diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..04115a2 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,56 @@ +name: Documentation + +on: + push: + branches: + - main + pull_request: + +jobs: + build: + runs-on: ubuntu-latest + steps: + - name: Checkout source + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4 + with: + fetch-depth: 0 + + - name: Cache tox + uses: actions/cache@5a3ec84eff668545956fd18022155c47e93e2684 # v4 + with: + path: .tox + key: tox-${{ hashFiles('pyproject.toml') }} + + - name: Set up Python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 + with: + python-version: "3.x" + cache: "pip" + cache-dependency-path: "pyproject.toml" + + - name: Install tox + run: python -m pip install tox + + - name: Build HTML documentation with tox + run: tox -e docs + + - name: Upload built docs as artifact + id: deployment + uses: actions/upload-pages-artifact@56afc609e74202658d3ffba0e8f6dda462b719fa # v3 + with: + path: site + + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + permissions: + pages: write + id-token: write + runs-on: ubuntu-latest + needs: build + if: github.event_name != 'pull_request' + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@d6db90164ac5ed86f2b6aed7e0febac5b3c0c03e # v4 diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml new file mode 100644 index 0000000..bf41326 --- /dev/null +++ b/.github/workflows/linting.yml @@ -0,0 +1,38 @@ +name: Linting + +on: + push: + branches: + - main + pull_request: + +jobs: + linting: + runs-on: ubuntu-latest + steps: + - name: Checkout source + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4 + + - name: Cache pre-commit + uses: actions/cache@5a3ec84eff668545956fd18022155c47e93e2684 # v4 + with: + path: ~/.cache/pre-commit + key: pre-commit-${{ hashFiles('.pre-commit-config.yaml') }} + + - name: Set up python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 + with: + python-version-file: ".python-version" + + - name: Install uv + uses: astral-sh/setup-uv@v6 + with: + # Install a specific version of uv + version: "0.9.0" + enable-cache: true + + - name: Install the project + run: uv sync --locked --dev + + - name: Run prek + run: uv run prek run --all-files --color always --verbose diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index e71a213..86a90e3 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -5,13 +5,12 @@ name: Python package on: push: - branches: [ "main" ] + branches: ["main"] pull_request: - branches: [ "main" ] + branches: ["main"] jobs: build: - runs-on: ubuntu-latest strategy: fail-fast: false @@ -19,24 +18,24 @@ jobs: python-version: ["3.11"] steps: - - uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v3 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - python -m pip install flake8 pytest - python -m pip install . - python -m pip install .[test] - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Test with pytest - run: | - pytest + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v3 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install flake8 pytest + python -m pip install . + python -m pip install .[test] + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + pytest diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 0000000..6f308bf --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,42 @@ +name: Tests + +on: + push: + branches: + - main + pull_request: + paths-ignore: + - "**.md" + +jobs: + tests: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: + - "3.11" + - "3.12" + - "3.13" + + steps: + - name: Checkout source + uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4 + + - name: Cache tox + uses: actions/cache@5a3ec84eff668545956fd18022155c47e93e2684 # v4 + with: + path: .tox + key: tox-${{hashFiles('pyproject.toml') }} + + - name: Set up python + uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5 + with: + python-version: ${{ matrix.python-version }} + cache: pip + cache-dependency-path: pyproject.toml + + - name: Install dependencies + run: python -m pip install tox tox-gh + + - name: Test with tox + run: tox run diff --git a/.gitignore b/.gitignore index 0a69720..cd13e67 100644 --- a/.gitignore +++ b/.gitignore @@ -101,7 +101,15 @@ ipython_config.py # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control #poetry.lock -# PEP 582; used by e.g. github.com/David-OConnor/pyflow +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff @@ -145,9 +153,14 @@ dmypy.json cython_debug/ # PyCharm -# JetBrains specific template is maintainted in a separate JetBrains.gitignore that can +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. -.idea/ -/.pdm-build/ +#.idea/ + +# package version +_version.py + +R-circumplex/ +.claude/ diff --git a/.markdownlint.yaml b/.markdownlint.yaml new file mode 100644 index 0000000..02907b2 --- /dev/null +++ b/.markdownlint.yaml @@ -0,0 +1,2 @@ +--- +MD013: false diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..bad482e --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,59 @@ +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.12.1 + hooks: + - id: ruff + - id: ruff-format + - repo: https://github.com/igorshubovych/markdownlint-cli + rev: v0.45.0 + hooks: + - id: markdownlint-fix + args: + - --dot + - repo: https://github.com/Lucas-C/pre-commit-hooks + rev: v1.5.5 + hooks: + - id: forbid-tabs + - repo: https://github.com/pappasam/toml-sort + rev: v0.24.2 + hooks: + - id: toml-sort-fix + # - repo: https://github.com/pre-commit/mirrors-mypy + # rev: v1.16.1 + # hooks: + # - id: mypy + - repo: local + hooks: + - id: ty + name: ty check + entry: ty check . --ignore unresolved-import + language: python + pass_filenames: false + args: [--python=.venv/] + additional_dependencies: [ty] + - repo: https://github.com/rbubley/mirrors-prettier + rev: v3.6.2 + hooks: + - id: prettier + args: + - --quote-props=as-needed + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: check-case-conflict + - id: check-merge-conflict + - id: check-toml + - id: end-of-file-fixer + - id: mixed-line-ending + args: + - --fix=lf + - id: trailing-whitespace + - repo: https://github.com/python-jsonschema/check-jsonschema + rev: 0.33.1 + hooks: + # Schemas taken from https://www.schemastore.org/json/ + - id: check-jsonschema + name: "Validate GitHub issue templates" + files: ^\.github/ISSUE_TEMPLATE/.*\.yml$ + exclude: ^\.github/ISSUE_TEMPLATE/config\.yml$ + args: ["--verbose", "--schemafile", "schemas/github-issue-forms.json"] diff --git a/.prettierignore b/.prettierignore new file mode 100644 index 0000000..dd44972 --- /dev/null +++ b/.prettierignore @@ -0,0 +1 @@ +*.md diff --git a/.python-version b/.python-version index 2419ad5..e4fba21 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.11.9 +3.12 diff --git a/.readthedocs.yaml b/.readthedocs.yaml index e13438d..e8ad8ac 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -16,12 +16,12 @@ build: # golang: "1.19" jobs: post_install: - - pip install mkdocs-material "mkdocstrings[python]" mkdocs-jupyter markdown pymdown-extensions + - pip install mike mkdocs-bibtex mkdocs-git-authors-plugin mkdocs-git-revision-date-localized-plugin mkdocs-include-markdown-plugin mkdocs-jupyter mkdocs-material mkdocs "mkdocstrings[python]" pymdown-extensions rich shtab - pip install . # Build documentation in the "docs/" directory with Sphinx mkdocs: - configuration: mkdocs.yml + configuration: mkdocs.yml # Optionally build your docs in additional formats such as PDF and ePub formats: @@ -33,4 +33,4 @@ formats: # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html # python: # install: -# - requirements: docs/requirements.txt \ No newline at end of file +# - requirements: docs/requirements.txt diff --git a/CITATION.cff b/CITATION.cff index d88cabe..383d813 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -12,14 +12,14 @@ authors: family-names: Mitchell email: andrew.mitchell.18@ucl.ac.uk affiliation: University College London - orcid: 'https://orcid.org/0000-0003-0978-5046' + orcid: "https://orcid.org/0000-0003-0978-5046" identifiers: - type: doi value: 10.17605/OSF.IO/FA5UR description: OSF Repo of the Package -repository-code: 'https://github.com/MitchellAcoustics/circumplex' -url: 'https://circumplex.readthedocs.io/en/latest/' -repository: 'https://osf.io/fa5ur/' +repository-code: "https://github.com/MitchellAcoustics/circumplex" +url: "https://circumplex.readthedocs.io/en/latest/" +repository: "https://osf.io/fa5ur/" license: GPL-3.0 -version: 0.1.4 -date-released: '2023-11-25' +version: 0.3.0 +date-released: "2023-11-25" diff --git a/LICENSE b/LICENSE index 089e1dc..64441c7 100644 --- a/LICENSE +++ b/LICENSE @@ -1,29 +1,595 @@ -BSD 3-Clause License - -Copyright (c) 2024, Andrew Mitchell -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -3. Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. 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Interpretation of Sections 15 and 16 + +If the disclaimer of warranty and limitation of liability provided above cannot be +given local legal effect according to their terms, reviewing courts shall apply local +law that most closely approximates an absolute waiver of all civil liability in +connection with the Program, unless a warranty or assumption of liability accompanies +a copy of the Program in return for a fee. + +_END OF TERMS AND CONDITIONS_ + +## How to Apply These Terms to Your New Programs + +If you develop a new program, and you want it to be of the greatest possible use to +the public, the best way to achieve this is to make it free software which everyone +can redistribute and change under these terms. + +To do so, attach the following notices to the program. It is safest to attach them +to the start of each source file to most effectively state the exclusion of warranty; +and each file should have at least the “copyright” line and a pointer to +where the full notice is found. + + + Copyright (C) 2018 Jeffrey Girard + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + +If the program does terminal interaction, make it output a short notice like this +when it starts in an interactive mode: + + ssm Copyright (C) 2018 Jeffrey Girard + This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type 'show c' for details. + +The hypothetical commands `show w` and `show c` should show the appropriate parts of +the General Public License. Of course, your program's commands might be different; +for a GUI interface, you would use an “about box”. + +You should also get your employer (if you work as a programmer) or school, if any, to +sign a “copyright disclaimer” for the program, if necessary. For more +information on this, and how to apply and follow the GNU GPL, see +<>. + +The GNU General Public License does not permit incorporating your program into +proprietary programs. If your program is a subroutine library, you may consider it +more useful to permit linking proprietary applications with the library. If this is +what you want to do, use the GNU Lesser General Public License instead of this +License. But first, please read +<>. diff --git a/README.md b/README.md index e069e0a..95a22ce 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,30 @@ -# circumplex +# circumplex + + + [![PyPI version](https://badge.fury.io/py/circumplex.svg)](https://badge.fury.io/py/circumplex) [![Documentation Status](https://readthedocs.org/projects/circumplex/badge/?version=latest)](https://circumplex.readthedocs.io/en/latest/?badge=latest) [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) -_circumplex_ is a Python package for analyzing and visualizing circumplex data. It provides a set of tools for analyzing and visualizing circumplex data, following the Structural Summary Method. This project is a Python implementation based on the R [circumplex](https://github.com/jmgirard/circumplex) package. Our goal is to provide a similar functionality and experience for Python users. +[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit) +[![Tests status][tests-badge]][tests-link] +[![Linting status][linting-badge]][linting-link] +[![Documentation status][documentation-badge]][documentation-link] +[![License][license-badge]](./LICENSE) + + +[tests-badge]: https://github.com/MitchellAcoustics/circumplex/actions/workflows/tests.yml/badge.svg +[tests-link]: https://github.com/MitchellAcoustics/circumplex/actions/workflows/tests.yml +[linting-badge]: https://github.com/MitchellAcoustics/circumplex/actions/workflows/linting.yml/badge.svg +[linting-link]: https://github.com/MitchellAcoustics/circumplex/actions/workflows/linting.yml +[documentation-badge]: https://github.com/MitchellAcoustics/circumplex/actions/workflows/docs.yml/badge.svg +[documentation-link]: https://github.com/MitchellAcoustics/circumplex/actions/workflows/docs.yml +[license-badge]: https://img.shields.io/badge/License-GPLv3-blue.svg + + + +_circumplex_ is a Python package for analyzing and visualizing circumplex data. It provides a set of tools for analyzing and visualizing circumplex data, following the Structural Summary Method. This project is a Python implementation based on the R [circumplex](https://github.com/jmgirard/circumplex) package. Our goal is to provide a similar functionality and experience for Python users. > [!WARNING] > This project is still under development. We're working hard to make it as good as possible, but there may be bugs or missing features. If you find any issues, please let us know by submitting an issue on Github. @@ -31,17 +51,19 @@ We welcome contributions from the community. If you're interested in contributin ## License -This project is licensed under the GNU GPLv3 License. For more information, please see the `license.md` file. +This project is licensed under the GNU GPLv3 License. For more information, please see the `LICENSE` file. ## Project layout +```text mkdocs.yml # The configuration file. docs/ index.md # The documentation homepage. about.md # The about page. - license.md # The license page. + LICENSE # The license page. tutorials/ # Tutorial pages. - Introduction to SSM Analysis.ipynb + Introduction to SSM Analysis.ipynb ... # Other markdown pages, images and other files. src/circumplex/ - circumplex.py + circumplex.py +``` diff --git a/docs/about.md b/docs/about.md deleted file mode 100644 index 95234db..0000000 --- a/docs/about.md +++ /dev/null @@ -1,39 +0,0 @@ -# Recepto nimios non cupidine secus oscula timuere - -## Ora boumque armorum vicit quae Athamas est - -Lorem markdownum silva at habendum en **tristis nasci ereptaque** Romane te -fuerit raucos **gelidus**. Levi caput superant ab facto; ac protinus avium -latebris Thybrin, Solis et. - -1. Hostilia illic thalamique avido sepulcro socerum retia -2. Quaerunt medias Aesone quas -3. Iamque fuit et quoque protegat -4. Est vocibus - -Villae intervenit recipit cruentatus vagas secretasque Cyllenius nec sinuantur -Glauci, Thaumantidos? Curru cadunt tenebat obsessum, iuga **fortuna exspes**! - -## Nisi duo et titubat mihi tantum non - -Saepe abstrahor Periphanta quid negabat tulit naturaeque quae, [hic -sine](http://iam.org/)! Iove mira fluxit credensque virtus, posuitque; adfore -merces est praesens ergo, Arcas, sucus. Ad [silvae tandem -exorata](http://agro-hoc.org/); silva hirsutus fuit Vulcanum de possit Titan -ulciscere verus quamquam navigat quibus; pes genitor! - -1. Nato tamen et humana et admoneo videtur -2. Tenebant insignia -3. Lucifer penetralia ut illis bimembres saturata membra - -Victus illis! Trepidoque quid habitata, suco esse **Sibyllae** loco superata -comitantibus illa vectus regionibus idonea arbore dextra **non** ferrum. - - var newbie_pup_jumper = metafile_bank(addGateway, mipsScrollWhite.affiliate( - fifoBatchClean)); - net(system, 5 - 1, cluster_icf_heap); - scrapingActiveBit.copyright = bookmark; - var drive_tutorial = frameworkSql; - -Et Themis uterque temptat et crimen adhibere vestem? Deus mens est pulverulenta -*fretum* nimios pharetras suspiria alti Phrygiae potest? diff --git a/docs/api/analysis/bootstrap.md b/docs/api/analysis/bootstrap.md new file mode 100644 index 0000000..cb73a54 --- /dev/null +++ b/docs/api/analysis/bootstrap.md @@ -0,0 +1,6 @@ +# Bootstrap + +::: circumplex.analysis.bootstrap + options: + heading_level: 2 +::: diff --git a/docs/api/analysis/corr_analysis.md b/docs/api/analysis/corr_analysis.md new file mode 100644 index 0000000..8218525 --- /dev/null +++ b/docs/api/analysis/corr_analysis.md @@ -0,0 +1,6 @@ +# Correlation Analysis + +::: circumplex.analysis.corr_analysis + options: + heading_level: 2 +::: diff --git a/docs/api/analysis/mean_analysis.md b/docs/api/analysis/mean_analysis.md new file mode 100644 index 0000000..addff39 --- /dev/null +++ b/docs/api/analysis/mean_analysis.md @@ -0,0 +1,6 @@ +# Mean Analysis + +::: circumplex.analysis.mean_analysis + options: + heading_level: 2 +::: diff --git a/docs/api/analysis/ssm_analyze.md b/docs/api/analysis/ssm_analyze.md new file mode 100644 index 0000000..b8f7705 --- /dev/null +++ b/docs/api/analysis/ssm_analyze.md @@ -0,0 +1,6 @@ +# SSM Analysis + +::: circumplex.analysis.ssm_analyze + options: + heading_level: 2 +::: diff --git a/docs/api/core/parameters.md b/docs/api/core/parameters.md new file mode 100644 index 0000000..6e2232c --- /dev/null +++ b/docs/api/core/parameters.md @@ -0,0 +1,6 @@ +# Parameters + +::: circumplex.core.parameters + options: + heading_level: 2 +::: diff --git a/docs/api/core/scores.md b/docs/api/core/scores.md new file mode 100644 index 0000000..5a43dd2 --- /dev/null +++ b/docs/api/core/scores.md @@ -0,0 +1,6 @@ +# Scores + +::: circumplex.core.scores + options: + heading_level: 2 +::: diff --git a/docs/api/data.md b/docs/api/data.md new file mode 100644 index 0000000..7a1bfcb --- /dev/null +++ b/docs/api/data.md @@ -0,0 +1,6 @@ +# Data + +::: circumplex.data + options: + heading_level: 2 +::: diff --git a/docs/api/index.md b/docs/api/index.md new file mode 100644 index 0000000..8e5f480 --- /dev/null +++ b/docs/api/index.md @@ -0,0 +1,7 @@ +# API Reference + +::: circumplex + options: + heading_level: 2 + members: [] +::: diff --git a/docs/api/models.md b/docs/api/models.md new file mode 100644 index 0000000..0979fe5 --- /dev/null +++ b/docs/api/models.md @@ -0,0 +1,6 @@ +# Instrument Models + +::: circumplex.instruments.models + options: + heading_level: 2 +::: diff --git a/docs/api/plots.md b/docs/api/plots.md new file mode 100644 index 0000000..4640b47 --- /dev/null +++ b/docs/api/plots.md @@ -0,0 +1,6 @@ +# Plots + +::: circumplex.visualization.plots + options: + heading_level: 2 +::: diff --git a/docs/api/ssm.md b/docs/api/ssm.md new file mode 100644 index 0000000..1a36d45 --- /dev/null +++ b/docs/api/ssm.md @@ -0,0 +1,6 @@ +# SSM Results + +::: circumplex.ssm + options: + heading_level: 2 +::: diff --git a/docs/api/utils/angles.md b/docs/api/utils/angles.md new file mode 100644 index 0000000..84d1496 --- /dev/null +++ b/docs/api/utils/angles.md @@ -0,0 +1,6 @@ +# Angles + +::: circumplex.utils.angles + options: + heading_level: 2 +::: diff --git a/docs/api/utils/contrast.md b/docs/api/utils/contrast.md new file mode 100644 index 0000000..5eca707 --- /dev/null +++ b/docs/api/utils/contrast.md @@ -0,0 +1,6 @@ +# Contrast + +::: circumplex.utils.contrast + options: + heading_level: 2 +::: diff --git a/docs/api/utils/tidying_functions.md b/docs/api/utils/tidying_functions.md new file mode 100644 index 0000000..e01156b --- /dev/null +++ b/docs/api/utils/tidying_functions.md @@ -0,0 +1,6 @@ +# Tidying Functions + +::: circumplex.utils.tidying_functions + options: + heading_level: 2 +::: diff --git a/docs/index.md b/docs/index.md index efcc831..d914143 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,50 +1,104 @@ +# Welcome to Circumplex + +[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit) +[![Tests status][tests-badge]][tests-link] +[![Linting status][linting-badge]][linting-link] +[![Documentation status][documentation-badge]][documentation-link] +[![License][license-badge]](./license.md) + + +[tests-badge]: https://github.com/MitchellAcoustics/python-circumplex/actions/workflows/tests.yml/badge.svg +[tests-link]: https://github.com/MitchellAcoustics/python-circumplex/actions/workflows/tests.yml +[linting-badge]: https://github.com/MitchellAcoustics/python-circumplex/actions/workflows/linting.yml/badge.svg +[linting-link]: https://github.com/MitchellAcoustics/python-circumplex/actions/workflows/linting.yml +[documentation-badge]: https://github.com/MitchellAcoustics/python-circumplex/actions/workflows/docs.yml/badge.svg +[documentation-link]: https://github.com/MitchellAcoustics/python-circumplex/actions/workflows/docs.yml +[license-badge]: https://img.shields.io/badge/License-GPL%20v3-blue.svg + + +## Overview + ![Image title](img/logo-light.png#only-light) ![Image title](img/logo-dark.png#only-dark) -# Welcome to Circumplex +The `circumplex` package provides tools for analyzing and visualizing circumplex data, particularly in psychology research. It implements the **Structural Summary Method (SSM)** with bootstrap confidence intervals for statistical analysis of circular data patterns commonly found in: -[![PyPI version](https://badge.fury.io/py/circumplex.svg)](https://badge.fury.io/py/circumplex) -[![Documentation Status](https://readthedocs.org/projects/circumplex/badge/?version=latest)](https://circumplex.readthedocs.io/en/latest/?badge=latest) -[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) +- Interpersonal functioning and behavior +- Mood and affect measurement +- Vocational preferences and interests +- Other psychological constructs with circular structure -_circumplex_ is a Python package for analyzing and visualizing circumplex data. It provides a set of tools for analyzing and visualizing circumplex data, following the Structural Summary Method. This project is a Python implementation based on the R [circumplex](https://circumplex.jmgirard.com/) package. Our goal is to provide a similar functionality and experience for Python users. + This project is a Python implementation based on the R [circumplex](https://circumplex.jmgirard.com/) package. Our goal is to provide a similar functionality and experience for Python users. !!! note This project is still under development. We're working hard to make it as good as possible, but there may be bugs or missing features. If you find any issues, please let us know by submitting an issue on Github. -## Getting Started +## Key Features -To get started with _circumplex_, you'll need to install it first. You can do this by running the following command: +- **SSM Analysis**: Mean-based and correlation-based structural summary methods +- **Bootstrap Confidence Intervals**: Robust statistical inference for SSM parameters +- **Visualization**: Publication-ready circular plots and curve plots +- **Built-in Instruments**: IIP-SC, CSIG, IPIP-IPC with normative data +- **Flexible Instrument System**: Easy registration of custom circumplex measures +- **Data Tidying**: Ipsatization, scoring, and normative standardization + +## Quick Start + +### Installation + +Install the latest development version: ```bash -pip install circumplex +pip install git+https://github.com/MitchellAcoustics/python-circumplex.git +``` + +### Basic Usage + +```python +from circumplex import load_dataset, ssm_analyze, OCTANTS + +# Load sample data +data = load_dataset('jz2017') + +# Define scale columns and their angular positions +scales = ['PA', 'BC', 'DE', 'FG', 'HI', 'JK', 'LM', 'NO'] +angles = OCTANTS # [90, 135, 180, 225, 270, 315, 360, 45] + +# Perform SSM analysis +results = ssm_analyze(data, scales=scales, angles=angles) + +# View results +results.summary() + +# Create visualizations +results.plot_circle() +results.plot_curve() ``` ## Documentation -This documentation is designed to help you understand and use _circumplex_ effectively. It's divided into several sections: +- **[API Reference](api/index.md)** - Complete API documentation +- **[GitHub Repository](https://github.com/MitchellAcoustics/circumplex)** - Source code and issue tracker + +## Requirements -- **Tutorials**: Practical examples showing how to use our project in real-world scenarios. -- **API Reference**: Detailed information about our project's API. -- **Contribute**: Information on how you can contribute to our project. +- Python 3.11, 3.12, or 3.13 +- NumPy, Pandas, SciPy, Matplotlib, Seaborn + +## Project Status + +This project is in active development. Core SSM analysis functionality is implemented with full numerical parity to the R package (validated to 3+ decimal places). Additional features and documentation are being added continuously. ## Contributing -We welcome contributions from the community. If you're interested in contributing, please get in touch or submit an issue on Github. +Contributions are welcome! Please see the [GitHub repository](https://github.com/MitchellAcoustics/python-circumplex) for guidelines. -## License +## Acknowledgments -This project is licensed under the GNU GPLv3 License. For more information, please see the `license.md` file. +This project is developed in collaboration with the [Centre for Advanced Research Computing](https://ucl.ac.uk/arc), University College London. -## Project layout +**Author:** Andrew Mitchell ([andrew.mitchell.research@gmail.com](mailto:andrew.mitchell.research@gmail.com)) - mkdocs.yml # The configuration file. - docs/ - index.md # The documentation homepage. - about.md # The about page. - license.md # The license page. - tutorials/ # Tutorial pages. - Introduction to SSM Analysis.ipynb - ... # Other markdown pages, images and other files. - src/circumplex/ - circumplex.py +**Based on:** The R circumplex package by Jeffrey Girard and colleagues + +## License diff --git a/docs/javascripts/katex.js b/docs/javascripts/katex.js new file mode 100644 index 0000000..0946ce0 --- /dev/null +++ b/docs/javascripts/katex.js @@ -0,0 +1,10 @@ +document$.subscribe(({ body }) => { + renderMathInElement(body, { + delimiters: [ + { left: "$$", right: "$$", display: true }, + { left: "$", right: "$", display: false }, + { left: "\\(", right: "\\)", display: false }, + { left: "\\[", right: "\\]", display: true }, + ], + }); +}); diff --git a/docs/javascripts/mathjax.js b/docs/javascripts/mathjax.js new file mode 100644 index 0000000..5209b3c --- /dev/null +++ b/docs/javascripts/mathjax.js @@ -0,0 +1,19 @@ +window.MathJax = { + tex: { + inlineMath: [["\\(", "\\)"]], + displayMath: [["\\[", "\\]"]], + processEscapes: true, + processEnvironments: true, + }, + options: { + ignoreHtmlClass: ".*|", + processHtmlClass: "arithmatex", + }, +}; + +document$.subscribe(() => { + MathJax.startup.output.clearCache(); + MathJax.typesetClear(); + MathJax.texReset(); + MathJax.typesetPromise(); +}); diff --git a/docs/license.md b/docs/license.md index 089e1dc..64441c7 100644 --- a/docs/license.md +++ b/docs/license.md @@ -1,29 +1,595 @@ -BSD 3-Clause License - -Copyright (c) 2024, Andrew Mitchell -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. 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If the Program specifies that +a certain numbered version of the GNU General Public License “or any later +version” applies to it, you have the option of following the terms and +conditions either of that numbered version or of any later version published by the +Free Software Foundation. If the Program does not specify a version number of the GNU +General Public License, you may choose any version ever published by the Free +Software Foundation. + +If the Program specifies that a proxy can decide which future versions of the GNU +General Public License can be used, that proxy's public statement of acceptance of a +version permanently authorizes you to choose that version for the Program. + +Later license versions may give you additional or different permissions. However, no +additional obligations are imposed on any author or copyright holder as a result of +your choosing to follow a later version. + +### 15. Disclaimer of Warranty + +THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. +EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES +PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER +EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF +MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE +QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE +DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. + +### 16. Limitation of Liability + +IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY +COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS +PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, +INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE +PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE +OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE +WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE +POSSIBILITY OF SUCH DAMAGES. + +### 17. Interpretation of Sections 15 and 16 + +If the disclaimer of warranty and limitation of liability provided above cannot be +given local legal effect according to their terms, reviewing courts shall apply local +law that most closely approximates an absolute waiver of all civil liability in +connection with the Program, unless a warranty or assumption of liability accompanies +a copy of the Program in return for a fee. + +_END OF TERMS AND CONDITIONS_ + +## How to Apply These Terms to Your New Programs + +If you develop a new program, and you want it to be of the greatest possible use to +the public, the best way to achieve this is to make it free software which everyone +can redistribute and change under these terms. + +To do so, attach the following notices to the program. It is safest to attach them +to the start of each source file to most effectively state the exclusion of warranty; +and each file should have at least the “copyright” line and a pointer to +where the full notice is found. + + + Copyright (C) 2018 Jeffrey Girard + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + +If the program does terminal interaction, make it output a short notice like this +when it starts in an interactive mode: + + ssm Copyright (C) 2018 Jeffrey Girard + This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type 'show c' for details. + +The hypothetical commands `show w` and `show c` should show the appropriate parts of +the General Public License. Of course, your program's commands might be different; +for a GUI interface, you would use an “about box”. + +You should also get your employer (if you work as a programmer) or school, if any, to +sign a “copyright disclaimer” for the program, if necessary. For more +information on this, and how to apply and follow the GNU GPL, see +<>. + +The GNU General Public License does not permit incorporating your program into +proprietary programs. If your program is a subroutine library, you may consider it +more useful to permit linking proprietary applications with the library. If this is +what you want to do, use the GNU Lesser General Public License instead of this +License. But first, please read +<>. diff --git a/docs/reference/api.md b/docs/reference/api.md deleted file mode 100644 index bf1412b..0000000 --- a/docs/reference/api.md +++ /dev/null @@ -1,11 +0,0 @@ -# Core functions - -::: circumplex.circumplex - options: - members: - - ssm_analyse - - ssm_parameters - - profile_plot - - - diff --git a/docs/reference/classes.md b/docs/reference/classes.md deleted file mode 100644 index 29c3f1a..0000000 --- a/docs/reference/classes.md +++ /dev/null @@ -1,9 +0,0 @@ -# Dealing with results - -## `SSMParams` - -::: circumplex.circumplex.SSMParams - -## `SSMResults` - -::: circumplex.circumplex.SSMResults \ No newline at end of file diff --git a/docs/reference/instruments.md b/docs/reference/instruments.md deleted file mode 100644 index f1b3424..0000000 --- a/docs/reference/instruments.md +++ /dev/null @@ -1,3 +0,0 @@ -# Survey Instruments - -::: circumplex.instrument diff --git a/docs/reference/utils.md b/docs/reference/utils.md deleted file mode 100644 index a330273..0000000 --- a/docs/reference/utils.md +++ /dev/null @@ -1,8 +0,0 @@ -# Utility functions - -::: circumplex.circumplex - options: - filters: - - ssm_analyse - - ssm_parameters - - profile_plot \ No newline at end of file diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css index 18b5dd8..677cfcc 100644 --- a/docs/stylesheets/extra.css +++ b/docs/stylesheets/extra.css @@ -4,12 +4,12 @@ --md-accent-color: #71e5e9; } - /* This will only apply if the user has chosen a light color scheme */ @media (prefers-color-scheme: light) { .only-dark { display: none; } + .only-light { display: block; margin: auto; @@ -21,8 +21,13 @@ .only-light { display: none; } + .only-dark { display: block; margin: auto; } -} \ No newline at end of file +} + +.md-grid { + max-width: 1440px; +} diff --git a/docs/tutorials/Intro_to_SSM_Analysis.ipynb b/docs/tutorials/Intro_to_SSM_Analysis.ipynb deleted file mode 100644 index ac8b0c0..0000000 --- a/docs/tutorials/Intro_to_SSM_Analysis.ipynb +++ /dev/null @@ -1,1375 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Introduction to SSM Analysis\n", - "\n", - "Reproduced from the introductory vignette for [R Circumplex](https://circumplex.jmgirard.com/articles/introduction-to-ssm-analysis.html), by Girard J, Zimmermann J, Wright A (2023). circumplex: Analysis and Visualization of Circular Data. https://github.com/jmgirard/circumplex, http://circumplex.jmgirard.com/.\n", - "\n", - "If you find this tutorial useful, **please cite Girard, Zimmermann, & Wright (2023)**. I am reproducing it here to demonstrate the equivalence between the R and Python versions of the package. The original R version of this vignette can be found [here](https://circumplex.jmgirard.com/articles/introduction-to-ssm-analysis.html)." - ], - "metadata": { - "collapsed": false - }, - "id": "817354cbea2e711b" - }, - { - "cell_type": "code", - "id": "initial_id", - "metadata": { - "collapsed": true, - "ExecuteTime": { - "end_time": "2024-06-08T12:09:56.081220Z", - "start_time": "2024-06-08T12:09:56.076995Z" - } - }, - "source": [ - "import circumplex\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "degree_sign = u'\\N{DEGREE SIGN}'" - ], - "outputs": [], - "execution_count": 30 - }, - { - "cell_type": "markdown", - "source": [ - "## 1. Background and Motivation\n", - "\n", - "### Circumplex models, scales, and data\n", - "\n", - "Circumplex models are popular within many areas of psychology because they offer a parsimonious account of complex psychological domains, such as emotion and interpersonal functioning. This parsimony is achieved by understanding phenomena in a domain as being a \"blend\" of two primary dimensions. For instance, circumplex models of emotion typically represent affective phenomena as a blend of *valence* (pleasantness versus unpleasantness) and *arousal* (activity versus passivity), whereas circumplex models of interpersonal functioning typically represent interpersonal phenomena as a blend of *communion* (affiliation versus separation) and *agency* (dominance versus submissiveness). These models are often depicted as circles around the intersection of the two dimensions (see figure). Any given phenomenon can be located within this circular space through reference to the two underlying dimensions (e.g. anger is a blend of unpleasantness and activity).\n", - "\n", - "Circumplex scales contain multiple subscales that attempt to measure different blends of the two primary dimensions (i.e., different parts of the circle). Although there have historically been circumplex scales with as many as sixteen subscales, it has become most common to use eight subscales: one for each “pole” of the two primary dimensions and one for each “quadrant” that combines the two dimensions. In order for a set of subscales to be considered circumplex, they must exhibit certain properties. Circumplex fit analyses can be used to quantify these properties.\n", - "\n", - "Circumplex data is composed of scores on a set of circumplex scales for one or more participants (e.g., persons or organizations). Such data is usually collected via self-report, informant-report, or observational ratings in order to locate psychological phenomena within the circular space of the circumplex model. For example, a therapist might want to understand the interpersonal problems encountered by an individual patient, a social psychologist might want to understand the emotional experiences of a group of participants during an experiment, and a personality psychologist might want to understand what kind of interpersonal behaviors are associated with a trait (e.g., extraversion). \n", - "\n" - ], - "metadata": { - "collapsed": false - }, - "id": "1d6f5b15838c0042" - }, - { - "cell_type": "code", - "source": [ - "angles = (90, 135, 180, 225, 270, 315, 360, 45)\n", - "alabel = (\"PA\", \"BC\", \"DE\", \"FG\", \"HI\", \"JK\", \"LM\", \"NO\")\n", - "\n", - "# Create plot ---------------------------------------------------------------\n", - "\n", - "fig, ax = plt.subplots(figsize=(4, 4), subplot_kw=dict(polar=True))\n", - "\n", - "ax.plot()\n", - "ax.set_xticks(np.radians(angles), labels=alabel, fontsize=14)\n", - "ax.set_yticks([])\n", - "ax.grid(True)\n", - "for i, angle in enumerate(angles):\n", - " ax.text(\n", - " np.radians(angle),\n", - " 0.6,\n", - " f\"{angle}{degree_sign}\",\n", - " ha=\"center\",\n", - " va=\"center\",\n", - " fontsize=12,\n", - ")\n", - "plt.show()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-08T12:09:57.004186Z", - "start_time": "2024-06-08T12:09:56.747142Z" - } - }, - "id": "b2b2c99827c47a27", - "outputs": [ - { - "data": { - "text/plain": [ - "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 31 - }, - { - "cell_type": "markdown", - "source": [ - "### The Structural Summary Method\n", - "\n", - "The Structural Summary Method (SSM) is a technique for analyzing circumplex data that offers practical and interpretive benefits over alternative techniques. It consists of fitting a cosine curve to the data, which captures the pattern of correlations among scores associated with a circumplex scale (i.e., mean scores on circumplex scales or correlations between circumplex scales and an external measure). By plotting a set of example scores below, we can gain a visual intuition that a cosine curve makes sense in this case. First, we can examine the scores with a bar chart ignoring the circular relationship among them." - ], - "metadata": { - "collapsed": false - }, - "id": "e7d40972d74b1c13" - }, - { - "cell_type": "code", - "source": [ - "from circumplex.datasets import JZ2017\n", - "import matplotlib.pyplot as plt\n", - "\n", - "jz_data = JZ2017\n", - "r = jz_data.ssm_analyse(measures = [\"NARPD\"])\n", - "plt.figure(figsize=(8, 5))\n", - "plt.bar(r.results[0].scores.index, r.results[0].scores.values, color='red')\n", - "plt.ylim(0, 0.5)\n", - "plt.ylabel(\"Score\")\n", - "plt.xlabel(\"Scale\")\n", - "plt.title(\"NARPD Scores\")\n", - "plt.grid(True)\n", - "plt.show()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-08T12:09:57.823770Z", - "start_time": "2024-06-08T12:09:57.670974Z" - } - }, - "id": "21f5a12726008489", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 32 - }, - { - "cell_type": "markdown", - "source": [ - "Next, we can leverage the fact that these subscales have specific angular displacements in the circumplex model (and that 0 and 360 degrees are the same) to create a path diagram.\n" - ], - "metadata": { - "collapsed": false - }, - "id": "cafd4ba1e88bf65f" - }, - { - "cell_type": "code", - "source": [ - "fig, ax = circumplex.profile_plot(r.results[0].amplitude, r.results[0].displacement, r.results[0].elevation, r.results[0].r2, r.results[0].angles, r.results[0].scores, r.results[0].label, incl_amp=False, incl_disp=False, incl_pred=False, incl_fit=False, reorder_scales=True);\n", - "\n", - "ax.grid(True)\n", - "plt.ylim(0, 0.5)\n", - "plt.xlabel(\"Angle\")\n", - "plt.title(\"Scores by Angle\")\n", - "plt.show()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-08T12:09:59.239947Z", - "start_time": "2024-06-08T12:09:59.105765Z" - } - }, - "id": "c90c1bcb4a07781b", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 33 - }, - { - "cell_type": "markdown", - "source": [ - "This already looks like a cosine curve, and we can finally use the SSM to estimate the parameters of the curve that best fits the observed data. By plotting it alongside the data, we can get a sense of how well the model fits our example data." - ], - "metadata": { - "collapsed": false - }, - "id": "933eea60765a4afe" - }, - { - "cell_type": "code", - "source": [ - "fig, ax = r.results[0].profile_plot(reorder_scales=True, incl_amp=False, incl_disp=False, incl_pred=True, incl_fit=False);\n", - "ax.grid(True)\n", - "plt.ylim(0, 0.5)\n", - "plt.xlabel(\"Angle\")\n", - "plt.title(\"Cosine curve estimated by SSM\")\n", - "plt.show()\n" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-08T12:10:01.258754Z", - "start_time": "2024-06-08T12:10:01.102717Z" - } - }, - "id": "c826947d0b98109e", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 34 - }, - { - "cell_type": "markdown", - "source": [ - "## Understanding the SSM Parameters\n", - "\n", - "The SSM estimates a cosine curve to the data using the following equation:\n", - "\n", - "$$\n", - "S_i = e + a \\times \\cos(\\theta_i - d)\n", - "$$\n", - "\n", - "where $S_i$ and $\\theta_i$ are the score and angle on scale $i$, respectively, and $e$, $a$, and $d$ are the elevation, amplitude, and displacement of the curve, respectively. Before we discuss these parameters, however, we can also estimate the fit of the SSM model. This is essentially how close the cosine curve is to the observed data points." - ], - "metadata": { - "collapsed": false - }, - "id": "dcc2aa0d761cfcf8" - }, - { - "cell_type": "code", - "source": [ - "from matplotlib import collections as mc\n", - "\n", - "fig, ax = r.results[0].profile_plot(reorder_scales=True, incl_amp=False, incl_disp=False, incl_pred=True, incl_fit=False, c_fit='black', c_scores='black');\n", - "\n", - "thetas = np.linspace(0, 360, 1000)\n", - "fit = circumplex.circumplex.cosine_form(thetas, r.results[0].amplitude, r.results[0].displacement, r.results[0].elevation)\n", - "angles, scores = circumplex.circumplex.sort_angles(r.results[0].angles, r.results[0].scores)\n", - "\n", - "lines = []\n", - "for i, angle in enumerate(angles):\n", - " idx = np.where(np.isclose(thetas, angle, atol=0.2))[0][-1]\n", - " lines.append([(angle, fit[idx]), (angle, scores[i])])\n", - " if angle == 360:\n", - " lines.append([(0, fit[0]), (0, scores[i])])\n", - "\n", - "lc = mc.LineCollection(lines, colors='red', linewidths=10)\n", - "ax.add_collection(lc)\n", - "\n", - "ax.grid(True)\n", - "plt.ylim(0, 0.5)\n", - "plt.xlabel(\"Angle\")\n", - "plt.title(f\"Fit = {round(r.results[0].r2, 2)}\")\n", - "plt.show()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-08T12:10:02.205870Z", - "start_time": "2024-06-08T12:10:01.977949Z" - } - }, - "id": "3d4382669e2bbfa8", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 35 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "If fit is less than 0.70, it is considered \"unacceptable\" and only the elevation parameter should be interpreted. If fit is between 0.70 and 0.80, it is considered \"adequate\", and if it is greater than 0.80, it is considered \"good\". Sometimes SSM model fit is called prototypicality or denoted using $R^2$. \n", - "\n", - "The first SSM parameter is elevation or $e$, which is calculated as the mean of all scores. It is the size of the general factor in the circumplex model and its interpretation varies from scale to scale. For measures of interpersonal problems, it is interpreted as generalized interpersonal distress. When using correlation-based SSM, $|e| \\geq 0.15$ is considered \"marked\" and $|e| \\leq .15$ is considered \"modest\"." - ], - "id": "897bd12afd585fde" - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "The second SSM is amplitude or $a$, which is calculated as the difference between the highest point of the curve and the curve's mean. It is interpreted as the distinctiveness or differentiation of a profile: how much it is peaked versus flat. Similar to elevation, when using correlation based SSM, $a \\geq 0.15$ is considered \"marked\" and $a \\leq 0.15$ is considered \"modest\".", - "id": "b8e0a65d8bf43c20" - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "The final SSM parameter is displacement or $d$, which is calculated as the angle at which the curve reaches its highest point. It is interpreted as the style of the profile. For instance, if $d = 90$ and we are using a circumplex scale that defines 90 degrees as \"domineering\", then the profile's style is domineering.", - "id": "83f2230678d86f83" - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "By interpreting these three parameters, we can understand a profile much more parsimoniously than by trying to interpret all eight subscales individually. This approach also leverages the circumplex relationship (i.e. dependency) among subscales. It is also possible to transform the amplitude and displacement parameters into estimates of distance from the x-axis and y-axis, which will be shown in the output discussed below.", - "id": "b1c952954bc8f60f" - }, - { - "cell_type": "markdown", - "source": [ - "## Example Data: jz2017\n", - "\n", - "To illustrate the SSM functions, we will use the example dataset `JZ2017`, which was provided by Zimmerman & Wright (2017). This dataset includes self-report data from 1166 undergraduate students. Students completed a circumplex measure of interpersonal problems with eight subscales (PA, BC, DE, FG, HI, JK, LM, and NO) and a measure of personality disorder symptoms with ten subscales (PARPD, SCZPD, SZTPD, ASPD, BORPD, HISPD, NARPD, AVPD, DPNPD, and OCPD). More information about these variables can be accessed by looking at the summary of the dataset with `jz_data.summary()`:" - ], - "metadata": { - "collapsed": false - }, - "id": "67084a8065287c69" - }, - { - "cell_type": "code", - "source": [ - "from circumplex.datasets import _jz2017_path\n", - "jz2017 = circumplex.instrument.load_instrument('IIPSC')\n", - "jz2017.attach_data(pd.read_csv(_jz2017_path))" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-08T12:10:04.469487Z", - "start_time": "2024-06-08T12:10:04.458544Z" - } - }, - "id": "d06f5b82ed8a562c", - "outputs": [ - { - "data": { - "text/plain": [ - "IIP-SC: Inventory of Interpersonal Problems Short Circumplex\n", - "32 Items, 8 Scales, 2 normative data sets\n", - "Soldz, Budman, Demby, & Merry (1995)\n", - "" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 36 - }, - { - "cell_type": "markdown", - "source": [ - "And we can view the accompanying dataset with:" - ], - "metadata": { - "collapsed": false - }, - "id": "c00508ca5abc4368" - }, - { - "cell_type": "code", - "source": "jz2017.data.head()\n", - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-08T12:10:05.287887Z", - "start_time": "2024-06-08T12:10:05.269950Z" - } - }, - "id": "3828a5802369696e", - "outputs": [ - { - "data": { - "text/plain": [ - " Gender PA BC DE FG HI JK LM NO PARPD SCZPD \\\n", - "0 Female 1.50 1.50 1.25 1.00 2.00 2.50 2.25 2.50 4 3 \n", - "1 Female 0.00 0.25 0.00 0.25 1.25 1.75 2.25 2.25 1 0 \n", - "2 Female 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 1 \n", - "3 Male 2.00 1.75 1.75 2.50 2.00 1.75 2.00 2.50 1 0 \n", - "4 Female 0.25 0.50 0.25 0.00 0.00 0.00 0.00 0.00 0 0 \n", - "\n", - " SZTPD ASPD BORPD HISPD NARPD AVPD DPNPD OCPD \n", - "0 7 7 8 4 6 3 4 6 \n", - "1 2 0 1 2 3 0 1 0 \n", - "2 0 4 1 5 4 0 0 1 \n", - "3 0 0 1 0 0 0 0 0 \n", - "4 0 0 1 0 0 1 0 0 " - ], - "text/html": [ - "
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GenderPABCDEFGHIJKLMNOPARPDSCZPDSZTPDASPDBORPDHISPDNARPDAVPDDPNPDOCPD
0Female1.501.501.251.002.002.502.252.504377846346
1Female0.000.250.000.251.251.752.252.251020123010
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" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 37 - }, - { - "cell_type": "markdown", - "source": [ - "The circumplex scales in `JZ2017` come from the Inventory of Interpersonal Problems - Short Circumplex (IIP-SC). These scales can be arranged into the following circular model, which is organized around the two primary dimensions of agency (y-axis) and communion (x-axis). Note that the two-letter scale abbreviations and angular values are based on convention. A high shore on PA indicates that one has interpersonal problems related to being \"domineering\" or too high on agency, whereas a high score on DE indicates problems related to being \"cold\" or too low on communion. Scales that are not directly on the y-axis or x-axis (i.e. BC, FG, JK, and NO) represent blends of agency and communion. " - ], - "metadata": { - "collapsed": false - }, - "id": "480a56f45e82f917" - }, - { - "cell_type": "code", - "source": "jz2017.demo_plot()", - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-08T12:10:06.174739Z", - "start_time": "2024-06-08T12:10:06.010592Z" - } - }, - "id": "4fca74a7a59559a", - "outputs": [ - { - "data": { - "text/plain": [ - "
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Z7Nu3D82bN+e7NCJHBAIBQkJCEBcXh++++w6zZ89Gy5YtcezYMbr9pwKy2F8R+aIQIRsSEgIdHR3k5eVVOs/IkSOhoaGBFy9e1GNlNVNYWIgFCxbg3LlzfJdSbeXl5di4cSNcXFywbds2hIeH4/Tp0/D39+e7NCLHVFVVMXbsWDx69AhhYWEYNWoUOnfujCtXrtT5c4mD6saNGzVaThG3R6KAmALYuXMnA8A2b95c4eMFBQVMV1eX9e3blwmFQlZUVMREIlGdPf/GjRsZAPb48WNuWklJCSspKanRev777z8GgM2fP1/qsbKyMlZUVPSBldYdkUjEDh48yFxdXZmDgwMLDw9n5eXlfJclt0pLS9nBgwdZaWkp36XIpZycHPbtt98yXV1dNmDAAHbv3r06W7d4+7x+/XqNlqtqe6wvsthfEfmiMEey+vr6CA8Pr/DxQ4cOoaCgACNHjoSqqiq0tLRkfupVQ0MDGhoadbY+NTU1aGlp1dn6PsSFCxfQvn17TJ48GVOnTsX9+/cxfPhwGh6N1JqhoSEWLVqExMREWFpaomXLlpg4cSKePn3Kd2nVVlBQUOfrrK/9FeGPQuw1tbW1MXDgQJw+fRpZWVlSj4eHh0NfXx8hISEVXuOws7NDnz59cOnSJfj7+0NLSwsODg7YsmWL1Lru3r2LLl26QFtbGzY2Nvjhhx8qbLhR0TXZ4uJiLFiwAC4uLtDS0oKlpSUGDhyIpKQkpKSkwMzMDACwcOFCCAQCCAQCLFiwAID0NVl3d3d07txZ6nlFIhGsra0xePBgiWnLly+Hm5sbtLS00LhxY0yZMgWvXr2q8n19V1xcHHr37o0+ffogODgYSUlJ+OSTT+r0ywRRbhYWFli9ejXi4+NRUFCAZs2aYdasWXj58mWdPcfYsWOhp6eH9PR09O/fH3p6ejAzM8PMmTO5e3nftz2K15GUlITg4GDo6+tj5MiRAN7sT8aOHSv1vBXtE1auXAk3Nzfo6OjA2NgYfn5+EgcL7+6v+vTpAwcHhwpfV9u2baUaGG7btg2+vr7Q1taGiYkJhg0bplBfXJSBQoQs8Oaaq1AoxO7duyWmv3z5EidOnMCAAQOgra1d6fKJiYkYPHgwunfvjqVLl8LY2Bhjx47F3bt3uXmePXuGzp07IyYmBrNnz8aMGTOwZcsWrFix4r31lZeXo0+fPli4cCF8fX2xdOlSTJ8+Hbm5ubhz5w7MzMywZs0aAMCAAQOwdetWbN26FQMHDqxwfaGhobhw4QKePXsmMf3SpUvIyMjAsGHDuGlTpkzBrFmz0L59e6xYsQLjxo3D9u3bERQUhLKysvfWXlJSgrlz56JNmzZo1qwZkpOT8e2338rFYOikYXJycsKOHTtw6dIlxMbGokWLFjhw4ECdrb+8vBxBQUFo1KgRfvvtNwQGBmLp0qVYt24dAFRrexQKhQgKCoK5uTl+++03DBo0qEY1rF+/HtOmTYOrqyuWL1+OhQsXwtvbG1evXq10mdDQUDx+/BjXr1+XmJ6amoorV65IbPc//vgjwsLCuPuTZ8yYgdOnT6Njx47IycmpUa1Ehvg+X11dQqGQWVpasrZt20pM/+uvvxgAduLECcZYxddPbW1tGQB24cIFblpWVhbT1NRkX375JTdtxowZDAC7evWqxHyGhoZS6wwMDGSBgYHc7//88w8DwJYtWyZVu/h6S1XXgObPn8/e/nM8fPiQAWArV66UmG/q1KlMT0+PFRYWMsYYu3jxIgPAtm/fLjHf8ePHK5z+rmvXrjFXV1fm6+vL4uPjq5yXVI6uydaeSCRi27ZtY8bGxmz48OEsOzu7Rsu/e012zJgxDAD7/vvvJebz8fFhvr6+3O9VbY/idcyePVvqMVtbWzZmzBip6e/uE/r168fc3NyqVbt435Kbmyu1X2KMsV9++YUJBAKWmprKGGMsJSWFqaqqsh9//FFivvj4eKampiY1nfBHYY5kVVVVMWzYMFy+fFniVHB4eDgaN26Mrl27Vrm8q6srOnTowP1uZmbGHbWJRUZGok2bNhItZ83MzLjTRFXZt28fTE1N8dlnn0k9VpvrLS4uLvD29sauXbu4aeXl5di7dy/69u3LHbXv2bMHhoaG6N69O7Kzs7kfX19f6Onp4ezZsxWuv6SkBN988w0CAwMxYsQIXL58Ge7u7jWuk5APJRAIMHLkSNy7dw+FhYVwc3PDwYMHP3i9H330kcTvHTp0kNjeq+Pjjz+u9fMbGRkhLS1N6qi0KgYGBujVqxd2794tccvTrl270KZNGzRt2hQAsH//fohEIgwdOlRiu7ewsICzs3Ol2z2pfwoTsgC4sBNf00hLS8PFixcxbNgwqKqqVrms+MP5NmNjY4nrlqmpqXB2dpaar1mzZu+tLSkpCc2aNavT3o5CQ0MRFRWF9PR0AMC5c+eQlZWF0NBQbp6EhATk5ubC3NwcZmZmEj/5+fkVXsO+efMm/Pz8cPz4cVy9ehXffPONwo6BShoOCwsLHDhwAL/99hvGjx+PkSNH1vqWPC0tLe6aq9i72/v7qKmpwcbGplbPDwBff/019PT04O/vD2dnZ3zyySeIiop673KhoaF4+vQpLl++DODNvuXmzZtS2z1jDM7OzlLb/f379yvc7gk/FCpkfX190bx5c+zYsQMAsGPHDjDGqnWkWVkIMzm+QT40NBSMMezZswcAsHv3bhgaGqJnz57cPCKRCObm5jh16lSFP99//z03b0lJCb799lt06NABoaGhuHLlCjw8POr9dRFSGYFAgFGjRuHu3bvIy8uDm5sbDh06VOP1vO9Ld3VoampW2KK+sjNT7w6Q0KJFCzx8+BA7d+5EQEAA9u3bh4CAAMyfP7/K5+3bty90dHS49ie7d++GiooKhgwZws0jEokgEAhw/PjxCrf7tWvX1vTlEhlRuE5mR44cie+++w5xcXEIDw+Hs7MzWrVqVSfrtrW1RUJCgtT0hw8fvndZR0dHXL16FWVlZZUeFdb0tLG9vT38/f2xa9cufPrpp9i/fz/69+8vMcC5o6Mj/v33X7Rv377Khl+3bt3CmDFjoK6ujitXrsDT07NGtRBSnywtLXHo0CFs27YN48aNQ+/evbFixYo6HSqxtrfNGBsbV9iwKDU1VaplsK6uLkJDQxEaGorS0lIMHDgQP/74I+bMmVPpLXu6urro06cP9uzZg2XLlmHXrl3o0KEDrKysuHkcHR3BGIO9vT1cXFxq9TpI/VCoI1ngf6eM582bh5iYmGodxVZXcHAwrly5gmvXrnHT/vvvP2zfvv29yw4aNAjZ2dlYtWqV1GPio2UdHR0AqFHLP/ER5z///IPs7GyJU0YAMHToUJSXl2PRokVSywqFQmRlZeG7775DQEAAhgwZgqtXr1LAEoUgEAgwevRo3L17F7m5uXBzc8Phw4frbP212R6BNwF35coViQEQjhw5InXrzLunujU0NODq6grG2Htb/YeGhiIjIwMbNmxAbGys1HY/cOBAqKqqYuHChVJn4xhjct3znbJRuCNZe3t7tGvXjjuFVJch+9VXX2Hr1q3o2bMnpk+fDl1dXaxbtw62trbvHZw6LCwMW7ZswRdffIFr166hQ4cOKCgowL///oupU6eiX79+0NbWhqurK3bt2gUXFxeYmJjA3d29ygZHQ4cOxcyZMzFz5kyYmJigW7duEo8HBgZiypQp+PnnnxETE4MePXpAXV0dCQkJ2L59O3R0dGBiYoLLly/Dy8urTt4nQuqT+Kh269atCAsLQ9++fbFixQoYGxt/0Hprsz0CwMSJE7F371707NkTQ4cORVJSErZt2wZHR0eJ+Xr06AELCwu0b98ejRs3xv3797Fq1Sr07t0b+vr6VT6H+N7cmTNnQlVVVer2IUdHR/zwww+YM2cOUlJS0L9/f+jr6+Px48c4cOAAJk+ejJkzZ9bujSF1i69mzR9i9erVDADz9/eXeqyyW3h69+4tNe+7Te4ZYywuLo4FBgYyLS0tZm1tzRYtWsT+/vvv997CwxhjhYWF7JtvvmH29vZMXV2dWVhYsMGDB7OkpCRunujoaObr68s0NDQkbh949xaet7Vv354BYBMnTqz0PVm3bh3z9fVl2traTE9Pj1lYWDA1NTX25Zdf0m0l9YBu4akf6enprHfv3szS0pJdvHiRMVbxLTy6urpSy1a0jVW2PVa2DrGlS5cya2trpqmpydq3b89u3LghtU9Yu3Yt69ixI2vUqBHT1NRkjo6ObNasWSw3N5ebp6L9ldjIkSMZANatW7dK69i3bx8LCAhgurq6TFdXlzVv3px98skn7OHDh5UuQ+qXgDE5bvlDaqyoqAgTJ07ExYsXceDAAfj6+vJdklIoKytDZGQkgoODqaW2jDHG8Ndff2HmzJlYsWIFJk6cyHdJhFRK4U4Xk8qJu5HT0NDA9evX0bhxY75LIqTOCQQCfPzxx3B1dcWgQYMQFxeHZcuW1entc4TUFYVr+EQqdvXqVfj5+cHT0xNnzpyhgCUNXmBgIK5fv45z586hZ8+eddr/MSF1hUK2AdiyZQu6dOmCOXPmYMOGDRK3+BDSkNnb2yM6OhoGBgbw9/fHvXv3+C6JEAkUsgqsvLwcs2bNwowZM3Dw4EFMmzaNhswiSkdPTw979+7FyJEj0bZtWxw5coTvkgjhUMgqqJycHPTp0wdHjhzB1atX0b17d75LIoQ3KioqWLhwITZs2IDhw4djyZIlct2bG1Ee1FJAAT169AghISHcTfGGhoZ8l9TgxcTESHTvp6qqCkNDQzg6OqJjx44Sp+gTEhIQHh4OPT09fPHFF3R2oR4NGTIETk5O6NevH+Li4rBhw4Yqe0IjRNboSFbBnDhxAq1bt0a/fv0QERFBAVvPOnXqhAEDBiA4OBhNmjTBjRs38Pfff0v04BMfHw8jIyPk5+fj8ePHPFarnHx8fHD9+nWkpqaiY8eO3AAbhPCBQlZBMMbw+++/Y9CgQVi5ciWWLFlSJ52gk5pxdnaGp6cnWrZsif79+6N169bIycnBo0ePAAClpaV48OAB2rRpAwsLC8THx/NcsXJq3LgxTp8+DS8vL/j5+eHKlSt8l0SUFIWsAmCM4auvvsIvv/yCM2fOYNSoUXyXRP6fvb09gP/1f/vo0SMIhUK4ubnB3d0d9+/fh1Ao5LFC5aWpqYn169djzpw56NatG06ePMl3SUQJUcjKOZFIhGnTpmHXrl24ePGixIDyhH/i8UnF1/3u3LkDOzs76Onpwd3dHSUlJdUaxYnIhkAgwLRp07B+/XoMHDiQWh6TekchK8dEIhGmTJmCyMhIXLhwAU5OTnyXpPSKi4tRWFiI169f486dOzh//jzU1NTg7OyMsrIypKSkcB3MGxoaokmTJnTKWA4MHz4cW7ZsQWhoKPbt28d3OUSJUOtiOSUUCjF+/HhcvXoV58+fh42NDd8lEQBbt26V+N3Q0BADBw6Evr4+cnJyIBAI0KJFC+5xd3d3nDx5EkVFRdTKlWcDBw6EhoYGhg0bhpKSEowYMYLvkogSoJCVQ2VlZRg1ahTu3r2L8+fPw8LCgu+SyP8LDg5Go0aNoKKiAl1dXZiamkIgEKCsrAyvXr2CpaUlioqKUFRUBACwsLBAeXk57t27R4M1yIE+ffpg//79GDhwIEpKSjBu3Di+SyINHIWsnCkrK0NoaCgePHiA8+fPw8zMjO+SyFusra1hZWUlNf3ly5coLCxEYWEhVq5cKfV4fHw8hayc6NGjBxe05eXlNIoPkSkKWTlSXl6OsLAwJCYm4ocffkB6ejp3pETk2507dyAQCNCvXz+poe6ePHmCq1evIjc3l+5rlgMlJSVQU1PD77//js8//xxaWlrUYp/IDIWsnBCJRJgwYQJiY2Nx7tw56OvrIyoqCrGxsfDy8qKglXN3796Frq4uXF1dpULWxsYGV69eRXx8PAICAniqkABvAjYqKgoGBgaYMGECHBwc0K9fP2hqamLIkCF8l0caIGpdLAcYY5g6dSqioqJw+vRpmJubQ1tbG+3bt0d2djZiY2OpH1Y5lpaWhlevXlV6lGpgYABLS0tqZcyztwO2ZcuWUFFRQdeuXbF7926MHTtWottMQuoKhSzPGGP4/PPPceLECZw5cwaWlpbcYxS0ikEcngYGBpXO4+LigqysLDx//ry+yiJvqShgxYKDg7F9+3aMGDECx48f57FK0hAJGO25eTV37lxs3boVFy5c4HoPeldRURGioqJgampKp47lVFlZGSIjIxEcHCx1upjwq6qAfduuXbswYcIEHD58GJ07d67nKklDRUeyPPrnn3+wdu1anD59utKABeiIlpDaqm7AAkBoaChWrlyJQYMGISEhoR6rJA0ZhSxPoqKi8Nlnn2Hv3r1wcXF57/wUtITUTE0CVmzcuHGYMGEC+vXrh9zc3HqokjR0FLI8ePLkCQYOHIhff/21RqelKGgJqZ7aBKzY4sWLYWtri5EjR6K8vFyGVRJlQCFbzwoKCtCvXz8MGDAAH3/8cY2Xp6AlpGofErAAoKqqih07diAxMRHffPONjKokyoJCth4xxjBu3DgYGBjgjz/+qHUDJgpaQir2oQErZmRkhIiICKxduxbbt2+v4yqJMqGQrUc//vgjrl27hr1790JDQ+OD1kVBS4ikugpYMRcXF+zcuRNTpkzB9evX66hKomwoZOvJgQMH8MsvvyAiIqLO+iOmoCXkjboOWLGgoCAsWrQI/fv3R0ZGRp2skygXCtl6EB8fjzFjxmDz5s3w9PSs03VT0BJlJ6uAFZsxYwaCgoIwYMAAFBcX1+m6ScNHIStj2dnZCAkJwaxZszBgwACZPAcFLVFWsg5YABAIBFizZg1UVVUxefJk2r5IjVDIylBZWRkGDx6MVq1a4dtvv5Xpc1HQEmVTHwErpqmpif379+Ps2bNYunSpzJ6HNDwUsjI0ffp05ObmYuPGjfXSFSIFLVEW9RmwYhYWFjh06BAWLlyIyMhImT8faRgoZGXkn3/+wd69e3Ho0CHo6urW2/NS0JKGjo+AFWvZsiU2bNiAESNGIDk5ud6elyguClkZSE1NxYwZM7B161Y0bdq03p+fgpY0VHwGrFhoaChGjhyJ8ePHQyQS1fvzE8VCIVvHGGOYOHEiQkNDERQUxFsdFLSkoZGHgBVbsmQJnjx5gtWrV/NWA1EMFLJ1bN26dXj48KFcNI6goCUNhTwFLADo6enhn3/+wZw5c5CUlMRrLUS+UcjWoZSUFMyaNQt///13lQN41ycKWqLo5C1gxTp16oRx48bRaWNSJfn4tDYAIpEIEyZMwPDhw9G9e3e+y5FAQUsUlbwGrNjixYuRlpaGVatW8V0KkVPy9YlVYGvXrkViYiJ+/fVXvkupEAUtUTTyHrAAoKuri3/++Qdz585FYmIi3+UQOSR/n1oF9PjxY3z11VdydZq4IhS0RFEoQsCKBQYGYvz48XTamFRIwGhP+0FEIhG6desGFxcX/PXXX3yXU6WYmBgcOnRIYpqOjg7Mzc3Rrl07ODs7SzyWn5+P6OhoPHr0CLm5uRAIBDA1NUXz5s3h7+8PLS2t+ixfrpWVlSEyMhLBwcFQV1fnuxzeiT9rqqqqmDZtmtSXz02bNqGwsBBTp07lppWXl+PGjRuIiYnBf//9BwAwNzeHl5cX/Pz8oKqqWq+voSYKCgrg5eWFzz77DNOnT+e7HCJH1PguQNH99ddfSE5OlgovedapUyfo6uri0aNH0NDQQFZWFsLDwzF8+HC4uLgAANLT0xEeHo7S0lJ4eHjAysoKAJCRkYFLly4hNTUVo0eP5vNlEAVQXl6OS5cuITg4uMr5SktLER4ejtTUVJiYmKB58+Zo2rQpkpOTcfz4cdy/fx8jRoz44CEiZUV82jg4OBjBwcFSX1iJ8qKQ/QCPHz/G119/jYMHD0JfX5/vcqrN2dkZVlZWcHNzQ1RUFJo2bYoTJ07gzp07cHFxQXFxMXbt2gWBQIApU6bA1NRUYvkuXbrg1q1bPFVPFImFhQVu3bqFDh06VLmNnDhxAqmpqXB0dETz5s25U8T+/v64du0ajh07hpMnT6JPnz71WH3NdOzYERMmTMC4ceNw/vx5uT7yJvVHfi90yDmRSITx48dj1KhR6Nq1K9/l1Ir4Gm1ubi5UVFS4/pVv3LiBvLw8BAUFSQUs8OYewY4dO9Z3uUQBBQQEgDGGS5cuVTrP69evcfv2bRgaGkoErJi/vz/s7Oxw+/ZtvH79uj7KrrWffvoJz549wx9//MF3KUROUMjW0po1a/D48WP88ssvfJdSY8XFxSgsLERBQQHy8vJQUFAAoVAIXV1dMMbw6NEjqKmpwdXVle9SiYIzNjaGp6cnbt26hby8vArnuX//PhhjsLOzq7SRk5eXF0Qikdy34NXV1cXGjRvx7bff4tGjR3yXQ+QAnS6uhezsbMyZMwf79+9XqNPEYlu3bpX4XVVVFcHBwcjNzUVsbCz+++8/NGrUiE53kTrRsWNHxMXF4dKlS+jVq5fEYyUlJYiLiwPw5oi1slbEjRs3BgCuQZQ869ChA8aPH4/p06fj2LFjfJdDeEYhWws//vgjOnTogG7duvFdSq0EBwejUaNGAN60II6Pj8fx48cREhKC//77DyUlJXLbwIQonrePZgMCArgvpowxREVFcV/mqmqtrqmpCeBNKCuCBQsWwMHBAefOnUOnTp34LofwiE4X11Bqair++usv/Pzzz3yXUmvW1tZwcHCAg4MDPD09MWLECJiZmeHff/9FmzZtoKqqitzcXLqPltSZjh07QiQScddmRSIRCgsLYWBgwLVcLy0trXR5cbiKw1beNWrUCF999RVmz55N25GSo5CtoXnz5mHw4MHw9PTku5Q6IxAIYGdnh/z8fBQWFsLc3Bz5+fm4ffs27SBInXj7aPbFixd4/fo1VFRU0LJlS5iZmQEAnj9/Xuny4sfE8yqCGTNmIDU1FQcPHuS7FMIjCtkauHPnDnbv3o3vv/+e71LqnLinmtLSUjRv3hwikQgPHz6knqFInenQoQNEIhH2798PVVVVaGtrQ0VFBU5OThAIBNy12YrExcVx8yoKXV1dzJs3D3PnzoVQKOS7HMITCtkamDt3LiZPngx7e3u+S6lT5eXlSEpKgqqqKszMzODn5wc9PT2kp6fj6dOnUkFbUFCACxcu8FgxUUS6urowMzPDs2fPuNvFAMDQ0BDe3t5ITk7G9evXpZa7ceMGHj9+DB8fH7nutrQiEydORFlZGTZv3sx3KYQn1PCpmi5duoSzZ89iw4YNfJfywRISEpCdnQ3gTWDGx8fj5cuXaN++PXfNKzQ0FOHh4YiLi0N6ejpSUlJgY2ODzMxM3LlzB02aNOHzJRAFI+6LuEWLFsjKysKLFy8kTv327NkTL168QGRkJJKSkuDo6AgASEpKwsOHD2Fra4sePXrwVX6tqaur44cffsDMmTMxYsQIaGtr810SqWcUstXAGMPs2bPx5ZdfwtzcnO9yPti5c+e4/6upqcHU1BS9e/eGr68vN93GxgYff/wxoqOj8fDhQ8THx+POnTswNzdHQEAA/P39eaicKKJ3O/t/9eoVYmNjJebR0NBAWFgYrl+/jri4OJw6dQoAYGpqiqCgILRq1UphbykbOnQofvnlF6xatQqzZs3iuxxSz2iAgGo4fPgwJkyYgKSkJIW8L7YuFBUVISoqCqampvDy8pI43UdogIDKKNJoOrJ08uRJDBs2DMnJyTAyMuK7HFKPlPMTXwPl5eWYM2cOvv32W6UNWICGySM1RwH7P927d4ePj49C9hBHPozyfuqradu2bSgoKMCUKVP4LoV3FLSkuihgJQkEAixevBgrVqxARkYG3+WQeqTcn/z3KC4uxrx587Bo0SKFuQle1ihoyftQwFasVatWCA4ObpC3AJLK0ae/CmvWrIGRkRFGjBjBdylyhYKWVIYCtmo//vgjNm/eTIMHKBHaAipRWFiIn376CT/99BPtKCpAQVs7jDGUl5fzXYZMUMC+n4uLC8LCwrBw4UK+SyH1hFoXV2LdunVYvXo1YmJiqCVtFRpqq+OMjAycOXMGT58+BfDmlqbu3bvDwsJCat6nT5/i5MmTSE9Ph46ODtzc3NC1a1epQRbi4uJw/PhxlJaWwsPDA3369FHY21LeRQFbfcnJyWjRogWSk5NhbW3NdzlExmhLqABjDH/88QemT5/eYEJDVhriEW1mZiY2btyIV69eITAwEB07dsTLly+xadMmrhMPsWfPnmHLli0oKyuDtbU1vLy8cPPmTezZs0divpycHBw9ehQBAQEYOHAgMjIycOXKlfp8WTJDAVszDg4O6NmzJ/7880++SyH1gLaGCpw+fRrPnz+na7HV1NCC9uzZs1BTU8OECRPQrl07tG/fHuPHjwdjDGfOnJGY9/Tp09DS0sKoUaNgamqKTp06ITg4GImJiUhKSuLmy8jIgIODA9q1awdXV1d07twZT548qe+XVucoYGtn+vTpWLt2LYqKivguhcgYbREVWLFiBaZMmVLl+JZEUkMK2tTUVDg4OEBHR4ebpq+vD1tbWzx69Igbkq2kpATJycnw9PSUaH3u5eUFDQ0N3L17l5tmbGyM1NRUJCUlITs7G7du3YKJiUn9vSgZoICtvc6dO8PS0hLh4eF8l0JkjLaKdyQmJuLkyZOYOnUq36UonIYStOXl5RX22qSuro7y8nJkZWUBeDP8mkgk4sZDFVNVVYWFhQWePXvGTbO0tISHhwe2bduG1atX4/Xr1wgICJDtC5EhCtgPIxAIMH36dKxYsUJhtxNSPbRlvGPlypUYNGiQ1I6TVE9DCNpGjRohLS2NG/4PeBO86enpAIDXr18DAPLz8wEAenp6UuvQ09NDXl6exLRevXph2rRpmDx5MiZPngxdXV1ZvQSZooCtGyNHjkRGRoZEX+Kk4aGt4y35+fnYuHEjpk2bxncpCk3Rg7ZVq1Z48eIFIiIi8N9//yErKwsHDhzgQlM8NmhZWRmAN4MsvEtNTY17/G3GxsawtLRU2GCigK072tramDx5MlauXMl3KUSGaBSet+zcuROOjo5o3bo136UoPHHQRkVFITY2VqFu7/Hz80Nubi6io6O50WKsrKzQvn17XLx4kbs1R3xKuaIBuYVCYYMbKIACtu5NnjwZLi4uyMzMhKWlJd/lEBmgkH3L+vXrMWnSJIUJA3mnyEHbtWtXtGvXDv/99x80NTXRuHFjnD59GsCb08nA/04Ti08bvy0/P79BDShBASsbdnZ26Ny5MzZt2oQ5c+bwXQ6RAdpS/l9cXBzi4+MxcuRIvktpUBT51LG2tjaaNm2Kxo0bA3jTiYCBgQFMTU0BAObm5lBRUZHq8L28vBzPnj3jllN0FLCyNWnSJGzYsEGiDQBpOGhr+X/r169HaGgoDA0N+S6lwVHkoBW7c+cOMjIy0Lp1a+5oXEtLCw4ODoiLi0NJSQk3b2xsLEpLS+Hm5sZXuXWGAlb2QkJCkJeXh7Nnz/JdCpEB6lYRb7oGtLKywtGjR9GuXTu+y2mwFKULxtTUVJw/fx6Ojo7Q1tZGWloaYmJi4OjoiOHDh0sETWZmJv7++2+YmppCXV0dTZs2xbVr12Bra4tRo0bx+Co+HAVs/fnqq6+QmpqKXbt28V0KqWMUsgC2bt2KxYsX486dO3K7428oFCFoX758icjISGRmZqKkpATGxsbw8vJC27ZtK+xr+MmTJzh16hTS09Ohra3N9V2syMMjUsDWr0ePHsHDwwNpaWkwMzPjuxxShyhkAXTp0gUhISGYMWMG36UoBUUI2poqKytDZGQkgoODFb5VMQUsPzp16oT+/fvTfqiBUfqt58WLF7h48SIGDx7MdylKoyFco22oKGD5M3ToUBw6dIjvMkgdU/ot6NixY/D09ISNjQ3fpSgVClr5QwHLrz59+uDixYt4+fIl36WQOqT0W1FERARCQkL4LkMp1UfQlpeXU4BXw9sB6+PjQ+8ZD5o2bQoPDw8cO3aM71JIHVLqzihKS0tx/PhxuglcRkpLSxEVFYX09HSkp6ejuLgY/fr1g7e3NzePtrY29PT0cPbsWRw/fhxCoRD6+vqws7NDYGAgjIyMJNa5cOHCCp+ra9euEh3uM8Zw4sQJ3LhxA2pqaujcuXOD78nrwoULOHv2LMzMzCQGuNi0aRNSU1Ol5nd0dMSoUaMkAlZNTQ1Lly5tkAPLK4KQkBAcPnyY7tdvQJQ6ZM+fPw9DQ0OJnT6pO4WFhbhw4QIMDQ1hYWGBlJSUCud78eIFmjZtivz8fBgZGUFfXx+3bt3Co0eP8NFHH0n1nOTg4AAvLy+JaRYWFhK/x8fH4/79+wgJCUFZWRn+/fdfWFtbN9jLAq9fv8alS5cqbXRlYGCArl27SkzT19eXCFgHBwesXbuW+3Jz/vx5XLlyBe3bt6+Pl0AA9O3bF8uXL0dpaSnXfSdRbEodshEREejbt2+DaN0qj/T09PDll19CT08PGRkZWL9+fYXz9e7dG8D/Wh2bmJhg+PDh2LBhA2JjY6WGhGvUqBE8PT2rfO60tDS0bduWm++///7DkydPGmzInjx5EjY2NhCJRCgsLJR6XFNTU+o9e/ca7IMHD7iB5QFARUUFt2/fppCtRy1btoSenh4uXLiAbt268V0OqQNKe02WMYbDhw+jb9++fJfSYKmpqVU4DFxl3r5GKx5Wrri4uMJ5y8rKKuyYX8zY2Bjx8fF49uwZ0tLS8OjRI67P4YYmNTUV9+7dQ1BQUJXziUQiiQHn323k1BAHllc0Kioq6NOnDw4fPsx3KaSOKO2RbHx8PLKzs9G5c2e+SyH/r7CwEIwxODg44MiRIwDedKD+rpiYGFy/fh0AYGpqio4dO8LDw0NiHj8/P9y7dw9r164FAHh4eMDFxUW2L4AHIpEIx44dQ8uWLavsK/nFixf46aefUF5eDl1dXZiYmMDd3V2iFfHbA8sDQOPGjdGvX796eR3kf0JCQvDpp59i+fLldJatAVDakI2IiEBQUBC0tLT4LoX8v2XLlqG8vBzAm6Nae3t75OfngzHG7WyaNGkCV1dXGBsbIy8vD9evX8f+/ftRXFyMVq1acetSV1fH+PHj8fz5c6ipqXGd+jc0N27cQE5ODkaPHl3pPMbGxrCzs0Pjxo1RWFiI6OhoPH36FPr6+vD395eYt1evXmjTpg2Ki4vRuHFjuo2HB126dEFWVhbu3Lkj9eWRKB6lDtm3W2AS/o0cORJCoRDZ2dmIi4tDkyZNuNt7xD1DjR8/XmIZHx8frFu3DmfOnIG3t7dEwx+BQCDVIKohKSwsxLlz59CxY0fo6upWOp/4aFR8irht27bIyMjA7du3kZaWJnWd2tjYWKZ1k6ppa2uje/fuiIiIoJBtAJTya2pmZiZu3brFNbgh8sHe3h7Ozs5o27YthgwZgujoaGhra1d5H62qqipatWqF4uJiZGZm8lA1f86cOQNtbe1q3Zr07jVYceOm5ORkWZdJakF8Kw9RfEoZskeOHEHr1q2pI245ZmJiAgsLCzx48OC9HVaIhycsKiqq7zJ58+LFC9y6dQv+/v7Iy8tDTk4OcnJyIBQKIRKJkJOTw70fFTVyUsb3TJH07t0b169fx7Nnz/guhXwgpTxdTL08KQahUAihUMi1Oo6KipI4dSz26tUrAICOjg5fpda7vLw8MMZw/PhxHD9+XOrxFStWoHXr1ujcuXOFXSUq43umSBo3bgx/f38cOXIEEydO5Lsc8gGULmQLCwvx77//4pdffuG7FII3rWNLSkqgra0tMT09PR3Pnz/nrklpa2vDx8cHt2/flgjakpISXLlyBTo6OrCysuLjJfDC3NwcoaGhUtPPnDmD0tJS9OzZE7q6ulyHK28HLGMMFy5cAAA4OTnVa92k+kJCQhAREUEhq+CULmSvXbuGRo0aoXnz5nyXohSuXbuG4uJi5OXlAXgzbubr168BgGvZ+vvvv8PNzQ3m5uZQV1dHVlYWYmJioKWlhY4dO3Lrio+PR0JCAlJTU5GSkgIjIyPExMQgNzcXAwYMUKru/3R0dCr8DF+5cgXAm+vbUVFRYIzh0qVLyMnJgYmJCcrKyvDgwQM8ffoULVu2hKWlZX2XTqqpR48eWLJkiUTreqJ4lC5kb968CT8/P/rQ1pPo6Gjk5uZyv9+/fx/3798HAHh6ekJfXx8tW7ZESkoK7t+/j7KyMujr68Pd3R0dO3aU6Lu4SZMmePr0KZ4/f4709HSoqamhSZMm6NevH+zt7ev7pcktxhh3itje3h6vX7/GgwcPkJ+fD4FAAFNTU/Tu3Ru+vr58l0qq4O7ujsLCQiQlJdEZBwWmlCFLO5f6U50BqHv27FmtdTk6OsLR0RGA5MDvFXVYoayGDx8udQ12yJAhfJdFakFTUxMeHh64efMmhawCU7rWxRSyDQONRyuNxoNteHx9fXHz5k2+yyAfQKm2wtevXyMhIYFCtoGgoP0fCtiGydfXF7du3eK7DPIBlGpLjImJgZWVVZV9vBLFQkFLAduQiUNWGT/XDYVSbY10qrhhUuagpYBt2Dw8PJCfn4/Hjx/zXQqpJaXaIilkGy5lDFoK2IZPU1MT7u7udF1WgSnVVnnz5k20bNmS7zKIjChT0FLAKo+WLVtSyCowpdky8/Ly8PDhQzqSbeCUIWgpYJULtTBWbEqzdcbExMDCwoJ6uFECDTloKWCVjzhkG9LnWJkozRZK12OVS0MMWgpY5eTp6Ym8vDykpKTwXQqpBaXZSm/dukUhq2QaUtBSwCovLS0tuLm50f2yCkpptlQ6klVODSFoKWAJXZdVXEqxtRYUFODBgwcUskpKkYOWApYAFLKKTCm22NTUVGhrayvVeKNEkiIGLQUsEXNxcUFycjLfZZBaUIqtNjMzk1oVE4UKWgpY8jZLS0tkZmbyXQapBaXYcilkiZgiBC0FLHmXlZUVCgoKkJeXx3cppIaUYuvNyMigU8WEI89BSwFLKmJkZARNTU1kZGTwXQqpIaXYgulIlrxLHoOWApZURiAQ0CljBaUUWzGFLKmIPAUtBSx5HwpZxaQUWzKFLKmMPAQtBSypDisrKwpZBaQUW3NGRgaFLKkUn0FLAUuqy9LSkq7JKiCl2KIzMzOp4ROpEh9BSwFLaoJOFyumBr9V5+XloaCggI5kyXvVZ9BSwJKaotPFiqnBb9mZmZnQ1NSEkZER36UQBVAfQUsBS2qDjmQVU4PfusXXYwUCAd+lEAUhy6ClgCW1RddkFVOD38LpeiypDVkELQUs+RCWlpZ4/fo1CgsL+S6F1ECD38rp9h1SW3UZtBSw5EM1atQI6urqdMpYwcjFlt6pUyd06tTpvfOdO3cOAoEA586dq/a6KWQVV3l5Od8l1EnQUsD+jzz8TRWViooK9PX14eTkhJSUFL7LIdWkVtsFk5KS8Msvv+DUqVPIyMiAhoYGPDw8MHToUEyePBna2tp1WWet5eXlwdTUlO8yAAAxMTE4dOgQ97uqqiq0tbXRuHFjODs7w9vbG5qamtzj586dw/nz5ytd35dffgk9PT2Z1FpaWoqoqCikp6cjPT0dxcXF6NevH7y9vaXmvXv3Li5fvozs7GyoqKjA3Nwc7dq1g4uLi8R8jDFER0fjxo0byMvLQ6NGjRAQEAAPDw+J+YRCIQ4dOoR79+5BW1sbvXr1gpubm0xeZ3WIgzYqKgqxsbHw8vKq9jV+eQrYrKwsnD9/HhkZGcjPz4e6ujrMzMzQrl07NGvWTGJexhhu3LiBmzdv4sWLF1BXV0fjxo0RFBQECwsLifnk9W8q3t4mTZpU4SWjnJwcrFixAgDQuXNndOzYkXvs7t27+PnnnwEAzs7OKCsrw+PHjzF37lzeP4t1aezYsdi8eTM8PDwQGxsr9bkWCAT45JNPsGrVqjp93j///BM6OjoYO3Zsna5XVu7du4fdu3dj7NixsLOzq9GytQrZo0ePYsiQIdDU1ERYWBjc3d1RWlqKS5cuYdasWbh79y7WrVtXm1XXOaFQCDW1Wn+XkIlOnTrB2NgY5eXlyM/PR2pqKo4fP47Lly9j+PDhaNy4scT8vXv3hoaGhtR6tLS0ZFZjYWEhLly4AENDQ1hYWFT6zfnq1as4fvw4nJ2d0a1bNwiFQsTExGDHjh0YOnQoWrRowc17+vRpREVFoWXLlrCyssLDhw+xf/9+CAQCuLu7c/NFR0cjKysLgwYNQk5ODiIiImBjYwNDQ0OZvd73qU3QylPAAkBubi5KSkrg5eUFfX19lJWV4f79+9i5cyf69OkDX19fbt5Dhw4hPj4enp6e8Pf3R2lpKZ49e4aCggKJdSry31RMTU0Nd+7c4UJ2//79GD58OMzNzTFhwgRunrNnz2Lv3r3YuXMnBgwYwEutqqqqMllvfHw89u/fj0GDBslk/e/6888/YWpqqlAhu3DhQnTq1En2Ifv48WMMGzYMtra2OHPmjMSp2E8++QSJiYk4evRoTVcrM/IYss7OzhLfrDt06IDHjx8jPDwcO3bswCeffAJ1dXXucVdXV+jo6NRrjXp6etyRckZGBtavX1/hfNeuXYOVlRWGDx/OhY6Pjw+WLVuG2NhYLmRfv36Ny5cvo1WrVggODgYAtGzZEps2bcKpU6fg6urKhVBaWho6d+6M5s2bAwCePn2K9PR03nfINQlaeQtY4M3nztnZWWKav78/1q1bh8uXL3Mhe/fuXcTGxkp9SXpXQ/ibAm/el/v373NfIkaPHg0HBwds2LABZ86cgZOTEx4/foy4uDh06NABo0ePRlxcHBwcHOqtxoKCAujq6srkc6StrY0mTZrg+++/x8CBA2V6J0ZhYaHM9mWMMRQXF8vNWVSxGv/FfvnlF+Tn5+Pvv/+u8Fqnk5MTpk+fDuBNwC1atAiOjo7Q1NSEnZ0d5s6di5KSkvc+T1paGvr37w9dXV2Ym5vj888/r9Zy7yorK5O7kK2Ivb09OnbsiNzcXMTFxfFdDtTU1Kp1KrqkpAS6uroSG6ampiY0NDQk3veHDx9CJBKhVatW3DSBQAA/Pz+8fv0aaWlp3HRjY2PcunUL2dnZSEpKQmpqKkxMTOrolX2Y6lyjlceArYyKigoMDQ1RXFzMTbt8+TKsra3RokULMMZQWlpa4bIN5W9qY2MDIyMjxMfH49dff0VhYSHWrVuH1NRUODk5cTttU1NTrF27FgUFBfjll18AAHv37oVAIKjwss7atWshEAhw584dbtqDBw8wePBgmJiYQEtLC35+foiIiJBYbtOmTdw6p06dCnNzc9jY2ACQPpIdM2YMTE1NUVZWJvX8PXr0kLoMUBEVFRV8++23iIuLw4EDB6qcV1zbu2e2Kmov06lTJ7i7u+PmzZvo2LEjdHR0MHfuXNjZ2eHu3bs4f/48BAIBBAIB1yZnwYIFFYZ8Rc9rZ2eHPn364MSJE/Dz84O2tjbWrl0LANi4cSO6dOkCc3NzaGpqwtXVFWvWrJFar3gdly5dgr+/P7S0tODg4IAtW7ZIPPeQIUMAvLmsIK65um2Darz1Hz58GA4ODmjXrt175504cSLmzZuHli1b4vfff0dgYCB+/vlnDBs2rMrlioqK0LVrV5w4cQKffvopvvnmG1y8eBFfffVVTcuFUCiUOCqUZ15eXgCA5ORkielFRUUoLCyU+Hl7p8gnOzs7JCYm4urVq8jJyUF2djaOHj2K4uJitG7dmpsvMzMT6urqUtfHra2tucfFAgIC8PLlS6xevRrbtm1Dy5YtJa4D8q2qoFWEgC0tLUVhYSFevnyJy5cvIyEhgTsqKykpQXp6OqysrHD69GksXrwYP//8M1asWIG7d+9KrKch/U3d3d1x9+5dHD58GHZ2dvD19UVSUpLUteWOHTvCzs6OO1vXu3dv6OnpYffu3VLr3LVrF9zc3LjT5nfv3kWbNm1w//59zJ49G0uXLoWuri769+9fYbhNnToV9+7dw7x58zB79mwAkPo8jR49Gi9evMCJEyckpj979gxnzpzBqFGjqvX6R4wYAWdnZ3z//fd1el/4ixcv0KtXL3h7e2P58uXo3Lkzli9fDhsbGzRv3hxbt27F1q1b8c0339Rq/Q8fPsTw4cPRvXt3rFixgmszsmbNGtja2mLu3LlYunQpmjRpgqlTp2L16tVS60hMTMTgwYPRvXt3LF26FMbGxhg7diz3ee/YsSOmTZsGAJg7dy5Xc1Vned5Wo0O8169fIz09Hf369XvvvLGxsdi8eTMmTpzInWoUfyv77bffcPbsWXTu3LnCZdetW4dHjx5h9+7d3DeISZMmcSFUE/J4urgyBgYG0NTUxMuXLyWmV9TooFGjRvj000/rq7RK9erVC4WFhTh+/DiOHz8OANDR0UFYWBiaNGnCzZefnw89PT2pb6n6+voA3jRQe3vaxx9/jOfPn0NHR0cue+t699Sxq6srgDfXqA0NDeU2YAHg5MmTuHnzJoA3R54tWrRAr169AID77N25cwcqKiro1q0btLS0cPXqVezduxeamppwcnIC0LD+ph4eHrh06RLU1NTg5eWFu3fvQk1NDc2aNUNiYqLEvJ6enoiIiEBeXh709fXRt29f7N27F3/88Qd3pPns2TOcP38eCxYs4JabPn06mjZtiuvXr3MNHKdOnYqAgAB8/fXXUtd5TUxMcPr0aYmj13ePZLt06QIbGxts27YNffr04abv2LEDIpGo2iGrqqqKb7/9FmPGjMHBgwfr7Jrzs2fP8Ndff2HKlCkS07/99luYmppWu77KJCYm4vjx4wgKCpKYfv78eYnTxp9++il69uyJZcuW4ZNPPpGY9+HDh7hw4QI6dOgAABg6dCiaNGmCjRs34rfffoODgwM6dOiAP/74A927d6/WnTBvq9Fe4PXr1wD+txFVJTIyEgDwxRdfSEz/8ssvAaDK67aRkZGwtLTE4MGDuWk6OjqYPHlyTcoFoFghCwAaGhpSp+eGDh2K0aNHS/xU54tOfVBXV0ejRo3g5eWFIUOGICQkhPtm//aXBaFQWGGjDfHfRigUSkxXVVWFlZWV3O2M3/b2Ea04tPT19eU6YAGgTZs2GD16NPr37w8nJyeIRCLu1hrxZ6+oqAjDhg1Dq1at4OHhgbCwMOjo6ODChQvcehrS39Tc3BwmJiZwd3eHvr4+4uPj0axZswrPgon3f+L9YWhoKLKysiROH+7duxcikQihoaEA3nx5OXPmDIYOHYq8vDxkZ2cjOzsbL168QFBQEBISEpCeni7xPJMmTZJ6f989ylRRUcHIkSO50Bfbvn072rVrB3t7+2q/ByNHjqzzo1lNTU2MGzeuTtZVEXt7e6mABSRbYefm5iI7OxuBgYFITk5Gbm6uxLyurq5cwAKAmZkZmjVrJnVGsbZqlD4GBgYAJL+hViY1NRUqKirct14xCwsLGBkZITU1tcplnZycpL4hV+f6wrvEG3pF1yzqm3hHJhQKK62ntLQUOjo6KCsr4+a3srKqsLFAfb0m8XtYXl4u9Zy7du2CiooKhg4dyk1zdHTEX3/9hX///Zf7Rqyqqlrh6xb/rqKiIhd/o5pSU1ODj48PLl26BABo3rw5ysvL5fp+UENDQ67BkaurK3bs2IHw8HCMHTuW2+aMjIzQuHFj7m8iEAjg5OSEO3fuoKSkBCoqKnL/N33f9iaeJv5cN2vWDE+fPsXjx4/x9OlTtG3bFmVlZRCJRBLzi3fSWlpaKCsrQ9euXWFoaIgdO3ZwLZR37twJLy8v2Nvbo6ysDA8ePABjDN999x2+++67CutNT0+Hubk5V3eTJk2k6n748KHUcmFhYViyZAkOHDiAsLAwPHz4EDdv3sRff/1Vo/dLFkez1tbWFd4ZUVcq+xIRFRWF+fPn4/Lly1I9ZOXm5ko0uGvatKnU8sbGxnj16lWd1FjjkLWyspK4kP8+fPcZrKqqiri4OO7Imk8vXrwA8OYDUFFolpaWoqSkBCUlJYiMjOSuaf3777+8Ho2LP6SxsbESjVlKSkqQnJyMJk2aSL2/6urqSExM5Ka/fPkSr1+/xtGjRyU+E+LGbE+ePJGLv9GHOnv2LN8l1FhZWRkyMzNx4MAB7siptLRU6u/x7NkziEQiHD16FKqqqnL/N33f9iau88GDB3j58iX3BdfY2Biqqqp4+PAhHj16hLS0NJSXl3Ov5dq1a2jUqBH3xQp406p6z549CA4ORk5ODqKjozFq1ChuGXE49u/fHz4+PhXWm5ycjMzMTMTGxgIAbt26xR0ti1lYWODZs2cS01xdXeHr64tt27YhLCwM27Ztg4aGhsQX3+oaOXIkFi1ahO+//x79+/eXeryy/XllXypr2tK3LtaflJSErl27onnz5li2bBmaNGkCDQ0NREZG4vfff+e+NIlVdltUXR3N13jP3adPH67Jf9u2bSudz9bWFiKRCAkJCRIXiJ8/f46cnBzY2tpWueydO3fAGJN40yv6Fvc+6urqaNasGXeLAZ/i4uLw9OlTtG/fvsKW2dHR0bh37x7atWsHb29vXLhwAc+fP0e3bt3q/Raet2VmZuLRo0fw8vKCp6cnNz0tLQ3379+Hm5sbWrZsKbHMrl278OzZM+59v3HjBk6ePAl/f3+YmZlx8929exf3799H586dK/xGKc9KSkpw9epV6Ovro0WLFjhz5gy0tbVhamoKd3d33r9gVte1a9eQlpaGNm3awMrKCikpKVBVVZXaZiIiIvDy5Uv06dMHAoFA7v+m79vecnJycP/+fTRv3hxt2rQBANy+fRvW1tZo3LgxevfuDeBNY8/8/HwEBwfj0qVLyMrKwqRJkyTeHxUVFYSEhEBTUxOvXr0CYwzffvstd6Tl5+eHr7/+Gi4uLpgzZ06VdWdnZwMA2rdvL3HvMvDmDMO7IQu8OZr94osvkJmZifDwcPTu3RvGxsY1eLfeEB/Njh07VqLjHDHxOnNyciSmV3VmsiKVbRtvr//tywo1Wf/hw4dRUlKCiIgIic/fh3wB/pBtucYXjr766ivo6upi4sSJeP78udTjSUlJWLFiBfcBXL58ucTjy5YtAwDuA1yR4OBgZGRkYO/evdw0cbP6mlJTUwNjDOrq6rz/iL8xqampST2WlpaGS5cuwcjICD4+PhLz8123+ChaVVVVYrq5uTkEAgEePHgg8ZqKiorw9OlTWFpactPc3NygoqKCmJgYifXevn0b+vr6sLe35/111uRHJBLh2rVrMDQ0hJ+fH9cxSJs2bfDy5Uvcu3evwr8znz+lpaVS01RUVLhGPuK/l7u7O16/fo0nT55w85WVlSEhIQH29vbQ0NBQiL9pVdub+Ofdz7U4SP/++2+8fv2ae4+AN5fJPv30U+jo6ODrr7+WWE/Pnj1hYmKCffv2Ye/evfD394eLiwv3uLW1NTp16oQNGzYgOztbqo6cnJxq1f3uUZiY+D716dOnIzk5+YMaFI0aNQpOTk5YuHCh1GOOjo4AIHFtvry8vMb7Zl1dXamgrmz9BQUF2Lx5c7XXLX7/3j4Szc3NxcaNG2tU49t0dXUBSH+5qI4aH8k6OjoiPDwcoaGhaNGihUSPT9HR0dizZw/Gjh2L6dOnY8yYMVi3bh1ycnIQGBiIa9euYfPmzejfv3+lLYuBNxf8V61ahbCwMNy8eROWlpbYunVrrY7m1NTUpBpg8C0hIQHZ2dkQiUTIz89HSkoKkpKSYGRkhOHDh0udGr53716F1zUcHBxk1q0i8OYIp7i4mLsG/+jRI+70lb+/P3R1deHt7Y3bt29jy5YtaNGiBUpKSnDjxg2UlZUhICCAW5eBgQHatGmD6OholJeXw9raGg8ePMCTJ08wcOBAuW4o9K6KbtMRn876kC4YZe3IkSMoKSlB06ZNYWBggPz8fMTHxyM7Oxs9evTgPmMBAQG4e/cudu/ejbZt20JTUxM3b95EeXk5unTpwq1PUf6mt2/flmohDFTcxiMgIACTJ0/GyJEj4eHhgQkTJsDAwAClpaXw8PBAdnY2duzYwYWBmLq6OgYOHIidO3eioKAAv/32m9S6V69ezXU5OWnSJDg4OOD58+e4fPky0tLSuNPEVanstKmZmRl69uyJPXv2wMjIqMqDmPdRVVXFN998U2GDJTc3N7Rp0wZz5szBy5cvYWJigp07d9Z4H+vr64s1a9bghx9+gJOTE8zNzdGlSxf06NEDTZs2xYQJEzBr1iyoqqrin3/+gZmZGZ48eVKtdYs/y3379sWUKVOQn5+P9evXw9zcvNaDK3h7e0NVVRVLlixBbm4uNDU1uftw36dWF/pCQkIQFxeHX3/9FYcOHcKaNWugqakJT09PLF26FJMmTQIAbNiwAQ4ODti0aRMOHDgACwsLzJkzB/Pnz69y/To6Ojh9+jQ+++wzrFy5Ejo6Ohg5ciR69eqFnj171qhWNTU1uWtQI26FKO672NzcHD179pTqu1isspbYY8aMkWnIRkdHS7TEu3//Pu7fvw/gzW0MWlpa6NOnDywsLHD79m2cPn0awJuGWv3795e6JCC+HeTmzZuIjY2FiYkJBgwYIHUvojyrzn2w8hq0bm5uuH37Nm7cuIGioiJoaGjAysoK3bp1kwgcPT09jB8/HidPnsSVK1dQXl6OJk2aYMCAAVL3tirC3/TGjRsVTq+se7whQ4agefPm+Pnnn/H333+jTZs2cHV1RWBgIObOnSvRXeTbQkNDsWHDBggEggqvh7q6uuLGjRtYuHAhNm3ahBcvXsDc3Bw+Pj6YN29etV5LVQ3qwsLCcOTIEQwdOrTC/UhNjBo1Cj/88AOSkpKkHtu+fTumTJmCxYsXw8jICBMmTEDnzp3RvXv3aq9/3rx5SE1NxS+//IK8vDwEBgaiS5cuUFdXx4EDBzB16lR89913sLCwwIwZM2BsbFztVsrNmjXD3r178e2332LmzJmwsLDAxx9/DDMzM4wfP77aNb7NwsICf/31F37++WdMmDAB5eXlOHv2bLVCVsDq8s5jOTRx4kTY2NhI3K9GSG1UFbBlZWWIjIxEcHAwdxqyqKgIUVFRMDU1lZugJYrNx8cH33//Pfr27Sv12KFDh9C/f3+Jez4J/+TjfI4MyePpYqJ4atOTkywGfifKrar7/tevXw8HBweJyzSEfw0+ZHV1daWawRNSEx/SVSIFLalLr1+/5hrhiO3cuRNz587F0aNHMX36dDpjImcafMhaWVnV+mI3IXXRFzEFLakLjDE8e/ZMamzc4cOHY+XKlZgwYQKmTp3KU3WkMorT32AtWVpaUsiSWqnLzv7ltTEUURziDjPeveeXvrTJtwZ/JEshS2pDFqPp0BEt+RCZmZnQ19eXOl1M5JtShGxGRgbt0Ei1yXK4OgpaUlsZGRkV9lxF5FuDD1krKysUFxdLjbxASEXqYzxYClpSG5mZmVLXY4n8a/Ahq6+vDx0dHTplTN6rPgdcp6AlNZWZmUlHsgqowYesQCCg67LkveozYMUoaElN0OlixdTgQxagxk+kanwErBgFLakuOpJVTEoRslZWVsjIyOC7DCKH+AxYMQpaUh10TVYxKUXI0pEsqYg8BKwYBS15HzqSVUwUskQpyVPAilHQksowxuiarILif89SDyhkydvkMWDFKGhJRXJzc1FcXEwhq4DkZ+8iQ9R/MRGT54AVo6Al78rMzISOjg4MDAz4LoXUkPztYWRA3OsTUW6KELBiFLTkbeJTxdTfteKR371MHbK1tUVBQQGeP3/OdymEJ4oUsGIUtEQsMTERdnZ2fJdBakH+9zR1QE9PDy4uLrh58ybfpRAeKGLAilHQEgC4efMmfH19+S6D1ILi7G0+kK+vL4WsElLkgBWjoCUUsopL8fY4tUQhq3waQsCKUdAqr5KSEsTHx1PIKijF3evUUMuWLSlklUhDClgxClrldOfOHejo6MDBwYHvUkgtKP6ep5p8fHyQlpaGrKwsvkshMtYQA1aMglb53Lx5Ey1btqSWxQqq4ex93sPQ0BDOzs50NNvANeSAFaOgVS50PVaxNbw9UBXoumzDpgwBK0ZBqzwoZBVbw90LVcDX1xe3bt3iuwwiA8oUsGIUtA1faWkpNXpScA1/T/QWOpJtmJQxYMUoaBu2u3fvQktLC46OjnyXQmpJefZGeNPC+MmTJ8jOzua7FFJHlDlgxShoGy5xoydl/Fw3FEr1lzM0NISTkxMdzTYQ1QnY8vJypQid+ghaZXkv5Qldj1V8anwXUN/E98sGBQXxXUqDl56ejtjYWKSkpCAnJwfa2tqwsbFBly5d0KhRIwBvxsmMjY3F/fv38ezZMxQVFcHIyAju7u5o164d1NQkP6ILFy6s8LmKi4sREBDA/c4Yw4kTJ3Djxg2oqamhc+fOaN26texerIxlZWXh/PnzyMjIQH5+PtTV1WFmZoZ27dqhWbNmAN4ErYODA06dOoVz584hLy8PIpEI8+fPr3Cdlb2XXbt2bdDvpSK5efMmZsyYwXcZ5AMoXcj6+vri6tWrfJehFKKiovD06VO4urqicePGyM/Px7Vr17B27VpMnDgR5ubmKCsrw6FDh2BjYwNfX1/o6uoiLS0N586dw+PHjxEWFiZ1f6CdnR00NTWhpaUFOzs7qKiowMLCQmKe+Ph43L9/HyEhISgrK8O///4La2tr2NjY1OdbUGdyc3NRUlICLy8v6Ovro6ysDPfv38fOnTvRp08f7mjnyZMnyMrKgra2NrS1tVFQUFDleh0cHODl5SUxraG/l4qirKwMcXFxdCSr4JQyZFevXs13GUqhbdu2GDRoEFRVVblpbm5uWLNmDS5duoSBAwdCVVUV48ePR5MmTbh5fH19YWRkxAXtuz3dlJSUwM3NrcprVWlpaWjbti08PT0BAP/99x+ePHmisMHg7OwMZ2dniWn+/v5Yt24dLl++zO2I/fz80L59ewiFQmzZsgUFBQVgjFXakUGjRo2496gyDe29VBR3796FhoaG1N+dKBaluiYLAK1bt8azZ8+QkJDAdykNXpMmTSQCFnizUzc3N+can6mqqkoErFjz5s0BvNmhi5WUlAAANDQ04OHhAZFIVOlzGxsbIz4+Hs+ePUNaWhoePXrEnaJuKFRUVGBoaIji4mJump6eHtTV1aGtrQ1LS0sAeO812rKyMgiFwkofV4b3Uh6dOnUKAQEB1OhJwSndkayenh66dOmCw4cP44svvuC7HKXDGEN+fj7Mzc2rnC8/Px8AoKOjA+B/jZyANwNYL168GABgamqKjh07wsPDQ2J5Pz8/3Lt3D2vXrgUAeHh4wMXFpU5fCx9KS0shFApRXFyMhw8fIiEhAe7u7hXOK76eLW4M5eXlJXVEGxMTg+vXrwNQvvdS3h0+fBgjRozguwzygZQuZAEgJCQEu3btopDlQXx8PPLy8tC5c+cq54uOjoampiacnZ0lWhHb2NjAzc0NxsbGyMvLw/Xr17F//34UFxejVatW3PLq6uoYP348nj9/DjU1NZiamsr6pdWLkydPcq3jBQIBWrRogV69elW5TPv27REVFSUVtE2aNIGrq6vSvpfyLDs7G9HR0QgPD+e7FPKBlDJk+/bti88++wwvX76EiYkJ3+UojezsbERGRsLGxkaqsc3bLl68iOTkZAQHB0MgEEjcpuPn5ycxr4+PD9atW4czZ87A29sb6urq3GMCgUCqEY+ia9OmDVxdXZGXl4e7d+9CJBKhvLy8ymXEt/e8G7Tjx4+XmE/Z3kt5FhkZCW9vb7ru3QAo5cl+GxsbeHp64tixY3yXojTy8/MRHh4OTU1NDB06tNLrTHfu3MGZM2fg4+MDT0/P994Hq6qqilatWqG4uBiZmZmyfhm8MzU15VoEjxgxAqWlpdixY8d771+tzn20yvZeyrPDhw+jb9++fJdB6oBShizw5pRxREQE32UoheLiYmzfvh3FxcUYNWoU9PX1K5wvKSkJBw8ehIuLC7p3717tnpwMDQ0BAEVFRTKpX565uroiIyMDL168eO+81QlaZX4v5UVJSQmOHz+OkJAQvkshdUCpQ/bYsWMoLS3lu5QGTSgUYseOHXjx4gWGDx8OMzOzCudLS0vDrl27YGVlhb59++Ly5cvV7irx1atXAP7XSEqZlJWVAfhfy+v3eV/QKvN7KS/OnTsHIyMjeHt7810KqQNKG7I+Pj4wMDDAhQsX+C6lwRKJRNi7dy/S0tIwZMiQCm/VAd7cphMeHg4jIyMMGjQI165dqzBgK+pYoaSkBFeuXIGOjg6srKxk9lr4VtFrLy8vR1xcHNTU1Cr98lIRbW1t+Pj4SAWtsryX8k58qpgGaW8YlLLhE/CmIUffvn0RERGBbt268V1Og3Ty5Ek8fPgQLi4uKCoqQlxcnMTjnp6eKCkpwbZt21BcXIzWrVvj2LFj0NLSgomJCe7cuQNjY2MunK9fv44HDx7AxcUFhoaGyM/Px+3bt5Gbm4sBAwZI3ZPbkBw5cgQlJSVo2rQpDAwMkJ+fj/j4eGRnZ6NHjx7Q0NAAAOTk5HDvc0ZGBgBwXyQNDQ25Bmfx8fFISEhAamoqUlJSYGRkhJiYGKV4L+UZYwwRERFYt24d36WQOqK0IQu8OWU8depUrFixgr41ysCzZ88AAI8ePcKjR4+kHvf09ERRURFev34N4M1pMrHY2FgAgJeXFxeyTZo0wdOnT3H79m0UFhZCQ0MD1tbW6NevH+zt7WX8avjl5uaG27dv48aNGygqKoKGhgasrKzQrVs3ru9i4E3Inj17VmJZ8e+2trZcyIrfy+fPnyM9PR1qampo0qSJUryX8iwuLg6vXr167y1uRHEImBIPq1FcXAxTU1NcvnxZ6gZ8Un8awnB1ZWVliIyMRHBwsMStL4qgqKgIUVFRMDU1rbDDClJ/Fi1ahJiYGOzbt4/vUkgdUby9WR3S0tJCjx49qJUxjxpCwCo6Go9WfkRERFCr4gZG6fdoISEhOHz4MN9lKCUKWPlBQcu/jIwM3L59G8HBwXyXQuqQ0u/VgoODcePGDe76IakfFLD/874em+oLBS2/jhw5gjZt2tSopTiRf0rd8AkAzM3N0bZtW+zbtw+ffPIJ3+UoBXkK2OoMLA9UPsA58GZM1mHDhnG/M8YQHR2NGzduIC8vD40aNUJAQIDUdX+hUIhDhw7h3r170NbWRq9eveDm5lb3L7IGKuuCkcjenj170K9fP77LIHVM6UMWAMaNG4eVK1di6tSptEORMXkKWKB6A8sDwIABA6SWzcjIwNWrV6XGuz19+jSioqLQsmVLWFlZ4eHDh9i/fz8EAoHEiDnR0dHIysrCoEGDkJOTg4iICNjY2HC9LvGFgrb+PX78GBcuXMDWrVv5LoXUMQpZAKGhoZgxYwZu3rwp1QE9qTvyFrBA9QaWB1DhwOYpKSkAIHGEmpeXh8uXL6NVq1bctbWWLVti06ZNOHXqFFxdXbnXnZaWhs6dO3Nj5z59+hTp6em8hyxAQVvf/v77b/Tu3ZsGYWiA+N/LyQFdXV2MGDGCbgCXIXkMWKB6A8tXRCgU4v79+7Czs4OBgQE3/dGjRxCJRBJDxQkEAvj5+eH169dIS0vjphsbG+PWrVvIzs5GUlISUlNT5WpUKLpGWz+EQiE2btyISZMm8V0KkQH52NPJgUmTJmHHjh3cYOGk7shrwFZGPLB8Vf33JiQkoLi4WOo66/Pnz6Guri415qq1tTUASIxuExAQgJcvX2L16tXYtm0bWrZsKXdHMhS0shcZGQk1NTX06NGD71KIDNDp4v/n6+sLZ2dn7Ny5ExMnTuS7nAZD0QIWqN7A8vHx8VBVVYWrq6vE9Pz8fOjp6UmdWhWPPJSXlycx7eOPP8bz58+ho6MDIyOjunsRdYhOHcvW+vXrMX78eOrKsoGS/z1ePZo0aRLWr1/PdxkNhiIGbHUGli8pKUFCQgKcnZ2hpaUl8VhZWVmFO0s1tTffZ4VCocR0VVVVWFlZyW3AitERrWykpaXhxIkTGD9+PN+lEBmR/71ePRoxYgTu3LmDW7du8V2KwlPEgK3uwPL37t2DUCissCtOdXX1Cu97FYerOGwVEQVt3duwYQN69OhR6QhVRPHJ/56vHhkaGiIsLAx//PEH36UoNEUM2OoOLA+8OVWsqakJFxcXqcf09PSQn58vFUDi08RVrVcRUNDWnZKSEqxZswafffYZ36UQGZL/vV89mzZtGnbu3Innz5/zXYpCUsSAre7A8sCbsExJSUGLFi0qPCo1NzdHWVmZVMvk9PR0AJC7hk21QUFbN3bt2gUTExNq8NTAyf8esJ61aNECgYGBWLt2Ld+lKBxFDNjqDiwvdufOHTDGKrxvFgBcXFygoqKC69evc9MYY7hx4wb09fUbzGlBCtoPwxjDihUrMG3aNGpE1sAp7gUiGZo+fTomTJiA2bNnc4Nhk6opYsAC1RtY/m3x8fHQ19eHnZ1dheszMDBAmzZtEB0djfLyclhbW+PBgwd48uQJBg4cqDDvS3VQq+Pai4qKQnJyMsLCwvguhcgYhWwFevbsCQMDA+zevRujRo3iuxy5p6gBC1RvYHmx7OxsZGZmok2bNlWGSbdu3aClpYWbN28iNjYWJiYmGDBgQIMcs5iCtnZWrFiBiRMnQldXl+9SiIwp9aDtVVm1ahU2b96Ma9eu0U6jCoocsHVJkQdtrws08Hv1PXnyBM7Oznj06BFsbW35LofImHLuEathzJgxSExMxOnTp/kuRW5RwBIxukZbfb/99hv69u1LAaskaK9YCX19fXz99deYM2cO7TAqQAFL3kVB+37JyclYv359lUMnkoaF9oxVmDZtGtLT07F3716+S5ErFLCkMhS0VZs3bx5CQ0N5HzeY1B/aO1ZBR0cH8+fPxzfffIOysjK+y5ELFLDkfShoKxYbG4t9+/bRUaySoT3ke4j7FN24cSPPlfCPApZUFwWttLlz5+Ljjz+ma7FKhloXV8OePXswffp0JCYmVjn8mSKIiYnBoUOHKnysffv26NatG4A3N8vHxcUhLi4OmZmZKCkpgaqqKgwNDdG6dWt4e3srdD+8dU3ZWxdXRtzquLCwELdv38akSZNgZWWFc+fO4fz585g1a5bENpWbm4vNmzejqKgIYWFhsLS05LH6unPhwgX07dsXSUlJUsMgkoaN9pLVMHjwYPzyyy/4448/MHv2bL7LqROdOnWCsbGxxDRzc3MAbwJj165dSEpKQpMmTeDv74+MjAyoqalBKBQiMjIS6enp6NevHx+lEwUiPqLdv38/AFR5RPv69WsuYEePHt1gApYxhq+//hqzZs2igFVCFLLVIBAIsHjxYgwaNAiTJ0+GiYkJ3yV9MGdnZ1hZWVX42IkTJ5CUlISgoCD4+PggKioKLi4u3CniFy9eIDk5uZ4rJopKW1sbzs7OSExMxKNHjyr83OXl5UkEbGWfTUV06NAhPH78GJ9//jnfpRAe0EW1auratSv8/f2xePFivkuRqdzcXNy6dQtOTk5cwL57DbZRo0Zo1aoVz5USRSLunjQnJ0fqGq04YAsKCjBq1KgGFbBCoRBz587FvHnzqHcnJUUhWwOLFy/GqlWrkJaWxncpH6y4uBiFhYUSPwCQmJgIxhhatGhBjZxInfPy8kJ2djY3ylV+fj62bNmC/Px8jBo1CtbW1jxXWLe2bt2KkpISTJw4ke9SCE/odHENtGzZEiEhIVi4cCHWr1/PdzkfZOvWrVLT5s+fzw3RlpaWBisrKy5gy8vLUVJSIjG/ojcCI/VPS0sL7du3x44dOwAA4eHh3Bi+NjY2PFdXt4qLizF//nwsWbKEBhpRYhSyNfTDDz/Aw8MDX375JZo3b853ObUWHByMRo0aSU0XH9EaGhpKHMEmJCRg165d3Hzq6uqYO3du/RRLGhRtbW00adIET58+RV5eHnR1daGnp8d3WXVu9erVMDU1RWhoKN+lEB7ROcAacnJywrhx4/DNN9/wXcoHsba2hoODg8RPSUkJdyTr7OwscYq4SZMmGD16NEaPHg1HR0e+yiYNhPg2J2dnZxQUFGDr1q0oKCjguaq6k5ubi59++gk///wzXWpRcvTXr4V58+bhxIkTuHTpEt+l1BlxRxPiltPisBXT1dXlwrghHnUQfnTv3h3NmjXDq1evsG3bNhQXF/NdUp348ccf4eXlhR49evBdCuEZhWwtWFhYYMGCBZgwYQKKior4LueDvd2TU5cuXSAQCBAfH893WUQJaGtro2/fvnBycsKzZ88QHh6u8F2YXr9+HatXr8Yff/xBQ/4RCtna+vzzz9GoUSN8++23fJfyQd7tKtHY2Bg+Pj5ITEzEtWvX+C6PKAFtbW0MGDAA9vb2ePr0KXbv3o3y8nK+y6qV4uJijB07Ft988w3c3d35LofIAWr4VEuqqqrYuHEjWrZsiQEDBiAgIIDvkmqssr6Ie/bsiZycHBw7dgx37tyBi4sLdHV1UVhYiKdPn+LRo0cVNpoipLa0tbUxZMgQ7NixA4mJiThw4AAGDRqkcEeCCxYsgI6ODr766iu+SyFygkL2AzRr1gzff/89xo0bh9jYWIW6paW0tLTS+2DV1dUxcuRIru/i6OholJSUQEtLC40bN0ZwcDC8vb35K54oHHHnE1WFpra2NoYPH46tW7fi7t270NLSQp8+feqrxA929epV/PHHH7h+/Tr16004NEDAByovL0fHjh3h7++P33//ne9yqoVG06l7NEBA1a5evYrjx4/js88+e2+3pOJBBUxNTeHl5aUQR7PFxcXw8fFBWFgY5syZw3c5RI7Q3vUDiU8br1u3DhcvXuS7nPeigCV8yMjIgLq6OoyMjN47ryIOkzd//nzo6+tj1qxZfJdC5AztYeuAi4sLFi1ahPHjx3OdOcgjClhS3+7du4fIyEjExcXBw8Oj2p85RQraK1euYOXKldi0aROdJiZSaC9bR6ZPn47GjRvLbS9IFLCED6dOncKdO3fg4+ODoKCgGi2rCEFbXFyMcePGYd68eXB1deW7HCKH6JpsHXr06BF8fHxw/PhxdOjQge9yOBSwskfXZGVHnq/RfvXVVzh//jyioqLoKJZUiPa2dcjFxQU//PADxo0bJzddxFHAEkUnr0e0V65cwerVq7Fx40YKWFIp2uPWsWnTpsHCwkIuThtTwJKGQt6CtqioCGPHjsX8+fPpNDGpEu1165i4tfHff/+N8+fP81YHBSxpaOQpaOfNmwcjIyN8+eWXvNVAFAPteWXA2dkZP//8M0aOHInMzMx6f34KWNJQyUPQRkRE4K+//sLGjRuhqqpa789PFAvtfWXk008/RefOnTFgwIB6HVmEApY0dHwG7d27dzFq1Cj8888/aNGiRb09L1FctAeWEYFAgPXr14Mxho8++qhedgQUsERZ8BG0L168QEhICD7//HMMGTJE5s9HGgbaC8uQlpYWDhw4gFOnTsm8y0UKWKJs6jNoy8rKMGTIEHh5eWH+/Pkyex7S8NCeWMasrKxw8OBBzJs3D8ePH5fJc1DAEmVVX0H7xRdfIDs7G1u2bKHti9QIfVrqQatWrbB27VoMGzYMDx8+rNN1U8ASZSfroF23bh127tyJiIgI6Onp1em6ScNHe+R6MnLkSHz00UcICQlBTk5OnayTApaQN2QVtBcuXMDnn3+OvXv3ws7Ork7WSZQL7ZXr0Y8//ghnZ2cMGzYM5eXlH7QuClhCJNV10KampmLQoEFYtmwZAgMD66hKomxoz1yPVFVVER4ejidPnuDrr7+u9XooYAmpWF0FbX5+PkJCQjB06FBMmTKljqskyoT2zvXMwMAAERER+Oeff7B58+YaL08BS0jVPjRoRSIRxo4dCxMTEyxfvlw2RRKlQXtoHjg5OWH37t2YOnUqrly5Uu3lKGAJqZ4PCdpFixbh1q1b2LNnD42oRD4Y7aV50q1bN/z888/o378/UlNT3zs/Bax8iImJwcKFC5GRkVHh49u2bcOff/7J/b58+XKEh4fXV3nkLbUJ2t27d2Pp0qWIiIiAqalpPVRJGjraU/Pos88+w+DBg9G1a1ekp6dXOh8FLCG1U5OgPXLkCMaNG4cdO3bA3d29HqskDRntrXkkEAjwxx9/IDAwEF27dsXz58+l5qGAJeTDVCdoT548iWHDhmHLli3o3bs3D1WShor22DxTUVHBunXr4Ovri27duiE7O5t7jAKWkLpRVdCeP38eAwcOxLp16zBo0CAeqyQNEe215YCqqio2b96MZs2aoUePHsjJyaGAlXPFxcUoLCyU+BEKhRCJRHyXRipRUdBGR0ejb9++WLlyJUaMGMF3iaQBUuO7APKGmpoawsPDMXjwYAQFBeG7776DhYUFBayc2rp1a6WPmZmZ1WMlpCbEQRsVFYXw8HB88sknWLJkCcaNG8d3aaSBopCVIxoaGti9ezdCQkLw7bff4uzZsxSwcio4OBiNGjXifhcKhbh27Rry8vJ4GUicVJ+2tjY0NDQwdepUzJ8/Hx9//DHfJZEGjPbgckZLSwsRERGwtrZGjx498PLlS75LIhWwtraGg4MD92Nvbw99fX1oaWnxXRp5j6ioKPTt2xfff/89vvjiC77LIQ0chawcEo9D26RJE3Tu3BlZWVl8l0RIg3D27Fn06tULv/76K6ZPn853OUQJUMjKKQ0NDezatQuurq7o1KkTMjMz+S6JEIV2/PhxrpHT5MmT+S6HKAkKWTmmrq6Obdu2wd/fHx07dsTTp0/5LokQhRQREYHBgwfj77//xpgxY/guhygRClk5p6qqin/++Qddu3ZFhw4dEB8fz3dJhCiUjRs3YsSIEdi6dStCQ0P5LocoGQpZBaCiooI1a9Zg3LhxaN++PQ4ePMh3SYTIPaFQiC+++AIzZ85EREQEBgwYwHdJRAkJGN1voFD27duHsWPH4uuvv8Y333wDgUDAd0kEQFlZGSIjIxEcHEwjt8iBV69eYdiwYUhLS0NERAQcHR35LokoKTqSVTCDBg3CpUuXsH79egwbNgyFhYV8l0SIXHnw4AFat24NTU1NXL58mQKW8IpCVgF5eXnh+vXryMzMREBAADWIIuT/HTt2DG3atMGQIUNw8OBBGBgY8F0SUXIUsgrK3Nwc//77L/z8/ODn54fo6Gi+SyKEN4wx/PbbbxgyZAjWrFmDH3/8kXpLI3KBulVUYBoaGli7di28vLzQvXt3rFy5EuPHj+e7LELqVXFxMSZPnowzZ87g3Llz8PPz47skQjgK81Vv7NixsLOzq9WydnZ2GDt2LPf7uXPnIBAIcO7cuTqpjS8pKSlQUVGBrq4uDh8+jFmzZuHzzz+HUCiUmvdD3j9C5FVGRgYCAwORkJCA69ev10nA1uW+pi506tSpWoPIp6SkQCAQYNOmTXX6/OTD1Cpk4+PjMXjwYNja2kJLSwvW1tbckZSy++qrryAQCOr9frwuXbrg2rVrOHnyJIKDg/Hq1at6fX4AiI6OxoIFC5CTk1Pvz00ank2bNkEgEFT6888//6BVq1Zo0aIFzp49C0tLS75LlnLv3j0sWLAAKSkpfJdCeFLj08XR0dHo3LkzmjZtikmTJsHCwgJPnz7FlStXsGLFCnz22WeyqBPr16+X+7E6GWPYsWMH7OzscPjwYeTl5UFfX19mz2dra4uioiLulhFHR0dcvnwZo0aNgr+/Pw4dOgRXV1eZPf+7oqOjsXDhQowdOxZGRkb19rykYfv+++9hb28vMS0qKgqfffYZFi1ahM8//1xubmV7+PChxLXge/fuYeHChejUqROdSVJSNQ7ZH3/8EYaGhrh+/brUjlSWHdkrwr2H586dQ1paGs6cOYOgoCDs379fpl24CQQCqVFfDAwMcPDgQXz33Xdo1aoVFi1aRB2hE4XWq1cv7jTwixcv8Omnn+LEiRPYt28fevbsyXN1kjQ1NfkugciZGp8uTkpKgpubW4VHKubm5hK/C4VCLFq0CI6OjtDU1ISdnR3mzp2LkpISqWWPHTuGwMBA6Ovrw8DAAK1atUJ4eDj3eEXXSX777Te0a9cOjRo1gra2Nnx9fbF3796aviTMnz8f6urq+O+//6Qemzx5MoyMjFBcXPze9Wzfvh2urq7o3LkzunXrhu3bt3OPPX/+HGpqali4cKHUcg8fPoRAIMCqVau4acnJyRgyZAhMTEygo6ODNm3a4OjRoxLLVXYNJiIiAocOHUJZWRnmzJkDd3d35ObmSj1vdd8/gUCATz/9FAcPHoS7uzs0NTXh5uaG48ePc/MsWLAAs2bNAgDY29tzp/ToNBmpKwcOHICrqyuKiopw79499OzZE/Pnz4eKigpOnz4tMe/kyZOhoaGB2NhYAP9rh7Fr1y7MnTsXFhYW0NXVRUhISLVugSsoKMCXX36JJk2aQFNTE82aNcNvv/0mNXbw29dkN23ahCFDhgAAOnfuzG0T4rYghw4dQu/evWFlZQVNTU04Ojpi0aJFKC8vr7CGmzdvol27dtDW1oa9vT3++uuvar1vDx48wODBg2FiYgItLS34+fkhIiKiWsuSOsBqqEePHkxfX5/Fx8e/d94xY8YwAGzw4MFs9erVLCwsjAFg/fv3l5hv48aNTCAQMHd3d/bjjz+y1atXs4kTJ7LRo0dLrMvW1lZiORsbGzZ16lS2atUqtmzZMubv788AsCNHjkjMZ2try8aMGcP9fvbsWQaAnT17ljHGWEJCAgPAVq5cKbFcSUkJMzY2ZuPHj3/vay0uLmZGRkZs0aJFjDHGtmzZwlRVVVlmZiY3T5cuXZirq6vUsgsXLmSqqqrs2bNnjDHGnj17xho3bsz09fXZN998w5YtW8a8vLyYiooK279/P7fc48ePGQC2ceNGbtqJEyeYiooKc3d3Z8uWLWNfffUV09DQYCoqKszY2JgJhcIav38AmJeXF7O0tGSLFi1iy5cvZw4ODkxHR4dlZ2czxhiLjY1lw4cPZwDY77//zrZu3cq2bt3K8vPz3/veNQSlpaXs4MGDrLS0lO9SGoyNGzcyAGzfvn1s0KBBzMjIiK1Zs4ZlZWVxn7vS0lLm4+PDbG1t2evXrxljjB0/fpwB4LZFxv63zXt4eDBPT0+2bNkyNnv2bKalpcVcXFxYYWEhN++7+xqRSMS6dOnCBAIBmzhxIlu1ahXr27cvA8BmzJghUfPb+5qkpCQ2bdo0BoDNnTuX2ybE23n//v3Z0KFD2a+//srWrFnDhgwZwgCwmTNnSqwzMDCQWVlZMXNzc/bpp5+yP/74gwUEBDAA7O+//+bmq2h/cOfOHWZoaMhcXV3ZkiVL2KpVq1jHjh2ZQCCQ2JcQ2alxyJ48eZKpqqoyVVVV1rZtW/bVV1+xEydOSO1cYmJiGAA2ceJEiekzZ85kANiZM2cYY4zl5OQwfX191rp1a1ZUVCQxr0gk4v5fUci+vWEw9maDc3d3Z126dJGY/r6QZYyxtm3bstatW0sst3//fqn5KrN3714GgCUkJDDGGHv9+jXT0tJiv//+OzfP2rVrGQCpLyiurq4SNc+YMYMBYBcvXuSm5eXlMXt7e2ZnZ8fKy8sZYxVvVN7e3szS0pLl5ORw006ePMkAMDU1NRYQEMDVWN33DwDT0NBgiYmJ3LTY2FipLya//vorA8AeP3783veroaGQrXvikK3oR1NTk5svPj6eaWhosIkTJ7JXr14xa2tr5ufnx8rKyrh5xNu8tbU1F8aMMbZ7924GgK1YsYKb9u6+5uDBgwwA++GHHyTqGzx4MBMIBBLbxbv7mj179lS6D3l3+2OMsSlTpjAdHR1WXFzMTQsMDGQA2NKlS7lpJSUlzNvbm5mbm3OfuYr2B127dmUeHh4S6xOJRKxdu3bM2dlZ6vlJ3avx6eLu3bvj8uXLCAkJQWxsLH755RcEBQXB2tpa4hREZGQkAOCLL76QWP7LL78EAO7U56lTp5CXl4fZs2dLXV98X2MGbW1t7v+vXr1Cbm4uOnTogFu3btX0ZSEsLAxXr15FUlISN2379u1o0qQJAgMD37v89u3b4efnBycnJwCAvr4+evfuLXHKeODAgVBTU8OuXbu4aXfu3MG9e/ckWiNHRkbC398fAQEB3DQ9PT1MnjwZKSkpuHfvXoU1ZGZmIiYmBmPGjIGhoSE3vXv37nB1dYWlpSV8fHzg7e2NFStWSFw/et/7161bN4nu6Tw9PWFgYIDk5OT3vjeE1NSLFy+wdu1aAMCYMWNw8uRJnDp1ivs5duwYN6+7uzsWLlyIDRs2ICgoCNnZ2di8eTPU1KSbnISFhUk0Rhw8eDAsLS25/VVFIiMjoaqqimnTpklM//LLL8EYk6ilJt7ef+Xl5SE7OxsdOnRAYWEhHjx4IDGvmpoapkyZwv2uoaGBKVOmICsrCzdv3qxw/S9fvsSZM2cwdOhQbv3Z2dl48eIFgoKCkJCQgPT09FrVTqqvVrfwtGrVCvv378erV69w7do1zJkzB3l5eRg8eDAXAKmpqVBRUeFCR8zCwgJGRkZITU0FAC7UqnMf2LuOHDmCNm3aQEtLCyYmJjAzM8OaNWsqvP74PqGhodDU1ORCMTc3F0eOHMHIkSPfG/Y5OTmIjIxEYGAgEhMTuZ/27dvjxo0bePToEQDA1NQUXbt2xe7du7lld+3aBTU1NQwcOJCblpqaimbNmkk9T4sWLbjHKyKe7uzsLPVYs2bNoKKigj/++ANHjx7FH3/8AU9PT3h7e1fr/WvatKnUNGNjY15uFSIN26FDh+Dm5sa1g/j000/RvXt3dOvWjfvp3LmzxDKzZs2Cl5cXrl27hvnz51faqv7dbUMgEMDJyanKtgOpqamwsrKSulPgfdvj+9y9excDBgyAoaEhDAwMYGZmhlGjRgGA1DZoZWUFXV1diWkuLi4AUGntiYmJYIzhu+++g5mZmcTP/PnzAci2sSp544N6fNLQ0ECrVq3QqlUruLi4YNy4cdizZw/3BwTefzRaWxcvXkRISAg6duyIP//8E5aWllBXV8fGjRslGkxVl7GxMfr06YPt27dj3rx52Lt3L0pKSrgPfVX27NmDkpISLF26FEuXLpV6fPv27VyDp2HDhmHcuHGIiYmBt7c3du/eja5du8LU1LTGNddWYGAg1qxZg6CgIKioqCA0NBQjR46EpqZmpe+fqqpqhetiNIgTqSMvX77E9OnTceTIEaxYsQLl5eXV7sEsOTkZCQkJAKAQYy7n5OQgMDAQBgYG+P777+Ho6AgtLS3cunULX3/9dZ3crihex8yZMxEUFFThPO8eBJG6V2fdKoqb2GdmZgJ4cw+nSCRCQkIC940PeNPKNicnB7a2tgDAnYK8c+dOjf7g+/btg5aWFk6cOCFx2nPjxo21fg1hYWHo168frl+/ju3bt8PHxwdubm7vXW779u1wd3eX+HIhtnbtWoSHh3Mh279/f0yZMoU7Zfzo0SPMmTNHYhlbW1s8fPhQal3iU0ji9+5d4uninc3b3l1fZGQktLW1cfDgQXz00UdIT0/HP//8876XWiV5uVeRKJ7Dhw9j8uTJ8PX1xd27d2FlZVXtnotEIhHGjh0LAwMDzJgxAz/99BMGDx4scXZI7N1tgzGGxMREeHp6Vrp+W1tb/Pvvv1L3vb9vewQq3ybOnTuHFy9eYP/+/ejYsSM3/fHjxxXOn5GRgYKCAomjWfEZssruv3VwcADw5vbHbt26VVojka0any4+e/ZshUcv4msa4tOcwcHBAIDly5dLzLds2TIAQO/evQEAPXr0gL6+Pn7++Wep22SqOkpSVVWFQCCQaO6ekpLyQQOa9+rVC6ampliyZAnOnz9fraPYp0+f4sKFCxg6dCgGDx4s9TNu3DgkJibi6tWrAAAjIyMEBQVh9+7d2LlzJzQ0NNC/f3+JdQYHB+PatWu4fPkyN62goADr1q2DnZ1dpafCLC0t4e3tjc2bN0ucbjp16pTUdVzx+xcQEIC4uDh4enrC09NT4lR2TYl3ANTjE6muhw8fYvDgwQgLC8PixYtx+PBhWFlZ1Wgdy5YtQ3R0NNatW4dFixahXbt2+Pjjj5GdnS0175YtW5CXl8f9vnfvXmRmZqJXr16Vrj84OBjl5eUSt9gBwO+//w6BQFDlspVtE+IzQ2/v40pLS/Hnn39WuB6hUMhdoxbPu3btWpiZmcHX17fCZczNzdGpUyesXbuWO/h5W0W3LJK6V+Mj2c8++wyFhYUYMGAAmjdvjtLSUkRHR2PXrl2ws7PDuHHjALwZjm3MmDFYt24dd2rk2rVr2Lx5M/r3789dUzEwMMDvv/+OiRMnolWrVhgxYgSMjY0RGxuLwsJCbN68ucI6evfujWXLlqFnz54YMWIEsrKysHr1ajg5OSEuLq5Wb4a6ujqGDRuGVatWQVVVFcOHD3/vMuHh4WCMISQkpMLHg4ODoaamhu3bt6N169YA3lz/HTVqFP78808EBQVJ3XM8e/Zs7NixA7169cK0adNgYmKCzZs34/Hjx9i3b1+Vo4v8/PPP6N27NwICAjB+/Hi8fPkSK1euhJubG/Lz87n53n3/zMzMoKmpCYFAAKFQiOXLl+Pjjz+u0c314o39m2++wbBhw6Curo6+fftKXUsiJD09HQsXLsTWrVsxZswY3Lt3r9JuEY8dOybVEAgA2rVrh5KSEnz33XcYO3Ys+vbtC+DN/ane3t6YOnWq1JdGExMTBAQEYNy4cXj+/DmWL18OJycnTJo0qdJa+/bti86dO+Obb75BSkoKvLy8cPLkSRw6dAgzZsyocrxab29vqKqqYsmSJcjNzYWmpia6dOmCdu3awdjYGGPGjMG0adMgEAiwdevWSg8srKyssGTJEqSkpMDFxQW7du1CTEwM1q1bV2VHPatXr0ZAQAA8PDwwadIkODg44Pnz57h8+TLS0tK4+4iJDNW0OfKxY8fY+PHjWfPmzZmenh7T0NBgTk5O7LPPPmPPnz+XmLesrIwtXLiQ2dvbM3V1ddakSRM2Z84ciebkYhEREaxdu3ZMW1ubGRgYMH9/f7Zjxw7u8Ypu4fn777+Zs7Mz09TUZM2bN2cbN25k8+fPZ+++rOrcwiN27do1BoD16NGjWu+Hh4cHa9q0aZXzdOrUiZmbm3O3FLx+/Zppa2szAGzbtm0VLpOUlMQGDx7MjIyMmJaWFvP395e6f7WiJvuMMbZv3z7WokULpqmpyVxdXdn+/fur/f7NmzePu5/Q1taWbd68mQFgn3zyiVSN776vjDG2aNEiZm1tzVRUVJTqdh66had6Xr58yb7++mumo6PDhgwZwh4+fFjpvFXdwgOAbdiwgbVq1YrZ2NhI3LLGGGMrVqxgANiuXbsYY//b5nfs2MHmzJnDzM3Nmba2NuvduzdLTU2VWLaibSUvL499/vnnzMrKiqmrqzNnZ2f266+/StxmyFjF28T69euZg4MDU1VVldjvREVFsTZt2jBtbW1mZWXF3Q757r4pMDCQubm5sRs3brC2bdsyLS0tZmtry1atWiXxPJXtD5KSklhYWBizsLBg6urqzNramvXp04ft3bu30vee1J0ah2xDJ76/d8uWLXyXwiuhUMi2bNnCbG1tmYeHBzt8+LDUDoX8D4Vs1QoLC9mSJUuYkZER69q1K7t+/Xq9Pr84ZPfs2VOvz0uIwgx1V1/Wr18PPT29ChtNKBNVVVWMHj0aDx8+xIQJEzBu3Dh07NiRBocnNSIUCrF+/Xo4OTlh9+7d2LNnD/79918a85UoDQrZ/3f48GEsWbIE69atw6RJk+g64v/T1NTE9OnTkZycjK5duyIoKAj9+vXD3bt3+S6NyDHGGPbt2wd3d3f88ssv+P3333Ht2jVq5UqUDoXs//vss8+wYMECBAcHV9iJv7LT19fHggULkJiYCFtbW/j5+WHs2LG1vhGfNFxnz55FmzZt8Omnn2LGjBm4d+8ehg4dWmWDPUIaKvrU/7+UlBQUFRXh4MGDMh0DVtE1btwYf/zxB+7evQuhUIjmzZtj6tSpFbb+JMpDJBLh2LFj6N69O/r3749+/fohMTERH330kVwMU9mpUycwxjB48GC+SyFKhkKW1IqDgwO2bduGK1euID8/H97e3ujZsyeOHTtWJ73VEMWQn5+P1atXw9XVFWPHjkX79u2RnJyMuXPn0iUXQkAhSz6Ql5cXtmzZgpSUFLRp0wZjx46Fq6sr/vzzT4n7cknD8vjxY3z55ZewsbHBP//8g7lz5+LJkydYsGABGjVqxHd5hMgNCllSJywsLLBgwQI8efIEc+fOxd9//w0bGxvMmDGDGkk1EOXl5YiMjET//v3RvHlzPH36FEePHsWNGzcQFhZWo45LCFEWFLKkTmlqaiIsLAw3btzAkSNH8OLFC/j5+aFdu3bYuHEjCgoK+C6R1JD4CNXe3h6TJk2Cu7s7EhISsHv3brRv3576rCakChSyRCbE/SJv3boVGRkZGD58OH7//XdYWVlh6tSpuH37Nt8lkiqUlZXh4MGD6N27N5ycnHDz5k2sWrUKqamp+OGHHyoc+pAQIo1ClsicsbExPvvsM8TGxuLkyZMoLS1Fhw4d4O7ujrlz5+Ly5csSAz0QfuTn5+PAgQMYP348rK2tMWPGDLRp0wbJyck4fPgwQkJCKhwInRBSOQFjNCAoqX95eXk4ceIEIiIicPToUaipqaFPnz7o27cvunfvrnAtU8vKyhAZGYng4GC5uGWlutLT03H48GEcPnwYp0+fhp2dHUJCQtC3b1+0b9+e7m0l5ANRyBLeCYVCREdH4/Dhwzh06BCePHmCrl27IiQkBH369IG1tTXfJb6XooQsYwwxMTGIiIhAREQEYmNj0b59ey5YXVxc+C6RkAaFQpbInYcPH+Lw4cOIiIhAdHQ0vL29ERISgpCQEHh5ecllQxt5Dtni4mKcPXuWO2LNzc1Fz549ERISgl69etEtN4TIEIUskWvZ2dk4duwYIiIicPz4cRgbGyM4OBitW7eGr68vXF1d5eI6oTyFbF5eHm7fvo2bN2/i4sWLOHnyJBo1asR9UQkMDISGhgavNRKiLChkicIoKSnBuXPncOLECdy4cQO3b9+GUCiEl5cXfH194evri5YtW8LNza3eg46vkH39+jUXqOKfR48ewdLSEr6+vmjdujX69OkDT09PuTwDQEhDRyFLFJZIJEJCQoJEwNy6dQulpaXw9PTkgtfX1xdubm4yPXqrj5DNzc3FrVu3JF5vQkICbGxsJL5k+Pr6wsLCQiY1EEJqhkKWNCgikQiJiYlc4Ir/LSoqgqenJzw8PGBlZQVLS0vuX0tLS1hYWHxQj0UfGrKMMeTl5SEzM1PiJyMjA0+ePMHt27eRmJiIJk2aSHx58PX1hbm5ea3rJoTIFoUsafBEIhGSk5Nx8+ZN3Lt3TyLAMjMz8fz5c4hEIpiYmEgEr/hHPM3U1BTq6upQV1eHmpoa96Ourg7GGI4fP44uXbpAIBBAKBRCKBSirKwMQqEQxcXFePbsmVSAvv17QUEBtLS0pJ7b2toa3t7eaNmyJczMzPh+OwkhNUAhS5ReeXk5/vvvP6nQe/f37OxsLjirS0VFBWpqatDU1ETjxo2lQvzd342MjOjaKSENCIUsIbUgEom4o1Rx8IoDVXykq6qqSp05EKLkKGQJIYQQGaGv2YQQQoiMUMgSQgghMkIhSwghhMgIhSwhhBAiIxSyhBBCiIxQyBJCCCEyQiFLCCGEyAiFLCGEECIjFLKEEEKIjFDIEkIIITJCIUsIIYTICIUsIYQQIiMUsoQQQoiMUMgSQgghMkIhSwghhMgIhSwhhBAiIxSyhBBCiIxQyBJCCCEyQiFLSAMgEAiwYMECvssghLyDQpbwYtOmTRAIBNDS0kJ6errU4506dYK7uzsPlcmvyMhIClJCFAyFLOFVSUkJFi9ezHcZCiEyMhILFy6s8LGioiJ8++239VwRIeR9KGQJr7y9vbF+/XpkZGTwXYrcKigoeO88WlpaUFNTq4dqCCE1QSFLeDV37lyUl5e/92hWKBRi0aJFcHR0hKamJuzs7DB37lyUlJRIzGdnZ4c+ffrg0qVL8Pf3h5aWFhwcHLBlyxaJ+V6+fImZM2fCw8MDenp6MDAwQK9evRAbGyv13CtXroSbmxt0dHRgbGwMPz8/hIeHc4/n5eVhxowZsLOzg6amJszNzdG9e3fcunVLYj1Xr15Fz549YWhoCB0dHQQGBiIqKkpingULFkAgEODevXsYMWIEjI2NERAQgLFjx2L16tUA3lx/Ff+IvX1Ndu/evRAIBDh//rzUa1m7di0EAgHu3LnDTXvw4AEGDx4MExMTaGlpwc/PDxEREVX9OQgh1UQhS3hlb2+PsLCw9x7NTpw4EfPmzUPLli3x+++/IzAwED///DOGDRsmNW9iYiIGDx6M7t27Y+nSpTA2NsbYsWNx9+5dbp7k5GQcPHgQffr0wbJlyzBr1izEx8cjMDBQoo7169dj2rRpcHV1xfLly7Fw4UJ4e3vj6tWr3DwfffQR1qxZg0GDBuHPP//EzJkzoa2tjfv373PznDlzBh07dsTr168xf/58/PTTT8jJyUGXLl1w7do1qdcwZMgQFBYW4qeffsKkSZMwZcoUdO/eHQCwdetW7qcivXv3hp6eHnbv3i312K5du+Dm5sZd77579y7atGmD+/fvY/bs2Vi6dCl0dXXRv39/HDhwoNK/ByGkmhghPNi4cSMDwK5fv86SkpKYmpoamzZtGvd4YGAgc3NzY4wxFhMTwwCwiRMnSqxj5syZDAA7c+YMN83W1pYBYBcuXOCmZWVlMU1NTfbll19y04qLi1l5ebnE+h4/fsw0NTXZ999/z03r168fV0dlDA0N2SeffFLp4yKRiDk7O7OgoCAmEom46YWFhcze3p51796dmzZ//nwGgA0fPlxqPZ988gmrbJMFwObPn8/9Pnz4cGZubs6EQiE3LTMzk6moqEi8vq5duzIPDw9WXFwsUW+7du2Ys7Nzla+bEPJ+dCRLeOfg4IDRo0dj3bp1yMzMlHo8MjISAPDFF19ITP/yyy8BAEePHpWY7urqig4dOnC/m5mZoVmzZkhOTuamaWpqQkXlzce/vLwcL168gJ6eHpo1ayZxmtfIyAhpaWm4fv16pfUbGRnh6tWrlR6Jx8TEICEhASNGjMCLFy+QnZ2N7OxsFBQUoGvXrrhw4QJEIpHEMh999FGlz1cdoaGhyMrKwrlz57hpe/fuhUgkQmhoKIA3p8zPnDmDoUOHIi8vj6vrxYsXCAoKQkJCQoUtvwkh1UchS+TCt99+C6FQWOG12dTUVKioqMDJyUliuoWFBYyMjJCamioxvWnTplLrMDY2xqtXr7jfRSIRfv/9dzg7O0NTUxOmpqYwMzNDXNz/tW9/IU3vYRzH3zqWyaYXlqaZjBwFJjFJ2EVWS5Bk6UWMcDfSMOjGKIKSkrK/KJgWXdRgYhdSQSOri7qISroKoS6CUyAiXVXQlGBCQWb1nAtxnDU7R+mME5zPCwb+vn73/T27er7/nj+Ynp5O9Tt69Chutxu/38+6devYv39/xjnq+fPnefXqFRUVFfj9fk6fPp2W0CcmJgCIRCIUFxenfQYHB5mZmUl7J8xto/+K+bPfeDyeaovH49TU1LB+/XpgblvdzOjq6sqI69SpUwBMTk7+Uhwi/3e6jii/hcrKSlpbWxkYGODYsWML9vnrRZ+/43A4Fmw3s9TfPT09dHV1sXfvXs6dO0dRURG5ubkcOnQobVVZVVXF+Pg49+/f58GDB9y+fZtoNMrJkydT5TQtLS1s3bqVu3fv8vDhQ/r6+ujt7eXOnTsEg8HUeH19fdTU1CwYm9vtTnvOz89f1G/9mby8vNS5ajQaJZFI8PTpU3p6elJ95uM6cuQIjY2NC47z48RGRJZGSVZ+GydOnOD69ev09vamtXs8Hr5//87ExARVVVWp9kQiQTKZxOPxLPldw8PD1NfXc/Xq1bT2ZDLJypUr09pcLhfhcJhwOMyXL18IhUJ0d3fT2dnJ8uXLASgrK6O9vZ329nYmJyfZtGkT3d3dBINBvF4vAIWFhTQ0NCw51nmLnWTMC4fDDA0NMTIywtjYGGaW2iqGuYkNgNPp/KW4ROTntF0svw2v10trayuxWIz379+n2nfu3AnApUuX0vpfvHgRmLtNu1QOhyNtZQtw69atjDPIDx8+pD0vW7aMDRs2YGbMzs7y7du3jK3ekpISVq9enSovqq2txev10t/fz8ePHzNimZqaWlTMLpcLmJsILEZDQwNFRUXE43Hi8Th+vz9tG7qkpITt27cTi8UWPAtfbFwi8nNaycpv5fjx41y7do3x8XGqq6sB8Pl8RCIRBgYGSCaTBAIBnj17xtDQELt27aK+vn7J72lububs2bO0tbWxefNmXr58yY0bN1Kru3k7duygtLSUuro6Vq1axdjYGJcvX6apqYmCggKSySRr1qxh9+7d+Hw+3G43jx8/5vnz51y4cAGA3NxcBgcHCQaDVFdX09bWRnl5Oe/evePJkycUFhZy7969f4y5trYWgIMHD9LY2IjD4ViwhGme0+kkFApx8+ZNPn36RH9/f0afK1eusGXLFjZu3Mi+ffuorKwkkUgwOjrK27dvF6wbFpEl+E/vNsv/1l9LeH4UiUQMSCudmZ2dtTNnztjatWvN6XRaRUWFdXZ2ppWemM2V8DQ1NWWMGQgELBAIpJ4/f/5shw8ftrKyMsvPz7e6ujobHR3N6BeLxWzbtm22YsUKy8vLM6/Xax0dHTY9PW1mZjMzM9bR0WE+n88KCgrM5XKZz+ezaDSaEcOLFy8sFAqlxvJ4PNbS0mIjIyOpPvMlPFNTUxnf//r1qx04cMCKi4stJycnrZyHH0p45j169MgAy8nJsTdv3mT838zs9evXtmfPHistLTWn02nl5eXW3Nxsw8PDC/YXkcXLMfthz0xERET+FTqTFRERyRIlWRERkSxRkhUREckSJVkREZEsUZIVERHJEiVZERGRLFGSFRERyRIlWRERkSxRkhUREckSJVkREZEsUZIVERHJEiVZERGRLPkTZ5apJuCmVZwAAAAASUVORK5CYII=" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 38 - }, - { - "cell_type": "markdown", - "source": [ - "## Mean-based SSM Analysis\n", - "\n", - "### Conducting SSM for a group's mean scores\n", - "\n", - "To begin, let's say that we want to use the SSM to describe the interpersonal problems of the average individual in the entire dataset. Although it is possible to analyze the raw scores contained in `jz2017`, our results will be more interpretable if we standardize the scores first. We can do this using the `standardize()` function. \n", - "\n", - "The first argument to this function is `data`, a dataframe containing the circumplex scales to be standardized. The second argment is `scales` and specifies where in `data` the circumplex scales are (either in terms of their variable names or their column numbers). The third argument is `angles` and specifies the angle of each of the circumplex scales included in `scales`. Note that the `scales` argument should be a `circumplex.Scales` object and `angles` should be a `np.array` that have the same ordering and length. Finally, the fourth argument is `norms`, a dataframe containing the normative data we will use to standardize the circumplex scales. Here, we will use normative data for the IIP-SC by loading the `iipsc` instrument." - ], - "metadata": { - "collapsed": false - }, - "id": "4113f134c1dabad1" - }, - { - "cell_type": "code", - "source": [ - "df = circumplex.utils.standardize(\n", - " data=jz2017.data, \n", - " scales=jz2017.scales, \n", - " angles=np.array((90, 135, 180, 225, 270, 315, 360, 45)), \n", - " instrument=circumplex.instrument.load_instrument('IIPSC'), \n", - " sample=1,\n", - ")\n", - "\n", - "jz2017s = jz2017.attach_data(df)\n", - "jz2017s.data" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-08T12:10:06.839534Z", - "start_time": "2024-06-08T12:10:06.793322Z" - } - }, - "id": "6f8df300b9442ef0", - "outputs": [ - { - "data": { - "text/plain": [ - " Gender PA BC DE FG HI JK LM NO PARPD ... \\\n", - "0 Female 1.50 1.50 1.25 1.00 2.00 2.50 2.25 2.50 4 ... \n", - "1 Female 0.00 0.25 0.00 0.25 1.25 1.75 2.25 2.25 1 ... \n", - "2 Female 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 ... \n", - "3 Male 2.00 1.75 1.75 2.50 2.00 1.75 2.00 2.50 1 ... \n", - "4 Female 0.25 0.50 0.25 0.00 0.00 0.00 0.00 0.00 0 ... \n", - "... ... ... ... ... ... ... ... ... ... ... ... \n", - "1161 Male 0.00 1.00 1.00 2.50 2.50 2.50 1.75 1.00 3 ... \n", - "1162 Female 0.00 0.00 0.00 0.00 0.00 0.00 2.25 0.00 1 ... \n", - "1163 Male 0.00 0.50 0.25 0.25 0.00 0.25 0.75 0.50 2 ... \n", - "1164 Female 0.50 0.25 0.00 0.25 0.25 0.25 0.25 0.50 3 ... \n", - "1165 Female 1.00 0.00 0.00 0.00 0.00 0.00 1.00 0.75 4 ... \n", - "\n", - " DPNPD OCPD PA_z BC_z DE_z FG_z HI_z JK_z \\\n", - "0 4 6 1.121212 1.025362 0.409357 -0.050132 0.633880 1.307918 \n", - "1 1 0 -1.151515 -0.786232 -1.052632 -0.841689 -0.185792 0.428152 \n", - "2 0 1 -1.151515 -1.148551 -1.052632 -1.105541 -1.551913 -1.624633 \n", - "3 0 0 1.878788 1.387681 0.994152 1.532982 0.633880 0.428152 \n", - "4 0 0 -0.772727 -0.423913 -0.760234 -1.105541 -1.551913 -1.624633 \n", - "... ... ... ... ... ... ... ... ... \n", - "1161 3 4 -1.151515 0.300725 0.116959 1.532982 1.180328 1.307918 \n", - "1162 0 0 -1.151515 -1.148551 -1.052632 -1.105541 -1.551913 -1.624633 \n", - "1163 0 1 -1.151515 -0.423913 -0.760234 -0.841689 -1.551913 -1.331378 \n", - "1164 0 2 -0.393939 -0.786232 -1.052632 -0.841689 -1.278689 -1.331378 \n", - "1165 1 5 0.363636 -1.148551 -1.052632 -1.105541 -1.551913 -1.624633 \n", - "\n", - " LM_z NO_z \n", - "0 0.951515 1.84375 \n", - "1 0.951515 1.53125 \n", - "2 -1.775758 -1.28125 \n", - "3 0.648485 1.84375 \n", - "4 -1.775758 -1.28125 \n", - "... ... ... \n", - "1161 0.345455 -0.03125 \n", - "1162 0.951515 -1.28125 \n", - "1163 -0.866667 -0.65625 \n", - "1164 -1.472727 -0.65625 \n", - "1165 -0.563636 -0.34375 \n", - "\n", - "[1166 rows x 27 columns]" - ], - "text/html": [ - "
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" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 39 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "Now we can use the `ssm_analyze()` function to perform the SSM analysis. The first three arguments are the same as the first three arguments to `standardize()`. We can pass the new `jz2017s` object that contains standardized data as `data` and the same vectors to `scales` and `angles` since these haven't changed.", - "id": "c16f217242651e9d" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-08T12:10:07.655949Z", - "start_time": "2024-06-08T12:10:07.639165Z" - } - }, - "cell_type": "code", - "source": [ - "results = circumplex.ssm_analyse(\n", - " data = jz2017s.data,\n", - " scales=['PA_z', 'BC_z', 'DE_z', 'FG_z', 'HI_z', 'JK_z', 'LM_z', 'NO_z'],\n", - " angles=(90, 135, 180, 225, 270, 315, 360, 45),\n", - ")" - ], - "id": "876dadadc07e23a9", - "outputs": [], - "execution_count": 40 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "The output of the function has been saved in the `results` object, and we can extract a table of the results using the `table` property. This will output a table of the elevation, amplitude, displacement, and fit for the cosine curve estimated by the SSM. The `table` property is a `pandas.DataFrame` object.", - "id": "356d62e50e3d757c" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-08T12:10:08.456914Z", - "start_time": "2024-06-08T12:10:08.442393Z" - } - }, - "cell_type": "code", - "source": "results.table", - "id": "7c587d1d7f7c35fd", - "outputs": [ - { - "data": { - "text/plain": [ - " label group measure elevation xval yval amplitude \\\n", - "SSM SSM None None -0.225058 0.130532 -0.014815 0.13137 \n", - "\n", - " displacement r2 PA_z BC_z DE_z FG_z HI_z JK_z LM_z NO_z \n", - "SSM 353.524846 0.709645 90 135 180 225 270 315 360 45 " - ], - "text/html": [ - "
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" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 41 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "That was pretty easy! We can now write up these results. However, the `circumplex` package has some features that can make what we just did even easier. ", - "id": "e2934188eaee0af" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-08T12:10:09.704555Z", - "start_time": "2024-06-08T12:10:09.414973Z" - } - }, - "cell_type": "code", - "source": "results.plot()", - "id": "17120bfc93c74a9d", - "outputs": [ - { - "data": { - "text/plain": [ - "(
, )" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 42 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-08T12:10:10.138441Z", - "start_time": "2024-06-08T12:10:09.946040Z" - } - }, - "cell_type": "code", - "source": "results.results[0].profile_plot()", - "id": "c5ae6bacbc5bb83d", - "outputs": [ - { - "data": { - "text/plain": [ - "(
,\n", - " )" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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JzIUQQgghhDABkpgLIYQQQghhAiQxF0IIIYQQwgRIYi6EEEIIIYQJkMRcCCGEEEIIEyB1zFNRZGQkjx8/JkuWLNGW7hZCCCGEEKZBURRevXpF3rx50eu17bOWxDwVPX78WBaYEEIIIYRIBx4+fEj+/Pk1jUES81SUJUsWQP1GOzg4aByNEEIIIYR4V0BAAAUKFDDmbVqSxDwVRQ1fcXBwkMRcCCGEEJlScHAw1apVA+DMmTPY2tpqHFHsTGHYsSTmQgghhBAi1SiKwvXr1437Im5SlUUIIYQQQggTIIm5EEIIIYQQJkAScyGEEEIIIUyAJOZCCCGEEEKYAEnMhRBCCCGEMAFSlSWjMBjg6FF48gTy5IHatcHMTOuohBBCCJHJ6XQ6ChUqZNyPk+QykphnCJs3w5Ah8OjRm2P588PPP0O7dtrFJYQQQohMz9bWlnv37sXfSHIZQIaypH+bN0OHDtF/kAG8vNTjmzdrE5cQQgghREJILmOkU6TSe6oJCAjA0dERf3//1Fn502CAwoVj/iBH0enUd5t372a6j4KEEEIIkQ6YQC6T6vlaIkiPeXp29GjcP8gAigIPH6rthBBCCCE08Pr1a6pVq0a1atV4/fp19Acll4lGxpinZ0+epGw7IYQQQogUFhkZydmzZ4370UguE430mKdnefKkbDshhBBCiLQkuUw0kpinZ7Vrq+Ou4io9pNNBgQJqOyGEEEIIUyO5TDSSmKdnZmZqGSGI+wd6zhyZ+CmEEEII0xSVy8RWiyQqt8lEuYwk5uldu3awcSPkyxfzsQYNMlXtTyGEEEKkQ+3awQcfxDyeP7+a42SiXEYmf2YE7dqBm9ub1bJevIBBg8DdHa5fhzJltI5QCCGEECJ2//4L58+r+7//Dnq9rPwp0jkzM6hX782/Dx6ELVtg9Gj4+2/NwhJCCCGEyJEjR9wPLligfm3RArp0SZuATJQMZcmopk5Vk/WtW+Gff7SORgghhBCZlJ2dHc+fP+f58+fY2dlFf/DVK1ixQt0fNCjtgzMxkphnVKVKQa9e6v7w4bFPqhBCCCGE0NLq1WpyXrIkNGmidTSak8Q8I5swAWxt4eRJdViLEEIIIYSpiIyEefPU/YED1bHlmZz8D2RkefLA0KHq/ujREBGhbTxCCCGEyHRev35NvXr1qFevHq9fv37zwP79cPMmZMkC3btrF6AJkcQ8oxs+HHLkgFu3YPlyraMRQgghRCYTGRnJ4cOHOXz4MJGRkW8emDtX/dqjBzg4aBKbqZHEPKNzcICxY9X9CRMgKEjTcIQQQgghuH0bdu5U9wcO1DYWE5JhEvMFCxZQuHBhrK2tqVGjBqdPn463/YYNG3BxccHa2pry5cuzM+qH4z+KojBu3Djy5MmDjY0NjRo14t9//03Np5B6vvwSihYFb2+YPVvraIQQQgiR2S1YoBamaNZMnfgpgAySmP/5558MHTqU8ePHc/78eSpWrEjTpk159uxZrO2PHz9O586d6dWrFxcuXKBt27a0bduWq1evGttMnz6duXPnsnjxYk6dOoWdnR1NmzYlJCQkrZ5WyrG0hMmT1f3p0+H5c23jEUIIIUTmFRj4Znjt4MHaxmJidIqS/uvo1ahRg2rVqjF//nxAHctUoEABBg0axKhRo2K0/+STTwgKCmL79u3GYx9++CGVKlVi8eLFKIpC3rx5GTZsGN988w0A/v7+5MqVi19//ZVOnTolKK6AgAAcHR3x9/fHQeuxU5GRUL06nDun1gmNGtclhBBCCJGKgoKCsLe3ByAwMBC71auhf38oUQI8PDSvxmJK+Vq67zEPCwvj3LlzNGrUyHhMr9fTqFEjTpw4Ees5J06ciNYeoGnTpsb2d+/exdvbO1obR0dHatSoEec1AUJDQwkICIi2mQy9Hn78Ud1fvFgd2yWEEEIIkZYURUokxiPd/2/4+PhgMBjIlStXtOO5cuXC29s71nO8vb3jbR/1NTHXBJg6dSqOjo7GrUCBAol+PqmqYUNo2hTCw+G777SORgghhBCZhK2tLba2tuDuDjdugL29Wo1FRJPuE3NTMnr0aPz9/Y3bw4cPtQ4pph9/BJ0O1q2Ds2e1jkYIIYQQGZydnR1BQUEEBQVht2yZelBKJMYq3SfmOXLkwMzMjKdPn0Y7/vTpU3Lnzh3rOblz5463fdTXxFwTwMrKCgcHh2ibyalYEbp0UfdHjlQ/UhJCCCGESG137kDU/D4pkRirdJ+YW1paUqVKFQ4cOGA8FhkZyYEDB3B1dY31HFdX12jtAfbt22dsX6RIEXLnzh2tTUBAAKdOnYrzmunKpElqpZaDB2HPHq2jEUIIIURmEFUisUkTKFVK62hMUrpPzAGGDh3KsmXLWLVqFTdu3KBfv34EBQXRs2dPALp168bo0aON7YcMGcLu3bv56aef8PDwYMKECZw9e5aB/7170+l0fPXVV/zwww9s3bqVK1eu0K1bN/LmzUvbtm21eIopq3DhN+9UR45UK7YIIYQQQqSCkJAQWjZtSsu5cwkBKZEYD3OtA0gJn3zyCc+fP2fcuHF4e3tTqVIldu/ebZy8+eDBA/RvzfqtWbMma9eu5bvvvuPbb7+lRIkS/PXXX5QrV87YZsSIEQQFBdGnTx/8/PyoVasWu3fvxtraOs2fX6r49lu1hujly7BmDXTtqnVEQgghhMiADAYDO/fuVfeLFIHmzTWOyHRliDrmpsqU6mLGato0GD0aChaEmzcho7zpEEIIIYTJCAoMxD5LFgACp03DbuRIjSOKzpTytQwxlEUk0ZAhkC8fPHigjvsSQgghhEhphw+/2ZdP6OMliXlmZmMD33+v7k+eDH5+moYjhBBCiAxo0aI3+46O2sWRDkhintl17w5ly8LLl+rQFiGEEEKIlHL3LuzcqXUU6YYk5pmdmRlMnaru//wzmOKiSEIIIYRInxYulDVTEkEScwGtWkHt2hASAuPHax2NEEIIITKCoCD45Reto0hXJDEXoNPB9Onq/qpVcPWqtvEIIYQQIv1bswb8/LArWhQlIgJFUbCzs9M6KpMmiblQffghtG+vLjb01mJMQgghhBCJpigwb566P2CAOnRWvJck5uKNyZPVX5zt2+HIEa2jEUIIIUR6deiQ+gm8rS18/rnW0aQbkpiLN0qVgt691f0RI2SyhhBCCCGSJqq3vFs3Qqyt6dixIx07diQkJETbuEycrPyZikxpJakE8/aGYsUgOBg2blSHtwghhBBCJNT9+1C0qDo89to1ggoVwt7eHoDAwECTG2duSvma9JiL6HLnhmHD1P3RoyE8XNt4hBBCCJG+LFyoJuUNG0KZMlpHk65IYi5iGj4cnJ3h33+lzJEQQgghEi44+E3uMGiQtrGkQ5KYi5iyZIFx49T9iRMhMFDbeIQQQgiRPqxdC76+ULiwuk6KSBRJzEXs+vRRx5o/fQqzZmkdjRBCCCFMnZRITDZJzEXsLC3V8okAM2bAs2faxiOEEEII03bkCFy+rJZI7NVL62jSJUnMRdw6doSqVdWhLN9/r3U0QgghhDBlUb3ln30GWbNqG0s6JYm5iJteD9Onq/tLloCnp7bxCCGEEMI0PXgAW7ao++9M+rS1tSUwMJDAwEBsbW01CC79kMRcxK9+fWjeHCIiYMwYraMRQgghhClatEgtkVi/PpQrF+0hnU6HnZ0ddnZ26HQ6jQJMHyQxF+83bRrodLB+PZw5o3U0QgghhDAlr1/D0qXq/uDB2saSzkliLt6vQgXo2lXdHzFCnXUthBBCCAHwxx9qicRChaB16xgPh4aG0qNHD3r06EFoaKgGAaYfkpiLhJk0Cays4NAh2L1b62iEEEIIYQoUBebOVffjKJEYERHBqlWrWLVqFREREWkcYPoiiblImIIF30zmGDkSDAZt4xFCCCGE9v75By5dAhsbKZGYAiQxFwk3ejQ4OcGVK/D771pHI4QQQgitRfWWf/YZZMumbSwZgCTmIuGyZVOTc4CxYyEkRNt4hBBCCKGdhw/jLJEokkYSc5E4gwZB/vzqL+P8+VpHI4QQQgitLF6sDm2tWxfKl9c6mgxBEnORODY2b1YBnTIFXr7UNh4hhBBCpL2QECmRmArSfWLu6+tLly5dcHBwwMnJiV69ehEYGBjvOSEhIQwYMIDs2bNjb29P+/btefr0abQ2Op0uxrZu3brUfCrpR7du6uIBL1/C1KlaRyOEEEKItLZuHfj4QIEC0KaN1tFkGOk+Me/SpQvXrl1j3759bN++nSNHjtCnT594z/n666/Ztm0bGzZs4PDhwzx+/Jh27drFaLdy5UqePHli3Nq2bZtKzyKdMTNTFx0CddLHw4faxiOEEEKItPNuiURz83ib29ra8uzZM549e4atrW0aBJh+6RQl/a4Wc+PGDcqUKcOZM2eoWrUqALt376ZFixY8evSIvHnzxjjH398fZ2dn1q5dS4cOHQDw8PCgdOnSnDhxgg8//BBQe8y3bNmSrGQ8ICAAR0dH/P39cXBwSPJ1TJKiqMvuHj4MPXrAypVaRySEEEKItHDsGNSqBdbW8OgRZM+udUTJYkr5WrruMT9x4gROTk7GpBygUaNG6PV6Tp06Fes5586dIzw8nEaNGhmPubi4ULBgQU6cOBGt7YABA8iRIwfVq1dnxYoVvO89TGhoKAEBAdG2DEungx9/VPdXrVJLKAohhBAi44vqLe/SJd0n5aYmXSfm3t7e5MyZM9oxc3NzsmXLhre3d5znWFpa4uTkFO14rly5op3z/fffs379evbt20f79u3p378/8+bNizeeqVOn4ujoaNwKFCiQtCeWXtSoAR06qL3no0ZpHY0QQgghUtujR7Bpk7qfwBKJoaGhDBgwgAEDBhAaGpqKwaV/JpmYjxo1KtbJl29vHh4eqRrD2LFj+eijj6hcuTIjR45kxIgRzJgxI95zRo8ejb+/v3F7mBnGXk+Zoo4t27kTDh3SOhohhBBCpKaoEol16kDFigk6JSIigoULF7Jw4UIiIiJSOcD0Lf7R+hoZNmwYPXr0iLdN0aJFyZ07N8+ePYt2PCIiAl9fX3Lnzh3reblz5yYsLAw/P79oveZPnz6N8xyAGjVqMGnSJEJDQ7Gysoq1jZWVVZyPZVglSkCfPrBwIYwcCSdPqsNchBBCCJGxvF0iURYUShUmmZg7Ozvj7Oz83naurq74+flx7tw5qlSpAsDBgweJjIykRo0asZ5TpUoVLCwsOHDgAO3btwfg5s2bPHjwAFdX1zjvdfHiRbJmzZr5Eu+EGDdOHWd++jRs3AgdO2odkRBCCCFS2p9/wvPn6kKDUqkuVZjkUJaEKl26NM2aNaN3796cPn2aY8eOMXDgQDp16mSsyOLl5YWLiwunT58GwNHRkV69ejF06FDc3d05d+4cPXv2xNXV1ViRZdu2bfzyyy9cvXoVT09PFi1axJQpUxgk7w5jlysXfPONuv/ttxAerm08QgghhEhZigJRc+36939viUSRNOk6MQdYs2YNLi4uNGzYkBYtWlCrVi2WRn3MAoSHh3Pz5k2Cg4ONx2bPnk2rVq1o3749derUIXfu3GzevNn4uIWFBQsWLMDV1ZVKlSqxZMkSZs2axfjx49P0uaUrw4ZBzpzg6QnLlmkdjRBCCCFS0smTcO4cWFlB795aR5Nhpes65qbOlOpipoUFn3zCjPXr8QYqVqnCvIULqV69epztr127xrhx4zh37hz3799n9uzZfPXVV9HaLFq0iEWLFnHv3j0AypYty7hx42jevHnqPREhhBBCRNe5s7raZ8+esGJFok4NCgrC3t4egMDAQOzs7FIjwiQzpXwt3feYC9Pw559/MvSvvxifMyfngYqKQtOmTWNMzn1bcHAwRYsWZdq0aXFOvM2fPz/Tpk3j3LlznD17lgYNGuDm5sa1a9dS6ZkIIYQQIprHj9U5ZCCTPlOZJOYZ0O7du6lVqxZOTk5kz56dVq1acfv27VS956xZs+jduzc958+nDLDYwwNba2tWxPOuulq1asyYMYNOnTrFOam2devWtGjRghIlSlCyZEkmT56Mvb09J0+eTKVnIoQQQohoFi+GiAh1tc/KlRN9uo2NDXfv3uXu3bvY2NikQoAZhyTmGVBQUBBDhw7l7NmzHDhwAL1ez//+9z8iIyPjPGfKlCnY29vHuz148CDWc8PCwjh37py6mmqHDlC9OvrgYBo5OsZYTTU5DAYD69atIygoKN4KOkIIIYRIIaGhsGSJup/E3nK9Xk/hwoUpXLgwer2knvGRKbUZUFQZyCgrVqzA2dmZ69evU65cuVjP+fLLL/n444/jvW5UpZt3+fj4YDAYyJUrl1rDfPp0qFePXLdu4ZEC5SWvXLmCq6srISEh2Nvbs2XLFsqUKZPs6wohhBDiPdavh2fPIF8++N//tI4mw5PEPAP6999/GTduHKdOncLHx8fYU/7gwYM4E/Ns2bKRLVu2lAmgbl1o0UJdDTQFVj8tVaoUFy9exN/fn40bN9K9e3cOHz4sybkQQgiRmhQF5s5V9/v1AwuLJF0mLCyMMWPGADB58mQsLS1TKsIMRz5PyIBat26Nr68vy5Yt49SpU5w6dQpQfzHikpyhLDly5MDMzIynT5++OThtGk+B3C9fwn/3TypLS0uKFy9OlSpVmDp1KhUrVuTnn39O1jWFEEII8R6nTsHZs2qJxD59knyZ8PBwZs6cycyZMwmXtU7iJT3mGcyLFy+4efMmy5Yto3bt2gD8888/7z0vOUNZLC0tqVKlCgcOHKDtfyuBRZYtywFbWwYGB8PIkeDurg5zSQGRkZGEhoamyLWEEEIIEYeoBYU6dYIErMgukk8S8wwma9asZM+enaVLl5InTx4ePHjAqFGj3ntecoeyDB06lO7du1O1alWqV6/OnDlzCDI3p6elJRw+DDt30u3PP8mXLx9Tp04F1B7869evG/e9vLy4ePEi9vb2FC9eHIDRo0fTvHlzChYsyKtXr1i7di2HDh1iz549SY5VCCGEEO/x5Ik6vhykRGIaksQ8g9Hr9axbt47BgwdTrlw5SpUqxdy5c6lXr16q3veTTz7h+fPnjBs3Dm9vbypVqsTuvXvJtWkTzJgBo0bxIFu2aLOxHz9+TOW3yi5FfcxVt25dDh06BMCzZ8/o1q0bT548wdHRkQoVKrBnzx4aN26cqs9HCCGEyNSWLFFLJNasCVWqaB1NpiErf6YiU1pJSjMvX0KxYurXlSuhRw+tIxJCCCFEfMLCoGBBePpUXe3zk0+SdTlZ+TPhZPKnSF1Zs8K336r7Y8fC69faxiOEEEKI+G3YoCblefNCu3ZaR5OpSGIuUt/AgVCgADx69GYiiRBCCCFMU9Tf6mSUSBRJI4m5SH3W1jBpkro/dSr4+mobjxBCCCFid/q0WibR0jJZJRLfZmNjw9WrV7l69So2NjYpcs2MShJzkTY++wzKlwc/PzU5F0IIIYTpebtEYs6cKXJJvV5P2bJlKVu2bLQiECIm+d8RacPMDH78Ud2fNw/iWKxICCGEEBrx9oY//1T3pUSiJiQxF2mnWTOoVw9CQ9WJoEIIIYQwHUuXQng4fPghVK2aYpcNCwtjwoQJTJgwId5VyIWUS0xVplR+x2ScOQPVq6urgF64ABUrah2REEIIIcLCoFAhtdd87Vro3DnFLi3lEhNOesxF2qpWDT7+GBQFRo/WOhohhBBCAGzapCbluXND+/ZaR5NpSWIu0t7kyWBuDrt2gbu71tEIIYQQYu5c9Wu/fmpFFqEJScxF2iteHPr2VfdHjIDISG3jEUIIITKzs2fh5Em1ZnkKlUgUSSOJudDGuHFgb6++GGzYoHU0QgghROYVVSLxk0/UoSxCM5KYC23kzAnDh6v7Y8aok06EEEIIkbaePYN169R9KZGoOUnMhXaGDoVcueD2bbVEkxBCCCHS1tKlaudYjRpq1TShKXOtA8gMgoKCMDMzi3HczMwMa2vraO3iotfroy1jm5i2wcHBxFUVU6fTYWtrm6S2r1+/JjKe8eFvl0OKta1OB6NGwddfY/f999CtGzg4EBISgsFgSNB139fW1tYWnU4HQGhoKBERESnS1sbGxrh6WVhYGOHh4SnS1tra2vizkpi24eHh8daGtbKywtzcPNFtIyIiCA0NjbOtpaUlFhYWiW5rMBgICQmJs62FhQWW/00+SkzbyMhIXr9+nSJtzc3NsbKyAkBRFIKDg1OkbWJ+7zP9a0QcbeU1Ql4j5DUi8W1jfY0ID4cFC9QDvXvDf+en9GuEwWDg8OHD6HS6aPGaymtEfP9vaU4Rqcbf318B4txatGgRrb2trW2cbevWrRutbY4cOeJsW758eSUiIsLYtlChQnG2LVOmTLTrlilTJs62hQoVita2atWqcbbNkSNHtLZ169aNs62tTqcooCjjximKoigtWrSI9//tbR06dIi3bWBgoLFt9+7d42377NkzY9v+/fvH2/bu3bvGtt988028ba9evWpsO378+Hjbnj592th2+vTp8bZ1d3c3tp0/f368bbdv325su3Llynjbrl+/3th2/fr18bZduXKlse327dvjbTt//nxjW3d393jbTp8+3dj29OnT8bYdP368se3Vq1fjbfvNN98Y2969ezfetv379ze2ffbsWbxtu3fvbmwbGBgYb9sOHTpE+xmOr21qvUZUrVo1WluTf42wtY3WVl4jVPIaoZLXiDe0eI0oVKiQsnLlSmXixInKF198oWTJkiXOtjqdTrGxsVEcHByUvHnzKlZWVnG2zZo1a7QY0uI1wt/fX9Ga9JhnQFeuXMHW1pbixYvj4uKCv7+/1iHFz9JSXQ30p5/UMk1CCCGEMBnx9VTfv3+fnj17Jug6iqLw+vVrXr9+TUBAQLxt4/sUJCNL9yt/+vr6MmjQILZt24Zer6d9+/b8/PPPxhWmYrN06VLWrl3L+fPnefXqFS9fvsTJySnZ131X1EpSjx8/jnUlqZT8mPrKlStMnjwZT09Pbt++HefHmwULFqRGjRrUqVOHOnXqUKxYsWgf6WjyMbWiYNeoEZw6BV9+Scjs2fIxdSLbysfU8jG1DGVJWlt5jVDJa0Ti26b714h//oHatdV1RTw8jNVYFEXh7t27HD9+nOPHj3PixAn+/fffWK9pbW1NoUKFKFSoEAULFqRAgQJkzZoVR0dH45YlSxYiIyP5448/0Ov19OnTB51OR2BgIC9evODVq1f4+/vz7Nkznjx5gre3N0+fPuXTTz/liy++MN4rNV8jXr58Sd68eU1i5c90n5g3b96cJ0+esGTJEsLDw+nZsyfVqlVj7dq1cZ4zZ84c4y/x6NGjY03Mk3Ldd2m1xKvBYODBgwfcunWLq1evcu7cOc6dO8etW7ditC1QoAANGjSgVatWNG3alCxZsqRZnNEcOQJ164KZGVy7BqVKaROHEEIIkRn06AGrVsGnn+K/cCEHDhxgz5497N27l3v37sVo7uLiQtWqVSlbtizlypWjbNmyFCpUyPimMj5BQUHGjs3AwMBoibEp0Cpfi026Tsxv3LhBmTJlOHPmDFWrVgVg9+7dtGjRgkePHpE3b954zz906BD169ePkZgn97pRTOkbDeDv78/Zs2c5cuQI7u7unDx5Mlpvi6WlJQ0aNMDNzQ03Nzfy5MmTtgG2bg3bt6tLAW/cmLb3FkIIITKLZ89QChRAFxbG166uLDh7NkY+8NFHH1GrVi1cXV2pUaMG2bJlS/LtJDFPuHSdmK9YsYJhw4bx8uVL47GIiAisra3ZsGED//vf/+I9P67EPKnXDQ0NjfYxXUBAAAUKFDCJb3RsgoKCOH78OHv27OHvv//G09PT+Jher6dRo0Z89tln/O9//0vUEJ4ku3oVKlZUVwI9cQI+/DD17ymEEEJkEkFBQWzcuJHISZPoefs2p4Ea/z1WqlQpmjZtStOmTalbt26KJs+SmCdcuq5j7u3tTc6cOaMdMzc3J1u2bHh7e6f5dadOnRptXFWBAgWSHENy9OjRA51Oh06nw8LCgiJFijBixIhoY/Du3bvH4MGD6dOnDwsWLCAyMpJ+/foxadIkatSoQWRkJHv37qVbt27kypWLLl26cOjQoTjHliZUSEgIAwYMIHv27Njb29O+fXuePn2qPliuHHTvru6PGAH/3Svquby7zZgxw3jdyZMnU7NmTWxtbWMMSxJCCCEyK0VR+Oeff+jVqxe5c+fmix49aHz7NgBbCxbkhx9+4MaNG3h4ePDzzz/TokULk0ucMxOTTMxHjRoVZzIWtXl4eGgdZgyjR4/G39/fuD18+FCzWJo1a8aTJ0+4c+cOs2fPZsmSJYwfP974uIeHB5GRkSxZsoRr164xe/ZsNm3ahJ+fHydPnsTT05OJEydSokQJgoODWbt2LfXr16ds2bLMnTsXPz+/JMX19ddfs23bNjZs2MDhw4d5/Pgx7dq1e9Pg++/B2hqOHlWHtQBPnjyJtq1YsQKdTkf79u2Np4WFhdGxY0f6SVUXIYQQgoCAAObNm4eLiwu1a9dmxYoVBAYG0i9XLvIDEdmz88OtW4wZMwYXFxetwxVR0rg8Y4I8e/ZMuXHjRrxbaGiosnz5csXJySnaueHh4YqZmZmyefPm994nqk7qy5cvox1P7nWjRNUxT+u6mN27d1fc3NyiHWvXrp1SuXLleM+bPn26UqRIkWjHIiMjlVOnTil9+vRR7OzsotUM7dOnj+Lh4ZHguPz8/BQLCwtlw4YNxmM3btxQAOXEiRNvGo4cqdY1L1NGUd6qxx7Fzc1NadCgQaz3WLlypeLo6JjgmIQQQoiM5ObNm8qgQYOi1RO3s7NTevbsqRw5ckSJrFVL/Rs7dmyaxfR27fa31w4wFVrla7ExyR5zZ2dnXFxc4t0sLS1xdXXFz8+Pc+fOGc89ePAgkZGR1KhRI547xC+1rquVq1evcvz4cWMJqLj4+/vHmNyh0+moXr06S5Ys4fHjx5QpUwa9Xk9wcDBLly7FxcUFc3NzbG1tsbe3p2zZsnFe/9y5c4SHh9OoUSPjMRcXFwoWLMiJEyfeNBw1CrJmhevX1Rnjb3n69Ck7duygV69eifgfEEIIITK2M2fO0LZtW0qVKsW8efN49eoVLi4uLFy4EG9vb1asWEFte3t0//yjlkj88kutQxaxMMnEPKFKly5Ns2bN6N27N6dPn+bYsWMMHDiQTp06GSuneHl54eLiwunTp43neXt7c/HiReNkxytXrnDx4kV8fX0TfF1Tt337duzt7bG2tqZ8+fI8e/aM4cOHx9ne09OTefPm0bdv3zjbODg4sHfvXjw8PFizZg0NGjQA1PKMr1+/xsXFhdGjR8c5Dt3b2xtLS8sYY8Bz5coVfey+kxOMGaPujxsHb9V7XbVqFVmyZIk+/EUIIYTIpI4cOUKTJk2oXr06f//9NzqdjlatWrF3716uX79Ov3793hRwmDdP/dqhA6RhPmNtbY27uzvu7u7R6q6LWGjdZZ9cL168UDp37qzY29srDg4OSs+ePZVXr14ZH49aUvftpYnjWvL47aWD33fdhNByKEujRo2Uf//9V7l48aLSvXt3pVevXnG2f/TokVKsWLF428Tl+vXryueff65YWFgY/x9r166tHDp0KEbbNWvWKJaWljGOV6tWTRkxYkT0g69fK0rBgurHbVOnGg+XKlVKGThwYJzxyFAWIYQQmcH+/fuVWrVqGf/2mpmZKd26dVNu3LgR+wnPnyuKlZX6d/X48bQN1sSZ0lCWdJ+YmzJTGWNuMBiUcuXKKb/88kuMtl5eXkqJEiWUrl27KgaD4b3XbtasmWJnZxdjs7W1VSwsLBSdTmd8kWjQoIFy7Ngx47kHDhyIdUx/wYIFlVmzZsW82erV6guIo6Oi+PgoR44cUQDl4sWLccYnibkQQoiM7Ny5c0rjxo2Nf2stLS2Vvn37Knfu3In/xClT1L+pVaooSmRk2gSbTphSYm6etv3zQgt6vZ5vv/2WoUOH8umnnxqX5PXy8qJ+/fpUqVKFlStXJmj1rl9++SXeJYpfvHjB6tWrWbZsGQcPHuTgwYO4ubkxffp0qlSpgoWFBQcOHDBWVLl58yYPHjzA1dU15sU+/RRmzoTLl2HKFJa/eEGVKlWoWLFi0v4jhBBCiHTK09OT7777jj///BMACwsL+vbty6hRo8iXL1/8J0dEwMKF6v7gwaDTpXK00YWHh7N06VIA+vTpg4WFRZreP13R+p1BRmYqPeaKolaVyZcvnzJjxgxFUdThK8WLF1caNmyoPHr0SHny5IlxSwn37t1TevfurZiZmSmAYm5urnz11VdKjx49lIIFCyoHDx5Uzp49q7i6uiqurq7Rzi1VqtSb6je7dikKKP4WFoqtjY2yaNGiWO93//595cKFC8rEiRMVe3t75cKFC8qFCxcSPfxICCGEMCUvXrxQBg4cqJibmyuAotPplC5duii3b99O+EU2bFB7y52dFSUkJPWCjYNUZUk4ScxTkSkl5oqiKFOnTlWcnZ2VwMBAZeXKlbGOs0/p92rXr19XWrRoYbx21qxZlVq1ailZs2ZVbG1tlf/9738x3gzw9nj/yEhFadBAWQKKjZmZ4ufnF+dzju25vD23QAghhEgvIiIilCVLlijZs2c3/k1r3ry5cuHChcRfrE4dNTH/7rsUjzMhJDFPOJ2iJHMpRxEnU1riVWt79+5l2LBhXL16FYCyZcuyaNEiateu/f6Tz56FatXUj97On4dKlVI3WCGEEEJDx48fZ9CgQZw/fx6AMmXKMHfuXBo2bJj4i126pP7dNDeHe/fgfcNeUkFQUJCxMkxgYKDJrSxqSvlaui6XKNKPJk2acOHCBRYvXkyOHDm4du0aderU4fPPP8fHxyf+k6tWhU8+AUVRa5wLIYQQGdCLFy/o0aMHH330EefPn8fR0ZE5c+Zw8eLFpCXl8KZEYvv2miTlInEkMRdpxtzcnL59++Lh4cEXX3wBwMqVKylVqhTLly8nMjIy7pMnTwYLC9izBw4cSKOIhRBCiNSnKApr167FxcWFVatWodPp+OKLL7h16xZDhgxJ+mTJFy9gzRp1f9CglAtYpBpJzEWay549O8uWLePYsWOUL18eX19fvvjiCxo0aMDt27djP6lYsTerlI0cCfEl8UIIIUQ6cf/+fVq0aEGXLl3w8fGhXLlyHD9+nGXLlpEzZ87kXfyXXyAkBCpXhpo1UyZgkaokMReaqVmzJufOnWPmzJnY2dlx+PBhKlSowLx582LvPR87FrJkgXPnYP36tA9YCCGESCGRkZHMnTuXsmXLsnv3bqysrPjhhx84d+4cH374YfJvoHGJRJE0kpgLTVlYWDBs2DAuX75MvXr1CA4OZvDgwdSvXx9PT8/ojZ2dYfhwdX/MGAgLS/uAhRBCiGR68OABjRo1YsiQIQQFBVGnTh0uXbrEmDFjsLS0TJmbbN0KDx5AjhzQqVPKXDOJrKys2L59O9u3b8fKykrTWEydJObCJBQtWpQDBw6wcOFC7OzsOHLkCBUqVGD+/PlEKxw0dCjkzg137sDixdoFLIQQQiSSoiisWrWK8uXL4+7ujq2tLYsWLcLd3Z1SpUql7M2iJn326QPW1il77UQyNzenZcuWtGzZEnNzWdsyPpKYC5Oh1+vp168fV65coX79+rx+/ZpBgwbRqlUrnj59qjays4MJE9T9SZMgIECzeIUQQoiEevbsGe3ataNHjx4EBARQs2ZNLl26xJdffpmglbcT5fJlOHQIzMygX7+UvbZIVZKYC5NTpEgR9u/fz9y5c7GysmLnzp1UqFCBnTt3qg169YKSJcHHB2bM0DZYIYQQ4j127NhB+fLl+euvv7CwsGDq1KkcOXKE4sWLp84N589Xv7ZrB/nzp849EiE8PJxff/2VX3/9lfDwcK3DMWmywFAqMqWC9enV1atX6dy5s3FhosGDB/Pjjz9ivXOnWpPV1hY8PSFPHo0jFUIIIaILCwtj9OjRzJo1C4By5crx22+/USk1F8rz9VWT8dev4cgRSMhCfqlMFhhKOOkxFyatXLlynDlzhsGDBwMwd+5cqlWrxvVSpcDVFYKD3wxtEUIIIUzEnTt3qFWrljEpHzx4MGfOnEndpBxg+XI1Ka9UCWrVSt17iRQnibkwedbW1vz888/s3LmTnDlzcvXqVapVr86+Ro3UBsuXg4eHtkEKIYQQ/1m/fj2VK1fmzJkzZM2alb/++ouff/4Z69SehGkwwIIF6v6gQVIiMR1KscTc09OTEydOcOvWrZS6pBDRNG/enMuXL9OwYUOCg4NpMmkSlwsVUl+Ivv1W6/CEEEJkciEhIXz55Zd88sknxgmeFy9exM3NLW0C2LYN7t+H7Nmhc+e0uadIUclKzA0GAz/88AO5c+emVKlS1KpVi2nTphkfX7NmDTVr1uTatWvJDlQIgFy5crFnzx7Gjh0LQKf79zEAbNkCx49rGpsQQojM68GDB9SuXZslS5ag0+n49ttvOXToEAULFky7IObOVb/27g02Nml3X5FikpyYGwwGWrVqxfjx43n58iWlS5fm3XmkH330ESdPnmTz5s3JDlSIKGZmZnz//ffs3LmTp9mysfK/475ffAEyl1kIIUQac3d3p0qVKpw9e5Zs2bKxa9cuJk+ejIWFRdoFcfUquLtLicR0LsmJ+eLFi9mzZw/169fn7t27xqoZbytcuDDFihVj7969yQpSiNg0b96cCxcu8HflyrwGst24wbouXYiMjNQ6NCGEEJmAoijMnDmTRo0a4ePjQ+XKlTl37hxNmzZN+2CiSiS2bQtp2UsvUlSSE/NVq1aRLVs2NmzYQN68eeNsV7p0aR48eJDU2wgRr4IFC7Lp5EmOfvABABX++INP2rcnMDBQ48iEEEJkZIGBgXTq1Inhw4cTGRlJt27dOHbsGIULF077YF6+hN9+U/f/q2JmSqysrFi/fj3r16/HyspK63BMWpLXRfXw8KBWrVpkzZo13naOjo48e/YsqbcR4r0sLS1pcuAAIfnzUyYoCMe//sLV1ZW///6bokWLah2eEEKIDOb27du4ublx7do1zM3N+fnnn+nXrx86raqgrFihlg+uUMEk6pa/y9zcnI4dO2odRrqQrDHmCXnX8+TJE3l3JFKfkxPWkyYBMEmv5/bVq1SrVo0DBw5oHJgQQoiM5PDhw1SvXp1r166RJ08eDh8+TP/+/bVLyg2GN8NYpERiupfkxLxQoUJcvnw53jbh4eFcvXqVEiVKJPU2QiRc//5QqBB5IiOZkS8fvr6+NG3alLlz58aYmCyEEEIk1ooVK2jcuDG+vr5Ur16dc+fOUbNmTW2D2rED7t2DbNng00+1jSUOERERbNiwgQ0bNhAREaF1OCYtyYl5s2bNuHfvHkuXLo2zzbx583j+/DktW7ZM6m2ESDgrK/jhBwD6v3pFv44dMRgMDBkyhP79+8uLgRBCiCQxGAwMHz6cXr16ER4ezieffMKhQ4fIkyeP1qG9KZH4xRdga6ttLHEIDQ3l448/5uOPPyY0NFTrcExakhPz4cOH4+joSP/+/fnqq684/l8N6aCgIM6fP8+oUaMYNWoUOXLkYODAgSkWsBDx+vRTqFQJXUAAC/LmZebMmeh0OhYvXkzr1q0JCAjQOkIhhBDpyKtXr2jbti0zZ84EYPz48fzxxx/YmEKd8OvX4cAB0OvVT41FuqdTkvEZ/5EjR2jXrh2+vr4xxlYpioKTkxNbt26lVq1ayQ40PQoICMDR0RF/f38cHBy0Difz2LsXmjYFCwu4eZO/L1/m008/JTg4mPLly7Njxw4KFCigdZRCCCFM3IMHD2jVqhVXrlzB2tqalStX0qlTJ63DeqNfP1i8GP73PzDhNWOCgoKwt7cH1Go2dnZ2GkcUnSnla8la+bNOnTpcu3aNESNGULZsWWxsbLCysqJ48eIMHjyYK1eupHpS7uvrS5cuXXBwcMDJyYlevXq9t1Te0qVLqVevHg4ODuh0Ovz8/GK0KVy4MDqdLtr29qqmwoQ1aQKNGkF4OIwdi5ubG4cPHyZ37txcuXKFGjVqcP78ea2jFEIIYcIuXrxIjRo1uHLlCrly5eLQoUOmlZT7+cHq1eq+CZZIFEmT5B7zBw8eoNPpNO95bN68OU+ePGHJkiWEh4fTs2dPqlWrxtq1a+M8Z86cOYSEhAAwevRoXr58iZOTU7Q2hQsXplevXvTu3dt4LEuWLIl6l2dK78AynXPnoGpVdX/pUrC356leT5NJk7h87Rq2trasW7eO1q1baxunEEIIk7Nv3z7atWtHYGAg5cqVY+fOnZrnOzHMmgXDhkG5cnD5sklXY5Ee80RQkkin0yk1a9ZM6ukp4vr16wqgnDlzxnhs165dik6nU7y8vN57vru7uwIoL1++jPFYoUKFlNmzZycrPn9/fwVQ/P39k3UdkUS1aikKRNsMefMqEytWVABFp9Mp8+fP1zpKIYQQJmTVqlWKubm5Aij169dX/Pz8tA4ppogIRSlaVP3btnSp1tG8V2BgoAIogBIYGKh1ODGYUr6W5KEsDg4OFClSJCXeGyTZiRMncHJyompUzyjQqFEj9Ho9p06dSvb1p02bRvbs2alcuTIzZsx4b1WP0NBQAgICom1CI5s3w7FjMQ7rnzxh7OXLLGrcGEVRGDhwIGPHjpVyikIIkckpisKUKVPo3r07ERERdO7cmV27duHo6Kh1aDHt3Al37kDWrNCli9bRiBSU5JU/y5Qpw8OHD1MylkTz9vYmZ86c0Y6Zm5uTLVs2vL29k3XtwYMH88EHH5AtWzaOHz/O6NGjefLkCbNmzYrznKlTpzJx4sRk3VekAIMBhgxR+8jfpSjodDr6enjgM2ECYydM4IcffsDb25tFixZhbp7kXwkhhBDpVEREBIMGDWLx4sUAjBgxgqlTp6LXJ2sqXuqZN0/9asIlEt9maWnJypUrjfsibkn+ievduzfHjh3jzJkzKRkPAKNGjYox8fLdzcPDI8Xv+7ahQ4dSr149KlSowJdffslPP/3EvHnz4q2/OXr0aPz9/Y2b1m9cMq2jR+HRo7gfVxR0Dx/yXd26LF26FL1ezy+//EL79u15/fp12sUphBBCc8HBwbRr147Fixej0+mYN28eP/74o+km5TduwL596apEooWFBT169KBHjx5YWFhoHY5JS3L3YM+ePblw4QJNmjRh+PDhtG/fnsKFC2NlZZXsoIYNG0aPHj3ibVO0aFFy587Ns2fPoh2PiIjA19eX3LlzJzuOt9WoUYOIiAju3btHqVKlYm1jZWWVIs9fJNOTJwlu17t3b5ydnencuTNbt26lSZMmbN26laxZs6ZujEIIITTn5+dHq1atOHbsGNbW1qxZs4Z27dppHVb85s9Xv7ZpA4ULaxqKSHlJTszNzMyM+2PHjmXs2LFxttXpdIladdHZ2RlnZ+f3tnN1dcXPz49z585RpUoVAA4ePEhkZCQ1atRI8P0S4uLFi+j1+hhDZ4QJSuhKbP+1a9u2LXv37qVNmzb8888/1K5dm927d5M/f/5UDFIIIYSWnj59StOmTbl06RKOjo5s377d9Ndd8feHVavU/UGDtI0lESIiItizZw8ATZs2lWGj8Ujy5zSKoiR4i4yMTMmYjUqXLk2zZs3o3bs3p0+f5tixYwwcOJBOnTqRN29eALy8vHBxceH06dPG87y9vbl48SKenp4AXLlyhYsXL+Lr6wuok0rnzJnDpUuXuHPnDmvWrOHrr7/ms88+k57U9KB2bcifP+7SUTodFCigtjOeUpujR4+SN29erl27Rs2aNbl582YaBSyEECIt3b9/n1q1anHp0iVy5crF4cOHTT8pB1i5EoKCoGxZqF9f62gSLDQ0lFatWtGqVat4hwSLZCTmkZGRidpSy5o1a3BxcaFhw4a0aNGCWrVqsXTpUuPj4eHh3Lx5k+DgYOOxxYsXU7lyZWON8jp16lC5cmW2bt0KqENS1q1bR926dSlbtiyTJ0/m66+/jnZdYcLMzODnn9X92JJzRYE5c9R2bylXrhzHjx+nVKlSPHz4kDp16nD58uXUj1cIIUSauXHjBh999BGenp4UKlSIo0ePUrFiRa3Der/IyDfDWAYNMum65SLpkrzAkHg/kypYnxlt3qxWZ4ltIuj+/dCwYaynPX/+nKZNm3LhwgWcnJzYvXt3ig+NEkIIkfbOnj1Ls2bNePHiBaVLl2bfvn3ky5dP67ASZscOaNUKnJzUv2smtkhPfGSBoYQz0SnHQqSAdu3g3j1wd4e1a9WvUTPYv/oK4pj34OzszMGDB6lZsyZ+fn40bNgQd3f3NAtbCCFEyjt06BD169fnxYsXVKtWjSNHjqSfpBzelEjs1StdJeUicZLdYx4REcHGjRtxd3fHy8sLgHz58lG/fn06dOiQqQf4m9I7MPEfX18oUUL9On8+DBgQZ9OgoCDc3Nw4cOAA1tbWbNy4kZYtW6ZhsEIIIVLCtm3b6NixI6GhodSvX5+///6bLFmyaB1Wwt28CS4u6vAVT08oWlTriBJFeswTLlmJ+cWLF+nQoQN3796NsXKiTqejaNGibNiwgUqVKiU3znTJlL7R4i0LF6oJedas8O+/kD17nE1DQkL45JNP2Lp1K+bm5qxdu5aOHTumYbBCCCGSY9OmTXTq1ImIiAjatGnDn3/+ibW1tdZhJc6gQWpnUps28PffWkeTaJKYJ1ySE/PHjx9TqVIlfHx8yJUrF506daJYsWIA3Llzh3Xr1uHt7Y2zszMXL14kT0JL2GUgpvSNFm+JiIAPPoArV2DgwDcfD8YhPDyc7t2788cff6DX61m2bBmff/55GgUrhBAiqf744w+6du2KwWCgc+fOrFq1Kv0tcBMQAPnyQWCgurBQo0ZaR5RokpgngpJEgwcPVnQ6ndK7d28lODg4xuOvX79Wevfureh0OuWrr75K6m3SNX9/fwVQ/P39tQ5FvOvAAUUBRTEzU5QrV97bPCIiQundu7cCKIAyf/78NAhSCCFEUv3666+KXq9XAKV79+5KRESE1iElzc8/q3+vSpdWlMhIraNJkrCwMGX+/PnK/PnzlbCwMK3DicGU8rUk95iXLFmSsLAwbt++HW2xobdFRERQvHhxLC0tuXXrVlLfO6RbJvUOTMTUvr1auaVhQ7UX4j2lpxRFYfjw4fz0008AzJkzhyFDhqRFpEIIIRLhl19+oU+fPiiKQu/evVm8eDF6fTqsdxEZqY4t//dfdRhmv35aR5QhmVK+luSf0ocPH1KzZs04k3IAc3NzXF1defjwYVJvI0TqmTEDrKzgwIEEjdnT6XTMmDGD0aNHA/DVV18xa9as1I5SCCFEIixcuJDevXujKAoDBgxIv0k5wJ49alLu6Ahdu2odjUgDSf5JtbKyIiAg4L3tXr16hZWVVVJvI0TqKVoUhg1T94cNg5CQ956i0+mYPHky33333X+nDWPGjBmpGaUQQogEmjNnDgP+q7Y1dOhQ5s2bl36TcngzB+rzz+G/MdrpkcFg4NChQxw6dAiDwaB1OCYtyT+tZcqUwd3dPd7e8AcPHuDu7k7ZsmWTehshUtfo0ZA3L9y5A7NnJ+gUnU7H999/z/jx4wEYMWIE06ZNS80ohRBCvMePP/7I119/DcDo0aOZOXMmuvS8OuatW7BrlzrMMp7SvulBSEgI9evXp379+oQkoBMsM0tyYt6tWzdev35No0aN2LlzZ4zHt2/fTuPGjQkJCaFbt27JClKIVGNvDz/+qO5PngyPHyfoNJ1Ox4QJE5g4cSKg/hGYPHlyakUphBAiHpMnT2bUqFEATJgwgcmTJ6fvpBxgwQL1a8uW8F/VO5HxJXnyp8FgoFmzZhw4cACdTke2bNkoUqQIAHfv3sXX1xdFUWjUqBG7d+9O3x8lJZEpTSYQ8YiMhI8+gpMnoVs3WLUqUae/PbRl4sSJjBs3LjWiFEIIEYtp06YZ5/5MnjyZb7/9VuOIUsCrV2qJxFevYO9eaNxY64iSRcolJlySs2UzMzN27NjBiBEjsLOz48WLF5w9e5azZ8/y4sUL7OzsGDlyJNu3b8+USblIR/R6mDtX3V+9Gk6dStTpY8aMYerUqQCMHz+eCRMmxFhwSwghRMp7e0L+lClTMkZSDmoH0atXakWWdFi3XCRdslb+jBIaGsrZs2fx8vICIF++fFStWjXTT/o0pXdgIgF69oRff4Xq1eHECTVhT4QZM2YwYsQI4E2CLoQQInXMmjWLYf9N4J80aZLxk8t0LzISSpdWx5jPn5/ux5eD9JgnRook5iJ2pvSNFgnw5AmULKmurrZqlTqsJZHe/kORYT5SFUIIE/Pzzz/z1VdfARmwI2TPHmjWDBwc4NEjyJJF64iSTRLzhJMxJkJEyZMHxo5V90eOVD9GTKShQ4fy43+TSceMGcPMmTNTMkIhhMj0FixYYEzKv/vuO2OFrAwjamhlz54ZIikXiZPkxHz+/PmYmZmxbdu2ONts27YNMzMzlixZktTbCJG2hgxRZ797e8OUKUm6xIgRI5g0aRIAw4cPZ27Ui6wQQohkWbx4MQMHDgRg1KhRfP/99+m/+srbPD3flEj873lmBBYWFkyfPp3p06djYWGhdTgmLclDWRo3bsyVK1d4/PhxnJM7DQYDefPmpXLlyuzevTtZgaZHpvTRiEiErVvBzQ0sLeH69SSXqRo3bpwxQV+4cCH9ZCllIYRIsqVLl9K3b19A7fT48ccfM1ZSDvD11zBnDrRoATt2aB1NpmFK+VqSe8w9PDwoV65cvBVXzMzMKF++PDdu3EjqbYRIe61bQ5MmEBYG33yT5MtMnDiRkSNHAtC/f3+WL1+eUhEKIUSmsmLFCmNSHjVkMMMl5YGBsGKFuj94sLaxCM0kOTF//vw5uXPnfm+73Llz8+zZs6TeRoi0p9Opq4CamcFff8H+/Um8jI6pU6caV6Lr3bs3q1evTsFAhRAi41u9ejVffPEFAEOGDEn/K3rGZfVqCAhQixCk87rl7zIYDJw5c4YzZ85gMBi0DsekJTkxz5IlC48TsEri48ePsbW1TepthNBGmTJvSlQNGQIREUm6jE6n46effmLAgAEoikLPnj35448/UjBQIYTIuDZv3kzPnj1RFIUBAwYwe/bsjJmUKwrMm6fuDxqU6HK9pi4kJITq1atTvXp1QkJCtA7HpCX5O1+xYkWOHz/Ow4cP42zz8OFDjh8/Tvny5ZN6GyG0M2ECZM+ujjNfvDjJl9HpdMydO5c+ffoQGRlJ165d2bhxY8rFKYQQGdC+ffvo3LkzkZGRfP7558ybNy9jJuWgfjLr4aFWYeneXetohIaSnJh/+umnhIWF0a5dO7y9vWM87u3tTfv27QkPD+fTTz9NVpBCaCJrVvjhB3V/3Dh48SLJl9Lr9SxatIgePXpgMBjo3LkzW7duTaFAhRAiYzlx4gRt27YlLCyMDh06sHTp0oyblIOUSBRGSa7KYjAYqFevHseOHcPGxoaWLVvi4uICqBNDd+7cSXBwMK6urhw+fBhzc/MUDTw9MKVZviKJDAb44AO4fBn694cFC5J5OQPdu3dnzZo1WFpasmPHDhrJcstCCGF06dIl6tWrh5+fH02bNuXvv//O2CuJ374NJUqow1lu3lTHmGcwssBQwiVr5c+AgAB69uzJli1b1Iv992426pJubm6sXLkSJyen5EeaDpnSN1okw6FDUL++Oubv4kVI5tCsiIgIPv74Y7Zs2YKtrS379u2jZs2aKRKqEEKkZ//++y+1atXi2bNnfPTRR+zZs8fkkrgUN3SoWnCgeXPYuVPraFKFJOYJl6zEPMrly5fZvXs39+/fB6BgwYI0a9aMihUrJjvA9MyUvtEimTp2hI0b1QT9wAG1cksyhIaG4ubmxp49e3B0dOTgwYN88MEHKRSsEEKkPw8fPqRWrVo8ePCASpUq4e7unvE79gIDIX9+8PdXk/LmzbWOKFVIYp5wKZKYi9iZ0jdaJNO9e+DiAqGhsGkTtGuX7EsGBwfTrFkzjh49So4cOTh8+DBlypRJfqxCCJHOPH/+nNq1a3Pz5k1KlizJ0aNHyZkzp9ZhpR6DAY4eVUskrlwJxYurw1gyWDWWKJKYJ1yK/QRERETw008/Ubt2bUqXLk3jxo1ZEVUoPxX5+vrSpUsXHBwccHJyolevXgQGBsbbftCgQZQqVQobGxsKFizI4MGD8ff3j9buwYMHtGzZEltbW3LmzMnw4cOJSGLJPJEBFC4Mw4er+8OGQQqUe7K1tWX79u1UrVoVHx8fGjduzJ07d5J9XSGESE/8/f1p2rQpN2/epECBAuzbty9jJ+WbN6t/U+rXV5NyAB8fdd2MDMrCwoLx48czfvx4LCwstA7HtCkJtGnTJsXZ2Vn59ttvYzxmMBiUxo0bK3q9XtHpdMZNr9cr3bt3T+gtkqRZs2ZKxYoVlZMnTypHjx5VihcvrnTu3DnO9leuXFHatWunbN26VfH09FQOHDiglChRQmnfvr2xTUREhFKuXDmlUaNGyoULF5SdO3cqOXLkUEaPHp2o2Pz9/RVA8ff3T/LzEyYkMFBR8uVTFFCUH35Iscv6+PgoZcuWVQClSJEiyqNHj1Ls2kIIYcqCgoKUWrVqKYDi7Oys3Lx5U+uQUtemTYqi06l/R97edDp127RJ6wgzJVPK1xKcmA8cOFDR6/XK+fPnYzy2ePFiYzLu5uamLFiwQBk5cqRiZ2en6PV6Zc+ePSkadJTr168rgHLmzBnjsV27dik6nU7x8vJK8HXWr1+vWFpaKuHh4YqiKMrOnTsVvV6veHt7G9ssWrRIcXBwUEJDQ+O8TkhIiOLv72/cHj58aDLfaJFC1qxRX0RtbRUlBRPox48fK8WLF1cAxcXFRXn69GmKXVsIIUxRaGio0qxZMwVQHB0dlQsXLmgdUuqKiFCU/PljJuVvJ+cFCqjtRJoypcQ8wUNZTp06RZ48eahcuXKMx5YsWYJOp6Nz58789ddf9O/fn2nTprF69WoUReG3335Lic79GE6cOIGTkxNVq1Y1HmvUqBF6vZ5Tp04l+DpRY4qiSjqeOHGC8uXLkytXLmObpk2bEhAQwLVr1+K8ztSpU3F0dDRuBQoUSMKzEiatc2eoWROCg2HUqBS7bJ48edi/fz/58+fHw8ODpk2b4ufnl2LXF0IIU2IwGPjss8/YvXs3tra27Nixg0qVKmkdVuoJD1dX9nz0KO42igIPH6pjzzOYyMhIrl27xrVr14iMjNQ6HJOW4MT8yZMnsf7S+Pj4cPHiRQCGR43B/U+7du0oXLhwopLkxPD29o4xDs3c3Jxs2bLFuuhRbHx8fJg0aRJ9+vSJdt23k3LA+O/4rjt69Gj8/f2NW3yroop0SqdTF4LQ6eD33+HEiRS7dKFChThw4AA5c+bk4sWLtGjRIt75EkIIkR4pikLfvn3ZsGEDFhYWbNmyhY8++kjrsFLeixfq34nOnSFnTvj664Sd9+RJ6salgdevX1OuXDnKlSvH69evtQ7HpCU4Mffx8SFr1qwxjp85cwYAZ2fnWBP3MmXK8Pjx40QFNWrUKHQ6Xbybh4dHoq4Zm4CAAFq2bEmZMmWYMGFCsq9nZWWFg4NDtE1kQFWqqKuzAQwZAin47r9kyZLs27ePrFmzcuLECdzc3AhJgYmmQghhChRF4ZtvvmH58uXo9Xr++OMPmjRponVYKUNR4OpVmDYNatVSk/GuXWHdOvDzg4TmBHnypGqYwrQleDlOMzMznj9/HuP4+fPnAeKswezk5JToaibDhg2jR48e8bYpWrQouXPn5tmzZ9GOR0RE4OvrS+7cueM9/9WrVzRr1owsWbKwZcuWaLOEc+fOzenTp6O1f/r0qfExIZg8GTZsgDNn1HJX7/l5TYwKFSqwa9cuGjVqxMGDB/n444/ZtGmTzGQXQqR7kydPZtasWQD88ssvtG/fXuOIkikkBNzdYccO2L4d/lvPxahiRWjVSt0++ACKFQMvLzWJf5dOp9Y0r107bWIXpimhg9HLlCmjODs7x5j8WL9+fUWv1ysTJkyI9bymTZsqBQsWTMYw+LhFTf48e/as8diePXveO/nT399f+fDDD5W6desqQUFBMR6Pmvz59gS8JUuWKA4ODkpISEiC4zOlyQQiFcyYoU7YyZVLUVLhe+zu7q5YW1srgNKlSxfFYDCk+D2EECKtzJ07VwEUQJkzZ47W4SSdl5eiLF2qKG3aqIUA3p7AaW2tKC1bKsqiRYry4EHMc6OqsrxbmSWDV2UJDAw0fu8DAwO1DicGU8rXEpyYDxgwQNHr9cqIESOMxw4ePKjo9XpFr9dHq4zytpw5cyoffvhh8iONQ7NmzZTKlSsrp06dUv755x+lRIkS0colPnr0SClVqpRy6tQpRVHU//waNWoo5cuXVzw9PZUnT54Yt4j/ZkJHlUts0qSJcvHiRWX37t2Ks7OzlEsU0YWGKkqJEuqL6lu/Fylp+/btirm5uQIoAwcOVCIjI1PlPkIIkZpWrVplTMzGjx+vdTiJYzAoyunTijJunKJ88EHMair58ytK376Ksm2bosTS2RfDpk0xq7MUKJBhk3JFkcQ8MRKcmP/777+KtbW1otfrlfz58yuVK1dWLCwsFL1er7i6usZ6zvHjxxWdTqcMGTIkpeKN4cWLF0rnzp0Ve3t7xcHBQenZs6fy6tUr4+N3795VAMXd3V1RFLUXMuqH493t7t27xvPu3bunNG/eXLGxsVFy5MihDBs2zFhOMaFM6RstUsm2beqLqoWFoty6lSq3WLNmjaLT6RRAGTduXKrcQwghUsuWLVsUMzMzBVCGDBmSPjoYAgLURLlnT/VT0Xd7tz/8UF3P4uJFRUnK84mIUBR3d0VZu1b9msFLJEpinnA6RYltoFPsNm/eTI8ePaJVisiXLx8HDx6kRIkSMdr36NGD1atX8/fff9O6deuEj6/JIExpiVeRShQFWrSA3buhTRv4++9Uuc2CBQsYOHAgAHPmzGHIkCGpch8hhEhJBw4coEWLFoSFhdGjRw/jpE+TdPv2m7Hihw6pJQ6jZMkCTZuqY8WbN1cndooECwoKwt7eHoDAwEDs7Ow0jig6U8rXEjz5E9Tyh7Vq1WL79u08ffqUggUL0rZt2zj/g6tXr07lypVp0KBBigQrhMnR6WDWLNi/H7Zuhb17IRUqDAwYMICXL18yduxYvvrqK7JmzUq3bt1S/D5CCJFSTp48iZubG2FhYbRr145ly5aZVlIeEQHHjr1Jxm/ciP548eLQujW0bKlOyLS01CbODMDCwoJvvvnGuC/ilqgec5E4pvQOTKSyr7+GOXOgdGm4dAlS4YVHURSGDRvG7NmzMTMzY9OmTbi5uaX4fYQQIrkuX75M3bp18fPzo3Hjxmzbtg0rKyutw1Jri+/erSbiu3erZQyjmJurCXhUFZWSJTULU0RXr149KlWqxJw5c1Ll+qaUr5nQW1ch0rHx4yFHDrXHZdGiVLmFTqdj5syZ9OjRA4PBwCeffIK7u3uq3EsIIZLK09OTJk2a4OfnR82aNdmyZYt2SXlUbfEff1ST7pw54bPP3tQWz5EDunWDP/+E58/h4EEYOlSS8kzmxH+LBRYuXBgbGxtcXFyYPXt2tDYTJkyIsaaOi4tLtDZ9+/alWLFi2NjY4OzsjJubW6LX3UnUUBYhRBycnNTa5n37qkl6587g7Jzit9Hr9Sxbtgw/Pz/++usv2rRpg7u7O1WrVk3xewkhRGI9evSIRo0a8fTpUypWrMiOHTvSfjxxSIg6Rnz79thri1eo8KZXvHp1MDNL2/gyocjISB48eABAwYIFTWtIE2BrawvArl27yJUrF//88w99+/bFzs4u2srwZcuWZf/+/cZ/m5tHT6OrVKlCly5dKFiwIL6+vkyYMIEmTZpw9+5dzBL4cyZDWVKRKX00ItKAwaCuCnrpEnz5Zar1nAOEhITQsmVLDh48SPbs2Tl69CilS5dOtfsJIcT7PH/+nDp16uDh4UGJEiU4evQouXLlSpubP34MO3eqifi+fRAc/OYxa2to0EBNxFu2hIIF0yYmYZSYyZ9BQUH069ePzZs3kyVLFr755hu2bduW5kNZ2rVrh52dHb/99hug9pj/9ddfXLx4McHXvXz5MhUrVsTT05NixYol6BzTessiRHpmZgZz56r7S5eqCXoqsba25q+//qJatWq8ePGCxo0bc//dXiEhhEgj/v7+NGvWDA8PDwoUKMD+/ftTNymPjFRXXh4/HqpWhXz5oHdvtTJWcLD67759Yds2dVz5jh3Qr58k5enA8OHDOXz4MH///Td79+7l0KFDxlXm43L06FHs7e3j3dasWZPgGC5cuMDx48epW7dutOP//vsvefPmpWjRonTp0sX4KUBsgoKCWLlyJUWKFKFAgQIJvrf0mKci6THPpD75BNavh3r11PGKOl2q3crHx4c6depw48YNSpQowT///ENOKeMlhEhDwcHBNGvWjKNHj+Ls7MzRo0cpVapUyt/o1Su1Atb27Wqi/fTpm8d0OnVYStQQlYoVU/W1VyROQnvMAwMDyZ49O7///jsdO3YEwNfXl/z589OnT584e8xfv36Nl5dXvDHkypWLLFmyxPpYVL6WN29efHx8iIiIYMKECYwdO9bYZteuXQQGBlKqVCmePHnCxIkT8fLy4urVq9Guu3DhQkaMGEFQUBClSpVix44dCe4tB0nMU5Uk5pnU/fvg4qKOc9ywATp0SNXbPXr0iFq1anH//n0qVarEoUOHcHR0TNV7CiEEQFhYGG3btmXXrl04ODhw6NAhKleunHI3uHMnem3xsLA3j0XVFm/ZUq0tnlbDZkSiJTQxv3TpEpUqVeL+/fsUfOvTjcqVK1O3bt1UH8py6dIldDodJ0+eZNSoUcyfP5/OnTvHeo6fnx+FChVi1qxZ9OrVy3jc39+fZ8+e8eTJE2bOnImXlxfHjh3D2to6QbHI5E8hUlqhQjBiBHz/PXzzjfpHw8Ym1W6XP39+9u3bR61atbh48SKtW7dm9+7dxsksQgiRGgwGA926dWPXrl3Y2NiwY8eOmEm5wQBHj8KTJ5Anj1oZJb5JcBERcPz4m4mb79YWL1ZMrS3eqpXUFhdGR48epXnz5vG2WbJkCV26dIm3TeHChXFwcKB8+fI8ffqUCRMmxJmYOzk5UbJkSTw9PaMdd3R0xNHRkRIlSvDhhx+SNWtWtmzZEud13iWJuRCpYeRIWLFC7T3/6Sf47rtUvV2JEiXYs2cP9erV4+jRo3Ts2JG//vpLFnIQQqQKRVHo168ff/75JxYWFmzevJlatWpFb7R5MwwZAo8evTmWPz/8/DO0a/fmmK/vm9riu3ZFry1uZhaztrgMUcmwihUrhoWFBadOnTL2mL98+ZJbt27FGO/9tqpVq753UmZi5zxERkYSGhoa5+OBgYHcvn2brl27xtlGURQURYn3Ou+SxFyI1GBrCzNmqGUTp06FHj3UP0ipqFKlSmzfvp0mTZqwc+dOunfvzu+//25yZamEEOmboiiMGDHCuJLnmjVraNasWfRGmzerw/jeHS3r5aUenzULQkPVZPz4cXUyZ5Ts2aFFCzURb9JELUcrMgV7e3t69erF8OHDyZ49Ozlz5mTMmDHv/TtmY2ND8eLFk3zfZcuWAXD79m3s7e05cuQIM2fOZPDgwcY233zzDa1bt6ZQoUI8fvyY8ePHY2ZmZuwJv3PnDn/++SdNmjTB2dmZR48eMW3aNGxsbGjRokWCY5HEXIjU8sknsGAB/POP2oOeiBnhSVWrVi02btyIm5sbf/zxB1mzZmX+/PnopIdJCJFCpk6dysyZMwE1oYmapGdkMKg95bFNYYs69vXX0Y+XL/+mV7xGDaktnsGYm5vTv39/4358ZsyYQWBgIK1btyZLliwMGzYMf3//VI0v8r83hrVq1cLc3JxixYrx448/0rdvX2ObR48e0blzZ168eIGzszO1atXi5MmTOP+3Zom1tTVHjx5lzpw5vHz5kly5clGnTh2OHz+eqKIMMvkzFcnkT8H582opL0VRE/SPPkqT2/7xxx906dIFRVEYO3Ys33//fZrcVwiRsS1YsICBAwcCMGvWLL5+N8EGdZJm/frvv1j16tC9uzoPp1ChlA1UiEQwpXxNPuMWIjV98AFEzdYeMiT6x7WpqHPnzixYsACASZMmxVhaWAghEuv33383JuXjxo2LPSkHdaJnQnz1FfTvL0m5EG+RxFyI1DZ5Mjg4wLlz8OuvaXbbfv36MXnyZACGDh3Kr2l4byFExrJ161Z69OgBwKBBg5gwYULcjeOoFR1DnjzJjkukD4qi8Pz5c54/f44M1IifJOZCpLacOdXV6QBGj4aAgDS79ejRoxk2bBgAvXr1YsuWLWl2byFExnDw4EE+/vhjY3nEOXPmxD1v5dQp+K9XPU46HRQooFZbEZlCcHAwOXPmJGfOnAQHB2sdjkmTxFyItDBwoFrm69kzmDQpzW6r0+mYMWMGPXv2JDIykk6dOnHgwIE0u78QIn07deoUbdq0ITQ0lLZt27J8+fLYK2RERsL06VCrllomNmqy27sJfNS/58yRCZ5CxEIScyHSgqUlRI3z/vlnuHUrzW6t0+lYunQp7dq1IywsDDc3N06ePJlm9xdCpE9Xr16lefPmBAUF0ahRI9atWxd7RY2nT9WVN0eOVBcI+uQT9TVu0ybIly962/z5YePG6HXMhRBGUpUlFZnSLF9hIlq0UBfQaNUKtm1L01uHhobSunVr9u3bh5OTE4cPH6ZChQppGoMQIn24ffs2tWrVwtvbmw8//JB9+/YZl1SPZv9++OwzNTm3sYF58+Dzz9/0jCd25U+RIQUFBRl/fgIDA7Gzs9M4ouhMKV+TxDwVmdI3WpiImzehXDm1V2nXLnh3UY5UFhQURJMmTTh+/Di5cuXin3/+SdaiDEKIjMfLy4tatWpx7949ypcvz+HDh8maNWv0RuHh6tyZadPUcrDlysGff0KZMtoELUyaJOYJJ0NZhEhLpUpB1EpiX3+t/nFLQ3Z2duzYsYOKFSvy9OlTGjVqxKO3l8sWQmRqPj4+NG7cmHv37lG8eHH27t0bMym/dw/q1lVXNVYU+PJLOH1aknIhUoAk5kKktbFjwdkZPDzUlUHTmJOTE3v27KFEiRLcv3+fxo0b8/z58zSPQwhhWgICAmjWrBk3btwgf/787N+/n9y5c0dvtGkTVKoEJ06AoyNs2ACLFqnDWIQQySaJuRBpzckJpkxR9ydMAA2S4ly5crFv3z7y58+Ph4cHzZo1S/Ulj4UQpuv169e0adOGc+fOkSNHDvbt20ehtxf+ef0a+vWDDh3A3x8+/BAuXlT/LcR7mJub0717d7p37x77BGJhJGPMU5EpjVkSJsZggGrV4MIF6NMHlizRJAwPDw/q1KnD8+fPqV27Nrt378bW1laTWIQQ2ggLC+N///sfO3fuxMHBAXd3dz744IM3DW7cUCutXLmiTuocORK+/x4sLLQLWogUZEr5mvSYC6EFMzO1bCLAsmVqz5MGXFxc2LNnDw4ODhw9epQOHToQFhamSSxCiLRnMBj47LPP2LlzJzY2Nmzfvv1NUq4osHw5VKmiJuU5c8KePerYcknKhUgV6T4x9/X1pUuXLjg4OODk5ESvXr0IDAyMt/2gQYMoVaoUNjY2FCxYkMGDB8f4GF+n08XY1q1bl9pPR2QmtWtDp07qH78hQ9SvGqhcuTI7duzAxsaGXbt20a1bNwwGgyaxCCHSTmRkJH369GHDhg1YWFiwZcsWaketxunvD59+Cl98oQ5jadwYLl1SvwqRSIqiEBQURFBQEDJQI37pPjHv0qUL165dY9++fWzfvp0jR47Qp0+fONs/fvyYx48fM3PmTK5evcqvv/7K7t276dWrV4y2K1eu5MmTJ8atbdu2qfhMRKb044/qpKkjR9RJVBqpVasWmzdvxsLCgj///JN+/frJi6cQGZiiKAwdOpQVK1ag1+tZt24dTZs2VR88cwY++ADWrQNzc/V1avdueHciqBAJFBwcjL29Pfb29gQHB2sdjmlT0rHr168rgHLmzBnjsV27dik6nU7x8vJK8HXWr1+vWFpaKuHh4cZjgLJly5Zkxefv768Air+/f7KuIzK4CRMUBRSlYEFFCQrSNJT169crer1eAZRvvvlGiYyM1DQeIUTqGDdunAIogPLrr7+qBw0GRZk5U1HMzdXXpMKFFeXECW0DFRlCYGCg8ectMDBQ63BiMKV8LV33mJ84cQInJyeqVq1qPNaoUSP0ej2nTp1K8HWiBvu/O1N4wIAB5MiRg+rVq7NixYr39iCGhoYSEBAQbRPivYYPh4IF4cEDmDlT01A6duzIsmXLAJg5cyZTp07VNB4hRMr76aef+P777wGYN28e3bt3h2fP1BWJv/lGXQCtQwd1cvqHH2ocrRCZS7pOzL29vcmZM2e0Y+bm5mTLlg1vb+8EXcPHx4dJkybFGP7y/fffs379evbt20f79u3p378/8+bNi/daU6dOxdHR0bgVKFAgcU9IZE62tjBjhro/bZqaoGvo888/Z9asWQCMGTOGBRrUWhdCpI6lS5fyzTffADBlyhQGDhwIBw+qtcl37QJra7VK1Pr1amlXIUTa0rrLPjYjR440fuQR13bjxg1l8uTJSsmSJWOc7+zsrCxcuPC99/H391eqV6+uNGvWTAkLC4u37dixY5X8+fPH2yYkJETx9/c3bg8fPjSZj0aEiYuMVJTatdWPjzt10joaRVHUn/mo37fffvtN63CEEMm0du1aRafTKYAycuRIRQkPV5QxYxRFp1Nfe8qUUZQrV7QOU2RAMpQl4UyyyvuwYcPo0aNHvG2KFi1K7ty5efbsWbTjERER+Pr6xlyt7B2vXr2iWbNmZMmShS1btmDxntJPNWrUYNKkSYSGhmJlZRVrGysrqzgfEyJeOp1aPrFKFXXCVf/+atUWDU2cOBE/Pz/mzZtHjx49cHBwoE2bNprGJIRImm3bttG1a1cURaFfv35M7dcP6tWDY8fUBr17w5w56id4QgjNmGRi7uzsjLOz83vbubq64ufnx7lz56hSpQoABw8eJDIykho1asR5XkBAAE2bNsXKyoqtW7dibW393ntdvHiRrFmzSuItUk/lyuofx6VL1fKJZ86o9c41otPpmDNnDv7+/qxevZqOHTuyfft2Gku5NCHSlYMHD9KxY0cMBgNdu3ZlfsOG6CpVAj8/cHBQ11L4+GOtwxRCkM7HmJcuXZpmzZrRu3dvTp8+zbFjxxg4cCCdOnUib968AHh5eeHi4sLp06cBNSlv0qQJQUFBLF++nICAALy9vfH29jbWbt62bRu//PILV69exdPTk0WLFjFlyhQGDRqk2XMVmcQPP4CjozrpauVKraNBr9ezfPly2rVrR1hYGG5ubhw+fFjrsIQQCXTy5EnatGlDaGgoHVu35ld7e/QdOqhJefXq6uJmkpSLVGZmZkaHDh3o0KEDZhp2OKULWo+lSa4XL14onTt3Vuzt7RUHBwelZ8+eyqtXr4yP3717VwEUd3d3RVEUxd3dPc5x63fv3lUURS25WKlSJcXe3l6xs7NTKlasqCxevFgxGAyJis2UxiyJdGTWLHW8p7Ozovj5aR2NoiiKEhoaqrRo0UIBFHt7e+X48eNahySEeI8LFy4oTk5OCqB8XrOmYihfXn1tAUUZPlxRQkO1DlEIk2BK+ZpOUWQVkdQSEBCAo6OjsRyjEAkSHg4VKoCHBwwbpnkJxSghISG0atWKAwcO4OjoyMGDB98s3S2EMClXr16lfv36+Pj48EOxYnz75Am64GBwdobffoOoxYSEECaVr6XroSxCZEgWFjB7trr/889w86a28fzH2tqav//+m9q1a+Pv70/jxo25cuWK1mEJId5x8+ZNGjVqRIiPD7uyZWPM7dtqUt6wIVy6JEm5ECZMEnMhTFGzZtCypbrQx9ChWkdjZGdnx/bt26lRowa+vr40bNgQDw8PrcMSQvzn9u3bNGjQgHxPn3LN0pJmvr7qJPIpU2DvXsiTR+sQRSYUFBSETqdDp9MRFBSkdTgmTRJzIUzVrFlq7/nOnepmIhwcHNi9ezeVK1fm+fPnNGzYkNu3b2sdlhCZ3v3792lQvz4fP37MSZ2OgmFh6qrCR47A6NGglz/5Qpg6+S0VwlSVLKmWTQT4+msIC9M2nrc4OTmxd+9eypUrx+PHj2nQoAH379/XOiwhMq1Hjx7RoW5dFjx8yGzAQlGgXTu16krNmlqHJ4RIIEnMhTBl330HOXPCrVswf77W0USTI0cO9u/fT8mSJXnw4AENGzbEy8tL67CEyHSePHnCaFdX/rp/n1aAYmUFCxfCxo2QNavW4QkhEkEScyFMmaMjTJ2q7k+cCO+sdKu1XLlyceDAAYoUKcLt27dp1KhRjNV4hRCp5/mTJ2ypUIFVjx6RDwgvXhzd6dPQr5+6orAQIl2RxFwIU9ejB1SpAgEBMGaM1tHEkD9/fg4ePEiBAgXw8PCgUaNG+Pj4aB2WEBme35UrPChRgv4+PuiBgI8/xuLiRbXcqhAiXZLEXAhTp9erZRMBli+H8+e1jScWhQsX5sCBA+TJk4crV67QsGFDSc6FSEWBa9dC5cpUCQrilU7Hk59+wuHPP8HOTuvQhBDJIIm5EOnBRx/Bp5+qa/YNGaJ+NTElSpTg4MGD5M6dm8uXL0tyLkRqCAkhtG9f7Lt0wclg4IK5OU927CCPCZVVFeJdZmZmtGjRghYtWmBmZqZ1OCZNVv5MRaa0kpTIAB49glKlIDgY/vgDOnXSOqJYeXh4UL9+fby9valQoQIHDhwgR44cWoclRPp38yYRHTti/t/CXgtsbKhz9Cjlq1TRODAh0jdTytekx1yI9CJ/fhg1St0fPlxN0E2Qi4sL7u7uxp7zBg0aSM+5EMm1ejVKlSqYX7nCc6CzgwN1Tp2SpFyIDEYScyHSk2++gUKF1N7z6dO1jiZOLi4uHDp0iNy5c3PlyhVJzoVIqlevoFs36N4dXVAQB4H6WbPy7T//UL58ea2jE0KkMEnMhUhPbGxg5kx1/8cf4cEDbeOJR6lSpWIk58+fP9c6LCHSj/Pn1YpMv/2GAfgO+DRHDtYdOSJJuUhXgoKCsLOzw87OjqCgIK3DMWmSmAuR3rRvD3XrQkiIOqTFhEUl529Xa5HkXIj3UBS1EpOrK/z7L08sLKgLLMuZkwOHDlGuXDmtIxQi0YKDgwk20SGYpkQScyHSG51O/aOt18P69XDkiNYRxatUqVK4u7tLci5EQvj4gJsbfPUVhIVx0MGBsuHheObKhbu7O2XLltU6QiFEKpLEXIj0qGJF6NNH3R8yBAwGbeN5j3eT83r16vHkyROtwxLCtBw5ApUqwbZtKJaWTM2Xj4YBAVj+l5SXKVNG6wiFEKlMEnMh0qvvvwcnJ7h4UV14yMRFDWvJly8f169fp06dOjww4THyQqQZgwEmToT69cHLi/BixWifLx/fenmR67+kvHTp0lpHKYRIA5KYC5FeOTvDhAnq/pgx4OenZTQJUrJkSY4cOULhwoXx9PSkdu3a3L59W+uwhNDOo0fQsKH6uxwZSWCHDlSJjGTL3bvkz5+fw4cPS1IuRCYiibkQ6Vn//lC6tDou9fvvtY4mQYoWLcqRI0coWbIkDx48oHbt2ty4cUPrsIRIe9u3q0NXDh8Ge3u8Z86kzKlTXLl7lyJFinD06FFKlSqldZRCiDQkibkQ6ZmFBcyere7PmwfpJMEtUKAAhw8fply5cjx58oS6dety6dIlrcMSIm2EhsLXX0Pr1vDiBXzwAbfWraPSjBk8fPgQFxcXjh49SuHChbWOVIgUodfrqVu3LnXr1kWvl9QzPjpFURStg8ioTGmJV5HBtWkD27ZB06awa5dauSUdePHiBU2aNOH8+fM4OTmxZ88eqlevrnVYQqSef/+FTp3UGuUAX33F+Y8/pknr1rx48YIKFSqwb98+cubMqW2cQmQippSvydsWITKCWbPU3vM9e2DnTq2jSbDs2bNz4MABXF1d8fPzo1GjRhw9elTrsIRIHb//Dh98oCbl2bPDtm0c69CB+s2a8eLFC6pXr467u7sk5UJkYpKYC5ERFC+ufjQO6tewMG3jSQQnJyf27t1L/fr1efXqFU2bNmXHjh1ahyVEygkMhB49oGtXdb9uXbh0iQM2NjRp0oSAgADq1KnDvn37yJYtm9bRCiE0JIm5EBnFmDGQK5f6UfncuVpHkyj29vbs2LGDFi1a8Pr1a9zc3Pj999+1DkuI5Lt4EapUgVWr1EXBJk6EAwfYcPw4LVq0IDg4mCZNmrBr1y7NP0IXIrUEBQXh7OyMs7MzQUFBWodj0iQxFyKjcHCAadPU/e+/h6dPtY0nkWxsbPjrr7/47LPPMBgMdO3alTlz5mgdlhDvZzDAoUPwxx/qV4MBFAXmz4caNeDWLciXD9zdYdw4Fi1dyieffEJYWBgdOnRg69at2Nraav0shEhVPj4++Pj4aB2GyZPEXIiMpFs3qFoVXr1Se9DTGQsLC1atWsVXX30FwNdff82YMWOQOerCZG3eDIULq4sDffqp+rVgQTUhHzRIHVbWujVcuoRSuzYTJ06kf//+KIrCl19+ybp167CystL6WQghTES6T8x9fX3p0qULDg4OODk50atXLwIDA+M9p2/fvhQrVgwbGxucnZ1xc3PDw8MjWpsHDx7QsmVLbG1tyZkzJ8OHDyciIiI1n4oQyafXvxnGsmIFnDunbTxJoNfrmTVrFpMnTwZgypQpfPnllxgMBo0jE+IdmzdDhw7qIkFve/wYzpwBc3P4+Wf4+28MTk4MHDiQCf8tCjZ+/HgWLlyImZlZ2scthDBZ6T4x79KlC9euXWPfvn1s376dI0eO0KdPn3jPqVKlCitXruTGjRvs2bMHRVFo0qSJ8Q+/wWCgZcuWhIWFcfz4cVatWsWvv/7KuHHj0uIpCZE8rq7QpYv6UfrgwerXdEan0/Htt9+yZMkS9Ho9S//76D80NDT1bx7bsAQh3mUwwJAh8f9+Zc8OAwYQGhbGp59+ysKFC9HpdMyfP58JEyagSydlTYUQaUhJx65fv64AypkzZ4zHdu3apeh0OsXLyyvB17l06ZICKJ6enoqiKMrOnTsVvV6veHt7G9ssWrRIcXBwUEJDQxN8XX9/fwVQ/P39E3yOECni0SNFsbVVFFCUtWu1jiZZNm7cqFhaWiqAUq9ePcXX1zf1brZpk6Lkz6/+v0Vt+fOrx4V4m7t79J+TOLagHTuURo0aKYBiYWGhrFu3TuvIhUhzgYGBCqAASmBgoNbhxGBK+Vq67jE/ceIETk5OVK1a1XisUaNG6PV6Tp06laBrBAUFsXLlSooUKUKBAgWM1y1fvjy5cuUytmvatCkBAQFcu3YtzmuFhoYSEBAQbRNCE/nywbffqvvDh0M6ngXfvn17du3aRZYsWTh06BAfffQR9+/fT/kbxTUswctLPb55c8rfU6Qv/v7qQl5ffw3duyfolO/79WP//v3Y2dmxY8cOPvnkk1QOUgiRnqXrxNzb2zvGQgzm5uZky5YNb2/veM9duHAh9vb22Nvbs2vXLvbt24elpaXxum8n5YDx3/Fdd+rUqTg6Ohq3qERfCE0MHapOSvPygh9/1DqaZGnQoAFHjx4lX7583Lhxgw8//JBzKTl+Pr5hCVHHvvpKhrVkNq9eqSvpjhgB1apBtmzqKrtz5sCDBwm6xMkHD8iVKxeHDh2icePGqRuvECZKr9dTtWpVqlatil6frlPPVGeS/zujRo1Cp9PFu707WTOxunTpwoULFzh8+DAlS5bk448/JiQkJFnXHD16NP7+/sbt4cOHybqeEMliYwM//aTuz5gB9+5pGk5yVaxYkZMnT1KhQgW8vb2pU6dOyi1EdORIzJ7ytykKPHwIsippxhYcDPv2qZ82ubpC1qzQooX6+3P2LERGQsmS0LcvrF0LefJAHOPEI4EHgI+LCydPnoz2ya4QmY2NjQ1nzpzhzJkz2NjYaB2OSTPXOoDYDBs2jB49esTbpmjRouTOnZtnz55FOx4REYGvry+5c+eO9/yoXu0SJUrw4YcfkjVrVrZs2ULnzp3JnTs3p0+fjtb+6X81oeO7rpWVlZS9Eqblf/9Ty7e5u6tDWjZs0DqiZMmfPz9Hjx6lQ4cO7Nu3jzZt2jB//nz69euXsAuEhKgLMHl4RN/iGaIWzaRJ4Omp9p6WLatW3RDpV0gInDih/n64u8OpUxAeHr1NkSLq71CDBlCvnjpMLIqVlTrMSaeL9mlL5H9ffylblqNHj5I1a9ZUfypCiIzBJP+qRK0O9T6urq74+flx7tw5qlSpAsDBgweJjIykRo0aCb6foigoimKs+ODq6srkyZN59uyZcajMvn37cHBwoEyZMkl4RkJoRKdTP3avXBk2blSrjNSrp3FQyePg4MCOHTvo27cvK1eupH///ty7d4+pU6eqH5EqCjx/HjP59vBQPzVITpWagwfVDdRPJD74QE3So7bixePsQRUmICxMTb6jEvETJ+DdSj8FCqiJeNRWqFDc12vXTv29GjIk2icuj4AtdeowZu9e6awRQiSKTlHSYS21tzRv3pynT5+yePFiwsPD6dmzJ1WrVmXt2rUAeHl50bBhQ1avXk316tW5c+cOf/75J02aNMHZ2ZlHjx4xbdo0jh07xo0bN8iZMycGg4FKlSqRN29epk+fjre3N127duWLL75gypQpCY4tICAAR0dH/P39Zalloa0BA2DhQqhQAc6fhwxQO1kJC2PR8OHsmTsXF6BpwYLUzZMHs1u34OXLuE90coLSpcHFBUqVUr+WKAFNmqj1p2N7SdTp1PHFn3+uDmk4e1YdfxzbtatWherV3yTrb/ewirQVHq7W8nd3V99QHTsGr19Hb5MnT/REvGjRRL+5Cn71iplubtxwd+cJUHfMGCZMmiTlEIX4T3BwsLFj8/r16ya30q0p5WvpPjH39fVl4MCBbNu2Db1eT/v27Zk7dy729vYA3Lt3jyJFiuDu7k69evV4/PgxX3zxBefOnePly5fkypWLOnXqMG7cOEqVKmW87v379+nXrx+HDh3Czs6O7t27M23aNMwT8dG1KX2jRSb34oWafL58CYsWwZdfah1Rwvn5venxvnnzzb6nJ8S16JdOpw5BiEq8396cnWNPvKKqskD05Dyq7caNag8pqGONb91SF5GJ2i5ciNn7CmriV63am2S9alU1yRcpz2BQvw9RPeJHj8K7C845O0dPxEuWTNanHI8ePaJt27acO3cOc3NzlixZwueff57MJyJExhIUFGTMywIDA7Gzs9M4ouhMKV9L94m5KTOlb7QQzJunLjiUPbs6ztqUxr1GRsL9+zGTbw8P+G9+R6zs7KBUKZ5nz87y48c5FxTEs6xZmbphAzUbNkx8HJs3xxiWQIEC6nCgqKQ8LmFhcPVq9GT96lX1ub2rWLHoQ2A++EB9LiJxIiPh8uU3ifiRI2pJw7dlywZ1674ZJ16mTIoNNzp58iRt27bl6dOn5MiRg02bNlGnTp0UubYQGYkk5gkniXkqMqVvtBBEREClSupExyFD1GQzrQUFqT3N7479vnVLnYgXl3z5YvZ8u7iox/9Lsh48eICbmxsXL17EwsKCRYsW0atXr8THaDCoPa1Pnqi93bVrJ33oT1CQ2oP7drLu6RmznV6vTiZ9O1kvXx7+K+Eq/qMo6s9vVCJ++DD4+kZv4+gIdeq86RGvUEH9/01hq1evpnfv3oSFhVG+fHm2bt1K4cKFU/w+QmQEkpgnnCTmqciUvtFCALB/PzRurCaaFy6oQ1xSIgF9m6Ko14xt8mV8JUQtLdVhBW8n3qVKqVuWLAm6dVBQED169GDjxo0ADBkyhJkzZyZqCFqq8/VVx6i/naw/fhyznZWV+kbq7WS9VKlUSTJNlqKob9qixogfOqRO7H2bvb36sxuViFeunKpzKAwGA6NGjWLmzJkAtG3blt9++82YdAghYpLEPOEkMU9FpvSNFsKobVv4+2818Xt7THT+/PDzz+8fshElNFTt/X03+b55M/aJkVGcnaMn3lH7hQunSEIVGRnJpEmTmDBhAgANGzbkjz/+SFClJ808fqwm6KdPv0nW/fxitnNwgCpVoifrBQtmnEowigJ37rzpEXd3V9/kvc3GBmrVepOIV6kCFhZpEt6LFy/47LPP2L17NwDfffcdEydOlAVThHgPScwTThLzVGRK32ghjBYuVKu0vCu2SY4APj4xE28PDzWBim38NKgJdrFiMSdfliqljnFPA5s2baJbt24EBweTP39+NmzYwIcffpgm9042RYHbt6Mn6+fPx6woAuobnberwFSrph5LL+7fj56Iv/upipWVuthP1Bjx6tU1GeJz9uxZOnTowP3797GxsWHlypV88sknaR6HEOmRJOYJJ4l5KjKlb7QQgDp+unDh+Fe5dHRUE/OoseAvXsTd1sEh9rHfxYqZxPjoa9eu0a5dO27duoWFhQWzZ8+mf//+6bOMXUQEXL8ePVm/ciX2yjSFCkWvBFOlSoKHA6U6L6/oifjdu9Eft7CAGjXe9Ii7uoK1tTaxoq5zsWzZMgYNGkRYWBjFixdn06ZNVKhQQbOYhEhvgoODqVatGgBnzpyRconxkMQ8FZnSN1oIQB2jW79+4s8rVCj2BDxXLpMfRhEQEECvXr2M484/++wzFi9ebHI9Nkny+jVcuvRm+Mvp0+onGu/S6dTv19vJesWKam90YiRlYuzTp+rPXdQ48X//jf64mZkaT1QiXrOmyVSoef36Nf379+fXX38FwM3NjV9//RUnJydN4xJCpCxTytckMU9FpvSNFgKAP/6ATz99f7v27aFjR3XoScmSYGK9G4mlKAqzZ89mxIgRGAwGypUrx4YNG3BxcdE6tJTn768uqvN2sh7bpFsLC7ViydtDYMqUiTvRjq2UZGzzEnx81GopUT3i169Hv45er5aHjErEa9Uynd78t/z777907NiRS5cuodfrmTJlCsOHD5fx5EJkQKaUr0linopM6RstBJDwHnN3d6hXL7WjSXNHjx7l448/xtvbG1tbW+bOncvnn3+ePoe2JMbTp9GrwJw5oybQ77KzU5Pmt5P1okVhyxZ18aV3/1xE/b+NGKGWu3R3V+uKv6tixTdjxGvXVldINWGrV6+mf//+BAUF4ezszLp162jQoIHWYQkhUokp5WuSmKciU/pGCwG8GWPu5RX30vP586vjflOx5JyWvL296dq1K/v37wfgk08+YfHixZlreIKiwL170RP1c+dirpIJ6kJUwcGxr2oal7Jl3/SI162bZhN+kysgIID+/fuzZs0aAOrWrcuaNWvIly+fxpEJkb7JGPOEk8Q8FZnSN1oIo8QsPZ9BRUZGMmPGDL777jsiIiIoXLgwa9euxdXVVevQtGMwqJN9307WL11SVzRNiNat4bPP1E9acuZM1VBTw+nTp+ncuTN37tzBzMyMCRMmMHr0aMwy6BtUIdKSVGVJOBksJ0Rm066dmny/2wuYP3+mSMoB9Ho9I0eO5J9//qFIkSLcu3eP2rVrM3nyZCJiq3KSGZiZqT3dPXrAggXq2PSAAPjhh4Sd37kzfPxxukvKDQYDP/74Ix999BF37tyhUKFCHDlyhO+++06SciFEmpPEXIjMqF07dSiDuzusXfumbF0mSMrfVqNGDS5cuEDnzp0xGAx899131KpVCw8PD61DMw1WVvDRRwlrmydP6saSCjw9PalTpw6jRo0iIiKCjh07cvHiRWrWrKl1aEKITEoScyEyKzMzddhB587q10zaO+jo6MiaNWtYtWoVjo6OnDp1isqVKzNr1iwMBoPW4Wmvdm3105S4JsjqdFCggNounYiMjGTBggVUrFiR48ePkyVLFn755Rf+/PPPzDXXQAhhciQxF0Jkejqdjm7dunH16lWaNGlCSEgIw4YNo169enh6emodnrbMzNSSiBAzOY/695w56eaN3f3792ncuDEDBw4kODiYBg0acOXKFXr16pXxq/MIIUyeJOZCCPGf/Pnzs3v3bpYsWYK9vT3//PMPFStWZM6cOZm79zwDzEswGAzMmzeP8uXLc/DgQWxtbZk/fz779u2jUKFCWocnhBCAVGVJVaY0y1cIkTj37t2jZ8+eHDp0CIAqVaqwZMkSqlSpom1gWkrKyp8m4NKlS/Tp04fTp08D8NFHH7Fy5UpKlCihcWRCZA7BwcGUKVMGgOvXr0u5xHhIYp6KTOkbLYRIvMjISJYuXcqoUaPw9/dHr9czaNAgJk2aRBYTXK1SRBcUFMTEiRON8wUcHBz48ccf6dOnj6zgKYQwMqV8TV6ZhBAiDnq9ni+//BIPDw86d+5MZGQkP//8M2XKlGHjxo1Iv4ZpUhSFLVu2UK5cOWbMmIHBYKBjx454eHjw5ZdfSlIuhDBZ8uokhBDvkTt3btauXcvu3bspWrQojx49omPHjtSvX5+LFy9qHZ54y5UrV2jUqBHt2rXj3r17FCxYkG3btrF+/XrypMOSjkKIzEUScyGESKCmTZty9epVxo4di7W1NYcPH+aDDz6gd+/ePH36VOvwMrUXL14wYMAAKlWqxMGDB7G2tmbs2LFcv36dVq1aaR2eEJna69evqVatGtWqVeP169dah2PSZIx5KjKlMUtCiJT14MEDRo4cybp16wDIkiULo0ePZvDgwSa33HRGFhwczLx585g2bRp+fn4AdOzYkenTp1O4cGFNYxNCqIKCgrC3twcgMDDQ5F4jTSlfkx5zIYRIgoIFC/LHH3/wzz//ULVqVV69esW3335LsWLFmDdvHqGhoVqHmKGFhYWxcOFCihUrxqhRo/Dz86NChQq4u7uzfv16ScqFEOmSJOZCCJEMH330EadOneK3336jaNGiPH36lMGDB1OyZEmWL19ORESE1iFmKBEREfz222+4uLgwYMAAvL29KVy4MKtXr+b8+fPUq1dP6xCFECLJJDEXQohk0uv1fPbZZ3h4eLB48WLy5s3LgwcP+OKLL3BxcWHJkiWEhIRoHWa6FhoayrJly3BxcaFbt27cvXuX3Llzs2DBAm7evEnXrl0xSwc11YUQIj6SmAshRAqxsLCgb9++eHp68tNPP5EjRw5u377Nl19+SZEiRZg+fToBAQFah5muBAYGMnv2bIoWLUqfPn24ffs22bNnZ+rUqXh6etK/f38sLS21DlMIIVKETP5MRaY0mUAIkfaCgoJYvnw5M2fO5OHDhwA4Ojry+eef079/f4oXL65xhKbr9u3bLFiwgBUrVuDv7w9Avnz5+Oabb+jdu7fJTR4TQsRNJn8mXLrvMff19aVLly44ODjg5OREr169CAwMjPecvn37UqxYMWxsbHB2dsbNzQ0PD49obXQ6XYwtqvqCEEIkhJ2dHYMHD8bT05Nff/2V0qVL4+/vz+zZsylRogTNmzdn+/btGAwGrUM1CZGRkezZs4dWrVpRokQJZs+ejb+/P8WLF2fZsmXcvn2br776yuT+qAsh3i9HjhzkyJFD6zBMXrrvMW/evDlPnjxhyZIlhIeH07NnT6pVq8batWvjPGfp0qW4uLhQsGBBfH19mTBhAhcvXuTu3bvGMYo6nY6VK1fSrFkz43lOTk5YW1snODZTegcmhNBeZGQke/fuZf78+ezcudO4cmjBggXp2rUr3bp1o2TJkhpHmfY8PT1ZtWoVq1atMn6yANCsWTMGDRpEs2bNZLVOIUSqMaV8LV0n5jdu3KBMmTKcOXOGqlWrArB7925atGjBo0ePyJs3b4Kuc/nyZSpWrIinpyfFihUD1MR8y5YttG3bNsnxmdI3WghhWu7cucOiRYtYvnw5L1++NB7/8MMP6datG+3atSNXrlwaRpi6nj59yl9//cXvv//OP//8Yzzu5OREt27dGDBgQKZ8kyKESHumlK+l68R8xYoVDBs27P/t3XtYVNX6B/DvjMBwvwQjd1BQApU0QsGDgkjgLVG8ayYX7THLOqFm/byhPpl6MrVSs0RBEyMJRU0BUcALlXlAziEVJETlIqIIIsMd1u8Pn9nHaWaAkcvM0Pt5nnmUtdZee+3lcnhnzdprS/xSa25uhra2NuLi4hAUFNRuHSKRCGvWrMGJEyeQm5vL3UTE4/FgZWWFhoYGODg44J133kFoaCh4PJ7cuhoaGiT2Lq6uroatra1K/EMTQlRTXV0dTp06hYMHDyI5OZlb1sLj8TBq1CgEBQUhKCioV+zLXVxcjGPHjiE+Ph6XL19Ga2srgGe72owbNw4hISEIDAxU6JtJQgjpLFUKzDWUevZOKisrQ9++fSXSNDQ08NJLL6GsrKzNY/fs2YOVK1dCJBLh5ZdfRkpKisSd/Rs3bsTYsWOhq6uLs2fP4t1330VNTQ0++OADuXVu3rwZGzZs6NxFEUL+VnR0dDBr1izMmjULZWVlOHLkCI4cOYLMzExcunQJly5dwrJly+Dq6gp/f3+8/vrrGD16NHcjlSqrra3FpUuXcPbsWaSkpCAnJ0ci393dHTNnzsSbb74Ja2trJbWSENLd6urqMGHCBABAYmIidHR0lNwi1aWSM+affPIJtm7d2maZmzdv4tixYzh48CDy8vIk8vr27YsNGzZgyZIlco9/8uQJysvLcf/+fWzbtg0lJSXIyMiQO1Ozbt06REVFSax//CuaMSeEdJV79+4hISEBx48fx8WLF7nZZeDZBISnpyf+8Y9/wMPDAyNGjICNjY0SW/vM/fv3ceXKFfz222/49ddfceXKFYn3RB6PBy8vL0yfPh3Tpk2DnZ2dEltLCOkptCtLx6lkYP7w4UNUVFS0WcbBwQGHDx/u9FIW4NmjnU1MTBAZGYm5c+fKLHP69Gm88cYbqK+vh0Ag6FC9qvQPTQhRX48ePUJqairOnTuHlJQU3LlzR6qMlZUVXn31Vbi4uHAvZ2dnGBsbt7kET1GMMVRVVeHPP//E9evXcePGDdy4cQM5OTm4d++eVHlbW1sEBAQgICAAfn5+MDU17bK2EELUAwXmHaeSS1mEQiGEQmG75UaOHImqqipkZmbitddeAwCkpqaitbUVHh4eHT4fYwyMMYmZnb/Kzs6GiYlJh4NyQgjpKmZmZtxyF+DZjaNpaWn4/fffceXKFeTk5KC0tBSlpaU4ffq0xLF6enqwtrbmXkKhEAYGBtxLX18ffD6fex8EgPr6ejx58oR7VVVVoaSkBEVFRSguLoZIJJLZTh6PhyFDhsDT0xOenp7w8vKCk5NTl34wIISQ3kwlZ8wVMWHCBDx48AB79+7ltkt0d3fntkssKSmBn58fDh06hBEjRuD27dv48ccfERAQAKFQiOLiYmzZsgUZGRm4efMm+vbti1OnTuHBgwfw9PSEtrY2UlJSsGLFCqxYsUKhNeSq9AmMENJ7iUQiZGVlIScnB7m5ubh58yZu3ryJkpKSbjunubk5Bg8ejEGDBnF/vvrqqzAwMOi2cxJC1BPNmHecSs6YKyImJgZLly6Fn58f+Hw+pk+fjq+++orLb2pqQl5eHmprawEA2trauHTpEnbu3InKykqYm5vD29sbv/zyC3cjqaamJnbv3o3w8HAwxjBgwABs374db7/9tlKukRBC2qKnp4fRo0dj9OjREukikQglJSUSr8ePH6O6uhpPnz7F06dPuQeyPf8wNS0tLRgZGUm8rKysYGtrCxsbG9jY2NDNW4QQ0g3UfsZclanSJzBCCCGEEGWgGfOOU/sZc0IIIYQQotp0dXWV3QS1QIE5IYQQQgjpNnp6enJvGieS+MpuACGEEEIIIYQCc0IIIYQQQlQCBeaEEEIIIaTb1NfXY9KkSZg0aRLq6+uV3RyVRmvMCSGEEEJIt2lpacGZM2e4vxP5aMacEEIIIYQQFUCBOSGEEEIIISqAAnNCCCGEEEJUAAXmhBBCCCGEqAAKzAkhhBBCCFEBtCtLN2KMAQCqq6uV3BJCCCGEEOV4/qmf1dXVKrczizhOE8dtykSBeTd6+vQpAMDW1lbJLSGEEEIIUT4rKytlN0Gup0+fwsjISKlt4DFV+HjQS7W2tqK0tBQGBgbg8Xjdfr7q6mrY2tqiqKgIhoaG3X4+dUH9Ih/1jWzUL/JR38hG/SIf9Y1s1C+qgzGGp0+fwsrKCny+cld504x5N+Lz+bCxsenx8xoaGtJ/chmoX+SjvpGN+kU+6hvZqF/ko76RjfpFNSh7plyMbv4khBBCCCFEBVBgTgghhBBCiAqgwLwXEQgEiIiIgEAgUHZTVAr1i3zUN7JRv8hHfSMb9Yt81DeyUb8QWejmT0IIIYQQQlQAzZgTQgghhBCiAigwJ4QQQgghRAVQYE4IIYQQQogKoMCcEEIIIYQQFUCBeS9w9epVTJw4EcbGxtDT04OnpyeOHj2q7GYpzdatW8Hj8cDj8fDbb79J5K1fv57Lk/W6c+eOchrdDVpbW7Fr1y64ublBV1cXhoaG8Pb2xsmTJ2WWr66uxrJly2Bvbw+BQIB+/frho48+Qk1NTQ+3vGscPnwYixcvhru7OwQCAXg8HqKjo2WWjYmJQVBQEBwdHWFgYAB9fX0MHjwY4eHhKCkpkSp/586dNsfR+vXru/fiOkGRfgGA/Px8hIaGYuDAgdDR0YG1tTX8/f3ljiPgWX+OGDECenp6MDExwRtvvIGsrKxuuJquU1JSgp07dyIgIAB2dnbQ0tKChYUFpk+fjitXrkiUbWpqQnx8PIKDg+Hi4gJ9fX0YGBjAw8MD33zzDVpaWqTqV9cxo0i/AGjzGsWvoqIiqePUcczU19dj2bJl8Pb2hpWVFbS1tWFhYQEvLy9ERUWhqalJonx2djZWrVqFcePGQSgUgsfjYcyYMXLrV9cxQzqHnvyp5tLS0jBu3Dhoa2tjzpw5MDAwQHx8PGbPno2ioiIsX75c2U3sUX/88QciIiKgp6cHkUgkt1xwcDD69esnlW5sbNx9jetBjDHMmjUL8fHxcHR0xMKFC9HQ0IATJ05gypQp+Prrr7F06VKuvEgkgo+PD7KzsxEQEIC5c+fi2rVr2LZtGy5cuICLFy9CW1tbiVekuDVr1uDu3bswMzODpaUl7t69K7dsbGws8vPz4enpCUtLSzDGkJ2djS+//BLR0dG4fPkyBg8eLHXc0KFDMXXqVKn0tn7ZKpsi/XLlyhX4+vqiqakJgYGBmD59OsrLy3Hs2DFMmTIF69evR0REhMQxmzZtwpo1a2Bvb4933nkHT58+RWxsLP7xj3/g/Pnz8PLy6u5LfCFff/01tm7dCkdHRwQEBEAoFCI/Px8JCQlISEjAkSNHMHv2bABAQUEBZsyYAX19ffj5+SEwMBBPnjzBqVOn8O677+LMmTM4efIkeDye1HnUbcwo0i8ApMaD2J9//omYmBgMGjQItra2EnnqOmZqamrwzTffYMSIEZg0aRKEQiEqKyuRmJiIsLAwxMbGIjExkXvEe0JCAjZv3gwtLS04OTnh0aNHHTqPuo0Z0kmMqK2mpibm6OjIBAIBu3btGpdeVVXFnJycmJaWFrtz547yGtjDGhsbmZubG/Pw8GDz589nANivv/4qUSYiIoIBYGlpacppZA+Ji4tjAJiXlxerra3l0h8+fMjs7e2ZQCBghYWFXPq6desYAPbxxx9L1PPxxx8zAOyzzz7rqaZ3mZSUFG78b968mQFgUVFRMsvW1dXJTI+MjGQA2IwZMyTSCwsLGQAWHBzclU3uEYr0y4QJExgAlpCQIJF+584dZmBgwHR0dFh9fT2XfuvWLaahocGcnJxYVVUVl37t2jUmEAiYi4sLa2lp6fqL6gLx8fEsPT1dKv3ixYtMU1OTmZiYcNdaXFzMdu/ezWpqaiTK1tTUMHd3dwaAHT16VCJPXceMIv3SlqVLlzIA7IsvvpBIV+cx09LSwhoaGqTSm5qa2JgxYxgA9vPPP3Ppf/zxB8vMzGSNjY3s/v37DADz8fGRW7+6jhnSObSURY2lpqaioKAA8+bNw7Bhw7h0IyMjrFq1Co2NjTh48KDyGtjDNm3ahOvXr+PAgQPo06ePspujVCdOnAAArFq1Cjo6Oly6mZkZwsPD0dDQgKioKADPZtcjIyOhr6+PtWvXStSzdu1a6OvrIzIysuca30Vef/112Nvbd6isvG8DZs6cCeDZbF9voUi/3L59GzweDxMmTJBIt7e3h6urK+rq6iSWOkVFRaG5uRmrV6+GkZERlz5s2DDMnTsXN2/exOXLl7vmQrrYtGnT4OPjI5U+evRo+Pr6orKyEjk5OQAAa2trvPvuu9DT05Moq6enh2XLlgEALly40P2N7gGK9Is89fX1iImJgZaWFt566y2JPHUeM3w+H1paWlLpGhoaCAoKAiD53jF48GC4ublBU1Ozx9pI1A8F5mosPT0dABAQECCVN27cOAC955dDe7KysrBp0yZERERg0KBB7Za/ePEitm7dis8//xwJCQlqu45anrKyMgBA//79pfLEaampqQCerSEuLS2Fl5eXzEDDy8sLt2/flrkutLc7ffo0AGDIkCEy80tLS7F792589tln2L9/PwoKCnqyed1uyJAhYIwhMTFRIv3evXvIycnB0KFDYWpqyqX31vckcSClodH+6s/2yvamMdPRfjl27BgqKysRGBgIoVAokdcbx0xrayuSkpIAyH/vUERvGjOkfbTGXI3l5+cDAAYOHCiVZ2FhAX19fa5Mb9bQ0IAFCxZg2LBhWLlyZYeO+es6SGNjY3z55ZdYsGBBdzSxx5mZmQEACgsL4eLiIpFXWFgIALh16xaAtseROD05ORn5+flSa0N7m6NHj+LGjRuora3F9evXkZycjP79+2Pjxo0yy6ekpCAlJYX7mcfj4c0338TevXulPuSoo08//RQZGRmYMWMGAgMD4eTkxK0xd3R0xI8//ihRPj8/H/r6+rCwsJCqSzy+1O096d69ezh37hwsLS3h6urabvkDBw4AkB1oAr1nzCjSL/v37wcALFq0SCqvN4yZxsZGfPbZZ2CMoaKiAufPn0dubi5CQ0Ph5+fX6fp7y5ghHUOBuRp78uQJAEh8/fc8Q0NDrkxvtm7dOuTn5yMzM7PdJSxDhw7FgQMHMGbMGFhaWqKsrAw///wz1q1bh5CQEBgbGyMwMLCHWt59JkyYgNjYWGzZsgVjx47llmpUVFRg586dAICqqioAHRtHz5frzY4ePYr4+HjuZ3d3d8TGxkp986Crq4u1a9di6tSpcHR0RGtrK7KysrB69WocPnwYtbW1EvWoK2dnZ/z222+YOXMmjh07xqWbmpoiNDQUjo6OEuWfPHmCvn37yqxLHcdRU1MT3nrrLTQ0NGDr1q3tvr989913SExMxNixYzFx4kSJvN40ZhTpl8LCQqSlpcHOzg7+/v5S+b1hzDQ2NmLDhg3czzweDytWrMDmzZs7VW9vGjNEAUpe4046wd/fnwFg+fn5MvOtrKyYoaFhD7eqZ/3yyy+Mz+ezjRs3SqQHBwfLvPlTnnPnzjEej8dcXV27o5k9rqmpifn6+jIAbMCAAWzp0qVs8eLFzNzcnL3yyisMANPW1maMMRYTE8MAsNWrV8usa9WqVQwAO3bsWE9eQpdq7ybHv6qsrGSpqals5MiRzMjIiJ0/f75Dx4lEIvbyyy8zACwzM7MTLe4Z7fXLlStXmKWlJQsICGCZmZlMJBKxgoICFh4ezgCwmTNnSpTX1NRk1tbWMuu6desWA8ACAwO7+jK6RUtLC5s3bx4DwN5+++12y586dYppamoye3t7Vlpa2uHzqNuYUbRf1qxZwwCwiIgImfm9bcwUFRWxPXv2MGNjY+bl5cWePHkis2xHbv6UR93GDFEMrTFXY+IZTnmzCdXV1XJnQXuD5uZmBAcH45VXXsEnn3zSqbr8/Pzg6OiInJwcVFdXd1ELlUdDQwOJiYlYv349+Hw+vvvuO26Lu59++gkAuFmqjoyj58v9HRgbG8PX1xdJSUnQ0dHBggULpPYklkVXV5e7uS0jI6O7m9mtmpqaMGfOHPD5fBw/fpzbD9/BwQHbt2/H1KlTERcXJ3GdRkZGvWIctba2IiwsDEeOHMH8+fOxd+/eNsufOXMGM2bMgLm5OVJTU2Fpadnhc6nTmFG0X1pbWxEdHQ0+n4+wsDCZZXrLmAGe3QxqY2ODJUuW4LvvvkNGRgY2bdrU5edRpzFDFEeBuRpra/1dWVkZampq5K4b7g1qamqQn5+P7OxsaGlpSTx4QbwbzciRI8Hj8ZCQkNBufeJ12bW1td3Z7B4jEAgQERGBvLw8NDQ0oLy8HN9++y33wBx3d3cA7a/jbG8Nem9maGgIT09PlJSUdHhnFvE4amsffXWQm5uLwsJCeHh4QFdXVyrf19cXAHDt2jUubeDAgaipqeFuPn6euoyj1tZWhIaG4uDBg5g7dy4XWMpz+vRpTJs2DWZmZkhLS4ODg4PC51SHMaNovwBAUlISiouL4e/vDzs7O5llesOYkUV8j4H45taupg5jhrwYCszVmHgLq7Nnz0rlJScnS5TpjQQCARYuXCjzJX4jDwwMxMKFC2U+TOh5IpEI169fh56eHveG11vFxMQAAObMmQPg2S89KysrZGRkSL3Ji0QiZGRkoH///r3+xk95SktLAaDDW5yJn4bY3phTdY2NjQCAhw8fyswXpwsEAi5N3d+TxMHnoUOHMHv2bHz//fdtrp8+ffo0pk+fjpdeeglpaWkYMGDAC51X1ceMov0i1tZNn2LqPmbkUfR9Q1GqPmZIJyh7LQ15cU1NTczBwaHNBww9/xCZvxNZa8yrq6tZXl6eVNna2lo2d+5cBoCFhob2ZDO7lay1jXFxcYzP57Phw4ez5uZmLr03PmDoeW2tpa6urma5ubkyj9u/fz8DwAYOHCiRnpWVxVpbW6XKx8fHMz6fz0xMTCQelqKq2uqX+vp6ZmhoyPh8PktOTpbIu3fvHhMKhYzH40n8n8rLy1Prh8WI3zdmzpzJmpqa2ix/5swZJhAImIWFhdzx8zx1HTOK9otYeXk509TUZEKhUOZDeMTUecxcv36diUQiqXSRSMTGjx/PALBNmzbJPLYja8zVdcyQzqFdWdSYhoYGIiMjMW7cOHh7e2POnDkwMDBAfHw87t69i23bttGn6edUVFTA2dkZw4cPh4uLCywsLPDgwQOcO3cOxcXFcHV1xeeff67sZnYZDw8P2NrawsXFBdra2vj999+Rnp4OBwcHxMXFScx4rVy5EidOnMDWrVtx7do1uLm5ISsrC2fPnsXw4cPx4YcfKu9CXlBkZCT3YBLxA1AiIyO5r5ZHjRqFRYsWoaKiAi4uLnB3d4ezszOsra1RWVmJq1evIisrC4aGhlIP6goPD0dBQQFGjhwJGxsbtLS0ICsrC5cvX4ZAIEB0dLTKrovtaL8IBAJ8/vnnWLx4MSZMmIA33ngDzs7OKCsrw7Fjx1BTU4Ply5fDycmJq9vJyQnr16/HmjVrMHToUEyfPp17vDoA7Nu3r93lD8qyceNGHDx4EPr6+nBycsKnn34qVWbq1KkYNmwYcnNzERQUhIaGBowZMwY//PCDVNl+/fohJCSE+1ldx4wi/fK8Q4cOcbu3yHoIj5g6j5mjR49i+/btGDVqFPr16wdDQ0OUlJQgMTERFRUVGD16NMLDw7nyubm52LJlCwCgrq6OS3t+nERHR3N/V9cxQzpJ2Z8MSOdduXKFjR8/nhkaGjIdHR02YsQIFhsbq+xmKZWsGfMnT56w9957jw0fPpwJhUKmoaHBDAwM2IgRI9i//vUviUfX9wYRERHM1dWVGRgYMG1tbebi4sLWrFkjd5eAqqoq9uGHHzJbW1umqanJ7Ozs2PLly1l1dXUPt7xriMeAvJf4Mdc1NTVs3bp1zNvbm1lYWDBNTU2mp6fHBg8ezMLDw1lRUZFU3fv27WPjx49ntra2TEdHhwkEAubg4MAWLVrEbt682cNXqpiO9ovY2bNn2aRJk5iZmRnr06cPMzIyYt7e3uzw4cNyz3H48GHm7u7OdHR0mJGREZs4caLK7x7RXr/guW8W0tLS2i3715lQdR0zivTL81xcXBgAduPGjQ6dRx3HzNWrV9nbb7/NBg8ezIyNjZmGhgYzNTVlvr6+7Ntvv5X6dqEj4+Z56jpmSOfwGGOs68N9QgghhBBCiCJU8/shQgghhBBC/mYoMCeEEEIIIUQFUGBOCCGEEEKICqDAnBBCCCGEEBVAgTkhhBBCCCEqgAJzQgghhBBCVAAF5oQQQgghhKgACswJIYQQQghRARSYE0IIIYQQogIoMCeEkC4wdOhQ8Hg8CAQCVFRUKLs5EqKjo8Hj8RASEtLt5woJCQGPx+NeGhoaCtcxZswY8Hg8pKend30DFeDs7CxxLWPGjFFqewghvZ/i75iEEEIkXL16Ff/9738BAI2NjTh8+DD++c9/KrlVyuXl5YUBAwagT58+ym7KCwsKCsL9+/dRVlaG5ORkZTeHEPI3QIE5IYR00v79+wEA1tbWKCkpwf79+//2gfmiRYt6ZIa+O23evBkAkJ6eToE5IaRH0FIWQgjphNraWvzwww8AgO+//x76+vrIycnB1atXldwyQggh6oYCc0II6YS4uDhUV1djyJAh8PX1xezZswH8bxZdlufXUGdnZ2PatGkwMzODQCDAoEGD8MUXX4AxJvNYkUiEtWvXYuDAgRAIBLCyskJYWBhKSkqwfv168Hg8rF+/XqFrKC0txbJly+Di4gJdXV0YGBhg+PDh2LVrF5qbmxWqq6OKiooQFhYGS0tLaGtrY+DAgVi9ejXq6uraPfann37C+PHjIRQKoaWlBWtra8yfPx83btyQe8zly5cxfvx4GBsbQ19fH8OHD8ehQ4cAgFtDTgghykaBOSGEdII4AA8LC5P4MzY2tt0gMzk5GR4eHsjNzYW/vz9GjhyJW7duYcWKFQgPD5cqLxKJ4Ovri08//RRlZWUICAjAqFGjkJSUBDc3N9y9e1fh9l+8eBFDhgzBjh07UF9fD39/f3h5eaGgoADvv/8+Jk2ahKamJoXrbUtubi7c3d0RFRUFHo+HwMBAODk5YceOHfDz80NjY6PM45qbmzF79mzMnDkT6enpcHJywtSpUyEUChETEwN3d3ckJSVJHRcbGwsfHx8kJyfDzs4OgYGB0NXVRWhoKD755JMuvTZCCOkURggh5IXk5eUxAExTU5OVl5dz6c7OzgwAO3TokMzjfHx8GAAGgO3du1ci7/z584zH47E+ffqwoqIiibzw8HAGgA0aNIiVlpZy6XV1dWzGjBlcnRERERLHRUVFMQAsODhYIv3+/fvM1NSU8Xg8tmfPHtbS0sLlPXr0iI0dO5YBYBs2bOhwnwQHBzMALCoqSm6Z4cOHMwBs1qxZrK6ujku/e/cuc3R05K4jLS1N4rhVq1YxAMzDw4Pdvn1bIi8uLo716dOHmZiYsMrKSi69pKSE6evrMwDsyy+/lDjmwoULTE9PjzufPGlpaQwA8/Hxaff6CSGkM2jGnBBCXtCBAwcAAIGBgRAKhVy6eNa8reUsADBt2jQsXrxYIm3s2LEYN24cWlpakJaWxqXX1dVh3759AIAdO3bA0tKSy9PW1saePXugq6urUPt37tyJiooKvPfee1iyZAn4/P/9SjA1NcWhQ4egqamJXbt2yV1ao6iMjAxcvXoVenp62LNnD7S1tbk8Ozs7bNu2TeZxjx8/xo4dO6CtrY34+Hj0799fIn/GjBlYvHgxKisrcfjwYS59//79qKmpwciRI/HBBx9IHOPt7Y0lS5Z0yXURQkhXoMCcEEJeQHNzMw4ePAjgf4G42IIFC6ChoYGLFy+ioKBAbh2TJ0+Wme7i4gIAKCkp4dIyMzNRU1MDMzMzBAQESB0jFArh7++v0DWcPn0aALh18X9lbW2NgQMH4uHDh8jPz1eobnnEe5OPHz8epqamUvlTpkyBkZGRVHpaWhrq6urg5eUFa2trmXWL9xn/5ZdfuLQLFy4AAN58802Zx8hLJ4QQZaDAnBBCXsDp06dRVlYGa2trjBs3TiLP3NwcEydOBGOMm1WXxc7OTma6oaEhAKC+vp5LKy4uBgD069dPbn1t5cly+/ZtAMDo0aMlHqTz/Et8Q+XDhw8Vqlse8XX8dcZbjMfjybwOcVvPnz8vt62zZs2Samt7/aZonxFCSHeifcwJIeQFiJep1NfXw8fHRypfPNsdHR2NjRs3ynzQzvNLRzqqrd1DFN1ZpLW1FcCzZSB6enptlpU1u92TxG0dMGAAvLy82izr7OwslSavb2g3FkKIKqHAnBBCFHT//n2cOXMGAFBRUYGMjAy5ZUtLS5GUlIRJkyZ16pzi5Rt37tyRW6atPFlsbW2Rn5+Pjz/+GO7u7p1oXcd15Dpk7S5ja2sLAHj55ZcRHR2t0Pny8vLknk/RPiOEkO5ES1kIIURB0dHRaGlpgYeHBxhjcl8rV64E0P5NoB3x2muvQVdXFw8fPsS5c+ek8h89eoSUlBSF6pwwYQIA4OjRo51uX0eJv11ISkrC48ePpfJPnjyJqqoqqXQ/Pz9oaWkhPT0d5eXlHT6ft7c3AHAPgfqrI0eOdLguQgjpbhSYE0KIgsTrxoODg9sst2DBAgDAzz//3Ok12rq6uli0aBEAIDw8HA8ePODyGhoasHTpUohEIoXq/Oijj2BsbIzt27fjiy++kLl/eGFhocQuJ501evRouLm5oaamBu+99x4aGhq4vKKiIqxYsULmcebm5nj//fchEokwefJk5OTkSJVpaGjAyZMnkZuby6UtXLgQurq6uHz5Mnbv3i1RPiMjA3v27OmiKyOEkM6jwJwQQhRw4cIF/PnnnxAIBJgzZ06bZQcPHgw3Nzc0NTVxT5nsjE2bNuG1117DH3/8gQEDBmDKlCmYPXs2HBwccP78ee6DgpaWVofqs7GxwYkTJ2BiYoIVK1bA1tYWfn5+mD9/PiZPnowBAwbAwcEBu3bt6nTbn/f9999DKBQiNjYWDg4OmD17NiZPngxnZ2eYmppi5MiRMo/bsmUL5s2bh99//x3Dhg2Dm5sbZsyYgTlz5mDUqFEwNTXFlClTJJan2NjY4NtvvwWfz8fSpUsxdOhQzJs3D2PGjIG3tzfeeecdAICmpmaXXiMhhLwICswJIUQB4mUpkydPhomJSbvlxbPmXbGcRV9fH+np6Vi1ahX69u2LpKQkXLx4EX5+fsjMzORuMDUzM+twnd7e3rh+/TrWrl0LGxsbXL16FXFxccjOzoa5uTkiIiK4/dO7yqBBg/Dvf/8bISEhaGlpQUJCAm7cuIH3338f58+fl/vBQkNDAzExMThz5gymTp2K8vJynDx5EsnJyXj8+DEmT56MI0eOcMtXxObPn4/U1FT4+/vjzp07OHHiBJ4+fYp9+/Zxe5sr0meEENJdeKyrnhpBCCFEaZqamjBkyBDcunULmZmZcHNzU0o7QkJCcPDgQURFRSEkJEQpbVDEoUOHEBwcjMmTJ+PkyZMyy6Snp8PX1xc+Pj7cPuyEENIdaFcWQghRI5mZmXj11VcltlqsqanB8uXLcevWLbzyyitKC8qfFxkZifT0dPTp06dLvi3ojHv37kFLSwsWFhYS6RkZGdya9tDQUKnj/u///g/3799HWVlZj7STEEIoMCeEEDUyffp01NbWwtXVFX379kV5eTmys7Px+PFjvPTSSwptJdidMjIykJGRoRKBeWpqKhYuXIihQ4fCzs4Offr0QUFBAf7zn/8AeBaUBwUFSR13/Phx5OXl9XRzCSF/Y7SUhRBC1MhXX32F48ePIzc3F5WVleDz+bC3t0dAQAB3AyeRlJubi23btuHSpUt48OABRCIRjI2NMWzYMISFhWHu3LnKbiIhhACgwJwQQgghhBCVQLuyEEIIIYQQogIoMCeEEEIIIUQFUGBOCCGEEEKICqDAnBBCCCGEEBVAgTkhhBBCCCEqgAJzQgghhBBCVAAF5oQQQgghhKgACswJIYQQQghRAf8PeKJaDp8ufIgAAAAASUVORK5CYII=" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 43 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-08T12:10:10.859562Z", - "start_time": "2024-06-08T12:10:10.815447Z" - } - }, - "cell_type": "code", - "source": [ - "from circumplex import OCTANTS\n", - "cor_res = circumplex.ssm_analyse(jz2017s.data, jz2017s.scales.abbrev, measures=['NARPD'])\n", - "cor_res.table" - ], - "id": "fb8c38caf15ab6fa", - "outputs": [ - { - "data": { - "text/plain": [ - " label group measure elevation xval yval amplitude \\\n", - "NARPD NARPD None NARPD 0.202476 0.178969 0.061508 0.189244 \n", - "\n", - " displacement r2 PA BC DE FG HI JK LM NO \n", - "NARPD 18.966744 0.957009 0 45 90 135 180 225 270 315 " - ], - "text/html": [ - "
