From 7b7159a39eeed2816d976a865fa3c31aa7dd78c1 Mon Sep 17 00:00:00 2001
From: James Montgomery <J.Montgomery@jpl.nasa.gov>
Date: Mon, 18 Nov 2024 10:16:17 -0800
Subject: [PATCH 1/2] Reworked the NEON example, cleaning it up and bringing it
 in line with recent isofit updates

---
 .gitignore                                    |    7 +-
 {isotuts/NEON => NEON}/data_prep.ipynb        |   27 +-
 NEON/neon.ipynb                               | 1297 +++++++++++++++++
 .../NEON => NEON}/neon_single_pixel.ipynb     |   28 +-
 isotuts/utils.py => NEON/utils/faker.py       |    8 +-
 NEON/utils/neon.py                            |   21 +
 isotuts/NEON/neon.ipynb                       |  573 --------
 isotuts/__init__.py                           |    0
 8 files changed, 1338 insertions(+), 623 deletions(-)
 rename {isotuts/NEON => NEON}/data_prep.ipynb (98%)
 create mode 100644 NEON/neon.ipynb
 rename {isotuts/NEON => NEON}/neon_single_pixel.ipynb (98%)
 rename isotuts/utils.py => NEON/utils/faker.py (97%)
 create mode 100644 NEON/utils/neon.py
 delete mode 100644 isotuts/NEON/neon.ipynb
 delete mode 100644 isotuts/__init__.py

diff --git a/.gitignore b/.gitignore
index 7439ee5..4ff75e4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,11 +1,14 @@
 # Local dev directory
+local/
 .idea/
 *.egg*/
 *__pycache__/
 
 # ISOFIT output directories
-isotuts/NEON/*
-!isotuts/NEON/*.ipynb
+NEON/output
+
+# Data directories
+NEON/data
 
 # Jupyter server files
 .ipynb*/
diff --git a/isotuts/NEON/data_prep.ipynb b/NEON/data_prep.ipynb
similarity index 98%
rename from isotuts/NEON/data_prep.ipynb
rename to NEON/data_prep.ipynb
index 36fae36..391d306 100644
--- a/isotuts/NEON/data_prep.ipynb
+++ b/NEON/data_prep.ipynb
@@ -86,26 +86,7 @@
    "outputs": [],
    "source": [
     "\n",
-    "\n",
-    "# Extract the image locations of each point of interest (POI)\n",
-    "# These are defined in the NEON report as pixel locations, so we round here to convert to indices\n",
-    "report = {}\n",
-    "report['173647'] = {           # Upp L Y  | Low R Y  | Upp L X | Low R X\n",
-    "    'WhiteTarp': np.round([2224.9626, 2230.9771, 316.0078, 324.9385,]).astype(int),\n",
-    "    'BlackTarp': np.round([2224.9626, 2231.0032, 328.0086, 333.9731,]).astype(int),\n",
-    "    'Veg'      : np.round([2245.0381, 2258.8103, 343.9006, 346.9423,]).astype(int),\n",
-    "    'RoadEW'   : np.round([2214.9905, 2216.9978, 348.9902, 373.0080,]).astype(int),\n",
-    "    'RoadNS'   : np.round([2205.9580, 2225.9612, 357.9536, 359.9608,]).astype(int)\n",
-    "}\n",
-    "report['174150'] = {           # Upp L Y | Low R Y | Upp L X  | Low R X\n",
-    "    'WhiteTarp': np.round([653.9626, 659.9771, 3143.0078, 3151.9385]).astype(int),\n",
-    "    'BlackTarp': np.round([653.9626, 660.0032, 3155.0086, 3160.9731]).astype(int),\n",
-    "    'Veg'      : np.round([674.0381, 687.8103, 3170.9006, 3173.9423]).astype(int),\n",
-    "    'RoadEW'   : np.round([643.9905, 645.9978, 3175.9902, 3200.0080]).astype(int),\n",
-    "    'RoadNS'   : np.round([634.9580, 654.9612, 3184.9536, 3186.9608]).astype(int)\n",
-    "}\n",
-    "# Converts numpy array to comma-separated string for ISOFIT\n",
-    "toString = lambda array: ', '.join(str(v) for v in array)"
+    "\n"
    ]
   },
   {
@@ -176,7 +157,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "isofit_env",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -190,9 +171,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/NEON/neon.ipynb b/NEON/neon.ipynb
new file mode 100644
index 0000000..4e34c8c
--- /dev/null
+++ b/NEON/neon.ipynb
@@ -0,0 +1,1297 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e30eef79",
+   "metadata": {},
+   "source": [
+    "# NEON\n",
+    "\n",
+    "This notebook is an excercise in executing ISOFIT on two dates from the NEON dataset and interpreting the outputs of ISOFIT.\n",
+    "\n",
+    "Prerequisites:\n",
+    "- Have ISOFIT installed and sRTMnet configured.\n",
+    "- Download the [sample data](https://avng.jpl.nasa.gov/pub/PBrodrick/isofit/tutorials/subset_data.zip) and place the unzipped `data` directory into the same directory of this notebook.\n",
+    "\n",
+    "Note: If you downloaded the [ISOFIT extra data](https://isofit.readthedocs.io/en/latest/custom/data.html) via `isofit download`, both sRTMnet and the NEON data files will be installed correctly and available with default settings for this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "44e2871f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Jupyter magics\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e9b8d164-38f5-456b-b6d2-94bed6d8e75c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Builtin\n",
+    "import os\n",
+    "import shutil\n",
+    "from types import SimpleNamespace\n",
+    "\n",
+    "# External\n",
+    "import matplotlib.pyplot  as plt\n",
+    "import matplotlib.patches as patches\n",
+    "import numpy as np\n",
+    "from spectral.io import envi\n",
+    "\n",
+    "# Internal\n",
+    "import isofit\n",
+    "from isofit.data import env\n",
+    "from isofit.utils.apply_oe import apply_oe \n",
+    "from isofit.utils.surface_model import surface_model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "14ccc3b5-019a-4c4c-b590-afa1b4760607",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using environment paths:\n",
+      "- data = /Users/jamesmo/projects/isofit/dev/local/data\n",
+      "- examples = /Users/jamesmo/projects/isofit/tutorials\n",
+      "- imagecube = /Users/jamesmo/projects/isofit/dev/local/imagecube\n",
+      "- srtmnet = /Users/jamesmo/projects/isofit/dev/local/srtmnet\n",
+      "- sixs = /Users/jamesmo/projects/isofit/dev/local/sixs\n",
+      "- modtran = /Users/jamesmo/projects/isofit/dev/local/modtran\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Below are the default values for the ISOFIT environment. Change these if your environment differs\n",
+    "\n",
+    "env.load('~/.isofit/isofit.ini') # Ini file to load\n",
+    "env.changeSection('DEFAULT')     # Section of the ini to use\n",
+    "# env.changeBase('~./isofit')      # Base path for ISOFIT extras (data, examples, etc)\n",
+    "# env.changePath('srtmnet', '/path/to/sRTMnet_v120.h5') # Overwrite the path to sRTMnet - copy this line for other products such as sixs if in non-default locations\n",
+    "\n",
+    "print('Using environment paths:')\n",
+    "for key, path in env.items():\n",
+    "    print(f\"- {key} = {path}\") \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "893fd5ac",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "ISOFIT needs at minimum three pieces as input:\n",
+    "\n",
+    "    1. Radiance measurements (rdn)\n",
+    "    2. Observation values    (obs)\n",
+    "    3. Location information  (loc)\n",
+    "\n",
+    "This sample dataset from NEON has radiance and observation data, but no location values (more recent NEON datasets include the location file).  However, we can 'fake' the location file with sufficient accuracy for ISOFIT to run successfully.  Note that there are data available for two dates:\n",
+    "\n",
+    "```\n",
+    "Radiance\n",
+    "├── 173647\n",
+    "│   ├── NIS01_20210403_173647_obs_ort\n",
+    "│   ├── NIS01_20210403_173647_obs_ort.hdr\n",
+    "│   ├── NIS01_20210403_173647_rdn_ort\n",
+    "│   └── NIS01_20210403_173647_rdn_ort.hdr\n",
+    "└── 174150\n",
+    "    ├── NIS01_20210403_174150_obs_ort\n",
+    "    ├── NIS01_20210403_174150_obs_ort.hdr\n",
+    "    ├── NIS01_20210403_174150_rdn_ort\n",
+    "    └── NIS01_20210403_174150_rdn_ort.hdr\n",
+    "```\n",
+    "\n",
+    "These files have corresponding in situ data as well, and below we've encoded the locations of each, which we can use to help subset data files.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e3f01d1a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from utils.neon import report\n",
+    "\n",
+    "# Which NEON date to process - change this to process a different date\n",
+    "neon_id  = list(report.keys())[0]\n",
+    "neon_str = f\"NIS01_20210403_{neon_id}\"\n",
+    "\n",
+    "# Select the locations from the neon id -- roi == Regions of Interest\n",
+    "roi = report[neon_id]\n",
+    "\n",
+    "from types import SimpleNamespace\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Set the paths for this tutorial\n",
+    "base = Path(env.path('examples', 'NEON'))\n",
+    "data = base / 'data'\n",
+    "\n",
+    "paths = SimpleNamespace(\n",
+    "    rdn = str(data / f'{neon_str}_rdn_ort'),\n",
+    "    loc = str(data / f'{neon_str}_loc_ort'),\n",
+    "    obs = str(data / f'{neon_str}_obs_ort'),\n",
+    "    insitu = data / 'FieldSpectrometer',\n",
+    "    output = base / 'output',\n",
+    "    working = base / f'output/NIS01_20210403_{neon_id}',\n",
+    "    surface = str(base / 'output/surface.mat'),\n",
+    "    surface_config = env.path('examples', '20171108_Pasadena', 'configs', 'ang20171108t184227_surface.json')\n",
+    ")\n",
+    "\n",
+    "paths.output.mkdir(exist_ok=True, parents=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "98252646",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# If you are missing either an OBS file or a LOC file, use these to create faked versions based off the radiance file\n",
+    "# This should not be needed if using the provided data\n",
+    "\n",
+    "# from utils import faker\n",
+    "\n",
+    "# paths.obs = faker.fakeOBS(paths.rdn)\n",
+    "# paths.loc = faker.fakeLOC(\n",
+    "#     rdn = paths.rdn,\n",
+    "#     lon = -105.237000,\n",
+    "#     lat = 40.125000,\n",
+    "#     elv = 1689.0\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a2cde13",
+   "metadata": {},
+   "source": [
+    "# Apply OE\n",
+    "\n",
+    "The next part walks through running the ISOFIT utility script `isofit/utils/apply_oe.py`. This is the first step of executing ISOFIT and will generate a default configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7357a326",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n",
+      "1 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n",
+      "2 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n",
+      "3 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n",
+      "4 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n",
+      "5 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n",
+      "6 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n",
+      "7 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First build a surface model\n",
+    "surface_model(\n",
+    "    config_path = paths.surface_config,\n",
+    "    output_path = paths.surface,\n",
+    "    wavelength_path = f\"{paths.rdn}.hdr\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "30a35242-c068-49a0-9aec-b860d54870f0",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function apply_oe in module isofit.utils.apply_oe:\n",
+      "\n",
+      "apply_oe(input_radiance, input_loc, input_obs, working_directory, sensor, surface_path, copy_input_files=False, modtran_path=None, wavelength_path=None, surface_category='multicomponent_surface', aerosol_climatology_path=None, rdn_factors_path=None, atmosphere_type='ATM_MIDLAT_SUMMER', channelized_uncertainty_path=None, model_discrepancy_path=None, lut_config_file=None, multiple_restarts=False, logging_level='INFO', log_file=None, n_cores=1, presolve=False, empirical_line=False, analytical_line=False, ray_temp_dir='/tmp/ray', emulator_base=None, segmentation_size=40, num_neighbors=[], atm_sigma=[2], pressure_elevation=False, prebuilt_lut=None, no_min_lut_spacing=False, inversion_windows=None)\n",
+      "    Applies OE over a flightline using a radiative transfer engine. This executes\n",
+      "    ISOFIT in a generalized way, accounting for the types of variation that might be\n",
+      "    considered typical.\n",
+      "    \n",
+      "    Observation (obs) and location (loc) files are used to determine appropriate\n",
+      "    geometry lookup tables and provide a heuristic means of determining atmospheric\n",
+      "    water ranges.\n",
+      "    \n",
+      "   \n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    input_radiance : str\n",
+      "        Radiance data cube. Expected to be ENVI format\n",
+      "    input_loc : str\n",
+      "        Location data cube of shape (Lon, Lat, Elevation). Expected to be ENVI format\n",
+      "    input_obs : str\n",
+      "        Observation data cube of shape:\n",
+      "            (path length, to-sensor azimuth, to-sensor zenith,\n",
+      "            to-sun azimuth, to-sun zenith, phase,\n",
+      "            slope, aspect, cosine i, UTC time)\n",
+      "        Expected to be ENVI format\n",
+      "    working_directory : str\n",
+      "        Directory to stage multiple outputs, will contain subdirectories\n",
+      "    sensor : str\n",
+      "        The sensor used for acquisition, will be used to set noise and datetime\n",
+      "        settings\n",
+      "    surface_path : str\n",
+      "        Path to surface model or json dict of surface model configuration\n",
+      "    copy_input_files : bool, default=False\n",
+      "        Flag to choose to copy input_radiance, input_loc, and input_obs locally into\n",
+      "        the working_directory\n",
+      "    modtran_path : str, default=None\n",
+      "        Location of MODTRAN utility. Alternately set with `MODTRAN_DIR` environment\n",
+      "        variable\n",
+      "    wavelength_path : str, default=None\n",
+      "        Location to get wavelength information from, if not specified the radiance\n",
+      "        header will be used\n",
+      "    surface_category : str, default=\"multicomponent_surface\"\n",
+      "        The type of ISOFIT surface priors to use.  Default is multicomponent_surface\n",
+      "    aerosol_climatology_path : str, default=None\n",
+      "        Specific aerosol climatology information to use in MODTRAN\n",
+      "    rdn_factors_path : str, default=None\n",
+      "        Specify a radiometric correction factor, if desired\n",
+      "    atmosphere_type : str, default=\"ATM_MIDLAT_SUMMER\"\n",
+      "        TODO\n",
+      "    channelized_uncertainty_path : str, default=None\n",
+      "        Path to a channelized uncertainty file\n",
+      "    model_discrepancy_path : str, default=None\n",
+      "        TODO\n",
+      "    lut_config_file : str, default=None\n",
+      "        Path to a look up table configuration file, which will override defaults\n",
+      "        choices\n",
+      "    multiple_restarts : bool, default=False\n",
+      "        TODO\n",
+      "    logging_level : str, default=\"INFO\"\n",
+      "        Logging level with which to run ISOFIT\n",
+      "    log_file : str, default=None\n",
+      "        File path to write ISOFIT logs to\n",
+      "    n_cores : int, default=1\n",
+      "        Number of cores to run ISOFIT with. Substantial parallelism is available, and\n",
+      "        full runs will be very slow in serial. Suggested to max this out on the\n",
+      "        available system\n",
+      "    presolve : int, default=False\n",
+      "        Flag to use a presolve mode to estimate the available atmospheric water range.\n",
+      "        Runs a preliminary inversion over the image with a 1-D LUT of water vapor, and\n",
+      "        uses the resulting range (slightly expanded) to bound determine the full LUT.\n",
+      "        Advisable to only use with small cubes or in concert with the empirical_line\n",
+      "        setting, or a significant speed penalty will be incurred\n",
+      "    empirical_line : bool, default=False\n",
+      "        Use an empirical line interpolation to run full inversions over only a subset\n",
+      "        of pixels, determined using a SLIC superpixel segmentation, and use a KDTREE of\n",
+      "        local solutions to interpolate radiance->reflectance. Generally a good option\n",
+      "        if not trying to analyze the atmospheric state at fine scale resolution.\n",
+      "        Mutually exclusive with analytical_line\n",
+      "    analytical_line : bool, default=False\n",
+      "        TODO\n",
+      "        Mutually exclusive with empirical_line\n",
+      "    ray_temp_dir : str, default=\"/tmp/ray\"\n",
+      "        Location of temporary directory for ray parallelization engine\n",
+      "    emulator_base : str, default=None\n",
+      "        Location of emulator base path. Point this at the model folder (or h5 file) of\n",
+      "        sRTMnet to use the emulator instead of MODTRAN. An additional file with the\n",
+      "        same basename and the extention _aux.npz must accompany\n",
+      "        e.g. /path/to/emulator.h5 /path/to/emulator_aux.npz\n",
+      "    segmentation_size : int, default=40\n",
+      "        If empirical_line is enabled, sets the size of segments to construct\n",
+      "    num_neighbors : list[int], default=[]\n",
+      "        Forced number of neighbors for empirical line extrapolation - overides default\n",
+      "        set from segmentation_size parameter\n",
+      "    atm_sigma : list[int], default=[2]\n",
+      "        TODO\n",
+      "    pressure_elevation : bool, default=False\n",
+      "        Flag to retrieve elevation\n",
+      "    prebuilt_lut : str, default=None\n",
+      "        Use this pre-constructed look up table for all retrievals. Must be an\n",
+      "        ISOFIT-compatible RTE NetCDF\n",
+      "    no_min_lut_spacing : bool, default=False\n",
+      "        TODO\n",
+      "    inversion_windows : list[float], default=None\n",
+      "        TODO\n",
+      "        Must be in 2-item tuples\n",
+      "    \n",
+      "   \n",
+      "    References\n",
+      "    ----------\n",
+      "    D.R. Thompson, A. Braverman,P.G. Brodrick, A. Candela, N. Carbon, R.N. Clark,D. Connelly, R.O. Green, R.F.\n",
+      "    Kokaly, L. Li, N. Mahowald, R.L. Miller, G.S. Okin, T.H.Painter, G.A. Swayze, M. Turmon, J. Susilouto, and\n",
+      "    D.S. Wettergreen. Quantifying Uncertainty for Remote Spectroscopy of Surface Composition. Remote Sensing of\n",
+      "    Environment, 2020. doi: https://doi.org/10.1016/j.rse.2020.111898.\n",
+      "    \n",
+      "   \n",
+      "    sRTMnet emulator:\n",
+      "    P.G. Brodrick, D.R. Thompson, J.E. Fahlen, M.L. Eastwood, C.M. Sarture, S.R. Lundeen, W. Olson-Duvall,\n",
+      "    N. Carmon, and R.O. Green. Generalized radiative transfer emulation for imaging spectroscopy reflectance\n",
+      "    retrievals. Remote Sensing of Environment, 261:112476, 2021.doi: 10.1016/j.rse.2021.112476.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# For reference, all of the available parameters to the apply_oe script\n",
+    "help(apply_oe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "81330d85-2453-4065-bfa7-f6a09374709a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-11-14 16:31:45,669\tINFO worker.py:1529 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Checking input data files...\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | ...Data file checks complete\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Setting up files and directories....\n",
+      "INFO:2024-11-14,16:31:46 || template_construction.py:__init__() | Flightline ID: NIS01_20210403_173647\n",
+      "INFO:2024-11-14,16:31:46 || template_construction.py:__init__() | no noise path found, proceeding without\n",
+      "INFO:2024-11-14,16:31:46 || template_construction.py:stage_files() | Staging /Users/jamesmo/projects/isofit/tutorials/NEON/output/surface.mat to /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/data/surface.mat\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | ...file/directory setup complete\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Using inversion windows: [[350.0, 1360.0], [1410, 1800.0], [1970.0, 2500.0]]\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | No wavelength file provided. Obtaining wavelength grid from ENVI header of radiance cube.\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Wavelength units of nm inferred...converting to microns\n",
+      "WARNING:2024-11-14,16:31:46 || template_construction.py:check_surface_model() | Center wavelengths provided in surface model file do not match wavelengths in radiance cube. Please consider rebuilding your surface model for optimal performance.\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Observation means:\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Path (km): 0.0\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | To-sensor azimuth (deg): 0.0\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | To-sensor zenith (deg): 0.0\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | To-sun azimuth (deg): 0.0\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | To-sun zenith (deg): 0.0\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Relative to-sun azimuth (deg): 0.0\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Altitude (km): 1.689\n",
+      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Segmenting...\n",
+      "2024-11-14 16:31:46,570\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
+      "\u001b[2m\u001b[36m(segment_chunk pid=42158)\u001b[0m INFO:2024-11-14,16:31:46 ||| 0: starting\n",
+      "INFO:2024-11-14,16:31:47 || apply_oe.py:apply_oe() | Extracting /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/input/NIS01_20210403_173647_subs_rdn\n",
+      "2024-11-14 16:31:47,132\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
+      "\u001b[2m\u001b[36m(segment_chunk pid=42158)\u001b[0m INFO:2024-11-14,16:31:47 ||| 0: completing\n",
+      "\u001b[2m\u001b[36m(extract_chunk pid=42158)\u001b[0m INFO:2024-11-14,16:31:47 ||| 0: starting\n",
+      "INFO:2024-11-14,16:31:47 || apply_oe.py:apply_oe() | Extracting /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/input/NIS01_20210403_173647_subs_obs\n",
+      "2024-11-14 16:31:47,196\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
+      "INFO:2024-11-14,16:31:47 || apply_oe.py:apply_oe() | Extracting /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/input/NIS01_20210403_173647_subs_loc\n",
+      "2024-11-14 16:31:47,253\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
+      "\u001b[2m\u001b[36m(extract_chunk pid=42158)\u001b[0m INFO:2024-11-14,16:31:47 ||| 0: starting\n",
+      "INFO:2024-11-14,16:31:47 || apply_oe.py:apply_oe() | Pre-solve H2O grid: [0.01 0.67 1.34 2.   2.67 3.33 4.   4.66 5.33 5.99]\n",
+      "INFO:2024-11-14,16:31:47 || apply_oe.py:apply_oe() | Writing H2O pre-solve configuration file.\n",
+      "INFO:2024-11-14,16:31:47 || apply_oe.py:apply_oe() | Run ISOFIT initial guess\n",
+      "WARNING:2024-11-14,16:31:47 || __init__.py:checkNumThreads() | \n",
+      "******************************************************************************************\n",
+      "! Number of threads is greater than 1, this may greatly impact performance\n",
+      "! Please set this the environment variables 'MKL_NUM_THREADS' and 'OMP_NUM_THREADS' to '1'\n",
+      "******************************************************************************************\n",
+      "INFO:2024-11-14,16:31:47 || configs.py:create_new_config() | Loading config file: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/config/NIS01_20210403_173647_h2o.json\n",
+      "INFO:2024-11-14,16:31:47 || configs.py:get_config_errors() | Checking config sections for configuration issues\n",
+      "INFO:2024-11-14,16:31:47 || configs.py:get_config_errors() | Configuration file checks complete, no errors found.\n",
+      "2024-11-14 16:31:47,316\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
+      "INFO:2024-11-14,16:31:47 || isofit.py:run() | Building first forward model, will generate any necessary LUTs\n",
+      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:__init__() | Loading from wavelength_file: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/data/wavelengths.txt\n",
+      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
+      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
+      "\u001b[2m\u001b[36m(extract_chunk pid=42158)\u001b[0m INFO:2024-11-14,16:31:47 ||| 0: starting\n",
+      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
+      "INFO:2024-11-14,16:31:47 || sRTMnet.py:preSim() | Creating a simulator configuration\n",
+      "INFO:2024-11-14,16:31:47 || sRTMnet.py:preSim() | Building simulator and executing (6S)\n",
+      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
+      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
+      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
+      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:runSimulations() | Executing parallel simulations\n",
+      "\u001b[2m\u001b[36m(streamSimulation pid=42158)\u001b[0m INFO:2024-11-14,16:31:47 ||| Note: NumExpr detected 10 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+      "\u001b[2m\u001b[36m(streamSimulation pid=42158)\u001b[0m INFO:2024-11-14,16:31:47 ||| Loaded ini from: /Users/jamesmo/.isofit/isofit.ini\n",
+      "INFO:2024-11-14,16:31:48 || common.py:__call__() |  20.00% simulations complete (elapsed: 0:00:01.156084, rate: 0:00:00.115608, eta: 0:00:10.404756)\n",
+      "INFO:2024-11-14,16:31:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:48 || common.py:__call__() |  30.00% simulations complete (elapsed: 0:00:01.233141, rate: 0:00:00.123314, eta: 0:00:04.932564)\n",
+      "INFO:2024-11-14,16:31:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:48 || common.py:__call__() |  40.00% simulations complete (elapsed: 0:00:01.290525, rate: 0:00:00.129052, eta: 0:00:03.011225)\n",
+      "INFO:2024-11-14,16:31:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:48 || common.py:__call__() |  50.00% simulations complete (elapsed: 0:00:01.345782, rate: 0:00:00.134578, eta: 0:00:02.018673)\n",
+      "INFO:2024-11-14,16:31:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:48 || common.py:__call__() |  60.00% simulations complete (elapsed: 0:00:01.399168, rate: 0:00:00.139917, eta: 0:00:01.399168)\n",
+      "INFO:2024-11-14,16:31:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:48 || common.py:__call__() |  70.00% simulations complete (elapsed: 0:00:01.451998, rate: 0:00:00.145200, eta: 0:00:00.967999)\n",
+      "INFO:2024-11-14,16:31:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:48 || common.py:__call__() |  80.00% simulations complete (elapsed: 0:00:01.504772, rate: 0:00:00.150477, eta: 0:00:00.644902)\n",
+      "INFO:2024-11-14,16:31:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:49 || common.py:__call__() |  90.00% simulations complete (elapsed: 0:00:01.558484, rate: 0:00:00.155848, eta: 0:00:00.389621)\n",
+      "INFO:2024-11-14,16:31:49 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:49 || common.py:__call__() | 100.00% simulations complete (elapsed: 0:00:01.612101, rate: 0:00:00.161210, eta: 0:00:00.179122)\n",
+      "INFO:2024-11-14,16:31:49 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:49 || radiative_transfer_engine.py:runSimulations() | Running any post-sim functions\n",
+      "INFO:2024-11-14,16:31:49 || radiative_transfer_engine.py:runSimulations() | Saving post-sim data to index zero of all dimensions except wl\n",
+      "INFO:2024-11-14,16:31:49 || luts.py:load() | Loading LUT into memory\n",
+      "WARNING:2024-11-14,16:31:49 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
+      "WARNING:2024-11-14,16:31:49 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
+      "INFO:2024-11-14,16:31:49 || sRTMnet.py:preSim() | Interpolating simulator quantities to emulator size\n",
+      "INFO:2024-11-14,16:31:49 || sRTMnet.py:preSim() | Loading and predicting with emulator\n",
+      "INFO:2024-11-14,16:31:51 || sRTMnet.py:preSim() | Saving intermediary prediction results to: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/lut_h2o/sRTMnet.predicts.nc\n",
+      "INFO:2024-11-14,16:31:51 || radiative_transfer_engine.py:runSimulations() | Saving pre-sim data to index zero of all dimensions except wl\n",
+      "INFO:2024-11-14,16:31:51 || radiative_transfer_engine.py:runSimulations() | Executing parallel simulations\n",
+      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  20.00% simulations complete (elapsed: 0:00:00.280150, rate: 0:00:00.028015, eta: 0:00:02.521350)\n",
+      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "\u001b[2m\u001b[36m(streamSimulation pid=42158)\u001b[0m INFO:2024-11-14,16:31:51 ||| Loading LUT into memory\n",
+      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  30.00% simulations complete (elapsed: 0:00:00.338177, rate: 0:00:00.033818, eta: 0:00:01.352708)\n",
+      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  40.00% simulations complete (elapsed: 0:00:00.388947, rate: 0:00:00.038895, eta: 0:00:00.907543)\n",
+      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  50.00% simulations complete (elapsed: 0:00:00.438956, rate: 0:00:00.043896, eta: 0:00:00.658434)\n",
+      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  60.00% simulations complete (elapsed: 0:00:00.489197, rate: 0:00:00.048920, eta: 0:00:00.489197)\n",
+      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  70.00% simulations complete (elapsed: 0:00:00.539138, rate: 0:00:00.053914, eta: 0:00:00.359425)\n",
+      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  80.00% simulations complete (elapsed: 0:00:00.589068, rate: 0:00:00.058907, eta: 0:00:00.252458)\n",
+      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  90.00% simulations complete (elapsed: 0:00:00.638226, rate: 0:00:00.063823, eta: 0:00:00.159556)\n",
+      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:52 || common.py:__call__() | 100.00% simulations complete (elapsed: 0:00:00.688654, rate: 0:00:00.068865, eta: 0:00:00.076517)\n",
+      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Running any post-sim functions\n",
+      "INFO:2024-11-14,16:31:52 || luts.py:load() | Loading LUT into memory\n",
+      "WARNING:2024-11-14,16:31:52 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
+      "WARNING:2024-11-14,16:31:52 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
+      "INFO:2024-11-14,16:31:52 || isofit.py:run() | Beginning 420 inversions in 100 chunks using 10 cores\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:31:55 ||| Worker 0 completed 1/~42.0:: 2.38% complete\n",
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+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker at start location (79,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker at start location (54,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker at start location (66,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker 0 completed 9/~42.0:: 21.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker 1 completed 10/~42.0:: 23.81% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker 5 completed 10/~42.0:: 23.81% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker 6 completed 9/~42.0:: 21.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker 2 completed 9/~42.0:: 21.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker at start location (83,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker 8 completed 10/~42.0:: 23.81% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker 3 completed 9/~42.0:: 21.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker at start location (71,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:01 ||| Worker 9 completed 9/~42.0:: 21.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:01 ||| Worker 4 completed 10/~42.0:: 23.81% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:01 ||| Worker at start location (109,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:01 ||| Worker at start location (92,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:01 ||| Worker at start location (96,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:01 ||| Worker at start location (117,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 5 completed 14/~42.0:: 33.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker at start location (88,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker at start location (113,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker at start location (100,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker at start location (105,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 1 completed 14/~42.0:: 33.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 0 completed 13/~42.0:: 30.95% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 8 completed 14/~42.0:: 33.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker at start location (126,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 7 completed 14/~42.0:: 33.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 2 completed 14/~42.0:: 33.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 3 completed 13/~42.0:: 30.95% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker at start location (122,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 6 completed 13/~42.0:: 30.95% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:03 ||| Worker 9 completed 14/~42.0:: 33.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:03 ||| Worker 4 completed 14/~42.0:: 33.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:03 ||| Worker at start location (143,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:03 ||| Worker at start location (130,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker at start location (134,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker at start location (151,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker 1 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker at start location (147,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker at start location (139,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker 8 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker at start location (155,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker 3 completed 17/~42.0:: 40.48% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker at start location (164,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker at start location (168,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker 7 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker 2 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker 0 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker 5 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker at start location (160,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker 4 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker at start location (172,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker 9 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:06 ||| Worker 6 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:06 ||| Worker 1 completed 22/~42.0:: 52.38% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:06 ||| Worker at start location (181,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:06 ||| Worker at start location (185,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:06 ||| Worker at start location (189,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:06 ||| Worker 8 completed 22/~42.0:: 52.38% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:06 ||| Worker at start location (198,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker at start location (202,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker 3 completed 21/~42.0:: 50.0% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker 7 completed 22/~42.0:: 52.38% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker at start location (206,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker at start location (194,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker at start location (177,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker at start location (215,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker 2 completed 22/~42.0:: 52.38% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker 4 completed 22/~42.0:: 52.38% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:08 ||| Worker 0 completed 23/~42.0:: 54.76% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:08 ||| Worker 5 completed 23/~42.0:: 54.76% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:08 ||| Worker 9 completed 22/~42.0:: 52.38% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:08 ||| Worker at start location (211,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:08 ||| Worker 1 completed 26/~42.0:: 61.9% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:08 ||| Worker 6 completed 23/~42.0:: 54.76% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:08 ||| Worker at start location (219,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:09 ||| Worker at start location (223,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:09 ||| Worker at start location (236,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:09 ||| Worker 8 completed 26/~42.0:: 61.9% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:09 ||| Worker at start location (232,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:09 ||| Worker 3 completed 25/~42.0:: 59.52% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:09 ||| Worker at start location (228,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:09 ||| Worker at start location (253,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker at start location (249,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker 2 completed 26/~42.0:: 61.9% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker 4 completed 26/~42.0:: 61.9% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker at start location (240,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker 7 completed 27/~42.0:: 64.29% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker at start location (245,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker 1 completed 30/~42.0:: 71.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker 5 completed 27/~42.0:: 64.29% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker at start location (257,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker 9 completed 26/~42.0:: 61.9% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:11 ||| Worker 0 completed 28/~42.0:: 66.67% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:11 ||| Worker 6 completed 27/~42.0:: 64.29% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:11 ||| Worker at start location (266,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:11 ||| Worker at start location (262,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:11 ||| Worker at start location (274,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:11 ||| Worker 3 completed 29/~42.0:: 69.05% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker at start location (270,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker 8 completed 31/~42.0:: 73.81% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker 2 completed 30/~42.0:: 71.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker at start location (283,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker at start location (287,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker at start location (279,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker at start location (295,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker at start location (291,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker 4 completed 30/~42.0:: 71.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker 1 completed 34/~42.0:: 80.95% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:13 ||| Worker 5 completed 31/~42.0:: 73.81% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker 7 completed 32/~42.0:: 76.19% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:13 ||| Worker 0 completed 32/~42.0:: 76.19% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:13 ||| Worker 9 completed 30/~42.0:: 71.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:13 ||| Worker at start location (304,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:13 ||| Worker at start location (300,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:13 ||| Worker 3 completed 33/~42.0:: 78.57% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker at start location (308,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker at start location (312,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker 6 completed 32/~42.0:: 76.19% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker at start location (325,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker at start location (321,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker 8 completed 35/~42.0:: 83.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker at start location (329,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker 2 completed 34/~42.0:: 80.95% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker at start location (317,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:15 ||| Worker at start location (338,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:15 ||| Worker 7 completed 36/~42.0:: 85.71% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:15 ||| Worker 1 completed 38/~42.0:: 90.48% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:15 ||| Worker 5 completed 35/~42.0:: 83.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:15 ||| Worker 4 completed 35/~42.0:: 83.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:15 ||| Worker at start location (342,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:15 ||| Worker 0 completed 36/~42.0:: 85.71% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:15 ||| Worker at start location (334,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:16 ||| Worker at start location (346,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:16 ||| Worker 9 completed 35/~42.0:: 83.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:16 ||| Worker at start location (355,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:16 ||| Worker 3 completed 37/~42.0:: 88.1% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:16 ||| Worker 6 completed 36/~42.0:: 85.71% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:16 ||| Worker at start location (359,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:16 ||| Worker 8 completed 39/~42.0:: 92.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:17 ||| Worker at start location (363,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:17 ||| Worker at start location (351,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:17 ||| Worker at start location (372,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:17 ||| Worker at start location (376,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:17 ||| Worker 7 completed 40/~42.0:: 95.24% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:17 ||| Worker 4 completed 39/~42.0:: 92.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:17 ||| Worker 1 completed 42/~42.0:: 100.0% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:17 ||| Worker 2 completed 39/~42.0:: 92.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:18 ||| Worker 0 completed 40/~42.0:: 95.24% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:18 ||| Worker at start location (368,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:18 ||| Worker at start location (380,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:18 ||| Worker at start location (389,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:18 ||| Worker at start location (393,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:18 ||| Worker 5 completed 40/~42.0:: 95.24% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:18 ||| Worker at start location (385,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:19 ||| Worker at start location (397,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:19 ||| Worker at start location (410,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:19 ||| Worker at start location (406,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:19 ||| Worker at start location (414,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:19 ||| Worker at start location (402,0) completed 4/5\n",
