,101.28,-0.264,5747 -5748,z3,-52.935,56.828,-0.592,0.576,0.028,0.854,7bda99c1885fe0b07f9acfd12018cf94a164c626,256,V6,7bda99c1885fe0b07f9acfd12018cf94a164c626_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx-52.935_ry+56.828_rz-00.592_tx+00.576_ty+00.028_s+00.854_7bda99c1885fe0b07f9acfd12018cf94a164c626_256x256.png,7e227a5d69f7c6ce9c7180945b9108836b135756,Cars,-52.935,146.828,-0.592,5748 -5749,z3,-54.264,-5.677,11.103,-0.391,-0.96,1.567,d721b5f8d4c57c7814feeac9b4a4286d5240b978,256,V6,d721b5f8d4c57c7814feeac9b4a4286d5240b978_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx-54.264_ry-05.677_rz+11.103_tx-00.391_ty-00.960_s+01.567_d721b5f8d4c57c7814feeac9b4a4286d5240b978_256x256.png,092ffe18940a0f06d2e792f9041c490a53f33aa3,Cars,-54.264,84.323,11.103,5749 -5750,z3,-54.823,86.269,84.744,-0.103,0.082,0.947,7f4ad2cd7c887496c1e7223c2c33146b2bf1cdeb,256,V6,7f4ad2cd7c887496c1e7223c2c33146b2bf1cdeb_V6,/braintree/home/jjpr/.skdata/HvMWithDiscfade_6eef6648406c333a4035cd5e60d0bf2ecf2606d7/Variation06_20110131/z3_rx-54.823_ry+86.269_rz+84.744_tx-00.103_ty+00.082_s+00.947_7f4ad2cd7c887496c1e7223c2c33146b2bf1cdeb_256x256.png,458e8bb4724af0a51f4678d7bda4da9cdca56d07,Cars,-54.823,176.269,84.744,5750 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" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "if (typeof Jupyter !== \"undefined\") { window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")};}else{ element.textContent = ' ';}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from vpython import canvas, vector, color, label, box, sphere, arrow" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "if (typeof Jupyter !== \"undefined\") { window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")};}else{ element.textContent = ' ';}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "scene = canvas()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "if (typeof Jupyter !== \"undefined\") {require.undef(\"nbextensions/vpython_libraries/glow.min\");}else{element.textContent = ' ';}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "if (typeof Jupyter !== \"undefined\") {require.undef(\"nbextensions/vpython_libraries/glowcomm\");}else{element.textContent = ' ';}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "if (typeof Jupyter !== \"undefined\") {require.undef(\"nbextensions/vpython_libraries/jquery-ui.custom.min\");}else{element.textContent = ' ';}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "if (typeof Jupyter !== \"undefined\") {require([\"nbextensions/vpython_libraries/glow.min\"], function(){console.log(\"GLOW LOADED\");});}else{element.textContent = ' ';}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "if (typeof Jupyter !== \"undefined\") {require([\"nbextensions/vpython_libraries/glowcomm\"], function(){console.log(\"GLOWCOMM LOADED\");});}else{element.textContent = ' ';}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "if (typeof Jupyter !== \"undefined\") {require([\"nbextensions/vpython_libraries/jquery-ui.custom.min\"], function(){console.log(\"JQUERY LOADED\");});}else{element.textContent = ' ';}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "box()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "sphere()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "mybox = box(pos=vector(0,0,0), length=51, height=50, width=39)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import tables" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Users/jjpr/Downloads/DiCarlo Lab/npc_v4_data.h5'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "h5 = tables.open_file(\"/Users/jjpr/Downloads/DiCarlo Lab/npc_v4_data.h5\")\n", - "h5.filename" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "synthetic_monkey_m_ohp_session_1\n", - "(51, 50, 39)\n", - "synthetic_monkey_m_ohp_session_2\n", - "(40, 45, 38)\n", - "synthetic_monkey_m_stretch_session_1\n", - "(51, 50, 39)\n", - "synthetic_monkey_m_stretch_session_2\n", - "(33, 80, 52)\n", - "synthetic_monkey_n_stretch_session_1\n", - "(31, 90, 20)\n", - "synthetic_monkey_n_stretch_session_2\n", - "(20, 5, 1)\n", - "synthetic_monkey_s_ohp_session_1\n", - "(47, 95, 22)\n", - "synthetic_monkey_s_stretch_session_1\n", - "(47, 95, 22)\n" - ] - } - ], - "source": [ - "for monkey in h5.root.neural.synthetic:\n", - " for setting in monkey:\n", - " for session in setting: \n", - " print(f\"synthetic_{monkey._v_name}_{setting._v_name}_{session._v_name}\")\n", - " print(session.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "synthetic_monkey_m_ohp_session_1\n", - "(51, 50, 39)\n" - ] - }, - { - "data": { - "application/javascript": [ - "if (typeof Jupyter !== \"undefined\") {require.undef(\"nbextensions/vpython_libraries/glow.min\");}else{element.textContent = ' ';}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "if (typeof Jupyter !== \"undefined\") {require.undef(\"nbextensions/vpython_libraries/glowcomm\");}else{element.textContent = ' ';}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "if (typeof Jupyter !== \"undefined\") {require.undef(\"nbextensions/vpython_libraries/jquery-ui.custom.min\");}else{element.textContent = ' ';}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "if (typeof Jupyter !== \"undefined\") {require([\"nbextensions/vpython_libraries/glow.min\"], function(){console.log(\"GLOW LOADED\");});}else{element.textContent = ' ';}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "if (typeof Jupyter !== \"undefined\") {require([\"nbextensions/vpython_libraries/glowcomm\"], function(){console.log(\"GLOWCOMM LOADED\");});}else{element.textContent = ' ';}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "if (typeof Jupyter !== \"undefined\") {require([\"nbextensions/vpython_libraries/jquery-ui.custom.min\"], function(){console.log(\"JQUERY LOADED\");});}else{element.textContent = ' ';}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "synthetic_monkey_m_ohp_session_2\n", - "(40, 45, 38)\n", - "synthetic_monkey_m_stretch_session_1\n", - "(51, 50, 39)\n", - "synthetic_monkey_m_stretch_session_2\n", - "(33, 80, 52)\n", - "synthetic_monkey_n_stretch_session_1\n", - "(31, 90, 20)\n", - "synthetic_monkey_n_stretch_session_2\n", - "(20, 5, 1)\n", - "synthetic_monkey_s_ohp_session_1\n", - "(47, 95, 22)\n", - "synthetic_monkey_s_stretch_session_1\n", - "(47, 95, 22)\n" - ] - } - ], - "source": [ - "x_offset = 0\n", - "for monkey in h5.root.neural.synthetic:\n", - " for setting in monkey:\n", - " for session in setting: \n", - " sh = session.shape\n", - " print(f\"synthetic_{monkey._v_name}_{setting._v_name}_{session._v_name}\")\n", - " print(sh)\n", - " box(pos=vector(x_offset, 0, 0), length=sh[2], height=sh[1], width=sh[0])\n", - " x_offset += 60" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scene.objects" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "list" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(scene.objects)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "objects is read-only", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscene\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobjects\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/anaconda/envs/dlvisuals/lib/python3.8/site-packages/vpython/vpython.py\u001b[0m in \u001b[0;36mobjects\u001b[0;34m(self, *args1, **args)\u001b[0m\n\u001b[1;32m 3069\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mobjects\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3070\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mobjects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0margs\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3071\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'objects is read-only'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3072\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3073\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mobjz\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: objects is read-only" - ] - } - ], - "source": [ - "scene.objects = []" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "mybox.delete()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(<60, 0, 0>, <38, 45, 40>),\n", - " (<240, 0, 0>, <20, 90, 31>),\n", - " (<360, 0, 0>, <22, 95, 47>),\n", - " (<420, 0, 0>, <22, 95, 47>),\n", - " (<300, 0, 0>, <1, 5, 20>),\n", - " (<180, 0, 0>, <52, 80, 33>),\n", - " (<0, 0, 0>, <39, 50, 51>),\n", - " (<0, 0, 0>, <51, 50, 39>),\n", - " (<0, 0, 0>, <1, 1, 1>),\n", - " (<120, 0, 0>, <39, 50, 51>),\n", - " (<0, 0, 0>, <2, 2, 2>)]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[(x.pos, x.size) for x in scene.objects]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "scene.objects[-1].delete()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "scene.objects[-3].delete()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "scene.objects[-4].delete()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "scene.delete()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "naturalistic_monkey_m_ohp_session_1\n", - "(38, 640, 39)\n", - "naturalistic_monkey_m_ohp_session_2\n", - "(36, 640, 38)\n", - "naturalistic_monkey_m_stretch_session_1\n", - "(38, 640, 39)\n", - "naturalistic_monkey_m_stretch_session_2\n", - "(36, 640, 52)\n", - "naturalistic_monkey_n_stretch_session_1\n", - "(34, 640, 20)\n", - "naturalistic_monkey_n_stretch_session_2\n", - "(34, 640, 1)\n", - "naturalistic_monkey_s_ohp_session_1\n", - "(33, 640, 22)\n", - "naturalistic_monkey_s_stretch_session_1\n", - "(33, 640, 22)\n" - ] - } - ], - "source": [ - "x_offset = 0\n", - "z_offset = 60\n", - "for monkey in h5.root.neural.naturalistic:\n", - " for setting in monkey:\n", - " for session in setting: \n", - " sh = session.shape\n", - " print(f\"naturalistic_{monkey._v_name}_{setting._v_name}_{session._v_name}\")\n", - " print(sh)\n", - " box(pos=vector(x_offset, 0, z_offset), length=sh[2], height=sh[1], width=sh[0])\n", - " x_offset += 60" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "naturalistic_monkey_m_ohp_session_1\n", - "(38, 640, 39)\n", - "naturalistic_monkey_m_ohp_session_2\n", - "(36, 640, 38)\n", - "naturalistic_monkey_m_stretch_session_1\n", - "(38, 640, 39)\n", - "naturalistic_monkey_m_stretch_session_2\n", - "(36, 640, 52)\n", - "naturalistic_monkey_n_stretch_session_1\n", - "(34, 640, 20)\n", - "naturalistic_monkey_n_stretch_session_2\n", - "(34, 640, 1)\n", - "naturalistic_monkey_s_ohp_session_1\n", - "(33, 640, 22)\n", - "naturalistic_monkey_s_stretch_session_1\n", - "(33, 640, 22)\n", - "synthetic_monkey_m_ohp_session_1\n", - "(51, 50, 39)\n", - "synthetic_monkey_m_ohp_session_2\n", - "(40, 45, 38)\n", - "synthetic_monkey_m_stretch_session_1\n", - "(51, 50, 39)\n", - "synthetic_monkey_m_stretch_session_2\n", - "(33, 80, 52)\n", - "synthetic_monkey_n_stretch_session_1\n", - "(31, 90, 20)\n", - "synthetic_monkey_n_stretch_session_2\n", - "(20, 5, 1)\n", - "synthetic_monkey_s_ohp_session_1\n", - "(47, 95, 22)\n", - "synthetic_monkey_s_stretch_session_1\n", - "(47, 95, 22)\n" - ] - } - ], - "source": [ - "x_offset = 0\n", - "z_offset = 60\n", - "for monkey in h5.root.neural.naturalistic:\n", - " for setting in monkey:\n", - " for session in setting: \n", - " sh = session.shape\n", - " print(f\"naturalistic_{monkey._v_name}_{setting._v_name}_{session._v_name}\")\n", - " print(sh)\n", - " box(pos=vector(x_offset, 0, 0), length=sh[2], height=sh[1], width=sh[0])\n", - " x_offset += 60\n", - "x_offset = 0\n", - "for monkey in h5.root.neural.synthetic:\n", - " for setting in monkey:\n", - " for session in setting: \n", - " sh = session.shape\n", - " print(f\"synthetic_{monkey._v_name}_{setting._v_name}_{session._v_name}\")\n", - " print(sh)\n", - " box(pos=vector(x_offset, 0, z_offset), length=sh[2], height=sh[1], width=sh[0])\n", - " x_offset += 60" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "for o in scene.objects:\n", - " o.visible = False\n", - " del o" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "b = scene.objects[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(<360, 0, 60>, <22, 95, 47>)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(b.pos, b.size)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<11, 47.5, 23.5>" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "b.size / 2" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "22.0" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "b.size.x" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def databox(*args, **kwargs):\n", - " b = box(*args, **kwargs)\n", - " b.pos = vector(b.pos + (b.size / 2))\n", - " return b" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "naturalistic_monkey_m_ohp_session_1: (38, 640, 39)\n", - "naturalistic_monkey_m_ohp_session_2: (36, 640, 38)\n", - "naturalistic_monkey_m_stretch_session_1: (38, 640, 39)\n", - "naturalistic_monkey_m_stretch_session_2: (36, 640, 52)\n", - "naturalistic_monkey_n_stretch_session_1: (34, 640, 20)\n", - "naturalistic_monkey_n_stretch_session_2: (34, 640, 1)\n", - "naturalistic_monkey_s_ohp_session_1: (33, 640, 22)\n", - "naturalistic_monkey_s_stretch_session_1: (33, 640, 22)\n", - "synthetic_monkey_m_ohp_session_1: (51, 50, 39)\n", - "synthetic_monkey_m_ohp_session_2: (40, 45, 38)\n", - "synthetic_monkey_m_stretch_session_1: (51, 50, 39)\n", - "synthetic_monkey_m_stretch_session_2: (33, 80, 52)\n", - "synthetic_monkey_n_stretch_session_1: (31, 90, 20)\n", - "synthetic_monkey_n_stretch_session_2: (20, 5, 1)\n", - "synthetic_monkey_s_ohp_session_1: (47, 95, 22)\n", - "synthetic_monkey_s_stretch_session_1: (47, 95, 22)\n" - ] - } - ], - "source": [ - "for o in scene.objects:\n", - " o.visible = False\n", - " del o\n", - " \n", - "# dimensions in H5: (repetition, image, neuroid)\n", - "# dimensions in diagram: (x: neuroid, y: image, z: repetition)\n", - "axes = [\n", - " arrow(axis=vector(200,0,0), color=color.red), \n", - " arrow(axis=vector(0,200,0), color=color.green), \n", - " arrow(axis=vector(0,0,200), color=color.blue)\n", - "]\n", - "axis_labels = [label(text=name, pos=axis.axis) for axis, name in zip(axes, [\"neuroid\", \"image\", \"repetition\"])]\n", - "x_offset = 0\n", - "for monkey in h5.root.neural.naturalistic:\n", - " for setting in monkey:\n", - " for session in setting: \n", - " sh = session.shape\n", - " size = vector(sh[2], sh[1], sh[0])\n", - " text = f\"naturalistic_{monkey._v_name}_{setting._v_name}_{session._v_name}: {sh}\"\n", - " print(text)\n", - " pos = vector(x_offset, 0, 0)\n", - " b = databox(pos=pos, size=size, color=color.cyan, opacity=0.5)\n", - " x_offset += b.size.x + 20\n", - "# l = label(pos=pos, text=text, pixel_pos=False)\n", - "x_offset = 0\n", - "y_offset = 0\n", - "z_offset = 60\n", - "for monkey in h5.root.neural.synthetic:\n", - " for setting in monkey:\n", - " for session in setting: \n", - " sh = session.shape\n", - " size = vector(sh[2], sh[1], sh[0])\n", - " text = f\"synthetic_{monkey._v_name}_{setting._v_name}_{session._v_name}: {sh}\"\n", - " print(text)\n", - " pos = vector(x_offset, y_offset, z_offset)\n", - " b = databox(pos=pos, size=size, color=color.magenta, opacity=0.5)\n", - " x_offset += b.size.x + 20\n", - " y_offset += b.size.y\n", - "# l = label(pos=pos, text=text, pixel_pos=False) " - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scene.visible" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dlvisuals", - "language": "python", - "name": "dlvisuals" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/packaging/notebooks/2021-01-26_gallant_v2.ipynb b/packaging/notebooks/2021-01-26_gallant_v2.ipynb deleted file mode 100644 index 23b2d0650..000000000 --- a/packaging/notebooks/2021-01-26_gallant_v2.ipynb +++ /dev/null @@ -1,21128 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "import pprint\n", - "import os\n", - "import json" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading lookup from /braintree/home/jjpr/dev/brainio_collection/brainio_collection/lookup.csv\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr\n", - "import jmespath\n", - "from brainio_collection import list_stimulus_sets, list_assemblies, get_stimulus_set, get_assembly" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.express as px" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from plotly.subplots import make_subplots" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.graph_objects as go" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from brainio_base.assemblies import NeuronRecordingAssembly" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "import scipy.io as sio" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "import re" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "source_netcdf_file = \"/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/crcns_v2-1_neuronal.nc\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "v2 = NeuronRecordingAssembly(xr.open_dataarray(source_netcdf_file))\n", - "v2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "v2_nans = np.count_nonzero(np.isnan(v2))\n", - "print(v2.size - v2_nans)\n", - "print(v2_nans)\n", - "print(v2.size)\n", - "v2_nans/v2.size" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "example_csv_path = Path(\"/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/V2Data10/NatRev/data/e0052.edrev3.001.p1.mat.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "example_mat_path = Path(\"/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/V2Data10/NatRev/e0052/e0052.edrev3.001.p1.mat\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example_csv = pd.read_csv(example_csv_path)\n", - "example_csv" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "example_mat = sio.loadmat(example_mat_path)\n", - "example_mat" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example_mat[\"psth\"].shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for k, v in example_mat.items():\n", - " if hasattr(v, \"shape\"):\n", - " print(f\"{k}: {v.dtype} {v.shape}\")\n", - " else: \n", - " print(f\"{k}: {type(v)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PosixPath('/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "v2_path = example_csv_path.parents[3]\n", - "v2_path" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "v2_path_glob = pprint.pformat(list(example_csv_path.parents[3].glob(\"**\")))\n", - "v2_path_glob" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "v2_path_walk = pprint.pformat(list(os.walk(v2_path)))\n", - "v2_path_walk" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(v2_path_glob)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(v2_path_walk)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "v2_tree_path = Path(\"/braintree/home/jjpr/dev/brainio_contrib/mkgu_packaging/gallant/v2/v2_dir_tree.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "v2_tree = json.load(v2_tree_path.open())\n", - "v2_tree" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(v2_tree)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "jmespath.search(\"[*].type\", v2_tree)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "jmespath.search(\"[*].keys(@)\", v2_tree)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "jmespath.search(\"[0].name\", v2_tree)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "jmespath.search(\"[0].contents.length(@)\", v2_tree)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "dir()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example_csv_df = pd.read_csv(example_csv_path)\n", - "example_csv_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example_mat[\"r\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(example_mat[\"r\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example_mat[\"r\"].shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example_mat[\"r\"][:10,:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "example_mat[\"r\"][:100,:10]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "example_mat[\"r\"][1000:1100,:10]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ex_nanmean = np.nanmean(example_mat[\"r\"], 1)\n", - "ex_nanmean" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ex_nanmean.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.isnan(ex_nanmean)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sum(np.isnan(ex_nanmean))" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "files = glob([directory, '/V2Data*/', stimuliType, '/*/*summary_file.mat']);\n", - "\n", - "summaries.celldata\n", - "\n", - " responseFile = [subDir, '/', summary.respfile];\n", - " response = respload(responseFile);\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "[f for f in v2_path.iterdir()]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[PosixPath('V2Data1/DGrat/e0022/e0022.edrev3.007.p1_summary_file.mat'),\n", - " PosixPath('V2Data1/DGrat/e0026/e0026.edrev3.005.p1_summary_file.mat'),\n", - 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" PosixPath('V2Data8/NatRev/e0036/e0036.edrev3.002.p1_summary_file.mat'),\n", - " PosixPath('V2Data8/NatRev/e0036/e0036.edrev3.003.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0037/e0037.edrev3.003.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0037/e0037.edrev3.004.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0039/e0039.edrev3.001.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0039/e0039.edrev3.002.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0040/e0040.edrev3.001.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0040/e0040.edrev3.002.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0043/e0043.edrev3.003.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0043/e0043.edrev3.004.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0043/e0043.edrev3.009.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0046/e0046.edrev3.001.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0046/e0046.edrev3.002.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0047/e0047.edrev3.001.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0047/e0047.edrev3.002.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0047/e0047.edrev3.006.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0047/e0047.edrev3.007.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0048/e0048.edrev3.002.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0048/e0048.edrev3.003.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0049/e0049.edrev3.002.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0050/e0050.edrev3.002.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0050/e0050.edrev3.003.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0050/e0050.edrev3.010.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0051/e0051.edrev3.001.p1_summary_file.mat'),\n", - " PosixPath('V2Data9/NatRev/e0051/e0051.edrev3.002.p1_summary_file.mat')]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "summaries_glob = sorted(v2_path.glob(\"V2Data*/**/*summary_file.mat\"))\n", - "[f.relative_to(v2_path) for f in summaries_glob]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "417" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(summaries_glob)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summaries_glob[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# re.split('(\\d+)', s.relative_to(v2_path).parts[0])\n", - "# (lambda s: s.relative_to(v2_path).parts[0] )(summaries_glob[0])\n", - "# (lambda s: re.split('(\\d+)', s.relative_to(v2_path).parts[0]) )(summaries_glob[0])\n", - "(lambda s: int(re.findall('(\\d+)', s.relative_to(v2_path).parts[0])[-1]) )(summaries_glob[0])\n", - "\n", - "# [f.relative_to(v2_path) for f in summaries_glob]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[PosixPath('/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/V2Data1/DGrat/e0022/e0022.edrev3.007.p1_summary_file.mat'),\n", - " PosixPath('/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/V2Data1/DGrat/e0026/e0026.edrev3.005.p1_summary_file.mat'),\n", - " PosixPath('/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/V2Data1/DGrat/e0031/e0031.edrev3.006.p1_summary_file.mat'),\n", - " PosixPath('/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/V2Data1/DGrat/e0031/e0031.edrev3.007.p1_summary_file.mat'),\n", - " PosixPath('/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/V2Data1/DGrat/e0033/e0033.edrev3.006.p1_summary_file.mat'),\n", - " PosixPath('/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/V2Data1/DGrat/e0033/e0033.edrev3.009.p1_summary_file.mat'),\n", - 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] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dir_key = lambda s: int(re.findall('(\\d+)', s.relative_to(v2_path).parts[0])[-1])\n", - "summaries_glob = sorted(summaries_glob, key=dir_key)\n", - "summaries_glob" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s = summaries_glob[-1]\n", - "mat = sio.loadmat(s)\n", - "for k, v in mat.items():\n", - " if hasattr(v, \"shape\"):\n", - " print(f\"{k}: {v.dtype} {v.shape}\")\n", - " else: \n", - " print(f\"{k}: {type(v)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mat[\"celldata\"].dtype" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(mat[\"celldata\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mat[\"celldata\"].shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mat[\"celldata\"][0].shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mat[\"celldata\"][0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mat[\"celldata\"].dtype.fields" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "[f\"{name}: {mat['celldata'][name]}\" for name in mat[\"celldata\"].dtype.names]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "summ_top_fields = {}\n", - "for s in summaries_glob:\n", - " rel_path = s.relative_to(v2_path)\n", - " mat = sio.loadmat(s)\n", - " fields = {}\n", - " for k, v in mat.items():\n", - " if hasattr(v, \"shape\"):\n", - " fields[k] = (v.dtype, v.shape)\n", - " else: \n", - " fields[k] = type(v)\n", - " summ_top_fields[rel_path] = fields\n", - " print(rel_path, fields.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "for k, v in summ_top_fields.items():\n", - " print(k, v[\"celldata\"][1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "for k, v in summ_top_fields.items():\n", - " print(k, v[\"celldata\"][0].names)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "for k, v in summ_top_fields.items():\n", - " print(v[\"celldata\"][0].names[:12])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "for k, v in summ_top_fields.items():\n", - " print(v[\"celldata\"][0].names[12:22])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "for k, v in summ_top_fields.items():\n", - " print(v[\"celldata\"][0].names[22:])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "list(summ_top_fields.values())[-1][\"celldata\"][0].names" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "('id',\n", - " 'cellid',\n", - " 'masterid',\n", - " 'rawid',\n", - " 'celldataid',\n", - " 'runclassid',\n", - " 'path',\n", - " 'resplen',\n", - " 'repcount',\n", - " 'respfile',\n", - " 'respvarname',\n", - " 'respfiletype',\n", - " 'nosync',\n", - " 'respfilefmt',\n", - " 'respfmtcode',\n", - " 'stimfile',\n", - " 'stimfiletype',\n", - " 'stimiconside',\n", - " 'stimfilecrf',\n", - " 'stimwindowsize',\n", - " 'stimfilefmt',\n", - " 'stimfmtcode',\n", - " 'addedby',\n", - " 'info',\n", - " 'lastmod',\n", - " 'stimpath',\n", - " 'stimspeedid',\n", - " 'spikes',\n", - " 'a_state',\n", - " 'singleid',\n", - " 'singlerawid',\n", - " 'area',\n", - " 'bad',\n", - " 'rawstimfile',\n", - " 'well')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sample_fields = [\n", - " 'cellid',\n", - " 'resplen',\n", - " 'repcount',\n", - " 'respfile',\n", - " 'respfilefmt',\n", - " 'respvarname',\n", - " 'respfiletype',\n", - " 'spikes',\n", - " 'area'\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{PosixPath('V2Data1/DGrat/e0022/e0022.edrev3.007.p1_summary_file.mat'): array((8846, 'e0022', 788, 2901, 0, 1, '/auto/data/archive/ed_data/e2004-03-18/', 7915, 1, 'e0022.edrev3.007.p1.mat', 'psth', 0, 0, 'PSTH', 0, 'gratrev2004-160.index60.1.pixel.imsm', 1, '160,160', 3.2, 160, 'pixel', 0, 'willmore', 'checkraw2cell.m', '2005-01-08 15:56:43', '/auto/data/archive/stimarchive/ED02/imsm/', 60, 5852, array([], dtype=float64), 773, 3796, 'V2', 0, 'index60.1', 1),\n", - 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" dtype=[('id', 'O'), ('cellid', 'O'), ('masterid', 'O'), ('rawid', 'O'), ('celldataid', 'O'), ('runclassid', 'O'), ('path', 'O'), ('resplen', 'O'), ('repcount', 'O'), ('respfile', 'O'), ('respvarname', 'O'), ('respfiletype', 'O'), ('nosync', 'O'), ('respfilefmt', 'O'), ('respfmtcode', 'O'), ('stimfile', 'O'), ('stimfiletype', 'O'), ('stimiconside', 'O'), ('stimfilecrf', 'O'), ('stimwindowsize', 'O'), ('stimfilefmt', 'O'), ('stimfmtcode', 'O'), ('addedby', 'O'), ('info', 'O'), ('lastmod', 'O'), ('stimpath', 'O'), ('stimspeedid', 'O'), ('spikes', 'O'), ('a_state', 'O'), ('singleid', 'O'), ('singlerawid', 'O'), ('area', 'O'), ('bad', 'O'), ('rawstimfile', 'O'), ('well', 'O')]),\n", - " PosixPath('V2Data20/NatRev/z0131/z0131.ezrev4.002.p1_summary_file.mat'): array((9289, 'z0131', 1076, 3888, 0, 2, '/auto/data/archive/zig_data/z2004-12-01/', 600, 12, 'z0131.ezrev4.002.p1.mat', 'psth', 0, 0, 'PSTH', 0, 'natrev2005-160.index60.conf.pixel.imsm', 1, '160,160', 3.2, 160, 'pixel', 0, 'willmore', 'checkraw2cell.m', '2005-01-08 19:24:41', '/auto/data/archive/stimarchive/ED02/imsm/', 60, 64, array([], dtype=float64), 1050, 4785, 'V2', 0, 'index60.conf', 5),\n", - " dtype=[('id', 'O'), ('cellid', 'O'), ('masterid', 'O'), ('rawid', 'O'), ('celldataid', 'O'), ('runclassid', 'O'), ('path', 'O'), ('resplen', 'O'), ('repcount', 'O'), ('respfile', 'O'), ('respvarname', 'O'), ('respfiletype', 'O'), ('nosync', 'O'), ('respfilefmt', 'O'), ('respfmtcode', 'O'), ('stimfile', 'O'), ('stimfiletype', 'O'), ('stimiconside', 'O'), ('stimfilecrf', 'O'), ('stimwindowsize', 'O'), ('stimfilefmt', 'O'), ('stimfmtcode', 'O'), ('addedby', 'O'), ('info', 'O'), ('lastmod', 'O'), ('stimpath', 'O'), ('stimspeedid', 'O'), ('spikes', 'O'), ('a_state', 'O'), ('singleid', 'O'), ('singlerawid', 'O'), ('area', 'O'), ('bad', 'O'), ('rawstimfile', 'O'), ('well', 'O')])}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "celldatas = {}\n", - "for s in summaries_glob:\n", - " rel_path = s.relative_to(v2_path)\n", - " mat = sio.loadmat(s, squeeze_me=True)\n", - " celldatas[rel_path] = mat[\"celldata\"]\n", - "celldatas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " len(celldatas)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summ_rel_path, celldata = list(celldatas.items())[223]\n", - "summ_rel_path, celldata" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldata.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldata[\"cellid\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(celldata[\"cellid\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldata[\"cellid\"].shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldata[\"cellid\"].item()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "{n: celldata[n].item() for n in celldata.dtype.names}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dict(summ_rel_path=summ_rel_path, **{n: celldata[n].item() for n in celldata.dtype.names})" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'summ_rel_path': PosixPath('V2Data1/DGrat/e0022/e0022.edrev3.007.p1_summary_file.mat'),\n", - " 'id': 8846,\n", - " 'cellid': 'e0022',\n", - " 'masterid': 788,\n", - " 'rawid': 2901,\n", - " 'celldataid': 0,\n", - " 'runclassid': 1,\n", - " 'path': '/auto/data/archive/ed_data/e2004-03-18/',\n", - " 'resplen': 7915,\n", - " 'repcount': 1,\n", - " 'respfile': 'e0022.edrev3.007.p1.mat',\n", - 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417 rows × 38 columns

