ground_truth_niches.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we devise a method to identify very coarse niche types in mouse brain. \n",
    "\n",
    "Main brain components:\n",
    "\n",
    "- Cortex, full of neurons like excitatory\n",
    "- Striatum, full of inhibitory neurons.\n",
    "- White matter, full of oligodendrocytes who generate myelin,\n",
    "\n",
    "Microglia are immune cells in the brain\n",
    "\n",
    "Astrocytes are everywhere but more close to boundaries,\n",
    "\n",
    "We can assume more macrophages in the old mice\n",
    "\n",
    "We can use this dataset to compare niches from different ages\n",
    "\n",
    "endothelial cells = surrounds the blood vessels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import anndata as ad\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from rich import print\n",
    "import os\n",
    "\n",
    "import scvi\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scanpy as sc\n",
    "\n",
    "from scib_metrics.benchmark import Benchmarker\n",
    "from scvi.strip_metrics import SpatialAnalysis\n",
    "\n",
    "from anndata import AnnData\n",
    "\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 34\n"
     ]
    }
   ],
   "source": [
    "scvi.settings.seed = 34"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adata_MERFISH_24w.h5ad\n",
      "adata_scvi_merfish4w.h5ad\n",
      "BrainAging_MERFISH_4weeks_major_ct.h5ad\n",
      "BrainAgingSpatialAtlas_MERFISH_24weeks.h5ad\n",
      "BrainAgingSpatialAtlas_MERFISH_4weeks.h5ad\n",
      "BrainAgingSpatialAtlas_MERFISH.h5ad\n",
      "BrainAgingSpatialAtlas_MERFISH.ipynb\n"
     ]
    }
   ],
   "source": [
    "# !ls /home/labs/nyosef/Collaboration/SpatialDatasets/merfish/BrainAgingSpatialAtlas_MERFISH\n",
    "\n",
    "!ls /home/nathanlevy/Data/BrainAgingSpatialAtlas_MERFISH/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "plots = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dirs = {\n",
    "    \"VIZGEN_Liver\": [\n",
    "        \"/home/nathanlevy/Data/Liver_VIZGEN/\",\n",
    "        \"liver-joint-scvi3-annotated.h5ad\",\n",
    "    ],\n",
    "    \"MERFISH_Brain\": [\n",
    "        # \"/home/labs/nyosef/Collaboration/SpatialDatasets/merfish/BrainAgingSpatialAtlas_MERFISH/\",\n",
    "        \"/home/nathanlevy/Data/BrainAgingSpatialAtlas_MERFISH/\",\n",
    "        \"adata_scvi_merfish4w.h5ad\",\n",
    "    ],\n",
    "    \"NANOSTRING_Lung\": [\n",
    "        \"/home/nathanlevy/Data/\",\n",
    "        \"nanostring_bis_finished_hotspot_densities.h5ad\",\n",
    "    ],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">adata_scvi_merfish4w\n",
       "</pre>\n"
      ],
      "text/plain": [
       "adata_scvi_merfish4w\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data_dir = \"/home/nathanlevy/Data/BrainAgingSpatialAtlas_MERFISH/\"\n",
    "# data_file = \"adata_scvi_merfish4w.h5ad\"\n",
    "\n",
    "data_dir, data_file = data_dirs[\"MERFISH_Brain\"]\n",
    "\n",
    "data_file_name = os.path.splitext(data_file)[0]\n",
    "print(data_file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_save = os.path.join(\"checkpoints\", data_file_name)\n",
    "os.makedirs(path_to_save, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">AnnData object with n_obs × n_vars = <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">123632</span> × <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">374</span>\n",
       "    obs: <span style=\"color: #008000; text-decoration-color: #008000\">'fov'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'center_x'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'center_y'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'min_x'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'max_x'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'min_y'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'max_y'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'age'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'clust_annot'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'slice'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'organism_ontology_term_id'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'sex_ontology_term_id'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'suspension_type'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'cell_type_ontology_term_id'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'assay_ontology_term_id'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'tissue_ontology_term_id'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'disease_ontology_term_id'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'self_reported_ethnicity_ontology_term_id'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'development_stage_ontology_term_id'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'donor_id'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'is_primary_data'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'cell_type_annot'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'cell_type'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'assay'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'disease'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'organism'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'sex'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'tissue'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'self_reported_ethnicity'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'development_stage'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'donor_slice'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'n_counts'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'cell_index'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'_scvi_batch'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'_scvi_labels'</span>\n",
       "    uns: <span style=\"color: #008000; text-decoration-color: #008000\">'_scvi_manager_uuid'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'_scvi_uuid'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'cell_type_colors'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'donor_id_colors'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'log1p'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'neighbors'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'pca'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'umap'</span>\n",
       "    obsm: <span style=\"color: #008000; text-decoration-color: #008000\">'X_pca'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'X_scVI'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'X_spVI'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'X_spatial'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'X_umap'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'centroids'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'neighborhood_composition'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'spatial'</span>\n",
       "    varm: <span style=\"color: #008000; text-decoration-color: #008000\">'PCs'</span>\n",
       "    layers: <span style=\"color: #008000; text-decoration-color: #008000\">'counts'</span>\n",
       "    obsp: <span style=\"color: #008000; text-decoration-color: #008000\">'connectivities'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'distances'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "AnnData object with n_obs × n_vars = \u001b[1;36m123632\u001b[0m × \u001b[1;36m374\u001b[0m\n",
       "    obs: \u001b[32m'fov'\u001b[0m, \u001b[32m'center_x'\u001b[0m, \u001b[32m'center_y'\u001b[0m, \u001b[32m'min_x'\u001b[0m, \u001b[32m'max_x'\u001b[0m, \u001b[32m'min_y'\u001b[0m, \u001b[32m'max_y'\u001b[0m, \u001b[32m'age'\u001b[0m, \u001b[32m'clust_annot'\u001b[0m, \u001b[32m'slice'\u001b[0m, \n",
       "\u001b[32m'organism_ontology_term_id'\u001b[0m, \u001b[32m'sex_ontology_term_id'\u001b[0m, \u001b[32m'suspension_type'\u001b[0m, \u001b[32m'cell_type_ontology_term_id'\u001b[0m, \n",
       "\u001b[32m'assay_ontology_term_id'\u001b[0m, \u001b[32m'tissue_ontology_term_id'\u001b[0m, \u001b[32m'disease_ontology_term_id'\u001b[0m, \n",
       "\u001b[32m'self_reported_ethnicity_ontology_term_id'\u001b[0m, \u001b[32m'development_stage_ontology_term_id'\u001b[0m, \u001b[32m'donor_id'\u001b[0m, \u001b[32m'is_primary_data'\u001b[0m, \n",