+      "INFO:2024-11-14,16:32:20 || isofit.py:run() | Inversions complete.  27.68s total, 15.1717 spectra/s, 1.5172 spectra/s/core\n",
+      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | Full (non-aerosol) LUTs:\n",
+      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | Elevation: None\n",
+      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | To-sensor zenith: None\n",
+      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | To-sun zenith: None\n",
+      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | Relative to-sun azimuth: None\n",
+      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | H2O Vapor: [0.6671 0.7777]\n",
+      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/output/NIS01_20210403_173647_subs_state\n",
+      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | Writing main configuration file.\n",
+      "INFO:2024-11-14,16:32:20 || template_construction.py:load_climatology() | Loading Climatology\n",
+      "INFO:2024-11-14,16:32:20 || template_construction.py:load_climatology() | Climatology Loaded.  Aerosol State Vector:\n",
+      "{'AOT550': {'bounds': [0.001, 1.0], 'scale': 1, 'init': 0.1009, 'prior_sigma': 10.0, 'prior_mean': 0.1009}}\n",
+      "Aerosol LUT Grid:\n",
+      "{'AOT550': [0.001, 0.1009, 0.2008, 0.3007, 0.4006, 0.5005, 0.6004, 0.7003, 0.8002, 0.9001, 1.0]}\n",
+      "Aerosol model path:/Users/jamesmo/projects/isofit/dev/local/data/aerosol_model.txt\n",
+      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:20 ||| Worker at start location (419,0) completed 4/5\n",
+      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | Running ISOFIT with full LUT\n",
+      "WARNING:2024-11-14,16:32:20 || __init__.py:checkNumThreads() | \n",
+      "******************************************************************************************\n",
+      "! Number of threads is greater than 1, this may greatly impact performance\n",
+      "! Please set this the environment variables 'MKL_NUM_THREADS' and 'OMP_NUM_THREADS' to '1'\n",
+      "******************************************************************************************\n",
+      "INFO:2024-11-14,16:32:20 || configs.py:create_new_config() | Loading config file: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/config/NIS01_20210403_173647_isofit.json\n",
+      "INFO:2024-11-14,16:32:20 || configs.py:get_config_errors() | Checking config sections for configuration issues\n",
+      "INFO:2024-11-14,16:32:20 || configs.py:get_config_errors() | Configuration file checks complete, no errors found.\n",
+      "2024-11-14 16:32:20,552\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
+      "INFO:2024-11-14,16:32:20 || isofit.py:run() | Building first forward model, will generate any necessary LUTs\n",
+      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:__init__() | Loading from wavelength_file: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/data/wavelengths.txt\n",
+      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
+      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
+      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
+      "INFO:2024-11-14,16:32:20 || sRTMnet.py:preSim() | Creating a simulator configuration\n",
+      "INFO:2024-11-14,16:32:20 || sRTMnet.py:preSim() | Building simulator and executing (6S)\n",
+      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
+      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
+      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
+      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:runSimulations() | Executing parallel simulations\n",
+      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  13.64% simulations complete (elapsed: 0:00:01.741504, rate: 0:00:00.079159, eta: 0:00:15.673536)\n",
+      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  22.73% simulations complete (elapsed: 0:00:01.848001, rate: 0:00:00.084000, eta: 0:00:07.392004)\n",
+      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  31.82% simulations complete (elapsed: 0:00:01.946866, rate: 0:00:00.088494, eta: 0:00:04.542687)\n",
+      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  40.91% simulations complete (elapsed: 0:00:02.040472, rate: 0:00:00.092749, eta: 0:00:03.060708)\n",
+      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  50.00% simulations complete (elapsed: 0:00:02.105106, rate: 0:00:00.095687, eta: 0:00:02.105106)\n",
+      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  63.64% simulations complete (elapsed: 0:00:02.173322, rate: 0:00:00.098787, eta: 0:00:01.448881)\n",
+      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  72.73% simulations complete (elapsed: 0:00:02.231235, rate: 0:00:00.101420, eta: 0:00:00.956244)\n",
+      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  81.82% simulations complete (elapsed: 0:00:02.286901, rate: 0:00:00.103950, eta: 0:00:00.571725)\n",
+      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:23 || common.py:__call__() |  90.91% simulations complete (elapsed: 0:00:02.341987, rate: 0:00:00.106454, eta: 0:00:00.260221)\n",
+      "INFO:2024-11-14,16:32:23 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:23 || common.py:__call__() | 100.00% simulations complete (elapsed: 0:00:02.396946, rate: 0:00:00.108952, eta: 0:00:00)\n",
+      "INFO:2024-11-14,16:32:23 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:23 || radiative_transfer_engine.py:runSimulations() | Running any post-sim functions\n",
+      "INFO:2024-11-14,16:32:23 || radiative_transfer_engine.py:runSimulations() | Saving post-sim data to index zero of all dimensions except wl\n",
+      "INFO:2024-11-14,16:32:23 || luts.py:load() | Loading LUT into memory\n",
+      "WARNING:2024-11-14,16:32:23 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
+      "WARNING:2024-11-14,16:32:23 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
+      "INFO:2024-11-14,16:32:23 || sRTMnet.py:preSim() | Interpolating simulator quantities to emulator size\n",
+      "INFO:2024-11-14,16:32:23 || sRTMnet.py:preSim() | Loading and predicting with emulator\n",
+      "INFO:2024-11-14,16:32:25 || sRTMnet.py:preSim() | Saving intermediary prediction results to: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/lut_full/sRTMnet.predicts.nc\n",
+      "INFO:2024-11-14,16:32:25 || radiative_transfer_engine.py:runSimulations() | Saving pre-sim data to index zero of all dimensions except wl\n",
+      "INFO:2024-11-14,16:32:25 || radiative_transfer_engine.py:runSimulations() | Executing parallel simulations\n",
+      "INFO:2024-11-14,16:32:25 || common.py:__call__() |  13.64% simulations complete (elapsed: 0:00:00.246199, rate: 0:00:00.011191, eta: 0:00:02.215791)\n",
+      "INFO:2024-11-14,16:32:25 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:25 || common.py:__call__() |  22.73% simulations complete (elapsed: 0:00:00.336718, rate: 0:00:00.015305, eta: 0:00:01.346872)\n",
+      "INFO:2024-11-14,16:32:25 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:25 || common.py:__call__() |  31.82% simulations complete (elapsed: 0:00:00.393345, rate: 0:00:00.017879, eta: 0:00:00.917805)\n",
+      "INFO:2024-11-14,16:32:25 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:25 || common.py:__call__() |  40.91% simulations complete (elapsed: 0:00:00.442589, rate: 0:00:00.020118, eta: 0:00:00.663883)\n",
+      "INFO:2024-11-14,16:32:25 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:25 || common.py:__call__() |  50.00% simulations complete (elapsed: 0:00:00.491846, rate: 0:00:00.022357, eta: 0:00:00.491846)\n",
+      "INFO:2024-11-14,16:32:25 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:25 || common.py:__call__() |  63.64% simulations complete (elapsed: 0:00:00.540206, rate: 0:00:00.024555, eta: 0:00:00.360137)\n",
+      "INFO:2024-11-14,16:32:25 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:25 || common.py:__call__() |  72.73% simulations complete (elapsed: 0:00:00.589028, rate: 0:00:00.026774, eta: 0:00:00.252441)\n",
+      "INFO:2024-11-14,16:32:25 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:26 || common.py:__call__() |  81.82% simulations complete (elapsed: 0:00:00.638072, rate: 0:00:00.029003, eta: 0:00:00.159518)\n",
+      "INFO:2024-11-14,16:32:26 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:26 || common.py:__call__() |  90.91% simulations complete (elapsed: 0:00:00.688201, rate: 0:00:00.031282, eta: 0:00:00.076467)\n",
+      "INFO:2024-11-14,16:32:26 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:26 || common.py:__call__() | 100.00% simulations complete (elapsed: 0:00:00.738618, rate: 0:00:00.033574, eta: 0:00:00)\n",
+      "INFO:2024-11-14,16:32:26 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-14,16:32:26 || radiative_transfer_engine.py:runSimulations() | Running any post-sim functions\n",
+      "INFO:2024-11-14,16:32:26 || luts.py:load() | Loading LUT into memory\n",
+      "WARNING:2024-11-14,16:32:26 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
+      "WARNING:2024-11-14,16:32:26 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
+      "INFO:2024-11-14,16:32:26 || isofit.py:run() | Beginning 420 inversions in 100 chunks using 10 cores\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:29 ||| Worker 4 completed 1/~42.0:: 2.38% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:29 ||| Worker 3 completed 1/~42.0:: 2.38% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:29 ||| Worker 8 completed 1/~42.0:: 2.38% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:29 ||| Worker 6 completed 1/~42.0:: 2.38% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:29 ||| Worker 2 completed 1/~42.0:: 2.38% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:29 ||| Worker 0 completed 1/~42.0:: 2.38% complete\n",
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+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:29 ||| Worker 5 completed 1/~42.0:: 2.38% complete\n",
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+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:29 ||| Worker 7 completed 1/~42.0:: 2.38% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:31 ||| Worker at start location (7,0) completed 3/4\n",
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+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:32 ||| Worker at start location (32,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:32 ||| Worker at start location (15,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:32 ||| Worker at start location (11,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:32 ||| Worker 4 completed 5/~42.0:: 11.9% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:32 ||| Worker 8 completed 5/~42.0:: 11.9% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:32 ||| Worker at start location (3,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:32 ||| Worker 0 completed 5/~42.0:: 11.9% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:32 ||| Worker 3 completed 5/~42.0:: 11.9% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:32 ||| Worker 6 completed 5/~42.0:: 11.9% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:32 ||| Worker 2 completed 5/~42.0:: 11.9% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:33 ||| Worker at start location (20,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:33 ||| Worker at start location (37,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:33 ||| Worker 7 completed 5/~42.0:: 11.9% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:33 ||| Worker 9 completed 5/~42.0:: 11.9% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:34 ||| Worker 5 completed 6/~42.0:: 14.29% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:34 ||| Worker 1 completed 6/~42.0:: 14.29% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:35 ||| Worker at start location (45,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:35 ||| Worker at start location (66,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:35 ||| Worker at start location (62,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:35 ||| Worker at start location (58,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:35 ||| Worker at start location (49,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:36 ||| Worker 8 completed 9/~42.0:: 21.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:36 ||| Worker at start location (75,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:36 ||| Worker 2 completed 9/~42.0:: 21.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:36 ||| Worker at start location (54,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:36 ||| Worker 4 completed 9/~42.0:: 21.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:36 ||| Worker 6 completed 9/~42.0:: 21.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:36 ||| Worker 0 completed 9/~42.0:: 21.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:36 ||| Worker at start location (79,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:36 ||| Worker at start location (83,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:37 ||| Worker 9 completed 9/~42.0:: 21.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:37 ||| Worker 3 completed 10/~42.0:: 23.81% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:37 ||| Worker at start location (71,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:37 ||| Worker 5 completed 10/~42.0:: 23.81% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:37 ||| Worker 1 completed 10/~42.0:: 23.81% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:37 ||| Worker 7 completed 10/~42.0:: 23.81% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:38 ||| Worker at start location (92,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:39 ||| Worker at start location (88,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:39 ||| Worker at start location (96,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:39 ||| Worker at start location (100,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:39 ||| Worker 2 completed 13/~42.0:: 30.95% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:39 ||| Worker at start location (109,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:39 ||| Worker at start location (113,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:39 ||| Worker at start location (117,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:40 ||| Worker at start location (105,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:40 ||| Worker 8 completed 14/~42.0:: 33.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:40 ||| Worker 6 completed 13/~42.0:: 30.95% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:40 ||| Worker 0 completed 13/~42.0:: 30.95% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:40 ||| Worker at start location (126,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:40 ||| Worker 9 completed 13/~42.0:: 30.95% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:40 ||| Worker 3 completed 14/~42.0:: 33.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:41 ||| Worker 4 completed 14/~42.0:: 33.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:41 ||| Worker at start location (122,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:41 ||| Worker 5 completed 14/~42.0:: 33.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:41 ||| Worker 7 completed 14/~42.0:: 33.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:42 ||| Worker at start location (130,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker at start location (134,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker at start location (143,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker at start location (151,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker at start location (147,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker 1 completed 15/~42.0:: 35.71% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker 2 completed 17/~42.0:: 40.48% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker at start location (139,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker at start location (164,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker at start location (155,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:44 ||| Worker 8 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker 6 completed 17/~42.0:: 40.48% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker 9 completed 17/~42.0:: 40.48% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:44 ||| Worker 3 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:44 ||| Worker 0 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:44 ||| Worker 7 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:44 ||| Worker at start location (160,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:44 ||| Worker 5 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:45 ||| Worker at start location (168,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:46 ||| Worker at start location (185,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:46 ||| Worker at start location (181,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:46 ||| Worker at start location (172,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:46 ||| Worker 4 completed 19/~42.0:: 45.24% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:46 ||| Worker at start location (198,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:46 ||| Worker 1 completed 19/~42.0:: 45.24% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:47 ||| Worker 6 completed 21/~42.0:: 50.0% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:47 ||| Worker 9 completed 21/~42.0:: 50.0% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:47 ||| Worker 2 completed 21/~42.0:: 50.0% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:47 ||| Worker at start location (189,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:47 ||| Worker at start location (177,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:47 ||| Worker at start location (202,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:47 ||| Worker 0 completed 22/~42.0:: 52.38% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:48 ||| Worker 3 completed 22/~42.0:: 52.38% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:48 ||| Worker 8 completed 23/~42.0:: 54.76% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:48 ||| Worker 5 completed 22/~42.0:: 52.38% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:49 ||| Worker at start location (194,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:49 ||| Worker at start location (206,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:49 ||| Worker at start location (219,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:49 ||| Worker at start location (215,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:49 ||| Worker at start location (223,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:49 ||| Worker 7 completed 23/~42.0:: 54.76% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker 4 completed 23/~42.0:: 54.76% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker at start location (211,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker 6 completed 25/~42.0:: 59.52% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker at start location (232,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker at start location (236,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker 2 completed 25/~42.0:: 59.52% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker 9 completed 25/~42.0:: 59.52% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker 1 completed 24/~42.0:: 57.14% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker at start location (240,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:51 ||| Worker 3 completed 26/~42.0:: 61.9% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:51 ||| Worker 8 completed 27/~42.0:: 64.29% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:51 ||| Worker 5 completed 26/~42.0:: 61.9% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:51 ||| Worker at start location (228,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:52 ||| Worker 0 completed 27/~42.0:: 64.29% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:53 ||| Worker at start location (249,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:53 ||| Worker at start location (253,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:53 ||| Worker at start location (257,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:53 ||| Worker at start location (245,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:53 ||| Worker at start location (266,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:53 ||| Worker 4 completed 27/~42.0:: 64.29% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:53 ||| Worker 6 completed 29/~42.0:: 69.05% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:54 ||| Worker at start location (270,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:54 ||| Worker 2 completed 29/~42.0:: 69.05% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:54 ||| Worker at start location (274,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:54 ||| Worker 7 completed 28/~42.0:: 66.67% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:54 ||| Worker at start location (262,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:54 ||| Worker 3 completed 30/~42.0:: 71.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:54 ||| Worker 1 completed 28/~42.0:: 66.67% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:55 ||| Worker 8 completed 31/~42.0:: 73.81% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:55 ||| Worker 9 completed 30/~42.0:: 71.43% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:55 ||| Worker at start location (283,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:55 ||| Worker at start location (279,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:56 ||| Worker 0 completed 31/~42.0:: 73.81% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:56 ||| Worker at start location (287,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:56 ||| Worker at start location (291,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:56 ||| Worker at start location (295,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:56 ||| Worker 5 completed 31/~42.0:: 73.81% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:57 ||| Worker 6 completed 33/~42.0:: 78.57% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:57 ||| Worker at start location (304,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:57 ||| Worker at start location (312,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:57 ||| Worker 2 completed 33/~42.0:: 78.57% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:57 ||| Worker at start location (308,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:57 ||| Worker at start location (300,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:57 ||| Worker 4 completed 31/~42.0:: 73.81% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:58 ||| Worker 1 completed 32/~42.0:: 76.19% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:58 ||| Worker 8 completed 35/~42.0:: 83.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:58 ||| Worker 7 completed 33/~42.0:: 78.57% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:58 ||| Worker 3 completed 34/~42.0:: 80.95% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:58 ||| Worker at start location (317,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:59 ||| Worker at start location (321,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:59 ||| Worker at start location (325,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:59 ||| Worker 9 completed 35/~42.0:: 83.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:59 ||| Worker at start location (329,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:33:00 ||| Worker 0 completed 35/~42.0:: 83.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:33:00 ||| Worker at start location (338,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:33:00 ||| Worker 5 completed 35/~42.0:: 83.33% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:33:00 ||| Worker 6 completed 37/~42.0:: 88.1% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:33:01 ||| Worker 2 completed 37/~42.0:: 88.1% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:33:01 ||| Worker at start location (342,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:33:01 ||| Worker at start location (334,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:33:01 ||| Worker at start location (355,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:33:01 ||| Worker at start location (346,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:33:02 ||| Worker 8 completed 39/~42.0:: 92.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:33:02 ||| Worker at start location (359,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:33:02 ||| Worker 3 completed 38/~42.0:: 90.48% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:33:02 ||| Worker 1 completed 36/~42.0:: 85.71% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:33:02 ||| Worker 4 completed 36/~42.0:: 85.71% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:33:02 ||| Worker at start location (351,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:33:02 ||| Worker at start location (363,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:33:03 ||| Worker 9 completed 39/~42.0:: 92.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:33:03 ||| Worker at start location (372,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:33:03 ||| Worker 7 completed 38/~42.0:: 90.48% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:33:03 ||| Worker 0 completed 39/~42.0:: 92.86% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:33:03 ||| Worker at start location (376,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:33:03 ||| Worker at start location (368,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:33:04 ||| Worker 6 completed 41/~42.0:: 97.62% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:33:04 ||| Worker 2 completed 41/~42.0:: 97.62% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:33:04 ||| Worker at start location (393,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:33:04 ||| Worker 5 completed 40/~42.0:: 95.24% complete\n",
+      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:33:04 ||| Worker at start location (380,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:33:05 ||| Worker at start location (389,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:33:05 ||| Worker at start location (397,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:33:05 ||| Worker at start location (385,0) completed 4/5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:33:06 ||| Worker at start location (406,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:33:06 ||| Worker at start location (410,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:33:06 ||| Worker at start location (414,0) completed 3/4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:33:06 ||| Worker at start location (402,0) completed 4/5\n",
+      "INFO:2024-11-14,16:33:07 || isofit.py:run() | Inversions complete.  41.27s total, 10.1773 spectra/s, 1.0177 spectra/s/core\n",
+      "INFO:2024-11-14,16:33:07 || apply_oe.py:apply_oe() | Analytical line inference\n",
+      "INFO:2024-11-14,16:33:07 || configs.py:create_new_config() | Loading config file: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/config/NIS01_20210403_173647_isofit.json\n",
+      "INFO:2024-11-14,16:33:07 || radiative_transfer_engine.py:__init__() | Loading from wavelength_file: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/data/wavelengths.txt\n",
+      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:33:07 ||| Worker at start location (419,0) completed 4/5\n",
+      "INFO:2024-11-14,16:33:07 || radiative_transfer_engine.py:__init__() | Prebuilt LUT provided\n",
+      "INFO:2024-11-14,16:33:07 || luts.py:load() | Loading LUT into memory\n",
+      "WARNING:2024-11-14,16:33:07 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
+      "WARNING:2024-11-14,16:33:07 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
+      "INFO:2024-11-14,16:33:07 || radiative_transfer_engine.py:__init__() | LUT grid loaded from file: OrderedDict([('AOT550', [0.001, 0.1009, 0.2008, 0.3007, 0.4006, 0.5005, 0.6004, 0.7003, 0.8002, 0.9001, 1.0]), ('H2OSTR', [0.6671, 0.7777])])\n",
+      "2024-11-14 16:33:07,997\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
+      "INFO:2024-11-14,16:33:08 || atm_interpolation.py:atm_interpolation() | Beginning atmospheric interpolation 10 cores\n",
+      "INFO:2024-11-14,16:33:10 || atm_interpolation.py:atm_interpolation() | Parallel atmospheric interpolations complete.  1.9962680339813232 s total, 2114.445519413397 spectra/s, 211.4445519413397 spectra/s/core\n",
+      "2024-11-14 16:33:10,084\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
+      "\u001b[2m\u001b[36m(Worker pid=42380)\u001b[0m INFO:2024-11-14,16:33:15 ||| Analytical line writing line 0\n",
+      "\u001b[2m\u001b[36m(Worker pid=42378)\u001b[0m INFO:2024-11-14,16:33:15 ||| Analytical line writing line 1\n",
+      "\u001b[2m\u001b[36m(Worker pid=42375)\u001b[0m INFO:2024-11-14,16:33:16 ||| Analytical line writing line 3\n",
+      "\u001b[2m\u001b[36m(Worker pid=42376)\u001b[0m INFO:2024-11-14,16:33:15 ||| Analytical line writing line 6\n",
+      "\u001b[2m\u001b[36m(Worker pid=42372)\u001b[0m INFO:2024-11-14,16:33:15 ||| Analytical line writing line 5\n",
+      "\u001b[2m\u001b[36m(Worker pid=42377)\u001b[0m INFO:2024-11-14,16:33:16 ||| Analytical line writing line 2\n",
+      "\u001b[2m\u001b[36m(Worker pid=42379)\u001b[0m INFO:2024-11-14,16:33:16 ||| Analytical line writing line 8\n",
+      "\u001b[2m\u001b[36m(Worker pid=42381)\u001b[0m INFO:2024-11-14,16:33:16 ||| Analytical line writing line 9\n",
+      "\u001b[2m\u001b[36m(Worker pid=42374)\u001b[0m INFO:2024-11-14,16:33:16 ||| Analytical line writing line 4\n",
+      "\u001b[2m\u001b[36m(Worker pid=42373)\u001b[0m INFO:2024-11-14,16:33:16 ||| Analytical line writing line 7\n",
+      "\u001b[2m\u001b[36m(Worker pid=42380)\u001b[0m INFO:2024-11-14,16:33:19 ||| Analytical line writing line 10\n",
+      "\u001b[2m\u001b[36m(Worker pid=42378)\u001b[0m INFO:2024-11-14,16:33:19 ||| Analytical line writing line 11\n",
+      "\u001b[2m\u001b[36m(Worker pid=42376)\u001b[0m INFO:2024-11-14,16:33:20 ||| Analytical line writing line 19\n",
+      "\u001b[2m\u001b[36m(Worker pid=42381)\u001b[0m INFO:2024-11-14,16:33:20 ||| Analytical line writing line 16\n",
+      "\u001b[2m\u001b[36m(Worker pid=42372)\u001b[0m INFO:2024-11-14,16:33:19 ||| Analytical line writing line 12\n",
+      "\u001b[2m\u001b[36m(Worker pid=42375)\u001b[0m INFO:2024-11-14,16:33:20 ||| Analytical line writing line 13\n",
+      "\u001b[2m\u001b[36m(Worker pid=42379)\u001b[0m INFO:2024-11-14,16:33:20 ||| Analytical line writing line 18\n",
+      "\u001b[2m\u001b[36m(Worker pid=42374)\u001b[0m INFO:2024-11-14,16:33:20 ||| Analytical line writing line 14\n",
+      "\u001b[2m\u001b[36m(Worker pid=42373)\u001b[0m INFO:2024-11-14,16:33:20 ||| Analytical line writing line 15\n",
+      "\u001b[2m\u001b[36m(Worker pid=42377)\u001b[0m INFO:2024-11-14,16:33:20 ||| Analytical line writing line 17\n",
+      "\u001b[2m\u001b[36m(Worker pid=42380)\u001b[0m INFO:2024-11-14,16:33:23 ||| Analytical line writing line 20\n",
+      "\u001b[2m\u001b[36m(Worker pid=42378)\u001b[0m INFO:2024-11-14,16:33:23 ||| Analytical line writing line 21\n",
+      "\u001b[2m\u001b[36m(Worker pid=42381)\u001b[0m INFO:2024-11-14,16:33:23 ||| Analytical line writing line 23\n",
+      "\u001b[2m\u001b[36m(Worker pid=42372)\u001b[0m INFO:2024-11-14,16:33:24 ||| Analytical line writing line 22\n",
+      "\u001b[2m\u001b[36m(Worker pid=42373)\u001b[0m INFO:2024-11-14,16:33:24 ||| Analytical line writing line 26\n",
+      "\u001b[2m\u001b[36m(Worker pid=42375)\u001b[0m INFO:2024-11-14,16:33:24 ||| Analytical line writing line 25\n",
+      "\u001b[2m\u001b[36m(Worker pid=42379)\u001b[0m INFO:2024-11-14,16:33:24 ||| Analytical line writing line 27\n",
+      "\u001b[2m\u001b[36m(Worker pid=42376)\u001b[0m INFO:2024-11-14,16:33:24 ||| Analytical line writing line 28\n",
+      "\u001b[2m\u001b[36m(Worker pid=42374)\u001b[0m INFO:2024-11-14,16:33:24 ||| Analytical line writing line 24\n",
+      "\u001b[2m\u001b[36m(Worker pid=42377)\u001b[0m INFO:2024-11-14,16:33:24 ||| Analytical line writing line 29\n",
+      "\u001b[2m\u001b[36m(Worker pid=42380)\u001b[0m INFO:2024-11-14,16:33:27 ||| Analytical line writing line 30\n",
+      "\u001b[2m\u001b[36m(Worker pid=42378)\u001b[0m INFO:2024-11-14,16:33:27 ||| Analytical line writing line 31\n",
+      "\u001b[2m\u001b[36m(Worker pid=42376)\u001b[0m INFO:2024-11-14,16:33:27 ||| Analytical line writing line 37\n",
+      "\u001b[2m\u001b[36m(Worker pid=42379)\u001b[0m INFO:2024-11-14,16:33:27 ||| Analytical line writing line 36\n",
+      "\u001b[2m\u001b[36m(Worker pid=42381)\u001b[0m INFO:2024-11-14,16:33:27 ||| Analytical line writing line 32\n",
+      "\u001b[2m\u001b[36m(Worker pid=42372)\u001b[0m INFO:2024-11-14,16:33:27 ||| Analytical line writing line 33\n",
+      "\u001b[2m\u001b[36m(Worker pid=42373)\u001b[0m INFO:2024-11-14,16:33:27 ||| Analytical line writing line 34\n",
+      "\u001b[2m\u001b[36m(Worker pid=42375)\u001b[0m INFO:2024-11-14,16:33:28 ||| Analytical line writing line 35\n",
+      "\u001b[2m\u001b[36m(Worker pid=42374)\u001b[0m INFO:2024-11-14,16:33:28 ||| Analytical line writing line 38\n",