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" - ], - "text/plain": [ - " summ_rel_path id cellid masterid \\\n", - "0 V2Data1/DGrat/e0022/e0022.edrev3.007.p1_summar... 8846 e0022 788 \n", - "1 V2Data1/DGrat/e0026/e0026.edrev3.005.p1_summar... 8857 e0026 795 \n", - "2 V2Data1/DGrat/e0031/e0031.edrev3.006.p1_summar... 8872 e0031 802 \n", - "3 V2Data1/DGrat/e0031/e0031.edrev3.007.p1_summar... 8873 e0031 802 \n", - "4 V2Data1/DGrat/e0033/e0033.edrev3.006.p1_summar... 8887 e0033 806 \n", - ".. ... ... ... ... \n", - "412 V2Data20/NatRev/z0128/z0128.ezrev4.002.p1_summ... 9284 z0128 1073 \n", - "413 V2Data20/NatRev/z0129/z0129.ezrev4.002.p1_summ... 9286 z0129 1074 \n", - "414 V2Data20/NatRev/z0129/z0129.ezrev4.003.p1_summ... 9287 z0129 1074 \n", - "415 V2Data20/NatRev/z0131/z0131.ezrev4.001.p1_summ... 9288 z0131 1076 \n", - "416 V2Data20/NatRev/z0131/z0131.ezrev4.002.p1_summ... 9289 z0131 1076 \n", - "\n", - " rawid celldataid runclassid path \\\n", - "0 2901 0 1 /auto/data/archive/ed_data/e2004-03-18/ \n", - "1 2929 0 1 /auto/data/archive/ed_data/e2004-03-21/ \n", - "2 2959 0 1 /auto/data/archive/ed_data/e2004-03-23/ \n", - "3 2960 0 1 /auto/data/archive/ed_data/e2004-03-23/ \n", - "4 2986 0 1 /auto/data/archive/ed_data/e2004-03-25/ \n", - ".. ... ... ... ... \n", - "412 3879 0 2 /auto/data/archive/zig_data/z2004-11-29/ \n", - "413 3884 0 2 /auto/data/archive/zig_data/z2004-12-01/ \n", - "414 3885 0 2 /auto/data/archive/zig_data/z2004-12-01/ \n", - "415 3887 0 2 /auto/data/archive/zig_data/z2004-12-01/ \n", - "416 3888 0 2 /auto/data/archive/zig_data/z2004-12-01/ \n", - "\n", - " resplen repcount ... spikes a_state singleid singlerawid area bad \\\n", - "0 7915 1 ... 5852 [] 773 3796 V2 0 \n", - "1 7842 1 ... 1555 [] 780 3824 V2 0 \n", - "2 2228 1 ... 19 [] 787 3855 V2 0 \n", - "3 1078 1 ... 11 [] 787 3856 V2 0 \n", - "4 7751 1 ... 1378 [] 791 3882 V2 0 \n", - ".. ... ... ... ... ... ... ... ... ... \n", - "412 600 12 ... 573 [] 1047 4776 V2 0 \n", - "413 7987 1 ... 653 [] 1048 4781 V2 0 \n", - "414 600 10 ... 43 [] 1048 4782 V2 0 \n", - "415 7964 1 ... 847 [] 1050 4784 V2 0 \n", - "416 600 12 ... 64 [] 1050 4785 V2 0 \n", - "\n", - " rawstimfile well respfile_rel_path \\\n", - "0 index60.1 1 V2Data1/DGrat/e0022/e0022.edrev3.007.p1.mat \n", - "1 index60.1 2 V2Data1/DGrat/e0026/e0026.edrev3.005.p1.mat \n", - "2 index60.1 2 V2Data1/DGrat/e0031/e0031.edrev3.006.p1.mat \n", - "3 index60.1 2 V2Data1/DGrat/e0031/e0031.edrev3.007.p1.mat \n", - "4 index60.1 2 V2Data1/DGrat/e0033/e0033.edrev3.006.p1.mat \n", - ".. ... ... ... \n", - "412 index60.conf 5 V2Data20/NatRev/z0128/z0128.ezrev4.002.p1.mat \n", - "413 index60.1 5 V2Data20/NatRev/z0129/z0129.ezrev4.002.p1.mat \n", - "414 index60.conf 5 V2Data20/NatRev/z0129/z0129.ezrev4.003.p1.mat \n", - "415 index60.1 5 V2Data20/NatRev/z0131/z0131.ezrev4.001.p1.mat \n", - "416 index60.conf 5 V2Data20/NatRev/z0131/z0131.ezrev4.002.p1.mat \n", - "\n", - " respfile_path \n", - "0 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "1 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "2 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "3 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "4 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - ".. ... \n", - "412 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "413 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "414 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "415 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "416 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "\n", - "[417 rows x 38 columns]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "celldatas_df[\"respfile_path\"] = celldatas_df.apply(lambda r: v2_path.joinpath(r[\"summ_rel_path\"].with_name(r[\"respfile\"])), axis=1)\n", - "celldatas_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df.iloc[223]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df[\"cellid\"].is_unique" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dir()\n", - "dir()\n", - "from pathlib import Path\n", - "import pprint\n", - "import os\n", - "import json\n", - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr\n", - "import jmespath\n", - "from brainio_collection import list_stimulus_sets, list_assemblies, get_stimulus_set, get_assembly\n", - "import plotly.express as px\n", - "from plotly.subplots import make_subplots\n", - "import plotly.graph_objects as go\n", - "from brainio_base.assemblies import NeuronRecordingAssembly\n", - "import scipy.io as sio\n", - "import re\n", - "source_netcdf_file = \"/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/crcns_v2-1_neuronal.nc\"\n", - "example_csv_path = Path(\"/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/V2Data10/NatRev/data/e0052.edrev3.001.p1.mat.csv\")\n", - "example_mat_path = Path(\"/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/V2Data10/NatRev/e0052/e0052.edrev3.001.p1.mat\")\n", - "v2_path = example_csv_path.parents[3]\n", - "v2_path\n", - "summaries_glob = sorted(v2_path.glob(\"V2Data*/**/*summary_file.mat\"))\n", - "[f.relative_to(v2_path) for f in summaries_glob]\n", - "len(summaries_glob)\n", - "dir_key = lambda s: int(re.findall('(\\d+)', s.relative_to(v2_path).parts[0])[-1])\n", - "summaries_glob = sorted(summaries_glob, key=dir_key)\n", - "summaries_glob\n", - "celldatas = {}\n", - "for s in summaries_glob:\n", - " rel_path = s.relative_to(v2_path)\n", - " mat = sio.loadmat(s, squeeze_me=True)\n", - " celldatas[rel_path] = mat[\"celldata\"]\n", - "celldatas\n", - "celldatas_l = [dict(summ_rel_path=summ_rel_path, **{n: celldata[n].item() for n in celldata.dtype.names}) for summ_rel_path, celldata in celldatas.items()]\n", - "celldatas_l\n", - "celldatas_df = pd.DataFrame(celldatas_l)\n", - "celldatas_df\n", - "celldatas_df[\"respfile_rel_path\"] = celldatas_df.apply(lambda r: r[\"summ_rel_path\"].with_name(r[\"respfile\"]), axis=1)\n", - "celldatas_df\n", - "celldatas_df[\"respfile_path\"] = celldatas_df.apply(lambda r: v2_path.joinpath(r[\"summ_rel_path\"].with_name(r[\"respfile\"])), axis=1)\n", - "celldatas_df\n", - "%history\n" - ] - } - ], - "source": [ - "%history" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df.groupby(\"cellid\").count()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df[\"respfile_rel_path\"].is_unique" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df[\"respfile_path\"].apply(lambda p: p.exists())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df[\"respfile_path\"].apply(lambda p: p.exists()).all()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df.iloc[223][\"respfile_path\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "example_resp_mat = sio.loadmat(celldatas_df.iloc[223][\"respfile_path\"], squeeze_me=True)\n", - "example_resp_mat" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "repr(example_resp_mat.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example_resp_mat[\"h\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "def structured_to_dict(arr: np.ndarray): # per https://stackoverflow.com/a/67324508/1760084\n", - " import numpy as np\n", - "\n", - " if np.ndim(arr) == 0:\n", - " if arr.dtype.names == None:\n", - " return arr.item()\n", - " # accessing by int does *not* work when arr is a zero-dimensional array!\n", - " return {k: structured_to_dict(arr[k]) for k in arr.dtype.names}\n", - " return [structured_to_dict(v) for v in arr]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "structured_to_dict(example_resp_mat[\"h\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "celldatas_df[\"respfile_path\"].apply(lambda p: repr(sio.loadmat(p, squeeze_me=True).keys())).nunique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "for p in celldatas_df[\"respfile_path\"]:\n", - " mat = sio.loadmat(p, squeeze_me=True)\n", - " print(Path(p.parts[-4:]), repr(sorted(mat.keys())))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sorted(example_resp_mat.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df[celldatas_df[\"respfile_rel_path\"].astype(\"str\")==\"V2Data15/NatRev/m0260/m0260.mac_review.001.mat\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df.iloc[308]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "for row in celldatas_df.itertuples():\n", - " p = row.respfile_path\n", - " try:\n", - " mat = sio.loadmat(p, squeeze_me=True)\n", - " print(row.respfile_rel_path, repr(sorted(mat.keys())))\n", - " except Exception as e:\n", - " print(row.respfile_rel_path, e)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"['__globals__', '__header__', '__version__', 'chanid', 'droptime', 'eyepos', 'fext', 'flist', 'fpath', 'freps', 'h', 'kHzdata', 'matfile', 'outpath', 'phototime', 'plxfn', 'psth', 'r', 'refreshperframe', 'rnz', 's', 'shortstimlist', 'sid', 'smoothspikes', 'spiketime', 'stimtime', 'trialcount', 'unitid', 'xpos', 'ypos']\"" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def get_resp_mat_keys(respfile_path):\n", - " try:\n", - " mat = sio.loadmat(respfile_path, squeeze_me=True)\n", - " return repr(sorted(mat.keys()))\n", - "# print(row.respfile_rel_path, repr(sorted(mat.keys())))\n", - " except Exception as e:\n", - " return repr(e)\n", - "# print(respfile_path, e)\n", - "get_resp_mat_keys(celldatas_df.iloc[223][\"respfile_path\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 ['__globals__', '__header__', '__version__', '...\n", - "1 ['__globals__', '__header__', '__version__', '...\n", - "2 ['__globals__', '__header__', '__version__', '...\n", - "3 ['__globals__', '__header__', '__version__', '...\n", - "4 ['__globals__', '__header__', '__version__', '...\n", - " ... \n", - "412 ['__globals__', '__header__', '__version__', '...\n", - "413 ['__globals__', '__header__', '__version__', '...\n", - "414 ['__globals__', '__header__', '__version__', '...\n", - "415 ['__globals__', '__header__', '__version__', '...\n", - "416 ['__globals__', '__header__', '__version__', '...\n", - "Name: resp_mat_keys, Length: 417, dtype: object" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "celldatas_df[\"resp_mat_keys\"] = celldatas_df.apply(lambda row: get_resp_mat_keys(row[\"respfile_path\"]), axis=1)\n", - "celldatas_df[\"resp_mat_keys\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df[\"resp_mat_keys\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df.iloc[223][\"respfile_path\"].name.split(\".\")[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 e0022\n", - "1 e0026\n", - "2 e0031\n", - "3 e0031\n", - "4 e0033\n", - " ... \n", - "412 z0128\n", - "413 z0129\n", - "414 z0129\n", - "415 z0131\n", - "416 z0131\n", - "Name: respload_esque_respvarname, Length: 417, dtype: object" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "celldatas_df[\"respload_esque_respvarname\"] = celldatas_df[\"respfile_path\"].apply(lambda p: p.name.split(\".\")[0])\n", - "celldatas_df[\"respload_esque_respvarname\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df.apply(lambda row: \"Unknown\" in row[\"resp_mat_keys\"], axis=1).value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df.apply(lambda row: row[\"respload_esque_respvarname\"] in row[\"resp_mat_keys\"], axis=1).value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df.apply(lambda row: row[\"respvarname\"] in row[\"resp_mat_keys\"], axis=1).value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df[\"respvarname\"].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df[\"respload_esque_respvarname\"].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df[celldatas_df[\"respfile\"].str.contains(\"andros\")][\"respfile\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(celldatas_df[\"cellid\"]==celldatas_df[\"respload_esque_respvarname\"]).value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df[[\"cellid\", \"respload_esque_respvarname\"]].loc[celldatas_df[\"cellid\"]!=celldatas_df[\"respload_esque_respvarname\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df[\"respfile_rel_path\"].apply(lambda p: p.parts[-2])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(celldatas_df[\"cellid\"]==celldatas_df[\"respfile_rel_path\"].apply(lambda p: p.parts[-2])).value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "r = example_resp_mat[\"r\"]\n", - "r" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "r.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nanmean_r = np.nanmean(r, axis=0)\n", - "nanmean_r" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nanmean_r.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "example_resp_mat.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df.iloc[223][\"respfile_rel_path\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fig01 = px.imshow(r, aspect=\"equal\")\n", - "# fig01.update_layout(autosize=False)\n", - "fig01.update_layout(width=r.shape[1]*1000)\n", - "fig01.update_layout(coloraxis_showscale=False)\n", - "# fig01.update_layout(title=str(celldatas_df.iloc[223][\"respfile_rel_path\"]))\n", - "fig01" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "def add_resp_info(s):\n", - " out_d = {}\n", - " if \"Unknown\" not in s[\"resp_mat_keys\"]:\n", - " mat = sio.loadmat(s[\"respfile_path\"], squeeze_me=True)\n", - " r = mat[\"r\"]\n", - " out_d[\"r\"] = r\n", - " out_d[\"r_shape\"] = r.shape\n", - " else:\n", - " out_d[\"r\"] = np.empty(shape=(0, 0))\n", - " out_d[\"r_shape\"] = (0, 0)\n", - " return pd.Series(out_d)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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417 rows × 42 columns

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" - ], - "text/plain": [ - " summ_rel_path id cellid masterid \\\n", - "0 V2Data1/DGrat/e0022/e0022.edrev3.007.p1_summar... 8846 e0022 788 \n", - "1 V2Data1/DGrat/e0026/e0026.edrev3.005.p1_summar... 8857 e0026 795 \n", - "2 V2Data1/DGrat/e0031/e0031.edrev3.006.p1_summar... 8872 e0031 802 \n", - "3 V2Data1/DGrat/e0031/e0031.edrev3.007.p1_summar... 8873 e0031 802 \n", - "4 V2Data1/DGrat/e0033/e0033.edrev3.006.p1_summar... 8887 e0033 806 \n", - ".. ... ... ... ... \n", - "412 V2Data20/NatRev/z0128/z0128.ezrev4.002.p1_summ... 9284 z0128 1073 \n", - "413 V2Data20/NatRev/z0129/z0129.ezrev4.002.p1_summ... 9286 z0129 1074 \n", - "414 V2Data20/NatRev/z0129/z0129.ezrev4.003.p1_summ... 9287 z0129 1074 \n", - "415 V2Data20/NatRev/z0131/z0131.ezrev4.001.p1_summ... 9288 z0131 1076 \n", - "416 V2Data20/NatRev/z0131/z0131.ezrev4.002.p1_summ... 9289 z0131 1076 \n", - "\n", - " rawid celldataid runclassid path \\\n", - "0 2901 0 1 /auto/data/archive/ed_data/e2004-03-18/ \n", - "1 2929 0 1 /auto/data/archive/ed_data/e2004-03-21/ \n", - "2 2959 0 1 /auto/data/archive/ed_data/e2004-03-23/ \n", - "3 2960 0 1 /auto/data/archive/ed_data/e2004-03-23/ \n", - "4 2986 0 1 /auto/data/archive/ed_data/e2004-03-25/ \n", - ".. ... ... ... ... \n", - "412 3879 0 2 /auto/data/archive/zig_data/z2004-11-29/ \n", - "413 3884 0 2 /auto/data/archive/zig_data/z2004-12-01/ \n", - "414 3885 0 2 /auto/data/archive/zig_data/z2004-12-01/ \n", - "415 3887 0 2 /auto/data/archive/zig_data/z2004-12-01/ \n", - "416 3888 0 2 /auto/data/archive/zig_data/z2004-12-01/ \n", - "\n", - " resplen repcount ... area bad rawstimfile well \\\n", - "0 7915 1 ... V2 0 index60.1 1 \n", - "1 7842 1 ... V2 0 index60.1 2 \n", - "2 2228 1 ... V2 0 index60.1 2 \n", - "3 1078 1 ... V2 0 index60.1 2 \n", - "4 7751 1 ... V2 0 index60.1 2 \n", - ".. ... ... ... ... .. ... ... \n", - "412 600 12 ... V2 0 index60.conf 5 \n", - "413 7987 1 ... V2 0 index60.1 5 \n", - "414 600 10 ... V2 0 index60.conf 5 \n", - "415 7964 1 ... V2 0 index60.1 5 \n", - "416 600 12 ... V2 0 index60.conf 5 \n", - "\n", - " respfile_rel_path \\\n", - "0 V2Data1/DGrat/e0022/e0022.edrev3.007.p1.mat \n", - "1 V2Data1/DGrat/e0026/e0026.edrev3.005.p1.mat \n", - "2 V2Data1/DGrat/e0031/e0031.edrev3.006.p1.mat \n", - "3 V2Data1/DGrat/e0031/e0031.edrev3.007.p1.mat \n", - "4 V2Data1/DGrat/e0033/e0033.edrev3.006.p1.mat \n", - ".. ... \n", - "412 V2Data20/NatRev/z0128/z0128.ezrev4.002.p1.mat \n", - "413 V2Data20/NatRev/z0129/z0129.ezrev4.002.p1.mat \n", - "414 V2Data20/NatRev/z0129/z0129.ezrev4.003.p1.mat \n", - "415 V2Data20/NatRev/z0131/z0131.ezrev4.001.p1.mat \n", - "416 V2Data20/NatRev/z0131/z0131.ezrev4.002.p1.mat \n", - "\n", - " respfile_path \\\n", - "0 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "1 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "2 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "3 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "4 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - ".. ... \n", - "412 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "413 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "414 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "415 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "416 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "\n", - " resp_mat_keys \\\n", - "0 ['__globals__', '__header__', '__version__', '... \n", - "1 ['__globals__', '__header__', '__version__', '... \n", - "2 ['__globals__', '__header__', '__version__', '... \n", - "3 ['__globals__', '__header__', '__version__', '... \n", - "4 ['__globals__', '__header__', '__version__', '... \n", - ".. ... \n", - "412 ['__globals__', '__header__', '__version__', '... \n", - "413 ['__globals__', '__header__', '__version__', '... \n", - "414 ['__globals__', '__header__', '__version__', '... \n", - "415 ['__globals__', '__header__', '__version__', '... \n", - "416 ['__globals__', '__header__', '__version__', '... \n", - "\n", - " respload_esque_respvarname \\\n", - "0 e0022 \n", - "1 e0026 \n", - "2 e0031 \n", - "3 e0031 \n", - "4 e0033 \n", - ".. ... \n", - "412 z0128 \n", - "413 z0129 \n", - "414 z0129 \n", - "415 z0131 \n", - "416 z0131 \n", - "\n", - " r r_shape \n", - "0 [[1.0, nan, nan, nan, nan, nan, nan, nan, nan,... (8000, 40) \n", - "1 [[1.0, nan, nan, nan, nan, nan, nan, nan, nan,... (8000, 36) \n", - "2 [[0.0, nan, nan, nan, nan, nan, nan, nan, nan,... (8000, 14) \n", - "3 [[0.0, nan, nan, nan, nan, nan, nan], [0.0, na... (8000, 7) \n", - "4 [[0.0, nan, nan, nan, nan, nan, nan, nan, nan,... (8000, 40) \n", - ".. ... ... \n", - "412 [[1.0, nan, 0.0, 1.0, nan, 1.0, 1.0, nan, 1.0,... (600, 39) \n", - "413 [[0.0, nan, nan, nan, nan, nan, nan, nan, nan,... (8000, 33) \n", - "414 [[0.0, nan, 1.0, 0.0, nan, 0.0, 0.0, nan, 0.0,... (600, 34) \n", - "415 [[0.0, nan, nan, nan, nan, nan, nan, nan, nan,... (8000, 28) \n", - "416 [[0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan,... (600, 41) \n", - "\n", - "[417 rows x 42 columns]" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "celldatas_df[[\"r\", \"r_shape\"]] = celldatas_df.apply(add_resp_info, axis=1)\n", - "celldatas_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "for row in celldatas_df[celldatas_df.index%20==0].itertuples():\n", - " mat = sio.loadmat(row.respfile_path, squeeze_me=True)\n", - " responses = mat[\"r\"]\n", - " fig = px.imshow(responses, title=str(row.respfile_rel_path), aspect=\"equal\")\n", - " fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df[\"r_shape\"].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rows=4\n", - "cols=4\n", - "fig02 = make_subplots(rows=rows, cols=cols)\n", - "fig02.update_layout(height=rows*200+250)\n", - "for i in range(rows*cols):\n", - "# print(i, i//cols, i%cols)\n", - " fig02.add_trace(px.imshow(celldatas_df.iloc[i].r.reshape((1000, -1))).data[0], row=i//cols+1, col=i%cols+1)\n", - "fig02.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig02 = px.imshow(responunique, title=str(row.respfile_rel_path), aspect=\"equal\")\n", - "fig01 = px.imshow(r, aspect=\"equal\")\n", - "# fig01.update_layout(autosize=False)\n", - "fig01.update_layout(width=r.shape[1]*1000)\n", - "fig01.update_layout(coloraxis_showscale=False)\n", - "# fig01.update_layout(title=str(celldatas_df.iloc[223][\"respfile_rel_path\"]))\n", - "fig01" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.count_nonzero(np.isnan(example_resp_mat[\"r\"]))/example_resp_mat[\"r\"].size" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "def get_nans_per(row):\n", - " r = row[\"r\"]\n", - " if hasattr(r, \"size\") and r.size > 0:\n", - " return np.count_nonzero(np.isnan(r))/r.size\n", - " return np.nan" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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summ_rel_pathidcellidmasteridrawidcelldataidrunclassidpathresplenrepcount...badrawstimfilewellrespfile_rel_pathrespfile_pathresp_mat_keysrespload_esque_respvarnamerr_shapenans_per_raw
0V2Data1/DGrat/e0022/e0022.edrev3.007.p1_summar...8846e0022788290101/auto/data/archive/ed_data/e2004-03-18/79151...0index60.11V2Data1/DGrat/e0022/e0022.edrev3.007.p1.mat/braintree/data2/active/users/jjpr/mkgu_packag...['__globals__', '__header__', '__version__', '...e0022[[1.0, nan, nan, nan, nan, nan, nan, nan, nan,...(8000, 40)0.974500
1V2Data1/DGrat/e0026/e0026.edrev3.005.p1_summar...8857e0026795292901/auto/data/archive/ed_data/e2004-03-21/78421...0index60.12V2Data1/DGrat/e0026/e0026.edrev3.005.p1.mat/braintree/data2/active/users/jjpr/mkgu_packag...['__globals__', '__header__', '__version__', '...e0026[[1.0, nan, nan, nan, nan, nan, nan, nan, nan,...(8000, 36)0.972628
2V2Data1/DGrat/e0031/e0031.edrev3.006.p1_summar...8872e0031802295901/auto/data/archive/ed_data/e2004-03-23/22281...0index60.12V2Data1/DGrat/e0031/e0031.edrev3.006.p1.mat/braintree/data2/active/users/jjpr/mkgu_packag...['__globals__', '__header__', '__version__', '...e0031[[0.0, nan, nan, nan, nan, nan, nan, nan, nan,...(8000, 14)0.979705
3V2Data1/DGrat/e0031/e0031.edrev3.007.p1_summar...8873e0031802296001/auto/data/archive/ed_data/e2004-03-23/10781...0index60.12V2Data1/DGrat/e0031/e0031.edrev3.007.p1.mat/braintree/data2/active/users/jjpr/mkgu_packag...['__globals__', '__header__', '__version__', '...e0031[[0.0, nan, nan, nan, nan, nan, nan], [0.0, na...(8000, 7)0.980429
4V2Data1/DGrat/e0033/e0033.edrev3.006.p1_summar...8887e0033806298601/auto/data/archive/ed_data/e2004-03-25/77511...0index60.12V2Data1/DGrat/e0033/e0033.edrev3.006.p1.mat/braintree/data2/active/users/jjpr/mkgu_packag...['__globals__', '__header__', '__version__', '...e0033[[0.0, nan, nan, nan, nan, nan, nan, nan, nan,...(8000, 40)0.974556
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412V2Data20/NatRev/z0128/z0128.ezrev4.002.p1_summ...9284z01281073387902/auto/data/archive/zig_data/z2004-11-29/60012...0index60.conf5V2Data20/NatRev/z0128/z0128.ezrev4.002.p1.mat/braintree/data2/active/users/jjpr/mkgu_packag...['__globals__', '__header__', '__version__', '...z0128[[1.0, nan, 0.0, 1.0, nan, 1.0, 1.0, nan, 1.0,...(600, 39)0.540256
413V2Data20/NatRev/z0129/z0129.ezrev4.002.p1_summ...9286z01291074388402/auto/data/archive/zig_data/z2004-12-01/79871...0index60.15V2Data20/NatRev/z0129/z0129.ezrev4.002.p1.mat/braintree/data2/active/users/jjpr/mkgu_packag...['__globals__', '__header__', '__version__', '...z0129[[0.0, nan, nan, nan, nan, nan, nan, nan, nan,...(8000, 33)0.964769
414V2Data20/NatRev/z0129/z0129.ezrev4.003.p1_summ...9287z01291074388502/auto/data/archive/zig_data/z2004-12-01/60010...0index60.conf5V2Data20/NatRev/z0129/z0129.ezrev4.003.p1.mat/braintree/data2/active/users/jjpr/mkgu_packag...['__globals__', '__header__', '__version__', '...z0129[[0.0, nan, 1.0, 0.0, nan, 0.0, 0.0, nan, 0.0,...(600, 34)0.530049
415V2Data20/NatRev/z0131/z0131.ezrev4.001.p1_summ...9288z01311076388702/auto/data/archive/zig_data/z2004-12-01/79641...0index60.15V2Data20/NatRev/z0131/z0131.ezrev4.001.p1.mat/braintree/data2/active/users/jjpr/mkgu_packag...['__globals__', '__header__', '__version__', '...z0131[[0.0, nan, nan, nan, nan, nan, nan, nan, nan,...(8000, 28)0.963817
416V2Data20/NatRev/z0131/z0131.ezrev4.002.p1_summ...9289z01311076388802/auto/data/archive/zig_data/z2004-12-01/60012...0index60.conf5V2Data20/NatRev/z0131/z0131.ezrev4.002.p1.mat/braintree/data2/active/users/jjpr/mkgu_packag...['__globals__', '__header__', '__version__', '...z0131[[0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan,...(600, 41)0.539878
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417 rows × 43 columns

\n", - "