       "\u001b[32m'cell_type_annot'\u001b[0m, \u001b[32m'cell_type'\u001b[0m, \u001b[32m'assay'\u001b[0m, \u001b[32m'disease'\u001b[0m, \u001b[32m'organism'\u001b[0m, \u001b[32m'sex'\u001b[0m, \u001b[32m'tissue'\u001b[0m, \u001b[32m'self_reported_ethnicity'\u001b[0m, \n",
       "\u001b[32m'development_stage'\u001b[0m, \u001b[32m'donor_slice'\u001b[0m, \u001b[32m'n_counts'\u001b[0m, \u001b[32m'cell_index'\u001b[0m, \u001b[32m'_scvi_batch'\u001b[0m, \u001b[32m'_scvi_labels'\u001b[0m\n",
       "    uns: \u001b[32m'_scvi_manager_uuid'\u001b[0m, \u001b[32m'_scvi_uuid'\u001b[0m, \u001b[32m'cell_type_colors'\u001b[0m, \u001b[32m'donor_id_colors'\u001b[0m, \u001b[32m'log1p'\u001b[0m, \u001b[32m'neighbors'\u001b[0m, \u001b[32m'pca'\u001b[0m, \n",
       "\u001b[32m'umap'\u001b[0m\n",
       "    obsm: \u001b[32m'X_pca'\u001b[0m, \u001b[32m'X_scVI'\u001b[0m, \u001b[32m'X_spVI'\u001b[0m, \u001b[32m'X_spatial'\u001b[0m, \u001b[32m'X_umap'\u001b[0m, \u001b[32m'centroids'\u001b[0m, \u001b[32m'neighborhood_composition'\u001b[0m, \u001b[32m'spatial'\u001b[0m\n",
       "    varm: \u001b[32m'PCs'\u001b[0m\n",
       "    layers: \u001b[32m'counts'\u001b[0m\n",
       "    obsp: \u001b[32m'connectivities'\u001b[0m, \u001b[32m'distances'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adata = ad.read_h5ad(data_dir + data_file)\n",
    "print(adata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'endothelial cell',\n",
       " 1: 'oligodendrocyte precursor cell',\n",
       " 2: 'neuron',\n",
       " 3: 'astrocyte',\n",
       " 4: 'inhibitory interneuron',\n",
       " 5: 'pericyte',\n",
       " 6: 'oligodendrocyte',\n",
       " 7: 'microglial cell',\n",
       " 8: 'vascular leptomeningeal cell',\n",
       " 9: 'medium spiny neuron',\n",
       " 10: 'macrophage',\n",
       " 11: 'T cell',\n",
       " 12: 'cell'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_types = adata.obs.cell_type.unique().tolist()\n",
    "int_to_cell_type = {i: cell_types[i] for i in range(len(cell_types))}\n",
    "int_to_cell_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs[\"niche_type\"] = \"unknown\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.14814815, 0.11111111, 0.55555556, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.18181818, 0.09090909, 0.54545455, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.13793103, 0.13793103, 0.51724138, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.4       , 0.05      , 0.1       , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.25      , 0.2       , 0.1       , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.2173913 , 0.17391304, 0.04347826, ..., 0.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obsm[\"neighborhood_composition\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cell_type\n",
       "neuron                            34553\n",
       "medium spiny neuron               25667\n",
       "oligodendrocyte                   13110\n",
       "astrocyte                         12830\n",
       "endothelial cell                  11376\n",
       "inhibitory interneuron            10330\n",
       "microglial cell                    6317\n",
       "oligodendrocyte precursor cell     4234\n",
       "pericyte                           3443\n",
       "vascular leptomeningeal cell       1137\n",
       "macrophage                          355\n",
       "cell                                235\n",
       "T cell                               45\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.cell_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>endothelial cell</th>\n",
       "      <th>oligodendrocyte precursor cell</th>\n",
       "      <th>neuron</th>\n",
       "      <th>astrocyte</th>\n",
       "      <th>inhibitory interneuron</th>\n",
       "      <th>pericyte</th>\n",
       "      <th>oligodendrocyte</th>\n",
       "      <th>microglial cell</th>\n",
       "      <th>vascular leptomeningeal cell</th>\n",
       "      <th>medium spiny neuron</th>\n",
       "      <th>macrophage</th>\n",
       "      <th>T cell</th>\n",
       "      <th>cell</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.148148</td>\n",
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       "      <td>0.181818</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.090909</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.103448</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
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       "      <td>0.150000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.550000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.130435</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123627</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123628</th>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123629</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123630</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123631</th>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123632 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        endothelial cell  oligodendrocyte precursor cell    neuron  astrocyte  \\\n",
       "0               0.148148                        0.111111  0.555556   0.037037   \n",
       "1               0.181818                        0.090909  0.545455   0.090909   \n",
       "2               0.137931                        0.137931  0.517241   0.068966   \n",
       "3               0.150000                        0.100000  0.550000   0.000000   \n",
       "4               0.130435                        0.173913  0.434783   0.086957   \n",
       "...                  ...                             ...       ...        ...   \n",
       "123627          0.400000                        0.050000  0.050000   0.050000   \n",
       "123628          0.350000                        0.150000  0.100000   0.050000   \n",
       "123629          0.400000                        0.050000  0.100000   0.050000   \n",
       "123630          0.250000                        0.200000  0.100000   0.050000   \n",
       "123631          0.217391                        0.173913  0.043478   0.043478   \n",
       "\n",
       "        inhibitory interneuron  pericyte  oligodendrocyte  microglial cell  \\\n",
       "0                     0.111111  0.000000         0.037037         0.000000   \n",
       "1                     0.045455  0.000000         0.045455         0.000000   \n",
       "2                     0.034483  0.000000         0.103448         0.000000   \n",
       "3                     0.100000  0.000000         0.000000         0.100000   \n",
       "4                     0.086957  0.000000         0.086957         0.000000   \n",
       "...                        ...       ...              ...              ...   \n",
       "123627                0.300000  0.150000         0.000000         0.000000   \n",
       "123628                0.150000  0.200000         0.000000         0.000000   \n",
       "123629                0.250000  0.150000         0.000000         0.000000   \n",
       "123630                0.300000  0.050000         0.000000         0.050000   \n",
       "123631                0.391304  0.086957         0.000000         0.043478   \n",
       "\n",
       "        vascular leptomeningeal cell  medium spiny neuron  macrophage  T cell  \\\n",
       "0                                0.0                  0.0         0.0     0.0   \n",
       "1                                0.0                  0.0         0.0     0.0   \n",
       "2                                0.0                  0.0         0.0     0.0   \n",
       "3                                0.0                  0.0         0.0     0.0   \n",
       "4                                0.0                  0.0         0.0     0.0   \n",
       "...                              ...                  ...         ...     ...   \n",
       "123627                           0.0                  0.0         0.0     0.0   \n",
       "123628                           0.0                  0.0         0.0     0.0   \n",
       "123629                           0.0                  0.0         0.0     0.0   \n",
       "123630                           0.0                  0.0         0.0     0.0   \n",
       "123631                           0.0                  0.0         0.0     0.0   \n",
       "\n",
       "        cell  \n",
       "0        0.0  \n",
       "1        0.0  \n",
       "2        0.0  \n",
       "3        0.0  \n",