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labelgroupmeasureelevationxvalyvalamplitudedisplacementr2PABCDEFGHIJKLMNO
NARPDNARPDNoneNARPD0.2024760.1789690.0615080.18924418.9667440.95700904590135180225270315
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" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 44 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-08T12:10:11.999274Z", - "start_time": "2024-06-08T12:10:11.817975Z" - } - }, - "cell_type": "code", - "source": "cor_res.results[0].profile_plot()", - "id": "11edf672affbfd8e", - "outputs": [ - { - "data": { - "text/plain": [ - "(
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 45 - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "\n", - "id": "efcee9158a27aa8d" - } - ], - "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": 5 -} diff --git a/docs/tutorials/Random_exs.ipynb b/docs/tutorials/Random_exs.ipynb deleted file mode 100644 index a93a6fe..0000000 --- a/docs/tutorials/Random_exs.ipynb +++ /dev/null @@ -1,967 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "initial_id", - "metadata": { - "collapsed": true, - "ExecuteTime": { - "end_time": "2024-02-15T14:42:04.126026Z", - "start_time": "2024-02-15T14:42:01.945946Z" - } - }, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%matplotlib inline\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "\n", - "import circumplex\n", - "\n", - "data = pd.read_excel(\n", - " \"/Users/mitch/Library/CloudStorage/OneDrive-UniversityCollegeLondon/_Fellowship/Papers - Drafts/J2308_APA_SATP-Main/data/SATP Dataset v1.4.xlsx\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error: array must not contain infs or NaNs | in Language = arb\n", - "Error: array must not contain infs or NaNs | in Language = cmn\n", - "Error: array must not contain infs or NaNs | in Language = ell\n", - "Error: array must not contain infs or NaNs | in Language = fra\n", - "Error: array must not contain infs or NaNs | in Language = ind\n", - "Error: array must not contain infs or NaNs | in Language = jpn\n", - "Error: array must not contain infs or NaNs | in Language = kor\n", - "Error: array must not contain infs or NaNs | in Language = nld\n", - "Error: array must not contain infs or NaNs | in Language = spa\n", - "Error: array must not contain infs or NaNs | in Language = vie\n", - "Error: array must not contain infs or NaNs | in Language = zsm\n" - ] - }, - { - "data": { - "text/plain": "(
, )" - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%autoreload\n", - "plt.style.use(\"ggplot\")\n", - "scales = [\"PAQ1\", \"PAQ2\", \"PAQ3\", \"PAQ4\", \"PAQ5\", \"PAQ6\", \"PAQ7\", \"PAQ8\"]\n", - "\n", - "ssm_res = circumplex.ssm_analyse(data, scales, [\"loud\"], [\"Language\"])\n", - "ssm_res.plot()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-02-15T14:42:04.419852Z", - "start_time": "2024-02-15T14:42:04.127613Z" - } - }, - "id": "7be48f6cc42a46b2" - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [ - { - "data": { - "text/plain": "SSMParams(deu_loud, scores=PAQ1 -0.735505\nPAQ2 -0.094355\nPAQ3 0.344259\nPAQ4 0.656572\nPAQ5 0.753815\nPAQ6 0.021532\nPAQ7 -0.394931\nPAQ8 -0.795127\ndtype: float64, angles=(0, 45, 90, 135, 180, 225, 270, 315))" - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ssm_res.results[0]" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-02-15T14:42:04.428488Z", - "start_time": "2024-02-15T14:42:04.419231Z" - } - }, - "id": "78230ac68ea161a9" - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error: array must not contain infs or NaNs | in Language = arb\n", - "Error: array must not contain infs or NaNs | in Language = cmn\n", - "Error: array must not contain infs or NaNs | in Language = ell\n", - "Error: array must not contain infs or NaNs | in Language = fra\n", - "Error: array must not contain infs or NaNs | in Language = ind\n", - "Error: array must not contain infs or NaNs | in Language = jpn\n", - "Error: array must not contain infs or NaNs | in Language = kor\n", - "Error: array must not contain infs or NaNs | in Language = nld\n", - "Error: array must not contain infs or NaNs | in Language = spa\n", - "Error: array must not contain infs or NaNs | in Language = vie\n", - "Error: array must not contain infs or NaNs | in Language = zsm\n" - ] - } - ], - "source": [ - "test = circumplex.ssm_analyse_grouped_corrs(data, scales, [\"loud\"], [\"Language\"])" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-02-15T14:42:05.992639Z", - "start_time": "2024-02-15T14:42:05.949054Z" - } - }, - "id": "83645267c6b51ea1" - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "data": { - "text/plain": "[SSMParams(deu_loud, scores=PAQ1 -0.735505\n PAQ2 -0.094355\n PAQ3 0.344259\n PAQ4 0.656572\n PAQ5 0.753815\n PAQ6 0.021532\n PAQ7 -0.394931\n PAQ8 -0.795127\n dtype: float64, angles=(0, 45, 90, 135, 180, 225, 270, 315)),\n SSMParams(eng_loud, scores=PAQ1 -0.606997\n PAQ2 -0.027397\n PAQ3 0.324510\n PAQ4 0.608231\n PAQ5 0.644103\n PAQ6 0.117573\n PAQ7 -0.314404\n PAQ8 -0.730986\n dtype: float64, angles=(0, 45, 90, 135, 180, 225, 270, 315)),\n SSMParams(hrv_loud, scores=PAQ1 -0.621366\n PAQ2 0.328738\n PAQ3 0.364106\n PAQ4 0.700794\n PAQ5 0.669574\n PAQ6 0.011157\n PAQ7 -0.321482\n PAQ8 -0.633544\n dtype: float64, angles=(0, 45, 90, 135, 180, 225, 270, 315)),\n SSMParams(ita_loud, scores=PAQ1 -0.516713\n PAQ2 0.126906\n PAQ3 0.249130\n PAQ4 0.617427\n PAQ5 0.690386\n PAQ6 -0.058512\n PAQ7 -0.191610\n PAQ8 -0.651333\n dtype: float64, angles=(0, 45, 90, 135, 180, 225, 270, 315)),\n SSMParams(por_loud, scores=PAQ1 -0.489250\n PAQ2 0.375296\n PAQ3 0.695722\n PAQ4 0.639984\n PAQ5 0.607933\n PAQ6 -0.223063\n PAQ7 -0.328263\n PAQ8 -0.621765\n dtype: float64, angles=(0, 45, 90, 135, 180, 225, 270, 315)),\n SSMParams(swe_loud, scores=PAQ1 -0.661008\n PAQ2 0.014031\n PAQ3 0.232966\n PAQ4 0.725853\n PAQ5 0.787736\n PAQ6 0.066319\n PAQ7 -0.298978\n PAQ8 -0.737212\n dtype: float64, angles=(0, 45, 90, 135, 180, 225, 270, 315)),\n SSMParams(tur_loud, scores=PAQ1 -0.502171\n PAQ2 0.087115\n PAQ3 0.580815\n PAQ4 0.562785\n PAQ5 0.758338\n PAQ6 -0.187736\n PAQ7 -0.584758\n PAQ8 -0.723721\n dtype: float64, angles=(0, 45, 90, 135, 180, 225, 270, 315))]" - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test.results" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:34.264776Z", - "start_time": "2023-11-25T18:35:34.219547Z" - } - }, - "id": "f155ed134be053e2" - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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t6X5VWwghxMd9+eWX5MmTB4CVK1cSGhpqsFjq1q0LRPUi2rJlS4LlNm3alGC7IK1I2yZtSdtGiIxFklJCZFDm5uZ069YNgCtXrjB79uw4ZSIiIujVqxdarTbB49SrV0+/Isrs2bMTXT4Xohod+/fvj3N/zZo1gagrSvFdrfH29mbgwIGJHjstfPbZZ7Rs2RKISoqtX78+ThmtVkv37t0JDw8H0K8yGNP7+j1//pytW7fG2f7u3btEl2UG6NOnj/6KVu/evePtDr9582YOHTr0kVplHObm5vTs2ROIWnVmypQpccooisKAAQP0q77E9/ymh/HjxwNRSyi3adMm0QRpWFgYS5YsMegPIiGEMDZqtZqxY8cCUT2UfvzxR8LCwhIsHxAQgKenZ5rE0rJlS/1cVRMnTox3IvO7d+8yadKkNHn8xEjbJm1J20aIDEYRQmRY/v7+Sp48eRRAAZQOHToohw8fVi5evKhs3bpV+eqrrxRAqVixor7Mw4cP4xzn3r17iqOjo75M06ZNlU2bNilnz55VLly4oBw6dEiZNm2a8vXXXyuAMnz48DjHuHHjhmJqaqoAioODgzJ//nzl/PnzysmTJxUPDw/F2dlZcXJyUooWLaoASs2aNVPteTh+/Lg+9uPHj8fZ/vTpU8XBwUEBFLVarfTs2VM5evSocuHCBWXTpk1KuXLl9Pu3a9cu3sd4+fKlYmtrqwCKhYWFMmnSJOXMmTPK2bNnlaVLlypFihRRLCwslPLlyyuAkj9//niPM2DAAP1jlShRQvHy8lIuXLigHDt2THF1dVXUanWs18vd3T1Zz4mXl5f+GF5eXinaP773TEwBAQFKoUKF9OVbt26tHDhwQLl48aKyc+dOpVatWvpt33zzjRIRERHnGA8fPkxSvDVr1kz0/fOx523w4MH6Ms7OzsrEiROV33//Xbl8+bLy999/K+vWrVN69Oihf7+8e/cu0boLIYRIXTqdTmnZsqX+XF24cGHFw8NDOXHihHL58mXlzz//VFasWKF06NBBsba2VpycnOIcw93dXb9/Yj7WftiyZYt+u5OTk+Lh4aGcPn1aOX36tDJr1izF0dFRsbe317dtatWqlVpPg7Rt4iFtG/d4t0vbRmR1kpQSIoO7ceOG4uzsrP8y+vBf165dk/QlfPv2baV06dIJHifmv0mTJsV7jHnz5iW4j6Ojo/LXX3999Is3OT7WcFMURbl06ZLy2WefJVqvVq1aKSEhIQk+zvbt2xUTE5N497W0tFR27NihdOnSJdGGm1arVVq1apVgDAULFlTu37+fqRpuihLV8CpRokSiz2/VqlWV169fJ7h/ejTcdDqdMmnSJH0CNbF/1tbWSnBw8EfrLoQQInVptVqlb9++ikql+ui5umDBgnH2T62klKIoytSpUxOMw8rKSjl48KBSvXp1BVAaNGiQGtVPcmzStkn+/tK2ESLzkOF7QmRwpUqV4p9//uGnn36iaNGimJubkz17dmrXrs2WLVvw8vJK0nGKFSvGlStX2LJlC61btyZfvnxYWlqi0WjInTs3tWrVYvz48Vy8eBE3N7d4jzF06FB+/fVX6tevj4ODA+bm5hQsWJD+/ftz+fJlqlevnppV/yTly5fn9u3bzJgxg8qVK2Nvb49Go+Gzzz6jVatW7Nu3j127dmFhYZHgMdq2bcupU6do2bIlOXLkQKPRkDdvXrp06cL58+dp06bNR+MwMzNj165dbNy4kerVq2NnZ4eVlRUlS5Zk7NixXLx4Mc5EpJlBgQIFuHr1Kp6entSsWRMnJyfMzMzIlSsXDRo0YOPGjfz111/pvjLNh1QqFW5ubty5c4effvqJihUr4ujoiImJCdmyZePzzz+nY8eOrF+/nhcvXmBpaWnQeIUQwhiZmZmxdOlSrl69ysCBAylTpgx2dnaYmJhgZ2dHuXLl6NGjBzt37uTWrVtpGsu4ceP4888/adGiBTlz5sTc3Jz8+fPTvXt3Lly4QKNGjQgICADAzs4uTWP5kLRt0pa0bYTIGFSKks4z9wkhhBBCCCFEJhAeHo6dnR0hISGMHz8+3vmHhBBCJJ/0lBJCCCGEEEKIePzyyy+EhIQA6FdCE0IIkXqkp5QQQgghhBDCKN27d48iRYrEu+3Ro0dUr16d//77j1y5cvHff/9hamqazhEKIUTWJmdVIYQQQgghhFEqUaIEjRo1okmTJpQqVQpra2tevnzJ8ePHWb58Of7+/gDMmTNHElJCCJEGpKeUECLN3bhxI1n75cmTB3t7+9QNRgghhBAimkqlSnS7Wq1m6tSpjBkzJtb90rYRQojUIUkpIUSa+1iDLyFeXl507do1dYMRQgghhIh24MABDh8+zKlTp/Dx8eH169eYm5vj4uJCrVq16N+/P6VLl46zn7RthBAidUgfVCGEEEIIIYRRatKkCU2aNDF0GEIIYbSkp5QQQgghhBBCCCGESHdqQwcghBBCCCGEEEIIIYyPDN9LoTdv3hAREZEmx86RIwe+vr5pcuyMRuqadRlTfaWuWZcx1deY6pqWTE1NcXBwMHQYyZZW7Rtje38ZU32lrlmXMdXXmOoKxlVfY6prWkqL9o0kpVIoIiKC8PDwVD/u+8kTIyIiyOojLKWuWVdK6hsSEkLr1q0B2LVrF5aWlqkeX2oyptfWmOoKxlVfY6qrSFxatG+M7f1lTPWVuiadtG8yLmOqKxhXfY2prpmRJKWEEBmSoihcvXpV/7cQQgghRGYn7RshhIhN5pQSQgghhBBCCCGEEOlOklJCCCGEEEIIIYQQIt1JUkoIIYQQQgghhBBCpLssM6fUr7/+yv79+/H39yd//vx0796dIkWKJFj+4MGDHDlyhFevXmFra0vlypX54Ycf0Gg06Ri1EEIIIYQQQgghhHHKEj2lTp06xYYNG2jTpg2zZs0if/78TJs2jbdv38Zb/u+//2bLli20bduW+fPn4+rqyunTp9m6dWs6Ry6EEEIIIYQQQghhnLJEUurAgQPUrVuX2rVrkydPHnr16oVGo+H48ePxlr99+zbFixenWrVq5MyZky+++IKqVaty7969dI5cCJEYR0dHHB0dDR2GEEIIIUSqkfaNEEL8v0yflIqIiODBgweUKVNGf59araZMmTLcuXMn3n2KFy/OgwcP9EkoHx8fLl++TPny5dMlZiHEx1lZWXH9+nWuX7+OlZWVocMRQgghhEgxad8IIURsmX5OqYCAAHQ6Hfb29rHut7e35/nz5/HuU61aNQICApgwYQIAkZGRfPfdd7Rq1SrBxwkPDyc8PFx/W6VSYWlpqf87tb0/ZlocOym8vLxYtmwZvr6+fP7550ydOjXRpF3r1q05ffp0nPvr1q3Lxo0bAfjss8/i3XfChAlMnjzZYHVNT4Z+XdObMdVX6pp1GVN9jamuIkp6tm+M7f1lTPWVumZdxlRfY6orGFd9jamumVGmT0olxz///MOePXvo2bMnRYsWxdvbGy8vL3bu3EmbNm3i3WfPnj3s3LlTf7tgwYLMmjWLHDlypGmszs7OaXr8+Gzbto1JkyaxfPlyKleuzIIFC+jYsSO3b98mZ86c8e5z4MABtFqt/vbr16/54osv6NSpE7lz5wbgxYsXsfY5fPgwPXr0oGvXroBh6mooxlRXMK76Sl2zLmOqrzHV1dgZon1jbO8vY6qv1DXrMqb6GlNdwbjqa0x1zUwyfVLK1tYWtVqNv79/rPv9/f3j9J56b9u2bdSoUYO6desCkC9fPkJDQ1m5ciWtWrVCrY47qrFly5Y0adJEf/t9ltXX15eIiIjUqUwMKpUKZ2dntmzZwoIFC7h9+zYmJiZUqFCByZMnU6BAgVR/zPdmzZrFDz/8QIMGDQBwd3dn//79LFiwgIEDBybpGLt27cLS0pLq1avHSUa9t23bNqpWrarvuuzt7Y2iKKlTiQzq/etqDHWFlNU3JCSETp06AbBp0yb9lfuMypheW2OqKxhXfY2prmnN1NQ0zS9cpYb0bN8Y2/vLmOordU06ad9kXMZUVzCu+hpTXdNaWrRvMn1SytTUlEKFCnHjxg0qVaoEgE6n48aNG/qkyofCwsLidN2LLxEVk5mZGWZmZvFuS8s3dnBwML1796ZkyZIEBQUxZ84cevTowZEjRxKMedGiRSxevDjR4544cQIXF5c492u1Wq5du8aAAQP09VKpVFSrVo2LFy8mua5bt26lefPmWFpaxruPr68vx44dY8GCBfrtiqIYzUnCmOoKyauvTqfTDwnV6XSZ5vkyptfWmOoKxlVfY6qrsTNE+8bY3l/GVF+p68dJ+ybjM6a6gnHV15jqmplk+qQUQJMmTViyZAmFChWiSJEiHDp0iLCwMGrVqgWAp6cnjo6O/PDDDwBUqFCBgwcPUrBgQf3wvW3btlGhQoWPJqfSW+PGjWN9cObNm6efxL1EiRLx7tO5c2eaNm2a6HFz5coV7/1+fn5ERkaSPXv2WPfnyJGD+/fvJynmy5cv8++//zJnzpwEy+zYsQMbGxsaNmyYpGMKkaFFRqI5dw60WjQaDWGVKoGJiaGjEkIIIYQQQogMLUskpapUqUJAQADbt2/H39+fAgUKMHbsWP3wvVevXsXqGdW6dWtUKhU///wzfn5+2NraUqFCBTp06GCgGiTswYMHzJ49m8uXL+Pn54dOpwPg2bNnCSalHBwccHBwSM8wY9m6dSslS5ZMdGL0n3/+mZYtW2JhYZGOkQmR+iwOHcLOzQ2T6GGqTkBk7ty8nTyZ0EaNDBucEEIIIYQQQmRgWSIpBdCgQYMEh+tNnDgx1m0TExPatm1L27Zt0yGylOnSpQt58uTBw8MDZ2dndDodderUibVSzodSMnzP0dERExMTXr16Fet+X1/fJI0dDQ4OZt++fYwYMSLBMmfPnuX+/fssW7bso8cTIiOzOHQIh9694YNuwGpvbxx69+bNypWSmBJCCCGEEEKIBGSZpFRW9Pr1a+7fv8/s2bOpXLkyAOfOnfvofikZvqfRaChbtix///23Psmn0+n4+++/6dat20cfe//+/Wi1Wlq1apVgma1bt1K2bFlKlSr10eMJkWFFRmLn5gaKwoeLy6oUBUWlwtbdndD69WUonxBCCCGEEELEQ5JSKRQaGkpYWFic+9VqdayhacHBwQkeQ6VSxVp5Izg4GJVKhY2NDQ4ODqxfvx5bW1ueP38eZ56mkJCQOJO1mZubkytXrjjHjVlWq9Wi1WrjjaFXr14MGTKEEiVKULZsWdatW0dwcDDNmjXT12P06NHkzp2bMWPGxDruli1b+Pbbb7GwsNCXfb+6HkT1uDpw4ACjR4/Wb1epVAQFBREcHBwr3tDQUP1wxfhYWlrqh2WGhYURGRmZKmUtLCz0c4tptdpEVx/6lLLm5uaYmprqyybW283c3ByT6ERGeHh4omU1Go3+uJ9SNiIiItZ74EMxJ7/9lLKRkZH6z0TM1/b9e8TU1BSNRhOn7Ic+/MzodDpCQ0MTjCHmcT9W1sTEBHNzcyBqwsOQkJBPK6vVYrVpE6EfrCxpArz/1KsUhbDnzwn/80/Cv/46znFTeo5Iatn4zhGpVTbm6/qhmJ/7xI77YdmPfe4/pWxqnSM+fB+n5Tni/ef+U8qm5jni/Xsd0uccEZ+kniM+LJuhzhHEfi6FEEIIIUT8JCmVQs2bN+fy5ctx7q9Tpw4bN27U3y5btmyCjdpvvvmGnTt36m9XrlwZPz8//e29e/eyd+9eAAoXLhxr31q1avHff//Fe9xixYpx/Phx/e1GjRpx586deMvmyZOHs2fP6us0bdo0Ro0aFSdOiBriV7x4cf0Prc6dO+tXEXnv4MGDQNQPvXv37unvb9++PSEhIbi7u+Pu7h4njmfPnun/HjRokP448bl7967+B+qoUaPYsWNHgmWvXbuGk5MTAJMmTWL9+vUJlj1z5gx58+YFYNasWSxfvjzBsn/88QfFixcHYPHixcybNy/BsgcPHtTPs7V69WqmTp2aYNkdO3ZQpUoVADZv3sy4ceMSLLt+/Xq+/fZbAHbv3s2wYcMSLLt8+XJ9L7rDhw/j6uqaYNl58+bRvn17IGq4Z5cuXRIsO23aNLp27QpEDc9MbGjs+PHj6du3LwBXr179aK8+tVrN4MGDATh06FCC5VxdXZkwYQIQ9T76Op5E0HtdunRh+vTpQNTk/mXLlk2wbNu2bVkwfz4mDx8ScfQoRSdPTrBsGyDmu9AGoHPneMum5jkipi+++CLW85QW5wiAGjVqcOHChXjLOjo6cv36df3t+M4R7314jujVqxd//PFHvGUh658jypUrB8CaNWsMco5YsWIFvXv3BjLGOeL69es0btw4wbLDhg1j+PDhQNTrXadOnQTLpuk5YsECICoBW7RoUQDKly/PpUuXEtxPCGG8Yl7kEUIIYydJqUzGxsYm1o+ytJI9e/ZEHyfmD+RP4eLiwu3bt5MblsgC/v33X8aPH8+VK1e4efPmR8vrdLpEk1Hv3bp1i2fPnsU7V1pKaC5eJGeVKpg+eUJQqh5ZCCFEelq3bh3Lli3D19eXzz//nClTpiS6KAvA27dvmTVrFocPH8bf3x8XFxcmTZpE3bp1gajefHPnzmX37t34+vqSK1cu2rZty5AhQ2ItsiPEe1ZWVrEuxgghhLFTKYmNqRAf9fTp0zQbvufs7Iy3t3esYS/pOTQnqcNtUjo0J2ZdjWH4Xu7cuXn8+LHRDN8LDAxkw4YNHDlyhBs3bsQbU+HChcmdOze5cuUiR44cZMuWDXNzcywsLDAxMSEoKIh3797x6tUrnj9/zrNnz3j69Clv376Nc7zChQtTs2ZN6tWrx5dffhnvj4J4h9vodJjevIn5n39i/vffaC5fRhUZiSlgDihmZoRVqMCbqlUJrVGDyKJFyVm7NmofH/2cUjGH7ykqFQG5cuF74kS8c0pl5uF7KpUKe3t7nj9/bjTD92Kej7P68L28efPy4sULwsPDZfgeKRu+975HXWbk6+ub6HslOVQqFblz5+bFixeJnhPSwt69exkyZAgzZ86kfPnyrF69mgMHDvDXX3+RPXv2ePfRarW0aNECJycnBg0ahLOzM//99x+2trb6eTEXLVrEypUrWbBgAcWLF+fq1asMGzaMUaNG0bNnT4PVN70Z8rVNb8ZUVzCu+hpTXcG46mtMdU1rZmZmSVoA7VNIT6kUev+j+WNi/pBKSlmVSoW1tTVWVlaJfnA+pftvRigb80f4ewnVNb6yCfmUuTs+paxGo9H/iEntsu9/oH1MzB9zqVnW1NRU/0M1NcuamJgQFhbGjh072LFjR5zeUEWLFqVKlSpUqlSJUqVKUbBgwSQfOyZFUXj+/DnXr1/nypUrnD59mkuXLnH//n3u37/P2rVryZs3L82bN6d9+/YUKlQozjHUL19i/uefOPz5J+Z//YXJ69extkcUKEBYrVoE1qyJtmpVFGtrICpJBRAxdWrU6ntEzSGljy0qQCKnTMEqW7Yk1edTzxFJlZaf+4+dn5Jz3E/53KfXOSKx83FaniOSWjY1zxExk7hpeY5I6nv4U8qq1eo0KatSqZJVNqmviTE6fvw4CxYs4Pbt26jVaipUqMDkyZMpUKBAmj3mqlWr+OGHH/RDTmfOnMmxY8f4+eefGTBgQLz7/Pzzz/j7+7N371796/lhovHChQvUr19fP0Q2b9687N27lytXrqRZXYQQQoisRJJSQohUc+XKFby8vDhw4IC+F4KJiQnVqlWjSZMm1K1bN8GVHz8UGhpKr169gKgfEx8mIFQqFS4uLri4uOhXinz79i1///03v/76K7/99htPnz7F09MTT09PateuTY9OnahnbY3FX39hceIEZh8kzHQ2NoRVrUpYzZqE1apFZP78icfYqBFvVq7Ezs0NkxiTnquA0Jo1CW3UKEl1FUIIYxIcHEzv3r0pWbIkQUFBzJkzh549e3LkyBF9z8IPLVq0iMWLFyd63BMnTsQ7hFur1XLt2rVYySe1Wk21atW4ePFigsc7evQoFSpUYNy4cfz22284OTnRokUL+vfvr78gWbFiRTZv3sz9+/cpXLgw//zzD+fOnYt33kwh4OPtGyGEMDaSlBJCpIiiKJw+fZpFixbxv//9T3//559/zo8//kjPnj3RarWf3FVWp9PpJ7xObIhWTHZ2djRu3JjGjRsTEhLC70ePcmrDBuxOn6be8ePUPn4c6w/20ZYtq09CaStUgE/s3RDaqBGh9etjfu4cTlotb+/dw87NDfPTp1H7+KBLYhJOCCGMRePGjWN9J8ybN48yZcpw584dSpQoEe8+nTt3/ujCGAld9PDz8yMyMjLOML0cOXJw//79BI/3+PFjTp48ScuWLdm4cSMPHz5k7NixRERE6BcMGDBgAIGBgdSsWRMTExMiIyMZNWoUrVq1SjRWYbyS074RQoisTJJSQohkO3fuHNOnT+f8+fNA1BCe5s2b07VrV8qXL49arcbJyYkXMXoRpTVVQADmJ09id+IEPf78kz5Pn8ba7g38BlzOkYNSQ4bQoHPnJA3BTZSJCdoqVSB3boKfP8di/37Mz5/HZsUKAtzcUnZsIYTIYh48eMDs2bO5fPkyfn5++h/mz549SzAp5eDggIODQ3qGiU6nw8nJCQ8PD0xMTChbtize3t4sX75cn5Tav38/u3fvZsmSJRQrVox//vkHd3d3cuXKpR8qKIQQQoiESVJKCPHJ7t+/z4wZMzh8+DAQNQfP999/T79+/ciTJ0/6BqPTYXbtGuYnTmD+559oLl5EFWOSakWjQfvVV4TVqoVfpUqsPH2a5StW8MbXF8aNo/TWrUyaNCnR5eE/iUpF4KBBmHfujNXGjbwbMADF0TF1ji2EEFlAly5dyJMnDx4eHjg7O6PT6ahTp06iE6unZPieo6MjJiYmvHr1Ktb9vr6+iU7WmitXLkxNTWNduChatCgvX75Eq9Wi0WiYMmUKAwYMoHnz5gCULFmS//77D09PT0lKCSGEEEkgSSkhRJIFBQUxd+5c1qxZQ0REBGq1mg4dOjBs2DCcnZ3TLQ61j48+CWX+11+YvHkTa3tEoUKE1qpFWM2aaKtUQYmeeFgDDKhYka7duuHl5cXSpUu5ceMGrVu3pnHjxri5uaVKUi2sdm20pUujuXEDm7VreTdiRIqPKYQQWcHr16+5f/8+s2fPpnLlykBUr9uPScnwPY1GQ9myZfn777/1cxDqdDr+/vtvunXrluDxKlasyC+//IJOp9PPdfXgwQNy5cqlX4ggJCQkziqvJiYmMixLCCGESCJJSgkhkuTo0aOMGzeOZ8+eAfDtt98ybtw4ihUrlvYPHhaG5uJFLP78E/MTJzC7dSvWZp2NDWHVqv3/BOX58iV6OBsbGwYOHMgPP/zA7Nmz2bx5MwcPHuT48eOMGjWKbt26pWxIn0pF4MCBOPbpg/XatQS6uqLY2CT/eEIIkc5CQ0MJCwuLc79arY41MXNwcHCCx1CpVLFW3wwODsbGxgYHBwfWr1+Pra0tz58/Z86cOXH2DQkJiTXvlLm5uT7p9OFx35fVarVotdp4Y+jVqxdDhw6lZMmSlClThnXr1hEcHEyzZs30dRg5ciR58uRhzJgxALRr1w4vLy/Gjh1L586defToEYsWLeLHH38kODgYKysrvvvuOxYtWkTOnDkpXLgwN2/eZMWKFbRp04bg4GCCgoIIDg6OFW9oaGiiSStLS0t9oissLIzIGL1/U1LWwsJCn1zTarVERESkSllzc3P96ptarTbRHm/m5ub679fw8PBEy2o0Gv1xP6VsREREnPdBTDFXIf2UspGRkYSFhaFSqfSva8z3qKmpqT5Z+b5sfD78zOh0Ov3iMPGJedyPlTUxMdGvIKsoCiEhISkuq1Kp4jxmYp/7lJ4jklr2w3NEapb98LWNKeZqrIkd98OyH/vcf0rZ1DpHfPheTstzxPvP/aeUTc1zRMyVldP6HJGQpJ4jPiyb0c4Rn7KidVKplE+dfVjE4uvrm+gHILlUKhW5c+fmxYsXnzxBdGYjdc3Y/Pz8GDNmDAcOHACilruePn06derU+ei+ya6vohB64waFo69oB1hYkC3GCVZRqQiPOUH5l19+8gTlMd26dYtx48Zx9uxZAMqXL8+cOXMSnNskPnHqGhlJjtq1Mbt/n4Bx4wjs1y/Z8WU0mfF9nBLGVF9jqmtaMzMzS3RoWEb35Zdfcvny5Tj316lTh40bN+pvFylSJMFG7TfffMPOnTv1t8uUKYOfn1+Cj7lmzRp9T6bKlSvz33//xVuuWLFiHD9+XH+7du3a3LlzJ96yefLk0Z/bvby8mDRpUoLtNlNTU1q2bMmCBQsAaNOmDadPn463rKWlJffu3SMwMBAPDw82bdqU6A+M9xd0AHr37s3BgwcTLHv37l39D9QhQ4awY8eOBMteu3YNJycnAMaOHcv69esTLHvmzBny5s0LwJQpU1i+fHmCZf/44w+KFy8OwNy5c5k3b16CZQ8ePEj58uXJnTs3EyZMYOrUqQmW3bFjB1WqVAFg3bp1jBs3LsGy69ev59tvvwVg27Zt+nm84rN8+XJ9T7r9+/fj6uqaYNl58+bph1b+/vvvdOnSJcGy06ZNo2vXrgCcOnWKtm3bJlh2/Pjx9OjRg9u3b3PkyJFEn7P3TE1Nsba25u3btwmWcXV1ZcKECQA8ffo00ekGunTpwvTp04GoXolly5ZNsGzbtm317/Xg4GCKFi2aYNk2bdqwaNEi/fdCfMNk30urc8QXX3zBoUOH9LfT4hyhUqlo1qwZFy5ciLeso6Mj169f199Oyjnivc6dO+snuI9PVj9HlCtXDoBly5YZ5ByxYsUKevfuzYsXL9i3b5/BzhF9+/YFolYsb9y4cYJlhw0bxvDhwwG4fft2or+70vscUb58eS5dupTgfskhPaWEEAk6ceIEw4YNw8fHBxMTE/r06cOwYcNiXVFKUGQkmnPnIHrejbBKlSCR3keqgADM//5bPywvLEZDQx0aSmTOnPokVFiNGuhScZ6mkiVLsnPnTjZv3sy0adO4fPkyDRs2ZNy4cfTo0SPO0IwkMTEhcMAAHIYOxXrlSgK7dYOkPG9CCGGEvvjiC31CKq1069aNHTt2cPXq1Xi329ra6hvgSWVjY8PkyZN5+PBhoj84Rda3du1aPDw8Eu1V8aGIiIhEE1IQ9SP93bt3VKtWjYIFC6Y0TCGEyHCkp1QKSU+plJO6ZjwhISFMmzYNLy8vIOoq1+LFixPNpsdkcegQdm5umMRYdS8yd27eTp5MaKNG0XdExp6g/NKluBOUV6qknxsqomRJSE5y6BO9ePGC0aNH8/vvvwNRV/zmzZv30R4P8b624eHkrFYN0//+w3/aNIKjr6RkdpnlfZxajKm+xlTXtJbZe0o9ffo01YfvhYSE4OzsjLe3d5z3V3oOzUnqcJuUDs1RqVT6+hrD8L3cuXPz+PHjLD9879mzZ5w8eZIdO3Zw7dq1OK+lnZ0dJUuWJH/+/OTJk4e8efPi6OiInZ0ddnZ2+ufLzMwMrVaLv78/L1++5M2bN/z33388fvyYp0+fcuvWrXh7DZUqVYpvv/2Whg0bUrhw4Vjb0mr4nouLC2/evNF/HrLq8D2VSoW9vT3Pnz83muF7Mc/JWX34Xt68eXnx4gXh4eEyfI+UDd9736MutUhSKoUkKZVyUteM5eHDh/Tq1Ytb0fM2devWjXHjxiWtdxRRCSmH3r1BUYiZQlJUKlAUgrp2xcTPD/O//kLt7x9r3/DChaN6QtWsifabb/QTlKc3RVFYv349U6ZMITQ0lOzZs7Nw4UJq1aqV4D4JvbZW69ZhP24cES4uvDx5MkXDDDOKzPA+Tk3GVF9jqmtay+xJqbRo3xjb+8uY6pvV6/r27Vt2797Nrl274gxrLVSoEDVq1KBSpUqUK1eOfPnyJa+H9QcUReH58+dcvXqVc+fO8b///Y9///03VpmSJUvSqlUr2rVrR/bs2VP8mHFERmJ+7hxOWi2vk9DrPbPL6u/jDxlTfY2prmktLdo3MnxPCKF35MgRBg8eTEBAANmzZ2fBggXUrl076QeIjMTOzS1OQgpAFf0FYLNunf4+XbZshFWv/v8TlKfCynepQaVS0bVrV7755hv69+/PrVu36NSpEz/99BMDBw78pMZmcPv2ZFuwANNnz7DcvZsQWSJcCCGEyBQuXbrEhg0b2L9/v773gVqtpkaNGtSrV49vv/020fmVUuJ9DyUXFxcaRfcy9/Hx4fjx4xw8eJC//vqLW7duMW3aNDw8PGjQoAGdO3emSpUqqZIU+7DXuxPx9HoXQohUoDZ0AEIIw4uMjGTmzJl069aNgIAAKlasyK+//vppCSlAc/YsJi9exElIfSi4dWte/fIL3jdu8GbVKoI7dYqTkAoNDaV379707t070W6oaal48eIcOHCAjh07oigKs2bNonfv3gQGBib9IJaWBPbpA4DNkiWQSBdrIYQQQhiWoigcP36c1q1b07RpU3bs2EFoaCglSpRg8uTJXLp0iePHj9O1a9dkJaRS0r7JlSsX33//PRs3buTKlSt4eHhQvnx5wsPD2b9/P+3ataNhw4bs27cv0SFdH/O+17s6xjQMAGpvbxx698YixoTjQgiRUpKUEsLIBQYG0q1bNxYvXgxAjx492LFjB7lz5/7kY5m8fJmkcmF16qD96iswTbizpk6n4+DBgxw8eDDRcfVpzcLCAg8PDzw8PNBoNBw6dIimTZvy8OHDJB8juHNndPb2mN2/Lw05IYQQIgNSFIUDBw5Qr149OnXqxJkzZzAzM6NNmzbs3buX33//nR49epAzZ84UPU5qtW8cHBzo2LEjBw4c4LfffqNz585YWFhw/fp1+vbtS40aNdi+ffunJ6eS0Ovd1t1dLrIJIVKNJKWEMGLPnz+nZcuWHDt2DAsLCzw9PZk8ebJ+srxPFZnE8cWRKWzQGULHjh3ZuXMnzs7O3Llzh6ZNm3L+/Pkk7avY2BDUvTsA2RYvBhnLLoQQQmQYp0+fpmnTpvTp04ebN29iZWVF7969OXXqFAsXLqRixYqpMiQurZQuXZqZM2dy/vx5hg0bhr29PY8ePWLo0KHUq1ePI0eOJDzRd2AgJvfuofn7byx37cJu7NhEe72rFAXT58/RnD2bdhUSQhgVmVNKCCN148YNunTpgre3N9mzZ8fLy4svv/wy+QeMiMBy165EiygqFZG5c6OtXDn5j2NAFSpU4PDhw3Tr1o0rV67Qvn17Fi9eTOPGjT+6b2D37lgvX47ZP/9g/scfhNWtmw4RCyGEECIhd+/eZcqUKRw7dgyIWgGtT58+9OjRAwcHBwNH9+kcHR0ZPnw4ffv2ZdPq1exZupRs//7LoW7deJMvH80rVcJZp8PkxQtMfHxQ+/igDgpK1mMltXe8EEJ8jCSlhDBCJ06coFevXgQHB1OsWDE2bNiQoqU9VcHBOLi6YnHsmH6VPVQqfTdviF59DwiYNClTr9ySM2dOduzYQb9+/Th69Ch9+vTBzc2N3r17J7qf4uBA8I8/YrN8OdkWLSKsTh3IwFddhRBCiKwqJCSEBQsWsHz5ciIiIjA1NaVjx44MHTo08VWlIiPRnDsHWi0aQ61GFxGB+uVLTHx8ohJL3t5x//b2ZqK/PxNj7vfkSdS/eOiyZSMyVy50uXKhmJhg8ddfHw0jM/Z6F0JkTJKUEsLI7N+/n4EDBxIeHk716tVZsWIFdnZ2yT6e+vVrHH/8Ec2VKygWFrxZulQ/H4FJjAkyI3PnJmDSpCyxYouVlRVr1qzBzc2NdevWMWnSJHx8fFi6dGmi+wX27o21lxeaCxfQnDmD9ptv0iliIYQQQkDUSsMTJkzgv//+A+C7777Dzc2NQoUKJbpfmq9Gp9OhfvMGdXQvpvc9mWL2ajLx8UHt6xvrol9iFAsLInPlIsTBgau+vpx79oznwDsbG+p17UrVdu1QnJ1RrK3/f6fISHJVroza2zvex8nsvd6FEBlPlklK/frrr+zfvx9/f3/y589P9+7dKVKkSILlg4KC2Lp1K+fOnSMwMJAcOXLQpUuXlA1fEiKD27JlC6NGjUKn09GsWTMWLlyY7PmjAEwePcKpY0dMHz0i0sEBv3XrCK9YEYDQ+vUxP3cOJ62W14a6mpiGTExMmDp1Knnz5mXKlCksX74cgPHjxyc474QuVy6C27fHesMGbBYvxk+SUkIIIUS68PPzY/z48ezduxcAFxcXpkyZQv369T+67/vV6D6cE/L9anRvVq5MODGlKKgCAvQ9mXQxeivZ9++P7atXUQmnly9RhYcnqS6KiQm6nDmJdHaO6uEU/X+sv52dUezs9L2yCwEvTp5k+bhx3L17l5WentS9dQsPDw+cYyalTEx4O3kyDr17o3zQ6/19fTJ7r3chRMaSJZJSp06dYsOGDfTq1YuiRYty8OBBpk2bxoIFC+LtARIREcHUqVOxtbVl2LBhODo68urVK6ysrAwQvRDpY9myZUydOhWATp06MX36dExS0KAwu3oVxx9/xOTVKyLy5uX1pk1ExkwEm5igrVIFcudG++JFlpzcW6VS4erqip2dHSNHjmT58uX4+voyb948TBNYWTCwXz+sNm/G4s8/MbtyhfBy5dI3aCGEEMLIHDt2jBEjRvDy5UtMTEzo06cPQ4cOTVrb/yOr0SmA3ejREBaGycuXmHh763s1vf9bHRKi3yfmDE6WR47w4aXByOzZ0UUnmCJz5/7/v98nnJyd0Tk6JispVLVqVY4cOcLKlSuZO3cux44do27dukydOpUWLVroL6qFNmrEm5Ur4/R6B9DZ2hL29def/NhCCJGQLJGUOnDgAHXr1qV27doA9OrVi0uXLnH8+HFatGgRp/wff/xBYGAgU6ZM0f9wTOnyrkJkZIsWLWLWrFkADBgwgNGjR6doFRnz48dx6N0bdXAw4aVK8XrjRnS5cqVWuABYWlpy9+5d/d8ZWYcOHbC2tmbgwIHs2rWL4OBgli5dGm8vtMi8eQlp2RKrnTux8fTkzerVBohYCCGEyPqCg4Nxd3dny5YtABQpUoSFCxdS7hMuCGnOno2TmIlJBZi8fo3jgAGJHkdnZ0dkrlyY5MzJyxw5iMyZE22ePITF6Nmky5EDUtCDPSk0Gg0DBgygXr16DBkyhKtXrzJgwAAOHz6Mh4cH9vb2QFRiKlavd5UKWzc3zO7exc7NDX9PzzSNUwhhPDJ9UioiIoIHDx7ESj6p1WrKlCnDnTt34t3n4sWLFC1alDVr1nDhwgVsbW2pWrUqLVq0QK1Wp1PkQqQPT09PfUJq1KhRDBo0KEXHs9y2DfuRI1FFRhJWvTp+q1ahZMuWGqHGolKpMlXvxebNm+Pi4kLbtm05fPgwffv2Zfny5ZiZmcUpGzhgAJa7dmF5+DDvbt8monhxA0QshBBCZF23b9+mT58+3L17F5VKRc+ePRk1atQnX+hK6ipz4UWKEF669P/3bHJ2jvo7+n8lnscNiec46aVYsWLs3buXJUuWMH/+fA4ePMi1a9dYunTp/09n8kGvd//588nerBlWe/YQ2qQJoQ0aGLAGQoisItMnpQICAtDpdPqs/nv29vY8f/483n18fHzw9fWlWrVqjBkzBm9vb1avXk1kZCRt27aNd5/w8HDCY4zzVqlU+i+1lPQ4Scj7Y6bFsTMaqWva8fT0ZMaMGQCMHj06ZQkpRcFm0SKyRSe4glu35u3cuaDRxOnO/p6xvbZNmzZl/fr1dOnShV9//ZWBAweyZMmSOEP5IosVI7RRIywPHsRmyRLeLl5soKiTx5heVzCu+hpTXUWU9GzfGNv7y5jqm9Hqum3bNsaMGUNoaCi5cuXC09OTqlWrJutYSe0JHjBzZlQCJwEZ45mJTaPRMHToUOrWrUufPn14/PgxLVu2ZOzYsfTp0weVShXrtY348kuC+vXDxtMTu9Gj0X79NYqDg4FrkXoy2vs4rRlTfY2prpmRSlEy90Qvfn5+uLq6MnXqVIoVK6a/f9OmTdy8eZPp06fH2Wfw4MFotVqWLFmi7xl14MAB9u3bx8qVK+N9nO3bt7Nz50797YIFC+p7nwiREc2ePZuffvoJgClTpjB+/PjkHywyEgYOhGXLom6PHg3Tp+snz0wLYWFh9OnTB4AVK1Zgbm6eZo+V2g4fPkyLFi3QarV06NCBjRs3xp2/69IlqFAhak6IO3fgI6v+CCFEWpD2jchKQkND6du3L+vWrQOiVtbbtGlTyqbpuHEDypeHiIj4t6tUkCcPPHyYpHmeMmr75u3bt/Tq1YsdO3YAUT3AN2zYgK2tbeyCoaFR7ZebN6FjR9i0yQDRCiGykkyflIqIiKBTp04MGzaMSpUq6e/39PQkODhY/6M8Jnd3d0xNTZkwYYL+vsuXLzNjxgy2bNkS7wTFCV1J9PX1JSKhL6kUUKlUODs74+3tTSZ/iT5K6pr61q5dq09CjRw5kqFDhyb/YCEhOPTvj8Wvv6KoVARMmUJw9+5J2jUl9Q0ODtavoHnv3r0MP5Tvw7r+9ttv9OrVi4iICNq2bcv8+fPjDA926NgRi+PHCercmYBM9CPQmD6zYFz1Naa6pjVTU1Ny5Mhh6DA+Kj3bN8b2/jKm+maEunp7e9OjRw8uX76MWq1mxIgRDBo0KEVTc5gfOYL9gAGoAwN5X6uYl+OU6Itz/qtWJbz63gcycvtGURQ2bdqEm5sbYWFhFCtWjPXr1/P111/Hem3NLl/GqWlTVDodfmvXEpZFhvFlhPdxejKm+hpTXdNaWrRvMv3wPVNTUwoVKsSNGzf0SSmdTseNGzdokMAJsnjx4pw8eRKdTqf/onrx4gUODg4JrphlZmYW79wwQJq+sRVFMZoPjtQ1dezevVufkBo6dChDhgxJ9mOp/Pxw6toVzcWLKObmvFm8mNDGjT95Jb3k1Ddm+cz03ngfa7169Vi6dCl9+/Zlx44dWFpaMn369FjdhgMHDcLi+HGstm3j3eDB6HLnNmDkny4zvS6pwZjqa0x1NXaGaN8Y2/vLmOprqLpeuXKFHj164O3tjb29PcuWLaNGjRr6mD7Z+ykLZs9GpSiEVa5McLt22M6ZE2vS88jcuQmYNInQhg2T3DbK6O2bTp06UapUKXr06MGdO3do2LAhO3bsoFSpUvpYteXKEdi3L9mWLMFu9GheVqqUpYbxZcTXJS0ZU32Nqa6ZSZaY1btJkyYcO3aMEydO8N9//7F69WrCwsKoVasWENVr6v2qGwD16tUjMDCQdevW8fz5cy5dusSePXuoX7++gWogROr4448/9L2ievTowfDhw5N9LJOnT8neogWaixfR2dnxeuvWqISUSLLGjRuzePFiVCoVGzZsYMGCBbG2aytVIqxyZVRaLTYrVhgmSCGEECIT27NnD61bt8bb25uiRYty8OBBfUIqOVRBQTj06YOthwcqRSHoxx95/fPPhHz/PT5nz/J6507YsoXXO3fy8syZJPeQykzKly/PoUOHKF++PP7+/tSvX5/Vq1fH+jH/btgwwosWxeTlS+zc3AwYrRAis8sSSakqVarQuXNntm/fzk8//cSjR48YO3asfvLzV69e8ebNG3357NmzM27cOO7fv8/IkSPx8vKiYcOGsVbwEyKzOX/+vH64WKtWrZg4cWKyJ/MzvXGD7M2aYXb/PhGffcarX35BW7lyKkdsHJo3b86UKVMAmDNnDhs2bIi1PTB68nmrTZtQ+/mle3xCCCFEZqQoCvPnz2fAgAGEhoZSt25d9u/fT4ECBZJ9TJMnT8jevDmWBw+imJnh7+HB2xkzQKOJLhC9Gl2HDlH/J2EOqczK2dmZnTt30q5dO3Q6HW5ubri7uxMZGRlVwMIC/3nzUNRqrHbvxvzIEcMGLITItDL98L33GjRokOBwvYkTJ8a5r1ixYkybNi2NoxIifdy6dYsuXbroG2Xz5s1L9hwKmv/9D8eePVEHBhJesiSvN27MdMPKMppu3brh6+vLwoULGTt2LE5OTjSO7nUWVrMm2rJl0Vy7hvXq1byLZx48IYQQQvy/iIgIxo4dy+bNmwHo168fo0ePjruoyCfQ/P03Dq6umLx5Q2SOHLxZtQrtV1+lVsiZkoWFBfPnz+err75i5MiRrFmzhhcvXrB48WIsLCwI//JLAl1dybZ0KfajRvHyq6+y1DA+IUT6yBI9pYQwZt7e3nTu3Jm3b9/y1VdfsWLFigTnB/kYy927cercGXVgIGHffMOr3bslIZVKRo4cSceOHVEUhQEDBnDq1KmoDSoVgQMHAmDt5YXq3TsDRimEEEJkbCEhIfTq1YvNmzejUqmYNm0a48aNS35CSlGwXr0apx9+wOTNG7RffIHvoUNGn5B6T6VSMWLECJYuXYpGo+HQoUN06NBBPwrl3fDhhBcpEjWMz93dwNEKITIjSUoJkYkFBQXRpUsXXrx4QZEiRVi3bh2WlpaffiBFwWbpUhwGDkQVHk5Is2a83rwZ5cNlgEWyqVQqpk+fToMGDdBqtfTs2ZN79+4BENqgAeFFi6IOCMB6/XoDRyqEEEJkTH5+frRv354jR45gbm7OqlWr6Nq1a/IPGBqK/dCh2Lm7o4qMJLh1a17t2oXus89SLeasokWLFmzevBlbW1vOnTtHy5YtefbsWexhfLt2yTA+IcQnk6SUEJlUZGQk/fv358aNGzg5ObFx40b9PGqfeCBs3d2xjR7OGti7N2+WLAFz89QN+BNZWlpy7do1rl27lrxEWwZkamqKp6cnFStW5O3bt/z444/4+fmBWk3ggAEAWK9ciSokxMCRCiGEEBnLy5cvadOmDRcvXsTOzo6ff/6Zhg0bJvt4am9vsrdpg9WOHShqNW/d3fFfuBDSuM2Rmds3VapUYffu3Tg7O3P37l1atWrF48ePCa9QgaA+fQCwHzUKVYy5fIUQ4mMkKSVEJjVp0iSOHj2KhYUFXl5e5MuX79MPEhqKQ9++2KxZA8BbNzcC3N0hmfNRpSaVSoWTkxNOTk7JnrA9I7K0tGTt2rXky5ePx48f06NHD8LCwghp3pyIvHkxef0aq61bDR2mEEIIkWE8e/aMVq1acfv2bZydndmzZw+VKlVK9vHMLlwgR8OGaC5fRmdvj9/mzQT17g3p0N7I7O2bkiVLsm/fPgoUKMB///1Hq1atuHfvHgEjRvz/ML545vMVQoiEGP6XpxDik61du5Y10YmkBQsWUKFChU8+hsrfH6eOHaNWmNFo8Fu6VH+VS6QtJycn1q9fT7Zs2Th37hwjRoxAMTUlsF8/AKyXLQOt1sBRCiGEEIb35MkTWrduzcOHD3FxcWHXrl0UL1482cez/Plnsrdti8nLl4QXL47vwYOE1aiRihFnfS4uLuzevZvixYvj7e1Nq1at+Of+/f8fxrdzpwzjE0IkmSSlhMhk/vjjD9yjJ5IcO3YsTZs2/eRjqJ89I3vLlpifOYMuWzZeb9pEaPPmqR1qioSFhTF27FjGjh1LWFiYocNJdcWKFWPlypWYmJiwe/duFixYQHC7dkTmyoXp8+dY7t5t6BCFEEIIg7p//z4tW7bk6dOnFChQgD179lCgQIHkHSw8HNvx43EYPhyVVktIw4a82rePyOQeL5mySvsmV65c7Ny5kzJlyvD69Wvatm3LeROTqB5ngP3o0aj8/Q0bpBAiU5CklBCZyP379+nfvz86nY4OHTrQL7pnzacwvXWLHM2aYXbnDpHOzrzaswdt1appEG3KREZGsn79etavX09kZKShw0kTNWrUYPr06QDMmTOHw8ePExjdmMvm6QlZtN5CCCHExzx48IA2bdrg7e1N0aJF2bVrFy4uLsk6lvr1a5w6dMDGywuAgBEjeLNyJYqNTWqGnCRZqX3j6OjItm3bqFChAm/fvuWHH37gdMOGhBcujImPjwzjE0IkiSSlhMgk3r17R48ePQgICOCrr75i+vTpnzwXgebUKbK3aoWJtzfhxYrxat8+IkqWTKOIRVJ06tSJHj16ADB48GCuffMNOnt7TB8+xOLAAQNHJ4QQQqS/J0+e0K5dO16+fEnJkiXZuXMnzs7OyTqW6Y0bZG/UCPPTp9FZW+O3di2BQ4dmiPkzswI7Ozu2bt3KV199xdu3b2nXpQtXBg2KGsa3Ywfmv/9u6BCFEBmcnI2FyAR0Oh1Dhgzh7t27ODs7s3LlSjQazScdw2LfPpw6dkQdEEBYpUq82r2byGRecRSpa8KECXzzzTcEBQXRpX9/XnfqBEC2xYtBUQwcnRBCCJF+nj17Rrt27Xjx4gVFihTh559/Jnv27Mk6lsXevWRv3hzT//4jokABXu3fT2j9+qkcsbC2tmbjxo2UL18ef39/Gk6ezNO2bYHo1fhkGJ8QIhGSlBIiE1i4cCG//vorGo2G1atXkzNnzk/a33rVKhz69YuaQ6FRI15v3Yri4JBG0YpPZWZmxooVK3BxceHhw4f0vnYNnbU1ZrduyRVGIYQQRsPHx4d27drp55Datm1b8hJSkZFkmzEDx379UIeGElqzJr4HDxKRggnSReKyZcvG5s2b9XNMVf/jD4Lz5sXE21uG8QkhEiVJKSEyuCNHjjBnzhwAZs6cSfny5ZO+s06H7eTJ2E2ciEpRCOzWjTfLl4OFRRpFK5LLycmJNWvWYGFhwS9//cXxEiUAyLZokfSWEkIIkeW9evWK9u3b8+jRI/Lmzcv27duTNWRPFRCAY9euUXMzAoF9++K3cSOKvX0qRyw+ZGdnx5YtWyhZsiRPfH1pHxSEolLJMD4hRKIkKSVEBnb//n0GDhwIQPfu3Wnfvn3Sdw4Lw37gQGxWrAAgYNw4AqZMAROTtAhVpIIyZcrg4eEBQMeLF4kwNUVz6RKaU6cMHJkQQgiRdt69e0fHjh25e/cuuXPnZvv27cma1Nz03j1yNG6MxR9/oFhY8GbxYgLGj5e2Tzp6P/l5sWLFOODnx+ps2YDoYXxv3xo4OiFERiRJKSEyqJCQEPr06UNgYCBff/01bm5uSd5XFRCAU+fOWP3yC4qpKW8WLiSwXz/4xInRRfpr3bo1PXv2xAdYE/16ZVu82LBBCSGEEGkkLCyMHj16cOPGDbJnz8727dvJly/fJx/H/OhRsjdpgumDB0R89hmv9uwhpFWrNIhYfIyTkxNbt24lb968DAoI4JFGI8P4hBAJkqSUEBnUhAkTuHXrFjly5GDp0qWYmZklaT+1tzfZW7XC/OTJqFVmNm4kpE2bNI429VlYWHDmzBnOnDmDhZENN5wwYQKVK1dmeng44YD5//6H2eXLhg5LCCGESFWRkZEMHDiQkydPYm1tzaZNmyhUqNCnHURRsFm0CMdu3VC/e0dY5cq8OnyY8LJl0yboFDKW9o2zszNbtmzB2smJjlotOsBq+3bMjx0zdGhCiAxGklJCZEDbt29n69atqNVqlixZQq5cuZK0n+ndu2Rv1gyzW7eIzJmTV7t3E1ajRhpHmzbUajV58+Ylb968qI1s2WZTU1OWLFlCkJMTm6Lvs5HeUkIIIbIQRVGYMGECBw8eRKPRsGbNGsqUKfNJx1AFB+Pg6ortrFmoFIWgH3/k9c8/o0vman3pwZjaN4UKFWLTpk1ctbZmfvR9diNHyjA+IUQsWftMKEQm9O+//zJmzBgAhg8fTtWqVZO0n+bcObK3aIHps2dEFCrEq717iShdOi1DFWkod+7ceHp6MgvQAZa//YbprVuGDksIIYRIFQsWLGD9+vWoVCoWLlxI9erVP2l/kydPyN6sGZYHDqCYmeE/axZvZ8wAjSaNIhbJUbZsWdauXcsUMzPuAKY+PthOmmTosIQQGYgkpYTIQIKCgujTpw+hoaHUqlWLQYMGJWk/i8OHcerQAbW/P9ovv+TV3r1EJmM+hoxEq9UyZcoUpkyZglarNXQ4BlGjRg0aDR3Kzvd3zJxpyHCEEEKIVLF161b9ysJTpkyhWbNmn7S/5uRJsjdqFNUzPEcOXm/fTnCnTmkRaqozxvZNtWrV8PD0pDtRF9qst23D/I8/DB2WECKDkKSUEBmEoiiMGjWKe/fu4ezszKJFi5LUrdtq3TocevVCFRpKSL16vN6+HZ2jYzpEnLYiIiJYvnw5y5cvJyIiwtDhGMzQoUM5/MUXADj9/jvh0ltKCCFEJva///2P0aNHAzBo0CC6deuW9J0VBes1a3Dq0AGTN2/QfvEFvocOoa1UKY2iTX3G2r5p0qQJDadOZUH0bfOBA2UYnxACkKSUEBnGzz//zJ49ezAxMWH58uU4OTklvoOikG3GDOzHjYuaR6FTJ96sWoViaZk+AYt0YWJiwpD16zmq0WAC3O/Tx9AhCSGEEMly9+5devfuTUREBC1atOCnn35K+s6hodgPG4admxuqyEiCW7Xi1a5d6D77LO0CFqmqW7du3OvShTuAjb8/4UkcESCEyNokKSVEBnDv3j0mTJgAwOjRo/nqq68S3yE8HPshQ8jm6QlAwMiRvJ05E0xN0zpUYQA5cuTANPr9Ue3+fY5v3GjgiIQQQohP8+rVK3788UcCAgL46quvmDt3LiqVKkn7qr29yd6mDVbbt6Oo1bx1c8N/0SKQC3GZzpgpU1hWsSI6IN/vvxOwbZuhQxJCGJgkpYQwMK1Wy4ABAwgJCaFatWq4uromWl4VGIhjly5Y7dyJYmLCm3nzCBwyBJLYsBOZU/Hu3bnr4oIGeOvmxrNnzwwdkhBCCJEkoaGhdO/enSdPnpA/f37WrFmDhYVFkvY1u3iRHI0aobl8GZ29PX6bNxPUp4+0ezIpExMT+m3ZwuboEQH2I0cS8PSpgaMSQhiSJKWEMDAPDw+uX7+Og4MDCxcuTHQeKfXLlzi1aYPFn3+is7TEb906Qtq3T8dohSHZzpgBQFetFvd+/YiMjDRwREIIIUTidDodQ4cO5eLFi9jZ2bFhw4aPT1EQzXLbNrK3aYOJjw/hxYvje/AgYTVqpHHEIq1ZW1tTeu9eHpiY4BwZyd1mzYxm0nchRFySlBLCgP766y+WLVsGwNy5c3F2dk6wrMn9+2Rv3hzN9etEOjnxeudOwurUSa9QRQYQWacO70qUwAqocuECS5YsMXRIQgghRKLmzp3Lvn37MDU1ZdWqVRQpUuTjO4WHYzthAg7DhqHSaglp2JBX+/YRWaBAmscr0keuggV57eGBDmj68iU7evQwdEhCCAPJMkmpX3/9lf79+9OxY0fGjh3LvXv3krTfyZMnadeuHR4eHmkcoRCx+fn5MWTIEAA6d+5M/fr1EyxrdvEi2Zs3x/TJEyIKFODVvn2ElyuXPoGKjEOlInzECAAGACtnz+bSpUuGjUkIIYRIwMGDB1mwYAEAs2bNomrVqh/dR+3nh9MPP2Czdi0AASNG8GblShQbm7QMVRiAy/ff82+9egB8/8cfbFu50sARCSEMIUskpU6dOsWGDRto06YNs2bNIn/+/EybNo23H1lm9OXLl2zcuJGSJUumU6RCRFEUheHDh+Pj40PRokVxd3dPsKz5kSM4tWunX/r41d69RnGl0MLCgj/++IM//vgjyfNOGIPQ+vUJL1YMe6CPTseAAQMIDAw0dFhCCCFELLdu3dJffOvZsyfff//9R/cxvXGD7A0bYn7qFDpra/zWrCFw6FBIZGqDzEbaN7E5LF3Ka3t78gK2kydz9uxZQ4ckhEhnWeIMf+DAAerWrUvt2rXJkycPvXr1QqPRcPz48QT30el0LF68mHbt2pEzZ850jFYI2LhxI0eOHEGj0eDp6YllAqvHWG3ejGOPHqhDQwmtU4fXO3agy549naM1DLVaTfHixSlevHii82wZHbWawAEDABiuVvPy8WPGjx9v4KCEEEKI//fmzRt69OhBcHAw1apV068wnBiLffvI3qIFpv/9F9UrfP9+Qhs0SIdo05e0b2JTLC3RrVmDDuihKGzp0kUWcxHCyGT69eMjIiJ48OABLVq00N+nVqspU6YMd+7cSXC/nTt3YmtrS506dbh169ZHHyc8PJzw8HD9bZVKpU8kJHU520/x/phpceyMxtjqeu/ePSZNmgTA2LFjKVOmTNyCioLNnDlkmz8fgODvv+ftrFlgZkZmepaM7bWN+X9aCm3Rgog5c8j+5Am9VCoW7dhBgwYNaNiwYZo/NhjX6wrGVV9jqquIkp7tG2N7fxlTfWPWNSIigr59+/L48WPy5cvH8uXLMTMzS3jnyEiyeXhgs3gxAGE1a/Jm2TIUe/sM2eYxptcV0qe+Ed98Q0CXLtivX8+8d+9o16ULm/bvx8rKKs0eMz7y2mZdxlTXzEilKIpi6CBSws/PD1dXV6ZOnUqxYsX092/atImbN28yffr0OPv8+++/LFiwAA8PD2xtbVmyZAlBQUH89NNPCT7O9u3b2blzp/52wYIFmTVrVupWRmR5kZGR1KxZk5MnT1K7dm1+//33uFfJIiLA1RXWrIm67eYGEyca3dLHWq1W//kdO3YsGo3GwBFlMCtWgKsr/tmykfPdO+xz5ODGjRvS81MI8UmkfSNS24gRI5g7dy5WVlacPn2asmXLJlz47Vvo2BEOHoy6PXIkzJgBJibpE6wBSPsmAUFBhJcqhdnjx6wCjrVvz9atWyWJIIQRyPQ9pT5VSEgIixcvpk+fPtja2iZ5v5YtW9KkSRP97fcnSF9fXyIiIlI9TpVKhbOzM97e3mTyvOFHGVNdly5dysmTJ7GxsWHWrFn4+PjE2q4KDsa+Tx8sjh1DUat5O3MmIZ06gbe3gSJOmZS8tsHBwfoeZZ07d073q2WfKt3fx/XrkzNXLux9fPgpd26mvXhB165dWb16dZo34IzpMwvGVV9jqmtaMzU1JUeOHIYO46PSs31jbO8vY6rv+7ouWbKEuXPnAjB//nxy5MjBixcv4t3H5N49HLt1w/T+fRQLC/znzCG0VSt4+TI9Q/9kKX1dpX2TMM38+Ti1akUvYMe2bUwoUoT+/fun6WPGZEyfWTCu+hpTXdNaWrRvMn1SytbWFrVajb+/f6z7/f39sbe3j1Pex8cHX1/fWFcB378xv//+exYsWICzs3Oc/czMzBLsepyWb2xFUYzmg5PV6/rvv//qV3mcNGkSLi4useqrfv0axx9/RHPlCjoLC94sW0ZYvXqQBZ6T5Ly2MctnpvdGusWq0RDYpw92kyczRqVijokJhw8fZteuXbRu3TrtH5/M9bqkBmOqrzHV1dgZon1jbO8vY6nvpUuXGBG9QuzAgQNp0qRJgvU2//13HAYMQP3uHZG5c+O3di3hZctmqjZPcl9Xad8kLKxyZQJ79MBmzRpWA2WnT6dcuXJUqVIlTR/3Q5npdUkNxlRfY6prZpLpZ9czNTWlUKFC3LhxQ3+fTqfjxo0bsYbzvffZZ58xZ84cPDw89P8qVKhAqVKl8PDwILuRTCIt0pdWq2Xw4MFotVoaN24cZwUak0ePyN6sGZorV4h0cOD1tm1RCSkhEhHcqRORDg5YP3/Omuj5pMaPH8/z588NHJkQQghj4u/vT5s2bQgNDaVu3boJT4mhKNgsWoRj166o370jrFIlfA8fjkpICQG8Gz2aiPz5yQd4KAp9+/bFO5OOGBBCJE2mT0oBNGnShGPHjnHixAn+++8/Vq9eTVhYGLVq1QLA09OTLVu2AKDRaMiXL1+sf9bW1lhYWJAvXz5MTTN95zGRAS1cuJAbN27g4ODAqlWrYg2vMrt6lezNm2P66BERefPy6pdfCK9Y0YDRisxCsbYmqEcPANreu8eX5coREBDAiBEj5CqQEEKIdKHT6Rg8eDAPHz4kX758LFq0KN5V5VTBwTj07YvtrFmoFIWgzp15vW0bukwwzFWkH8XKCv/oIaC9gfKvXuHq6hprQQYhRNaSJZJSVapUoXPnzmzfvp2ffvqJR48eMXbsWP3wvVevXvHmzRvDBimM1pUrV1i8eDFqYF3XruQ+cQLNqVMQGYn5H3/g1KYNJq9eoS1dmlf79hFZpIihQxaZSFC3buhsbND8+y/r27XDwsKCP//8kw0bNhg6NCGEEEZg2bJlHD16FHNzc1auXBnv9BkmT5+SvXlzLPfvRzEzw3/WLN7OnAkyybeIh/abbwjs3h2ANSoV/54/z7Rp0wwclRAirWSZbkENGjSgQYMG8W6bOHFiovum5wR6wriEhIQwePBgmkVGstLCguzz5wPgBOjs7FC9e4dKpyO0Rg3erFqFYmNj2IBFpqPY2xPUpQvZliyh2I4djBk9GveJE5kyZQo1atSgYMGChg5RCCFEFnXq1ClmzpwJwOLFiylbtmycnrqaU6dw6N0bkzdviMyenTerVqGtVMkQ4YpM5N2YMVgcO0bex4+ZDbiuWkWFChVo2rSpoUMTQqSyLNFTSoiMau7cuZS5d4+dgFNoaKxt6rdvUel0hH39NX7r10tCSiRbUK9eKBYWaC5fpm+JEnzzzTeEhIQwYsQIdDqdocMTQgiRBfn4+NCvXz90Oh1t27alZ8+esQsoCtZr1+L0/feYvHmDtmxZfA8dkoSUSBLFygr/OXMA6APUBYYPH869e/cMGpcQIvVJUkqINHLlyhVWLV/OQkAV/e9DCmDy5AmYmKRvcJmAubk5Bw8e5ODBg5ibmxs6nAxNlyMHQR06AGC7eDHz5s3D0tKSM2fOsHHjRgNHJ4QQIquJiIigX79++Pr6UqpECeY3b47q55/10xMQFob98OHYTZiAKjKS4FateLV7NzoXF0OHbnDSvkk6bZUqBHbrBsAmjQZ1UBA9e/YkKCjIwJEJIVKTJKWESANarZYRI0ZQVVHIS/wJKaLvN33+HM3Zs+kYXeZgYmJCuXLlKFeuHCaStPuooL59UUxNMT95ksK+vowZMwaAadOm8ezZMwNHJ4QQIivx8PDgzJkz/GBhwcXXr3Hp1Al++AGnNm3IVbEiOerWxWrbNhS1mrcTJuC/aBFYWho67AxB2jef5t2YMUTky4ezVstiS0vu3r3LuHHjDB2WECIVSVJKiDTg6enJrVu3KGptnaTyJi9fpnFEIquLdHEhpHVrALItXky3bt2oWLEiQUFBjBo1SlbjE0IIkSqOHTvGkiVLaAlsCg1F4+sba7v65UvMHj5EZ2WF3+bNBLm6giqhy3NCJE6xttavxtclJITvVCp27NjBzp07DRyZECK1SFJKiFT277//smjRIgCa9OqVpH0ic+ZMy5AyJa1Wy7Jly1i2bBlardbQ4WQK7/r3R1GpsDh6FM2//zJ37lzMzc05fvy4NN6EEEKkmLe3N0OGDEENrLayAuL2BlcRNT2BYmNDWNWq6Rxhxiftm0+nrVKFoK5dAfg5WzZsgDFjxsj8UkJkEZKUEiIVRUREMHz4cMLDw6lXrx4Vhw4lMmdOEuqjoqhURHz2GdrKldM1zswgIiKCqVOnMnXqVCIiIgwdTqYQWbgwoU2aAGDj6UmRIkUYOnQoELUK6UvpkSeEECKZIiMjGTRoEH5+fvxYoACOwcGJTk9g8vKlTE8QD2nfJE/A2LFE5MuHY0AAG3LlIjg4mL59+xL6wUJCQojMR5JSQqSi1atXc+XKFWxtbZkxYwaq8HB0lpb6q4YxKdFd2QMmTZKJzkWqeTdwIACW+/dj8uABrq6ulC5dGn9/f5mDQQghRLJ5enpy8uRJrKys+Klz5yTtI9MTiNSiWFvrV+Nr6eNDC1tbbt68yZQpUwwcmRAipSQpJUQqefDgAbNnzwbAzc0N51y5sB89GrPHj4m0tkb3wRC9yNy5ebNyJaGNGhkiXJFFRZQqRWjduqh0OmyWLsXMzIy5c+diamrKoUOHOHjwoKFDFEIIkcmcP3+eudHz+kybNo0cZcsmaT+ZnkCkJm3VqgR16QLARo0GG2DdunUcOnTIsIEJIVJEklJCpAKdTsfIkSMJDQ2levXqfP/991h7eWG1cyeKWs2btWvxuXCB1zt3wpYtvN65k5dnzkhCSqSJd4MGAWC1cyfqZ88oXbo0/fr1A2DcuHG8efPGkOEJIYTIRN68eUP//v2JjIykVatWtG3bFm2lSuii55SKj0xPINJKwLhxROTNi82rVxwsVQqAESNG8PTpUwNHJoRILklKCZEKNm3axJkzZ7C0tMTDwwPzM2ewnTgRgIDx49FWqwYmJmirVIEOHaL+lyF7Io2EV6xI2DffoAoPx2bFCgCGDBlC0aJF8fX1ZdKkSQaOUAghRGagKAojR47k2bNnFChQIGpqAsB28mTUwcFRZT7cR6YnEGko5jC+Gv/8Q+8iRXj79i39+vUjPDzcwNEJIZJDklJCpJCPjw8zZswAYPTo0RQwMcGhTx9UkZEEt2xJUO/eBo5QGCN9b6nNm1G/eoW5uTlz5sxBFb2U8smTJw0coRBCiIxuw4YNHD58GDMzM5YtW4aNtTW27u7YrFkDQFCXLuhy5461j0xPINKatlo1gn78EYBFgYF8li0bly5dYk50skoIkblIUkqIFHJ3dycgIIAvvviCbh064NirFyavXxNeqhRvZ88GVUJr0wiRdrTVq6MtVw51aCjWq1YBULFiRX6MbsSNGjVKVqwRQgiRoJs3b+p71o4bN46yZcpgO3GiPiHlP3s2b6dPx+fsWZmeQKS7gPHjicibF3Nvb458+SUAS5Ys4cyZMwaOTAjxqSQpJUQKHDt2jP3796NWq/GYNQun8ePRXL2Kzt4evzVrUCwtDR1ipmVubs6OHTvYsWMH5ubmhg4n81GpCIzuLWW9fj2qt2+BqN58OXPm5OHDh3h6ehoyQiGEEBlUSEgI/fv3JywsjLp169KzRw9sJ03CZvVqAPw9PAj+4YeowjI9wSeR9k3qiDmMr9SffzK5Vi0URWHgwIG8jW7zCCEyB0lKCZFMwcHBjB07FoCePXtS6eJFrLZvR1Gr8Vu2jMi8eQ0cYeZmYmJClSpVqFKlCibSwE2W0O++I7x4cdTv3mG9bh0Atra2TJ48GYha3vvu3bsGjFAIIURGNGPGDO7cuUPOnDmZP28edlOmYBPd69Z/1iyCO3Y0cISZl7RvUo+2WjWCOncGYPTdu3yeLx/Pnz9nzJgxKMqHs50JITIqSUoJkUzz58/nv//+47PPPmN8zZrYubsDUauCaGvUMHB0QgBqNYEDBwJgvWoVquhJaZs0aULdunUJDw9n1KhR6HQ6Q0YphBAiAzlx4gRroofozZs7l4JLl2KzciUA/jNnEtypkyHDEyKWgPHjiciTB7NnzzhYujQmJibs3buX3bt3Gzo0IUQSSVJKiGS4efMmK6JXNZs/bBifDR6MKiKC4BYtCOrTx8DRZQ3h4eGsW7eOdevWyWoqKRDStCkR+fNj8uYNVps3A6BSqZg2bRqWlpacPXuW7du3GzhKIYQQGYGfnx9Dhw4FoFvXrjQ/eVK/iqv/jBkER/dKEckn7ZvUpdjY6IfxFTh0CM/WrYGoedCePHliyNCEEEkkSSkhPpFOp+Onn34iMjKS5vXr02LTJkxevSL88895O2eOTGyeSsLDwxk3bhzjxo2TRltKmJoS2L8/ADbLl0NYGAB58+ZlxIgRAEyZMoVXr14ZLEQhhBCGpygKo0aN4uXLlxQtUoS5Gk3U9wbgP306wdELZYiUkfZN6tNWr64fxtfz9GlqfPkl7969Y9CgQURERBg4OiHEx0hSSohPtHHjRi5fvoyNtTWrNRo0V67IxOYiQwtu04ZIZ2dMvL2x2rlTf3/Pnj0pVaoU/v7++hWWhBBCGKft27dz6NAhzExN+fXLL3F4P2Rv2jSCu3QxcHRCJC5g/HgiXFwwffqUbQULYmNjw/nz52VRFyEyAUlKCfEJvL29mTFjBgA76tQh+/79/z+xeb58Bo5OiASYmxPo6gqAzZIlEH3V0NTUFA8PD1QqFbt37+avv/4yZJRCCCEM5NGjR0yYMAGAo199RaHoYd3+06YR3LWrASMTImliDuNz3rWL9d26ATBv3jwuXbpkyNCEEB8hSSkhPoG7uzvv3r2jW5Ei1D98GJCJzUXmENyxI5GOjpg+fozl/v36+8uVK0e36IbbmDFjCAkJMVSIQgghDCAiIoJBgwYRFBTEus8+o+bp04AkpETmo61Rg6Doifib7d3L902aEBkZycCBAwkKCjJwdEKIhEhSSogkOnbsGAcOHCCfWs2yV6+iJjZv3lwmNheZgmJlRVDPngDYLF4MMVbc++mnn3B2dubRo0fSzV0IIYzM4sWLuXjxIh4aDV2ePwfAf+pUSUiJTEk/jO/JE5bY2uLi4sKjR4+YMmWKoUMTQiRAklJCJEFISAjjx4/HHDiRPTvm/v6ElywpE5uLTCWoa1d02bJhdvs2FkeP6u/Pli2bfk6ppUuX8uDBA0OFKIQQIh1dvnyZ+fPmMQUYqdUC8HbKFIKje9AKkdko2bJFtc8Bxy1b2Bh9QW7jxo2cOHHCgJEJIRIiSSkhkmDJkiU8efIEL0tLCr58+f8Tm1tZGTo0IZJMsbMjKHqyWptFi0BR9NsaN25MzZo10Wq1TJgwASXGNiGEEFlPSEgIAwcMwF2nY3z0fW8nTyaoe3eDxiVESoXVqEFQx44AVF+3jr7RK0cOHz4cf39/A0YmhIiPqaEDSC2//vor+/fvx9/fn/z589O9e3eKFCkSb9nff/+dv/76i6dPnwJQqFAhOnTokGB5YdwePnzI0qVLcQU6hISgqNW8WbaMyPz5DR1alqbRaFi/fr3+b5E6gnr1wnr1ajRXrqD53//086GpVCqmTp1K3bp1OXHiBIcOHaJx48YGjlYIIURamTF9Ol0fPWJC9O23kyYR1KOHQWMyBtK+SR8BEyZgfuIEpo8fMx34rVAhHjx4wPjx42WqAiEymCzRU+rUqVNs2LCBNm3aMGvWLPLnz8+0adN4+/ZtvOVv3rxJ1apVcXd3Z+rUqTg5OTF16lT8/PzSOXKR0SmKgpubGxXDwlgcPUzv3ZgxhMnE5mnO1NSUb7/9lm+//RZT0yyTPzc4XfbsBEdfPcy2aFGsbYUKFaJv375A1KT+wcHB6R6fEEKItHfy5Enyrl2LW/Ttt+7u+nkHRdqS9k36iDmMz37DBjb27IlarWbPnj3sj7HgixDC8LJEUurAgQPUrVuX2rVrkydPHnr16oVGo+H48ePxlh80aBD169enQIECuLi44OrqiqIoXL9+PZ0jFxndkSNH+PePP9gJmCoKIU2bEhj9o12IzCqwTx8UMzPMT5/G7Pz5WNsGDhxI3rx5efHiBQsWLDBMgEIIIdJMYGAgT3v0wD369ls3N4J69zZoTEKkhZjD+CqtWMFwV1cgarXhly9fGjI0IUQMmT49HxERwYMHD2jRooX+PrVaTZkyZbhz506SjhEWFkZERAQ2NjYJlgkPDyc8PFx/W6VSYWlpqf87tb0/ZlocO6PJqHUNDg5m6oQJ7AKcIWpi8/nzUamTn8vNqHVNKympb3h4OLt37wagVatWmJmZpWpsqS0zvbZKnjyEtGmD1datZPP05M2GDfptVlZWTJkyha5du7JixQratWtH0aJFY+2fmeqaGoypvsZUVxElPds3xvb+yqj1vd6mDcPevQPAd9QoIlxdSWmEGbWuaSGldZX2Tfp65+aGxfHjmD5+zPjgYA6XLs2NGzcYOXIk69evj1WvzF7XT2VM9TWmumZGKiWTz2br5+eHq6srU6dOpVixYvr7N23axM2bN5k+ffpHj7F69WquXr3K3LlzExzbvX37dnbu3Km/XbBgQWbNmpXyCogMa/y4cRSYPp2egOLggOrCBShUyNBhGY2goCB9ojgwMBBra2sDR5TF3L0LJUqATgeXL0O5crE2N2vWjP3791O7dm2OHTsmX+JCZFHSvjEudzt3puimTQDcd3Wl8LJlBo7I+Ej7xgCOHIH69QF44OVFyT590Gq1rFmzhu4ysb8QBpfpe0ql1C+//MLJkyeZOHFiopMNtmzZkiZNmuhvv/+B5uvrS0RERKrHpVKpcHZ2xtvbO8uvgpUR63r//n38Z82iJ6BTqXizZAlaS0t48SJFx82IdU1LKalvzPmMvL29scrgKx1mutfWxgb7pk2x3LuXEDc3/FesiLV53LhxHD16lOPHj7Ns2TJatmyp35bp6ppCxlRfY6prWjM1NSVHjhyGDuOj0rN9Y2zvr4xWX9MZM/QJqR2VKlHNzY0XKWzXvJfR6pqWUlpXad8YQJky2P3wA1ZbtpBv0iTGDh7MxNmzGTRoEKVLlyZv3rxAFqnrJzCm+hpTXdNaWrRvMn1SytbWFrVaHWd5T39/f+zt7RPdd9++ffzyyy9MmDCB/B9ZSc3MzCzB7rVp+cZWFMVoPjgZpa6KovDzwIHMi4wE4N3o0YTVrAmpGFtGqWt6SU59Y5bPTM9XZor13YABWO7di8WBA6jv3SOycGH9trx58zJw4EBmz57N5MmTqVu3LtmyZYu1f2aqa2owpvoaU12NnSHaN8b2/soI9bVZsADbxYsBmOngQNstW9IkpoxQ1/SS3LpK+8Yw3rq5oTlxAtNHjxj26hUHv/qK8+fPM2TIELZv3446xvQcmb2un8qY6mtMdc1MMv1E56amphQqVIgbN27o79PpdNy4cSPWcL4P7d27l127djF27FgKx/ghJsSfW7cy7soVNIBv7doE9e9v6JCESBMRn39O6HffoVIUsi1ZEmd73759KViwID4+PsyJXsFGCCFE5mKzcCG2s2cDMFql4vN16/TzhglhLJRs2Xgb/TnI5uXF2q5dsbKy4vTp06xZs8bA0Qlh3DJ9UgqgSZMmHDt2jBMnTvDff/+xevVqwsLCqFWrFgCenp5s2bJFX/6XX35h27Zt9O3bl5w5c+Lv74+/vz+hoaEGqoHIKILfvKH42LHkBp5nz07EihUgc+mILOzdwIEAWO7ahcmzZ7G2mZubM23aNAC8vLy4efNmuscnhBAi+WwWLcLWwwOA0UBQ//5UrFjRsEEJYSBhtWoR1KEDACVmz2bSqFEAzJw5kwcPHhgyNCGMWpZISlWpUoXOnTuzfft2fvrpJx49esTYsWP1w/devXrFmzdv9OWPHj1KREQE8+bNo3fv3vp/+/btM1ANREbh064dFcLDeatSEb5tG4pMPimyuPAKFQirWhVVRATW8Ux4W7NmTRo3bkxkZCRjx46VLs9CCJFJ2CxejG30pPVjgL0lSjBs2DDDBiWEgQW4uRGZOzemjx7h+uQJ1atXJzQ0lGHDhhEZPXWHECJ9Zfo5pd5r0KABDRo0iHfbxIkTY91eEs8wFSEC582j6s2b6IBzQ4dSqkQJQ4ckRLp4N2gQ5idPYr11K4GDB6P7YPJCd3d3jh8/zvnz59mzZw+tW7c2UKRCCCGSwmbJEmxnzgRgLDDH1JQDCxZgbm5u2MCEMDDF1hb/2bNx6tQJ67VrWb5sGZUuXeL8+fOsXbsWNzc3Q4cohNHJEj2lhEgps/PnKThvHgBrCxfmc7mSaHAajYbly5ezfPnyRFfGFCmnrVoVbfnyqEJDsV61Ks52FxcXBg0aBMDUqVMJDAxM7xCFEEIkkc3SpdhOnw7AVAsLZgCDBw+mTJkyhg1MANK+yQjCatcm6PvvUSkKxWbO1A/jmzFjBnfv3jVwdEIYn1RLSnl7e3Pnzh2eP3+eWocUIl2ovb2x/vFHzBSFXWo1pTZs0C+JLQzH1NSUpk2b0rRpU0xNs0ynzoxJpeJddNLJev16VB+sZgrQu3dvChQogI+PDwsXLkznAIUQ8ZG2l/iQ9bJl2L6fC7BQISaEhlKmTBkGRs8fKAxP2jcZQ4C7u34YX58nT6hWrRqhoaF0794dnU5n6PCEMCopSkrpdDp27dpFr169GDx4MBMmTOCXX37Rb//f//7H+PHjefr0aUrjFCJthIVh37MnVgEB3ADO9+1L/gIFDB2VEOku7NtvCS9ZEnVgINZeXnG2m5ub64dCr1y5kjt37qRzhEIIkLaXSJj18uXYTZ0KwJlGjej+4AEajYYFCxZgZmZm4OiEyFgUW1v8oxcBsF6zhuU//EADCwvy/P03xyZMAJlfSoh0k+yklE6nY+bMmWzfvp2goCDy5MkTp0zx4sW5e/cuZ8+eTVGQQqQVOzc3LC5f5g3QO0cOeg0dauiQRLSIiAj279/P/v37iYiIMHQ4WZ9arV+Jz2b1alRBQXGKfPvtt9SpU4fw8HCGymdFiHQnbS+REOvly7GbMgWAF66uNDx1CoChQ4dSQubIzFCkfZNxhNWpox/G9/nAgRwODWUr8KOXF04VKmBx6JChQxTCKCQ7KXXkyBGuXr1K6dKl8fT0ZO7cuXHK5MyZk1y5cnHt2rUUBSlEWrDavBnrTZvQAT8AnSdNwtLS0tBhiWharRZXV1dcXV3RarWGDscohDZpQkSBAqj9/bHatCnOdpVKxcSJEzEzM+PQoUMcPXrUAFEKYbyk7SXiY71ihT4hFTBsGD0eP8bf35/SpUvTt29fA0cnPiTtm4xF+803KIDqg55RZr6+OPTuLYkpIdJBspNSf/75JzY2NgwdOhRHR8cEy7m4uPDq1avkPowQacLswgXsxo0DYDzwpnJlmjVrZtighDA0ExMCBwwAwGbFCggLi1OkcOHC9OrVC4halS8snjJCiLQhbS/xIeuVK7GbPBmAd8OGsbVYMQ4fPoypqSnz5s2TYXtCJCYyUr9K5YfUAIqCrbu7DOUTIo0lOyn1/PlzihQpgo2NTaLlrKysePv2bXIfRohUp/bxwbF3b1Th4ewEZgKTJ0+Wyc2FAIJbtyYyd25MfHyw2r493jJDhgwhd+7cPHr0iJUrV6ZzhEIYL2l7iZisV67EbtIkAN4NHcqjrl0ZF33BbeDAgZQqVcqQ4QmR4WnOnsXkxQsS+gWgAkyfP0cjw6GFSFMpmlMqKStG+Pv7y1UakXFotTj27o2Jjw93zc3pCvzQsSOlS5c2dGRCZAwaDYHRwz1sli6FeOa7sLGxwSN6ctCFCxfKyl9CpBNpe4n3rFet+v+E1JAhvBs+HDc3N16/fk2JEiUYFL2iqhAiYSYvXyapnMrbO40jEcK4JTsplT17dp48eZJomYiICJ48eULu3LmT+zBCpCo7Nzc0Fy4QamFBo7AwTGxtGTVqlKHDEiJDCf7hByKdnDB98gTLvXvjLdOxY0cqVqxISEgI06KXHxdCpC1pewkA69WrsYteDfXdoEG8GzGC344c4ZdffkGtVjNv3jw0Go1hgxQiE4jMmTNJ5Q5dvpzGkQhh3JKdlCpXrhwvX77k999/T7DMr7/+SkBAAOXLl0/uwwiRaqy2bMF640YUlYquZmbcA4YNG4aTk5OhQxMiQ1EsLQmKnjfKxtMTdLo4ZVQqFVOnTkWlUvHLL7/ISl9CpANpewnrNWuwc3cH4N3Agbz76Sf8375lzJgxAPTt25cvvvjCkCEKkWloK1cmMndulASm8FCAJ0DfLVt4/PhxusYmhDFJdlKqWbNmWFlZsXr1atatW8ft27cBCAsL48GDB2zevJnNmzdja2tLgwYNUi1gIZLD7OJF/cTmeytUYNu7dxQpUoSuXbsaNjAhMqigLl3Q2dpiducOFr/9Fm+ZsmXL8sMPPwAwfvx4ImUiUCHSlLS9jJv12rXYubkB8G7AAN6NGgUqFZMmTcLHx4fChQszbNgwA0cpRCZiYsLb6IUCEkpM7cyfn6DQUIYPH44unot0QoiUS3ZSysHBgZEjR2Jtbc3hw4dxi/6SPHPmDGPGjGHfvn1YWFgwfPhwbG1tUy1gIT6V+uXLqInNtVp8a9SgbXQX3EmTJsmcGxmYmZkZ8+bNk9WDDESxtSUoOmlrs2gRKEq85UaPHo2dnR03b95k06ZN6RihEMZH2l7Gy8rLC7sJE4DohNTo0aBScfz4cbZv345KpWLu3LlYWFgYOFLxMdK+yVhCGzXizcqV6JydY92vaDSogL46HbktLTl9+jQbNmwwTJBCZHHJTkoBfP7558ydO5dmzZqRN29eNBoNZmZmODs707BhQ+bOnUuJEiVSK1YhPp1Wi0Pv3ph4exNerBhdFYWIyEi+++47atWqZejoRCLMzMxo37497du3l0abgQT17InO0hLNtWuY//VXvGUcHR0ZOXIkAB4eHvj5+aVniEIYHWl7GR+rdeuwHz8egHf9++sTUu/evdOff3v06MFXX31lyDBFEkn7JuMJbdQIn7Nneb1zJ2zZwuudO/E5c4YIFxcsnz7lf3nyoAamTZvG06dPDR2uEFnOx5dwScCrV6+AqEk3O3bsSMeOHVMtKCFSi527O+bnz6OztWV/9+4cGj0aMzMz3KPnYxBCJEzn5ERwx47YrF6NzaJFhNWsGW+5zp07s3nzZm7dusXs2bOZMWNGOkcqhHGQtpfxsVq3Dvvo6Qfe9evHuzFjIHqY0dSpU3nx4gX58+eXRVuESCkTE7RVqkDu3GhfvEBRFPzWriV78+YUvnuXdblz8+OLF4wcOZKtW7eiSmC4nxDi0yW7p1T//v1ZuHBhasYiRKqy2roV6w0bUFQqfOfPZ/jy5QD06tWLggULGjg68TERERH8/vvv/P7770RERBg6HKMV2KcPipkZ5mfOoDl3Lt4ypqamTJkyBYBNmzZx48aN9AxRCKMhbS/jYrV+vT4hFdi3L+/GjtUnpP7++2/9kOk5c+ZgZWVlsDjFp5H2TeYRUbo0/vPmAdD5xQt+NDPjf//7H1u2bDFwZEJkLclOSllaWpIjR47UjEWIVGN26RJ2Y8cC8G7ECDwfPuTRo0fkzJmTwYMHGzg6kRRarZYuXbrQpUsXtFqtocMxWrrPPiO4XTsAbBYvTrDcN998Q7NmzdDpdLi5uaEkMAeVECL5pO1lPKw2bMA+uh0T6OpKwLhx+oRUUFCQftjejz/+SJUqVQwWp/h00r7JXEKbN+fdgAEArFYUvgQmT57Ms2fPDBuYEFlIspNSefLk4fXr16kZixCpQv3yJY69eqHSaglp0IAH33/PggULABgzZgw2NjaGDVCITCawb18UtRqLP/7ANJFeUOPHj8fCwoKzZ8+yb9++dIxQCOMgbS/jYLVxI/ZjxgBRvVUDxo/XJ6QAZs2axZMnT3BxcWFcdE8qkfW1adNGv7iBSF/vfvqJ0Dp1MIuI4KCZGZaBgYwaNUouwAmRSpKdlKpbty63b9/m3r17qRmPECmj1eLQp0/UxOZFi+K/cCEzZs0iMDCQ8uXL06ZNG0NHKESmE1mwICHNmwOQbdGiBMu5uLgwIPpq4pQpUwgODk6X+IQwFlml7TVkyBBcXFxwcXEhf/78fP3110ydOpXQ0FB9madPnzJ8+HC+/vprChcuTJUqVZgzZ06a9ywJDQ1l7NixlCpViqJFi9KrVy98fX0T3cfX15chQ4bw5ZdfUrhwYTp27MiDBw9ilXn58iUDBw6kXLlyFClShPr163Pw4ME4x7LatAn70aMBCOzdm4AJE2IlpM6dO8fatWsBmD17tlxoE6nGx8eH/v37U61aNfLkyfPRBNjevXtxcXGhe/fuse5//9n+8N+yZcvSMvy0ZWLCmyVLCC9cGOfwcHarVPwdvfKlECLlkp2Uql27NvXq1WPatGns3r2b58+fEx4enpqxCfHJ7CZOxPzcOXTZsuG3ejWX793Tf2FMnjwZtTpFC04KYbQCo5NNFocOYXL3boLlXF1dyZs3Ly9evGDJkiXpFZ4QRiErtb1q167N5cuXOXXqFBMnTmTTpk3MnTtXv/3evXvodDpmzZrFH3/8wcSJE9m4cSMzZ85M07gmTpzI0aNHWbFiBbt27cLb25uePXsmWF5RFLp3786TJ09Yu3Ytv/32Gy4uLnz//fexEvODBw/mwYMHeHl5cezYMRo2bIirq2usOfisNm/GPnrC8sBevQhwc4uVkAoJCWH48OEoisL3339PzQQWnxAiObRaLU5OTgwePJjPP/880bJPnz5l8uTJVK5cOc62y5cvx/o3b948VCoVjRo1SqvQ04Via4vf2rXosmWjmqKwEJg0aRLe3t6GDk2ITC/Zq++1b99e//e2bdvYtm1bgmVVKhU///xzch9KiCSx/PlnrNevR1GpeLN4MeGFCjEhundHmzZt+PLLLw0coRCZV0SJEoTUr4/lb79h6+4OvXuj0WgIq1QJTEz05SwtLZkwYQK9e/dm2bJltG/fnnz58hkwciGyjqzU9tJoNOTMmROI6llRvXp1/vrrL/1wtNq1a1O7dm19+fz583P//n02bNiQZkOYAgIC+Pnnn/H09KRatWoAzJ8/n5o1a3Lx4kUqVKgQZ58HDx5w6dIl/vjjD4oXLw7AzJkzKVeuHHv27GHEiBEAXLhwgRkzZlC+fHkgqrfYqlWruHbtGqVLl8Zqyxbsf/oJgMCePQlwd4+VkAKYO3cuDx48wNnZWYZxZXHBwcGMHj2aw4cPY2NjQ58+fdL8MfPmzcvkyZMBEj23REZGMmDAAEaMGMHZs2cJCAiItf395/q93377jSpVqpA/f/7UDzqdRRYpwhtPTxy7dqWvonDl7VtGjx6Nl5eXrMYnRAqkS7cRGW+bfNLFPeEu7jGZXb6sn3/h3fDhhH33Hbt37+bSpUtYWVkxJnqbECL5tNE/yCxOnIAffsCpTRtyVa6MxaFDsco1atSIqlWrEhYWpl+VTwiRvjJT2+vff//lwoULmJmZJVouICAAe3v7RMt06tSJokWLJvgvZqLrQ9euXSM8PJzq1avr7ytSpAguLi5cvHgx3n3et7XMzc3196nVajQaDefPn9ffV7FiRfbt28ebN2/Q6XTs3buXsLAwvvnmG6y2bsU+euLywB49CJg4MU5C6vLly6xYsQKISnrZ2dkl+jyIzG3KlCmcOXOGtWvXsmXLFk6fPs3169cT3efs2bOJvveLFi3K7t27Uxzb/PnzyZ49Ox06dPhoWV9fX44dO5aksplF2Lff8i56iK0nEHL0KHv27DFsUEJkcsnuKZVYBl2krtq1azNv3jzCw8O5fv06Q4YMQaVS6a8mxuziXqBAAW7fvs3IkSMJDg5O0ytpEydO5NixY6xYsQJbW1vGjRtHz5492bt3b7zl33dxNzMzY+3atdjY2LBy5Urat2/P7du39eUGDx5MQEAAXl5eODo6smfPHlxdXTl8+DClS5eOc1y1ry+OPXvqJzYPHDyYoKAgpk+frj+es7Nz2jwJIsNp06YNn3/+uf5qn0gdFocOYTtjBgoQ86eS2tsbh969ebNyJaHRXfNVKhWTJ0+mXr16HDp0iL///lvf60AIkXxZqe31+++/U7RoUSIjIwkLC0OtVjN16tQEyz98+BAvLy8mTJiQ6HFnz54d68LdhxJLfPn6+qLRaOIkfHLkyJHgRbf3SasZM2Ywa9YsrKysWLVqFS9evMDHx0dfbvny5fTt25fSpUtjamqKpaUla9as4fOzZ7GLmZCaNClOQiosLIzhw4ej0+lo1aoV3333XaLPgcjcgoKC+Pnnn1m0aJE+QbpgwQIqVqyY6H5ly5blyJEjiZZJ6eqd586dY+vWrRw9ejRJ5Xfs2IGNjQ0NGzZM0eNmNIH9+2P2zz9Y7tvHLqDuuHFUq1YtTi8xIUTSJDspJdKPMXRx37p1K40bNwY+3sU9lpgTmxcpgv+CBaBWs2jRInx8fChQoAC9evVKk+dApC0zMzOmTZum/zuj8PHxYfLkyVy9epVHjx7RvXv3RBNge/fupV+/ftSvX18/OS1EfZbjM378ePr27ZvqcadIZCR2bm6gKHzYOV2lKCgqFbbu7oTWr68fyleiRAl+/PFHvLy8cHNz48iRI5iayleOECJKlSpVmDFjBsHBwaxatQpTU1N9O+BDL168oFOnTjRp0oSOHTsmetzcuXOnRbgJMjMzY/Xq1QwfPpxSpUphYmJC9erVqVOnTqzearNnz9a3nRwdHfntt99w7d6d/4WG4kDCCSmAhQsXcvv2bbJnz86kSZPSsXYiLXysffPo0SO0Wm2saSccHBwoXLhwose1tLSkYMGCqRtsDIGBgQwaNIjZs2fj6OiYpH1+/vlnWrZsiYWFRZrFZRAqFf5z52Jy7x45b95kXUAAk0ePZvGaNTKMT4hkyDK/EH799Vf279+Pv78/+fPnp3v37hQpUiTB8qdPn2bbtm34+vri7OxMx44dM8WcQ++7uCf0g/a9pHZxP3v2bILb8+TJw/Hjx+Pd9rEu7vElpRLr4v7333/rG6Pvu7jXrVsXOzs79u/fr+/i/iHbyZMxP3s2amLzNWtQsmXj0aNHrFy5EgB3d/dYjycyDzMzM7p27WroMOKIORHoqlWrEi37sYlAYzp+/DjDhw/PkBOBas6exeTFiwS3qxQF0+fP0Zw9i7ZKFf39w4cP55dffuH27dts2LAhzgo9QgjjZWVlpf8BPW/ePL777ju2bt0aZ5iPt7c3bdu2pUKFCnh4eHz0uClp2+TIkQOtVsvbt29j9Zby9fVNtIdJ2bJlOXr0KAEBAYSHh+Pk5ESTJk0oW7YsEJVk8PLyinVRruKNG1wJDWUpMKd79wQTUjdu3MDT0xOA6dOnJzkZIDKutGrfnD17lk6dOiVaZtasWbRq1SpZx3/06BFPnz6NFbtOpwMgX758/PXXXxQoUCBWPPfv38/cq+4lQrGy4o2XFw716lHh7Vta//Yb+/fto1n0fLZCiKRLcVIqMjKSM2fO8M8//+Dn5weAo6MjpUqV4uuvv8YkxgS4aeXUqVNs2LCBXr16UbRoUQ4ePMi0adNYsGBBvGPub9++zcKFC/nhhx/48ssv+fvvv5k9ezazZs3KkBPyGkMX9xcxfvAm1MX9w6s/ltu2YePlBcCbRYuIjE5CTp48Ga1WS40aNaSLexYnE4GmD5OXL5NVzsHBgZ9++okxY8YwZ84cWrRoIT+ohEgFGaHtlZrUajUDBw5k0qRJtGjRAktLSyCqh1Tbtm0pW7Ys8+fPT9IKuilp25QtWxYzM7NYF8ru3bvHs2fP4r3Y9iFbW1sgqmf41atXGRk9LC8kJERfTwDL7duxHz4cEyCsWDECJk+ONyEVHh7OsGHDiIyMpHHjxgn2JBNZS4ECBTAzM+PSpUv6i9D+/v48ePCAr7/+OsH90nr4XpEiRTh27Fis+zw8PAgMDGTy5Ml89tlnsbZt3bqVsmXLUqpUqWQ/ZkYXmScPAWvW4NCuHR11OtyHD+d1tWo4OTkZOjQhMpUUJaUePXrE3LlzeRnPD5Zjx46xbds2hg0bFitrnhYOHDhA3bp19UPYevXqxaVLlzh+/DgtWrSIU/7QoUOUK1eOZs2aAfD9999z/fp1fv31V3r37p2msSaHMXRxj9nQjLeLu6sru3fvpmTJklHHunJFP7F5wPDhhNWrB8Bff/3Fb7/9homJCZMmTZIutJlYZGSk/mp35cqV4/2RFXMi0OzZszNz5kyuX7+e6FLGaX0l8b2YE4EmdtUe/n8i0AULFqToMdNKZBLnSIivXMeOHdm4cSM3b97Ew8MjzZdzFyKryyhtr9TWpEkTpkyZwvr163F1deXFixe0adOGPHnyMGHCBF6/fq0vm9i8LSlp29ja2vL9998zadIk7O3tyZYtG+PHj6dChQqxklI1atRgzJgx+nly9u//P/buOzyK6mvg+Hd203tCCyT03q1EQBQRRWkCEqr0Lr13Qu9NOghIkY5IE/1ZkA4BFdAgLRSpAdJ72d15/wjsS0gCISTZTfZ8noeHZObO7jmZ3ezJnTv37iNfvnx4eXlx6dIlJkyYwCeffELdunWB5D/mS5QowciRI5lasyYlFi5kLfALsH7s2DQ7pACWLVvGhQsXcHNzM97uJXK/F9U3jo6OtGnThqlTp+Lu7k7+/PmZNWvWCztls+L2vYCAACB5XqvQ0FACAgKwtbWlcOHC2NnZUaFChRTtn3TEPrs9KiqK/fv3W8QqkYk1axI+cSL5JkzALy6OyT170vO770wdlhC5SqY7pUJDQ5k6dSpRUVG4urpSq1Yt42TSDx484MSJEzx48IBp06Yxe/Zs3N3dsyzop+l0Oq5fv56i80mj0VC1alWuXLmS5jFXrlyhcePGKbZVr149xSopz0pKSiIpKcn4vaIoxit52dHx8fRjOjg4UKpUKSD5D9369euzZcsW2rVrl+KYoKAgWrVqxVtvvcWcOXNeGFf79u1fOMT90KFDae4rWLAgiYmJREZGphriXrBgwXSfu3r16vz6668phrg3atSImjVroiiKcYj777//bhziXqVKFfz9/Vm/fn3yh/KTic0TEoj/+GNiBg9GURSSkpLw8/MDoEuXLsbjzcmTn4uldJa9Sr6JiYn4+voCyVeqHRwcUux/MhHo4sWLee+994DkeTfefPNNFEV57mvwRRN0FihQIMMxP3mup3P19/dn69at/Pzzz6n2peXJRKANGzY0y9dG0jvvoC9cGE1QEEoaK3qpgCFfPpLeeSdV/FZWVkydOpUWLVqwadMmOnTokOaCBebMkt63lpRrbpQdtVdO1jfPPt7T31tbW9OlSxeWLVtGp06dOHr0KDdv3uTmzZupJni+d+9elsb1tEmTJqHRaOjZsycJCQnUrVuXGTNmpIj12rVrREVFGbc9fPiQSZMmERwcTMGCBfH19TUuSgPJc4N+++23zPryS9ovXEg0UMrdna8mTqR+OiO6r1y5YrxQMXXqVLOfQNmSfne8aq4vqm8AJkyYQGxsLJ07dzaOBH/ymsvOn3GDBg2MX//99998//33eHt7c/v27TSfN72fxd69e1FVlebNm+eq10Rmz21it27cPnmSoj/+yKBTp/h13Tpqd+mSHSFmKXnfCrOhZtLatWvVVq1aqStWrFATEhJS7U9ISFBXrFihtmrVSv3mm28y+zQvFBISovr6+qqXL19OsX3jxo3q6NGj0zymTZs26tGjR1Ns++mnn9Tu3bun+zzbtm1TfX19jf9GjBjx6sFnQKdOndTPPvssxbbNmzernp6eamxsrHHbnTt31LJly6pt2rRRdTpdhh77zp076tWrV9P9d/PmzXSPDQ8PV62trdWdO3cat126dEkF1JMnT2Y4vytXrqgajUb93//+p6qqqv79998qoP77778p2n388cdqjx49VDUxUVXfe09VQVXLl1fViAhjm6+++koF1Hz58qmhoaEZjkGYp+joaJXk/g41Ojo61f5z586pgPrff/+l2P7aa6+pAwcOzJEY33///VTPFRkZqZYoUUI9cOCAcVta7+OnlS9fXu3Xr182RZlFvvtOVRUl+R+k/ufoqKrP/B5+WuvWrVVArVOnjmowGHIwcCHyjuyovUxV3+R5Op2q/v67qm7enPz/unX///uzd29V1eufc6hO9fHxUQG1cePG8jszj3lRfSNyqYQE9aa3t6qCelmrVUNu3DB1RELkGpkeKXXu3Dny589Pjx490hxOamNjQ/fu3fn77785e/asWU5Y/DKaN2+eYnTVk17WR48eodPpsvz5FEXB09OTuLg44uPjU8y59O6776IoCjNmzKBPnz7GIe5eXl4MHz7cOPQWnj/EXaPR4Ojo+Nw47j9ncuO2bdsycOBAVFXF2dmZsWPH8uabb1K8eHHjcXXq1GHMmDFpDnG/ePGicYj7xx9/TFBQEK6urpQsWZIuXbowYcIE3N3d+emnn/jll1/YsGEDMb1743jkCAYnJ4JXrUIfEwMxMYSEhBiHCI8YMSLVz8xcPDmvQUFBKVblyateJd/Y2Fjj10FBQamuJD6Zu+zBgwcp5ghJSkoiJiYm3fPv7+//wltbZ8+enaHb9xITE43P9SRXf39/bt68SZMmTYztnkwEamVlxdGjR1NNBHr58mWWLFlilq9Zo5o1sfv6a1zGj08x6bm+cGEM9vZYX7+O7tNPCd63DzWN0RnDhg1jz549HD16lJUrV/JZLpoI1JLet5aUa3azsrJ65eXXn5UdtVdO1jeW8vqyO3Ag1e9KFVCAmA4diBw3Dh48SPf4lStX4u/vj7OzM5MmTSIoKCj7g35FlnJu4dVzfVF9Y27k3GacZtcugmrVopxOxx81a5Lwxx+QgbnwTEXOrciM7KhvMt0pFRwcTI0aNZ57f7NWq6Vs2bLPvS3uVbm4uKDRaAgPD0+xPTw8PN3V59zc3IiIiEixLSIi4rmr1VlbW6c7OWZOvLCffg6tVkvnzp1ZtmwZHTt25PDhw9y4cYMbN26kmojz7t272RaTn58fiqLQo0cP4xD36dOnp4j12rVrREREGLc9ePCAiRMnGoe4t2zZksGDBxtztLKyYsOGDcyYMYNOnToRExNDiRIlWLhwIY0ePcJx7VoAwhYvRlemTPIYDZI7ESIiIqhUqRJt27Y1+182qqqafYxZKTP5Pt0+reOLFy/+3IlA03u+qlWrZmgi0IzG+2xspUuXfu5EoIULF07RfvPmzVSrVo1KlSqZ/Wsi7tNPifv4Y2xPnyZfYiIhNjYk1KiBJjSU/I0aYXX9Ou49ehCyeTM88/uySJEi9OvXj7lz5zJlyhTq169v9oX4syzpfWtJueYm2VF7maK+ycuvL7sDB3Dr2dNYnzyhkNwxlVCnDqqipNr/xI0bN5g1axaQfAuXp6dnrvpZ5eVz+6zM5vqi+sZc5aZYX1Vmc9V6eRE4Zw6ugwfzVlAQf/fpQ/4VK7Ihwqwl51aYWqY7paytrY2riTxPfHz8c1c6eVVWVlaUKlWKgIAAatSoASSPSggICOCTTz5J85hy5crxzz//pJgs/O+//6Zs2bLZFmdmLVy4MM03Tr9+/ejXrx8ArVu3pnXr1jkdGnZ2dkyfPp3p06en2+bZTrFu3brRrVu3FNuevbe3VKlSfP311ym2WZ8/j1vz5gBEDRlinNgc4MKFC2zatAlInvg6t606JDJHJgI1Ea2WxFq1oHBhEu/fB1XFUKAAoevXk/+zz7A9cQLXsWOJmDUr1eS9vXv3ZuvWrdy5c4elS5caV6YSQmSMudReIh16Pa4TJoCqkuasJYqCy8SJxH/yCaRRqxgMBoYPH058fDzvvvsubdu2zfaQhRBZq1SrVmzbv5/Ov/1GtX37uPPBB2hM8HeaELlJpscTent7c+HCBYKDg9NtExwczIULFyhatGhmnyZDGjduzG+//cahQ4e4c+cOq1evNo7cAViyZAmbN282tm/YsCHnz59n37593L17l+3bt3Pt2rV0O7GEaWmCg/Ho1i15YvOPPiLq8cgqSO7t9vPzw2Aw0KRJk+culSvynvHjx1OjRg06d+5MmzZtqFGjBtWqVcv2523QoAENGjQwTgLaoEGDF67ol5Y9e/agqmqaq4TmNrqKFQlbtgxVo8Fx0yYcn+lYhuQOwScdcCtWrOD27ds5HaYQuZo51V4iNRt/f7T376fdIQUoqorVvXvYpLPIzMaNGzl58iT29vYZWrBGCGGe6qxcyerHC0HlHz4cqwsXTByREOYt0yOl3nvvPdasWcOUKVPo1KkTb7zxRor9f/75Jxs2bCAxMdG4MlZ2qVWrFpGRkWzfvp3w8HBKlCjBmDFjjLfjBQcHp/hgL1++PAMGDGDr1q1s2bKFwoULM3z4cIoVK5atcYpMSErCvXdvtPfvk1S6NGGLFqW4N3v//v2cPHkSOzs7xo8fb8JAhSk4OjqyePHiFNv69OmT7c+b1m2xz/vj4ckKSs/64osvMtWZZa4S6tcncvx4XCdNwmXyZHQlS5LwzMpSDRs2pFatWpw4cYLJkyenGhUphEifOdVeIjXtw4eZbnfnzh2mTZsGwJgxY6QmFSIXs7e3p+Datfzv889poNeja9eO6N9/x+DhYerQhDBLme6Uql+/Pv7+/gQEBDBr1iycnJyMk2o/fPiQ6OhoIHn+lg8//DBron2OTz75JN2RThMnTky1rWbNmtSsWTOboxKvymXKFGxPnsTg5ETY2rWoj2+DAoiLi2PKlCkA9O3b1zivkMgbrKysGDdunPFrkTvE9OiBVWAgjps24d63L8G7d6OrVMm4X1EUJk+eTIMGDThw4ADHjh3j3XffNWHEQuQe5lZ7iZT0z1lc5nntVFVl5MiRxMTE8Pbbb+f6xYHE80l9YxneeucdZnXoQOmNGykTHIzSvTsR27almnNTCPEKt+9pNBpGjx5N06ZNsbOzIzo6muvXr3P9+nWio6Oxs7Pjs88+Y9SoUS+c40WItNjv2IHTmjUAhC9alDyx+VOWL1/O3bt3KVKkSI6MjhE5y8bGhj59+tCnTx9sbGxMHY7IKEUhYto0EmrXRhMTg0fnzmger5T4RMWKFenYsSOQvGBCdqxgKkReJLWXeUv08UFfsCDpTaGrKgq6IkVI9PFJsX379u0cOnQIW1tb5s6dK+cuj5P6xnIM8PPjyyJFiAIc/f1xmTzZ1CEJYZZeqXveysqK9u3b06pVK65du0ZoaCgAHh4elC5dWibZFJlm/fffuI0aBUDU4MHEN2iQYv/du3dZunQpkDyvkL29fY7HKIRIh7U1oStXUqBJE6xu3MCja1eCd+wAOztjk6FDh7J7924uXbrExo0b6dKliwkDFiL3kNrLjGm16EqUwPbhQ1RIMbeU+vgW78hJk1JMcv7gwQMmTZoEwLBhwyjzzAU4IUTuZW9vT69Fi/iiZUv2AE5r15JUuTJxbdqYOjQhzEqWXIqxtramQoUK1KpVi1q1alGhQgUpikSmaUJCcO/WDSU+nvj69YkaMiRVm6lTpxIfH88777xDkyZNTBClyG56vZ5z585x7tw59Hq9qcMRL0l1dydk/XoMbm7Y/PUXbkOHplgC3d3dnREjRgAwZ84c4x/WQoiMkdrL/NgcO4bt6dOoioIhf/4U+/SFCxO2ahXxDRsat6mqyujRo4mIiKBatWr07Nkzp0MWJiD1jWWpWbMmHp0782SdZddRo7D+4w+TxiSEuZHxwcK8JCXh3qsXVvfuoStVirDFi1NMbA5w6tQp9u7di0ajYdKkSbI6TR6VkJBAo0aNaNSoEQkJCaYOR2SCvnRpQletQrWywmH3bpyemfC9ffv2VKpUiYiICGbPnm2aIIUQIiskJuI6diwAsZ068eCvvwjZuRM2byZk504enjqVokMKYO/evfzvf//D2tqa+fPny/xCFkLqG8szZswY1nt78x2gSUrCo0cPNPfvmzosIcxGpjulfvrpJ1q3bs0fz+np/eOPP2jdujW//PJLZp9G5HV6PTYnTsCWLdicOIHL5MnJE5s7OhK6Zk2Kic2Tm+uNS8q3a9eOKlWqmCJqIUQGJdauTcSMGQC4zJ2L3d69xn1arda4WMGmTZu4IEsmC/FcUnuZL6evv8Y6MBB9vnxEjhgBWi2JtWpB27bJ/z91yx5ASEiIcdXgAQMGULFiRVOELYTIAY6OjsyeO5dOwD8kr8Dp0b07xMebOjQhzEKmO6XOnDmDi4tLquWIn/bGG2/g4uLC6dOnM/s0Ig+zO3CAQj4+5GvZEtq1I1/LljitXQtA+FdfoStXLtUxW7Zs4cKFC7i6uhpv/RFCmLfYdu2IfnxbivvgwVifPWvc984779C0aVMMBgMTJkxAVdObIlgIIbWXedLcvYvTggUARI4bh+rq+sJjJkyYQEhICBUrVqRfv37ZHaIQwsTq1KlDs/bt+QwI02iwOXcuef5cqXuEyHyn1L179yhWrNhzVwjRaDQUK1aMu3fvZvZpRB5ld+AA7j17pjl0VYU0f0FHREQwa9YsAIYMGUK+fPmyOUohRFaJHDeO+Pr1UeLj8ejSBc1Tnwvjxo3Dzs7OeGuuECJtUnuZJ9eJE9HExZFQowZxvr4vbP/zzz+ze/duNBoN8+bNkxXYhLAQ48ePJ6FIEVoaDOgVBYcdO3BcvdrUYQlhcpnulIqMjMQ1A1eCXF1diYiIyOzTiLxIr8d1wgRQVdKcDUpRcPHzg2cmf5w3bx6hoaGULVuWTp065UioQogsotUStnQpSRUron30iHydO6PExADg5eVlHCkwdepU4uLiTBmpEGZLai/zY3voEPYHDqBqtURMnw4vmOcyIiKCUY9XF+7duzfVq1fPiTCFEGbA2dmZOXPmcBAY+vgCvMvkydgcOWLawIQwsUx3StnZ2REWFvbCdmFhYdja2mb2aUQeZOPvj/b+/bQ7pABFVbG6dw8bf3/jtitXrrBu3ToAJk+eLCsMCZELqU5OhK5bhz5/fqz//Re3fv2Mnc+9e/fG29ube/fusXTpUhNHKoR5ktrLzMTHGyc3j+naFV0G5oWaPHkyDx48oFSpUgxJY3VhIUTeVrduXVq3bs1XwHdOTigGAx59+qC9edPUoQlhMpnulCpRogRXrlwhODg43TbBwcFcuXKFYsWKZfZpRB6kffjwpdqpqsqECRPQ6/U0aNCA9957LzvDE0JkI723N6Fr16La2mL/88+4PJ4E3d7e3riIwfLly7l9+7YpwxTCLEntZV6cVqzA6uZN9IUKETV06AvbHzlyhK1bt6IoCvPnz8fe3j4HohRCmBs/Pz88PT1pHx3NjYIF0YSH49G1K0p0tKlDE8IkMt0pVbt2bXQ6HfPmzSM8PDzV/vDwcObNm4dOp+Pdd999lRhFHqMvWPCl2v3vf//j6NGj2NjYGP9oFXmflZUVQ4YMYciQIbJMdh6T9OabhM+fD4DT8uXYb90KQMOGDalVqxbx8fFMnjzZlCEKYZak9jIf2lu3cF68GIDICRNQnZ2f2z46Oprhw4cD0KVLF95+++1sj1GYJ6lvhKurKzNnziQBqPPoEfEeHlhfvozboEFgMJg6PCFyXKZ/E9atW5dDhw5x+fJl+vfvz+uvv46XlxcAd+/e5ezZsyQmJlKuXDk++OCDLAtY5H6JPj7oCxdGk84tfKqioC9cmEQfH+Lj45k0aRIAPXv2pESJEjkaqzAdGxsbhmbgyrPIneKaNcMqMBDnBQtwGzkSffHiJNasyeTJk/n44485cOAAx44dkz+shXiK1F7mw3XCBJT4eBJq1SLus89e2H7GjBncuXOHokWLGueUEpZJ6hsB8NFHH9GiRQt27dpFR0dHtkVHY//jjyR99RXRgwebOjwhclSmR0ppNBpGjRrF22+/TWJiIv7+/uzatYtdu3bh7+9PYmIib731FqNGjUKr1WZlzCK302qJTKcgUx9PEBo5aRJotaxatYpbt27h6enJgAEDcjJKIUQ2ixo6lLimTVF0Ojy6d0d74wYVK1Y0LmTg5+eHTqczcZRCmA+pvcyD7c8/Y/fLL6jW1hma3PzEiRPGeTFnz56No6NjDkQphDB3kydPpkCBAuy4fZvNdeoA4DJ3LnY//WTiyITIWa80ZtTBwYFhw4bx33//ce7cOR49egRA/vz5ee2112RUi0iX1dWrKIBqZYXy1B+d+sKFiZw0ifiGDbl//z6LHw+NHzt2rBRxFsZgMHD16lUAypYt+9wl0EUupSiEzZ+P9vZtbM6exaNTJ4L37mXo0KHs3r2bS5cusXHjRrp06WLqSIUwG1J7mZYSF5e8gjAQ3bMnurJln9s+OjqawY9HPbRv317mxRRS3wgjd3d3ZsyYQffu3el06BC1P/uMEnv24DZgAMF796KrUMHUIQqRI7LkRubixYtTvHjxrHgoYQE09+7htHo1AKErV4KrK/kSEwmxsSGhRg14fHV3+vTpxMbG8tZbb9G8eXNThixMID4+nnr16gFw9epVHBwcTByRyBb29oSuXUv+Ro2wvnYNj969UTduZPjw4YwZM4a5c+fy2Wef4eHhYepIhTArUnuZhtOSJVjdvo2+cGGiBw58YfuRI0dy+/ZtvL29ZV5MAUh9I1L69NNPadq0KXv37qXp5cv416qF/YkTeHTtyqMffkB1dzd1iEJkuyybXU+v13PgwAHOnDlDVFQUHh4e1K5d2/hLV4gnXObOTZ6HwceHhAYNUDQaKFyYxPv3QVUBOHPmDLt27UJRFKZMmYLygqHxQojcy1CwIKHr1pG/WTNsjx7Fdfx4vpg6lW+//ZZ///2X2bNnM3PmTFOHKYTZkdorZ2mvX8dp2TIAIiZNQn3BCO5jx46x7HH7uXPn4uTklO0xCiFyn6lTp3L8+HH+uXSJGX36MOH2baz++w/3L78kdONGkAnxRR6X4fGi/v7+dO/enS1btqTaZzAYmDFjBt9++y2XL1/m3r17BAQEsHLlSpYuXZqlAYvczeriRey3bwcgcty4NOdhMBgMxquJbdq0oVq1ajkaoxAi5+kqVyZs6VJURcFx40Zc1q9nypQpAGzatIkLFy6YOEIhcp7UXmZEVXEdPx4lMZH4unWJb9jwuc2jo6MZMmQIAJ06daLO4/lihBDiWfny5WPq1KkAzPj6a86MHYvB3h67I0dwmTbNxNEJkf0y3Cl14cIFoqKieOedd1Lt++233/jnn38AePPNN+natStNmzbFxsaGI0eOcP78+ayLWORqLtOno6gqcY0bk/TGG2m22bZtG3///TfOzs6yQo0QFiTh44+TO6sBl4kTeT82liZNmhg7qtXHIymFsBRSe5kPuwMHsDt0CNXGhogpU144ufmUKVO4c+cOJUqUYNzj32tCCJGeJk2a8Omnn6LT6ei5ZAkh8+cD4LRqFfY7dpg4OiGyV4Y7pa5evYq7uzslS5ZMte/XX38FoHbt2owYMYIGDRrQvn17+vXrB8CRI0eyKFyRm9kcPYrdwYOoVlbprr4XERHBjBkzABg8eDD58+fPyRCFECYW06sXMW3aoBgMuPfpw/R27bCzs+PUqVPs27fP1OEJkaOk9jIPSmwsrn5+AER/+SX6UqWe2/7IkSN8++23AKxdu1YWahFCvJCiKEyfPh03NzcCAgKYe+MGUY/nrXMbORLrc+dMG6AQ2SjDnVLh4eFprugSGRnJzZs3AWjatGmKfT4+PhQoUIDAwMBXClLkAQaDcfhpTMeO6NMosAEWLFhASEgIpUuXlhW3hLBEikLEjBkk1KyJJjqaCiNGMPLx74IpU6YQFxdn4gCFyDlSe5kHp4UL0d6/j65oUaIfd/qlJyoqiqFDhwLQuXNnPvjgg5wIUQiRBxQsWJDJkycDsHDhQv5o0oT4jz5CSUjAo1s3NA8emDhCIbJHhjulIiMj07zSc+3aNQBcXFzSLJy8vb0JDQ3NfIQiT7Dfswebf/7B4ORE9KBBaba5evUq33zzDQCTJk3CxsYmByMUQpgNGxtCV61CV6IEVrdvM9Lfn9JeXty7d0/myhEWRWov07O6ehWnlSsBiJg8GdXe/rntp0yZwr179yhWrBhjx47NiRCFEHlIixYtqF+/PomJiQwZNoxHCxaQVLYs2qAgPHr0gIQEU4coRJbLcKeURqMhMjIy1fYbN24AUCqdocwODg4YDIZMhifyhIQEnGfNAiC6b18M+fKlaqKqKn5+fuh0OurXry9XFgVWVlb07t2b3r17YyWrjlgc1cOD0PXrMbi6Yv/XX/zo7Q3A8uXLuX37tomjEyJnSO1lYqqK65gxKDod8R99RMLHHz+3+aFDh9i0aRMA8+fPl9v2RJqkvhHPoygKM2fOxMXFhXPnzrFyyxZC167F4OqKzZ9/4jp2rHG1ciHyigx3ShUoUIAbN26g0+lSbH8yyWaZMmXSPC4qKgpXV9dXCFHkdo7r12N1+zZ6T09ievRIs83+/fs5dOgQNjY2TJw4MWcDFGbJxsaG8ePHM378eBk1Z6F0ZcoQumIFqlZLWX9/VhQrRnx8vHFouxB5ndRepmW/Zw+2J06g2tkR8YLfO5GRkQwbNgyArl27UrNmzZwIUeRCUt+IFylcuLDx76G5c+dy2WAgbNkyVI0Gxy1bcFi/3rQBCpHFMtwpVblyZaKioti6datxW0BAAP/++y8Ab6SzktrNmzdxd3d/xTDTFx0dzaJFi+jUqROdO3dm+fLlxMfHP7f92rVrGThwIO3bt6dPnz6sXbuW2NjYbIvRkinh4Th/9RUAkcOHpznsPSEhgcGDBwPQo0ePNCd0FUJYpsT33iPi8TLJvW7dwldROHDgAMePHzdxZEJkP3OtvSyBEhWFy+OOqKh+/dAXK/bc9pMnT+b+/fuUKFGC0aNH50SIQog8rFWrVtStW5eEhASGDBlCbJ06RD6+Jdh1wgRsTpwwcYRCZJ0Mjxlt1KgRBw8eZN++fRw/fhwXFxdu3boFQNmyZSldunSqY65cuUJkZCS1a9fOuoifsWjRIsLCwhg3bhx6vZ5ly5axcuVKBj5ereBZoaGhhIaG0qFDB7y9vQkODubrr78mLCzMODGlyDpOS5eiCQ8nqXx54nx902zz9ddfc+3aNQoVKsSAAQNyOEJhrgwGA3fv3gXAy8sLjSbDfegij4nt2BGrwECc1qxho0bDdb2eCRMm8L///U9ufRB5mrnWXpbAed48tA8eoCtRgug+fZ7b9uDBg2zZsgVFUZg/fz4ODg45FKXIjaS+ERmhKAqzZ8+mXr16/Pnnn6xZs4aevXphfeECDrt24d6rF8EHDqAvWtTUoQrxyjL8W9DT05MBAwZgZ2dHaGgoN2/exGAw4OHhQd++fdM85pdffgGgSpUqWRPtM+7cucO5c+fo3bs3ZcuWpUKFCnTt2pUTJ06kO8FnsWLFGDZsGG+99Raenp5UqVKFNm3a8Oeff6LX67MlTkulvXsXpzVrAJJ79rXaVG3u37/PV49HUo0ZMwYnJ6ccjVGYr/j4eN555x3eeeed545+FJYh0s+P+Hr1sNXr2acoRF26xMaNG00dlhDZyhxrL0tgdfEijmvXAhAxbRrY2aXbNjw8nOHDhwPJt+35+PjkSIwi95L6RmSUl5cX48ePB2DWrFncuHmT8NmzSaxaFW1oKB5du6LI3T4iD3ipS8w+Pj5UqFCBP//8k4iICPLnz8/bb7+NXTof1mXKlKFkyZLZVhhduXIFR0fHFFcKq1atiqIoBAYGUqNGjQw9TmxsLPb29mjT6DR5IikpiaSkJOP3iqJg//hWNEVRMplB+p48ZnY8dk5xnjMHJSGBhNq1SfzwwzRzmTJlCjExMbzzzju0bNkyV+ebEXnhvL6MV8n36WMURTH7n5klnVuT5GplRfjy5eRr2pTCly+zF2gyZw7NmjXDw8MjW59azq0wpeyuvXKyvskVry9VxW3MGBS9nrhGjUj84AOeF+3YsWMJCgqiVKlSjB49OtVn19P/52WS68sf/+Rrc/+Zybk1rS+++IJ9+/Zx7Ngxhg0bxs6dOwlbu5b8n36K9b//4jZkCOErVsAr1NrmlG92saRccyNFVXPv9P27du3i8OHDxpE2T3Tv3p1WrVrx8QtWSYHkiSlHjRpFnTp1aNu2bbrttm/fzs6dO43flyxZklmPV5QTaTh3Dt54I3l1iDNn4K23UjX5/fffqVevHoqi8Mcff6Q7N4awTDExMcaRc9HR0bKKkUh28yZqjRoojx7xPfBr794sXb7c1FEJkWtJffOM9euhc2dwcIBLl+A5t8Zs27aNNm3aoNVqOX78uIySEhki9Y14WTdv3qRKlSrExMSwePFi+vXrB8eOQb16kJQE06eDzGUncjGznIxj06ZN7Nmz57ltFixY8MrPExsby8yZM/H29sY3nfmOnmjevDmNGzc2fv+kl/XRo0epVsXJCoqi4OnpSVBQELmx39Bj0CBsVZW4zz4j3MsL7t9PsT8pKYnevXsD0LFjR954441cm+vLyO3n9WW9Sr5PLz4QFBRk9nN0WNK5NWmutrZYr16N++ef01yn4/KKFfz6+edUrlw5255Szq3IDCsrKwoUKGDqMF4oJ+sbc399KRERFBg6FC0QOWgQMVZWqeqXJ+7fv2+sY/r370+xYsW4/0xbc883K0muGSf1jfky11xtbW0ZM2YMY8eOZcSIEbz++uuUKl0a+2nTcBsxAnXsWMK8vEj46KOXelxzzTc7WFKu2S076huz7JRq0qQJdevWfW6bQoUK4ebmRmRkZIrter2e6Oho3Nzcnnt8XFwc06dPx97enmHDhr1wslxra2usra3T3JedL2xVVXPdG8f28GFsDx9GtbYmcuTINONfs2YNV65cwd3dnREjRgC5M9fMsqRcIXP5Pt0+N/28clOsr8pUuSa+9RaRCxbg3r8/o4ApvXtT8fDhbB+SLedW5EWmqG/M9fXlMmsW2pAQksqWJbpHj+TR3mlQVZWhQ4cSHh5OtWrVGDRo0HPzMdd8s4PkmrHjXvUxTCE3xfqqzDHXjh07Glcf7t+/P7t370Zt3x7rgAAcN2zArW9fgn/4AV2ZMi/92OaYb3axpFxzE7Nc7sHFxQUvL6/n/rOysqJcuXLExMRw/fp147EBAQGoqkqZ57whY2NjmTp1KlZWVowYMQIbG5ucSMsyGAy4PF6+PaZTJ/TFi6dq8uDBA+bPnw8kT24uy1YLIV5WXIsW3OvSBYCR165x5vHvFCGEyAzrf/7BYcMG4PHk5s+pDdevX8+hQ4ews7Nj0aJF6XbqCSFEVtFoNCxYsAAXFxfOnj3LokWLAIiYNIkEHx800dF4dOmCEhFh4kiFeHlm2SmVUd7e3rz22musXLmSwMBALl26xNq1a6lVq5Zx4tvQ0FAGDRpEYGAgkNwhNW3aNBISEujduzdxcXGEh4cTHh6OwWAwZTp5gv2uXVj/+y8GFxeiBg5Ms83UqVOJjo7m9ddfp02bNjkcoRAiz5g8mfPlymEDvLdwIUmXLpk6IiFEbmQw4Dp6NIrBQGyzZiTWrp1u02vXrjFlyhQg+cJa2bJlcypKIYSF8/LyYvr06QAsXLiQs2fPgo0NYatWoStSBKvr13Hv1w9kRXmRy5jl7XsvY8CAAaxZs4bJkyejKAo+Pj507drVuF+n03Hv3j0SEhIAuHHjBlevXjUe+7QlS5ZQsGDBnAs+r4mPx/nx5KjR/fqhprEi1qlTp9i1axeKojBt2jQ0mlzdLyqykVarpVOnTsavhUhFo8H5u+84/8YbVE9KItHXF/X4cVQXF1NHJoTIRRy2bsXm7FkMTk5EPl5+PS06nY6BAwcSHx/Pu+++S5fHozWFeBlS34hX0bx5c3755Rf27NlD//79+fnnn3HIn5+wtWvJ16wZdgcP4jxrFlFjxpg6VCEyLNd3Sjk5OTEwnRE5AAULFmT79u3G7ytXrpzie5F1HL/5Bqt799AXLkz0Ux2DT+h0OsaNGwdAu3btqF69ek6HKHIRW1tb49UgIdJj5+HBialTyTdyJN6hoUR07kzM9u3wgnkChRACQAkNxfnxZ03U0KEYPD3Tbbt48WLOnj2Li4sL8+fPlwtrIlOkvhGvavr06fj7+3Pjxg0mT57MzJkzSapalYh583Dv2xfnpUvRVapEXLNmpg5ViAyRT1ORJZSwMJwXLwYgcsQIsLdP1Wb9+vVcvHgRNzc3Ro0aldMhCiHyqA/at2dctWrEAK7+/rj6+Zk6JCFELuEycybasDCSKlYkJo0Lak/8/fffLFy4EIBp06bh5eWVQxEKIURKbm5uxt9HGzdu5LfffgMgrlkzovr2BcB16FCs//nHVCEK8VKkU0pkCefFi9FERJBUsSJxn3+eav+jR4+YM2cOAKNGjTLO+SVEelRVJSQkhJCQEFklQzyXoih0mD+fjo9X33Nctw6HdetMG5QQwuxZ//UXDps3AxAxfXq6Iyzj4uLo378/Op2Oxo0b07x585wMU+QxUt+IrFCnTh26d+8OwNChQwkJCQEgauRI4uvVQxMfj3vXrmiCg00ZphAZIp1S4pVpb9/G8ZtvAIgcNw7SuD9+2rRpREVFUa1aNdq1a5fTIYpcKC4ujmrVqlGtWjXi4uJMHY4wcxUrVsSlUydGPv7edcIEbA8dMmVIQghzptfjOmYMiqoS6+tLYo0a6TadPn06gYGBFCpUiBkzZqA87gAXIjOkvhFZZfTo0ZQrV45Hjx4xYsSI5E5OrZawJUvQlSqF1b17uPfsCYmJpg5ViOeSTinxypxnz0ZJTCShTh0S3n8/1f4zZ86wY8cOILlzSiZ1FEJkh2HDhrHK1ZV1gKLX4967N1ZXrpg6LCGEGXLYuBGbf/7B4OJC5Nix6bb79ddfWbt2LQDz5s2Tkd5CCLNhZ2fH4sWLsba25qeffmLbtm0AqK6uhH7zDQZnZ2xlWgORC0inlHgl1v/8g8OuXQBEjBsHz1w91Ol0jH1c7LVt25Y33ngjx2MUQlgGd3d3xowdSy/guEaDJioKj06d0Dwe0i6EEACa4GBcZs8GIHLkSAwFCqTZ7uHDhwwePBiA7t2788EHH+RYjEIIkRFVqlRh+PDhAEyYMIH//vsPAF2ZMoQtXoyqKDhu2IDDxo2mDFOI55JOKZF5qorLlCkAxLZoga5KlVRN1q1bx4ULF3B1dWX06NE5HaEQwsK0bduWqm++yWcGA/cdHLC6dQv37t0hIcHUoQkhzITL1KloIiJIrFqV2A4d0mxjMBgYNGgQoaGhVKpUiTGyvLoQwkz17t0bHx8fYmJi6NevH0lJSQAkfPQRUSNGAOA6bhw2p0+bMkwh0iWdUiLTbA8dwvb4cVQbG6JGjky1//79+8x+fCVyzJgx5MuXL6dDFEJYGI1Gw4wZMwjXaqkXG0uigwO2p0/jNnIkyISyQlg8m9OncdixA1VRkic3T2dKgdWrV3P48GHs7OxYunQptra2ORypEEJkjFarZdGiRbi4uPDXX38xb948477o/v2Ja9wYRafDvUcPNHfvmjBSIdImnVIic/R6XKZNAyCmSxf03t6pmvj5+RETE8Mbb7whk5sLIXJM5cqV6datG5eAbk5OqFotDjt24LRsmalDE0KYkk6H6+MRT7Ht2pGUzpQCAQEBTJ8+HUiuZcqVK5djIQohRGZ4e3sbBwMsWbKEY8eOJe9QFMIXLCCpUiW0wcF4dOuGEhcHej02J07Ali3J/+v1JoxeWDrplBKZYr9zJ9YXL2JwdSWqf/9U+3/77Td++OEHtFotM2fORKORl5oQIucMHToUT09Pvn34kO/eew8Al+nTsfvxRxNHJoQwFcdvvkmuXdzciBo1Ks02sbGx9O3bl6SkJBo0aECHdG7vE0IIc9OkSRPatWuHqqoMGDCA0NBQAFQHB0LXrkXv4YHNP//g0a4dhXx8yNeyJbRrR76WLSnk44PdgQMmzkBYKukpEC8vLs44QWjUgAGo7u7P7I5j3LhxQPLEoJUrV87xEEXup9Vq8fX1xdfXV1ZsFC/NycmJKY/nvGt37Bh3mzcHwK1/f6z/+ceUoQkhTEATFITz3LkARI4ZgyGdVfQmTpxIYGAgnp6ezJ07F+WZBVyEeFVS34jsNGnSJMqUKcODBw8YPHgw6uOpC/RFixK2ciWqRoPt6dNo7t9PcZwmKAj3nj2lY0qYhHRKiZfmtGYN2qAgdF5exHTunGr/V199xa1btyhcuDBDhw7N+QBFnmBra8vChQtZuHChzOUhMuXTTz+lXr16JCUl0e7BA+Lffx9NXBwenTujCQoydXhCiBzkMmUKmuhoEl9/ndi2bdNs8+OPP7Jp0yYURWHhwoV4pNNxJcSrkPpGZCcHBweWLVuGra0tv/76K2vXrjXuS/TxQXVyAuDZ7nblceeVi5+f3Moncpx0SomXogkNxWnJEoDkyc3t7FLsv3LlCitWrABg6tSpODo65niMQggBoCgKU6dOxc7OjiMnTrCxcWOSypVDGxSER5cuyXMqCCHyPJtjx3DYvRtVoyFixgxIY0qBu3fvMmzYMAD69OlDnTp1cjpMIYTIEpUrV2b8+PFA8t9jAQEBANj4+6OJjEz3OEVVsbp3Dxt//xyJU4gnpFNKvBSnhQvRREWRVLkycY9vh3lCVVVGjx5NUlISH330EQ0aNDBRlCIvUFWV2NhYYmNjjUOPhXhZxYsXZ+DAgQCMmTWL/xYvTp5T4e+/cRswAAwGE0cohMhWiYm4Pp5SILZjR5KqVk3VJCkpiS+//JLw8HCqVavG8OHDczpKYUGkvhE5oXPnznz00UckJiby5ZdfEhsbi/bhwwwdm9F2QmQV6ZQSGaa9eRPHDRsAiBg3LtWVxh07dnDq1Cns7OyYMmWKzMMgXklcXBxly5albNmyxMmIFvEKevfuTZkyZQgODmbq5s2ErV6Nam2N/YEDOM+ZY+rwhBDZyGn1aqyvXkWfLx+R6XQ2zZkzhz/++ANnZ2dWrFiBjY1NDkcpLInUNyInKIrC/Pnz8fT05Nq1a0yYMAF9wYIZOjaj7YTIKtIpJTLMZdYslKQk4uvWJfHxalZPhIaGGicVHjJkCEWLFjVFiEIIkYqNjY1xefcNGzbgb2ND+OPFGpwXLcL+u+9MGZ4QIpto7t7Faf58ACLHjUN1c0vV5rfffmPp0qUAzJs3j+LFi+dkiEIIkW08PDxYtGgRiqKwZcsWtt27h75wYdR0Bg6oioKuSBESfXxyOFJh6aRTSmSI9blz2O/di6ooRI4Zk2r/jBkzCA0NpXz58vTs2dMEEQohRPpq167N559/jqqqjBo1iqgWLYjq1w8At2HDsDlzxsQRCiGymuvEiWji4kioUYM4X99U++/evWu8vbdr1640atQop0MUQohsVbt2bePvuRGjR3OpTx+AtDumVJXISZNAVoUUOUw6pcSLqSouU6cCENeyJbrKlVPs9vf3Z/PmzUBy55S1tXWOhyiEEC8yYcIE3NzcCAgI4OuvvyZq5EjiPv0UJTER927d0N66ZeoQhRBZxPbQIewPHEDVaomYNg2e+QPsyTxSYWFhVKtWjXGP550SQoi8ZsiQIdSuXZvY2FhafPstQYsXY/D0TN1QUVCfWcRKiJwgnVLihWx/+w3bkydRbW1TzccQHx9vnBC0bdu2+MhwTyGEmcqfPz8TJkwAYO7cudy8dYvwRYtIrFoVbUgIHp07o0RFmThKIcQrS0gwTm4e06ULukqVUjV5dh4pW1vbnI5SCCFyhFarZenSpRQqVIgrV67Q7+BBgk6dImTnTti8mZAdO4hp3RpFVXHv0werS5dMHbKwMNIpJZ5Pp8Nl2jQAort3x+DllWL3okWLuHbtGgULFpSrjEIIs9eqVStq165NfHw8I0eOxGBvT+g336D39MT68mXcv/wSdDpThymEeAVOy5djdeMG+kKFiBo2LNV+mUdKCGFpChQowPLly9FqtezatYtNW7eSWKsWtG1LYu3aRMycSULNmmiio/Ho1AlNcLCpQxYWRDqlxHM57NiB9ZUrGNzciO7bN8W+ixcvGou6qVOn4pbGBKJCCGFOFEVh9uzZ2NnZcezYMbZv346hcGFCv/kGg50ddgcP4jJ5sqnDFEJkkvbWLZwXLwYgcsIEVGfnFPtlHikhhKXy8fFh1KhRQPKUBn///ff/77SxIXTVKnQlSmB15w4e3bpBfLyJIhWWRjqlRLqU2Fic584FIGrgQFRXV+M+vV7P8OHD0el0fPLJJzRs2NBUYYo8SqPR0KhRIxo1aoRGI7+qRNYpUaIEwx6Pnpg8eTKPHj0iqVo1wr/6CgCnNWtw2LDBlCEKITLJdcIElPh4EmrVIu6zz1Lsi4+Pp2fPnoSFhVG9enUZ4S1MQuobYUq9e/fmo48+IiEhgV69ehEeHm7cp3p4ELp+PQZXV2z++AO34cNBVU0XrLAY8ptQpMvx66/RBgWhK1qUmE6dUuz75ptvOHv2LM7OzkydOhUlnaVFhcgsOzs7Vq1axapVq7CTSRdFFuvRowdVqlQhPDwcPz8/AOIbNyZy5EgAXMeNw+bIEdDrsTlxArZsSf5frzdl2EKI57D9+WfsfvkF1cqKiOnTU01uPn78eM6dO4ebmxsrV66UeaSESUh9I0xJo9GwcOFCihYtyn///UeXLl1Qn+p40pUpQ+iKFahaLQ67duG0aJEJoxWWItd3SkVHR7No0SI6depE586dWb58OfEZHGqoqirTp0+nVatWnD59OpsjzV00wcE4LVsGQNSoUfBU4Xb79m1mzZoFwNixYylcuLBJYhRCiMyysrJi7ty5aLVa9uzZwy+//AJAdP/+xH7+OYpej0fXrhR6803ytWwJ7dqRr2VLCvn4YHfggImjF0KkEheH6+MO5uiePdGVLZti96ZNm9i8eTMajYbly5dTtGhRU0QphBAm96Rj3sbGht27d7Ps8d98TyS+9x4Rj1ded5k9G7v9+00RprAgub5TatGiRdy+fZtx48YxatQoLl68yMqVKzN07A8//CAjfNLhtHAhmuhoEqtVI65pU+N2VVUZNWoUsbGx+Pj40L59exNGKYQQmVe1alV69uwJwOjRo4mOjgZFIXzOHJJKl0YTF4fm0aMUx2iCgnDv2VM6poQwM85LlmB16xb6woWJHjQoxb6zZ88ab9UbMWIE7733ngkiFEII81G9enUmP55Dc8aMGRw+fDjF/tiOHYnu1g0At4EDsT5/PsdjFJYjV3dK3blzh3PnztG7d2/Kli1LhQoV6Nq1KydOnCA0NPS5x968eZP9+/fTp0+fHIo299Bev47jxo0ARI4bB0/d7/79999z6NAhbG1tmT17ttwLL7JNbGwsXl5eeHl5ERsba+pwRB41dOhQihcvzv3795k5c2byRisrNNHRqMCzly2Ux0PcXfz85FY+IcyE9vp14+juiEmTUB0djfuCg4Pp0aMHiYmJfPrpp/Tr189UYQoBSH0jzEeHDh3o1q0bBoOBL7/8kps3b6bYH+nnR3y9emji4/Ho0gXNvXumCVTkebm6R+HKlSs4OjpSunRp47aqVauiKAqBgYHpHpeQkMBXX31Ft27dZMW4NLjMnImi0xFfrx6JtWsbt4eGhhrnXhk0aBBlypQxVYhCCJEl7O3tjZ1R69at48yZM9j4+6N98CBVh9QTiqpide8eNv7+OReoECJtqorr+PEoiYnE161L/FMLr+h0Onr37s39+/cpXbo0CxYskBHyQgjxmKIoLF26lDfffJPw8HC6d+9OTEzM/zfQaglbtoyk8uXRPniAR5cuKNKRKrKBlakDeBXh4eG4uLik2KbVanFyckqxksCz1q9fT/ny5Xn77bcz/FxJSUkkJSUZv1cUBXt7e+PXWe3JY+Z08WT955/Y//ADqkZD1LhxKZ5/7NixhIaGUrFiRb788sssi81UuZqCJeUKr5bv08coimL2PzNLOrd5Ldf333+fVq1asX37doYOHcrxDI6k0D58mGd+Bk/ktXMrXiwn65vseH3Z/fgjdocOodrYEDl1KspTI7inT5/OyZMncXR0ZM2aNalqxuxmSe8nyfXlj3/ytbn/zOTc5l2KomBra8vq1atp0KABFy9eZMiQIaxcufL/fwYuLoRt2EC+hg2xCQjAfcAAwr7+OsWdNLmBpZ3b3MYsO6U2bdrEnj17nttmwYIFmXrsP/74g4CAAGbPnv1Sx33//ffs3LnT+H3JkiWZNWsWBQoUyFQcGeXp6Zmtj5+CqsLjCcyVzp0p8MEHxl3fffcde/fuRavVsnHjRooVK5blT5+juZqYJeUKmcv36Ss1np6eOD51O4Y5s6Rzm5dyXbFiBUePHuXatWvsOnmSjNzY7V6pEuTRhR7y0rkVz2eK+ibLXl8xMTBpEgDKiBEUfGp094YNG4xzjK5fv573338/a54zEyzp/SS5vpjUN+bPknIFeO211/j++++pW7cu+/fvZ8OGDYwaNer/GxQuDHv2QL162P34I4WXLIEZM0wX8CuwtHObWyjq02tAmonIyEiioqKe26ZQoUIcOXKEjRs38s033xi36/V62rdvz5AhQ6hRo0aq49atW8ePP/6YopfUYDCgKAoVK1Zk4sSJaT5felcSHz16hE6ne8kMX0xRFDw9PQkKCiKnTpHtTz/h0bUrqp0dD48dw1CkCAAhISHUrVuXkJAQBg4cyMjHS6ZnFVPkaiqWlCu8Wr6xsbHGW0QDAwNxcHDIjhCzjCWd27ya66+//krHjh3RAlH58mEXGmqcQ+ppKmAoUoSH/v6g1eZ4nNkpr55bU7Cyssr2C1dZISfrm6x+fTlPm4bT0qXoihbl0e+/w+PPiT/++IOWLVuSmJhI//79GT169Cs/V2ZY0vtJcs04qW/MlyXlCqnz/fbbbxkxYgSKovDtt9/ywVMDFADsv/sOt/79AQhfuJC4Vq1MEXamWNq5zU7ZUd+Y5UgpFxeXDA2xLleuHDExMVy/fp1SpUoBEBAQgKqq6c531KxZM+rVq5di27Bhw+jUqRNvvfVWus9lbW2NtbV1mvuy84WtqmrOvHF0OpynTwcgukcP9IULJ4+cInlVqpCQECpUqMDAgQOzLZ4cy9UMWFKukLl8n26fm35euSnWV5XXcv3www/x9fVlx44dDNFqWQaoipJmx1R09+6oGo3x92Rek9fOrUifKeqbrHh9WV29iuPjkVARkyej2tuDqnL37l26du1KYmIin3zyCSNGjDD5a9mS3k+Sa8aOe9XHMIXcFOursqRc4f/zbd++PefPn2fTpk306dOHffv2pfibOrZFC7RXr+K8aBGuw4ejK1aMRB8fE0b+8izt3OYWuetm0Gd4e3vz2muvsXLlSgIDA7l06RJr166lVq1aeHh4AMmTcw8aNMg48bmbmxvFihVL8Q8gf/78FCxY0GS5mJrD1q1YBwai9/Ag+ssvjdv37dvH/v370Wq1LFy4EFtbWxNGKYQQ2WvSpEl4enqy4uFDVnz4IYZnhnmrNjYogNOqVbIKjRCmoqq4jh2bvCjLRx+R8PHHQPJtUZ07dyY4OJhKlSqxaNEiWSVYCCFewtSpU3n77beJjIykU6dOqVa0jxo+nLhGjVCSknDv1g3tf/+ZKFKRl+T6T+oBAwZQpEgRJk+ezIwZMyhfvjy9evUy7tfpdNy7d4+EhAQTRmnelJgYnOfNAyB68GDUx6PUgoODGTNmDAD9+/enatWqJotRWB6NRkO9evWoV6+e/FEhcoyrqytz5swBoO9vv7F30SJCdu6EzZsJ2bmToD/+IKlsWbRBQeTr2BHlBbeaCyGynt3evdgeP45qZ0fE5MlA8lQMAwcO5N9//yV//vysW7cu18zVIyyL1DfCnNnY2LB69WqKFi3KzZs36d69e8q/ozUawr/6isRq1dCGheHRqRNKZKTpAhZ5glnevvcynJycGDhwYLr7CxYsyPbt25/7GC/an9c5rlqF9uFDdCVKEPPFF8btY8aMMa6297yfsRDZwc7Ojo0bN5o6DGGB6tWrR+vWrdm2bRtDhg/n119/JV/p0iTev4+qqoRu3Ej+pk2xvngR9549Cd2wAdK5/UkIkbWUqChcH09uHtWvH/rHI97nzJnDjz/+aPyDysvLy5RhCpEuqW+EucufPz8bNmygadOm+Pv7M2LECBYuXGick1m1tyf0m28o0KgR1lev4t67d3ItZJXruxaEiUj3vIXTPHqE07JlAESOGgU2NgDs3buXH374ASsrKxYuXIjN4+1CCGEJ/Pz8KFy4MDdv3mTmzJkp9umLFiV0/XoMDg7YHTmC24gReXZuKSHMjfO8eWgfPEBXogTRfZLXydyxYweLFi0CYPbs2bz99tumDFEIIXK9cuXKsXLlSrRaLTt37jT+jn3C4OlJ6Lp1GOztsTt8GJd0FgsTIiOkU8rCOc+fjyY2lsTXXye+cWMAHj58yNixY4Hk2/aqVKliyhCFECLHubq6MnfuXABWr17N4cOHU+xPqlaNsBUrUDUaHLZvx2nBAlOEKYRFsbp4Ece1awGImDYN7Ow4evQow4YNA6Bv3774+vqaMkQhhMgz3n//faZOnQokd/jv3bs3xf6kqlUJX7wYAKdvvsFh3bqcDlHkEdIpZcG0gYE4bNoEQOTYsaAoqKrK0KFDCQ0NpVKlSgwYMMDEUQpL9WTJ5DJlyhAbG2vqcIQFqlu3Lm3btgWgc+fORD0zf1TChx8SMWMGAC7z5mG/bVuOxyiExVBVXMeMQdHriWvYkIS6dbl48SI9evRAp9Px2WefMWrUKFNHKcQLSX0jcpOOHTvSvXt3AAYPHswff/yRYn/8p58SOXo0AK4TJmD7zEU8ITJCOqUsmMvMmSh6PfEffURizZoAbNiwgYMHD2Jra8uSJUvktj1hUnFxccTFxZk6DGHB/Pz8jJN9jh8/PtX+2C++IKpfPwDcRozA9siRnA5RCItgv3MntqdPY7C3J2LiRO7du8cXX3xBVFQUNWvWZMGCBTJptMg1pL4RucmECROoX78+8fHxdOrUybiq/RPRffsS27Ilil6Pe69eWF29aqJIRW4ln94WyubMGex//BFVoyHy8Qp7gYGBTH68is3o0aMpX768KUMUQgiTc3Z2Ni4rv337dvbt25eqTdTIkcQ2b46i0+HeowdW//5rgkiFyLuUiAhcHt9CEj14MOHOznTs2JGgoCDKli3L6tWrsbW1NXGUQgiRN2m1WpYvX87rr79OeHg47dq1Iygo6P8bKArhs2eTUKMGmqgoPDp1QhMaarqARa4jnVKWSFVxmTIFgNi2bdGVK0dSUhIDBgwgPj6ed999l27dupk4SCGEMA8+Pj6Mfjw0fdSoUdy7dy9lA42G8HnzSKhZE010NPk6dEDzbBshRKa5zJ6NNjiYpLJlCenYkZ49e3Lx4kUKFizIt99+i5ubm6lDFEKIPM3BwYENGzZQqlQp7t69yxdffEFERMT/N7C1JWzNGnTFimH133+4d+8OCQmmC1jkKtIpZYHsfvwRmz//xGBvT9SQIQAsXLiQ8+fP4+rqKkPghRDiGX5+flSvXp3w8HAGDx6MwWBI2cDWltDVq0kqVw5tUBD5OnZEiYw0TbBC5CHW//yDw4YNAIROmkS/IUM4evSo8Q8kb29vE0cohBCWwcPDg02bNlGwYEEuXrxIt27diI+PN+43eHgkr07s7Iytvz9uo0bJ6sQiQ6TnwdIkJeEyfToAMb16YfD05MyZM8ZlPmfOnEmRIkVMGaEQQpgda2trFi9ejL29PceOHWP16tWp2qhuboRu3Ii+YEGsL17Eo2dPSEw0QbRC5BEGA66jR6MYDMR+9hmD9u7lwIED2NjYsGbNGqpWrWrqCIUQwqIUK1aMjRs34uTkxMmTJ+nfvz96vd64X1euXMrViZcvN2G0IreQTikL47BpE1Y3bqDPl4/oPn2Ijo5m4MCBGAwGWrRoQdOmTU0dohBCmKUyZcrg5+cHwIwZM7h48WKqNnpvb0I3bMDg4IDt0aO4jRghVwmFyCSHrVuxOXsWg5MTE52c2Lp1KxqNhqVLl/Lee++ZOjwhhLBIVapUYc2aNdjY2HDgwAGGDRuWYgR5Qt26RDyep9h5+nTsfvrJVKGKXEI6pSyIEh2N8/z5AEQNGYLB0ZHRo0fz33//4eXlxbRp00wcoRD/T1EUatasSc2aNVEUxdThCAHAF198Qf369UlMTKRfv34phq0/kVS1KmErV6JqtTjs2GH8vSuEyDglNBTnxyO7f3j7beZs2gTA3LlzadiwoSlDE+KVSH0j8oJ3332XpUuXotVq2b59O+PGjUN96iJcbJcuxHTqhKKquPXrh1VAgAmjFeZOOqUsiNPy5WhDQtCVLEls+/bs2LGDXbt2odFoWLx4MS4uLqYOUQgje3t7du7cyc6dO7G3tzd1OEIAyX9MzJs3j/z583Pp0iWmPF404lkJ9eoR8fgPauf587Hfti0nwxQi13OZORNtWBgPChWi+e+/AzBx4kRat25t4siEeDVS34i8omHDhixcuBBFUVi/fj1Tp05N0TEVMXky8e+/jyYujnydOqF5esU+IZ4inVIWQvPgAY4rVwIQOWYMgf/9x5gxYwAYOnQoPj4+pgxPCCFyjfz587NgwQIA1q1bx4EDB9JsF/vFF0T17w+A24gR2B4+nGMxCpGbWf/1Fw6bNwPQ8sED9MDgwYPp0aOHaQMTQgiRQosWLZg1axYAK1asYP7To8OtrAhbvpykMmXQBgXh0bUrSlyciSIV5kw6pSyE87x5aOLiSHzzTcI/+IDevXsTFxdH7dq16f/4jyYhhBAZU69ePb788ksguWP/1q1babaLGjmS2BYtUHQ63Hv0kOHrQryIXo/rmDEoqsp64BjQq1cvhg4daurIhBBCpKF9+/ZMmjQJgPnz57NkyRLjPtXVldD169G7u2Nz/jxugwbBsysYC4snnVIWwOrqVRy2bAEgcvx4Jk+ZwsWLF8mXLx+LFy9Gq9WaOEIhUouNjaVq1apUrVqV2NhYU4cjRCojRozgzTffJDIyki+//JLEtFbaUxTC580joVYtNDExycPX797N+WCFyCUcNm7E5p9/CAdGAD179mT8+PEy947IM6S+EXlR9+7dGTVqFJC8GMzChQuN+/QlShC2Zg2qtTX2+/fjPG+eiaIU5ko6pSyA8/TpKAYDcZ98wvcPH7J+/XoAFi1aRKFChUwcnRDpCw0NJTQ01NRhCJEma2trli1bhpubG2fPnmXmzJlpN7SxIXT1apLKlUMbFES+jh1RIiNzNlghcgFNcDB2U6cCMBb4rEcPJkyYIB1SIs+R+kbkRf3792f48OEAzJkzhzlz5hjnmEr08SH88W1+zgsXYr9rl8niFOZHOqXyOJtTp7D/+WdUrZYrnTsbf1F8+eWX1K1b17TBCSFELuft7W2cP2HlypX88ssvabZTXV0J/fZb9IUKYX3pEh49ekBaI6uEsGBBnTphFxfHn0BS1674+flJh5QQQuQigwYNYty4cQAsXLiQ6dOnGzum4lq3JqpvXwDchg3D+o8/TBanMC/SKZWXqSouj684RrVuTacZM4iIiOCNN95gxIgRJg5OCCHyhgYNGtCtWzcguRi7m87teXovL0I2bMDg4IDtsWO4DR8OT61SI4SlUlWVXUOH8tq5cwD80qwZfpMnS4eUEELkQn369DHOMbVs2TL8/PwwPJ5HKmrUKOI++QQlIQGPbt3Q3r5tylCFmZBOqTzMbv9+bM6exeDgwOj4eM6fP4+bmxvLly/H2tra1OEJIUSeMW7cOKpXr054eDi9e/cmISEhzXa6KlUIW7UKVavFYedOnOfOzeFIhTAvBoOByRMmUGfrVgD8q1Wjw5Il0iElhBC5WPfu3ZkxYwYAa9asYfDgwSQlJYFGQ/iiRSRVrow2OBiPzp1RoqJMHK0wNemUyqsSE3F5PL+Jf506LNu1C0VRWLZsGd7e3iYOTggh8hYbGxuWL1+Om5sbf/31FxMnTky3bcIHHxDx+Pez88KFxoUohLA0Op2OYcOGYb92LdWBWHt7im/aJB1SQgiRB3Ts2JEFCxag1WrZuXMnXbt2JTY2FtXRkZB164xTGrj37Qt6vanDFSYknVJ5lOO332J18yYJ7u40/v13AIYNG8b7779v4siEECJvKl68OIsXL0ZRFDZs2MC2bdvSbRvbrh1RAwcC4DpyJLaHDuVQlEKYh5iYGLp06cKRbduY/Hhb4qRJGDw8TBqXEEKIrNOqVSvWrl2LnZ0dBw8epFWrVoSGhmIoUoTQtWtR7eyw++03XKZMMXWowoSkUyoPUiIjcVqwAICJQGhiIvXr12fAgAEmjUuIl6EoCtWrV6d69epy1VzkGvXq1WPo0KEAjB49mr///jvdtlHDhxP7+ecoej3uPXtiFRCQU2EKYVJBQUG0aNGCgwcPMl+jwQVIfP11Ytu2NXVoQmQ7qW+Epalfvz7btm0zrlbcrFkzbt26RdJrrxG2cCEATl9/jcO335o2UGEy0imVBzktW4Y2NJRbDg7MCQujePHifPXVV2g0crpF7mFvb8+BAwc4cOAA9vb2pg5HiAwbOHAgH330EQkJCfTo0SP9Zb8VhfC5c0moVQtNTAz5OnZEm84k6ULkFf/88w+NGzcmICCAz1xcaGMwoCoKEdOng9QpwgJIfSMs0VtvvcXu3bspUqQI165do1GjRpw+fZr4Jk2IfLw6vOvYsdgcO2biSPMGnU5n6hBeinz65zGae/dw+vprAPrHxmJtZ8eqVatwc3MzbWBCCGEhNBoNixYtomTJkty5c4cvv/wSfXpzJdjYELp6NUnly6N98ACPDh1QIiJyNmAhcsjhw4epXbs29+7do0KpUmx+fKtebMeOJFWrZuLohBBCZKeyZcuyd+9eqlatSmhoKK1atWLbtm1EDxxIbIsWKDodHj17or12zdSh5mpbt26lQYMGROSiejLXd0pFR0ezaNEiOnXqROfOnVm+fDnx8fEvPO7KlStMmjSJDh060KlTJ/z8/EhMTMyBiLOX87x5KPHxHAX2ArNnz6ZKlSqmDksIISyKi4sLq1evxt7enqNHjzJ16tR026quroRu3Ije0xPry5fx6N4d8sDnkRBPMxgMzJkzh6ioKGrWrMnvzZrhcPMm+nz5iBwxwtThCSGEyAGFCxfm+++/p1GjRiQlJTFkyBCmTptGyMyZJL75JpqICPJ16oQSFmbqUHOdxMRERo8ezdChQ7l06RIbN240dUgZlus7pRYtWsTt27cZN24co0aN4uLFi6xcufK5x1y5coVp06ZRvXp1pk+fzowZM2jQoEGuv6/b6uJFHLZvB2A40K9fPz7//HPTBiVEJsXFxeHj44OPjw9xcXGmDkeIl1ahQgUWPJ7fb9WqVWzevDndtnovL0LWr8fg6IjtiRO4DR0KqppToQqR7TSqytbevdnw6afsbteOgsuXAxA5bhyqjOYWFkTqG2Hp7O3tWbFiBYMHDwZg+fLldOrVi2tz56Lz9sbqxg08evaEpCQTR5p7PHz4kFatWrFhwwYURWH48OF8+eWXpg4rw3J1p9SdO3c4d+4cvXv3pmzZslSoUIGuXbty4sSJ9OfwANavX8+nn35Ks2bNKFq0KEWKFKFWrVpYW1vnYPRZz8bPD8VgYAfg8tFHjBw50tQhCZFpqqpy584d7ty5gyp/nItcqkmTJgwbNgxInvj8+PHj6bbVValC2KpVqFotDrt24TxnTk6FKUS2sjtwgEI+PpTt2ZMOP/5Iwf790cTFkVSmDHG+vqYOT4gcJfWNEMlTHQwbNoxly5ZhZ2fH77//zkdffMHxkSONF+hcx4yRC3QZcObMGT799FPOnDmDi4sL69atY9CgQblqPuncE2karly5gqOjI6VLlzZuq1q1KoqiEBgYmOYxERERXL16FVdXV8aNG0ePHj3w8/Pj0qVLORV2tlAPHsTt+HGSgDWlSrFkyZJc9UIUQoi8atCgQTRr1gydTkfPnj25fv16um0T6tYlYtYsAJy/+gqH54yuEiI3sDtwAPeePdHcv59iuwpYBQZi9+OPpglMCCGEyX322Wfs37+fkiVLcvfuXT4aMoTtzZujajQ4bt6M46pVpg7RbOn1er766is+//xzgoKCKFeuHD/88AP169c3dWgvzcrUAbyK8PBwXFxcUmzTarU4OTkRHh6e5jEPHjwAYMeOHXTo0IESJUpw+PBhJk+ezLx58yhcuHCaxyUlJZH01BBCRVGMK2Zkx21/Tx4zI49t0OmI7dcPgPW2tkzavBlnZ+csjym7vEyuuZ0l5Qqvlu/TxyiKYvY/M0s6t5aUK7x6voqiMG/ePG7dusVff/1F586d2bdvX7oLUMS1a4f2zh2cFy7EddQoDEWKkPDBB5kN/6Vjffp/kfdla32j1+M6YQKoKs8+kgKoioKrnx8Jn3wCWu2rPZcZsqT3k+T68sc/+drcf2ZybvMuc8m3UqVK/PTTTwwdOpT9+/fT9ttvCS9fnt6XL+MyZQr60qVJ+OijV3oOc8k1qwQFBdG/f3/jCPwWLVowc+ZMnJycTBxZ5phlp9SmTZvYs2fPc9s8mafjZT0ZJlu/fn0+eFzklyxZkoCAAH7//XfatWuX5nHff/89O3fuNH5fsmRJZs2aRYECBTIVR0Z5enq+sM2mJk1oHxFBFFB52zZ8fHyyNabskpFc8wpLyhUyl29MTEyK4x0dHbMypGxjSefWknKFV8/3hx9+oEaNGly7do0BAwZw4MCB9G8bnz8fQkJQNm7Eo1cvOHIEXn/9lZ7/ZVjaubVk2VrfHDoEz4yQepqiqmjv3aNwYCDUrfvqz2emLOn9JLm+mNQ35s+ScgXzyLdw4cLs3buXxYsXM3z4cPpcvoyDnR0d4+Px6NsXjh+HLFil1RxyfVXfffcdvXv3Jjg4GEdHR5YuXUrHjh1zdYebWXZKNWnShLovKE4KFSqEm5sbkZGRKbbr9Xqio6PTvQLt7u4OgLe3d4rtXl5eBAcHp/t8zZs3p3Hjxsbvn5z0R48eodPpnhtrZiiKgqenJ0FBQc+933zdypW03r8fgICGDSlRowb3n1MAmqOM5poXWFKu8Gr5xsbGGr8OCgrCwcEhq8PLUpZ0bi0pV8jafNeuXctnn33Gr7/+Svv27Vm4cGH6RcSUKXjcuIHtsWPoP/2U4H37MDzz2ZXVLO3cZicrK6tsv3CVFbKzvrH791/cM9Au7N9/iS9f/pWeyxxZ0vtJcs04qW/MlyXlCuaZr6+vL5UrV6Z///50u3gRL+DD6GiSPv2U0B9/xJDJz1VzzPVlhYSEMGbMGPbt2wdA5cqVWb58OWXKlCEoKCjH4siO+sYsO6VcXFxS3ZaXlnLlyhETE8P169cpVaoUAAEBAaiqSpkyZdI8pkCBAri7u3Pv3r0U2+/fv89rr72W7nNZW1une0U7O1/Yqqqm+/gHDhzg4aRJlAIinZwo8dVXufZNBs/PNa+xpFwhc/k+3T43/bxyU6yvypJyhazJt1KlSqxYsYIuXbqwY8cOChUqxOjRo9NubG1N6Ndfk795c6wvXcLjiy8I3r0b1dX1lWLICEs7t5YsO+sbfcGCGW6Xl19vlvR+klwzdtyrPoYp5KZYX5Ul5Qrml2/FihXZv38/c+bMwXfFCk4C5e/dg2bNMPzyC8rjW8wzw9xyzQhVVdm3bx9jx44lNDQUrVZL3759GTRoELa2trkun7Tk6pmwvb29ee2111i5ciWBgYFcunSJtWvXUqtWLTw8PAAIDQ1l0KBBxonPFUWhadOm/Pjjj5w6dYqgoCC2bt3K3bt3qVevninTeSlnzpxhXL9+jHv8vX7CBFQzv9IixMtQFIVy5cpRrly5XD0cVYhnffjhh8x5vLLekiVLWLt2bbptVRcXQjZsQO/pifWVK3h06wYJCTkVqhCvJNHHB33hwqjp/A5XFQVdkSIk5tJpB4TIDKlvhHgxOzs7xo8fz9c7d9LH25tQoNCNG1yuXZs7t2+bOrwcExgYSLt27ejTpw+hoaHGDruRI0dia2tr6vCyTK7ulAIYMGAARYoUYfLkycyYMYPy5cvTq1cv436dTse9e/dIeKqIb9SoEc2bN2f9+vUMHz6cgIAAxo8fn2vuMb169SqdO3dmUEIC+YDEsmWJa93a1GEJkaXs7e35/fff+f33342T7gqRV7Ru3ZoRI0YAMGHCBONQ7LQYvLwI2bABg5MTtidP4jZsmCyRLHIHrZaIyZMBUnVMPfk+ctKkPDnJuRDpkfpGiIyrWbMmqw8fZsvnn5ME1HvwgJ/efZfZs2cTHR1t6vCyTVRUFJMnT+bDDz/kyJEj2NraMmTIEA4cOEC1LJhby9yY5e17L8PJyYmBAwemu79gwYJs37491fZmzZrRrFmzbIwse/z333+0adMG5/BwBisKqCpRY8eCVa4/lUIIYVEGDBjAgwcPWL9+PQMGDMDNzY06deqk2VZXuTJhq1bh0aEDDrt2off2JmrkyByOWIiXF9+wIWGrVuE6YQLap+a81BcuTOSkScQ3bGjC6IQQQpg7Ozs7mi9axPWSJSk/dy4TdDpaffUVtTdtYtiwYbRt2xarPPK3cHx8PBs3bmTJkiXG+a4/+ugjJk6cSIkSJUwbXDbK9SOlLMm9e/do3bo1QUFBfOXigq2qklCzJgn165s6NCGEEC9JURSmTJlCw4YNSUxMpEuXLpw5cybd9gnvv0/47NkAOC9ahMOmTTkVqhCvJL5hQx74+xOycyds3kzIzp08PHVKOqSEEEJkmPPgwUT17AnABkWhZHAwo0aNom7dumzbto2kpCQTR5h5SUlJbNq0iXfffZeJEycSHBxMiRIlWL9+PevWrcvTHVIgnVK5RnBwMG3btuX27dt8WqQIzaKiAIgcOxbkfnSRB8XFxfHBBx/wwQcfEBcXZ+pwhMgWWq2WJUuW8P777xMXF8cXX3zBuXPn0m0f16YNUYMHA+A6ejS2Bw/mUKRCvCKtlsRataBt2+T/5ZY9YaGkvhEi86LGjSO+fn3sVJWDTk5UdnHhxo0bDBkyhDp16rBhwwbi4+NNHWaGRUdHs2rVKmrXrs2IESO4f/8+np6ezJo1i0OHDlHfQgafSKdULhAREUG7du0IDAykSJEibC5WDEVViWvalKTXXzd1eEJkC1VVuXLlCleuXMkTq0oIkR5bW1vWrFlDzZo1iY6Opn379ly4cCHd9lFDhxLr64ui1+PeqxfW//yTg9EKIYR4FVLfCPEKtFrCli4lqWJFnKKj+aNIESYNH07+/Pm5ffs2o0eP5q233mLq1KncvHnT1NGmKzAwkClTpvD2228zadIk7t69S/78+Zk4cSLHjx/niy++SHdl3LxIOqXMXFhYGG3btuXChQsUKFCAn4YOxe3UKVRrayJlPhEhhMgT7O3tWbduHW+++Sbh4eG0bduWK1eupN1YUQifPZuEOnXQxMbi0bEj2jt3cjZgIYQQQggTUJ2cCF23Dn3+/NhdusTw8+c5dfw4U6ZMwcvLi7CwMJYvX867775L27Zt2bZtG5GRkaYOm4iICLZt20azZs14//33WbFiBZGRkZQuXZrZs2dz6tQpevTogZ2dnalDzXHSKWXGgoODadWqFefPn8fDw4MtmzZR4fHS4TEdO6LP4/eWCiGEJXFycmLjxo1UrVqVkJAQWrZsyb///pt2YxsbQletIqliRbQPH+LxxRco4eE5Gq8QQgghhCnovb0JXbsW1dYW+59/ptDChXTt2pWTJ0/yzTffULduXVRV5ciRIwwZMoTq1avTvHlzNm/ezP2nFt3Ibvfv32fTpk188cUXVK9enSFDhnDmzBk0Gg3169fnm2++4dChQ7Rv396iV+PMG9PU50HBwcHGWzjy58/Ptm3beP3CBawvXMDg7Ez0oEGmDlEIIUQWc3V1ZfPmzbRt25aAgAB8fX3ZtGkTr732Wqq2qosLIRs2UKBJE6yvXsWje3dCNm0CW9ucDzyXMhgMaDRyfU4IIYTIbZLefJPw+fNx79sXp+XLSSpThrg2bfj444/5+OOPuXnzJt9//z179uzh6tWr7N69m927dwNQsWJFfHx8eP3113n99dcpVaoUyivO06zT6bh+/ToBAQH4+/tz/Phxbty4kaJNuXLlaNGiBb6+vnh6er7S8+Ul0illhh4+fEjr1q25cuUKBQsWZPv27ZQtWhTnTp0AiO7XD4OHh4mjFEIIkR08PDzYvn07X3zxBX/99RetW7dm48aN1KhRI1VbQ5EihGzYQP4WLbA9eRK3oUMJX7QIpKPlhS5fvkzfvn1Zvnw5ZcuWNXU4QgghhHhJcc2aYRUYiPOCBbiNHIm+eHESa9YEoESJEgwePJhBgwZx8eJFjh07xr59+zh79iwXL17k4sWLrFu3DgAHBwdKlSpFqVKlKF68OPnz5ydfvnx4eHhgY2ODlZUVWq2WpKQkoqKiiI6OJjQ0lDt37nDnzh1u377NlStXUk2yrtFoqFatGp988gmffvopZcqUyekfUa4gnVJmaPHixVy5cgUvLy+2bt1KqVKlcFyxAqs7d9B7ehLdrZupQxRCCJGNXF1d2bJlC507d+bkyZO0a9eOtWvX8t5776Vqq6tcmbCvv8ajQwccvv8evZcXUaNHmyDq3OPMmTN07tyZ8PBwJk6cyKZNm0wdkhBCCCEyIWroUKyuXcN+7148unfn0f796EuWNO5XFIXKlStTv359evXqRUhICMeOHeOvv/7i7NmzBAQEEBsbS0BAAAEBAa8Ui6OjI5UqVaJ69erUrl0bHx8fXF1dXzXFPE86pczQ2LFjiYuLY8qUKTg4OEBoKM6LFgEQOWIEWPD9psJyKIqCt7e38WshLM2TOaZ69OjB77//TqdOnViwYAHNmjVL1TbhvfcInz0b9yFDcF6yBL23N7EdOuR80LnAr7/+Sq9evYiPj+fNN99kyZIlpg5JCGFBpL4RIospCmHz56O9fRubs2fx6NSJ4L17Ud3c0mzu4eFB06ZNadq0KQBJSUn8999/XL9+nevXr3P79m1CQkIIDQ0lNDSUpKQkdDoder0ea2trnJ2dcXR0xM3NjSJFiuDt7Y23tzfly5enRIkSMi1AJkinlBmys7Nj3rx5FC5cmPv37+O8ZAmaiAiSKlQgrmVLU4cnRI6wt7fH39/f1GEIYVL29vasWbOG/v3788MPP9C3b1/u3r3Ll19+meqPmbjWrdHevYvLvHm4jhmDvkgREj780ESRm6ft27czbNgw9Ho99erVY9WqVRY9sagQIudJfSNENrC3J3TNGgo0aoT1tWt49O5NyMaNYG39wkOtra0pU6aM3FpnQtKNZ+a0t2/j+HjFvcixY0GrNXFEQgghcpKtrS0rVqygR48eAEyfPp0xY8ag0+lStY0ePJjYVq1QDAbce/fG+u+/czpcs2QwGJgxYwaDBw9Gr9fTsmVL1q5dKx1SQgghRB5hKFSIkHXrMDg4YHv0KK4TJoCqmjoskQHSKWXmnGbPRklMJKF2bRI++MDU4QghhDABjUbDxIkTmThxIoqisGHDBrp06UJERETKhopC+OzZxL/3HprYWDw6dUJ7+7ZpgjYTsbGx9OzZ03ibXv/+/VmwYAHWGbh6KoQQQojcQ1elCmFLl6IqCo4bNuD4zTeg12Nz4gRs2ZL8v15v6jDFM6RTyhw9eePMmIHDd98BEDluHMh958KCxMXF0bBhQxo2bEhcXJypwxHCLPTo0YOVK1diZ2fHwYMHadSoEVevXk3ZyNqasFWrSKpYEe3Dh3h06IASHm6SeE3t3r17NG/enB9//BEbGxu++uorRo0aJfM9CCFMRuobIbJXwscfJ99hBLhMmECh114jX8uW0K4d+Vq2pJCPD3YHDpg4SvE0qcrMjN2BAxTy8Ul+44wZA4DBzg7tnTsmjkyInKWqKufPn+f8+fOoMvRWCKNGjRqxe/duihQpwo0bN2jcuDH/+9//UrRRnZ0J2bABvacn1lev4tGtGyQkmChi0zhy5AiffPIJAQEBeHh4sH37dlrKvIxCCBOT+kaI7BfTuzfxtWujqCqa0NAU+zRBQbj37CkdU2ZEOqXMiN2BA7j37Inm/v0U25X4eHnjCCGEMKpatSo//vgjNWvWJDo6mq5duzJjxgySkpKMbQxFihDy7bcYnJ2xPXUKt8GDwWAwYdQ5w2AwsGDBAtq1a0dISAiVKlXihx9+4O233zZ1aEIIIYTICQYD1teuoQLP3mukPO4MdvHzk1v5zIR0SpkLvd44GVuqN87j/+WNI4QQ4on8+fOzZcsWunXrBsCSJUto3rw5//33n7GNrmJFQletQrWywmHPHpxnzTJVuDkiODiYDh06MHfuXFRVpV27duzdu5dixYqZOjQhhBBC5BAbf3+0QUGp/q5+QlFVrO7dw0ZWwjQL0illJmz8/dHevy9vHCGEEBlmbW3N5MmTWblyJa6urpw9e5aPP/6Y77//3tgm8b33CJ8zBwDnJUtw2LDBVOFmq//973/Uq1ePQ4cOYWdnx8KFC5kzZ46ssCeEEEJYGO3Dh1naTmQv6ZQyE/LGEUIIkVmNGzfml19+oUaNGkRHR9OvXz969uzJw8efGXGtWhE5bBgArmPHYvvLL6YMN0tFRUUxePBgunbtSkhICBUrVuSHH37A19fX1KEJIYQQwgT0BQtmaTuRvaRTykzIG0cIIcSr8PLyYseOHQwZMgStVssPP/zABx98wI4dO1BVlehBg4ht3RrFYMC9Tx+sz583dcivRFVV9u/fT926ddm+fTuKotC3b19++OEHKlSoYOrwhBBCCGEiiT4+6AsXRk1n9XpVUdAVKUKij08ORybSIp1SZkLeOEKk5uHhgYeHh6nDECLXsLKyYujQoRw4cIAqVaoQHh7OoEGDaNWqFRcvXSJ81izi338fTVwcHh07or11y9QhZ8qtW7fo2LEjvXr1IigoiBIlSrBr1y7GjBmDra2tqcMTQojnkvpGiGym1RIxeTJAqr+vn3wfOWkSaLU5HppITTqlzIW8cYRIwcHBgX/++Yd//vkHBwcHU4cjRK5SpUoV9u/fb+ykOXHiBB9//DFj/Py4PnMmSZUqoQ0OxqNDB5SwMFOHm2Hh4eFMnTqVunXrcvDgQWxsbBg8eDC//vorNWrUMHV4QgjxQlLfCJEz4hs2JGzVKgyenim26wsXJmzVKuIbNjRRZOJZ0illRuSNI4QQIqtYW1vTt29fDh06RMOGDTEYDKxfv56aDRow4913SfL0xDowEI/u3SEhwdThPldcXBwrV66kdu3aLF++nISEBN59911++eUXhg0bJpOZCyGEECKV+IYNeeDvT8jOnbB5MyE7d/Lw1Cn5u9rMWJk6AJFSfMOGxDdogO3p0+RLTCTExoaEGjVkhJQQQohMKVasGF9//TUnTpzAz8+Pf//9F79Vq/jJyYnfbGywP3UKt8GDCf/qK2z++AMSE7Exk8+eyMhI1q9fz+rVqwkODgagfPnyjB07lnr16qGkc8u7EEIIIQQAWi2JtWpB4cIk3r8PqmrqiMQzcn2nVHR0NGvXruXPP/9EURR8fHzo0qULdnZ26R4THh7Oxo0b+fvvv4mPj6dIkSI0b96cd955Jwcjfw554whBXFwcHTp0AGDjxo0yEkKIV1SrVi3+97//sX//fhYsWMDJK1doDPwEOOzZg83PP2MVFwdAPpJH6UZMnmySq4mBgYFs2rSJLVu2EBUVBYC3tzcDBw6kVatWWFnl+vJFCGGhpL4RQoiUcn1Vt2jRIsLCwhg3bhx6vZ5ly5axcuVKBg4cmO4xS5YsISYmhpEjR+Ls7MyxY8dYsGABM2fOpGTJkjkYvRAiPaqqcvLkSePXQohXp9FoaNq0KY0aNWLfvn0sW7aM5RcuMACMHVLGtkFBuPfsmWO3j0dGRvLzzz+zdetW43sfoFy5cvTt25fPPvsMa2vrbI9DCCGyk9Q3QgiRUq6eU+rOnTucO3eO3r17U7ZsWSpUqEDXrl05ceIEoaGh6R53+fJlPv30U8qUKUOhQoX4/PPPcXR05Pr16zkYvRBCCGEaWq2WZs2a8b8DB+ieLx9p/VmkqCqoKg5jx4Jeny1x3Llzhy1bttChQweqVavGwIEDOXnyJBqNho8++oj169fz22+/0bJlS+mQEkIIIYTIg3L1SKkrV67g6OhI6dKljduqVq2KoigEBgamuxJP+fLlOXHiBG+88QYODg6cPHmSpKQkKleunFOhCyGEECZne/o0DiEh6e5XALuHDxn81lsk1qpF9erVee211yhTpgzu7u4vNadTWFgYly9f5tKlS5w/f55Tp05x69atFG3Kli3LZ599RuvWrSlSpEhm0xJCCCGEELlEru6UCg8Px8XFJcU2rVaLk5MT4eHh6R43ePBgFi5cSNeuXdFqtdjY2DBs2DA8n1n17mlJSUkkJSUZv1cUxXgPeHZMtPrkMS1hElfJNe96lXyfPkZRFLP/mVnSubWkXCFv56t9+DBD7TQPH7J79252795t3Obk5ESxYsUoVKgQTk5OODs7Y2dnh16vJzExkcTERIKDg3n48CEPHjxIcwSzVqulevXq1K9fn0aNGlG2bNmsSk1kUE7WN3n5vZQWS8pXcn354598be4/Mzm3eZcl5WtJueZGZtkptWnTJvbs2fPcNgsWLMj042/bto2YmBjGjx+Ps7MzZ86cYcGCBUyePJlixYqlecz333/Pzp07jd+XLFmSWbNmUaBAgUzHkRHP6yjLayTXvCsz+cbExKQ43tHRMStDyjaWdG4tKVfIo/lWqpShZkNmz6ZKYiJnzpzhzz//5M6dO0RHR/Pvv//y77//ZvjpihcvTpUqVahevTrvvfcetWrVwtnZObPRiyxgivomT76XnsOS8pVcX0zqG/NnSbmCZeVrSbnmJmbZKdWkSRPq1q373DaFChXCzc2NyMjIFNv1ej3R0dG4ubmleVxQUBA//fQT8+bNo2jRogCUKFGCS5cu8dNPP9GzZ880j2vevDmNGzc2fv+kl/XRo0fodLoMZpZxiqLg6elJUFBQnp8EUXLNu14l39jYWOPXQUFBODg4ZHV4WcqSzq0l5Qp5PN8yZShYuDCaoKDkOaSeoSoKhsKF8W7blq5aLV27dgWSV4+6c+cOt2/fJiQkhMjISKKjo4mPj8fKygpra2usra3x8PCgUKFCFCxYkKJFi6bqgIqOjiY6OjpHUs1pVlZW2X7hKivkZH2Tp99LabCkfCXXjJP6xnxZUq5gWflaUq7ZLTvqG7PslHJxcUl1W15aypUrR0xMDNevX6dUqVIABAQEoKoqZcqUSfOYxMREIPXQPY1G89wX6JMCOy3Z+cJWVdVi3jiSa96VmXxVVTXeQpKbfl65KdZXZUm5Qh7NV6MhYvJk3Hv2RFWUFB1T6uPPyYhJk1A1Gnhqn52dHWXKlEn3szY9ee7nlweYor7Jk++l57CkfCXXjB0n9Y15s6RcwbLytaRcc5Ncvfqet7c3r732GitXriQwMJBLly6xdu1aatWqhYeHBwChoaEMGjSIwMBAAIoUKYKnpydff/01gYGBBAUFsW/fPv7++2/efvttU6YjhHiKg4MDgYGBBAYGmv1VRCFys/iGDQlbtQrDM0Pa9YULE7ZqFfENG5ooMiGEyHukvhFCiJTMcqTUyxgwYABr1qxh8uTJKIqCj4+P8fYCAJ1Ox71790hISACSh5uNHj2aTZs2MWvWLOLj4/H09KRv37688cYbpkpDCCGEMJn4hg2Jb9AA29OnyZeYSIiNDQk1aoBWa+rQhBBCCCFEHpbrO6WcnJwYOHBguvsLFizI9u3bU2wrXLgww4YNy+7QhBBCiNxDqyWxVi0oXJjE+/dT3K4nhBBCCCFEdsjVt+8JIfKu+Ph4OnToQIcOHYiPjzd1OEIIIYQQr0zqGyGESCnXj5QSQuRNBoOBgwcPGr8WQgghhMjtpL4RQoiUZKSUEEIIIYQQQgghhMhx0iklhBBCCCGEEEIIIXKcdEoJIYQQQgghhBBCiBwnnVJCCCGEEEIIIYQQIsdJp5QQQgghhBBCCCGEyHGy+t4rsrLK3h9hdj++OZFc867M5Gtra8vrr79u/Nra2jqrw8oWlnRuLSlXsKx8LSnX7JLbf4bZGX9u/9m8LEvKV3J9MalvzJ8l5QqWla8l5ZpdsuNnqKiqqmb5owohhBBCCCGEEEII8Rxy+56ZiouLY+TIkcTFxZk6lGwnueZdlpSv5Jp3WVK+lpSryHmW9vqypHwl17zLkvK1pFzBsvK1pFxzI+mUMlOqqnLjxg0sYSCb5Jp3WVK+kmveZUn5WlKuIudZ2uvLkvKVXPMuS8rXknIFy8rXknLNjaRTSgghhBBCCCGEEELkOOmUEkIIIYQQQgghhBA5TjqlzJS1tTUtW7bMNStyvArJNe+ypHwl17zLkvK1pFxFzrO015cl5Su55l2WlK8l5QqWla8l5Zobyep7QgghhBBCCCGEECLHyUgpIYQQQgghhBBCCJHjpFNKCCGEEEIIIYQQQuQ46ZQSQgghhBBCCCGEEDlOOqWEEEIIIYQQQgghRI6zMnUAIqXAwEB27NjB5cuX0ev1FCtWjEaNGlGrVi1Th5Zldu/ezebNmwGYOnUq5cqVM+7bvn07O3fuTPfYJUuWULBgwWyPMTMMBgM///wzv//+O3fv3kWr1VKiRAmaNGnCW2+9lap9bGwsO3bswN/fn/DwcNzd3XnnnXfw9fXFzs7OBBmkduTIES5dusT169e5desWOp2OL7/8krp166Zqe/ToUU6dOsWtW7eIiIhAVVUKFChAtWrVaNq0KR4eHinaP3z4kH79+qX73C1btqRVq1ZZndJzvUy+APfv3+f777/n0qVLhISE4OTkhLe3N59++mma5xySf04HDhzgzp07WFlZUb58eVq1akWpUqWyMbOUQkNDOXnyJGfPnuXu3buEh4fj5ORE+fLl+eyzzyhbtqyxrU6n448//uCPP/7g2rVrBAcHoygK3t7e1K1bl/r166PRpLy+YW7n9mXyBTIU27Jly8ifP3+KbeZwbhMTE9myZQvXr18nKCiI6OhoHBwc8PT0pF69etSpUwcrq///6L958yYnTpzgxo0bXL9+naioKCpVqsTEiRPTfHxzO7ci98jr9U1erW1A6pvcXt9YSm0DllXfWFJtA1Lf5HXSKWVGAgICmDZtGjY2NtSqVQt7e3v8/f1ZuHAhISEhNGnSxNQhvrJbt26xY8cObG1tSUhISLfd+++/T4ECBVJtd3R0zM7wMk1VVRYsWIC/vz+FChWiXr16JCUl8ccffzB79my6du3KJ598YmwfHx/PxIkTuXnzJtWrV6d27drcvHmTffv28e+//zJp0iRsbGxMmFGybdu28ejRI5ydnXF3d+fRo0fptj1+/DhBQUGULVsWNzc3IPkD4ccff+Tw4cNMnjyZokWLpjquePHivP3226m2V65cOcvyyKiXyffq1atMmjQJvV7Pm2++iY+PDxEREZw+fZrZs2fj6+uLr69vimN27drF1q1bKVCgAB999BFxcXGcOHGC8ePHM378eCpUqJDdKQLw448/smfPHgoVKkT16tVxcXHh/v37nDlzhjNnzjBw4EDjH4oPHjxg/vz52NnZUaVKFd58801iY2P5888/Wb16NX/99RcjR45EUZRUz2Mu5/Zl8oXkwiMtQUFBHDt2DG9v71RFm7mc2/j4eH7++WfKlCnD66+/jouLCzExMZw7d47ly5dz4sQJRo8ebSy0T58+ze7du7GysqJw4cJERUVl6HnM5dyK3CGv1zd5tbYBqW8g99c3llLbgGXVN5ZU24DUN3meKsyCTqdT+/Xrp7Zr1069ceOGcXtMTIw6YMAAtW3bturDhw9NF2AWSEpKUkeMGKGOGTNGXbRokerr66tevnw5RZtt27apvr6+akBAgImizJyTJ0+qvr6+6rhx49SEhATj9oiICPXLL79U27Vrpz548MC4/Ume3377bYrH+fbbb1VfX191165dORb785w/f974uvv+++9VX19f9ffff0+z7dN5P+23335TfX191Xnz5qXY/uDBA9XX11ddsmRJlsb8Kl4m3+nTp6u+vr7q6dOnU2x/+PCh2rFjR7V9+/ZqYmKicfu9e/fUNm3aqAMGDFBjYmKM22/cuKG2a9dOHTRokKrX67M+qTScOnVKvXDhQqrt//77r9qmTRu1c+fOxthDQkLUn376SY2Li0vRNi4uTh01apTq6+urnjhxIsU+czu3L5Pv86xZs0b19fVV9+3bl2K7OZ1bvV6vJiUlpdqu0+nUiRMnqr6+vuqff/5p3H7r1i312rVralJSkhoWFqb6+vqqfn5+6T6+uZ1bYf7yen2Tl2sbVZX6RlVzf31jKbWNqlpWfWNJtY2qSn2T18mcUmYiICCABw8eULt2bUqUKGHc7uDgQPPmzdHpdBw+fNh0AWaBXbt2cefOHfr06ZNqOGxud+bMGQCaN2+e4gqgi4sLjRo1IikpiUOHDgHJVx1/++037Ozs+Pzzz1M8zueff46dnR0HDx7Msdifp1q1amle1U1Lelc+33nnHSD5Soy5e5l8Hzx4gKIovP766ym2FyhQgGLFipGYmEh8fLxx+++//45er6dFixY4ODgYt5coUYLatWtz9+5dLl26lDWJvICPjw+VKlVKtb1ixYpUqVKFmJgYbt26BYCHhwcNGjRIdcuFnZ0djRo1AuDff//N/qBfwcvkm57ExESOHj2KlZUV7733Xop95nRuNRpNiuHrT2i1WuOVv6ffi0WLFqVUqVJpHiNEVsjr9U1erm1A6hvI/fWNpdQ2YFn1jSXVNiD1TV6X9z49c6kLFy4AUL169VT7XnvtNcC8fzG+yPXr1/n+++9p2bIl3t7eL2x/8eJFdu/ezd69ezl9+nSKD0BzFB4eDpDmnBBPtgUEBADJ9+qHhYVRvnz5ND8Iy5cvz4MHDwgODs7eoHPIX3/9BZDm0HaAsLAwfvrpJ3bt2sXBgwfNvrh7omjRoqiqytmzZ1NsDw4O5tatWxQvXhxnZ2fj9ifv37Te40+2mcN7XKvVpvj/eZ580KfXNjec24zme/r0aWJiYnjrrbdwcXFJsS83nFuDwcD58+eB9N+LLyM3nFthHvJyfZPXaxuQ+uZ58mJ9k1drG7Cs+sZSahuQ+iavkK5DM/HkBV+4cOFU+9zc3LCzs+P+/fs5HVaWSEpKYunSpZQoUYLPPvssQ8ds3749xfeOjo507tyZ999/PztCfGVPPqAfPnyYqjB9+PAhgPH8Pe9cP9l+/vx5goKCUt3bnRucOHGCO3fukJiYyO3btzl//jwFCxakdevWabb/+++/+fvvv43fK4rCu+++S48ePcxmQtS0tGnThsuXLzN//nzefPNNihQpYpx3oVChQgwaNChF+/v372NnZ2ecj+JpT14Lpv7gCw4O5p9//sHd3Z1ixYq9sP3vv/8OJF+FTYu5n9uXyffJ1f169eql2meO51an07Fr1y4AoqKiCAgI4O7du9StW5eqVau+8uOb+7kV5iOv1jeWUNuA1DdPs4T6Ji/WNmBZ9U1erm1A6pu8SjqlzERsbCxAiuGRT7O3tze2yW22bdvG/fv3mTlz5guHtpcoUYI+ffpQuXJl3NzcCA8PPBsH2wABAABJREFU56+//mLbtm0sW7YMR0fHdFf+MKXXX3+dEydOsGfPHqpUqWIc6h0VFcUPP/wAQExMDJCxc/10u9zm5MmT+Pv7G78vXbo0AwcOTHWV1dbWls8//5y3334bT09PDAYDN27cYOvWrRw9epSEhASGDRuW0+FnmJeXF9OmTWP+/PmcPn3auN3Z2Zm6devi6emZon1sbCyurq5pPpY5nHOdTsfixYtJSkqiffv2L3yv/vrrr5w9e5YqVarwxhtvpNiXG87ty+T78OFDLly4QP78+dMsUM3x3Op0uhSrfSmKQpMmTWjXrt0rPW5uOLfCvOTV+sYSahuQ+uZpllDf5LXaBiyrvsnrtQ1IfZNXSaeUyFZXrlxh3759+Pr6ZujKRI0aNVJ8X7BgQT755BO8vLyYOnUqW7duNcvC7d133+XQoUNcuHCBYcOG8dprr6HT6Thz5ozxCkNaq3fkRUOHDgWSi9Qnv8xHjRrFsGHDqFKlirGdq6trqquLVatWpVy5cowcOZLTp09z/fr1HF9yNqMCAwOZM2cOxYoVY+bMmXh5eREeHs5PP/3EunXruHTpEkOGDDF1mBliMBhYtmwZFy9e5MMPP0w1r8Cz/vzzT9asWUOBAgXo379/qv3mfm5fNt+DBw+iqip169bNNXPG2NnZsX37dgwGA2FhYfz5559s2bKFK1euMHr06HT/aHwRcz+3QuQES6ltQOqbp1lCfZOXahuwrPrGEmobkPomr8o9r8A87skbKL0e57i4uEy/yUxFr9ezdOlSihcvTrNmzV7psapWrUqhQoW4deuWya+4pEWr1TJmzBh8fX1RFIVff/2V06dP8/bbbxs/vJ9cbcjIuX66XW7l6OhIlSpVGDNmDDY2NixZsgSdTvfC42xtbY0fpJcvX87uMDNFp9OxcOFCFEVh2LBhlCpVCltbWwoVKkSnTp14++23OXXqVIoJIB0cHMzynBsMBpYvX86xY8eoU6cOPXr0eG77v/76i/nz5+Pm5saECRNwd3fP8HOZw7l92XwNBgOHDx9GUZQ0h7eD+Z5bSJ4YNF++fHz88cf07NmTy5cvG4e9ZyVzOLfCPOW1+saSahuQ+iYtebW+yUu1DVhWfWNptQ1IfZPXyEgpM/FkOOz9+/dT9cCGh4cTHx9PmTJlTBFapsXHxxvnGUhvSOW4ceMAGDZsWKoric9ydnYmKCiIxMREsyxorK2t8fX1xdfXN8X2J5O8PjmvT5/rtDzZ/uwQ6dzKwcGBsmXLcubMGYKCgjI0GeyTOSwSEhKyO7xMuXfvHg8fPqRGjRrY2tqm2l+5cmXOnDnDzZs3qVChApB8//2VK1cIDw9PdX++qc75k6tqR44coXbt2vTt2/e5V8v++usv5s2bh7OzM35+fhQqVOiln9OU5/Zl8wU4d+4cISEhVK9ePd05UMzx3KYluycmNff3rTCNvFbfWFptA1LfpCev1Td5pbYBy6pvLL22Aalv8gIZKWUmnizp+WT1gKedO3cuRZvcwtramnr16qX578kEeW+99Rb16tVLc1WXp8XHx3Pnzh1sbW1TrPqRGxw9ehSA2rVrA8m/5N3d3bl8+XKqlXfi4+O5fPkyBQsWzJWTgKYnLCwMIMPLsl69ehUgw0sY57QnV0SjoqLS3B8ZGQmkzPd57/En23LyPf50EVOrVi369++foYLNyckJPz+/TBcipjq3L5vvE08mAf3www/TbWNu5zY9oaGhQMZWHsoMc3/fCtPIa/WN1Db/T+qbvFXf5IXaBiyrvpHaJpnUN7mfdEqZiSdDuI8fP87NmzeN22NjY/n++++xsrJ64b3B5sbGxobevXun+a9cuXIANGvWjN69e1OiRAni4uK4d+9eqsdJTExk5cqVxMXFUbNmzWz7hfOq0hrieurUKX7//XdKly6Nj48PkDz3wocffkh8fDzfffddivbfffcd8fHxz/2QMEfpnTtI/uALDAykcOHCKT7ob9y4gaqqqdr7+/tz+PBhHB0def3117Mt5ldRtGhR7O3tuXTpUqoP6+DgYH799VcURUnxYV23bl20Wi27du1K8Vq5efMmx48fx8vLy3jlMbs9GeZ95MgR3nnnnRcWMWfPnmXevHk4Ojri5+eX7spKT5jbuX3ZfJ+IjIzkzz//xMXF5bnzvZjTub1z506aV/ISEhLYsGEDwCv97M3t3Arzl9fqG0urbUDqG0upb3J7bQOWVd9YUm0DUt/kdXL7npnQarX06tWLadOm4efnR61atbC3t8ff359Hjx7RoUOHF15xy+2ioqIYPHgwpUuXxsvLCzc3NyIiIvjnn38ICQmhWLFidOjQwdRhpmvs2LHky5cPLy8vrK2tuXbtGhcuXKBQoUIMGTIkxQdF06ZNOXPmDHv27OHmzZuULFmSGzducP78eUqXLk2jRo1MmMn/++2334xzB9y6dcu47cmQ/QoVKvDhhx8az12pUqXw8vLCw8OD6Ohorl27xo0bN7C3t6dv374pHnv9+vU8ePCAsmXLki9fPuMqF5cuXcLa2pq+ffvm+K0MGc3X2tqaDh06sGrVKqZPn25cNjk8PJzTp08THx9P48aNKVKkiPGxixQpgq+vL1u3bmX48OH4+PgQFxfHiRMnAOjVq1eOTTS5c+dODh8+jJ2dHUWKFEn1xwMkT8xbokQJ7t69y9y5c0lKSqJSpUocO3YsVduCBQtSt25d4/fmdm5fJt+nHT58GL1ez3vvvffcq+DmdG5PnDjBDz/8QIUKFShQoAD29vaEhoZy7tw5oqKiqFixIo0bNza2v3v3Lrt37waS/0h+sm3p0qXGNk+/d83t3ArzZ+n1TW6vbUDqm9xe31hKbQOWVd9YUm0DUt/kddIpZUaqVKnClClT2L59OydOnECv11OsWDHat29PrVq1TB1etnNycuLjjz8mMDCQs2fPEhMTg42NDV5eXnz66ad88sknxqWIzVHNmjU5ffo0V69eRafTUbBgQVq0aEHTpk1T/RKzs7Nj0qRJbN++HX9/fwICAnB3d6dx48b4+vqaTZ6XLl3i8OHDKbZdvnw5xUR/H374IS4uLnz++edcuHCBv//+m6ioKKysrChYsCCNGjWicePG5MuXL8Xj1KlTB39/f65evcpff/2Fqqp4eHhQr149mjRpgpeXV47k+LSM5gtQv359ChYsyIEDB7h8+TJ//fUXdnZ2lCxZkvr161OnTp1Uj9+iRQsKFCjAgQMH+Pnnn7GysqJChQq0bt06R1fzePToEZB8O0V6k0IWLFiQEiVKEB4eTlJSEoCxEHlWpUqVUhRt5nZuXybfpz0Z3p7eJKBPM5dz++abbxIWFsaVK1e4cuUK8fHxODg4UKxYMWrXrs0HH3yQYkRGeHh4qtd8REREim1PF23mdm5F7mDJ9U1ur21A6pvcXt9YSm0DllXfWFJtA1Lf5HWKmtY4NSGEEEIIIYQQQgghspHMKSWEEEIIIYQQQgghcpx0SgkhhBBCCCGEEEKIHCedUkIIIYQQQgghhBAix0mnlBBCCCGEEEIIIYTIcdIpJYQQQgghhBBCCCFynHRKCSGEEEIIIYQQQogcJ51SQgghhBBCCCGEECLHSaeUEEIIIYQQQgghhMhxVqYOQAiRtw0fPpz//vsPKysrVq5cibOzs6lDMjp06BDLli3j/fffp2/fvtn6XEuXLuXw4cPG7zUaDVu3bn2px5g4cSL//vsvfn5+VK5cOatDzLBBgwZx79494/eVKlVi4sSJJotHCCGEyGlS3yST+kYI8aqkU0oIkW0CAwP577//ANDpdBw9epSGDRuaOCrTKl++PJ6enmg0uXegao0aNQgLCyM8PJzz58+bOhwhhBAiR0l9k5rUN0KIzJJOKSFEtjl48CAAHh4ehIaGcvDgQYsv2j788EPq1q1r6jBeSbt27QC4cOGCFG1CCCEsjtQ3qUl9I4TIrNzblS2EMGsJCQkcP34cgH79+mFnZ8etW7cIDAw0cWRCCCGEEJkj9Y0QQmQtGSklhMgWJ0+eJC4ujqJFi1KlShVq1arFwYMHOXjwIGXKlEnzmKfnFHB0dGTnzp1cvHiRuLg4ChUqRL169WjcuDGKoqQ6Nj4+nt27d3PixAmCg4NxdnamevXqtGnThl9//ZWdO3fSsmVLWrVqleEcQkND2bdvH+fOnePRo0doNBq8vLx4//33+eijj9BqtZn++aQnODiY7du3c+7cOWJiYsiXLx81a9bk888/f+Gxp06d4rfffuP69evExsbi4uJC5cqVadGiBd7e3mkec+nSJb777juuXr2KXq/H29v7/9i76+gojy6Aw7+VbAxCBAkuLaW0uJdStKUUp1iBIsWt0EKAFilQ3CkEdy/uFCju1uLuRRIkIQnRzcr3R8h+CSQhgZUke59zOCS7887cWZ3cd94ZatWqRZUqVUyP1Zo1a8zaRyGEECKtkvHNu5HxjRAiMZKUEkJYxP79+wGoVq2a6f99+/Zx7Ngx2rVrh0ajSfTY8+fPs23bNrJly0bRokUJCgri2rVrLFu2jICAANq1axevfGRkJMOHD+f27ds4OTlRrFgxNBoN58+f5+zZs5QsWTLF8V+5coUJEyYQFhZGlixZKFasGNHR0dy6dYuFCxfyzz//MGDAANRq832MPnr0iGHDhhEcHIyHhwelS5cmKiqK7du3c/ny5USP0+v1TJs2jePHj+Pg4ECBAgXw8PDAz8+PI0eOcOrUKXx8fChRokS8444ePcq0adMwGo3kyZOH3LlzExgYyMyZM3n48KHZ+iWEEEKkFzK+STkZ3wghkiJJKSGE2T1+/JirV6+iUqmoXLkyELMAZs6cOXn06BEnTpww3Z6QTZs20alTJ7766ivTbZcuXWLEiBHs3LmTevXq4eXlZbpv9erV3L59m1y5cjFkyBA8PDwA0Gq1+Pr6cuDAgRTFHxQUxKRJkwgPD6djx458+eWXpoU7X758yZQpUzh//jybNm2iSZMmKao7Kb6+vgQHB/PZZ5/Ro0cP08D2+fPnDB8+nCdPniR43Jo1azh+/DgFCxakd+/eZM2a1XTfiRMnmDp1Kn/88Qe+vr64uroCMWdJ58yZg9FopF27dvHWwrhy5QpjxowxW7+EEEKI9EDGN+9GxjdCiKTImlJCCLOLPYtYpkwZ3NzcTLfHnlWMXSA0MeXKlYs3YAMoUqQIxYsXx2AwxDurptVq2bt3LwBt27Y1DdgANBoNHTt2xNHRMUXxb9++nZcvX/L1119Ts2bNeDvJZMyYkZ49e6JSqdi5cydGozFFdSfm2rVr3L59G0dHRzp27BjvTGvmzJlp3bp1gseFhoayfft2HBwc6Nu3b7wBG0CFChX48ssvCQsL4/Dhw6bb9+3bR2RkJB999NEbi7N+8skn1KxZ0yz9EkIIIdILGd+knIxvhBBvI0kpIYRZ6fV6Dh48CPx/kBarcuXKqFQqrl69ir+/f6J1lClTJsHbc+bMCcScBYt1584dIiMjTWssvM7NzY2iRYumqA///vsvABUrVkzwfk9PT7Jnz05ISAh+fn4pqjsxV65cAaBEiRJkzJjxjfvLli2Li4vLG7dfunQJrVbLxx9/jKenZ4J1f/rppwBcv37ddNvVq1cBqFSpUoLHfPHFFynrgBBCCJGOyfjm3cj4RgjxNnL5nhDCrP7991+CgoLw9PR84xp/d3d3SpYsyZkzZ9i/fz8tWrRIsI7MmTMneLuzszMQc/YwVkBAAMAbZ9DiSuq+hDx9+hSA33777a1lQ0JCyJEjR4rqT8jb+qFQKMiSJQv379+Pd3tsrBcvXnzrIqchISHJbi9LlizJC1wIIYSwAzK+eTcyvhFCvI0kpYQQZhU7dT06OpqhQ4e+cX/sWcADBw7QvHnzeFPHYyW0+4w1GQwGIGZq+Numxid01s+aYmP19vamUKFCSZaNPRObHLZ+DoQQQojURMY31iXjGyHshySlhBBm8+LFC86ePQvELJgZdzp1QmXPnTtHqVKl3qvN2Cndz549S7RMUvclJHPmzPj5+dGgQQM++OCD94ovuZLTj+fPn79xW+xZ1xw5ctCjR48Utff48eNE24s9QymEEELYOxnfvDsZ3wgh3kaSUkIIszlw4AAGg4GCBQsyatSoRMstX76cLVu2sG/fvvcetBUoUABHR0dCQkK4cOECxYoVi3d/7O0pUaJECfz8/Dh+/LjVBm2ffPIJAOfOnSM0NJQMGTLEu//MmTOEhYW9cVyRIkVQq9VcvnyZ4OBgMmXKlKz2ChcuzKVLlzh69Chff/31G/cfOXLkHXohhBBCpD8yvnl3Mr4RQryNLHQuhDCb2F1pqlSpkmS52Pv/+eefeOsAvAtHR0eqV68OwJIlSwgKCjLdFx0dzcKFC4mKikpRnfXr18fV1ZVt27axdetWdDrdG2WePn3KoUOH3iv2uAoXLkz+/PmJjIxk/vz5REdHm+57/vw5y5YtS/A4d3d3atWqRVRUFOPGjeO///57o0x0dDRnzpzh0aNHptuqV6+Oo6Mj165dY+fOnfHKX7t2jd27d5upZ0IIIUTaJuObdyfjGyHE28hMKSGEWVy5cgV/f38cHBwS3dUlVu7cucmfPz93797l4MGD1KtX773abtGiBdevX+fOnTv06tWLIkWK4ODgwLVr19DpdFSpUoWDBw+iVifvI8/Ly4t+/foxadIkli1bxpYtW8idOzfu7u6Eh4fz6NEjnjx5QsGCBalcufJ7xR7Xjz/+yLBhwzh27BhXr17l448/JioqisuXL5MnTx4++ugjbty48cZxrVq1IigoiCNHjtCvXz/y5ctH1qxZUalUBAQEcO/ePaKiohg4cKBp3QUvLy86derEjBkzWLhwIXv37iVXrly8ePGCq1evUrduXbZu3YpKpTJb/4QQQoi0RsY370/GN0KIpMhMKSGEWcQuAFq6dOk3pmYnJPZsYuxx78PJyYlhw4bRqFEjMmXKxLlz57h69SpFixZl3LhxpsVG3dzckl3nJ598wuTJk2ncuDGenp7cvn2bEydOcP/+fdzd3WnSpAldunR579jjypUrF2PHjqVq1aoYDAZOnz7No0ePqFWrFr/99luig06VSkWvXr349ddfKVu2LMHBwZw5c4bz588TGhpK6dKl6dWrF4ULF453XOXKlRk6dCjFihXj6dOnnDlzhoiICLp06cI333wDpOwxE0IIIdIbGd+8PxnfCCGSojAajUZbByGEEJai0+no27cvfn5+jB07lgIFCtgkjhkzZnDw4EG6d+9O1apVbRJDShw8eJAZM2ZQunRpBgwYkGCZy5cvM3z4cD755BOGDRtm3QCFEEIIOybjm3cj4xshUh+5fE8IkS7cuXOHfPnyxduCOTIykqVLl+Ln50fevHltNmCLa+/evVy+fBmlUkm3bt1sGsvz589Rq9W4u7vHu/3atWumNR6qVav2xnErV67kxYsX8da3EEIIIYT5yfgm5WR8I0TaIkkpIUS6MGnSJKKiosiTJw9ubm6EhIRw7949004v3bt3t3WIAFy/fp3r16+nikHbpUuXmDVrFvny5cPLywulUsmTJ0+4f/8+AFWrVqVcuXJvHHfq1CkeP35s7XCFEEIIuyPjm5ST8Y0QaYtcvieESBd27NhhWqMgLCwMhUJBlixZKFasGPXq1SNz5sy2DjHVefToEVu3buXq1asEBwcTFRWFi4sL+fLlo1q1alSqVMnWIQohhBB2TcY3KSfjGyHSFklKCSGEEEIIIYQQQgirk933hBBCCCGEEEIIIYTVSVJKCCGEEEIIIYQQQlidJKWEEEIIIYQQQgghhNVJUkoIIYQQQgghhBBCWJ0kpYQQQgghhBBCCCGE1UlSSgghhBBCCCGEEEJYnSSlhBBCCCGEEEIIIYTVSVJKCCGEEEIIIYQQQlidJKWEEEIIIYQQQgghhNVJUkoIIYQQQgghhBBCWJ0kpYQQQgghhBBCCCGE1UlSSgghhBBCCCGEEEJYnSSlhBBCCCGEEEIIIYTVSVJKCCGEEEIIIYQQQlidJKWESCOGDRuGQqFAoVDYOhSriO3rsGHDbB2KWVStWhWFQkHVqlVtHYpVBQQE4OPjQ+HChXF2djY9r1OnTgXe/rq218dNCCHs2eLFi03fDffu3bN1OO8sX758KBQK2rVrZ+tQzKJdu3YoFAry5ctn61CsKiIiguHDh1O8eHFcXV1Nr82ffvoJePvr1V4fNyGSS23rAIQQQrzdvXv3yJ8/f4L3OTo64uXlRbFixWjYsCGtW7fGxcXFyhG+KTg4mM8++4ybN2/aOhQhhBBCpAKJnYRycHDAw8ODIkWK8M0339ChQwc8PDysHN2boqOj+fLLLzl27JitQxEi3ZKZUkIIkcZFRUXx+PFjdu7cSdeuXSlRogRXr161dVjMmDHDlJDq378/hw8f5uLFi1y8eJHWrVvbODohhBBplcw8SX+io6N5+vQp+/bto1+/fnzyySccOXLE1mGxdu1aU0KqXbt27N+/3zSW+eWXX2wcnRDpg8yUEkKINKZBgwaMHDnS9HtERAQXL15k6tSpXLx4kZs3b/LNN99w5coVm86Y2rNnDwBlypRh3LhxCZYZNmxYurlEUwghhHm0a9cu3VzyJhJWpkwZFi1aZPpdq9Vy48YNZs6cyeHDh/H396devXpcunSJnDlz2izO2LGMt7c38+fPR6VSvVFGXq9CvB+ZKSWEEGmMu7s7RYoUMf0rW7Ys7du35/Tp01SsWBGA+/fvs2DBApvG+ejRIwA++ugjm8YhhBBCiNTF1dU13limVKlSfPfddxw4cICmTZsCEBQUxOTJk20aZ+xYpkCBAgkmpIQQ70+SUkIIkU44OjoyYsQI0+87d+60YTQxlxVCzDoRQgghhBBvo1QqGTt2rOl3GcsIkf5JUkqINCoyMpIJEyZQqlQpMmbMSMaMGSlXrhy+vr7odLpEj3t9J5h//vmHdu3akT9/fhwdHU0LUH7wwQcoFAo+//zzt8by6NEjVCoVCoWC/v37m6V/yWEwGFi+fDm1a9fG29sbjUZDlixZqFatGjNnzkSr1SZ6bHLXo0juDkAnTpygadOmeHt74+TkRP78+encuTPXr19/x969m3Llypl+vn//vunne/fumfqxePFiADZs2EDt2rXJkSMHarU6wR3utm7dSpMmTciVK5dpQfXPPvuMsWPHEhoa+kb5AwcOmNqJbX/JkiWm217fSc9cu0r6+/szaNAgypQpg6enJ46OjuTOnZtmzZqZpt4LIYRIGxL77o39zliyZAkQ8z0X9/sloe8TrVbL1q1b6dmzJ2XLlsXDwwMHBwe8vLwoX748w4YN4/nz59bs3htS+l0bK7nfoXG/mw8cOJBouatXr9KuXTty586Nk5MTuXPnpmXLlpw+ffpdu/ZOChQogJeXFxB/LANv7s68b98+mjZtSu7cuXFwcEhwXHfkyBFat25Nvnz5cHJywt3dnZIlSzJ48GCePXv2Rvm4Y6aDBw8CcPDgwXivsbjtmGu3yODgYMaMGcPnn39OlixZ0Gg0ZM+enXr16rFu3TqMRuM71y1EaiZrSgmRBj158oRatWpx7ty5eLefPn2a06dPs3v3bjZt2oRSmXTeefbs2fz4448JJrFatmzJyJEjOX78OPfu3UsyebNq1SoMBgMArVq1SnF/3kVgYCD169fn6NGj8W5//vw5Bw4c4MCBA/j6+vLXX3+RN29ei8YyZcoUfHx8TI8BxAxo5s2bx8qVK1mzZo1F248r7pk8vV6fYBmj0UibNm1YtmxZovVERkbSsmVLNm7cGO/2wMBATpw4wYkTJ5g+fTrbt2+nRIkSZon9Xa1YsYIuXboQFhYW7/aHDx+ydu1a1q5dS4cOHZg9ezZqtXztCSGEPencubMpiRVXYGAgp06d4tSpU/j6+rJ58+ZknYgzp9T0XbtmzRratGljmhkEMd+jq1atYu3atcyePdui7b8udjyT2FgGYNCgQYwePTrR+w0GA7169WLGjBnxbo+KiuLcuXOcO3cOX19f1q5dy1dffWWewN/R3r17ad68OQEBAfFu9/f3Z9u2bWzbto3atWuzevVqMmTIYKMohbAMmSklRBr07bffcuXKFXr16sXff//NP//8w8qVKylcuDAQc8Zt3rx5SdZx+vRpevbsSa5cufD19eXEiRMcOXKEMWPGAP9PLhmNRlauXJlkXbH3f/rppxQvXvx9u/dWer2eunXrmhJSVapUYe3atZw5c4YtW7bQsGFDIOaMX40aNZI8y/i+Nm7cSJ8+fTAYDGTKlInRo0dz7Ngxjh07xsiRI1GpVLRq1YrHjx9bLIa4Ll68aPo5R44cCZaZOnUqy5Yt44svvmDlypWcOXOGPXv2xNsRr23btqZBcvHixVm6dCmnT59m165d/PDDDygUCh4/fkyNGjVM6y0AlC1b1rQrTWz7DRo0MN128eLFeAubvq81a9bQunVrwsLCKFCgAJMnT2bnzp38888/rF+/ntq1awOwYMECq87iE0IIYX7du3fn4sWLNGjQAIj5nov7/RL7Ly6dTkeBAgXo27cvq1ev5vjx45w+fZp169bRtWtXNBoNAQEBNGrUiKdPn1q1P+/6XWtup0+fplWrVkRFReHo6Mgvv/zCoUOHOHnyJNOmTSNz5sx069btjZOhlvLs2TOePHkCJD6W2bBhA6NHj6Zo0aIsXLiQU6dOcfDgQfr06WMq88svv5gSUvnz52f27NmcOnWK/fv38/PPP+Pg4EBwcDB169bl/PnzpuNy5sxpei2VKVMGiFmYPe5rbPfu3Wbr79GjR/nmm28ICAggW7ZsjBw5kq1bt/LPP/+wdetWvv/+ewB27NhB27ZtzdauEKmGUQiRJgwdOtQIGAGjg4ODcf/+/W+UCQgIMGbLls0IGIsVK5ZgPXnz5jXVU7RoUeOLFy8SbbNUqVJGwPjpp58mWubq1aum+kaPHp3SbiUqts6hQ4e+cZ+vr6/p/jZt2hgNBsMbZQYOHGgq079//zfub9u2rREw5s2bN8k4Fi1aZKrn7t278e6Liooy5siRwwgYM2XKZLxy5cobx1+8eNHo5uZmqqNKlSpJtpeYu3fvmupo27ZtouWaNGliKvf7778neHxSj5vRaDRu27bNVK5GjRrGqKioN8rMnTvXVKZZs2YJ1hP7Wksq3riv64RUqVIl0cft2bNnxkyZMhkBY/v27Y3R0dEJ1hH7WlAqlcZr164lGosQQojUIanvXqMx+d/hRqPReOvWrUS/74xGo/HChQvGDBkyGAHj4MGD3yPqNyX1PWiO79q3fYfG2r9/v6lcQuPHMmXKmMaXBw8efOP+hw8fGnPlymWqIzmPe2KSMx7y8fExlWvfvn2Cx8c+bpGRkQnWceHCBaNSqTQCxiJFiiQ43v3rr79MZcqVK5dgPUmNQ2K9z+tVq9Ua8+XLZwSMtWrVMoaFhSXYRtzXwu7duxONRYi0SGZKCZEG/fjjjwmu/+Pp6ckPP/wAxMyYCQ4OTrKeGTNm4O7unuj9sbOlLl++HO8MUlwrVqwAYq7xb9myZTKif3+xZ72yZMmCr69vgmspDB8+nI8//hiAefPmxZuObi6bN282zYAaMmSIaaZaXEWKFGHQoEFmbzuuyMhITp8+TZMmTVi3bh0Abm5udOnSJcHy7u7uiT5u8P/H18HBgUWLFqHRaN4o06lTJ7788ksg5myln5+fObqSIrNmzSI4OJicOXMyc+bMRC/NGz58ODlz5sRgMLB06VIrRymEEMKWYtfITEzRokXp2LEjAJs2bbJSVKnnu/b06dOcOXMGgC5dulC5cuU3yuTMmZNJkyaZve24tFotly5domvXrkycOBEAtVrNzz//nGB5pVLJ/PnzcXR0TPD+WbNmmZZVmD9/foLj3Vq1atG+fXsATp06ZfW1swD+/PNP7t27h5OTE0uXLsXFxSXBcp06dTKtGxq7NqgQ6YUkpYRIg5Jat6l06dJAzGV3d+/eTbRc7ty5+eKLL5Js57vvvjOtS5XYJXyrVq0C4PPPP7f42k0Ajx8/5urVqwA0a9aMjBkzJlhOrVabEnQvXrzg33//NXsssQtoKxSKJKdTx07BN5fXFw53dnamXLlyrF+/HoAMGTKwdu1asmbNmuDx9erVS/Rx0+l0pkU9a9asSe7cuRONo1OnTqZjklo41VK2bNkCQN26dRMdlELMa+Gzzz4D4Pjx41aJTQghROr04sULbt++zeXLl7l06RKXLl0yJSyuXLlCdHS0xWNITd+1cTcDiR03JaRRo0ZJnshMqdcXDnd0dKRo0aLMmTMHiEnWzZ8/nyJFiiR4/Oeff57keqex/fr0008pX758ouViH9+4x1hT7FimSpUqZMmSJcmysQlDGcuI9EaSUkKkQbEzgBLi6elp+vnly5eJlitWrNhb28mRIwfVqlUDYpJPxtd2/Th58iS3b98GrLfA+aVLl0w/JzXIeP3+uMeZS+y6Ffnz5ydz5syJlsuSJctbd/kzhxw5ctCtWzcuXLhAzZo1Ey2X1HN/584dwsPDAds/vknR6/WmtS3mzJmT4O5Lcf/FziDz9/e3apxCCCFs7+LFi7Rv357s2bPj6enJhx9+SJEiRShatChFixY17eRmMBh48eKFxeNJTd+1sWMZjUaT5LqgDg4OlCxZ0uztvy5z5sx8//33nDlzJskTfkmNZaKiorh58ybw9se3ZMmSpkXVrT2WAUyz1Hbt2vXWsUzsDDIZy4j0RrYhEiINSmxqLxBvx72kdizx8PBIVlutWrVi7969PHjwgEOHDlGlShXTfbGX7jk4ONC0adNk1fe+AgMDTT8nNhMolre3d4LHmTuWt8UBkC1btiRnrqVEgwYNGDlypOl3R0dHPD09Tdsnv01Sz31qenyTEhgYmOCukW8T+0eAEEII+7BgwQK6du2a7O+MiIgIC0eUur5rY+v09PREpVIlWTZbtmxma7dMmTLxNj5xcHDAw8MjWWMqSHosEzex+Lb6HBwc8PLywt/f3+pjGeCdFte3xmtUCGuSpJQQduptA49YjRs3pnv37kRGRrJy5UpTUkqv17NmzRog5pr85CZEzMmcl8S9D2vH4e7unuh09uRI7nOfWh7fhMRNuHbs2JHevXsn67iE1uwQQgiRPl27ds2UkMqaNSv9+vWjevXq5MuXj4wZM5pmyCxcuJAOHToAvDEr3NJSy3etteNwdXW1+7EM/H8888033zB+/HgbRyOEbUhSSgiRJDc3N+rWrcu6detYt24dvr6+ODg4sHfvXtN2vda6dA/iX54Y235i4k5vjnsc/H9GWewimIkJCwtL9L7Ys3RviyO5ZVIDcz2+lha3PaPR+F4DWyGEEOnT4sWL0el0qFQqDh48mOjyB9aeIWPusQzEjGfi/h5XcsYyAQEB6PX6JJM9aWUsE3cW1dti1ul0BAQEANYfywB4eXnx+PFjtFqtjGWE3ZI1pYQQbxWbdAoMDOSvv/4C/n/pXsaMGalfv77VYon7hX3y5Mkky546dSrB4wDTQt9BQUFJ1nHjxo1E7ytatCgAd+/eNQ1oEvLs2TPu3buXZDupRYECBUyXh77P42tpGo2GTz/9FICjR49atW0hhBC2ldzZL5cvXwagePHiSa7HGbuuj7WY67s27qYlSa2FlZyxjFarTXSnZYhJ3sSu5ZjaOTo6UrBgQeDtj+/Zs2dNi9vbIikUu07XmTNn0Gq1Vm9fiNRAklJCiLeqXbu26azTihUriIyMZOPGjUDMbizOzs5WiyVHjhwULlwYgDVr1hAaGppgOb1eb9oy18PDg1KlSsW7P3/+/EDMYvDXr19PsA6tVmva0S4hsds0G41Gli5dmmi5xYsXW/1ygHelVqtNl2j+/fffPHz4MNGy8+fPNx1TtWpVa4QXT2wy9Nq1a+zatcvq7QshhLANJycnIGZB66TEriOV1EwhPz8/0w5o1mKu79rYsQwknVj7888/E70vdiwDMbv7Jmbjxo1WWQTeXGL7dfny5XiJvdfFPr5xj7Gm2LFMcHBwvDW2hLAnkpQSQryVRqOhSZMmAGzdupWVK1eadvaz5qV7sXr06AHEzEDq1atXgmWGDx/OlStXgJjtfh0dHePdH3fB9kmTJiVYR58+fXj06FGicTRs2JDs2bMDMGLEiASTW1euXGHUqFFJ9Cb1iX18tVotHTp0SHB77IULF7J7924Avv32W9PjYE29e/cmQ4YMQMw21rFnxBOzfft2Lly4YI3QhBBCWFDsd87Tp0+T3Gk4drbMzZs3OXbs2Bv3h4eH07JlS5ssHG2O79qKFSuiVsesxjJlypQET4BNmDAhyaRMuXLlTCfuZs2axZEjR94o4+fnh4+PTzJ7ljp069bNdDlj586dCQkJeaPM7t27WbBgARDzOJQtW9aqMQK0bduW3LlzA+Dj48OhQ4eSLH/kyBEOHjxojdCEsBpJSgkhkiU2+RQREUHfvn2BmF1YatSoYfVYunbtymeffQbAokWLqFGjBuvXr+fff/9l+/btNG7cmBEjRgDwwQcfMGTIkDfqKFmypKmOefPm0a5dO/bv38+///7L6tWrqVGjBjNmzKBixYqJxqHRaJg+fToQM22+QoUKjB07lhMnTnD8+HHGjBljOv7DDz8062NgSXXq1DHtprh7924qVKjAihUr+Oeff9izZw8dO3akY8eOQMz6C5MnT7ZJnNmyZWPJkiUoFAr8/PwoU6YM3bp1Y8uWLfz777+cPHmS9evXM2DAAD744APq1q3Lf//9Z5NYhRBCmE/sd6vBYKBr166cOHGCW7dumf7Fat26talcnTp1GD16NIcOHeLUqVPMmjWLEiVKcODAAT7//HOr98Ec37VZs2Y11bFr1y7q16/Pzp07OXv2LJs3b6ZJkyb0798/ybEMwMyZM1Gr1URHR/PVV18xcOBAjhw5wunTp/H19aV06dL4+flRvHhxMz8KllO0aFHTePX8+fOUKlWKefPmcebMGQ4ePIiPjw9169ZFr9ej0WiYM2eOTeJ0dHRkzZo1ODo6EhoaSvXq1fn+++9Zt24d//zzD6dPn2bLli0MHTqUYsWK8cUXX3Dx4kWbxCqExRiFEGnC0KFDjYDxbW/b/fv3m8rt37//jfvz5s1rBIxt27ZNUfsGg8GYO3duU92AsXfv3imqIyVi2xg6dGiC9wcEBBg///zzePG8/q9w4cLGe/fuJdrG1atXjVmzZk30eB8fH+OiRYtMv9+9ezfBeiZMmGBUKBQJ1uHi4mLctm2bsUqVKkbAWKVKlXd6PO7evWuqM6XP3evHL1q06K3lIyIijI0aNUry8c2RI4fx7NmzidaRnNfa217XyXnctmzZYvT09EwyVsCoVCqN+/bte2vfhRBC2Nbbvnv1er2xQoUKiX7exzV8+PAkvxv69u2brO/6d/G270FzfNf6+/sbCxYsmOjx3333nXHPnj1Jjg2NRqNx5cqVRo1Gk2AdarXaOHfuXGPbtm2NgDFv3rzv/JjE1vmu46G3jQ/j0uv1xu7duyf5+GbKlMm4a9euROtIzjjkba+f5Dxux48ff2Ocndi/JUuWvLXvQqQlMlNKCJEsCoWCFi1axLvNFpfuxfL09OTQoUMsXbqUWrVqkS1bNhwcHPDy8qJq1ar4+vpy7tw58ubNm2gdH3/8Mf/++y/dunUjb968aDQasmTJQq1atdi+fTsTJkxIViw+Pj4cOXKEb7/9lqxZs+Lo6EjevHlp3749Z86coU6dOubqttU4OTmxYcMGtmzZwrfffkuOHDnQaDR4eHhQvnx5xowZw/Xr1ylRooStQ6VevXrcvXuXiRMnUr16ddNrwdnZmfz581O3bl0mT57MvXv3qFatmq3DFUII8Z6USiW7d+9m8ODBFC9enAwZMiS6+Plvv/3G9u3bqVmzJh4eHmg0GnLlysW3337L7t27mThxopWj/z9zfNdmy5aNkydPMmDAAAoWLIijoyOenp5UrlyZ5cuXs2rVqiR31IvVokULzp49S+vWrU1x5MyZk2bNmnHkyBE6depkxp5bh1KpZMaMGRw6dIhWrVqRJ08eHB0dcXNzo0SJEgwcOJCbN29Ss2ZNW4dKhQoVuHnzJrNnz6ZOnTqm58DJyYncuXNTs2ZNRo0axbVr12jTpo2twxXCrBRGYxpZfVcIIYQQQgghhBBCpBsyU0oIIYQQQgghhBBCWJ0kpYQQQgghhBBCCCGE1UlSSgghhBBCCCGEEEJYndrWAQgh0p+7d+8SFhaW4uM8PDzImTOnBSISQgghhEi+R48e8eLFixQf5+rqSv78+S0QkRBCpE+y0LkQwuyqVq3KwYMHU3xc27ZtWbx4sfkDEkIIIYRIgXbt2rFkyZIUH1elShUOHDhg/oCEECKdksv3hBBCCCGEEEIIIYTVyUwpIYQQQgghhBBCCGF1MlNKCCGEEEIIIYQQQlidJKWEEEIIIYQQQgghhNXJ7nvv6cWLF+h0OovUnSVLFp49e2aRulMb6Wv6lSVLFh4+fIiPjw8AEyZMwMnJycZRWYY9Pbf21Fewr/7aU18tSa1W4+HhYesw3pmlxjf29vqyp/5KX+OLjIykX79+QNof+8hzm37ZU3/tqa+WZInxjSSl3pNOpyM6Otrs9SoUClP96X3ZL+lr+hXb36ioKFavXg3AyJEjUalUtgzLIuzpubWnvoJ99dee+iqSZonxjb29vuypv9LXN6WXsY88t+mXPfXXnvqaFsnle0IIIYQQQgghhBDC6iQpJYQQQgghhBBCCCGsTpJSQgghhBBCCCGEEMLq0s2aUjt37mTr1q0EBQWRN29e2rdvz4cffpho+e3bt7N7926eP3+Om5sb5cuXp2XLlmg0GitGLYQQQgghhBBCCGGf0sVMqWPHjrF06VKaNGnCuHHjyJs3L6NGjSI4ODjB8keOHGHlypU0bdqUKVOm0LVrV44fP86qVausHLkQQgghhBBCCCGEfUoXSalt27ZRo0YNqlWrRq5cuejUqRMajYb9+/cnWP769esUKlSISpUqkTVrVooXL87nn3/OrVu3rBy5EPbD09MTT09PW4chhBBCCGEVMvYRQoi3S/NJKZ1Ox507dyhatKjpNqVSSdGiRblx40aCxxQqVIg7d+6YklBPnjzh7NmzlCxZ0ioxC2FvXFxcuHjxIhcvXsTFxcXW4QghhBBCWJSMfYQQInnS/JpSISEhGAwG3N3d493u7u7O48ePEzymUqVKhISEMGTIEAD0ej1fffUV3377baLtREdHEx0dbfpdoVDg7Oxs+tncYuu0RN2pjfQ1/bKn/kpf0y976q899VXEsOb4xt5eX/bUX+lr+mVP/bWnvoJ99dee+poWpfmk1Lu4fPkyGzdupGPHjhQsWBB/f38WLVrEunXraNKkSYLHbNy4kXXr1pl+z58/P+PGjSNLliwWjdXb29ui9SdmxowZTJgwAX9/f4oXL8706dMpV65ckscEBQUxaNAgNmzYQGBgIHnz5mXq1KnUrl0biEn+DRs2jOXLl+Pv70+OHDlo164dgwcPBmzXV1uwp76CffVX+pp+2VN/7amv9s4W4xt7e33ZU3+lr+mXPfXXnvoK9tVfe+prWpLmk1Jubm4olUqCgoLi3R4UFPTG7KlYq1evpnLlytSoUQOAPHnyEBkZydy5c/n2229RKt+8qrFRo0bUrVvX9HtslvXZs2fodDrzdCYOhUKBt7c3/v7+GI1Gs9eflM2bN9OnTx/Gjh1LqVKlmDdvHjVr1uTw4cNkzpw5wWO0Wi0NGjQgc+bMzJ49m+zZs/Pw4UM0Gg1+fn4ATJs2jTlz5vDHH39QqFAhzp8/z88//4xCoWDw4ME26au12fJ5tYXY/t69e5dWrVoBsHz5ctNZ+PTEnp5be+or2Fd/7amvlqZWqy1+4socrDm+sbfXlz31V/r6poiICL7//nsgbY995LlNv+ypv/bUV0uzxPgmzSel1Go1BQoU4NKlS6aZPAaDgUuXLlGrVq0Ej4mKinpj6l5Ciai4HBwccHBwSPA+S76w9+3bx9SpU7l+/TpKpZLSpUvz+++/ky9fPou1OXfuXFq2bEnz5s0BGDt2LHv37mXVqlX07NkzwWNWrVpFUFAQmzdvNj1OuXLlAv7/+Jw+fZqvv/7alAzMlSsXmzZt4uzZs6Zy9vIhYU99hZj35PHjx00/p+e+29Nza099Bfvqrz311d7ZYnxjb68ve+qv9PX/0tvYR57b9Mue+mtPfU1L0vxC5wB169Zl7969HDhwgIcPHzJ//nyioqKoWrUqAL6+vqxcudJUvnTp0vz9998cPXqUp0+fcuHCBVavXk3p0qXfmpyytvDwcDp37syOHTtYvXo1SqWSjh07YjAYEj1m2rRpFCxYMMl/jx49SvBYrVbLhQsX+OKLL0y3KZVKKlWqxD///JNom3///TelS5dm0KBBFC9enOrVqzNt2jT0er2pTJkyZThy5Ai3b98GYi6jPHXqFNWrV0/pwyKEEEIIIYQQQog0Ls3PlAKoWLEiISEhrFmzhqCgIPLly8fAgQNNl+89f/483syoxo0bo1Ao+PPPPwkMDMTNzY3SpUvTokULG/UgcXXq1ImXzZ08ebJpZ8GPP/44wWNat25NvXr1kqw3W7ZsCd4eGBiIXq9/4zK9LFmymJJJCbl//z5Hjx6lUaNGLFu2jLt37zJw4EB0Oh19+vQBoGfPnoSGhlKlShVUKhV6vZ4BAwYkucC8EEIIIYQQQggh0qd0kZQCqFWrVqKX6w0bNize7yqViqZNm9K0aVMrRPZ+7ty5w4QJEzh79iyBgYGmGVKPHj1KNCnl4eGBh4eHNcPEYDDg5eXF+PHjUalUFCtWDH9/f2bPnm1KSm3dupUNGzYwY8YMPvroIy5fvszQoUPx9vamd+/eVo1XCCGEEEIIIYQQtpVuklLpVdu2bcmVKxfjx4/H29sbg8FA9erV423f/Lpp06Yxffr0JOs9cOAAOXPmfON2T09PVCoVz58/j3f7s2fPklzQLFu2bKjValQqlem2ggUL8vTpU7RaLRqNhhEjRtCzZ08aNGgAQOHChXn48CHTp0+XpJQQQgghhBBCCGFnJCn1niIjI4mKinrjdqVSiZOTk+n38PDwROtQKBTxduQIDw9HoVDw33//cfv2bUaMGEHp0qUB3ljXKSIi4o3F2po0acJXX30FEC+GyMhI088ZM2aMF1NsDBqNhmLFinHgwAEqV64MxMyCOnz4MK1btzYd4+LiEi+GEiVKsHXrVkJDQ03rcl27do2sWbOi0WhM5fR6fbx2DQYDer2esLAwwsPD4z0OkZGRSa6d5ezsbLosMyoqKt76Ve9T1snJydQHrVab5O5DKSnr6OiIWq02lU0qsejo6GhK8EVHRydZVqPRmOpNSVmdTodWq020bNzFb1NSVq/Xm94TCoXC9NzG0mq1ptdP3LIJUavVptePwWCI9xp+n7IqlQpHR0cgZsHDiIiI9y6rUCjeaDOp9/37fkYkt2xCnxHmKhseHp5o+dc/I5JaVDJu2be971NS1lyfEXFfx0aj0aKfEbHv+5SUNednROxrHazzGZGQuO/ltPwZEfexFEIIIYQQCZOk1Htq0KCBafe4uKpXr86yZctMvxcrVizRQe1nn33GunXrTL+XL1+ewMBA0+8tW7Y0/fz6drJVq1bl4cOHCdb70UcfsX//ftPv1apV48aNGwmWzZUrFydPngSgU6dO9OjRI178ABMnTmTixIl4enpSrVo1smfPzq+//krr1q1Nu4sUKlQo3jGxfwgBfPXVV4waNYrffvvtjfYzZMgAEG8B9l69erF9+/YE4wW4efOm6Q/UAQMGsHbt2kTLXrhwAS8vLwCGDx/OkiVLEi174sQJcufODcC4ceOYPXt2omX37dtn6vP06dOZPHlyomW3b99OyZIlAZg/fz4jR45MtOzatWupWLEiACtWrGDQoEGJll2yZAlffvklABs2bDBdLpmQ2bNnm9Yb++uvv+jatWuiZSdPnmzagfHAgQO0bds20bLDhw+nQoUK3L9/n0OHDrF8+fJEy37xxRfkzZsXDw8PDAYDBw4cSLRsnz596Nu3LxDzfCe1KH7Xrl0ZMmQIEPM6qlChQqJl27Zty+jRo4GYddSKFSuWaNmmTZsydepUICa5UrBgwUTLNmnShGnTppl+T6qsOT8j4ipevDg7duww/Z6Sz4jatWsn6zMCoHLlypw5cybBsp6enly8eNH0e9zPiNc5Oztz69Yt0++dOnVi3759CZaF9P8ZUaJECQAWLFhgk8+IOXPm0LlzZ8C8nxGjRo2iXbt2AJw8eTLJy+cHDx5Mt27dALh48SJ16tRJtGxq/owoWbIk//77b6LHCSHSv9fH7UIIId4kSak0JkeOHEkuOG4ODRo0YNSoUYnu0Afw+PHjZO1UGDcpNXLkSI4fP57oH8gi7Ro+fHiSM1biCgwMTDSh8roHDx4QFRUlMw6EEEIIkaa4uLjEO+kihBAiYQpjUtdUiLeK/aP5dea4fM/b2xt/f/94l71Y89Kc5F5u876X5sTtqz1cvpc9e3bu37+fpi7f02q1nDx5kr/++ovDhw9z586dN8q7u7tToEABcufOTfbs2cmWLRtubm7kz5+fqKgojEYjBoPBdJlbUFAQAQEBPHz4kPv37/Pff//x33//vfHcODs789lnn1GtWjW+/PLLN3aGjJUaLt/LmTMnL168ML0f0uvlewqFAnd3dx4/fmw3l+/F/TxO75fv5c6dGz8/P6Kjo+XyPd7v8r3YGXVp0bNnz5J8rbwLhUJB9uzZ8fPzS/IzIb2wp/5KX9Mve+qvPfUV7Ku/9tRXS3NwcEhyrel3ITOl3pOTk1O8xb0TE/cPqeSUVSgUuLq64uLikuQbJyXTglND2bh/hMdKrK8JlU1MSmbSpKSsRqMx/RFj7rKxf6C9Tdw/5sxZVq1Wx5vJlpDLly+zevVqtm/fjr+/v+l2hUJB8eLFqVixIiVKlKBYsWLkypXL9Ed93HIp+QKIiIjg6tWrnD9/nn///ZfDhw/z7Nkz9u3bx759+xg6dCifffYZ9evXp0GDBmTMmDHBepRKZbLfcwqFwixlFQrFG6/ZlL7vLVHWku/7t30+vUu9KXnfW+szIqnPY0t+RiS3rDk/I+K+h5PzGfEuZVUqVbJfwykpm5L3vTU+I5L7nAghhBBC2DNJSgkh4gkPD2fTpk2sWLGCc+fOmW7PmDEjX331FbVq1aJixYp4eHgkXZFej+bUKdBqMQBt5s8HYN68eYkmE5ydnSlVqhSlSpXihx9+wGg0cvXqVQ4cOMCOHTs4e/YsR48e5ejRowwfPpxGjRrx/fffJ7neixBCCCGEtUVGRtKpUycg6bGPEELYO0lKCSGAmLWeFi1axKJFi3jx4gUQc6a/Vq1aNG7cmMqVKyd7tonTjh1k+u03VH5+ALgDsUtXJ3ftKYiZdfDJJ5/wySef0L17dx48eMDWrVtZvXo1t27dYsWKFaxYsYKyZcvSs2dPatSo8cZsLSGEEEIIazMYDKaNO1Iy9hFCCHsjSSkh7Jyfnx8zZ85k5cqVpjVW8ubNS5s2bWjSpEmiazglxmnHDjw6d4ZELuty3LULGjV6p1hz585N9+7d6datGydPnmT58uVs376d06dP07ZtWwoXLsyPP/5IvXr1krUQvxBCCCGESEScWe8ajYaocuUgGcuWCCFESshfbULYqeDgYMaMGUOlSpVYuHAhkZGRFC1alFmzZnH48GG6du2a4oQUej2ZfvsNjEYSm6/kNnIkJLHgdHIoFAoqVKiAr68vJ06coFu3bri6unL16lW6d+9OrVq1OHDggCxkKIQQQgjxDpx27CBb+fJ4NWkCLVvi1aQJ2cqXx2nHDluHJoRIZyQpJYSd0Wq1zJkzh4oVK+Lr60tkZCRly5Zl1apV/PXXX9SvXz9Zi/cnRHPyJCo/v0QTUgBqf38yf/MN7t274/bbb2SYNg2XVatw/PtvHM6dQ/XoESSxK9brsmXLxuDBgzl16hQ+Pj5kzJiRy5cv06pVK5o1a8bFixffqS9CCCHSl8WLF1O+fHkKFChA3bp1OXv27FuPCQ4OZuDAgZQsWZL8+fNTqVIl9u7da7r/xIkTtG3bllKlSpEzZ0527txpyS4IYRWxs96Vr5ZhiKX098ejc2dJTAkhzEou3xPCjhw9epRBgwZx8+ZNAD766CN+/fVXvvrqK7OsxaR6+jRZ5TSXL6O5fDnJMoaMGTFkzow+SxYMmTNjyJIFfebM8X9+dZ/R1RV3d3d+/vln2rZty7Rp01iyZAnHjh2jdu3atGnThv79+5MpU6b37mOCZHq7EEKkaps3b2b48OGMHTuWkiVLMn/+fFq1asWhQ4cSnRWs1Wpp0aIFXl5ezJ07F29vbx4+fIibm5upTHh4OJ988gnfffcdHTt2tFZ3hLCcJGa9K4xGjAoFbkOHEvn11zLWEUKYhSSlhLADT58+ZcSIEWzYsAGAzJkz8+uvv9K0adN3nhWVEH2WLMkq97JXLwweHiifP0f17BnK58/j/ayIjkb58iXKly9R37371voMTk6mBJVHlixMyZyZoa1asePMGf6+eJE7ixfTftMmfhgwgDrff4/CjOtNvb6ouxegz56d4N9/J7J2bbO1I4QQ6cX+/fuZOnUq169fR6lUUrp0aX7//Xfy5ctnsTbnzZtHy5Ytad68OQBjx45l7969/Pnnn/Ts2TPBY/7880+CgoLYvHkzDg4OQMzahnFVr16d6tWrWyxuIawiOhqHa9dwOHcOx927TWOahCiMRtSPH6M5eRJtxYpWDFIIkV5JUkqIdMxoNLJhwwaGDBlCcHAwCoWCNm3aMGDAAPPPGjIYcN648a3FdN7ehPn4JH52zWhEERyM6lWiSvkqUWVKXj17Fv++iAiUkZEoHzyABw9M1bgCnV79AyAoCH79leiBAzFmyQLZssUksry8Ep2NZfD0TPIsYGKLusdOb38xd64kpoQQ4jXh4eF07tyZwoULExYWxsSJE+nYsSO7d+9OdJOKadOmMX369CTrPXDgADlz5nzjdq1Wy4ULF+Iln5RKJZUqVeKff/5JtL6///6b0qVLM2jQIHbt2oWXlxcNGzakR48eZj2hI4RVGQyo7txBc+4cDufPx/x/5QqKFCydAMmfHS+EEG8jSSkh0qnnz5/zyy+/8NdffwFQrFgxxo4dS/Hixc3fmE6He58+uKxfj1GhiEnSKBQoXiVrXAHDq8sDX4wYkfR0b4UCo7s7Ond3+PDDtzatCAuLSU49e4YqICB+EuvZM5QBASiePkX/+DHOUVE4GI3w9GnMv7cwKpUYPD1Ns7DiXT7o6Ynb6NEyvV0IIVKoTp068TaimDx5MkWLFuXGjRt8/PHHCR7TunVr6tWrl2S92bJlS/D2wMBA9Hr9G5fpZcmShdu3byda3/379zl69CiNGjVi2bJl3L17l4EDB6LT6ejTp0+SsQjh4uLCo0ePbBuE0Yjy8WM058/HJKDOnsXhwgWUL1++UdSQKRPa4sUxeHnhkoyTjPqsWS0RsRDCDklSSoh0aOfOnfTr14/AwEAcHBz4+eef6dGjB2q1Bd7yWi0ePXvivH07RrWaF9OmgYNDvEvaIOaStpDhw80+c8jo6ore1RV9vnxEv6XsmcuXGfPzzzy7fJmsQI1PP6VljRpkCAv7fyIrNrH14gUKgwHV8+eonj9PcVwyvV0IIRJ2584dJkyYwNmzZwkMDMRgMADw6NGjRJNSHh4eeHh4WDNMDAYDXl5ejB8/HpVKRbFixfD392f27NmSlBKpkiIwMCYBde6cKRGV0Iwmg5MTuiJF0JYoQXSJEmiLF0efPz8oFKDX43jiBEp/f9PJxbiMCgX67NnRli9vjS4JIeyAJKWESEeioqIYMWIEixYtAqBw4cJMnTqVIkWKWKbByEg8u3TBac8ejBoNL2bPjpkZBER+/TWOp07hpdUSkEoW//7g00+ZvWMHs2bNYtKkSey4fJkJAQHMmDGDChUqxC+s05kSVAnNwFJfuYLm6tW3tinT24UQIr62bduSK1cuxo8fj7e3NwaDgerVqxMdnfiphfe5fM/T0xOVSsXz104wPHv2jCxJrIWYLVs21Gp1vEv1ChYsyNOnT9G+2thCCFtRhIfjcPFiTALq1aV46vv33yhnVKnQffxxTAKqeHG0JUqgK1QIEjtRqVIR/PvveHTujDHOrHeA2J9Chg+3+ZhOCJF+SFJKiHTi3r17dOvWjQsXLgDQrVs3+vXrh6Ojo0XaU4SH49m+PY6HD2N0ciJwwQKiqlb9fwGVKmaGUPbshNy9y4/dugExf1g4OTlZJKbkUKvV/Pjjj1SvXp3u3btz69YtmjZtio+PDz/++OP/1zNRqzFky4YhWzZ0CdSjOXaMzE2bvrU9md4uhBD/FxAQwO3bt5kwYQLlX820OHXq1FuPe5/L9zQaDcWKFePIkSPUqlULiJkFdeTIEX744YdE6ytTpgybNm3CYDCYvhvu3LlDtmzZJCEl3ioyMpJevXoBZhj7aLWmhcgdzp9Hc/486uvXUbyaZRiXLn9+tCVLxiSgihdHV6QIRmfnlMVeuzYv5s59Y9a7AnjZs6eslymEMCtJSgmRDvz111/8/PPPvHz5Eg8PD/744w9q1KhhsfYUL1/i2bYtjidPYnBxIXDpUrSffZZoeYPBwPbt2wGYOnWqxeJKiU8//ZQdO3YwcOBA1q1bx/jx4zl58iS+vr54enq+9Xht+fLos2eX6e1CiHQpMjKSqKioN25XKpXx/rgODw9PtA6FQoFznD+Gw8PDyZAhAx4eHixZsgQ3NzceP37MxIkT3zg2IiIi3rpTjo6OpqTT6/XGltVqtWi12gRj6NSpEz///DOFCxemaNGiLF68mPDwcOrXr2/qQ79+/ciVKxe//vorAM2aNWPRokUMHDiQ1q1bc+/ePaZNm0abNm0IDw/HxcWFsLAw7t69a2r39u3bnDlzBnd3d3LmzElYWBjh4eHx4o2MjDRdspgQZ2dnFK/WYYyKikKv15ulrJOTkym5ptVq0ekSOuWS8rKOjo6m5QG0Wm2SM94cHR1NM8+io6OTLKvRaEz1pqSsTqd743UQl4ODg2k3xZSU1ev1REVFoVAoTM+r8bXv/5CQEJ49e0ZwcDDPnj0zjX2++uorXF1dcXR0xMPDA29vb7JmzYparcZgMBAZd5FxgwHV3bs4XLiAw4ULuF68iOu1ayiiojAAEYD21T991qxoixUjumhRtMWKYSxRAs2rk2FGo5GIiIiYdT4TeJ+qVCrTiUtT2TjCq1YlcP9+NGfOkFWnw7h+Pc5bt6I5eTLJ9/37fkYkt+zrnxHmLJvQcxvLxcUlWfW+XvZt7/uUlDXXZ8Trr2VLfkbEvu9TUtacnxFxT9Jb+jMiMWq12nRCIyVl3/iMeI+yb3vfJ6esJSY8KIxJvZPEWz179izJN8C7UigUZM+eHT8/vyQ/7NID6eu7MxgMTJkyhcmTJwNQtmxZZs6cSY4cOd677sQoXrzAq3VrNGfPYnBzI2D5cqJLl0647Kv+3r59mw9fLVp+8+bNeF+8qcHq1asZOHAgkZGR5M2bl4ULFya6rklcpt33IMHp7S/mzUuXZxPt6T0L9tVfe+qrpTk4OCR5aVhqV6pUKc6ePfvG7dWrV2fZsmWm3z/88MNEB7WfffYZ69atM/1etGhRAgMDE21zwYIFpplM5cuX5+HDhwmW++ijj9i/f7/p92rVqnHjxo0Ey+bKlYuTJ08CsGjRIoYPH57ouE2tVtOoUSPTyZMmTZpw/PjxBMs6Oztz69Ytjh07RtNkzJqNu+B1586dTcmKhMT9nvzpp59Yu3ZtomUvXLiAl5cXAAMHDmTJkiWJlj1x4gS5c+cGYMSIEcyePTvRsvv27aNQoUIATJo0yTTOSMj27dspWbIk2bNnZ8iQIYwcOTLRsmvXrqXiq3UWFy9ezKBBgxItu2TJEr788ksg5ns6qXW8Zs+ebZpJt3XrVrp27Zpo2cmTJ9O8eXMA9uzZQ9u2bRMtO2rUKNq1awfw1udaoVCk6HNToVDgnS0bhT092XflSqLlfIAJgMHdnZuFCvHxq9dzQtq2bcvo0aOBmFmJxYoVS7Rs06ZNTa/18PBwChYsmGjZJk2a4DtwIFkrVECh1b6xwUtclvqMKF68ODt27DD9bonPCIVCQf369Tlz5kyCZT09Pbl48aLp9+R8RsRq3bo1+/btS7AspP/PiBIlSgAwa9Ysm3xGzJkzh86dO+Pn58eWLVts8hkxePBgur26cuTcuXPUqVMn0bJ9+vShb9++AFy/fp3q1asnWrZr164MGTIEgAcPHry5JEkc5viMKFmyJP/++2+ix72LhPfdFUKkemFhYXTp0sX0JdCxY0fWrVtn0YSUMiCAzM2aoTl7Fr2HBwFr1iSakEpLmjdvzrZt28iTJw/379+nfv367Nq1663HxU5vN3h7x7tdAUR98UW6TEgJIYQlFC9e3JSQspQffviBTz75JNH73dzcUjybt2LFijx69CjJPxhE2hYSEsK2bduYO3dukuWMRiNKpZKsWbMmuLZZXJWBzUYj//r7My2JhBRAaNWqPDlyBP9Llwj644+Uhm82Bm9vwps0sVn7Qoj0S2ZKvSeZKfX+pK8p9+DBA3744QeuXr2KRqNh7Nixpoy+pSj9/fH67jscbt5EnyULAX/+ie4ts4nSykypWIGBgXTp0oVjx44BMZdy9O7d2zTtOVF6vWlR9+Dbt8k0ZAhGpZJn+/eje9Xv9MSe3rNgX/21p75aWlqfKfXgwQOzX74XERGBt7c3/v7+b7y+rHlpTnIvt3nfS3MUCoWpv/Zw+V727Nm5f/9+urh87+HDh/z1118cPHiQf/75B51OR0NgLJArTrkXrq6catwYRcOG5MqVC29vb1QqFXq9nqBHjyj2ammDezVr4n7lCupXM3scgNhVyXQKBbe9vLjk4MCBly/ZHxrKVSD2FaJWqylbtix169alVq1auLm5Jdo3c1ya8zqFQkHOnDl58eIFylu3yFqlCuFGI0+3bUP/aqZMXGn58j2FQoG7uzuPHz+2m8v34n4mp/fL93Lnzo2fnx/R0dFy+R7vd/le7Iw6c5Gk1HuSpNT7k76mzLlz52jTpg0BAQFkyZKFefPmUbZsWTNHGp/q4UO8mjdHfe8e+uzZeb56NfoPPnjrcWktKQUxX2rDhw837WD47bffMmnSpLcuahv3ufVo2xanv/8mok4dXrzlzGpaZE/vWbCv/tpTXy0trSelLDG+sbfXlz31Nz309cWLF2zbto0NGza8sfh+t2zZmPHkCUC8S9eMr/7YfzFzJvp8+XA4exbN+fM4nD9P1PXrZHz1WIQCrq+O0RUoELMTXokSaIsXJ/rTTyFOAsXf35/Tp09z+PBhDh8+zH///We6T6FQUKFCBRo1akSDBg3IkCGD2R+H173+3Hp07ozz9u2EN25M0LRpFm/fmtLD6zgl7Km/9tRXS7PE+CbdLHS+c+dOtm7dSlBQEHnz5qV9+/amP4ITEhYWxqpVqzh16hShoaFkyZKFtm3bUqpUKStGLUTK7Nmzh65duxIREUGRIkVYuHDhW6eIvy/V3bsxCalHj9DlyUPA6tXo8+SxaJu25ODgwMiRIylcuDC//vorGzZs4NmzZ8ybN4+MGTMmq46QX37Bcc8enLdvJ/TcOaJfXUcvhBBCiNTl3LlzLFy4kK1bt5pmRCgUCj777DO++eYbqlepQvlXs9FfnzetMBoxAh7dur1xX9y5FSF9+hBRtizRxYtjzJQpyXi8vb2pV6+eaY2se/fusWvXLrZu3crZs2c5fvw4x48fZ/jw4TRq1IjWrVtTpEiRd38AUii0Rw+ct2/HedMmXvbvjz5XrrcfJIQQSUgXa0odO3aMpUuX0qRJE8aNG0fevHkZNWoUwcHBCZbX6XSMHDmSZ8+e0adPH6ZOnUqXLl2SteOWELayatUq2rdvT0REBFWqVGH9+vUWT0ipb94kc+PGMQmpAgV4vn59uk5IxdWqVSuWLFmCi4sLhw8fpnHjxjx5dZb0bXQff0xE48YAuI0ZY8kwhRBCCJFCOp2ODRs2ULduXerUqcP69evRarUULlyYIUOGcOrUKdauXUv79u356MkTVH5+iS7urXj1z+DqSmSVKrzs3ZuARYt4cvSoqUxYt25oK1d+a0IqIfny5aNLly5s27aNEydOMHDgQAoUKEBYWBjLly/n66+/pmHDhvz9999JXvJlLtHFixNVqRIKvR7XdDgbXAhhfekiKbVt2zZq1KhBtWrVyJUrF506dUKj0cTbdSGuffv2ERoaSr9+/fj444/JmjUrn3zyCfny5bNu4EIkg9FoZMqUKfj4+KDX62nSpAlLliyx+JRt9eXLeDVujOrJE6ILF+b5hg0Y3nERdWdnZ27evMnNmzfjXeOf2lWrVo3169eTOXNmLl++TP369ePtpJKUlz4+GB0ccDxyBM2hQxaOVAghhBBvEx0dzZ9//kmVKlX48ccfOXv2LBqNhsaNG7Nt2zbTjPS4m8aonj5NVt3B48YRuHIlL/v3J6pmTRzz5jX72Cd37tz06NGDQ4cOsWbNGurXr4+DgwOnT5+mXbt2fPXVV6xfvz7JNXvM4WWPHgC4rFiBMokdNYUQIjnSfFJKp9Nx584dihYtarpNqVRStGjRRLf+/OeffyhYsCALFiygU6dO9O3blw0bNljl7IIQKWE0Ghk6dCgTJ04E4Mcff2Tq1KmmBfUsxeHsWTI3a4YqIABtsWI8X7MGw3tcO6xQKHBxccHFxeXti4anMsWKFWPLli3kz5+fhw8f8u2333Lp0qW3HqfPnZuwNm2AV7Ol5Pp1IYQQwiZ0Oh0rV66kUqVK9O3bl3v37uHp6YmPjw+nT59m2rRplCxZMsFj9VmzJqsNfbZs8X635NhHoVDw+eefM2vWLE6ePEn37t3JkCED165do1evXlSrVo1t27ZZbO0c7RdfoC1WDGVkJK4LF1qkDSGE/Ujza0qFhIRgMBhwd3ePd3vszgkJefLkCc+ePaNSpUr8+uuv+Pv7M3/+fPR6PU2bNk3wmNdX9H99pwZzi60zrf0B/y6krwnT6/X88ssvrFixAoCRI0fSvn17i8YH4HDyJJ6tW6MMDUVbpgyBy5eDm1ui09aTkl6e23z58rFlyxZatWrFhQsXaNasGStWrIi3Bl1CfQ3r3RuXP/9Ec+ECzjt2EFm3rtVjt4T08rwmlz311576KmJYc3xjb68ve+pvau2r0Whk3759jBgxwnSyOkuWLHTr1o02bdoka+OV6AoVMLi5oQwJSbgNhQJD9uxEV6hgk/57e3szePBgfvzxR5YuXcqcOXO4c+cOXbp0oXjx4gwcOJAvvvjinetP8LlVKAjr0QNNly64LlpEWPfuGF1dE6kh7Uitr2NLsaf+2lNf06I0v/teYGAgXbt2ZeTIkXz00Uem25cvX86VK1cYPXr0G8f07t0brVbLjBkzTFtZbtu2jS1btjA3kWuj16xZw7p160y/58+fn3Hjxpm5N0LE0Ol0tGvXjhUrVqBUKlmwYAHt2rWzfMN79kCDBhAeDtWqwZYtYIbLBKOioujSpQsAc+bMMW0vmtYEBwdTu3Ztjh07RoYMGdi+fTuVK1dO+qBhw2D4cPjoI7h8GdRp/lyAECIdkfGNSK8uXLhAnz592Lt3LwCenp4MHjyYrl27puxyugULoGPHhO+L/QN33Tr49tt4d9lq7PPy5UsmTZrEpEmTCA0NBaBevXpMnTqVAgUKmK8hvR4+/hhu3YLJk+Hnn81XtxDCrqT5pJROp+P777+nT58+lCtXznS7r68v4eHh9O/f/41jhg4dilqtZsiQIabbzp49y5gxY1i5ciXqBP5oTOxM4rNnzyxy3bZCocDb2xt/f/90v22l9DU+rVZLjx492L59O2q1munTp9OgQQOLx+a4Zw8enTqhiIoislo1XsyfH2+L4ncR2987d+7wwQcfAHDr1q1knZlMrcLCwvjhhx84cuQITk5OLFy4kKpVqyb63CpeviTLZ5+hCgwkaMIEIlq1smH05mFP71mwr/7aU18tTa1Wm33LZEuw5vjG3l5f9tTf1NTXsLAwJk6caLoKQqPR0KFDB3r16kWmFC407rRpE+49eqAwGon46is0ly6h8vMz3a/PkYOQ338nsnbtN44NDw837QRui7HP8+fP+eOPP1iyZAk6nQ5HR0d69OhBjx49UpSUS+q5dV6xAvd+/dBnz87T48dBozF3N6wqNb2OrcGe+mtPfbU0S4xv0vwpe7VaTYECBbh06ZIpKWUwGLh06RK1atVK8JhChQpx9OhRDAaDaaaUn58fHh4eCSakIGab+MTW8bHkC9toNNrNG0f6GvPHQZcuXdi1axcajYY5c+ZQs2ZNiz8uTtu349GjB4roaCK++YYXM2aAo6PZ1kGKG39af55dXFxYvHgxnTt3Zt++ffzwww8sWrSIqlWrAm/2z5ghA6G9epFp2DAyTppEeKNG753sSy3S+nOZUvbUX3vqq72zxfjG3l5f9tRfW/bVaDSya9cuBg8ejN+rxFHt2rX57bffyJ07t6lMcjnu3o17r14ojEbCWrcmeMwYMBhwPHUKL62WAI2GqHLlQKVKcLxk67GPl5cXv//+O61bt2bw4MEcOXKEyZMns27dOsaPH5/iS/oS6kN448ZknDgRlZ8fThs2ENG8uTm7YDP29J4F++qvPfU1LUnzC50D1K1bl71793LgwAEePnzI/PnziYqKMv2R6Ovry8qVK03la9asSWhoKIsXL+bx48f8+++/bNy4ka+//tpGPRAiZtZfz5492bVrF46OjixatIiaNWtavF3n9evx6NoVRXQ04Q0b8mLWrJiElEiUs7MzCxYsoFatWkRFRdG+fXuOHTuWaPmwNm3Q5cyJyt8f18WLrReoEEIIYQcCAgLo0qULHTp0wM/Pjzx58rB06VLmzZtnSkilhObQITy7dEGh0xH+7bcEjx4dc6meSoW2YkVo0SLmf5XKAr0xr4IFC/Lnn38ye/ZssmfPzn///cd3333HgAEDePny5ftV7uhIaKdOAGSYORNk0yghxDtIF0mpihUr0rp1a9asWUP//v25d+8eAwcONC1+/vz5c168eGEqnzlzZgYNGsTt27fp168fixYt4ptvvqFhw4a26YCwe3q9np9//plt27bh4ODAvHnzTElVS3JZsQL33r1RGAyEN29O0LRpYOGd/dILjUbDrFmzqFGjBpGRkbRp04ajR48mXNjRkZc+PgBk9PVFERxsxUiFEEKI9Gv37t1Ur17dtOxBz5492bdvHzVq1Hin+jSnTuHZvj0KrZaIb74haMoUUKbtP5kUCgX16tXj4MGDpjVKly9fTvXq1Tl48OB71R3+/fcY3NxwuHULp927zRCtEMLepPnL92LVqlUr0cv1hg0b9sZtH330EaNGjbJwVEK8ncFgoF+/fmzYsAG1Ws2cOXPeeSCVEq4LFpDpt98ACGvXjuARI9L8oMvaNBoNc+fOpX379hw8eJBvvvmGVatWJbitdETjxmSYNQuHGzfIMHMmL3/91QYRCyGEEOlDSEgIv/32G2vXrgVilueYOnUqxYoVe+c6HS5cwLNNG5QRETHra86Yka42KHF1dWXUqFHUqVMHHx8f7t+/T8uWLWnbti1DhgxJ2QLwrxgzZiSsTRsy+vqSwdeXyK+//v8C8EIIkQzyF6gQNmQ0Ghk4cCCrV69GqVTi6+trlctIM/j6mhJSoV27EjxypCSk3pGTkxMLFizg888/5+XLl7Rs2ZILFy68WVCl4uUvvwDgOn8+yidPrBypEEIIkT6cPXuWmjVrsnbtWhQKBd27d2fHjh3vlZBSX7+OZ8uWKF++JKpCBV7Mm5dulzOoWLEie/bsoX379gAsWbKEevXqcePGjXeqL6xjR4yOjmjOnkVz4oQ5QxVC2AH5K1QIGxozZgzLli1DoVDwxx9/UK9ePcs2aDSSccIE3MaMAeBlnz6EDB4sZ7Tek7OzM4sXL+bzzz8nJCSEVq1acevWrTfKRdasibZ0aZSRkWScOtX6gQohhBBpmNFoZN68eTRq1IgHDx6QJ08eNm7cyKBBg3BycnrnelV37+L13XeoXrxAW7IkgUuWYEwnm5IkxsXFhREjRrBixQoyZ87M1atX+eabb1ixYkWKF4I2ZMlC+KtFzjPMmGGJcIUQ6ZgkpYSwkblz5zLj1Rf3+PHj+fbbby3boNGI24gRpmRIyMCBvOzb1yoJKWdnZy5cuMCFCxfeaWp4WuDq6mo6SxsYGEirVq3w9/ePX0ihIGTgQABcVq5EdfeuDSIVQggh0p6goCA6dOjAsGHDiI6Opk6dOuzatYuyZcu+V72qR4/wat4c1dOnRBcuTMCyZRgzZHjveNPK2Kdq1ar8/fffVK5cmcjISPr370+PHj0IDw9PUT2hXbtiVCpx2r8f9aVLFopWCJEeSVJKCBtYv349w4cPB+CXX36hZcuWlm3QYCDToEFkmDMHgOARIwjt0cOybcahUCjw8vLCy8sLRTqeleXm5sby5cvJnz8/Dx8+pFWrVgQFBcUro61Qgcjq1VHodGScMME2gQohhBBpyOXLl6lVqxa7du1Co9EwatQo5syZg5ub23vVq3z6FK9mzVA/eoSuQAECVq3C6OFhlpjT0tgna9asrFixgkGDBqFWq9m8eTP169fn3r17ya5DnzcvEa9m/GeYOdNCkQoh0iNJSglhZX/99Rc///wzAB06dKBnz56WbVCvx93HB9clSzAqFARNmEDYqzUEhPllzpyZlStXki1bNq5du0a7du2IiIiIVyZkwAAAXDZvlrOJQgghRBK2bt1KgwYNePDgAXnz5mXLli20a9fuvRM9isBAvFq0QH3vHrrcuXm+ejWGLFnMFHXao1Qq6d69O2vWrDFdzle7dm3279+f7DpCu3cHwHnrVlQpSGgJIeybJKWEsKJ///2XJk2aoNPpaNSoEcOGDbPs2bPoaNx//BGX1asxqlQETZtGuKVnZSUgKiqKgQMHMnDgQKKioqzevrXlyZOH5cuX4+bmxunTp+natSs6nc50v65IEcIbNQLAbexYW4UphBBCpFoGg4Hx48fTtWtXIiIiqFKlCjt27KBo0aLvXbfi5Uu8vv8eh2vX0GfLRsCff2LIkcMMUf9fWh37lC9fnp07d1KyZEmCg4P5/vvvGT16dLLWmdIVKUJktWooDAbT7HwhhHgbSUoJYSV37tyhdevWhIeHU7VqVSZPnozSkjveRUXh0bUrLps3Y3Rw4MWsWURYet2qROj1epYsWcKSJUvQ6/U2icHaPvnkExYvXoyTkxN79uxh4MCB8QZ0L318MKrVOO3fj+bYMRtGKoQQQqQuoaGhdOjQgT/++AOArl27snTpUtzd3d+7bkV4OJ5t2qA5fx69pycBf/6JPl++9673dWl57JM9e3bWr19Pq1atMBqNDBo0iN69eycruRa7PITL6tUonz2zdKhCiHRAklJCWEFgYCBt2rThxYsXlC1blnnz5qHRaCzWniIiAs8OHXDeuROjoyOBCxYQWaeOxdoTCStfvjyzZs1CqVSyYsUKZs+ebbpPny8f4a1aAeA2ejSkcKcbIYQQIj3y9/fn22+/Zffu3Tg6OjJt2jSGDBmCWq1+/8qjovDo0AHHU6cwuLkRsGoVuo8+ev960yFHR0fGjx/P2LFjUalUrFu3jpYtWxIYGJjkcdoKFdCWKoUiKgrX+fOtFK0QIi2TpJQQFhYVFUXHjh25e/cuuXLlYsuWLbi6ulqsPUVYGJ6tW+O0fz8GZ2cCliwhqkYNi7UnklazZk2GDRsGwMiRI9m2bZvpvpc//YTB2RnN2bM47dplowiFEEKI1OH69evUr1+fy5cvkzlzZtavX0/jxo3NU3l0NB7duuF06BAGFxcCli5FV6SIeepOx9q0acP27dvJkCEDJ06coH79+txNavdghcI0W8p1yRIUISFWilQIkVZJUkoICzIajfj4+HDy5EkyZszIsmXL8Pb2tlh7iuBgvFq0wPH4cQwZMhC4ciXaL76wWHsieTp06ED7V4vL9+7dm3///RcAQ9ashHXqBEDGsWMhjU3vF0IIIczl6NGjNGzYkEePHlGgQAG2bNlCyZIlzVO5Xo/7zz/jvGtXzAzyRYuILlvWPHXbga+//potW7aQM2dO7t69S926dTlz5kyi5SNr1iT6ww9RvnyJ6/LlVoxUCJEWSVJKCAuaMmUKGzZsQKVSMXfuXAoVKmSxthSBgXg1b47mn38wuLsTsHo12nLlLNaeSJlhw4ZRo0YNIiMj+eGHH3jw4AEAod26YXB3x+HmTZzXrbNxlEIIIYT1bdy4kVatWhESEkLZsmXZvHkzefPmNU/lRiOZfvkFl40bMarVBM6di7ZSJfPUbUc+/vhjtm3bRokSJQgKCuK7775LfGc+pdK0E5/rvHkQGWnFSIUQaY0kpYSwkPXr1zNp0iQAxowZQ+XKlS3WlvLpUzI3bYrm4kX0Xl48X7OG6BIlLNaeSDmVSsWsWbP49NNPef78OW3atCE4OBijmxsvf/wRgIwTJ8rATQghhF1ZuHAhPXv2JDo6mrp16/Lnn3/i6elpnsqNRtyGDcN15UqMSiUvpk8n6ssvzVO3HcqaNStr166latWqRERE0K5dOzZt2pRg2YhGjdBnz47q6VNc1q+3bqBCiDRFklJCWMDp06fp27cvAN27d6fVqwWtLUH5+DGZGzeO2dbY25uA9evRffqpxdoT787V1ZUlS5bg7e3NjRs36Nq1KzqdjrC2bdFnz4768WNcly61dZhCCCGExRmNRv744w+GDBkCxFzqPmvWLJycnMzWRsaJE8nwarHtoIkTiaxf32x12ysXFxcWLVpEgwYN0Ol09OzZk8WLF79ZUKMhtEsXADLMnClLFAghEiVJKSHM7PHjx3Tq1Ino6Ghq167Nr7/+arG2VP/9R+bGjVHfuYMuZ06er1+PrmBBi7X3rpycnDhx4gQnTpww62AzLcqePTtLlizB2dmZQ4cOMXr0aHB25uWrJGaGadNQvHxp4yiFEEIIyzEajYwaNYrx48cD0KdPH4YPH45Sab4/TTLMnEnGqVMBCBo5kojmzc1Wd3Kk57GPRqPB19eXdu3aYTQaGTRoEJMnT8b42k7C4S1bYnB3R33vHk47dtgoWiFEaidJKSHMKCIigo4dO/Ls2TMKFy7M1KlTzTrAikt1+zaZGzVC/d9/6PLlI2DjRvT58lmkrfelVCrJnTs3uXPnttjjkZYUKVKEKVOmADBnzhzWrVtHeNOmRH/wAaoXL8gwe7aNIxRCCCEsQ6/X88svvzBr1iwAhg4dSt++fVEoFGZrw2XxYtxGjQIgZOBAwn/4wWx1J1d6H/solUpGjhxJnz59AJg0aRJjxoyJl5gyuroS9uqxzzBjBryWtBJCCJCklBBmYzQaGTBgAOfPn8fDw4OFCxfi6upqkbbU166RuXFjVP7+RH/0Ec83bECfM6dF2hKWUa9ePXr37g1A//79OXfpEi8HDADAde5clM+e2TI8IYQQwuz0ej0//fQTy5cvR6FQMGHCBDp37mzWNpzXrMF90CAAXvbqRWiPHmatX/yfQqGgb9++DB8+HIAZM2YwYsSIeImpsPbtMTg5obl4Ec3hw7YKVQiRiklSSggzmTdvHuvXr0elUjF79mzy5MljkXYcLl6MSUg9e0b0p58SsG4dhmzZLNKWuWi1WkaMGMGIESPQarW2DifV8PHx4auvviIqKooOHTpwv3RptCVKoAwPJ8O0abYOTwghhDCb2ITUhg0bUKvVzJgxg5YtW5q1Dadt23B/dTl8aIcOvOzf36z1p4Q9jX06duzIqFcz0+bMmcPQoUNNiSmDpyfhr9ZWzThjhs1iFEKkXpKUEsIMDh06xIgRI4CYaeiVLLTVsMOZM3g1a4YyKAhtyZI8X7MGg5eXRdoyJ51Ox+zZs5k9ezY6nc7W4aQaSqWS6dOnU7BgQfz9/enUuTMBPj4AuC5bhuq//2wcoRBCCPH+9Ho9P//8sykhNXv2bBo0aGDWNhz37sWjRw8UBgNhLVoQMnw4mPGSwJSyt7FPu3btGDduHAALFixg0KBBGAwGAMI6d8aoUuF45AgO587ZMEohRGokSSkh3tO9e/fo1q0bBoOBZs2a0b59e4u0ozl2DK8WLVCGhBBVvjwBq1ZhdHe3SFvCejJmzMjChQvJlCkT//zzD323byeycmUU0dFknDDB1uEJIYQQ70Wv19OnTx/TbPJZs2bxzTffmLUNzdGjeHbujEKnI7xBA4LHjbNpQspeff/990yaNAmFQsGSJUsYOHAgRqMRfa5cRDRsCLxaW0oIIeKQpJQQ7yE8PJyOHTsSFBREyZIlGTNmjFkX6ozleOAAXq1bowwPJ7JyZQJXrMCYMaPZ2xG2UaBAAWbOnIlSqWTVqlWsLVUKAOeNG1FfuWLj6IQQQoh3o9fr8fHxYd26dahUKmbOnEnt2rXN2obDmTN4tmuHIjKSiJo1CfrjD1CpzNqGSL7vvvuOyZMno1AoWLZsGcOGDcNoNBLavTsATn/9herWLRtHKYRITSQpJcQ7MhqN/PLLL1y9epWsWbMyf/58i2z567RrF54//IAiMpLIr74icNEijM7OZm9H2FbVqlXp/2rti06zZuFXuTIKoxG3V1PhhRBCiLTEYDDQv39/1qxZg0qlYsaMGdStW9esbagvXTKdtIv64gtezJoFDg5mbUOkXLNmzZg0aRIA8+fPZ/z48eg+/pjIr75CYTSSYc4cG0cohEhNJCklxDtasWJFvKno3t7eZm/DafNmPDp1QqHVElG3LoFz54IFEl8idejRo4dp4fPvbtzAqFLhtGcPmlOnbB2aEEIIkWxGo5GhQ4fy559/olKpmD59OvXq1TNrG+qbN/Fq2TJmWYOyZQlcuFDGSKlI8+bNTYufT5s2jenTp/Py1U6ILmvXovTzs2V4QohURJJSQryDCxcuMGTIEAB+/fVXKlSoYPY2nFevxqNnTxR6PeGNG/NixgzQaMzejkg9lEolU6dOJW/evBzy92f7q10VM44eDXG2VxZCCCFSs0mTJrFw4UIApkyZYvZFzVX37+P13XeoAgLQFitG4NKlGF1czNqGeH/t2rVj8ODBAIwdO5ZZ588TVa4ciuhoMsyfb+PohBCpRbpJSu3cuZMePXrQqlUrBg4cyK1kXqt89OhRmjVrxvjx4y0coUgvXrx4QefOndFqtdSqVYuuXbuavQ2XxYvx6NMnZgeZVq0ImjoV1GqztyNSH3d3d+bOnYuTkxNdHj8mWq3G8fRpHPfssXVoQgghxFvNmzePKVOmADBq1CgaN25s1vqVjx/HJKT8/YkuVChmnU03N7O2IcynW7du9OnTB4jZoXrbp58C4LJsGYqgIBtGJoRILdJFUurYsWMsXbqUJk2aMG7cOPLmzcuoUaMIDg5O8rinT5+ybNkyChcubKVIRVpnMBjo3bs3Dx48IF++fKaFHM3JdfZs3AcNAiC0Q4eYHWSUafut6uTkxL59+9i3b59F1t1Kb4oUKcLo0aN5DEx+tY2029ixoNfbNjAhhBAiCX/++SfDhg0DoH///rRr186s9SufP8fru+9Q//cfunz5CFi1CoOnp1nbMBcZ+/xfnz596NatGwBNFy0iMEcOlGFhuC5ZYuPIhBCpQdr+S/eVbdu2UaNGDapVq0auXLno1KkTGo2G/fv3J3qMwWBg+vTpNGvWjKxZs1oxWpGWzZgxg7179+Lk5MScOXPIlCmT+So3GskwZQqZRowA4OWPPxIyfHi62NJYqVRSqFAhChUqhDKNJ9ispXnz5rRs2ZKxQJBCgcO1azhv2mTrsIQQQogEbdiwAR8fHwC6dOlCr169zFq/IigIrxYtcLh9G12OHASsXo3h1WXuqZGMff5PoVAwaNAgWrdujRH4+ckTAFwXLICICNsGJ4SwuTT/CanT6bhz5w5FixY13aZUKilatCg3btxI9Lh169bh5uZG9erVrRGmSAeOHj1qusxz1KhRFClSxHyVG41kHDsWt4kTAQjp35+Xv/ySLhJS4t2NGDGC3EWLMvbVelIZx4+HqCgbRyWEEELEd+TIEVq0aIHBYKBFixYMGTLErDPJFaGheH3/PQ5XrqDPkoWA1avR58pltvqF5SkUCkaNGkXt2rVZoddzT6FAFRCAy+rVtg5NCGFjaX6RmpCQEAwGA+7u7vFud3d35/Hjxwkec+3aNfbt25eidaSio6OJjo42/a5QKHB2djb9bG6xdVqi7tQmLfT1yZMndO/eHYPBQPPmzWnRosU71ZNgX41G3IYOjTlbBIQMG0ZY586k3kcj+WL7GR0dzR9//AFAr1690KTDBdst8Tp2dnZm3rx5NPjqK3q9fEmOhw9xXbGC8A4dzNbGu0gL71lzsqf+2lNfRQxrjm/s7fVlL/29cuUKHTp0QKvVUrduXcaPH2/emUEREXj+8AOas2cxeHgQuHo1hg8+sNk4KbnPq1arZdq0aUDaHvuY83WsVqvx9fWlVatWTDx+HF/AydeXiNatU8Xaqfbyno1lT/21p76mRQqjMW1v6RQYGEjXrl0ZOXIkH330ken25cuXc+XKFUaPHh2vfEREBD4+PnTs2JGSJUsCMZdkhYWF0b9//0TbWbNmDevWrTP9nj9/fsaNG2fm3ojUyGAwULNmTfbu3UuxYsU4ceKEacD+3vR66NYN5s2L+X3WLLDAwum2FhYWRoYMGQAIDQ3F1dXVxhGlLevWrePvpk2ZA2gzZULz4AFkzGjrsIQQ6YCMb8T7ePDgAZ999hmPHj2icuXK7Nq1y7zrJ2m10LAh/PVXzPfevn1Qpoz56rcgGfskLjg4mK+/+IKtFy+SBQieOZNMr9acEkLYnzSflNLpdHz//ff06dOHcuXKmW739fUlPDz8jUTTvXv36N+/f7wzOLEPgUKhYOrUqXh7e7/RTmJnEp89e4bu1ULE5qRQKPD29sbf3580/hS9VWrv6/Tp0xkzZgzOzs7s3LmTggULvltFej2Op07hGRVFoKMjUaVKkcnHB5f16zEqlQRPnkxEs2bmDd7GYp/bO3fu8MEHHwBw69YtXNLhts2Wfh0P6t+fX5cvpyDg1707vNpi2RZS+3vW3Oypv/bUV0tTq9VkyZLF1mG8lTXHN/b2+krv/Q0ODqZhw4Zcv36dQoUKcfz4caKioszXV50O927dcN6+HaOTEwGrVhFdvrx56n4PyX1ew8PD+fDDD4G0Pfax1Ov4yZMn/F2lCv1CQrju5ITy/Hky2PiEW3p/z77OnvprT321NEuMb2w/T/I9qdVqChQowKVLl0xJKYPBwKVLl6hVq9Yb5XPkyMHEV+v2xPrzzz+JjIykXbt2ZM6cOcF2HBwccHBwSPA+S76wjUaj3bxxUmNfz5w5E28dqQ8//PCdYnTasYNMv/2Gys8PAE/A4OSEMjISo1rNi+nTiaxfH1JZ/80l7mOWGp9nc7JU/wYNG8aM/fuZ/OgRbnPmENSpEwobb9KQ3p/L19lTf+2pr/bOFuMbe3t9pcf+RkVF0b59e65fv463tzfLly/Hw8MDPz8/8/TVYMC9T5+YhJRGQ+DChWjLlUtV46S3Pa/pbexj7j5kzZqVSqtWEVqnDoUiI/m1WTO6bdmCOhVcxpcenq+UsKf+2lNf05I0v9A5QN26ddm7dy8HDhzg4cOHzJ8/n6ioKKpWrQrEzJpauXIlABqNhjx58sT75+rqipOTE3ny5EkVH4QidQgODqZHjx7o9XoaNWpEs3ecxeS0YwcenTujfJWQiqWMjMQIhHbpEpOQEiIJzs7ONFy+nLMKBa56PXc6dbJ1SEIIIeyQwWDgp59+4vjx42TMmJFly5aRM2dO8zVgNJJp0CBc1q3DqFLxYvZsoqpUMV/9ItXIW6IETxs1AqDW+fP89ttvkjAQwg6li6RUxYoVad26NWvWrKF///7cu3ePgQMHmhY/f/78OS9evLBtkCJNMRqN+Pj48PDhQ/Lly8eYMWPebWE8vZ5Mv/0GRmOiC3I6b9wYs7aUEG/x4UcfcbdzZwDKnTnDhW3bbByREEIIezNq1Ci2bNmCg4MD8+fP55NPPjFf5UYjbqNG4bp0KUaFgqBp04j8+mvz1S9SHZdBg9CrVFQBbixZwrzYdVaFEHYj3UwLqlWrVoKX6wEMGzYsyWN79OhhgYhEWrZ8+XJ27NiBg4MDM2fOJGNS17hHRKB6+hTVkyco/f1j/n/6FJW/P+obN0yX7CVEAagfP0Zz8iTaihXN3xGR7lQYMoSLGzZQ9Nkzgn76icCKFfH09LR1WEIIIezAsmXLmD17NgCTJ0+mUqVKZq0/w9SpZJg1C4Dg8eOJaNjQrPWL1MeQPTuRTZvi+uefDAAa/f47uXPn5ptvvrF1aEIIK0k3SSkhzOXatWsMGzYMDTCma1fKaLWotm+PSTY9eYIqNvH05Amqp09RBgW9d5uqp0/fuw5hJxQKMs2cCU2b0jQigi6dOzN87VrZ4lYIIYRFHT58mEGDBgHQv39/vv32W7PW7zp3Lm6v1n0NHjaM8JYtzVq/SL3CunXDZfVqGhiNFDYa6dmzJ+vXr6dEiRK2Dk0IYQWSlBK2pdejOXUKtFo0Gg1R5cqBSmXZNqOjY2YyPXnyRqIJPz9ynjzJA62WzADTp8f8ewuDkxMGb2/02bJhyJYNfdas6L29UYaEkHHatLcer7fxgtWW5ujoyPbt200/i/ejrliRJ59/TrajR6lz/DjLli2jTZs2tg5LCCFEOnXr1i26dOmCXq/n22+/pVevXmat32X5cjINHw5ASL9+hKWDdRNl7JN8ug8/JPKbb3DesYPJ3t7U8venXbt2bN26ldy5c9s6PCGEhUlSStjM6zvSeQH67NkJ/v13ImvXTnmFOh3KZ8/+n2zy94+ZyfTqd5W/f8zPAQFJVlMozs9GR8eYRFPWrOizZUPv7R2TdHr1L/Zno5sbJDRTRa/HZe1alP7+KBJYuNGoUKDPnh1tKtji2JJUKpWc7TIzxahRGKpVo5HRSJWhQ6lYsaJp62khhBDCXAIDA2nbti3BwcGUKVOGCRMmmHV2rvOGDWT65RcAXvboQWjv3mar25Zk7JMyod2747xjBzWfP6f6hx+y79Yt2rRpw5YtW5JeRkMIkeZJUkrYROyOdK9v7av098ejc2dezJ37/8SUXo/y+fP/z2p6bYaT6TK6Z88STPwkxOjggD5r1pik0qsZTtdDQpi+fj1+wM8TJlD8m28wursnnGxKLpWK4N9/x6NzZ4wKRbz4jK/qDRk+3PKzw0S6oytYkIhmzXBdvZrhWi09e/Rgy9ataDQaW4cmhBAindBqtXTu3Jl79+6RO3duFixYgJOTk9nqd/rrL9x/+gmF0UhYu3a8/PXX9xt3iTQrumRJoipWxPHYMVaWKUOJ0FBu3LhBjx49WLRoESoZKwuRbklSSlhfEjvSKYxGjIB7z57oChWKSTY9fYrCYEhW1UaVKmZWU+yldAnMcDJ4e2Nwdwfl/zeffPr0KTVr1CAQ6Nq1K8VatsRcG9JG1q7Ni7lz480Kg5hZYSHDh7/brLA0RqvVMn/+fAA6dOggiRMzedm3L84bN1JVqyXHpUtMnDiRgQMH2josIYQQ6YDRaGTgwIEcP36cDBkysHjxYjJnzmy2+h0PHMCje3cUej3hTZsSPGJEukpIabVaFixYAMjYJ7lCe/bE8dgxsmzezNKFC6n/ww/s3buXcePGyfhGiHRMklLC6jQnT751RzpFVBSaCxdMtxmVSgxZssS7ZC6hS+kMXl7xkk3JYTQa6du3L4GBgRQuXJj+/fu/a9cSFVm7NpFff43jqVN4abUEWGv9rFRCp9MxcuRIANq2bSsDMzMx5MxJ+A8/kGHOHMYAZWbMoEqVKnz++ee2Dk0IIUQaN2fOHFatWoVSqWTmzJl8/PHHZqtbc+IEHh06oNBqiahTh6CJE1M8fkvtZOyTclGVK6MtUgTNpUuUP3OGiRMn0rNnT2bMmMHHH39s9sX1hRCpgySlhNUld6e5l126ENmwYUzCKXNmiyVwli5dyr59+3B0dMTX19dyi1GqVGgrVoTs2dH6+b1x6aIQ7+Jlz564rFxJyZcvaQr07t2bPXv24O7ubuvQhBBCpFF79+41JVSGDh1KjRo1zFa3w7lzeLZtizIyksjq1Xnh6wtq+ZNEAAoFoT164NmtGxkWLODb06e51rMnvr6++Pj4kD9/fkqWLGnrKIUQZpa+TkmINCG5O81Fffkl0cWKYciWzWIJqVu3bvH7778DMHDgQLOeBRTCGoyenoR27QrAWLWaZ35+DBgwAKMkPYUQQryD27dv07NnT4xGI61ataJDhw5mq1t95QperVqhDA0lqmJFAufOBZlBJOKIrFMHXb58KIOCcFm5kgEDBvDVV18RFRVFhw4d8Pf3t3WIQggzk6SUsDpt+fLos2dPdM0mo0KBLkcOi+9Ip9Vq+fHHH4mMjKRy5cq0b9/eou0JYSlhnTqhz5KFfDodXZRKtm3bxtq1a20dlhBCiDTm5cuXdOjQgZCQEMqWLcvIkSPNttOe6vZtvFq0QBkUhLZUKQIXLwZnZ7PULdIRlcp0ss11zhyUOh2+vr4UKlSIJ0+e0KFDByIiImwcpBDCnCQpJazv1Y50CbHmjnRTpkzhwoULuLu7M2XKFJTpbC0DYT+Mrq68/OknAEY7O+MCDB48mPv379s0LiGEEGmHwWCgd+/e3Lx5E29vb+bOnWu2dZBUDx6QuXlzVM+fE/3ppwQsW4bR1dUsdYv0J7xpU/RZsqB+/BjnTZvIkCEDixYtwsPDg3PnztGvXz+ZES5EOiJ/hQubiPzmG/TZsr1xuz57dl7MnWvxHelOnTqFr68vAOPGjcPb29ui7QlhaeEtW6LLkwe3sDAm5MpFWFgYvXv3Rq/X2zo0IYQQacDUqVPZtWsXGo2G+fPnkzWZyy28jfLJE7y++w6Vnx/RH35IwKpVGGXdQ5EUJyfCOnUCIMOsWWAwkDdvXubOnYtarWbjxo3MnTvXxkEKIcxFklLCJhzOnUP95AkGR0cCly6FlSsJWLeOpydOWDwh9fLlS3r16oXBYKBp06bUrVvXou0JYRUaDS/79QOgc1AQuV1dOX36tAzahBBCvNWuXbuYNGkSAGPHjjXbYtLKwEC8vvsO9b176PLkIeDPP2N2ShbiLcJat8aQMSMON27guGcPABUrVmT48OEAjBo1iqNHj9oyRCGEmUhSStiEy7p1AETWrk3Ul19CixYxO9NZ+JI9gCFDhvDgwQNy587NiBEjLN6eAEdHR9auXcvatWstt7uhIKJhQ6ILF0YdGsr6smUBGD9+PFevXrVxZEIIIVKrGzdu8OOPPwLQvn17mjdvbpZ6FSEheLZsicONG+i9vQlYvRpD9uxmqTstkLHP+zG6uRHWpg0AGadPN+1a3bZtW5o0aYJer6dbt248evTIlmEKIcxAklLC+qKicN60CYCIpk2t2vTOnTtZu3YtSqWSadOmkTFjRqu2b69UKhUVK1akYsWKqKyQeLRbSiUhv/4KQJnjx2n++edotVp69+6NVqu1cXBCCCFSm+DgYNq3b09YWBifffYZv/32m1nqVYSH49W6NZqLF9F7eRGwejX6PHnMUndaIWOf9xfWoQNGR0c0//6L5uRJABQKBWPHjuXTTz8lICCAzp07ExkZaeNIhRDvQ5JSwuqc9u1DGRSE3tubqEqVrNZuQEAAAwYMAKBbt26UK1fOam0LYS1R1asTVb48iqgofLNlw8PDg8uXLzN16lRbhyaEECIVMRgM/Pjjj9y9e5ecOXMyZ84cHBwcUl6RXo/m2DFYtSrm/7AwPH/4Ac2ZMxgyZSJg1Sp0H35o/g6IdM+QLRvhr05gZ5gxw3S7s7Mz8+fPx93dnXPnzpktmSqEsA1JSgmrc361VX34t99a5XI9AKPRyK+//srz588pVKgQffv2tUq7IkZ0dDSLFy9m8eLFREdH2zqc9E2hMM2W8tq8mZm9ewMwffp0/v33X1tGJoQQIhWZNm0ae/fuxcnJiQULFuD1Dms9Oe3YQbby5fFq0gRatsSrSROyFy2K45EjGFxdCVi+HN2nn1og+tRPxj7mEdq1K0alEqd9+1BfuWK6PU+ePMycOROFQsGKFStYsWKFDaMUQrwPSUoJq1IGBuK0dy8AEY0bW63dLVu2sH37dtRqNX/88Ydc229l0dHRDBo0iEGDBsnAzAqiy5YlomZNFHo9DU6fplGjRqatviMiImwdnhBCCBs7dOgQEydOBGD06NEULVo0xXU47diBR+fOKP384t2uiIrCCIR260Z0qVLmCDdNkrGPeejz5yfy1aZEGWbOjHdflSpVTFdBDB48mLNnz1o9PiHE+5OklLAq502bUOh0aIsWRffxx1Zp88mTJwwcOBCA3r17v9PAS4i05uWAARgVCpy3b2di8+Z4e3tz584dxowZY+vQhBBC2NCjR4/o3r07RqORVq1avdvC5no9mX77DYxGFIkUcVm5EvT694pVCICXPXoA4Lx5M6r79+Pd17NnT7755hu0Wi2dOnXi+fPntghRCPEeJCklrMr51a571lrg3Gg00q9fP4KCgihatKhpdxkh0jvdxx+bZiPmnDHDdEZ8wYIFHD582JahCSGEsJGoqCi6dOnCixcvKFq0KL///vs71aM5eRKVn1+iCSkFoH782LQ4tRDvQ1ekCJFVqqAwGMgwZ068+xQKBVOmTOGDDz7Az8+Prl27otPpbBSpEOJdSFJKWI36xg00589jVKuJaNjQKm2uXr2avXv3otFo+OOPP95tAU8h0qiXPj4YNRocDx/ma5WK1q1bA9CnTx9CQkJsHJ0QQghr+/333zl79iyZMmVi7ty5ODk5pbwSoxHN6dPJKqp6+jTl9QuRgNBXs6VcVq9G+dpsqIwZM7JgwQJcXV05fvw448ePt0WIQoh3JEkpYTWxs6Qiq1fH8A6LaabUw4cPGTp0KAD9+vWjUKFCFm9TiNREnzs3Ya8SUW5jxzJk8GDy5s3L48ePZacaIYSwMxs3bmTx4sVAzCLnefLkSdHx6lu3yDhxIlm/+AK3ZP7Rr8+aNaVhCpEgbcWKaEuWRBEZieuCBW/cX7BgQSZPngzAjBkz+Pvvv60dohDiHUlSSliHXo/L+vUARDRpYvHmDAYDffr0ITQ0lNKlS9OlSxeLtylEahTaqxcGV1c058/jdeAAU6dORaFQsHbtWnbv3m3r8IQQQljB9evX6devHwC9evXiyy+/TNZxykePcJ01iyw1a5K1ShUyTpmC+u5dDI6OGJycMCZynFGhQJcjB9ry5c3UA2H3FArTbCnXJUtQvHz5RpG6devSoUMHAH766ScePHhg1RCFEO9GklLCKhyPHkXl74/B3Z3IZA6E3sfSpUs5evQoTk5OTJ06FZVKZfE2hUiNDJkzE/YqKes2bhzlSpUyJWl/+eUXgoKCbBidEEIIS3v58iUdO3YkIiKCSpUq4ePjk2R5ZUAALkuW4NWoEd7lypFp5EgcLl/GqFYTWaMGL6ZP58nFiwRNnw4KBUZF/JWlYn8PGT4cZPwlzCjy66+J/uADlMHBuKxYkWCZwYMHU7JkSYKCgujatStRUVFWjlIIkVJqWwdgLjt37mTr1q0EBQWRN29e2rdvz4cffphg2T179nDo0CFT9rxAgQK0aNEi0fLi/TmvXQtARP364Oho0bbu3r3LyJEjARg0aBAFChSwaHvi7TQaDUuWLDH9LKwrtHNnXBYvRn3nDi5r1uDj48Pff//N7du3GTp0KH/88YetQxRCCGEBRqORvn37cufOHbJnz87MmTMTPFGnePkSp507cd68GcdDh1C82jXPqFCgrVCBiAYNiKhTB6Onp+mYyNq1eTF3Lpl++w2Vn5/pdn327IQMH05k7dqW72AqJmMfC1AqCe3eHY++fckwdy5hP/zwxt8VGo2GOXPmULNmTc6dO8eIESNMfxcIIVKndDFT6tixYyxdupQmTZowbtw48ubNy6hRowgODk6w/JUrV/j8888ZOnQoI0eOxMvLi5EjRxIYGGjlyO2DIjQUp7/+AiDcwpfu6fV6fv75ZyIiIqhYsSLt2rWzaHsiedRqNV9++SVffvklanW6yYWnGcaMGQnt3RuAjJMm4QxMnjwZpVLJunXrZN0FIYRIp+bNm8f27dtRq9XMnj0br7hrekZG4rRjBx6dO+NdogQeP/2E0/79KPR6tMWKETxkCE9OnSJg3TrCW7eOl5AyVVG7Nk9OniRg3TpYuZKAdet4euKE3SekQMY+lhLRqBF6b29UT56YlgZ5Xc6cOU0n3BYtWsTmzZutGaIQIoXSRVJq27Zt1KhRg2rVqpErVy46deqERqNh//79CZbv1asXX3/9Nfny5SNnzpx07doVo9HIxYsXrRy5fXDavh1lRAS6AgWILlXKom3NmzeP06dP4+rqavqjWwgBYa1bo8uZE5W/P66LF1OmTBk6d+4MwIABA+QyPiGESGfOnj3LqFGjABg6dChlypQBnQ7Hgwdx//lnvEuUwLNTJ5y3b0cRGUn0Bx8Q4uPDk0OHeP7XX4R17YohR463N6RSoa1YEVq0iPlfLtkTluToSOir8UuGWbPg1ay+13355Zf07NkTiNnw6Pbt21YLUQiRMmn+L3adTsedO3coWrSo6TalUknRokW5ceNGsuqIiopCp9ORIUMGS4Vp11xe7boX3qQJvLbugDndvn2bCRMmADGDr9y5c1usLZEy0dHRrF69mtWrVxMdHW3rcOyToyMvX60jktHXF0VwMD4+PhQoUIAnT54wbNgw28YnhBDCbEJCQujevTs6nY46tWvTpWhR3AYPJlvp0ni1bInLmjUoX75Enz07od268XTXLp4dPEjozz+j/+ADW4efLsjYx3LCW7XC4O6O+s4dnHbuTLRcv379+OyzzwgLC6NLly5ERERYMUohRHKl+bmkISEhGAwG3N3d493u7u7O48ePk1XHihUr8PT0jJfYel10dHS8LxSFQoGzs7PpZ3OLrdMSdVuT6uFDHI8dA2J23UuoP+boq8FgwMfHh8jISL744gtatWqVKh+79PK8JldsP3U6HX369AGgfv366XJthbTw3EY2aUL07Nk4XL9Oxtmz4ZdfmDJlCg0bNmTt2rXUq1cvWTsypYW+mpM99dee+ipiWHN8Y2+vL1v112g00r9fPzL99x/TM2aky7lzODRsaLpf7+lJZL16RDRsSHTZsvBqVvn7RGlPz21y+5pexj6p8rnNmJGwdu3IOHUqGWbMIKpOnQRPfDs4ODBz5kxq1qzJ1atXGTRoEFOmTEm02lTZVwuyp/7aU1/TIoXRaExsN9c0ITAwkK5duzJy5Eg++ugj0+3Lly/nypUrjB49OsnjN23axObNmxk2bBh58+ZNtNyaNWtY92rGD0D+/PkZN27c+3cgvRs1CgYPhmrVYN8+izUzffp0evXqhaurK5cuXSJfvnwWa0ukXFhYmGkmYmhoKK6urjaOyI5t2QINGoCzM9y+Ddmz4+Pjw6RJk8iRIweXLl3Cw8PD1lEKIaxExjfpzO3bnOnbF+fNm/k07u0ZMkCjRtCiBXz5JTg42CpCuyFjHwt79gzy5oWICNizB2rUSLTo/v37+fLLLzEYDCxcuJAffvjBioEKId4mzc+UcnNzQ6lUvrEeSlBQ0Buzp163ZcsWNm3axJAhQ5JMSAE0atSIunXrmn6PzbI+e/YMnU73TrEnRaFQ4O3tjb+/P2k2b2g0kmXhQtRAUP36RMTZmSWu9+3r/fv3GTBgABCz256joyN+ibRla+nieU2B2P4+efLEdJu/vz8uLi42jMoy0sxzW6YMXqVLo/nnH8J+/ZWQMWPo3r07Gzdu5M6dO3Tt2pWpU6cmWUWa6auZ2FN/7amvlqZWq8mSJYutw3gra45v7O31Za3+Kp88wWnLFpw3bUJz9ixlXt2uU6nQ1axJRMOGRH75ZczJCIDnz80egz09t8nta3h4uOnntDz2Sc3Prdt33+G6aBFRw4cT+MkniZb7+OOP6devH+PGjaN79+7kzZuXwoULv1EuNffVEuypv/bUV0uzxPgmzSel1Go1BQoU4NKlS5QrVw6IuZTr0qVL1KpVK9HjNm/ezIYNGxg0aBAfJOPaeQcHBxwSOatkyRe20WhMs28ch3/+QX3nDgZnZyJq135rP96lr7FbHUdERPDZZ5/RunXrNPF4peXn9V3E7Wt673ta6F/IwIFkbtwYlxUrCO3UCaf8+Zk8eTKNGjVizZo11KlTJ1mX8aWFvpqTPfXXnvpq72wxvrG315cl+qsICsJ5x46YRNSxYyhe1a8H9gL/fPQRbTZuRBH3BK0VHnN7em7f1tf0NvZJjX0I7dIFl6VLcTx8GPX580QXK5Zo2Z49e3Lq1Cn2799Ply5d+OuvvxJNFKbGvlqSPfXXnvqalqT5hc4B6taty969ezlw4AAPHz5k/vz5REVFUbVqVQB8fX1ZuXKlqfymTZtYvXo13bp1I2vWrAQFBREUFERkZKSNepA+xS5wHvnNNxgttIj88uXLOXbsGE5OTkyYMEF22xMiGbQVKhBZvToKnY6MrzYHKFu2LJ06dQJiduMLDg62ZYhCCCFeowgPx2nzZjzbtcO7RAnc+/XD8ehRFEYj2tKlWVq2LDmA77NkofaaNfETUkKkQ/rcuYlo0ACADDNmJFlWqVTyxx9/4O3tza1btxg8eLA1QhRCJEOanykFULFiRUJCQlizZg1BQUHky5ePgQMHmi7fe/78ebxFzf7++290Oh2TJ0+OV0+TJk1o1qyZNUNPv6KicN6yBYDwpk0t0sSjR48YOXIkEPNHdP78+S3SjhDpUcgvv+C0bx8umzcT2r07uiJF6N+/P3v27OHOnTsMGzYsycVAhRBCWIFWi+OBAzhv3ozTrl0o4+weFl24MBENGxLRoAGbz5+nS5cuAKz84480cemoEOYQ2r07Lhs24LR9O6o7d9AXKJBoWS8vL6ZPn07z5s1ZvXo1lSpV4ttvv7VitEKIhKSLpBRArVq1Er1c7/Wtzme8JZMu3p/Tnj0og4LQe3uj/fxzs9dvNBrp378/oaGhlC5dmg4dOpi9DSHSM92nnxLeqBEuGzfiNnYsgcuX4+zsHO8yvrp161IjiYVDhRBCWIBej+bECZw3b8Z5+3aUcdZN1eXNS0SDBkQ0bIiuUCEAHjx4QL9+/QDo0aMHVapUsUXUQtiErnBhImvUwGnvXjLMnk3w+PFJlq9YsSI//fQTkydP5pdffqFEiRIUSCKRJYSwPLnWSViEy9q1AIQ3aQIqldnrX7NmDQcOHMDR0ZHJkyejskAbwnw0Gg2zZ89m9uzZaXZL5PTopY8PRrUap/370Rw7BsS/jK9///5yGZ8QQliD0YjDuXO4DRtGtnLlyNysGa4rVsSc4MualdAOHXi2dStPjx7l5YABpoRUdHQ0PXr0ICQkhJIlS5qSU8L2ZOxjPaE9ewIxf38o42yuk5iffvqJzz77jLCwMLp160ZUVJSlQxRCJMFsSSl/f39u3LjB48ePzVWlSKOUz5/juH8/ABGNG5u9fn9/f4YPHw5A3759+fDDD83ehjAvtVpNvXr1qFevHmp1upmgmebp8+Uj/PvvAXAbPdq0CG7//v3Jnz9/vPeaECL1kbFXKqbXxyT7V62K+V+vT7CY+uZNMk6YQNZKlchSpw4Z5s1D5e+PIVMmwlq04Pnq1Tw5c4aQ338nulQpiLMcBcDEiRP5559/cHNzY+bMmYkuWi+sT8Y+1qMtV46osmVRaLW4zp//1vIqlYrp06fj6enJpUuXTMuBCCFs472SUgaDgfXr19OpUyd69+7NkCFD2LRpk+n+w4cPM3jwYB48ePC+cYo0xHnzZhQ6HdrixdF99JFZ6zYajfzyyy8EBwdTvHhx0/oJQoh387J3bwzOzmjOnsVp1y4AnJ2dmTJlCgqFgtWrV7P/VZJZCGF7MvZK/Zx27CBb+fJ4NWkCLVvi1aQJ2cqXx2nHDgBUjx6RYeZMsnz1FVmrViXj1Kmo793D4ORERP36BCxahP/ZswRPnIi2UqVEZ5wfOnTItCTF+PHjyZMnj9X6KERqE9q9OwCuS5eiSMYs7+zZszN16lQAFi5cyM6dOy0ZnhAiCe+clDIYDIwdO5Y1a9YQFhZGrly53ihTqFAhbt68ycmTJ98rSJG2OMdeumeBBc43bdrE33//jYODA5MnT5YzT2mETqdj69atbN26FZ1OZ+twRByGrFkJe3W5XsaxY01n88uWLUv79u0B6NevHy9fvrRZjEKIGDL2Sv2cduzAo3NnlH5+8W5X+vvj0akTWSpXJlu5criNGoXDlSsY1Woiv/ySF76+PLlwgRezZhFVsyY4OibZzrNnz+jVqxdGo5FWrVpRr149S3ZLvAMZ+1hX1JdfEl2oEMrQUFyXLk3WMTVq1DCd4O7bty8PHz60ZIhCiES8c1Jq9+7dnD9/niJFiuDr68ukSZPeKJM1a1ayZcvGhQsX3itIkXaor11Dc/EiRgcHIl9t0fo/9u47PKria+D4d0t6CEkIkBCkdwRBQJpUkSqdgHSkFwWl99577yK9hEgVBBQVVCAggvxQWkCUkkAgvWzK7n3/IOQlkkASstns7vk8j4+wd+7dc3I3YTJ35kxWCQ4OTt6+ddiwYZQpUyZLry+MJz4+noEDBzJw4EDi4+NNHY74j6hBgzC4umJz6xYOfn7Jr48dO5YiRYoQGBjIjBkzTBihEAKk75Xj6fXknjwZFAXVfw6pkl6zuX0bBYirWZOwefMIunSJkC1biG3bFsXJKV1vYzAYGDZsGMHBwZQuXVqWWedQ0vfJZmr1/8+W2rgRXtip8lXGjh1L5cqVCQsLY8iQITKAKIQJZHpQ6tSpUzg7O/PFF1/g7u6eZjtvb2+ePHmS2bcRZsYx6Rda3QcfYHjF5yIzJkyYQFhYGOXLl+fTpIKGQog3p7i4EPnZZwDkWrgQdDoAHB0dWbhwIQA7duzg9OnTJotRCCF9r5zO1t8fTWDgSwNS/xW6di1P/fyI6dYNJRN9pXXr1nHq1Cns7e1Zs2YNDg4OmQtYCAsT27o1id7eaJ48wdHXN13n2NrasmrVKnLlysWFCxeYMmWKkaMUQvxXpgelHj58SIkSJXB2dn5lO0dHR9m9yVro9Tjs2wdAbIcOWXrpb775hiNHjqDValm8eLEU8rQSHTp0YPLkyaYOwypE9+yJ3ssL7cOHKaa916xZk169egHPlvFFRUWZKEIhhPS9cjbN48fpaqdKo+h5evz+++/MnTsXgOnTp1M6aRc+IQRgY0P0wIEAOK9bB+mc9VS4cGEWLFgAwJw5c+QhnBDZ7I1qSqWnnk9YWJgMIFgJu59/RvPoEQZXV3QffJBl1w0JCWHChAkADBkyhLfffjvLri2s26NHjxgyZAjvv/8+BQsWTHUA7OjRozRr1oyyZctSokQJPvzwQ/xeWOIGz2YlFChQAJVKRYECBfD29sbb25s1a9ZkVypvzsGByBEjAHBevhzVCzWkxo8fz1tvvcX9+/eZPXu2qSIUwupJ3ytn0+fLl6Xt/is8PJzBgweTmJhIy5Yt6dKlS6auI4Qli/n4Y/Rubmj/+Qf7I0fSfV7Lli3p3r07iqLw2WefERwcbMQohRAvyvSglIeHB//+++8r2yQmJvLvv//i5eWV2bcRZuR5LZrYNm3A1jbLrjt58mSePHlC6dKlGTZsWJZdV4j4+Hjy5MnDsGHDKFeuXKptXF1dGTp0KIcOHeL777+nU6dODB8+nJ9++im5zaVLl7h8+TKBgYFcvnyZxYsXo1KpaN68eTZlkjVifHxIKFECTWjosyeMSZycnJKfIG7ZsoUzZ86YKkQhrJr0vXK2+OrV0Xt5oaRxXFGpSCxQgPjq1TN8bUVRGD16NPfu3eOtt95i/vz5qFSvWygohPVRHB2J7tMHgFwrV4KS1nfky6ZOnUqFChWSNxIwGAzGClMI8YJMD0pVqlSJx48f8/3336fZ5tixY0RERFC5cuXMvo0wE6rISOy//RaAmCxcunfixAn279+PWq1m8eLF2L1mNxphvmJiYhg6dCglS5akcuXKrF271ujv+dZbbzF9+nR8fHxwcXFJtU2tWrVo1qwZJUuWpEiRIvTt25eyZcty/vz55Db58uUjX758eHp6ki9fPo4fP06tWrUoXLiw0XPIUlotkWPGAOC0bh3qF54S1qlTh65duwLPdqiJjo42SYhCWDPpe+VwGg0xLVuigpcGppSkAaSIadNAo8nwpXfu3Mk333yDVqtl9erVaf6bJYR4VpLA4OiIzV9/YffCQ8TXcXBwYM+ePTg4OHD69GlWrVplvCCFEMkyPSjVqlUrHB0d2bhxI5s3b+bGjRsAxMXFcefOHXbs2MGOHTtwcXGhadOmWRawyJnsjxxBrdORUKIECZUqZck1w8LCGDt2LAADBw6kUhZdV+RMM2bM4Ny5c2zatImdO3dy9uxZ/ve//73yHH9/f0qWLPnK//Yl1TnLCoqi8PPPP3P79m1q1KiRapvg4GBOnjxJ586ds+x9s5OuWTPiK1VCHROD8/LlKY5NmjSJAgUK8M8//yQvqRVCZB/pe+Vw8fE4HD8OgPKful96Ly9C169Hl4kZtDdu3EheXj5mzBjefffdN49VCAumuLsTk/QgzTmDA0tly5Zl1qxZACxYsIDffvsty+MTQqT0+sIEaXBzc2PUqFEsWrSIb7/9lm+TZsmcO3eOc+fOAc8KbY4YMUKe5liB57vuxXboAFk0nXzatGk8evSIYsWKMXz48Cy5pjANGxsbFi9enPzn/4qOjmb37t0sX76cOnXqALB06VKqVq36yutWrFiREydOvLJN3rx5Mxn1/4uIiKBKlSrEx8ej0WiYPXs2devWTbWtr68vzs7ONGvW7I3f1yRUKiLGjcOjUyectm0jul8/9IUKAZArVy4WLFhA165dWb58OQ0aNKBatWomDlgI6yF9r5zNaft2tP/8gz5fPh6fOoXtn3+SJz6ep7a2xL33XqZmSMXGxjJo0CB0Oh316tVjYFIRZ5Hzva7v8yodOnSgXLlyTJ8+3RihWYWo/v1x2rwZu7Nnsbl4kYQqVdJ9bqdOnfj555/Zv38/Q4YM4fjx47i6uhovWCGsXKZnSgGUK1eORYsW0apVK9566y1sbW2xsbHB09OTZs2asWjRIsqUKZNVsVqlzz//PLlocuHChalRowYzZ85El7RlO8C9e/cYMWIENWrUoHjx4tSqVYuFCxcSHx9v1Nh0Oh3jx4/n7bJlyXP2LO2Bf+rVe+U5wcHBfP7557z77rsUL16crl27cufOneTjoaGhTJw4kSpVquCbtJVr2bJlSUhIMGYqwshsbGzo1KkTnTp1SrVjdvfuXeLj41M8/XVzc6N48eKvvK6DgwNFixZ95X+v26UqPZydnTlx4gRHjhxh9OjRTJs2Lc26Srt376Zt27bY29u/8fuaSvz776OrVw9VQgK5Fi5Mcax+/fp8/PHHKIrC8OHDiY2NNVGUQlgn6XvlTKrISJyXLAEgcvhwFBcX4mvVgs6dn/0/EwNS8KzGzY0bN8ibNy/Lli1DrX6jrrvIRq/r+5hSejZ6edHu3bspUKAAvXv3TvH6okWLqFu3LiVKlKBcuXJ06tSJ33//3Zihp5uhQAFi27YFwHn16gydq1KpmDNnDkWKFOH+/fuMGjUKJQO1qYQQGZPpmVJPnjwBnhXd7Nq1a3KtEZH1GjRowOLFi0lISOB///sfn3/+OSqVKnn5TEBAAAaDgXnz5lGkSBFu3LjBqFGjiImJee0/Mm9i6tSpnDx5ki1Nm1LA15chuXLRe9IkDh48mGp7RVHo3bs3NjY2bNq0CWdnZ9avX0+nTp2SlyA8evSI+/fvJw+oNWvWjL/++osRI0awYcMGo+UizJO/vz/dunV7ZZt58+bRrl27N3oftVpN0aJFAXj77bcJCAhg5cqV1KpVK0W750v7zGrXvTREjh2L/alTOOzbR9TAgSS+UAh+ypQpnD59mjt37rBw4UImTZpkwkiFsB7S98q5nFevRhMSQkLx4sRk0fLtw4cPs337dlQqFcuXL8+Smb9CQMqNXl7Xv7537x4jR46keioF+osVK8bMmTMpXLgwOp2ODRs20KVLF3799Vfy5MljrPDTLWrwYBx9fXE4dozIW7dILFky3efmypWL1atX07p1a44ePcq2bdvo0aOHEaMVwnpl+nHLkCFDWLZsWVbGItJga2tLvnz58Pb2pmnTptSpU4fTp08nH2/QoAFLliyhXr16FC5cmMaNGzNw4MDkaf3GEBERwe7du5kyeTLNzp+nCrDss8/47bffuHjxYqrn3Llzh99//505c+ZQqVIlSpQowdy5c9HpdOzatQuAMmXKkD9/fkJCQihcuDDLly9nzJgxfP/99yQmJhotH2FciYmJfP/992nexyJFimBjY5Pi6VpYWFiKWXSpeb5871X/NW7cOMvzMRgMqc5E/PLLL6lYsSLly5fP8vfMbgkVKxLbsiUqRcFl3rwUx3Lnzs369esBWL9+fZrf80KIrCV9r5xJHRSEU9LPxMjx40Gb6We+ye7du8fo0aOBZ/c9rSXjIud6Xd/nuZy60QuAXq9nyJAhTJs2LdXNW9q2bUvdunUpXLgwpUuXZsqUKURGRvLXX38ZM/x0SyxZktik+noZnS0F8M477zBu3DjgWVmRa9euZWl8QohnMj0o5eDgIE9sTOD69ev89ttvr50GHBER8dq1z926dXtlgegGDRqkee6VK1dISEigobMz2rt3MTg6UqhXL7y9vdP8BfX5L/Ev7qCnVquxtbXll19+AeDXX39l+/btwLPigo6OjkRGRuLs7Iw2Czp5wjTi4+Pp2bMnPXv2THUwx8nJiY8//piZM2fyyy+/cP36db744ovXLlPIiuV7V69e5erVq0RHRxMSEsLVq1e5efNm8vEVK1Zw+vRp/vnnH27dusXatWv5+uuvX5p9FRkZyd69e+nSpUsGvjI5W8SoUSgaDfbff4/tC7sNArRo0YIOHTpgMBgYPnx4iiXFQgjjsJS+lzmUJihfvjwlS5akX79+BL+wE2lqYmbOpLdORwEbG7yHDHmpNAHA9u3b6dChA6VLl8bb25vw8PA0r5eQkMDgwYOJiIjg3XffZeTIkVmSm8her+v7PJeTN3pZsmQJHh4e9OnT57Vt4+PjkzdayEkP56IGDwbAYd8+1A8eZPj8fv360bBhQ3Q6HYMGDSImJiarQxTC6mX6t/yCBQvy9OnTrIxFpOH777+nZMmS6PV64uLiUKvVzJw5M832f//9N1999dVrl9QsWLDglb9IvmrgKzg4GFtbW7yOHQNA17w5ipMTefPmTbPzVqJECby9vZkzZw7z5s3D0dGRDRs2EBgYSGBgIDExMYwaNQqA7t27U7t2bUJCQli6dKksUbACkyZNIjo6ml69euHs7MyAAQOIjIw0+vs2adIk+c9Xrlxh//79FCxYEH9/f+DZE8xx48YRFBSEvb09xYsXZ/ny5bRu3TrFdQ4ePIiiKLRp08boMWcXfdIyFKft28k1ezZP9+9PsZHBtGnTOH36NAEBASxZsiT5aaIQwjgsqe+V00sTrFu3DhcXFyZMmEDfvn3TLE2guXGDrvv3YwNsnj0b+/feY/369Xz88cecOnUquV1sbCz169enfv36zJkz55UxLFy4kN9//x0XFxdWr16d4+oRiayTkzd6OX/+PLt27eK77757ZbvvvvuOwYMHExsbS/78+dm1axfu7u5v9N5ZKaFKFeJq1sTu7FmcN2wgYurUDJ2vVqtZunQpH374Ibdu3WLKlCksWLDAOMEKYaUyPSj1wQcfsG7dOgICAihRokRWxiT+o1atWsyZM4eYmBg2bNiAVqulRYsWqbYNDAykW7dufPTRR68dyPHy8nrj2BwOHwYgxsfntW1tbGzYuHEjI0aMoHz58mg0GurUqUPDhg1Rq9XMmzePf/75hwIFCjBhwgQiIyPp0aMHpUqVYsSIEW8cq8jZnJycWLFiRYrXBg0aZPT3ffCap2ZjxoxhzJgxr71Ot27dGDVqFIGBgRZVDDPyiy9w9PPD7sIF7L7/nrgPP0w+5ubmxpw5c+jTpw9r1qyhefPmvPPOOyaMVgjLZkl9r+elCQC8vb2TSxM8H5Rq0KBBihnbhQsX5vbt22zdutVog1LPSxOsXLmS999/HyC5PMLFixepksruXY8mTeIc8Nv77+OVNFN27ty5VKpUif379yfPcurXrx9AmptkPHfq1ClWrlwJPHt4+NZbb2VVeiIHetONXowlKiqKoUOHsmDBgtfWhqpduzYnTpwgJCSEnTt3MnDgQL755hs8PDyMFl9GRQ0Zgt3Zszju2EHk0KEoGRw0y5MnDytWrKBTp07s3LmT999//6WHk0KIzMv08r0GDRrQuHFjZs2axb59+3j48KHskGYkjo6OFC1alPLly7N48WIuXbqUXIPpRUFBQfj4+FClShXmz5//2uu+yfK9vHnzEh8fT0R4OIkFCjzbWYZnM6he9WSmYsWKfPfdd1y7do1Lly6xY8cOQkNDcXR0ZOPGjQDMnz8flUpF165dcXJyYuPGjfKUUAgTMXh6EpU0bd9l7lzQ61Mcb9q0Ka1bt0av1zN8+HDi4uJMEaYQVsFS+145rTTB8xkr8P+zvFMrTWDr74/q118BSExaIgT/X5rgwoULr4z1vx4/fszQoUOBZzPGP/roowydL6yHsZfv3b17l3v37tGrVy/eeusttFote/fu5cSJExQqVIi7d+8mt33+e0qVKlVYtGgRGo0m1d9TTCmufn0SypVDHROD05YtmbpG7dq1k78/x4wZwz///JOVIQph1TI9U6pTp07Jf96zZw979uxJs61KpWL37t2ZfSvxArVazWeffca0adNo06YNDg4OwLMZUj4+PlSsWJElS5aka8vgN1m+V7FiRWxUKk4qCo3btwe1moCAAB48eJDqk8T/el5U8c6dO/zxxx8EBwejKAo+Pj5UrVqVzp07Y2dnx+bNm7G3t3/t9YQQxhM1eDBO27djc/06DgcOoOvQIcXxF2uBLV++PHkZrhAia1lS3ysnlybInTt3itdTLU2gKLjMmEFu4C0nJ2bu3Mm8d95JUZrg0aNHr4z1RQaDgWHDhvHkyRPKlCnDlClT0n2uMF8vbvTi7e0N/P9GLzVq1EjzPGMv3ytRogQnT54Env0syZs3LyNHjiQqKorp06dToECBNM9VFMXotd8yTKUi8tNPcR88GKcvvyR6wAAUR8cMX2b48OGcOXOGCxcuMGTIEPbt24etra0RAhbCumRL5WhLWsqSE3z00UfMmDGDLVu2MHDgQAIDA+nQoQMFCxZk0qRJKepNPJ8an5o3Wb7nGhdHb2A4sLhYMZyuXGHixIlUqVIlxaBU3bp1GTduHM2aNQOebW+cJ08evL29uX79OpMnT6Zo0aLcvn2bfPnyMWLECDp37oxOp2PFihVERkYm1xXKkycPGo0m0zELITJHcXUlasgQXGbPJteCBehatUpx3N3dndmzZzNgwABWrlxJs2bNePvtt00UrRACcn7fKyeXJkgP+yNHsL10CYOjIxvWr2f4rFkvlSbIyD1Yu3Ytp0+fxt7enjVr1iQ/dBSW7cWNXtzc3PDw8GDevHnp3ujlTVy9ehUgxUYvtra2lCpVCnt7e8qUKQM8G5Ty8vJKHqx9/npMTAzLli2jcePGyTtnb968maCgoBw5y0/XogWJhQuj/ecfHHftIjodxdv/S6vVsmrVKho3bsylS5eYP38+EydONEK0QliXTA9KverpnDAurVbLJ598wurVq+nRowenT5/m7t273L1796XCiK+rl5NZDgcOsFRRwMODvtOmERcXR/369Zk9e3aKdrdv3yYiIiL5748fP2batGk8efKEfPnyUbdu3eTP0pw5c7h37x6XLl0Cnk2TfdG5c+ektoIQJhLduzdOX36J9t49ck2fDo0aYWtrS9x774FGw0cffUSLFi04cuQIX3zxBUeOHJGnh0JkMUvqez1f8gOwePFiPvzwQ3bt2kXnzp1TtMtMaYLnG1WkpmDBgvz444+pHntemiA8PDzFbKmXShMkJDxbzgxEDxhAhfr1+a5+fSIiIkhISCBPnjx89NFHVKxY8bXxAly8eJF58+YBz3ZiK1WqVLrOE5Yhp2708jpqtZrbt2/Tv39/QkJCcHNz45133mHfvn2ULl3aWGFnnlZL1MCBuI4bh9O6dUT36AGZKA/i7e3NokWLkutp1q5d+5XLgoUQr6dScvqjtBwuODjYKPUcnj+VyKlFk/M2bozNn38SNmsWMb16Zeoa8fHxNG/enGvXrtGpUyeWLFmSI3PNSjn9vma15/n++++/bN++HYCuXbtaZI0wa7i3uceMwSnpPj6n9/IifPp0dM2b8+TJE+rXr09oaCgjR47kiy++MFGkWcsa7u1z1pSrsdnY2Lzx7lemZIz+zfPPV6dOnQgPD2fTpk3Jxw4cOMC0adM4c+ZMqqUJVqxYka7Z0oGBga9dvlewYMFUj0VERFCxYkVWrVqVPGsrICCAevXqcejQoeSZ4I6bN+M6YQJ6Dw8e//orirNziuvcuXOHevXqsX37djp37pzi++nMmTP4+Pjw119/kTt3bsLDw2nSpAn37t2jVatWrF69GtULu5yaC2v62ZHeXBMSEtixYwdg3n0fi7q3sbHkr1EDzZMnhC5bRux/yhFkJNcJEyawefNm8uTJw3fffUf+/PmNGblRWNS9fQ1rytXYjNG/yZble9nh2LFjHD58mLCwMAoXLkzv3r1fuTPN2bNn2bNnD8HBwXh6etK1a9cUO1+ItGn/+gubP/9EsbEh9j/LeDJi1apVXLt2DXd3d1asWEFiYmIWRilyEhsbG3plcvBS5Az2R4/iuGMHCvDir0vqoCDc+vcndP16PJo3Z+bMmQwZMoRly5bRtGlTypYta6qQhRBmJCeUJnBxceHjjz9m2rRpuLq6kitXrpdKE6iioqgxaRJzgAZffIHi7JxqaYKmTZtSv3795Gs/fvyYx48fJxeIvn79Ok5OTixatIh79+5RqFAh5s2bZ5YDUiJ10vfJgRwciO7bF5e5c3FevZrYdu0gHXV4UzNp0iT8/f25du0aQ4cOZdeuXemq6SuEeNkbD0rp9XrOnTvHn3/+SUhICPCsvkj58uWpUaNGttQAOnPmDFu3bqVfv36ULFmSI0eOMGvWLJYuXfpSsUqAGzdusGzZMrp06cK7777LL7/8woIFC5g3bx6FChUyerzmztHPDwDdhx9meEvV565fv86yZcuAZ0WS8+bNS2BgYJbFKITIQno9uSdPBkXhv78uqRQFRaXCZcoUdE2a0Lp1aw4dOsTx48cZPnw4hw8fRqu1mOcfQuQIOaHvldVyQmkCgKlTp6JWq+nfv3+qpQmc167lhsFAiIcHMUn1rf5bmqBDhw58/vnnKa67bds2Fi9enPz3du3aJf9Zq9WyevXq5E1ghBDGE92jB84rV2Jz4wZ2339PXOPGmbqOvb09a9eupWnTpvzyyy+sWrWKzz77LIujFcI6vNHyvbt377Jo0SIeP36c6vH8+fMzfPhwihQpktm3SJfx48dTvHhx+iQVrDMYDAwaNIhmzZrRpk2bl9ovWbKEuLg4xo4dm/zahAkTKFy4MP3798/Qe1vd8r3ERPJXq4bm8WNCNm1C98J69PRfIpHWrVtz+fJlGjduzFdffUWBAgVyXq5GkGPvq5E8z/f+/fucO3cOgOrVq5vlL0yvY8n31vbMGTx8fF7b7snevcTXqsWjR49o2LAhYWFhjB071uw7aZZ8b//LmnI1NmMt38uuvpcxl++Z6+dL/egR+WrXRh0bS8j69ejSKMz+3KvyvXHjBs2bN0en0zFx4kQGDRpkzNCNztzvbUakN1e9Xp9cn8mc+z6WeG9zzZpFrtWriatWjacHDiS/nplc9+zZw/Dhw9FoNHz99ddUq1bNSFFnPUu8t2mxplyNLUct3wsJCWHmzJlERkaSO3duatWqhaenJwCPHj3izJkzPHr0iFmzZjF//nzc3NyyLOgXJSYmcufOnRSDT2q1mgoVKnDz5s1Uz7l58+ZLu0K88847XLhwIc33SUhISNE5U6lUyTUPjDHV+vk1c9o0bruff0bz+DF6d3fiGjbMVHwbNmzg8uXLuLi4MHfu3OSprjktV2PIqffVWJ7nGR8fj0/SoEZAQACOmdiGN6ez5HurSeOX39TaqVQqPD09mTZtGsOGDWPx4sU0bdrUrAv3WvK9/S9rytUcGaPvlZ39G3P/fOVasgR1bCzxVaoQ16LFa/NIK9+YmBgGDRqETqejfv36DBw40Gy/Js+Z+73NiPTmail9H0u8tzH9+uG8cSN2Fy5ge/48CdWrA5nLtVOnTvz888/s37+fIUOG8N133+Hq6mqMsLOcJd7btFhTruYo04NSBw8eJDIykoYNG/LJJ5+8tMtS586d+eqrr/jhhx84ePCg0dZUR0REYDAYXvrmd3V15eHDh6meExYW9tKyvty5cxMWFpbm++zfvx+/pGVrAEWLFmXevHlGL2L6vLOZY3zzDQCarl3xKlw4w6ffuHGDBQsWALB06VIqV66cfCzH5WpE1pQrkKL4o6enJ05OTiaMxrgs8t6WK5euZm7lykFSPZfPPvuM48ePc/ToUcaMGcOvv/5qtk+Jn7PIe5sGa8rVnBij72WK/o1Zfr6uX4edOwGwXbIErwIF0n3qf/MdMGAAN27cwNPTkz179ryyRpa5Mct7m0mvyzU6OjpFW3Pv+1jUvfXygp49YcMGPDZuhP+srMlorps3b+bKlSvcvn2bSZMmsXfvXrMa/LCoe/sa1pSrOcn0oNTly5fx8PCgX79+qRZ1s7W1pW/fvly5coVLly6ZfaG/tm3bpphd9fwHTXBwsFEKdD+fbRAUFJRjphiqIiLIf+AAKiC4RQsSM1gDymAw0KNHD+Li4qhXrx5NmjQhMDAwR+ZqLNaUK/x/vo8ePUp+LSgoyGyfFr6KRd/bEiXI5+WFOigIVRq5KRoNwba26F/4uTBjxgx++eUX/P39mTZtmtkuT7Hoe/sf1pSrsWm12iwf2DFG3ys7+zfm/Ply++IL7PV6dI0bE1qiBKSjD5RavocOHWL9+vWoVCqWLl2KXq+3iJqa5nxvMyq9ucbExCT/2Zz7PpZ6bzU9e5J340ZUR44QfPIkieXKvVGuK1eupFWrVnz99dfMnz+fHj16GCnyrGOp9zY11pSrsRmjf5PpQaknT57w3nvvvXKXAY1GQ8mSJV+5LO5Nubi4oFarX5rlFBYWlubUSVdXV8LDw1O8Fh4e/sqpljY2Nmlu5WrMD7aiKDnmG8fh8GFUOh0JpUqR8PbbkMG4Nm/ezPnz53FycmL+/PlAyq9dTsrV2KwpV7Cu+2yR+anVhE+fjlv//igqVYqBqed/Uun15Gnfnqe7d5NYsiTw7GnUlClTGDFiBPPnz6dRo0av3BU1p7PIe5sGa8rVnBij72WK/o25fb5sL1zA/tgxFLWaiHHjMhz783z//fdfRo0aBcCnn35KnTp1zOrrkB7mdm/fxOtytbS+jyXk8KLEokXRtWiBwzff4LR6NWErViQfy0yuFStWZPz48UybNo0pU6ZQpUoVyqVzprmpWdq9fRVrytWcZHrfShsbG2JjY1/bTqfTpdnZyQparZZixYpx9erV5NcMBgNXr15Ns4ZJqVKl+N///pfitStXrlAy6RcpkTqHpOn9sR06QAanpP7777/Ju9eMHz+eggULZnl8Qgjj0TVvTuj69Rj+M+1ZX6AAYQsWkFCqFJqgIPK0a4f2hZ/HnTp1on79+sTFxTFixAj0en12hy6ExcgpfS+roii4zJgBQEznziRmsj5eQkICgwcPJjIykqpVqzJixIisjFIIkQlRQ4YA4HDwIJp79974ev369aNhw4bExcUxePDgFLPlhBBpy/SgVMGCBfnzzz958uRJmm2ePHnCn3/+yVtvvZXZt0mXjz76iJMnT/LTTz9x//59Nm7cmLyNLzybTrkzqQ4AQPPmzfnjjz84fPgwDx48wNfXl9u3b9O0aVOjxmnONP/8g52/P4pKRcwL2xinh6IojBo1ipiYGGrUqGEW01mFEC/TNW/OI39/nvr5wc6dPPXz4/G5c8R26cLTr78mvkIFNCEheHTsiM1vvwHPpkvPnz8fZ2dnfvvtNzZt2mTiLIQwXzmp72Ut7I8dw/biRQz29kQOH57p6yxYsIBLly6RO3duVq1aJYOGQuQACRUrElenDiq9Hqd16974es+X5ebPn59bt24xefLkLIhSCMuX6UGpunXrEh8fz4wZM/j9999fOn7x4kVmzJhBfHw8devWfaMgX6dWrVp0794dX19fRo8ezd27dxk/fnzycrwnT54QGhqa3L506dIMHTqU77//nlGjRuHv78+oUaMoVKiQUeM0Zw5ffw1AXJ06GJIKGafXrl27+OWXX7C3t2fhwoWvXHYghMjhNBria9WCzp2f/T+peLnB3Z2nvr7EVauGOjycPJ07Y/vLLwB4e3szceJEAObOncvff/9tsvCFMGc5qe9lFRIScEma5R09YMBLM0XT66effmLVqlXAs8EpmS0uRM4RmTRbymnnTuy+/RZ27cL2zBnI5MzuPHnysGLFClQqFbt27eLgwYNZGa4QFinTNaUaNWqEv78/V69eZd68eTg7OyfvHvL48WOioqIAqFChAh988EHWRPsKTZs2TXOm09SpU196rWbNmtSsWdPIUVkIRcHx+dK9pK1t0+vhw4dMnz4dgNGjR1O0aNEsD0/kfFqtNnlQQqvN9I8dkcMpLi6E7NyJW58+2J8+TZ4ePQhZt464Dz+kW7duHD58mF9//ZWRI0eyd+9eGaAWIoNyWt/L0jnu2oX2zh307u5EZXKjhqCgIIYOHQpAjx49aNGiRVaGKHIw6fuYh/j33yehcGFs/vkH9z59AMgD6L28CJ8+HV3z5hm+Zu3atRk2bBhLly5l9OjRVKpUicKZ2LVcCGuR6d8I1Go148aNo1WrVtjb2xMVFcWdO3e4c+cOUVFR2Nvb07p1a8aOHSu/eJg52wsX0P7zDwYnJ3QZWOKoKArjxo0jMjKSypUr07dvXyNGKXIyW1tbBg0axKBBg17awlxYFsXRkZCvviK2SRNUcXG49+2L/aFDqFQqFi5ciKOjI+fOnWPr1q2mDlUIsyN9r+yjio4m1+LFAER98QVKrlwZvobBYKB79+48efKEsmXLylIeKyN9H/Ng/+23aP/556XX1UFBuPXvj/3Ro5m67hdffMF7771HVFQUgwcPJj4+/k1DFcJivdGwvVarpWvXrnTs2JHbt28TEhICgLu7O8WLF5f18hbieYFzXYsWKBnYznbfvn18//332NrasnjxYjRJy3yEEBbO3p7QdetQvvgCx/37cRsyhLCYGAp9/DHjx49n4sSJzJo1i4YNG8qyaSEySPpe2cNp/Xo0wcEkFilCdLdumbrGypUr+f7773FwcGDNmjU4ODhkcZRCiDei15M7jcFilaKgqFS4TJmCrkmT5HIF6aXValm5ciWNGzfm8uXLzJs3j0mTJmVF1EJYnCyZS2pjY0OZMmWy4lIip4mNxeHQIQBiMrB079GjR8lPBD///PM0d0IU1kGv13PlyhXg2bISGaC0AjY2hC1bhuLoiNOOHbiNGIE6Opqen3zCN998w7lz5xg5ciR79uxBlcHdPIUQ0vcyJnVwMM6rVwMQMWYMZGKWy4ULF1iwYAEAs2bNkh2erZBer0/e7Vv6PjmTrb8/msDANI+rFAXtw4fY+vs/q6OZQd7e3ixatIg+ffqwdu1a3n//fRo0aPAmIQthkWRut3gl+xMnUEdGkliwIPE1aqTrHEVRGDNmDGFhYVSsWJEhSQUEhfWKi4ujRYsWtGjRgri4OFOHI7KLRkP4vHlE9e8PQO7Jk3FZuZKFCxdib2/Pr7/+yvbt200cpBBCpJRryRLUMTHEV6qErmXLDJ8fGhrK4MGD0ev1dO3alU6dOhkhSpHTSd8n59M8fpyl7VLTtGlTPvnkEwCGDRvGo0ePMn0tISxVpgeljh07RqdOnfgtadvv1Pz222906tSJ7777LrNvI0wsucB5+/aQzvoUfn5+fPfdd9ja2rJkyRIp7iiENVOpiJg8OXkrdZd586i4ezdjx4wBYObMmTx48MCUEQphNqTvZXya27dxTBosj5g4ETI4k1NRFIYPH87Dhw8pVqwYa9askdmgQuRQ+qSNIl7H6csvsf/2W8hkXaiJEydSrlw5nj59ymeffYY+kzv7CWGpMj0odeHCBVxcXHj33XfTbPPuu+/i4uLC+fPnM/s2woTUjx9j99NPAMS0b5+ucwIDA5kyZQoAw4cPl6UFQghQqYgcMYLwpFoKuVauZPjdu1SrUoWoqChGjRqFoigmDlKInE/6XsbnMncuKr0e3QcfEJ+JXZo3bdrEiRMnsLW1Ze3ateTKRIF0IUT2iK9eHb2XF0oaA8fPeya2v/+Oe9++5K9SBZcpU9D+9VeG3sfe3j65rtyvv/7KqlWr3jByISxLpgelHj58SKFChV65u4taraZQoULyFNxMOezfj8pgIL5KFfTFi7+2vaIojB49mvDwcCpVqsSgTG6fLISwTNEDBxI2dy6KSkWuLVs4lDcvTnZ2nDp1ij179pg6PCFyPOl7GZfNxYs4HD2KolYTMX58hs+/cuUKM2fOBGDy5Mm8/fbbWR2iECIraTSET58O8NLAlKJSgUpF+IwZRA4ejD5fPjQhIThv3Ei+Dz/Eo1kzHDdvRhUamq63KlGiBLNmzQJg4cKFXLhwIWtzEcKMZXpQKiIigty5c7+2Xe7cuQkPD8/s2wgTcty7F4CYDh3S1d7X15cffvhBlu0JIdIU0737swLoGg2ex45xrlgxbIBp06YR+Ipio0II6XsZlaLgkjSgFNOxI4kZnOkdGRnJoEGDiI+Pp1mzZvTq1csIQQohspqueXNC16/H4OmZ4nW9lxeh69cT3bs3kRMm8OjCBZ5u2UJs8+YoNjbYXrmC64QJeL77Lm4DB2L344/wmmV5HTt2pF27duj1eoYMGUJYWJgRMxPCfGR6UMre3p7QdIwMh4aGYmdnl9m3ESai/fNPbK5dQ7G1JTYdRT4fPnyYvGxv1KhRstueECJNse3bE7puHYqtLW9fu8YPLi7ER0QwZswYWcYnxCtI38t47L77Drvz51Hs7YkcMSJD5yqKwtixY7l79y7e3t4sXLhQ6kgJYUZ0zZvzyN+fp35+sHMnT/38eHzuHLrmzf+/kVZLXKNGhG7YwKPffyd82jQSypZFFR+Pw+HD5OnWjfzVq5Nr7lw0d+6k+j4qlYo5c+ZQpEgRHjx4IOULhEiS6UGpIkWKcPPmTZ48eZJmmydPnnDz5k0KFSqU2bcRJvJ8lpTuww9R3Nxe2VZRFEaNGkVkZCSVK1dmwIAB2RGiEMKM6Zo1I2TzZgz29rwfEcExlQr/kyf5+uuvTR2aEDmW9L2MJDERl6RlNVF9+2IoUCBDp+/evZsDBw6g0WhYtWoVrq6uRghSCGFUGg3xtWpB587P/q/RpNnU4O5OdN++BH/3HcHHjhH1yScYXF3RBAaSa8UK8tepQ562bXHYswdVdHSKc52dnVmzZg02NjYcPXqUrVu3GjszIXK8TA9K1a5dm8TERBYtWpTq1MOwsDAWLVpEYmIi77///pvEKLJbYiIO+/cD6Vu6t3v3bn766Sfs7OxYunQpmlf8EBfWSavVMnz4cIYPHy7LOkWyuHr1CNm5E4OzM/UUhe+BxRMnynbJQqRB+l7G4bhnDzYBAejd3IgaMiRD5964cYOJEycCMHr0aKpVq2aMEIUZkr6PFVCpSKhQgYiZMwn6/XdC1q5F16ABilqN3fnzuA0fTv5KlXD94gtsz52DpFlRFStWZHxS3bpp06bxVwYLpwthaTL9E7J+/fr89NNP3Lhxg88++4zKlSvj7e0NwIMHD7h06RLx8fGUKlWKBg0aZFnAwvjsTp1C8+QJ+jx5iHvNvXvw4AFTp04FnnXGSpQokQ0RCnNja2vLiAwuhxDWIb56dZ76+uLepQvVw8I4GBnJnOHDWbR9uyx/EeI/pO+V9VQxMeRatAiAqM8/R3FxSfe5sbGxDBo0CJ1OR7169Rg8eLCxwhRmSPo+VsbODl3LluhatkQdGIijnx+Oe/ag/ftvHH19cfT1JbFIEWI6diSmQwf69evHL7/8wsmTJxk8eDBHjx7F0dHR1FkIYRKZnimlVqsZO3Ys1apVIz4+Hn9/f/bt28e+ffvw9/cnPj6eqlWrMnbsWJk5Y2aeL92LbdMGbGzSbKcoCiNHjiQqKooqVarQr1+/bIpQCGFJEt55h6dff02cuzvvANN++omTmzebOiwhchzpe2U9p/Xr0Tx6RGKhQkR3756hc6dMmcKNGzfIly8fy5Yte+WuiEII62Hw8iLqs894/PPPPNm/n+iPP8bg5IT27l1c5s8nf/Xq5OnalU1NmlA4f35u3brF5MmTTR22ECbzRnNJHR0dGTlyJP/88w+XL18mODgYAA8PDypVqkSRIkWyIkaRjVTh4difOAE8233mVXbs2MHp06ext7dnyZIl0gEWaTIYDNy8eROAkiVLSsddvCSxTBnCDh3CtlkzSkdG4jB5MuEVK5K7ShVThyZEjiJ9r6yjfvIE5zVrAIgcMwYyUBz+wIED7NixA5VKxfLly8mbN6+xwhRmymAwcOvWLUD6PlZLpSL+vfeIf+89IqZPx/7IERx9fbE7exb7U6codOoUN52c2Ahs2rWLA7Vr06ZtW1NHLUS2y5IFzoULF6Zw4cJZcSlhYg6HD6OKiyOhTBkSy5dPs939+/eZPn06AGPGjKF48eLZFaIwQzqdjoYNGwJw69YtmZ4sUqUvWpToY8cIb9CAIvHxPPXxQf/ttySWLm3q0ITIcaTv9eacly5FHRVFfMWKxLZqle7zbt26xahRowAYNmwYderUMVaIwoxJ30e8SHFyIrZjR2I7dkRz9y6Ovr447N2L7cOHDAYGA38OHUrcjRs49OuHIU8eU4csRLbJsqp7er2eo0ePcuHCBSIjI3F3d6d27drJP4yFeXDw8wOSCpynUc/FYDDwxRdfEB0dTbVq1ejTp092hiiEsGDqIkW4u2ULkZ07UyEuDl3LlkT6+ZFQsaKpQxMix5G+V+Zp/v4bp23bAIiYMAHSOYslJiaG/v37ExMTQ+3atRk+fLgxwxRCWCB9kSJEjh5N5IgR2P36K/a7dqE9fJjyBgOsWIGyZg26Ro2I6dTpWX3fV5RTEcISpHseqb+/P3379mXXrl0vHTMYDMyZM4ft27dz48YNHj58yNWrV1m3bh2rVq3K0oCF8Wj+/hu7CxdQ1GpiXzF1dOPGjZw5cwYHBwdZtieEyHKl6tZl14AB+AP20dG4+/hge/68qcMSIttJ38t4XObNQ5WYiK5BA+LTuVOhoiiMHTuWmzdvkj9/flatWiV9ICFE5mk0xNWtS/iaNfz53XeMcHDgPKBKTMTh2DHyfPIJ+atVw2XGDLRJZTCEsETpHpT6888/iYyMpEaNGi8dO3nyJP/73/8AqFKlCr1796ZVq1bY2tpy+vRp/vjjj6yLWBiN49dfAxBXty4GT89U29y4cYO5c+cCzwp8Fi1aNNviE0JYj35jxjC4ZElOAZqoKNw7d8bu9GlThyVEtpK+l3HYXLqEw+HDKCoVEUnbsqfHrl27+Prrr9FoNKxevVrqSAkhsoxn2bK8vWoV1YG3gT+bNEGfJw+a4GCc164lX4MGeHz0EY7btqGKiDB1uEJkqXQPSt26dQs3N7dUByG+//57AGrXrs3o0aNp0qQJXbt25dNPPwXgtPwikfMZDMlL92J9fFJtEh8fz9ChQ4mLi6Nhw4Z069YtOyMUQlgROzs7Zi5bxkdqNd8Cap0O9549sT9+3NShCZFtpO9lBIqCy6xZAMR26EBiuXLpOu3q1atMnDgReFZLM7WBQiGEeBNNmjShd+/e/AnUPneO3w4cIGTTJmIbN0bRaLC9dAnXsWPxrFwZ108/xfb0aTAYTB22EG8s3YNSYWFhqe7oEhERwd27dwFo9Z8ikdWrVydv3rwEBAS8UZDC+GzPn0d77x4GZ2d0TZqk2mbJkiVcvXoVNzc3Fi5ciCqNmlNCCJEV3nnnHfoNG0Yb4KBWiyo+Hrd+/XDYv9/UoQmRLaTvlfXsTp7E7uxZFDs7IpKKlb9OREQEAwYMIC4ujkaNGjFo0CAjRymEsFaTJk2icuXKhIeH02/wYMLq1SP0q694dPEi4ZMmkVC6NCqdDsf9+/Ho3Jl8NWuSa+FCNP/+a+rQhci0dA9KRURE4OTk9NLrt2/fBsDFxSXVjlPBggUJCQnJfIQiWyTPkmrZEsXB4aXjv/32GytXrgRg7ty55M+fP1vjE0JYp2HDhlGuUiXaJybybb58qPR6XD/7DMft200dmhBGJ32vLKbX4zJ7NgBRffpg8PZ+7SmKojBixAju3r1LwYIFWbp0Kep0FkUXQoiMsrW1Zd26dbi5ufG///2PyZMnA2DIm5fogQMJPnmS4CNHiO7eHYOLC9r798m1ZAn5a9YkT4cOOOzdiyomxsRZCJEx6f5XVa1WE5HK+tW///4bgGLFiqV6nqOjIwaZVpijqWJjcTh8GHg2lf2/oqOjGTZsGAaDgXbt2vHRRx9ld4jCzGm1WgYOHMjAgQPRarNs009hBWxsbFi+fDk29va0ePyYC1WrolIUXMeMwWndOlOHJ4RRSd8razns3YvNjRsYXF2JGjIkXeds3LiRo0ePYmNjw9q1a3FzczNylMJSSN9HZJa3tzerV69GpVKxY8cO9uzZ8/8HVSoSKlUifO5cgn7/ndBVq9DVrYuiUmF39ixun39O/sqVyT1qFDYXLoCipP4mej22Z87Arl3P/q/XZ09yQqQi3YNSefPm5e+//yYxMTHF68+LbJYoUSLV8yIjI8mdO/cbhCiMzf74cdRRUSS+9Rbx77330vHp06dz9+5dvLy8mDlzpgkiFObO1taWSZMmMWnSJGxtbU0djjAzxYsXZ/LkyShAnStX+OfjjwHIPX06zosXp93hEsLMSd8r66hiY3FZsACAyKFDUVxdX3vOb7/9ltzvmTJlCpUrVzZmiMLCSN9HvIm6desyYsQIAMaPH8+ff/75ciMHB2LbtCFk1y4e+/sTMXIkiYULo46KwmnnTvK2aUPeevVwXrUKdVBQ8mn2R4+Sv3p18nToAF26kKdDB/JXr4790aPZlZ4QKaR7UKp8+fJERkaye/fu5NeuXr3KX3/9BcC7776b6nl379416lOlqKgoli9fTs+ePenVqxdr1qxBp9O9sv2mTZsYNmwYXbt2ZdCgQWzatIkYK57m6LB3L5A0S+o/U9JPnjzJ9qRlMkuWLJFOrhDCJHr06EHDhg2Ji4+nxZUrhCZ11FwWLcJlxgwZmBIWKaf2vcyR08aNaIKCSCxYkOhevV7bPiQkhIEDB5KYmEjLli3plY5zhBAiKw0bNoyGDRui0+no378/4eHhabbVe3sT9cUXPP7lF57s3UtMhw4YHBywuX0bl9mzyV+tGu7du5Nr+nTc+vdHHRiY4nx1UBBu/fvLwJQwiXQPSrVo0QKtVsvhw4cZNGgQY8aMYVbS7iUlS5akePHiL51z8+ZNIiIiKFmyZNZF/B/Lly/n3r17TJw4kbFjx3Lt2jXWvWJJR0hICCEhIXTv3p1FixYxZMgQ/vjjD9asWWO0GHMydVBQ8jbrMe3bpzgWEhLCyJEjAejTpw916tTJ9viEZTAYDNy7d4979+7JkhKRKSqVikWLFuHm5saff/3FJJ2O8GnTAHBet47cY8fKDjTC4uTUvpe5UYeE4LxqFQCRo0eDnd0r2+v1egYPHkxgYCDFihWTzV1EpkjfR7wptVrN8uXLKViwIHfv3uWLL75Aed1DOLWa+Fq1CFu2jEeXLhG2cCHxVauiMhiw/+EHcq1bB4rCf3+iqZKu6zJliizlE9ku3YNSnp6eDB06FHt7e0JCQrh79y4GgwF3d3eGpLEu/7vvvgPg7bffzppo/+P+/ftcvnyZgQMHUrJkScqUKUPv3r05c+ZMmgU+CxUqxMiRI6latSqenp68/fbbfPzxx1y8eBG9FX4DOhw4gMpgIK5aNfQvbDmtKApjx47l8ePHlCxZknHjxpkwSmHudDodNWrUoEaNGq+cySjEq+TLl48FSctvVq9ezQ8VKhC2cCGKSoXT9u24DhsG/1nmJIQ5y4l9L3PkvHQp6shIEsqXJ7Zt29e2nzdvHj///DMODg5s2LABZ2fnbIhSWBrp+4is4Obmxvr167G1teX48eMZmkih5MpFTOfOPDl4kEenThHTpg3ASwNSz6kUBe3Dh9j6+7954EJkQIaq7lWvXp0yZcpw8eJFwsPD8fDwoFq1atjb26favkSJEhQtWtRoHaObN2/i5OSU4klhhQoVUKlUBAQE8F4q9ZFSExMTg4ODAxqNJs02CQkJJCQkJP9dpVLhkLRLnTGenj2/plGfzCkKjs+X7vn4pHgvX19fjhw5glarZcWKFTg6OhotjGzJNYewplwh9XxVKpVF5m9N99aUuTZv3pxOnTqxZ88ehg4dysmTJ1GcnJ7tyLdvH+qYGELXrHntTIiMkHsrTMnYfa/s7N+Y4vOl+ecfnLZuBSBi4kRUr+jrARw5coRVSbOqlixZQtmyZTP93tb0/SS5pt3u+Z/N9Wsj99b0KlWqxIwZMxgzZgxz5syhcuXK1KpVK0PXMJQsSdyHH+J44MBr22oeP85xX4M3lVPvrXhGpbx2DmDOtW/fPk6dOsWyZctSvN63b186duxI48aNX3uNiIgIxo4dS506dejcuXOa7Xx9ffHz80v+e9GiRZk3b17mg88JLl2Cd9999stbUBAkFf0MCAigUqVKREdHM2vWLMaPH2/aOIXZi46OTn7SHBUVleoW50KkV0REBO+88w53796lV69efPXVV3D4MPj4QFwcNG4M+/eDEQfThbAUFtm/eVHnzrB797OfC8ePv7LpX3/9RfXq1YmKimLkyJHJMzOFyAzp+4ispCgKvXr1YuvWreTPn5/ff/+dAgUKZOwiP/0EDRq8vt2PP0L9+pkJU4hMyZH7k+7YsYODBw++ss2SJUve+H1iYmKYO3cuBQsWxMfH55Vt27Zty0cffZT89+ejrMHBwS/tipMVVCoVnp6eBAUFvX7tcCa5rF6NExDbuDFhsbEQG0t8fDwdOnQgOjqaWrVq0aNHDwL/Uwgvq2VHrjmFNeUK/5/vo0ePkl8LCgoy6sw7U7Gme5sTcl2yZAnt27dn8+bN1KpVi48++gjbrVtx69UL9YkTxDdoQMjWrSguLm/8Xjkh3+xiTbkam1arJW/evKYO47Wys3+T3Z8vmz/+wGP3bhSViicjR5L4iv5MREQELVu2JCoqitq1azN06NA37v9Y0/eT5PqyFzdRMue+j9zbnGPy5MlcuHCBa9eu0apVK/z8/LDLyMzwEiXI5+WFOigouYbUixSVCoOXF49LlAAj//6X3XL6vTUnxujf5MhBqZYtW1L/NaOz+fPnx9XVlYiIiBSv6/V6oqKicH3NVr+xsbHMnj0bBwcHRo4ciVb76i+FjY0NNjY2qR4z5gdbURTjXD8hAfv9+wGI6dAh+T0WLlzIH3/8gaurK8uWLUOtVmfbN67Rcs2BrClXSPk9Yum5W3p+LzJlru+99x6DBw9m5cqVjBo1ikqVKuH9/vs83bWLPD16YHv+PO4dO/J0xw4Ud/cseU+5t8ISmaJ/ky2fL0Uh14wZAMS2bUtC+fJp7tJpMBgYNmwYd+7coUCBAqxZswaNRpNlMVrT95PkmvJ4etuaA0vIIb1yaq4ODg5s3LiR5s2bc/HiRSZMmMD8+fPTvyRNrSY8afc9RaV6eWBKUQifNg1FrbbYXY1z6r21dukudJ6dXFxc8Pb2fuV/Wq2WUqVKER0dzZ07d5LPvXr1KoqiUKJEiTSvHxMTw8yZM9FqtYwePRpbW9vsSCtHsfvpJzRPn6L38CAuaQDwl19+Sa6jMH/+/IxPCRVCiGw0cuRIKleuTHh4OJ9++imJiYkkVKvGk7170bu7Y3vlCh4dOqB+YaaeEMI62P34I3ZnzqDY2hI5Zswr265YsYLjx49ja2vLhg0byJMnTzZFKYQQGVOkSBFWr16NWq1m586dbNu2LUPn65o3J3T9egyeni8dU4HFDkaJnC1HDkqlV8GCBalUqRLr1q0jICCA69evs2nTJmrVqoV70pPxkJAQPv/8cwICAoBnA1KzZs0iLi6OgQMHEhsbS1hYGGFhYVa1XWtygfO2bUGrJSQkhGHDhqEoCl27dqVFixYmjlAIIV7NxsaGlStX4uzszPnz55PrCya+/TZP9+1D7+mJzY0beLRrh+b+fRNHK4TINno9LrNnAxD9ySfoCxZMs+mPP/6YXDtq9uzZVKpUKTsiFEKITKtfvz5jx44FYNKkSZw/fz5D5+uaN+eRvz9P/fxg506e+vkRNWAAAK7Dh6N5YcKHENkhRy7fy4ihQ4fy5ZdfMn36dFQqFdWrV6d3797JxxMTE3n48CFxcXEA/P3339y6dSv53BetXLmSfPnyZV/wJqIKC8M+acvoGB8fFEVh1KhRBAUFUbx4caZOnWraAIXF0Wg09OzZM/nPQmSVIkWKMHfuXD799FOWLl1K7dq1qVGjBoklS/Jk3z7ydOqE9u5d8rRty9Pdu9G/sFurEMIyOfj5YXPtGobcuYn87LM02/3zzz98+umnyQ/kXrXhjRAZJX0fYUyDBw/mf//7H4cPH6Z///4cPXo0Y6tcNBria9UCLy/iAwOJq1oVm0uXsDt/Hvf+/Qk+fBiSdmIVwtjMflDK2dmZYcOGpXk8X758+Pr6Jv+9fPnyKf5ujRwOHUIVH09C2bIkli/P9m3bOHbsGDY2NqxevdpsCzGKnMvOzo7ZSU+thchqbdu25dSpU+zdu5dPP/2U7777Djc3N/SFC/Nk/37yfPwxNgEBeLRvz9OdO0ksV87UIQshjCU2FpekmU+Rn32G4uaWarOoqCh69+5NWFgYlStXZkZS/Skhsor0fYQxqVQqFi9eTEBAANeuXaNfv358/fXX2NvbZ+6CNjaErl5N3iZNsLl2jdyTJhG+cGHWBi1EGsx6+Z7IHMekrZ9jOnTg1q1byTOjxo4dy9tvv23CyIQQInNmzpxJ0aJFCQwMZNSoUclFLA1eXjz9+msSypdHExyMh48PNpcumThaIYSxOH/1FZrAQBILFCD6k09SbWMwGPjss8+4fv06+fPnZ8OGDRnbwUoIIXIAR0dHNm3ahKurK5cvX2bcuHFvVMTb4OVF6KpVKCoVTrt24WDlEzlE9pFBKSujuX0b24sXUdRqQps3Z9CgQeh0OurWrUv//v1NHZ6wUIqi8PTpU54+fSo7XgijcHZ2ZvXq1djY2PDtt9+mKPxp8PDgia8v8e++izosjDydOmF79qwJoxVCGIMqJATnFSsAiBw9GtKYMTBv3jxOnDiBnZ0dX375JV5eXtkZprAS0vcR2aFQoUKsWbMGtVqNr68vX3311RtdL75OHSJHjAAg97hxaK9dy4owhXglGZSyMo5ffw1AXP36TFy5kmvXruHh4cHSpUtRq+XjIIwjNjaWihUrUrFiRWJjY00djrBQFStWZNy4cQBMmzaN69evJx9TXF15uns3cbVro46OJk+3btj9+KOpQhVCGEGuFStQR0SQULYsse3apdpm//79rFy5EoCFCxdSuXLl7AxRWBHp+4jsUrduXSZMmADA1KlTOX369BtdL2rYMHT166PW6XAbMABVVFRWhClEmmQUwpoYDDgkDUqdKlyYHTt2oFKpWLFiBfnz5zdxcEII8eb69etHw4YN0el0DB48OMUvAoqTE0+3bkXXqBEqnQ73Tz7B/sgRE0YrhMgqmnv3cNq8GYCICRMglcLSly9fZuTIkQAMGTKEdmkMXAkhhLkZMGAA7du3R6/XM3DgwOSd5zNFrSZsxQr0Xl7Y3L6N68iRILP9hBHJoJQVsT13Du39+yQ6O9N1zx4Ahg0bRt26dU0cmRBCZA21Ws2SJUvImzcvN27cSH5ymMzenpCNG4lt2RJVQgJuAwfisHevaYIVQmSZXPPno4qPJ+7994mrX/+l40FBQfTu3RudTkejRo0YM2ZM9gcphBBGolKpWLBgAVWrViU8PJyePXsSEhKS6esZ3N0JWbsWRavF4fBhHJMG/YUwBhmUsiLPC5zv02p5GhNDzZo1GT58uImjEkKIrOXh4cGqVatQq9Xs2bOHPUmD8MlsbAhdtYrojz9GZTDg9vnn0tkSwoxpr17Fcd8+IGmWlEqV4nhsbCx9+vTh0aNHlC5dmpUrV6JJZSaVEEKYs+d18t566y3u3r1L//79iY+Pz/T1EqpWffYzFcg9bZpsFCOMRgalrIQqJgb7b74BYHlYWPIvbdIpE0JYotq1azMiqVDn+PHjufbfQp0aDeELFhDVpw8ArhMm4Lx6dXaHKYTIArlnzgQgpm1bEipWTHHMYDAwYsQILl++jKurK1999RW5cuUyRZhCCGF0Hh4ebN68GWdnZ86ePcv48ePfqNB+dL9+xDZvnjy7XBUamoXRCvGMDEpZCftjx1BHR3MbOANSR0oIYfGGDh1KgwYN0Ol09O/fn8jIyJQN1Goipk0jcuhQAFxmzSLXvHlSN0EIM2J36hR2P/+MYmv7bMe9/5g/fz4HDx5Eq9Wyfv16ChcubIIohRAi+5QpU4bVq1ejVqvZtWsX69aty/zFVCrCFi0isUgRtPfv4zZsGBgMWResEMiglNVQtm4FYCvw+RdfSB0pIYTFU6vVLF++HC8vL+7cucOoUaNeflqoUhE5ZgwRSbv25Vq+HJcpU2RgSghzYDDgkjRLKrpHD/SFCqU4vHv3blasWAE8G5yqXbt2tocohBCm8MEHHzB58mQAZs6cyYkTJzJ9LcXFhZB161Ds7LA/eVJmlossJ4NSViDuzh1yX7gAwJ+VK/PFF1+YOCJhbTQaDT4+Pvj4+MiSUZGt3N3dWbt2LVqtlsOHD7Nly5ZU20V9+ilhs2YB4Pzll+QeNQr0etDrsT1zBnbtevZ/vT47wxdCvILDvn3Y/PUXBhcXIocNS3Hs9OnTycXMhw0bRqdOnUwRorBi0vcRpta3b1+6deuGoigMGTKEq1evZvpaiW+/TXjSQ4Bc8+Y96xMJkUVkUMrCKYrCzwMHogHOarVM+PJL+YdRZDs7OzuWLl3K0qVLsbOzM3U4wspUrVqViRMnAjB16lQuX76caruYXr0IXbIERa3Gadcu8rRrR/7q1cnToQN06UKeDh3IX7069kePZmP0QohU6XTkmj8fgKghQ1Dc3ZMPXb9+nf79+5OYmEjbtm0ZNWqUqaIUVkz6PsLUVCoVM2fO5P333ycmJobu3btz//79TF8vpnNnYjp0eLZJzODBqB8/zsJohTWTQSkLt+nLL6ny558A2PXrJ3WkhBBWqW/fvjRv3pyEhAQGDBhAaBqFOmM7diR0zRoUjQa7335DHRiY4rg6KAi3/v1lYEoIE3PavBntgwfoPT2TNywAePz4MT169CAyMpLq1auzaNEiVP/ZjU8IIayFjY0NGzZsoGzZsjx+/Jhu3bql2Qd6LZWK8DlzSChdGk1wMG6DB0NiYtYGLKySDEpZMH9/f76ZPp3yQIJWi1dSMV8hspuiKMTExBATE/NGO4AIkVkqlYpFixZRpEgR7t+/z5AhQ9CnsRRP16wZhty5UYD//iqrSvr8ukyZIkv5hDARVVgYuZJqRUWMHg0ODgDExMTQq1cvHjx4QNGiRdm4caPMUBEmI30fkVO4uLiwdetWvLy8uHXrFn369EGn02XqWoqjI6Hr12NwcsLu7FlyLVyYxdEKaySDUhYqKCiIAQMG0DXpl6aEFi1QXFxMHJWwVrGxsZQsWZKSJUsSGxtr6nCElXJxcWHDhg04ODhw6tQp5s6dm2o7W39/NCEhLw1IPadSFLQPH2Lr72+8YIUQacq1ciXqsDASypQhtkMHgORZkH/88Qfu7u5s27YN9xeW9AmR3aTvI3KSAgUKsH37dlxcXPD392fYsGEYMrmLXmKJEoQtWABArhUrsDt5MitDFVZIBqUsUHx8PP379ycsOJhuSfWjnnfahBDCmpUrV47FixcDsHr1ag4ePPhSG006aySkt50QIutoHjzAadMmACLGjweNBoPBwIgRI/jhhx+wt7dn06ZNFC1a1MSRCiFEzlKmTBk2btyIjY0N33zzDTNmzMj0tXStWxPdqxcAbkOHonnwIIuiFNZIBqUs0NSpU7l48SLtHBxw1+vR58tHXN26pg5LCCFyhFatWjF48GAARowYwZ9Jdfee0+fLl67rpLedECLr5Jo/H1VcHHE1axLXsCEAs2bN4uuvv0aj0bBu3TqqVatm4iiFECJnql27NkuWLAFg3bp1LF26NNPXCp88mfhKlVCHheE2YADEx2dRlMLayKCUhfH19U3e8nxe+fIAxLZtC1qtKcMSQogcZezYsdSrV4/Y2Fj69u1LSEhI8rH46tXRe3mhpFEcWQH07u7EV6+eTdEKIQC0f/6Jw9dfAxAxcSKoVKxdu5a1a9cCsHDhQho1amTKEIUQIsdr27YtEyZMAOCLL77Az88vcxeysyN07VoMrq7YXrqEy8yZWRilsCYyKGVBLly4wJgxYwCYMGgQhf74A4AYHx9ThiWEEDmORqNh1apVFCpUiH///ZchQ4aQ+HwHGY2G8OnTAV4amHpe/FwdFobDvn3ZG7QQVs5l9mxUikJsq1YkVKqEn59f8vKTCRMm0LFjRxNHKIQQ5mHQoEH069cPeDYwdfz48UxdR//WW4QmzbZy/vJL7A8fzqoQhRWRQSkL8eDBA/r27Ut8fDzNmjVjhLc3qoQEEsqXJ7FsWVOHJ4QQOY6bmxtffvklDg4OnD59OkXhc13z5s92l/H0THGO3suLuOrVURkMuH3+Oc7Ll4PsqiSE0dmePo39Tz+h2NgQMWYMJ0+eZMSIEQD079+fQYMGmThCIYQwHyqViilTptCrVy/0ej2DBg3i119/zdS14j78kMghQwBwHTkSze3bWRmqsAIyKGUBoqOj6dWrF0+ePKFcuXIsW7YMp6Tp7TJLSggh0vZi4fM1a9bg6+ubfEzXvDmP/P156ucHO3fy1M+Px0l/j0yqSeUybx65J0yApJ1OhRBGYDDgMmsWANE9enDq/n369+9PYmIi7dq1Y9KkSajSWG4rhBAidWq1mg0bNtCsWTPi4uL45JNPuHTpUqauFTl6NHE1aqCOisJ9wABUsuOkyAAZlDJzBoOBYcOG8ddff+Hh4cHmzZtxCQzE9tIlFI2G2DZtTB2iEKjValq0aEGLFi1Qq+XHjshZWrVqxdChQwEYPXo0586d+/+DGg3xtWpB587P/q/RgFpN5IQJhE+fjqJS4bRlC279+4N0wIQwCoeDB7G9ehWDszM/163LJ598gk6no3HjxixevFj+XRE5kvR9hDnQarWsWrWK999/n+joaLp168bNmzczcyFCV69GnzcvNteuPXtgJ0Q6yU9IM7dgwQK+/fZbbG1t2bhxI97e3jgmFauLq18fQ968Jo5QCLC3t2f9+vWsX78ee3t7U4cjxEtGjRpFixYtSEhIoE+fPvz999+vPSe6Tx9C165FsbPD4dgxPD7+GNULBdOFEFkgLo5c8+YBENCuHZ0+/ZSYmBjq1avHmjVrsLGxMXGAQqRO+j7CXNjb2/Pll19SuXJlwsLC6Ny5c7r6Qf9lyJ+f0FWrUNRqHPfswWHPHiNEKyyRDEqZsf3797N8+XIA5s+f/2wLZIMheWcaWbonhBDpo1arWbZsGZUqVSIsLIwePXoQFhb22vN0H33E0507MeTOje1vv+HRpg2ae/eMH7AQVsJpyxa09+6hy5OH+gcPEhkZSY0aNfjyyy/lF30hhMgizs7ObNu2jTJlyhAUFISPjw93797N8HXia9cmcuRIAFzHj0f7119ZHKmwRGY/KBUVFcXy5cvp2bMnvXr1Ys2aNeh0unSdqygKs2fPpmPHjpw/f97IkWYtf39/hg8fDsDgwYPxSRqAsj1zBu3Dhxhy50b34YemDFEIIcyKg4MDX331FQUKFODOnTv079+fhISE154XX6MGT/bvR+/lhc3t23i0aoX26tVsiFgIy6YKDyfXsmUAjIuLIzA8nMqVK7NlyxYcHBxMHJ0QQlgWNzc3du/eTcmSJQkMDMTHx4d//vknw9eJ+uwzdA0botLpcO/fH1VkpBGiFZbE7Aelli9fzr1795g4cSJjx47l2rVrrFu3Ll3nHjlyxCwLYwYEBNC7d2/i4+Np0qQJY8eOTT72fOlebMuWIE8QRQ4RExODt7c33t7exMTEmDocIdKUL18+Nm/ejJOTE7/++ivjx49HScfueomlSxN86BAJZcuiefwYj/btsT19OhsiFsJyOa9ahTosjJsaDSuioihfvjw7duzA2dnZ1KEJ8VrS9xHmKG/evPj6+lKiRAkePnyIj48P9zI6A1ytJnTZMhILFED799+4jhghOxWLVzLrQan79+9z+fJlBg4cSMmSJSlTpgy9e/fmzJkzhLymrsfdu3f55ptvzG4L4cePH9OtWzfCwsJ49913WbVqFRqNBgBVdDT2R44AENOhgynDFEIIs1W+fHlWrVqFWq1m586drFixIl3nGQoU4Mm+fcTVrIk6Koo83bvjsG+fkaMVwjKpHzzAccMGAEbo9ZQsU4Zdu3aRO3duE0cmhBCWLV++fPj6+lKsWDEePHiAj48P9+/fz9A1FHf3Z3U3bWxwOHIEp02bjBStsARaUwfwJm7evImTkxPFixdPfq1ChQqoVCoCAgJ47733Uj0vLi6OZcuW0adPH1xdXdP1XgkJCSmWcahUquSp48aYbfX8mi9eOyYmhp49e3Lv3j2KFCnCli1bcHR0TD7ucOwY6pgYEosWJbFaNbOZBZZarpbKmnKF1PNVqVQWmb813VtryLVx48ZMmzaNSZMmMXfuXEqXLk3Tpk1ff2Lu3ITs3InrsGE4HDqE22efoQkKInrwYDCDr5c13FuRUnb2bzLy+TJMmoQmPp7TwJ2yZdnr60uePHmyNB5js6bvJ8k17XbP/2yuXxu5t5brVfl6enri5+dHhw4duHPnDj4+Pvj5+VGwYMF0Xz+xalUiJk0i9+TJuMyYQcK775Lw7rtZFn9GWNu9NTdmPSgVFhaGi4tLitc0Gg3Ozs6vLFC7ZcsWSpcu/awweDrt378fv6SlcQBFixZl3rx55DXy7naenp4AJCYm0qZNG65cuYKHhwcnTpygZMmSKRsfOgSA9pNP8CpQwKhxGcPzXK2BNeUKkD9//uQ/e3p64uTkZMJojMua7q2l5zpx4kSioqKYN28effv25dChQzRv3jx9J+/fD6NGweLFuMyahUtEBCxZAkkzW3M6S7+34v+Zon/zus/X9b17KXX8OABfli7Nz7/8gru7u9HiMTZr+n6SXP9fdHR0irbm3veRe2u50srXy8uL06dPU79+fQICAmjfvj3ff/89pUqVSv/FJ06EK1dQ+fnhMWgQXLoEJnzAYG331lzkyEGpHTt2cPDgwVe2WbJkSaau/dtvv3H16lXmz5+fofPatm3LRx99lPz356OswcHBJCYmZiqWV1GpVHh6ehIUFITBYGDUqFEcOXIEe3t7vvrqK5ydnQkMDExur37wgHwnT6ICHjdpgv6FYzndi7mmp3aLObOmXOH/83306FHya0FBQSlm+FkKa7q31pTr0KFDuX37dvLTQj8/PypXrpy+k0eOxMnFBZepU2HFCmLv3CFsxYocXe/Pmu6tsWm1WqM/uMoK2dm/Sc/n6/Lly2i6dKEM8J2rK+MOHCAuLi5Fn8dcWNP3k+T6shfrSJlz30fureVKT75qtZo9e/bQqVMnAgICqF27Nrt27aJ8+fLpf59Zs/D4/Xe0d+6g69iR0K1bQZ29VYSs7d4akzH6NzlyUKply5bUr1//lW3y58+Pq6srERERKV7X6/VERUWluSzv6tWrPHr0iF69eqV4fdGiRZQtW5apU6emep6NjQ02NjapHjPmB1tRFGbNmsXOnTtRq9WsXLmSd99996X3dNi3D5WiEFezJokFC5plMTlFUazmh4Q15Qopv0csPXdLz+9F1pLrokWLiIyM5Pjx43Tv3p0DBw6kWDb+KlH9+pGYLx9un3+Ow5EjqJ88IWTTJpR0Lh03FWu5t8I0/Zu0Pl+nT59m6yefcDQxkUSg2K5dOLi4mP1n0Zq+nyTXlMfT29YcWEIO6WVNucLr8/X09GTfvn106dKFq1ev0qFDB7Zt20aVKlXSd31nZ0LWrSNvy5bY//ADTitWEDV0aFaFnyHWdm/NRY4clHJxcXlpWV5qSpUqRXR0NHfu3KFYsWLAs0EnRVEoUaJEque0adOGhg0bpnht5MiR9OzZk6pVq7558FlsxYoVrF69GoD58+fTrFmzlxspCg579wJS4FwIIbKajY0Nfn5+vP/++/zxxx907dqVffv2USCdy6R1rVvzNG9e3Pv0wc7fH4+2bQnZvh29t7eRIxfCfBw+fJhhn33Gz0n1raK6dsWhYkUTRyWEEAIgT548+Pr60rNnTy5cuMDHH3/MV199xfvvv5+u8xPLlSNs1izcRowg14IFxFepQnzt2kaOWpgLs959r2DBglSqVIl169YREBDA9evX2bRpE7Vq1UquPRASEsLnn39OQEAAAK6urhQqVCjFfwAeHh7ky5fPZLmkZt26dcyZMweASZMm0blz51Tb2fzxBzYBARjs7dG1aJGdIQqRLmq1moYNG9KwYUPU2TxdV4is4OzszLZt2yhSpAj37t3j448/Jjg4ON3nx9eqxZN9+9B7emJz8yYerVqh/esvI0YshPnYunUrgwYNonVCAtUAg5MTutGjTR2WEG9E+j7C0uTOnZudO3dSp04dYmJi6NGjB4cPH073+bEff0xMx46oDAbchgxB/UJ5D2HdzP4n5NChQylQoADTp09nzpw5lC5dmgEDBiQfT0xM5OHDh8TFxZkwyow7cOAAgwYNAuCzzz5j4MCBabZ1TJolpWvWDCVXrmyJT4iMsLe3Z9u2bWzbtg37HFxPR4hX8fDwYM+ePRQoUIDbt2/TuXNnQkND031+YtmyBB86RELp0miCgvBo1w7bX381YsTida5fv27qEKyaoigsWbKEcePGoVUUljs7AxA1aBAGDw8TRyfEm5G+j7BEjo6ObN68mWbNmhEXF8egQYNYv359us8Pnz2bhLJl0QQH4zZ4MBihNrMwP2Y/KOXs7MywYcPYunUrW7ZsYfDgwSl+8OfLlw9fX99XFmPz9fXlvffey45w0+XkyZMMHToURVHo2bMnY8aMSbtxfDwOBw4AECtL94QQwqgKFiyIr68v+fLl49q1a3Tr1o3IyMh0n2/w9ubJvn3E1aiBOjKSPN26Yf+ajT2EcWzevJkPPviATZs2mToUq5SQkMCYMWNYuHAhALvr1SN/VBT6fPmI7t/fxNEJIYRIi729PevWreOTTz5BURSmTZvGlClTMBgMrz1XcXAgZN06DM7O2J07R64FC7IhYpHTmf2glCV68OABiYmJdO7cmVmzZiXvhJMa+x9+QB0Whj5/fuLq1MnGKIUQwjoVLVqU3bt34+bmxuXLl+nZs2eKXZZeR3F15emOHcS2aIEqPh73wYNxWrfOiBGL/9qyZQsTJkwASLE7qMge4eHhdO/enR07dqBSqZg/YQKtr1wBIHL4cBQnJxNHKIQQ4lU0Gg0zZsxg0qRJAGzcuJEBAwYQGxv72nP1xYsTlvRAItfKldh9951RYxU5nwxK5UA9evRgz549bNmy5bVr0J8XOI9t1w40muwIT4gMi4mJoUSJEpQoUSJDv7wLkVOVLl2aXbt24eLigr+/P5988km6OmLJ7O0JXbuWqD59AMg9fTouU6dCOp4yijezZcsWxo8fD8CAAQMYO3asiSOyLn///TetW7fm559/xtHRkU2bNjEwIgJNaCgJxYsTk0b9TCHMjfR9hKVTqVQMHDiQ1atXY2try9GjR+nUqROPHz9+7bm6li2J6t0bALfPP0dz756xw7Uq5rbDoAxK5VB16tRJc4vm59QhIdifPAnIrnsi54uNjc3YL+1C5HAVKlRg27ZtODk58csvv9C9e3eio6PTfwG1mohp0whPesrovGHDs/oKZlYD0Zy8OCA1cOBAJk2a9MrZyCJrXbx4kerVq3Pz5k08PT3Zv38/TStUwGnDBgAix48HbY7cGFqITJG+j7AGrVu3ZufOneTOnZuLFy/SvHlzriTNfn2ViEmTiK9cGXVYGG4DB0r/J4t8++23tGvXzqwGw2VQyozZHzyIKiGB+AoVSCxTxtThCCGE1alatSo7duwgV65cnD17lq5du2aoxhQqFdEDBxK6ciWKjQ0Ohw+Tp2tXVOHhxgvaSv13QGrixIkyIJWNDAYD48ePJzg4mLfffptvvvmGt99+m1yLFqHW6YivWhVdkyamDlMIIUQm1KxZk2+++YYSJUoQGBhI27ZtOZBU9zhNtraErl2LwdUV28uXcZkxI1titVR6vZ65c+fSt29fzp8/z4akBz7mQAalzJijnx8AsT4+Jo5ECCGsV7Vq1di1axe5c+fmwoULdO7cmfAMDirFtm3L023bnhX+PHsWj3btUD98aKSIrc/69euTB6QGDRokA1ImoFarWb9+Pb169WL//v14eXmhvXkTxz17AAifOBHkngghhNkqVqwYhw8f5oMPPkCn0zFkyBBmz55N4it22NMXLEjosmUAOH/1lWz+kkmhoaH07NmTFStWANCvXz+GDBli4qjSTwalzJT21i1sL19G0WqJbd3a1OEIIYRVq1y5Mnv27MHV1ZVLly7RqVMnnj59mqFrxNepw5N9+9Dnz4/N9evkbdUK7Y0bRorYOiiKwvz585k2bRoAQ4YMYcKECTIgZSKFCxfmq6++wimpkLnL7NmoDAZimzUjoVo1E0cnhBDiTbm4uPDVV1/x6aefArBq1So+/vjjV24qEteoEZFJ7V1HjUITEJAtsVqKq1ev0qJFC3788Ufs7e1ZuXIlU6dORWtGy+FlUMpMOSTNkopr0ACDh4eJoxFCCFGhQgX27t1Lnjx5+N///kfr1q25l8HCnYnly/Pk0CESSpRAExiIR9u22J47Z6SILZvBYGDixIksS3oCO27cOMaPHy8DUjmE7blz2H/3HYpGQ4QUmxdCCIuh0WgYN24cq1evxsnJibNnz9K4cWNOnz6d5jmRo0YRV7Mm6uho3AcMQCW12F5LURS++uorWrVqxT///EOhQoU4ePAgbdu2NXVoGSaDUuZIr8fx668BiJGle0IIkWOUK1eOffv2UbBgweRdxv76668MXUNfsCBPDhwgrlo11OHh5OncGfvDh40UsWVKSEhg6NChbN68GZVKxZw5c5Kf2oocQFFwmTkTgJguXdCXKGHigIQQQmS11q1bc/ToUcqWLcuTJ0/o0qULCxYsSH05n1ZL6OrV6PPlw+b6dXKPGwdmtoNcdgoNDaVPnz5MnDiRuLg4GjVqxJEjR3j77bdNHVqmyKCUGbL99Vc0gYEYXF3RNWpk6nCEeC2VSkXNmjWpWbOmzFIQFq9EiRIcOHCAMmXK8OjRI9q3b4+/v3+GrqG4ufF01y5imzVDFR+P26BBOH35pZEitiwRERH07NmT/fv3o9VqWbVqFT169DB1WEKvx/bMGdi1C+dFi7C9dAmDoyORw4ebOjIhjEL6PkI86xMdPnyYrl27oigKS5cupV27dty5c+eltoZ8+QhdtQpFrcZx714cd+82QcQ5n7+/P40bN+b48ePY2toyffp0Nm/ejLu7u6lDyzQZlDJDyQXOW7UCOzsTRyPE6zk4OODn54efnx8ODg6mDkcIo/Py8uLrr7/mvffeIyIigi5dunDo0KGMXcTBgdB164ju1QuVopB78uRns0sMBuMEbQEePHhA27ZtOXXqFA4ODmzatInWUnfR5OyPHiV/9erk6dABunQh1+LFAMR98AGGfPlMHJ0QxiF9HyGecXBwYP78+axcuZJcuXJx8eJFGjduzObNm1H+MxsqvlYtIkePBiD3xIlo//zTFCHnSLGxsUyZMoX27dvz8OFDihYtyqFDh+jTp4/ZD3zLoJSZUUVFYX/0KAAxHTqYOBohhBBpcXV1ZefOnTRu3BidTsegQYNYsmTJSx2wV9JoCJ85k4hx4wBwXrMG16FDIT7eSFGbrytXrvDRRx9x/fp18uXLx759+/jggw9MHZbVsz96FLf+/VEHBqZ4XQHsv/kmuU8jhBDCsrVt25aTJ09Su3ZtYmNjmTBhAl26dOHBgwcp2kUNGYKuYUNUOh3u/fujiogwUcQ5x/nz52nUqBEbN25EURQ+/vhjjh07RoUKFUwdWpaQQSkzY3/0KOrYWBKLFSPh3XdNHY4QQohXcHBwYOPGjfTr1w+AhQsXMmTIEGIzUsBTpSLq008JXbYMRavFcf9+8nTvLp20Fxw/fpx27drx+PFjypQpwzfffEPFihVNHZbQ68k9eTIoCv99hvv87y5TpoBen92RCSGEMAFvb292797NjBkzsLe35/Tp0zRo0ID169f/f60ptZrQZctI9PZGe/curiNGWG19qaioKCZPnky7du24e/cunp6ebNu2jUWLFuHs7Gzq8LKMDEqZmedL92I6dAAzn6YnrEdMTAwVKlSgQoUKxMTEmDocIbKVRqNh6tSpzJ8/H61Wy8GDB/Hx8Xnl9sipie3QgZCtWzE4OWH3yy94tGuHOijISFGbB4PBwMKFC+nduzexsbHUrVuX/fv34+3tberQBGDr748mMPClAannVIqC9uFDbDNYc00IcyB9HyFSp1ar6d27N8ePH6dq1apER0czbdo0mjVrxsWLFwFQ3N0JXbcOxcYGh6NHcdq40cRRZy9FUThw4AB169blyy+/TJ4d9cMPP9CwYUNTh5flZFDKjGgePHhWJBSIbd/exNEIkTEhISGEhISYOgwhTKZr167s3LkTV1dXLl26RNOmTTl79myGrhFXrx5P9u1DnzcvNteu4dGqFdpbt4wUcc4WHh5Or169WLJkCQC9evVi69atuLi4mDgy8Zzm8eMsbSeEuZG+jxBpK1GiBPv372fhwoW4urry119/0bp1a0aNGsXjx49JqFyZ8ClTAHCZOROb334zccTZ48aNG3Ts2JEhQ4bw6NEjihQpwvbt21m0aBG5c+c2dXhGIYNSZsTh669RKQpxtWqhL1jQ1OEIIYTIoNq1a3P48GHKlCnD48eP6dSpE6tXr85QnanEt9/myaFDJBYrhvbBAzzatMH2wgUjRp3zXLt2jebNm3Py5Ens7e1ZsmQJs2bNwsbGxtShiRfo01nEPL3thBBCWBa1Wk3nzp05ffo0HTt2RFEUdu7cSe3atVm8eDHBPj7EtmyJKjER94EDUVvwIO/Dhw8ZOXIkjRo14syZM9jb2zNq1ChOnjxJgwYNTB2eUcmglLlQFBz37gWkwLkQQpizYsWKcfjwYdq3b49er2fWrFn06dOHsLCwdF9DX6gQTw4eJP7dd1GHhZHn44+x//Zb4wWdQyiKwubNm2nRogV3797F29ubAwcO0LFjR1OHJlIRX706ei8vlDTKDSgqFYkFChBfvXo2RyaEECInyZMnD0uWLGH//v1UrlyZmJgYFi1axPt16rCqcmXiixZFExiI62efWdwuxKGhocyaNYs6deqwa9cuDAYDzZo146effuLzzz/H3t7e1CEanQxKmQmbS5fQ3rmDwcEBXYsWpg5HCCHEG3B0dGTZsmXMmzcPW1tbjh8/zgcffMDPP/+c7msY3N156utLbOPGqHQ63Pr1w3HzZuMFbWIhISH07t2bCRMmEBcXR8OGDS1q5xmLpNEQPn06wEsDU8//HjFtGmg02R6aEEKInOe9997j8OHDrFmzhsKFC/P48WNGTZ9Ok4gIErRa7H/6Cefly00dZpYICgpi+vTpVK9endWrV6PT6ahevToHDx5k48aNvPXWW6YOMdvIoJSZeF7gXNesGYoFVdoXQghrpVKp6NatGwcPHqRYsWIEBQXx8ccfM3XqVHQ6XbquoTg4ELphA9HduqFSFFwnTCDXnDkWt0vNTz/9xIcffsiJEyewtbVl2rRpbN26FXd3d1OHJl5D17w5oevXY/D0TPG63suL0PXr0TVvbqLIhBBC5EQqlYpWrVrx008/MWvWLAoUKMBPT5/SL2l3PueFC4k+dMjEUWbezZs3GT16NDVr1mTdunVER0dTrlw5tmzZwtdff03VqlVNHWK2k0EpcxAXh8PBgwDE+PiYOBghhBBZqWLFihw/fpzu3bsDsGHDBpo3b87vv/+evgtotYTPnUvE6NEA5Fq5EtfPP4f4eCNFnH3CwsIYPnw4Xbt2JSgoiOLFi3P48GH69u2LSnagNRu65s155O/PUz8/2LmTp35+PD53TgakhBBCpMnW1pZevXrx66+/smDBAn4qXJgvAbWi4DJoEON79eLcuXMZqstpKnFxcRw4cID27dvToEEDduzYQXx8PO+99x5bt27lxIkTNGrUyGr7NlpTByBez/7kSdRhYeg9PYmvXdvU4QiRYSqVinfeeSf5z0KIlBwdHZk7dy6NGjVixIgR3Lhxg1atWtGrVy/GjBlDrly5Xn0BlYqoYcPQe3riOmoUjn5+qIODCV2/3mxn1544cYKxY8fy6NEjVCoVvXv3ZuzYsTg6Opo6NJEZGg3xtWqBlxfxgYEWN5tPiP+Svo8QWcPW1pYuXbrQsWNHju3fz61x4ygZG0vv776j4Xff4V2oEG3atKFdu3aULFnS1OEm0+v1nD9/nkOHDvHNN98k78Sp0Wj48MMP6d+/P9WlpiIgg1JmweF5gfP27aXugjBLDg4OHD161NRhCJHjNWrUiB9//JFp06bh5+fHV199xbfffsuMGTNo1qzZa3+xie3UCUPevLgNGID9qVPkad+ekG3bMJjR7mZ///03U6dO5fvvvweeFYZftGgR7733nokjE0KI9JO+jxBZS6vV8pGPD5oqVUhs0oS6MTHMt7FhxL//snz5cpYvX07ZsmX54IMPaNiwIVWqVEGrzd7hjujoaM6ePctPP/3Et99+S1BQUPIxT09PunTpQufOnSlQoEC2xpXTyaBUDqd++hT7H34AIFZ23RNCCIvn7u7OsmXLaN++PbuImBcAAQAASURBVOPGjePu3bv069ePmjVrMmXKlNcW9o5r2JCnfn649+iB7dWreLRqxdPt29GXKJFNGWROdHQ0y5cvZ/369cTHx6PVaunfvz/Dhw/HwcHB1OEJIYQQIgfQFytGxNKluPfvz/CEBMoOHMiS27f58ccfuXbtGteuXWPlypW4uLhQs2ZNqlSpQpUqVWjSpEmWxxISEsLly5e5fPkyZ86c4bfffiMhISH5uIuLC82aNaNVq1a8//772T5IZi7kq5LD2e/fjyoxkfh33iGxVClThyOEECKb1K1bl++//54VK1awbt06zp49S7NmzWjfvj2jR4/G29s7zXMT3nmHJwcPkqdrV7R37+LRpg0hmzeTkAOLZ8bFxbFz506WL1/O48ePAahXrx7Tp0+nRA4fSBNCCCFE9tO1aEFUnz44f/klTXbt4t1jxwh2dubHH39M/i8sLIzjx49z/PhxANRqNUWKFKFEiRIUL16cwoULky9fPvLmzYuHhweOjo7Y2dlhZ2eHXq8nPj6e+Ph4IiIiePLkCcHBwTx69Ii///6bO3fuEBAQwIMHD16K7a233qJevXp88MEH1KtXDzs7u+z+8pgdsx+UioqKYtOmTVy8eBGVSkX16tX55JNPsLe3f+V5N2/eZNeuXQQEBCR/QCdMmICtrW02RZ4+Dkm77kmBc2HOYmJiqF+/PvBsFy2Z9SBE+jg4ODB69Gi6du3K3Llz2bdvH35+fhw8eJCOHTvy6aefUqhQoVTP1RcpwpNDh3Dv2RPbS5fw6NSJkDVriGvcOJuzSF1CQgK+vr4sXbqUhw8fAlCoUCGmTp1K48aNpQaLEMKsxcbGSt9HCCOKmDgR20uXsP39d9wGDkS/fz/t27enffv26PV6Ll++zIULF/j999/5/fffCQwM5M6dO9y5cydL4yhevDiVKlWiSpUq1K1blyJFikgfJoPMflBq+fLlhIaGMnHiRPR6PatXr2bdunUMGzYszXNu3rzJrFmzaNu2Lb1790aj0XD37t2c9+H5809sr1xBsbFB17q1qaMR4o3cv38fwCx2yBAip/H29mbFihX07t2bWbNmcfbsWXbs2MHu3btp3749AwcOpHTp0i+dZ8iTh6e+vrgNHIj9yZO49+lD+OzZxCTt9GcKERER7Ny5k02bNiU/YfT09GTo0KF07tw5xz0cEkKIzFAURfo+QhiTrS2ha9eSt3FjbP/4g9zTphE+ezbwrJj482V78GyzAUVR+Pnnn7l9+zYBAQHcv3+f4OBggoODefr0KTqdLtW3cXZ2xsPDAw8PD/Lly0fRokUpVqwYRYsWpUyZMuTOnTvbUrZUZj0odf/+fS5fvsycOXMoXrw4AL1792bOnDl0794dd3f3VM/bsmULzZo1o02bNsmv5chiY1u3AqD74AMMaeQihBDCelSuXBk/Pz/8/f1ZunQpp0+fxtfXF19fX2rWrEmvXr1o0qQJNjY2yecojo6EbNpE7nHjcNq5E9exY9EEBhI5ahRk48OY69evs3PnTvbs2UNUVBQAHh4efPrpp3Tr1k1mEQghhBAiQ/Te3oSuWEGe7t1x2rKF+GrViG3bNtW2BQoUoG7dutSpUyfV4waDgfj4eHQ6HVqtFltbW2xsbHLexBULZNaDUjdv3sTJySl5QAqgQoUKqFQqAgICUt2pJzw8nFu3bvH+++8zceJEHj16RIECBejcuTNlypRJ870SEhJSFC1TqVTJHegs/6Dq9didPQvr1wMQ2769RX8zPM/NknN8zppyhdTzValUFpm/Nd1ba8oVcma+NWrUYPfu3fz++++sXr2a48ePc/bsWc6ePYuHhwetWrWiXbt2VK5c+VncNjZELFiAwcuLXIsWkWvZMjRBQYTPnw8vDGBlda5PnjzhyJEj7N69mz/++CP59VKlStG/f3/atWv32uX2wriys3+TE7+XjMma8pVc0273/M/m+rWRe2u5LCXf+A8+IPLzz8m1dCm5R48msUIFEkuWTNEmPblqNBocHBzkIZkJqBQznk+6b98+Tp06xbJly1K83rdvXzp27EjjVOpm3Lx5k4kTJ+Ls7Ez37t0pUqQIp06d4sSJEyxatAgvL69U38vX1xe/pPpOAEWLFmXevHlZmxDAvn0wbBgkTfcFwNsbli+Hdu2y/v2EyAbR0dE4OzsDz+rAOTk5mTgiISzLvXv3WLduHRs2bEguFg5QrFgxPvroI5o1a0a9evWedbQ2boSBA0Gvh6ZNYe9eSPr+fFOKonDjxg2OHj3K/v37+fXXX5OXrWi1Wlq2bEn//v1p0qSJ2XeCLUW29W+EsDLS9xEiG+n10Lgx/PADlCsH58+DfM+ZjRw5KLVjxw4OHjz4yjZLlizB398/w4NSN27cYNKkSbRp04YuXbokvz5y5EjefffdFK+9KK0nicHBwSQmJmYkvTTZHz2Ka79+oCi82FVXkjruYRs2oGvePEveKydRqVR4enoSFBRk8WvurSlX+P9879y5kzyjMSAgAEdHRxNHlvWs6d5aU65gXvkmJCRw6tQp9u/fz7Fjx4iNjU0+Zm9vT+XKlalSpQqtNBoarluHRqcjvmJFQrdtw+Dujt3587jHxRFiZ0fce++BRvPK9wsPD+f69etcvXqV8+fPc+7cOYKDg1O0qVChAu3bt6ddu3Z4eHgYJe+cSKvVkjdvXlOH8VrZ0b958drm8r2UFawpX8n1ZTExMck7iJpz30fureWytHzVwcF4NG6M5tEjYjp0IHzZsuQyBZaWqykZo3+TI5fvtWzZMnm3irTkz58fV1dXIiIiUryu1+uJiorC1dU11fPc3NwAKFiwYIrXvb29efLkSZrvZ2Njk6JGx4uy5IOt1+MyadJLA1IAKkVBUanINXkysY0bv/aXBHOlKIrV/JCwplwh5feIpedu6fm9yJpyBfPIV6vV8sEHH/DBBx8QHR3Nzz//zA8//MDJkycJCgpKXuK3EqgGHAHyXrmCbbVqqDQaXJMGsdyBSFdXznbqxK0KFYiKiiI6OprQ0FAePnzIw4cPuXfvXqpbIdvZ2VG1alWaNGlC06ZN8fb2Tj6W079+1sjo/Zs0rmtNnwVryldyTXk8vW3NgSXkkF7WlCtYTr56Dw9CV68mT8eOOPr5Ef/ee8R07ZqijaXkamly5KCUi4sLLi4ur21XqlQpoqOjuXPnDsWKFQPg6tWrKIqS/GTiv/LmzYubm1vy9tPPBQYGUqlSpTeOPbNs/f3RBAameVylKGgfPsTW35/4WrWyMTIhskapUqUA81+3LoS5cHJyomnTpjRt2hRFUbh16xYXL17k999/5+LFi1y6fZtaiYn8DHjGx/PfLppTWBiN1q1jLbD/Fe9ToEABypUrR6VKlahZsyaVKlWSOlFCCKunUqmk7yNENouvUYPIMWNwmT2b3JMmEf/OOyS+/bapwxKvkSMHpdKrYMGCVKpUiXXr1tGvXz8SExPZtGkTtWrVSt55LyQkhOnTp/Ppp59SokQJVCoVrVq1wtfXlyJFilCkSBF++uknHjx4wPDhw02Wi+aFGiBZ0U6InMTR0ZEff/zR1GEIYbWe/3JUqlQpOnfuDDxbtvXPnTvkatsWJTz8pVm6asAArLG1RVe/Pg7Ozri4uODt7U2BAgXw9vamZMmSac5MFkIIa+bg4CB9HyFMIGrQIGzPn8f+++9xHzCA4MOHsb1xA+LjsbW1TVd5ApG9zHpQCmDo0KF8+eWXTJ8+HZVKRfXq1endu3fy8cTERB4+fEhcXFzyay1atCAhIYEtW7YQFRVF4cKFmTRpEp6enqZIAQB9vnxZ2k4IIYR4FRsbG8o9fYpTeHiabdRA/vh4tvbrJ7N0hRBCCJHzqdWELl1K3mbN0N69S/6qVVEnjQXkAfReXoRPn26RtZrNldkPSjk7OzNs2LA0j+fLlw9fX9+XXm/Tpg1t2rQxYmQZE1+9OnovL9RBQahSWeeqqFTovbyIr17dBNEJIYSwRDJLVwghhBCWRnFzI7pHD1xmzUoekHpOHRSEW//+hK5fLwNTOYTa1AGIJBoN4dOnA/+/295zz/8eMW2aTDUUZikmJoYGDRrQoEGDFLuBCSFMS2bpCiGEccTGxkrfRwhT0etx3rQp1UPPJ4C4TJkCen12RiXSIINSOYiueXNC16/H8J9lhHovLxnJFWbv5s2b3Lx5U3a8ECIHeT5L978PQ55TVCoSCxSQWbpCCJFBiqJI30cIE3m+iVhaWwy8uImYMD2zX75naXTNm6Nr0gS78+fJEx/PUynGJoQQwliSZum69e+PolKlWD4us3SFEEIIYY6kPIF5kZlSOZFG86ygbOfOz/4vvwwIIYQwEpmlK4QQQghLIuUJzIvMlBJCCCGsnMzSFUIIIYSlkE3EzIvMlBJCCCGEzNIVQgghhGWQTcTMigxKCSGEEEIIIYQQwmJIeQLzIcv3hBDZomDBggCo0tjlSwghhBDCUqhUKun7CGFiUp7APMiglBDC6BwdHfGXLVeFEEIIYSUcHByk7yNETvC8PIGXF/GBgZBKjSlhWrJ8TwghhBBCCCGEEEJkOxmUEkIIIYQQQgghhBDZTgalhBBGFxsbS/PmzWnevDmxsbGmDkcIIYQQwqik7yOEEOkjNaWEEEanKAp//PFH8p+FEEIIISyZ9H2EECJ9ZKaUEEIIIYQQQgghhMh2MiglhBBCCCGEEEIIIbKdDEoJIYQQQgghhBBCiGwng1JCCCGEEEIIIYQQItvJoJQQQgghhBBCCCGEyHay+54QIlu4u7ubOgQhhBBCiGwjfR8hhHg9GZQSQhido6Mj//vf/0wdhhBCCCFEtpC+jxBCpI8s3xNCCCGEEEIIIYQQ2U4GpYQQQgghhBBCCCFEtjP75XtRUVFs2rSJixcvolKpqF69Op988gn29vZpnhMWFsa2bdu4cuUKOp2OAgUK0LZtW2rUqJGNkQthPWJjY+nWrRsA27Ztw8HBwcQRCSGEEEIYT2xsLN27dwek7yOEEK9i9oNSy5cvJzQ0lIkTJ6LX61m9ejXr1q1j2LBhaZ6zcuVKoqOjGTNmDLly5eKXX35hyZIlzJ07l6JFi2Zj9EJYB0VROHv2bPKfhRBCCCEsmfR9hBAifcx6+d79+/e5fPkyAwcOpGTJkpQpU4bevXtz5swZQkJC0jzvxo0bNGvWjBIlSpA/f37at2+Pk5MTd+7cycbohRBCCCGEEEIIIayXWQ9K3bx5EycnJ4oXL578WoUKFVCpVAQEBKR5XunSpTlz5gxRUVEYDAZ+/fVXEhISKF++fHaELYQQQgghhBBCCGH1zHr5XlhYGC4uLile02g0ODs7ExYWluZ5X3zxBUuXLqV3795oNBpsbW0ZOXIknp6eaZ6TkJBAQkJC8t9VKlXy2nCVSvVmiaTi+TWNce2cRnK1XKnlq1KpLDJ/a7q31pQrWFe+1pSreCY7+zfW9vmypnwl17TbPf+zuX5t5N5aLmvK15pyNUc5clBqx44dHDx48JVtlixZkunr79mzh+joaCZNmkSuXLm4cOECS5YsYfr06RQqVCjVc/bv34+fn1/y34sWLcq8efPImzdvpuNIj1cNlFkaydVy5c+fP/nPnp6eODk5mTAa47Kme2tNuYJ15WtNuVo7U/RvrO3zZU35Sq7/Lzo6OkVbc+/7yL21XNaUrzXlak5y5KBUy5YtqV+//ivb5M+fH1dXVyIiIlK8rtfriYqKwtXVNdXzgoKCOHbsGIsWLeKtt94CoEiRIly/fp1jx47Rv3//VM9r27YtH330UfLfn4+yBgcHk5iYmM7M0k+lUuHp6UlQUJDFF0eUXC3X83wfPXqU/FpQUBCOjo4mjMo4rOneWlOuYF35WlOuxqbVao3+4CorZGf/xto+X9aUr+T6spiYmOQ/m3PfR+6t5bKmfK0pV2MzRv8mRw5Kubi4vLQsLzWlSpUiOjqaO3fuUKxYMQCuXr2KoiiUKFEi1XPi4+OBl6fuqdXqV35AbWxssLGxSfWYMT/YiqJYzTeO5Gq5FEVJXg5i6blben4vsqZcwbrytaZcrZ0p+jfW9vmypnwl15THLanvYwk5pJc15QrWla815WpOzLrQecGCBalUqRLr1q0jICCA69evs2nTJmrVqoW7uzsAISEhfP7558mFzwsUKICnpycbNmwgICCAoKAgDh8+zJUrV6hWrZop0xHCYjk6OhIQEEBAQIDZPikUQgghhEgv6fsIIUT65MiZUhkxdOhQvvzyS6ZPn45KpaJ69er07t07+XhiYiIPHz4kLi4OeDbdbNy4cezYsYN58+ah0+nw9PRkyJAhvPvuu6ZKQwghhBBCCCGEEMKqmP2glLOzM8OGDUvzeL58+fD19U3xmpeXFyNHjjR2aEIIIYQQQgghhBAiDWa9fE8IYR50Oh3du3ene/fu6HQ6U4cjhBBCCGFU0vcRQoj0MfuZUkKInM9gMPDDDz8k/1kIIYQQwpJJ30cIIdJHZkoJIYQQQgghhBBCiGwng1JCCCGEEEIIIYQQItvJoJQQQgghhBBCCCGEyHYyKCWEEEIIIYQQQgghsp0MSgkhhBBCCCGEEEKIbCe7770hrda4X0JjXz8nkVwtl62tLZUrVwbAzs4OGxsbE0dkPNZ0b60pV7CufK0pV2Mx96+hMeM3969NRllTvpLr/7Ozs7Oovo/cW8tlTflaU67GYoyvoUpRFCXLryqEEEIIIYQQQgghxCvI8r0cKjY2ljFjxhAbG2vqUIxOcrVc1pSv5Gq5rClfa8pVZD9r+3xZU76Sq+WypnytKVewrnytKVdzJINSOZSiKPz9999Yw0Q2ydVyWVO+kqvlsqZ8rSlXkf2s7fNlTflKrpbLmvK1plzBuvK1plzNkQxKCSGEEEIIIYQQQohsJ4NSQgghhBBCCCGEECLbyaBUDmVjY0OHDh3MfqeO9JBcLZc15Su5Wi5ryteachXZz9o+X9aUr+RquawpX2vKFawrX2vK1RzJ7ntCCCGEEEIIIYQQItvJTCkhhBBCCCGEEEIIke1kUEoIIYQQQgghhBBCZDsZlBJCCCGEEEIIIYQQ2U4GpYQQQgghhBBCCCFEttOaOgCRUkBAAHv37uXGjRvo9XoKFSpEixYtqFWrlqlDyzIHDhxg586dAMycOZNSpUolH/P19cXPzy/Nc1euXEm+fPmMHmNmGAwGTpw4wY8//siDBw/QaDQUKVKEli1bUrVq1Zfax8TEsHfvXvz9/QkLC8PNzY0aNWrg4+ODvb29CTJ42enTp7l+/Tp37tzh33//JTExkcGDB1O/fv2X2v7888+cO3eOf//9l/DwcBRFIW/evFSsWJFWrVrh7u6eov3jx4/59NNP03zvDh060LFjx6xO6ZUyki9AYGAg+/fv5/r16zx9+hRnZ2cKFixIs2bNUr3n8OzrdPToUe7fv49Wq6V06dJ07NiRYsWKGTGzlEJCQjh79iyXLl3iwYMHhIWF4ezsTOnSpWndujUlS5ZMbpuYmMhvv/3Gb7/9xu3bt3ny5AkqlYqCBQtSv359GjVqhFqd8vlGTru3GckXSFdsq1evxsPDI8VrOeHexsfHs2vXLu7cuUNQUBBRUVE4Ojri6elJw4YNqVOnDlrt///Tf/fuXc6cOcPff//NnTt3iIyMpFy5ckydOjXV6+e0eyvMh6X3byy1bwPSvzH3/o219G3Auvo31tS3AenfWDoZlMpBrl69yqxZs7C1taVWrVo4ODjg7+/P0qVLefr0KS1btjR1iG/s33//Ze/evdjZ2REXF5dmu3r16pE3b96XXndycjJmeJmmKApLlizB39+f/Pnz07BhQxISEvjtt9+YP38+vXv3pmnTpsntdTodU6dO5e7du7zzzjvUrl2bu3fvcvjwYf766y+mTZuGra2tCTN6Zs+ePQQHB5MrVy7c3NwIDg5Os+2vv/5KUFAQJUuWxNXVFXj2D8K3337LqVOnmD59Om+99dZL5xUuXJhq1aq99Hr58uWzLI/0yki+t27dYtq0aej1eqpUqUL16tUJDw/n/PnzzJ8/Hx8fH3x8fFKcs2/fPnbv3k3evHn58MMPiY2N5cyZM0yaNIlJkyZRpkwZY6cIwLfffsvBgwfJnz8/77zzDi4uLgQGBnLhwgUuXLjAsGHDkn9RfPToEYsXL8be3p63336bKlWqEBMTw8WLF9m4cSO///47Y8aMQaVSvfQ+OeXeZiRfeNbxSE1QUBC//PILBQsWfKnTllPurU6n48SJE5QoUYLKlSvj4uJCdHQ0ly9fZs2aNZw5c4Zx48Yld7TPnz/PgQMH0Gq1eHl5ERkZma73ySn3VpgHS+/fWGrfBqR/A+bfv7GWvg1YV//Gmvo2IP0bi6eIHCExMVH59NNPlS5duih///138uvR0dHK0KFDlc6dOyuPHz82XYBZICEhQRk9erQyfvx4Zfny5YqPj49y48aNFG327Nmj+Pj4KFevXjVRlJlz9uxZxcfHR5k4caISFxeX/Hp4eLgyePBgpUuXLsqjR4+SX3+e5/bt21NcZ/v27YqPj4+yb9++bIv9Vf7444/kz93+/fsVHx8f5ccff0y17Yt5v+jkyZOKj4+PsmjRohSvP3r0SPHx8VFWrvw/9u46PIrra+D4dyUuRLHgxYt7geIFSqHF3YtDgQKlLaVIkUILFEnw4sXhR7FSwYu7uxYIkJCEEN1kd98/wu6bJa6bZM/neXhIZu7Mnrszu3Ny58693ukac1qkpL7Tp0/Xd+jQQX/69GmT5S9fvtT37NlT361bN71GozEuf/bsmb5z58764cOH60NDQ43LHzx4oO/atat+5MiReq1Wm/6VisfJkyf1165di7P8+vXr+s6dO+t79+5tjP3Vq1f6ffv26cPDw03KhoeH67/55ht9hw4d9MePHzdZl9WObUrqm5hff/1V36FDB/2uXbtMlmelY6vVavVRUVFxlkdHR+snTZqk79Chg/7cuXPG5Y8fP9bfu3dPHxUVpQ8MDNR36NBBP3HixAT3n9WOrcj6cnp+k5NzG71e8hu9PvvnN5aS2+j1lpXfWFJuo9dLfpPTyZhSWcTVq1d58eIFderUoUiRIsbl9vb2tGnThujoaA4fPmy+ANPB9u3befLkCYMHD47THTa7O3PmDABt2rQxuQPo7OzMJ598QlRUFIcOHQJi7jru378fW1tb2rVrZ7Kfdu3aYWtry4EDBzIt9sRUqFAh3ru68UnozmetWrWAmDsxWV1K6vvixQsUCgWVK1c2We7p6UmhQoXQaDREREQYlx88eBCtVkvbtm2xt7c3Li9SpAh16tTh6dOn3Lx5M30qkoSaNWtStmzZOMvLlClDuXLlCA0N5fHjxwC4ubnRrFmzOI9c2Nra8sknnwBw/fr1jA86DVJS34RoNBqOHj2KWq2mXr16Juuy0rFVKpUm3dcNVCqV8c5f7M9iwYIFKVasWLzbCJEecnp+k5NzG5D8BrJ/fmMpuQ1YVn5jSbkNSH6T0+W8q2c2de3aNQAqVqwYZ12lSpWArP3FmJT79+/zv//9j/bt21OgQIEky9+4cYMdO3awc+dOTp8+bXIBzIqCgoIA4h0TwrDs6tWrQMyz+oGBgZQqVSreC2GpUqV48eIF/v7+GRt0Jjl//jxAvF3bAQIDA9m3bx/bt2/nwIEDWT65MyhYsCB6vZ4LFy6YLPf39+fx48cULlwYJycn43LD5ze+z7hhWVb4jKtUKpP/E2O40CdUNjsc2+TW9/Tp04SGhlKtWjWcnZ1N1mWHY6vT6bh06RKQ8GcxJbLDsRVZQ07Ob3J6bgOS3yQmJ+Y3OTW3AcvKbywltwHJb3IKaTrMIgwnfL58+eKsc3FxwdbWFl9f38wOK11ERUXh4+NDkSJF+Oyzz5K1zebNm01+d3BwoHfv3tSvXz8jQkwzwwX65cuXcRLTly9fAhiPX2LH2rD80qVLPH/+PM6z3dnB8ePHefLkCRqNhv/++49Lly6RO3duOnXqFG/5y5cvc/nyZePvCoWCunXr0r9//ywzIGp8OnfuzK1bt5gzZw5Vq1Ylf/78xnEX8uTJw8iRI03K+/r6YmtraxyPIjbDuWDuC5+/vz9XrlzB1dWVQoUKJVn+4MGDQMxd2Phk9WObkvoa7u43atQozrqseGyjo6PZvn07AG/evOHq1as8ffqUBg0aUL58+TTvP6sfW5F15NT8xhJyG5D8JjZLyG9yYm4DlpXf5OTcBiS/yamkUSqLCAsLAzDpHhmbnZ2dsUx2s2nTJnx9fZkxY0aSXduLFCnC4MGDef/993FxcSEoKIjz58+zadMmFi5ciIODQ4Izf5hT5cqVOX78OL///jvlypUzdvV+8+YNe/bsASA0NBRI3rGOXS67OXHiBKdOnTL+/t577zFixIg4d1ltbGxo164d1atXJ2/evOh0Oh48eMDGjRs5evQokZGRjBkzJrPDTzYvLy+mTZvGnDlzOH36tHG5k5MTDRo0IG/evCblw8LCyJUrV7z7ygrHPDo6mgULFhAVFUW3bt2S/Kz+888/XLhwgXLlylGlShWTddnh2Kakvi9fvuTatWt4eHjEm6BmxWMbHR1tMtuXQqGgVatWdO3aNU37zQ7HVmQtOTW/sYTcBiS/ic0S8pucltuAZeU3OT23AclvcipplBIZ6vbt2+zatYsOHTok685EjRo1TH7PnTs3zZs3x8vLi6lTp7Jx48YsmbjVrVuXQ4cOce3aNcaMGUOlSpWIjo7mzJkzxjsM8c3ekRONHj0aiElSDV/m33zzDWPGjKFcuXLGcrly5Ypzd7F8+fKULFmSr7/+mtOnT3P//v1Mn3I2ue7evcvPP/9MoUKFmDFjBl5eXgQFBbFv3z5WrVrFzZs3GTVqlLnDTBadTsfChQu5ceMGjRs3jjOuwLvOnTvHr7/+iqenJ1988UWc9Vn92Ka0vgcOHECv19OgQYNsM2aMra0tmzdvRqfTERgYyLlz59iwYQO3b9/m22+/TfCPxqRk9WMrRGawlNwGJL+JzRLym5yU24Bl5TeWkNuA5Dc5VfY5A3M4wwcooRbn8PDwVH/IzEWr1eLj40PhwoVp3bp1mvZVvnx58uTJw+PHj81+xyU+KpWKcePG0aFDBxQKBf/88w+nT5+mevXqxou34W5Dco517HLZlYODA+XKlWPcuHFYW1vj7e1NdHR0ktvZ2NgYL6S3bt3K6DBTJTo6mrlz56JQKBgzZgzFihXDxsaGPHny0KtXL6pXr87JkydNBoC0t7fPksdcp9OxaNEi/v33Xz788EP69++faPnz588zZ84cXFxcmDBhAq6ursl+raxwbFNaX51Ox+HDh1EoFPF2b4ese2whZmBQd3d3mjZtyoABA7h165ax23t6ygrHVmRNOS2/saTcBiS/iU9OzW9yUm4DlpXfWFpuA5Lf5DTSUyqLMHSH9fX1jdMCGxQUREREBMWLFzdHaKkWERFhHGcgoS6V48ePB2DMmDFx7iS+y8nJiefPn6PRaLJkQmNlZUWHDh3o0KGDyXLDIK+G4xr7WMfHsPzdLtLZlb29PSVKlODMmTM8f/48WYPBGsawiIyMzOjwUuXZs2e8fPmSGjVqYGNjE2f9+++/z5kzZ3j48CGlS5cGYp6/v337NkFBQXGezzfXMTfcVTty5Ah16tRh6NChid4tO3/+PLNnz8bJyYmJEyeSJ0+eFL+mOY9tSusLcPHiRV69ekXFihUTHAMlKx7b+GT0wKRZ/XMrzCOn5TeWltuA5DcJyWn5TU7JbcCy8htLz21A8pucQHpKZRGGKT0NswfEdvHiRZMy2YWVlRWNGjWK959hgLxq1arRqFGjeGd1iS0iIoInT55gY2NjMutHdnD06FEA6tSpA8R8ybu6unLr1q04M+9ERERw69YtcufOnS0HAU1IYGAgQLKnZb1z5w5AsqcwzmyGO6Jv3ryJd31wcDBgWt/EPuOGZZn5GY+dxNSuXZsvvvgiWQmbo6MjEydOTHUiYq5jm9L6GhgGAW3cuHGCZbLasU1IQEAAkLyZh1Ijq39uhXnktPxGcpv/J/lNzspvckJuA5aV30huE0Pym+xPGqWyCEMX7mPHjvHw4UPj8rCwMP73v/+hVquTfDY4q7G2tmbQoEHx/itZsiQArVu3ZtCgQRQpUoTw8HCePXsWZz8ajYYlS5YQHh7OBx98kGFfOGkVXxfXkydPcvDgQd577z1q1qwJxIy90LhxYyIiIti2bZtJ+W3bthEREZHoRSIrSujYQcyF7+7du+TLl8/kQv/gwQP0en2c8qdOneLw4cM4ODhQuXLlDIs5LQoWLIidnR03b96Mc7H29/fnn3/+QaFQmFysGzRogEqlYvv27SbnysOHDzl27BheXl7GO48ZzdDN+8iRI9SqVSvJJObChQvMnj0bBwcHJk6cmODMSgZZ7dimtL4GwcHBnDt3Dmdn50THe8lKx/bJkyfx3smLjIxkzZo1AGl677PasRVZX07LbywttwHJbywlv8nuuQ1YVn5jSbkNSH6T08nje1mESqVi4MCBTJs2jYkTJ1K7dm3s7Ow4deoUfn5+9OjRI8k7btndmzdv+PLLL3nvvffw8vLCxcWF169fc+XKFV69ekWhQoXo0aOHucNM0HfffYe7uzteXl5YWVlx7949rl27Rp48eRg1apTJheLTTz/lzJkz/P777zx8+JCiRYvy4MEDLl26xHvvvccnn3xixpr8v/379xvHDnj8+LFxmaHLfunSpWncuLHx2BUrVgwvLy/c3NwICQnh3r17PHjwADs7O4YOHWqy79WrV/PixQtKlCiBu7u7cZaLmzdvYmVlxdChQzP9UYbk1tfKyooePXqwdOlSpk+fbpw2OSgoiNOnTxMREUHLli3Jnz+/cd/58+enQ4cObNy4ka+++oqaNWsSHh7O8ePHARg4cGCmDTS5detWDh8+jK2tLfnz54/zxwPEDMxbpEgRnj59yqxZs4iKiqJs2bL8+++/ccrmzp2bBg0aGH/Pasc2JfWN7fDhw2i1WurVq5foXfCsdGyPHz/Onj17KF26NJ6entjZ2REQEMDFixd58+YNZcqUoWXLlsbyT58+ZceOHUDMH8mGZT4+PsYysT+7We3YiqzP0vOb7J7bgOQ32T2/sZTcBiwrv7Gk3AYkv8nppFEqCylXrhxTpkxh8+bNHD9+HK1WS6FChejWrRu1a9c2d3gZztHRkaZNm3L37l0uXLhAaGgo1tbWeHl58fHHH9O8eXPjVMRZ0QcffMDp06e5c+cO0dHR5M6dm7Zt2/Lpp5/G+RKztbVl8uTJbN68mVOnTnH16lVcXV1p2bIlHTp0yDL1vHnzJocPHzZZduvWLZOB/ho3boyzszPt2rXj2rVrXL58mTdv3qBWq8mdOzeffPIJLVu2xN3d3WQ/H374IadOneLOnTucP38evV6Pm5sbjRo1olWrVnh5eWVKHWNLbn0BmjRpQu7cudm7dy+3bt3i/Pnz2NraUrRoUZo0acKHH34YZ/9t27bF09OTvXv38tdff6FWqyldujSdOnXK1Nk8/Pz8gJjHKRIaFDJ37twUKVKEoKAgoqKiAIyJyLvKli1rkrRltWObkvrGZujentAgoLFllWNbtWpVAgMDuX37Nrdv3yYiIgJ7e3sKFSpEnTp1aNiwoUmPjKCgoDjn/OvXr02WxU7astqxFdmDJec32T23Aclvsnt+Yym5DVhWfmNJuQ1IfpPTKfTx9VMTQgghhBBCCCGEECIDyZhSQgghhBBCCCGEECLTSaOUEEIIIYQQQgghhMh00iglhBBCCCGEEEIIITKdNEoJIYQQQgghhBBCiEwnjVJCCCGEEEIIIYQQItNJo5QQQgghhBBCCCGEyHTSKCWEEEIIIYQQQgghMp00SgkhhBBCCCGEEEKITKc2dwBCiJztq6++4tGjR6jVapYsWYKTk5O5QzI6dOgQCxcupH79+gwdOjRDX8vHx4fDhw8bf1cqlWzcuDFF+5g0aRLXr19n4sSJvP/+++kdYrKNHDmSZ8+eGX8vW7YskyZNMls8QgghRGaT/CaG5DdCiLSSRikhRIa5e/cujx49AiA6OpqjR4/SokULM0dlXqVKlSJv3rwoldm3o2qNGjUIDAwkKCiIS5cumTscIYQQIlNJfhOX5DdCiNSSRikhRIY5cOAAAG5ubgQEBHDgwAGLT9oaN25MgwYNzB1GmnTt2hWAa9euSdImhBDC4kh+E5fkN0KI1Mq+TdlCiCwtMjKSY8eOATBs2DBsbW15/Pgxd+/eNXNkQgghhBCpI/mNEEKkL+kpJYTIECdOnCA8PJyCBQtSrlw5ateuzYEDBzhw4ADFixePd5vYYwo4ODiwdetWbty4QXh4OHny5KFRo0a0bNkShUIRZ9uIiAh27NjB8ePH8ff3x8nJiYoVK9K5c2f++ecftm7dSvv27enYsWOy6xAQEMCuXbu4ePEifn5+KJVKvLy8qF+/Ph999BEqlSrV709C/P392bx5MxcvXiQ0NBR3d3c++OAD2rVrl+S2J0+eZP/+/dy/f5+wsDCcnZ15//33adu2LQUKFIh3m5s3b7Jt2zbu3LmDVqulQIECNG/enPr16xvfq82bN6drHYUQQojsSvKb1JH8RgiREGmUEkJkiIMHDwLQsGFD4/8HDhzg+PHj9O7dG2tr6wS3vXTpErt37yZPnjyUL1+eoKAgbt68ydq1a3n16hW9e/c2KR8REcHkyZO5d+8etra2VKhQAWtray5dusSFCxeoXLlyiuO/fv06P//8M6GhoXh6elKhQgWioqK4e/cuK1as4Ny5c3z99deo1en3Nfr06VMmTZrE69evcXV1pWrVqkRGRrJnzx6uXbuW4HZarZb58+dz4sQJrKysKFasGK6urvj6+vLvv/9y+vRpxowZQ6VKlUy2O3bsGPPnz0ev11OoUCEKFixIQEAACxcu5MmTJ+lWLyGEECKnkPwm5SS/EUIkRhqlhBDp7tmzZ9y4cQOVSkW9evWAmAEwvby8ePr0KSdPnjQuj8+OHTvo378/H330kXHZ1atXmTJlCvv27aNVq1a4u7sb123atIl79+5RoEABvv/+e1xdXQHQaDR4e3tz6NChFMUfFBTE7NmzCQsLo1+/fjRp0sQ4cOebN2/45ZdfuHTpEjt27KB9+/Yp2ndivL29ef36NR988AFDhw41Jrb+/v5MnjyZFy9exLvd5s2bOXHiBCVKlGDEiBHkzp3buO7kyZPMnTuXefPm4e3tjYODAxBzl3TJkiXo9Xp69+5tMhbG9evX+fHHH9OtXkIIIUROIPlN6kh+I4RIjIwpJYRId4a7iNWqVcPZ2dm43HBX0TBAaEJq1KhhkrABlCtXjooVK6LT6Uzuqmk0Gvbv3w9Ar169jAkbgLW1Nf369cPGxiZF8e/Zs4c3b97QrFkzmjZtajKTjJOTE8OGDUOlUrFv3z70en2K9p2Qmzdvcu/ePWxsbOjXr5/JnVYPDw969OgR73YhISHs2bMHKysrRo8ebZKwAdSqVYsmTZoQGhrK0aNHjcsPHDhAREQEJUuWjDM4a9myZWnatGm61EsIIYTIKSS/STnJb4QQSZFGKSFEutJqtRw+fBj4/yTNoF69eqhUKm7cuMHz588T3Ee1atXiXe7l5QXE3AUzuH//PhEREcYxFt7l7OxM+fLlU1SH8+fPA1C7du1417u5uZEvXz6Cg4Px9fVN0b4Tcv36dQAqVaqEk5NTnPXVq1fH3t4+zvKrV6+i0WgoXbo0bm5u8e77/fffB+DWrVvGZTdu3ACgbt268W7z4YcfpqwCQgghRA4m+U3qSH4jhEiKPL4nhEhX58+fJygoCDc3tzjP+Lu4uFC5cmXOnj3LwYMH6dKlS7z78PDwiHe5nZ0dEHP30ODVq1cAce6gxZbYuvi8fPkSgAkTJiRZNjg4mPz586do//FJqh4KhQJPT08ePXpkstwQ65UrV5Ic5DQ4ODjZr+fp6Zm8wIUQQggLIPlN6kh+I4RIijRKCSHSlaHrelRUFBMnToyz3nAX8NChQ3Tq1Mmk67hBfLPPZCadTgfEdA1Pqmt8fHf9MpMh1rx581KqVKlEyxruxCaHuY+BEEIIkZVIfpO5JL8RwnJIo5QQIt0EBgZy4cIFIGbAzNjdqeMre/HiRapUqZKm1zR06fbz80uwTGLr4uPh4YGvry+fffYZ7733XpriS67k1MPf3z/OMsNd1/z58zN06NAUvd6zZ88SfD3DHUohhBDC0kl+k3qS3wghkiKNUkKIdHPo0CF0Oh0lSpRg2rRpCZZbt24dO3fu5MCBA2lO2ooVK4aNjQ3BwcFcvnyZChUqmKw3LE+JSpUq4evry4kTJzItaStbtiwAFy9eJCQkBEdHR5P1Z8+eJTQ0NM525cqVQ61Wc+3aNV6/fk2uXLmS9XplypTh6tWrHDt2jGbNmsVZ/++//6aiFkIIIUTOI/lN6kl+I4RIigx0LoRIN4ZZaerXr59oOcP6c+fOmYwDkBo2NjY0atQIgNWrVxMUFGRcFxUVxYoVK4iMjEzRPj/99FMcHBzYvXs3u3btIjo6Ok6Zly9fcuTIkTTFHluZMmUoWrQoERERLF++nKioKOM6f39/1q5dG+92Li4uNG/enMjISGbOnMnjx4/jlImKiuLs2bM8ffrUuKxRo0bY2Nhw8+ZN9u3bZ1L+5s2b/PXXX+lUMyGEECJ7k/wm9SS/EUIkRXpKCSHSxfXr13n+/DlWVlYJzupiULBgQYoWLcqDBw84fPgwrVq1StNrd+nShVu3bnH//n2GDx9OuXLlsLKy4ubNm0RHR1O/fn0OHz6MWp28rzx3d3e++uorZs+ezdq1a9m5cycFCxbExcWFsLAwnj59yosXLyhRogT16tVLU+yxffHFF0yaNInjx49z48YNSpcuTWRkJNeuXaNQoUKULFmS27dvx9muW7duBAUF8e+///LVV19RpEgRcufOjUql4tWrVzx8+JDIyEjGjRtnHHfB3d2d/v374+Pjw4oVK9i/fz8FChQgMDCQGzdu0LJlS3bt2oVKpUq3+gkhhBDZjeQ3aSf5jRAiMdJTSgiRLgwDgFatWjVO1+z4GO4mGrZLC1tbWyZNmkSbNm3IlSsXFy9e5MaNG5QvX56ZM2caBxt1dnZO9j7Lli3LnDlzaNeuHW5ubty7d4+TJ0/y6NEjXFxcaN++PQMHDkxz7LEVKFCAGTNm0KBBA3Q6HWfOnOHp06c0b96cCRMmJJh0qlQqhg8fzrfffkv16tV5/fo1Z8+e5dKlS4SEhFC1alWGDx9OmTJlTLarV68eEydOpEKFCrx8+ZKzZ88SHh7OwIED+fjjj4GUvWdCCCFETiP5TdpJfiOESIxCr9frzR2EEEJklOjoaEaPHo2vry8zZsygWLFiZonDx8eHw4cPM2TIEBo0aGCWGFLi8OHD+Pj4ULVqVb7++ut4y1y7do3JkydTtmxZJk2alLkBCiGEEBZM8pvUkfxGiKxHHt8TQuQI9+/fp0iRIiZTMEdERLBmzRp8fX0pXLiw2RK22Pbv38+1a9dQKpUMHjzYrLH4+/ujVqtxcXExWX7z5k3jGA8NGzaMs9369esJDAw0Gd9CCCGEEOlP8puUk/xGiOxFGqWEEDnC7NmziYyMpFChQjg7OxMcHMzDhw+NM70MGTLE3CECcOvWLW7dupUlkrarV6+yaNEiihQpgru7O0qlkhcvXvDo0SMAGjRoQI0aNeJsd/r0aZ49e5bZ4QohhBAWR/KblJP8RojsRR7fE0LkCHv37jWOURAaGopCocDT05MKFSrQqlUrPDw8zB1ilvP06VN27drFjRs3eP36NZGRkdjb21OkSBEaNmxI3bp1zR2iEEIIYdEkv0k5yW+EyF6kUUoIIYQQQgghhBBCZDqZfU8IIYQQQgghhBBCZDpplBJCCCGEEEIIIYQQmU4apYQQQgghhBBCCCFEppNGKSGEEEIIIYQQQgiR6aRRSgghhBBCCCGEEEJkOmmUEkIIIYQQQgghhBCZThqlhBBCCCGEEEIIIUSmk0YpIYQQQgghhBBCCJHppFFKCCGEEEIIIYQQQmQ6aZQSQgghhBBCCCGEEJlOGqWEEEIIIYQQQgghRKaTRikhhBBCCCGEEEIIkemkUUoIIYQQQgghhBBCZDpplBJCCCGEEEIIIYQQmU4apYQQQgghhBBCCCFEppNGKSFEljRp0iQUCgUKhcLcoaSLQ4cOGetz6NAhc4eTqfbs2UOzZs3w8PBApVKhUChwcXExrje8L5MmTYqzrSW/b0IIIXK2nHiNS+yanpNdvXqV7t27U7BgQaytrY3vw8WLFwFo0KABCoWCBg0axLu9pb5vQoA0SgkhRLbRu3dvY9IS+59SqcTFxYWKFSsydOhQYwKUFSxcuJCWLVvy119/8erVK3Q6nblDEkIIIUQWtWrVqnhzHYVCgaOjI8WLF6dz587s3r3b3KEanTt3jho1avDbb7/x5MkToqKizB2SENmKNEoJIUQ2p9fref36NZcvX2bhwoVUrVqV7777ztxhERYWxrhx4wAoXbo0W7du5cKFC1y5coUTJ06YOTohhBBCZCehoaHcu3ePTZs20apVKz7++GNCQkLMHRbffvst4eHhODs7s3DhQk6fPs2VK1e4cuUKpUuXNnd4QmR5anMHIIQQIuX+/PNP8ufPD4BOp+PFixfs2bMHHx8foqOjmT59Ol5eXgwZMsRsMZ49e5bXr18DMGvWLD755JN4y+n1+swMSwghhBDZwNSpU/nss8+MvwcFBXHs2DFmz56Nn58f+/bto2/fvmzevNlsMUZFRXH48GEABgwYwODBg+Mtl1MezxQiI0ijlBBCZEMlS5akSJEiJss++ugjGjduzKeffgrEjMs1cOBAVCqVGSKEp0+fGn8uWbKkWWIQQgghRPbk5eVFuXLlTJbVrVuXTp06UbVqVQICAtiyZQuXL1+mQoUKZonR398fjUYDSK4jRGrJ43tCCJGDtGrVig8//BAAPz8/zp8/b7ZYIiMjjT9bWVmZLQ4hhBBC5BxFihRh6NChxt///PNPs8UiuY4QaSeNUkJkE+/ORhcUFMTEiRN5//33cXR0xM3NjYYNG7Jhw4Zk7e/hw4d8+eWXvP/++zg5OWFvb0+JEiUYOHAgV65cSXTbd2cIOXDgAB06dKBgwYJYWVnF6cGTkVJbj4cPHxrrsWrVqkRfo0iRIigUCnr37p1gmfDwcKZPn07FihVxcHDA3d2dOnXqsGzZskwf3LtGjRrGnx89emT82TBQuuH4+Pr68vXXXxvfu/hm//Hz82P8+PFUrlwZFxcXbG1tKVKkCD169ODff/+N9/UNM8z06dPHuKxo0aImg5XGfp30mnFmx44ddOjQgUKFCmFra4uLiwvVqlVj8uTJBAYGpmnfQggh0sezZ8/45ptvqFKlCrly5cLKyoo8efJQvnx5unTpwqpVqwgODjaWP3v2rPE6sW/fvnj3abjuKBQK5s6dG2+ZQYMGoVAoyJcvX4Kx3b17ly+//JLy5cuTK1cu7OzsKFasGL179+bs2bNpqndahISEMGPGDD744APc3NywsbGhQIECtG/fPskBv5OTw0DcHCEh69evp0GDBri6uuLo6Ei5cuWYOHEiQUFBKatUGiWU68QeKP3hw4dERkYyd+5catWqhYeHR7z5hkajYeHChTRs2BBPT0+sra3JmzcvLVq0YN26dfHmcYa8vGjRosZlffr0Mcl1Yr9OUrPvJdf58+cZNGgQpUqVwtHREQcHB0qVKsXgwYO5fft2mvYthLnI43tCZEMPHjzgo48+4t69e8ZloaGhHDp0iEOHDrFjxw5+++031Or4P+Jr1qxhwIABJnd3ICYZu3v3Lr/++itTpkzh22+/TTKW7777junTp6etQqmUnvVIi+fPn9OoUSNu3LhhXBYWFsbx48c5fvw427ZtY9SoURkaQ2yx79Rptdp4y5w8eZJWrVrh7++f4H7++usvOnToYPLHAcQkf48ePWLdunUMHTqU+fPno1Sa7x5HYGAg7du358CBAybLIyMjOXfuHOfOnWPhwoX8/vvv1KpVy0xRCiGEOHr0KC1btoxzXXn58iUvX77k6tWrbNy4EQ8PD1q2bAlA5cqVcXZ2Jjg4mEOHDtG8eXOTbSMjIzl16pTx90OHDjFy5Mg4r20Y96d+/frxxjZr1izGjRsXZ+a0Bw8e8ODBA9asWcP48eP54YcfUlzvtLhw4QItW7bk2bNnJsufPn3Ktm3b2LZtG23btuW3337D1tY2w+KIjo6ma9eubNmyxWT5tWvXuHbtGuvWreOff/7JsNd/V3JyHX9/f9q0aZPorMQPHz7k448/5ubNmybLX7x4wR9//MEff/zBkiVL+P3333Fzc0uX2FNDp9MxZswY5s6dG2csztu3b3P79m2WL1+Oj48PAwYMMFOUQqSONEoJkQ116tSJBw8eMGjQINq3b0+uXLm4fPkyM2fO5Pbt22zevJn8+fPzyy+/xNl2z5499O7dG71ej6OjI6NHj6ZJkyao1WqOHz/Ojz/+iL+/P+PGjcPFxSXBARsBtm/fzpUrVyhfvjxffvkl5cqVIzw8PNGLf3pJz3qkRXR0NC1btjQ2SDVt2pTBgwdTsGBBHj9+zMKFC/nzzz8JCAjIkNePT+weYobB0GMLCQmhXbt2RERE8N133/HRRx9hb2/PlStXjHeQL168SKtWrdBoNFhZWTFs2DA+/fRTHBwcuHDhAjNmzODBgwf4+Pjg4ODAzJkzjftfuXIloaGh/P7774wfPx4wHZgdMLmzmBaRkZE0adKE8+fPo1Kp6Nq1Ky1atKBo0aJERUVx5MgR5syZw8uXL2nRogUXLlygcOHC6fLaQgghki8yMpLOnTsTHByMk5MTgwcPpmHDhuTOnRuNRsODBw84fvw4//vf/0y2U6lU1K1bl71798Y7WPTJkyeJiIgw/n7kyBF0Op3JzZIXL14YGx3ia5T6+eefGTt2LAAVKlRg8ODBlChRAhcXF27duoW3tzcnTpxgypQpeHh4MHz48PR4S5L09OlTGjduTGBgoLG3U+fOnXF3d+f69evMnj2bS5cusX37dnr37s3GjRszLJYxY8YYG6RKlSrF2LFjqVChAq9fv2bLli0sW7aMTp06ZdjrvyupXAfg888/58qVK/Ts2ZNOnTqRN29eHj9+jI2NDRCTDzVu3Jj79+8D0Lp1a/r27Uv+/Pl58OAB3t7eHD58mH///ZdWrVpx5MgR4zidQ4YMoX379jx79oxmzZoBcQdmz507d7rV94svvmDhwoUA1KtXj969e1OsWDHs7e25dOkSc+fO5dq1awwcOJC8efMaxxcVIlvQCyGyhYkTJ+oB47/169fHKRMcHKyvWLGiHtArlUr9lStXTNZrNBp9/vz59YDe0dFRf+HChTj7ePjwoT5fvnx6QG9vb6/38/OLUyZ2HI0bN9ZHRESkWz0NYtf3XelRjwcPHhj3v3LlykRjKVy4sB7Q9+rVK846b29v434GDBgQ7/Z9+/Y1ec8OHjyY6OslpFevXsZ9PHjwIN4yFy9e1KtUKmO9w8PD493e0dFRf/HixQRfq3r16npAr1Kp9H/++Wec9QEBAfqyZcsaz7WrV6/GKbNy5cok49Xr//98mjhxYpx1Bw8eTPR9GzdunB7Qu7i46M+ePRvv/mOfC127dk0wDiGEEBln//79xu/zXbt2JVguKipK//r1a5NlM2fO1AN6tVqtf/Pmjcm6yZMn6wF9kyZN9HZ2dnogTl6wadMm42tfv37dZN21a9f0VlZWxuuQTqeLE5NWq9V3797deP0MCAhIYe3jl9Q1rn379sb1y5cvj7M+IiJC37BhQ2OZvXv3ximTWA4TmyFHKFy4cJx1ly9f1iuVSj2gr1KlSpxjoNfr9atXrzbJdeK7pidH7NwhofwsODhYX7RoUWO5I0eOxLt9Qu+bwZgxY4zlxo8fH2e9TqfTd+vWzVhm4cKFccokN5+sX7++HtDXr18/3vWJvW9//fVXkvUJDw/XN2rUyHgMo6KiEoxFiKxGxpQSIhtq2bIlXbp0ibPcycmJpUuXAjHdfBcvXmyy/n//+5+x+/f48eOpVKlSnH0ULlyYn3/+GYh5BG3lypUJxqFUKlm+fLnxjlNmSe96pIXhrlWePHni7ZkGMG/ePDw9PTPk9QH0ej3Pnz9n+fLlNGnSxNiNffjw4Ql25R87diwVK1aMd93p06c5c+YMAP3796dp06Zxyri6upqca4b3ITOFhITg4+MDwJQpU6hatWq85QoXLsz3338PwJYtWwgNDc20GIUQQsR4/vy58ed69eolWE6tVuPs7GyyzDAOT3R0dJzxDA29p5o1a2Z8RPvdHlWG33Pnzk2ZMmVM1s2ePZuoqCiqVavGxIkTjWN3xqZUKlmwYAE2NjaEhISwdevWBONPL8+ePTP2GmvevDmff/55nDI2NjasWLHCOFyDt7d3hsSyePFi47hKS5cuxdHRMU6Znj178vHHH2fI6xsEBwezd+9ePvzwQx48eABArVq1jBO8vKtRo0bxvm8Q03Nv+fLlALz//vvxjmupUChYuHAh7u7uQMa9v0mZMWMGAO3atUuwPra2tsb4Hj16xMGDBzMtPiHSShqlhMiGYg8g/a4aNWrw/vvvA8R5tt/wu0KhoG/fvgnuo0OHDuTKlSvefcRWp06dTB3U3CC965Favr6+XL9+HYCOHTtib28fbzlHR0c6duyYrq8de+BwpVJJvnz56N+/v3GMqE8++STRcS+6deuW4LrY71VCyQ/EHH9Dcp+Z40gYHD58mNevXwPQvn37RMsa/gCKiori3LlzGR6bEEIIU7EHGE/pjaIqVarg5OQEmDY4aTQaTp48CcQ0XBkar95tlEpsPKldu3YBMX/wx9cgZeDi4kL58uUBOHHiRIriT41Dhw4ZbzIldi0uUqQIH330UZxt0pPhGl++fPkEbwABieZkqfHuwOG5cuXik08+4dKlSwC89957bN68OcHtE8t1zp07ZxycvXfv3sbH8t7l7OxszOGuX7+Or69vKmuTOoax1CDpXKdMmTJ4eHgAmXOOCpFepFFKiGyoevXqia43zEhy+/ZtNBqNcfnVq1eBmAaNxHruWFtbU7lyZZNt4lOhQoVkx5ye0rseqRV7PIPkHpOMZG1tTZ06dVi9ejW7du1KcGpiR0dHihUrluB+DO+VtbV1vL3QYqtZsyYAd+7cMTnXMkPsmZDy5ctnkri++69cuXLGsrHv1gshhMgcdevWNV57Ro4cSY0aNfjxxx85duxYktcPtVpNnTp1ANMGp1OnThEeHk6uXLmoXLmysVHKMK4UxMwia7iB9G6j1KNHj/Dz8wPg22+/TfQ6olAojNedzLiOxM5bDNfahBjWh4WFGcdHSi+RkZHcuXMHyBq5jkKhoEyZMkyZMoWLFy9SsGDBBMsmlqem5v19d7vMcOHCBeO53KVLlyTPUcPNScl1RHYiA50LkQ0lNXBinjx5gJjHugIDA42/GwbbTs7Ai3nz5jXZJj6urq7Jije9pXc90hpHcmIxHIP0EnvgcKVSiaOjI3nz5sXa2jrJbV1cXBJdb6iXm5tbgjM4Ghje33fPtczw8uXLVG0XFhaWzpEIIYRIipWVFbt27aJ9+/bcuHGDM2fOGB8Vt7Ozo169esYBqePrtVK/fn327dvHuXPnCAkJwdHR0dgDqm7duqhUKmrWrImtrS2BgYFcvnyZSpUqGcvA/z8GaJCVryMpyTEM1+J3t0sPgYGBxtneMjvXiT1wuEKhwN7eHk9Pz3gfH4xPYnlqVnl/k5KVz1Eh0os0SgmRDSXWvTwztjdIqKtzZkmveqSHzI6lZMmSqX50MrnHLSu9v/GJ/YjC+fPnE+wZ9q4CBQpkVEhCCCESUbZsWa5cucKuXbvYtWsXR44c4e7du4SHh/Pnn3/y559/MmfOHPbu3RunoeDdcaWaN29ubHAyrLOxsaFWrVocOnSIQ4cOmTRKeXp6UrZsWZN9xr6OTJgwgQ4dOiSrHg4ODqmpfqplletxZsfh5eVl0tM5pXJCvhP7HF2yZAm1a9dO1nbmunEsRGpIo5QQ2dCLFy8S7a784sULIOYiG/ui5ObmZrI+MYZuv4ZtspL0qEfsqaIN3aITktDA2LHf26RiSU6sWYXhvXr16hXR0dGJ9pYyvL/vnmuZwTDwKMT8sSGNTUIIkfWpVCpat25N69atgZjxGfft24ePjw/nzp3j3LlzDBw40DjIt0G1atVwcHAgNDSUQ4cO0bhxY44fPw6Y9oBq0KCBsVFq5MiRxsf96tWrF6fxIfZ1xMrKKk0NIOktdt6SVN4X+1GthPKd1OY6sXtX58RcB2LiLlmyZIJlE3t/M1rsc9Te3j5LnaNCpBcZU0qIbMjQ3T2p9SVKlDB5nMtwIXvw4IFxDIX4REVFceHCBZNtspL0qIdhwFSI6ZqekICAAF69ehXvOsOAp5D8Y5IdGN4rjUbDxYsXEy17+vRpIO65lhkM44UBHDt2LFNfWwghRPrIly8fffr04cSJE1SpUgWA3bt3Ex4eblLu3XGlzpw5Q1hYmHE8KYPY40r5+flx7do1IP5BzosVK2acECWrXUdi5y2nTp1KtKzhWmxvbx9nzEhDvpNYrgMx45DGx9bWlhIlSgA5M9eB5L+/726XGSpVqmRsTM1q56gQ6UUapYTIhlavXp3gujNnzhgHYWzSpInJOsPver0+0Zlvtm7dapzV7N19ZAXpUQ9XV1fj3b/YA2a/a+PGjcaxFN6VP39+4+xzW7ZsiZNAG4SGhiY6O0xWE/u9WrFiRYLlTpw4YRw81hznSZMmTYwzHs6fPz/B4ySEECLrs7KyMjYcRUdHG2dGi82w/ty5c+zevRv4//GkDGrVqmUcV2rBggXGa8O740lBTK+tFi1aAPDXX39x48aN9KxSmjRo0MBYr8SuxY8fP+bvv/+Os41B0aJFgZjH3BO6Tl67do3Lly8n+BqGa/yVK1eMN/vik1icWU3VqlWNeeDq1asT7En25s0bYw5XtmxZk1kkM4Onpye1atUCYP369YnejBUiu5JGKSGyoZ07d8bbyBESEsLAgQOBmO7ahp8NWrdubRwce9q0aSazxxn8999/jBkzBoi549anT5/0Dj/N0qse9erVA+D333/n3r17cdbfunWL77//PtFYBg8eDMR07R49enS8Zb788stUD1RpDjVq1KBatWoALFu2jP3798cp8/r1a5NzzfA+ZCYXFxeGDRsGwPHjx/nyyy8TfTzhxYsXLF++PLPCE0IIEcvRo0e5e/dugus1Go1x/CdHR8d4Z9eNPa7UwoULTZYZ2NjYGGdLmz9/PhDzCFRCPVy+/fZbVCoVOp2O9u3b8+TJkwRj1Gq1/Pbbb4mWSS/58+enTZs2APzxxx/x3pDUaDT07duXqKgoAOM1MTZDQ96zZ8/YsGFDnPVv3rzh888/TzSWgQMHGnvrDBgwIN5H/X777Tf27t2bRK2yDhsbG/r16wfEzKg3ZcqUOGX0ej3Dhg0zzmgX3/ubGcaPHw9AcHAw7du3j7fB1iAyMhIfHx8iIiIyKToh0k4apYTIhqpVq0bXrl0ZOnQoBw8e5Ny5c6xcuZJq1aoZ72ANHTo0zlS41tbWLF26FIVCQXBwMHXq1GHKlCkcP36cU6dO8csvv1CtWjWePXsGwKxZs/Dw8Mj0+iUlveoxZMgQAMLDw2nQoAG//vor58+f58iRI0ycOJGaNWvi5uYWb2JsMHjwYONjA4sWLeLjjz/m999/5/z58/z+++80a9aMZcuWGRt5sotly5ZhbW1NdHQ0LVq0YMyYMRw+fJizZ8+ybNkyqlSpYmwMHDNmjNke8/zhhx+Mf3zMmzePKlWq4OPjw7Fjx7h48SIHDx7E29ub1q1bU6hQIRYvXmyWOIUQwtLt37+fUqVK0aBBA37++Wf+/PNPzp8/z7Fjx1i5ciUffvgh58+fB+Dzzz+PdzzD6tWrG3vIGnpCx9cDyrDMUCa+8aQMypcvz6xZswC4fv065cqVY+zYsezbt48LFy5w4sQJNmzYwPDhwylYsCDdu3dPtFEgPf3yyy/G8Rr79u1L//79+eeffzh37hy//fYbNWvWNN446tixIx9//HGcfXTv3h1nZ2cg5n394YcfOHXqFKdPn2bRokVUqVKFS5cumTwC+a6KFSsydOhQIKZ3ebVq1Vi1ahXnzp3jwIEDDB48mJ49e2a7XGfChAnGxx0nTZpE+/bt2bNnD+fPn2fbtm00atSINWvWAPDBBx8wYMAAs8TZokULRowYAcQ8llqmTBkmT57M/v37uXjxIseOHWP16tX069ePfPnyMWzYMKKjo80SqxCpohdCZAsTJ07UA3pAf//+fX3RokWNv7/7r127dvqoqKgE97Vq1Sq9jY1NgturVCr99OnTE9zeUG7ixIkZUNMYseubkLTWQ6/X64cPH57g9oUKFdJfv35dX7hwYT2g79WrV7z7ePr0qb5UqVIJ7qdp06b6P//80/j7wYMHU/We9OrVy7iPBw8epHr7woULJ6v8n3/+qXd2dk6wXoB+6NCheq1WG+/2K1euTFa8iZ1PBw8eTPJ9Cw4O1rdt2zbROA3/GjZsmKy6CyGESF+xr+uJ/fvss8/0YWFhCe6nSZMmxrK5cuXSR0dHxykT+9oB6OfOnZtkfEuXLtXb29snGZ+1tbX+zp07aXov4oszoWvc+fPn9fnz5080prZt2+rDw8MTfJ3NmzfrVSpVvNva2dnpt2zZkmSOoNFoEr3WFi1aVH/v3r0054ixc4eVK1emafvk5EoPHjzQly5dOtH3t06dOvpXr14luH1y4q1fv74e0NevXz/e9Um9bzqdTj958mS9Wq1O8hx1cHBI9DMkRFYjPaWEyIaKFi3KuXPnGDduHGXKlMHe3p5cuXJRr1491q1bx9atWxOdMa1Xr17cvHmTESNGUKZMGRwcHLCzs+O9996jf//+XLhwgW+//TYTa5Q66VGPefPmsX79eurVq4ezszN2dnaUKlWKb775hvPnzxvHjEpM/vz5uXDhAlOnTqVcuXLY2dnh4uJCrVq1WLhwIX/88UemDwKeHpo2bcrdu3cZN24clSpVwtnZGRsbGwoVKkS3bt04evQo3t7eJjMZmoOTkxPbtm3j6NGj9OvXj1KlSuHk5IRarcbNzY3q1aszdOhQ9u7daxx3QwghROYaM2YM27ZtY/DgwdSqVYtChQpha2uLra0tRYoUoWPHjuzevZsdO3ZgZ2eX4H5iD1j+7nhSBrVq1cLGxsb4e3y9qd7Vv39/7t+/z+TJk6lTpw4eHh6o1WocHBwoWbIk7dq1Y/HixTx9+pTixYunrPJpULlyZW7dusWPP/5IzZo1cXFxwdramvz589O2bVt27tzJtm3bsLW1TXAfHTp04Pjx47Rp0wZPT0+sra0pWLAgvXr14syZM7Rv3z7JOKysrNi2bRtr167lww8/JFeuXNjb21OmTBnGjRvHuXPn4gyynh0UKVKES5cu4e3tTf369XF3d8fKyoo8efLQvHlz1q5dy5EjR8w+G7VCoWDChAncvn2bsWPHUq1aNdzc3FCpVDg5OVG2bFm6devG6tWr8fX1TfQzJERWo9DrZWRYIbKDSZMmMXnyZAAZ0FkIIYQQQgghRLYnPaWEEEIIIYQQQgghRKaTRikhhBBCCCGEEEIIkemkUUoIIYQQQgghhBBCZLqER0IWQohUCg0N5cGDB6natlSpUlhZWaVzREIIIYQQ6evq1aup2q5AgQK4uLikbzBCCJFNSaOUECLdnTlzhoYNG6Zq2wcPHlCkSJH0DUgIIYQQIp2VL18+VdutXLmS3r17p28wQgiRTcnje0JkE5MmTUKv18vMe0IIIYQQQgghcgSFXv7CFUIIIYQQQgghhBCZTHpKCSGEEEIIIYQQQohMJ2NKpVFgYCDR0dEZsm9PT0/8/PwyZN9ZjdQ1a4qIiOCrr74C4Oeff8bW1jbF+8hO9U0rqWvOZUn1taS6ZiS1Wo2rq6u5w0i1jMpvLO38sqT6Sl1TJj1yrMwixzbnsqT6WlJdM1JG5DfSKJVG0dHRREVFpft+FQqFcf85/QlLqWvWFRkZyaZNmwCYOnUqKpUqRdtnt/qmhdQ157Kk+lpSXUXiMiK/sbTzy5LqK3VNubTmWJlFjm3OZUn1taS6Zkfy+J4QQgghhBBCCCGEyHTSKCWEEEIIIYQQQgghMp00Sol4rVq1ipo1a1KsWDFatmzJhQsXEi3fvn17vLy84vzr0aOHsYxer+fnn3+mcuXKvPfee3Tq1In79+9ndFWEEEIIIYQQQgiRBUmjlIjj999/Z/LkyYwaNYp9+/ZRtmxZunXrhr+/f4LbLFu2jAsXLhj/HThwAJVKRcuWLY1lFi5cyIoVK5gxYwa7du3C3t6ebt26ERERkRnVEkIIIYQQQgghRBaSYwY637dvH7t27SIoKIjChQvTt29fihcvnmD5PXv28Ndff+Hv74+zszM1a9aka9euWFtbZ2LUSTt48CBz587l1q1bKJVKqlatyg8//ECRIkUy7DWXLVtG165d6dSpEwAzZsxg//79bNy4kWHDhsW7zbsj8P/+++/Y2dnRqlUrIKaX1PLlyxkxYgTNmjUDYN68eVSqVIl9+/YxePDgDKuPEEIIIYQQQgghsp4c0Sh1/Phx1qxZQ//+/SlRogR79uxh2rRpzJ07l1y5csUp/++//7J+/XoGDx5MyZIl8fX1ZeHChSgUCnr16mWGGiQsLCyMAQMGUKZMGUJDQ5k1axb9+vXjr7/+QqmMv6Pb/PnzWbBgQaL7PXToEF5eXnGWazQaLl++bNL4pFQqqVu3LufOnUt23Bs3buSzzz7D3t4egMePH/Py5Uvq1q1rLOPs7EzlypVTtF+R+dzc3MwdghBCCCFEjpPlcyytFuvTp0GjwdramsgaNSCLzhIohMi+ckSj1O7du2ncuDENGzYEoH///pw/f56DBw/SunXrOOVv3bpFqVKljA0kuXPnpk6dOty5cyczw06WTz75xGTayjlz5lC+fHlu375N6dKl492mR48exh5KCcmTJ0+8ywMCAtBqtXh4eJgs9/T05N69e8mK+cKFC9y8eZNZs2YZl718+dK4n9g8PDyM60TWY29vz5UrV8wdhhBCCJFmq1atYtGiRfj5+VG2bFmmTJlC5cqVEyzfvn17Tpw4EWd5o0aNWLt2bZzlX3/9NevWrWPSpEn0798/XWMXOU9Wz7Fs9+4l14QJqHx9AXAHtPny8fqHH4ho0cK8wQkhcpRs3ygVHR3N/fv3TRqflEqlseEmPqVKleLo0aPcvXuX4sWL8+LFCy5cuMCHH36Y4OtERUURFRVl/F2hUGBnZ2f8Ob0Z9vngwQN++uknLly4QEBAADqdDoBnz55RpkyZeLd1c3NL9Z0Xw+sqFIp465Wcum7cuJEyZcpQpUqVJPcb+/eMeB+zGkuqK1hWfaWuOZcl1deS6ipiZGZ+Y87zyzBe5owZM6hSpQrLli2jW7duHD16NM6NOIPly5ebvDeBgYE0adKEVq1axanDH3/8wfnz58mbN68xt7Gkz5PUNWex3bsXlwEDINaNcQDl8+e4DhhA0LJlObJhyhKObWyWVF9Lqmt2lO0bpYKDg9HpdLi4uJgsd3Fx4dmzZ/FuU7duXYKDg/n+++8B0Gq1fPTRR7Rt2zbB1/nf//7H1q1bjb8XLVqUmTNnxun5k9769u1L4cKFWbFiBfnz50en01GuXDkcHR3Jly9fvNtMnz6d6dOnJ7rf69evU6hQoTjL3d3dUalUaLVak/2HhoZSsGDBBF8zdrmdO3fyww8/mJQtV66c8efYy1+/fk2lSpUAyJs3b6L7zkksqa5gWfWVuuZcllRfS6qrpTNHfnPx4kWmTp3K1atXUalUfPDBB8ybN4/33nsvw15z5cqV9O/fny+//BKADz/8kIIFC7Jnzx6++eabeLd5N+eZO3cu9vb29O/fHwcHB+Pyp0+fMmHCBP78808++eQTnJ2dTba1pM+T1DUH0Gph0qQ4DVIACr0eFApcJ0+G3r1z7KN8OfbYJsCS6mtJdc1Osn2jVGpcu3aN//3vf/Tr148SJUrw/PlzVq5cydatW2nfvn2827Rp08ZkJjlDK+t///2HRqOJU16pVGJra2v8PSwsLMF4Yt+VNJRVKBSo1Wpu3brFpEmTjAObG8ZfCggIwNfXN979NmvWjJo1awKY7Dc8PNzk53cfxzOM/1ShQgW2b99O+fLlAdDpdPz555/06NHDuI2hrGFfhkcMt23bRkREBHXq1DEpa2trS+7cudmyZQtOTk4AvHnzhpMnT9K2bVtCQ0N58eKFSbwRERHGnmHxsbOzMx6HyMhItFptupS1tbU1jtel0WiIjo5Ol7I2Njao1Wry5s3L48ePTe6+xldW9fZC/+5d7HdZW1ujVqtTXDY6Otrk3NXr9cbzKiAggMjISIKDg1myZAkAn3/+OQ4ODri6uuLu7o6Hhwdubm4olUqsrKywsrICYhp5IyMjgZhzO0+ePLx48cJ4jqjVauOEArHLxid2WZ1Ol+hMjSkpq1KpsLGxMdY79mcjtWUVCgX58+cnKCjIWNfEPvdp/Y5Ij7Jg+llOblmFQkGuXLnw9fU1ebw4of3G/o5IqmxSn/uUlE2v74h3z+OM/I4wfO5TUjY9vyNsbGwoUKAAz58/JyoqKt7rm0Hsz/273yeJlU3J5z47f0dYW1tTsGDBBLfLKhLKb/z8/BI9B1NDoVCQN29enjx5Qp8+fYzjZf7888+0atWKv//+O9HxMufPn5/o/g8dOkSBAgXiLNdoNJw7d46BAwfi+/ZRJIA6depw8ODBZI8numTJEj799FOCg4MJDg4GYs6lTp06MXDgQNzc3NBqtQQHB+Pr62us7/PnzxP9DswJpK4pFx4eTvfu3QFYt26dybXbnKyPHsX9yZOEC+j18N9/vNqxA03t2pkXWCawpPMYLKu+llTXjKZWq9P9xlW2b5RydnZGqVQSFBRksjwoKChO7ymDTZs2Ua9ePRo3bgxAoUKFiIiIYOnSpbRt2zbehCh2Qh3bZ599xoULF+Isf3e8gfLlyyeY1H7wwQcmdylr1KhBQECA8fcuXboYf459wdLr9TRo0IAnCVw4SpYsycGDB42/N2zYMMFHGgsUKMCpU6eAmDG5hg4dGme8hFmzZjFr1izc3Nxo2LAh+fLl49tvv6V79+5xxlyoXr26Md67d+8C0K9fP3766ac4vbhGjhzJyJEjgZi7jQZffPEFe/bsiTdegDt37hj/QB07dixbtmxJsOzly5dxd3cHYNKkSaxevTrBsidPnjT+ITFjxgwWL16cYNkDBw5QqlQpICZhnjNnToJl9+zZYxy7YtmyZUydOjXBslu2bKH22wv9unXr+O677xIsu3r1apo0aQLENAqOGjUqwbKLFy+mVatWBAYGsnjxYry9vRMs+67EYqhYsSIff/wxVatWJTIy0phkxWf8+PHG2RYvX77MJ598kmDZUaNGMXr0aABu375No0aNEiw7aNAgY+/HJ0+eUKtWrQTL9urVy3gevnr1igoVKiRYtkOHDsydOxeIabQpUaJEgmXbt2/P/PnzjRe7xGYATc/viNgqVqzI3r17jb+n5DuiRYsWyfqOAKhXrx5nz56Nt6ybm5vJOBnxfUcYxP6OgJjviQMHDsRbFnL+d4Sh5+jy5cvN8h2xZMkSBgwYgF6vZ+/evQwaNCjBsnPmzDHO0prUH/bTpk2jd+/eQMz716FDhwTL5pTviMqVK3P+/PkEt8sqEspvgAxL3BMaL/PWrVsJjpfZvXt3k8az+OTJkyfemF+9emUcLzP2eg8PD+7evZusesYeLzN2eW9vb1QqFX379jUu1+v1JmXe/T0nk7omn06nM14bdTpd5r9vej1KPz+sbtxAfeMGVjdvor55E6sbN5K1uTLWDcecxpLOY7Cs+lpSXbOTbN8opVarKVasGFevXqVGjRpAzBf71atXad68ebzbREZGxnmeNKE7c1lN/vz5kz3geGp99tlnTJs2zeSPv3c9e/Ysxe/ZkCFDWL9+PQ8fPkxjhCK1tm7dire3N9euXUvyC7lo0aIULFiQI0eOJLnfS5cucenSJSD7fJaEEEKYx/379/n555/jjJf59OnTBBulXF1dcXV1zcwwTWzYsIEyZcqYDIx++fJlfv31V/bt2yfjlIgsTREWhvrWrZgGqLcNT+qbN1ElcIMrObS5c6djhEIIS6bQ54CmwuPHj+Pj40P//v0pXrw4e/fu5cSJE/zyyy+4uLjg7e2Nm5sbXbt2BWDz5s3s2bOHAQMGGB/fW7ZsGcWKFTOONZBc//33X7yPFqTH43vxdTF8t2xij8VkVFlI30dzYtfVEh7fy5cvH48ePcrwx/c0Gg1Hjx7ljz/+4MCBA7x588Zku6JFi/L+++9TtmxZSpcuTaFChcibN6/xMRQrKyuioqKMd/0vXbpkciyjoqJ4/PgxDx484MGDB9y4cYMzZ87w/Plzk9dxc3OjUaNGfPbZZ9SoUQNra+sc8WjOuxQKBV5eXgQGBmapx/cy4jtCoVAYx+2zlMf3Yn8f5/TH9woWLIivr688vpfG7wjDe5ld+fn5JXqupIZCoSBfvnwUL16cAgUKMHjwYPLmzYtOp6NRo0b8+uuvCd5QnD9/PgsWLEh0/4cOHcLLyyvOco1GQ/HixVm6dKnJ/keMGEFwcDArV65MdL9hYWFUqVKFMWPG0K9fP+PyZcuWMXnyZJObMVqtFqVSSf78+Tl9+jT58uVL9FHnnMJwbKWuyRe7Z2Xsnr1potWievAAq1gNT1Y3b6J69ChmPKh36JVKtEWKEFWmDFFlyhBdujRRJUrg0bkzyufP499GoUCbLx8vT57McWNKWdJ5DJZVX0uqa0azsrKSx/fiU7t2bYKDg9m8eTNBQUEUKVKEcePGGR/f8/f3N7mD1a5dOxQKBRs3biQgIABnZ2eqVq1q8phcctna2hr/MEhMSi409vb2KBQKHBwcsLe3T/SDk5Lnz7NC2dh/hBskVNf4yibE8AdBepeN3YCS3mUTelziXYk9WhFf2UePHrFx40a2bNmCv7+/cV3u3Llp3LgxdevW5YMPPiBPnjxJ7i/2HyT29vZxzuPy5csbxx4zePr0KSdPnmT//v0cOnSIgIAAtm7dytatW/Hy8qJdu3Z0794dLy8vVCpVsj8bSqUyQ8oqFIp0KatQKOKcsyn93GdE2Yz83Cf1/ZSa/abkc59Z3xGJfR9n5HdEcsum9DsisbKxr5VqtdrYmJWUlJRNyec+O39HJPeYWJpXr15x7949fv75Z+P4l6dPn05yux49etCqVatEyyR0XbO2tqZChQr8+++/xkYpnU7Hv//+S58+fZJ87V27dqHRaOJMiNOuXbs4Mzd369aNdu3a0bFjxyT3K0SqGR69u3kz5tE7QwPUnTsoEmhw13p6xjQ6lSlDVOnSRJcpQ1SJEhDPNfr1Dz/gOmAAeoXCpGHK8FPw5Mk5rkFKCGE+OaJRCqB58+YJ3l2bNGmSye8qlYoOHTokOqaFENmJXq/nyJEjLFq0iKNHjxqX586dm1atWvHJJ59QrVq1ZDWgppWh4aldu3ZotVru3r3L8uXL2b17N0+fPmX+/Pn4+Pjw8ccf07dvX2rUqCGPPQghRBYTERGRIT3BHR0dcXV1ZfXq1Tg7O/Ps2TNmzZoVZ9t3e1ja2NgYG50S6uWp0Wji9NgzlDXMvFemTBnKly/PqlWrCAsL49NPPzXW4auvvqJAgQJ8++23Jvtdv349TZo0wdbW1qS+bm5uuLm5Gd8vnU6HSqUiV65c5M+fn7CwMEJDQwkLC7OInuCGslltIpd3pbWXp0KhMB7X2OdoSnt5xpbY58hKo8H+be8n5fXrRF+7hvr2bVSBgcYyeiDq7f9WdnZElyqFplQpgosXJ6pkSbSlSqF7O26iQWI9QsMaNCB0wQJyTZmC6sULVIAtgEJB0Jw5BDRoAAnEnJ17ghvKvntsY8tpPcFjn8s5vSe4QUZ/RyQkJ/UET285plFKCEuk0+nYtWsX3t7eXL9+HYhJBho1akTXrl1p3LhxsnsvZAS1Wk3Dhg0pXbo0U6ZM4a+//mLt2rUcP36c3bt3s3v3bqpVq8aoUaOoV6+eNE4JIUQWkdyJXCpUqJCqSRp+//13fv/99wRfP70nafjss88ICAjgu+++M/nD5YMPPjD+rFaradOmjfH3Hj16mEzSEHtihXcnaejfv79xkobp06fHmdQl9jidw4cPT/YkDV9//XWyJ2mYPHlysidpmDlzZrInaViwYEGyJ3JJySQNv/32W7Inadi+fXuyJnIB+OOPP5I9ScOhQ4eSPUnDqVOnkj1Jw5UrVxKdpMHT0xNHR0fj7xUrVkyw7GjA0Gz7ECiaYEno3aYN0+bPB6UyRZM0hIeHJz6RS40abHj5EvXDh6gfP060bFq+I2rWrJmtJnJ59zsitsS+I+KT078jDBO5/Prrr2b5jjBM5ALZ4zsi9kQud+7cSfZELk+fPk32RC4BAQGp+o7IiIlcZERiIbIhvV7PP//8Q9OmTRkyZAjXr1/Hzs6Ozz//nOPHj7N69WqaNWuWLg1SdnZ26TJNsa2tLZ9++ilbtmzhn3/+oVu3btjY2HD27Fm6du3Kp59+atLLSwghhOWoWLFigj3e00ufPn0oW7ZsguudnZ2NCbgQKRUZGck///zDlClTkhyjNtTPjyIPHqAm6R4CCsBPpeJK3rxcj9WIGh+9szNkxIQzhQrx5m3jgMOKFem/fyGERcsRA52bU0YMBAqWNRib1DVlzp49y7Rp04xjcDg5OTFw4EB69eplfIwgq0iqvi9evGDhwoWsW7fO2NW0SZMmfP/99xQvXjyzw00TOY9zLkuqryXVNaNlxECgmSkjJnIJDw+PdxKXhMrKRC7Z6/G9zJrIJamyGfVojtXu3dhMmIDqxQvjeo2nJ3saNGDBo0ecPXs2znF1tbamWaFC1HBwoLRWS+GQEHL7++McEoJh1EAdYOhHFKVW89zNjfv29lzR6Tji78/psDD8MVWqVCmaNWtG8+bNTXotZehELq9e4dGwIVZ37uA7ahShb3t8vCs7P74nE7nk7Mf3ZCKXrDuRizRKpZE0SqWd1DV5/P39mTZtGps3bwZiLgZ9+/ZlyJAhZp0mOzHJre/Lly9ZsGABa9asITo6GrVaTa9evRg1apRxwoKsTs7jnMuS6mtJdc1o2b1RKiNn37OU88uS6pvT62q7dy+uAwaAXk/sgQYMzQYdgctAE09PmuTJQ0WlkvwBAdg+fZrwDHZFixL1duDx6NKliSpdGm3hwiYDiOv1enx9fbl69SonTpzg2LFjXLt2zWRfJUuWpFOnTnTs2DFDbk7GPra227bh+sUXaN3ceHnqFPr0mDEwC8np5/G7LKm+llTXjJYR+Y00SqWRNEqlndQ1cVqtlnXr1jFz5kxev34NQKdOnfjqq6/Ily9fRoabZimt7927d5kyZQr//PMPEDPmwpQpU2jZsmWWH29KzuOcy5Lqa0l1zWjSKBWXpZ1fllTfHF1XrZY8NWui9PUlvkzEUNuEshSth0fMTHexGqCiS5ZEn8qhEQICAvj777/Zs2cPR48eNfbisLa2pnnz5vTt25fq1aunat/xMTm2UVHkrl8f9cOHvJ4wgdCBA9PtdbKCHH0ex8OS6mtJdc1oGZHfyEDnQmRh9+7d48svv+TcuXMAlCtXjunTp1O1atVMef2IiAj69+8PwLJly0y6Y2eE4sWLs3r1ao4cOcL333/P3bt3GTRoEE2bNmXatGnkz58/Q19fCCGEECK2Jxs2kN/XN8H1hsYonZUV0WXLmvR8ii5TBp2HR7zbpTbHcnNzo1OnTnTq1Ing4GB2797NunXruHTpEjt37mTnzp3UqFGDoUOH0rhx4/S9qadW8+aLL3AdPRrHxYsJ7dULMjg3FELkfDLQuRCZRavF+vhx2LAh5v9Env/W6XQsX76cpk2bcu7cOZycnJg6dSp79+7NtAYpQxwHDhzgwIEDiT7bnt7q1avHX3/9xZdffomVlRV//fUXDRs2ZNOmTXJ3QwghhBAZ7vr16/Ts2ZNfvv46WeVfz5mD/969vJ49m9D+/dF8+GGCDVKQPjmWs7MzXbt2Ze/evezbt48uXbpgZWXF6dOn6dWrF02bNuXAgQPpmjuFt21LtJcXqpcvsd+4Md32K4SwXNIoJUQmsN27lzw1a+Levj107Yp7+/bkqVkT21hT2xo8ffqUjh07MnHiRCIiIqhXrx779++nT58+xoH/LIGNjQ1jxozhzz//pEqVKoSEhDBq1CgGDhxIYGCgucMTQgghRA7033//8cUXX9C0aVP279/Py2TOZqfNmzeDI0tc+fLlmTVrFidOnGDQoEE4ODhw/fp1evToQYcOHbhw4UL6vJC1NSFDhwLg6OMDiQwCLYQQySGNUkJkMMPgmMp3un4rnz/HdcAAk4apv/76i6ZNm3LixAns7e358ccfWb9+PV5eXpkddpZRqlQpduzYwbfffotarWbPnj189NFHHDt2zNyhCSGEECKHiIiI4JdffqFBgwZs374dvV7Pp59+yuQDB9Dmy0dCfY30CgXR+fOjqVkzU+NNSL58+fj+++85efIkAwcOxNramhMnTtCyZUtGjBiBn59fml8jrFMntHnyoH72DPutW9MhaiGEJZNGKSEyklZLrgkT4szWAhhnY3GeOBFNeDiTJk2iT58+BAUFUblyZf7++2969uyZ5Qf4zgwqlYphw4axc+dOihYtiq+vL506deKXX37J1McKhRBCCJHzHDx4kMaNGzNr1iwiIiKoU6cOf/zxB4sWLaJYiRJEfPghCojTMKV/m6MFT55sMmteVuDm5saECRP4999/6dChAwBbt26lXr16rFq1Cm0iw0gkydaWkEGDAHD09obo6PQIWQhhoaRRSogMZH3qFKoEZmuBmIYp9bNn/FmnDieXLcMGGDBgANu3b6dIkSKZGGn2ULFiRf766y+6dOmCXq9n1qxZ9OnTxzgroRBCCCFEcgUEBDBkyBC6d+/Ow4cPyZs3LwsXLmTTpk1UqFABAPX169jv2AGAPlcuk+21+fIRuHQpES1aZHboyebl5cXcuXPZuXMn5cqVIzg4mO+++47WrVtz7969VO83rHt3tO7uqB89wu7339MxYiGEpZFGKSEykOrly2SVG/jiBZeBMIUCn7/+Im///jhPmYLdxo1YnT2LQhpdjOzt7Zk1axZz5szBxsaGf/75hxYtWnDt2jVzhyaEEEKIbGL//v00btyY33//HZVKxYABAzh8+DCfffbZ//dSDw/HddgwFBoNER99xPPLl3m1dSusX8+rrVt5efJklm6Qiq1q1ars3buXadOm4eTkxPnz52natCnLly9PVa9zvb09oQMGAOA4f36iE/gIIURi1OYOQIicTJs7d7LK3bG2pqiNDeo3b1A+fIj64UP45x/TfXl6El28ONElShj/jypeHF2+fGCBj/h16tSJsmXL0r9/fx4+fMinn36Kj48PzZs3N3doQgghhMiiQkJCmDx5MuvXrwegePHizJs3j0qVKsUp6zx9Ola3bqH19CRo9mxQq9HUrg358qHx9YVsNiOwSqWid+/efPTRR4wePZqjR48yceJE9u3bx7x581I8hmlor144LlqE1d272O7dS0SrVhkUuRAiJ5NGKSEykNbdHb1SiSKBO1A64JWtLVy4wEsnJ5T+/qjv3In5d+8e6jt3sLpzB5WvLyo/P1R+fticOGG6DweHmEaqdxqsogsXBiurNMVvb2/P06dP07SPjFS+fHn27t3L0KFDOXLkCP369WP8+PEMHDhQxuISQgghhIkbN24wYMAA7t+/j0KhoH///owdOxY7O7s4ZW3278dxxQoAgn75BZ27e7rGYs4cy8vLiw0bNrBmzRqmTp3KiRMnaNq0KXPnzuWjjz5K9n70Tk6EfP45zrNn4zRvHhEtW1rkjVIhRNpIo5QQGcTq4kXcevRAodMZB8aMfZnWvf1dMX8+Ds7OMcs8PdF4esbchYtFERJibKRS37mD+u7dmH8PH6IMDcX60iWsL10y2UavVhNdpIhpQ9Xbxiu9g0OG1Tuzubm5sXbtWr7//nvWrFnDlClTePDgAVOnTsUqjY1yQgghhMgZNm3axLhx44iIiCBfvnwsWLCADz74IN6ySn9/XEaNAiDk88+JbNgwM0PNFAqFgl69elG/fn2GDh3KxYsX6d27N4MHD+brr79Odg4V2qcPjkuWYHXjBjZ//01k06YZHLkQIqeRRikhMoDNkSO4fv45yrAwNBUrEtqrFw4zZ2L94oWxTJirK5qffkKTjLEI9I6ORFWsSFTFiqYrNBrUjx79f0NVrAYrZVgYVnfvYnX3bpz9RefPH7dnVfHi6Dw80u8Ol1aL9enToNFgbW1NZI0aGTYzjVqtZvr06RQrVozJkyezbt06/vvvP5YsWYKTk1OGvKYQQgghsr7w8HDGjRvH5s2bAWjYsCHz58/Hzc0t/g30elxGjULl709U6dIEjxuXidFmviJFirB9+3amTZvGr7/+yqJFizhz5gxLly4lT548SW6vd3UltHdvnLy9cZo3j8iPPpLeUkKIFJFGKSHSme3OnbgOH44iKorIDz8kYPlyHgcG0t3eHi+ghIMDvb/9lqI9e6a9kcbaOqZBqUQJ0+U6HUpfX6zeaahS37mDyt8f9bNnqJ89gyNHTDdzcSG6eHGi3jZUhRQqxKD169Hb2TF/wQJsbW2T9x7s3UuuCRNQ+foC4E7MDDWvf/ghwwYENXTDL1y4MEOGDOHw4cN07NiRdevW4Z7OXe6FEEIIkfU9f/6cvn37cunSJZRKJV999RXDhg1DqUx4rif71aux3b8fvY0Ngd7ekMzcJ6UiIiIYPnw4APPnz092jpURbGxs+OGHH6hVqxajR4/m7NmztGjRghUrVsQ71ta7QgcMwGH5cqwvXsTmyBEi69fP+KCFEDmGNEoJkY7sV60i1/jxKPR6wlu1InDePK7euUOPHj14+fIl4V5eLPr7b1xcXNBn5OCYSiU6Ly8ivbziJAaKwEDUb3tQxR6/SvX4McqgIKzPnsX67Fkg5gti99vt1t27h02pUiaNVtHFisVJ1mz37sV1wIA4g38qnz/HdcCADJ86uWnTpmzfvp3u3btz+fJl2rRpw4YNG1I8eKcQQgghsq9Lly7Rt29fnj9/jouLC0uWLKFu3bqJbqO+fZtcU6YAEPzdd0SXKZNh8el0Ovbs2QPA3LlzM+x1UqJFixaUKVOGPn36cOfOHdq2bcsvv/zCoEGDEt1O5+5OWI8eOC5bhuPcuUTWqye9pYQQySaNUkKkB70epzlzcJozB4iZjeT1lCmcvXCBHj16EBwcTJkyZVi3bh1lypTB920PIrOE6upKVPXqRFWvbroiPBz1/fsmDVZRt2/DrVsAWN26hd3bn437UijQFir0/48AFiuG08yZoNfzbiqi0OvRKxQ4T5xIRLNmGfYoH0CFChXYvn07Xbp04d69e7Ru3ZoNGzZQvHjxDHtNIYQQQmQNO3fu5MsvvyQiIoKSJUuyatUqChcunPhGkZG4Dh2KIiKCiIYNCe3bN3OCzWKKFi3Krl27GDp0KPv372fw4ME8ffqUwYMHJ7pdyKBBOKxZg83p01ifPIkmgfG6hBDiXQn3XRVCJI9WS67vvjM2SAWPHs3radM4eeYMXbt2JTg4mBo1arBt2zby5ctn5mATYWdH9PvvE/HZZ7wZPZrAxYvx373buPrVkiW8Hj+e0M6d0VStii5XLhR6PepHj7Ddvx/HxYtxGTsW1atXcRqkDBR6Pepnz7DbvBmlvz9otRlWneLFi7Njxw6KFy/Os2fPaNOmDVeuXMmw1xNCCCGE+S1evJjBgwcTERFBo0aN2LlzZ9INUoDzzJlYXb+O1s2NoDlzLLqnj5OTEytXrjQ2RE2dOpWRI0cSFRWV4Da6vHkJ69w5Zvt58zIlTiFEziA9pYRIi8hIXEeMwG7XLvQKBa+nTiWsd2+OHDlCnz59iIiIoG7duqxcuRJ7e3tzR5smmkaNCI1dB70epZ+fyQDr1sePY33zZpL7ch0zJmYXSiU6Dw90Hh5oc+c2+V/n6YnW0xOdpye63LnRubhAImNAxMfLy8vkUb5OnTqxceNGKlSokKL9CCGEECJr0+l0TJ06lSVLlgDQr18/JkyYgCoZPbOtjxzB8e12QbNno8udO0NjzQ5UKhXjx4+nRIkSfPXVV2zZsgU/Pz+WLl2KQwKzOIcMGYL9b79hc/QoVufOEVW1aiZHLYTIjqRRSohUUoSE4NavHzZHj6K3siJw/nwiPv2U/fv3079/fyIjI2nUqBFLly7Fzs7O3OGmP4UCXe7caHLnRlO7NgDWx4/j0aFDkptqnZxQvXmDQqdD9fIlqpcvsbp+PdFt9CpVTKOVoaEqdqOVpydaDw90uXOj9fBA7+pqvMPp7u7O5s2b6d69O2fPnqVz585s2LCBiu/OZJgWmTjToBBCCCFMRUVFMWrUKLZv3w7A999/n+Q4SAaKgABcR44EILRnTyKbNs2oMLOlzp07U6pUKdq3b8+hQ4do3749a9aswdPTM05ZbYEChHXogMOGDTjNm0fAmjVmiFgIkd1Io5QQqaB89Qq3Hj2wvnQJnb09Ab/+iqZePQ4cOMDnn39OVFQUzZo1Y9GiRdjY2Jg73EyjqVkTbb58KJ8/RxHPQO56hQJtvny8PHkyZobAV69Q+fmhfPsvzs/+/qhevkQZFIRCq0X14gWqFy+SjENvZYXO3d3YaOXi6cm+KlVY9eIFp//7D58OHRg7ezYlPvwQfa5caeqib46ZBoUQQggRIywsjAEDBnDw4EFUKhWzZ8+mQzJukAGg18cMPfDiBVHFixM8YULGBptNtWjRgq1bt9KjRw8uX75M69at2bx5c7yTyIQMHYr9pk3Y7t+P1ZUrRJUvb4aIhRDZSY5plNq3bx+7du0iKCiIwoUL07dv30QHNQ4NDWXDhg2cPn2akJAQPD096dWrF1WqVMnEqEV2pHryBPcuXVDfv4/WzY2AtWuJqlSJf//9l/79+xMVFcUnn3yCj48PVlZW5g43c6lUvP7hB1wHDECvUJg0TOnfNvwET54c04tIpUKXNy+6vHmT3q9GE9NA5e+P8uXLmJ/ja8jy949pwIqKQvX8Oarnz427sAdGG34JDYW3d1D11tYxvaw8PU0fH3zb6yp2jyy9s7NJA5a5ZxoUQgghLFlISAg9e/bk1KlT2NnZsXTpUho1apTs7e03bMDujz9ierz7+KDPiT3b00nlypXZsWMH3bp14+HDh7Rr147NmzdTqFAhk3LaokUJb90a++3bcZw/n8Bly8wUsRAiu8gRjVLHjx9nzZo19O/fnxIlSrBnzx6mTZvG3LlzyZUrV5zy0dHRTJ06FWdnZ0aNGoWbmxv+/v7ZfswfkfHUt27h3rUrqufPifby4tX69WiLF+fMmTPGMaSaNm2aYxqk7OzsuHPnjvHn5Iho0YLApUtNeg9BTO+h4MmTU9dIY22NLn9+dPnzJ102MtK00Sp2Q9bLl+hfvMDv8mVyRUbiAig0GtTPnsGzZ0nuWm9jY3xMUOfujvXx42afaVAIIYSwRG/evDE+mu/s7MzatWupVq1asrdX3buH89ueUcHffEN0uXIZFWq8UpNjmdt7773Htm3b6NixIw8fPqRt27Zs3ryZYsWKmZQL+eIL7P73P+z27uXNrVtElyplpoiFENlBjmiU2r17N40bN6Zhw4YA9O/fn/Pnz3Pw4EFat24dp/yBAwcICQlhypQpqNUxb0FuGdBQJMHqzBnce/dGGRREVKlSvPrtN3T58nHp0iV69OhBWFgY9evXZ9GiRTmiQQpAoVCkqrE2okULIpo1w+b0adw1Gl5l5jhLNjbovLzQxdOl3CAyJITGXbpw7fx53nd3Z8WPP1LAyir+3leGn0NCUERGon76FJ4+TTIMw0yD1qdOGcfcEkIIIUTavX79mm7dunHhwgVy5cqV8rEiNRpchw1DGR5OZJ06hA4YkHHBJiC1OZa5GSaR6dSpE3fu3KFdu3Zs2rSJkiVLGstElyxJRIsW2O3Zg+P8+QT5+JgxYiFEVpftG6Wio6O5f/++SeOTUqmkfPny3L59O95tzp07R4kSJfj111+Nd1fq1KlD69atUSYwu1dUVJTJNKgKhcJ4V0ORAVPGGvaZEfvOarJDXW3278e1f38UERFoqlYlYM0a9K6u3Lh+na5du/LmzRtq1arFr7/+mujdruxQ13SjVhNVpw7kzUtUAmNMmYuTkxPr1q2jXbt2nL9xg1ZTprBjxw7y5cuX8Ebh4f/f68rPD9u//8Z+w4YkX0v18mWOO94WdR5jWfW1pLqKGJmZ31ja+WVJ9c3MugYFBdGlSxcuXbqEq6srGzdupHwKxy1ymj0b68uX0bm4EDR/PooU3DSzpOMK8dc3b968bNu2jU6dOnHjxg3at2/Ptm3bTBqmQkaMwG7PHux27iRk9Gi0772X6bGnlBzbnMuS6podKfT6LPSXYioEBAQwaNAgpk6davJFuG7dOq5fv8706dPjbDNy5Ej8/PyoW7cuzZo14/nz5yxfvpyPP/44wYERN2/ezNatW42/Fy1alJkzZ6Z/hUTWs3Yt9OkDWi20aAFbtoC9Pffv36d27dq8ePGCmjVr8vfff+Pk5GTuaNNVZGQkAwcOBGDJkiU5btD258+fU7duXe7du0fZsmU5fPgwHh4eydv40CF42zszUQcPQoMGaQlTCCEyjOQ3IjsJDg6mcePGnD17Fg8PD/7555+Uz6Z76BA0ahQzHuTWrdCuXYbEmpSckGO9evWKpk2bcv78efLly8eRI0dMx/T99FPYtSsmj16xwnyBCiGyNItslBoxYgQajQYfHx9jz6jdu3ezc+dOli5dGu/rJHQn0c/Pj+jo6HSuVcz+8+bNy/Pnz8nmhyhJWbmuDkuX4jxpEgBh7drxes4csLLC39+fzz77jAcPHlC2bFm2bdsW7/hl78rKdY1PWFiYMbm4e/duiruZZ4f6/vfff7Ru3RpfX18qVqzI5s2bk9e4qNWSu0aNRGca1OXLx8tTp3LcmFLZ4bimJ0uqryXVNaOp1ep4p0zPajIzv7G088uS6psZdQ0LC6N79+6cPHkSNzc3tm7dSunSpVMWZ1AQno0bo/L1JaxLF17Pnp3iONKrrmnNsTJLUvUNCAigffv23Lx5Ey8vL/73v/9RoEABAKzOn8ejZUv0ajV+x46hLVgws8NPEUv6zIJl1deS6prRMiK/yfaP7zk7O6NUKgkKCjJZHhQUhIuLS7zbuLi4oFarTR7V8/LyIigoiOjoaOM4U7FZWVklOE5QRp7Yer3eYj44Waquej1OM2bg5O0NQEj//jHTBCuVhL6d6eXBgwcUKFCAdevW4ezsnKLYs1RdExE7xrTEnJXrW6BAATZs2ECbNm24dOkSn3/+OWvXrsXa2jrxDZXKhGcafPv/68mT0SuVcWbnyymy8nHNCJZUX0uqq6UzR35jaeeXJdU3o+oaGRlJ//79OXnyJE5OTqxfv55SpUql7LX0elzGjkXl60t0kSIx1+g0xJrWuqZXjpVZEorR8Ahl27ZtuX//Ph07dmT79u3kyZMHTeXKRNSrh+2RIzh4e/N6xgwzRJ5y2eF4pCdLqq8l1TU7iX8ApWxErVZTrFgxrl69alym0+m4evWqSc+p2EqVKsXz58/R6XTGZb6+vri6usbbICUsTHQ0ucaMMTZIBY8bR/DEiaBUEhUVxcCBA7l48SKurq789ttv5MmTx8wBi7QqUaIE69evx97enn///ZevvvoqWRcsw0yDurx5TZYrgODvvkvdTINCCCGEMIqOjmbYsGEcPHgQOzs71q5dm+IxpADstmzBbvdu9Go1gT4+6B0cMiBay+Tp6cmmTZsoVKgQDx8+pHPnzgQEBAAQMnIkAPabNqGMNSuzEEIYZPtGKYCWLVuyf/9+Dh06xJMnT1i+fDmRkZE0eDuOi7e3N+vXrzeWb9q0KSEhIaxatYpnz55x/vx5/ve//9GsWTMz1UBkGeHhuA4YgMPGjeiVSoJmzSJk6FBQKNDr9YwZM8aYFK1Zs8b0uXmRrVWoUIElS5agUqnYunUrs2bNStZ2ES1a8OLUKV5t3Qrr1xNZty4AVm+neRZCCCFE6hhyr71792Jtbc2KFSuoXr16ivejeviQXOPHA/Bm9GiiKlVK50hF/vz52bRpE3nz5uX27dv07NmTsLAwNDVrElmrFgqNBsdFi8wdphAiC8oRjVK1a9emR48ebN68mbFjx/Lw4UPGjRtnfHzP39+fwMBAY3kPDw++++477t27x1dffcXKlSv5+OOPTWbwE5ZHERyMe/fu2P35J3obGwKXLSOsSxfj+p9++omtW7eiUqlYvHgxVapUMWO0IiM0atSIGW+7ls+dO9ekMTtRKhWa2rWhSxfejB0LgN2OHSje3iUUQgghRMrNmDGDLVu2oFKpWLRoEfXq1Uv5TqKicB02DGVoKJE1a8bcbBQZolChQmzatAlXV1cuXLjAwIEDiYqKImTECADsf/sNpZ+fmaMUQmQ1OeZZtebNm9O8efN41016O1B1bCVLlmTatGkZHJXILpQvX+LerRtW16+jc3IiYOVKNB98YFy/ZcsW5s+fD8Q0TjVp0sRcoYoM1rVrV548ecK8efP45ptvyJcvHw2TM8veW1FVq6IpXx7rK1dw2LiRkCFDMjBaIYQQImdavXo13m+HUvj5558TzPOT4jRvHtYXLqBzdiZo/vwcN/lIVlO8eHFWr15Nx44dOXDgAF9//TWzZ81CU7ky1hcu4LBsGW/GjTN3mEKILCRH9JQSIi1UDx/i0bo1Vtevo/X0xH/rVpMGqVOnTvHVV18BMHz4cDp37myuUEUm+eqrr2jXrh1arZaBAweajFmXJIWC0D59ALBfvRq02gyKUgghhMiZ/vzzT8a/fdxuzJgxdOrUKVX7sT5zBsd58wAImjED7dtZ4UTGqlq1KosXL0alUrFp0yZ++vln3rztLeWwapX0JBdCmJBGKWHR1Fev4tG6NepHj4guXBj/HTuILlfOuP7hw4d8/vnnREVF8cknnxgbpyyFnZ0dly9f5vLly9jZ2Zk7nEyjUCiYNWsWdevWJTQ0lD59+uCXgu7m4Z9+is7FBfWTJ9js35+BkQohhBA5y7lz5xgyZAg6nY6uXbsy8u1A2SmlCA7G5YsvUOh0hLVrR8Rnn6VvoGmU03Osjz76iJkzZwIwf/58lj59StT776MMDcVxxQozRyeEyEqkUUpYLOsTJ/Bo3x6Vnx9RZcviv2MH2iJFjOtfv35Nr169CAwMpGLFisybNw+l0rI+MgqFAnd3d9zd3VEoFOYOJ1NZW1uzZMkSihUrxrNnz/j888+JjIxM3sZ2doR27QrE3BEUQgghRNLu379Pr169iIiIoFGjRvz444+pzj9yffcd6v/+I7pQIV5nwSE7LCHH6tKlC2PGjAFg/Pffc6JxYwAcfv0VRXCwOUMTQmQhlvUXthBv2e7bh3u3bijfvCGyVi38t21Dlzu3cX1UVBSDBg3i7t275MuXj5UrV+bIu1gicS4uLqxatYpcuXJx7tw5vvnmG/R6fbK2DevZE71Cge3hw6ju3s3gSIUQQojsLTAwkB49ehAYGEiFChVYvHgxanXqhr+1+9//sN++Hb1SSeD8+eidnNI5WpFcI0eOpFOnTuh0OlqtWEFo4cIog4Plpp0QwkgapYTFsd+wAdf+/VFERhLerBmvfvsNvbOzSZkJEyZw5MgR7O3tWbVqFXny5DFTtOYVGRnJuHHjGDduXPJ7CeUw7733nnFchM2bN7NkyZJkbactWJCIjz4CwGHNmowMUQghhMjWDDcDHz58iJeXF2vWrMHBwSFV+1L99x+5vv0WgJCRI4mqXj09Q003lpJjKRQKZsyYQa1atQgOCeGbN28AcFi6FEVYmJmjE0JkBdIoJSyHXo+jjw8uY8ag0OkI7dyZwKVLwdbWpNhvv/3GmjVrUCgU+Pj4UC7WGFOWRqvVsnr1alavXo3WggfsrlevHhMnTgRg6tSp7E/mOFFhhgHPN29GERKSYfEJIYQQ2dnEiRP5999/jTcDPT09U7cjrRaX4cNRvnmDpkoV4+DaWZEl5VjW1tYsW7aMIkWKsCgggMc2NqgCA7GXm3ZCCKRRSlgKnQ7nH37Aefp0AN4MG8brWbPgnW7h586dM872MnbsWJo2bZrpoYqsqW/fvnTr1g29Xs/QoUO5fft2kttE1q1LdLFiKN+8wW7btkyIUgghhMheVq1axerVq1EoFHh7e1O2bNlU78vR2xub06fROTgQ6O0dJ88T5uPm5sbq1atxzJWLSW97hjkuWQLh4WaOTAhhbtIoJXK+qChcRo7EcelSAF5PmMCbb7+FdwaVfPnyJQMGDECj0dCiRQu++OILc0QrsiiFQsHUqVOpVasWb9684fPPPyc4qUE6lUpCe/cG3g54nszxqIQQQghLcPToUSZMmADAN998Q7NmzVK9L6sLF3CaPRuA19OmoS1cOF1iFOmnePHiLFmyhPVKJY8A1cuX2G/aZO6whBBmJo1SIkdThIfj1rcv9tu2oVepCJw3j9CBA+OU02g0DBgwgOfPn1OyZEl++eWXHDsTikg9a2trli5dSv78+bl//z5ffvklOp0u0W3COnRAZ2+P1e3bWB8/nkmRCiGEEFnb/fv3GTRoEFqtlnbt2jF06NBU70sRGorrsGEotFrCP/2U8Pbt0zFSkZ4+/PBDJk2bxoy3v1vNmQMajVljEkKYlzRKiRxLERiIe+fO2B44gM7WloAVKxJMUiZNmsSZM2dwdnbm119/xdHRMZOjFdmFu7s7y5Ytw9ramn379uHj45Noeb2zs/G8k5lmhBBCCAgNDaVfv34EBQVRpUoVfvrppzTdDHSeMAH1w4dE589P0I8/xukNL7KWnj17EtG5M88Ah1evCFu82NwhCSHMSBqlRI6k9PXFo107rM+eRZcrF682biSySZN4y27cuNE4lsGCBQsoVqxYJkcrsptKlSoxdepUAH766SeOHDmSaHnDI3y2+/ahevo0o8MTQgghsiy9Xs+YMWO4desWuXPnZvny5di+M+lMStju3o3Dxo3oFQqCFixA7+KSfsGKDDNh+nTWe3kBYD1nDmFJDYkghMixpFFK5Diqu3fx+OwzrG7dQps3L/7btyc4HfCVK1f49u20waNHj6ZJAg1XQryra9eudO7cGZ1Ox5AhQ3jy5EmCZaNLlSKydm0UOh32a9dmYpRCCCFE1rJ8+XJ27tyJWq1myZIl5MmTJ9X7Uj57hsvXXwMQMmwYmlq10itMkcFsbGxovHEjfgoFhaKi2NW1K3oZe1MIiySNUiJHsbp0CY82bVA/fUp0sWL4//470aVLx1v29evXDBw4EI1GQ9OmTRmRhacNNhdbW1tOnjzJyZMn03QXMydSKBRMmzaNChUqEBgYSP/+/YmIiEiwfGifPgDY//YbJFJOCCGEyKlOnjzJlClTAJgwYQI1atRI/c50OlxHjEAZFISmYkXejB6dTlFmDsmxIE+xYrzs1g2A5hcusHTRIjNHJIQwB2mUEjmG9ZEjuHfogCogAE3Fivjv2IG2QIF4y+r1ekaPHs2jR48oWLAgv/zyC0qlfBzepVQqKViwIAULFpT3Jx62trYsW7YMFxcXLl26xPDhwxMsG9G0Kdp8+VAFBGC3e3cmRimEEEKY3/Pnz40Dm7dp04a+ffumaX8OS5Zgc/w4Ojs7Ar29wcoqnSLNHJJjxXAbP55wOzvKADenT+fYsWPmDkkIkcks9xtQ5Ci2O3fi3rMnytBQIuvW5dXmzejc3RMsv3z5cv744w+sra1ZsmQJLjL+gEilAgUKsHDhQhQKBcuWLWP79u3xF1SrCe3ZE5ABz4UQQlgWwyzHfn5+lClTJs0Dm1tduYLzzJkABE+ZglbGA8229E5ORA0eDMA4vZ5hQ4bw8uVLM0clhMhM0iglsj371atxHTIERVQU4S1b8mrNGvSJzJ539uxZ4yDVEydOpGLFipkVaraj0WiYMmUKU6ZMQSPT9Saofv36jBw5EoCxY8dy9+7deMuFde2K3toa6wsXsLpwIRMjFEIIIcxnypQpnDt3DmdnZ5YtW4a9vX2q96UID8dl6NCYvK9FC8I6d07HSDOP5Fj/L/Tzz9E5OFARqOnvz9ChQ9FqteYOSwiRSaRRSmRfej2Oc+bgMm4cCr2e0J49CVy4EGxsEtwkICCAQYMGER0dTatWrejVq1cmBpz9REdHs3jxYhYvXkx0dLS5w8nSRo0aRYMGDQgLC2PQoEGEh4fHKaPz8CC8VStAeksJIYSwDH/88QcrVqwAYN68eRQtWjRN+3OePBmre/fQ5s1L0MyZkIYeV+YkOdb/07u4GMfenKhUcvz4cebMmWPmqIQQmUUapUT2pNWS67vvcJ49G4A3o0bxevp0UKkS3ESn0zFixAh8fX0pWrQoP//8c5q6jgsRm0ql4rfffsPd3Z0bN24wadKkeMsZki67nTtRvnqViREKIYQQmeu///5j9NsByAcPHkzTpk3TtD+bv/7C4e0stoFz56J3c0tzjCJrCB0wAJ2dHVV1OpoS04B5+PBhc4clhMgE0iglsp/ISFyHDsVh9Wr0CgVB06bFzLiSRAOTj48PBw4cwNbWliVLluDk5JRJAQtLkT9/fhYsWIBCoWDdunX8/vvvccpEVa6MplIlFBoN9uvXmyFKIYQQIuNFRUUxePBgXr9+TZUqVfj666/TtD/lixe4vG3gChk0CM2HH6ZHmCKL0Lm7E9ajBwDzPT3R6/UMGzYMX19fM0cmhMho0iglshVFSAjuvXpht2sXeisrAn18COvdO8ntzp49y88//wzEjGvw/vvvZ3CkwlI1aNCAL774AogZX+rBgwdxyoS+PWft16wBC++yL4QQImeaOXMmFy5cIFeuXCxcuBCrtMyOp9Ph8uWXqAICiHr/fYLHjk2/QEWWETJwIHobG0r5+dG7SBECAgIYMmSIxT/eKEROJ41SIttQvnqFe6dO2Bw9is7enoA1a4j47LMktwsODmbYsGFotVpat25Nly5dMiFaYclGjx5NzZo1CQkJYdCgQURERJisD2/VCq2bG+pnz7D9+28zRSmEEEJkjP3797No0SIAZs+eTcGCBdO0P4cVK7A9fBi9rS2BPj6Jjh8qsi9d3rzGget/cXfH0dGR06dPM2vWLDNHJoTISNIoJbIF1ZMnuLdpg/XFi2hdXXm1ZQuR9eoluZ1er+fbb7/lv//+o2DBgvz4448yjpTIcGq1Gh8fH9zc3Lh69SrTp083LWBrS1jXrgA4rFxphgiFEEKIjPHs2TNGjBgBQN++ffn444/TtD/19es4T5sGwOsJE4guUSLNMYqsK2ToUPRqNS7nzrF2yBAAvL29OXHihJkjE0JklBzTKLVv3z6GDh1Kt27dGDduXIJTsr/r2LFjdOzYkZ9++imDIxSppb51C4/PPsPq3j2ivbx4tWMHUZUqJWvbLVu2sGPHDlQqFT4+Pjg7O2dssEK8lS9fPubNmwfAr7/+yoEDB0zWh/XsiV6pxObYMdS3b5sjRCGEECJdRUdHM2zYMAIDAylfvjzjx49P2w7Dw3H94gsUGg0RTZoQ1rNn+gQqsiytlxdhHToA0PzsWbp06YJer+eLL74gMDDQzNEJITJCjmiUOn78OGvWrKF9+/bMnDmTwoULM23aNF6/fp3odi9fvmTt2rWUKVMmkyIVKWV19iwebduiev6cqJIl8d+xg+jixZO17f379/nuu++AmMepqlatmpGh5ki2trYcOHDAOEC8SJlGjRrx+eefA/Dll1/i5+dnXKf18iKiWTMAHFatMkd4QgghRLqaN28ep06dwtHRkUWLFmGTxsfsnH/8EaubN9F6ehI0e3aSk9pkJ5JjJSxk6FD0SiW2Bw4wo2NHihUrhq+vL2PHjkWv15s7PCFEOssRjVK7d++mcePGNGzYkAIFCtC/f3+sra05ePBggtvodDoWLFhAx44dyZ07dyZGK5LL5sAB3Dt1QhkUhKZqVfy3b0eXP3+yttVoNAwdOpSwsDA++OADhg0blsHR5kxKpZJSpUpRqlQplMoc8XWR6caNG0fp0qXx9/dn1KhRJsmUYcBzuy1bUAQHmylCIYQQIu1OnjzJ3LlzAfjxxx8pWrRomvZnc+AAjr/+CkDQL7+g8/BIa4hZiuRYCdMWLUp469YAeC5bho+PD1ZWVuzdu5f1MnOxEDlOtv8GjI6O5v79+5QvX964TKlUUr58eW4n8kjM1q1bcXZ2plGjRpkRpkghu23bcOvTB2VEBBGNGvFq40b0rq7J3v6nn37i8uXLuLi4MH/+fFQqVQZGK0TCbG1t8fHxwcbGhgMHDrAqVq8oTZ06RJUogTIsDPutW80XpBBCCJEGISEhdOvWDa1WS5s2bWjbtm2a9qf098dl1KiYfX/+OZENG6ZHmCIbCfniC/QKBXZ791LF2ppvvvkGgAkTJiR7mBYhRPagNncAaRUcHIxOp8PFxcVkuYuLC8+ePYt3m5s3b3LgwIEUjSMVFRVFVFSU8XeFQoGdnZ3x5/Rm2GeOH5Rbq8Xm9GmIjMTGxobIGjWwX7GCXBMnAhDWrh2v58wBKyuS+04cPnzYZMYXLy+vDAo+5bLbcdVoNMyfPx+A4cOHY21tnaLts1t90yKxupYpU4bx48fz/fffM2XKFGrXrk3p0qVBoSCsTx9yjRuHw6pVhPXtmy0eTbCk4wqWVV9LqquIkZn5jaWdX5ZU34kTJ3L//n28vLyYPn162uqs1+MyejQqPz+iSpfmzXffZan3ML2Oa1pzrMxirvNYW6oUEZ98gt3u3TgtWMBAb28OHTrE0aNHGTJkCLt3707z46HvsqTPLFhWfS2prtmRQp/NH8wNCAhg0KBBTJ06lZIlSxqXr1u3juvXr8eZ9So8PJwxY8bQr18/KleuDICPjw+hoaGMHTs2wdfZvHkzW2P1ZChatCgzZ85M59pYmO3bYcQIePLk/5c5OcGbNzE/jxwJs2dDCro0+/v7U758eZ4/f87gwYNZuHBh+sZsYUJDQ3F0dARi7oI6ODiYOaLsS6/X07JlS/bu3Uu5cuU4c+ZMzBgSb96Al1fM/3//DU2amDtUIYQFkfxGpNX27dtp164dCoWCgwcPUr9+/bTtcNEiGDIEbGzgzBmI9TRETiI5VjJcugSVKsX8LXDjBr5OTlSoUME4JMLs2bPNHaEQIh1k+0ap6OhounfvzqhRo6hRo4Zxube3N2FhYXEamh4+fMjYsWNNnt02vAUKhYK5c+eSN2/eOK+T0J1EPz8/oqOj07taKBQK8ubNy/Pnz3PkgH62e/fi0r8/6PXx9oAKa9uW1wsWpKjXiF6vZ8CAAezZs4eSJUvyxx9/GO/2ZhXZ7biGhYVR/O3A8nfv3sXe3j5F22e3+qZFcurq5+dH48aN8ff3p1+/fvzwww8AOI8fj8OKFUQ0a0bgypWZGXaqWNJxBcuqryXVNaOp1Wo8PT3NHUaSMjO/sbTzyxLq6+vrS5MmTQgMDOSbb75hxIgRaaqr+s4dPJo1QxERwesffiCsX790jDZ9pNdxTWuOlVnMfR679uqF7d9/E9axI6/nzuWvv/6i99sxOTdu3Ei9evXS7bXMXdfMZkn1taS6ZrSMyG+y/eN7arWaYsWKcfXqVWOjlE6n4+rVqzRv3jxO+fz58zNr1iyTZRs3biQiIoLevXvjkcAgilZWVlhZWcW7LiNPbL1en/M+OFotzt9/n2CDlB6wPnkSvVYLKRgLauvWrezZswe1Ws2CBQuwtbXNsu9ddjmusWNMS8zZpb7pIbG6enh4MGfOHHr27Mny5ctp0KABDRs2JLRXLxxWrMDm779RPn6MtmDBTI46dSzpuIJl1deS6mrpzJHfWNr5lVPrq9PpGDlyJIGBgZQvX57Jkyfz6tWr1Nc1MhKXIUNQREQQ0aABoX36QBZ+39J6XNMrx8os5orxzYgR2P79N3bbtvFm1Cg++ugjevbsyZo1a/jyyy/Zv38/uXLlStfXzA7HIz1ZUn0tqa7ZSbYf6BygZcuW7N+/n0OHDvHkyROWL19OZGQkDRo0AGJ6TRlmarC2tqZQoUIm/xwcHLC1taVQoUKo1dm+nS7Lsz51CpWvb4JjRCkA9bNnWJ86lex9Pn36lPHjxwMwatQoypUrl/ZAhcgAjRs3pm/fvkDMuRoQEEB08eJEfvghCp0O+7VrzRyhEEIIkbTly5dz9OhRbG1t8fb2TvOYSM4//YTVtWto3dwImjMnRcM3iJwrqnJlIurXR6HV4ujtDcD3339PkSJF8PX15fvvvzdzhEKItMoR3/a1a9emR48ebN68mbFjx/Lw4UPGjRtnHPzc39+fwMBA8wYpjFQvX6ZrOcOdujdv3lC1alWGDh2alvCEyHDjxo2jRIkSvHz5knHjxgHE3BEG7Nevh/Bwc4YnhBBCJOrWrVv8+OOPQMwg5yVKlEjT/qyPHMFx8WIAgmbPRpcnT5pjFDlHyIgRANhv3ozy2TPs7e2ZO3cuSqWSbdu2sXfvXjNHKIRIixzTLah58+bxPq4HMGnSpES3lUaMzKXNnTtdyy1fvpzjx49jb2/PvHnzpLebyPLs7OyYN28erVq1YteuXXz88cd81rIl0V5eqJ8+xW7nTsI7dTJ3mEIIIUQcUVFRjBw5Eo1GQ6NGjejRo0ea9qcICMB15EgAQnv0ILJp03SIUuQkmpo1iaxVC5uTJ3FcvJjgH36gevXqDBkyBG9vb77++muqV6+eLcbxE0LElSN6SonsRVOzJtp8+dAnMIi5XqEgOn9+NDVrJrmvW7duMWPGDAAmTJhA0aJF0zVWITJKxYoVGfk2CR83bhzP/fwI69ULAIeVK7P0OBpCCCEsl7e3N5cvX8bFxYVZs2albYp1vR6XsWNRvXhB1HvvETxxYvoFKnKUN297Szn89htKPz8gZhiEMmXKEBAQwFdffSVjBQmRTUmjlMh8KhWvf/gh3j+6DQ1VwZMnJznIuUajYfjw4URGRtKoUSO6d++eIeFaMhsbG/bs2cOePXuwsbExdzg5zhdffEGFChUICgpizJgxhHbujN7GBusrV7A6f97c4QkhhBAmrl69yty5cwGYOnUqedL4mJ39hg3Y/fEHeisrgnx80GexWZMzkuRYKaP58EM0lSujiIjAYelSIOY9nD9/PlZWVvz9999s3rzZzFEKIVJDGqWEWUQ0a4bO3T3Ocm2+fAQuXUpEixZJ7mPOnDlcvXoVV1dXZs+enbY7dSJeKpWKSpUqUalSJVQpmAlRJI+VlRXz58/HxsaGgwcPsmbvXsI/+wwAh1WrzBucEEIIEYtGo2HkyJFER0fTokULWrdunab9qe7dw3nCBACCv/6aqPLl0yHK7ENyrBRSKHjztoe5w+rVKAICAChbtixfffUVEPPUxH///WeuCIUQqSSNUsIsbA4fRvXqFTpnZ1799husX8+rrVt5efJkshqkzpw5g4+PDwAzZ84kdzLHnxIiqylRogTffPMNAD/88AN3mjUDwG7XLmP3dCGEEMLcfvnlF27cuIGbmxs//vhj2m4GajS4DhuGMjycyDp1CB04MP0CFTlWZOPGRL3/PsrQUBx//dW4fNCgQVSrVo2QkBC+/PJLdDqdGaMUQqSUNEoJs7Bftw6AsA4d0DRsCF26oKldO8lH9gDCwsIYOXIkOp2Odu3a8cknn2R0uBZLo9GwaNEiFi1ahEajMXc4OVa/fv344IMPCAsLo//ixURWqYIiKgr7334zd2hCCCEEFy9eNN4MnDFjBh4eHmnan9Ps2VhfvozOxYXAuXNBaXl/kkiOlQoKxf+PLbViBYrgYCCm19ncuXOxs7PjxIkTrFy50pxRCiFSyPKuAMLslM+eYfv33wCEpWLGlunTp/Pw4UPy58/P1KlT0zs8EUt0dDRTp05l6tSpREdHmzucHEupVPLLL7/g6OjImTNn2FmwIAAOa9dCVJSZoxNCCGHJIiIiGDlyJFqtltatW6f5ZqD1iRM4vm3gCvrpJ3T586dHmNmO5FipE/Hxx0SVLIkyONhkqIOiRYsyfvx4AH788UcePXpkpgiFECkljVIi0zls2IBCpyPygw+ILlEiRdvGvvsxe/ZsnJ2dMyJEITJdwYIFmTx5MgB99+5F4+KC6vlzbP/808yRCSGEsGQ///wzd+7cIXfu3Gm+GagICsJl+HAUej2hnTsTIb3dRUoplYQMHw6Aw9KlKEJDjat69uzJBx98QHh4OGPGjJHH+ITIJqRRSmSu6Gjs168HIDSFvaTCwsIYPXo0AN26daNevXrpHp4Q5tSpUyeaNGlCSFQUK9RqQAY8F0IIYT5nzpxhyZIlAPz000+4urqmfmd6PS7ffIP62TOiixQh+Icf0ilKYWnCW7UiukgRVIGB2K9da1yuVCqZNWsWdnZ2HD9+nHVvhwsRQmRt0iglMpXt/v2onj9H6+ZGRPPmKdp2+vTpPHr0CC8vL77//vsMilAI81EoFPz888+4uLgw1d8frUKBzYkTqG/cMHdoQgghLEx4eDhffvkler2ejh078tFHH6Vpf3Zbt2K3axd6lYpAb2/0Dg7pFKmwOGo1b972lnJcsgTCw42rihQpYpxAZurUqTx58sQsIQohkk8apUSmMtzNCOvcGWxskr1d7Mf2Zs2ahZOTU4bEJ4S5GR6PeArseLtMeksJIYTIbL/88gsPHjwgb968TJo0KU37Uj18SK7vvgPgzejRRFWunA4RCksW3rYt0V5eqF6+xH7jRpN1ffr0oVq1aoSGhjJ27Fj0er2ZohRCJIc0SolMo3r0CJtDhwAI69Yt2dvJY3vC0rRu3ZqmTZsy/20SZbdtG4rXr80clRBCCEtx+fJlFi9eDMQMGp0rV67U7yw6GtcvvkAZGkpkjRqEDBuWTlEKi2ZlRcjQoQA4+fhArBkMVSoVs2fPxtbWlsOHD7Np0yZzRSmESAZplBKZxn79ehR6PRH166MtUiTZ28lje8LSKBQKZsyYwSVnZ64AyvBw7DdvNndYQgghLEBUVBSjR49Gq9Xy6aef0rRp0zTtz2nePKzPn0fn5ETQggWgUqVTpMLShXXqhDZvXlS+vthv2WKyrnjx4owZMwaAyZMn4+vra44QhRDJII1SInNoNMautWEpGOD8+PHj8tieGdnY2LBlyxa2bNmCTQoetxRplydPHib/8APeb3+3WrYMZBYZIYQQGWzhwoVcv34dV1dXpkyZkqZ9WZ05g+PcuQC8njEDbYEC6RBhziA5VjqwtSVk0CAAHL29ITraZHX//v2pVKkSwcHBfPPNN/IYnxBZlDRKiUxhu28fKn9/tHnyENGkSbK2kcf2zE+lUlG7dm1q166NSu5sZrr27dvzpF49ggD7p09RHzhg7pCEEELkYHfu3GHu20akH374AQ8Pj1TvS/HmDa5ffIFCpyOsbVvCW7dOnyBzCMmx0kdY9+5o3d1RP36M3Y4dJuvUajVz5szB2tqaf/75h+3bt5snSCFEoqRRSmQKB8MA5126gJVVsraZPn06jx8/lsf2hMVSKBRMnj2b395+ZgLTeMdaCCGESIhWq2X06NFoNBoaNWpEmzZt0rS/XN99h/q//4guWJDX/8fefYc1eb0NHP8mIRCmgAsFN+5Rrdb1s9ZRtXVr3btu616oVdwL96wbJ1p3raOtWuuqVmvd1lG11okLkRFWxvuHwCsKyEwI3J/r6lVIzvM89zEBTs5zzn1Pm5ZGUQoRl9HWltA+fQBwWLQI9Po4zxcvXpwhQ4YAMH78eF68eGHqEIUQHyCTUiLdWd2+jc2pUxiVSrQdOiTpGNm2lzFERUWxbt061q1bR1RUlLnDyZLy5s2LbfSKwaK3b/Pw2DEzRySEECIzWr9+PX/99Rf29vbMnDkThUKR4nPZ/vADdjt3YlQqCVy8GKOTUxpGmjnIGCvthHbtisHZGfWdO2j273/v+W+++YbSpUsTGBjIhAkTzBChECIxMikl0p3dpk0ARNSti97d/YPtZdtexhEVFcXYsWMZO3asDJjMqMGAAZxxdUUJ3Bo6FP07dwGFEEKI1Hjw4AEzZswAYOzYsbgnYbyWENXDh2QbMwaAkMGDifzkkzSJMbORMVbaMTo4ENKzJwCOixa9l4NTrVYzZ84clEolP/zwA0ckHYIQGYpMSon0FRYWWw0jtFOnJB0ya9Ys7t+/T968eWXbnhC82cbnMm4cAI2ePmVDdJluIYQQIrWMRiOjRo1Cq9VSpUoVOiejIA0Aej3Wp07Bli1YnzyJ88CBKIOCiPz4Y4Kjt00Jkd5Cv/4ag4MD6uvX0Rw69N7z5cqVo2f0xNXo0aMJDQ01dYhCiATIpJRIV7b796MMDETn7k5E7dofbH/x4kXWrFkDgI+Pj2zbEyKaQ6tWvHJ1xQV4OGsWd+/eNXdIQgghMoHt27dz7NgxbGxsmD17Nkpl0j8eaA4cIHeVKmRv1Qo6dCB7mzbYnD2LwcaGV4sXg5VVOkYuxP8zOjsT2q0bAA4LF0I8lfZGjhyJh4cHjx49YtasWSaOUAiREJmUEukqNsF5x47wgcoiUVFRjBgxAoPBQIsWLahTp44pQhTCMqhUKPv3B6C3TseI4cMxvLM8XQghhEiOly9fMmnSJACGDx9OkSJFknys5sABXHr3RvnkyXvPKSIiUP/9d5rFKURShPbujcHWFutLl7CJJwennZ0dM2fOBMDX15eLFy+aOEIhRHxkUkqkG6vr17E+dw6jlRXadu0+2H758uVcv34dFxeX2AGSEOL/adu2xWBjQwVAdfYsm6LztQkhhBApMXXqVAIDAylVqhR9oiuYJYleT7bx48FoJN506AoFThMmvFcJTYj0ZMieHW309lOHBQviXS1Vu3ZtWrRogcFgYMSIEZLPS4gMQCalRLqxj/7AHF6/PobcuRNt+88//zBv3jwAJkyYQPbs2dM9PiEsjdHFhbCWLQEYAEybNo3Hjx+bNyghhBAW6cyZM2zbtg2AGTNmYJWMrXbWZ86gevIk/gkpQGE0YvX4MdZnzqRBpEIkXUjfvhhtbLD580+sT5+Ot83EiRNxdnbm+vXrrFixwsQRCiHeJZNSIl0oQkOx3bEDgNAPJMw0GAz06tWLiIgIatasSatWrUwRohAWKSZfQivAISSEb7/9FmM8dwKFEEKIhERFRTEmukJex44dqVSpUrKOVz17lqbthEgrhty50bZvD4DjwoXxtsmRIwcTJkwAYP78+fz7778mi08I8b5Mk33w559/Zu/evQQGBlKgQAG6d++Op6dnvG0PHz7M8ePHefDgAQCFCxemffv2CbYXyWe7Zw/KkBB0BQsSWaNGom23bNnCsWPHsLW1xcfHB4UioftuwtSsra1Zv3597NfC/HRlyhDxySfY/Pkn/ZRKJhw6xN69e2natKm5QxNCCGEhVq1axc2bN3F1dY2dnEoOYxKToetz5Ur2ubMKGWOln5BvvsFu0yZsTp5Efe4cUfFMurZu3Zpdu3Zx4sQJRo8ezffffy+fQYQwk0yxUurUqVNs2LCBVq1a4ePjQ4ECBZg2bRqvX7+Ot/3ff//N//73PyZMmMDUqVPJnj07U6dOJSAgwMSRZ1520QnOQzt3hkQGLk+fPmXq1KkAeHl5kT9/fpPEJ5LGysqKzz//nM8//zxZy/pF+gr9+msAhtjaoga8vb3l95cQQogkefjwYWzKhHHjxuHi4pL0g3U67FeuxHnYMAASWqdrVCjQ5c1LZJUqqYw285IxVvrRu7ujbd0aSHi1lEKhYObMmWg0Gk6ePBm7lVUIYXqZYlJq37591K1bl9q1a+Ph4UGvXr2wtrbmt99+i7f9oEGDaNCgAQULFsTd3Z2+fftiNBq5cuWKiSPPnNSXLmF9+TJGa2vC2rRJtK23tzevX7+mYsWK9OjRw0QRCmHZwr/8En2uXDiFhvKNmxsvXrxg8uTJ5g5LCCGEBRg/fjxhYWFUqVKFNh8Yp71NfekSORo1ItukSSjDwoiKrtRnfGd1Scz3QZMmfbDyshDpJWTAAIxKJZojR1Bfvhxvm4IFCzJ8+HAAJk+ezIsXL0wZohAimsVPSul0Ou7evUvZsmVjH1MqlZQtW5Zbt24l6RwRERHodDocHBzSK8wsxS46wXlYo0YYXF0TbPfzzz+zf/9+VCoVq1evlrtEGVBUVBRbt25l69atUp0kI7G2RtupEwDerq4oFAq2b9/O8ePHzRyYEEKIjOzgwYP88ssvWFlZMWPGjCRtV1IEB+M0fjw5GjfG+upVDNmyEThrFs+PHuXVqlUY3NzitNfnycOrlSsJb9gwvbqRKcgYK33pCxYkrHlzABwWLUqwXe/evSldujSBgYGxeaaEEKZl8bMAQUFBGAwGnJ2d4zzu7Oyc5KpUfn5+uLq6xpnYeldUVFScPxgKhQJbW9vYr9NazDktbW+zIigI2927AdB26ZJg/EFBQYwdOxaAfv36Ub58efz9/U0Wp7lY2uuq0+kYFr1Ev2nTpsnOeWBp/U0NU/dV26kTDosWkf3vvxnfrBmT9uzBy8uL3377DTs7u3S9dlZ6XSFr9Tcr9VW8YcrxTVZ7f2W0/mq1Wry9vYE3H8RLlCiR+AFGI5qffsJp3DhU0WO0sJYtCZowAUPOnCiAiEaNePbFF9icPYtrRAQBNjZEVK4MKlWClfksXVq9rqkdY5lKRnsfJ0fo4MHY7t6N7U8/EXLzJrp43vNqtZo5c+bQqFEjdu/ezeHDhylTpowZojU9S35tkysr9dUSWfykVGr98MMP/P7770ycODHRPwa7d+9mR3Q1OYBChQrh4+NDzpw50zU+t3fuPmV4u3ZBWBiUKkWOZs0ggR/8KVOm4O/vj6enJ7NmzQIssK+pYCl9DQ0Njf3azc0Ne3v7FJ3HUvqbFkzW1zx54KuvYOtWvnV2xjdfPh48eMDSpUtjc4Wkt6z0ukLW6m9W6mtWZ47xTVZ7f2WU/o4ZM4aHDx+SP39+Zs2alfjf9Pv3YcAA2Lv3zfdFisCyZdjWq4dtfO09PABIeH185pPa1zWtxlimklHex8mSJw+0agXbt5Nz5UrYsiWBZnno378/ixcv5ptvvuHy5ctoNBoTB2s+FvnaplBW6qslURgtvJa4TqejU6dODBs2jMqVK8c+vmTJErRaLV5eXgke++OPP7Jr1y68vb0pEr0vPiEJ3Ul8/vw5Op0u9R15h0KhwM3NDX9/f8sp9240kqNuXdQ3bvB6yhS0CeSIOnPmDC1atABg+/bt1KhRw/L6mkKW9rpqtdrYqpS3b99O9gocS+tvapijr+ozZ8jRogVGjYYdCxbQpm9flEole/fupUKFCul23az0ukLW6m9W6mt6s7KySvcbV2nBlOObrPb+ykj9vXXrFp9//jk6nQ5fX1+++OKL+BvqdNivWYPD7NkotVqMVlaE9O9PyKBBYBvvdBSQsfqa3tKqr6kdY5mKpb+2VteukbNePYwKBc+PHUOfQLX1oKAgPvvsM54+fcqIESNiV7FlZpb+2iZHVuprekuP8Y3Fr5SysrKicOHCXL16NXZSymAwcPXq1YT/4AJ79uxh165djB079oMTUvBmaadarY73ufR8YxuNRov5wVH/+SfqGzcwaDRov/oq3rgjIiIYOXIkAO3bt6d69eqx7Sypr6llKX19O8bUxGwp/U0Lpuxr5CefEFWqFOq//6b+o0e0bNmSXbt2MWLECA4cOJDuWwGy0usKWau/WamvWZ05xjdZ7f1l7v4ajUbGjBmDTqejXr16NGjQIN541Bcv4uzlhfraNQAiPvmE1z4+6IoXjzlRkq6VVV7b1PY1rcZYpmIJMcYnqlQpwuvVQ3PoEA5LlhA4f3687RwdHZk0aRJ9+/Zl0aJFNGvWjMKFC5s4WvOw1Nc2JbJSXy2JxSc6B2jcuDG//vorR48e5eHDh6xevZqIiAhq1aoFvFk1tXnz5tj2P/zwA1u3bqVfv37kypWLwMBAAgMDCQ8PN1MPMgf7jRsBCG/WDGO2bPG2Wbx4Mbdv3yZnzpyMGzfOlOEJkfkoFIR+/TUA9hs2MNHbGxcXF65fv853331n5uCEEEJkBDt27OD06dNoNBqmTJny3vOK4GCcvL3J0bgx6mvX3iQynz2bl7t2/f+ElBAWLHjwYABsd+5Edf9+gu2aNGlCgwYNiIyM5Ntvv5XJCyFMJFNMSlWvXp3OnTuzbds2vLy8uHfvHt9++21s8vMXL17w6tWr2PaHDh1Cp9Mxb948evfuHfvfjz/+aKYeWD5FQAC2+/YBENq5c7xtbty4wZIlS4A3OaXeTU4vhEi+sBYtMGTLhtV//5H38mUmT54MwMKFC7l9+7aZoxNCCGFOr169ip2IGjp0KPny5fv/J41GNAcOkKtWLRx8fVEYjWhbtuTZsWNoO3QAZab4mCAEURUqEP7ZZyj0ehyWLk2wnUKhYOnSpWg0Gk6cOMEPP/xguiCFyMIsfvtejC+++CLB7XoTJ06M8/3SRH4ZiZSx274dRUQEkWXKEFW+/HvP6/V6Ro4cSVRUFPXr16dx48amD1KITMhoa4u2XTscVqzAft06WmzcyO7duzly5AgjR45k586dKOWDhRBCZEkzZ87k5cuXFCtWjN69e8c+rnr0iGxjx6I5dAgAXcGCvJ4xg4iaNc0VqhDpKmTIEDTHjmG3bRvBgwdjyJs33nZFihRh8ODB+Pj4MHHiRGrXri030oVIZ/JJRaSe0Yj9pk3AmzL18VXc27BhA+fPn8fBwYFp06ZJOU4LYW1tzfLly1m+fHmGLVUsILRLF4wKBZrffsPq33+ZMWMGdnZ2nD17lo3R22qFEEJkLefPn8fPzw+A6dOnv/k7rtNhv2IFOWvVQnPoEEa1muBBg3h2+LBMSJmYjLFMK7JyZSKqVUMRGYnD8uWJtu3bty+enp68ePECHx8fE0UoRNaVZpNS/v7+3Lp1i8ePH6fVKYWFsD51Cqu7dzHY2xMWXVXvbY8ePWLGjBnAm3LEeRO4MyEyHisrK5o0aUKTJk2wsso0CyszHX3BgkTUqQOA/fr1eHh4MGbMGODNB5FHjx6ZMzwhRDqRsZdIiE6nY/To0RiNRlq1akW1atVQX7xIzoYNyTZ5MkqtlojKlXn+yy8EjxqVaGU9kT5kjGV6wYMGAWDv54fy+fME29nY2MR+dtm4cSPnz583SXxCZFWpmpQyGAzs3LmTXr16MXjwYLy9vePsvT1x4gTjxo3jwYMHqY1TZGAxCc7DWrbE6OAQ57mYii+hoaFUqlSJLl26mCNEITK9mITndtu2oQgNpWvXrlSsWJGQkBBJ1ilEJiJjL5EU69at49q1azg7OzNh6FCcxo37/0Tmzs4EzpnDy507JZG5yFIiP/2UyI8/RhEejsOKFYm2rV69Oq1atcJoNDJ69Gh0Op2JohQi60nxpJTBYGDmzJls27aN0NBQPDw83mtTvHhx/vnnH86cOZOqIEXGpXz+HM1PPwHxJzj/8ccf+fXXX7G2tmb27NmS28bC6HQ69u7dy969e5P9x7hVq1aMHz8+nSIT74r47DN0BQuiDArCdtcuVCoVs2fPRq1Wc/jwYfbu3WvuEIUQqSRjL5EU/v7+zJ49GwDfRo0o+dVXOKxdGzeRefv2ksjczFIzxhIppFDEVuKzW78eRUBAos29vb1xdnbm2rVr+Pr6miJCIbKkFP81OnjwIJcuXaJMmTIsWbKEuXPnvtcmV65c5M6dm8uXL6cqSJFx2W3dikKnI/Ljj9GVLh3nuYCAALy9vQEYOHAgxYoVM0eIIhUiIyPp27cvffv2JTIy0tzhxPH06VP69+9PjRo18PDwiHcCzM/PjxYtWlCqVClKlSpF27ZtuXDhQpw2c+fOpWbNmnh6esa2schl2kolod26AWC/bh0YjRQvXpyBAwcCbwZWb1chFUJYHhl7iaSYOHEiriEhHHNyooWfHyp/f3QFC/JiyxYCFy/GkCOHuUMUZOwxVmYWUbcukWXKoNRqcVizJtG2OXLkYOzYsQDMnj1b0iEIkU5SPCl17NgxHBwcGDp0KK6urgm2c3d358WLFym9jMjIDAbsohOch3bq9N7TU6ZMia340r9/f1NHJzK5yMhIsmfPzuDBgylVqlS8bU6fPk2zZs3Ytm0bP/74I3nz5qVDhw48efIktk3hwoWZOnUqv/76K7t37yZfvnx06NCBly9fmqoraUbbpg0GW1vUN25g/ccfAAwYMICiRYvy4sULpk6dauYIhRCpIWMv8SHHjxyh6N69/A3UDAp6k8h88GCeHT5MpCQyFwIUCkKiV0vZ+/qiCApKtHm7du2oVKkSWq32vYruQoi0keJJqcePH+Pp6YnDOzmE3mVnZ8fr169TehmRgdkcO4bVgwcYsmUjvGnTOM8dP36cbdu2oVAomD17NjY2NmaKUpiCVqtl0KBBFC1alAoVKrD8A1VN0kK+fPmYPHkyrVu3xsnJKd42S5YsoVu3bpQpUwZPT0/mzJmDwWDg5MmTsW1atGhBzZo1KVCgAMWLF2fChAkEBwfz999/p3sf0poxWzbCvvoKAPu1a4E3yTpjtnF8//33nDhxwmzxCSFSR8ZeIjHGM2co26MHcwF7eJPI/OBBgr28JJG5EG8J/+ILoooVQxkUFDteSohSqWTmzJmoVCoOHDjAoUOHTBSlEFlHqnJKJaVSRGBgIGq1OqWXERmYXXSCc22rVhjfGuyEhYUxevRoALp160alSpXMEp8wnSlTpvDHH3/g6+vL5s2bOX36NFeuXEn0mDNnzlC0aNFE/9u1a1eaxhkWFoZOp8PZ2Tne5yMjI/Hz88PJyYnS72xHtRQxW/g0P/+MMroi1yeffELXrl0BGD16NGFhYeYKTwiRCjL2EvFRBAfjNG4ceVq2pHRkJK8UCvynTn2TyFxSJwjxPqWSkJhKfKtWoQgNTbR5yZIl6d27NwDjxo1Dq9Wme4hCZCUpnpTKkSMH9+/fT7SNTqfj/v375MmTJ6WXERmU8vFjNNF3CrTvJDifO3cu//33H3ny5ImdnBKZV2hoKN9//z3e3t58+umnlCxZkgULFnwwaWe5cuU4ePBgov/Vr18/TWOdNm0auXPn5tNPP43z+KFDhyhatCiFCxdm1apVbNmyJdGtMRmZrmRJIqpVQ6HXYx+9vRZgzJgxuLm5ce/ePebPn2/GCIUQKSVjLxGH0Yhm/35y1aqFw9q1KIGNwJ6ZMzF8/bUkMhciEWFNm6IrVAjVq1exN9oTM2zYMNzd3Xn48CELFy40QYRCZB0p/mtVvnx5nj17xuHDhxNs8/PPPxMUFESFChVSehmRQdl9/z0Kg4GIqlXRFS0a+/iVK1dYEV1idcaMGR/cYiAs371794iMjOTjjz+OfczFxYUiRYokepytrS2FChVK9L+0fP8sWbKEH3/8kdWrV6PRaOI897///Y+DBw+yZ88eatWqRd++fS06H0to9KooOz8/iIgAwNHRkRkzZgCwfPlyrl69arb4hBApI2MvEUP18CGu3brh2rs3Kn9/Hmo0fA6s+ewz6nXsaO7whMj4VCqCo4vBOCxfDh9YRW5nZ8eUKVOAN+OoW7dupXuIQmQVKZ6Uatq0KXZ2dqxevZp169Zx8+ZNACIiIrh79y5+fn6x22C++OKLNAtYZAA6HfZ+fkDcVVI6nY4RI0ZgMBho2rQp9erVM1eEwgKYcvve8uXLWbp0KZs3b443KbqdnR2FChWiYsWKzJ07F5VKxZYtW9Lk2uYQ/sUX6N3cUL14ge2BA7GP169fnyZNmqDX6xkxYoSUoBbCwsjYS6DTYb98OTlr1UJz+DBGtZpzX36JZ3g4J21smDZtGgqFwtxRCmERwlq2ROfhger5c+y+//6D7Rs0aEC9evXQ6XR8++23GI1GE0QpROb34cQECXBxcWHkyJHMnTuXn376iZ9++gmAP/74gz+iqz7Z2dkxfPjwBJMQiw8bMmQI27ZtA8DKyoo8efLQuHFjRowYEbva48GDByxYsIDff/+d58+fkzt3blq2bMmgQYOwtrZO85g0v/6Kyt+fUBcXhp4+zZ6xY4mMjCR//vzcuHEDZ2dnJk+eHO+xz58/Z9q0aRw/fpzXr19TtWpVpk6dGmebgZeXFydPnuTp06fY2dlRqVIlxo4di6enZ5r3RSROrVYzb9682K/jU7BgQdRqNefPn8fd3R14k8/k7t27VKtWLcFzx2zfS0zOnDlTGPn/++6771i0aBF+fn589NFHSTrGaDRadnlmtZrQTp1wmjMH+7VrCWvRIvapyZMnc/z4ca5cucLq1avp27evGQMVQiSHjL2yNvWFCzh7eaGOLsQRUaUKj8ePp3H37kQAwwcMoFChQuYNUiRZUsZYIp2p1YT074/zmDE4Ll1KWBJWGU6ZMoUTJ05w+vRpduzYQevWrU0QqBCZW4onpQBKlSrF3Llz2b9/PxcuXODp06cYjUayZ89OhQoVaNq0qcXmZclIateuzbx584iKiuLKlSsMGTIEhULB2LFjAbh9+zYGgwEfHx8KFizIzZs3GTlyJFqtlvHjx6d5PDH7rgfnzcuhI0dYsWIFISEh9OzZE4Dx48fHO5lgNBrp3r07arUaX19fHBwcWLlyJW3bto292wtvJitatmyJu7s7gYGBzJ07l/bt2/PHH3+gUqnSvD8iYWq1mrZt2ybaxt7ennbt2jF16lRcXFzIkSMHPj4+KD+QyyJm+15qxGxBCw0NJSAggKtXr2JtbU2x6MSuS5cuZc6cOSxZsoR8+fLx7Nmz2Jjt7e3RarUsXLiQ+vXrkzt3bgICAli3bh3+/v40btw4VbGZm7ZjRxwXLsT6r79QX75MVLlyAOTKlYvx48czfPhwZs+ezZdffkmBAgXMHK0QIqlk7JX1KIKCcJw1C/t161AYjRicnXnt7U1YmzZMmziRp0+fUrBgQb755htzhyqSISljLJH+tG3a4LhwIaonT3D08YGaNbG2tiaicmWI53NHvnz5GDp0KDNmzGDKlCnUq1cvwQI6QoikURhTuO4wJt9Kjhw50jQgS/P8+XOioqLS/LwKhYI8efLQtm1bXr9+ja+vb+xzvXr14v79+/zyyy8JHr9s2TI2bNjA6dOn0zQu1f375KpenSCjkZxWVixZupRGjRrRrl07Tp48CcCePXvirbh3584datasyZEjRyhevDjwppJQ+fLlmTlzJo0aNYp3Gezff/9NvXr1+P333ylYsGCa9sfUYl7XJ0+eZKolv6GhoYwePZqffvoJBwcH+vTpw6+//krp0qVZtWpVuvU3ZmXW2zw8PDhz5gwAVapU4eHDh++1GTZsGMOHDyc8PJwBAwZw4cIFAgICcHFx4aOPPmLw4MGUL18+WbFkxNfWecAA7HbvRtu2LYHRd2PhzQRx27Zt+f333/n000/ZsmVLsrZ7ZMS+pqes1N+s1Nf0plar02S159tMOfZKj/FNVnt/pbq/0YnMs02YgMrfHwDtV18RNH48hhw5uHLlCg0bNsRgMLBlyxZq1qyZxj1Iuqz02malvkLW6G+2oUOxj96ZEkOfJw+vJ08mvGHD99pHRkbSoEEDbt26RefOnZk5c6apQk1TWeG1jZGV+pre0mN8k+KVUv3796dYsWKxCd+Eady4cYNz587F+2H8bUFBQR+cte/UqVPsh/f4eHh48Ntvv8V5zM7PD4XRyOmyZYm6coVPP/2Ubdu2cfLkSTQaDdmyZeP8+fPxTkrFbIeysbGJfUypVGJtbc3Jkydp1KjRe8dotVq2bt1K/vz5yZs3b6L9EWlPp9Nx9OhRAGrVqpVgKXJ7e3sWL14c57F+/fqle16LR48eJfp8Yu9vAI1Gw+rVq9MypAwltFs37HbvxvaHH3g9bhzG6NUTCoUCHx8fPv/8c06cOMH27dtp06aNmaMVQnyIjL2yDtWDB2QbOxbNr78CoCtYkMCZM4mMrh6r1+sZM2ZMbB5Pc05IiZRJ6hjrXa1ataJUqVIJpsoQyaM5cAC77dsxAm+PWpX+/rj07s2rlSvfm5iytrZm+vTptGrVik2bNtG2bVspLiFEKqQ40bmtrW2az5CJ+B0+fDi2XH3dunV58eIF/fr1S7D9v//+y9q1a+nUqVOi5509ezYHDx5M8L+N75ZHjYyMTQJ4v0oVrK2tiYyMjP2jOHz4cPLkycPz58/jvZ6npyfu7u7MmDGDwMBAIiMjWbp0KU+ePOHJkydx2q5bty422fVvv/3Gli1b0iU/lkhcZGQkXbt2pWvXrpadYymLiqpYkciyZVFERGD/TgLPQoUKMWzYMAAmTZpk0dUGhcgqZOyVBURFvUlkXrs2ml9/xahWEzxkCM9+/TV2QgrAz8+PCxcu4ODgwIQJE8wYsEipjDrGevr0Kf3796dGjRp4eHh8MBXInj17cHd3p3v37nEeDw0NZezYsVSsWJEiRYpQq1YtNmzYkJ6hJ59eT7bx48Fo5N3bqIro1TROEyaAXv/eodWqVaNVq1YYjUZGjx4txWOESIUUT0p5eHjw8uXLtIxFJKB69eocPHiQvXv30rp1a9q2bRvvqiKAJ0+e0KlTJxo3bkzHDyTry5MnD4UKFUrwPw8PjzjtNT//jOrFC/S5cxNVtiwAEyZMIDAwkDJlytC7d+9Er6dWq1m9ejV3796ldOnSeHp6curUKerUqfNe/qGWLVvyyy+/sHPnTgoXLkzfvn0JDw//0D+VEOJtCgWhX38NgN369e8Nqnr37k3p0qUJDAyUDzVCWIDMMvYaMmQI7u7uuLu7U6BAgdiiJ2//nX/w4AHDhw+natWqFClShOrVqzNnzpx0//AeHh7Ot99+S+nSpSlatCi9evVK8GZbjOfPnzNkyBA+/vhjihQpQseOHbl79+577c6dO0fr1q3x9PSkePHitGzZkrC3ytCrz58nZ8OGZJsyBWVYGBFVqvD80CGCR46E6OI2MdeL2S7k5eWFm5tbGvVeiDeTZdmzZ2fw4MHxVix+24MHD5g8eTJVqlR577lJkyZx9OhRFi9ezNGjR+nZsyfjxo37YJEbU7I+cwbVkyfvTUjFUBiNWD1+jHUCK++9vb3Jli0bV69eZf369ekXqBCZXIonperWrcvNmze5fft2WsYj4hFTrr506dLMmzePCxcuxFuu3t/fn9atW1OxYkVmzZr1wfN26tQpdjVSfP/Vrl07Tnv7TZsA0LZvT043NyIjI9mzZw8qlYo5c+ZgZWXF8+fPE72LW65cOQ4dOsT169e5cOECfn5+vHr1isKFC8dp5+TkROHChalatSorV67k9u3b/Pzzz0n55xJCvCWsaVMMzs5YPXyITfQ2kBhqtZo5c+agVCr54YcfOHz4sJmiFEIkRWYae9WuXZsLFy5w6tQpJk6cyKZNm5g7d27s828XcTly5AgTJ05k48aN6Z67ZeLEiRw6dIgVK1awc+dO/P39Ywu5xCemiMv9+/fx9fXll19+wd3dnXbt2qHVamPbnTt3jk6dOvHZZ5+xf/9+9u/fT7du3VAqlSiCgsg2diw5mjZF/fffGJydeTV3Li937EBXtOh715wyZQqvX7+mTJkydO3aNV3+HUTGoNVqGTRoEEWLFqVChQosX7483a+ZL18+Jk+eTOvWrROt4qnX6xkwYAAjRowgf/787z1/7tw5WrVqRfXq1cmXLx+dOnWiVKlSXLhwIT3DTxZVdAGclLbLkSMHo0ePBmDWrFn4R+d+E0IkT4onpWrXrk39+vWZNm0au3bt4vHjx+mS8FvEpVQqGThwILNmzYpzd+3Jkye0atWKcuXKMX/+/A9WPoPkbd9T3b6Nze+/Y1Qq0XboQJEiRWKf69WrF2XLluX27ds8evSIihUrfvDaTk5OZM+enbt373Lp0iWaNWuWYFuj0YjRaCQiIuKD5xVCvMPWltAOHQCwX7v2vafLlSsXu8pxzJgxhISEmDQ8IUTSZaaxl7W1Nbly5cLd3Z0vvviCTz/9lOPHj8c+X7t2bebPn89nn31GgQIFqF+/Pn379uWnn35Kt5iCgoL4/vvvmTBhAjVq1IgdU507d46//vor3mPu3r3L+fPnmTFjBuXLl8fT05OZM2cSHh7O7t27Y9tNnDiR7t27M2DAAIoXL46npydNmzQh26FD5KpVK7aynrZVK54dP05Yu3YQz1ju1KlT7Ny5E4VCwcyZM5Och0hYpilTpvDHH3/g6+vL5s2bOX36NFeuXEn0mDNnziR607lo0aLs2rUr1bHNnz+fHDly0L59+3ifr1SpEocOHYpNLP37779z9+5dPvvss1RfO63oc+VKdbtOnTpRoUIFQkJCmDRpUlqFJkSWkuK/ZG+XMN26dStbt25NsK1CoeD7d/KZiJRr3LgxU6ZMYf369fTt2zd2QsrDwwNvb+84S/tzJfJLNE+ePEm+pr2fHwARdeqgd3dn6bhxAKhUKv73v/9x+fJlxo0bR8WKFeNMStWsWZMxY8bw5ZdfArB3716yZ8+Ou7s7N27cYPz48XzxxRfUr1+fJ0+e8N9///Hjjz/y2WefkT17dh4/fszSpUvRaDTUrVs3Wf9OQog3tF264LBsGZrjx7G6fRudp2ec50eMGMGBAwe4f/8+Pj4+kkRZiAwqs469zF3EJcbly5eJiori07dyN8Xkw/zrr7/ivemWWBGXP//8E3hTNfHChQu0bNmSpk2b8t9//+GZLx9TlUrqRk926QoVepPIvEaNBGOPjIzk22+/je2nJFbO3EJDQ/n+++9ZtGhR7HtywYIF8RYTelu5cuU+uEUutbnpzp49y5YtWzh06FCCbaZMmYKXlxeVKlXCysoKpVLJrFmzqFq1aqqunZYiq1RBnycPSn//2BxS7zI4OhIZz/bEGEqlkpkzZ/Lll1/y448/0r59eyk8IEQymeT2ipRdTFtWVlZ8/fXXfPfdd3Tp0oXjx49z79497t27994fqg9VJ0uS8HDsosukhnbuzLlz51i3bh0AderUYeDAgURERFCrVi2mT58e59A7d+4QFBQU+/2zZ89ikyrnypWLVq1aMXTo0NjnbWxsOHv2LKtXr+b169fkyJGDqlWrsmfPHpOUwBYiM9Lny0d4vXrYHjyI3fr1BL0z6WRra4uPjw/t27dn7dq1NG/ePEkrHoUQGVdGH3vFFHHR6/VERESgVCqZOnVqgu1jirh4e3snet7Zs2cnmoNSrVYn+Nzz58+xtrYmW7ZscR7PmTNnkoq4+Pj4YGdnx6pVq3jy5AnPb96ELVt4fO8eAHPnzmX8t99S8dYtdq1dS0O9nitWVuQZOJDgAQPi5I2Kz4oVK/jnn3/ibBkSmde9e/eIjIzk448/jn3MxcUlzm6F+Nja2lKoUKF0iyskJIRBgwYxe/ZsXKOr+sZn7dq1nD9/nrVr1+Lh4cGZM2cYO3YsuXPnzjiTNioVrydPxqV3b4wKRZyJqZhqfMrgYBxWrCDkm28SPE2ZMmX4+uuvWbNmDWPGjOHXX39F84GfZyHE/0vxpFRid+dE2lmwYEG8A8sBAwYwYMAA4M2d07fvnqY12/37UQYGonN3J/h//2Nkw4YYjUZat27NggULEj323UmxHj160KNHjziPKRT/n17Qzc3t/ap/QohU03799ZtJqW3bCB41CqODQ5zna9asSevWrdm+fTsjR47k559/loqXQmQwmWnsVb16dWbMmIFWq2XVqlVYWVmlWREXU4op4jJ8+HBKly6NSqXisxIl+MLGBi5dgg4dcIxu271kSQasW4f677+pCfxqZ8eS5s0ZPWLEB69z//792DGXt7f3B1eMiazrzJkzH6zA7ePjQ8uWLVN0/nv37vHgwQO6desW+5jBYAAgf/78HD9+nNy5czNz5kxWr17N559/DkCpUqW4du0aK1asyDiTUkB4w4a8WrmSbOPHo3qrGrg+b14iq1TBbvdunKZNw2Briza6eEx8Ro4cyb59+7h37x7Lli2Lc9NdCJE42YguPsguepJI26EDS5cv59atW2TPnv2DJWKF5VOr1UybNi32a2G5ImrUIKpIEdR37mC7YwfatwaTMcaPH8+RI0e4efMmS5culQGVECLdxBRxAZg3bx716tVjy5Yt7+WnSUkRl5Ru38uZMyeRkZG8fv06zmqppBZxCQoKQvnTTxQdPpyqRiMxa9djpsnKnz6NGjA4O/N6/HgK/fYbD9/KD5oQo9GIt7c34eHhVKtWja+++uqDx4iM70NjrIIFC6JWqzl//nzs1tbAwEDu3r2b6Ba49N6+5+npya/vFE6ZNWsWISEhTJ48mbx58xIREUFUVNR7OW6VSmXsBFZGEt6wIeENGmBz9izZIyN5aW1NROXKoFKhz5cPx0WLcB43DqOt7Zt8b/FwdHRkwoQJfPPNNyxevJjmzZun64o1ITKTTDMp9fPPP7N3714CAwMpUKAA3bt3x/OdvClvO336NFu3buX58+e4ubnRsWPHOMtjxRtW169j8+efGFUqrlWtyqLoweKUKVMSXbIrMge1Wh3nTpiwYEol2m7dyObtjf26dWi7dgVF3CLIrq6uTJ48mf79+7No0SIaN25M0XgqPwkhRFqKKeIyadIkmjdvjq2tLfBmhVTr1q2TXcQlpdv3ypUrh1qt5uTJk7GrtpJVxMXentyzZ/OP0cg5IGajdEEgL3ALMNja8uy33zDkysXdNWveq3Qcn19++YXDhw+jVquZMWNGnBXmwnJ9aIxlb29Pu3btmDp1Ki4uLuTIkQMfH58P/hykxfa9q1evAm/yWr18+ZKLFy/y+vVrihYtikajoUSJEnHax1Tpi3nc2tqaatWqMXXqVDQaDR4eHpw+fZqdO3dm3JvaKhWR1atDnjxEPnkC0TtVgr28UISF4bBqFc4jRoBGQ1jz5vGeomnTpnz//fccP36ccePGsWnTJvl5FSIJUj0ppdfr+eOPP7h27RoBAQHAmw82pUuXpmrVqqhUqlQH+SGnTp1iw4YN9OrVi6JFi7J//36mTZvGggUL3ssLAHDz5k0WLlxIhw4d+Pjjjzl58iSzZ8/Gx8cn3pKmWZn9pk0AhDVowOCZM4mMjKRu3bo0bdrUzJEJIZJL27o1jjNnov7nH6x//z3ehLrNmjVj586dHDlyBC8vL3bu3JmkD4JCCNPJCGOvtGaOIi7vcnJyol27dkyaNAlnZ2ccHR2TVcSl4NOn/PnkCYOB5kD96PYKYCQwAfgoLIz8J06w+c4d7ty5w8qVKxONKTQ0NDaPVp8+feRGQRbj7e1NaGgo3bp1w8HBgT59+hAcHJzu123QoEHs15cvX2b37t2xeaGS6rvvvmPGjBkMHDiQwMBA3N3d8fLyokuXLukRcvpRKAiaMAFFeDj2GzfiPGgQRhsbwqN//uM2VTBt2jTq1q3L0aNH2bdvH02aNDFD0EJYllRNSt27d4+5c+fy7Nmz95779ddf2bp1K8OGDaNgwYKpucwH7du3j7p168beberVqxfnz5/nt99+o3k8M9kHDhygfPnysRMr7dq148qVK/z888+xpdEFKLRabHfuBOCH3Ln588AB7O3t5S5dFqLX62MHIFWqVLHIDzri/xkdHQlr1Qr79euxX7cu3kmpmDLjtWrV4uzZs2zatMnyBpBCZGIZZeyV1kxexCUBEydORKlU0rt37+QXcXn2jDxAF+DddOxDgHBgKBAwYgQly5Zly5YtH3yd5s+fz+PHj8mXLx9DhgxJbfdEBpKUMZa9vT2LFy+O81i/fv3SPba3f8YUCgV58uThyZMnCRZQiC/HbK5cuZg/f356hWhaCgWvp09HERaG3Y4duPTrR8DatUTEs9KxcOHC9O/fn/nz5zNx4kRq1aqFo6NjPCcVQsRQGFNYniUgIAAvLy+Cg4PJli0b1atXx83NDYCnT59y6tQpAgMDcXJyYtasWbi4uKRp4DF0Oh2dOnVi2LBhVK5cOfbxJUuWoNVq8fLyeu+Yfv360bhx4zgJNbdt28aff/7J7Nmzk3X958+fExUVlfIOJCApfwDSm92WLTiPGEG4hwe5Xr0iODSUqVOn8nUiSf5SIiP01VQsra9arTb2ruw///yDnZ1dso63tP6mhqX01erWLXLVro1RqeTZH3+gT6AEu6+vL97e3jg4OHD06NE4qw8spa9pJSv1Nyv1Nb2p1epUl11/lynHXukxvsns7y/rU6fI0br1B9u92L79zTahD7hx4wYNGjRAp9Oxbt066tWrlxZhpovM/tq+La36mtoxlqnIa/sWnQ6X/v2x3bcPo0bDy40b4/1ZDg8Pp27duty7d4+ePXsyadIkE0SffPLaipRIj/FNildK7dmzh+DgYOrUqcPXX3/9XpWmmNLiR44cYc+ePemWlyYoKAiDwfBeFRJnZ2ceP34c7zGBgYHvbevLli0bgYGBCV4nKioqzuBMoVDE5jxIj1VDMec054qkmATnGzQagkNDqVixIl27dk3zmDJCX03F0vr6dpwKhSLZcVtaf1PDUvqqL16ciBo1sDl5EvuNGwkeMybedt26dWP37t2cP3+esWPH4uvr+14fM3pf00pW6m9W6qslSo+xlynHN5n9/RVVtSr6PHlQPnlCfD00KhQY8uQhqmrVD/4bGAwGxowZg06n44svvqB+/fqJtje3zP7avi2t+praMZapyGv7FrWawCVLUISHozl8GNeuXQn4/nui3lnNaWtry/Tp0+nQoQO+vr60adOGMmXKpHf4ySavrcgoUjwpdfHiRXLkyEGvXr3izTdibW1Nz549uXz5MhcuXLD4ZMm7d+9mx44dsd8XKlQIHx+fNJ8lfFfMHVCTO3cOLl1Cb2XF2Nu3UavVbNiwAQ8Pj3S7pNn6agaW0tfQ0NDYr93c3LC3t0/ReSylv2nBIvo6bBicPInDli04zJoFGk28zdavX0+FChX45ZdfOH369HsVnyyir2koK/U3K/XVkqTH2Msc45tM/f6aOhV69Hj/cYUCBaBavJg8SRhLrV27lrNnz2JnZ8eKFStSlSvLlDL1a/uO1PY1rcZYpiKv7Vv27oUmTVAePkyOTp3gt9/gnYJZ7du3Z/fu3Wzfvp3x48fz+++/Z9gcnfLaCnNL8aTUixcvqFy5cqI/XCqViqJFi/Lnn3+m9DIf5OTkhFKpfG+VU2Bg4Hurp2I4Ozvz+vXrOI+9fv06wfYALVq0oHHjxrHfx8yyPn/+HJ1Ol6LYE6NQKHBzc8Pf398sSwyzzZ+PHbBbqeQFMHzQIFxcXHjy5EmaX8vcfTUlS+urVquN/drf3z9F2/csqb+pYVF9rVSJXHnzonr8mMCVKwlLYLtJ9uzZGTBgAAsWLOCbb76hVKlSODs7W1Zf00BW6m9W6mt6s7KySvOJnfQYe5lyfJMV3l9Op09jDxjVahRvrUDT58lD0OTJhFerBh8YSwUEBDB8+HAAhg0bhlqtTpfxV1rKCq9tjLTqa2rHWKYir20CbZcvx6VjR2zOnMHw+ee83LkT3TtVCUePHs2BAwf4448/mDdvHh07dkzP8JNNXluREukxvknxpJRarSYsLOyD7cLDwxMtv5taVlZWFC5cmKtXr8bmlDIYDFy9epUvvvgi3mOKFSvGlStX4uSUunz5cqIVTdRqdYL9SM83ttFoNPkPjiIoCM0PPwCwMDKS4sWLM2DAgHSPwxx9NRdL6evbMaYmZkvpb1qwiL6qVIR26YLTzJnYrV2LtlWrBJsOGjSIffv2cfv2baZOnRon755F9DUNZaX+ZqW+WpL0GHuZY3yTWd9fykePsNuwAYCX69ahsLEhe2QkL62tiahcGVSq2DLziZk+fTqvXr2iRIkS9OzZ06L+rTLraxuf1PY1rcZYpmIJMaaVpPTVaGtLwPr1ZG/fHusLF3Bt25YXO3eiL1Ikto2bmxsjR45k4sSJTJs2jQYNGpA9e/b0Dj/Z5LUV5pbiNYQeHh5cu3aNFy9eJNjmxYsXXLt2jXz58qX0MknSuHFjfv31V44ePcrDhw9ZvXp1bMUUeJP0fPPmzbHtGzZsyKVLl9i7dy+PHj1i27Zt3LlzJ8FJrKzGdtculFotfwO/A7Nnz34vb4UQwrJpO3TAaG2N9cWLqC9cSLCdjY1N7ETU5s2bOXXqlKlCFEK8IyONvcT7HBcuRBEZSUS1akR+9tmbBMjt27/5fxKr1/7555+xY9YZM2ak641dIUTqGB0deblxI1GlSqF6/pwcbduievAgTpuvv/6a0qVLExgYyNSpU80UqRAZW4onpWrWrElkZCRTpkzh/Pnz7z3/119/MWXKFCIjI6lZs2aqgvyQ6tWr07lzZ7Zt24aXlxf37t3j22+/jd2O9+LFC169ehXbvnjx4gwaNIjDhw8zcuRIzpw5w8iRI8mfP3+6xmkRjEZso+/yLQe69+hBxYoVzRuTECLNGbJnJ6xJEwDs165NtG3lypXp3LkzAF5eXklaqSGESHsZaewl4lL9+y92338PQNCoUZCCZLpRUVGMiS4+0bZt2zhVpYUQGZPRxYWXW7YQ5emJ6skTsrdpg/Kt7bZWVlbMmDEDhULBtm3bOHPmjBmjFSJjSvH2vc8//5wzZ85w9epVfHx8cHBwIFeuXAA8e/aMkJAQAMqWLUvdunXTJtpEfPHFFwmudJo4ceJ7j1WrVo1q1aqlc1SWR33uHDY3b6IFfs2Thx9GjTJ3SMKMrKysGDduXOzXInMJ/fpr7HbuxHbvXoLGj8eQI0eCbb/99lsOHTrEv//+y4IFC1i0aJEJIxVCQMYbe4n/5zhvHgq9nvA6dYj65JMUnWPNmjVcv34dZ2fn2L+9IvOSMVbmYciRg5dbt5Ljq6+wuneP7G3b8nLnTgzReXcqVqxIhw4d8PPzY8yYMfzyyy+yClKIt6R4pZRSqWTMmDE0bdoUjUZDSEgId+/e5e7du4SEhKDRaGjWrBmjR4/OsJUGxPvCoz9ofg98O3t2hq8EItKXtbU1/fr1o1+/frKFMxOKqlCByPLlUURGYvfWFuf4ODk5MX36dAC+++47Ll26ZIoQhRBvkbFXxmR14wa2u3cD0aukUuDRo0fMnTsXgLFjx+Lq6ppm8YmMScZYmYvBzY2XW7eiy5sX9Z07ZG/fHsVbO3XGjBmDq6srN2/eZNWqVWaMVIiMJ1XT8lZWVnTs2JE2bdpw584dAgICAHB1daVIkSIyA2xhdM+ekfu33wC4VasWg2rXNnNEQoj0FtqtG9ZDhmC/YQMh33wDidytbdCgAQ0bNuTAgQP07NmT3bt3ywdfIUxMxl4Zj+OcOSiMRsIaNUJXpkyKzjFx4kS0Wi0VK1akXbt2aRyhEMIU9B4esSum1Nevk71jR15+/z1GJydcXFwYN24cw4YNY968eTRt2hQPDw9zhyxEhpAmnybUajUlSpSgevXqVK9enRIlSsigyAJdGjYMG6ORyyoVnWRrjgD0ej0XL17k4sWL6PV6c4cj0kFYkyboXV1RPXmC5tChD7afOnUqTk5OnDt3jjVr1pggQiFEfGTslTGoL1/G9qefMCoUBI8YkaJz/Prrrxw4cACVSsXMmTNlsj+LkDFW5qQvXJiXW7eid3XF+tIlXLt0QaHVAtCmTRuqVKlCWFgYEyZMMHOkQmQc8ldPAPDPrVsUj14lFdC6Na4ZsFypML2IiAgaNWpEo0aNiIiIMHc4Ij1oNGg7dAA+nPAcIHfu3Hh7ewPg4+PD/fv30zU8IYTIyBxnzQIgrGVLdMWKJfv4sLCw2LxCPXr0oFSpUmkan8i4ZIyVeemKFePlli0YsmXD5s8/ce3WDcLCUCgUzJgxAysrK37++WcOJeFmoBBZQYonpX7++Wfatm3LuXPnEmxz7tw52rZtKz9wGZzBYOD7vn0pBmhVKorFkxheCJF5abt0wahUYvP771jduvXB9h06dKBWrVqEhYUxevRojEajCaIUQsjYK2OxPnsWzW+/YbSyInj48BSdY9GiRdy/fx83NzeGp/AcQoiMR1emDC83bsRgb4/N77/j2rs3REZSvHhxevfuDYC3t7dUNBaCVExK/fnnnzg5OfHxxx8n2Objjz/GycmJs2fPpvQywgQ2bNjAZzdvAhDStCk4Opo5IiGEKend3Qlv0AAA+3XrPtheoVCwcuVKbGxsOHbsGDt37kznCIUQIGOvDMVoxNHHBwBtu3boCxRI9ilu377NsmXLAJg8eTIODg5pGqIQwryiKlYkYP16DBoNmiNHcOnfH3Q6hg4diru7Ow8ePGDBggXmDlMIs0vxpNTjx4/Jnz9/ovvelUol+fPn59GjRym9jEhnjx49YvW0abSMeaBvX3OGI4Qwk9Bu3QCw3b4dRVDQB9sXLVqUYcOGAW8S9L58+TI9wxNCIGOvjMTm+HFs/vgDo40NwYMHJ/t4o9HImDFjiIqKok6dOjRs2DAdohRCmFtktWq8WrMGo7U1tgcO4Dx0KHY2NkyZMgWAFStW8M8//5g5SiHMK8WTUkFBQWTLlu2D7bJly8br169TehmRjoxGI15eXrTRarEGIipUSHHVGCGEZYv83/+IKloUpVaL3Y4dSTqmb9++lCxZklevXjFRtv0Kke5k7JVBvLVKKrRLFwx58yb7FLt37+bUqVNoNBqmTp2KQqFI6yiFEBlERK1aBKxYgdHKCrtdu8g2ZgwN6tenXr16REVFMWbMGEmFILK0FE9KaTQaXr169cF2r169wsbGJqWXEelo27ZtHDt6lD7R32s7dzZrPEIIM1IoYldL2a9dCwbDBw9Rq9XMmTMHpVLJrl27OHLkSDoHKUTWJmOvjEHzyy9YX7qEwc6OkAEDkn18YGAgkyZNAmDgwIEUSMHWPyGEZYmoX59XixZhVCqx9/PDacIEpkyejEaj4fTp05IKQWRpKZ6UKliwILdu3eLFixcJtnnx4gW3bt0if/78Kb2MSCdPnjxh4sSJ1AMKAQYnJ8KbNjV3WEIIMwpr1QqDgwNWd+9ic/Jkko4pX748PXv2BGD06NGEhoamZ4hCZGky9soA9HocZ88GILRnTww5ciT7FD4+Prx48QJPT0/69euX1hEKITKo8GbNCJwzBwCHNWso5efHkCFDgDd55QIDA80XnBBmlOJJqf/973/odDrmzp0b7w9QYGAgc+fORafTUaNGjdTEKNKY0Whk9OjRBAUFMSp6G4C2dWuMtrZmjkxkNFZWVgwbNoxhw4ZhZWVl7nBEOjM6OKBt0wYAu7Vrk3zcyJEjyZcvH48ePWLmzJnpFZ4QWZ6MvczP9scfUd+4gSFbNkJSkIfz/PnzbNy4EYAZM2bIirYsTMZYWVNY27YETpsGgOOSJXhFRVG0aFFevnyJT/S2YCGymhT/BqxVqxZHjx7l5s2bDBw4kAoVKuDu7g68SZ594cIFIiMjKVasGLVr106zgEXq7dq1i8OHD1NIraZWcDAA2k6dzByVyIisra2lRHUWo+3aFQdfXzSHDqF68AB9vnwfPMbOzo5Zs2bRvn17fH19adiwIdWqVTNBtEJkLTL2MrOoKByjVzmE9O2LMQn5vd6m0+kYNWoURqORVq1aUb169fSIUlgIGWNlXdpu3VCEh5NtyhRc5s5le5culP/nHzZu3EibNm2oUKGCuUMUwqRSvFJKqVQyevRoPvnkEyIjIzlz5gy7du1i165dnDlzhsjISCpVqsTo0aNRqVRpGbNIhadPnzJ+/HgAVlSujNJgIKJqVXTFipk5MiFERqDz9CS8Zk0URiN2GzYk+biaNWvSsWNHAIYPH45Wq02vEIXIsmTsZV52O3Zgde8e+uzZCe3RI9nH+/r68vfff+Ps7Bw7FhNCZE2hffsSNGIEAB9t2MCKChViq3Lq9XozRyeEaaVqraidnR0jRozgv//+4+LFizx//hyAHDlyUL58eQoWLJgWMYo0YjQa+fbbbwkMDOSj0qWpffcuIAnORcIMBkNsmdqiRYsmWoZcZB6hX3+N5vhx7DdvJnjYMEji1l5vb29+++03/vvvP2bMmBFb7lgIkXZk7GUmERE4zJ8PQMiAARjt7ZN1+KNHj5gdnYtq7NixZM+ePc1DFJZFxlgiZMgQFGFhOC5dSu8LF7hga8vyK1dYv3493bt3N3d4QphMmmxgLlCggFQOsQA//vgjP//8M1ZWVqxr0warCRPQu7oS9uWX5g5NZFDh4eHUqVMHgH/++Qc7OzszRyRMIaJuXXQeHlg9fIjtjz8S1rZtko5zdHRkzpw5dOjQQbbxCZHOZOxlWvZ+flg9eoTezY3QLl2SffyECRPQarV88skntGvXLh0iFJZGxlgChYLgMWNQhIXh4OvL0vBwXgKzZs2iUaNG5M6d29wRCmESaZZVT6/Xc+DAAf7880+Cg4NxdXXlf//7X+wvW2FeL168YOzYsQAMHjyYEseOAW+S7SFJNoUQb1Op0HbtitO0adj7+hLWpg0oFEk69LPPPqNjx474+fkxfPhwDh8+LANtIdKJjL1MQ6HV4rBoEQDBQ4aARpOs4w8ePMhPP/2ElZUVM2bMkBUxQoj/p1AQNGkSirAw7LdswQ9oGRzMpEmT+O6778wdnRAmkeS/imfOnKFnz55s2bLlvecMBgMzZsxg06ZN3Lx5k8ePH3P16lVWrFjB0qVL0zRgkTLffvstr169omTJkgxp3hyb334DIDQ6B4wQQrwttF07jBoN1levov7rr2Qd6+3tjbu7O//99x/Tp09PpwiFyPxk7JUx2K9di+r5c3QFCqBN5iqn0NBQxo0bB0Dv3r0pWbJkeoQohLBkSiWvfXzQtmiBGtgBhO7Zw/Hjx80dmRAmkeRJqWvXrhEcHEzVqlXfe+7XX3/lypUrAFSsWJHu3bvTtGlTrK2tOX78OJcuXUq7iEWy7du3j/3796NSqZg/fz7Ztm1DYTQSXrMm+kKFzB2eECIDMrq6EtasGQD269Yl69iYbXwAa9eu5fTp02kdnhBZgoy9zE8RFIRD9GqF4GHDQK1O1vGTJ0/m4cOHuLu7M3To0PQIUQiRGahUBC5YQNiXX2ID7AF2Dx1KeHi4uSMTIt0leVLqn3/+wcXFhULxTGIcPnwYgP/97394eXnRoEEDOnbsyIABAwBklteMAgIC+PbbbwHo378/ZYsXx+777wHQdupkztCEEBlc6NdfA2C7bx/KZ8+SdaxU4xMi9WTsZX4OK1eiDAwkqlgxwlq0SNax169fZ968eQBMnTpVtjILIRJnZcWrpUsJ+fRT7IDV/v7sk0qdIgtI8qRUYGBgvBVdgoKCuHfvHgBNmzaN81yVKlXImTMnt2/fTlWQImViyoq+fPmS4sWLM2TIEDS//ILq+XP0uXIRXr++uUMUQmRgUWXLElmxIoqoKOz8/JJ9vGzjEyJ1ZOxlXsqAAOxXrgQgeMQIUKmSfKzBYGD06NHodDq+/PJL6suYSwiRFDY2BK1dy6NixXAEOvv58ezgQXNHJUS6SvKkVFBQEPbxlL+9c+cOAE5OTvEOnDw8PAgICEh5hCLF9uzZw759+7CysmLBggXY2Nhgv3EjANr27ZO9BF0IkfXErJay37QJoqKSdey72/hOnTqV5vEJkZnJ2Mu8HJYuRRkaSmSZMoQ3bJisY7du3cqff/6Jvb09kydPTqcIhRCZkq0tir17ueLkhAtQsHdvVDdvmjsqIdJNkiellEolQUFB7z3+77//AlC4cOF4j7Ozs8NgMKQwPJFST548id22N3jwYMqVK4fqzh1sfv8do1KJVhKciySwsrKib9++9O3bFyurNCvWKSxIWKNG6HPmROXvj+bnn5N9vGzjEyLlZOxlPkp//9h8esFeXkmuQArw8uVLpk6dCrzJKeXu7p4eIQoLJ2MskSgHB4K//56/FAqco6JwaNECVfTvfiEymyRPSuXMmZN///0XnU4X5/GYJJuenp7xHhccHEy2bNlSEaJILqPRyMiRI3n9+jXlypVj4MCBANhHb7+JqF0bvQyQRBJYW1vj7e2Nt7c31tbW5g5HmIO1NdoOHYDkJzyPEbON7/79+7Ef1IQQHyZjL/NxXLQIRXg4kZUqEVGnTrKOnTJlCoGBgZQqVYpBgwalU4TC0skYS3xIwY8+YnefPlwB7F+/xrVNG1QPH5o7LCHSXJInpUqXLk1wcDDfRyfJBrh69Sp///03AB9//HG8x927dw8XF5dUhpmwkJAQFi1aRNeuXenWrRvLli1LtEpBSEgIvr6+DB48mI4dO9KvXz98fX0z1d37TZs28dtvv2FjY8OiRYtQq9UQHo7d1q0AhHbubOYIhRCWJLRTJ4wqFTZ//IFV9O/85Hh7G9/69ev57bff0jpEITKljDr2yuxUDx5gt3kzAEGjRiVrldTJkyfZvn07CoWCWbNmyQoYIUSqdB85ku4eHtwE1I8fk71tW5T+/uYOS4g0leRJqUaNGmFlZcXevXvp168fo0aNYtq0aQAULVqUIkWKvHfMrVu3CAoKomjRomkX8TsWLVrEgwcPGDduHKNHj+b69eusWLEiwfYBAQEEBATQuXNn5s6dS//+/bl06RLLli1LtxhN6d69e7G5C0aPHh37b2+7fz/KwEB0efMm+46fyLoMBgMPHjzgwYMHshUkCzPkzUv4F18AKV8tVbNmTbp37w682cYn+W6E+LCMOvbK7Bznz0cRFUXEp58SWb16ko8LCwtj1KhRAHTu3DnBSUMhQMZYImk0Gg1DfXyoC9wFrO7dI3u7dihfvjR3aEKkmSRPSrm5uTFo0CA0Gg0BAQHcu3cPg8GAq6sr/fv3j/eYQ4cOAVCmTJm0ifYdDx8+5OLFi/Tt25eiRYtSokQJunfvzqlTpxL8wJM/f35GjBhBpUqVcHNzo0yZMrRr146//voLvV6fLnGail6vZ+jQoWi1WqpVq0bPnj1jn7OLSXDesWOyqseIrC08PJyqVatStWrVRFcgiswvJuG57a5dKAIDU3SOb7/9Fk9PT54+fcqYMWMwGo1pGKEQmU9GHHtldqrbt7Hdvh2IXiWVDAsWLODevXu4ubkxZsyY9AhPZCIyxhJJVatWLSo0bkxd4Klajfqff8jevn2Kx2NCZDTJWlNcpUoVSpQowV9//cXr16/JkSMHn3zyCRqNJt72np6eFCpUKN0GRrdu3cLe3j7OncKyZcuiUCi4ffs2lStXTtJ5tFottra2qCx8smbVqlWcPXsWe3t75s2bh1L5Zs7R6sYNbP78E6NK9abqnhBCJFNk1apElSiB+sYN7LZtgwkTkn0OW1tbFi1aRNOmTdm3bx+7d++mZcuW6RCtEJlHRht7ZXZOc+eiMBgIq1+fqAoVknzctWvXYlfdT58+HScnp/QKUQiRBU2cOJHPfvuNmqGhnHdwwP7aNbJ36sTLLVswOjqaOzwhUiXZG92zZctGnSRu/2rQoEGyA0qOwMDA9/7oq1QqHBwcCEzizHFQUBA7d+7k888/T7RdVFQUUW+VQ1coFNja2sZ+ndZizpnUc9+4cQMfHx8AJk2aRIECBWKfs9+0CYDwBg0wurmR9tGmTnL7asksra9vx6lQKJIdt6X1NzUyfV8VCrRff022UaOwX7YMPD2xsbUlonLlZK2+LF++PEOHDmX27NmMHTuWqlWrZvjKVJn+tX1LVuqrJUnPsZcpxzcZ/f1lde0atj/+iFGhIMTLK8lx6vV6vLy80Ov1NGrUiC+itztn9P6mJelrys8T83VG/beT1zZjyJs3LyNHjmTixInUVyo5li0b1hcu4Nq1KwF+fmBnl+xzZuT+prWs1FdLpDBmwP0Tfn5+7NmzJ9E28+fP58yZMxw7doyFCxfGea5nz560adOG+vXrJ3oOrVbL1KlTcXBwwMvLK9FklNu2bWPHjh2x3xcqVCh2EsjcIiMjqVq1KhcuXKBRo0bs3bv3/3/gQkMhb14ICoKDB6FePfMGKyxKaGgoDg4OwJsiAfb29maOSJiVnx907gxv/9nw8ICFCyEZK550Oh01atTgzJkz1KlTh0OHDsWu7BRCmFZGHt+YXNOmsHcvtGsHW7Yk+bAFCxYwdOhQsmXLxvXr18mTJ086BikyCxljieTS6XRUqlSJS5cuMbFxYyYcP/7mM169evDjj5DAClohMroMOSkVFBREcHBwom1y587N8ePH2bhxI2vXro19XK/X07FjR4YNG5bo9r2wsDCmTZuGjY0No0aN+mAp1oTuJD5//vy9Us1pQaFQ4Obmhr+//wfzrvj4+LBw4UJcXFw4cuQIuXPnjn3OdvNmnEeMQFewIM9PnoQM+MEvOX21dJbWV61WG1ty/Pbt29gl8y6MpfU3NTJ7XzUHDuDcqxcYjXFWWxqjJ8ADV60ivGHDJJ/vzp071K9fn7CwMCZNmkSvXr3SOOK0k9lf27dlpb6mNysrK3LmzGnuMD7IlOObjPz+Uv/1FzmaNMGoUvH86FH08SSRj8/Dhw+pVasWWq2WWbNm0alTp9jnMnJ/05r0NflSO8YyFXltM5Zz587RrFkzjEYjhydNovbMmSjDwgivV49Xq1eDWp3kc1lCf9NKVuprekuP8U2GrFPr5OSUpL34xYoVIzQ0lLt371K4cGHgTalko9EY+0s+PlqtlmnTpqFWq/Hy8vrghBSAWq1GncAPeXq+sY1GY6Ln/+OPP1i0aBEAM2bMIFeuXHHa20Vv3Qvt1OnNh8cM/EP4ob5mJpbS17djTE3MltLftJAp+6rX4+Tt/d6EFIDCaMSoUOA4fjxh9esneStf4cKF8fb25ttvv2X69OnUrFmTYsWKpX3saShTvrYJyEp9zerMMb7JiO8vx+jVYdrWrdEVLpyk8ZLRaGT06NFotVqqVKlC+/bt4+1XRuxvepG+Ju/4tDqXKVhCjGklI/e1YsWKdOvWjbVr19J9zRpOrVhB3l690Bw6hPOAAbxaujTZRa0ycn/TWlbqqyXJeMtmksHDw4Py5cuzYsUKbt++zY0bN/D19aV69eq4uroCEBAQwJAhQ7h9+zbw/xNSERER9O3bl7CwMAIDAwkMDLS4cqyvX79m0KBBGI1G2rZtS5MmTeI8r758GeuLFzFaWxPWpo2ZohRCWDrrM2dQPXmSYD46hdGI1ePH2Bw5kqzzdunShdq1axMREcHAgQOJjIxMfbBCCJFM1r//js3JkxjVakKGDEnycXv27OHIkSNYW1sza9Ys2YYshDCJ0aNH4+7uzv3795l04gQBq1ZhVKux3bsX5+HDwcI+0wqRIVdKJcegQYNYs2YNkydPRqFQUKVKFbp37x77vE6n4/Hjx0RERADw77//8s8//8Qe+7YlS5aQK1cu0wWfCkajkTFjxvDo0SMKFizI5MmT32sTs0oqrGFDDNmzmzpEkQmoVCq6du0a+7XImlTPniWpnevXXxNZtSrh9eoRXq8e+ugVrAlRKBTMnTuXOnXqcPXqVebNm8fo0aPTImQhhEgaoxGnmFVSHTuiz5cvSYcFBAQwfvx44M14MrEV+kLER8ZYIqUcHBzw8fGhU6dOrF69mqZNm1L9u+9w6dsXu+3bMWo0vJ4xAySpt7AQFj8p5eDgwODBgxN8PleuXGzbti32+9KlS8f53lLt3LmTPXv2oFKpWLx4cWyixBiK4GBsd+8GQNu5szlCFJmAjY0N06dPN3cYwsz0SZysVxiN2Jw+jc3p02SbPJmoIkWIiJ6giqxUCeIpJpE7d258fHzo06cPS5YsoWbNmlSvXj2tuyCEEPGy+fVXrP/6C4NGQ/A7NysTM3nyZF6+fEnx4sXp379/OkYoMisZY4nUqF27Nl999RU7d+5kxIgR/PzzzygWLMB50CDsN27EaGtL0PjxMjElLIKsM7ZA//33H2PHjgVg+PDhfPzxx++1sd21C6VWS1TRokRWqWLqEIUQmUhklSro8+SJTWr+LqNCgS5vXp4eP87riROJqFEDo5UV6jt3cFi+nBxffYXbRx/hPHAgmj17ULx+Hef4xo0bx+ZiGThwIAEBAabolhAiqzMYcJw9GwDt119jeKtQTGJOnDjB9u3bUSgUzJo1K0m5SYUQIq1NnDiR7Nmzc/PmTZYsWUJYy5a8njULAIeVK3GcM8fMEQqRNDIpZWF0Oh0DBgwgJCSEypUrM2DAgPcbGY3Yb9wIgLZTJ5khFylmNBp5+fIlL1++lKSAWZlKxevoLcLvTkzFfB80aRL6IkUI7dWLl1u34n/lCgHLlqFt2RKDszPKwEDsdu3C9ZtvcCtXjuytW2O/ciWqf/8F3qw6KFKkCP7+/owYMULeb0KIdKc5cADrq1cxODgQ/M03STomNDQULy8vALp27UqlSpXSM0SRickYS6SWq6srU6ZMAWDRokXcuHEDbYcOsWM2xwULcFiyxJwhCpEkMillYRYsWMD58+dxcnJi8eLF8e5BV//1F+rr1zFqNGhbtTJDlCKzCAsLo1y5cpQrV46wsDBzhyPMKLxhQ16tXInBzS3O4/o8eXi1ciXhDRvGedzo5ER406YELl6M/6VLvNi1i+BvviGqaFEUOh02p06RbdIkcteoQc7PPsNt7ly29OuHrVrNL7/8wvr1603ZPSFEVqPXx64iCO3dG2N0gZwPmTlzJvfv3ydv3rySA0+kioyxRFpo2rQp9evXJyoqihEjRqDX6wnt0YOgb78FwGnGDOzXrDFzlEIkzuJzSmUlf/75JwsXLgTeDIo8PDzibRezSiqsaVOMzs6mCk8IkcmFN2xIeIMG2Jw9S/bISF5aWxNRufKHSw9bWRFZpQqRVaoQPHYsqnv30Bw6hObQIazPnEF9+zbq27f5BHhha8uuqCh+njCBf0qXpugnn5ikb0KIrMV21y7U//yDwdmZkN69k3TMmTNn8PX1BWD27Nk4OjqmZ4hCCPFBCoWC6dOnc/r0aS5cuICvry+9evUipH9/FFotjgsWkG38eIy2tmg7dDB3uELES1ZKWYhXr17Rv39/DAYDX331Fc2aNYu3neLVK2z37gUgVBKcCyHSmkpFZPXq0L79m/+noGKQvmDBN9v8tm17b5ufXVgYnYBNOh3VmzfH5auv4mzzE0KIVIuMxHHePABC+vfHmITJpbCwMIYNGwZAu3btqFWrVnpGKIQQSZYnTx68vb0B8PHx4b///gMgeMQIQvr0ASCblxe2u3aZLUYhEiOTUhbAaDQybNgwHj16RMGCBZk2bVqCbe127EAREUFU6dJEVahgwiiFECL54tvm96xbN26qVKgB2z/+iLPNz2nqVKzPnAGdztyhCyEslN3332N1/z76nDkJ/frrJB0za9Ys7t27h5ubG+PHj0/nCIUQInk6dOhAtWrVCAsLw8vL602eMoWCIG9vQrt0QWE04jxkCJr9+98coNdjfeoUbNny5v96vXk7ILI0mZSyAKtXr+bgwYNYW1uzYsWKhJeLG43YRW/dC5UE50IISxO9zU83bRoXNm3CExgCPC5R4k01v9u3cVi2jBwtW8at5hcUZObAhRAWIywMx+hUCCGDBmG0tf3gIefOnWPVqlXAm8mpbNmypWuIQgiRXAqFgtmzZ6PRaDh58iRbtmyJeYLX06ahbd0ahV6PS//+OM6cSe4qVcjeqhV06ED2Vq3IXaUKmgMHzNsJkWXJpFQGd/HixdiVURMmTKBMmTIJtrU+fRr1nTsY7O0Ja9nSVCEKIUSaq1mzJg2++YaFQGl/f/766ScCvvsu/mp+ZcuSvU0b7FetQnXvnrlDF0JkYPYbNqDy90fn7k5ox44fbB8eHs7w4cMxGo20atWKunXrmiBKIYRIvkKFCjFy5EgAJk2axKNHj948oVQSOHcuYU2aoIiKwmHxYpRPnsQ5Vunvj0vv3jIxJcxCJqUysMDAQPr06UNUVBQNGzaka9euibaPTXDeogVGBwdThCiEEOlm5MiRVKhQgcDAQHqNHEnQl1/G2eYX0q8fUZ6eb6r5/f472SZOJPf//ifb/IQQ8VKEhMSWRw8ZOhRsbD54zLx587h9+za5cuVi4sSJ6RyhEEKkTq9evahUqRIhISGxE+oAqFS8WrAAg40NCuDd/TSK6HZOEybIVj5hcjIplUEZjUZ69OjBgwcPyJ8/P3PmzEGRyHY85YsXaH76CZAE5yLtqFQqWrduTevWrVGlIKG1EKlhbW3N8uXLcXZ25uLFi0yZMuXNE9Hb/ILGjeP5sWM8PXmS1xMmEFG9esLb/H78Ubb5CZHF2a9ejSogAF2hQmhbt/5g+4sXL7Js2TLgTdVjFxeX9A5RZCEyxhLpQaVSMW/ePDQaDSdOnGDDhg2xz1mfP48yIiLBYxVGI1aPH7+5qSeECVmZOwARv3Xr1rFr1y7UajXLli37YP4Cu61bUURFEVmhArpEtvgJkRw2NjYsWLDA3GGILMzDw4OFCxfStWtXfH19qVy5Mk2aNInTRl+oEKG9exPauzeK16+xOXoUzeHDaI4cid3mZ7drF8boyazwevUIr1cPfcGCHw5Ar8f67FmIjMTa2pqIypVTVHFQZEzPnj0jV65c5g5DmIAiMBCHFSuANxWpsEp8CBwREcGwYcMwGAw0b96cBg0amCJMkYXIGEuklyJFijBmzBgmTJjA1KlTqVWrFgUKFED17FmSjk9qOyHSiqyUyoCuXLnCpEmTABg3bhzly5dP/ACDAbtNmwBZJSWEyHw+//xzBgwYAMCIESO4c+dOgm2N2bIR3qzZ/2/z27mTkL5949/mV6sWjtOmJbjNT3PggCQCzcR+/vlnqlWrxtatW80dijABh+XLUQYFEVWyJGFNm36w/fz587l58yY5cuT4/1WaQghhIbp37061atXQarUMHz4cg8GAPok3YZLaToi0IpNSGdDBgweJjIykadOm9OzZ84PtbY4fx+r+fQzRpdWFSCtGoxGtVotWq/3/PelCmMHIkSOpWrUqISEh9OnTh7CwsA8fZGVFZNWqBHl7x7/N759/cPzuu3i3+WkOHMCld29JBJpJ3blzhyFDhhAeHs7169fNHY5IZ8oXL7BfswaA4JEjQZn48PfcuXMsXboUgBkzZuDq6pruMYqsR8ZYIj0plUrmzp2LnZ0dp0+fZu3atURWqYI+Tx6MCaSEMSoU6PLmJbJKFRNHK7I6mZTKgIYPH87y5ctZu3ZtonmkYsSsktK2bp2k0sZCJFVYWBhFixalaNGiSZsEECKdWFlZsXTpUnLkyMH169cZP358ss8Rs83v5fbt+F++HH81v379cCtTBpf+/cFolESgmZBWq6VXr14EBwdTpUoVxo4da+6QRDpzWLwYpVZLZIUKhNevn2hbrVbL4MGDMRgMfPXVVzRs2NBEUYqsRsZYIr0VKFCAcePGATB9+nTu3LvH68mTAeKfmDIaCZo0SdIUCJOTSakMqmnTpkm6M6d88gTNwYMAaDt1Su+whBDCbNzc3FiyZAkKhYLNmzenattVgtv8ihRBodejiIx8b0IqhiRR0wFSAAEAAElEQVQCtVxGo5GRI0dy8+ZNcufOzfLly1Gr1eYOS6Qj5ePHsdWJg7284AM3+6ZOncq9e/fIkyePbNsTQli8Ll268OmnnxIeHs7QoUMJbdCAVytXYnBze7+xlRV6d3fTBymyPJmUsnB233+PQq8nokoVdMWKmTscIYRIV59++inDhw8HYMyYMVy+fDn1J317m9/x47yeMCFJh0kiUMvj6+vLDz/8gJWVFcuXL5ck51mA44IFKCIiiKhWjYhPP0207dGjR1m/fj3wJqfUh4rMCCFERqdQKJg7dy4ODg789ddfrFq1ivCGDXl65gwvd+yAzZt5uW0b4Z9/jkKnw7VHD5QvXpg7bJHFyKSUJdPpsPfzA0ArCc6FEFnE4MGDqVevHhEREfTo0YMXaTx4ikpiBVNJBGpZzp49y+TobQvjx4+ncuXKZo5IpDfVvXvYRa+o/NAqqVevXsVOePfo0YNPPzCBJYQQlsLd3Z2JEycCMGvWLG7cuAEqFZHVq0P79kTWqMGrxYvRFS6M6skTXPr0gago8wYtshSZlLJgNkeOoHryBL2rK2GS80AIkUUolUoWLVpEkSJFePz4MX379iUqDQdPH0wEypsJKUkEajmePn1Knz590Ol0NG/enO7du5s7JGECjnPnotDpCK9dm8gPTEKOGzcOf3//2FLqQgiRmbRr1466desSERHBgAEDiIiIiPO80cmJAF9fDA4O2PzxB06yfVmYkExKWbCYHAlhbduCjY2ZoxFCCNNxcnLC19cXBwcHTp8+nba5X1SqBBOBGgEFoAgJwfqPP9LumiLdRERE0KdPH549e0aJEiWYPXt2koqICMtmdfMmtrt3A9GrpBKxZ88efvjhB1QqFQsXLsRWisYIITKZmG182bNn5/r168ycOfO9NrqiRQlctAgAhzVrsN2+3dRhiixKJqUslOrBA2x++w2A0I4dzRyNEEKYnqenJ4uiB09r1qxhexoOnsIbNow3Eaghd26iChVCqdWSvUOH2OqnImMyGo2MGTOGP//8EycnJ1atWoWdnZ25wxIm4DhnDgqjkbCGDYkqVy7Bdv7+/nz77bcADBo0iAoVKpgqRCGEMKmcOXMyd+5cAFauXMnx48ffaxPeoAHBQ4cC4DxqFOpLl0wao8iaZFLKQtn5+aEwGon49FP0hQqZOxyRSSmVSho1akSjRo1QKuXXhch4GjRowNDowdPo0aPTJvF5tPcSge7YwdM//+T5oUNomzdHodPhPGoUThMmgF6fZtcVaWfVqlVs3boVpVLJ8uXLKVy4sLlDEiagvnwZ2wMHMCoUBI8YkWA7g8HAsGHDCAwMpGzZsgwePNiEUYqsTsZYwhzq1atH5+hcxEOGDOHly5fvtQkeNuxN4vOICFx69pTE5yLdyW9ASxQVhd333wMQKgnORTrSaDSsXLmSlStXotFozB2OEPEaNmwY9erVIzw8nK+//hp/f/+0O/nbiUCrVweVCmxtCVyyhKCRIwFwWL0a127dUAQHp911RaodPXo0dlvn+PHj+eyzz8wckTAVx9mzAQhr0QJd8eIJtlu1ahXHjh1Do9GwaNEi1Gq1qUIUQsZYwmwmTJhAkSJF8Pf3p0+fPhiNxrgNlMrYxOdWjx/j0revJD4X6UompSyQ5pdfUD1/jj5XLsLr1zd3OEIIYVYxic+LFi2Kv78/3bt3JywsLH0vqlAQMmQIAcuXY9Bo0Bw5Qo6mTVHdv5++1xVJcvv2bfr164fBYKBdu3b07NnT3CEJE7H+8080R45gtLIiOLqaXnyuXLnCjBkzAJg4cSLFihUzVYhCCGFWtra2LFmyBCsrK3bu3Mm2bdveaxMn8fnp05L4XKQri5+UCgkJYdGiRXTt2pVu3bqxbNkywsPDk3Ss0Whk+vTptGnThrNnz6ZzpGknJsG5tl07kLt6QgiBk5MT69evx9XVlUuXLjFo0CAMBkO6Xze8SRNe7tqF3s0N9a1b5GjUCGsL+nuSGQUGBtKtWzeCgoL45JNPmD59uiQ2zyqMRhx9fADQtm2LvmDBeJtptVq++eYboqKi+PLLL+nUqZMJgxRCCPMrV64cI6NXfI8bN4579+6910YSnwtTsfhJqUWLFvHgwQPGjRvH6NGjuX79OitWrEjSsfv377e4garqzh1sTp7EqFCglQTnIp1ptVrc3d1xd3dHq9WaOxwhElWgQAHWrFmDtbU1Bw4cwCf6w2l6i/roI57v20dk2bKoAgLI3qYNtlu3muTaIq6oqCj69evHv//+i7u7O6tXr8ZGqtNmGTYnTmBz+jRGGxuChwxJsN2ECRO4e/cubm5uzJo1y+LGgiJzkDGWMLdvvvmGmjVrEhoaysCBA9HpdO+1eS/xeRrm7hQihkVPSj18+JCLFy/St29fihYtSokSJejevTunTp0iICAg0WPv3bvHvn376Nevn4miTRv2fn4ARNSpg97Dw8zRCCFExlK5cmXmzJkDwJIlS9hqoskhQ548vNy9m7CGDVFEReEybBiO06aBCVZriTdiKu0dP34cW1tbfH19yZEjh7nDEqby1iqp0M6dMeTNG2+zffv2sXnzZhQKBYsWLcLV1dWUUQohRIahUqnYsGEDTk5OnD9/PrYy37viJD7v0UMSn4s0Z2XuAFLj1q1b2NvbU6RIkdjHypYti0Kh4Pbt21SuXDne4yIiIli4cCE9evTA2dk5SdeKiooi6q0EbwqFAltb29iv01rMOeOcOzwcu+g9v9ouXTLNnb14+5pJWVpf345ToVAkO25L629qSF8zjlatWnH37l0WLFjAqFGjKFCgANWqVUvx+ZLcXzs7AleuRDdnDo4LFuD43Xeo79whcMkSjPb2Kb6+KWX01zYxCxcuZMuWLbGV9sqWLWvukCyCKcc36fn+svnlF6wvXsRga0vowIHxXuPhw4d4eXkBMGDAAGrUqJHmcbzNkn+ekkv6mvLzxHydUf/t5LXNvBQKBQUKFGDWrFn07duXxYsXU61atfcLg6hUBC5ZQo6GDbG6exeXvn0J+P57i0ojk9VeW0ujML6Xbt9y7Nq1i2PHjrFw4cI4j/fs2ZM2bdpQP4Ek4CtXrsRgMNC3b18A2rRpw4gRIxKcxALYtm0bO3bsiP2+UKFCJtsaEsvPDzp1gnz54N9/31SBEiIdhYaG4uDgALzJ32ZvIR+shYhJcL19+3ZcXFz4/fffKVmypOkC2LwZuneHiAj46CP48UfIn990189iNmzYQNeuXQFYtmxZ7N938WEZYnyTWgYDlC8PV67AmDEwffp7TfR6PbVr1+bEiRNUrlyZkydPSrU9YVYyxhIZSZ8+fVi5ciW5cuXi4sWL5MmT5/1G169D5coQEgKDB8OCBSaPU2ROGXKllJ+fH3v27Em0zfz581N07nPnznH16lVmzZqVrONatGhB48aNY7+PmWV9/vx5vPtvU0uhUODm5oa/v39smc7sixZhDQS3a0fIs2dpfk1zia+vmZWl9fXtHAf+/v7Y2dkl63hL629qSF8znpkzZ3Lnzh3Onz/P559/zt69e+MfZH1Aivpbuzbq7dtx6d4d1aVL6CtV4pWvL1EVKyb7+qZkKa/t206cOEGPHj0A6N+/P82aNePJkydmjgqsrKzImTOnucP4IFOOb9Lr/aXZvRuXK1cwODnxrHNnjPG8/rNmzeLEiRPY29uzYMECXphg+4kl/jyllPQ1+VI7xjIVeW0zr7f7O2rUKE6cOMH169dp06YNW7ZsQfXuAghnZ2wWLsS1Rw9YuJDAIkUIa9XKPMEnU1Z7bdNTeoxvMuSkVJMmTahVq1aibXLnzo2zszNBQUFxHtfr9YSEhCS4Le/q1as8ffqUbt26xXl87ty5lCxZkokTJ8Z7nFqtTvCOWnq+sY1GI0ajEaubN7E+exajSkVou3aZ8ocppq9ZgaX09e0YUxOzpfQ3LUhfMw6NRsP69etp3rw5d+7coWPHjuzcuTPJ27bfldz+RlasyIv9+3Ht1g319etkb9WKwHnzCGvePEXXN6WM/trGuHHjBj179kSn09GsWTNGjx5tEXFnJOYY36Tp+0unwzE6j1xInz4YsmWDd8599OjR2FX1Pj4+FChQwKTvE0v5eUoL0tfkHZ9W5zIFS4gxrWSlvsKb/mo0GpYvX84XX3zByZMnWbhwIUOjE5y/LfyLLwgeMgTHBQvI5uVFVNGiRJUrZ4aoUyarvbaWIkNOSjk5OeHk5PTBdsWKFSM0NJS7d+9SuHBh4M2kk9FoxNPTM95jmjdvTp06deI8NmLECLp27UqlSpVSH3w6sdu0CXhTAcHg5mbmaIQQwjK4urqyefNmmjZtyo0bN+jevTt+fn6xOXPSm97Dgxd79uA8YAC2Bw/i0r8/Vv/8Q/Dw4aC06FojZnf//n06duxIcHAwVatWZf78+Sjl3zTLsdu+Hat//0Xv6kpoz57vPf/48WMGDhyI0Wikc+fOtGjRwgxRCiFExufp6cmMGTMYMmQI8+bNo2rVqvHm5AwePhz11atoDh/GpUcPXvz0EwYpLCJSwaJHbx4eHpQvX54VK1Zw+/Ztbty4ga+vL9WrV4+tphIQEMCQIUO4ffs2AM7OzuTPnz/OfwA5cuQgV65cZutLYhRaLXbR+R60nTubORqRlSiVSurUqUOdOnXkw56wWB4eHmzatAlHR0fOnDnDwIED0ev1Jru+0d6eV6tXExJd7dVxwQJc+vVDERZmshgym6dPn9K+fXv8/f0pXrw4q1evxsbGxtxhCVOLiMAhOp1DyIABGKPz88SIiorim2++ISAggDJlyiS4Gl4Ic5AxlsiIWrduTZs2bTAYDPTv35+XL1++30ip5NXixegKF8bq8WNc+vaFtwpmCJFcFv8bcNCgQeTNm5fJkyczY8YMihcvTp8+fWKf1+l0PH78mIiICDNGmTqaH39EGRSErkABItK5UowQb9NoNGzcuJGNGzei0WjMHY4QKVaqVCl8fX2xtrbmp59+YsyYMaZdvq1SETRuHK/mzcOoVmO7bx/ZW7ZEmQFyH1mawMBAOnbsyL1798ifPz+bN2/GxcXF3GEJM7DbvBmrR4/Qu7kR2qXLe8/PnDmTP//8E0dHR1asWCF/x0SGImMskVFNmzYNT09Pnj59yqBBgzAYDO+1MTo5EbBmDQZ7e2xOn8ZpyhQzRCoyC4uflHJwcGDw4MFs2LCB9evX880338T5xZ4rVy62bdtG6dKlEzzHtm3bEq28Z272GzcCoO3USbZ7CCFEClWvXp0lS5agUCjw8/Nj4sSJJs8rENa2LS+3bkXv4oL15cvkbNwY9eXLJo3Bkmm1Wrp06cL169fJlSsXW7ZswU22tGdJirAwHBctAiB48GB4Z0vuL7/8wvLlywGYN28eBQsWNHWIQghhkezs7GIn8o8ePcq8efPibacrVozA6N/DDmvWYPtWJVchkkNmODI4q8uXsb54EaNajbZtW3OHI4QQFq1Ro0bMnTsXgNWrVzNz5kyTT0xFVqnCi/37iSpWDJW/P9lbtECzb59JY7BEERER9OjRg7/++gtnZ2e2bNkiEw1ZmP3ataiePUOXPz/adu3iPHf//v3YBL09e/akYcOG5ghRCCEsVokSJZg5cybwpur9oUOH4m0Xk/gcwHnUKLnRJlJEJqUyuJgE52GNGmHInt3M0YisRqvV4unpiaenZ5zSxUJYsrZt2zJt2jQAlixZwoIFC0weg75AAV7s2UN47doow8Nx7dMHhwUL3qsaJt6IjIykb9++HD9+HDs7OzZs2ECJEiXMHZYwE0VQEA5LlwIQPHQoWFvHPhcWFkbPnj15/fo1FSpUYOzYseYKU4hEyRhLZHStW7eOrVg/aNAg/v3333jbBQ8fTnjduijCw3Hp2RNlfHmohEiETEplZEFB2O7aBURv3RPCDMLCwgiThMwik+nWrRvjx48HYM6cObHbfEzJ6OREwLp1hPToAYDT7Nk4DxwI4eEmjyUji4yMpE+fPhw8eBCNRsOaNWuoWLGiucMSZuSwahXKwECiPD0J++qr2MeNRiMjR47k2rVrZM+enRUrVmD91oSVEBmNjLFERjdhwgQqVapEUFAQPXv2jH8CNSbxeaFCWD16hEufPpL4XCSLTEplZH5+KLVaojw9iaxa1dzRCCFEptKnTx9GjhwJwJQpU1i1apXpg7CyImjyZAJnzsRoZYXd7t3kaN0a5fPnpo8lA3p3QsrX15eaNWuaOyxhRoqAAOxXrgQgeMQIUKlin1u1ahW7d+9GpVKxYsUK3N3dzRWmEEJkCtbW1qxYsYKcOXNy48YNRo4cGW/aA2O2bAT4+kric5EiMimVEen1WP/+O8yYAYC2Y0dQKMwclBBCZD5Dhgxh4MCBAEycOJElS5aYJQ5t58689PPD4OyM9fnz5GjYEKtr18wSS0YRs2Xv4MGD2NjY4Ovry2effWbusISZOX73HcqQEKJKlya8UaPYx0+ePMnUqVOBN3f2q1WrZq4QhRAiU3Fzc2PFihVYWVnxww8/sGbNmnjb6YoVI3DhQkASn4vkkUmpDEZz4AC5q1Qhe+vW8OABAA7Ll6M5cMDMkQkhROY0atSo2KTIM2bMYM6cOSZPfg4QWaMGz3/8EV3hwlg9fkyO5s2xOXjQ5HFkBOHh4fTu3ZtffvkFGxsb1q5dKxNSAuXTp9itXQtAkJdXbEXihw8f0rdvX/R6Pa1ataJ79+7mDFMIITKdKlWq4O3tDcDkyZP5/fff420X/uWXbyqiEp34/MoVk8UoLJdMSmUgmgMHcOndG+WTJ3EeVz57hkvv3jIxJYQQ6UChUDBixAjGjBkDvKkyM23aNLNMTOmLFOH53r1E1KiBUqvFtXt37Jcty1IJ0IODg+nUqROHDh1Co9HIhJSI5bhoEcrwcCIrViSibl3gTU6eHj168OrVK8qVK8fMmTNRyOpyIYRIcz169KBly5bo9Xp69+7N3bt3420XPGLE/yc+79FDEp+LD5JJqYxCryfb+PFgNPLuUEoR/WHEacIE0OtNH5sQQmQBAwYMYNKkSQAsW7aMsWPHojfD71yjszMvN20itHNnFEYj2aZOxXn4cIiMNHksphYQEEDbtm05ffo0Dg4ObNq0SSakBACqBw+w8/MDIGjUKFAoMBgMDBo0iKtXr5I9e3ZWr16Nra2tmSMVQojMSaFQMGvWLCpUqEBgYCDdunXj9evX7zeML/G5Tmf6gIXFkEmpDML6zBlUT568NyEVQ2E0YvX4MdZnzpg0LpG1KRQKqlWrRrVq1eTOs8gSevbsiY+PDwqFgvXr19O3b1/CzVENT63m9YwZvJ4yBaNSid3WrWRv1w5lQIDpYzGRx48f06JFCy5duoSrqyvbt2+XvEAilsOCBSiiooioUYPI//0PAB8fHw4cOIBarWbVqlWS2FxYFBljCUtka2vLmjVryJMnD3fu3KFfv37o4plwksTnIjlkUiqDUD17lqbthEgLtra27Nixgx07dsjdZ5FldOrUiaVLl2Jtbc2BAwdo3749r169Mn0gCgWh3bsTsGEDBkdHbM6cIUejRljdumX6WNLZjRs3aN68Obdv3yZPnjzs3r2bcuXKmTsskUGo7tzBbvt2IHqVFLBt27bYwgSzZ8+mSpUqZotPiJSQMZawVLlz52bdunXY2tpy7NgxJk+eHG+7OInPV6+WxOciQTIplUHoc+VK03ZCCCFSrlmzZvj5+eHk5MSZM2eoUaMGDx8+NEssEbVr8+LHH9EVKIDV/fvkaNoUm99+M0ss6eHYsWM0b96cR48eUbhwYX744Qc8PT3NHZbIQBznzkWh1xNerx5RH3/MH3/8gZeXFwADBw6kdevWZo5QCCGyljJlyrBo0SIA1qxZw4YNG+JtJ4nPRVLIpFQGEVmlCvo8eTAmsHzXqFCgy5uXSLkTKIQQJlG9enV27dpFnjx5+Pvvv2natCkXL140Syy6YsV4sW8fEVWrogwOxrVLF+zXrLH4BOh+fn507tyZ4OBgqlatyp49e/Dw8DB3WCIDsfr7b+z27AEgaMQI/v33X3r06EFUVBSNGjWKnZwSQghhWg0bNmTkyJEAjBs3jiNHjsTbLnjECMLr1JHE5yJBMimVUahUvI5e+vjuxFTM90GTJoFKZfLQRNal1WopW7YsZcuWRavVmjscIUyuZMmS/Pjjj5QuXRp/f3+++uordu3aZZZYDK6uvNyyBW3btigMBrKNH0+20aMhKsos8aSGwWBg2rRpeHl5odfradmyJZs3b8bV1dXcoYkMxnH2bADCmjThWd68dOnShcDAQMqXL8/ChQtRKmUoKyyTjLFEZjB48GC++uqr2Ip8ly5der+RUsmrJUv+P/F5376S+FzEIX/JM5Dwhg15tXIlBje3OI/r8+Th1cqVhDdsaKbIRFYWEBBAQCZOrizEh7i7u3Pq1Ck+//xzwsPDGThwINOmTTNLZT6srQmcO5fX3t4YFQrsN20ie8eOKMyR8yqFXr16RdeuXfnuu+8AGD58OIsWLcLGxsbMkYmMRn3+PLYHD2JUKnnWvz9dunTh7t275M2bF19fX8nDIyyejLGEpVMoFMyZM4eaNWsSFhZGly5d+O+//95rFyfx+alTkvhcxCGTUhlMeMOGPD1zhpc7dsDmzbzcsYNnf/whE1JCCGFGTk5OrF27lgEDBgDw3Xff0a1bNwIDA00fjEJBaN++/z+4+/13cjZpgurOHdPHkkxXr16lYcOGHDlyBI1Gw+LFixk2bJhUnhLxcpo1C4DQli3pMXs2Fy5cwNnZmc2bN5M7d24zRyeEEALA2tqaVatWUbp0aV68eEHHjh15Gc8WvfcSn+/caepQRQYlk1IZkUpFZPXq0L79m//Llj0hhDA7lUrFmDFj+O6779BoNBw5coQGDRpw/vx5s8QTUb8+L374AZ27O1b//kvOJk2wPnHCLLEkxY4dO2jWrBn3798nf/787Nmzh5YtW5o7LJFBWZ86hc2JExjVasaEh/Prr7+i0WhYt24dRYsWNXd4Qggh3uLg4MDGjRvx8PDg33//pWvXroSFhb3XLk7icy8vSXwuAJmUEkIIIZKlWbNm/PDDDxQoUICHDx/SokULli9fjsFgMHksulKleLF/P5EVK6J8/ZrsHTtil0AFHHMJCQlh8ODBDB48mPDwcGrXrs2BAwcoU6aMuUMTGZXRiJOPDwAnS5Rgyb59KJVKli1bxieffGLm4IQQQsQnd+7c+Pn54ezszIULF+jduzeRkZHvtQsePlwSn4s4ZFJKCCGESKayZcvy888/06RJE3Q6HVOmTKFr1648e/bM5LEYcubkxbZtaFu2RKHX4zxmDE7jx2eIJKLnz5+nfv367NixA6VSyfDhw1m/fj0uLi7mDk1kYDZHjmB97hxRVla0jb6L7uPjQ/369c0cmRBCiMR4enqybt262BXlgwYNej8Hp0olic9FHDIpJYQQQqSAk5MTy5YtY+bMmdjY2HDkyBFq167Nnj17MBqNpg1GoyFw0SKCRo0CwGHNGly7dkURFGTaOKJFRUUxf/58mjdvzn///Ye7uzs7d+5k2LBhqGRLukiMwYBjdC6pBTodT4BRo0bRoUMH88YlhBAiST755BPWrFmDWq1m7969jBw58r3V5O8lPp861UzRioxAJqWEEAlSKBR89NFHfPTRR5KIWIh4KBQKOnfuzP79+ylTpgyBgYF888039OnTJ94kn+kcDCGDBhGwahUGW1s0R4+So2lTVPfumTSMS5cu8eWXXzJnzhz0ej1Nmzbl0KFDVK5c2aRxCMuk+eknrK9eJRjwAQYOHMigQYPMHZYQaU7GWCIzq1WrFsuWLUOlUrF161YmTJjw3g07XbFiBC5YAIDDqlWS+DwLk0kpIUSCbG1tOXDgAAcOHJDS20IkomTJkuzbt4/hw4djZWXF/v37qVmzJn5+fibPNRXesCEvd+9G7+aG+p9/yNG4MdZ//JHu1w0LC2Py5Mk0btyY69ev4+LiwuLFi/nuu+/Ili1bul9fZAJ6PYwfD8B8oGWvXoyKXv0nRGYjYyyR2X355ZfMnz8fhUKBr68vM2fOfG9iKrxhQ4KjbzxI4vOsSyalhBBCiDSgVqsZNmwY+/fvp2TJkgQGBuLl5UXTpk25dOmSSWOJKluW5/v3E/nRR6hevSJ7u3bYbt2aLtcyGo388MMP1KxZkxUrVmAwGGjRogXHjh2jZcuWsgJAJNnNCRNw9fcnAHjUti0TJkyQ948QQliwr776ihkzZgCwZMmSeCemgkeMiJv4PCDAHKEKM5JJKSGEECINlSlThp9++omJEyfi4ODAhQsXaNSoEcOHD+fRo0cmi8Pg5sbLnTsJa9IERVQULsOG4TRlypvVKGnkwoULNGvWjP79+/P48WPc3d1Zv349S5YsIXv27Gl2HZH5/bB9O/nXrgXgpzJl8J4zRyakhBAiE+jcuTNTpkwB3kxMTZ48Oe7EVEzi84IFJfF5FmXxk1IhISEsWrSIrl270q1bN5YtW0Z4ePgHj7t16xaTJk2ic+fOdO3alQkTJsRbslKIrCwsLIwqVapQpUoVwsLCzB2OEBZDrVbTq1cvjh8/TsuWLTEajXz//ffUqFGDCRMmmCzflNHWllfLlhE8bBgADsuX49qjB4qQkFSd98aNG/Tp04fGjRvz119/YWdnh5eXF8eOHePzzz9Pi9BFFrJlyxYuDxlCESBQo+Gz6GqNQmRmMsYSWUn37t2ZPn06ACtXrmT8+PFxJqbiJD7//XdJfJ7FWPxf/EWLFvHgwQPGjRvH6NGjuX79OitWrEj0mFu3bjFt2jQ++ugjpk+fzowZM2jQoIHckRPiHUajkYcPH/Lw4UPTVxMTIhPInTs3ixcvZs+ePVSrVo3IyEhWr15NtWrVmDp1Kk+ePEn/IBQKgocPJ+C77zDa2KA5dIgczZujevgw2ae6du0affr0oW7duuzbtw+A1q1bc+LECQYPHix5UUSyrVu3jrEjRjAu5oExY1A6OpozJCFMQsZYIqvp2rUrc6JXwfr6+jJmzJg4eTd1xYvHTXy+a5eZIhWmZtGTUg8fPuTixYv07duXokWLUqJECbp3786pU6cISGQv6vr16/nyyy9p3rw5+fLlI2/evFSvXh21Wm3C6IUQQmQVlSpVYvv27WzZsoWPPvqI0NBQli1bRtWqVRk8eDBXr15N9xjCmzXjxY4d6HPlQn39OjkaNUJ97twHj9Pr9fzyyy+0atWK+vXrx05GNW7cmF9//ZUFCxbg5uaW3uGLTGjZsmWMHTuWfoAHoMubF23nzuYOSwghRDpp37498+bNQ6FQsHHjRgYMGEBERETs83ESn48ciZUJxkfC/KzMHUBq3Lp1C3t7e4oUKRL7WNmyZVEoFNy+fTve8tOvX7/mn3/+oUaNGowbN46nT5+SN29e2rdvT4kSJRK8VlRUFFFRUbHfKxSK2DvC6bHCKuacWWH1lvQ143o7ToVCkey4La2/qSF9zbzSqr8KhYLPPvuMmjVrcvjwYZYvX87p06fZsWMHO3bsoFy5cnTo0IHmzZvj5OSUFqG/R1exIi/278e1WzfU166Ro3VrXs+dS9hXX4Fej83ZsxARgY2NDTdz5WLH7t1s376dBw8eAKBSqWjUqBFDhgxJ9G+msBymHN/EnM9oNDJp0iRWrlyJPTDZ1hbCwggZOhSFRpOm1zSnrPS7Uvqa8vPEfJ1R/+3ktc28zNXftm3bYmNjw+DBg9mzZw8vX75kzZo1OEavkg0ZORL1tWtofv0V1x49ePHTTxhTmacyq722lkZhtOD1ort27eLYsWMsXLgwzuM9e/akTZs21K9f/71jbt26xbhx43BwcKBz584ULFiQY8eOcfDgQebOnUuePHnivda2bdvYsWNH7PeFChXCx8cnbTskRAYTGhqKg4MD8CZ/m729vZkjEiJzOXv2LPPmzWPXrl2xEwM2NjbUr1+fr776iiZNmuDq6pr2Fw4Jgc6d4Ycf3nzfsiWcPQtvbel7AAwGdgMuLi706dOHb775hnz58qV9PMJsTD2+iYiIoGvXrmyNrgZ5vEEDPv3lF/D0hL//Blm1LrIIGWOJrO7QoUO0bNmSkJAQKlSowIEDB/5/5XVgIHzyCdy+DXXqwC+/gJVFr6cRiciQk1J+fn7s2bMn0Tbz58/nzJkzyZ6UunnzJt7e3jRv3pwOHTrEPj5ixAg+/vjjOI+9LaE7ic+fP0eXDtUBFAoFbm5u+Pv7Z/p95tLXjEur1eLp6QnA7du3sbOzS9bxltbf1JC+Zl6m6O/Lly/ZtWsXmzdv5ubNm7GPq1QqypcvT/Xq1alWrRqVKlWK/RCTagYDeHuTJ7rimRF4+/6hIfr7n3v2pNjo0cn++c/qrKysyJkzp7nD+CBTjm+Cg4Pp06cPR48eRa1W8920aXSfOhVlUBCvliwhvGXLNL2euWWl35XS1+RL7RjLVOS1zbwyQn8vX75Mx44defnyJfnz58fPzy92F5TVzZtkb9wYZWgoIX36EDxhQoqvkxH6aiqPHz9m586dDBgwIF1WhqXH+CZDTjc2adKEWrVqJdomd+7cODs7ExQUFOdxvV5PSEgIzs7O8R7n4uICgIeHR5zH3d3defHiRYLXU6vVCeacSs83ttFozPQ/ODGkrxlPnKoYqYjZUvqbFqSvmVd69tfV1ZWePXvSo0cPbty4wU8//cSBAwe4fv06f/31F3/99ReLFy8GoECBApQqVYrixYvj4eFB3rx5yZMnD46OjtjZ2WFvb49CoSAyMpLIyEhCQkJ48eIFz54948mTJ9y+fZt//vmHW7du8czfn2eAK3EnpOBN0kmjQkG9Awd49k6VHJF5mGp8YzAYaN++PefPn8fBwYFVq1bR6NQplEFBRJUoQVizZpBJ32NZ6Xel9DV5x6fVuUzBEmJMK1mpr2De/pYtW5Y9e/bQsWNH/vvvP5o0acKyZcuoWbMmUcWKEbhgAa69euGwYgVRZcsS1qJFqq6X2V/bv/76i549e/Ls2TMcHR3p2rWruUNKkgw5KeXk5JSkfBrFihUjNDSUu3fvUrhwYQCuXr2K0WiMvfPwrpw5c+Li4sLjx4/jPP7kyRPKly+f6tiFyEwUCgXFihWL/VoIkb4UCgUlS5akZMmSDBs2jIcPH/L7779z+vRpTp8+zcOHD/nvv//477//+Omnn1J9vdoKBdkTGZwpjEasHj/G+swZIqtXT/X1RNalVCoZMGAA3t7erF27lrK5c2PfowcAwSNGgNKia+8IkWwyxhLijUKFCrFnzx66d+/O+fPn6dSpE5MmTaJbt25vEp8PHIjj4sU4jxhBVNGi6MqUMXfIGdKOHTvw8vIiIiKCkiVLUqdOHXOHlGQZclIqqTw8PChfvjwrVqygV69e6HQ6fH19qV69emwOjoCAACZPnsyAAQPw9PREoVDQtGlTtm3bRsGCBSlYsCBHjx7l0aNHDBs2zMw9EiJjsbW15bfffjN3GEJkWR4eHrRt25a2bdsCb/6m/f333/z999/cvn2bJ0+e8PjxY/z9/QkODkav1793DisrK3LkyEGuXLnIlSsXhQsXplixYnh6elL5zh0YPvyDcaiePUvzvoms54svvqBdu3YEBgbiMGECSq2WyI8+IvyLL8wdmhAmJ2MsIf5fzpw52b59O15eXuzcuZNx48Zx48YNpkyZQnBM4vMjR2ITnxvSI9+mhdLpdMycOZNly5YB0KBBAxYvXmxReeoselIKYNCgQaxZs4bJkyejUCioUqUK3bt3j31ep9Px+PHjOKUmGzVqRFRUFOvXryckJIQCBQrg7e0tJa2FEEJkaK6urtSoUYMaNWq895zRaCQyMpLQ0FDgTcL0mK1ZCd2FV7+VSygx+ly5Uh60EG+xtbUl6O+/sd+wAYBgLy+QVSJCCJHlaTQaFi5cSMmSJZk2bRqbNm3i2rVrLFu2DMWSJeRs2BCre/dw6duXl5s3S+JzwN/fn/79+/PHH38Ab+ZGRo4cidLCVh9b/Cvp4ODA4MGDE3w+V65cbNu27b3H/4+9+45vqvr/OP5K091S2rJa9t5TFJChbBABEaigyFccIIiiAg5QNqjIEBVQRAVFZQ9liCgby5C9QZYItECBUuhOk98f0PyoHRQ6kibv5+PBg+bec+/9nJwkPf3knHM7depEp06dcjAyERGR3GMwGPDw8MDDwyPTxyTUr09ScDAu4eEY0pjGZzEYSAoOJqF+/ewMVZxRUhLuO3ZAQgJ+s2ZhiI8nvn594h991NaRiYiInTAYDPTr148KFSrw+uuvs2fPHtq0acOUKVNo9+23FGzfHo8//8Rv3DiisrDwuSPYvHkzr776KhEREfj4+DBx4kQ6duxo67DuS95KoYlIroqNjaVZs2Y0a9aM2NhYW4cjItnNaOT66NHArQTUnZIfR40aBUZjrocmjsNz1SqK1K9Pga5d4Zln8Pr9d4BbCSmNkhInpT6WSPpatmzJb7/9Rp06dbh+/TrPP/8878+dy+UJEwDw/eorvJYutXGUtpGYmMjEiRN5+umniYiIoEqVKvz66695NiEFSkqJSAYsFgvHjx/n+PHjDn2nChFnFteuHde++grzf6awJwUHc+2rr4hr185GkYkj8Fy1ioA+fXAJC0ux3QLkmzABz1WrbBOYiI2pjyWSseLFi7NkyRJ69+4NwMyZM2n6+eec7N4dgPyDB+N68KAtQ8x1x48fp2PHjnzyySdYLBaeeeYZli9fTrly5WwdWpbk+el7IiIikjVx7doR16YNHjt2UCAhgSvu7sTXq6cRUpI1SUnkHz4cLBb+Ox7KwK3ElN+IEcS1aaPXmoiIpOLu7s7IkSNp2LAhb731FkePHqXq33+zt0wZqpw+7TQLnyclJTFz5kw+/vhj4uPj8ff3Z9y4cQ6zHJFGSomIiAgYjSQ0bAhPP33rfyUJJIvct2/HGBaWKiGVzGCx4HrhAu7bt+dqXCIikre0bt2adevW8fjjj5OQlETD06c56+6O67lzBPTtCyaTrUPMMQcPHqRTp06MGTOG+Ph4mjdvztq1ax0mIQVKSomIiIhIDjBeupSt5URExHkVKFCAGTNmMH36dPD3p11CAjcBjz//xG3YMFuHl+1u3LjB8OHDeeyxx9i9ezc+Pj58/PHHfP/99wT9Z8mFvE5JKRERERHJdkmFC2drORERcW4Gg4EnnniCjRs3Ur1bN567vb3Q99+z/fXXiY+Pt2l82SExMZHvv/+eJk2a8M0332A2m+nQoQMbN26kR48eGBzwBiFKSomIiIhItkuoX5+k4OBUd3ZMZjEYMBUtSkL9+rkcmYiI5GUFCxZk8uTJPPfzz3xVsCAAbRYtot/DD7Nw4UKSkpJsHOG9M5vNrFy5kubNmzNkyBAuX75MmTJl+Omnn/jyyy8JDg62dYg5RkkpEUmXwWCgePHiFC9e3CGz8iIikoOMRq6PHg2QKjGV/Dhq1CitXyZOSX0skax78MEHeeyvvzhZqRLewJcXLzLmjTdo0aIF8+fPzxMjp0wmE0uWLKFVq1b06dOHU6dOUaBAAcaOHcu6det49NFHbR1ijlNSSkTS5eXlxfbt29m+fTteXl62DkdERPKYuHbtuPbVV5j/s/5FUnAw1776irh27WwUmYhtqY8lkj2M7u54L1lCYsmSlAYWubpy6u+/GThwIA0bNmT69OlcuXLF1mGmcu3aNWbOnEmTJk147bXXOHr0KL6+vrzxxhv8+eefPP/887i7u9s6zFzhausARERERMRxxbVrR1ybNnjs2EGBhASuuLsTX6+eRkiJiEi2sPj7c23WLAp26EDTmBg2NGhAyJkzhIeHM27cOD7++GM6derEk08+SePGjTHa6PeP2Wxmx44dzJ07lxUrVhAXFwdAYGAgL730Er169SJ//vw2ic2WlJQSERERkZxlNJLQsCEEB5MQFgYWi60jEhERB2KqXJnIKVMI7NOHxtu2sX/KFH4wm5k1axYHDhxg4cKFLFy4kEKFCtG6dWvatWtHw4YNc3w0UmJiIrt27WLlypWsXLmSixcvWvdVrVqVZ599lqeeesqpR0wqKSUi6YqNjaVLly4ALF682Kk/LEVERESyi/pYItkv7vHHufHqq+SbOpUC775Lj59/ptvq1Rw8eJBffvmFH374gcuXL/Pjjz/y448/4u3tzYMPPkiDBg1o0KABVatWJV++fFmK4caNGxw5coTdu3fz559/sm3bNmJiYqz7/fz8eOyxx+jRowcPPPCA1pRDSSkRyYDFYmHfvn3Wn0VEREQk69THEskZN95+G7fDh/Fct47Al17i8vLl1L1xg9aNGvF+48asN5lY9dtvrFmzhkuXLrFp0yY2bdpkPb5YsWJUrlyZkiVLUqRIEQoXLkxAQAAeHh54eHhgNBqJjY0lJiaG6OhoLl68yPnz5zl//jx///03Z86cSRVTYGAgLVq0oH379jRp0gQPD49cfEbsn5JSIiIiIiIiIpL3GY1c+/xzCj3+OK5nzhBUrx6GhAQAgoCngoNpM3o0MR9+yLFjx9i2bRtbt25l165dhIeHWxNMWREcHEz16tV5+OGHady4MVWqVMHFRfeYS4+SUiIiIiIiIiLiECz+/kT36oXfyJHWhFQyl/BwAvr0ga++okq7dlSpUoXnn38euHVHvOPHj3P06FHCwsK4ePEi4eHhREVFER8fT0JCAiaTCW9vb7y8vPD29qZw4cIUK1aMYsWKUapUKapXr05gYKAtqp1nKSklIiIiIiIiIo4hKQnfGTPS3GWwWLAYDPiNGEFcmzYp7gQbEBBA/fr1qV+/fm5FKoDGkImIiIiIiIiIQ3Dfvh1jWBjpLSFusFhwvXAB9+3bczUuSZuSUiIiIiIiIiLiEIyXLmVrOclZmr4nIhnSnGgRERGR7Kc+lkjOSCpcOFvLSc5SUkpE0uXt7c2BAwdsHYaIiIiIQ1EfSyTnJNSvT1JwMC7h4RgsllT7LQYDScHBJGjtKLug6XsiIiIiIiIi4hiMRq6PHg3cSkDdKflx1KhRKRY5F9tRUkpEREREREREHEZcu3Zc++orzEFBKbYnBQdz7auviGvXzkaRyX9p+p6IpCs2NpaePXsCMGfOHLy8vGwckYiIiEjepz6WSM6La9eOuDZt8NixgwIJCVxxdye+Xj2NkLIzeT4pdfPmTb799lt27dqFwWCgfv36PP/883h6eqZ7TGRkJHPmzGH//v3ExcVRtGhRnnzySRo0aJCLkYvYP4vFwtatW60/i4iIiEjWqY8lkkuMRhIaNoTgYBLCwkDvN7uT56fvffbZZ/z777+8//77vPvuuxw5coQZM2ZkeMzUqVO5cOEC77zzDhMnTqRevXp88sknnD59OpeiFhERERERERFxbnk6KXXu3Dn27t1L3759qVChApUrV+aFF14gNDSUq1evpnvcsWPHeOyxxyhfvjxFihShS5cu+Pj4cOrUqVyMXkRERERERETEeeXp6XvHjx/Hx8eHcuXKWbfVqFEDg8HAiRMnqFevXprHVapUidDQUB544AG8vb3ZunUriYmJVKtWLd1rJSYmkpiYaH1sMBisc78N/1nRPzsknzMnzm1vVFf7dWecBoPhnuPOa/XNCtXVcTlTfZ2prnJLbvZvnO315Uz1VV3v/zzJP9vrc6e2dVzOVF9nqmteZLDk4UnMS5YsYePGjXz66acptr/00ks89dRTtG7dOs3joqOjmTJlCvv27cNoNOLu7s7AgQOpVatWutdasGABixYtsj4uU6YM48ePz56KiNip6OhofH19gVvrt/n4+Ng4IhERyU7q34jYhvpYIiK32OVIqR9//JGff/45wzKffPLJfZ9//vz5REdHM2zYMPLly8dff/3FJ598wujRoylZsmSaxzz55JO0b9/e+jg5y3r58mVMJtN9x5Ieg8FAUFAQ4eHhDr/4oepqv2JiYqw/h4eH4+3tfU/H57X6ZoXq6ricqb7OVNec5urqSqFChWwdxl3lZv/G2V5fzlRf1fXeZbWPlVvUto7LmerrTHXNaTnRv7HLpFSHDh1o2rRphmWKFCmCv78/UVFRKbYnJSVx8+ZN/P390zwuPDyc1atXM2nSJEqUKAFA6dKlOXr0KKtXr6ZPnz5pHufm5oabm1ua+3LyhW2xWJzmjaO62h+LxWKdxpGVmPNKfbOD6uq4nKm+zlRXZ2eL/o2zvb6cqb6q670dnx19rNySF2LMLs5UV3Cu+jpTXfMSu0xK+fn54efnd9dyFStWJDo6mlOnTlG2bFkADh48iMVioXz58mkek5CQAKSeT+ri4qIXqMh/eHt7c+LECVuHISIiIuJQ1McSEbklT999r3jx4tSuXZsZM2Zw4sQJjh49yrfffkvDhg0JDAwE4OrVq7zxxhvWD/2iRYsSFBTEzJkzOXHiBOHh4Sxfvpz9+/fz0EMP2bI6IiIiIiIiIiJOwy5HSt2LAQMG8M033zB69GgMBgP169fnhRdesO43mUxcuHCB+Ph44NYcyCFDhvDjjz8yfvx44uLiCAoKon///jzwwAO2qoaIiIiIiIiIiFPJ80kpX19fXn/99XT3Fy5cmAULFqTYFhwczODBg3M6NJE8Ly4ujt69ewMwc+ZMPD09bRyRiIiISN6nPpaIyC15PiklIjnHbDazbt06688iIiIiknXqY4mI3JKn15QSEREREREREZG8SUkpERERERERERHJdUpKiYiIiIiIiIhIrlNSSkREREREREREcp2SUiIiIiIiIiIikut0970scnXN2acwp89vT1RX++Ph4UGdOnWsP7u5ud3XefJKfbOD6uq4nKm+zlTXnJLXn8OcjD+vPzf3ypnqq7pmXnb1sXKL2tZxOVN9namuOSUnnkODxWKxZPtZRUREREREREREMqDpe3YqNjaWd955h9jYWFuHkuNUV8flTPVVXR2XM9XXmeoquc/ZXl/OVF/V1XE5U32dqa7gXPV1prrmRUpK2SmLxcLp06dxhoFsqqvjcqb6qq6Oy5nq60x1ldznbK8vZ6qv6uq4nKm+zlRXcK76OlNd8yIlpUREREREREREJNcpKSUiIiIiIiIiIrlOSSk75ebmRteuXe3+ThzZQXV1XM5UX9XVcTlTfZ2prpL7nO315Uz1VV0dlzPV15nqCs5VX2eqa16ku++JiIiIiIiIiEiu00gpERERERERERHJdUpKiYiIiIiIiIhIrlNSSkREREREREREcp2SUiIiIiIiIiIikutcbR2ApHTixAkWLlzIsWPHSEpKomTJkjz++OM0bNjQ1qFlm2XLlvHTTz8BMHbsWCpWrGjdt2DBAhYtWpTusVOnTqVw4cI5HuP9MJvNrFmzhvXr13P+/HmMRiOlS5emQ4cOPPjgg6nKx8TEsHDhQrZv305kZCQBAQE0aNCAkJAQPD09bVCD1DZt2sTRo0c5deoUZ8+exWQy8corr9C0adNUZTdv3sy2bds4e/Ys169fx2KxUKhQIWrWrEnHjh0JDAxMUf7SpUu8+uqr6V67a9euPPXUU9ldpQzdS30BwsLCWLp0KUePHuXKlSv4+vpSvHhxHnvssTTbHG49T6tWreLcuXO4urpSqVIlnnrqKcqWLZuDNUvp6tWrbN26lT179nD+/HkiIyPx9fWlUqVKPPHEE1SoUMFa1mQysXPnTnbu3MnJkyeJiIjAYDBQvHhxmjZtSsuWLXFxSfn9hr217b3UF8hUbNOnT6dgwYIpttlD2yYkJDB37lxOnTpFeHg4N2/exNvbm6CgIJo3b06TJk1wdf3/X/1nzpwhNDSU06dPc+rUKW7cuEHVqlUZOXJkmue3t7aVvMPR+zeO2rcB9W/yev/GWfo24Fz9G2fq24D6N45OSSk7cvDgQcaNG4e7uzsNGzbEy8uL7du3M2XKFK5cuUKHDh1sHWKWnT17loULF+Lh4UF8fHy65R599FEKFSqUaruPj09OhnffLBYLn3zyCdu3b6dIkSI0b96cxMREdu7cyccff8wLL7xA27ZtreXj4uIYOXIkZ86coVatWjRq1IgzZ86wfPlyDh8+zKhRo3B3d7dhjW6ZP38+ly9fJl++fAQEBHD58uV0y/7555+Eh4dToUIF/P39gVu/EH799Vc2btzI6NGjKVGiRKrjSpUqxUMPPZRqe7Vq1bKtHpl1L/X9+++/GTVqFElJSdStW5f69etz/fp1duzYwccff0xISAghISEpjlmyZAnz5s2jUKFCtGrVitjYWEJDQxk2bBjDhg2jcuXKOV1FAH799Vd+/vlnihQpQq1atfDz8yMsLIy//vqLv/76i9dff936h+LFixeZPHkynp6eVK9enbp16xITE8OuXbv4+uuv2b17N++88w4GgyHVdeylbe+lvnCr45GW8PBwtmzZQvHixVN12uylbePi4lizZg3ly5enTp06+Pn5ER0dzd69e/niiy8IDQ1lyJAh1o72jh07WLZsGa6urgQHB3Pjxo1MXcde2lbyBkfv3zhq3wbUv4G8379xlr4NOFf/xpn6NqD+jcOziF0wmUyWV1991fLMM89YTp8+bd0eHR1tGTBggOXpp5+2XLp0yXYBZoPExETL22+/bRk6dKjls88+s4SEhFiOHTuWosz8+fMtISEhloMHD9ooyvuzdetWS0hIiOX999+3xMfHW7dfv37d8sorr1ieeeYZy8WLF63bk+v5ww8/pDjPDz/8YAkJCbEsWbIk12LPyL59+6yvu6VLl1pCQkIs69evT7PsnfW+09q1ay0hISGWSZMmpdh+8eJFS0hIiGXq1KnZGnNW3Et9P/jgA0tISIhlx44dKbZfunTJ8r///c/So0cPS0JCgnX7hQsXLN27d7cMGDDAEh0dbd1++vRpyzPPPGN54403LElJSdlfqTRs27bNcujQoVTbDx8+bOnevbulV69e1tivXLliWb16tSU2NjZF2djYWMu7775rCQkJsYSGhqbYZ29tey/1zcg333xjCQkJsSxfvjzFdntq26SkJEtiYmKq7SaTyTJy5EhLSEiIZdeuXdbtZ8+etZw8edKSmJhouXbtmiUkJMQyYsSIdM9vb20r9s/R+zeO3LexWNS/sVjyfv/GWfo2Fotz9W+cqW9jsah/4+i0ppSdOHjwIBcvXqRRo0aULl3aut3b25snn3wSk8nExo0bbRdgNliyZAnnzp2jX79+qYbD5nV//fUXAE8++WSKbwD9/Px4/PHHSUxMZMOGDcCtbx3Xrl2Lp6cnXbp0SXGeLl264Onpybp163It9ozUrFkzzW9105LeN58NGjQAbn0TY+/upb4XL17EYDBQp06dFNsLFSpEyZIlSUhIIC4uzrp9/fr1JCUl0blzZ7y9va3bS5cuTaNGjTh//jxHjx7NnorcRf369alatWqq7VWqVKF69epER0dz9uxZAAIDA2nTpk2qKReenp48/vjjABw+fDjng86Ce6lvehISEti8eTOurq488sgjKfbZU9u6uLikGL6ezGg0Wr/5u/O9WKJECcqWLZvmMSLZwdH7N47ctwH1byDv92+cpW8DztW/caa+Dah/4+gc77dnHnXo0CEAatWqlWpf7dq1Afv+YLybU6dOsXTpUrp27Urx4sXvWv7IkSMsW7aMX375hR07dqT4BWiPIiMjAdJcEyJ528GDB4Fbc/WvXbtGpUqV0vxFWKlSJS5evEhERETOBp1Ldu/eDZDm0HaAa9eusXr1apYsWcK6devsvnOXrESJElgsFvbs2ZNie0REBGfPnqVUqVLky5fPuj35/ZvWezx5mz28x41GY4r/M5L8iz69snmhbTNb3x07dhAdHc2DDz6In59fin15oW3NZjP79u0D0n8v3ou80LZiHxy5f+PofRtQ/yYjjti/cdS+DThX/8ZZ+jag/o2jUOrQTiS/4IODg1Pt8/f3x9PTk7CwsNwOK1skJiYybdo0SpcuzRNPPJGpYxYsWJDisY+PD7169eLRRx/NiRCzLPkX9KVLl1J1TC9dugRgbb+M2jp5+759+wgPD081tzsvCA0N5dy5cyQkJPDvv/+yb98+ChcuTLdu3dIsv3//fvbv3299bDAYaNy4Mb1797abBVHT0r17d44dO8bkyZOpW7cuRYsWta67UKRIEd54440U5cPCwvD09LSuR3Gn5NeCrX/xRUREcODAAQICAihZsuRdy69fvx649S1sWuy9be+lvsnf7jdv3jzVPntsW5PJxJIlSwC4ceMGBw8e5Pz58zRt2pQaNWpk+fz23rZiPxy1f+MMfRtQ/+ZOztC/ccS+DThX/8aR+zag/o2jUlLKTsTExACkGB55Jy8vL2uZvGb+/PmEhYXx0Ucf3XVoe+nSpenXrx/VqlXD39+fyMhIdu/ezfz585k+fTo+Pj7p3vnDlurUqUNoaCg///wz1atXtw71vnHjBitXrgQgOjoayFxb31kur9m6dSvbt2+3Pi5Xrhyvv/56qm9ZPTw86NKlCw899BBBQUGYzWZOnz7NvHnz2Lx5M/Hx8QwePDi3w8+0YsWKMW7cOCZPnsyOHTus2/Ply0fTpk0JCgpKUT4mJob8+fOneS57aHOTycTnn39OYmIiPXr0uOt79Y8//mDPnj1Ur16dBx54IMW+vNC291LfS5cucejQIQoWLJhmB9Ue29ZkMqW425fBYKBDhw4888wzWTpvXmhbsS+O2r9xhr4NqH9zJ2fo3zha3wacq3/j6H0bUP/GUSkpJTnq+PHjLF++nJCQkEx9M1GvXr0UjwsXLkzbtm0pVqwYY8eOZd68eXbZcWvcuDEbNmzg0KFDDB48mNq1a2Mymfjrr7+s3zCkdfcORzRo0CDgVic1+cP83XffZfDgwVSvXt1aLn/+/Km+XaxRowYVK1bknXfeYceOHZw6dSrXbzmbWSdOnGDChAmULFmSjz76iGLFihEZGcnq1auZPXs2R48eZeDAgbYOM1PMZjPTp0/nyJEjtGjRItW6Av+1a9cuvvnmGwoVKsRrr72War+9t+291nfdunVYLBaaNm2aZ9aM8fT0ZMGCBZjNZq5du8auXbuYO3cux48fZ8iQIen+0Xg39t62IrnBWfo2oP7NnZyhf+NIfRtwrv6NM/RtQP0bR5V3XoEOLvkNlF7GOTY29r7fZLaSlJTEtGnTKFWqFJ06dcrSuWrUqEGRIkU4e/aszb9xSYvRaGTo0KGEhIRgMBj4448/2LFjBw899JD1l3fytw2Zaes7y+VVPj4+VK9enaFDh+Lu7s7UqVMxmUx3Pc7Dw8P6i/TYsWM5HeZ9MZlMTJkyBYPBwODBgylbtiweHh4UKVKE5557joceeoht27alWADS29vbLtvcbDbzxRdfsGXLFpo0aULv3r0zLL97924mT56Mv78/w4cPJyAgINPXsoe2vdf6ms1mNm7ciMFgSHN4O9hv28KthUELFChA69at6dOnD8eOHbMOe89O9tC2Yp8crX/jTH0bUP8mLY7av3Gkvg04V//G2fo2oP6No9FIKTuRPBw2LCwsVQY2MjKSuLg4ypcvb4vQ7ltcXJx1nYH0hlS+//77AAwePDjVN4n/lS9fPsLDw0lISLDLDo2bmxshISGEhISk2J68yGtyu97Z1mlJ3v7fIdJ5lbe3NxUqVOCvv/4iPDw8U4vBJq9hER8fn9Ph3ZcLFy5w6dIl6tWrh4eHR6r91apV46+//uLMmTNUrlwZuDX//vjx40RGRqaan2+rNk/+Vm3Tpk00atSI/v37Z/ht2e7du5k0aRL58uVjxIgRFClS5J6vacu2vdf6Auzdu5crV65Qq1atdNdAsce2TUtOL0xq7+9bsQ1H6984W98G1L9Jj6P1bxylbwPO1b9x9r4NqH/jCDRSyk4k39Iz+e4Bd9q7d2+KMnmFm5sbzZs3T/Nf8gJ5Dz74IM2bN0/zri53iouL49y5c3h4eKS460desHnzZgAaNWoE3PqQDwgI4NixY6nuvBMXF8exY8coXLhwnlwEND3Xrl0DyPRtWf/++2+ATN/COLclfyN648aNNPdHRUUBKeub0Xs8eVtuvsfv7MQ0bNiQ1157LVMdNl9fX0aMGHHfHRFbte291jdZ8iKgLVq0SLeMvbVteq5evQpk7s5D98Pe37diG47Wv1Hf5v+pf+NY/RtH6NuAc/Vv1Le5Rf2bvE9JKTuRPIT7zz//5MyZM9btMTExLF26FFdX17vODbY37u7u9O3bN81/FStWBKBTp0707duX0qVLExsby4ULF1KdJyEhgRkzZhAbG8vDDz+cYx84WZXWENdt27axfv16ypUrR/369YFbay+0aNGCuLg4Fi9enKL84sWLiYuLy/CXhD1Kr+3g1i++EydOEBwcnOIX/enTp7FYLKnKb9++nY0bN+Lj40OdOnVyLOasKFGiBF5eXhw9ejTVL+uIiAj++OMPDAZDil/WTZs2xWg0smTJkhSvlTNnzvDnn39SrFgx6zePOS15mPemTZto0KDBXTsxe/bsYdKkSfj4+DBixIh076yUzN7a9l7rmywqKopdu3bh5+eX4Xov9tS2586dS/ObvPj4eL7//nuALD339ta2Yv8crX/jbH0bUP/GWfo3eb1vA87Vv3Gmvg2of+PoNH3PThiNRl5++WXGjRvHiBEjaNiwIV5eXmzfvp3Lly/Ts2fPu37jltfduHGDN998k3LlylGsWDH8/f25fv06Bw4c4MqVK5QsWZKePXvaOsx0vffeexQoUIBixYrh5ubGyZMnOXToEEWKFGHgwIEpflF07NiRv/76i59//pkzZ85QpkwZTp8+zb59+yhXrhyPP/64DWvy/9auXWtdO+Ds2bPWbclD9itXrkyLFi2sbVe2bFmKFStGYGAgN2/e5OTJk5w+fRovLy/69++f4tzfffcdFy9epEKFChQoUMB6l4ujR4/i5uZG//79c30qQ2br6+bmRs+ePfnqq6/44IMPrLdNjoyMZMeOHcTFxdG+fXuKFi1qPXfRokUJCQlh3rx5vPXWW9SvX5/Y2FhCQ0MBePnll3NtoclFixaxceNGPD09KVq0aKo/HuDWwrylS5fm/PnzTJw4kcTERKpWrcqWLVtSlS1cuDBNmza1Pra3tr2X+t5p48aNJCUl8cgjj2T4Lbg9tW1oaCgrV66kcuXKFCpUCC8vL65evcrevXu5ceMGVapUoX379tby58+fZ9myZcCtP5KTt02bNs1a5s73rr21rdg/Z+/f5PW+Dah/k9f7N87StwHn6t84U98G1L9xdEpK2ZHq1aszZswYFixYQGhoKElJSZQsWZIePXrQsGFDW4eX43x9fWndujUnTpxgz549REdH4+7uTrFixXjsscdo27at9VbE9ujhhx9mx44d/P3335hMJgoXLkznzp3p2LFjqg8xT09PRo0axYIFC9i+fTsHDx4kICCA9u3bExISYjf1PHr0KBs3bkyx7dixYykW+mvRogV+fn506dKFQ4cOsX//fm7cuIGrqyuFCxfm8ccfp3379hQoUCDFeZo0acL27dv5+++/2b17NxaLhcDAQJo3b06HDh0oVqxYrtTxTpmtL0DLli0pXLgwq1at4tixY+zevRtPT0/KlClDy5YtadKkSarzd+7cmUKFCrFq1SrWrFmDq6srlStXplu3brl6N4/Lly8Dt6ZTpLcoZOHChSldujSRkZEkJiYCWDsi/1W1atUUnTZ7a9t7qe+dkoe3p7cI6J3spW3r1q3LtWvXOH78OMePHycuLg5vb29KlixJo0aNaNasWYoRGZGRkale89evX0+x7c5Om721reQNzty/yet9G1D/Jq/3b5ylbwPO1b9xpr4NqH/j6AyWtMapiYiIiIiIiIiI5CCtKSUiIiIiIiIiIrlOSSkREREREREREcl1SkqJiIiIiIiIiEiuU1JKRERERERERERynZJSIiIiIiIiIiKS65SUEhERERERERGRXKeklIiIiIiIiIiI5DolpUREREREREREJNcpKSUiIiIiIiIiIrnO1dYBiIhje+utt/jnn39wdXVlxowZ5MuXz9YhWW3YsIHp06fz6KOP0r9//xy91rRp09i4caP1sYuLC/Pmzbunc4wcOZLDhw8zYsQIqlWrlt0hZtobb7zBhQsXrI+rVq3KyJEjbRaPiIhIblP/5hb1b0Qkq5SUEpEcc+LECf755x8ATCYTmzdvpl27djaOyrYqVapEUFAQLi55d6BqvXr1uHbtGpGRkezbt8/W4YiIiOQq9W9SU/9GRO6XklIikmPWrVsHQGBgIFevXmXdunVO32lr0aIFTZs2tXUYWfLMM88AcOjQIXXaRETE6ah/k5r6NyJyv/JuKltE7Fp8fDx//vknAK+++iqenp6cPXuWEydO2DgyERERkfuj/o2ISPbSSCkRyRFbt24lNjaWEiVKUL16dRo2bMi6detYt24d5cuXT/OYO9cU8PHxYdGiRRw5coTY2FiKFClC8+bNad++PQaDIdWxcXFxLFu2jNDQUCIiIsiXLx+1atWie/fu/PHHHyxatIiuXbvy1FNPZboOV69eZfny5ezdu5fLly/j4uJCsWLFePTRR2nVqhVGo/G+n5/0REREsGDBAvbu3Ut0dDQFChTg4YcfpkuXLnc9dtu2baxdu5ZTp04RExODn58f1apVo3PnzhQvXjzNY44ePcrixYv5+++/SUpKonjx4rRt25ZHH33U+lwtWLAgW+soIiKSV6l/c3/UvxGR9CgpJSI5Yv369QA0a9bM+v+6desIDQ2lV69euLu7p3vsvn37WLFiBUWKFKFGjRpERkZy9OhR5syZw5UrV+jVq1eK8nFxcYwaNYqTJ0/i6elJzZo1cXd3Z9++fezZs4c6dercc/yHDx9mwoQJREdHU6hQIWrWrEliYiInTpzg22+/ZdeuXbzzzju4umbfx+j58+cZOXIk169fJyAggLp16xIfH8/KlSs5dOhQusclJSXx2WefsXXrVtzc3ChbtiwBAQGEhYWxZcsWduzYweDBg6ldu3aK4/78808+++wzLBYLJUuWpESJEly9epXp06dz7ty5bKuXiIiIo1D/5t6pfyMiGVFSSkSy3YULFzhy5AhGo5FHHnkEuLUAZrFixTh//jzbtm2zbk/LsmXL6N27N61atbJuO3jwIGPGjGH16tV06NCBAgUKWPfNnz+fkydPUrx4cYYNG0ZAQAAACQkJTJ06lQ0bNtxT/JGRkUyaNImYmBheeuklWrZsaV2488aNG3zyySfs27ePZcuW0bVr13s6d0amTp3K9evXefjhh+nfv7+1YxsREcGoUaO4ePFimsctWLCArVu3UqFCBV5//XUKFy5s3bdt2zamTJnCp59+ytSpU/Hx8QFufUs6Y8YMLBYLvXr1SrEWxuHDh/nwww+zrV4iIiKOQP2b+6P+jYhkRGtKiUi2S/4W8cEHH8TPz8+6PflbxeQFQtNTr169FB02gOrVq1OrVi3MZnOKb9USEhJYu3YtAM8995y1wwbg7u7OSy+9hIeHxz3Fv3LlSm7cuEGbNm1o3bp1ijvJ5MuXj1dffRWj0cjq1auxWCz3dO70HD16lJMnT+Lh4cFLL72U4pvWggUL0rNnzzSPu3nzJitXrsTNzY1Bgwal6LABNGjQgJYtWxIdHc3mzZut29etW0dcXBwVK1ZMtThr1apVad26dbbUS0RExFGof3Pv1L8RkbtRUkpEslVSUhIbN24E/r+TluyRRx7BaDRy5MgRwsPD0z3Hgw8+mOb2YsWKAbe+BUt26tQp4uLirGss/Jefnx81atS4pzrs3r0bgIYNG6a5PzAwkODgYKKioggLC7unc6fn8OHDANSuXZt8+fKl2v/QQw/h7e2davvBgwdJSEigcuXKBAYGpnnuatWqAXDs2DHrtiNHjgDQuHHjNI9p0qTJvVVARETEgal/c3/UvxGRu9H0PRHJVrt37yYyMpLAwMBUc/z9/f2pU6cOO3fuZP369Tz99NNpnqNgwYJpbvfy8gJufXuY7MqVKwCpvkG7U0b70nLp0iUAhg8ffteyUVFRFC1a9J7On5a71cNgMFCoUCH++eefFNuTYz1w4MBdFzmNiorK9PUKFSqUucBFREScgPo390f9GxG5GyWlRCRbJQ9dT0xMZMSIEan2J38LuGHDBrp165Zi6HiytO4+k5vMZjNwa2j43YbGp/WtX25KjjUoKIhKlSplWDb5m9jMsHUbiIiI2BP1b3KX+jcizkNJKRHJNteuXWPPnj3ArQUz7xxOnVbZvXv38sADD2TpmslDui9fvpxumYz2paVgwYKEhYXxxBNPUK5cuSzFl1mZqUdERESqbcnfuhYtWpT+/fvf0/UuXLiQ7vWSv6EUERFxdurf3D/1b0TkbpSUEpFss2HDBsxmMxUqVGDcuHHplvvhhx/45ZdfWLduXZY7bWXLlsXDw4OoqCj2799PzZo1U+xP3n4vateuTVhYGFu3bs21TlvVqlUB2Lt3Lzdv3sTX1zfF/p07dxIdHZ3quOrVq+Pq6sqhQ4e4fv06+fPnz9T1qlSpwsGDB/nzzz9p06ZNqv1btmy5j1qIiIg4HvVv7p/6NyJyN1roXESyTfJdaR599NEMyyXv37VrV4p1AO6Hh4cHzZs3B+C7774jMjLSui8xMZFvv/2W+Pj4ezpnx44d8fHxYcWKFSxfvhyTyZSqzKVLl9i0aVOWYr9TlSpVKFOmDHFxcXz99dckJiZa90VERDBnzpw0j/P396dt27bEx8czfvx4zp49m6pMYmIiO3fu5Pz589ZtzZs3x8PDg6NHj7J69eoU5Y8ePcqaNWuyqWYiIiJ5m/o390/9GxG5G42UEpFscfjwYcLDw3Fzc0v3ri7JSpQoQZkyZTh9+jQbN26kQ4cOWbr2008/zbFjxzh16hQDBgygevXquLm5cfToUUwmE48++igbN27E1TVzH3kFChTgrbfeYtKkScyZM4dffvmFEiVK4O/vT0xMDOfPn+fixYtUqFCBRx55JEux3+m1115j5MiRhIaGcuTIESpXrkx8fDyHDh2iZMmSVKxYkePHj6c6rkePHkRGRrJlyxbeeustSpcuTeHChTEajVy5coUzZ84QHx/P0KFDresuFChQgN69ezNt2jS+/fZb1q5dS/Hixbl27RpHjhyhffv2LF++HKPRmG31ExERyWvUv8k69W9EJCMaKSUi2SJ5AdC6deumGpqdluRvE5OPywpPT09GjhzJk08+Sf78+dm7dy9HjhyhRo0ajB8/3rrYqJ+fX6bPWbVqVSZPnkyXLl0IDAzk5MmTbNu2jX/++Qd/f3+6du3Kyy+/nOXY71S8eHE++ugjmjZtitls5q+//uL8+fO0bduW4cOHp9vpNBqNDBgwgCFDhvDQQw9x/fp1du7cyb59+7h58yZ169ZlwIABVKlSJcVxjzzyCCNGjKBmzZpcunSJnTt3Ehsby8svv8xjjz0G3NtzJiIi4mjUv8k69W9EJCMGi8VisXUQIiI5xWQyMWjQIMLCwvjoo48oW7asTeKYNm0aGzdu5JVXXqFp06Y2ieFebNy4kWnTplG3bl3eeeedNMscOnSIUaNGUbVqVUaOHJm7AYqIiDgx9W/uj/o3IvZH0/dExCGcOnWK0qVLp7gFc1xcHN9//z1hYWGUKlXKZh22O61du5ZDhw7h4uJCv379bBpLREQErq6u+Pv7p9h+9OhR6xoPzZo1S3XcTz/9xLVr11KsbyEiIiLZT/2be6f+jUjeoqSUiDiESZMmER8fT8mSJfHz8yMqKoozZ85Y7/Tyyiuv2DpEAI4dO8axY8fsotN28OBBvvjiC0qXLk2BAgVwcXHh4sWL/PPPPwA0bdqUevXqpTpux44dXLhwIbfDFRERcTrq39w79W9E8hZN3xMRh7Bq1SrrGgXR0dEYDAYKFSpEzZo16dChAwULFrR1iHbn/PnzLF++nCNHjnD9+nXi4+Px9vamdOnSNGvWjMaNG9s6RBEREaem/s29U/9GJG9RUkpERERERERERHKd7r4nIiIiIiIiIiK5TkkpERERERERERHJdUpKiYiIiIiIiIhIrlNSSkREREREREREcp2SUiIiIiIiIiIikuuUlBIRERERERERkVynpJSIiIiIiIiIiOQ6JaVERERERERERCTXKSklIiIiIiIiIiK5TkkpERERERERERHJdUpKiYiIiIiIiIhIrlNSSkREREREREREcp2SUiIiIiIiIiIikuuUlBIRERERERERkVynpJSI2I2RI0diMBgwGAy2DiVbbNiwwVqfDRs22DqcXLVy5UratGlDwYIFMRqNGAwG/P39rfuTn5eRI0emOtaZnzcREREREWeipJSIiB3r1auXNUFz5z8XFxf8/f2pVasW/fv3Z+/evbYO1Wr69Om0b9+eNWvWcOXKFcxms61DEhERERERO6SklEgeNHv2bGty4syZM7YOR2zAYrFw/fp19u/fz/Tp06lbty7vvfeercMiJiaGoUOHAlC5cmUWLVrEnj17OHDgAFu3brVxdCIiIiIiYk9cbR2AiIhkzm+//UbRokUBMJvNXLx4kZUrVzJt2jRMJhMffPABxYoV45VXXrFZjDt37uT69esATJw4kccffzzNchaLJTfDEhERERERO6SklIhIHlGxYkVKly6dYlurVq1o0aIFHTt2BG6ty/Xyyy9jNBptECGcP3/e+nPFihVtEoOIiIiIiOQNmr4nIpLHdejQgSZNmgBw+fJldu/ebbNY4uPjrT+7ubnZLA4REREREbF/SkqJ5CHJdyV7/vnnrdvKlCmTahHsO+9Y1rRpUwwGA02bNs3w3He7891/75a2bt06QkJCKFGiBG5ubqlG8OSkM2fO8Oabb1KtWjXy5cuHt7c3FSpU4OWXX+bAgQMZHpdcj9mzZ2d4jdKlS2MwGOjVq1e6ZWJjY/nggw+oVasWPj4+FChQgEaNGjFz5sxcX9y7Xr161p//+ecf68/JC6Unt09YWBjvvPOO9blL6w53ly9f5v3336dOnTr4+/vj6elJ6dKl6dmzJ1u2bEnz+smvs4xem3deJ6O7792LZcuWERISQsmSJfH09MTf358HH3yQUaNGce3atSydW0REREREcpam74nIPXvvvff44IMPbHLt77//nj59+qQYkQNw4sQJTpw4wTfffMOYMWMYMmRIjsYRHh5O8+bNOXLkiHVbTEwMoaGhhIaGsnjxYgYOHJijMdzpzlFJSUlJaZbZtm0bHTp0ICIiIt3zrFmzhpCQEKKiolJs/+eff/jnn3/44Ycf6N+/P5999hkuLrb7XuPatWt07dqVdevWpdgeHx/Prl272LVrF9OnT+fnn3+mQYMGNopSREREREQyoqSUSB7y0EMPceDAAX7++Wfef/99IOXi18nKlCmTYzEsWbKEAwcOUKNGDd58802qV69ObGwse/fuzbFrJlu5ciW9evXCYrHg6+vLoEGDaNmyJa6uroSGhvLhhx8SERHB0KFD8ff3p1+/fjkSh8lkon379taEVOvWrenXrx8lSpTg7NmzTJ8+nd9++42rV6/myPXTcucIsf++HgBu3rxJly5diIuL47333qNVq1Z4e3tz4MABgoODAdi7dy8dOnQgISEBNzc3Xn31VTp27IiPjw979uzho48+4vTp00ybNg0fHx/Gjx9vPf+sWbOIjo7O8LWZXa/L+Ph4WrZsye7duzEajTzzzDO0a9eOMmXKkJiYyKZNm5g8eTKXLl2iXbt27Nmzh1KlSmXLtUVEREREJPsoKSWSh/j4+FC9enV27txp3ZbW4tc56cCBA7Ro0YKVK1fi4eFh3f7II4/k6HUTExPp06ePNSG1efNmateubd3foEEDunTpwsMPP0xYWBiDBw8mJCSEggULZnssM2bMYNeuXQD06dOHGTNmWPfVrVuXJ598khdffJFvv/0226+dln379rF69WoAvL29eeihh1KVuXLlCr6+vmzZsoVatWpZt99Ztk+fPiQkJGA0GlmxYgWtW7dOUS4kJITGjRtz+PBhJk6cyP/+9z+qVasG/H/CKTdem6NHj2b37t34+/vzxx9/ULdu3RT7GzduTI8ePayvhaFDh/Ljjz9mexwiIiIiIpI1WlNKRO6Ji4sLX3/9dYqEVG5YunQpFy5cAOD9999PkZBKVqpUKSZMmADcmko3a9asHIll+vTpABQpUoRPPvkkzTKffvophQoVypHrA1gsFsLDw/n6669p2bKldcregAED8PT0TPOYt99+O0VC6k47duzgr7/+AqB3794pElLJAgIC+OqrrwAwm83W5yE33bx5k2nTpgEwZsyYVAmpZKVKlWLYsGEALFy4kOjo6FyLUUREREREMkdJKRG5J40aNcrVkVnJ/vjjD+DWAtkvvPBCuuVCQkLInz9/imOyU1hYGIcPHwbgqaeewtvbO81yvr6+PPXUU9l67TsXDndxcSE4OJjevXtb14h6/PHHGT16dLrH9+jRI919dz5XL774YrrlGjVqRJUqVVIdk1s2btzI9evXAejatWuGZZNH7yUmJlpHtomIiIiIiP1QUkpE7knNmjVtct2DBw8CtxIzGY1Acnd3p06dOimOyU53rt2U1jS5O915R7yc4u7uTqNGjfjuu+9Yvnx5igXP7+Tr60vZsmXTPU/yc+Xu7p7mKLQ71a9fH4C///6bhISE+wv8Pt05PTA4ODjVnSfv/Fe9enVr2fDw8FyNU0RERERE7k5rSonIPQkICLDJdZMXDS9cuPBdywYFBaU4JifiyEwsRYoUydZr37lwuIuLC76+vgQFBeHu7n7XY/39/TPcn1yvwMBAXF0z/tWQ/PxaLBauXbuW7fXMyKVLl+7ruJiYmGyOREREREREskpJKRG5J0aj0abXNxgMNr3+nXI7lqwsHJ7ZdrOn5zctyWtnAezevTvdkWH/Vbx48ZwKSURERERE7pOSUiIOzsXl1ixds9mcYTl7Xwg6MDAQgIsXL961bPJUreRjkiU/F3D/z8edI8XuFktmYrUXyc/VlStXMJlMGY6WSn5+DQZDro+cK1CggPXnQoUKKdkkIiIiIpKHaU0pkTzoXkaz5MuXD4Br165lWO748eNZiimnJa8PdPr0aS5fvpxuucTERPbs2ZPimGTJzwVk/HxcvXqVK1eupLmvRo0a1p+T71aXnrvttyfJz1VCQgJ79+7NsOyOHTsAqFChQqamDman5PXCAP78889cvbaIiIiIiGQvJaVE8iBPT0/rz/Hx8RmWLVOmDHAr6XTjxo00y0RERPD7779nX4A5oGXLlsCtdYxmzZqVbrlFixZZ786WfEyygIAA69pKdy6Y/V/z5s3DYrGkua9o0aLWu88tXLiQ2NjYNMtFR0ezYMGCdK9hb+58rr799tt0y23dutV698H/Pr+5oWXLltY7Hn722WfptpOIiIiIiNg/JaVE8qDg4GDrzydPnsyw7KOPPgrcGgHz+eefp9qfmJjISy+9lG5yxV506tTJusj3uHHjUtwFL9m///7L4MGDAfD29ub5559PVeaRRx4B4Oeff07zuTt27BjDhg3LMJZ+/foBt6axDRo0KM0yb7755n0vym0L9erV48EHHwRg5syZrF27NlWZ69ev8/LLLwO3pkImPw+5yd/fn1dffRWA0NBQ3nzzzQynYl68eJGvv/46t8ITEREREZF7oKSUSB5Up04d62ipYcOG8fvvv3P8+HFOnDjBiRMnUiSYHn/8cUqVKmUtO3DgQLZs2cLOnTv57rvvaNCgAb/88gsNGjSwSV0yy93dna+++gqDwUBUVBSNGjVizJgxhIaGsn37dj755BMefPBBLly4AMDEiRMpWLBgqvO88sorAMTGxtK0aVO++eYbdu/ezaZNmxgxYgT169cnMDCQQoUKpRtLv379rNPIvvjiCx577DF+/vlndu/ezc8//0ybNm2YOXOmNcmTV8ycORN3d3dMJhPt2rVj8ODBbNy4kZ07dzJz5kweeOABazJw8ODBqaZH5pbRo0dTv359AD799FMeeOABpk2bxp9//snevXtZv349U6dOpVOnTpQsWZIvv/zSJnGKiIiIiMhdWEQkT3r77bctQJr/1q9fn6Ls5s2bLT4+PmmWNRqNlk8//dQyYsQI67a0JO8bMWJEjtXpbjFYLBbL7NmzLR4eHunW3Wg0Wj744IMMrzNgwIB0jy9ZsqTl8OHDllKlSlkAy3PPPZfmOc6fP2+pVKlSuudp3bq15bfffku3TTLrueees57j9OnT9318qVKlMlX+t99+s/j5+aVbL8DSv39/S1JSUprHz5o1K1PxZvR6Wr9+/V2ft6ioKEvnzp0zjDP5X7NmzTJVdxERERERyV0aKSWSR3300UfMnDmTJk2aEBgYiNFoTLds48aN2bVrFz179qRo0aK4ubkRHBxMly5d2LRpEwMGDMjFyLPmueee4+jRo7z++utUqVIFHx8fvLy8KFeuHL1792bPnj0MGTIkw3N8+umn/PTTTzzyyCP4+fnh5eVFpUqVePfdd9m9e7d1zaiMFC1alD179jB27FiqV6+Ol5cX/v7+NGjQgOnTp/Prr7/m+iLg2aF169acOHGCoUOHUrt2bfz8/PDw8KBkyZL06NGDzZs3M3Xq1BR3MrSFfPnysXjxYjZv3sxLL71EpUqVyJcvH66urgQGBvLQQw/Rv39/Vq1aZffrpYmIiIiIOCuDxaJVYkVEREREREREJHdppJSIiIiIiIiIiOQ6JaVERERERERERCTXKSklIiIiIiIiIiK5ztXWAYiIY4iOjub06dP3dWylSpVwc3PL5ohERERERETEnikpJSLZ4q+//qJZs2b3dezp06cpXbp09gYkIiIiIiIidk3T90REREREREREJNcZLBaLxdZBiIiIiIiIiIiIc9FIKRERERERERERyXVaUyqLrl27hslkypFzFypUiMuXL+fIue2N6uq4nKm+qqvjcqb6OlNdc5KrqysBAQG2DuO+5WT/RrJG71H7pzayb2of+6c2sl850b9RUiqLTCYTiYmJ2X5eg8FgPb+jz7BUXfO22NhYunTpAsDixYvx8vKy7nPE+qZHdXVczlRfZ6qrZCyn+jeSNXqP2j+1kX1T+9g/tZHzUVJKRLLEYrGwb98+688iIiIiIiIimaE1pUREREREREREJNcpKSUiIiIiIiIiIrlOSSkREREREREREcl1DrOm1OrVq1m+fDmRkZGUKlWKF154gfLly6dbfuXKlaxZs4aIiAj8/PyoX78+zzzzDO7u7rkYtYiIiIiIiIiIc3KIkVKhoaF8//33dO3alfHjx1OqVCnGjRvH9evX0yy/ZcsWfvrpJ0JCQvjkk0/o27cvW7duZe7cubkcuYiIiIiIiIiIc3KIpNSKFSto0aIFzZo1o3jx4vTu3Rt3d3fWr1+fZvljx45RqVIlGjduTOHChalVqxaNGjXixIkTuRy5iGMIDAwkMDDQ1mGIiIiIiIhIHpLnp++ZTCZOnTpFp06drNtcXFyoUaMGx48fT/OYSpUqsXnzZk6cOEH58uW5ePEie/bsoUmTJuleJzExkcTEROtjg8GAl5eX9efslnzOnDh3ZsyaNYsvvviCy5cvU7VqVcaOHUudOnXSLd+lSxe2bt2aanuLFi2YM2cOAG+88QYLFixIsb9p06bWEWq2qmtusnW75gQfHx8OHjyY5j5HrG96VFfH5Uz1daa6yi3p9W8MBoNeB3ZI71H7pzayb2of+6c2sm850S55PikVFRWF2WzG398/xXZ/f38uXLiQ5jGNGzcmKiqKYcOGAZCUlESrVq3o3LlzutdZunQpixYtsj4uU6YM48ePp1ChQlmvRAaCgoJy9PxpmT9/PqNGjeLLL7+kfv36TJkyhR49enDs2DEKFy6c5jErVqwgISHB+vjKlSvUqlWLZ599luDgYAC8vLxo27Yts2bNspbz8PAgICAAsE1dbcWZ6grOVV/V1XE5U32dqa7OLr3+TcGCBW0YldyN3qP2T21k39Q+9k9t5DzyfFLqfhw6dIilS5fy0ksvUaFCBcLDw5k1axaLFi2ia9euaR7z5JNP0r59e+vj5Azh5cuXMZlM2R6jwWAgKCiIn376iSlTpnDs2DGMRiN169Zl9OjRlC5dOtuvmWz8+PE888wztG3bFoARI0awfPlypkyZwmuvvZapcyxevBgvLy+aNGlCWFgYALGxsVgsFiwWi7VcXFwc4eHhBAUFER4enmKfI0puV2eoKzhXfVVXx+VM9XWmuuY0V1fXHP/iKjuk17+JiIhIMYJK7IPeo/ZPbWTf1D72T21k39zc3LL9i6s8n5Ty8/PDxcWFyMjIFNsjIyNTjZ5KNn/+fB555BFatGgBQMmSJYmLi+Orr76ic+fOuLikXmrLzc0NNze3NM+Xk2+WmJgY+vTpQ5UqVYiOjmbixIm8+OKLrFmzJs04AT777DM+//zzDM+7YcMGihUrlmp7QkIC+/fv59VXX7XWy2Aw0LhxY3bt2pXpus6dO5cnnngCLy8v6zEWi4WtW7dSo0YN8ufPT6NGjXj77bcpUKCAdb+zfPA4Ul1jY2Pp2bMnAHPmzLFOa72TI9X3blRXx+VM9XWmujq79Po3eg3YN7WP/VMb2Te1j/1TG9mnnGiTPJ+UcnV1pWzZshw8eJB69eoBYDabOXjwoHWkz3/Fx8enmguZXoLH1h5//PEUDT958mTrelmVK1dO85iePXvSoUOHDM9bpEiRNLdfvXqVpKSkVNnPQoUKcfLkyUzFvGfPHo4ePcrEiRNTbG/WrBnt2rWjRIkS/PPPP3z00Uf07NmT5cuXZ+q8Yp+Sk43JPzulpCTcd+yAhATc3d2Jr1cPjEZbRyUiIiIiImLX8nxSCqB9+/ZMmzaNsmXLUr58eVatWkV8fDxNmzYFYOrUqQQGBvLMM88AULduXVauXEmZMmWs0/fmz59P3bp17S45derUKSZMmMCePXu4evUqZrMZgPPnz6eblAoICLCu02QLc+fOpUqVKqkWRn/iiSesP1epUoUqVarQsGFDQkNDeeqpp3I7TJFs4blqFfmHD8d4e5pqASApOJjro0cT166dbYMTERERERGxYw6RlGrYsCFRUVEsWLCAyMhISpcuzdChQ63T9yIiIlKMjOrSpQsGg4F58+Zx9epV/Pz8qFu3Lk8//bSNapC+5557juLFi/Pxxx8TFBSE2WymefPmGa7zkJXpe4GBgRiNRiIiIlJsv3z5cqbWxoiJieGXX35h8ODBdy1bqlQpAgMDOXPmzF3Litgjz1WrCOjTB/4zQswlPJyAPn249tVXSkyJiIiIiIikwyGSUgBt27ZNd7reyJEjUzw2Go2EhIQQEhKSC5HdvytXrnDy5EkmTJhA/fr1AdixY8ddj8vK9D13d3dq1qzJli1brM+n2Wxmy5YtPP/883e99vLly0lISMjwTobJLly4wLVr19K9o5+IXUtKIv/w4WCx8N8boxosFiwGA34jRhDXpo2m8omIiIiIiKTBYZJSthIXF0d8fHyq7S4uLnh6elofx8TEpHsOg8GQYnHomJgYDAYDvr6+BAQE8N133+Hn58eFCxdSrdOUfEe7O3l4eFCkSJFU572zbEJCAgkJCWnG0Lt3b9544w0qV65MzZo1mT17NjExMXTs2NFaj3fffZfg4GCGDBmS4rw//fQTLVu2xNPT01rW29ub6OhoJk+eTMuWLSlQoABnz57l448/plSpUtSrV4/o6GhiYmJSxBsXF2edrpgWLy8v6wi4+Ph4kpKSsqWsp6endRpnQkJChndXvJeyHh4euLq6WstmNNrNw8MD4+1ERmJiYoZl3d3dree9l7ImkynFa+C/7lz89r9lLRYL165dIyIigmvXrlm3b926lYCAANzc3PDz86NgwYLWtk1+jbi6uuLu7g5AUlJSmu+fZHeWNZvNxMXFZUtZo9GIh4eHtS6xsbH3XNZt2zbibk/Zs5YFkt/1BouF+AsXSNy4kcQGDVKdN6ufEZktm9ZnRHaVvbNd/8vb2ztT5/1v2bu97++lbHZ9RhgMhhSv45z8jEh+399L2ez8jEh+rUPWPiMyKnsv7/u8/Blx53MpIiIiImkzWJx2ZeLs8cADD7Bnz55U25s3b86cOXOsj8uXL59up/bhhx9m0aJF1sc1atTg6tWraZYtV64cJ0+e5JtvvqFt27bUr1+fc+fOpVm2YsWKrF+/3vq4WbNmHD9+PM2yxYsXZ/v27dbH9erV4/z582mWDQwMpFKlShQvXpwpU6bQtWtX60LX/+Xl5cWJEyeIjY3lxRdfJDQ0NMM/iO68Zp8+fVi5cmW6Zf/++2/rH6hvvPEGCxcuTLfs/v37rXf5Gzp0KN999126Zbdt20aJEiUAGDNmDF9++WW6ZdetW0elSpUAmDRpEpMnT0637MqVK6lTpw7BwcEMGzaMsWPHplt24cKFNGzYEIDZs2fz3nvvpVv2u+++o2XLlsCtO0sOHDgw3bJffvmldRTd8uXL6du3b7plJ0+eTEhICMeOHWPevHl8/fXX6Za9Fy+//DLvvfceRqORvXv38vjjj6dbduDAgQwaNAiAY8eO0bx583TL9u3bl2HDhgHw77//0iCNRFCy5557jg8++AC4NSKxZs2a6ZYNCQlhypQpwK1kUIUKFdIt2xW481X43xFUd8qpz4hatWqxatUq6+Oc+IwwGAx07NiRnTt3plk2MDCQAwcOWB9n5jMiWc+ePVm3bl2aZcHxPyNq164NwBdffGGTz4gZM2bQp08fwsLC+OWXX+76GdGtWzcA/vjjD5577rl0y44bN45evXoBEBoamuFI5ffff59+/foB5OnPiDp16rB79+50j7N3ly9fzvD3tdiGwWAgODiYsLAw5725iJ1TG9k3tY/9UxvZNzc3t0wt63MvNFIqj/H19U03WZSdChYsmOF17vwDOTO8vLz46aef7voHp9iPb7/9ltGjRxMZGZmp8gaDgXLlynHt2jWuXLmSbrkZM2Ywd+5cGjVqRJkyZbIpWhEREREREclrNFIqi/79998cm74XFBREeHh4igxxbk7Nyex0m6xOzbmzrs4wfS84OJh//vnHrqbv/fvvv/z888/8/vvvHD58OMUx3t7e1KpViwoVKlCxYkXKly9PcHAwBQoUsE5PSWtqjslk4tKlS8TExLB3716OHDnCwYMHOXz4cKppM1WqVKF9+/Y8/vjjKRbgt6upOYmJeH//PS6TJ2O8naizuLlBYqJ1RNSd0/csBgNRRYpwecOGNNeUysvT9wwGA/7+/ly4cMFppu/d+Xns6NP3SpQoQVhYGImJiZq+R9am7yWPqMuLNFLKPmkEgf1TG9k3tY/9UxvZt5wYKaWkVBblVKfNmd6MqqttxMfHs3r1aubOncvmzZut211cXKhfvz7NmzenQYMG1KhRw/rH5L1Kq75JSUkcOHCAjRs3smnTJnbu3Jnij++HH36YXr160aZNm/u+brayWPBcswa/sWNxPXUKgMQKFYgaNgxDXBwBL78M3FpDynoIgMHgsHffs6fXcW5wpvo6U11zWk502nKTklL2Se9R+6c2sm9qH/unNrJvmr4nIll2/fp15syZwzfffMOlS5es25s0aUKnTp1o1aqVdW2dnGA0Gqlduza1a9fm9ddf5+rVq6xatYply5axbds2tm7dytatWwkKCqJnz548//zz5M+fP8fiyYjb/v34jR6Nx+31kJIKFODG4MHEPPMM3B55cu2rr8g/fDjG/yx6HjllikMmpERERERERLKLklIiTuLq1atMnz6dOXPmcPPmTQCCgoLo3r073bp1o2TJkvd13ri4OHr37g3AzJkzU0xJy4zAwECeffZZnn32Wc6fP88PP/zATz/9RHh4OBMmTODLL7/k+eefp3fv3gQGBt5XjPfK5fx5/D76CO8lSwCweHpys3dvbvbvjyVfvhRl49q1I65NGzx27KBAfDymt9/G9fx5DBlMZRIRERERERElpUQcXnR0NDNnzuTLL7/kxo0bAFSqVIl+/frxxBNPWNdYuV9ms9m6eH1G6/tkRrFixXjnnXd44403WLlyJdOmTePo0aN89tlnfPPNN/Tr14+XX345xbpC2clw8ya+U6fiO3MmhtvrzcR07syNd98l6Y61rlIxGklo2BCCg4k5dAi/Dz7Aa+HCWyOqREREREREJE0utg5ARHKG2Wxm3rx5NGrUiAkTJnDjxg2qV6/O7NmzWbt2LSEhIVlOSOUUDw8POnfuzO+//87MmTOpVq0a0dHRTJw4kSZNmrBgwYIsJ8BSMJnwnjOHwo0ake/zzzHExRHfoAGXV60i8vPPM05I/Udsly5YXFzw2LED45kz2RejiIiIiIiIg1FSSsQBHTx4kCeeeIJBgwZx+fJlSpcuzfTp0/n1119p1aqV9S5j9s7FxYV27drx22+/8cUXX1CiRAnCw8N588036dChA4cOHcraBSwWPNato1CrVvi/+y7GiAhMZcpw9ZtvuLJoEYm1at3zKc3BwcQ3aQKA9+LFWYtPRERERETEgSkpJeJAYmNjGTFiBI899hi7d+/Gx8eH4cOHs2HDBp544gnrrenzGoPBQMeOHdmwYQPvv/8+vr6+7N27l8cee4yxY8cSExNzz+d0PXSIAk8/TYGePXE7fpykgACujxnDpfXriWvbFrKQuIsNCQHAa9EiyM4RXSIiIiIiIg4kb/6FKiKp/PXXX7Rq1Yqvv/4as9nME088waZNm3j55Zdxc3OzdXjZwtPTk379+rFx40batWtHUlISX3zxBS1atOCvv/7K1DlcwsPJP2gQhdq0wWPzZizu7tzs25dLf/5J9AsvQDY8V3Ft22L29cX17Fncd+zI8vlEREREREQckZJSInlcXFwcY8eOpXPnzpw+fZqgoCB++OEHpk+fTlBQkK3DyxFBQUHMnDmTWbNmUbRoUc6ePUvnzp35+OOPSUxMTPMYQ0wMvpMnU7hxY3zmzcNgsRDboQOXNm4katgwLPnzZ1t8Fi8vYtu3B8Br4cJsO6+IiIiIiIgjUVJKJA87ffo0HTt25IsvvsBsNtO1a1fWrVtHs2bNbB1armjdujVr166la9eumM1mPv30U5544glOnDjx/4WSkvCaP5/CTZrgN2kSLrGxJNSty+Wff+bal1+SVLJkjsRmncK3YgWG2NgcuYaIiIiIiEhepqSUSB61fPly2rZty6FDhwgMDOTbb7/l008/JX82jvjJDG9vb86fP8/58+fx9vbO1WsD+Pn58emnn/LFF1/g7+/Pvn37aNeuHcuXL8d90yYKtW1LwMCBGMPDMZUsydUvvyTi559JfPDBHI0roV49TCVL4nLzJp6rV+fotURERERERPIiJaVE8pj4+Hjee+89+vbty82bN6lfvz5r1qyhTZs2tg7Npjp27Mgff/zBww8/TMnoaIr37UvBp5/G7fBhzPnzc33YMC5t2EBchw5ZWsQ801xciO3aFdAUPhERERERkbQoKSWSh0RERPDUU08xe/ZsAF599VUWLFhAcHCwbQOzE8Xc3PitXDkOGAw8DiQC84sU4eDSpUT37QseHrkaT8ztpJTH5s24hIXl6rVFRERERETsnZJSInnEoUOHaNeuHTt37sTPz485c+YwZMgQXF1dbRpXXFwcffr0oU+fPsTFxdkmiNhYfD//nMKNGpHvhx8wWiycqVOHet7edL94kbY9enDo0KFcDyupVCni69XDYDbjvWRJrl9fRERERETEnikpJZIH/PrrrzzxxBOcP3+esmXLsnz5cpo3b27rsAAwm82sXLmSlStXYjabc/vieC1eTOFHHsHvo49wuXmThFq1iFi8GPcVK/hs9WrKlStHWFgYTz75JGvXrs3d+LhjwfOFC8FiyfXri4iIiIiI2CslpUTs3MyZM3nppZeIjY3lkUceYfny5ZQvX97WYdmc+7ZtFGzfnoABA3C9cAFT0aJc+/xzIlasIKFBAwDKlSvHL7/8QsOGDYmOjqZXr17MmjUrV+OMbd8ei6cnbn//jdu+fbl6bREREREREXumpJSInbJYLIwbN46RI0cC0KtXL+bMmYO/v79N47I148mTBLz4IgW7dMF93z7Mvr5EvfsulzZtIrZzZ3BJ+bHm7+/Pjz/+SPfu3TGbzbz//vuMHj0aSy6NWrL4+RHbti0AXosW5co1RURERERE8gIlpUTsUGJiIm+88QbTp08HYMiQIYwdO9bm60fZkuHqVfyGDaNw8+Z4rV6NxWgk+n//49Kff3LztdfAyyvdY93d3Zk4cSJDhgwBYMaMGQwaNAiTyZQrsVun8C1bBgkJuXJNERERERERe+e8f+GK2KnY2Fj69OnDunXrMBqNTJgwgW7dutk6LNuJj8dn1izyffopLlFRAMS1aEHU++9jqlgx06cxGAy8+uqrFCpUiMGDBzN//nyuX7/OtGnT8PT0zKnoAYhv0oSkIkUwXryI59q1xD32WI5eT0REREREJC9wmKTU6tWrWb58OZGRkZQqVYoXXnghw3V3oqOjmTt3Ljt27ODmzZsUKlSI5557jgceeCAXoxZJKSYmhueee47Q0FA8PT2ZMWMGLVu2tHVYtmGx4Ll8OX4ffojr2bMAJFatyvXhw0lo0uS+T9utWzfy58/PK6+8wurVq+nZsyezZs3C19c3uyJPzWgktnNnfL/4Aq+FC5WUEhERERERwUGm74WGhvL999/TtWtXxo8fT6lSpRg3bhzXr19Ps7zJZGLs2LFcvnyZgQMHMmXKFF5++WUCAwNzOXKR/3fz5k2effZZQkND8fHxYe7cuU6bkHLbuZOCHTsS2K8frmfPklSkCNcmT+by6tVZSkgla9u2LXPmzMHX15fQ0FB69uzJzZs3syHy9MXcnsLnuXYtLleu5Oi1RERERERE8gKHSEqtWLGCFi1a0KxZM4oXL07v3r1xd3dn/fr1aZZft24dN2/e5K233qJy5coULlyYqlWrUrp06dwNXOS2qKgonnnmGbZv346fnx9z586lXr16tg4rU7y8vPj777/5+++/8cpgXafMMP7zDwEvv0yhJ57AffduzF5eRA0ezKUtW4jt1g2MxmyKGho1asT8+fPx8/Njx44dOZ6YMlWqRELNmhhMpltrS4mIiIiIiDi5PD99z2QycerUKTp16mTd5uLiQo0aNTh+/Hiax+zatYsKFSrwzTffsHPnTvz8/GjUqBGdOnXCxSXtPF1iYiKJiYnWxwaDwfoHuMFgyL4K3XH+nDq3vXGauiYl4bFjB8TH4+HhQXy9emA0cv36dbp3786+ffvw9/dn7ty51KpVy9bRZprBYMDHxyfdfXf+n+45IiPx/fRTfGbNwpCQgMVgILZ7d268/TbmIkVulcnesAGoU6cO8+fPp1u3btbE1A8//HBfU/kyU9fYkBDc9+/Ha9EiYl566b7jtjWnec/e5kz1daa6yi3p9W8MBoNeB3ZI71H7pzayb2of+6c2sm850S55PikVFRWF2WzG398/xXZ/f38uXLiQ5jEXL17k8uXLNG7cmCFDhhAeHs7XX39NUlISIben2PzX0qVLWXTH7dzLlCnD+PHjKVSoULbVJS1BQUE5en574tB1XbIEXn8dzp0DIBCgeHFiP/qIzlOnsm/fPgoWLMjvv/9O7dq1bRlpjki3bRMS4MsvYdQouHr11rZWrTBMnIh3zZp450JswcHBrF27lpYtW7Jjxw5eeOEFVq1aRb58+e7rfBm+jvv2hVGjcN+/n+CrV6FatfuM2j449Hs2Dc5UX2eqq7NLr39TsGBBG0Yld6P3qP1TG9k3tY/9Uxs5jzyflLofFosFPz8/Xn75ZVxcXChbtixXr17ll19+STcp9eSTT9K+fXvr4+QM4eXLl3PktvIGg4GgoCDCw8OxWCzZfn574uh19Vy1Cv/evcFiSTHax3L+PJ7PPkswt5Ko8+bNo0iRIoSFhdkq1PsSHx/PO++8A8D48ePx8PCw7ku3bS0WPFavxm/cOFxPnQIgsWJFbgwfTnyzZmAwQC4+D8WKFWPevHl069aNLVu20KZNG3744Yd7mo6Y2ddxQIsWeP72GzenTePGsGHZEX6uc/T37H85U32dqa45zdXVNce/uMoO6fVvIiIiUoygEvug96j9UxvZN7WP/VMb2Tc3N7ds/+Iqzyel/Pz8cHFxITIyMsX2yMjIVKOnkvn7++Pq6ppiql6xYsWIjIzEZDLh6pr6aXFzc8PNzS3N8+Xkm8VisTjNm9Eh65qUhN+wYakSUgAGiwUz8AVwdMwYKvv5Yb52DYuvL6TxGrRXJpOJBQsWADBu3Djc3d1v7UhKwn3HDkhIwM3d3Tpd0W3fPvxGj8Zj27ZbxQoW5MbgwcQ8/fT/19sGr4OaNWsyb948nnrqKbZu3Urfvn2ZOXNmuu/79NztdRwTEoLnb7/htWQJUe++m6fa+r8c8j2bAWeqrzPV1dml17/Ra8C+qX3sn9rIvql97J/ayD7lRJvk3b+GbnN1daVs2bIcPHjQujC02Wzm4MGDtG3bNs1jKlWqxJ9//onZbLYmpsLCwggICEgzISVyv9y3b8eYwYgfF6AIUOS111JsN3t6YvH1vfXPxwfz7Z/Nd2y787E5jW0WX1/MPj7g6Xlr5FEu8ly1ivzDh1vrXgBIKlwYU5kyeGzfDoDF05Obffpws3//W4k4O1CrVi1mz57Ns88+y++//87AgQP59NNP011r7n7EtWhBUkAAxosX8di8+dbIMBERERERESfkEBmY9u3bM23aNMqWLUv58uVZtWoV8fHxNG3aFICpU6cSGBjIM888A0Dr1q357bffmD17Nm3btiU8PJylS5fy2GOP2bAW4oiMly5lqpzZxwdDYiKGhAQAXOLiIC4OIiKyHIPFaLQmqCz58qVIcll8fDDf3na35FZy+bvdAc9z1SoC+vRJNdrJ5dIlPG4/HzFduhD1zjuYixXLcv2y28MPP8yXX37Jiy++yJIlS8ifPz9jxozJvkX93N2J7dQJ31mz8Fq4UEkpERERERFxWg6RlGrYsCFRUVEsWLCAyMhISpcuzdChQ63T9yIiIlL8QVmwYEHee+89vvvuO9566y0CAwN57LHHUtzBTyQ7JBUunKlyV2fPJqFhQ0hIwHDzJi43b976PzoaQ/LPt/9PsT06GpcbN/7/59v/G27cwCUmBgBDUhKG69dxuX49W+pk9vZOMYLr5h3rLvm99x7+K1emPV0RsADmQoWI/OSTuya3bKlVq1ZMmTKFAQMGMGvWLPz9/Rk8eHC2nT82JORWUuq337geFYXFzy/bzi0iIiIiIpJXOERSCqBt27bpTtcbOXJkqm0VK1Zk3LhxORyVOLuE+vVJKlIEl4sXUyVpACwGA0nBwSTUr39rg7s7lsBAkgIDs35xs/lWgurOJNaNGykTXbcTWNaE1p3Jrf8mwG4v6O8SEwO3E14AHndc0mfBAjKa6GYAjJcv4759+60knB3r3LkzUVFRvPfee3zyyScUK1aMp59+OlvOnVizJokVK+J2/DheK1YQc3sUp4iIiIiIiDNxmKSUiF1yccFUtCgeFy9igZR337s9ei9q1KicGTXk4nJrul6+fJizei6LBeLj00xWxVy5Am++CUBM69b4rFlz19NldlqjrfXq1YtLly7x6aef8s477xAcHGydFpwlBgOxXbvi9sEHeC1cqKSUiIiIiIg4pexbvVdEUvGeNw+PPXtIBC7+Z19ScDDXvvqKuHbtbBHavTEYwNMTc8GCJJUujal6dRLq1ye+RQvi77iVeEzPnpk6XWanNdqDt956i86dO5OUlESfPn04dOhQtpw3pnNnLC4ueOzYgfHMmWw5p4iIiIiISF6ipJRIDjGeOkW+998HYCjQvXFjwufOhZ9+4sqiRVzati1vJKTuwsvLi/3797N//35cH3mEpOBg6yiw/7IYDJiKFv3/6Yp5gMFgYNKkSTRs2JDo6Gj+97//ceHChSyf1xwcTHyTJgB4L1qU5fOJiIiIiIjkNUpKieSExETyv/IKxrg41gG/VqnCjK+/xvLoo/D007fWU7Ljhb7vhcFgoECBAhQoUACDqyvXR48GSJWYyvHpijnI3d2dr7/+mooVKxIeHs7//vc/bt68meXzxoaEAOC1aBGYszzJUkREREREJE9RUkokB/hOmoTngQNcBQYVKMDs778nX758tg4rV8S1a8e1r77CHBSUYnuemq6Yhvz58zNnzhwKFy7MkSNHeO211zBnMZEU17YtZl9fXP/9F/cdO7IpUhERERERkbxBSSmRbOa+bRu+n38OwKuuroyZPZuiRYvaOKqcEx8fz9ChQxk6dCjx8fHArcTUxe3bubJokUNNVyxevDjffPMNHh4erFmzho8//jhL57N4eRHboQMAXgsXZkeIIiIiIiIieYaSUiLZyHD9Ou4vvYQL8C3Q8JNPeOCBB2wdVo5KSkriu+++47vvviMpKen/dxiNt6YpOth0xQceeIAJEyYA8Pnnn7Ns2bIsnS+2a1cAvFaswBAbm9XwRERERERE8gwlpUSyUVK/fvhdu8YJ4HCfPnTu3NnWIUkO6NKlC6+88goAgwYNYv/+/fd9roR69TCVLInLzZt4/vprdoUoIiIiIiJi95SUEskm8d9+S/GNGzEBU+vX541hw2wdkuSgd999l+bNmxMXF8fzzz/PpUuX7u9ELi7/P1pKU/hERERERMSJKCklkg3Mp05RaORIAKYXLEj/OXNwcdHby5EZjUamTZtG+fLlCQ8P58UXXyQhIeG+zhVzOynlsXkzLmFh2RmmiIiIiIiI3dJfzSJZZTIR26ULvklJbDMaqbtoET4+PraOSnKBn58fs2bNIn/+/OzatYtBgwbd13mSSpUivn59DBYL3kuWZHOUIiIiIiIi9klJKZEsOvvKK1S4dIko4NyHH1KmQgVbhyS5qGzZsnz66acATJ06lSX3mVRKMYXPYsm2+EREREREROyVklIiWXBu8WIeXLkSgJ/btKFhjx42jkhsoVWrVrz++usAvPXWWxw9evSezxHbvj0WT0/c/v4bt337sjtEERERERERu6OklMh9unHhAoUHDsQV+KNwYZp+9ZWtQ7IJT09Ptm3bxrZt2/D09LR1ODYzePBgWrVqRWxsLL179+bGjRv3dLzFz4/Ytm0B8NaC5yIiIiIi4gSUlBK5D2azmdNPPEFpk4lzRiNFly7F6Opq67BswsXFhRIlSlCiRAmnXtzdaDTy008/UbRoUU6dOsXAgQOx3OM0vNiQEAC8li2D+PgciFJERERERMR+OO9fkCJZsPn112l74QJm4NwHH+BfurStQxI7ULBgQb766ivc3NxYtWoVM2bMuKfj45s0ISkoCJfISDzXrcuhKEVEREREROyDklIi9+jQb7/R5vZi1tubNaPks8/aOCLbSkhIYMyYMYwZM4aEhARbh2NzDzzwAKNGjQLgww8/ZPfu3Zk/2GgkpnNn4PaC5yIiIiIiIg5MSSmRexB59Spe/foRCPzt70/Jb7+1dUg2ZzKZ+PLLL/nyyy8xmUy2Dscu/O9//6N9+/aYTCZeeeUVrl+/nuljk+/C57l2LS5XruRUiCIiIiIiIjanpJRIJlksFraGhNA4Pp4YgwHj3LkY3N1tHZbYIYPBwIQJEyhRogT//vsvb7/9dqbXlzJVqkRCzZoYTKZba0uJiIiIiIg4KCWlRDJp9Ycf0vPoUQBOvvYanjVr2jgisWd+fn588cUXuLq6smLFCn788cdMH2td8FxT+ERERERExIEpKSWSCYd37qTR9Om4A0cqV6bA22/bOiTJA+rUqcOQIUMAGDFiBEeOHMnUcbGdOmFxc8P9wAFcbydCRUREREREHI2SUiJ3ER0dTVjPnlS2WLji7o7/woVgMNg6LMkj+vTpQ/PmzYmLi6Nfv37ExMTc9RhzYCBxLVoA4L1oUU6HKCIiIiIiYhNKSoncxdIXX6RnVBQAUZ9/jiUw0MYRSV7i4uLCJ598QpEiRfj7778ZPnx4po6zTuFbsgS0gLyIiIiIiDggh0lKrV69mv79+9OjRw+GDh3KiRMnMnXcn3/+yVNPPcXHH3+cwxFKXrTup5/otXkzAH936IBH+/Y2jkjyooIFC/LZZ59hMBiYO3cuq1atuusxcc2bkxQQgPHiRTxuvwZFREREREQciUMkpUJDQ/n+++/p2rUr48ePp1SpUowbN+6ut2G/dOkSc+bMoUqVKrkUqeQl4WFhBA0ZQmHgfIEC+Hz6qa1Dskuenp6sW7eOdevW4enpaetw7Fbjxo155ZVXAHj77be5ePFixge4uxPbqROgBc9FRERERMQxOURSasWKFbRo0YJmzZpRvHhxevfujbu7O+vXr0/3GLPZzOeff85TTz1F4cKFczFayQvMZjObunWjpclEnMEAP/0EHh62Dssuubi4UKlSJSpVqoSLi0N8pOSYwYMHU61aNa5du8agQYOwWCwZlrdO4fvtNwy3p5CKiIiIiIg4CldbB5BVJpOJU6dO0en2iAK49UdyjRo1OH78eLrHLVq0CD8/P5o3b56pO2IlJiaSmJhofWwwGPDy8rL+nN2Sz5kT57Y39ljXFePH0/fkSQDODRiAT40a2XJee6xrTnKm+mamrh4eHkybNo22bduyfv16vvvuO55//vl0y5tq1SKxYkXcjh/Ha8UKYnv0yPa474cztSs4V32dqa5yS3r9G4PBoNeBHdJ71P6pjeyb2sf+qY3sW060S55PSkVFRWE2m/H390+x3d/fnwsXLqR5zNGjR1m3bt09rSO1dOlSFt1xF6wyZcowfvx4ChUqdF9xZ1ZQUFCOnt+e2EtdD+3eTcNp0/AE/qlWjfKffJLtd9uzl7pmh4SEBD744AMAhg4diru7e6oyjlTfu7lbXYODg/n4448ZMGAAY8aM4cknn8x4CvGLL8I77+D/88/4Dx6czdFmjTO1KzhXfZ2prs4uvf5NwYIFbRiV3I3eo/ZPbWTf1D72T23kPPJ8UupexcbG8vnnn/Pyyy/j5+eX6eOefPJJ2t+xyHVyhvDy5cuYcuDOWAaDgaCgIMLDw+86xSevs6e6xsfH81erVvSyWLjm5obHjz8SFh6ebee3p7pml5iYGEaNGgVAz5498fb2tu5zxPqm517q2rlzZxYvXszGjRvp1q0by5cvTzOZB+DSsiWFXVwwbNnCpa1bSSpdOgeivzfO1K7gXPV1prrmNFdX1xz/4io7pNe/iYiISDGCSuyD3qP2T21k39Q+9k9tZN/c3Nyy/YurPJ+U8vPzw8XFhcjIyBTbIyMjU42eArh48SKXL19m/Pjx1m3JL/bu3bszZcqUNLOybm5uuLm5pRlDTr5ZLBaL07wZ7aGuP7/6Kq9fvQrAtYkT8SxUCHIgJnuoa3a5sx7p1cuR6ns3mamrwWBg8uTJtGjRggMHDjBx4kSGDBmSZtmkoCDimzTBc+NGvBYu5IYdjZZypnYF56qvM9XV2aXXv9FrwL6pfeyf2si+qX3sn9rIPuVEm+T5VYldXV0pW7YsBw8etG4zm80cPHiQihUrpipftGhRJk6cyMcff2z9V7duXapVq8bHH3+s4epObNdvvxGyciUAR5o1w7NrVxtHJI4sKCiICRMmADBt2jS2b9+eblnrgueLFoHZnCvxiYiIiIiI5LQ8n5QCaN++PWvXrmXDhg2cO3eOr7/+mvj4eJo2bQrA1KlT+emnnwBwd3enZMmSKf75+Pjg6elJyZIlcXXN84PH5D5E37yJW//+FAXO+fnhP3OmrUMSJ9CuXTueeuopLBYLb775JjExMWmWi2vbFrOvL67//ot7BskrERERERGRvMQhklINGzakZ8+eLFiwgLfffpszZ84wdOhQ6/S9iIgIrl27Ztsgxa5t7tWLtrGxJACm777DcvvOiiI5bfTo0RQtWpR//vnHumD8f1m8vIjt0AG4PVpKRERERETEATjMsKC2bdvStm3bNPeNHDkyw2P79++fAxFJXrFn/ny6bd0KwKEePShSr56NIxJnki9fPiZNmsTTTz/NrFmzaNeuHQ0bNkxVLjYkBJ+5c/FasYKosWOVOBURERERkTzPIUZKidyvG1evUuydd/ABDgUFUeSjj2wdkjihRx55hGeffRaAgQMHEh0dnapMwkMPYSpZEpebN/H89dfcDlFERERERCTbKSklTu1o9+7UTkwk0sUF7wULwEVviXvl4eHBypUrWblyJR4eHrYOJ88aNmwYxYsX599//2XcuHGpC7i4EHt78X2vhQtzOToREREREZHsp7/AxWkd/vJLOhw6BMCRN9/Eo1w5G0eUNxmNRmrXrk3t2rUxGo22DifP8vX1ZeLEiQB89913bNmyJVWZmNtJKY/Nm3G5cCFX4xMREREREcluSkqJU7p57hxVxo3DBdhcoQKlBg60dUgiNGnShP/9738ADBo0iJs3b6bYn1SqFPH162OwWPBessQWIYqIiIiIiGQbJaXE+VgsRISEUMxs5rSrK0U1FSpLEhIS+OKLL/jiiy9ISEiwdTh53vvvv0+JEiU4d+4cY8aMSbU/NiQEuH0XPoslt8MTERERERHJNkpKidM5PXYsDc+exQT8M3YsXoUK2TqkPM1kMjF27FjGjh2LyWSydTh5no+PD5MnTwbghx9+YNOmTSn2x7Zvj8XTE7e//8Zt3z5bhCgiIiIiIpItlJQSpxJ35Ag1Z8wAYEXdulTs2dPGEYmk1rBhQ55//nkABg8enOJufJZ8+Yht2xYAb43yExERERGRPExJKXEeJhNJzzxDPouFHR4eVP/xR1tHJJKuoUOHUqJECc6fP8/48eNT7LNO4Vu2DOLjbRCdiIiIiIhI1ikpJU7j+rvvUuHSJa4DFydMwDtfPluHJJIub29vazLq22+/ZefOndZ98U2akBQUhEtkJJ5r19oqRBERERERkSxRUkqcw7ZtVJg7F4Dv69enbpcuNg5I5O4effRRQkJCsFgsvPXWW8Qnj4oyGonp3Bm4veC5iIiIiIhIHqSklDg8w82buL/wAq7AYg8Pmn/zja1DEsm04cOHU6BAAY4fP860adOs22O7dgXAc+1aXK5csVV4IiIiIiIi901JKXF4lgEDKHj9OmeAqA8/JCAgwNYhiWRaYGAgY8aMAeCzzz7j2LFjAJgqVSKhZk0MJtOttaVERERERETyGCWlxKG5//wzRX/7jSTg83r1aP3UU7YOyeF4eHiwcOFCFi5ciIeHh63DcUgdO3akVatWJCYmMnjwYJKSkoA7FjzXXfhERERERCQPUlJKHJbL+fP4DBwIwGR3d56ZPh2DwWDjqByP0WikYcOGNGzYEKPRaOtwHJLBYOCDDz7A19eX3bt3M3v2bABiO3XC4uaG+4EDuB49atsgRURERERE7pGSUuKYkpLw6tsXr7g4dgCGkSMJDg62dVQi961o0aIMHToUgI8++ohz585hDgwkrkULALw1WkpERERERPIYJaXEIfl8+SX5d+/mJjCxdm269+xp65AcVmJiIrNnz2b27NkkJibaOhyH1rNnT+rXr09MTAzvvPMOFovl/6fwLVkCJpONIxQREREREck8JaXE4bgdOIDv+PEADHZ1ZcDnn+Piopd6TklMTOS9997jvffeU1Iqh7m4uPDxxx/j4eHBhg0bWLp0KXHNm5MUEIDx0iU8Nm+2dYgiIiIiIiKZpr/UxaEYYmPx69sXY1ISi4HAwYMpW7asrcMSyTbly5fn9ddfB2DkyJFci44mtlMnQAuei4iIiIhI3qKklDgUv1Gj8DhzhvPAxAoVeLlvX1uHJJLt+vXrR8WKFbly5Qoffvjh/0/h++03DFFRNo5OREREREQkc5SUEofhsWYNPnPmAPAc8O6ECbi5udk2KJEc4O7uzkcffQTAjz/+SGh8PIkVK2KIi8Nr+XIbRyciIiIiIpI5SkqJQ3C5dAn/QYMAmAgE9ejBQw89ZNugRHJQ/fr16d69OwDvvPsuN558EtAUPhERERERyTuUlJK8z2zG/803MV69yl7gkwIFGDp0qK2jEslx7733HoGBgRw7dowZ0dFYXFzw+OsvjGfO2Do0ERERERGRu1JSSvI8n1mz8NywgVjgGWDIyJH4+/vbOCqRnBcYGMjw4cMBGP3111y/PTrQe9EiW4YlIiIiIiKSKa62DiC7rF69muXLlxMZGUmpUqV44YUXKF++fJpl//jjDzZt2sS///4LQNmyZXn66afTLS/2y/XoUfzGjQNgMFCwSROevD2NSXKHu7s73333nfVnyV1du3ZlwYIFhIaGMvXGDd4HvBYt4sbAgeCi7x1ERERERMR+OcRfLKGhoXz//fd07dqV8ePHU6pUKcaNG8f169fTLH/48GEaNWrEiBEjGDt2LAUKFGDs2LFcvXo1lyOXLImLI+DVVzHEx7MS+MbdnQ8++ACDwWDryJyKq6srLVu2pGXLlri6OkyeO88wGAx8+OGHuLu788HhwyR4euL677+4b99u69BEREREREQy5BBJqRUrVtCiRQuaNWtG8eLF6d27N+7u7qxfvz7N8gMGDKBNmzaULl2aYsWK0bdvXywWCwcOHMjlyCUr/D78ELcjR7hsMPAC8NqAAZQtW9bWYYnkuvLly/Pqq68SCyQvc+6tBc9FRERERMTO5flhDSaTiVOnTtGpUyfrNhcXF2rUqMHx48czdY74+HhMJhO+vr7plklMTCQxMdH62GAw4OXlZf05uyWf0xlG/dxPXd03bMD3668BeM5iIV+5cvTv39/uny9HbNfExESWLFkCQOfOnXFzc7Puc8T6psfWdX311VdZtmwZX546RQ/Ac8UKXMaNw+Ltne3XsnVdc5sz1deZ6iq3pNe/MRgMeh3YIb1H7Z/ayL6pfeyf2si+5US75PmkVFRUFGazOdXC1v7+/ly4cCFT5/jxxx8JDAykRo0a6ZZZunQpi+5YPLhMmTKMHz+eQoUK3VfcmRUUFJSj57cnma5rRAQMHAjANOBXYN3MmZQuXTqnQst2jtSu0dHRvPnmmwD07t0bHx+fVGUcqb53Y8u6zpw5kxYtWnASKBcdTdC2bdCjR45dz5naFZyrvs5UV2eXXv+mYMGCNoxK7kbvUfunNrJvah/7pzZyHnk+KZVVy5Yt488//2TkyJEZLtL85JNP0r59e+vj5Azh5cuXMZlM2R6XwWAgKCiI8PBwLBZLtp/fntxTXS0WAl54Ac/wcE66uzM4IYGQkBAqV65MWFhY7gScBY7YrjExMdafw8PD8b5jZI4j1jc99lDXKlWq0KVLF75fvJhRQNyMGVxr3jzbr2MPdc1NzlRfZ6prTnN1dc3xL66yQ3r9m4iIiBQjqMQ+6D1q/9RG9k3tY//URvbNzc0t27+4yvNJKT8/P1xcXIiMjEyxPTIyMtXoqf/65ZdfWLZsGcOGDaNUqVIZlnVzc0sxLelOOflmsVgsTvNmzExdvX/4Ac/ffsNkNNIlIQFPf3+GDRuW554jR2rXO+uRXr0cqb53Y+u6Dh8+nP+tWcOoGzfw2LIFw/nzmIsWzZFr2bquuc2Z6utMdXV26fVv9Bqwb2of+6c2sm9qH/unNrJPOdEmeX6hc1dXV8qWLcvBgwet28xmMwcPHqRixYrpHvfzzz+zePFihg4dSrly5XIjVMki44kT+I0cCcAwo5F9wHvvvUeBAgVsGpeIPSlYsCDdhw5lE2CwWDB//72tQxIREREREUlTnk9KAbRv3561a9eyYcMGzp07x9dff018fDxNmzYFYOrUqfz000/W8suWLWP+/Pn069ePwoULExkZSWRkJHFxcTaqgdxVQgIBr72GS2ws+woVYnxCAnXq1KF79+62jkzE7vTo0YN1xYsDYJ49G/Qtk4iIiIiI2KE8P30PoGHDhkRFRbFgwQIiIyMpXbo0Q4cOtU7fi4iISLFK/O+//47JZGLy5MkpztO1a1eeeuqp3AxdMinfpEm4799PvK8v7S5fBoOBDz74ABcXh8irimQro9FIoylTiOnaleI3brDuhx+o3LOnrcMSERERERFJwSGSUgBt27albdu2ae4beXvKV7Jp06blQkSSXdy3bsX3dpsN8vPjws2b9Hz2WWrWrGnjyETsV7WHH2Zv6dI0PHOGsI8+olz37umuiyciIiIiImILGmYids0QGYn/gAEYLBZ216nDtAsXCAgI4J133rF1aHKbu7s7X375JV9++WWGd7CU3Fd0yBAA2kZGMnvGDBtHIyIiIiIiklK2jZQKDw8nKioKX19fiubQnZ7EyVgs+L/7Lq4XLhBXogTtjh8HYMiQIQQEBNg4OEnm6upKhw4dbB2GpMH9sce46edHgagojk6axIXOnfX5LCIiIiIidiNLI6XMZjOLFy+md+/evP766wwbNoxly5ZZ92/evJn333+ff//9N6txihPyWrQIr+XLsbi6MrxcOS5GR1O7dm2efvppW4cmkjcYjVh69ACge0ICo0ePtnFAIiIiIiIi/+++k1Jms5mPPvqIBQsWEB0dTfHbd3q6U6VKlfj777/Zvn17loIU52P85x/yv/8+AIefeooJGzZgMBgYN26cFje3MyaTieXLl7N8+XJMJpOtw5H/iL1984Z2wNbly9m0aZNtAxIREREREbntvv+6X7NmDfv27aN69epMnTqVSZMmpSpTuHBhihQpwv79+7MUpDgZk4mA117D5eZN4h56iG67dwPwzDPPULt2bdvGJqkkJCTQt29f+vbtS0JCgq3Dkf8wVaxIQq1auAFPA++99x7x8fG2DktEREREROT+k1IbN27E19eXN998k8DAwHTLFStWjIiIiPu9jDgh388+w33XLsz58vFVkyYcOnoUf39/3n33XVuHJpInxYSEAPCiqyunTp3iq6++snFEIiIiIiIiWUhKXbhwgfLly+Pr65thOW9vb65fv36/lxEn47ZzJ/k++QSAf4cO5f2ZMwF45513Mkx+ikj64p54AoubG7VMJqoBU6ZM4dy5c7YOS0REREREnFyW1pRydb37zfsiIyNxc3O738uIo0tKwj00FObOxeOPPwh49VUMZjMxnTvz1u7d3Lhxgxo1atDj9mLNInLvzIGBxLVoAcCQ4GDi4uIYMWKEjaMSERERERFnd99JqYIFC3L27NkMy5hMJs6ePUtwcPD9XkYcmOeqVRSpX58CXbvCM88Q+L//4frvvyQVKMD6Ll1YuHAhAB988AFGo9HG0YrkbbG3p/B1jY/H3cWF1atXs3btWhtHJSIiIiIizuy+k1K1a9fm0qVL/PHHH+mWWb16NVFRUdSpU+d+LyMOynPVKgL69MElLCzFdgvgcuUKv99eP+rpp5/mgQcesEGEIo4lrnlzkgIC8Lh6lYlt2gAwbNgw4uLibByZiIiIiIg4q/tOSnXs2BFvb2++/vprZs+ezbFjxwCIj4/n1KlT/Pjjj/z444/4+fnRtm3bbAtYHEBSEvmHDweLBcN/diU/HvjvvwT4+TFkyJDcjk7EMbm7E/vkkwA85+JCUFAQ//zzD9OnT7dxYCIiIiIi4qzuOykVEBDAW2+9hY+PD7/++ivDhw8HYNu2bQwZMoRffvkFT09PBg0ahJ+fX7YFLHmf+/btGMPCUiWkkhmAksBnISEUKFAgFyOT++Hm5sbkyZOZPHmy1o+zc8lT+PKtXcu4t98GYOrUqfzzzz+2DEtERERERJzUfSelAKpWrcqkSZPo2LEjJUqUwN3dHTc3N4KCgnjssceYNGkSlStXzq5YxUEYL13KVLnHatfO2UAkW7i5udGtWze6deumpJSdS6xRg8SKFTHExfFkYiKNGzcmPj5ei56LiIiIiIhN3P32eemIiIgAbi143qNHD90dTTItqXDhTJWzBAXlcCQiTsZgIDYkBLdx4/BetIixEybQsmVLfv/9d37//XdatWpl6whFRERERMSJ3PdIqf79+/Ppp59mZyziJBLq1ycpOBhLOvvNgKloURLq18/NsOQ+mUwm/vjjD/744w9MJpOtw5G7iOncGYuLCx5//UVlV1f69OkDwPDhw4mNjbVxdCIiIiIi4kzuOynl5eVFoUKFsjMWcRZGIzdffjnNXWbAYDAQNWoUGI25G5fcl4SEBJ577jmee+45EhISbB2O3IU5KIj4Rx4BwHvxYt544w2CgoI4e/YsX3zxhY2jExERERERZ3LfSanixYtz5cqV7IxFnIXFgudvv2EAzJ6eKXbdyJ+fa199RVy7draJTXJV165drTdJkNyTvOC516JF+Hh5WdeU0qLnIiIiIiKSm+47KdWiRQuOHTvGiRMnsjMe+Y833niDYsWKUaxYMUqVKkWDBg0YO3YscXFx1jL//vsvgwYNokGDBpQrV46GDRsyceLEHB+1EhcXx9ChQ6lWrRoVKlSgd+/eXL58OcNjLl++zKCuXSmzdSveQMtatXizYUOeBnqVKsX1PXto/+231jon/3vnnXdytC7imC5evEj//v1p3LgxxYsXTzMBtmrVKh577DGqVKlC+fLladWqFYsWLUpV5umnn6ZatWoUK1aMgwcP5lYVckRcmzaYfX1x/fdf3Ldvp0OHDlr0XEREREREct19J6WaNWtG69atGTduHEuWLOHChQskJiZmZ2xyW7NmzdizZw+hoaGMHDmSH374gUmTJln3nzhxArPZzPjx41m3bh0jR45kzpw5fPTRRzka18iRI/n999+ZMWMGixcvJjw8nJdeeind8haLhRd79eLcrl38DGx+4QU8AgKYEhrKPKDTlCm4engA0KNHD/bs2WP99/777+doXcQxJSQkUKBAAV5//XWqVq2aZhl/f38GDBjAL7/8wh9//EG3bt0YOHAgGzZssJaJiYmhXr16vPfee7kUec6yeHkR26EDAN4LF2IwGBg7diyurq7WRc9FRERERERy2n3ffa9bt27Wn+fPn8/8+fPTLWswGJg3b979Xsrpubu7U/j2HeuKFStGkyZN2LRpk/UP5GbNmtGsWTNr+VKlSnHy5Em+//77HJsaFRUVxbx585g6dSqNGzcG4JNPPuHRRx9l165d1K1bN9Uxp06dYtfevRwEKpUqRfiQIYR17QpAgwYNqF+/PhbLreXPPT09rXUWxxATE8O7777Lr7/+iq+vLy+ns65YdipRogSjR48GSPczqmHDhikev/TSSyxcuJAdO3bQtGlT4NY0Q7g1KtFRxIaE4DN3Lp4rVmAYO5YKFSrQp08fpk+fzvDhw2ncuDFeXl62DlNERERERBzYfY+UuhfJiQbJuqNHj7Jz507c3NwyLBcVFYW/v3+GZZ599lkqVKiQ7r87E13/tX//fhITE2nSpIl1W/ny5SlWrBi7du1K85ik21M9PYHro0Yxd+lS9u3bh8FgoGTJkinKLl26lOrVq9O8eXM+/PBD3RXMAYwZM4Zt27bx7bff8tNPP7F161YOHDiQ4THbt2/P8DVaoUIFlixZkm0xWiwWNm/ezMmTJ2nQoEG2ndceJdSrh6lUKVyio/H89VcALXouIiIiIiK56r5HSmU0Mkqy1x9//EGFChVISkoiPj4eFxcXxo4dm27506dPM2vWLIYNG5bheSdMmJBibar/yijxdfnyZdzd3cmfP3+K7YUKFUp7XSmLhbqzZlESeLtwYQZVqMAHb7xxe5eFyMhIa9FOnTpRvHhxihQpwpEjRxg3bhwnT57k66+/zrA+Yr+io6OZN28en332mTWROWXKFB588MEMj6tZsyZr1qzJsEx23AU0KiqKunXrkpCQgNFo5IMPPuCR23eoc1gGAzFdu+I3aRJeixYR26ULPj4+jBgxgn79+jF16lS6dOlCqVKlbB2piIiIiIg4qPtOStmb1atXs3z5ciIjIylVqhQvvPAC5cuXT7f81q1bmT9/PpcvXyYoKIgePXrwwAMP5GLEmdewYUM+/PBDYmJimDlzJq6urjz++ONplg0LC+PZZ5+lffv29OjRI8PzBgcH50S4afJcvZp8mzez2NWVXn5+NGrUCAAfHx/q1auHi8v/D9p79tlnrT9XqVKFwoUL061bN86cOUPp0qVzLWbJHDc3N8aNG2f9OS1nzpwhISEhxXssICCAcuXKZXhuLy8vypQpk33BpsPX15c1a9YQHR3Nli1bGDVqFCVLlkw1tc/RxHbpgt+kSXhs3ozLhQuYixalQ4cO/Pjjj2zZsoURI0Ywe/ZsW4cpIiIiIiIOKlem7+W00NBQvv/+e7p27cr48eMpVaoU48aN4/r162mWP3bsGJ9++inNmzdn/PjxPPTQQ0yYMIGzZ8/mcuSZ4+3tTZkyZahWrRqTJ09mz549zJ07N1W58PBwQkJCqFu3Lh9//PFdz5uV6XuFChUiISEh1XN8+fLlVCNXDLGx+I0cCUCl/v2Z+Pnn1n3ff/89kZGRlC1bNt1rJScyzpw5c9c6Se5zc3OjV69e9OrV667TSu9Vbk3fc3FxoUyZMlSvXp2+ffvy+OOPM3Xq1GyogX1LKlWK+Pr1MVgseN9+Hg0GA+PGjcPNzY3ff//9riPVRERERERE7leWR0olJSWxbds2Dh06xNWrVwEIDAykWrVqNGjQAKPRmOUg72bFihW0aNHCmkTp3bs3u3fvZv369XTq1ClV+VWrVlG7dm06duwIQPfu3Tlw4ACrV6+mT58+OR5vVri4uPDaa68xatQoOnXqZF2IOCwsjJCQEGrWrMknn3ySYuRRerIyfa9mzZq4ubmxZcsW66itEydOcP78+VSLnPtOm4bruXOYihUjqn9/ht5eJP/JJ5+kcOHC7Nu3L8M7BR46dAhAC5/nYaVLl8bNzY3du3dTrFgxACIjIzl16lSGazfl1vS9/zKbzSQkJGT7ee1RbEgIHtu347VwITf79weDgfLly9O7d2+mT5/OiBEjaNKkiRY9FxERERGRbJelpNSZM2eYNGkSly5dSrVv7dq1zJ8/n4EDB+bolCuTycSpU6dSJJ9cXFyoUaMGx48fT/OY48eP0759+xTbatWqxV9//ZVjcWan9u3bM2bMGL777jv69u1LWFgYXbt2pXjx4gwbNowrV65Yy2aUyMnK9D0/Pz+6d+/OqFGj8Pf3J1++fLz//vvUrVs3RVLq0YcfZvyFC3QGokaMYOjYsezZswcvLy8aN27M008/Tdu2bWndujVhYWGcOXOGpUuX0qJFCwICAjhy5AgjR46kQYMGVK1a9b7jlZyTlJTE9u3bAahfv36aiWgfHx+6d+/O2LFjCQgIoGDBgowfP/6uydPsmL538OBB4Na6VlevXuXgwYO4u7tTsWJFAD7//HNq1apFqVKlSEhIYO3atf/X3r3H51z/fxx/XLt23syMMac1ZzlHCIUkOZUORviiRBEhKeScU0jSF32VFFFCjpHkfGzO5GwOlR0w28zOp+v3h3b9rG1ss+26tj3vt1u3dn0+78/ner322jXXXtf78/7w008/MW3aNPM5wsLCCAgI4Pr16wBcunQJuPv6yu/N0piOHXEbMwY7f3/sjh8n4bHHgLuLnq9evdq86PmwYcMsHKmIiIiIiBQ02W5KhYaGMnnyZO7cuUPRokVp2rQpXl5eAFy/fp39+/dz/fp1pkyZwowZMyhWrFiOBX2viIgIkpOT09xpzt3dncDAwHSPCQ8PT7NAd9GiRVMttv1vCQkJJCQkmB8bDAbzzAGDwZC94O/j3+e897GdnR2vv/468+fPp3fv3uzZs4erV69y9erVNAtHZ/Q9yAkTJ07ExsaGN998k7i4OFq2bMm0adNSxer/119EAHFPPcX1Zs1Y9c/i5nFxcXz66af4+vry7rvvmnO0t7dn7969fP3110RHR1OmTBnat2/P0KFDc+X7nNdScigIuaSIj4/H19cXuDtbztnZ2bzv3nzHjRtHdHQ0r732Gq6urrz11lvcuXMHg8GQq9+P5557zvz1yZMnWbNmDeXKlePgwYMAxMTE8OGHHxIUFISjoyOVKlXiv//9L506dTIf99tvv5l/TgHefvttAIYNG8bw4cPT5JqvuLkR164dTmvW4LxyJRH/XC7r6urKhAkT6N+/P3PnzqVz587mRc/zba7ZVJjyLUy5yl0Zvb/J7d/Nkj16jVo/1ci6qT7WTzWybrnS+zCZTKbsHPjNN9+wefNmWrVqxeuvv469vX2q/fHx8XzzzTds376ddu3a8dprr+VEvGmEhobSv39/Jk+ebJ75ALB06VLOnDnD1KlT0xzTrVs3Bg4cyJNPPmne9uuvv7Jq1Sq++uqrdJ9nxYoVrFq1yvy4QoUKTJ8+PQczKYA2boSOHcHWFv74g0Fz5zJv3jxq1KjB8ePHc3z9IbGMqKgoXF1dAYiMjMTFxcXCEUmWbdkCzz0HHh4QGAgODsDdO2M+++yzbNu2jeeff57169dbOFARyWl6fyMiIiKWlO2ZUsePH6dEiRL069cv3Utw7O3t6du3LydPnuTYsWO51pRyc3PDxsYmzSyn8PDwNLOnUri7u6dZoPv27dsZjoe76x/de8lfSofw5s2bJCYmZiv2+zEYDHh5eREcHEw2+4aWFRuL56BB2AKR/fpx4O+/+eKLL4C7M6xCQkLMQ/N9rllQEHONjo42fx0cHJxmplRByzcj+TrXGjUo6eWFMTiYsKVLiW3f3rxr3Lhx7N69mw0bNrB48WLatGmTv3PNhsKUb2HKNbfZ2trmypp3OS2j9zchISGpZlCJddBr1PqpRtZN9bF+qpF1s7Ozo0SJEjl6zmw3pUJCQmjUqNF914QxGo1UqVIlV9dqsrW1pWLFipw6dYpGjRoBdxcpPnXqFG3btk33mKpVq/LHH3+YF+iGu5f1VKlSJcPnsbOzy3BmT26+WEwmU758MbouWIDt1asklSrF7cGD+bBnT5KTk+nUqRNNmzZNN6f8mmt2FKRc780jo7wKUr4Pki9ztbEh+uWXKTJ/Po4rVhDTrp15172Lno8bN44nn3zS3HjMl7k+hMKUb2HKtbDL6P2Nfgasm+pj/VQj66b6WD/VyDrlRk0efIu2DNjZ2RETE/PAcbGxsbl+mVbHjh3Ztm0bO3fu5Nq1ayxcuNC8xhHA3Llz+f77783j27dvz4kTJ9iwYQMBAQGsWLGCS5cuZdjEkqwxBgTgOmcOABFjx7Jy82YOHz6Mi4sLY8eOtXB0IpKemH/WBXPcvh2be26WAHcXPffy8jIvei4iIiIiIpITst2UKleuHKdPn051Gda/hYSEcPr0acqXL5/dp8mUpk2b0rNnT1asWMEHH3zA1atX+fDDD82X44WEhBAWFmYeX61aNQYPHszWrVt5//338fPz4/3338fb2ztX4yws3CZOxCY2lrgnniD46aeZPHkyAO++++5D3fFPRHJPYtWqxNetiyExEac1a1Ltc3FxYcKECcDdJv+ff/5pgQhFRERERKSgyfble82bN+frr79m0qRJ9O7dm/r/3LEpxZEjR1iyZAnx8fE0b978oQN9kLZt22Y40ynlj6l7NWnShCZNmuRyVIWP/e7dOG3ciMlo5PakSXwyaxa3bt2icuXKvPHGG5YOT0TuI9rXF/sTJ3BauZKovn1T7evYsSNPPvkke/fuZezYsfz2228WilJERERERAqKbDelWrdujZ+fH6dOnWL69Om4urpSsmRJAG7cuEFkZCQAtWvX5plnnsmZaMW6xcdT9J/L86Jee40TJhPffvstAJMnT05zh0YpGGxtbRkzZoz5a8m/Yjt1wjRxIvanTmF79iyJjz5q3mcwGJgyZQqtW7dm69atbNiwgccff9yC0YqIiIiISH6X7cv3bGxsGDVqFC+88AKOjo5ERkZy+fJlLl++TGRkJI6OjnTq1ImRI0fedzF0KThcFi3Czt+fpOLFiRg2jDFjxpCcnEzHjh156qmnLB2e5BJ7e3sGDBjAgAED1HjM55I9PIj950ME53tuEZ8iZdFzgCFDhmRqXUEREREREZGMPNS0BltbW3r06EGXLl24dOkSoaGhAHh4eFCpUqVcX+BcrIdNcDBFPv0UgIjRo1m1dSsHDx7EycmJcePGWTg6EcmsGF9fnDZvxmn1aiJGjYJ/zX4bOnQoa9as4cqVK8yfP59hw4ZZKFIREREREcnvcmQKk52dHdWrV6dp06Y0bdqU6tWrqyFVyLhNmYJNVBTx9etz/bnnzIubDx06lLJly1o4OslNSUlJHD9+nOPHj5OUlGTpcOQhxbZqRVKxYhhv3MBh9+40+7XouYiIiIiI5BRdVycPzf7333FevRqTwcDtKVOYNXs2N2/epGLFirz55puWDk9yWVxcHB06dKBDhw7ExcVZOhx5WPb2xLz0EgDOK1emO6Rjx44888wzxMXFaSakiIiIiIhkW7abUps3b6Zr164cPnw4wzGHDx+ma9euuktTQZaYSNF/FrmO7tGDk3Z2fPPNN4AWNxfJr2J8fQFw/PVXDLdvp9lvMBiYO3cudnZ2bN26lS1btuR1iCIiIiIiUgBkuyl16NAh3NzcqF+/foZj6tevj5ubGwcPHszu04iVc1myBLuzZ0l2dydixAhGjx5NUlIS7du3p0WLFpYOT0SyIaF2bRKqVcMQF4fTzz+nO6Z69ermmZDjx4/XouciIiIiIpJl2W5KBQYG4u3tfd8769nY2ODt7U1AQEB2n0asmE1ICEVmzgQgYuRIVu3YgZ+fH05OTuY1Z0QkHzIYiOncGQCnDC7hg7trxpUuXZq//vqL+fPn51V0IiIiIiJSQGS7KRUREUHRokUfOK5o0aLcTufyD8n/ikybhk1EBPG1axPcsSOTJk0CtLi5SEEQ/fLLmGxscDh0COOVK+mOcXFxYfz48QDMmzdPi56LiIiIiEiWZLsp5ejoSFhY2APHhYWF4eDgkN2nEStld/QoLsuXA3B78mRmffaZFjcXKUCSvbyIa94cAOdVqzIc17FjR5588kktei4iIiIiIlmW7aaUj48PFy5cICQkJMMxISEhXLhwAW9v7+w+jVijpCSKjh4NQHSXLpxwdtbi5iIFUMqC506rVkFycrpjDAYDU6ZM0aLnIiIiIiKSZdluSjVr1ozExERmzZpFeHh4mv3h4eHMmjWLxMREnnzyyYeJUayM8w8/YH/yJMlubtweNcq8uHmHDh20uHkhZGtry7Bhwxg2bBi2traWDkdyUMxzz5FcpAi2165h7+eX4bjKlStr0XMREREREcmybP8F2bJlS3bu3Mn58+d55513eOyxx8zrCAUEBHDs2DHi4+OpWrUqTz/9dI4FLJZlCA3Fbdo0AO4MH87K3bs5ePAgTk5O5rVlpHCxt7fnvffes3QYkhucnIh5/nlcvv8e55UriW/SJMOhQ4YMYfXq1eZFz/UzISIiIiIiD5LtmVI2NjaMHDmShg0bEh8fj5+fH6tXr2b16tX4+fkRHx/P448/zsiRIzEajTkZs1iQ28yZ2ISHk1C9OkEvvcTkyZMBLW4uUlCl3IXP8eefMURHZzhOi56LiIiIiEhWPdS1Ns7OzgwfPpw///yT48ePc/PmTQBKlChBvXr18PHxyYkYxUrY/fEHzt99B8DtKVP45J/FzStVqqTFzQux5ORkLl68CECVKlWwscl2r1usUHyjRiQ+8gi2f/6J4y+/EPPKKxmO7dixI0899RR79uxh3LhxLF68OA8jFRERERGR/CZHFoB55JFHeOSRR3LiVGKtkpMpOno0BpOJ6Jde4ribmxY3FwBiY2Np1aoVABcvXsTZ2dnCEUmOMhiI7twZt1mzcF658r5NKYPBwOTJk2ndurV50fM2bdrkYbAiIiIiIpKf5NiUhqSkJDZs2MC4ceN49913mTRpEtu3b8+p04uFOa1ahf2RIyS7uHB79GhGjx5NcnIyHTp0oPk/t40XkYIp5RI++717sQkIuO9YLXouIiIiIiKZlemmlJ+fH3379uWHH35Isy85OZlp06axdOlSzp8/T2BgIKdOnWLBggXMmzcvRwOWvGeIiMBtyhQA7rz7Liv37dPi5iKFSJK3N3FPPIHBZMJ5zZoHjh8yZAilS5c2L3ouIiIiIiKSnkw3pU6fPs2dO3d44okn0uzbtm0bf/zxBwANGjSgT58+vPDCC9jb27N7925OnDiRcxFLnivyyScYQ0JIqFSJwM6dmTRpEqDFzUUKk5TZUk4rV4LJdN+xWvRcREREREQyI9NNqYsXL1KsWDEqVKiQZt/WrVsBaNasGR988AHPPfccPXr0YNCgQQDs3r07h8KVvGZ79iwu334LQMTkycz6738JCQnR4uYihUxMx44kOzpi5++P3fHjDxyfsuh5XFwc48aNy/0ARUREREQk38l0Uyo8PDzdu+lFRERw9epVAF544YVU+xo3boynpyf+/v4PFaRYiMlE0bFjMSQlEdO+PUeLF9fi5iKFlKlIEWLbtQPAacWKB45PWfTczs7OvOi5iIiIiIjIvTLdlIqIiMDFxSXN9kuXLgHg5uaWbtOqXLlyhIaGZj9CsRjH9etxOHCAZEdHwseO1eLmIoVcjK8vcPfGByxZgv3+/ZCUlOF4LXouIiIiIiL3k+mmlI2NDREREWm2X7lyBYCKFSume5yzszPJycnZDE8sxRAZSdGPPgIg8p13+GH/fg4dOoSzs7MWN5dUbG1t6d+/P/3798fW1tbS4UguMty5g8nGBpuoKOjdm+KdO1OqcWMcN23K8Bgtei4iIiIiIhnJ9F+Qnp6eXLlyhcTExFR/eKYscF65cuV0j7tz5w5FixZ9yDAzFhkZyaJFizhy5AgGg4HGjRvz+uuv4+jomOH4FStWcOLECUJCQnBzc6Nhw4a8+uqrODs751qc+Y3rnDkYg4NJ9PHh765dmdS6NQDvvfeeFjeXVOzt7Rk7dqylw5Bc5rhpE8X690+zyLlNcDDF3nyTsC+/JLZ9+zTHpSx63r9/f+bNm8crr7yS7qxaEREREREpfDI9U6pmzZrcuXOH5cuXm7edOnWKM2fOAFC/fv10j7t69SrFihV7yDAz9vnnn/P3338zZswYRo4cydmzZ1mwYEGG40NDQwkNDaVnz57MmjWLgQMHcuLECb744otcizG/sfX3x/WrrwC4PXEik2bOJDw8nEcffZQ33njDwtGJSJ5LSqLouHFgMmH41y7DP00qt/HjM7yU795FzzXTUkREREREUmS6KdWhQwdsbW3ZsGEDAwYMYMSIEUyZMgWAKlWqUKlSpTTHXLhwgYiICKpUqZJzEd/j2rVrHD9+nP79+1OlShWqV69Onz592L9/f4brWHl7ezN8+HAef/xxvLy8qFWrFq+++ipHjhwh6T5roxQaJhNuY8diSEggtnVrdhcpwo8//gjAtGnTsLOzs3CAYm2Sk5P5+++/+fvvv3WpbgFl7+eHMSgoTUMqhcFkwjYwEHs/v/T3a9FzERERERFJR6abUl5eXgwePBhHR0dCQ0O5evUqycnJeHh4MHDgwHSP+e233wCoVatWzkT7LxcuXMDFxSVVQ6x27doYDIYs3fEvOjoaJycnjEZjboSZrzj+8guOu3djsrcnZMwYRo0aBUCPHj1o2LChhaMTaxQbG8sTTzzBE088QWxsrKXDkVxgvHHjocdp0XMREREREfm3LK1K3LhxY6pXr86RI0e4ffs2JUqUoGHDhhmu31S5cmUqVKiQa02p8PBw3NzcUm0zGo24uroSHh6eqXNERETw008/0fqfNZMykpCQQEJCgvmxwWDAycnJ/HVOSzlnbpw7Q9HRuE2YAEDk22/zxZYtnD9/Hg8PDz788MNci8UiuVpIQcz13lwMBkOax/8eU1AV5FyTS5XK9Lj75T906FBWr15tXvR8+PDhORVirirItf23wpSr3JXR+5t//z4X66DXqPVTjayb6mP9VCPrliu9D5PpX6vWWoFly5axbt26+46ZPXs2fn5+7Nq1izlz5qTa17dvX7p06UKbNm3ue47o6GgmT56Mq6srH3zwwX3vHLZixQpWrVplflyhQgWmT5+eiWzykXHjYNIk8Pbmz82bebRBA2JiYli8eDG9evWydHRipaKionB1dQXu3kjAxcXFwhFJjktKAh8fCAhIs9C5WcmSEBgID5hxunLlSrp06YKDgwOnT59O99JvEck7heL9jYiIiFgtq7x/+/PPP0/Lli3vO6ZUqVK4u7sTERGRantSUhKRkZG4u7vf9/iYmBimTp2Kk5MTw4cPf+Ct7F966SU6duxofpzSIbx58yaJiYn3PTY7DAYDXl5eBAcHkxd9Q+PVq3jOmIEBCB07lr6DBxMTE0OTJk1o3bo1QUFBufbceZ2rJRXEXKOjo81fBwcHp7qLZUHMNyMFPVfHCRNw79cPDAbz4uYAJsAAJEdGcmvvXhKrVr3veZo1a8ZTTz3Fnj176N+/P0uWLMndwHNAQa/tvQpTrrnN1tYWT09PS4fxQBm9vwkJCUk1g0qsg16j1k81sm6qj/VTjaybnZ0dJUqUyNFzWmVTys3NLc1leempWrUqUVFRXL58mYoVKwJ37whoMpmoXLlyhsdFR0czZcoU7Ozs+OCDD7C3t3/gc9nZ2WW4yHduvlhMJlOevBjdxo3DEBdHbPPmrDUY2Lp1K3Z2dkybNs0cR27Lq1ytQUHK9d48MsqrIOX7IAU115h27TB9+SVFx43DeE+TOql0aUxOTthdvkyxHj0IWb+eZC+v+55r8uTJtG7dmq1bt/Lrr78+cFartSiotU1PYcq1sMvo/Y1+Bqyb6mP9VCPrpvpYP9XIOuVGTTK90Lk1KleuHPXq1WPBggX4+/tz7tw5Fi1aRNOmTfHw8AAgNDSUoUOHmhc+T2lIxcXF0b9/f2JiYggPDyc8PLzQ3jnM4bffcNy6FZOdHcGjRjF23DgA810NRUQAYtu357qfH7dWrYLvv+fWqlXc8PPj1rp1JFasiG1AAMV79sRw5859z6NFz0VEREREBKx0plRWDB48mK+//pqPPvoIg8FA48aN6dOnj3l/YmIigYGBxMXFAXDlyhUuXrxoPvZec+fOpWTJknkXvDWIjaVoyuLm/foxfd06AgMDKV++PEOGDLFsbCJifYxG4ps2hdKliQ8KApOJZA8Pbi1dSokXXsDuzBmKvfkmoUuWQAazSwGGDBliXvT8888/Z8SIEXmYhIiIiIiIWIN835RydXW9b/OkZMmSrFixwvy4Zs2aqR4Xdq4LFmB79SpJXl4cfu45vnr5ZQCmTJlivrugyP0YjUZ69+5t/loKp6RHHiF0yRKKv/IKjrt34/7++4TPng0Z3KHDxcWFSZMm0bdvX+bPn0+nTp2oXr16HkctIiIiIiKWlK8v35OHYwwIwPXzzwEIHz2a9z/6iKSkJNq3b88zzzxj4egkv3BwcGDq1KlMnToVBwcHS4cjFpRQty5hCxZgMhpxXrmSIp98ct/x7dq1o23btiQmJvL+++8X2kuoRUREREQKKzWlCjG3CROwiY0lrkkTFty+zZEjR3BxcWHixImWDk1E8qm4Z57h9j83SCjy2Wc4L1t23/GTJk3C1dWVo0eP5os78YmIiIiISM5RU6qQcti9G6dNmzAZjfgPHsy0jz8GYNSoUZQpU8bC0Ul+YjKZuHXrFrdu3dIdMgSA6B49uDN0KABFR43CYdu2DMeWKVOGUaNGATBt2jQCAwPzIkQREREREbECakoVRvHxuI0dC0DUa6/x3rffEhkZSf369enVq5eFg5P8JiYmhjp16lCnTh3dRU3M7gwfTrSvL4akJIq99RZ2J05kOLZnz57Ur1+fyMhIxv7zu0lERERERAo+NaUKIZdFi7Dz9yepRAl+ql2bX3/9FTs7O2bOnKmFqkUkZxgMhM+YQWzz5tjExODRqxfGP/9Md6jRaGTGjBnY2tqyefNmfvnllzwOVkRERERELEFNqULGJjiYIp9+CsD1d9/lg6lTAXj77bd15ysRyVn29oR9+SUJNWpgDAmh+H/+gyE0NN2hjz76KAMGDABgzJgxRERE5GWkIiIiIiJiAWpKFTJukydjExVFfP36jDh9mhs3blCpUiUGDx5s6dBEpAAyFSnCre++I7FMGWwvX6b4669DBpd5DhkyBB8fH4KDg/n4n3XuRERERESk4FJTqhCx//13nNeswWQwsOfVV1n6/fcAzJw5E0dHRwtHJyIFVbKXF6FLl5JctCj2hw9T7J13ICkpzTgnJydmzJgBwJIlSzh06FBehyoiIiIiInlITanCIjGRomPGAHCnWzfe/N//AOjRoweNGze2ZGQiUggkVqtG6NdfY7K3x+mXX3CbMAHSuVtjs2bN6Nq1KyaTiREjRhAfH5/3wYqIiIiISJ5QU6qQcFm8GLuzZ0l2d+djV1cuX75MqVKlGD16tKVDE5FCIr5JE8I++wwA10WLcFmwIN1xY8aMoXjx4pw/f5758+fnYYQiIiIiIpKX1JQqBGxu3qTIzJkAXHjtNWYuWgTApEmTKFq0qCVDkwLAaDTi6+uLr6+v7t4oDxTbqRO3x44FoOikSTiuW5dmjIeHBxMnTgRgzpw5XLx4MU9jFBERERGRvKGmVCHgNm0aNnfuEF+7Nj137SIxMZHnnnuO9u3bWzo0KQAcHBz47LPP+Oyzz3BwcLB0OJIPRL31FpFvvAFAsaFDsT9wIM2YF198kVatWhEfH8+wYcNISmcNKhERERERyd/UlCrg7I4cwfnHHwFY0qgRh48dw9XVlSlTpmAwGCwcnYgUSgYDEePHE9O+PYb4eDzeeAPb8+f/NcTA9OnTKVKkCEePHuWrr76yULAiIiIiIpJb1JQqyJKSKPrPmlHX27fnnWXLABg/fjylS5e2ZGRSgJhMJqKjo4mOjsaUzsLVIukyGgn7/HPiH38cm9u38fjPf7AJDk41pEyZMowbNw64e5fQy5cvWyJSERERERHJJWpKFWDO33+P/R9/kOzmRu+gIGJjY2nevDndunWzdGhSgMTExFClShWqVKlCTEyMpcOR/MTJiVvffENixYrYBgZSvGdPDHfupBrSrVs3nnrqKWJjYxk+fDjJyckWClZERERERHKamlIFlCE0FLePPwZgy5NP8uuxY7i4uDBz5kxdticiVsPk4cGtZctIKlECuzNn8OjXD+LjzfsNBgMzZ87E2dkZPz8/vv32W8sFKyIiIiIiOUpNqQLKbcYMbMLDiapUic7btgF3b7Nerlw5C0cmIpJakrc3oUuWkOzsjMOePbi//z7ccylo+fLlGf3PpchTp07lr7/+slSoIiIiIiKSg9SUKoDsTp7EeelSAIY7OBAVF0ezZs34z3/+Y+HIRETSl1C3LmH/+x8moxHnVasoMnNmqv29evWiSZMmxMTEMHz4cK1fJiIiIiJSAKgpVdAkJ1N09GgMJhOn6tblf2fO4OzszCeffIKNjcotItYr7plnuP3PZcdF5swxN9cBbGxs+OSTT3B0dGTfvn0svWefiIiIiIjkT+pSFDBOK1dif/QoSc7OdPrnFuujR4/G29vbwpGJiDxYdPfu3Hn3XQCKjhqFw2+/mff5+PgwcuRIACZPnkxAQIBFYhQRERERkZyhplQBYrh9G7epUwH4wtOTy7GxNGnShF69elk4MhGRzLvz3ntEd+mCITmZYgMGYHf8uHlfnz59ePzxx4mMjOS9997T3fhERERERPIxNaUKkCKzZmEMCSHE05Nhf/6Jk5OTLtuTXGdjY0OHDh3o0KGDftYkZxgMhM+YQWyLFtjExODRqxfGq1cBMBqNfPrppzg6OrJnzx4WL15s2VhFRERERCTb9BdkAWF75gwu33wDQO/wcBK4e7c9Hx8fi8YlBZ+joyNffvklX375JY6OjpYORwoKOzvCvvyShJo1Md66RfH//Aeb0FAAKlWqxNixY4G7l/H5+/tbMlIREREREcmmfN+UioyM5PPPP6d379689tprfPHFF8TGxmbqWJPJxNSpU+nSpQsHDx7M5UhzkclE0bFjMSQn81vRomxKSKBFixb07t3b0pGJiGSbydWVW999R2LZstheuYJH794YYmKAu3fja968ObGxsQwZMoTExEQLRysiIiIiIlmV75tSn3/+OX///Tdjxoxh5MiRnD17lgULFmTq2I0bN2IwGHI5wtzntG4dDr//TrytLW/cvo27uzuzZs0qELmJSOGWXKoUocuWkVy0KPZHj+I+aBAkJWFjY8OsWbMoWrQox48f57///a+lQxURERERkSzK102pa9eucfz4cfr370+VKlWoXr06ffr0Yf/+/YT+c5lHRq5evcrPP//MgAED8ija3GGIjMRt0iQAJiUl8TcwdepUSpcubdnApNCIjo6mbNmylC1blujoaEuHIwVQYpUqhH7zDSZ7e5w2b6bouHFgMlGmTBmmTJkCwOzZszlx4oSFIxURERERkazI102pCxcu4OLiQqVKlczbateujcFguO8aI3FxccyZM4c33ngDd3f3PIg09xT57DOMwcH8aWvLTJOJTp060alTJ0uHJSKSo+IbNybs888BcPn2W1z+9z8AXnzxRZ5//nmSkpIYPHgwMf9c3iciIiIiItbP1tIBPIzw8HDc3NxSbTMajbi6uhIeHp7hcYsXL6ZatWo0bNgw08+VkJBAQkKC+bHBYMDJycn8dU5LOef9zm28eBGXr74C4O3ERIp5eTF16tR8d9leZnItKApirvfmYjAY0jz+95iCSrnmvrgXXiAiOBi3CRMoOnkyyaVLE/vSS0ybNo2DBw/i7+/Pxx9/zEcffZSjz6vaSkGW0fubf/8+F+ug16j1U42sm+pj/VQj65YbdbHKptSyZctYt27dfcfMnj07W+c+fPgwp06dYsaMGVk6bs2aNaxatcr8uEKFCkyfPh1PT89sxZFZXl5e6e8wmaBXL0hMZD2wCfh18WJq1KiRq/HkpgxzLYAKUq5RUVHmr728vHBxcUkzpiDl+yDKNZeNHw9hYTBnDsXefRdq1KB0y5Z88803tG/fnoULF9KtWzdatWqV40+t2kpBlNH7mxIlSlgwKnkQvUatn2pk3VQf66caFR4Gk8lksnQQ/xYREcGdO3fuO6ZUqVLs3r2b7777jm+++ca8PSkpiR49ejBs2DAaNWqU5rhvv/2WX375JVWHLzk5GYPBwKOPPsqECRPSfb6MPkm8efNmrtz1yWAw4OXlRXBwMOmVyHHTJor17UssUBNo/tprTJ06NcfjyAsPyrUgKYi5RkdHU7lyZQD8/f1xdnY27yuI+WZEueahpCTc+/fHaeNGkt3cuLVuHYnVqjFixAi+++47SpcuzW+//YaHh0eOPJ3F881DhSnX3GZra5vrH1zlhIze34SEhKTaLtZBr1HrpxpZN9XH+qlG1s3Ozi7HP7iyyplSbm5uaS7LS0/VqlWJiori8uXLVKxYEYBTp05hMpnMfyT/24svvpjmE/Thw4fTu3dvHn/88Qyfy87ODjs7u3T35eaLxWQypTm/ISaGIuPHAzADMFSqxJgxY/L9iza9XAuqgpTrvXlklFdByvdBlGsesLEhbM4cbG7cwOHQIYr16EHI+vWMHTuWffv2cfnyZYYPH87ChQtzdIqxaisFUUbvb/QzYN1UH+unGlk31cf6qUbWKTdqkq8XOi9Xrhz16tVjwYIF+Pv7c+7cORYtWkTTpk3Nn5CHhoYydOhQ88Ln7u7ueHt7p/oPoESJEpQsWdJiuWSF63//i21AAFeBT+3smD9/vnl9KxGRQsHJidBFi0ioVAnbwECK9+yJa3Iy8+fPx87Ojs2bN7NkyRJLRykiIiIiIveRr5tSAIMHD6ZMmTJ89NFHTJs2jWrVqvHWW2+Z9ycmJhIYGEhcXJwFo8w5xitXcJk/H4B3gffGjqVWrVqWDUoKNRsbG1q1akWrVq2wscn3v1IkHzF5eBC6dClJnp7YnT2LR79+1K5WjQ8//BCAiRMncvbsWQtHKSIiIiIiGbHKy/eywtXVlSFDhmS4v2TJkqxYseK+53jQfmviOnYsNgkJ/ArcadWKPn36WDokKeQcHR357rvvLB2GFFJJ3t6ELllC8VdewWHPHtyHD6fv7Nns2bOH7du3M3DgQDZu3KjZpCIiIiIiVkjTGvIRh99+w2XHDuKBiR4ezP7sM90qU0QKvYQ6dQhbsACT0YjzTz9R9JNPmD17Np6enpw/f56PPvrI0iGKiIiIiEg61JTKL2JjsXv/fQA+AwbPm0fx4sUtGpKIiLWIa9WK29OnA1Dk88/x/uUX5syZA8CSJUv45ZdfLBmeiIiIiIikQ02pfCJ5xgzcbt4kAAju14/mzZtbOiQRAKKjo6lcuTKVK1cmOjra0uFIIRbdrRt3hg0DoOiHH9ImPp4BAwYAd++yGhAQYMnwRERERETkX9SUygeSLl+m+JdfAvBfHx+GjB5t4YhEUouJiSEmJsbSYYhwZ9gwort2xZCcTLEBAxjTpg1169YlPDycAQMGEB8fb+kQRURERETkH2pK5QPX//MfnEwm9hqNvLx8OXZ2dpYOSUTEOhkMhE+fTmzLltjExFCqb18WjR6Nm5sbR44cYcqUKZaOUERERERE/qGmlJU7PHUqj//5J4lA2KRJlCtf3tIhiYhYNzs7whYsIL5WLYy3blHngw/44p/FzhcuXMjPP/9s4QBFRERERATUlLJOSUnY799PwPTp1J07F4A99erRoHdvCwcmIpI/mFxdCV2yhMRy5bC9epXOS5YwpF8/AN577z0uXbpk4QhFRERERERNKSvjuGkTpRo3pnjnzpQdORJvIAmorYaUiEiWJJcqRejSpSS7u2N/9ChT//yTpo0bExkZyVtvvaV10ERERERELExNKSviuGkTxd58E5ugoFTbbYDiw4bhuGmTZQITEcmnEqtUIfSbbzA5OOC8ZQtrvb3xLFGCs2fPMnLkSEwmk6VDFAtJTExk1apV+hkQERERsSA1paxFUhJFx40DkwnDv3alPHYbPx6SkvI6MpH7MhgMNGnShCZNmmAw/PunV8Ty4hs1IuzzzzEZDHiuXMnWtm2xsbFh1apVLFu2zNLhiYVMmTKFIUOGMHToUEuHIiIiIlJoqSllJez9/DAGBaVpSKUwmEzYBgZi7+eXp3GJPIiTkxOrVq1i1apVODk5WTockXTFduxIxPjxANRZupTvO3YEYMyYMRw6dMiSoYkFrFmzhi+//BKANm3aWDgaERERkcJLTSkrYbxxI0fHiYhIalH9+hH5z2LnXX75hZGNG5OQkEC/fv0IDAy0cHSSV06dOsXw4cMBGDRoEB06dLBwRCIiIiKFl5pSViKpZMkcHSciImlFjBtHTMeOGBISmHz6NC9UqMDNmzfp27evFj4vBG7evMnrr79ObGwsLVu25IMPPrB0SCIiIiKFmppSViK+cWOSSpfGlMGaPCaDgcQyZYhv3DiPIxO5v+joaGrXrk3t2rWJjo62dDgi92djQ9icOcQ1aoQxMpIVUVHUcHPjxIkTfPDBB1r0ugCLi4ujb9++BAYGUrFiRebNm4fRaLR0WCIiIiKFmppS1sJo5PZHHwGkaUylPI6YOBH0BlqsUGhoKKGhoZYOQyRzHB0JXbSIhMqVcbhxg33u7hSzsWH16tXmdYakYDGZTIwcOZLDhw9TtGhRvv32W9zd3S0dloiIiEihp6aUFYlt356wL78k2csr1fak0qUJ+/JLYtu3t1BkIiIFi6lYMUKXLiWpZEnc//qLIz4+2AGTJ09mx44dlg5PctiCBQtYsWIFRqOR//3vf1SqVMnSIYmIiIgIYGvpACS12PbtiX3uORwOHqR4fDy37O2Ja9RIM6RERHJYUvny3PruO0q8/DIVLl9m+yOP8NSff9K/f39Wr15NzZo1LR2i5IBt27YxefJkACZMmEDz5s0tHJGIiIiIpNBMKWtkNBLftCl063b3/2pIiYjkisRatQj78ktMRiNP/vkni8uWJTIykl69ehEUFGTp8OQhnTlzhrfffhuTyUSPHj14/fXXLR2SiIiIiNxDTSkRESnU4lq2JHzmTAB6BQQwztOT4OBgevfuTWRkpIWjk+wKDAykZ8+eREZG0qRJE6ZMmYIhg5uJiIiIiIhlqCklIiKFXkzXrkQMHw7AhFu36FGkCKdPn2bAgAEkJiZaODrJqoiICHr27ElwcDBVq1Zl4cKF2NnZWTosEREREfkXNaVE5KEYDAbq1q1L3bp1NQtB8rXIoUOJ6tYNQ3Iyi+PjedLenp3bt7OsXz9M33+P/f79kJRk6TDlAeLj43njjTc4d+4cpUqVYunSpbrTnoiIiIiV0kLnIvJQnJyc2LRpk6XDEHl4BgO3p03DGByM444dbHV0JBQo/euv8OuvFOfu3VBvf/SR7oZqpZKTk3nvvffYv38/Li4uLFmyhLJly1o6LBERERHJgGZKiYiIpLCzI2zBAhK8vXGIjcXrX7ttgoMp9uabOKoRa3VMJhOTJk1i9erV2Nra8tVXX1GrVi1LhyUiIiIi95HvZ0pFRkayaNEijhw5gsFgoHHjxrz++us4Ojre97gLFy7www8/4O/vj42NDT4+PowePRp7e/s8ilxERKyRydERm7g4TMC/L0g1mEyYDAbcxo8n9rnndHdUK/LZZ5/x5ZdfAjBz5kxatGhh4YhERERE5EHyfVPq888/JywsjDFjxpCUlMT8+fNZsGABQ4YMyfCYCxcuMGXKFF566SX69OmD0Wjk6tWrWg9HJBtiYmJo2bIlADt37sTJycmyAYk8JHs/P4zXr2e432AyYRsYiL2fH/FNm+ZhZJKRhQsX8sknnwAwceJEunTpYuGIRERERCQz8vXle9euXeP48eP079+fKlWqUL16dfr06cP+/fsJDQ3N8LjFixfTrl07XnzxRcqXL0+ZMmVo2rSp7swjkg0mk4lr165x7do1TCaTpcMReWjGGzcyN+4+jSvJO8uXL2f8+PEADB8+nL59+1o4IhERERHJrHzdlLpw4QIuLi5UqlTJvK127doYDAb8/f3TPeb27dtcvHiRokWLMmbMGPr168f48eM5d+5cXoUtIiJWLKlkyUyNs5s0CZdFizCEh+duQJKh9evX8/777wPw5ptvMnToUMsGJCIiIiJZkq8v3wsPD8fNzS3VNqPRiKurK+EZ/JFw/Z9PtleuXEnPnj3x8fFh165dfPTRR8yaNYvSpUune1xCQgIJCQnmxwaDwXyZUm5c9pdyzsJwSaFyzd/uzcVgMKR5/O8xBZVyLTgSnniCpNKlsQkOxpDO7D/TP/+5Xr8OY8fiNmUKMc8/T3T37iQ0agT5+PuSn2q7bt06Bg0aRHJyMt27d2f8+PH5Im5rk9H7m3//PhfrkJ9eo4WVamTdVB/rpxpZt9yoi1U2pZYtW8a6devuO2b27NnZOnfK5UWtW7fm6aefBqBChQqcOnWKHTt20L1793SPW7NmDatWrTI/rlChAtOnT8fT0zNbcWSWl9e/7/1UcCnX/CkqKsr8tZeXFy4uLmnGFKR8H0S5FhBz50LnzncbTPc2pv75h3h69eoEnD1LfxsbasXG4rxyJc4rV8Kjj0K/ftCrFxQvbqHgH5611/b7779n4MCBJCcn06tXLxYtWoRRi85nS0bvb0qUKGHBqORBrP01KqqRtVN9rJ9qVHhYZVPq+eefNy+cnJFSpUrh7u5OREREqu1JSUlERkbi7u6e7nHFihUDoFy5cqm2ly1blpCQkAyf76WXXqJjx47mxykdwps3b5KYmHjfWLPDYDDg5eVFcHBwgV+nR7nmb9HR0eavg4ODcXZ2Nj8uiPlmRLkWME2a4PjVV7iNHYsxKMi8Oal0aSI++oguLVvSu3dvau/bR0snJ7598knK792LzdmzMGwYppEjiW3fnuj//If4Jk3yzeyp/FDbn376iSFDhpCcnEzXrl2ZMmUKNzK5DlhesrW1zfUPrnJCRu9vQkJCUs2gEuuQH16jhZ1qZN1UH+unGlk3Ozu7HP/gyiqbUm5ubmkuy0tP1apViYqK4vLly1SsWBGAU6dOYTKZqFy5crrHeHp6UqxYMQIDA1NtDwoKol69ehk+l52dXYYLoefmi8VkMhWaF6NyzZ/uzSOjvApSvg+iXAuOmHbtiGnTBoeDBykeH88te3viGjUCoxEn4Ntvv6VXr17sPHCAGvv2sXjuXFrfuIHz999j/8cfOK1di9PatSRWqEBUjx7E+PqSnE9mn1hrbVesWMGwYcMwmUx0796d6dOnY2NjY5Wx5hcZvb+x1p8BuUv1sX6qkXVTfayfamSdcqMm+Xqh83LlylGvXj0WLFiAv78/586dY9GiRTRt2hQPDw8AQkNDGTp0qHnhc4PBwAsvvMAvv/zC77//TnBwMMuXLycgIIBWrVpZMh2RfMlgMFC1alWqVq2qa7+l4DEaiW/aFLp1u/v/ey4Rc3Z2ZsmSJTz11FNER0fTY8AAVpUoQcjmzdz85Rei/vMfkl1csL1yhaKTJ1Pq8ccp9tZbOOzeDcnJFkwqf/rqq6949913MZlM9OzZ09yQEhEREZH8yypnSmXF4MGD+frrr/noo48wGAw0btyYPn36mPcnJiYSGBhIXFyceVuHDh1ISEhg8eLFREZG8sgjjzB27FhdtyqSDU5OTuzYscPSYYhYhLOzM4sXL2bQoEFs2rSJt956i5kzZ/Lqq69yu04dIsaNw2n9epyXLcP+2DGcfv4Zp59/JtHbm+hu3Yju2pXkUqUsnYZVM5lMfPzxx8ydOxeAN954g4kTJ6oJLiIiIlIAGEyaE/dQbt68mStrLhgMBkqXLk1QUFCBn7aoXAuuwpSvci24MpNvUlISI0aM4IcffgBgzJgx9O/fP1XjxPb0aVy+/x6n1aux+Wc9RJPRSOyzzxLdowdxLVqkmollCdZW28TEREaMGMHy5csBGDlyJIMGDcoXDSk7O7t8saZURnLr/Y08HGt7jUpaqpF1U32sn2pk3XLj/Y3mvYuIiDwko9HIzJkzefvttwGYPHkyH374YaobYSTWrMntKVO4fvQoYbNnE9ewIYakJJw2b6Z4z56UbNIE19mzsfnXmoeFVWRkJG+88QbLly/HxsaGmTNn8s477+SLhpSIiIiIZI6aUiLyUGJiYnj66ad5+umniYmJsXQ4IhZjMBgYPXo048aNw2AwsGTJEnr16pXmLrEmJydiunTh1tq13Ni+nci+fUl2d8c2IAC3Tz6hVOPGePTujcOWLZALd3fND65du8aLL77I1q1bcXBw4KuvvqJ79+6WDktEREREcpiaUiLyUEwmExcuXODChQuaYisCvPXWWyxcuBAnJyd27dpFp06d+Pvvv9Mdm1itGhETJxJ85Ahhc+cS16QJhuRkHLdupfjrr1OqcWOKzJyJ8dq1PM7Cco4cOUKHDh04e/Ysnp6erFq1irZt21o6LBERERHJBWpKiYiI5LC2bduyZs0avLy8uHDhAu3atWP37t0ZH+DoSMxLL3Fr1Squ79pFZP/+JHl4YAwOpshnn1HyiSfw+M9/cNy0CQrwOj8rV67E19eXkJAQatSowcaNG6lfv76lwxIRERGRXKKmlIiISC6oXbs2GzZsoHbt2oSFhdG9e3fmzJlDcnLyfY9LqlyZiLFjuX74MKFffEHcU09hMJlw3LEDj379KNWwIUWmTcN49WreJJIHYmNj+eCDDxg6dChxcXG0adOGtWvXUrZsWUuHJiIiIiK5SE0pERGRXFKmTBnWrl1Lt27dMJlMzJgxgz59+hAeHv7ggx0ciH3hBW4tX871ffu4M2gQSZ6eGG/epMjcuZRq1oziXbviuH49xMfnei655c8//6RTp04sW7YMg8HAe++9x8KFC3FxcbF0aCIiIiKSy9SUEhERyUWOjo588sknfPLJJzg4OPDbb7/x7LPPsm/fvkyfI8nHhzujRnH90CFCFy4k9umnMRkMOOzdi8eAAZRq0AC3SZMwXrqUi5nkvHXr1tG2bVtOnTqFh4cH33//PcOGDcNoNFo6NBERERHJA2pKiYiI5IFu3bqxbt06fHx8CAwMpGvXrkyaNIm4uLjMn8TOjth27QhdupQbBw5wZ+hQkry8MIaG4vq//1GqeXOKd+6M05o1EBube8k8pLCwMN5++23efvttIiIiePzxx/n1119p3ry5pUMTERERkTykppSIPBSDwUC5cuUoV64cBoPB0uGIWLXatWuzZcsWevTogclk4n//+x8dOnTgjz/+yPK5ksqX587773Pdz49b33xDbOvWmGxscDhwgGKDBuHVoAFu48dje+FCLmSSfTt27OCZZ55h3bp1GI1Ghg0bxqpVqyhTpoylQxMRERGRPKamlIg8FCcnJ/z8/PDz88PJycnS4YhYPRcXF2bMmME333xD8eLFOXv2LO3bt2f8+PFERkZm/YS2tsS1aUPo4sVc9/MjYvhwEsuWxSY8HNeFCyn59NMUf/FFnFaswBATk/MJZdKNGzcYNGgQ//nPf7h+/TqVKlVi/fr1vPfee9jZ2VksLhERERGxHDWlRERELKBNmzZs27aNTp06kZyczMKFC2nZsiW//PILJpMpW+dMLlOGyHff5caBA9z67jti2rXDZDTicOgQxd59l1L161N09GhsT59Oe3BSEvb798MPP9z9f1LSQ2aYctokvv32W1q0aMGaNWswGAy88cYb/Prrr9SrVy9HnkNERERE8ic1pURERCzE09OT+fPns2zZMry9vQkKCqJv3774+vpy4sSJ7J/YaCSuVSvCFi7k+qFDRIwcSaK3NzYREbh8+y0l27ShRMeOOP/wA4aoKBw3baJU48YU79wZuneneOfOlGrcGMdNm7IdgslkYvv27Tz33HOMHj2aiIgI6tSpw8aNG/noo480s1JERERE1JQSkYcTExND+/btad++PTEWvDRIJD9r2bIl27dv55133sHBwYEDBw7Qvn17Bg4cyOXLlx/q3MmlShH5zjvc2LePkB9+IKZjR0x2dtgfO4b78OGUql2bYv36YRMUlOo4m+Bgir35ZrYaU8eOHaNr16707NmTs2fPUrRoUaZMmcLPP/9M3bp1HyofERERESk41JQSkYdiMpk4ceIEJ06cyPYlRyJyd322kSNHsnv3bl555RUMBgNr166lRYsWDBgwgNPpXXKXFTY2xDdvTtiCBVw/fJjbY8aQ6OODTVwcBuDftykw/PN6dhs/PlOX8plMJvbu3UvXrl3p2LEj+/btw97env79+7Nv3z5ee+01jEbjw+UgIiIiIgWKmlIiIiJWpFy5cnz++eds3ryZ1q1bk5yczPr162nTpg09evTg119/JTEx8aGeI7lECaIGDCB8xoz7jjOYTNgGBmLv55fhmJiYGH788Uc6duxI165d2bt3L0ajEV9fX/bs2cPYsWMpVqzYQ8UrIiIiIgWTraUDEBERkbRq1arF4sWLOX36NPPmzWPDhg3s3LmTnTt34uXlRbdu3ejUqRNVqlTJ9nMYb97M3LgbN1I9NplMHD9+nNWrV/PTTz9x+/ZtABwdHenWrRv9+/enXLly2Y5LRERERAoHNaVERESsWM2aNZk/fz4jRoxg2bJl/PDDDwQHBzN79mxmz55NtWrV6NixI08//TR16tTJ0iVySSVLZnpcXFwcR48eZcuWLWzcuJGAgADzfm9vb7p37063bt0oUaJElnMUERERkcJJTSkREZF84JFHHuHDDz/kvffe45dffuGnn35iz549nD9/nvPnzzNr1izc3Nx44oknaNiwIbVq1aJGjRr3bRLFN25MUunS2AQHm9eQupcJCC9ShM5z5uB3+DCxsbHmfc7OzrRu3ZquXbvSvHlzbGy0IoCIiIiIZI2aUiIiIvmIg4MDL774Ii+++CLh4eFs2bKFX3/9lf379xMREcGWLVvYsmWLeXzx4sUpW7YsZcqUoXTp0ri4uODk5ISTkxPJyclUbtiQXuvXk0zqhSaT//n/G3fusGvvXgA8PT156qmnaN++PS1btsTJySnP8hYRERGRgkdNKRF5aB4eHpYOQaRQcnd3p0uXLnTp0oWkpCT++OMP9u3bx8mTJzlz5gxXrlzh1q1b3Lp1i5MnT2Z4nnXAHKD8PdsCgJlly2Jo3JjJ9evTrFkzqlSpgsHw7/v0iYiIiIhkj5pSIvJQnJ2d+eOPPywdhkihZzQaqVevHvXq1TNvi4qK4sqVKwQGBhIYGEhwcDDR0dHExsYSHR2NjY0Njo6OODo6MtvFhUZxcVQvWpTkUqUo9sILjHR2tlxCIiIiIlLgqSklIiJSQLm4uFCrVi1q1aqVqfEGg4HSpUsTFBSEKZ01pkREREREcpJWJRURERERERERkTyX72dKRUZGsmjRIo4cOYLBYKBx48a8/vrrODo6ZnhMeHg43333HSdPniQ2NpYyZcrw0ksv8cQTT+Rh5CIFQ0xMDD179gTgu+++08LHIiIiIiIikin5vin1+eefExYWxpgxY0hKSmL+/PksWLCAIUOGZHjM3LlziYqKYsSIERQpUoS9e/cye/ZsPv74YypUqJCH0YvkfyaTiQMHDpi/FhEREREREcmMfH353rVr1zh+/Dj9+/enSpUqVK9enT59+rB//35CQ0MzPO78+fO0a9eOypUrU6pUKV555RVcXFy4fPlyHkYvIiIiIiIiIlJ45euZUhcuXMDFxYVKlSqZt9WuXRuDwYC/vz+NGjVK97hq1aqxf/9+6tevj7OzMwcOHCAhIYGaNWtm+FwJCQkkJCSYHxsMBvNlSrlxe+yUcxaGW28r1/zt3lwMBkOax/8eU1Ap14KrMOVbmHKVuzJ6f/Pv3+diHfQatX6qkXVTfayfamTdcqMu+bopFR4ejpubW6ptRqMRV1dXwsPDMzzu3Xff5bPPPqNPnz4YjUbs7e0ZPnw4Xl5eGR6zZs0aVq1aZX5coUIFpk+fjqen50PncT/3i6mgUa75U1RUlPlrLy8vXFxc0owpSPk+iHItuApTvoUp18Iuo/c3JUqUsGBU8iB6jVo/1ci6qT7WTzUqPKyyKbVs2TLWrVt33zGzZ8/O9vl//PFHoqKiGDt2LEWKFOHQoUPMnj2bjz76CG9v73SPeemll+jYsaP5cUqH8ObNmyQmJmY7lowYDAa8vLwIDg4u8Ov0KNf8LTo62vx1cHAwzs7O5scFMd+MKNeCqzDlW5hyzW22tra5/sFVTsjo/U1ISEiqGVRiHfQatX6qkXVTfayfamTd7OzscvyDK6tsSj3//PO0bNnyvmNKlSqFu7s7ERERqbYnJSURGRmJu7t7uscFBwezefNmZs2aRfny5QHw8fHh3LlzbN68mTfffDPd4+zs7LCzs0t3X26+WEwmU6F5MSrX/OnePDLKqyDl+yDKteAqTPkWplwLu4ze3+hnwLqpPtZPNbJuqo/1U42sU27UxCqbUm5ubmkuy0tP1apViYqK4vLly1SsWBGAU6dOYTKZqFy5crrHxMfHA2mvhbSxsdEPvUg2payvJiIiIiIiIpJZ+frue+XKlaNevXosWLAAf39/zp07x6JFi2jatCkeHh4AhIaGMnToUPz9/QEoU6YMXl5efPXVV/j7+xMcHMyGDRs4efIkDRs2tGQ6IvmSs7Mz/v7++Pv7p7p0T0REREREROR+rHKmVFYMHjyYr7/+mo8++giDwUDjxo3p06ePeX9iYiKBgYHExcUBd9d4GDVqFMuWLWP69OnExsbi5eXFwIEDqV+/vqXSEBEREREREREpVPJ9U8rV1ZUhQ4ZkuL9kyZKsWLEi1bbSpUszfPjw3A5NREREREREREQykK8v3xMRy4uNjaVnz5707NmT2NhYS4cjIiIiIiIi+US+nyklIpaVnJzM9u3bzV+LiIiIiIiIZIZmSomIiIiIiIiISJ5TU0pERERERERERPKcmlIiIiIiIiIiIpLn1JQSEREREREREZE8p6aUiIiIiIiIiIjkOd197yHZ2ubutzC3z29NlGv+5ODgwGOPPWb+2s7OLs2YgpTvgyjXgqsw5VuYcs0t+f17mN/jL+hUH+unGlk31cf6qUbWKTfqYjCZTKYcP6uIiIiI5DsJCQnpfrggIiIikiIn3y/o8j0rFRMTw4gRI4iJibF0KLlOuRZchSlf5VpwFaZ8C1Oukr6EhATmzJmjnwErpdeo9VONrJvqY/1UI+sWExPDnDlzSEhIyLFzqillpUwmE1euXKEwTGRTrgVXYcpXuRZchSnfwpSrZGzfvn36GbBSeo1aP9XIuqk+1k81sm4mk4l9+/bl6DnVlBIRERERERERkTynppSIiIiIiIiIiOQ5NaWslJ2dHZ07dy4Ui40q14KrMOWrXAuuwpRvYcpV0qefAeum+lg/1ci6qT7WTzWybrlRH919T0RERERERERE8pxmSomIiIiIiIiISJ5TU0pERERERERERPKcmlIiIiIiIiIiIpLnbC0dgKTm7+/PypUrOX/+PElJSXh7e9OhQweaNm1q6dByzNq1a/n+++8BmDx5MlWrVjXvW7FiBatWrcrw2Llz51KyZMlcjzE7kpOT2bJlCzt27CAgIACj0YiPjw/PP/88jz/+eJrx0dHRrFy5Ej8/P8LDwylWrBhPPPEEvr6+ODo6WiCDtHbv3s25c+e4fPkyf/31F4mJibz99tu0bNkyzdg9e/bw+++/89dff3H79m1MJhOenp7UqVOHF154AQ8Pj1Tjb9y4waBBgzJ87s6dO9OlS5ecTum+spIvQFBQEGvWrOHcuXPcunULV1dXypUrR7t27dKtOdz9Pm3atIlr165ha2tLtWrV6NKlCxUrVszFzFILDQ3lwIEDHDt2jICAAMLDw3F1daVatWp06tSJKlWqmMcmJiZy+PBhDh8+zKVLlwgJCcFgMFCuXDlatmxJ69atsbFJ/fmGtdU2K/kCmYpt/vz5lChRItU2a6htfHw8P/zwA5cvXyY4OJjIyEicnZ3x8vKiVatWPPXUU9ja/v8//VevXmX//v1cuXKFy5cvc+fOHWrUqMGECRPSPb+11VayZ/PmzWzYsIHw8HAeeeQR+vTpQ+XKlTMcf+DAAX788Udu3ryJl5cXPXr0oH79+nkYceGSlfps3bqV3bt38/fffwNQsWJFunXrdt96ysPL6msoxb59+5gzZw6PP/44H3zwQR5EWjhltT5RUVH88MMPHDx4kMjISDw9Pendu7d+z+WSrNZn48aNbNmyhZCQENzc3GjcuDHdu3fH3t4+D6MuPM6cOcP69eu5cuUKYWFhDB8+nEaNGt33mNOnT7NkyRL+/vtvihcvziuvvJLh30/pUVPKipw6dYopU6Zgb29P06ZNcXJyws/Pj88++4xbt27x/PPPWzrEh/bXX3+xcuVKHBwciIuLy3BcixYt8PT0TLPdxcUlN8PLNpPJxOzZs/Hz86NUqVK0atWKhIQEDh8+zIwZM+jTpw9t27Y1j4+NjWXChAlcvXqVunXr0qxZM65evcqGDRs4c+YMEydOtIpftCl/hBQpUoRixYpx8+bNDMfu27eP4OBgqlSpgru7O3D3D95ffvmFXbt28dFHH1G+fPk0xz3yyCM0bNgwzfaaNWvmWB6ZlZV8L168yMSJE0lKSqJBgwY0btyY27dvc/DgQWbMmIGvry++vr6pjlm9ejXLly/H09OTZ599lpiYGPbv38/YsWMZO3Ys1atXz+0UAfjll19Yt24dpUqVom7duri5uREUFMShQ4c4dOgQQ4YMMTfCr1+/zqeffoqjoyO1atWiQYMGREdHc+TIERYuXMjRo0cZMWIEBoMhzfNYS22zki/cbaykJzg4mL1791KuXLk0DSlrqW1sbCxbtmyhcuXKPPbYY7i5uREVFcXx48f54osv2L9/P6NGjTI3Eg8ePMjatWuxtbWldOnS3LlzJ1PPYy21lazbv38/S5YsoV+/flSpUoWNGzcyZcoUPvvsM4oWLZpm/Pnz55kzZw7du3enfv367N27l5kzZzJ9+nS8vb0tkEHBltX6nDlzhmbNmlGtWjXs7OxYt24dkydP5tNPP03zYZDkjKzWKMWNGzf47rvvePTRR/Mw2sInq/VJTExk8uTJuLm5MWzYMDw8PAgJCcHZ2dkC0Rd8Wa3P3r17+f777xkwYABVq1YlKCiI+fPnYzAY6N27twUyKPji4uLw8fGhVatWfPLJJw8cf+PGDT7++GOeffZZ3nnnHU6dOsX//vc/3N3dqVevXuae1CRWITEx0TRo0CBT9+7dTVeuXDFvj4qKMg0ePNjUrVs3040bNywXYA5ISEgwffDBB6YPP/zQ9Pnnn5t8fX1N58+fTzXmxx9/NPn6+ppOnTploSiz58CBAyZfX1/TmDFjTHFxcebtt2/fNr399tum7t27m65fv27enpLn0qVLU51n6dKlJl9fX9Pq1avzLPb7OXHihPnnbs2aNSZfX1/Tjh070h17b9732rZtm8nX19c0a9asVNuvX79u8vX1Nc2dOzdHY34YWcl36tSpJl9fX9PBgwdTbb9x44apV69eph49epji4+PN2wMDA02vvvqqafDgwaaoqCjz9itXrpi6d+9uGjp0qCkpKSnnk0rH77//bjp9+nSa7WfOnDG9+uqrptdee80c+61bt0ybN282xcTEpBobExNjGjlypMnX19e0f//+VPusrbZZyfd+vv76a5Ovr69pw4YNqbZbU22TkpJMCQkJabYnJiaaJkyYYPL19TUdOXLEvP2vv/4yXbp0yZSQkGAKCwsz+fr6msaPH5/h+a2ttpJ1o0aNMi1cuND8OCkpyfTmm2+a1qxZk+74Tz/91DRt2rRU2z788EPTggULcjPMQiur9fm3pKQkU69evUw7d+7MpQglOzVKSkoyjRkzxrRt2zbT3LlzTdOnT8+DSAunrNbn119/NQ0aNCjdfzsl52W1PgsXLjRNnDgx1bbFixebxowZk5thyj98fX1Nfn5+9x3z3XffmYYNG5Zq2+zZs02TJ0/O9PNoTSkrcerUKa5fv06zZs3w8fExb3d2duall14iMTGRXbt2WS7AHLB69WquXbvGgAED0lzuk98dOnQIgJdeeinVDCc3Nzc6dOhAQkICO3fuBO7Oqtq2bRuOjo688sorqc7zyiuv4OjoyPbt2/Ms9vupU6dOujPW0pPRzK4nnngCuDvLxNplJd/r169jMBh47LHHUm339PTE29ub+Ph4YmNjzdt37NhBUlISL7/8cqpP33x8fGjWrBkBAQGcO3cuZxJ5gMaNG1OjRo002x999FFq1apFVFQUf/31FwAeHh4899xzaS4pdXR0pEOHDsDdT+qtWVbyzUh8fDx79uzB1taW5s2bp9pnTbW1sbFJdXleCqPRaJ7ZdO9rsXz58lSsWDHdY6TgSUxM5PLly9SuXdu8zcbGhtq1a3PhwoV0j7lw4UKq8QB169bl4sWLuRprYZSd+vxbXFwciYmJuLq65laYhVp2a7Rq1Src3Nxo1apVXoRZaGWnPkeOHKFKlSp8/fXX9OvXj/fee4/Vq1eTnJycV2EXGtmpT7Vq1bh8+TL+/v7A3fffx44dS/P+Wyzn4sWL6b5PyOy/W6DL96zG6dOngbsF/LeUaW/W/off/Vy+fJk1a9bQpUsXypUr98DxZ8+e5eLFi9jY2ODl5UWdOnWsZp2l9ISHhwOku95VyrZTp07RpUsXgoKCCAsLo27duun+oV+tWjVOnDhBSEhImkuE8qOjR48CpHvpHkBYWBibN28mOjoad3d3atSogZeXV16GmC3ly5cnMDCQY8eOpbqMKSQkhL/++otHHnmEIkWKmLenvH7Te43XrVuXnTt3cubMmXSbJ3nJaDSm+v/9pDQyMhqbH2qb2XwPHjxIVFQUTzzxBG5ubqn25YfaJicnc+LECSDj12JW5IfaSloREREkJyebL7FO4e7uTmBgYLrHhIeHp7mkomjRouZ/9yTnZKc+/7Zs2TI8PDzS/IEgOSM7NTp37hzbt29nxowZeRBh4Zad+ly/fp2bN2/y5JNPMmrUKIKDg1m4cCFJSUlplmGQh5Od+jz55JNEREQwduxYAJKSknj22Wd5+eWXcztcyaSM3ifExMQQHx+fqSVp1JSyEimfXJcuXTrNPnd3dxwdHQkKCsrrsHJEQkIC8+bNw8fHh06dOmXqmBUrVqR67OLiwmuvvUaLFi1yI8SHltJ8uHHjRpqm240bNwDM9btfrVO2nzhxguDg4HzZlNq/fz/Xrl0jPj6ev//+mxMnTlCyZEm6du2a7viTJ09y8uRJ82ODwcCTTz5Jv379rLoR+eqrr3L+/Hk+/fRTGjRoQJkyZcxrSpUqVYqhQ4emGh8UFISjo2Oaf4jh/38WLD2bLCQkhD/++INixYplaq2YHTt2AHdnmKXH2mublXxTZi+m9ym3NdY2MTGR1atXA3Dnzh1OnTpFQEAALVu2zJE/Vq29tiKF0dq1a9m3bx8TJkywinUpBWJiYvjvf//LW2+9leYDDbEOJpMJNzc33nrrLWxsbKhYsSKhoaGsX79eTSkrcPr0adasWUPfvn2pUqUKwcHBfPPNN6xatSrDNUAl/1FTykpER0cDZLionpOTk3lMfvPjjz8SFBTExx9//MDL9nx8fBgwYAA1a9bE3d2d8PBwjh49yo8//sj8+fNxcXHJ8K5mlvTYY4+xf/9+1q1bR61atcxvBu/cucPGjRuBu3f2gMzV+t5x+c2BAwfw8/MzP65UqRJDhgxJM4vMwcGBV155hYYNG+Ll5UVycjJXrlxh+fLl7Nmzh7i4OIYPH57X4Wda2bJlmTJlCp9++ikHDx40by9SpAgtW7ZMM2skOjo6wwVQraHmiYmJ/Pe//yUhIYEePXo88LW6detWjh07Rq1atdLcnSY/1DYr+d64cYPTp09TokSJdBtw1ljbxMTEVHcyNRgMPP/883Tv3v2hzpsfaisZc3Nzw8bGJs0sp/Dw8HSbqnD3g7Hbt2+n2nb79u0Mx0v2Zac+KdavX8/atWsZO3YsjzzySO4FWchltUYps3CmT59u3mYymYC7H2599tlnmmWag7L7O87W1jbV+4CyZcsSHh5OYmKiLm/PQdmpz48//kjz5s155plnAPD29iY2NpYvv/ySl19+ucAtCZMfZfQ+wcnJKdMfkOhVJrnqwoULbNiwAV9f30zNvPj37SZLlixJ27ZtKVu2LJMnT2b58uVW2ZR68skn2blzJ6dPn2b48OHUq1ePxMREDh06ZP4lm97dyQqi9957D7jbhEv5Y3XkyJEMHz6cWrVqmccVLVo0zeyp2rVrU7VqVUaMGMHBgwe5fPkyFStWzNP4M8vf35+ZM2fi7e3Nxx9/bH4Ds3nzZr799lvOnTvHsGHDLB1mpiQnJzN//nzOnj3LM888k2bNpH87cuQIX3/9NZ6enrzzzjtp9lt7bbOa7/bt2zGZTLRs2TLfvPlxdHRkxYoVJCcnExYWxpEjR/jhhx+4cOECo0aNyvZdhay9tnJ/tra2VKxYkVOnTpn/vU1OTubUqVOp7hB7r6pVq/LHH3+Y15CDuzPlqlSpkicxFybZqQ/AunXrWL16NaNHj6ZSpUp5FW6hlNUalSlTJs3dq5YvX05sbCyvvfZavpwRb82y8xqqVq0a+/btIzk52fxvfFBQEMWKFVNDKodlpz5xcXFp/obKL+/FCosqVapw7NixVNtOnjxJ1apVM30OVdRKpPyBkNGn123XggAAFXlJREFU6TExMfnu1qRJSUnMmzePRx55hBdffPGhzlW7dm1KlSrFX3/9ZZUziIxGIx9++CG+vr4YDAa2bt3KwYMHadiwobkxkTKTIjO1vndcfuXi4kKtWrX48MMPsbe3Z+7cuSQmJj7wOAcHB3OT4Pz587kdZrYkJiby2WefYTAYGD58OBUrVsTBwYFSpUrRu3dvGjZsyO+//55qcWtnZ2errHlycjJffPEFe/fu5amnnqJfv373HX/06FE+/fRT3N3dGTduHMWKFcv0c1lDbbOab3JyMrt27cJgMGS4QK211hbuvnErXrw4bdq04c033+T8+fPmy/pykjXUVjKnY8eObNu2jZ07d3Lt2jUWLlxIXFwcLVu2BGDu3Ll8//335vHt27fnxIkTbNiwgYCAAFasWMGlS5fu2ySR7MtqfdauXcuPP/7IgAEDKFmyJOHh4YSHh6e60YbkrKzUyN7eHm9v71T/ubi44OjoiLe3t5oeuSCrr6E2bdoQGRnJt99+S2BgIEePHmXNmjU899xzFsqgYMtqfRo0aMBvv/3Gvn37uHHjBidPnuTHH3+kQYMGak7lktjYWK5evcrVq1eBu1cMXL16lZCQEAC+//575s6dax7fpk0bbty4wdKlSwkICODXX3/lwIEDqT7MehD9JrQSKVN3g4KC0nzCnPLmonLlypYILdtiY2PN6yhldMnImDFjABg+fHiaWVL/VqRIEYKDg4mPj7fKho2dnR2+vr5prj9PWcQ+pa731jo9KdsLynRuZ2dnqlSpwqFDhwgODs7UQvcpa3TFxcXldnjZEhgYyI0bN2jUqBEODg5p9tesWZNDhw5x9epVqlevDtxdW+jChQvpTlG2VM1TZgzt3r2bZs2aMXDgwPv+A3/06FFmzZpFkSJFGD9+PKVKlcryc1qytlnNF+D48ePcunWLunXrZviJtjXWNj0pC7Hn1k0zrP11K3c1bdqUiIgIVqxYQXh4OD4+Pnz44Yfmn92QkJBUn0pXq1aNwYMHs3z5cn744QdKly7N+++/n6nZz5J1Wa3Pb7/9RmJiIp9++mmq83Tu3JkuXbrkZeiFRlZrJHkrq/UpUaIEo0ePZvHixbz//vt4eHjQrl27h/5AXdKX1fq88sorGAwGli9fTmhoKG5ubjRo0IBu3bpZKIOC79KlS0ycONH8eMmSJQC0aNGCgQMHEhYWZm5Qwd0rm0aOHMnixYvZtGkTxYsXp3///uabtWWGmlJWokaNGqxdu5YTJ07QrFmzVPuOHz9uHpOf2NnZZTiz4OzZswQFBfH444/j5uaW7l3r7hUbG8u1a9dwcHBIdUez/GDPnj0A5rqWLl2aYsWKcf78eWJjY1MtChwbG8v58+cpWbJkgZrSHRYWBpDpTwRTbjXu6emZazE9jJQZX3fu3El3f0REBJA63xo1anDhwgVOnDiRZsH+lLui5eVr/N4GTdOmTXnnnXcy1ZBydXVl/Pjx2W6yWKq2Wc03RcoC5ylrGaTH2mqbkdDQUCBzd1bMDmt/3cr/a9u2bYYznSZMmJBmW5MmTWjSpEkuRyUpslKfefPm5UFE8m9ZfQ3da+DAgbkQkdwrq/WpWrUqU6ZMyeWoJEVW6mM0GtP90F9yT82aNdPcdOxe6f0Oq1mz5kPdYVRz3qxEyuVp+/btM0+Vg7uXeK1ZswZbW9sHrntibezt7enfv3+6/6VcY/riiy/Sv39/fHx8iImJSfd2oPHx8SxYsICYmBiaNGmSa39QPaz0Lt/5/fff2bFjB5UqVaJx48bA3bWlnnnmGWJjY/npp59Sjf/pp5+IjY297x/A1iij2sHdP+r9/f0pXbp0qkbGlStXzIt93svPz49du3bh4uLCY489lmsxP4zy5cvj5OTEuXPnzE2HFCEhIWzduhWDwZCqEdGyZUuMRiOrV69O9bNy9epV9u3bR9myZc2zqnJbyiVsu3fv5oknnnhgg+bYsWPMmjULFxcXxo8fn+GdI1NYW22zmm+KiIgIjhw5gpub233XsrOm2l67di3dmUpxcXHmT7oe5ntvbbUVERERkfxNM6WshNFo5K233mLKlCmMHz+epk2b4uTkhJ+fHzdv3qRnz54PnE2U3925c4d3332XSpUqUbZsWfNK/n/88Qe3bt3C29ubnj17WjrMDI0ePZrixYtTtmxZ7OzsuHTpEqdPn6ZUqVIMGzYs1R/BL7zwAocOHWLdunVcvXqVChUqcOXKFU6cOEGlSpWydA1ubtq2bZt5XaS//vrLvC3lksTq1avzzDPPmGtXsWJFypYti4eHB5GRkVy6dIkrV67g5OSUpqu+ePFirl+/TpUqVShevLj5Ll7nzp3Dzs6OgQMH5vllmpnN187Ojp49e/Lll18ydepUGjRoQJkyZQgPD+fgwYPExsbSsWNHypQpYz53mTJl8PX1Zfny5bz//vs0btyYmJgY9u/fD2C+FXFeWLVqFbt27cLR0ZEyZcqkaY7C3ZsO+Pj4EBAQwCeffEJCQgI1atRg7969acaWLFnSvBYAWF9ts5LvvXbt2kVSUhLNmze/7yw/a6rt/v372bhxI9WrV8fT0xMnJydCQ0M5fvw4d+7c4dFHH6Vjx47m8QEBAaxduxa4+wFAyrZ7Z1/c+9q1ttqKiIiISP6mppQVqVWrFpMmTWLFihXs37+fpKQkvL296dGjB02bNrV0eLnO1dWVNm3a4O/vz7Fjx4iKisLe3p6yZcvSrl072rZtm+nbSlpCkyZNOHjwIBcvXiQxMZGSJUvy8ssv88ILL6T5I83R0ZGJEyeyYsUK/Pz8OHXqFMWKFaNjx474+vpaTZ7nzp1j165dqbadP38+1ULGzzzzDG5ubrzyyiucPn2akydPcufOHWxtbSlZsiQdOnSgY8eOFC9ePNV5nnrqKfz8/Lh48SJHjx7FZDLh4eFBq1ateP755ylbtmye5HivzOYL0Lp1a0qWLMmmTZs4f/48R48exdHRkQoVKtC6dWueeuqpNOd/+eWX8fT0ZNOmTWzZsgVbW1uqV69O165d8/RuZTdv3gTuXi6a0aLXJUuWxMfHh/DwcBISEgDMTZZ/q1GjRqqmlLXVNiv53ivl0r2MLkO+l7XUtkGDBoSFhXHhwgUuXLhAbGwszs7OeHt706xZM55++ulUs03Dw8PT/Mzfvn071bZ7m1LWVlsRERERyd8MpvTm4YuIiIiIiIiIiOQirSklIiIiIiIiIiJ5Tk0pERERERERERHJc2pKiYiIiIiIiIhInlNTSkRERERERERE8pyaUiIiIiIiIiIikufUlBIRERERERERkTynppSIiIiIiIiIiOQ5NaVERERERERERCTPqSklIiIiIiIiIiJ5ztbSAYhIwfb+++/z559/Ymtry4IFCyhSpIilQzLbuXMn8+fPp0WLFgwcODBXn2vevHns2rXL/NjGxobly5dn6RwTJkzgzJkzjB8/npo1a+Z0iJk2dOhQAgMDzY9r1KjBhAkTLBaPiIiIiIjkT2pKiUiu8ff3588//wQgMTGRPXv20L59ewtHZVnVqlXDy8sLG5v8O1G1UaNGhIWFER4ezokTJywdjoiIiIiI5FNqSolIrtm+fTsAHh4ehIaGsn379kLflHrmmWdo2bKlpcN4KN27dwfg9OnTakqJiIiIiEi25d+P6kXEqsXFxbFv3z4ABg0ahKOjI3/99Rf+/v4WjkxERERERESsgWZKiUiuOHDgADExMZQvX55atWrRtGlTtm/fzvbt26lcuXK6x9y7ZpKLiwurVq3i7NmzxMTEUKpUKVq1akXHjh0xGAxpjo2NjWXt2rXs37+fkJAQihQpQt26dXn11VfZunUrq1atonPnznTp0iXTOYSGhrJhwwaOHz/OzZs3sbGxoWzZsrRo0YJnn30Wo9GY7e9PRkJCQlixYgXHjx8nKiqK4sWL06RJE1555ZUHHvv777+zbds2Ll++THR0NG5ubtSsWZOXX36ZcuXKpXvMuXPn+Omnn7h48SJJSUmUK1eOtm3b0qJFC/P3asWKFTmao4iIiIiICKgpJSK5ZMeOHQA8/fTT5v9v376d/fv389prr2Fvb5/hsSdOnODnn3+mVKlS1K5dm/DwcM6dO8d3333HrVu3eO2111KNj42NZeLEiVy6dAlHR0fq1KmDvb09J06c4NixYzz22GNZjv/MmTPMnDmTqKgoPD09qVOnDgkJCfj7+7No0SKOHDnCiBEjsLXNuV+jAQEBTJgwgdu3b1OsWDEaNGhAXFwcGzdu5PTp0xkel5SUxOeff86BAwews7OjYsWKFCtWjKCgIPbu3cvBgwcZPnw49erVS3Xcvn37+PzzzzGZTHh7e1O+fHlCQ0OZP38+165dy7G8RERERERE0qOmlIjkuMDAQM6ePYvRaKR58+bA3QW+y5YtS0BAAL///rt5e3rWrl1Lv379ePbZZ83bTp06xaRJk9i8eTPPP/88xYsXN+/78ccfuXTpEuXKlWPs2LEUK1YMgPj4eObOncvOnTuzFH94eDizZs0iOjqavn370rp1a/PC5Hfu3GH27NmcOHGCtWvX0rlz5yyd+37mzp3L7du3adKkCQMHDjQ37kJCQpg4cSLXr19P97gVK1Zw4MABqlSpwpAhQyhZsqR53++//85nn33GnDlzmDt3Li4uLsDdWWALFizAZDLx2muvpVrr68yZM0ybNi3H8hIREREREUmP1pQSkRyXMkvq8ccfx83Nzbw9ZdZUygLoGWnUqFGqhhRArVq1qFu3LsnJyalmDcXHx7Nt2zYAevfubW5IAdjb29O3b18cHByyFP/GjRu5c+cOzz33HG3atEl1p7wiRYowaNAgjEYjmzdvxmQyZencGTl37hyXLl3CwcGBvn37pppJVqJECXr27JnucZGRkWzcuBE7Ozvee++9VA0pgCeeeILWrVsTFRXFnj17zNu3b99ObGwsVatWTbP4fI0aNWjTpk2O5CUiIiIiIpIRNaVEJEclJSWxa9cu4P+bUCmaN2+O0Wjk7NmzBAcHZ3iOxx9/PN3tZcuWBe7O8klx+fJlYmNjzWtI/Zubmxu1a9fOUg5Hjx4FoGnTpunu9/DwoHTp0kRERBAUFJSlc2fkzJkzANSrV48iRYqk2d+wYUOcnZ3TbD916hTx8fFUr14dDw+PdM9ds2ZNAM6fP2/edvbsWQCefPLJdI956qmnspaAiIiIiIhIFunyPRHJUUePHiU8PBwPD480axi5u7vz2GOPcfjwYXbs2EG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0.567405 0.752296 \n\n displacement r2 PAQ1 PAQ2 PAQ3 PAQ4 PAQ5 PAQ6 PAQ7 \\\ndeu_loud 147.050614 0.978323 0 45 90 135 180 225 270 \neng_loud 147.186772 0.982669 0 45 90 135 180 225 270 \nhrv_loud 137.315498 0.916134 0 45 90 135 180 225 270 \nita_loud 143.332361 0.912120 0 45 90 135 180 225 270 \npor_loud 123.804373 0.928637 0 45 90 135 180 225 270 \nswe_loud 148.746958 0.955507 0 45 90 135 180 225 270 \ntur_loud 131.041796 0.947249 0 45 90 135 180 225 270 \n\n PAQ8 \ndeu_loud 315 \neng_loud 315 \nhrv_loud 315 \nita_loud 315 \npor_loud 315 \nswe_loud 315 \ntur_loud 315 ", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
labelgroupmeasureelevationxvalyvalamplitudedisplacementr2PAQ1PAQ2PAQ3PAQ4PAQ5PAQ6PAQ7PAQ8
deu_louddeu_louddeuloud-0.030467-0.6494430.4209380.773928147.0506140.97832304590135180225270315
eng_loudeng_loudengloud0.001829-0.5751450.3708430.684336147.1867720.98266904590135180225270315
hrv_loudhrv_loudhrvloud0.062247-0.5024740.4634180.683547137.3154980.91613404590135180225270315
ita_loudita_louditaloud0.033210-0.4932850.3672500.614981143.3323610.91212004590135180225270315
por_loudpor_loudporloud0.082074-0.3915680.5848200.703804123.8043730.92863704590135180225270315
swe_loudswe_loudsweloud0.016213-0.6300650.3823790.737018148.7469580.95550704590135180225270315
tur_loudtur_loudturloud-0.001167-0.4939640.5674050.752296131.0417960.94724904590135180225270315
\n
" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ssm_res.table" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:34.896960Z", - "start_time": "2023-11-25T18:35:34.888084Z" - } - }, - "id": "99ab18f9960d34a2" - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error: array must not contain infs or NaNs | in Language = arb\n", - "Error: array must not contain infs or NaNs | in Language = cmn\n", - "Error: array must not contain infs or NaNs | in Language = ell\n", - "Error: array must not contain infs or NaNs | in Language = fra\n", - "Error: array must not contain infs or NaNs | in Language = ind\n", - "Error: array must not contain infs or NaNs | in Language = jpn\n", - "Error: array must not contain infs or NaNs | in Language = kor\n", - "Error: array must not contain infs or NaNs | in Language = nld\n", - "Error: array must not contain infs or NaNs | in Language = spa\n", - "Error: array must not contain infs or NaNs | in Language = vie\n", - "Error: array must not contain infs or NaNs | in Language = zsm\n" - ] - }, - { - "data": { - "text/plain": "(
, )" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "lang_angles = {\n", - " 'arb': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'cmn': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'deu': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'ell': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'eng': (0, 46, 93, 138, 182, 228, 272, 340),\n", - " 'fra': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'hrv': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'ind': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'ita': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'jpn': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'kor': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'nld': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'por': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'spa': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'swe': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'tur': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'vie': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " 'zsm': (0, 65, 97, 131, 182, 255, 281, 335),\n", - " }\n", - "\n", - "corr_res = circumplex.ssm_analyse(data, scales, [\"loud\"], [\"Language\"], grouped_angles = lang_angles)\n", - "corr_res.plot()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:35.119549Z", - "start_time": "2023-11-25T18:35:34.900761Z" - } - }, - "id": "a33ca93dde25b889" - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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0.998362 0 65 97 131 182 255 281 \neng_loud 149.805988 0.981556 0 46 93 138 182 228 272 \nhrv_loud 152.892397 0.963337 0 65 97 131 182 255 281 \nita_loud 159.356518 0.974369 0 65 97 131 182 255 281 \npor_loud 138.874937 0.973591 0 65 97 131 182 255 281 \nswe_loud 164.467842 0.989495 0 65 97 131 182 255 281 \ntur_loud 146.874258 0.969585 0 65 97 131 182 255 281 \n\n PAQ8 \ndeu_loud 335 \neng_loud 340 \nhrv_loud 335 \nita_loud 335 \npor_loud 335 \nswe_loud 335 \ntur_loud 335 ", - "text/html": "
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labelgroupmeasureelevationxvalyvalamplitudedisplacementr2PAQ1PAQ2PAQ3PAQ4PAQ5PAQ6PAQ7PAQ8
deu_louddeu_louddeuloud0.008102-0.7573200.2321370.792100162.9584160.99836206597131182255281335
eng_loudeng_loudengloud0.005455-0.5858920.3409150.677859149.8059880.98155604693138182228272340
hrv_loudhrv_loudhrvloud0.089558-0.6130450.3138140.688697152.8923970.96333706597131182255281335
ita_loudita_louditaloud0.062258-0.5958950.2244980.636781159.3565180.97436906597131182255281335
por_loudpor_loudporloud0.099232-0.5147930.4494790.683405138.8749370.97359106597131182255281335
swe_loudswe_loudsweloud0.054330-0.7356130.2044480.763495164.4678420.98949506597131182255281335
tur_loudtur_loudturloud0.023584-0.6159720.4019420.735513146.8742580.96958506597131182255281335
\n
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "new_ang_results.profile_plot()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:36.030606Z", - "start_time": "2023-11-25T18:35:35.916368Z" - } - }, - "id": "c2b3a77b1350c969" - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "data": { - "text/plain": "34.87191446710762" - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_ang_results.displacement" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:36.031454Z", - "start_time": "2023-11-25T18:35:36.026395Z" - } - }, - "id": "64f3cd8bb92fe5b9" - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [ - { - "data": { - "text/plain": "42.91989829477362" - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_ang_results.elevation" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:36.039183Z", - "start_time": "2023-11-25T18:35:36.032692Z" - } - }, - "id": "a00685d5a11367bb" - }, - { - "cell_type": "code", - "execution_count": 16, - "outputs": [], - "source": [ - "import soundscapy as sspy\n", - "\n", - "ss_data = sspy.isd.load()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:37.135583Z", - "start_time": "2023-11-25T18:35:36.036804Z" - } - }, - "id": "1a75672166b1fd02" - }, - { - "cell_type": "code", - "execution_count": 17, - "outputs": [ - { - "data": { - "text/plain": " LocationID SessionID GroupID RecordID Language \\\n441 MonumentoGaribaldi MonumentoGaribaldi1 MG101 2.0 Italian \n442 MonumentoGaribaldi MonumentoGaribaldi1 MG101 1.0 Italian \n443 MonumentoGaribaldi MonumentoGaribaldi1 MG102 206.0 English \n444 MonumentoGaribaldi MonumentoGaribaldi1 MG103 3.0 English \n445 MonumentoGaribaldi 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0.410 0.2390 \n442 0.0283 0.410 0.2390 \n443 0.0312 0.529 0.2490 \n444 0.0111 0.480 0.1140 \n445 0.0156 0.771 0.0851 \n.. ... ... ... \n521 0.0594 0.383 0.4750 \n522 0.0435 0.381 0.3810 \n523 0.0576 0.407 0.4150 \n524 0.0303 0.453 0.2990 \n525 0.0496 0.422 0.3530 \n\n LZeq_L(dB(SPL)) LAeq_L(A)(dB(SPL)) LA10_LA90(dB(SPL)) \\\n441 60.95 53.66 8.08 \n442 60.95 53.66 8.08 \n443 62.36 54.93 9.13 \n444 59.74 50.01 4.61 \n445 56.51 50.68 5.14 \n.. ... ... ... \n521 62.07 57.41 12.76 \n522 58.19 52.56 10.78 \n523 57.02 53.97 13.76 \n524 57.23 53.72 16.18 \n525 60.17 57.01 16.93 \n\n LCeq_LAeq(dB(SPL)) LC10_LC90(dB(SPL)) RA_2D_cp(cPa) PA(Zwicker) \n441 6.73 4.82 9.86 10.758803 \n442 6.73 4.82 9.86 10.758803 \n443 7.01 4.89 10.40 11.670954 \n444 9.15 3.65 9.10 9.744015 \n445 4.99 3.37 9.81 8.246837 \n.. ... ... ... ... \n521 1.18 8.60 9.53 12.503792 \n522 1.56 6.77 8.60 7.972545 \n523 0.41 8.33 9.03 8.887960 \n524 1.40 8.44 8.41 8.912749 \n525 1.32 8.87 9.65 12.126460 \n\n[85 rows x 78 columns]", - "text/html": "
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LocationIDSessionIDGroupIDRecordIDLanguageLockdownstart_timeend_timelatitudelongitude...FS_Avg,arith(vacil)I_HM_Avg,arith(iu)Ton_HM_Avg,arith(tuHMS)LZeq_L(dB(SPL))LAeq_L(A)(dB(SPL))LA10_LA90(dB(SPL))LCeq_LAeq(dB(SPL))LC10_LC90(dB(SPL))RA_2D_cp(cPa)PA(Zwicker)
441MonumentoGaribaldiMonumentoGaribaldi1MG1012.0Italian003/03/2019 11:1803/03/2019 11:2245.43183712.354908...0.02830.4100.239060.9553.668.086.734.829.8610.758803
442MonumentoGaribaldiMonumentoGaribaldi1MG1011.0Italian003/03/2019 11:1803/03/2019 11:2245.43183712.354908...0.02830.4100.239060.9553.668.086.734.829.8610.758803
443MonumentoGaribaldiMonumentoGaribaldi1MG102206.0English003/03/2019 11:1803/03/2019 11:2845.43183712.354908...0.03120.5290.249062.3654.939.137.014.8910.4011.670954
444MonumentoGaribaldiMonumentoGaribaldi1MG1033.0English003/03/2019 11:2303/03/2019 11:2945.43183712.354908...0.01110.4800.114059.7450.014.619.153.659.109.744015
445MonumentoGaribaldiMonumentoGaribaldi1MG1044.0English003/03/2019 11:2203/03/2019 11:3345.43183712.354908...0.01560.7710.085156.5150.685.144.993.379.818.246837
..................................................................
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522MonumentoGaribaldiMonumentoGaribaldi3MG331NaNNaN1NaNNaNNaNNaN...0.04350.3810.381058.1952.5610.781.566.778.607.972545
523MonumentoGaribaldiMonumentoGaribaldi3MG332NaNNaN1NaNNaNNaNNaN...0.05760.4070.415057.0253.9713.760.418.339.038.887960
524MonumentoGaribaldiMonumentoGaribaldi3MG333NaNNaN1NaNNaNNaNNaN...0.03030.4530.299057.2353.7216.181.408.448.418.912749
525MonumentoGaribaldiMonumentoGaribaldi3MG334NaNNaN1NaNNaNNaNNaN...0.04960.4220.353060.1757.0116.931.328.879.6512.126460
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85 rows × 78 columns

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" - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ss_data.query(\"LocationID == 'MonumentoGaribaldi'\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:37.153649Z", - "start_time": "2023-11-25T18:35:37.134917Z" - } - }, - "id": "9f0b5e4a63535199" - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Renaming PAQ columns.\n", - "Checking PAQ data quality.\n", - "Identified 627 samples to remove.\n", - "[95, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 152, 168, 171, 190, 198, 204, 222, 230, 247, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 302, 308, 337, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 557, 580, 583, 607, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 818, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 870, 876, 885, 889, 905, 930, 958, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 1000, 1009, 1023, 1028, 1041, 1063, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1213, 1220, 1251, 1271, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1433, 1448, 1468, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1553, 1586, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1712, 1713, 1714, 1715, 1716, 1717, 1718, 1719, 1720, 1721, 1722, 1723, 1724, 1725, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1733, 1734, 1735, 1736, 1737, 1738, 1739, 1740, 1744, 1747, 1752, 1753, 1755, 1759, 1765, 1785, 1798, 1799, 1830, 1846, 1851, 1865, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908]\n" - ] - } - ], - "source": [ - "ss_data, excl_data = sspy.isd.validate(ss_data)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:37.312644Z", - "start_time": "2023-11-25T18:35:37.151777Z" - } - }, - "id": "ce971cc7b2f46236" - }, - { - "cell_type": "code", - "execution_count": 19, - "outputs": [ - { - "data": { - "text/plain": " LocationID SessionID GroupID RecordID Language Lockdown \\\n95 CamdenTown CamdenTown4 CT411 NaN NaN 0 \n106 CamdenTown CamdenTown5 CT501 NaN NaN 1 \n107 CamdenTown CamdenTown5 CT502 NaN NaN 1 \n108 CamdenTown CamdenTown5 CT503 NaN NaN 1 \n109 CamdenTown CamdenTown5 CT504 NaN NaN 1 \n... ... ... ... ... ... ... \n1904 TorringtonSq TorringtonSq5 TS537 NaN NaN 1 \n1905 TorringtonSq TorringtonSq5 TS538 NaN NaN 1 \n1906 TorringtonSq TorringtonSq5 TS539 NaN NaN 1 \n1907 TorringtonSq TorringtonSq5 TS540 NaN NaN 1 \n1908 TorringtonSq TorringtonSq5 TS541 NaN NaN 1 \n\n start_time end_time latitude longitude ... FS_Avg,arith(vacil) \\\n95 NaN NaN NaN NaN ... 0.04410 \n106 NaN NaN NaN NaN ... 0.00700 \n107 NaN NaN NaN NaN ... 0.00656 \n108 NaN NaN NaN NaN ... 0.01390 \n109 NaN NaN NaN NaN ... 0.01820 \n... ... ... ... ... ... ... \n1904 NaN NaN NaN NaN ... 0.00550 \n1905 NaN NaN NaN NaN ... 0.00916 \n1906 NaN NaN NaN NaN ... 0.00967 \n1907 NaN NaN NaN NaN ... 0.00902 \n1908 NaN NaN NaN NaN ... 0.00930 \n\n I_HM_Avg,arith(iu) Ton_HM_Avg,arith(tuHMS) LZeq_L(dB(SPL)) \\\n95 0.369 0.608 85.82 \n106 0.361 0.175 74.52 \n107 0.339 0.287 78.27 \n108 0.370 0.181 84.71 \n109 0.462 0.177 73.67 \n... ... ... ... \n1904 0.399 0.203 71.77 \n1905 0.359 0.230 68.85 \n1906 0.363 0.481 79.33 \n1907 0.445 0.257 67.57 \n1908 0.404 0.246 78.01 \n\n LAeq_L(A)(dB(SPL)) LA10_LA90(dB(SPL)) LCeq_LAeq(dB(SPL)) \\\n95 75.37 4.79 10.00 \n106 59.44 12.32 9.35 \n107 69.67 15.15 7.19 \n108 72.93 21.23 6.32 \n109 62.28 8.99 7.76 \n... ... ... ... \n1904 52.90 5.61 12.97 \n1905 56.30 10.09 9.27 \n1906 64.32 14.80 13.95 \n1907 49.33 2.64 12.12 \n1908 59.17 16.54 14.17 \n\n LC10_LC90(dB(SPL)) RA_2D_cp(cPa) PA(Zwicker) \n95 6.34 18.80 54.379887 \n106 8.93 11.70 18.410488 \n107 11.39 12.90 37.721660 \n108 9.70 15.50 40.301608 \n109 5.27 12.80 20.047791 \n... ... ... ... \n1904 8.31 10.80 9.987229 \n1905 8.16 9.99 14.359218 \n1906 20.39 10.90 30.033977 \n1907 5.04 9.62 7.882057 \n1908 17.02 11.10 20.284137 \n\n[627 rows x 78 columns]", - "text/html": "
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LocationIDSessionIDGroupIDRecordIDLanguageLockdownstart_timeend_timelatitudelongitude...FS_Avg,arith(vacil)I_HM_Avg,arith(iu)Ton_HM_Avg,arith(tuHMS)LZeq_L(dB(SPL))LAeq_L(A)(dB(SPL))LA10_LA90(dB(SPL))LCeq_LAeq(dB(SPL))LC10_LC90(dB(SPL))RA_2D_cp(cPa)PA(Zwicker)
95CamdenTownCamdenTown4CT411NaNNaN0NaNNaNNaNNaN...0.044100.3690.60885.8275.374.7910.006.3418.8054.379887
106CamdenTownCamdenTown5CT501NaNNaN1NaNNaNNaNNaN...0.007000.3610.17574.5259.4412.329.358.9311.7018.410488
107CamdenTownCamdenTown5CT502NaNNaN1NaNNaNNaNNaN...0.006560.3390.28778.2769.6715.157.1911.3912.9037.721660
108CamdenTownCamdenTown5CT503NaNNaN1NaNNaNNaNNaN...0.013900.3700.18184.7172.9321.236.329.7015.5040.301608
109CamdenTownCamdenTown5CT504NaNNaN1NaNNaNNaNNaN...0.018200.4620.17773.6762.288.997.765.2712.8020.047791
..................................................................
1904TorringtonSqTorringtonSq5TS537NaNNaN1NaNNaNNaNNaN...0.005500.3990.20371.7752.905.6112.978.3110.809.987229
1905TorringtonSqTorringtonSq5TS538NaNNaN1NaNNaNNaNNaN...0.009160.3590.23068.8556.3010.099.278.169.9914.359218
1906TorringtonSqTorringtonSq5TS539NaNNaN1NaNNaNNaNNaN...0.009670.3630.48179.3364.3214.8013.9520.3910.9030.033977
1907TorringtonSqTorringtonSq5TS540NaNNaN1NaNNaNNaNNaN...0.009020.4450.25767.5749.332.6412.125.049.627.882057
1908TorringtonSqTorringtonSq5TS541NaNNaN1NaNNaNNaNNaN...0.009300.4040.24678.0159.1716.5414.1717.0211.1020.284137
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627 rows × 78 columns

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- }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%autoreload\n", - "ss_res = circumplex.ssm_analyse(ss_data, scales, measures=[\"Appropriate\"], grouping=[\"LocationID\"])\n", - "ss_res.plot()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:37.558409Z", - "start_time": "2023-11-25T18:35:37.290756Z" - } - }, - "id": "575b5a5e7851ae08" - }, - { - "cell_type": "code", - "execution_count": 22, - "outputs": [ - { - "data": { - "text/plain": " label \\\nCamdenTown_Appropriate CamdenTown_Appropriate \nEustonTap_Appropriate EustonTap_Appropriate \nMarchmontGarden_Appropriate MarchmontGarden_Appropriate \nMonumentoGaribaldi_Appropriate MonumentoGaribaldi_Appropriate \nPancrasLock_Appropriate PancrasLock_Appropriate \nRegentsParkFields_Appropriate RegentsParkFields_Appropriate \nRegentsParkJapan_Appropriate RegentsParkJapan_Appropriate \nRussellSq_Appropriate RussellSq_Appropriate \nSanMarco_Appropriate SanMarco_Appropriate \nStPaulsCross_Appropriate StPaulsCross_Appropriate \nStPaulsRow_Appropriate StPaulsRow_Appropriate \nTateModern_Appropriate TateModern_Appropriate \nTorringtonSq_Appropriate TorringtonSq_Appropriate \n\n group measure elevation \\\nCamdenTown_Appropriate CamdenTown Appropriate -0.012624 \nEustonTap_Appropriate EustonTap Appropriate -0.053329 \nMarchmontGarden_Appropriate MarchmontGarden Appropriate 0.035732 \nMonumentoGaribaldi_Appropriate MonumentoGaribaldi Appropriate 0.033375 \nPancrasLock_Appropriate PancrasLock Appropriate -0.015507 \nRegentsParkFields_Appropriate RegentsParkFields Appropriate 0.008437 \nRegentsParkJapan_Appropriate RegentsParkJapan Appropriate -0.017871 \nRussellSq_Appropriate RussellSq Appropriate -0.037783 \nSanMarco_Appropriate SanMarco Appropriate 0.043938 \nStPaulsCross_Appropriate StPaulsCross Appropriate 0.064805 \nStPaulsRow_Appropriate StPaulsRow Appropriate -0.077965 \nTateModern_Appropriate TateModern Appropriate -0.023393 \nTorringtonSq_Appropriate TorringtonSq Appropriate -0.024703 \n\n xval yval amplitude displacement \\\nCamdenTown_Appropriate 0.009983 0.198888 0.199139 87.126609 \nEustonTap_Appropriate 0.088189 0.076755 0.116913 41.034683 \nMarchmontGarden_Appropriate 0.247035 -0.167318 0.298364 325.890005 \nMonumentoGaribaldi_Appropriate 0.165411 0.157678 0.228524 43.628852 \nPancrasLock_Appropriate 0.350502 -0.049884 0.354034 351.899919 \nRegentsParkFields_Appropriate 0.412197 -0.098031 0.423694 346.622152 \nRegentsParkJapan_Appropriate 0.222595 -0.099201 0.243699 335.979462 \nRussellSq_Appropriate 0.416207 0.070529 0.422140 9.617804 \nSanMarco_Appropriate 0.155632 0.227018 0.275243 55.567422 \nStPaulsCross_Appropriate 0.312216 -0.055789 0.317161 349.868872 \nStPaulsRow_Appropriate 0.382289 -0.014567 0.382566 357.817845 \nTateModern_Appropriate 0.326791 0.097733 0.341092 16.650245 \nTorringtonSq_Appropriate 0.219167 0.135520 0.257681 31.730213 \n\n r2 PAQ1 PAQ2 PAQ3 PAQ4 PAQ5 PAQ6 \\\nCamdenTown_Appropriate 0.748778 0 45 90 135 180 225 \nEustonTap_Appropriate 0.318162 0 45 90 135 180 225 \nMarchmontGarden_Appropriate 0.965836 0 45 90 135 180 225 \nMonumentoGaribaldi_Appropriate 0.474805 0 45 90 135 180 225 \nPancrasLock_Appropriate 0.909881 0 45 90 135 180 225 \nRegentsParkFields_Appropriate 0.920718 0 45 90 135 180 225 \nRegentsParkJapan_Appropriate 0.822661 0 45 90 135 180 225 \nRussellSq_Appropriate 0.943055 0 45 90 135 180 225 \nSanMarco_Appropriate 0.940069 0 45 90 135 180 225 \nStPaulsCross_Appropriate 0.891766 0 45 90 135 180 225 \nStPaulsRow_Appropriate 0.954414 0 45 90 135 180 225 \nTateModern_Appropriate 0.956939 0 45 90 135 180 225 \nTorringtonSq_Appropriate 0.945766 0 45 90 135 180 225 \n\n PAQ7 PAQ8 \nCamdenTown_Appropriate 270 315 \nEustonTap_Appropriate 270 315 \nMarchmontGarden_Appropriate 270 315 \nMonumentoGaribaldi_Appropriate 270 315 \nPancrasLock_Appropriate 270 315 \nRegentsParkFields_Appropriate 270 315 \nRegentsParkJapan_Appropriate 270 315 \nRussellSq_Appropriate 270 315 \nSanMarco_Appropriate 270 315 \nStPaulsCross_Appropriate 270 315 \nStPaulsRow_Appropriate 270 315 \nTateModern_Appropriate 270 315 \nTorringtonSq_Appropriate 270 315 ", - "text/html": "
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labelgroupmeasureelevationxvalyvalamplitudedisplacementr2PAQ1PAQ2PAQ3PAQ4PAQ5PAQ6PAQ7PAQ8
CamdenTown_AppropriateCamdenTown_AppropriateCamdenTownAppropriate-0.0126240.0099830.1988880.19913987.1266090.74877804590135180225270315
EustonTap_AppropriateEustonTap_AppropriateEustonTapAppropriate-0.0533290.0881890.0767550.11691341.0346830.31816204590135180225270315
MarchmontGarden_AppropriateMarchmontGarden_AppropriateMarchmontGardenAppropriate0.0357320.247035-0.1673180.298364325.8900050.96583604590135180225270315
MonumentoGaribaldi_AppropriateMonumentoGaribaldi_AppropriateMonumentoGaribaldiAppropriate0.0333750.1654110.1576780.22852443.6288520.47480504590135180225270315
PancrasLock_AppropriatePancrasLock_AppropriatePancrasLockAppropriate-0.0155070.350502-0.0498840.354034351.8999190.90988104590135180225270315
RegentsParkFields_AppropriateRegentsParkFields_AppropriateRegentsParkFieldsAppropriate0.0084370.412197-0.0980310.423694346.6221520.92071804590135180225270315
RegentsParkJapan_AppropriateRegentsParkJapan_AppropriateRegentsParkJapanAppropriate-0.0178710.222595-0.0992010.243699335.9794620.82266104590135180225270315
RussellSq_AppropriateRussellSq_AppropriateRussellSqAppropriate-0.0377830.4162070.0705290.4221409.6178040.94305504590135180225270315
SanMarco_AppropriateSanMarco_AppropriateSanMarcoAppropriate0.0439380.1556320.2270180.27524355.5674220.94006904590135180225270315
StPaulsCross_AppropriateStPaulsCross_AppropriateStPaulsCrossAppropriate0.0648050.312216-0.0557890.317161349.8688720.89176604590135180225270315
StPaulsRow_AppropriateStPaulsRow_AppropriateStPaulsRowAppropriate-0.0779650.382289-0.0145670.382566357.8178450.95441404590135180225270315
TateModern_AppropriateTateModern_AppropriateTateModernAppropriate-0.0233930.3267910.0977330.34109216.6502450.95693904590135180225270315
TorringtonSq_AppropriateTorringtonSq_AppropriateTorringtonSqAppropriate-0.0247030.2191670.1355200.25768131.7302130.94576604590135180225270315
\n
" - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ss_res.table" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:37.573844Z", - "start_time": "2023-11-25T18:35:37.562415Z" - } - }, - "id": "a08facba2975a1f0" - }, - { - "cell_type": "code", - "execution_count": 23, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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" 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "circumplex.profile_plot(\n", - " 0.22,\n", - " 332.3,\n", - " 0,\n", - " 0.777,\n", - " ss_res.query_label('RegentsParkJapan_Appropriate').angles,\n", - " ss_res.query_label('RegentsParkJapan_Appropriate').scores,\n", - " \"Appropriate\",\n", - " )\n", - "plt.show()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:39.462712Z", - "start_time": "2023-11-25T18:35:38.956960Z" - } - }, - "id": "9a081d5c6031399b" - }, - { - "cell_type": "code", - "execution_count": 25, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sspy.plotting.scatter(ss_res.table, x='xval', y='yval')\n" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:39.463289Z", - "start_time": "2023-11-25T18:35:39.110359Z" - } - }, - "id": "e614fdebb98c3cad" - }, - { - "cell_type": "markdown", - "source": [ - "## Instruments" - ], - "metadata": { - "collapsed": false - }, - "id": "82e8807763eff74f" - }, - { - "cell_type": "code", - "execution_count": 26, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SATP-eng: Soundscape Attributes Translation Project - English Translation\n", - "8 Items, 8 Scales\n", - "Aletta, Mitchell, et.al. (2024)\n", - "<>\n", - "\n", - "The SATP-eng contains 8 circumplex scales.\n", - "PAQ1: pleasant (0°)\n", - "PAQ2: vibrant (46°)\n", - "PAQ3: eventful (93°)\n", - "PAQ4: chaotic (138°)\n", - "PAQ5: annoying (178°)\n", - "PAQ6: monotonous (228°)\n", - "PAQ7: uneventful (272°)\n", - "PAQ8: calm (340°)\n", - "\n", - "The SATP-eng is rated using the following 5-point scale.\n", - "0. Strongly disagree\n", - "25. Somewhat disagree\n", - "50. Neither agree nor disagree\n", - "75. Somewhat agree\n", - "100. Strongly agree\n", - "\n", - "The SATP-eng contains 8 items (open-access).\n", - "None\n" - ] - } - ], - "source": [ - "from circumplex.datasets import SATP_ENG\n", - "\n", - "satp_eng = SATP_ENG\n", - "satp_eng.summary()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:43.674594Z", - "start_time": "2023-11-25T18:35:41.803575Z" - } - }, - "id": "bbcca11c33dfca66" - }, - { - "cell_type": "code", - "execution_count": 27, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "satp_eng.demo_plot()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:48.047926Z", - "start_time": "2023-11-25T18:35:47.955539Z" - } - }, - "id": "da11116471121fef" - }, - { - "cell_type": "code", - "execution_count": 28, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================\n", - "Measure: loud\n", - "Group: None\n", - "Scales: ['PAQ1', 'PAQ2', 'PAQ3', 'PAQ4', 'PAQ5', 'PAQ6', 'PAQ7', 'PAQ8']\n", - "Scale Angles: (0, 46, 93, 138, 178, 228, 272, 340)\n", - "\n", - "Profile [All]:\n", - " Estimate\n", - "Elevation: 0.002\n", - "X-Value: -0.58\n", - "Y-Value: 0.342\n", - "Amplitude: 0.674\n", - "Displacement: 149.477\n", - "R2: 0.983\n" - ] - } - ], - "source": [ - "satp_eng_res = satp_eng.ssm_analyse(measures=['loud'])\n", - "print(satp_eng_res)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-25T18:35:58.920878Z", - "start_time": "2023-11-25T18:35:58.891366Z" - } - }, - "id": "4c7f315bfc5df6d4" - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "data": { - "text/plain": "(
, )" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "satp_eng_res.plot()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-16T15:56:02.454078Z", - "start_time": "2023-11-16T15:56:02.124062Z" - } - }, - "id": "6431fbd8bb17bac" - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "satp_eng_res.results[0].profile_plot();" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-16T15:56:26.744168Z", - "start_time": "2023-11-16T15:56:26.539583Z" - } - }, - "id": "bb2fc8e49a953a75" - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-11-16T15:56:27.051707Z", - "start_time": "2023-11-16T15:56:27.020638Z" - } - }, - "id": "8a4a01ca2ed0058c" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - }, - "id": "2a99ea4bcc555038" - } - ], - "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": 5 -} diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md deleted file mode 100644 index 2fea9a8..0000000 --- a/docs/tutorials/index.md +++ /dev/null @@ -1,4 +0,0 @@ -# Tutorials - -Each of the tutorials provided will walk you through the basic functionality of _circumplex_ as well as the basic concepts of structural summary method analysis. - diff --git a/docs/tutorials/using-instruments.ipynb b/docs/tutorials/using-instruments.ipynb deleted file mode 100644 index 4083590..0000000 --- a/docs/tutorials/using-instruments.ipynb +++ /dev/null @@ -1,980 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Using Circumplex Instruments\n", - "\n", - "Source: https://circumplex.jmgirard.com/articles/using-instruments.html" - ], - "metadata": { - "collapsed": false - }, - "id": "81339325eef33e59" - }, - { - "cell_type": "code", - "id": "initial_id", - "metadata": { - "collapsed": true, - "ExecuteTime": { - "end_time": "2024-06-22T14:13:32.855401Z", - "start_time": "2024-06-22T14:13:32.839738Z" - } - }, - "source": [ - "import circumplex\n", - "import pandas as pd\n", - "\n", - "%load_ext autoreload\n", - "%autoreload 2" - ], - "outputs": [], - "execution_count": 3 - }, - { - "cell_type": "markdown", - "source": [ - "## Overview of Instrument-related Functions\n", - "\n", - "Although the circumplex package is capable of analyzing and visualizing data in a \"source-agnostic\" manner (i.e., without knowing what the numbers correspond to), it can be helpful to both the user and the package to have more contextual information about which information/questionnaire the data come from. For example, knowing the specific instrument used can enable the package to automatically score item-level responses and standardize these scores using normative data. Furthermore, a centralized repository of information about circumplex instruments would provide a convenient and accessible way for users to discover and begin using new instruments.\n", - "\n", - "The first part of this tutorial will discuss how to preview the instruments currently available in the circumplex package, how to load information about a specific instrument for use in analysis, and how to extract general and specific information about that instrument. The following functions will be discussed: `instruments()`, `instrument()`, `print()`, `summary()`, `scales()`, `items()`, `anchors()`, `norms()`, and `View()`.\n", - "\n", - "The second part of this tutorial will discuss how to use the information about an instrument to transform and summarize circumplex data. It will demonstrate how to ipsatize item-level responses (i.e. apply deviation scoring across variables), how to calculate scale scores from item-level responses (with or without imputing/prorating missing values), and how to standardize scale scores using normative/comparison data. The following functions will be discussed: `ipsatize()`, `score()`, and `standardize()`." - ], - "metadata": { - "collapsed": false - }, - "id": "a813bc61f5051b2d" - }, - { - "cell_type": "markdown", - "source": [ - "## 2. Loading and Examining Instrument Objects\n", - "\n", - "### Previewing the available instruments\n", - "\n", - "You can preview the list of currently available instruments using the `instruments()` function. This function will print the abbreviation, name, and (in parentheses) the \"code\" for each available instrument. We will return to the code in the next section." - ], - "metadata": { - "collapsed": false - }, - "id": "8f738e559a4a4e5e" - }, - { - "cell_type": "code", - "source": [ - "circumplex.instrument.instruments()" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-08T11:58:38.323890Z", - "start_time": "2024-06-08T11:58:38.304523Z" - } - }, - "id": "afc9791b88c08ae9", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The circumplex package currently includes 4 instruments:\n", - "1. CSIP: Circumplex Scales of Interpersonal Problems (CSIP)\n", - "2. IIPSC: Inventory of Interpersonal Problems Short Circumplex (IIP-SC)\n", - "3. SSQP-eng: Swedish Soundscape Quality Protocol - English Translation (SSQP-eng)\n", - "4. SATP-eng: Soundscape Attributes Translation Project - English Translation (SATP-eng)\n" - ] - } - ], - "execution_count": 7 - }, - { - "cell_type": "markdown", - "source": [ - "### Loading a specific instrument\n", - "\n", - "To reduce loading time and memory usage, instrument information is not loaded into memory when the circumplex package is loaded. Instead, instruments should be loaded into memory on an as-needed bases. As demonstrated below, this can be done by passing an instrument's code (which we saw how to find in the last section) to the `load_instrument()` function. We can then examine that instrument data using the `print()` function." - ], - "metadata": { - "collapsed": false - }, - "id": "1b8d60c17024bb84" - }, - { - "cell_type": "code", - "source": [ - "csip = circumplex.instrument.load_instrument('CSIP')\n", - "print(csip)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-06-08T12:02:08.358157Z", - "start_time": "2024-06-08T12:02:07.769628Z" - } - }, - "id": "8ff381ed31a876fa", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CSIP: Circumplex Scales of Interpersonal Problems\n", - "64 Items, 8 Scales\n", - "Boudreaux, Ozer, Oltmanns, & Wright (2018)\n", - "\n" - ] - } - ], - "execution_count": 18 - }, - { - "cell_type": "markdown", - "source": [ - "### Examining an instrument in-depth\n", - "\n", - "To examine the information available about a loaded instrument, there are several options. To print a long list of formatted information about the instrument, use the `summary()` function. This will return the same information returned by `print()`, followed by information about the instrument's scales, rating scale anchors, items, and normative data set(s). The summary of each instrument is also available from the package reference page." - ], - "metadata": { - "collapsed": false - }, - "id": "127364e5d1f2bd67" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-08T12:02:09.294138Z", - "start_time": "2024-06-08T12:02:09.274364Z" - } - }, - "cell_type": "code", - "source": "csip.summary()", - "id": "a98b0bc63b71e63b", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CSIP: Circumplex Scales of Interpersonal Problems\n", - "64 Items, 8 Scales\n", - "Boudreaux, Ozer, Oltmanns, & Wright (2018)\n", - "\n", - "\n", - "The CSIP contains 8 circumplex scales.\n", - "PA: Domineering (90°)\n", - "BC: Self-Centered (135°)\n", - "DE: Distant (180°)\n", - "FG: Socially Inhibited (225°)\n", - "HI: Nonassertive (270°)\n", - "JK: Exploitable (315°)\n", - "LM: Self-Sacrificing (360°)\n", - "NO: Intrusive (45°)\n", - "\n", - "The CSIP is rated using the following 4-point scale.\n", - "0. Not a problem\n", - "1. Minor problem\n", - "2. Moderate problem\n", - "3. Serious problem\n", - "\n", - "The CSIP contains 64 items (open-access).\n", - "1. Bossing around other people too much\n", - "2. Acting rude and inconsiderate toward others\n", - "3. Pushing away from other people who get too close\n", - "4. Difficulty making friends\n", - "5. Lacking self-confidence\n", - "6. Letting other people boss me around too much\n", - "7. Putting other people's needs before my own too much\n", - "8. Being overly affectionate with others\n", - "9. Verbally or physically abusing others\n", - "10. Acting selfishly with others\n", - "11. Difficulty showing love and affection to others\n", - "12. Having trouble fitting in with others\n", - "13. Getting easily embarrassed in front of others\n", - "14. Acting overly submissive with others\n", - "15. Giving too much to others\n", - "16. Difficulty keeping personal matters private from others\n", - "17. Starting arguments and conflicts with others\n", - "18. Being unable to feel guilt or remorse\n", - "19. Being unable to enjoy the company of others\n", - "20. Avoiding people or social situations\n", - "21. Difficulty taking the lead\n", - "22. Being unable to express anger toward others\n", - "23. Forgiving people too easily\n", - "24. Talking too much\n", - "25. Trying to influence or control other people too much\n", - "26. Lacking respect for other people's beliefs, attitudes, or opinions\n", - "27. Feeling emotionally disconnected from others\n", - "28. Being unable to keep conversations going\n", - "29. Having trouble asserting myself\n", - "30. Being too concerned about what other people think\n", - "31. Being overly sentimental or tender-hearted\n", - "32. Flirting with other people too much\n", - "33. Dominating or intimidating others\n", - "34. Having trouble getting along with others\n", - "35. Difficulty developing close and lasting relationships\n", - "36. Feeling like an outsider in most social situations\n", - "37. Feeling weak and insecure around dominant others\n", - "38. Being easily taken advantage of\n", - "39. Being easily affected by the pain and suffering of others\n", - "40. Having trouble respecting other people's privacy\n", - "41. Acting aggressively toward others\n", - "42. Being insensitive to the thoughts, feelings, and needs of others\n", - "43. Being unable to fully connect with others\n", - "44. Being unable to be myself around others\n", - "45. Being unable to stand up to others\n", - "46. Compromising with other people too much\n", - "47. Trusting people too easily\n", - "48. Exaggerating so that other people will respect me\n", - "49. Manipulating other people to get what I want\n", - "50. Disliking most people\n", - "51. Difficulty opening up to others\n", - "52. Feeling fearful or nervous in social situations\n", - "53. Avoiding confrontation when problems arise\n", - "54. Being easily influenced by others\n", - "55. Trying to solve other people's problems too much\n", - "56. Confronting people too quickly about problems\n", - "57. Acting superior or condescending toward others\n", - "58. Having trouble giving emotional or moral support to others\n", - "59. Feeling uncomfortable with being close or intimate with others\n", - "60. Acting shy around others\n", - "61. Letting other people make decisions too often\n", - "62. Being unable to say 'no'\n", - "63. Getting too attached to others\n", - "64. Needing to be the center of attention\n", - "\n", - "No data has been loaded for this instrument. Use attach_data() to load data.\n" - ] - } - ], - "execution_count": 19 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "Specific subsections of this output can be returned individually by printing the `scales`, `anchors`, `items`, and `norms` attributes of the instrument object. These functions are especially useful when you need to know a specific bit of information about an instrument and don't want the console to be flooded with unneeded information. ", - "id": "5478d2bce6a5a2dc" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-08T12:02:56.736978Z", - "start_time": "2024-06-08T12:02:56.714448Z" - } - }, - "cell_type": "code", - "source": "csip.anchors.show()", - "id": "feca079741ba597c", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0. Not a problem\n", - "1. Minor problem\n", - "2. Moderate problem\n", - "3. Serious problem\n" - ] - } - ], - "execution_count": 23 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "Some of these attributes also have additional methods to customize their output. For instance, the `scales` attribute has a `.show()` method which includes the option to display the items for each scale.", - "id": "87ccb43033467e56" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-08T12:04:34.780774Z", - "start_time": "2024-06-08T12:04:34.760906Z" - } - }, - "cell_type": "code", - "source": "csip.inst_items.show(n=5)", - "id": "b58a3bb64fb04fd7", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1. Bossing around other people too much\n", - "2. Acting rude and inconsiderate toward others\n", - "3. Pushing away from other people who get too close\n", - "4. Difficulty making friends\n", - "5. Lacking self-confidence\n", - "\n", - "...and 59 more items.\n" - ] - } - ], - "execution_count": 27 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-08T12:03:00.996825Z", - "start_time": "2024-06-08T12:03:00.976837Z" - } - }, - "cell_type": "code", - "source": "csip.scales.show(inst_items=True)", - "id": "abbc4313ebd35297", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PA: Domineering (90°)\n", - "\t1: Bossing around other people too much\n", - "\t9: Verbally or physically abusing others\n", - "\t17: Starting arguments and conflicts with others\n", - "\t25: Trying to influence or control other people too much\n", - "\t33: Dominating or intimidating others\n", - "\t41: Acting aggressively toward others\n", - "\t49: Manipulating other people to get what I want\n", - "\t57: Acting superior or condescending toward others\n", - "BC: Self-Centered (135°)\n", - "\t2: Acting rude and inconsiderate toward others\n", - "\t10: Acting selfishly with others\n", - "\t18: Being unable to feel guilt or remorse\n", - "\t26: Lacking respect for other people's beliefs, attitudes, or opinions\n", - "\t34: Having trouble getting along with others\n", - "\t42: Being insensitive to the thoughts, feelings, and needs of others\n", - "\t50: Disliking most people\n", - "\t58: Having trouble giving emotional or moral support to others\n", - "DE: Distant (180°)\n", - "\t3: Pushing away from other people who get too close\n", - "\t11: Difficulty showing love and affection to others\n", - "\t19: Being unable to enjoy the company of others\n", - "\t27: Feeling emotionally disconnected from others\n", - "\t35: Difficulty developing close and lasting relationships\n", - "\t43: Being unable to fully connect with others\n", - "\t51: Difficulty opening up to others\n", - "\t59: Feeling uncomfortable with being close or intimate with others\n", - "FG: Socially Inhibited (225°)\n", - "\t4: Difficulty making friends\n", - "\t12: Having trouble fitting in with others\n", - "\t20: Avoiding people or social situations\n", - "\t28: Being unable to keep conversations going\n", - "\t36: Feeling like an outsider in most social situations\n", - "\t44: Being unable to be myself around others\n", - "\t52: Feeling fearful or nervous in social situations\n", - "\t60: Acting shy around others\n", - "HI: Nonassertive (270°)\n", - "\t5: Lacking self-confidence\n", - "\t13: Getting easily embarrassed in front of others\n", - "\t21: Difficulty taking the lead\n", - "\t29: Having trouble asserting myself\n", - "\t37: Feeling weak and insecure around dominant others\n", - "\t45: Being unable to stand up to others\n", - "\t53: Avoiding confrontation when problems arise\n", - "\t61: Letting other people make decisions too often\n", - "JK: Exploitable (315°)\n", - "\t6: Letting other people boss me around too much\n", - "\t14: Acting overly submissive with others\n", - "\t22: Being unable to express anger toward others\n", - "\t30: Being too concerned about what other people think\n", - "\t38: Being easily taken advantage of\n", - "\t46: Compromising with other people too much\n", - "\t54: Being easily influenced by others\n", - "\t62: Being unable to say 'no'\n", - "LM: Self-Sacrificing (360°)\n", - "\t7: Putting other people's needs before my own too much\n", - "\t15: Giving too much to others\n", - "\t23: Forgiving people too easily\n", - "\t31: Being overly sentimental or tender-hearted\n", - "\t39: Being easily affected by the pain and suffering of others\n", - "\t47: Trusting people too easily\n", - "\t55: Trying to solve other people's problems too much\n", - "\t63: Getting too attached to others\n", - "NO: Intrusive (45°)\n", - "\t8: Being overly affectionate with others\n", - "\t16: Difficulty keeping personal matters private from others\n", - "\t24: Talking too much\n", - "\t32: Flirting with other people too much\n", - "\t40: Having trouble respecting other people's privacy\n", - "\t48: Exaggerating so that other people will respect me\n", - "\t56: Confronting people too quickly about problems\n", - "\t64: Needing to be the center of attention\n" - ] - } - ], - "execution_count": 25 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "## 3. Instrument-related tidying functions\n", - "\n", - "It is a good idea in practice to digitize and save each participant's response to each item on an instrument, rather than just their scores on each scale. Having access to item-level data will make it easier to spot and correct mistakes, will enable more advanced analysis of missing data, and will enable latent variable models that account for measurement error (e.g. structural equation modelling). Furthermore, the functions described below will make it easy to transform and summarize such item-level data into scale scores.\n", - "\n", - "First, however, we need to make sure the item-level data is in the expected format. Your data should be stored in a data frame where each row corresponds to one observation (e.g. participant, organization, or timepoint) and each column corresponds to one variable describing these observations (e.g. item responses, demographic characteristics, scale scores). The `pandas` package provides excellent tools for getting your data into this format from a variety of different file types and formats.\n", - "\n", - "For the purpose of illustration, we will work with a small-scale data set, which includes item-level responses to the Inventory of Interpersonal Problems, Short Circumplex (IIP-SC) for just 10 participants. As will become important later on, this data set contains a small amount of missing values (represented as `NA`). This data set is included as part of the circumplex package and can be loaded and previewed as follows:\n" - ], - "id": "fa50458a60f80003" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-22T14:13:36.636970Z", - "start_time": "2024-06-22T14:13:36.617817Z" - } - }, - "cell_type": "code", - "source": [ - "from circumplex.datasets import _raw_iipsc_path\n", - "raw_iipsc = pd.read_csv(_raw_iipsc_path)\n", - "print(raw_iipsc.head())" - ], - "id": "66641d626662906c", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " IIP01 IIP02 IIP03 IIP04 IIP05 IIP06 IIP07 IIP08 IIP09 IIP10 ... \\\n", - "0 0 0 0 0.0 1 0 1 0 2 1 ... \n", - "1 1 1 0 0.0 3 2 2 1 0 1 ... \n", - "2 1 0 1 0.0 1 1 1 3 0 1 ... \n", - "3 3 2 3 NaN 2 3 2 3 2 3 ... \n", - "4 0 0 0 1.0 0 0 1 1 0 1 ... \n", - "\n", - " IIP23 IIP24 IIP25 IIP26 IIP27 IIP28 IIP29 IIP30 IIP31 IIP32 \n", - "0 0 0 3 3 3 0 0 0 1 0 \n", - "1 0 0 0 0 0 1 0 0 0 2 \n", - "2 3 1 1 1 1 0 3 2 3 2 \n", - "3 3 2 1 2 3 2 3 2 3 2 \n", - "4 1 1 2 1 0 0 0 0 0 0 \n", - "\n", - "[5 rows x 32 columns]\n" - ] - } - ], - "execution_count": 4 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-22T14:14:22.299841Z", - "start_time": "2024-06-22T14:14:22.285164Z" - } - }, - "cell_type": "code", - "source": [ - "iipsc = circumplex.instrument.load_instrument('IIPSC')\n", - "iipsc.summary()" - ], - "id": "6e68baa5510c4f4b", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IIP-SC: Inventory of Interpersonal Problems Short Circumplex\n", - "32 Items, 8 Scales\n", - "Soldz, Budman, Demby, & Merry (1995)\n", - "\n", - "\n", - "The IIP-SC contains 8 circumplex scales.\n", - "PA: Domineering (90°)\n", - "BC: Vindictive (135°)\n", - "DE: Cold (180°)\n", - "FG: Socially Avoidant (225°)\n", - "HI: Nonassertive (270°)\n", - "JK: Exploitable (315°)\n", - "LM: Overly Nurturant (360°)\n", - "NO: Intrusive (45°)\n", - "\n", - "The IIP-SC is rated using the following 5-point scale.\n", - "0. Not at all\n", - "1. Somewhat\n", - "2. Moderately\n", - "3. Very\n", - "4. Extremely\n", - "\n", - "The IIP-SC contains 32 items (partial text).\n", - "1. ...point of view...\n", - "2. ...supportive of another...\n", - "3. ...show affection to...\n", - "4. ...join in on...\n", - "5. ...stop bothering me...\n", - "6. ...I am angry...\n", - "7. ...my own welfare...\n", - "8. ...keep things private...\n", - "9. ...too aggressive toward...\n", - "10. ...another person's happiness...\n", - "11. ...feeling of love...\n", - "12. ...introduce myself to...\n", - "13. ...confront people with...\n", - "14. ...assertive without worrying...\n", - "15. ...please other people...\n", - "16. ...open up to...\n", - "17. ...control other people...\n", - "18. ...too suspicious of...\n", - "19. ...feel close to...\n", - "20. ...socialize with other...\n", - "21. ...assertive with another...\n", - "22. ...too easily persuaded...\n", - "23. ...other people's needs...\n", - "24. ...noticed too much...\n", - "25. ...argue with other...\n", - "26. ...revenge against people...\n", - "27. ...at a distance...\n", - "28. ...get together socially...\n", - "29. ...to be firm...\n", - "30. ...people take advantage...\n", - "31. ...another person's misery...\n", - "32. ...tell personal things...\n", - "\n", - "No data has been loaded for this instrument. Use attach_data() to load data.\n" - ] - } - ], - "execution_count": 5 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-22T14:36:34.235809Z", - "start_time": "2024-06-22T14:36:34.221077Z" - } - }, - "cell_type": "code", - "source": [ - "def score(data, instrument, na_rm=True, prorate=True):\n", - " \"\"\"Score item-level data using an instrument object.\n", - " \n", - " Args:\n", - " data (pandas.DataFrame): A data frame where each row corresponds to one observation\n", - " (e.g. participant, organization, timepoint) and each column corresponds to one variable\n", - " describing these observations (e.g. item responses, demographic characteristics, scale scores).\n", - " instrument (circumplex.instrument.Instrument): An instrument object loaded using the load_instrument() function.\n", - " impute (bool): Whether to impute missing values before scoring.\n", - " prorate (bool): Whether to prorate scale scores when missing values are present.\n", - " \n", - " Returns:\n", - " pandas.DataFrame: A data frame where each row corresponds to one observation and each column\n", - " corresponds to one variable describing these observations (e.g. item responses, demographic characteristics, scale scores).\n", - " \"\"\"\n", - " # Step 1: Impute missing values\n", - " # if impute:\n", - " # data = circumplex.impute(data)\n", - " \n", - " # assert set(data.columns).issubset(instrument.inst_items.keys()), \"Invalid item(s) selected.\"\n", - " \n", - " for i, scale in enumerate(instrument.scales.abbrev):\n", - " items = list(instrument.scales.inst_items[i].keys())\n", - " data[scale] = data.loc[:, items].mean(axis=1)\n", - " \n", - " return data" - ], - "id": "5926c67e18f9c763", - "outputs": [], - "execution_count": 12 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-22T14:36:35.017729Z", - "start_time": "2024-06-22T14:36:34.991573Z" - } - }, - "cell_type": "code", - "source": [ - "import numpy as np\n", - "raw_iipsc.columns = np.arange(1,len(raw_iipsc.columns)+1).astype(str)\n", - "\n", - "score(raw_iipsc, iipsc)" - ], - "id": "aadb417f8d90a221", - "outputs": [ - { - "data": { - "text/plain": [ - " 1 2 3 4 5 6 7 8 9 10 ... 31 32 PA BC DE FG \\\n", - "0 0 0 0 0.0 1 0 1 0 2 1 ... 1 0 1.75 2.00 1.25 0.000000 \n", - "1 1 1 0 0.0 3 2 2 1 0 1 ... 0 2 0.25 0.50 0.25 0.500000 \n", - "2 1 0 1 0.0 1 1 1 3 0 1 ... 3 2 1.00 0.75 0.75 0.000000 \n", - "3 3 2 3 NaN 2 3 2 3 2 3 ... 3 2 1.75 2.25 2.50 2.333333 \n", - "4 0 0 0 1.0 0 0 1 1 0 1 ... 0 0 0.50 0.75 0.00 1.000000 \n", - "5 0 0 0 0.0 0 0 1 1 0 0 ... 0 1 0.25 0.00 0.00 0.000000 \n", - "6 1 0 0 0.0 2 1 1 0 1 0 ... 0 0 1.00 0.00 0.00 0.000000 \n", - "7 1 0 1 0.0 1 1 2 1 1 0 ... 2 1 1.00 0.25 0.75 0.000000 \n", - "8 0 0 2 2.0 0 1 3 0 1 0 ... 3 0 0.75 0.50 1.50 0.750000 \n", - "9 0 0 0 0.0 0 0 2 0 0 0 ... 0 0 0.00 0.00 0.00 0.000000 \n", - "\n", - " HI JK LM NO \n", - "0 0.50 0.250000 1.50 0.75 \n", - "1 2.00 1.750000 1.25 1.00 \n", - "2 2.25 2.000000 2.50 2.00 \n", - "3 2.50 2.000000 2.50 2.50 \n", - "4 0.50 0.250000 1.25 0.75 \n", - "5 0.00 0.000000 1.00 1.00 \n", - "6 1.00 1.000000 0.75 0.25 \n", - "7 0.50 0.666667 1.75 1.00 \n", - "8 0.00 1.000000 2.75 0.50 \n", - "9 0.00 0.500000 1.00 0.25 \n", - "\n", - "[10 rows x 40 columns]" - ], - "text/html": [ - "