" - ], - "text/plain": [ - " summ_rel_path id cellid masterid \\\n", - "0 V2Data1/DGrat/e0022/e0022.edrev3.007.p1_summar... 8846 e0022 788 \n", - "1 V2Data1/DGrat/e0026/e0026.edrev3.005.p1_summar... 8857 e0026 795 \n", - "2 V2Data1/DGrat/e0031/e0031.edrev3.006.p1_summar... 8872 e0031 802 \n", - "3 V2Data1/DGrat/e0031/e0031.edrev3.007.p1_summar... 8873 e0031 802 \n", - "4 V2Data1/DGrat/e0033/e0033.edrev3.006.p1_summar... 8887 e0033 806 \n", - ".. ... ... ... ... \n", - "412 V2Data20/NatRev/z0128/z0128.ezrev4.002.p1_summ... 9284 z0128 1073 \n", - "413 V2Data20/NatRev/z0129/z0129.ezrev4.002.p1_summ... 9286 z0129 1074 \n", - "414 V2Data20/NatRev/z0129/z0129.ezrev4.003.p1_summ... 9287 z0129 1074 \n", - "415 V2Data20/NatRev/z0131/z0131.ezrev4.001.p1_summ... 9288 z0131 1076 \n", - "416 V2Data20/NatRev/z0131/z0131.ezrev4.002.p1_summ... 9289 z0131 1076 \n", - "\n", - " rawid celldataid runclassid path \\\n", - "0 2901 0 1 /auto/data/archive/ed_data/e2004-03-18/ \n", - "1 2929 0 1 /auto/data/archive/ed_data/e2004-03-21/ \n", - "2 2959 0 1 /auto/data/archive/ed_data/e2004-03-23/ \n", - "3 2960 0 1 /auto/data/archive/ed_data/e2004-03-23/ \n", - "4 2986 0 1 /auto/data/archive/ed_data/e2004-03-25/ \n", - ".. ... ... ... ... \n", - "412 3879 0 2 /auto/data/archive/zig_data/z2004-11-29/ \n", - "413 3884 0 2 /auto/data/archive/zig_data/z2004-12-01/ \n", - "414 3885 0 2 /auto/data/archive/zig_data/z2004-12-01/ \n", - "415 3887 0 2 /auto/data/archive/zig_data/z2004-12-01/ \n", - "416 3888 0 2 /auto/data/archive/zig_data/z2004-12-01/ \n", - "\n", - " resplen repcount ... bad rawstimfile well \\\n", - "0 7915 1 ... 0 index60.1 1 \n", - "1 7842 1 ... 0 index60.1 2 \n", - "2 2228 1 ... 0 index60.1 2 \n", - "3 1078 1 ... 0 index60.1 2 \n", - "4 7751 1 ... 0 index60.1 2 \n", - ".. ... ... ... .. ... ... \n", - "412 600 12 ... 0 index60.conf 5 \n", - "413 7987 1 ... 0 index60.1 5 \n", - "414 600 10 ... 0 index60.conf 5 \n", - "415 7964 1 ... 0 index60.1 5 \n", - "416 600 12 ... 0 index60.conf 5 \n", - "\n", - " respfile_rel_path \\\n", - "0 V2Data1/DGrat/e0022/e0022.edrev3.007.p1.mat \n", - "1 V2Data1/DGrat/e0026/e0026.edrev3.005.p1.mat \n", - "2 V2Data1/DGrat/e0031/e0031.edrev3.006.p1.mat \n", - "3 V2Data1/DGrat/e0031/e0031.edrev3.007.p1.mat \n", - "4 V2Data1/DGrat/e0033/e0033.edrev3.006.p1.mat \n", - ".. ... \n", - "412 V2Data20/NatRev/z0128/z0128.ezrev4.002.p1.mat \n", - "413 V2Data20/NatRev/z0129/z0129.ezrev4.002.p1.mat \n", - "414 V2Data20/NatRev/z0129/z0129.ezrev4.003.p1.mat \n", - "415 V2Data20/NatRev/z0131/z0131.ezrev4.001.p1.mat \n", - "416 V2Data20/NatRev/z0131/z0131.ezrev4.002.p1.mat \n", - "\n", - " respfile_path \\\n", - "0 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "1 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "2 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "3 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "4 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - ".. ... \n", - "412 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "413 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "414 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "415 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "416 /braintree/data2/active/users/jjpr/mkgu_packag... \n", - "\n", - " resp_mat_keys \\\n", - "0 ['__globals__', '__header__', '__version__', '... \n", - "1 ['__globals__', '__header__', '__version__', '... \n", - "2 ['__globals__', '__header__', '__version__', '... \n", - "3 ['__globals__', '__header__', '__version__', '... \n", - "4 ['__globals__', '__header__', '__version__', '... \n", - ".. ... \n", - "412 ['__globals__', '__header__', '__version__', '... \n", - "413 ['__globals__', '__header__', '__version__', '... \n", - "414 ['__globals__', '__header__', '__version__', '... \n", - "415 ['__globals__', '__header__', '__version__', '... \n", - "416 ['__globals__', '__header__', '__version__', '... \n", - "\n", - " respload_esque_respvarname \\\n", - "0 e0022 \n", - "1 e0026 \n", - "2 e0031 \n", - "3 e0031 \n", - "4 e0033 \n", - ".. ... \n", - "412 z0128 \n", - "413 z0129 \n", - "414 z0129 \n", - "415 z0131 \n", - "416 z0131 \n", - "\n", - " r r_shape \\\n", - "0 [[1.0, nan, nan, nan, nan, nan, nan, nan, nan,... (8000, 40) \n", - "1 [[1.0, nan, nan, nan, nan, nan, nan, nan, nan,... (8000, 36) \n", - "2 [[0.0, nan, nan, nan, nan, nan, nan, nan, nan,... (8000, 14) \n", - "3 [[0.0, nan, nan, nan, nan, nan, nan], [0.0, na... (8000, 7) \n", - "4 [[0.0, nan, nan, nan, nan, nan, nan, nan, nan,... (8000, 40) \n", - ".. ... ... \n", - "412 [[1.0, nan, 0.0, 1.0, nan, 1.0, 1.0, nan, 1.0,... (600, 39) \n", - "413 [[0.0, nan, nan, nan, nan, nan, nan, nan, nan,... (8000, 33) \n", - "414 [[0.0, nan, 1.0, 0.0, nan, 0.0, 0.0, nan, 0.0,... (600, 34) \n", - "415 [[0.0, nan, nan, nan, nan, nan, nan, nan, nan,... (8000, 28) \n", - "416 [[0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan,... (600, 41) \n", - "\n", - " nans_per_raw \n", - "0 0.974500 \n", - "1 0.972628 \n", - "2 0.979705 \n", - "3 0.980429 \n", - "4 0.974556 \n", - ".. ... \n", - "412 0.540256 \n", - "413 0.964769 \n", - "414 0.530049 \n", - "415 0.963817 \n", - "416 0.539878 \n", - "\n", - "[417 rows x 43 columns]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "celldatas_df[\"nans_per_raw\"] = celldatas_df.apply(get_nans_per, axis=1)\n", - "celldatas_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "celldatas_df.iloc[::10]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "[np.unique(r[~np.isnan(r)]) for r in celldatas_df[\"r\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "r = celldatas_df.iloc[223][\"r\"]\n", - "paper_height = r.shape[0]\n", - "paper_width = r.shape[1]\n", - "margin = 100\n", - "height = 2*margin + paper_height\n", - "width = 2*margin + paper_width\n", - "fig = px.imshow(r, aspect=\"equal\")\n", - "fig.update_layout(height=height, width=width, margin=dict(l=margin, r=margin, t=margin, b=margin))\n", - "fig.show()\n", - "fig.show(\"json\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(fig.layout.width)\n", - "# fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig.data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig.layout" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(celldatas_df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "list(celldatas_df.iloc[::10].index)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "celldatas_df.iloc[::10].iloc[11]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "step = 10\n", - "df = celldatas_df.iloc[::step]\n", - "cols = 4\n", - "rows = len(df)//cols + (1 if len(df)%cols else 0)\n", - "margin = 50\n", - "max_height = 500\n", - "height = 2*rows*margin + rows*max_height\n", - "\n", - "fig = make_subplots(rows=rows, cols=cols, vertical_spacing=)\n", - "for i in range(len(df)):\n", - " r = df.iloc[i].r\n", - " fig.add_trace(px.imshow(r[:max_height, :]).data[0], row=i//cols+1, col=i%cols+1)\n", - "fig.update_layout(height=height)\n", - "fig.update_layout(coloraxis_showscale=False)\n", - "fig.update_yaxes(autorange=\"reversed\")\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(8000*73)\n", - "print(8000*73//500)\n", - "print(500*225)\n", - "print((8000*73)//(500*225) + (1 if (8000*73)%(500*225) else 0))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def reshape(r, paper_height, paper_width):\n", - " orig_aspect = r.shape[1]/r.shape[0]\n", - " paper_aspect = paper_width/paper_height\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "step = 10\n", - "df = celldatas_df.iloc[::step]\n", - "cols = 4\n", - "rows = len(df)//cols + (1 if len(df)%cols else 0)\n", - "margin = 50\n", - "paper_height = 500\n", - "total_height = (2*margin) + ((rows-1)*margin) + (rows*paper_height)\n", - "paper_width = 225\n", - "total_width = (2*margin) + ((cols-1)*margin) + (cols*paper_width)\n", - "\n", - "fig = make_subplots(rows=rows, cols=cols)\n", - "fig.update_layout(height=total_height, width=total_width, margin=dict(l=margin, r=margin, t=margin, b=margin))\n", - "\n", - "for i in range(len(df)):\n", - " r = df.iloc[i].r\n", - " fig.add_trace(go.Heatmap(z=r[:max_height, :]), row=i//cols+1, col=i%cols+1)\n", - "fig.update_layout(height=height)\n", - "fig.update_layout(coloraxis_showscale=False)\n", - "fig.update_yaxes(autorange=\"reversed\")\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "((500-2*100-3*50)/(3-1))/500" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = make_subplots(3, 2, vertical_spacing=((500-2*100-3*50)/(3-1))/500) # should give 75 pixels of spacing\n", - "fig.update_layout(height=500)\n", - "fig.update_layout(margin=dict(t=100, b=100, l=100, r=100))\n", - "for i in range(2):\n", - " for j in range(3):\n", - " fig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=j+1, col=i+1)\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cols = 2\n", - "rows = 3\n", - "margin = 50\n", - "paper_height = 100\n", - "total_height = (rows+1)*margin + rows*paper_height\n", - "vertical_spacing=margin/(total_height - 2*margin)\n", - "paper_width = 225\n", - "total_width = (cols+1)*margin + cols*paper_width\n", - "horizontal_spacing = margin/(total_width - 2*margin)\n", - "\n", - "fig = make_subplots(rows, cols, vertical_spacing=vertical_spacing, horizontal_spacing=horizontal_spacing) \n", - "fig.update_layout(height=total_height, width=total_width)\n", - "fig.update_layout(margin=dict(t=margin, b=margin, l=margin, r=margin))\n", - "fig.update_yaxes(autorange=\"reversed\")\n", - "for i in range(cols):\n", - " for j in range(rows):\n", - " fig.add_trace(go.Heatmap(z=np.identity(10), showscale=False), row=j+1, col=i+1)\n", - "fig.show()\n", - "print(f\"cols: {cols} rows: {rows}\")\n", - "print(f\"margin: {margin} paper height: {paper_height} paper width: {paper_width}\")\n", - "print(f\"total height: {total_height} total width: {total_width}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.identity(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cols: 4 rows: 11\n", - "margin: 50 paper height: 500 paper width: 225\n", - "total height: 6100 total width: 1150\n" - ] - } - ], - "source": [ - "# raise Exception\n", - "\n", - "step = 10\n", - "df = celldatas_df.iloc[::step]\n", - "cols = 4\n", - "rows = len(df)//cols + (1 if len(df)%cols else 0)\n", - "margin = 50\n", - "paper_height = 500\n", - "total_height = (rows+1)*margin + rows*paper_height\n", - "vertical_spacing=margin/(total_height - 2*margin)\n", - "paper_width = 225\n", - "total_width = (cols+1)*margin + cols*paper_width\n", - "horizontal_spacing = margin/(total_width - 2*margin)\n", - "\n", - "fig = make_subplots(rows, cols, vertical_spacing=vertical_spacing, horizontal_spacing=horizontal_spacing) \n", - "fig.update_layout(height=total_height, width=total_width)\n", - "fig.update_layout(margin=dict(t=margin, b=margin, l=margin, r=margin))\n", - "fig.update_yaxes(autorange=\"reversed\")\n", - " \n", - "# for i in range(len(df)):\n", - "for i in range(1):\n", - " r = df.iloc[i].r\n", - " fig.add_trace(go.Heatmap(z=r, showscale=False), row=i//cols+1, col=i%cols+1)\n", - "\n", - "# fig.show()\n", - "fig.show(renderer=\"png\", height=total_height, width=total_width)\n", - "print(f\"cols: {cols} rows: {rows}\")\n", - "print(f\"margin: {margin} paper height: {paper_height} paper width: {paper_width}\")\n", - "print(f\"total height: {total_height} total width: {total_width}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['In',\n", - " 'Out',\n", - " '_',\n", - " '_1',\n", - " '__',\n", - " '___',\n", - " '__builtin__',\n", - " '__builtins__',\n", - " '__doc__',\n", - " '__loader__',\n", - " '__name__',\n", - " '__package__',\n", - " '__spec__',\n", - " '_dh',\n", - " '_i',\n", - " '_i1',\n", - " '_i2',\n", - " '_ih',\n", - " '_ii',\n", - " '_iii',\n", - " '_oh',\n", - " 'exit',\n", - " 'get_ipython',\n", - " 'quit']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dir()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dir()\n", - "dir()\n", - "from pathlib import Path\n", - "import pprint\n", - "import os\n", - "import json\n", - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr\n", - "import jmespath\n", - "from brainio_collection import list_stimulus_sets, list_assemblies, get_stimulus_set, get_assembly\n", - "import plotly.express as px\n", - "from plotly.subplots import make_subplots\n", - "import plotly.graph_objects as go\n", - "from brainio_base.assemblies import NeuronRecordingAssembly\n", - "import scipy.io as sio\n", - "import re\n", - "source_netcdf_file = \"/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/crcns_v2-1_neuronal.nc\"\n", - "example_csv_path = Path(\"/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/V2Data10/NatRev/data/e0052.edrev3.001.p1.mat.csv\")\n", - "example_mat_path = Path(\"/braintree/data2/active/users/jjpr/mkgu_packaging/crcns/v2-1/V2Data10/NatRev/e0052/e0052.edrev3.001.p1.mat\")\n", - "v2_path = example_csv_path.parents[3]\n", - "v2_path\n", - "summaries_glob = sorted(v2_path.glob(\"V2Data*/**/*summary_file.mat\"))\n", - "[f.relative_to(v2_path) for f in summaries_glob]\n", - "len(summaries_glob)\n", - "dir_key = lambda s: int(re.findall('(\\d+)', s.relative_to(v2_path).parts[0])[-1])\n", - "summaries_glob = sorted(summaries_glob, key=dir_key)\n", - "summaries_glob\n", - "celldatas = {}\n", - "for s in summaries_glob:\n", - " rel_path = s.relative_to(v2_path)\n", - " mat = sio.loadmat(s, squeeze_me=True)\n", - " celldatas[rel_path] = mat[\"celldata\"]\n", - "celldatas\n", - "celldatas_l = [dict(summ_rel_path=summ_rel_path, **{n: celldata[n].item() for n in celldata.dtype.names}) for summ_rel_path, celldata in celldatas.items()]\n", - "celldatas_l\n", - "celldatas_df = pd.DataFrame(celldatas_l)\n", - "celldatas_df\n", - "celldatas_df[\"respfile_rel_path\"] = celldatas_df.apply(lambda r: r[\"summ_rel_path\"].with_name(r[\"respfile\"]), axis=1)\n", - "celldatas_df\n", - "celldatas_df[\"respfile_path\"] = celldatas_df.apply(lambda r: v2_path.joinpath(r[\"summ_rel_path\"].with_name(r[\"respfile\"])), axis=1)\n", - "celldatas_df\n", - "%history\n", - "def structured_to_dict(arr: np.ndarray): # per https://stackoverflow.com/a/67324508/1760084\n", - " import numpy as np\n", - "\n", - " if np.ndim(arr) == 0:\n", - " if arr.dtype.names == None:\n", - " return arr.item()\n", - " # accessing by int does *not* work when arr is a zero-dimensional array!\n", - " return {k: structured_to_dict(arr[k]) for k in arr.dtype.names}\n", - " return [structured_to_dict(v) for v in arr]\n", - "def get_resp_mat_keys(respfile_path):\n", - " try:\n", - " mat = sio.loadmat(respfile_path, squeeze_me=True)\n", - " return repr(sorted(mat.keys()))\n", - "# print(row.respfile_rel_path, repr(sorted(mat.keys())))\n", - " except Exception as e:\n", - " return repr(e)\n", - "# print(respfile_path, e)\n", - "get_resp_mat_keys(celldatas_df.iloc[223][\"respfile_path\"])\n", - "celldatas_df[\"resp_mat_keys\"] = celldatas_df.apply(lambda row: get_resp_mat_keys(row[\"respfile_path\"]), axis=1)\n", - "celldatas_df[\"resp_mat_keys\"]\n", - "celldatas_df[\"respload_esque_respvarname\"] = celldatas_df[\"respfile_path\"].apply(lambda p: p.name.split(\".\")[0])\n", - "celldatas_df[\"respload_esque_respvarname\"]\n", - "def add_resp_info(s):\n", - " out_d = {}\n", - " if \"Unknown\" not in s[\"resp_mat_keys\"]:\n", - " mat = sio.loadmat(s[\"respfile_path\"], squeeze_me=True)\n", - " r = mat[\"r\"]\n", - " out_d[\"r\"] = r\n", - " out_d[\"r_shape\"] = r.shape\n", - " else:\n", - " out_d[\"r\"] = np.empty(shape=(0, 0))\n", - " out_d[\"r_shape\"] = (0, 0)\n", - " return pd.Series(out_d)\n", - "celldatas_df[[\"r\", \"r_shape\"]] = celldatas_df.apply(add_resp_info, axis=1)\n", - "celldatas_df\n", - "def get_nans_per(row):\n", - " r = row[\"r\"]\n", - " if hasattr(r, \"size\") and r.size > 0:\n", - " return np.count_nonzero(np.isnan(r))/r.size\n", - " return np.nan\n", - "celldatas_df[\"nans_per\"] = celldatas_df.apply(get_nans_per, axis=1)\n", - "celldatas_df\n", - "step = 10\n", - "df = celldatas_df.iloc[::step]\n", - "cols = 4\n", - "rows = len(df)//cols + (1 if len(df)%cols else 0)\n", - "margin = 50\n", - "paper_height = 500\n", - "total_height = (rows+1)*margin + rows*paper_height\n", - "vertical_spacing=margin/(total_height - 2*margin)\n", - "paper_width = 225\n", - "total_width = (cols+1)*margin + cols*paper_width\n", - "horizontal_spacing = margin/(total_width - 2*margin)\n", - "\n", - "fig = make_subplots(rows, cols, vertical_spacing=vertical_spacing, horizontal_spacing=horizontal_spacing) \n", - "fig.update_layout(height=total_height, width=total_width)\n", - "fig.update_layout(margin=dict(t=margin, b=margin, l=margin, r=margin))\n", - "fig.update_yaxes(autorange=\"reversed\")\n", - " \n", - "for i in range(len(df)):\n", - " r = df.iloc[i].r\n", - " fig.add_trace(go.Heatmap(z=r, showscale=False), row=i//cols+1, col=i%cols+1)\n", - "\n", - "fig.show()\n", - "print(f\"cols: {cols} rows: {rows}\")\n", - "print(f\"margin: {margin} paper height: {paper_height} paper width: {paper_width}\")\n", - "print(f\"total height: {total_height} total width: {total_width}\")\n", - "raise Exception\n", - "\n", - "step = 10\n", - "df = celldatas_df.iloc[::step]\n", - "cols = 4\n", - "rows = len(df)//cols + (1 if len(df)%cols else 0)\n", - "margin = 50\n", - "paper_height = 500\n", - "total_height = (rows+1)*margin + rows*paper_height\n", - "vertical_spacing=margin/(total_height - 2*margin)\n", - "paper_width = 225\n", - "total_width = (cols+1)*margin + cols*paper_width\n", - "horizontal_spacing = margin/(total_width - 2*margin)\n", - "\n", - "fig = make_subplots(rows, cols, vertical_spacing=vertical_spacing, horizontal_spacing=horizontal_spacing) \n", - "fig.update_layout(height=total_height, width=total_width)\n", - "fig.update_layout(margin=dict(t=margin, b=margin, l=margin, r=margin))\n", - "fig.update_yaxes(autorange=\"reversed\")\n", - " \n", - "for i in range(len(df)):\n", - " r = df.iloc[i].r\n", - " fig.add_trace(go.Heatmap(z=r, showscale=False), row=i//cols+1, col=i%cols+1)\n", - "\n", - "fig.show()\n", - "print(f\"cols: {cols} rows: {rows}\")\n", - "print(f\"margin: {margin} paper height: {paper_height} paper width: {paper_width}\")\n", - "print(f\"total height: {total_height} total width: {total_width}\")\n", - "raise Exception\n", - "\n", - "step = 10\n", - "df = celldatas_df.iloc[::step]\n", - "cols = 4\n", - "rows = len(df)//cols + (1 if len(df)%cols else 0)\n", - "margin = 50\n", - "paper_height = 500\n", - "total_height = (rows+1)*margin + rows*paper_height\n", - "vertical_spacing=margin/(total_height - 2*margin)\n", - "paper_width = 225\n", - "total_width = (cols+1)*margin + cols*paper_width\n", - "horizontal_spacing = margin/(total_width - 2*margin)\n", - "\n", - "fig = make_subplots(rows, cols, vertical_spacing=vertical_spacing, horizontal_spacing=horizontal_spacing) \n", - "fig.update_layout(height=total_height, width=total_width)\n", - "fig.update_layout(margin=dict(t=margin, b=margin, l=margin, r=margin))\n", - "fig.update_yaxes(autorange=\"reversed\")\n", - " \n", - "# for i in range(len(df)):\n", - "for i in range(1):\n", - " r = df.iloc[i].r\n", - " fig.add_trace(go.Heatmap(z=r, showscale=False), row=i//cols+1, col=i%cols+1)\n", - "\n", - "fig.show()\n", - "print(f\"cols: {cols} rows: {rows}\")\n", - "print(f\"margin: {margin} paper height: {paper_height} paper width: {paper_width}\")\n", - "print(f\"total height: {total_height} total width: {total_width}\")\n", - "# raise Exception\n", - "\n", - "step = 10\n", - "df = celldatas_df.iloc[::step]\n", - "cols = 4\n", - "rows = len(df)//cols + (1 if len(df)%cols else 0)\n", - "margin = 50\n", - "paper_height = 500\n", - "total_height = (rows+1)*margin + rows*paper_height\n", - "vertical_spacing=margin/(total_height - 2*margin)\n", - "paper_width = 225\n", - "total_width = (cols+1)*margin + cols*paper_width\n", - "horizontal_spacing = margin/(total_width - 2*margin)\n", - "\n", - "fig = make_subplots(rows, cols, vertical_spacing=vertical_spacing, horizontal_spacing=horizontal_spacing) \n", - "fig.update_layout(height=total_height, width=total_width)\n", - "fig.update_layout(margin=dict(t=margin, b=margin, l=margin, r=margin))\n", - "fig.update_yaxes(autorange=\"reversed\")\n", - " \n", - "# for i in range(len(df)):\n", - "for i in range(1):\n", - " r = df.iloc[i].r\n", - " fig.add_trace(go.Heatmap(z=r, showscale=False), row=i//cols+1, col=i%cols+1)\n", - "\n", - "fig.show()\n", - "print(f\"cols: {cols} rows: {rows}\")\n", - "print(f\"margin: {margin} paper height: {paper_height} paper width: {paper_width}\")\n", - "print(f\"total height: {total_height} total width: {total_width}\")\n", - "# raise Exception\n", - "\n", - "step = 10\n", - "df = celldatas_df.iloc[::step]\n", - "cols = 4\n", - "rows = len(df)//cols + (1 if len(df)%cols else 0)\n", - "margin = 50\n", - "paper_height = 500\n", - "total_height = (rows+1)*margin + rows*paper_height\n", - "vertical_spacing=margin/(total_height - 2*margin)\n", - "paper_width = 225\n", - "total_width = (cols+1)*margin + cols*paper_width\n", - "horizontal_spacing = margin/(total_width - 2*margin)\n", - "\n", - "fig = make_subplots(rows, cols, vertical_spacing=vertical_spacing, horizontal_spacing=horizontal_spacing) \n", - "fig.update_layout(height=total_height, width=total_width)\n", - "fig.update_layout(margin=dict(t=margin, b=margin, l=margin, r=margin))\n", - "fig.update_yaxes(autorange=\"reversed\")\n", - " \n", - "# for i in range(len(df)):\n", - "for i in range(1):\n", - " r = df.iloc[i].r\n", - " fig.add_trace(go.Heatmap(z=r, showscale=False), row=i//cols+1, col=i%cols+1)\n", - "\n", - "# fig.show()\n", - "fig.show(renderer=\"png\")\n", - "print(f\"cols: {cols} rows: {rows}\")\n", - "print(f\"margin: {margin} paper height: {paper_height} paper width: {paper_width}\")\n", - "print(f\"total height: {total_height} total width: {total_width}\")\n", - "%history\n" - ] - } - ], - "source": [ - "%history" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cols: 4 rows: 11\n", - "margin: 50 paper height: 500 paper width: 225\n", - "total height: 6100 total width: 1150\n" - ] - } - ], - "source": [ - "step = 10\n", - "df = celldatas_df.iloc[::step]\n", - "cols = 4\n", - "rows = len(df)//cols + (1 if len(df)%cols else 0)\n", - "margin = 50\n", - "paper_height = 500\n", - "total_height = (rows+1)*margin + rows*paper_height\n", - "vertical_spacing=margin/(total_height - 2*margin)\n", - "paper_width = 225\n", - "total_width = (cols+1)*margin + cols*paper_width\n", - "horizontal_spacing = margin/(total_width - 2*margin)\n", - "\n", - "fig = make_subplots(rows, cols, vertical_spacing=vertical_spacing, horizontal_spacing=horizontal_spacing) \n", - "fig.update_layout(height=total_height, width=total_width)\n", - "fig.update_layout(margin=dict(t=margin, b=margin, l=margin, r=margin))\n", - "fig.update_yaxes(autorange=\"reversed\")\n", - " \n", - "for i in range(len(df)):\n", - " r = df.iloc[i].r\n", - " fig.add_trace(go.Heatmap(z=r, showscale=False), row=i//cols+1, col=i%cols+1)\n", - "\n", - "for k in fig.layout:\n", - " if k.startswith(\"yaxis\"):\n", - " ax = fig.layout[k]\n", - " ax[\"scaleanchor\"] = ax[\"anchor\"]\n", - " ax[\"constrain\"] = \"range\"\n", - " ax[\"constraintoward\"] = \"top\"\n", - "\n", - "for k in fig.layout:\n", - " if k.startswith(\"xaxis\"):\n", - " ax = fig.layout[k]\n", - " ax[\"constrain\"] = \"range\"\n", - " ax[\"constraintoward\"] = \"left\"\n", - "\n", - "fig.show(renderer=\"png\", height=total_height, width=total_width)\n", - "print(f\"cols: {cols} rows: {rows}\")\n", - "print(f\"margin: {margin} paper height: {paper_height} paper width: {paper_width}\")\n", - "print(f\"total height: {total_height} total width: {total_width}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[('yaxis21', 'x21'),\n", - " ('yaxis41', 'x41'),\n", - " ('yaxis3', 'x3'),\n", - " ('yaxis35', 'x35'),\n", - " ('yaxis36', 'x36'),\n", - " ('yaxis2', 'x2'),\n", - " ('yaxis11', 'x11'),\n", - " ('yaxis40', 'x40'),\n", - " ('yaxis22', 'x22'),\n", - " ('yaxis14', 'x14'),\n", - " ('yaxis19', 'x19'),\n", - " ('yaxis5', 'x5'),\n", - " ('yaxis38', 'x38'),\n", - " ('yaxis7', 'x7'),\n", - " ('yaxis31', 'x31'),\n", - " ('yaxis6', 'x6'),\n", - " ('yaxis15', 'x15'),\n", - " ('yaxis18', 'x18'),\n", - " ('yaxis20', 'x20'),\n", - " ('yaxis23', 'x23'),\n", - " ('yaxis26', 'x26'),\n", - " ('yaxis34', 'x34'),\n", - " ('yaxis8', 'x8'),\n", - " ('yaxis', 'x'),\n", - " ('yaxis4', 'x4'),\n", - " ('yaxis13', 'x13'),\n", - " ('yaxis30', 'x30'),\n", - " ('yaxis9', 'x9'),\n", - " ('yaxis32', 'x32'),\n", - " ('yaxis43', 'x43'),\n", - " ('yaxis29', 'x29'),\n", - " ('yaxis17', 'x17'),\n", - " ('yaxis16', 'x16'),\n", - " ('yaxis39', 'x39'),\n", - " ('yaxis28', 'x28'),\n", - " ('yaxis25', 'x25'),\n", - " ('yaxis12', 'x12'),\n", - " ('yaxis24', 'x24'),\n", - " ('yaxis10', 'x10'),\n", - " ('yaxis27', 'x27'),\n", - " ('yaxis33', 'x33'),\n", - " ('yaxis37', 'x37'),\n", - " ('yaxis44', 'x44'),\n", - " ('yaxis42', 'x42')]" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[(k, fig.layout[k][\"anchor\"]) for k in fig.layout if k.startswith(\"yaxis\")]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Heatmap({\n", - " 'showscale': False,\n", - " 'xaxis': 'x',\n", - " 'yaxis': 'y',\n", - " 'z': array([[ 1., nan, nan, ..., nan, nan, 0.],\n", - " [ 0., nan, nan, ..., nan, nan, 0.],\n", - " [ 1., nan, nan, ..., nan, nan, 1.],\n", - " ...,\n", - " [nan, nan, nan, ..., nan, nan, 1.],\n", - " [nan, nan, nan, ..., nan, nan, 0.],\n", - " [nan, nan, nan, ..., nan, nan, 2.]])\n", - "})" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fig.data[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "brainio_contrib", - "language": "python", - "name": "brainio_contrib" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - }, - "toc-autonumbering": false, - "toc-showcode": true, - "toc-showmarkdowntxt": false, - "toc-showtags": false - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/packaging/notebooks/2021-04-12_darren_heatmap.ipynb b/packaging/notebooks/2021-04-12_darren_heatmap.ipynb deleted file mode 100644 index 2db7d188f..000000000 --- a/packaging/notebooks/2021-04-12_darren_heatmap.ipynb +++ /dev/null @@ -1,1116 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "INSTALLED VERSIONS\n", - "------------------\n", - "commit: None\n", - "python: 3.7.2 (default, Dec 29 2018, 06:19:36) \n", - "[GCC 7.3.0]\n", - "python-bits: 64\n", - "OS: Linux\n", - "OS-release: 4.4.0-179-generic\n", - "machine: x86_64\n", - "processor: x86_64\n", - "byteorder: little\n", - "LC_ALL: None\n", - "LANG: en_US.UTF-8\n", - "LOCALE: en_US.UTF-8\n", - "libhdf5: 1.10.2\n", - "libnetcdf: 4.4.1.1\n", - "\n", - "xarray: 0.12.0\n", - "pandas: 0.24.0\n", - "numpy: 1.15.4\n", - "scipy: 1.2.0\n", - "netCDF4: 1.4.2\n", - "pydap: None\n", - "h5netcdf: None\n", - "h5py: None\n", - "Nio: None\n", - "zarr: None\n", - "cftime: 1.0.3.4\n", - "nc_time_axis: None\n", - "PseudonetCDF: None\n", - "rasterio: None\n", - "cfgrib: None\n", - "iris: None\n", - "bottleneck: None\n", - "dask: None\n", - "distributed: None\n", - "matplotlib: None\n", - "cartopy: None\n", - "seaborn: None\n", - "setuptools: 40.6.3\n", - "pip: 18.1\n", - "conda: None\n", - "pytest: 4.2.0\n", - "IPython: 7.2.0\n", - "sphinx: None\n" - ] - } - ], - "source": [ - "xr.show_versions()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class FooSub(xr.DataArray):\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from brainio_collection import list_stimulus_sets, list_assemblies, get_stimulus_set, get_assembly" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['dicarlo.MajajHong2015', 'gallant.David2004',\n", - " 'dicarlo.MajajHong2015.temporal', 'tolias.Cadena2017',\n", - " 'movshon.FreemanZiemba2013.noaperture',\n", - " 'dicarlo.Rajalingham2018.public',\n", - " 'dicarlo.Rajalingham2018.private', 'dicarlo.Kar2019',\n", - " 'dicarlo.MajajHong2015.temporal-10ms', 'dicarlo.Kar2018hvm',\n", - " 'dicarlo.Kar2018cocogray', 'dicarlo.MajajHong2015.public',\n", - " 'dicarlo.MajajHong2015.private',\n", - " 'dicarlo.MajajHong2015.temporal.public',\n", - " 'dicarlo.MajajHong2015.temporal.private',\n", - " 'movshon.FreemanZiemba2013.noaperture.public',\n", - " 'movshon.FreemanZiemba2013.noaperture.private',\n", - " 'klab.Zhang2018search_obj_array',\n", - " 'movshon.FreemanZiemba2013.public',\n", - " 'movshon.FreemanZiemba2013.private', 'movshon.FreemanZiemba2013',\n", - " 'dicarlo.SanghaviMurty2020', 'dicarlo.SanghaviJozwik2020',\n", - " 'dicarlo.Sanghavi2020', 'dicarlo.SanghaviMurty2020THINGS1',\n", - " 'dicarlo.SanghaviMurty2020THINGS2', 'dicarlo.Rajalingham2020',\n", - " 'dicarlo.Seibert2019', 'aru.Kuzovkin2018',\n", - " 'dicarlo.Rust2012.single', 'dicarlo.Rust2012.array'], dtype=object)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list_assemblies()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "  - neuroid_id       (neuroid) object 'I2_IT_00' 'I2_IT_01' ... 'HT_IT_46'\n",