+      "\u001b[2m\u001b[36m(Worker pid=42377)\u001b[0m INFO:2024-11-14,16:33:28 ||| Analytical line writing line 39\n",
+      "\u001b[2m\u001b[36m(Worker pid=42380)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 40\n",
+      "\u001b[2m\u001b[36m(Worker pid=42381)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 42\n",
+      "\u001b[2m\u001b[36m(Worker pid=42373)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 41\n",
+      "\u001b[2m\u001b[36m(Worker pid=42376)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 44\n",
+      "\u001b[2m\u001b[36m(Worker pid=42375)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 47\n",
+      "\u001b[2m\u001b[36m(Worker pid=42379)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 45\n",
+      "\u001b[2m\u001b[36m(Worker pid=42372)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 43\n",
+      "\u001b[2m\u001b[36m(Worker pid=42374)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 48\n",
+      "\u001b[2m\u001b[36m(Worker pid=42378)\u001b[0m INFO:2024-11-14,16:33:32 ||| Analytical line writing line 46\n",
+      "\u001b[2m\u001b[36m(Worker pid=42377)\u001b[0m INFO:2024-11-14,16:33:32 ||| Analytical line writing line 49\n",
+      "\u001b[2m\u001b[36m(Worker pid=42380)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 50\n",
+      "\u001b[2m\u001b[36m(Worker pid=42376)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 53\n",
+      "\u001b[2m\u001b[36m(Worker pid=42373)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 51\n",
+      "\u001b[2m\u001b[36m(Worker pid=42379)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 54\n",
+      "\u001b[2m\u001b[36m(Worker pid=42381)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 52\n",
+      "\u001b[2m\u001b[36m(Worker pid=42372)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 56\n",
+      "\u001b[2m\u001b[36m(Worker pid=42375)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 55\n",
+      "\u001b[2m\u001b[36m(Worker pid=42378)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 59\n",
+      "\u001b[2m\u001b[36m(Worker pid=42377)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 58\n",
+      "\u001b[2m\u001b[36m(Worker pid=42374)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 57\n",
+      "\u001b[2m\u001b[36m(Worker pid=42380)\u001b[0m INFO:2024-11-14,16:33:37 ||| Analytical line writing line 60\n",
+      "INFO:2024-11-14,16:33:38 || analytical_line.py:analytical_line() | Analytical line inversions complete.  27.99s total, 150.777 spectra/s, 15.0777 spectra/s/core\n",
+      "INFO:2024-11-14,16:33:38 || apply_oe.py:apply_oe() | Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add a ray shutdown, just in case this is being re-called\n",
+    "import ray\n",
+    "ray.shutdown()\n",
+    "\n",
+    "# Cleanup any previous runs; comment this out if you want to preserve a previous run's output\n",
+    "if Path(paths.working).exists():\n",
+    "    shutil.rmtree(paths.working)\n",
+    "\n",
+    "apply_oe(\n",
+    "    input_radiance    = paths.rdn, # Radiance\n",
+    "    input_loc         = paths.loc, # Location\n",
+    "    input_obs         = paths.obs, # Observations\n",
+    "    working_directory = str(paths.working), # Output directory\n",
+    "    sensor            = \"neon\", \n",
+    "    surface_path      = paths.surface, # Surface priors - often changes\n",
+    "    emulator_base     = f\"{env.srtmnet}/sRTMnet_v120.h5\",\n",
+    "    surface_category  = \"multicomponent_surface\",\n",
+    "\n",
+    "    modtran_path    = None,\n",
+    "    atmosphere_type = \"ATM_MIDLAT_SUMMER\", # MODTRAN\n",
+    "    aerosol_climatology_path = None,       # MODTRAN\n",
+    "    \n",
+    "    rdn_factors_path       = None, # RCC update used 'on the fly'\n",
+    "    model_discrepancy_path = None, # Model discrepancy term - handle things like unknown radiative transfer model effects\n",
+    "    channelized_uncertainty_path = None, # Channelized uncertainty - if you have an instrument model\n",
+    "\n",
+    "    multiple_restarts = False, # Useful if the AOD conditions are really challenging\n",
+    "    \n",
+    "    presolve        = True,  # Attempts to solve for the right wv range\n",
+    "    empirical_line  = False, # wavelength-specific local linear interpolation between radiance and reflectance\n",
+    "    analytical_line = True,  # mathematical representation of OE given that the atmsophere is known\n",
+    "    \n",
+    "    segmentation_size  = 10,\n",
+    "    num_neighbors      = [5],\n",
+    "    atm_sigma          = [0.5, 0.5],\n",
+    "    pressure_elevation = False,\n",
+    "\n",
+    "    n_cores = os.cpu_count(),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ea06539-8f6a-4806-b24e-c85fbcc18b28",
+   "metadata": {},
+   "source": [
+    "# Plotting\n",
+    "\n",
+    "Below plots the regions of interest defined by a NEON report. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d07cc4e0-2b61-4c8f-998a-2b3f1b064b48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load in the ISOFIT reflectance output\n",
+    "ds = envi.open(paths.working / f\"output/{neon_str}_rfl.hdr\")\n",
+    "rfl = ds.open_memmap(interleave='bip')\n",
+    "rgb = rfl[:, :, [60, 40, 30]].copy()\n",
+    "wl = np.array(ds.metadata['wavelength'], dtype=float)\n",
+    "\n",
+    "# Find the bounding box for all regions of interest (RoI)\n",
+    "regions = report[neon_id]\n",
+    "bounds = np.vstack(list(regions.values()))\n",
+    "y = bounds[:, 0].min() - 5 # , bounds[:, 1].max() + 5\n",
+    "x = bounds[:, 2].min() - 5 # , bounds[:, 3].max() + 5\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6dcf2273-ebd0-480b-b3de-260a67814809",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x14f7f1ae0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the RoIs\n",
+    "fig, ax = plt.subplots(figsize=(7, 7))\n",
+    "\n",
+    "ax.imshow(rgb / np.max(rgb, axis=(0, 1))) # Dividing brightens the image\n",
+    "ax.set_title(neon_str)\n",
+    "\n",
+    "for i, (roi, region) in enumerate(regions.items()):\n",
+    "    rect = patches.Rectangle(\n",
+    "        (region[2] - x, region[0] - y), \n",
+    "        region[3] - region[2], \n",
+    "        region[1] - region[0], \n",
+    "        linewidth = 1, \n",
+    "        edgecolor = f'C{i}', \n",
+    "        facecolor = 'none',\n",
+    "        label = roi\n",
+    "    )\n",
+    "    ax.add_patch(rect)\n",
+    "ax.legend(loc='lower right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f358f265-0ddd-47bf-9407-4c30623dd454",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x1500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(len(regions), sharex=True, figsize=(10, 3*len(regions)))\n",
+    "\n",
+    "for i, (roi, region) in enumerate(regions.items()):\n",
+    "    ax = axes[i]\n",
+    "    \n",
+    "    in_situ = np.genfromtxt(paths.insitu / f'{roi}01/Data/{roi}01_Refl.dat', skip_header=3)\n",
+    "    ax.plot(in_situ[:, 0], in_situ[:, 1], label='In Situ', c='red', ls='-')\n",
+    "\n",
+    "    mean_rfl = np.mean(\n",
+    "        rfl[\n",
+    "            region[0] - y : region[1] - y,\n",
+    "            region[2] - x : region[3] - x,\n",
+    "        ],\n",
+    "        axis = (0, 1)\n",
+    "    )\n",
+    "    \n",
+    "    ax.plot(wl, mean_rfl, label='Isofit', c='black')\n",
+    "    \n",
+    "    ax.set_ylabel('Reflectance')\n",
+    "    ax.set_title(roi)\n",
+    "    ax.legend()\n",
+    "\n",
+    "ax.set_xlabel('Wavelength')\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "411124d3",
+   "metadata": {},
+   "source": [
+    "We can plot out the mapped reflectance (as above), but also the interpolated atmospheric conditions.  The windows size is small enough here (and the atmospheric parameters are chosen in such a way) that the map is going to be pretty static...but we can still see it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "82b2055e-5309-4974-b621-fb7685ef734d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x14fc0e230>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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aO3ZsoxNEvguJDwAAUaw+8Vm7/aKIJD7j++1q9sQnkkh8AABwgfr3aTl7jujPShzr+CxbtkwLFy7UwYMHdfHFF2vJkiW64oorvvd7wWBQZWVliouLY2l6AEDUMU1T1dXVSk5OtjVAF5HlSMfn6aefVk5OjpYtW6bhw4dr+fLlGj16tHbt2qUuXbp853fLysqUmprqRLMAAIiY0tLSBuNVnFRnGqozHX5Xl8P1R4IjY3yGDBmiAQMGKD8/39rWu3dv3XjjjcrLy/vO71ZWVqpdu3ZKCfxGMd94kSMAANEg+OWX+jhwvz777DMlJCQ4fr76MT7//dYlERnjc1v/HYzx+aba2lpt27atwSjr+uXnv0/9462YNm3o+AAAolakh2vURWAdnzrG+DR0+PBh1dXVKSkpKWR7UlKSysvLGxxfU1MTsvBQVVVVczcJAABAkoMvKf12T9c0zUZ7v3l5eUpISLAK43sAAAhf0IyJSIl2zX4FiYmJatGiRYN0p6KiokEKJElz5sxRZWWlVUpLS5u7SQAAAJIc6Pi0bt1aAwcOVGFhYcj2wsJCDRs2rMHxPp/PeueH3Xd/AAAANIUj09lnzZql2267TYMGDbLesLp//35NmzbNidMBAOB5DG62x5GOz09+8hMdOXJE8+fP18GDB9WnTx+9+OKL6tq1qxOnAwAAsMWxlZunT5+u6dOnO1U9AAD4hqCcX2Aw6GjtkRH9w7MBAABs4iWlAAC4QGReUhr9eUn0XwEAAIBNJD4AALhAnRmjOocXGHS6/kiI/isAAACwicQHAAAXCMpQUE7P6orsi1edQOIDAAA8g8QHAAAXYIyPPdF/BQAAADaR+AAA4AKReVdX9Ocl0X8FAAAANpH4AADgAkHTUNDpd3U5XH8kkPgAAADPIPEBAMAFghEY48O7ugAAAKIIHR8AAOAZPOoCAMAFgmaMgg4vMOh0/ZEQ/VcAAABgE4kPAAAuUCdDdQ6/RNTp+iOBxAcAAHgGiQ8AAC7AGB97ov8KAAAAbCLxAQDABerk/BicOkdrjwwSHwAA4BkkPgAAuABjfOyJ/isAAACwicQHAAAXqDNjVOdwIuN0/ZEQ/VcAAABgE4kPAAAuYMpQ0OFZXSYrNwMAAEQPEh8AAFyAMT72RP8VAAAA2ETiAwCACwRNQ0HT2TE4TtcfCSQ+AADAM0h8AABwgTrFqM7hPMPp+iMh+q8AAADAJjo+AADAM3jUBQCACzC42R4SHwAA4BkkPgAAuEBQMQo6nGc4XX8kRP8VAAAA2ETiAwCAC9SZhuocHoPjdP2RQOIDAAA8g8QHAAAXYFaXPSQ+AADAM0h8AABwAdOMUdB0Ns8wHa4/EqL/CgAAAGwi8QEAwAXqZKhODs/qcrj+SCDxAQAAnkHiAwCACwRN52ddBU1Hq48IEh8AAOAZJD4AALhAMAKzupyuPxKi/woAAABsIvEBAMAFgjIUdHjWldP1R0LYic+mTZs0ZswYJScnyzAMrV+/PmS/aZoKBAJKTk5WbGysMjMztXPnzuZqLwAAQJOF3fE5duyYLr30Ui1durTR/QsWLNDixYu1dOlSFRcXy+/3a8SIEaqurj7txgIAAJyOsB91jR49WqNHj250n2maWrJkiebOnauxY8dKklatWqWkpCStWbNGU6dOPb3WAgCARtWZhuocns7udP2R0KyDm/fu3avy8nKNHDnS2ubz+ZSRkaHNmzc3+p2amhpVVVWFFAAAACc0a8envLxckpSUlBSyPSkpydr3bXl5eUpISLBKampqczYJAABPqJ/O7nSJdo5cgWGERmGmaTbYVm/OnDmqrKy0SmlpqRNNAgAAaN7p7H6/X9LJ5Kdz587W9oqKigYpUD2fzyefz9eczQAAwHOCMpx/ZYUXp7N/l7S0NPn9fhUWFlrbamtrVVRUpGHDhjXnqQAAAMIWduLz+eefq6SkxPq8d+9ebd++XR06dFCXLl2Uk5Oj3NxcpaenKz09Xbm5uWrbtq0mTJjQrA0HAAD/ZEZgAUPTBYlP2B2frVu3Kisry/o8a9YsSdLEiRP1xBNPaPbs2Tp+/LimT5+uo0ePasiQISooKFBcXFzztRoAAKAJwu74ZGZmyjRP/V56wzAUCAQUCAROp10AACAMQTMCY3xYxwcAACB68JJSAABcIBLr7LCODwAAQBQh8QEAwAUY42MPiQ8AAPAMEh8AAFwgGIF1fFi5GQAAIIqQ+AAA4AKM8bGHxAcAAHgGiQ8AAC5A4mMPiQ8AAPAMOj4AAMAzeNQFAIAL8KjLHhIfAADgGSQ+AAC4AImPPSQ+AADAM0h8AABwAVPOv1LCdLT2yCDxAQAAnkHiAwCACzDGxx4SHwAA4Bl0fAAAcIH6xMfp4oR9+/Zp8uTJSktLU2xsrHr06KF58+aptra20eOPHDmilJQUGYahzz77LKxz8agLAACcUe+9956CwaCWL1+unj176p133tGUKVN07NgxLVq0qMHxkydPVt++fXXgwIGwz0XHBwAAF4jmMT6jRo3SqFGjrM/du3fX7t27lZ+f36Djk5+fr88++0z33nuvXnrppbDPRccHAACcdSorK9WhQ4eQbbt27dL8+fP1xhtvaM+ePU2ql44PAAAuEMnEp6qqKmS7z+eTz+drtvN8+OGHevjhh/Xggw9a22pqanTzzTdr4cKF6tKlS5M7PgxuBgAAYUlNTVVCQoJV8vLyGj0uEAjIMIzvLFu3bg35TllZmUaNGqWbbrpJt99+u7V9zpw56t27t2699dbTajuJDwAALmCahkyHE5/6+ktLSxUfH29tP1Xak52drfHjx39nnd26dbN+LisrU1ZWloYOHaoVK1aEHPfqq69qx44deuaZZ75uy8l1pBMTEzV37lzdd999tq6Bjg8AAAhLfHx8SMfnVBITE5WYmGirzgMHDigrK0sDBw7UypUrFRMT+lDq2Wef1fHjx63PxcXF+tnPfqbXX39dPXr0sN12Oj4AALhAUIbj7+pyqv6ysjJlZmaqS5cuWrRokQ4dOmTt8/v9ktSgc3P48GFJUu/evdWuXTvb56LjAwAAzqiCggKVlJSopKREKSkpIfvqH2k1FwY3AwCAM2rSpEkyTbPRciqZmZkyTTOstEci8QEAwBWieQHDSCLxAQAAnkHiAwCAC0RyOns0I/EBAACeQeIDAIALMMbHHhIfAADgGSQ+AAC4AGN87CHxAQAAnkHiAwCAC5gRGOND4gMAABBFSHwAAHABU1Izv9aq0XNEOxIfAADgGSQ+AAC4QFCGDDm8jo/D9UcCiQ8AAPAMEh8AAFyAdXzsIfEBAACeQeIDAIALBE1DBu/q+l4kPgAAwDNIfAAAcAHTjMA6Pi5YyIfEBwAAeAYdHwAA4Bk86gIAwAWYzm4PiQ8AAPCMsDo+eXl5Gjx4sOLi4tSpUyfdeOON2r17d8gxpmkqEAgoOTlZsbGxyszM1M6dO5u10QAAIFR94uN0iXZhdXyKioo0Y8YMbdmyRYWFhTpx4oRGjhypY8eOWccsWLBAixcv1tKlS1VcXCy/368RI0aourq62RsPAAAQjrDG+GzYsCHk88qVK9WpUydt27ZNV155pUzT1JIlSzR37lyNHTtWkrRq1SolJSVpzZo1mjp1avO1HAAAWFjA0J7TGuNTWVkpSerQoYMkae/evSovL9fIkSOtY3w+nzIyMrR58+ZG66ipqVFVVVVIAQAAcEKTOz6maWrWrFm6/PLL1adPH0lSeXm5JCkpKSnk2KSkJGvft+Xl5SkhIcEqqampTW0SAACeVb+AodMl2jW545Odna23335bf/zjHxvsM4zQKMw0zQbb6s2ZM0eVlZVWKS0tbWqTAAAAvlOT1vG566679Pzzz2vTpk1KSUmxtvv9fkknk5/OnTtb2ysqKhqkQPV8Pp98Pl9TmgEAAL52MpFxeh0fR6uPiLASH9M0lZ2drXXr1unVV19VWlpayP60tDT5/X4VFhZa22pra1VUVKRhw4Y1T4sBAACaKKzEZ8aMGVqzZo3+/Oc/Ky4uzhq3k5CQoNjYWBmGoZycHOXm5io9PV3p6enKzc1V27ZtNWHCBEcuAAAAsHKzXWF1fPLz8yVJmZmZIdtXrlypSZMmSZJmz56t48ePa/r06Tp69KiGDBmigoICxcXFNUuDAQAAmiqsjo9p4+GeYRgKBAIKBAJNbRMAAAiT+XVx+hzRjnd1AQAAz+Dt7AAAuABjfOwh8QEAAJ5B4gMAgBswyMcWEh8AAOAZdHwAAIBn8KgLAAA3iMDgZjG4GQAAIHqQ+AAA4AInX1Lq/DmiHYkPAADwDBIfAABcgAUM7SHxAQAAnkHiAwCAG5iG87OuSHwAAACiB4kPAAAuwKwue0h8AACAZ5D4AADgBryk1BYSHwAA4BkkPgAAuADr+NhD4gMAADyDxAcAALdwwRgcp5H4AAAAzyDxAQDABRjjYw+JDwAA8AwSHwAA3IB1fGwh8QEAAJ5BxwcAAHgGj7oAAHAF4+vi9DmiG4kPAADwDBIfAADcgMHNtpD4AAAAzyDxAQDADUh8bCHxAQAAnkHiAwCAG5jGyeL0OaIciQ8AAPAMEh8AAFzANE8Wp88R7Uh8AACAZ5D4AADgBszqsoXEBwAAeAaJDwAAbsCsLltIfAAAgGeQ+AAA4AKGebI4fY5oR+IDAAA8g8QHAAA3YFaXLSQ+AADAM+j4AAAAz+BRFwAAbsB0dltIfAAAgGeQ+AAA4AYMbraFxAcAAHgGiQ8AAG5A4mMLiQ8AAPAMEh8AANyAxMcWEh8AAOAZYXV88vPz1bdvX8XHxys+Pl5Dhw7VSy+9ZO03TVOBQEDJycmKjY1VZmamdu7c2eyNBgAA31K/jo/TJcqF1fFJSUnR7373O23dulVbt27VVVddpRtuuMHq3CxYsECLFy/W0qVLVVxcLL/frxEjRqi6utqRxgMAAIQjrI7PmDFj9MMf/lAXXHCBLrjgAv3Hf/yHzj33XG3ZskWmaWrJkiWaO3euxo4dqz59+mjVqlX64osvtGbNGqfaDwAAJBlmZEq0a/IYn7q6Oq1du1bHjh3T0KFDtXfvXpWXl2vkyJHWMT6fTxkZGdq8efMp66mpqVFVVVVIAQAAcELYHZ8dO3bo3HPPlc/n07Rp0/Tcc8/poosuUnl5uSQpKSkp5PikpCRrX2Py8vKUkJBgldTU1HCbBAAAzAiVKBd2x+fCCy/U9u3btWXLFt15552aOHGidu3aZe03jNCBT6ZpNtj2TXPmzFFlZaVVSktLw20SAACALWGv49O6dWv17NlTkjRo0CAVFxfr97//vX71q19JksrLy9W5c2fr+IqKigYp0Df5fD75fL5wmwEAABC2017HxzRN1dTUKC0tTX6/X4WFhda+2tpaFRUVadiwYad7GgAAgNMWVuJzzz33aPTo0UpNTVV1dbXWrl2rjRs3asOGDTIMQzk5OcrNzVV6errS09OVm5urtm3basKECU61HwAASDLk/Kyr6F/FJ8zE55NPPtFtt92mCy+8UFdffbXeeOMNbdiwQSNGjJAkzZ49Wzk5OZo+fboGDRqkAwcOqKCgQHFxcY40HgAARL99+/Zp8uTJSktLU2xsrHr06KF58+aptrY25DjDMBqURx55JKxzhZX4PPbYY9+53zAMBQIBBQKBsBoBAAC867333lMwGNTy5cvVs2dPvfPOO5oyZYqOHTumRYsWhRy7cuVKjRo1yvqckJAQ1rl4SSkAAG4QiVdKOFT/qFGjQjoz3bt31+7du5Wfn9+g49OuXTv5/f4mn4uXlAIAgLNOZWWlOnTo0GB7dna2EhMTNXjwYD3yyCMKBoNh1UviAwCAG0RigcGv6//2Wxaae2maDz/8UA8//LAefPDBkO3//u//rquvvlqxsbF65ZVX9Mtf/lKHDx/Wb37zG9t1k/gAAICwpKamhrx1IS8vr9HjAoFAowOSv1m2bt0a8p2ysjKNGjVKN910k26//faQfb/5zW80dOhQ9evXT7/85S81f/58LVy4MKy2k/gAAOAGEUx8SktLFR8fb20+VdqTnZ2t8ePHf2eV3bp1s34uKytTVlaWhg4dqhUrVnxvcy677DJVVVXpk08++c7Fkr+Jjg8AAAhLfHx8SMfnVBITE5WYmGirzgMHDigrK0sDBw7UypUrFRPz/Q+l3nrrLbVp00bt2rWzdQ6Jjg8AAK5gmBFYwNCh+svKypSZmakuXbpo0aJFOnTokLWvfgbXCy+8oPLycg0dOlSxsbF67bXXNHfuXN1xxx1hjS+i4wMAAM6ogoIClZSUqKSkRCkpKSH7TPNkb6tVq1ZatmyZZs2apWAwqO7du2v+/PmaMWNGWOei4wMAgBtEcIxPc5s0aZImTZr0ncd8e62fpmJWFwAA8AwSHwAA3CCKE59IIvEBAACeQeIDAIALRPOsrkgi8QEAAJ5B4gMAgBtE8dvZI4nEBwAAeAaJDwAAbsCsLltIfAAAgGeQ+AAA4ALM6rKHxAcAAHgGHR8AAOAZPOoCAMANGNxsC4kPAADwDBIfAADcIAKDm0l8AAAAogiJDwAAbsAYH1tIfAAAgGeQ+AAA4AYkPraQ+AAAAM8g8QEAwAV4ZYU9JD4AAMAz6PgAAADPoOMDAAA8gzE+AAC4AbO6bCHxAQAAnkHiAwCACzCryx4SHwAA4BkkPgAAuIULEhmnkfgAAADPoOMDAAA8g0ddAAC4AdPZbSHxAQAAnkHiAwCACzCd3R4SHwAA4BkkPgAAuAFjfGwh8QEAAJ5B4gMAgAswxsceEh8AAOAZJD4AALgBY3xsIfEBAACeQeIDAIAbkPjYQuIDAAA8g8QHAAAXYFaXPaeV+OTl5ckwDOXk5FjbTNNUIBBQcnKyYmNjlZmZqZ07d55uOwEAAE5bkzs+xcXFWrFihfr27RuyfcGCBVq8eLGWLl2q4uJi+f1+jRgxQtXV1afdWAAAcApmhEqUa1LH5/PPP9ctt9yiRx99VO3bt7e2m6apJUuWaO7cuRo7dqz69OmjVatW6YsvvtCaNWuardEAAABN0aSOz4wZM3TdddfpmmuuCdm+d+9elZeXa+TIkdY2n8+njIwMbd68+fRaCgAATo3Ex5awBzevXbtWf//731VcXNxgX3l5uSQpKSkpZHtSUpI++uijRuurqalRTU2N9bmqqircJgEAANgSVuJTWlqqX/ziF3ryySfVpk2bUx5nGEbIZ9M0G2yrl5eXp4SEBKukpqaG0yQAAKB/zupyukS7sDo+27ZtU0VFhQYOHKiWLVuqZcuWKioq0kMPPaSWLVtaSU998lOvoqKiQQpUb86cOaqsrLRKaWlpEy8FAADgu4X1qOvqq6/Wjh07Qrb927/9m3r16qVf/epX6t69u/x+vwoLC9W/f39JUm1trYqKivTAAw80WqfP55PP52ti8wEAAOwLq+MTFxenPn36hGw755xz1LFjR2t7Tk6OcnNzlZ6ervT0dOXm5qpt27aaMGFC87UaAACE4pUVtjT7ys2zZ8/W8ePHNX36dB09elRDhgxRQUGB4uLimvtUAAAAYTntjs/GjRtDPhuGoUAgoEAgcLpVAwAAm3hlhT28pBQAAHgGLykFAMANGONjC4kPAADwDBIfAADcgMTHFhIfAADgGSQ+AAC4gPF1cfoc0Y7EBwAAeAaJDwAAbsAYH1tIfAAAgGeQ+AAA4AKs3GwPiQ8AAPAMEh8AANyAMT62kPgAAADPIPEBAMAtXJDIOI3EBwAAeAYdHwAA4Bk86gIAwAWYzm4PiQ8AAPAMEh8AANyA6ey2kPgAAADPIPEBAMAFGONjD4kPAADwDBIfAADcgDE+tpD4AAAAzyDxAQDABRjjYw+JDwAA8AwSHwAA3IAxPraQ+AAAAM8g8QEAwA1IfGwh8QEAAJ5B4gMAgAswq8seEh8AAOAZJD4AALgBY3xsIfEBAACeQeIDAIALGKYpw3Q2knG6/kgg8QEAAJ5BxwcAAHgGj7oAAHADBjfbQuIDAAA8g8QHAAAXYAFDe0h8AACAZ5D4AADgBozxsYXEBwAAnFH79u3T5MmTlZaWptjYWPXo0UPz5s1TbW1tg2OfeOIJ9e3bV23atJHf71d2dnZY5yLxAQDABaJ5jM97772nYDCo5cuXq2fPnnrnnXc0ZcoUHTt2TIsWLbKOW7x4sR588EEtXLhQQ4YM0Zdffqk9e/aEdS46PgAA4IwaNWqURo0aZX3u3r27du/erfz8fKvjc/ToUf3mN7/RCy+8oKuvvto69uKLLw7rXDzqAgDADcwIlQiprKxUhw4drM+FhYUKBoM6cOCAevfurZSUFI0bN06lpaVh1UvHBwAAhKWqqiqk1NTUNGv9H374oR5++GFNmzbN2rZnzx4Fg0Hl5uZqyZIleuaZZ/Tpp59qxIgRjY4FOhU6PgAAuED9GB+niySlpqYqISHBKnl5eY22KRAIyDCM7yxbt24N+U5ZWZlGjRqlm266Sbfffru1PRgM6quvvtJDDz2ka6+9Vpdddpn++Mc/6oMPPtBrr71m+z4xxgcAAISltLRU8fHx1mefz9focdnZ2Ro/fvx31tWtWzfr57KyMmVlZWno0KFasWJFyHGdO3eWJF100UXWtvPOO0+JiYnav3+/7bbT8QEAwA0iuI5PfHx8SMfnVBITE5WYmGir6gMHDigrK0sDBw7UypUrFRMT+lBq+PDhkqTdu3crJSVFkvTpp5/q8OHD6tq1q+1L4FEXAAA4o8rKypSZmanU1FQtWrRIhw4dUnl5ucrLy61jLrjgAt1www36xS9+oc2bN+udd97RxIkT1atXL2VlZdk+F4kPAAAuEa3v0iooKFBJSYlKSkqsNKeeaf7zolavXq2ZM2fquuuuU0xMjDIyMrRhwwa1atXK9rlIfAAAwBk1adIkmabZaPmm+Ph4PfbYYzp69KiOHDmidevWKTU1NaxzkfgAAOAGpnmyOH2OKBdW4tPYtDS/32/tN01TgUBAycnJio2NVWZmpnbu3NnsjQYAAGiKsB91XXzxxTp48KBVduzYYe1bsGCBFi9erKVLl6q4uFh+v18jRoxQdXV1szYaAACgKcJ+1NWyZcuQlKeeaZpasmSJ5s6dq7Fjx0qSVq1apaSkJK1Zs0ZTp049/dYCAIBGRfNLSiMp7MTngw8+UHJystLS0jR+/Hjrrah79+5VeXm5Ro4caR3r8/mUkZGhzZs3N1+LAQAAmiisxGfIkCFavXq1LrjgAn3yySe6//77NWzYMO3cudOaa5+UlBTynaSkJH300UenrLOmpibkHR9VVVXhNAkAAEgRXcAwmoXV8Rk9erT18yWXXKKhQ4eqR48eWrVqlS677DJJkmEYId8xTbPBtm/Ky8vTfffdF04zAAAAmuS01vE555xzdMkll+iDDz6wxv18c5VFSaqoqGiQAn3TnDlzVFlZaZVwXy8PAAAkIxiZEu1Oq+NTU1Ojd999V507d1ZaWpr8fr8KCwut/bW1tSoqKtKwYcNOWYfP57Pe+WH33R8AAABNEdajrrvvvltjxoxRly5dVFFRofvvv19VVVWaOHGiDMNQTk6OcnNzlZ6ervT0dOXm5qpt27aaMGGCU+0HAAASY3xsCqvj8/HHH+vmm2/W4cOHdd555+myyy7Tli1brLeizp49W8ePH9f06dN19OhRDRkyRAUFBYqLi3Ok8QAAAOEIq+Ozdu3a79xvGIYCgYACgcDptAkAAISJdXzs4SWlAADAM3hJKQAAbsBLSm0h8QEAAJ5B4gMAgAswxsceEh8AAOAZJD4AALgB6/jYQuIDAAA8g8QHAAAXYIyPPSQ+AADAM0h8AABwA9bxsYXEBwAAeAYdHwAA4Bk86gIAwAUY3GwPiQ8AAPAMEh8AANyABQxtIfEBAACeQeIDAIALMMbHHhIfAADgGSQ+AAC4QdA8WZw+R5Qj8QEAAJ5B4gMAgBswq8sWEh8AAOAZJD4AALiAoQjM6nK2+ogg8QEAAJ5B4gMAgBuY5sni9DmiHIkPAADwDBIfAABcgJWb7SHxAQAAnkHiAwCAG7COjy0kPgAAwDPo+AAAAM/gURcAAC5gmKYMh6ebO11/JJD4AAAAzyDxAQDADYJfF6fPEeVIfAAAgGeQ+AAA4AKM8bGHxAcAAHgGiQ8AAG7AAoa2kPgAAADPIPEBAMANTPNkcfocUY7EBwAAeAaJDwAALmCYJ4vT54h2JD4AAMAzSHwAAHADxvjYQuIDAAA8g8QHAAAXMIIni9PniHYkPgAAwDNIfAAAcAPG+NhC4gMAADyDxAcAADfgXV22kPgAAADPoOMDAAA8g0ddAAC4gGGaMhwefOx0/ZFA4gMAADwj7I7PgQMHdOutt6pjx45q27at+vXrp23btln7TdNUIBBQcnKyYmNjlZmZqZ07dzZrowEAwLfUT2d3ukS5sDo+R48e1fDhw9WqVSu99NJL2rVrlx588EG1a9fOOmbBggVavHixli5dquLiYvn9fo0YMULV1dXN3XYAAICwhDXG54EHHlBqaqpWrlxpbevWrZv1s2maWrJkiebOnauxY8dKklatWqWkpCStWbNGU6dObZ5WAwCAUKYkp18pEf2BT3iJz/PPP69BgwbppptuUqdOndS/f389+uij1v69e/eqvLxcI0eOtLb5fD5lZGRo8+bNjdZZU1OjqqqqkAIAAOCEsDo+e/bsUX5+vtLT0/Xyyy9r2rRp+vnPf67Vq1dLksrLyyVJSUlJId9LSkqy9n1bXl6eEhISrJKamtqU6wAAwNPqZ3U5XaJdWB2fYDCoAQMGKDc3V/3799fUqVM1ZcoU5efnhxxnGEbIZ9M0G2yrN2fOHFVWVlqltLQ0zEsAAACwJ6yOT+fOnXXRRReFbOvdu7f2798vSfL7/ZLUIN2pqKhokALV8/l8io+PDykAACBMpiIwq+tMX+TpC6vjM3z4cO3evTtk2/vvv6+uXbtKktLS0uT3+1VYWGjtr62tVVFRkYYNG9YMzQUAAGi6sGZ1zZw5U8OGDVNubq7GjRunN998UytWrNCKFSsknXzElZOTo9zcXKWnpys9PV25ublq27atJkyY4MgFAAAARWadHReM8Qmr4zN48GA999xzmjNnjubPn6+0tDQtWbJEt9xyi3XM7Nmzdfz4cU2fPl1Hjx7VkCFDVFBQoLi4uGZvPAAAQDjCflfX9ddfr+uvv/6U+w3DUCAQUCAQOJ12AQCAcAQlNT6PqHnPEeV4VxcAAPAM3s4OAIAL8HZ2e0h8AACAZ5D4AADgBszqsoXEBwAAeAYdHwAA4Bk86gIAwA141GULiQ8AAPAMEh8AANyAxMeWs67jY359U4NffnmGWwIAQPjq//4yXdBJcKOzruNTXV0tSfo4cP8ZbgkAAE1XXV2thISEyJ2QV1bYctZ1fJKTk1VaWqq4uDgZhqGqqiqlpqaqtLRU8fHxZ7p5Eef165e4BxL3QOIeSNyDaLl+0zRVXV2t5OTkM90UNOKs6/jExMQoJSWlwfb4+Piz+g+607x+/RL3QOIeSNwDiXsQDdcf0aTna7yywh5mdQEAAM846xIfAADQBMzqsuWsT3x8Pp/mzZsnn893pptyRnj9+iXugcQ9kLgHEvfA69eP5mGYzLcDACBqVVVVKSEhQdf0yFHLFs52Ck/U1eivHy5RZWXlWT/O6lTO+sQHAACguTDGBwAAN2CMjy0kPgAAwDPO6o7PsmXLlJaWpjZt2mjgwIF6/fXXz3STHLNp0yaNGTNGycnJMgxD69evD9lvmqYCgYCSk5MVGxurzMxM7dy588w01gF5eXkaPHiw4uLi1KlTJ914443avXt3yDFuvwf5+fnq27evtUbJ0KFD9dJLL1n73X79jcnLy5NhGMrJybG2uf0+BAIBGYYRUvx+v7Xf7dcvSQcOHNCtt96qjh07qm3bturXr5+2bdtm7ffCPWga85+pj1NFziQ++/bt0+TJk5WWlqbY2Fj16NFD8+bNU21trXXME0880eD/jfpSUVFh+1xnbcfn6aefVk5OjubOnau33npLV1xxhUaPHq39+/ef6aY54tixY7r00ku1dOnSRvcvWLBAixcv1tKlS1VcXCy/368RI0ZYr/iIdkVFRZoxY4a2bNmiwsJCnThxQiNHjtSxY8esY9x+D1JSUvS73/1OW7du1datW3XVVVfphhtusH6hu/36v624uFgrVqxQ3759Q7Z74T5cfPHFOnjwoFV27Nhh7XP79R89elTDhw9Xq1at9NJLL2nXrl168MEH1a5dO+sYt98DL3rvvfcUDAa1fPly7dy5U//5n/+pRx55RPfcc491zE9+8pOQ/y8OHjyoa6+9VhkZGerUqZPtc521s7qGDBmiAQMGKD8/39rWu3dv3XjjjcrLyzuDLXOeYRh67rnndOONN0o6+a+b5ORk5eTk6Fe/+pUkqaamRklJSXrggQc0derUM9haZxw6dEidOnVSUVGRrrzySk/eA0nq0KGDFi5cqJ/97Geeuv7PP/9cAwYM0LJly3T//ferX79+WrJkiSf+HAQCAa1fv17bt29vsM8L1//rX/9af/vb306Z8HvhHoTLmtWVdpdaxjg8qytYo7/ufTgis7oWLlyo/Px87dmzp9H9hw4d0vnnn6/HHntMt912m+16z8rEp7a2Vtu2bdPIkSNDto8cOVKbN28+Q606c/bu3avy8vKQ++Hz+ZSRkeHa+1FZWSnp5F/8kvfuQV1dndauXatjx45p6NChnrv+GTNm6LrrrtM111wTst0r9+GDDz5QcnKy0tLSNH78eOsXvxeu//nnn9egQYN00003qVOnTurfv78effRRa78X7gFOqqystP4OaMzq1avVtm1b/fjHPw6r3rOy43P48GHV1dUpKSkpZHtSUpLKy8vPUKvOnPpr9sr9ME1Ts2bN0uWXX64+ffpI8s492LFjh84991z5fD5NmzZNzz33nC666CLPXL8krV27Vn//+98bTXa9cB+GDBmi1atX6+WXX9ajjz6q8vJyDRs2TEeOHPHE9e/Zs0f5+flKT0/Xyy+/rGnTpunnP/+5Vq9eLckbfwaaLGhGpuhkyvTNUlNT06yX8uGHH+rhhx/WtGnTTnnM448/rgkTJig2Njasus/Kjk89wzBCPpum2WCbl3jlfmRnZ+vtt9/WH//4xwb73H4PLrzwQm3fvl1btmzRnXfeqYkTJ2rXrl3Wfrdff2lpqX7xi1/oySefVJs2bU55nJvvw+jRo/Wv//qvuuSSS3TNNdfof/7nfyRJq1atso5x8/UHg0ENGDBAubm56t+/v6ZOnaopU6aEDHuQ3H0PokFqaqoSEhKscqohKI0N1v922bp1a8h3ysrKNGrUKN100026/fbbG633//7v/7Rr1y5Nnjw57Laflev4JCYmqkWLFg167xUVFQ16+V5QP6OjvLxcnTt3tra78X7cddddev7557Vp0yalpKRY271yD1q3bq2ePXtKkgYNGqTi4mL9/ve/t8YyuP36t23bpoqKCg0cONDaVldXp02bNmnp0qXWTD+334dvOuecc3TJJZfogw8+sMb9ufn6O3furIsuuihkW+/evfXss89K8s7vgrNdaWlpyBifU71GJDs7W+PHj//Ourp162b9XFZWpqysLA0dOlQrVqw45Xf+8Ic/qF+/fiG/K+w6KxOf1q1ba+DAgSosLAzZXlhYqGHDhp2hVp05aWlp8vv9IfejtrZWRUVFrrkfpmkqOztb69at06uvvqq0tLSQ/V64B40xTVM1NTWeuf6rr75aO3bs0Pbt260yaNAg3XLLLdq+fbu6d+/uifvwTTU1NXr33XfVuXNnT/w5GD58eIOlLN5//3117dpVknd/F9hiBiNTJGvZjfpyqo5PYmKievXq9Z2lPt09cOCAMjMzNWDAAK1cuVIxMY13UT7//HP96U9/alLaI52liY8kzZo1S7fddpsGDRpk9fz279//nc/7otnnn3+ukpIS6/PevXu1fft2dejQQV26dFFOTo5yc3OVnp6u9PR05ebmqm3btpowYcIZbHXzmTFjhtasWaM///nPiouLs9K+hIQExcbGWmu5uPke3HPPPRo9erRSU1NVXV2ttWvXauPGjdqwYYMnrl+S4uLirHFd9c455xx17NjR2u72+3D33XdrzJgx6tKliyoqKnT//ferqqpKEydO9MSfg5kzZ2rYsGHKzc3VuHHj9Oabb2rFihXWv/69cA+8qKysTJmZmerSpYsWLVqkQ4cOWfu+uY6VdHK5mxMnTuiWW25p0rnO2o7PT37yEx05ckTz58/XwYMH1adPH7344otWr99ttm7dqqysLOvzrFmzJEkTJ07UE088odmzZ+v48eOaPn26jh49qiFDhqigoEBxcXFnqsnNqv75fWZmZsj2lStXatKkSZLk+nvwySef6LbbbtPBgweVkJCgvn37asOGDRoxYoQk91+/XW6/Dx9//LFuvvlmHT58WOedd54uu+wybdmyxfrd5/brHzx4sJ577jnNmTNH8+fPV1pampYsWRLyl5zb70GTRfErKwoKClRSUqKSkpKQYQ4nTxl6zscee0xjx45V+/btm3Sus3YdHwAA8P2sdXxS74zMOj6l+VH9dvazNvEBAABhCDr3SonQc0S3s3JwMwAAgBNIfAAAcIMoHuMTSSQ+AADAM0h8AABwA1MRSHycrT4SSHwAAIBnkPgAAOAGjPGxhcQHAAB4BokPAABuEAxKCkbgHNGNxAcAAHgGiQ8AAG7AGB9bSHwAAIBnkPgAAOAGJD62kPgAAADPoOMDAAA8g0ddAAC4QdCU4++UCPKoCwAAIGqQ+AAA4AKmGZRpOrvAoNP1RwKJDwAA8AwSHwAA3MA0nR+Dw3R2AACA6EHiAwCAG5gRmNVF4gMAABA9SHwAAHCDYFAyHJ51xawuAACA6EHiAwCAGzDGxxYSHwAA4BkkPgAAuIAZDMp0eIwPKzcDAABEERIfAADcgDE+tpD4AAAAzyDxAQDADYKmZJD4fB8SHwAA4BkkPgAAuIFpSnJ65WYSHwAAgKhBxwcAAHgGj7oAAHABM2jKdHhws8mjLgAAgOhB4gMAgBuYQTk/uJlXVgAAAEQNEh8AAFyAMT72kPgAAADPIPEBAMANGONjCx0fAABc4IS+cvzl7Cf0lbMniAA6PgAARLHWrVvL7/frf8tfjMj5/H6/WrduHZFzOcEw3TBSCQAAD/vyyy9VW1sbkXO1bt1abdq0ici5nEDHBwAAeAazugAAgGfQ8QEAAJ5BxwcAAHgGHR8AAOAZdHwAAIBn0PEBAACeQccHAAB4xv8HSveWiakPpZkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 700x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dat = envi.open(paths.working / f\"output/{neon_str}_atm_interp.hdr\")\n",
+    "atm = dat.open_memmap(interleave='bip').copy()\n",
+    "\n",
+    "plt.figure(figsize=(7, 7))\n",
+    "plt.title('AOD')\n",
+    "plt.imshow(atm[..., 0])\n",
+    "plt.colorbar()\n",
+    "\n",
+    "plt.figure(figsize=(7, 7))\n",
+    "plt.title('Water Vapor')\n",
+    "plt.imshow(atm[..., 1])\n",
+    "plt.colorbar()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "isofit",
+   "language": "python",
+   "name": "isofit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/isotuts/NEON/neon_single_pixel.ipynb b/NEON/neon_single_pixel.ipynb
similarity index 98%
rename from isotuts/NEON/neon_single_pixel.ipynb
rename to NEON/neon_single_pixel.ipynb
index 9e11b7a..07ded6f 100644
--- a/isotuts/NEON/neon_single_pixel.ipynb
+++ b/NEON/neon_single_pixel.ipynb
@@ -59,27 +59,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "\n",
-    "# Extract the image locations of each point of interest (POI)\n",
-    "# These are defined in the NEON report as pixel locations, so we round here to convert to indices\n",
-    "report = {}\n",
-    "report['173647'] = {           # Upp L Y  | Low R Y  | Upp L X | Low R X\n",
-    "    'WhiteTarp': np.round([2224.9626, 2230.9771, 316.0078, 324.9385,]).astype(int),\n",
-    "    'BlackTarp': np.round([2224.9626, 2231.0032, 328.0086, 333.9731,]).astype(int),\n",
-    "    'Veg'      : np.round([2245.0381, 2258.8103, 343.9006, 346.9423,]).astype(int),\n",
-    "    'RoadEW'   : np.round([2214.9905, 2216.9978, 348.9902, 373.0080,]).astype(int),\n",
-    "    'RoadNS'   : np.round([2205.9580, 2225.9612, 357.9536, 359.9608,]).astype(int)\n",
-    "}\n",
-    "report['174150'] = {           # Upp L Y | Low R Y | Upp L X  | Low R X\n",
-    "    'WhiteTarp': np.round([653.9626, 659.9771, 3143.0078, 3151.9385]).astype(int),\n",
-    "    'BlackTarp': np.round([653.9626, 660.0032, 3155.0086, 3160.9731]).astype(int),\n",
-    "    'Veg'      : np.round([674.0381, 687.8103, 3170.9006, 3173.9423]).astype(int),\n",
-    "    'RoadEW'   : np.round([643.9905, 645.9978, 3175.9902, 3200.0080]).astype(int),\n",
-    "    'RoadNS'   : np.round([634.9580, 654.9612, 3184.9536, 3186.9608]).astype(int)\n",
-    "}\n",
-    "# Converts numpy array to comma-separated string for ISOFIT\n",
-    "toString = lambda array: ', '.join(str(v) for v in array)"
+    "\n"
    ]
   },
   {
@@ -211,7 +191,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "isofit_env",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -225,9 +205,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/isotuts/utils.py b/NEON/utils/faker.py
similarity index 97%
rename from isotuts/utils.py
rename to NEON/utils/faker.py
index b81b6ae..3bcd59e 100644
--- a/isotuts/utils.py
+++ b/NEON/utils/faker.py
@@ -27,6 +27,7 @@ def getMetadata(file, remove=['fwhm', 'band names', 'wavelength', 'wavelength un
 
     return metadata
 
+
 def fakeLOC(rdn, lon, lat, elv, output=None, **kwargs):
     """
     Creates a fake LOC file
@@ -66,6 +67,9 @@ def fakeLOC(rdn, lon, lat, elv, output=None, **kwargs):
 
     del ds, loc
 
+    return output
+
+
 def fakeOBS(rdn, param0=0, sea=0, sez=0, soa=0, soz=0, phase=0, slope=0, aspect=0, cosi=0, param9=0, param10=0, output=None, **kwargs):
     """
     Creates a fake OBS file
@@ -104,7 +108,7 @@ def fakeOBS(rdn, param0=0, sea=0, sez=0, soa=0, soz=0, phase=0, slope=0, aspect=
     """
     if not output:
         if 'rdn' in rdn:
-            output = rdn.replace('rdn', 'loc')
+            output = rdn.replace('rdn', 'obs')
         else:
             Logger.error('No ouput file specified and cannot generate a unique name')
             return False
@@ -129,3 +133,5 @@ def fakeOBS(rdn, param0=0, sea=0, sez=0, soa=0, soz=0, phase=0, slope=0, aspect=
     obs[..., 10] = param10
 
     del ds, obs
+
+    return output
diff --git a/NEON/utils/neon.py b/NEON/utils/neon.py
new file mode 100644
index 0000000..cae44f3
--- /dev/null
+++ b/NEON/utils/neon.py
@@ -0,0 +1,21 @@
+import numpy as np
+
+# Extract the image locations of each point of interest (POI)
+# These are defined in the NEON report as pixel locations, so we round here to convert to indices
+report = {}
+report['173647'] = {           # Upp L Y  | Low R Y  | Upp L X | Low R X
+    'WhiteTarp': np.round([2224.9626, 2230.9771, 316.0078, 324.9385,]).astype(int),
+    'BlackTarp': np.round([2224.9626, 2231.0032, 328.0086, 333.9731,]).astype(int),
+    'Veg'      : np.round([2245.0381, 2258.8103, 343.9006, 346.9423,]).astype(int),
+    'RoadEW'   : np.round([2214.9905, 2216.9978, 348.9902, 373.0080,]).astype(int),
+    'RoadNS'   : np.round([2205.9580, 2225.9612, 357.9536, 359.9608,]).astype(int)
+}
+report['174150'] = {           # Upp L Y | Low R Y | Upp L X  | Low R X
+    'WhiteTarp': np.round([653.9626, 659.9771, 3143.0078, 3151.9385]).astype(int),
+    'BlackTarp': np.round([653.9626, 660.0032, 3155.0086, 3160.9731]).astype(int),
+    'Veg'      : np.round([674.0381, 687.8103, 3170.9006, 3173.9423]).astype(int),
+    'RoadEW'   : np.round([643.9905, 645.9978, 3175.9902, 3200.0080]).astype(int),
+    'RoadNS'   : np.round([634.9580, 654.9612, 3184.9536, 3186.9608]).astype(int)
+}
+# Converts numpy array to comma-separated string for ISOFIT
+toString = lambda array: ', '.join(str(v) for v in array)
diff --git a/isotuts/NEON/neon.ipynb b/isotuts/NEON/neon.ipynb
deleted file mode 100644
index 1593bb1..0000000
--- a/isotuts/NEON/neon.ipynb
+++ /dev/null
@@ -1,573 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e30eef79",
-   "metadata": {},
-   "source": [
-    "# NEON\n",
-    "\n",
-    "This notebook is an excercise in executing ISOFIT on two dates from the NEON dataset and interpreting the outputs of ISOFIT. \n",
-    "\n",
-    "Prerequisites:\n",
-    "- Download sample data from https://avng.jpl.nasa.gov/pub/PBrodrick/isofit/tutorials/subset_data.zip.  This dataset was prepped already from the data_prep notebook.  Place the dataset into the NEON folder in this repo and unzip it, which will create the 'data' folder which includes the 'subsets' directory.\n",
-    "- Have a working installation of ISOFIT, with sRTMnet installed and configured (see environment variable specification on the next line)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "44e2871f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Data subset directory at: /Users/brodrick/repos/isofit-tutorials/isotuts/NEON/data/subsets\n",
-      "Surface model at: /Users/brodrick/repos/isofit/examples/20171108_Pasadena/configs/ang20171108t184227_surface.json\n",
-      "sRTMnet emulator path (required): /Users/brodrick/isofit_support/sRTMnet_v120.h5\n",
-      "6s path (required): /Users/brodrick/6s/\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Jupyter magics\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Setup logging\n",
-    "import logging\n",
-    "import os\n",
-    "\n",
-    "from types import SimpleNamespace\n",
-    "import scipy\n",
-    "\n",
-    "import isofit\n",
-    "from isofit.utils.apply_oe import apply_oe \n",
-    "from isofit.utils.surface_model import surface_model\n",
-    "\n",
-    "# Enable the ISOFIT logger\n",
-    "logging.getLogger().setLevel(logging.INFO)\n",
-    "\n",
-    "# Find where we're running the tutorial from\n",
-    "home = os.path.abspath(os.getcwd())\n",
-    "\n",
-    "# Path to the input NEON data\n",
-    "indata = os.path.join(home, 'data') \n",
-    "subset_dir = os.path.join(indata, 'subsets')\n",
-    "print(f'Data subset directory at: {subset_dir}')\n",
-    "\n",
-    "# Path to write isofit output\n",
-    "output = os.path.join(home,'outputs')\n",
-    "if os.path.isdir(output) is False:\n",
-    "    os.mkdir(output)\n",
-    "\n",
-    "if os.path.isdir(subset_dir) is False:\n",
-    "    os.mkdir(subset_dir)\n",
-    "\n",
-    "surface_model_path = os.path.join(isofit.root, 'examples/20171108_Pasadena/configs/ang20171108t184227_surface.json')\n",
-    "print(f'Surface model at: {surface_model_path}')\n",
-    "neon_id = '173647'\n",
-    "\n",
-    "\n",
-    "# Optionally set some environment variables as needed\n",
-    "#os.environ['EMULATOR_PATH'] = '/Users/brodrick/isofit_support/sRTMnet_v120.h5'\n",
-    "#os.environ['SIXS_DIR'] = '/Users/brodrick/6s/'\n",
-    "\n",
-    "print(f'sRTMnet emulator path (required): {os.environ[\"EMULATOR_PATH\"]}')\n",
-    "print(f'6s path (required): {os.environ[\"SIXS_DIR\"]}')\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "893fd5ac",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "ISOFIT needs at minimum three pieces as input:\n",
-    "\n",
-    "    1. Radiance measurements (rdn)\n",
-    "    2. Observation values    (obs)\n",
-    "    3. Location information  (loc)\n",
-    "\n",
-    "This sample dataset from NEON has radiance and observation data, but no location values (more recent NEON datasets include the location file).  However, we can 'fake' the location file with sufficient accuracy for ISOFIT to run successfully.  Note that there are data available for two dates:\n",
-    "\n",
-    "```\n",
-    "Radiance\n",
-    "├── 173647\n",
-    "│   ├── NIS01_20210403_173647_obs_ort\n",
-    "│   ├── NIS01_20210403_173647_obs_ort.hdr\n",
-    "│   ├── NIS01_20210403_173647_rdn_ort\n",
-    "│   └── NIS01_20210403_173647_rdn_ort.hdr\n",
-    "└── 174150\n",
-    "    ├── NIS01_20210403_174150_obs_ort\n",
-    "    ├── NIS01_20210403_174150_obs_ort.hdr\n",
-    "    ├── NIS01_20210403_174150_rdn_ort\n",
-    "    └── NIS01_20210403_174150_rdn_ort.hdr\n",
-    "```\n",
-    "\n",
-    "These files have corresponding in situ data as well, and below we've encoded the locations of each, which we can use to help subset data files.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "be6e2d53",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "# Extract the image locations of each point of interest (POI)\n",
-    "# These are defined in the NEON report as pixel locations, so we round here to convert to indices\n",
-    "report = {}\n",
-    "report['173647'] = {           # Upp L Y  | Low R Y  | Upp L X | Low R X\n",
-    "    'WhiteTarp': np.round([2224.9626, 2230.9771, 316.0078, 324.9385,]).astype(int),\n",
-    "    'BlackTarp': np.round([2224.9626, 2231.0032, 328.0086, 333.9731,]).astype(int),\n",
-    "    'Veg'      : np.round([2245.0381, 2258.8103, 343.9006, 346.9423,]).astype(int),\n",
-    "    'RoadEW'   : np.round([2214.9905, 2216.9978, 348.9902, 373.0080,]).astype(int),\n",
-    "    'RoadNS'   : np.round([2205.9580, 2225.9612, 357.9536, 359.9608,]).astype(int)\n",
-    "}\n",
-    "report['174150'] = {           # Upp L Y | Low R Y | Upp L X  | Low R X\n",
-    "    'WhiteTarp': np.round([653.9626, 659.9771, 3143.0078, 3151.9385]).astype(int),\n",
-    "    'BlackTarp': np.round([653.9626, 660.0032, 3155.0086, 3160.9731]).astype(int),\n",
-    "    'Veg'      : np.round([674.0381, 687.8103, 3170.9006, 3173.9423]).astype(int),\n",
-    "    'RoadEW'   : np.round([643.9905, 645.9978, 3175.9902, 3200.0080]).astype(int),\n",
-    "    'RoadNS'   : np.round([634.9580, 654.9612, 3184.9536, 3186.9608]).astype(int)\n",
-    "}\n",
-    "# Converts numpy array to comma-separated string for ISOFIT\n",
-    "toString = lambda array: ', '.join(str(v) for v in array)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e3f01d1a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Which NEON date to process - change this to process a different date\n",
-    "neon_id = list(report.keys())[0]\n",
-    "\n",
-    "# Select the locations from the neon id -- roi == Regions of Interest\n",
-    "roi = report[neon_id]\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b0faef2",
-   "metadata": {},
-   "source": [
-    "## Loc file generation\n",
-    "\n",
-    "NEON doesn't distribute (?) a loc file, so let's fake one for now.  We'll do this for the full file and for the subset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "98252646",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Key 'band names' not found in the metadata, skipping\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Key 'band names' not found in the metadata, skipping\n"
-     ]
-    }
-   ],
-   "source": [
-    "from isotuts.utils import fakeLOC\n",
-    "\n",
-    "fakeLOC(\n",
-    "    rdn = os.path.join(indata,f'NIS01_20210403_{neon_id}_rdn_ort.hdr'),\n",
-    "    lon = -105.237000,\n",
-    "    lat = 40.125000,\n",
-    "    elv = 1689.0\n",
-    ")\n",
-    "\n",
-    "\n",
-    "fakeLOC(\n",
-    "    rdn = os.path.join(subset_dir,f'NIS01_20210403_{neon_id}_rdn_ort.hdr'),\n",
-    "    lon = -105.237000,\n",
-    "    lat = 40.125000,\n",
-    "    elv = 1689.0\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8a2cde13",
-   "metadata": {},
-   "source": [
-    "# Apply OE\n",
-    "\n",
-    "The next part walks through running the ISOFIT utility script `isofit/utils/apply_oe.py`. This is the first step of executing ISOFIT and will generate a default configuration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "7357a326",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 ['../../../data/reflectance/surface_model_ucsb']\n",
-      "1 ['../../../data/reflectance/surface_model_ucsb']\n",
-      "2 ['../../../data/reflectance/surface_model_ucsb']\n",
-      "3 ['../../../data/reflectance/surface_model_ucsb']\n",
-      "4 ['../../../data/reflectance/surface_model_ucsb']\n",
-      "5 ['../../../data/reflectance/surface_model_ucsb']\n",
-      "6 ['../../../data/reflectance/surface_model_ucsb']\n",
-      "7 ['../../../data/reflectance/surface_model_ucsb']\n"
-     ]
-    }
-   ],
-   "source": [
-    "output_surface_file = os.path.join(output, 'surface.mat')\n",
-    "surface_model(**{\n",
-    "    'config_path': surface_model_path,\n",
-    "    'output_path': output_surface_file,\n",
-    "    'wavelength_path': os.path.join(subset_dir,f'NIS01_20210403_{neon_id}_rdn_ort.hdr')\n",
-    "}\n",
-    "\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "81330d85-2453-4065-bfa7-f6a09374709a",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-07-22 09:56:47,340\tINFO worker.py:1724 -- Started a local Ray instance.\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | namespace(input_radiance='/Users/brodrick/repos/isofit-tutorials/isotuts/NEON/data/subsets/NIS01_20210403_173647_rdn_ort', input_loc='/Users/brodrick/repos/isofit-tutorials/isotuts/NEON/data/subsets/NIS01_20210403_173647_loc_ort', input_obs='/Users/brodrick/repos/isofit-tutorials/isotuts/NEON/data/subsets/NIS01_20210403_173647_rdn_obs_ort', working_directory='/Users/brodrick/repos/isofit-tutorials/isotuts/NEON/outputs/NIS01_20210403_173647', sensor='neon', surface_path='/Users/brodrick/repos/isofit-tutorials/isotuts/NEON/outputs/surface.mat', emulator_base='/Users/brodrick/isofit_support/sRTMnet_v120.h5', modtran_path=None, n_cores=4, copy_input_files=False, wavelength_path=None, surface_category='multicomponent_surface', aerosol_climatology_path=None, atmosphere_type='ATM_MIDLAT_SUMMER', rdn_factors_path=None, channelized_uncertainty_path=None, model_discrepancy_path=None, lut_config_file=None, multiple_restarts=False, logging_level='INFO', log_file=None, num_cpus=1, memory_gb=-1, presolve=True, empirical_line=False, analytical_line=True, ray_temp_dir='/tmp/ray', segmentation_size=10, num_neighbors=[5], atm_sigma=[0.5, 0.5], pressure_elevation=False, prebuilt_lut=None)\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Checking input data files...\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | ...Data file checks complete\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Setting up files and directories....\n",