       "4        0.0  \n",
       "...      ...  \n",
       "123627   0.0  \n",
       "123628   0.0  \n",
       "123629   0.0  \n",
       "123630   0.0  \n",
       "123631   0.0  \n",
       "\n",
       "[123632 rows x 13 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhood_composition = pd.DataFrame(\n",
    "    adata.obsm[\"neighborhood_composition\"], columns=cell_types\n",
    ")\n",
    "neighborhood_composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scvi.nearest_neighbors import pynndescent\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 1/10 [00:00<00:00,  9.44it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:01<00:00,  9.44it/s]\n"
     ]
    }
   ],
   "source": [
    "mean_1_nn = {}\n",
    "mean_dist = {}\n",
    "\n",
    "for sample in tqdm(adata.obs[\"donor_slice\"].unique()):\n",
    "\n",
    "    adata_sample = adata[adata.obs[\"donor_slice\"] == sample].copy()\n",
    "\n",
    "    coordinates = adata_sample.obsm[\"centroids\"].values\n",
    "\n",
    "    k_nn, d_k_nn = pynndescent(coordinates, n_neighbors=2, return_distance=True)\n",
    "\n",
    "    mean_1_nn[sample] = d_k_nn[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_1_nn\n",
    "\n",
    "plt.hist(mean_1_nn[\"Donor_8-slice_2\"], bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Niche1 = neuron\n",
    "# Niche2 = medium spiny neuron\n",
    "# Niche3 = astrocyte\n",
    "# Niche4 = oligodendrocyte\n",
    "# Niche5 = microglia\n",
    "\n",
    "# cell_types = [\"microglial cell\"]\n",
    "\n",
    "# for sample in adata.obs.donor_slice.unique().tolist()[:]:\n",
    "#     sc.pl.spatial(\n",
    "#         adata=adata[\n",
    "#             adata.obs[\"cell_type\"].isin(cell_types)\n",
    "#             & (adata.obs[\"donor_slice\"] == sample)\n",
    "#         ],\n",
    "#         spot_size=40,\n",
    "#         color=[\"cell_type\"],\n",
    "#         ncols=2,\n",
    "#         frameon=False,\n",
    "#         title=sample,\n",
    "#     )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Niche1 = neuron  2\n",
    "# Niche2 = medium spiny neuron  9\n",
    "# Niche3 = astrocyte 3\n",
    "# Niche4 = oligodendrocyte 6\n",
    "# Niche5 = microglia 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2368268/3167995157.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  adata.obs.niche_type[\n"
     ]
    }
   ],
   "source": [
    "# filter rows that have adata.obsm['neighborhood_composition'] column index 2 > 0.2 and retrieve the row indexes:\n",
    "\n",
    "treshold = 0.3333\n",
    "\n",
    "cell_type_index = 2\n",
    "cell_type = int_to_cell_type[cell_type_index]\n",
    "\n",
    "niches = [2, 3, 6, 9]\n",
    "\n",
    "neighborhood_composition_niches = neighborhood_composition.iloc[:, niches]\n",
    "\n",
    "for cell_type_index in niches:\n",
    "    cell_type = int_to_cell_type[cell_type_index]\n",
    "    adata.obs.niche_type[\n",
    "        adata.obsm[\"neighborhood_composition\"][:, cell_type_index] > treshold\n",
    "    ] = cell_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "niche_type\n",
       "neuron                 39008\n",
       "unknown                34948\n",
       "medium spiny neuron    31317\n",
       "oligodendrocyte        13448\n",
       "astrocyte               4911\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.niche_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['neuron', 'unknown', 'oligodendrocyte', 'astrocyte', 'medium spiny neuron']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "niche_types = adata.obs.niche_type.unique().tolist()\n",
    "niche_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nathanlevy/mambaforge/envs/phenospace_23/lib/python3.11/site-packages/anndata/_core/anndata.py:1222: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  df[key] = c\n"
     ]
    },
    {
     "data": {
      "image/png": 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lTYPPYy+pPdXrDRj1xgYUV9sOYs7GjH4BEEWgtsGAib18cd2QEKQV12Dqe1vsvodCJmDH0xPh66KxfTIRUTviCBOZEUURz/19DN/vOpOFeWCoO767bRic1QroDUbMHhSE7cnFZouDHZRy1OkMZvdak1iAlKJqRPo4X7D+09k7kVdlV7DkpFagxsoapr7BbkjMqYBB4tewfw6fKbuy4UQhdqaU4IFJPeHqoEBlne01UQCgN4rYmVLCqV8iuuAYMJGZnaklZsESABzILMeibWmI8nXGS/8kIr9SC2e1HLMGBKFvsBsGh3lixsfbJO+XV65lwNRF+Lio7Trnyv6B+HprmuTxspoGyWBJyr8JeUgqqLI7WGri58rRJSK68JiHicxsO2W5mBsAVh/Lx/0/HUT+6fVL1fUG/HkwB36uGvQNdsPAUHeLaxxVcsSHuJ3P7lI7CnR3wOXxAa2eU1rTYDVYAgBnddt+B0tuNkrZ3K2jwjE51nJnnUYhQ3wQv6eI6MJjwERmFFaqx9frjTBIFGb980BjPbAXrugN12Z1yAQBeH5GHFw19iU4pM7h7Wv64Z5xkXBWSy/Wl/oeaBLh7YTega5Wj7fFraPCIVWsTqs34s7v9yG/QnrjARHR+cIpOTL5ZlsaPtqQbNEulwmID3KTHA3Qn/4BGh/sjq1PTMR/R/NQU6/HlDg/hHk5nfc+U/vSKOWQywRU1xtatMug1VnunHNWy3HfxJ4IdHfA1Dg/JORU4I8DOXY9a2b/QPx1KFfy2DWf70RBZb3ksW3JJZj92Q6sfGgMA3IiumA4wkQAgLyKOrz233G0/J3e00mJb28dijvG9oAgMfh0Zf8z9cDcHJWYMzQUt4/pwWCpC/tlX5ZFm1SwBAANBhH5FVoMCfeARinHkHBPvDAjDi6np+YclDJMjfNFgJv5uiNvZxUclNb/+bEWLDXJKa/D31aCLSKi84EjTAQA2JNWKjndojOIGN3TGwDw5ux4vPbfcZTX6qBWyHDb6AjM5G6lbqe+xW7HJj4uKhRVme+ia9AbsWRHOlYfy8d/D4yBh5MKt4yKwLVDQpBRUotgDwesOpqPNYmFZtcVVzfgnyP559TP7HZIqElEZC8GTAQAVvPa+DfbkXTt4BBc0S8QacU1CHRzgJudBVipa5keH4Cf9piPMinlAsprdFavyavQ4vovd+HDOQMQ4+8CR5UCsQGN65mO5VZKXqMznFtyzB4+HMUkoguHU3IEABgW4Yl+wZa7j+4ca15RXqOUIzbAlcFSN/bUpbEYc3pUsYnOIELXyoJvAEgqqMK1X+xEYZX5guyeftJpJWIDXM6pn3UN0iNhRETnAwMmAgDIZAK+nT8Ut4wMR5iXI/qHuOOD6/vjmsGW9b+oe3NzUOL724bh7wWjoJRL75q0pqJOh9/3Z5u1zewfhJ6+5kGTu4MSfYLcLNY2tYWrA4N2IrpwWBqFiCRtTy7GDV/vbvN1fYJc8f51/RHle2YEqaJWhx92Z+BQVjl8nNVYeTQPZbXWp/hs8XJSYcsTE+DUxrxPRERniyNMRCQpxMPxrK47mlOJSz/Yis0ni0xtBlFE70BXPH1pLwgCzilYAoBPbxjIYImILiiOMBGRVQ//cgjLDprnVRLQmJsr2MMBaqUcSflVktfGBrjih9uG4pV/E7HiSB50p2umeDgqJQOmCTE+2JhUZNEuxVElx7fzh2BIuFfbXoiI6CwxYCIiq3QGI749nTagut6A43mWO94C3dTIrZDOmyQXYHdtucFhHtiXUWZ33+QyAUtvH4bhPRg0EdH5xyk5IrJKKZfh9jE98NvdI6FWSP9zYS1YAqwHS/IWJXgGhLojyN2hTX0zGEU89PNB6M8xPQERkT24CICI7FJQaX/9NgGwyBrfnJ+LGjMHBOFAZhl6+bvg0SkxOJhVjr8Pty17d35lPU7kV6EPC/IS0XnGESYissuoKG/bJ6Gx7MkLV/Ru9RwvZxV+3JOJXamlWLIjA5d/vA0hno547vI4uFgp/GuNG9MLENEFwICJiOzy6NRohHvZ3jlXXN0AQQAiW8nEnVlah/JmC78zSmrx9J9HMDnWt0273ybH+iLE8+x28xERtQUXfROR3er1Bvx5IBsfrk9GXoXtKbpe/s5ILqyBvlmW8ClxflibWCB5vgyAvSuSonydsOzeUXDRcISJiM4/jjARkd32pZfhjZVJdgVLAHAiv9oULMllAh6dEo0nL+ll9fy2LN9+YlovBktEdMEwYCIiuzTojXjw54OoqDu7pJMGo4gP1p+Cq0aBEeeYCmDO0BBMjvU7p3sQEbUFAyYissuR7HIUVzec0z30RhFrjxfghSt6W6QWsNfC2X3x+qx4yM7yeiKis8G0AkRkF1u70RQywWytkjXP/30Mvq5qGOw410EpR73eAKMIqOQyPDApCtcOCbW7z0RE7YUjTERkl55+Lhjew9Pq8ZbBkpuD9O9jeqOI3HLra6Cc1XL08nfBZX380XA6WAIAESJ6M98SEXUQBkxEZLfPbxyEawYFw57JsIo6PQQAHm3IkxTr74yDz03F3/eNwp70UrNM4TqDiOf+PgqjHSNTRETtjQETEdnN3VGFt67ph9mDgu06XwTg2IZElFlldVDIBKQW1Uiul8oqrUNeGzKOExG1FwZMRNRmT0yLQbSvs13n5pRr7a4TV9vQOAVXUl0PQWIYy1Elh4cjUwkQ0YXHgImI2szXVYOXr+pj17mhno64aUSYXeeOivLG/owy3LpkL6RS6s4fFQFHFfeqENGFx4CJiM7KtlPFNs8RBOCxaTHwclbbPFcpF/DGrL74ZGMydAbLaOmWkeF4dGr0WfWViOhc8Vc1ImozURTxx/5syWMDQ90R4O4AB4UMcYGuOJxVDrVCBqVckAyEAEAlF/DVTYMR5OGItOIayXNCPB0hSM3TERFdAAyYiKjNjCKQa6U8yl1jIzGtjz9eXpGIl1YcN7VrFDIoVTLUNhgAAIPDPDC1tx8cVApc2scf3qdHofoEuiKztNbivgND3dv/RYiI7MSAiYjaTC4TMDDUHQcyy83aVQoZhkR4Iq24Bt9sSzM7ptUbMTLSC/eMj4S3sxqxAa4W9xVFEVlldRbtsf4u6B/i3p6vQETUJlzDRERn5dnL4+CsNv+d64lpMfB0UuFwVrnkNfvSS+HnqpEMlgAgMa8SCTkVFu25FVrJReBERBcKR5iI6KwMDPXAxsfGY/nhXFRr9ZgS54e4wMZAqIePk+Q1DQYR097fgjdnx+PawSEwGkX8tj8LK4/mQ6OQo4+VTN4VdToYRBEyu1JmEhG1P0EU+XsbEbWvrNJajFm40epxmQDMGhAEgwgsO5hjdkytkKFebzRrGxvtg+/mDz0vfSUisgdHmIio3SXmVbZ63CgCvx/IkTzmoJRDQOOaJwAI8XTAS1f0bu8uEhG1CQMmImp3UXZmAZdSXqfD9qcm4mBmGZzUCoyO8oZSzuWWRNSxGDARUbuL9HHG7IHB+OOAdK6m1q91QqCbBkHxgeehZ0REZ4drmIjovDAYRfx5IBtrjuWjUqtHRkkt8m0UzlXJZfjipkGYEON7gXpJRGQfBkxEdEFUanWY8+UuHMuVXt8U7O6AH24fhnBv6R12REQdiQETEV0wOoMRKxPy8PzyYyir1Zkd++bmwZgU69dBPSMiah0DJiK64AoqtXh/3SnsSCmGn6sGd47pgclxDJaIqPNiwERERERkA/fqEhEREdnAgImIiIjIBgZMRERERDYwYCIiIiKygQETERERkQ0MmIiIiIhsYMBEREREZAMDJiIiIiIbGDARERER2cCAiYiIiMgGBkxERERENjBgIiIiIrKBARMRERGRDQyYLiJ6gxGJuZXIr9B2dFeIiIi6FEVHd4DOr8JKLdKKa1Ba04CXViQir0ILQQCm9w3A29f0g0Yp7+guEhERdXoMmLopo1HEK/8ex7c702EwimbHRBFYcSQPQR4OePrS2A7qIRERUdfBKbluavnhXCzanmYRLDW34nDeBewRERFR18WAqZtaedR2MCSXCRegJ0RERF0fA6ZuqrxWZ/Oc2QODL0BPiIiIuj6uYeqGft2Xhd1ppVaPqxUyXD8kBPdOiLyAvSIiIuq6GDB1M6Io4oN1pySPvXxlb0zr7Q8ntQJOan7piYiI7MWfmt2MVmdATnmd5LEQT0f4umoucI+IiIi6PgZMXcD25GIsP5QLmUzA7IFBGBzuCVEUIQiWi7Z/258teQ+FTEDfILfz3VUiIqJuiQFTJ7dw1Ql8uinF9PFPezIR6eOEtOIaBLo74N7xUZg7LNR0/Nsd6ZL3GRTmAS9n9fnuLhERUbfEXXKd2CcbT5kFS01SimpgFIHssjr8b1kCVhzJBQAUV9cjtbhG8l5jenqf174SERF1ZwyYOqm8ijq8s+akXed+vzMDALA3rRSilTyV03r7t1fXiIiILjoMmDqp3amlaCVJt5lKrR4A4O9mfUH3VZ/uwOpj+e