-       "  * presentation     (presentation) MultiIndex\n",
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-       "  * time_bin         (time_bin) MultiIndex\n",
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-       "  - time_bin_end     (time_bin) int64 170\n",
-       "Attributes:\n",
-       "    stimulus_set_identifier:  dicarlo.hvm\n",
-       "    stimulus_set:                     id                             backgrou...\n",
-       "    identifier:               dicarlo.Seibert2019
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'IT' 'IT' 'IT'\n", - " - electrode (neuroid) int64 0 1 2 3 4 5 6 7 ... 39 40 41 42 43 44 45 46\n", - " - neuroid_id (neuroid) object 'I2_IT_00' 'I2_IT_01' ... 'HT_IT_46'\n", - " * presentation (presentation) MultiIndex\n", - " - image_id (presentation) object 'f1ae7c2160c6cbb309b940a0dc127a8aa...\n", - " - repetition (presentation) int64 0 0 0 0 0 0 0 ... 52 52 52 52 52 52 52\n", - " - stimulus (presentation) int64 0 1 2 3 4 ... 2555 2556 2557 2558 2559\n", - " - id (presentation) int64 610 224 185 522 ... 3366 5125 5605\n", - " - background_id (presentation) object '01c0425e982e4edc276fe5373dbf8e5e4...\n", - " - s (presentation) float64 1.0 1.0 1.0 ... 1.394 1.039 0.875\n", - " - image_file_name (presentation) object 'Apple_Fruit_obj_rx-90.000_ry+00.0...\n", - " - filename (presentation) object 'Apple_Fruit_obj_rx-90.000_ry+00.0...\n", - " - rxy (presentation) float64 -0.0 -0.0 -0.0 ... -51.99 11.54\n", - " - tz (presentation) float64 0.0 0.0 0.0 ... 0.244 0.402 -0.072\n", - " - category_name (presentation) object 'Fruits' 'Fruits' ... 'Cars' 'Cars'\n", - " - rxz_semantic (presentation) float64 0.0 0.0 0.0 ... -86.07 -86.88 -87.96\n", - " - ty (presentation) float64 0.0 0.0 0.0 ... -0.283 -0.014 0.145\n", - " - ryz (presentation) float64 -0.0 -0.0 -0.0 ... 38.99 51.39 79.69\n", - " - object_name (presentation) object 'apple' 'apple' ... 'car_z3' 'car_z3'\n", - " - variation (presentation) int64 0 0 0 0 0 0 0 0 0 ... 6 6 6 6 6 6 6 6\n", - " - size (presentation) float64 256.0 256.0 256.0 ... 256.0 256.0\n", - " - rxy_semantic (presentation) float64 -0.0 -0.0 -0.0 ... 154.4 38.01 101.5\n", - " - ryz_semantic (presentation) float64 -0.0 -0.0 -0.0 ... 38.99 51.39 79.69\n", - " - rxz (presentation) float64 0.0 0.0 0.0 ... -86.07 -86.88 -87.96\n", - " * time_bin (time_bin) MultiIndex\n", - " - time_bin_start (time_bin) int64 70\n", - " - time_bin_end (time_bin) int64 170\n", - "Attributes:\n", - " stimulus_set_identifier: dicarlo.hvm\n", - " stimulus_set: id backgrou...\n", - " identifier: dicarlo.Seibert2019" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "darren = get_assembly(\"dicarlo.Seibert2019\")\n", - "darren" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAANQklEQVR4nO3cX4il9X3H8fenuxEak0aJk5DurmRb1pi90KITI6VpTUObXXuxBLxQQ6QSWKQx5FIpNLnwprkohKBmWWSR3GQvGkk2ZRMplMSCNd1Z8N8qynSlOl3BNYYUDFRWv704p51hnHWenXNmZp3v+wUD85znNzPf+TH73mfPznlSVUiStr7f2ewBJEkbw+BLUhMGX5KaMPiS1ITBl6QmDL4kNbFq8JMcSfJakmfPcz5JvptkPsnTSa6b/piSpEkNucJ/GNj3Huf3A3vGbweB700+liRp2lYNflU9BrzxHksOAN+vkSeAy5J8YloDSpKmY/sUPscO4JUlxwvjx15dvjDJQUb/CuDSSy+9/uqrr57Cl5ekPk6ePPl6Vc2s5WOnEfys8NiK92uoqsPAYYDZ2dmam5ubwpeXpD6S/OdaP3Yav6WzAOxacrwTODOFzytJmqJpBP8YcMf4t3VuBH5TVe96OkeStLlWfUonyQ+Am4ArkiwA3wI+AFBVh4DjwM3APPBb4M71GlaStHarBr+qblvlfAFfm9pEkqR14SttJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJamJQ8JPsS/JCkvkk965w/iNJfpLkqSSnktw5/VElSZNYNfhJtgEPAPuBvcBtSfYuW/Y14Lmquha4CfiHJJdMeVZJ0gSGXOHfAMxX1emqegs4ChxYtqaADycJ8CHgDeDcVCeVJE1kSPB3AK8sOV4YP7bU/cCngTPAM8A3quqd5Z8oycEkc0nmzp49u8aRJUlrMST4WeGxWnb8ReBJ4PeBPwLuT/J77/qgqsNVNVtVszMzMxc4qiRpEkOCvwDsWnK8k9GV/FJ3Ao/UyDzwEnD1dEaUJE3DkOCfAPYk2T3+j9hbgWPL1rwMfAEgyceBTwGnpzmoJGky21dbUFXnktwNPApsA45U1akkd43PHwLuAx5O8gyjp4DuqarX13FuSdIFWjX4AFV1HDi+7LFDS94/A/zldEeTJE2Tr7SVpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDUxKPhJ9iV5Icl8knvPs+amJE8mOZXkF9MdU5I0qe2rLUiyDXgA+AtgATiR5FhVPbdkzWXAg8C+qno5ycfWaV5J0hoNucK/AZivqtNV9RZwFDiwbM3twCNV9TJAVb023TElSZMaEvwdwCtLjhfGjy11FXB5kp8nOZnkjpU+UZKDSeaSzJ09e3ZtE0uS1mRI8LPCY7XseDtwPfBXwBeBv0ty1bs+qOpwVc1W1ezMzMwFDytJWrtVn8NndEW/a8nxTuDMCmter6o3gTeTPAZcC7w4lSklSRMbcoV/AtiTZHeSS4BbgWPL1vwY+FyS7Uk+CHwWeH66o0qSJrHqFX5VnUtyN/AosA04UlWnktw1Pn+oqp5P8jPgaeAd4KGqenY9B5ckXZhULX86fmPMzs7W3NzcpnxtSXq/SnKyqmbX8rG+0laSmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmBgU/yb4kLySZT3Lve6z7TJK3k9wyvRElSdOwavCTbAMeAPYDe4Hbkuw9z7pvA49Oe0hJ0uSGXOHfAMxX1emqegs4ChxYYd3XgR8Cr01xPknSlAwJ/g7glSXHC+PH/l+SHcCXgEPv9YmSHEwyl2Tu7NmzFzqrJGkCQ4KfFR6rZcffAe6pqrff6xNV1eGqmq2q2ZmZmYEjSpKmYfuANQvAriXHO4Ezy9bMAkeTAFwB3JzkXFX9aBpDSpImNyT4J4A9SXYD/wXcCty+dEFV7f6/95M8DPyTsZeki8uqwa+qc0nuZvTbN9uAI1V1Ksld4/Pv+by9JOniMOQKn6o6Dhxf9tiKoa+qv558LEnStPlKW0lqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSE4OCn2RfkheSzCe5d4XzX07y9Pjt8STXTn9USdIkVg1+km3AA8B+YC9wW5K9y5a9BPxZVV0D3AccnvagkqTJDLnCvwGYr6rTVfUWcBQ4sHRBVT1eVb8eHz4B7JzumJKkSQ0J/g7glSXHC+PHzuerwE9XOpHkYJK5JHNnz54dPqUkaWJDgp8VHqsVFyafZxT8e1Y6X1WHq2q2qmZnZmaGTylJmtj2AWsWgF1LjncCZ5YvSnIN8BCwv6p+NZ3xJEnTMuQK/wSwJ8nuJJcAtwLHli5IciXwCPCVqnpx+mNKkia16hV+VZ1LcjfwKLANOFJVp5LcNT5/CPgm8FHgwSQA56pqdv3GliRdqFSt+HT8upudna25ublN+dqS9H6V5ORaL6h9pa0kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNDAp+kn1JXkgyn+TeFc4nyXfH559Oct30R5UkTWLV4CfZBjwA7Af2Arcl2bts2X5gz/jtIPC9Kc8pSZrQkCv8G4D5qjpdVW8BR4EDy9YcAL5fI08AlyX5xJRnlSRNYPuANTuAV5YcLwCfHbBmB/Dq0kVJDjL6FwDA/yR59oKm3bquAF7f7CEuEu7FIvdikXux6FNr/cAhwc8Kj9Ua1lBVh4HDAEnmqmp2wNff8tyLRe7FIvdikXuxKMncWj92yFM6C8CuJcc7gTNrWCNJ2kRDgn8C2JNkd5JLgFuBY8vWHAPuGP+2zo3Ab6rq1eWfSJK0eVZ9SqeqziW5G3gU2AYcqapTSe4anz8EHAduBuaB3wJ3Dvjah9c89dbjXixyLxa5F4vci0Vr3otUveupdknSFuQrbSWpCYMvSU2se/C9LcOiAXvx5fEePJ3k8STXbsacG2G1vViy7jNJ3k5yy0bOt5GG7EWSm5I8meRUkl9s9IwbZcCfkY8k+UmSp8Z7MeT/C993khxJ8tr5Xqu05m5W1bq9MfpP3v8A/gC4BHgK2Ltszc3ATxn9Lv+NwC/Xc6bNehu4F38MXD5+f3/nvViy7l8Y/VLALZs99yb+XFwGPAdcOT7+2GbPvYl78bfAt8fvzwBvAJds9uzrsBd/ClwHPHue82vq5npf4XtbhkWr7kVVPV5Vvx4fPsHo9Qxb0ZCfC4CvAz8EXtvI4TbYkL24HXikql4GqKqtuh9D9qKADycJ8CFGwT+3sWOuv6p6jNH3dj5r6uZ6B/98t1y40DVbwYV+n19l9Df4VrTqXiTZAXwJOLSBc22GIT8XVwGXJ/l5kpNJ7tiw6TbWkL24H/g0oxd2PgN8o6re2ZjxLipr6uaQWytMYmq3ZdgCBn+fST7PKPh/sq4TbZ4he/Ed4J6qent0MbdlDdmL7cD1wBeA3wX+LckTVfXieg+3wYbsxReBJ4E/B/4Q+Ock/1pV/73Os11s1tTN9Q6+t2VYNOj7THIN8BCwv6p+tUGzbbQhezELHB3H/grg5iTnqupHGzLhxhn6Z+T1qnoTeDPJY8C1wFYL/pC9uBP4+xo9kT2f5CXgauDfN2bEi8aaurneT+l4W4ZFq+5FkiuBR4CvbMGrt6VW3Yuq2l1Vn6yqTwL/CPzNFow9DPsz8mPgc0m2J/kgo7vVPr/Bc26EIXvxMqN/6ZDk44zuHHl6Q6e8OKypm+t6hV/rd1uG952Be/FN4KPAg+Mr23O1Be8QOHAvWhiyF1X1fJKfAU8D7wAPVdWWu7X4wJ+L+4CHkzzD6GmNe6pqy902OckPgJuAK5IsAN8CPgCTddNbK0hSE77SVpKaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrifwHXe3WluIZOawAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
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\n", - "text/plain": [ - "
" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ax.imshow(darren[:, 150000:152500, :].squeeze().transpose())\n", - "fig" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'AxesSubplot' object has no attribute 'get_cmap'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_size_inches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_bad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"black\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'AxesSubplot' object has no attribute 'get_cmap'" - ] - } - ], - "source": [ - "fig.set_size_inches(15,25)\n", - "ax.get_cmap().set_bad(color=\"black\")\n", - "fig" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/braintree/home/jjpr/anaconda3/envs/brainio/lib/python3.7/site-packages/ipykernel_launcher.py:1: MatplotlibDeprecationWarning: You are modifying the state of a globally registered colormap. In future versions, you will not be able to modify a registered colormap in-place. To remove this warning, you can make a copy of the colormap first. cmap = copy.copy(mpl.cm.get_cmap(\"viridis\"))\n", - " \"\"\"Entry point for launching an IPython kernel.\n" - ] - } - ], - "source": [ - "ax.get_children()[-2].get_cmap().set_bad(color=\"black\")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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}, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dir()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "brainio", - "language": "python", - "name": "brainio" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/packaging/notebooks/image_meta_dataframe.pkl b/packaging/notebooks/image_meta_dataframe.pkl deleted file mode 100644 index bc16adc42..000000000 Binary files a/packaging/notebooks/image_meta_dataframe.pkl and /dev/null differ diff --git a/packaging/notebooks/image_meta_scratch.db b/packaging/notebooks/image_meta_scratch.db deleted file mode 100644 index 07e33cb5e..000000000 Binary files a/packaging/notebooks/image_meta_scratch.db and /dev/null differ diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..33310af02 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,90 @@ +################################################################ +#### project config #### +################################################################ + +[project] +name = "brainscore_vision" +version = "2.0" +description = "The Brain-Score library enables model comparisons to behavioral and neural experiments" +authors = [] +license = { 'file' = 'LICENSE' } +readme = "README.md" +requires-python = ">=3.7" + +dependencies = [ + "numpy>=1.17", + "brainscore_core @ git+https://github.com/brain-score/core", + "result_caching @ git+https://github.com/brain-score/result_caching", + "importlib-metadata<5", # workaround to https://github.com/brain-score/brainio/issues/28 + "scikit-learn", # for metric_helpers/transformations.py cross-validation + "scipy", # for benchmark_helpers/properties_common.py + "h5py", + "tqdm", + "gitpython", + "fire", + "jupyter", + "pybtex", + "peewee", + "psycopg2-binary", + "networkx", +] + +[project.optional-dependencies] +test = [ + "pytest", + "pytest_check", + "torch", + "torchvision", + "tensorflow==1.15", + "keras==2.3.1", +] + +[build-system] +requires = [ + "setuptools>=65.*", + "wheel" +] + +[project.scripts] +brainio_brainscore = "brainscore_vision.entrypoint:brainio_brainscore" + + +################################################################ +#### mypy type-checking config #### +################################################################ + +[tool.mypy] +ignore_missing_imports = true + + +[tool.pytest.ini_options] +testpaths = [ + "tests" +] + +filterwarnings = [ + "ignore::DeprecationWarning", + "ignore::FutureWarning" +] + +markers = [ + "memory_intense", + "travis_slow", + "slow", + "private_access", +] + + +################################################################ +#### setuptools packaging config #### +################################################################ + +[tool.setuptools] +py-modules = ['brainscore_vision'] + +[tool.setuptools.package-data] +# include non-python files (e.g. 'requirements.txt') in package install +"brainscore_vision.benchmarks" = ["**"] +"brainscore_vision.data" = ["**"] +"brainscore_vision.metrics" = ["**"] +"brainscore_vision.models" = ["**"] diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 000000000..148c7ba2a --- /dev/null +++ b/pytest.ini @@ -0,0 +1,4 @@ +# This is a temporary specification that should be removed when finalizing https://github.com/brain-score/brain-score/tree/integrate_core + +[pytest] +addopts = --ignore=todotests diff --git a/setup.py b/setup.py deleted file mode 100644 index 53a72f36a..000000000 --- a/setup.py +++ /dev/null @@ -1,69 +0,0 @@ -""" -Releasing a new version (adopted from https://github.com/huggingface/transformers/blob/master/setup.py) - -1. Change the version in `setup.py` and `docs/source/conf.py`. - -2. Commit these changes with the message: "release: " - -3. Mark the release with a git tag - -""" - -from setuptools import setup, find_packages - -with open('README.md') as readme_file: - readme = readme_file.read() - -requirements = [ - "numpy>=1.17", - "brainio @ git+https://github.com/brain-score/brainio", - "importlib-metadata<5", # workaround to https://github.com/brain-score/brainio/issues/28 - "scikit-learn<0.24", # 0.24 breaks pls regression - "scipy", - "h5py", - "tqdm", - "gitpython", - "fire", - "networkx", - "matplotlib", - "tensorflow", - "result_caching @ git+https://github.com/brain-score/result_caching", - "fire", - "jupyter", - "pybtex", - "peewee", - "pillow<9.0.0", - "psycopg2-binary" -] - -setup( - name='brain-score', - version='1.3', - description="A framework for the quantitative comparison of mindlike systems.", - long_description=readme, - author="Brain-Score Team", - author_email='mschrimpf@mit.edu, jjpr@mit.edu', - url='https://github.com/brain-score/brain-score', - packages=find_packages(exclude=['tests']), - include_package_data=True, - install_requires=requirements, - license="MIT license", - zip_safe=False, - keywords='brain-score', - classifiers=[ - 'Development Status :: 5 - Production/Stable', - 'Intended Audience :: Developers', - 'Intended Audience :: Education', - 'Intended Audience :: Science/Research', - 'License :: OSI Approved :: MIT License', - 'Natural Language :: English', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.7', - ], - test_suite='tests', - entry_points={ - 'brainio_lookups': [ - 'brainio_brainscore = brainscore.entrypoint:brainio_brainscore', - ], - }, -) diff --git a/test_setup.sh b/test_setup.sh deleted file mode 100755 index 77d54d244..000000000 --- a/test_setup.sh +++ /dev/null @@ -1,54 +0,0 @@ -#!/bin/bash - -# get directory of this script (i.e. tests), following https://stackoverflow.com/a/246128/2225200 -SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" - -for f in \ - alexnet-freemanziemba2013.aperture-private.nc \ - alexnet-majaj2015.private-features.12.nc \ - CORnetZ-rajalingham2018public.nc \ - cornet_s-kar2019.nc \ - alexnet-sanghavi2020-features.12.nc \ - alexnet-sanghavijozwik2020-features.12.nc \ - alexnet-sanghavimurty2020-features.12.nc \ - alexnet-rajalingham2020-features.12.nc \ - resnet-50-pytorch-3deg-Geirhos2021_colour.nc \ - resnet-50-pytorch-3deg-Geirhos2021_contrast.nc \ - resnet-50-pytorch-3deg-Geirhos2021_cue-conflict.nc \ - resnet-50-pytorch-3deg-Geirhos2021_edge.nc \ - resnet-50-pytorch-3deg-Geirhos2021_eidolonI.nc \ - resnet-50-pytorch-3deg-Geirhos2021_eidolonII.nc \ - resnet-50-pytorch-3deg-Geirhos2021_eidolonIII.nc \ - resnet-50-pytorch-3deg-Geirhos2021_false-colour.nc \ - resnet-50-pytorch-3deg-Geirhos2021_high-pass.nc \ - resnet-50-pytorch-3deg-Geirhos2021_low-pass.nc \ - resnet-50-pytorch-3deg-Geirhos2021_phase-scrambling.nc \ - resnet-50-pytorch-3deg-Geirhos2021_power-equalisation.nc \ - resnet-50-pytorch-3deg-Geirhos2021_rotation.nc \ - resnet-50-pytorch-3deg-Geirhos2021_silhouette.nc \ - resnet-50-pytorch-3deg-Geirhos2021_sketch.nc \ - resnet-50-pytorch-3deg-Geirhos2021_stylized.nc \ - resnet-50-pytorch-3deg-Geirhos2021_uniform-noise.nc \ - resnet-50-pytorch-8deg-Geirhos2021_colour.nc \ - resnet-50-pytorch-8deg-Geirhos2021_contrast.nc \ - resnet-50-pytorch-8deg-Geirhos2021_cue-conflict.nc \ - resnet-50-pytorch-8deg-Geirhos2021_edge.nc \ - resnet-50-pytorch-8deg-Geirhos2021_eidolonI.nc \ - resnet-50-pytorch-8deg-Geirhos2021_eidolonII.nc \ - resnet-50-pytorch-8deg-Geirhos2021_eidolonIII.nc \ - resnet-50-pytorch-8deg-Geirhos2021_false-colour.nc \ - resnet-50-pytorch-8deg-Geirhos2021_high-pass.nc \ - resnet-50-pytorch-8deg-Geirhos2021_low-pass.nc \ - resnet-50-pytorch-8deg-Geirhos2021_phase-scrambling.nc \ - resnet-50-pytorch-8deg-Geirhos2021_power-equalisation.nc \ - resnet-50-pytorch-8deg-Geirhos2021_rotation.nc \ - resnet-50-pytorch-8deg-Geirhos2021_silhouette.nc \ - resnet-50-pytorch-8deg-Geirhos2021_sketch.nc \ - resnet-50-pytorch-8deg-Geirhos2021_stylized.nc \ - resnet-50-pytorch-8deg-Geirhos2021_uniform-noise.nc -do - aws --no-sign-request s3 cp s3://brain-score-tests/tests/test_benchmarks/${f} ${SCRIPT_DIR}/tests/test_benchmarks/ -done - -pip install git+https://github.com/brain-score/model-tools --no-deps -pip install torch torchvision diff --git a/tests/README.md b/tests/README.md index 3d7d31b7b..64f1f9e5a 100644 --- a/tests/README.md +++ b/tests/README.md @@ -1,11 +1,4 @@ # Unit Tests -## Precomputed files -Some files have been pre-computed, but are too large to add to git. -They are on S3 instead and are automatically downloaded by executing `bash test_setup.sh`. - -When adding new files, they need to be made publicly accessible on S3: -select all files that you have added > Actions > Make public using ACL > Make public. - ## Markers Unit tests have various markers that denote possible issues in the travis build: @@ -24,3 +17,29 @@ def test_something(...): To skip a specific marker, run e.g. `pytest -m "not memory_intense"`. To skip multiple markers, run e.g. `pytest -m "not private_access and not memory_intense"`. + + +## Precomputed features +For many benchmark tests, it can be useful to evaluate on "reasonable" features that have been computed beforehand. +They are on S3 instead and are automatically downloaded by executing `bash test_setup.sh`. +Often these precomputed features are taken from models that were run on the benchmark. + +To capture a model's activations, you can use the following steps: +1. in the benchmark, put a breakpoint right after `candidate.look_at`. + E.g. if you run a benchmark with `predictions = candidate.look_at(stimulus_set)`, capture the `predictions` +2. run the model you want on the benchmark, stopping at the breakpoint +3. store the model predictions to netcdf: + ```python + from brainio.packaging import write_netcdf + + write_netcdf(predictions, '.nc') + ``` +4. upload the `.nc` file to the S3 brain-score-tests + [bucket](https://s3.console.aws.amazon.com/s3/buckets/brain-score-tests?region=us-east-1&prefix=tests/test_benchmarks/&showversions=false) + (drag and drop in browser is likely easiest; you might have to ask an admin to upload.) + In the upload, make sure to make the file(s) publicly accessible: + under Permissions > Predefined ACLs > Grant public-read access +5. add filename to [`test_setup.sh`](https://github.com/brain-score/brain-score/blob/master/test_setup.sh) +6. write your unit test (see e.g. + [here](https://github.com/brain-score/brain-score/blob/9ba55450a9d1c2b695c393df92aba2102ccdb169/tests/test_benchmarks/test_geirhos2021.py#L73) + for an example) diff --git a/tests/conftest.py b/tests/conftest.py index 8989c6cc7..95efb0e17 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,7 +1,7 @@ import brainio import pytest -from brainscore.benchmarks import screen +from brainscore_vision.benchmark_helpers import screen @pytest.fixture diff --git a/tests/test_anatomy.py b/tests/test_anatomy.py deleted file mode 100644 index 8068934aa..000000000 --- a/tests/test_anatomy.py +++ /dev/null @@ -1,34 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -import networkx as nx - -from brainscore.metrics.anatomy import EdgeRatioMetric - - -class TestEdgeRatio: - def test_2nodes_equal(self): - graph = nx.DiGraph() - graph.add_edge('A', 'B') - assert 1 == EdgeRatioMetric()(graph, graph) - - def test_2nodes_different(self): - graph1 = nx.DiGraph() - graph1.add_edge('A', 'B') - graph2 = nx.DiGraph() - graph2.add_edge('B', 'A') - assert 0 == EdgeRatioMetric()(graph1, graph2) - - def test_3nodes_equal(self): - graph = nx.DiGraph() - graph.add_edge('A', 'B') - graph.add_edge('B', 'C') - assert 1 == EdgeRatioMetric()(graph, graph) - - def test_3nodes_1edge_missing(self): - graph1 = nx.DiGraph() - graph1.add_edge('A', 'B') - graph1.add_edge('B', 'C') - graph2 = nx.DiGraph() - graph2.add_edge('A', 'B') - graph2.add_edge('C', 'B') - assert 0.5 == EdgeRatioMetric()(graph1, graph2) diff --git a/tests/test_assemblies.py b/tests/test_assemblies.py deleted file mode 100644 index 6f1c222ef..000000000 --- a/tests/test_assemblies.py +++ /dev/null @@ -1,484 +0,0 @@ -import numpy as np -import pytest -from PIL import Image -from pathlib import Path -from pytest import approx - -import brainio - -import brainscore - - -@pytest.mark.parametrize('assembly', ( - 'dicarlo.MajajHong2015', - 'dicarlo.MajajHong2015.private', - 'dicarlo.MajajHong2015.public', - 'dicarlo.MajajHong2015.temporal', - 'dicarlo.MajajHong2015.temporal.private', - 'dicarlo.MajajHong2015.temporal.public', - 'dicarlo.MajajHong2015.temporal-10ms', - 'gallant.David2004', - 'tolias.Cadena2017', - 'movshon.FreemanZiemba2013', - 'movshon.FreemanZiemba2013.private', - 'movshon.FreemanZiemba2013.public', - 'dicarlo.Rajalingham2018.public', 'dicarlo.Rajalingham2018.private', - 'dicarlo.Kar2019', - 'dicarlo.Kar2018hvm', - 'dicarlo.Kar2018cocogray', - 'klab.Zhang2018search_obj_array', - 'aru.Kuzovkin2018', - 'dicarlo.Rajalingham2020', - 'dicarlo.SanghaviMurty2020', - 'dicarlo.SanghaviJozwik2020', - 'dicarlo.Sanghavi2020', - 'dicarlo.SanghaviMurty2020THINGS1', - 'dicarlo.SanghaviMurty2020THINGS2', - 'aru.Kuzovkin2018', - 'dicarlo.Seibert2019', - 'aru.Cichy2019', - 'dicarlo.Rust2012.single', - 'dicarlo.Rust2012.array', - 'dicarlo.BashivanKar2019.naturalistic', - 'dicarlo.BashivanKar2019.synthetic', - 'movshon.Cavanaugh2002a', - 'devalois.DeValois1982a', - 'devalois.DeValois1982b', - 'movshon.FreemanZiemba2013_V1_properties', - 'shapley.Ringach2002', - 'schiller.Schiller1976c', - 'brendel.Geirhos2021_colour', - 'brendel.Geirhos2021_contrast', - 'brendel.Geirhos2021_cue-conflict', - 'brendel.Geirhos2021_edge', - 'brendel.Geirhos2021_eidolonI', - 'brendel.Geirhos2021_eidolonII', - 'brendel.Geirhos2021_eidolonIII', - 'brendel.Geirhos2021_false-colour', - 'brendel.Geirhos2021_high-pass', - 'brendel.Geirhos2021_low-pass', - 'brendel.Geirhos2021_phase-scrambling', - 'brendel.Geirhos2021_power-equalisation', - 'brendel.Geirhos2021_rotation', - 'brendel.Geirhos2021_silhouette', - 'brendel.Geirhos2021_stylized', - 'brendel.Geirhos2021_sketch', - 'brendel.Geirhos2021_uniform-noise', -)) -def test_list_assembly(assembly): - l = brainio.list_assemblies() - assert assembly in l - - -@pytest.mark.parametrize('assembly_identifier', [ - pytest.param('gallant.David2004', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.MajajHong2015', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.MajajHong2015.public', marks=[]), - pytest.param('dicarlo.MajajHong2015.private', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.MajajHong2015.temporal', marks=[pytest.mark.private_access, pytest.mark.memory_intense]), - pytest.param('dicarlo.MajajHong2015.temporal.public', marks=[pytest.mark.memory_intense]), - pytest.param('dicarlo.MajajHong2015.temporal.private', - marks=[pytest.mark.private_access, pytest.mark.memory_intense]), - # pytest.param('dicarlo.MajajHong2015.temporal-10ms', marks=[pytest.mark.private_access, pytest.mark.memory_intense]), - pytest.param('tolias.Cadena2017', marks=[pytest.mark.private_access]), - pytest.param('movshon.FreemanZiemba2013', marks=[pytest.mark.private_access, pytest.mark.memory_intense]), - pytest.param('movshon.FreemanZiemba2013.public', marks=[pytest.mark.memory_intense]), - pytest.param('movshon.FreemanZiemba2013.private', marks=[pytest.mark.private_access, pytest.mark.memory_intense]), - pytest.param('dicarlo.Rajalingham2018.public', marks=[]), - pytest.param('dicarlo.Rajalingham2018.private', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.Kar2019', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.Kar2018hvm', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.Kar2018cocogray', marks=[pytest.mark.private_access]), - pytest.param('klab.Zhang2018search_obj_array', marks=[pytest.mark.private_access]), - pytest.param('aru.Kuzovkin2018', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.Rajalingham2020', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.SanghaviMurty2020', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.SanghaviJozwik2020', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.Sanghavi2020', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.SanghaviMurty2020THINGS1', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.SanghaviMurty2020THINGS2', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.Seibert2019', marks=[pytest.mark.private_access]), - pytest.param('aru.Cichy2019', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.Rust2012.single', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.Rust2012.array', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.BashivanKar2019.naturalistic', marks=[pytest.mark.private_access]), - pytest.param('dicarlo.BashivanKar2019.synthetic', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_colour', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_contrast', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_cue-conflict', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_edge', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_eidolonI', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_eidolonII', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_eidolonIII', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_false-colour', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_high-pass', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_low-pass', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_phase-scrambling', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_power-equalisation', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_rotation', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_silhouette', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_stylized', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_sketch', marks=[pytest.mark.private_access]), - pytest.param('brendel.Geirhos2021_uniform-noise', marks=[pytest.mark.private_access]), -]) -def test_existence(assembly_identifier): - assert brainio.get_assembly(assembly_identifier) is not None - - -@pytest.mark.private_access -def test_klab_Zhang2018search(): - assembly = brainio.get_assembly('klab.Zhang2018search_obj_array') - assert set(assembly.dims) == {'presentation', 'fixation', 'position'} - assert len(assembly['presentation']) == 4500 - assert len(set(assembly['stimulus_id'].values)) == 300 - assert len(set(assembly['subjects'].values)) == 15 - assert len(assembly['fixation']) == 8 - assert len(assembly['position']) == 2 - assert assembly.stimulus_set is not None - - -class TestFreemanZiemba: - @pytest.mark.parametrize('identifier', [ - pytest.param('movshon.FreemanZiemba2013.public', marks=[]), - pytest.param('movshon.FreemanZiemba2013.private', marks=[pytest.mark.private_access]), - ]) - def test_v1_v2_alignment(self, identifier): - assembly = brainio.get_assembly(identifier) - v1 = assembly[{'neuroid': [region == 'V1' for region in assembly['region'].values]}] - v2 = assembly[{'neuroid': [region == 'V2' for region in assembly['region'].values]}] - assert len(v1['presentation']) == len(v2['presentation']) - assert set(v1['stimulus_id'].values) == set(v2['stimulus_id'].values) - - @pytest.mark.parametrize('identifier', [ - pytest.param('movshon.FreemanZiemba2013.public', marks=[]), - pytest.param('movshon.FreemanZiemba2013.private', marks=[pytest.mark.private_access]), - ]) - def test_num_neurons(self, identifier): - assembly = brainio.get_assembly(identifier) - assert len(assembly['neuroid']) == 205 - v1 = assembly[{'neuroid': [region == 'V1' for region in assembly['region'].values]}] - assert len(v1['neuroid']) == 102 - v2 = assembly[{'neuroid': [region == 'V2' for region in assembly['region'].values]}] - assert len(v2['neuroid']) == 103 - - @pytest.mark.parametrize('identifier', [ - pytest.param('movshon.FreemanZiemba2013.public', marks=[]), - pytest.param('movshon.FreemanZiemba2013.private', marks=[pytest.mark.private_access]), - ]) - def test_nonzero(self, identifier): - assembly = brainio.get_assembly(identifier) - nonzero = np.count_nonzero(assembly) - assert