-      "INFO:2024-07-22,09:56:47 || template_construction.py:__init__() | Flightline ID: NIS01_20210403_173647\n",
-      "INFO:2024-07-22,09:56:47 || template_construction.py:__init__() | no noise path found, proceeding without\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | ...file/directory setup complete\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | No wavelength file provided. Obtaining wavelength grid from ENVI header of radiance cube.\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Wavelength units of nm inferred...converting to microns\n",
-      "WARNING:2024-07-22,09:56:47 || template_construction.py:check_surface_model() | Center wavelengths provided in surface model file do not match wavelengths in radiance cube. Please consider rebuilding your surface model for optimal performance.\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Observation means:\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Path (km): 1.0036078691482544\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | To-sensor azimuth (deg): 153.4481201171875\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | To-sensor zenith (deg): 178.3806858062744\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | To-sun azimuth (deg): 39.8218994140625\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | To-sun zenith (deg): 39.8218994140625\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Relative to-sun azimuth (deg): 31.813383102416992\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Altitude (km): 2.692207074296544\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Existing h2o-presolve solutions found, using those.\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Full (non-aerosol) LUTs:\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Elevation: None\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | To-sensor zenith: [177.0325 179.0392]\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | To-sun zenith: None\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Relative to-sun azimuth: [3.80000e-03 4.12002e+01 8.23965e+01]\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | H2O Vapor: [0.6083 0.6485]\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | /Users/brodrick/repos/isofit-tutorials/isotuts/NEON/outputs/NIS01_20210403_173647/output/NIS01_20210403_173647_subs_state\n",
-      "INFO:2024-07-22,09:56:47 || apply_oe.py:apply_oe() | Analytical line inference\n",
-      "INFO:2024-07-22,09:56:47 || configs.py:create_new_config() | Loading config file: /Users/brodrick/repos/isofit-tutorials/isotuts/NEON/outputs/NIS01_20210403_173647/config/NIS01_20210403_173647_isofit.json\n",
-      "INFO:2024-07-22,09:56:47 || radiative_transfer_engine.py:__init__() | Loading from wavelength_file: /Users/brodrick/repos/isofit-tutorials/isotuts/NEON/outputs/NIS01_20210403_173647/data/wavelengths.txt\n",
-      "INFO:2024-07-22,09:56:47 || radiative_transfer_engine.py:__init__() | Prebuilt LUT provided\n",
-      "WARNING:2024-07-22,09:56:47 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
-      "WARNING:2024-07-22,09:56:47 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
-      "INFO:2024-07-22,09:56:47 || radiative_transfer_engine.py:__init__() | Resampling LUT to instrument spectral response.\n",
-      "2024-07-22 09:56:52,015\tINFO worker.py:1558 -- Calling ray.init() again after it has already been called.\n",
-      "INFO:2024-07-22,09:56:52 || atm_interpolation.py:atm_interpolation() | Beginning atmospheric interpolation 4 cores\n",
-      "INFO:2024-07-22,09:56:53 || atm_interpolation.py:atm_interpolation() | Parallel atmospheric interpolations complete.  1.8515851497650146 s total, 2279.6683158404508 spectra/s, 569.9170789601127 spectra/s/core\n",
-      "2024-07-22 09:56:53,938\tINFO worker.py:1558 -- Calling ray.init() again after it has already been called.\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:56:58 ||| Analytical line writing line 1\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:57:05 ||| Analytical line writing line 9\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:57:11 ||| Analytical line writing line 17\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:57:18 ||| Analytical line writing line 27\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:57:25 ||| Analytical line writing line 33\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:57:31 ||| Analytical line writing line 43\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:57:38 ||| Analytical line writing line 50\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
-      "\u001b[36m(Worker pid=25201)\u001b[0m INFO:2024-07-22,09:57:45 ||| Analytical line writing line 59\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
-      "INFO:2024-07-22,09:57:51 || analytical_line.py:analytical_line() | Analytical line inversions complete.  57.43s total, 73.4973 spectra/s, 18.3743 spectra/s/core\n",
-      "INFO:2024-07-22,09:57:51 || apply_oe.py:apply_oe() | Done.\n",
-      "\u001b[36m(Worker pid=25202)\u001b[0m INFO:2024-07-22,09:57:48 ||| Analytical line writing line 61\u001b[32m [repeated 5x across cluster]\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Add a ray shutdown, just in case this is being re-called\n",
-    "import ray\n",
-    "ray.shutdown()\n",
-    "\n",
-    "args = SimpleNamespace(**{\n",
-    "        'input_radiance': os.path.join(subset_dir,f'NIS01_20210403_{neon_id}_rdn_ort'), # Radiance\n",
-    "        'input_loc': os.path.join(subset_dir,f'NIS01_20210403_{neon_id}_loc_ort'), # Location\n",
-    "        'input_obs': os.path.join(subset_dir,f'NIS01_20210403_{neon_id}_rdn_obs_ort'), # Observations\n",
-    "        'working_directory': os.path.join(output, f'NIS01_20210403_{neon_id}'), # Output directory\n",
-    "        'sensor': 'neon', \n",
-    "\n",
-    "        \"surface_path\": output_surface_file, # Surface priors - often changes\n",
-    "\n",
-    "        'emulator_base': os.environ['EMULATOR_PATH'],\n",
-    "        \"modtran_path\": None,\n",
-    "        'n_cores': 4,\n",
-    "        \"copy_input_files\": False,\n",
-    "        \"wavelength_path\": None,\n",
-    "        \"surface_category\": \"multicomponent_surface\",\n",
-    "        \"aerosol_climatology_path\": None, # MODTRAN\n",
-    "        \"atmosphere_type\": \"ATM_MIDLAT_SUMMER\", # MODTRAN\n",
-    "        \"rdn_factors_path\": None, # RCC update used 'on the fly'\n",
-    "        \"channelized_uncertainty_path\": None, # Channelized uncertainty - if you have an instrument model\n",
-    "        \"model_discrepancy_path\": None, # Model discrepancy term - handle things like unknown radiative transfer model effects\n",
-    "\n",
-    "        \"lut_config_file\": None,\n",
-    "        \"multiple_restarts\": False, # Useful if the AOD conditions are really challenging\n",
-    "        \"logging_level\": \"INFO\",\n",
-    "        \"log_file\": None,\n",
-    "        \"num_cpus\": 1,\n",
-    "        \"memory_gb\": -1,\n",
-    "        \"presolve\": True, # Attempts to solve for the right wv range\n",
-    "\n",
-    "        \"empirical_line\": False, # wavelength-specific local linear interpolation between radiance and reflectance\n",
-    "        \"analytical_line\": True, # mathematical representation of OE given that the atmsophere is known\n",
-    "\n",
-    "        \"ray_temp_dir\": \"/tmp/ray\",\n",
-    "        \"segmentation_size\": 10,\n",
-    "        \"num_neighbors\": [5],\n",
-    "        \"atm_sigma\": [0.5, 0.5],\n",
-    "        \"pressure_elevation\": False,\n",
-    "        \"prebuilt_lut\": None\n",
-    "    })\n",
-    "\n",
-    "apply_oe(args)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "61cea885",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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DhgHw4MEDbt26Rf/+/Rk4cKD+mPj4eNzd3c1y/R49ejB+/HjCwsKYOXMm+fPn54033gDg6tWrxMXF0bhxY/3+Dg4O1K9fnwsXLugfW7p0Kd9//z03b94kKiqK2NhYatasCUBoaCjBwcH4+fnp98+TJw9169ZN0S2k+14YKliwoNHzw4cPJy4ujoCAAH33V7Nmzfj2228BtZvIlMyNyDpTu4R0Y5BAzcgY3hdC5F658i+Bq6srERERVru2Kdzc3HjppZcAWLBgAS1atOCLL75g6tSpJCYmAmq3UIMGDYyOM9c4AHd3d/31f/75Z6pUqcIPP/xA//799QFF8i4uRVH0j61fv55PPvmEOXPm4OfnR758+fj66685cuSIyW0x/F6kpkWLFjx79oxjx46xZ88eRo4cCagBS+/evXn8+DGHDh2iT58+Jl9bZF5muoR0YmNjJWARQgC5NGDRaDS4ublZuxmZMmnSJNq3b8/777+Pj48PxYoV49q1a/To0SPV/XVjVswxJsDBwYFx48YxduxY3nnnHV566SUcHR3Zv38/7777LqB+OB0/fpzhw4cDEBgYSKNGjfjggw/057l69ap+293dHW9vbw4fPszLL78MqP+Fnzhxgtq1a5vUvrJly+Lr68sff/zB6dOnadasGQDe3t6UKlWKOXPmEB0dLeNXspmpAYthgBIbG2tykC9ykdBQ+PZbuHgRTp+GBg3gvffUrzJ4+4Ujs4RymObNm1OlShW++uorACZPnsz06dOZP38+ly5d4uzZs6xYsYK5c+cCULRoUVxcXNi+fTv37t0jNDQUgE2bNlGxYkWTr//uu++i0WhYvHgxbm5uvP/++4wcOZLt27dz/vx5Bg4cSGRkJP379wfgpZde4vjx4+zYsYNLly4xceJEo1lEAB9//DEzZsxg06ZN/Pfff3zwwQc8ffo0xbVjYmIICQkxuj18+NBonxYtWrB48WJeeuklPD099Y/ruoXKlClDiRIlTH7dIvNM7RKys0v6sxQbG2uRNokc7upVePttKFAAJk6En3+Gf/+FH36Axo2hSRMICjI+JjYWjh4FK024EFknAUsONGLECJYvX86tW7cYMGAA33//PStXrqRatWo0a9aMlStX6qcz58mThwULFrBs2TJ8fHzo3LkzoI4duXjxosnXdnR0ZOjQocyaNYuIiAhmzJjBG2+8Qa9evahduzZXrlxhx44d+unGQ4YMoWvXrnTv3p0GDRrw6NEjo2wLwKeffkrv3r3p27evvtvo9ddfT3Ht7du34+3tbXRr0qSJ0T4tWrQgPDw8xfTtZs2aER4eLtkVKzA1w2K4jwQswkhcHEyaBBUrwrp1oCjg7Q2tW4OfH2izqhw+DBUqQMmS4OYGrq7g5KRmXsqVg4AAq74MkUmZGdG7aNEipVSpUoqTk5NSu3ZtZd++fWnuGxgYqDRq1EgpWLCg4uzsrFSoUMFolouiKMqKFStSnVFjyiwLU2YJCWFJ8vNm7L333tP/Tq9du/a5+3t4eOj3v3r1aja0UNi8+HhF+eEHRSlTRlHUMEVR2rZVlO3bFSUy0njfK1cUpV69pP1Su9nZKYp2tqOwPovNElq3bh3Dhw9n8eLFNG7cmGXLltG+fXvOnz+faqrdzc2NoUOHUr16ddzc3Ni/fz+DBw/Gzc2NQYMG6ffLnz9/iv/4nZ2dTW2eEMLGGGZYMtIlZJhhscYyEMKGhIXBmDHw66+g6/4tVAi++QZ69Ur9mLJl4eBB+OkntVsob15ITIRGjaBIERgwAA4cgP79oWFDKFYs+16PyBKTA5a5c+fSv39/BgwYAMC8efPYsWMHS5YsSbVce61atahVq5b+fqlSpdi4cSOBgYFGAYtGo8HLyyszr0EIYcOkS0hkSmIi9OgBf/6p3ndxgaFDYexYdexKevLkUQOS1AQEqGNcjhyB2bPV4EfkCCaNYYmNjeXEiRO0adPG6PE2bdpw8ODBDJ3j1KlTHDx4UD+DQyciIoKSJUtSvHhxOnbsyKlTp9I9T0xMDGFhYUY3IYTtMXXQrQQsgvh4NTj5809wdIRZs9SBtrNmPT9YeZ48eeDzz9Xtn36CqKist1dkC5MClocPH5KQkGA0+wLA09OTkJCQdI8tXrw4Tk5O1K1blw8//FCfoQGoWLEiK1eu5I8//mDNmjU4OzvTuHFjLl++nOb5pk+fjru7u/7m6+tryksRQmQTUzMsuvpCIAFLrjVmDOiKRP74I4wcqQ6uNZe2baFECXjyBDZsMN95hUVlapZQeoXC0hIYGMjx48dZunQp8+bNM1ptuGHDhvTs2ZMaNWrQtGlT1q9fT/ny5fXVSVMzduxYQkND9bdbt25l5qUIISxMuoSESfbvB21ZBhYsULuFzM3eHnTVwZctM//5hUWYNIalcOHC2Nvbp8im3L9/P0XWJTndNNtq1apx7949Jk+ezDvvvJPqvnZ2dtSrVy/dDIuTkxNOTk6mND/VFYCFMDf5OTNmSpeQoiiSYcnNzp6FN99U5/L06wfaZUgsol8/tWto/364eVOdAi1smkkZFkdHR+rUqYO/v7/R4/7+/jRq1CjD51EUJd3R/4qicPr0abzNlALUlfqWP34iO+hWAjdcVTs3MyXDYhisgPzOpisqCk6cUMdhTJsG778PffrAuHHqLJmcRlHgnXfg3j21+2fOHMter1ixpLot69db9lrCLEyeJTRixAh69epF3bp18fPz47vvviMoKIghQ4YAalfNnTt3WLVqFQCLFi2iRIkS+qqq+/fvZ/bs2foF/AC++OILGjZsSLly5QgLC2PBggWcPn2aRYsWmeM1kidPHlxdXXnw4AEODg5GlTSFMBdFUYiMjOT+/ft4eHiYbT2nnM6UgCX58zKtGfWD/MQJ+P13uHZNnd57/TpcuaI+l5rp09XsxPz58JzuepuxZw+cOwcODhAYmPXBtRnx9tvqrKG1a9VxMsKmmRywdO/enUePHjFlyhSCg4OpWrUqW7dupaQ2nRYcHEyQQUnkxMRExo4dy/Xr18mTJw9ly5ZlxowZDB48WL/P06dPGTRoECEhIbi7u1OrVi327dtH/fr1zfAS1TE33t7eXL9+nZs3b5rlnEKkxcPDQ6boGzClSyh5wJJrMizR0WpmQXe7f1/9evSoWrX13r3UjytUCKpVU2uPFC0K+fKp03V//11dY6dQIbUybE6wcKH6dcAA9fVkhzfegA8/hJMn4dIlKF8+e64rMkWjvCAd7mFhYbi7uxMaGkr+/PlT3ScxMTH3/AEUVuHg4CCZlWTq1q3LiRMnAPjqq68YO3ZsmvtGRESQL18+/f0VK1bQt29fSzcx+yQmwpkzsHOnmlG4dg1CQtRF/NLj5ASdO6vBia+v2p1RrZoapKSWQVm4MGn8x8qValeRLfv556RCcOfOQeXK2Xft9u1h+3aYMkVdl0hku4x8fkMuW63Zzs5OqucKkc0Msyq5cgzLtWtqPZHdu9UBno8epb6fgwN4eoKXl1qR1d0d6tVTq7HWqqUWTsuooUPhzh2YMUMtoJaQoK5ibIsSE9VpzKC+3uwMVgC6d1cDlnXrJGCxcbkqYBFCZD9TSvM/r0soKCiI27dvU7t2bdv+5+POHVixAlavhgsXjJ/LmxeaN4dWraBmTTVA8fQEDw/zjjf58ku4do3E9euJ798fx7p1oXp1853fXP79V/1+AWzblv3X79IFBg9WMzsXLkClStnfBpEhMvpUCGFRWRl0O2zYMH0V7cOHD1OyZEkaN25Mv379zN/QrIqPVzMpr72mFiWbOFH9ALS3VwOUGTPU2TuPH8OWLTB8uPp4xYrqAFNzD461s+O311+nSJ48NADiW7e2zaquv/+ufm3bVh1zk908PJJmCyWbAStsiwQsQgiLMqVLKLXnp0yZAsChQ4f0j/37779map0ZPHgAkydDqVLQqZMajCQmQtOm6pTjhw/V8SqjR4Ofn9r1kw0ePnxIz759eRwfz2lg9/370LNntlw7w44eVceOQNqLGWaHVq3Ur7t2Wa8N4rmkS0gIYVFZ6RICtf4TYDTD7+nTp+ZpXFYEB6uL5y1dCtraOxQqpA5wHTDA6l0LV69eNZoWvg5os3EjHDqkBk7W9vixWnclPh7q1lXHkliLLmAJCFDbk0c+Gm2RZFiEEBaVlS4hSBrHYjMBS1CQOqi1dGm1hHxkJNSpA2vWqGMx5syxerACcPfuXaP7Gx0diYOksvfW9PSpGjRdu6YOMN6xw7pBQq1aatdQWJha80bYJAlYhBAWlZkuIWdnZzZt2gTA48ePAeOAJSIiwigQyhZXriTVCFm0CGJioFEjdaDosWNqETITlwuxpDvagaydO3emUKFCPI2N5TjAxo1w44ZZrnH9+nVWrVrFsWPHMn5QQoK6PtClS2pGav16KFjQLO3JNHt7aNFC3ZZuIZslAYsQwqJM6RLSTWu2t7enSJEiADx58gTAqCAlQOjzapeYy6VL6viKChXghx/ULoOWLZOmKbdrZ1PVZBVF4fPPP2fp0qUA+Pr60rx5cwB2lyihjq/Zsyfdc9y+fZthw4Zx6dKlNPcJDQ2latWq9OnTh5YtWxKV0QG98+bB1q3g7KxmVrRtszoZx2LzJGARQlhUZrqE7O3tKaAtzf748WOePXvGo2T1S3SBjMU8fQqffQZVqqiFzRIToUMHOHBA/VBr0cKmAhUdf39/pk6dyrlz5wAoVqwYLVu2BGC3LmC8di3dc3Tr1o2FCxemu0bc0aNH9etmRUREcP369ec3LjZWHfcD8M03aleardAFLAcOqJWHhc2RgEUIYVGZKc1vb29PQW03wZMnT/Qfhh4eHvj6+gIWHMcSHw+LF0O5cup4lPh4NVA5fhz++kvtBrJhISEhRvd9fHz0AcuB+/eJArh6Nd1zHDlyBCBFkGgoeTfQtecEQQB8/71a2dfb2/YK2VWoAIULq119//xj7daIVEjAIoSwGEVRspxhURSFs2fPAlCyZEn94xYJWA4fhtq11fVlHj5UB8/+9Zd6s6VsQCoSEhJYuHAhu5J1afj4+FChQgVKlChBTHw8WyH1MSxRUWom6eFDNBnIHCUPWK4+Jwji/Hn49FN1e/Ro0M7+yopz587x5Zdfmmc8k0aT9B7LwFubJAGLEMJikgcoGQ1Y7OzscHJyws3NDYAzZ84AUKJECTw8PAAzdwk9fQoffKBmT86eVQeDLlqk/qfdoYP5rmNBq1evZtiwYaxatcro8WLFiqHRaOiunTa8FpIqyxr6/HN1rM6bb2ZoRXtdwNKwYUPgOQFLYqI6YDk6Gtq0SVrnKIuqVq3KhAkTWLx4sVnOR9266lcJWGySBCxCCItJ3gVkSpcQoM+m/PfffwB4enrqAxazZFgURZ2lUqkSLFmi3u/XD/77Tw1gclA9DsPCeoZ8fHwAePvttwH4E7h3544aRBhatkz9unfvcwOWe/fucefOHTQaDW+++SbwnC6hOXPU+i9586oDlzMQEKVFt17v+fPn9Y/purAM9wkICCAiIsK0k0uGxaZJwCKEsJjkqXpTuoQA/TgWXcBStGhRfRCT5QzL9evw6qtqwbKQEHUMw5498OOP6liGHCatbhzd6re1atWiVs2aRAM9ExJQ7t0z3tFglXE7g4JzqdGNkylatCg1atQA0smwXLuWtLjh119D8eLPeSVpe++996hYsSLh4eGsWbNG//itW7eM9hs1ahQtWrRg8uTJpl2gdm3167lzkN3T5sVzScAihLCY5BmVjK7WnDxguXjxIgBFihTJeoYlMRHmz1dn/2zbpo6lmDwZzpyxnSm2mZB82neFChWYOXOmPpDRaDT8/MsvOAM7gTO7dxufwN1dv/m8DwbdlPL8+fNTpkwZQK3Jkny1bQB++UX9njdvri4ymAUrVqzg0qVLrF27lj/++EP/+P79+xk+fDjLly9Ho9EwWzsTac6cOaZdoEQJyJ9fDVa0P3PCdkjAIoSwmOQZFlO7hAomKyhmmGHJVMASEqKOSRk+XB1k2ry5Ok5l0iSbKvpmKC4u7vkDWiHFtOLdu3czatQoo8cqV65MdVdXAG7Mm2d8AoOAxShXs39/imuFhYVpD3GnRIkSODs7ExMTk7Jui6KoAQtA375ZmgZuGOxevnxZPxBbZ/78+QwaNMjoMXd3d30XUoZoNFC1qrotM4VsjgQsQgiLyWqXkC440THMsJjcJRQQADVrqsXKnJ3VQbW7d6tdQTZs8ODBvPTSS/z9999p7qMoSooxJJ6enqnu66sNWG4dPw664nuKolag1TL6YGjaFFauNDqHYYYlT548NGjQAIB9+/YZX+zUKTVT4ewMr7+eZvt1IiMjadiwIZ/qZhMZMAxQt27diqIolC5dOtWusKFDh+rbmXya93NVr65+TRYQCeuTgEUIYTGmdgkZzhKC1DMsmeoSWrBALQx27x5Uq6bWVPngA5ss/JbcihUrAFL9ENe5d+9eikqz9gZjUgwVb9IEgOGAX+PGhLzyCgF2dow8d46n2n1SfDD8+KPRXcMMC0CzZs2AVAIWXXalUye1q+U59u7dy5EjR5g7dy737983ek63RAOgL4rXqFEjZs+ebdQ1NWPGDL799lvKlSsHwIULF557XSPVqqlfJcNicyRgEUJYTFa7hLy9vY2eL1KkiD7rkl5RMz1FgYkT4eOP1XEUffqotVaqVMnoS7AZly9fNrr/9OlTRo8ezbVr1/TdQfny5aNGjRrpDjb11QYsicDhc+fw3rWLFsBsYLl2nxRh3IkTagE9LV2GRRewvPzyy4AacOi7YBITYe1adbtHjwy9RsPVpXVrSekYBiw6fn5+jBgxgidPnnDx4kUOHz6s7warpF2A0nA2UYaYK8MSFqau6C3MRgIWIYTF6FZa1jG1S6ia7r9drcKFC1OxYkUATp48yZUrV9I+WWKiWgBu2jT1/rRpsGIFaLtEcgLD8RcxMTFG389+/foxa9Ys2rZtqx+UXKdOHU6fPs2kSZPSPKeuUnBqArVfU3wwREaCQaZCl2HRzUDy8/PD3t6e27dvE6z7kP73X7h7F9zc1PWWMsAwKPnll1+MXn9qXYCvvPKK2l47O33XlK6LqHLlykDqAUtgYGDamRfdGJZbt8DUbseICFi9Grp1U8cE+fioWbwuXdTvocgSCViEEBaTvJvC1ICluu6/XcDR0RFHR0fKly9Phw4dSExMZObMmYAaGBllcxRFnZGyZIn6gbFkCYwfnyO6gAwlryNy8uRJ/fbmzZsBuHLlCkePHgXUgOV5UgtYSmm/7gcSL182/mDQ7a+9BqTMsLi6uuq77/SZL90Ci02aZHhAs2FQEhgYyMyZM2nSpAkvv/wyDx48MNq3cuXKVEhn/FEVbRYt+eDc27dv8/LLL1O5cmUePnyY8kAPDyhWTN02ZabQ48fqGKkePeDXX42f+/13dSxQsm4uYRoJWIQQFpM8YDFltWZQx6zoGGYXxo4dC8APP/zAL7/8QtmyZalWrVrSh+UPP6jr1tjZqf/xDhmS5deSXWJjY4nWLr6X/EN606ZN7Ny5k8HJpgfv0QYHusGv6SluUAfFC5gAnAJcgSfAhWfPsDMM7LSF4fj3X/1DyTMskBS86FfR1k2bbtFCv090dDR79uxJkXnTSZ5FGTt2LAcOHCAwMDBFcbivvvoq3ddZq1YtAE6fPm003fofg7EpaZ5DO/6FZN1waVq/HkqXNl6jqXJl+O47dXaUkxOcPJlUj0ZkigQsQgiLyWqGJS1NmjRhyJAhKIpCz549uX37NhcvXuTtt98m/tgx0M4S4csvQVvhNafo0KEDJUqU4PHjxykCllmzZtG6dWu+++47o8d1hfXq16//3PMbjgt6A5gKeAANtY/tP3wYuyJF9Pskli+vbhhkG5JnWCApeAkNDVWDlT//VJ9o00a/zyeffELLli3TDBR0XUITJ06klW71ZC1dUPbKK6+wZ88eOnfunO7rrFChAi4uLkRERBh1HRpOEV+6dGnqP5OmBCxnz6pZlbAwNTtz7Jia4Tt3DgYOVLshdes7rVyp1vsRmSIBixDCYiKT9dtnJmAppkvPJzN37lyaawu9aTQaXFxc2LlzJ+Nbt1ZX3O3YEZLVIbF1N27cYNeuXTx48IDdu3frZ8rUrFmTIgZBRGo8PT0pUaLEc6+Rx2C5gcoGj+sCluPHj6Mx2CdGO/sGbVAEKWcJGW6HPnmifoAnJqpVhLWZDlADBIAvvvgi1bbpMiwFCxZMEdToxqLUrl1b/76nJ0+ePPoqvIZdaYbBS1RUFDdv3kx5sC5gSW+MFKivceBAdUBypUpqhkW3HpGhxo3V74WiqDV/RKZIwCKEsBhTu4SST2sG2LBhA56envrpvTouLi7s2LGDWbNmsWbNGlZqp97OCg3lnI8P/PRTltassQbDWiuXLl3SZ1i8vb2ZO3cuFStWZMOGDUyYMAEw/j698sorGVplGeCHTp14B+hv8Jhu9MvJkyeNzhv90kvqxvXr+lWeDeuw6OgDllOn1AJ9rq5ql4iW4QDafPnypdouXYalYMGC1K9fn02bNtGlSxejfZJPdU+PrlvIMGBJPtsqRbE7AN1rfl6GZds2OHJEXSPJ3x/Sa5tu5tYffzw/EBKpylm/zUKIHEUXsOj+qzfMsPz999/Mnj3b6IMstQxLgwYNCAkJoW/fvinO7+joyMiRI+nevTvdLl2iq/bx2bVrp//hYaN27Nih3z506BCnTp0C1OncPXv25MKFC3Tt2pUpU6Zw4sQJzp8/T7169RgyZAgLFizI8HXea9SI1YB+KGzFimhX0eHs2bNG04tjXF3Bz0+9s3Ur8JwMy6FDJAAxXboY1V4JNpjimzdv3lTbpcuw6Kaud+nShZEjRxrtY0rAohuEfPjwYf1jugyL7hqpBiyGXULpVcpdskT9Onhw0kDdtFSsqK5dpSjq0hDCZBKwCCEsRtclpPuP2jDD0rZtW0aOHMlugzVtMjqGJYW1a2HSJHQdQL/s2JGiVL2tO3DgAFu1AQHAn3/+ycKFCwHjwcegdoHVrl2bChUqcPToUZYsWWLSB7nR1O527WDnTkrWrEkBFxfi4uKMxs5ER0dD27bqHe36PellWJYfPUoJoOjmzdzQZmQAzhiM3Xjw4EGq3YO6DIthhePq1asbZY6SVz9OTwvtgN9Dhw4RFhZGfHy8/ueiQ4cOQNI6VUbKllW/hoZCWvV+nj2DnTvV7X79MtagTz5Rv/74ozqrSJhEAhYhhMXoMiy6gEX3IWWYVbl9+7Z+2+SAJTQUJkzQFyZrMGwYrVq1Ii4ujvHjx2e5/dklIiKCLl26EB0dTW3disEGnjd+xWQuLknbnp5QrBiaU6eo3ahRil1jYmKgdWv1TkAASmJiuhmWawkJ3AXCIiP1U6/BOGCJj483yrjoGI5h0cmbN6/RAFxTArMyZcrw0ksvER8fz549e7h58ybx8fE4OTnRsmVLII0Mi4tL0qrSqT0PahdQTIw6O6hy5dT3Sa5lS7WSbmQkJCuMJ55PAhYhhMXoAhZdF4AuIDGsL+JkUKMj+bTmdP3yizrW4MsvkwY/fvMNX3/9NRqNhjVr1phelt1KVq1axcOHDylTpgyBgYEpnnd0dDTvBQ0zLAZZktTqmkRHR6v1RQBiYoi+f19f8ya1gMWQYfbsxIkTRs8lH+yamJiYoktIZ4jBtHTd0gwZ1U5btG7btm0cOHAAUAsS6goQphqwAOhmR6U1jmXLFvVrp04Zr++j0UBXbcfl9u0ZO0boScAihLCYtLqEDAt2GXYNZDjD8v330LMnPHyojg349VdYtgzs7alVqxavvvoqAP/73//M9loyIzo6moULF9KiRQvWrVuX6j6KoujHn3z88ce4urqyZcsWo/L6GSkIZxIHh6Rtg+9/amNLoqOj1QBH+x6GaRdZ1Gg0uLm56fdzT6U43N69e4mPj0dRFPZrV33WvbfJA5awsDB95i15wPLaa69RoEABozWDMqpjx44ArF69mtWrVwPQpk0bymsDkqCgoNQX0kxvarOiwF9/6RpnUnto31796u8PyZauEOmTgEUIYTFpdQkZBizh4eH67dRmCaVw927SWIBPP1UXqXvzTaP/cnv16gWo5d0Ni4Zlpz179lC1alWGDRtGQEAAPXv2ZN26dQQHB/PDDz/w66+/EhERwaFDh7h48SJubm76gcUdO3Zk0qRJ3L59mz/++IOmTZuat3GG3THaTAOoFWuT0w/A9fICIFQ7BiR//vxG75O7QWE5Ly8vPDw8CAsL4+TJk1y9epWQkBAcHR15U1uIrkePHvypq9VC0vgVFxcXnJ2djdrg4ODAlStXuHDhgskZltatW1O7dm3Cw8P1g5pbt25N4cKF9eX7d+7cadRNCSRlWFLLwNy8qS6k6eCgVvI1Rd26ULiw2p2ZfLFIkS4JWIQQFpNWl9DzApZ0Myzjx6trtjRsCLNmGWcLtDp16kT+/PkJCgpiiy51n02io6P56KOPaNmyJVevXtUXaouPj+ftt9/Gx8eHAQMG0K1bN8qWLatfrK9r165Gg1hBrUHTqVMn8zfSoPosffroNw0zJoavB9AHLP7abh6jEv8JCbgvW6a/6+vrq6+Vsnv3bn12pV69evjpZhwBv/32m347tfErhgoWLIiPj8/zXlkKdnZ2+iUcAJydnfVt0HUXdevWDUdHR+rXr59UOyiVgnl6x4+rX6tXz/CyA3r29uraQqCW7BcZJgGLEMJikmdYUusS0g3ghAwELOfOqfVVQJ0amkYmxsXFhQ8//BBQy7s/r/6LOY0ZM4Zvv/0WUMdeXLx4kaioKEaNGqUPCMqWLUuJEiW4f/++flxFz549s62NVK+uVlx99MhoDEu6GRZPT+KBOdoPWcNxJYwcieEIlmLFiukHte7Zs4d92kxCkyZNeP/99/VVap89e6Y/RhewmJpByYhXXnmFbdu20aJFC6ZNm6YfN9XOYFHG+Ph4jh07xk7dzB/dit4XLqTsujl2TP2aWpG4jNBV/927N3PHZ5OoqCijfyisTQIWIYTFJB/DogtIHhlMFTUpw/LFF+r4ga5d4Tll6EeNGkXBggW5cOECLi4u1KtXz6gwmzkk72568uQJy5cvB9QxE0uWLCFfvnw4Ozszc+ZMnj59SmRkJFeuXOHy5cvMnDkTNzc3qlevrv+AzzbVq6eoVZNawGKYYTkOXH/0CA8PD/rppvLevQsLFxoFLD4+PvrXExgYqO/6ad68OY6OjvqAJTIyktDQUHr37s3GjRuBtIvKZVW7du3YvXs3n376qf6xpk2bUqRIEZycnPDSZpD0PyMlS6rjduLiUmZZdBmWzAYsui6+s2fVkv42SFEUihQpQv78+ZN+BqxMAhYhhMWYtUvon3/UwbUajRq4PIeHhwdz584F1P+ejx8/rq/9cvHixZRjFkwQHx/Pm2++SYkSJTh16hTBwcFMmTKF5s2bExkZSbVq1Xg7lTWM8uTJg4t2SrGjoyOjRo3i8ePHHDlyxKhkvrWk2yVUtCi6tYbLly+fFNxs2QJxceQ3KMHv7e1N5cqVKVq0KFFRUTx48ABnZ2eaNWsGJAVGz549Y+rUqfzvf/9jibYIm6UCltQ4Oztz/Phxzp8/r182QB+w2NmpQR2oP3s6iYmgm/FUr17mLuzlBSVKqMG3QRVeWxITE6PPgKW6fIEVWP83RAjxwspIl5BhwJLutGZdkNKtG1StmqHr9+7dm4MHD/Lbb7/RrFkzNm3axOzZs5k9ezYVK1akbt26VKpUievXr+Pi4sKZM2do27YtY8aM4cmTJ+TNmxcnJyeCgoKIj4+nTJky3Lhxg3HjxrFhwwZA/c89NDRU33Wi0WiYPHlyhsvkm33Kchak2yVUuDC6UmdG40y0Y1rcO3QAbWVed3d3NBoNb7zxhj4Q8fPz0wdrusDo2bNnRnV4IO0quJaiW3+pUKFC2Nvbc/nyZYKCgtTHq1eHAwfU7rN331UPuHJFHTDr7Jzx+iupqVcPgoLU4nMZWBspuxmWHnBIZZyYNUjAIoQwn4cP1f8Yz5yB//4jUjujxNRZQikCljt3QFeE7PPPM9wcjUbDsmXLWLJkCXZ2dmzevJk5c+Zw7Ngx/vvvP/0qx4b27dvH1KlTiY6OpmrVqpQrV45N2iJf+fLlS9Gnr1ug0M7Ojs8//5w33niDqhkMqGxNul1CqQUsiYn6gMW1bVu1Jg5JQcdXX33Fd999R0JCAl119UcwDliSzwjK7oBFx93dnfLly3PhwgUuX76cFLCAcYZFO+aI2rVTHfCdYX5+sGGD+j2bNClr57IAw4DFWjPtkpOARQiRNRcvqrMdNm+Gw4eN1l7RLX2YXpdQaoNuU0xr/vln9cOxSZNM/VerO1+XLl3o0qULoaGh7Nq1i927d7N27VoaNGjAiRMnePz4MXFxcfoP6X///Zd/DabrhoeHY2dnR8uWLRk3bhzlypWjQ4cOnD9/no0bN/KaqTU5bIzJGZa9e9Ug1c0NTYMG+mN0AZuHhwfnz5/njz/+YNCgQfrn0wtYsrNLKDlvb28uXLhASEiI+kBqAYuusF9Wp5r37g2ffZZ0zuwew/QchoF5nI3Ui5GARQhhunv3YNUqWLkSzp83fq58eahRAwoVIko7LiDfvXvA87uE0sywrF+vfu3d2yzNd3d3p2vXrnTt2lW/Xo9he27cuMHx48d57733cHZ2Zvv27dSqVYu7d+9SuHBhChcurN//1KlT3L9/Xz99OSdLdwxL8oDl9OmkD9mmTcHRkX379nHz5k3qGYztKF++PJ/pPpiTXScyMtJmMiyAfuCtftmAatXUr3fvqitQe3mZL2ApUkSdUv7TT7Bjh80FLIYZltjYWCu2JIkELEKIjNu/H+bMUQda6iqkOjiof2w7d1arfhqsWhu5ahVERpLP3x9QAxJFUUwLWO7eVbuZNBr1GhaWL18+qlWrRrVq1ShXrhxFixbVV0VNXidF19YXIViBjHcJFbC3N67wOnQooM66yUiRO8NBt7YUsOjeR32GJV8+Nfg+cwb27FHr11y5ov4sNm6c9Qu2aKEGLAcPZv1cZmYYsEiGRQiRc5w8CcOHJ/13CWrhtv794a23wGAdGUVR+Pvvv6latSpR2n75vLp+f9Qsi0kBi24F43r1INmqxZbWxNQqpjlchruE1q+HW7fU4PTgQXXGiwl0GZaoqKgUAzqt2SWky7DoAxaAVq3UgGX37qRFI6tUAXPUi9FNzd+/PykgshG2GLDItGYhRNpCQ9X/nuvVU4MVR0cYNAj+/RcOHYIBA4yCFQB/f3/atWtHrVq1iNKmkgsZ/MG7efOm0fpBGQ5YtOsDCctJN8Pi4MBj7ftS8Nw59bENG0wOVsC468mweBzYWJcQJHXV7N6tdoMBmGttpwoV1NWeddc5etQ85zUDW+wSkoBFCJG6AwfUQYeLFqkDXt9+G65eVRcZ1FUBTcV27Sq0Dx480E9rLgQU1P4nfVxbdEs3EDY2Nlb/X3yKac2KkpTVad3arC9PpJRuhgV4rJ2qXRDU9XCeU7wvLbrpzQBPnz41es4WAhajDEuTJmoX0LVr8Mcf6mMGNWeyxM4uaXyW7lq6pQGs7IXJsCxevJjSpUvj7OxMnTp1Ul0OXWf//v00btyYQoUK4eLiQsWKFfnmm29S7LdhwwYqV66Mk5MTlStX1k8jFEJks8REdZxK8+ZqnYgyZdRaEWvWQPHizz08tQJoLkAJbfbkmLas+UsvvaR/fsuWLURFRXHkyBHAYJbQlSvqLBQnJ3UaqbCo1N47wwzYY+0MsIIAHToYLThpCjs7O33QkjxgsfYsIUgWsLi7J9X90daZ4ZVXzHfRunWTSv3HxakD2W2ALc4SMjlgWbduHcOHD2f8+PGcOnWKpk2b0r59e4KCglLd383NjaFDh7Jv3z4uXLjAhAkTmDBhAt99951+n0OHDtG9e3d69erFmTNn6NWrF926ddP/8RJCZJPbt9V1Tj77DOLjoXt3NQ3eqpUJp7id4jEXZ2dKarMnR7Vpbx8fH/2aLm+99Raurq7s1tb00GdYdIMR69Y1fZE5YRbXtbV04uPjCdUGnQXBaJXnzNB1C+nWENKxhQzL48ePjTJLaFf/BqBdu3QzjJlSty7o/pHXrZVlZS9El9DcuXPp378/AwYMoFKlSsybNw9fX199NcPkatWqxTvvvEOVKlUoVaoUPXv2pG3btkZZmXnz5tG6dWvGjh1LxYoVGTt2LK1atWLevHmZfmFCCBP5+0PNmrBrF7i6ql0/a9aoMyVMkLyMd548eXCoXRvdSAddhsXToCsoOX3AovunxWCFX5G9Ll++DBhnQgqAutZOFugCFlvqEipYsKB+EPA97VR8AEaMgAUL4OuvjbtwzOmdd9QuoqNH1e4nK8vxXUKxsbGcOHGCNrqVJrXatGnDwQxOyzp16hQHDx7UrykBaoYl+Tnbtm2b4XMKIbIgMRGmTYO2bdXVe2vXVlPfgwZlKuV/48YNo/suLi5Qt64+YNEFKZ7p/H7rA5azZ9Wv5hozIEz26NEjnjx5wuPH6hyhfIADQKlSWTpvWhkWa3YJaTSa1Afe2tvDsGFq5tFS7fP0TBrgu26dZa5hghwfsDx8+JCEhAQ8PT2NHvf09DTu80tF8eLFcXJyom7dunz44YcMGDBA/1xISIjJ54yJiSEsLMzoJoQw0ePH0KkTTJyoDnAdOFAdbKutO2KqmJgY4z/0aNchqVOH5HNJPIH/AX3LlqVPnz5Gz9nb26vt0VWZzaGl7nMyJycnfHx8ADXLogss9KsIVaiQpfPrBvjaUpcQJK0tpOsKy1a6BTN//DGpzpGVvBBdQkCKRb0URXnuQl+BgYEcP36cpUuXMm/ePNasWZOlc06fPh13d3f9zdfX18RXIUQu988/6vTMrVvVhdx+/BG++07dzqRbt26hKAp2dna8/fbbODo6qoXEGjRIEbB41a9PT2DF1aus6NWLVatW6Z+zt7dX1w96+lT97zaLH47CdHFxcZQrVw4wDlgKgDrotFChLJ1fl2FJ/s+mtQOWCtqftYsXL2b/xbt1g4IF1cHm2sU1rSXHZ1gKFy6Mvb19iszH/fv3U2RIkitdujTVqlVj4MCBfPLJJ0yePFn/nJeXl8nnHDt2LKGhofrbrVu3THkpQuRu27aplTpv3FBnAR06BP36Zfm0uu6gChUqsGbNGsLDw9VVjStUoKRuXRYtz7Fj9duab7+lpMGYCHt7+6TsSoUKMuDWChITE40CFl1gkb9hQ7XScRaltgxAeo9nF11V40uXLmX/xfPlgyFD1O3Vq7P/+gZy/CwhR0dH6tSpg7+2zLaOv78/jRo1yvB5FEUxGmzn5+eX4px///13uud0cnIif/78RjchRAYsWQIdO0JEhFpZ8/hxdbCtGRzQVrStVKkSoP7N0I1H8Xz3XaMsi2fx4klByV9/4e3oqH/Ozs5OuoNsgC5guXjxov4DLG/BglnKwumkFZikWEcqm1k1YAF1Zh7A9u1gxaEOL0SX0IgRI/j+++/58ccfuXDhAp988glBQUEM0UaFY8eOpbfBAmWLFi1iy5YtXL58mcuXL7NixQpmz55Nz5499ft8/PHH/P3338ycOZP//vuPmTNnsnPnToYPH571VyiEUCUkwKefwgcfqANt+/ZV/ygWKGC2S2zR/ufdsWPHFM/ZjRjBTwsW6O/7+vqq00Pr14f4eLz37NE/FxUVJQGLDaiuzYqdPHlSH7CYa1BsakXqbIFhl5BisPJ4tqlWDcqVg5gYdZaeldhil5DJawl1796dR48eMWXKFIKDg6latSpbt27Vp3ODg4ONarIkJiYyduxYrl+/Tp48eShbtiwzZsxg8ODB+n0aNWrE2rVrmTBhAhMnTqRs2bKsW7eOBgbLlQshsuDRI3XapC6T+eWXMHZspgt/JRcUFMRrr73GmTNn0Gg0vJpaGX0HB5oPG8b28uUJCwtL6vLt1w+OHiWvQQr8/v37STOEJGCxGt2qy5cuXdJ3u5srYLF2109aypYti0ajITw8nHv37ulnDaXn5s2beHt742iQJdRRFEU/ritDNBp1VfKJE2H5cjD4rMxOthiwoLwgQkNDFUAJDQ21dlOEsC0nTypKyZKKAori6qooa9ea/RLvv/++AiiA4ufnZ9rBT54oipOTomiPB5QunTsrirOz2ubLl83eXpE2DN4HRVGUMmXKKIBSs2ZNBVA++eQTs1xn5MiRRtcyvKa1lS5dWgGUgICAdPcLDQ1VOnbsqABKrVq1lM2bNys3b97UP5+QkKA0aNBAKVeunPL06dOMNyAkRP3ZB0W5cyfp8bNnFeX99xVlxgxFCQtT7z96ZOrLyxAPDw/9ezJ58mSLXEMno5/fspaQEC+y//0PGjWCmzehbFl1cK2uj9xMYmJiWLt2LQD169c3qmKdIR4e0L690UP3bt6E6Gi1gF2ZMmZqqciIqVOnAjBt2jRAfU8BTmsX/rNkhuWzzz4zy7mzqoq2ku0///yT7n6zZ8/mzz//BNQaY126dKFBgwb61ciPHDnCkSNHuHz5MhMnTtSvrfVcnp7qgqMAf/+tfo2LU9fTWrIExoyB/PnV7qOqVeH+fdNfZDoURbHJDIsELEK8iGJj1UJXvXurH/wdOqjrlSSbqZMVN27c4NNPP2Xq1Kk8efKEYsWKcfDgQapmpgvn9dcBeFn7IdZb98e6ShW1+qfINuPHj+f69euMGzcOSApYdCwVsGzfvp2vv/7aLOfOqtradatOnjyZ5j7R0dEsXboUgA8++EA/HTskJISuXbty8eJFNm7cqN//22+/pVixYhmfLq0rIrd3r7qeVo8ekFptsuBgdaxLXBxcuqTmZbIoJiaG+Ph4/f0cO+hWCGHjbtyAZs1g4UL1/qRJ6jRUMw6uBRg6dChz587lyy+/BGDw4MGZn+HRsSPY27P12TP2r1vHIN34lmrVzNRakVEajYZSpUrp62CVSZbhstSgW8MVnK1NF7Cc0i12mIoNGzbw4MEDfH19mT9/Pg8ePOD48eM4OzsTGBhIxYoVmT17ttExT5484d13381YxqJtW/Xrr7+qWdJff1Xvf/45jB5tvO/27TBypFoCYMSIDL/OtCSvjSMZFiGE+W3erE5RPnxYXWX2999h8mSzZyn+/fdf/vrrL/39Zs2aMWbMmMyfsGBBaN4cN6BxUBB22u4HCVisr3Dhwkb3LZVhSW3AqrXU0i4Fce7cOaKjo1Pd57fffgOgb9++5MmTB2dnZ+rUqcPJkydp3ry5fj9vb2/Cw8MJCgrCw8ODkydPsnfv3uc3onlztVvo2TPQrueEg4O6ZMaXX8IffyQFMdu3w/z56va8eRAamolXnUQCFiGEZS1YAF27qn+sGjZUV1l+7TWLXOrbb78FoH379mzatIm//vpLv2hcpmm7hfj1V9i3T91u2jRr5xRZlhsDFl9fXwoVKkR8fDz/6qbXG3j27Bnbt28H4I033jB6rlKlSvz99998+eWXTJgwgUOHDpE3b158fX1pq82a6BYATZdGA3PnJt0vXBju3YNixdTqz506QZcukFpmShe8ZFJosoBHuoSEEOahKOogvI8/Vrc//FD9wM/i4nRpiY2N1f93+emnn9KlSxfzTFHt3Fn9evSoWjCrUCGzFbQTmZcbAxaNRkOdOnUAdeBsctu3byc6OpoyZcroa9UYcnBwYNy4cUydOtWogrNumniGAhZQu4Jeflnd7tEjZbdunjxqbSVQl0vQDpRm0iR13FomMyPJAxbJsAghzGPiRJg5U93+6iv49ls1dWwhO3fu5PHjx3h5eRmlvrOseHH1j6zOm2+q/0kKqypQoIBRDRFLjWHJcobOzJo0aQKo6+AlFxAQAMCrr7763HX0DNWtWxeA48ePZ+wAOzu1u+evv5KCkeSmTFHXBdu+XR3bostKbtsGu3dnuG2GpEtICGF+q1er/dkAy5aZtRhcahITE/UDCbt162b+MuqzZqnrqXh6wvjx5j23yBQ7OzsKGSx0mBsyLIC6aCdqwKIkm3mjW4JCF9RkVO3atdFoNNy6dYt79+5l7CAXFzWQT2tRSI1GHetlb69mXLZvhyJF1Of27zepfTrSJSSEMK89e9Ty+qDOEBg0yKKXUxSFyZMns2fPHlxdXRk2bJj5L1KlCgQFwdWrICuw24zcGLA0aNAABwcH7t69y/Xr1/WPR0REcObMGQCT1tAD9XtXuXJlAP0YGLNzdQVtLR39WDATSZeQEMI8FEUtCNe5s9pH/dZbMGOGRS/57NkzBg8erC8qNn/+fF566SXLXMzDA2y0bHtuZdh9k1sCFhcXF/2Yk30GH/xHjx4lMTGREiVKULx4cZPP+8477wCwZMkS8zQ0NX5+6td9+0A73swUui6hPHnU1XskYBFCmO7RI3VsR+/eEB6uFpdatcqixdX++OMPKlasyPLlywGYM2cOAwYMsNj1hO1xcnLSb5trDSBbH8MCxt1COocPHwbATxcUmGjAgAE4ODhw5MiRdOu8ZEm1akldw2+9BQZF4DJCl2HRDbiWLiEhhGlCQtQBdRs3qn3VX34JO3aAs7PFLrlkyRK6dOnC7du3KVmyJP7+/owwQ2EqkbMYBiymDDJNj61nWCApYDHMsOiWKNDNIjKVp6cnnTp1ApJWNzc7jUadbadj4gKKyQMWybAIITLu3j1o1QouXFDrMBw9CuPGqYGLBSiKwhdffMEHH3yAoigMGjSICxcu8Morr1jkesK2GQYs5uLs7GwU/NhiwNK4cWM0Gg1XrlwhODgYQD9+pUaNGpk+r64ey9+6dYIsoW5dddYgwI8/Qir1ZNKi6xKSgEUI8Xznz6szZqZPV6cqVq+uPlasmLq2iLYSp7nFxMQwf/58mjZtyuTJkwGYNGkSS5cutanS6SJ7WSJg0Wg0+iyLRqMx/4wzM/Dw8NDXWQkMDOTZs2dc1ladzUrA0rp1a0DtXko+hdisxo5NWljUhODIVruELPPvmRAicy5dUqfzpjZQrkoV2LRJXXXZjGJjY3n48CG7d+/m888/18+I0Gg0LFiwgKFDh5r1eiLn8fDwsMh5XV1diYiIwMHBwWxdTebWrFkzzpw5Q0BAACVLlkRRFIoWLYqnbr2rTChdujQvvfQSV65cYcuWLfTo0cOMLU6mYUO1JsvZsxk+RDIsQoi03b2r9jNXrqwGKxoNvPoq9OwJQ4aoKd1jx6BcOf0hx44d47fffktRI8IU27Ztw9vbm2LFitGrVy+uX7+Oj48PM2fO5MSJExKsCACmTZuGr68vX+m6GMxEl2Gxxe4gnZbaVZN3795tlu4gnV69egHw+eefExERkeXzpUm3evrKlZDBmUm2OoYF5QURGhqqAEpoaKi1myJExiUmKsrChYri4qIo6oRlRenYUVH++Sfdw27cuKG4uLgogNK7d28lPj7+uZdKSEhQNm/erPTq1UsZOHCgUrt2bQXQ38qUKaN88cUXSnh4uLlenRDpqlq1qgIoBQsWtHZT0vTkyRPFzs5OAZSWLVsqgDJ69Ogsnzc8PFzx8vJSAMXDw0Px9/c3Q2tTcft20t8WUJSwMCUsLEyZNGmS8u+//6Z6SPHixRVAmTdvngIo5cqVs0zbtDL6+S0BixDWcuuWorRunfSHxM9PUfbtS3P3O3fuKFevXlUSExOVzp07GwUb33zzTarHREZGKitXrlS6du2qVKpUyegYQHFwcFA++OADJTo62kIvUoi0NWjQQAEULy8vazclXXXr1jX6vdmXzu+pKXbv3q2UKVNGARR3d3flypUrZjlvCsOHJ/2dOXhQGTZsmP61pCZ//vwKoPz8888KoJQqVcoy7dLK6Oe3jGERwhp+/VXtAnryBJyc1MG1w4enWVb/xIkTvPzyy0RGRuofy5MnD4MHD2bRokWMGzeOatWqER8fz+7du3n06BGJiYls2rSJp0+f6o9xc3Ojd+/eODg4UK5cOd5+++0Ui9sJkV1yQpcQQJcuXfTr/zg6Oppc4TYtLVq04Pz58zRv3pzDhw+zZMkS/dIXZvXNN+osoZ074cIFDh48mOauiYmJhIeHA8ZdQnFxcVavlSMBixDZKSwMhg1Ti70B1KsHP/8M5cuneUhUVBSvv/66UbACMHv2bD766COuXLnCjh070pxyXKpUKXr37o2vry/t27enWLFiZns5QmSFrnictT8In2fEiBFMmDABgNdee82sM5qcnJz48MMPOXz4cKoLLZpNlSpqwPKc9YXCw8P14+IMZwk1bNiQoKAgNmzYwMu6FaSzmQQsQmSX/fuhVy+4cUOtTDtuHHz++XNXVj569Ci3bt2iaNGiHD9+nDVr1uDk5MRHH32ERqNhw4YNvPPOO2zZsgUvLy86deqEt7c3jx49omPHjrRu3domp4wKkVMyLC4uLpw5c4ZZs2YxZcoUs59ft4jiyZMnefbsmdmqCRvp0AHmz4eVK9HoBuKm4uHDh4AaTObVLrgYFxfHjRs3ePz4MQUKFDB/2zJIAhYhLC0uDr74Qu32SUyEUqXUrErjxhk6XFe+28/PD19fX0aNGmX0vJubG3/88QfR0dE4W7DqrRDmllMCFoDq1avz888/W+TcJUuWpHjx4ty+fZsjR47oZyaZVatWULMmnD6NxnCK8xdfQKdOULs2AHfv3gWgWLFiOGgLUxp2K5csWdL8bcsgmdYshCVdugSNGqll9BMToU8fOHMmw8EKJJUCr/WcYnESrIicJicFLJak0Wj0WZY9e/ZY5iL29qDtNjYaKTd5Mnz8sf6uLmDx0WhwrFvX6BQFChQgf/78lmlfBkjAIoSlrFihVqQ9fhwKFID169VaCCb+wusyLDVr1jR/G4WwopwyhiU7dOjQAYA1a9ZkqbZSukqXTvGQAmp3tfaa+oDl0iUcDDIroI6HsyYJWIQwt6goGDgQ3nsPIiPVVOw//6irppooJiaG8+fPAxKwiBePZFiSvP7667i5uXH16lX9itBml0qGJUa3cfs2AHfu3AHAB0geRlqzOwgkYBHCvB49Uv8ofP+9OkV56lR1DY/ixTN1unPnzhEfH0+BAgUoUaKEmRsrhHVJwJIkb968dO3aFYBvv/021X1OnTqlDyiS+/zzz2nbtm36VXPLl4cnT1AMAo8I3QKq69cDBhkWIPm7IhkWIV4Uhw+rXUAHD4KHB+zYARMmqDOCMkk3fqVmzZo2u9aKEJmVL18+wDKLK+ZEw4cPB9RuoZMnTxo9d+HCBerVq0ejRo2IiYkhISGBt956izfeeIOwsDCmTp3K33///fyBwR4eRBksZBrevLm68dlncOQId2/dAsDH2TlFhkUCFiFeBD/+CM2awa1b6no/+/eDdkXWrNCNX3negFshcqJXX32VDh06MGTIEGs3xSbUrl1bvxDi/PnzjZ7bvHkzCQkJBAUFsXLlSlauXMlvv/3Gxo0b+e677/T7BQQEPPc6ETExSdvaWitngAcNG3JXW6fFJz7e5rqEZFqzEFmhKDBnDowcqd7v2lUdWKv9zzGrDDMsQrxofHx8+Ouvv6zdDJvSu3dvfvnlF/YnK/Dm7++v3x42bBh2Bpnb6dOn67fXrVuHk5MTTZo04b333ku1BpNht1F4q1b8s3YtNQFPQPdMsfj4FBmNsmZeKd5UkmERIis++SQpWBkzRi25b6ZgJTExMcNTmoUQL4YGDRqg0Wi4du0aISEhgBpgGAYwcXFxxBhkSR4/fmx0jlWrVjFo0KA0g0HDgCWiRAm29OsHwD3gmfZx72TH1KlTh6rpFJzLDhKwCJFZN26olSMBZs+Gr77K0niV5C5fvkxERAROTk5UqFDBbOcVQtgud3d3qlWrBqBf82fDhg3ExcVRtmxZjh8/jrOzMwUKFOCLL77QH+fs7Mzq1asZNmyYfrzbiRMnUpw/ISGBqKgo/f3w8HCiPD2N9vEAXLXbnwEd2rZl165dVh9HJwGLEJkRHJxU06B8efj00zQXLsysGTNmANC4cWOpUyFELqJbXPHAgQMoisK8efMAGDhwIHXq1OHKlStcvXqVd999V39Mv379eOedd1iwYAFz5swB1FmGySVfkywiIoLQZDOLfAy2vwb+WrcOd3f3rL+wLJKARQhTbd4MPga/0nPnmv0SK1asYOXKlQB89dVXZj+/EMJ26RYX/P333+ncuTOnT5/GxcWFgQMHAmrZ/AIFClC2bFkqVapEwYIF9YszAvqum3///TfFuZNPew4PDycoKMjoMR+SiY/P4isyDxl0K4QpgoPh9deT7k+aBK++atZLbNiwgffeew+A999/nwYNGpj1/EII29a6dWs0Gg1Xr17l6tWr2NvbM2PGDAoWLGi0n0aj4ciRI8TExOhXVoakgOXy5csp1hhLHrBERERw48YNo8dSBCwJCVl+TeYgGRYhMiouDt55J+n+Tz+p63CY0aNHjxg6dCigzgRYuHChWc8vhLB9hQsXNhpov2LFCj766KNU982XL59RsALg5eVFwYIFSUxM5L///jN6LrUMy82bN40es9UMiwQsQmRETAw4OsLeveDkBIcOQe/eZr1EcHAwzZs3JyQkhAoVKvD1118bTV0UQuQeusUQAbp3727SsRqNRj9w9/PPPyc6Olr/XPKA5fbt24SGhho9Viz5CSXDIkQOoShguBLy5MnQsKFZL3H9+nWaNm3Kv//+i7e3Nxs3bpTqn0LkYhMmTOD1119ny5YtmVq6YMyYMTg5ObFlyxYWL16sfzx5wJLawFyfAQOMu74lwyJEDqEdoQ9AtWowerRZT3/u3DkaN27M1atXKVOmDPv376dy5cpmvYYQImcpUqQIGzdupGPHjpk6vl27dsyePRuAtWvX6h9PHrDoaj0Z8unfHzZuTKopJRkWIXKAzZthxAh1u3lzddVlM01fDgwMpF27dlSrVo3g4GCqVq3K/v37KVOmjFnOL4TI3d566y3s7Ow4duwYN27cIDo6OkXAkpBKMOKjmwWpWxhRMixC2LgHD5LSok2bgkFp7KzauXMn7du3Z8eOHSiKQoMGDdi7dy/e3snrSwohROZ4enrSrFkzAHr06EHevHn1WZeiRYsa7dtctwgi6qBdAHRl/SXDIoQNi48Hw5VJf/gh6b+NLHjy5An9+/endevWPHv2jJo1a7J+/Xr27NmTYsqiEEJklW7w7sGDB0lISOD8+fMAjB071mhQf9u2bQEoXrx40pgZ3d88GwlYpA6LEKmZOhV0FSF/+UVdgTmLNmzYwIcffsi9e/cAGDx4MHPmzMHNzS3L5xZCiNSktqzHiBEj+Pjjj1m+fLk+gOnSpQuFCxemnOHfOl2GxUa6hCRgESK5r7+GKVPU7YYNwaD8dWYEBQUxfPhwNm3aBEDFihX5/vvvady4cVZbKoQQ6UoesJw+fZoaNWoA6lgVXcBStmxZKlasaHywjWVYpEtICEPz58OoUeq2pyds3ZrpU0VFRdG3b19Kly7Npk2byJMnDxMmTODUqVMSrAghskXygMVwBmKVKlX026muV2ZjGZZMBSyLFy+mdOnSODs7U6dOHQIDA9Pcd+PGjbRu3ZoiRYqQP39+/Pz82LFjh9E+K1euRKPRpLgZFrsRwuIuX04KVpyc1NWYCxQw+TS3b9/m7bffplChQvz0008kJibSokULTpw4wdSpU43KZAshhCXl001N1jIMTCZPnsyrr77KqlWrUj/YxgbdmtwltG7dOoYPH87ixYtp3Lgxy5Yto3379pw/f54SJUqk2H/fvn20bt2ar776Cg8PD1asWEGnTp04cuSIUenh/Pnzc/HiRaNj5Q+7yDZ37kCnThAbq45XOXcOMrFC8u7du3nrrbd4/PgxAIUKFWL9+vW0bNnS3C0WQogs8fDw4M8//0x7Bxub1oxiovr16ytDhgwxeqxixYrKmDFjMnyOypUrK1988YX+/ooVKxR3d3dTm2IkNDRUAZTQ0NAsnUfkIlFRijJ2rKIUK6Yoaj1bRfH1VZSbNzN1uqNHjyqOjo4KoNSpU0fZu3evEhkZaeZGCyGEaV555RUFUN59913TDqxWTf27+PfflmmYVkY/v03KsMTGxnLixAnGjBlj9HibNm04ePBghs6RmJhIeHh4iimcERERlCxZkoSEBGrWrMnUqVONMjDJxcTEEBMTo78fFhZmwisRuV5CAvTpA+vXJz3m5AS7d0MqmcLn2bdvH++++y6xsbG8+uqr/Pbbb5IhFELYhF9++YW1a9fSq1cv0w7U/Q0z+Ky1JpPGsDx8+JCEhAQ8PT2NHvf09CQkJCRD55gzZw7Pnj2jW7du+scqVqzIypUr+eOPP1izZg3Ozs40btyYy5cvp3me6dOn4+7urr/5+vqa8lJEbhYXBwMGqMGKRgNvvgnvvQcHDsBLL6V6iKIo3L9/H0VRUjx38OBBWrZsyZ07d6hQoQK//PKLBCtCCJtRtGhRPvroIwqYOibPxUX9GhVl/kZlQqYG3WqSlSZXFCXFY6lZs2YNkydPZt26dUZV9ho2bEjPnj2pUaMGTZs2Zf369ZQvX55vv/02zXONHTuW0NBQ/e3WrVuZeSkitwkKgtdeg5Ur1WDll1/g11/VwnB16qTY/ccff8TV1RU3Nzc8PT0ZO3as0fPnzp2jV69eJCQk0LlzZ44cOYK7u3s2vRghhLCgnBywFC5cGHt7+xTZlPv376fIuiS3bt06+vfvz/r163nllVfSb5SdHfXq1Us3w+Lk5ET+/PmNbkKk6+JFaNQItm9XR7///DO8806qu8bHx7N+/Xo++OADoqKiiNL+ws6cORN/f38iIyP57LPPqFmzJteuXaNEiRL89NNPEqwIIV4cukxxTgxYHB0dqVOnDv7J1lTx9/enUaNGaR63Zs0a+vbty+rVq3n11Vefex1FUTh9+rSsqyLMIy4OFi9Wi8DduQMVK8LBg2kWhIuLi6NLly50796dmJgYmjRpwn///cfAgQMBtYR19erVmTNnDvHx8XTp0oXAwEAJVoQQLxZdhsVWSoyYOpp37dq1ioODg/LDDz8o58+fV4YPH664ubkpN27cUBRFUcaMGaP06tVLv//q1auVPHnyKIsWLVKCg4P1t6dPn+r3mTx5srJ9+3bl6tWryqlTp5R+/fopefLkUY4cOZLhdsksIZGqffsUpWLFpFlANWsqyv376R4yevRoBVBcXFyUzz77TP+zGhYWpnh6eiqAAije3t7Kn3/+mR2vQgghst+gQerfzY8/tuhlMvr5bXLAoiiKsmjRIqVkyZKKo6OjUrt2bWXv3r365/r06aM0a9ZMf79Zs2b6P/CGtz59+uj3GT58uFKiRAnF0dFRKVKkiNKmTRvl4MGDJrVJAhZhJDZWUUaNUhQ7O/UXrkABRfnqK0UJD0/3sOjoaKVAgQIKoKxduzbF8z/99JMCKDVr1lRCQkIs1XohhLC+339X/362bGnRy2T081ujKKlMe8iBwsLCcHd3JzQ0VMaz5HaXLqlTlg8fVu/37q2W3PfweO6h69evp3v37hQrVoybN29ir6v0aODKlSuULFky9VLWQgjxooiLg1OnoF49dZKChWT081vWEhIvjsRE+OYbqFpVDVby54eNG+GnnzIUrKxYsYLu3bsD0LNnz1SDFYCXXnpJghUhxIvPwQHq17dosGIKWa1ZvBiCgqBvX9izR73fvLkaqGSwCNx///3H4MGDAXWxsGHDhlmmnUIIITJFMiwiZ1MUNTCpVk0NVlxdYckSkyvWjho1iri4OP26WMWKFbNgo4UQQphKMiwi57p/HwYPhs2b1ft+frBqVZrVatOSkJDA7t27Afjqq6+ws5M4XgghbI38ZRY5065dalZl82a1n/Wrr2DfPpODFYDLly/z7NkzXF1dqVatmvnbKoQQIsskwyJyFkWB6dNh0iR1yfOqVeF//4OaNTN9ypMnTwJQs2bNNAfaCiGEsC4JWETOERcHH34Iy5er93v0gO+/TyofnUm6gCW91cGFEEJYlwQsImd48EANUPz9wc4O5s2DoUPNMt3uxIkTANSuXTvL5xJCCGEZErAI23f7NnToAGfPqmtbrFsHnTqZ5dSBgYHs3bsXUFcNF0IIYZtk0K2wbX/8AVWqqMGKl5daEM4MwcqyZcto2bIlbdq0QVEU+vXrR+XKlc3QYCGEEJYgGRZhuxYuhI8/VivYNmyo1lspXz7Lp924cSNDhgzR32/UqBHffPNNls8rhBDCciTDImzT1KkwbJgarAwcCIGBZglWDhw4wIABAwB47bXXOHnyJPv378fd3T3L5xZCCGE5kmERtmfuXPj8c3V72jQYN84sg2t37drFq6++SkxMDA0aNGD9+vU4OTll+bxCCCEsTzIswrZ89x18+qm6PW0ajB9vlmDl7t27dO7cmZiYGDp27MiuXbskWBFCiBxEMizCdvz6K+jGlowerWZWzGThwoU8e/aMevXq8dtvv0mwIoQQOYxkWIRtOHEC+vRRK9kOGaJWszXTkuZXrlxh6dKlAIwbN06CFSGEyIEkYBHWFxICXbpAVBS0b6/ODjJTsBIYGEidOnV48uQJlSpVopOZ6rcIIYTIXhKwCOuKi4M331SLw1WoAGvWgJnW8wkICKBdu3aEhYXRuHFj/v77b1krSAghcigZwyKsa/JkOHAA8udXi8SZaXrxrl276NSpE1FRUbRp04bNmzfj4uJilnMLIYTIfpJhEdaze7c6VgXUBQ3NUGfl8OHDNGzYkFdeeYWoqCjat2/P77//LsGKEELkcJJhEdbx4AH07KkOsh04ELp1y/Ipd+zYQZcuXYiOjgbg9ddfZ82aNTLIVgghXgCSYRHW8fHHEBwMlSurKy9nQUxMDEuXLuWNN94gOjqaDh06cP78eTZs2CDBihBCvCAkwyKy35Yt6uBajQb+9z9wdc30qe7evcvrr7/O0aNHAWjVqhWbNm3C0dHRXK0VQghhAyTDIrLXjRvQu7e6/f77ULt2pk4TExPDunXraNy4MUePHqVAgQJ88803bNmyRYIVIYR4AUmGRWQf3XiVp0/V1ZczuUJyWFgYbdq04ciRIwCUKVMGf39/ypQpY8bGCiGEsCWSYRHZZ+1a2LkTnJzg55/BxEzI2bNnGThwIGXKlOHIkSO4uLgwZMgQAgMDJVgRQogXnGRYRPZ4+hRGjFC3J0yAsmVNOvzGjRs0a9aMJ0+eAFCqVCk2bNhA7Ux2KQkhhMhZJMMisseECWoJ/goVYORIkw69fv06HTp04MmTJ1SsWJHt27dz5coVCVaEECIXkQyLsLzjx2HxYnV78WK1SyiDEhISaN++PRcvXsTHx4ft27dTsmRJCzVUCCGErZKARVhWQoK6+rKiQI8e0LKlSYf/9ddfXLx4kQIFCnD06FGKFStmoYYKIYSwZdIlJCxryRI4cUJdI2jOHJMO3bdvH4MGDQJg4MCBEqwIIUQuJhkWYTl378K4cer29Ong6ZnhQwMDA2nVqhXx8fHkz5+fDz/80EKNFEIIkRNIhkVYzpgxEB4O9euDNlOSEfv27aNLly7Ex8fTsWNHTpw4QYkSJSzYUCGEELZOMizCMo4eVcvuAyxaBPb2GTrs+PHjtG3blujoaBo2bMjatWtxc3OzYEOFEELkBJJhEeanKDB8uLrdpw/UrZuhw0JDQ+natSvR0dG0a9eO3bt3S7AihBACkIBFWMK6dXDokLqo4VdfZfiwSZMmcevWLcqWLcvatWtxcXGxYCOFEELkJBKwCPO6fz+pMNzYseDjk6HD/ve///Htt98CsHjxYtzd3S3VQiGEEDmQjGER5vXxx3D7Nrz0Enz6aYYOWb16Nb21KzgPGjSINm3aWLKFQgghciDJsAjz2bdPXeBQo1G7hTLQpXPixAn69u0LwIcffsiSJUss3EghhBA5kQQswjzi42HYMHV70CDIwDo/cXFx9O/fn7i4OLp06cKCBQuws5MfSSGEECnJp4Mwj2XL4J9/oEAB+PLLDB2ydu1azpw5Q8GCBVm2bJkEK0IIIdIknxAi6x4+hIkT1e1p06BQoQwdtli7IOKIESMoWrSopVonhBDiBSABi8i68ePhyROoUQMGD87QIf/88w+HDx8mT5489O/f38INFEIIkdNlKmBZvHgxpUuXxtnZmTp16hAYGJjmvhs3bqR169YUKVKE/Pnz4+fnx44dO1Lst2HDBipXroyTkxOVK1dm06ZNmWmayG4nT8Ly5er2t99muKLt1q1bAejQoQNeXl6Wap0QQogXhMkBy7p16xg+fDjjx4/n1KlTNG3alPbt2xMUFJTq/vv27aN169Zs3bqVEydO0KJFCzp16sSpU6f0+xw6dIju3bvTq1cvzpw5Q69evejWrRtHjhzJ/CsTlqco6jRmRYF33oGmTTN86L59+wBo2bKlpVonhBDiBaJRFEUx5YAGDRpQu3Zto+mnlSpVokuXLkyfPj1D56hSpQrdu3fn888/B6B79+6EhYWxbds2/T7t2rWjQIECrFmzJkPnDAsLw93dndDQUPLnz2/CKxKZtm4dvP22On354kXw9X3uIZGRkfz4448M084oOnnyJLVq1bJ0S4UQQtiojH5+m5RhiY2N5cSJEykKe7Vp04aDBw9m6ByJiYmEh4dTsGBB/WOHDh1Kcc62bdume86YmBjCwsKMbiIbRUQkFYYbMyZDwQrA4MGD9cEKQPXq1S3ROiGEEC8YkwKWhw8fkpCQgKenp9Hjnp6ehISEZOgcc+bM4dmzZ3Tr1k3/WEhIiMnnnD59Ou7u7vqbbwY/MIWZTJ0Kd+5AmTIwalSGDtm8eTM///yz/n7nzp2xz+CYFyGEELlbpkrzazQao/uKoqR4LDVr1qxh8uTJ/P777ymmsZp6zrFjxzJixAj9/bCwMAlassu5czB3rro9fz44Oz/3kIcPHzJYO4Po448/pm7durRo0cKSrRRCCPECMSlgKVy4MPb29ikyH/fv30+RIUlu3bp19O/fn19//ZVXXnnF6DkvLy+Tz+nk5ISTk5MpzRfmoCjwwQdqZdsuXaBjx+ceEhMTw1tvvcX9+/epXLkyM2bMwDkDQY4QQgihY1KXkKOjI3Xq1MHf39/ocX9/fxo1apTmcWvWrKFv376sXr2aV199NcXzfn5+Kc75999/p3tOYSWrVqlrBrm6qtmV57h//z7t2rUjICCAvHnzsnr1aglWhBBCmMzkLqERI0bQq1cv6tati5+fH9999x1BQUEMGTIEULtq7ty5w6pVqwA1WOnduzfz58+nYcOG+kyKi4sL7u7ugNpF8PLLLzNz5kw6d+7M77//zs6dO9m/f7+5Xqcwh8ePYeRIdXvSJChRIt3d/f396devH3fu3CFv3rxs2rSJGjVqZENDhRBCvHCUTFi0aJFSsmRJxdHRUaldu7ayd+9e/XN9+vRRmjVrpr/frFkzBUhx69Onj9E5f/31V6VChQqKg4ODUrFiRWXDhg0mtSk0NFQBlNDQ0My8JJERgwcrCihK5cqKEhub7q5jxozRv9cVKlRQzp8/n02NFEIIkZNk9PPb5DostkrqsFjYkSPg56eOYdm7F15+Oc1dAwMDeVn7/EcffcTUqVPlPRFCCJGqjH5+Z2qWkMhl4uPh/ffVYKVPn3SDlYSEBD744AMA+vfvz/wMjHMRQgghnkcWPxTPt3gxnDoFBQrArFnp7vrDDz/w77//4uHhwazn7CuEEEJklAQsIn1378KECer29OmQrH6OoS+//FI/+HrcuHFG1YyFEEKIrJAuIZG+4cMhPBzq14cBA9LcbfPmzUzQBjYDBgzg448/zqYGCiGEyA0kwyLStnUr/Por2NvDsmXq11TMnz+fN998E4BPPvmE5cuX4+jomJ0tFUII8YKTDItIXWQkfPihuj18ONSsmepuZ8+eZcSIESQmJvLuu+8yY8aMbGuiEEKI3EMCFpG6SZPgxg11FebJk1PdRVEUfbDyxhtv8Msvv2RrE4UQQuQe0iUkUjp+PGlxw8WLIW/eVHf766+/2LlzJ46OjjIjSAghhEVJwCKMxcVB//6QmAjvvJPm4oaxsbF8+umngLq0QpkyZbKzlUIIIXIZCViEsdmz4Z9/oFChdBc3nD17NpcuXaJo0aKMHz8+GxsohBAiN5IxLCLJ9eswZYq6/c03UKRIil0URWHkyJHM1XYZzZkzR7+IpRBCCGEpErAIlaLAsGEQHQ0tWkDPnqnu9r///Y85c+YAMGjQIHr06JGdrRRCCJFLSZeQUG3eDH/9BQ4O6kBbjcbo6fj4eGbMmMGwYcMAtartsmXL0CTbTwghhLAEybAIiIiAjz5St0eOhIoVjZ6+c+cOffr0YdeuXQD4+fkxcuTI7G6lEEKIXEwyLAK++AJu34ZSpSDZANqTJ09StWpVdu3ahZubGytWrGDfvn04ODhYp61CCCFyJQlYcruzZ9UBtgALF4KrKwCXL1+mU6dO1KlTh6dPn1K7dm2OHj1K3759yZNHEnNCCCGyl3zy5GaJifD++5CQAK+/Dq++CsD169dp1qwZwcHBAFStWpXdu3fLbCAhhBBWIxmW3Gz1ajhwANzc9DVXEhIS6NOnD8HBwVSsWJENGzZw7NgxCVaEEEJYlWRYcqvo6KTxKuPHg68vCQkJDB06lMDAQPLmzcu2bdsoVaqUVZsphBBCgAQsudeCBRAUBMWLw/DhxMTE0LNnT3777Tc0Gg1Lly6VYEUIIYTNkIAlN3r0CL76St2eNo2wuDhef/11du/ejYODA7/88gtvvfWWddsohBBCGJCAJTeaNg1CQ6FGDS43aECn+vW5ePEiefPmZfPmzbRq1craLRRCCCGMyKDb3Ob+fVi6FABl5kx69e3LxYsXKV68OAEBARKsCCGEsEmSYcltFi5UB9zWq8dPd+9y5MgR3NzcOHToEMWLF7d264QQQohUSYYlN4mIUAMWYGH16vR77z0ARo4cKcGKEEIImyYBS27yww/w5An3Spdm9Jo1AAwZMoRx48ZZuWFCCCFE+iRgyS0SE/XZlWklSxIZGUn9+vVZvHixrAskhBDC5knAklvs3g1XrnAtb16WHTgAwIwZM9BoNFZumBBCCPF8ErDkFtqZQeO8vIiLi6NNmza0aNHCyo0SQgghMkYCltzg7l3YvJm1wLorV7Czs2P69OnWbpUQQgiRYRKw5Abr1pGQkMBnjo4ATJgwgdq1a1u5UUIIIUTGScCSBQsWLOC9997j4MGD1m5K+n7/nQDgTmwsHh4eMitICCFEjiMBSxZs376dFStWcPHiRWs3JW0PH0JgIP/T3u3WrRtOTk5WbZIQQghhKglYssDFxQWAqKgoK7ckHX/9RWRiIhvs1Le6V69eVm6QEEIIYToJWLJAF7BER0dbuSXp2LyZ34GIxERKly5N48aNrd0iIYQQwmQSsGSBzWdYIiNhxw5+1t7t2bOn1F0RQgiRI0nAkgU2H7Ds3ElYVBR/a+/26NHDqs0RQgghMksClixwdnYGbDhg2byZXUA8UK5cOSpUqGDtFgkhhBCZIgFLFtj0GJaEBNiyhe3au+3bt7dqc4QQQoiskIAlC2y6S+jgQRIfPmSbdsxKu3btrNwgIYQQIvMkYMkCmw5Yfv+dvcAtRSFfvnw0a9bM2i0SQgghMk0Cliyw2TEsigKbN/Oj9u4777yDq6urVZskhBBCZIUELFlgsxmW8+eJvHqV37R3+/fvb9XmCCGEEFmVqYBl8eLFlC5dGmdnZ+rUqUNgYGCa+wYHB/Puu+9SoUIF7OzsGD58eIp9Vq5ciUajSXGzycGsBmx20O3mzewDogFfX1/q1atn7RYJIYQQWWJywLJu3TqGDx/O+PHjOXXqFE2bNqV9+/YEBQWlun9MTAxFihRh/Pjx1KhRI83z5s+fn+DgYKObrsvFVtlshmXzZvy1m61bt5ZicUIIIXI8kwOWuXPn0r9/fwYMGEClSpWYN28evr6+LFmyJNX9S5Uqxfz58+nduzfu7u5pnlej0eDl5WV0s3U2OYbl9m04fpyd2rutW7e2anOEEEIIczApYImNjeXEiRO0adPG6PE2bdpw8ODBLDUkIiKCkiVLUrx4cTp27MipU6eydL7sYJMZlj/+4C7wj/Zuq1atrNkaIYQQwixMClgePnxIQkICnp6eRo97enoSEhKS6UZUrFiRlStX8scff7BmzRqcnZ1p3Lgxly9fTvOYmJgYwsLCjG7ZzSbHsPz+O1u0mw0aNKBIkSJWbY4QQghhDpkadJt8TISiKFkaJ9GwYUN69uxJjRo1aNq0KevXr6d8+fJ8++23aR4zffp03N3d9TdfX99MXz+zbC7DEhoKe/bwu/bua6+9ZtXmCCGEEOZiUsBSuHBh7O3tU2RT7t+/nyLrkqVG2dlRr169dDMsY8eOJTQ0VH+7deuW2a6fUTY3hmXbNkLj4tilDR47d+5s5QYJIYQQ5mFSwOLo6EidOnXw9/c3etzf359GjRqZrVGKonD69Gm8vb3T3MfJyYn8+fMb3bKbYYZFUZRsv34KmzfzPRCrKFSpUoXKlStbu0VCCCGEWeQx9YARI0bQq1cv6tati5+fH9999x1BQUEMGTIEUDMfd+7cYdWqVfpjTp8+DagDax88eMDp06dxdHTUf6B+8cUXNGzYkHLlyhEWFsaCBQs4ffo0ixYtMsNLtBxdwALqgGQnJyfrNSYmhri//mK+9u4nn3wi05mFEEK8MEwOWLp3786jR4+YMmUKwcHBVK1ala1bt1KyZElALRSXvCZLrVq19NsnTpxg9erVlCxZkhs3bgDw9OlTBg0aREhICO7u7tSqVYt9+/ZRv379LLw0yzMMWKKioqwbsAQEEBARwS3UrrsePXpYry1CCCGEmWkUm+jLyLqwsDDc3d0JDQ3Ntu4hRVGwt7dHURTu3r2bbheWxQ0ezEfffce3qKX4v//+e+u1RQghhMigjH5+y1pCWaDRaGxjplB8PMrGjfyhvSuzg4QQQrxoJGDJIpuoxRIYyJmHD7mJOnPplVdesV5bhBBCCAuQgCWLbCLDsmEDP2s3O3TogKurq/XaIoQQQliABCxZZPVaLImJxBsELH369LFOO4QQQggLkoAli6yeYdm3j20hIdxDnR3Uvn1767RDCCGEsCAJWLLI6mNYVq9Gt052v379cHBwsE47hBBCCAuSgCWLrJphiYnhytq1bEedsTR48ODsb4MQQgiRDSRgySKrjmHZto3Z4eEoQPt27Shbtmz2t0EIIYTIBhKwZJE1MyzB33/PCu32mLFjs/36QgghRHaRgCWL3NzcAHj27Fn2XjgkhLXbthEL+NWoQdOmTbP3+kIIIUQ2koAli/LlywdAeHh49l74hx/YkpgIQPd+/bL32kIIIUQ2k4Ali6wSsCQk8HTpUgK1dzt27Jh91xZCCCGsQAKWLLJKwLJ1K9tu3yYeqFSxogy2FUII8cKTgCWLrBKwLF7MKu3mG2++mX3XFUIIIaxEApYsyvaA5do17m7fzt/au1KKXwghRG4gAUsWZXvAsmwZPwOJQJMmTXjppZey57pCCCGEFUnAkkXZGrDExKD88AMrtXcluyKEECK3kIAli7I1YPntN44/esQF1IJ1b731luWvKYQQQtgACViyKDsDFmXxYiZot7t27Yq7u7vFrymEEELYAglYsijbApZ//mH9wYP8DTg5OTF58mTLXk8IIYSwIRKwZJEuYImMjCQhIcFi1wmdOZNPtNvjxo2TwbZCCCFyFQlYskgXsABERERY5iJ37jBt7VqCgXK+vowePdoy1xFCCCFslAQsWeTk5ESePHkAy3ULhXz1FYu06wZ9s2QJTk5OFrmOEEIIYaskYMkijUZj2XEsYWHM+v57ooAG5cvToUMH819DCCGEsHESsJiBJQOW4LlzWRIbC8AX8+ah0WjMfg0hhBDC1knAYgYWC1ji4pg2Zw7RgF/ZsrRp18685xdCCCFyCAlYzMBSAcvpWbNYqh3I++WiRZJdEUIIkWtJwGIGFglYFIVxM2eSCHSrWpUWbdua79xCCCFEDiMBixlYImC58tNPbAsPRwN8tXKl2c4rhBBC5EQSsJiBrkT+48ePzXK+6Kgoxo0cCUD7kiUpW6eOWc4rhBBC5FR5rN2AF0H58uUBOH/+fJbPpSgKb7ZowV8PHwLw2axZWT6nEEIIkdNJhsUMqlWrBsDZs2ezfK6fVq7kryNHcAL+7NSJFt26ZfmcQgghRE4nAYsZ6AKW//77j1htzZTM2Lx5M4MGDQLgizx5eHXZMrO0TwghhMjpJGAxA19fX9zd3YmPj+fixYuZOsfjx4/p06cPcfHxvAl8+skn4O1t3oYKIYQQOZQELGag0WioWrUqkPluoa+//pqwsDCqA2sLFSLP+PFmbKEQQgiRs8mgWzOpWrUqBw4cyPDA25iYGMaPH09YWBheXl58/fXXAEwF7L/6CrQzj4QQQgghAYvZlC5dGoCgoKA091EUhZ9++okbN26wbds2jh49avR8d6BTkyYwYIAlmyqEEELkOBKwmImvry8At27dSnOfPXv20K9fP/19jUZDnTp14NEjaly/zgIHBzTLl4Od9NQJIYQQhiRgMZMSJUoA6WdYFi1apN+eNGkS7733HiUAtLOMmDwZKla0XCOFEEKIHEoCFjMxzLAkJiZilyxLcvPmTX7//XdAHZhbtWpVSEyEV16BsDBo2BBGjcr2dgshhBA5gfQ9mImPjw92dnbExcVx//79FM+PGjWKhIQEWrVqpZ9RxKJFsGcPuLrCqlWQR+JHIYQQIjUSsJiJg4MD3tq6KVOmTCE4OBhQB9pOnDiR9evXY2dnx9y5c9UDLl2C0aPV7a+/hnLlrNFsIYQQIkeQgMWMdONYlixZQp06dTh79iwrVqxg2rRpAHz++edUr14dFAUGDYKoKGjdGt5/35rNFkIIIWye9EGYUXR0tH47ODiYDh066Ev1T5s2jfG6YnAbNsDeveDsDMuXg0ZjjeYKIYQQOUamMiyLFy+mdOnSODs7U6dOHQIDA9PcNzg4mHfffZcKFSpgZ2fH8OHDU91vw4YNVK5cGScnJypXrsymTZsy0zSratWqFQCurq4UKVKE27dvc//+fXx9ffn000/VnaKi4LPP1O3Ro6FkSSu1VgghhMg5TA5Y1q1bx/Dhwxk/fjynTp2iadOmtG/fPs3pvDExMRQpUoTx48dTo0aNVPc5dOgQ3bt3p1evXpw5c4ZevXrRrVs3jhw5YmrzrGr06NHMmDGDmzdvsmDBAjQaDeXKlWPdunU4OzurOy1eDDdvgq+vzAoSQgghMkijKIpiygENGjSgdu3aLFmyRP9YpUqV6NKlC9OnT0/32ObNm1OzZk3mzZtn9Hj37t0JCwtj27Zt+sfatWtHgQIFWLNmTYbaFRYWhru7O6GhoeTPnz/jL8iCQkJCKFy4MHl0s3+ioqB0abh3D77/Hvr3t24DhRBCCCvL6Oe3SRmW2NhYTpw4QZs2bYweb9OmDQcPHsxcS1EzLMnP2bZt23TPGRMTQ1hYmNHN1nh5eSUFK6AGKffuqd1AvXpZr2FCCCFEDmNSwPLw4UMSEhLw9PQ0etzT05OQkJBMNyIkJMTkc06fPh13d3f9TVe4zWbFxMDMmer2mDHg6Gjd9gghhBA5SKYG3WqSzWpRFCXFY5Y+59ixYwkNDdXf0lvDxyasWAF37kCxYmCwnpAQQgghns+kac2FCxfG3t4+Rebj/v37KTIkpvDy8jL5nE5OTjg5OWX6mtkqLg5043tGj4ac0m4hhBDCRpiUYXF0dKROnTr4+/sbPe7v70+jRo0y3Qg/P78U5/z777+zdE6b8uuvEBQERYvCgAHWbo0QQgiR45hcOG7EiBH06tWLunXr4ufnx3fffUdQUBBDhgwB1K6aO3fusGrVKv0xp0+fBiAiIoIHDx5w+vRpHB0dqVy5MgAff/wxL7/8MjNnzqRz5878/vvv7Ny5k/3795vhJVqZosCcOer20KHg4mLd9gghhBA5kMkBS/fu3Xn06JF+vZyqVauydetWSmoLoAUHB6eoyVKrVi399okTJ1i9ejUlS5bkxo0bADRq1Ii1a9cyYcIEJk6cSNmyZVm3bh0NGjTIwkuzEfv2wcmTalVbKcEvhBBCZIrJdVhslS3WYQHg1Vdh61YYPBiWLrV2a4QQQgibYpE6LMJEhw6pwYq9PehK8wshhBDCZBKwWNLnn6tf+/SBcuWs2xYhhBAiB5OAxVL8/WHnTnBwgIkTrd0aIYQQIkeTgMUS4uNBtyr1++9DqVLWbI0QQgiR40nAYglLl8L581CoEEyebO3WCCGEEDmeBCzm9vBh0tiVadOgQAHrtkcIIYR4AUjAYm7jx8OTJ1C9ulS1FUIIIcxEAhZzOnAAli9Xt7/9FvKYXJdPCCGEEKmQgMVcYmLgvffUUvx9+sDLL1u7RUIIIcQLQwIWc5k/Hy5dAi8vmDfP2q0RQgghXigSsJjD3bswdaq6PXMmeHhYtTlCCCHEi0YCFnOYNg0iIqBhQ+jZ09qtEUIIIV44ErBkVVAQfP+9uj1jBtjJt1QIIYQwN/l0zaovv4S4OGjRApo1s3ZrhBBCiBeSBCxZcf06/Pijuv3FF9ZtixBCCPECk4AlK6ZOVdcNat0amja1dmuEEEKIF5YELJl1+TKsWqVuT5li3bYIIYQQLzgJWDJr4kRISIAOHdTZQUIIIYSwGAlYMuPoUVi3DjQaddCtEEIIISxKAhZThYVBr17qdq9eULOmVZsjhBBC5AYSsJhCUaB/f7UEf/HiMHu2tVskhBBC5AoSsJjiq6/gt9/AwQF+/RWKFLF2i4QQQohcQQKWjFq+HCZMULfnzpWBtkIIIUQ2koAlI9avh8GD1e1Ro+DDD63bHiGEECKXyWPtBti8LVuge3d1+623YPp0dXaQEEIIIbKNZFjSExmpDrIFdTbQ99/L4oZCCCGEFcinb3pcXWHzZnjzTThyBPLnt3aLhBBCiFxJuoSep1Ej9SaEEEIIq5EMixBCCCFsngQsQgghhLB5ErAIIYQQwuZJwCKEEEIImycBixBCCCFsngQsQgghhLB5ErAIIYQQwuZJwCKEEEIImycBixBCCCFsngQsQgghhLB5ErAIIYQQwuZJwCKEEEIImycBixBCCCFs3guzWrOiKACEhYVZuSVCCCGEyCjd57buczwtL0zAEh4eDoCvr6+VWyKEEEIIU4WHh+Pu7p7m8xrleSFNDpGYmMjdu3fJly8fGo3G2s0xm7CwMHx9fbl16xb58+e3dnNEMvL+2DZ5f2ybvD+2LzveI0VRCA8Px8fHBzu7tEeqvDAZFjs7O4oXL27tZlhM/vz55Rfahsn7Y9vk/bFt8v7YPku/R+llVnRk0K0QQgghbJ4ELEIIIYSweRKw2DgnJycmTZqEk5OTtZsiUiHvj22T98e2yftj+2zpPXphBt0KIYQQ4sUlGRYhhBBC2DwJWIQQQghh8yRgEUIIIYTNk4BFCCGEEDZPApZsNnnyZDQajdHNy8tL/7yiKEyePBkfHx9cXFxo3rw5586dMzpHTEwMw4YNo3Dhwri5ufHaa69x+/bt7H4pL4x9+/bRqVMnfHx80Gg0bN682eh5c70nT548oVevXri7u+Pu7k6vXr14+vSphV9dzve896dv374pfqcaNmxotI+8P5Yzffp06tWrR758+ShatChdunTh4sWLRvvI75D1ZOT9ySm/QxKwWEGVKlUIDg7W386ePat/btasWcydO5eFCxdy7NgxvLy8aN26tX6tJIDhw4ezadMm1q5dy/79+4mIiKBjx44kJCRY4+XkeM+ePaNGjRosXLgw1efN9Z68++67nD59mu3bt7N9+3ZOnz5Nr169LP76crrnvT8A7dq1M/qd2rp1q9Hz8v5Yzt69e/nwww85fPgw/v7+xMfH06ZNG549e6bfR36HrCcj7w/kkN8hRWSrSZMmKTVq1Ej1ucTERMXLy0uZMWOG/rHo6GjF3d1dWbp0qaIoivL06VPFwcFBWbt2rX6fO3fuKHZ2dsr27dst2vbcAFA2bdqkv2+u9+T8+fMKoBw+fFi/z6FDhxRA+e+//yz8ql4cyd8fRVGUPn36KJ07d07zGHl/stf9+/cVQNm7d6+iKPI7ZGuSvz+KknN+hyTDYgWXL1/Gx8eH0qVL8/bbb3Pt2jUArl+/TkhICG3atNHv6+TkRLNmzTh48CAAJ06cIC4uzmgfHx8fqlatqt9HmI+53pNDhw7h7u5OgwYN9Ps0bNgQd3d3ed/MICAggKJFi1K+fHkGDhzI/fv39c/J+5O9QkNDAShYsCAgv0O2Jvn7o5MTfockYMlmDRo0YNWqVezYsYPly5cTEhJCo0aNePToESEhIQB4enoaHePp6al/LiQkBEdHRwoUKJDmPsJ8zPWehISEULRo0RTnL1q0qLxvWdS+fXt++eUXdu/ezZw5czh27BgtW7YkJiYGkPcnOymKwogRI2jSpAlVq1YF5HfIlqT2/kDO+R16YVZrzinat2+v365WrRp+fn6ULVuWn376ST/ISaPRGB2jKEqKx5LLyD4i88zxnqS2v7xvWde9e3f9dtWqValbty4lS5bkr7/+omvXrmkeJ++P+Q0dOpR//vmH/fv3p3hOfoesL633J6f8DkmGxcrc3NyoVq0aly9f1s8WSh6N3r9/X//fiZeXF7GxsTx58iTNfYT5mOs98fLy4t69eynO/+DBA3nfzMzb25uSJUty+fJlQN6f7DJs2DD++OMP9uzZQ/HixfWPy++QbUjr/UmNrf4OScBiZTExMVy4cAFvb29Kly6Nl5cX/v7++udjY2PZu3cvjRo1AqBOnTo4ODgY7RMcHMy///6r30eYj7neEz8/P0JDQzl69Kh+nyNHjhAaGirvm5k9evSIW7du4e3tDcj7Y2mKojB06FA2btzI7t27KV26tNHz8jtkXc97f1Jjs79DZhm6KzLs008/VQICApRr164phw8fVjp27Kjky5dPuXHjhqIoijJjxgzF3d1d2bhxo3L27FnlnXfeUby9vZWwsDD9OYYMGaIUL15c2blzp3Ly5EmlZcuWSo0aNZT4+HhrvawcLTw8XDl16pRy6tQpBVDmzp2rnDp1Srl586aiKOZ7T9q1a6dUr15dOXTokHLo0CGlWrVqSseOHbP99eY06b0/4eHhyqeffqocPHhQuX79urJnzx7Fz89PKVasmLw/2eT9999X3N3dlYCAACU4OFh/i4yM1O8jv0PW87z3Jyf9DknAks26d++ueHt7Kw4ODoqPj4/StWtX5dy5c/rnExMTlUmTJileXl6Kk5OT8vLLLytnz541OkdUVJQydOhQpWDBgoqLi4vSsWNHJSgoKLtfygtjz549CpDi1qdPH0VRzPeePHr0SOnRo4eSL18+JV++fEqPHj2UJ0+eZNOrzLnSe38iIyOVNm3aKEWKFFEcHByUEiVKKH369EnxvZf3x3JSe28AZcWKFfp95HfIep73/uSk3yGN9gUJIYQQQtgsGcMihBBCCJsnAYsQQgghbJ4ELEIIIYSweRKwCCGEEMLmScAihBBCCJsnAYsQQgghbJ4ELEIIIYSweRKwCCGEEMLmScAihBBCCJsnAYsQQgghbJ4ELEIIIYSweRKwCCGEEMLm/R8bf4MXPbVb5AAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from spectral.io import envi\n",
-    "import matplotlib.pyplot as plt\n",
-    "for _key, key in enumerate(report.keys()):\n",
-    "  if _key < 1:\n",
-    "\n",
-    "    rfl_ds = envi.open(os.path.join(output, f'NIS01_20210403_{key}','output',f'NIS01_20210403_{key}_rfl.hdr'))\n",
-    "    rfl_rgb = rfl_ds.open_memmap(interleave='bip')[:,:,np.array([60,40,30])].copy()\n",
-    "    wl = np.array([float(x) for x in rfl_ds.metadata['wavelength']])\n",
-    "\n",
-    "    miny = np.min([np.min([i[0],i[1]]) for k,i in report[key].items()])-5\n",
-    "    maxy = np.max([np.max([i[0],i[1]]) for k,i in report[key].items()])+5\n",
-    "    minx = np.min([np.min([i[2],i[3]]) for k,i in report[key].items()])-5\n",
-    "    maxx = np.max([np.max([i[2],i[3]]) for k,i in report[key].items()])+5\n",
-    "\n",
-    "    plt.figure()\n",
-    "    plt.imshow(rfl_rgb / np.max(rfl_rgb,axis=(0,1)))\n",
-    "    plt.title(f'NIS01_20210403_{key}')\n",
-    "    for k,i in report[key].items():\n",
-    "        plt.plot([i[2]-minx,i[3]-minx,i[3]-minx,i[2]-minx,i[2]-minx],[i[0]-miny,i[0]-miny,i[1]-miny,i[1]-miny,i[0]-miny],label=k)\n",
-    "\n",
-    "    for k,i in report[key].items():\n",
-    "        plt.figure()\n",
-    "        in_situ = np.genfromtxt(f'data/FieldSpectrometer/{k}01/Data/{k}01_Refl.dat', skip_header=3)\n",
-    "        plt.plot(in_situ[:,0], in_situ[:,1], label=f'In Situ {k}',c='red',ls='-')\n",
-    "        mean_rfl = np.mean(rfl_ds.open_memmap(interleave='bip')[i[0]-miny:i[1]-miny,i[2]-minx:i[3]-minx,:],axis=(0,1))\n",
-    "        plt.plot(wl, mean_rfl, label=f'Ret. {k}', c='black')\n",
-    "        plt.legend()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "411124d3",
-   "metadata": {},
-   "source": [
-    "We can also plot out the mapped reflectance (as above), but also the interpolated atmospheric conditions.  The windows size is small enough here (and the atmospheric parameters are chosen in such a way) that the map is going to be pretty static...but we can still see it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "7b27704a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for _key, key in enumerate(report.keys()):\n",
-    "  if _key < 1:\n",
-    "\n",
-    "    rfl_ds = envi.open(os.path.join(output, f'NIS01_20210403_{key}','output',f'NIS01_20210403_{key}_rfl.hdr'))\n",
-    "    rfl_rgb = rfl_ds.open_memmap(interleave='bip')[:,:,np.array([60,40,30])].copy()\n",
-    "    wl = np.array([float(x) for x in rfl_ds.metadata['wavelength']])\n",
-    "\n",
-    "    miny = np.min([np.min([i[0],i[1]]) for k,i in report[key].items()])-5\n",
-    "    maxy = np.max([np.max([i[0],i[1]]) for k,i in report[key].items()])+5\n",
-    "    minx = np.min([np.min([i[2],i[3]]) for k,i in report[key].items()])-5\n",
-    "    maxx = np.max([np.max([i[2],i[3]]) for k,i in report[key].items()])+5\n",
-    "\n",
-    "    plt.figure()\n",
-    "    plt.imshow(rfl_rgb / np.max(rfl_rgb,axis=(0,1)))\n",
-    "    plt.title(f'NIS01_20210403_{key}')\n",
-    "    for k,i in report[key].items():\n",
-    "        plt.plot([i[2]-minx,i[3]-minx,i[3]-minx,i[2]-minx,i[2]-minx],[i[0]-miny,i[0]-miny,i[1]-miny,i[1]-miny,i[0]-miny],label=k)\n",
-    "\n",
-    "    plt.figure()\n",
-    "    atm_ds = envi.open(os.path.join(output, f'NIS01_20210403_{key}','output',f'NIS01_20210403_{key}_atm_interp.hdr'))\n",
-    "    atm = atm_ds.open_memmap(interleave='bip').copy()\n",
-    "    plt.imshow(atm[...,0])\n",
-    "    plt.title('AOD')\n",
-    "    plt.colorbar()\n",
-    "\n",
-    "    plt.figure()\n",
-    "    plt.imshow(atm[...,1])\n",
-    "    plt.title('Water Vapor')\n",
-    "    plt.colorbar()\n",
-    "\n",
-    "    plt.imshow"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/isotuts/__init__.py b/isotuts/__init__.py
deleted file mode 100644
index e69de29..0000000

From a65c05fe8c0d79a1ed985139b241823f23ce8b6e Mon Sep 17 00:00:00 2001
From: James Montgomery <J.Montgomery@jpl.nasa.gov>
Date: Wed, 20 Nov 2024 15:44:57 -0800
Subject: [PATCH 2/2] Updated example with docker paths

---
 NEON/neon.ipynb | 1108 ++++++++++++++++++-----------------------------
 1 file changed, 411 insertions(+), 697 deletions(-)

diff --git a/NEON/neon.ipynb b/NEON/neon.ipynb
index 4e34c8c..adc2d40 100644
--- a/NEON/neon.ipynb
+++ b/NEON/neon.ipynb
@@ -65,12 +65,12 @@
      "output_type": "stream",
      "text": [
       "Using environment paths:\n",
-      "- data = /Users/jamesmo/projects/isofit/dev/local/data\n",
-      "- examples = /Users/jamesmo/projects/isofit/tutorials\n",
-      "- imagecube = /Users/jamesmo/projects/isofit/dev/local/imagecube\n",
-      "- srtmnet = /Users/jamesmo/projects/isofit/dev/local/srtmnet\n",
-      "- sixs = /Users/jamesmo/projects/isofit/dev/local/sixs\n",
-      "- modtran = /Users/jamesmo/projects/isofit/dev/local/modtran\n"
+      "- data = /home/mambauser/data\n",
+      "- examples = /home/mambauser/examples\n",
+      "- imagecube = /home/mambauser/imagecube\n",
+      "- srtmnet = /home/mambauser/srtmnet\n",
+      "- sixs = /home/mambauser/sixs\n",
+      "- modtran = /home/mambauser/modtran\n"
      ]
     }
    ],
@@ -160,25 +160,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 21,
    "id": "98252646",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:2024-11-18,10:32:18 || faker.py:getMetadata() | Key 'band names' not found in the metadata, skipping\n",
+      "WARNING:2024-11-18,10:32:18 || faker.py:getMetadata() | Key 'band names' not found in the metadata, skipping\n"
+     ]
+    }
+   ],
    "source": [
     "# If you are missing either an OBS file or a LOC file, use these to create faked versions based off the radiance file\n",
     "# This should not be needed if using the provided data\n",
+    "# Using this may cause the below plots to not generate the same results\n",
     "\n",
     "# from utils import faker\n",
     "\n",
-    "# paths.obs = faker.fakeOBS(paths.rdn)\n",
+    "# paths.obs = faker.fakeOBS(\n",
+    "    # f\"{paths.rdn}.hdr\",\n",
+    "    # sea = 153.4481201171875,\n",
+    "    # sez = 178.3806858062744,\n",
+    "    # soa = 39.8218994140625,\n",
+    "    # soz = 39.8218994140625,\n",
+    "    # slope = 31.813383102416992\n",
+    "# )[:-4] # Remove the .hdr extension from the return\n",
     "# paths.loc = faker.fakeLOC(\n",
-    "#     rdn = paths.rdn,\n",
+    "#     rdn = f\"{paths.rdn}.hdr\",\n",
     "#     lon = -105.237000,\n",
     "#     lat = 40.125000,\n",
     "#     elv = 1689.0\n",
-    "# )"
+    "# )[:-4]"
    ]
   },
   {
@@ -193,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "7357a326",
    "metadata": {},
    "outputs": [
@@ -201,14 +218,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n",
-      "1 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n",
-      "2 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n",
-      "3 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n",
-      "4 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n",
-      "5 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n",
-      "6 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n",
-      "7 ['/Users/jamesmo/projects/isofit/dev/local/data/reflectance/surface_model_ucsb']\n"
+      "0 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n",
+      "1 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n",
+      "2 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n",
+      "3 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n",
+      "4 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n",
+      "5 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n",
+      "6 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n",
+      "7 ['/home/mambauser/data/reflectance/surface_model_ucsb']\n"
      ]
     }
    ],
@@ -223,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "30a35242-c068-49a0-9aec-b860d54870f0",
    "metadata": {
     "scrolled": true
@@ -362,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "81330d85-2453-4065-bfa7-f6a09374709a",
    "metadata": {
     "scrolled": true
@@ -372,679 +389,376 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-11-14 16:31:45,669\tINFO worker.py:1529 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Checking input data files...\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | ...Data file checks complete\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Setting up files and directories....\n",
-      "INFO:2024-11-14,16:31:46 || template_construction.py:__init__() | Flightline ID: NIS01_20210403_173647\n",
-      "INFO:2024-11-14,16:31:46 || template_construction.py:__init__() | no noise path found, proceeding without\n",
-      "INFO:2024-11-14,16:31:46 || template_construction.py:stage_files() | Staging /Users/jamesmo/projects/isofit/tutorials/NEON/output/surface.mat to /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/data/surface.mat\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | ...file/directory setup complete\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Using inversion windows: [[350.0, 1360.0], [1410, 1800.0], [1970.0, 2500.0]]\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | No wavelength file provided. Obtaining wavelength grid from ENVI header of radiance cube.\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Wavelength units of nm inferred...converting to microns\n",
-      "WARNING:2024-11-14,16:31:46 || template_construction.py:check_surface_model() | Center wavelengths provided in surface model file do not match wavelengths in radiance cube. Please consider rebuilding your surface model for optimal performance.\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Observation means:\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Path (km): 0.0\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | To-sensor azimuth (deg): 0.0\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | To-sensor zenith (deg): 0.0\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | To-sun azimuth (deg): 0.0\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | To-sun zenith (deg): 0.0\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Relative to-sun azimuth (deg): 0.0\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Altitude (km): 1.689\n",
-      "INFO:2024-11-14,16:31:46 || apply_oe.py:apply_oe() | Segmenting...\n",
-      "2024-11-14 16:31:46,570\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
-      "\u001b[2m\u001b[36m(segment_chunk pid=42158)\u001b[0m INFO:2024-11-14,16:31:46 ||| 0: starting\n",
-      "INFO:2024-11-14,16:31:47 || apply_oe.py:apply_oe() | Extracting /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/input/NIS01_20210403_173647_subs_rdn\n",
-      "2024-11-14 16:31:47,132\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
-      "\u001b[2m\u001b[36m(segment_chunk pid=42158)\u001b[0m INFO:2024-11-14,16:31:47 ||| 0: completing\n",
-      "\u001b[2m\u001b[36m(extract_chunk pid=42158)\u001b[0m INFO:2024-11-14,16:31:47 ||| 0: starting\n",
-      "INFO:2024-11-14,16:31:47 || apply_oe.py:apply_oe() | Extracting /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/input/NIS01_20210403_173647_subs_obs\n",
-      "2024-11-14 16:31:47,196\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
-      "INFO:2024-11-14,16:31:47 || apply_oe.py:apply_oe() | Extracting /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/input/NIS01_20210403_173647_subs_loc\n",
-      "2024-11-14 16:31:47,253\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
-      "\u001b[2m\u001b[36m(extract_chunk pid=42158)\u001b[0m INFO:2024-11-14,16:31:47 ||| 0: starting\n",
-      "INFO:2024-11-14,16:31:47 || apply_oe.py:apply_oe() | Pre-solve H2O grid: [0.01 0.67 1.34 2.   2.67 3.33 4.   4.66 5.33 5.99]\n",
-      "INFO:2024-11-14,16:31:47 || apply_oe.py:apply_oe() | Writing H2O pre-solve configuration file.\n",
-      "INFO:2024-11-14,16:31:47 || apply_oe.py:apply_oe() | Run ISOFIT initial guess\n",
-      "WARNING:2024-11-14,16:31:47 || __init__.py:checkNumThreads() | \n",
+      "2024-11-20 23:30:40,305\tINFO worker.py:1819 -- Started a local Ray instance.\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Checking input data files...\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | ...Data file checks complete\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Setting up files and directories....\n",
+      "INFO:2024-11-20,23:30:40 || template_construction.py:__init__() | Flightline ID: NIS01_20210403_173647\n",
+      "INFO:2024-11-20,23:30:40 || template_construction.py:__init__() | no noise path found, proceeding without\n",
+      "INFO:2024-11-20,23:30:40 || template_construction.py:stage_files() | Staging /home/mambauser/examples/NEON/output/surface.mat to /home/mambauser/examples/NEON/output/NIS01_20210403_173647/data/surface.mat\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | ...file/directory setup complete\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Using inversion windows: [[350.0, 1360.0], [1410, 1800.0], [1970.0, 2500.0]]\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | No wavelength file provided. Obtaining wavelength grid from ENVI header of radiance cube.\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Wavelength units of nm inferred...converting to microns\n",
+      "WARNING:2024-11-20,23:30:40 || template_construction.py:check_surface_model() | Center wavelengths provided in surface model file do not match wavelengths in radiance cube. Please consider rebuilding your surface model for optimal performance.\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Observation means:\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Path (km): 1.0036078691482544\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | To-sensor azimuth (deg): 153.4481201171875\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | To-sensor zenith (deg): 1.619314193725586\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | To-sun azimuth (deg): 145.23248291015625\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | To-sun zenith (deg): 39.8218994140625\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Relative to-sun azimuth (deg): 31.813383102416992\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Altitude (km): 2.6922070875167847\n",
+      "INFO:2024-11-20,23:30:40 || apply_oe.py:apply_oe() | Segmenting...\n",
+      "2024-11-20 23:30:40,906\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "\u001b[36m(segment_chunk pid=299)\u001b[0m INFO:2024-11-20,23:30:41 ||| 0: starting\n",
+      "INFO:2024-11-20,23:30:41 || apply_oe.py:apply_oe() | Extracting /home/mambauser/examples/NEON/output/NIS01_20210403_173647/input/NIS01_20210403_173647_subs_rdn\n",
+      "2024-11-20 23:30:41,625\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "\u001b[36m(segment_chunk pid=299)\u001b[0m INFO:2024-11-20,23:30:41 ||| 0: completing\n",
+      "\u001b[36m(extract_chunk pid=299)\u001b[0m INFO:2024-11-20,23:30:41 ||| 0: starting\n",
+      "INFO:2024-11-20,23:30:41 || apply_oe.py:apply_oe() | Extracting /home/mambauser/examples/NEON/output/NIS01_20210403_173647/input/NIS01_20210403_173647_subs_obs\n",
+      "2024-11-20 23:30:41,678\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "INFO:2024-11-20,23:30:41 || apply_oe.py:apply_oe() | Extracting /home/mambauser/examples/NEON/output/NIS01_20210403_173647/input/NIS01_20210403_173647_subs_loc\n",
+      "2024-11-20 23:30:41,715\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "INFO:2024-11-20,23:30:41 || apply_oe.py:apply_oe() | Pre-solve H2O grid: [0.01 0.67 1.34 2.   2.67 3.33 4.   4.66 5.33 5.99]\n",
+      "INFO:2024-11-20,23:30:41 || apply_oe.py:apply_oe() | Writing H2O pre-solve configuration file.\n",
+      "\u001b[36m(extract_chunk pid=299)\u001b[0m INFO:2024-11-20,23:30:41 ||| 0: starting\n",
+      "INFO:2024-11-20,23:30:41 || apply_oe.py:apply_oe() | Run ISOFIT initial guess\n",
+      "\u001b[36m(extract_chunk pid=299)\u001b[0m INFO:2024-11-20,23:30:41 ||| 0: starting\n",
+      "WARNING:2024-11-20,23:30:41 || __init__.py:checkNumThreads() | \n",
       "******************************************************************************************\n",
       "! Number of threads is greater than 1, this may greatly impact performance\n",
       "! Please set this the environment variables 'MKL_NUM_THREADS' and 'OMP_NUM_THREADS' to '1'\n",
       "******************************************************************************************\n",
-      "INFO:2024-11-14,16:31:47 || configs.py:create_new_config() | Loading config file: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/config/NIS01_20210403_173647_h2o.json\n",
-      "INFO:2024-11-14,16:31:47 || configs.py:get_config_errors() | Checking config sections for configuration issues\n",
-      "INFO:2024-11-14,16:31:47 || configs.py:get_config_errors() | Configuration file checks complete, no errors found.\n",
-      "2024-11-14 16:31:47,316\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
-      "INFO:2024-11-14,16:31:47 || isofit.py:run() | Building first forward model, will generate any necessary LUTs\n",
-      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:__init__() | Loading from wavelength_file: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/data/wavelengths.txt\n",
-      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
-      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
-      "\u001b[2m\u001b[36m(extract_chunk pid=42158)\u001b[0m INFO:2024-11-14,16:31:47 ||| 0: starting\n",
-      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
-      "INFO:2024-11-14,16:31:47 || sRTMnet.py:preSim() | Creating a simulator configuration\n",
-      "INFO:2024-11-14,16:31:47 || sRTMnet.py:preSim() | Building simulator and executing (6S)\n",
-      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
-      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
-      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
-      "INFO:2024-11-14,16:31:47 || radiative_transfer_engine.py:runSimulations() | Executing parallel simulations\n",
-      "\u001b[2m\u001b[36m(streamSimulation pid=42158)\u001b[0m INFO:2024-11-14,16:31:47 ||| Note: NumExpr detected 10 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
-      "\u001b[2m\u001b[36m(streamSimulation pid=42158)\u001b[0m INFO:2024-11-14,16:31:47 ||| Loaded ini from: /Users/jamesmo/.isofit/isofit.ini\n",
-      "INFO:2024-11-14,16:31:48 || common.py:__call__() |  20.00% simulations complete (elapsed: 0:00:01.156084, rate: 0:00:00.115608, eta: 0:00:10.404756)\n",
-      "INFO:2024-11-14,16:31:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:48 || common.py:__call__() |  30.00% simulations complete (elapsed: 0:00:01.233141, rate: 0:00:00.123314, eta: 0:00:04.932564)\n",
-      "INFO:2024-11-14,16:31:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:48 || common.py:__call__() |  40.00% simulations complete (elapsed: 0:00:01.290525, rate: 0:00:00.129052, eta: 0:00:03.011225)\n",
-      "INFO:2024-11-14,16:31:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:48 || common.py:__call__() |  50.00% simulations complete (elapsed: 0:00:01.345782, rate: 0:00:00.134578, eta: 0:00:02.018673)\n",
-      "INFO:2024-11-14,16:31:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:48 || common.py:__call__() |  60.00% simulations complete (elapsed: 0:00:01.399168, rate: 0:00:00.139917, eta: 0:00:01.399168)\n",
-      "INFO:2024-11-14,16:31:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:48 || common.py:__call__() |  70.00% simulations complete (elapsed: 0:00:01.451998, rate: 0:00:00.145200, eta: 0:00:00.967999)\n",
-      "INFO:2024-11-14,16:31:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:48 || common.py:__call__() |  80.00% simulations complete (elapsed: 0:00:01.504772, rate: 0:00:00.150477, eta: 0:00:00.644902)\n",
-      "INFO:2024-11-14,16:31:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:49 || common.py:__call__() |  90.00% simulations complete (elapsed: 0:00:01.558484, rate: 0:00:00.155848, eta: 0:00:00.389621)\n",
-      "INFO:2024-11-14,16:31:49 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:49 || common.py:__call__() | 100.00% simulations complete (elapsed: 0:00:01.612101, rate: 0:00:00.161210, eta: 0:00:00.179122)\n",
-      "INFO:2024-11-14,16:31:49 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:49 || radiative_transfer_engine.py:runSimulations() | Running any post-sim functions\n",
-      "INFO:2024-11-14,16:31:49 || radiative_transfer_engine.py:runSimulations() | Saving post-sim data to index zero of all dimensions except wl\n",
-      "INFO:2024-11-14,16:31:49 || luts.py:load() | Loading LUT into memory\n",
-      "WARNING:2024-11-14,16:31:49 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
-      "WARNING:2024-11-14,16:31:49 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
-      "INFO:2024-11-14,16:31:49 || sRTMnet.py:preSim() | Interpolating simulator quantities to emulator size\n",
-      "INFO:2024-11-14,16:31:49 || sRTMnet.py:preSim() | Loading and predicting with emulator\n",
-      "INFO:2024-11-14,16:31:51 || sRTMnet.py:preSim() | Saving intermediary prediction results to: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/lut_h2o/sRTMnet.predicts.nc\n",
-      "INFO:2024-11-14,16:31:51 || radiative_transfer_engine.py:runSimulations() | Saving pre-sim data to index zero of all dimensions except wl\n",
-      "INFO:2024-11-14,16:31:51 || radiative_transfer_engine.py:runSimulations() | Executing parallel simulations\n",
-      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  20.00% simulations complete (elapsed: 0:00:00.280150, rate: 0:00:00.028015, eta: 0:00:02.521350)\n",
-      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "\u001b[2m\u001b[36m(streamSimulation pid=42158)\u001b[0m INFO:2024-11-14,16:31:51 ||| Loading LUT into memory\n",
-      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  30.00% simulations complete (elapsed: 0:00:00.338177, rate: 0:00:00.033818, eta: 0:00:01.352708)\n",
-      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  40.00% simulations complete (elapsed: 0:00:00.388947, rate: 0:00:00.038895, eta: 0:00:00.907543)\n",
-      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  50.00% simulations complete (elapsed: 0:00:00.438956, rate: 0:00:00.043896, eta: 0:00:00.658434)\n",
-      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  60.00% simulations complete (elapsed: 0:00:00.489197, rate: 0:00:00.048920, eta: 0:00:00.489197)\n",
-      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  70.00% simulations complete (elapsed: 0:00:00.539138, rate: 0:00:00.053914, eta: 0:00:00.359425)\n",
-      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  80.00% simulations complete (elapsed: 0:00:00.589068, rate: 0:00:00.058907, eta: 0:00:00.252458)\n",
-      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:52 || common.py:__call__() |  90.00% simulations complete (elapsed: 0:00:00.638226, rate: 0:00:00.063823, eta: 0:00:00.159556)\n",
-      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:52 || common.py:__call__() | 100.00% simulations complete (elapsed: 0:00:00.688654, rate: 0:00:00.068865, eta: 0:00:00.076517)\n",
-      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:31:52 || radiative_transfer_engine.py:runSimulations() | Running any post-sim functions\n",
-      "INFO:2024-11-14,16:31:52 || luts.py:load() | Loading LUT into memory\n",
-      "WARNING:2024-11-14,16:31:52 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
-      "WARNING:2024-11-14,16:31:52 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
-      "INFO:2024-11-14,16:31:52 || isofit.py:run() | Beginning 420 inversions in 100 chunks using 10 cores\n",
-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:31:55 ||| Worker 0 completed 1/~42.0:: 2.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:31:55 ||| Worker 8 completed 1/~42.0:: 2.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:31:55 ||| Worker 5 completed 1/~42.0:: 2.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:31:55 ||| Worker 7 completed 1/~42.0:: 2.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:31:55 ||| Worker 4 completed 1/~42.0:: 2.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:31:55 ||| Worker 1 completed 1/~42.0:: 2.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:31:55 ||| Worker 6 completed 1/~42.0:: 2.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:31:55 ||| Worker 3 completed 1/~42.0:: 2.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:31:55 ||| Worker 2 completed 1/~42.0:: 2.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:31:56 ||| Worker 9 completed 1/~42.0:: 2.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:31:57 ||| Worker at start location (41,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:31:57 ||| Worker at start location (15,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:31:57 ||| Worker at start location (7,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:31:57 ||| Worker at start location (11,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:31:57 ||| Worker at start location (37,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:31:57 ||| Worker at start location (24,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:31:57 ||| Worker at start location (28,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:31:57 ||| Worker at start location (32,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:31:58 ||| Worker 7 completed 5/~42.0:: 11.9% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:31:58 ||| Worker 8 completed 5/~42.0:: 11.9% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:31:58 ||| Worker 0 completed 5/~42.0:: 11.9% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:31:58 ||| Worker at start location (20,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:31:58 ||| Worker 6 completed 5/~42.0:: 11.9% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:31:58 ||| Worker at start location (3,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:31:58 ||| Worker 1 completed 6/~42.0:: 14.29% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:31:58 ||| Worker 3 completed 5/~42.0:: 11.9% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:31:58 ||| Worker 4 completed 5/~42.0:: 11.9% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:31:58 ||| Worker 2 completed 5/~42.0:: 11.9% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:31:58 ||| Worker 5 completed 6/~42.0:: 14.29% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:31:59 ||| Worker 9 completed 5/~42.0:: 11.9% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:31:59 ||| Worker at start location (58,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker at start location (49,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker 7 completed 9/~42.0:: 21.43% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker at start location (62,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker at start location (45,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker at start location (75,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker at start location (79,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker at start location (54,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker at start location (66,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker 0 completed 9/~42.0:: 21.43% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker 1 completed 10/~42.0:: 23.81% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker 5 completed 10/~42.0:: 23.81% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker 6 completed 9/~42.0:: 21.43% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker 2 completed 9/~42.0:: 21.43% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker at start location (83,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker 8 completed 10/~42.0:: 23.81% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker 3 completed 9/~42.0:: 21.43% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:00 ||| Worker at start location (71,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:01 ||| Worker 9 completed 9/~42.0:: 21.43% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:01 ||| Worker 4 completed 10/~42.0:: 23.81% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:01 ||| Worker at start location (109,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:01 ||| Worker at start location (92,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:01 ||| Worker at start location (96,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:01 ||| Worker at start location (117,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 5 completed 14/~42.0:: 33.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker at start location (88,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker at start location (113,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker at start location (100,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker at start location (105,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 1 completed 14/~42.0:: 33.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 0 completed 13/~42.0:: 30.95% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 8 completed 14/~42.0:: 33.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker at start location (126,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 7 completed 14/~42.0:: 33.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 2 completed 14/~42.0:: 33.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 3 completed 13/~42.0:: 30.95% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker at start location (122,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:02 ||| Worker 6 completed 13/~42.0:: 30.95% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:03 ||| Worker 9 completed 14/~42.0:: 33.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:03 ||| Worker 4 completed 14/~42.0:: 33.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:03 ||| Worker at start location (143,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:03 ||| Worker at start location (130,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker at start location (134,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker at start location (151,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker 1 completed 18/~42.0:: 42.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker at start location (147,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker at start location (139,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker 8 completed 18/~42.0:: 42.