3RNSIioosOA6ZOytfV/vVGU+L8AAD9Q9wxNNxT8pzqej0e+eUQqrS2E1oSERGROQZMndTwCC/EB1vuahsd5QVFs5Im3s4qTD0dMAmCgK9vGYw7x/aAVNGTmgYDdqVaT2hJRERE0hgwdVIymYDv5g/FLSPDEebliAGh7vjg+v54fFovNJ+pK65uwLxvdqO4uh4A4KpR4n+XxcLbRXqEys1BeQF6T0RE1L0IomhtmTB1Ro/8cgh/HsyxaL95ZBhOFVTjSHYFIn2d0cPbCctanNfL3wX/PTAGMhbdJSIiahPmYepiCqq0ku1Ld2VCf3qV+OGschzNqcDcoSFYe7wQlXU6TI7zw/9Nj2OwREREdBYYMHUxY3r6YHtyiUW7vsWWOoNRRL1exN5nJl+orhEREXVbXMPUSRiNIn7YlYFZn27HzE+2Y8n2NBgk8grcNCIMwyLMd8JF+TpL3rOstuG89JWIiOhiwxGmTuKtNUn4rFlW70NZ5UgvqcULV/Q2O89RpcBPdwzHztQSpBbXYECIO4qr63HL4r0W95zQy/e895uIiOhiwEXfnUB5bQOGvbYe9XqjWbtKLsOeZybB3VHV6vWiKOLFfxKxpFkduUt6++OjuQOglHMQkYiI6FwxYOpgG08U4rHfDqOkRnr6bM3DYxHt52LXvVKLqpGQU4FIH2f0CbLM4URERERnh1NyHai0pgH3LN0Prc4oedzHRY0Ibye779fDxxk9fKTXMxEREdHZ43xNB1qbmG81WFLKBbx8ZR9OqREREXUCHGHqQDJBOieSj7MKf903GkHuDhe4R0RERCSFwxcdaGpvf7ioLWNWrd6IugZ9B/SIiIiIpDBg6kBuDkp8euNAtEy+XaXV47m/j3VMp4iIiMgCA6YOJhcESOSnxI6UEtTrDRe+Q0RERGSBAVMH83SWzrHk5qCEUsYvDxERUWfAn8gdrJe/K0ZGelm03zIynIVyiYiIOgkmruwEKrU6vL06CauO5sNJrcD1Q0Jwx5geDJiIiIg6CQZMRERERDZwSo6IiIjIBgZMRERERDYwYCIiIiKygQETERERkQ0MmM6CKIqoqNVBb5AunEtERETdC4vvttHWU0V48Z9EJBdWw8tJhQUTojB/dERHd4uIiIjOI6YVaIOc8jpMfHsT6vXmI0uf3TAQl/YN6KBeERER0fnGKbk2+PtQjkWwBAC/7MvqgN4QERHRhcKAqQ20Ouk1S/VW2omIiKh7YMBkp73ppVh/vEDy2GXxnI4jIiLqzrjo2w6ZJbWY981uixEmmQBcNyQEc4eGdlDPiIiI6EJgwGSH3/dnSU7HTevtj9dnxXdAj4iIiOhCYsBkh4o6nWR7w+kF4EVV9ViyIw3HcisR4+eCW0dFwN9NcyG7SEREROcR0wrYYfPJIty8aI9F+8z+gdAo5VhxJBfV9QZTu4NSjiv6BeDaIaEYFOZxIbtKRERE5wEDJjuIoohX/z2Or7eltfnaF6/ojZtHhrd/p4iIiOiC4S65Vmh1BhzNqUBZrQ7/uywWgWcxzfb26iTU1OvPQ++IiIjoQuEaJgDJhdX4YVcGCqu0GBXljasHBeOfw3l4eUUiKup0UMoFXNEvELkV2jbfu6pej7TiGvQJcjsPPSciIqIL4aKfkjuUVY7rv9xptgvOy0mFkpqGdrm/WiHDnmcmw81B2S73IyIiogvvop+Se2PlcYuUAWcbLAW5W07Z3TUuksESERFRF3dRT8nVNRiwJ630nO4xva8/FHIZruwfiIm9/LApqRB/HMiBwWjEjPhAXNLHv516S0RERB3log6Y1h4vgPEcJiRHRnrhkxsGmbWNj/HF+Bjfc+wZERERdSbdNmA6nFWOI9nliPR1xogeXhAEAQBQVtOAjUmFUCvkKK2ut+tearmABoOI5rFViIcDXruqL7Q6A5YdzMHhrHJE+jjj2sEhcHPkFBwRdQ16gxF708tgFEUMCfeESnHRr9QgktTtAiajUcRjvx3GnwdzTG2jorzwzc1DsPVUMe7/6YBpzZKHowoyAa2OMo3p6Y1dqSVoeUqMvwv83TS4/stdOJRVbmr/blc6lt07Ct7O6nZ8KyKi9neqoArzv92LrNI6AICfqxpf3TQY8cHuHdsxok6o2/0qse54gVmwBADbk0vw3Y50PPH7YbMF3mW1DQhyd8DpwScLPi4qXNU/CDqDZUS1KakIyw/nmgVLAJBVWocl29PP9TWIiM67x347bAqWAKCgsh73Lj0A47msVSDqprpdwLQtuViyfeXRfJTVWtaEyyqrg1RihZn9g7D8vtHIr5LOvSQTBBzNqZA8dsRKOxFRZ1FQqcXhbMt/q7LL6vDP4dwO6BFR59btAiYnlVyyPcBKlm4rg0sYGeWFADcHZJXWSh53UMnQ09dZ8li0lXYios5Co5RDZuUfwB92Z1zYzhB1Ad0mYKqo0+HO7/bh882pFsdUchkWTIzCpF6Wu9c8rCzQ3p1Sgt2pJUiQ+A0MAPqFuOOqgcGI9HEya/d2VuHW0RFn8QZERBeOm4MS/ULcJY+lFtVc2M4QdQHdJtP3Qz8fxF+HzIeR5TIBI3p44oFJ0Rga4Ymaej3eXXsS/x7OhUohw+Q4P2w9VYxThdVW76tRyiwSWwLAE9NicO+EKJTXNuD7nRk4fHpH3s0jwhHo7tDu70dE1N72pJXg2i92WbSPivLC0tuHd0CPiDqvbrFLrkFvxL8JeRbtBqOIR6fGYECoh6mtqKoehdX1MIrAtzszYLCxuFEqWAKAKXF+AAB3RxXun9TzHHpPRNQxhkZ4YXrfALN/P9UKGR6eHN2BvSLqnLpFwARAcuE2ANTU601/fn3lcSxvtpjRVrDUxEklR02DwfTxPeMj0dPP5ew6SkTUiXxwfX9M7OWLzSeL4OWswg3DQhHly3/fiFrqNlNyD/x00CwYahLm6YA/7x0FTycVej+/GrXNAh97PTS5J0I9HVFQWY8xPb3RJ8itPbpMREREXUS3GWF6+co+OFVYheN5VWbtGaV1WLIjHQ9P7gnjWcSGnk4qzB0WCl8X6V12RERE1P11m4DJzVGJIeGeFgETAGw5VYyVR/OtrkeSopQLuKS3Px6eEs1giYiI6CLXbQImAIj0kc5/dDy3Eg0G+4MlANAZREyM9UUPK/ckIiKii0e3ycMEALMGBiHC2zwvkqNK3uZgqcmJfMvRKiIiIrr4dKsRJheNEn/cMxJLtqfhUHYFIn2cEOLhgJdWHLd6jUYpw8BQD+xIKbE41ieQi7uJiIiomwVMQOMi7UemxgAAdAYj7vvxgOR5CpmAqwYE4bYxEXDRKHHVJ9tRWFVvOj44zAPTevtfkD4TERFR59btAqbmftmbhdXHCiSP6Y0iruwfhF7+rgCAfx8Yg1/2ZiKtuBaDwjwwa2AQVIpuNWNJREREZ6nbBUwNeiM+2nAKfx7IQXF1favnltScOe7josZ9E5mxm4iIiCx1u4Dp+eXH8NOeTJvnKWRAuLcjRFGEIFgp2U1ERESELprpe9nBbHy5JRX5FVqM6emDpy7thUB3B1TU6TD4lbXQGVp/JQGATCbAYBQR6eOEN2fHY3C454XpPBEREXU5XS5g+vtQDh78+ZBZm6ejCrv+NwlpxTWY9v4WyevUChnCvBzh4ajC7rRSs2OuGgW2PzURLhrl+eo2ERERdWFdbkru4w3JFm2ltQ14b10SiqsaJK8J9tBg8+MTIZcJGPfWRovjlVo9NpwoxJX9g9q9v0RERNT1dbltYLnldZLtX2xOxV+HciSP3ToqAnJZ4zola/XkDmSWtU8HiYiIqNvpcgFTkLuDZLtRhNW1S72bJaCcaWUU6dsdGdh4ovDcO0hERETdTpcLmJ68tFebzg/2cMDgMA/Tx/dNjMKAEHfJcxfvSD+HnhEREVF31eUCpkmxfnjxijirxweFeeD07Bt6B7rim5uHQCE/85pqhRzjon0kry2taT1vExEREV2cutyibwAYH+MLINGiXQDw4fX94aBSoE5ngLuDAsXVDdAbjKagyWAUse64dPbv8dG+57HXRERE1FV1uREmADieVyXZLgL4v7+PwcNRiV/3ZmHIq+sx7q1NGP3mRqxMyAMAbDxRiKO5lRbXejmpcPf4yPPZbSIiIuqiumTAFOPvYvXYxqRC/Lg7Ex+sP4XaBgMAIL9Si/t+Ooi04hqcyLcMlgDA21kFZ3WXHHAjIiKi86xLBkwR3k6Y3jdA8pgoAiuO5Fm0G4wi/jmci7hAV8nrege5SbYTERERdcmACWhcq+TlpJI8llFSI9l+sqAKv+7NsrjOzUGJe8dHtXsfiYiIqHvocqVRmrt18R5sTCqy61wBjWucmosLdMWkXr6YMzQUgVbyOxERERF12REmAHC2s/abQiZAKRcs2tOKqjElzg8Bbpr27hoRERF1I106YJo10L7ab3qjiAaJLOB1OiOu+Hg7Lnl/K04VSO+8IyIistemTZsgCALKy8sBAEuWLIG7u/t5f256ejoEQcChQ4fO+7MEQcBff/113p/T2XTpgGlCjC+enxEHNwf7RpqsSSqowt0/7IfR2GVnJ4mIqBO67rrrcPLkyY7uRrfTMjC9ELp0wAQ0Ftb9457h0Chbf5Wm4rvWpBTVIDFPOuUAERF1TgajiJ0pJfj7UA52ppTA0Ml+8XVwcICv78WVFFmn03V0F86LThUw7UwpwR3f7cOVn2zHO2uSUKlt/ZNepdXh9m/3YfK7W6HVGVs998p+gVhx/2jcOioc1kInhcQ6JyIi6pxWHc3D6Dc3YM5Xu/Dgz4cw56tdGP3mBqw6aplapj3V19fjgQcegK+vLzQaDUaPHo29e/dKnis1JffKK6/A19cXLi4uuP322/HUU0+hf//+puNGoxEvvfQSgoODoVar0b9/f6xatcrsHnv27MGAAQOg0WgwePBgHDx40OLZiYmJuOyyy+Ds7Aw/Pz/MmzcPxcXFpuPjx4/HAw88gCeeeAKenp7w9/fHCy+8YHaPU6dOYezYsdBoNIiLi8PatWvNjjdNBf76668YP348NBoNfvjhB7veITs7G9dffz08PT3h5OSEwYMHY/fu3UhPT4dMJsO+ffvMzv/oo48QFhaGtLQ0TJgwAQDg4eEBQRBwyy23AABEUcTChQvRo0cPODg4oF+/fvj9998lvzZt1WkCpk1JhZj79S6sTSzA4axyfLQhGUNeWYc7vt2LhOwKyWte/fe4RZkTmdCYJsDTsXGaThCAab398MKVvdEnyA3Pz+iNy/sFWtzL01EJT0fpNAVERNS5rDqah3t+OIC8Cq1Ze36FFvf8cOC8Bk1PPPEE/vjjD3z77bc4cOAAoqKiMG3aNJSWltq8dunSpXj11Vfx5ptvYv/+/QgNDcVnn31mds4HH3yAd955B2+//TaOHDmCadOm4YorrsCpU6cAADU1Nbj88ssRExOD/fv344UXXsBjjz1mdo+8vDyMGzcO/fv3x759+7Bq1SoUFBTg2muvNTvv22+/hZOTE3bv3o2FCxfipZdeMgVFRqMRs2bNglwux65du/D555/jySeflHyvJ598Eg888ACOHz+OadOm2XyH6upqjBs3Drm5uVi+fDkOHz6MJ554AkajEeHh4Zg8eTIWL15s9ozFixfjlltuQWhoKP744w8AQFJSEvLy8vDBBx8AAJ599lksXrwYn332GY4dO4aHH34YN954IzZv3mzza2NLp0krcO0XO7EnTfqbzUEpxz/3j0aUr7OpTRRF9H5+tSmbd3PPz4jDLSPDkVNeB41SDm9ntdnxijod7vhuL/aklZm1R3g7YeWDY6BRytvhjYiI6HwwGEWMfnODRbDURADg76bBticn2lyO0VY1NTXw8PDAkiVLMHfuXACNU1Dh4eF46KGHMGTIEEyYMAFlZWVwd3fHkiVL8NBDD5nW2gwfPhyDBw/Gxx9/bLrn6NGjUV1dbVqwHRQUhAULFuB///uf6ZyhQ4diyJAh+OSTT/Dll1/i6aefRlZWFhwdHQEAn3/+Oe655x4cPHgQ/fv3x3PPPYfdu3dj9erVpntkZ2cjJCQESUlJiI6Oxvjx42EwGLB161az50ycOBFvvPEG1qxZg8suuwzp6ekIDg4GAKxatQqXXnopli1bhpkzZyI9PR0RERF4//338eCDD5ruY887PPbYY0hPT4enp6fF5/nXX3/F3Xffjby8PKjVahw+fBgDBgxAamoqwsPDsWnTJrPPc9PXxtvbGxs2bMCIESNM97r99ttRW1uLH3/8sU1f65Y6zQhTVmmt1WN1OgO+25lu0S4XpP8ivLQiEWsTCxDo5oATeY3JKtOLzySzdHNQwlFlWQYlrbgGq4/lt73zRER0wexJK7UaLAGNOffyKrRWfwk/FykpKdDpdBg1apSpTalUYujQoTh+/LjN65OSkjB06FCztuYfV1ZWIjc31+z+ADBq1CjT/Y8fP45+/fqZgiUAZgECAOzfvx8bN26Es7Oz6b9evXqZ3qFJfHy82XUBAQEoLCw0PSc0NNQULEk9p8ngwYPb9A6HDh3CgAEDJIMlAJg5cyYUCgWWLVsGAFi0aBEmTJiA8PBwyfOBxilIrVaLKVOmmL33d999Z/bOZ6vTFE8bEu6J5YdzrR7/ZW8WLuntj5FR3gAatzXOHhSMJTvSLc4VReCNVSfw8cZkHGk2nffgpJ54eEo0ACC3vE7yObnl1v8SEhFRxyussu/faXvPa4umSRmhxS/soihatFkjda095zS12TMxZDQaMWPGDLz55psWxwICzpQWUyrNd5kLggCj0Wh3v5o4OTnZPLf5Ozg4tJ4sWqVSYd68eVi8eDFmzZqFH3/8Ee+//36r1zT1+99//0VQkHnaIbVaLXVJm3SaEaZHpkTD18X6C9Xrjbjrh/2ortdjR3IxnlmWAINRRHywdG241KIas2AJAD5YfwqJuY074Ub08JK8bmSkdDsREXUOvi72JRu297y2iIqKgkqlwrZt20xtOp0O+/btQ2xsrM3rY2JisGfPHrO25oubXV1dERgYaHZ/ANixY4fp/nFxcTh8+DDq6s784r9r1y6z8wcOHIhjx44hPDwcUVFRZv9JBTdS4uLikJmZidzcM4MZO3futHmdPe8QHx+PQ4cOtbru6/bbb8e6devw6aefQqfTYdasWaZjKlXjmmOD4cyynLi4OKjVamRmZlq8c0hIiF3v3JpOEzCFezth3aPj8PKVvdHDW/qLWaXV45llCZj79W4s3Z2J73dlICG70uquNykLV53A6mP5mDUwCIoWc9sR3k6ID2YRXiKizmxohCcC3DRW/+0XAAS4aTA0Qnq651w4OTnhnnvuweOPP45Vq1YhMTERd9xxB2pra3HbbbfZvP7+++/HN998g2+//RanTp3CK6+8giNHjpiNxjz++ON488038csvvyApKQlPPfUUDh06ZFojNHfuXMhkMtx2221ITEzEf//9h7ffftvsOQsWLEBpaSnmzJmDPXv2IDU1FWvWrMH8+fPNgozWTJ48GTExMbjppptw+PBhbN26Fc8884xd19p6hzlz5sDf3x8zZ87E9u3bkZqaij/++MMsIIuNjcXw4cPx5JNPYs6cOWajUmFhYRAEAStWrEBRURGqq6vh4uKCxx57DA8//DC+/fZbpKSk4ODBg/jkk0/w7bff2tXv1nSagAkAXDVKzBsRjvWPjoOnk3Qyyn+PmO98EAE4qc1nFlsLoDadLMJd3+/HrUv2Qd8iX0dacQ12pbb/nDcREbUfuUzA8zPiAFj+e9/08fMz4tp9wXeTN954A7Nnz8a8efMwcOBAJCcnY/Xq1fDw8LB57Q033ICnn34ajz32GAYOHIi0tDTccsst0GjOjIY98MADePTRR/Hoo4+ib9++WLVqFZYvX46ePXsCAJydnfHPP/8gMTERAwYMwDPPPGMx9RYYGIjt27fDYDBg2rRp6NOnDx588EG4ublBJrPvR79MJsOyZctQX1+PoUOH4vbbb8err75q17W23kGlUmHNmjXw9fXFZZddhr59++KNN96AXG6+6eq2225DQ0MD5s+fb9YeFBSEF198EU899RT8/Pxw3333AQBefvllPPfcc3j99dcRGxuLadOm4Z9//kFERIRd/W5Np9kl19K7a5Lw4YZkszZ3RyXKa6VzM71/XX/sTCmBs0aBtcfykVkmvUbJlkenROP+ST3P6loiIrpwVh3Nw4v/JJotAA9w0+D5GXG4pE9AK1d2LlOmTIG/vz++//77ju5Kp/Pqq6/i559/RkJCQkd3pfMs+m7pvok9UVGnw097MtFgEBHorsEDE3vimb+OWmRyddUoMKNfIGYOCMInG5PPOlgCgGDP1heiERFR53BJnwBMifPHnrRSFFZp4evSOA13vkaW2kNtbS0+//xzTJs2DXK5HD/99BPWrVtnkRDyYlddXY3jx4/jo48+wssvv9zR3QHQiUeYgMZ8STd8tQtHc8+ULFHIBIupNLlMwK6nJ0IUgZFvbLA43hZbn5iAEE9H2ycSERG1UV1dHWbMmIEDBw6gvr4eMTExePbZZ80WNBNwyy234KeffsLMmTPx448/WkzVdYROHTC9vToJH29Mtn0igGERnrhtdATu/H7/OT3z+9uGYkxPn3O6BxEREXUvnXZKDgC2JhfbPum03WmlcNac++ss3ZXJgImIiIjMdKpdci21lpdJyvrjhef8zNKahnO+BxEREXUvnTpgum10BM5l7V58kBuuGRxs+8RmBoW7n/0DiYiIqFvq1AHT8B5eeGa67cyp1oyI9MIzl8XC21ll9zUJ2ZW2TyIiIqKLSqcOmADA2/ns67/8czgXRrExIaa9tiUXo6JOOtcTERERXZw61aJvg1HE0t0Z+PtQLuSCgFkDgzAqyguC0FhQt61yK7R447/jSC2usfsahUywKJlCREREF7dOlVbgxX+OYfH2dLO2BRMioZTL8P66U6Y2D0clRkZ64b+EfNjqvJuDsk0jRlf0C8SHcwa0oddERETU3XWagKm8tgFDX12PBoPRrN1RJcfeZyYjtagGG04UwlElw4ojeTicXQGgsW5QkIcDss8hu3eTy+MD8Nqsvm2awiMiIqLur9OsYSqorLcIlgCgtsGA0poG9A12w4OTe6Kgqt4ULAGNxXezy+oQ7ed0Ts+/d3wPfDx3IIMlIiIistBpAqYIbyfJBd7BHg4IdHeA3mDEyysS8c3WNMnrTxa0vk4pxt8F8cFuUMmlX/nLLWnYcrKo7R0nIiKibq/TBEwqhQwvXdnbbMG1St7YJpcJ+GprGr7ZlmZzzZI1L13RG8vvG437JkZJHtcbRbz23/GzvDsRERF1Z51ql9xlfQPQN8gN/x7JRWZpHXxc1HBWKyGKIn7fn3XW971hWCiGRngCAO6bEIX8ijr8uMfyfifyq6A3GKGwMgpFREREF6dOFTABgI+LGttTSrD1VGMduQ/Wn8K03n7QS6xvaiIAVkeeBAB1OgN0BhEqhQCZTMDAME/JgCnIw4HBEhEREVnodNHBD7syTMFSk9XHCtDL39XqNSIApVw6d5II4M8DOfh8c4qpbeMJ6Zpz/m2sXUdEREQXh04VMOkNRny+KUXymCAA4Z6OVq8dFOoOtcL666w4kmv6c3mtdIHdjNJaO3tKREREF5NOMyX3y95MLFyVhJIa6WBmTWIBjK2s+HZzUKFeb33aTi4IOJxVDie1HNF+LtieUmJxTkl1A0RRhCAw0zcRERGd0SkCph3JxXjyj4RWz2k9WFKiuLq+1etzK7S48pPtAIC+QW6S58T4uzBYIiIiIgudYkrut/3ZZ3Wdm0NjvFdRp8P+zHKr5ylkgll5lIScCoR4OpidI5cJeGxqzFn1g4iIiLq3TjHCJJXh2x4VdXqLNrkgwNCi2oteYngqt1yLd6/th/UnCuGqUWLO0BDEB7ufVT+IiIioe+sUI0zT+wa0ejw+2HIKzVrGbpkAfDd/iNXjza+/sn8QPpk7EK/P6stgiYiIiKzqFAHTpX38ce/4SEitHuoT5IrPbxxoFjT5u2rwydwBkvfydlHDzUFlc9TKw1EJGZcrERERkR06xZScIAhYMCEKKxPykFZyZmu/Ui7g5Sv7INDdEcvvG42UomroDSIq6hrwtZWacpV1OkT4OMFBKUedzmD1mbkVWny3Mx0n8quhNxgxo18gxkb7tPu7ERERUdcniKJ4tuXZ2tX3uzLwf38dtWi/b0IUHpt2ZjH20t0ZeGaZ5XnNeTqqoJALKKxqfedcS49Pi8GCCdK15oiIiOji1SFTcg16I1Ym5OGbbWk4nlcJAKb/t7Q/owxfb03F97sykFdeh7dWJ9m8f2ltgylYGhLugdAWO+Ks+XhDMiq1OtsnEhER0UXlgk/JFVZpMefLXUgpqjG13TW2B2L9XSTP35Vagp2pjUkmX7MxzSblYGY5evm7QKOsh1bX+rqmOp0BGcW16CuxyJyIiIguXhd8hOmj9clmwRIAfLElFbEBroj2czZrb1lUt63BEtCYUuBobiW0OqNF7qWWHJRyhHlbL79CREREF6cLPsK0PaVYsv1gZjl+v2ckftqdiYScCjiq5Ph139kltLQmp6yu1eP3TYyCq0bZrs8kIiKiru+CB0wBbhqkthhhAgB/Nw1cNUrcNS4SAJBTXtfuAZO18ioKmYCHJvfkgm8iIiKSdMGn5O4Y0wMty7W5OyjxzpokTHtvC77emgqjUUSQuwOGhHm067PDPKWn2/RGER9uSMbnm1NQUctF30RERGSuQ9IKbEwqxFdbUpFbXgejCGSW1podv3d8JJ64pBc2nCjA/CX72uWZYV6O+HLeILy8IhHbkkusnhfi6YA/7xkFHxd1uzyXiIiIur4OzcOUU16HUW9ssGh3Uslx/4QofLIpGVX1bV/o3dzwHp64cVgYpvT2g1ohhyiK6PvCGlTXW9aha9IUsBEREREBHVwapchKYsmaBgPeWJ10zsESAFzRLwiX9wuEWiEH0JhV/MbhYa1ek5BTcc7PJSIiou6jQ0ujFFS2vmutPRglBtAenRoNuQxYtC0NdRK5maJ8nS3aiIiI6OLVYSNMBqOIl1ccb9M1g8M8MCzCEz3tDGjUChmm9fZDYm4ljudVomn2USmX4fFpvfDLXSOgVph/CjwclZg/KqJN/SIiIqLurcNGmPIq6pBtIy9SS09f1guDwjxhNIr4dFMyvt6ahvI6811tggCIIhDopsEDk3rixq/3IKmgCgDQy98Fn984COHeTjAaRTz8yyHU681HmAaGeSDEym46IiIiujh12AiTl5MaTip5m665ZdFevP7fcRhEEfdN7Il/7h+NuIAzJVWi/Zyx6sExWPfIOGx+fDx+3JNpCpYA4ER+FRb8dAAAcDS3wiLjOABsTiqC9iwyihMREVH31WEBk4NKjttGt23qq6pejy+2pOKdNSchiiLu++kgEvPOBEQnC6rx+soTiPJ1Rn5lPY5kWy7ePpZTif0ZZei4vYFERETU1XToLrmHp0Tjzdl9MTTCE0PCPfD0pTFY9eBom9f9uDsDacU1OJxVbnFsU1IRPt2YDKVcsLzwtKW7M9A3yA09fJwsjl3Sxx8aZdtGvoiIiKh769BdcoIg4LohobhuSKhZ+7AIT+xOK7V6XW2DQXL3W5OFq5Pg4qBEuJcj0ktqLY6fyKuCTCbgy3mD8dAvB3E0pxIAMKmXL16Z2ecs34aIiIi6qw4NmKx5Znosbvh6N6q00sklp8T5IdLHGf2C3XBYYtoNAJZsT8Pd4yLx1J8JFsc0ShlEUUSUrzP+uW80ssvqoFbI4OuqMZ2jNxjx4YZk/LQnE3UNBlzSxx/PXBYLDydV+7wkERERdRkdmum7NSXV9Vh+OBcJ2RVYd7wAlaeDpz5Brvjm5iHwc9Ugq7QW877ZLTmKpJLL8Oe9I/HE70eQmFdpcfzOsT3wv8tirT7/9ZXH8cXmVLO2oRGeWHTLEHy4/hRWH8uHk0qBucNCccOwUAgtC+QRXUB/H8rBZ5tSkFtehxGRXnjykl7o4cN8YkRE7aXTBkzNaXUG7M8og7NagfhgN7PgpKiqHiNeXw+90fI1XDQK/HLncFz7xS6LUigKmYCdT0+SrBmnNxjR/6W1kuVTYgNccLzZQnMAeOrSXrh7XOTZvh7ROVl1NB93/7DfrM3XRY2Nj42Hk7pTDiITEXU5Hbro214apRyjorwRH+yGlKJqpBZVm455O6swPsZH8roqrR7LDuZKBj56o4iMEsu0Ak3HrNWaaxksAcDXW1PRBeJO6oYa9EYsXH3Cor2wqh4rj+Z3QI+IiLqnLvPrZ2pRNRb8eBDHT0+vDQx1x8dzB2JPWinWHS+0el1pTT3CvByR0WLaTiET8OOeDOSU1+GyvgFQys/EjhqlHKOjvLEtudiuvhVXN0BvFFvdmUfU3oxGEXd+vw+pEvnEAKCgUnuBe0RE1H11iREmURTNgiUAOJBZjsd/P4xlB3NavbZvsBuevKQX5DLzYEZvFPHngVw8+PMh3PndPhhbTOm9MrMPNEr7Pj3De3iaBVxEF8L2lGJsSiqyevxIVjlHPomI2kmX+CmfUlRjFiw12Z5cgga9ZfHcJnKZgBeWJ+LNVSfw4hW9cefYHoiSyL20MakIG5PMR6nCvZ3wyORom33TKGQI9XS0Or1HdL4k5lr+nWhudWIBLv1gK7JKLTdFEBFR23SJgMnaVJcgANPj/a1eZzg9apRRUouXViTilpHhUFgZCXrw50NYf7wAAFBW04CdKSWY1scfY6Ol10c10eqN+HVfNqa9vwX7M6znjiJqbzH+LjbPOZFfhSd+P3IBekNE1L11iYApzMsJQ8I9LNonx/rhhmFhuH9iFBxOZ+f2dlajp6/lduoGvRH/HM5FTz/pHzLV9Xrc+f1+vLziGIa9vh5zvtqFCW9vQriXIybH+trso1ZnxNurT7bxzYjO3tiePhgV5WXzvJ2pJSirabgAPSIi6r66RMAEAB/NGWga7ZEJwLTeflg4Ox6CIODRqTHY88wkrH90HHY8NRHujkrJe+iNIu4dH2m16K/BKOKbbemmaT6jCHy3MwO1DfYV4z2WK51Ek+h8kMkEfHPzELx2VV+MivKCm4P0971CJkCp6DJ/1YmIOqUukYepubKaBshkgtUfDgDw3c50PPf3MbM2mQCsf3Q8IrydkFpUjas/34lSO3/rVsgEyTxPLQW6afDD7cOYMJA6hCiKuOrTHTjUosbiVQOC8N51/TukT0RE3UWX+LUzpagaL69IxIM/H8TGpEKrI0RNbhgWhptGhEFxemecu6MS71zbDxHejQu+ndQKDJWY4rPGnmAJAHIrtJjy3hasTMiz+95E7UUQBHx102BM6+0HmdCY7f7qQcF4mfURiYjOWacfYdqfUYq5X+1GfbPdcNPjA/DJ3IEW52p1Bry39iT+PJgDUWyctpvZPwh9g92gOb3GadXRPNz/00HoDOfvtQPdNNjyxASrC8yJzjetzgBBANSK1n+5ICIi+3T6n+jvrj1pFiwBwL9H8nAkuxxA4w+GtOIaaHUGPPvXUXyxJRVFVfUorq7H0t2Z+GF3hilYqtcb8Myyo3YHSz7OKskF5LbkVmiRV8GkgdRxNEo5gyUionbUqTN9nyyowv6MMsljx/MqcTCzHO+uPYmKOh1cNArUSJQzWX4oF89Mj4WviwYn86tR0obdQu9c2x9qhQxzvtoFO2flAAAuagW8nS1r1BEREVHX1ClHmAortXh2WQIueX8LtDrpxJQ6g4jnlx9DRZ0OQGPdOKmgRgSweHs6AMDXVQ2ZndVLXDQKDAn3xLAeXni/jQtm7xrXAw421lkRERFR19HpAqYtJ4swZuFG/LA70+qozuXxAUjItn8L/w87G2vG+blqMCTc065rbh8dYQp6Lu0bAHvirCB3B3w4ZwAWTIiyu29ERETU+XWqKTmjUcT//X3UYs1Sc0PDPRHp42R1qk5KVb0esz7djkemRGN3mn3ZuH/YlQkntQK3jY7A4axytDYjFxfginvGR+Ly+AAIAgvwEhERdTedapdcXkUdRry+4ZzuIZcJppIoLankMjQYrAdjUibE+ODRqTG4/KNtksfvGtsDoZ6OGBjmgdgA1zb3l4iIiDq/TjUl5+6gspljyZapcX5W1ym1NVgCGgvzygSgb5CbxTFfFzW+2JKKZ/46iks/2Ipn/0pgdXgiIqJuqFMFTA4qOeaPjrBoD/F0sPseV/YPxC0jw9v8bE8n65nD/zmci75BblCdLi8hlwHxwW4orKo3O++HXZnYkVLS5mcTERFR59appuSAxvIOP+3Jwh8HsqE3iriiXyBmDwjCyDc32Kzp1jvABaFeTkjKr0JBpRY1dtSAU8oE6GzkDOgb5IqEnEqzNo1CBq3EWqu7xvbA05fF2nwuERERdR2dLmBqSRRF3LRoD7aeKrY4dllff0T6OCOztBYR3k74ckuqWVAlE2B1p92EGB/UNRiwy85F4Pb636W9cOe4yHa9JxEREXWsTrVLTsrBrHLJYCnATYMPrx9gKj/ywvJjFiNQRhEQAMkdbsfzKpFfWS9x5NykldS0+z2JiIioY3WqNUxSMqwEIBV1OrNabbnldZLnWRs+Ox/BEgAsP5yHhlbSIhAREVHX0+kDpoGhHpLttQ0GfLMtzfTxiEivC9WlVtXU61FQyTpy1P0lF1bh70M5SMqv6uiuEBGdd51+Si7Mywl3jeuBLzanWhx7Z00SZvYPxJGcCng7qzEo1AP7M+1PaHk+OKvl8HVlHTnqvoxGEf9bloCf92aZ2mYNCMJb1/SD3N7aQ0REXUynX/QNAEVVWgx5db3kMW9nFYqrGwvqumoUuHVUBD7blIwGw/l7rQhvR0T7uaCsRoc96ZaLxp+dHovbx/Q4b88n6kj/JeTh3qUHLNrfvbYfZg0M7oAeERGdf51+Sg4A3B1V8HZWWbQLgClYAoBKrR4/783EtYNDzmt/XrqiN76YNxg/3zlcsl/vrj2JSq3uvPaB6EIqqNSirKbx79qGE4WS57y/7hRyrKwlJCLq6rpEwKSUy/DQ5GiLdqkxpILKepvTAmqFgHPJJ37/T4fwwvJjWH+iwCxga1LbYEByYfU5PIGoc0grrsFVn27HsNfWY9Ara3Hv0v1wtJKNP7O0Fld/tgMVtfxlgYi6ny4RMAHAjcPD8N38oZjRLxATYnzgKzGy08RZI700y9dFjasGBCHSxwW2U1paV16nw5Id6bjju/2SpVyUcgFhno7n8ASijmc0irjju304mFne+LEI/JeQj7zyOlPW+5byKrRYdjD7AvaSiOjC6DIBEwCMjfbBR3MGINrPBYUSIztAY4HdTzamWLSPj/HBjcPDsOpoPhLzKiWuPDtS2b7nj4qAlzMXflPXlpBTITlSuulkERbfPAQuVn4xyS7jtBwRdT+dfpeclF2p0vXanNRy1NSbjx0JAB6Z0hMNBhHvrj3Z7n0xGEU8MS0GR3IqIIoipscHYkZ8QLs/h+hC25chveNUbxCxOjEfM/sH4ftdGRbHh0V4nu+uERFdcF0yYAr2cMTh7AqL9pbBEtC4zumdtaesrrs4VwKAL7akoqJOh7gAVwS4aSAI3FpNXU+D3ojy2gbT6OiibZapPIDGv1Pf7cyAtaWCfx/OxcRYP6YYIKJupUtNyTW5Y2wPKOXm/xgHuWmgklt/ndYK9/byd4Gnk/U1UQqZgPeu7Ye3Zve1OCaiMes4ACTmVeKWRXtQXH1+soi3pgtkh6BO7Pud6Rjx+noMfW09xry5Ad/tTEdOeesJWK3VaVxxJA9rE/PPQy+JiDpOl8jDJOVAZhm+3JyKjNJaDIvwRHltA/46lGv1fEeVXDJo8nfV4MHJPSGXCXjyjyNo/tm4bnAI+oe6Y2IvX/i5avD7/mw89tthm32bMzQEr87sC9kF+A37t31Z+HhjMjJLazE03BPPTI9FfLD7eX8udR+bTxbh5kV72vWec4eF4rWrLH/BICLqqrpswNRS3+dXo6peb/V4oJsGIhp38Ui5ZlAwbh4Zjr8O5sAoAlf2D0SkrzPeWZOElQn5UCsbR68ySmrt6s/Vg4Lx9jX92vwebbHmWD7u/H6/WZuLRoFNj43nonOy2/0/HcQ/h63/snE2HpkSjQcm9WzXexIRdaQuOSUnRSFvfTQnt0KLFfePxpfzBqGXv4vF8d/2Z0MuE/Ds5XF4bkYcAtw1uOeH/Vi8PR35lVpklNTaHSwBwO/7s3E0x3KdVXv6YXemRVuVVo/l7fzDj7ovURRRUNG+u9pcNYrznjyWiOhC65KLvqVcPSgYX21Ns3pcKRfgqFJgam9/PPVnguQ5CTkVcFIp8Ohvh7A33b6adAKkE2gCwNGcCvQJcrPrPk1EUcShrHKU1jRgaIQnXDRKq+dWWckm/sbKE1h5NB9BbhroRWBsT2/MHBAEZStrvKj7Kq6ux+LtaTiSXYFIH2fcNjoCIZ6O0OoMuOO7fdjThu/1Jy+JwY6UEmw5VWxxXCkXMKmXHx6eEg1/N007vwURUcfqNgHTo1NjUF1vwO/7sqCTWI06o18gHE7vlOvp64zdaZY14Hr6OuOuH/bjuJ15mnxd1HhuRhwWbU3Dgaxyy/v5OZv+bDCK0BmM0Cit79Yrqa7H/G/34fDpezmp5Fh4dT9Mt5KmYEqcnympYHP1eiP2NHu/fw7nYvWxAnx10yAAwMGscpRUNwZkbg7WAzLq+iq1Osz+bIdpdHTrqWL8fSgHy+8bjU1JhdgqEfhY8+Cknrh7fBRmDgjCqDc2wtBiNv/hydG4d0JUu/afiKiz6DYBk0Ypx+uz+uLZ6bHIKq3FW6uTsCGpEEqZDFf0D8SLV/Q2nfvQ5GjctGg3dM0K9E6J84NaIbM7WOrh44TVD42FUi6Dt7Ma874xv9/4aG/E+LnAaDTiiy1p+HJLCspqdYjwdsTC2f0wRCJXzRsrT5iCJQCoaTDgkV8PYVSUF9wdLXfxzR8VgYOZ5VibWGCzv+uOF2BjUiE+2pBsCrIcTn/OZg4Isuudqev5Y3+2xVRyWa0Oi7enI6fc/ilmAFCezu79b0K+RbAENC4eZ8BERN1VtwmYmjipFegV4IpvbhmC2gY9ZIJgMaozItILi24Zgqf/TEB2WR0UMgHOanmbftsur22AwShCKQeG9/DCXwtG4YddGSiqqoe3sxo7UkrQ54U18HRUobT2TFbytOJaXPvFTvx4xzCMiPQ2u+fa45aBT73eiC2ninFFv0CLYxqlHK/M7IO5Q0Pw5qoknMivarXPn25KMRuRqtMZ8MTvR9DDxwmpRTUI8XTEwFB35pHqRlKKpGsaphRVI9RK+R4nlRw1EjtKe/o2jpjqDJbZ7VtrJyLqDrr1ohZHlcLqFNgH606ZSjjojSKWHczF6ytP2H3v0hod3mh2fu9ANzw/ozc8HFX4eW8WMksbf3tvHiw1EQE88uthi9xJrlbWK7lKlKCoqNXhtiV7Mey19bh1yT6U1kiXimlOahF6g8GIKz/ejod+OYTZn+3ADV/vRm2D9d2G1LX0s5Jiol+IO24aEQZ1i5pwLhoFPrtxkEWNxEFhHpjYyxcAcGkff8mklZfHWwb1RETdRbdJK9AWyYVVmPzuFrvOHR3ljZFRXli4KsnimINSjuuGhGB/RhlCPR1RUafDtmT7R6l2Pj0RAW4Opo+/3JKC1/4zD9qUcgHRvi7QGY2o1xsxpqc3HpocjZdXJOLvFnmnNAoBWr31L2drC9Sb45bwrk9vMKK0pgEOKjnmL9lrtomhh7cTfr9nJDydVDiYWYaPNiQjKb8KfYPc8ODknogNcEVibgXeXJWEwkotpvb2w13jIuGoOhO4LzuYjReWJ6KiTge5TMC1g4Px8pV9oODGAiLqpi7KgCkhuwIzPt7W6jlymYDNj49HsIcjdqaUYM5Xu9q1D0q5gAP/NwUuGiUKq7SorNMj3NMRX2xNxZLtaSiyUlwYAII9NMgr18Jwnr5y/UPc8deCUefn5nTe/bE/GwtXn0BBZT18XNR4cFJPOKrkOJxVjihfZ1w1MBjOauuz8ScLqnDD17tRVNWYsV4pF/D2Nf1wZX/ztW5anQFJ+VUIcNPA15W74oioe+t2a5jsERfoilBPR9O0mRRXjQJB7o2jPzF+zvBwUqKsRnob/9m4ZnAI5DIBC348gP8S8iCKQIS3E164Ig73jI/CSysSrV6bXabF2SwzkgnWy1k011qZGOp4lVodVh3Nh1ZnwJQ4PwS4OUBnMEIhE3AoqxyPNstGX1RVj2f/Oopf7xqBWQODLe619VQRFm1LQ0FlPQaFu8NZpcRv+7NQ3Cxg1xlEPLPsKCbF+pkFWhqlHP1C3M/ruxIRdRYXZcAklwn4ZO5A3P3DfuSUSyftu2FYGEQReGPlcSzZkY56vdEs4PBxVrU6CtTc/FHhqNTqsO54IdQKOa4ZFIwHJvXEa/8dx79H8kznpRXX4OZFe+26p1ImQ0MbF9kqZAIa7BiWunlkeJvuSxfO4axy3LRoj6l+4QvLjyHQ3QHZZXUI9XREsIeD5HV/7M/G0BY7M9cnFuC27/aZPk5sZYdodb0eBzPLMKanTzu8BRFR13NRBkwA0DfYDZsfH4/D2RWortfh5z2ZWJtYAI1SjghvZ+iNRny44RS+2HKmYrtRbAy2vpo3CBuSCvHDLstM21L6hbhbTGcAOKeM3A0GI6bE+mLdiULYO6lqLVgaFuGJxNxKhHg64r6JURjb0xsn8ishio2FiZt2zYmiCL1RhEImcCddB3lu+TFTsAQ0fk82bV7ILK21OmqqMxrRoDfix90ZWH+iEI4qObacLGrTs31dOO1GRBevizZgAgCFXIZBYR4AgHHRvnhz5XF8tjkVCTkVSMipgFRIYDCKSC6qxtEc+/I1AcBnm1IkA6ZzCTliA1zx1c1DUFxdj3q9EZd/uBVltW2fMpQJwKc3DDTVnsssqcX0D7eZRhti/Fzw2Y0DkVxYjYWrk5BcWI0IbydcMygY1w8N5fTdBVRTrzfL09UWg8M88PAvh/BvQp7tkyWM6emNGImSQkREF4uLOmBqLq+izmw0CbC+o0yrM+BUQes5j5qz9lv/1N5++GlPlt33aaJSyPC/y3pBqzNAo5RDKZPZFSx5OSlR0mId1swBQWaFeh/59ZDZ1ExSQRXu+G4fUotqTJ+PtOIaLFydhHfXnsSCCVF4aHJPjjhdAGqFDO6OSpTb+FqHezsiu7QO+mYL1v637OhZPdPdUYmrBgRBo5Rh6nuboVbIce3gYNw4PIxfcyK6qDBgOi0xt9KuBdFKuYDCqnrJxH7W1DYY8NKKY/i/6XFmP2QemRKNX/Zm2fXc5mL8nLEyIQ+3f7sP9XojRvbwglohQ72+9TVNpTU6XD84GNtSSiCKwJX9A83SB+RXaLEvw7KuWEpRjeT99EYRH6w/hQGh7hgf42v1uTX1evx1KAenCqrRJ8gNl8cHtFoihswZjCJWH8vHpqRCRPu5mJW9kVJeozMLls5WbIAr/rxnJJ744wj+aTZ9nJBTgZKaBjw0Ofqcn0FE1FUwYDrN2jZrpVyATBBQrzfC31WDF6/sjffWnmzz/RdtS0esvyuuaVbF3cdFgxuGheH7XRltuldCTiUSmk0J7kgtgbuD0mbAJAI4kV+NEI/GxcEz+gWaAhdRFLH+hO0SK1J+2pNpCpga9EZU1+vh4aiEIAioqNXh6s934FThmYzTS3dn4Kc7hjNostPjvx/GnwdyTB8r5QLCPB2RXV4Hrc78ay4XBJTXnf1uTg9HJUZFeWNAqAeuGxKCkpp6s2CpyTfb0nDv+CioFMy7REQXBwZMp2VYmTbTKOXY/tREFFfVI9TTESlFNVbLTdjy3c4Ms4AJAJ6bEYfaBgP+OJB9VvdsUl6nw9WDgrDlZDEKT+fPkXIou9z05+WHc/Hb3SMQH+yOTzel4K3Vlsk57bEvvQyZJTX461Auvt6aikqtHjF+Lnjpyt7Yl1FmFiwBwMHMciw7mIM5Q0PP6nkXk2O5FWbBEtC4zd/TWY1kiZE/qRpv1kglMi2r1WF63wBc2rex4HOSlXI7VVo96hoMDJiI6KLBgOk0PyuJ94I9HOGqUZrKljz391GzIrttUSZRJkUpl+HWUeHnHDABwKwBwejh7YyFdgY+9Xoj7vhuHxZMiMJnG5Mlz3F3UNocsSipacDYtzaZtSUVVGH+kr0YFGZZZBho3B7PgOmMSq0OX25OxeaTRXBzUCLC2wkuGoXVAOhkfhUEAXbvkGwpxNMBpdUNklPLzdfc9Q50hZuD0mxnXlO7qwP/+SCiiwd/PTxtdJQ3eknsArpjTITpz1qdAbttrB9pzaRe0ut8ege6mnbr2SPAzTK483ZWI8rXGZ9YCXysKaisx3N/H0O1lTVZD55DiZSaBgOMVpbO9/BxOuv7nm/5FVrsTS9Flbb1QDGvog5Ld2fg70M551R/z2gUccuiPfh4YzISciqwLbkY3+/KwKebUvDF5lTJa1w0CkyW+H6yd8An2tfF6jq8YT28TH/WKOV49ao+UDQrHueiUeClK/tw0TcRXVT4KyIa192cLKjCe9f1xw+7MrApqQjeLmrMHxWO8TG+OJxVjnBvJzirFXbtUpLSP8QdD0+RXiQrCAK+umkwXvvvOP5LyINRFKFRyCVHdkI9HXDn2Ei8sPyY2cLeG4eF4q9DOW1ajG6P4T28MG94KLYll6CmXgcRgqlkhj0GhrjjeG4lSpoVBw5yd8C1LaYmOwODUcT//X0UP+/JhFEEHFVyPH1pL8wbEW5x7p8HsvHE70dMXwNvZxV+uH0Yevm7Iq+iDr/uzUZRtRZje/pgcqwfZFLVak/bmVqMA5nlbeprVlkdLuntjxn9Ak9nihcxNc4f42J88MyyBKsbCQQBuGl4GLacks7BNCzCE/2C3czaLo8PxOAwT6xJzIdaIcMlvQPg5ihdKJqIqLu6KGvJNbf+eAGe+jMBRVX1EATg8r4BCPFyxNpjBajU6lBa0wCdQYRaISDC2xmpRTV2Z9iWC8DTl8aiX6g7Bod5oE5nQLVWDx8Xtc3fzr/emopX/j1u93u4qBWY1scfv+8/96m95ppnNw/xdMCSW4Zib3opXl95HBV1rY+qNNXjAxoXCScXVqN3oBvmjw7vlEkQf9iVgWf/stx+/98DYxAX6Gr6uFKrw/DX1qO2RXA6oocXnr08Ftd/sQtV9Wc+N7MHBuOda/uZPs4uq8V7a09hV2oJvJ1VyCytPascWkqZgN5Bbujp64z5o8MRG9AY6GSV1uLlFYlYkyi9iD/K1xnVWj3yK7UWx966Ot5inR0REV3kI0wl1fW4d+kB0+4yUQT+OSKd2K9eL+JEiwWwUotmmzOIQEGVFgNC3PHKv8exdHcGtDojYvxc8Nqsvq1Ow7W1mGnV6Z1p7a35SEVWaR3eXpOEz24cBAjAU38kWL3ORaPAKzP7INjDEQDw/Ize7d639rbyqPTXftXRPLOA6VBmuUWwBAA7U0vwzpqTZsESAPxxIBu3jgpHnyA31Dbocd0Xu0wleayV5rGHzijiUFY5DmWVY+3xAvz7wBgEuTsg2MMBx1spc5JcKL1pwUXTGHQTEZGli3oN07rjBTa34rdG3so0S5MqrR5fbU3DN9vSTFvAkwqqcOviPa2ukZka54dQT8c29WdyrB8GhbpLHuvp64Tr2mHkYFNSIf49kodvd6RbPSfCyxH3TYhCYm4ltp4qQlcZxFQrpNMcqFukP7C2QcDbWYWjORWSx45kN7b/l5B/TkGSNeW1Ony/szE9RV6FFllltp/h56qGw+l3i/FzwZJbh5g2NxARkbmLOmBSyM7t9e1JDjixly9+32+ZzbtSq8e649bzHmmUcvx853DMGmhZUsWat1YnYekdwzAo1HLkytNJjTevjseye0ZiSLgH5Gf56oIgYMGPB3A8T3q7uVIuIKdci9dXnsAXW1Ix75s9ePpP6yNRHam4uh47kouRX9E4NSVVuFalkOHK/oFmbTH+LhjT09vi3DvG9EBPP2fJZwW6NwZZhVWW02D2shWeZ5Y2phlwUinsCuYdVQoc+L8p2PPMJKx6aIzVHY1ERHSRT8lN6e0Hl38UqNKe/Q4nW77ckmJ1zZOt9ASB7g5499r+cFDKsXS37UK/+zLKcCS7AifyLadjdqeV4qpPtkGjVGBvumU2b3tJTUU11/hO5u/1894sJBdVY97wMFzRL7BT7K56f91JfLIxGTqDCLlMwLQ4P/x3NN/ivJE9vHDdF7ug1RlwSR9/PHlpL7hqlPj8xkH4cP0prDqaD4VcwJyhobhtdAT6h7hjd2qpRTD9yopEDArzwJgoHyxE62kf4oNccSK/2uL7ZsGEKER4O2F/Rhl+3GP5/dAU8Lg5KjEg1B37bHydPR2V+P1AFq7qH9QpviZERJ3ZRT3C5KpRYsmtQxDj15hOwEEpx8RePmj+y3nTHx1UZ5eVel9GOcK9LLfQa5QyTIn1Q1lNA5buzsDi7WkWUzU55XX4ZGMy3ByU8LazyG16cY3VnXIHsyqwM7Wk7S8BwN9Vjcv6nv36ln3pZXjw50N4f92ps75He9meXIz3150yBawGoygZLAHAppNFyCmvQ0lNA5buzsSCpQcAAE5qBaL9XFCp1SGlqLG23usrT2BIuKfkDsDkohr8vj8bfYPdcNe4HmbHXDUKyITG77UwL0ck5FSaBUsKGTB3WCgenNwTswcF47kZcRgabj4a1C/EHdcPaXzu7/uzLYIlB6UM8hZB0f7McvzfX8fQ78U1+GxT29JREBFdbC76XXJAY1mQoup6uKiVcFDJkVteh7WJBXBQyjEpzhdGI3Akqxy3fbfvrO4/uZcvciu1SMxtHPnRKGV49aq+yCmrxccbzoxAyWUC3r22H67sH4RdqSW4ZfEei9IXrVErZJgS54sVR6R/+J+LKXF+mDssFLcu3ntO93FQyrHnmUlw6cC1Ms/+lYAfdtkesbNm3SPjGrfxv7/FInHkW1fHY21igeQOtWsHB2Ph1Y275U4WVGF3agmCPBwwtqcPGgxGbEsuxp3f7be47v+mx+K2MeZBls5gxJpjBTiWW4EYfxdc0sfftAbrkve3WGxQAIAPru+PtOIaq0Hrt/OHYly0j12fAyKii81FPSXXRBAEs23uge4OuHlkuNk5o3p6wUklbzXPkUImSK5rKq1tMAVLAKDVGfH4b4ctcuUYjCKe+/sYpsb545V/E9sULMllAp6YFoPXVp6w+5qWHJQy+LlqkF5iWSbG01GFsT19MDjMQ7JAr73qdAYUVNZ3WMCUUVKD3alnn3wUaMzYvu1UsWSW7eWHczEg1EMyYOrlf2anXbSfC6L9ziRKVchl2JEsPfq3I6XEImBSymWYHh+A6fEBFudbK42jM4iQtTL19sveTAZMRERWXNRTcm2xeHuGzaSQd4/rYbG131EpQ6pEzS9r68Ur6nQ4nF2GoznWt4U3F+iuweuz+mL7kxMxLsYXBhsL0RUyweriYSe1QjJYkssE3DA8FHKZgGBPy4XRbeHmoMSpgiqLUhttUV7bgLqzSNBZUavDNZ/vtKht11zT50ajlEkuAvd0UiE+2M3qomqZIOCmEWEIcje/NsrXGdcMDm61f64a6d9f3BzaFlyOlViQDgBG0WjRr+Z2p5Z2mR2NREQXGkeY7LRoe5rNcz7fnIq3ro7HtuQSHM2pQLS/C+4e1wMzPtrWpmf5uKgR5O5g1/bz3HItZsQHYNmhXBzNKbc5Ctbazr7iastad32DXPHYtF6ID3ZHSXU9/jksnatILhMgiqLVQLBJRZ0O9yw9AI1Shteu6otZA1sPIpo7VVCFJ/44goOZ5VArZLh2cAj+7/I4mwVgRVHEsoM5+HxzSquFiYHG5eoyoXF6ytNRhZsW7UHe6V10Tio53rm2H9QKOYaES+fQOppTgaf+SMALV8QhKb8KSQXV6BvkiuuGhNocVbt6UAi+3JpqNrIoCI3rl9riiUt6YVtyscXX85llR/HPfaPh76pGfqXl56GkpgEHMsvbVKaHiOhiwTVMdhBFEVHPrLQ5egM0lv3Y8sQEUwCxPbkE//d3AtKKLUdurPF0UuHSPv527YyL9HGCSiFvNVHhuXh9Vh9cNSAYyYXVqNPpcc3nuyTPUysE1Ovb9q2klAvY/tREu7J+N+iNGP/WRuRWmG/Lv2d8JJ68pJfkNScLqvDh+lPYeqq4zSNaN48Iw4tX9kGD3oitp4pQpzNgbLSPKU/RxxtO4e01J61eL5cJ+OG2YRgR6WX1HCm7U0vw5qoTOJJdgShfZzw8JRpje/rgz4PZOJJVgUhfJ1w7OATujq1vArh50R5sPmlZ/uThydGYMzQEI95YD6nNm29f0w9XD7I/iCUiulhwhMkOgiBgXLQPNpwotHluTnkdkgurEenjhHuXHrBanqI1pTUN+GVvFt6aHY81xwtQ26DHtN7+SMqvMguiZAIwOMwDv+xr33IozS0/nIu3Vp9EaU0D5DIBKrmAhhbpEHxdVCisshydskVnEPFfQh5uGRlh89wdKcUWwRIA/LYvyyJgMhhF/HUoB88sS2jTOrDmmrJ1qxQyTIr1M7XnV2jxyr+JWG1lV13zPnyxJcVqwGQ0ivh1XxaWH86FXCZg9sBgXNk/EMN6eOHPe0eZztPqDLjui504nH0mIeb3uzKw7N5R8HZWW32+0crvQXqjEb6uGozt6YONSZYB1QAriU+JiC52DJjs9NzlcTiYWWaz5pcgAO6OSny2KeWsgqUmeqOI8jodvrppMHQGo2lL+PAeXliTWABntQLXDwnBd6ezO7cU5O6A6no91EoZCiWmX+y1M+XMAmmDUYTUZN+gME+stBFAaJQyyeDlr4O5dgVM1nJWtczUXlRVj3nf7JbcJSbFy0llVhi4yaRefhZtBqOImxbtxskC62ugmstuJdv2a/8dx9fbzkzzbj1VjKzSWtw/qafZeX8dzDELloDGEjVLtqfjsWkxVu8/Iz4QW08Vm7UJAjA9PgA55XXQKOVmdQIBYP6oCET6SCfeJCK62DFgslO4txNWPjgGYxZubDXh5GV9A+DuqMQn7ZDXprpeh1sX78Gmk0VwVitww7AwPDo1GjP6nck83dNKXqXZg4LxyJRoAMB3O9OxcFUSquvPT4LOoiot4gJckWhlWjDazxlTe/vh4w0pFscOZ5ejolYHNxt18EZFecHNQWkxtXZ5vHkW7nfXJtkVLDUtsJYKluQyAQ/+fBArj+bhxSt6w+v0SM7utBK7gyUAGBYhnTm7rKZBMtB9b91J/Lg7A9P6BOCRqdFw1ShxNFe61EqClRIsTa4eFIykgip8tzMdOoMIV40Cz06PQ4CbAy55f4tpXRYAyAUBr1zVG9cPadtaKSKiiwl3ybWBv5sDvpg3CH6ujT9AZQLQy98F/q5q+LiocdvoCLx