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" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 13 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-06-22T14:34:19.353285Z", - "start_time": "2024-06-22T14:34:19.088690Z" - } - }, - "cell_type": "code", - "source": "raw_iipsc[['1', '9', '17', '25']]\n", - "id": "a3415037988572aa", - "outputs": [ - { - "ename": "KeyError", - "evalue": "\"None of [Index(['1', '9', '17', '25'], dtype='object')] are in the [columns]\"", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mKeyError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[10], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43mraw_iipsc\u001B[49m\u001B[43m[\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m1\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m9\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m17\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m25\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m]\u001B[49m\n", - "File \u001B[0;32m~/Documents/GitHub/circumplex/.venv/lib/python3.11/site-packages/pandas/core/frame.py:3902\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m 3900\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_iterator(key):\n\u001B[1;32m 3901\u001B[0m key \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(key)\n\u001B[0;32m-> 3902\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_indexer_strict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcolumns\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m[\u001B[38;5;241m1\u001B[39m]\n\u001B[1;32m 3904\u001B[0m \u001B[38;5;66;03m# take() does not accept boolean indexers\u001B[39;00m\n\u001B[1;32m 3905\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mgetattr\u001B[39m(indexer, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdtype\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m) \u001B[38;5;241m==\u001B[39m \u001B[38;5;28mbool\u001B[39m:\n", - "File \u001B[0;32m~/Documents/GitHub/circumplex/.venv/lib/python3.11/site-packages/pandas/core/indexes/base.py:6114\u001B[0m, in \u001B[0;36mIndex._get_indexer_strict\u001B[0;34m(self, key, axis_name)\u001B[0m\n\u001B[1;32m 6111\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 6112\u001B[0m keyarr, indexer, new_indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_reindex_non_unique(keyarr)\n\u001B[0;32m-> 6114\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_raise_if_missing\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkeyarr\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mindexer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis_name\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 6116\u001B[0m keyarr \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtake(indexer)\n\u001B[1;32m 6117\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(key, Index):\n\u001B[1;32m 6118\u001B[0m \u001B[38;5;66;03m# GH 42790 - Preserve name from an Index\u001B[39;00m\n", - "File \u001B[0;32m~/Documents/GitHub/circumplex/.venv/lib/python3.11/site-packages/pandas/core/indexes/base.py:6175\u001B[0m, in \u001B[0;36mIndex._raise_if_missing\u001B[0;34m(self, key, indexer, axis_name)\u001B[0m\n\u001B[1;32m 6173\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m use_interval_msg:\n\u001B[1;32m 6174\u001B[0m key \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(key)\n\u001B[0;32m-> 6175\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNone of [\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mkey\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m] are in the [\u001B[39m\u001B[38;5;132;01m{\u001B[39;00maxis_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m]\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 6177\u001B[0m not_found \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(ensure_index(key)[missing_mask\u001B[38;5;241m.\u001B[39mnonzero()[\u001B[38;5;241m0\u001B[39m]]\u001B[38;5;241m.\u001B[39munique())\n\u001B[1;32m 6178\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnot_found\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m not in index\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "\u001B[0;31mKeyError\u001B[0m: \"None of [Index(['1', '9', '17', '25'], dtype='object')] are in the [columns]\"" - ] - } - ], - "execution_count": 10 - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "6e8791c5039e6d3e" - } - ], - "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": 5 -} diff --git a/examples/.gitignore b/examples/.gitignore new file mode 100644 index 0000000..e33609d --- /dev/null +++ b/examples/.gitignore @@ -0,0 +1 @@ +*.png diff --git a/examples/Intro_to_SSM_Analysis.ipynb b/examples/Intro_to_SSM_Analysis.ipynb new file mode 100644 index 0000000..f97a954 --- /dev/null +++ b/examples/Intro_to_SSM_Analysis.ipynb @@ -0,0 +1,1765 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "817354cbea2e711b", + "metadata": { + "collapsed": false + }, + "source": [ + "# Introduction to SSM Analysis\n", + "\n", + "Reproduced from the introductory vignette for [R Circumplex](https://circumplex.jmgirard.com/articles/introduction-to-ssm-analysis.html), by Girard J, Zimmermann J, Wright A (2023). circumplex: Analysis and Visualization of Circular Data. https://github.com/jmgirard/circumplex, http://circumplex.jmgirard.com/.\n", + "\n", + "If you find this tutorial useful, **please cite Girard, Zimmermann, & Wright (2023)**. I am reproducing it here to demonstrate the equivalence between the R and Python versions of the package. The original R version of this vignette can be found [here](https://circumplex.jmgirard.com/articles/introduction-to-ssm-analysis.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T18:37:02.172464Z", + "start_time": "2024-07-11T18:37:01.505069Z" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import circumplex\n", + "from circumplex import OCTANTS, get_instrument, load_dataset, octants\n", + "\n", + "# %matplotlib inline\n", + "degree_sign = \"\\N{DEGREE SIGN}\"" + ] + }, + { + "cell_type": "markdown", + "id": "1d6f5b15838c0042", + "metadata": { + "collapsed": false + }, + "source": [ + "## 1. Background and Motivation\n", + "\n", + "### Circumplex models, scales, and data\n", + "\n", + "Circumplex models are popular within many areas of psychology because they offer a parsimonious account of complex psychological domains, such as emotion and interpersonal functioning. This parsimony is achieved by understanding phenomena in a domain as being a \"blend\" of two primary dimensions. For instance, circumplex models of emotion typically represent affective phenomena as a blend of *valence* (pleasantness versus unpleasantness) and *arousal* (activity versus passivity), whereas circumplex models of interpersonal functioning typically represent interpersonal phenomena as a blend of *communion* (affiliation versus separation) and *agency* (dominance versus submissiveness). These models are often depicted as circles around the intersection of the two dimensions (see figure). Any given phenomenon can be located within this circular space through reference to the two underlying dimensions (e.g. anger is a blend of unpleasantness and activity).\n", + "\n", + "Circumplex scales contain multiple subscales that attempt to measure different blends of the two primary dimensions (i.e., different parts of the circle). Although there have historically been circumplex scales with as many as sixteen subscales, it has become most common to use eight subscales: one for each “pole” of the two primary dimensions and one for each “quadrant” that combines the two dimensions. In order for a set of subscales to be considered circumplex, they must exhibit certain properties. Circumplex fit analyses can be used to quantify these properties.\n", + "\n", + "Circumplex data is composed of scores on a set of circumplex scales for one or more participants (e.g., persons or organizations). Such data is usually collected via self-report, informant-report, or observational ratings in order to locate psychological phenomena within the circular space of the circumplex model. For example, a therapist might want to understand the interpersonal problems encountered by an individual patient, a social psychologist might want to understand the emotional experiences of a group of participants during an experiment, and a personality psychologist might want to understand what kind of interpersonal behaviors are associated with a trait (e.g., extraversion). \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b2b2c99827c47a27", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T18:37:02.338368Z", + "start_time": "2024-07-11T18:37:02.173524Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "angles = octants(3)\n", + "alabel = (\"PA\", \"BC\", \"DE\", \"FG\", \"HI\", \"JK\", \"LM\", \"NO\")\n", + "\n", + "# Create plot ---------------------------------------------------------------\n", + "\n", + "fig, ax = plt.subplots(figsize=(4, 4), subplot_kw={\"polar\": True})\n", + "\n", + "ax.plot()\n", + "ax.set_xticks(np.radians(angles), labels=alabel, fontsize=14)\n", + "ax.set_yticks([])\n", + "ax.grid(visible=True)\n", + "for angle in angles:\n", + " ax.text(\n", + " np.radians(angle),\n", + " 0.6,\n", + " f\"{angle}{degree_sign}\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " fontsize=12,\n", + " )\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e7d40972d74b1c13", + "metadata": { + "collapsed": false + }, + "source": [ + "### The Structural Summary Method\n", + "\n", + "The Structural Summary Method (SSM) is a technique for analyzing circumplex data that offers practical and interpretive benefits over alternative techniques. It consists of fitting a cosine curve to the data, which captures the pattern of correlations among scores associated with a circumplex scale (i.e., mean scores on circumplex scales or correlations between circumplex scales and an external measure). By plotting a set of example scores below, we can gain a visual intuition that a cosine curve makes sense in this case. First, we can examine the scores with a bar chart ignoring the circular relationship among them." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "21f5a12726008489", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T18:37:04.774698Z", + "start_time": "2024-07-11T18:37:02.341401Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scales = [\"PA\", \"BC\", \"DE\", \"FG\", \"HI\", \"JK\", \"LM\", \"NO\"]\n", + "\n", + "jz_data = load_dataset(\"jz2017\")\n", + "r = circumplex.ssm_analyze(jz_data, scales, angles=OCTANTS, measures=[\"NARPD\"])\n", + "plt.figure(figsize=(8, 5))\n", + "plt.bar(scales, r.scores[scales].to_numpy()[0], color=\"red\")\n", + "plt.ylim(0, 0.5)\n", + "plt.ylabel(\"Score\")\n", + "plt.xlabel(\"Scale\")\n", + "plt.title(\"NARPD Scores\")\n", + "plt.grid(visible=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cafd4ba1e88bf65f", + "metadata": { + "collapsed": false + }, + "source": [ + "Next, we can leverage the fact that these subscales have specific angular displacements in the circumplex model (and that 0 and 360 degrees are the same) to create a path diagram.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c90c1bcb4a07781b", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T18:37:04.841796Z", + "start_time": "2024-07-11T18:37:04.775420Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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IeVq61PO+DG5jCqiKF2dEnSxPT0REgp9ffKJwpKdt27aoWrUqqlWrZlbEXbx40ayyozZt2piRH06nsc5T2SgjF9myZTM/3a/v0aMHhg0bhpIlS+L222/HgAEDUKBAgWj1niQZ7d4NxFYOwh4tevdd4PbbrVO9ep7HnD7tGUjZP/fsYWQMsPZX1PpfqVMDJUrcDKTsoOrOO4HMmX30REVEJFj5ReDUrFkzHD9+3BSsZPI2R4kWLVoUmdy9b98+s9IuIfr06WOCrxdffBFnzpzB/fffbx6TgZc44PBhoH59TiRbo0CXLwOHDt28nUOv48cDTZrE/hjZswM1algnd0zq5whV1KCKp0uXrMs8RcXfGdMoFad6A3zaT0REfCOFi0WPJBpO7bGEwdmzZ32S43TgwIHQma/maFCtWgBXQXL055dfgJw5zSq7k0wY50ghp/HCwpL29zL3jav43Een7J9xFVfNmjXmgIr1wnw07RdSr4c4qB9uUl9Y1A8W9YNv+yEhn/l+MeIkQYyjQRxFYtCUJw+weLH1k2rXxmUGUr56M+Ao5W23WSeOdrnjygx7VMo9qOK039mzwK+/Wqeo034lS0YPqjjtl9jVfqxZtXKlWXEIXwWQIiKSZBQ4ie9wxKddOyuRm4HFd99Zozb+IEcO4N57rZM75mDt2hV9lIonTi/+9Zd1iqpw4ZhHqTjdHNu0H6uksyDogQM3yzQwiGSeV1xTliIi4hgFTuI7vXtzF2ZreotBQpUq8HvMgePIT9TSCQwCWZwz6pQff3Laj7fxtGSJ5/24cME9Kd3+yRG4Zs2iV1G3t5754gsFTyIifkiBk/jG2LHchNA6/8knwMMPI6Bx2q9IEesU27Rf1IDqn3+AM2eAtWutkzcYSHGEilvPNGqkaTsRET+jwEmS3qefAr16WedHjwZatkRQi2vaz17t5x5U8eS2vU+sW89wi5ratX3efBER8Z4CJ0lanKpiXhNx1OTVVxGyOO1Xrpx1cjdnDtCqlXclHERExK8krDiSSFw2bLDycq5ft/J3OF2nekjRsTq6N9y2CxIREf+gwEmSBpfxP/oocOEC8OCDwIwZVl6QRMeSA1w9F1dQyVV6PE5ERPyKPtnk1nHvOSZMcy+5ChWAhQu5+Z/TrfJfTPhmyQGKLXjiaJ0Sw0VE/I4CJ7k1HGF67DGr9hFXnH3/vbXhrsSNU5osORB12s4epYursrmIiDhGgZMkHnOZnnkG+O03awsVVgVXXk7Cgqe9e4Fly3By4kTz0+zXRwMGACdOON1CERGJQoGTJA6XzHfoYI0wpU8PfPONtfWIJAyn47j1zJNPWqUHOnUCypcHTp8G3njD6daJiEgUCpwkcfr3txLA+cH/+efAPfc43aLgwCrr771nnZ86FVi/3ukWiYiIGwVOknD8YH/7bev8hx8Cjz/udIuCC1fTPfusNarXtau13YuIiPgFBU6SMExo7tbNOj90KPDcc063KDiNGgVkzAisWQPMnu10a0RE5D8KnMR7K1ZY26dwJOSll5SD40tcbccEcerTBzh3zukWiYiIAifx2p9/WpvOXrsGMJGZ03WqCu5b3LKmZEmrPtabbzrdGhERUeAkXtm3D2jQADh7Frj/fmsTXxVn9D0WEbULZfInNwcWERFHKXCSuJ06BTzyCHDwIFCmDPD111b5AUkeDFgbNgRu3AC6d7emSUVExDEKnCR2ly8DTzxhjXQw52bRIiB7dqdbFXreeccafVqyBPjyS6dbIyIS0hQ4Scw4wtGiBfDLL0C2bFbQxI1nJfkVLw706mWdf+UV4NIlp1skIhKyFDhJdJwO6tIF+Oora6SDP8uWdbpVoa1fPytw/fdfq1SBiIg4QoGTRDdsGPDBB9aqOSaCP/CA0y0S1nQaM8Y6P3KktcediIgkOwVO4umjj4CBA63zLDnAjWjFPzz9NFCnDnDlCtCzp9OtEREJSQqc5Kb//Q/o2NE6z+KWnTs73SJxxxHACROsUhALF1rJ4iIikqwUOImFW3s0a2bti9a+vbWdivgf5pox/4y49Q0LkoqISOgFTpMmTULRokWRLl06VK9eHevWrYv12AULFqBq1arIli0bMmbMiIoVK2LWrFkex7Rr1w4pUqTwOD3CekQS3fbt1ka9LD/w6KM385vEPw0eDOTObf3dJk50ujUiIiHFLwKnefPmoWfPnhg0aBA2bNiAChUqoH79+jh27FiMx+fIkQNvvPEG1qxZg82bN6N9+/bmtHjxYo/jGCgdPnw48vTZZ58l0zMKIIcOWQUuWeiyWjXg88+B1KmdbpXEheUhRoywzg8ZAhw+7HSLRERChl8ETuPGjUOHDh1M8FOmTBlMmTIFGTJkwPTp02M8vnbt2mjcuDFKly6N4sWLo3v37ihfvjxWrVrlcVzatGmRL1++yFN2FW/0xC1UWJmaS9y5J9o331irt8T/tWtnBbrnzwOvveZ0a0REQobjgdO1a9ewfv161K1bN/K6lClTmsscUYqPy+XC0qVLsWPHDjwQZdn88uXLkSdPHtx5553o1KkTTp486ZPnEJCuXgUaNwY2bwby5QM4WsfpHwkMKVPenKabORNYvdrpFomIhATHA6cTJ04gPDwcefPm9biel48cORLr/c6ePYtMmTIhTZo0eOyxxzBx4kQ8/PDDHtN0M2fONEHVyJEjsWLFCjRo0MD8rphcvXoV586d8zgFLSaAt2kDLFsGZM4MfPcdcPvtTrdKEoojTs89Z53v2hWI5bUtIiJJJxUCVObMmbFx40ZcuHDBBEfMkSpWrJiZxqPmzZtHHluuXDkzlcdpPY5CPfTQQ9Eeb/jw4RjCfJEoDh486JMg6hRzipzgciHrkCHI/PnncKVOjRMffICrHGk6cMCR5jjWD34msf2QsmtX5PviC6TcsAGnx4zBxZYtEcj0erhJfWFRP1jUD77th/NMewiUwClXrlwICwvD0aNHPa7nZeYlxYbTeSVKlDDnuapu27ZtJvixA6eoGFTxd+3atSvGwKlfv34m+LIxWCpcuDAKFiyILFmywBcKFSqEZDd6NDBtmjmbYsYM5OZ+dA5zpB/8UKL6gfd5802gRw9kHz0a2Tt04OoJBDK9Hm5SX1jUDxb1g+/6ISEDJI5P1XGqrUqVKmbUyBYREWEu16hRw+vH4X043RabAwcOmByn/Pnzx3g7E8kZILmfgg5LNvTpY50fO9baxFcCHwuV3nUXwBw+u+q7iIj4hOOBE3GkZ+rUqZgxY4YZOWIi98WLF80qO2rTpo0ZEbJxZGnJkiXYs2ePOX7s2LGmjlOrVq3M7Zy+6927N9auXYu9e/eaIKxRo0ZmhIplDkLSDz/czId59VVt2RFMWD7CThSfPBnYtMnpFomIBC3Hp+qoWbNmOH78OAYOHGgSwjn1tmjRosiE8X379pmpORuDqs6dO5tRpPTp06NUqVKYPXu2eRzi1B/rOzEQO3PmDAoUKIB69eph6NChZmQp5Pz+u7Xn3I0bwLPPAqNGOd0iSWrcw4572c2fbyWKr1ihIqYiIj6QwsX1/BLjfGfWrFnN6j1fTNsx6EuW+erdu4F77wVYTJQlH779lvOj8BfJ1g9+Lkn6Yd8+oFQpqwL8nDlWkBxg9Hq4SX1hUT9Y1A++7YeEfOb7xVSd+AiDJU5N8melSsD//Z9fBU2SxG67DXj9det8796cs3a6RSIiQUeBU7Dih+Zjj1kjTqzRxFpNwZjwLp569eISUmsrnWHDnG6NiEjQUeAUjK5fB5o2tXKbcuUCFi2yqoNL8EuXDhg/3jo/bhzw999Ot0hEJKgocAo2TFl7/nlrC5UMGaycpjvucLpVkpwef9zag5ABdI8e1mtCRESShAKnYMOyDazXFBZmrbDithwSWriajqNOLFPw/ffW5s0iIpIkFDgFkwkTgJEjrfMffQQ8+qjTLRKncJTRrtXFUacrV5xukYhIUFDgFCw+/9z6gKS33gLatXO6ReK0/v2BAgWAPXusSvEiInLLFDgFg+XLgdatrVyWl1+2putEMmWy9iakt98G9u93ukUiIgFPgVOg27wZaNQIuHYNeOop4N13VTFabuJ+hDVrApcuWaUKRETklihwCmT//gs88ghLngIPPADMnm0lhYvYGERzHztuWcTp3GXLnG6RiEhAU+AUqE6etIKmw4eBsmWBr76yaviIRFWhAvDSS9b5bt2sPQtFRCRRFDgFIk67NGwIbN8OcM8eLjnPls3pVok/GzoUyJkT2LIFeP99p1sjIhKwFDgFGo4WMG9lzRoge3ar0KU2fpT45MhhrbakgQOt/QtFRCTBFDgFEq6a69wZ+Ppra1qOP8uUcbpVEiheeAGoXBk4e1YrL0VEEkmBUyAZMgSYOtVK9P3sM+D++51ukQQSLhxgojhNnw6sW+d0i0REAo4Cp0Dx4YdW4ESTJgFPPul0iyQQ3Xsv0KaNdb5LFyAiwukWiYgEFAVOgYAr5jp1ss4PGHBzhZRIYnBbnsyZgd9+Az75xOnWiIgEFAVO/m71aqB5c2tk4Pnnb446iSRWvnzAoEHW+ddeA86ccbpFIiIBQ4GTP9u2DXj8cWuDVv6cMkVVwSVpdO0KlCoFHD8ODB7sdGtERAKGAid/dfAgUL8+cPo0cM89wLx5QKpUTrdKgkWaNMCECdb5996z6juJiEi8FDj5I06dNGhgbcp6553A//4HZMjgdKsk2Dz8MNC4MRAeblUUZ7kLERGJkwInf8NpOa6Y+/NPKxdl0SIgVy6nWyXBatw4qyYY97D74gunWyMi4vcUOPkTfvNv3RpYsQLIksUKmooWdbpVEsz4+urb1zr/6qvAxYtOt0hExK8pcPIXnCbp0cP61p86NbBwobU5q4ivMXAqUsSaGh4xwunWiIj4NQVO/lRbh0m6NGsW8OCDTrdIQkX69NaUHY0eDezZ43SLRET8lgInfzBjxs29w8aPB5o1c7pFEmqYJF63LnD1KvDKK063RkTEb/lN4DRp0iQULVoU6dKlQ/Xq1bEujn20FixYgKpVqyJbtmzImDEjKlasiFkcpXHjcrkwcOBA5M+fH+nTp0fdunWxc+dO+EUe0/LlSP/ll+YnvvnGKmxJvXsD3bs73UIJRawPxvIELHnBzaO//97pFomI+CW/CJzmzZuHnj17YtCgQdiwYQMqVKiA+vXr49ixYzEenyNHDrzxxhtYs2YNNm/ejPbt25vT4sWLI48ZNWoUJkyYgClTpuDXX381ARYf8wpXrTllwQIrGbdOHeRkAcI6dYAnnrCCqVatlF8izipd+mbgzp8cfRIREf8LnMaNG4cOHTqY4KdMmTIm2MmQIQOmcwf3GNSuXRuNGzdG6dKlUbx4cXTv3h3ly5fHqlWrIkebxo8fj/79+6NRo0bmtpkzZ+LQoUP4kiM9TgVNTZsCBw54Xm/XzmFl8JR+8eeQUDZwoFUGg6OznDYWEREPjn9SX7t2DevXrzdTabaUKVOayxxRig+DpKVLl2LHjh144IEHzHX//PMPjhw54vGYWbNmNVOA3jxmkuOIEr/Bx1VgkNN0PE7ESSyDwYUKNHSoVcFeRET8J3A6ceIEwsPDkTdvXo/reZnBT2zOnj2LTJkyIU2aNHjssccwceJEPMxKyEDk/RLymFevXsW5c+c8Tklm5croI01RcSk4jxNxGqeNa9Swajr16eN0a0RE/ErAbn6WOXNmbNy4ERcuXDAjTsyRKlasmJnGS4zhw4djyJAh0a4/ePDgLQdR6bdsQU4vjju5ZQsulyiBUHLq1Cmnm+AX/K0fUg8YgDyPPYYUn36KY02a4Fr16iHZD05SX1jUDxb1g2/74fz584ETOOXKlQthYWE4evSox/W8nI+5FrHgdF6J/4IMrqrbtm2bCX4YONn342NwVZ37Y/LYmPTr188EXzYGS4ULF0bBggWRhdMXt6JsWa8Oy8njChVCqCkUgs/Z7/uBbenQAfjwQ+ThlN369UBYWOj1g8PUFxb1g0X94Lt+SMgAieNTdZxqq1Klihk1skVERJjLNThd4CXeh9NtdPvtt5vgyf0x2SlcXRfbY6ZNm9YESO6nJFOzpvVBxCXfMeH1hQtbx4n4i7feArJnBzZtAj74wOnWiIj4BccDJ+JIz9SpUzFjxgwzctSpUydcvHjRrLKjNm3amBEhG0eWlixZgj179pjjx44da+o4tWJuholDUqBHjx4YNmwYvv76a/z555/mMQoUKIAnuYFucuM39Xfftc5HDZ7sy1zBlEzf6EW8ws2lOdpE/fszIdHpFomIOM7xqTpq1qwZjh8/bgpWMnmb02mLFi2KTO7et2+fmZqzMajq3LkzDhw4YIpblipVCrNnzzaPY+vTp4857sUXX8SZM2dw//33m8dkgU1HNGli7UPH1XXuieIciWLQxNtF/E3Hjma6Dps3W8HTlClOt0hExFEpXFzPL9Fwao8lDLh6L0mn7VhyYOVKkwhucpo4PRfCI00MfjVv7+f98PPPQK1a1ujo778DlSuHZj8kM/WFRf1gUT/4th8S8pnvF1N1IYVBUu3auMwpQ64ADOGgSQIE66O1aGHVIWPFe33XEpEQpsBJROI3ejSQMSOwejUwe7bTrRERcYwCJxGJX8GCVo4TsShmUhaIFREJIAqcRMQ7r7wClCzJ0vw3V9uJiIQYBU4i4p20aW+W1eBK0O3bnW6RiEiyU+AkIt5r0ABo2BC4cQPo1k2J4iISchQ4iUjCvPOONfq0ZAnw5ZdOt0ZEJFkpcBKRhCleHOjVyzrP/R0vX3a6RSIiyUaBk4gkHLdA4v6Ke/cCo0Y53RoRkWSjwElEEo41ncaMsc6PGGEFUCIiIUCBk4gkztNPA3XqAFeuAK++6nRrRESShQInEUkc7l03YYK1bdCCBcCPPzrdIhERn1PgJCKJx42qX37ZOs/yBNevO90iERGfUuAkIrdmyBAgd25g2zZg4kSnWyMi4lMKnETk1mTLBgwfbp0fPNjakkVEJEgpcBKRW9e+PXD33cD588BrrzndGhERn1HgJCK3LmVK4L33rPMzZgBr1jjdIhERn1DgJCJJo1o14LnnrPNduwLh4U63SEQkySlwEpGkw1ynrFmB9euB6dOdbo2ISJJT4CQiSSdPHmuVnb0ty6lTTrdIRCRJKXASkaTVuTNw113AyZPAwIFOt0ZEJEkpcBKRpJU69c16TpMnA5s2Od0iEZEko8BJRJIe97DjXnYREVaiuMvldItERJKEAicR8Y0xY4D06YGVK4G5c51ujYhIklDgJCK+cdttwOuvW+d79QIuXHC6RSIit0yBk4j4DgOmYsWAQ4eAt95yujUiIsETOE2aNAlFixZFunTpUL16daxbty7WY6dOnYqaNWsie/bs5lS3bt1ox7dr1w4pUqTwOD3yyCPJ8ExEJFK6dMA771jnx44Fdu50ukUiIoEfOM2bNw89e/bEoEGDsGHDBlSoUAH169fHsWPHYjx++fLlaNGiBZYtW4Y1a9agcOHCqFevHg4ePOhxHAOlw4cPR54+++yzZHpGIhKpYUP+MwLXrwM9ejjdGhGRwA+cxo0bhw4dOqB9+/YoU6YMpkyZggwZMmB6LJWH58yZg86dO6NixYooVaoUPvroI0RERGDp0qUex6VNmxb58uWLPHF0SkSSWYoUwLvvWmUKvvsO+OYbp1skIhK4gdO1a9ewfv16M91mS5kypbnM0SRvXLp0CdevX0eOHDmijUzlyZMHd955Jzp16oSTLMgnIsnvjjuAnj2t8xx1unLF6RaJiDgTOO3fvx+rV6/GxYsXE3X/EydOIDw8HHnz5vW4npePHDni1WP07dsXBQoU8Ai+OE03c+ZMMwo1cuRIrFixAg0aNDC/KyZXr17FuXPnPE4ikoT69wcKFAB27+Yws9OtERFJlFSJuxvw4YcfYsiQIZHBzW+//YbKlSujcePGqF27Nrp3747kMGLECMydO9eMLjGx3Na8efPI8+XKlUP58uVRvHhxc9xDDz0U7XGGDx9unk9UzJvyRRB1Snt4GeqH0OqH9K+9hpzduiFi2DAcrVsX4QykQrAfvKG+sKgfLOoH3/bD+fPnfRs4jR8/3ozyMKGbQQgTs20MmubPn+914JQrVy6EhYXh6NGjHtfzMvOS4jJmzBgTOP34448mMIpLsWLFzO/atWtXjIFTv379zPOxMVhi0nnBggWRJUsW+EKhQoV88riBRv0QQv3QpQswfz5SrlyJ/Bx1iqEwZkj0g5fUFxb1g0X94Lt+SMgASaKm6iZOnIgBAwaYUZo63FrBDfOJduzY4fVjpUmTBlWqVPFI7LYTvWvUqBHr/UaNGoWhQ4di0aJFqFq1ary/58CBAybHKX/+/DHezkRyBkjuJxHxQaI497FLmZLLaZmI6HSLREQSJFGBE6ev7r333hhvS506NS4ksEIwR3pYm2nGjBnYtm2bSeRmzhRX2VGbNm3MiJCNOUsM3LjqjrWfOF3Ik/17+bN3795Yu3Yt9u7da4KwRo0aoUSJEqbMgYg4qEIF4KWXrPPcx+7GDadbJCLi28CpSJEisRao/PXXX3EHV9AkQLNmzcy028CBA02JgY0bN5qRJDthfN++faYOk23y5MlmNV7Tpk3NCJJ94mMQp/42b96MJ554wrTl+eefN6NaK1euNCNLIuKwoUOBnDmBLVuA9993ujUiIr7NcWLNpcGDByN37txo0qSJuY7lAL799luMHj0abyVia4UuXbqYU0yY0O2Oo0hxSZ8+PRYvXpzgNohIMmHpEL5PcORp4ECu5gDy5HG6VSIivhlx6tWrF5577jm8+OKLJnii++67z0yHtW7d2hSnFBGJ0wsvAJUrA2fP3twMWEQkWMsRTJgwAT169DAr2liLicUnuVqtZMmSSdtCEQlOYWFWovh99wHcJaBqVaRnvlPZskDNmtbtIiKBHjhduXLF5B7Nnj0bDRs2NKNOIiKJwkUmtWoBK1YAnTohp309lxtzm5b/UgFERAJ2qo5FJrmPXKpUiR6sEhGxLFhgBU1RccPupk2t20VEAj3HqW3btmZjXRGRROP2R7EVynW5bu5rF8s2SSIiTkjUsFH27NlNjSRW6+aecJy6S8HCdv/h+VdeeSUp2ykiwWblSlamjf12Bk/791vH1a6dnC0TEUnawMkuRsnaSltYhyUKBU4iEi+32mxJcpyIiL8GTtwSRUTklsSy/VGijxMR8dccJxGRW8aSA1w95zbNHw1v53EiIn4i0UvjuJfcJ598glWrVuHUqVOmjlPNmjVN4njGjBmTtpUiEnxYp4klB7h6jsGTnRDujntLqp6TiAT6iNP+/ftNYni3bt2wY8cOpEyZ0vzk5QoVKpjbRUTixTpNX3wBFCzoeX22bNbP2bOBrVsdaZqISJIFTj179jQ///rrL2zYsAHff/+9+bl161aTGP7qq68m5mFFJFSDJ+4/uWwZTrKS+LJlwPHjQIMGwNWrQMuW1k8RkUANnJYsWYK3334bd955p8f1vDx06FD88MMPSdU+EQkFnI6rXRuXn3zSKj3AArvchiVXLmDTJqB/f6dbKCKS+MDpxo0bSJ8+fYy38fpwFawTkVuVL58VPNHYscBPPzndIhGRxAVO9913H4YNG4az3NXcDS+/9dZb5nYRkVvWsCHQsaOVON6mDXDqlNMtEpEQl6hVdWPHjsUDDzyAwoUL48EHHzSVw48dO4alS5ciderUmG5/SxQRuVUcbWLe099/Ay+9BMybF3cJAxERfxtxKlu2LDZv3owXXngBhw4dwk8//WR+dujQAZs2bTK3i4gkCZY3mTPHynuaPx+YNcvpFolICEt0HadChQph3LhxSdsaEZGYVK0KDBkCvPEG0KWLVRTz9tudbpWIhKBE13Fi+YGY8PoDcW3cKSKSGH37AvffD5w/D7RuzVUqTrdIREJQogKnTp06YVYsw+WffvopXn755Vttl4hI9JIFfN/JkgX45RdgxAinWyQiIShRgdOvv/5qksJjUqdOHaxZs+ZW2yUiEl3RosCkSdb5wYOBdeucbpGIhJhEBU4XLlwwq+difMCUKXGeQ+kiIr7ASuLNmwOsF8fzFy443SIRCSGJCpxKly6NhQsXxnjbV199Fa2iuIhIkmEpgvffBwoXBnbt4h5QTrdIREJIolbV9ejRA+3atUNYWBiee+45FChQwJQj+PjjjzF16lTVcRIR38qeHZg5E2DKwNSpwKOPAtyuRUTEHwOnNm3a4OjRoxgyZAg++OADj+1WRowYgbZt2yZlG0VEouOedr17A6NGAS+8AFSvDuTP73SrRCTIJbqOU+/evdGxY0eTCH7y5EnkzJkTNWrUQBaueBERSQ5DhwLcVHzjRqB9e+C775ho6XSrRCSIJTpwIgZJ9evXT7rWiIgkRJo0rIECVK4MLF5srbjr2tXpVolIEPP6q9mJEyfMNitR8bqmTZvirrvuwkMPPYT//e9/iWrIpEmTULRoUaRLlw7Vq1fHujiWGTOPqmbNmsiePbs51a1bN9rxLpcLAwcORP78+c0UIo/ZuXNnotomIn6sdGlgzBjrPKfutm51ukUiEsS8Dpz69etnEsLd/fvvvyaA4Uo6BidbtmxB48aN8fPPPyeoEfPmzUPPnj0xaNAgU3m8QoUKZiSLGwfHZPny5WjRogWWLVtmpgq52XC9evVw8ODByGNGjRqFCRMmYMqUKabuVMaMGc1jXrlyJUFtE5EA0LmzlSB+9Srw7LPWTxERX3B5qXTp0q4xY8Z4XNe9e3dXypQpXYsXLzaXL1265Lrvvvtcjz76qCshqlWr5nr55ZcjL4eHh7sKFCjgGj58uFf3v3Hjhitz5syuGTNmmMsRERGufPnyuUaPHh15zJkzZ1xp06Z1ffbZZ1495tmzZ13sHv70hf379/vkcQON+sGifkiCfjhyxOXKndvl4ttar16uQKfXhEX9YFE/+LYfEvKZ7/WIE0dzypYt63Edp+UqVqxoRnuIo05dunSJcUovNteuXcP69evNVJp7EU1e9rYC+aVLl3D9+nXkyJHDXP7nn39w5MgRj8fMmjWrmQKM7TGvXr2Kc+fOeZxEJIDkzQtMm2adHzsW+Oknp1skIqGcHJ4iRQpzsrEcAQMU1nRyV6hQIZMP5S0eGx4ejrx803PDy9u3b/fqMfr27WtqSdmBEoMm+zGiPqZ9W1TDhw835RViChh9EUSdOnUqyR8zEKkfLOqHJOqHSpWQrVUrZJo9GzdatsTRH36AizWfApBeExb1g0X94Nt+SMiOJ14HTqwG/uOPP0aOLn3zzTcmkLIv2w4fPozcuXMjubBu1Ny5c03eExPLE4s5XMyzsjFYYu5UwYIFfVZigUGmqB9s6ock6ocpU8wedqn+/hsFWa5g3jyr2ngA0mvCon6wqB981w8JGSDxOnDq1q2bKXx5+vRp5MuXD5MnT0aJEiU8psNo8eLFKFeunNcNyJUrl6lAzhEsd7zM3xOXMWPGmMCJAV358uUjr7fvx8fgqjr3x+TUYkzSpk1rTiIS4DJmBObMAWrUAObPBx5/nFV7nW6ViAQJr3OcWrZsaaazFi1ahHfeeceUH1iwYAFSpboZe3EVHPOeGjZs6HUD0qRJgypVqmDp0qWR10VERJjLLKgZG66aGzp0qGlP1apVPW67/fbbTfDk/piMJrm6Lq7HFJEgwfcEe+q9SxcmPjrdIhEJxQKYffr0MafY5MmTJ9rIkTc4RcZtWhgAVatWDePHj8fFixfRnpWA/9vihVNmDNxo5MiRpkbTp59+amo/2XlLmTJlMidOITL3atiwYShZsqQJpAYMGGDyoJ7UflYioaFvX2DRImDlSqBVK2DFCsDti56ISGL4xbtIs2bNcPz4cRMMMQjidBpHkuzk7n379pmVdjZOE3I1HgtvumMdqMGDB5vzDPAYfL344os4c+YM7r//fvOYt5IHJSIBJCzM2gi4QgVg9WquAAEGDHC6VSIS4FKwJoHTjfBHnNpjCYOzZ8/6JDn8wIEDSvRTP0RSP/iwH2bPBlq3tgIpBlDVqiEQ6DVhUT9Y1A++7YeEfOZrN0wRCW4tWwLNmwPh4db5CxecbpGIBDAFTiIS3FiKYPJkoHBhYNcu4JVXnG6RiAQwBU4iEvyyZbPynRhEffQRsHCh0y0SkQClwElEQkPt2kDv3tb5Dh1YrdfpFolIAFLgJCKhg5XEK1UCTp4EWO4kIsLpFolIgFHgJCKhI00aq6o4y5IsXgy8957TLRKRAKPASURCS+nS3K/JOs+Cvlu3Ot0iEQkgCpxEJPR07gw8+ihw9Srw7LPWTxERLyhwEpHQw9V106cDuXMDmzcDb7zhdItEJEAocBKR0MQtnaZNs86PHQu4bQouIhIbBU4iEroaNgQ6drTOt20LnDrldItExM8pcBKR0MbRpjvuAA4eBF56CdD2nSISBwVOIhLaMma0ShSkSgXMn29VGBcRiYUCJxGRqlWBIUOs8126AHv2ON0iEfFTCpxERKhvX6BmTeDCBaB1a+DGDadbJCJ+SIGTiAiFhQGzZgFZsgCrVwPDhzvdIhHxQwqcRERsRYoA779vnefU3a+/Ot0iEfEzCpxERNyxknjz5kB4ONCqlTV1JyLyHwVOIiJRq4pPngwULgzs2gW88orTLRIRP6LASUQkqmzZrHwnBlEffQQsXOh0i0TETyhwEhGJSa1aQJ8+1vkOHYBDh5xukYj4AQVOIiKxefNNoFIl4ORJoH17ICLC6RaJiMMUOImIxCZNGquqeLp0wA8/AO+953SLRMRhCpxEROJSurS1nx1x6m7LFqdbJCIOUuAkIhKfTp2ARx8Frl4FWra0fopISFLgJCISH66umz4dyJ0b2LwZeOMNp1skIqEcOE2aNAlFixZFunTpUL16daxbty7WY7du3YqnnnrKHJ8iRQqMHz8+2jGDBw82t7mfSpUq5eNnISJBLW9eK3giTt0tXep0i0QkFAOnefPmoWfPnhg0aBA2bNiAChUqoH79+jh27FiMx1+6dAnFihXDiBEjkC9fvlgf96677sLhw4cjT6tWrfLhsxCRkPD448BLL1nn27YFTp1yukUiEmqB07hx49ChQwe0b98eZcqUwZQpU5AhQwZMt7/ZRXH33Xdj9OjRaN68OdKmTRvr46ZKlcoEVvYpV65cPnwWIhIyxowB7rgDOHgQ6NgRcLmcbpGIhErgdO3aNaxfvx5169a92aCUKc3lNWvW3NJj79y5EwUKFDCjUy1btsS+ffviPP7q1as4d+6cx0lEJJqMGYFPP+W3M+CLL4CZM51ukYgko1Rw0IkTJxAeHo68zB1ww8vbt29P9OMyT+qTTz7BnXfeaabphgwZgpo1a2LLli3InDlzjPcZPny4OS6qgwcP+iSIOqUhfkP9YFE/BFg/5M2LzK++iqwjRyLi5ZdxtEQJhBcpEpp94WPqB4v6wbf9cP78+cAInHylQYMGkefLly9vAqkiRYrg888/x/PPPx/jffr162dyrWwMlgoXLoyCBQsiS5YsPmlnoUKFfPK4gUb9YFE/BFg/vPUWsHo1Uq5cify9ewM//2yNQoViX/iY+sGifvBdPyRkgMTRqTrmHYWFheHo0aMe1/NyXInfCZUtWzbccccd2MWdzmPBfCkGSO4nEZFYhYVZGwHzvYKpBcOHO90iEUkGjgZOadKkQZUqVbDUbVlvRESEuVyjRo0k+z0XLlzA7t27kT9//iR7TBERcHru/fet85zq//VXp1skIsG+qo7TY1OnTsWMGTOwbds2dOrUCRcvXjSr7KhNmzZmGs09oXzjxo3mxPPMQeJ599GkXr16YcWKFdi7dy9Wr16Nxo0bm5GtFi1aOPIcRSSIsZI431vCw4FWrfhNzekWiYgPOZ7j1KxZMxw/fhwDBw7EkSNHULFiRSxatCgyYZyr4bjSznbo0CFU4m7l/xkzZow51apVC8uXLzfXHThwwARJJ0+eRO7cuXH//fdj7dq15ryISJLjqBNrxfELXI8ewEcfOd0iEQnWwIm6dOliTjGxgyEbK4a74qmbMnfu3CRtn4hInLJls/Kd6tQBpk0DHnsMaNzY6VaJSDBO1YmIBIVatYA+fazzHTpweNzpFomIDyhwEhFJKm++CTCV4ORJgHmaERFOt0hEkpgCJxGRpJImDTBnDpAuHfDDD8DEiU63SESSmAInEZGkVLo0MHasdb5vX2DLFqdbJCJJSIGTiEhS69QJePRRboJplSvgTxEJCgqcRESSWooUwPTpAEugbN4MvP660y0SkSSiwElExBdYi47BE40bB/z4o9MtEpEkoMBJRMRXHn8ceOkl63zbttza3ekWicgtUuAkIuJLTBS/806rrlPHjkA8BXxFxL8pcBIR8aUMGawSBalSAV98AcyY4XSLROQWKHASEfG1KlWs4pjUtSuwZ4/TLRKRRFLgJCKSHLgdywMPABcuAK1aATduON0iEUkEBU4iIskhLAyYORPIkgVYswZ4+22nWyQiiaDASUQkuRQpArz/vnWeU3dr1zrdIhFJIAVOIiLJiZXEW7QAwsOtKTtO3YlIwFDgJCKS3DjqVLgwsHs30KOH060RkQRQ4CQiktyyZQNmzbK2Zpk2DVi40OkWiYiXFDiJiDihVi1rpR298IJVIFNE/J4CJxERpzBBvHJlayuWdu2AiAinWyQi8VDgJCLilDRprKri6dMDS5YAEydaSePLlyP9l1+an+ayiPgNBU4iIk4qVQoYM8Y637s3ULAgUKcOcrLCeJ06QNGiwIIFTrdSRP6jwElExGmdOlnbsly/Dhw96nnbwYNA06YKnkT8hAInERGnMbcptuRwl8v6ybIFmrYTcZwCJxERp61cCRw+HPvtDJ7277eOExFHKXASEXFaXEFTYo4TEZ9R4CQi4rT8+ZP2OBEJ7sBp0qRJKFq0KNKlS4fq1atj3bp1sR67detWPPXUU+b4FClSYPz48bf8mCIijqpZEyhUyKokHlfpguzZk7NVIuKPgdO8efPQs2dPDBo0CBs2bECFChVQv359HDt2LMbjL126hGLFimHEiBHIly9fkjymiIijwsKAd9+1zscWPF27BlStCvTvD1y5kqzNExE/CpzGjRuHDh06oH379ihTpgymTJmCDBkyYPr06TEef/fdd2P06NFo3rw50qZNmySPKSLiuCZNgC++sOo4ueNmwB9+CDRqBNy4Abz1FlC+vFUcU0RCK3C6du0a1q9fj7p1695sUMqU5vKaNWuS9TGvXr2Kc+fOeZxERJI9eNq7F1i2DCdZRXzZMuCff4AOHayNgP/v/6w8p507reKY3OOO27WISLJJBQedOHEC4eHhyJs3r8f1vLx9+/Zkfczhw4djyJAh0a4/ePCgT4KoU3qzM9QPFvWDRf3wnxIlcCpHDlzOkcNzJV21akjx44/IOmIEMs2aBUybhvCvvsKZwYNx+Ykn4s6RClB6TVjUD77th/PnzwdG4ORP+vXrZ/KibAyWChcujIIFCyJLliw++Z2FmAwq6of/qB8s6gcv+mLmTKBjRzMSFbZtG3J26QJ8+y0weTJQpAiCjV4TFvWD7/ohIQMkjk7V5cqVC2FhYTgaZYsBXo4t8dtXj8l8KQZI7icREb91333AH38AHCnnirvvvwfKlAHeecfKhRIRn3A0cEqTJg2qVKmCpUuXRl4XERFhLteoUcNvHlNExC9xgczAgcCmTVZJg0uXAI6c33OPFVSJSPCtquP02NSpUzFjxgxs27YNnTp1wsWLF82KOGrTpo2ZRnNP/t64caM58TxzkHh+165dXj+miEhQKVXKWmU3dSqQLRuwfj2XIAN9+ljBlIgkGcdznJo1a4bjx49j4MCBOHLkCCpWrIhFixZFJnfv27fPrIqzHTp0CJUqVYq8PGbMGHOqVasWlv+3PDe+xxQRCTp8n+Qqu8cfB7p3Bz7/HBg92ipxMGUKUK+e0y0UCQopXC57622JmiiWNWtWnD171if5TgcOHFCin/ohkvrBon5Iwr745hugc2drc2Bq1YpF7oDcuRFI9JqwqB982w8J+cx3fKpORER8gCNPW7dao08sUzB7NlC6tLUiT9+XJdCEh5vp6PRffmlNS/OyQxQ4iYgEq8yZAe7nuXatVW385EmgbVvg4YcBt7xQEb+2YAFQtKgp+pqza1er+Csv83oHKHASEQl21aoBv/8OjBgBpEsHcNVxuXLW5evXnW6dSOwYHDVtyjk6z+sPHrSudyB4UuAkIhIKUqcG+vYFtmwBHnrI2iiYK5a5cfC6dU63TiQ6TsdxqjmmqWX7uh49kn3aToGTiEgoKV4cWLIEmDEDyJkT2LzZqvvED6AEbDsh4nMrV0YfaYoaPHHxA49LRgqcRERCDZPF27QBtm0DWre2PoDefRe46y5rNZ6Ik/btAz74AOjVy7vj3fdzTAYKnEREQhVLE3CV3eLFwO23W9/eGzYEnnkm2T+MJIRduwb89BPQuzdQtqy13+JLL1mFXL2RPz+SkwInEZFQx+KYzH3iB1dYGDB/vlW6gJXIIyKcbp0Eo/37gQ8/BBo3tqaMmXc3ZoxVQoPFXO+9F3jzTSBPnrhHTgsXtrYbCqXK4SIi4gcyZABGjQJatAA6dLC+7b/4olX/idMm3NZFJLG4evOXX4DvvrM2pGag7o4B0iOPAI8+apXLyJHDup7Tx1w9R+5J4gyaiOU2GOwnIwVOIiJyE7e0Yt2n994D+vcHfv4ZqFABeOMNa1UeNxYW8QZLBjBI4okLEtwXHzDw4aKEBg2sYImvO7ft1SI1aWJtG8TVde6J4qwezqCJtyczBU4iIuIpVSprlR2nUTp1sj74Bg0C5s61plfuv9/pFoq/jiqtXn0zWOKKzag5dRxVYrDE6WFO0XmDwVGjRmb13MktW5CTeVCcnkvmkSabAicREYkZk3S//dbaMLhbN2sVHj+wmLjL4plZszrdQnHaoUOeo0rnznmOKrH4KkeUGCxVqRLzqJI3GCTVro3LJUpYo00OUuAkIiKx44dfs2ZW3kmfPsC0acCUKcBXX1nTeRyVsvNNJPjduAGsWWMFSsxX2rTJ8/ZcuYD69a1giaNKvBxkFDiJiEj8mKz70UdAq1ZAx47A338DTz1lTaEwgHJ4FEB8iKUpFi2ygqUffgDOnr15G4Pmu+++mavEUSWHptCSiwInERHxXu3a1ijDW29Z03UceWINnrfftvKhgvxDM2RGlbhAwJ6C++MPz9tz5rRGlRgs8Sdzl0KIAicREUkYbhQ8dKg1hceSBZy64a71c+ZYyePcQFgCy5EjnqNKZ8543m6PKjVoYJ0P4QBZgZOIiCQOVzetWmXlPL32mjVKUbmyVbaApQwYYIl/4sa4v/56s67Shg3Rp2aZo8TpN44qxVWIMsQocBIRkcTjKqnOna1cpy5dgC+/tKbxuBKPhTPr1HG6hWI7duzmqBK32Tl92vN25ifZuUpcDRfCo0pxUeAkIiK3rmBBYOFC68QAaudO4MEHgfbtgdGjva/ZI0k7qrRu3c1cpd9/97w9e3ZrVInBEusr5c3rVEsDigInERFJOixPwICpXz9g8mTg44+Bb74B3n0XaN5cpQsSE/ysXIn03KLEm8KPx497jiqdOuV5O6dS7Vyl6tWtYqeSIOoxERFJWiyM+f77QMuWVvL4X38Bzz4LzJxpBVNFizrdwsCwYEHkViOR43Us+8Ag1N5qhIEVR5Lsuko8776nG/8Wdq4SR5Xy5XPimQQVBU4iIuIb991nLWXn5sFchceREG7ayvOsRK7RjriDJm5u6x4E2fu/sX4WA6oTJ6w+PXnS85iKFW/mKnE/OPVzklJvioiI76RJY62we/ppa/SJmwa/+qpVumDqVGvqSDxxFImBUdSgiezrOOrkPqrEyu52rlKBAsnX1hCkwElERHzvzjuBZcuA6dOB3r2t5e9cufXKK8DgwUDGjAipwIi5Rxwxcj9x5Ig/mc904ED8j8PpT1Zxr1EDSJ06OVouCpxERCRZSxe88ALw+ONAjx7AvHnAmDHAF19YtaBYLyixSdFOiYiwikXGFgTFdGIZgJhGkxKK/fjAA0nxLCQBFDiJiEjyYoLy3LlA69ZWDai9e60pJiaTjxtnFdWMLynaFxjMnDuXsCCItzF4Soxs2axNcKOe2AZWYI9P/vyJ+70SHIHTpEmTMHr0aBw5cgQVKlTAxIkTUY3DuLGYP38+BgwYgL1796JkyZIYOXIkHmUi3H/atWuHGTNmeNynfv36WMREOhERcd5jjwFbtwIDBgATJlh5T9z77sKF6McyKZrJ0hyd8iZ4YhB08WLCgiCeuE9bYmTOHHMQFNuJlbljS9rmaBtXyPE5xzQyxZIODCQ5CiehGTjNmzcPPXv2xJQpU1C9enWMHz/eBDk7duxAnhjKvK9evRotWrTA8OHD8fjjj+PTTz/Fk08+iQ0bNqAsh3T/88gjj+Bj1hD5T9q0aZPtOYmIiBcyZQLeecfK1+nQwdpAOCZ2AMERqgwZYp4ei3q6ejVxbeLjJzQISsrPF05JcnSNgSKDJPfgya6DNX68f05dhoAULldSTLTeGgZLd999N9577z1zOSIiAoULF0bXrl3xGvc/iqJZs2a4ePEivmFRtf/cc889qFixogm+7BGnM2fO4EuW/0+Ec+fOIWvWrDh79iyyZMmCpHbgwAEU4jeGEKd+sKgfLOqHEO+LH3+0VoclJQY0CQmCWOE8fXr4Wx2nSIULW0GTL6csQ/D/4lwCPvMdH3G6du0a1q9fj36sMvuflClTom7duljDHbdjwOs5QuWOI1RRg6Tly5ebEavs2bPjwQcfxLBhw5AzlrL/V69eNSf3ThQRkWTEqtfeYPBQvHj8ARB/crVeoFYrZ3DEPQBXrsTJLVuQ05+T5EOI44HTiRMnEB4ejrxR9sjh5e3bt8d4H+ZBxXQ8r3efpmvSpAluv/127N69G6+//joaNGhggq6wGF50nPYbMmRItOsPHjzokyDqVNQy+CFK/WBRP1jUD6HdF2lTpUJuL447PnYsrnIJfnw4ncdToCtRAqdy5MBlTgkePoxQdspH/xfnz58PnMDJV5pzT6T/lCtXDuXLl0fx4sXNKNRDDz0U7XiOeLmPYjFY4nRhwYIFfTJVRyE3DB8L9YNF/WBRP4RwX3CEhc85nqTo3DwuBEddQu71EAtfTdV5KyUclitXLjMCdPToUY/reTlfLHvq8PqEHE/FihUzv2vXrl0x3s7EcQZI7icREUlGdlI0RZ1eU1K0+AnHA6c0adKgSpUqWLp0aeR1TA7n5RqxDMXyevfjacmSJbEebyeUnTx5EvlV90JExH9xNIklBwoW9LyeowzeliIQ8SG/mKrjFFnbtm1RtWpVU7uJ5Qi4aq59+/bm9jZt2pgpM+YhUffu3VGrVi2MHTsWjz32GObOnYvff/8dH/5XMOzChQsmX+mpp54yo1DMcerTpw9KlChhkshFRMSPKSla/JhfBE4sL3D8+HEMHDjQJHizrAALVdoJ4Pv27TMr7Wz33nuvqd3Uv39/k/TNAphcUWfXcOLU3+bNm00BTJYkKFCgAOrVq4ehQ4eqlpOISCBgkFS7Ni6XKGGNNon4Cb8InKhLly7mFBMmdEf19NNPm1NM0qdPj8WLFyd5G0VERCS0OZ7jJCIiIhIoFDiJiIiIeEmBk4iIiIiXFDiJiIiIBFpyuL+x9z721Z51LO+u/fDUDzb1g0X9cJP6wqJ+sKgffNsP9mPan/1xUeAUz7413HZFREREgh8/+7NmzRrnMSlc3oRXIYjVyw8dOoTMmTMjRRLvrG3vg7d///6Q3tpF/WBRP1jUDzepLyzqB4v6wff9wFCIQRPrPrrXjYyJRpxiwY7z9YaK2hPPon6wqB8s6oeb1BcW9YNF/eDbfohvpMmm5HARERERLylwEhEREfGSAicHcL+8QYMGhfy+eeoHi/rBon64SX1hUT9Y1A/+1Q9KDhcRERHxkkacRERERLykwElERETESwqcRERERLykwElERETESwqcRERERLykwMkHQn2hYqg//6jUH5ZQ74dQf/429YMn9Ufg9YO2XEkCf//9N5YvX47w8HB06tQpyfe2CyTbt2/HF198gTRp0uCFF15Ajhw5EIpOnTplXg+ZMmVC+vTpzd6H8e1/FIyOHz+O06dPI3/+/GbfR745huL/x19//YXx48eb/4dKlSqhWbNmCEXqB4veHwK7H/y/hX7u8OHDqFOnDjZs2ID33nvPBAuh6tixY3jkkUdw5MgRLF68GL169UIo2rZtG2rUqIHOnTujcuXKOHPmTEC8GfgiiL777rvx6quvoly5cmZjzlAMmvjh0Lp1a/NlokiRInjnnXdw4MABhBr1g0XvD4HfD4HRSj/GIKFatWqYMmUKli5dagKpkydPIhRdvnzZ9AUDyAULFmDHjh3Yu3cvQgm/MU2bNg0NGjTA/PnzUbNmTTMCF4q++uorPP744/jf//6HJ598ErNnz0ao/l9ky5YNvXv3NiPSdOPGDYQa9YPeH4KlHxQ43aLcuXPj559/xvDhw3H//febUvA5c+ZEKMqQIQN++eUXvPHGG2YYnsPxRYsWRSjhN6ZUqVKZKSpOS/30008h9+Fgu379upmauXLlCr799tuA+Tbpi/eIihUrom/fvihTpgwyZswYcv8XpH7Q+0Ow9IO2XEkEDinym5Nt/fr15tt0unTpTABFoZrLwVEmjjbx+b/++ush0xfuz5GjbD169DA/8+bNa6YtQ4mdp8DchUaNGuHs2bMmv+m7775DqOAHgh0s0M6dO7Fx40b8+++/kVPYgZLPkdTTt3/++WfI9YPeH27i+0JYWFhA94MCpwTim1/Xrl3NdFSFChUi/yHc//lD4Y2AmLPCeWrmNj366KMmcOSok/2PESp9wWBx7dq1aNmypfkWRdeuXTMfEuXLlw+ZfuBUDEdcOdLkvgkn3xjtkYVQ6AfmO/L/gVMPHIWOSSh8meDffcmSJebvXbBgQZP/GIr9oPcHy9WrVyPfH1KnTm2u4/mtW7eakchA6gf/b6Ef2bRpk8nhYUIbgyay/+nt+JM/A+EPf6s2b95s+oLfGl9++WUzNWcnezJoYj+EQl/wNVG6dGkzqmK/KTJwZAKs/aYYCv2wZcsWNGzYEPfddx+qVKliRmAZWJMdNIXK66FWrVpo3rx5jEETPxgo2IMFjipVrVoV06dPN6PwTz/9tFk4c+jQoZAKmvT+YGFw1KJFCzz88MPmfWLFihVmCp8BlB00BVQ/cMRJ4rdlyxZX+vTpXQMHDjSXIyIiXCdPnnTt2bPHFWpOnTrlqly5sqtPnz6uo0ePusLDw12vvvqqq3r16q42bdq49u7dG9lHwWzTpk2ujBkzunr37u1xvf28b9y44QoFu3fvdmXPnt318ssvuyZOnOjq2rWrufziiy+61q9f7woVmzdvdmXNmjXy9cD/iz///NO1Zs0a19atWyOP4/XB7Pz5864aNWqY1wEdPnzY9f3337ty5MjheuSRR1y7du1yhQK9P1j+/vtvV5YsWcz7AfuiadOmrhQpUrgGDRrk+vfff12BSIGTF06cOOEqUaKEq1KlSpHXtW/f3lWlShVX/vz5XQ888IDrjz/+CPpAwcYXe5EiRVw//vijx/X80OQbZqdOnVzHjx93BbMdO3a4MmfObN4M7A/DKVOmmDcGnnbu3OkKFWPGjDH/A+7mzJnjKl++vAmkGTwEu+vXr7tKlSplPiivXLliXg9PPPGEeY/gdbfddptr8ODBkccH83vFpUuXzBeruXPnRvufyZUrl+vJJ5+MDBqCtR/0/nBT//79XfXq1XO7xuWaMGGCK2fOnK6+ffu6jhw54go0ATIu5iyukqtXr55ZBTJ48GAzRcWyAx07dsT7779v5mm53Hr37t0ew/HBisPrLFZmD7vbqyG6dOmCJk2amGKgXF0XzH2xbt06XLhwAaVKlTK5HA8++CDmzJljVljy+ZctW9asJAvmPnB37tw50x/2c3322WcxYMAArFmzBnPnzjXXB3M6Jadh/u///s/8X3BaitN1nIrg9DWTXlnLaujQoRg7dqw5PhinqOy/L6eijh49anJ7bHyPvOOOO0zJFuY92YtogrEfSO8PnrmPNvuzgnnCb731lskVXrhwYeD1g9ORmz87dOiQGW61vfLKK668efO6HnvssWhR8l133eVq27atK1jxG5P7FMPTTz/tKleunOvMmTOR37htDRo0cNWpU8cV7MaPH+8qUKCAGU3g6ML+/fvNaMPly5ddL730kitbtmyuAwcOuILdZ599ZqaxN2zYYC5fvXo18rbJkye70qRJY6a6Q8Fff/1lRpgqVqzo8R7B10WPHj1c9913n5nqDraRFk7Pf/fdd5GXx40b5ypUqJDrf//7X+R1165dMz+HDRtmpvWZ6hBs/eDu3Xff1fuDyxpd4ujbwYMHo70/DBkyxJUpUybXvn37XIFEgVMs+ILmUGLjxo1NjoJt1KhRrv/7v/+LNk/91FNPmbnbYMT8jNatW5tgiFOUfIM8duyYq0KFCuY6938EO6CoWbNm0M3h828e9TnxTYEfAr///nu0D1C+fr7++mtXsLl48aL58OeUjK1Ro0auwoULm5w34geEjdPc7KdgnMLftm2bx/uDPZX98ccfRwYKttdff9119913e3zJCAacik2VKpWrbNmyHq//Zs2amfeBxYsXexzPKavSpUub11Gw4XuE+xdMpi+E2vvD7t27XStWrIi8zPeJ2rVru+655x7zP0MMHu38N75vLFiwwBVINFUXC9Zd4UoIniZPnoxff/3VXM+qt6x2ag8x2yvIeJlF3SiYpiS4ZJarg7gKhFWgOT3HKTkOs3KakqUIOAzN/uLUhL2ihnV7OGQfTPsRvvLKK6Yu0Ztvvmmetz3k/MEHH0T723NqIk+ePGaPtmBbHcP9xbh6jtNxrApOfD3cdtttuOeee8zKSrscwaVLl8xrIXv27AgmfI2z5ACn6PmacF9qz35o165d5JJr24kTJ8xKqoCakvCiPAv/5vXr1zdTMjNnzjTXcyUZUxn4d+eULadr7f+LPXv2mP+NYHp/sHeL4OeAXZ6G+F4ZSu8PmzdvNp8XfB3Y75GcvrZrdvG9g1vvsHQN8X2CKTBR/1f8ntORm7/iMDKHVz/44AOT6NiyZUuzaobcv1Hw2yOT35gkHmwJfxw14PPu1q1b5HX8psBpCK6KaNGihekTfqMqVqyYq1q1ambkgUOv7lOcgY7PMU+ePGZEsWPHjmbqyT3RNyZMeqxataoZmQumkUd79RxHDTjtxNeA/U37t99+M0ninILg/w2n8F577TXzzZrfQoPF9u3bTZIznxtHmziiwtd/v379Yjye36rfeOMN0w/uq+sC3caNG10ZMmRwDRgwwIyucUSBI9PufvnlF7NYhCNSHKHmMXwNcTFNsODfNCwszPxfxDbyFArvD3v27HHly5fPJL9HnYJlXyxcuNB8Rtx+++3mf+ann34yn528T6CtrlPgFANOx/AFfccdd5gpOw4jcoi9Q4cOrnvvvddMy9GiRYtcDRs2NH94O78j2Dz00EORQYI9vMoyBE2aNDErhiZNmhQ5JM0PEi4x5QdLML0ZFC1a1ONDkf3RuXPnaFMx9gcFA00GD/xgCRYcbudqqO7du0de99VXX5mpbAYG9tQcf/L5cyrmzjvvNEF1MP1vcKn9M888Y/7+7h8KXHrPL1oxvR64spC5LsEULPBLIr88MSC0zZ8/35U2bVrXsmXLovUZA8yhQ4eagDuYvmAyb4fBAIMgfmG0SzBQTPlbwfr+QDNnzjSfC8T3xhEjRriee+45897J5038bGjevLkrd+7c5vOVucGBWLLEqsglHliEi1slcGd3FvZr3LixGVJs27atqX7aoUMHc1zx4sXNkPSoUaPM6olgwqCaQ++scMvVglwNweHVgwcPYt68eRg0aJDZX4hD8NzdmkPSwYZTCVwpxanZ1157LfJ6TkVxysou9sgpGxZ140rLZcuWYdWqVabAm13gLhjw9c/pCBY6ta1cuRJ//PGHKXRYsmRJUxj27bffxrvvvmteJxyCJ/ftiYIBpx7ton32+wWnJ7haiv8vnKqxpx44FcPXD/9fihUrhmDB9wJubP7iiy9Gvl/w78/Xwtdff43atWub9wyuNuTrgNN5PAUTTsfxb16kSBGzdQjfFzhFSxMmTIi2owT/J/ieGYzvD8T3AnsFHVeh83+BfcMtuPi++Nxzz5nPzs8++8ykgGTJksWkgOTKlQsBx+nIzZ/xmyJHUej55583Q8xlypQxUfS6devM9cGWAB3VqlWrXClTpjRTMByG54qhF154ITIplKslmCAbrHVZuBLGPfmX35o5LM9v2kx45kjkgw8+GDnkzpVUdgJksOCIytmzZ13169c3I0wcZeS3SK6kYxI0ixtydQyntO0kz2At8sjnZRd4dX+9z5s3z6wydWcnPwdrX8SEBYL5Pmn/DwTb+0FUXA3mnuBtrzCNbeSJ9e2Ctcbdxx9/bEalWb+rbt26katKOSLNFeecvbDfJwP9daHAKQb2H/WTTz4xU0+co2cOE6dt+MFQvHhxk+tiT10FOwaJrVq1MgGTPTVnT9VwSsYuSRAsYguG+WHAJeUMFNxXx3DKwn0pdrD2w9q1a03l52effdZMw02bNi3yNr5Jctn18OHDXcHmwoULrnPnzpng0Z17QMRpKk472Hr27GnKlgTTF6vY+sH9PZNBAd8T+IUz0D8c48p/5f89i1xGxb83Awf34InXzZgxI+gKwbIf+KWZlcHtXNB06dKZQtH2lJ2NU3R8n4y6wjJQKXCKA5dU8o/NHCb35aRMcgu1rVZiehPs1auXWWYa0xtpoOKbISths4ZXTOxRBDv5k28WHGmxFw4Eez/ww5MfBKwQz1EWG3MaHn744cjAOlg+NJn4y6rH/DBgntLs2bNjfH7ffvutCSbJHo2LWqYgFPqBi2VYsoSvj5hyAAMdgx/2AUcXuUiEI9B8zu79wMv837CDJ+Y0MTk+0BKgE9IPg//Lg+WiED5XLiByXxDCL53MD+YCkmCgwCkO/Mfnt2p7hViwfBjcKgYJTI7l/kPBlODIpFXup8VgmR9+7kPq9t8+6muAtXmYAB1Mq2Pi6gcGiwye+Jy5mur06dMm+Zfn7VHZYAoWuBKOhW+5hQxHkVKnTh1jkjdHX7lijK8HfpAEYsLrrfaD/b/B1wBfO/wQDSZ2P/ALI8/ziwWfZ0zFG/nl4tNPPzW3c+oyah2nYOyHf//918zCjBw50qR3MNXl559/NqPRXD3HRTZ2EcxAp8ApHqGUn+ANrpridCVXRgRTyQEGA8xda9eunRk14RsBl9XGlo/ANwy+GTB4DMV+4Ddq3saVMQyiuHdhMK2e4zQER1jcS3EQR1jtKRj3INruj2D7kExoP/D9ktN5vM2ewgkGfP0zz9N9VSmfN6euV69ebYJI9wCKgRPzYpkDymm9YO+H+vXrm37gF2nmAHIEtmDBgmanDU7dBtv7g1bVxcNeESE3V1dxFZm9d18w/Z25Qo77ErJIG1d6NG/e3NzWp08fj5Uf+/btQ//+/c3KEO49FUyrY7zth2eeeQYFCxY0q4p4HQsgFi1aFMGCBQrPnDmDpk2bmsv26qjbb7/dFPCLus8a+4wr6yZNmoRy5cohVPuBt3HV4ejRoyOLoAYDPkcWObX7gYYNG2b2ITxy5IgpbnrXXXeZ9wW+DrgfH/83uIqOK6+DvR9++OEH0w98TXCFOVdcrl+/Hv/8849ZXcdVt0FV7NPpyE3En0Zb3DHJk6MIHJK2VwnxmyS3FeFqO55CrR/skSdOYwfr6iCb+4iJna/DUcaoRR45VRlTv4VqPwQrjqS5r57j/wRHGjkqx3xYrrC1c304PcXVZKHWD8uXLzf1/bi6MphpxEnkP/YIGus38ZszR1w4nc2tRfhNi7VaxowZY75FsRaJvW1AqPYDd32fNWsWMmTIEJS73PNbsj3KYtdlYj/YW0nQ8OHDTS0a9kkwjcAmph84wtStWzdTuykYcSTNxppVv//+OypXrmwuP/DAA2YLFV5HefPmRbDKHEc/1KpVC/ny5cOGDRsQzILzFS5yC+z9B/lBwWkqBgWtW7c2hf1YDHTdunVBGzQlpB9+++23oA0W3DF4tPejtC/TwIEDzTQFC/+xr4KdN/0QrEFTVCzsyBPx/4PTUZkyZQqqaXtvFAnRflACj0gM7M06+UHBEZeaNWvi+PHj5puUe/XsUO4H9+rZwc7enJWBQeHChc2IG3cM4LftChUqIFSoH6JjAMmK+WvWrMHTTz+NUJUyhPohNL4eiCQCAwZOV/Xu3dtsGcCd4IMp8ddb6oeboyucqpo6darZLoJbZ9hTFKFC/eBp/vz5ZvsUbj3FhHB7WjPUzA+xftCIk0g8uFqGIyzBPvwcH/UDzOpBWr16tdmXLVSpHyxlypQxI7DctzGURqJDvR9SMEPc6UaI+DP3vI5Qpn6wXLx4MSRyu+KjfrhZssFOmg9l10OoHxQ4iYiIiHhJU3UiIiIiXlLgJCIiIuIlBU4iIiIiXlLgJCIiIuIlBU4iIiIiXlLgJCIiIuIlBU4iEjS47QdrTbEQny8VLVoUXbp08envEBH/pMBJRILC1q1bsXnzZnP+008/dbo5IhKkFDiJSFCYM2eO2UutTp06Zu8sVjIWEUlqCpxEJOBxA4TPPvsMDz74IHr27ImTJ09i0aJFkbcvX77cTOFxA9Jnn30WmTNnRpEiRTBq1Khoj/XBBx+Y2zJkyICHH34Yf/zxh7nvJ598EmcbuCs8fz+3IcmaNav5PceOHfPJ8xUR5yhwEpGAx81m9+7da4IVbkCbM2fOGKfrXnrpJdxxxx1YuHAhGjZsiL59+3oEWF9//bU5pl69euaYunXr4plnnon39zNoql27tgmY5s2bhw8//BC//fYbGjVqlOTPVUSclcrh3y8icssYJKVLlw5NmjQxG402bdoUs2bNwoULF5ApU6bI45566ikMHjzYnH/ooYfw7bff4osvvsAjjzxirhs2bJgZNZo6daq5zCCMU34DBgyI8/e/9tprqFq1KhYsWBC5EXK5cuVQtmxZfPfdd3j00Ud9+OxFJDlpxElEAtqNGzdMThODE474EEeeLl26ZEaN3HEkycYAp3Tp0jhw4IC5HB4ebqblnnjiCY/7xDdqxN/zyy+/4OmnnzaPwfbwxJGtwoULm5EnEQkeCpxEJKD98MMPOH78uJl6O3PmjDlxtCd//vzRpuuyZcvmcTlNmjS4cuWKOc/HYMCTO3duj2Py5MkT5+8/ffq0CZheeeUVM9rlftq3bx/279+fZM9VRJynqToRCWh2cNS+fXtzcsdgyNsEbQZMqVKlMvdxF9/9GYxx9Or111/Hk08+Ge32XLlyefX7RSQwKHASkYDFabKvvvrKBCzdu3f3uO3IkSNo0aKFSdbmCFR8wsLCUKlSJfN47o/15Zdfxnk/rqKrUaMGtm3bZnKkRCS4KXASkYDFIIcJ4N26dTOr2qJiuQGOSA0fPtyrx+vfv7/JaerQoYPJWWLO04wZM8xtrBEVm9GjR5uk8mbNmqF58+bInj27yZ1i+QOOgsXUNhEJTMpxEpGAxaDotttuizUwadu2LdauXYvdu3d79XhMDJ88eTIWL15sAqjvv//eXCY78Twm9957L1atWmWCOAZKTFR/8803TS2oEiVKJPLZiYg/SuFi5TgREYnRtGnT8MILL+Cff/4xe9SJSGjTVJ2IyH9OnTqFIUOGmGk3VhdnKYG33nrLjD4paBIRUuAkIvIflhDgtB6nAFnWgCvtWrdujZEjRzrdNBHxE5qqExEREfGSksNFREREvKTASURERMRLCpxEREREvKTASURERMRLCpxEREREvKTASURERMRLCpxEREREvKTASURERMRLCpxERERE4J3/B/T8Oz1R5cAEAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = r.plot_curve(incl_pred=False)\n", + "\n", + "plt.xlabel(\"Angle\")\n", + "plt.title(\"Scores by Angle\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "933eea60765a4afe", + "metadata": { + "collapsed": false + }, + "source": [ + "This already looks like a cosine curve, and we can finally use the SSM to estimate the parameters of the curve that best fits the observed data. By plotting it alongside the data, we can get a sense of how well the model fits our example data." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c826947d0b98109e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T18:37:04.908090Z", + "start_time": "2024-07-11T18:37:04.843764Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = r.plot_curve()\n", + "\n", + "plt.xlabel(\"Angle\")\n", + "plt.title(\"Cosine curve estimated by SSM\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "dcc2aa0d761cfcf8", + "metadata": { + "collapsed": false + }, + "source": [ + "## Understanding the SSM Parameters\n", + "\n", + "The SSM estimates a cosine curve to the data using the following equation:\n", + "\n", + "$$\n", + "S_i = e + a \\times \\cos(\\theta_i - d)\n", + "$$\n", + "\n", + "where $S_i$ and $\\theta_i$ are the score and angle on scale $i$, respectively, and $e$, $a$, and $d$ are the elevation, amplitude, and displacement of the curve, respectively. Before we discuss these parameters, however, we can also estimate the fit of the SSM model. This is essentially how close the cosine curve is to the observed data points." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3d4382669e2bbfa8", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T18:37:04.974845Z", + "start_time": "2024-07-11T18:37:04.908734Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from matplotlib import collections as mc\n", + "\n", + "fig = r.plot_curve(c_fit=\"black\", c_scores=\"black\")\n", + "thetas = np.linspace(0, 360, 1000)\n", + "fit = circumplex.utils.cosine_form(\n", + " np.deg2rad(thetas),\n", + " r.results[\"a_est\"][0],\n", + " np.deg2rad(r.results[\"d_est\"][0]),\n", + " r.results[\"e_est\"][0],\n", + ")\n", + "angles, scores = circumplex.utils.sort_angles(\n", + " np.array(r.details.angles), r.scores[scales].to_numpy()[0]\n", + ")\n", + "\n", + "lines = []\n", + "for i, angle in enumerate(angles):\n", + " idx = np.where(np.isclose(thetas, angle, atol=0.2))[0][-1]\n", + " lines.append([(angle, fit[idx]), (angle, scores[i])])\n", + " if angle == 360:\n", + " lines.append([(0, fit[0]), (0, scores[i])])\n", + "\n", + "lc = mc.LineCollection(lines, colors=\"red\", linewidths=10)\n", + "fig.axes[0].add_collection(lc)\n", + "\n", + "plt.xlabel(\"Angle\")\n", + "plt.title(f\"Fit = {round(r.results.fit_est[0], 2)}\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "897bd12afd585fde", + "metadata": {}, + "source": [ + "If fit is less than 0.70, it is considered \"unacceptable\" and only the elevation parameter should be interpreted. If fit is between 0.70 and 0.80, it is considered \"adequate\", and if it is greater than 0.80, it is considered \"good\". Sometimes SSM model fit is called prototypicality or denoted using $R^2$. \n", + "\n", + "The first SSM parameter is elevation or $e$, which is calculated as the mean of all scores. It is the size of the general factor in the circumplex model and its interpretation varies from scale to scale. For measures of interpersonal problems, it is interpreted as generalized interpersonal distress. When using correlation-based SSM, $|e| \\geq 0.15$ is considered \"marked\" and $|e| \\leq .15$ is considered \"modest\"." + ] + }, + { + "cell_type": "markdown", + "id": "b8e0a65d8bf43c20", + "metadata": {}, + "source": [ + "The second SSM is amplitude or $a$, which is calculated as the difference between the highest point of the curve and the curve's mean. It is interpreted as the distinctiveness or differentiation of a profile: how much it is peaked versus flat. Similar to elevation, when using correlation based SSM, $a \\geq 0.15$ is considered \"marked\" and $a \\leq 0.15$ is considered \"modest\"." + ] + }, + { + "cell_type": "markdown", + "id": "83f2230678d86f83", + "metadata": {}, + "source": [ + "The final SSM parameter is displacement or $d$, which is calculated as the angle at which the curve reaches its highest point. It is interpreted as the style of the profile. For instance, if $d = 90$ and we are using a circumplex scale that defines 90 degrees as \"domineering\", then the profile's style is domineering." + ] + }, + { + "cell_type": "markdown", + "id": "b1c952954bc8f60f", + "metadata": {}, + "source": [ + "By interpreting these three parameters, we can understand a profile much more parsimoniously than by trying to interpret all eight subscales individually. This approach also leverages the circumplex relationship (i.e. dependency) among subscales. It is also possible to transform the amplitude and displacement parameters into estimates of distance from the x-axis and y-axis, which will be shown in the output discussed below." + ] + }, + { + "cell_type": "markdown", + "id": "67084a8065287c69", + "metadata": { + "collapsed": false + }, + "source": [ + "## Example Data: jz2017\n", + "\n", + "To illustrate the SSM functions, we will use the example dataset `JZ2017`, which was provided by Zimmerman & Wright (2017). This dataset includes self-report data from 1166 undergraduate students. Students completed a circumplex measure of interpersonal problems with eight subscales (PA, BC, DE, FG, HI, JK, LM, and NO) and a measure of personality disorder symptoms with ten subscales (PARPD, SCZPD, SZTPD, ASPD, BORPD, HISPD, NARPD, AVPD, DPNPD, and OCPD). More information about these variables can be accessed by looking at the summary of the dataset with `jz_data.summary()`:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d06f5b82ed8a562c", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T18:37:04.979706Z", + "start_time": "2024-07-11T18:37:04.975364Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "IIP-SC: Inventory of Interpersonal Problems Short Circumplex\n", + "32 items, 8 scales, 2 normative data sets\n", + "Soldz, Budman, Demby, & Merry (1995)\n", + "< https://doi.org/10.1177/1073191195002001006 >" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iipsc = get_instrument(\"iipsc\")\n", + "iipsc" + ] + }, + { + "cell_type": "markdown", + "id": "c00508ca5abc4368", + "metadata": { + "collapsed": false + }, + "source": [ + "And we can view the accompanying dataset with:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3828a5802369696e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T18:37:04.986807Z", + "start_time": "2024-07-11T18:37:04.980309Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "Gender", + "rawType": "object", + "type": "string" + }, + { + "name": "PA", + "rawType": "float64", + "type": "float" + }, + { + "name": "BC", + "rawType": "float64", + "type": "float" + }, + { + "name": "DE", + "rawType": "float64", + "type": "float" + }, + { + "name": "FG", + "rawType": "float64", + "type": "float" + }, + { + "name": "HI", + "rawType": "float64", + "type": "float" + }, + { + "name": "JK", + "rawType": "float64", + "type": "float" + }, + { + "name": "LM", + "rawType": "float64", + "type": "float" + }, + { + "name": "NO", + "rawType": "float64", + "type": "float" + }, + { + "name": "PARPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "SCZPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "SZTPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "ASPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "BORPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "HISPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "NARPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "AVPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "DPNPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "OCPD", + "rawType": "int64", + "type": "integer" + } + ], + "ref": "2f1cd42a-748c-42a1-b036-f906a26f551e", + "rows": [ + [ + "0", + "Female", + "1.5", + "1.5", + "1.25", + "1.0", + "2.0", + "2.5", + "2.25", + "2.5", + "4", + "3", + "7", + "7", + "8", + "4", + "6", + "3", + "4", + "6" + ], + [ + "1", + "Female", + "0.0", + "0.25", + "0.0", + "0.25", + "1.25", + "1.75", + "2.25", + "2.25", + "1", + "0", + "2", + "0", + "1", + "2", + "3", + "0", + "1", + "0" + ], + [ + "2", + "Female", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0", + "1", + "0", + "4", + "1", + "5", + "4", + "0", + "0", + "1" + ], + [ + "3", + "Male", + "2.0", + "1.75", + "1.75", + "2.5", + "2.0", + "1.75", + "2.0", + "2.5", + "1", + "0", + "0", + "0", + "1", + "0", + "0", + "0", + "0", + "0" + ], + [ + "4", + "Female", + "0.25", + "0.5", + "0.25", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0", + "0", + "0", + "0", + "1", + "0", + "0", + "1", + "0", + "0" + ] + ], + "shape": { + "columns": 19, + "rows": 5 + } + }, + "text/html": [ + "
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" + ], + "text/plain": [ + " Gender PA BC DE FG HI JK LM NO PARPD SCZPD \\\n", + "0 Female 1.50 1.50 1.25 1.00 2.00 2.50 2.25 2.50 4 3 \n", + "1 Female 0.00 0.25 0.00 0.25 1.25 1.75 2.25 2.25 1 0 \n", + "2 Female 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 1 \n", + "3 Male 2.00 1.75 1.75 2.50 2.00 1.75 2.00 2.50 1 0 \n", + "4 Female 0.25 0.50 0.25 0.00 0.00 0.00 0.00 0.00 0 0 \n", + "\n", + " SZTPD ASPD BORPD HISPD NARPD AVPD DPNPD OCPD \n", + "0 7 7 8 4 6 3 4 6 \n", + "1 2 0 1 2 3 0 1 0 \n", + "2 0 4 1 5 4 0 0 1 \n", + "3 0 0 1 0 0 0 0 0 \n", + "4 0 0 1 0 0 1 0 0 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "jz2017 = load_dataset(\"jz2017\")\n", + "jz2017.head()" + ] + }, + { + "cell_type": "markdown", + "id": "480a56f45e82f917", + "metadata": { + "collapsed": false + }, + "source": [ + "The circumplex scales in `JZ2017` come from the Inventory of Interpersonal Problems - Short Circumplex (IIP-SC). These scales can be arranged into the following circular model, which is organized around the two primary dimensions of agency (y-axis) and communion (x-axis). Note that the two-letter scale abbreviations and angular values are based on convention. A high shore on PA indicates that one has interpersonal problems related to being \"domineering\" or too high on agency, whereas a high score on DE indicates problems related to being \"cold\" or too low on communion. Scales that are not directly on the y-axis or x-axis (i.e. BC, FG, JK, and NO) represent blends of agency and communion. " + ] + }, + { + "cell_type": "markdown", + "id": "4113f134c1dabad1", + "metadata": { + "collapsed": false + }, + "source": [ + "## Mean-based SSM Analysis\n", + "\n", + "### Conducting SSM for a group's mean scores\n", + "\n", + "To begin, let's say that we want to use the SSM to describe the interpersonal problems of the average individual in the entire dataset. Although it is possible to analyze the raw scores contained in `jz2017`, our results will be more interpretable if we standardize the scores first. We can do this using the `standardize()` function. \n", + "\n", + "The first argument to this function is `data`, a dataframe containing the circumplex scales to be standardized. The second argment is `scales` and specifies where in `data` the circumplex scales are (either in terms of their variable names or their column numbers). The third argument is `angles` and specifies the angle of each of the circumplex scales included in `scales`. Note that the `scales` argument should be a `circumplex.Scales` object and `angles` should be a `np.array` that have the same ordering and length. Finally, the fourth argument is `norms`, a dataframe containing the normative data we will use to standardize the circumplex scales. Here, we will use normative data for the IIP-SC by loading the `iipsc` instrument." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "735290eb", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "Gender", + "rawType": "object", + "type": "string" + }, + { + "name": "PA", + "rawType": "float64", + "type": "float" + }, + { + "name": "BC", + "rawType": "float64", + "type": "float" + }, + { + "name": "DE", + "rawType": "float64", + "type": "float" + }, + { + "name": "FG", + "rawType": "float64", + "type": "float" + }, + { + "name": "HI", + "rawType": "float64", + "type": "float" + }, + { + "name": "JK", + "rawType": "float64", + "type": "float" + }, + { + "name": "LM", + "rawType": "float64", + "type": "float" + }, + { + "name": "NO", + "rawType": "float64", + "type": "float" + }, + { + "name": "PARPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "SCZPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "SZTPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "ASPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "BORPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "HISPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "NARPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "AVPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "DPNPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "OCPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "PA_z", + "rawType": "float64", + "type": "float" + }, + { + "name": "BC_z", + "rawType": "float64", + "type": "float" + }, + { + "name": "DE_z", + "rawType": "float64", + "type": "float" + }, + { + "name": "FG_z", + "rawType": "float64", + "type": "float" + }, + { + "name": "HI_z", + "rawType": "float64", + "type": "float" + }, + { + "name": "JK_z", + "rawType": "float64", + "type": "float" + }, + { + "name": "LM_z", + "rawType": "float64", + "type": "float" + }, + { + "name": "NO_z", + "rawType": "float64", + "type": "float" + } + ], + "ref": "754b69b1-8e17-4f41-9893-a68d51fb1f4d", + "rows": [ + [ + "0", + "Female", + "1.5", + "1.5", + "1.25", + "1.0", + "2.0", + "2.5", + "2.25", + "2.5", + "4", + "3", + "7", + "7", + "8", + "4", + "6", + "3", + "4", + "6", + "1.121212121212121", + "1.0253623188405798", + "0.4093567251461988", + "-0.050131926121372135", + "0.6338797814207651", + "1.3079178885630498", + "0.9515151515151514", + "1.84375" + ], + [ + "1", + "Female", + "0.0", + "0.25", + "0.0", + "0.25", + "1.25", + "1.75", + "2.25", + "2.25", + "1", + "0", + "2", + "0", + "1", + "2", + "3", + "0", + "1", + "0", + "-1.1515151515151514", + "-0.7862318840579711", + "-1.0526315789473684", + "-0.8416886543535621", + "-0.18579234972677588", + "0.42815249266862165", + "0.9515151515151514", + "1.53125" + ], + [ + "2", + "Female", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0", + "1", + "0", + "4", + "1", + "5", + "4", + "0", + "0", + "1", + "-1.1515151515151514", + "-1.1485507246376812", + "-1.0526315789473684", + "-1.1055408970976255", + "-1.5519125683060109", + "-1.6246334310850439", + "-1.7757575757575759", + "-1.2812499999999998" + ], + [ + "3", + "Male", + "2.0", + "1.75", + "1.75", + "2.5", + "2.0", + "1.75", + "2.0", + "2.5", + "1", + "0", + "0", + "0", + "1", + "0", + "0", + "0", + "0", + "0", + "1.8787878787878787", + "1.38768115942029", + "0.9941520467836257", + "1.5329815303430079", + "0.6338797814207651", + "0.42815249266862165", + "0.6484848484848484", + "1.84375" + ], + [ + "4", + "Female", + "0.25", + "0.5", + "0.25", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0", + "0", + "0", + "0", + "1", + "0", + "0", + "1", + "0", + "0", + "-0.7727272727272727", + "-0.42391304347826086", + "-0.760233918128655", + "-1.1055408970976255", + "-1.5519125683060109", + "-1.6246334310850439", + "-1.7757575757575759", + "-1.2812499999999998" + ] + ], + "shape": { + "columns": 27, + "rows": 5 + } + }, + "text/html": [ + "
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GenderPABCDEFGHIJKLMNOPARPD...DPNPDOCPDPA_zBC_zDE_zFG_zHI_zJK_zLM_zNO_z
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" + ], + "text/plain": [ + " Gender PA BC DE FG HI JK LM NO PARPD ... DPNPD \\\n", + "0 Female 1.50 1.50 1.25 1.00 2.00 2.50 2.25 2.50 4 ... 4 \n", + "1 Female 0.00 0.25 0.00 0.25 1.25 1.75 2.25 2.25 1 ... 1 \n", + "2 Female 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 ... 0 \n", + "3 Male 2.00 1.75 1.75 2.50 2.00 1.75 2.00 2.50 1 ... 0 \n", + "4 Female 0.25 0.50 0.25 0.00 0.00 0.00 0.00 0.00 0 ... 0 \n", + "\n", + " OCPD PA_z BC_z DE_z FG_z HI_z JK_z LM_z \\\n", + "0 6 1.121212 1.025362 0.409357 -0.050132 0.633880 1.307918 0.951515 \n", + "1 0 -1.151515 -0.786232 -1.052632 -0.841689 -0.185792 0.428152 0.951515 \n", + "2 1 -1.151515 -1.148551 -1.052632 -1.105541 -1.551913 -1.624633 -1.775758 \n", + "3 0 1.878788 1.387681 0.994152 1.532982 0.633880 0.428152 0.648485 \n", + "4 0 -0.772727 -0.423913 -0.760234 -1.105541 -1.551913 -1.624633 -1.775758 \n", + "\n", + " NO_z \n", + "0 1.84375 \n", + "1 1.53125 \n", + "2 -1.28125 \n", + "3 1.84375 \n", + "4 -1.28125 \n", + "\n", + "[5 rows x 27 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "jz2017s = iipsc.norm_standardize(data=jz2017, sample_id=1)\n", + "jz2017s.head()" + ] + }, + { + "cell_type": "markdown", + "id": "c16f217242651e9d", + "metadata": {}, + "source": [ + "Now we can use the `ssm_analyze()` function to perform the SSM analysis. The first three arguments are the same as the first three arguments to `standardize()`. We can pass the new `jz2017s` object that contains standardized data as `data` and the same vectors to `scales` and `angles` since these haven't changed." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "876dadadc07e23a9", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T18:37:08.478026Z", + "start_time": "2024-07-11T18:37:05.122651Z" + } + }, + "outputs": [], + "source": [ + "results = circumplex.ssm_analyze(\n", + " data=jz2017s,\n", + " scales=[\"PA_z\", \"BC_z\", \"DE_z\", \"FG_z\", \"HI_z\", \"JK_z\", \"LM_z\", \"NO_z\"],\n", + " angles=octants(3),\n", + " boots=2000,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "356d62e50e3d757c", + "metadata": {}, + "source": [ + "The output of the function has been saved in the `results` object, and we can extract a table of the results using the `table` property. This will output a table of the elevation, amplitude, displacement, and fit for the cosine curve estimated by the SSM. The `table` property is a `pandas.DataFrame` object." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7c587d1d7f7c35fd", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T18:37:08.480559Z", + "start_time": "2024-07-11T18:37:08.478703Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Statistical Basis:   Mean Scores\n",
+       "Bootstrap Resamples: 2000\n",
+       "Confidence Level:    0.95\n",
+       "Listwise Deletion:   True\n",
+       "Scale Displacements: [135.0, 180.0, 225.0, 270.0, 315.0, 0.0, 45.0, 90.0]\n",
+       "\n",
+       "\n",
+       "                  Profile[All]                   \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ -0.225   │ -0.259   │ -0.192   │\n",
+       "│ X-Value      │ 0.103    │ 0.065    │ 0.139    │\n",
+       "│ Y-Value      │ 0.082    │ 0.052    │ 0.113    │\n",
+       "│ Amplitude    │ 0.131    │ 0.099    │ 0.165    │\n",
+       "│ Displacement │ 38.525   │ 24.081   │ 54.462   │\n",
+       "│ Model Fit    │ 0.71     │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "
\n" + ], + "text/plain": [ + "Statistical Basis: Mean Scores\n", + "Bootstrap Resamples: \u001b[1;36m2000\u001b[0m\n", + "Confidence Level: \u001b[1;36m0.95\u001b[0m\n", + "Listwise Deletion: \u001b[3;92mTrue\u001b[0m\n", + "Scale Displacements: \u001b[1m[\u001b[0m\u001b[1;36m135.0\u001b[0m, \u001b[1;36m180.0\u001b[0m, \u001b[1;36m225.0\u001b[0m, \u001b[1;36m270.0\u001b[0m, \u001b[1;36m315.0\u001b[0m, \u001b[1;36m0.0\u001b[0m, \u001b[1;36m45.0\u001b[0m, \u001b[1;36m90.0\u001b[0m\u001b[1m]\u001b[0m\n", + "\n", + "\n", + "\u001b[3m Profile[All] \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ -0.225 │ -0.259 │ -0.192 │\n", + "│ X-Value │ 0.103 │ 0.065 │ 0.139 │\n", + "│ Y-Value │ 0.082 │ 0.052 │ 0.113 │\n", + "│ Amplitude │ 0.131 │ 0.099 │ 0.165 │\n", + "│ Displacement │ 38.525 │ 24.081 │ 54.462 │\n", + "│ Model Fit │ 0.71 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "e2934188eaee0af", + "metadata": {}, + "source": [ + "That was pretty easy! We can now write up these results. However, the `circumplex` package has some features that can make what we just did even easier. " + ] + }, + { + "cell_type": "markdown", + "id": "dd96575f63243c4", + "metadata": {}, + "source": [ + "## Visualizing the results with a table and figure\n", + "\n", + "Next, we can produce a table to display our results. With only a single set of parameters, this table is probably overkill, but in future analyses we will see how this function saves a lot of time and effort. To create the table, simply pass the " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d131c20346a3427f", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T18:37:08.484858Z", + "start_time": "2024-07-11T18:37:08.481101Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "Label", + "rawType": "object", + "type": "string" + }, + { + "name": "Group", + "rawType": "object", + "type": "string" + }, + { + "name": "Measure", + "rawType": "object", + "type": "unknown" + }, + { + "name": "e_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "e_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "e_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "x_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "x_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "x_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "y_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "y_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "y_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "a_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "a_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "a_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "d_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "d_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "d_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "fit_est", + "rawType": "float64", + "type": "float" + } + ], + "ref": "ac957a2e-bb5a-4747-94c5-23831895dc1b", + "rows": [ + [ + "0", + "All", + "All", + null, + "-0.22505848844945509", + "-0.25866704120024875", + "-0.19220594024797213", + "0.10277608027608404", + "0.06489126402886365", + "0.1385936244749129", + "0.0818245776233467", + "0.052354240482066", + "0.1130213949266876", + "0.1313704083123561", + "0.09886118278035817", + "0.16459711340023672", + "38.524845806749205", + "24.081368525347546", + "54.46219641626787", + "0.709644509037654" + ] + ], + "shape": { + "columns": 19, + "rows": 1 + } + }, + "text/html": [ + "
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LabelGroupMeasuree_este_lcie_ucix_estx_lcix_uciy_esty_lciy_ucia_esta_lcia_ucid_estd_lcid_ucifit_est
0AllAllNone-0.225058-0.258667-0.1922060.1027760.0648910.1385940.0818250.0523540.1130210.131370.0988610.16459738.52484624.08136954.4621960.709645
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" + ], + "text/plain": [ + " Label Group Measure e_est e_lci e_uci x_est x_lci \\\n", + "0 All All None -0.225058 -0.258667 -0.192206 0.102776 0.064891 \n", + "\n", + " x_uci y_est y_lci y_uci a_est a_lci a_uci \\\n", + "0 0.138594 0.081825 0.052354 0.113021 0.13137 0.098861 0.164597 \n", + "\n", + " d_est d_lci d_uci fit_est \n", + "0 38.524846 24.081369 54.462196 0.709645 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.results" + ] + }, + { + "cell_type": "markdown", + "id": "1ca7363331b47a53", + "metadata": {}, + "source": [ + "Next, let's leverage the fact that we are working within a circumplex space by creating a nice-looking circular plot by mapping the amplitude parameter to the points' distance from the center of the circle and the displacement parameter to the points' rotation from due-east (as is conventional). This, again, is as simple as passing the results object to the `ssm_plot()` function." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "17120bfc93c74a9d", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T18:37:08.601881Z", + "start_time": "2024-07-11T18:37:08.489077Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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"2024-07-09T21:39:29.760651Z", + "start_time": "2024-07-09T21:39:26.635185Z" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%matplotlib inline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "import circumplex\n", + "from circumplex import octants\n", + "\n", + "data = pd.read_excel(\n", + " \"/Users/mitch/Library/CloudStorage/OneDrive-UniversityCollegeLondon/_Fellowship/Papers - Drafts/J2308_APA_SATP-Main/data/SATP Dataset v1.4.xlsx\" # noqa: E501\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7be48f6cc42a46b2", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-09T21:43:34.827932Z", + "start_time": "2024-07-09T21:43:28.062484Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%autoreload\n", + "plt.style.use(\"ggplot\")\n", + "scales = [\"PAQ1\", \"PAQ2\", \"PAQ3\", \"PAQ4\", \"PAQ5\", \"PAQ6\", \"PAQ7\", \"PAQ8\"]\n", + "\n", + "ssm_res = circumplex.ssm_analyze(\n", + " data, scales, angles=octants(), grouping=\"Language\", measures=[\"loud\"]\n", + ")\n", + "fig = ssm_res.plot_circle()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "78230ac68ea161a9", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-09T21:43:34.831685Z", + "start_time": "2024-07-09T21:43:34.829007Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Statistical Basis:   Correlation Scores\n",
+       "Bootstrap Resamples: 2000\n",
+       "Confidence Level:    0.95\n",
+       "Listwise Deletion:   True\n",
+       "Scale Displacements: [0.0, 45.0, 90.0, 135.0, 180.0, 225.0, 270.0, 315.0]\n",
+       "\n",
+       "\n",
+       "                     Profile                     \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ -0.03    │ -0.043   │ -0.018   │\n",
+       "│ X-Value      │ -0.649   │ -0.683   │ -0.614   │\n",
+       "│ Y-Value      │ 0.421    │ 0.366    │ 0.471    │\n",
+       "│ Amplitude    │ 0.774    │ 0.743    │ 0.805    │\n",
+       "│ Displacement │ 147.051  │ 143.151  │ 151.255  │\n",
+       "│ Model Fit    │ 0.978    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                     Profile                     \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.002    │ -0.012   │ 0.016    │\n",
+       "│ X-Value      │ -0.575   │ -0.619   │ -0.53    │\n",
+       "│ Y-Value      │ 0.371    │ 0.32     │ 0.422    │\n",
+       "│ Amplitude    │ 0.684    │ 0.647    │ 0.722    │\n",
+       "│ Displacement │ 147.187  │ 142.638  │ 151.806  │\n",
+       "│ Model Fit    │ 0.983    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                     Profile                     \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.062    │ 0.049    │ 0.075    │\n",
+       "│ X-Value      │ -0.502   │ -0.545   │ -0.459   │\n",
+       "│ Y-Value      │ 0.463    │ 0.417    │ 0.508    │\n",
+       "│ Amplitude    │ 0.684    │ 0.647    │ 0.718    │\n",
+       "│ Displacement │ 137.315  │ 132.946  │ 141.561  │\n",
+       "│ Model Fit    │ 0.916    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                     Profile                     \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.033    │ 0.014    │ 0.053    │\n",
+       "│ X-Value      │ -0.493   │ -0.533   │ -0.454   │\n",
+       "│ Y-Value      │ 0.367    │ 0.315    │ 0.418    │\n",
+       "│ Amplitude    │ 0.615    │ 0.577    │ 0.654    │\n",
+       "│ Displacement │ 143.332  │ 138.42   │ 148.371  │\n",
+       "│ Model Fit    │ 0.912    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                     Profile                     \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.082    │ 0.069    │ 0.095    │\n",
+       "│ X-Value      │ -0.392   │ -0.419   │ -0.365   │\n",
+       "│ Y-Value      │ 0.585    │ 0.557    │ 0.614    │\n",
+       "│ Amplitude    │ 0.704    │ 0.676    │ 0.732    │\n",
+       "│ Displacement │ 123.804  │ 121.532  │ 126.017  │\n",
+       "│ Model Fit    │ 0.929    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                     Profile                     \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.016    │ 0.003    │ 0.03     │\n",
+       "│ X-Value      │ -0.63    │ -0.664   │ -0.595   │\n",
+       "│ Y-Value      │ 0.382    │ 0.331    │ 0.432    │\n",
+       "│ Amplitude    │ 0.737    │ 0.708    │ 0.766    │\n",
+       "│ Displacement │ 148.747  │ 144.514  │ 153.013  │\n",
+       "│ Model Fit    │ 0.956    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                     Profile                     \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ -0.001   │ -0.019   │ 0.017    │\n",
+       "│ X-Value      │ -0.494   │ -0.528   │ -0.458   │\n",
+       "│ Y-Value      │ 0.567    │ 0.523    │ 0.609    │\n",
+       "│ Amplitude    │ 0.752    │ 0.72     │ 0.784    │\n",
+       "│ Displacement │ 131.042  │ 127.782  │ 134.461  │\n",
+       "│ Model Fit    │ 0.947    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "
\n" + ], + "text/plain": [ + "Statistical Basis: Correlation Scores\n", + "Bootstrap Resamples: \u001b[1;36m2000\u001b[0m\n", + "Confidence Level: \u001b[1;36m0.95\u001b[0m\n", + "Listwise Deletion: \u001b[3;92mTrue\u001b[0m\n", + "Scale Displacements: \u001b[1m[\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m45.0\u001b[0m, \u001b[1;36m90.0\u001b[0m, \u001b[1;36m135.0\u001b[0m, \u001b[1;36m180.0\u001b[0m, \u001b[1;36m225.0\u001b[0m, \u001b[1;36m270.0\u001b[0m, \u001b[1;36m315.0\u001b[0m\u001b[1m]\u001b[0m\n", + "\n", + "\n", + "\u001b[3m Profile\u001b[0m\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ -0.03 │ -0.043 │ -0.018 │\n", + "│ X-Value │ -0.649 │ -0.683 │ -0.614 │\n", + "│ Y-Value │ 0.421 │ 0.366 │ 0.471 │\n", + "│ Amplitude │ 0.774 │ 0.743 │ 0.805 │\n", + "│ Displacement │ 147.051 │ 143.151 │ 151.255 │\n", + "│ Model Fit │ 0.978 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile\u001b[0m\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.002 │ -0.012 │ 0.016 │\n", + "│ X-Value │ -0.575 │ -0.619 │ -0.53 │\n", + "│ Y-Value │ 0.371 │ 0.32 │ 0.422 │\n", + "│ Amplitude │ 0.684 │ 0.647 │ 0.722 │\n", + "│ Displacement │ 147.187 │ 142.638 │ 151.806 │\n", + "│ Model Fit │ 0.983 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile\u001b[0m\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.062 │ 0.049 │ 0.075 │\n", + "│ X-Value │ -0.502 │ -0.545 │ -0.459 │\n", + "│ Y-Value │ 0.463 │ 0.417 │ 0.508 │\n", + "│ Amplitude │ 0.684 │ 0.647 │ 0.718 │\n", + "│ Displacement │ 137.315 │ 132.946 │ 141.561 │\n", + "│ Model Fit │ 0.916 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile\u001b[0m\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.033 │ 0.014 │ 0.053 │\n", + "│ X-Value │ -0.493 │ -0.533 │ -0.454 │\n", + "│ Y-Value │ 0.367 │ 0.315 │ 0.418 │\n", + "│ Amplitude │ 0.615 │ 0.577 │ 0.654 │\n", + "│ Displacement │ 143.332 │ 138.42 │ 148.371 │\n", + "│ Model Fit │ 0.912 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile\u001b[0m\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.082 │ 0.069 │ 0.095 │\n", + "│ X-Value │ -0.392 │ -0.419 │ -0.365 │\n", + "│ Y-Value │ 0.585 │ 0.557 │ 0.614 │\n", + "│ Amplitude │ 0.704 │ 0.676 │ 0.732 │\n", + "│ Displacement │ 123.804 │ 121.532 │ 126.017 │\n", + "│ Model Fit │ 0.929 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile\u001b[0m\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.016 │ 0.003 │ 0.03 │\n", + "│ X-Value │ -0.63 │ -0.664 │ -0.595 │\n", + "│ Y-Value │ 0.382 │ 0.331 │ 0.432 │\n", + "│ Amplitude │ 0.737 │ 0.708 │ 0.766 │\n", + "│ Displacement │ 148.747 │ 144.514 │ 153.013 │\n", + "│ Model Fit │ 0.956 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile\u001b[0m\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ -0.001 │ -0.019 │ 0.017 │\n", + "│ X-Value │ -0.494 │ -0.528 │ -0.458 │\n", + "│ Y-Value │ 0.567 │ 0.523 │ 0.609 │\n", + "│ Amplitude │ 0.752 │ 0.72 │ 0.784 │\n", + "│ Displacement │ 131.042 │ 127.782 │ 134.461 │\n", + "│ Model Fit │ 0.947 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ssm_res.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e8504273f20f6030", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-09T21:43:35.668664Z", + "start_time": "2024-07-09T21:43:34.832557Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = ssm_res.plot_curve(incl_fit=True)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "83645267c6b51ea1", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-15T14:42:05.992639Z", + "start_time": "2024-02-15T14:42:05.949054Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Statistical Basis:   Correlation Scores\n",
+       "Bootstrap Resamples: 2000\n",
+       "Confidence Level:    0.95\n",
+       "Listwise Deletion:   True\n",
+       "Scale Displacements: [0.0, 45.0, 90.0, 135.0, 180.0, 225.0, 270.0, 315.0]\n",
+       "\n",
+       "\n",
+       "                     Profile                     \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ -0.03    │ -0.043   │ -0.017   │\n",
+       "│ X-Value      │ -0.649   │ -0.682   │ -0.614   │\n",
+       "│ Y-Value      │ 0.421    │ 0.367    │ 0.471    │\n",
+       "│ Amplitude    │ 0.774    │ 0.744    │ 0.804    │\n",
+       "│ Displacement │ 147.051  │ 143.049  │ 151.4    │\n",
+       "│ Model Fit    │ 0.978    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                     Profile                     \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.002    │ -0.013   │ 0.016    │\n",
+       "│ X-Value      │ -0.575   │ -0.619   │ -0.525   │\n",
+       "│ Y-Value      │ 0.371    │ 0.318    │ 0.422    │\n",
+       "│ Amplitude    │ 0.684    │ 0.646    │ 0.722    │\n",
+       "│ Displacement │ 147.187  │ 142.359  │ 152.087  │\n",
+       "│ Model Fit    │ 0.983    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                     Profile                     \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.062    │ 0.05     │ 0.075    │\n",
+       "│ X-Value      │ -0.502   │ -0.543   │ -0.459   │\n",
+       "│ Y-Value      │ 0.463    │ 0.415    │ 0.512    │\n",
+       "│ Amplitude    │ 0.684    │ 0.648    │ 0.718    │\n",
+       "│ Displacement │ 137.315  │ 132.725  │ 141.726  │\n",
+       "│ Model Fit    │ 0.916    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                     Profile                     \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.033    │ 0.014    │ 0.052    │\n",
+       "│ X-Value      │ -0.493   │ -0.532   │ -0.45    │\n",
+       "│ Y-Value      │ 0.367    │ 0.315    │ 0.418    │\n",
+       "│ Amplitude    │ 0.615    │ 0.575    │ 0.653    │\n",
+       "│ Displacement │ 143.332  │ 138.393  │ 148.466  │\n",
+       "│ Model Fit    │ 0.912    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                     Profile                     \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.082    │ 0.069    │ 0.095    │\n",
+       "│ X-Value      │ -0.392   │ -0.419   │ -0.362   │\n",
+       "│ Y-Value      │ 0.585    │ 0.554    │ 0.614    │\n",
+       "│ Amplitude    │ 0.704    │ 0.676    │ 0.732    │\n",
+       "│ Displacement │ 123.804  │ 121.435  │ 126.105  │\n",
+       "│ Model Fit    │ 0.929    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                     Profile                     \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.016    │ 0.002    │ 0.029    │\n",
+       "│ X-Value      │ -0.63    │ -0.666   │ -0.596   │\n",
+       "│ Y-Value      │ 0.382    │ 0.33     │ 0.435    │\n",
+       "│ Amplitude    │ 0.737    │ 0.708    │ 0.766    │\n",
+       "│ Displacement │ 148.747  │ 144.483  │ 153.22   │\n",
+       "│ Model Fit    │ 0.956    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                     Profile                     \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ -0.001   │ -0.019   │ 0.016    │\n",
+       "│ X-Value      │ -0.494   │ -0.527   │ -0.458   │\n",
+       "│ Y-Value      │ 0.567    │ 0.524    │ 0.609    │\n",
+       "│ Amplitude    │ 0.752    │ 0.719    │ 0.785    │\n",
+       "│ Displacement │ 131.042  │ 127.857  │ 134.336  │\n",
+       "│ Model Fit    │ 0.947    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "
\n" + ], + "text/plain": [ + "Statistical Basis: Correlation Scores\n", + "Bootstrap Resamples: \u001b[1;36m2000\u001b[0m\n", + "Confidence Level: \u001b[1;36m0.95\u001b[0m\n", + "Listwise Deletion: \u001b[3;92mTrue\u001b[0m\n", + "Scale Displacements: \u001b[1m[\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m45.0\u001b[0m, \u001b[1;36m90.0\u001b[0m, \u001b[1;36m135.0\u001b[0m, \u001b[1;36m180.0\u001b[0m, \u001b[1;36m225.0\u001b[0m, \u001b[1;36m270.0\u001b[0m, \u001b[1;36m315.0\u001b[0m\u001b[1m]\u001b[0m\n", + "\n", + "\n", + "\u001b[3m Profile\u001b[0m\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ -0.03 │ -0.043 │ -0.017 │\n", + "│ X-Value │ -0.649 │ -0.682 │ -0.614 │\n", + "│ Y-Value │ 0.421 │ 0.367 │ 0.471 │\n", + "│ Amplitude │ 0.774 │ 0.744 │ 0.804 │\n", + "│ Displacement │ 147.051 │ 143.049 │ 151.4 │\n", + "│ Model Fit │ 0.978 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile\u001b[0m\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.002 │ -0.013 │ 0.016 │\n", + "│ X-Value │ -0.575 │ -0.619 │ -0.525 │\n", + "│ Y-Value │ 0.371 │ 0.318 │ 0.422 │\n", + "│ Amplitude │ 0.684 │ 0.646 │ 0.722 │\n", + "│ Displacement │ 147.187 │ 142.359 │ 152.087 │\n", + "│ Model Fit │ 0.983 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile\u001b[0m\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.062 │ 0.05 │ 0.075 │\n", + "│ X-Value │ -0.502 │ -0.543 │ -0.459 │\n", + "│ Y-Value │ 0.463 │ 0.415 │ 0.512 │\n", + "│ Amplitude │ 0.684 │ 0.648 │ 0.718 │\n", + "│ Displacement │ 137.315 │ 132.725 │ 141.726 │\n", + "│ Model Fit │ 0.916 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile\u001b[0m\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.033 │ 0.014 │ 0.052 │\n", + "│ X-Value │ -0.493 │ -0.532 │ -0.45 │\n", + "│ Y-Value │ 0.367 │ 0.315 │ 0.418 │\n", + "│ Amplitude │ 0.615 │ 0.575 │ 0.653 │\n", + "│ Displacement │ 143.332 │ 138.393 │ 148.466 │\n", + "│ Model Fit │ 0.912 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile\u001b[0m\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.082 │ 0.069 │ 0.095 │\n", + "│ X-Value │ -0.392 │ -0.419 │ -0.362 │\n", + "│ Y-Value │ 0.585 │ 0.554 │ 0.614 │\n", + "│ Amplitude │ 0.704 │ 0.676 │ 0.732 │\n", + "│ Displacement │ 123.804 │ 121.435 │ 126.105 │\n", + "│ Model Fit │ 0.929 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile\u001b[0m\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.016 │ 0.002 │ 0.029 │\n", + "│ X-Value │ -0.63 │ -0.666 │ -0.596 │\n", + "│ Y-Value │ 0.382 │ 0.33 │ 0.435 │\n", + "│ Amplitude │ 0.737 │ 0.708 │ 0.766 │\n", + "│ Displacement │ 148.747 │ 144.483 │ 153.22 │\n", + "│ Model Fit │ 0.956 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile\u001b[0m\u001b[3m \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ -0.001 │ -0.019 │ 0.016 │\n", + "│ X-Value │ -0.494 │ -0.527 │ -0.458 │\n", + "│ Y-Value │ 0.567 │ 0.524 │ 0.609 │\n", + "│ Amplitude │ 0.752 │ 0.719 │ 0.785 │\n", + "│ Displacement │ 131.042 │ 127.857 │ 134.336 │\n", + "│ Model Fit │ 0.947 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "test = circumplex.ssm_analyze(\n", + " data, scales, angles=octants(), measures=[\"loud\"], grouping=\"Language\"\n", + ")\n", + "test.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f155ed134be053e2", + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-25T18:35:34.264776Z", + "start_time": "2023-11-25T18:35:34.219547Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "Label", + "rawType": "object", + "type": "string" + }, + { + "name": "Group", + "rawType": "object", + "type": "string" + }, + { + "name": "Measure", + "rawType": "object", + "type": "string" + }, + { + "name": "e_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "e_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "e_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "x_est", + "rawType": "float64", + "type": "float" + }, + { + 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LabelGroupMeasuree_este_lcie_ucix_estx_lcix_uciy_esty_lciy_ucia_esta_lcia_ucid_estd_lcid_ucifit_est
0loud: deudeuloud-0.030-0.043-0.017-0.649-0.682-0.6140.4210.3670.4710.7740.7440.804147.051143.049151.4000.978
1loud: engengloud0.002-0.0130.016-0.575-0.619-0.5250.3710.3180.4220.6840.6460.722147.187142.359152.0870.983
2loud: hrvhrvloud0.0620.0500.075-0.502-0.543-0.4590.4630.4150.5120.6840.6480.718137.315132.725141.7260.916
3loud: itaitaloud0.0330.0140.052-0.493-0.532-0.4500.3670.3150.4180.6150.5750.653143.332138.393148.4660.912
4loud: porporloud0.0820.0690.095-0.392-0.419-0.3620.5850.5540.6140.7040.6760.732123.804121.435126.1050.929
5loud: swesweloud0.0160.0020.029-0.630-0.666-0.5960.3820.3300.4350.7370.7080.766148.747144.483153.2200.956
6loud: turturloud-0.001-0.0190.016-0.494-0.527-0.4580.5670.5240.6090.7520.7190.785131.042127.857134.3360.947
\n", + "
" + ], + "text/plain": [ + " Label Group Measure e_est e_lci e_uci x_est x_lci x_uci y_est \\\n", + "0 loud: deu deu loud -0.030 -0.043 -0.017 -0.649 -0.682 -0.614 0.421 \n", + "1 loud: eng eng loud 0.002 -0.013 0.016 -0.575 -0.619 -0.525 0.371 \n", + "2 loud: hrv hrv loud 0.062 0.050 0.075 -0.502 -0.543 -0.459 0.463 \n", + "3 loud: ita ita loud 0.033 0.014 0.052 -0.493 -0.532 -0.450 0.367 \n", + "4 loud: por por loud 0.082 0.069 0.095 -0.392 -0.419 -0.362 0.585 \n", + "5 loud: swe swe loud 0.016 0.002 0.029 -0.630 -0.666 -0.596 0.382 \n", + "6 loud: tur tur loud -0.001 -0.019 0.016 -0.494 -0.527 -0.458 0.567 \n", + "\n", + " y_lci y_uci a_est a_lci a_uci d_est d_lci d_uci fit_est \n", + "0 0.367 0.471 0.774 0.744 0.804 147.051 143.049 151.400 0.978 \n", + "1 0.318 0.422 0.684 0.646 0.722 147.187 142.359 152.087 0.983 \n", + "2 0.415 0.512 0.684 0.648 0.718 137.315 132.725 141.726 0.916 \n", + "3 0.315 0.418 0.615 0.575 0.653 143.332 138.393 148.466 0.912 \n", + "4 0.554 0.614 0.704 0.676 0.732 123.804 121.435 126.105 0.929 \n", + "5 0.330 0.435 0.737 0.708 0.766 148.747 144.483 153.220 0.956 \n", + "6 0.524 0.609 0.752 0.719 0.785 131.042 127.857 134.336 0.947 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test.results.round(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8632928f988b2c91", + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-25T18:35:34.885920Z", + "start_time": "2023-11-25T18:35:34.222066Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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LabelGroupMeasuree_este_lcie_ucix_estx_lcix_uciy_esty_lciy_ucia_esta_lcia_ucid_estd_lcid_ucifit_est
0loud: deudeuloud-0.030-0.043-0.018-0.649-0.683-0.6140.4210.3660.4710.7740.7430.805147.051143.151151.2550.978
1loud: engengloud0.002-0.0120.016-0.575-0.619-0.5300.3710.3200.4220.6840.6470.722147.187142.638151.8060.983
2loud: hrvhrvloud0.0620.0490.075-0.502-0.545-0.4590.4630.4170.5080.6840.6470.718137.315132.946141.5610.916
3loud: itaitaloud0.0330.0140.053-0.493-0.533-0.4540.3670.3150.4180.6150.5770.654143.332138.420148.3710.912
4loud: porporloud0.0820.0690.095-0.392-0.419-0.3650.5850.5570.6140.7040.6760.732123.804121.532126.0170.929
5loud: swesweloud0.0160.0030.030-0.630-0.664-0.5950.3820.3310.4320.7370.7080.766148.747144.514153.0130.956
6loud: turturloud-0.001-0.0190.017-0.494-0.528-0.4580.5670.5230.6090.7520.7200.784131.042127.782134.4610.947
\n", + "
" + ], + "text/plain": [ + " Label Group Measure e_est e_lci e_uci x_est x_lci x_uci y_est \\\n", + "0 loud: deu deu loud -0.030 -0.043 -0.018 -0.649 -0.683 -0.614 0.421 \n", + "1 loud: eng eng loud 0.002 -0.012 0.016 -0.575 -0.619 -0.530 0.371 \n", + "2 loud: hrv hrv loud 0.062 0.049 0.075 -0.502 -0.545 -0.459 0.463 \n", + "3 loud: ita ita loud 0.033 0.014 0.053 -0.493 -0.533 -0.454 0.367 \n", + "4 loud: por por loud 0.082 0.069 0.095 -0.392 -0.419 -0.365 0.585 \n", + "5 loud: swe swe loud 0.016 0.003 0.030 -0.630 -0.664 -0.595 0.382 \n", + "6 loud: tur tur loud -0.001 -0.019 0.017 -0.494 -0.528 -0.458 0.567 \n", + "\n", + " y_lci y_uci a_est a_lci a_uci d_est d_lci d_uci fit_est \n", + "0 0.366 0.471 0.774 0.743 0.805 147.051 143.151 151.255 0.978 \n", + "1 0.320 0.422 0.684 0.647 0.722 147.187 142.638 151.806 0.983 \n", + "2 0.417 0.508 0.684 0.647 0.718 137.315 132.946 141.561 0.916 \n", + "3 0.315 0.418 0.615 0.577 0.654 143.332 138.420 148.371 0.912 \n", + "4 0.557 0.614 0.704 0.676 0.732 123.804 121.532 126.017 0.929 \n", + "5 0.331 0.432 0.737 0.708 0.766 148.747 144.514 153.013 0.956 \n", + "6 0.523 0.609 0.752 0.720 0.784 131.042 127.782 134.461 0.947 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ssm_res.results.round(3)" + ] + }, + { + "cell_type": "markdown", + "id": "a33ca93dde25b889", + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-25T18:35:35.119549Z", + "start_time": "2023-11-25T18:35:34.900761Z" + }, + "collapsed": false + }, + "source": [ + "lang_angles = {\n", + " \"arb\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"cmn\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"deu\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"ell\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"eng\": (0, 46, 93, 138, 182, 228, 272, 340),\n", + " \"fra\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"hrv\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"ind\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"ita\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"jpn\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"kor\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"nld\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"por\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"spa\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"swe\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"tur\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"vie\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + " \"zsm\": (0, 65, 97, 131, 182, 255, 281, 335),\n", + "}\n", + "\n", + "# Updated to use ssm_analyze instead of ssm_analyse\n", + "corr_res = circumplex.ssm_analyze(\n", + " data, scales, measures=[\"loud\"], grouping=\"Language\", grouped_angles=lang_angles\n", + ")\n", + "corr_res.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "bf9f01e61ec25305", + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-25T18:35:36.030123Z", + "start_time": "2023-11-25T18:35:35.906154Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Statistical Basis:   Mean Scores\n",
+       "Bootstrap Resamples: 2000\n",
+       "Confidence Level:    0.95\n",
+       "Listwise Deletion:   True\n",
+       "Scale Displacements: [0.0, 65.0, 97.0, 131.0, 182.0, 255.00000000000003, 281.0, 335.0]\n",
+       "\n",
+       "\n",
+       "                  Profile[All]                   \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 45.041   │ 44.485   │ 45.596   │\n",
+       "│ X-Value      │ 26.004   │ 24.778   │ 27.186   │\n",
+       "│ Y-Value      │ 19.611   │ 18.378   │ 20.826   │\n",
+       "│ Amplitude    │ 32.57    │ 31.542   │ 33.661   │\n",
+       "│ Displacement │ 37.021   │ 34.702   │ 39.371   │\n",
+       "│ Model Fit    │ 0.934    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "
\n" + ], + "text/plain": [ + "Statistical Basis: Mean Scores\n", + "Bootstrap Resamples: \u001b[1;36m2000\u001b[0m\n", + "Confidence Level: \u001b[1;36m0.95\u001b[0m\n", + "Listwise Deletion: \u001b[3;92mTrue\u001b[0m\n", + "Scale Displacements: \u001b[1m[\u001b[0m\u001b[1;36m0.0\u001b[0m, \u001b[1;36m65.0\u001b[0m, \u001b[1;36m97.0\u001b[0m, \u001b[1;36m131.0\u001b[0m, \u001b[1;36m182.0\u001b[0m, \u001b[1;36m255.00000000000003\u001b[0m, \u001b[1;36m281.0\u001b[0m, \u001b[1;36m335.0\u001b[0m\u001b[1m]\u001b[0m\n", + "\n", + "\n", + "\u001b[3m Profile[All] \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 45.041 │ 44.485 │ 45.596 │\n", + "│ X-Value │ 26.004 │ 24.778 │ 27.186 │\n", + "│ Y-Value │ 19.611 │ 18.378 │ 20.826 │\n", + "│ Amplitude │ 32.57 │ 31.542 │ 33.661 │\n", + "│ Displacement │ 37.021 │ 34.702 │ 39.371 │\n", + "│ Model Fit │ 0.934 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "lang_data = data[data[\"Language\"] == \"deu\"]\n", + "rec_data = data[data[\"Recording\"] == \"CG01\"]\n", + "angles = [0, 65, 97, 131, 182, 255, 281, 335]\n", + "new_ang_results = circumplex.ssm_analyze(rec_data, scales, angles=angles)\n", + "\n", + "new_ang_results.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c2b3a77b1350c969", + "metadata": { + "ExecuteTime": { + "end_time": "2023-11-25T18:35:36.030606Z", + "start_time": "2023-11-25T18:35:35.916368Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Loading Example Data: jz2017\n", + "\n", + "We'll use the `jz2017` dataset from Zimmermann & Wright (2017). This dataset includes self-report data from 1166 undergraduate students who completed:\n", + "\n", + "- A circumplex measure of interpersonal problems (IIP-SC) with eight subscales: PA, BC, DE, FG, HI, JK, LM, NO\n", + "- A measure of personality disorder symptoms with ten subscales: PARPD, SCZPD, SZTPD, ASPD, BORPD, HISPD, NARPD, AVPD, DPNPD, OCPD" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "Gender", + "rawType": "object", + "type": "string" + }, + { + "name": "PA", + "rawType": "float64", + "type": "float" + }, + { + "name": "BC", + "rawType": "float64", + "type": "float" + }, + { + "name": "DE", + "rawType": "float64", + "type": "float" + }, + { + "name": "FG", + "rawType": "float64", + "type": "float" + }, + { + "name": "HI", + "rawType": "float64", + "type": "float" + }, + { + "name": "JK", + "rawType": "float64", + "type": "float" + }, + { + "name": "LM", + "rawType": "float64", + "type": "float" + }, + { + "name": "NO", + "rawType": "float64", + "type": "float" + }, + { + "name": "PARPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "SCZPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "SZTPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "ASPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "BORPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "HISPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "NARPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "AVPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "DPNPD", + "rawType": "int64", + "type": "integer" + }, + { + "name": "OCPD", + "rawType": "int64", + "type": "integer" + } + ], + "ref": "16eaf014-04a9-404b-ba2f-e7ae9fd16d7c", + "rows": [ + [ + "0", + "Female", + "1.5", + "1.5", + "1.25", + "1.0", + "2.0", + "2.5", + "2.25", + "2.5", + "4", + "3", + "7", + "7", + "8", + "4", + "6", + "3", + "4", + "6" + ], + [ + "1", + "Female", + "0.0", + "0.25", + "0.0", + "0.25", + "1.25", + "1.75", + "2.25", + "2.25", + "1", + "0", + "2", + "0", + "1", + "2", + "3", + "0", + "1", + "0" + ], + [ + "2", + "Female", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0", + "1", + "0", + "4", + "1", + "5", + "4", + "0", + "0", + "1" + ], + [ + "3", + "Male", + "2.0", + "1.75", + "1.75", + "2.5", + "2.0", + "1.75", + "2.0", + "2.5", + "1", + "0", + "0", + "0", + "1", + "0", + "0", + "0", + "0", + "0" + ], + [ + "4", + "Female", + "0.25", + "0.5", + "0.25", + "0.0", + "0.0", + "0.0", + "0.0", + "0.0", + "0", + "0", + "0", + "0", + "1", + "0", + "0", + "1", + "0", + "0" + ] + ], + "shape": { + "columns": 19, + "rows": 5 + } + }, + "text/html": [ + "
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GenderPABCDEFGHIJKLMNOPARPDSCZPDSZTPDASPDBORPDHISPDNARPDAVPDDPNPDOCPD
0Female1.501.501.251.002.002.502.252.504377846346
1Female0.000.250.000.251.251.752.252.251020123010
2Female0.000.000.000.000.000.000.000.000104154001
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" + ], + "text/plain": [ + " Gender PA BC DE FG HI JK LM NO PARPD SCZPD \\\n", + "0 Female 1.50 1.50 1.25 1.00 2.00 2.50 2.25 2.50 4 3 \n", + "1 Female 0.00 0.25 0.00 0.25 1.25 1.75 2.25 2.25 1 0 \n", + "2 Female 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 1 \n", + "3 Male 2.00 1.75 1.75 2.50 2.00 1.75 2.00 2.50 1 0 \n", + "4 Female 0.25 0.50 0.25 0.00 0.00 0.00 0.00 0.00 0 0 \n", + "\n", + " SZTPD ASPD BORPD HISPD NARPD AVPD DPNPD OCPD \n", + "0 7 7 8 4 6 3 4 6 \n", + "1 2 0 1 2 3 0 1 0 \n", + "2 0 4 1 5 4 0 0 1 \n", + "3 0 0 1 0 0 0 0 0 \n", + "4 0 0 1 0 0 1 0 0 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "jz2017 = load_dataset(\"jz2017\")\n", + "jz2017.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Mean-Based SSM Analysis\n", + "\n", + "### Single Group Analysis\n", + "\n", + "Let's analyze the interpersonal problems of the average individual in the dataset using `ssm_analyze()`. The function returns an SSM object with results, scores, and analysis details." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Statistical Basis:   Mean Scores\n",
+       "Bootstrap Resamples: 2000\n",
+       "Confidence Level:    0.95\n",
+       "Listwise Deletion:   True\n",
+       "Scale Displacements: [90.0, 135.0, 180.0, 225.0, 270.0, 315.0, 360.0, 45.0]\n",
+       "\n",
+       "\n",
+       "                  Profile[All]                   \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.917    │ 0.887    │ 0.946    │\n",
+       "│ X-Value      │ 0.351    │ 0.325    │ 0.379    │\n",
+       "│ Y-Value      │ -0.252   │ -0.283   │ -0.222   │\n",
+       "│ Amplitude    │ 0.432    │ 0.403    │ 0.464    │\n",
+       "│ Displacement │ 324.292  │ 320.937  │ 327.7    │\n",
+       "│ Model Fit    │ 0.878    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "
\n" + ], + "text/plain": [ + "Statistical Basis: Mean Scores\n", + "Bootstrap Resamples: \u001b[1;36m2000\u001b[0m\n", + "Confidence Level: \u001b[1;36m0.95\u001b[0m\n", + "Listwise Deletion: \u001b[3;92mTrue\u001b[0m\n", + "Scale Displacements: \u001b[1m[\u001b[0m\u001b[1;36m90.0\u001b[0m, \u001b[1;36m135.0\u001b[0m, \u001b[1;36m180.0\u001b[0m, \u001b[1;36m225.0\u001b[0m, \u001b[1;36m270.0\u001b[0m, \u001b[1;36m315.0\u001b[0m, \u001b[1;36m360.0\u001b[0m, \u001b[1;36m45.0\u001b[0m\u001b[1m]\u001b[0m\n", + "\n", + "\n", + "\u001b[3m Profile[All] \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.917 │ 0.887 │ 0.946 │\n", + "│ X-Value │ 0.351 │ 0.325 │ 0.379 │\n", + "│ Y-Value │ -0.252 │ -0.283 │ -0.222 │\n", + "│ Amplitude │ 0.432 │ 0.403 │ 0.464 │\n", + "│ Displacement │ 324.292 │ 320.937 │ 327.7 │\n", + "│ Model Fit │ 0.878 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define circumplex scales and their angular positions\n", + "scales = [\"PA\", \"BC\", \"DE\", \"FG\", \"HI\", \"JK\", \"LM\", \"NO\"]\n", + "angles = [90, 135, 180, 225, 270, 315, 360, 45]\n", + "\n", + "# Run SSM analysis\n", + "results = ssm_analyze(data=jz2017, scales=scales, angles=angles)\n", + "\n", + "# Display summary\n", + "results.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Accessing Results\n", + "\n", + "The SSM object provides easy access to results, scores, and analysis details:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "Label", + "rawType": "object", + "type": "string" + }, + { + "name": "Group", + "rawType": "object", + "type": "string" + }, + { + "name": "Measure", + "rawType": "object", + "type": "unknown" + }, + { + "name": "e_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "e_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "e_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "x_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "x_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "x_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "y_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "y_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "y_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "a_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "a_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "a_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "d_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "d_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "d_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "fit_est", + "rawType": "float64", + "type": "float" + } + ], + "ref": "22d14d0a-83c2-48a2-9c72-31ddec395a1d", + "rows": [ + [ + "0", + "All", + "All", + null, + "0.9168096054888508", + "0.8868963068181818", + "0.9458130092195539", + "0.3510608932309254", + "0.32460614165810453", + "0.378622951857467", + "-0.2523391128831408", + "-0.282768915130183", + "-0.2222968679265911", + "0.4323410443697726", + "0.4027535526363061", + "0.4637861621161283", + "324.291791715271", + "320.9370971905794", + "327.6999403209235", + "0.8776189329590804" + ] + ], + "shape": { + "columns": 19, + "rows": 1 + } + }, + "text/html": [ + "
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LabelGroupMeasuree_este_lcie_ucix_estx_lcix_uciy_esty_lciy_ucia_esta_lcia_ucid_estd_lcid_ucifit_est
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" + ], + "text/plain": [ + " Label Group Measure e_est e_lci e_uci x_est x_lci \\\n", + "0 All All None 0.91681 0.886896 0.945813 0.351061 0.324606 \n", + "\n", + " x_uci y_est y_lci y_uci a_est a_lci a_uci \\\n", + "0 0.378623 -0.252339 -0.282769 -0.222297 0.432341 0.402754 0.463786 \n", + "\n", + " d_est d_lci d_uci fit_est \n", + "0 324.291792 320.937097 327.69994 0.877619 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# View results DataFrame with parameter estimates and confidence intervals\n", + "results.results" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "Label", + "rawType": "object", + "type": "string" + }, + { + "name": "PA", + "rawType": "float64", + "type": "float" + }, + { + "name": "BC", + "rawType": "float64", + "type": "float" + }, + { + "name": "DE", + "rawType": "float64", + "type": "float" + }, + { + "name": "FG", + "rawType": "float64", + "type": "float" + }, + { + "name": "HI", + "rawType": "float64", + "type": "float" + }, + { + "name": "JK", + "rawType": "float64", + "type": "float" + }, + { + "name": "LM", + "rawType": "float64", + "type": "float" + }, + { + "name": "NO", + "rawType": "float64", + "type": "float" + } + ], + "ref": "d5927066-f8ce-4738-a03d-7f8cd9ceed0c", + "rows": [ + [ + "0", + "All", + "0.5501715265866209", + "0.5834048027444254", + "0.6239279588336192", + "0.7645797598627787", + "1.2109777015437393", + "1.2144082332761579", + "1.4843481989708405", + "0.9026586620926244" + ] + ], + "shape": { + "columns": 9, + "rows": 1 + } + }, + "text/html": [ + "
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" + ], + "text/plain": [ + " Label PA BC DE FG HI JK LM \\\n", + "0 All 0.550172 0.583405 0.623928 0.76458 1.210978 1.214408 1.484348 \n", + "\n", + " NO \n", + "0 0.902659 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# View scores DataFrame\n", + "results.scores" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Analysis type: mean\n", + "Angles: [90.0, 135.0, 180.0, 225.0, 270.0, 315.0, 360.0, 45.0]\n", + "Bootstrap resamples: 2000\n", + "Confidence level: 0.95\n" + ] + } + ], + "source": [ + "# View analysis details\n", + "print(f\"Analysis type: {results.type}\")\n", + "print(f\"Angles: {results.details.angles}\")\n", + "print(f\"Bootstrap resamples: {results.details.boots}\")\n", + "print(f\"Confidence level: {results.details.interval}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Visualizing SSM Results\n", + "\n", + "The circumplex package provides three visualization functions:\n", + "\n", + "1. **Circle Plot**: Shows amplitude and displacement on a circular plot\n", + "2. **Curve Plot**: Shows fitted cosine curves overlaid on observed scores \n", + "3. **Contrast Plot**: Shows parameter differences between groups (for contrast analyses)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Circle Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AuW/cM+BkgVwITvWocpMd7kRd+GKTlucoRKhI0AuRIjiKRjS4xlCIBVcmkthdxCcGGWGNGOLYPNdrXgxxl0+UkgiYneBI8i/lC0nCpKETArqqRL/6kn0/cunUqZONGXcQ74yod79PWASvbNh0IPYbCs8CAeL+L8pd8gJEFALFJ/C6z5gxw5b6dF1Ds5tTOTgZohNtVRBCwiYu95mxQeWe1MYTSx4D/zelWUnc5JXk/azLGEwSThLYQJEMzIaa17e+9a0Lfu///J//Y0/Tik3anqMQoaFOsUKkDMJWqPuMOKgquQ7RT61mwiDwOhOLjAjj2ByhjRcfY14IzxvXQ11qOsUixEggxNuGN43NRTbEjVMxg58Ts058L9eF15IQBWqWc/R/1VVX1ekaqDzyjW98w1ZE4ZiexFn+LjXFEeXU+F+0aJHp2bNn5t9w/zi+p+Y4nWG5Bk4TCIXh1OKb3/ymDQGoqnpIXUOl8Fwj5vh/uU947Ymlb0j30CTheVOt6B/+4R+sN5WqJK4zKqEbJH9SWQcvdVUwDmfOnGmfN8+K1/Tp063Iry5ZN9+mktMCwnfwjjPOef5cE6crdHklhKS6vI2GUp8xmDT0EuD0gMRarpln5TpK8z44Kfva175WsutJw3MUIlQuonZl0hchhEgPJFd+6UtfynytJUIIIYRIN/LQCyGEEEII4TES9EIIIYQQQniMBL0QQgghhBAeoxh6IYQQQgghPEYeeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGgl4IIYQQQgiPkaAXQgghhBDCYyTohRBCCCGE8JiLk74AIYQQ9eP48ePmlVdeMWvXrjUnTpwwPXv2NDfddJPp2LGj/XlFRYV54YUXzObNm03jxo3NiBEjzA033GA/B/7NjBkzzPbt283AgQPNrbfeaho1kp9HCCF846Jz586dS/oihBBC1J3HH3/cfPDBB+auu+4yTZs2teJ+5cqV5g/+4A9MkyZNzHe+8x1TXl5ubr75Ziv+f/azn5k+ffpY0Q+vv/66FfBXXnmlef75502vXr3M6NGjk35bQggh6ohcMUII4SGnTp0ya9asMddcc4255JJLzMUXX2yuu+46c+TIEbNq1Srrld+3b5/1yDdr1sy0bdvWXHvttWbRokV2EwBnz541+HSyPwohhPAPCXohhPAURHj2Iav7fOfOnWbXrl2mXbt2Vuw7unfvbk6fPm32799vv77sssvM1q1bzb/8y79YMX/ppZcm8C6EEEI0FMXQCyGEhxBi069fP/PGG2+YO++8MxNyg6g/efKkfTVv3rzSv3Ff8zNA7H/84x9P5PqFEEIUDgl6IYTwFGLnX375ZfPd737XCvmxY8fahFiEOmE2JL1m477mZ0IIIcJBgl4IITylZcuW5o477sh8fezYMfPOO+9Yzz3JrgcPHjTvv/9+Juxmx44dNlm2Q4cOCV61EEKIQqMYeiGE8BSSXhHxcODAAfPMM8+Yvn37WkHfu3dv662fNWuWDbGhhCVVbcaMGWMTaIUQQoSDylYKIYSnULEGkU5JSrzw1Jmnko0T7IcOHTIvvvii2bRpk/0eP7/++usl6IUQIjAk6IUQQgghhPAYhdwIIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMapdJoQQHkOhsg8++MC++DzfCy666KK8L0pY8uJzIYQQfiJBL4QQCYIQP3LkiDl69Kj9SE35U6dO2WZQfHSv7K9zf9bQ6sOI+aZNm2ZezZo1y/t59tctWrQwZWVlplWrVvajatsLIURyqA69EEIUgdOnT1cS6u7F19nfO3HiRFH+/2wv/NmzZ+3HbI99oWnevLkV9tki333M/rxJkyZF+f+FECJmJOiFEKIB3vUDBw6Y/fv3X/B6//33a/13GjdunBG+dHytjbc89/PssJnc8Jn33nvPTJw4MfO1W/ZduE5V3v/qTgV4f25TcubMmVq/V95fhw4dLni1b99eXn4hhKgnWj2FEKIaEL0VFRV5RfuhQ4eq/bcI1Np4rfFulzKGPVv4u01BQ+4Ppwy5pxHu8+zvsXlgI8Br69atF/yttm3b5hX7bdq0UYy/EEJUgwS9EEL8FjzNe/bsMTt37jQ7duywH/kaIVoViOGOHTteIEIRp6UW6knA+yOenlfnzp1rFP5sgtgM7du3L3O6wed4/fkZr/Xr11+wMeJvd+3a1XTr1s1+5GtONoQQQijkRggRKfnE++7du/OGjzRq1MiGhOTzHrds2TL1oj035CZtYIaOHTuW9xQE0U8OQC6I+S5dukjkCyGEBL0QIgZY5hDrCPeaxDtedcRh9qtdu3ZW1PtK2gV9dSDmDx48aJ+Ze/EMiemvSeTz4uu0b7iEEKKhSNALIYIV8Js2bbKvzZs3560mky3enZcX8R6aAPRZ0Ff1fJ3Idxs0XlU94969e5s+ffrYlwS+ECJEJOiFEN7DMkb4jBPwvHLFHbHu3bt3D168xyDoayvyt2/fbmPzsyHWP1vgE6YTwxgQQoSNBL0QwnsBjweehky5Ar5Xr14Z4YaA9zlspiHEIOirCtdB2LtxsmXLlrwCn/HhRL4EvhDCRyTohRBegMd93bp1Zs2aNbYKSm6ddxoW5Qp4JUjGLehzIWciV+DTACy3Tn7//v3NoEGDzIABA2zIjhBCpB0JeiFEaiGEAgG/evVq64XPrnbiBDye1b59+0rAV4MEfdUCn/Acd8qTK/A50WFziLgfPHiwLUUqhBBpRIJeCJEaWI4QWAh4XoTVZEO9d8QVrx49ekjA1xIJ+toL/G3bttlNJC/q42dDOA7Cnhd5GArNEUKkBQl6IUSi4BHdsGGDFfBr1661nUUdCCa88AgoRDx130XdkaCvH9TBZ1wi7vHeZ5tLuvy6zWW/fv3siZEQQiSFBL0QouTQeRXxvnTpUvsxuxMryazELiPi+UhMs2gYEvQNh5wNcjgQ+HzMTq6lk+3AgQPNyJEj7Ue+FkKIUiJBL4QoCSw1xCkvWbLErFixolJjoDZt2mTilIlZVihNYZGgLyxsQBnLzntfUVGR+RlJtMOGDbPinvwOheUIIUqBBL0QoqgQB4+Ixxt/+PDhzPfLysqs6BkxYoQpLy+X8CkiEvTFAxO6a9cus2zZMjvGjxw5kvlZ69at7RgfNWqUjb8XQohiIUEvhCg4CHfEDS86tjqaNWuW8V7iiZeILw0S9KWBKkzuFGrlypWVTqHoUIuwZwOL0BdCiEIiQS+EKAiIF0JpEPEbN26sVPqPcBpEPB8VX1x6JOiTSfYmPwRxz8fskquUWWU+sLllkyuEEA1Fgl4I0SAIN5g3b54V8tk1vKlOg2gZPny47cYpkkOCPlnoYrx8+XI7R6iW46AyDnNkwoQJNuxMCCHqiwS9EKJeSYF44+fPn2+2bt2a+T5lJS+99FIrUtSEJz1I0KeHQ4cOWWH/m9/8xpbFdPTs2dMK+6FDh+oUSwhRZyTohRB1EiOI+EWLFtkyfi6kBhEyfvx4VfVIKRL06a36xHwi3t6F5FCmdezYsWbcuHHaFAshao0EvRCiWlgiqLuN8KBEX3aVGkQH4oPPRXqRoE83VMZZuHChWbBgQaUqOeSc4LXv37+/NspCiGqRoBdC5AUPPJ54RMbBgwcz36crJt54asbjnRfpR4LeD/DSU9uezTPdkx3t2rWzc2706NFqtCaEyIsEvRCiEsT1vv3227Y6x5kzZzLNchATiAri5IVfSND7x759+6ywX7x4cab8JQ3XKH05ZcoUzUMhRCUk6IUQlp07d1ohT7KrWxa6du1qj/ypnU1FDuEnEvT+curUKdu0ikpSVJQCwm8oeXnFFVeoOo4QwiJBL0TEMP0pozd79mwbJ58du4sXkMobit31Hwn6MOYqFaWYq9S1dwwYMMAKe8rEaq4KES8S9EJECNOeBFc88q7sJGIATzxCnq6WIhwk6MOC7ssIe2rbOxPO5hthP3DgQAl7ISJEgl6IyJLuOL5HyO/ZsycTl0t8PEKe5DsRHhL0YXLgwAHzzjvv2Dh7l+/SuXNnK+xp6KakdSHiQYJeiEgaQVGxBuNPLXlo2rSpTXKdPHmyyk4GjgR92FDqcs6cOTaJlph7YHN++eWX2826GlUJET4S9EIEDF476lu/9dZb5ujRo/Z7lL2bNGmSTXZt0aJF0pcoSoAEfRwcP37cJs/OnTs30/itVatW5qqrrrL9IjiNE0KEiQS9EAHCtCa+9rXXXsvUkG/durX12GHYVbEmLiTo4wIvvTuRO3z4sP1e+/btzbXXXmtDcRRjL0R4SNALERBMZxrSvPLKK5kSdy1btrQeOrq6ykMXJxL08Z7Q0RiOE7pjx45lStFOmzbNNoiTsBciHCTohQiE7du3WyG/adOmTIw8HvnLLrvMfi7iRYI+bmhMRYw9HnsXY9+3b18r7Lt375705QkhCoAEvRABdJQktGblypX2a7zwxMdT6QLvvBAS9ALw0v/617+2ybOuKs7QoUPN1KlTTceOHZO+PCFEA5CgF8JTiI194403bMk6pjHH55deeqm5+uqrTdu2bZO+PJEiJOhFNlS6Yu34zW9+Y79m7RgzZoxdO8i1EUL4hwS9EJ5x4sQJ62VDpFGOEgYPHmy9bNSgFiIXCXqRD3pRcLq3evVq+zXlLRknV155pWnevHnSlyeEqAMS9EJ4AlN1yZIl5uWXX84kuNHu/brrrrNdIoWoCgl6UR1btmwxr776qv0IhOpNnz7djBo1SomzQniCBL0QHkDFmhdffNFs3brVft2hQwdz/fXXq827qBUS9KImkAJr1641s2bNMvv37884DG666SZTXl6e9OUJIWpAgl6IlIfXvP7667ZZDFOV+vGUoKRyjUpQitoiQS9qC2F8VMSh1OXp06etw4Ake2rYKwxHiPQiQS9ECmFakrBGGUoXXkNDGLzySloTdUWCXtSViooK661fsWJFJgyH8D4S73UqKET6kKAXImXs3LnThtds27bNfk05OY69aQQjRH2QoBf1hUZ1M2fOtOVxgXydm2++WWE4QqQMCXohUsLx48dtxQk6O7rwGsrITZ48WeE1okFI0IuGQM16wnDefPPNTBjO+PHjbRhOixYtkr48IYQEvRDJwxSkljzhNe+//7793ogRI2yVCYXXiEIgQS8K1fuCMJzly5fbry+55BIbhjN69GiF4QiRMBL0QiTc4OWXv/yl2bhxo/26U6dONryGtuxCFAoJelFIWK8IC3RhOIQD3nrrrWpoJ0SCSNALkQBMO0JrqCl/6tQp29CF4+tJkyYpvEYUHAl6UYwwnLlz59oqXFTGadq0qT1VHDdunLz1QiSABL0QCXvlqfV822232dryQhQDCXpRLKhZ/+yzz2Z6ZMhbL0QySNALkaBXftq0adYrL4+WKCYS9KKYnD171o4xus3KWy9EMkjQC1EC5JUXSSJBL0qBvPVCJIcEvRBFRF55kQYk6EWpkLdeiGSQoBeiSMgrL9KCBL0oNfLWC1FaJOiFKDBMqUWLFpmXXnpJXnmRCiToRVq89TfccIMZM2aM1kIhCowEvRAF5OTJk+a5557LNF6RV16kAQl6kSZvPY3zbrnlFtOsWbOkL02IYJCgF6JA7Nixwzz11FPm4MGDplGjRmbq1Knm8ssvlydKJI4EvUiDt/7dd9+13npkR7t27cw999xjunXrlvSlCREEEvRCNBCmEA1WSHzFaLVp08Yaqh49eiR9aUJYJOhFWsBL//TTT5uKigrr+CBhVuGIQjQcCXohGsD7779vE19Xr15tvx4yZIgNsWnRokXSlyZEBgl6kSaOHz9u181Vq1bZrwcPHmxuv/12rZtCNAAJeiHqyZYtW6yn6fDhw6Zx48bm+uuvNxMmTJCnSaQOCXqRNpAe8+bNM7NmzTJnzpwxrVu3NnfffbfNOxJC1B0JeiHqCFNm9uzZ5vXXX7eft2/f3obYdO3aNelLEwFBVRCSrHnxOaKHj+6V+zXhXlWxefNm07t377w/I+yBSkzuxeY039ckMPLicyEKxc6dO23u0YEDB6wz5NprrzVXXHGFHCNC1BEJeiHqwNGjR82MGTPMhg0b7NcjR440H/nIR1StQdQahDghBwj1EydO2Jf7HGHuQEg3b97clvrLFtj5RDcvhHl9PPRsBLI3Bvk2C7wowco18jMH/y/XyPjno/uc0AmuUYjawPh/4YUXzNKlSzM16++8807TqlWrpC9NCG+QoBeiliDin3nmGXPs2DHTpEkTc9NNN5nRo0fLkyQugGUV0c4GkPHCR74GhDeCN1sAu88RwcUYT8UIueE9Iu5zNyW8eK/uxID3ijBr2bKl/cjXmjMi33havHixefHFF+0GkvFy1113WXEvhKgZCXohaoApMmfOHFvFhs87d+5sQ2w6deqU9KWJhGE8INjpCnzkyBH7OSIXwXrJJZdYUZIGIZtUDH3uxoYXieR8n80L96asrMx2D+VzCX2xd+9eG4KzZ88eOx6ogjN58mSNDSFqQIJeiGrAU/T888+b3/zmN/ZrPPI333yz9dCLeMU7Jff4HBCilColqY/P0xhqksakWDY+3EOSyt395B6z+eF+SuTHy+nTp62nHo+9W3cJbVT+hhBVI0EvRBXgcX3yySfNtm3brKigZTmiSAIjDvAkk6iXT7z7JjbTKOhru2nKFvkkoHPyIeLp70EVHD6nr8e9995rT3SEEBciQS9EFV1fH3/8cSvqiW0mxKZ///5JX5YosoCnRT0v4sEJkUFA+ibefRb0NYl8NliE8JB30KFDB/uSwA+b9evX2xAccjMQ8/fff7+6ywqRBwl6IXKg0gJNTwi36dixozUgCAcRFghDxPu+ffsyAt6JRDZxIeGzoM8H4s5tvpzAZ67y7NScKDx4zjhYmKuE3dC8jwpjQogPkaAX4rdQleO1114zb7/9tv164MCBttGJSlKGARs0Eu5ItkMQhizgQxf0NQl8nifJ6ySuK+46nGdMlbG1a9far6lVP3XqVK9PzoQoJBL0Qvy2DjLGYs2aNfbrKVOmWGNRXW1v4YcI2L17txXyLHUIvC5dukTnxQ1d0OeCqHfPHcHnnnvoG7fYnC6DBg2ypS3ldBFCgl4IG5f7s5/9TMe5AcByRtUUxNzBgwetoUfIxe6pjU3Q5zuZYUywcW/Xrp0dE1QlknfXT5YsWWLDIqmUxNwmLJJ8FyFiRoJeRM3GjRttJRuXcHXfffeZ7t27J31Zog6whBFqQQt5kiephoJgQ7hJsJ0nZkGfO1bY6O3atctu/Eh27tq1qw270ljxi+3bt5snnngiU7iACjh9+/ZN+rKESAwJelFvli1bZubNm2eNI23h/+qv/ioTooLRnDFjhvV640XBcF566aXmqquuyhjON954w7z11luVPKeDBw+2ceuOd99917zzzjtWbN9+++1WqBXy+rlGjnER8Yh5lUTzyxOPUecjggxhplbx+ZGgzw8Nr9gIsiHEY886IM+9PyDmEfWsA9ieO++804wYMaIo/xf/z6pVq8zHP/5x272Wqkvf/OY3L+hJ8oUvfCET2oVtfPbZZ62jgTDOSZMmFeXahIB4z6BFg2HRGj9+vD3S5vgzG0rJEbrCMSgLLQL/pz/9qf032YsatYU//elPVxkKw9Hq5z//ebswzpw50zz88MMFuXY6v7700kv28+HDh5s77rgj6pAMn0pLYryzBdjQoUMlwES9YANI8vuAAQPsxpCeE4hENoiMLZXETDc4YLAJv/jFL8zy5cvN008/bcVzoYUzjQVpdpWPRx55pMpwH2zWjTfeaMrLy82jjz5qY/45ORSiGEjBiHqDEYRNmzZd8DNil3MTlRBdCLG6eGF54UHnVYjDJP7Gq6++mkmqwmvJgitBmF4wpPQFIAa6adOmVmgx9vTMRKFgLBGqxYs1gpNFEuQ5eeRUkLrn6g6dTnDEcKrLKTAnUb/61a/spmzatGkFWSPY6JGIi+PpX//1X+v0b3PtlwIiRDGRoBdF5Yc//KEVY3jx8ahOmDCh0s/xvH/961+3xrJnz562sozzYOAlGzZsmPnWt76VCblpCIT+PP/885l24vxflD6TMEwfGD4SGbdu3Wo/R1CNGzfONG7cOOlLE4HjquLwYs1gI4mHlu+zRvF9rRnpgueBY4YTF1cFB0/9rbfe2qBKZaw9hMwQKspmLx943rFv2KvLL7/cnhg66C7O6TUni5dddpkSd0VRkaAXReVTn/qU9U4QJoHHCy+KA7E+evRou1DiUXnllVfMj3/8Y3uEiScWWEh5FcLL+/Of/9zWMGbxZ6EfM2ZMg/+uKCx4RBHx1IqnURDxsCpJJ5KCDSSbSV5UyNmyZYvtXEqNe8S9W6dE8rCuX3nlldbGOMcNQpou3/U9XZk/f779iDMhF8Kx8NozNrBxK1assCE/5GIRxgWcJn7uc59r4DsTonaoyLYoOnhIMH7Ez7PQOjCKbdu2tQsx3nti7jneRNAVEhb1H/3oR1bMczzLgisxny5IMMMAkzOBl23y5MnWKErMi7TAWGRMMjYRjXjteTF2RXoYO3asXeNZ63EisfbTl6CukMNF0QacP/lgM4ddY9PHhoGiDzggWMOESAJ56EXJ4Pia2NSqQNjzKmScYUVFhXnsscfs/8uG4mMf+5hdhEU6xgPhWJzesKFDLGWf4AiRRlijSHLkRVjH5s2bzerVqzOefIWFJQ/V0qhGQ38REp1/8IMfmIceeqjKsJl8cBqDM+i73/1upe9T5phCCvmEvkKxRJJI0It645J9EGZAHCHeeAzahg0brAeDUoJ8D6M3d+5cG2LjoCoBdYM5uqR83Msvv2wFXaEEN2EbiHnCeRCMDz74oD0VEMmCkSSRmueCACKvQiJI+AjrFaGDbnNKiAb5Pn369FGFnITp1auXDfl0Dh1EfV1sAKKd8pTZfOMb3zC33HKL6d+/v7VpPGNi53FCEXKzdOlS89GPfrRI70iI6lEdelFvCJEgYSiXT37yk/aIkzrzlKtE0GPk6L5KEqpLUnLeE+KmW7RoYXr37m2uvfbagiQO4V3h79MwiljsunpnROFBwBN/zCYQQ0m4lSgNqkNfOgjBwaHBOofwU2+LZCnkKe2XvvSlTB36hQsXmtmzZ1tnFA4JhD2Jr2wEhEgCCXoRHIjGxx9/3J4YUOeeBZwNg0hO4PBMiGlF4Kj5U+mRoC89CD3GPesQ414b2GRPBZ0DiXXo/vvvt89EiJCQoBdBsW7dOivmOQInJpvjT9WPLj2ulvfGjRttWAIeLW2qkkOCPjk4rcRjT7w9IYacGCrWuvRkVzrDo46od71UhAgBCXoRDFQ0IGEJMU9SFGJesdmlheWE3gLEl9JPAAGj0n7JI0GfPIQWssElDJHwQpJqJexLC6clTz31lE1ixjbce++9tnurECEgQS+CYNWqVdb7Qnw2jT3oHCgxXzpYRnbu3GmFPElnCBaOtkU6kKBPl6hknpC0zzyhcICEfenA4UO9+JUrV9o8B0Q9DiAhfEeCXngPCzNeF8Q8CUl33nmnxHwJoaMrscJ00KS6h+59+pCgT6ewpNoT84d4buaPKN29nzFjhq20hqin+VR2h1chfESCXngv5gmzAaro3HHHHQ1q9S3qluxKmBMlQREkylVILxL06Y7tZkNMFSjyfpQ8WxpwACHqly1bZr/GUy9RL3xGykd4HWaDZx44tr7++usl5ktUvYOSbXT0HTVqlBkyZIjEvBD1hLnDHMIhwZxibjHHRHHBVtxwww3WdgC2BJsihK/IQy+8BM/wE088Yb0sGELEPE09aL+thi7FgZr+3HeOq0kkU1dXf5CH3h+ohsM8I3SNeUbtdFGcUpa/+c1vrP146aWXrKcekX/fffcpUVZ4iQS98Lo0JTHzd911l12I3QItUV/4JD5KveE1xNCpQZd/SND7GdLGvKNvA6E4SjIvHLm2AscQibJ0e1VJS+Erik8QXjaNQswT70gCrAuzYWFmgWahZsEWDYO9/vbt2828efNs7ewJEyZIzAtRIoilZ84x95iDzEX53xpOPscPNgTHEDYF24KNoXeAED4hQS+8YcuWLRkxT8xpvtKUEvWFgQQ9RARNcSZNmqQKHEIkBHOPOchcZE4yN0X9qO4UF1uCTaGEJTaGzrLkNAjhCxL0wguo2cwCS/gHx8+UGauqPKJEff3h/lLKjbAmYks5dlaisRDJwhxkLjInmZvMUeaqqD21CcnEptCQEBvD/f3pT39qy4oK4QOy1CL1VFRUmJ/85Cc2KbNHjx616gArUV83OMrftm2b9QB26dLFjBkzxrRo0SLpyxJCZMGcZG4yR5mrzFmF4dRMXfKrnKjH1mBzHnvsMWuDhEg7EvQi9QsxC+rhw4dtLOnHPvaxWpdIlKivHdxbkiZPnjxpj/a5z0KI9MIcZa4yZxH2zGGRn/oUS8DGPPDAA/Y+c2+xQYQ8CZFmJOhFqhuuEGazb98+27zooYceqrPXWKK+aogTpaoDicbcI5pDKbxGCD9grjJn6QXBHGYuM6fFhzSk8hm/j80pKyuzNojwG2ySEGlF1lukEgzTz3/+c3ukTB3mBx98sN4VViTqL+TgwYPWK9+hQwd7hK9a10L4CXOXOcxcZk4zt0XDxLwDm4Oo5x5ji2g+RYlLIdKIBL1IHcSEPv/887YGM7WXCbPp3Llzg/6mRH1lr/zmzZvN+PHjbSyuEMJ/mMvMaeZ27N76QvYkwfYQfoMtouHXc889p7wFkUok6EXqePXVV83ixYvNRRddZKvZ9OzZsyB/N3ZRn+2VHz16dK1zEYQQfsCcZm7H7K0vRoPBXr16WVuETcI2vfbaawX5u0IUEgl6kSrmzJlj3n77bfv5rbfeamsCF5IYRb288kLEBXN83Lhx0Xnri9ktHFt0yy232M9nz55t5s6dW9C/L0RDkaAXqWHZsmXmpZdesp9PnTrVxoUWg5hEvbzyQsRJ06ZNo/LWF1PMO8aOHWttE/zqV7+yNkuItCBBL1LBxo0bzYwZM+znEydONFdccUVR/7/QRT2JWytXrpRXXojIyfbWsyaEmNRZCjHvwDZhowCbhe0SIg1I0IvEOXDggHnyySetoRk+fLi58cYbbaxisQlV1PNeqE1NhQZ55YUQzlvPmsDaENp6VyoxD9gmbBS2CpuF7cKGCZE0EvQiUWiMQq15OvJ1797d3HHHHSUR86GK+p07d5olS5bYFvHdunVL+nKEECmCNYG1gTVi165dxndKLeYd2ChsFTYL2/X4449bWyZEkkjQi8TAu/HMM8/Yph0077jvvvtsabBSE4KoJ+mNeE48RRwHl9K4CSH8gbWBNWL//v12zfA1YTYpMe/AVmGzsF179+61tkzlLEWSSNCLxHj99ddtXd/shTEpfBb1R48etcfonTp1ssfA6vYqhKgO1gjWCtYM1o5jx44Zn0hazDucI6px48bWlqmcpUgSWX6RCEuXLrWlv+C2226zR5dJ46Oo3759u1m+fLm9biW+CiHqAmsGaweeetYSH0iLmHdgu7BhgE3DtgmRBBL0ouTs2LHD/PKXv7SfT5kyxcZ0pgVfRP0HH3xgr/HIkSP2+LxFixZJX5IQwkNYO1hDWEuIrU9zCE7axLxj1KhR5vLLL7efY9uwcUKUGgl6UVIwGiQQIUgHDhyYqembJtIu6l0VGxLchgwZUtIkYiFEeLCGsJZ07drV1qxP67qXRjHvmDZtmrVp2DZsHKGQQpQSCXpRMljonnjiCSvqO3bsaO6+++7UxnunVdSTyOaMGvGvQghRKFhT3LrHWpMW0i7mAVt21113WduGjcPWYfOEKBXpVFMiOMj+f/75522cZvPmzc0DDzxgmjVrZtJM2kT9pk2bbHOYCRMmpNaoCSH8hrWFNYa1hlfS+CDmHdi2+++/337ctm2beeGFF1T5RpQMCXpREubMmWMXZY52P/rRj5r27dsbH0iDqKe8J4lWp06dMmPGjEmktKcQIh5YY1hrqK3O2pNUd1mfxLyjQ4cO5p577rG2bvHixdb2CVEKJOhF0dmwYYN5+eWX7efXX3+96devn/GJJEU9In7+/Pn2GHfQoEGKlxdClATWGtYc1h7WINaiUuKjmHf079/f2jrA9mEDhSg2EvSiqJAY5Bpu0Hp80qRJxkeSEPWHDx82CxYsyCSrCSFEqWHtYQ1iLWJNKgU+i3kHtg6bh+3DBipJVhQbCXpRNFjIZsyYYZuWdO7c2dx8881ee5hLKep37txpVq1aZcaOHWtat25d1P9LCCGqgzWItWjlypVm165dRf2/QhDzgK3D5pFojA3EFiqeXhQTCXpRNGiywVEj8ZjEFDZp0iTpS/JC1K9fv962Eh8/fnzqE4eFEHHAWkSy7J49e+waVQxCEfMObB45Y9hAbKFrpihEMZCgF0Vhy5Yt5vXXX7efOy9FKBRL1OO9WbFihW3sQrOttJb0FOmDsUOJvBMnTlhvIGXzCI/gVVFRYb/vvuZn/A7f49/IayhqC2sSaxNrFGtVIcdOaGLege3DBgI2EdsoRDG46JxWc1FgWJj/67/+y4oHFv8777zT61CbUhggqkjQpbFdu3amd+/eBbtG4bc4p8oIH7M/z9fJk/nVuHFj6wnkI8LLzTk+rl271gwYMCDz9xlv/B3+Hz7mMwP8Hcrv4ZnlY/bn/D8hzmlReyhpefDgQdsltaHOh1DFfG74KRWDCF965JFH1N1bFBwJelFQGE401Fi9erUtTfmZz3wm6LCRQhgiRNWiRYtMz549TXl5ecGvUaRznlA1hEQ5vOV8ZCy58oAc1ecT0nysT9lSun9OnDixzuPSbSJyNxanT5+2v4OQY9y3atXKtGzZ0n5s2rSpxH4kkOtDvfWGlNMNXcw7mDff/e53zYEDB8zgwYPNfffdp3kiCooEvSgo1Nx96aWXrHfvd3/3d6MQqA0xSCzyiHlahlO/WIQHwpjTqkOHDtnwFyeGEedOBPORV7HCrOoj6GsD3n3Gv9uU8JEx7TYlbdq0MW3btrVeSfVPCBM6ynICRNIsm7m6EIuYz94Aff/737fz5sYbb/S26ptIJ1phRcHYsWNHpXrzMYj53Jj6uhgmZ8yGDx+uSjaBine88GxuEba8unfvHtSJFe+trKzMvnJB2HMPEHwkBCJiEHwS+WGBI4LNG2Ut67P+xSLmXQlQbOPMmTPNrFmz7Klst27dkr4sEQjy0IuCwFE8x4nEVFKz+N57743uOLEuBgrRt3z58qiMWagCnjG/b98++0yzxTuvtIj3Ynno64oT+e6FyOc+IQrJH5HA95e6OChiFPMOJNeTTz5pyxIz5j/72c+mZp0QfiNBLxoMQ+jpp5+2AhXjzAIVa8JPbQwVMZRr1qyp1xG1SIeAx+uMIEXAkyuCIEXEpHUTmxZBn2/tYCPE/WReSOD7jQshpMMs8yIfMYt5x/Hjx23hCNaQESNGmLvuuiu1a4fwBwl60WAWLlxonnvuORv/+6lPfcr06NHDxEx1BgvhQg1nxLzEij/Gd/fu3bY3ACBUOnbsmGoB74ugr0rgc+KBwHdl/7p06RKtk8DHTS82oX///hfkBUnMf8jWrVvND3/4Qzvmb731VmsThGgIEvSiQRAr/O1vf9vGCl933XVmypQpSV9SKshnuBCEmzZtsgs3nl2RblFJR0zGN8fhCEqEpa+bMF8EfT5xyLxhQ4X3l9h7cnN82kzFCCctxNT369fPbn5BYv5CaDT16quv2pPaz33uc3Z8C1FfJOhFvWHo/PjHPzYbN240vXr1Mg8//LCMbBbZBowKINRtlphP71gmlGb79u22UgthH4h4wj5CGNO+Cvp8zwhxT6gCVYFIMg7lGYUo6vHU9+nTxz4rifkLoUzto48+ar31bH4eeughjWVRbyToRb2ZP3++eeGFF6zXkkYZKruYX9S//fbb1qBRokxiPl2w0aKONkIRYUi4GGUkQyMEQV/dsyMMCnEf4rPzXdTPnTvXbpI5vZWYvxDCML/zne/Y06hbbrnFjBs3LulLEp7i5/mxSBxCEVyJymnTpknMVyM6EPNAyIAMWvLwHPDEE8rB80AI0uhFnjG/QLxTUct57jkpZANNaFRo5UF9xfUkYA1E1Gv9uxBs59SpU20ZS17kHij0RtQHCXpRZzCgv/zlL23cPKE2ao6RHxL7CLPh/mDYdOSc7Jjds2ePPdomeRvBRyhAsRo5idLBRgwPPS9CGNioUXGLz6nz3blzZ23WEgw5HD16tN1cEX7Dc3Ax9eJDsBErV6606xMFJhR6I+qDrJmoMyQ74Q0j1Oa2227TwlPFMSr3yMXMZzefwtCJ0sBGat26dbaDMaclo0aNss+E+HiJ+fDgmfJsecY86yNHjthnzxhw3mJRfHITYFkDeSY0GGNtFBeO29tvv93aVO4Rmx8h6oosmqgTCrWpGcrtudKU2THzEvWlwYVgUA976dKltiLK5MmT7VG26v7HA896wIAB9tkzBhgLixcvtmNDqWPFo6pqNqyFxIezNrqSpOLC0Bsg9AZbK0RdkKAXtUahNjVDuUPXNCpfAqxEffEgxIIkSZLwKDlJXPz48eMVchE5PHvGAGOBhkeMDcYIY4UxIwpHTaUpnaeeNZK1UlQGm0qYGDaW0BttPEVdkKAXtUahNjUbM2J3a2oaJVFf+Eoa1PdHpPH5hAkTzNChQ5WrIC6AMcHYYIy4CiyMHT4XDaO2deZZG8eMGWPXSq1/lVHojWgIEvSiVijUpnqyk15rE9YhUd9wTp8+bdauXWvmzZtnmjRpYkMrevfurdKgokYYI4wVxgziibKejCXGlKg7dW0aRZKsW//wRosPUeiNqC8S9KJGFGpTPdQPJl57+PDhdfIKS9TXf/NERQi8V8RGMx6pWqMTI1FXGDP0HnBx9owpxpYSaGtPfTvA8rusmdxz1lDxIQq9EfVBgl7UCIlkCrXJDzG4iPmBAwdaQVBXJOprD97TVatW2XtFTDSNkqhoovEoGgpjiLHEmGJsMcYYa/LYF0fMO1gzWTuxMcpnqDr0hvsjRE1I0ItqOX78uHnllVfs59dee61CbbLAa7JkyRLrSWnIfZGorx7im6mMQQ4H95n4Zz5KyItCw5jKHmOMOcaeYuwLL+Yd3GdO2FhL5YmufF+uueYa+zk2GFssRHVI0Itqef311zPdFxVqUxmO5tu1a2fKy8sb/Lck6i8E475lyxYb39yiRQs7/hiHEvKi2DDG3JrH2GMMMhYlOAsr5h1du3a1aylrqvgQQsFoxMX9xhYLUR0S9KJKdu7caebPn28/v+mmm5RsmAVeO45DSawrFBL150E0UVqQhkB8jqjq1q2bhLwoOYw5xh5jkLHImGRsxizsCy3mHaylrKmsreI82Nybb77Zfo4tZuwJURUS9CIvGKwXX3zRfhwxYoTp27dv0peUqo3OsWPHbOxnoYld1NPZk6o1fCTsASOvjq4iaRiDjEXGZPYYjY1iiXkHayprK2usOA+2l+ThbJssRD5kKUVeWLRpvEIJxunTpyd9OamBZigcvbPJKZbHOEZRT5WLFStW2NKBI0eOtIa9ulr+QiQBY5KxyfxnrDJmY6nQUmwxD6yp3FvWWDWe+pDrr7/elubdunWrfQZC5EOCXlzAiRMnMjXnr7rqqnpVbwkRSojRDGX06NFF9xrHIurxNm3fvt16PIlZpikXMctCpBnmJ2OVMcvYZQyH7DkthZh3sLayxrLWqkb9ebDBV199dSZBFhstRC4S9KLKRFiScUjKEefLU1I6jKNPmqKUgtBFvQtdoHqDS3gVwidc4ixjONQwnFKKeQdrLGutyllemCBLSJISZEU+JOhFJUi6wTCBEmE/BG8R5SlLfVoRoqjHQFPj24XXDBgwQHHywlsYu4xhxjJjmrEdighNQsw7WGtZc1l7xfkEWWwyYKN3796d9CWJlCErKjJwZDxz5kz7cdiwYaZfv35JX1Iq2LRpk/UYUVotCUIS9bQxnzt3rmnTpo3Ca0RQMJYZ04xtxnhFRYXxmSTFvIM1l7V38+bNifz/aQObjG1WgqzIhwS9yEBjD5KRSL654YYbkr6cVLB//35z4MCBolS0iUnUO688HYcRPUltjoQoNoxtxjjlF3311qdBzDtYe1mHeYkPE2Sx1UuXLk36ckSKkKAXFiXC5jdqHKGPGjUqFTXQfRX1eCppzIPncsyYMSXLQRAiKRjjvnrr0yTmgbWXNXjNmjVerXvFgjGFjQZsthJkhUOCXlh+/etf22Qb2k1fdtllJnYoRYdRw5CkqXyiT6Iez+Tq1avNhg0brJCXV17E7K1nLqTdW582Me9gDXbr3pkzZ0zsYKOx1UePHjWzZ89O+nJESpCgF7beLx5Ud5ynRNjzSbAkuqXJqPkk6rkuErfKysrklRdR47z1zAXmRJrnbBrFvINrYk1Wkuz5BFlsNXACpJr9AiTohXnjjTesR7pXr16Jx4qnARpqYYTTXEYxzaKeLo/kY1D1o1u3bklfjhCpgLnAnGBupK0TatrFvIM1mWaH1P2PHWw1Nhvb/eabbyZ9OSIFSNBHzr59+2ytX7juuutSESueJIQdYSwGDx5s0k7aRD1H4cuWLTMHDx40EydOTLUwECIJmBPMDRLtmStpCB/xRcw7WJtxurBWxwy2etq0afbzRYsWWVsu4kaCPnJee+01W/qKRZKavzGDcaVqQFqSYH0S9cRyum6vlFVTXXkh8sPcoGmS6zKbpDD1TcxnJ8myVqc9J6HY4KEfNGiQteFqNiVkdSMGT/TKlSvt51OnTjWxw73o27evd7XRkxb1jKMVK1bYa+jSpUvJ/38hfIS5wpwhJjyJEBIfxbyDNZq1mnUndpyXnnuhUKS4kaCPFHb0r7zyiv2cBb1z584mZohpxfPjqyBNQtQzhjAiJGRNmDDBu42QEEnDnGHuMIeYS6VqFOSzmHewVrNm0908ZrDdPEd49dVXk74ckSAS9JFCKUE6oJItf80115iYwbjRiXDo0KHGZ0op6k+fPm3mz59v+xVw33wJURIibTB3mEPMpQULFti5VUxCEPMO7ht2LA05REmCDceW07iPEqkiTiToI/fOjx8/3rRt29bECjGYLm4+hLjvUoh64uUR81RZ6NGjR1H+DyFig7lEWUbmFnOsGIQk5oE1m8pBscfTY8Ox5c5LX6qTHpEu/Fcwos4Qs8kxJeW/XMe5WKHZC8nAIRi3Uoj6PXv22PFDbfmYN4JCFAPmFHOLOcZcKyShiXlHy5Yt7RpOJ9mYufLKK61NJ3xUtfrjRII+wkouVLaByy+/PKiFva5QXvHkyZNB1kovtKjH48NR7o4dO6wnqHnz5gW5TiFEZZhbzDHmGqGRhfC2hirmHazhJ06csGt6rLCxwaYDFW/SUBJVlBYJ+shYuHChXfSY/LSPjhUWu1WrVtnycaFSKFGPoKBmNh/5e+okLERxYY4x1wgjcXOvvoQu5h2Uy2VNj1nITp482T5j+hxQm17EhQR9RNBR7q233rKfE2rD8VzMoTb9+vUzTZo0MSHTUFGPcWQT2K5dOxvfq+RXIUoDc405x9xDnNVHqMYi5gF7xpoec+gNHc5dGC3dY7H5Ih4k6COCjrAkW1FNYdy4cSZWOKE4deqUtyUqSyXqXSUbmpco+VWIZGDu8aprBZyYxLyDNZ0wyphDb7Dt2HhsvesCL+JAgj4SOLp9++237efE2cUaNhFDqE0hRD3xqIh5OgjT0VIIkWytcTqCMieZmzURo5h3xB56c/HFF2fCad95552oq//EhgR9JJD1fujQIbu4jx071sRKLKE2DRH1eHY44qccnCrZCJEOmIvMSeZmdWUtYxbz2aE3rPWxgo2naRknFap4Ew8S9BFAQtXs2bPt55MmTYpSzMYYalMfUc+mj5rOlM5r1apVItcohMgPc5K5yRxlruYSu5h3sMaz1scaesOmBlsPnMyrLn0cSNBHwNq1a21NYyY5bcZjJNZQm7qIeowfXi2VpRQi/WUtmavZglVivjKs9TGH3kycONHa/N27d1sNIMJHgj4i7zxGgGO4GIk51KY2op4yZ1SHIKFK90iIdMMcZa4yZ5m7EvP571HMoTfYelf8wmkAETYS9IGzZcsWs3XrVpsES43aGDl8+LCtfBBzqE11op7EqRUrVtjFn4QqIUT6Ya4yZ4mRZg5LzFdd9QYbECMkx2L70QCbN29O+nJEkZGgDxy3Mx89erQpKyszMZ5QcOw6dOjQpC8llVAxgyN8al4TcyqE8AfmbKNGjewcrk31mxhh7ccGxBhHjs1nowfy0oePBH3A7Ny506xbt86KNdcSOja2b99uOnTooJjwPBB/S2wlJzck2jW0o6wQonS4MBvmLnOYuRxrEmh1sPZjA3bs2GFiZMqUKVYDoAV27dqV9OWIIiJBHzCu7vyIESNM+/btTWzQJY+jxr59+yZ9KamDChkuZp6j+4Z2lBVClI7cmHkXfsOczlf9JnawAYSfxtg5FdvvikHISx82EvSBsn//fhsX7XboMUIy1MCBA+2RtPgQalivXLnS1irOjpmXqBci/VSVAMtcZk4zt6urUx8j2ABsQawJsldccYX9iCYgiVqEiZROoLjas3QXjDEZ9MiRIza+tGPHjklfSqogztbVmc9XzUaiXoj0UlM1G+a0q1OvmPrKYAtIkMU2xAYagA0NmsCd3IvwkKAPdNFfsmRJtN55Fi28VEOGDEn6UlLF6dOnMx1gq8spkKgXIn3UtjQlc9t1lGXOi8oJstiGGBNknZcebaB1PUxUoy5AFi9ebJtpdO3a1fTs2dPEBslPxA3GWnM/H4yHhQsXWoNWmw6w2aJe5fDSCaIEjyOeWF58zqnU2bNn7c/cR14kyFPekOQ4wg/cRxrPNGvWzIpAXnzOz0S6qGudeeY4cx1RT2w9pQvF+drs2AZsRPfu3U1MoAXQBKwFaIRYC2WEjAR9YGC858+fbz+nK2xsxpmkJ5KfXNtrcX5MsIDTZKVt27a1/ncS9cmP5YqKChsP7UQ7gt15F5nbToy7j61bt64k2PnIi0TJPn36VBL6fOTv8Xep0+02Bdl/H8HvxD4isU2bNupVUGLq2zSKuc4zd5VwYrMFVcE6OHfuXBuGEtNY5vnTXPK5556zGoEa9RoTYRHPaI6E9evX29JlGGCq28QGpdsGDBigRNjfgjhbtmyZNV6dOnWq87+XqC+teHcvhDZiA4HOC3HGnEZg18cI8+9atmxZ57HjBD8vkuk2bdpkr5W/h7h3r5iEUSlpaAfYzp0722fIGoA9kIA7nyDbv39/ayti60/CGJg1a5bVCGgFbKUIB63CgTFv3jz7EQOQL+kxZBAdeDNjW6SrY8OGDfaYuUePHvX+GxL1hQXPOB5zXrniHeHOs0IwJ407AeCFaM9OrueaufZ8Ip/3wEub6mTFvIPxxNq4ceNG650W5zc6dE51jfVigTlKk0lOKPDSS9CHhQR9gLXFgaO12OC9U9VHnGfPnj22ooPrFNgQJOobBsmJPI/du3fbz4njTZN4rytcMyc+2ac+TuRTMhfvJ7+DcOIVm3MhLWLegUeav8cY5HkIY6u+YDNGjRplYgJtgKB3PQvqEoYp0o0EfUAsWLDAfsQLE1u5RjzzJH7iIRTn7wceORbvQh2zS9TXXZTRmXHfvn02KREhRYMXPN4hkivyicdHQFJVg7nJmlReXq5xU2IxD6wBVL7hBJe/WZvE+NBByHKCyVoZ0/1gHtJoC/uAZpg2bVrSlyQKhAR9IHDkTRWTmL3zgwcPTvoyUgEeYFdrvtDVLSTqq485x+OFFx5PNaFOCNjevXtHWWWEjQuVNXgh6PHc034ewYqYIoSHj4rrLq6YdzAGCbeg8g02QqcmxtoMmk3RkCsmKJiBoEczXH311cqBCQQ9xUCgAxzGoKysLDphi4jCONU16S9UUYkgII+gWLGhEvWVwRNNZSU88YTSIOKZgxKqH+JOKHgxRtnwsPFZtWqV9Rj26tUr2JOLNIh5B2sCawP/D+UsYx+j2AzEbGyhJ6xPaAVCMqnLz+mN8B9lLQWCK1XJIh1bMhrxusRDCmMXZwRlsY1T7M2nEKUkhOLtpIII93vy5MnWUMrrXD3cG+4R94p7xufcQ+4l9zTGpj+lEPMO7jdrBGuFMDbvChsSE2gEtEJ2IQ3hP3Epv0AhTnfr1q12ksZ2dLh3717raYipUkFVbN++3YqlhlS0qQsxinpC26iOQVIZ8eF0I8YwEjcuEV93uGfcO+4h95J7yr3lHnOvY6CUYt7BGsG9Z82IHWwHNgRbEhNoBTQD2gENIfxHgj4A3A6bo1QWpljAk0ctXSo4xA6JXRhnRFEpiUXUczRNXgIxpxzRT5w40d5rdSMuHNxL7in3lnvMveaec+9DJQkx7+Beb9u2zRw7dszEDjYEWxLT6RBawdkLd8Iv/EaCPoD4XYxejMmwtLDGuxd7chcJh4QsEAeZhJc4ZFFPCAgbZmqtk9yK2KRlfGxhbaWEe8s95l5zz7n3PAOeRUgkKeaBtYKSjdgPeiPEDDYEW4JNiS05FqhEhZYQfiOrFEAyLFVNSCzD+MUCnhSO5WltHjvEwlKGLElvcWiiHq8wJd0w8IgeNks0fhKlhXvOvecZ7Nixwz6TEDz2SYt5B2sGawd2JHawJdiUmLz0aIYOHTpYDaGcCv+RoPcc553H4MUUw0vMHxUzYiwHmA2CE49mdhfPpAhB1DuhxfE7IWwh1433CZ4Bbet5JjybEMZY0mLewdqB7YjNO11VJaaY4sndKY3z0gu/kaD3mMOHD9tashBT2SnnnY/pRKIqYUC5xFLHzYco6ulyunz5cuupxGNJve40iC1RGZ4Jz8Z5lXlmPDtfSJuYd7BRYk31ac4WA2wKa2pMXnqnHdASaArhLxL0AXjnqeEcUw1d6n23a9cu6mYYxLzy/FmM0xbP7ZOop5IKTckomdi1a1ebh6LQmvTDM+JZ8cx4dpQdTHtVnLSKeWANUTy9sTaFbuM0QYsFbCnN34BcLOEv6VICol6CPibvvPMk4KGLGQQMi3DahIEvoh4PHJ44qjtgwEnApCmU8AueGc8Ogc+zTKt3Nc1i3sF1sabEVpM9F2yLO/mOBRd24zSF8BMJek+hyyIvPCvE+cYCHf3o7te0aVMTK3TZRCB069bNpJm0inquBfGHR3fSpEmZGGLhJzw7niHPkmfKs03beEu7mHewplDGkjUm5nwNkoWxNbEwbNgwqyXIH6AXhPATCXpPcTtpOqTGVAubhLh+/fqZWOE4nNhhFmAfSJOod7kXzB3uH+NIQj4ceJY8U54tzzgNFUt8EvMOHESsMTGH3ri69LHA2HTd1pUc6y8S9B6CkcqubhMLlKsjxjGmDUwuxHtTXs2nyitpEPXOK0/NfkI0OOURYcKz5RnzrHnmx48fT+Q6fBTzwNpCcmjMoTfYGGxNCCVSa4sL3SWOPumNsKgfEvQegueJbHQW3kGDBplYiL0rLEfAiBMSAX0jKVEvr3ycZHvr8TiW2lvvq5h3KPQmPi89WgJNwTMnF0X4hwS9h7gjMUqNxVLpBQPJEXCrVq1MjPDeafzhS6hNGkS9vPIiCW+972LeEXvoDbaGcZPUCU8S3XLRFKCwGz+RoPcMkr5cV7+Ywm1o/x5z7LyPoTZJiXpXwUZeeZHtrUesIFSK6X0MRcwDaw1rTsyhN3jpY6p44zQFGiPtZWDFhUjQewaL68mTJ22ZNhbbGMBLQixjTLX2s+G9IxR8DLUptah39flPnDghr7y4wOPKmGBsFKPeekhi3sGaQ+hNTLHk2WBzeO/YoBggd6KsrMzOkZg3cr4iQe8ZLhmWNuixeB137NiR+hKNxQJvM6E27ig0FIoh6tnozps3z3Tq1MnGg8YyP0TtYUwwNjp27GhDcBgzhSBEMe9g7WENijVREtuDDYoBSle65FjVpPcPCXqPOH36dGbXjKCPhZgFPe+d5jkhVvYppKgnSXzhwoVWfIRykiGKB2NkyJAhdsw0tN19yGIeWHvoJrpz504TIzEJ+mxtgdZAcwh/kKD3iA0bNti4NjpblpeXm1gqu3AE2LhxYxMbPGvifUPOHSiEqEdorFq1yowbN86GoglRGxgrjBnGTn3FauhiPjuWnEpBMcZVY3sI14ql0RTagrnBs44pfyAEJOg9YvXq1fZjTOEEJMMS1xdrIuyAAQPsMWjI1FfUEwLAPdq3b58ZP3581N2DRf1gzDB2GEOMpbqElcQi5oE1CFEfa1w1+WpsaGIAbTF48OBKmkP4QdhKISAwNG4xdZMtdE6dOmWP/GJMbCQRi8Qk4sFjoK6iHu/RokWLbCUOYj5D3/SI4scNI+4ZU7XxQsck5h2dO3e2JRxjTJDFBmGPeMWA0xh13eSKZJEV9ARi+I4ePWqNTizVbbZu3Wp69eplYiPURNhCiXpEBcmvnNzEenojCg/rKuOJsVVd7fEYxbyDNYkQpRhFXs+ePc22bdtMDDAP0BpojpjyB3xHgt4T3NEXIRgxxJNjMPbs2WO9QrGxe/dum4QWYiJsQ0U9JfQWL15sf6dDhw6JXKMIF8YUY4sxxljLJWYxD6xJ5HCxRsVGly5d7PuOYTNDw0q0Bijsxh8k6D3BTapYwm327t1rS8vFkivgwFiQiNS3b18TK1WJerxFNAYaM2ZMlGJKlAbG1ujRo+1YY8w5YhfzDpL0WaNiELbZYIuwSeRbxAC5ei7sRviBBL0HHDx40HqrWVAGDhxoYiDWcBveN2XS8JDETK6op7QgdZHHjh1rmjdvnvTliQg80Yw1xhxjT2L+Q1ibKPsZS/hJNtikYnYaThNoDTQHpxKxVPjxHQl6D3A7ZBaTGMIwSDzC+0PCY0zQjXD79u02VlN8KOrnzp1rQyAoMRjbmBDJwVhjzDH2GIMS8x+CLULQx9JBNXtMYJtiqM/OWHdONYXd+IEEvUeCPpZwG2pCx9hIypXoVMWWD6HiCDkj3JMYa2CLZGHMMfYYgxp/H8I9QeyxZsUGpxOxJIoq7MYvpBxSDqUL3aLpJlfocMRHAlJM4PEhb0BdTj+E8njLly83kydPtuEPhegoK0RtcWE2jD3GIGMxO6Y+dnC6sGbF4K3ObbwUS1KwcyKiQdAiIt1I0KecdevWmbNnz9pknBiqemBEKZcVQyWfbNavX28bt8SWBFwVVBhZtmyZFVOMh0J0lBWituTGzDMGXUx9vuo3McJaxZrF2hUT2CbGQ3WlTUMBzcELDRLbc/YRCfqU4466YvHOE0PevXt3ExMnT560iXds2sT5OvOumk12zLxEvSgFVSXAMhZd9ZsYxFxtYM1i7WINiwlsFLYqBtQ11h8k6FMMyTduVxyDoOf97t+/Pzphu2HDBnnnfwtxyk5M5atmI1EviklN1WwoSuDGn2LqP/TSU8YyJlz5yhhKdzrtgRaJ4f36jAR9iqFUJQamSZMmpkePHiZ08PSUlZVFJWyJP+V9t2/f3sQOxgLvJ+XSqqsmIlEvikFtS1PyM8YoY1UCx9i1q6KiIqpYemwUtoo8n9BBe6BBmB/kTIj0IkGfYlwyLNUEYogp5wgzho1L7jOm5XxMm5iqWLt2bSZmsyYk6kUhqWudecYoQpYxGzusXaxhmzdvNjGBrYqhFj/aw5VSjrGqkU9I0KcYt0BSyjB08HThqW7durWJBWo4E2LUuXNnEzuUKiUOty5jXaJeFIL6No1CxDJmGbuxwxpGCEpMdemxVdisGE5pGOsgQZ9uJOhTCouEmzxuMoUMwhavV0yearrC4vmI6T3nA6PIvRg+fHid/61EvWgIDe0Ay5hl7DKGY4Y1DI819yKm94zNwnbFJOhj2MD4igR9iuPnqaRA7FoMTZZiayZFGbDY3nM+8HBS35vqIfVtqCVRL5IQ88CY5d8zhmOr9JKv8gtrWkyCj74hu3btMqGDnUKLoEnQJiKdSNCnlJji5zEA1HZu2bKliQU6DbJIxuydZ1OzePFi6+WkrnNDkKgXpRbz2eUsGcOMZcZ0rLCWIXBjKecIrVq1ss3GQt/EoEHQIqCwm/QiQZ9SYgq3iS12nsWfZKrYEoBz7wGNozAShXr2EvWi1GLewRhmLOOpjxlCCFnbQhe4uc8+hmo3iqNPPxL0KSS2+HnaaNNOOxYOHTpk2rRpE/zJS3UQa0udeTx6hUSiXpRazDsYy3jrt2zZYmKFNY21jTUuFrp06RJF2I3TIhTriGnD5hMS9CkVuCdOnLBhCIUWPGnk4MGDpl27diYW2KzFULmoOlFFrC21vIuBRL0otZh3MKYZ2zF3kmVti6mEJeVLsWGhgxZRHH26kaBPITHFz7NxwasVSyw5iXOUdiuWoEg7eHYISxgxYkRRn7lEvSi1mAfGNPH0hJPF6sXk/tJF99SpUyYGeObYMGxZyKBFnCMqts7AviBBn0JiC7fhyDKmUBOXXBQjhCNQ6q0UCdAS9aKUYj47UZIxHnPoDWtcTO8fG4YtCx0n6GM6gfEJCfqUgVfHTZYYBD2tpDt16mRiebYxvd98wopY0759+5bs/5Soj5tSi3kHY5yxHmvoDWsca10spxTu/YaO6tGnGwn6lBFT/DzHsiwKF198sYkB4g7pqBhLeFESoTb5kKiPk6TEPMQeesP7j0XkAjaM54xNCxlKLaNN0CiKo08fEvQprE/umnTUt9GOL8TmrXadYWOklKE2+ZCoj4skxbwj9tCb2MJusGX79u0zIYMmQZtATP0GfCFsxeixoA/dOw/s8GOJnycZlsWwoQ2UfCSJUJt8SNTHQRrEvCPm0BvWOta8WDroxhJH77QJ1ZxEupCgTxluknC0FToc27Vo0cLEAN4M59mIiSRDbfIhUR82aRLzwJhn7McaeoMdc06q0MGWhV7pJlvQx/JcfUKCPkVQztDt8EP30OOxikXMxxhelB1mlGSoTT4k6sMkbWLewdhnDjAXYoOcoZhirWmWF/ppjHM2olXQLCI9SNCnCBY+JgiLQuiNlvbv32+NXAwcPXrUCozQcyJyIUGMk4mkQ23yIVEfFmkV8w7mAB7N0JMmc2HNw3HDGhgD2DRsW8igTai7j1aJJenZF+IoL+JZuA3e+TSEJxQTkoeGDBliYmDbtm1Rhtts2LDB9OvXL7VjOVvUp1UIVgchHMeOHTOHDx+2XkGO+4lXzhaN3HtXCtd9nl2ZA8OMAwHR1bp1a+tNTuvz8lXMA/eUkn805ClWh+S00qNHD7uxHzx4sAmdjh07mtWrV9v3HCqMZTQKpSvZpJaXlyd9SeK3SNCniJgSYhEeCInQQUAdOnQoCmOWDV0iaYeedvHik6hnzrARrqioyHg8ud42bdqYtm3b2vmEQK+qDOyECRMqfY3wd5sARDEGmg0CBpsKLfxdBAp/M634IOazkybZWDE3YkqOx6O7Zs0auxb6tlmsK8zBWOLoWS+UGJsuJOhTRCwJsRjhWOLnEbWIrdANWS5r1661Yt6H951WUY8AQrhTJeXAgQNWBJKHQelTBHdD7y3Cn7/jyiu6kqru/2XjsGLFCitA27dvbz1xhfh/YxTzwH1jTjA3qFEfC7xv1kAcG6GHkgK2LfQcMadRlBibLiToU0JMCbExxc+nNYa82EILg4YI9IU0iXruH55cBHVZWZkV0v379y9ZDgYCjP+XF6EDZ8+etRtTaoofOXLEeu5pAZ/0PUrDs6orzAlC0UIXfLkQcohHNwZB7+LoQw67yU2Mbdy4cdKXJCTo00NsCbExxM+7GGe8mjHB8fqgQYOSvgyvRD1jhQQzRDOecwQzcyQN3nA2EogUXi6EjDhh1iuaB3FqUMrr9FXMO5gbzBGuPxbYHLIWxhB2wzxZtWpV0ILeJcYSrse6pTj6dCBBnxJiSoiNJX6eZEW8mTGBBxdIsPSRUot6RDHeeDxdCOORI0emOmadtQljzot5TCnG9evX2/hwNiHF9tT5Lubd3EDYMlcQurHAWhjDmohtC72ZlhJj00lcdfRSTCwJsTHFzxP7HEsnXN+986UuaYmgQwy/9957VsBPnjzZDBgwINViPheulWvm2vmc98J7KlYDpRDEfK6XPiZi6aQK2LjQy+GqY2z6kKBPoYc+ZEju8ym2uiHEkgSW/WwRdr6LrWKKesQuG705c+ZY7/ykSZNsfLHPp3JcO++B98J74r3xHgsp7EMS88B7YK4wZ2KBtZBcjBjAxoX+bJUYmz4k6FMAhs910wtd0JPoR8WD0CHpDYPts1CrK4RepL1MZZKinr8xf/58G2pBCUnqkofUbIz3wnvivfEeea+Fum8hiXkHc4U5EwushayJoXdSBcKKsHUh47QK2qVYp3KiboRjTTyGiU9NaAxi6B5dEqNoXhM6HC3HFG6DgHN10EOiEKLeNXZatmyZGTZsmBVyVdWKDwHeG++R97p06VL73utr8EMV88Bc4eXyTmKANdE5r0KGQgjYupBBq6BZ0C7kRojkkaBPAa5VNMd0IXnscnFGPQavNZn/JDnGAslRJEWGSENEvfPKU/oRz3UMm1kH73XixIn2vc+bN69e9y5UMe/gRIMNTyywJsYg6J2NC9lzne2AdBpGJEu46tEj3GQIvTZ7LN55PBYQshc29/1yjO5rZZtiiXryYvBQ46mmF0EMG9lceM+8dxopcS9qm0AXg5gH5gzrolszQsetiTG8X2xd6F56OkkDHaxF8kjQp4BYBD1JoqGXLAMSv2JJ/HXNs0iKDJ3ainq8ctRpx8jF5pWvCu4B94J7QnWX6jyXsYh5B/XKmUOxwNoYQ3JsDHH0zs7JQ58OJOhTQCyCPpaEWJ6n81yEDsIMr2voydy1FfV4HhctWmTzCagpH3IIXV3hXrg6+9yjfF7a2MQ8MHeYQyGHZ+TrpBqDoMeJFTJOs4Re0ccXZG1SQCyCPpaQGzYuIYefZIOnjU1aTMK1KlHP58SKk0sQaj5BIXD3h3uVXfEkRjEPzB3mUAxe61g817EkxjrNEsMGzQfiscIpBS+V28WHLOhjSYjledItM/T36SChL0bxmivqMdxOjIY8jwsF94h7tXjxYnvvYhXzDuZQLMmxrI2skaHH0ceQGOtOotEwoT9PH4gjay/FuKMqjqFD9l7H4p2PKX6e9uZUMIml829Vop4OqRhvGisRaiNqf//GjBlj5s6da0UPFXFiFPPAHGIuMadCK/1aXRx96JXAXGIs3vpQ31/Tpk3NqVOnonieaUce+hSF24Ts1Y0lIZakv1g8tFu2bDG9evUyMYMQZd4SNoEgE3WDe8a94x6G7MmsDcwl5lQMxBRHH3J4EfNWYTfpQYI+YWKJn6d5Sgxx5TTYiOF9Ir5iSv7NB2EiS5YssZ75cePGFayjbCy4MBvuHd557mUMXUSrgrnEnIphY8MaGbLQzX6foTddkqBPDxL0KWoqFTIxhNzEFD/PiQtNRWJ4r1U9axfzTZhNITrKxkRuzDwhJy6mPtZYXOYSybExCN1Y4uhjqEXvBL1q0SePBH1KBH3ons4zZ87YBTxk8MTEEFYEu3fvtm3cYwQPKmJ00KBBlWK+JeprR1UJsHzOPcVTH4OXOh/l5eVm165dJgZYK0P3XmPzsH0ho9KV6UGCPmFiCLlxccahE0ueAOBFjOW95kJjJDbg+easRH311FTNhnvKa+3atSZGQo+5jvG9hp4fIg99epCgT5DTp09njH7IDZeIi42hekUsIpcxS4hEDJu0XGgAREWH6kp1StTnp7alKbm3VHvhXscGc4q5FcO4iaHxEvA8Q84NIfQSGLNoGpEcEvQJJ4rCxRdfHHS5u6NHjwYfPw8IvRhKzhESQGhAbGCwqEIyfPjwGn9Xor4yda0zzz3mXsd47whlI6QtdFgrWTNDJ/QGU2gXNIyz9SI5JOgTxA3+srKyoL2dMSXExkBMpTkdHJkvW7bMjBgxotZdcSXqz1OfplHcY+718uXLgw5XyAfhXHv37jUxEEtibMhCF+3i6uw7J6VIBgn6BHGDH0EfMixmoTbWiC0hliNVxFYsmxcH3mKaptR1Yxq7qG9IB1juNRvHWGqzO5hbzLEYwhdiSIwN3UOfrWEk6JNFgj5B3OAPXewSPxh6N9FYEmL37NkTXXUbRCkhEH369KnXv49V1DdEzDv69u1rQ7xium/QuXNnO9dCJ4bE2BhyIpygD/kkwgck6FMSchM6IYcUxZQQi8hAbMQC4R6EfRDT3ZAxHJuoL4SYB+55jKE3bJpjEfShJ8aGbvtAITfpQII+QWLw0FODt7Yxxz7D8XjoCbFnz56NJvHXgWeeClSFyAGJRdQXSsw7uPcIvxgSRXMTRplzob/PGEKLsIEh16OXhz4dhK+0UkwMHvoYwm1i8RziSQu9o3Huc924caMN+ygUoYv6Qot5R79+/eyziGWuAXMtdO+1I/TnGnrpSsXQpwMJ+gSJISmWetIhl+SEWLzWiIuQ+yXksm3bNtO1a9dMSbZCEaqoL5aYB54Bz4JnEgvMtRgEfdOmTYMvX4kNxBaGikJu0oEEfYLEEHJz4sSJ4AV9LHX2Y8kTAI7HEY+9evUqyt8PTdQXU8w7eBY8k5BDF2JLGHX2L/RQDRw+2MJQUchNOpCgTwhq77oJHrKHnvcYuveakmQhb8oceNHwpsXA5s2brYAsZv5HKKK+FGIeeBY8E55NDMTguQacIaGXdQzdQ+80DGFFofcVSDMS9Al756k5HLIHO4aQmxg89CzShQ49SXM8LwmY3bp1K/r/5buoL5WYd/BMeDahx1w7mHOhC6QY6rRjA0P20PP+XG8SeemTQ4I+IWLpEhtDyA2iJnRBz9F/69atTQzQpZMmUqWal76K+lKLeeCZ8Gxi6aTKnAu98RJjJ3RBH3rIDfNSibHJI0GfEDEkxALepdC7ilJaLvTSnAj6WBJi6UxarNj5UER9EmLe0bNnz2i6x8ZQpx37EHp5zlhOWkCCPjnCViEpxpWwKrUxTIKQTyBiOfqPJSEWoYrxTSJXwBdRn6SYd95ORGCa71GhYBMdQ2Js6GtpyDbQ4daCkE8i0o4EfUK4ZKdYkgxDBa9LkyZNTOjEkhBLwmXv3r0T+//TLuqTFvMOnlEMybGxJMayhobuwQ4dV/wi5OTftCNBnxBu0IcskmIIt4mhik8sCbF4CNMQWpRWUZ8WMQ/t2rWzzypkr25M4Rqhx5gDtjDkkqvOsRXDBjStSNAnRAwe+hgq3MTwHmNJiCVRPS1J6mkT9WkS89lJeDFU1IghMTb0so4xVLqRhz55JOgTFvQhe3eZ2CFvWGKp4oNoikHQ79q1y5SXl5u0kBZRnzYx7+jSpYt9ZqHD3As90TAGD33o4VPO1of8HtOOBH1CxOChjyFUI4aQmxg2LXDgwAEbypEmkhb1aRXz0L59e/vMQid0z24sHvrQQ6ck6JNHgj4hYoihJ14wdEEfQ8hNDILenSalsfxoUqI+zWIeeFY8s9CFIA6D0N9jDJuW0AW9c2xJ0CdH+qxXJMQQciMPfRjEUOFm3759tmFRWim1qE+7mHd07NjRPruQiUHQx/AeQxf08tAnjwR9QijkJgxiOIWgkkgaEkVjr7NfKlHvi5iPpU47cy/0aj6hi11X5Sbk9+i0TOgbszQjQZ8QMYTcxFC2UoST+Os6HcYs6n0S88Azi6HSjfCf0DctCrlJHgn6hIgh5CYG73XoxOCd5z369D6LJep9E/PZ3uvQPdjdu3cP/j2GTsuWLVOXdF9IFHKTPBL0CaGQG+HLSVLIm044duyYNbY+UWhR76OYd/Ds0lCrv9iC3pcNp8gP6yg5H6GikJvkkaBPADwtEvTCB2KocEPTnrTHzxdT1Pss5oFnF3ocvRA+hdzoNCkZJOgTErpuwIfs/Qxd0PsUplFfYqjic/z4cS+FbCFEve9iHrhunqHwHwlBf3HOSZ5hyLkCaUaCPgGyB3vIgvfs2bOprOtdyByB0JN+VWc/XFEfgpiPpctoDGALWVOFn2RrGT3HZAhXbXnihQjdwxsyoZ9AxFKDPoQ8gbqK+lDEfCxdRmNAgt5vsrWMTlqSIWw1ksOyZcvMvHnzzK5du6xQ+au/+qtKHuQlS5aYt99+2xw6dMga+GHDhpnrrrsuI9oQcC+99JJZvny5XXh69+5tPvKRj1SKv501a5b9OzSpueOOO/LG5mqwh0EMHvrQT1lC2phli/rqhHpIYt7XcoDYgL/92781//3f/23j/8eNG2f+8z//04wYMSLv7992221mwYIF5siRIzYJ+MYbbzT/7//9P9OhQ4fM7zz11FPmL//yL83mzZtNnz59zFe+8hVz1113Gd/qtPuyua7rM4RHH33U/PM//7PZsGGDfY7333+/+bd/+7cgnmFtBP0bb7xh9RFrEHalW7duVmOVl5dnfocx8Oabb5qlS5fa32ONuvbaa+165f42f2fhwoV2I9+1a1erwzp37pz5G++++6555513TFlZmbn99ttNly5dTAyEbanzeHLGjx9vF8NcEPkzZswwV155pfniF79oPv3pT5v169fbgeVAzG/ZssV85jOfMV/4whdMixYtzOOPP54ZvBs3bjR79+41f/zHf2xGjx5tXn/99bzXIQ99GMQgdmPIEwiJmjz1oYl5X0GM/+AHP7A2hU63U6ZMMTfccEOVNfX//u//3qxbt84mcK9YscLmDGCHHHPnzjUPPfSQFYD8zpe//GXz4IMPmvnz5xtfYC1lTQ31GSLkv/SlL1nRzwYAvfDwww8H8wxrI+jZ7Pze7/2e1Vh/8id/Yvr162cee+yxSs/95z//udmxY4f5xCc+Yf6//+//s79PlScHQn3x4sX2Xv3Zn/2Z6dmzp/0bp35baOTAgQN20/D5z3/ePo+ZM2eaWAhbjeQwYMAAM3LkyLy1YA8ePGgFPwOOgUkHwoEDB1qh73aNDCJ2ivwMLwKDZc+ePVbkA4PS1UR2n+fDfV9CyW9iELsxbFpiEfUS8+kBUfenf/qn1h7hGEKwI0hwKuWDZ8bvOZiTq1evznz9ne98x9x0003m7rvvNk2aNLEfcVx9+9vfNr7g21pal2eIQP+bv/kb643HacipEh76sWPHBvkMq9I+lO1045jfYRxTNtgltXNywYtTifbt29u/x33KLvfJBueyyy6zXnfu09SpU+1p+cqVKzN/12mw6nRYiMhSZ4l9BhA7OwYBu7w1a9aYIUOG2J+zA0fUZ+8UMYqIeyf62W2yWfjXf/3XjPjPhwS98IUYNi0hvr9cUY/QCFnM+/QM8c5u2rTJTJw4MfM9BN6YMWPMokWLqvx3eCsJIcBO/eIXv7AC0YG9yf57MGHChGr/XhrxRXzV9RniVUa4oilwFBIegkOQORnSM6xJ0AP34B//8R/tSQSnG5MnT870AUHMo6lmz55tTzS+8Y1vmGeffTbjmCD5nZDobB3GpoCQnV2/1WGEoREu/a1vfcuGQLNJigX/A0cLBDs9dsscz7BYMiAxfkxQcElXudUw2G26nzGYieXiVR0S9GEQg9iN5T2GiBP1hA3u3r3b3HvvvUGKed+eId5aQLhkgzPI/Swf//AP/2BfhN4Qiz1o0KBKf7Oufy+tXX9DfIY4BAFtQfw3mzLi7/HAr1q1yubahfIMobrQKcYtITd45dnEtG7dOvMzhDv3qm/fvuYP//APMycevAg/qo0Og6uuusq+YkOC/rcwsF555RWbpNKrVy8bB/fcc8+ZZ555xtxzzz2ZRB12iIh/B4Oyvkk8DPr33nvPhAqJPSHDWGCxDbmpzc6dO61HJORKNyGPUwwiYp4XSWShPse0PkNObXO7gzoBw7zKDfvM9jxWd5pMkiwe3q1bt1p7xN/M9/eyxVI2eH1Pnz5t0gThq4zTtJWQxZtOgmpDnqH7/T//8z/P/PyrX/2q+fd//3frvceLXNdnyMaOSII0UZccCEQ43vmvfe1r1quOl91pqenTp9txzXp1zTXX2FwFxmu2DssGHVZWVmZiR4L+t5CEQdUaXsDgIGudrHNgUeZIjd8bPHhwZjfJBMzO0K7LLpaPuUdsoRHy+0PMMx5cWFaIUNEJY+aOREOEecjRdmi4mHk884h5qoiQIxSil96nZ4g3ljlFxTVigbNztD7+8Y/X6m8gbhC/OBOwTRRh4O9lQ6yxO2HOparvJwmeaqqeVCVgfX6G7n5Xd9pZ12fIxi5tEELjYuNrA79L/DsbE3QUFWuqO71hs8cpBnaXZFi3iSDcZtSoUSZ2ooqh58Ez6VytWz53XVsR8nh58HjwNfFuGEE3wBDzrnINiyjHO8RnUZ4Sj36h48yESAM+HYPXlxDfX24CLJ6uhnSUTTu+PcPf//3ft1VSKKWMd5F4eDySd955Z96YY06KcSDwPkmGpboHGxjn/f/sZz9rXnzxRRuagNjnI+GjjzzyiPEJn8L76vIMEZ+UscYrz0YM/UDZbEJqqI4TyjOsKZx4zpw5mSpAaKwXXnjBOhqcOMc5xobu1VdftdqMtYqQQfIO3OkilQo51eBEh/uEJuNvDB061MROVB56El5JsHAQjwif/OQnzfDhw22NX37ORyYmIj+7BixHnOxA/+u//ssONnboDzzwQJ0XIQn6MIhB7PpWSk5UXc2mtnXqRfGhOgp2hhrcCHVEyq9+9SvTqlUrWzWNpD7EHBVRWGP+5V/+xZZSxu4g4rFFlEB0ELrw4x//2CbOYpOwTZTy8+XUAnxbS+vyDOF//ud/bElrRCvrKs8GPeFOJEJ6hlVpIpJeSXglFJDwGU5kKE/pwmUQ7ZxwcN/+6Z/+yf4OYp4QHMfll19u//2PfvQjuzHibxBf3zTQcMK6cNE532ZRALAzZWcPf/3Xf+2VV6IukB8QcsgNz5FKB2wGQwVvIEeh+RqkhUJI47QqMZ/9HkMsXxnSM4yVGML7QgYp+Xd/93f2c06QQllbfCKqkJu0EKqAjw2O+UJvVR6Dh97HTqP5qK1Qr6n5lG+E0uk3dlhL9Rz9RQ0zk0eCPqUd1UT6CUUIVgfHmK4DX6hwrJtd8sxH6up1D0nUU/GivpXGRHpgLcVJIvxEgj55JOgTINsLEbIgDN27G4OHHqGUWyIsNKic4PN7rG8ITSiins1Y2kodirojQe832VpGzzEZJOgTEvRuB+u7ZzBmD3YMSbEIpZDHqKuH7KugbWg8fAiinut27eSF38iz6y/uJJdnqNCpZJCgTwAGvMvIDjmcIXRBHwO+e69rA1UmfGwOVqjkVt9FPc8u5KRtIXzAOX7QNtqYJYMEfUJI0AsfCCG+vCaoqkHFIp8odKUan0U9zy70ihrbt28P/jQwdFhH9+3bZ0LFaRnlsySHBH3Cgj5ksUQcnQS938QQVsR79Ol9FqvspI+inmfmnl/ogj709xg6bDwPHjxoQhf0qgefHBL0CeF2saF76ENPGhVhQDMY18EwzRS7hrxvop5nxrMTIu2EXl41O+RGJIMEfUIo5CYMYjiF8Ml7XV+IwU57HH2pGkL5JOoPHToUfPy8O4UImdDFbgx19hVykzwS9AkRQ8hNDII+hiowMdSi79ixo9m7d69JK6Xu7uqLqCcmmWcXMqwvoYukGN5j6JsWhdwkjwR9QsQQchOD91p12sN5jszFNPZNKLWY90XU86x4ZqELwRjELutL6L0EQhf0CrlJHgn6hFDITRjE4KGPQdBD+/btU5e0lpSY90HUHzhwwD6z0IlB7MbQ7Td0QS8PffJI0CdEDCE3zusZMjGIXZIODx8+bEKnvLzc7Nq1y6SFpMV82kX97t277TMLHeZeWVmZCZkYuv1iC0MWuxL0ySNBnxAxeOhjCEeJ4T2SdBiDoGfjcuTIkVQkAKdFzKdV1POMeFYxVLhh7tH8LGRi8NCHftLinJOhP8c0I0GfEDHE0MdQtjKGkJsYQqeASiJsXqickiRpE/NpFPWERvGsQq/+EkOoRiweemwheWWhcvr0aftRHvrkkKBPiBg89DGAoXULWcjEUOkGevfubTZv3pzY/59WMZ82Uc8z4lmFTuhhGg7W0NA3LaEjD33ySNAnRIsWLezHpD1dpSANIQzFIgYPoS912gslWPGIJrF5SbuYT4uoRzjg7UzzPSoUMdTZj2EtDdkGOtxaEPpJS5qRoE8Il+REHGjIxBB206hRo1SWOywkaQhFKRW9evUyW7ZsKen/6YuYT4Oo37p1q31GMcAmum3btiZksA+soSETQ9iU67QdegJ3mgl7FqUYl8yVliS8YhFDFRjEzbFjx0zIxJIYC506dbJNpko1L30T80mKep4Jz4ZnFAMxJMQydlq2bGlCJvQcAZekDhL0ySFBnxBu0OOdCFnwxlAFhs1Z6II+lsRYd/TfpUsXs2PHjqL/X76K+aREPc+EZxNyeEaMnt3QBX3oFW54f+4kPobKU2lFgj4hWKTdBA857CaGKjAYI3fcGDKxJMYCCZeE3RQzlMp3MV9qUc+z4JnEkAwbU0IszpAYBH3IyaJOw5AbGPoGNM1I0KfASx+yGIwh5CYGD31MibFAebkePXoULZY+FDFfSlHPs+CZhFz6LxvmWgwJsdi/0L26oYfcOEEf+nNMOxL0CRJDYmwMITd40UI/hQCS82JJjAXE486dOwseahSamC+FqOcZ8Cx4JrHAXAs9ITaWk4jQPfRKiE0HEvQpSYwNFY7gjh8/bkImlnhexMWBAwdMLPBc+/btazZu3FiwvxmqmC+2qN+wYYN9FrHMNWCuxSDoIfTnig10papDRAmx6UCCPkFiCLnheDz0ko7QpEmT4L30lJaL5TTCQQImntJChFSFLuaLJeq594Sf8CxigTnGXAu9nCPvk7UzdLCBIYeKKeQmHYS9WqScGDz0jpBLc8YUX965c2ezZ88eEwt4DocPH26WL1/eoDEci5gvtKjnni9btsw+g9C9uNns3r3bzrXQiaHOfui2DxRykw4k6BMkBg99LGE3GCUJ+nDFKd7hTZs21evfxybmCynqCXcqLy+P6r4BcywWQR964i+2L/TxKw99OpCgT5AYkmLdJA9900LzlxgEPcfj1BsOvftvLnQmpaFRXUNvYhXzhRD13Ov9+/dH0xXWwdwiRCOGUBTWzNAbZ8VQZ18x9OlAgj5BYukWy2IWellHau/GInI7duxohVZMEO4xYsQIG/5R25yQ2MV8Q0Q99zjGUBvYt29fNJ1wWTNDr1uO7QvZc412UchNOpCgTxA3+CnJFnJpx1jqtMeSMEoIxK5du0yMwhRvMfH0NSEx3zBRzz3mXsd474ifjyEB2CX+hk7oHnq0iyvtG/LGxQck6BOEI1VnsEKu700MfSnawidNLImxjFniQkM+VaqKrl27WhGyefPmKn9HYr5hop5cBWp2c69jgzkVQ8x1LAmxMZSsPHjwoP3ImI0hTCzNSNAnTIcOHezHkEMYODKPQfzFkhgb0+YlH4MGDbJhEfnmrMR8w0Q995T66wMHDjQxEkOSaGzvFdsXctiYWwcJxRTJIkGfEkGPQAgZavCGHmMeS2IsEBJAaECMYJwRpWvWrKkkSiXmGybq+Zx7OmrUqKAFUHUQykZIWwzEkBCLzQu5/ny2oG/fvn3SlxI9EvQpEfShd+CMKTE2ltMIxmwM77WqZz169GgrSokhlZhvmKgnLIHPuaehJ0lWBXOJ8IVYvNaxJMSGHD8P8tCnh7BnkwfEEHLjEoAPHz4cvEeG98f7DN0o40FlAY+pIkcuxMXiTZ47d67t6Dlu3DiJ+XqI+gULFtiqNuPHjw861rgmmEvMqRhOJ2JYIyEGm+e0i9MyIjnkoU+RoA/Z2xlLfHlMJR2pQrJlyxYTMy4/BEGKqBd1v3/cu9DjjGsDcymWmvuskTEIwNDzBJi3EvTpQRYoYVzcGSW8Qg5JiSHkBtq1axd8+JSDSiSI2NC7AFeFC7OZOHGi9S4vWrQoimpOhYJ7tXjxYnvvuIcN6SjrO8wh5hJzKgZYI1krQyf0kBve36lTp+xmPIbnmXYk6BOGGEJXuitkz67zvoV8ChFbHD307t272hKOoZIbM4/RdjH1Ic/jQsE9cjHz3LuGdJQNAeYQcykGYomfdzYg5JMnV8wDDRP68/QBCfoUEEscfSxeeo5YiZ2MAbwy9FCobffUEKgqAZb47wkTJlhxFuMmp7a4+8O9yo6Zj1XUM3eYQ7F4OEMPQ4mloRQo3CZdSNCngFgEPYt4yA20sp9n6GVIHXifaAC0c+dOEwM1VbPBSzVmzBgbQrd06dKoNjo1wb3gnnBvuEf5PHoxinrmDnMoZE9urPHzoTfOUsnKdCFBnwJiEfSxJMbGFEcP3bt3N9u3bzehU9vSlAgzmk+RID1v3rwoTqVqgnvAveCecG+qE6+xifpt27bZORQLscTPx3AS4excDBs0H5CgTwGxCPpYQm6c5/GDDz4wMcD7JXQi5DCj+tSZx+s6cuRIs2LFCrNx48Zo8iqy4T1v2LDB3gPuBfekNsQi6pkzrIuxxB+7NTGG9xt6Qiy4k2jVoE8HEvQpay4V8hF9LImxQG32vXv3mljo06dPsHHjDWkaxe9TxYUKJrF563mv7733nu2UyT2oz70LXdRv2rQpmmRYYE3s3LmzCZ0YEmLRKjRCA3no04EEfQrgWA6PRfYECZVYvPRdunQxu3fvNrFA4zA6phIfHRKF6ACLUUe0jRgxwnqq16xZE/TpDe+N9+i88rz3+gqbkEU9c4UXcycWWBNjEPQxJMSiVdAsaJfQm2f5ggR9CsDYuUUu9OTCWBJjCUHBWMdwGuHo37+/Wbt2rQmFQoj5fN56jB/eeryzIZ3I8V54T7w33mN9vPIxiXrmyoABA0wssBayJsbQDTiG+HmnVdAuIZ9E+IQEfUpwsaWhC3qy4WNJGCUJOPQTl9xni8EOQXQVWsw7MHzl5eVm8uTJNhRl7ty5NqHY540f18574L3wnnhvvMdCGvnQRD3vgbkSQ3Kog7UwlveLjQu98suOHTvsx27duiV9KeK3SNCnBDcpQhf0GOZYOosiamIKuwEqmBBu4TPFEvPZIHZ79uxpO6Qi7ObMmWPWrVvnVcgS18o1c+18PmnSJPueiuWtC0nUM0eYKzHBWkgoYgxg44q1dqQFp1Vqm+guik/4qeYeeujxeIV8hEV7c+KtmzdvbkKGsIOVK1eamHDxwFTv8DGushRiPhs82v369TN9+/a1CYPUaed7xJ3jzUzbOsDahKeVBGi6ffbq1cuGWpXqOrNFfameUaFhbnC/Yoqdd2EoQ4YMMaGDbcPGhQzrgBP08tCnBwn6lEAcGoacxQCDGfJxHRnxlOgMvfYyRpvEKBKkWrVqZWIBzyMJkcRQ+0SpxXy+PBpeXAeCGS8uog+vJusBlXKSio0nhAAP65EjR2xs8ODBgxMT076Lep7r8OHDTUwwblgL07ZBLQbYttDLOKJROJVDs1DRTaQDCfqUwMTAcBOXxs43dEHPUX3ogh54jzSOicEz5UBgkfjmUxxpkmI+F/7/oUOHWi8Ym8Fdu3aZ9evXm6ZNm1qhQG4GG8RiiSP3/5K8Tp3pU6dO2eeIN76Y/28Moh6xx9yIITE0G3IsevToYWKAZzxw4EATQ/w8mgXtItKBBH3Kwm6YKLxC9uDEFEdP2AQeudDDqHLBoC1atMjGiKf9fadJzGfjwjJcaAYeMQT21q1breBmTOH1xGPOdXPMTxhbbZv2UF7SlRrlHhASQUlZ/l+EO3932LBhqQ0f8E3U87xwZIwZM8bEBO+bzSGnOjGAbQt9w6aE2HQiQZ8imBwLFiwIPjE2pjh6xJGrduOLt7oQ4E1mM7Nnz55UJ8KlVcxXNWc48XEnWwglJ8QRTE6cZ9e4Z/zxe4TwuM8dCH+3CUCAEMfPPUj7BsxXUU/IEmsAcyMmXHUbn8ZVfYnBpoESYtOJBH2KiCkxltCBGOLogaPmjRs3RiXogWRPapKntU6xT2K+uhyN2jawmTBhggkRH0Q96zk1+n3LKykEhByyFsQAJ2ihd01VQmx6UdnKFCfGhoxLjI0BwhcQjyE1EaoNeIDZsLGZSRu+i3nhV0lL5gDip7bhUKHAmkcISixFAbBpoQt6JcSmFwn6FCbGQuhhNxzxxxJHDyx8lCWMDeqSY+SIzU4LEvNhklZRz9hnDjAXYoOQO9cFPQZwxsUSP6+E2PQhQZ/SsBs3aUKGWMNYRD2eaio9xAZhISR4L1u2LBXdUCXmwyZtop4xz9gfMWJEKsPOig12LJawDGxZTPHzsTxXn5CgTxmxdIwFPDexdFIl+ZDjZ0oAxiiy6JqbdOiNxHwcpEnUM+YZ+6F7bfPBWseal9YqSYUmlk64SohNLxL0KRX0eHNDj7mOLQyFI3dKDsYINcyTDL2RmI+LNIh6Sosy5hn7MbJly5ao3ju2LPSGUmgSd9IcQ0EL35CgTxns8Dm2w7sRupeeBDGOobPL7IV+IkFMaRpCT2IKvZGYj5MkRT1jfPny5dGG2vD+EbixJE1iw3jOoSc9E0KFNkGjxJQb4QsS9CmDRaF37972c8qchU5MXnqebUzvNw2hNxLzcZOUqI851AacmI9lMxPL5sVpkj59+kTzbH1Cgj6FMFliEfScSMQSR+/CbjiKjpVSht5IzIskRH3soTYxhtvEEj9PgzpwTkeRLiToUyzoWRTPnDljQoajO2raxhKGQoIYpb6STtYLPfRGYl4kIepjD7UB7i+hJ7F0xOWZY8NCr3CDFnGCnq7SIn1I0KeQmOLogbbgoTfSyt2wuYUxVnFFhYS1a9cW5e9LzIukRD1jmrEda6gNsLbF5ME9cOCAtWGhgxY5ffq0HduKn08nEvQpBM9ObGE3u3btMrHQtm1bU1FREfzpS02hRzRhKfSGVWJeJCXqGct4amMKNcmFNY21jTUupnAb8iVCx2kRNmuxnj6lHQn6lBKToG/durU5fPiwiQUWwx49epht27aZWOEeEJZAWFmhnr3EvEhK1DOGGcuEk8UMZXlZ22ISfDz7srIyE1NCrEgnEvQpJaY4ehb/li1b2mSymPoNUAIsltyBfDRq1MiMHj3axhw3tOGWxLxIStTjlWcMM5YZ07HCWsYpRUz1ybFZrVq1Cn4DgwZxxRwk6NNLvKtPyiFGjVg1YtYQfqFD3GkM+QIODD/vOYZnW1OSMF7NxYsX17uRmsS8SErUM2b594zhWDqiVgUNh1jTQhe32WCzYgi3wU4pfj79SNCnlNji6Dt06GBLvcXksXadY2N6z1WFXHEv8HLWFYl5kaSoZ8wydhnDMcMaRggh9yKm94zNwnaFjurP+4EEfYpxlQJiqIjCIhFbLD3lKzEGdI+NHTx7eDjrMtYl5kWSoh6Rw5hl7MYOa1jHjh3tmhYL2CpsVgwCV/HzfiBBn2JiiqMHYi9jSxTlGbNYxu6lh4EDB1qPF6+akJgXSYp6xijlChmzscPaxRoWU6lKwFaRABw6aA9OkkGCPt1I0KcYYtUwNMSuxSB08XYcOXIkKnHbpEkT+74RB7GDp2vUqFG2lnd1okpiXiQp6vkZY5SxGoN3tiZYu9q0aWPXsljARmGrYqhug/ZAgzA/OnXqlPTliGqQoE8xGIv+/fvbz9esWWNieL+EoOzbt8/ERL9+/cz69euj2shUBR0mnaiiTn0uEvMiSVF//PjxzPhjrMYOaxZrV2ydQ7FRhBjFsKFz2gMtEsP79RkJ+pQzaNCgaAS9C7uhWkJMEIeLlz62jUxVUEkB7+eiRYtsSUCHxLxIUtQzFqnGxNiMuRNsNqxZrF2xVfjBRsVSnnP16tX24+DBg5O+FFEDEvQpZ8CAAbbEIQtnbWKLQzCm1CSPIWcgG7wf8tJ/CH0JRo4caRYuXGjHg8S8SFLUMwYZi4xJxqb40DvvTpFjAdvEeIhhU+dymtAgsT1nH5GgTznNmzfPJKLE4qXv0qWLbacdE8SfEp8YUy3+mqBhC/W958yZY8WUxLxIQtQz9hiDjEXGpPiwNjlrVkyx87Br1y5ro2LyzqNB0CIi3UjQexR24yZXLF1UY4NFk7KN9W2wFCLEKeMR454oZlmUGsowMvYYg76Ov7Nnz5lth46bUx8Ubl3hnlB9LcaqJzhdsFEx4JyIToOIdCNB7wFuMrGANrRNuQ/g8SH5Jjt+OhbxQFymKxEWOy7MZtKkSWb06NFmwYIF0Y0JkRyMNbzzY8aMsWOwIR1lk2TBtgrz7Xc3mx/O22o27i/M9WOLKNkYU915NyawTTGcSjDWec6g+Hk/kKD3gHbt2tkSlsQsrlu3zsQAHQfdYhITvG9OJz744AMTM7kx8yTeuZj6fNVvhCgkVLNxMfOUJmxoR9mk2H/slHll7V7zwZmzZuWeo+Z7720xM1fuMUdP1n99YW3CSx1DDfZcsEm9evUyMUBpVjQH4UVt27ZN+nJELZCg9wS3Q44l7IbYTBKBY0sSxftDCbiNGzeaWKkqAZb4ZVf9xidRJfyCseWq2WTHzPsm6gm1QczvOHzS9O1wiRnWpZVp2bSxeXHlbvO9uVvMsp2H67W+btiwwa5RsZUw5F65cpUxoHAb/5Cg90zQ46GPoQIMxoJTCVqKxwYekYMHD1ovYWzUVM2GCiOE3yxZsiSKqk+itDCmGH+MsXzVbHwS9Ut3HTHvbTlkerdrYRr9Vnx3bNnUDC1vZXYcPmEenb/NPLVkpznw/qla/03ec0VFRTRJodlQqIH3HcNGhlMYFw2gcBt/kKD3BJJw8BZRLos22zEQa9gNBmPo0KFm5cqVJiZqW5qScnHjx4+3CcS8hCgErKuMpwkTJlRbktAHUY9I/9WqPaZJ40amdfPKybwXN2pkBnRsacrLmpq3Nhww335ns3lvy0EbllMTq1atMkOGDIlC1OZCblMsYUbMA7QGmiOWBOAQkKD3BBbQgQMHRhV207RpU5t8dOzYMRMbxO1SJmzv3r0mBupaZ56KIyQrkqS2dOlSVQYS9YaxwxhCwDCmalPNJs2i/szZc1bMb6s4Yfq0r3pj0rp5EzO8vJU5fvqM+dmiHeaxhdttNZyq4LSUjQ5rU2xgg7BHvGLAaQzCbWLcvPmKBL1HuKMvYttiiS13pRxjhMWUY8/QxWp9m0ZhaLhHxLTOnz/fCjIh6gIbQsYOY6iu4iWtop4wm3lbK0y/Dh+G2mSD7Th66IDZt3Oreb/ioOnZtrkV/ou2V5jvztliXlu715w4XTmskzWIJlLOqRTj6U3v3r1NDDA+1B3WT/wsrBsp/fr1s94jYhhpbtG1a1cTOmTXs7iQNxBbiTSeNRUVSEKjY3CIFKIDLPOAeGfKWtL8h4o4QtTE4cOHzfLlyxs0ZrJFfRoan+Fhf2n1Hhtm07JpZfP+/pEK8+6LT5vXn37M7N26IfP9Tj37mWvvfshcdvPd5oi5yMxYttus3HPMTB/U0QzqdD4pGDGPoPW1Fn9DwPYcPXo0mkovaAvmBs+a5GfhD/LQewThJ85DsmzZMhMLsTaacu/9wIEDQSbIFkLMOxBkY8eOtTG+6rYraoIxwlgZN25cgzeAafHUv3/qjHluxW5z6MRp0611s0o/Wz7nTfPFO6aYJ//tK2bvJd2NueX/GnPP39uPfM33+fm+5XPM4M4tzYb9x8wP3ttqfrlsl9l98IhN0o/BgZQPbE9MceROW6A1Yqi3HxIS9J5BXWQg5jOWsJuYBX2oCbKFFPOOZs2a2WRZSsvFFJYmag9jgrHBGGGsFComOmlRz/t6de1es2L3EZvwmh06hJj/jz/9HXOqfKgxn/mhMbf8H2MGX2FM79HnP/L1Z35of87vrZ33azO4cyvTrkUT8/LavearLywyp9v1NLFOp5gEvcsnydYawh8k6D2DXTPJkkeOHImm2g2hNiRiHTp0yMSIa2wTiue5GGLe0ahRI2uImCPvvfeePSoXAhgLjAnGBmOEsVJIkhT1S3YeMW9uOGB6tG1hmjZuVCnM5r/+4g/Mud5jzLnb/8KYlu3y/4GW7ezP+T1+n3/X7pImpluzs+b4ucbmmZUHzOOLt5s9R+Lq1IzNYf2NJdyTfDW0BXMk1nwJn5Gg9wzi2vDYArW4Y4HkWGLJY4WEPTZwJPH5TDHFfDbkHtAYiJMNxo289fHCs2cMMBYYE8Xs9JmEqN9RccI8v2K3adL4IutVz4aY+ZMnjptz1/+hMY1qEKWNGptz0z9vf3/OzGfM6dMfmEMHD5ix/bub7m2amzmbD5nvvLvZvL3xgDldixKXIUDuQExx5E5TDBs2LMp8Cd+RoPcQjBJgoGgAEQMYSjxqsXpcee9s5FasWGF8pVRiPrdePd41PLMxlj+NHeeVZwwwFqqrL++jqD968gPzi2W7zJ6jJ20DqdyNDAmwZuDlVXvmc2nV3piBl5nXnvqx2bFju+nWratp1Ogi06rZxbbE5emzZ20zqpW7j0Yxdhg3pRgzaeD06dOZ0E6nMYRfSNB7CNUGSOTCW+vaM8dA//79rcckVqiygHHxMfSm1GLeQSwx8wUDxWZI3vo4vfKMgVLW0y6FqKcR1Asrd9u4+UGdWl5QovJYxcHz1WwQ9HVh4OX23505ebKSmOX+MXU6tWpqPfahg63B5sQCWgJN0aZNm6KeYoniIUHvISysLmElprAbYhk5kQix4kvIoTdJifls5K2PhyS88kmI+pV7jpp3Nx0yvdq1sB1hczlx/LdjvPn50pO1ptn53y9rWfm+HTt1xhw99YGZPrCT6dAy7AZL2BhsTUxNtFwy7IgRI9RMylMk6D3FHYmtXbs2KoGLxyTmWHpCb4hv9CX0Jg1iPtdbz2ZY3vrwSNorX2pR36VVM9O/wyVm66HjtmRlLs1btDz/yYk6hsecPP/7LVq1qnRvNx5434zt3saM7dHGhE5s3nnGJloCFG7jLxL0ntK5c2fTpUsXW2aK5igxhZ3gXY25KyhHogiFtJfyTJOYz4ZrwXNL0tfcuXPN7t27Jew9hmdHM5w5c+bYZ5qkV76Uor5zWTPzyQk9zMSe7cyG/e+bA+9XXhNbtmlnm0aZte/U7Q+vfcf+u5atP2yktOPwSdPhkibm+kGdTONGYXtvOf3ESRZLIynAwYGWKC8vt9pC+IkEfSA16WOCqgMbN240MUNJsa1bt6aq5bwPYt6B55Y4UcQfXREJ0aCBl/ALnhnPjlJ7EyZMsM80aa98KUV96+ZNzH2ju5mbhnQ2+46dNlsOfnhay32gA6wV9McO1u4PHj1gzNp3zdR7Pp65j4TaVBz/wEwf1MmUtw4/dh7bElNlm+zQXdWe9xsJeo9xk2/Lli1R1Wjv2LGj7VwYS4Wf6uqts5nDs5Im0i7ms8Gjy+ZozJgxNtl4/vz5VuCLdMMz4lnxzHh2PMO0l9krlqhvenEjc+OQTub+0d3MxY0uMqv2HDVnzp4/cbrs5rtNs+YtzEWz/t2YsxeG5VTi7Blz0cv/YX9/8k13nf8WYUz73zfje7YxE3uG77HGplRUVJgOHTqYWMCW4hxy8fPCXyToPYZKN86TEJOX3sVC0wQjZhAIeCRpYZ8WfBLz2dAxdPjw4faFh27x4sWpPf2IGZ4Jz4Zn5J5Xobq9+izqWRPH92xrPjm+p+nRprlZsfuoOXH6jLmkrI357Fe+ZS7avMhc9OxXznvg83H0gLno2S/b33vkq/9p/x1sPnDcVrS5cUhnc3GexNvQwKak9ZSnWDjtgJZAUwh/CX+GRuKlx0DEFAdMrN+ePXvMmTM1eJ0Cp2vXrtZDTxx40vgq5rMh9prrJyGO5Mply5Z5VVEoVHgGPAueCc+GZ5SmOPm0JMr27XCJeXhCTzOmW2uzdt/7puL4aTN88tXm8//v+6bprpXG/PenjXn+a8as/rUxmxad/8jX//1p03TXKvOH//wDM2zSVfZvHTp+2pw+e86G83QMvKoNYEuwKdiWWEAzuHAbJcP6T7rPKEWNUPFk5syZZv/+/da7QEfVmLz0lHCMqRpBPmg4NW/ePOtdSUrkhCDms6Fc3bhx42yMNgavWbNmdm7Jg1X60BpXppV53r59exMC2aK+0HOm/SVNzcfGdjcdWjYxb244YGPgEfX/+Iu3bQdYmkbtff6fMr/ftrynmf6//sJM+cg9pkWr8+ObTrBbD50w0wZ0MKO6xlG6kXGWhspIpQTNgHZo0qRJpgO98BcJes9BaOClX7hwoY0pjUXQO+80VUo4ImVBihXqbRP7iPCcOHFiyQ1SaGI+GwQkL5IuMX5UWOrRo4cde+QxiMLDiROx8du2bTMtW7a0oQAh1gMvpqhv3qSxuXVYuenUqpl5ceUes2bvMTOgY2sz9d5PmWs/+rA5dviQOX7sqNm7b7/pN2ioad68WaV/v27f+2Zo51Y2ETYGgUuX1L1795pJkyaZmMAR5LzzaAnhN7JIAUB1B+A4GuERCxia2LvHOlq1amWFZqnj6UMW89kgKNk04bUncY7KKtzrmHpAFBvuJfeUe8s95l5zz0MU86UIv2nU6CJzeZ/25uPjephOLZvauPpTH5y162arNu3MBxc1MV179rlAzG+vOGFaN7/Y3DKsi2nZLA6fn6s7H8PmxYFWcPaCal/CfyToA4CYv549e1rPFp76mOjUqZM9lj9x4oSJne7du9uYSDybpSAWMZ8NlVQ4lseTR71mDOKCBQusdy+mHJZCwT0jbpnTRe4l95R7yz1Oe9UaXzrKDu7cynxqQk8zvEuZWb33mDly8gNb2YR7365d5co1R09+YCpOnDY3Du5sO9DGALYDcYstiQm0ApoB7RBT3kDISNAHgtthIy7SVsaw2AwaNCjT5S52iIOkyU6xy5jGKOazwZNHKA4lE/Eic78J/1q9erX9XOK+arg33CMEPM2gKBNI2CD3knsak5e0VKKeJlR46q/q196s23XIbNx90IaNZUPcPN1gL+vdzkzsFX6JSseaNWts2dOYIAEYrZB9wi/8R4I+oORYjAKeBkRFTNDRjxhI4ptjBzGEMCD8qlinFrGL+VyIPUUQ4FmmezMbKsJGyGmg+lDslZiAe8C94J5wb7hHeAUnT55s753id4sv6i9p2tjcPLCdGd78qGndobMV727jyce1+46ZoZ3LzM1DugTfDdaBzSC8K6ausG4Tg1ZgzCkZNhziONOMAI6nx44da2bPnm2Pr2ObpHjpWaTw8sUOCcJ4PBEGnNyQNFsoJOar30whDJw44F4hYjna5meEkyD4YxGvVKbh/RNSg2CkIdyAAQM0bhJKlGVTtXTJb8zD08aZTUfOmmeX7zYr9xw1Azu2NNsqTphOLZuZ20d0MWXN45EF2AxsR2y4ZFg0QyyhbTGgJxkQJJEh6Dds2GD27dtnDWhMSaEIV47yY/O2VHU/qA5C0xCEQSHCGCTm6wb3iGfAixMkhO2KFSvMqVOnbGgJ47RNmzZeNUaqDt4X4TPMQcp98r7YxDBeYq5ClQZRz4aKtaBfv352bRjRyph2lzQxM5btMst3HTVlzaiK08V0bxNH3DwwTrEZ3I+YQBvQmA2bgGYQ4SBBHxAIBOepxkt/4403mpjgvWO0FBN4HsQUx6pUcMAz2hAk5hsGgpakZV7kuCAmeJHAjBDGS0aNe19EPtfM9SPgSUonbIFr5to7dOhgK4aorGd6RD1rAOOLNcGBeP/EuB7mlTX7TKdWTc2obnH1WCDvyjVmjAm0gbOXcn6FhQR9YCBmEfQYgmnTpkXlGWvevLn1tlBxJLaKBVWBR44OmwhHylrWB4n5woLQdfXtHQhihHE+kc+LsU2oDq9SJY3i1SVshhf5GFxfPvHOCYSO7dMr6hlPlAQleTuX1s2bmLtGVU6OjQFOy7AVzKuYYF1ZvHix/VylKsNDq3Bg4Blr166dLUuGkIstppwEO+IDERryEJ6P68aQL1q0KBMCURck5ksDgrgqkc8pC0IfUY1BdomMPFsn8rMFP+Oen/Fyn/PvSADkdIB/z4vPswW7+7yqvy/x7p+oR7jywg7EWD0oH4x7TixiPMlFEzDH0Qixd1gPEa3MgcGizc775ZdftsJ29OjRUS3kiA06x5JH0NAwk9Aq31CmDFFf22NWifn0ifx8HnQnxl097VzRzkfau9PaPlfsMx4Q63jb3aYgpvUiZFHPJpBnTpy0numHYBuwEbFtTFkHXDIsGkFjIjziGtGRgIh/7bXXbPv0rVu32sUrJrp162YXLo6ZW7SIJ8mrOkj+wktH/CRxozUlgknMpx8MMiK8NmEDPM/hw4eX5LpE8qL+6NGjts4/Yr6QVa58B5tAwnaM3nm0AOVi2cigEUR4KCYhQFjsR40aZT9/++23TYxCh7Kdrq21OA/5FIh6Eoerq1EvMS+Ev3XqmdvMcURbTDlUtYH+HNiGGL3TVMADtIHW9TCRoA+UKVOm2EWLBFlqQcdGWVmZDSegRJf4ELy5eOiJqaeUYi4S80L4K+qZ08xt5nhsCZ81QbEEQsqwDbGBBqCqD5oAbSDCRII+UEhgo3tsrF56GDx4sF3EiCMWH0K4DV4qGh6ReOmQmBfCX1HPXGZOM7djq61eE9iAdevWWZsQI847jyaoKidH+I8EfcC4nTiZ7cQNxgaxgj179rRNNET+ngUkyiIEJOaF8FfUM4eZy6otnh9sQIyJsIDtX758uf38iiuuSPpyRBGRoA+Yrl272kovZLe/8847JkZo5EOFj+pixmOF0mWU+ZwzZ449ppeYF8I/Uc/cZQ4zl5nT4sJEWGwAxRJihBN6NABaoLy8POnLEUVEgj5w3I6cZhKUtIsNYgaHDBlik6HEhRBny2aHBT/t3UmFEJVhzhJOwhxWzHx+KI4QayIsNp9THJB3Pnwk6AOHY0bCTs6cOWO9ODFCp02SoWJMDq4OF2Zz+eWX29hKF34jhEg/LsyGcqTM4Zqq38QIa36sibDw7rvvWtuPBujdu3fSlyOKjAR94OCVcDtzapBz/BgjJEPRUCRfZZcYyY2ZJ1HKxdTrHgmRbpijLmaeuVvbkpYxQXdk1vxYE2Gx9YwRkHc+DiToI4DYys6dO9sFznWKiw2aqxB645KDYqaqBFjibzF+bPyUcyBEOmFuMkeZq9kx8xL1lVmxYoVd82NtrPXee+9Zm9+lSxerAUT4SNBH5qWfO3dutB5YjB8xpzGH3tRUzYYKGa5OPd0mhRDpgTnp6sznq2YjUX8e1njW+liThBHy2PrsnjQifCToI4E4SxY3FnlqFcdKzKE3tS1NSQ1r11H20KFDJb1GIUR+mIvMSeZmdXXmYxf1sYfaADaekBtsPrZfxIEEfSQ0atTIJk4BJSxJlImRWENv6lpnnooZ48ePN6tXrzZ79uwpyTUKIfLDHKTrN3OyNtVsYhb1sYfakCxNMixg87H9Ig70pCNi9OjR1rNz+PDhTLJMjMQWelPfplFNmjSxAmLbtm32JYQoPW7+jRs3zs7J2hKjqHdVbWINtQFsOzYeW4/NF/EgQR8RdMm76qqr7OdvvfWWPZqMlVhCbxraARYvF0f8Bw8etK3TqVcvhCg+zDXmHHOPOVgfj3NMot6F2lD5J1ZOnjxpbTtcffXVUXbGjRkJ+sgYO3as9V4cO3YscywXIzGE3jRUzDtIqBoxYoQ9uuXvxRquJUSpYI4x15hzzL2GJDXGIupjD7UBes3wjCllyiZQxIUEfWSw2E2dOjUTS4+wjxU2NhzP7tixw4RGocS8A0HRr18/2z6d0qcqaylEcWBuMceYa8y5QlQoCV3Us4aTWxBzqA22HJsO1157bdQbm1iRoI8Qst7Ly8vtEeWvf/1rEzOE3mzdujUoI1doMZ8N/QzwGFI6TxVwhCgszCnmFnOMuVZIQhX1CFnW8JhDbQBbjk3v2rWrKttEigR9hODxue666+znNCiJWZhxpE1N5yVLlpizZ88a3ymmmHeQbEWyLPG9SpYVojAwl5hTzK3qylI2hNBEPWs2pTxZw2Ou5oINx5bDtGnTVHc+UuKdAZHDUW7fvn1trOYbb7xhYgYj17t3b7Ny5UrjM6UQ8w6qbVB1g2oK3DclywpRP5g7xH8zl+paySZ2Uc/a06dPn6Kvd2kHG44tx6b3798/6csRCSFBHyns4NnJAwt77LXGOabEsPpayrKUYj57DA0bNsy0bt3axvzSyEQIUXuYM++9955p06aNnUul8qyGIOpZq1mzCR+NGWw3zxGcTRdxIkEfMd27dzdDhw61n7/22msmdrgXGzdu9E6YJiHmc8cRMZtcg68bIiFKDXOFOUO8PHOo1Pgs6lmjWavZBMXOq6++aj9yL5IYRyI9SNBHDhVv8ArREXTLli0mZqgK4OLpfQkhSVrMO1q2bGkmTpxo9u7da0uBhpCPIEQxYG4wR5grzBnmTlL4KOpZm1mjY4+bB2w2HYSx4VS2EXET92wQpmPHjplucuz0fRGyxQLj2qNHD7vBSTtpEfMOVzObGsiEEfgiEIQoZUUW5gZzxPV2SBrfRD1rM2t0khuhNICtfuWVV+zn1JzHlou4SX41EYlzzTXX2I5y7PbXrl1rYodjSzru4UFLK2kT87n5CKNGjbLVJ0Ks8S9EfWAuLFu2zM4N5kia8EXUsyZTmlGhJcbaasp1YrvpCiuEBL2wSY0c/cKsWbPUCdQY6z2jhFwajVuaxbyD65owYYI5cuSIWbhwod0gCREjjH3mAHOBOZHmOZtmUc81sSarxroxH3zwgbXVMGnSJGvDhZCgF5Yrr7zSHmHu37/fvPvuuyZ2iKfHuBGryeKZFnwQ8w7CCWjcRRk1BM3OnTuTviQhSu6VZ+wzB5gLaQix8VHUswa7dU8dUI2ZM2eOtdX0K7jiiiuSvhyREtK9uoiSQdvs6dOn28/feustU1FRYWIH4zZw4MDUJMn6JOazoSQfXiTqbMtbL2LzyjP2mQO+kDZR75Jg6QTr07pXLLDN2GjAZmO7hQAJepGB2M5evXqZ06dPZ47zYqdDhw72lXRuga9i3iFvvYgF37zyaRf1rL1uHRbnw2Kx0dhqKv0I4fBvpRFFg9JXN910k/1I58INGzYkfUmpgC6yeNySEqG+i/nqvPW+1fwXoioYy7565dMq6llzWXtZg4WxNhnbjI2++eabS9aITPiBBL2oBF33SNyCmTNnKkH2t5CIRUUBhGgpCUnM53rrCWeiEg6JbqpbL3yFsYsXmbHMmPbVK582Uc9ay5qrJNjzYItffPFF+zk2ukuXLklfkkgZYaw6oqDQoIIE2X379tnkG3FehFKvn4YwpYoBD1HMZ1NWVpap+jF37txUlwkVIh979uyxY5f1krHMmA6NJEQ9ayxrLWtuKJujQiXCMtbURErkQzNFXABJNtddd539/M033yy5VzqtNG3a1HqLFi9eXHSPcuhi3sGRcbdu3awYQtArDEf4APNzwYIFVmAxdhnDIYc/lFLUs7ayxrLWsuaK86cV2GLANisRVuRDgl7khcWbbnwk37z88stJX05qoN4vyUg0iClW5ZtYxHw2NEcZNmxYJgyHEIY0lQsVAhiTa9assd5jqq4MHTrUjt0YKIWoZ01lbSVmXrXVL0yE7dmzp30GQuRDgl7kJTvphgV248aNSV9SaqDLI8eexah8E6OYzxeGw8d58+aZTZs2Kb5eJA5jkLHImERojh8/PsjwmqRFPWsqayu5XOLDRFg2kEqEFTUhQS+qFa4YLlCCbGUoSYe3bvPmzQX7m7GLeQcGC4M+efJk20SGGGVKAaahF4CIC8bc9u3b7RhkLDImGZsxi6piiXrWUtZU1lZxHmwuthewxdroiOqQoBfVQvINCzjxzRg18SEctx88eLAg5Swl5i8E0cQR88SJE21cvUuclbAXxYYx5hJeSdBkDDIWYxbyxRT1rKGspayponIiLMUpuN9Tp05N+nJEypGgF9XSokWLTILs66+/bpPAxHkw7jTj2rZtW4Pui8R89eAZxWs3btw4e58Je+CjhL0oNIwpN8YOHDhgx1y/fv3sGBTFEfXcb05BWEu1Yap8X9544w37uRJhRW2QoBc1Qumwvn372uPQX/7ylxJSWVBSbcyYMTb2sz7VgCTma0+TJk3MkCFD7L3CU//ee++Z3bt3azyKBsMY2rVrlx1TjC3GGGONMSeKJ+pZM1k7VZ7ywpyNZ5991tpcNpTcHyFqQjNI1Ahek9tuu82WENuyZYtCb3KgysXYsWNt4lJdjJrEfP1o1qyZFVvcc7pyMh45JZGwF3WFMcPYIbTh6NGjdkwxthhjoriint9lzeSex1IpqLawptFUC5t766236uRC1AoJelEr2rZta6ZPn24/f/XVVxV6kwMLrzNqp06dqvH3JeYbDt7TAQMG2Ko4eLIQZVQiUfK2qAnGCGOFMcPYIUaesSSPfGlEPXkJbv1TrfnKYFtfe+01+/n1119vba8QtUGCXtQa4kkVelO9UaMZCs2RqquhLjFfWIhv7tOnj5k0aZL19BH/vHLlypK3qhfphzHB2GCMMFaoWsPYUYx86UQ9a+OiRYvsWqn1r/pQG04vhKgtEvSi1ij0pmaoUU3DGUR9Pk+xxHzxIAaXZmgIe8q70QBo/vz5tlqJNp/xwrMn14KxwJhgbDBGGCsKZSitqEeosjayRqpx1IUo1EY0BAl6UScUelMz7du3t1VZaA2fLeol5ksDRrBdu3Y2kWzkyJE28Y7QinXr1tUqHEqEAWEdPHOePbkWVFFhTDA2JJRKL+pZCxHzrI2skaIyCrURDUWCXtQZhd7UTIcOHeyRqfPUS8wnA8mNxEYTWoFHcMmSJfaZUNFEHWjDg2fKs+UZL1261D5znj1jQLHayYl6J+ZZE1kbRWUUaiMKwUXnpMZEPTh06JD59re/bT2eN9xwgzWa4kIogUdZNsA7KDGfDs8tnWcJxaHPAqEXoXttKcdI4meIYMJoSkS1GhqQde7c2XTr1k2ValIAYn7x4sX284EDB5pOnTolfUmp5N133zWzZs2ym87Pfe5z8s6LeqFaUaJBoTcvvPCCDb1hsZbn5UJatmxpjh07Zj9KYKQDngMnTLwoVUhTm9WrV1tR3717d1NWVpb0JYpaQBgNzw4xTwgHns1WrVolfVkiC7fmuTVQXIhCbUShkKAXDQq9WbFihdm4caMNvXn44YeD9nLWFRdmM2XKFCscOXLmKFUVNdIDAnDw4MHWy8upE6UMER9t2rQxXbp0Cd5z76MnngTXiooKKxA5XeH56RmlDxdmg7OHZ6WQwwtRqI0oJAq5EQULvaE9NeJV5E+A3bdvn938SNSnG5ZEEmkRjghIvIyIe8IFfG2A42vIDUKHsDWeBaFSbLB4FsTGS8SnW8xTFACR2rFjR/s95RFdyOzZs+0Jt0JtRCHw0zqJ1MACRAz9c889Z48Ne/fubb1mMVOV4cKwIUIwdOqOmF54RnjoeQFx2cTbUzsbCO8gvIyfS1QWfjOF950whAMHDtjvEROPF558B5F+XGlKqtlkh2FmJ8pK1BtbntKF2mBDJeZFQ5GHXjQYhtDTTz9t23gjcj772c9Ga3xr44VCqFAPG1Gvyhv+iRW89ghOhCcnLU7gp9lrnFYPvTsNcQIezy5rCPcTb7w2vX7BKQobX+rMV1WaUp76806C//qv/7JryIgRI8xdd92V2rVD+IMEvSjYQs4ChdgZMmSIuffee6NboOpiqBAxbIBiNmqhCnzn3eeVlkTotAh61gnuk3tJwIe3/tEBtqamUTGLeiTXk08+aVatWmXHPA6wtKwTwm8k6EXBoBTg97//fZvoc9NNN6VCQJSK+hiouhhA4Y/AZ7OGWCW/hNySNIj8JAR9PvHOiRShBdwHxrwEfBjUx0ERq6hnLs6cOdN2tv6d3/kdW2JViEIgQS8KCl0ZX3rpJStiWKy6du1qQqchhgnB5ypBqOxnHCL/9OnT1kuHsKfKDhVA+MjYKVaydLEEvWuaRhUnqgPxESHP6VyTJk0k3iOA0yl6bdQnhDA2Ub9z507r9GLe3HjjjWbSpElJX5IICAl6UVAYTk888YSt600M5Wc+85mgjxMLYZAQfMSdkkwcwwZInJ8nbOacEHYv170WMcy8ad68eeaj+7w+wrg+gp5xiTg/ceKEfbnP+cimBPAysiFxLzYmiLrYwu1iBYFKLwCa5tV3wxaLqGfefPe737W5IiR533fffZonoqBI0IuiJPx85zvfsV7JkSNHmjvvvDPIhauQhgght2TJEhtTSaUgES8syYjpXBHtPvKzfCCoeOHlR2i7OcdHPKgDBgzI/H3GG15C/lZ1fy93U+E+8rMQ57SoPZs3b7b5I6NGjbLjrSGELuqZczNmzDBLly61p1WPPPJItIUjRPGQoBdFYcuWLebRRx+1C9ltt91mxowZY0KiGAaIe7Vy5UorlgjBkWASdRk7CHQn0p2nn+/zYqziRXVjCgHmxD8vjTVRWxhPbBAZZ0OHDi3Y2AlZ1HMCS/NF7tWnPvUp07Nnz6QvSQRIw7bVQlRBr169zLXXXms/f/HFF21zmFAoluFhsR82bJgVWHhynCgTojZjx3nUCX0pKyuzL7yBxLDjVedz931+x4XvSMyL2sKaxNrEGsVaVcixk12nnjU2FOhhgQ0EbKLEvCgWEvSiaFxxxRW2UyCenKeeeioTd+szpfAi0ZCFrqTz5s2zIRZCCJE0rEWsSTT6Yo0qBqGJemwetg8biC3EJgpRLCToRdHAe0P8PN5A56XwOcKrlEfCJMdynE0FHHIRhBAiKViDWItYk8rLy4v6f4Ui6rF17nSaZPFQc8lEepCgF0WFhcx1wVu8eLGZO3eu8ZEk4jsJkRg3bpxtQEI1CSGEKDWsPaxBrEWl6pcRgqjH1mHznGMLWyhEMZGgF0WHo8bp06fbz2fNmmXWr19vfCLJZC1KAI4fP97s27fPrFmzxusTDiGEP7DWUH6YtYc1qK415mMW9dg4bB1g+7CBQhQbCXpREiZPnmwXZ4wEMYU0I/GBNFReoCIJ5T9JYqRaQlVlBoUQohC43hgkU7P2NLQsZUyiHtuGjcPWUVkK2ydEKZCgFyWBY8dbbrnFNk+invbjjz9uP6aZNIj5bKhPz4vENF+MmxDCL1hbWGPcepM0Pon6bNuGrfvIRz6iuHlRMiToRcmgRN69995ry+ZxjPvMM8+ktjRj2sS8o0OHDhnjFlIpUCFE8rCmuHWPtSYt+CDqsWXYNGwbuQZ0gq1v91wh6oMEvSgpiPn777/fLnQ0J3nttddM2kirmHdwTRMnTswkqymuXgjREFhDXPI9a0ta1700i/pXX33V2jRsG2JeSbCi1EjQi5LTrVs32z0W3n77bduoJC2kXcw7aOxCy3U2SO+99545fvx40pckhPAQ1g7WENYS1hTWlrSSVlG/ZMkS884779jPb7/9dmvjhCg1EvQiEUi0ck02aIm9ffv2pC/JGzGfTffu3c2IESPsde/evTvpyxFCeARrBmsHawhriQ+kTdRju7BhgE3jXgqRBBL0IjGmTp1qBg0aZCsqPPHEE+bIkSOJXYuPYt5B4y6OyYl/Xb58eWrzEoQQ6YA1grWCNYO1gzXEJ9Ii6rFZ2K4zZ85YW4ZNEyIpJOhFYpD9T9Opjh07ZhbGJEoy+izmHZSVwzPUvn17e3x+7NixpC9JCJFCWBtYI0h6Zc1IqiSl76I+2xHVqVOnTANFIZLCz5ksgoHa6g888ICtd8zR5S9+8YuSJnmGIOaz6dq1q42DXbZsmdmxY0fSlyOESBGsCawNrBHl5eXGd5IS9dgobBU2C9tFoQdsmRBJIkEvEgevMuUs8RRxDPyrX/2qJKI+NDHv4L1MmDDBHD582LYeP3XqVNKXJIRIENYA1gLWBNaG0Na7Uop6bNPMmTOtrcJmYbuwYUIkjQS9SAV9+/Y1d955p/2c4+DZs2cX9f8LVcw7MDRDhgyxjWEWLFighFkhIoW5zxrAWsCa4GuITVpEPbaJxluAzcJ2CZEGwpvZwluI57zhhhvs59SnX7hwYVH+n9DFfDbt2rWzSW+0I8dDd/r06aQvSQhRQq/8gQMH7BrAWhAypRD12CTXO+XGG29URRuRKiToRaqYPHmymTJliv38+eefN6tXry7o349JzDuoKz1s2DDroZs/f7689UJE5JUfOnRoqmvL+yLqsUXYJFeectKkSQX9+0I0FAl6kTqmTZtmRo8ebWMVn3rqKbN169aC/N0YxXw28tYLETbM6Zi88qUS9Vu2bLG2CJuEbVJ5SpFGJOhF6qD016233pqpUf/Tn/7U7Nmzp0F/M3Yx75C3XogwYS4zp2Pzyhdb1GN7fvazn1lbhE3CNqk8pUgjEvQilZC4dc8995gePXqYEydOmJ/85CemoqKiXn9LYr56b/2iRYvsPRZC+AdzlznMXI7VK18sUY/Neeyxx+w9xhZhk0JMKhZhoJEpUkuTJk1sjXoaT1FujYX1+PHjdfobEvM1e+v79+9vlixZYtavX68us0J4AnN13bp1du4yh5nLMXvlCy3q+X1sDo2jsEEf+9jHrE0SIq1I0IvUL8gPPfSQad26tdm3b58Nv6lt7LfEfO3g3lKbmsYoc+fOtfdZCJFemKPM1RYtWti5yxwWhRP1VAgizIb7zL3FBnGvhUgzEvQi9bRp08Y8+OCDtiPftm3bzM9//nNz5syZav+NxHzdICaUI2XEAbG4lGer62mIEKK4MCeZm8xR5mr37t0Vz11gUU+sPAmw2BpsDmIeGyRE2pGgF17QuXNne+R58cUXm7Vr11Yr6iXm6w/3d/jw4WbgwIFm6dKl9khfYThCpCO8hjnJ3GSOMldFYUU9NgUxj43h/mJzOnXqVPJrFaI+SNALb+jZs6e5//77bZwoNYGffvrpC0S9xHxhKCsrsx5Ajpk52t+7d2/SlyRElFBlJTu8hrkpCi/qsSXYFGwLYp78LWyOEL5w0TkKqwrhEXiqHn/8cbsA46m66667bOUBifniwBE0HqujR49a72Dbtm2TviRRR9577z1bAUX4w6FDh+y8a9WqlZ138sgXjlxbwQkIYn7FihXWYYTjaMCAAUlfphB1QoJeeMmaNWvME088YRfikSNHmuuvv94eR0vMFw9Kt3HfEfiDBw82LVu2TPqSRC2RoPcHNs7MMwQ8dc+J4xbFE/XYj5deesksW7bMOoYQ82yghPANCXrhLatWrbKx9Ij6rl272nhHvFmiuEhw+IcEvV8bZuaV1rLSrGVUTtu5c6cV8/fee691VgjhI4qhF94yZMgQ2+gDWJBnzZqlBM4SgNAYO3as6dWrlz0VYWNV21KiQojKMHdWrlxp5xJzirklMV98sBV45rEdgC2RmBc+Iw+98B6MIZUJWKCJqb/zzjvVYKWEkDBLUyqqQfTp00f3PoXIQ58+yAHauHGjrXVOYyhVUyntvX/mmWdszLzrSj506NCkL0uIBiFBL4ILv2FhvvvuuyUsSwjLCJ6uzZs32xKjvXv3VhJfipCgTw+E1DBPqF7DPCFcULXkS4erZoMjSGE2IiQk6EUwEH/65JNP2gWbBfqjH/2oRH2JYTnZtWuX2bJli23G0q9fP9O0adOkLyt6JOiT5+TJk9YjX1FRYYV8ly5dJORLjGsaRWlKbANinnwFIUJAgl4EW9KSSgWI+iZNmiR9WdHBsrJ//34rYKifTUiBWqcnhwR9st1dCUnjY9++fU2HDh0k5BPKVeAUl1KgKk0pQkSCXgQHxhNRjzemR48etkGISlkmW0+bZ0IIDsJeCX+lR4I+mQoqjHvWIca9+jckW6LyZz/7mdm2bVumaRSnh0KEhAS9qBevvPKK9XQg1gipIBnyuuuus2EWgHf2tddeM1u3brVHza4yyuWXX57xTm3atMn8z//8TyUPOiUQv/CFL2S+3rBhg3nxxRetx52/T9JrbSDkgwWcUnAdO3Y0Dz30UObaRDIcOXLEChzyHDCmEjilQ4K+9BtYvMAIeXV2TRZCnB577DGbfIx9obxxbTrAvvHGG2bJkiV2M0Csfbdu3awNKi8vz3j8f/GLX9gQwwMHDpgrr7zSTJ069YK/8dZbb1XKJyIclBwvx7vvvmveeecdO05uv/12G4olRH1Q1pqoN27xYWF74YUXrIB+5JFH7M84XqYE2w033GAXKhY9fo6Rmzx5cqW/88UvftEumPng7+JNYSH+wQ9+YOMdaxNCw//9qU99yvzkJz+xC/n3v/99K+pJ2BTJwDgYPXq0NZBs5ohjxUjyUq6D8BkcDjt27LAvxjmJ+ToVTB4SjxHzOBNat25tbUBtqwmNGDHCTJo0yYYK8nznzp1r/xYOJ+wVjilOgMePH29effXVKv8Ov/PpT38678/YCLBp+PznP29t5MyZM83DDz9c7/cr4kZ16EW9wFPhhBhie8qUKWb37t1WyLtFjMWQRZSFj0oOw4YNs0KuLnCAxAuvbl1rzCPeWUjx0LOg//CHP7See5EsCB3GAoYQ5s+fb5YvX26OHTuW9KUJUScYs4xdxjAwphnbEvPJw1rPms/aj4jHFtSlNCh2w+X9YIMQ8TxvZ+Pwul922WU2L6K+Fb1y7ZsCJkRDkIdeFASOmAlpqSrxkcUKMU8zqFy++c1vWg8IAvyqq66y4TuOG2+80Xr2+ffTpk2rc4Ir18RCTjdA4id//OMfq4FISmAzyNE3L8IUSGg+deqU/VoVQERaQXThvCCckHBDKtYofCxdcPpHNRvyF1hPOOWtT1I+ldOoV0/YKHC63LJlyzr9DTzvX//6163t4loIy2nXrp39GQnSbAC/9a1vZUJuhKgviqEXDYY4d5JQKQGWr2oAQ+y5556zBvB3f/d3TbNmzTJJY7wQ8oTtLFiwwMbd8zsuTrEYFQ4QirfeeqsZM2ZMQf8P0XAQ9Gy8EEx4yAidcuNF1B/F0DccRB1eX0L42HByCqmSrOlj4cKF5vnnn7d2hxBNHDgNrXSGV37x4sX2xDlfHtejjz5q16rcGHpCfhgjOJY4KSD3jPWN0FSNHVFoFHIjClL7ne6s+cQ8nvVf/vKXZvv27eaTn/xkJXFGoizCnaNMvk/CLEaSI+xCw4J+33332RhuFnqu6de//rWOOFMGRo6EWTxheD2XLVtmwxkYP5ziCFFKGHOMPcYgY5ExydhUf4X0wVpOAirOIz5nrWfNL0TZYrz7PHf+Nh732oKzijGDE4nNwG233WYOHz5snVtCFBqF3Ih6QzIPFWjwgOQT8xx30pGPBYxEn9ocebLwFUtkE+LBgspGYvbs2fY0gBMCwnoU3pEueB7Eu/LidIVkQ05wEFHdu3e33ns9M1EMWH/wwiPkOTHCG3/ppZeqn0XKnxkJpfPmzbNfX3HFFdZbXsg1gv+DDR6JrPU9QeZ6imnjRNxI0It6H+G//vrrNjaRGNJcMISE4eCh/8QnPpE3bIKYaWII8WAg/hFseC6mT59etOtmMSUWnzjIl156yb4PEp3uuOOOeic2ieKCkGKM8aJCDuKenA1iTjnRcYnXQtQXBBaOB8IhCI1gw0iejZqhpR9sB+Uj3ckuDhoKMjSUOXPm2Eo3OICwETiAXN5P9v8NLrGVr1mLXNUuromkWZKkcR69/PLL1vbUpmymEHVFMfSiXnzpS1+yoTK55QYffPBBK7yIN3z22WetSM4WW4j33//937efv/nmmzbekfhEfg9PGEmxLIClgCP0GTNm2IUYry/Hs6oZ7ZcAw4vKRzaGVFJS06r8KIY+P4isnTt32r4ZbAzZILIGaIPoB2y+nnjiCbsOYI8I/USEFwIKKeA8wDmFQ4qqbldffbX96PjXf/1XW+c+G+yfKz3pmlnxN9gc8rNrr73WtG/fviDXKEQ2EvQiajZu3GhzAGhAhSFH1CPuhT+whCHIiG1FoCHM2BxiNCXMziNB/+FYIWSCpGs2gi6Phw2hxopfIOIR84h6SidTlKFUziAh0ogEvYgeDDzhQXv37s3E2Y8aNSrpyxL1gOUMA4+4P3jwoPWsIe6JxY85pCpmQU8YBHMbEU+lGkoGIuLlifc7f4vCBsS0M7fvv/9+eb1F9EjQC/HbknTUG6ZqD9Aoi6SqqjrYCj/g5AUhh6BjqcP4I/Bji42OTdATxueeu0uw5rnjyRX+QngkXVnfeecd+zVlKe+66y6VthVCgl6IysaCRF8q4MDAgQOtsZAICMdTS/UShB5Cn+dKqAUJkKE/49AFPc+TZ0volXu2CHiebcwnMyHBc8XpQi+RYlWyEcJnJOiFyGHp0qX2OBcBiCDgOBfhJ8Lz4iIAnQjEa89z5hWawA9N0PO83LPjObrNGa/YTl9igOdMWCSbNjZohEWOHDky6csSIlVI0AuRB6obYEBcwhW19vv375/0ZYkiQklM8ikQDYRgIQyJy6UyE6XmfPYE+izoMVEkO1NNhOeDgCfEgs22BHz4UKL2qaeeyhQuwMGSXWlGCHEeCXohqgAxTwUcyo4h5q6//npb39hnYSfqLvARktShBoQ9bdx9E/m+CPps8Z7vvrPBoqa3CB/Gwty5c82sWbPs55QUpQqZStMKkR8JeiGqgbCbF154wdbVB9qJ33zzzeoaGSEslQjMQ4cO5RWblMvk89zeDGkgjYKeCiXcQ8pH5t5PNkzcU582TaJw0B2aLuTZ6+5HPvIR5UMIUQ0S9ELUAFOEroF0+XOVUj760Y/ajyJunMhHkCJM+RyhigglFARvIqKUj3ydlDhNStBzfwiRwevOveEjX/N9Nj7cGzZCEu/CQWWin//855kKRToZFaJ2SNALUUs2bNhgqywgTPAU4anHcyRDI6oSsk7E8pEQHqAUKuKe3AxiwfnoPs/trJxmQc975ASLfAPim3m5z3nvVI0CQmTcpoaPSW5sRHphPOGRxzPPuGKsUGWsX79+SV+aEF4gQS9EHUCczZgxw4p7oNICR8GqgyxqCx58BK8Tv9mCGCHjwION0G/atKkV+u7F97O/dt9jo1CVUK5K0LP8I7z5f7kuPrpX7te0r+ca+b6D/zt7Q+I+ItrTGHok0glzgNBGKowBIv7OO+9UvLwQdUCCXog6wpShVj016/mcRD2q4HTt2jXpSxMBgXBGQCOks4V1VYLbecTzsXnzZtO7d++8P2MjUNOGgRcbCwS7hLooJDt37rRVbEhAZ0NKbXka++kUR4i6IUEvRD3ZsmWLefrpp23sNCKHWM8JEybIEInUkcakWBE3SA/GJblJbE7Jpbj77rtNr169kr40IbxEgl6IBkDoxLPPPmtWr15tvx4yZIhteqLa2CJNSNCLtK2bNO9btWqV/Xrw4MHm9ttv17opRAOQoBeiQPWS8TQR9kDFDjxNPXv2TPrShLBI0Iu0sHXrVnuySWUoTjanT59ux6ZONoVoGBL0QhSwuyyxoAcPHszEgl5++eU2RlmIJJGgF0mDs+Odd94xr732mnWCtGvXzuYeqeurEIVBgl6IAldreP75582yZcvs18SDEoJDi3ohkkKCXiTJ/v37bWgi3nkYMWKEueWWW1QdTIgCIkEvRIFhSi1atMi89NJLtkIJFUKmTZum5igiMSToRVJeecIR8cpTiYlKSTfccIMZM2aM1kIhCowEvRBF4tChQ+a5557L1KyXt14khQS9SNorT235W2+91bRt2zbpSxMiSCTohSgiTK8FCxbYhFl560VSSNCLJL3ylPQdO3as1jwhiogEvRAlQN56kSQS9KIUyCsvRHJI0AtRIuStF0khQS+KibzyQiSPBL0QJUbeelFqJOhFsZBXXoh0IEEvREq89ddcc42ZPHmybbYiRCGRoBeF5syZM2bOnDnmjTfekFdeiBQgQS9Eirz1HTt2NDfffLPp27dv0pcmAkKCXhSSjRs3mhdffNHs27fPfi2vvBDJI0EvRMIwBRcvXmxeeeUV8/7779vvDR8+3Hq7WrdunfTliQCQoBeF4PDhw2bWrFlm+fLl9utLLrnEXHfddWb06NHyyguRMBL0QqSE48ePm9dff93Mnz/fivwmTZqYq6++WmE4osFI0ItChNe8+eab5vTp01a8jx8/3lx77bWmRYsWSV+eEEKCXoj0sWvXLnuc7ZLMCMO56aab7LG2EPVBgl7UF8IBZ86cmQmv6dmzpw0LLC8vT/rShBBZSNALkUKYlr/5zW9sGM6xY8fs94YNG2bDcNq0aZP05QnPkKAXdaWiosKG16xYscJ+3bJlSzN9+nQzatQohdcIkUIk6IVIMSdOnLBhOPPmzcuE4Vx11VXmsssuUxiOqDUS9KK2ULHm3XffNb/+9a8z4TUTJkyw4TXNmzdP+vKEEFUgQS+EJ2E4HHtv2bLFfk3Nerz1AwcOlLdM1IgEvagJpMDatWutV57a8q5HBuE1Xbp0SfryhBA1IEEvhCcwVZcsWWJr17swHAwu3Wb5KERVSNCL6sBR8Oqrr2YcBq1atbLhNSNHjpTDQAhPkKAXwsMwnNmzZ9tW6xyPw6BBg6yw79y5c9KXJ1KIBL3Ix549e6yQX7Nmjf2aBneTJk0yV1xxhcJrhPAMCXohPK4JTRm5RYsWWe89XHrppbbjrBq8iGwk6EVuQzs6vJJ4D3jhx4wZY8vkqveFEH4iQS+E51BOjsRZV42CZFlqRF955ZW2MoUQEvQCCNUj2ZVeF9SWd9WzSHilPK4Qwl8k6IUIhO3bt9vjc9qyQ9OmTc3ll19uG1M1a9Ys6csTCSJBHzcnT560lWt4nTp1yn6vb9++Nkyve/fuSV+eEKIASNALERBMZxrBIOx37tyZac9OqUu89ip1GScS9HFCjs2CBQvMW2+9Zd5//337va5du1oh379//6QvTwhRQCTohQgQpvXy5cttKM6BAwfs94iNpX792LFjrfdexIMEfVzghV+4cKH1yJNrA+3btzdTp061ITaqXCNEeEjQCxEwxMmSNEvy7NGjRzMeeypZ0CymRYsWSV+iKAES9HFw/Phx+6ypgMXnrgQlya4kveqETohwkaAXIpKj98WLF5u3337bVrgAvPSE4RBjX1ZWlvQliiIiQR82R44csd54wmtcjHy7du1sDs3o0aNtOUohRNhI0AsREWfPnrWhONSxpwY14LXD6GP8OZYX4SFBHyaE07FJp/ykq1pDV9cpU6aY4cOHm0aNGiV9iUKIEiFBL0TEbd4R9lu3brXfI64WEUBTGbV6DwsJ+rDYtWuXFfJszp0J79mzp527AwcOVIy8EBEiQS9E5GzevNkK+3Xr1mW+hyhAHCASJA78R4LefzDVbL6Zq2zGHQMGDLBztXfv3olenxAiWSTohRAZrx9igQZVblmgxB1x9iNHjjRNmjRJ+hJFPZGg9xdi4pctW2bmzZtn5yiwyaZaDUK+vLw86UsUQqQACXohRI1xuTSmIs4eca+Okv4hQe9nB2g6upLMTmMoILl11KhRNkZe+S5CiGwk6IUQeaERDWICUXHw4MHM9/v162eF/eDBg5V05wkS9P4kra9evdp6413HZ1exhjnHppqys0IIkYsEvRCiWlgi1q9fb0XGmjVrMt+n1OW4ceNsoyqVvUw3EvTpLztJIyjKTvK5C6sZNGiQFfJ0dVUuixCiOiTohRC1hhr2iA7Eh2slj5d+yJAhtlEViXkSHulDgj59YHpJSGejvGrVKuudBzzwbJLZLLdt2zbpyxRCeIIEvRCiXo2qVq5cacWIK3sJHTp0sDG+JNESJiDSgQR9eiB8benSpWbJkiVm//79me/36tXLeuOHDh2qRlBCiDojQS+EaBBU3iDOHoFy+vTpzPcpeYm4pxqH4n6TRYI+WTjNonoUcyR7A0zlKOYIQl7VaoQQDUGCXghREKjEgdce0ZKd0EdIDnXt8doTE6zyl6VHgr70sLkl5wRvPHXjXUgN9O3b1wp5vPFUkBJCiIYiQS+EKDiHDx+2tbMRM652NiBeEDGIGeLtVSWnNEjQlwZEO3HxbGrZ3Lpyk4AHnk3tiBEjTOvWrRO9TiFEeEjQCyGKyp49e6zAQeBXVFRkvk9lHCdwEDtKpi0eEvTFAxPKptVtYF2VGmjTpo0d47w6d+6c6HUKIcJGgl4IURJYarZs2WLFPfHEJ06cyPwMjyV17XnhuVdSYGGRoC98UvimTZtsSA114zmRcjRv3tzmjXAKRaKrNqpCiFIgQS+ESEQQEVfs4ov52tG0aVMzYMAAG29P7L0SahuOBH1hElsZq4j4devWmVOnTmV+xgbU5YnwURtSIUSpkaAXQiSePEgSLZ5OxNLRo0czP8O7iZcTcY/3nrKYou5I0NcPykq6ccnpUra5bNWqVWZckuSqZG8hRJJI0AshUgPL0Y4dOzIiavfu3ZV+jqBHQCGkevToYRo3bpzYtfqEBH3tOHPmjNm2bVsmlCa7Tjx06dIlI+K7deumcBohRGqQoBdCpBY60zpxT8xyduk/PKLUuu/Tp499IbAk8PMjQV+1gGcDydjiRY347F4KVGFibLlNpDq3CiHSigS9EMILSKJdv369Ffh8JKY5GwQ+4TlO4Hft2lUC/7dI0H8o4Hfu3GlDvCgvSRhNtoAHcjb69+9vRTwfSXIVQoi0I0EvhPAOlq29e/dmPKu8jh8/Xul3SK5F4FM1x3nwY617H6ug50Qn2wOfT8C3aNEiswnk1alTJ4XSCCG8Q4JeCOE9LGPUu3fCDe9rPoHfvXt367nnhcBv165dFOItBkHPGDh48KAV8HjheW3fvr1SNRon4N0mjxf14WMYA0KIsJGgF0IEK/BdaAUiP7vufXbnWifuQxb5oQn6bPGeLeCzO7M6CJlBuCPiqUYjAS+ECBEJeiFE8LDMUTHHiT8+8jUx1VWJ/Gyhj8j3OVzHZ0FP2Azi3T236sQ7ORNUonHPjRMZCXghRAxI0AshogQxTxx+bUQ+Yh5R37FjR9O+fXtbPpMXX7ds2TL1gjHtgh4zRP8BykS614EDB8y+ffusmM+ublSVeOcj8e9KhBZCxIja2QkhogThV15ebl/ViXxCd+hk64RmLsTmO4Gf/WIDQLhH2sV+KUU7YU8I9Gzh7l65se7Z0HkVT7vEuxBC5EeCXgghqhH5CNHDhw9b0YnH2HmP+UidfISoCwPJJ0TpKFpWVmZfVX3us/B3Qv3IkSOZF9727I/uczZGVcH7p847myF3CsIJCB9bt27t7f0RQohSIEEvhBDVgJBs06aNffXr16/SzxCo2R5nBL8LFaFOPj9H9POqaSPhRD510Injp64+HzkB4FXT52weuFb3qkmEuxfXyKaEmHQ+1vQ5ZR/5yPtzgj1fmFJV8P6cUM890eA9CCGEqDuKoRdCiCKA8M31UOfzWueW1ywU2eKeGHQ+OhFfDCgHme8EIvd7bFSEEEIUFgl6IYRIEDzk2SKf8BXnFc/2jlfnPW/oMo7Yr+4UIN/PCBPKFuryrgshRHJI0AshhMe4sBnCXrJDadzLVYihUk+21969CPdx4TpCCCH8RIJeCCGEEEIIj/G3U4oQQgghhBBCgl4IIYQQQgifkaAXQgghhBDCYyTohRBCCCGE8BgJeiGEEEIIITxGhYOFEMIDXnnlFbN27VrbdZZa8H369DHXXXed7WALmzdvNj/5yU8q/RtKWdLI6Ytf/GLme++995555513zLFjx2zH1htvvNH07t078/OlS5eaV1991f67j3zkI/b/EUIIkW5UtlIIITwR9MOGDTNdunSxXWhfeOEFs3fvXvPII49U+W+++93vmh49epibb77Zfr18+XLz3HPPmfvvv9/07NnTLFiwwP7dP/iDP7AbA/7uf/7nf5rf+Z3fsQ2unnjiCfszIYQQ6UYhN0II4QF447t162YbQdGldcqUKWb37t3m+PHjeX9/27ZtZufOnWbChAmZ782fP9+MHj3aet35OxMnTjQdOnQwixcvtj/PbkaV3ZRKCCFEulHIjRBCeMj69eutV71FixZ5fz5v3jwr3Dt16pT53q5du8zYsWMr/R6bBL4PhPJcffXV5nvf+579nJAbIYQQ6UeCXgghPGPDhg3mzTffNPfee2/en7///vtmxYoV5o477qj0/ZMnT1rvfjZ8ffDgwczXY8aMsS8hhBD+IEEvhBAesWbNGvPMM8+YO++80wwYMCDv7yxatMgK9aFDh1b6frNmzWxsfDZ8zfeFEEL4i2LohRDCE5YsWWLF/D333HOBWHcQ+06yK6E1jRpVXuLLy8vNjh07Kn2Pr/m+EEIIf5GgF0IID6Dc5MyZM80DDzxQpWce1q1bZyoqKsy4ceMu+Nn48eOt954Sl5S0JM5+//79NlFWCCGEvyjkRgghPAAxj8c9t9b8gw8+WKmOPJVsBg8ebFq3bn3B3xg+fLitPz9jxoxMHfqPfexjmVr2Qggh/ER16IUQQgghhPAYhdwIIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQgjhMRL0QgghhBBCeIwEvRBCCCGEEB4jQS+EEEIIIYTHSNALIYQQQghh/OX/By/yPVKLsuSHAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create circle plot\n", + "fig = results.plot_circle(title=\"Interpersonal Problems Profile\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Curve Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create curve plot showing observed scores and fitted curve\n", + "fig = results.plot_curve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Correlation-Based SSM Analysis\n", + "\n", + "To examine how personality disorder symptoms relate to interpersonal problems, we can perform correlation-based SSM analysis by specifying external measures." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Statistical Basis:   Correlation Scores\n",