nonzero > 0 - - @pytest.mark.parametrize('identifier, image_id, expected_amount_gray, ratio_gray', [ - pytest.param('movshon.FreemanZiemba2013.public', '21041db1f26c142812a66277c2957fb3e2070916', - 31756, .3101171875, marks=[]), - pytest.param('movshon.FreemanZiemba2013.private', 'bfd26c127f8ba028cc95cdc95f00c45c8884b365', - 31585, .308447265625, marks=[pytest.mark.private_access]), - ]) - def test_aperture(self, identifier, image_id, expected_amount_gray, ratio_gray): - """ test a random image for the correct amount of gray pixels """ - assembly = brainio.get_assembly(identifier) - stimulus_set = assembly.stimulus_set - image_path = Path(stimulus_set.get_stimulus(image_id)) - assert image_path.is_file() - # count number of gray pixels in image - image = Image.open(image_path) - image = np.array(image) - amount_gray = 0 - for index in np.ndindex(image.shape[:2]): - color = image[index] - gray = [128, 128, 128] - if (color == gray).all(): - amount_gray += 1 - assert amount_gray / image.size == approx(ratio_gray, abs=.0001) - assert amount_gray == expected_amount_gray - - -class TestSeibert: - @pytest.mark.private_access - def test_dims(self): - assembly = brainio.get_assembly('dicarlo.Seibert2019') - # neuroid: 258 presentation: 286080 time_bin: 1 - assert assembly.dims == ("neuroid", "presentation", "time_bin") - assert len(assembly['neuroid']) == 258 - assert len(assembly['presentation']) == 286080 - assert len(assembly['time_bin']) == 1 - - @pytest.mark.private_access - def test_coords(self): - assembly = brainio.get_assembly('dicarlo.Seibert2019') - assert len(set(assembly['stimulus_id'].values)) == 5760 - assert len(set(assembly['neuroid_id'].values)) == 258 - assert len(set(assembly['animal'].values)) == 3 - assert len(set(assembly['region'].values)) == 2 - assert len(set(assembly['variation'].values)) == 3 - - @pytest.mark.private_access - def test_content(self): - assembly = brainio.get_assembly('dicarlo.Seibert2019') - assert np.count_nonzero(np.isnan(assembly)) == 19118720 - assert assembly.stimulus_set_identifier == "dicarlo.hvm" - hvm = assembly.stimulus_set - assert hvm.shape == (5760, 19) - - -class TestRustSingle: - @pytest.mark.private_access - def test_dims(self): - assembly = brainio.get_assembly('dicarlo.Rust2012.single') - # (neuroid: 285, presentation: 1500, time_bin: 1) - assert assembly.dims == ("neuroid", "presentation", "time_bin") - assert len(assembly['neuroid']) == 285 - assert len(assembly['presentation']) == 1500 - assert len(assembly['time_bin']) == 1 - - @pytest.mark.private_access - def test_coords(self): - assembly = brainio.get_assembly('dicarlo.Rust2012.single') - assert len(set(assembly['stimulus_id'].values)) == 300 - assert len(set(assembly['neuroid_id'].values)) == 285 - assert len(set(assembly['region'].values)) == 2 - - -class TestRustArray: - @pytest.mark.private_access - def test_dims(self): - assembly = brainio.get_assembly('dicarlo.Rust2012.array') - # (neuroid: 296, presentation: 53700, time_bin: 6) - assert assembly.dims == ("neuroid", "presentation", "time_bin") - assert len(assembly['neuroid']) == 296 - assert len(assembly['presentation']) == 53700 - assert len(assembly['time_bin']) == 6 - - @pytest.mark.private_access - def test_coords(self): - assembly = brainio.get_assembly('dicarlo.Rust2012.array') - assert len(set(assembly['stimulus_id'].values)) == 300 - assert len(set(assembly['neuroid_id'].values)) == 296 - assert len(set(assembly['animal'].values)) == 2 - assert len(set(assembly['region'].values)) == 2 - - -@pytest.mark.parametrize('assembly,shape,nans', [ - pytest.param('dicarlo.BashivanKar2019.naturalistic', (24320, 233, 1), 309760, marks=[pytest.mark.private_access]), - pytest.param('dicarlo.BashivanKar2019.synthetic', (21360, 233, 1), 4319940, marks=[pytest.mark.private_access]), -]) -def test_BashivanKar2019(assembly, shape, nans): - assy = brainio.get_assembly(assembly) - assert assy.shape == shape - assert np.count_nonzero(np.isnan(assy)) == nans - - -@pytest.mark.private_access -class TestMarques2020V1Properties: - @pytest.mark.parametrize('identifier,num_neuroids,properties,stimulus_set_identifier', [ - ('movshon.Cavanaugh2002a', 190, [ - 'surround_suppression_index', 'strongly_suppressed', 'grating_summation_field', 'surround_diameter', - 'surround_grating_summation_field_ratio'], - 'dicarlo.Marques2020_size'), - ('devalois.DeValois1982a', 385, [ - 'preferred_orientation'], - 'dicarlo.Marques2020_orientation'), - ('devalois.DeValois1982b', 363, [ - 'peak_spatial_frequency'], - 'dicarlo.Marques2020_spatial_frequency'), - ('movshon.FreemanZiemba2013_V1_properties', 102, [ - 'absolute_texture_modulation_index', 'family_variance', 'max_noise', 'max_texture', 'noise_selectivity', - 'noise_sparseness', 'sample_variance', 'texture_modulation_index', 'texture_selectivity', - 'texture_sparseness', 'variance_ratio'], - 'movshon.FreemanZiemba2013_properties'), - ('shapley.Ringach2002', 304, [ - 'bandwidth', 'baseline', 'circular_variance', 'circular_variance_bandwidth_ratio', 'max_ac', 'max_dc', - 'min_dc', 'modulation_ratio', 'orientation_selective', 'orthogonal_preferred_ratio', - 'orthogonal_preferred_ratio_bandwidth_ratio', 'orthogonal_preferred_ratio_circular_variance_difference'], - 'dicarlo.Marques2020_orientation'), - ('schiller.Schiller1976c', 87, [ - 'spatial_frequency_selective', 'spatial_frequency_bandwidth'], - 'dicarlo.Marques2020_spatial_frequency'), - ]) - def test_assembly(self, identifier, num_neuroids, properties, stimulus_set_identifier): - assembly = brainio.get_assembly(identifier) - assert set(assembly.dims) == {'neuroid', 'neuronal_property'} - assert len(assembly['neuroid']) == num_neuroids - assert set(assembly['neuronal_property'].values) == set(properties) - assert assembly.stimulus_set is not None - assert assembly.stimulus_set.identifier == stimulus_set_identifier - - -class TestGeirhos2021: - - # test stimulus_set data alignment with assembly: - @pytest.mark.parametrize('identifier', [ - 'colour', - 'contrast', - 'cue-conflict', - 'edge', - 'eidolonI', - 'eidolonII', - 'eidolonIII', - 'false-colour', - 'high-pass', - 'low-pass', - 'phase-scrambling', - 'power-equalisation', - 'rotation', - 'silhouette', - 'stylized', - 'sketch', - 'uniform-noise', - ]) - @pytest.mark.parametrize('field', [ - 'image_id', - 'condition', - 'truth', - ]) - def test_stimulus_set_assembly_alignment(self, identifier, field): - full_name = f"brendel.Geirhos2021_{identifier}" - assembly = brainscore.get_assembly(full_name) - assert assembly.stimulus_set is not None - assert assembly.stimulus_set.identifier == full_name - assert set(assembly.stimulus_set[field]) == set(assembly[field].values) - - # test the number of subjects: - @pytest.mark.parametrize('identifier, num_subjects', [ - ('colour', 4), - ('contrast', 4), - ('cue-conflict', 10), - ('edge', 10), - ('eidolonI', 4), - ('eidolonII', 4), - ('eidolonIII', 4), - ('false-colour', 4), - ('high-pass', 4), - ('low-pass', 4), - ('phase-scrambling', 4), - ('power-equalisation', 4), - ('rotation', 4), - ('silhouette', 10), - ('stylized', 5), - ('sketch', 7), - ('uniform-noise', 4), - ]) - def test_num_subjects(self, identifier, num_subjects): - assembly = brainscore.get_assembly(f"brendel.Geirhos2021_{identifier}") - assert len(np.unique(assembly['subject'].values)) == num_subjects - - # test the number of images - @pytest.mark.parametrize('identifier, num_images', [ - ('colour', 1280), - ('contrast', 1280), - ('cue-conflict', 1280), - ('edge', 160), - ('eidolonI', 1280), - ('eidolonII', 1280), - ('eidolonIII', 1280), - ('false-colour', 1120), - ('high-pass', 1280), - ('low-pass', 1280), - ('phase-scrambling', 1120), - ('power-equalisation', 1120), - ('rotation', 1280), - ('silhouette', 160), - ('stylized', 800), - ('sketch', 800), - ('uniform-noise', 1280), - ]) - def test_num_images(self, identifier, num_images): - assembly = brainscore.get_assembly(f"brendel.Geirhos2021_{identifier}") - assert len(np.unique(assembly['image_id'].values)) == num_images - - # tests assembly dim for ALL 17 sets: - @pytest.mark.parametrize('identifier, length', [ - ('colour', 5120), - ('contrast', 5120), - ('cue-conflict', 12800), - ('edge', 1600), - ('eidolonI', 5120), - ('eidolonII', 5120), - ('eidolonIII', 5120), - ('false-colour', 4480), - ('high-pass', 5120), - ('low-pass', 5120), - ('phase-scrambling', 4480), - ('power-equalisation', 4480), - ('rotation', 5120), - ('silhouette', 1600), - ('stylized', 4000), - ('sketch', 5600), - ('uniform-noise', 5120), - ]) - def test_length(self, identifier, length): - assembly = brainscore.get_assembly(f"brendel.Geirhos2021_{identifier}") - assert len(assembly['presentation']) == length - - # test assembly coords present in ALL 17 sets: - @pytest.mark.parametrize('identifier', [ - 'colour', - 'contrast', - 'cue-conflict', - 'edge', - 'eidolonI', - 'eidolonII', - 'eidolonIII', - 'false-colour', - 'high-pass', - 'low-pass', - 'phase-scrambling', - 'power-equalisation', - 'rotation', - 'silhouette', - 'stylized', - 'sketch', - 'uniform-noise', - ]) - @pytest.mark.parametrize('field', [ - 'image_id', - 'image_id_long', - 'choice', - 'truth', - 'condition', - 'response_time', - 'trial', - 'subject', - 'session', - ]) - def test_fields_present(self, identifier, field): - assembly = brainscore.get_assembly(f"brendel.Geirhos2021_{identifier}") - assert hasattr(assembly, field) - - # tests assembly coords for the 2 "abnormal" sets: - @pytest.mark.parametrize('identifier', [ - 'edge', - 'silhouette', - ]) - @pytest.mark.parametrize('field', [ - 'image_id', - 'image_category', - 'truth', - 'image_variation', - 'condition', - ]) - def test_fields_present_abnormal_sets(self, identifier, field): - assembly = brainscore.get_assembly(f"brendel.Geirhos2021_{identifier}") - assert hasattr(assembly, field) - - # tests assembly coords for the cue-conflict different set: - @pytest.mark.parametrize('identifier', [ - 'cue-conflict', - ]) - @pytest.mark.parametrize('field', [ - 'image_id', - 'original_image', - 'truth', - 'category', - 'conflict_image', - 'original_image_category', - 'original_image_variation', - 'conflict_image_category', - 'conflict_image_variation', - 'condition', - ]) - def test_fields_present_cue_conflict(self, identifier, field): - assembly = brainscore.get_assembly(f"brendel.Geirhos2021_{identifier}") - assert hasattr(assembly, field) \ No newline at end of file diff --git a/tests/test_benchmark_helpers/__init__.py b/tests/test_benchmark_helpers/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/test_benchmark_helpers/rgb1-10to12.png b/tests/test_benchmark_helpers/rgb1-10to12.png new file mode 100644 index 000000000..21608ccad Binary files /dev/null and b/tests/test_benchmark_helpers/rgb1-10to12.png differ diff --git a/tests/test_benchmarks/rgb1.jpg b/tests/test_benchmark_helpers/rgb1.jpg similarity index 100% rename from tests/test_benchmarks/rgb1.jpg rename to tests/test_benchmark_helpers/rgb1.jpg diff --git a/tests/test_benchmarks/test_screen.py b/tests/test_benchmark_helpers/test_screen.py similarity index 82% rename from tests/test_benchmarks/test_screen.py rename to tests/test_benchmark_helpers/test_screen.py index 9680251ca..bffba3241 100644 --- a/tests/test_benchmarks/test_screen.py +++ b/tests/test_benchmark_helpers/test_screen.py @@ -1,17 +1,15 @@ -import os -from tempfile import TemporaryDirectory - import numpy as np from PIL import Image from pathlib import Path from pytest import approx +from tempfile import TemporaryDirectory -from brainscore.benchmarks.screen import ImageConverter +from brainscore_vision.benchmark_helpers.screen import ImageConverter class TestImageConverter: def test_identical(self): - image_path = Path(os.path.dirname(__file__)) / "rgb1.jpg" + image_path = Path(__file__).parent / "rgb1.jpg" source_image = np.array(Image.open(image_path)) with TemporaryDirectory('screen') as target_dir: image_converter = ImageConverter(target_dir=target_dir) @@ -21,7 +19,7 @@ def test_identical(self): assert (source_image == converted_image).all() def test_crop_shape(self): - image_path = Path(os.path.dirname(__file__)) / "rgb1.jpg" + image_path = Path(__file__).parent / "rgb1.jpg" with TemporaryDirectory('screen') as target_dir: image_converter = ImageConverter(target_dir=target_dir) converted_image_path = image_converter.convert_image(image_path, source_degrees=10, target_degrees=8) @@ -29,7 +27,7 @@ def test_crop_shape(self): np.testing.assert_array_equal(converted_image.size, [1855 * .8, 1855 * .8]) def test_enlarge_shape(self): - image_path = Path(os.path.dirname(__file__)) / "rgb1.jpg" + image_path = Path(__file__).parent / "rgb1.jpg" with TemporaryDirectory('screen') as target_dir: image_converter = ImageConverter(target_dir=target_dir) converted_image_path = image_converter.convert_image(image_path, source_degrees=10, target_degrees=12) @@ -37,16 +35,16 @@ def test_enlarge_shape(self): np.testing.assert_array_equal(converted_image.size, [1855 * 1.2, 1855 * 1.2]) def test_enlarge_match(self): - image_path = Path(os.path.dirname(__file__)) / "rgb1.jpg" + image_path = Path(__file__).parent / "rgb1.jpg" with TemporaryDirectory('screen') as target_dir: image_converter = ImageConverter(target_dir=target_dir) converted_image_path = image_converter.convert_image(image_path, source_degrees=10, target_degrees=12) converted_image = Image.open(converted_image_path) - target_image = Image.open(Path(os.path.dirname(__file__)) / "rgb1-10to12.jpg") - np.testing.assert_array_equal(np.array(converted_image), np.array(target_image)) + target_image = Image.open(Path(__file__).parent / "rgb1-10to12.png") + np.testing.assert_array_equal(converted_image, target_image) def test_enlarge_gray_background(self): - image_path = Path(os.path.dirname(__file__)) / "rgb1.jpg" + image_path = Path(__file__).parent / "rgb1.jpg" with TemporaryDirectory('screen') as target_dir: image_converter = ImageConverter(target_dir=target_dir) converted_image_path = image_converter.convert_image(image_path, source_degrees=8, target_degrees=10) @@ -60,10 +58,10 @@ def test_enlarge_gray_background(self): if (color == gray).all(): amount_gray += 1 assert amount_gray / converted_image.size == approx(.11658, abs=.0001) - assert amount_gray == 1880753 + assert amount_gray == approx(1_880_000, rel=.0005) # depending on Pillow version for some reason def test_enlarge_image_centered(self): - image_path = Path(os.path.dirname(__file__)) / "rgb1.jpg" + image_path = Path(__file__).parent / "rgb1.jpg" with TemporaryDirectory('screen') as target_dir: image_converter = ImageConverter(target_dir=target_dir) converted_image_path = image_converter.convert_image(image_path, source_degrees=8, target_degrees=10) diff --git a/tests/test_benchmarks/rgb1-10to12.jpg b/tests/test_benchmarks/rgb1-10to12.jpg deleted file mode 100644 index 300f9151f..000000000 Binary files a/tests/test_benchmarks/rgb1-10to12.jpg and /dev/null differ diff --git a/tests/test_benchmarks/test___init__.py b/tests/test_benchmarks/test___init__.py deleted file mode 100644 index 5f41c0f70..000000000 --- a/tests/test_benchmarks/test___init__.py +++ /dev/null @@ -1,588 +0,0 @@ -import os -from pathlib import Path -from typing import List, Tuple - -import numpy as np -import pytest -from PIL import Image -from pytest import approx - -from brainio.assemblies import BehavioralAssembly, NeuroidAssembly, PropertyAssembly -from brainscore.benchmarks import benchmark_pool, public_benchmark_pool, evaluation_benchmark_pool, \ - engineering_benchmark_pool -from brainscore.model_interface import BrainModel -from tests.test_benchmarks import PrecomputedFeatures - - -class TestPoolList: - """ ensures that the right benchmarks are in the right benchmark pool """ - - @pytest.mark.parametrize('benchmark', [ - 'movshon.FreemanZiemba2013.V1-pls', - 'movshon.FreemanZiemba2013public.V1-pls', - 'dicarlo.MajajHong2015.IT-pls', - 'dicarlo.MajajHong2015public.IT-pls', - 'dicarlo.Rajalingham2018-i2n', - 'dicarlo.Rajalingham2018public-i2n', - 'fei-fei.Deng2009-top1', - ]) - def test_contained_global(self, benchmark): - assert benchmark in benchmark_pool - - @pytest.mark.parametrize('benchmark', [ - 'movshon.FreemanZiemba2013public.V1-pls', - 'dicarlo.MajajHong2015public.IT-pls', - 'dicarlo.Rajalingham2018public-i2n', - 'fei-fei.Deng2009-top1', - ]) - def test_contained_public(self, benchmark): - assert benchmark in public_benchmark_pool - - def test_exact_evaluation_pool(self): - assert set(evaluation_benchmark_pool.keys()) == { - # V1 - 'movshon.FreemanZiemba2013.V1-pls', - 'dicarlo.Marques2020_Ringach2002-or_bandwidth', - 'dicarlo.Marques2020_Ringach2002-or_selective', - 'dicarlo.Marques2020_Ringach2002-circular_variance', - 'dicarlo.Marques2020_Ringach2002-orth_pref_ratio', - 'dicarlo.Marques2020_Ringach2002-cv_bandwidth_ratio', - 'dicarlo.Marques2020_DeValois1982-pref_or', - 'dicarlo.Marques2020_Ringach2002-opr_cv_diff', - 'dicarlo.Marques2020_Schiller1976-sf_bandwidth', - 'dicarlo.Marques2020_Schiller1976-sf_selective', - 'dicarlo.Marques2020_DeValois1982-peak_sf', - 'dicarlo.Marques2020_FreemanZiemba2013-texture_sparseness', - 'dicarlo.Marques2020_FreemanZiemba2013-texture_selectivity', - 'dicarlo.Marques2020_FreemanZiemba2013-texture_variance_ratio', - 'dicarlo.Marques2020_Ringach2002-modulation_ratio', - 'dicarlo.Marques2020_Cavanaugh2002-grating_summation_field', - 'dicarlo.Marques2020_Cavanaugh2002-surround_diameter', - 'dicarlo.Marques2020_Cavanaugh2002-surround_suppression_index', - 'dicarlo.Marques2020_FreemanZiemba2013-texture_modulation_index', - 'dicarlo.Marques2020_FreemanZiemba2013-abs_texture_modulation_index', - 'dicarlo.Marques2020_FreemanZiemba2013-max_noise', - 'dicarlo.Marques2020_FreemanZiemba2013-max_texture', - 'dicarlo.Marques2020_Ringach2002-max_dc', - # V2 - 'movshon.FreemanZiemba2013.V2-pls', - # V4 - 'dicarlo.MajajHong2015.V4-pls', - 'dicarlo.Sanghavi2020.V4-pls', - 'dicarlo.SanghaviJozwik2020.V4-pls', - 'dicarlo.SanghaviMurty2020.V4-pls', - # IT - 'dicarlo.MajajHong2015.IT-pls', - 'dicarlo.Sanghavi2020.IT-pls', - 'dicarlo.SanghaviJozwik2020.IT-pls', - 'dicarlo.SanghaviMurty2020.IT-pls', - 'dicarlo.Kar2019-ost', - # behavior - 'dicarlo.Rajalingham2018-i2n', - 'brendel.Geirhos2021colour-error_consistency', - 'brendel.Geirhos2021contrast-error_consistency', - 'brendel.Geirhos2021cueconflict-error_consistency', - 'brendel.Geirhos2021edge-error_consistency', - 'brendel.Geirhos2021eidolonI-error_consistency', - 'brendel.Geirhos2021eidolonII-error_consistency', - 'brendel.Geirhos2021eidolonIII-error_consistency', - 'brendel.Geirhos2021falsecolour-error_consistency', - 'brendel.Geirhos2021highpass-error_consistency', - 'brendel.Geirhos2021lowpass-error_consistency', - 'brendel.Geirhos2021phasescrambling-error_consistency', - 'brendel.Geirhos2021powerequalisation-error_consistency', - 'brendel.Geirhos2021rotation-error_consistency', - 'brendel.Geirhos2021silhouette-error_consistency', - 'brendel.Geirhos2021stylized-error_consistency', - 'brendel.Geirhos2021sketch-error_consistency', - 'brendel.Geirhos2021uniformnoise-error_consistency', - } - - def test_engineering_pool(self): - assert set(engineering_benchmark_pool.keys()) == { - 'fei-fei.Deng2009-top1', - 'katz.BarbuMayo2019-top1', - 'dietterich.Hendrycks2019-noise-top1', 'dietterich.Hendrycks2019-blur-top1', - 'dietterich.Hendrycks2019-weather-top1', 'dietterich.Hendrycks2019-digital-top1', - 'brendel.Geirhos2021colour-top1', - 'brendel.Geirhos2021contrast-top1', - 'brendel.Geirhos2021cueconflict-top1', - 'brendel.Geirhos2021edge-top1', - 'brendel.Geirhos2021eidolonI-top1', - 'brendel.Geirhos2021eidolonII-top1', - 'brendel.Geirhos2021eidolonIII-top1', - 'brendel.Geirhos2021falsecolour-top1', - 'brendel.Geirhos2021highpass-top1', - 'brendel.Geirhos2021lowpass-top1', - 'brendel.Geirhos2021phasescrambling-top1', - 'brendel.Geirhos2021powerequalisation-top1', - 'brendel.Geirhos2021rotation-top1', - 'brendel.Geirhos2021silhouette-top1', - 'brendel.Geirhos2021stylized-top1', - 'brendel.Geirhos2021sketch-top1', - 'brendel.Geirhos2021uniformnoise-top1', - 'kornblith.Hermann2020cueconflict-shape_bias', - 'kornblith.Hermann2020cueconflict-shape_match', - } - - -@pytest.mark.private_access -class TestStandardized: - @pytest.mark.parametrize('benchmark, expected', [ - pytest.param('movshon.FreemanZiemba2013.V1-pls', approx(.873345, abs=.001), - marks=[pytest.mark.memory_intense]), - pytest.param('movshon.FreemanZiemba2013.V2-pls', approx(.824836, abs=.001), - marks=[pytest.mark.memory_intense]), - pytest.param('movshon.FreemanZiemba2013.V1-rdm', approx(.918672, abs=.001), - marks=[pytest.mark.memory_intense]), - pytest.param('movshon.FreemanZiemba2013.V2-rdm', approx(.856968, abs=.001), - marks=[pytest.mark.memory_intense]), - pytest.param('dicarlo.MajajHong2015.V4-pls', approx(.89503, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.MajajHong2015.IT-pls', approx(.821841, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.MajajHong2015.V4-rdm', approx(.936473, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.MajajHong2015.IT-rdm', approx(.887618, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.Sanghavi2020.V4-pls', approx(.8892049, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.Sanghavi2020.IT-pls', approx(.868293, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.SanghaviJozwik2020.V4-pls', approx(.9630336, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.SanghaviJozwik2020.IT-pls', approx(.860352, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.SanghaviMurty2020.V4-pls', approx(.9666086, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.SanghaviMurty2020.IT-pls', approx(.875714, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.Rajalingham2020.IT-pls', approx(.561013, abs=.001), - marks=[pytest.mark.memory_intense, pytest.mark.slow]), - # V1 properties Ceilings - pytest.param('dicarlo.Marques2020_DeValois1982-pref_or', approx(0.962, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_Ringach2002-circular_variance', approx(0.959, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_Ringach2002-or_bandwidth', approx(0.964, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_Ringach2002-orth_pref_ratio', approx(0.962, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_Ringach2002-or_selective', approx(0.994, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_Ringach2002-cv_bandwidth_ratio', approx(0.968, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_Ringach2002-opr_cv_diff', approx(0.967, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_DeValois1982-peak_sf', approx(0.967, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_Schiller1976-sf_selective', approx(0.963, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_Schiller1976-sf_bandwidth', approx(0.933, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_Cavanaugh2002-grating_summation_field', approx(0.956, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_Cavanaugh2002-surround_diameter', approx(0.955, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_Cavanaugh2002-surround_suppression_index', approx(0.958, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_FreemanZiemba2013-texture_modulation_index', approx(0.948, abs=.005), - marks=[]), - pytest.param('dicarlo.Marques2020_FreemanZiemba2013-abs_texture_modulation_index', approx(0.958, abs=.005), - marks=[]), - pytest.param('dicarlo.Marques2020_Ringach2002-modulation_ratio', approx(0.959, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_FreemanZiemba2013-texture_selectivity', approx(0.940, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_FreemanZiemba2013-texture_sparseness', approx(0.935, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_FreemanZiemba2013-texture_variance_ratio', approx(0.939, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_Ringach2002-max_dc', approx(0.968, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_FreemanZiemba2013-max_texture', approx(0.946, abs=.005), marks=[]), - pytest.param('dicarlo.Marques2020_FreemanZiemba2013-max_noise', approx(0.945, abs=.005), marks=[]), - ]) - def test_ceilings(self, benchmark, expected): - benchmark = benchmark_pool[benchmark] - ceiling = benchmark.ceiling - assert ceiling.sel(aggregation='center') == expected - - @pytest.mark.parametrize('benchmark, visual_degrees, expected', [ - pytest.param('movshon.FreemanZiemba2013.V1-pls', 4, approx(.668491, abs=.001), - marks=[pytest.mark.memory_intense]), - pytest.param('movshon.FreemanZiemba2013.V2-pls', 4, approx(.553155, abs=.001), - marks=[pytest.mark.memory_intense]), - pytest.param('tolias.Cadena2017-pls', 2, approx(.577474, abs=.005), - marks=pytest.mark.private_access), - pytest.param('dicarlo.MajajHong2015.V4-pls', 8, approx(.923713, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.MajajHong2015.IT-pls', 8, approx(.823433, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.Sanghavi2020.V4-pls', 8, approx(.9727137, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.Sanghavi2020.IT-pls', 8, approx(.890062, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.SanghaviJozwik2020.V4-pls', 8, approx(.9739177, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.SanghaviJozwik2020.IT-pls', 8, approx(.9999779, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.SanghaviMurty2020.V4-pls', 5, approx(.978581, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.SanghaviMurty2020.IT-pls', 5, approx(.9997532, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.Rajalingham2020.IT-pls', 8, approx(.693463, abs=.005), - marks=[pytest.mark.memory_intense, pytest.mark.slow]), - ]) - def test_self_regression(self, benchmark, visual_degrees, expected): - benchmark = benchmark_pool[benchmark] - score = benchmark(PrecomputedFeatures(benchmark._assembly, visual_degrees=visual_degrees)).raw - assert score.sel(aggregation='center') == expected - raw_values = score.attrs['raw'] - assert hasattr(raw_values, 'neuroid') - assert hasattr(raw_values, 'split') - assert len(raw_values['split']) == 10 - - @pytest.mark.parametrize('benchmark, visual_degrees, expected', [ - pytest.param('movshon.FreemanZiemba2013.V1-rdm', 4, approx(1, abs=.001), - marks=[pytest.mark.memory_intense]), - pytest.param('movshon.FreemanZiemba2013.V2-rdm', 4, approx(1, abs=.001), - marks=[pytest.mark.memory_intense]), - pytest.param('dicarlo.MajajHong2015.V4-rdm', 8, approx(1, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param('dicarlo.MajajHong2015.IT-rdm', 8, approx(1, abs=.001), - marks=pytest.mark.memory_intense), - ]) - def test_self_rdm(self, benchmark, visual_degrees, expected): - benchmark = benchmark_pool[benchmark] - score = benchmark(PrecomputedFeatures(benchmark._assembly, visual_degrees=visual_degrees)).raw - assert score.sel(aggregation='center') == expected - raw_values = score.attrs['raw'] - assert hasattr(raw_values, 'split') - assert len(raw_values['split']) == 10 - - -@pytest.mark.private_access -class TestPrecomputed: - @pytest.mark.memory_intense - @pytest.mark.parametrize('benchmark, expected', [ - ('movshon.FreemanZiemba2013.V1-pls', approx(.466222, abs=.005)), - ('movshon.FreemanZiemba2013.V2-pls', approx(.459283, abs=.005)), - ]) - def test_FreemanZiemba2013(self, benchmark, expected): - self.run_test(benchmark=benchmark, file='alexnet-freemanziemba2013.aperture-private.nc', expected=expected) - - @pytest.mark.memory_intense - @pytest.mark.parametrize('benchmark, expected', [ - ('dicarlo.MajajHong2015.V4-pls', approx(.490236, abs=.005)), - ('dicarlo.MajajHong2015.IT-pls', approx(.584053, abs=.005)), - ]) - def test_MajajHong2015(self, benchmark, expected): - self.run_test(benchmark=benchmark, file='alexnet-majaj2015.private-features.12.nc', expected=expected) - - @pytest.mark.memory_intense - @pytest.mark.slow - @pytest.mark.parametrize('benchmark, expected', [ - ('dicarlo.Sanghavi2020.V4-pls', approx(.551135, abs=.015)), - ('dicarlo.Sanghavi2020.IT-pls', approx(.611347, abs=.015)), - ]) - def test_Sanghavi2020(self, benchmark, expected): - self.run_test(benchmark=benchmark, file='alexnet-sanghavi2020-features.12.nc', expected=expected) - - @pytest.mark.memory_intense - @pytest.mark.slow - @pytest.mark.parametrize('benchmark, expected', [ - ('dicarlo.SanghaviJozwik2020.V4-pls', approx(.49235, abs=.005)), - ('dicarlo.SanghaviJozwik2020.IT-pls', approx(.590543, abs=.005)), - ]) - def test_SanghaviJozwik2020(self, benchmark, expected): - self.run_test(benchmark=benchmark, file='alexnet-sanghavijozwik2020-features.12.nc', expected=expected) - - @pytest.mark.memory_intense - @pytest.mark.parametrize('benchmark, expected', [ - ('dicarlo.SanghaviMurty2020.V4-pls', approx(.357461, abs=.015)), - ('dicarlo.SanghaviMurty2020.IT-pls', approx(.53006, abs=.015)), - ]) - def test_SanghaviMurty2020(self, benchmark, expected): - self.run_test(benchmark=benchmark, file='alexnet-sanghavimurty2020-features.12.nc', expected=expected) - - @pytest.mark.memory_intense - @pytest.mark.slow - @pytest.mark.parametrize('benchmark, expected', [ - ('dicarlo.Rajalingham2020.IT-pls', approx(.147549, abs=.01)), - ]) - def test_Rajalingham2020(self, benchmark, expected): - self.run_test(benchmark=benchmark, file='alexnet-rajalingham2020-features.12.nc', expected=expected) - - def run_test(self, benchmark, file, expected): - benchmark = benchmark_pool[benchmark] - precomputed_features = Path(__file__).parent / file - precomputed_features = NeuroidAssembly.from_files( - precomputed_features, - stimulus_set_identifier=benchmark._assembly.stimulus_set.identifier, - stimulus_set=None) - precomputed_features = precomputed_features.stack(presentation=['stimulus_path']) - precomputed_paths = list(map(lambda f: Path(f).name, precomputed_features['stimulus_path'].values)) - # attach stimulus set meta - stimulus_set = benchmark._assembly.stimulus_set - expected_stimulus_paths = [stimulus_set.get_stimulus(image_id) for image_id in stimulus_set['stimulus_id']] - expected_stimulus_paths = list(map(lambda f: Path(f).name, expected_stimulus_paths)) - assert set(precomputed_paths) == set(expected_stimulus_paths) - for column in stimulus_set.columns: - precomputed_features[column] = 'presentation', stimulus_set[column].values - precomputed_features = PrecomputedFeatures(precomputed_features, - visual_degrees=10, # doesn't matter, features are already computed - ) - # score - score = benchmark(precomputed_features).raw - assert score.sel(aggregation='center') == expected - - @pytest.mark.memory_intense - @pytest.mark.private_access - @pytest.mark.slow - def test_Kar2019ost_cornet_s(self): - benchmark = benchmark_pool['dicarlo.Kar2019-ost'] - precomputed_features = Path(__file__).parent / 'cornet_s-kar2019.nc' - precomputed_features = NeuroidAssembly.from_files( - precomputed_features, - stimulus_set_identifier=benchmark._assembly.stimulus_set.identifier, - stimulus_set=benchmark._assembly.stimulus_set) - precomputed_features = PrecomputedFeatures(precomputed_features, visual_degrees=8) - # score - score = benchmark(precomputed_features).raw - assert score.sel(aggregation='center') == approx(.316, abs=.005) - - def test_Rajalingham2018public(self): - benchmark = benchmark_pool['dicarlo.Rajalingham2018public-i2n'] - # load features - precomputed_features = Path(__file__).parent / 'CORnetZ-rajalingham2018public.nc' - precomputed_features = BehavioralAssembly.from_files( - precomputed_features, - stimulus_set_identifier=benchmark._assembly.stimulus_set.identifier, - stimulus_set=benchmark._assembly.stimulus_set) - precomputed_features = PrecomputedFeatures(precomputed_features, - visual_degrees=8, # doesn't matter, features are already computed - ) - # score - score = benchmark(precomputed_features).raw - assert score.sel(aggregation='center') == approx(.136923, abs=.005) - - @pytest.mark.memory_intense - @pytest.mark.slow - @pytest.mark.parametrize('benchmark, expected', [ - ('dicarlo.Marques2020_DeValois1982-pref_or', approx(.895, abs=.01)), - ('dicarlo.Marques2020_DeValois1982-pref_or', approx(.895, abs=.01)), - ('dicarlo.Marques2020_Ringach2002-circular_variance', approx(.830, abs=.01)), - ('dicarlo.Marques2020_Ringach2002-or_bandwidth', approx(.844, abs=.01)), - ('dicarlo.Marques2020_Ringach2002-orth_pref_ratio', approx(.876, abs=.01)), - ('dicarlo.Marques2020_Ringach2002-or_selective', approx(.895, abs=.01)), - ('dicarlo.Marques2020_Ringach2002-cv_bandwidth_ratio', approx(.841, abs=.01)), - ('dicarlo.Marques2020_Ringach2002-opr_cv_diff', approx(.909, abs=.01)), - ('dicarlo.Marques2020_DeValois1982-peak_sf', approx(.775, abs=.01)), - ('dicarlo.Marques2020_Schiller1976-sf_selective', approx(.808, abs=.01)), - ('dicarlo.Marques2020_Schiller1976-sf_bandwidth', approx(.869, abs=.01)), - ('dicarlo.Marques2020_Cavanaugh2002-grating_summation_field', approx(.599, abs=.01)), - ('dicarlo.Marques2020_Cavanaugh2002-surround_diameter', approx(.367, abs=.01)), - ('dicarlo.Marques2020_Cavanaugh2002-surround_suppression_index', approx(.365, abs=.01)), - ('dicarlo.Marques2020_FreemanZiemba2013-texture_modulation_index', approx(.636, abs=.01)), - ('dicarlo.Marques2020_FreemanZiemba2013-abs_texture_modulation_index', approx(.861, abs=.01)), - ('dicarlo.Marques2020_Ringach2002-modulation_ratio', approx(.371, abs=.01)), - ('dicarlo.Marques2020_FreemanZiemba2013-texture_selectivity', approx(.646, abs=.01)), - ('dicarlo.Marques2020_FreemanZiemba2013-texture_sparseness', approx(.508, abs=.01)), - ('dicarlo.Marques2020_FreemanZiemba2013-texture_variance_ratio', approx(.827, abs=.01)), - ('dicarlo.Marques2020_Ringach2002-max_dc', approx(.904, abs=.01)), - ('dicarlo.Marques2020_FreemanZiemba2013-max_texture', approx(.823, abs=.01)), - ('dicarlo.Marques2020_FreemanZiemba2013-max_noise', approx(.684, abs=.01)), - ]) - def test_Marques2020(self, benchmark, expected): - self.run_test_properties( - benchmark=benchmark, - files={'dicarlo.Marques2020_blank': 'alexnet-dicarlo.Marques2020_blank.nc', - 'dicarlo.Marques2020_receptive_field': 'alexnet-dicarlo.Marques2020_receptive_field.nc', - 'dicarlo.Marques2020_orientation': 'alexnet-dicarlo.Marques2020_orientation.nc', - 'dicarlo.Marques2020_spatial_frequency': 'alexnet-dicarlo.Marques2020_spatial_frequency.nc', - 'dicarlo.Marques2020_size': 'alexnet-dicarlo.Marques2020_size.nc', - 'movshon.FreemanZiemba2013_properties': 'alexnet-movshon.FreemanZiemba2013_properties.nc', - }, - expected=expected) - - def run_test_properties(self, benchmark, files, expected): - benchmark = benchmark_pool[benchmark] - from brainscore import get_stimulus_set - - stimulus_identifiers = np.unique(np.array(['dicarlo.Marques2020_blank', 'dicarlo.Marques2020_receptive_field', - 'dicarlo.Marques2020_orientation', - benchmark._assembly.stimulus_set.identifier])) - precomputed_features = {} - for current_stimulus in stimulus_identifiers: - stimulus_set = get_stimulus_set(current_stimulus) - path = Path(__file__).parent / files[current_stimulus] - features = PropertyAssembly.from_files(path, - stimulus_set_identifier=stimulus_set.identifier, - stimulus_set=stimulus_set) - features = features.stack(presentation=['stimulus_path']) - precomputed_features[current_stimulus] = features - precomputed_paths = [Path(f).name for f in precomputed_features[current_stimulus]['stimulus_path'].values] - # attach stimulus set meta - expected_stimulus_paths = [stimulus_set.get_stimulus(stimulus_id) - for stimulus_id in stimulus_set['stimulus_id']] - expected_stimulus_paths = list(map(lambda f: Path(f).name, expected_stimulus_paths)) - assert set(precomputed_paths) == set(expected_stimulus_paths) - for column in stimulus_set.columns: - precomputed_features[current_stimulus][column] = 'presentation', stimulus_set[column].values - - precomputed_features = PrecomputedFeatures(precomputed_features, visual_degrees=8) - # score - score = benchmark(precomputed_features).raw - assert score.sel(aggregation='center') == expected - - -class TestVisualDegrees: - @pytest.mark.parametrize('benchmark, candidate_degrees, image_id, expected', [ - pytest.param('movshon.FreemanZiemba2013.V1-pls', 14, 'c3a633a13e736394f213ddf44bf124fe80cabe07', - approx(.31429, abs=.0001), marks=[pytest.mark.private_access]), - pytest.param('movshon.FreemanZiemba2013.V1-pls', 6, 'c3a633a13e736394f213ddf44bf124fe80cabe07', - approx(.22966, abs=.0001), marks=[pytest.mark.private_access]), - pytest.param('movshon.FreemanZiemba2013public.V1-pls', 14, '21041db1f26c142812a66277c2957fb3e2070916', - approx(.314561, abs=.0001), marks=[]), - pytest.param('movshon.FreemanZiemba2013public.V1-pls', 6, '21041db1f26c142812a66277c2957fb3e2070916', - approx(.23113, abs=.0001), marks=[]), - pytest.param('movshon.FreemanZiemba2013.V2-pls', 14, 'c3a633a13e736394f213ddf44bf124fe80cabe07', - approx(.31429, abs=.0001), marks=[pytest.mark.private_access]), - pytest.param('movshon.FreemanZiemba2013.V2-pls', 6, 'c3a633a13e736394f213ddf44bf124fe80cabe07', - approx(.22966, abs=.0001), marks=[pytest.mark.private_access]), - pytest.param('movshon.FreemanZiemba2013public.V2-pls', 14, '21041db1f26c142812a66277c2957fb3e2070916', - approx(.314561, abs=.0001), marks=[]), - pytest.param('movshon.FreemanZiemba2013public.V2-pls', 6, '21041db1f26c142812a66277c2957fb3e2070916', - approx(.23113, abs=.0001), marks=[]), - pytest.param('dicarlo.MajajHong2015.V4-pls', 14, '40a786ed8e13db10185ddfdbe07759d83a589e1c', - approx(.251345, abs=.0001), marks=[pytest.mark.private_access]), - pytest.param('dicarlo.MajajHong2015.V4-pls', 6, '40a786ed8e13db10185ddfdbe07759d83a589e1c', - approx(.0054886, abs=.0001), marks=[pytest.mark.private_access]), - pytest.param('dicarlo.MajajHong2015public.V4-pls', 14, '8a72e2bfdb8c267b57232bf96f069374d5b21832', - approx(.25071, abs=.0001), marks=[]), - pytest.param('dicarlo.MajajHong2015public.V4-pls', 6, '8a72e2bfdb8c267b57232bf96f069374d5b21832', - approx(.00460, abs=.0001), marks=[]), - pytest.param('dicarlo.MajajHong2015.IT-pls', 14, '40a786ed8e13db10185ddfdbe07759d83a589e1c', - approx(.251345, abs=.0001), marks=[pytest.mark.private_access]), - pytest.param('dicarlo.MajajHong2015.IT-pls', 6, '40a786ed8e13db10185ddfdbe07759d83a589e1c', - approx(.0054886, abs=.0001), marks=[pytest.mark.private_access]), - pytest.param('dicarlo.MajajHong2015public.IT-pls', 14, '8a72e2bfdb8c267b57232bf96f069374d5b21832', - approx(.25071, abs=.0001), marks=[]), - pytest.param('dicarlo.MajajHong2015public.IT-pls', 6, '8a72e2bfdb8c267b57232bf96f069374d5b21832', - approx(.00460, abs=.0001), marks=[]), - pytest.param('dicarlo.Kar2019-ost', 14, '6d19b24c29832dfb28360e7731e3261c13a4287f', - approx(.225021, abs=.0001), marks=[pytest.mark.private_access]), - pytest.param('dicarlo.Kar2019-ost', 6, '6d19b24c29832dfb28360e7731e3261c13a4287f', - approx(.001248, abs=.0001), marks=[pytest.mark.private_access]), - pytest.param('dicarlo.Rajalingham2018-i2n', 14, '0223bf9e5db0edad21976b16494fe9396a5ef145', - approx(.225023, abs=.0001), marks=[pytest.mark.private_access]), - pytest.param('dicarlo.Rajalingham2018-i2n', 6, '0223bf9e5db0edad21976b16494fe9396a5ef145', - approx(.002244, abs=.0001), marks=[pytest.mark.private_access]), - pytest.param('dicarlo.Rajalingham2018public-i2n', 14, '0020cef91bd626e9fbbabd853494ee444e5c9ecb', - approx(.22486, abs=.0001), marks=[]), - pytest.param('dicarlo.Rajalingham2018public-i2n', 6, '0020cef91bd626e9fbbabd853494ee444e5c9ecb', - approx(.00097, abs=.0001), marks=[]), - pytest.param('tolias.Cadena2017-pls', 14, '0fe27ddd5b9ea701e380063dc09b91234eba3551', approx(.32655, abs=.0001), - marks=[pytest.mark.private_access]), - pytest.param('tolias.Cadena2017-pls', 6, '0fe27ddd5b9ea701e380063dc09b91234eba3551', approx(.29641, abs=.0001), - marks=[pytest.mark.private_access]), - ]) - def test_amount_gray(self, benchmark, candidate_degrees, image_id, expected, brainio_home, resultcaching_home, - brainscore_home): - benchmark = benchmark_pool[benchmark] - - class DummyCandidate(BrainModel): - class StopException(Exception): - pass - - def visual_degrees(self): - return candidate_degrees - - def look_at(self, stimuli, number_of_trials=1): - image = stimuli.get_stimulus(image_id) - image = Image.open(image) - image = np.array(image) - amount_gray = 0 - for index in np.ndindex(image.shape[:2]): - color = image[index] - gray = [128, 128, 128] - if (color == gray).all(): - amount_gray += 1 - assert amount_gray / image.size == expected - raise self.StopException() - - def start_task(self, task: BrainModel.Task, fitting_stimuli): - pass - - def start_recording(self, recording_target: BrainModel.RecordingTarget, time_bins=List[Tuple[int]]): - pass - - candidate = DummyCandidate() - try: - benchmark(candidate) # just call to get the stimuli - except DummyCandidate.StopException: # but stop early - pass - - -class TestNumberOfTrials: - @pytest.mark.private_access - @pytest.mark.parametrize('benchmark_identifier', [ - # V1 - 'movshon.FreemanZiemba2013.V1-pls', - 'dicarlo.Marques2020_Ringach2002-or_bandwidth', - 'dicarlo.Marques2020_Ringach2002-or_selective', - 'dicarlo.Marques2020_Ringach2002-circular_variance', - 'dicarlo.Marques2020_Ringach2002-orth_pref_ratio', - 'dicarlo.Marques2020_Ringach2002-cv_bandwidth_ratio', - 'dicarlo.Marques2020_DeValois1982-pref_or', - 'dicarlo.Marques2020_Ringach2002-opr_cv_diff', - 'dicarlo.Marques2020_Schiller1976-sf_bandwidth', - 'dicarlo.Marques2020_Schiller1976-sf_selective', - 'dicarlo.Marques2020_DeValois1982-peak_sf', - 'dicarlo.Marques2020_FreemanZiemba2013-texture_sparseness', - 'dicarlo.Marques2020_FreemanZiemba2013-texture_selectivity', - 'dicarlo.Marques2020_FreemanZiemba2013-texture_variance_ratio', - 'dicarlo.Marques2020_Ringach2002-modulation_ratio', - 'dicarlo.Marques2020_Cavanaugh2002-grating_summation_field', - 'dicarlo.Marques2020_Cavanaugh2002-surround_diameter', - 'dicarlo.Marques2020_Cavanaugh2002-surround_suppression_index', - 'dicarlo.Marques2020_FreemanZiemba2013-texture_modulation_index', - 'dicarlo.Marques2020_FreemanZiemba2013-abs_texture_modulation_index', - 'dicarlo.Marques2020_FreemanZiemba2013-max_noise', - 'dicarlo.Marques2020_FreemanZiemba2013-max_texture', - 'dicarlo.Marques2020_Ringach2002-max_dc', - # V2 - 'movshon.FreemanZiemba2013.V2-pls', - # V4 - 'dicarlo.MajajHong2015.V4-pls', - 'dicarlo.Sanghavi2020.V4-pls', - 'dicarlo.SanghaviJozwik2020.V4-pls', - 'dicarlo.SanghaviMurty2020.V4-pls', - # IT - 'dicarlo.MajajHong2015.IT-pls', - 'dicarlo.Sanghavi2020.IT-pls', - 'dicarlo.SanghaviJozwik2020.IT-pls', - 'dicarlo.SanghaviMurty2020.IT-pls', - 'dicarlo.Kar2019-ost', - # behavior - 'dicarlo.Rajalingham2018-i2n', # Geirhos2021 are single-trial, i.e. not included here - ]) - def test_repetitions(self, benchmark_identifier): - """ Tests that benchmarks have repetitions in the stimulus_set """ - benchmark = benchmark_pool[benchmark_identifier] - - class AssertRepeatCandidate(BrainModel): - class StopException(Exception): - pass - - def identifier(self) -> str: - return 'assert-repeat-candidate' - - def visual_degrees(self): - return 8 - - def look_at(self, stimuli, number_of_trials=1): - assert number_of_trials > 1 - raise self.StopException() - - def start_task(self, task: BrainModel.Task, fitting_stimuli): - pass - - def start_recording(self, recording_target: BrainModel.RecordingTarget, time_bins=List[Tuple[int]]): - pass - - candidate = AssertRepeatCandidate() - try: - benchmark(candidate) # just call to get the stimuli - except AssertRepeatCandidate.StopException: # but stop early - pass - - -def lstrip_local(path): - parts = path.split(os.sep) - brainio_index = parts.index('.brainio') - path = os.sep.join(parts[brainio_index:]) - return path diff --git a/tests/test_benchmarks/test_cadena2017.py b/tests/test_benchmarks/test_cadena2017.py deleted file mode 100644 index c50185960..000000000 --- a/tests/test_benchmarks/test_cadena2017.py +++ /dev/null @@ -1,15 +0,0 @@ -import pytest - -from brainscore.benchmarks.cadena2017 import AssemblyLoader -from . import check_standard_format - - -@pytest.mark.private_access -class TestAssembly: - def test(self): - loader = AssemblyLoader() - assembly = loader() - check_standard_format(assembly) - assert assembly.attrs['stimulus_set_identifier'] == 'tolias.Cadena2017' - assert len(assembly['presentation']) == 6249 - assert len(assembly['neuroid']) == 166 diff --git a/tests/test_benchmarks/test_freemanziemba2013.py b/tests/test_benchmarks/test_freemanziemba2013.py deleted file mode 100644 index 7f8b587f2..000000000 --- a/tests/test_benchmarks/test_freemanziemba2013.py +++ /dev/null @@ -1,24 +0,0 @@ -import pytest - -from brainscore.benchmarks.freemanziemba2013 import load_assembly -from . import check_standard_format - - -@pytest.mark.memory_intense -@pytest.mark.private_access -class TestAssembly: - def test_V1(self): - assembly = load_assembly(region='V1', average_repetitions=True) - check_standard_format(assembly) - assert assembly.attrs['stimulus_set_identifier'] == 'movshon.FreemanZiemba2013.aperture-private' - assert set(assembly['region'].values) == {'V1'} - assert len(assembly['presentation']) == 315 - assert len(assembly['neuroid']) == 102 - - def test_V2(self): - assembly = load_assembly(region='V2', average_repetitions=True) - check_standard_format(assembly) - assert assembly.attrs['stimulus_set_identifier'] == 'movshon.FreemanZiemba2013.aperture-private' - assert set(assembly['region'].values) == {'V2'} - assert len(assembly['presentation']) == 315 - assert len(assembly['neuroid']) == 103 diff --git a/tests/test_benchmarks/test_hermann2020.py b/tests/test_benchmarks/test_hermann2020.py deleted file mode 100644 index bb203804f..000000000 --- a/tests/test_benchmarks/test_hermann2020.py +++ /dev/null @@ -1,38 +0,0 @@ -from pathlib import Path -import pytest -from pytest import approx -from brainio.assemblies import BehavioralAssembly -from brainscore import benchmark_pool -from tests.test_benchmarks import PrecomputedFeatures - - -class TestEngineering: - @pytest.mark.parametrize('model, expected_shape_bias', [ - ('resnet-50-pytorch', approx(0.21392405, abs=0.001)), - ]) - def test_shape_bias(self, model, expected_shape_bias): - benchmark = benchmark_pool["kornblith.Hermann2020cueconflict-shape_bias"] - # load features - precomputed_features = Path(__file__).parent / f'{model}-3deg-Geirhos2021_cue-conflict.nc' - precomputed_features = BehavioralAssembly.from_files(file_path=precomputed_features) - precomputed_features = PrecomputedFeatures(precomputed_features, visual_degrees=None) - # score - score = benchmark(precomputed_features) - assert score.sel(aggregation='center') == expected_shape_bias - - - @pytest.mark.parametrize('model, expected_shape_match', [ - ('resnet-50-pytorch', approx(0.14083333, abs=0.001)), - ]) - def test_shape_match(self, model, expected_shape_match): - benchmark = benchmark_pool["kornblith.Hermann2020cueconflict-shape_match"] - # load features - precomputed_features = Path(__file__).parent / f'{model}-3deg-Geirhos2021_cue-conflict.nc' - precomputed_features = BehavioralAssembly.from_files(file_path=precomputed_features) - precomputed_features = PrecomputedFeatures(precomputed_features, visual_degrees=None) - # score - score = benchmark(precomputed_features) - assert score.sel(aggregation='center') == expected_shape_match - - - \ No newline at end of file diff --git a/tests/test_benchmarks/test_kar2019.py b/tests/test_benchmarks/test_kar2019.py deleted file mode 100644 index 9402c926f..000000000 --- a/tests/test_benchmarks/test_kar2019.py +++ /dev/null @@ -1,52 +0,0 @@ -import numpy as np -import pytest -from numpy.random.mtrand import RandomState -from pytest import approx - -from brainio.assemblies import DataAssembly -from brainscore.benchmarks.kar2019 import DicarloKar2019OST -from tests.test_benchmarks import PrecomputedFeatures - - -@pytest.mark.memory_intense -@pytest.mark.private_access -def test_no_time(): - benchmark = DicarloKar2019OST() - rnd = RandomState(0) - stimuli = benchmark._assembly.stimulus_set - source = DataAssembly(rnd.rand(len(stimuli), 5, 1), coords={ - 'stimulus_id': ('presentation', stimuli['stimulus_id']), - 'image_label': ('presentation', stimuli['image_label']), - 'truth': ('presentation', stimuli['truth']), - 'neuroid_id': ('neuroid', list(range(5))), - 'layer': ('neuroid', ['test'] * 5), - 'time_bin_start': ('time_bin', [70]), - 'time_bin_end': ('time_bin', [170]), - }, dims=['presentation', 'neuroid', 'time_bin']) - source.name = __name__ + ".test_notime" - score = benchmark(PrecomputedFeatures(source, visual_degrees=8)) - assert np.isnan(score.sel(aggregation='center')) # not a temporal model - assert np.isnan(score.raw.sel(aggregation='center')) # not a temporal model - assert score.attrs['ceiling'].sel(aggregation='center') == approx(.79) - - -@pytest.mark.memory_intense -@pytest.mark.private_access -def test_random_time(): - benchmark = DicarloKar2019OST() - rnd = RandomState(0) - stimuli = benchmark._assembly.stimulus_set - source = DataAssembly(rnd.rand(len(stimuli), 5, 5), coords={ - 'stimulus_id': ('presentation', stimuli['stimulus_id']), - 'image_label': ('presentation', stimuli['image_label']), - 'truth': ('presentation', stimuli['truth']), - 'neuroid_id': ('neuroid', list(range(5))), - 'layer': ('neuroid', ['test'] * 5), - 'time_bin_start': ('time_bin', [70, 90, 110, 130, 150]), - 'time_bin_end': ('time_bin', [90, 110, 130, 150, 170]), - }, dims=['presentation', 'neuroid', 'time_bin']) - source.name = __name__ + ".test_notime" - score = benchmark(PrecomputedFeatures(source, visual_degrees=8)) - assert np.isnan(score.sel(aggregation='center')) # not a temporal model - assert np.isnan(score.raw.sel(aggregation='center')) # not a temporal model - assert score.attrs['ceiling'].sel(aggregation='center') == approx(.79) diff --git a/tests/test_benchmarks/test_majajhong2015.py b/tests/test_benchmarks/test_majajhong2015.py deleted file mode 100644 index 33b421599..000000000 --- a/tests/test_benchmarks/test_majajhong2015.py +++ /dev/null @@ -1,23 +0,0 @@ -import pytest - -from brainscore.benchmarks.majajhong2015 import load_assembly -from . import check_standard_format - - -@pytest.mark.private_access -class TestAssembly: - def test_majajhong2015_V4(self): - assembly = load_assembly(average_repetitions=True, region='V4') - check_standard_format(assembly) - assert assembly.attrs['stimulus_set_identifier'] == 'dicarlo.hvm-private' - assert set(assembly['region'].values) == {'V4'} - assert len(assembly['presentation']) == 2560 - assert len(assembly['neuroid']) == 88 - - def test_majajhong2015_IT(self): - assembly = load_assembly(average_repetitions=True, region='IT') - check_standard_format(assembly) - assert assembly.attrs['stimulus_set_identifier'] == 'dicarlo.hvm-private' - assert set(assembly['region'].values) == {'IT'} - assert len(assembly['presentation']) == 2560 - assert len(assembly['neuroid']) == 168 diff --git a/tests/test_benchmarks/test_public_benchmarks.py b/tests/test_benchmarks/test_public_benchmarks.py deleted file mode 100644 index be9d2f3aa..000000000 --- a/tests/test_benchmarks/test_public_benchmarks.py +++ /dev/null @@ -1,52 +0,0 @@ -import pytest -from pytest import approx - -from brainscore.benchmarks.public_benchmarks import list_public_assemblies, \ - FreemanZiembaV1PublicBenchmark, FreemanZiembaV2PublicBenchmark, \ - MajajHongV4PublicBenchmark, MajajHongITPublicBenchmark -from tests.test_benchmarks import PrecomputedFeatures - - -@pytest.mark.parametrize('benchmark_ctr, visual_degrees, expected', [ - pytest.param(FreemanZiembaV1PublicBenchmark, 4, approx(.679954, abs=.001), - marks=[pytest.mark.memory_intense]), - pytest.param(FreemanZiembaV2PublicBenchmark, 4, approx(.577498, abs=.001), - marks=[pytest.mark.memory_intense]), - pytest.param(MajajHongV4PublicBenchmark, 8, approx(.897956, abs=.001), - marks=pytest.mark.memory_intense), - pytest.param(MajajHongITPublicBenchmark, 8, approx(.816251, abs=.001), - marks=pytest.mark.memory_intense), -]) -def test_self(benchmark_ctr, visual_degrees, expected): - benchmark = benchmark_ctr() - source = benchmark._assembly.copy() - source = {benchmark._assembly.stimulus_set.identifier: source} - score = benchmark(PrecomputedFeatures(source, visual_degrees=visual_degrees)).raw - assert score.sel(aggregation='center') == expected - - -def test_list(): - assemblies = list_public_assemblies() - assert set(assemblies) == {'dicarlo.MajajHong2015.public', - 'dicarlo.MajajHong2015.temporal.public', - 'movshon.FreemanZiemba2013.public', - 'movshon.FreemanZiemba2013.noaperture.public', - 'dicarlo.Rajalingham2018.public', - 'brendel.Geirhos2021_colour', - 'brendel.Geirhos2021_contrast', - 'brendel.Geirhos2021_cue-conflict', - 'brendel.Geirhos2021_edge', - 'brendel.Geirhos2021_eidolonI', - 'brendel.Geirhos2021_eidolonII', - 'brendel.Geirhos2021_eidolonIII', - 'brendel.Geirhos2021_false-colour', - 'brendel.Geirhos2021_high-pass', - 'brendel.Geirhos2021_low-pass', - 'brendel.Geirhos2021_phase-scrambling', - 'brendel.Geirhos2021_power-equalisation', - 'brendel.Geirhos2021_rotation', - 'brendel.Geirhos2021_silhouette', - 'brendel.Geirhos2021_stylized', - 'brendel.Geirhos2021_sketch', - 'brendel.Geirhos2021_uniform-noise', - } diff --git a/tests/test_benchmarks/test_rajalingham2018.py b/tests/test_benchmarks/test_rajalingham2018.py deleted file mode 100644 index 0b192f2f5..000000000 --- a/tests/test_benchmarks/test_rajalingham2018.py +++ /dev/null @@ -1,54 +0,0 @@ -import numpy as np -import os -from pathlib import Path - -import pandas as pd -import xarray as xr -import pytest -from pytest import approx - -from brainio.assemblies import BehavioralAssembly -from brainscore.benchmarks.rajalingham2018 import DicarloRajalingham2018I2n -from brainscore.model_interface import BrainModel - - -@pytest.mark.private_access -class TestRajalingham2018: - def test_ceiling(self): - benchmark = DicarloRajalingham2018I2n() - ceiling = benchmark.ceiling - assert ceiling.sel(aggregation='center') == approx(.479, abs=.0064) - - @pytest.mark.parametrize(['model', 'expected_score'], - [ - ('alexnet', .253), - ('resnet34', .37787), - ('resnet18', .3638), - ]) - def test_precomputed(self, model, expected_score): - benchmark = DicarloRajalingham2018I2n() - probabilities = Path(__file__).parent.parent / 'test_metrics' / f'{model}-probabilities.nc' - probabilities = BehavioralAssembly.from_files(probabilities, stimulus_set_identifier=benchmark._assembly.stimulus_set.identifier, stimulus_set=benchmark._assembly.stimulus_set) - candidate = PrecomputedProbabilities(probabilities) - score = benchmark(candidate) - assert score.raw.sel(aggregation='center') == approx(expected_score, abs=.005) - assert score.sel(aggregation='center') == approx(expected_score / np.sqrt(.479), abs=.005) - - -class PrecomputedProbabilities(BrainModel): - def __init__(self, probabilities): - self.probabilities = probabilities - - def visual_degrees(self) -> int: - return 8 - - def start_task(self, task: BrainModel.Task, fitting_stimuli): - assert task == BrainModel.Task.probabilities - assert len(fitting_stimuli) == 2160 - - def look_at(self, stimuli, number_of_trials=1): - assert set(self.probabilities['stimulus_id'].values) == set(stimuli['stimulus_id'].values) - image_ids = self.probabilities['stimulus_id'].values.tolist() - probabilities = self.probabilities[[image_ids.index(image_id) for image_id in stimuli['stimulus_id'].values], :] - assert all(probabilities['stimulus_id'].values == stimuli['stimulus_id'].values) - return probabilities diff --git a/tests/test_metric_helpers/__init__.py b/tests/test_metric_helpers/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/test_metrics/test_transformations.py b/tests/test_metric_helpers/test_transformations.py similarity index 98% rename from tests/test_metrics/test_transformations.py rename to tests/test_metric_helpers/test_transformations.py index f495aa71c..e530d34cb 100644 --- a/tests/test_metrics/test_transformations.py +++ b/tests/test_metric_helpers/test_transformations.py @@ -1,8 +1,8 @@ import numpy as np from brainio.assemblies import NeuroidAssembly, DataAssembly -from brainscore.metrics import Metric, Score -from brainscore.metrics.transformations import CartesianProduct, CrossValidation, \ +from brainscore_core.metrics import Metric, Score +from brainscore_vision.metric_helpers.transformations import