1dTz2pZed9VRQE7VCwN+HcrExqQii2FiT7vPNKXj9P/O0AdcPCUFIi51r/q4a3DDsTPLEm0aEY/f/JmFizPnZMq5RKrBk/hCr0zlje/rgyn7SJV5EsXGayBZHlQJfzBuEALcz650mxPjg6cvMp+M2SUwztaSQCQj1dERliwzvTfveDEYReqOIFUfysODHA6bjFVZGF9VyAVf0N9/eH+LpgPsnnhktKq1pwLtrkjDvm914fvkxyezvRhHIq6zHkh3puPv7xnxMzVMPNBfj39herzegoFILY4v1dTKZgP+7PA67/zcZK+4fjd3/m4xrh4Rg2YFss2AJAAyiiP0Z5cz2TUTUCo4wtdHEXn7Y9qQPThVUw9tFJblgWW1j15YtEd5OmDs0FK/+d9zi2KLtaais02Hh1fGQyQS4O6qw7N5R+G5nBhJzKyCXCTiZX4Xhr29AjJ8LRkZ6oV+wO/5NyMMGO4KJs3HN4GD4umjw0x3DMfuzHTjWLOeUj4sa80dHIMBNg17+LhajPyMjvUwjOLb4uWrw0ORoiKKIEZFeCJPIoN7ajrkgdwdc0S8AVw8OwaUfbLU4LjVuuCu1FKlF1ejh44wRkV6SU4tjo33N0kDIBOD20RHwd9Mgu6wWWaW1+N+yo0grPpNeQrDyvCY7UkpwPK8SswYG49sd6UhplprC21mNm0eE4YvNKfh0Uwoq6nQI8XTAc5f3xpQ486lETycVPJtlibe28zK7zP5NCUREFyMGTGdBKZchLtDV6vGBoR6I9HEy+yHXFq/O7IM6nfXUAL8fyMbW5CJ8ceMg9A/1gLezGo9Micbe9FJc+8VOU0LFE/lVdq/lsddVA4KQkFOB5MJquDsqcevIcIyOasz7o1HK8ctdI/DL3iwcyipHhLcTbhwWCl/XxqDy47kDce/S/aZprf4h7nj7mn42nymKIt5YdQJfbE41tY2M9MLXNw+Go8r8W9jJSgmbMVFe+Hb+MMhkAjYlFcJgpb6flNoGA+oaDPj7UC56+bsiIafctMOsl78L5DLBLNeWUQRe+fc4Np0owkaJnWpAY7BkK2g6kl2O2ABX/HHPSCzdnYlDWeWI8nXGzSPCsT+jDK83S1KaVVqHe5fux7pHxkkGkk2GRXjhq62WKTKG92jbbj4ioosN0wqcJ5kltXh62RFsTy6BWiFDoLuD2QhDa+4a2wMPTu6JYa+tt1kY+K6xPfDUpb0gCAIe+vkg/jqU2x7dl9Q70BX/PjAGoigivaQG76w5iZVH82EwihgW4Ym3r+mHEE/HVu8hiiKSCqqgkMkQ6eNk1zTQ/oxSzP5sp0X7Y1OjMSXOH/V6A3oHNiaTnPnxdhzKLrc4V62Q4fDzU3Egowxzv94t+RxHpQy1LUaPgj0csO6Rcbjx691mGc4dlHK8fGUfzBoYhIGvrEW5jc0AUvzd1CisrLeau2pyL198fcsQyWO3Lt4jucvtkSnReKDFwvHmDEYRD/x0EP8mnMmnFRvgip/vHN7mBJlERBcTjjCdJ6Fejlh6+3BUaXVQymW47du9dgdMnk4qOKoU+HjuQNz13T5o9dZHQ77Ykooh4R6YHOdvM7hqyc9VjYI27KC7f2JPHM2pwEsrErEnzby8yO60Usz6dDsm9PLF4HBPXNk/0FTbrDlBEMxKhACNweWGEwVwc1RiWm9/i1GjjSekR2k+25RiyoUU6umIT28YiNgAF8mAqV5vxGv/HW9159qkWD8cz69C8ulM4P6uGnw8dyA2JRValIOp0xmwI7UYVw8OhpeT6qwCpvyKelhJGA4A2JdpvQSNtQSkehsjZ3KZgI/nDsDclFAczCxDuLcTpsb520z+SUR0sWPAdJ41ZXd2Udv3qXbRKHDVwMYF0uOiffDXglG47MOtrWbQfmNVEibH+WNynB/W25ErCgAcVfJWg6UANzWu7B+E3Wml8HJS45aR4egb7IYxb26wWCzdpKi6Ab/uy8av+7Lx54Fs/HDbMCjk1n8Q1zUY8Nhvh81GO3xcTuCnO4YhyvfMYufma3Caa57RPLO0Fjct2o3P5g7ET3uzJM//Y382wr2tj4DlV2rx7GWxcHZQQG8QMSjMA0q5DO8nSX9Ok05Pd946KgLP/nXU7JhSLrS6m7JJa1/XIHcHbDtVjE83JSOjpBaDwz3w8ORohHs7tZI2INDK3ZqfJ2BUlDdGRUmXUCEiIkv8tfICGRHZ+g8nR5UcY6N98NMdw80WkvcKcMUX8wZBKbc+FJFSWI2f9mTi70M58Ha2ngHaQdk44tPT1xmTYy3zDDV338SeeOrSWCy7dxQ+uWEASmrq8dDPB60GSy3tSi3F2kTruZn0BiOu/3KnWbAENOZRevXf4yis1OLNVSdw86I9yCiphZuVOmvNldbocN1X0tNtQOMo08hWgoS96WW4ZclefLwh2RQsAUBcgPR6td6n17Fd2scfA0LdIRMaF3z3DXLDbaNt55ZqEuHlKDnSNK23P25evAc7UkqQU16Hvw/l4povdqKiToerBwXjttERpu8LV40Cb86KN+2eIyKi9sU1TBdIbYMe13+5C0eypfPnrLh/NMK9nfDbvixTWYw5Q0NNoyupRdWY981u5JRbZrtumYAQAMZH+8DDSYW96aXwcFThkj5+qKrTY9H2NDQYRChkQqt15VbcPxp9gtxQqdXh+i92Wc2x1JoYPxcsv3+U5NTcqqP5uPuH/ZLXKWQCfF3UZpm9vZxUiAt0wZHsSoR6OiAxr8quUjXNTY8PwKsz++CGr3eb7eSTMiXOD4PCPLA3vRRbThZBgGCWCsDDUYll946C3mjEzE92WOS4undcD+iMIhZtT7fZz8enxeDGYaH4bHMKtpwshpezCreOCsfv+7PxX4Jl0Pnylb0xb0Q4gMZ0BTlldYjydYaDlQXvRER07hgwXUBanQH3/3gAa4+bT/EMDHXHt7cOwXVf7jYLTNQKGeKD3XDN4BBcMygYoggMe209iqrNp9IEAWj5VVQrZNjzzGT8dTAHb61uW9LKmf0D8f71AwDYrplmi5eTCl/eNAiDwsyTOH6w7hTeWyd9X6kElQDwwKSepmScj/12GL/vt78kzMBQd3wxbxB8XDQwGEVsOVmEjNIa/HckD3vSra8Vaik+2A0Te/liztBQeDiqMOqNDRZfjyazBgbhukHBuHnxXqvr0MK9HLHs3lHwkJh2nP3ZDuzPsOzbggmReHyadP08IiI6P7iG6QLSKOX4fN5gfLoxGT/uyUSVVodQLyekFtcg/sW1FlvM6/VG7E0vw970MqQW1eDJS2Lw3IxYvLHyhNlIk1TIW683Yup7m20u6o4PcoOzRoEqrR6B7hpMjw/E9L5nkjC2JZiQUlLTgAd+OmQqSNwkNsD61FG4l6NFORAASCk6k2X7xSt6o0qrw+pjtsvPqBQCDmSWY/ZnO/DYtF64ol8gJvTyBQBklNS26R3La3V4aHJj0LbhRIHVYAkA/jyQA1eNEssWjMLXW9OQUVKDfiHucNUokF5Sixh/F8wZEmo1y/noKG/JgGlMz/OTgJSIiKxjwHSByWUC7p/UE/dP6onPN6fgjZUnbF8EYPH2NOSU1eKfI3m2Tz7Nnh1wfYLd8NpVfc3aKmp1+HlvJk7kV6Fa2/bdXy3llNfhWG4F4oPdATTmF3prdZLFeXKZgCemxUApFyQDpvggN9OfndQKzOgXaFfA1KBvjCgzS+vwwE8HEeCmwZDwxhGvm0aE49e9WWYLyFuTXVaLt1cn4Ybhoai145o/DmTjhSt6451rbeebaiKKInamlMAoigjzckRGyZmkkvOGh1ktuUJEROcPA6YOtHS3dOFcKfV6Y5uCJXtdPSjY7OPy2gZc9ekOu1Mg2Ktpt6BWZ8D8JftQ3GJkZkCIOz6fNwh+rhpU1+vx+/4cs+nJGD8XzBlmXuss2KP1nE/W/LI3yxQwRXg74fd7RuKTjcnYl16G/ErLNWLNGUXg443JWLo7A9/NHwpHlbzVwKllyRJbjEYRD/x8ECuafa1DPB1ww9AwjIn2Ru9At1auJiKi84UBUweqbkPeJAelvNXs32dj5oBADAz1MGtbujtTMljyc1VDgNBqQDEgxB0NBqPFgupRUV6I8G7MPr0jpdgiWAKAjNJa+J3OCO6sVuD3e0bg70O5OJZbgV7+rrhqQBCcWqRm6BfshtFR3tiWXGxxv9bUtQhwYgNc8fHcgdDqDOjz/OpWF8M3KavVYenuTHw8dwAe+fWw1TxMV/SXrqFnzaaThWbBEtCYxbtWZ2CwRETUgRgwdaApcX74dZ/thcsCgJ5+zlZ32J2ttKIabDxRiGh/F3y5OQXHcitRWCU9jVdQWY+hER5WAyZvZzXemB2PMC9HLFyVhN/2ZUGrNyDI3QH1OgMWrjqB20ZHSK63AhqnoU4VVOHNVUnYl1GKEA9HLJgQhVdm9pW+AI35hL68aRA+3pCMzzanWL13S1N7++FgZhmW7EhHYWU9xkb74OaRYXBUKXBFvwD8edC+bOnH86vwxux47Hp6Eg5llaOwSot315xE+ukptClxfvjfZW1bnL0rtdRKe0mb7kNERO2Lu+Q6UGlNA+74bp/kwl6gsZBsQ7PdVbZSAZwtF7UCVXbsorP2fE9HJTY8Nh7ujmd2ep0qqMTMT3aYrQ3q4e2E3+8eganvb0FxdYPZPVRymdm2/Sbf3zbU5iLn5IIqTH5vi83+A8AtI8MxqZcvblmy12y7/7AITzw8JRq3LN5jUVzXmsmxvvj6ZvPSJUajiJSiarholPB3syzMbMt3O9Px3N/HLNpnDQzCu9f2b/P9iIiofTBg6mCiKOKXvVl47u9jZgFDtJ+zqUitLT28nZBRWmuR78dBKUOdnT/8z9UdYyKwK7UUKUXV6B/iDie1AmsTLRdkv3ZVX8QFuuKRXw8htajGZgFaqaCkpffWnsQH609ZPe6iUeC6wSG4eVQYQjyccP2XOyVHcgLdNGa5n2zxdVHhh9uHI9qv/ZJFVmp1uOS9LWb9UClk+POekegTxCk5IqKOwoCpk8gqrcVv+7NRWafDpFhf/N9fR01TO7bEBbjgm5uH4ObFeyyCrOZrn9wdlCiXyG/UHloGPlLJNIHGwOqZ6XEQRREnC6pwxcfbUK+3/i3YP8Qdfy0YZdaWVVqLjzacwsHMcvTwccLRnErklFuvERfs7oBtT000fTzstXVtqqHXGpVchg/m9MfgME/4uKjb5Z455XX4ZGMyDmSUIcLbCXePi0S/EPd2uTcREZ0drmHqJEI8HU1JGYuq6pFVaj0AaCkxrwpzv94tuVi7TmfANzcPhoeTCiEeDhj95kbUt1LM92y1DHmszRw2LTIXBAEB7g6tBksAMCHG1+zjspoGzP5sh2mt1anCarRSvxYAUN9iqm9gqAdWHrVetqUtGgxG3PPDAQDAyEgvvHNtPwS4OQBo3BFoMIoWi9VtCXJ3sEj1QEREHYu15DqhNYn5MEgM/Dko5bhvQhQEiQihtTQAUb7OGBjqAR8XDR6fFmN2TKWQIcS97Wtt7OHaov7bmJ7eCHTXYFNSIaq0OrhqlBjaSk4hD0cl1ibmY+Tr63HHd/twNKcCfx7MsViYbmuItKiqHiXNduY9MiUaKkX7f+vvSCnBAz8dRG2DHo//dhjxL6xB7+dX4+ZFe5BXYX8ATEREnQ+n5Dqhn/dk4qk/EyzaPRyVMIpAg94AQRDsSpzo76bB7IFBuGpAEKJ8G9faHMkux8qj+XBQyjGttx+u/3IXyqxsiz8XX980GAVVWqQV1aBXgAt+3ZeNPWmNa4fUChl6+DjBWa1AalENSmoaF4FrlDI06IyQGgNTygT4tKgxZw+VXIZDz0+Bo6oxgCutacBLK47hL4ndcHeP64GCynpU1ulQWFWPhJy270yc3jfAoqhw3yA3LL9vFASpaJeIiDo9BkydUFlNA0a9ucGugKilvkFuOJ5XabGbTSET8MkNAzGtt79Z+18Hc/DQL4ck7+XhqER1vR46gwhBACb38kVPPxd8uinF4tyWa5b6Brni8Wkx+PNADnallsIgiiiykrKg8VkKlNXan5eqLdwdlNjx9EQ4qhRIyK7ADV/vQqVEDqzL+vrjg+sHQClvHH16ecUxfLMtvc3PU8oF6AyWf63+e2AM4gJd23w/IiLqeFzD1Al5OKnw9U2D8cQfR5BdVgeNUoZ6ndHm1JNKLsM1g4OxeHu6xRSd3ijilX8TMSXWD7JmNd1aS4bZNOrk6ajCT3cMR4iXAy55X3r7/swBQdAZjPjncOPISkJOJW5atNeOt2161vkJlgCgvE6He384ABcHJfamlVoES3KZgG9vHYLRLdIXOCjkVu/polHASSVHfovF4/FBbkjIlR6V0urbN/EoERFdOFzD1Akdy63A88uPIbuscd1Ln0BXq8FS0wyPt7MKd4ztgef+PmZ1PVNWaR1Ka83zH02K9YVS3vo0UWltA3amFuOXvVnIlFiM7u2swm2jIiwyVHcmm04W4Z/DuZKJNw1GERUSuwfXnSi0er8qrR4jo7wwJPxMpvR+wW74eO5ATGyxUB1oXMjd73QtPSIi6no4wtTJ6A1G3PndfrNt8vsyyiXPFQSYslvHB7vhcJb0eU28nFRwc1Catfm6aPDedf3xvz8TJKepmqSX1OKvQzmSx/xdNcgur7M703Zn9OQfCfB2VmNYDy9TW1PAas2WkyXY9+xkZJXWni6U21j+5bVZfXHX9/tx6PTXI9jDAZ/eMBByGdcvERF1VQyYOpl1xwtazSnUXPMAZcOJIjjb2L7+4OSepvU5zV0eH4hJvfzw6aZkfLQhWfJaoyharZd2Ir8KG1sZjTkbrhpFqwFcE1uJL+1VXa/HI78expYnJpgCG0eVHNWtZEBvWv4X4mleBNjPVYNl947EyYJq1OkM6BvkxmCJiKiL45RcJ1JQqcXTErvj7GXth/vISC8sumUwbhoRbvVaB5UcW09JF7EdHOYBbYP1wEFvFPHz3qw29bU1QyM8ccfYHnad69pixAwANAoZ+oU0ZsV2VFlfh9RSTnkdThZUmT6ePSi41fMr6nSSU3lAY56pGH8X9A9xZ7BERNQNcISpE1m8Pd3q9v5gDwebU0QtCWjMOXT/pJ52nV9ULb1dv7ZBj9/2S0/HtcZJJUd8iDt2pthfOFYG4IlpMXbXzJMKWDydVPh7wWhUaXVQK2QY9MpaVGntW3Dt7ngmAHtwUk+sP15gtUSN3igiIbsCo3t623VvIiLqujjC1Ikk5VdKtod5OmLlg2MwINS9TffTqGSmYCmrtBa/78/GjpRiGCWCkb8P5aCoqsGiHWjMJH42016OKjl+vH0Y3ru2n90jPUYAi7anYViEJ8ZGt15015qmhe0uGiWWH85DXYN9mc3VChk2JRWZPtYo5fjm5iFwVln/axJ4npJ+EhFR58IRpk7Ew1El2X7r6HDIBAHJhfYV421S12BEWnEN/j2Si3fWnjSteeof4o5v5w81LQBPLarGw78cslrO5GzJZQIMRhHrTxS2KadUXoUWgiDgq5sGYeq7W5BRal9NvSbDTy/cziypxRO/H5Z8r8vjA+CiUeCnPWemEuv1Rjz9ZwL8XTWY0Ktxp1ulVoebR/XAf0dykdaitt+lffzRw8e5TX0jIqKuiQFTJ1JULZ3YUS4I2HCiEFV2LIJuadOJQry95qRZ26Gscny6MRlPXxYLncGI+Uv2tnuwBAD5lfXo8/xqaNtYu250ZGPAo1bIMSLSq00Bk5eTCk9fGgugscSM1HuFejriw+sH4MMNpyTv8fPeTIyP8cGL/yRiyY50U7ufixrBHg6oNxhxSW9/3D7GvnVWRETU9TFg6kSsZcL+v7+Poadv20cyBFgPwjYmFeLpy2Kx6mg+0kvaNoLTFm0NlgBAkJ2ZAquzc2TKx1mFByb1xBX9A+HmoMKpgiqzBdzNeTurIJMJVpN21umM2J9RZhYsAUBBVT1cHBRY9eBYKCR2GxIRUffFf/U7kWGtFKI91cbpOOD0lJiV5EheTmoAMOUKspfKSqDQnvHD4u1p0BmMKK6ux7Zk6Z17LRXXNOD//j6G6R9uxXVf7MSU97bg133ZkudePzQUAHBJizIxTS7t4291x2ByYQ3+OmRZg46IiLo3BkydyIIJUQjzcrR94mm2tqvrjSJWJuTDz0VtcexAZhmmf7jV7hGcJpf08ZNsN7R9IMmqKq0etQ0GfLsj3VSU15amuDC7TIvdpwv8tuTppMLTl/bCNafTBQwI9cCz02OhVjT+NZAJwJyhobh2cAj8XK0v5t6U1L45p4iIqPPjlFwn4uuqwaoHx+KfI7l46o8jNtcVuWkUmDkgGJtPFiKnrE5y+iuztBY/3zEc3+/KwLbkIlRp9TCKjQucj+VWIjGvEn6uahRUWi+M20QhE3DDsDC4O6rw/a4MiGJjkBHt54IT+dLTX83JALg6Kq0mwGzi56qGq0aBoznSNdnig1xxJEd6R6E1l/