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker at start location (155,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker 3 completed 17/~42.0:: 40.48% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:04 ||| Worker at start location (164,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker at start location (168,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker 7 completed 18/~42.0:: 42.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker 2 completed 18/~42.0:: 42.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker 0 completed 18/~42.0:: 42.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker 5 completed 18/~42.0:: 42.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker at start location (160,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker 4 completed 18/~42.0:: 42.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker at start location (172,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:05 ||| Worker 9 completed 18/~42.0:: 42.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:06 ||| Worker 6 completed 18/~42.0:: 42.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:06 ||| Worker 1 completed 22/~42.0:: 52.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:06 ||| Worker at start location (181,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:06 ||| Worker at start location (185,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:06 ||| Worker at start location (189,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:06 ||| Worker 8 completed 22/~42.0:: 52.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:06 ||| Worker at start location (198,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker at start location (202,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker 3 completed 21/~42.0:: 50.0% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker 7 completed 22/~42.0:: 52.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker at start location (206,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker at start location (194,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker at start location (177,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker at start location (215,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker 2 completed 22/~42.0:: 52.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:07 ||| Worker 4 completed 22/~42.0:: 52.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:08 ||| Worker 0 completed 23/~42.0:: 54.76% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:08 ||| Worker 5 completed 23/~42.0:: 54.76% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:08 ||| Worker 9 completed 22/~42.0:: 52.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:08 ||| Worker at start location (211,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:08 ||| Worker 1 completed 26/~42.0:: 61.9% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:08 ||| Worker 6 completed 23/~42.0:: 54.76% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:08 ||| Worker at start location (219,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:09 ||| Worker at start location (223,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:09 ||| Worker at start location (236,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:09 ||| Worker 8 completed 26/~42.0:: 61.9% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:09 ||| Worker at start location (232,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:09 ||| Worker 3 completed 25/~42.0:: 59.52% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:09 ||| Worker at start location (228,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:09 ||| Worker at start location (253,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker at start location (249,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker 2 completed 26/~42.0:: 61.9% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker 4 completed 26/~42.0:: 61.9% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker at start location (240,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker 7 completed 27/~42.0:: 64.29% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker at start location (245,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker 1 completed 30/~42.0:: 71.43% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker 5 completed 27/~42.0:: 64.29% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:10 ||| Worker at start location (257,0) completed 3/4\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:11 ||| Worker 0 completed 28/~42.0:: 66.67% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:11 ||| Worker 6 completed 27/~42.0:: 64.29% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:11 ||| Worker at start location (266,0) completed 3/4\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:11 ||| Worker 3 completed 29/~42.0:: 69.05% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker at start location (270,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker 8 completed 31/~42.0:: 73.81% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker 2 completed 30/~42.0:: 71.43% complete\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker 1 completed 34/~42.0:: 80.95% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:13 ||| Worker 5 completed 31/~42.0:: 73.81% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:12 ||| Worker 7 completed 32/~42.0:: 76.19% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:13 ||| Worker 0 completed 32/~42.0:: 76.19% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:13 ||| Worker 9 completed 30/~42.0:: 71.43% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:13 ||| Worker at start location (304,0) completed 3/4\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker at start location (308,0) completed 3/4\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker 6 completed 32/~42.0:: 76.19% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker at start location (325,0) completed 3/4\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker 8 completed 35/~42.0:: 83.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker at start location (329,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker 2 completed 34/~42.0:: 80.95% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:14 ||| Worker at start location (317,0) completed 4/5\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:15 ||| Worker 1 completed 38/~42.0:: 90.48% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:15 ||| Worker 5 completed 35/~42.0:: 83.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:15 ||| Worker 4 completed 35/~42.0:: 83.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:15 ||| Worker at start location (342,0) completed 3/4\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:16 ||| Worker 9 completed 35/~42.0:: 83.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42221)\u001b[0m INFO:2024-11-14,16:32:16 ||| Worker at start location (355,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:16 ||| Worker 3 completed 37/~42.0:: 88.1% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42219)\u001b[0m INFO:2024-11-14,16:32:16 ||| Worker 6 completed 36/~42.0:: 85.71% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:16 ||| Worker at start location (359,0) completed 3/4\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:17 ||| Worker at start location (363,0) completed 3/4\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:17 ||| Worker at start location (376,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:17 ||| Worker 7 completed 40/~42.0:: 95.24% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:17 ||| Worker 4 completed 39/~42.0:: 92.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:17 ||| Worker 1 completed 42/~42.0:: 100.0% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:17 ||| Worker 2 completed 39/~42.0:: 92.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:18 ||| Worker 0 completed 40/~42.0:: 95.24% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:18 ||| Worker at start location (368,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42216)\u001b[0m INFO:2024-11-14,16:32:18 ||| Worker at start location (380,0) completed 3/4\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:18 ||| Worker 5 completed 40/~42.0:: 95.24% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42222)\u001b[0m INFO:2024-11-14,16:32:18 ||| Worker at start location (385,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42220)\u001b[0m INFO:2024-11-14,16:32:19 ||| Worker at start location (397,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42217)\u001b[0m INFO:2024-11-14,16:32:19 ||| Worker at start location (410,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42215)\u001b[0m INFO:2024-11-14,16:32:19 ||| Worker at start location (406,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42213)\u001b[0m INFO:2024-11-14,16:32:19 ||| Worker at start location (414,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42214)\u001b[0m INFO:2024-11-14,16:32:19 ||| Worker at start location (402,0) completed 4/5\n",
-      "INFO:2024-11-14,16:32:20 || isofit.py:run() | Inversions complete.  27.68s total, 15.1717 spectra/s, 1.5172 spectra/s/core\n",
-      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | Full (non-aerosol) LUTs:\n",
-      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | Elevation: None\n",
-      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | To-sensor zenith: None\n",
-      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | To-sun zenith: None\n",
-      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | Relative to-sun azimuth: None\n",
-      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | H2O Vapor: [0.6671 0.7777]\n",
-      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/output/NIS01_20210403_173647_subs_state\n",
-      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | Writing main configuration file.\n",
-      "INFO:2024-11-14,16:32:20 || template_construction.py:load_climatology() | Loading Climatology\n",
-      "INFO:2024-11-14,16:32:20 || template_construction.py:load_climatology() | Climatology Loaded.  Aerosol State Vector:\n",
+      "INFO:2024-11-20,23:30:41 || configs.py:create_new_config() | Loading config file: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/config/NIS01_20210403_173647_h2o.json\n",
+      "INFO:2024-11-20,23:30:41 || configs.py:get_config_errors() | Checking config sections for configuration issues\n",
+      "INFO:2024-11-20,23:30:41 || configs.py:get_config_errors() | Configuration file checks complete, no errors found.\n",
+      "2024-11-20 23:30:41,772\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "INFO:2024-11-20,23:30:41 || isofit.py:run() | Building first forward model, will generate any necessary LUTs\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:__init__() | Loading from wavelength_file: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/data/wavelengths.txt\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
+      "INFO:2024-11-20,23:30:41 || sRTMnet.py:preSim() | Creating a simulator configuration\n",
+      "INFO:2024-11-20,23:30:41 || sRTMnet.py:preSim() | Building simulator and executing (6S)\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
+      "INFO:2024-11-20,23:30:41 || radiative_transfer_engine.py:runSimulations() | Executing parallel simulations\n",
+      "\u001b[36m(streamSimulation pid=299)\u001b[0m INFO:2024-11-20,23:30:42 ||| Loaded ini from: /home/mambauser/.isofit/isofit.ini\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  20.00% simulations complete (elapsed: 0:00:03.106160, rate: 0:00:00.310616, eta: 0:00:27.955440)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  30.00% simulations complete (elapsed: 0:00:03.196983, rate: 0:00:00.319698, eta: 0:00:12.787932)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  40.00% simulations complete (elapsed: 0:00:03.265636, rate: 0:00:00.326564, eta: 0:00:07.619817)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  50.00% simulations complete (elapsed: 0:00:03.339618, rate: 0:00:00.333962, eta: 0:00:05.009427)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  60.00% simulations complete (elapsed: 0:00:03.406902, rate: 0:00:00.340690, eta: 0:00:03.406902)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  70.00% simulations complete (elapsed: 0:00:03.475695, rate: 0:00:00.347570, eta: 0:00:02.317130)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  80.00% simulations complete (elapsed: 0:00:03.543349, rate: 0:00:00.354335, eta: 0:00:01.518578)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() |  90.00% simulations complete (elapsed: 0:00:03.609925, rate: 0:00:00.360992, eta: 0:00:00.902481)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || common.py:__call__() | 100.00% simulations complete (elapsed: 0:00:03.678686, rate: 0:00:00.367869, eta: 0:00:00.408743)\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:45 || radiative_transfer_engine.py:runSimulations() | Running any post-sim functions\n",
+      "INFO:2024-11-20,23:30:46 || radiative_transfer_engine.py:runSimulations() | Saving post-sim data to index zero of all dimensions except wl\n",
+      "INFO:2024-11-20,23:30:46 || luts.py:load() | Loading LUT into memory\n",
+      "WARNING:2024-11-20,23:30:46 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
+      "WARNING:2024-11-20,23:30:46 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
+      "INFO:2024-11-20,23:30:46 || sRTMnet.py:preSim() | Interpolating simulator quantities to emulator size\n",
+      "INFO:2024-11-20,23:30:46 || sRTMnet.py:preSim() | Loading and predicting with emulator\n",
+      "INFO:2024-11-20,23:30:53 || sRTMnet.py:preSim() | Saving intermediary prediction results to: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/lut_h2o/sRTMnet.predicts.nc\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Saving pre-sim data to index zero of all dimensions except wl\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Executing parallel simulations\n",
+      "\u001b[36m(streamSimulation pid=299)\u001b[0m INFO:2024-11-20,23:30:53 ||| Loading LUT into memory\n",
+      "INFO:2024-11-20,23:30:53 || common.py:__call__() |  20.00% simulations complete (elapsed: 0:00:00.183867, rate: 0:00:00.018387, eta: 0:00:01.654803)\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:53 || common.py:__call__() |  30.00% simulations complete (elapsed: 0:00:00.260936, rate: 0:00:00.026094, eta: 0:00:01.043744)\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:53 || common.py:__call__() |  40.00% simulations complete (elapsed: 0:00:00.333105, rate: 0:00:00.033310, eta: 0:00:00.777245)\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:53 || common.py:__call__() |  50.00% simulations complete (elapsed: 0:00:00.405420, rate: 0:00:00.040542, eta: 0:00:00.608130)\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:53 || common.py:__call__() |  60.00% simulations complete (elapsed: 0:00:00.471780, rate: 0:00:00.047178, eta: 0:00:00.471780)\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:53 || common.py:__call__() |  70.00% simulations complete (elapsed: 0:00:00.548214, rate: 0:00:00.054821, eta: 0:00:00.365476)\n",
+      "INFO:2024-11-20,23:30:53 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:54 || common.py:__call__() |  80.00% simulations complete (elapsed: 0:00:00.616973, rate: 0:00:00.061697, eta: 0:00:00.264417)\n",
+      "INFO:2024-11-20,23:30:54 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:54 || common.py:__call__() |  90.00% simulations complete (elapsed: 0:00:00.684282, rate: 0:00:00.068428, eta: 0:00:00.171070)\n",
+      "INFO:2024-11-20,23:30:54 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:54 || common.py:__call__() | 100.00% simulations complete (elapsed: 0:00:00.754454, rate: 0:00:00.075445, eta: 0:00:00.083828)\n",
+      "INFO:2024-11-20,23:30:54 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:30:54 || radiative_transfer_engine.py:runSimulations() | Running any post-sim functions\n",
+      "INFO:2024-11-20,23:30:54 || luts.py:load() | Loading LUT into memory\n",
+      "WARNING:2024-11-20,23:30:54 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
+      "WARNING:2024-11-20,23:30:54 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
+      "INFO:2024-11-20,23:30:55 || isofit.py:run() | Beginning 420 inversions in 100 chunks using 10 cores\n",
+      "\u001b[36m(Worker pid=890)\u001b[0m INFO:2024-11-20,23:31:00 ||| Worker 1 completed 1/~42.0:: 2.38% complete\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:31:07 ||| Worker 9 completed 1/~42.0:: 2.38% complete\u001b[32m [repeated 9x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.)\u001b[0m\n",
+      "\u001b[36m(Worker pid=883)\u001b[0m INFO:2024-11-20,23:31:10 ||| Worker at start location (7,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=883)\u001b[0m INFO:2024-11-20,23:31:13 ||| Worker 8 completed 5/~42.0:: 11.9% complete\n",
+      "\u001b[36m(Worker pid=887)\u001b[0m INFO:2024-11-20,23:31:14 ||| Worker 6 completed 5/~42.0:: 11.9% complete\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:31:17 ||| Worker at start location (3,0) completed 3/4\u001b[32m [repeated 9x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:31:20 ||| Worker 9 completed 5/~42.0:: 11.9% complete\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=883)\u001b[0m INFO:2024-11-20,23:31:24 ||| Worker at start location (45,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:31:24 ||| Worker at start location (58,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:31:27 ||| Worker 4 completed 9/~42.0:: 21.43% complete\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:31:31 ||| Worker at start location (83,0) completed 3/4\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:31:34 ||| Worker 9 completed 9/~42.0:: 21.43% complete\u001b[32m [repeated 9x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=891)\u001b[0m INFO:2024-11-20,23:31:37 ||| Worker at start location (96,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:31:37 ||| Worker at start location (92,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:31:41 ||| Worker 4 completed 13/~42.0:: 30.95% complete\n",
+      "\u001b[36m(Worker pid=886)\u001b[0m INFO:2024-11-20,23:31:42 ||| Worker at start location (105,0) completed 4/5\u001b[32m [repeated 6x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=884)\u001b[0m INFO:2024-11-20,23:31:47 ||| Worker 0 completed 15/~42.0:: 35.71% complete\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=891)\u001b[0m INFO:2024-11-20,23:31:51 ||| Worker at start location (130,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:31:48 ||| Worker 9 completed 13/~42.0:: 30.95% complete\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:31:55 ||| Worker 4 completed 17/~42.0:: 40.48% complete\n",
+      "\u001b[36m(Worker pid=892)\u001b[0m INFO:2024-11-20,23:31:56 ||| Worker at start location (139,0) completed 4/5\u001b[32m [repeated 6x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=892)\u001b[0m INFO:2024-11-20,23:32:00 ||| Worker 3 completed 18/~42.0:: 42.86% complete\u001b[32m [repeated 6x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:32:01 ||| Worker at start location (164,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:32:04 ||| Worker 9 completed 17/~42.0:: 40.48% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=892)\u001b[0m INFO:2024-11-20,23:32:09 ||| Worker at start location (198,0) completed 3/4\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=887)\u001b[0m INFO:2024-11-20,23:32:10 ||| Worker 6 completed 22/~42.0:: 52.38% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=890)\u001b[0m INFO:2024-11-20,23:32:15 ||| Worker at start location (194,0) completed 4/5\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=884)\u001b[0m INFO:2024-11-20,23:32:15 ||| Worker 0 completed 23/~42.0:: 54.76% complete\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:32:19 ||| Worker at start location (215,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:32:22 ||| Worker 4 completed 25/~42.0:: 59.52% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=886)\u001b[0m INFO:2024-11-20,23:32:25 ||| Worker at start location (236,0) completed 3/4\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=892)\u001b[0m INFO:2024-11-20,23:32:25 ||| Worker 3 completed 26/~42.0:: 61.9% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:32:30 ||| Worker at start location (253,0) completed 3/4\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=891)\u001b[0m INFO:2024-11-20,23:32:31 ||| Worker 7 completed 27/~42.0:: 64.29% complete\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=892)\u001b[0m INFO:2024-11-20,23:32:35 ||| Worker at start location (270,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=887)\u001b[0m INFO:2024-11-20,23:32:36 ||| Worker 6 completed 30/~42.0:: 71.43% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=890)\u001b[0m INFO:2024-11-20,23:32:40 ||| Worker at start location (287,0) completed 3/4\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=890)\u001b[0m INFO:2024-11-20,23:32:44 ||| Worker 1 completed 31/~42.0:: 73.81% complete\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:32:43 ||| Worker at start location (295,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=887)\u001b[0m INFO:2024-11-20,23:32:49 ||| Worker 6 completed 34/~42.0:: 80.95% complete\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=888)\u001b[0m INFO:2024-11-20,23:32:49 ||| Worker at start location (312,0) completed 3/4\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=888)\u001b[0m INFO:2024-11-20,23:32:52 ||| Worker 5 completed 35/~42.0:: 83.33% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=886)\u001b[0m INFO:2024-11-20,23:32:54 ||| Worker at start location (317,0) completed 4/5\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=889)\u001b[0m INFO:2024-11-20,23:33:00 ||| Worker 9 completed 34/~42.0:: 80.95% complete\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=885)\u001b[0m INFO:2024-11-20,23:33:01 ||| Worker at start location (346,0) completed 3/4\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=888)\u001b[0m INFO:2024-11-20,23:33:05 ||| Worker 5 completed 39/~42.0:: 92.86% complete\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=890)\u001b[0m INFO:2024-11-20,23:33:06 ||| Worker at start location (363,0) completed 3/4\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=886)\u001b[0m INFO:2024-11-20,23:33:11 ||| Worker 2 completed 40/~42.0:: 95.24% complete\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=887)\u001b[0m INFO:2024-11-20,23:33:12 ||| Worker at start location (380,0) completed 3/4\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=891)\u001b[0m INFO:2024-11-20,23:33:13 ||| Worker 7 completed 40/~42.0:: 95.24% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=890)\u001b[0m INFO:2024-11-20,23:33:20 ||| Worker at start location (406,0) completed 3/4\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "INFO:2024-11-20,23:33:28 || isofit.py:run() | Inversions complete.  152.69s total, 2.7506 spectra/s, 0.2751 spectra/s/core\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | Full (non-aerosol) LUTs:\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | Elevation: None\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | To-sensor zenith: [0.9608 2.9675]\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | To-sun zenith: None\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | Relative to-sun azimuth: [3.80000e-03 4.12002e+01 8.23965e+01]\n",
+      "\u001b[36m(Worker pid=891)\u001b[0m INFO:2024-11-20,23:33:28 ||| Worker at start location (419,0) completed 4/5\u001b[32m [repeated 4x across cluster]\u001b[0mINFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | H2O Vapor: [0.6083 0.6485]\n",
+      "\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | /home/mambauser/examples/NEON/output/NIS01_20210403_173647/output/NIS01_20210403_173647_subs_state\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | Writing main configuration file.\n",
+      "INFO:2024-11-20,23:33:28 || template_construction.py:load_climatology() | Loading Climatology\n",
+      "INFO:2024-11-20,23:33:28 || template_construction.py:load_climatology() | Climatology Loaded.  Aerosol State Vector:\n",
       "{'AOT550': {'bounds': [0.001, 1.0], 'scale': 1, 'init': 0.1009, 'prior_sigma': 10.0, 'prior_mean': 0.1009}}\n",
       "Aerosol LUT Grid:\n",
       "{'AOT550': [0.001, 0.1009, 0.2008, 0.3007, 0.4006, 0.5005, 0.6004, 0.7003, 0.8002, 0.9001, 1.0]}\n",
-      "Aerosol model path:/Users/jamesmo/projects/isofit/dev/local/data/aerosol_model.txt\n",
-      "\u001b[2m\u001b[36m(Worker pid=42218)\u001b[0m INFO:2024-11-14,16:32:20 ||| Worker at start location (419,0) completed 4/5\n",
-      "INFO:2024-11-14,16:32:20 || apply_oe.py:apply_oe() | Running ISOFIT with full LUT\n",
-      "WARNING:2024-11-14,16:32:20 || __init__.py:checkNumThreads() | \n",
+      "Aerosol model path:/home/mambauser/data/aerosol_model.txt\n",
+      "INFO:2024-11-20,23:33:28 || apply_oe.py:apply_oe() | Running ISOFIT with full LUT\n",
+      "WARNING:2024-11-20,23:33:28 || __init__.py:checkNumThreads() | \n",
       "******************************************************************************************\n",
       "! Number of threads is greater than 1, this may greatly impact performance\n",
       "! Please set this the environment variables 'MKL_NUM_THREADS' and 'OMP_NUM_THREADS' to '1'\n",
       "******************************************************************************************\n",
-      "INFO:2024-11-14,16:32:20 || configs.py:create_new_config() | Loading config file: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/config/NIS01_20210403_173647_isofit.json\n",
-      "INFO:2024-11-14,16:32:20 || configs.py:get_config_errors() | Checking config sections for configuration issues\n",
-      "INFO:2024-11-14,16:32:20 || configs.py:get_config_errors() | Configuration file checks complete, no errors found.\n",
-      "2024-11-14 16:32:20,552\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
-      "INFO:2024-11-14,16:32:20 || isofit.py:run() | Building first forward model, will generate any necessary LUTs\n",
-      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:__init__() | Loading from wavelength_file: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/data/wavelengths.txt\n",
-      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
-      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
-      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
-      "INFO:2024-11-14,16:32:20 || sRTMnet.py:preSim() | Creating a simulator configuration\n",
-      "INFO:2024-11-14,16:32:20 || sRTMnet.py:preSim() | Building simulator and executing (6S)\n",
-      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
-      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
-      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
-      "INFO:2024-11-14,16:32:20 || radiative_transfer_engine.py:runSimulations() | Executing parallel simulations\n",
-      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  13.64% simulations complete (elapsed: 0:00:01.741504, rate: 0:00:00.079159, eta: 0:00:15.673536)\n",
-      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  22.73% simulations complete (elapsed: 0:00:01.848001, rate: 0:00:00.084000, eta: 0:00:07.392004)\n",
-      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  31.82% simulations complete (elapsed: 0:00:01.946866, rate: 0:00:00.088494, eta: 0:00:04.542687)\n",
-      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  40.91% simulations complete (elapsed: 0:00:02.040472, rate: 0:00:00.092749, eta: 0:00:03.060708)\n",
-      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  50.00% simulations complete (elapsed: 0:00:02.105106, rate: 0:00:00.095687, eta: 0:00:02.105106)\n",
-      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  63.64% simulations complete (elapsed: 0:00:02.173322, rate: 0:00:00.098787, eta: 0:00:01.448881)\n",
-      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  72.73% simulations complete (elapsed: 0:00:02.231235, rate: 0:00:00.101420, eta: 0:00:00.956244)\n",
-      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:32:22 || common.py:__call__() |  81.82% simulations complete (elapsed: 0:00:02.286901, rate: 0:00:00.103950, eta: 0:00:00.571725)\n",
-      "INFO:2024-11-14,16:32:22 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:32:23 || common.py:__call__() |  90.91% simulations complete (elapsed: 0:00:02.341987, rate: 0:00:00.106454, eta: 0:00:00.260221)\n",
-      "INFO:2024-11-14,16:32:23 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
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-      "INFO:2024-11-14,16:32:23 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
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-      "INFO:2024-11-14,16:32:23 || luts.py:load() | Loading LUT into memory\n",
-      "WARNING:2024-11-14,16:32:23 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
-      "WARNING:2024-11-14,16:32:23 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
-      "INFO:2024-11-14,16:32:23 || sRTMnet.py:preSim() | Interpolating simulator quantities to emulator size\n",
-      "INFO:2024-11-14,16:32:23 || sRTMnet.py:preSim() | Loading and predicting with emulator\n",
-      "INFO:2024-11-14,16:32:25 || sRTMnet.py:preSim() | Saving intermediary prediction results to: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/lut_full/sRTMnet.predicts.nc\n",
-      "INFO:2024-11-14,16:32:25 || radiative_transfer_engine.py:runSimulations() | Saving pre-sim data to index zero of all dimensions except wl\n",
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-      "INFO:2024-11-14,16:32:26 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
-      "INFO:2024-11-14,16:32:26 || radiative_transfer_engine.py:runSimulations() | Running any post-sim functions\n",
-      "INFO:2024-11-14,16:32:26 || luts.py:load() | Loading LUT into memory\n",
-      "WARNING:2024-11-14,16:32:26 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
-      "WARNING:2024-11-14,16:32:26 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
-      "INFO:2024-11-14,16:32:26 || isofit.py:run() | Beginning 420 inversions in 100 chunks using 10 cores\n",
-      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:29 ||| Worker 4 completed 1/~42.0:: 2.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:29 ||| Worker 3 completed 1/~42.0:: 2.38% complete\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:37 ||| Worker 1 completed 10/~42.0:: 23.81% complete\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:40 ||| Worker 6 completed 13/~42.0:: 30.95% complete\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:41 ||| Worker at start location (122,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:41 ||| Worker 5 completed 14/~42.0:: 33.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:41 ||| Worker 7 completed 14/~42.0:: 33.33% complete\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker at start location (143,0) completed 3/4\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker 1 completed 15/~42.0:: 35.71% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker 2 completed 17/~42.0:: 40.48% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker at start location (139,0) completed 4/5\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker at start location (155,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:44 ||| Worker 8 completed 18/~42.0:: 42.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker 6 completed 17/~42.0:: 40.48% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:43 ||| Worker 9 completed 17/~42.0:: 40.48% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:44 ||| Worker 3 completed 18/~42.0:: 42.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:44 ||| Worker 0 completed 18/~42.0:: 42.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:44 ||| Worker 7 completed 18/~42.0:: 42.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:44 ||| Worker at start location (160,0) completed 4/5\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:46 ||| Worker 4 completed 19/~42.0:: 45.24% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:46 ||| Worker at start location (198,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:46 ||| Worker 1 completed 19/~42.0:: 45.24% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:47 ||| Worker 6 completed 21/~42.0:: 50.0% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:47 ||| Worker 9 completed 21/~42.0:: 50.0% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:47 ||| Worker 2 completed 21/~42.0:: 50.0% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:47 ||| Worker at start location (189,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:47 ||| Worker at start location (177,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:47 ||| Worker at start location (202,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:47 ||| Worker 0 completed 22/~42.0:: 52.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:48 ||| Worker 3 completed 22/~42.0:: 52.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:48 ||| Worker 8 completed 23/~42.0:: 54.76% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:48 ||| Worker 5 completed 22/~42.0:: 52.38% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:49 ||| Worker at start location (194,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:49 ||| Worker at start location (206,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:49 ||| Worker at start location (219,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:49 ||| Worker at start location (215,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:49 ||| Worker at start location (223,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:49 ||| Worker 7 completed 23/~42.0:: 54.76% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker 4 completed 23/~42.0:: 54.76% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker at start location (211,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker 6 completed 25/~42.0:: 59.52% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker at start location (232,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker at start location (236,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker 2 completed 25/~42.0:: 59.52% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker 9 completed 25/~42.0:: 59.52% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker 1 completed 24/~42.0:: 57.14% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:50 ||| Worker at start location (240,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:51 ||| Worker 3 completed 26/~42.0:: 61.9% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:51 ||| Worker 8 completed 27/~42.0:: 64.29% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:51 ||| Worker 5 completed 26/~42.0:: 61.9% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:51 ||| Worker at start location (228,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:52 ||| Worker 0 completed 27/~42.0:: 64.29% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:53 ||| Worker at start location (249,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:53 ||| Worker at start location (253,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:53 ||| Worker at start location (257,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:53 ||| Worker at start location (245,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:53 ||| Worker at start location (266,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:53 ||| Worker 4 completed 27/~42.0:: 64.29% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:53 ||| Worker 6 completed 29/~42.0:: 69.05% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:54 ||| Worker at start location (270,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:54 ||| Worker 2 completed 29/~42.0:: 69.05% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:54 ||| Worker at start location (274,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:54 ||| Worker 7 completed 28/~42.0:: 66.67% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:54 ||| Worker at start location (262,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:54 ||| Worker 3 completed 30/~42.0:: 71.43% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:54 ||| Worker 1 completed 28/~42.0:: 66.67% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:55 ||| Worker 8 completed 31/~42.0:: 73.81% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:55 ||| Worker 9 completed 30/~42.0:: 71.43% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:55 ||| Worker at start location (283,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:55 ||| Worker at start location (279,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:56 ||| Worker 0 completed 31/~42.0:: 73.81% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:56 ||| Worker at start location (287,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:56 ||| Worker at start location (291,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:56 ||| Worker at start location (295,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:56 ||| Worker 5 completed 31/~42.0:: 73.81% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:57 ||| Worker 6 completed 33/~42.0:: 78.57% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:57 ||| Worker at start location (304,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:57 ||| Worker at start location (312,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:32:57 ||| Worker 2 completed 33/~42.0:: 78.57% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:57 ||| Worker at start location (308,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:57 ||| Worker at start location (300,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:32:57 ||| Worker 4 completed 31/~42.0:: 73.81% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:32:58 ||| Worker 1 completed 32/~42.0:: 76.19% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:32:58 ||| Worker 8 completed 35/~42.0:: 83.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:32:58 ||| Worker 7 completed 33/~42.0:: 78.57% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:32:58 ||| Worker 3 completed 34/~42.0:: 80.95% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:58 ||| Worker at start location (317,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:32:59 ||| Worker at start location (321,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:32:59 ||| Worker at start location (325,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:32:59 ||| Worker 9 completed 35/~42.0:: 83.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:32:59 ||| Worker at start location (329,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:33:00 ||| Worker 0 completed 35/~42.0:: 83.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:33:00 ||| Worker at start location (338,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:33:00 ||| Worker 5 completed 35/~42.0:: 83.33% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:33:00 ||| Worker 6 completed 37/~42.0:: 88.1% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:33:01 ||| Worker 2 completed 37/~42.0:: 88.1% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:33:01 ||| Worker at start location (342,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:33:01 ||| Worker at start location (334,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:33:01 ||| Worker at start location (355,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:33:01 ||| Worker at start location (346,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:33:02 ||| Worker 8 completed 39/~42.0:: 92.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:33:02 ||| Worker at start location (359,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:33:02 ||| Worker 3 completed 38/~42.0:: 90.48% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:33:02 ||| Worker 1 completed 36/~42.0:: 85.71% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:33:02 ||| Worker 4 completed 36/~42.0:: 85.71% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:33:02 ||| Worker at start location (351,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:33:02 ||| Worker at start location (363,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:33:03 ||| Worker 9 completed 39/~42.0:: 92.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:33:03 ||| Worker at start location (372,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:33:03 ||| Worker 7 completed 38/~42.0:: 90.48% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:33:03 ||| Worker 0 completed 39/~42.0:: 92.86% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:33:03 ||| Worker at start location (376,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:33:03 ||| Worker at start location (368,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:33:04 ||| Worker 6 completed 41/~42.0:: 97.62% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:33:04 ||| Worker 2 completed 41/~42.0:: 97.62% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42314)\u001b[0m INFO:2024-11-14,16:33:04 ||| Worker at start location (393,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:33:04 ||| Worker 5 completed 40/~42.0:: 95.24% complete\n",