CartesianProduct, CrossValidation, \ CrossValidationSingle @@ -169,4 +169,3 @@ def __call__(self, assembly, *args, **kwargs): assert 'division_coord' not in result.raw.dims # no dimension assert hasattr(result.raw, 'division_coord') # but a level assert result.raw["dim"].variable.level_names == ["dim_id", "division_coord"] - diff --git a/tests/test_metrics/test_xarray_utils.py b/tests/test_metric_helpers/test_xarray_utils.py similarity index 98% rename from tests/test_metrics/test_xarray_utils.py rename to tests/test_metric_helpers/test_xarray_utils.py index 87b4ef3b4..c79a962cf 100644 --- a/tests/test_metrics/test_xarray_utils.py +++ b/tests/test_metric_helpers/test_xarray_utils.py @@ -4,7 +4,7 @@ from sklearn.linear_model import LinearRegression from brainio.assemblies import NeuroidAssembly -from brainscore.metrics.xarray_utils import XarrayRegression, XarrayCorrelation +from brainscore_vision.metric_helpers.xarray_utils import XarrayRegression, XarrayCorrelation class TestXarrayRegression: diff --git a/tests/test_metrics/__init__.py b/tests/test_metrics/__init__.py deleted file mode 100644 index 5e404feca..000000000 --- a/tests/test_metrics/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -import brainscore - - -def load_hvm(group=lambda hvm: hvm.multi_groupby(['object_name', 'stimulus_id'])): - assembly = brainscore.get_assembly(name="dicarlo.MajajHong2015").sel(variation=6) - assembly.load() - assembly = group(assembly) - assembly = assembly.mean(dim="presentation").squeeze("time_bin").T - return assembly diff --git a/tests/test_metrics/it_rdm.p b/tests/test_metrics/it_rdm.p deleted file mode 100644 index 10e07ab08..000000000 Binary files a/tests/test_metrics/it_rdm.p and /dev/null differ diff --git a/tests/test_metrics/test___init__.py b/tests/test_metrics/test___init__.py deleted file mode 100644 index c30a14147..000000000 --- a/tests/test_metrics/test___init__.py +++ /dev/null @@ -1,42 +0,0 @@ -import numpy as np -from brainio.assemblies import DataAssembly - -from brainscore.metrics import Score - - -class TestScoreRaw: - def test_sel(self): - score = Score([1, 2], coords={'a': [1, 2]}, dims=['a']) - score.attrs['raw'] = DataAssembly([0, 2, 1, 3], coords={'a': [1, 1, 2, 2]}, dims=['a']) - sel_score = score.sel(a=1) - np.testing.assert_array_equal(sel_score.raw['a'], [1, 1]) - - def test_isel(self): - score = Score([1, 2], coords={'a': [1, 2]}, dims=['a']) - score.attrs['raw'] = DataAssembly([0, 2, 1, 3], coords={'a': [1, 1, 2, 2]}, dims=['a']) - sel_score = score.isel(a=0) - np.testing.assert_array_equal(sel_score.raw['a'], [1, 1]) - - def test_sel_no_apply_raw(self): - score = Score([1, 2], coords={'a': [1, 2]}, dims=['a']) - score.attrs['raw'] = DataAssembly([0, 2, 1, 3], coords={'a': [1, 1, 2, 2]}, dims=['a']) - sel_score = score.sel(a=1, _apply_raw=False) - np.testing.assert_array_equal(sel_score.raw['a'], [1, 1, 2, 2]) - - def test_squeeze(self): - score = Score([[1, 2]], coords={'s': [0], 'a': [1, 2]}, dims=['s', 'a']) - score.attrs['raw'] = DataAssembly([[0, 2, 1, 3]], coords={'s': [0], 'a': [1, 1, 2, 2]}, dims=['s', 'a']) - sel_score = score.squeeze('s') - np.testing.assert_array_equal(sel_score.raw.dims, ['a']) - - def test_mean(self): - score = Score([1, 2], coords={'a': [1, 2]}, dims=['a']) - score.attrs['raw'] = DataAssembly([0, 2, 1, 3], coords={'a': [1, 1, 2, 2]}, dims=['a']) - mean_score = score.mean('a') - np.testing.assert_array_equal(mean_score.raw['a'], [1, 1, 2, 2]) - - def test_mean_apply_raw(self): - score = Score([1, 2], coords={'a': [1, 2]}, dims=['a']) - score.attrs['raw'] = DataAssembly([0, 2, 1, 3], coords={'a': [1, 1, 2, 2]}, dims=['a']) - mean_score = score.mean('a', _apply_raw=True) - assert mean_score.raw == 1.5 diff --git a/tests/test_metrics/test_behavior.py b/tests/test_metrics/test_behavior.py deleted file mode 100644 index 8f9559734..000000000 --- a/tests/test_metrics/test_behavior.py +++ /dev/null @@ -1,38 +0,0 @@ -import os -from pathlib import Path - -import pandas as pd -import xarray as xr -import pytest -from pytest import approx - -from brainio.assemblies import BehavioralAssembly -from brainscore.benchmarks.rajalingham2018 import load_assembly -from brainscore.metrics.image_level_behavior import I2n - - -@pytest.mark.private_access -class TestI2N: - @pytest.mark.parametrize(['model', 'expected_score'], - [ - ('alexnet', .253), - ('resnet34', .37787), - ('resnet18', .3638), - ]) - def test_model(self, model, expected_score): - # assemblies - objectome = load_assembly() - probabilities = Path(__file__).parent / f'{model}-probabilities.nc' - probabilities = BehavioralAssembly.from_files(probabilities, stimulus_set_identifier=objectome.attrs['stimulus_set_identifier'], stimulus_set=objectome.attrs['stimulus_set']) - # metric - i2n = I2n() - score = i2n(probabilities, objectome) - score = score.sel(aggregation='center') - assert score == approx(expected_score, abs=0.005), f"expected {expected_score}, but got {score}" - - def test_ceiling(self): - objectome = load_assembly() - i2n = I2n() - ceiling = i2n.ceiling(objectome) - assert ceiling.sel(aggregation='center') == approx(.4786, abs=.0064) - assert ceiling.sel(aggregation='error') == approx(.00537, abs=.0015) diff --git a/tests/test_metrics/test_cka.py b/tests/test_metrics/test_cka.py deleted file mode 100644 index d342519bd..000000000 --- a/tests/test_metrics/test_cka.py +++ /dev/null @@ -1,31 +0,0 @@ -import numpy as np -from brainio.assemblies import NeuroidAssembly -from pytest import approx - -from brainscore.metrics.cka import CKACrossValidated, CKAMetric - - -class TestCKACrossValidated: - def test_small(self): - assembly = NeuroidAssembly((np.arange(30 * 25) + np.random.standard_normal(30 * 25)).reshape((30, 25)), - coords={'stimulus_id': ('presentation', np.arange(30)), - 'object_name': ('presentation', ['a', 'b', 'c'] * 10), - 'neuroid_id': ('neuroid', np.arange(25)), - 'region': ('neuroid', [None] * 25)}, - dims=['presentation', 'neuroid']) - metric = CKACrossValidated() - score = metric(assembly1=assembly, assembly2=assembly) - assert score.sel(aggregation='center') == approx(1) - - -class TestCKAMetric: - def test_equal30(self): - assembly = NeuroidAssembly((np.arange(30 * 25) + np.random.standard_normal(30 * 25)).reshape((30, 25)), - coords={'stimulus_id': ('presentation', np.arange(30)), - 'object_name': ('presentation', ['a', 'b', 'c'] * 10), - 'neuroid_id': ('neuroid', np.arange(25)), - 'region': ('neuroid', [None] * 25)}, - dims=['presentation', 'neuroid']) - similarity = CKAMetric() - score = similarity(assembly, assembly) - assert score == approx(1.) diff --git a/tests/test_metrics/test_temporal.py b/tests/test_metrics/test_temporal.py deleted file mode 100644 index b85b5255d..000000000 --- a/tests/test_metrics/test_temporal.py +++ /dev/null @@ -1,67 +0,0 @@ -import numpy as np -import scipy.stats - -from brainio.assemblies import NeuroidAssembly -from brainscore.metrics.regression import pls_regression, pearsonr_correlation -from brainscore.metrics.temporal import TemporalRegressionAcrossTime, TemporalCorrelationAcrossImages, \ - TemporalCorrelationAcrossTime -from brainscore.metrics.xarray_utils import XarrayCorrelation - - -class TestTemporalRegressionAcrossTime: - def test_small(self): - values = (np.arange(30 * 25 * 5) + np.random.standard_normal(30 * 25 * 5)).reshape((30, 25, 5)) - assembly = NeuroidAssembly(values, - coords={'stimulus_id': ('presentation', np.arange(30)), - 'object_name': ('presentation', ['a', 'b', 'c'] * 10), - 'neuroid_id': ('neuroid', np.arange(25)), - 'region': ('neuroid', ['some_region'] * 25), - 'time_bin_start': ('time_bin', list(range(5))), - 'time_bin_end': ('time_bin', list(range(1, 6))), - }, - dims=['presentation', 'neuroid', 'time_bin']) - regression = TemporalRegressionAcrossTime(pls_regression()) - regression.fit(source=assembly, target=assembly) - prediction = regression.predict(source=assembly) - assert all(prediction['stimulus_id'] == assembly['stimulus_id']) - assert all(prediction['neuroid_id'] == assembly['neuroid_id']) - assert all(prediction['time_bin'] == assembly['time_bin']) - - -class TestTemporalCorrelation: - def test_across_images(self): - values = (np.arange(30 * 25 * 5) + np.random.standard_normal(30 * 25 * 5)).reshape((30, 25, 5)) - assembly = NeuroidAssembly(values, - coords={'stimulus_id': ('presentation', np.arange(30)), - 'object_name': ('presentation', ['a', 'b', 'c'] * 10), - 'neuroid_id': ('neuroid', np.arange(25)), - 'region': ('neuroid', ['some_region'] * 25), - 'time_bin_start': ('time_bin', list(range(5))), - 'time_bin_end': ('time_bin', list(range(1, 6))), - }, - dims=['presentation', 'neuroid', 'time_bin']) - correlation = TemporalCorrelationAcrossImages(pearsonr_correlation()) - score = correlation(assembly, assembly) - np.testing.assert_array_equal(score.dims, ['neuroid']) - np.testing.assert_array_equal(score['neuroid_id'].values, list(range(25))) - np.testing.assert_array_almost_equal(score.values, [1.] * 25) - assert set(score.raw.dims) == {'neuroid', 'time_bin'} - - def test_across_time(self): - values = (np.arange(30 * 25 * 5) + np.random.standard_normal(30 * 25 * 5)).reshape((30, 25, 5)) - assembly = NeuroidAssembly(values, - coords={'stimulus_id': ('presentation', np.arange(30)), - 'object_name': ('presentation', ['a', 'b', 'c'] * 10), - 'neuroid_id': ('neuroid', np.arange(25)), - 'region': ('neuroid', ['some_region'] * 25), - 'time_bin_start': ('time_bin', list(range(5))), - 'time_bin_end': ('time_bin', list(range(1, 6))), - }, - dims=['presentation', 'neuroid', 'time_bin']) - correlation = XarrayCorrelation(scipy.stats.pearsonr, correlation_coord='time_bin') - correlation = TemporalCorrelationAcrossTime(correlation) - score = correlation(assembly, assembly) - np.testing.assert_array_equal(score.dims, ['neuroid']) - np.testing.assert_array_equal(score['neuroid_id'].values, list(range(25))) - np.testing.assert_array_almost_equal(score.values, [1.] * 25) - assert set(score.raw.dims) == {'neuroid', 'presentation'} diff --git a/tests/test_model_helpers/README.md b/tests/test_model_helpers/README.md new file mode 100644 index 000000000..e0520dc04 --- /dev/null +++ b/tests/test_model_helpers/README.md @@ -0,0 +1,16 @@ +# Unit Tests +## Markers +Unit tests have various markers that denote possible issues in the travis build: + +* **private_access**: tests that require access to a private ressource, such as assemblies on S3 (travis pull request builds can not have private access) +* **memory_intense**: tests requiring more memory than is available in the travis sandbox (currently 3 GB, https://docs.travis-ci.com/user/common-build-problems/#my-build-script-is-killed-without-any-error) + +Use the following syntax to mark a test: +``` +@pytest.mark.memory_intense +def test_something(...): + assert False +``` + +To skip a specific marker, run e.g. `pytest -m "not memory_intense"`. +To skip multiple markers, run e.g. `pytest -m "not private_access and not memory_intense"`. diff --git a/tests/test_model_helpers/__init__.py b/tests/test_model_helpers/__init__.py new file mode 100644 index 000000000..12fe48049 --- /dev/null +++ b/tests/test_model_helpers/__init__.py @@ -0,0 +1,6 @@ +import logging +import sys + +logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) +for disable_logger in ['s3transfer', 'botocore', 'boto3', 'urllib3', 'peewee', 'PIL']: + logging.getLogger(disable_logger).setLevel(logging.WARNING) diff --git a/tests/test_model_helpers/activations/__init__.py b/tests/test_model_helpers/activations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/test_model_helpers/activations/alexnet-rgb-1000.nc b/tests/test_model_helpers/activations/alexnet-rgb-1000.nc new file mode 100644 index 000000000..2242b8aa9 Binary files /dev/null and b/tests/test_model_helpers/activations/alexnet-rgb-1000.nc differ diff --git a/tests/test_model_helpers/activations/alexnet-rgb-None.nc b/tests/test_model_helpers/activations/alexnet-rgb-None.nc new file mode 100644 index 000000000..de372bd2e Binary files /dev/null and b/tests/test_model_helpers/activations/alexnet-rgb-None.nc differ diff --git a/tests/test_model_helpers/activations/grayscale.png b/tests/test_model_helpers/activations/grayscale.png new file mode 100644 index 000000000..3c48afd5c Binary files /dev/null and b/tests/test_model_helpers/activations/grayscale.png differ diff --git a/tests/test_model_helpers/activations/grayscale2.jpg b/tests/test_model_helpers/activations/grayscale2.jpg new file mode 100644 index 000000000..17360f0a2 Binary files /dev/null and b/tests/test_model_helpers/activations/grayscale2.jpg differ diff --git a/tests/test_model_helpers/activations/grayscale_alpha.png b/tests/test_model_helpers/activations/grayscale_alpha.png new file mode 100644 index 000000000..98a011624 Binary files /dev/null and b/tests/test_model_helpers/activations/grayscale_alpha.png differ diff --git a/tests/test_model_helpers/activations/palletized.png b/tests/test_model_helpers/activations/palletized.png new file mode 100644 index 000000000..628fcbc1c Binary files /dev/null and b/tests/test_model_helpers/activations/palletized.png differ diff --git a/tests/test_model_helpers/activations/rgb.jpg b/tests/test_model_helpers/activations/rgb.jpg new file mode 100644 index 000000000..c9efdae08 Binary files /dev/null and b/tests/test_model_helpers/activations/rgb.jpg differ diff --git a/tests/test_model_helpers/activations/test___init__.py b/tests/test_model_helpers/activations/test___init__.py new file mode 100644 index 000000000..86a1ee788 --- /dev/null +++ b/tests/test_model_helpers/activations/test___init__.py @@ -0,0 +1,334 @@ +import functools +import numpy as np +import os +import pytest +import xarray as xr +from pathlib import Path + +from brainio.stimuli import StimulusSet +from brainscore_vision.model_helpers.activations import KerasWrapper, PytorchWrapper, TensorflowSlimWrapper +from brainscore_vision.model_helpers.activations.core import flatten +from brainscore_vision.model_helpers.activations.pca import LayerPCA + + +def unique_preserved_order(a): + _, idx = np.unique(a, return_index=True) + return a[np.sort(idx)] + + +def pytorch_custom(): + import torch + from torch import nn + from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images + + class MyModel(nn.Module): + def __init__(self): + super(MyModel, self).__init__() + self.conv1 = torch.nn.Conv2d(in_channels=3, out_channels=2, kernel_size=3) + self.relu1 = torch.nn.ReLU() + linear_input_size = np.power((224 - 3 + 2 * 0) / 1 + 1, 2) * 2 + self.linear = torch.nn.Linear(int(linear_input_size), 1000) + self.relu2 = torch.nn.ReLU() # can't get named ReLU output otherwise + + def forward(self, x): + x = self.conv1(x) + x = self.relu1(x) + x = x.view(x.size(0), -1) + x = self.linear(x) + x = self.relu2(x) + return x + + preprocessing = functools.partial(load_preprocess_images, image_size=224) + return PytorchWrapper(model=MyModel(), preprocessing=preprocessing) + + +def pytorch_alexnet(): + from torchvision.models.alexnet import alexnet + from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images + + preprocessing = functools.partial(load_preprocess_images, image_size=224) + return PytorchWrapper(model=alexnet(pretrained=True), preprocessing=preprocessing) + + +def pytorch_alexnet_resize(): + from torchvision.models.alexnet import alexnet + from brainscore_vision.model_helpers.activations.pytorch import load_images, torchvision_preprocess + from torchvision import transforms + torchvision_preprocess_input = transforms.Compose([transforms.Resize(224), torchvision_preprocess()]) + + def preprocessing(paths): + images = load_images(paths) + images = [torchvision_preprocess_input(image) for image in images] + images = np.concatenate(images) + return images + + return PytorchWrapper(alexnet(pretrained=True), preprocessing, identifier='alexnet-resize') + + +def pytorch_transformer_substitute(): + import torch + from torch import nn + from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images + + class MyTransformer(nn.Module): + def __init__(self): + super(MyTransformer, self).__init__() + self.conv = torch.nn.Conv1d(in_channels=3, out_channels=2, kernel_size=3) + self.relu1 = torch.nn.ReLU() + linear_input_size = (224 ** 2 - 2) * 2 + self.linear = torch.nn.Linear(int(linear_input_size), 1000) + self.relu2 = torch.nn.ReLU() # logit out needs to be 1000 + + def forward(self, x): + x = x.view(*x.shape[:2], -1) + x = self.conv(x) + x = self.relu1(x) + x = x.view(x.shape[0], -1) + x = self.linear(x) + x = self.relu2(x) + + return x + + preprocessing = functools.partial(load_preprocess_images, image_size=224) + return PytorchWrapper(model=MyTransformer(), preprocessing=preprocessing) + + +def keras_vgg19(): + import keras + from keras.applications.vgg19 import VGG19, preprocess_input + from brainscore_vision.model_helpers.activations.keras import load_images + keras.backend.clear_session() + preprocessing = lambda image_filepaths: preprocess_input(load_images(image_filepaths, image_size=224)) + return KerasWrapper(model=VGG19(), preprocessing=preprocessing) + + +def tfslim_custom(): + from brainscore_vision.model_helpers.activations.tensorflow import load_resize_image + import tensorflow as tf + slim = tf.contrib.slim + tf.compat.v1.reset_default_graph() + + image_size = 224 + placeholder = tf.compat.v1.placeholder(dtype=tf.string, shape=[64]) + preprocess = lambda image_path: load_resize_image(image_path, image_size) + preprocess = tf.map_fn(preprocess, placeholder, dtype=tf.float32) + + with tf.compat.v1.variable_scope('my_model', values=[preprocess]) as sc: + end_points_collection = sc.original_name_scope + '_end_points' + # Collect outputs for conv2d, fully_connected and max_pool2d. + with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], + outputs_collections=[end_points_collection]): + net = slim.conv2d(preprocess, 64, [11, 11], 4, padding='VALID', scope='conv1') + net = slim.max_pool2d(net, [5, 5], 5, scope='pool1') + net = slim.max_pool2d(net, [3, 3], 2, scope='pool2') + net = slim.flatten(net, scope='flatten') + net = slim.fully_connected(net, 1000, scope='logits') + endpoints = slim.utils.convert_collection_to_dict(end_points_collection) + + session = tf.compat.v1.Session() + session.run(tf.compat.v1.initialize_all_variables()) + return TensorflowSlimWrapper(identifier='tf-custom', labels_offset=0, + endpoints=endpoints, inputs=placeholder, session=session) + + +def tfslim_vgg16(): + import tensorflow as tf + from nets import nets_factory + from preprocessing import vgg_preprocessing + from brainscore_vision.model_helpers.activations.tensorflow import load_resize_image + tf.compat.v1.reset_default_graph() + + image_size = 224 + placeholder = tf.compat.v1.placeholder(dtype=tf.string, shape=[64]) + preprocess_image = lambda image: vgg_preprocessing.preprocess_image( + image, image_size, image_size, resize_side_min=image_size) + preprocess = lambda image_path: preprocess_image(load_resize_image(image_path, image_size)) + preprocess = tf.map_fn(preprocess, placeholder, dtype=tf.float32) + + model_ctr = nets_factory.get_network_fn('vgg_16', num_classes=1001, is_training=False) + logits, endpoints = model_ctr(preprocess) + + session = tf.compat.v1.Session() + session.run(tf.compat.v1.initialize_all_variables()) + return TensorflowSlimWrapper(identifier='tf-vgg16', labels_offset=1, + endpoints=endpoints, inputs=placeholder, session=session) + + +models_layers = [ + pytest.param(pytorch_custom, ['linear', 'relu2']), + pytest.param(pytorch_alexnet, ['features.12', 'classifier.5'], marks=pytest.mark.memory_intense), + pytest.param(pytorch_transformer_substitute, ['relu1']), + pytest.param(keras_vgg19, ['block3_pool'], marks=pytest.mark.memory_intense), + pytest.param(tfslim_custom, ['my_model/pool2'], marks=pytest.mark.memory_intense), + pytest.param(tfslim_vgg16, ['vgg_16/pool5'], marks=pytest.mark.memory_intense), +] + + +@pytest.mark.parametrize("image_name", ['rgb.jpg', 'grayscale.png', 'grayscale2.jpg', 'grayscale_alpha.png', + 'palletized.png']) +@pytest.mark.parametrize(["pca_components", "logits"], [(None, True), (None, False), (5, False)]) +@pytest.mark.parametrize(["model_ctr", "layers"], models_layers) +def test_from_image_path(model_ctr, layers, image_name, pca_components, logits): + stimuli_paths = [os.path.join(os.path.dirname(__file__), image_name)] + + activations_extractor = model_ctr() + if pca_components: + LayerPCA.hook(activations_extractor, pca_components) + activations = activations_extractor.from_paths(stimuli_paths=stimuli_paths, + layers=layers if not logits else None) + + assert activations is not None + assert len(activations['stimulus_path']) == 1 + assert len(np.unique(activations['layer'])) == len(layers) if not logits else 1 + if logits and not pca_components: + assert len(activations['neuroid']) == 1000 + elif pca_components is not None: + assert len(activations['neuroid']) == pca_components * len(layers) + import gc + gc.collect() # free some memory, we're piling up a lot of activations at this point + return activations + + +def _build_stimulus_set(image_names): + stimulus_set = StimulusSet([{'stimulus_id': image_name, 'some_meta': image_name[::-1]} + for image_name in image_names]) + stimulus_set.stimulus_paths = {image_name: os.path.join(os.path.dirname(__file__), image_name) + for image_name in image_names} + return stimulus_set + + +@pytest.mark.parametrize("pca_components", [None, 5]) +@pytest.mark.parametrize(["model_ctr", "layers"], models_layers) +def test_from_stimulus_set(model_ctr, layers, pca_components): + image_names = ['rgb.jpg', 'grayscale.png', 'grayscale2.jpg', 'grayscale_alpha.png', 'palletized.png'] + stimulus_set = _build_stimulus_set(image_names) + + activations_extractor = model_ctr() + if pca_components: + LayerPCA.hook(activations_extractor, pca_components) + activations = activations_extractor.from_stimulus_set(stimulus_set, layers=layers, stimuli_identifier=False) + + assert activations is not None + assert set(activations['stimulus_id'].values) == set(image_names) + assert all(activations['some_meta'].values == [image_name[::-1] for image_name in image_names]) + assert len(np.unique(activations['layer'])) == len(layers) + if pca_components is not None: + assert len(activations['neuroid']) == pca_components * len(layers) + + +@pytest.mark.memory_intense +@pytest.mark.parametrize("pca_components", [None, 1000]) +def test_exact_activations(pca_components): + activations = test_from_image_path(model_ctr=pytorch_alexnet_resize, layers=['features.12', 'classifier.5'], + image_name='rgb.jpg', pca_components=pca_components, logits=False) + path_to_expected = Path(__file__).parent / f'alexnet-rgb-{pca_components}.nc' + expected = xr.load_dataarray(path_to_expected) + assert (activations == expected).all() + + +@pytest.mark.memory_intense +@pytest.mark.parametrize(["model_ctr", "internal_layers"], [ + (pytorch_alexnet, ['features.12', 'classifier.5']), + (keras_vgg19, ['block3_pool']), + (tfslim_vgg16, ['vgg_16/pool5']), +]) +def test_mixed_layer_logits(model_ctr, internal_layers): + stimuli_paths = [os.path.join(os.path.dirname(__file__), 'rgb.jpg')] + + activations_extractor = model_ctr() + layers = internal_layers + ['logits'] + activations = activations_extractor(stimuli=stimuli_paths, layers=layers) + assert len(np.unique(activations['layer'])) == len(internal_layers) + 1 + assert set(activations['layer'].values) == set(layers) + assert unique_preserved_order(activations['layer'])[-1] == 'logits' + + +@pytest.mark.memory_intense +@pytest.mark.parametrize(["model_ctr", "expected_identifier"], [ + (pytorch_custom, 'MyModel'), + (pytorch_alexnet, 'AlexNet'), + (keras_vgg19, 'vgg19'), +]) +def test_infer_identifier(model_ctr, expected_identifier): + model = model_ctr() + assert model.identifier == expected_identifier + + +def test_transformer_meta(): + model = pytorch_transformer_substitute() + activations = model(stimuli=[os.path.join(os.path.dirname(__file__), 'rgb.jpg')], layers=['relu1']) + assert hasattr(activations, 'channel') + assert hasattr(activations, 'embedding') + assert len(set(activations['neuroid_id'].values)) == len(activations['neuroid']) + + +def test_convolution_meta(): + model = pytorch_custom() + activations = model(stimuli=[os.path.join(os.path.dirname(__file__), 'rgb.jpg')], layers=['conv1']) + assert hasattr(activations, 'channel') + assert hasattr(activations, 'channel_x') + assert hasattr(activations, 'channel_y') + assert len(set(activations['neuroid_id'].values)) == len(activations['neuroid']) + + +def test_conv_and_fc(): + model = pytorch_custom() + activations = model(stimuli=[os.path.join(os.path.dirname(__file__), 'rgb.jpg')], layers=['conv1', 'linear']) + assert set(activations['layer'].values) == {'conv1', 'linear'} + + +@pytest.mark.timeout(300) +def test_merge_large_layers(): + import torch + from torch import nn + from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images + + class LargeModel(nn.Module): + def __init__(self): + super(LargeModel, self).__init__() + self.conv = torch.nn.Conv2d(in_channels=3, out_channels=4, kernel_size=3) + self.relu = torch.nn.ReLU() + + def forward(self, x): + x = self.conv(x) + x = self.relu(x) + return x + + preprocessing = functools.partial(load_preprocess_images, image_size=224) + model = PytorchWrapper(model=LargeModel(), preprocessing=preprocessing) + activations = model(stimuli=[os.path.join(os.path.dirname(__file__), 'rgb.jpg')] * 64, layers=['conv', 'relu']) + assert len(activations['neuroid']) == 394272 + assert len(set(activations['neuroid_id'].values)) == len(activations['neuroid']) + assert set(activations['layer'].values) == {'conv', 'relu'} + + +class TestFlatten: + def test_flattened_shape(self): + A = np.random.rand(2560, 256, 6, 6) + flattened = flatten(A) + assert np.prod(flattened.shape) == np.prod(A.shape) + assert flattened.shape[0] == A.shape[0] + assert len(flattened.shape) == 2 + + def test_indices_shape(self): + A = np.random.rand(2560, 256, 6, 6) + _, indices = flatten(A, return_index=True) + assert len(indices.shape) == 2 + assert indices.shape[0] == np.prod(A.shape[1:]) + assert indices.shape[1] == 3 # for 256, 6, 6 + + def test_match_flatten(self): + A = np.random.rand(10, 256, 6, 6) + flattened, indices = flatten(A, return_index=True) + for layer in range(A.shape[0]): + for i in range(np.prod(A.shape[1:])): + value = flattened[layer][i] + index = indices[i] + assert A[layer][tuple(index)] == value + + def test_inverse(self): + A = np.random.rand(2560, 256, 6, 6) + flattened = flatten(A) + A_ = np.reshape(flattened, [flattened.shape[0], 256, 6, 6]) + assert A.shape == A_.shape + assert (A == A_).all() diff --git a/tests/test_model_helpers/brain_transformation/__init__.py b/tests/test_model_helpers/brain_transformation/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/test_model_helpers/brain_transformation/rgb1.jpg b/tests/test_model_helpers/brain_transformation/rgb1.jpg new file mode 100644 index 000000000..c9efdae08 Binary files /dev/null and b/tests/test_model_helpers/brain_transformation/rgb1.jpg differ diff --git a/tests/test_model_helpers/brain_transformation/rgb2.jpg b/tests/test_model_helpers/brain_transformation/rgb2.jpg new file mode 100644 index 000000000..cc2e84a2f Binary files /dev/null and b/tests/test_model_helpers/brain_transformation/rgb2.jpg differ diff --git a/tests/test_model_helpers/brain_transformation/test___init__.py b/tests/test_model_helpers/brain_transformation/test___init__.py new file mode 100644 index 000000000..49fb55b1c --- /dev/null +++ b/tests/test_model_helpers/brain_transformation/test___init__.py @@ -0,0 +1,9 @@ +from brainscore_vision.model_helpers.brain_transformation import ModelCommitment +from brainscore_vision.model_helpers.utils import fullname + + +class TestVisualDegrees: + def test_standard_commitment(self): + brain_model = ModelCommitment(identifier=fullname(self), activations_model=None, + layers=['dummy']) + assert brain_model.visual_degrees() == 8 diff --git a/tests/test_model_helpers/brain_transformation/test_behavior.py b/tests/test_model_helpers/brain_transformation/test_behavior.py new file mode 100644 index 000000000..915ed69a8 --- /dev/null +++ b/tests/test_model_helpers/brain_transformation/test_behavior.py @@ -0,0 +1,127 @@ +import functools +import os + +import numpy as np +import pytest +from pytest import approx + +from brainio.assemblies import BehavioralAssembly +from brainio.stimuli import StimulusSet +from brainscore_vision.benchmark_helpers.screen import place_on_screen +from brainscore_vision.model_helpers.activations import PytorchWrapper +from brainscore_vision.model_helpers.brain_transformation import ModelCommitment +from brainscore_vision.model_interface import BrainModel + + +def pytorch_custom(): + import torch + from torch import nn + from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images + + class MyModel(nn.Module): + def __init__(self): + super(MyModel, self).