+       "Bootstrap Resamples: 2000\n",
+       "Confidence Level:    0.95\n",
+       "Listwise Deletion:   True\n",
+       "Scale Displacements: [90.0, 135.0, 180.0, 225.0, 270.0, 315.0, 360.0, 45.0]\n",
+       "\n",
+       "\n",
+       "                 Profile[NARPD]                  \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.202    │ 0.168    │ 0.235    │\n",
+       "│ X-Value      │ -0.062   │ -0.094   │ -0.03    │\n",
+       "│ Y-Value      │ 0.179    │ 0.145    │ 0.211    │\n",
+       "│ Amplitude    │ 0.189    │ 0.157    │ 0.223    │\n",
+       "│ Displacement │ 108.967  │ 99.266   │ 118.299  │\n",
+       "│ Model Fit    │ 0.957    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "
\n" + ], + "text/plain": [ + "Statistical Basis: Correlation Scores\n", + "Bootstrap Resamples: \u001b[1;36m2000\u001b[0m\n", + "Confidence Level: \u001b[1;36m0.95\u001b[0m\n", + "Listwise Deletion: \u001b[3;92mTrue\u001b[0m\n", + "Scale Displacements: \u001b[1m[\u001b[0m\u001b[1;36m90.0\u001b[0m, \u001b[1;36m135.0\u001b[0m, \u001b[1;36m180.0\u001b[0m, \u001b[1;36m225.0\u001b[0m, \u001b[1;36m270.0\u001b[0m, \u001b[1;36m315.0\u001b[0m, \u001b[1;36m360.0\u001b[0m, \u001b[1;36m45.0\u001b[0m\u001b[1m]\u001b[0m\n", + "\n", + "\n", + "\u001b[3m Profile[NARPD] \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.202 │ 0.168 │ 0.235 │\n", + "│ X-Value │ -0.062 │ -0.094 │ -0.03 │\n", + "│ Y-Value │ 0.179 │ 0.145 │ 0.211 │\n", + "│ Amplitude │ 0.189 │ 0.157 │ 0.223 │\n", + "│ Displacement │ 108.967 │ 99.266 │ 118.299 │\n", + "│ Model Fit │ 0.957 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Analyze narcissistic personality disorder symptoms\n", + "results_corr = ssm_analyze(\n", + " data=jz2017,\n", + " scales=scales,\n", + " angles=angles,\n", + " measures=\"NARPD\",\n", + ")\n", + "\n", + "results_corr.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize correlation-based results\n", + "fig = results_corr.plot_circle(title=\"NARPD Correlations\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Group Comparisons\n", + "\n", + "### Multiple Groups" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Statistical Basis:   Mean Scores\n",
+       "Bootstrap Resamples: 2000\n",
+       "Confidence Level:    0.95\n",
+       "Listwise Deletion:   True\n",
+       "Scale Displacements: [90.0, 135.0, 180.0, 225.0, 270.0, 315.0, 360.0, 45.0]\n",
+       "\n",
+       "\n",
+       "                 Profile[Female]                 \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.946    │ 0.907    │ 0.983    │\n",
+       "│ X-Value      │ 0.459    │ 0.42     │ 0.5      │\n",
+       "│ Y-Value      │ -0.31    │ -0.354   │ -0.269   │\n",
+       "│ Amplitude    │ 0.554    │ 0.51     │ 0.6      │\n",
+       "│ Displacement │ 325.963  │ 321.974  │ 329.968  │\n",
+       "│ Model Fit    │ 0.889    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                  Profile[Male]                  \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.884    │ 0.84     │ 0.927    │\n",
+       "│ X-Value      │ 0.227    │ 0.192    │ 0.262    │\n",
+       "│ Y-Value      │ -0.186   │ -0.225   │ -0.147   │\n",
+       "│ Amplitude    │ 0.294    │ 0.258    │ 0.33     │\n",
+       "│ Displacement │ 320.685  │ 313.789  │ 327.926  │\n",
+       "│ Model Fit    │ 0.824    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "
\n" + ], + "text/plain": [ + "Statistical Basis: Mean Scores\n", + "Bootstrap Resamples: \u001b[1;36m2000\u001b[0m\n", + "Confidence Level: \u001b[1;36m0.95\u001b[0m\n", + "Listwise Deletion: \u001b[3;92mTrue\u001b[0m\n", + "Scale Displacements: \u001b[1m[\u001b[0m\u001b[1;36m90.0\u001b[0m, \u001b[1;36m135.0\u001b[0m, \u001b[1;36m180.0\u001b[0m, \u001b[1;36m225.0\u001b[0m, \u001b[1;36m270.0\u001b[0m, \u001b[1;36m315.0\u001b[0m, \u001b[1;36m360.0\u001b[0m, \u001b[1;36m45.0\u001b[0m\u001b[1m]\u001b[0m\n", + "\n", + "\n", + "\u001b[3m Profile[Female] \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.946 │ 0.907 │ 0.983 │\n", + "│ X-Value │ 0.459 │ 0.42 │ 0.5 │\n", + "│ Y-Value │ -0.31 │ -0.354 │ -0.269 │\n", + "│ Amplitude │ 0.554 │ 0.51 │ 0.6 │\n", + "│ Displacement │ 325.963 │ 321.974 │ 329.968 │\n", + "│ Model Fit │ 0.889 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile[Male] \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.884 │ 0.84 │ 0.927 │\n", + "│ X-Value │ 0.227 │ 0.192 │ 0.262 │\n", + "│ Y-Value │ -0.186 │ -0.225 │ -0.147 │\n", + "│ Amplitude │ 0.294 │ 0.258 │ 0.33 │\n", + "│ Displacement │ 320.685 │ 313.789 │ 327.926 │\n", + "│ Model Fit │ 0.824 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Analyze by gender\n", + "results_group = ssm_analyze(\n", + " data=jz2017,\n", + " scales=scales,\n", + " angles=angles,\n", + " grouping=\"Gender\",\n", + ")\n", + "\n", + "results_group.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare groups on circle plot\n", + "fig = results_group.plot_circle(colors=\"Set2\", title=\"Interpersonal Problems by Gender\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare groups with curve plots\n", + "fig = results_group.plot_curve(angle_labels=scales)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multiple Measures" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Statistical Basis:   Correlation Scores\n",
+       "Bootstrap Resamples: 2000\n",
+       "Confidence Level:    0.95\n",
+       "Listwise Deletion:   True\n",
+       "Scale Displacements: [90.0, 135.0, 180.0, 225.0, 270.0, 315.0, 360.0, 45.0]\n",
+       "\n",
+       "\n",
+       "                 Profile[NARPD]                  \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.202    │ 0.169    │ 0.236    │\n",
+       "│ X-Value      │ -0.062   │ -0.094   │ -0.028   │\n",
+       "│ Y-Value      │ 0.179    │ 0.146    │ 0.213    │\n",
+       "│ Amplitude    │ 0.189    │ 0.156    │ 0.225    │\n",
+       "│ Displacement │ 108.967  │ 99.019   │ 118.283  │\n",
+       "│ Model Fit    │ 0.957    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                  Profile[ASPD]                  \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.124    │ 0.09     │ 0.157    │\n",
+       "│ X-Value      │ -0.099   │ -0.134   │ -0.064   │\n",
+       "│ Y-Value      │ 0.203    │ 0.168    │ 0.238    │\n",
+       "│ Amplitude    │ 0.226    │ 0.19     │ 0.263    │\n",
+       "│ Displacement │ 115.927  │ 107.57   │ 124.018  │\n",
+       "│ Model Fit    │ 0.964    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                 Profile[BORPD]                  \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.277    │ 0.242    │ 0.311    │\n",
+       "│ X-Value      │ -0.036   │ -0.068   │ -0.002   │\n",
+       "│ Y-Value      │ 0.097    │ 0.058    │ 0.139    │\n",
+       "│ Amplitude    │ 0.103    │ 0.065    │ 0.147    │\n",
+       "│ Displacement │ 110.358  │ 91.269   │ 128.695  │\n",
+       "│ Model Fit    │ 0.872    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "
\n" + ], + "text/plain": [ + "Statistical Basis: Correlation Scores\n", + "Bootstrap Resamples: \u001b[1;36m2000\u001b[0m\n", + "Confidence Level: \u001b[1;36m0.95\u001b[0m\n", + "Listwise Deletion: \u001b[3;92mTrue\u001b[0m\n", + "Scale Displacements: \u001b[1m[\u001b[0m\u001b[1;36m90.0\u001b[0m, \u001b[1;36m135.0\u001b[0m, \u001b[1;36m180.0\u001b[0m, \u001b[1;36m225.0\u001b[0m, \u001b[1;36m270.0\u001b[0m, \u001b[1;36m315.0\u001b[0m, \u001b[1;36m360.0\u001b[0m, \u001b[1;36m45.0\u001b[0m\u001b[1m]\u001b[0m\n", + "\n", + "\n", + "\u001b[3m Profile[NARPD] \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.202 │ 0.169 │ 0.236 │\n", + "│ X-Value │ -0.062 │ -0.094 │ -0.028 │\n", + "│ Y-Value │ 0.179 │ 0.146 │ 0.213 │\n", + "│ Amplitude │ 0.189 │ 0.156 │ 0.225 │\n", + "│ Displacement │ 108.967 │ 99.019 │ 118.283 │\n", + "│ Model Fit │ 0.957 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile[ASPD] \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.124 │ 0.09 │ 0.157 │\n", + "│ X-Value │ -0.099 │ -0.134 │ -0.064 │\n", + "│ Y-Value │ 0.203 │ 0.168 │ 0.238 │\n", + "│ Amplitude │ 0.226 │ 0.19 │ 0.263 │\n", + "│ Displacement │ 115.927 │ 107.57 │ 124.018 │\n", + "│ Model Fit │ 0.964 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile[BORPD] \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.277 │ 0.242 │ 0.311 │\n", + "│ X-Value │ -0.036 │ -0.068 │ -0.002 │\n", + "│ Y-Value │ 0.097 │ 0.058 │ 0.139 │\n", + "│ Amplitude │ 0.103 │ 0.065 │ 0.147 │\n", + "│ Displacement │ 110.358 │ 91.269 │ 128.695 │\n", + "│ Model Fit │ 0.872 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare multiple personality disorder symptoms\n", + "results_multi = ssm_analyze(\n", + " data=jz2017,\n", + " scales=scales,\n", + " angles=angles,\n", + " measures=[\"NARPD\", \"ASPD\", \"BORPD\"],\n", + ")\n", + "\n", + "results_multi.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize multiple measures\n", + "fig = results_multi.plot_circle(\n", + " colors=[\"red\", \"blue\", \"green\"], title=\"Personality Disorder Symptoms\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Contrast Analyses\n", + "\n", + "### Group Contrasts\n", + "\n", + "To test whether groups differ significantly on SSM parameters, use `contrast=True`:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Statistical Basis:   Mean Scores\n",
+       "Bootstrap Resamples: 2000\n",
+       "Confidence Level:    0.95\n",
+       "Listwise Deletion:   True\n",
+       "Scale Displacements: [90.0, 135.0, 180.0, 225.0, 270.0, 315.0, 360.0, 45.0]\n",
+       "\n",
+       "\n",
+       "                 Profile[Female]                 \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.946    │ 0.907    │ 0.983    │\n",
+       "│ X-Value      │ 0.459    │ 0.42     │ 0.497    │\n",
+       "│ Y-Value      │ -0.31    │ -0.352   │ -0.266   │\n",
+       "│ Amplitude    │ 0.554    │ 0.51     │ 0.597    │\n",
+       "│ Displacement │ 325.963  │ 322.158  │ 329.859  │\n",
+       "│ Model Fit    │ 0.889    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                  Profile[Male]                  \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.884    │ 0.842    │ 0.926    │\n",
+       "│ X-Value      │ 0.227    │ 0.19     │ 0.263    │\n",
+       "│ Y-Value      │ -0.186   │ -0.225   │ -0.15    │\n",
+       "│ Amplitude    │ 0.294    │ 0.257    │ 0.329    │\n",
+       "│ Displacement │ 320.685  │ 313.368  │ 327.546  │\n",
+       "│ Model Fit    │ 0.824    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "             Profile[Male - Female]              \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ -0.062   │ -0.118   │ -0.004   │\n",
+       "│ X-Value      │ -0.232   │ -0.286   │ -0.176   │\n",
+       "│ Y-Value      │ 0.124    │ 0.065    │ 0.181    │\n",
+       "│ Amplitude    │ -0.261   │ -0.318   │ -0.203   │\n",
+       "│ Displacement │ -5.278   │ 346.016  │ 3.136    │\n",
+       "│ Model Fit    │ -0.066   │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "
\n" + ], + "text/plain": [ + "Statistical Basis: Mean Scores\n", + "Bootstrap Resamples: \u001b[1;36m2000\u001b[0m\n", + "Confidence Level: \u001b[1;36m0.95\u001b[0m\n", + "Listwise Deletion: \u001b[3;92mTrue\u001b[0m\n", + "Scale Displacements: \u001b[1m[\u001b[0m\u001b[1;36m90.0\u001b[0m, \u001b[1;36m135.0\u001b[0m, \u001b[1;36m180.0\u001b[0m, \u001b[1;36m225.0\u001b[0m, \u001b[1;36m270.0\u001b[0m, \u001b[1;36m315.0\u001b[0m, \u001b[1;36m360.0\u001b[0m, \u001b[1;36m45.0\u001b[0m\u001b[1m]\u001b[0m\n", + "\n", + "\n", + "\u001b[3m Profile[Female] \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.946 │ 0.907 │ 0.983 │\n", + "│ X-Value │ 0.459 │ 0.42 │ 0.497 │\n", + "│ Y-Value │ -0.31 │ -0.352 │ -0.266 │\n", + "│ Amplitude │ 0.554 │ 0.51 │ 0.597 │\n", + "│ Displacement │ 325.963 │ 322.158 │ 329.859 │\n", + "│ Model Fit │ 0.889 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile[Male] \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.884 │ 0.842 │ 0.926 │\n", + "│ X-Value │ 0.227 │ 0.19 │ 0.263 │\n", + "│ Y-Value │ -0.186 │ -0.225 │ -0.15 │\n", + "│ Amplitude │ 0.294 │ 0.257 │ 0.329 │\n", + "│ Displacement │ 320.685 │ 313.368 │ 327.546 │\n", + "│ Model Fit │ 0.824 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile[Male - Female] \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ -0.062 │ -0.118 │ -0.004 │\n", + "│ X-Value │ -0.232 │ -0.286 │ -0.176 │\n", + "│ Y-Value │ 0.124 │ 0.065 │ 0.181 │\n", + "│ Amplitude │ -0.261 │ -0.318 │ -0.203 │\n", + "│ Displacement │ -5.278 │ 346.016 │ 3.136 │\n", + "│ Model Fit │ -0.066 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Contrast analysis between genders\n", + "results_contrast = ssm_analyze(\n", + " data=jz2017,\n", + " scales=scales,\n", + " angles=angles,\n", + " grouping=\"Gender\",\n", + " contrast=True,\n", + ")\n", + "\n", + "results_contrast.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "Label", + "rawType": "object", + "type": "string" + }, + { + "name": "Group", + "rawType": "object", + "type": "string" + }, + { + "name": "Measure", + "rawType": "object", + "type": "unknown" + }, + { + "name": "e_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "e_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "e_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "x_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "x_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "x_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "y_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "y_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "y_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "a_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "a_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "a_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "d_est", + "rawType": "float64", + "type": "float" + }, + { + "name": "d_lci", + "rawType": "float64", + "type": "float" + }, + { + "name": "d_uci", + "rawType": "float64", + "type": "float" + }, + { + "name": "fit_est", + "rawType": "float64", + "type": "float" + } + ], + "ref": "24cace74-1e5d-4b97-8ed8-b249b88f6ed1", + "rows": [ + [ + "0", + "Female", + "Female", + null, + "0.9457897909967846", + "0.9067009143890676", + "0.9832207094051446", + "0.4593176682777965", + "0.41964463719648665", + "0.49710156404391115", + "-0.3102451433597703", + "-0.3516049131983191", + "-0.26608707009701194", + "0.5542786026633866", + "0.510352566902124", + "0.597080495604968", + "325.96302454734587", + "322.15825635936517", + "329.8590848011998", + "0.8894488937356706" + ], + [ + "1", + "Male", + "Male", + null, + "0.8836741727941176", + "0.8423684512867647", + "0.9258990119485293", + "0.22728200705601015", + "0.1902394409426811", + "0.26290749134248953", + "-0.18613037950728867", + "-0.22465241728529523", + "-0.15015752618031636", + "0.29377138885013215", + "0.2574487184693303", + "0.32940395981089093", + "320.6845735000274", + "313.36818117423877", + "327.5455577467306", + "0.8238287760741773" + ], + [ + "2", + "Male - 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LabelGroupMeasuree_este_lcie_ucix_estx_lcix_uciy_esty_lciy_ucia_esta_lcia_ucid_estd_lcid_ucifit_est
0FemaleFemaleNone0.9457900.9067010.9832210.4593180.4196450.497102-0.310245-0.351605-0.2660870.5542790.5103530.597080325.963025322.158256329.8590850.889449
1MaleMaleNone0.8836740.8423680.9258990.2272820.1902390.262907-0.186130-0.224652-0.1501580.2937710.2574490.329404320.684574313.368181327.5455580.823829
2Male - FemaleMale - FemaleNone-0.062116-0.118318-0.003855-0.232036-0.286291-0.1760580.1241150.0652280.180890-0.260507-0.318480-0.203398-5.278451346.0158473.135694-0.065620
\n", + "
" + ], + "text/plain": [ + " Label Group Measure e_est e_lci e_uci \\\n", + "0 Female Female None 0.945790 0.906701 0.983221 \n", + "1 Male Male None 0.883674 0.842368 0.925899 \n", + "2 Male - Female Male - Female None -0.062116 -0.118318 -0.003855 \n", + "\n", + " x_est x_lci x_uci y_est y_lci y_uci a_est \\\n", + "0 0.459318 0.419645 0.497102 -0.310245 -0.351605 -0.266087 0.554279 \n", + "1 0.227282 0.190239 0.262907 -0.186130 -0.224652 -0.150158 0.293771 \n", + "2 -0.232036 -0.286291 -0.176058 0.124115 0.065228 0.180890 -0.260507 \n", + "\n", + " a_lci a_uci d_est d_lci d_uci fit_est \n", + "0 0.510353 0.597080 325.963025 322.158256 329.859085 0.889449 \n", + "1 0.257449 0.329404 320.684574 313.368181 327.545558 0.823829 \n", + "2 -0.318480 -0.203398 -5.278451 346.015847 3.135694 -0.065620 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# View contrast results (last row shows Male - Female differences)\n", + "results_contrast.results" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize contrasts\n", + "fig = results_contrast.plot_contrast()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Simplified contrast plot (drop X and Y parameters)\n", + "fig = results_contrast.plot_contrast(drop_xy=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Measure Contrasts" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Statistical Basis:   Correlation Scores\n",
+       "Bootstrap Resamples: 2000\n",
+       "Confidence Level:    0.95\n",
+       "Listwise Deletion:   True\n",
+       "Scale Displacements: [90.0, 135.0, 180.0, 225.0, 270.0, 315.0, 360.0, 45.0]\n",
+       "\n",
+       "\n",
+       "                 Profile[NARPD]                  \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.202    │ 0.169    │ 0.235    │\n",
+       "│ X-Value      │ -0.062   │ -0.094   │ -0.029   │\n",
+       "│ Y-Value      │ 0.179    │ 0.145    │ 0.212    │\n",
+       "│ Amplitude    │ 0.189    │ 0.155    │ 0.224    │\n",
+       "│ Displacement │ 108.967  │ 99.689   │ 118.438  │\n",
+       "│ Model Fit    │ 0.957    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "                  Profile[ASPD]                  \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ 0.124    │ 0.089    │ 0.158    │\n",
+       "│ X-Value      │ -0.099   │ -0.134   │ -0.065   │\n",
+       "│ Y-Value      │ 0.203    │ 0.169    │ 0.238    │\n",
+       "│ Amplitude    │ 0.226    │ 0.19     │ 0.262    │\n",
+       "│ Displacement │ 115.927  │ 107.728  │ 124.75   │\n",
+       "│ Model Fit    │ 0.964    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "              Profile[ASPD - NARPD]              \n",
+       "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃              ┃ Estimate ┃ Lower CI ┃ Upper CI ┃\n",
+       "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│ Elevation    │ -0.079   │ -0.115   │ -0.041   │\n",
+       "│ X-Value      │ -0.037   │ -0.078   │ -0.0     │\n",
+       "│ Y-Value      │ 0.024    │ -0.014   │ 0.06     │\n",
+       "│ Amplitude    │ 0.037    │ -0.003   │ 0.075    │\n",
+       "│ Displacement │ 6.96     │ 356.694  │ 17.513   │\n",
+       "│ Model Fit    │ 0.007    │          │          │\n",
+       "└──────────────┴──────────┴──────────┴──────────┘\n",
+       "
\n" + ], + "text/plain": [ + "Statistical Basis: Correlation Scores\n", + "Bootstrap Resamples: \u001b[1;36m2000\u001b[0m\n", + "Confidence Level: \u001b[1;36m0.95\u001b[0m\n", + "Listwise Deletion: \u001b[3;92mTrue\u001b[0m\n", + "Scale Displacements: \u001b[1m[\u001b[0m\u001b[1;36m90.0\u001b[0m, \u001b[1;36m135.0\u001b[0m, \u001b[1;36m180.0\u001b[0m, \u001b[1;36m225.0\u001b[0m, \u001b[1;36m270.0\u001b[0m, \u001b[1;36m315.0\u001b[0m, \u001b[1;36m360.0\u001b[0m, \u001b[1;36m45.0\u001b[0m\u001b[1m]\u001b[0m\n", + "\n", + "\n", + "\u001b[3m Profile[NARPD] \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.202 │ 0.169 │ 0.235 │\n", + "│ X-Value │ -0.062 │ -0.094 │ -0.029 │\n", + "│ Y-Value │ 0.179 │ 0.145 │ 0.212 │\n", + "│ Amplitude │ 0.189 │ 0.155 │ 0.224 │\n", + "│ Displacement │ 108.967 │ 99.689 │ 118.438 │\n", + "│ Model Fit │ 0.957 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile[ASPD] \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ 0.124 │ 0.089 │ 0.158 │\n", + "│ X-Value │ -0.099 │ -0.134 │ -0.065 │\n", + "│ Y-Value │ 0.203 │ 0.169 │ 0.238 │\n", + "│ Amplitude │ 0.226 │ 0.19 │ 0.262 │\n", + "│ Displacement │ 115.927 │ 107.728 │ 124.75 │\n", + "│ Model Fit │ 0.964 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n", + "\u001b[3m Profile[ASPD - NARPD] \u001b[0m\n", + "┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mEstimate\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mLower CI\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mUpper CI\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n", + "│ Elevation │ -0.079 │ -0.115 │ -0.041 │\n", + "│ X-Value │ -0.037 │ -0.078 │ -0.0 │\n", + "│ Y-Value │ 0.024 │ -0.014 │ 0.06 │\n", + "│ Amplitude │ 0.037 │ -0.003 │ 0.075 │\n", + "│ Displacement │ 6.96 │ 356.694 │ 17.513 │\n", + "│ Model Fit │ 0.007 │ │ │\n", + "└──────────────┴──────────┴──────────┴──────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare two measures\n", + "results_meas_contrast = ssm_analyze(\n", + " data=jz2017,\n", + " scales=scales,\n", + " angles=angles,\n", + " measures=[\"NARPD\", \"ASPD\"],\n", + " contrast=True,\n", + ")\n", + "\n", + "results_meas_contrast.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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8eGFfb9CgQbbMmTM7bHvq1ClbzZo1bfHjx7elSpXK1q1bN9vIkSNtyZMnd1gP+4btjc6cOSPLN2/ebF+G5zeuh8fM61itB3/88YetVq1atqRJk9oSJEhgy5s3r23ixIn2xw8cOGArU6aMPJY+fXrbgAEDbNOmTZPnx74Yn7t169ZhPiesN2PGDPv9o0eP2ipVqiSvZ/X+jJ49e2ZLly6dfDZGeD5su23bNtn3hAkTyufYp08f2/Pnz23ecOXKlXDXqVChQpi/Lfz000+2ggULynGB42PIkCEO+3Xjxg3bO++8Y8uUKZMtXrx48tkWLVpUjp2XL1+Geb727dvbSpYsafO3YD5fcZxZXQ7hGMbfyXy+4O8wfvx4W86cOW1x4sSxpU2b1ta5c2fbnTt3HLZfuXKlLX/+/HIuW51z2oULF2yfffaZfEfgWI0dO7bt1VdftdWvX9+2d+/ecM/TuXPn2nLlyiX7inN06dKltsKFC9t69OgR5rwwnpf6vRuPU/39YFwP5675WLZaDyZNmiTvGfuSIkUKW5UqVeR7QpswYYItS5Ys8pkUL17c9ssvv8i5gpv5uc2fl34PRtOnT7flyJFD/g5W+xPe7wwRUSCJgf/4u3GAiEIX5rvV03yZe8HQ4+lvmOYLPWP+hN4m9Jyiyjv9f+gZRBrv4cOH7ctQVApVrZHKa+5djm6QwouMAvSstm3b1q/7wvM1cCA1PEeOHFLNH9kX5Px3hogokDCVnYgCEsY54yI7EPYjKiHIQlomUmmREooLSsyhjfnKKey0YhMnTpTPBo0XoQaBF1LgkXLtb6F6vvobPnPMHY4x3hj6gqKMSCdHKjuGYBARUfBgYE5EAQnjE0Ox5wvjIzFWG4XNkNCE8eroFTbP6UtKxoujeBrmfA5FqFWAMdDmglr+EKrnq7/hb48ihRiHjjoImNMe0zSiDgT/HkREwYWp7ETkV0wxJCIiX+LvDBEFA06XRkRERERERORHDMyJiIiIiIiI/IiBOREREREREZEfMTAnIiIiIiIi8iP/l3IlopCnpwUjIiLyxW8MpqEkIgpkDMyJyK94sURERL7+neFvDREFOk6XRkRERERERORHHGNORERERERE5EcMzImIiIiIiIj8iIE5ERERERERkR8xMCciIiIiIiLyIwbmRERERERERH7EwJyIiIiIiIjIjxiYExEREREREfkRA3MiIiIiIiIiP2JgTkRERERERORHDMyJiIiIiIiI/IiBOREREREREZEfMTAnIiIiIiIi8iMG5kREQerChQuqX79+qnTp0ip16tQqTpw4KlmyZKp48eKqV69e6uDBg37Zr7Nnz6oYMWLYb1u2bFGBzLivM2fO9Nnr/Pzzzw6vhdvcuXNdbnPo0CHVunVrlTVrVhU/fnyVKFEilSlTJlWqVCnVsWNHNX/+fJefvfGGbXPnzq0++ugjderUqXC3w/GUNGlSlS1bNlWtWjU1bNgwdeXKFRVVrPZp7969DutcvHjR7b9fiRIlHNbNlSuX03XxPFafIT4TnGvlypVT48ePV48fPw6z7fvvv2+5bezYsWXbKlWqqBkzZqiXL1+63C5WrFgqQYIEKk2aNKpIkSKqVatWas2aNcpms0Xo8yQiogBnIyKioDNmzBhbnDhxcIXu9JY5c2a/7NuZM2cc9mPz5s22QGbc1xkzZvjsdRo3bhzmb1S9enWn669cuTLcv3G+fPlcfvbObgkSJLCtW7fO4+3ixo1r++qrr3z2GYX3Xsyf14ULF9z6+x05csTy/ezcudNyfTyPO59H2bJlbc+fP3fYtnXr1m5tW6NGDduTJ0883q5kyZLy2RARUfQS298NA0RE5JnBgwerL774wn4fvXj16tVTBQsWVM+fP1eHDx9W69atU8Hs3r17KkmSJCq6uH37tlq5cmWY5Rs3bpRe6Ndee81h+YsXL1SHDh3Us2fP5D56yRs2bCg9rnfu3FH//e9/1W+//Rbu66Knu3r16vI8v//+u1qxYoUsf/TokfTEo1caPfHOtrt79676448/1Pr162Wfnj59qnr27CnLBw4cqKLahg0b1Pbt21XZsmU92m7WrFmWy2fPni0ZJ+Hp1KmTyp49u/rnn3/Ujz/+KP8C9mX16tXqnXfecbrtmDFj5N9///1XXk9nHeAcnTRpknyezrbD3w1ZAci2OHfunCzfs2ePvH9kD5iPGyIiCmL+bhkgIiL3HTp0yBYzZkx771m6dOlshw8fDrPenTt3bN98802Y5Tt27LA1bdrUliFDBun9TJo0qa148eK2L7/80nb37t0w65t7I1etWmV78803pcf1lVdesTVv3tx2/fp1+/oVKlRw2duHx616Qzdt2mSbNGmSLW/evLJf6D2EvXv32jp06CD7+Nprr9nixYtnix8/vi1btmy2li1b2g4cOBBmn+/du2cbOHCgrWDBgrZEiRLZYseObXv11VdtRYsWtXXq1Mm2bds2t3oojRkHgwYNilQmwuTJk+3b4zNPkyaN/f7o0aPDrP/nn3867Mu5c+fCrPP48WP53IzMnyv22wifmfHxjRs3urUd9gefv348RowYciz6krNe/IoVK3rUY/7ixQtb+vTp7evkzJnT/v84hvE5htdjbsz6QKaB8bERI0Y4bGs+roxOnjwpn51+rFy5cm5t9+zZM1u3bt0cHm/UqFEEP1kiIgpEHGNORBREJk6c6DA2dfLkySpfvnxh1sPY4G7dujksGz16tPS0LViwQHrh0PuJns99+/bJWPWiRYvKuHVnpk6dqurWrat27twpPa63bt2SMdJYFtlxr/3791edO3dWR48elf3SMD4dr4t9RE/jkydPZFzv33//LT2XJUuWlN5co9q1a6shQ4bI+OwHDx5IFsG1a9fU/v375fNasmSJimrGHlv0rqL3W0Mvqhl6p43wXszixYunKlWq5NF+vPHGGw73r1696tZ2hQoVkr+Dhr83jsWolDZtWvsxgUwDd/3666/q0qVLDuPHY8b8v8sfHMOrVq3yaD/SpUvncD9VqlRub5sjRw6H9d39/DE+/euvv5b6EdpPP/3k8L6IiCi4MZWdiCiIbN682f7/KVKkUHXq1HFru02bNqk+ffrY77/++uuqUaNGEqAjwEWwj4JgzZs3d5oivWvXLilChcAXRagQ6MLu3bvlsTfffFN9+OGHqkyZMmr48OFh0oAhY8aMls+NYB9BS4MGDeQ+itgB0qzxvIULF1YpU6ZUCRMmVDdv3pT04b/++ktSfbt3766OHTsm6yOw1/uP4AsFtfDa169fV6dPn3YoRNe0aVOVP39+9emnn9qXvfvuu/bgR+9DZJ08eVI+H+NrIE3/u+++k/sYeoDPEg0jGoq0ofAXGkAAjR/4fBBYY/8qVKggn4mnjPsBKCzmLvzd8Te4ceOG3I/qon49evSQIRxomBkwYIAUUXOHseEDnx2OJ3x++lzC4zgX3IEU9nHjxtnv42/k7jkIOMeQ0h6Rzx8F4VAADo1UunFk69at6r333nP7OYiIKHAxMCciCiLGHrKcOXPKxbo70NumvfrqqzI+VY/hzps3rz1o37ZtW5ggUUNguGPHDglGUPUdPX+6ZxfBAgIeBJ2oGG4MzLGsYsWKLvcP7wVjmc3jyrt06SI96X/++acEsAjKsf81a9aUwByOHz+uzp8/L+Ow0aNubHyYPn26w2eEQF6PD65Ro4bcjIE57iOY9yZjYPjKK6/I2G1U3EbP6+XLl+3rGD9zNECgCjo+Z2NQh9ucOXPsf7dvvvlGVa1a1elro8Fj7Nix8r4xNnn58uX2xzBeHY0o7sLniEYOHZhHdW9t+vTppZEHxzIaGNA4hLoK4dUqWLZsmcOxCE2aNLEH5hjrjYYbfB7OWGUm4O+HTAjdk+8MPn9AQI5GMGN2Sf369ZUncA4asceciCj6YGBORBQC0KutoVCcMQBu0aKFQ286ioRZBebNmjWToBySJ08ugbkOclHcLDIwhZdVsTc0ILRs2VKCb1cQoCAwz5Mnj71XF4E7An708mNqLPyLomYZMmTweP/QU2ssuOcuBGE6kNaBGIr1AdLZdTr4vHnzJIBDyrL28ccfSzA6atQodeDAgTDPjewA9NbiMbxvK7/88ovcrNLgMWWXVeG38N6PJ44cOaLWrl0bZjkacXDzVN++fdW0adNkiAJ6zXUxO2cwbOHhw4f2hgUE5PqzR6MPGpbQaIHPH5kX7sLfCX8fV40imrHhx6hy5crS6OQJTpVGRBR9cYw5EVEQQaCmoffU3Qt1jKXV0ONsZL5vXNfInIaO4E4zz8nsKQTQZgiokMIdXlAOuqccgeaiRYskSAekryM4Qw9+48aNpZcTQVhUQaoxKp9rOjA09t4CemytAlisgwwGNDwsXrxYgkdjwwLet06JDw8+G3zOH3zwgWQgIDXdEzjW8HlaHYvOoGEFgan5hurqEYFjFQE14HMx9oaHN7YfmRz6uEDvuLEX3GqcvxF66ocOHSrPAahb8Mknn6gvv/zS7X3H0AoMP0H2yJQpU+QzMJ5D7jDPP+/O34CIiIIDA3MioiBiDCbQK4xplNyBFGoNhdCMzPeN6xrpnl7N3TR6dyRKlCjMMowV1z3ygLG96JlHgIieWGfQE3nmzBkJCtG7imyAcuXK2YN9BKb3799XUcE8TRdS5fG54Wae8stVcIgGBYyDRho3Gip0gGkVrBkNGjRIPi/cMF79xIkTUsQNY9g9hXH9GEpg/Jz9oXfv3lLcEIxDJszQIGKsl4CsEf3Z44aicBqCfFfHFBpIUKAQQz2MBfQQrBsbXqzozx+98zhnkUKPqfAwnMETeA6kwmt4D+ENESEiouDBwJyIKIgg9dUYEKMnT4+1NkK19QkTJtjvG+dqxjhjjL3VjKnWVpW7PWUO4HUqsaeMQSC0adPGXpANveJWUBgMnwd6J1Hoq3379mrkyJHSc623xf4Ye+GN6ePO9hVp7Dqgy5Ili1v7j+dC5Wx3oTq4zlbA2HP0jqNwnFncuHHlpnmrSJ0rqArfsWNH+318vl27dg13O4zX14Gp8RaRYQEaep1RCC68qubm8dzhCa/XXB/b48ePd8hYcNU44C3oocd71oXfQGeAEBFR9MAx5kREQQTTVqHnDj11gBRnVOfGuPECBQpIrxyKpKGgFcZa6ynTEOStXLnS3kOOacYwzhbbGwOS8uXLy1jsyECaMAIYjN0FTMWG1GkEk+jhM0755ArGhRsh9RpF3xAkOpvyDD3qKIqGKeTwHhG4YFz89u3b1Z07d+zrYYy8MR343Llz9l559GpiG3wO7lb+trJ06VKHBpBatWqFGUePhgQ9ThpB3sKFC6WxBVPGoWEFN/x90bCCFHb0eiPl3dhL/tZbbylv00XjsP8oyofjyTiFG44/q2n6ogrGd2N8vrNhF2A8rpFhYGyc0hDo6vR8NFCNGDHCPpWaM3geVHVHY4/OikBmgrfTyvH5IyDHzAnIVtDHqB5WgsJ/REQUjfh7InUiIvLciBEjbLFjx0Z3oNNb5syZHbYZOXKkLUaMGE7Xz5Ejh+38+fMO2xgfnzFjhsNjeH792KBBgxweq1+/vuVrjBkzRh4/c+aMw/LNmzdbvs/q1atbPk/Lli0tt79y5YrLzwS3evXqObxGz549Ldfr3LmzfR28P2efqzPVqlWzb5MhQwbbixcvLNfLnTu3fb3SpUtbfj7OblWrVrU9e/bM/lzm7cx/F2fcfb148eLZJkyYYIsK5n368ccfHR7/8ssvw+yfPkZ37NjhsHzq1KmWrzF//nyH9davXy/L8Tyujs+1a9c6PN6jRw/7Y61bt3Z4zF3m7Zzd3njjDdu5c+c8+iyJiCjwMZWdiCgIffbZZ9JriirV6BlGei9SsjH2FhXV0atuLiaGsdYYI4vxsujdQ6924sSJVbFixWRqLvSMOptn3FMY2926dWuZpzm8HkhXkAqO9H0U/UKhLFQfnzRpkhoyZIjl+hgfj55UzFGOdXEfY3nRQ46eTqQhm9PgUcALmQX4TDwd9+sMMhE2btxov4/PwtnngMc0TAOG9HX08KKXf/DgwTKWG0XbkLKOvzGq4aPWwOTJk+VvbEzF9yZ8Fjg+kLqP6uP4nNBr604Ke1RAFoizKc6MY/uR/WAstGeETBNj9oQ76ey6VoBxHnmM2zfOT+4NGDaBLBO8R7wW5jDH3xvZDMYaA0REFD3EQHTu750gIiIiIiIiClXsMSciIiIiIiLyIwbmRERERERERH7EwJyIiIiIiIjIjxiYExEREREREfkRA3MiIiIiIiIiP2JgTkRERERERORHDMyJiIiIiIiI/IiBOREREREREZEfMTAnIiIiIiIi8iMG5kRERERERER+xMCciIiIiIiIyI8YmBMRERERERH5EQNzIiIiIiIiIj9iYE5ERERERETkRwzMiYiIiIiIiPyIgTkRERERERGRHzEwJyIiIiIiIvIjBuZEREREREREfsTAnIiIiIiIiMiPGJgTERERERER+REDcyIiIiIiIiI/YmBORERERERE5EcMzImIiIiIiIj8iIE5ERERERERkR8xMCciIiIiIiLyIwbmRERERERERH7EwJyIiIiIiIjIjxiYExEREREREfkRA3MiIiIiIiIiP2JgTkRERERERORHDMyJiIiIiIiI/IiBOREREREREZEfMTAnIiIiIiIi8iMG5kRERERERER+xMCciIiIiIiIyI8YmBMRERERERH5EQNzIiIiIiIiIj9iYE5ERERERETkRwzMiYiIiIiIiPyIgTkRERERERGRHzEwJyIiIiIiIvIjBuYkKlasqLJkyaKCRYwYMdT777/v790g8gmcizgniULNF198Id/vZ8+e9feuEHkVrllwbPv72ovnWPSyZcsW+XvOnDnTZ6+BYwWvgWOHfIuBeYA7fPiwnAy4HT9+PEI/As5uvjyJveHrr79W06dP9/duUDQ9R+7du6cyZcqksmfPrh4+fBjm8WnTpslzjho1ynL7Tz75RB7funWr09e4c+eOSpAggSpbtqzb+0UUVefBhQsXVLJkydQbb7yhXr58GebxR48eqVy5cql06dKpW7duhXmtNm3auHz+Zs2ayXqnTp2KwDuiUBSZax6r7+fSpUurYMNrn+Dmzet2XD/g+7dq1apyLXL9+nWf7Td5DxowlixZEqFtGZgHOATPsWPHVjFjxoxwIP2f//xH/fjjj2Fu5cuXV8H644QLRgRORBE9R5IkSaKmTJmi/v77b/X55587PHbx4kW5sCtevLj8a0VnbMyePdvpayxcuFA9fvyY2R0UkOdBxowZ1ZgxY9Tvv/+uvvrqqzCPDxo0SJ08eVLOk1deecW+PH/+/KpYsWLqp59+smzU0o1Sy5cvV2XKlFE5cuSIxDujUOKNa57nz5+ruXPnqmzZsqndu3dHKsD3tQ0bNoTZPwbmwc2b1+2TJk1SXbp0kefr27evev3119WaNWsc1sW1PK6JW7Zs6aV3QJE1ePDgCAfmykYB69mzZ7a0adPaateubatcubItffr0thcvXri9fevWrW34Ez969CjcdStUqGDLnDmzLZBgf0qVKuXv3aBofI5Aq1atbDFjxrTt2LHDvqxGjRq2OHHi2A4dOuRy22LFitmSJElie/DggeXjb775pi1hwoS2O3fueHzs45wkiorzoGrVqrYECRLYTp48aV+2b98+W6xYsWwtWrSw3GbixIny+/Ljjz9aPj516lR5fNq0aR69l0GDBsl2Z86c8Wg7Cn7e+D6HVatWyTG0detWW+LEiW2fffaZLRDoazJ/X/vwHAvO6/adO3faXnnlFfmu/uuvv2xRCccK9gvHDoUPn9W7775riwj2mAewdevWqatXr6rmzZurFi1aqEuXLqlff/01Svdh586dqlatWtJbEj9+fFWoUCFpyTO2TKdJk0ZVqVLFcvtWrVrJdjoNEj0zWIYeFKTo4Hnx/Pv27XPYDik8586dk/WNaT3Gx829kE+fPlVDhgyR1Mt48eKptGnTqrZt26rLly87rIcWTGy/ceNGWR/pzNhH9AD99ttvXvncKHjOEfQUvvrqq6pdu3bSu43jA8/bv39/VaBAAZfb4hhESvyyZcvCPIb0XZw/9evXV0mTJnX72Pd0fJezMYrhnbsUfUT2PEDvXKxYsVT79u0RNahnz57J+ZA6dWr1zTffWG7z3nvvqbhx46pZs2ZZPo7lOM6bNGki50i/fv0kAyVFihRyPOLc+vbbb+X1wuPsGNff5RhjaXTjxg3Vo0cP2Qb7iFTQjz76yCEdn6LvNQ+Oi9y5c0tPYsOGDdWcOXMsh2ro4wrf1fiuRBYVrmcGDhwox+W///4r+4FjFt/h+jfC6jnQ6/3WW2+pxIkTq1SpUqkPP/xQjntPj21X1z4R+R347rvvVM6cOeWcy5cvn/TCOnP+/Hl5jzhfcN7g+dBLa37P5J/rdgzLwPUKesdHjhzpcow5MpnwnYu/vb7eKFy4sJowYUKY7WbMmCGZU1mzZpXjpGjRovI+wuPp9zq+lz/++GMZPqiv0evUqaP++OOPCF27xPhfHIB9xT7gfeL6f/HixfL4/v37VaVKlVSiRInkmB4/frzl+1i9erWcPzj/EyZMKEO7Vq1a5bCO8dzD8xcsWFDeAz6z77//Psx6OmNSn78e1ZGIUDhPUaJhw4bS2oveOPS4xY8f39asWTOPW94uXrxou379epibsRXPqsd82bJlttixY9tKlixpGz16tO3bb7+1Va9eXZ7zyy+/tK/XtWtX6XG8dOmSw/bYb+x//fr17cvQcl2+fHnb4MGDpSdlwIABtldffTVMCyB6YVKlSmXLnj27/L++adgHvD+jd955R5bjX+xr9+7dpdczU6ZMtn///de+3owZM2S94sWL20qUKGEbP368bfjw4bYUKVLYkiVLZrt165bbnzEF9zmi/fTTT3JMtG3b1pY8eXJboUKFbE+fPg13uxs3btjixo0r54UZjm0854YNGzw69q16zF21Vkfm3KXowRvnwXfffSfHx6RJk2xDhw6V/8dxFN7r4rsfvzFGp06dku3fe+89uY/jGz1HPXv2lOf/6quvbOXKlZN1vvjii3B785xldOnv8s2bN9uX3bx505YzZ07pWerTp49typQp8rr4TAoWLGh7+PChR58LBddxrL+Thw0bJvd/+eUXOUbWr18fZl0cV7jOyJIli+3DDz+Uc6BatWqy/pgxY2yFCxe2NW/eXJaj9wvL+/XrZ/kcOL6RXYLjG/9iXfSYvnz50mWPufnYdnXt4+nvAN4D1kfv+9dffy2/OTgvihQpEuYcO336tPwe4X3g+XHetG/fXrJm3nrrLYf3Qb6/bneW6frkyRN5zjRp0tiX4fsP2+D70Pg8uAbo0qWLXG/g79+hQwfZP/N2uN7BOTBixAi54f+x7caNG+3rWh17nnyvI+bAMY3fizZt2sg5NXLkSFutWrVsEyZMiNC1i1LKVqBAATlusV/ffPONfPfjNRYtWiTLP/30U9k3XOtjfXwfGOG1sRznPfYfnxPOlxgxYtjmzJkT5v0jSzJdunS2IUOGyLb4TTH+Bt2/f1/OVyx744037OdveL+lDu/L7TUpSukfl5YtW9qXNW7cWC7ib9++7dEJ7ux24cIFp1/quHjBjwPSccxfyDix8cWgg93ff/9dnm/s2LEO682bN0+WI+jRrFJ+jx8/Lu8VXxrupnOZA/M1a9bIMvyQGC1ZskSW9+jRwzIwR9qRhhMHy/GFQaFxjhg1atRI/v74Ufjjjz/c3s4qOME5gx+3jBkz2hvAPD32IxqYe3LuUvDz1nmAY6VSpUpyURkvXjy3LiZ1yjAu5owGDhzo0CiFi0njd61+PQQuGAqCx70VmH/00UfynGgcsNpXXOhR9D2OMcQCF9Vnz56V+/j+RfBgdTzjuDIfE2iQfe211+Q5evfu7bA+rhlSp05t+RzmdXEfy5cuXepRYO7q2seT3wE0UOGzwz4bz68///xTfq/M5xgCpAwZMoT5bdBDVlavXh3mNcl31+2uhqDqYFAPkbMKzNHBgMYmV/R2SZMmtV25csW+HP+PZQjYXR17nnyv49rcWWOvvk7x9NpFKSXH8sGDB+3LDh8+LMtx/iIu0LAdtsffQ8M1G/5e5s/p+fPn0jCAIQn4f+P7T5QokcO13tWrV+U5mjRp4vAcTGWPhubNmyep2UiH0ZAWgxSWRYsWefRcKBTxyy+/hLkhTdEZPI40rtatW0v6Cf5f32rXri2pTdu3b5d1S5YsKekjKLZihPSx5MmTy/oa0kS0+/fvy3MjBQYFLfbu3asiauXKlfKvuYgX0tiwbytWrAizTadOnaSghlahQgX5lxWEQ+8cAX0+YGhDeCnsRkilQpokjncNldqR0oRiLCgA48tjPzLnLgU/b50HSLf74YcfZHgS0nEnTpwY7jY1atSQdERjOjuuSVAQMUOGDPYhTkiL1d+12NebN2/KsYlKw0iHPHbsmPIGvPaCBQvkeVFt3njsIz0RKY1RPRyMovY4RjpvuXLlVObMmeU+vn8x7AKFCO/evRtmfQzh+OCDD+z348SJI9c0OJZwjWCEQoaoim2Vot6rVy/L++aU2KgsKofPrmvXrnL+aUgLrlatmsO6t2/flnRgXC/pFH59q169uqzD8ybqr0mcQco1uBoqgWtvDIdwZ0o87C++xzX8P5YdPHhQhjc44+73Oq6PkP6N7+B69eqFeR6d+h2Ra5cyZcpIWrmG4Rr47kdh05o1a9qXp0yZUq61Tp8+bV+G4qXYbwwxNL4Whjzh9TAkARX2jTA0MX369Pb7GPqCYTPejBsYmAco/LjgD44DXMNBhoPL0yqPGGOB5zHfMD7CGX1CYXwgAhbjDeO24dq1a/b1cRIfOHBA/fXXX3IfP174YWjcuLHD61y5ckVOOgQk+HLBWCw853//+1/5cYioM2fOyPgSjPcwy5Mnj4zZMo95MY/50FWH8eVCoXWOIJCePHmyXLSgSvvw4cMdHscPK76kjTd8oRuDE2N1dh2o4Fj39bEf2XOXgps3zwN8f+K5UAcB22vmYx8V1wEXZfjuxzG3Z88eWYY6HeZGKXz3otI0LmAwZhDPjeNRN6R66/jH7w6+v1HzwXzs4/bgwQMe+9H4OMZFNMar4poHF8r69uabb8p3OMZ8mr322msOgasOanQjrdVy8zUCrh1Qp8QI97E+rk38Qb8uzjmrayKjEydOSPCEehLmcwbBDPC8idrvYld0QI66B85gLDr+rvhOxywa3bp1czq1q9Uxov/uro5fd7/X8b2M3wxjAO2ta5fM/2uAM8J5Zz539XLjuatfD2P3za+HGUmsXs9qrDjOf2/GDf+/u5AChv5x6d69u7TmGltyccCi0ACmsEFRB1/RQSyKOOgT1NWXO1oFcSCj13zYsGHyA4ieFyzX8MWP1lf8UKIABApMIEDBxRsK9aAXMSoZP1sjd4oRUfQ5R1AkRRe8wQ8XelcQmDdq1EhaXwHHs3nO5s2bN0vBEB2cjBs3Tgq55c2bV1pi8WWPbA1vHPvGwodmL168iNS5S8Erqn4rELwYoYFJX2giYwTHPhqm0NOoG6iMxTlRWKhPnz7SC/HZZ5/JxSv2EdlcuIC0KszlzvHv7NivW7euXIhaQW8KRc/jWB+TmKoIN6vHjb3jrq4DAvEawZPfAU/o94PPBp+3FWOPKvnvu/jJkydSaBB/D91zbgU902ggRWEzXKtg6i5kQSHARWaUN0T2e90sItcusZyco+6cu/r/0ZuvG93MihQp4vHzRhYD8wCkf1yM6TAaluEER48cAmBf0fPO4iLG2PrnDOYLRSCCVB7sFwJ0tGQhpUxDzyC+vFDVULdGaUgdwQnt7o+QGVoF169fL6175l5z9OKjlcuT56PQOUdQVRTpTUg5xPGOOZsRkCNYR3VQBM+otos0KyP0rms6OMFrlipVSlq0jYGJp8e+mc7msOpZxI+v8cfC03OXgldU/VaYj300YpnnNJ8/f768Di5yjI1SgMfwvYxzzPg9vGnTJrdeH8c/MrLMzGma6OnAcY/GNh77oXUc67nLcc1h1SiDWViQFeWLTg18h6Nnzdhrjvv4vrbK4guPs2sVT34H9OuiVxANZkY6s1FDlWy8JgIpnjeB/V2MoToIzo1p2s6gBxuNqLih4QZZTKhujmEW6EDQrIYSIfgHV8evu9/r+F5G4IvUeFei+tolx/9eD40cZcuWVYGCqewBRv+44KKmRIkSYR7HeAqcCJjywtPWKE8gEEGq7Zdffmk5jgXjMMyvj95xBMboMdm9e7f0PBpPVv2jYd4OY3PNU5oBxjm6O73NO++8I/+OGjXKYTlSGpHOox+n4OfNc2TXrl0yfQiOVUzbARgbi+MI47P0VFHoMTQPBdEXSebgBFNPIa3r3XffjfCxb4aUNVz0maeFQu2EixcvRvrcpeATlb8V5mPfeFEHaIRCKh8as3DMGYdwODv+MYbQ3en7EEghFdIYnCPLxDxVGxrRmjVrJheG5sYEwMUpXpei33Gsp6lCjyCyncy3Tz/9VNZzNr1fZKFh1uq+/l3xhLNrH09+B5Chhd8h9JLqYVeA4Mh8buD3Ar8b+DuYx9QCAkE9fIX8912M6+qePXtKvRr0UDuD7zlz4w2+g3UqufnYwr7/888/9vs4j7AMtXasUsKNz+nO9zq+l3EO4poKtR6c9TZH9bVL48aNZRgLpkc0niNaZIZveBK/mLHHPMDoHxecxMZ5Cs2tTxjPh4sPd1qVECxY9cghmMC8hs4OKsxtiGIgSB3BhRd6nXGg/vnnn/JDgC9qfPFrCESQltulSxe5b0xjB4xDQXrK6NGjpYgDWmmR+ou0X/S4m1Ox0POIlBvMJ40eTAT5TZs2tdxfjPNF8I3eTuwjPhekDU+aNEm+WNArStGDt84RHIO4iEOrsnmu5o4dO0rLNI49pIS50+uBcwSFdpAOj+PUmDLr6bFvBfPiIj0Txznm+EQrN/YRz4ULg8icuxR8fPFbEVFo2EIvzNKlS+W4Mn9P4xwaMGCABCn4fxyLU6dOlSI6xgtCZ5Bii0AHhXeQKop51nHxh56OCxcuOKyLYSjbtm2THiX8BuFCGecXsmKwf/gtMBf1ouA/jtFjiWFFb7/9tuXj+J5FYILgaMiQIfb6B96AYAKBDOqIoMAVAii8DgrK4pj1lKtrH3d/B9BwjAwtBHCYzx0NVmg8Q6COzwG/BUbo0UUAid51DNtCUIbME3RsIA0ajci4zqKovW7Hdx2eG7U7ULcJPc+4bjBmJJkhqEVWE44RXOPj+EQPOK6HrRoPsN84bnHdA7iORj0Oc2OTmSff6/heRtYKAnQUW8M+4DXwvtCIhLghqq9dMmXKJB0zOKdwTuB3DJ8bOkvQiIBGLHODlyfnMK4F0cmD7GEUHsUQK7dEqJY7+QymBHA1xZnxhvk1IzNdGuZ4DW86mv3798v0ApgPEHOCY/6+qlWryhQaxnnQtbp168pzY55MK5jCButgLk1My4PnOnDggOXrY8oGzEmOucX1PruaxxxTM2DuRMyViH3FPr///vth5le3mmLH1fNS9DxHcPxjnQULFlg+fuLECZnmpEqVKh5NlYLnXLduXaSOffN0afr4xpykKVOmlP2qWLGiTH3jrXOXQve3wt1pKt3Zn6ZNm4Z5DFPqYAo1PDemYsudO7cch1bfxVbTpQGm3cR2OJbxHY8prpx9l9+9e1fmm3799dfl9TB1EKYY+uSTT8I8LwX/cay/e8P7rtbHlp7L2Nl3p9W0ZuFN5Xfs2DGZbxnTKaVIkUKmwNTTWbl6Xk+vfTz9HcB5hvMFn0+ePHlss2fPdnqOYeqnrl272rJmzSrr4zUw3RrO3WvXrrn8bEOZL6/b8f2FabtwbI8aNUrmAzczT5eGYwTXN5hzG9cbeA4cA927d5e/sXm7//znP/LcOH7wdy9cuLDt559/dngNq+nSPPleBxxDOHYxjSy+xzEXO66JzNPT7nfz2sXZ9brV9RM4O0c2bdok0wXis8L7x/7VqVPHch5zd6YqBHwfYNo4XOthO6vXdSbG/94cERERERG5CQVAMb7bnWmpiAIJhkRgBgP0Uhtr4pB/cYw5ERERERERkR8xMCciIiIiIiLyIwbmRERERERERH7EMeZEREREREREfsQecyIiIiIiIiI/YmBORERERERE5EcMzImIiIiIiIj8iIE5ERERERERkR8xMCciIiIiIiLyIwbmRERERERERH7EwJyIiIiIiIjIjxiY+9jbb78tN6JQxXOAQhmPfwplPP4plPH4J0/F9ngL8sjp06f9vQtEfsVzgEIZj38KZTz+KZTx+CdPsceciIiIiIiIyI8YmBMRERERERH5EQNzIiIiIiIiIj9iYE5ERERERETkRwzMiYiIiIiIiPwo6APzo0ePqipVqqiECROqdOnSqYEDB6oXL16Eu92dO3dUmzZt1CuvvKKSJUummjdvrm7cuBFmvRUrVqgCBQqo+PHjq7x586qFCxf66J0QRQzPASIiIiKi4BbUgfmtW7dU1apVVYwYMSR4QEAybtw4NWjQoHC3bdKkidqyZYuaPn26mjlzptq7d6+qV6+ewzrbt29XDRs2VJUqVVJr165VtWvXVs2aNVMbNmzw4bsich/PAQp1vm6YwrKOHTuqtGnTqgQJEqjcuXOr2bNn++jdEBERUagK6nnMJ0+erB49eqSWLl2qkiZNqqpVq6bu3r2rvvjiC9W7d29ZZmXXrl0SWGzdulWVL19elqVPn16VKlVK/frrrxLowNChQ+XxCRMmyH0EJ0eOHFFDhgxR1atXj8J3SmSN5wCFMt0whUwONExhzthevXqply9fqmHDhoXbMHXixAlpmIoZM6bq06ePNExt27bNvg7OJRz/iRMnVhMnTlSpUqWShoCnT59GwbsjIiKiUBLUPebowXvrrbccgo+mTZtKoIKAw9V2adKksQckULJkSZU1a1Z5DJ48eaI2b94sF29GeH4ENehtIfI3ngMUyowNU2iU6tSpk2SLjB8/XoJqZ3TD1KxZsyQjpH79+mrOnDmSIYKGKW348OFyHiCzpHHjxtIw1blzZ9W+ffsoeodERESBDVlqa9askd/TwoULqxw5csi/uI/laCynEAjMjx07JmmFRpkyZZKURjzmyXaQJ08e+3boeXn27FmY9bAODjD0tBD5G88BCmW+bJiCGTNmqHbt2kkKOxERETkG5F9//bXKmTOnDHVEI/m5E8fUs5v/yL+4j+UI1LGeO8PMQl3sYE9jTJ48eZjlGDOIxyKy3d9//21fB8zrYR3j42boXcFNQwCDm3HsIsYD42az2eQW2eXmlihny5GuaX4OZ8u9vY+h8J5SpkypohrPgeA9XqLbe/LH8Y9GpMqVKzttmKpbt26EG6bOnDmjrl27Jsd/rVq1pCcdY9FbtmypRo4cqeLGjatCXbZs2SK0nf6OIQpmPP4plGXJkkVdv35dPXz4UCWMG1t1KP266vhmblUo3f+/Fjh4+YaasvOYmvPHadWzZ0/Vr18/9e+//7KxO7oG5oFoxIgRavDgwQ7LMmTIoDZu3Oi3faKoYU75DlWhcg507do1QtthrHJ05I/j35cNU1evXpV/UasBvfDr1q1TBw8eVJ9//rmKHTu2Gj16tNsNU+YGlejSiOPMhQsX5N+MGTNaPq4/k0B8TxFdjtcjcuf4Jwp26PnWQXmVnOnU/FaVVIqE8cKshyD9u0Zl1LBaxVXT2ZvUppNXVIsWLdSiRYtUrFix/LLvgS6oA3NcRFmNc8VFl+7Vc7YdDihX2+l/zc+vL/acPX/fvn3Vxx9/bL+PYlq4kEDV4Oh2UcbewrCvG9V4DvjveHHW4nvp0iV7MT0r+nMIxPcUjOeAr+j3lS9fPjVt2jT5f/TO37t3T8aeo8AieubdaZhC0bibN2/a78eLF08lSZJE3b9/3yGIx/PhhvHxGEaiofgcpiu8ffu2QyogUvjRc49z0vh3QKMD/v7G14QUKVLI8YPn0fB3RLYDXs84Lh8XTTjHsX/YTy1OnDiSOYDhAnv27AnznvD5YHpFwOP6PeF7RL8n7FegvidcaEbk74S/MYUWZz3fuiedPeMUXaGDQQflq9pXU3Fjuw6yEbSvbl9d1Zm+QdLbsX2PHj2ibH+DSVAH5khFNI+jRUslDharNEXjdsbKuxqeS08XlT17dvmxxrIKFSo4rIOLg1y5clk+N37IcdOwLm7+SPOk6I/ngP+cPXvWcjkvyqJXwxQKvhkhOEeBOdRg0AFoeA1TCBQRQGq4DwhOEyVKFGY5glNzQ4gOTq2Wm9+rbjgxvibgPLRaDjjXrfYR57IxbV8vR8MUAmur96R7j/F80eU9ufN3IiKK7tCQipl6EsWLIz3l4QXlGtZb0KqyyjJ0oWzfrVs3ZhpZCOpPpGbNmmr9+vXSQq8tXLhQflyNgYTVdkhTRAVebd++fXIhjcf0DzcuyBYvXuywLZ6/dOnS0rpO5G88ByiURaZhyqo4onHsORqmELyZA6/wUpdx3iBg0zcdOOoGKn0fvLXcuMzVcqvncLY8MvuoRaf3FN5yIqJQgGtO1GBpXjSbZfq6K1i/edHssj2eh8IK6l8TTI2Di6AGDRpIYZ6pU6dKeiF6K4xVelENEJV1NQQVmIO5VatWklKxfPly1bx5c1W2bFn7/M0wYMAAmSYH6Rb4F2MNUfZ/4MCBUf5eiazwHKBQ5suGKQTlmIINUwYaoVYC0phxThEREYWSH374Qf5FobeI0Nvp56FoFJgj1Q0XSUirQPVdpBei6p95fN/z58/DlOjHxRsu3Nq2bSvBSbFixdSyZcsc1kGQsmTJEgl4MCXPypUr1bx58ySgIQoEPAcolPm6YQoNUAcOHFBt2rSRec/Hjh0rFdlRAM44XIOIiKKfo0ePSl0YNMamS5dOfhNCfcovDONKniCeQ/V1TxROn1K2x/NQWDFsHCDlUygcBEeOHPH3rhD5RaidAxxjHvUXTl26dFG7du2S8crt27eX4NxY8RXTulSsWFHNnDnTvgzFwtCIhcYoFA+rU6eOjHszF/FCjzzGjeP4ffXVV1WHDh1kyhd305dD7fgHngOk8finYIWaIzh+8+bNq/r06SOBZK9eveR3Y9iwYSF7/KOhG/OU/90/4jOxZB26UMVNmVadOnXKq/sWHQR18TciIgptuGjatGmTx4X6EMTPmDFDbq4gUwQ3IiIKHZMnT5aZGpBVhQwsDG3CTAxo+MWwPmNWVihBMcxzly9G6jnuPXmusiRJ4rV9ik6COpWdiIiIiIjIm9auXSuNssYAvGnTphKsb926VYUqFEa9/eiJOnj5RoS2//PSDdkez0NhsceciIiIiIjIMEsHpsc0ypQpk4w3x2Oo62P25MkTuWkYJoXbjRs3wkz9iJHE5qkiI7Icz2/kbDmGX5mfw9lyV6/57rvvShbBlJ3H1HeNyihPYTto0qSJunnzZkC8pxheWO6tKYEZmBMRERERERnGmGPIk1XRXTxmZcSIEWGK72bIkEGK9EYXCI5Rb2XuH6fVsFrFPZoy7ebDJ2ru/tOyPZ4HRVujiyZNIj7m3oiBORERERERUSSgUChmBdFKlSolASgqu0eXHnPcunfvLkVQm87epFa3r67ixv7/xVadefr8haz/8Olz1a97dxkmEEjvyRbJ5d7CwJyIiIiIiMjQM37nzp0wy9FbjsesYBpN41SaCBBx81aac6BAlfo//vhDUtrrTN+gFrSq7LLnHD3lCMo3nbyiGjZsqD777DO3ZzYJNfxUiIiIiIiI/id37twyltzowoUL6uHDh/JYKMN0pHPmzFENGjSQYDvrsEXqw8U7whSEw30szzJ0oayH9bEdg3Ln2GNORERERET0PzVr1lRjxoxR9+7dU0n+N7XXwoULVYIECVSFChVUqMPnsGjRIjVx4kQ1YcIENW33cbklix9XJYkfR91/8lyqr0PWrFlVt27dVNeuXSWoJ+fYZEFERERERPQ/nTp1krR09PKiSNnUqVNlDnOMIQ/VOczNEGT36NFDnTp1Sq1Zs0bS1B+9VOrq/Scqy+t55D6W43Gsx6A8fOwxJyIiIiIi+h+MI0c19S5dusjUaKjQ3rNnTwnOyRFS05FhgFu2bNlk2YEDB/y9W0GJgTkREREREZFB3rx51aZNm/y9GxRCmMpORERERERE5EcMzImIiIiIiIj8iIE5ERERERERkR8xMCciIiIiIiLyIwbmRERERERERH7EwJyIiIiIiIjIjxiYExEREREREfkRA3MiIiIiIiIiP2JgTkRERERERORHDMyJiIiIiIiI/IiBOREREREREZEfMTAnIiIiIiIi8iMG5kRERERERER+xMCciIiIiIiIyI8YmBMRERERERH5UdAH5tOmTVM5c+ZU8ePHV8WKFVMbN250a7sdO3aoUqVKyXZZs2ZVEyZMcHj8+PHjqnPnzipPnjwqYcKEKlu2bKp79+7q9u3bPnonREREREREFIqCOjCfP3++6tSpk2rVqpVau3atypcvn6pTp446fPiwy+1OnTql3nrrLQnI16xZozp27Kg+/vhjNX36dPs6v/zyiwTvH374oazTv39/tXjxYlW9enX18uXLKHh3REREREREFApiqyD2xRdfqNatW6sBAwbI/QoVKqgDBw6okSNHqjlz5jjdbsyYMSpdunSyTuzYsVXlypXV+fPn1eDBg1W7du1UjBgxVLNmzaTHHP8PFStWVBkyZJCAftu2bfJaRERERERERCHbY/7333+rEydOqCZNmtiXxYwZUzVu3Fh6z13B4w0aNJCgXGvatKm6ePGivbc9ZcqU9qBcK1KkiPx7+fJlL78bIiIiIiLyNXS24RrffHv8+LHDepcuXVL169dXSZIkUalSpVJdunRRDx8+9Nt+U/QXtD3mx44dk39z587tsBxjwm/evKmuX7+uUqdOHWa7Bw8eqAsXLlhup5+3QIEClq+5a9cu+TdXrlxO9+vJkydy05D2jpPdmP6uvwBsNpvcIrvcnFrvbDkaLszP4Wy5t/cxFN4TXpOIiIiIAlulSpXU8OHDHZbFixfP/v/Pnj2TLNm4ceOqBQsWSI0pDHvFv66ycolCMjC/deuW/Js8eXKH5a+88or9cavAXBdvc7WdFbSQ9enTR1LYUWTOmREjRkhKvBFa2dBYYDzx0fp2//59hyAeReZwu3v3rnwhaIkTJ5Yiddj3Fy9e2JcnTZpUvjCwz8YAEe8NQaLxNSFFihQS2BoL2CHARHYAXg+vq8WKFUs+E+wf9lOLEyeOSpYsmXr06JFDqyHf0//9nYmIiKLK0aNHVdeuXaXjAL+T7du3V4MGDZLfO1fu3LmjevTooZYvXy6/oajPgyK4+O00unHjhvr888/VihUrZJvMmTPLfdT2IQpmuH584403nD6+ZMkS9ddff0ldKtSk0teLyLDFOYbC00TROjDHl/6VK1fCXc/c2+1rCBAx9vzatWvq559/drlu3759pUVNQ+V3BIr4AtB0ijyC00SJEoVZjuDU3EML+NG1Wq4bFYzLza8JCGytlusvG6t9RHCKQNm8PEGCBBJY8z059tQTERFFBTRgV61aVeXNm1cC59OnT6tevXpJoD1s2DCX22IYIIYDougtfkfR8VCvXj2poaOh8bl8+fLyGzhx4kRpfEZDwNOnT6Pg3RH5F4a9lihRwh6UA84RXD+uW7eOgTlF/8AcVc8/+OCDcNdDMKQDNwTzxt5v3eNtDuw0vS62M3K1HX6wli1bJpXaMW2aKwj6jKkwOr3ZKs1ZB5uRXe4shdqXr+nr5cH8noiIiHxt8uTJkum1dOlSaSiuVq2aBNMojNu7d29ZZgW96xs2bFBbt26VwBvSp08vHQm//vqrBPuANF9ki+3bt08arnX6L1F0gHMA2Y9Qrlw5KQxdsGBB++MY2opGLyME5dmzZ7cPpzXjcFZbmPceHd6TLQqHswZUYI4ULNzcoXvNcXIgtUrDffSSWqWxA3o+M2bMGOakcjZm/auvvlJjx46V8SU4cYmIiIgCoUcPY2CNATjSbNGZgKC7bt26TrdLkyaNPSiHkiVLSs8gHtOB+YwZMyTdXQflRNEFhqViVqccOXKoc+fOqS+//FKu8Q8ePKiyZMli77AzD3vVHXjOhr1yOOt9e/CM148u7+lJFA5nDajA3BPouUYRNvSy44cJ8MfB/Zo1a7rcFo+jBxypXnoc1sKFCyVgz58/v329uXPnSlrY+PHjHaq/ExEREfkTOhQw3atRpkyZ5KIRjzkLzPGY1ZBAFMHVnRRnzpyR4Xu4IK5Vq5b0pOPitWXLljIlrXE4GFGwDYU1Bs8IyNEYhce+/vpruUUUh7Mmtvcc4/miy3tKFIXDWYM2MAeka7Vo0UJat8qUKaNmzZqlTp48qebNm2dfB63GVapUURs3brTPPf7pp59K0I0fGKTO7927V02ZMkV9//339g8c27Vp00ZVr15dikPs3r3b/pyYzxw3IiIiIn+ISI9eeNthKlq4evWq/IuUePTCY0wtehNR+A1TzY4ePdryuZnKy1TeQB8KayVt2rQSR+zfv9/hfDAPe9XnT6FChSyfh8NZY1i+frC/pxhROJw1qAPzZs2aSYrBqFGj1NChQ1W+fPnU6tWrHXq9cRIiTcJ4MiJ1BT8yaNVC7zlOyHHjxjmk0W/evFlSFNavXy83I1RjRKMAERERUXSjr5lwXTVt2jT5f/TO37t3T8ae4xpIj881YiovU3kDfSisM+ZACz3o5mGvKHyIxqtOnTpF6rWIomVgDmghc9VKVrFiRcsWsrJly6o9e/Y43Q4/Ogy+iYiIKBC56tFzVgBXb3f9+nWX2+l/zcXeEJyjcwIV4AsUKBDmOZjKy1TeYIQMke3bt6u2bdval6HjDhm4GIOua1mtXLlSGilq1Kjhx72l6CzoA3MiIiKiUGPVo3fhwgXp9XQ1rSweM06LpuG5MB0UoPI0gjdz4KXvO0s1ZSovU3kD3aFDh6QBqXHjxhJwnz9/XjI98Nmi2KHWqFEjKQrXoEEDycpFI1jPnj3Ve++9x6nSyGf8NyCEiIiIiCIEPXoYaof0cg2FbNHzqWvqONtO9xBqmBINKbq6eC6Ccky/hmF9RqjXgzRmDAkkCkZI80cDE4JzFI9GhgeGbOzcuVOKJxozDjDsFYWhUQC6S5cuqmHDhmrq1Kl+3X+K3thjTkRERBRkMM51woQJ0qOHKdIQWGMIHgIN4xRqCKIRqP/www9yv3Tp0lLYtlWrVjIdLHoKsT2G+Omp0mDgwIGyDIVwUdMHPY2oyD5gwACHXnGiYJI+fXq1Zs0at9ZFoefly5f7fJ+INPaYExEREQUZjENGDzaKh2FqNIz9Rqqtufja8+fPHQqM6Z51BOsYU4sAvVixYjKNrBHmNl+1apVUY8fzf/PNN6pfv37S00hERN7HHnMiIiKiIJQ3b161adMml+ucPXs2zDIUHZsxY4bcXEGqL25EROR77DEnIiIiIiIi8iMG5kRBDPPLojoopjhBKiLSGt2xY8cOmcYG22XNmlXGKRodP35cde7cWeXJk0cK/WTLlk11797dYa5UIiIiIiLyDgbmREFq/vz5UvwH4wPXrl0rVUXr1KmjDh8+7HK7U6dOSWoiAnIUQOnYsaMUC5o+fbp9nV9++UWC9w8//FDW6d+/v1q8eLEUDHr58mUUvDsiIiIiotDBMeZEQQrVd1u3bi0VcgGFfA4cOCBVc+fMmeN0uzFjxqh06dLJOrFjx1aVK1eWeTxRMKhdu3YyLykq8KLHXM9RWrFiRalOioAe89+6moqHiIiIiIg8wx5zoiCEaXFOnDghc2tqmPKmcePG0nvuCh7H9DoIyrWmTZuqixcv2nvbMc+nDsq1IkWKyL+XL1/28rshirijR4+qKlWqyJALNDhhiidzBWord+7ckWmgUNk6WbJkqnnz5urGjRtO11+xYoWcE8WLF/fyOyAiIiJijzlRUDp27Jj8mzt3boflGBN+8+ZNdf36dZU6deow2z148EBduHDBcjv9vAUKFLB8zV27dsm/uXLlcrlvT548kZuG1HcENMYUeNzHzWazyS2yy83p9c6Wo/HC/BzOlkd0X4zvO7q8J3eW4/Wi2q1bt2TeZVSmRuB8+vRp1atXL/mMhg0b5nJbNGqhcQtDOPQ8zvXq1ZOMELPHjx/LNFRp0qTx4bshIiKiUMbAnCgIISDRU94YofdPP24VmOviba62s/Lw4UMJXJDCjiJzrowYMSLMPLqpUqWSBgMtXrx4KkmSJOr+/fsOQTx6PXG7e/euevbsmX154sSJpVAd9t/YG5o0aVIVN25c2W9j4Ij3h2DL+JqQIkUKCdqMRewQYCJDAK+H19VixYolnwv2D/upxYkTR3pYHz16JJ+L+T3pYFW/dnR4T+78nfA3jmqTJ0+WfV66dKl8btWqVZN9wjCP3r17yzJnjUwbNmxQW7duVeXLl5dl6dOnl4KIv/76qwT75uEfeDx79uzh1nAgIiIiiggG5kQBAqm1V65cCXc9c2+3ryE4xNjza9euqZ9//jnc9fv27SvF5DQEOwgUEUBqumcZwWmiRInCLEdAZe6h1cGp1XLdsGBcbn5NQGBrtVwHp1b7iOAUgbJ5eYIECSSwNi83v3Z0eE/u/p2iGoZloO6BMQDHsAw0IiHorlu3rtPt0Putg3IoWbKkFETEY8bAHPUXRo8eLc9nnr2AiIiIyFsYmBMFCFQ9/+CDD8JdD4GQDtoQzBt7v3WPtzmo0/S62M7I1XYIcpYtWyaV2jFtWngQ9OGm6RRnq1Rnc/p3RJc7S6P25Ws6W+7stYP5PUX0M/A1DL1A8UKjTJkySW8+HnMWmOMxqwYuDOnQw0Q0pMYj7b1o0aJu7VOoD+UwLtPvPTq8p0AcykFERNELA3OiANG+fXu5uUMHFQgiMmfObF+O++ghtUpjB/R6ZsyYMUzw4WzM+ldffaXGjh2rFixYoMqVK+fxeyLyJTQomYdl6AYmZ8MywtsOhRW1TZs2Sco7xqK7K9SHcmBdHTzj9aPLewrEoRxERBS9MDAnCkLouUYRNvSyI5UXcHGK+zVr1nS5LR5HDziKY+FCFRYuXCgBe/78+e3rzZ07V3oLx48f71D9nSgUPH/+XHXr1k3169fPo6JvoT6UA+9J9x7j+aLLewrEoRxERBS9MDAnClIocNWiRQuVJUsWVaZMGTVr1ix18uRJNW/ePPs6GBeLqaQ2btxon3v8008/laC7ZcuWkjq/d+9eNWXKFPX999/bLzixHaaSql69unrjjTfU7t277c+J+cxxI/I3BG/mYRmA3lZnwzn0dpi5wNV206ZNk+d+//337b2xT58+ld5d3EeghsDPLNSHchiXGV8/2N9TIA7lICKi6IWBOVGQatasmaRYjho1Sg0dOlTly5dPrV692qHXG705CCSMvTo5cuRQ69atk1499J6nTZtWjRs3ziGNfvPmzZKiuX79erkZDRo0SBoFiPwNQy/MwzIwHSDSkV0VScRjVtOi4bkwZRocP35cXbx40bK3HMH7jz/+KA1jRERERN7AwJwoiKHH21XBuIoVK1qmWpYtW1bt2bPH6XYIvBl8U6BDwxKmMrt3756MBdbDMpCSrDNEnG2Hxqzt27fLuQD79u2T8eV6KEiXLl3sQbo2cuRIdebMGckwQaE4IiIiooAMzNHDZpXaR0RE5G2dOnWSKcwaNGggswcgsEaDErJBjFOoIUsEgfoPP/wg90uXLi3DNFq1aiXFDZEeje0RpOup0rANbkYzZ85U//77rzR4EREREXlTzMgWx0FvRaFChWRMHXopHj9+LHMet23bVlIKiYiIfAEp5aifgOEamBoNwyx69uwZpio6fquMlb91zzqCdfxWIUAvVqyYFEUkIiIiCqrAHAE4ehY+++wzdfjwYektR8osKpqeO3dOClHhwoeIiMhX8ubNK9OaYaqrK1euSIq6nm1AO3v2rPR2G6Ea+IwZM6SQG6a9QtHE8Ka8wnMg5Z2IiAIP4g5kUL322mtSmNH8va9dunRJ1a9fX4ZA4XsfQ5eMUyVqKAKaM2dOiW3QeIuGYKKADMxHjx6tfvvtNwnGzWNYq1WrJstWrVrljX0kIiIiIiJyasmSJdIQW6dOHafroCMR08yiE3HBggXqm2++kalmO3To4LDe/PnzZbgUMqrWrl0rBXbxvOiMJAq4MeboXUBrVO3atVXHjh0ljVDT4/JQJIeIiIiIiMjXPeaoGYIZa6ZPn+40eP/rr7/UqVOnVNasWWUZ6mM1bdpUhkOhhxxQr6R169ZqwIABch9Dnw4cOCBFQOfMmROF74pCSYR7zNEiBV27dpWUQCN9/9q1a5HdPyIiIiIiIpcQlIcHvd8lSpSwB+WAGTjixo0rU8kCComeOHFCNWnSxOG5GzduLNsTBVyPecKECdWdO3fU5cuXw1SuPXTokPxrrIpLRERERETkL8eOHZPaJEYIyrNnzy6P6XUgd+7cDuthmsybN2+q69evq9SpU4d57idPnshNe/nypWQX418N93EzDwWO6HLjc7tajoYFq+HHVssjs4/m9x4d3pPNjeXuNAq5I8LPgiII2KF+/frZW5hg9uzZUnwHO40WKV+LaGGGHTt2qFKlSsl2aDXDlDuuoEgE3tO3337rpT0nIiIiIqKocuvWrTCZvnqWDzym1wHzeljH+LjZiBEjVLJkyew3BPgI4hHM6xvS7AH/GpejgCmgGKlxuQ70UajUuBxj5fW+GJdjBhIEsMZluGEZHjMu0+8Dz2VcjtcCvLZxOfYNsK/O3hNeR79+dHlPN934O/m9xxwVDBEEowru8OHDJWiFNm3aSMCO+1jHl3RhBowDwfyzqLCLwgx79+5V+fPnd7odxpWg8APWxUm0Z88emfcWWQDt27cPs/6GDRvUrl27fPpeiIiIiIjo/yAzF3FGeMw92/7St29fiSc0dAAiHkqRIoV9mY6XEidOrBIlShRmObKNzT20upHAarluLDAuN7+m7tG1Wq7H2FvtI6bCRjaBeTmmx0bHptV70j3HeL7o8p4SufF38ntg/s4776j+/furYcOGWT6OYgk1a9ZUvhTRwgyYez1dunSyTuzYsVXlypXV+fPnZe5bzMGuP3Td4tK9e3f15ZdfWgbtRERERETkXaiW/sEHH4S7nicBEoI+BPxm6GktVKiQfR3AesZec90baw4cNQR9uGk6SLVKc9bBZmSXO0uh9uVrulpu9frB/p5ieLA8siKVED9kyBD1+++/S+CKIBw3/D+WIWj2pcgUZsDjmOcQQbmGaowXL14MMw0CplFAKwoyAYiIiIiIyPfQIabH8rq6eQK963oMufb06VOJK3TPu/7XvB7uoxfWanw5kV97zDWMI4+KseRmES3M8ODBA3XhwgXL7fTzFihQQP7/6tWrMl4e87G7O6ifhR9C9z15q/ADEREREXkfOhEx5TPmMc+cObMsW7lypVy716hRQ+5ny5ZN5cqVS3rsMfQVcP2J+77OBqbQFuHAHJXX//zzT8nTR2+zeew3UsALFy6sChYsqHzBncIMVoG5HvzvTkGH3r17ywlZvnx5t/cLY9aREm+UKlUqh8IASHNJkiSJFBQwBvEY444bCgroAgh6fAPGPWDfUeRAw/gGfP7YZ2OAiPeGINFcjACtfPhi0Z8BIMBMmTKlvJ4ugACxYsWSzwT7pwsg6DEbKGiB4gcPHz7kezK8J/ydiYiIiCjqHT16VG6PHz+W+/v27ZPrTcQDGO4KjRo1kuGpyJxF5xvS1Xv27Knee+89+xzmgMzfFi1aqCxZsqgyZcqoWbNmqZMnT0pQTxRwgTkKHKAaO8Z+mAPz7du3q8mTJ6tatWpJb3MwFnlAsbclS5aov/76y6PtWPghdN8TEREREfnHokWLHDrHJk2aJDcE5Vu2bLFfGyJ+QYFqDIfFNSHiGNSfMmrWrJl0zIwaNUoC+Hz58qnVq1e7LC5N5LfAfP/+/fKvTvEwqlatmvr++++lpcpXRR4iWphBr2su/GDerkePHqpjx47Sk2rsjUWvKrbF8lAv/IA5HyMC43jCe/5QLvxARERERJ5BL7c7Na4yZMigli9fHu56iEnciUuIvCXCg2J1SjHSg50FT57O6+ZJkYeIFmZAz2fGjBkttzM+7/Hjx9XXX38tgbq+6fR2pEmTcxjDjxsRERERERH5sMcc42lRHG327Nnq7bffdngMy8CXAWxkCjPg8WXLlslUb7phYeHChRKw6xQVpKs8f/7cYbtKlSqpbt26ybgUcuz5Nv9tXD1OREREREREXgjMUQgBY7AR4JYrV07Vrl1blq9Zs0bt2LFDUnyxji+5U5hh69atqkqVKmrjxo32wg+ffvqpmjt3rmrZsqWkqOzdu1dNmTJF0u91anLZsmUtXxOFIfTzEBEREREREfktMEeBMwTl6KXeuXOn3DSkm6MnulevXsqX3CnMgH1B1W9jka4cOXJI4Qe8B/Sep02bVo0bN05S6YmIiIiIiIiCIjB/44031NSpU9VHH33kMJUUoPjZt99+K+v4WniFGSpWrGhZORs94nv27PHotViBm4iIiIiIiAImMIc2bdrI+G6M6z5x4oQsw7jvxo0bq3Tp0nlrH4mIiIiIiIiirUgF5oAAvHv37t7ZGyIiIiIiIqIQE+nA/K+//lKnTp2Sub6tUr1btWoV2ZcgIiIiIiIiirYiHJifP39eKqKjArszqHDOwJyIiIiIiIjIB4F5p06d1Pbt2yO6ORERERERERFFJjDfsmWL9IgnS5ZMNW3aVKVMmVLFjh3pzHgiIiIiIiKikBLhSDpJkiQyTdqkSZNkPnEiIiIiIiIi8lxMFUFNmjSRfx88eBDRpyAiIiIiIiIKeRHuMe/QoYNau3at+uSTT9SjR49U+fLl1SuvvBJmvUyZMkV2H4mIiIiIiIiirQgH5oULF5Z/MUVajx49LNfBGPTnz59HfO+IiIiIiIiIorkIp7Ib5yzH/zu7EREREZH3HT16VFWpUkUlTJhQpUuXTg0cOFC9ePEi3O3u3Lmj2rRpI5mOKOLbvHlzdePGDafrr1ixQjpbihcv7uV3QEREke4xR+o6vqSJiIiIKGrdunVLVa1aVeXNm1cC59OnT6tevXqply9fqmHDhoVbJ+jEiRNq+vTpKmbMmKpPnz6qXr16atu2bWHWffz4serZs6dKkyaND98NERFFaro0IiIiIop6kydPlho/S5cuVUmTJlXVqlVTd+/eVV988YXq3bu3LLOya9cutWHDBrV161bpZIH06dOrUqVKqV9//VWCfaMxY8bI49mzZ1eHDx+OkvdGRBSKIpzKbuXZs2fefDoioqCA1NE1a9aohg0bqkuXLqkLFy5IHQ7cx3L0YBFFZzwHoh4K8L711lsOAXjTpk0lWEfQ7Wo79H7roBxKliypsmbNKo8ZnT9/Xo0ePVp98803PnoXRN6zcOFC1aBBA/Xaa69JVu/MmTMtOxbxmPn22WefhVl32rRpKmfOnCp+/PiqWLFiauPGjVH0TihURSowR2E3tKQWKlRIxYsXTyVIkEBSntq1a6fatm0rP8xERNE5GPn666/lh7t27drSc5UgplKvJY6nzp04JvexPEeOHLKeO2M/iYL+HEiQQMY7nzt3jueADx07dkzlzp07zEw4GG+OxzzZDvLkyRNmO6TGI+29aNGiXtxzIt9YsmSJOnv2rKpTp064686dO1eyR/Stc+fODo/Pnz9fderUSbVq1UoarPLlyyfPy6wRCshUdgTgNWrUsI9HQqE3tDihVQk/xps3b5ZxT5hOjYgoukGvVIsWLSTwSBQvjupQ+nXV8c3cqlC6lPZ1Dl6+oabsPKbm7v9bxmji+3LOnDkSuBBFq3MgUSLVsWNH9eGHH0pjvXbw4EH1/fffy3HPc8D7Y8yTJ08eZjkKuuGxiGz3999/2+9v2rRJUt4xFt1dT548kZuGTAlcGxozJnQPpblIcESXm7MxnC3HWHqrwsRWyyOzj+b3Hh3ekzvL8ZqB0GOO/bh//77UT3ClYMGCKn/+/E4fx5CQ1q1bqwEDBsj9ChUqqAMHDqiRI0fKdxhRQAXmSG367bffLB/DOCd8oa9atYqBORFFO+j1QxXjZcuWqSo506n5rSqpFAnjhVkPQfp3jcqoYbWKq6azN0kAg0Bm0aJFKlasWH7ZdyJvnwMYk4wL4hQpUoRZD0E6xkIPHz5cel55DgQHZER269ZN9evXz6OibyNGjFCDBw92WJYqVSp18+ZN+31kWCZJkkSCJ2MQj55+3DBO3jg0MnHixNLpc/v2bYeMC6Twx40bVxoajAEiGh0QnBlfE3B8IrDF82gIMFOmTCmvh9fVcGyioQL7h/3U4sSJI1Xs0Sj18OFD+/LYsWOrnTt3qilTpqiLFy/K/iDwQ6ZIhw4dVOnSpR32PRjek6d/J/yd/c1bjQNooEKDlHEIB567cePGHNZBgRmYz5s3T05+pKihlbxu3br2x/BFBGfOnPHOXhIRBZCJEyfag/JV7aupuLFdBxgI2le3r67qTN8ggQm279GjR5TtL5GvzgEE5T///LMEE64ggMBY81q1avEc8BIEWZj2zAxBHR5ztd3169ddboextXju999/3x70PX36VIJI3EeGBAI6s759+6qPP/7Yfh8F5XCtaGy00TP6IDjF85iXIzg199Dq4NRqufm96h5dc0MRAiur5YD3YrWPCE6Nx7ZejowPBNb4PL799ls5nvU1L4Jc3FBr4ciRI1IxH+P3u3btqrp06SIBciC/p4j+nYJN5cqVZYrAjBkzqvbt28uxqxsL9ZAO85APDPdA4wjOn9SpU4d5TmaMMGPEb4E5xnAAvmjQcmakU6SuXbsW2f0jIgoouBCbMGGCpK+jpzy8oFzDegtaVVZZhi6U7dEbFQipf0QRPgcSJZKe8vCCcg3roaccF8I8ByIPQYN5TDhq+6DX02oMuXE7q2nR8FyYMg2OHz8uPb9WveUIGn/88UfJfDBD0Iebpv++Vn9nfYEb2eXOjiFfviaWYUinJ0M50GCxfft2h6EcgfaevLE80KHRBIXeypUrJ99Jq1evVoMGDZJgW/eG66Eg5iEfusEEj1sF5qGaMWJ8Tzp4xutHl/f0JAozRiIcmGOn0Jp6+fJlew+5dujQIfnX2VQdRETBav369dIzgjHlVunrrmD95kWzq2m7j8vz1KxZ02f7SeTrcwCBiFVPnStYH8HM1KlTeQ5EEj47FOC9d++eXEACGkoQ9GE8rKvthg4dKkFi2bJlZdm+ffskfVf/PdCzq4N0DWNr8XdHujZ6DkMZh3JEDcQZV65cCXc9Vw1RZkWKFJGbhr8fgrDx48fLePLIBFihljFi9Z504xGeL7q8p0RRmDES4aZqTBuAHcL4o3Xr1tmXz549W77wseMlSpTw1n4SEQWEH374Qf5FobeI0Nvp5yEKNvrYRe9gROjteA5EDipG4yIT00Nh/nE0dqBgFQIDY8cIOk8wW46G8c7Vq1eXatMIFJcvXy5BJoJ0PYc5tqlYsaLDLW3atNIAgP/3ZNx5KAzlCK+BSg/lqFKlin0oB4Vv8eLF0ggU3i2yGjVqJHUVdMeiDgzNQ0V0T7qzoSI4H3Hu6ZsOHPGvvunAzlvLjctcLbd6DmfLI7OPWnR6TzHdWO4tEX4mtKYCWrLQEqh3tE2bNvaUAr0OEVF0cfr0aZU8QTyH6uueKJw+pWyP5yEK2nMgeXKHlF1PYH5zbM9zIHIQHGBeZfTeos4P0nFR+d6cSouAwzxNHXp40auOqW0RoKOzBYEm+X4oBzJOsb15vCyFhbHfeiyvq1tkmQNL3QNvHiqC+2hksUpjJ/JrKvs777yj+vfvr4YNG2b5ONJBmKJGRNENxhsljR+26JEnksSLLemnREF7DkRyqBp6XnkORB6mpcUsOO7UBDJCw8iMGTPk5q6ZM2dGaB+jGw7liJ7zn6OyPirpQ7Zs2VSuXLmkx/6tt96SZWhIwX3+zSggA3MYMmSIevvtt9XcuXPt81ziQH7vvfeYxk5E0RLGG527fDFSz3HvyXOV5X9jQomC8hw4dy5Sz4GgPEuWLF7bJ6JgGsqBwBzPwyDPu44ePSo3FObTtRPwfYUebl13AZ8/7iNOQRYDhhigsj5miUDRMA3DQtCIgu+pMmXKqFmzZqmTJ0/KrFREvhKhVHZUrsNYctyQ9vHVV1/JGBvc8P8MyomiDqa1yZkzpxStQDoiUhvdsWPHDilMgu0wlQtS61ypX7++nO/4AQtl2bNnV7cfPVEHL9+I0PZ/Xroh2+N5iIL2HLh9WypOR8Sff/4p2/McoGDEoRyBC0MFMNd4y5Yt5f6kSZPkPoZ5aBiTjroK6ERE5yLqM4wbN06NHTvW4bmaNWsmhfuQKVKjRg0Zf44K7vnz54/y90WhI0KBOSrXYdwHxpNbpUhFp6Dkt99+k1Y2jCXCFyn+//z5815+F0QRM3/+fCkAhDGCa9euVfny5VN16tRRhw8fdrndqVOnJD0Lxz5ai5GSh4JB06dPt1x/w4YNateuXT56F8FFF1GastNx7Jm79HbGYkxEwUQfu5gGKiL0djwHKBhxKEfgQi+31Rj0LVu22NfBNI0IsvH5YxoszDWP3nKrqd8++OADuV7Cevv375fifUS+FOHib7igB3eLXgRjUIJq86i4iVbRlStXytyTqGaqU2SIAuFHqHXr1lLToVKlStKyi2q6mNbGFUyxky5dOjmmK1euLHN6dujQQYoGmQupYJ7G7t27qy+//NLH7yY46O+Oufv/Vjcf/v+5Ld2B9efuPy3b63FrRMF6DuD7wzyvbHiwPrbjOUDBCqnRxvmRI8I4xR0RUaQDcwSyuIBHmoe/Kkv6MihBMIKWsk8//VR609FKhqAfz41x9ET+hjlnUdsBc6NqmLIBaVtoqHIFj2OKHRQ70Zo2baouXrwYpmHrm2++kSwZZMiQkrln0eL+4Mkz1XT2JvX0uWO1Y2ewHtZ/+PS5bO/N6TWI/HIOPHgg3z9Pnz51azush/UfPnzIc4CCFodyEFHAFX+7evWqVC1ErzKCYYy/wLyW5lSQgQMHKl8GJQgazEGJcZmzoARjS8xBCdLrEJQUKFBA/fLLLxKkdO7c2Sf7TxRZehoPPa2HcfwUeqWuX79uOaUHLqYvXLhguZ1+XpwD+jwfOnSoWrVqldsX0Uj5wk1Dwx2+F4wNeLiPm3mqk4guNzcOOluO92A1vYrVclev2bVrV7Vt2zaZj7bO9A1qQavKKkXCeC57yhGUbzp5RRpEMJUknjOQ3lNklzPICi3Gc6BWrVoyttNVhWp8JyEox3Czhg0bSmBOFIwwBAPHPa4Z0TnlKQ7lICKvB+boXdZBOKqzTpkyxXI9XwXmvg5Kfv/9d6nOuHv3btWnTx+ZGgPbjBgxQuYLJfK3W7duyb+ofWCe21Y/bnUOoKU+vO203r17S7pp+fLl3d4vnCPmeXRTpUrlkPIaL148SePDWD1jEI/5XXFDmiCyVoypg6gHgX03zseLcX4YToN9NgaOeG8IFM1ptggcENjqzwDwPYZzHa9nTE9EryA+E+wf9lOLEyeOSpYsmVTVxWMoepl16CL1XtFsqlOZ3A7zm6NA3OQdxyR9HT3ltWvXloZDvH4gvicU9kRvZkT+Tvgb+wMq8CJIRA0EfEaof4JCP3ivrty5c0fGFaIIED4/ZEQhO0pX5cXfBMWAUOwHrwGoY4IhHSxw+n/HErLOULUYQUqmTJlU8+bN1UcffeRQFAu9it99952si2MLDVP4fzbkUHQYyjF8+HCPpkzjUA4i8tl0aeYeGjOrQgrBEpSgpxBBPFLccSGGlCNciKMy9YEDB+w9iqHYWxjecvN7jw7vyZ3l3rjQRLBw5cqVcNczNyz5AgIdzO35119/ebRd3759ZaiLhiKL+LyMFy/6uwHBKQormpcjODV/5vq8tVquz1/jcvNr6r+R1XIdnFrtI4JTYy0NvRzrLlu2TKrUT5w4QU3bfVxuyeLHVUnix1H3nzyX6uuAizAEj+gp1wFjIL4nDFlAY0FE/05RDd/XqAOCuZxXrFghVY579eol3xPDhg1zuS16b5F1hdoi+AzRAFuvXj3pBQY0UmDoEoZw4JjWMxKULVtW7dy5U4L0UIfjBT3lEydOlEYN/EbihkYeHBsYR6t/c3EOoJcc50F4jSZEwTCUo2fPnvI9glpF7tRb4lAOIvJZYD5jxgzlbYEUlOBiE0XeMP0bisMBxrHjtUePHq1+/PHHkO4tdNWzpoNnvH50eU/u/J280WO4ePFiqW0QHnw2OnDDeWNsaNKNS+bATtPrYjsj83boTcSxj8/G+Pnic8K2WG4Fnxtumr74sLoI0cFmZJc7u8Dx5WtiGY4dXJyhON769etlXlqk/T+4/0SmVEGDHtIV0TPibB8D7T15Y3lUQRopjkf02OK7o1q1anJeov4Isj2cVU5GoxNmGti6das9GyR9+vTSiISpcxDsI+jEkCnjeYRaI6gxggDdF7+BwQjfq/iuQKDhcA48eOD2OUAUbDiUg4h8whZApk2bhkgs3Bv8/PPP8v9nz551eI5FixbJ8mvXrlm+xv379+XxmTNnOizH+liO7aF3795y/9ixYw7rtWvXzla0aFGn7+Hx48e2O3fu2G+5c+e25cmTx/bixQv77eXLl7Iu/vXGcuMyV8utnsPZ8sjsY9asWeUWnd6TO8uj2unTp+UYXbduncPyIUOG2FKkSOFy24wZM9o+++wzh2W//fabPN+hQ4fkfrJkyZyeg7FixXJ7P/PmzSu3UKGPf/K9cuXK2d59912HZefOnZNjdOXKlU63GzBggC1NmjRhluPv9vHHH7t8zVq1atmqV6/u9j6G2vEPPAcoFI7/hw8f2ho0aCDfN4kSJbJ16NDB9ueffzqsg/tYnjBhQlkP6z969Mhv+0xRKzof/87w+z9yvNJ8jdTuefPmyZzikYGxgVbzD5pvxl5zPdZcw320WlqlsQPSMTNmzGi5nfF59Zhzc5om7rtq9UdPIXpp9E2nmeJffdM9TN5ablzmarnVczhbHpl91KLTe3JneVRD8UX03qGXXUP2AO7XrFnT5bZ4HGnYxmyFhQsXyrmBXi7A2NrNmzc73AAt/Wj1J/I3fG+bM6gw1hkZLebv+PC209/7rrbTc9lyZg4i0kM5kFn56quvyjCOwoULS1YavoeQbYP7WI7iyFgP6xuHCxEReW2M+b59+2T8nS6Mg0ClZcuWMhUZ0gmRElixYkXl66BEF9DwNCjBGEQ91s0clOA5UbV906ZN9gs4BDFIffTVeyLyFFJ2UXwpS5YsqkyZMmrWrFnq5MmT0lCm4ZhFCi6C6QoVKsgyTAM4d+5cOV+ROr93714p4IhqsbrhAWNpreTMmdP+PET+hOEX5nohgAtiYxFDT7ZD+rozqDeClFTUCnCGdUZCs84I0/RDE4dyEFFABOboVcAc4PjyMf5goSUQBXQwpziCZF8Gsb4MSl577TWZKg1znOP9YUo4tHpiCjUUAiIKBM2aNZMx8KNGjZJpzfLlyyc93bqBCXD8olHJeJ7ieMZUhyjShoaqtGnTqnHjxknWChGFher7CMxxnrz++utO12OdkdCsM+KvWQkoMODYxG8pbug40tmkRERREpgjKMaPFX7oSpYsKcV0NBTQQWC+fft2FcxByZgxYyT1HT3ruBgoUqSItIiiBZQoUKBxyVXBODSOWVXORo/4nj17PHotf1bgJjJDkGUuYggI6pwVP9TbYUpNd7dD4+27776rOnXqJL1jroTKrASuKvjrnkE8X3R5T4E4KwEREUUvEQ7MMd4UP07oHShdurQqV66c/TH0YAN6l4M5KMGPOnpIcCMiosCCYUbmMeEXLlyQXk9Xs3fgMT0tmhGeCxlfRphSDfPPI/MKU4KFJ5RmJXC23Or1g/09BeKsBEREFL1EeMCL7qVAL7KZTu0ypoQRERF5EzKekMWE+bI11AtBz6erOgjY7urVqw5ZXaiZgvHlxholmL4TY0ORJTV//nzOv01ERESBF5gj/RswF6yZrhKdIUOGyOwbERGRU0gtR+90gwYNpNgo6oBgmBVSyY1zmGP4Egowacjyql69umrVqpXMQ7x8+XLVvHlzyaTCHOaA8cYI0pHe3r9/f3Xo0CG1e/duuXHsKBEREQVMKnu1atWk+uTYsWPlgkhDQbgtW7ZIehcufIiIiHwB45BR2BNV0uvWrSvjlXv27CnBudHz588dCozpnnWs27ZtWykeVqdOHYdU9X/++UcdPHhQ/h+PGWXOnFmdPXvWp++NiIiIQkuEA/N+/fqpn376SSqh/vnnn/ZxVqiCDrhAQkVzIiIiX8mbN69Ma+mKVRCN36gZM2bIzQpqpbCwFxEREQV8KjsuWtBTjkroei5PfUNVdDyGecGJiIiIiIiIyAs95nouUMzpqXvHixYtqv773/9Kuh8q10KuXLlUoUKF3H1aIiIiIiIiopDmdo850v4wH6ierzxr1qxSqXb//v0SiDdu3FhuDMqJiIiIiCiqoANx0KBBqmTJkipZsmRSpLp+/fr2jkPzzFJt2rSROiVYF8U/b9y4EWa9FStWqAIFCqj48ePLsCnUJiEKmFR2pKnrAjrnzp2TcXuPHz/21b4RERERERG5dP78eTVt2jSZ4nLJkiVqypQpMuVlqVKl1IULFxzWbdKkiRSqnj59upo5c6bau3evqlevnsM6mE6zYcOGqlKlSmrt2rWqdu3aqlmzZpazURFFeSp74sSJ1YMHD9TXX38tc71qOFhPnTrldDtMR0NEREREROQLyOQ9ffq0SpAggX1ZuXLlVKZMmdR//vMf6U0HZP4iuEax6vLly8uy9OnTSwCP+lh6ysyhQ4fK43q2DgToR44cUUOGDOGsU+T/wBwpHGhRwnyvuOlx5sOHD3e6DdZhYE5ERERERL6SKFGiMMswBBfTW16+fNmhQzFNmjT2oByQ/o7AHo8hMH/y5InavHmzwxSa0LRpU0mBRyo8UuCJ/JbKjhaiePHihanAHt6NiIiIiIgoKl2/fl2yelGYWjt27JjKnTt3mHXz5MkjjwF63p89exZmPazz8uVLy3HrgIAeY931DesiFsK/+qZjI28tNy5ztdzqOZwtj8w+atHpPb10Y3mU95gjbeOvv/6SNA+0PCElBD3i77//vqSJEBERERERBYJevXrJUFzEKtqtW7ekoLUZCsHpobpYB8zrYR3j42YjRoxQgwcPdliWKlUqdfPmTft9dHJihqv79+9LIK8lTJhQbgjo0SigYf9RfO727dv2Ol+QNGlSFTduXNkXY0CMfY4ZM6bDa+rsAQSQeB4NcVzKlCnl9fTsWxArVix5r9g/7KcWJ04cyRR49OiRevjwoeV70kEqXj+6vKcnbvyd8HeO0sAckA7Srl07+X89VgP333zzTa/sDBEREREREVLGUcAtPFY94N9//72aM2eO+umnnyRQiwp9+/ZVH3/8sf0+xq0jUEQAqemhwAhOjen3ejmCU2NQqpcjOLVarhsLjMvNrwkIbK2W6+DUah8RnCJQNi/HOH4E1lbvCa8DeL7o8p4SufF38ha3A3PMWY6dmTFjhipYsKBq3bq13Mc4DSIiIiIiIm9ZvHix+uCDD8JdzxwgrVy5UnXt2lWNGjVKpkwzQtCHFHcz9NLqgFD/i4YB8zrGx80Q9OGm6SBV/2sVbEZ2udVz+/o1XS23ev1gf08xPFgeZWPM//zzT7np7v9Zs2bJ7Z9//vH6ThERERERUehq3769x/WsduzYIUXaOnXqpD799FPL3nU9ltzIOPY8e/bs0uNqXg/3ERwax6wT+SUw193+GGOOOcy1q1evytyBzm5ERERERES+hOnM6tatq2rUqBGmorpWs2ZNiV0wT7m2b98+GV+OxwC93pgeDT32RgsXLlSlS5dmRXbyGbdT2TGNAKoQoqgBbrr7vnHjxk63wTrPnz/3zp4SERERERGZXLt2TQJyjAnu1q2b2rNnj/0xjAfGtM+AwBoFrTGd89ixY6UHvE+fPqps2bL2OcxhwIABqmLFiqpHjx6qXr16as2aNXJbt26dX94fhQa3A/OOHTs6FDQgIiIiIiLyt6NHj6qLFy/K/6O326hChQpqy5YtDj3fPXv2VG3btpWq3nXq1AnTw45AfcmSJap///5SSA4dlPPmzZOgnsjvgTlajNKmTat+/vlnmS5t8+bN0iNeqFAhpnQQEREREZFfoHfb3SrZqAaOYta4uYKectyIoopH06WhmAJuxsp43377LadLIyIiIiIiIoqKwNwIPeaQP3/+iD4FRSMvXrxQ69evVz/88IO6dOmSpAYVLlxYKltirnuM+3E2/QEREREREVEoi3BgjvEaRAjIJ06cKGNzzpw5I8uSxY+rkiaMp86dOKYOHjyoli5dKmNzUIwD80rGihXL37tNREREREQUfIE5ejtx++233yR13Z3gilXZo7dHjx6pFi1aSOCdKF4c1aH066rjm7lVoXQp7escvHxDTdl5TM3d/7cU2ti2bZuaM2eOSpAggV/3nYiIiIiIKFB4lFtsLKqA/3fnRtG3p7x58+YSlFfJmU6d6d9EfdeojENQDriP5Xi8cs7XZH0E89ieiIiIiIiIPOgxz5Qpk/SAx48f3+E+hSakry9btkyC8lXtq6m4sV1nUKRIGE+tbl9d1Zm+QYJzbI9K/0RERERERKHO7cD87NmzLu9T6EBvN8aUI319fqtK4QblGtZb0KqyyjJ0oWyPMecsCEdERERERKEuZmSqsnfo0EGVLFlSvf766/Jvx44d1aZNm1RUmjZtmsqZM6f05BcrVkxt3LjRre127NihSpUqJduhMBkCRbOTJ0+qhg0bqjRp0qikSZPK2Pp169apUIfq6yj01rxoNukJ9wTWb140u2yP5yEiIiIiIgp1Hgfm9+/fVw0aNFBVq1aVqbH++OMPCWDx7/Tp01W1atVU/fr11YMHD5SvzZ8/X3Xq1Em1atVKrV27VuXLl0/VqVNHHT582OV2p06dUm+99ZYE5GvWrJEGhY8//lj2X7t37568l7///lt9//33asmSJSpdunSqbt26as+ePSqU4e8OKPQWEXo7/TxEREREREShzOPp0lDwa9WqVTK+3FwMTlu5cqV677331IoVK5QvffHFF6p169ZqwIAB9incDhw4oEaOHCmVv50ZM2aMBNlYJ3bs2Kpy5crq/PnzavDgwTLnNt4betTPnTsn77VAgQKyHdZLnz69+umnnyRDIFSdPn1aJU8QL0yhN3cVTp9StsfzEBERERERhTqPesyRemwMytFrjqD4u+++U/369VMVK1aU9fDY6tWrpRfbV9CTfeLECdWkSRP7MoxXbty4cbivi8fR64+gXGvatKm6ePGivbf92bNn8m+yZMns62D9RIkShXy1eWRNJI0fJ1LPkSRebMlKICIioog5evSoqlKlikqYMKF0OAwcONCtWU/u3Lmj2rRpo1555RW5zkGny40bN+yP4zlGjRqlypUrp1KmTCm36tWrq7179/r4HRERhS6Pesxnzpwp/8aNG1dSu2vXrh1mneXLl0uQi8B21qxZqmbNmsoXjh07Jv/mzu2YTp0nTx518+ZNdf36dZU6deow2yHF/sKFC5bb6edFDzl+6LJkyaI++eQTNW7cOAnIJ0+erK5du6bef/99FcoSJ06szl2+GKnnuPfkucqSJInX9omIiCiU3Lp1SzpI8ubNKxmKyELr1auXevnypRo2bJjLbdGpgc4NDOFDp0afPn1UvXr11LZt2+TxR48eSfYhgve+fftKh8y3336rypYtq3bu3Ck1fYiIyI+B+cGDB+XLGWOyrYJywBf7Bx98oCZNmiTr+/IHCZInT+6wHK2/+nGrwPz27dvhbgdofd6yZYuqVauWTA0HKACHHz/8CDrz5MkTuWn4gcRnhn813NdZB8be94guNz63q+X48bWaX95quavXzJ49u/xtD16+EaF09j8v3VC3Hz2R5wmU9+SN5awwT0REUQWdBQigMQUprk9QF+fu3bsyzK93796yzMquXbvUhg0b1NatW1X58uVlGYbpoSDur7/+KsF+ggQJJDNRXxsBOixy5colAfqMGTOi7H0SEYUKjwLzK1euyL/40nYF6U4IzPX67kJqlTvbmHu7fQE960iLx48SgnEE6nPnzpUq7ahIX6RIEcvtRowYIWPVjVKlSiW9+Fq8ePFUkiRJJCXcGMTjNXDDD6tOpdc91Kgej0YFY4oafnSRvYDGBGOAiEYHBInG14QUKVJIYKsbJwABJlLU8Hp4XS1WrFjy3rF/2E8tTpw4Mg4fFwJTdh5T3zUqozyF7QDPEyjvCal8uMB5+PBhhP9O+DsTERFFBQzLQyFbYwCOjEX0fiPoRrFaZ9ththkdlAPq5qAgLh7DNZ7+vTTCbzOK7F6+fNmH74qIKHR5FJjrSusIhlzRj3tamX3x4sXS2x4eBGz6BwPBvLH3W/d4m39QNL0utjMyb4eK4Ri7hXHnehv8WCHVfdCgQVLgzgpSvlDhXUMLNAJF42eG+zrgRoq8eTl+ZM09tHrfrZab36vu0TX/nRDYWi3XwanVPiI4xY+xcbmuaD93/99qWK3iHk2ZdvPhEzV3/2nZHs9jLiLor/cE6CFAA0hE/05ERERRBdcjKEprhAw/NBzjMWeBOR6z6uDAkD49TNAKGqj379+vGjVq5IW9JyKiSAXmz58/l6Bk+PDh6tVXX3W6HsZhgzntODzt27eXmzv0jwp+RDJnzmxfjvsIxqzS2AEBVsaMGcP8+JjHrOvnNae8o6ccLdHOIOjDzZzebJXmrIPNyC53lkLtq9dES3q3bt1Uz549VdPZm9Tq9tVV3NixVHiePn8h6z98+ly21/sXCO/Jm8uJiIh8DR0K5msU3bCtOxs83Q7p6858+eWXkrXWpUuXkB7OF95y83uPDu+Jw/mIAnS6NPBltXV3ZcuWTcY6oZcdPa+ALyzcD6/gHB5ftmyZFEdBkAkLFy6UgD1//vxyH0H52bNn5QfM2HuL+dpRFC7Ude3aVYrEIKW9zvQNakGryi57ztFTjqB808krMhwAgTkREREFvp9//lkCcxTDff31152uFwrD+cIb+qaDZ7x+dHlPHM5HFKCBeSCl7qLASYsWLSRQLlOmjFSBP3nypJo3b559HfRuo2DJxo0bZZ5z+PTTT2W8eMuWLSV1HtN/TJkyRX3//ff2HlDMw47MABR/QxEVfAlh3vM9e/bID1Sowxc8Pg98/gjOsw5bpN4rkk11KpPboSAcCsRN3nFM0tfRU45p6rAdW1aJiIgiDkGWeVgemDsUrLbDzDXubodrpHfffVd16tRJ9ejRw+U+hcJwvvCGvunrGzxfdHlPHM5HFICBOcZWB5JmzZpJSx7m2hw6dKgUJcH86brXG/ClgdZI45dHjhw51Lp16+THA73nadOmlVZgYxo9es9R5A3zs6MK/ePHjyXNHdPEIVin//sSX7RokZo4caKaMGGCmrb7uNySxY+rksSPo+4/eS7V1wFjytFLjp52naVAREREEYNrEvOwPEwHi15PV0Vy8ZieFs0Iz4WZdYwwpRpm4UEHB37nwxMKw/nCW271+sH+noJhOB968HEtj6ze48ePyzVq6dKlJUZAhq2GbFhck5qh8WnBggUOy1D8uX///tLph0xdxEFYj8hXgjowB/R4uyoYV7FiRcsWPczFid5vV4oWLRoQafuBDEE2WtARdK9fv16K5q1atUo9uP9EGkgwJRqqr2O4AXvJiYiIvAMdC2PGjFH37t2TlGM9LA8Bic4QdLYdOjO2b98u10Kwb98+GV9uHAqIWXLw243f8fnz57NRnQLa+fPn1bRp0+SaE8Mu0ECFoRXI2jh06JB0uBmNHTtWsm2dpeLj/MDQy48++kgapdasWSMdgshmwOxTRL4Qw8Y8FJ9CLz4cOXJEhQq0KoKrIjIUOkLtHODxT6F8/APPgaiB1PO8efNKIzimSMPnjUxANJajho4xSxCBOhrONQTc6AVEcIJGc2yPor66Jx3jjdHbiN5FDP3DmGUNPeLOpow14/FPUQUzQeFYRsOUhjH5mKkAQ1h156LuMUcnUp06dZw+H84RjKHftGmTfRkyZtEzj6DdHTz+KUqKvxERERGR/6DnDvVzUCUdU6NhvDJmS0H9HfOMOsYCY7pnHeu2bdtWiochQDGmqv/zzz/q4MGD8v/m4EUXxyUKJMZx8BrG2ON4vXz5skfPhWJ3GM5qHr7RtGlT1aZNG6ntgIJ5RN7GwJyIiIgoCKHH3NijZ8UqiEYQP2PGDLlZQVFdJlRSsEORw1OnTkkDlBkCbPSoI1MEKepIf9e97adPn5becnOthjx58khDFmovlChRIsxzcrpAThcYWQzMiYiIiIgoWunVq5dUlX///fcdhmJ07txZxomjsvyWLVukQByCcRR708NEdAOWka6Wrx8343SBnC4wshiYEwU5FDsZPXq0VOPFeCb8PyrohmfHjh0yHhHpiq+99pqkNVrNL//bb7+pAQMGSHEgfIEVKlRI/fjjjzJui4iIiMgXkDKOIoThsZqFAFMgY3ren376yaFGAq53vv32W4ci0WnSpJEib7gewjVORHG6QE4XGFkMzImCGCrlYm5ZjClEdV2kJWI8IOadNU4baIbULhQ2wbpo4cUMBfgxQSugcdpATCv49ttvy2sMHDhQWhRR9ATTBxIRERH5yuLFi13OvKSZA6SVK1fK9LzoCa9fv3642zdq1EgC8z/++EMCcx0YomHASPeUmwNHjdMFcrrAyGJgThTEEJC3bt1aerQBlXcPHDigRo4cKS3FzmCKnXTp0sk6sWPHVpUrV5apRpCChalG8GWDFB38IKKaKcZeaa6qmBIRERF5AzoKjJ0F7kA2IIq0oUMB1y/u0AGW/hdTBKLH9dixYw5TD+I+gkPjvOhE3sSJpYmCFKaiQAGSJk2a2JfhB6Nx48Zq7dq1LrfF4w0aNJCgXMMP2cWLF9Xhw4fl/i+//CL3MRaLiIiIKJBhWjLMUFCjRo0wFdVdWbJkifxbrFgx+Re93pUqVZIee/NsBphGkBXZyVfYY04UpNByC1ZVQ1EgA9VIU6dObTnXJ8ajW22nn7dAgQLq999/l3FZu3fvljluz5w5I9sg9R0/fERERESB4Nq1axKQY0ww6uVgiJ6G8cCYwUBnGt67d0+VKVNGlqOODrII0VlRsGBB+zbIRMT48x49eqh69eqpNWvWyA1D/Ih8hYE5UZByp2qoVWCuq1eGV2306tWrEsR36NBBUtmR2jV16lQZr4V0eQTvVkJ9uhDj+44u78md5d6aKoSIiMhTR48elSw/QG+3EdLRUX0d0MEwduxYNX36dKmbg0K2SHnv16+fwzao24Oe9P79+0shuaxZs6p58+ZJNXciX2FgThRNKpB6GwIuFHn76quvVMeOHe0/dnhtVH5HZXYroT5diA5W9WtHh/fkzt/JW1OFEBEReQq92+5UycawPdzcgZ5y3IiiCgNzoiCtQGqsGmrs/Q6vaqheN7xqo/pfY8szAjtdYM6ZUJ8uxPza0eE9RfV0IUREREShhrmHRAEE1Ud1j6urm7HXXI8113AfwZhVGjsgwMqYMaPldsbn1WPOzYEX7rtKW0bQh4BN33TgiH/1zRjEemO5cZmr5VbP4Wx5RPdFi07vyZ3l/kxfrFKlivTgY6YBTOtnzEBwBg1Tbdq0kQYQZAs0b95c3bhxI8x6K1askGEbaLDAGEUU/yEiIiLyNgbmREEqW7ZsMmWHsWooUppxv2bNmi63xePLli1zCGAQcCBg1/OfY55zVG3ftGmTfR2sv3XrVpnnk8jfkOVRtWpVaSBAAI2gfNy4cWrQoEHhbovZDDDmEOMMZ86cqfbu3RsmZXH79u2qYcOGkjWCmQxq166tmjVrpjZs2ODDd0VEREShiKnsREEM1UVbtGihsmTJIhVGZ82apU6ePCkFSjQE0uhR3Lhxo30+ThQ6mTt3rmrZsqWkziMomTJlihQ40b2hr732mkyV9tlnn0kveY4cOaT4G4qrIF2dyN8mT54s4+KXLl0q2RnVqlWTce84L3r37i3LrOzatUuCa5wb5cuXl2Xp06eXYRe//vqrBPswdOhQeVxPu4MAHdPxDBkyhAWAiIiIyKvYY04UxNB7h+AEPX6YJuTQoUNq9erV9l5vQFCNnm5jSjqCbEz5cerUKek9/+6776SnEan0RphCpGvXrmrYsGHq7bffVpcuXVLr16+XCu1E/oZebGR2GANwFPVBsI6g29V2adKksQflULJkSam6i8cAxe42b94sPetGeH4E9uYaDURERESRwR5zoiCHHm9XBeOcVSrFVCDGeT6toHgYpkrDjSjQoC5C5cqVHZZh6huMN8djdevWdbqd1cwGqKugay2cPn1aKs6b18M6GDJy4sQJVaJECbemC8TNOH49Ok8XiGX6dfGeo8t7Cm85ZkEgIiKKDAbmREQUtGPMjTMSaCjopmcZ8HS7v//+274OmNfT1fKdPb/VdIEZMmSQoSShAhkLEErv2ZxZQURE5CkG5kRERF5iNV0genlR5yFUeswx7R7gPUeX9+TOciIioshgYE5EREEJvddWY73Rm22eB9683fXr111up/81P7/uKXf2/JguEDdNTycXSqnOevq8UHrPREREkcXib0REFJQw/luPCdcuXLigHj58aDmG3NV25rHnKHCIGgvm9XAfgSemKiQiIiLyFgbmREQUlDCjAGYJuHfvnn3ZwoULJZVaTw3obLurV6/KPOXavn37ZHw5HgP0emN6tMWLFztsi+cvXbq0SpYsmU/eExEREYUmBuZERBSUOnXqJAF0gwYNZP7xqVOnyhzmGONtnEIN0wO2a9fOfh+BNeYhb9WqlcyBvnz5ctW8eXOZqUDPYQ4DBgxQW7ZsUT169JB/MTf6mjVr1MCBA6P8vRIREVH0xsCciIiCEsZ5o/L3ixcvZGq0QYMGqZ49e4apiv78+XNZx9zzjV71tm3bSoBerFgxtWzZMod1EKgvWbJEgn7Ml75y5Uo1b948CeqJiIiIvInF34iIKGjlzZtXbdq0yeU6Z8+eDbMM06DNmDFDbq7Uq1dPbkRERES+xB5zIiIiIiIiIj8K+sB82rRpKmfOnCp+/PiSioi0xvCgyM/777+vXn/9damui/+38uTJE9WrVy/16quvqkSJEqnatWtb9rwQERERERERhWRgPn/+fCn+g/GBa9euVfny5VN16tRRhw8fdrndjh07pBpviRIlVNq0aZ2u161bNzVz5kw1duxYGWf477//qmrVqqnHjx/74N0QERERERFRKArqwBzVd1u3bi2VczGtDYJoVN8dOXKky+26du2qTp06pebMmaPSpUtnuc7FixfVDz/8oL766isJ/DGFDqr3njt3TrYjIiIiIqLAgM663Llzq8SJE0tx0PLly0vxTrNLly6p+vXrqyRJkqhUqVKpLl26qIcPH3olK5coJANzzDd74sQJ1aRJE/sypKU3btxYes9dwXrh2bBhg/yLaXi09OnTS5Xe8J6fiIiIiIiizqNHjyTIxgwb6ERD0I2Otd27d9vXefbsmcyygY62BQsWqG+++UYtXrxYdejQwStZuUQhWZX92LFj8i9axozy5Mmjbt68qa5fv65Sp04dqefPkCGDtLqZnx/z2TqDcem4aS9fvlQxYsSQfzXcx81ms8ktssuNz+1qORokzM/hbHlk9tH83qPDe3JnuTsNPkRERETkfbNmzXK4X6NGDZU1a1aZ5vKNN96QZRia+tdff0nmLB6DOHHiqKZNm8qUm+ghN2flAqbXPHDggGTlMnOWfCVoA/Nbt27Zp7wxQuqKfjwygTm2Nz+3fn792lZGjBgRZg5dtNihsUCLFy+epM/cv3/fIYhPmDCh3O7evSstehoaB5BGc/v2bYe5eJMmTarixo0r+2MMELHfCBKNrwkpUqSQwBbPoyHATJkypbweXleLFSuWvFfsH/ZTw5dXsmTJpFXSmPZjfE86eMbrR5f35M7fCX9nIiIiIvI/XPfh+vHp06f2Zej9Ro0pHZQDpsTEtee6deskMNdZuehNN2flGpcRRevA/M6dO+rKlSvhrmfuJQ8kffv2VR9//LH9fqlSpSRQRACp4b4OTlHt3bwcwam5hxbw5WK1XDdGGJebX1N/qVgt18Gp1T4iOMWXlXl5ggQJJLC2ek+65xjPF13ekzt/JyIiIiLyH1yXocMHMcXs2bPVyZMn1fTp0x0yYvPmzeuwDa4Js2fPbs/G9XVWLlFQBOYY4/HBBx+4ddLpwA0nnrFnW/dmmwM7T2F7PLcZnt/VcyPow03TQapVmrMONiO73FkKtS9f09Vyq9cP9vfkyXIiIiIiinoLFy5UzZo1k/9HpwrulyxZ0qOM2Ihm5XI4K4ezRqvAvH379nJzh27FQqtW5syZ7ctxH72kkW3JwvNfuHBBPXjwwKG3FM8fyD32RERERETBLiKZtCjstnfvXpnieO7cuTJ2fM2aNapixYo+3lsOZ+Vw1mgWmHsiW7ZsKleuXNLLjpMQ8MfBfVRgjKzq1avLv6js2KJFC/n/y5cvq23btqnvvvsu0s9PRERERESRz6TVEHwVL17cXvwN1+4DBw5Uv/32W7gZsYUKFbKvE5GsXA5n5XDWkA3MdcVEBM1ZsmRRZcqUkWqMGEuC6ova1q1bVZUqVWTuQVRUBIwNwXJ9kmHKBFRphEaNGsm/qMjerl071aNHD/ng0QOP10PvvA7UiYiIiIjIv5m0zhQpUkSmRTP2rusx5BqKw6HgG6ZH0+tEJCuXw1k5nDWkA3OMIUGKwahRo9TQoUNljsHVq1er/PnzhykCYWzVOHLkiFRW1HAy6inQjOtNmDBBWknQ+oX0BgT2mNfQ2KJCRERERESBBdf0u3btcqjAjqxadOChU04H3StXrpR0ZfSwR0VWLlG0DMwBKS6u0lwwpsScamC1zApavcaPHy83IiIiIiIKPBhqiuv1+vXrq0yZMqkbN25IJu3u3bvVqlWr7OshM/bLL79UDRo0kE49pKv37NlTvffee/Y5zN3NyiXytqAPzImIiIiIKHRlzJhRCnx9/vnn9unMChcurLZv365Kly7tMPYY85V36dJFNWnSRDrhUCBuzJgxHmflEnkbA3MiIiIiIgpa6NnW9aLCgzpSy5cvj3RWLpG3eWfSNSIiIiIiIiKKEAbmRERERERERH7EwJyIiIiIiIjIjxiYExEREREREfkRA3MiIiIiIiIiP2JgTkRERERERORHDMyJiIiIgtDRo0dVlSpVVMKECVW6dOnUwIED1YsXL8Ld7s6dO6pNmzbqlVdeUcmSJVPNmzdXN27cCLPeihUrVIECBVT8+PFV3rx51cKFC330ToiIiIE5ERERUZC5deuWqlq1qooRI4YE0AjKx40bpwYNGhTutk2aNFFbtmxR06dPVzNnzlR79+5V9erVc1hn+/btqmHDhqpSpUpq7dq1qnbt2qpZs2Zqw4YNPnxXREShK7a/d4CIiIiIPDN58mT16NEjtXTpUpU0aVJVrVo1dffuXfXFF1+o3r17yzIru3btkuB669atqnz58rIsffr0qlSpUurXX3+VYB+GDh0qj0+YMEHuI0A/cuSIGjJkiKpevXoUvlMiotDAHnMiIiKiIINe7LfeesshAG/atKkE6wi6XW2XJk0ae1AOJUuWVFmzZpXH4MmTJ2rz5s3Ss26E50dgj1R4IiLyLgbmREREREHm2LFjKnfu3A7LMmXKJOPN8Zgn20GePHns250+fVo9e/YszHpY5+XLl+rEiRNeex9ERPR/mMpOREREFIRjzJMnTx5mOQq64bGIbPf333/b1wHzeljH+LgZetpx0xDE42YsLIcx8bjZbDa5RXY5nt/I2fKYMWOGeQ5nyyOzj/p18Z6jy3tyZ3nKlCkd9oGIPMfAnIiIiIgibcSIEWrw4MEOyzJkyKA2btyoQgWGEkAovWcwD3sgIs8xMCciIiIKMui9thrrjd5s3bPtbLvr16+73E7/a35+3VPu7Pn79u2rPv74Y/t9FJRDLy+mdAuVHvMECRLIMrzn6PKe3FlORJHHwJyIiIgoyGD8t3ks+YULF9TDhw8tx5Abt9u2bVuY5XguPWVa9uzZVZw4cWRZhQoVHNZB0JcrVy7L544XL57cNKyLWyilOeP9Qii9ZyLyDhZ/IyIiIgoyNWvWVOvXr1f37t2zL1u4cKH02BqDaavtrl69KvOUa/v27ZPx5XgMEFxjerTFixc7bIvnL126tEqWLJlP3hMRUShjYE5EREQUZDp16iQBdIMGDWT+8alTp8oc5kglN06hliNHDtWuXTv7fQTWmIe8VatWMgf68uXLVfPmzVXZsmXtc5jDgAED1JYtW1SPHj3kX8yNvmbNGjVw4MAof69ERKGAgTkRERFRkME4bxQYe/Hihapbt64aNGiQ6tmzZ5jia8+fP5d1zD3f6FVv27atBOjFihVTy5Ytc1gHgfqSJUsk6Md86StXrlTz5s2ToJ6IiLwvho2VG3wqX7588u+RI0dUdJMtWzbL5RjjBhkzZrR8XE/HQqEhup4DPP4plI9/4DlA4eHxHxaPf99mkSC74+LFi1IjoUCBApLhYcwEweMYpmHWp08fNXLkSIdl06ZNU6NHj5a/KY5l/L+xkGF4ePyHxePfNRZ/I69zdjIShQIe/xTqeA5QKOPx79+p6rp06aJef/119fTpU/XDDz9I3QQUO3zjjTcc1p07d65DcJk+fXqHx+fPny+BPoaHIHtkxowZqk6dOmrv3r0qf/78Ufaegg2P/8hhj7mPRefWMiJ38BygUMbjn0IZj3/yJwzhyJo1q8w2MGHCBIce8//+978uA2wE92XKlFH/+c9/5D6mqitUqJDc5syZ49br8/gnT3GMOVGQQ6pVzpw5Vfz48WWcIMYcumPHjh0yxyy2ww+X/tEyOnnypGrYsKFKkyaNFBN688031bp163zwLoiIiIi8J1asWCp58uTSe+4JpFufOHFCNWnSxGEavMaNG6u1a9f6YE+J/g8Dc6IgplOtULwHPxZonUWq1eHDh11ud+rUKSnmg4AcVXY7duwolXynT59uXwdT8FSrVk1+oL7//nspApQuXTopMrRnz54oeHdERERE7kMiMAoe3rhxQ3311VfSwYAih2aVK1eWwD1Llixq2LBhDgUSjx07Jv/mzp3bYZs8efKomzdvquvXr0fBO6FQFDMUewsxX+f7778vaSpoAcP/mx0/flx17txZTsKECRPKOJTu3bur27dv++idEHkOY59at24t09ogNWvmzJkyNY65gInZmDFjJMhGOhZ+nD777DPVoUMHqearR7egR/3cuXNq9uzZMh0PKvEuWLBApUiRQv30009R9A6JiIiI3IMZB1D4LVWqVHJthPslS5a0P54sWTK55sH10vr16yXNHTMaoHNCu3XrlvyL3nbzTAjGx82ePHmi7t69a78h/R3XVPhX3/Q1lreWG5e5Wm71HM6We3sfQ+E9eUtQF3+LaGEGBBzbt2+XQhDoFbTyyy+/yHoffvihKliwoPQa9u/fX+3atUvt3r1bAnoif9KpVt98802YVCvjMivoXX/vvfdU7Nj//yugadOm0jOO3nZUMn327Jn9R0zD+okSJbJ/MRERERH5wp07d9SVK1fCXc/Ys41sQMQB//77rxR4w7UNMgMrVqwojxcpUkRuGiq2x4sXT40fP14CeQT0ETVixIgw0xXi+dDLruG1kiRJou7fvy+BvIZOQNwQ0OvrL0icOLF0PqJj0Nirj+GFcePGlUYC4zUZGhNwLWh8TUCnCgJIYwdjjBgxVMqUKeX18LoaMgnQCIH9w35qaPDANSGK7D18+JDv6dn/f0+ROW6iTfG3iBZmwHo6sC5evLgE8Wg5M0IKDP7g+ANrGzZskBMehSMw/6c7WPiBfAU/NLVr11ZnzpyRVCxt8eLFMi7q2rVrKnXq1GG2e/DggXwpoiHLmC2C1KxXX31VLVq0SIJ7fEHh+C1RooQaN26cBOSTJ09Ww4cPl1T2vHnzWu4XvsCMX2IYx47zyJhej/u44evH+BUU0eXm1kpny3Hem5/D2XJv72N0f09srLTG3wAKZTz+KTIwvO6DDz4Idz1XoQymN0MA9dtvvzldB4E8etWRdYssQn19dfbsWZU5c2a3r694/RO67ymml66BYodib6E7Hx5aW8x0C9vly5cjtM9E3uROqpXVD4duWQwvRQstgmiEqlWrlsqUKZO9NXPFihVOg3Jgi3FovidvtRYTERFB+/bt5RYZuHbHMDxXdCec/lf3wGOsuTEwx3385lpdW+nfTNzM8YZV3KEDvMgudxbT+PI1fb08ZhC/p5ANzN0pzODsxIkopLFDrly5vPq8RJFJ2/IV9KyjoQuBHIJxBGJIC0OV9s2bNzukghn17dvXYayWbjHGj5mmv8wQnKIn3rwcwam5hVIHp1bLdaOCcbn5NfWXutVyHZxa7SN+ZBEom5cnSJBAAmu+p6BNuiIiomgMv0+4dkehW1dQ3BZD9TB0FVBXCtf66CFHpiygARz3MS86ka/EDrXewohCL1KfPn0khR1F5pwxp7HgRDanXzDlI3q+J2+kseBL3920LX2sI5g3ngf63DAHdppeF9sZmbf74Ycf1NGjR9XFixft22AsFhrFUChl5cqVls/PFmO+JyIioqi0bds2GSdev359yfLDkNRZs2ZJXahVq1bZ10PtKMQHGKaHBmqkrX/77beqR48eDtmyqF/VokULGSqIYbN4LlR4nzdvnp/eIYWCgArMA6m30BwEtWvXTsaU/Pzzzy7XZRpv6L4nb6TyepK2FdFUK/R8ZsyY0Z51YtzO6nnNjV/oKd+6dauH74yIiIjIN3Bdg+u8zz//3J41W7hwYSn2XLp0aYfMWoxdx1RqmN8cM9mgjg5mXjJq1qyZXP+NGjVKDR06VOolrF692mVxaaJoFZhHRW9hRKCnfNmyZVKpHektrjCNN3TfU1SLTKoVHscxjbk78UMGmFIEP2z6RwdBOQqf4Jwy/j3++OMPh2JzRERERP6E6xKkpIenW7ducnMHYhJ34hKiaBmYR0VvoafQojZ27FgpHFGuXLlw1zen8WKOdWAab+i8p6jkTqoVerdRlRTVRvVsAp9++qmMF2/ZsqX86KAi6ZQpU2S6NP2eMJ0aKrCj+Fvv3r0lSwCzHaAie3iZI0bZs2f3wTsnCg48/imU8finUMbjn0JuujQEy0hJ0b2FSFtB8QZX06UZOZsuDXTggjErGHtCFIimTZsmqVYXLlyQVKsxY8ZIIK6hsnqlSpWkYJuexxOQ3oXsjkOHDqm0adPK/5tbkffv36/69esnveSPHz+WBjFkkKAAHBEREREReUdQB+bz58+X3kKM6da9hUjHRe+fTse16i3E2BM9RhZBBopEdO7cWe43atTIvl21atVkPkP0ShplyJBBbkRERERERETRKpXdU+4UZkC7A4qLGdsfjhw5ItNAGedER6+iXh/Qu4jCXuvXr5ebESpSm4N1IiIiIiIiopDrMSciIiIiIiIKdpGfeJmIiIiIiIiIIoyBOREREREREZEfMTAnIiIiIiIi8iMG5kRERERERER+xMCciIiIiIiIyI8YmBMRERERERH5EQNzIiIiIiIiIj9iYE5ERERERETkRwzMiYiIiIiIiPyIgTkRERERERGRHzEwJyIiIiIiIvIjBuZEREREREREyn/+H0jEYq/2PWPtAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize measure contrasts\n", + "fig = results_meas_contrast.plot_contrast()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Customizing Visualizations\n", + "\n", + "All plot functions support extensive customization:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Circle plot with custom options\n", + "fig = results_group.plot_circle(\n", + " colors=\"husl\", # Color palette\n", + " fontsize=14, # Font size\n", + " figsize=(10, 10), # Figure size\n", + " title=\"Custom Circle Plot\",\n", + " angle_labels=scales, # Custom angle labels\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Curve plot with custom options\n", + "fig = results_group.plot_curve(\n", + " angle_labels=scales,\n", + " base_size=12,\n", + " figsize=(12, 5),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Contrast plot with custom colors\n", + "fig = results_contrast.plot_contrast(\n", + " sig_color=\"darkred\",\n", + " ns_color=\"lightgray\",\n", + " fontsize=14,\n", + " drop_xy=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. Saving Plots\n", + "\n", + "All plots return matplotlib Figure objects that can be saved:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Save plots\n", + "fig1 = results.plot_circle()\n", + "fig1.savefig(\"circle_plot.png\", dpi=300, bbox_inches=\"tight\")\n", + "\n", + "fig2 = results.plot_curve()\n", + "fig2.savefig(\"curve_plot.png\", dpi=300, bbox_inches=\"tight\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "The circumplex package provides a comprehensive toolkit for SSM analysis:\n", + "\n", + "- **`ssm_analyze()`**: Performs mean-based or correlation-based SSM analysis\n", + "- **`plot_circle()`**: Visualizes amplitude/displacement on circular plots\n", + "- **`plot_curve()`**: Shows fitted curves with observed data\n", + "- **`plot_contrast()`**: Displays parameter differences between groups\n", + "\n", + "All functions support:\n", + "- Single or multiple groups\n", + "- Single or multiple measures\n", + "- Bootstrap confidence intervals\n", + "- Contrast analyses\n", + "- Extensive customization options" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python-circumplex (3.12.5)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/ssm_analysis_demo.py b/examples/ssm_analysis_demo.py new file mode 100644 index 0000000..d7f3954 --- /dev/null +++ b/examples/ssm_analysis_demo.py @@ -0,0 +1,362 @@ +""" +# Structural Summary Method (SSM) Analysis Demo. + +This notebook demonstrates the core functionality of the `circumplex` package +for analyzing circumplex data using the Structural Summary Method (SSM). + +The SSM provides six key parameters that summarize circular data patterns: +- **Elevation ($e$)**: Mean level across all scales +- **X-value ($x$)**: Projection onto the x-axis (cosine component) +- **Y-value ($y$)**: Projection onto the y-axis (sine component) +- **Amplitude ($a$)**: Vector length (prototypicality of the pattern) +- **Displacement ($d$)**: Angular position in degrees $[0, 360)$ +- **Fit ($R^2$)**: Proportion of variance explained by the model + +Bootstrap confidence intervals are computed for all parameters except fit. +""" + +# %% +# ## Setup + +import numpy as np +from rich.console import Console + +from circumplex import load_dataset, ssm_analyze + +console = Console() + +# Set random seed for reproducible bootstrap results +np.random.seed(12345) + +# %% +# ## Example 1: Single-Group Mean-Based Analysis + +# Load a small example dataset (5 observations, 8 octant scales) +aw2009 = load_dataset("aw2009") +console.print(f"[bold]aw2009 dataset shape:[/bold] {aw2009.shape}") +console.print("\n[bold]First few rows:[/bold]") +console.print(aw2009.head()) + +# %% +# ### Run SSM Analysis +# +# For mean-based analysis, we analyze the profile of mean scale scores. +# The `ssm_analyze()` function automatically uses octant angles [90, 135, 180, 225, 270, 315, 360, 45] +# when 8 scales are provided and angles=None. + +results_single = ssm_analyze( + aw2009, + scales=list(range(8)), # Use all 8 scales (or specify column names) + boots=2000, # Number of bootstrap resamples + interval=0.95, # 95% confidence intervals + seed=12345, # For reproducibility +) + +console.print("\n[bold]SSM Parameters:[/bold]") +console.print( + results_single.results[ + ["Label", "e_est", "x_est", "y_est", "a_est", "d_est", "fit_est"] + ] +) + +console.print("\n[bold]Confidence Intervals:[/bold]") +console.print( + results_single.results[ + ["Label", "e_lci", "e_uci", "a_lci", "a_uci", "d_lci", "d_uci"] + ] +) + +# %% +# ### Interpretation +# +# - **Elevation (e_est)**: 0.423 - The mean level across all scales is positive +# - **Amplitude (a_est)**: 0.981 - Strong prototypicality (high consistency with circular model) +# - **Displacement (d_est)**: 344.4° - The peak is near 360° (quadrant IV) +# - **Fit (fit_est)**: 0.954 - The circular model explains 95.4% of variance +# +# The 95% CIs show the uncertainty in these estimates based on bootstrap resampling. + +# %% +# ## Example 2: Multi-Group Mean-Based Analysis + +# Load a larger dataset with multiple groups +jz2017 = load_dataset("jz2017") +console.print(f"[bold]jz2017 dataset shape:[/bold] {jz2017.shape}") +console.print(f"\n[bold]Columns:[/bold] {jz2017.columns.tolist()}") +console.print("\n[bold]Gender distribution:[/bold]") +console.print(jz2017["Gender"].value_counts()) + +# %% +# ### Compare Groups +# +# Analyze interpersonal circumplex profiles separately for females and males. + +results_groups = ssm_analyze( + jz2017, + scales=list(range(1, 9)), # Columns 1-8 (PA, BC, DE, FG, HI, JK, LM, NO) + grouping="Gender", # Split by gender + boots=2000, + seed=12345, +) + +console.print("\n[bold]SSM Parameters by Group:[/bold]") +console.print( + results_groups.results[ + ["Label", "e_est", "x_est", "y_est", "a_est", "d_est", "fit_est"] + ] +) + +console.print("\n[bold]Mean Scale Scores by Group:[/bold]") +console.print(results_groups.scores) + +# %% +# ### Group Comparison +# +# Both groups show similar displacement (around 320-326°) but differ in amplitude: +# - **Female**: Amplitude = 0.553 (moderate prototypicality) +# - **Male**: Amplitude = 0.299 (lower prototypicality) +# +# This suggests females show a more consistent interpersonal pattern than males +# in this sample. + +# %% +# ## Example 3: Multi-Group Analysis with Contrast + +# Compute the difference between groups (Male - Female) +results_contrast = ssm_analyze( + jz2017, + scales=list(range(1, 9)), + grouping="Gender", + contrast=True, # Add contrast row + boots=2000, + seed=12345, +) + +console.print("\n[bold]SSM Parameters with Contrast:[/bold]") +console.print( + results_contrast.results[["Label", "e_est", "x_est", "y_est", "a_est", "d_est"]] +) + +console.print("\n[bold]Contrast Interpretation:[/bold]") +contrast_row = results_contrast.results.iloc[2] +console.print(f" Elevation difference: {contrast_row['e_est']:.3f}") +console.print(f" Amplitude difference: {contrast_row['a_est']:.3f}") +console.print(f" Angular difference: {contrast_row['d_est']:.3f}°") + +# %% +# ### Contrast Interpretation +# +# The contrast row shows Male - Female differences: +# - **Elevation**: -0.062 (males slightly lower overall) +# - **Amplitude**: -0.254 (males show much less prototypicality) +# - **Displacement**: -5.28° (minimal angular difference) +# +# The key finding is that males have significantly lower amplitude, indicating +# their interpersonal patterns are less differentiated/prototypical. + +# %% +# ## Example 4: Correlation-Based Analysis + +# Analyze how personality disorder symptoms (PARPD) relate to interpersonal scales +results_corr = ssm_analyze( + jz2017, + scales=list(range(1, 9)), + measures="PARPD", # Paranoid personality disorder scale + boots=2000, + seed=12345, +) + +console.print("\n[bold]Correlation-Based SSM Parameters:[/bold]") +console.print( + results_corr.results[ + ["Label", "e_est", "x_est", "y_est", "a_est", "d_est", "fit_est"] + ] +) + +console.print("\n[bold]Correlation Profile:[/bold]") +console.print(results_corr.scores) + +# %% +# ### Correlation Interpretation +# +# The correlation profile shows how PARPD symptoms relate to interpersonal behavior: +# - **Elevation (e_est)**: 0.250 - PARPD is positively correlated with interpersonal problems overall +# - **Amplitude (a_est)**: 0.150 - Moderate differentiation in the correlation pattern +# - **Displacement (d_est)**: 128.9° - Peak correlation around 135° (Quadrant II) +# - **Fit (fit_est)**: 0.802 - The circular model explains 80% of the correlation variance +# +# This suggests PARPD is most strongly associated with interpersonal behaviors +# in the cold-dominant quadrant (quadrant II). + +# %% +# ## Example 5: Multi-Measure Correlation with Contrast + +# Compare correlation profiles for two different personality disorders +results_measure_contrast = ssm_analyze( + jz2017, + scales=list(range(1, 9)), + measures=["ASPD", "NARPD"], # Antisocial and Narcissistic PD + contrast=True, + boots=2000, + seed=12345, +) + +console.print("\n[bold]Multi-Measure SSM Parameters:[/bold]") +console.print( + results_measure_contrast.results[["Label", "e_est", "a_est", "d_est", "fit_est"]] +) + +console.print("\n[bold]Measure Contrast:[/bold]") +contrast_row = results_measure_contrast.results.iloc[2] +console.print(f" Elevation difference (NARPD - ASPD): {contrast_row['e_est']:.3f}") +console.print(f" Amplitude difference: {contrast_row['a_est']:.3f}") +console.print(f" Angular difference: {contrast_row['d_est']:.1f}°") + +# %% +# ### Multi-Measure Interpretation +# +# Comparing ASPD vs NARPD interpersonal profiles: +# - **ASPD**: Displacement at ~315° (dominant-cold), moderate amplitude +# - **NARPD**: Displacement at ~82° (dominant-warm), higher amplitude +# - **Contrast**: NARPD shows 0.148 higher amplitude (more differentiated pattern) +# and is ~127° apart (nearly opposite on the circle) +# +# This reveals distinct interpersonal correlates: ASPD associates with cold-dominance +# while NARPD associates with warm-dominance. + +# %% +# ## Example 6: Group x Measure Correlation + +# Analyze how NARPD relates to interpersonal behavior differently by gender +results_group_corr = ssm_analyze( + jz2017, + scales=list(range(1, 9)), + measures="NARPD", + grouping="Gender", + contrast=True, # Compare genders + boots=2000, + seed=12345, +) + +console.print("\n[bold]Group x Measure SSM Parameters:[/bold]") +console.print( + results_group_corr.results[["Label", "e_est", "a_est", "d_est", "fit_est"]] +) + +# %% +# ### Group x Measure Interpretation +# +# NARPD-interpersonal relationships by gender: +# - **Female**: Higher elevation (0.334) and amplitude (0.231) +# - **Male**: Lower elevation (0.281) and amplitude (0.160) +# - **Contrast**: Females show stronger and more differentiated NARPD-interpersonal associations +# +# Both groups have similar angular positions (~73-82°), suggesting the _location_ +# of NARPD correlates is similar, but the _strength_ differs by gender. + +# %% +# ## Example 7: Custom Angles + +# You can specify custom angular positions for non-octant circumplex models +# For example, using 4 scales at quadrant positions: + +results_custom = ssm_analyze( + jz2017, + scales=["PA", "DE", "HI", "LM"], # 4 scales at quadrant positions + angles=[90, 180, 270, 360], # Quadrant angles in degrees + boots=1000, # Fewer boots for speed + seed=12345, +) + +console.print("\n[bold]Custom Angles SSM Parameters:[/bold]") +console.print(results_custom.results[["Label", "e_est", "a_est", "d_est", "fit_est"]]) + +# %% +# ## Key Features Summary + +console.print(""" +[bold cyan]circumplex Package Features[/bold cyan] + +✓ [green]Mean-based SSM:[/green] Analyze profiles of mean scale scores +✓ [green]Correlation-based SSM:[/green] Analyze how measures correlate with circumplex scales +✓ [green]Multi-group designs:[/green] Compare profiles across groups (e.g., gender, diagnosis) +✓ [green]Contrast analysis:[/green] Test differences between 2 groups or 2 measures +✓ [green]Bootstrap CIs:[/green] Percentile confidence intervals via resampling +✓ [green]Circular statistics:[/green] Proper handling of angular data +✓ [green]Flexible angles:[/green] Support for any circumplex configuration (OCTANTS, QUADRANTS, etc.) +✓ [green]Numerical parity:[/green] Results match R circumplex package to 3+ decimal places + +[bold cyan]Analysis Types Supported[/bold cyan] + +1. Single-group mean profile +2. Multi-group mean profiles +3. Multi-group mean profiles with contrast +4. Single-group correlation profile +5. Multi-group correlation profiles +6. Multi-measure correlation profiles with contrast +7. Group x measure correlation with contrast + +[bold cyan]Bootstrap Implementation[/bold cyan] + +• Stratified sampling when groups are present +• Circular quantile method for displacement CIs +• Listwise or pairwise deletion options +• Reproducible with seed parameter + +[bold cyan]Next Steps[/bold cyan] + +• Visualization components (coming soon) +• Instrument registry for standard scales (coming soon) +• Export/reporting functions (coming soon) +""") + +# %% +# ## Technical Notes + +console.print(""" +[bold cyan]Parameter Calculation Details[/bold cyan] + +The SSM parameters are calculated using the following formulas: + +[bold]Elevation:[/bold] e = mean(scores) + +[bold]X-value:[/bold] x = (2/n) x Σ(scores x cos(angles)) + +[bold]Y-value:[/bold] y = (2/n) x Σ(scores x sin(angles)) + +[bold]Amplitude:[/bold] a = √(x² + y²) + +[bold]Displacement:[/bold] d = arctan2(y, x) [converted to degrees, [0, 360)] + +[bold]Fit:[/bold] R² = 1 - (SS_residual / SS_total) + +where predicted scores = e + a x cos(angles - d) + +[bold cyan]Bootstrap Confidence Intervals[/bold cyan] + +CIs are computed using the percentile method: +• Lower CI: 2.5th percentile of bootstrap distribution (for 95% CI) +• Upper CI: 97.5th percentile of bootstrap distribution + +For displacement (circular data), a special circular quantile method is used +that accounts for angular wrapping at 0°/360°. + +[bold cyan]Contrast Calculations[/bold cyan] + +Contrasts are computed as second minus first (e.g., Male - Female): +• For linear parameters: simple subtraction +• For displacement: circular distance using angle_dist() + - Returns shortest angular distance in [-180°, 180°] + +[bold cyan]Data Requirements[/bold cyan] + +• [bold]Scales:[/bold] Must be equally spaced around the circle (or specify custom angles) +• [bold]Sample size:[/bold] Minimum ~30 for stable bootstrap CIs (n=5 works but CIs are wide) +• [bold]Missing data:[/bold] Listwise deletion (default) or pairwise deletion available +• [bold]Grouping:[/bold] Groups are treated as categorical factors (alphabetical ordering) +""") + +# %% +console.print( + "\n✓ [bold green]Demo complete! The circumplex package is ready for SSM analysis.[/bold green]" +) diff --git a/examples/using-instruments-v1.ipynb b/examples/using-instruments-v1.ipynb new file mode 100644 index 0000000..3588042 --- /dev/null +++ b/examples/using-instruments-v1.ipynb @@ -0,0 +1,663 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "81339325eef33e59", + "metadata": { + "collapsed": false + }, + "source": [ + "# Using Circumplex Instruments\n", + "\n", + "Source: https://circumplex.jmgirard.com/articles/using-instruments.html" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T14:46:02.630893Z", + "start_time": "2024-07-11T14:46:02.614580Z" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "import circumplex\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "id": "a813bc61f5051b2d", + "metadata": { + "collapsed": false + }, + "source": [ + "## Overview of Instrument-related Functions\n", + "\n", + "Although the circumplex package is capable of analyzing and visualizing data in a \"source-agnostic\" manner (i.e., without knowing what the numbers correspond to), it can be helpful to both the user and the package to have more contextual information about which information/questionnaire the data come from. For example, knowing the specific instrument used can enable the package to automatically score item-level responses and standardize these scores using normative data. Furthermore, a centralized repository of information about circumplex instruments would provide a convenient and accessible way for users to discover and begin using new instruments.\n", + "\n", + "The first part of this tutorial will discuss how to preview the instruments currently available in the circumplex package, how to load information about a specific instrument for use in analysis, and how to extract general and specific information about that instrument. The following functions will be discussed: `instruments()`, `instrument()`, `print()`, `summary()`, `scales()`, `items()`, `anchors()`, `norms()`, and `View()`.\n", + "\n", + "The second part of this tutorial will discuss how to use the information about an instrument to transform and summarize circumplex data. It will demonstrate how to ipsatize item-level responses (i.e. apply deviation scoring across variables), how to calculate scale scores from item-level responses (with or without imputing/prorating missing values), and how to standardize scale scores using normative/comparison data. The following functions will be discussed: `ipsatize()`, `score()`, and `standardize()`." + ] + }, + { + "cell_type": "markdown", + "id": "8f738e559a4a4e5e", + "metadata": { + "collapsed": false + }, + "source": [ + "## 2. Loading and Examining Instrument Objects\n", + "\n", + "### Previewing the available instruments\n", + "\n", + "You can preview the list of currently available instruments using the `instruments()` function. This function will print the abbreviation, name, and (in parentheses) the \"code\" for each available instrument. We will return to the code in the next section." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "afc9791b88c08ae9", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T14:46:04.574244Z", + "start_time": "2024-07-11T14:46:04.554387Z" + }, + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
          The circumplex package currently includes 3 instruments          \n",
+       "┏━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃   ┃ Abbreviation ┃ Name                                                 ┃\n",
+       "┡━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ 1 │ CSIG         │ Circumplex Scales of Interpersonal Goals             │\n",
+       "│ 2 │ IIPSC        │ Inventory of Interpersonal Problems Short Circumplex │\n",
+       "│ 3 │ IPIPIPC      │ IPIP Interpersonal Circumplex                        │\n",
+       "└───┴──────────────┴──────────────────────────────────────────────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[3m The circumplex package currently includes 3 instruments \u001b[0m\n", + "┏━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mAbbreviation\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mName \u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", + "│ 1 │\u001b[36m \u001b[0m\u001b[36mCSIG \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35mCircumplex Scales of Interpersonal Goals \u001b[0m\u001b[35m \u001b[0m│\n", + "│ 2 │\u001b[36m \u001b[0m\u001b[36mIIPSC \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35mInventory of Interpersonal Problems Short Circumplex\u001b[0m\u001b[35m \u001b[0m│\n", + "│ 3 │\u001b[36m \u001b[0m\u001b[36mIPIPIPC \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35mIPIP Interpersonal Circumplex \u001b[0m\u001b[35m \u001b[0m│\n", + "└───┴──────────────┴──────────────────────────────────────────────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "circumplex.show_instruments()" + ] + }, + { + "cell_type": "markdown", + "id": "1b8d60c17024bb84", + "metadata": { + "collapsed": false + }, + "source": [ + "### Loading a specific instrument\n", + "\n", + "To reduce loading time and memory usage, instrument information is not loaded into memory when the circumplex package is loaded. Instead, instruments should be loaded into memory on an as-needed bases. As demonstrated below, this can be done by passing an instrument's code (which we saw how to find in the last section) to the `load_instrument()` function. We can then examine that instrument data using the `print()` function." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8ff381ed31a876fa", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T14:46:06.307446Z", + "start_time": "2024-07-11T14:46:06.291792Z" + }, + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CSIG: Circumplex Scales of Interpersonal Goals\n", + "32 items, 8 scales, 1 normative data sets\n", + "Lock (2014)\n", + "< https://doi.org/10.1177/0146167213514280 >\n" + ] + } + ], + "source": [ + "csig = circumplex.get_instrument(\"csig\")\n", + "print(csig)" + ] + }, + { + "cell_type": "markdown", + "id": "127364e5d1f2bd67", + "metadata": { + "collapsed": false + }, + "source": [ + "### Examining an instrument in-depth\n", + "\n", + "To examine the information available about a loaded instrument, there are several options. To print a long list of formatted information about the instrument, use the `summary()` function. This will return the same information returned by `print()`, followed by information about the instrument's scales, rating scale anchors, items, and normative data set(s). The summary of each instrument is also available from the package reference page." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a98b0bc63b71e63b", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T14:46:07.525697Z", + "start_time": "2024-07-11T14:46:07.512705Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
CSIG: Circumplex Scales of Interpersonal Goals\n",
+       "32 items, 8 scales, 1 normative data sets\n",
+       "Lock (2014)\n",
+       "< https://doi.org/10.1177/0146167213514280 >\n",
+       "\n",
+       "\n",
+       "The CSIG contains 8 scales:\n",
+       "├── PA (90°): Be authoritative\n",
+       "├── BC (135°): Be tough\n",
+       "├── DE (180°): Be self-protective\n",
+       "├── FG (225°): Be wary\n",
+       "├── HI (270°): Be conflict-avoidant\n",
+       "├── JK (315°): Be cooperative\n",
+       "├── LM (360°): Be understanding\n",
+       "└── NO (45°): Be respected\n",
+       "\n",
+       "\n",
+       "The CSIG is rated using the following 5-point scale:\n",
+       "  0. It is not at all important that...\n",
+       "  1. It is somewhat important that...\n",
+       "  2. It is moderately important that...\n",
+       "  3. It is very important that...\n",
+       "  4. It is extremely important that...\n",
+       "\n",
+       "\n",
+       "\n",
+       "The CSIG currently has 1 normative data set(s):\n",
+       "\n",
+       "1. 665 MTurkers from US, Canada, and India about interactions between nations\n",
+       "   Lock (2014)\n",
+       "   https://doi.org/10.1177/0146167213514280\n",
+       "\n",
+       "
\n" + ], + "text/plain": [ + "CSIG: Circumplex Scales of Interpersonal Goals\n", + "\u001b[1;36m32\u001b[0m items, \u001b[1;36m8\u001b[0m scales, \u001b[1;36m1\u001b[0m normative data sets\n", + "Lock \u001b[1m(\u001b[0m\u001b[1;36m2014\u001b[0m\u001b[1m)\u001b[0m\n", + "\u001b[1m<\u001b[0m\u001b[39m \u001b[0m\u001b[4;94mhttps://doi.org/10.1177/0146167213514280\u001b[0m\u001b[39m \u001b[0m\u001b[1m>\u001b[0m\n", + "\n", + "\n", + "\u001b[1;36mThe CSIG contains 8 scales:\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mPA (90°): Be authoritative\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mBC (135°): Be tough\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mDE (180°): Be self-protective\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mFG (225°): Be wary\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mHI (270°): Be conflict-avoidant\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mJK (315°): Be cooperative\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mLM (360°): Be understanding\u001b[0m\n", + "\u001b[2m└── \u001b[0m\u001b[1mNO (45°): Be respected\u001b[0m\n", + "\n", + "\n", + "\u001b[1;36mThe CSIG is rated using the following 5-point scale:\u001b[0m\n", + " 0. It is not at all important that...\n", + " 1. It is somewhat important that...\n", + " 2. It is moderately important that...\n", + " 3. It is very important that...\n", + " 4. It is extremely important that...\n", + "\n", + "\n", + "\n", + "\u001b[1;36mThe CSIG currently has 1 normative data set(s):\u001b[0m\n", + "\n", + "1. 665 MTurkers from US, Canada, and India about interactions between nations\n", + " Lock (2014)\n", + " https://doi.org/10.1177/0146167213514280\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "csig.info()" + ] + }, + { + "cell_type": "markdown", + "id": "5478d2bce6a5a2dc", + "metadata": {}, + "source": [ + "Specific subsections of this output can be returned individually by printing the `scales`, `anchors`, `items`, and `norms` attributes of the instrument object. These functions are especially useful when you need to know a specific bit of information about an instrument and don't want the console to be flooded with unneeded information. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "feca079741ba597c", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T14:46:08.592671Z", + "start_time": "2024-07-11T14:46:08.582386Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
The CSIG is rated using the following 5-point scale:\n",
+       "  0. It is not at all important that...\n",
+       "  1. It is somewhat important that...\n",
+       "  2. It is moderately important that...\n",
+       "  3. It is very important that...\n",
+       "  4. It is extremely important that...\n",
+       "\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;36mThe CSIG is rated using the following 5-point scale:\u001b[0m\n", + " 0. It is not at all important that...\n", + " 1. It is somewhat important that...\n", + " 2. It is moderately important that...\n", + " 3. It is very important that...\n", + " 4. It is extremely important that...\n", + "\n" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "csig.info_anchors()" + ] + }, + { + "cell_type": "markdown", + "id": "87ccb43033467e56", + "metadata": {}, + "source": [ + "Some of these attributes also have additional methods to customize their output. For instance, the `scales` attribute has a `.show()` method which includes the option to display the items for each scale." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "abbc4313ebd35297", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T14:46:11.978006Z", + "start_time": "2024-07-11T14:46:11.962478Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
The CSIG contains 8 scales:\n",
+       "├── PA (90°): Be authoritative\n",
+       "│   ├── 8. We are assertive\n",
+       "│   ├── 16. We appear confident\n",
+       "│   ├── 24. We are decisive\n",
+       "│   └── 32. They see us as capable\n",
+       "├── BC (135°): Be tough\n",
+       "│   ├── 5. We show that we can be tough\n",
+       "│   ├── 12. They not get angry with us\n",
+       "│   ├── 21. We are aggressive if necessary\n",
+       "│   └── 29. We not show our weaknesses\n",
+       "├── DE (180°): Be self-protective\n",
+       "│   ├── 2. We are the winners in any argument or dispute\n",
+       "│   ├── 10. We do whatever is in our best interest\n",
+       "│   ├── 18. We are better than them\n",
+       "│   └── 26. We keep our guard up\n",
+       "├── FG (225°): Be wary\n",
+       "│   ├── 7. We let them fend for themselves\n",
+       "│   ├── 15. They stay out of our business\n",
+       "│   ├── 23. We not trust them\n",
+       "│   └── 31. We not get entangled in their affairs\n",
+       "├── HI (270°): Be conflict-avoidant\n",
+       "│   ├── 4. We avoid conflict\n",
+       "│   ├── 12. They not get angry with us\n",
+       "│   ├── 20. We not get into arguments\n",
+       "│   └── 28. We not make them angry\n",
+       "├── JK (315°): Be cooperative\n",
+       "│   ├── 1. We are friendly\n",
+       "│   ├── 9. We celebrate their achievements\n",
+       "│   ├── 17. They feel we are all on the same team\n",
+       "│   └── 25. We are cooperative\n",
+       "├── LM (360°): Be understanding\n",
+       "│   ├── 6. We appreciate what they have to offer\n",
+       "│   ├── 14. We understand their point of view\n",
+       "│   ├── 22. We show concern for their welfare\n",
+       "│   └── 30. We are able to compromise\n",
+       "└── NO (45°): Be respected\n",
+       "    ├── 3. They respect what we have to say\n",
+       "    ├── 11. We get the chance to express our views\n",
+       "    ├── 19. They listen to what we have to say\n",
+       "    └── 27. They see us as responsible\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;36mThe CSIG contains 8 scales:\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mPA (90°): Be authoritative\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m8. We are assertive\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m16. We appear confident\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m24. We are decisive\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m32. They see us as capable\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mBC (135°): Be tough\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m5. We show that we can be tough\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m12. They not get angry with us\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m21. We are aggressive if necessary\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m29. We not show our weaknesses\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mDE (180°): Be self-protective\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m2. We are the winners in any argument or dispute\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m10. We do whatever is in our best interest\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m18. We are better than them\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m26. We keep our guard up\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mFG (225°): Be wary\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m7. We let them fend for themselves\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m15. They stay out of our business\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m23. We not trust them\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m31. We not get entangled in their affairs\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mHI (270°): Be conflict-avoidant\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m4. We avoid conflict\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m12. They not get angry with us\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m20. We not get into arguments\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m28. We not make them angry\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mJK (315°): Be cooperative\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m1. We are friendly\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m9. We celebrate their achievements\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m17. They feel we are all on the same team\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m25. We are cooperative\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mLM (360°): Be understanding\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m6. We appreciate what they have to offer\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m14. We understand their point of view\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m22. We show concern for their welfare\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m30. We are able to compromise\u001b[0m\n", + "\u001b[2m└── \u001b[0m\u001b[1mNO (45°): Be respected\u001b[0m\n", + "\u001b[2m \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m3. They respect what we have to say\u001b[0m\n", + "\u001b[2m \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m11. We get the chance to express our views\u001b[0m\n", + "\u001b[2m \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m19. They listen to what we have to say\u001b[0m\n", + "\u001b[2m \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m27. They see us as responsible\u001b[0m\n" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "csig.info_scales(items=True)" + ] + }, + { + "cell_type": "markdown", + "id": "fa50458a60f80003", + "metadata": {}, + "source": [ + "## 3. Instrument-related tidying functions\n", + "\n", + "It is a good idea in practice to digitize and save each participant's response to each item on an instrument, rather than just their scores on each scale. Having access to item-level data will make it easier to spot and correct mistakes, will enable more advanced analysis of missing data, and will enable latent variable models that account for measurement error (e.g. structural equation modelling). Furthermore, the functions described below will make it easy to transform and summarize such item-level data into scale scores.\n", + "\n", + "First, however, we need to make sure the item-level data is in the expected format. Your data should be stored in a data frame where each row corresponds to one observation (e.g. participant, organization, or timepoint) and each column corresponds to one variable describing these observations (e.g. item responses, demographic characteristics, scale scores). The `pandas` package provides excellent tools for getting your data into this format from a variety of different file types and formats.\n", + "\n", + "For the purpose of illustration, we will work with a small-scale data set, which includes item-level responses to the Inventory of Interpersonal Problems, Short Circumplex (IIP-SC) for just 10 participants. As will become important later on, this data set contains a small amount of missing values (represented as `NA`). This data set is included as part of the circumplex package and can be loaded and previewed as follows:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "66641d626662906c", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T14:46:14.198824Z", + "start_time": "2024-07-11T14:46:14.177613Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Gender PA BC DE FG HI JK LM NO PARPD SCZPD \\\n", + "0 Female 1.50 1.50 1.25 1.00 2.00 2.50 2.25 2.50 4 3 \n", + "1 Female 0.00 0.25 0.00 0.25 1.25 1.75 2.25 2.25 1 0 \n", + "2 Female 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 1 \n", + "3 Male 2.00 1.75 1.75 2.50 2.00 1.75 2.00 2.50 1 0 \n", + "4 Female 0.25 0.50 0.25 0.00 0.00 0.00 0.00 0.00 0 0 \n", + "\n", + " SZTPD ASPD BORPD HISPD NARPD AVPD DPNPD OCPD \n", + "0 7 7 8 4 6 3 4 6 \n", + "1 2 0 1 2 3 0 1 0 \n", + "2 0 4 1 5 4 0 0 1 \n", + "3 0 0 1 0 0 0 0 0 \n", + "4 0 0 1 0 0 1 0 0 \n" + ] + } + ], + "source": [ + "from circumplex import load_dataset\n", + "\n", + "raw_jz2017 = load_dataset(\"jz2017\")\n", + "print(raw_jz2017.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6e68baa5510c4f4b", + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-11T14:46:15.069758Z", + "start_time": "2024-07-11T14:46:15.053171Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
IIP-SC: Inventory of Interpersonal Problems Short Circumplex\n",
+       "32 items, 8 scales, 2 normative data sets\n",
+       "Soldz, Budman, Demby, & Merry (1995)\n",
+       "< https://doi.org/10.1177/1073191195002001006 >\n",
+       "\n",
+       "\n",
+       "The IIP-SC contains 8 scales:\n",
+       "├── PA (90°): Domineering\n",
+       "│   ├── 1. ...point of view...\n",
+       "│   ├── 9. ...too aggressive toward...\n",
+       "│   ├── 17. ...control other people...\n",
+       "│   └── 25. ...argue with other...\n",
+       "├── BC (135°): Vindictive\n",
+       "│   ├── 2. ...supportive of another...\n",
+       "│   ├── 10. ...another person's happiness...\n",
+       "│   ├── 18. ...too suspicious of...\n",
+       "│   └── 26. ...revenge against people...\n",
+       "├── DE (180°): Cold\n",
+       "│   ├── 3. ...show affection to...\n",
+       "│   ├── 11. ...feeling of love...\n",
+       "│   ├── 19. ...feel close to...\n",
+       "│   └── 27. ...at a distance...\n",
+       "├── FG (225°): Socially avoidant\n",
+       "│   ├── 4. ...join in on...\n",
+       "│   ├── 12. ...introduce myself to...\n",
+       "│   ├── 20. ...socialize with other...\n",
+       "│   └── 28. ...get together socially...\n",
+       "├── HI (270°): Nonassertive\n",
+       "│   ├── 5. ...stop bothering me...\n",
+       "│   ├── 13. ...confront people with...\n",
+       "│   ├── 21. ...assertive with another...\n",
+       "│   └── 29. ...to be firm...\n",
+       "├── JK (315°): Exploitable\n",
+       "│   ├── 6. ...I am angry...\n",
+       "│   ├── 14. ...assertive without worrying...\n",
+       "│   ├── 22. ...too easily persuaded...\n",
+       "│   └── 30. ...people take advantage...\n",
+       "├── LM (360°): Overly nurturant\n",
+       "│   ├── 7. ...my own welfare...\n",
+       "│   ├── 15. ...please other people...\n",
+       "│   ├── 23. ...other people's needs...\n",
+       "│   └── 31. ...another person's misery...\n",
+       "└── NO (45°): Intrusive\n",
+       "    ├── 8. ...keep things private...\n",
+       "    ├── 16. ...open up to...\n",
+       "    ├── 24. ...noticed too much...\n",
+       "    └── 32. ...tell personal things...\n",
+       "\n",
+       "\n",
+       "The IIP-SC is rated using the following 5-point scale:\n",
+       "  0. Not at all\n",
+       "  1. Somewhat\n",
+       "  2. Moderately\n",
+       "  3. Very\n",
+       "  4. Extremely\n",
+       "\n",
+       "\n",
+       "\n",
+       "The IIP-SC currently has 2 normative data set(s):\n",
+       "\n",
+       "1. 872 American college students\n",
+       "   Hopwood, Pincus, DeMoor, & Koonce (2011)\n",
+       "   https://doi.org/10.1080/00223890802388665\n",
+       "2. 106 American psychiatric outpatients\n",
+       "   Soldz, Budman, Demby, & Merry (1995)\n",
+       "   https://doi.org/10.1177/1073191195002001006\n",
+       "\n",
+       "
\n" + ], + "text/plain": [ + "IIP-SC: Inventory of Interpersonal Problems Short Circumplex\n", + "\u001b[1;36m32\u001b[0m items, \u001b[1;36m8\u001b[0m scales, \u001b[1;36m2\u001b[0m normative data sets\n", + "Soldz, Budman, Demby, & Merry \u001b[1m(\u001b[0m\u001b[1;36m1995\u001b[0m\u001b[1m)\u001b[0m\n", + "\u001b[1m<\u001b[0m\u001b[39m \u001b[0m\u001b[4;94mhttps://doi.org/10.1177/1073191195002001006\u001b[0m\u001b[39m \u001b[0m\u001b[1m>\u001b[0m\n", + "\n", + "\n", + "\u001b[1;36mThe IIP-SC contains 8 scales:\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mPA (90°): Domineering\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m1. ...point of view...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m9. ...too aggressive toward...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m17. ...control other people...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m25. ...argue with other...\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mBC (135°): Vindictive\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m2. ...supportive of another...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m10. ...another person's happiness...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m18. ...too suspicious of...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m26. ...revenge against people...\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mDE (180°): Cold\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m3. ...show affection to...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m11. ...feeling of love...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m19. ...feel close to...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m27. ...at a distance...\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mFG (225°): Socially avoidant\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m4. ...join in on...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m12. ...introduce myself to...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m20. ...socialize with other...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m28. ...get together socially...\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mHI (270°): Nonassertive\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m5. ...stop bothering me...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m13. ...confront people with...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m21. ...assertive with another...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m29. ...to be firm...\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mJK (315°): Exploitable\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m6. ...I am angry...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m14. ...assertive without worrying...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m22. ...too easily persuaded...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m30. ...people take advantage...\u001b[0m\n", + "\u001b[2m├── \u001b[0m\u001b[1mLM (360°): Overly nurturant\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m7. ...my own welfare...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m15. ...please other people...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m23. ...other people's needs...\u001b[0m\n", + "\u001b[2m│ \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m31. ...another person's misery...\u001b[0m\n", + "\u001b[2m└── \u001b[0m\u001b[1mNO (45°): Intrusive\u001b[0m\n", + "\u001b[2m \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m8. ...keep things private...\u001b[0m\n", + "\u001b[2m \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m16. ...open up to...\u001b[0m\n", + "\u001b[2m \u001b[0m\u001b[2m├── \u001b[0m\u001b[1;2m24. ...noticed too much...\u001b[0m\n", + "\u001b[2m \u001b[0m\u001b[2m└── \u001b[0m\u001b[1;2m32. ...tell personal things...\u001b[0m\n", + "\n", + "\n", + "\u001b[1;36mThe IIP-SC is rated using the following 5-point scale:\u001b[0m\n", + " 0. Not at all\n", + " 1. Somewhat\n", + " 2. Moderately\n", + " 3. Very\n", + " 4. Extremely\n", + "\n", + "\n", + "\n", + "\u001b[1;36mThe IIP-SC currently has 2 normative data set(s):\u001b[0m\n", + "\n", + "1. 872 American college students\n", + " Hopwood, Pincus, DeMoor, & Koonce (2011)\n", + " https://doi.org/10.1080/00223890802388665\n", + "2. 106 American psychiatric outpatients\n", + " Soldz, Budman, Demby, & Merry (1995)\n", + " https://doi.org/10.1177/1073191195002001006\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "iipsc = circumplex.get_instrument(\"iipsc\")\n", + "iipsc.info(items=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e8791c5039e6d3e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "circumplex (3.12.5)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/using-instruments.ipynb b/examples/using-instruments.ipynb new file mode 100644 index 0000000..db3b7e9 --- /dev/null +++ b/examples/using-instruments.ipynb @@ -0,0 +1,3206 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "33d6280b", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext rich\n", + "import numpy as np\n", + "\n", + "from circumplex import (\n", + " get_instrument,\n", + " ipsatize,\n", + " norm_standardize,\n", + " score,\n", + " show_instruments,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cd871616", + "metadata": {}, + "source": [ + "# Using Circumplex Instruments\n", + "\n", + "## 1. Overview of Instrument-related Functions" + ] + }, + { + "cell_type": "markdown", + "id": "bc25eb0a", + "metadata": {}, + "source": [ + "## 2. Loading and Examining Instrument Objects\n", + "\n", + "### Previewing the available instruments\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4552fc97", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
          The circumplex package currently includes 3 instruments          \n",
+       "┏━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃   ┃ Abbreviation ┃ Name                                                 ┃\n",
+       "┡━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ 1 │ CSIG         │ Circumplex Scales of Interpersonal Goals             │\n",
+       "│ 2 │ IIPSC        │ Inventory of Interpersonal Problems Short Circumplex │\n",
+       "│ 3 │ IPIPIPC      │ IPIP Interpersonal Circumplex                        │\n",
+       "└───┴──────────────┴──────────────────────────────────────────────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[3m The circumplex package currently includes 3 instruments \u001b[0m\n", + "┏━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mAbbreviation\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mName \u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", + "│ 1 │\u001b[36m \u001b[0m\u001b[36mCSIG \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35mCircumplex Scales of Interpersonal Goals \u001b[0m\u001b[35m \u001b[0m│\n", + "│ 2 │\u001b[36m \u001b[0m\u001b[36mIIPSC \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35mInventory of Interpersonal Problems Short Circumplex\u001b[0m\u001b[35m \u001b[0m│\n", + "│ 3 │\u001b[36m \u001b[0m\u001b[36mIPIPIPC \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35mIPIP Interpersonal Circumplex \u001b[0m\u001b[35m \u001b[0m│\n", + "└───┴──────────────┴──────────────────────────────────────────────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show_instruments()" + ] + }, + { + "cell_type": "markdown", + "id": "7df02ac6", + "metadata": {}, + "source": [ + "### Loading a specific instrument" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d3596a68", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "\u001b[1;35mInstrument\u001b[0m\u001b[1m(\u001b[0m\n",
+       "    \u001b[32m'CSIG: Circumplex Scales of Interpersonal Goals'\u001b[0m,\n",
+       "    \u001b[32m'32 items, 8 scales, 1 normative data sets'\u001b[0m,\n",
+       "    \u001b[32m'Lock \u001b[0m\u001b[32m(\u001b[0m\u001b[32m2014\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m,\n",
+       "    \u001b[32m'\u001b[0m\u001b[32m<\u001b[0m\u001b[32m https://doi.org/10.1177/0146167213514280 \u001b[0m\u001b[32m>\u001b[0m\u001b[32m'\u001b[0m\n",
+       "\u001b[1m)\u001b[0m"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "csig = get_instrument(\"csig\")\n",
+    "csig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f1450aa9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
CSIG: Circumplex Scales of Interpersonal Goals\n",
+       "32 items, 8 scales, 1 normative data sets\n",
+       "Lock (2014)\n",
+       "< https://doi.org/10.1177/0146167213514280 >\n",
+       "\n",
+       "\n",
+       "The CSIG contains 8 scales:\n",
+       "├── PA (90°): Be authoritative\n",
+       "├── BC (135°): Be tough\n",
+       "├── DE (180°): Be self-protective\n",
+       "├── FG (225°): Be wary\n",
+       "├── HI (270°): Be conflict-avoidant\n",
+       "├── JK (315°): Be cooperative\n",
+       "├── LM (360°): Be understanding\n",
+       "└── NO (45°): Be respected\n",
+       "\n",
+       "\n",
+       "The CSIG is rated using the following 5-point scale:\n",
+       "  0. It is not at all important that...\n",
+       "  1. It is somewhat important that...\n",
+       "  2. It is moderately important that...\n",
+       "  3. It is very important that...\n",
+       "  4. It is extremely important that...\n",
+       "\n",
+       "\n",
+       "\n",
+       "The CSIG currently has 1 normative data set(s):\n",
+       "\n",
+       "1. 665 MTurkers from US, Canada, and India about interactions between nations\n",
+       "   Lock (2014)\n",
+       "   https://doi.org/10.1177/0146167213514280\n",
+       "\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "CSIG: Circumplex Scales of Interpersonal Goals\n",
+       "\u001b[1;36m32\u001b[0m items, \u001b[1;36m8\u001b[0m scales, \u001b[1;36m1\u001b[0m normative data sets\n",
+       "Lock \u001b[1m(\u001b[0m\u001b[1;36m2014\u001b[0m\u001b[1m)\u001b[0m\n",
+       "\u001b[1m<\u001b[0m\u001b[39m \u001b[0m\u001b[4;94mhttps://doi.org/10.1177/0146167213514280\u001b[0m\u001b[39m \u001b[0m\u001b[1m>\u001b[0m\n",
+       "\n",
+       "\n",
+       "\u001b[1;36mThe CSIG contains 8 scales:\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mPA (90°): Be authoritative\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mBC (135°): Be tough\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mDE (180°): Be self-protective\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mFG (225°): Be wary\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mHI (270°): Be conflict-avoidant\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mJK (315°): Be cooperative\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mLM (360°): Be understanding\u001b[0m\n",
+       "\u001b[2m└── \u001b[0m\u001b[1mNO (45°): Be respected\u001b[0m\n",
+       "\n",
+       "\n",
+       "\u001b[1;36mThe CSIG is rated using the following 5-point scale:\u001b[0m\n",
+       "  0. It is not at all important that...\n",
+       "  1. It is somewhat important that...\n",
+       "  2. It is moderately important that...\n",
+       "  3. It is very important that...\n",
+       "  4. It is extremely important that...\n",
+       "\n",
+       "\n",
+       "\n",
+       "\u001b[1;36mThe CSIG currently has 1 normative data set(s):\u001b[0m\n",
+       "\n",
+       "1. 665 MTurkers from US, Canada, and India about interactions between nations\n",
+       "   Lock (2014)\n",
+       "   https://doi.org/10.1177/0146167213514280\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "csig.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9ab8ac2",
+   "metadata": {},
+   "source": [
+    "## 3. Instrument-related Tidying Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7e0c229c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
IIP-SC: Inventory of Interpersonal Problems Short Circumplex\n",
+       "32 items, 8 scales, 2 normative data sets\n",
+       "Soldz, Budman, Demby, & Merry (1995)\n",
+       "< https://doi.org/10.1177/1073191195002001006 >\n",
+       "\n",
+       "\n",
+       "The IIP-SC contains 8 scales:\n",
+       "├── PA (90°): Domineering\n",
+       "├── BC (135°): Vindictive\n",
+       "├── DE (180°): Cold\n",
+       "├── FG (225°): Socially avoidant\n",
+       "├── HI (270°): Nonassertive\n",
+       "├── JK (315°): Exploitable\n",
+       "├── LM (360°): Overly nurturant\n",
+       "└── NO (45°): Intrusive\n",
+       "\n",
+       "\n",
+       "The IIP-SC is rated using the following 5-point scale:\n",
+       "  0. Not at all\n",
+       "  1. Somewhat\n",
+       "  2. Moderately\n",
+       "  3. Very\n",
+       "  4. Extremely\n",
+       "\n",
+       "\n",
+       "\n",
+       "The IIP-SC currently has 2 normative data set(s):\n",
+       "\n",
+       "1. 872 American college students\n",
+       "   Hopwood, Pincus, DeMoor, & Koonce (2011)\n",
+       "   https://doi.org/10.1080/00223890802388665\n",
+       "2. 106 American psychiatric outpatients\n",
+       "   Soldz, Budman, Demby, & Merry (1995)\n",
+       "   https://doi.org/10.1177/1073191195002001006\n",
+       "\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "IIP-SC: Inventory of Interpersonal Problems Short Circumplex\n",
+       "\u001b[1;36m32\u001b[0m items, \u001b[1;36m8\u001b[0m scales, \u001b[1;36m2\u001b[0m normative data sets\n",
+       "Soldz, Budman, Demby, & Merry \u001b[1m(\u001b[0m\u001b[1;36m1995\u001b[0m\u001b[1m)\u001b[0m\n",
+       "\u001b[1m<\u001b[0m\u001b[39m \u001b[0m\u001b[4;94mhttps://doi.org/10.1177/1073191195002001006\u001b[0m\u001b[39m \u001b[0m\u001b[1m>\u001b[0m\n",
+       "\n",
+       "\n",
+       "\u001b[1;36mThe IIP-SC contains 8 scales:\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mPA (90°): Domineering\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mBC (135°): Vindictive\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mDE (180°): Cold\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mFG (225°): Socially avoidant\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mHI (270°): Nonassertive\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mJK (315°): Exploitable\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mLM (360°): Overly nurturant\u001b[0m\n",
+       "\u001b[2m└── \u001b[0m\u001b[1mNO (45°): Intrusive\u001b[0m\n",
+       "\n",
+       "\n",
+       "\u001b[1;36mThe IIP-SC is rated using the following 5-point scale:\u001b[0m\n",
+       "  0. Not at all\n",
+       "  1. Somewhat\n",
+       "  2. Moderately\n",
+       "  3. Very\n",
+       "  4. Extremely\n",
+       "\n",
+       "\n",
+       "\n",
+       "\u001b[1;36mThe IIP-SC currently has 2 normative data set(s):\u001b[0m\n",
+       "\n",
+       "1. 872 American college students\n",
+       "   Hopwood, Pincus, DeMoor, & Koonce (2011)\n",
+       "   https://doi.org/10.1080/00223890802388665\n",
+       "2. 106 American psychiatric outpatients\n",
+       "   Soldz, Budman, Demby, & Merry (1995)\n",
+       "   https://doi.org/10.1177/1073191195002001006\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "iipsc = get_instrument(\"iipsc\")\n",
+    "iipsc.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8781def3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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IIP01IIP02IIP03IIP04IIP05IIP06IIP07IIP08IIP09IIP10...IIP23IIP24IIP25IIP26IIP27IIP28IIP29IIP30IIP31IIP32
00000.0101021...0033300010
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40001.0001101...1121000000
\n",
+       "
5 rows × 32 columns
\n",
+       "
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+      ],
+      "text/plain": [
+       "\n",
+       "   IIP01  IIP02  IIP03  IIP04  IIP05  IIP06  IIP07  IIP08  IIP09  IIP10  \u001b[33m...\u001b[0m  \\\n",
+       "\u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m    \u001b[1;36m0.0\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m2\u001b[0m      \u001b[1;36m1\u001b[0m  \u001b[33m...\u001b[0m   \n",
+       "\u001b[1;36m1\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m0\u001b[0m    \u001b[1;36m0.0\u001b[0m      \u001b[1;36m3\u001b[0m      \u001b[1;36m2\u001b[0m      \u001b[1;36m2\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m1\u001b[0m  \u001b[33m...\u001b[0m   \n",
+       "\u001b[1;36m2\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m1\u001b[0m    \u001b[1;36m0.0\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m3\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m1\u001b[0m  \u001b[33m...\u001b[0m   \n",
+       "\u001b[1;36m3\u001b[0m      \u001b[1;36m3\u001b[0m      \u001b[1;36m2\u001b[0m      \u001b[1;36m3\u001b[0m    NaN      \u001b[1;36m2\u001b[0m      \u001b[1;36m3\u001b[0m      \u001b[1;36m2\u001b[0m      \u001b[1;36m3\u001b[0m      \u001b[1;36m2\u001b[0m      \u001b[1;36m3\u001b[0m  \u001b[33m...\u001b[0m   \n",
+       "\u001b[1;36m4\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m    \u001b[1;36m1.0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m1\u001b[0m  \u001b[33m...\u001b[0m   \n",
+       "\n",
+       "   IIP23  IIP24  IIP25  IIP26  IIP27  IIP28  IIP29  IIP30  IIP31  IIP32  \n",
+       "\u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m3\u001b[0m      \u001b[1;36m3\u001b[0m      \u001b[1;36m3\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m0\u001b[0m  \n",
+       "\u001b[1;36m1\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m2\u001b[0m  \n",
+       "\u001b[1;36m2\u001b[0m      \u001b[1;36m3\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m3\u001b[0m      \u001b[1;36m2\u001b[0m      \u001b[1;36m3\u001b[0m      \u001b[1;36m2\u001b[0m  \n",
+       "\u001b[1;36m3\u001b[0m      \u001b[1;36m3\u001b[0m      \u001b[1;36m2\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m2\u001b[0m      \u001b[1;36m3\u001b[0m      \u001b[1;36m2\u001b[0m      \u001b[1;36m3\u001b[0m      \u001b[1;36m2\u001b[0m      \u001b[1;36m3\u001b[0m      \u001b[1;36m2\u001b[0m  \n",
+       "\u001b[1;36m4\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m2\u001b[0m      \u001b[1;36m1\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m      \u001b[1;36m0\u001b[0m  \n",
+       "\n",
+       "\u001b[1m[\u001b[0m\u001b[1;36m5\u001b[0m rows x \u001b[1;36m32\u001b[0m columns\u001b[1m]\u001b[0m"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "raw_iipsc = pd.read_csv(\n",
+    "    \"/Users/mitch/Documents/GitHub/python-circumplex/src/circumplex/data/raw_iipsc.csv\"\n",
+    ")\n",
+    "raw_iipsc.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f811e679",
+   "metadata": {},
+   "source": [
+    "### Ipsatizing item-level data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0e76e58e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+         "type": "float"
+        },
+        {
+         "name": "2_i",
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+        },
+        {
+         "name": "3_i",
+         "rawType": "float64",
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+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ips_iipsc = ipsatize(\n",
+    "    data=raw_iipsc,\n",
+    "    items=np.arange(0, 32),\n",
+    "    append=False,\n",
+    ")\n",
+    "ips_iipsc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0dbd5c56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "dtype: float64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(raw_iipsc.mean(axis=1, skipna=True), 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6bdf3124",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "\u001b[1;36m7\u001b[0m    \u001b[1;36m0.0\u001b[0m\n",
+       "\u001b[1;36m8\u001b[0m    \u001b[1;36m0.0\u001b[0m\n",
+       "\u001b[1;36m9\u001b[0m    \u001b[1;36m0.0\u001b[0m\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(ips_iipsc.mean(axis=1, skipna=True), 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3971ab9d",
+   "metadata": {},
+   "source": [
+    "### Scoring item-level data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "cc2939a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
The IIP-SC contains 8 scales:\n",
+       "├── PA (90°): Domineering\n",
+       "├── BC (135°): Vindictive\n",
+       "├── DE (180°): Cold\n",
+       "├── FG (225°): Socially avoidant\n",
+       "├── HI (270°): Nonassertive\n",
+       "├── JK (315°): Exploitable\n",
+       "├── LM (360°): Overly nurturant\n",
+       "└── NO (45°): Intrusive\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[1;36mThe IIP-SC contains 8 scales:\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mPA (90°): Domineering\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mBC (135°): Vindictive\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mDE (180°): Cold\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mFG (225°): Socially avoidant\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mHI (270°): Nonassertive\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mJK (315°): Exploitable\u001b[0m\n",
+       "\u001b[2m├── \u001b[0m\u001b[1mLM (360°): Overly nurturant\u001b[0m\n",
+       "\u001b[2m└── \u001b[0m\u001b[1mNO (45°): Intrusive\u001b[0m\n"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iipsc.info_scales()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ceb1b5d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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PABCDEFGHIJKLMNO
01.752.001.250.0000000.500.2500001.500.75
10.250.500.250.5000002.001.7500001.251.00
21.000.750.750.0000002.252.0000002.502.00
31.752.252.502.3333332.502.0000002.502.50
40.500.750.001.0000000.500.2500001.250.75
50.250.000.000.0000000.000.0000001.001.00
61.000.000.000.0000001.001.0000000.750.25
71.000.250.750.0000000.500.6666671.751.00
80.750.501.500.7500000.001.0000002.750.50
90.000.000.000.0000000.000.5000001.000.25
\n",
+       "
"
+      ],
+      "text/plain": [
+       "\n",
+       "     PA    BC    DE        FG    HI        JK    LM    NO\n",
+       "\u001b[1;36m0\u001b[0m  \u001b[1;36m1.75\u001b[0m  \u001b[1;36m2.00\u001b[0m  \u001b[1;36m1.25\u001b[0m  \u001b[1;36m0.000000\u001b[0m  \u001b[1;36m0.50\u001b[0m  \u001b[1;36m0.250000\u001b[0m  \u001b[1;36m1.50\u001b[0m  \u001b[1;36m0.75\u001b[0m\n",
+       "\u001b[1;36m1\u001b[0m  \u001b[1;36m0.25\u001b[0m  \u001b[1;36m0.50\u001b[0m  \u001b[1;36m0.25\u001b[0m  \u001b[1;36m0.500000\u001b[0m  \u001b[1;36m2.00\u001b[0m  \u001b[1;36m1.750000\u001b[0m  \u001b[1;36m1.25\u001b[0m  \u001b[1;36m1.00\u001b[0m\n",
+       "\u001b[1;36m2\u001b[0m  \u001b[1;36m1.00\u001b[0m  \u001b[1;36m0.75\u001b[0m  \u001b[1;36m0.75\u001b[0m  \u001b[1;36m0.000000\u001b[0m  \u001b[1;36m2.25\u001b[0m  \u001b[1;36m2.000000\u001b[0m  \u001b[1;36m2.50\u001b[0m  \u001b[1;36m2.00\u001b[0m\n",
+       "\u001b[1;36m3\u001b[0m  \u001b[1;36m1.75\u001b[0m  \u001b[1;36m2.25\u001b[0m  \u001b[1;36m2.50\u001b[0m  \u001b[1;36m2.333333\u001b[0m  \u001b[1;36m2.50\u001b[0m  \u001b[1;36m2.000000\u001b[0m  \u001b[1;36m2.50\u001b[0m  \u001b[1;36m2.50\u001b[0m\n",
+       "\u001b[1;36m4\u001b[0m  \u001b[1;36m0.50\u001b[0m  \u001b[1;36m0.75\u001b[0m  \u001b[1;36m0.00\u001b[0m  \u001b[1;36m1.000000\u001b[0m  \u001b[1;36m0.50\u001b[0m  \u001b[1;36m0.250000\u001b[0m  \u001b[1;36m1.25\u001b[0m  \u001b[1;36m0.75\u001b[0m\n",
+       "\u001b[1;36m5\u001b[0m  \u001b[1;36m0.25\u001b[0m  \u001b[1;36m0.00\u001b[0m  \u001b[1;36m0.00\u001b[0m  \u001b[1;36m0.000000\u001b[0m  \u001b[1;36m0.00\u001b[0m  \u001b[1;36m0.000000\u001b[0m  \u001b[1;36m1.00\u001b[0m  \u001b[1;36m1.00\u001b[0m\n",
+       "\u001b[1;36m6\u001b[0m  \u001b[1;36m1.00\u001b[0m  \u001b[1;36m0.00\u001b[0m  \u001b[1;36m0.00\u001b[0m  \u001b[1;36m0.000000\u001b[0m  \u001b[1;36m1.00\u001b[0m  \u001b[1;36m1.000000\u001b[0m  \u001b[1;36m0.75\u001b[0m  \u001b[1;36m0.25\u001b[0m\n",
+       "\u001b[1;36m7\u001b[0m  \u001b[1;36m1.00\u001b[0m  \u001b[1;36m0.25\u001b[0m  \u001b[1;36m0.75\u001b[0m  \u001b[1;36m0.000000\u001b[0m  \u001b[1;36m0.50\u001b[0m  \u001b[1;36m0.666667\u001b[0m  \u001b[1;36m1.75\u001b[0m  \u001b[1;36m1.00\u001b[0m\n",
+       "\u001b[1;36m8\u001b[0m  \u001b[1;36m0.75\u001b[0m  \u001b[1;36m0.50\u001b[0m  \u001b[1;36m1.50\u001b[0m  \u001b[1;36m0.750000\u001b[0m  \u001b[1;36m0.00\u001b[0m  \u001b[1;36m1.000000\u001b[0m  \u001b[1;36m2.75\u001b[0m  \u001b[1;36m0.50\u001b[0m\n",
+       "\u001b[1;36m9\u001b[0m  \u001b[1;36m0.00\u001b[0m  \u001b[1;36m0.00\u001b[0m  \u001b[1;36m0.00\u001b[0m  \u001b[1;36m0.000000\u001b[0m  \u001b[1;36m0.00\u001b[0m  \u001b[1;36m0.500000\u001b[0m  \u001b[1;36m1.00\u001b[0m  \u001b[1;36m0.25\u001b[0m"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scale_scores = score(\n",
+    "    data=raw_iipsc, items=np.arange(0, 32), append=False, instrument=\"iipsc\"\n",
+    ")\n",
+    "scale_scores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "942a523d",
+   "metadata": {},
+   "source": [
+    "### Standardizing scale-level data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a8942359",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
The IIP-SC currently has 2 normative data set(s):\n",
+       "\n",
+       "1. 872 American college students\n",
+       "   Hopwood, Pincus, DeMoor, & Koonce (2011)\n",
+       "   https://doi.org/10.1080/00223890802388665\n",
+       "2. 106 American psychiatric outpatients\n",
+       "   Soldz, Budman, Demby, & Merry (1995)\n",
+       "   https://doi.org/10.1177/1073191195002001006\n",
+       "\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[1;36mThe IIP-SC currently has 2 normative data set(s):\u001b[0m\n",
+       "\n",
+       "1. 872 American college students\n",
+       "   Hopwood, Pincus, DeMoor, & Koonce (2011)\n",
+       "   https://doi.org/10.1080/00223890802388665\n",
+       "2. 106 American psychiatric outpatients\n",
+       "   Soldz, Budman, Demby, & Merry (1995)\n",
+       "   https://doi.org/10.1177/1073191195002001006\n",
+       "\n"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iipsc.info_norms()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ca6ac236",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+        {
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+        {
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+        {
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PABCDEFGHIJKLMNO
01.5000001.7500000.409357-1.105541-1.005464-1.3313780.042424-0.34375
1-0.772727-0.423913-0.760234-0.5778360.6338800.428152-0.260606-0.03125
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60.363636-1.148551-1.052632-1.105541-0.459016-0.451613-0.866667-0.96875
70.363636-0.786232-0.175439-1.105541-1.005464-0.8426200.345455-0.03125
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9-1.151515-1.148551-1.052632-1.105541-1.551913-1.038123-0.563636-0.96875
\n",
+       "
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+      "text/plain": [
+       "\n",
+       "         PA        BC        DE        FG        HI        JK        LM  \\\n",
+       "\u001b[1;36m0\u001b[0m  \u001b[1;36m1.500000\u001b[0m  \u001b[1;36m1.750000\u001b[0m  \u001b[1;36m0.409357\u001b[0m \u001b[1;36m-1.105541\u001b[0m \u001b[1;36m-1.005464\u001b[0m \u001b[1;36m-1.331378\u001b[0m  \u001b[1;36m0.042424\u001b[0m   \n",
+       "\u001b[1;36m1\u001b[0m \u001b[1;36m-0.772727\u001b[0m \u001b[1;36m-0.423913\u001b[0m \u001b[1;36m-0.760234\u001b[0m \u001b[1;36m-0.577836\u001b[0m  \u001b[1;36m0.633880\u001b[0m  \u001b[1;36m0.428152\u001b[0m \u001b[1;36m-0.260606\u001b[0m   \n",
+       "\u001b[1;36m2\u001b[0m  \u001b[1;36m0.363636\u001b[0m \u001b[1;36m-0.061594\u001b[0m \u001b[1;36m-0.175439\u001b[0m \u001b[1;36m-1.105541\u001b[0m  \u001b[1;36m0.907104\u001b[0m  \u001b[1;36m0.721408\u001b[0m  \u001b[1;36m1.254545\u001b[0m   \n",
+       "\u001b[1;36m3\u001b[0m  \u001b[1;36m1.500000\u001b[0m  \u001b[1;36m2.112319\u001b[0m  \u001b[1;36m1.871345\u001b[0m  \u001b[1;36m1.357080\u001b[0m  \u001b[1;36m1.180328\u001b[0m  \u001b[1;36m0.721408\u001b[0m  \u001b[1;36m1.254545\u001b[0m   \n",
+       "\u001b[1;36m4\u001b[0m \u001b[1;36m-0.393939\u001b[0m \u001b[1;36m-0.061594\u001b[0m \u001b[1;36m-1.052632\u001b[0m \u001b[1;36m-0.050132\u001b[0m \u001b[1;36m-1.005464\u001b[0m \u001b[1;36m-1.331378\u001b[0m \u001b[1;36m-0.260606\u001b[0m   \n",
+       "\u001b[1;36m5\u001b[0m \u001b[1;36m-0.772727\u001b[0m \u001b[1;36m-1.148551\u001b[0m \u001b[1;36m-1.052632\u001b[0m \u001b[1;36m-1.105541\u001b[0m \u001b[1;36m-1.551913\u001b[0m \u001b[1;36m-1.624633\u001b[0m \u001b[1;36m-0.563636\u001b[0m   \n",
+       "\u001b[1;36m6\u001b[0m  \u001b[1;36m0.363636\u001b[0m \u001b[1;36m-1.148551\u001b[0m \u001b[1;36m-1.052632\u001b[0m \u001b[1;36m-1.105541\u001b[0m \u001b[1;36m-0.459016\u001b[0m \u001b[1;36m-0.451613\u001b[0m \u001b[1;36m-0.866667\u001b[0m   \n",
+       "\u001b[1;36m7\u001b[0m  \u001b[1;36m0.363636\u001b[0m \u001b[1;36m-0.786232\u001b[0m \u001b[1;36m-0.175439\u001b[0m \u001b[1;36m-1.105541\u001b[0m \u001b[1;36m-1.005464\u001b[0m \u001b[1;36m-0.842620\u001b[0m  \u001b[1;36m0.345455\u001b[0m   \n",
+       "\u001b[1;36m8\u001b[0m \u001b[1;36m-0.015152\u001b[0m \u001b[1;36m-0.423913\u001b[0m  \u001b[1;36m0.701754\u001b[0m \u001b[1;36m-0.313984\u001b[0m \u001b[1;36m-1.551913\u001b[0m \u001b[1;36m-0.451613\u001b[0m  \u001b[1;36m1.557576\u001b[0m   \n",
+       "\u001b[1;36m9\u001b[0m \u001b[1;36m-1.151515\u001b[0m \u001b[1;36m-1.148551\u001b[0m \u001b[1;36m-1.052632\u001b[0m \u001b[1;36m-1.105541\u001b[0m \u001b[1;36m-1.551913\u001b[0m \u001b[1;36m-1.038123\u001b[0m \u001b[1;36m-0.563636\u001b[0m   \n",
+       "\n",
+       "        NO  \n",
+       "\u001b[1;36m0\u001b[0m \u001b[1;36m-0.34375\u001b[0m  \n",
+       "\u001b[1;36m1\u001b[0m \u001b[1;36m-0.03125\u001b[0m  \n",
+       "\u001b[1;36m2\u001b[0m  \u001b[1;36m1.21875\u001b[0m  \n",
+       "\u001b[1;36m3\u001b[0m  \u001b[1;36m1.84375\u001b[0m  \n",
+       "\u001b[1;36m4\u001b[0m \u001b[1;36m-0.34375\u001b[0m  \n",
+       "\u001b[1;36m5\u001b[0m \u001b[1;36m-0.03125\u001b[0m  \n",
+       "\u001b[1;36m6\u001b[0m \u001b[1;36m-0.96875\u001b[0m  \n",
+       "\u001b[1;36m7\u001b[0m \u001b[1;36m-0.03125\u001b[0m  \n",
+       "\u001b[1;36m8\u001b[0m \u001b[1;36m-0.65625\u001b[0m  \n",
+       "\u001b[1;36m9\u001b[0m \u001b[1;36m-0.96875\u001b[0m  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z_scales = iipsc.norm_standardize(\n",
+    "    data=scale_scores,\n",
+    "    scales=np.arange(0, 8),\n",
+    "    sample_id=1,\n",
+    "    append=False,\n",
+    ")\n",
+    "z_scales"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "804b5b83",
+   "metadata": {},
+   "outputs": [
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PA_zBC_zDE_zFG_zHI_zJK_zLM_zNO_z
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9-1.151515-1.148551-1.052632-1.105541-1.551913-1.038123-0.563636-0.96875
\n",
+       "
"
+      ],
+      "text/plain": [
+       "\n",
+       "       PA_z      BC_z      DE_z      FG_z      HI_z      JK_z      LM_z  \\\n",
+       "\u001b[1;36m0\u001b[0m  \u001b[1;36m1.500000\u001b[0m  \u001b[1;36m1.750000\u001b[0m  \u001b[1;36m0.409357\u001b[0m \u001b[1;36m-1.105541\u001b[0m \u001b[1;36m-1.005464\u001b[0m \u001b[1;36m-1.331378\u001b[0m  \u001b[1;36m0.042424\u001b[0m   \n",
+       "\u001b[1;36m1\u001b[0m \u001b[1;36m-0.772727\u001b[0m \u001b[1;36m-0.423913\u001b[0m \u001b[1;36m-0.760234\u001b[0m \u001b[1;36m-0.577836\u001b[0m  \u001b[1;36m0.633880\u001b[0m  \u001b[1;36m0.428152\u001b[0m \u001b[1;36m-0.260606\u001b[0m   \n",
+       "\u001b[1;36m2\u001b[0m  \u001b[1;36m0.363636\u001b[0m \u001b[1;36m-0.061594\u001b[0m \u001b[1;36m-0.175439\u001b[0m \u001b[1;36m-1.105541\u001b[0m  \u001b[1;36m0.907104\u001b[0m  \u001b[1;36m0.721408\u001b[0m  \u001b[1;36m1.254545\u001b[0m   \n",
+       "\u001b[1;36m3\u001b[0m  \u001b[1;36m1.500000\u001b[0m  \u001b[1;36m2.112319\u001b[0m  \u001b[1;36m1.871345\u001b[0m  \u001b[1;36m1.357080\u001b[0m  \u001b[1;36m1.180328\u001b[0m  \u001b[1;36m0.721408\u001b[0m  \u001b[1;36m1.254545\u001b[0m   \n",
+       "\u001b[1;36m4\u001b[0m \u001b[1;36m-0.393939\u001b[0m \u001b[1;36m-0.061594\u001b[0m \u001b[1;36m-1.052632\u001b[0m \u001b[1;36m-0.050132\u001b[0m \u001b[1;36m-1.005464\u001b[0m \u001b[1;36m-1.331378\u001b[0m \u001b[1;36m-0.260606\u001b[0m   \n",
+       "\u001b[1;36m5\u001b[0m \u001b[1;36m-0.772727\u001b[0m \u001b[1;36m-1.148551\u001b[0m \u001b[1;36m-1.052632\u001b[0m \u001b[1;36m-1.105541\u001b[0m \u001b[1;36m-1.551913\u001b[0m \u001b[1;36m-1.624633\u001b[0m \u001b[1;36m-0.563636\u001b[0m   \n",
+       "\u001b[1;36m6\u001b[0m  \u001b[1;36m0.363636\u001b[0m \u001b[1;36m-1.148551\u001b[0m \u001b[1;36m-1.052632\u001b[0m \u001b[1;36m-1.105541\u001b[0m \u001b[1;36m-0.459016\u001b[0m \u001b[1;36m-0.451613\u001b[0m \u001b[1;36m-0.866667\u001b[0m   \n",
+       "\u001b[1;36m7\u001b[0m  \u001b[1;36m0.363636\u001b[0m \u001b[1;36m-0.786232\u001b[0m \u001b[1;36m-0.175439\u001b[0m \u001b[1;36m-1.105541\u001b[0m \u001b[1;36m-1.005464\u001b[0m \u001b[1;36m-0.842620\u001b[0m  \u001b[1;36m0.345455\u001b[0m   \n",
+       "\u001b[1;36m8\u001b[0m \u001b[1;36m-0.015152\u001b[0m \u001b[1;36m-0.423913\u001b[0m  \u001b[1;36m0.701754\u001b[0m \u001b[1;36m-0.313984\u001b[0m \u001b[1;36m-1.551913\u001b[0m \u001b[1;36m-0.451613\u001b[0m  \u001b[1;36m1.557576\u001b[0m   \n",
+       "\u001b[1;36m9\u001b[0m \u001b[1;36m-1.151515\u001b[0m \u001b[1;36m-1.148551\u001b[0m \u001b[1;36m-1.052632\u001b[0m \u001b[1;36m-1.105541\u001b[0m \u001b[1;36m-1.551913\u001b[0m \u001b[1;36m-1.038123\u001b[0m \u001b[1;36m-0.563636\u001b[0m   \n",
+       "\n",
+       "      NO_z  \n",
+       "\u001b[1;36m0\u001b[0m \u001b[1;36m-0.34375\u001b[0m  \n",
+       "\u001b[1;36m1\u001b[0m \u001b[1;36m-0.03125\u001b[0m  \n",
+       "\u001b[1;36m2\u001b[0m  \u001b[1;36m1.21875\u001b[0m  \n",
+       "\u001b[1;36m3\u001b[0m  \u001b[1;36m1.84375\u001b[0m  \n",
+       "\u001b[1;36m4\u001b[0m \u001b[1;36m-0.34375\u001b[0m  \n",
+       "\u001b[1;36m5\u001b[0m \u001b[1;36m-0.03125\u001b[0m  \n",
+       "\u001b[1;36m6\u001b[0m \u001b[1;36m-0.96875\u001b[0m  \n",
+       "\u001b[1;36m7\u001b[0m \u001b[1;36m-0.03125\u001b[0m  \n",
+       "\u001b[1;36m8\u001b[0m \u001b[1;36m-0.65625\u001b[0m  \n",
+       "\u001b[1;36m9\u001b[0m \u001b[1;36m-0.96875\u001b[0m  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "norm_standardize(\n",
+    "    data=scale_scores,\n",
+    "    instrument=\"iipsc\",\n",
+    "    scales=np.arange(0, 8),\n",
+    "    sample_id=1,\n",
+    "    append=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25fd8e2d",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python-circumplex (3.12.5)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/visualization_demo.py b/examples/visualization_demo.py
new file mode 100644
index 0000000..4d65e92
--- /dev/null
+++ b/examples/visualization_demo.py
@@ -0,0 +1,292 @@
+"""Circumplex Visualization Demo.
+
+This script demonstrates the visualization capabilities of the `circumplex` package,
+showing how to create publication-ready plots of SSM analysis results.
+
+The package provides three main plot types:
+- Circle plots: Show amplitude/displacement on circumplex circle
+- Curve plots: Show fitted curves overlaid on observed scores
+- Contrast plots: Show parameter differences between groups
+"""
+
+# %%
+# ## Setup
+
+import numpy as np
+
+from circumplex import load_dataset, plot_circle, ssm_analyze
+
+# Set random seed for reproducible results
+np.random.seed(12345)
+
+# %%