-      "\u001b[2m\u001b[36m(Worker pid=42312)\u001b[0m INFO:2024-11-14,16:33:04 ||| Worker at start location (380,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42315)\u001b[0m INFO:2024-11-14,16:33:05 ||| Worker at start location (389,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42320)\u001b[0m INFO:2024-11-14,16:33:05 ||| Worker at start location (397,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42319)\u001b[0m INFO:2024-11-14,16:33:05 ||| Worker at start location (385,0) completed 4/5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42311)\u001b[0m INFO:2024-11-14,16:33:06 ||| Worker at start location (406,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42317)\u001b[0m INFO:2024-11-14,16:33:06 ||| Worker at start location (410,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42313)\u001b[0m INFO:2024-11-14,16:33:06 ||| Worker at start location (414,0) completed 3/4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42318)\u001b[0m INFO:2024-11-14,16:33:06 ||| Worker at start location (402,0) completed 4/5\n",
-      "INFO:2024-11-14,16:33:07 || isofit.py:run() | Inversions complete.  41.27s total, 10.1773 spectra/s, 1.0177 spectra/s/core\n",
-      "INFO:2024-11-14,16:33:07 || apply_oe.py:apply_oe() | Analytical line inference\n",
-      "INFO:2024-11-14,16:33:07 || configs.py:create_new_config() | Loading config file: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/config/NIS01_20210403_173647_isofit.json\n",
-      "INFO:2024-11-14,16:33:07 || radiative_transfer_engine.py:__init__() | Loading from wavelength_file: /Users/jamesmo/projects/isofit/tutorials/NEON/output/NIS01_20210403_173647/data/wavelengths.txt\n",
-      "\u001b[2m\u001b[36m(Worker pid=42316)\u001b[0m INFO:2024-11-14,16:33:07 ||| Worker at start location (419,0) completed 4/5\n",
-      "INFO:2024-11-14,16:33:07 || radiative_transfer_engine.py:__init__() | Prebuilt LUT provided\n",
-      "INFO:2024-11-14,16:33:07 || luts.py:load() | Loading LUT into memory\n",
-      "WARNING:2024-11-14,16:33:07 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
-      "WARNING:2024-11-14,16:33:07 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
-      "INFO:2024-11-14,16:33:07 || radiative_transfer_engine.py:__init__() | LUT grid loaded from file: OrderedDict([('AOT550', [0.001, 0.1009, 0.2008, 0.3007, 0.4006, 0.5005, 0.6004, 0.7003, 0.8002, 0.9001, 1.0]), ('H2OSTR', [0.6671, 0.7777])])\n",
-      "2024-11-14 16:33:07,997\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
-      "INFO:2024-11-14,16:33:08 || atm_interpolation.py:atm_interpolation() | Beginning atmospheric interpolation 10 cores\n",
-      "INFO:2024-11-14,16:33:10 || atm_interpolation.py:atm_interpolation() | Parallel atmospheric interpolations complete.  1.9962680339813232 s total, 2114.445519413397 spectra/s, 211.4445519413397 spectra/s/core\n",
-      "2024-11-14 16:33:10,084\tINFO worker.py:1370 -- Calling ray.init() again after it has already been called.\n",
-      "\u001b[2m\u001b[36m(Worker pid=42380)\u001b[0m INFO:2024-11-14,16:33:15 ||| Analytical line writing line 0\n",
-      "\u001b[2m\u001b[36m(Worker pid=42378)\u001b[0m INFO:2024-11-14,16:33:15 ||| Analytical line writing line 1\n",
-      "\u001b[2m\u001b[36m(Worker pid=42375)\u001b[0m INFO:2024-11-14,16:33:16 ||| Analytical line writing line 3\n",
-      "\u001b[2m\u001b[36m(Worker pid=42376)\u001b[0m INFO:2024-11-14,16:33:15 ||| Analytical line writing line 6\n",
-      "\u001b[2m\u001b[36m(Worker pid=42372)\u001b[0m INFO:2024-11-14,16:33:15 ||| Analytical line writing line 5\n",
-      "\u001b[2m\u001b[36m(Worker pid=42377)\u001b[0m INFO:2024-11-14,16:33:16 ||| Analytical line writing line 2\n",
-      "\u001b[2m\u001b[36m(Worker pid=42379)\u001b[0m INFO:2024-11-14,16:33:16 ||| Analytical line writing line 8\n",
-      "\u001b[2m\u001b[36m(Worker pid=42381)\u001b[0m INFO:2024-11-14,16:33:16 ||| Analytical line writing line 9\n",
-      "\u001b[2m\u001b[36m(Worker pid=42374)\u001b[0m INFO:2024-11-14,16:33:16 ||| Analytical line writing line 4\n",
-      "\u001b[2m\u001b[36m(Worker pid=42373)\u001b[0m INFO:2024-11-14,16:33:16 ||| Analytical line writing line 7\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42374)\u001b[0m INFO:2024-11-14,16:33:20 ||| Analytical line writing line 14\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42380)\u001b[0m INFO:2024-11-14,16:33:23 ||| Analytical line writing line 20\n",
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-      "\u001b[2m\u001b[36m(Worker pid=42381)\u001b[0m INFO:2024-11-14,16:33:23 ||| Analytical line writing line 23\n",
-      "\u001b[2m\u001b[36m(Worker pid=42372)\u001b[0m INFO:2024-11-14,16:33:24 ||| Analytical line writing line 22\n",
-      "\u001b[2m\u001b[36m(Worker pid=42373)\u001b[0m INFO:2024-11-14,16:33:24 ||| Analytical line writing line 26\n",
-      "\u001b[2m\u001b[36m(Worker pid=42375)\u001b[0m INFO:2024-11-14,16:33:24 ||| Analytical line writing line 25\n",
-      "\u001b[2m\u001b[36m(Worker pid=42379)\u001b[0m INFO:2024-11-14,16:33:24 ||| Analytical line writing line 27\n",
-      "\u001b[2m\u001b[36m(Worker pid=42376)\u001b[0m INFO:2024-11-14,16:33:24 ||| Analytical line writing line 28\n",
-      "\u001b[2m\u001b[36m(Worker pid=42374)\u001b[0m INFO:2024-11-14,16:33:24 ||| Analytical line writing line 24\n",
-      "\u001b[2m\u001b[36m(Worker pid=42377)\u001b[0m INFO:2024-11-14,16:33:24 ||| Analytical line writing line 29\n",
-      "\u001b[2m\u001b[36m(Worker pid=42380)\u001b[0m INFO:2024-11-14,16:33:27 ||| Analytical line writing line 30\n",
-      "\u001b[2m\u001b[36m(Worker pid=42378)\u001b[0m INFO:2024-11-14,16:33:27 ||| Analytical line writing line 31\n",
-      "\u001b[2m\u001b[36m(Worker pid=42376)\u001b[0m INFO:2024-11-14,16:33:27 ||| Analytical line writing line 37\n",
-      "\u001b[2m\u001b[36m(Worker pid=42379)\u001b[0m INFO:2024-11-14,16:33:27 ||| Analytical line writing line 36\n",
-      "\u001b[2m\u001b[36m(Worker pid=42381)\u001b[0m INFO:2024-11-14,16:33:27 ||| Analytical line writing line 32\n",
-      "\u001b[2m\u001b[36m(Worker pid=42372)\u001b[0m INFO:2024-11-14,16:33:27 ||| Analytical line writing line 33\n",
-      "\u001b[2m\u001b[36m(Worker pid=42373)\u001b[0m INFO:2024-11-14,16:33:27 ||| Analytical line writing line 34\n",
-      "\u001b[2m\u001b[36m(Worker pid=42375)\u001b[0m INFO:2024-11-14,16:33:28 ||| Analytical line writing line 35\n",
-      "\u001b[2m\u001b[36m(Worker pid=42374)\u001b[0m INFO:2024-11-14,16:33:28 ||| Analytical line writing line 38\n",
-      "\u001b[2m\u001b[36m(Worker pid=42377)\u001b[0m INFO:2024-11-14,16:33:28 ||| Analytical line writing line 39\n",
-      "\u001b[2m\u001b[36m(Worker pid=42380)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 40\n",
-      "\u001b[2m\u001b[36m(Worker pid=42381)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 42\n",
-      "\u001b[2m\u001b[36m(Worker pid=42373)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 41\n",
-      "\u001b[2m\u001b[36m(Worker pid=42376)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 44\n",
-      "\u001b[2m\u001b[36m(Worker pid=42375)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 47\n",
-      "\u001b[2m\u001b[36m(Worker pid=42379)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 45\n",
-      "\u001b[2m\u001b[36m(Worker pid=42372)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 43\n",
-      "\u001b[2m\u001b[36m(Worker pid=42374)\u001b[0m INFO:2024-11-14,16:33:31 ||| Analytical line writing line 48\n",
-      "\u001b[2m\u001b[36m(Worker pid=42378)\u001b[0m INFO:2024-11-14,16:33:32 ||| Analytical line writing line 46\n",
-      "\u001b[2m\u001b[36m(Worker pid=42377)\u001b[0m INFO:2024-11-14,16:33:32 ||| Analytical line writing line 49\n",
-      "\u001b[2m\u001b[36m(Worker pid=42380)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 50\n",
-      "\u001b[2m\u001b[36m(Worker pid=42376)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 53\n",
-      "\u001b[2m\u001b[36m(Worker pid=42373)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 51\n",
-      "\u001b[2m\u001b[36m(Worker pid=42379)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 54\n",
-      "\u001b[2m\u001b[36m(Worker pid=42381)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 52\n",
-      "\u001b[2m\u001b[36m(Worker pid=42372)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 56\n",
-      "\u001b[2m\u001b[36m(Worker pid=42375)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 55\n",
-      "\u001b[2m\u001b[36m(Worker pid=42378)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 59\n",
-      "\u001b[2m\u001b[36m(Worker pid=42377)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 58\n",
-      "\u001b[2m\u001b[36m(Worker pid=42374)\u001b[0m INFO:2024-11-14,16:33:35 ||| Analytical line writing line 57\n",
-      "\u001b[2m\u001b[36m(Worker pid=42380)\u001b[0m INFO:2024-11-14,16:33:37 ||| Analytical line writing line 60\n",
-      "INFO:2024-11-14,16:33:38 || analytical_line.py:analytical_line() | Analytical line inversions complete.  27.99s total, 150.777 spectra/s, 15.0777 spectra/s/core\n",
-      "INFO:2024-11-14,16:33:38 || apply_oe.py:apply_oe() | Done.\n"
+      "INFO:2024-11-20,23:33:28 || configs.py:create_new_config() | Loading config file: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/config/NIS01_20210403_173647_isofit.json\n",
+      "INFO:2024-11-20,23:33:28 || configs.py:get_config_errors() | Checking config sections for configuration issues\n",
+      "INFO:2024-11-20,23:33:28 || configs.py:get_config_errors() | Configuration file checks complete, no errors found.\n",
+      "2024-11-20 23:33:28,781\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "INFO:2024-11-20,23:33:28 || isofit.py:run() | Building first forward model, will generate any necessary LUTs\n",
+      "INFO:2024-11-20,23:33:28 || radiative_transfer_engine.py:__init__() | Loading from wavelength_file: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/data/wavelengths.txt\n",
+      "INFO:2024-11-20,23:33:28 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
+      "INFO:2024-11-20,23:33:28 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
+      "INFO:2024-11-20,23:33:29 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
+      "INFO:2024-11-20,23:33:29 || sRTMnet.py:preSim() | Creating a simulator configuration\n",
+      "INFO:2024-11-20,23:33:29 || sRTMnet.py:preSim() | Building simulator and executing (6S)\n",
+      "INFO:2024-11-20,23:33:29 || radiative_transfer_engine.py:__init__() | No LUT store found, beginning initialization and simulations\n",
+      "INFO:2024-11-20,23:33:29 || radiative_transfer_engine.py:__init__() | Initializing LUT file\n",
+      "INFO:2024-11-20,23:33:29 || radiative_transfer_engine.py:runSimulations() | Running any pre-sim functions\n",
+      "INFO:2024-11-20,23:33:29 || radiative_transfer_engine.py:runSimulations() | Executing parallel simulations\n",
+      "INFO:2024-11-20,23:33:35 || common.py:__call__() |  10.61% simulations complete (elapsed: 0:00:05.884491, rate: 0:00:00.044579, eta: 0:00:52.960419)\n",
+      "INFO:2024-11-20,23:33:35 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:33:38 || common.py:__call__() |  20.45% simulations complete (elapsed: 0:00:08.586929, rate: 0:00:00.065052, eta: 0:00:34.347716)\n",
+      "INFO:2024-11-20,23:33:38 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:33:41 || common.py:__call__() |  30.30% simulations complete (elapsed: 0:00:11.628591, rate: 0:00:00.088095, eta: 0:00:27.133379)\n",
+      "INFO:2024-11-20,23:33:41 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:33:45 || common.py:__call__() |  40.15% simulations complete (elapsed: 0:00:15.907742, rate: 0:00:00.120513, eta: 0:00:23.861613)\n",
+      "INFO:2024-11-20,23:33:45 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:33:48 || common.py:__call__() |  50.00% simulations complete (elapsed: 0:00:18.644423, rate: 0:00:00.141246, eta: 0:00:18.644423)\n",
+      "INFO:2024-11-20,23:33:48 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:33:50 || common.py:__call__() |  60.61% simulations complete (elapsed: 0:00:21.166233, rate: 0:00:00.160350, eta: 0:00:14.110822)\n",
+      "INFO:2024-11-20,23:33:50 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:33:54 || common.py:__call__() |  70.45% simulations complete (elapsed: 0:00:25.395109, rate: 0:00:00.192387, eta: 0:00:10.883618)\n",
+      "INFO:2024-11-20,23:33:54 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:33:57 || common.py:__call__() |  80.30% simulations complete (elapsed: 0:00:28.367045, rate: 0:00:00.214902, eta: 0:00:07.091761)\n",
+      "INFO:2024-11-20,23:33:57 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:00 || common.py:__call__() |  90.15% simulations complete (elapsed: 0:00:30.988081, rate: 0:00:00.234758, eta: 0:00:03.443120)\n",
+      "INFO:2024-11-20,23:34:00 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:03 || common.py:__call__() | 100.00% simulations complete (elapsed: 0:00:34.498852, rate: 0:00:00.261355, eta: 0:00:00)\n",
+      "INFO:2024-11-20,23:34:03 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:04 || radiative_transfer_engine.py:runSimulations() | Running any post-sim functions\n",
+      "INFO:2024-11-20,23:34:04 || radiative_transfer_engine.py:runSimulations() | Saving post-sim data to index zero of all dimensions except wl\n",
+      "INFO:2024-11-20,23:34:04 || luts.py:load() | Loading LUT into memory\n",
+      "WARNING:2024-11-20,23:34:04 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
+      "WARNING:2024-11-20,23:34:04 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
+      "INFO:2024-11-20,23:34:04 || sRTMnet.py:preSim() | Interpolating simulator quantities to emulator size\n",
+      "INFO:2024-11-20,23:34:04 || sRTMnet.py:preSim() | Loading and predicting with emulator\n",
+      "INFO:2024-11-20,23:34:09 || sRTMnet.py:preSim() | Saving intermediary prediction results to: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/lut_full/sRTMnet.predicts.nc\n",
+      "INFO:2024-11-20,23:34:09 || radiative_transfer_engine.py:runSimulations() | Saving pre-sim data to index zero of all dimensions except wl\n",
+      "INFO:2024-11-20,23:34:09 || radiative_transfer_engine.py:runSimulations() | Executing parallel simulations\n",
+      "INFO:2024-11-20,23:34:09 || common.py:__call__() |  10.61% simulations complete (elapsed: 0:00:00.340668, rate: 0:00:00.002581, eta: 0:00:03.066012)\n",
+      "INFO:2024-11-20,23:34:09 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:10 || common.py:__call__() |  20.45% simulations complete (elapsed: 0:00:00.548051, rate: 0:00:00.004152, eta: 0:00:02.192204)\n",
+      "INFO:2024-11-20,23:34:10 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:10 || common.py:__call__() |  30.30% simulations complete (elapsed: 0:00:00.857750, rate: 0:00:00.006498, eta: 0:00:02.001417)\n",
+      "INFO:2024-11-20,23:34:10 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:10 || common.py:__call__() |  40.15% simulations complete (elapsed: 0:00:01.085104, rate: 0:00:00.008220, eta: 0:00:01.627656)\n",
+      "INFO:2024-11-20,23:34:10 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:10 || common.py:__call__() |  50.00% simulations complete (elapsed: 0:00:01.297816, rate: 0:00:00.009832, eta: 0:00:01.297816)\n",
+      "INFO:2024-11-20,23:34:10 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:11 || common.py:__call__() |  60.61% simulations complete (elapsed: 0:00:01.656411, rate: 0:00:00.012549, eta: 0:00:01.104274)\n",
+      "INFO:2024-11-20,23:34:11 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:11 || common.py:__call__() |  70.45% simulations complete (elapsed: 0:00:01.842987, rate: 0:00:00.013962, eta: 0:00:00.789852)\n",
+      "INFO:2024-11-20,23:34:11 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:11 || common.py:__call__() |  80.30% simulations complete (elapsed: 0:00:02.095662, rate: 0:00:00.015876, eta: 0:00:00.523916)\n",
+      "INFO:2024-11-20,23:34:11 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:11 || common.py:__call__() |  90.15% simulations complete (elapsed: 0:00:02.374645, rate: 0:00:00.017990, eta: 0:00:00.263849)\n",
+      "INFO:2024-11-20,23:34:11 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:12 || common.py:__call__() | 100.00% simulations complete (elapsed: 0:00:02.542582, rate: 0:00:00.019262, eta: 0:00:00)\n",
+      "INFO:2024-11-20,23:34:12 || radiative_transfer_engine.py:runSimulations() | Flushing netCDF to disk\n",
+      "INFO:2024-11-20,23:34:12 || radiative_transfer_engine.py:runSimulations() | Running any post-sim functions\n",
+      "INFO:2024-11-20,23:34:12 || luts.py:load() | Loading LUT into memory\n",
+      "WARNING:2024-11-20,23:34:12 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
+      "WARNING:2024-11-20,23:34:12 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
+      "INFO:2024-11-20,23:34:12 || isofit.py:run() | Beginning 420 inversions in 100 chunks using 10 cores\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:34:20 ||| Worker 4 completed 1/~42.0:: 2.38% complete\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:34:40 ||| Worker at start location (15,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:34:22 ||| Worker 0 completed 1/~42.0:: 2.38% complete\u001b[32m [repeated 9x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2229)\u001b[0m INFO:2024-11-20,23:34:45 ||| Worker at start location (3,0) completed 3/4\u001b[32m [repeated 7x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:34:48 ||| Worker 6 completed 5/~42.0:: 11.9% complete\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:34:49 ||| Worker at start location (20,0) completed 4/5\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:34:56 ||| Worker 1 completed 6/~42.0:: 14.29% complete\u001b[32m [repeated 8x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:35:09 ||| Worker at start location (58,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:34:56 ||| Worker 5 completed 6/~42.0:: 14.29% complete\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:35:15 ||| Worker at start location (79,0) completed 3/4\u001b[32m [repeated 6x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:35:16 ||| Worker 4 completed 9/~42.0:: 21.43% complete\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:35:19 ||| Worker at start location (54,0) completed 4/5\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:35:22 ||| Worker 1 completed 10/~42.0:: 23.81% complete\u001b[32m [repeated 6x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:35:39 ||| Worker at start location (96,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:35:27 ||| Worker 2 completed 10/~42.0:: 23.81% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:35:44 ||| Worker at start location (117,0) completed 3/4\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:35:45 ||| Worker 0 completed 13/~42.0:: 30.95% complete\n",
+      "\u001b[36m(Worker pid=2229)\u001b[0m INFO:2024-11-20,23:35:47 ||| Worker at start location (105,0) completed 4/5\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:35:51 ||| Worker 5 completed 14/~42.0:: 33.33% complete\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2233)\u001b[0m INFO:2024-11-20,23:35:52 ||| Worker at start location (122,0) completed 4/5\n",
+      "\u001b[36m(Worker pid=2233)\u001b[0m INFO:2024-11-20,23:35:56 ||| Worker 7 completed 15/~42.0:: 35.71% complete\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:36:04 ||| Worker at start location (130,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:36:09 ||| Worker 6 completed 17/~42.0:: 40.48% complete\n",
+      "\u001b[36m(Worker pid=2229)\u001b[0m INFO:2024-11-20,23:36:10 ||| Worker at start location (164,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:36:13 ||| Worker 3 completed 17/~42.0:: 40.48% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:36:15 ||| Worker at start location (139,0) completed 4/5\u001b[32m [repeated 4x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2233)\u001b[0m INFO:2024-11-20,23:36:21 ||| Worker 7 completed 19/~42.0:: 45.24% complete\u001b[32m [repeated 5x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2233)\u001b[0m INFO:2024-11-20,23:36:17 ||| Worker at start location (168,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:36:24 ||| Worker at start location (160,0) completed 4/5\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:36:22 ||| Worker 8 completed 18/~42.0:: 42.86% complete\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:36:30 ||| Worker 2 completed 19/~42.0:: 45.24% complete\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:36:27 ||| Worker at start location (172,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:36:31 ||| Worker at start location (181,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:36:34 ||| Worker 0 completed 21/~42.0:: 50.0% complete\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:36:37 ||| Worker 3 completed 21/~42.0:: 50.0% complete\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:36:35 ||| Worker at start location (198,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:36:42 ||| Worker 4 completed 22/~42.0:: 52.38% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2233)\u001b[0m INFO:2024-11-20,23:36:41 ||| Worker at start location (206,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:36:49 ||| Worker 8 completed 22/~42.0:: 52.38% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:36:43 ||| Worker at start location (194,0) completed 4/5\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:36:50 ||| Worker 5 completed 23/~42.0:: 54.76% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:36:55 ||| Worker at start location (215,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:37:00 ||| Worker 2 completed 24/~42.0:: 57.14% complete\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:37:00 ||| Worker at start location (223,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:37:02 ||| Worker 0 completed 25/~42.0:: 59.52% complete\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:37:04 ||| Worker at start location (232,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:37:07 ||| Worker 3 completed 25/~42.0:: 59.52% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:37:12 ||| Worker at start location (249,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:37:11 ||| Worker 4 completed 26/~42.0:: 61.9% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2229)\u001b[0m INFO:2024-11-20,23:37:18 ||| Worker 9 completed 27/~42.0:: 64.29% complete\n",
+      "\u001b[36m(Worker pid=2233)\u001b[0m INFO:2024-11-20,23:37:19 ||| Worker 7 completed 27/~42.0:: 64.29% complete\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:37:20 ||| Worker at start location (245,0) completed 4/5\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:37:28 ||| Worker 8 completed 27/~42.0:: 64.29% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:37:23 ||| Worker at start location (257,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:37:31 ||| Worker 2 completed 28/~42.0:: 66.67% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:37:33 ||| Worker at start location (274,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:37:39 ||| Worker 1 completed 30/~42.0:: 71.43% complete\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:37:38 ||| Worker at start location (262,0) completed 4/5\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:37:41 ||| Worker 6 completed 30/~42.0:: 71.43% complete\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:37:44 ||| Worker at start location (287,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:37:46 ||| Worker 3 completed 30/~42.0:: 71.43% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:37:50 ||| Worker at start location (291,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:37:52 ||| Worker 5 completed 31/~42.0:: 73.81% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:37:52 ||| Worker at start location (295,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:38:00 ||| Worker 2 completed 32/~42.0:: 76.19% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:38:00 ||| Worker at start location (300,0) completed 4/5\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:38:07 ||| Worker 1 completed 34/~42.0:: 80.95% complete\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:38:03 ||| Worker at start location (312,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:38:10 ||| Worker 0 completed 34/~42.0:: 80.95% complete\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:38:16 ||| Worker at start location (317,0) completed 4/5\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:38:11 ||| Worker 6 completed 34/~42.0:: 80.95% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:38:21 ||| Worker at start location (338,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:38:23 ||| Worker 3 completed 35/~42.0:: 83.33% complete\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:38:28 ||| Worker at start location (346,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:38:29 ||| Worker 2 completed 36/~42.0:: 85.71% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:38:31 ||| Worker at start location (342,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:38:35 ||| Worker 8 completed 36/~42.0:: 85.71% complete\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:38:35 ||| Worker at start location (355,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2230)\u001b[0m INFO:2024-11-20,23:38:37 ||| Worker 1 completed 38/~42.0:: 90.48% complete\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:38:42 ||| Worker at start location (363,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:38:40 ||| Worker 0 completed 38/~42.0:: 90.48% complete\n",
+      "\u001b[36m(Worker pid=2231)\u001b[0m INFO:2024-11-20,23:38:44 ||| Worker 6 completed 38/~42.0:: 90.48% complete\n",
+      "\u001b[36m(Worker pid=2229)\u001b[0m INFO:2024-11-20,23:38:48 ||| Worker at start location (372,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:38:50 ||| Worker 4 completed 39/~42.0:: 92.86% complete\n",
+      "\u001b[36m(Worker pid=2238)\u001b[0m INFO:2024-11-20,23:38:53 ||| Worker 5 completed 39/~42.0:: 92.86% complete\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:38:56 ||| Worker at start location (376,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:38:57 ||| Worker at start location (368,0) completed 4/5\n",
+      "\u001b[36m(Worker pid=2229)\u001b[0m INFO:2024-11-20,23:38:57 ||| Worker 9 completed 40/~42.0:: 95.24% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2237)\u001b[0m INFO:2024-11-20,23:39:01 ||| Worker at start location (380,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:39:06 ||| Worker 3 completed 40/~42.0:: 95.24% complete\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2236)\u001b[0m INFO:2024-11-20,23:39:07 ||| Worker at start location (389,0) completed 3/4\n",
+      "\u001b[36m(Worker pid=2232)\u001b[0m INFO:2024-11-20,23:39:16 ||| Worker at start location (397,0) completed 3/4\u001b[32m [repeated 3x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2229)\u001b[0m INFO:2024-11-20,23:39:25 ||| Worker at start location (410,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=2234)\u001b[0m INFO:2024-11-20,23:39:31 ||| Worker at start location (414,0) completed 3/4\u001b[32m [repeated 2x across cluster]\u001b[0m\n",
+      "INFO:2024-11-20,23:39:40 || isofit.py:run() | Inversions complete.  327.73s total, 1.2815 spectra/s, 0.1282 spectra/s/core\n",
+      "INFO:2024-11-20,23:39:40 || apply_oe.py:apply_oe() | Analytical line inference\n",
+      "INFO:2024-11-20,23:39:40 || configs.py:create_new_config() | Loading config file: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/config/NIS01_20210403_173647_isofit.json\n",
+      "\u001b[36m(Worker pid=2235)\u001b[0m INFO:2024-11-20,23:39:40 ||| Worker at start location (419,0) completed 4/5\n",
+      "INFO:2024-11-20,23:39:41 || radiative_transfer_engine.py:__init__() | Loading from wavelength_file: /home/mambauser/examples/NEON/output/NIS01_20210403_173647/data/wavelengths.txt\n",
+      "INFO:2024-11-20,23:39:41 || radiative_transfer_engine.py:__init__() | Prebuilt LUT provided\n",
+      "INFO:2024-11-20,23:39:41 || luts.py:load() | Loading LUT into memory\n",
+      "WARNING:2024-11-20,23:39:41 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_upwelling\n",
+      "WARNING:2024-11-20,23:39:41 || luts.py:load() | Detected NaNs in the following LUT variable and may cause issues: thermal_downwelling\n",
+      "INFO:2024-11-20,23:39:41 || radiative_transfer_engine.py:__init__() | LUT grid loaded from file: OrderedDict([('AOT550', [0.001, 0.1009, 0.2008, 0.3007, 0.4006, 0.5005, 0.6004, 0.7003, 0.8002, 0.9001, 1.0]), ('H2OSTR', [0.6083, 0.6485]), ('observer_zenith', [0.9608, 2.9675]), ('relative_azimuth', [0.0038, 41.2002, 82.3965])])\n",
+      "2024-11-20 23:39:42,027\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "INFO:2024-11-20,23:39:42 || atm_interpolation.py:atm_interpolation() | Beginning atmospheric interpolation 10 cores\n",
+      "INFO:2024-11-20,23:39:43 || atm_interpolation.py:atm_interpolation() | Parallel atmospheric interpolations complete.  1.1534008979797363 s total, 3659.612201961505 spectra/s, 365.9612201961505 spectra/s/core\n",
+      "2024-11-20 23:39:43,191\tINFO worker.py:1652 -- Calling ray.init() again after it has already been called.\n",
+      "\u001b[36m(Worker pid=3155)\u001b[0m INFO:2024-11-20,23:40:03 ||| Analytical line writing line 4\n",
+      "\u001b[36m(Worker pid=3155)\u001b[0m INFO:2024-11-20,23:40:22 ||| Analytical line writing line 10\u001b[32m [repeated 10x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=3155)\u001b[0m INFO:2024-11-20,23:40:42 ||| Analytical line writing line 20\u001b[32m [repeated 10x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=3155)\u001b[0m INFO:2024-11-20,23:41:02 ||| Analytical line writing line 30\u001b[32m [repeated 10x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=3155)\u001b[0m INFO:2024-11-20,23:41:25 ||| Analytical line writing line 40\u001b[32m [repeated 10x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=3155)\u001b[0m INFO:2024-11-20,23:41:46 ||| Analytical line writing line 50\u001b[32m [repeated 10x across cluster]\u001b[0m\n",
+      "\u001b[36m(Worker pid=3155)\u001b[0m INFO:2024-11-20,23:42:08 ||| Analytical line writing line 60\u001b[32m [repeated 10x across cluster]\u001b[0m\n",
+      "INFO:2024-11-20,23:42:08 || analytical_line.py:analytical_line() | Analytical line inversions complete.  145.51s total, 29.0084 spectra/s, 2.9008 spectra/s/core\n",
+      "INFO:2024-11-20,23:42:08 || apply_oe.py:apply_oe() | Done.\n",
+      "\u001b[36m(Worker pid=3160)\u001b[0m INFO:2024-11-20,23:42:08 ||| Analytical line writing line 61\n"
      ]
     }
    ],
@@ -1102,7 +816,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "d07cc4e0-2b61-4c8f-998a-2b3f1b064b48",
    "metadata": {},
    "outputs": [],
@@ -1122,23 +836,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "6dcf2273-ebd0-480b-b3de-260a67814809",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x14f7f1ae0>"