__init__() + np.random.seed(0) + torch.random.manual_seed(0) + self.conv1 = torch.nn.Conv2d(in_channels=3, out_channels=2, kernel_size=3) + self.relu1 = torch.nn.ReLU() + linear_input_size = np.power((224 - 3 + 2 * 0) / 1 + 1, 2) * 2 + self.linear = torch.nn.Linear(int(linear_input_size), 1000) + self.relu2 = torch.nn.ReLU() # can't get named ReLU output otherwise + + def forward(self, x): + x = self.conv1(x) + x = self.relu1(x) + x = x.view(x.size(0), -1) + x = self.linear(x) + x = self.relu2(x) + return x + + preprocessing = functools.partial(load_preprocess_images, image_size=224) + return PytorchWrapper(model=MyModel(), preprocessing=preprocessing) + + +class TestLabelBehavior: + @pytest.mark.parametrize(['model_ctr'], [(pytorch_custom,)]) + def test_imagenet_creates_synset(self, model_ctr): + activations_model = model_ctr() + brain_model = ModelCommitment(identifier=activations_model.identifier, activations_model=activations_model, + layers=None, behavioral_readout_layer='dummy') # not needed + stimuli = mock_stimulus_set() + brain_model.start_task(BrainModel.Task.label, 'imagenet') + behavior = brain_model.look_at(stimuli) + assert isinstance(behavior, BehavioralAssembly) + assert set(behavior['stimulus_id'].values) == {'1', '2'} + assert len(behavior['synset']) == 2 + assert behavior['synset'].values[0].startswith('n') + + @pytest.mark.parametrize(['model_ctr'], [(pytorch_custom,)]) + def test_choicelabels(self, model_ctr): + activations_model = model_ctr() + brain_model = ModelCommitment(identifier=activations_model.identifier, activations_model=activations_model, + layers=['relu1', 'relu2'], behavioral_readout_layer='relu2') + stimuli = mock_stimulus_set() + choice_labels = ['dog', 'cat', 'bear', 'bird'] + brain_model.start_task(BrainModel.Task.label, choice_labels) + behavior = brain_model.look_at(stimuli) + assert isinstance(behavior, BehavioralAssembly) + assert set(behavior['stimulus_id'].values) == {'1', '2'} + assert all(choice in choice_labels for choice in behavior.squeeze().values) + # these two labels do not necessarily make sense since we're working with a random model + assert behavior.sel(stimulus_id='1').values.item() == 'bear' + assert behavior.sel(stimulus_id='2').values.item() == 'bird' + + +def mock_stimulus_set(): + stimuli = StimulusSet({'stimulus_id': ['1', '2'], 'filename': ['rgb1', 'rgb2']}) + stimuli.stimulus_paths = {'1': os.path.join(os.path.dirname(__file__), 'rgb1.jpg'), + '2': os.path.join(os.path.dirname(__file__), 'rgb2.jpg')} + stimuli.identifier = 'TestLabelBehavior.rgb_1_2' + return stimuli + + +class TestLogitsBehavior: + """ + legacy support for LogitsBehavior; still used in old candidate_models submissions + https://github.com/brain-score/candidate_models/blob/fa965c452bd17c6bfcca5b991fdbb55fd5db618f/candidate_models/model_commitments/cornets.py#L13 + """ + + def test_import(self): + # noinspection PyUnresolvedReferences + from brainscore_vision.model_helpers.brain_transformation.behavior import LogitsBehavior + + def test_imagenet(self): + from brainscore_vision.model_helpers.brain_transformation.behavior import LogitsBehavior + + activations_model = pytorch_custom() + logits_behavior = LogitsBehavior( + identifier='pytorch-custom', activations_model=activations_model) + + stimuli = mock_stimulus_set() + logits_behavior.start_task(BrainModel.Task.label, 'imagenet') + behavior = logits_behavior.look_at(stimuli) + assert isinstance(behavior, BehavioralAssembly) + assert set(behavior['stimulus_id'].values) == {'1', '2'} + assert len(behavior['synset']) == 2 + assert behavior['synset'].values[0].startswith('n') + + +class TestProbabilitiesMapping: + def test_creates_probabilities(self): + activations_model = pytorch_custom() + brain_model = ModelCommitment(identifier=activations_model.identifier, activations_model=activations_model, + layers=None, behavioral_readout_layer='relu2') + fitting_stimuli = StimulusSet({'stimulus_id': ['rgb1', 'rgb2'], 'image_label': ['label1', 'label2']}) + fitting_stimuli.stimulus_paths = {'rgb1': os.path.join(os.path.dirname(__file__), 'rgb1.jpg'), + 'rgb2': os.path.join(os.path.dirname(__file__), 'rgb2.jpg')} + fitting_stimuli.identifier = 'test_probabilities_mapping.creates_probabilities' + fitting_stimuli = place_on_screen(fitting_stimuli, target_visual_degrees=brain_model.visual_degrees(), + source_visual_degrees=8) + brain_model.start_task(BrainModel.Task.probabilities, fitting_stimuli) + probabilities = brain_model.look_at(fitting_stimuli) + np.testing.assert_array_equal(probabilities.dims, ['presentation', 'choice']) + np.testing.assert_array_equal(probabilities.shape, [2, 2]) + np.testing.assert_array_almost_equal(probabilities.sel(stimulus_id='rgb1', choice='label1').values, + probabilities.sel(stimulus_id='rgb2', choice='label2').values) + assert probabilities.sel(stimulus_id='rgb1', choice='label1') + \ + probabilities.sel(stimulus_id='rgb1', choice='label2') == approx(1) diff --git a/tests/test_model_helpers/brain_transformation/test_neural.py b/tests/test_model_helpers/brain_transformation/test_neural.py new file mode 100644 index 000000000..bbe298f9a --- /dev/null +++ b/tests/test_model_helpers/brain_transformation/test_neural.py @@ -0,0 +1,70 @@ +import functools +import os +from pathlib import Path + +import numpy as np +import pytest + +from brainio.stimuli import StimulusSet +from brainscore_vision.model_helpers.activations import PytorchWrapper +from brainscore_vision.model_helpers.brain_transformation import ModelCommitment, LayerMappedModel + + +def pytorch_custom(): + import torch + from torch import nn + from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images + + class MyModel(nn.Module): + def __init__(self): + super(MyModel, self).__init__() + self.conv1 = torch.nn.Conv2d(in_channels=3, out_channels=2, kernel_size=3) + self.relu1 = torch.nn.ReLU() + linear_input_size = np.power((224 - 3 + 2 * 0) / 1 + 1, 2) * 2 + self.linear = torch.nn.Linear(int(linear_input_size), 1000) + self.relu2 = torch.nn.ReLU() # can't get named ReLU output otherwise + + def forward(self, x): + x = self.conv1(x) + x = self.relu1(x) + x = x.view(x.size(0), -1) + x = self.linear(x) + x = self.relu2(x) + return x + + preprocessing = functools.partial(load_preprocess_images, image_size=224) + return PytorchWrapper(model=MyModel(), preprocessing=preprocessing) + + +@pytest.mark.memory_intense +class TestLayerSelection: + @pytest.mark.parametrize(['model_ctr', 'layers', 'expected_layer', 'region'], + [(pytorch_custom, ['linear', 'relu2'], 'relu2', 'IT')]) + def test_commit_record(self, model_ctr, layers, expected_layer, region): + activations_model = model_ctr() + brain_model = ModelCommitment(identifier=activations_model.identifier, activations_model=activations_model, + layers=layers) + + brain_model.start_recording(region, [(70, 170)]) + predictions = brain_model.look_at([Path(__file__).parent / 'rgb1.jpg']) + assert set(predictions['region'].values) == {region} + assert set(predictions['layer'].values) == {expected_layer} + + +class TestLayerMappedModel: + @pytest.mark.parametrize(['model_ctr', 'layers', 'region'], [ + (pytorch_custom, 'relu2', 'IT'), + (pytorch_custom, ['linear', 'relu2'], 'IT'), + ]) + def test_commit(self, model_ctr, layers, region): + activations_model = model_ctr() + layer_model = LayerMappedModel(identifier=activations_model.identifier, activations_model=activations_model, + region_layer_map={region: layers}) + + layer_model.start_recording(region) + stimulus_set = StimulusSet([{'stimulus_id': 'test'}]) + stimulus_set.stimulus_paths = {'test': os.path.join(os.path.dirname(__file__), 'rgb1.jpg')} + stimulus_set.identifier = self.__class__.__name__ + predictions = layer_model.look_at(stimulus_set) + assert set(predictions['region'].values) == {region} + assert set(predictions['layer'].values) == {layers} if isinstance(layers, str) else set(layers) diff --git a/tests/test_model_helpers/brain_transformation/test_temporal.py b/tests/test_model_helpers/brain_transformation/test_temporal.py new file mode 100644 index 000000000..c4ab48e3c --- /dev/null +++ b/tests/test_model_helpers/brain_transformation/test_temporal.py @@ -0,0 +1,50 @@ +import numpy as np + +from brainio.assemblies import NeuroidAssembly +from brainscore_vision.model_helpers.brain_transformation import TemporalIgnore +from brainscore_vision.model_interface import BrainModel + + +class LayerMappedModelMock: + def __init__(self): + # attributes that TemporalIgnore copies for outside use + self.region_layer_map = None + self.activations_model = None + self.start_task = None + + def start_recording(self, *args, **kwargs): + pass + + def look_at(self, *args, **kwargs): + return NeuroidAssembly([[1, 2, 3], [1, 2, 3]], coords={'stimulus_id': ('presentation', ['image1', 'image2']), + 'object_name': ( + 'presentation', ['number', 'number']), + 'neuroid_id': ('neuroid', [1, 2, 3]), + 'region': ('neuroid', ['IT'] * 3), }, + dims=['presentation', 'neuroid']) + + +class TestTemporalIgnore: + def test_single_timebin(self): + model = TemporalIgnore(layer_model=LayerMappedModelMock()) + model.start_recording(recording_target=BrainModel.RecordingTarget.IT, time_bins=[(70, 170)]) + recordings = model.look_at('dummy') + assert set(recordings.dims) == {'presentation', 'neuroid'} # squeezed time-bin + + def test_two_timebins(self): + layer_model = LayerMappedModelMock() + model = TemporalIgnore(layer_model=layer_model) + model.start_recording(recording_target=BrainModel.RecordingTarget.IT, time_bins=[(70, 170), (170, 270)]) + recordings = model.look_at('dummy') + assert set(recordings.dims) == {'presentation', 'neuroid', 'time_bin'} + np.testing.assert_array_equal(recordings['time_bin_start'].values, [70, 170]) + np.testing.assert_array_equal(recordings['time_bin_end'].values, [170, 270]) + + def test_18_timebins(self): + model = TemporalIgnore(layer_model=LayerMappedModelMock()) + time_bins = [(70 + i * 10, 80 + i * 10) for i in range(18)] + model.start_recording(recording_target=BrainModel.RecordingTarget.IT, time_bins=time_bins) + recordings = model.look_at('dummy') + assert set(recordings.dims) == {'presentation', 'neuroid', 'time_bin'} + np.testing.assert_array_equal(recordings['time_bin_start'].values, [start for start, end in time_bins]) + np.testing.assert_array_equal(recordings['time_bin_end'].values, [end for start, end in time_bins]) diff --git a/tests/test_model_helpers/test_imports.py b/tests/test_model_helpers/test_imports.py new file mode 100644 index 000000000..148849407 --- /dev/null +++ b/tests/test_model_helpers/test_imports.py @@ -0,0 +1,10 @@ +# noinspection PyUnresolvedReferences +class TestImport: + def test_model_helpers(self): + import brainscore_vision.model_helpers + + def test_activations_extractor(self): + from brainscore_vision.model_helpers.activations.core import ActivationsExtractorHelper + + def test_model_commitment(self): + from brainscore_vision.model_helpers.brain_transformation import ModelCommitment diff --git a/tests/test_stimuli.py b/tests/test_stimuli.py deleted file mode 100644 index e362a91d0..000000000 --- a/tests/test_stimuli.py +++ /dev/null @@ -1,251 +0,0 @@ -import os - -import brainscore -import pytest -import numpy as np - -import brainio - - -@pytest.mark.parametrize('stimulus_set', ( - 'dicarlo.hvm', - 'dicarlo.hvm-public', - 'dicarlo.hvm-private', - 'gallant.David2004', - 'tolias.Cadena2017', - 'movshon.FreemanZiemba2013', - 'movshon.FreemanZiemba2013-public', - 'movshon.FreemanZiemba2013-private', - 'dicarlo.objectome.public', - 'dicarlo.objectome.private', - 'dicarlo.Kar2018cocogray', - 'klab.Zhang2018.search_obj_array', - 'dicarlo.Rajalingham2020', - 'dicarlo.Rust2012', - 'dicarlo.BOLD5000', - 'dicarlo.THINGS1', - 'dicarlo.THINGS2', - 'aru.Kuzovkin2018', - 'dietterich.Hendrycks2019.noise', - 'dietterich.Hendrycks2019.blur', - 'dietterich.Hendrycks2019.weather', - 'dietterich.Hendrycks2019.digital', - 'katz.BarbuMayo2019', - 'fei-fei.Deng2009', - 'aru.Cichy2019', - 'dicarlo.BashivanKar2019.naturalistic', - 'dicarlo.BashivanKar2019.synthetic', - 'dicarlo.Marques2020_blank', - 'dicarlo.Marques2020_receptive_field', - 'dicarlo.Marques2020_orientation', - 'dicarlo.Marques2020_spatial_frequency', - 'dicarlo.Marques2020_size', - 'movshon.FreemanZiemba2013_properties', - 'brendel.Geirhos2021_colour', - 'brendel.Geirhos2021_contrast', - 'brendel.Geirhos2021_cue-conflict', - 'brendel.Geirhos2021_edge', - 'brendel.Geirhos2021_eidolonI', - 'brendel.Geirhos2021_eidolonII', - 'brendel.Geirhos2021_eidolonIII', - 'brendel.Geirhos2021_false-colour', - 'brendel.Geirhos2021_high-pass', - 'brendel.Geirhos2021_low-pass', - 'brendel.Geirhos2021_phase-scrambling', - 'brendel.Geirhos2021_power-equalisation', - 'brendel.Geirhos2021_rotation', - 'brendel.Geirhos2021_silhouette', - 'brendel.Geirhos2021_stylized', - 'brendel.Geirhos2021_sketch', - 'brendel.Geirhos2021_uniform-noise', -)) -def test_list_stimulus_set(stimulus_set): - l = brainio.list_stimulus_sets() - assert stimulus_set in l - - -@pytest.mark.private_access -def test_klab_Zhang2018search(): - stimulus_set = brainio.get_stimulus_set('klab.Zhang2018.search_obj_array') - # There are 300 presentation images in the assembly but 606 in the StimulusSet (explanation from @shashikg follows). - # For each of the visual search task out of total 300, you need two images (one - the target image, - # second - the search space image) plus there are 6 different mask images to mask objects - # present at 6 different locations in a specified search image. - # Therefore, a total of 300 * 2 + 6 images are there in the stimulus set. - assert len(stimulus_set) == 606 - assert len(set(stimulus_set['stimulus_id'])) == 606 - - -@pytest.mark.private_access -@pytest.mark.slow -class TestDietterichHendrycks2019: - def test_noise(self): - stimulus_set = brainio.get_stimulus_set('dietterich.Hendrycks2019.noise') - assert len(stimulus_set) == 3 * 5 * 50000 - assert len(set(stimulus_set['synset'])) == 1000 - - def test_blur(self): - stimulus_set = brainio.get_stimulus_set('dietterich.Hendrycks2019.blur') - assert len(stimulus_set) == 4 * 5 * 50000 - assert len(set(stimulus_set['synset'])) == 1000 - - def test_weather(self): - stimulus_set = brainio.get_stimulus_set('dietterich.Hendrycks2019.weather') - assert len(stimulus_set) == 4 * 5 * 50000 - assert len(set(stimulus_set['synset'])) == 1000 - - def test_digital(self): - stimulus_set = brainio.get_stimulus_set('dietterich.Hendrycks2019.digital') - assert len(stimulus_set) == 4 * 5 * 50000 - assert len(set(stimulus_set['synset'])) == 1000 - - -@pytest.mark.private_access -def test_Katz_BarbuMayo2019(): - stimulus_set = brainio.get_stimulus_set('katz.BarbuMayo2019') - assert len(stimulus_set) == 17261 - assert len(set(stimulus_set['synset'])) == 104 - -@pytest.mark.private_access -def test_feifei_Deng2009(): - stimulus_set = brainio.get_stimulus_set('fei-fei.Deng2009') - assert len(stimulus_set) == 50_000 - assert len(set(stimulus_set['label'])) == 1_000 - - -@pytest.mark.private_access -class TestMarques2020V1Properties: - @pytest.mark.parametrize('identifier,num_stimuli', [ - ('dicarlo.Marques2020_blank', 1), - ('dicarlo.Marques2020_receptive_field', 3528), - ('dicarlo.Marques2020_orientation', 1152), - ('dicarlo.Marques2020_spatial_frequency', 2112), - ('dicarlo.Marques2020_size', 2304), - ('movshon.FreemanZiemba2013_properties', 450), - ]) - def test_num_stimuli(self, identifier, num_stimuli): - stimulus_set = brainio.get_stimulus_set(identifier) - assert len(stimulus_set) == num_stimuli - - -@pytest.mark.slow -class TestGeirhos2021: - # test stimulus_set data: - @pytest.mark.parametrize('identifier', [ - 'colour', - 'contrast', - 'cue-conflict', - 'edge', - 'eidolonI', - 'eidolonII', - 'eidolonIII', - 'false-colour', - 'high-pass', - 'low-pass', - 'phase-scrambling', - 'power-equalisation', - 'rotation', - 'silhouette', - 'stylized', - 'sketch', - 'uniform-noise', - ]) - def test_stimulus_set_exist(self, identifier): - full_name = f"brendel.Geirhos2021_{identifier}" - stimulus_set = brainio.get_stimulus_set(full_name) - assert stimulus_set is not None - assert stimulus_set.identifier == full_name - - # test the number of images - @pytest.mark.parametrize('identifier, num_images', [ - ('colour', 1280), - ('contrast', 1280), - ('cue-conflict', 1280), - ('edge', 160), - ('eidolonI', 1280), - ('eidolonII', 1280), - ('eidolonIII', 1280), - ('false-colour', 1120), - ('high-pass', 1280), - ('low-pass', 1280), - ('phase-scrambling', 1120), - ('power-equalisation', 1120), - ('rotation', 1280), - ('silhouette', 160), - ('stylized', 800), - ('sketch', 800), - ('uniform-noise', 1280), - ]) - def test_num_images(self, identifier, num_images): - stimulus_set = brainscore.get_stimulus_set(f"brendel.Geirhos2021_{identifier}") - assert len(np.unique(stimulus_set['image_id'].values)) == num_images - - # tests stimulus_set coords for the 14 "normal" sets: - @pytest.mark.parametrize('identifier', [ - 'colour', - 'contrast', - 'eidolonI', - 'eidolonII', - 'eidolonIII', - 'false-colour', - 'high-pass', - 'low-pass', - 'phase-scrambling', - 'power-equalisation', - 'rotation', - 'stylized', - 'sketch', - 'uniform-noise', - ]) - @pytest.mark.parametrize('field', [ - 'image_id', - 'image_id_long', - 'image_number', - 'experiment_code', - 'condition', - 'truth', - 'category_ground_truth', - 'random_number', - ]) - def test_fields_present(self, identifier, field): - stimulus_set = brainscore.get_stimulus_set(f"brendel.Geirhos2021_{identifier}") - assert hasattr(stimulus_set, field) - - # tests assembly coords for the 2 "abnormal" sets: - @pytest.mark.parametrize('identifier', [ - 'edge', - 'silhouette', - ]) - @pytest.mark.parametrize('field', [ - 'image_id', - 'image_category', - 'truth', - 'image_variation', - 'condition', - ]) - def test_fields_present2(self, identifier, field): - stimulus_set = brainscore.get_assembly(f"brendel.Geirhos2021_{identifier}") - assert hasattr(stimulus_set, field) - - # test assembly fields for cue-conflict's odd stimulus_set: - @pytest.mark.parametrize('identifier', [ - 'cue-conflict', - ]) - @pytest.mark.parametrize('field', [ - 'image_id', - 'original_image', - 'truth', - 'category', - 'conflict_image', - 'original_image_category', - 'original_image_variation', - 'conflict_image_category', - 'conflict_image_variation', - 'condition', - ]) - def test_fields_present3(self, identifier, field): - stimulus_set = brainscore.get_assembly(f"brendel.Geirhos2021_{identifier}") - assert hasattr(stimulus_set, field) - - - diff --git a/tests/test_utils.py b/tests/test_utils.py index ab5944ee5..9c5569e37 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,4 +1,4 @@ -from brainscore.utils import recursive_dict_merge +from brainscore_vision.utils import recursive_dict_merge class TestRecursiveDictMerge: diff --git a/tests/test_examples.py b/todotests/test_examples.py similarity index 100% rename from tests/test_examples.py rename to todotests/test_examples.py diff --git a/tests/test_submission/base_model.py b/todotests/test_submission/base_model.py similarity index 100% rename from tests/test_submission/base_model.py rename to todotests/test_submission/base_model.py diff --git a/tests/test_submission/configs/submission_33.json b/todotests/test_submission/configs/submission_33.json similarity index 100% rename from tests/test_submission/configs/submission_33.json rename to todotests/test_submission/configs/submission_33.json diff --git a/tests/test_submission/configs/submission_33.zip b/todotests/test_submission/configs/submission_33.zip similarity index 100% rename from tests/test_submission/configs/submission_33.zip rename to todotests/test_submission/configs/submission_33.zip diff --git a/tests/test_submission/configs/submission_35.json b/todotests/test_submission/configs/submission_35.json similarity index 100% rename from tests/test_submission/configs/submission_35.json rename to todotests/test_submission/configs/submission_35.json diff --git a/tests/test_submission/configs/submission_35.zip b/todotests/test_submission/configs/submission_35.zip similarity index 100% rename from tests/test_submission/configs/submission_35.zip rename to todotests/test_submission/configs/submission_35.zip diff --git a/tests/test_submission/configs/submission_36.json b/todotests/test_submission/configs/submission_36.json similarity index 100% rename from tests/test_submission/configs/submission_36.json rename to todotests/test_submission/configs/submission_36.json diff --git a/tests/test_submission/configs/submission_36.zip b/todotests/test_submission/configs/submission_36.zip similarity index 100% rename from tests/test_submission/configs/submission_36.zip rename to todotests/test_submission/configs/submission_36.zip diff --git a/tests/test_submission/configs/submission_40.json b/todotests/test_submission/configs/submission_40.json similarity index 100% rename from tests/test_submission/configs/submission_40.json rename to todotests/test_submission/configs/submission_40.json diff --git a/tests/test_submission/configs/submission_40.zip b/todotests/test_submission/configs/submission_40.zip similarity index 100% rename from tests/test_submission/configs/submission_40.zip rename to todotests/test_submission/configs/submission_40.zip diff --git a/tests/test_submission/test_db.py b/todotests/test_submission/test_db.py similarity index 88% rename from tests/test_submission/test_db.py rename to todotests/test_submission/test_db.py index e883a6a22..634fa9d11 100644 --- a/tests/test_submission/test_db.py +++ b/todotests/test_submission/test_db.py @@ -3,9 +3,9 @@ import pytest -from brainscore.submission.database import connect_db -from brainscore.submission.evaluation import run_evaluation -from brainscore.submission.models import User, Score, Model, BenchmarkInstance, BenchmarkType, Submission, Reference +from brainscore_vision.submission.database import connect_db +from brainscore_vision.submission.evaluation import run_evaluation +from brainscore_vision.submission.models import User, Score, Model, BenchmarkInstance, BenchmarkType, Submission, Reference def init_user(): diff --git a/tests/test_submission/test_integration.py b/todotests/test_submission/test_integration.py similarity index 92% rename from tests/test_submission/test_integration.py rename to todotests/test_submission/test_integration.py index 04c1da43f..ce03e8df3 100644 --- a/tests/test_submission/test_integration.py +++ b/todotests/test_submission/test_integration.py @@ -6,10 +6,10 @@ import pytest from pytest import approx -from brainscore.submission.database import connect_db -from brainscore.submission.evaluation import run_evaluation -from brainscore.submission.models import Score, Model, Submission -from tests.test_submission.test_db import clear_schema, init_user, init_benchmark_parents +from brainscore_vision.submission.database import connect_db +from brainscore_vision.submission.evaluation import run_evaluation +from brainscore_vision.submission.models import Score, Model, Submission +from todotests.test_submission.test_db import clear_schema, init_user, init_benchmark_parents logger = logging.getLogger(__name__) @@ -53,7 +53,7 @@ def test_competition_field(self, tmpdir): def test_competition_field_none(self, tmpdir): working_dir = str(tmpdir.mkdir('sub')) config_dir = str(os.path.join(os.path.dirname(__file__), 'configs/')) - submission = Submission.create(id=33, submitter=1, timestamp=datetime.now(), + submission = Submission.create(id=33, jenkins_id=33, submitter=1, timestamp=datetime.now(), model_type='BaseModel', status='running') model = Model.create(name='alexnet', owner=submission.submitter, public=False, submission=submission) @@ -87,7 +87,7 @@ def test_evaluation(self, tmpdir): def test_rerun_evaluation(self, tmpdir): working_dir = str(tmpdir.mkdir('sub')) config_dir = str(os.path.join(os.path.dirname(__file__), 'configs/')) - submission = Submission.create(id=33, submitter=1, timestamp=datetime.now(), + submission = Submission.create(id=33, jenkins_id=33, submitter=1, timestamp=datetime.now(), model_type='BaseModel', status='running') model = Model.create(name='alexnet', owner=submission.submitter, public=False, submission=submission) diff --git a/tests/test_submission/test_submission.py b/todotests/test_submission/test_submission.py similarity index 88% rename from tests/test_submission/test_submission.py rename to todotests/test_submission/test_submission.py index 41234f91e..ad4422989 100644 --- a/tests/test_submission/test_submission.py +++ b/todotests/test_submission/test_submission.py @@ -6,15 +6,15 @@ import pytest from _pytest import tmpdir -from brainscore.submission.configuration import object_decoder, BaseConfig -from brainscore.submission.database import connect_db -from brainscore.submission.evaluation import get_reference, get_benchmark_instance, get_ml_pool, \ +from brainscore_vision.submission.configuration import object_decoder, BaseConfig +from brainscore_vision.submission.database import connect_db +from brainscore_vision.submission.evaluation import get_reference, get_benchmark_instance, get_ml_pool, \ run_submission -from brainscore.submission.models import Reference, BenchmarkType, Submission, Model, BenchmarkInstance, Score -from brainscore.submission.repository import prepare_module, extract_zip_file, find_submission_directory +from brainscore_vision.submission.models import Reference, BenchmarkType, Submission, Model, BenchmarkInstance, Score +from brainscore_vision.submission.repository import prepare_module, extract_zip_file, find_submission_directory from model_tools.brain_transformation import ModelCommitment -from tests.test_submission import base_model -from tests.test_submission.test_db import clear_schema, init_user +from todotests.test_submission import base_model +from todotests.test_submission.test_db import clear_schema, init_user logger = logging.getLogger(__name__) database = 'brainscore-ohio-test' # test database @@ -78,7 +78,7 @@ def test_get_benchmark_instance(self): assert instance2.benchmark.parent.identifier == 'IT' def get_test_models(self): - submission = Submission.create(id=33, submitter=1, timestamp=datetime.now(), + submission = Submission.create(id=33, jenkins_id=33, submitter=1, timestamp=datetime.now(), model_type='BaseModel', status='running') model_instances = [] model_instances.append( @@ -129,6 +129,8 @@ def test_base_config(self): assert not submission_config.public def test_resubmit_config(self): + Submission.create(id=33, jenkins_id=33, submitter=1, timestamp=datetime.now(), + model_type='BaseModel', status='running') model = Model.create(id=19, name='alexnet', public=True, submission=33, owner=1) config = { "model_ids": [model.id], @@ -166,7 +168,7 @@ def tear_down_method(self): def test_prepare_module(self): config = BaseConfig(TestRepository.working_dir, 33, '', TestRepository.config_dir) - submission = Submission.create(id=33, submitter=1, timestamp=datetime.now(), + submission = Submission.create(id=33, jenkins_id=33, submitter=1, timestamp=datetime.now(), model_type='BaseModel', status='running') module, repo = prepare_module(submission, config) assert hasattr(module, 'get_model_list')