+# ## Example 1: Single Profile - Circle Plot
+
+# Load data and run analysis
+aw2009 = load_dataset("aw2009")
+results_single = ssm_analyze(
+    aw2009,
+    scales=list(range(8)),
+    boots=1000,
+    seed=12345,
+)
+
+# Display summary
+results_single.summary()
+
+# Create basic circle plot using SSM object method
+fig = results_single.plot_circle()
+
+# %%
+# ## Example 2: Single Profile - Curve Plot
+
+# Create curve plot showing fitted curve overlaid on observed scores
+fig = results_single.plot_curve()
+
+# %%
+# ## Example 3: Customized Single Profile
+
+# Customize circle plot appearance
+fig = results_single.plot_circle(
+    colors=None,  # Single blue color
+    figsize=(10, 10),
+    fontsize=14,
+    title="Interpersonal Profile (aw2009 dataset)",
+)
+
+
+# %%
+# ## Example 4: Multi-Group Comparison - Circle Plot
+
+# Analyze by gender
+jz2017 = load_dataset("jz2017")
+results_groups = ssm_analyze(
+    jz2017,
+    scales=list(range(1, 9)),
+    grouping="Gender",
+    boots=1000,
+    seed=12345,
+)
+
+# Plot comparison with default palette
+fig = results_groups.plot_circle()
+
+# %%
+# ## Example 5: Multi-Group - Custom Colors
+
+# Use custom colors for groups
+fig = results_groups.plot_circle(
+    colors=["#FF6B6B", "#4ECDC4"],  # Custom hex colors
+    figsize=(10, 10),
+    title="Gender Comparison: Interpersonal Profiles",
+)
+
+# %%
+# ## Example 6: Multi-Group - Curve Plots
+
+# Compare groups with curve plots
+scale_names = ["PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"]
+fig = results_groups.plot_curve(angle_labels=scale_names)
+
+# %%
+# ## Example 7: Group Contrast Analysis
+
+# Include contrast in analysis
+results_contrast = ssm_analyze(
+    jz2017,
+    scales=list(range(1, 9)),
+    grouping="Gender",
+    contrast=True,
+    boots=1000,
+    seed=12345,
+)
+
+# Plot all three profiles (Female, Male, Male - Female)
+fig = results_contrast.plot_circle()
+
+# Plot contrast differences
+fig = results_contrast.plot_contrast()
+
+# %%
+# ## Example 8: Simplified Contrast Plot (Drop X/Y)
+
+# Plot contrasts without X and Y parameters for simpler view
+fig = results_contrast.plot_contrast(drop_xy=True)
+
+# %%
+# ## Example 9: Correlation-Based Profile
+
+# Analyze correlation with personality disorder measure
+results_corr = ssm_analyze(
+    jz2017,
+    scales=list(range(1, 9)),
+    measures="PARPD",
+    boots=1000,
+    seed=12345,
+)
+
+# Plot correlation profile
+fig = results_corr.plot_circle(
+    colors=["purple"],
+    title="PARPD Correlations with Interpersonal Scales",
+)
+
+# %%
+# ## Example 10: Multi-Measure Comparison
+
+# Compare two personality disorder measures
+results_measures = ssm_analyze(
+    jz2017,
+    scales=list(range(1, 9)),
+    measures=["ASPD", "NARPD"],
+    boots=1000,
+    seed=12345,
+)
+
+# Circle plot comparison
+fig = results_measures.plot_circle(
+    colors=["#E74C3C", "#3498DB"],
+    title="Personality Disorder Profiles: ASPD vs NARPD",
+)
+
+# Curve plots comparison
+fig = results_measures.plot_curve(
+    angle_labels=scale_names,
+    colors=["#E74C3C", "#3498DB"],
+)
+
+# %%
+# ## Example 11: Measure Contrast
+
+# Compare measures with contrast
+results_measure_contrast = ssm_analyze(
+    jz2017,
+    scales=list(range(1, 9)),
+    measures=["ASPD", "NARPD"],
+    contrast=True,
+    boots=1000,
+    seed=12345,
+)
+
+# Plot measure contrast
+fig = results_measure_contrast.plot_contrast(drop_xy=True)
+# fig.savefig("examples/output/contrast_measures.png", dpi=300, bbox_inches="tight")  # noqa: ERA001
+
+# %%
+# ## Example 12: Profile Selection
+
+# Plot only specific profiles from multi-profile results
+# Using the imported plot_circle function directly
+
+# Plot only first profile from measure results
+fig = plot_circle(
+    results_measures.results,
+    results_measures.details.angles,
+    profile_indices=[0],
+    colors=["#E74C3C"],
+    title="ASPD Profile Only",
+)
+
+# %%
+# ## Example 13: Seaborn Palettes
+
+# Try different seaborn color palettes
+results_multi = ssm_analyze(
+    jz2017,
+    scales=list(range(1, 9)),
+    measures=["ASPD", "NARPD", "BORPD"],
+    boots=500,  # Fewer boots for speed
+    seed=12345,
+)
+
+# Using 'husl' palette
+fig = results_multi.plot_circle(colors="husl", title="HUSL Palette")
+
+# Using 'colorblind' palette
+fig = results_multi.plot_circle(colors="colorblind", title="Colorblind Palette")
+
+# %%
+# ## Visualization Features Summary
+
+print(
+    """
+### Circumplex Visualization Features
+
+✓ **Three Plot Types**:
+  - Circle plots: Amplitude/displacement on circumplex circle
+  - Curve plots: Fitted cosine curves overlaid on observed scores
+  - Contrast plots: Parameter differences with confidence intervals
+
+✓ **SSM Object Methods**: Convenient plotting directly from results
+  - results.plot_circle()
+  - results.plot_curve()
+  - results.plot_contrast()
+
+✓ **Customization**: Colors, sizes, fonts, titles
+✓ **Confidence intervals**: Bootstrap CIs displayed as arc bars (circle)
+  or error bars (contrast)
+✓ **Publication-ready**: High-resolution output (300 dpi)
+✓ **Flexible colors**: Seaborn palettes or custom color lists
+
+### Plot Types in Detail
+
+**Circle Plot (plot_circle)**:
+- Shows amplitude and displacement on circular plot
+- Profile points at (x, y) Cartesian coordinates
+- Arc bars show confidence intervals for amplitude and displacement
+- Supports single or multiple profiles
+- Automatic legend for multiple profiles
+
+**Curve Plot (plot_curve)**:
+- Faceted plots (one subplot per profile)
+- Fitted cosine curve overlaid on observed scale scores
+- Black points and lines show observed data
+- Colored curve shows SSM model fit
+- Dashed curves indicate low fit (R² < 0.70)
+
+**Contrast Plot (plot_contrast)**:
+- Shows differences between two profiles/groups
+- One subplot per SSM parameter (elevation, amplitude, displacement, etc.)
+- Points colored by significance (CI excludes zero)
+- Optional drop_xy parameter for simplified plot
+
+### Customization Options
+
+**Common Parameters**:
+- colors: Seaborn palette name ('Set2', 'husl', etc.) or list of custom colors
+- figsize: Figure dimensions in inches (width, height)
+- fontsize/base_size: Font size for labels and text
+- drop_lowfit: Omit profiles with fit < 0.70
+
+**Circle Plot Specific**:
+- amax: Maximum amplitude for scaling
+- angle_labels: Custom labels for angles
+- title: Plot title
+
+**Curve Plot Specific**:
+- angle_labels: Labels for x-axis (scale names)
+
+**Contrast Plot Specific**:
+- drop_xy: Omit x-value and y-value parameters
+- sig_color: Color for significant contrasts
+- ns_color: Color for non-significant contrasts
+
+### Usage Patterns
+
+**Method 1: SSM Object Methods (Recommended)**
+```python
+results = ssm_analyze(data, scales=[...])
+fig = results.plot_circle()
+fig = results.plot_curve()
+```
+
+**Method 2: Direct Function Calls (Advanced)**
+```python
+from circumplex import plot_circle, plot_curve, plot_contrast
+fig = plot_circle(results.results, results.details.angles)
+fig = plot_curve(results.results, results.scores, results.details.angles)
+```
+
+Both patterns support the same customization options.
+"""
+)
diff --git a/export_test_fixtures.R b/export_test_fixtures.R
new file mode 100644
index 0000000..35720c9
--- /dev/null
+++ b/export_test_fixtures.R
@@ -0,0 +1,358 @@
+#!/usr/bin/env Rscript
+
+# Export R circumplex test fixtures for Python regression testing
+# This script generates JSON files containing expected outputs from R analyses
+# to validate numerical parity in the Python port
+
+library(circumplex)
+library(jsonlite)
+
+# Create output directory
+output_dir <- "python_test_fixtures"
+dir.create(output_dir, showWarnings = FALSE)
+
+# Helper function to convert results to serializable format
+prepare_results <- function(res) {
+    list(
+        results = as.data.frame(res$results),
+        scores = as.data.frame(res$scores),
+        details = res$details,
+        type = res$type
+    )
+}
+
+# Load example datasets
+data("jz2017")
+data("aw2009")
+
+cat("Generating test fixtures for Python regression tests...\n\n")
+
+# =============================================================================
+# Test 1: Single-group mean-based SSM
+# =============================================================================
+cat("1. Single-group mean-based SSM (aw2009)...\n")
+set.seed(12345)
+res1 <- ssm_analyze(aw2009, scales = 1:8, boots = 2000, interval = 0.95)
+
+# Extract key values for validation
+fixture1 <- list(
+    dataset = "aw2009",
+    analysis_type = "single_group_mean",
+    seed = 12345,
+    input = list(
+        scales = names(aw2009)[1:8],
+        boots = 2000,
+        interval = 0.95,
+        listwise = TRUE
+    ),
+    expected = list(
+        # Parameter estimates
+        e_est = round(res1$results$e_est, 3),
+        x_est = round(res1$results$x_est, 3),
+        y_est = round(res1$results$y_est, 3),
+        a_est = round(res1$results$a_est, 3),
+        d_est = as.numeric(round(res1$results$d_est, 3)),
+        fit_est = round(res1$results$fit_est, 3),
+        # Confidence intervals
+        e_lci = round(res1$results$e_lci, 3),
+        e_uci = round(res1$results$e_uci, 3),
+        x_lci = round(res1$results$x_lci, 3),
+        x_uci = round(res1$results$x_uci, 3),
+        y_lci = round(res1$results$y_lci, 3),
+        y_uci = round(res1$results$y_uci, 3),
+        a_lci = round(res1$results$a_lci, 3),
+        a_uci = round(res1$results$a_uci, 3),
+        d_lci = as.numeric(round(res1$results$d_lci, 3)),
+        d_uci = as.numeric(round(res1$results$d_uci, 3)),
+        # Scale scores
+        scores = as.list(round(res1$scores[1, names(aw2009)[1:8]], 3))
+    )
+)
+write_json(fixture1, file.path(output_dir, "ssm_single_group_mean.json"),
+    auto_unbox = TRUE, pretty = TRUE
+)
+
+# =============================================================================
+# Test 2: Multiple-group mean-based SSM
+# =============================================================================
+cat("2. Multiple-group mean-based SSM (jz2017)...\n")
+set.seed(12345)
+res2 <- ssm_analyze(jz2017, scales = 2:9, grouping = "Gender", boots = 2000)
+
+fixture2 <- list(
+    dataset = "jz2017",
+    analysis_type = "multi_group_mean",
+    seed = 12345,
+    input = list(
+        scales = names(jz2017)[2:9],
+        grouping = "Gender",
+        boots = 2000,
+        interval = 0.95
+    ),
+    expected = list(
+        # Parameter estimates (2 groups)
+        e_est = round(res2$results$e_est, 3),
+        x_est = round(res2$results$x_est, 3),
+        y_est = round(res2$results$y_est, 3),
+        a_est = round(res2$results$a_est, 3),
+        d_est = as.numeric(round(res2$results$d_est, 3)),
+        fit_est = round(res2$results$fit_est, 3),
+        labels = as.character(res2$results$Label),
+        # Confidence intervals
+        e_lci = round(res2$results$e_lci, 3),
+        e_uci = round(res2$results$e_uci, 3),
+        x_lci = round(res2$results$x_lci, 3),
+        x_uci = round(res2$results$x_uci, 3),
+        y_lci = round(res2$results$y_lci, 3),
+        y_uci = round(res2$results$y_uci, 3),
+        a_lci = round(res2$results$a_lci, 3),
+        a_uci = round(res2$results$a_uci, 3),
+        d_lci = as.numeric(round(res2$results$d_lci, 3)),
+        d_uci = as.numeric(round(res2$results$d_uci, 3)),
+        # Scale scores (both groups)
+        scores_female = as.list(round(res2$scores[1, names(jz2017)[2:9]], 3)),
+        scores_male = as.list(round(res2$scores[2, names(jz2017)[2:9]], 3))
+    )
+)
+write_json(fixture2, file.path(output_dir, "ssm_multi_group_mean.json"),
+    auto_unbox = TRUE, pretty = TRUE
+)
+
+# =============================================================================
+# Test 3: Multiple-group mean-based SSM with contrast
+# =============================================================================
+cat("3. Multiple-group mean-based SSM with contrast (jz2017)...\n")
+set.seed(12345)
+res3 <- ssm_analyze(jz2017,
+    scales = 2:9, grouping = "Gender",
+    contrast = TRUE, boots = 2000
+)
+
+fixture3 <- list(
+    dataset = "jz2017",
+    analysis_type = "multi_group_mean_contrast",
+    seed = 12345,
+    input = list(
+        scales = names(jz2017)[2:9],
+        grouping = "Gender",
+        contrast = TRUE,
+        boots = 2000
+    ),
+    expected = list(
+        # All 3 rows: Female, Male, Male - Female
+        e_est = round(res3$results$e_est, 3),
+        x_est = round(res3$results$x_est, 3),
+        y_est = round(res3$results$y_est, 3),
+        a_est = round(res3$results$a_est, 3),
+        d_est = as.numeric(round(res3$results$d_est, 3)),
+        fit_est = round(res3$results$fit_est, 3),
+        labels = as.character(res3$results$Label),
+        # CIs for all rows
+        e_lci = round(res3$results$e_lci, 3),
+        e_uci = round(res3$results$e_uci, 3),
+        a_lci = round(res3$results$a_lci, 3),
+        a_uci = round(res3$results$a_uci, 3),
+        d_lci = as.numeric(round(res3$results$d_lci, 3)),
+        d_uci = as.numeric(round(res3$results$d_uci, 3))
+    )
+)
+write_json(fixture3, file.path(output_dir, "ssm_multi_group_contrast.json"),
+    auto_unbox = TRUE, pretty = TRUE
+)
+
+# =============================================================================
+# Test 4: Single-group correlation-based SSM
+# =============================================================================
+cat("4. Single-group correlation-based SSM (jz2017)...\n")
+set.seed(12345)
+res4 <- ssm_analyze(jz2017, scales = 2:9, measures = "PARPD", boots = 2000)
+
+fixture4 <- list(
+    dataset = "jz2017",
+    analysis_type = "single_group_correlation",
+    seed = 12345,
+    input = list(
+        scales = names(jz2017)[2:9],
+        measures = "PARPD",
+        boots = 2000
+    ),
+    expected = list(
+        e_est = round(res4$results$e_est, 3),
+        x_est = round(res4$results$x_est, 3),
+        y_est = round(res4$results$y_est, 3),
+        a_est = round(res4$results$a_est, 3),
+        d_est = as.numeric(round(res4$results$d_est, 1)),
+        fit_est = round(res4$results$fit_est, 3),
+        e_lci = round(res4$results$e_lci, 3),
+        e_uci = round(res4$results$e_uci, 3),
+        x_lci = round(res4$results$x_lci, 3),
+        x_uci = round(res4$results$x_uci, 3),
+        y_lci = round(res4$results$y_lci, 3),
+        y_uci = round(res4$results$y_uci, 3)
+    )
+)
+write_json(fixture4, file.path(output_dir, "ssm_single_group_correlation.json"),
+    auto_unbox = TRUE, pretty = TRUE
+)
+
+# =============================================================================
+# Test 5: Multi-group correlation-based SSM (no contrast)
+# =============================================================================
+cat("5. Multi-group correlation-based SSM (jz2017)...\n")
+set.seed(12345)
+res5 <- ssm_analyze(jz2017,
+    scales = 2:9, measures = "PARPD",
+    grouping = "Gender", boots = 2000
+)
+
+fixture5 <- list(
+    dataset = "jz2017",
+    analysis_type = "multi_group_correlation",
+    seed = 12345,
+    input = list(
+        scales = names(jz2017)[2:9],
+        measures = "PARPD",
+        grouping = "Gender",
+        boots = 2000
+    ),
+    expected = list(
+        # Both groups
+        e_est = round(res5$results$e_est, 3),
+        x_est = round(res5$results$x_est, 3),
+        y_est = round(res5$results$y_est, 3),
+        a_est = round(res5$results$a_est, 3),
+        d_est = as.numeric(round(res5$results$d_est, 1)),
+        fit_est = round(res5$results$fit_est, 3),
+        labels = as.character(res5$results$Label)
+    )
+)
+write_json(fixture5, file.path(output_dir, "ssm_multi_group_correlation.json"),
+    auto_unbox = TRUE, pretty = TRUE
+)
+
+# =============================================================================
+# Test 6: Measure-contrast correlation-based SSM
+# =============================================================================
+cat("6. Measure-contrast correlation-based SSM (jz2017)...\n")
+set.seed(12345)
+res6 <- ssm_analyze(jz2017,
+    scales = 2:9, measures = c("ASPD", "NARPD"),
+    contrast = TRUE, boots = 2000
+)
+
+fixture6 <- list(
+    dataset = "jz2017",
+    analysis_type = "measure_contrast_correlation",
+    seed = 12345,
+    input = list(
+        scales = names(jz2017)[2:9],
+        measures = c("ASPD", "NARPD"),
+        contrast = TRUE,
+        boots = 2000
+    ),
+    expected = list(
+        # All 3 rows: ASPD, NARPD, NARPD - ASPD
+        e_est = round(res6$results$e_est, 3),
+        x_est = round(res6$results$x_est, 3),
+        y_est = round(res6$results$y_est, 3),
+        a_est = round(res6$results$a_est, 3),
+        d_est = as.numeric(round(res6$results$d_est, 1)),
+        fit_est = round(res6$results$fit_est, 3),
+        labels = as.character(res6$results$Label),
+        # Some CIs
+        e_lci = round(res6$results$e_lci, 3),
+        e_uci = round(res6$results$e_uci, 3),
+        a_lci = round(res6$results$a_lci, 3),
+        a_uci = round(res6$results$a_uci, 3),
+        # Scores for all three
+        scores_aspd = as.list(round(res6$scores[1, names(jz2017)[2:9]], 3)),
+        scores_narpd = as.list(round(res6$scores[2, names(jz2017)[2:9]], 3)),
+        scores_contrast = as.list(round(res6$scores[3, names(jz2017)[2:9]], 3))
+    )
+)
+write_json(fixture6, file.path(output_dir, "ssm_measure_contrast_correlation.json"),
+    auto_unbox = TRUE, pretty = TRUE
+)
+
+# =============================================================================
+# Test 7: Group-contrast correlation-based SSM
+# =============================================================================
+cat("7. Group-contrast correlation-based SSM (jz2017)...\n")
+set.seed(12345)
+res7 <- ssm_analyze(jz2017,
+    scales = 2:9, measures = "NARPD",
+    grouping = "Gender", contrast = TRUE, boots = 2000
+)
+
+fixture7 <- list(
+    dataset = "jz2017",
+    analysis_type = "group_contrast_correlation",
+    seed = 12345,
+    input = list(
+        scales = names(jz2017)[2:9],
+        measures = "NARPD",
+        grouping = "Gender",
+        contrast = TRUE,
+        boots = 2000
+    ),
+    expected = list(
+        # All 3 rows: Female, Male, Male - Female
+        e_est = round(res7$results$e_est, 3),
+        x_est = round(res7$results$x_est, 3),
+        y_est = round(res7$results$y_est, 3),
+        a_est = round(res7$results$a_est, 3),
+        d_est = as.numeric(round(res7$results$d_est, 1)),
+        fit_est = round(res7$results$fit_est, 3),
+        labels = as.character(res7$results$Label),
+        # CIs
+        e_lci = round(res7$results$e_lci, 3),
+        e_uci = round(res7$results$e_uci, 3),
+        a_lci = round(res7$results$a_lci, 3),
+        a_uci = round(res7$results$a_uci, 3)
+    )
+)
+write_json(fixture7, file.path(output_dir, "ssm_group_contrast_correlation.json"),
+    auto_unbox = TRUE, pretty = TRUE
+)
+
+# =============================================================================
+# Test 8: SSM parameters function (no bootstrap)
+# =============================================================================
+cat("8. SSM parameters calculation...\n")
+
+# Test data
+test_scores <- c(0.374, -0.572, -0.520, 0.016, 0.688, 1.142, 1.578, 0.678)
+test_angles <- octants()
+
+params <- ssm_parameters(test_scores, test_angles)
+
+fixture8 <- list(
+    function_name = "ssm_parameters",
+    input = list(
+        scores = test_scores,
+        angles = as.numeric(test_angles)
+    ),
+    expected = list(
+        elevation = round(params[[1]], 6),
+        x_value = round(params[[2]], 6),
+        y_value = round(params[[3]], 6),
+        amplitude = round(params[[4]], 6),
+        displacement = round(params[[5]], 6), # radians
+        fit = round(params[[6]], 6)
+    )
+)
+write_json(fixture8, file.path(output_dir, "ssm_parameters.json"),
+    auto_unbox = TRUE, pretty = TRUE
+)
+
+# =============================================================================
+# Export example datasets as CSV
+# =============================================================================
+cat("\nExporting datasets to CSV...\n")
+write.csv(jz2017, file.path(output_dir, "jz2017.csv"), row.names = FALSE)
+write.csv(aw2009, file.path(output_dir, "aw2009.csv"), row.names = FALSE)
+
+cat("\n✓ Test fixtures exported to", output_dir, "\n")
+cat("✓ Datasets exported as CSV\n")
+cat("\nFiles created:\n")
+list.files(output_dir, full.names = FALSE)
diff --git a/mkdocs.yml b/mkdocs.yml
index ad91a46..b439a5a 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -1,71 +1,136 @@
-# yaml-language-server: $schema=https://squidfunk.github.io/mkdocs-material/schema.json
 site_name: "Circumplex"
-site_url: https://circumplex.readthedocs.io/en/latest/
-site_dir: site
+site_description: "Documentation website for Circumplex"
+site_author: "Andrew Mitchell"
+site_url: https://drandrewmitchell.com/circumplex/
+copyright: "Copyright © 2025 Andrew Mitchell"
+repo_url: "https://github.com/MitchellAcoustics/circumplex/"
 repo_name: "MitchellAcoustics/circumplex"
-repo_url: https://github.com/MitchellAcoustics/circumplex
-copyright: Copyright © 2023 Andrew Mitchell
-nav:
-    - Home: index.md
-    - About:
-        - 'License': license.md
-    - Tutorials:
-        - tutorials/index.md
-        - 'Using Circumplex Instrument': tutorials/using-instruments.ipynb
-        - 'Introduction to SSM': tutorials/Intro_to_SSM_Analysis.ipynb
-        - 'Random Examples': tutorials/Random_exs.ipynb
-    - 'API reference':
-        - 'Core functions': reference/api.md
-        - 'Dealing with results': reference/classes.md
-        - 'Survey Instruments': reference/instruments.md
-        - 'Utils': reference/utils.md
-    - 'News': news.md
+edit_uri: edit/main/docs/
+
+validation:
+  omitted_files: warn
+  absolute_links: warn
+  unrecognized_links: warn
+
 theme:
-  name: material
-  logo: img/small-logo.png
-  favicon: img/favicon.ico
+  name: "material"
   features:
+    - content.action.edit
+    - content.action.view
     - navigation.tabs
-    - navigation.expand
-    - navigation.path
+    # - navigation.tabs.sticky
     - navigation.top
+    - navigation.expand
+    - navigation.path # waiting for general release
+    - navigation.indexes
+    - navigation.instant
+    - content.code.copy
   palette:
-    # Palette toggle for light mode
+    - media: "(prefers-color-scheme)"
+      toggle:
+        icon: material/brightness-auto
+        name: Switch to light mode
     - media: "(prefers-color-scheme: light)"
       scheme: default
       toggle:
         icon: material/brightness-7
         name: Switch to dark mode
-
-    # Palette toggle for dark mode
     - media: "(prefers-color-scheme: dark)"
       scheme: slate
       toggle:
         icon: material/brightness-4
-        name: Switch to light mode
-extra_css:
-  - stylesheets/extra.css
+        name: Switch to system preference
+  icon:
+    repo: fontawesome/brands/github
+
+nav:
+  - Home: index.md
+  - API Reference:
+      - api/index.md
+      - SSM Results: api/ssm.md
+      - Analysis:
+          - SSM Analysis: api/analysis/ssm_analyze.md
+          - Bootstrap: api/analysis/bootstrap.md
+          - Mean Analysis: api/analysis/mean_analysis.md
+          - Correlation Analysis: api/analysis/corr_analysis.md
+      - Core:
+          - Parameters: api/core/parameters.md
+          - Scores: api/core/scores.md
+      - Data: api/data.md
+      - Instruments: api/models.md
+      - Utilities:
+          - Angles: api/utils/angles.md
+          - Contrast: api/utils/contrast.md
+          - Tidying Functions: api/utils/tidying_functions.md
+      - Visualization: api/plots.md
+  - License: license.md
+
+markdown_extensions:
+  - admonition
+  - footnotes
+  - pymdownx.details
+  - pymdownx.arithmatex:
+      generic: true
+  - pymdownx.tasklist
+  - pymdownx.highlight:
+      anchor_linenums: true
+  - pymdownx.superfences
+  - pymdownx.tabbed:
+      alternate_style: true
+  - pymdownx.details
+  - pymdownx.snippets
 
 plugins:
-    - search
-    - mkdocstrings:
-        default_handler: python
-        handlers:
-          python:
-            paths: [src.circumplex]
-            options:
-              docstring_section_style: spacy
-              docstring_style: "google"
-              separate_signature: true
-              show_if_no_docstring: false
-              merge_init_into_class: true
-              show_symbol_type_heading: true # waiting for general release
-              show_symbol_type_toc: true
+  - search
+  - autorefs
+  - git-authors
+  - git-revision-date-localized:
+      enable_creation_date: true
+      type: timeago
+  - mkdocs-jupyter:
+      execute: true
+      include_source: true
+      allow_errors: false
+      theme: default
+  - mkdocstrings:
+      default_handler: python
+      handlers:
+        python:
+          inventories:
+            - "https://docs.python.org/3/objects.inv"
+          options:
+            docstring_style: numpy
+            docstring_section_style: spacy
+            members_order: source
+            merge_init_into_class: true
+            modernize_annotations: true
+            overloads_only: true
+            separate_signature: true
+            show_signature: true
+            show_signature_annotations: true
+            show_signature_type_parameters: true
+            show_symbol_type_heading: true
+            show_symbol_type_toc: true
+            show_source: true
+            show_overloads: true
+            show_submodules: true
+            show_root_heading: true
+            summary: true
+          paths: [src]
+  - include-markdown:
+      opening_tag: "{!"
+      closing_tag: "!}"
 
-    - mkdocs-jupyter:
-        include_source: true
-        theme: default
-#        execute: true
+extra:
+  social:
+    - icon: fontawesome/brands/github
+      link: "https://github.com/MitchellAcoustics"
 
-markdown_extensions:
-  - admonition
\ No newline at end of file
+extra_css:
+  - stylesheets/extra.css
+  - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.css
+
+extra_javascript:
+  - javascripts/katex.js
+  - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/katex.min.js
+  - https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.7/contrib/auto-render.min.js
diff --git a/pyproject.toml b/pyproject.toml
index d0cbc5d..056e97c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,42 +1,188 @@
+[build-system]
+build-backend = "setuptools.build_meta"
+requires = ["setuptools", "setuptools-scm"]
+
+[dependency-groups]
+dev = [
+    "build>=1.3.0",
+    "prek>=0.2.3",
+    "ruff>=0.13.3",
+    "tox>=4.30.3",
+    "twine>=6.2.0",
+    "ty>=0.0.1a21",
+    {include-group = "docs"},
+    {include-group = "test"},
+]
+docs = [
+    "mike>=2.1.3",
+    "mkdocs-bibtex>=4.4.0",
+    "mkdocs-git-authors-plugin>=0.10.0",
+    "mkdocs-git-revision-date-localized-plugin>=1.4.7",
+    "mkdocs-include-markdown-plugin>=7.2.0",
+    "mkdocs-jupyter>=0.25.0",
+    "mkdocs-material>=9.6.21",
+    "mkdocs>=1.6.1",
+    "mkdocstrings[python]>=0.30.1",
+    "rich>=14.1.0",
+    "shtab>=1.7.2",
+]
+test = [
+    "hypothesis>=6.140.3",
+    "pytest-cov>=7.0.0",
+    "pytest>=8.4.2",
+    "xdoctest>=1.3.0",
+]
+
 [project]
-name = "circumplex"
-version = "0.1.4"
-description = "A Python package for analyzing and visualizing circumplex data"
 authors = [
-    {name = "Andrew Mitchell", email = "andrew.mitchell.18@ucl.ac.uk"},
+    {email = "andrew.mitchell.research@gmail.com", name = "Andrew Mitchell"},
+]
+classifiers = [
+    "Operating System :: POSIX",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Typing :: Typed",
 ]
 dependencies = [
-    "numpy>=1.25.2",
-    "matplotlib>=3.8.1",
-    "scipy>=1.9.3",
-    "pandas[excel,performance]>=2.2.2",
+    "matplotlib>=3.10.7",
+    "nptyping>=2.0.1",
+    "numpy>=2.3.3",
+    "pandas[excel]>=2.3.3",
+    "scipy>=1.16.2",
+    "seaborn>=0.13.2",
+]
+description = "Python package for the analysis and visualization of circumplex data."
+dynamic = ["version"]
+keywords = [
+    "circular statistics",
+    "circumplex",
+    "interpersonal",
+    "psychology",
+    "ssm",
+    "structural summary method",
 ]
+license = "MIT"
+license-files = ["LICENSE"]
+name = "circumplex"
 readme = "README.md"
-requires-python = ">= 3.8"
+requires-python = ">=3.11"
 
 [project.urls]
-repository = "https://github.com/MitchellAcoustics/circumplex"
 documentation = "https://circumplex.readthedocs.io/en/latest/"
+homepage = "https://github.com/MitchellAcoustics/circumplex"
+repository = "https://github.com/MitchellAcoustics/circumplex"
 
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
-
-[tool.rye]
-managed = true
-dev-dependencies = [
-    "pytest>=8.2.2",
-    "jupyter>=1.0.0",
-    "mkdocs>=1.6.0",
-    "mkdocs-material>=9.5.26",
-    "mkdocstrings[python]>=0.25.1",
-    "mkdocs-jupyter>=0.24.7",
-    "markdown>=3.6",
-    "pymdown-extensions>=10.8.1",
-]
-
-[tool.hatch.metadata]
-allow-direct-references = true
-
-[tool.hatch.build.targets.wheel]
-packages = ["src/circumplex"]
+[tool.coverage]
+omit = ["*/__pycache__/*", "*/tests/*"]
+run = {branch = true, parallel = true, source = ["circumplex"]}
+paths.source = ["src", ".tox*/*/lib/python*/site-packages"]
+
+[tool.coverage.report]
+exclude_lines = [
+    "@abstractmethod",
+    "def __repr__",
+    "if TYPE_CHECKING:",
+    "if __name__ == .__main__.:",
+    "pragma: no cover",
+    "raise AssertionError",
+    "raise NotImplementedError",
+]
+
+[tool.mypy]
+explicit_package_bases = true
+
+[tool.pytest.ini_options]
+addopts = [
+    "--color=yes",
+    "--cov",
+    "--cov-report=term-missing",
+    "--import-mode=importlib",
+    "--strict-markers",
+    "--verbose",
+]
+markers = [
+    "integration: Integration tests for workflows",
+    "regression: Regression tests against R package output",
+    "slow: Tests that take significant time to run",
+    "unit: Unit tests for individual functions",
+]
+testpaths = ["tests"]
+
+[tool.ruff]
+fix = true
+force-exclude = true
+lint.ignore = [
+    "ANN401", # Dynamically typed expressions (typing.Any) are disallowed in `**kwargs`
+    "COM812", # trailing commas (ruff-format recommended)
+    "D417", # argument description in docstring (unreliable)
+    "ISC001", # simplify implicit str concatenation (ruff-format recommended)
+    "NPY002", # legacy np.random (required for R parity)
+    "PERF401", # list.extend suggestion (readability over micro-optimization)
+    "PLR0912", # too many branches (acceptable for RTHOR algorithm complexity)
+    "PLR0913", # too many arguments (acceptable for algorithm functions)
+    "PLR2004", # magic value comparison (acceptable for algorithm constants)
+    "TC002", # type checking blocks (would break runtime imports)
+    "TC003", # type checking blocks (would break runtime imports)
+    "UP037", # Remove quotes from type annotation
+]
+lint.per-file-ignores = {"*.ipynb" = [
+    "T201", # print statement found
+], "*demo.py" = [
+    "E501", # line-too-long
+    "INP001", # File is part of an implicit namespace package.
+    "T201", # print statement found
+], "tests*" = [
+    "ANN001", # missing type annotation for function (test code)
+    "ANN201", # missing type annotation for self in methods (test code)
+    "D100", # missing docstring in public module (test code)
+    "D103", # missing docstring in public function (test code)
+    "ERA001", # assertRaises(Exception) (test code)
+    "INP001", # File is part of an implicit namespace package.
+    "PLR2004", # magic value comparison (test code)
+    "S101", # Use of `assert` detected
+]}
+lint.select = ["ALL"]
+lint.isort.known-first-party = ["circumplex"]
+lint.mccabe.max-complexity = 18
+lint.pep8-naming.classmethod-decorators = ["classmethod"]
+lint.pydocstyle.convention = "numpy"
+
+[tool.setuptools_scm]
+local_scheme = "no-local-version"
+write_to = "src/circumplex/_version.py"
+
+[tool.tomlsort]
+all = true
+spaces_indent_inline_array = 4
+trailing_comma_inline_array = true
+overrides."project.classifiers".inline_arrays = false
+overrides."tool.coverage.paths.source".inline_arrays = false
+overrides."tool.tox.env.docs.commands".inline_arrays = false
+overrides."tool.tox.env_run_base.commands".inline_arrays = false
+
+[tool.tox]
+env_list = ["py311", "py312", "py313"]
+env_run_base = {commands = [
+    [
+        "pytest",
+        "--cov",
+        "--cov-report=xml",
+    ],
+], dependency_groups = [
+    "test",
+]}
+env.docs = {commands = [
+    [
+        "mkdocs",
+        "build",
+        "--strict",
+    ],
+], dependency_groups = [
+    "docs",
+]}
+gh.python."3.11" = ["py311"]
+gh.python."3.12" = ["py312"]
+gh.python."3.13" = ["py313"]
diff --git a/requirements-dev.lock b/requirements-dev.lock
deleted file mode 100644
index 5ad66fd..0000000
--- a/requirements-dev.lock
+++ /dev/null
@@ -1,436 +0,0 @@
-# generated by rye
-# use `rye lock` or `rye sync` to update this lockfile
-#
-# last locked with the following flags:
-#   pre: false
-#   features: []
-#   all-features: false
-#   with-sources: false
-#   generate-hashes: false
-
--e file:.
-anyio==4.4.0
-    # via httpx
-    # via jupyter-server
-argon2-cffi==23.1.0
-    # via jupyter-server
-argon2-cffi-bindings==21.2.0
-    # via argon2-cffi
-arrow==1.3.0
-    # via isoduration
-asttokens==2.4.1
-    # via stack-data
-async-lru==2.0.4
-    # via jupyterlab
-attrs==23.2.0
-    # via jsonschema
-    # via referencing
-babel==2.15.0
-    # via jupyterlab-server
-    # via mkdocs-material
-beautifulsoup4==4.12.3
-    # via nbconvert
-bleach==6.1.0
-    # via nbconvert
-bottleneck==1.3.8
-    # via pandas
-certifi==2024.6.2
-    # via httpcore
-    # via httpx
-    # via requests
-cffi==1.16.0
-    # via argon2-cffi-bindings
-charset-normalizer==3.3.2
-    # via requests
-click==8.1.7
-    # via mkdocs
-    # via mkdocstrings
-colorama==0.4.6
-    # via griffe
-    # via mkdocs-material
-comm==0.2.2
-    # via ipykernel
-    # via ipywidgets
-contourpy==1.2.1
-    # via matplotlib
-cycler==0.12.1
-    # via matplotlib
-debugpy==1.8.1
-    # via ipykernel
-decorator==5.1.1
-    # via ipython
-defusedxml==0.7.1
-    # via nbconvert
-    # via odfpy
-et-xmlfile==1.1.0
-    # via openpyxl
-executing==2.0.1
-    # via stack-data
-fastjsonschema==2.19.1
-    # via nbformat
-fonttools==4.53.0
-    # via matplotlib
-fqdn==1.5.1
-    # via jsonschema
-ghp-import==2.1.0
-    # via mkdocs
-griffe==0.45.2
-    # via mkdocstrings-python
-h11==0.14.0
-    # via httpcore
-httpcore==1.0.5
-    # via httpx
-httpx==0.27.0
-    # via jupyterlab
-idna==3.7
-    # via anyio
-    # via httpx
-    # via jsonschema
-    # via requests
-iniconfig==2.0.0
-    # via pytest
-ipykernel==6.29.4
-    # via jupyter
-    # via jupyter-console
-    # via jupyterlab
-    # via mkdocs-jupyter
-    # via qtconsole
-ipython==8.25.0
-    # via ipykernel
-    # via ipywidgets
-    # via jupyter-console
-ipywidgets==8.1.3
-    # via jupyter
-isoduration==20.11.0
-    # via jsonschema
-jedi==0.19.1
-    # via ipython
-jinja2==3.1.4
-    # via jupyter-server
-    # via jupyterlab
-    # via jupyterlab-server
-    # via mkdocs
-    # via mkdocs-material
-    # via mkdocstrings
-    # via nbconvert
-json5==0.9.25
-    # via jupyterlab-server
-jsonpointer==2.4
-    # via jsonschema
-jsonschema==4.22.0
-    # via jupyter-events
-    # via jupyterlab-server
-    # via nbformat
-jsonschema-specifications==2023.12.1
-    # via jsonschema
-jupyter==1.0.0
-jupyter-client==8.6.2
-    # via ipykernel
-    # via jupyter-console
-    # via jupyter-server
-    # via nbclient
-    # via qtconsole
-jupyter-console==6.6.3
-    # via jupyter
-jupyter-core==5.7.2
-    # via ipykernel
-    # via jupyter-client
-    # via jupyter-console
-    # via jupyter-server
-    # via jupyterlab
-    # via nbclient
-    # via nbconvert
-    # via nbformat
-    # via qtconsole
-jupyter-events==0.10.0
-    # via jupyter-server
-jupyter-lsp==2.2.5
-    # via jupyterlab
-jupyter-server==2.14.1
-    # via jupyter-lsp
-    # via jupyterlab
-    # via jupyterlab-server
-    # via notebook
-    # via notebook-shim
-jupyter-server-terminals==0.5.3
-    # via jupyter-server
-jupyterlab==4.2.1
-    # via notebook
-jupyterlab-pygments==0.3.0
-    # via nbconvert
-jupyterlab-server==2.27.2
-    # via jupyterlab
-    # via notebook
-jupyterlab-widgets==3.0.11
-    # via ipywidgets
-jupytext==1.16.2
-    # via mkdocs-jupyter
-kiwisolver==1.4.5
-    # via matplotlib
-llvmlite==0.42.0
-    # via numba
-markdown==3.6
-    # via mkdocs
-    # via mkdocs-autorefs
-    # via mkdocs-material
-    # via mkdocstrings
-    # via pymdown-extensions
-markdown-it-py==3.0.0
-    # via jupytext
-    # via mdit-py-plugins
-markupsafe==2.1.5
-    # via jinja2
-    # via mkdocs
-    # via mkdocs-autorefs
-    # via mkdocstrings
-    # via nbconvert
-matplotlib==3.9.0
-    # via circumplex
-matplotlib-inline==0.1.7
-    # via ipykernel
-    # via ipython
-mdit-py-plugins==0.4.1
-    # via jupytext
-mdurl==0.1.2
-    # via markdown-it-py
-mergedeep==1.3.4
-    # via mkdocs
-    # via mkdocs-get-deps
-mistune==3.0.2
-    # via nbconvert
-mkdocs==1.6.0
-    # via mkdocs-autorefs
-    # via mkdocs-jupyter
-    # via mkdocs-material
-    # via mkdocstrings
-mkdocs-autorefs==1.0.1
-    # via mkdocstrings
-mkdocs-get-deps==0.2.0
-    # via mkdocs
-mkdocs-jupyter==0.24.7
-mkdocs-material==9.5.26
-    # via mkdocs-jupyter
-mkdocs-material-extensions==1.3.1
-    # via mkdocs-material
-mkdocstrings==0.25.1
-    # via mkdocstrings-python
-mkdocstrings-python==1.10.3
-    # via mkdocstrings
-nbclient==0.10.0
-    # via nbconvert
-nbconvert==7.16.4
-    # via jupyter
-    # via jupyter-server
-    # via mkdocs-jupyter
-nbformat==5.10.4
-    # via jupyter-server
-    # via jupytext
-    # via nbclient
-    # via nbconvert
-nest-asyncio==1.6.0
-    # via ipykernel
-notebook==7.2.1
-    # via jupyter
-notebook-shim==0.2.4
-    # via jupyterlab
-    # via notebook
-numba==0.59.1
-    # via pandas
-numexpr==2.10.0
-    # via pandas
-numpy==1.26.4
-    # via bottleneck
-    # via circumplex
-    # via contourpy
-    # via matplotlib
-    # via numba
-    # via numexpr
-    # via pandas
-    # via scipy
-odfpy==1.4.1
-    # via pandas
-openpyxl==3.1.3
-    # via pandas
-overrides==7.7.0
-    # via jupyter-server
-packaging==24.0
-    # via ipykernel
-    # via jupyter-server
-    # via jupyterlab
-    # via jupyterlab-server
-    # via jupytext
-    # via matplotlib
-    # via mkdocs
-    # via nbconvert
-    # via pytest
-    # via qtconsole
-    # via qtpy
-paginate==0.5.6
-    # via mkdocs-material
-pandas==2.2.2
-    # via circumplex
-pandocfilters==1.5.1
-    # via nbconvert
-parso==0.8.4
-    # via jedi
-pathspec==0.12.1
-    # via mkdocs
-pexpect==4.9.0
-    # via ipython
-pillow==10.3.0
-    # via matplotlib
-platformdirs==4.2.2
-    # via jupyter-core
-    # via mkdocs-get-deps
-    # via mkdocstrings
-pluggy==1.5.0
-    # via pytest
-prometheus-client==0.20.0
-    # via jupyter-server
-prompt-toolkit==3.0.46
-    # via ipython
-    # via jupyter-console
-psutil==5.9.8
-    # via ipykernel
-ptyprocess==0.7.0
-    # via pexpect
-    # via terminado
-pure-eval==0.2.2
-    # via stack-data
-pycparser==2.22
-    # via cffi
-pygments==2.18.0
-    # via ipython
-    # via jupyter-console
-    # via mkdocs-jupyter
-    # via mkdocs-material
-    # via nbconvert
-    # via qtconsole
-pymdown-extensions==10.8.1
-    # via mkdocs-material
-    # via mkdocstrings
-pyparsing==3.1.2
-    # via matplotlib
-pytest==8.2.2
-python-calamine==0.2.0
-    # via pandas
-python-dateutil==2.9.0.post0
-    # via arrow
-    # via ghp-import
-    # via jupyter-client
-    # via matplotlib
-    # via pandas
-python-json-logger==2.0.7
-    # via jupyter-events
-pytz==2024.1
-    # via pandas
-pyxlsb==1.0.10
-    # via pandas
-pyyaml==6.0.1
-    # via jupyter-events
-    # via jupytext
-    # via mkdocs
-    # via mkdocs-get-deps
-    # via pymdown-extensions
-    # via pyyaml-env-tag
-pyyaml-env-tag==0.1
-    # via mkdocs
-pyzmq==26.0.3
-    # via ipykernel
-    # via jupyter-client
-    # via jupyter-console
-    # via jupyter-server
-    # via qtconsole
-qtconsole==5.5.2
-    # via jupyter
-qtpy==2.4.1
-    # via qtconsole
-referencing==0.35.1
-    # via jsonschema
-    # via jsonschema-specifications
-    # via jupyter-events
-regex==2024.5.15
-    # via mkdocs-material
-requests==2.32.3
-    # via jupyterlab-server
-    # via mkdocs-material
-rfc3339-validator==0.1.4
-    # via jsonschema
-    # via jupyter-events
-rfc3986-validator==0.1.1
-    # via jsonschema
-    # via jupyter-events
-rpds-py==0.18.1
-    # via jsonschema
-    # via referencing
-scipy==1.13.1
-    # via circumplex
-send2trash==1.8.3
-    # via jupyter-server
-six==1.16.0
-    # via asttokens
-    # via bleach
-    # via python-dateutil
-    # via rfc3339-validator
-sniffio==1.3.1
-    # via anyio
-    # via httpx
-soupsieve==2.5
-    # via beautifulsoup4
-stack-data==0.6.3
-    # via ipython
-terminado==0.18.1
-    # via jupyter-server
-    # via jupyter-server-terminals
-tinycss2==1.3.0
-    # via nbconvert
-tornado==6.4.1
-    # via ipykernel
-    # via jupyter-client
-    # via jupyter-server
-    # via jupyterlab
-    # via notebook
-    # via terminado
-traitlets==5.14.3
-    # via comm
-    # via ipykernel
-    # via ipython
-    # via ipywidgets
-    # via jupyter-client
-    # via jupyter-console
-    # via jupyter-core
-    # via jupyter-events
-    # via jupyter-server
-    # via jupyterlab
-    # via matplotlib-inline
-    # via nbclient
-    # via nbconvert
-    # via nbformat
-    # via qtconsole
-types-python-dateutil==2.9.0.20240316
-    # via arrow
-typing-extensions==4.12.2
-    # via ipython
-tzdata==2024.1
-    # via pandas
-uri-template==1.3.0
-    # via jsonschema
-urllib3==2.2.1
-    # via requests
-watchdog==4.0.1
-    # via mkdocs
-wcwidth==0.2.13
-    # via prompt-toolkit
-webcolors==24.6.0
-    # via jsonschema
-webencodings==0.5.1
-    # via bleach
-    # via tinycss2
-websocket-client==1.8.0
-    # via jupyter-server
-widgetsnbextension==4.0.11
-    # via ipywidgets
-xlrd==2.0.1
-    # via pandas
-xlsxwriter==3.2.0
-    # via pandas
diff --git a/requirements.lock b/requirements.lock
deleted file mode 100644
index 694a662..0000000
--- a/requirements.lock
+++ /dev/null
@@ -1,73 +0,0 @@
-# generated by rye
-# use `rye lock` or `rye sync` to update this lockfile
-#
-# last locked with the following flags:
-#   pre: false
-#   features: []
-#   all-features: false
-#   with-sources: false
-#   generate-hashes: false
-
--e file:.
-bottleneck==1.3.8
-    # via pandas
-contourpy==1.2.1
-    # via matplotlib
-cycler==0.12.1
-    # via matplotlib
-defusedxml==0.7.1
-    # via odfpy
-et-xmlfile==1.1.0
-    # via openpyxl
-fonttools==4.53.0
-    # via matplotlib
-kiwisolver==1.4.5
-    # via matplotlib
-llvmlite==0.42.0
-    # via numba
-matplotlib==3.9.0
-    # via circumplex
-numba==0.59.1
-    # via pandas
-numexpr==2.10.0
-    # via pandas
-numpy==1.26.4
-    # via bottleneck
-    # via circumplex
-    # via contourpy
-    # via matplotlib
-    # via numba
-    # via numexpr
-    # via pandas
-    # via scipy
-odfpy==1.4.1
-    # via pandas
-openpyxl==3.1.3
-    # via pandas
-packaging==24.0
-    # via matplotlib
-pandas==2.2.2
-    # via circumplex
-pillow==10.3.0
-    # via matplotlib
-pyparsing==3.1.2
-    # via matplotlib
-python-calamine==0.2.0
-    # via pandas
-python-dateutil==2.9.0.post0
-    # via matplotlib
-    # via pandas
-pytz==2024.1
-    # via pandas
-pyxlsb==1.0.10
-    # via pandas
-scipy==1.13.1
-    # via circumplex
-six==1.16.0
-    # via python-dateutil
-tzdata==2024.1
-    # via pandas
-xlrd==2.0.1
-    # via pandas
-xlsxwriter==3.2.0
-    # via pandas
diff --git a/schemas/github-issue-forms.json b/schemas/github-issue-forms.json
new file mode 100644
index 0000000..b890ff1
--- /dev/null
+++ b/schemas/github-issue-forms.json
@@ -0,0 +1,2377 @@
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+
+  "$id": "https://json.schemastore.org/github-issue-forms.json",
+
+  "$comment": "https://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-issue-forms",
+
+  "additionalProperties": false,
+
+  "definitions": {
+    "type": {
+      "description": "A form item type\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#keys",
+
+      "type": "string",
+
+      "enum": ["checkboxes", "dropdown", "input", "markdown", "textarea"]
+    },
+
+    "id": {
+      "type": "string",
+
+      "pattern": "^[a-zA-Z0-9_-]+$",
+
+      "examples": ["SampleId"]
+    },
+
+    "validations": {
+      "title": "validation options",
+
+      "type": "object",
+
+      "properties": {
+        "required": {
+          "description": "Specify whether require a form item",
+
+          "type": "boolean",
+
+          "default": false
+        }
+      },
+
+      "additionalProperties": false
+    },
+
+    "assignee": {
+      "type": "string",
+
+      "maxLength": 39,
+
+      "pattern": "^[a-zA-Z0-9](-?[a-zA-Z0-9])*$",
+
+      "examples": ["SampleAssignee"]
+    },
+
+    "label": {
+      "type": "string",
+
+      "minLength": 1,
+
+      "examples": ["Sample label"]
+    },
+
+    "description": {
+      "type": "string",
+
+      "default": "",
+
+      "examples": ["Sample description"]
+    },
+
+    "placeholder": {
+      "type": "string",
+
+      "default": "",
+
+      "examples": ["Sample placeholder"]
+    },
+
+    "value": {
+      "type": "string",
+
+      "minLength": 1,
+
+      "examples": ["Sample value"]
+    },
+
+    "form_item": {
+      "title": "form item",
+
+      "description": "A form item\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#about-githubs-form-schema",
+
+      "type": "object",
+
+      "required": ["type"],
+
+      "properties": {
+        "type": {
+          "$ref": "#/definitions/type"
+        }
+      },
+
+      "allOf": [
+        {
+          "if": {
+            "properties": {
+              "type": {
+                "const": "markdown"
+              }
+            }
+          },
+
+          "then": {
+            "$comment": "For `additionalProperties` to work `type` must also be present here.",
+
+            "title": "markdown",
+
+            "description": "Markdown\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#markdown",
+
+            "type": "object",
+
+            "required": ["type", "attributes"],
+
+            "properties": {
+              "type": {
+                "$ref": "#/definitions/type"
+              },
+
+              "attributes": {
+                "title": "markdown attributes",
+
+                "description": "Markdown attributes\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes",
+
+                "type": "object",
+
+                "required": ["value"],
+
+                "properties": {
+                  "value": {
+                    "description": "A markdown code\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes",
+
+                    "type": "string",
+
+                    "minLength": 1,
+
+                    "examples": ["Sample code"]
+                  }
+                },
+
+                "additionalProperties": false
+              }
+            },
+
+            "additionalProperties": false
+          }
+        },
+
+        {
+          "if": {
+            "properties": {
+              "type": {
+                "const": "textarea"
+              }
+            }
+          },
+
+          "then": {
+            "$comment": "For `additionalProperties` to work `type` must also be present here.",
+
+            "title": "textarea",
+
+            "description": "Textarea\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#textarea",
+
+            "type": "object",
+
+            "required": ["type", "attributes"],
+
+            "properties": {
+              "type": {
+                "$ref": "#/definitions/type"
+              },
+
+              "id": {
+                "$ref": "#/definitions/id",
+
+                "description": "A textarea id\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#keys"
+              },
+
+              "attributes": {
+                "title": "textarea attributes",
+
+                "description": "Textarea attributes\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-1",
+
+                "type": "object",
+
+                "required": ["label"],
+
+                "properties": {
+                  "label": {
+                    "$ref": "#/definitions/label",
+
+                    "description": "A short textarea description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-1"
+                  },
+
+                  "description": {
+                    "$ref": "#/definitions/description",
+
+                    "description": "A long textarea description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-1"
+                  },
+
+                  "placeholder": {
+                    "$ref": "#/definitions/placeholder",
+
+                    "description": "A textarea placeholder\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-1"
+                  },
+
+                  "value": {
+                    "$ref": "#/definitions/value",
+
+                    "description": "A textarea value\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-1"
+                  },
+
+                  "render": {
+                    "description": "A textarea syntax highlighting mode\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-1",
+
+                    "type": "string",
+
+                    "enum": [
+                      "1C Enterprise",
+
+                      "4D",
+
+                      "ABAP CDS",
+
+                      "ABAP",
+
+                      "ABNF",
+
+                      "AFDKO",
+
+                      "AGS Script",
+
+                      "AIDL",
+
+                      "AL",
+
+                      "AMPL",
+
+                      "ANTLR",
+
+                      "API Blueprint",
+
+                      "APL",
+
+                      "ASL",
+
+                      "ASN.1",
+
+                      "ASP.NET",
+
+                      "ATS",
+
+                      "ActionScript",
+
+                      "Ada",
+
+                      "Alloy",
+
+                      "Alpine Abuild",
+
+                      "Altium Designer",
+
+                      "AngelScript",
+
+                      "Ant Build System",
+
+                      "ApacheConf",
+
+                      "Apex",
+
+                      "Apollo Guidance Computer",
+
+                      "AppleScript",
+
+                      "Arc",
+
+                      "AsciiDoc",
+
+                      "AspectJ",
+
+                      "Assembly",
+
+                      "Astro",
+
+                      "Asymptote",
+
+                      "Augeas",
+
+                      "AutoHotkey",
+
+                      "AutoIt",
+
+                      "AutoIt3",
+
+                      "AutoItScript",
+
+                      "Avro IDL",
+
+                      "Awk",
+
+                      "BASIC",
+
+                      "Ballerina",
+
+                      "Batchfile",
+
+                      "Beef",
+
+                      "Befunge",
+
+                      "BibTeX",
+
+                      "Bicep",
+
+                      "Bison",
+
+                      "BitBake",
+
+                      "Blade",
+
+                      "BlitzBasic",
+
+                      "BlitzMax",
+
+                      "Boo",
+
+                      "Boogie",
+
+                      "Brainfuck",
+
+                      "Brightscript",
+
+                      "Browserslist",
+
+                      "C",
+
+                      "C#",
+
+                      "C++",
+
+                      "C-ObjDump",
+
+                      "C2hs Haskell",
+
+                      "CIL",
+
+                      "CLIPS",
+
+                      "CMake",
+
+                      "COBOL",
+
+                      "CODEOWNERS",
+
+                      "COLLADA",
+
+                      "CSON",
+
+                      "CSS",
+
+                      "CSV",
+
+                      "CUE",
+
+                      "CWeb",
+
+                      "Cabal Config",
+
+                      "Cabal",
+
+                      "Cap'n Proto",
+
+                      "Carto",
+
+                      "CartoCSS",
+
+                      "Ceylon",
+
+                      "Chapel",
+
+                      "Charity",
+
+                      "ChucK",
+
+                      "Cirru",
+
+                      "Clarion",
+
+                      "Classic ASP",
+
+                      "Clean",
+
+                      "Click",
+
+                      "Clojure",
+
+                      "Closure Templates",
+
+                      "Cloud Firestore Security Rules",
+
+                      "CoNLL",
+
+                      "CoNLL-U",
+
+                      "CoNLL-X",
+
+                      "ColdFusion CFC",
+
+                      "ColdFusion",
+
+                      "Common Lisp",
+
+                      "Common Workflow Language",
+
+                      "Component Pascal",
+
+                      "Containerfile",
+
+                      "Cool",
+
+                      "Coq",
+
+                      "Cpp-ObjDump",
+
+                      "Crystal",
+
+                      "Csound Document",
+
+                      "Csound Score",
+
+                      "Csound",
+
+                      "Cuda",
+
+                      "Cue Sheet",
+
+                      "Cycript",
+
+                      "Cython",
+
+                      "D-ObjDump",
+
+                      "DIGITAL Command Language",
+
+                      "DM",
+
+                      "DTrace",
+
+                      "Dafny",
+
+                      "Darcs Patch",
+
+                      "Dart",
+
+                      "DataWeave",
+
+                      "Dhall",
+
+                      "Diff",
+
+                      "Dlang",
+
+                      "Dockerfile",
+
+                      "Dogescript",
+
+                      "Dylan",
+
+                      "E",
+
+                      "E-mail",
+
+                      "EBNF",
+
+                      "ECL",
+
+                      "ECLiPSe",
+
+                      "EJS",
+
+                      "EQ",
+
+                      "Eagle",
+
+                      "Earthly",
+
+                      "Easybuild",
+
+                      "Ecere Projects",
+
+                      "EditorConfig",
+
+                      "Eiffel",
+
+                      "Elixir",
+
+                      "Elm",
+
+                      "Emacs Lisp",
+
+                      "EmberScript",
+
+                      "Erlang",
+
+                      "F#",
+
+                      "F*",
+
+                      "FIGfont",
+
+                      "FIGlet Font",
+
+                      "FLUX",
+
+                      "Factor",
+
+                      "Fancy",
+
+                      "Fantom",
+
+                      "Faust",
+
+                      "Fennel",
+
+                      "Filebench WML",
+
+                      "Filterscript",
+
+                      "Fluent",
+
+                      "Formatted",
+
+                      "Forth",
+
+                      "Fortran Free Form",
+
+                      "Fortran",
+
+                      "FreeBasic",
+
+                      "Frege",
+
+                      "Futhark",
+
+                      "G-code",
+
+                      "GAML",
+
+                      "GAMS",
+
+                      "GAP",
+
+                      "GCC Machine Description",
+
+                      "GDB",
+
+                      "GDScript",
+
+                      "GEDCOM",
+
+                      "GLSL",
+
+                      "GN",
+
+                      "Game Maker Language",
+
+                      "Gemfile.lock",
+
+                      "Genie",
+
+                      "Genshi",
+
+                      "Gentoo Eclass",
+
+                      "Gerber Image",
+
+                      "Gettext Catalog",
+
+                      "Gherkin",
+
+                      "Git Config",
+
+                      "Glyph Bitmap Distribution Format",
+
+                      "Glyph",
+
+                      "Gnuplot",
+
+                      "Go Checksums",
+
+                      "Go Module",
+
+                      "Go",
+
+                      "Golo",
+
+                      "Gosu",
+
+                      "Grace",
+
+                      "Gradle",
+
+                      "Grammatical Framework",
+
+                      "Graph Modeling Language",
+
+                      "GraphQL",
+
+                      "Graphviz (DOT)",
+
+                      "Groovy Server Pages",
+
+                      "Groovy",
+
+                      "HAProxy",
+
+                      "HCL",
+
+                      "HTML",
+
+                      "HTML+ECR",
+
+                      "HTML+EEX",
+
+                      "HTML+ERB",
+
+                      "HTML+PHP",
+
+                      "HTML+Razor",
+
+                      "HTTP",
+
+                      "HXML",
+
+                      "Hack",
+
+                      "Haml",
+
+                      "Handlebars",
+
+                      "Harbour",
+
+                      "HashiCorp Configuration Language",
+
+                      "Haskell",
+
+                      "Haxe",
+
+                      "HiveQL",
+
+                      "HolyC",
+
+                      "Hy",
+
+                      "IDL",
+
+                      "IGOR Pro",
+
+                      "IPython Notebook",
+
+                      "Idris",
+
+                      "Ignore List",
+
+                      "ImageJ Macro",
+
+                      "Inform 7",
+
+                      "Io",
+
+                      "Ioke",
+
+                      "Isabelle ROOT",
+
+                      "Isabelle",
+
+                      "J",
+
+                      "JAR Manifest",
+
+                      "JFlex",
+
+                      "JSON with Comments",
+
+                      "JSON",
+
+                      "JSON5",
+
+                      "JSONLD",
+
+                      "JSONiq",
+
+                      "Jasmin",
+
+                      "Java Properties",
+
+                      "Java Server Pages",
+
+                      "Java",
+
+                      "JavaScript",
+
+                      "JavaScript+ERB",
+
+                      "Jest Snapshot",
+
+                      "Jinja",
+
+                      "Jison Lex",
+
+                      "Jison",
+
+                      "Jolie",
+
+                      "Jsonnet",
+
+                      "Julia",
+
+                      "Jupyter Notebook",
+
+                      "Kaitai Struct",
+
+                      "KakouneScript",
+
+                      "KiCad Layout",
+
+                      "KiCad Legacy Layout",
+
+                      "KiCad Schematic",
+
+                      "Kit",
+
+                      "Kotlin",
+
+                      "Kusto",
+
+                      "LFE",
+
+                      "LLVM",
+
+                      "LOLCODE",
+
+                      "LSL",
+
+                      "LTspice Symbol",
+
+                      "LabVIEW",
+
+                      "Lark",
+
+                      "Lasso",
+
+                      "Lean",
+
+                      "Less",
+
+                      "Lex",
+
+                      "LilyPond",
+
+                      "Limbo",
+
+                      "Linker Script",
+
+                      "Linux Kernel Module",
+
+                      "Liquid",
+
+                      "Literate Agda",
+
+                      "Literate CoffeeScript",
+
+                      "Literate Haskell",
+
+                      "LiveScript",
+
+                      "Logos",
+
+                      "Logtalk",
+
+                      "LookML",
+
+                      "LoomScript",
+
+                      "Lua",
+
+                      "M",
+
+                      "M4",
+
+                      "M4Sugar",
+
+                      "MATLAB",
+
+                      "MAXScript",
+
+                      "MLIR",
+
+                      "MQL4",
+
+                      "MQL5",
+
+                      "MTML",
+
+                      "MUF",
+
+                      "Macaulay2",
+
+                      "Makefile",
+
+                      "Mako",
+
+                      "Markdown",
+
+                      "Marko",
+
+                      "Mathematica",
+
+                      "Max",
+
+                      "Mercury",
+
+                      "Meson",
+
+                      "Metal",
+
+                      "Microsoft Developer Studio Project",
+
+                      "Microsoft Visual Studio Solution",
+
+                      "MiniD",
+
+                      "Mirah",
+
+                      "Modelica",
+
+                      "Modula-2",
+
+                      "Modula-3",
+
+                      "Module Management System",
+
+                      "Monkey",
+
+                      "Moocode",
+
+                      "MoonScript",
+
+                      "Motoko",
+
+                      "Motorola 68K Assembly",
+
+                      "Muse",
+
+                      "Myghty",
+
+                      "NASL",
+
+                      "NCL",
+
+                      "NEON",
+
+                      "NPM Config",
+
+                      "NSIS",
+
+                      "NWScript",
+
+                      "Nearley",
+
+                      "Nemerle",
+
+                      "NeoSnippet",
+
+                      "NetLinx",
+
+                      "NetLinx+ERB",
+
+                      "NetLogo",
+
+                      "NewLisp",
+
+                      "Nextflow",
+
+                      "Nginx",
+
+                      "Ninja",
+
+                      "Nit",
+
+                      "Nix",
+
+                      "NumPy",
+
+                      "Nunjucks",
+
+                      "ObjDump",
+
+                      "Object Data Instance Notation",
+
+                      "ObjectScript",
+
+                      "Objective-C",
+
+                      "Objective-C++",
+
+                      "Objective-J",
+
+                      "Odin",
+
+                      "Omgrofl",
+
+                      "Opa",
+
+                      "Opal",
+
+                      "Open Policy Agent",
+
+                      "OpenCL",
+
+                      "OpenEdge ABL",
+
+                      "OpenQASM",
+
+                      "OpenRC runscript",
+
+                      "OpenSCAD",
+
+                      "OpenStep Property List",
+
+                      "OpenType Feature File",
+
+                      "Org",
+
+                      "Ox",
+
+                      "Oxygene",
+
+                      "Oz",
+
+                      "P4",
+
+                      "PEG.js",
+
+                      "PHP",
+
+                      "PLpgSQL",
+
+                      "POV-Ray SDL",
+
+                      "Pan",
+
+                      "Papyrus",
+
+                      "Parrot Assembly",
+
+                      "Parrot Internal Representation",
+
+                      "Parrot",
+
+                      "Pascal",
+
+                      "Pawn",
+
+                      "Pep8",
+
+                      "Perl",
+
+                      "Pickle",
+
+                      "PicoLisp",
+
+                      "PigLatin",
+
+                      "Pike",
+
+                      "PlantUML",
+
+                      "Pod 6",
+
+                      "Pod",
+
+                      "PogoScript",
+
+                      "Pony",
+
+                      "PostCSS",
+
+                      "PostScript",
+
+                      "PowerShell",
+
+                      "Prisma",
+
+                      "Processing",
+
+                      "Proguard",
+
+                      "Prolog",
+
+                      "Promela",
+
+                      "Propeller Spin",
+
+                      "Protocol Buffer",
+
+                      "Protocol Buffers",
+
+                      "Public Key",
+
+                      "Pug",
+
+                      "Puppet",
+
+                      "Pure Data",
+
+                      "PureBasic",
+
+                      "PureScript",
+
+                      "Python",
+
+                      "Q#",
+
+                      "QMake",
+
+                      "Qt Script",
+
+                      "Quake",
+
+                      "R",
+
+                      "RAML",
+
+                      "RDoc",
+
+                      "REALbasic",
+
+                      "REXX",
+
+                      "RMarkdown",
+
+                      "RPC",
+
+                      "RPM Spec",
+
+                      "Racket",
+
+                      "Ragel",
+
+                      "Raw token data",
+
+                      "ReScript",
+
+                      "Readline Config",
+
+                      "Reason",
+
+                      "Rebol",
+
+                      "Record Jar",
+
+                      "Red",
+
+                      "Redirect Rules",
+
+                      "Regular Expression",
+
+                      "RenderScript",
+
+                      "Rich Text Format",
+
+                      "Ring",
+
+                      "Riot",
+
+                      "RobotFramework",
+
+                      "Roff",
+
+                      "Rouge",
+
+                      "Rscript",
+
+                      "Ruby",
+
+                      "Rust",
+
+                      "SAS",
+
+                      "SCSS",
+
+                      "SELinux Kernel Policy Language",
+
+                      "SELinux Policy",
+
+                      "SMT",
+
+                      "SPARQL",
+
+                      "SQF",
+
+                      "SQL",
+
+                      "SQLPL",
+
+                      "SRecode Template",
+
+                      "SSH Config",
+
+                      "STON",
+
+                      "SVG",
+
+                      "SWIG",
+
+                      "Sage",
+
+                      "SaltStack",
+
+                      "Sass",
+
+                      "Scala",
+
+                      "Scaml",
+
+                      "Scheme",
+
+                      "Scilab",
+
+                      "Self",
+
+                      "ShaderLab",
+
+                      "Shell",
+
+                      "ShellCheck Config",
+
+                      "Sieve",
+
+                      "Singularity",
+
+                      "Slash",
+
+                      "Slice",
+
+                      "Slim",
+
+                      "SmPL",
+
+                      "Smalltalk",
+
+                      "SnipMate",
+
+                      "Solidity",
+
+                      "Soong",
+
+                      "SourcePawn",
+
+                      "Spline Font Database",
+
+                      "Squirrel",
+
+                      "Stan",
+
+                      "Standard ML",
+
+                      "Starlark",
+
+                      "StringTemplate",
+
+                      "Stylus",
+
+                      "SubRip Text",
+
+                      "SugarSS",
+
+                      "SuperCollider",
+
+                      "Svelte",
+
+                      "Swift",
+
+                      "SystemVerilog",
+
+                      "TI Program",
+
+                      "TLA",
+
+                      "TOML",
+
+                      "TSQL",
+
+                      "TSV",
+
+                      "TSX",
+
+                      "TXL",
+
+                      "Tcl",
+
+                      "Tcsh",
+
+                      "TeX",
+
+                      "Tea",
+
+                      "Terra",
+
+                      "Texinfo",
+
+                      "Text",
+
+                      "TextMate Properties",
+
+                      "Textile",
+
+                      "Thrift",
+
+                      "Turing",
+
+                      "Turtle",
+
+                      "Twig",
+
+                      "Type Language",
+
+                      "TypeScript",
+
+                      "UltiSnip",
+
+                      "UltiSnips",
+
+                      "Unified Parallel C",
+
+                      "Unity3D Asset",
+
+                      "Unix Assembly",
+
+                      "Uno",
+
+                      "UnrealScript",
+
+                      "Ur",
+
+                      "Ur/Web",
+
+                      "UrWeb",
+
+                      "V",
+
+                      "VBA",
+
+                      "VCL",
+
+                      "VHDL",
+
+                      "Vala",
+
+                      "Valve Data Format",
+
+                      "Verilog",
+
+                      "Vim Help File",
+
+                      "Vim Script",
+
+                      "Vim Snippet",
+
+                      "Visual Basic .NET",
+
+                      "Vue",
+
+                      "Wavefront Material",
+
+                      "Wavefront Object",
+
+                      "Web Ontology Language",
+
+                      "WebAssembly",
+
+                      "WebVTT",
+
+                      "Wget Config",
+
+                      "Wikitext",
+
+                      "Windows Registry Entries",
+
+                      "Wollok",
+
+                      "World of Warcraft Addon Data",
+
+                      "X BitMap",
+
+                      "X Font Directory Index",
+
+                      "X PixMap",
+
+                      "X10",
+
+                      "XC",
+
+                      "XCompose",
+
+                      "XML Property List",
+
+                      "XML",
+
+                      "XPages",
+
+                      "XProc",
+
+                      "XQuery",
+
+                      "XS",
+
+                      "XSLT",
+
+                      "Xojo",
+
+                      "Xonsh",
+
+                      "Xtend",
+
+                      "YAML",
+
+                      "YANG",
+
+                      "YARA",
+
+                      "YASnippet",
+
+                      "Yacc",
+
+                      "ZAP",
+
+                      "ZIL",
+
+                      "Zeek",
+
+                      "ZenScript",
+
+                      "Zephir",
+
+                      "Zig",
+
+                      "Zimpl",
+
+                      "abl",
+
+                      "abuild",
+
+                      "acfm",
+
+                      "aconf",
+
+                      "actionscript 3",
+
+                      "actionscript3",
+
+                      "ada2005",
+
+                      "ada95",
+
+                      "adobe composite font metrics",
+
+                      "adobe multiple font metrics",
+
+                      "advpl",
+
+                      "ags",
+
+                      "ahk",
+
+                      "altium",
+
+                      "amfm",
+
+                      "amusewiki",
+
+                      "apache",
+
+                      "apkbuild",
+
+                      "arexx",
+
+                      "as3",
+
+                      "asm",
+
+                      "asp",
+
+                      "aspx",
+
+                      "aspx-vb",
+
+                      "ats2",
+
+                      "au3",
+
+                      "autoconf",
+
+                      "b3d",
+
+                      "bash session",
+
+                      "bash",
+
+                      "bat",
+
+                      "batch",
+
+                      "bazel",
+
+                      "blitz3d",
+
+                      "blitzplus",
+
+                      "bmax",
+
+                      "bplus",
+
+                      "bro",
+
+                      "bsdmake",
+
+                      "byond",
+
+                      "bzl",
+
+                      "c++-objdump",
+
+                      "c2hs",
+
+                      "cURL Config",
+
+                      "cake",
+
+                      "cakescript",
+
+                      "cfc",
+
+                      "cfm",
+
+                      "cfml",
+
+                      "chpl",
+
+                      "clipper",
+
+                      "coccinelle",
+
+                      "coffee",
+
+                      "coffee-script",
+
+                      "coldfusion html",
+
+                      "console",
+
+                      "cperl",
+
+                      "cpp",
+
+                      "csharp",
+
+                      "csound-csd",
+
+                      "csound-orc",
+
+                      "csound-sco",
+
+                      "cucumber",
+
+                      "curlrc",
+
+                      "cwl",
+
+                      "dcl",
+
+                      "delphi",
+
+                      "desktop",
+
+                      "dircolors",
+
+                      "django",
+
+                      "dosbatch",
+
+                      "dosini",
+
+                      "dpatch",
+
+                      "dtrace-script",
+
+                      "eC",
+
+                      "ecr",
+
+                      "editor-config",
+
+                      "edn",
+
+                      "eeschema schematic",
+
+                      "eex",
+
+                      "elisp",
+
+                      "emacs muse",
+
+                      "emacs",
+
+                      "email",
+
+                      "eml",
+
+                      "erb",
+
+                      "fb",
+
+                      "fish",
+
+                      "flex",
+
+                      "foxpro",
+
+                      "fsharp",
+
+                      "fstar",
+
+                      "ftl",
+
+                      "fundamental",
+
+                      "gf",
+
+                      "git-ignore",
+
+                      "gitattributes",
+
+                      "gitconfig",
+
+                      "gitignore",
+
+                      "gitmodules",
+
+                      "go mod",
+
+                      "go sum",
+
+                      "go.mod",
+
+                      "go.sum",
+
+                      "golang",
+
+                      "groff",
+
+                      "gsp",
+
+                      "hbs",
+
+                      "heex",
+
+                      "help",
+
+                      "html+django",
+
+                      "html+jinja",
+
+                      "html+ruby",
+
+                      "htmlbars",
+
+                      "htmldjango",
+
+                      "hylang",
+
+                      "i7",
+
+                      "ignore",
+
+                      "igor",
+
+                      "igorpro",
+
+                      "ijm",
+
+                      "inc",
+
+                      "inform7",
+
+                      "inputrc",
+
+                      "irc logs",
+
+                      "irc",
+
+                      "java server page",
+
+                      "jq",
+
+                      "jruby",
+
+                      "js",
+
+                      "jsonc",
+
+                      "jsp",
+
+                      "kak",
+
+                      "kakscript",
+
+                      "keyvalues",
+
+                      "ksy",
+
+                      "lassoscript",
+
+                      "latex",
+
+                      "leex",
+
+                      "lhaskell",
+
+                      "lhs",
+
+                      "lisp",
+
+                      "litcoffee",
+
+                      "live-script",
+
+                      "ls",
+
+                      "m2",
+
+                      "m68k",
+
+                      "mIRC Script",
+
+                      "macruby",
+
+                      "mail",
+
+                      "make",
+
+                      "man page",
+
+                      "man",
+
+                      "man-page",
+
+                      "manpage",
+
+                      "markojs",
+
+                      "max/msp",
+
+                      "maxmsp",
+
+                      "mbox",
+
+                      "mcfunction",
+
+                      "mdoc",
+
+                      "mediawiki",
+
+                      "mf",
+
+                      "mma",
+
+                      "mumps",
+
+                      "mupad",
+
+                      "nanorc",
+
+                      "nasm",
+
+                      "ne-on",
+
+                      "nesC",
+
+                      "nette object notation",
+
+                      "nginx configuration file",
+
+                      "nixos",
+
+                      "njk",
+
+                      "node",
+
+                      "npmrc",
+
+                      "nroff",
+
+                      "nush",
+
+                      "nvim",
+
+                      "obj-c",
+
+                      "obj-c++",
+
+                      "obj-j",
+
+                      "objc",
+
+                      "objc++",
+
+                      "objectivec",
+
+                      "objectivec++",
+
+                      "objectivej",
+
+                      "objectpascal",
+
+                      "objj",
+
+                      "octave",
+
+                      "odin-lang",
+
+                      "odinlang",
+
+                      "oncrpc",
+
+                      "ooc",
+
+                      "openedge",
+
+                      "openrc",
+
+                      "osascript",
+
+                      "pandoc",
+
+                      "pasm",
+
+                      "pcbnew",
+
+                      "perl-6",
+
+                      "perl6",
+
+                      "pir",
+
+                      "plain text",
+
+                      "posh",
+
+                      "postscr",
+
+                      "pot",
+
+                      "pov-ray",
+
+                      "povray",
+
+                      "progress",
+
+                      "protobuf",
+
+                      "pwsh",
+
+                      "pycon",
+
+                      "pyrex",
+
+                      "python3",
+
+                      "q",
+
+                      "ql",
+
+                      "qsharp",
+
+                      "ragel-rb",
+
+                      "ragel-ruby",
+
+                      "rake",
+
+                      "raw",
+
+                      "razor",
+
+                      "rb",
+
+                      "rbx",
+
+                      "reStructuredText",
+
+                      "readline",
+
+                      "red/system",
+
+                      "redirects",
+
+                      "regex",
+
+                      "regexp",
+
+                      "renpy",
+
+                      "rhtml",
+
+                      "robots txt",
+
+                      "robots",
+
+                      "robots.txt",
+
+                      "rpcgen",
+
+                      "rs",
+
+                      "rs-274x",
+
+                      "rss",
+
+                      "rst",
+
+                      "rusthon",
+
+                      "salt",
+
+                      "saltstate",
+
+                      "sed",
+
+                      "sepolicy",
+
+                      "sh",
+
+                      "shell-script",
+
+                      "shellcheckrc",
+
+                      "sml",
+
+                      "snippet",
+
+                      "sourcemod",
+
+                      "soy",
+
+                      "specfile",
+
+                      "splus",
+
+                      "squeak",
+
+                      "terraform",
+
+                      "tl",
+
+                      "tm-properties",
+
+                      "troff",
+
+                      "ts",
+
+                      "udiff",
+
+                      "vb .net",
+
+                      "vb.net",
+
+                      "vb6",
+
+                      "vbnet",
+
+                      "vdf",
+
+                      "vim",
+
+                      "vimhelp",
+
+                      "viml",
+
+                      "visual basic 6",
+
+                      "visual basic for applications",
+
+                      "visual basic",
+
+                      "vlang",
+
+                      "wasm",
+
+                      "wast",
+
+                      "wdl",
+
+                      "wgetrc",
+
+                      "wiki",
+
+                      "winbatch",
+
+                      "wisp",
+
+                      "wl",
+
+                      "wolfram lang",
+
+                      "wolfram language",
+
+                      "wolfram",
+
+                      "wsdl",
+
+                      "xBase",
+
+                      "xbm",
+
+                      "xdr",
+
+                      "xhtml",
+
+                      "xml+genshi",
+
+                      "xml+kid",
+
+                      "xpm",
+
+                      "xsd",
+
+                      "xsl",
+
+                      "xten",
+
+                      "yas",
+
+                      "yml",
+
+                      "zsh"
+                    ]
+                  }
+                },
+
+                "additionalProperties": false
+              },
+
+              "validations": {
+                "$ref": "#/definitions/validations",
+
+                "title": "textarea validations",
+
+                "description": "Textarea validations\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#validations"
+              }
+            },
+
+            "additionalProperties": false
+          }
+        },
+
+        {
+          "if": {
+            "properties": {
+              "type": {
+                "const": "input"
+              }
+            }
+          },
+
+          "then": {
+            "$comment": "For `additionalProperties` to work `type` must also be present here.",
+
+            "title": "input",
+
+            "description": "Input\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#input",
+
+            "type": "object",
+
+            "required": ["type", "attributes"],
+
+            "properties": {
+              "type": {
+                "$ref": "#/definitions/type"
+              },
+
+              "id": {
+                "$ref": "#/definitions/id",
+
+                "description": "An input id\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#keys"
+              },
+
+              "attributes": {
+                "title": "input attributes",
+
+                "description": "Input attributes\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-2",
+
+                "type": "object",
+
+                "required": ["label"],
+
+                "properties": {
+                  "label": {
+                    "$ref": "#/definitions/label",
+
+                    "description": "A short input description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-2"
+                  },
+
+                  "description": {
+                    "$ref": "#/definitions/description",
+
+                    "description": "A long input description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-2"
+                  },
+
+                  "placeholder": {
+                    "$ref": "#/definitions/placeholder",
+
+                    "description": "An input placeholder\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-2"
+                  },
+
+                  "value": {
+                    "$ref": "#/definitions/value",
+
+                    "description": "An input value\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-2"
+                  }
+                },
+
+                "additionalProperties": false
+              },
+
+              "validations": {
+                "$ref": "#/definitions/validations",
+
+                "title": "input validations",
+
+                "description": "Input validations\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#validations-1"
+              }
+            },
+
+            "additionalProperties": false
+          }
+        },
+
+        {
+          "if": {
+            "properties": {
+              "type": {
+                "const": "dropdown"
+              }
+            }
+          },
+
+          "then": {
+            "$comment": "For `additionalProperties` to work `type` must also be present here.",
+
+            "title": "dropdown",
+
+            "description": "dropdown\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#dropdown",
+
+            "type": "object",
+
+            "required": ["type", "attributes"],
+
+            "properties": {
+              "type": {
+                "$ref": "#/definitions/type"
+              },
+
+              "id": {
+                "$ref": "#/definitions/id",
+
+                "description": "A dropdown id\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#keys"
+              },
+
+              "attributes": {
+                "title": "dropdown attributes",
+
+                "description": "Dropdown attributes\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-3",
+
+                "type": "object",
+
+                "required": ["label", "options"],
+
+                "properties": {
+                  "label": {
+                    "$ref": "#/definitions/label",
+
+                    "description": "A short dropdown description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-3"
+                  },
+
+                  "description": {
+                    "$ref": "#/definitions/description",
+
+                    "description": "A long dropdown description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-3"
+                  },
+
+                  "multiple": {
+                    "description": "Specify whether allow a multiple choices\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-3",
+
+                    "type": "boolean",
+
+                    "default": false
+                  },
+
+                  "options": {
+                    "description": "Dropdown choices\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-3",
+
+                    "type": "array",
+
+                    "minItems": 1,
+
+                    "uniqueItems": true,
+
+                    "items": {
+                      "type": "string",
+
+                      "minLength": 1,
+
+                      "examples": ["Sample choice"]
+                    }
+                  },
+
+                  "default": {
+                    "description": "Index of the default option\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-3",
+
+                    "type": "integer",
+
+                    "examples": [0]
+                  }
+                },
+
+                "additionalProperties": false
+              },
+
+              "validations": {
+                "$ref": "#/definitions/validations",
+
+                "title": "dropdown validations",
+
+                "description": "Dropdown validations\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#validations-2"
+              }
+            },
+
+            "additionalProperties": false
+          }
+        },
+
+        {
+          "if": {
+            "properties": {
+              "type": {
+                "const": "checkboxes"
+              }
+            }
+          },
+
+          "then": {
+            "$comment": "For `additionalProperties` to work `type` must also be present here.",
+
+            "title": "checkboxes",
+
+            "description": "Checkboxes\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#checkboxes",
+
+            "type": "object",
+
+            "required": ["type", "attributes"],
+
+            "properties": {
+              "type": {
+                "$ref": "#/definitions/type"
+              },
+
+              "id": {
+                "$ref": "#/definitions/id",
+
+                "description": "Checkbox list id\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#keys"
+              },
+
+              "attributes": {
+                "title": "checkbox list attributes",
+
+                "description": "Checkbox list attributes\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-4",
+
+                "type": "object",
+
+                "required": ["label", "options"],
+
+                "properties": {
+                  "label": {
+                    "$ref": "#/definitions/label",
+
+                    "description": "A short checkbox list description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-4"
+                  },
+
+                  "description": {
+                    "$ref": "#/definitions/description",
+
+                    "description": "A long checkbox list description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-4"
+                  },
+
+                  "options": {
+                    "description": "Checkbox list choices\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-4",
+
+                    "type": "array",
+
+                    "minItems": 1,
+
+                    "items": {
+                      "title": "checkbox list choice",
+
+                      "description": "Checkbox list choice\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-4",
+
+                      "type": "object",
+
+                      "required": ["label"],
+
+                      "properties": {
+                        "label": {
+                          "description": "A short checkbox list choice description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-4",
+
+                          "type": "string",
+
+                          "minLength": 1,
+
+                          "examples": ["Sample label"]
+                        },
+
+                        "required": {
+                          "description": "Specify whether a choice is required\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-githubs-form-schema#attributes-4",
+
+                          "type": "boolean",
+
+                          "default": false
+                        }
+                      },
+
+                      "additionalProperties": false
+                    }
+                  }
+                },
+
+                "additionalProperties": false
+              }
+            },
+
+            "additionalProperties": false
+          }
+        }
+      ]
+    }
+  },
+
+  "properties": {
+    "name": {
+      "description": "An issue template name\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-issue-forms#top-level-syntax",
+
+      "type": "string",
+
+      "minLength": 1,
+
+      "examples": ["Sample name"]
+    },
+
+    "description": {
+      "description": "An issue template description\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-issue-forms#top-level-syntax",
+
+      "type": "string",
+
+      "minLength": 1,
+
+      "examples": ["Sample description"]
+    },
+
+    "body": {
+      "description": "An issue template body\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-issue-forms#top-level-syntax",
+
+      "type": "array",
+
+      "minItems": 1,
+
+      "items": {
+        "$ref": "#/definitions/form_item"
+      }
+    },
+
+    "assignees": {
+      "description": "An issue template assignees\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-issue-forms#top-level-syntax",
+
+      "oneOf": [
+        {
+          "$ref": "#/definitions/assignee"
+        },
+
+        {
+          "type": "array",
+
+          "minItems": 1,
+
+          "uniqueItems": true,
+
+          "items": {
+            "$ref": "#/definitions/assignee"
+          }
+        }
+      ]
+    },
+
+    "labels": {
+      "description": "An issue template labels\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-issue-forms#top-level-syntax",
+
+      "type": "array",
+
+      "minItems": 1,
+
+      "uniqueItems": true,
+
+      "items": {
+        "type": "string",
+
+        "minLength": 1,
+
+        "examples": [
+          "Sample label",
+
+          "bug",
+
+          "documentation",
+
+          "duplicate",
+
+          "enhancement",
+
+          "good first issue",
+
+          "help wanted",
+
+          "invalid",
+
+          "question",
+
+          "wontfix"
+        ]
+      }
+    },
+
+    "title": {
+      "description": "An issue template title\nhttps://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-issue-forms#top-level-syntax",
+
+      "type": "string",
+
+      "minLength": 1,
+
+      "examples": ["Sample title", "Bug: ", "Feature: "]
+    }
+  },
+
+  "required": ["name", "description", "body"],
+
+  "title": "GitHub issue forms config file schema",
+
+  "type": "object"
+}
diff --git a/src/circumplex/__init__.py b/src/circumplex/__init__.py
index b8cfd5d..3b17255 100644
--- a/src/circumplex/__init__.py
+++ b/src/circumplex/__init__.py
@@ -1,5 +1,75 @@
-from circumplex.circumplex import *
+"""Circumplex: Analysis and visualization of circular data in Python.
 
-# from circumplex import datasets
-from circumplex import instrument
-from circumplex import utils
\ No newline at end of file
+This package provides tools for analyzing circular data using the Structural
+Summary Method (SSM), particularly for interpersonal functioning, mood/affect,
+and vocational preferences.
+
+Main functions:
+
+- ssm_analyze: Perform SSM analysis with bootstrap confidence intervals
+- plot_circle: Plot SSM profiles on a circumplex circle
+- plot_curve: Plot SSM fitted curves with observed scores
+- plot_contrast: Plot SSM parameter contrasts between groups
+- load_dataset: Load built-in example datasets
+- OCTANTS, QUADRANTS, POLES: Standard angle sets
+
+Examples
+--------
+>>> from circumplex import ssm_analyze, plot_circle
+>>> from circumplex.data import load_dataset
+>>>
+>>> jz2017 = load_dataset('jz2017')
+>>> results = ssm_analyze(jz2017, scales=['PA', 'BC', 'DE', 'FG',
+...                                         'HI', 'JK', 'LM', 'NO'])
+>>> print(results.results)
+>>> fig = results.plot_circle()
+>>> fig.savefig('profile.png')
+
+"""
+
+# Core analysis functions
+# Utility functions
+from circumplex import utils
+from circumplex.analysis import ssm_analyze
+
+# Data loading
+from circumplex.data import load_dataset
+from circumplex.utils.angles import OCTANTS, POLES, QUADRANTS, Degree, Radian, octants
+
+# Visualization functions
+from circumplex.visualization import plot_circle, plot_contrast, plot_curve
+
+from ._version import __version__  # noqa: F401
+from .instruments import (
+    csig,
+    get_instrument,
+    iipsc,
+    ipipipc,
+    register_instrument,
+    show_instruments,
+)
+from .utils.tidying_functions import ipsatize, norm_standardize, score
+
+__all__ = [
+    "OCTANTS",
+    "POLES",
+    "QUADRANTS",
+    "Degree",
+    "Radian",
+    "csig",
+    "get_instrument",
+    "iipsc",
+    "ipipipc",
+    "ipsatize",
+    "load_dataset",
+    "norm_standardize",
+    "octants",
+    "plot_circle",
+    "plot_contrast",
+    "plot_curve",
+    "register_instrument",
+    "score",
+    "show_instruments",
+    "ssm_analyze",
+    "utils",
+]
diff --git a/src/circumplex/_utils.py b/src/circumplex/_utils.py
new file mode 100644
index 0000000..bb8fc5e
--- /dev/null
+++ b/src/circumplex/_utils.py
@@ -0,0 +1,13 @@
+import importlib.util
+
+
+def is_package_installed(package_name: str) -> bool:
+    """Check if a package is installed."""
+    package_spec = importlib.util.find_spec(package_name)
+    return package_spec is not None
+
+
+if is_package_installed("rich"):
+    from rich.console import Console
+
+    console = Console()
diff --git a/src/circumplex/analysis/__init__.py b/src/circumplex/analysis/__init__.py
new file mode 100644
index 0000000..24c8caf
--- /dev/null
+++ b/src/circumplex/analysis/__init__.py
@@ -0,0 +1,5 @@
+"""SSM analysis components."""
+
+from circumplex.analysis.ssm_analyze import ssm_analyze
+
+__all__ = ["ssm_analyze"]
diff --git a/src/circumplex/analysis/bootstrap.py b/src/circumplex/analysis/bootstrap.py
new file mode 100644
index 0000000..3a1cfb3
--- /dev/null
+++ b/src/circumplex/analysis/bootstrap.py
@@ -0,0 +1,261 @@
+"""Bootstrap confidence interval calculation for SSM analysis.
+
+This module implements bootstrap resampling with confidence interval
+calculation, including special handling for circular displacement data.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+import pandas as pd
+
+if TYPE_CHECKING:
+    from nptyping import Float, NDArray, Shape
+
+
+def circular_quantile(
+    angles: NDArray[Any, Float],
+    probs: list[float] | NDArray[Shape[Any], Float],
+) -> NDArray[Shape[Any], Float]:
+    """Calculate quantiles for circular data in radians.
+
+    Implements a circular quantile method that accounts for the periodic
+    nature of angular data. Centers angles around their mean direction,
+    calculates linear quantiles, then transforms back.
+
+    Parameters
+    ----------
+    angles
+        Array of angles in radians, shape (n,)
+    probs
+        Probability points at which to calculate quantiles (e.g., [0.025, 0.975])
+
+    Returns
+    -------
+    Quantiles at the requested probability points
+
+    Examples
+    --------
+    >>> angles = np.array([0.1, 0.2, 6.2, 6.3])  # Two near 0, two near 2π
+    >>> circular_quantile(angles, [0.25, 0.75])
+    array([6.25..., 0.15...])
+
+    Notes
+    -----
+    This function mirrors the quantile.circumplex_radian method from the
+    R package (R/ssm_bootstrap.R lines 72-82). It:
+    1. Computes mean direction using atan2
+    2. Centers all angles around the mean
+    3. Calculates linear quantiles on centered data
+    4. Transforms back to [0, 2π)
+
+    """
+    # Calculate mean direction
+    mean_angle = np.arctan2(np.mean(np.sin(angles)), np.mean(np.cos(angles)))
+
+    # Center angles around mean direction
+    angles_centered = (angles - mean_angle + np.pi) % (2 * np.pi) - np.pi
+
+    # Calculate quantiles on centered data
+    quantiles_centered = np.quantile(angles_centered, probs)
+
+    # Transform back
+    return (quantiles_centered + mean_angle) % (2 * np.pi)
+
+
+def ssm_bootstrap(
+    data: pd.DataFrame,
+    bootstrap_fn: Callable[
+        [pd.DataFrame, NDArray[Shape[Any], Float]], NDArray[Shape[Any], Float]
+    ],
+    boots: int = 2000,
+    grouping_col: str | None = None,
+    *,
+    seed: int | None = None,
+) -> dict[str, Any]:
+    """Perform stratified bootstrap with confidence intervals.
+
+    Executes bootstrap resampling with stratification by group (if specified),
+    calculates point estimates and and bootstrap replicats;
+    use `calculate_confidence_intervals()` to derive confidence intervals.
+
+    Parameters
+    ----------
+    data
+        DataFrame containing all data for bootstrap sampling
+    bootstrap_fn
+        Function that takes (data, resample_indices) and returns
+        flat array of parameters for all groups/measures
+    boots
+        Number of bootstrap resamples
+    grouping_col
+        Name of grouping column for stratified sampling.
+        If None, uses simple random sampling.
+    seed
+        Random seed for reproducibility
+
+    Returns
+    -------
+    Dictionary containing:
+    - 't0': Point estimates (observed parameters)
+    - 't': Bootstrap matrix (boots x n_params)
+    - 'n_params': Number of parameters per profile
+    - 'n_profiles': Number of profiles (groups/measures)
+
+    Examples
+    --------
+    >>> def simple_mean(df, indices):
+    ...     return np.array([df.iloc[indices]['x'].mean()])
+    >>> data = pd.DataFrame({'x': [1, 2, 3, 4, 5]})
+    >>> result = ssm_bootstrap(data, simple_mean, boots=100, seed=123)
+    >>> result['t0']  # Observed mean
+    array([3.])
+
+    Notes
+    -----
+    This function mirrors ssm_bootstrap() from the R package
+    (R/ssm_bootstrap.R lines 1-55). It uses stratified sampling
+    when a grouping variable is provided to ensure each bootstrap
+    sample maintains the original group proportions.
+
+    """
+    if seed is not None:
+        np.random.seed(seed)
+
+    n_obs = len(data)
+
+    # Calculate observed parameters (t0)
+    observed_indices = np.arange(n_obs)
+    t0 = bootstrap_fn(data, observed_indices)
+
+    # Initialize bootstrap matrix
+    n_params_total = len(t0)
+    t_matrix = np.zeros((boots, n_params_total))
+
+    # Perform bootstrap resampling
+    for b in range(boots):
+        if grouping_col is not None:
+            # Stratified sampling: sample within each group
+            resample_indices = _stratified_resample(data, grouping_col)
+        else:
+            # Simple random sampling with replacement
+            resample_indices = np.random.choice(n_obs, size=n_obs, replace=True)
+
+        # Calculate parameters for this resample
+        t_matrix[b] = bootstrap_fn(data, resample_indices)
+
+    return {
+        "t0": t0,
+        "t": t_matrix,
+        "n_params": 6,  # Always 6 SSM parameters per profile
+        "n_profiles": n_params_total // 6,
+    }
+
+
+def _stratified_resample(data: pd.DataFrame, grouping_col: str) -> np.ndarray:
+    """Perform stratified resampling within groups.
+
+    Parameters
+    ----------
+    data
+        DataFrame with grouping column
+    grouping_col
+        Name of column containing group labels
+
+    Returns
+    -------
+    Array of resampled indices
+
+    """
+    indices = []
+    groups = data[grouping_col].unique()
+
+    for group in groups:
+        # Get indices for this group
+        group_mask = data[grouping_col] == group
+        group_indices = np.where(group_mask)[0]
+
+        # Sample with replacement from this group
+        n_group = len(group_indices)
+        resampled = np.random.choice(group_indices, size=n_group, replace=True)
+        indices.extend(resampled)
+
+    return np.array(indices)
+
+
+def calculate_confidence_intervals(
+    bootstrap_results: dict[str, Any],
+    interval: float = 0.95,
+) -> pd.DataFrame:
+    """Calculate confidence intervals from bootstrap results.
+
+    Computes percentile confidence intervals for all parameters, with
+    special circular handling for displacement parameters.
+
+    Parameters
+    ----------
+    bootstrap_results
+        Dictionary from ssm_bootstrap() containing 't0' and 't'
+    interval
+        Confidence level (e.g., 0.95 for 95% CI)
+
+    Returns
+    -------
+    DataFrame with columns:
+    - e_est, x_est, y_est, a_est, d_est, fit_est (point estimates)
+    - e_lci, x_lci, y_lci, a_lci, d_lci (lower CI)
+    - e_uci, x_uci, y_uci, a_uci, d_uci (upper CI)
+    Note: fit has no confidence intervals
+
+    Notes
+    -----
+    This function mirrors the CI calculation in R's ssm_bootstrap()
+    (R/ssm_bootstrap.R lines 38-54). It uses percentile method for
+    linear parameters and circular_quantile() for displacement.
+
+    """
+    t0 = bootstrap_results["t0"]
+    t_matrix = bootstrap_results["t"]
+    n_params = bootstrap_results["n_params"]
+    n_profiles = bootstrap_results["n_profiles"]
+
+    # Calculate probability points for CI
+    alpha = 1 - interval
+    lower_prob = alpha / 2
+    upper_prob = 1 - alpha / 2
+
+    # Initialize results DataFrame
+    param_names = ["e", "x", "y", "a", "d", "fit"]
+    results = []
+
+    for profile_idx in range(n_profiles):
+        # Extract parameters for this profile
+        param_start = profile_idx * n_params
+        profile_params = {}
+
+        for param_idx, param_name in enumerate(param_names):
+            obs_value = t0[param_start + param_idx]
+            boot_values = t_matrix[:, param_start + param_idx]
+
+            # Point estimate
+            profile_params[f"{param_name}_est"] = obs_value
+
+            # Confidence intervals (skip fit)
+            if param_name != "fit":
+                if param_name == "d":
+                    # Use circular quantile for displacement
+                    ci = circular_quantile(boot_values, [lower_prob, upper_prob])
+
+                else:
+                    # Use regular quantile for other parameters
+                    ci = np.quantile(boot_values, [lower_prob, upper_prob])
+
+                profile_params[f"{param_name}_lci"] = ci[0]  # type: ignore[non-subscriptable]
+                profile_params[f"{param_name}_uci"] = ci[1]  # type: ignore[non-subscriptable]
+
+        results.append(profile_params)
+
+    return pd.DataFrame(results)
diff --git a/src/circumplex/analysis/corr_analysis.py b/src/circumplex/analysis/corr_analysis.py
new file mode 100644
index 0000000..6c1fdd1
--- /dev/null
+++ b/src/circumplex/analysis/corr_analysis.py
@@ -0,0 +1,288 @@
+"""Correlation-based SSM analysis.
+
+This module implements correlation-based SSM analysis with bootstrap confidence
+intervals, supporting single/multi-group and single/multi-measure designs with
+optional contrast analysis.
+"""
+
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+from circumplex.analysis.bootstrap import calculate_confidence_intervals, ssm_bootstrap
+from circumplex.core.scores import corr_scores, group_parameters
+from circumplex.utils.contrast import param_diff_array
+
+
+def ssm_analyze_corrs(  # noqa: C901, PLR0915
+    data: pd.DataFrame,
+    scales: list[str] | list[int],
+    angles: np.ndarray,
+    measures: list[str] | str,
+    grouping: str | None = None,
+    boots: int = 2000,
+    interval: float = 0.95,
+    measures_labels: list[str] | None = None,
+    *,
+    contrast: bool = False,
+    listwise: bool = True,
+    seed: int | None = None,
+) -> dict[str, Any]:
+    """Perform correlation-based SSM analysis.
+
+    Calculates SSM parameters from correlations between measures and scales,
+    optionally stratified by group, with bootstrap confidence intervals.
+    Supports contrast analysis for comparing two groups or two measures.
+
+    Parameters
+    ----------
+    data
+        DataFrame containing circumplex scales and measures
+    scales
+        Column names or indices for circumplex scales (length n_scales)
+    angles
+        Angular positions in radians (length n_scales)
+    measures
+        Column name(s) for measure variable(s). Can be string or list.
+    grouping
+        Column name for grouping variable. If None, analyzes all data as one group.
+    boots
+        Number of bootstrap resamples
+    interval
+        Confidence level (e.g., 0.95 for 95% CI)
+    measures_labels
+        Optional custom labels for measures (same length as measures)
+    contrast
+        If True, calculate difference between two groups or two measures
+        (requires exactly 2 groups OR 2 measures, not both)
+    listwise
+        If True, use listwise deletion. If False, use pairwise deletion.
+    seed
+        Random seed for reproducibility
+
+    Returns
+    -------
+    Dictionary with keys:
+    - 'results': DataFrame with parameters and confidence intervals
+    - 'scores': DataFrame with correlation scores
+    - 'details': Dict with analysis metadata
+    - 'type': 'correlation'
+
+    Raises
+    ------
+    ValueError
+        If contrast=True but requirements not met (2 groups XOR 2 measures)
+
+    Examples
+    --------
+    >>> from circumplex.data import load_dataset
+    >>> from circumplex.utils.angles import OCTANTS, degrees_to_radians
+    >>> data = load_dataset('jz2017')
+    >>> angles = degrees_to_radians(OCTANTS)
+    >>> results = ssm_analyze_corrs(data, scales=['PA', 'BC', 'DE', 'FG',
+    ...                                             'HI', 'JK', 'LM', 'NO'],
+    ...                              angles=angles, measures='PARPD',
+    ...                              boots=2000, seed=12345)
+
+    Notes
+    -----
+    This function mirrors ssm_analyze_corrs() from the R package
+    (R/ssm_analysis.R lines 280-406).
+
+    """
+    # Convert measures to list if single string
+    if isinstance(measures, str):
+        measures = [measures]
+
+    # Convert scale indices to names if needed
+    if isinstance(scales[0], int):
+        scale_names = [data.columns[i] for i in scales]
+    else:
+        scale_names = scales
+
+    # Use custom labels or measure names
+    if measures_labels is None:
+        measures_labels = measures
+
+    n_measures = len(measures)
+
+    # Handle grouping
+    if grouping is None:
+        group_labels = np.array(["All"] * len(data))
+        group_col = "All"
+        n_groups = 1
+    else:
+        group_col = data[grouping]
+        if not isinstance(group_col.dtype, pd.CategoricalDtype):
+            group_col = pd.Categorical(group_col)
+        else:
+            group_col = group_col.astype("category")
+
+        group_labels = np.array(group_col)
+        unique_groups = group_col.categories
+        n_groups = len(unique_groups)
+
+    # Validate contrast
+    if contrast:
+        group_mean_contrast = n_measures == 0 and n_groups == 2
+        group_corr_contrast = n_measures == 1 and n_groups == 2
+        measure_corr_contrast = n_measures == 2 and n_groups == 1
+
+        if not (group_mean_contrast or group_corr_contrast or measure_corr_contrast):
+            msg = (
+                "Contrast can only be TRUE when comparing exactly 2 groups "
+                "(with 1 measure) or exactly 2 measures (with 1 group)"
+            )
+            raise ValueError(msg)
+
+    # Prepare bootstrap input data
+    bs_input = data[scale_names + measures].copy()
+    bs_input["__group__"] = group_labels
+
+    # Apply listwise deletion if requested
+    if listwise:
+        bs_input = bs_input.dropna()
+
+    # Convert groups to integer codes
+    if grouping is None:
+        group_codes = np.zeros(len(bs_input), dtype=int)
+        unique_groups = ["All"]
+    else:
+        group_categories = pd.Categorical(bs_input["__group__"])
+        group_codes = group_categories.codes
+        unique_groups = group_categories.categories
+
+    # Calculate observed correlation scores
+    observed_scores = corr_scores(
+        bs_input[scale_names].values,
+        bs_input[measures].values,
+        group_codes,
+        listwise=listwise,
+    )
+
+    # Add contrast row if requested
+    if contrast:
+        if n_measures == 2 and n_groups == 1:
+            # Measure contrast: compare two measures within single group
+            contrast_scores = observed_scores[1] - observed_scores[0]
+        elif n_groups == 2:
+            # Group contrast: compare two groups for single measure
+            contrast_scores = observed_scores[1] - observed_scores[0]
+        observed_scores = np.vstack([observed_scores, contrast_scores])
+
+    # Define bootstrap function
+    def bootstrap_fn(df: pd.DataFrame, indices: np.ndarray) -> np.ndarray:
+        """Calculate SSM parameters for a bootstrap resample."""
+        resampled = df.iloc[indices]
+        scale_vals = resampled[scale_names].to_numpy()
+        measure_vals = resampled[measures].to_numpy()
+
+        # Get group codes for resample
+        if grouping is None:
+            grp_codes = np.zeros(len(resampled), dtype=int)
+        else:
+            grp_categories = pd.Categorical(
+                resampled["__group__"], categories=unique_groups
+            )
+            grp_codes = grp_categories.codes
+
+        # Calculate correlation scores for this resample
+        scores_r = corr_scores(scale_vals, measure_vals, grp_codes, listwise=listwise)
+
+        # Calculate SSM parameters
+        params = group_parameters(scores_r, angles)
+
+        # Add contrast if requested
+        if contrast:
+            contrast_params = param_diff_array(params)
+            params = np.concatenate([params, contrast_params])
+
+        return params
+
+    # Perform bootstrap
+    bs_results = ssm_bootstrap(
+        bs_input,
+        bootstrap_fn,
+        boots=boots,
+        grouping_col="__group__" if grouping is not None else None,
+        seed=seed,
+    )
+
+    # Calculate confidence intervals
+    results_df = calculate_confidence_intervals(bs_results)
+
+    # Convert displacement from radians to degrees
+    results_df["d_est"] = np.degrees(results_df["d_est"])
+    if "d_lci" in results_df.columns:
+        results_df["d_lci"] = np.degrees(results_df["d_lci"])
+        results_df["d_uci"] = np.degrees(results_df["d_uci"])
+
+    # Create labels combining measure and group information
+    labels = []
+    profile_groups = []
+    profile_measures = []
+
+    if n_groups == 1:
+        # Single group: labels are measure names
+        for m_label in measures_labels:
+            labels.append(m_label)
+            profile_groups.append(unique_groups[0])
+            profile_measures.append(m_label)
+
+        if contrast and n_measures == 2:
+            contrast_label = f"{measures_labels[1]} - {measures_labels[0]}"
+            labels.append(contrast_label)
+            profile_groups.append(unique_groups[0])
+            profile_measures.append(contrast_label)
+    else:
+        # Multiple groups: labels combine measure and group
+        for group in unique_groups:
+            for m_label in measures_labels:
+                # Always use "MEASURE: GROUP" format for multi-group correlation
+                label = f"{m_label}: {group}"
+                labels.append(label)
+                profile_groups.append(group)
+                profile_measures.append(m_label)
+
+        if contrast:
+            if n_measures == 1:
+                # Format: "MEASURE: GROUP1 - GROUP2"  # noqa: ERA001
+                contrast_label = (
+                    f"{measures_labels[0]}: {unique_groups[1]} - {unique_groups[0]}"
+                )
+            else:
+                contrast_label = (
+                    f"{unique_groups[1]} - {unique_groups[0]}: {measures_labels[0]}"
+                )
+            labels.append(contrast_label)
+            profile_groups.append(contrast_label)
+            profile_measures.append(
+                measures_labels[0] if n_measures == 1 else contrast_label
+            )
+
+    # Add metadata columns
+    results_df.insert(0, "Label", labels)
+    results_df.insert(1, "Group", profile_groups)
+    results_df.insert(2, "Measure", profile_measures)
+
+    # Create scores DataFrame
+    scores_df = pd.DataFrame(observed_scores, columns=scale_names)
+    scores_df.insert(0, "Label", labels)
+
+    # Prepare details
+    details = {
+        "boots": boots,
+        "interval": interval,
+        "listwise": listwise,
+        "angles": np.degrees(angles).tolist(),
+        "contrast": contrast,
+        "score_type": "correlation",
+    }
+
+    return {
+        "results": results_df,
+        "scores": scores_df,
+        "details": details,
+        "type": "correlation",
+    }
diff --git a/src/circumplex/analysis/mean_analysis.py b/src/circumplex/analysis/mean_analysis.py
new file mode 100644
index 0000000..62b5d92
--- /dev/null
+++ b/src/circumplex/analysis/mean_analysis.py
@@ -0,0 +1,222 @@
+"""Mean-based SSM analysis.
+
+This module implements mean-based SSM analysis with bootstrap confidence
+intervals, supporting single-group and multi-group designs with optional
+contrast analysis.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+from circumplex.analysis.bootstrap import calculate_confidence_intervals, ssm_bootstrap
+from circumplex.core.scores import group_parameters, mean_scores
+from circumplex.utils.contrast import param_diff_array
+
+
+def ssm_analyze_means(  # noqa: PLR0915
+    data: pd.DataFrame,
+    scales: list[str] | list[int],
+    angles: np.ndarray,
+    grouping: str | None = None,
+    boots: int = 2000,
+    interval: float = 0.95,
+    *,
+    contrast: bool = False,
+    listwise: bool = True,
+    seed: int | None = None,
+) -> dict[str, Any]:
+    """Perform mean-based SSM analysis.
+
+    Calculates SSM parameters from mean scale scores, optionally stratified
+    by group, with bootstrap confidence intervals. Supports contrast analysis
+    for comparing two groups.
+
+    Parameters
+    ----------
+    data
+        DataFrame containing circumplex scale scores
+    scales
+        Column names or indices for circumplex scales (length n_scales)
+    angles
+        Angular positions in radians (length n_scales)
+    grouping
+        Column name for grouping variable. If None, analyzes all data as one group.
+    contrast
+        If True, calculate difference between two groups (requires exactly 2 groups)
+    boots
+        Number of bootstrap resamples
+    interval
+        Confidence level (e.g., 0.95 for 95% CI)
+    listwise
+        If True, use listwise deletion. If False, use pairwise deletion.
+    seed
+        Random seed for reproducibility
+
+    Returns
+    -------
+    Dictionary with keys:
+    - 'results': DataFrame with parameters and confidence intervals
+    - 'scores': DataFrame with mean scale scores per group
+    - 'details': Dict with analysis metadata
+    - 'type': 'mean'
+
+    Raises
+    ------
+    ValueError
+        If contrast=True but number of groups is not 2
+
+    Examples
+    --------
+    >>> from circumplex.data import load_dataset
+    >>> from circumplex.utils.angles import OCTANTS, degrees_to_radians
+    >>> data = load_dataset('jz2017')
+    >>> angles = degrees_to_radians(OCTANTS)
+    >>> results = ssm_analyze_means(data, scales=['PA', 'BC', 'DE', 'FG',
+    ...                                             'HI', 'JK', 'LM', 'NO'],
+    ...                              angles=angles, boots=2000, seed=12345)
+
+    Notes
+    -----
+    This function mirrors ssm_analyze_means() from the R package
+    (R/ssm_analysis.R lines 179-276).
+
+    """
+    # Convert scale indices to names if needed
+    if isinstance(scales[0], int):
+        scale_names = [data.columns[i] for i in scales]
+    else:
+        scale_names = scales
+
+    # Handle grouping
+    if grouping is None:
+        # Create synthetic "All" group
+        group_labels = np.array(["All"] * len(data))
+        group_col = "All"
+        n_groups = 1
+    else:
+        group_col = data[grouping]
+        # Convert to categorical and get labels
+        if not isinstance(group_col.dtype, pd.CategoricalDtype):
+            group_col = pd.Categorical(group_col)
+        else:
+            group_col = group_col.astype("category")
+
+        group_labels = np.array(group_col)
+        unique_groups = group_col.categories
+        n_groups = len(unique_groups)
+
+    # Validate contrast
+    if contrast and n_groups != 2:
+        msg = "Contrast can only be TRUE when comparing exactly 2 groups"
+        raise ValueError(msg)
+
+    # Prepare bootstrap input data
+    bs_input = data[scale_names].copy()
+    bs_input["__group__"] = group_labels
+
+    # Apply listwise deletion if requested
+    if listwise:
+        bs_input = bs_input.dropna()
+
+    # Convert groups to integer codes for internal use
+    if grouping is None:
+        group_codes = np.zeros(len(bs_input), dtype=int)
+    else:
+        group_categories = pd.Categorical(bs_input["__group__"])
+        group_codes = group_categories.codes
+        unique_groups = group_categories.categories
+
+    # Calculate observed mean scores
+    observed_scores = mean_scores(
+        bs_input[scale_names].values, group_codes, listwise=listwise
+    )
+
+    # Add contrast row if requested
+    if contrast:
+        contrast_scores = observed_scores[1] - observed_scores[0]
+        observed_scores = np.vstack([observed_scores, contrast_scores])
+
+    # Define bootstrap function
+    def bootstrap_fn(df: pd.DataFrame, indices: np.ndarray) -> np.ndarray:
+        """Calculate SSM parameters for a bootstrap resample."""
+        resampled = df.iloc[indices]
+        scale_vals = resampled[scale_names].to_numpy()
+
+        # Get group codes for resample
+        if grouping is None:
+            grp_codes = np.zeros(len(resampled), dtype=int)
+        else:
+            grp_categories = pd.Categorical(
+                resampled["__group__"], categories=unique_groups
+            )
+            grp_codes = grp_categories.codes
+
+        # Calculate mean scores for this resample
+        scores_r = mean_scores(scale_vals, grp_codes, listwise=listwise)
+
+        # Calculate SSM parameters
+        params = group_parameters(scores_r, angles)
+
+        # Add contrast if requested
+        if contrast:
+            contrast_params = param_diff_array(params)
+            params = np.concatenate([params, contrast_params])
+
+        return params
+
+    # Perform bootstrap
+    bs_results = ssm_bootstrap(
+        bs_input,
+        bootstrap_fn,
+        boots=boots,
+        grouping_col="__group__" if grouping is not None else None,
+        seed=seed,
+    )
+
+    # Calculate confidence intervals
+    results_df = calculate_confidence_intervals(bs_results)
+
+    # Convert displacement from radians to degrees
+    results_df["d_est"] = np.degrees(results_df["d_est"])
+    if "d_lci" in results_df.columns:
+        results_df["d_lci"] = np.degrees(results_df["d_lci"])
+        results_df["d_uci"] = np.degrees(results_df["d_uci"])
+
+    # Add metadata columns
+    if grouping is None:
+        results_df.insert(0, "Label", ["All"])
+        group_labels_list = ["All"]
+    else:
+        group_labels_list = list(unique_groups)
+        if contrast:
+            contrast_label = f"{group_labels_list[1]} - {group_labels_list[0]}"
+            group_labels_list.append(contrast_label)
+        results_df.insert(0, "Label", group_labels_list)
+
+    results_df.insert(1, "Group", group_labels_list)
+    results_df.insert(2, "Measure", [None] * len(results_df))
+
+    # Create scores DataFrame
+    scores_df = pd.DataFrame(observed_scores, columns=scale_names)
+    scores_df.insert(0, "Label", group_labels_list)
+
+    # Prepare details
+    details = {
+        "boots": boots,
+        "interval": interval,
+        "listwise": listwise,
+        "angles": np.degrees(angles).tolist(),
+        "contrast": contrast,
+        "score_type": "mean",
+    }
+
+    return {
+        "results": results_df,
+        "scores": scores_df,
+        "details": details,
+        "type": "mean",
+    }
diff --git a/src/circumplex/analysis/ssm_analyze.py b/src/circumplex/analysis/ssm_analyze.py
new file mode 100644
index 0000000..8b9daba
--- /dev/null
+++ b/src/circumplex/analysis/ssm_analyze.py
@@ -0,0 +1,261 @@
+"""Main SSM analysis function.
+
+This module provides the primary user-facing function for performing
+Structural Summary Method (SSM) analysis on circumplex data.
+"""
+
+from __future__ import annotations
+
+from typing import cast
+
+import numpy as np
+import pandas as pd
+
+from circumplex.analysis.corr_analysis import ssm_analyze_corrs
+from circumplex.analysis.mean_analysis import ssm_analyze_means
+from circumplex.ssm import SSM
+from circumplex.utils.angles import OCTANTS, degrees_to_radians
+
+
+def ssm_analyze(
+    data: pd.DataFrame,
+    scales: list[str] | list[int],
+    angles: np.ndarray | list[float] | None = None,
+    measures: list[str] | str | None = None,
+    grouping: str | None = None,
+    boots: int = 2000,
+    interval: float = 0.95,
+    measures_labels: list[str] | None = None,
+    *,
+    contrast: bool = False,
+    listwise: bool = True,
+    seed: int | None = None,
+) -> SSM:
+    """Perform Structural Summary Method (SSM) analysis on circumplex data.
+
+    This is the main entry point for SSM analysis. It automatically determines
+    whether to perform mean-based or correlation-based analysis based on the
+    `measures` parameter, calculates SSM parameters, and computes bootstrap
+    confidence intervals.
+
+    Parameters
+    ----------
+    data
+        DataFrame containing circumplex scale scores (and optionally measures
+        and grouping variables)
+    scales
+        Column names or indices for circumplex scales. Must be ordered according
+        to their angular positions (matching the order in `angles`).
+    angles
+        Angular positions for the scales in degrees. If None, uses standard
+        octant angles [90, 135, 180, 225, 270, 315, 360, 45]. Length must
+        match the number of scales.
+    measures
+        Column name(s) for external measure variable(s) to correlate with scales.
+        - If None: Performs mean-based analysis (profile analysis)
+        - If string or list: Performs correlation-based analysis
+    grouping
+        Column name for grouping variable. If None, treats all data as a
+        single group. The variable will be converted to a categorical factor.
+    contrast
+        If True, calculates differences between groups or measures.
+        - Requires exactly 2 groups (with 0 or 1 measures), OR
+        - Requires exactly 2 measures (with 1 group)
+        Raises ValueError if requirements not met.
+    boots
+        Number of bootstrap resamples for confidence interval calculation.
+        Default: 2000.
+    interval
+        Confidence level for bootstrap intervals (e.g., 0.95 for 95% CI).
+        Default: 0.95.
+    listwise
+        Missing data handling:
+        - If True: Listwise deletion (remove rows with any missing value)
+        - If False: Pairwise deletion (use all available data for each calculation)
+        Default: True.
+    measures_labels
+        Optional custom labels for measures (same length as measures).
+        If None, uses the column names.
+    seed
+        Random seed for reproducibility of bootstrap results. If None, results
+        will vary across runs.
+
+    Returns
+    -------
+    dict
+        Dictionary containing:
+
+        - **results** : pd.DataFrame
+            SSM parameters and confidence intervals with columns:
+
+            - Label: Profile label (group/measure name or combination)
+            - Group: Group identifier
+            - Measure: Measure identifier (None for mean-based analysis)
+            - e_est, x_est, y_est, a_est, d_est, fit_est: Point estimates
+            - e_lci, x_lci, y_lci, a_lci, d_lci: Lower confidence intervals
+            - e_uci, x_uci, y_uci, a_uci, d_uci: Upper confidence intervals
+
+            Note: Displacement (d) is in degrees. No CIs for fit parameter.
+
+        - **scores** : pd.DataFrame
+            Mean scores (mean-based) or correlation scores (correlation-based)
+            for each scale, with Label column.
+
+        - **details** : dict
+            Analysis metadata including boots, interval, listwise, angles,
+            contrast, and score_type.
+
+        - **type** : str
+            Analysis type: 'mean' or 'correlation'
+
+    Raises
+    ------
+    ValueError
+        - If angles length doesn't match scales length
+        - If contrast=True but requirements not met
+        - If data contains no valid observations after missing data handling
+
+    Examples
+    --------
+    **Mean-based analysis (single group)**
+
+    >>> from circumplex import ssm_analyze
+    >>> from circumplex.data import load_dataset
+    >>> aw2009 = load_dataset('aw2009')
+    >>> results = ssm_analyze(aw2009, scales=list(range(8)), seed=12345)
+    >>> print(results['results'][['Label', 'e_est', 'a_est', 'd_est']])
+         Label  e_est  a_est      d_est
+    0      All  0.423  0.981  344.358
+
+    **Mean-based analysis (multiple groups with contrast)**
+
+    >>> jz2017 = load_dataset('jz2017')
+    >>> results = ssm_analyze(jz2017, scales=list(range(1, 9)),
+    ...                        grouping='Gender', contrast=True, seed=12345)
+    >>> print(results['results'][['Label', 'e_est', 'a_est']])
+              Label  e_est  a_est
+    0        Female  0.635  0.158
+    1          Male  0.596  0.192
+    2  Male - Female -0.039  0.034
+
+    **Correlation-based analysis (single measure)**
+
+    >>> results = ssm_analyze(jz2017, scales=list(range(1, 9)),
+    ...                        measures='PARPD', seed=12345)
+    >>> print(results['results'][['Label', 'e_est', 'a_est', 'd_est']])
+       Label  e_est  a_est   d_est
+    0  PARPD  0.250  0.150  128.9
+
+    **Correlation-based analysis (measure contrast)**
+
+    >>> results = ssm_analyze(jz2017, scales=list(range(1, 9)),
+    ...                        measures=['ASPD', 'NARPD'],
+    ...                        contrast=True, seed=12345)
+    >>> print(results['results'][['Label', 'e_est', 'a_est']])
+                 Label  e_est  a_est
+    0             ASPD  0.253  0.055
+    1            NARPD  0.311  0.203
+    2  NARPD - ASPD     0.058  0.148
+
+    Notes
+    -----
+    This function is a Python port of ssm_analyze() from the R circumplex
+    package (Zimmermann & Wright, 2017). It maintains numerical parity with
+    the R implementation to at least 3 decimal places.
+
+    **SSM Parameters:**
+
+    - **elevation (e)**: Mean of all scale scores
+    - **x_value (x)**: Projection onto x-axis (cosine component)
+    - **y_value (y)**: Projection onto y-axis (sine component)
+    - **amplitude (a)**: Vector length (prototypicality)
+    - **displacement (d)**: Angular position in degrees [0, 360)
+    - **fit**: Model fit (R²), proportion of variance explained
+
+    **Bootstrap Confidence Intervals:**
+
+    Uses percentile method with stratified sampling when groups are present.
+    Displacement CIs use circular statistics to handle angular wrapping.
+
+    See Also
+    --------
+    load_dataset : Load example datasets
+    OCTANTS : Standard octant angles for 8-scale circumplex
+
+    References
+    ----------
+    Zimmermann, J., & Wright, A. G. C. (2017). Beyond description in
+    interpersonal construct validation: Methodological advances in the
+    circumplex Structural Summary Approach. *Assessment, 24*(1), 3-23.
+    https://doi.org/10.1177/1073191115621795
+
+    Zimmermann, J., & Wright, A. G. C. (2017). The circumplex package
+    [Computer software]. https://cran.r-project.org/package=circumplex
+
+    """
+    # Validate and process scales
+    if isinstance(scales[0], int):
+        scale_names = [data.columns[i] for i in scales]
+    else:
+        scale_names = scales
+
+    n_scales = len(scale_names)
+
+    # Process angles
+    if angles is None:
+        # Use default octant angles
+        if n_scales != 8:
+            msg = (
+                f"When angles=None, exactly 8 scales are required (got {n_scales}). "
+                "Please provide custom angles for non-octant circumplex models."
+            )
+            raise ValueError(msg)
+        angles_deg = OCTANTS
+    else:
+        # Convert to numpy array if needed
+        angles_deg = np.array(angles, dtype=float)
+
+        # Validate length
+        if len(angles_deg) != n_scales:
+            msg = (
+                f"Length of angles ({len(angles_deg)}) must match "
+                f"number of scales ({n_scales})"
+            )
+            raise ValueError(msg)
+
+    # Convert angles to radians for internal use
+    # Ensure numpy array type for downstream functions
+    angles_rad = cast("np.ndarray", degrees_to_radians(angles_deg))
+
+    # Route to appropriate analysis function
+    if measures is None:
+        # Mean-based analysis
+        return SSM.from_dict(
+            ssm_analyze_means(
+                data=data,
+                scales=scale_names,
+                angles=angles_rad,
+                grouping=grouping,
+                contrast=contrast,
+                boots=boots,
+                interval=interval,
+                listwise=listwise,
+                seed=seed,
+            )
+        )
+    # Correlation-based analysis
+    return SSM.from_dict(
+        ssm_analyze_corrs(
+            data=data,
+            scales=scale_names,
+            angles=angles_rad,
+            measures=measures,
+            grouping=grouping,
+            contrast=contrast,
+            boots=boots,
+            interval=interval,
+            listwise=listwise,
+            measures_labels=measures_labels,
+            seed=seed,
+        )
+    )
diff --git a/src/circumplex/circumplex.py b/src/circumplex/circumplex.py
deleted file mode 100644
index 893b947..0000000
--- a/src/circumplex/circumplex.py
+++ /dev/null
@@ -1,529 +0,0 @@
-# %%
-
-import matplotlib.pyplot as plt
-from matplotlib import colormaps
-from cycler import cycler
-import numpy as np
-import pandas as pd
-import math
-from scipy.optimize import curve_fit
-
-OCTANTS = (0, 45, 90, 135, 180, 225, 270, 315)
-BOUNDS = ([0, 0, -np.inf], [np.inf, 360, np.inf])
-
-
-class SSMParams(object):
-    """
-    A class to store the results of a single SSM analysis.
-
-    Attributes:
-        scores (np.array): A numeric vector (or single row dataframe) containing one score for each of a
-            set of circumplex scales.
-        angles (tuple): A numeric vector containing the angular displacement of each circumplex scale
-            included in `scores`.
-        scales (list): A list of the names of the circumplex scales included in `scores`.
-        group (list): A list of the names of the groups included in `scores`.
-        measure (list): A list of the names of the measures included in `scores`.
-        elevation (float): The elevation of the SSM curve.
-        xval (float): The x-value of the SSM curve.
-        yval (float): The y-value of the SSM curve.
-        amplitude (float): The amplitude of the SSM curve.
-        displacement (float): The displacement of the SSM curve.
-        r2 (float): The R2 fit of the SSM curve.
-    """
-
-    def __init__(
-        self,
-        scores: np.array,
-        scales: list,
-        angles: tuple = OCTANTS,
-        group: list = None,
-        measure: list | str = None,
-        bounds: tuple = BOUNDS,
-    ):
-        self.scores = scores
-        self.angles = angles
-        self.scales = scales
-        self.group = group
-        self.measure = measure
-        (
-            self.elevation,
-            self.xval,
-            self.yval,
-            self.amplitude,
-            self.displacement,
-            self.r2,
-        ) = ssm_parameters(self.scores, self.angles, bounds=bounds)
-
-    @property
-    def label(self) -> str:
-        """Return a label for the SSMParams object."""
-        if self.group is not None and self.measure is not None:
-            return f"{self.group}_{self.measure}"
-        elif self.measure is not None:
-            return self.measure
-        elif self.group is not None:
-            return self.group
-        else:
-            return "SSM"
-
-    @property
-    def table(self) -> pd.DataFrame:
-        """Return a table of the results."""
-        scale_angle = {scale: angle for scale, angle in zip(self.scales, self.angles)}
-        return pd.DataFrame(
-            self.params | scale_angle,
-            index=[self.label],
-        )
-
-    @property
-    def params(self) -> dict:
-        return {
-            "label": self.label,
-            "group": self.group,
-            "measure": self.measure,
-            "elevation": self.elevation,
-            "xval": self.xval,
-            "yval": self.yval,
-            "amplitude": self.amplitude,
-            "displacement": self.displacement,
-            "r2": self.r2,
-        }
-
-    def __repr__(self):
-        # TODO: Add param results
-        return f"SSMParams({self.label}, scores={self.scores}, angles={self.angles})"
-
-    def __str__(self):
-        # TODO: Add param results
-        return (
-            "====================================\n"
-            f"Measure:          {self.measure}\n"
-            f"Group:            {self.group}\n"
-            f"Scales:           {self.scales}\n"
-            f"Scale Angles:     {self.angles}\n"
-            f"\nProfile [All]:\n"
-            f"                  Estimate\n"
-            f"Elevation:        {round(self.elevation, 3)}\n"
-            f"X-Value:          {round(self.xval, 3)}\n"
-            f"Y-Value:          {round(self.yval, 3)}\n"
-            f"Amplitude:        {round(self.amplitude, 3)}\n"
-            f"Displacement:     {round(self.displacement, 3)}\n"
-            f"R2:               {round(self.r2, 3)}\n"
-        )
-
-    def profile_plot(self, ax=None, **kwargs) -> plt.Axes:
-        """
-        Plot the SSM profile.
-
-        Returns:
-            plt.Axes: A tuple containing the figure and axis objects.
-        """
-        return profile_plot(
-            self.amplitude,
-            self.displacement,
-            self.elevation,
-            self.r2,
-            self.angles,
-            self.scores,
-            self.label,
-            ax=ax,
-            **kwargs,
-        )
-
-    def plot(self) -> plt.Axes:
-        """
-        Plot the results in the circumplex
-
-        Returns:
-            plt.Axes: A tuple containing the figure and axis objects.
-        """
-        fig, ax = plt.subplots(subplot_kw={"projection": "polar"})
-        ax.plot(
-            np.deg2rad(self.displacement),
-            self.amplitude,
-            color="black",
-            marker="o",
-            markersize=10,
-        )
-
-
-class SSMResults(object):
-    """
-    A class to store the results of a SSM analysis.
-
-    Attributes:
-        results (list[SSMParams]): A list containing the SSMParams results of the SSM analysis.
-        measures (list): A list of the names of the measures included in `scores`.
-        grouping (list): A list of the names of the groups included in `scores`.
-    """
-
-    def __init__(
-        self, results: list[SSMParams] | SSMParams, measures=None, grouping=None
-    ):
-        if isinstance(results, SSMParams):
-            measures = [results.measure]
-            grouping = [results.group]
-            results = [results]
-
-        self.results = results
-        self.measures = measures
-        self.grouping = grouping
-
-    def __str__(self):
-        return "\n".join([str(res) for res in self.results])
-
-    @property
-    def groups(self) -> set:
-        """Return a list of the groups included in the analysis."""
-        return set(val.group for val in self.results if val.group is not None)
-
-    @property
-    def labels(self) -> set:
-        """Return a list of the labels included in the analysis."""
-        return set(val.label for val in self.results)
-
-    @property
-    def table(self) -> pd.DataFrame:
-        """
-        Return a table of the results.
-
-        Returns:
-            pd.DataFrame: A dataframe containing the results of the SSM analysis.
-        """
-        df = pd.DataFrame()
-        for val in self.results:
-            df = pd.concat([df, val.table])
-        return df
-
-    def query_label(self, label: str) -> SSMParams:
-        """Query the results for a specific SSMParams by label."""
-        for res in self.results:
-            if res.label == label:
-                return res
-        raise ValueError(
-            f"No results found for {label}"
-        )  # Raised if the label is never found
-
-    def plot(self, colors=None, legend=True, *args, **kwargs) -> plt.Axes:
-        """
-        Plot the results in the circumplex
-
-        Returns:
-            plt.Axes: A tuple containing the figure and axis objects.
-        """
-        fig, ax = plt.subplots(subplot_kw={"projection": "polar"})
-        if colors is None:
-            colors = colormaps.get_cmap("tab20").colors
-        colors = iter(colors)
-        for res in self.results:
-            ax.plot(
-                np.deg2rad(res.displacement),
-                res.amplitude,
-                color=next(colors),
-                marker="o",
-                markersize=10,
-                label=res.label,
-            )
-        fig.legend(loc="upper right", bbox_to_anchor=(1.2, 1))
-        return fig, ax
-
-    def profile_plots(self, axes=None) -> plt.Axes:
-        """
-        Plot the SSM profiles.
-
-        Returns:
-            plt.Axes: A tuple containing the figure and axis objects.
-        """
-        if axes is None:
-            fig, axes = plt.subplots(
-                nrows=len(self.results),
-                figsize=(8, 4 * len(self.results)),
-            )
-        for i, res in enumerate(self.results):
-            # BUG: Raises error if we only need a single plot
-            fig, ax = res.profile_plot(ax=axes.flatten()[i])
-        plt.tight_layout()
-        # plt.show()
-        return fig, axes
-
-
-# %%
-
-
-def ssm_analyse(
-    data: pd.DataFrame,
-    scales: list,
-    measures: list | None = None,
-    grouping: list | None = None,
-    angles: tuple = OCTANTS,
-    grouped_angles: dict = None,
-) -> SSMResults:
-    """
-    Analyse a set of data using the SSM method.
-
-    Args:
-        data (pd.DataFrame): A dataframe containing the data to be analysed.
-        scales (list): A list of the names of the circumplex scales to be included in the analysis.
-        measures (list, optional): A list of the names of the measures to be included in the analysis. Defaults to None.
-        grouping (list, optional): A list of the names of the groups to be included in the analysis. Defaults to None.
-        angles (tuple, optional): A tuple containing the angular displacement of each circumplex scale
-            included in `scores`. Defaults to OCTANTS.
-        grouped_angles (dict, optional): A dictionary containing the angular displacement of each circumplex scale
-            included in `scores` for each group. Defaults to None.
-
-    Returns:
-        SSMResults: A SSMResults object containing the results of the analysis.
-
-    """
-    if grouping is not None and measures is not None:
-        return ssm_analyse_grouped_corrs(
-            data, scales, measures, grouping, angles, grouped_angles
-        )
-    elif measures is not None:
-        return ssm_analyse_corrs(data, scales, measures, angles)
-    elif grouping is not None:
-        return ssm_analyse_means(data, scales, grouping, angles, grouped_angles)
-    else:
-        ssm = SSMParams(data[scales].mean(), scales, angles)
-        # ssm.param_calc()
-        return SSMResults(ssm)
-
-
-def ssm_analyse_grouped_corrs(
-    data: pd.DataFrame,
-    scales: tuple,
-    measures: list,
-    grouping: list,
-    angles: tuple = OCTANTS,
-    grouped_angles: dict = None,
-) -> SSMResults:
-    """
-    Perform SSM analysis of correlations for a set of grouped data.
-
-    Args:
-        data (pd.DataFrame): A dataframe containing the data to be analysed.
-        scales (tuple): A list of the names of the circumplex scales to be included in the analysis.
-        measures (list): A list of the names of the measures to be included in the analysis.
-        grouping (list): A list of the names of the groups to be included in the analysis.
-        angles (tuple, optional): A tuple containing the angular displacement of each circumplex scale
-            included in `scores`. Defaults to OCTANTS.
-
-    Returns:
-            SSMResults: A SSMResults object containing the results of the analysis.
-    """
-    res = []
-    for group_var in grouping:
-        for group, group_data in data.groupby(group_var):
-            if grouped_angles is not None:
-                angles = grouped_angles[group]  # grouped angles will override angles
-            try:
-                res.append(
-                    ssm_analyse_corrs(
-                        group_data, scales, measures, angles, group=group
-                    ).results[0]
-                )
-            except ValueError as e:
-                print(f"Error: {e} | in {group_var} = {group}")
-
-    return SSMResults(res, measures, grouping)
-
-
-def ssm_analyse_corrs(
-    data: pd.DataFrame,
-    scales: tuple,
-    measures: list,
-    angles: tuple = OCTANTS,
-    group: str | None = None,
-) -> SSMResults:
-    """
-    Perform SSM analysis of correlations for a set of data.
-
-    Args:
-        data (pd.DataFrame): A dataframe containing the data to be analysed.
-        scales (tuple): A list of the names of the circumplex scales to be included in the analysis.
-        measures (list): A list of the names of the measures to be included in the analysis.
-        angles (tuple, optional): A tuple containing the angular displacement of each circumplex scale
-            included in `scores`. Defaults to OCTANTS.
-        group (str, optional): The name of the group to be included in the analysis. Defaults to None.
-
-    Returns:
-        SSMResults: A SSMResults object containing the results of the analysis.
-    """
-    res = []
-    for measure in measures:
-        r = data[scales].corrwith(data[measure])
-        ssm = SSMParams(r, scales, angles, measure=measure, group=group)
-        # ssm.param_calc()
-        res.append(ssm)
-
-    return SSMResults(res, measures)
-
-
-def ssm_analyse_means(
-    data: pd.DataFrame,
-    scales: tuple,
-    grouping: list,
-    angles: tuple = OCTANTS,
-    grouped_angles: dict = None,
-) -> SSMResults:
-    """
-    Perform SSM analysis of means for a set of data.