+       "<matplotlib.legend.Legend at 0xffff84626a40>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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+P/r06YP58+dj2rRpuP3221MfpeXn51f7kVBGRgaaNm2Ko446KvWa3+/HxIkTce655+LSSy/FmWeeia+//hrXXXcdBg0alBbI+Oc//xmNGjVC3759kZeXh40bN+KFF17A888/j7/+9a+pr/bbmjdvHoqKilBYWAig7ApZxXafcMIJyMrKSpv+scceQ2ZmZlrIaWUPP/ww3nnnHQwePBht2rRBUVER3nnnHTzwwAM47LDD0j4a/dOf/oSHH34Yw4cPx8aNG3HggQfijTfewJQpUzB8+HC0a9cOANC5c+e01HEAqeT5fffdN60tKyxevBgrVqzADTfcUOO3FkV+VfZMPJTIr09FuOXSpUurlJ111lkuAGO45eeff+5ee+21bu/evd3c3Fw3EAi4TZo0cQcOHJgWHFmhsLDQveqqq9z8/Hw3FAq5Bx98sPvcc89Vme7NN990AVT7M3DgQOvtW7VqVY3LAeCef/75Vea577773P33398NhUJu27Zt3ZtvvtmNRqNp07Rr167GZbZr1y5t2oo2tll/NBp1b775Zrdt27ZuKBRy999/f/f++++32tbqwi0rzJgxwz344IPdUCjk5ufnu1dddZVbWFiYNs3jjz/uHnHEEW6zZs3cQCDgNm7cuMb9aFufmrZ7x6DSH374wfX5fO55551X4/L++9//uieddJLbsmVLNxQKuVlZWe4hhxzi3nbbbW5RUVGV6Tdt2uReeumlbl5enhsMBt3999/fveuuu9LCRqtT0WdqCre85JJLXMdx0sI0RX7NHNe1/L60iIiIyF5M9zSJiIiIWNA9TSJ7gUQiYQzhdBxH95zUsWQyWe2N/5XV9pErIrJn6UqTyF5g3333RTAYrPHn2GOP3dNV/M259dZbjW0eDAZTN1KLyK+D7mkS2Qt8+umnqRDH6jRs2BAHHHDAbqzRb99PP/2En376yTjNwQcfnPrGpIjUfxo0iYiIiFjQx3MiIiIiFurdXYjJZBI//fQTGjZsuFseSioiIiJ7N9d1UVhYiJYtW1Z5OPmOE+4SU6ZMcdu3b++Gw2G3Z8+e7ttvv2013+rVq40Be/rRj370ox/96Ec/u+Jn9erVxjHKLrnS9Pzzz2PEiBF48MEHcfjhh+Phhx/GkCFD8Pnnn6Nt27bGeRs2bAgAGNC7BwKBnfsKdDLpGst9PvMVrICffGppcwHMXAWAXEVLJMxfVU6QrzKzbaTIrW7xhLnctbhVzm8azQO0nVkd2E5gTcRb0DwFrR3tI7QC9GqsS44Fl9SSLd9PjpVY3NxPXddc7ic7yefwOwxCAfMyWB1ZG2QEyXmKdTTSDxKJhLGcne+ipBzkXGKDnTMdj/0sSQ4Wn8f5WRvSUw2AOGnHDNIPWfxElOwmhxxLoQA5VnzmfrytOGosZ/EaYYtIEx/pRwFyLLH9aOqG8UQC7yxbnhqD1GSX3Ajer18/9OzZM/X0cADo0qULTjnlFIwfP944b0FBAXJycnDUob13OsOE7TzjpTdYnAB2w6ApXu8HTezN0GbQRA4iOmhiJ3sNmnb9oMm8D2Nx8xu+S/qx318XgyY2sDPXkbVBZpCcpzwOmuKeB03kONGgqW4GTeR8lBHc04MmNrhng6aav30LAEmy/WGLiyB80GQu54Ommsvj8QTeXLIMW7duRaNGjWquo3kNtReNRrFs2TIMHjw47fXBgwfjvffeqzJ9JBJBQUFB2o+IiIhIfVPng6aNGzcikUggLy8v7fW8vDysW7euyvTjx49HTk5O6qdNmzZ1XSURERERz3ZZ5MCOl1td1632Euzo0aOxdevW1M/q1at3VZVEREREdlqd3wjerFkz+P3+KleV1q9fX+XqEwCEw2GEw+G6roaIiIhInarzQVMoFEKvXr2wYMECnHrqqanXFyxYgJNPPtl6OY5T873S9KZC9u04emMmuWnQ4g5ddmMiR7aRzE1XT+6XC5KbEh3HfHNqgt2QB4ub9gj2zSuG3cQcZN+iJKLsW1nkJmeb5vF6Mzm7YT9I2ojdYMv2Eas+7yO8DyTIzaM+cgMs+2JJnLVB3NtN0OwmZLJ4OORmedeiDb2eznykHznsSxVJ8/mG8vh1J5svtrAvp2WG2TmV3AQdMD9uJytsLnfINhQUlRjLM0JBY3mCfenD5jtn7Ist7L2ZnG8ShoOJf7GozC6JHBg5ciTOPfdc9O7dG/3798e0adPwww8/YNiwYbtidSIiIiK73C4ZNJ1++unYtGkTbr31VqxduxbdunXDq6++inbt2u2K1YmIiIjscrvsMSrDhw/H8OHDd9XiRURERHYrPbBXRERExIIGTSIiIiIWNGgSERERsaBBk4iIiIiFXXYjuFc+x6kx68jPHtrHHu5IHy5plsECOQC4SfJkdfoQTm/5NmwClg3jkDb20wf6xs0VAOA4O/9wRQAIkJwjUyYHwPczy/NiD4tlsSRR9lBmi1wTmtdFysNBkr1C+mEsbt7PCbKNLM+M9UP2oFiAP7yaPRCXZRSxfJcQyTxLsCyrOn+k+g7rt/i3c4C0ATvn0swzso0+11u2HjsQ6IOpzbMD4H2xhDxxN0GyqFySjReNmI/FJFk+fWgx6ec8O5CHfcVJtl0pOVZZPwgazwV2OU260iQiIiJiQYMmEREREQsaNImIiIhY0KBJRERExIIGTSIiIiIWNGgSERERsaBBk4iIiIiFepvTVBqNI1BDboTX2BIfyX5xSdZDdgZvNpY3wXKYHJ85GYTl59DcEpLJAR/Jy4hEjeVBFuwCgCV7RGN2uRk1YbkjjEvqVxonuSmknGF5ZADvqyw/JxpjeVre2pAda5THbgwAPpbbZpEf47UOJgm2AJJn5pBGYqcK1kdssAwedj4Mhcg5ldTRZedTdrpjOU1+fi5iuzFGcuNYP/CRjYgkzedkdiz6SZ6Zw/LOWANY9LMEy2Ei2BpKDVlTLG+tgq40iYiIiFjQoElERETEggZNIiIiIhY0aBIRERGxoEGTiIiIiAUNmkREREQsaNAkIiIiYqHe5jT5/D74/NVnFdFcFRa7QvIiHDKWpNE2ACIkl4Rtg89bdIvnLKtYNGYsDwTMOVL+GvZdGtIGARKbwbJhYmRHFUfN5SUxc2YI24csS4tmdTn83zQsBom1EYtOYfOzg41lv/j9JBuGLN8mY4jlr8Ti5n7AsqzC4ZCxnNXQJVOwXpBgGUYu2QcWOVUsC4ptJetHrJydb3wwl0di5vMZO5bjSd5GXo8lh53UCZbr5rrsPcnchg45pydd8/mSZcoBQJDsZ5YVxbL5TFlZtq2vK00iIiIiFjRoEhEREbGgQZOIiIiIBQ2aRERERCxo0CQiIiJiQYMmEREREQsaNImIiIhY0KBJRERExEK9DbcMBfw1BpqxmDGf3zwW9JHQQBZ0ZpEFh0DcHNLFgw9J1BYJGvOREDAWNOY1qI0GkFrUIUD2I1uDP4OEDpI2ZqGDrI1C5N8kcbZ+i7Q1nj3pLZySBeLRwDyPYXQk6w5gxwl4+CPTsGEDcxXIscyQJuTnCtJPWeihRYYqfGQdCbYfyTuNSwJI6TmdbGODoLkCJaURY3mYzA8AxRGSeuw13JIUx0k/YSGm9H2PnCuC5FgvjZvDLwEexMrqyI500/w++o5SMZ2IiIiIUBo0iYiIiFjQoElERETEQr29p0lERPZOGYEchPxZqb/ZPUs7Phg6mihGJFGwS+omezcNmkREpN7ICOTgiPZXIeAzf5HDJJGM4v0fp2jgJHVOgyYREak3Qv4sBHwhfLz2BWyLbgBQuytNWcFcdGs+FCF/lgZNUuc0aBIRkXpnW3QDCiJrAQB+EjnAYhVE6kq9HTT5/b4ac3pYVgP7V0mAZByx/KBYgudN8Jwkc3mChLc4HnOYWBtRZPksxwnguR5sG2vK8apQFDXnpiRILgrL7WCbGCf5O2z+He/TqI63BCIec8SOFb8vaF4+y/ch+TwOyxhiIUcAAqQZA37zNsA1H+/sWAsGzcuPkvyaWMLbsRYIkIwjizZk2XYuyH4ky698rFf87vic1O8+0g8DleavGED5fdvfQ9ixxKK8gmTQBgBh0s6JGMlRIutgmWasjeNJcz+LFMWM5TQvjPSjUIAcZ+BtQPMDyfJN78vsPTs1ndVUIiIiIns5DZpERERELGjQJCIiImJBgyYRERERCxo0iYiIiFjQoElERETEggZNIiIiIhbqbU6TAxdODRkv4YC52iy7Jp4w5/fESHYMy2UBQEN4WCYEiUWpsW22l5PlmxcPl7aiudy1yH4Jkf1Is65IbghILgn7FwNrARKfw7OyyE6wSQ1J0uwU8/x+kn/DMoBo/g3ZRTw/h2QgkayuulASM9chI2iug9d8nRBZPjsdsTYOWmS2uWQbXLIVLBeuprkrXk+yc7Jvexu5lf5b8XvSY25dScT8ngEADbLMj31JuFHzOmLm81WC7AM/OVbp6dJcjATraGQBkag5BwoA/H5zG7DzFevrpmMxbpG/COhKk4iIiIgVDZpERERELGjQJCIiImJBgyYRERERCxo0iYiIiFjQoElERETEggZNIiIiIhbqbU5TNBavMWOGZbc4JKshHjcHSsRJoIVVThPNx9m53JJKlTDPT4bDLEeJZSSxzA7Hoo1iJC8LJDYjTrJbonHz8ll2S4IEMQUDZB+SneAj5Ra9zCJHyTx/gE1AsGMlQPJvXNdczrJTaFYX+LHGjjaW/RIgeWOMk2DHmnkbg6QPBH3mnhQi/RgAonGWEUTOB+SElnYoVgpaSr3umvtBrFKWVtwpmzYeTyAWKzsHBOixaq4fKweA4hJzDlHjbHOOk1MUMZaXxkhfZ+cCl2T3sX7kN/dz9r6asDhWWR4XzaUj5xtTHpfV+zp0pUlERETEigZNIiIiIhY0aBIRERGxoEGTiIiIiAUNmkREREQsaNAkIiIiYkGDJhEREREL9TanKZ5wAaf6TAaWz+MjeRMuScBxawqIqmCR2cEyLxJkHQ6rI1k/iXbhWVcsUoPMTyJBAAAlpA2iMXPO0pZtxWR+c7YLz14xT9C0UZax3E/ycdj6WcYRAIRCJDuFdIRYkrQRWT/rRzGSqxIM+I3lfpa7YhOgQ7H9ZF4Hy/vyk/lJEyBM877M87MsrjjJIwPM+TYAeG4c6UmVm6iivR3HqZQXZ39Or/jdLf8fwDPXeJYXR48FknWV2zjbWM7eE37ZRnKeIuZjPQFzR2TvWZlBcxv6/fxYTcbNdWTHGjsWTNl5cbLu1DqsphIRERHZy2nQJCIiImJBgyYRERERCxo0iYiIiFjQoElERETEggZNIiIiIhY0aBIRERGxUG9zmjIzAggGqq9egmS/sFwVlltiynIAQDNJyhdCFmFeRmkkZl48Wz0pd8kmJkkWFmtjlq8DAC7dj+b5Q4EgKTd3bx8J9WDbECYZSSxDiOWCsIwjgP+rJxw0Z68kYV4H6+me87zYsUT6gE0eGMtuYSsJk/wZx2G5b+b9THYR7YcsYyjOMtvMxVbTsBwndr6onEFUcVz6fNtzmtg5uXI/qqiLz+eDz1fWuOxcw+ofCFhkDLHsPbIOl/ZDc0dp2dR8PvxhfYGxPE5y7YJsH5AmYrl5ANAo07wN7HQTie587pxt5FutrzS9/fbb+P3vf4+WLVvCcRzMnTs3rdx1XYwdOxYtW7ZEZmYmjjrqKKxYsaK2qxERERGpV2o9aCoqKsIhhxyCyZMnV1s+ceJETJo0CZMnT8bSpUuRn5+PQYMGobCw0HNlRURERPaUWn88N2TIEAwZMqTaMtd1ce+992LMmDEYOnQoAGD69OnIy8vDjBkzcOmll3qrrYiIiMgeUqc3gq9atQrr1q3D4MGDU6+Fw2EMHDgQ7733XrXzRCIRFBQUpP2IiIiI1Dd1Omhat24dACAvLy/t9by8vFTZjsaPH4+cnJzUT5s2beqySiIiIiJ1YpdEDuz4TQnXdWv89sTo0aOxdevW1M/q1at3RZVEREREPKnTyIH8/HwAZVecWrRokXp9/fr1Va4+VQiHwwiHw3VZDREREZE6V6eDpg4dOiA/Px8LFixAjx49AADRaBQLFy7EhAkTarWsJg2zEAruZPVI3oKP5TCRVBKW6QEASZZPQ+YvLokay2lOk8ecKIZtn80lTFaFBqSV9mnEMn7MbcBylEjsCs15YvuA2VZUSqdh64iQbWAZQAmykzJIVpXXTDWG5e8AfD/7SM4Szwsjy/eZs2dYjhLrhwl2tJEwLZ+f7wOv+4md8AJ+f5XfA35/KqvPpevfvgJ/wJ/6b8Atz2lKeDsfOvSMC3rSS5AdGSW5beycGyDno7zG2cbySCxuLN9aZH5PSpKdzHLzACCWMC+DlfO8MEMZCy8sV+tRybZt2/DNN9+k/l61ahWWL1+OffbZB23btsWIESMwbtw4dOrUCZ06dcK4ceOQlZWFs846q7arEhEREak3aj1o+uCDD3D00Uen/h45ciQA4Pzzz8eTTz6J6667DiUlJRg+fDg2b96Mfv36Yf78+WjYsGHd1VpERERkN6v1oOmoo44yXsp0HAdjx47F2LFjvdRLREREpF7RA3tFRERELGjQJCIiImJBgyYRERERCxo0iYiIiFio05ymupSdnYVQyJxvUjNvGUlsApvIEo8xSAjTbfeYm0K4dPmkjS22n2WjuCyghiGbQLeQ9gNvK2DLz8wImScAeGcmuSUsw4gt3nseGClnGUUWOU2sHyXZMth+JnWMxs35NywnKUHmd0gFWBaX5wwmWOxndqg4VX93nPTfzYvfvo0VfdrnOPCXZ/IlaTSfx/cMAH5SztooQboh6+ssk82l+YPmRs7KNIdQ0xwpi9xFmodFOkKCZF05hm2MkZyqCrrSJCIiImJBgyYRERERCxo0iYiIiFjQoElERETEggZNIiIiIhY0aBIRERGxoEGTiIiIiIV6m9OUlRWuMauI5V0kSS4LiaNAXWQgsVyPeILkSZBdw7OmzFOwPAyW6UEzliyCTdh+4vExHoOYWH4Pa0NSQZ7jRDJHEjaHp7d8GZqLwuanWVTesrZYH7FJ0EmQZdCsJ5Z/Q/NpzAk+fB+Y83HYuYSxy0OjjeBl7rQpKrJ0HJ9TKTuIHGuVfq+Yxeds/93vN+8D11ssXfkkHt93HPM1DJrzRIKeWD9neWXsOOK5ezFjOWDOUQIAh7RRMGAuDwRrzj9kmXWp6aymEhEREdnLadAkIiIiYkGDJhERERELGjSJiIiIWNCgSURERMSCBk0iIiIiFjRoEhEREbGgQZOIiIiIhXobbpkRDiAcqqF6NGfNW0iX32ceS9LQQgsuag7ZAoBYnATu0eWbec733A3hll6zK2kVWFgbW77H8EsfCXKLxXhooef9zOb32Ncdm1RAD1igIACw7MpIxBy6l2Cd2VsxDbeMkw0Ikn1Eg2pZA8EmuJAdS+x8sX0bKtrDgUPDDKtXKRCzvG3osVgn53Rv50RWBR9pCz85n7gwB3wmaTgmC5Um4ZkWbwr0UCPv7XFyyozHIzWWxeJx88zldKVJRERExIIGTSIiIiIWNGgSERERsVBv72kSEfktahiNIbPSzRfsniZ2Lwl/aHNd3NPkbYLa3O8TCpXdY9Y0GkPDaJTWbUfVzc/vWfJ+T5P3h2czdfBUYQN6T5LHB5xXV17s92FrwHyvVX2jQZOIyG7SMBrDhV/+gCAdCO29ChuEsbQVMHTtZjTctn63zy+7T9QB7mvd7Fc1cNKgSURkN8mMJxBMuvhX2zxsyij7Bq2uNO14pakJ2gCY3aIJotGav+1Uk+rm15UmbndfaWoei+O0DQXISiQ1aBIRkZptyghifVYGAO+RA54HLPUicmD779mBINoA2BQKosgXonXbUXXzs0FT3cTI7NrIAdZPvA7a6kPkwK9BvR00ua5rOFC9NT79V4dTBzuXrCJAsqACfvPIuw6OcW9IE9VFJgfDTnRec5YqZ8dUXwG2fFIBwu/nhyfNKfIWhWWB/euUveF7WzvbhwDg+M1byfJtoiQvKxI157s41fzupL1OcuPI+cglxwG7QmC1CzxmUbFuULmfJH1u+WsuEuVv5HQ3V2qjhK9snkQyiXiibN+x7D12vrU6UMj5gmUM0UERKyY5TnTIRb4W5mP91E8WsMNODJXv21DAj3Cw7Fznejyp+sixbmpjdh5IrcNqKhEREZG9nAZNIiIiIhY0aBIRERGxoEGTiIiIiAUNmkREREQsaNAkIiIiYkGDJhEREREL9TanKRKJ1pjJYJPNsivxdFka2UEzOQIB865xSGYGD0ojvE5QB/uIZj3VQVaUFzQHimTTBIJkH1vEhjhkG0l0i+f9lEiQDKOIOcMoEo0Zy4tLzeU2ATrBoDmDhx3PPAnZvKMrBz/G4/HUf2Oxsm1jeWM+kh/D8n/oPrbqA96Wwfpy5Yyfiu31+ZzU6yxCx1cphylUfu4MBQPIcILl83vNQLI455M2SNBzJilmYVc09JxlGJEMJK+hbjusv6JNHcdJ/c7ytOh+8FBHJ2l3DUlXmkREREQsaNAkIiIiYkGDJhEREREL9faeJhGR36qmke33arH7NPj9NOwZiuxBq2TxFsug5XwVKaFQWds0jcbQMBoFwO+nqfzctdT8Efv5d8s9TQnPD1o0l3u+UdXbA4drqxl5ZmN9pUGTiMhuUuL3I+o4+H9rNu3pqtRbhQ3CWNoKGLp2MxpuW78T8wextAVw8ppNaLht3S6oodSVqOOgmD3ot57RoElEZDcpCAXw6H4tkVnpW4e60pQuFGqCNgBmt2iCaDQCoLZXmpqiJYCXWjVFtPzbmbrSZDPB7r3SBADFfh8KyLeI65tfV21FRH7lCkIBFFQ69XqPHPA6aOLvhl6XUZv32+xAEG0AbAoFUeQLAahd5EBWIIiWADaFgyj2V8xfDyIHEiwawtvyaRXJBLwfkeXvJertoKk0Gkeyhp7MY0e8ZYqwvmGT08QH9eRflz5zPg07SWWEWM4TwU7kXjM7UDcnc5MEO5GT9Xs90bIJEklzxhGrP+D9REZPxGx+j8tnAwKWsWTVAORSSpRkTcXj5nJ+pchYTPtZgtz64ZA2pN2oLt4NWQ4Ta6NKgx5/wJ/6bwB+m8Wn7YOK9vQ5Tmow5bKrKLyRSDlsRi3eZvd4JYi1Aa0fG7yTpVshOU08G898rJu2wbb+v64PE0VERET2EA2aRERERCxo0CQiIiJiQYMmEREREQsaNImIiIhY0KBJRERExIIGTSIiIiIW6m1O09qff0EwsJPV8xgu4zVbxobXDCKWR5GVlWksp9kxJC+DN7HF9pFJkmQbaZgcaeMEWz7bR57zecxtnJPTwLwAC8ldnIXFk6BZDpS3YEebyDQWKliRGr2z81N1EJxoRHYhPV+RYx3g/Yj1ZcdP9mPl5Vf87rqp32nCUOVtdCr9t/x3H6m/n9TP5ihh5xMfWQc730Xj5sCuSIzkjZF+HCeJ5awPxGLm+ln1czJJPG7eBj/pywlDJhvLY6ugK00iIiIiFjRoEhEREbGgQZOIiIiIBQ2aRERERCxo0CQiIiJiQYMmEREREQsaNImIiIhYqLc5TQXbIggEqs99YJkZPpIHwXJLWHaNTUyTQ/Ii2DqCfvP8YVKJzVuLzOs3lvIcJq/RMmVYO5tXwnJRWAZQjOSWxEhuR4jsI9bGAb/fWF4SMecH2WBtxPY0jfChGUFkAaQf+T1mYQEWxytdBpmAnZBoLpx5H7FzRSBg7kc+Un+aRwaLPC4fy/ixX37F9iaTbiprjZ6zKx2ryfIjL5lIpl4Psowksn022PnKTZrPJ1GSc1RYHDGWbyuOGsv5NpLzGcvFI+fTBNl+gLchSB3YOdV0PowbMpwq05UmEREREQsaNImIiIhY0KBJRERExIIGTSIiIiIWNGgSERERsaBBk4iIiIgFDZpERERELNTbnKZE0oVTY7gHy9ww50V4zXlKWgQ1+ViuCSmPk+Esj5Qw1zHAMoZIsEqCbZ9F9gtbB9uPFgE4xmKWpeU45vK4S3JZWK4JyS2Jxdn284ihQNCcWxIKmk8BMZIdw7KsWLYLi2Vhx2Jd5IWxY4FhGUJBkqPEMtloGxhLeT+04bBzIskDIzFJcCq1geO4qf9W/O4jx6JTaRt9lf5b8XucZAix7QuwDQAQi5tz1bZu85azFGfnZHq6YLl35mOZnQ/9pJ87Sd6GLtmIOMvmc3Y+L8ziLQtALa80jR8/Hn369EHDhg3RvHlznHLKKfjqq6/SpnFdF2PHjkXLli2RmZmJo446CitWrKjNakRERETqnVoNmhYuXIjLL78cixYtwoIFCxCPxzF48GAUFW1Pn544cSImTZqEyZMnY+nSpcjPz8egQYNQWFhY55UXERER2V1q9fHca6+9lvb3E088gebNm2PZsmU48sgj4bou7r33XowZMwZDhw4FAEyfPh15eXmYMWMGLr300rqruYiIiMhu5OnD/K1btwIA9tlnHwDAqlWrsG7dOgwePDg1TTgcxsCBA/Hee+9Vu4xIJIKCgoK0HxEREZH6ZqcHTa7rYuTIkRgwYAC6desGAFi3bh0AIC8vL23avLy8VNmOxo8fj5ycnNRPmzZtdrZKIiIiIrvMTg+arrjiCnzyySd47rnnqpTt+E0E13Vr/HbC6NGjsXXr1tTP6tWrd7ZKIiIiIrvMTkUOXHnllXj55Zfx9ttvo3Xr1qnX8/PzAZRdcWrRokXq9fXr11e5+lQhHA4jHA7vTDVEREREdptaDZpc18WVV16JOXPm4K233kKHDh3Syjt06ID8/HwsWLAAPXr0AABEo1EsXLgQEyZMqFXFfI5TYz6Jj+TrJHhghZHfb86bCIf4IC8YMjctyzVhuSQJMn8kYs4EYU0UIJkbmSRbJmaxD1iGD9tGlgXF5k+QHCS2j1j8TTgUNE9AE3Ys+jGpRCRizo6JRs05TOxYYjlJLunHLIPIJbkrQXKsAkCC5H2xDB/WDxiWt5XBsrTIsciw/Bm2D8qm8ZZ5xvp65fNdRR6Q4/hSr7vsWK5UXvF7IplM5fqwbWRRXfG4+TgBgI1bio3lJVFzDlI8YW5jP80DM5dHYyRTjWVtkfUnSf1tsEyzjLD5nMrOyVHDfnRpgmOZWg2aLr/8csyYMQMvvfQSGjZsmLpPKScnB5mZmXAcByNGjMC4cePQqVMndOrUCePGjUNWVhbOOuus2qxKREREpF6p1aBp6tSpAICjjjoq7fUnnngCF1xwAQDguuuuQ0lJCYYPH47NmzejX79+mD9/Pho2bFgnFRYRERHZE2r98RzjOA7Gjh2LsWPH7mydREREROodPbBXRERExIIGTSIiIiIWNGgSERERsaBBk4iIiIiFnQq33B2isVjNuREkc4PlODk+cxZE2/YdjOUXX3qZsRzY/jy+mkRi5vycf700x1gejUSN5T4SPMLayEfaKKdRA2N5cUmJsRwAXv7Xa8byjRs3GctZdoqfZQCR7zWwfB+mphT8CvnNmhjLszJ4HhjLz/H5SNYUySYJkgwhlt3SpEGGsTwSM6+/oNjcz23+2RckpznWBixyLJFg+Tck44f0M9LENNsGrrcsLABwyDJY1JOPbETl8opMJZ/jpF53SQZQTUuveN0h66cZRDanApJJFk+Y+wHLrWO7iWX7sXN6gMwfIueCeJwdB7yfBYLkWGVtRPphwG9YPunjFXSlSURERMSCBk0iIiIiFjRoEhEREbGgQZOIiIiIBQ2aRERERCxo0CQiIiJiQYMmEREREQsaNImIiIhYqLfhltmZGQgEqq8eCypjGVp+vzmkq3lenrG8Y8eO5hUAaJTT2Fi+dds2Y/lZF1xkLI/HzEFpQRISFgqFjOXGEDDwsLpPP/3EWA4ATz5vDvDcXFhsLE+QsDgWWkjDzFj6JUOS1raQ7fOTgFIAaJvX1Fie0zDLWM7yO1mmXzxunmLD1lJjOetH4ZD5WE1YBOax0D2GBeqxbsJqyNowGjCXB0g/IZmENFQRsGhnEoxIg2YrLd/1lf3uum7qdbb6ymG+Fb/7/D743LLf2aEUiZn7yPrN5vM1ABRHzOejOElJpf0sSdoQ5m3wk3DLIGkk1k3CYXMYLwsjBvg5z6kxxrR8HSSFNGA639hlW+pKk4iIiIgNDZpERERELGjQJCIiImJBgyYRERERCxo0iYiIiFjQoElERETEggZNIiIiIhbqbU5TKOBHMFB9rgRLXQmTjCF/MGgsb9KksXn5GRmkBhaZFCQjiOUkZdI6mJcfi8XI+s1L95HcFb+Pj8cTCfOeZOkxLDeEZXqwYo8pTVRdLD9MQnhCZD+4ZDeVkjwwmvFjkaNkEo16XD94/k2S1JFlVdH1k47Gll8aMR8nYZJtk2T5O2T9NtwkO5bN/bRyplrF7275/ypeNc7vVv69Us5T+e8kvgcFxeY8sVKS41S2DnMd2bGaFTKf80vIsRiNkzYibRgjOVIB8qaQJPPbvCcE2Xs36ct+8p5i2kcOOxmW05UmEREREQsaNImIiIhY0KBJRERExEK9vafp12ZtQQRbSrZ/5sye81MSiRrL2f0+7PNldsMOu+cqFDTf88Q+W/5ua9X6Nwg5aJa568bprRok0bTSrV70UUL00XNe7/awfJhRJZtKgTXb9G8ZEZH6SIOmOrC2III/PP4JSmNebxn9bQv5gTuPyNwlA6dWDZJ4/8xSZJvv8a/3imJA/+cyNHASEamHNGiqA1tK4iiNJXH7ifuiQ9NMAL+FK03m0Qe70vTtN1+n/f3TNhcPfRLBtqiLZpnGWXdK0wwgOwhcuiCE/20uq9uv7UrT/k2SeHhQFE0zgDX8oeoiIrKbadBUhzo0zUSXvGwAgJ98dbKoJGIsTyTMg5pgkO06b5ED4VDIWB4gg6bEhh0Hdfwru3Xhf5t9+GTjr3PQJCIi9Vv9HTT5fGU/1aCVJhlCLGOoYYOGxvLkDqEfbvnfbjKZKguHzbXMyTEPSlgI0Y512FGMXEny+cxtwMYLCbL+wm1FaX8XlVT8txSF5eOpWNQ8cHNdbx93slwSh2RlWayAFJvr71Tph5UzaVz4LHJNHDINa0HWAiFyRTPBMo489uMgawNyLAO8DdixQIqRYHUgbVBTHl2FJDkOiiLmY539A6dqP6xmGtIIbAmsDdOOpfTDoGx+ljdWuR9VM3+EZBxFSA6T47Ar+0AoxOpIk+fMpSyXjp2P2Psi3cfmFcRJVpefdgLvxyJjOpbYcZaqg7cqiIiIiOwdNGgSERERsaBBk4iIiIiF+ntPk4iI7LUyA01Tv7P7rirf05QZaLbL6iSiQZOIiNQb8WQJEm4U++X8v51eRiIZRTxZUoe1EimjQZOIiNQb0WQBPtn4KAK+7YFutbnSBJQNvKLJgl1SP9m7adAkIiL1SjRZkDboqe2gSWRXqbeDpngsAdSQo+MjuSNuwpy2nZmZYSxvmmv+TNy3Q9q3U/634/OnyuIk0Zvdgc8SuVloRzhs3kaW6O33m3NJIhFzOGdJafql8UjEAZCBSKQUJYGyurMMH5r+4lTzu1Ppd5qj5DG8klbPW6iIzdwsc4zmJJEmSJIJWMSPn+ZIkewXUn+2fADwsfwbshEsXybgJ0GycfO5gB8F3vZxnIwnfA4fcPhgPh+QByDQQQ3bRholRcpZDpNLMtv4uQpwE94yybaSHcUGjiyzjfVj9p4QCpiHC/GEuf4s081mGT6Sl8W6SdKw/CTZf6k6WE0lIiIispfToElERETEggZNIiIiIhY0aBIRERGxoEGTiIiIiAUNmkREREQsaNAkIiIiYqHe5jRlZ2UgWEMuhI/kTSRI1kOMlDdpso+xfMcMo4qsGL/PlyoLBsw5SywRguU8xUn2SyBg3sZIxFyedEnmRjxuLP/pp7Vpf2+IhgC0xoYNG+HbWpajFY/HjMtwWTZK5fKK312XZljVFVY/mtPEMo5IbgpgkdPEQv9Y9gupI1u/xyQuWgHaRwC4ZC0skwzkWODdlOQo0Xwe8/JZllZxxHycZYfJ9oP3I5e0ocsyesg5neUksTaOxsznK7Z8q8w0crzyzDRzeYBUgrUB66c044hmpnkPIC2OmDMWk675fZVUAUFDP3Us3zd0pUlERETEggZNIiIiIhY0aBIRERGxoEGTiIiIiAUNmkREREQsaNAkIiIiYkGDJhEREREL9TanyS3/X3UcxzzWYzlOTtKccTTjiUeN5ZnhjLS/vysoy5/43xcrEF1TVrfehx5uriPJ9IjFzNkqSbINRUURMr85MyMUNHeNaNScp1FYuC3t7+JEZtl/i0tQGCkBYJHdUquUH6fSfyt+95bX5DmHia6ghr/dsh+HBfQASFhkn5iwdTg+cz+le9Bjvg7jI/UDQANoWB0SpJ/SGniLKPKM5dbZvA2wPK4AOZ+xctqLSRvSDCS2D+n2WWRZkTwvdjibMoQAXkfWD72ebeMkm49mqpHlAxbbSNqQ9XVTHeLKaRIRERGpOxo0iYiIiFjQoElERETEggZNIiIiIhY0aBIRERGxoEGTiIiIiAUNmkREREQs1NucppghE4JmbpDsFj8JewiHwsbyRo1z0v7OdmMAtiC7YQM0ahwEAMTj5hyj4m3mHKWGDbKN5cmkeRsyMzOM5YGAORMkGTfnQLkkH2j9ho1pf29xGgAh4JfNm5F0yzKceEYPyRBy3Eq/b/9vxe8eI4CscpJMaM7TjnljO2xEiOwjAAgGyCFMNsHHJiBtkGA5TCxHimYokXKLnCq2H/0B8m9HUgd/wLz8UDBoLC8pNWeysfoHQyQDibSRzWFSU2ZeBa9ZU2x2h6wgQSKEkqT+sThpI4tG4mczcj7jrWAsZUdCgmT7JWMkZ4rsA5YzZZfTZC6Pk/cllqkWMKzAUU6TiIiISN3RoElERETEggZNIiIiIhY0aBIRERGxoEGTiIiIiAUNmkREREQsaNAkIiIiYqHe5jQ55f+rnjlPIemSxApS7A+FjOW+HbJxfH63/L+BVFlxSal5JQTNZiEZPp7XTzJBiouKjOWbftmU9ve2QALIBbZu3YpEfAsAi5wmlgFUze9upd9pdAzLYSLVY/2M7UPfDuUVESI+p6ws4Of/ptkx6mlHfpJZ5vOZ+xFtwwTJ82IV9MgqWoVmQZFsF7IfwqSNWT9Ihs05TrEYaWPSBixLK57gWVfBgLmOrJ94jGSjbZiwyOsyoflAJP8H4PmALikHOZ+wY5XVkOUbsjZmxwHbBzyXD/CR9zXaxqQVXMMmupa5fLU6o02dOhUHH3wwGjVqhEaNGqF///6YN2/e9pW6LsaOHYuWLVsiMzMTRx11FFasWFGbVYiIiIjUS7UaNLVu3Rp33nknPvjgA3zwwQc45phjcPLJJ6cGRhMnTsSkSZMwefJkLF26FPn5+Rg0aBAKCwt3SeVFREREdpdaDZp+//vf44QTTsD++++P/fffH3fccQcaNGiARYsWwXVd3HvvvRgzZgyGDh2Kbt26Yfr06SguLsaMGTN2Vf1FREREdoudvqcpkUjghRdeQFFREfr3749Vq1Zh3bp1GDx4cGqacDiMgQMH4r333sOll15a7XIikQgike3PYSsoKNjZKu1xqwu2PwDpF6fY07IamG8Zop8Pl0bMz75j2D1NJSUlxvJtgcZpfxcHGnmqj639m2z/XJ0+S2iP39OUvoJOTTw+LE9ERHapWg+aPv30U/Tv3x+lpaVo0KAB5syZgwMPPBDvvfceACAvLy9t+ry8PHz//fc1Lm/8+PG45ZZbaluNeqVR2Iew38Gk97dUenVjTZPvHXIHVXnJl4wjkDQ/qHhnbSoFimLAw8d5GyzuaUUxYFOpx6efiojILlHrQdMBBxyA5cuXY8uWLZg1axbOP/98LFy4MFW+47+uXdc1/ot79OjRGDlyZOrvgoICtGnTprbV2qNys/148MRcFES2X3lo0jTX0zIbZGcZy/f8lSbzlbQpUx+q8logGUFG0nyFamet2eZD/5kZaJqx/bX6f6WpavmmUgdrtikJRESkPqr1oCkUCmG//fYDAPTu3RtLly7Ffffdh+uvvx4AsG7dOrRo0SI1/fr166tcfaosHA4jHA7Xthr1Tm62H7nZ278u2by5edDDNGrU0FjOBk27PnLAvP4G5bECu9OabT6s2bb9710/aGKLZ4MmDY5ERH5NPOc0ua6LSCSCDh06ID8/HwsWLECPHj0AANFoFAsXLsSECRNqX7GAD4FA9W8qAb85yyFBMzXMb2YOebf88nMeo/DLxvXG8pDPnAUVysgwlseT5itJSbKNHffd31juJs3ZMNGo+WO2om3bjOUAH7P4WHgK3c1kP1vmctTET9qYV48MqliuC4BEnFztMh8q1V7t2qES5uWTgSkdl5JG8pM+ELAYeLpkGTGSU8TqmCQTBMn6E2T9CXJF002Y1+/30xAkczl43hfHMtlYHcnsZB+wjCL2nsH2cXkl2ATmOpD5fexYY8cKyVmKkcy1GDnXsNN144bZ5gkA9Dykk7H8y29+NJav37TVWJ40ZJ7FyfZVqNWg6YYbbsCQIUPQpk0bFBYWYubMmXjrrbfw2muvwXEcjBgxAuPGjUOnTp3QqVMnjBs3DllZWTjrrLNqsxoRERGReqdWg6aff/4Z5557LtauXYucnBwcfPDBeO211zBoUNlNv9dddx1KSkowfPhwbN68Gf369cP8+fPRsKH5oyYRERGR+q5Wg6bHHnvMWO44DsaOHYuxY8d6qZOIiIhIvaM7UUVEREQsaNAkIiIiYkGDJhERERELGjSJiIiIWNCgSURERMSC53DLXSUWi9eYBRYhjwhxSOCdQ4La1q1bayx/4eknjeUAD0vjYdXmpLDO3Q4yltNgRBKo5ydBaPGYeR+0zm1iXj+ANbmNzROwNqJrIKF/pI1YGByLsvtp4xYyhVk4GLSYioX2kcC2uLnY5/MWaMfaiOXQxskEDqlf2UpYmC2ZnZS75FiNk33A1u9jYby0n3p/EDQ7H3mLE+ZLYCGs9JxP129mEzTLsL7uJ6HN7GCLxMjBTEJQWThmdth8PmL7qLCIP0Lr3+8sN6+DtREJgg0Y9iMLwU3VwWoqERERkb2cBk0iIiIiFjRoEhEREbGgQZOIiIiIBQ2aRERERCxo0CQiIiJiQYMmEREREQv1NqcpIxhEMFB99fyk1vGEuTwWJ8krJETJTZIVAEiQdbAcpyjJUVq8aLGxnERmwE8mCAXMeRgJkqsSZ20MoFFWprGcZUWx7JQ4yS1xyDY0yg4byyMx8zaGg+aOmiD7mOWeADyHySV9mcXPuKSf+gJkH9F8H3P9veY4AbyNWB5XkJSz+cOknIVlxRPm8w3LUGL9rLCI5PuA96Oc7AxjuY+cT7xKkn1MIorAkpx8Nhk+ZB0uWQTbz26c5CyxXDu/uQKmDCOAv6ew4yTh8j6QZOcrGt5H9qOh3DKmSVeaRERERGxo0CQiIiJiQYMmEREREQsaNImIiIhY0KBJRERExIIGTSIiIiIWNGgSERERsVBvc5oisQSSNQRbxEpIbgkJXHBIJodDAi+iLAgKQJxkowRYtgqJo2B5FqQYLgv1SLA2Ms/uZwFA4JkbLBuGbSTLVmFZVKVR835m+TcsP4fGgljkNLF1uCS/JkqyrFgsSohkr/hIdgvbxawFMi3yfyLkeGX5OEFSCV/SPAHLXEuS8qxQ0FgeY4eJuZiWAxZ92WM5j99heV9mAdJPWHZfzOKcz+oYJvvRiZmXz+rAmpidkZMk8ywSI21A4w95T2PHO8vmY/Mbs6Rcu2tIutIkIiIiYkGDJhERERELGjSJiIiIWNCgSURERMSCBk0iIiIiFjRoEhEREbGgQZOIiIiIhXqb0+T4nJrzlkgYQzRuzp5heRQs38ciYYdmRbl0GeY6sm1gXMfbeJnN7ffzNmLtGCAZPyycJRonOU1k+SyPi80fDpoPr1KfuZ/WRTZMkrQx6+ouD9AxFtN8HJL9wvLIYiSHCgBcUsdQwLyfgkHzNvhJIxaWRI3lcbKNAZAsKpJdwzLTkhZtyLPtbI73mrGzWZwcC40aZBrLi0rN+4BlCNlkDDEs1y1J8sIclktHdgFZPZKueQKWR8ZynByLRDD2vpJBzqk+0g9NbUTf9iums5tMREREZO+mQZOIiIiIBQ2aRERERCxo0CQiIiJiQYMmEREREQsaNImIiIhY0KBJRERExEK9zWkyYZkgXiM1HDJ/guRZADwvgubfkOwUmq/jLV6H5uOESbYNy+exqQOLzSiJxozlPLODZAyRfJtIjOSBsZwlj/UDAD9p5zjMdWRZVKyfspwlugVkG/0kTyxp0UZBkqeVFTL35QjJzymNk2OV5Y0lzG1MiuEn5XFSf5sMIo8xTOA9wVwHOjfZhkyyj4tLvWX7AUCIZAix3Dk/uYbB5o/Fvb3xsffVUnK+Y+8JpRFzVhYA+Ei+H9vP7L05buhJcRZkVU5XmkREREQsaNAkIiIiYkGDJhERERELGjSJiIiIWNCgSURERMSCBk0iIiIiFjRoEhEREbFQb3OakolEjRksDslqCPrN2TUuyXJIkIwkv0VoCcvUoLkfNEPIY8YPzZEyY9tnkzHEREm+jEPSW4IBcx3jZB/EST9wyfpZEyTI+n0kJwrg2TA004wsn+Wi+EiOUoAci6yN2D5wXN7PWObYtlJzfoxDjrUgyacpKSE5TyRvjJ5LSBuzfsbOJfUBTSAi+5jNn5Vh3oeFRSRzzWItLC8sljDvB3Io0GOdYTlFbOnsfTEnO5PWwWXnXLaNLGPRsHyLKC4AutIkIiIiYkWDJhERERELGjSJiIiIWNCgSURERMSCBk0iIiIiFjRoEhEREbGgQZOIiIiIhXqb0+Q4To25Ey4JY2A5TCz/huX/+EjeRnkljMXxpDn3g0VGmJNZvOc0JUj9o3Fz/UMWPYutIxKLG8tZLkg8QVOIjKU0Sovl25D5A36SoWSRu0LrSPpyZkbYXAeSlcUykNg2svQXtgdtslV8LLuFlPtsA1xqwKKkfKQfsfwcGnZF+C3OZw7J46Lzs3M2nZ+vwYSdD1kGEsu6AoBMmgtnXkk07m0/020kbeRnmWrkfTNJ9qJNdp9L1sGy9fg53ZTTZHec60qTiIiIiAUNmkREREQsaNAkIiIiYkGDJhERERELGjSJiIiIWNCgSURERMSCBk0iIiIiFupxTpOvxmyQYJDkLJFckyQL5XDMeQ2ORZ4Dy/UgNYBL1pEk4S8sESNAsmFKIuaMpBjM+T1Rli0Dvo20DUjuB00IIhOwTBBWP4btgyDNOAJKIjFPdXCSJLuFZQzR/Bt2HHjL7wmQXBcACJIcopjHvDB2vmFtFAqaT8OsDfm5wlsemc062LHEsvFcGjhG+hnJ5mMZSX7S0bPDIWM5AESj5n7SJCfbWJ4gOU2sH7D8QBYrxw42lqtH3jbpPgAAl+4nc5YUi7pyDe+bprLKdKVJRERExIIGTSIiIiIWNGgSERERsaBBk4iIiIgFDZpERERELGjQJCIiImJBgyYRERERC/U2pykSjSJRQ2YDy2pIkNwUh+amBD0tH+AZO6Uxc76Oy3JJyPozSGYHSzFi+Tcs+yUWN2eWAKgxh6uCnwSLhAPmfhAgbRAn+zGeMG+Dn7QRy+dh+T+lUR6gw/JvWL4NyxxLkn20ZVuxsZztwwySf8Pyf5IsHAY8l81H+lGDrLCxPBIzZ5axYy1G+oGPtCE7nzE25zOWO+eQbWR1dEhIEM1cY+sn5SwnKpZg+xgIkHN+kuZ5kRWQCVg/YVlbcbKNNJaO7ONSkmMFWOQLknIvcV+2h5GnK03jx4+H4zgYMWJE6jXXdTF27Fi0bNkSmZmZOOqoo7BixQovqxERERHZ43Z60LR06VJMmzYNBx98cNrrEydOxKRJkzB58mQsXboU+fn5GDRoEAoLCz1XVkRERGRP2alB07Zt23D22WfjkUceQZMmTVKvu66Le++9F2PGjMHQoUPRrVs3TJ8+HcXFxZgxY0adVVpERERkd9upQdPll1+OE088Eccdd1za66tWrcK6deswePDg1GvhcBgDBw7Ee++9V+2yIpEICgoK0n5ERERE6pta3wg+c+ZMfPjhh1i6dGmVsnXr1gEA8vLy0l7Py8vD999/X+3yxo8fj1tuuaW21RARERHZrWp1pWn16tW4+uqr8cwzzyAjI6PG6Xb8poTrujV+e2L06NHYunVr6mf16tW1qZKIiIjIblGrK03Lli3D+vXr0atXr9RriUQCb7/9NiZPnoyvvvoKQNkVpxYtWqSmWb9+fZWrTxXC4TDCYfNXekVERET2tFpdaTr22GPx6aefYvny5amf3r174+yzz8by5cvRsWNH5OfnY8GCBal5otEoFi5ciMMOO6zOKy8iIiKyu9TqSlPDhg3RrVu3tNeys7PRtGnT1OsjRozAuHHj0KlTJ3Tq1Anjxo1DVlYWzjrrrFpVLOECNebWkbA6kKA0FtJVGjUHT7JQRJuVxEkKFws6c0moX8ymjgYJUn+WA0aD0MAD7ZKkjeJJcy1YIB8Le6NhcaT+bP2MTdYaC25kAaIBEi7ZtGkzY3nCXWcsT5LgRhZ+yfqATSOxvuySUD8fC150SXglC3ol20DbiDQRC6IN0yBcIBg0B4CykFV2PmDbQHIj6TayflQX/czxmduInZNZaHOQtDF7X4qSY7GmMOkKAVI/GrLKgngBBMkkLPSZVSEWr3k/27xnAbsgEfy6665DSUkJhg8fjs2bN6Nfv36YP38+GjZsWNerEhEREdltPA+a3nrrrbS/HcfB2LFjMXbsWK+LFhEREak39MBeEREREQsaNImIiIhY0KBJRERExIIGTSIiIiIWNGgSERERsVDnkQN1JRTwIRiofkxHIoqQJGENLI4hljBPEbDIm4iT7BeWJ8HydxJkK+Kkjn5SAZaLwjItfGwDASRc8zb6SP4Ny1kKkvwZlpUV8psPD9YGLtk+cw/hfQTg+TIOyfiJRKPG8tVr1hjLQyy7pYZjuEKMZSSRfmyTZcWmSZDj3fF5zPgh2LHCcpoSJJ+H1s8qU42Us/MFWwnL0iLr9yoUMB/r0Tg7Wvk52+8zryMeN8/vNXsvGDSvP0D2QYDl1rHzMcmxAgA/2dMx0pcjHt53bc63gK40iYiIiFjRoElERETEggZNIiIiIhY0aBIRERGxoEGTiIiIiAUNmkREREQsaNAkIiIiYqHe5jQlEi6cGgKZHJbawUNDzLOTCRIWuSwsUSNAMoSSdAlk/SQzxKVtZJ6fZXZYDceTLCvKXB4k5UmSG+JnGUBk+YGgOXeE5ZaURMwZSa5N/g/LJCOLSJBck63bSozlTXOyjeUsg8ghCUAsOsUqD4y0I8sQollRrJ855Fhn/SQaN5azfsaaKECytGyW4TVIic3O+jHLokqQ82E8TnLvSJYXABogWFwaM5azPC52OmDnK8dr5hnrZ2T2qEXOVIgEBPoD5nNuiLRhNFbz+Y69paWms5tMREREZO+mQZOIiIiIBQ2aRERERCxo0CQiIiJiQYMmEREREQsaNImIiIhY0KBJRERExEK9zWmKu26NuRAsy8FHclFYrolL8oPiJMMI4BlDPErKWwZRkGRyxEluCc/P8ZavA/CsKp4FRTKKvEVd8Swt0obRuDkDifUBli0D8OwVlv0SyswwlmdnhI3lybi5jn6WAUQaIUpypKx2McuyYhk/JF+G5uPQrCqSE0XOZ6QYcVJ/mwwilpPElsC20WO0HpLkYGfnywQpT1oEUfk8XoNg+4n1I7aP4JqPJZonRhZfF/mGcVaHhDmzjB2rPn/NOU8sjy01ndVUIiIiIns5DZpERERELNTbj+dERHZGIjuJZMb2jwL4IzjII4d2OEv6Ig4CRfr3psjeSIMmEfnNSGQnsWFoEdzgrluHEwOaz22ggZPIXkiDJhH5zUhmuHCDQOO3MhDYWjao8XqlKVDpIaHxxklsPrIEybALFHmuroj8ymjQJCK/OYGtPgQ3lQ12+KDJ/K2kYND8ZHUR2Xvo+rKIiIiIhXp7pcl13RrzlCJx8z8d/T5STvMu2PJtUojMeIYPyTAKmP/1myTb6CMZRA7513mctBHLOLLB4mPiJCPIR9ogxNqAZcew7BrysQ/LZbEKu2KZY6SOsZg594S1Acs2ockspIJsHyZ3bMOKLJikm/qdHa4sfyZWqZ9V7LN4IgknXrZglp8TJ9kybH4/ywujfYBkEFnkgbH94DVnifVTtnzWhvRgopFw/GAMsnMy2w8kk4ydU30+8/ot0riMpSxnicUc2bxvsvdFJsbeVw3n3LjlunWlSURERMSCBk0iIiIiFjRoEhEREbGgQZOIiIiIBQ2aRERERCxo0CQiIiJiQYMmEREREQv1NqfJ7/PBX0PwQ5LkSdDMD5YXQTKSXJfnmiRI9gnL7PCRoCQ2v8MeQspSkmkIEamfeW4rLD+GZQSxVBCWCeL3mCFUU/+twHKoWJ4YAPj95mliMdIPk+Y2YN2ARMPAcc31C5BjkWYM7VBe8XfSdVO/s38Z0oyfSsUVkzrO9t9ZHVk/ZeVsJwTI/EmS7xMK8rcBn8f9xI5GniHklbesKtciNI11oyB5X4nR/ECWB8a2wVsr0/OdYz4ZWMXO0TqYy9l7u6nU4nRbVge7yURERET2bho0iYiIiFjQoElERETEggZNIiIiIhY0aBIRERGxoEGTiIiIiAUNmkREREQs1NucpoDfQbCGDJqYOVoGoQDJWSLrZhlJNCwCAIn9QDJJcpZIHeIkYwgs+4WgGUNkfrZ9gH0uRo1INoxLVhAk+TVsC/ykHySTJKOIrZ9m3/BsFpB+xDKK2D7i+T3m+dkWhkgbxXb4O+kvW6Lf70u1bywWNy6D5mVVqoNb3q/dpJv6nXZ1jx2dZb6xfuiS+dnyAbu+6InHnCevi0+wnWix/fScTk7JDbPCnqpQHNnxaEgXJ/Xz+1gunTmHqYSs32YP+snxzrKiwgHzkMaYP2jZx3WlSURERMSCBk0iIiIiFjRoEhEREbGgQZOIiIiIBQ2aRERERCxo0CQiIiJiQYMmEREREQv1NqcpmXQN2RnmPIUYyyUhGUcso4hl2wBA0G/OtGABNYGAef5YwrwNrI4s74JFVrCMIVY/AHBJPIzXOoZCQWN5w8wMY3lJSYmxnP2LIxw0H16lcXN+kE3WVUaIHcLmZZRGzfspQI4FY+4JgCRZf5BkDMXJ8nfsIxV/+xwn9XsWaaMoCWqKJbe3UUVekeu6cMs7MDvWWMZRNGY+EJIsSIrk0vnIPmTnmrJlsEwyUkd2ymR5YKyNSTnL/wmQjKI4CwcE38/xuLm8kGSuOaSReNKVeX52yo673t5z2D4A+Hsvz4Vjx1rN51yafVhRB6upRERERPZyGjSJiIiIWNCgSURERMSCBk0iIiIiFurtjeAiIiJ7SigcRqNGOXAcB5Go+WG0gN0N9V7wG8G9PXSY3UTNyumXjyweIu/1RvCatjHpuigs2IoSi/3IaNAkIiJSznEcHH3cYPQ5tD8CgSAcx6EDhrL5dkPlPKBbwDfRE7v22VWN6CIWi2HRe//FG6/Ps9qfNdGgSUREpNzRxw3GEUcfgyaNm8Dn98EBv8oCWETR8EyAXYtdiNrFoyabqJ5dKZlI4qhjjgMALHjt1Z1eTv0dNLluzZcTPe5bli0TN2Q5AHY7n11FTCRJ5kXCvACvHZCdA9gBVHOGVhmb+vEpPJ5lSPBISUkxWbz5UjHLgYqQbBfWRiwLCwBA9kOUha+QJo6R7JgkyURj+Toxkv3iJxlE0R3WnyjPTkq4ScTdsnWTeBx6PkhUWkeyfPlJN4mKpgn6zafRJOmmLAsrTgLNWD9Kkj6QtMincV3zNvJBhbd8HbZ0Nn+C9OOK8104nIE+h/ZHk8ZNEKyUs1Yng6Y9zeM5n26d10Gj1Up2fna/z4d99mmC/ocNwNL330U0Ekkrj5PcvAq6EVxERARAw0aNEAgE4bP5B4v86vh8fgSCATRs2Gjnl1GH9REREfnVchwHjrPrPymTPcQp+38O+yjIQIMmEREREQv1954mERGRemL9tji2Rmq+/6su72nKCfvRvMGuf3vufOCBmHz//TjuuOOqLV+yZAnOv+BPWLzofeQ02vmPtH5LNGgSERExWL8tjkte+hER9q2COhIOOHj0lDbWA6eZM2di4l13Y8niRQgEyuYpKipCv0P745BDDsazzzyTmvaDDz7AOeedh3mv8m+Qde/eHW8vfAsNGzYEAMyeMwfj75yApYsXWW/LqNE3YO7cucZpvvz8c+vl7WkaNImIiBhsjSQQibu47ohctMmp/luzdXWlafWWKCa8swFbIwnrQVPffv1QXFyMzz5bge7dDwEALFu2DM2aNcOnn36GkpISZGZkAgCWLF2K5s2bo0P79nS5oVAIubm5O70tADDmhtG4duQ1qW/PHTFwIMbdcQeOGDBgp5aXSCTgOI5VWOauoEGTiIiIhTY5QXRqGq62bE9GDnTs0AHNmzfHkqVLUoOmJUuW4phjjsGSxYvx0Ucf4bD+h5W/vgT9+vZNzbt5yxZcceWVePe//0Xz5s1x/XV/xTHHHJOatuLjuS+//BI3jLkRAND5wK4AgMuHD8eVV1yOaDSK++5/AP985RUUFhaiU6f9cO2116Jf375o2LBh2ZWqShfpGjVsmBqMPfHkk5g9Zw5+/PFH5OTk4OijjsJf/nItsrOzAZRf3Rp/JyZOmIC777kb3333PV5/bR4mT3kQhYUF6NKlC2bMeA6RSAQnnXgCbhwzBqFQaJe1tW4EFxER+ZXr27cPFi9ekvp78ZIl6Nu3D/r06YPFS8pej0ajWP7xx+jXr19quikPPojjjz8ec+fMxsAjj8Rfr7seW7ZsqbL8Ht2744bRo9CgQQO8s/AtvLPwLVz4pwsAADeMuREffvQRJt1zN16aOwe/+93vcMklf8Z3331H6+3z+TDmhhvwz5dewp3jx2HR4sW4++570qYpLS3BtEcewW233YZ//vNl7LPPPgCA999fhJXfrsT0J5/APXffjQVv/BtTHnywli1XO7W60jR27Fjccsstaa/l5eVh3bp1AMoCwG655RZMmzYNmzdvRr9+/TBlyhR07dq11hULBfwI1vAsHxZUFiOBe+wZPj6HPYPHvHwASLhsHebxapIG2pHxrsd/9Tik/l5i6CuwUEH2rVC2iQmyfJIvCsdnnj9WXGpePwmeZN9rDljsw1iC1JEEF/KebMZqSCMPySV21oSJHba/4u9EwoWv/HcWwMn7oVPld5/jpH7PCJqfOcb6QZTsI9rPyfZ5DY4sr4W52GNwIg3brUUAaXVYH/CXN3LZf8t+r/UZlB3uXrMfyQR9+/TFnXfeiXgsjtJIBF988QX69OqNZCKJp595BoCLjz/5GKWlpejXt09qgaeecjJOOvEEAA6uGTECzzz7LD779DMcccQR2N4WDkKhMBo0aAjHcZCb2zy13h9++AH/evVVLHzrTeQ1L3v94gsvxLvvvIs5c+Zg5DXXlFW/8vZvb2acf/55qZdbt26Fq6+8EmNvvRU3/+2m8u12EYvFcfNNN6JLly5piwgFgxh3x+3IzMzE/p064aorr8DEu+7G1VddVf3HdxX9yEXVTmf5nlbrj+e6du2KN954I/W337/9hDFx4kRMmjQJTz75JPbff3/cfvvtGDRoEL766qvUjWQiIiJSt/r17YvikhJ8+tlnKNi6Fe3btUPTpk3Rp3dvXH/99SguLsaSJUvQskULtGnTJjXfAfsfkPo9KysL2dnZ2PTLL9br/fzzz+G6Lo4fMiTt9Wg0hsaNG9P5Fy1ejGkPT8M3336Lbdu2IZFIIBKJoLi4GFlZWQCAYDCIAw44oMq8B3TujMzMzNTfPbp3R3FxMdauXYtWrVpZb0Nt1HrQFAgEkJ+fX+V113Vx7733YsyYMRg6dCgAYPr06cjLy8OMGTNw6aWXeq+tiIiIVNGuXTvk5+dj8eLFKCgoQJ8+fQAAubm5aNW6NT786CMsXrIk7aM5AKlv21VwHIdemass6brw+/2Y9eKL8O9wdadi0FOTNWvW4NJLh+GM00/HVVddhZycRvhw2YcYc9NNaY81ycjIqNU9Y7vy/rJa39P09ddfo2XLlujQoQPOOOMMrFy5EgCwatUqrFu3DoMHD05NGw6HMXDgQLz33ns1Li8SiaCgoCDtR0RERGqnX9++WLJ0KZYsXYq+5YMmAOjTpw/effe/+PjjT9CvX1/DEsyCwSASOzzLsEuXLkgkEvhl0ya0a9cu7Yd98+6zFSuQSCRw/fXXoXv3Q9ChfXus37Deuj5fffklSku33yax/OOPkZWVVe2FnbpSq0FTv3798NRTT+H111/HI488gnXr1uGwww7Dpk2bUvc15eXlpc1T+Z6n6owfPx45OTmpn8qXDUVERMRO37598eGHH+LLL79MXWkCgL69e+OFF19EJBJJ++ZcbbVq1QrFxcV4//33sXnzZpSUlKBD+/b4/Ukn4bpRozF//gKs/vFHfPLpp5j2yKNYuHChcXlt27RBPB7HM888i9WrV+Oll1/GzOf/YV2faCyGMTfehG+++QYL334bD0yejHPOPmuXxhHU6uO5IZU+szzooIPQv39/7Lvvvpg+fToOPfRQAFUvi7mua7xUNnr0aIwcOTL1d0FBgQZOIiJS76zeGquxjH3ByPZG8NVborWs1Xb9+vVDaWkpOnbsiGbNmqVe79OnD4qKitC2TRu0aNFip5ffs0cPnHH66bjm2muxZcuW8siBKzDujjvw0MMP4c6JE7F+/c9onNMY3bt3x8CBRxqX16VLF4y6/no88uijmPT3v6N3714Yec0IXD9qtFV9+vc/FO3atcXZ556HaDSKE084AVdeccVOb58Nx/X4NahBgwZhv/32w1//+lfsu++++PDDD9GjR49U+cknn4zGjRtj+vTpVssrKChATk4Ohhx5KIKB6sd0Xr89FyffOGLfjrNpMpcdQGwB7JtjbCRNjlC6fo/fZrHpVGwZ/Ntz5glCNXz70nZ+2sakn+2Ob88l6Tez4sbyXf3tOR/ZhiB7mjzpBJFY+kcFsaYJbB5aiiazMxDcVLb/vX97bvvv8WZJbPlDFI1nhRDYWFb37LA5E8brt+fitbi/pDoumT8ro/qwxsoaZmcYyzPIseaSfsDOBX6y/Bjp579sLSbrL6tfbvM8XHL51cjLa552f866wtieTwT3vGr6XVZPS/f87UCAfoOt8jl71OgbUFBYgAcnT7ZZMoCyb1n+/PN6PPrg/di4/ue0sng8jn+/txRbt25FI8MjYzyFW0bKv9Z4xBFHoEOHDsjPz8eCBQtSg6ZoNIqFCxdiwoQJXlYjIiKyxzRvEMAjJ7c2P3uujq40Abvv2XNSe7XaK3/5y1/w+9//Hm3btsX69etx++23o6CgAOeffz4cx8GIESMwbtw4dOrUCZ06dcK4ceOQlZWFs846q9YVi7sunBpGnSRCiGZ2xBMkoIddILD4vNRH/oUci5n/ZcRG5a5rnj8YNP/rccdvOVRdPsuJ8v7thACpw65O2GUXDB3ST2LkCgK7ChQgfcQJ8O0P+NnlOPMhHiH9kOWBsUPBz/ahx2M5sMOVqqS/rM39fl+qLEHW4WNXTWuoQ8Vid7wxtsp0pJF8fvNVFHacxMk+ZG3MzgVlSBt5vXTtUZx8+hCNs6t1ZRsQSyRRUde0GrtA8+wAmmfXfDx5Pl95Pd15/fjC4y7iOVMWn9DUIqzKhQu4O16l5Fc0XddFSWkURSWRtDI6LihXq0HTjz/+iDPPPBMbN25Ebm4uDj30UCxatAjt2rUDAFx33XUoKSnB8OHDU+GW8+fPV0aTiIiI1Jk7x43bI+ut1aBp5syZxnLHcTB27FiMHTvWS51ERERE6h09e05ERETEggZNIiIiIhY0aBIRERGxoEGTiIiIiAUNmkREREQs1Nv0LJ/jg6+GjJhYouYoewCIR825JSxhd8fslyos8iZYCjBLKWYpvv4a0tIrLYGUekz0Jm0QINkzAA97Y23IE8VZIrh5fprDxNJrSYZSKGROkg5Z5DSVRszHQjBI2oBkRfH0fHM5mR0+lpXFkqTNs1stg/Wzyn05WX5q8Pt88Je/niDLzwyaj9UY2YesF7AcKBa6aJMPRIMbyfzslEmfMEDmZ5loLDePha47PiBQtA7+yFbzhAa1efZGIpyDeHYtHzpLlr9mzRocN2gwZs96EV26dKlSzvZxde8ZXQ7sigfuvx/HHXcsHBbWZZFj5Xh8X7KJhXMcIBhwEAqkHzc+y6CqejtoEhERqQ8CRevQ4dUz4UuU7pb1Jf0ZWDnkuVoNnEbfcAPmzn0p9XdOTg4OOqgb/nLttTjggAN2RTVr9OOaNThu0CDjNJdfPnyXPyduV9CgSURExMAf2QpfohQ/HToW0Ubtd2oZtleawgXfoeXisfBHttb6atMRAwbgjjtuBwBs3LgR993/AC67bDj+859/17a6nrTIz8c7Cxem/n78iSfw7rvv4vHHH0u9lpWVVatlRqNRBMnV+d1BgyYREREL0UbtEdmnpqs23h5KXBdCoRByc3MBALm5ubj4ootw7nnn4ZdffqkybSKRwN9uHovFixdj48aNaNGiBc4680ycd+65adPNmjULTzz5JL7/4Qfk5ORg8OBBuOnGG6td/5QHH8SM557Do9OmpX0EmJWVBX/An6rbDz/8gJvHjsXHH3+CkpJidOy4L0ZeMwKH9e+fmueY4wbhtD/+Ad//8APeeOPfOPbYY3DlFVfguEGDcffdd+GZZ57F559/jjZt2uBvN92Ivn37em4/Gxo0iYiI/MYUFRXhlVdeQdu2bdG4cWOUlJSklSeTSeTn5eHvk+5BkyZNsPyj5fjb2LHIbdYMQ4YMAQA8N3Mm7pwwAdeOHIkBRwzAtsJCfPjRR1XW5bouxo0fj3//+9949umn0b59e2PdiouLceSRR+Lqq69GOBTC3Lkv4bLhl2Pev15By5YtU9M99vgTuGzYMFx26aVp89999z0YPep67Lvvvnhy+nQMv/wKLFgwH/s0abKTrWVPgyYREZHfgLcWLkSvXr0BAMUlJcjNzcXUqQ/CV82XBYLBIK68cvs9RW1at8FHy5dj3uuvpwZNUx96CH+64AKcd+65qRvBDzrooLTlJBJxXD9qNFasWIEZzz6L/Hz+kWLnzp3RuXPn1N8jRlyNN954A/95802cc/bZqdcP7dcPF134p9TfP65ZAwA466wzMXjwYADAzX/7G95997+YNWsWLrn4YrpurzRoEhER+Q3o27cvbv7bTQCArVu34rmZM3HppcPwfA3PjZ0583m8OGsWfvrpJ0RKSxGLxdC5/GO1TZs2Yf369Tj00EON67xzwkSEgkE8P3Mmmlhe6SkuLsaUKQ/irYVvYf36DUgk4igtjWDt2rVp03Xr2rXa+bt37576PRAIoGvXrlj57UqrdXulnCYREZHfgKzMTLRr1w7t2rXDwQcfjNtvuw0lxcV44cUXqkw7b95ruHPCBAwdeioefeQRzJk9G6eeeipi0SgAIJyRYbXOw/r3x8/r1+Pdd9+1ruddd92N+QsWYMTVI/DMM09jzuzZ2H//TojF0uM3MrMyrZfJokXqSr290hRPJAyhC+bG8bGcpWSCrJtkJJH8HQBwasiYqhAImPNzXJjLAySbxU/agOXvMCzfJ8GCT2DTyUk2DJmd3XhJol1o/di/OJKkjbcVm7++HCYZSwDvq07cXB5k/dAleWLGUj5BnCw/GDC3ciyefiwnytsjkUgiVeTx/tvKOU4VzZ1IAr7y11kvLomZzzdebw9mmW/kVIF43Fw/wOImZm/FHMvfIecjH8vqqlTD6upasfmuW/O34Fi+Hzsnem3j6jiOA8fnQ6Q0UqVs2bJl6NG9O84688yyaQGsXr06tbIG2Vlo1aoVFi16H4f261tjjtMxRx+No48+Gn/561/h8/tx4gkn1Fz/8m34YNkynHrKKRh03HEAyu6/WrPmJ7h90jez0ixpPvn4Y/TtXfYxZDwex+crVuDss8+yDnLyOb4qH1n6WGhcuXo7aBIRERF70WgUGzZsAAAUFBTg2RkzUFxcjKOPPqrKtG3btcVLL7+Md999F61at8Y/X34Zn372GVq3apWa5orLh2PsLbdin32a4sgjjkBRcTE+/PBDnHvOOWnLGnTccZhw5524ftQo+P1+HP+73xnr2a5tWyxYsABHH3UUHMfBfQ88QAf/lc2Y8RzatWuHfTt2xJPTn8LWggL8YehQ6/m90KBJRETEQrjguxrLvF9p4utg3nn3XRw58CgAQHZ2Njp27IB7/z4Jffv2xZrym6grnHH66fjyiy8x8tq/wHEcnHjCEJx5xhl45513UtOcesopiESimP7UU7jrrrvQuEkT/K78BuwdHf+738FNJnH9qFHw+XwYbAi3HDVqFMbceCPOPPtsNGncGBdffDG2bdtmvZ0jR16DRx99DJ9/8QXatmmDByc/YH0/lVeOuzvCI2qhoKAAOTk5OPbwvgjU9KgQUuVo1PxYgkjMXM6uBfttHhHCrol7/NhjT388xx5LYMPrx3OslfweH8FBH+HBPv7z+BiWOvl4jmwD+3hux4+/duT14zn28R/7eC66Q/1iTZPYPLQUTWZnILjJZ1UH9u/byi0Ya5rElj+UovGs7ctnHw2x8wX9FzapfzRmfmwUOxWFSRsDQKNs8/0tGeRRMayN2bHkJ8sviUSN5VsLzR+Fx8rPh82b5+Gyq0YgPy8v7eMb/7a16DhvDyeC0xOWt3XaPORkV2OP96p4FMycGh4Fw+qYdJP4+eefMe2Be7Fh/c9pZfF4Am8tXoatW7eiUaNGNS5DV5pEREQM4tn5WDnkOeOz5+rqShOwk8+ek91CgyYREREinp1vHMgkyBVFzzeCS72gQZOIiIjUe61btcKXn6/Yo3VQTpOIiIiIhXp7pSkRT9R4S1dG0DzWK6U3l5KbFsllUtciRItdavX5zJdy/Sx3hKyf3SjO7yokbWCZh2HCLlfXlAtSgd2LXt2jAypjN6eyfsCzacwTOKTc5iu4bBo/aYMI+dIE/cDAY6AcO07o9xV2XL9T6b/lZTRriqyjcj+qWJ3jbL/Jnh1rWRnmJ7NHo+Ybudk+YlheWGnSvH4ACAbMdWD387DcOno2Yseax4++Ko4Tn89Hzzs7uw6vH7/xQ81brp1F8J25mCzeBmt7b18NqjgtOAgG/Ajt8EUbx7HbAl1pEhEREbGgQZOIiIiIBQ2aRERERCxo0CQiIiJiod7eCC4iIlJfbIhsRGG8oMZy+sWTWjxbrWGgEXLDzaynl91HgyYRERGDDZGNuObTkYgkI7tlfWFfGH8/aJL1wGnY8OGIlEbwxOOPVyn7aPlynHnWWZj94ovo2vXAuq7qXkeDJhEREYPCeAEiyQiu6ngFWmW2qnaaurrStKZkDe5fORmF8QLrQdMfh/4BV159NdasWYNWrdLrN3v2bHTp3FkDpjpSbwdN4YxQjXlKpaXm0T6Ns2AP2CS3epHnsJatgqwjTB6UGiUPYk2QSrgkhYjl99DlW+aemPhILoiPZLskSf4OzdtiD9xlWVuk/iy7hj1olT2MFwDCIXM/YifySNT8QF4fST6J2xwMBqyfsCyuHdfuq/Tfit9rTnyraSk7lrqVft/+34rXYwlzGxYVm89XrJ+x5bNzDXtwtE0uEdsP9OHbHo9FlrXFzlch0s/c8nNN0F/eGo5T7Ta1ymyFjtkdq12G10GTU81/02pgaOOjjj4KTZvugzkvzcUVl1+eer2kpASvzpuHa64ZgQ8/+gj33DMJn372GZo0aYJBxx2Lkddcg6ysLADAzz+vx0033YRFixejWbNmGDHiavz97/fi/PPOw/nnn1cHuXQW+Yaen0pM+lGy7HgricZRVJp+/o2Th5NX0I3gIiIiv2KBQAAn/7+TMWfO3LQB6Guvv45YLIbOBxyAiy6+BIMGDcLLc+fg75PuwbJlH+K2229PTTtq1Cis37AeT01/Evffdx/+8Y8X8Msvv+yJzanXNGgSERH5lfvDH4ZizZo1WLxkSeq1WbNmY9CgQXjhhRfx+5NOxAXnn4f27dujZ48eGDPmBsx96WVEIhF8u3Il3nv/fdx666045JBD0LXrgbj9tltRWlq6B7eofqq3H8+JiIiInY4dO6JHjx6YPWs2Du3XDz/88AOWLVuGxx59BOPGjcf3P/yAf77ySmp61y37yPDHH3/Equ++QyAQQNcDt9/31K5dO+TkNNoTm1KvadAkIiLyG/DHPwzFbbffgb9tuwmzZ89By5Yt0b9/fyRdF2ec/n8495xzqszTokULrFq1qtrleXxc3m+SPp4TERH5DTj++OPh8/nwyiuvYO5LL2HoqafCcRwceGAXfP3NN2jXrl2Vn1AohA4dOyIej+PzL75ILev7779HQUHNuVR7Kw2aREREfgOys7NxwpAh+Pu992L9+vU49dRTAACXXHwxli//GLfcehu++OILfPfdd/j3f/6TuhF8344dcVj//vjb327GJ598gs8//xx/u3ksMjIy+Dcj9zL6eE5ERMTCmpI1NZbVVeTAj4Z12PjDH/6AF2fNwuGHH46WLVsCAA444AA8/dR03HvvfTjrnHMBuGjTpi1OGHJ8ar4777wTN954I8459zw0a9YMI0deg2+++QahcMhTfX5r6u2gKRaL1/x5Khn41pTvVMElI2c/CSWJxnl+DhudswweGoRGlu+S+WOkPEhypGicBp+AthGLevKRC6XsMirLGGKf57ss78tvbkM3as5pckjOEwAEAkFjeYSsg2dRkfX7ST8ky2f9zCEV2HHxjs8t/68v9SbGMstYceV8mfQMnbK/WE4SK09YnE+8YMeZRTdDgJ0PapmnVdsJXHK+zM4MG8uTZP54rCyjx3GTAFw4btlPhYaBRgj7wrh/5WRzRetI2BdGg0CjtGZxLG8w6tH9EHz5+YqyPyrNc1C3g/DYo49Wmb5ikua5uZj28MOp19etW4dNmzahXZu2Nqdzimcw7fpluHCB8hvgd3yPZbl/FertoElERKQ+yA03w98PmmR89lxd2hPPnlu0aBGKi4ux//77Y/2GDbj7nnvQqlUr9O7de7fWo77ToElERITIDTf7TT9ENxaP4+/33ovVP/6I7Oxs9OjeHXdNmIBg0Hw1e2+jQZOIiMhe7ogBA3DEgAF7uhr1nr49JyIiImJBgyYRERERCxo0iYiIiFjQoElERETEQr29ETyeTAI1ZAllkGARHymPJ0jWA8k1YZElAM/4icbN+Tks34YFqbFsGIbNzdbPMpBsJMl+CgTMdWBtGCA5Sizbhf2LIxoz72MfyTiy0SA701heGtlqLPezQD6SXcLagPUTFgDjd0j9djhWfeV/+xwn9XuS9Gavgce0DUkmWoJlqpEcpwA537Ht81sENbGj2SXHOzsdOGQNLtmHfP2kvNJ/3Uo/2+vHOwnLEGJ5Wex8xfaB17NJXeQo7WlsPznl/8/nc6q8R/qSdi2oK00iIiIiFurtlSYREZH6wt2wATA8wJZep2Gp6ZUX0KghnNxc26rtUZMnT8G///1vzJkze09XZbfQoElERMTA3bAB7pVXAZHIzi+jNhOHw3AfuK9WA6dRN9yAuXNfAlD2CKfmubkYOHAgrhlxNXJycmpXWQ8mT56CKQ8+WOX1Dh064NV/vYJ7Jk3Cf/7zJv71yj9TZd9++y1O+v3/w+9//3tMnHBn6vWXXn4ZN954Exa9/x6ys7N3S/0ZDZpERERMCgqASATOiKuB1q2rnYTec2R7penHH4F77wMKCoFaXm06YsAAjLvjdiQSCXzz7bcYc+NNKCgswKS7767Vcrzab7/98Phj6c+5C5Q/E7Zf37549NHHsGHDBuSWb9+SpUvRIj8fS5YsSZtnyZKlOOigbvVmwARo0CQiImKndWs4++5bQ6G3G8ErRk1ebscOhUKpgUh+fj6GHH885sydC6DsCwlTH3oI/3jhBfzyyy/Yt2NHXDtyJI444ojU/Hffcw/eeOPf+Pnnn9GsWTOcdNKJGH7ZZWmPUnnkkUcw/amnUVJSguOPPx777NOkSj0Cfn+qHjvq2bMngoEAlixdihNPOAFA2eDozLPOxLRpj+D7779Hu3btAABLly7FCScM8dAidU83gouIiPzGrF69Gu+++y6C5Vd4nnr6aTzx5JO47q9/xUtz52LAgAEYfvnl+O6771LzZGdnY/y4O/DKP1/GDaNH4cUXXsT0p55Klc+b9xoemDwFV199FV584R/IzW2G556bWat6ZWVlodtB3bBk8farSh8sXYr+hx6KHj16YHH51aa1a9di9erV6Ne3n4dWqHu60iQiIvIb8NbChejZqzcSySQi5fdfjbr+OgDA4088gYsvuih1decv116LxUuW4Kmnn8bfbroJAHDZsGGpZbVq1QoXXLAK8157DRdfdBGAsoHX0KFDcdof/wgAGHH11Xj//UWI7nCv1/++/hq9evVOe23ICSfg9ttuBQD07dMXr8+fDwD45ptvUBqJoEuXLujTpzeWLlmK/zvtNCxesgShUAg9enSvyybyrN4OmsIBH4I15PDU9HqF0og5HyfJMj1o4gW/gMqyW7JC5qaPxBPGcpY1FSeZHyESNsXayGFZWRbXMFmWVJy0QYLkKLHck2CIPL07YV4/y+KiWVckg8hnESD0y5ZCYznLr0mQHCaWb+OSOgZJFlaC5uuQY3mH+lXkSiXdJBJuWd1oM5I6VM6vqehTruvCLV9XguS7JEg/Yv2YnW/Y8v3kOHMt82lMaCzbLs4gYllX7FxSMXtNx4ub9t9dlWdU3VZWfo2s1wX69u2Lm2+6CaWlpXhx1ix89913OPuss1FYuA3r169Hj5490vpzjx7d8dVXX6Vee/311/HU00/jh+9/QHFxMeKJBBo0aJBa/sqVK3H6//1fWlW6H3JIlXuROrRvjylTJqe91iC7QSpHqV/fvnh42jSsX78BS5d+gF49eyLgD6Bvn7545pln4cDB0iVLccghhyAzw5xFVzvlNXB8cHY4/zqO3X7Vx3MiIiK/AVmZmWjXrh0OOOAAjLnhBkSj0bRvsu14X5XruqmBzPKPP8a1f/krjhhwBKZOfRCzZs3CpZf+GbFYrNb1CAaDaNeuXdpPs2bNUuU9e/ZEMBjEkiVLsHjxYvTp0wcA0K1rV2wrLMSq777D4iVL0K9v351phl1KgyYREZHfoMuHD8cTTz6J4uJiNG/eHB8u+zCtfPny5ei4b0cAwEcffoSWLVti2LBL0a1bN7Rv3w4//bQ2bfqOHTvi448/Tnvt408+qXW9MjIycMjBB2PJ0qX4YNky9C0fNAUCAXTv0QMvvfQS1qxZg3796tf9TEA9/nhOREREdl7fvn2x33774eFp03DhhX/C5MlT0KZtG3Tp3Bmz58zBl198iYkTJgIA2rZti7Vr1+Jfr76Kg7p1w8KFb+ONN95IW96555yD0TfcgG7duqFnz5545ZVX8M0336DNDjEM8UQCGzZsSHvN5/jSrjb17ds3dZP5gQceuP313r3xyGOPpQZW9Y0GTSLymxPPqXSPC7lVIUHuD6x8D0iiCbv/SH7Tfvxx5+9oqk1OUx264LzzcMONN2Leq69i27ZtmDjxLvyyaRP23XdfTJkyGe3bl329/9hjj8F5552L22+/A9FoFAMHDsRllw3DlCnbP947YcgQrF69GvdMmoRIJILBgwbhjNNPx3//+9+0dX7zzTc4cuBRaa+FQiF8snx56u9+/frhwalTccSAAakMJwDo06cP7r3/fvQ/9FCEQqE6bYu64LjsbtndrKCgADk5ORg0oE/qq5I7+i3cCM6ep+v1RnC2fHYjOOsVfnKDr83DH73eCM4ezuj1RvBI1PxZvs2N2iashWyWz9owwW6mZw8yJeV+jzeCu2QTWRPsWL9EdhIbhhbDJff4exID9vlHGP6ismOcHQvsRm32QF52kzPrJexG8HCQP4G8QXbYWB6q4Vyd4vFthj2EnfXjgsJiY3lFE+c2z8MlV4xAXl5e2jk8sX49cOXVnhLBayUcBh64f4dEcH4juJH3J/p6Wr7NQ493tWQyiZ/X/4wH778XG37+Oa0sHk/g3aXLsHXrVjRq1KjGZehKk4j8ZviLfMidnYVkRqUzfB1eaQIAXylSAybZOzi5uXAfKE/p3tll1OrZc41+Nc+e29to0CQivyn+Ih/8RZVeIIMmH4uu2GVfMZdfEyc3t9aPNUmbn46adnrRshvpn0siIiIiFurtlaZILImEW/29AJGY+R4Bds8TCxVkn8+75B4DAIiSbAsWCsiw2YPkHgA/aYM4CT2Mxc33jdF/VQEIBcw3nsRB7gUh94rQWwA83rMUIPetsSsUMXKFg24fPN8qQu8y4IFv5iUkST9ix2KSfXRmcRix+65cmyRWA1YFdv8hWwI7lmg/JPvABj+evYVXMuyUm0yS8EoWRFt+X1rZf52yXVKLXMm6QLuyx/cMz/uI3n/4K7iSVl5FH6re98vuA05NV6cVEhEREfmN0qBJRERExIIGTSIiIiIWNGgSERERsaBBk4iIiIiFevvtORERkfqitCCBaOnOfxOxNt8uC2X4kNGIJ7XXB5MnT8G///1vzJ07Z09XZbfQoElERMSgtCCB95/eiGR893xv3hdwcNi5ubUaOI264QbMnTsXQNmjfZrn5mLgwIG4ZsQI5OTk7KKaVvXA5MmYMuVBnH76/+GWsWNTr3/xxRc49Q9/wBsLFqB1q1YAgNfnz8djjz2GlatWIZlMomWLFhgwYABGXX/9bqtvbdV60LRmzRpcf/31mDdvHkpKSrD//vvjscceQ69evQCUPe/rlltuwbRp07B582b069cPU6ZMQdeuXWu1Htd1a3x2GOu2LP+GhbvE4ubnC2WE+UME2fPrkqyOBHumF8tpCpD5kSDPNGPZLxYBQqWRqLGc5ds4JFiD5SSxZ3qxnCbSRLSfsue6JVi4DPjz9VjmGHsuGVs+2wfsX9esDWgbWRxHcXIssuf3sQsE7DEsDOun7DmW9DggTcTmB/ixwJ7nyfoBzyjy1k+Yiky0sv+6Zf9XaZHR0iSScRddf5eDrH129lqDXRsU/RLHite3IFqarPXVpiOOGIBxd9yBRCKBb779FmPG3IiCwkJMuuduvn6Pj7arLBwOY9as2bjg/AvQoUP78hVg+38d4L333sO1f/kLrhlxNY4+5hg4cPDtt9/g/UWLdvo5eXS28o3wO2U/aUWW66zV3t+8eTMOP/xwHH300Zg3bx6aN2+Ob7/9Fo0bN05NM3HiREyaNAlPPvkk9t9/f9x+++0YNGgQvvrqKzRs2LA2qxMREak3svYJoFHznX0atMeBo4VQKITc8ke95OfnY8iQ4zFnzlwAZQ+rnTr1IfzjhRfwyy+/YN99O+LakSNxxBFHpOa/+557sOCNN/Dzzz+jWbNm+P1JJ2H4ZZchGNy+zY888gimP/UUSkpKcPzxx2OfffapUo8OHdpjn32a4r7778O9f/97tXV9662F6NmzJy666KK0+Y477rg6aIldp1Y3gk+YMAFt2rTBE088gb59+6J9+/Y49thjse+++wIo+1fpvffeizFjxmDo0KHo1q0bpk+fjuLiYsyYMWOXbICIiIikW716Nd59510Eg2XXRp566mk88eSTuO6vf8VLL83FgMMHYPjll+O7775LzZOdnY3x48bhlX/+EzeMHo0XXngB0596KlU+b948PDB5Mq6++mq8+MILyM3NxXPPPVft+q8dORLz5y/Ap59+Wm15s9xm+Oabb/C//31ddxu9G9Rq0PTyyy+jd+/eOO2009C8eXP06NEDjzzySKp81apVWLduHQYPHpx6LRwOY+DAgXjvvfeqXWYkEkFBQUHaj4iIiNTOW28tRM9evXBI9x4YNPh3+Obbb3HxRRcDAB5/4glcfPFFOPHEE9CxQwf85S/XonPnLnjqqadT8182bBh69uiB1q1a4Zijj8afLrgA8157LVX+1NNPY+jQoTjtj39Ehw4dMOLqq1MXTXbUteuBOP7443HPpEnVlp9z9tk4qFs3/L+TT8Yxxx6HkSOvxaxZsxCNmm/b2NNqNWhauXIlpk6dik6dOuH111/HsGHDcNVVV+Gp8pHounXrAAB5eXlp8+Xl5aXKdjR+/Hjk5OSkftq0abMz2yEiIrJX69e3L+bMno1/PD8T55xzNgYMGIBzzjkb27Ztw/r169GzR8+06Xv26IFvV65M/f3a66/jrHPOwYAjjkDPXr1w3wMPYO3atanylStXonv37mnL2PHvykZcfRWWLVuGd//73yplWVlZePjhhzD/9ddw2bBhyMrKwoSJd+G0/zsdJSUlO9cAu0GtBk3JZBI9e/bEuHHj0KNHD1x66aW45JJLMHXq1LTpdrzpz3XdGm8EHD16NLZu3Zr6Wb16dS03QURERDKzMtGuXTsccMABuHHMGESjUUyZ8mCqvPr35rLfl3/8Ma79y19w5IABmDp1KmbPmoVhl16KGHn4vEnbtm1x2h9Pw6R7JtX4pZK2bdvitNP+iNtvvw2zZ72Ib7/9Fq/Om7fT69zVajVoatGiBQ488MC017p06YIffvgBQNmNZwCqXFVav359latPFcLhMBo1apT2IyIiIt5cfvlwPP7EEygqLkbz5s2x7MNlaeUfLV+Ojh3LPl778MMP0bJlSwwbNgwHdeuG9u3b46effkqbvmPHjvj444/TXtvx7x0NH34Zvvv+O7z6Kh8ItWrVChkZGfX6SlOtvj13+OGH46uvvkp77X//+x/atWsHAOjQoQPy8/OxYMEC9OjRAwAQjUaxcOFCTJgwoY6qLCIiIky/vn2x33774eGHp+GiCy/EA5Mno22btujcpTPmzJ6DL7/8AndNLHtvbte2LdauXYt/vfoqDurWDW8tXIgFb7yRtrxzzz0Xo0ePRreuXdGzVy+88s9/4ptvvkGb1q1rrEOzZs1wwfkX4LHHH097/YHJk1FaUoojBx6JVi1boqCgAE8/8wzi8TgOO+ywum+MOlKrQdM111yDww47DOPGjcP//d//YcmSJZg2bRqmTZsGoOzS34gRIzBu3Dh06tQJnTp1wrhx45CVlYWzzjqrVhULBfwI1JAxwzKIEmTZLNslnjAvgeULAcB+7VoZy3/a8IuxvKik1FjO8nVCIfPXYoMkv8eJxY3l0bi5PEkykADAR75km3DInmQZQjSfx7x49iVgnk1DFk/qz/KDAJ6twraR5W35HPMCWCYa2wcs/YXlA9lkDLG4K5bxw7fBXO7fMRCmSgVYPyPHEsuJIqvn28exPC8f64hsftIPvX5h3y3vJC7pLMW/mM97JrSdy4uLNse3/13bzXKrNuUF55+PG8aMwbxXX0XhtkJMmDgRv2zahH333RdTJk9Gu3bt4Loujjn2WJx33nm47fbbEY1GMXDgQFx22WWYMmVK+VHq4oQhx2P1Dz/gnkmTEIlEMHjwIJxxxun477v/3X4ou5V+yl144YV4buZMRCKRVFmf3n0w47nnMOr6Udi4aRNyGjVCly5d8Ngjj6Bj+w61C4aqQA9VBw4c+AJ++Hd4D7RdneOy3r6DV155BaNHj8bXX3+NDh06YOTIkbjkkku2r7g83PLhhx9OC7fs1q2b1fILCgqQk5ODYw/rU28HTTatu6sHTUHyZpFJAjjZoCm6GwZN7A0/yvYDX4Ox1Ougic5NZo+TfmhzYDrk8A0EzOF4bMDA3qziZD/v6kFT0qKV2KCJn2e9pZi6MLcRy+dkxxLvxuYKZpJ/YAFAw+yweQLSj/wsTJf1QzJ/hNx3s63YHFgcKx945jbPw/CrrkFeXl7aQK9ka3z3J4KfV7tEcP5OzibwdqzWxeDbKxpE67pYv+5nPDr1fmxcvz6tLB6P443/LsHWrVuNtwnVOtr0pJNOwkknnVRjueM4GDt2LMZWik8XERH5tcpo5Ef/c5t5e/ac5ZUm4Nf17Lm9jZ49JyIiQmQ08nsayNRm0CT1V62+PSciIiKyt9KgSURERMSCBk0iIiIiFjRoEhERAeC6Sbiua/FNNPlVcl24cJFkX1k1qLc3gjs+X43ZHuyr2rGk+avqPp/5Zj72dXybr9Ov22iOFGBf3/ST78O75LuVAfIVXZYBxL5mzZIq4ux73uDREUHHvA3xuHk/s6+fshsz2fzsa9QJ+nV8s4BFBlF2pvmr4KVRczSES2Jn2Ff6WXwH62cB0gdohhINw+L/MmTbkCRZVkEW60C+ps7Wz5ZPoy1IPEiCnC8BIEYiSLzWkX0bnqY+eBzlVERbbN28GQUFBchu0ADZWdlwfGVnCZvls/OJyzJW2Co8DuQswjmMpWz7kh7nL5/Imxo20kVZBldxcTG2bt2KTZs2IrFDpA2NGipXbwdNIiIiu1MikcCTj0zF7078f+jU6QD4/H44jk0GEixCRL0twPU6amJ2bYwTHzhb2blKuC6QTCTwzddfYf6r/6wyYKoNDZpERETKFWzdihefewZZWdnIzMqC4ziIxPibLLvaRsN2WZArueLJsKu2Lrkyzj794EG5FsNGMg39BKaGOriui5LiYpSUFHm+KqlBk4iISCWu66KoaBuKirYB4B9zAxaPpiIDAjaoYB/3M3t60OS3GTSRj/N3dtCUWn4dXO3SjeAiIiIiFjRoEhEREbGgQZOIiIiIhXp3T1PFZ5LGr8mSz17Zk9d9Po9PVrf4bDkW9/Z0efY1Yfb5OF1/0jxejpH1s/qxWAgAcFxvn0/H4+QrrqSNaCSBx685s3sQWP1di8gBz/uJfB3epV9n9xY5AHJzK0uusLlHgd33mUiYJ6CxByyWgbQRO1+xTaSRA+SbQn5+uw7tZ+yrU/TmW7KjfWR+fhyY24Cdrmy+ju41woTf0+TtBmZ6TxM5Fl2yfrr8OrinyfWROnq4p6mij7Bl1LtBU2FhIQDgrUXL9nBNREREZG9SWFiInJycGssd1+v37+pYMpnETz/9hIYNG6auFBQUFKBNmzZYvXo1GjVqtIdr+Ouj9vNObeid2tA7taF3akPvfott6LouCgsL0bJlyxqDtYF6eKXJ5/OhdevW1ZY1atToN7OD9gS1n3dqQ+/Uht6pDb1TG3r3W2tD0xWmCroRXERERMSCBk0iIiIiFn4Vg6ZwOIybb74Z4bD54aRSPbWfd2pD79SG3qkNvVMberc3t2G9uxFcREREpD76VVxpEhEREdnTNGgSERERsaBBk4iIiIgFDZpERERELNT7QdODDz6IDh06ICMjA7169cI777yzp6tUb7399tv4/e9/j5YtW8JxHMydOzet3HVdjB07Fi1btkRmZiaOOuoorFixYs9Utp4aP348+vTpg4YNG6J58+Y45ZRT8NVXX6VNo3Y0mzp1Kg4++OBU8F3//v0xb968VLnar3bGjx8Px3EwYsSI1GtqQ27s2LFwHCftJz8/P1WuNuTWrFmDc845B02bNkVWVha6d++OZcu2P+Jsb2zDej1oev755zFixAiMGTMGH330EY444ggMGTIEP/zww56uWr1UVFSEQw45BJMnT662fOLEiZg0aRImT56MpUuXIj8/H4MGDUo970+AhQsX4vLLL8eiRYuwYMECxONxDB48GEVFRalp1I5mrVu3xp133okPPvgAH3zwAY455hicfPLJqZOp2s/e0qVLMW3aNBx88MFpr6sN7XTt2hVr165N/Xz66aepMrWh2ebNm3H44YcjGAxi3rx5+Pzzz3HPPfegcePGqWn2yjZ067G+ffu6w4YNS3utc+fO7qhRo/ZQjX49ALhz5sxJ/Z1MJt38/Hz3zjvvTL1WWlrq5uTkuA899NAeqOGvw/r1610A7sKFC13XVTvurCZNmriPPvqo2q8WCgsL3U6dOrkLFixwBw4c6F599dWu66oP2rr55pvdQw45pNoytSF3/fXXuwMGDKixfG9tw3p7pSkajWLZsmUYPHhw2uuDBw/Ge++9t4dq9eu1atUqrFu3Lq09w+EwBg4cqPY02Lp1KwBgn332AaB2rK1EIoGZM2eiqKgI/fv3V/vVwuWXX44TTzwRxx13XNrrakN7X3/9NVq2bIkOHTrgjDPOwMqVKwGoDW28/PLL6N27N0477TQ0b94cPXr0wCOPPJIq31vbsN4OmjZu3IhEIoG8vLy01/Py8rBu3bo9VKtfr4o2U3vac10XI0eOxIABA9CtWzcAakdbn376KRo0aIBwOIxhw4Zhzpw5OPDAA9V+lmbOnIkPP/wQ48ePr1KmNrTTr18/PPXUU3j99dfxyCOPYN26dTjssMOwadMmtaGFlStXYurUqejUqRNef/11DBs2DFdddRWeeuopAHtvPwzs6QowjuOk/e26bpXXxJ7a094VV1yBTz75BO+++26VMrWj2QEHHIDly5djy5YtmDVrFs4//3wsXLgwVa72q9nq1atx9dVXY/78+cjIyKhxOrWh2ZAhQ1K/H3TQQejfvz/23XdfTJ8+HYceeigAtaFJMplE7969MW7cOABAjx49sGLFCkydOhXnnXdearq9rQ3r7ZWmZs2awe/3Vxmxrl+/vsrIVriKb42oPe1ceeWVePnll/Hmm2+idevWqdfVjnZCoRD2228/9O7dG+PHj8chhxyC++67T+1nYdmyZVi/fj169eqFQCCAQCCAhQsX4v7770cgEEi1k9qwdrKzs3HQQQfh66+/Vj+00KJFCxx44IFpr3Xp0iX1Ray9tQ3r7aApFAqhV69eWLBgQdrrCxYswGGHHbaHavXr1aFDB+Tn56e1ZzQaxcKFC9WelbiuiyuuuAKzZ8/Gf/7zH3To0CGtXO24c1zXRSQSUftZOPbYY/Hpp59i+fLlqZ/evXvj7LPPxvLly9GxY0e14U6IRCL44osv0KJFC/VDC4cffniVuJX//e9/aNeuHYC9+Fy4p+5AtzFz5kw3GAy6jz32mPv555+7I0aMcLOzs93vvvtuT1etXiosLHQ/+ugj96OPPnIBuJMmTXI/+ugj9/vvv3dd13XvvPNONycnx509e7b76aefumeeeabbokULt6CgYA/XvP647LLL3JycHPett95y165dm/opLi5OTaN2NBs9erT79ttvu6tWrXI/+eQT94YbbnB9Pp87f/5813XVfjuj8rfnXFdtaOPaa69133rrLXflypXuokWL3JNOOslt2LBh6v1DbWi2ZMkSNxAIuHfccYf79ddfu88++6yblZXlPvPMM6lp9sY2rNeDJtd13SlTprjt2rVzQ6GQ27Nnz9RXv6WqN9980wVQ5ef88893XbfsK6I333yzm5+f74bDYffII490P/300z1b6XqmuvYD4D7xxBOpadSOZhdeeGHqmM3NzXWPPfbY1IDJddV+O2PHQZPakDv99NPdFi1auMFg0G3ZsqU7dOhQd8WKFalytSH3z3/+0+3WrZsbDofdzp07u9OmTUsr3xvb0HFd190z17hEREREfj3q7T1NIiIiIvWJBk0iIiIiFjRoEhEREbGgQZOIiIiIBQ2aRERERCxo0CQiIiJiQYMmEREREQsaNImIiIhY0KBJRERExIIGTSIiIiIWNGgSERERsaBBk4iIiIiF/w/XrIrUKRsA8gAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
        "<Figure size 700x700 with 1 Axes>"
       ]
@@ -1170,13 +884,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "f358f265-0ddd-47bf-9407-4c30623dd454",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1000x1500 with 5 Axes>"
       ]
@@ -1222,23 +936,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "82b2055e-5309-4974-b621-fb7685ef734d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x14fc0e230>"
+       "<matplotlib.colorbar.Colorbar at 0xffff8422aaa0>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 700x700 with 2 Axes>"
       ]
@@ -1248,7 +962,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 700x700 with 2 Axes>"
       ]
@@ -1289,7 +1003,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,