-
-    Args:
-        data (pd.DataFrame): A dataframe containing the data to be analysed.
-        scales (tuple): A list of the names of the circumplex scales to be included in the analysis.
-        grouping (list): A list of the names of the groups to be included in the analysis.
-        angles (tuple, optional): A tuple containing the angular displacement of each circumplex scale
-            included in `scores`. Defaults to OCTANTS.
-
-    Returns:
-        SSMResults: A SSMResults object containing the results of the analysis.
-    """
-    means = data.groupby(grouping)[scales].mean()
-    res = []
-    for group, scores in means.iterrows():
-        if grouped_angles is not None:
-            angles = grouped_angles[group]
-        scores = means.loc[group]
-        ssm = SSMParams(scores, scales, angles, group=group)
-        # ssm.param_calc()
-        res.append(ssm)
-
-    return SSMResults(res, grouping=grouping)
-
-
-def cosine_form(theta, ampl, disp, elev):
-    """Cosine function with amplitude, dispersion and elevation parameters."""
-    return elev + ampl * np.cos(np.deg2rad(theta - disp))
-
-
-def _r2_score(y_true: np.array, y_pred: np.array):
-    """Calculate the R2 score for a set of predictions."""
-    ss_res = np.sum(np.square(y_true - y_pred))
-    ss_tot = np.sum(np.square(y_true - np.mean(y_true)))
-    return 1 - (ss_res / ss_tot)
-
-
-def ssm_parameters(scores, angles, bounds=BOUNDS) -> tuple:
-    """Calculate SSM parameters (without confidence intervals) for a set of scores.
-
-    Args:
-        scores (np.array): A numeric vector (or single row dataframe) containing one score for each of a
-            set of circumplex scales.
-        angles (tuple): A numeric vector containing the angular displacement of each circumplex scale
-            included in `scores`.
-        bounds (tuple, optional): The bounds for each of the parameters of the curve optimisation.
-            Defaults to ([0, 0, -1], [np.inf, 360, 1]).
-
-    Returns:
-        tuple: A tuple containing the elevation, x-value, y-value, amplitude, displacement, and R2 fit of the SSM curve.
-
-    Examples:
-        >>> scores = np.array([-0.5, 0, 0.25, 0.51, 0.52, 0.05, -0.26, -0.7])
-        >>> angles = OCTANTS
-        >>> results = ssm_parameters(scores, angles)
-        >>> [round(i, 3) for i in results]
-        [-0.016, -0.478, 0.333, 0.582, 145.158, 0.967]
-    """
-
-    # noinspection PyTupleAssignmentBalance
-    # NOTE: Bug - Sometimes returns displacement at the trough, not the crest, so 180 degrees off
-    # This was addressed by setting the lower bound of amplitude to 0, not -np.inf. Need a less hard-coded solution
-    param, covariance = curve_fit(
-        cosine_form, xdata=angles, ydata=scores, bounds=bounds
-    )
-    r2 = _r2_score(scores, cosine_form(angles, *param))
-    ampl, disp, elev = param
-
-    def polar2cart(r, theta):
-        x = r * np.cos(theta)
-        y = r * np.sin(theta)
-        return x, y
-
-    xval, yval = polar2cart(ampl, np.deg2rad(disp))
-    return elev, xval, yval, ampl, disp, r2
-
-
-def profile_plot(
-    amplitude,
-    displacement,
-    elevation,
-    r2,
-    angles,
-    scores,
-    label,
-    ax=None,
-    reorder_scales=True,
-    incl_pred=True,
-    incl_fit=True,
-    incl_disp=True,
-    incl_amp=True,
-    c_scores='red',
-    c_fit='black',
-) -> plt.Axes:
-    """
-    Plot the SSM profile.
-
-    Args:
-        reorder_scales:
-        incl_pred:
-        incl_fit:
-        incl_disp:
-        incl_amp:
-
-    Returns:
-        plt.Axes: A tuple containing the figure and axis objects.
-    """
-
-    if ax is None:
-        fig, ax = plt.subplots(figsize=(8, 4))
-    else:
-        fig = ax.get_figure()
-
-    if reorder_scales:
-        angles, scores = sort_angles(angles, scores)
-        if angles[-1] == 360:
-            angles = (0,) + angles
-            scores = (scores[-1],) + scores
-
-    if incl_pred:
-        thetas = np.linspace(0, 360, 1000)
-        fit = cosine_form(thetas, amplitude, displacement, elevation)
-        ax.plot(thetas, fit, color=c_fit)
-
-    ax.plot(angles, scores, color=c_scores, marker="o")
-    # ax.scatter(self.angles, self.scores, marker="o", color="black")
-    if incl_disp:
-        ax.axvline(displacement, color="black", linestyle="--")
-        ax.text(
-            displacement + 5,
-            elevation,
-            f"d = {int(displacement)}",
-        )
-    if incl_amp:
-        ax.axhline(amplitude + elevation, color="black", linestyle="--")
-        ax.text(0, amplitude + elevation * 0.9, f"a = {amplitude:.2f}")
-
-    if incl_fit:
-        ax.text(0, elevation * 0.5, f"R2 = {r2:.2f}")
-
-    ax.set_xticks(OCTANTS)
-    ax.set_xticklabels(
-        ["0", "45", "90", "135", "180", "225", "270", "315"], fontsize=14
-    )
-    ax.set_xlabel("Angle [deg]", fontsize=16)
-    ax.set_ylabel("Score", fontsize=16)
-    ax.set_title(f"{label} Profile", fontsize=20)
-    return fig, ax
-
-
-def sort_angles(angles, scores):
-    """Sort angles, scores, and scales in order of scale."""
-    # Zip the values and labels together
-    zipped = list(zip(angles, scores))
-    # Sort the list of zipped tuples
-    zipped.sort(key=lambda x: x[0])
-    # Unzip the list of tuples
-    angles, scores = zip(*zipped)
-    return angles, scores
diff --git a/src/circumplex/core/__init__.py b/src/circumplex/core/__init__.py
new file mode 100644
index 0000000..0d7740e
--- /dev/null
+++ b/src/circumplex/core/__init__.py
@@ -0,0 +1,5 @@
+"""Core circumplex analysis functions."""
+
+from circumplex.core.parameters import ssm_parameters
+
+__all__ = ["ssm_parameters"]
diff --git a/src/circumplex/core/parameters.py b/src/circumplex/core/parameters.py
new file mode 100644
index 0000000..719cc36
--- /dev/null
+++ b/src/circumplex/core/parameters.py
@@ -0,0 +1,107 @@
+"""SSM parameter calculation engine.
+
+This module implements the core Structural Summary Method parameter
+calculations, ported from the C++ implementation in the R package.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, cast
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from typing import Any
+
+    from nptyping import NDArray, Shape
+
+
+def ssm_parameters(
+    scores: NDArray[Shape[Any], float],
+    angles: NDArray[Shape[Any], float],
+) -> dict[str, float]:
+    """Calculate SSM parameters for a profile of circumplex scores.
+
+    Direct port of ssm_parameters_cpp from the R circumplex package.
+    Computes the six core SSM parameters: elevation, x-value, y-value,
+    amplitude, displacement, and model fit.
+
+    Parameters
+    ----------
+    scores
+        Array of circumplex scale scores (length n_scales)
+    angles
+        Angular positions in radians (length n_scales)
+
+    Returns
+    -------
+    Dictionary with keys:
+
+    - elevation: Mean of all scale scores
+    - x_value: Projection onto x-axis (cosine component)
+    - y_value: Projection onto y-axis (sine component)
+    - amplitude: Vector length in 2D space
+    - displacement: Angular position in radians [0, 2π)
+    - fit: Model fit (R²), proportion of variance explained
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from circumplex.utils.angles import OCTANTS, degrees_to_radians
+    >>>
+    >>> scores = np.array([0.374, -0.572, -0.520, 0.016, 0.688, 1.142, 1.578, 0.678])
+    >>> angles = degrees_to_radians(OCTANTS)
+    >>> params = ssm_parameters(scores, angles)
+    >>> print(f"Elevation: {params['elevation']:.3f}")
+    Elevation: 0.423
+    >>> print(f"Amplitude: {params['amplitude']:.3f}")
+    Amplitude: 0.981
+
+    Notes
+    -----
+    This function implements the Structural Summary Method as described in
+    Zimmermann & Wright (2017). The displacement is returned in radians;
+    convert to degrees if needed.
+
+    References
+    ----------
+    Zimmermann, J., & Wright, A. G. C. (2017). Beyond description in
+    interpersonal construct validation: Methodological advances in the
+    circumplex Structural Summary Approach. Assessment, 24(1), 3-23.
+
+    """
+    n = len(scores)
+
+    # Elevation: mean of scores
+    elevation: float = cast("float", np.mean(scores))
+
+    # X and Y values: projections onto axes
+    x_value: float = (2 / n) * np.dot(scores, np.cos(angles))
+    y_value: float = (2 / n) * np.dot(scores, np.sin(angles))
+
+    # Amplitude: vector length
+    amplitude: float = np.sqrt(x_value**2 + y_value**2)
+
+    # Displacement: angular position
+    # Use atan2 for proper quadrant, then modulo to ensure [0, 2π)
+    displacement: float = np.arctan2(y_value, x_value) % (2 * np.pi)
+
+    # Model fit (R²)
+    # Predicted scores from cosine model
+    predicted: NDArray[Any, float] = elevation + amplitude * np.cos(
+        angles - displacement
+    )
+    ss_residual: float = np.sum((scores - predicted) ** 2)
+    ss_total: float = cast("float", np.var(scores, ddof=1) * (n - 1))
+
+    # Avoid division by zero
+    fit: float = 1.0 if ss_total == 0 else 1 - ss_residual / ss_total
+
+    return {
+        "elevation": elevation,
+        "x_value": x_value,
+        "y_value": y_value,
+        "amplitude": amplitude,
+        "displacement": displacement,
+        "fit": fit,
+    }
diff --git a/src/circumplex/core/scores.py b/src/circumplex/core/scores.py
new file mode 100644
index 0000000..de3f9e0
--- /dev/null
+++ b/src/circumplex/core/scores.py
@@ -0,0 +1,251 @@
+"""Score calculation functions for SSM analysis.
+
+This module implements the core score calculation functions for both mean-based
+and correlation-based SSM analysis, ported from the C++ implementation in the
+R circumplex package.
+"""
+
+import numpy as np
+
+from circumplex.core.parameters import ssm_parameters
+
+
+def mean_scores(
+    data: np.ndarray,
+    groups: np.ndarray | None = None,
+    *,
+    listwise: bool = True,
+) -> np.ndarray:
+    """Calculate mean scale scores by group.
+
+    Port of mean_scores() from R circumplex C++ implementation. Computes
+    mean values for each scale, optionally stratified by group, with
+    listwise or pairwise deletion of missing data.
+
+    Parameters
+    ----------
+    data
+        Array of circumplex scale scores, shape (n_obs, n_scales)
+    groups
+        Group indicators as integers (0-indexed), shape (n_obs,).
+        If None, treats all observations as a single group.
+    listwise
+        If True, use listwise deletion (remove any row with any NA).
+        If False, use pairwise deletion (compute mean per scale ignoring NAs).
+
+    Returns
+    -------
+    Array of mean scores, shape (n_groups, n_scales)
+
+    Examples
+    --------
+    >>> data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+    >>> mean_scores(data)
+    array([[4., 5., 6.]])
+
+    >>> groups = np.array([0, 0, 1])
+    >>> mean_scores(data, groups)
+    array([[2.5, 3.5, 4.5],
+           [7. , 8. , 9. ]])
+
+    Notes
+    -----
+    This function mirrors the behavior of mean_scores() in the R package's
+    C++ code (src/parameters.cpp lines 62-93).
+
+    """
+    # Handle single group case
+    if groups is None:
+        groups = np.zeros(len(data), dtype=int)
+
+    # Get unique groups (sorted)
+    unique_groups = np.unique(groups)
+    n_groups = len(unique_groups)
+    n_scales = data.shape[1]
+
+    # Initialize output
+    result = np.zeros((n_groups, n_scales))
+
+    for i, group_id in enumerate(unique_groups):
+        # Extract data for this group
+        group_mask = groups == group_id
+        group_data = data[group_mask]
+
+        if listwise:
+            # Listwise deletion: remove rows with any NA
+            complete_rows = ~np.isnan(group_data).any(axis=1)
+            clean_data = group_data[complete_rows]
+            if len(clean_data) > 0:
+                result[i] = np.mean(clean_data, axis=0)
+            else:
+                result[i] = np.nan
+        else:
+            # Pairwise deletion: compute mean per column ignoring NAs
+            result[i] = np.nanmean(group_data, axis=0)
+
+    return result
+
+
+def corr_scores(
+    scales: np.ndarray,
+    measures: np.ndarray,
+    groups: np.ndarray | None = None,
+    *,
+    listwise: bool = True,
+) -> np.ndarray:
+    """Calculate correlation scores between measures and scales by group.
+
+    Port of corr_scores() from R circumplex C++ implementation. Computes
+    correlations between measure variables and circumplex scales, optionally
+    stratified by group, with listwise or pairwise deletion.
+
+    Parameters
+    ----------
+    scales
+        Array of circumplex scale scores, shape (n_obs, n_scales)
+    measures
+        Array of measure variables, shape (n_obs, n_measures)
+    groups
+        Group indicators as integers (0-indexed), shape (n_obs,).
+        If None, treats all observations as a single group.
+    listwise
+        If True, use listwise deletion (remove any row with any NA).
+        If False, use pairwise deletion (compute correlation per pair ignoring NAs).
+
+    Returns
+    -------
+    Array of correlation scores, shape (n_groups * n_measures, n_scales).
+    Rows are ordered by group, then by measure within each group.
+
+    Examples
+    --------
+    >>> scales = np.array([[1, 2], [3, 4], [5, 6]])
+    >>> measures = np.array([[0], [1], [2]])
+    >>> corr_scores(scales, measures)
+    array([[1., 1.]])
+
+    Notes
+    -----
+    This function mirrors the behavior of corr_scores() in the R package's
+    C++ code (src/parameters.cpp lines 113-160).
+
+    The output is organized as:
+    - Single group: [measure1_corrs, measure2_corrs, ...]
+    - Multiple groups: [g1_m1_corrs, g1_m2_corrs, ..., g2_m1_corrs, g2_m2_corrs, ...]
+
+    """
+    # Ensure measures is 2D
+    if measures.ndim == 1:
+        measures = measures.reshape(-1, 1)
+
+    # Handle single group case
+    if groups is None:
+        groups = np.zeros(len(scales), dtype=int)
+
+    # Get dimensions
+    unique_groups = np.unique(groups)
+    n_groups = len(unique_groups)
+    n_measures = measures.shape[1]
+    n_scales = scales.shape[1]
+
+    # Initialize output: (n_groups * n_measures) x n_scales
+    result = np.zeros((n_groups * n_measures, n_scales))
+
+    for g_idx, group_id in enumerate(unique_groups):
+        # Extract data for this group
+        group_mask = groups == group_id
+        group_scales = scales[group_mask]
+        group_measures = measures[group_mask]
+
+        if listwise:
+            # Listwise deletion: remove rows with any NA in scales or measures
+            all_data = np.column_stack([group_scales, group_measures])
+            complete_rows = ~np.isnan(all_data).any(axis=1)
+            clean_scales = group_scales[complete_rows]
+            clean_measures = group_measures[complete_rows]
+
+            # Compute correlations for all measure-scale pairs
+            for m_idx in range(n_measures):
+                row_idx = g_idx * n_measures + m_idx
+                if len(clean_scales) > 1:  # Need at least 2 observations
+                    for s_idx in range(n_scales):
+                        result[row_idx, s_idx] = np.corrcoef(
+                            clean_measures[:, m_idx], clean_scales[:, s_idx]
+                        )[0, 1]
+                else:
+                    result[row_idx] = np.nan
+        else:
+            # Pairwise deletion: compute correlation per pair ignoring NAs
+            for m_idx in range(n_measures):
+                row_idx = g_idx * n_measures + m_idx
+                for s_idx in range(n_scales):
+                    # Get measure and scale vectors
+                    m_vec = group_measures[:, m_idx]
+                    s_vec = group_scales[:, s_idx]
+
+                    # Remove pairs where either is NA
+                    valid_mask = ~(np.isnan(m_vec) | np.isnan(s_vec))
+                    m_clean = m_vec[valid_mask]
+                    s_clean = s_vec[valid_mask]
+
+                    if len(m_clean) > 1:  # Need at least 2 observations
+                        result[row_idx, s_idx] = np.corrcoef(m_clean, s_clean)[0, 1]
+                    else:
+                        result[row_idx, s_idx] = np.nan
+
+    return result
+
+
+def group_parameters(
+    scores: np.ndarray,
+    angles: np.ndarray,
+) -> np.ndarray:
+    """Calculate SSM parameters for multiple groups.
+
+    Applies ssm_parameters() to each row of a score matrix, returning
+    a flat array of all parameters.
+
+    Parameters
+    ----------
+    scores
+        Array of scale scores, shape (n_groups, n_scales)
+    angles
+        Angular positions in radians, shape (n_scales,)
+
+    Returns
+    -------
+    Flat array of parameters, length (n_groups * 6).
+    Order: [e1, x1, y1, a1, d1, f1, e2, x2, y2, a2, d2, f2, ...]
+
+    Examples
+    --------
+    >>> from circumplex.core.parameters import ssm_parameters
+    >>> from circumplex.utils.angles import OCTANTS, degrees_to_radians
+    >>> scores = np.array([[1, 2, 3, 4, 5, 6, 7, 8],
+    ...                     [8, 7, 6, 5, 4, 3, 2, 1]])
+    >>> angles = degrees_to_radians(OCTANTS)
+    >>> params = group_parameters(scores, angles)
+    >>> len(params)
+    12
+
+    Notes
+    -----
+    This function mirrors group_parameters() in the R package's C++ code
+    (src/parameters.cpp lines 37-45).
+
+    """
+    n_groups = scores.shape[0]
+    result = np.zeros(n_groups * 6)
+
+    for i in range(n_groups):
+        params = ssm_parameters(scores[i], angles)
+        result[i * 6 : (i + 1) * 6] = [
+            params["elevation"],
+            params["x_value"],
+            params["y_value"],
+            params["amplitude"],
+            params["displacement"],
+            params["fit"],
+        ]
+
+    return result
diff --git a/src/circumplex/data/SATP Dataset v1.4.xlsx b/src/circumplex/data/SATP Dataset v1.4.xlsx
deleted file mode 100644
index cffd75f..0000000
Binary files a/src/circumplex/data/SATP Dataset v1.4.xlsx and /dev/null differ
diff --git a/src/circumplex/data/__init__.py b/src/circumplex/data/__init__.py
index e69de29..1b4ba86 100644
--- a/src/circumplex/data/__init__.py
+++ b/src/circumplex/data/__init__.py
@@ -0,0 +1,96 @@
+"""Example datasets and data loading utilities.
+
+This module provides access to the built-in example datasets that
+are used for testing and demonstration purposes.
+"""
+
+from pathlib import Path
+from typing import Literal
+
+import pandas as pd
+
+# Type for available datasets
+DatasetName = Literal["jz2017", "aw2009"]
+
+
+def load_dataset(name: DatasetName) -> pd.DataFrame:
+    """Load a built-in example dataset.
+
+    Parameters
+    ----------
+    name : str
+        Dataset name. Available datasets:
+        - 'jz2017': Interpersonal problems and personality disorder data
+        - 'aw2009': Standardized octant scores
+
+    Returns
+    -------
+    pd.DataFrame
+        The requested dataset
+
+    Raises
+    ------
+    ValueError
+        If dataset name is not recognized
+
+    Examples
+    --------
+    >>> from circumplex.data import load_dataset
+    >>> jz2017 = load_dataset('jz2017')
+    >>> print(jz2017.shape)
+    (1166, 19)
+    >>> print(jz2017.columns[:9].tolist())
+    ['Gender', 'PA', 'BC', 'DE', 'FG', 'HI', 'JK', 'LM', 'NO']
+
+    Notes
+    -----
+    The jz2017 dataset contains data from Zimmermann & Wright (2017):
+    - 1166 observations
+    - 8 IIP-SC octant scales (PA through NO)
+    - 10 PDQ-4+ personality disorder scales
+    - Gender grouping variable
+
+    The aw2009 dataset contains data from Wright et al. (2009):
+    - 5 observations
+    - 8 standardized circumplex scales
+
+    """
+    valid_datasets = {"jz2017", "aw2009"}
+
+    if name not in valid_datasets:
+        msg = (
+            f"Unknown dataset '{name}'. Valid options are: {', '.join(valid_datasets)}"
+        )
+        raise ValueError(msg)
+
+    # Path to data files
+    data_dir = Path(__file__).parent
+    filepath = data_dir / f"{name}.csv"
+
+    if not filepath.exists():
+        msg = (
+            f"Dataset file not found: {filepath}. "
+            f"This may indicate a corrupted package installation."
+        )
+        raise FileNotFoundError(msg)
+
+    return pd.read_csv(filepath)
+
+
+def list_datasets() -> list[str]:
+    """List all available built-in datasets.
+
+    Returns
+    -------
+    list of str
+        Names of available datasets
+
+    Examples
+    --------
+    >>> from circumplex.data import list_datasets
+    >>> datasets = list_datasets()
+    >>> print(datasets)
+    ['aw2009', 'jz2017']
+
+    """
+    return ["aw2009", "jz2017"]
diff --git a/src/circumplex/data/aw2009.csv b/src/circumplex/data/aw2009.csv
new file mode 100644
index 0000000..4dc4a74
--- /dev/null
+++ b/src/circumplex/data/aw2009.csv
@@ -0,0 +1,6 @@
+"PA","BC","DE","FG","HI","JK","LM","NO"
+-1.09,-1.04,-0.97,0.61,1.41,2.49,1.78,0.27
+1.13,-1.04,-0.97,-0.79,-0.56,0.79,1.78,1.52
+0.91,-0.65,-0.8,-0.96,-0.23,-0.34,1.24,0.27
+0.47,-0.45,-0.29,0.26,1.57,1.36,1.6,0.48
+0.45,0.32,0.43,0.96,1.25,1.41,1.49,0.85
diff --git a/src/circumplex/datasets.py b/src/circumplex/datasets.py
deleted file mode 100644
index 54ef1aa..0000000
--- a/src/circumplex/datasets.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# %%
-import pandas as pd
-from circumplex import instrument
-from importlib.resources import files
-
-_jz2017_path = str(files("circumplex.data").joinpath("jz2017.csv"))
-JZ2017 = instrument.load_instrument("CSIP").attach_data(pd.read_csv(_jz2017_path))
-
-_raw_iipsc_path = str(files("circumplex.data").joinpath("raw_iipsc.csv"))
-# RAW_IIPSC = instrument.load_instrument("IIPSC").attach_data(pd.read_csv(_raw_iipsc_path))
-
-_satp_path = str(files("circumplex.data").joinpath("SATP Dataset v1.4.xlsx"))
-satp_data = pd.read_excel(_satp_path)
-SATP_ENG = instrument.load_instrument("SATP-eng").attach_data(satp_data.query("Language == 'eng'"))
diff --git a/src/circumplex/instrument.py b/src/circumplex/instrument.py
deleted file mode 100644
index dc1506d..0000000
--- a/src/circumplex/instrument.py
+++ /dev/null
@@ -1,355 +0,0 @@
-# %%
-from __future__ import annotations
-
-import json
-from dataclasses import dataclass
-from importlib.resources import files
-from typing import Any
-
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-
-from circumplex import SSMResults, ssm_analyse
-
-INSTRUMENT_JSONS = {
-    "CSIP": str(files("circumplex.instruments").joinpath("CSIP.json")),
-    "IIPSC": str(files("circumplex.instruments").joinpath("IIPSC.json")),
-    "SSQP-eng": str(files("circumplex.instruments").joinpath("SSQP-eng.json")),
-    "SATP-eng": str(files("circumplex.instruments").joinpath("SATP-eng.json")),
-}
-
-
-def instruments() -> None:
-    """
-    Print a list of the instruments included in the circumplex package.
-
-    Args:
-        None
-
-    Returns:
-        None
-    """
-    ins = {name: load_instrument(name) for name in INSTRUMENT_JSONS.keys()}
-    print(f"The circumplex package currently includes {len(ins)} instruments:")
-    i = 1
-    for name, inst in ins.items():
-        print(f"{i}. {name}: {inst.details.name} ({inst.details.abbrev})")
-        i += 1
-
-    return None
-
-
-def from_dict(inst_dict: dict) -> Instrument:
-    """
-    Compose an Instrument object from a dictionary.
-
-    Typically this would be used to load an instrument from one of our built in JSON files.
-    Args:
-        inst_dict: A dictionary containing the instrument's details, scales, anchors, and items.
-
-    Returns:
-        Instrument: An Instrument object.
-    """
-    items_exist = sum(
-        ["inst_items" in scale.keys() for scale in inst_dict["scales"].values()]
-    )
-    scales = Scales(
-        abbrev=list(inst_dict["scales"].keys()),
-        label=[scale["label"] for scale in inst_dict["scales"].values()],
-        angle=[scale["angle"] for scale in inst_dict["scales"].values()],
-        inst_items=[
-            inst_dict["scales"][scale]["inst_items"] for scale in inst_dict["scales"].keys()
-        ]
-        if items_exist
-        else None,
-    )
-    anchors = Anchors(
-        value=[int(key) for key in inst_dict["anchors"].keys()],
-        label=list(inst_dict["anchors"].values()),
-    )
-    norms = Norms(
-        table=pd.DataFrame.from_dict(inst_dict["norms"]),
-        src=pd.DataFrame.from_dict(inst_dict["norms_src"])
-    ) if "norms" in inst_dict else None
-    details = InstrumentDetails(**inst_dict["details"])
-    return Instrument(scales, anchors, details, norms)
-
-
-def load_instrument(instrument: str) -> Instrument:
-    """
-    Load an instrument from one of our built-in JSON files.
-
-    Args:
-        instrument: The name of the instrument to load. Must be one of the following:
-            - CSIP
-
-    Returns:
-        Instrument: An Instrument object.
-    """
-    with open(INSTRUMENT_JSONS[instrument], "r") as f:
-        instrument = json.load(f)
-
-    return from_dict(instrument)
-
-
-@dataclass
-class Anchors:
-    value: list[int]
-    label: list[str]
-
-    def __post_init__(self):
-        assert len(self.value) == len(self.label)
-
-    def __repr__(self):
-        return f"Anchors({self.value}, {self.label})"
-
-    def __str__(self):
-        return "\n".join(
-            [f"{value}. {label}" for value, label in zip(self.value, self.label)]
-        )
-
-    def show(self):
-        return print(self)
-
-
-@dataclass
-class Items:
-    data: dict
-
-    def __getitem__(self, key: Any) -> Any:
-        return self.data[key]
-
-    def __setitem__(self, key: Any, value: Any) -> None:
-        self.data[key] = value
-
-    def __delitem__(self, key: Any) -> None:
-        del self.data[key]
-
-    def keys(self) -> list:
-        return list(self.data.keys())
-
-    def values(self) -> list:
-        return list(self.data.values())
-
-    def inst_items(self) -> list:
-        return list(self.data.items())
-
-    def __str__(self):
-        return "\n".join([f"{number}. {text}" for number, text in self.data.items()])
-
-    def show(self, n=10):
-        n = len(self.data) if n is None or n > len(self.data) else n
-        p = "\n".join([f"{number}. {text}" for number, text in list(self.data.items())[:n]])
-        if n < len(self.data):
-            p += f"\n\n...and {len(self.data) - n} more items."
-
-        return print(p)
-
-
-@dataclass
-class Scales:
-    abbrev: list[str]
-    label: list[str]
-    angle: list[float]
-    inst_items: list[dict] | None = None
-
-    def __post_init__(self):
-        assert len(self.abbrev) == len(self.angle)
-        assert len(self.abbrev) == len(set(self.abbrev)), "Abbreviations must be unique"
-        assert (
-            max(self.angle) <= 360 and min(self.angle) >= 0
-        ), "Angles must be between 0 and 360"
-
-    def __str__(self):
-        return "\n".join(
-            [
-                f"{abbrev}: {label} ({angle}°)"
-                for abbrev, label, angle in zip(self.abbrev, self.label, self.angle)
-            ]
-        )
-
-    def show(self, inst_items: bool = True):
-        if inst_items is False:
-            return print(self)
-        else:
-            p = []
-            for i, abbrev in enumerate(self.abbrev):
-                p.append(f"{abbrev}: {self.label[i]} ({self.angle[i]}°)")
-                p.append(
-                    "\n".join([f"\t{key}: {val}" for key, val in self.inst_items[i].items()])
-                )
-            return print("\n".join(p))
-
-
-@dataclass
-class Norms:
-    table: pd.DataFrame
-    src: pd.DataFrame
-
-    def get_sample(self, sample: int) -> pd.DataFrame:
-        return self.table.query("sample == @sample")
-
-    def show(self):
-        return print(self.src)
-
-
-@dataclass
-class InstrumentDetails:
-    name: str
-    abbrev: str
-    inst_items: int | None = None
-    scales: int | None = None
-    prefix: str | None = None
-    suffix: str | None = None
-    status: str | None = None
-    construct: str | None = None
-    reference: str | None = None
-    url: str | None = None
-
-    def __str__(self):
-        return (
-            f"{self.abbrev}: {self.name}\n"
-            f"{self.inst_items} Items, {self.scales} Scales\n"
-            f"{self.reference}\n"
-            f"<{self.url}>"
-        )
-
-
-@dataclass
-class Instrument:
-    """
-    A class for representing circumplex instruments.
-
-    Attributes:
-        scales: Scales
-        anchors: Anchors
-        details: InstrumentDetails
-        inst_items: Items | None = None
-        _data: pd.DataFrame | None = None
-    """
-
-    scales: Scales
-    anchors: Anchors
-    details: InstrumentDetails
-    norms: Norms | None = None
-    _data: pd.DataFrame | None = None
-
-    def __repr__(self):
-        return (
-            f"{self.details.abbrev}: {self.details.name}\n"
-            f"{self.details.inst_items} Items, {self.details.scales} Scales\n"
-            f"{self.details.reference}\n"
-            f"<{self.details.url}>"
-        ) if self.norms is None else (
-            f"{self.details.abbrev}: {self.details.name}\n"
-            f"{self.details.inst_items} Items, {self.details.scales} Scales, {len(self.norms.src)} normative data sets\n"
-            f"{self.details.reference}\n"
-            f"<{self.details.url}>"
-        )
-
-    @property
-    def data(self):
-        if self._data is None:
-            raise UserWarning(
-                "No data has been loaded for this instrument. Use attach_data() to load data."
-            )
-        else:
-            return self._data
-
-    @property
-    def inst_items(self):
-        if self.scales.inst_items is None:
-            raise UserWarning("No items have been defined for this instrument.")
-        else:
-            item_dict = {}
-            for val in self.scales.inst_items:
-                for key, value in val.items():
-                    item_dict[int(key)] = value
-            item_dict = {k: v for k, v in sorted(item_dict.items())}
-
-            return Items(item_dict)
-
-    def summary(self):
-        print(self.details)
-        print(
-            f"\nThe {self.details.abbrev} contains {self.details.scales} circumplex scales."
-        )
-        print(self.scales)
-        print(
-            f"\nThe {self.details.abbrev} is rated using the following {len(self.anchors.value)}-point scale."
-        )
-        print(self.anchors)
-        print(
-            f"\nThe {self.details.abbrev} contains {self.details.inst_items} items ({self.details.status})."
-        )
-        try:
-            print(self.inst_items)
-        except UserWarning:
-            print("\nNo items have been defined for this instrument.")
-        try:
-            print(self.data)
-        except UserWarning:
-            print(
-                "\nNo data has been loaded for this instrument. Use attach_data() to load data."
-            )
-
-
-    def attach_data(self, data: pd.DataFrame, scales: list | dict = None) -> Instrument:
-        # check scales
-        assert set(self.scales.abbrev).issubset(data.columns), (
-            f"Data is missing scales. "
-            f"Missing scales: {set(self.scales.abbrev) - set(data.columns)}"
-        )
-        self._data = data
-        return self
-
-    def ssm_analyse(
-        self, measures: list[str] = None, grouping: list[str] = None
-    ) -> SSMResults:
-        return ssm_analyse(
-            self.data,
-            self.scales.abbrev,
-            measures=measures,
-            grouping=grouping,
-            angles=tuple(self.scales.angle),
-        )
-
-    def demo_plot(self):
-        # alabel = self.scales.label
-        # angles = self.scales.angle
-        degree_sign = "\N{DEGREE SIGN}"
-
-        # Create plot ---------------------------------------------------------------
-        fig, ax = plt.subplots(figsize=(4, 4), subplot_kw=dict(polar=True))
-
-        ax.plot()
-        ax.tick_params(axis="both", pad=10)
-        ax.set_xticks(
-            np.radians(self.scales.angle),
-            labels=self.scales.label,
-            fontsize=12,
-        )
-
-        ax.set_yticks([])
-        ax.grid(True)
-        for i, angle in enumerate(self.scales.angle):
-            ax.text(
-                np.radians(angle),
-                0.4,
-                f"{angle}{degree_sign}",
-                ha="center",
-                va="center",
-                fontsize=12,
-                color="gray",
-            )
-            ax.text(
-                np.radians(angle),
-                0.75,
-                self.scales.abbrev[i],
-                ha="center",
-                va="center",
-                fontsize=12,
-                color="gray",
-            )
-        plt.show()
diff --git a/src/circumplex/instruments/CSIP.json b/src/circumplex/instruments/CSIP.json
deleted file mode 100644
index 66b67e1..0000000
--- a/src/circumplex/instruments/CSIP.json
+++ /dev/null
@@ -1,134 +0,0 @@
-{
-  "details": {
-    "name": "Circumplex Scales of Interpersonal Problems",
-    "abbrev": "CSIP",
-    "inst_items": 64,
-    "scales": 8,
-    "prefix": "",
-    "suffix": "",
-    "status": "open-access",
-    "construct": "interpersonal problems",
-    "reference": "Boudreaux, Ozer, Oltmanns, & Wright (2018)",
-    "url": "https://doi.org/10.1037/pas0000505"
-  },
-  "anchors": {
-    "0": "Not a problem",
-    "1": "Minor problem",
-    "2": "Moderate problem",
-    "3": "Serious problem"
-  },
-  "scales": {
-    "PA": {
-      "label": "Domineering",
-      "angle": 90,
-      "inst_items": {
-        "1": "Bossing around other people too much",
-        "9": "Verbally or physically abusing others",
-        "17": "Starting arguments and conflicts with others",
-        "25": "Trying to influence or control other people too much",
-        "33": "Dominating or intimidating others",
-        "41": "Acting aggressively toward others",
-        "49": "Manipulating other people to get what I want",
-        "57": "Acting superior or condescending toward others"
-      }
-    },
-    "BC": {
-      "label": "Self-Centered",
-      "angle": 135,
-      "inst_items": {
-        "2": "Acting rude and inconsiderate toward others",
-        "10": "Acting selfishly with others",
-        "18": "Being unable to feel guilt or remorse",
-        "26": "Lacking respect for other people's beliefs, attitudes, or opinions",
-        "34": "Having trouble getting along with others",
-        "42": "Being insensitive to the thoughts, feelings, and needs of others",
-        "50": "Disliking most people",
-        "58": "Having trouble giving emotional or moral support to others"
-      }
-    },
-    "DE": {
-      "label": "Distant",
-      "angle": 180,
-      "inst_items": {
-        "3": "Pushing away from other people who get too close",
-        "11": "Difficulty showing love and affection to others",
-        "19": "Being unable to enjoy the company of others",
-        "27": "Feeling emotionally disconnected from others",
-        "35": "Difficulty developing close and lasting relationships",
-        "43": "Being unable to fully connect with others",
-        "51": "Difficulty opening up to others",
-        "59": "Feeling uncomfortable with being close or intimate with others"
-      }
-    },
-    "FG": {
-      "label": "Socially Inhibited",
-      "angle": 225,
-      "inst_items": {
-        "4":  "Difficulty making friends",
-        "12": "Having trouble fitting in with others",
-        "20": "Avoiding people or social situations",
-        "28": "Being unable to keep conversations going",
-        "36": "Feeling like an outsider in most social situations",
-        "44": "Being unable to be myself around others",
-        "52": "Feeling fearful or nervous in social situations",
-        "60": "Acting shy around others"
-      }
-    },
-    "HI": {
-      "label": "Nonassertive",
-      "angle": 270,
-      "inst_items": {
-        "5": "Lacking self-confidence",
-        "13": "Getting easily embarrassed in front of others",
-        "21": "Difficulty taking the lead",
-        "29": "Having trouble asserting myself",
-        "37": "Feeling weak and insecure around dominant others",
-        "45": "Being unable to stand up to others",
-        "53": "Avoiding confrontation when problems arise",
-        "61": "Letting other people make decisions too often"
-      }
-    },
-    "JK": {
-      "label": "Exploitable",
-      "angle": 315,
-      "inst_items": {
-        "6": "Letting other people boss me around too much",
-        "14": "Acting overly submissive with others",
-        "22": "Being unable to express anger toward others",
-        "30": "Being too concerned about what other people think",
-        "38": "Being easily taken advantage of",
-        "46": "Compromising with other people too much",
-        "54": "Being easily influenced by others",
-        "62": "Being unable to say 'no'"
-      }
-    },
-    "LM": {
-      "label": "Self-Sacrificing",
-      "angle": 360,
-      "inst_items": {
-        "7": "Putting other people's needs before my own too much",
-        "15": "Giving too much to others",
-        "23": "Forgiving people too easily",
-        "31": "Being overly sentimental or tender-hearted",
-        "39": "Being easily affected by the pain and suffering of others",
-        "47": "Trusting people too easily",
-        "55": "Trying to solve other people's problems too much",
-        "63": "Getting too attached to others"
-      }
-    },
-    "NO": {
-      "label": "Intrusive",
-      "angle": 45,
-      "inst_items": {
-        "8": "Being overly affectionate with others",
-        "16": "Difficulty keeping personal matters private from others",
-        "24": "Talking too much",
-        "32": "Flirting with other people too much",
-        "40": "Having trouble respecting other people's privacy",
-        "48": "Exaggerating so that other people will respect me",
-        "56": "Confronting people too quickly about problems",
-        "64": "Needing to be the center of attention"
-      }
-    }
-  }
-}
\ No newline at end of file
diff --git a/src/circumplex/instruments/IIPSC.json b/src/circumplex/instruments/IIPSC.json
deleted file mode 100644
index e3cd2a9..0000000
--- a/src/circumplex/instruments/IIPSC.json
+++ /dev/null
@@ -1,126 +0,0 @@
-{
-  "details": {
-    "name": "Inventory of Interpersonal Problems Short Circumplex",
-    "abbrev": "IIP-SC",
-    "inst_items": 32,
-    "scales": 8,
-    "prefix": "",
-    "suffix": "",
-    "status": "partial text",
-    "construct": "interpersonal problems",
-    "reference": "Soldz, Budman, Demby, & Merry (1995)",
-    "url": "https://doi.org/10.1177/1073191195002001006"
-  },
-  "anchors": {
-    "0": "Not at all",
-    "1": "Somewhat",
-    "2": "Moderately",
-    "3": "Very",
-    "4": "Extremely"
-  },
-  "norms": {
-    "sample": [1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2],
-    "scale": ["PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO", "PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"],
-    "angle": [90, 135, 180, 225, 270, 315, 360, 45, 90, 135, 180, 225, 270, 315, 360, 45],
-    "m": [0.76, 0.7925, 0.9, 1.0475, 1.42, 1.385, 1.465, 1.025, 0.99, 0.97, 1.30, 1.33, 1.81, 1.92, 2.14, 1.43],
-    "sd": [0.66, 0.69, 0.855, 0.9475, 0.915, 0.8525, 0.825, 0.8, 0.82, 0.85, 1.07, 0.98, 0.89, 0.89, 0.90, 1.05]
-  },
-  "norms_src": {
-    "sample": [1, 2],
-    "size": [872, 106],
-    "population": [
-      "American college students",
-      "American psychiatric outpatients"
-    ],
-    "reference": [
-      "Hopwood, Pincus, DeMoor, & Koonce (2011)",
-      "Soldz, Budman, Demby, & Merry (1995)"
-    ],
-    "url": [
-      "https://doi.org/10.1080/00223890802388665",
-      "https://doi.org/10.1177/1073191195002001006"
-    ]
-  },
-  "scales": {
-    "PA": {
-      "label": "Domineering",
-      "angle": 90,
-      "inst_items": {
-        "1": "...point of view...",
-        "9": "...too aggressive toward...",
-        "17": " ...control other people...",
-        "25": " ...argue with other..."
-      }
-    },
-    "BC": {
-      "label": "Vindictive",
-      "angle": 135,
-      "inst_items": {
-        "2": "...supportive of another...",
-        "10": "...another person's happiness...",
-        "18": "...too suspicious of...",
-        "26": "...revenge against people..."
-      }
-    },
-    "DE": {
-      "label": "Cold",
-      "angle": 180,
-      "inst_items": {
-        "3": "...show affection to...",
-        "11": "...feeling of love...",
-        "19": "...feel close to...",
-        "27": "...at a distance..."
-      }
-    },
-    "FG": {
-      "label": "Socially Avoidant",
-      "angle": 225,
-      "inst_items": {
-        "4": "...join in on...",
-        "12": "...introduce myself to...",
-        "20": "...socialize with other...",
-        "28": "...get together socially..."
-      }
-    },
-    "HI": {
-      "label": "Nonassertive",
-      "angle": 270,
-      "inst_items": {
-        "5": "...stop bothering me...",
-        "13": "...confront people with...",
-        "21": "...assertive with another...",
-        "29": "...to be firm..."
-      }
-    },
-    "JK": {
-      "label": "Exploitable",
-      "angle": 315,
-      "inst_items": {
-        "6": "...I am angry...",
-        "14": "...assertive without worrying...",
-        "22": "...too easily persuaded...",
-        "30": "...people take advantage..."
-      }
-    },
-    "LM": {
-      "label": "Overly Nurturant",
-      "angle": 360,
-      "inst_items": {
-        "7": "...my own welfare...",
-        "15": "...please other people...",
-        "23": "...other people's needs...",
-        "31": "...another person's misery..."
-      }
-    },
-    "NO": {
-      "label": "Intrusive",
-      "angle": 45,
-      "inst_items": {
-        "8": "...keep things private...",
-        "16": "...open up to...",
-        "24": "...noticed too much...",
-        "32": "...tell personal things..."
-      }
-    }
-  }
-}
\ No newline at end of file
diff --git a/src/circumplex/instruments/SATP-eng.json b/src/circumplex/instruments/SATP-eng.json
deleted file mode 100644
index 2488392..0000000
--- a/src/circumplex/instruments/SATP-eng.json
+++ /dev/null
@@ -1,55 +0,0 @@
-{
-  "details": {
-    "name": "Soundscape Attributes Translation Project - English Translation",
-    "abbrev": "SATP-eng",
-    "inst_items": 8,
-    "scales": 8,
-    "prefix": "For each of the 8 scales below, to what extent do you agree or disagree that the present surrounding sound environment is...",
-    "suffix": "",
-    "status": "open-access",
-    "construct": "soundscape circumplex",
-    "reference": "Aletta, Mitchell, et.al. (2024)",
-    "url": ""
-  },
-  "anchors": {
-    "0": "Strongly disagree",
-    "25": "Somewhat disagree",
-    "50": "Neither agree nor disagree",
-    "75": "Somewhat agree",
-    "100": "Strongly agree"
-  },
-  "scales": {
-    "PAQ1": {
-      "label": "pleasant",
-      "angle": 0
-    },
-    "PAQ2": {
-      "label": "vibrant",
-      "angle": 46
-    },
-    "PAQ3": {
-      "label": "eventful",
-      "angle": 93
-    },
-    "PAQ4": {
-      "label": "chaotic",
-      "angle": 138
-    },
-    "PAQ5": {
-      "label": "annoying",
-      "angle": 178
-    },
-    "PAQ6": {
-      "label": "monotonous",
-      "angle": 228
-    },
-    "PAQ7": {
-      "label": "uneventful",
-      "angle": 272
-    },
-    "PAQ8": {
-      "label": "calm",
-      "angle": 340
-    }
-  }
-}
\ No newline at end of file
diff --git a/src/circumplex/instruments/SSQP-eng.json b/src/circumplex/instruments/SSQP-eng.json
deleted file mode 100644
index 80e63c5..0000000
--- a/src/circumplex/instruments/SSQP-eng.json
+++ /dev/null
@@ -1,55 +0,0 @@
-{
-  "details": {
-    "name": "Swedish Soundscape Quality Protocol - English Translation",
-    "abbrev": "SSQP-eng",
-    "inst_items": 8,
-    "scales": 8,
-    "prefix": "For each of the 8 scales below, to what extent do you agree or disagree that the present surrounding sound environment is...",
-    "suffix": "",
-    "status": "open-access",
-    "construct": "soundscape circumplex",
-    "reference": "Axelsson, Nilsson, & Berglund (2012)",
-    "url": "https://doi.org/10.1121/1.4709112"
-  },
-  "anchors": {
-    "1": "Strongly disagree",
-    "2": "Somewhat disagree",
-    "3": "Neither agree nor disagree",
-    "4": "Somewhat agree",
-    "5": "Strongly agree"
-  },
-  "scales": {
-    "PAQ1": {
-      "label": "pleasant",
-      "angle": 0
-    },
-    "PAQ2": {
-      "label": "vibrant",
-      "angle": 46
-    },
-    "PAQ3": {
-      "label": "eventful",
-      "angle": 93
-    },
-    "PAQ4": {
-      "label": "chaotic",
-      "angle": 138
-    },
-    "PAQ5": {
-      "label": "annoying",
-      "angle": 178
-    },
-    "PAQ6": {
-      "label": "monotonous",
-      "angle": 228
-    },
-    "PAQ7": {
-      "label": "uneventful",
-      "angle": 272
-    },
-    "PAQ8": {
-      "label": "calm",
-      "angle": 340
-    }
-  }
-}
\ No newline at end of file
diff --git a/src/circumplex/instruments/__init__.py b/src/circumplex/instruments/__init__.py
index e69de29..3c0dca2 100644
--- a/src/circumplex/instruments/__init__.py
+++ b/src/circumplex/instruments/__init__.py
@@ -0,0 +1,27 @@
+"""Circumplex instrument registry and management.
+
+This package provides classes and functions for managing circumplex instruments,
+including their scales, normative samples, and response items.
+"""
+
+from .models import (
+    Instrument,
+    InstrumentScale,
+    NormativeSample,
+    ResponseAnchor,
+    ResponseItem,
+    get_instrument,
+    register_instrument,
+    show_instruments,
+)
+
+__all__ = [
+    "Instrument",
+    "InstrumentScale",
+    "NormativeSample",
+    "ResponseAnchor",
+    "ResponseItem",
+    "get_instrument",
+    "register_instrument",
+    "show_instruments",
+]
diff --git a/src/circumplex/instruments/csig.py b/src/circumplex/instruments/csig.py
new file mode 100644
index 0000000..2e24076
--- /dev/null
+++ b/src/circumplex/instruments/csig.py
@@ -0,0 +1,113 @@
+"""Circumplex Scales of Intergroup Goals (CSIG) instrument definition.
+
+Reference:
+    Lock, B. D. (2014). Circumplex scales of intergroup goals: An interpersonal circle
+    model of goals for interactions between groups. Personality and Social
+    Psychology Bulletin, 40(4), 433-449. https://kennethlocke.org/CSIG/CSIG.html
+"""
+
+import pandas as pd
+
+from circumplex.instruments.models import (
+    Instrument,
+    InstrumentScale,
+    NormativeSample,
+    ResponseAnchor,
+    ResponseItem,
+    register_instrument,
+)
+
+# Define scales
+SCALES = (
+    InstrumentScale("PA", 90, items=(8, 16, 24, 32), label="Be authoritative"),
+    InstrumentScale("BC", 135, items=(5, 12, 21, 29), label="Be tough"),
+    InstrumentScale("DE", 180, items=(2, 10, 18, 26), label="Be self-protective"),
+    InstrumentScale("FG", 225, items=(7, 15, 23, 31), label="Be wary"),
+    InstrumentScale("HI", 270, items=(4, 12, 20, 28), label="Be conflict-avoidant"),
+    InstrumentScale("JK", 315, items=(1, 9, 17, 25), label="Be cooperative"),
+    InstrumentScale("LM", 360, items=(6, 14, 22, 30), label="Be understanding"),
+    InstrumentScale("NO", 45, items=(3, 11, 19, 27), label="Be respected"),
+)
+
+ANCHORS = (
+    ResponseAnchor(0, "It is not at all important that..."),
+    ResponseAnchor(1, "It is somewhat important that..."),
+    ResponseAnchor(2, "It is moderately important that..."),
+    ResponseAnchor(3, "It is very important that..."),
+    ResponseAnchor(4, "It is extremely important that..."),
+)
+
+ITEMS = (
+    ResponseItem(1, "We are friendly"),
+    ResponseItem(2, "We are the winners in any argument or dispute"),
+    ResponseItem(3, "They respect what we have to say"),
+    ResponseItem(4, "We avoid conflict"),
+    ResponseItem(5, "We show that we can be tough"),
+    ResponseItem(6, "We appreciate what they have to offer"),
+    ResponseItem(7, "We let them fend for themselves"),
+    ResponseItem(8, "We are assertive"),
+    ResponseItem(9, "We celebrate their achievements"),
+    ResponseItem(10, "We do whatever is in our best interest"),
+    ResponseItem(11, "We get the chance to express our views"),
+    ResponseItem(12, "They not get angry with us"),
+    ResponseItem(13, "We not appear vulnerable"),
+    ResponseItem(14, "We understand their point of view"),
+    ResponseItem(15, "They stay out of our business"),
+    ResponseItem(16, "We appear confident"),
+    ResponseItem(17, "They feel we are all on the same team"),
+    ResponseItem(18, "We are better than them"),
+    ResponseItem(19, "They listen to what we have to say"),
+    ResponseItem(20, "We not get into arguments"),
+    ResponseItem(21, "We are aggressive if necessary"),
+    ResponseItem(22, "We show concern for their welfare"),
+    ResponseItem(23, "We not trust them"),
+    ResponseItem(24, "We are decisive"),
+    ResponseItem(25, "We are cooperative"),
+    ResponseItem(26, "We keep our guard up"),
+    ResponseItem(27, "They see us as responsible"),
+    ResponseItem(28, "We not make them angry"),
+    ResponseItem(29, "We not show our weaknesses"),
+    ResponseItem(30, "We are able to compromise"),
+    ResponseItem(31, "We not get entangled in their affairs"),
+    ResponseItem(32, "They see us as capable"),
+)
+
+
+norm_stats = pd.DataFrame(
+    {
+        "scale": ["PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"],
+        "angle": [90, 135, 180, 225, 270, 315, 360, 45],
+        "mean": [2.96, 2.53, 2.02, 1.88, 2.24, 2.89, 2.97, 2.96],
+        "sd": [0.68, 0.86, 0.88, 0.74, 0.90, 0.76, 0.71, 0.68],
+    }
+)
+
+NORMS = NormativeSample(
+    sample_id=1,
+    size=665,
+    population="MTurkers from US, Canada, and India about interactions between nations",
+    reference="Lock (2014)",
+    url="https://doi.org/10.1177/0146167213514280",
+    statistics=norm_stats,
+)
+
+# Create Instrument
+csig = Instrument(
+    name="Circumplex Scales of Interpersonal Goals",
+    abbrev="CSIG",
+    construct="interpersonal intergroup goals",
+    reference="Lock (2014)",
+    url="https://doi.org/10.1177/0146167213514280",
+    status="open-access",
+    scales=SCALES,
+    anchors=ANCHORS,
+    items=ITEMS,
+    norms=(NORMS,),
+    prefix=(
+        "In dealing with other groups, how important is it"
+        "that we act or appear or are treated this way?"
+    ),
+    suffix="",
+)
+
+register_instrument("csig", csig)
diff --git a/src/circumplex/instruments/iipsc.py b/src/circumplex/instruments/iipsc.py
new file mode 100644
index 0000000..0897106
--- /dev/null
+++ b/src/circumplex/instruments/iipsc.py
@@ -0,0 +1,126 @@
+"""Inventory of Interpersonal Problems Short Circumplex (IIP-SC) instrument definition.
+
+Reference:
+    Soldz, S., Budman, S., Demby, A., & Merry, J. (1995). A short form of the Inventory
+    of Interpersonal Problems Circumplex Scales. Assessment, 2(1), 53-63.
+    https://doi.org/10.1080/00223890802388665
+"""
+
+import numpy as np
+import pandas as pd
+
+from circumplex.instruments.models import (
+    Instrument,
+    InstrumentScale,
+    NormativeSample,
+    ResponseAnchor,
+    ResponseItem,
+    register_instrument,
+)
+
+SCALES = (
+    InstrumentScale("PA", 90, items=(1, 9, 17, 25), label="Domineering"),
+    InstrumentScale("BC", 135, items=(2, 10, 18, 26), label="Vindictive"),
+    InstrumentScale("DE", 180, items=(3, 11, 19, 27), label="Cold"),
+    InstrumentScale("FG", 225, items=(4, 12, 20, 28), label="Socially avoidant"),
+    InstrumentScale("HI", 270, items=(5, 13, 21, 29), label="Nonassertive"),
+    InstrumentScale("JK", 315, items=(6, 14, 22, 30), label="Exploitable"),
+    InstrumentScale("LM", 360, items=(7, 15, 23, 31), label="Overly nurturant"),
+    InstrumentScale("NO", 45, items=(8, 16, 24, 32), label="Intrusive"),
+)
+
+ANCHORS = (
+    ResponseAnchor(0, "Not at all"),
+    ResponseAnchor(1, "Somewhat"),
+    ResponseAnchor(2, "Moderately"),
+    ResponseAnchor(3, "Very"),
+    ResponseAnchor(4, "Extremely"),
+)
+
+ITEMS = (
+    ResponseItem(1, "...point of view..."),
+    ResponseItem(2, "...supportive of another..."),
+    ResponseItem(3, "...show affection to..."),
+    ResponseItem(4, "...join in on..."),
+    ResponseItem(5, "...stop bothering me..."),
+    ResponseItem(6, "...I am angry..."),
+    ResponseItem(7, "...my own welfare..."),
+    ResponseItem(8, "...keep things private..."),
+    ResponseItem(9, "...too aggressive toward..."),
+    ResponseItem(10, "...another person's happiness..."),
+    ResponseItem(11, "...feeling of love..."),
+    ResponseItem(12, "...introduce myself to..."),
+    ResponseItem(13, "...confront people with..."),
+    ResponseItem(14, "...assertive without worrying..."),
+    ResponseItem(15, "...please other people..."),
+    ResponseItem(16, "...open up to..."),
+    ResponseItem(17, "...control other people..."),
+    ResponseItem(18, "...too suspicious of..."),
+    ResponseItem(19, "...feel close to..."),
+    ResponseItem(20, "...socialize with other..."),
+    ResponseItem(21, "...assertive with another..."),
+    ResponseItem(22, "...too easily persuaded..."),
+    ResponseItem(23, "...other people's needs..."),
+    ResponseItem(24, "...noticed too much..."),
+    ResponseItem(25, "...argue with other..."),
+    ResponseItem(26, "...revenge against people..."),
+    ResponseItem(27, "...at a distance..."),
+    ResponseItem(28, "...get together socially..."),
+    ResponseItem(29, "...to be firm..."),
+    ResponseItem(30, "...people take advantage..."),
+    ResponseItem(31, "...another person's misery..."),
+    ResponseItem(32, "...tell personal things..."),
+)
+
+norm_1_stats = pd.DataFrame(
+    {
+        "scale": ["PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"],
+        "angles": [90, 135, 180, 225, 270, 315, 360, 45],
+        "mean": np.array([3.04, 3.17, 3.60, 4.19, 5.68, 5.54, 5.86, 4.10]) / 4,
+        "sd": np.array([2.64, 2.76, 3.42, 3.79, 3.66, 3.41, 3.30, 3.20]) / 4,
+    }
+)
+
+NORM1 = NormativeSample(
+    sample_id=1,
+    size=872,
+    population="American college students",
+    reference="Hopwood, Pincus, DeMoor, & Koonce (2011)",
+    url="https://doi.org/10.1080/00223890802388665",
+    statistics=norm_1_stats,
+)
+
+norm_2_stats = pd.DataFrame(
+    {
+        "scale": ["PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"],
+        "angles": [90, 135, 180, 225, 270, 315, 360, 45],
+        "mean": [0.99, 0.97, 1.30, 1.33, 1.81, 1.92, 2.14, 1.43],
+        "sd": [0.82, 0.85, 1.07, 0.98, 0.89, 0.89, 0.90, 1.05],
+    }
+)
+
+NORM2 = NormativeSample(
+    sample_id=2,
+    size=106,
+    population="American psychiatric outpatients",
+    reference="Soldz, Budman, Demby, & Merry (1995)",
+    url="https://doi.org/10.1177/1073191195002001006",
+    statistics=norm_2_stats,
+)
+
+iipsc = Instrument(
+    name="Inventory of Interpersonal Problems Short Circumplex",
+    abbrev="IIP-SC",
+    construct="interpersonal problems",
+    reference="Soldz, Budman, Demby, & Merry (1995)",
+    url="https://doi.org/10.1177/1073191195002001006",
+    status="partial text",
+    scales=SCALES,
+    anchors=ANCHORS,
+    items=ITEMS,
+    norms=(NORM1, NORM2),
+    prefix="",
+    suffix="",
+)
+
+register_instrument("iipsc", iipsc)
diff --git a/src/circumplex/instruments/ipipipc.py b/src/circumplex/instruments/ipipipc.py
new file mode 100644
index 0000000..b758b23
--- /dev/null
+++ b/src/circumplex/instruments/ipipipc.py
@@ -0,0 +1,109 @@
+"""IPIP Interpersonal Circumplex (IPIP-IPC) instrument definition.
+
+Reference:
+    Markey, P. M., & Markey, C. N. (2009). A brief assessment of the
+    interpersonal circumplex: The IPIP-IPC. Assessment, 16(4), 352-361.
+    https://doi.org/10.1177/1073191109340382
+"""
+
+import pandas as pd
+
+from circumplex.instruments.models import (
+    Instrument,
+    InstrumentScale,
+    NormativeSample,
+    ResponseAnchor,
+    ResponseItem,
+    register_instrument,
+)
+
+# Define scales
+SCALES = (
+    InstrumentScale("PA", 90, (6, 14, 22, 30), "Assured-Dominant"),
+    InstrumentScale("BC", 135, (7, 15, 23, 31), "Arrogant-Calculating"),
+    InstrumentScale("DE", 180, (8, 16, 24, 32), "Cold-Hearted"),
+    InstrumentScale("FG", 225, (1, 9, 17, 25), "Aloof-Introverted"),
+    InstrumentScale("HI", 270, (2, 10, 18, 26), "Unassured-Submissive"),
+    InstrumentScale("JK", 315, (3, 11, 19, 27), "Unassuming-Ingenuous"),
+    InstrumentScale("LM", 360, (4, 12, 20, 28), "Warm-Agreeable"),
+    InstrumentScale("NO", 45, (5, 13, 21, 29), "Gregarious-Extraverted"),
+)
+
+ANCHORS = (
+    ResponseAnchor(1, "Very Inaccurate"),
+    ResponseAnchor(2, "Moderately Inaccurate"),
+    ResponseAnchor(3, "Neither Accurate nor Inaccurate"),
+    ResponseAnchor(4, "Moderately Accurate"),
+    ResponseAnchor(5, "Very Accurate"),
+)
+
+ITEMS = (
+    ResponseItem(1, "Am quiet around strangers"),
+    ResponseItem(2, "Speak softly"),
+    ResponseItem(3, "Tolerate a lot from others"),
+    ResponseItem(4, "Am interested in people"),
+    ResponseItem(5, "Feel comfortable around people"),
+    ResponseItem(6, "Demand to be the center of interest"),
+    ResponseItem(7, "Cut others to pieces"),
+    ResponseItem(8, "Believe people should fend for themselves"),
+    ResponseItem(9, "Am a very private person"),
+    ResponseItem(10, "Let others finish what they are saying"),
+    ResponseItem(11, "Take things as they come"),
+    ResponseItem(12, "Reassure others"),
+    ResponseItem(13, "Start conversations"),
+    ResponseItem(14, "Do most of the talking"),
+    ResponseItem(15, "Contradict others"),
+    ResponseItem(16, "Don't fall for sob-stories"),
+    ResponseItem(17, "Don't talk a lot"),
+    ResponseItem(18, "Seldom toot my own horn"),
+    ResponseItem(19, "Think of others first"),
+    ResponseItem(20, "Inquire about others' well-being"),
+    ResponseItem(21, "Talk to a lot of different people at parties"),
+    ResponseItem(22, "Speak loudly"),
+    ResponseItem(23, "Snap at people"),
+    ResponseItem(24, "Don't put a lot of thought into things"),
+    ResponseItem(25, "Have little to say"),
+    ResponseItem(26, "Dislike being the center of attention"),
+    ResponseItem(27, "Seldom stretch the truth"),
+    ResponseItem(28, "Get along well with others"),
+    ResponseItem(29, "Love large parties"),
+    ResponseItem(30, "Demand attention"),
+    ResponseItem(31, "Have a sharp tongue"),
+    ResponseItem(32, "Am not interested in other people's problems"),
+)
+
+norm_stats = pd.DataFrame(
+    {
+        "scale": ["PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"],
+        "angle": [90, 135, 180, 225, 270, 315, 360, 45],
+        "mean": [2.66, 2.27, 2.46, 2.68, 3.20, 3.64, 4.37, 3.64],
+        "sd": [0.71, 0.69, 0.58, 0.79, 0.63, 0.58, 0.47, 0.78],
+    }
+)
+
+NORMS = NormativeSample(
+    sample_id=1,
+    size=274,
+    population="American college students",
+    reference="Markey & Markey (2009)",
+    url="https://doi.org/10.1177/1073191109340382",
+    statistics=norm_stats,
+)
+
+INSTRUMENT = Instrument(
+    name="IPIP Interpersonal Circumplex",
+    abbrev="IPIP-IPC",
+    construct="interpersonal traits",
+    reference="Markey & Markey (2009)",
+    url="https://doi.org/10.1177/1073191109340382",
+    status="open-access",
+    scales=SCALES,
+    anchors=ANCHORS,
+    items=ITEMS,
+    norms=(NORMS,),
+    prefix="",
+    suffix="",
+)
+
+
+register_instrument("ipipipc", INSTRUMENT)
diff --git a/src/circumplex/instruments/models.py b/src/circumplex/instruments/models.py
new file mode 100644
index 0000000..671d57b
--- /dev/null
+++ b/src/circumplex/instruments/models.py
@@ -0,0 +1,442 @@
+"""Instrument data models."""
+
+from __future__ import annotations
+
+from collections.abc import Iterable
+from dataclasses import dataclass
+
+import numpy as np
+import pandas as pd
+
+from circumplex._utils import is_package_installed
+
+console = None  # Will be set if rich is available
+if is_package_installed("rich"):
+    from rich.console import Group
+    from rich.table import Table
+    from rich.text import Text
+    from rich.tree import Tree
+
+    from circumplex import _utils
+
+    console = getattr(_utils, "console", None)
+
+
+@dataclass(frozen=True)
+class InstrumentScale:
+    """Single scale within an instrument."""
+
+    abbrev: str
+    angle: float  # degrees
+    items: tuple[int, ...]  # immutable
+    label: str
+
+
+@dataclass(frozen=True)
+class ResponseAnchor:
+    """Response option for an instrument."""
+
+    value: int
+    label: str
+
+
+@dataclass(frozen=True)
+class ResponseItem:
+    """Single item within an instrument."""
+
+    item_id: int
+    text: str
+
+
+@dataclass(frozen=True)
+class NormativeSample:
+    """Normative sample metadata and statistics."""
+
+    sample_id: int
+    size: int
+    population: str
+    reference: str
+    url: str
+    statistics: pd.DataFrame  # columns: scale, mean, sd, etc.
+
+
+@dataclass(frozen=True)
+class Instrument:
+    """Circumplex instrument definition."""
+
+    # Metadata
+    name: str
+    abbrev: str
+    construct: str
+    reference: str
+    url: str
+    status: str  # "open-access" or "limited"
+
+    # Structure
+    scales: tuple[InstrumentScale, ...]
+    anchors: tuple[ResponseAnchor, ...]
+    items: tuple[ResponseItem, ...] | None = None  # Full item text (if available)
+
+    # Normative data
+    norms: tuple[NormativeSample, ...] = ()
+
+    # Formatting
+    prefix: str = ""
+    suffix: str = ""
+
+    @property
+    def n_scales(self) -> int:
+        """Number of scales in the instrument."""
+        return len(self.scales)
+
+    @property
+    def n_items(self) -> int:
+        """Number of items in the instrument."""
+        return len(self.items) if self.items else 0
+
+    @property
+    def scale_labels(self) -> list[str]:
+        """Get names of all scales."""
+        return [scale.label for scale in self.scales]
+
+    @property
+    def scale_abbrevs(self) -> list[str]:
+        """Get abbreviations of all scales."""
+        return [scale.abbrev for scale in self.scales]
+
+    def get_angles(self) -> list[float]:
+        """Get angular positions for all scales."""
+        return [scale.angle for scale in self.scales]
+
+    def get_scale(self, abbrev: str) -> InstrumentScale:
+        """Get scale by abbreviation."""
+        for scale in self.scales:
+            if scale.abbrev == abbrev:
+                return scale
+        msg = (
+            f"Scale with abbreviation '{abbrev}' not found in instrument "
+            f"'{self.abbrev}'."
+        )
+        raise ValueError(msg)
+
+    def get_item(self, item_id: int) -> ResponseItem:
+        """Get item by item ID."""
+        if self.items is None:
+            msg = f"Instrument '{self.abbrev}' does not have item text available."
+            raise ValueError(msg)
+        for item in self.items:
+            if item.item_id == item_id:
+                return item
+        msg = f"Item with ID '{item_id}' not found in instrument '{self.abbrev}'."
+        raise ValueError(msg)
+
+    def __repr__(self) -> str:
+        """Return a human-readable multi-line summary of the instrument."""
+        lines = [
+            f"{self.abbrev}: {self.name}",
+            (
+                f"{self.n_items} items, {self.n_scales} scales, "
+                f"{len(self.norms)} normative data sets"
+            ),
+            f"{self.reference}",
+            f"< {self.url} >",
+        ]
+        return "\n".join(lines)
+
+    def __rich_repr__(self) -> Iterable[str]:
+        """Yield lines for rich-rendered representation."""
+        yield f"{self.abbrev}: {self.name}"
+        yield (
+            f"{self.n_items} items, {self.n_scales} scales, "
+            f"{len(self.norms)} normative data sets"
+        )
+        yield f"{self.reference}"
+        yield f"< {self.url} >"
+
+    def info(
+        self,
+        *,
+        scales: bool = True,
+        anchors: bool = True,
+        items: bool = False,
+        norms: bool = True,
+        rich_print: bool = True,
+    ) -> None:
+        """Print instrument information."""
+        if is_package_installed("rich") and rich_print:
+            info_sections = [
+                *self.__rich_repr__(),
+            ]
+            if scales:
+                info_sections.append("\n")
+                info_sections.append(
+                    self.info_scales(items=items, rich_print=rich_print)
+                )
+            if anchors:
+                info_sections.append("\n")
+                info_sections.append(self.info_anchors(rich_print=rich_print))
+            if norms:
+                info_sections.append("\n")
+                info_sections.append(self.info_norms(rich_print=rich_print))
+            info_group = Group(*info_sections)
+            if console is not None:
+                console.print(info_group)
+        else:
+            print(self)  # noqa: T201
+            print()  # noqa: T201
+            if scales:
+                print(self.info_scales(items=items, rich_print=rich_print))  # noqa: T201
+                print()  # noqa: T201
+            if anchors:
+                print(self.info_anchors(rich_print=rich_print))  # noqa: T201
+                print()  # noqa: T201
+            if norms:
+                print(self.info_norms(rich_print=rich_print))  # noqa: T201
+                print()  # noqa: T201
+
+    def info_scales(
+        self, *, items: bool = False, rich_print: bool = True
+    ) -> str | Tree:
+        """Return information about instrument scales, optionally with items."""
+        if is_package_installed("rich") and rich_print:
+            tree = Tree(
+                f"[cyan]The {self.abbrev} contains {self.n_scales} scales:",
+                style="bold",
+                guide_style="dim",
+            )
+            for scale in self.scales:
+                scale_branch = tree.add(
+                    f"{scale.abbrev} ({scale.angle}°): {scale.label}"
+                )
+                if items and self.items is not None:
+                    for item_id in scale.items:
+                        scale_branch.add(
+                            f"[dim]{item_id}. {self.items[item_id - 1].text}"
+                        )
+            return tree
+
+        text = [f"The {self.abbrev} contains {self.n_scales} scales:"]
+        for scale in self.scales:
+            text.append(f"  {scale.abbrev}: {scale.label} ({scale.angle}°)")
+            if items and self.items is not None:
+                for item_id in scale.items:
+                    text.append(f"    {item_id}. {self.items[item_id - 1].text}")
+        return "\n".join(text)
+
+    def info_anchors(self, *, rich_print: bool = True) -> str | Text:
+        """Return the response anchors for the instrument."""
+        lines = [
+            (
+                f"The {self.abbrev} is rated using the following "
+                f"{len(self.anchors)}-point scale:"
+            )
+        ]
+        for anchor in self.anchors:
+            lines.append(f"  {anchor.value}. {anchor.label}")
+        if is_package_installed("rich") and rich_print:
+            text = Text()
+            for i, line in enumerate(lines):
+                if i == 0:
+                    text.append(line + "\n", style="bold cyan")
+                else:
+                    text.append(line + "\n")
+            return text
+
+        return "\n".join(lines)
+
+    def info_norms(self, *, rich_print: bool = True) -> str | Text:
+        """Return information about available normative samples."""
+        lines = [
+            (
+                f"The {self.abbrev} currently has {len(self.norms)} "
+                "normative data set(s):\n"
+            )
+        ]
+        for norm in self.norms:
+            lines.append(f"{norm.sample_id}. {norm.size} {norm.population}")
+            lines.append(f"   {norm.reference}")
+            lines.append(f"   {norm.url}")
+        if is_package_installed("rich") and rich_print:
+            text = Text()
+            for i, line in enumerate(lines):
+                if i == 0:
+                    text.append(line + "\n", style="bold cyan")
+                else:
+                    text.append(line + "\n")
+            return text
+
+        return "\n".join(lines)
+
+    def score(
+        self,
+        data: pd.DataFrame,
+        items: Iterable[str | int],
+        prefix: str = "",
+        suffix: str = "",
+        *,
+        na_rm: bool = True,
+        append: bool = True,
+    ) -> pd.DataFrame:
+        """Compute mean scale scores for the instrument.
+
+        Parameters
+        ----------
+        data
+            DataFrame containing item-level data.
+        items
+            Iterable of item names or integer indices in `data`.
+        prefix, suffix
+            String affixes to add to resulting scale column names.
+        na_rm
+            If True, ignore missing values when computing means.
+        append
+            If True, append scores to `data`; else return only scores.
+        """
+        # Extract item data from the provided dataframe
+        if all(
+            isinstance(item, (int, np.integer, float, np.floating)) for item in items
+        ):
+            item_data = data.iloc[:, items].copy()
+        elif all(isinstance(item, str) for item in items):
+            item_data = data.loc[:, items].copy()
+        else:
+            msg = "All items in 'items' must be either strings or integers."
+            raise TypeError(msg)
+
+        scores = pd.DataFrame(index=data.index, columns=self.scale_abbrevs, dtype=float)
+        for scale in self.scales:
+            scale_items = [self.get_item(i).item_id - 1 for i in scale.items]
+            scale_data = item_data.iloc[:, scale_items]
+
+            scale_score = scale_data.mean(axis=1, skipna=na_rm)
+            scores.loc[:, scale.abbrev] = scale_score
+
+        scores.columns = [f"{prefix}{col}{suffix}" for col in scores.columns]
+
+        if append:
+            return pd.concat([data, scores], axis=1)
+        return scores
+
+    def norm_standardize(
+        self,
+        data: pd.DataFrame,
+        sample_id: int,
+        scales: Iterable[str | int] | None = None,
+        prefix: str = "",
+        suffix: str = "_z",
+        *,
+        append: bool = True,
+    ) -> pd.DataFrame:
+        """Standardize scale-level data using a normative sample.
+
+        Parameters
+        ----------
+        data
+            DataFrame containing at least circumplex scales.
+        sample_id
+            The ID of the normative sample to use for standardization.
+        scales
+            The variable names or column numbers for the scales in `data`. If None,
+            all scales in the instrument are standardized.
+        prefix
+            Prefix to add to standardized scale names.
+        suffix
+            Suffix to add to standardized scale names.
+        append
+            If True, append standardized scales to `data`. If False,
+            return only standardized scales.
+
+        Returns
+        -------
+        DataFrame
+            DataFrame with standardized scale-level data.
+
+        Raises
+        ------
+        ValueError
+            If `sample_id` is not found in the instrument's normative samples.
+        """
+        norm_sample = None
+        for norm in self.norms:
+            if norm.sample_id == sample_id:
+                norm_sample = norm
+                break
+        if norm_sample is None:
+            msg = (
+                f"Normative sample with ID '{sample_id}' not found in instrument "
+                f"'{self.abbrev}'."
+            )
+            raise ValueError(msg)
+        if scales is None:
+            scales = self.scale_abbrevs
+
+        scores = pd.DataFrame(index=data.index, columns=[], dtype=float)
+        for scale in scales:
+            if isinstance(scale, (int, np.integer)):
+                scale_abbrev = self.scales[scale].abbrev
+            else:
+                scale_abbrev = scale
+
+            scale_data = data.loc[:, scale_abbrev]
+            norm_stats = norm_sample.statistics
+            mean_row = norm_stats.loc[norm_stats["scale"] == scale_abbrev]
+            if mean_row.empty:
+                msg = (
+                    f"Normative statistics for scale '{scale_abbrev}' not found in "
+                    f"sample ID '{sample_id}'."
+                )
+                raise ValueError(msg)
+            mean = mean_row["mean"].to_numpy()[0]
+            sd = mean_row["sd"].to_numpy()[0]
+
+            standardized_score = (scale_data - mean) / sd
+            scores[f"{prefix}{scale_abbrev}{suffix}"] = standardized_score
+
+        if append:
+            return pd.concat([data, scores], axis=1)
+        return scores
+
+
+# Global registry
+_INSTRUMENTS: dict[str, Instrument] = {}
+
+
+def register_instrument(abbrev: str, instrument: Instrument) -> None:
+    """Register an instrument in the global registry."""
+    _INSTRUMENTS[abbrev.lower()] = instrument
+
+
+def get_instrument(abbrev: str) -> Instrument:
+    """Retrieve an instrument by its abbreviation."""
+    key = abbrev.lower()
+    if key not in _INSTRUMENTS:
+        available = ", ".join(_INSTRUMENTS.keys())
+        msg = f"Instrument '{abbrev}' not found. Available instruments: {available}"
+        raise ValueError(msg)
+    return _INSTRUMENTS[key]
+
+
+def show_instruments(*, rich_print: bool = True) -> None:
+    """List all registered instrument abbreviations."""
+    if is_package_installed("rich") and rich_print:
+        table = Table(
+            title=(
+                "The circumplex package currently includes "
+                f"{len(_INSTRUMENTS)} instruments"
+            )
+        )
+        table.add_column("", no_wrap=True)
+        table.add_column("Abbreviation", style="cyan", no_wrap=True)
+        table.add_column("Name", style="magenta")
+        for index, (abbrev, inst) in enumerate(_INSTRUMENTS.items(), start=1):
+            table.add_row(str(index), abbrev.upper(), inst.name)
+        if console is not None:  # safety if rich not actually available
+            console.print(table)
+
+    else:
+        print(  # noqa: T201
+            "The circumplex package currently includes "
+            f"{len(_INSTRUMENTS)} instruments:"
+        )
+        for index, (abbrev, inst) in enumerate(_INSTRUMENTS.items(), start=1):
+            print(f"  {index}. {abbrev.upper()}: {inst.name}")  # noqa: T201
diff --git a/docs/news.md b/src/circumplex/py.typed
similarity index 100%
rename from docs/news.md
rename to src/circumplex/py.typed
diff --git a/src/circumplex/ssm.py b/src/circumplex/ssm.py
new file mode 100644
index 0000000..033bd2e
--- /dev/null
+++ b/src/circumplex/ssm.py
@@ -0,0 +1,341 @@
+"""Structural Summary Method (SSM) analysis results and configuration classes.
+
+This module provides dataclasses for representing SSM analysis results:
+
+- SSMDetails: Configuration and parameters used in SSM analysis
+- SSM: Complete SSM analysis results with estimates and confidence intervals
+"""
+
+from __future__ import annotations
+
+from collections.abc import Iterable
+from dataclasses import dataclass
+from typing import Any
+
+import pandas as pd
+from matplotlib.figure import Figure
+
+from circumplex._utils import is_package_installed
+from circumplex.visualization import plot_circle, plot_contrast, plot_curve
+
+console = None
+if is_package_installed("rich"):
+    from rich.console import Group
+    from rich.table import Table
+
+    from circumplex import _utils
+
+    console = getattr(_utils, "console", None)
+
+
+@dataclass
+class SSMDetails:
+    """Details of SSM analysis configuration and parameters.
+
+    Attributes
+    ----------
+    boots
+        Number of bootstrap resamples used for confidence intervals.
+    interval
+        Confidence interval level (e.g., 0.95 for 95% CI).
+    listwise
+        Whether listwise deletion was used for missing data.
+    angles
+        Angular displacements of the circumplex scales in degrees.
+    contrast
+        Whether the analysis involves contrasts between groups.
+    score_type
+        Type of scores used in analysis ('mean' or 'correlation').Pterm
+    """
+
+    boots: int
+    interval: float
+    listwise: bool
+    angles: list[float]
+    contrast: bool
+    score_type: str
+
+    @classmethod
+    def from_dict(cls, data: dict) -> SSMDetails:
+        """Create SSMDetails instance from dictionary.
+
+        Parameters
+        ----------
+        data
+            Dictionary containing SSM analysis parameters with keys:
+            boots, interval, listwise, angles, contrast, score_type.
+
+        Returns
+        -------
+        New SSMDetails instance populated from dictionary data.
+        """
+        return cls(
+            boots=data["boots"],
+            interval=data["interval"],
+            listwise=data["listwise"],
+            angles=data["angles"],
+            contrast=data["contrast"],
+            score_type=data["score_type"],
+        )
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert SSMDetails instance to dictionary.
+
+        Returns
+        -------
+        Dictionary containing all SSMDetails attributes.
+        """
+        return self.__dict__.copy()
+
+    def __rich_repr__(self) -> Iterable[str]:
+        """Generate rich console representation of SSM analysis details."""
+        yield f"Statistical Basis:   {self.score_type.capitalize()} Scores"
+        yield f"Bootstrap Resamples: {self.boots}"
+        yield f"Confidence Level:    {self.interval}"
+        yield f"Listwise Deletion:   {self.listwise}"
+        yield f"Scale Displacements: {self.angles}"
+
+
+@dataclass
+class SSM:
+    """Results from a Structural Summary Method (SSM) analysis.
+
+    Attributes
+    ----------
+    results
+        DataFrame containing SSM parameter estimates and confidence intervals.
+    scores
+        DataFrame containing the circumplex scale scores used in the analysis.
+    details
+        Configuration and parameters used in the SSM analysis.
+    type
+        Type of SSM analysis performed (e.g., 'profile', 'contrast').
+    """
+
+    results: pd.DataFrame
+    scores: pd.DataFrame
+    details: SSMDetails
+    type: str
+
+    @classmethod
+    def from_dict(cls, data: dict) -> SSM:
+        """Create SSM instance from dictionary.
+
+        Parameters
+        ----------
+        data
+            Dictionary containing SSM analysis results with keys:
+            results, scores, details, type.
+
+        Returns
+        -------
+        New SSM instance populated from dictionary data.
+        """
+        return cls(
+            results=data["results"],
+            scores=data["scores"],
+            details=SSMDetails.from_dict(data["details"]),
+            type=data["type"],
+        )
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert SSM instance to dictionary.
+
+        Returns
+        -------
+        Dictionary containing all SSM attributes with details converted to dict format.
+        """
+        d = self.__dict__.copy()
+        d["details"] = self.details.to_dict()
+        return d
+
+    def summary(self, *, rich_print: bool = True) -> None:
+        """Print a formatted summary of SSM analysis results.
+
+        Parameters
+        ----------
+        rich_print
+            Whether to use rich console formatting for output, by default True.
+            If False or rich is not installed, falls back to standard printing.
+        """
+        summ_secs = []  # Initialize summ_secs here
+        if is_package_installed("rich") and rich_print:
+            summ_secs = [*self.details.__rich_repr__()]
+            summ_secs.append("\n")
+
+            for _, row in self.results.iterrows():
+                tbl = Table(title=f"Profile[{row.Label}]")
+                tbl.add_column("")
+                tbl.add_column("Estimate")
+                tbl.add_column("Lower CI")
+                tbl.add_column("Upper CI")
+
+                for label, val in zip(
+                    ["Elevation", "X-Value", "Y-Value", "Amplitude", "Displacement"],
+                    ["e", "x", "y", "a", "d"],
+                    strict=False,
+                ):
+                    tbl.add_row(
+                        label,
+                        str(round(row[f"{val}_est"], 3)),
+                        str(round(row[f"{val}_lci"], 3)),
+                        str(round(row[f"{val}_uci"], 3)),
+                    )
+                tbl.add_row("Model Fit", str(round(row.fit_est, 3)))
+
+                summ_secs.append(tbl)
+
+            summ_group = Group(*summ_secs)  # This will now work correctly
+            if console is not None:
+                console.print(summ_group)
+        else:
+            # Non-rich version of the summary
+            print(f"Statistical Basis: {self.details.score_type.capitalize()} Scores")  # noqa: T201
+            print(f"Bootstrap Resamples: {self.details.boots}")  # noqa: T201
+            print(f"Confidence Level: {self.details.interval}")  # noqa: T201
+            print(f"Listwise Deletion: {self.details.listwise}")  # noqa: T201
+            print(f"Scale Displacements: {self.details.angles}")  # noqa: T201
+            print("\nResults:")  # noqa: T201
+            for _, row in self.results.iterrows():
+                print(f"Profile[{row.Label}]:")  # noqa: T201
+                print(  # noqa: T201
+                    f"  Elevation: {round(row.e_est, 3)}, "
+                    f"Lower CI: {round(row.e_lci, 3)}, "
+                    f"Upper CI: {round(row.e_uci, 3)}"
+                )
+                print(  # noqa: T201
+                    f"  X-Value: {round(row.x_est, 3)}, "
+                    f"Lower CI: {round(row.x_lci, 3)}, "
+                    f"Upper CI: {round(row.x_uci, 3)}"
+                )
+                print(  # noqa: T201
+                    f"  Y-Value: {round(row.y_est, 3)}, "
+                    f"Lower CI: {round(row.y_lci, 3)}, "
+                    f"Upper CI: {round(row.y_uci, 3)}"
+                )
+                print(  # noqa: T201
+                    f"  Amplitude: {round(row.a_est, 3)}, "
+                    f"Lower CI: {round(row.a_lci, 3)}, "
+                    f"Upper CI: {round(row.a_uci, 3)}"
+                )
+                print(f"  Displacement: {round(row.d_est, 3)}")  # noqa: T201
+                print(f"  Model Fit: {round(row.fit_est, 3)}\n")  # noqa: T201
+
+    def plot_circle(self, **kwargs: Any) -> Figure:
+        """Generate a circular SSM plot for the analysis results.
+
+        Convenience method that passes SSM data to the plot_circle() function.
+        Automatically plots all profiles in the results.
+
+        Parameters
+        ----------
+        **kwargs
+            Additional plotting options passed to plot_circle(). See plot_circle()
+            documentation for available options (e.g., amax, angle_labels, colors,
+            fontsize, drop_lowfit, figsize, title).
+
+        Returns
+        -------
+        Matplotlib Figure object.
+
+        Examples
+        --------
+        >>> results = ssm_analyze(data, scales=list(range(8)))
+        >>> fig = results.plot_circle()
+        >>> fig.savefig('profile.png')
+
+        >>> fig = results.plot_circle(colors="husl", fontsize=14)
+
+        See Also
+        --------
+        circumplex.visualization.plot_circle : Full function documentation
+        """
+        return plot_circle(
+            results_df=self.results,
+            angles=self.details.angles,
+            **kwargs,
+        )
+
+    def plot_curve(self, **kwargs: Any) -> Figure:
+        """Generate SSM curve plots for the analysis results.
+
+        Convenience method that passes SSM data to the plot_curve() function.
+        Creates faceted plots showing fitted curves overlaid on observed scores.
+
+        Parameters
+        ----------
+        **kwargs
+            Additional plotting options passed to plot_curve(). See plot_curve()
+            documentation for available options (e.g., angle_labels, colors,
+            base_size, drop_lowfit, figsize).
+
+        Returns
+        -------
+        Matplotlib Figure object.
+
+        Examples
+        --------
+        >>> results = ssm_analyze(data, scales=list(range(8)))
+        >>> fig = results.plot_curve()
+        >>> fig.savefig('curves.png')
+
+        >>> fig = results.plot_curve(angle_labels=['PA', 'BC', 'DE', 'FG',
+        ...                                         'HI', 'JK', 'LM', 'NO'])
+
+        See Also
+        --------
+        circumplex.visualization.plot_curve : Full function documentation
+        """
+        return plot_curve(
+            results_df=self.results,
+            scores_df=self.scores,
+            angles=self.details.angles,
+            **kwargs,
+        )
+
+    def plot_contrast(self, **kwargs: Any) -> Figure:
+        """Generate SSM parameter contrast plots for the analysis results.
+
+        Convenience method that passes SSM data to the plot_contrast() function.
+        Only available for contrast analyses (when contrast=True in ssm_analyze).
+
+        Parameters
+        ----------
+        **kwargs
+            Additional plotting options passed to plot_contrast(). See
+            plot_contrast() documentation for available options (e.g., drop_xy,
+            sig_color, ns_color, linewidth, fontsize, figsize).
+
+        Returns
+        -------
+        Matplotlib Figure object.
+
+        Raises
+        ------
+        ValueError
+            If this SSM object does not contain contrast results.
+
+        Examples
+        --------
+        >>> results = ssm_analyze(data, scales=list(range(8)),
+        ...                       grouping='condition', contrast=True)
+        >>> fig = results.plot_contrast()
+        >>> fig.savefig('contrasts.png')
+
+        >>> fig = results.plot_contrast(drop_xy=True)
+
+        See Also
+        --------
+        circumplex.visualization.plot_contrast : Full function documentation
+        """
+        if not self.details.contrast:
+            msg = (
+                "plot_contrast() requires contrast results. "
+                "Run ssm_analyze() with contrast=True."
+            )
+            raise ValueError(msg)
+
+        return plot_contrast(
+            results_df=self.results,
+            **kwargs,
+        )
diff --git a/src/circumplex/utils.py b/src/circumplex/utils.py
deleted file mode 100644
index 9aaac52..0000000
--- a/src/circumplex/utils.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import numpy as np
-import pandas as pd
-from circumplex.instrument import Scales, Instrument
-
-
-def standardize(data: pd.DataFrame, scales: Scales, angles: np.array, instrument: Instrument, sample: int = 1,
-                prefix: str = "", suffix: str = "_z"):
-    scale_names = scales.abbrev
-    assert len(scale_names) == len(angles)
-
-    key = instrument.norms.get_sample(sample)
-    assert len(scale_names) == len(key)
-
-    for i in range(len(angles)):
-        scale_i = scale_names[i]
-        new_var = f"{prefix}{scale_i}{suffix}"
-        index_i = key['angle'][i]
-        m_i = key['m'][i]
-        s_i = key['sd'][i]
-        data[new_var] = (data[scale_i] - m_i) / s_i
-
-    return data
diff --git a/src/circumplex/utils/__init__.py b/src/circumplex/utils/__init__.py
new file mode 100644
index 0000000..e9d712c
--- /dev/null
+++ b/src/circumplex/utils/__init__.py
@@ -0,0 +1,31 @@
+"""Utilities package."""
+
+from .angles import (
+    OCTANTS,
+    POLES,
+    QUADRANTS,
+    Degree,
+    Radian,
+    cosine_form,
+    degrees_to_radians,
+    octants,
+    radians_to_degrees,
+    sort_angles,
+)
+from .tidying_functions import ipsatize, norm_standardize, score
+
+__all__ = [
+    "OCTANTS",
+    "POLES",
+    "QUADRANTS",
+    "Degree",
+    "Radian",
+    "cosine_form",
+    "degrees_to_radians",
+    "ipsatize",
+    "norm_standardize",
+    "octants",
+    "radians_to_degrees",
+    "score",
+    "sort_angles",
+]
diff --git a/src/circumplex/utils/angles.py b/src/circumplex/utils/angles.py
new file mode 100644
index 0000000..22685c3
--- /dev/null
+++ b/src/circumplex/utils/angles.py
@@ -0,0 +1,219 @@
+"""Angle utilities and predefined angle sets.
+
+This module provides tools for working with circular angles, including
+conversion between degrees and radians, and standard angle sets for
+circumplex models.
+"""
+
+from __future__ import annotations
+
+from enum import IntEnum, auto
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from nptyping import Float, NDArray, Shape
+
+
+OCTANTS: NDArray[Shape["8"], Float] = np.array(
+    [90, 135, 180, 225, 270, 315, 360, 45], dtype=float
+)
+"""Standard octant angles in degrees.
+
+Returns the eight standard positions on a circumplex circle,
+spaced 45 degrees apart, starting from 90 degrees (North).
+"""
+
+QUADRANTS: NDArray[Shape["4"], Float] = np.array([90, 180, 270, 360], dtype=float)
+"""Standard quadrant angles in degrees.
+
+Returns the four standard quadrant positions on a circumplex circle,
+spaced 90 degrees apart.
+"""
+
+POLES: NDArray[Shape["2"], Float] = np.array([90, 270], dtype=float)
+"""Standard pole angles in degrees.
+
+Returns the two primary axis positions (vertical poles)
+on a circumplex circle.
+"""
+
+
+class AngleStart(IntEnum):
+    """Enumeration for angle starting positions."""
+
+    EAST = 0  # 0 degrees
+    NE = auto()  # 45 degrees
+    NORTH = auto()  # 90 degrees
+    NW = auto()  # 135 degrees
+    WEST = auto()  # 180 degrees
+    SW = auto()  # 225 degrees
+    SOUTH = auto()  # 270 degrees
+    SE = auto()  # 315 degrees
+
+
+def octants(start: AngleStart | int = AngleStart.EAST) -> NDArray[Shape["8"], Float]:
+    """Get octant angles starting from a specified position.
+
+    Parameters
+    ----------
+    start
+        Starting position for the octants (default is 90 degrees).
+
+    Returns
+    -------
+    np.ndarray
+        Array of octant angles in degrees starting from the specified position.
+
+    Examples
+    --------
+    >>> octants(AngleStart.ONE)
+    array([  0.,  45.,  90., 135., 180., 225., 270., 315.])
+    >>> octants(AngleStart.FIVE)
+    array([180., 225., 270., 315.,   0.,  45.,  90., 135.])
+
+    """
+    if isinstance(start, int):
+        start = AngleStart(start)
+
+    base_angles = np.array([0, 45, 90, 135, 180, 225, 270, 315], dtype=float)
+    start_index = start
+    return np.roll(base_angles, -start_index)
+
+
+class Degree(float):
+    """Angular measurement in degrees.
+
+    A float subclass representing an angle in degrees, with
+    conversion methods to radians.
+
+    Examples
+    --------
+    >>> angle = Degree(90)
+    >>> angle.to_radians()
+    Radian(1.5707963267948966)
+
+    """
+
+    def to_radians(self) -> "Radian":
+        """Convert to radians.
+
+        Returns
+        -------
+        Angle in radians
+
+        """
+        return Radian(np.radians(self))
+
+    def __repr__(self) -> str:
+        """Return a compact string representation, e.g., '90°'."""
+        return f"{float(self):.0f}°"
+
+
+class Radian(float):
+    """Angular measurement in radians.
+
+    A float subclass representing an angle in radians, with
+    conversion methods to degrees.
+
+    Examples
+    --------
+    >>> angle = Radian(np.pi/2)
+    >>> angle.to_degrees()
+    Degree(90.0)
+
+    """
+
+    def to_degrees(self) -> Degree:
+        """Convert to degrees.
+
+        Returns
+        -------
+        Angle in degrees
+
+        """
+        return Degree(np.degrees(self))
+
+    def __repr__(self) -> str:
+        """Return a compact string representation, e.g., '1.571 rad'."""
+        return f"{float(self):.3f} rad"
+
+
+def degrees_to_radians(degrees: float | np.ndarray) -> float | np.ndarray:
+    """Convert degrees to radians.
+
+    Parameters
+    ----------
+    degrees
+        Angle(s) in degrees
+
+    Returns
+    -------
+    Angle(s) in radians
+
+    Examples
+    --------
+    >>> degrees_to_radians(180)
+    3.141592653589793
+    >>> degrees_to_radians(OCTANTS)
+    array([1.57..., 2.35..., 3.14..., ...])
+
+    """
+    return np.radians(degrees)
+
+
+def radians_to_degrees(radians: float | np.ndarray) -> float | np.ndarray:
+    """Convert radians to degrees.
+
+    Parameters
+    ----------
+    radians
+        Angle(s) in radians
+
+    Returns
+    -------
+    Angle(s) in degrees
+
+    Examples
+    --------
+    >>> radians_to_degrees(np.pi)
+    180.0
+
+    """
+    return np.degrees(radians)
+
+
+def cosine_form(
+    theta: NDArray[Shape[Any], Float], ampl: float, disp: float, elev: float
+) -> NDArray[Shape[Any], Float]:
+    """
+    Cosine function with amplitude, displacement and elevation parameters.
+
+    This is the mathematical model used in the Structural Summary Method.
+
+    Parameters
+    ----------
+    theta
+        Angular positions in radians.
+    ampl
+        Amplitude of the cosine curve.
+    disp
+        Angular displacement in radians.
+    elev
+        Elevation (mean level) of the cosine curve.
+
+    Returns
+    -------
+    np.ndarray
+        Predicted values at each theta position.
+    """
+    return elev + ampl * np.cos(theta - disp)
+
+
+def sort_angles(
+    angles: NDArray[Shape[Any], Float], scores: NDArray[Shape[Any], Float]
+) -> tuple[NDArray[Shape[Any], Float], NDArray[Shape[Any], Float]]:
+    """Sort angles and corresponding scores in ascending order."""
+    sorted_indices = np.argsort(angles)
+    return np.array(angles)[sorted_indices], np.array(scores)[sorted_indices]
diff --git a/src/circumplex/utils/contrast.py b/src/circumplex/utils/contrast.py
new file mode 100644
index 0000000..06a80d4
--- /dev/null
+++ b/src/circumplex/utils/contrast.py
@@ -0,0 +1,170 @@
+"""Contrast and circular difference utilities for SSM analysis.
+
+This module provides functions for computing parameter differences and
+circular distance calculations, particularly for contrast analyses.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from nptyping import Float, Int, NDArray, Shape
+
+
+def angle_dist(angle1: float, angle2: float) -> float:
+    """Calculate circular distance between two angles.
+
+    Computes the signed difference between two angles in radians,
+    returning a value in the range [-π, π]. This handles the circular
+    nature of angular data correctly.
+
+    Parameters
+    ----------
+    angle1
+        First angle in radians
+    angle2
+        Second angle in radians
+
+    Returns
+    -------
+    Signed difference (angle1 - angle2) in radians, range [-π, π]
+
+    Examples
+    --------
+    >>> angle_dist(0.1, 0.05)  # Small positive difference
+    0.05
+    >>> angle_dist(0.1, 6.2)  # Wraps around circle
+    0.18318...
+    >>> angle_dist(np.pi, -np.pi)  # Opposite sides of circle
+    0.0
+
+    Notes
+    -----
+    This function mirrors angle_dist() from the R package (R/utils.R lines 32-34).
+    The formula ((x - y + π) mod 2π) - π ensures the result is in [-π, π].
+
+    """
+    diff = angle1 - angle2
+    # Wrap to [-π, π]
+    return ((diff + np.pi) % (2 * np.pi)) - np.pi
+
+
+def param_diff(
+    params1: dict[str, float], params2: dict[str, float]
+) -> dict[str, float]:
+    """Calculate difference between two sets of SSM parameters.
+
+    Computes element-wise differences between parameter sets, with special
+    handling for the displacement parameter (circular difference).
+
+    Parameters
+    ----------
+    params1
+        First parameter set with keys: elevation, x_value, y_value,
+        amplitude, displacement, fit
+    params2
+        Second parameter set with same keys
+
+    Returns
+    -------
+    Parameter differences (params1 - params2). The displacement difference
+    uses circular distance to handle angular wrapping correctly.
+
+    Examples
+    --------
+    >>> p1 = {'elevation': 1.0, 'x_value': 0.5, 'y_value': 0.3,
+    ...       'amplitude': 0.6, 'displacement': 0.1, 'fit': 0.9}
+    >>> p2 = {'elevation': 0.8, 'x_value': 0.4, 'y_value': 0.2,
+    ...       'amplitude': 0.5, 'displacement': 6.2, 'fit': 0.85}
+    >>> diff = param_diff(p1, p2)
+    >>> diff['elevation']
+    0.2
+    >>> diff['displacement']  # Uses circular distance
+    0.18318...
+
+    Notes
+    -----
+    This function mirrors param_diff() from the R package (R/utils.R lines 13-19).
+    For displacement, it uses angle_dist() instead of simple subtraction.
+
+    """
+    return {
+        "elevation": params1["elevation"] - params2["elevation"],
+        "x_value": params1["x_value"] - params2["x_value"],
+        "y_value": params1["y_value"] - params2["y_value"],
+        "amplitude": params1["amplitude"] - params2["amplitude"],
+        "displacement": angle_dist(params1["displacement"], params2["displacement"]),
+        "fit": params1["fit"] - params2["fit"],
+    }
+
+
+def param_diff_array(
+    params: NDArray[Shape["12", Float | Int]],
+) -> NDArray[Shape["6", Float | Int]]:
+    """Calculate parameter differences from a flat parameter array.
+
+    Given a flat array containing parameters for two groups/measures,
+    computes the difference between them (first - second) with circular
+    handling for displacement.
+
+    Parameters
+    ----------
+    params
+        Flat array of parameters, length 12:
+        [e1, x1, y1, a1, d1, f1, e2, x2, y2, a2, d2, f2]
+
+    Returns
+    -------
+    Array of parameter differences, length 6:
+    [e_diff, x_diff, y_diff, a_diff, d_diff, f_diff]
+
+    Examples
+    --------
+    >>> params = np.array([1.0, 0.5, 0.3, 0.6, 0.1, 0.9,
+    ...                    0.8, 0.4, 0.2, 0.5, 6.2, 0.85])
+    >>> diff = param_diff_array(params)
+    >>> diff[0]  # Elevation difference
+    0.2
+
+    Notes
+    -----
+    This is a convenience wrapper around param_diff() for use with
+    flat arrays returned by group_parameters().
+
+    """
+    # Extract first set (indices 0-5) - first group
+    p1 = {
+        "elevation": params[0],
+        "x_value": params[1],
+        "y_value": params[2],
+        "amplitude": params[3],
+        "displacement": params[4],
+        "fit": params[5],
+    }
+
+    # Extract second set (indices 6-11) - second group
+    p2 = {
+        "elevation": params[6],
+        "x_value": params[7],
+        "y_value": params[8],
+        "amplitude": params[9],
+        "displacement": params[10],
+        "fit": params[11],
+    }
+
+    # Compute difference as second - first (matches R convention)
+    diff = param_diff(p2, p1)  # type: ignore[invalid-argument-type]
+
+    return np.array(
+        [
+            diff["elevation"],
+            diff["x_value"],
+            diff["y_value"],
+            diff["amplitude"],
+            diff["displacement"],
+            diff["fit"],
+        ]
+    )
diff --git a/src/circumplex/utils/tidying_functions.py b/src/circumplex/utils/tidying_functions.py
new file mode 100644
index 0000000..7edbbc0
--- /dev/null
+++ b/src/circumplex/utils/tidying_functions.py
@@ -0,0 +1,227 @@
+"""Utility functions for data tidying and instrument scoring."""
+
+from collections.abc import Iterable
+
+import numpy as np
+import pandas as pd
+
+from circumplex.instruments import get_instrument
+from circumplex.instruments.models import Instrument
+
+
+def ipsatize(
+    data: pd.DataFrame,
+    items: Iterable[str | int],
+    prefix: str = "",
+    suffix: str = "_i",
+    *,
+    na_rm: bool = True,
+    append: bool = True,
+) -> pd.DataFrame:
+    """Ipsatize item-level data by centering within individuals.
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        DataFrame containing item-level data.
+    items : tuple[str]
+        Tuple of column names corresponding to item-level data to ipsatize.
+    prefix : str, optional
+        Prefix to add to ipsatized column names, by default "".
+    suffix : str, optional
+        Suffix to add to ipsatized column names, by default "".
+    na_rm : bool, optional
+        Whether to remove NAs when computing individual means, by default True.
+    append : bool, optional
+        Whether to append ipsatized columns to the original DataFrame,
+        or return only the ipsatized columns, by default True.
+
+    Returns
+    -------
+    DataFrame with ipsatized item-level data.
+
+    Raises
+    ------
+    TypeError
+        If `data` is not a DataFrame or `items` is not a sequence.
+    ValueError
+        If any item in `items` is not a column in `data`.
+    """
+    if not isinstance(data, pd.DataFrame):
+        msg = "Input 'data' must be a pandas DataFrame."
+        raise TypeError(msg)
+    if isinstance(items, str):
+        msg = "Input 'items' must be a sequence of column names."
+        raise TypeError(msg)
+    if not isinstance(items, Iterable):
+        msg = "Input 'items' must be a sequence of column names."
+        raise TypeError(msg)
+
+    if all(isinstance(item, str) for item in items):
+        if all(item in data.columns for item in items):
+            item_data = data.loc[:, list(items)].copy()
+        else:
+            msg = "All items in 'items' must be valid column names in 'data'."
+            raise ValueError(msg)
+    elif all(isinstance(item, (int, np.integer, float, np.floating)) for item in items):
+        numeric_items = [int(item) for item in items]
+        if all(0 <= idx < data.shape[1] for idx in numeric_items):
+            item_data = data.iloc[:, numeric_items].copy()
+        else:
+            msg = "All items in 'items' must be valid indices in 'data'."
+            raise ValueError(msg)
+    else:
+        msg = "All items in 'items' must be either strings or integers."
+        raise TypeError(msg)
+
+    rmean = item_data.mean(axis=1, skipna=na_rm)
+    scores = item_data.subtract(rmean, axis=0)
+    scores.columns = [f"{prefix}{item}{suffix}" for item in items]
+
+    if append:
+        return pd.concat([data, scores], axis=1)
+    return scores
+
+
+def score(
+    data: pd.DataFrame,
+    items: Iterable[str | int],
+    instrument: Instrument | str,
+    prefix: str = "",
+    suffix: str = "",
+    *,
+    na_rm: bool = True,
+    append: bool = True,
+) -> pd.DataFrame:
+    """Score item-level data using a circumplex instrument.
+
+    Parameters
+    ----------
+    data
+        DataFrame containing at least circumplex scales.
+    items
+        The variable names or column numbers for the variables in `data`
+        that contain all the circumplex items from a single circumplex measure,
+        in ascending order from item 1 to item N.
+    instrument
+        An instrument object from the package. To see the available
+    prefix : str, optional
+        Prefix to add to scored column names, by default "".
+    suffix : str, optional
+        Suffix to add to scored column names, by default "".
+    na_rm : bool, optional
+        Whether to remove NAs when computing individual means, by default True.
+    append : bool, optional
+        Whether to append scored columns to the original DataFrame,
+        or return only the scored columns, by default True.
+
+    Returns
+    -------
+    DataFrame with scored scale-level data.
+
+    Raises
+    ------
+    TypeError
+        If `data` is not a DataFrame or `items` is not a sequence.
+    ValueError
+        If any item in `items` is not a column in `data`.
+    """
+    # Validate inputs
+    # -- validate data and items first (before trying to get instrument)
+    if not isinstance(data, pd.DataFrame):
+        msg = "Input 'data' must be a pandas DataFrame."
+        raise TypeError(msg)
+    if isinstance(items, str):
+        msg = "Input 'items' must be a sequence of column names."
+        raise TypeError(msg)
+    if not isinstance(items, Iterable):
+        msg = "Input 'items' must be a sequence of column names or indices."
+        raise TypeError(msg)
+
+    # -- get instrument if a string is provided
+    if isinstance(instrument, str):
+        instrument = get_instrument(instrument)
+    if not isinstance(instrument, Instrument):
+        msg = "Input 'instrument' must be an Instrument instance."
+        raise TypeError(msg)
+
+    return instrument.score(
+        data,
+        items,
+        prefix=prefix,
+        suffix=suffix,
+        na_rm=na_rm,
+        append=append,
+    )
+
+
+def norm_standardize(
+    data: pd.DataFrame,
+    instrument: Instrument | str,
+    sample_id: int,
+    scales: Iterable[str | int] | None = None,
+    prefix: str = "",
+    suffix: str = "_z",
+    *,
+    append: bool = True,
+) -> pd.DataFrame:
+    """Standardize scale-level data using normative sample statistics.
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        DataFrame containing scale-level data.
+    scales : tuple[str | int]
+        Tuple of column names or indices corresponding to scale-level data
+        to standardize.
+    instrument : Instrument | str
+        An instrument object from the package. To see the available
+        instruments, use `show_instruments()`.
+    sample_id : int | str
+        The ID of the normative sample to use for standardization.
+    prefix : str, optional
+        Prefix to add to standardized column names, by default "".
+    suffix : str, optional
+        Suffix to add to standardized column names, by default "_z".
+    append : bool, optional
+        Whether to append standardized columns to the original DataFrame,
+        or return only the standardized columns, by default True.
+
+    Returns
+    -------
+    DataFrame with standardized scale-level data.
+
+    Raises
+    ------
+    TypeError
+        If `data` is not a DataFrame or `scales` is not a sequence.
+    ValueError
+        If any scale in `scales` is not a column in `data`.
+    """
+    # Validate inputs
+    # -- validate data and scales first (before trying to get instrument)
+    if not isinstance(data, pd.DataFrame):
+        msg = "Input 'data' must be a pandas DataFrame."
+        raise TypeError(msg)
+    if isinstance(scales, str):
+        msg = "Input 'scales' must be a sequence of column names."
+        raise TypeError(msg)
+    if not isinstance(scales, Iterable) and scales is not None:
+        msg = "Input 'scales' must be a sequence of column names or indices."
+        raise TypeError(msg)
+
+    # -- get instrument if a string is provided
+    if isinstance(instrument, str):
+        instrument = get_instrument(instrument)
+    if not isinstance(instrument, Instrument):
+        msg = "Input 'instrument' must be an Instrument instance."
+        raise TypeError(msg)
+
+    return instrument.norm_standardize(
+        data,
+        int(sample_id),
+        scales=scales,
+        prefix=prefix,
+        suffix=suffix,
+        append=append,
+    )
diff --git a/src/circumplex/visualization/__init__.py b/src/circumplex/visualization/__init__.py
new file mode 100644
index 0000000..069ecf2
--- /dev/null
+++ b/src/circumplex/visualization/__init__.py
@@ -0,0 +1,13 @@
+"""Circumplex visualization module.
+
+This module provides functions for visualizing SSM analysis results using
+matplotlib. Three main plot types are available:
+
+- plot_circle: Circular plot showing amplitude and displacement
+- plot_curve: Faceted plot showing fitted cosine curves with observed scores
+- plot_contrast: Faceted plots for parameter contrasts between groups
+"""
+
+from circumplex.visualization.plots import plot_circle, plot_contrast, plot_curve
+
+__all__ = ["plot_circle", "plot_contrast", "plot_curve"]
diff --git a/src/circumplex/visualization/_utils.py b/src/circumplex/visualization/_utils.py
new file mode 100644
index 0000000..c9cd4ac
--- /dev/null
+++ b/src/circumplex/visualization/_utils.py
@@ -0,0 +1,104 @@
+"""Helper utilities for circumplex visualization."""
+
+import numpy as np
+
+
+def ggrad(degrees: float | np.ndarray) -> float | np.ndarray:
+    """Convert degrees to ggplot2-style radians.
+
+    ggplot2 uses a coordinate system where:
+    - 90° is at the top (North)
+    - Angles proceed clockwise
+
+    This function converts standard circumplex degrees to this system.
+
+    Parameters
+    ----------
+    degrees
+        Angle(s) in degrees (0-360).
+
+    Returns
+    -------
+    Angle(s) in radians with ggplot2 orientation.
+
+    Examples
+    --------
+    >>> ggrad(90)   # Top (North)
+    0.0
+    >>> ggrad(0)    # Right (East)
+    1.5707963267948966
+    >>> ggrad(180)  # Left (West)
+    -1.5707963267948966
+
+    """
+    return (degrees - 90) * (-np.pi / 180)
+
+
+def pretty_max(values: np.ndarray) -> float:
+    """Find a nice maximum value for amplitude scaling.
+
+    Selects from a predefined set of "pretty" values that are slightly
+    larger than the maximum data value, providing appropriate buffer space.
+
+    Parameters
+    ----------
+    values
+        Array of amplitude values (typically upper CI bounds).
+
+    Returns
+    -------
+    A nice round number suitable for the amplitude scale maximum.
+
+    Examples
+    --------
+    >>> pretty_max(np.array([0.42, 0.38, 0.45]))
+    0.75
+    >>> pretty_max(np.array([1.2, 1.5, 1.3]))
+    2.5
+
+    """
+    amax = np.nanmax(values)
+
+    options = np.array(
+        [
+            -5.00,
+            -4.00,
+            -3.00,
+            -2.50,
+            -2.00,
+            -1.50,
+            -1.25,
+            -1.00,
+            -0.75,
+            -0.50,
+            -0.25,
+            -0.20,
+            -0.15,
+            -0.10,
+            -0.05,
+            0.00,
+            0.05,
+            0.10,
+            0.15,
+            0.20,
+            0.25,
+            0.50,
+            0.75,
+            1.00,
+            1.25,
+            1.50,
+            2.00,
+            2.50,
+            3.00,
+            4.00,
+            5.00,
+        ]
+    )
+
+    # If negative, use smaller buffer; if positive, use larger buffer
+    scalar = 0.5 if amax < 0 else 1.5
+
+    # Find the first option larger than amax * scalar
+    idx = np.searchsorted(options, amax * scalar)
+
+    return options[min(idx, len(options) - 1)]
diff --git a/src/circumplex/visualization/plots.py b/src/circumplex/visualization/plots.py
new file mode 100644
index 0000000..033ed00
--- /dev/null
+++ b/src/circumplex/visualization/plots.py
@@ -0,0 +1,1103 @@
+"""Matplotlib-based plotting functions for SSM results."""
+
+from __future__ import annotations
+
+import warnings
+from typing import Any, cast
+
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from matplotlib.colors import to_rgb
+from matplotlib.figure import Figure
+from matplotlib.patches import Patch
+
+from circumplex.visualization._utils import pretty_max
+
+
+def _validate_results_df(results_df: pd.DataFrame) -> None:
+    """Validate that results DataFrame has required columns.
+
+    Parameters
+    ----------
+    results_df
+        DataFrame to validate.
+
+    Raises
+    ------
+    ValueError
+        If required columns are missing.
+
+    """
+    required_cols = ["Label", "e_est", "x_est", "y_est", "a_est", "d_est", "fit_est"]
+    missing = [col for col in required_cols if col not in results_df.columns]
+    if missing:
+        msg = f"results_df missing required columns: {missing}"
+        raise ValueError(msg)
+
+
+def _prepare_plot_data(
+    results_df: pd.DataFrame,
+    profile_indices: list[int] | None,
+    *,
+    drop_lowfit: bool,
+) -> pd.DataFrame:
+    """Extract and filter profiles to plot.
+
+    Parameters
+    ----------
+    results_df
+        Full results DataFrame.
+    profile_indices
+        Indices of profiles to plot, or None for all.
+    drop_lowfit
+        Whether to drop profiles with fit < 0.70.
+
+    Returns
+    -------
+    Filtered DataFrame ready for plotting.
+
+    Raises
+    ------
+    IndexError
+        If profile index is out of range.
+    ValueError
+        If no profiles remain after filtering.
+
+    """
+    # Determine which profiles to plot
+    if profile_indices is None:
+        profile_indices = list(range(len(results_df)))
+
+    if len(profile_indices) == 0:
+        msg = "No profiles to plot"
+        raise ValueError(msg)
+
+    # Validate indices
+    for idx in profile_indices:
+        if idx >= len(results_df):
+            msg = f"Profile index {idx} out of range (max: {len(results_df) - 1})"
+            raise IndexError(msg)
+
+    # Extract profiles
+    plot_df = results_df.iloc[profile_indices].copy()
+
+    # Handle low-fit profiles
+    if drop_lowfit:
+        low_fit_mask = plot_df["fit_est"] < 0.70
+        if low_fit_mask.any():
+            dropped = plot_df.loc[low_fit_mask, "Label"].tolist()
+            warnings.warn(
+                f"Dropping profiles with fit < 0.70: {dropped}",
+                stacklevel=3,
+            )
+            plot_df = plot_df[~low_fit_mask]
+            if len(plot_df) == 0:
+                msg = "All profiles dropped due to low fit"
+                raise ValueError(msg)
+
+    # Add line style column based on fit
+    plot_df["linestyle"] = plot_df["fit_est"].apply(
+        lambda fit: "solid" if fit >= 0.70 else "dashed"
+    )
+
+    return plot_df
+
+
+def _setup_colors(
+    n_profiles: int,
+    colors: str | list[str] | None,
+) -> tuple[list[tuple[float, float, float]], bool]:
+    """Set up colors and determine if legend should be shown.
+
+    Parameters
+    ----------
+    n_profiles
+        Number of profiles to plot.
+    colors
+        Color specification. Can be:
+        - None: single blue color, no legend
+        - str: seaborn palette name (e.g., "Set2", "husl")
+        - list: custom colors as names, hex codes, or RGB tuples
+
+    Returns
+    -------
+    tuple of (colors list, show_legend boolean)
+
+    Raises
+    ------
+    ValueError
+        If custom color list is provided but empty.
+
+    """
+    # No colors specified or single profile with default
+    if colors is None or (colors == "Set2" and n_profiles == 1):
+        return [(0.0, 0.45, 0.70)] * n_profiles, False
+
+    # Palette name (string)
+    if isinstance(colors, str):
+        if n_profiles > 1:
+            color_list = sns.color_palette(colors, n_profiles)
+            return color_list, True
+        # Single profile with named palette
+        color_list = sns.color_palette(colors, 1)
+        return color_list, False
+
+    # Custom color list
+    if len(colors) == 0:
+        msg = "colors list cannot be empty"
+        raise ValueError(msg)
+
+    # Convert all colors to RGB tuples
+    color_list: list[tuple[float, float, float]] = []
+    for color in colors:
+        try:
+            color_list.append(to_rgb(color))
+        except ValueError as e:
+            msg = f"Invalid color specification: {color}"
+            raise ValueError(msg) from e
+
+    # Cycle colors if not enough provided
+    if len(color_list) < n_profiles:
+        warnings.warn(
+            f"Only {len(color_list)} colors provided for {n_profiles} profiles. "
+            f"Colors will be cycled.",
+            stacklevel=4,
+        )
+        # Repeat colors to cover all profiles
+        multiplier = (n_profiles // len(color_list)) + 1
+        color_list = (color_list * multiplier)[:n_profiles]
+
+    return color_list, n_profiles > 1
+
+
+def _get_contrast_row(results_df: pd.DataFrame) -> pd.Series:
+    """Get the contrast row (last row) from results_df."""
+    if len(results_df) < 3:
+        msg = (
+            "Contrast plot requires at least 3 rows in results_df "
+            "(two profiles + contrast)"
+        )
+        raise ValueError(msg)
+    return results_df.iloc[-1]
+
+
+def _build_contrast_plot_data(
+    contrast_row: pd.Series, *, drop_xy: bool
+) -> list[dict[str, Any]]:
+    """Build per-parameter contrast plot data from a results row.
+
+    Parameters
+    ----------
+    contrast_row
+        The contrast row from results_df (typically last row).
+    drop_xy
+        Whether to omit X and Y parameters.
+
+    Returns
+    -------
+    List of dictionaries with keys: parameter, estimate, lci, uci, significant.
+    """
+    param_names = ["e", "x", "y", "a", "d"]
+    param_labels = [
+        "Δ Elevation",
+        "Δ X-Value",
+        "Δ Y-Value",
+        "Δ Amplitude",
+        "Δ Displacement",
+    ]
+
+    plot_data: list[dict[str, Any]] = []
+    for param, label in zip(param_names, param_labels, strict=False):
+        est_col = f"{param}_est"
+        lci_col = f"{param}_lci"
+        uci_col = f"{param}_uci"
+
+        if est_col not in contrast_row:
+            msg = f"Missing required column: {est_col}"
+            raise ValueError(msg)
+
+        lci = contrast_row[lci_col]
+        uci = contrast_row[uci_col]
+        significant = not (lci <= 0 <= uci)
+
+        plot_data.append(
+            {
+                "parameter": label,
+                "estimate": contrast_row[est_col],
+                "lci": lci,
+                "uci": uci,
+                "significant": significant,
+            }
+        )
+
+    if drop_xy:
+        plot_data = [
+            d for d in plot_data if d["parameter"] not in ["Δ X-Value", "Δ Y-Value"]
+        ]
+
+    return plot_data
+
+
+def _draw_contrast_subplot(
+    *,
+    ax: plt.Axes,
+    data: dict[str, Any],
+    sig_color: str,
+    ns_color: str,
+    linewidth: float,
+    fontsize: float,
+    is_leftmost: bool,
+) -> None:
+    """Render a single contrast subplot."""
+    # Draw horizontal line at zero
+    ax.axhline(0, color="darkgray", linewidth=linewidth * 0.8, zorder=1)
+
+    # Determine color based on significance
+    color = sig_color if data["significant"] else ns_color
+
+    # Draw error bar
+    lower_error = abs(data["estimate"] - data["lci"])
+    upper_error = abs(data["uci"] - data["estimate"])
+    ax.errorbar(
+        0,
+        data["estimate"],
+        yerr=[[lower_error], [upper_error]],
+        fmt="none",
+        ecolor="black",
+        elinewidth=linewidth,
+        capsize=5,
+        capthick=linewidth,
+        zorder=2,
+    )
+
+    # Draw point
+    ax.scatter(
+        0,
+        data["estimate"],
+        s=linewidth * 120,
+        c=[color],
+        edgecolors="black",
+        linewidths=linewidth,
+        zorder=3,
+    )
+
+    # Set title
+    ax.set_title(data["parameter"], fontsize=fontsize * 1.05, pad=10)
+
+    # Remove x-axis
+    ax.set_xlim(-0.4, 0.4)
+    ax.set_xticks([])
+
+    # Style y-axis - only show ylabel on leftmost plot
+    if is_leftmost:
+        ax.set_ylabel("Difference", fontsize=fontsize, fontweight="bold")
+    ax.tick_params(axis="y", labelsize=fontsize * 0.9)
+
+    # Apply seaborn style
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+    ax.spines["bottom"].set_visible(False)
+    ax.spines["left"].set_linewidth(1.2)
+    ax.grid(axis="y", alpha=0.25, linestyle="--", linewidth=0.8)
+    ax.set_axisbelow(True)
+
+
+def _add_contrast_legend(
+    fig: Figure, sig_color: str, ns_color: str, fontsize: float
+) -> None:
+    """Add a legend to the contrast plot."""
+    legend_elements = [
+        Patch(
+            facecolor=sig_color,
+            edgecolor="black",
+            linewidth=1.5,
+            label="Significant (p < .05)",
+        ),
+        Patch(
+            facecolor=ns_color,
+            edgecolor="black",
+            linewidth=1.5,
+            label="Not Significant",
+        ),
+    ]
+    legend = fig.legend(
+        handles=legend_elements,
+        loc="upper center",
+        bbox_to_anchor=(0.5, 1.0),
+        ncol=2,
+        frameon=True,
+        fontsize=fontsize * 0.95,
+        edgecolor="gray",
+        fancybox=False,
+    )
+    legend.get_frame().set_linewidth(1.2)
+
+
+def _draw_circle_base(
+    ax: plt.Axes,
+    angles: list[float] | np.ndarray,
+    amax: float,
+    fontsize: float = 12,
+    labels: list[str] | None = None,
+) -> None:
+    """Draw the base circumplex circle with scales.
+
+    Parameters
+    ----------
+    ax
+        Matplotlib axes to draw on.
+    angles
+        Angular positions in degrees for displacement scale.
+    amax
+        Maximum amplitude value for scale.
+    fontsize
+        Font size for labels in points.
+    labels
+        Labels for each angle. If None, uses degree symbols (e.g., "90°").
+
+    """
+    # Default labels: degree symbols
+    if labels is None:
+        labels = [f"{int(angle)}°" for angle in angles]
+    elif len(labels) != len(angles):
+        msg = f"labels must have same length as angles ({len(angles)})"
+        raise ValueError(msg)
+
+    # Set up axes
+    ax.set_aspect("equal")
+    ax.set_xlim(-6.5, 6.5)
+    ax.set_ylim(-6.5, 6.5)
+    ax.axis("off")
+
+    # Draw outer circle (radius 5)
+    outer_circle = mpatches.Circle(
+        (0, 0),
+        5,
+        fill=True,
+        facecolor="white",
+        edgecolor="gray",
+        linewidth=1.5,
+    )
+    ax.add_patch(outer_circle)
+
+    # Draw radial segments for displacement scale
+    angles_rad = np.radians(angles)
+    for angle_rad in angles_rad:
+        x_end = 5 * np.cos(angle_rad)
+        y_end = 5 * np.sin(angle_rad)
+        ax.plot([0, x_end], [0, y_end], color="gray", linewidth=0.5, alpha=0.6)
+
+    # Draw amplitude circles (radii 1-4)
+    for radius in range(1, 5):
+        circle = mpatches.Circle(
+            (0, 0),
+            radius,
+            fill=False,
+            edgecolor="gray",
+            linewidth=0.5,
+            alpha=0.6,
+        )
+        ax.add_patch(circle)
+
+    # Draw amplitude scale labels (at positions 2 and 4 on x-axis)
+    amp_values = np.linspace(0, amax, 6)
+    for radius, amp_val in zip([2, 4], [amp_values[2], amp_values[4]], strict=False):
+        ax.text(
+            radius,
+            0,
+            f"{amp_val:.2f}",
+            ha="center",
+            va="center",
+            fontsize=fontsize * 0.8,
+            bbox={
+                "boxstyle": "round,pad=0.3",
+                "facecolor": "white",
+                "edgecolor": "none",
+            },
+        )
+
+    # Draw displacement scale labels (at radius 5.1)
+    for label, angle_rad in zip(labels, angles_rad, strict=False):
+        x_label = 5.1 * np.cos(angle_rad)
+        y_label = 5.1 * np.sin(angle_rad)
+
+        # Determine alignment based on position
+        if np.abs(x_label) < 0.5:
+            ha = "center"
+        elif x_label > 0:
+            ha = "left"
+        else:
+            ha = "right"
+
+        if np.abs(y_label) < 0.5:
+            va = "center"
+        elif y_label > 0:
+            va = "bottom"
+        else:
+            va = "top"
+
+        ax.text(
+            x_label,
+            y_label,
+            label,
+            ha=ha,
+            va=va,
+            fontsize=fontsize * 0.8,
+            color="gray",
+        )
+
+
+def plot_circle(
+    results_df: pd.DataFrame,
+    angles: list[float] | np.ndarray,
+    *,
+    profile_indices: list[int] | None = None,
+    amax: float | None = None,
+    angle_labels: list[str] | None = None,
+    colors: str | list[str] | None = "Set2",
+    fontsize: float = 12,
+    drop_lowfit: bool = False,
+    figsize: tuple[float, float] = (8, 8),
+    title: str | None = None,
+) -> Figure:
+    """Plot SSM profiles on a circumplex circle.
+
+    Creates a circular plot showing amplitude and displacement of SSM profiles,
+    with arc bars representing confidence intervals. Automatically handles both
+    single and multiple profiles.
+
+    Parameters
+    ----------
+    results_df
+        DataFrame with SSM results. Must contain columns:
+        - Label: str, profile name
+        - e_est, x_est, y_est, a_est, d_est: float, parameter estimates
+        - e_lci, x_lci, ..., d_uci: float, confidence intervals
+        - fit_est: float, model fit (0-1)
+    angles
+        Angular positions of scales in degrees
+        (e.g., [90, 135, 180, 225, 270, 315, 360, 45]).
+    profile_indices
+        Which rows of results_df to plot. If None, plots all profiles.
+    amax
+        Maximum amplitude for scaling. If None, auto-computed using pretty_max().
+    angle_labels
+        Labels for each angle. If None, shows degree symbols (e.g., "90°").
+        Pass empty strings to hide labels.
+    colors
+        Colors for profiles. Can be:
+        - Seaborn palette name: "Set2", "husl", "deep", etc.
+        - List of color specifications: ['red', 'blue'] or ['#FF0000', '#0000FF']
+        - None: single blue color with no legend
+    fontsize
+        Base font size in points.
+    drop_lowfit
+        If True, omit profiles with fit < 0.70. If False, show with dashed borders.
+    figsize
+        Figure size in inches (width, height).
+    title
+        Title for the plot. If None, no title is added.
+
+    Returns
+    -------
+    Matplotlib Figure object.
+
+    Examples
+    --------
+    Plot all profiles from an SSM analysis:
+
+    >>> from circumplex import ssm_analyze
+    >>> results = ssm_analyze(data, scales=list(range(8)))
+    >>> fig = plot_circle(results.results, results.details.angles)
+    >>> fig.savefig('profiles.png')
+
+    Plot specific profiles with custom styling:
+
+    >>> fig = plot_circle(
+    ...     results.results,
+    ...     results.details.angles,
+    ...     profile_indices=[0, 1],
+    ...     colors="husl",
+    ...     fontsize=14,
+    ...     figsize=(10, 10),
+    ... )
+
+    Use custom colors:
+
+    >>> fig = plot_circle(
+    ...     results.results,
+    ...     results.details.angles,
+    ...     colors=['red', 'blue', 'green'],
+    ... )
+
+    See Also
+    --------
+    plot_curve : Plot SSM fitted curves with observed scores
+    plot_contrast : Plot SSM parameter contrasts
+
+    """
+    # Validate and prepare data
+    _validate_results_df(results_df)
+    plot_df = _prepare_plot_data(results_df, profile_indices, drop_lowfit=drop_lowfit)
+
+    # Determine amax
+    if amax is None:
+        if "a_uci" in plot_df.columns:
+            amax = pretty_max(plot_df["a_uci"].values)
+        else:
+            amax = pretty_max(plot_df["a_est"].values)
+
+    # Create figure and draw base
+    fig, ax = plt.subplots(figsize=figsize)
+    _draw_circle_base(ax, angles, amax, fontsize, angle_labels)
+
+    # Scale factor: radius 5 corresponds to amax
+    scale_factor = 5.0 / amax
+
+    # Set up colors and legend
+    n_profiles = len(plot_df)
+    color_list, show_legend = _setup_colors(n_profiles, colors)
+
+    # Plot each profile
+    for i, (_idx, row) in enumerate(plot_df.iterrows()):
+        color = color_list[i]
+        label = row["Label"]
+
+        # Scale parameters to circle coordinates
+        x_plot = row["x_est"] * scale_factor
+        y_plot = row["y_est"] * scale_factor
+
+        # Draw confidence interval arc if available
+        ci_cols = ["a_lci", "a_uci", "d_lci", "d_uci"]
+        has_ci = all(col in row and not pd.isna(row[col]) for col in ci_cols)
+
+        if has_ci:
+            a_lci_plot = row["a_lci"] * scale_factor
+            a_uci_plot = row["a_uci"] * scale_factor
+            d_lci = row["d_lci"]
+            d_uci = row["d_uci"]
+
+            # Handle displacement wrapping (CI crosses 0/360)
+            if d_uci < d_lci:
+                d_uci += 360
+
+            # Draw arc bar (wedge)
+            wedge = mpatches.Wedge(
+                center=(0, 0),
+                r=a_uci_plot,
+                theta1=d_lci,
+                theta2=d_uci,
+                width=a_uci_plot - a_lci_plot,
+                facecolor=color,
+                edgecolor=color,
+                alpha=0.4,
+                linestyle=row["linestyle"],
+                linewidth=1.0,
+            )
+            ax.add_patch(wedge)
+
+        # Draw point at (x, y)
+        ax.scatter(
+            x_plot,
+            y_plot,
+            s=100,
+            facecolor=color,
+            edgecolor="black",
+            linewidth=1.0,
+            zorder=10,
+            label=label if show_legend else None,
+        )
+
+    # Add legend if multiple profiles
+    if show_legend:
+        ax.legend(
+            title="Profile",
+            fontsize=fontsize * 0.9,
+            title_fontsize=fontsize,
+            loc="upper left",
+            bbox_to_anchor=(1.02, 1),
+            frameon=True,
+        )
+
+    # Add title if provided
+    if title is not None:
+        fig.suptitle(title, fontsize=fontsize * 1.2, fontweight="bold")
+
+    plt.tight_layout()
+
+    return fig
+
+
+def _plot_single_curve(
+    ax: plt.Axes,
+    angles: np.ndarray,
+    angles_sorted: np.ndarray,
+    observed_scores: np.ndarray,
+    scores_sorted: np.ndarray,
+    result_row: pd.Series,
+    c_scores: str,
+    c_fit: str,
+    tick_labels: list[str],
+    xlabel: str,
+    base_size: float,
+    *,
+    incl_pred: bool,
+    incl_fit: bool,
+    incl_disp: bool,
+    incl_amp: bool,
+    incl_elev: bool,
+) -> None:
+    """Plot a single SSM curve on an axes.
+
+    Parameters
+    ----------
+    ax
+        Matplotlib axes to plot on.
+    angles
+        Angular positions (original order).
+    angles_sorted
+        Angular positions (sorted order).
+    observed_scores
+        Observed scores (original order).
+    scores_sorted
+        Observed scores (sorted order).
+    result_row
+        Row from results DataFrame with SSM parameters.
+    color
+        RGB color tuple for the fitted curve.
+    tick_labels
+        Labels for x-axis ticks.
+    xlabel
+        Label for x-axis.
+    base_size
+        Base font size.
+
+    """
+    # Plot observed scores as points (at original positions)
+    ax.plot(
+        angles,
+        observed_scores,
+        "o",
+        color=c_scores,
+        # markersize=6,  # noqa: ERA001
+        # zorder=3,  # noqa: ERA001
+        label="Observed",
+    )
+
+    # Connect observed scores with lines (using sorted order)
+    ax.plot(
+        angles_sorted,
+        scores_sorted,
+        "-",
+        color=c_scores,
+        # linewidth=0.8,  # noqa: ERA001
+        # alpha=0.5,  # noqa: ERA001
+        zorder=2,
+    )
+
+    # Generate fitted curve
+    amplitude = result_row["a_est"]
+    displacement = result_row["d_est"]
+    elevation = result_row["e_est"]
+    r2 = result_row["fit_est"]
+
+    if incl_pred:
+        angle_range = np.linspace(min(angles), max(angles), 100)
+        fitted_scores = elevation + amplitude * np.cos(
+            np.radians(angle_range - displacement)
+        )
+
+        # Determine line style based on fit
+        linestyle = "solid" if r2 >= 0.70 else "dashed"
+
+        # Plot fitted curve
+        ax.plot(
+            angle_range,
+            fitted_scores,
+            linestyle=linestyle,
+            color=c_fit,
+            # linewidth=2.0,  # noqa: ERA001
+            # zorder=4,  # noqa: ERA001
+            label="Fitted",
+        )
+
+    # Annotate parameters
+    ymin, ymax = ax.get_ylim()
+    y_offset = (ymax - ymin) * 0.2
+    curve_min = displacement - 180 if displacement >= 180 else displacement + 180
+
+    if incl_disp:
+        ax.axvline(displacement, color="gray", linestyle="--", linewidth=1.0, alpha=0.7)
+        ax.text(
+            displacement,
+            max(fitted_scores) - y_offset,
+            f"d = {int(displacement)}°",
+            horizontalalignment="center",
+            verticalalignment="center",
+            bbox={"facecolor": "white"},
+            fontsize=base_size * 0.8,
+        )
+    if incl_amp:
+        ax.axhline(
+            amplitude + elevation,
+            color="gray",
+            linestyle="--",
+            linewidth=1.0,
+            alpha=0.7,
+        )
+        ax.text(
+            curve_min,
+            elevation + amplitude,
+            f"a = {amplitude:.2f}",
+            horizontalalignment="center",
+            verticalalignment="center",
+            bbox={"facecolor": "white"},
+            fontsize=base_size * 0.8,
+        )
+    if incl_fit:
+        ax.text(
+            curve_min,
+            elevation + (amplitude * 0.5),
+            f"R² = {r2:.2f}",
+            horizontalalignment="center",
+            verticalalignment="center",
+            bbox={"facecolor": "white"},
+            fontsize=base_size * 0.8,
+        )
+    if incl_elev:
+        ax.axhline(elevation, color="gray", linestyle="--", linewidth=1.0, alpha=0.7)
+        ax.text(
+            curve_min,
+            elevation,
+            f"e = {elevation:.2f}",
+            horizontalalignment="center",
+            verticalalignment="center",
+            bbox={"facecolor": "white"},
+            fontsize=base_size * 0.8,
+        )
+
+    # Set x-axis ticks and labels
+    ax.set_xticks(angles)
+    ax.set_xticklabels(tick_labels, rotation=45, ha="right")
+
+    # Add title with profile label
+    ax.set_title(result_row["Label"], fontsize=base_size * 1.1, fontweight="bold")
+
+    # Add axis labels
+    ax.set_xlabel(xlabel, fontsize=base_size)
+    ax.set_ylabel("Score", fontsize=base_size)
+
+    # Apply seaborn style
+    ax.grid(axis="x", alpha=0.5, linestyle="-", linewidth=0.5)
+    ax.grid(axis="y", alpha=0.5, linestyle="-", linewidth=0.5)
+    ax.set_axisbelow(True)
+
+
+def plot_curve(
+    results_df: pd.DataFrame,
+    scores_df: pd.DataFrame,
+    angles: list[float] | np.ndarray,
+    *,
+    profile_indices: list[int] | None = None,
+    angle_labels: list[str] | None = None,
+    c_scores: str = "red",
+    c_fit: str = "black",
+    base_size: float = 11,
+    drop_lowfit: bool = False,
+    figsize: tuple[float, float] | None = None,
+    incl_pred: bool = True,
+    incl_fit: bool = False,
+    incl_disp: bool = False,
+    incl_amp: bool = False,
+    incl_elev: bool = False,
+) -> Figure:
+    """Plot SSM fitted curves with observed scores.
+
+    Creates a faceted plot showing the fitted cosine curve overlaid on the
+    observed circumplex scale scores. Each profile is shown in a separate subplot.
+
+    Parameters
+    ----------
+    results_df
+        DataFrame with SSM results. Must contain columns:
+        - Label: str, profile name
+        - e_est, a_est, d_est: float, parameter estimates
+        - fit_est: float, model fit (0-1)
+    scores_df
+        DataFrame with observed circumplex scores. Must have columns:
+        - Label: str, profile name (matching results_df)
+        - Scale columns: float, one column per circumplex scale
+    angles
+        Angular positions of scales in degrees, matching score column order.
+    profile_indices
+        Which rows to plot. If None, plots all profiles.
+    angle_labels
+        Labels for each angle on x-axis. If None, shows degree symbols (e.g., "90°").
+    c_scores
+        Color for observed scores (points and lines).
+    c_fit
+        Color for fitted curve.
+    base_size
+        Base font size in points for labels and text.
+    drop_lowfit
+        If True, omit profiles with fit < 0.70. If False, show with dashed curves.
+    figsize
+        Figure size in inches (width, height). If None, auto-computed based on
+        number of profiles.
+
+    Returns
+    -------
+    Matplotlib Figure object.
+
+    Examples
+    --------
+    Plot curves from an SSM analysis:
+
+    >>> from circumplex import ssm_analyze
+    >>> results = ssm_analyze(data, scales=list(range(8)))
+    >>> fig = plot_curve(results.results, results.scores, results.details.angles)
+    >>> fig.savefig('curves.png')
+
+    Use custom angle labels:
+
+    >>> fig = plot_curve(
+    ...     results.results,
+    ...     results.scores,
+    ...     results.details.angles,
+    ...     angle_labels=['PA', 'BC', 'DE', 'FG', 'HI', 'JK', 'LM', 'NO'],
+    ... )
+
+    See Also
+    --------
+    plot_circle : Plot SSM profiles on a circumplex circle
+    plot_contrast : Plot SSM parameter contrasts
+
+    """
+    # Validate results
+    _validate_results_df(results_df)
+
+    # Prepare plot data (filter profiles, handle low fit)
+    plot_results = _prepare_plot_data(
+        results_df, profile_indices, drop_lowfit=drop_lowfit
+    )
+
+    # Filter scores to match plot_results
+    plot_scores = scores_df[scores_df["Label"].isin(plot_results["Label"])].copy()
+
+    n_profiles = len(plot_results)
+    if n_profiles == 0:
+        msg = "No profiles to plot"
+        raise ValueError(msg)
+
+    # Determine subplot layout
+    ncols = min(3, n_profiles)
+    nrows = (n_profiles + ncols - 1) // ncols
+
+    # Auto-compute figure size if not provided
+    if figsize is None:
+        figsize = (ncols * 6, nrows * 4)
+
+    # Set up colors
+    # color_list, _ = _setup_colors(n_profiles, colors)  # noqa: ERA001
+
+    # Prepare angle labels for x-axis
+    if angle_labels is None:
+        xlabel = "Angle"
+        tick_labels = [f"{int(a)}°" for a in angles]
+    else:
+        xlabel = "Scale"
+        if len(angle_labels) != len(angles):
+            msg = f"angle_labels must have same length as angles ({len(angles)})"
+            raise ValueError(msg)
+        tick_labels = angle_labels
+
+    # Create figure with subplots
+    fig, axes = plt.subplots(nrows, ncols, figsize=figsize, squeeze=False)
+    # Convert ndarray of Axes to a plain list with proper typing for type checkers
+    axes_flat: list[plt.Axes] = [cast("plt.Axes", a) for a in axes.flatten()]
+
+    # Hide unused subplots
+    for idx in range(n_profiles, len(axes_flat)):
+        axes_flat[idx].set_visible(False)
+
+    # Get scale column names (everything except Label, Model, Fit)
+    info_cols = {"Label", "Model", "Fit"}
+    scale_cols = [col for col in plot_scores.columns if col not in info_cols]
+
+    if len(scale_cols) != len(angles):
+        msg = (
+            f"Number of scale columns ({len(scale_cols)}) must match "
+            f"angles ({len(angles)})"
+        )
+        raise ValueError(msg)
+
+    # Prepare angles array for sorting
+    angles_array = np.array(angles)
+
+    # Plot each profile
+    for idx, (_ridx, result_row) in enumerate(plot_results.iterrows()):
+        ax = axes_flat[idx]
+        label = result_row["Label"]
+
+        # Get scores for this profile
+        score_row = plot_scores[plot_scores["Label"] == label].iloc[0]
+        observed_scores = score_row[scale_cols].to_numpy().astype(float)
+
+        # Sort angles and scores together for proper line connection
+        sorted_indices = np.argsort(angles_array)
+        angles_sorted = angles_array[sorted_indices]
+        scores_sorted = observed_scores[sorted_indices]
+
+        # Plot using helper function
+        _plot_single_curve(
+            ax,
+            angles_array,
+            angles_sorted,
+            observed_scores,
+            scores_sorted,
+            result_row,
+            c_scores,
+            c_fit,
+            tick_labels,
+            xlabel,
+            base_size,
+            incl_pred=incl_pred,
+            incl_fit=incl_fit,
+            incl_disp=incl_disp,
+            incl_amp=incl_amp,
+            incl_elev=incl_elev,
+        )
+
+    plt.tight_layout()
+
+    return fig
+
+
+def plot_contrast(
+    results_df: pd.DataFrame,
+    *,
+    drop_xy: bool = False,
+    sig_color: str = "#fc8d62",
+    ns_color: str = "white",
+    linewidth: float = 1.25,
+    fontsize: float = 12,
+    figsize: tuple[float, float] | None = None,
+) -> Figure:
+    """Plot SSM parameter contrasts with confidence intervals.
+
+    Creates a faceted plot showing the difference between two profiles for each
+    SSM parameter (elevation, x-value, y-value, amplitude, displacement). Points
+    are colored based on statistical significance (whether CI includes zero).
+
+    This function requires results from a contrast analysis (e.g., comparing two
+    groups or measures).
+
+    Parameters
+    ----------
+    results_df
+        DataFrame with SSM contrast results. Must contain the contrast row
+        (typically the last row) with columns:
+        - Label: str, contrast label (e.g., "Group 1 - Group 2")
+        - e_est, x_est, y_est, a_est, d_est: float, parameter differences
+        - e_lci, x_lci, ..., d_uci: float, confidence intervals
+    drop_xy
+        Whether to omit x-value and y-value parameters from the plot. This can
+        simplify the plot when only interested in elevation, amplitude, and
+        displacement (default = False).
+    sig_color
+        Color for significant contrasts (CI excludes zero).
+    ns_color
+        Color for non-significant contrasts (CI includes zero).
+    linewidth
+        Width of error bars and point outlines in points.
+    fontsize
+        Base font size in points for labels and text.
+    figsize
+        Figure size in inches (width, height). If None, uses (10, 4) for all
+        parameters or (7, 4) if drop_xy=True.
+
+    Returns
+    -------
+    Matplotlib Figure object.
+
+    Examples
+    --------
+    Plot contrasts from an SSM analysis:
+
+    >>> from circumplex import ssm_analyze
+    >>> results = ssm_analyze(
+    ...     data, scales=list(range(8)),
+    ...     grouping='condition', contrast=True
+    ... )
+    >>> fig = plot_contrast(results.results)
+    >>> fig.savefig('contrasts.png')
+
+    Drop x and y parameters for simpler plot:
+
+    >>> fig = plot_contrast(results.results, drop_xy=True)
+
+    Use custom colors:
+
+    >>> fig = plot_contrast(
+    ...     results.results,
+    ...     sig_color='red',
+    ...     ns_color='lightgray',
+    ... )
+
+    See Also
+    --------
+    plot_circle : Plot SSM profiles on a circumplex circle
+    plot_curve : Plot SSM fitted curves with observed scores
+
+    """
+    # Build data
+    contrast_row = _get_contrast_row(results_df)
+    plot_data = _build_contrast_plot_data(contrast_row, drop_xy=drop_xy)
+
+    n_params = len(plot_data)
+
+    # Set figure size
+    if figsize is None:
+        figsize = (7, 4) if drop_xy else (10, 4)
+
+    # Create figure with subplots
+    fig, axes = plt.subplots(
+        1, n_params, figsize=figsize, sharey=False, constrained_layout=False
+    )
+
+    # Normalize axes into a list of Axes for consistent typing/iteration
+    if n_params == 1:
+        axes_list: list[plt.Axes] = [cast("plt.Axes", axes)]
+    else:
+        axes_list = [cast("plt.Axes", a) for a in cast("np.ndarray", axes)]
+
+    # Plot each parameter
+    for i, (ax, data) in enumerate(zip(axes_list, plot_data, strict=False)):
+        _draw_contrast_subplot(
+            ax=ax,
+            data=data,
+            sig_color=sig_color,
+            ns_color=ns_color,
+            linewidth=linewidth,
+            fontsize=fontsize,
+            is_leftmost=(i == 0),
+        )
+
+    # Add legend with better positioning
+    _add_contrast_legend(fig, sig_color, ns_color, fontsize)
+
+    # Add contrast label as suptitle
+    contrast_label = contrast_row["Label"]
+    fig.suptitle(
+        f"Contrast: {contrast_label}",
+        fontsize=fontsize * 1.15,
+        fontweight="bold",
+        y=0.88,
+    )
+
+    plt.tight_layout(rect=[0, 0, 1, 0.86])
+
+    return fig
diff --git a/tests/__init__.py b/tests/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..d48871e
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,209 @@
+"""Shared pytest fixtures for circumplex tests."""
+
+import json
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from circumplex.data import load_dataset
+
+
+@pytest.fixture(scope="session")
+def jz2017_data() -> pd.DataFrame:
+    """Load jz2017 dataset once per test session."""
+    return load_dataset("jz2017")
+
+
+@pytest.fixture(scope="session")
+def aw2009_data() -> pd.DataFrame:
+    """Load aw2009 dataset once per test session."""
+    return load_dataset("aw2009")
+
+
+def load_regression_fixture(fixture_name: str) -> dict[str, Any]:
+    """Load a regression test fixture from JSON file.
+
+    Parameters
+    ----------
+    fixture_name
+        Name of fixture file (without .json extension)
+
+    Returns
+    -------
+    Dictionary containing fixture data with keys:
+        - dataset: Name of dataset to use
+        - analysis_type: Type of analysis
+        - seed: Random seed for reproducibility
+        - input: Input parameters for analysis
+        - expected: Expected results from R package
+
+    """
+    fixture_path = (
+        Path(__file__).parent / "regression" / "fixtures" / f"{fixture_name}.json"
+    )
+    with fixture_path.open() as f:
+        return json.load(f)
+
+
+@pytest.fixture
+def ssm_parameters_fixture() -> dict[str, Any]:
+    """Load ssm_parameters regression test fixture."""
+    return load_regression_fixture("ssm_parameters")
+
+
+@pytest.fixture
+def single_group_mean_fixture() -> dict[str, Any]:
+    """Load single-group mean-based SSM regression test fixture."""
+    return load_regression_fixture("ssm_single_group_mean")
+
+
+@pytest.fixture
+def multi_group_mean_fixture() -> dict[str, Any]:
+    """Load multi-group mean-based SSM regression test fixture."""
+    return load_regression_fixture("ssm_multi_group_mean")
+
+
+@pytest.fixture
+def multi_group_contrast_fixture() -> dict[str, Any]:
+    """Load multi-group contrast SSM regression test fixture."""
+    return load_regression_fixture("ssm_multi_group_contrast")
+
+
+@pytest.fixture
+def single_group_correlation_fixture() -> dict[str, Any]:
+    """Load single-group correlation-based SSM regression test fixture."""
+    return load_regression_fixture("ssm_single_group_correlation")
+
+
+@pytest.fixture
+def multi_group_correlation_fixture() -> dict[str, Any]:
+    """Load multi-group correlation-based SSM regression test fixture."""
+    return load_regression_fixture("ssm_multi_group_correlation")
+
+
+@pytest.fixture
+def measure_contrast_correlation_fixture() -> dict[str, Any]:
+    """Load measure-contrast correlation-based SSM regression test fixture."""
+    return load_regression_fixture("ssm_measure_contrast_correlation")
+
+
+@pytest.fixture
+def group_contrast_correlation_fixture() -> dict[str, Any]:
+    """Load group-contrast correlation-based SSM regression test fixture."""
+    return load_regression_fixture("ssm_group_contrast_correlation")
+
+
+def assert_parameters_close(
+    result: dict[str, float],
+    expected: dict[str, float],
+    *,
+    rtol: float = 1e-3,
+    convert_displacement: bool = True,
+) -> None:
+    """Assert SSM parameters match expected values within tolerance.
+
+    Parameters
+    ----------
+    result
+        Computed SSM parameters
+    expected
+        Expected SSM parameters from R (supports both fixture formats)
+    rtol
+        Relative tolerance for comparison (default: 0.1%)
+    convert_displacement
+        If True, convert displacement from radians to degrees for comparison
+
+    """
+    # Handle both fixture formats:
+    # - ssm_parameters.json uses keys like "elevation", "x_value", etc.
+    # - ssm_*_mean/correlation.json use keys like "e_est", "x_est", etc.
+    elevation_key = "elevation" if "elevation" in expected else "e_est"
+    x_key = "x_value" if "x_value" in expected else "x_est"
+    y_key = "y_value" if "y_value" in expected else "y_est"
+    amp_key = "amplitude" if "amplitude" in expected else "a_est"
+    disp_key = "displacement" if "displacement" in expected else "d_est"
+    fit_key = "fit" if "fit" in expected else "fit_est"
+
+    # Standard parameters
+    assert np.isclose(result["elevation"], expected[elevation_key], rtol=rtol), (
+        "Elevation mismatch: "
+        f"{result['elevation']:.3f} vs {expected[elevation_key]:.3f}"
+    )
+    assert np.isclose(result["x_value"], expected[x_key], rtol=rtol), (
+        f"X-value mismatch: {result['x_value']:.3f} vs {expected[x_key]:.3f}"
+    )
+    assert np.isclose(result["y_value"], expected[y_key], rtol=rtol), (
+        f"Y-value mismatch: {result['y_value']:.3f} vs {expected[y_key]:.3f}"
+    )
+    assert np.isclose(result["amplitude"], expected[amp_key], rtol=rtol), (
+        f"Amplitude mismatch: {result['amplitude']:.3f} vs {expected[amp_key]:.3f}"
+    )
+
+    # Displacement needs special handling (convert from radians to degrees)
+    if convert_displacement:
+        displacement_deg = np.degrees(result["displacement"])
+        assert np.isclose(displacement_deg, expected[disp_key], rtol=rtol), (
+            "Displacement mismatch: "
+            f"{displacement_deg:.3f}° vs {expected[disp_key]:.3f}°"
+        )
+    else:
+        assert np.isclose(result["displacement"], expected[disp_key], rtol=rtol)
+
+    assert np.isclose(result["fit"], expected[fit_key], rtol=rtol), (
+        f"Fit mismatch: {result['fit']:.3f} vs {expected[fit_key]:.3f}"
+    )
+
+
+def assert_confidence_intervals_close(
+    result: dict[str, tuple[float, float]],
+    expected: dict[str, float],
+    *,
+    rtol: float = 1e-3,
+    convert_displacement: bool = True,
+) -> None:
+    """Assert confidence intervals match expected values within tolerance.
+
+    Parameters
+    ----------
+    result
+        Computed confidence intervals (dict mapping parameter to (lower, upper))
+    expected
+        Expected CI values from R (dict with keys like 'e_lci', 'e_uci')
+    rtol
+        Relative tolerance for comparison (default: 0.1%)
+    convert_displacement
+        If True, convert displacement CIs from radians to degrees
+
+    """
+    # Map parameter names to their lower/upper CI keys in expected
+    ci_params = {
+        "elevation": ("e_lci", "e_uci"),
+        "x_value": ("x_lci", "x_uci"),
+        "y_value": ("y_lci", "y_uci"),
+        "amplitude": ("a_lci", "a_uci"),
+        "displacement": ("d_lci", "d_uci"),
+    }
+
+    for param, (lci_key, uci_key) in ci_params.items():
+        if lci_key not in expected or uci_key not in expected:
+            continue  # Skip if CIs not in expected results
+
+        lower_computed, upper_computed = result[param]
+
+        # Convert displacement from radians to degrees if needed
+        if param == "displacement" and convert_displacement:
+            lower_computed = np.degrees(lower_computed)
+            upper_computed = np.degrees(upper_computed)
+
+        lower_expected = expected[lci_key]
+        upper_expected = expected[uci_key]
+
+        assert np.isclose(lower_computed, lower_expected, rtol=rtol), (
+            f"{param} lower CI mismatch: {lower_computed:.3f} vs {lower_expected:.3f}"
+        )
+        assert np.isclose(upper_computed, upper_expected, rtol=rtol), (
+            f"{param} upper CI mismatch: {upper_computed:.3f} vs {upper_expected:.3f}"
+        )
diff --git a/tests/regression/fixtures/ssm_group_contrast_correlation.json b/tests/regression/fixtures/ssm_group_contrast_correlation.json
new file mode 100644
index 0000000..d897942
--- /dev/null
+++ b/tests/regression/fixtures/ssm_group_contrast_correlation.json
@@ -0,0 +1,25 @@
+{
+  "dataset": "jz2017",
+  "analysis_type": "group_contrast_correlation",
+  "seed": 12345,
+  "input": {
+    "scales": ["PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"],
+    "measures": "NARPD",
+    "grouping": "Gender",
+    "contrast": true,
+    "boots": 2000
+  },
+  "expected": {
+    "e_est": [0.172, 0.244, 0.072],
+    "x_est": [-0.08, -0.029, 0.051],
+    "y_est": [0.202, 0.146, -0.056],
+    "a_est": [0.217, 0.149, -0.068],
+    "d_est": [111.7, 101.2, -10.4],
+    "fit_est": [0.972, 0.902, -0.071],
+    "labels": ["NARPD: Female", "NARPD: Male", "NARPD: Male - Female"],
+    "e_lci": [0.126, 0.194, 0.005],
+    "e_uci": [0.217, 0.295, 0.142],
+    "a_lci": [0.17, 0.105, -0.133],
+    "a_uci": [0.265, 0.195, -0.003]
+  }
+}
diff --git a/tests/regression/fixtures/ssm_measure_contrast_correlation.json b/tests/regression/fixtures/ssm_measure_contrast_correlation.json
new file mode 100644
index 0000000..10dc1eb
--- /dev/null
+++ b/tests/regression/fixtures/ssm_measure_contrast_correlation.json
@@ -0,0 +1,54 @@
+{
+  "dataset": "jz2017",
+  "analysis_type": "measure_contrast_correlation",
+  "seed": 12345,
+  "input": {
+    "scales": ["PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"],
+    "measures": ["ASPD", "NARPD"],
+    "contrast": true,
+    "boots": 2000
+  },
+  "expected": {
+    "e_est": [0.124, 0.202, 0.079],
+    "x_est": [-0.099, -0.062, 0.037],
+    "y_est": [0.203, 0.179, -0.024],
+    "a_est": [0.226, 0.189, -0.037],
+    "d_est": [115.9, 109, -7],
+    "fit_est": [0.964, 0.957, -0.007],
+    "labels": ["ASPD", "NARPD", "NARPD - ASPD"],
+    "e_lci": [0.087, 0.169, 0.042],
+    "e_uci": [0.158, 0.238, 0.117],
+    "a_lci": [0.191, 0.154, -0.077],
+    "a_uci": [0.264, 0.227, 0.003],
+    "scores_aspd": {
+      "PA": 0.368,
+      "BC": 0.354,
+      "DE": 0.187,
+      "FG": 0.045,
+      "HI": -0.073,
+      "JK": -0.045,
+      "LM": -0.018,
+      "NO": 0.173
+    },
+    "scores_narpd": {
+      "PA": 0.4,
+      "BC": 0.385,
+      "DE": 0.234,
+      "FG": 0.108,
+      "HI": 0.051,
+      "JK": 0.058,
+      "LM": 0.084,
+      "NO": 0.3
+    },
+    "scores_contrast": {
+      "PA": 0.031,
+      "BC": 0.032,
+      "DE": 0.047,
+      "FG": 0.063,
+      "HI": 0.124,
+      "JK": 0.103,
+      "LM": 0.101,
+      "NO": 0.127
+    }
+  }
+}
diff --git a/tests/regression/fixtures/ssm_multi_group_contrast.json b/tests/regression/fixtures/ssm_multi_group_contrast.json
new file mode 100644
index 0000000..e42880a
--- /dev/null
+++ b/tests/regression/fixtures/ssm_multi_group_contrast.json
@@ -0,0 +1,26 @@
+{
+  "dataset": "jz2017",
+  "analysis_type": "multi_group_mean_contrast",
+  "seed": 12345,
+  "input": {
+    "scales": ["PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"],
+    "grouping": "Gender",
+    "contrast": true,
+    "boots": 2000
+  },
+  "expected": {
+    "e_est": [0.946, 0.884, -0.062],
+    "x_est": [0.459, 0.227, -0.232],
+    "y_est": [-0.31, -0.186, 0.124],
+    "a_est": [0.554, 0.294, -0.261],
+    "d_est": [325.963, 320.685, -5.278],
+    "fit_est": [0.889, 0.824, -0.066],
+    "labels": ["Female", "Male", "Male - Female"],
+    "e_lci": [0.907, 0.839, -0.122],
+    "e_uci": [0.984, 0.928, -0.002],
+    "a_lci": [0.511, 0.256, -0.318],
+    "a_uci": [0.6, 0.33, -0.205],
+    "d_lci": [321.834, 313.386, -13.521],
+    "d_uci": [329.805, 327.985, 3.029]
+  }
+}
diff --git a/tests/regression/fixtures/ssm_multi_group_correlation.json b/tests/regression/fixtures/ssm_multi_group_correlation.json
new file mode 100644
index 0000000..c4cfff7
--- /dev/null
+++ b/tests/regression/fixtures/ssm_multi_group_correlation.json
@@ -0,0 +1,20 @@
+{
+  "dataset": "jz2017",
+  "analysis_type": "multi_group_correlation",
+  "seed": 12345,
+  "input": {
+    "scales": ["PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"],
+    "measures": "PARPD",
+    "grouping": "Gender",
+    "boots": 2000
+  },
+  "expected": {
+    "e_est": [0.262, 0.243],
+    "x_est": [-0.146, -0.035],
+    "y_est": [0.12, 0.114],
+    "a_est": [0.189, 0.12],
+    "d_est": [140.5, 106.9],
+    "fit_est": [0.87, 0.648],
+    "labels": ["PARPD: Female", "PARPD: Male"]
+  }
+}
diff --git a/tests/regression/fixtures/ssm_multi_group_mean.json b/tests/regression/fixtures/ssm_multi_group_mean.json
new file mode 100644
index 0000000..52b3b0b
--- /dev/null
+++ b/tests/regression/fixtures/ssm_multi_group_mean.json
@@ -0,0 +1,50 @@
+{
+  "dataset": "jz2017",
+  "analysis_type": "multi_group_mean",
+  "seed": 12345,
+  "input": {
+    "scales": ["PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"],
+    "grouping": "Gender",
+    "boots": 2000,
+    "interval": 0.95
+  },
+  "expected": {
+    "e_est": [0.946, 0.884],
+    "x_est": [0.459, 0.227],
+    "y_est": [-0.31, -0.186],
+    "a_est": [0.554, 0.294],
+    "d_est": [325.963, 320.685],
+    "fit_est": [0.889, 0.824],
+    "labels": ["Female", "Male"],
+    "e_lci": [0.907, 0.839],
+    "e_uci": [0.984, 0.928],
+    "x_lci": [0.422, 0.191],
+    "x_uci": [0.498, 0.262],
+    "y_lci": [-0.357, -0.225],
+    "y_uci": [-0.266, -0.147],
+    "a_lci": [0.511, 0.256],
+    "a_uci": [0.6, 0.33],
+    "d_lci": [321.834, 313.386],
+    "d_uci": [329.805, 327.985],
+    "scores_female": {
+      "PA": 0.519,
+      "BC": 0.504,
+      "DE": 0.589,
+      "FG": 0.685,
+      "HI": 1.33,
+      "JK": 1.361,
+      "LM": 1.645,
+      "NO": 0.933
+    },
+    "scores_male": {
+      "PA": 0.585,
+      "BC": 0.674,
+      "DE": 0.664,
+      "FG": 0.856,
+      "HI": 1.075,
+      "JK": 1.047,
+      "LM": 1.3,
+      "NO": 0.868
+    }
+  }
+}
diff --git a/tests/regression/fixtures/ssm_parameters.json b/tests/regression/fixtures/ssm_parameters.json
new file mode 100644
index 0000000..e98a9cb
--- /dev/null
+++ b/tests/regression/fixtures/ssm_parameters.json
@@ -0,0 +1,15 @@
+{
+  "function_name": "ssm_parameters",
+  "input": {
+    "scores": [0.374, -0.572, -0.52, 0.016, 0.688, 1.142, 1.578, 0.678],
+    "angles": [90, 135, 180, 225, 270, 315, 360, 45]
+  },
+  "expected": {
+    "elevation": 0.423,
+    "x_value": 0.9445,
+    "y_value": -0.2645,
+    "amplitude": 0.9808,
+    "displacement": 344.3576,
+    "fit": 0.9541
+  }
+}
diff --git a/tests/regression/fixtures/ssm_single_group_correlation.json b/tests/regression/fixtures/ssm_single_group_correlation.json
new file mode 100644
index 0000000..5c8a1b7
--- /dev/null
+++ b/tests/regression/fixtures/ssm_single_group_correlation.json
@@ -0,0 +1,24 @@
+{
+  "dataset": "jz2017",
+  "analysis_type": "single_group_correlation",
+  "seed": 12345,
+  "input": {
+    "scales": ["PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"],
+    "measures": "PARPD",
+    "boots": 2000
+  },
+  "expected": {
+    "e_est": 0.25,
+    "x_est": -0.094,
+    "y_est": 0.117,
+    "a_est": 0.15,
+    "d_est": 128.9,
+    "fit_est": 0.802,
+    "e_lci": 0.218,
+    "e_uci": 0.282,
+    "x_lci": -0.128,
+    "x_uci": -0.062,
+    "y_lci": 0.081,
+    "y_uci": 0.153
+  }
+}
diff --git a/tests/regression/fixtures/ssm_single_group_mean.json b/tests/regression/fixtures/ssm_single_group_mean.json
new file mode 100644
index 0000000..365cb32
--- /dev/null
+++ b/tests/regression/fixtures/ssm_single_group_mean.json
@@ -0,0 +1,39 @@
+{
+  "dataset": "aw2009",
+  "analysis_type": "single_group_mean",
+  "seed": 12345,
+  "input": {
+    "scales": ["PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"],
+    "boots": 2000,
+    "interval": 0.95,
+    "listwise": true
+  },
+  "expected": {
+    "e_est": 0.423,
+    "x_est": 0.945,
+    "y_est": -0.264,
+    "a_est": 0.981,
+    "d_est": 344.358,
+    "fit_est": 0.954,
+    "e_lci": 0.129,
+    "e_uci": 0.708,
+    "x_lci": 0.654,
+    "x_uci": 1.251,
+    "y_lci": -0.946,
+    "y_uci": 0.3,
+    "a_lci": 0.662,
+    "a_uci": 1.403,
+    "d_lci": 316.48,
+    "d_uci": 17.191,
+    "scores": {
+      "PA": 0.374,
+      "BC": -0.572,
+      "DE": -0.52,
+      "FG": 0.016,
+      "HI": 0.688,
+      "JK": 1.142,
+      "LM": 1.578,
+      "NO": 0.678
+    }
+  }
+}
diff --git a/tests/regression/test_parameters_regression.py b/tests/regression/test_parameters_regression.py
new file mode 100644
index 0000000..c2e817e
--- /dev/null
+++ b/tests/regression/test_parameters_regression.py
@@ -0,0 +1,162 @@
+"""Regression tests for SSM parameter calculations against R circumplex package.
+
+These tests validate that the Python implementation produces numerically identical
+results to the R package's ssm_parameters() function.
+"""
+
+import numpy as np
+import pytest
+
+from circumplex.core.parameters import ssm_parameters
+from tests.conftest import assert_parameters_close
+
+
+@pytest.mark.regression
+class TestSSMParametersRegression:
+    """Validate ssm_parameters() numerical parity with R package."""
+
+    def test_ssm_parameters_basic(self, ssm_parameters_fixture):
+        """Test ssm_parameters() matches R output exactly.
+
+        This test validates the core parameter calculation function against
+        the R package output using the aw2009 dataset (first row).
+
+        Reference: R-circumplex/tests/testthat/test-ssm_analysis.R::test_that("ssm_parameters works")
+        """  # noqa: E501
+        # Extract input data from fixture
+        scores = np.array(ssm_parameters_fixture["input"]["scores"])
+        angles_deg = np.array(ssm_parameters_fixture["input"]["angles"])
+
+        # Convert angles from degrees to radians (Python implementation uses radians)
+        angles_rad = np.radians(angles_deg)
+
+        # Calculate parameters
+        result = ssm_parameters(scores, angles_rad)
+
+        # Validate against expected R output
+        expected = ssm_parameters_fixture["expected"]
+        assert_parameters_close(result, expected, rtol=1e-3)
+
+    def test_ssm_parameters_from_raw_data(self, aw2009_data):
+        """Test ssm_parameters() on raw (non-standardized) dataset first row.
+
+        This validates the function works with real pandas DataFrame data
+        extracted the same way as in the R package test.
+        Reference: R-circumplex/tests/testthat/test-ssm_analysis.R line 402-418
+        """
+        # Get first row of raw data (same as R test line 404)
+        scales = ["PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"]
+        scores = aw2009_data[scales].iloc[0].values
+
+        # Use standard octant angles
+        angles_deg = np.array([90, 135, 180, 225, 270, 315, 360, 45])
+        angles_rad = np.radians(angles_deg)
+
+        # Calculate parameters
+        result = ssm_parameters(scores, angles_rad)
+
+        # Expected values from R test (rounded to 2 decimals in R)
+        # These are for RAW (non-standardized) scores
+        # Reference: test-ssm_analysis.R lines 411-416
+        assert np.isclose(result["elevation"], 0.43, atol=1e-2)
+        assert np.isclose(result["x_value"], 1.25, atol=1e-2)
+        assert np.isclose(result["y_value"], -1.31, atol=1e-2)
+        assert np.isclose(result["amplitude"], 1.81, atol=1e-2)
+
+        # Displacement in degrees
+        displacement_deg = np.degrees(result["displacement"])
+        assert np.isclose(displacement_deg, 313.71, atol=1e-1)
+
+        assert np.isclose(result["fit"], 0.97, atol=1e-2)
+
+    def test_ssm_parameters_second_row(self, aw2009_data):
+        """Test ssm_parameters() on second row of aw2009 dataset.
+
+        This is tested indirectly in the R package via ssm_score() function.
+        Reference: test-ssm_analysis.R::test_that("ssm_score works")
+        """
+        # Get second row of data
+        scales = ["PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"]
+        scores = aw2009_data[scales].iloc[1].values
+
+        # Use standard octant angles
+        angles_deg = np.array([90, 135, 180, 225, 270, 315, 360, 45])
+        angles_rad = np.radians(angles_deg)
+
+        # Calculate parameters
+        result = ssm_parameters(scores, angles_rad)
+
+        # Expected values from R ssm_score test (rounded to 2 decimals)
+        # Using absolute tolerance for comparison with rounded values
+        assert np.isclose(result["elevation"], 0.23, atol=5e-3)
+        assert np.isclose(result["x_value"], 1.42, atol=5e-3)
+        assert np.isclose(result["y_value"], 0.51, atol=5e-3)
+        assert np.isclose(result["amplitude"], 1.51, atol=5e-3)
+
+        displacement_deg = np.degrees(result["displacement"])
+        assert np.isclose(displacement_deg, 19.67, atol=5e-2)
+
+        assert np.isclose(result["fit"], 0.92, atol=5e-3)
+
+    def test_ssm_parameters_validates_input_length(self):
+        """Test that ssm_parameters() requires matching array lengths."""
+        scores = np.array([1.0, 2.0, 3.0])
+        angles = np.array([0.0, np.pi / 2])  # Mismatched length
+
+        # This should work fine - the function doesn't explicitly validate
+        # But the calculation will fail with numpy broadcasting error
+        with pytest.raises((ValueError, IndexError)):
+            ssm_parameters(scores, angles)
+
+    def test_ssm_parameters_zero_variance(self):
+        """Test ssm_parameters() with zero variance (constant scores).
+
+        When all scores are identical, variance is zero and fit should be 1.0.
+        This tests the edge case handling in the fit calculation.
+        """
+        scores = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
+        angles = np.radians([90, 135, 180, 225, 270, 315, 360, 45])
+
+        result = ssm_parameters(scores, angles)
+
+        # With constant scores:
+        # - Elevation should be the constant value
+        # - x_value and y_value should be 0 (no variation around circle)
+        # - Amplitude should be 0
+        # - Fit should be 1.0 (perfect fit, handled by division by zero check)
+        assert np.isclose(result["elevation"], 1.0)
+        assert np.isclose(result["x_value"], 0.0, atol=1e-10)
+        assert np.isclose(result["y_value"], 0.0, atol=1e-10)
+        assert np.isclose(result["amplitude"], 0.0, atol=1e-10)
+        assert result["fit"] == 1.0
+
+    def test_ssm_parameters_displacement_range(self):
+        """Test that displacement is always in [0, 2π) range."""
+        # Test various angle configurations to ensure displacement wraps correctly
+        test_cases = [
+            # (scores, expected_displacement_deg)
+            ([1, 0, 0, 0, 0, 0, 0, 0], 90),  # Peak at 90°
+            ([0, 0, 0, 0, 1, 0, 0, 0], 270),  # Peak at 270°
+            ([0, 0, 0, 0, 0, 0, 1, 0], 0),  # Peak at 360° (should wrap to ~0°)
+        ]
+
+        angles = np.radians([90, 135, 180, 225, 270, 315, 360, 45])
+
+        for scores, expected_deg in test_cases:
+            result = ssm_parameters(np.array(scores, dtype=float), angles)
+            displacement_deg = np.degrees(result["displacement"])
+
+            # Displacement should be in [0, 360) range
+            # (allowing for floating point at boundary)
+            assert 0 <= displacement_deg <= 360, (
+                f"Displacement {displacement_deg} outside valid range for scores "
+                f"{scores}"
+            )
+
+            # Note: 360° and 0° are equivalent due to circular nature
+            # For the 360° angle case, we expect close to 0° or 360°
+            if expected_deg == 0:
+                # Allow either near 0 or near 360 due to modulo operation
+                assert np.isclose(displacement_deg, 0, atol=1.0) or np.isclose(
+                    displacement_deg, 360, atol=1.0
+                ), f"Expected displacement near 0° or 360°, got {displacement_deg}°"
diff --git a/tests/regression/test_ssm_analysis_regression.py b/tests/regression/test_ssm_analysis_regression.py
new file mode 100644
index 0000000..b2662e3
--- /dev/null
+++ b/tests/regression/test_ssm_analysis_regression.py
@@ -0,0 +1,544 @@
+"""Regression tests for SSM analysis against R circumplex package.
+
+These tests validate that the Python implementation of ssm_analyze() produces
+numerically identical results to the R package, including all data preprocessing,
+parameter calculations, and bootstrap confidence intervals.
+
+Key understanding:
+- Fixtures are generated from RAW data (e.g., aw2009 dataset as-is)
+- ssm_analyze() must handle all preprocessing internally:
+  1. Standardize/normalize circumplex scale scores (produces profile scores)
+  2. Calculate SSM parameters from standardized scores
+  3. Compute bootstrap confidence intervals
+- The "scores" in fixtures are the STANDARDIZED profile scores, not raw data
+
+NOTE: Most of these tests are now covered by tests/test_ssm_analyze.py
+This file is kept for the unique tests (standardization, listwise/pairwise).
+"""
+
+import numpy as np
+import pytest
+
+from circumplex import ssm_analyze
+from circumplex.utils.angles import OCTANTS
+
+
+@pytest.mark.regression
+class TestSSMAnalysisMeanBased:
+    """Regression tests for mean-based SSM analysis."""
+
+    def test_single_group_mean_based(self, aw2009_data, single_group_mean_fixture):
+        """Test single-group mean-based SSM matches R output exactly.
+
+        This is the most basic SSM analysis:
+        - Input: Raw aw2009 dataset (5 rows x 8 scales)
+        - Process: ssm_analyze() standardizes data and computes mean profile
+        - Output: SSM parameters + bootstrap CIs for the group mean profile
+
+        Reference:
+        - R code: ssm_analyze(aw2009, scales = 1:8, boots = 2000)
+        - R test: test-ssm_analysis.R::
+            test_that("Single-group mean-based SSM results are correct")
+        - Export script: export_test_fixtures.R lines 31-73
+
+        Note: This test is now covered by
+        test_ssm_analyze.py::test_ssm_single_group_mean
+        """
+        # Extract test parameters
+        input_params = single_group_mean_fixture["input"]
+        expected = single_group_mean_fixture["expected"]
+
+        # Run analysis (angles should be in degrees)
+        angles = OCTANTS
+        result = ssm_analyze(
+            aw2009_data,
+            scales=input_params["scales"],
+            angles=angles,
+            boots=input_params["boots"],
+            interval=input_params["interval"],
+            listwise=input_params["listwise"],
+            seed=single_group_mean_fixture["seed"],
+        )
+
+        # Get actual results from first row
+        actual = result.results.iloc[0]
+
+        # Validate parameter estimates (displacement already in degrees in SSM object)
+        # Use both rtol and atol to handle fixture rounding to 3 decimals
+        assert np.isclose(actual["e_est"], expected["e_est"], rtol=1e-3, atol=1e-3)
+        assert np.isclose(actual["x_est"], expected["x_est"], rtol=1e-3, atol=1e-3)
+        assert np.isclose(actual["y_est"], expected["y_est"], rtol=1e-3, atol=1e-3)
+        assert np.isclose(actual["a_est"], expected["a_est"], rtol=1e-3, atol=1e-3)
+        assert np.isclose(actual["d_est"], expected["d_est"], rtol=1e-3, atol=1e-3)
+        assert np.isclose(actual["fit_est"], expected["fit_est"], rtol=1e-3, atol=1e-3)
+
+        # Validate conf intervals (use relaxed tolerance for bootstrap variability)
+        # With n=5, bootstrap CIs are highly variable
+        assert np.isclose(actual["e_lci"], expected["e_lci"], rtol=0.20, atol=0.20)
+        assert np.isclose(actual["e_uci"], expected["e_uci"], rtol=0.20, atol=0.20)
+        assert np.isclose(actual["x_lci"], expected["x_lci"], rtol=0.20, atol=0.20)
+        assert np.isclose(actual["x_uci"], expected["x_uci"], rtol=0.20, atol=0.20)
+        assert np.isclose(actual["y_lci"], expected["y_lci"], rtol=0.20, atol=0.20)
+        assert np.isclose(actual["y_uci"], expected["y_uci"], rtol=0.20, atol=0.20)
+        assert np.isclose(actual["a_lci"], expected["a_lci"], rtol=0.20, atol=0.20)
+        assert np.isclose(actual["a_uci"], expected["a_uci"], rtol=0.20, atol=0.20)
+        assert np.isclose(actual["d_lci"], expected["d_lci"], rtol=0.20, atol=0.20)
+        assert np.isclose(actual["d_uci"], expected["d_uci"], rtol=0.20, atol=0.20)
+
+        # Validate standardized profile scores
+        actual_scores = result.scores.iloc[0][input_params["scales"]].to_dict()
+        for scale, expected_score in expected["scores"].items():
+            # Use both rtol and atol for fixture rounding to 3 decimals
+            assert np.isclose(
+                actual_scores[scale], expected_score, rtol=1e-3, atol=1e-3
+            ), f"Profile score mismatch for {scale}"
+
+    def test_multi_group_mean_based(self, jz2017_data, multi_group_mean_fixture):
+        """Test multi-group mean-based SSM matches R output.
+
+        Tests grouping functionality:
+        - Input: jz2017 dataset with Gender grouping variable
+        - Process: Compute separate SSM profiles for each gender
+        - Output: Two rows of results (Female, Male)
+
+        Reference:
+        - R code: ssm_analyze(jz2017, scales = 2:9, grouping = "Gender", boots = 2000)
+        - R test: test-ssm_analysis.R::
+            test_that("Multiple-group mean-based SSM results are correct")
+
+        Note: This test is now covered by test_ssm_analyze.py::test_ssm_multi_group_mean
+        """
+        # Extract test parameters
+        input_params = multi_group_mean_fixture["input"]
+        expected = multi_group_mean_fixture["expected"]
+
+        # Run analysis
+        angles = OCTANTS
+        result = ssm_analyze(
+            jz2017_data,
+            scales=input_params["scales"],
+            angles=angles,
+            grouping=input_params["grouping"],
+            boots=input_params["boots"],
+            interval=input_params["interval"],
+            seed=multi_group_mean_fixture["seed"],
+        )
+
+        # Validate both groups
+        assert len(result.results) == 2
+
+        for idx in range(2):
+            actual = result.results.iloc[idx]
+
+            # Validate parameter estimates (use both rtol and atol for fixture rounding)
+            assert np.isclose(
+                actual["e_est"], expected["e_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["x_est"], expected["x_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["y_est"], expected["y_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["a_est"], expected["a_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["d_est"], expected["d_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["fit_est"], expected["fit_est"][idx], rtol=1e-3, atol=1e-3
+            )
+
+            # Validate confidence intervals (relaxed tolerance for bootstrap)
+            assert np.isclose(
+                actual["e_lci"], expected["e_lci"][idx], rtol=0.05, atol=0.05
+            )
+            assert np.isclose(
+                actual["e_uci"], expected["e_uci"][idx], rtol=0.05, atol=0.05
+            )
+            assert np.isclose(
+                actual["a_lci"], expected["a_lci"][idx], rtol=0.05, atol=0.05
+            )
+            assert np.isclose(
+                actual["a_uci"], expected["a_uci"][idx], rtol=0.05, atol=0.05
+            )
+
+    def test_multi_group_contrast(self, jz2017_data, multi_group_contrast_fixture):
+        """Test multi-group contrast SSM matches R output.
+
+        Tests contrast functionality:
+        - Input: jz2017 with Gender grouping + contrast=TRUE
+        - Process: Compute profiles for each gender + Male - Female difference
+        - Output: Three rows (Female, Male, Male - Female)
+
+        Reference:
+        - R code: ssm_analyze(jz2017, scales = 2:9, grouping = "Gender",
+            contrast = TRUE)
+        - R test: test-ssm_analysis.R::
+            test_that("Multiple-group mean-based SSM contrast is correct")
+
+        Note: This test is now covered by
+        test_ssm_analyze.py::test_ssm_multi_group_mean_contrast
+        """
+        # Extract test parameters
+        input_params = multi_group_contrast_fixture["input"]
+        expected = multi_group_contrast_fixture["expected"]
+
+        # Run analysis
+        angles = OCTANTS
+        result = ssm_analyze(
+            jz2017_data,
+            scales=input_params["scales"],
+            angles=angles,
+            grouping=input_params["grouping"],
+            contrast=input_params["contrast"],
+            boots=input_params["boots"],
+            seed=multi_group_contrast_fixture["seed"],
+        )
+
+        # Validate all 3 rows (Female, Male, Male - Female)
+        assert len(result.results) == 3
+
+        for idx in range(3):
+            actual = result.results.iloc[idx]
+
+            # Validate parameter estimates (use both rtol and atol for fixture rounding)
+            assert np.isclose(
+                actual["e_est"], expected["e_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["x_est"], expected["x_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["y_est"], expected["y_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["a_est"], expected["a_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["d_est"], expected["d_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["fit_est"], expected["fit_est"][idx], rtol=1e-3, atol=1e-3
+            )
+
+            # Validate confidence intervals (relaxed tolerance for bootstrap)
+            assert np.isclose(
+                actual["e_lci"], expected["e_lci"][idx], rtol=0.05, atol=0.05
+            )
+            assert np.isclose(
+                actual["e_uci"], expected["e_uci"][idx], rtol=0.05, atol=0.05
+            )
+            assert np.isclose(
+                actual["a_lci"], expected["a_lci"][idx], rtol=0.05, atol=0.05
+            )
+            assert np.isclose(
+                actual["a_uci"], expected["a_uci"][idx], rtol=0.05, atol=0.05
+            )
+
+        # Verify labels
+        assert result.results.iloc[0]["Label"] == expected["labels"][0]
+        assert result.results.iloc[1]["Label"] == expected["labels"][1]
+        assert result.results.iloc[2]["Label"] == expected["labels"][2]
+
+
+@pytest.mark.regression
+class TestSSMAnalysisCorrelationBased:
+    """Regression tests for correlation-based SSM analysis."""
+
+    def test_single_group_correlation(
+        self, jz2017_data, single_group_correlation_fixture
+    ):
+        """Test single-group correlation-based SSM matches R output.
+
+        Tests correlation-based analysis:
+        - Input: jz2017 with measures=["PARPD"] (external variable)
+        - Process: Correlate each scale with PARPD, use correlations as profile
+        - Output: SSM parameters describing correlation profile
+
+        Key difference from mean-based:
+        - Profile scores are correlations, not standardized means
+        - Scores represent association strength with external measure
+
+        Reference:
+        - R code: ssm_analyze(jz2017, scales = 2:9, measures = "PARPD", boots = 2000)
+        - R test: test-ssm_analysis.R::
+            test_that("Single-group correlation-based SSM results are correct")
+
+        Note: This test is now covered by
+        test_ssm_analyze.py::test_ssm_single_group_correlation
+        """
+        # Extract test parameters
+        input_params = single_group_correlation_fixture["input"]
+        expected = single_group_correlation_fixture["expected"]
+
+        # Run analysis
+        angles = OCTANTS
+        result = ssm_analyze(
+            jz2017_data,
+            scales=input_params["scales"],
+            angles=angles,
+            measures=input_params["measures"],
+            boots=input_params["boots"],
+            seed=single_group_correlation_fixture["seed"],
+        )
+
+        # Validate results
+        actual = result.results.iloc[0]
+
+        # Validate parameter estimates (use both rtol and atol for fixture rounding)
+        assert np.isclose(actual["e_est"], expected["e_est"], rtol=1e-3, atol=1e-3)
+        assert np.isclose(actual["x_est"], expected["x_est"], rtol=1e-3, atol=1e-3)
+        assert np.isclose(actual["y_est"], expected["y_est"], rtol=1e-3, atol=1e-3)
+        assert np.isclose(actual["a_est"], expected["a_est"], rtol=1e-3, atol=1e-3)
+        # Note: displacement in fixture is rounded to 1 decimal for correlations
+        assert np.isclose(actual["d_est"], expected["d_est"], rtol=0.1, atol=0.1)
+        assert np.isclose(actual["fit_est"], expected["fit_est"], rtol=1e-3, atol=1e-3)
+
+        # Validate confidence intervals (relaxed tolerance for bootstrap)
+        assert np.isclose(actual["e_lci"], expected["e_lci"], rtol=0.05, atol=0.05)
+        assert np.isclose(actual["e_uci"], expected["e_uci"], rtol=0.05, atol=0.05)
+        assert np.isclose(actual["x_lci"], expected["x_lci"], rtol=0.05, atol=0.05)
+        assert np.isclose(actual["x_uci"], expected["x_uci"], rtol=0.05, atol=0.05)
+        assert np.isclose(actual["y_lci"], expected["y_lci"], rtol=0.05, atol=0.05)
+        assert np.isclose(actual["y_uci"], expected["y_uci"], rtol=0.05, atol=0.05)
+
+    def test_multi_group_correlation(
+        self, jz2017_data, multi_group_correlation_fixture
+    ):
+        """Test multi-group correlation-based SSM matches R output.
+
+        Combines correlation mode with grouping:
+        - Separate correlation profiles for each gender
+
+        Reference:
+        - R test: test-ssm_analysis.R (multi-group correlation test)
+
+        Note: This test is now covered by
+        test_ssm_analyze.py::test_ssm_multi_group_correlation
+        """
+        # Extract test parameters
+        input_params = multi_group_correlation_fixture["input"]
+        expected = multi_group_correlation_fixture["expected"]
+
+        # Run analysis
+        angles = OCTANTS
+        result = ssm_analyze(
+            jz2017_data,
+            scales=input_params["scales"],
+            angles=angles,
+            measures=input_params["measures"],
+            grouping=input_params["grouping"],
+            boots=input_params["boots"],
+            seed=multi_group_correlation_fixture["seed"],
+        )
+
+        # Validate both groups
+        assert len(result.results) == 2
+
+        for idx in range(2):
+            actual = result.results.iloc[idx]
+
+            # Validate parameter estimates (use both rtol and atol for fixture rounding)
+            assert np.isclose(
+                actual["e_est"], expected["e_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["x_est"], expected["x_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["y_est"], expected["y_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["a_est"], expected["a_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["d_est"], expected["d_est"][idx], rtol=0.1, atol=0.1
+            )
+            assert np.isclose(
+                actual["fit_est"], expected["fit_est"][idx], rtol=1e-3, atol=1e-3
+            )
+
+        # Verify labels
+        assert result.results.iloc[0]["Label"] == expected["labels"][0]
+        assert result.results.iloc[1]["Label"] == expected["labels"][1]
+
+    def test_measure_contrast_correlation(
+        self, jz2017_data, measure_contrast_correlation_fixture
+    ):
+        """Test measure-contrast correlation-based SSM matches R output.
+
+        Tests contrasting two external measures:
+        - Input: measures = ["ASPD", "NARPD"], contrast = TRUE
+        - Output: Three profiles (ASPD, NARPD, NARPD - ASPD)
+
+        Reference:
+        - R code: ssm_analyze(jz2017, scales = 2:9, measures = c("ASPD", "NARPD"),
+            contrast = TRUE)
+        - R test: test-ssm_analysis.R::
+            test_that("Measure-contrast correlation-based SSM results are correct")
+
+        Note: This test is now covered by
+        test_ssm_analyze.py::test_ssm_measure_contrast_correlation
+        """
+        # Extract test parameters
+        input_params = measure_contrast_correlation_fixture["input"]
+        expected = measure_contrast_correlation_fixture["expected"]
+
+        # Run analysis
+        angles = OCTANTS
+        result = ssm_analyze(
+            jz2017_data,
+            scales=input_params["scales"],
+            angles=angles,
+            measures=input_params["measures"],
+            contrast=input_params["contrast"],
+            boots=input_params["boots"],
+            seed=measure_contrast_correlation_fixture["seed"],
+        )
+
+        # Validate all 3 rows (ASPD, NARPD, NARPD - ASPD)
+        assert len(result.results) == 3
+
+        for idx in range(3):
+            actual = result.results.iloc[idx]
+
+            # Validate parameter estimates (use both rtol and atol for fixture rounding)
+            assert np.isclose(
+                actual["e_est"], expected["e_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["x_est"], expected["x_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["y_est"], expected["y_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["a_est"], expected["a_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["d_est"], expected["d_est"][idx], rtol=0.1, atol=0.1
+            )
+            assert np.isclose(
+                actual["fit_est"], expected["fit_est"][idx], rtol=1e-3, atol=1e-3
+            )
+
+            # Validate confidence intervals (relaxed tolerance for bootstrap)
+            assert np.isclose(
+                actual["e_lci"], expected["e_lci"][idx], rtol=0.05, atol=0.05
+            )
+            assert np.isclose(
+                actual["e_uci"], expected["e_uci"][idx], rtol=0.05, atol=0.05
+            )
+            assert np.isclose(
+                actual["a_lci"], expected["a_lci"][idx], rtol=0.05, atol=0.05
+            )
+            assert np.isclose(
+                actual["a_uci"], expected["a_uci"][idx], rtol=0.05, atol=0.05
+            )
+
+        # Verify labels
+        assert result.results.iloc[0]["Label"] == expected["labels"][0]
+        assert result.results.iloc[1]["Label"] == expected["labels"][1]
+        assert result.results.iloc[2]["Label"] == expected["labels"][2]
+
+        # Check scores for all three profiles
+        score_names = input_params["scales"]
+        actual_scores_aspd = result.scores.iloc[0][score_names].to_dict()
+        actual_scores_narpd = result.scores.iloc[1][score_names].to_dict()
+        actual_scores_contrast = result.scores.iloc[2][score_names].to_dict()
+
+        # Use both rtol and atol for fixture rounding to 3 decimals
+        for scale, expected_val in expected["scores_aspd"].items():
+            assert np.isclose(
+                actual_scores_aspd[scale], expected_val, rtol=1e-3, atol=1e-3
+            )
+        for scale, expected_val in expected["scores_narpd"].items():
+            assert np.isclose(
+                actual_scores_narpd[scale], expected_val, rtol=1e-3, atol=1e-3
+            )
+        for scale, expected_val in expected["scores_contrast"].items():
+            assert np.isclose(
+                actual_scores_contrast[scale], expected_val, rtol=1e-3, atol=1e-3
+            )
+
+    def test_group_contrast_correlation(
+        self, jz2017_data, group_contrast_correlation_fixture
+    ):
+        """Test group-contrast correlation-based SSM matches R output.
+
+        Tests contrasting groups in correlation mode:
+        - Input: measures = "NARPD", grouping = "Gender", contrast = TRUE
+        - Output: Three profiles (Female, Male, Male - Female)
+
+        Reference:
+        - R code: ssm_analyze(jz2017, scales = 2:9, measures = "NARPD",
+          grouping = "Gender", contrast = TRUE)
+        - R test: test-ssm_analysis.R::
+          test_that("Group-contrast correlation-based SSM results are correct")
+
+        Note: This test is now covered by
+        test_ssm_analyze.py::test_ssm_group_contrast_correlation
+        """
+        # Extract test parameters
+        input_params = group_contrast_correlation_fixture["input"]
+        expected = group_contrast_correlation_fixture["expected"]
+
+        # Run analysis
+        angles = OCTANTS
+        result = ssm_analyze(
+            jz2017_data,
+            scales=input_params["scales"],
+            angles=angles,
+            measures=input_params["measures"],
+            grouping=input_params["grouping"],
+            contrast=input_params["contrast"],
+            boots=input_params["boots"],
+            seed=group_contrast_correlation_fixture["seed"],
+        )
+
+        # Validate all 3 rows (Female, Male, Male - Female)
+        assert len(result.results) == 3
+
+        for idx in range(3):
+            actual = result.results.iloc[idx]
+
+            # Validate parameter estimates (use both rtol and atol for fixture rounding)
+            assert np.isclose(
+                actual["e_est"], expected["e_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["x_est"], expected["x_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["y_est"], expected["y_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["a_est"], expected["a_est"][idx], rtol=1e-3, atol=1e-3
+            )
+            assert np.isclose(
+                actual["d_est"], expected["d_est"][idx], rtol=0.1, atol=0.1
+            )
+            assert np.isclose(
+                actual["fit_est"], expected["fit_est"][idx], rtol=1e-3, atol=1e-3
+            )
+
+            # Validate confidence intervals (relaxed tolerance for bootstrap)
+            assert np.isclose(
+                actual["e_lci"], expected["e_lci"][idx], rtol=0.05, atol=0.05
+            )
+            assert np.isclose(
+                actual["e_uci"], expected["e_uci"][idx], rtol=0.05, atol=0.05
+            )
+            assert np.isclose(
+                actual["a_lci"], expected["a_lci"][idx], rtol=0.05, atol=0.05
+            )
+            assert np.isclose(
+                actual["a_uci"], expected["a_uci"][idx], rtol=0.05, atol=0.05
+            )
+
+        # Verify labels
+        assert result.results.iloc[0]["Label"] == expected["labels"][0]
+        assert result.results.iloc[1]["Label"] == expected["labels"][1]
+        assert result.results.iloc[2]["Label"] == expected["labels"][2]
diff --git a/tests/test_analysis.py b/tests/test_analysis.py
deleted file mode 100644
index 24a4803..0000000
--- a/tests/test_analysis.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import numpy as np
-import pandas as pd
-import pytest
-
-import circumplex
-
-SCALES = ("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8")
-
-
-def fixed_data(angles=circumplex.OCTANTS, ampl=0.5, disp=180, elev=0):
-    return circumplex.cosine_form(np.array(angles), ampl, disp, elev)
-
-
-@pytest.fixture
-def ssm_params():
-    scores = fixed_data()
-    ssm_params = circumplex.SSMParams(
-        scores, SCALES, circumplex.OCTANTS, group="group", measure="measure"
-    )
-    return ssm_params
-
-
-def test_ssm_parameters():
-    # elev, xval, yval, ampl, disp, r2
-    fix_data = fixed_data()
-    fixed_ssm = circumplex.ssm_parameters(fix_data, circumplex.OCTANTS)
-    np.testing.assert_allclose(fixed_ssm, (0.0, -0.5, 0, 0.5, 180, 1.0), atol=1e-4)
-
-    fix_data = fixed_data(ampl=0.5, disp=45, elev=0)
-    fixed_ssm = circumplex.ssm_parameters(fix_data, circumplex.OCTANTS)
-    np.testing.assert_allclose(
-        fixed_ssm, (0.0, 0.35355, 0.35355, 0.5, 45, 1.0), atol=1e-4
-    )
-
-    fix_data = fixed_data(ampl=0.3, disp=90, elev=0.1)
-    fixed_ssm = circumplex.ssm_parameters(fix_data, circumplex.OCTANTS)
-    np.testing.assert_allclose(fixed_ssm, (0.1, 0.0, 0.3, 0.3, 90, 1.0), atol=1e-4)
-
-
-def test_label(ssm_params):
-    assert ssm_params.label == "group_measure"
-
-
-def test_table(ssm_params):
-    assert isinstance(ssm_params.table, pd.DataFrame)
-
-
-def test_params(ssm_params):
-    params = ssm_params.params
-    assert isinstance(params, dict)
-    assert len(params) == 9
diff --git a/tests/test_models.py b/tests/test_models.py
new file mode 100644
index 0000000..6f85e30
--- /dev/null
+++ b/tests/test_models.py
@@ -0,0 +1,554 @@
+"""Unit tests for instrument data models."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from circumplex.instruments.models import (
+    _INSTRUMENTS,
+    Instrument,
+    InstrumentScale,
+    NormativeSample,
+    ResponseAnchor,
+    ResponseItem,
+    get_instrument,
+    register_instrument,
+    show_instruments,
+)
+
+
+# Fixtures
+@pytest.fixture
+def sample_scales() -> tuple[InstrumentScale, ...]:
+    """Create sample instrument scales."""
+    return (
+        InstrumentScale(abbrev="PA", angle=90.0, items=(1, 2), label="Positive Affect"),
+        InstrumentScale(
+            abbrev="NA", angle=270.0, items=(3, 4), label="Negative Affect"
+        ),
+    )
+
+
+@pytest.fixture
+def sample_anchors() -> tuple[ResponseAnchor, ...]:
+    """Create sample response anchors."""
+    return (
+        ResponseAnchor(value=1, label="Strongly Disagree"),
+        ResponseAnchor(value=2, label="Disagree"),
+        ResponseAnchor(value=3, label="Neutral"),
+        ResponseAnchor(value=4, label="Agree"),
+        ResponseAnchor(value=5, label="Strongly Agree"),
+    )
+
+
+@pytest.fixture
+def sample_items() -> tuple[ResponseItem, ...]:
+    """Create sample response items."""
+    return (
+        ResponseItem(item_id=1, text="I feel happy"),
+        ResponseItem(item_id=2, text="I feel joyful"),
+        ResponseItem(item_id=3, text="I feel sad"),
+        ResponseItem(item_id=4, text="I feel anxious"),
+    )
+
+
+@pytest.fixture
+def sample_norms() -> tuple[NormativeSample, ...]:
+    """Create sample normative data."""
+    stats = pd.DataFrame(
+        {
+            "scale": ["PA", "NA"],
+            "mean": [3.5, 2.0],
+            "sd": [0.8, 0.6],
+        }
+    )
+    return (
+        NormativeSample(
+            sample_id=1,
+            size=500,
+            population="College Students",
+            reference="Doe et al. (2020)",
+            url="https://example.com/norms",
+            statistics=stats,
+        ),
+    )
+
+
+@pytest.fixture
+def sample_instrument(
+    sample_scales: tuple[InstrumentScale, ...],
+    sample_anchors: tuple[ResponseAnchor, ...],
+    sample_items: tuple[ResponseItem, ...],
+    sample_norms: tuple[NormativeSample, ...],
+) -> Instrument:
+    """Create a sample instrument."""
+    return Instrument(
+        name="Test Affect Scale",
+        abbrev="TAS",
+        construct="Affect",
+        reference="Test et al. (2023)",
+        url="https://example.com/tas",
+        status="open-access",
+        scales=sample_scales,
+        anchors=sample_anchors,
+        items=sample_items,
+        norms=sample_norms,
+        prefix="TAS_",
+        suffix="_score",
+    )
+
+
+@pytest.fixture
+def sample_data() -> pd.DataFrame:
+    """Create sample item-level data for scoring."""
+    return pd.DataFrame(
+        {
+            "item1": [5, 4, 3, 2, 1],
+            "item2": [5, 4, 3, 2, 1],
+            "item3": [1, 2, 3, 4, 5],
+            "item4": [1, 2, 3, 4, 5],
+        }
+    )
+
+
+@pytest.fixture(autouse=True)
+def clear_registry():
+    """Clear the global instrument registry before each test."""
+    _INSTRUMENTS.clear()
+    yield
+    _INSTRUMENTS.clear()
+
+
+# Test InstrumentScale
+class TestInstrumentScale:
+    """Tests for InstrumentScale dataclass."""
+
+    def test_creation(self):
+        """Test creating an InstrumentScale."""
+        scale = InstrumentScale(
+            abbrev="PA", angle=90.0, items=(1, 2, 3), label="Positive Affect"
+        )
+        assert scale.abbrev == "PA"
+        assert scale.angle == 90.0
+        assert scale.items == (1, 2, 3)
+        assert scale.label == "Positive Affect"
+
+    def test_immutable(self):
+        """Test that InstrumentScale is immutable."""
+        scale = InstrumentScale(abbrev="PA", angle=90.0, items=(1, 2), label="Test")
+        with pytest.raises(AttributeError):
+            scale.abbrev = "NA"  # type: ignore[invalid-assignment]
+
+
+# Test ResponseAnchor
+class TestResponseAnchor:
+    """Tests for ResponseAnchor dataclass."""
+
+    def test_creation(self):
+        """Test creating a ResponseAnchor."""
+        anchor = ResponseAnchor(value=1, label="Strongly Disagree")
+        assert anchor.value == 1
+        assert anchor.label == "Strongly Disagree"
+
+    def test_immutable(self):
+        """Test that ResponseAnchor is immutable."""
+        anchor = ResponseAnchor(value=1, label="Test")
+        with pytest.raises(AttributeError):
+            anchor.value = 2  # type: ignore[invalid-assignment]
+
+
+# Test ResponseItem
+class TestResponseItem:
+    """Tests for ResponseItem dataclass."""
+
+    def test_creation(self):
+        """Test creating a ResponseItem."""
+        item = ResponseItem(item_id=1, text="I feel happy")
+        assert item.item_id == 1
+        assert item.text == "I feel happy"
+
+    def test_immutable(self):
+        """Test that ResponseItem is immutable."""
+        item = ResponseItem(item_id=1, text="Test")
+        with pytest.raises(AttributeError):
+            item.text = "New text"  # type: ignore[invalid-assignment]
+
+
+# Test NormativeSample
+class TestNormativeSample:
+    """Tests for NormativeSample dataclass."""
+
+    def test_creation(self):
+        """Test creating a NormativeSample."""
+        stats = pd.DataFrame({"scale": ["PA"], "mean": [3.5], "sd": [0.8]})
+        norm = NormativeSample(
+            sample_id=1,
+            size=100,
+            population="Students",
+            reference="Doe (2020)",
+            url="https://example.com",
+            statistics=stats,
+        )
+        assert norm.sample_id == 1
+        assert norm.size == 100
+        assert norm.population == "Students"
+        assert isinstance(norm.statistics, pd.DataFrame)
+
+
+# Test Instrument
+class TestInstrument:
+    """Tests for Instrument class."""
+
+    def test_creation(self, sample_instrument: Instrument):
+        """Test creating an Instrument."""
+        assert sample_instrument.name == "Test Affect Scale"
+        assert sample_instrument.abbrev == "TAS"
+        assert sample_instrument.construct == "Affect"
+        assert sample_instrument.n_scales == 2
+        assert sample_instrument.n_items == 4
+
+    def test_n_scales_property(self, sample_instrument: Instrument):
+        """Test n_scales property."""
+        assert sample_instrument.n_scales == 2
+
+    def test_n_items_property(self, sample_instrument: Instrument):
+        """Test n_items property."""
+        assert sample_instrument.n_items == 4
+
+    def test_n_items_no_items(
+        self,
+        sample_scales: tuple[InstrumentScale, ...],
+        sample_anchors: tuple[ResponseAnchor, ...],
+    ):
+        """Test n_items when no items are provided."""
+        inst = Instrument(
+            name="Test",
+            abbrev="TST",
+            construct="Test",
+            reference="Test",
+            url="https://test.com",
+            status="open-access",
+            scales=sample_scales,
+            anchors=sample_anchors,
+            items=None,
+        )
+        assert inst.n_items == 0
+
+    def test_scale_labels_property(self, sample_instrument: Instrument):
+        """Test scale_labels property."""
+        labels = sample_instrument.scale_labels
+        assert labels == ["Positive Affect", "Negative Affect"]
+
+    def test_scale_abbrevs_property(self, sample_instrument: Instrument):
+        """Test scale_abbrevs property."""
+        abbrevs = sample_instrument.scale_abbrevs
+        assert abbrevs == ["PA", "NA"]
+
+    def test_get_angles(self, sample_instrument: Instrument):
+        """Test get_angles method."""
+        angles = sample_instrument.get_angles()
+        assert angles == [90.0, 270.0]
+
+    def test_get_scale_success(self, sample_instrument: Instrument):
+        """Test get_scale with valid abbreviation."""
+        scale = sample_instrument.get_scale("PA")
+        assert scale.abbrev == "PA"
+        assert scale.label == "Positive Affect"
+
+    def test_get_scale_failure(self, sample_instrument: Instrument):
+        """Test get_scale with invalid abbreviation."""
+        with pytest.raises(ValueError, match="Scale with abbreviation 'XX' not found"):
+            sample_instrument.get_scale("XX")
+
+    def test_get_item_success(self, sample_instrument: Instrument):
+        """Test get_item with valid item ID."""
+        item = sample_instrument.get_item(1)
+        assert item.item_id == 1
+        assert item.text == "I feel happy"
+
+    def test_get_item_failure(self, sample_instrument: Instrument):
+        """Test get_item with invalid item ID."""
+        with pytest.raises(ValueError, match="Item with ID '99' not found"):
+            sample_instrument.get_item(99)
+
+    def test_get_item_no_items(
+        self,
+        sample_scales: tuple[InstrumentScale, ...],
+        sample_anchors: tuple[ResponseAnchor, ...],
+    ):
+        """Test get_item when items are not available."""
+        inst = Instrument(
+            name="Test",
+            abbrev="TST",
+            construct="Test",
+            reference="Test",
+            url="https://test.com",
+            status="open-access",
+            scales=sample_scales,
+            anchors=sample_anchors,
+            items=None,
+        )
+        with pytest.raises(ValueError, match="does not have item text available"):
+            inst.get_item(1)
+
+    def test_repr(self, sample_instrument: Instrument):
+        """Test __repr__ method."""
+        repr_str = repr(sample_instrument)
+        assert "TAS: Test Affect Scale" in repr_str
+        assert "4 items, 2 scales, 1 normative data sets" in repr_str
+        assert "Test et al. (2023)" in repr_str
+        assert "https://example.com/tas" in repr_str
+
+    def test_rich_repr(self, sample_instrument: Instrument):
+        """Test __rich_repr__ method."""
+        rich_repr = list(sample_instrument.__rich_repr__())
+        assert len(rich_repr) == 4
+        assert "TAS: Test Affect Scale" in rich_repr[0]
+
+    def test_info_plain(self, sample_instrument: Instrument, capsys):
+        """Test info method with plain output."""
+        sample_instrument.info(rich_print=False)
+        captured = capsys.readouterr()
+        assert "TAS: Test Affect Scale" in captured.out
+        assert "Positive Affect" in captured.out
+
+    def test_info_scales_plain(self, sample_instrument: Instrument):
+        """Test info_scales with plain output."""
+        result = sample_instrument.info_scales(rich_print=False)
+        assert isinstance(result, str)
+        assert "The TAS contains 2 scales:" in result
+        assert "PA: Positive Affect (90.0°)" in result
+        assert "NA: Negative Affect (270.0°)" in result
+
+    def test_info_scales_with_items_plain(self, sample_instrument: Instrument):
+        """Test info_scales with items displayed."""
+        result = sample_instrument.info_scales(items=True, rich_print=False)
+        assert isinstance(result, str)
+        assert "1. I feel happy" in result
+        assert "3. I feel sad" in result
+
+    def test_info_anchors_plain(self, sample_instrument: Instrument):
+        """Test info_anchors with plain output."""
+        result = sample_instrument.info_anchors(rich_print=False)
+        assert isinstance(result, str)
+        assert "5-point scale:" in result
+        assert "1. Strongly Disagree" in result
+        assert "5. Strongly Agree" in result
+
+    def test_info_norms_plain(self, sample_instrument: Instrument):
+        """Test info_norms with plain output."""
+        result = sample_instrument.info_norms(rich_print=False)
+        assert isinstance(result, str)
+        assert "1 normative data set(s):" in result
+        assert "500 College Students" in result
+        assert "Doe et al. (2020)" in result
+
+    def test_score_with_column_names(
+        self, sample_instrument: Instrument, sample_data: pd.DataFrame
+    ):
+        """Test scoring with column names."""
+        result = sample_instrument.score(
+            sample_data,
+            items=["item1", "item2", "item3", "item4"],
+            prefix="",
+            suffix="",
+            append=False,
+        )
+        assert result.shape == (5, 2)
+        assert "PA" in result.columns
+        assert "NA" in result.columns
+        # PA is mean of items 1, 2; NA is mean of items 3, 4
+        assert result["PA"].iloc[0] == 5.0
+        assert result["NA"].iloc[0] == 1.0
+
+    def test_score_with_indices(
+        self, sample_instrument: Instrument, sample_data: pd.DataFrame
+    ):
+        """Test scoring with column indices."""
+        result = sample_instrument.score(
+            sample_data,
+            items=[0, 1, 2, 3],
+            append=False,
+        )
+        assert result.shape == (5, 2)
+        assert "PA" in result.columns
+        assert "NA" in result.columns
+
+    def test_score_with_prefix_suffix(
+        self, sample_instrument: Instrument, sample_data: pd.DataFrame
+    ):
+        """Test scoring with prefix and suffix."""
+        result = sample_instrument.score(
+            sample_data,
+            items=[0, 1, 2, 3],
+            prefix="pre_",
+            suffix="_suf",
+            append=False,
+        )
+        assert "pre_PA_suf" in result.columns
+        assert "pre_NA_suf" in result.columns
+
+    def test_score_append(
+        self, sample_instrument: Instrument, sample_data: pd.DataFrame
+    ):
+        """Test scoring with append=True."""
+        result = sample_instrument.score(
+            sample_data,
+            items=[0, 1, 2, 3],
+            append=True,
+        )
+        assert result.shape == (5, 6)  # 4 original + 2 scores
+        assert "item1" in result.columns
+        assert "PA" in result.columns
+
+    def test_score_with_missing_data(self, sample_instrument: Instrument):
+        """Test scoring with missing values."""
+        data = pd.DataFrame(
+            {
+                "item1": [5, 4, np.nan, 2, 1],
+                "item2": [5, 4, 3, np.nan, 1],
+                "item3": [1, 2, 3, 4, 5],
+                "item4": [1, 2, 3, 4, 5],
+            }
+        )
+        result = sample_instrument.score(
+            data, items=[0, 1, 2, 3], na_rm=True, append=False
+        )
+        assert not np.isnan(result["PA"].iloc[2])  # Should compute from remaining item
+        assert not np.isnan(result["PA"].iloc[3])
+
+    def test_score_mixed_item_types_error(
+        self, sample_instrument: Instrument, sample_data: pd.DataFrame
+    ):
+        """Test scoring with mixed item types raises error."""
+        with pytest.raises(TypeError, match="must be either strings or integers"):
+            sample_instrument.score(sample_data, items=["item1", 1])
+
+    def test_norm_standardize_success(self, sample_instrument: Instrument):
+        """Test norm_standardize with valid sample ID."""
+        data = pd.DataFrame(
+            {
+                "PA": [4.3, 3.5, 2.7],
+                "NA": [2.6, 2.0, 1.4],
+            }
+        )
+        result = sample_instrument.norm_standardize(
+            data,
+            scales=["PA", "NA"],
+            sample_id=1,
+            append=False,
+        )
+        assert "PA_z" in result.columns
+        assert "NA_z" in result.columns
+        # Check standardization: (4.3 - 3.5) / 0.8 = 1.0
+        assert np.isclose(result["PA_z"].iloc[0], 1.0)
+        # Check standardization: (3.5 - 3.5) / 0.8 = 0.0
+        assert np.isclose(result["PA_z"].iloc[1], 0.0)
+
+    def test_norm_standardize_with_indices(self, sample_instrument: Instrument):
+        """Test norm_standardize with scale indices."""
+        data = pd.DataFrame(
+            {
+                "PA": [4.3, 3.5, 2.7],
+                "NA": [2.6, 2.0, 1.4],
+            }
+        )
+        result = sample_instrument.norm_standardize(
+            data,
+            scales=[0, 1],  # Use indices instead of names
+            sample_id=1,
+            append=False,
+        )
+        assert "PA_z" in result.columns
+        assert "NA_z" in result.columns
+
+    def test_norm_standardize_append(self, sample_instrument: Instrument):
+        """Test norm_standardize with append=True."""
+        data = pd.DataFrame(
+            {
+                "PA": [4.3, 3.5, 2.7],
+                "NA": [2.6, 2.0, 1.4],
+            }
+        )
+        result = sample_instrument.norm_standardize(
+            data,
+            scales=["PA", "NA"],
+            sample_id=1,
+            append=True,
+        )
+        assert "PA" in result.columns
+        assert "PA_z" in result.columns
+
+    def test_norm_standardize_custom_affix(self, sample_instrument: Instrument):
+        """Test norm_standardize with custom prefix and suffix."""
+        data = pd.DataFrame(
+            {
+                "PA": [4.3],
+                "NA": [2.6],
+            }
+        )
+        result = sample_instrument.norm_standardize(
+            data,
+            scales=["PA", "NA"],
+            sample_id=1,
+            prefix="std_",
+            suffix="_score",
+            append=False,
+        )
+        assert "std_PA_score" in result.columns
+        assert "std_NA_score" in result.columns
+
+    def test_norm_standardize_invalid_sample_id(self, sample_instrument: Instrument):
+        """Test norm_standardize with invalid sample ID."""
+        data = pd.DataFrame({"PA": [4.3], "NA": [2.6]})
+        with pytest.raises(
+            ValueError, match="Normative sample with ID '999' not found"
+        ):
+            sample_instrument.norm_standardize(data, scales=["PA"], sample_id=999)
+
+
+# Test Registry Functions
+class TestRegistryFunctions:
+    """Tests for global registry functions."""
+
+    def test_register_instrument(self, sample_instrument: Instrument):
+        """Test registering an instrument."""
+        register_instrument("TAS", sample_instrument)
+        assert "tas" in _INSTRUMENTS
+        assert _INSTRUMENTS["tas"] == sample_instrument
+
+    def test_get_instrument_success(self, sample_instrument: Instrument):
+        """Test retrieving a registered instrument."""
+        register_instrument("TAS", sample_instrument)
+        retrieved = get_instrument("TAS")
+        assert retrieved == sample_instrument
+
+    def test_get_instrument_case_insensitive(self, sample_instrument: Instrument):
+        """Test that get_instrument is case-insensitive."""
+        register_instrument("TAS", sample_instrument)
+        retrieved = get_instrument("tas")
+        assert retrieved == sample_instrument
+
+    def test_get_instrument_not_found(self):
+        """Test get_instrument with unregistered instrument."""
+        with pytest.raises(ValueError, match="Instrument 'XYZ' not found"):
+            get_instrument("XYZ")
+
+    def test_show_instruments_empty(self, capsys):
+        """Test show_instruments with empty registry."""
+        show_instruments(rich_print=False)
+        captured = capsys.readouterr()
+        assert "0 instruments" in captured.out
+
+    def test_show_instruments_with_instruments(
+        self, sample_instrument: Instrument, capsys
+    ):
+        """Test show_instruments with registered instruments."""
+        register_instrument("TAS", sample_instrument)
+        show_instruments(rich_print=False)
+        captured = capsys.readouterr()
+        assert "1 instruments" in captured.out
+        assert "TAS" in captured.out
+        assert "Test Affect Scale" in captured.out
diff --git a/tests/test_ssm.py b/tests/test_ssm.py
new file mode 100644
index 0000000..f2a9e7c
--- /dev/null
+++ b/tests/test_ssm.py
@@ -0,0 +1,134 @@
+from unittest.mock import patch
+
+import pandas as pd
+
+from circumplex.ssm import SSM, SSMDetails
+
+
+def test_ssm_details_from_dict():
+    data = {
+        "boots": 2000,
+        "interval": 0.95,
+        "listwise": True,
+        "angles": [0, 90, 180, 270],
+        "contrast": False,
+        "score_type": "mean",
+    }
+    details = SSMDetails.from_dict(data)
+    assert details.boots == 2000
+    assert details.interval == 0.95
+    assert details.listwise is True
+    assert details.angles == [0, 90, 180, 270]
+    assert details.contrast is False
+    assert details.score_type == "mean"
+
+
+def test_ssm_details_to_dict():
+    details = SSMDetails(
+        2000,
+        0.95,
+        listwise=True,
+        angles=[0, 90, 180, 270],
+        contrast=False,
+        score_type="mean",
+    )
+    data = details.to_dict()
+    assert data["boots"] == 2000
+    assert data["interval"] == 0.95
+    assert data["listwise"] is True
+    assert data["angles"] == [0, 90, 180, 270]
+    assert data["contrast"] is False
+    assert data["score_type"] == "mean"
+
+
+def test_ssm_from_dict():
+    data = {
+        "results": pd.DataFrame(
+            {
+                "Label": ["A"],
+                "e_est": [1.0],
+                "x_est": [0.5],
+                "y_est": [0.5],
+                "fit_est": [0.9],
+            }
+        ),
+        "scores": pd.DataFrame({"Score": [1, 2, 3]}),
+        "details": {
+            "boots": 2000,
+            "interval": 0.95,
+            "listwise": True,
+            "angles": [0, 90, 180, 270],
+            "contrast": False,
+            "score_type": "mean",
+        },
+        "type": "profile",
+    }
+    ssm = SSM.from_dict(data)
+    assert ssm.type == "profile"
+    assert ssm.results.shape[0] == 1
+    assert ssm.details.boots == 2000
+
+
+def test_ssm_to_dict():
+    details = SSMDetails(
+        2000,
+        0.95,
+        listwise=True,
+        angles=[0, 90, 180, 270],
+        contrast=False,
+        score_type="mean",
+    )
+    results = pd.DataFrame(
+        {
+            "Label": ["A"],
+            "e_est": [1.0],
+            "x_est": [0.5],
+            "y_est": [0.5],
+            "fit_est": [0.9],
+        }
+    )
+    scores = pd.DataFrame({"Score": [1, 2, 3]})
+    ssm = SSM(results, scores, details, "profile")
+    data = ssm.to_dict()
+    assert data["type"] == "profile"
+    assert data["results"].shape[0] == 1
+    assert data["details"]["boots"] == 2000
+
+
+def test_ssm_summary():
+    results = pd.DataFrame(
+        {
+            "Label": ["A"],
+            # Estimates + CIs expected by summary
+            "e_est": [1.0],
+            "e_lci": [0.8],
+            "e_uci": [1.2],
+            "x_est": [0.5],
+            "x_lci": [0.3],
+            "x_uci": [0.7],
+            "y_est": [0.5],
+            "y_lci": [0.3],
+            "y_uci": [0.7],
+            "a_est": [0.71],
+            "a_lci": [0.6],
+            "a_uci": [0.8],
+            "d_est": [0.0],
+            "d_lci": [-0.1],
+            "d_uci": [0.1],
+            "fit_est": [0.9],
+        }
+    )
+    scores = pd.DataFrame({"Score": [1, 2, 3]})
+    details = SSMDetails(
+        2000,
+        0.95,
+        listwise=True,
+        angles=[0, 90, 180, 270],
+        contrast=False,
+        score_type="mean",
+    )
+    ssm = SSM(results, scores, details, "profile")
+
+    with patch("builtins.print") as mocked_print:
+        ssm.summary(rich_print=False)
+        mocked_print.assert_called()  # Ensure print was called
diff --git a/tests/test_ssm_analyze.py b/tests/test_ssm_analyze.py
new file mode 100644
index 0000000..a1295b4
--- /dev/null
+++ b/tests/test_ssm_analyze.py
@@ -0,0 +1,586 @@
+"""Regression tests for ssm_analyze against R package output.
+
+These tests validate numerical parity with the R circumplex package
+using pre-computed test fixtures from R analyses.
+
+All tests updated to use the SSM object API instead of dict access.
+"""
+
+import json
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pytest
+
+from circumplex import ssm_analyze
+from circumplex.data import load_dataset
+from circumplex.utils.angles import OCTANTS
+
+FIXTURES_DIR = Path(__file__).parent / "regression" / "fixtures"
+
+
+def load_fixture(filename: str) -> dict:
+    """Load a test fixture JSON file."""
+    with (FIXTURES_DIR / filename).open() as f:
+        return json.load(f)
+
+
+def assert_close(
+    actual: float, expected: float, rtol: float = 1e-3, atol: float = 1e-3
+) -> None:
+    """Assert two values are close to 3 decimal places."""
+    if expected is None or np.isnan(expected):
+        assert np.isnan(actual) or actual is None
+    else:
+        np.testing.assert_allclose(actual, expected, rtol=rtol, atol=atol)
+
+
+def assert_close_ci(
+    actual: float, expected: float, rtol: float = 0.05, atol: float = 0.05
+) -> None:
+    """Assert CI values are close (relaxed tolerance for bootstrap variability)."""
+    # Bootstrap CIs can vary due to RNG implementation differences between R and Python.
+    # Even with the same seed, R and Python generate different random sequences.
+    # Default: 5% relative tolerance or 0.05 absolute tolerance for CIs.
+    # Can be overridden for tests with very small samples or high variability.
+    if expected is None or np.isnan(expected):
+        assert np.isnan(actual) or actual is None
+    else:
+        np.testing.assert_allclose(actual, expected, rtol=rtol, atol=atol)
+
+
+@pytest.mark.regression
+def test_ssm_single_group_mean() -> None:
+    """Test single-group mean-based SSM analysis (aw2009 dataset).
+
+    Note: This test uses a very small sample (n=5), which leads to high
+    bootstrap variability. CI tolerances are wider than other tests.
+    """
+    fixture = load_fixture("ssm_single_group_mean.json")
+
+    # Load data
+    data = load_dataset("aw2009")
+
+    # Run analysis (angles should be in degrees)
+    angles = OCTANTS
+    results = ssm_analyze(
+        data,
+        scales=fixture["input"]["scales"],
+        angles=angles,
+        boots=fixture["input"]["boots"],
+        interval=fixture["input"]["interval"],
+        listwise=fixture["input"]["listwise"],
+        seed=fixture["seed"],
+    )
+
+    # Validate results
+    expected = fixture["expected"]
+    actual = results.results.iloc[0]
+
+    # Check point estimates
+    assert_close(actual["e_est"], expected["e_est"])
+    assert_close(actual["x_est"], expected["x_est"])
+    assert_close(actual["y_est"], expected["y_est"])
+    assert_close(actual["a_est"], expected["a_est"])
+    assert_close(actual["d_est"], expected["d_est"])
+    assert_close(actual["fit_est"], expected["fit_est"])
+
+    # Check confidence intervals (use extra-relaxed tolerance for small sample)
+    # With n=5, bootstrap CIs are highly variable even with same algorithm
+    assert_close_ci(actual["e_lci"], expected["e_lci"], rtol=0.20, atol=0.20)
+    assert_close_ci(actual["e_uci"], expected["e_uci"], rtol=0.20, atol=0.20)
+    assert_close_ci(actual["x_lci"], expected["x_lci"], rtol=0.20, atol=0.20)
+    assert_close_ci(actual["x_uci"], expected["x_uci"], rtol=0.20, atol=0.20)
+    assert_close_ci(actual["y_lci"], expected["y_lci"], rtol=0.20, atol=0.20)
+    assert_close_ci(actual["y_uci"], expected["y_uci"], rtol=0.20, atol=0.20)
+    assert_close_ci(actual["a_lci"], expected["a_lci"], rtol=0.20, atol=0.20)
+    assert_close_ci(actual["a_uci"], expected["a_uci"], rtol=0.20, atol=0.20)
+
+    # Check displacement CIs (may wrap around circle)
+    assert_close_ci(actual["d_lci"], expected["d_lci"], rtol=0.20, atol=0.20)
+    assert_close_ci(actual["d_uci"], expected["d_uci"], rtol=0.20, atol=0.20)
+
+    # Check scores
+    actual_scores = results.scores.iloc[0][fixture["input"]["scales"]].to_dict()
+    for scale, expected_val in expected["scores"].items():
+        assert_close(actual_scores[scale], expected_val)
+
+
+@pytest.mark.regression
+def test_ssm_multi_group_mean() -> None:
+    """Test multi-group mean-based SSM analysis (jz2017 by Gender)."""
+    fixture = load_fixture("ssm_multi_group_mean.json")
+
+    # Load data
+    data = load_dataset("jz2017")
+
+    # Run analysis
+    angles = OCTANTS
+    results = ssm_analyze(
+        data,
+        scales=fixture["input"]["scales"],
+        angles=angles,
+        grouping=fixture["input"]["grouping"],
+        boots=fixture["input"]["boots"],
+        interval=fixture["input"]["interval"],
+        seed=fixture["seed"],
+    )
+
+    # Validate results for both groups
+    expected = fixture["expected"]
+    actual_df = results.results
+
+    # Check both groups
+    for idx in range(2):
+        actual = actual_df.iloc[idx]
+
+        # Check point estimates
+        assert_close(actual["e_est"], expected["e_est"][idx])
+        assert_close(actual["x_est"], expected["x_est"][idx])
+        assert_close(actual["y_est"], expected["y_est"][idx])
+        assert_close(actual["a_est"], expected["a_est"][idx])
+        assert_close(actual["d_est"], expected["d_est"][idx])
+        assert_close(actual["fit_est"], expected["fit_est"][idx])
+
+        # Check CIs (use relaxed tolerance for bootstrap variability)
+        assert_close_ci(actual["e_lci"], expected["e_lci"][idx])
+        assert_close_ci(actual["e_uci"], expected["e_uci"][idx])
+        assert_close_ci(actual["a_lci"], expected["a_lci"][idx])
+        assert_close_ci(actual["a_uci"], expected["a_uci"][idx])
+
+
+@pytest.mark.regression
+def test_ssm_multi_group_mean_contrast() -> None:
+    """Test multi-group mean-based SSM with contrast."""
+    fixture = load_fixture("ssm_multi_group_contrast.json")
+
+    # Load data
+    data = load_dataset("jz2017")
+
+    # Run analysis
+    angles = OCTANTS
+    results = ssm_analyze(
+        data,
+        scales=fixture["input"]["scales"],
+        angles=angles,
+        grouping=fixture["input"]["grouping"],
+        contrast=fixture["input"]["contrast"],
+        boots=fixture["input"]["boots"],
+        seed=fixture["seed"],
+    )
+
+    # Validate all 3 rows (Female, Male, Male - Female)
+    expected = fixture["expected"]
+    actual_df = results.results
+
+    assert len(actual_df) == 3
+
+    for idx in range(3):
+        actual = actual_df.iloc[idx]
+
+        # Check point estimates
+        assert_close(actual["e_est"], expected["e_est"][idx])
+        assert_close(actual["x_est"], expected["x_est"][idx])
+        assert_close(actual["y_est"], expected["y_est"][idx])
+        assert_close(actual["a_est"], expected["a_est"][idx])
+        assert_close(actual["d_est"], expected["d_est"][idx])
+        assert_close(actual["fit_est"], expected["fit_est"][idx])
+
+        # Check some CIs (use relaxed tolerance for bootstrap variability)
+        assert_close_ci(actual["e_lci"], expected["e_lci"][idx])
+        assert_close_ci(actual["e_uci"], expected["e_uci"][idx])
+        assert_close_ci(actual["a_lci"], expected["a_lci"][idx])
+        assert_close_ci(actual["a_uci"], expected["a_uci"][idx])
+
+    # Verify labels
+    assert actual_df.iloc[0]["Label"] == expected["labels"][0]
+    assert actual_df.iloc[1]["Label"] == expected["labels"][1]
+    assert actual_df.iloc[2]["Label"] == expected["labels"][2]
+
+
+@pytest.mark.regression
+def test_ssm_single_group_correlation() -> None:
+    """Test single-group correlation-based SSM analysis."""
+    fixture = load_fixture("ssm_single_group_correlation.json")
+
+    # Load data
+    data = load_dataset("jz2017")
+
+    # Run analysis
+    angles = OCTANTS
+    results = ssm_analyze(
+        data,
+        scales=fixture["input"]["scales"],
+        angles=angles,
+        measures=fixture["input"]["measures"],
+        boots=fixture["input"]["boots"],
+        seed=fixture["seed"],
+    )
+
+    # Validate results
+    expected = fixture["expected"]
+    actual = results.results.iloc[0]
+
+    # Check point estimates
+    assert_close(actual["e_est"], expected["e_est"])
+    assert_close(actual["x_est"], expected["x_est"])
+    assert_close(actual["y_est"], expected["y_est"])
+    assert_close(actual["a_est"], expected["a_est"])
+    # Note: displacement in fixture is rounded to 1 decimal for correlations
+    assert_close(actual["d_est"], expected["d_est"], rtol=0.1)
+    assert_close(actual["fit_est"], expected["fit_est"])
+
+    # Check confidence intervals (use relaxed tolerance for bootstrap variability)
+    assert_close_ci(actual["e_lci"], expected["e_lci"])
+    assert_close_ci(actual["e_uci"], expected["e_uci"])
+    assert_close_ci(actual["x_lci"], expected["x_lci"])
+    assert_close_ci(actual["x_uci"], expected["x_uci"])
+    assert_close_ci(actual["y_lci"], expected["y_lci"])
+    assert_close_ci(actual["y_uci"], expected["y_uci"])
+
+
+@pytest.mark.regression
+def test_ssm_multi_group_correlation() -> None:
+    """Test multi-group correlation-based SSM analysis."""
+    fixture = load_fixture("ssm_multi_group_correlation.json")
+
+    # Load data
+    data = load_dataset("jz2017")
+
+    # Run analysis
+    angles = OCTANTS
+    results = ssm_analyze(
+        data,
+        scales=fixture["input"]["scales"],
+        angles=angles,
+        measures=fixture["input"]["measures"],
+        grouping=fixture["input"]["grouping"],
+        boots=fixture["input"]["boots"],
+        seed=fixture["seed"],
+    )
+
+    # Validate both groups
+    expected = fixture["expected"]
+    actual_df = results.results
+
+    assert len(actual_df) == 2
+
+    for idx in range(2):
+        actual = actual_df.iloc[idx]
+
+        # Check point estimates
+        assert_close(actual["e_est"], expected["e_est"][idx])
+        assert_close(actual["x_est"], expected["x_est"][idx])
+        assert_close(actual["y_est"], expected["y_est"][idx])
+        assert_close(actual["a_est"], expected["a_est"][idx])
+        assert_close(actual["d_est"], expected["d_est"][idx], rtol=0.1)
+        assert_close(actual["fit_est"], expected["fit_est"][idx])
+
+    # Verify labels
+    assert actual_df.iloc[0]["Label"] == expected["labels"][0]
+    assert actual_df.iloc[1]["Label"] == expected["labels"][1]
+
+
+@pytest.mark.regression
+def test_ssm_measure_contrast_correlation() -> None:
+    """Test measure-contrast correlation-based SSM analysis."""
+    fixture = load_fixture("ssm_measure_contrast_correlation.json")
+
+    # Load data
+    data = load_dataset("jz2017")
+
+    # Run analysis
+    angles = OCTANTS
+    results = ssm_analyze(
+        data,
+        scales=fixture["input"]["scales"],
+        angles=angles,
+        measures=fixture["input"]["measures"],
+        contrast=fixture["input"]["contrast"],
+        boots=fixture["input"]["boots"],
+        seed=fixture["seed"],
+    )
+
+    # Validate all 3 rows (ASPD, NARPD, NARPD - ASPD)
+    expected = fixture["expected"]
+    actual_df = results.results
+
+    assert len(actual_df) == 3
+
+    for idx in range(3):
+        actual = actual_df.iloc[idx]
+
+        # Check point estimates
+        assert_close(actual["e_est"], expected["e_est"][idx])
+        assert_close(actual["x_est"], expected["x_est"][idx])
+        assert_close(actual["y_est"], expected["y_est"][idx])
+        assert_close(actual["a_est"], expected["a_est"][idx])
+        assert_close(actual["d_est"], expected["d_est"][idx], rtol=0.1)
+        assert_close(actual["fit_est"], expected["fit_est"][idx])
+
+        # Check some CIs (use relaxed tolerance for bootstrap variability)
+        assert_close_ci(actual["e_lci"], expected["e_lci"][idx])
+        assert_close_ci(actual["e_uci"], expected["e_uci"][idx])
+        assert_close_ci(actual["a_lci"], expected["a_lci"][idx])
+        assert_close_ci(actual["a_uci"], expected["a_uci"][idx])
+
+    # Verify labels
+    assert actual_df.iloc[0]["Label"] == expected["labels"][0]
+    assert actual_df.iloc[1]["Label"] == expected["labels"][1]
+    assert actual_df.iloc[2]["Label"] == expected["labels"][2]
+
+    # Check scores for all three profiles
+    score_names = fixture["input"]["scales"]
+    actual_scores_aspd = results.scores.iloc[0][score_names].to_dict()
+    actual_scores_narpd = results.scores.iloc[1][score_names].to_dict()
+    actual_scores_contrast = results.scores.iloc[2][score_names].to_dict()
+
+    for scale, expected_val in expected["scores_aspd"].items():
+        assert_close(actual_scores_aspd[scale], expected_val)
+    for scale, expected_val in expected["scores_narpd"].items():
+        assert_close(actual_scores_narpd[scale], expected_val)
+    for scale, expected_val in expected["scores_contrast"].items():
+        assert_close(actual_scores_contrast[scale], expected_val)
+
+
+@pytest.mark.regression
+def test_ssm_group_contrast_correlation() -> None:
+    """Test group-contrast correlation-based SSM analysis."""
+    fixture = load_fixture("ssm_group_contrast_correlation.json")
+
+    # Load data
+    data = load_dataset("jz2017")
+
+    # Run analysis
+    angles = OCTANTS
+    results = ssm_analyze(
+        data,
+        scales=fixture["input"]["scales"],
+        angles=angles,
+        measures=fixture["input"]["measures"],
+        grouping=fixture["input"]["grouping"],
+        contrast=fixture["input"]["contrast"],
+        boots=fixture["input"]["boots"],
+        seed=fixture["seed"],
+    )
+
+    # Validate all 3 rows (Female, Male, Male - Female)
+    expected = fixture["expected"]
+    actual_df = results.results
+
+    assert len(actual_df) == 3
+
+    for idx in range(3):
+        actual = actual_df.iloc[idx]
+
+        # Check point estimates
+        assert_close(actual["e_est"], expected["e_est"][idx])
+        assert_close(actual["x_est"], expected["x_est"][idx])
+        assert_close(actual["y_est"], expected["y_est"][idx])
+        assert_close(actual["a_est"], expected["a_est"][idx])
+        assert_close(actual["d_est"], expected["d_est"][idx], rtol=0.1)
+        assert_close(actual["fit_est"], expected["fit_est"][idx])
+
+        # Check some CIs (use relaxed tolerance for bootstrap variability)
+        assert_close_ci(actual["e_lci"], expected["e_lci"][idx])
+        assert_close_ci(actual["e_uci"], expected["e_uci"][idx])
+        assert_close_ci(actual["a_lci"], expected["a_lci"][idx])
+        assert_close_ci(actual["a_uci"], expected["a_uci"][idx])
+
+    # Verify labels
+    assert actual_df.iloc[0]["Label"] == expected["labels"][0]
+    assert actual_df.iloc[1]["Label"] == expected["labels"][1]
+    assert actual_df.iloc[2]["Label"] == expected["labels"][2]
+
+
+@pytest.mark.unit
+def test_ssm_analyze_basic_usage() -> None:
+    """Test basic usage of ssm_analyze without bootstrap (fast test)."""
+    data = load_dataset("aw2009")
+
+    # Run with minimal bootstraps for speed
+    results = ssm_analyze(data, scales=list(range(8)), boots=10, seed=123)
+
+    # Check SSM object structure
+    assert hasattr(results, "results")
+    assert hasattr(results, "scores")
+    assert hasattr(results, "details")
+    assert hasattr(results, "type")
+
+    # Check results DataFrame structure
+    df = results.results
+    assert "Label" in df.columns
+    assert "Group" in df.columns
+    assert "Measure" in df.columns
+    assert "e_est" in df.columns
+    assert "a_est" in df.columns
+    assert "d_est" in df.columns
+    assert "fit_est" in df.columns
+
+    # Check that we have one row for single-group analysis
+    assert len(df) == 1
+
+
+@pytest.mark.unit
+def test_ssm_analyze_with_grouping() -> None:
+    """Test ssm_analyze with grouping variable."""
+    data = load_dataset("jz2017")
+
+    results = ssm_analyze(
+        data, scales=list(range(1, 9)), grouping="Gender", boots=10, seed=123
+    )
+
+    # Should have 2 rows (Female, Male)
+    assert len(results.results) == 2
+    assert results.type == "mean"
+
+
+@pytest.mark.unit
+def test_ssm_analyze_with_contrast() -> None:
+    """Test ssm_analyze with contrast=True."""
+    data = load_dataset("jz2017")
+
+    results = ssm_analyze(
+        data,
+        scales=list(range(1, 9)),
+        grouping="Gender",
+        contrast=True,
+        boots=10,
+        seed=123,
+    )
+
+    # Should have 3 rows (Female, Male, Male - Female)
+    assert len(results.results) == 3
+    assert "Male - Female" in results.results["Label"].values
+
+
+@pytest.mark.unit
+def test_ssm_analyze_correlation() -> None:
+    """Test correlation-based SSM analysis."""
+    data = load_dataset("jz2017")
+
+    results = ssm_analyze(
+        data, scales=list(range(1, 9)), measures="PARPD", boots=10, seed=123
+    )
+
+    # Check that it's correlation-based
+    assert results.type == "correlation"
+    assert len(results.results) == 1
+
+
+@pytest.mark.unit
+def test_ssm_analyze_invalid_contrast() -> None:
+    """Test that invalid contrast raises ValueError."""
+    data = load_dataset("jz2017")
+
+    # Should fail: contrast with 3+ groups
+    # Create a dummy 3-group dataset
+    data_modified = data.copy()
+    data_modified["Group3"] = ["A", "B", "C"] * (len(data) // 3) + ["A"] * (
+        len(data) % 3
+    )
+    with pytest.raises(ValueError, match="Contrast can only be TRUE"):
+        ssm_analyze(
+            data_modified,
+            scales=list(range(1, 9)),
+            grouping="Group3",
+            contrast=True,
+            boots=10,
+        )
+
+
+@pytest.mark.unit
+def test_ssm_analyze_custom_angles() -> None:
+    """Test ssm_analyze with custom angles."""
+    data = load_dataset("jz2017")
+
+    # Use custom quadrant angles (4 scales)
+    custom_angles = [90, 180, 270, 360]
+    results = ssm_analyze(
+        data, scales=["PA", "DE", "HI", "LM"], angles=custom_angles, boots=10, seed=123
+    )
+
+    assert len(results.results) == 1
+    assert results.details.angles == custom_angles
+
+
+@pytest.mark.unit
+def test_plot_circle_basic() -> None:
+    """Test that plot_circle() works and returns matplotlib Figure."""
+    data = load_dataset("aw2009")
+    results = ssm_analyze(data, scales=list(range(8)), boots=10, seed=123)
+
+    fig = results.plot_circle()
+
+    assert fig is not None
+    assert hasattr(fig, "savefig")  # Check it's a matplotlib Figure
+    plt.close(fig)
+
+
+@pytest.mark.unit
+def test_plot_curve_basic() -> None:
+    """Test that plot_curve() works and returns matplotlib Figure."""
+    data = load_dataset("aw2009")
+    results = ssm_analyze(data, scales=list(range(8)), boots=10, seed=123)
+
+    fig = results.plot_curve()
+
+    assert fig is not None
+    assert hasattr(fig, "savefig")
+    plt.close(fig)
+
+
+@pytest.mark.unit
+def test_plot_contrast_basic() -> None:
+    """Test that plot_contrast() works with contrast results."""
+    data = load_dataset("jz2017")
+    results = ssm_analyze(
+        data,
+        scales=list(range(1, 9)),
+        grouping="Gender",
+        contrast=True,
+        boots=10,
+        seed=123,
+    )
+
+    fig = results.plot_contrast()
+
+    assert fig is not None
+    assert hasattr(fig, "savefig")
+    plt.close(fig)
+
+
+@pytest.mark.unit
+def test_plot_contrast_requires_contrast() -> None:
+    """Test that plot_contrast() raises error without contrast results."""
+    data = load_dataset("jz2017")
+    results = ssm_analyze(
+        data, scales=list(range(1, 9)), grouping="Gender", boots=10, seed=123
+    )
+
+    with pytest.raises(ValueError, match="requires contrast results"):
+        results.plot_contrast()
+
+
+@pytest.mark.unit
+def test_plot_multiple_profiles() -> None:
+    """Test plotting with multiple profiles."""
+    data = load_dataset("jz2017")
+    results = ssm_analyze(
+        data,
+        scales=list(range(1, 9)),
+        measures=["ASPD", "NARPD"],
+        boots=10,
+        seed=123,
+    )
+
+    # All three plot types should work
+    fig1 = results.plot_circle()
+    fig2 = results.plot_curve()
+
+    assert fig1 is not None
+    assert fig2 is not None
+
+    plt.close(fig1)
+    plt.close(fig2)
diff --git a/tests/test_tidying_functions.py b/tests/test_tidying_functions.py
new file mode 100644
index 0000000..5ff8b9f
--- /dev/null
+++ b/tests/test_tidying_functions.py
@@ -0,0 +1,296 @@
+"""Tests for data tidying and instrument scoring utility functions."""
+
+from unittest.mock import Mock
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from circumplex.instruments import Instrument
+from circumplex.utils.tidying_functions import ipsatize, norm_standardize, score
+
+
+class TestIpsatize:
+    """Tests for the ipsatize function."""
+
+    def test_ipsatize_basic_with_string_items(self):
+        """Test basic ipsatization with string column names."""
+        data = pd.DataFrame(
+            {
+                "item1": [1, 2, 3],
+                "item2": [2, 3, 4],
+                "item3": [3, 4, 5],
+            }
+        )
+        result = ipsatize(data, ["item1", "item2", "item3"], append=False)
+
+        # Expected: each row centered to mean of 0
+        expected = pd.DataFrame(
+            {
+                "item1_i": [-1.0, -1.0, -1.0],
+                "item2_i": [0.0, 0.0, 0.0],
+                "item3_i": [1.0, 1.0, 1.0],
+            }
+        )
+        pd.testing.assert_frame_equal(result, expected)
+
+    def test_ipsatize_with_integer_indices(self):
+        """Test ipsatization with integer column indices."""
+        data = pd.DataFrame(
+            {
+                "item1": [1, 2, 3],
+                "item2": [2, 3, 4],
+                "item3": [3, 4, 5],
+            }
+        )
+        result = ipsatize(data, [0, 1, 2], append=False)
+
+        expected = pd.DataFrame(
+            {
+                "0_i": [-1.0, -1.0, -1.0],
+                "1_i": [0.0, 0.0, 0.0],
+                "2_i": [1.0, 1.0, 1.0],
+            }
+        )
+        pd.testing.assert_frame_equal(result, expected)
+
+    def test_ipsatize_with_prefix_suffix(self):
+        """Test ipsatization with custom prefix and suffix."""
+        data = pd.DataFrame(
+            {
+                "item1": [1, 2],
+                "item2": [2, 3],
+            }
+        )
+        result = ipsatize(
+            data, ["item1", "item2"], prefix="pre_", suffix="_post", append=False
+        )
+
+        assert "pre_item1_post" in result.columns
+        assert "pre_item2_post" in result.columns
+
+    def test_ipsatize_append_true(self):
+        """Test that append=True keeps original columns."""
+        data = pd.DataFrame(
+            {
+                "id": [1, 2],
+                "item1": [1, 2],
+                "item2": [2, 3],
+            }
+        )
+        result = ipsatize(data, ["item1", "item2"], append=True)
+
+        assert "id" in result.columns
+        assert "item1" in result.columns
+        assert "item2" in result.columns
+        assert "item1_i" in result.columns
+        assert "item2_i" in result.columns
+
+    def test_ipsatize_append_false(self):
+        """Test that append=False returns only ipsatized columns."""
+        data = pd.DataFrame(
+            {
+                "id": [1, 2],
+                "item1": [1, 2],
+                "item2": [2, 3],
+            }
+        )
+        result = ipsatize(data, ["item1", "item2"], append=False)
+
+        assert "id" not in result.columns
+        assert "item1" not in result.columns
+        assert "item2" not in result.columns
+        assert "item1_i" in result.columns
+        assert "item2_i" in result.columns
+
+    def test_ipsatize_with_na_values_na_rm_true(self):
+        """Test ipsatization with NA values when na_rm=True."""
+        data = pd.DataFrame(
+            {
+                "item1": [1, 2, np.nan],
+                "item2": [2, 3, 4],
+                "item3": [3, np.nan, 5],
+            }
+        )
+        result = ipsatize(data, ["item1", "item2", "item3"], na_rm=True, append=False)
+
+        # First row: mean of [1, 2, 3] = 2
+        assert np.isclose(result.iloc[0, 0], -1.0)
+        # Second row: mean of [2, 3] = 2.5 (ignoring NaN)
+        assert np.isclose(result.iloc[1, 0], -0.5)
+
+    def test_ipsatize_with_na_values_na_rm_false(self):
+        """Test ipsatization with NA values when na_rm=False."""
+        data = pd.DataFrame(
+            {
+                "item1": [1, 2, np.nan],
+                "item2": [2, 3, 4],
+            }
+        )
+        result = ipsatize(data, ["item1", "item2"], na_rm=False, append=False)
+
+        # Third row should have NaN mean
+        assert pd.isna(result.iloc[2, 0])
+
+    def test_ipsatize_invalid_data_type(self):
+        """Test that non-DataFrame input raises TypeError."""
+        with pytest.raises(TypeError, match="Input 'data' must be a pandas DataFrame"):
+            ipsatize([1, 2, 3], ["item1"])  # type: ignore[invalid-argument-type]
+
+    def test_ipsatize_invalid_items_type(self):
+        """Test that non-iterable items raises TypeError."""
+        data = pd.DataFrame({"item1": [1, 2]})
+        with pytest.raises(TypeError, match="Input 'items' must be a sequence"):
+            ipsatize(data, "item1")
+
+    def test_ipsatize_invalid_column_names(self):
+        """Test that invalid column names raise ValueError."""
+        data = pd.DataFrame({"item1": [1, 2]})
+        with pytest.raises(
+            ValueError, match="All items in 'items' must be valid column names"
+        ):
+            ipsatize(data, ["item1", "invalid"])
+
+    def test_ipsatize_invalid_column_indices(self):
+        """Test that invalid column indices raise ValueError."""
+        data = pd.DataFrame({"item1": [1, 2]})
+        with pytest.raises(
+            ValueError, match="All items in 'items' must be valid indices"
+        ):
+            ipsatize(data, [0, 5])
+
+    def test_ipsatize_mixed_item_types(self):
+        """Test that mixed string/int items raises TypeError."""
+        data = pd.DataFrame({"item1": [1, 2], "item2": [2, 3]})
+        with pytest.raises(
+            TypeError, match="All items in 'items' must be either strings or integers"
+        ):
+            ipsatize(data, ["item1", 1])
+
+
+class TestScore:
+    """Tests for the score function."""
+
+    def test_score_invalid_data_type(self):
+        """Test that non-DataFrame input raises TypeError."""
+        with pytest.raises(TypeError, match="Input 'data' must be a pandas DataFrame"):
+            score([1, 2, 3], ["item1"], "csig")  # type: ignore[invalid-argument-type]
+
+    def test_score_invalid_items_type(self):
+        """Test that non-iterable items raises TypeError."""
+        data = pd.DataFrame({"item1": [1, 2]})
+        with pytest.raises(TypeError, match="Input 'items' must be a sequence"):
+            score(data, "item1", "csig")
+
+    def test_score_invalid_instrument_type(self):
+        """Test that invalid instrument type raises TypeError."""
+        data = pd.DataFrame({"item1": [1, 2]})
+        with pytest.raises(
+            TypeError, match="Input 'instrument' must be an Instrument instance"
+        ):
+            score(data, ["item1"], 123)  # type: ignore[invalid-argument-type]
+
+    def test_score_delegates_to_instrument(self):
+        """Test that score delegates to instrument.score method."""
+        data = pd.DataFrame({"item1": [1, 2], "item2": [2, 3]})
+        mock_instrument = Mock(spec=Instrument)
+        mock_instrument.score.return_value = pd.DataFrame({"scale1": [1.5, 2.5]})
+
+        result = score(
+            data,
+            ["item1", "item2"],
+            mock_instrument,
+            prefix="pre_",
+            suffix="_post",
+            na_rm=False,
+            append=False,
+        )
+
+        mock_instrument.score.assert_called_once_with(
+            data,
+            ["item1", "item2"],
+            prefix="pre_",
+            suffix="_post",
+            na_rm=False,
+            append=False,
+        )
+        assert "scale1" in result.columns
+
+
+class TestNormStandardize:
+    """Tests for the norm_standardize function."""
+
+    def test_norm_standardize_invalid_data_type(self):
+        """Test that non-DataFrame input raises TypeError."""
+        with pytest.raises(TypeError, match="Input 'data' must be a pandas DataFrame"):
+            norm_standardize([1, 2, 3], ["scale1"], "csig", 1)  # type: ignore[invalid-argument-type]
+
+    def test_norm_standardize_invalid_scales_type(self):
+        """Test that non-iterable scales raises TypeError."""
+        data = pd.DataFrame({"scale1": [1, 2]})
+        with pytest.raises(TypeError, match="Input 'scales' must be a sequence"):
+            norm_standardize(
+                data,
+                "csig",
+                1,
+                scales="scale1",
+            )
+
+    def test_norm_standardize_invalid_instrument_type(self):
+        """Test that invalid instrument type raises TypeError."""
+        data = pd.DataFrame({"scale1": [1, 2]})
+        with pytest.raises(
+            TypeError, match="Input 'instrument' must be an Instrument instance"
+        ):
+            norm_standardize(data, 123, 1, scales=["scale1"])  # type: ignore[invalid-argument-type]
+
+    def test_norm_standardize_delegates_to_instrument(self):
+        """Test norm_standardize delegates to instrument.norm_standardize method."""
+        data = pd.DataFrame({"scale1": [1, 2], "scale2": [2, 3]})
+        mock_instrument = Mock(spec=Instrument)
+        mock_instrument.norm_standardize.return_value = pd.DataFrame(
+            {
+                "scale1_z": [-1.0, 1.0],
+                "scale2_z": [-1.0, 1.0],
+            }
+        )
+
+        result = norm_standardize(
+            data,
+            mock_instrument,
+            1,
+            scales=["scale1", "scale2"],
+            prefix="pre_",
+            suffix="_post",
+            append=False,
+        )
+
+        mock_instrument.norm_standardize.assert_called_once_with(
+            data,
+            1,
+            scales=["scale1", "scale2"],
+            prefix="pre_",
+            suffix="_post",
+            append=False,
+        )
+        assert "scale1_z" in result.columns
+
+    def test_norm_standardize_converts_sample_id_to_int(self):
+        """Test that sample_id is converted to int."""
+        data = pd.DataFrame({"scale1": [1, 2]})
+        mock_instrument = Mock(spec=Instrument)
+        mock_instrument.norm_standardize.return_value = pd.DataFrame(
+            {"scale1_z": [-1.0, 1.0]}
+        )
+
+        norm_standardize(
+            data,
+            mock_instrument,
+            "1",  # type: ignore[invalid-argument-type]
+            scales=["scale1"],
+        )
+
+        # Check that int(sample_id) was passed
+        call_args = mock_instrument.norm_standardize.call_args
+        assert call_args[0][1] == 1
+        assert isinstance(call_args[0][1], int)
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..2581e4c
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,3252 @@
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+    "python_full_version >= '3.12'",
+    "python_full_version < '3.12'",
+]
+
+[[package]]
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