bxx8dzLbNt3z6mB64eFIzEvEqEezkh0N0BADCjXwBe/TcRNRIBpZeT9OJ8IiLqvjjC1Mk4qOS4dnAILo8PtHluqJcTnpsRh0gfZ6trhdQKGfoEu+GTGwZiwYQoiyBMFIE+QW64Z3wkRvTwQi9/F6vP0xtF3Lv0AB6Y1BNbHp+AxbcOwfanJuKqAUF2vZsRwPMz4tA70LXV8+YODUVBZT2irfQl2r/166X4uqitjsi5O6owMtLbFCwBjSkJvrppMOSC+TUqhQw3DA9r8/OJiKhrY8DUSb1wRW8M72F9TRMApBVXY11iAdYeL7B6zo3Dw0wlU6wlxayp1+PJS3rhpzuHY96I1oOBkpoG3PfjATipFZgQ44sANwdcOzgEgW725SPSG0QMCvOQPCYTGv97b90pjHxjPUqrG+DTYjrRzUGB6DYugFfKBcwZFtqmawBgZJQ3fr17OEZFecHdUYnhPTzx/fyhiPazHlQSEVH3JIhiVy6Z2v2lFFVj2YFsfLwxRfK4Si6gwSD9Jbx7XA88Pq2XaWRlb3oprvl8p8V5/7usF+4cGwmgsbzKjI+2Ia24ptV+OSjlWHzrEFPOo4ziGlz9+Q4UVdtec6SSAXbmkkS0nxPyyutR1azsi6NSBkEQJKfLAGBqnB9KaxqQmFeJXv4ueHRqDEZFMRs3ERGdPa5h6uTcHJRIzLO+PshasBQb4IonL+kFodmU0uAwD9wzPhKfb04xLZKeEONjVmPOWa3Ab3ePwNdb0/DvkVxkWSnHUqcz4KZv9mD9o+MQ4umI7SkldgVLgP3BEgCcLLAM3Gp10jfwdFLi9Vl9Ma13gP0PICIisgNHmDqx6no9Lv9wa5vzJDko5egX4obbR/fA5DjLXW1ZpbU4kFmGCG8nxAe7W71PTb0eNy/ag30ZZVbPuX9iFB6dGoOn/jjSrgV4z5arRoGtT040ZTEnIiJqD1zD1IktO5hzVkkl63QG7Eotxe3f7cPPezItjod4OuLK/kGtBksA4KRW4Je7RmByrK/Vc4pPjypFnUVizfOhUqvHqqN5Hd0NIiLqZhgwdWJpRa2vI7LHh+tP4VwGEeUyAa9d1RcejtIjNhNP11y7ZlAIQj3tzyF1PpXYOTVIRERkLwZMndiAUHfJ9huHheKJaTF23SO3QgudlXVO9vJ11WDjY+PRp0U6gDlDQzHpdMDk5qjEn/eOxH0TIjEkzAPOKvk5PfNc/HM495yCRCIiopa46LsTu6SPP0ZHeZuVB+nh7YSHp0TDy1mNHj5OeHfNSZxspWxKfLAbVIpzj4vdHVVY8cAYHM2pwMmCKvQNckPPFtvrc8rq8M+RPGS0U226ASHu8HNVY9Ux62kTpBzPr8Le9DIMbaXUDBERUVtw0XcnpzMYsfpYPg5mlqOHjxNm9g+Ck/pMnJtfocXw19dLXqtRyvDd/GFtDhwMRhEFlVp4OqmgUdo3UtSgN2LMwg12ZQy3l0ouw46nJ2LWpzuQWdq2IOzjuQPsSv5JRERkD44wdXJKuQyXxwda/eHv76bB5FhfrDtuXt8sNsAFi28ZCn87E0o2WX0sHy/9k4ic8jq4aBS4e1wk7h0faZaeQMq+9NJ2DZYAoMFgREGlFotuGYxHfz2Mw9kVEAB4O6taTWGglAum/FBERETtgQFTN/DOtf3x8opELD+cC5kAXDUgGM9OjzUbibJHWnENFiw9AP3p+ilVWj3eWp2EUE9HzOjX+miNQn5u034qhQwNLcq7uDsqEenjDI1Sjr/vG438Ci0clHIUVGkx96tdph16zcmExizp3s6WBYeJiIjOFqfkuhHj6UBHZqVmmi0frDuF99adtGgfH+ODJbcObfVag1HE5Hc3W2QI7x/ijqT8KtTpDHBUybFgQhQOZJRh/YkzI2KDwzxw88hwPPzLIVOwJgjAW1f3w9WDgiWfV1Ovx6qj+ajU6jCmpzdOFVSjvE6HcdE+ZjXhiIiI2gNHmLqRsw2UmhisxM6GlhV7JchlAr65eTCe/OMI9qaXQSkXcGX/ILx8ZR/ojEZkltQizMsRLholjEYR21OKkZBTgWhfF0zo5Qu5TEDfIDcsP5wLvVHE5fEBrdZsc1IrMLtZMBXly/puRER0/nCEiUxOFlRh2vtb0PI7YuHV8bh2cIjd9ymtaYBKITMV/SUiIurqmIeJTKL9XPDW1f3gfjpJpUouwx1jInD1QOlpMWs8nVQMloiIqFvhCBNZ0OoMSC2qQaC7Bu6Oqo7uDhERUYdjwERERERkA6fkiIiIiGxgwERERERkAwMmIiIiIhsYMBERERHZwICJiIiIyAYGTEREREQ2MGAiIiIisoEBExEREZENDJiIiIiIbGDARERERGQDAyYiIiIiGxgwEREREdnAgImIiIjIBgZMRERERDYwYCIiIiKygQETERERkQ0MmIiIiIhsYMBEREREZAMDJiIiIiIbGDARERER2cCAiYiIiMgGBkxERERENjBgIiIiIrKBARMRERGRDQyYiIiIiGz4f/6NRFJAlqs/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nathanlevy/mambaforge/envs/phenospace_23/lib/python3.11/site-packages/anndata/_core/anndata.py:1222: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  df[key] = c\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nathanlevy/mambaforge/envs/phenospace_23/lib/python3.11/site-packages/anndata/_core/anndata.py:1222: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  df[key] = c\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nathanlevy/mambaforge/envs/phenospace_23/lib/python3.11/site-packages/anndata/_core/anndata.py:1222: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  df[key] = c\n"
     ]
    },
    {
     "data": {
      "image/png": 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4dmRAVw+NEEIIIYR0IYZlWbarB9Fao1yJivom2FmIaIaVEEIIIYR0z6CVEEIIIYSQlrpNTishhBBCCCH6UNBKCCGEEEK6PQpaCSGEEEJIt0dBKyGEEEII6fYoaCWEEEIIId0eBa2EEEIIIaTbo6CVEEIIIYR0exS0EkIIIYSQbo+CVkIIIYQQ0u1R0EoIIYQQQro9CloJIYQQQki3R0ErIYQQQgjp9ihoJYQQQggh3R4FrYQQQgghpNujoJUQQgghhHR7FLQSQgghhJBuj4JWQgghhBDS7VHQSgghhBBCuj0KWgnpJE0KFZQqtquHQQghhNwVBF09AELuBnKlCjKFChYiPmKyKvB/+67jfEY5zIR8TOvrjlcm9oCVRNjVw+w02WX1yK2sR4CTFE6Wkq4eDiGEkLsQw7IsTQUR0k61MgU++e8qtsfmoUGuhL+jBbLK6qFoNcMa6W2LrU8NBMMwXTTSzlHVIMeLfybg4NUiAICAx2BmlCfevb8nhHxayCGEENJx6FOFkFvw9B+x+ONsNhrkSgBAWkmdTsAKADFZFTibXn67h9fpVmy7pAlYAUChYrHxXDZWHkzpwlERQgi5G1HQSkg7Xc6twomUUpOPT8yv6sTR3H4FVQ3Yl1TIed8f57KgUKpu84gIIYTczShoJaSdrhZUt+l4D1uzThpJ1yisaoS+5KLKerlm9pkQQgjpCBS0EtJOHnamB6HuNmYY1cO5E0dz+/k5SiERcr+F+NibQyqmfZ6EEEI6DgWthLRTtK89erhYct5nLuJr/t/TzQrrFvSHSHB3/blZmwnx+GBfzvueGxV41206I4QQ0rVoKoSQduLxGIwIdkRyUQ2a917xGOCp4f5YOjoQV/KqYCkRIshZetcGcC+NC4atuRBrTmWioKoRgU5SLBkZgKkR7l09NEIIIXcZKnlFSDv9e6kASzbG6dzuaWeGIy+OgOAeKvnEsiyUKvaees2EEEJuL/qEIaSdfj+TyXl7TnkDjl4vub2D6WIMw1DASgghpFPRpwwh7VRY3aj3vgID9xFCCCGk7ShoJaSdwtyt9d7Xx0P/fYQQQghpOwpaCWmnp4b7c1YEGBnsiN4eNrd/QIQQQshdjDZiEXILYrPK8dWBFJxOK4WNuQgP9fPAC2OCYNai5BUhhBBCbh0FrYR0AJZl79qyVoQQQkh3QOkBhHQAClgJIYSQzkVBKyGEEEII6fYoaCWEEEIIId0eBa2EEEIIIaTbo6CVEEIIIYR0exS0EkIIIYSQbo+CVkIIIYQQ0u1R0EoIIYQQQro9QVcPgBBiWGpxLQ5dLYKAz8PEXi5wszHr6iERQgghtx11xCKkm2JZFp/uvY4fj6VpbuMxwLv398S8gT5dNzBCCCGkC1B6ACHd1PGUUq2AFQBULPDmP4lILa7tolERQgghXYOCVkK6qR1xuXrv++di3m0cCSGEENL1KGglpJuqlSkN3Ke4jSMhhBBCuh4FrYR0U8ODHAzc53gbR0IIIYR0PQpaCemmZvTzQJi7tc7to3o4YVggBa2EEELuLVQ9gJBurFamwPozWTiQVAghn4fJfdwwK9ITIgFdbxJCCLm3UNBKCCGEEEK6PWouQAi5LVKKavDVwWQcu14CM5EA0yLc8PyYIEjF9DZECCHEOJppJYR0upzyekz+9iSqGuRat/f3scPmxdHg8ZguGhkhhJA7BQWthJBOVVzdiGVbLuJUWhnn/d525jAT8TE21BmLhvjB2lx4m0dICCHkTkBBKyGk0/x6Ih2f/HcNCpVpbzO+9hZ47b4e6OVuDVdrs04eHSGEkDsJBa2EkE4Rn12Bad+fbtdjeQwwo68HPpjWC2IBv4NHRggh5E5EdXMIIZ1iW6z+NrTGqFhga2wuvtyf3IEjIoQQciejoJUQ0ikqW226ao+N57MhV6o6YDSEEELudBS0EkI6xSB/+1s+R02jAnUyRQeMhhBCyJ2OglZCSKeYFuEOXwfzWzqHt705rCRUTYAQQgg1FyCEdJKk/GrkVjTc0jmeGxV4T9dwZVkWJ1NLsS+xEHyGwaQwV/T3tQPD3LvfE0LIvYuqBxBCOlR1oxxJ+dX4aM9VXMqtavd5XhofjCUjAzpwZHcWlYrF8q0J+Cs+T+v2+YN88PaUUApcCSH3HJppJYR0CJZl8d3hVPxwLA31TcpbPt/wIMcOGNWd69C1Yp2AFQDWns7Efb1dEeVj1wWjIoSQrkM5rYSQDvFXXB6+OJDcIQGrl505Ql2tOmBUd6ayWhl+Opam9/5/LxXcxtEQQkj3QDOthJAOse5MZoecRyLk4ePpYfdsLuvqkxn45L9raDJQ6ouyuggh9yIKWgkhHSK/sv2brvwdLdDTzRpeduaYFeUJT7tbqzpwp4rNKsd7u5OMHje+l8ttGA0hhHQvFLQSQjpEqJs1jieXtOuxGaV1WL9wANxszDp4VHeWLRdyjB7jbCnGQL9br4FLCCF3GsppJYTcEoVShePJJQj3sEZ7V/RVLHAhs7xjB3YHKq8z3kWsqEbWIXnDhBByp6GZVkJIu13KrcRT62ORX9UIAOAxgJOlGMU1MvAYdTBqKltzUSeN8s4xwNcOB68WGTyGxwA8KndFCLkH0UwrIaRdZAolFq6L0QSsgDpILa6R4dMZvXH85ZHo73uzLJOIz8Degjswdbcx65C2r3e6mVGe8LE3nM87qoczzET82zQiQgjpPihoJYS0y+GrxSipkXHetzUmBx625vjtsUjM6OsOPo9Bk5JFWV2TzrGu1hL8/Gg/CPj0dmRtJsSfTw3E44N94GQpRusJVTdrCd6aHNo1gyOEkC5G6QGEkHYp5QhAm5XVNaGsVoYHfzyNjNJ6zmO87MwwNsQFy8YGQioRdtYw7zhOlhK8PaUn3p7SE6W1MmyPzUV2eT16uFphWoQ7pGJ62yaE3JuojSshpF2uFlRj4soTnPc92M8DdhYi/Hw83eh5vOzMsWlxNNzv8coBhBBCDKP1OEJIu4S4WmFyb1ed26ViAZ4a7o8DSYY3FDXLLq/HOzsTO3p4hBBC7jK0zkQ4qVQsLuZWQiZXIcLLBhJh+zZ+1Dcp8N/lQhRWN6KPhw0G+dvfs52O7kZfzQpHqJsVtsXkorJBjkH+9nhuVCACnKTgt+HnfOhqEWplClr6JoQQohelBxAdsVkVWLblIrLL1bmINuZCvHlfKGb082jTeS7nVmH+mvNam28ivW2x5vEoWFIO413v64PJ+PpgisnHJ7w1Dtbm9HtBCCGEG6UHEC1V9XLMX3NeE7ACQGW9HMu3JSA+uwIAUNMoR1F1o8H+5yoVi6Wb43V2i8dkVeDLA8mdM3jSrTwx1A+R3rYmHRvpbUsBK+nWcivq8fXBZLzx92XsiM+FTEENHgi53WimlWhZdzoTb+vJL5zc2xUsC+xNLIRSxSLASYpXJvTAmFBnnWPjsisw/fvTnOexNhMi4e1xHTpu0j0plCp8uCcJa05l6T3GQsTH+kUD0NfLtACXkNttX2IhntsYjyalSnNbDxdLbF4cDRtqikHIbUMzrURLQYtC8a0dulaEfy8XQHmjzVFqcS0Wr4/BhcwyreNYlsXl3Cq956mTKQzO0pK7h4DPwysTQxDsbKlzn4jPw5z+ntjz/FAKWEm31ShX4uVtl7QCVgC4VliDlYe001/qZAr8cDQNU1edwvTvT+GX4+lolNOMLCEdhXY9EC1h7tZ672toUuncpmKBub+ex7KxQVg81A/JxTV4bmM8Uopr9Z5neJAjGGpDec8QC/jYtDgaXx64jt2XCqBQshgX6oxlY4PgaWe4+xMhXYVlWTAMgxPJJahqkHMe8++lArw9pSeq6uX4+2IeVh1JRXGLhhtx2ZU4eLUIfywaACE1zyDkllF6ANEiV6oQ9eFBVNZzv0kb8vRwf/wVn4uiau4uSYC6HNKWJ6PR001/cEwIIV3leHIJvj6YjPicSthbiMBjGK1AtCU7CxG+mhWOp9fHoF6ue1Hf7PtH+mJSmG55OEJI21DQSnQ89OMZXMgsb/PjJAIeGhXcb9yWEgEeCHfDwiF+8HWwuNUhEkJIhzudWoq5v52DysRPRWcrMepkCtTKDKcAPBzliU9m9O6AERJyb6P1CqIjyFnarsfpC1gBwN3GDB9MDaOAlRDSbX17ONXkgBUAiqplRgNWAKinPH5COgQFrURDJlfis73XcCa9DB2dcdrbg9IBCCHd2+U8/RtIb8XOSwWYuuoUiqr1b3QlhBhHQSsBAFzIKEPE+wfw/dE0pJfUofWcgN8tzJAyAIYFOt7S+AghpLO5Wks67dwJuVVYtuVip52fkHsBBa0EJTUyzFt9HvVN3MtcS0cF4LVJIe0+Pwvgvd1JkCmUtERGCOmWlCoW/X3tOvU5TqeVIbO0rlOfg5C7GQWtBNvjctFoYOfrxZxKDAtyhKOluN3PUVwjQ8R7B+D/2h7M++0cLuZUtvtchBDSkRqalHjk17PYcC6705+rtFZ/dRVCiGFUp/UupVKx+DMmB1tjc1FZ34RB/g5YPMyPsy5mTouWrVyyy+shEvDw49x+WPx7jE5rVlM1z+SeSCnFhcxy/LNkCIJddIvOE0JIZ/v3UgHWns5Adnk9zEUCZJg4AyrgMVC0ZbdWCxYiPnq4WrXrsYQQKnl113r1r8vYdF571sBBKsKOZwYDADadz0ZORQNCXC0h4DH4aM81g+dzshRj/mAfzB3gja8PJmNXQj5KatsXvDab0dcDX8zsc0vnIISQtlp9MgPv7U5q12OfHRmAxPwqHLle0ubHTuzlgh/m9mvX8xJCaKb1rpRaXKsTsAJAaW0T3tmViJMppZDdKE+1KwGwtxDB2UpssClAcY0Mn+29jm0xuUjvoJysy3mVHXIeQggxVUOTUqf9qiEetmYorGqEh60Z5g/ywWODfPDlgeR2Ba0d9d5JyL2Kgta70Jn0Mr33HbteorO0VVbXhEH+9qhuUKDBSJ/sjnzTdbE267BzEUKIIeV1Tdh8IRvHDbRlbY3PY7D96UFwttKuKnClnaWxUotq8fzmeAQ6STEryuuW9gkQci+ioPUuZG0m1HufvlysM+lleGFMIL46YPoMxK2aF+19256LEHLvSsqvwqOrz6O0jSlN/xsbpBOwAuC8zRRKlsU/F/MBAL+ezMCGRQOopTUhbUDVA+4SBVUN+PZQCl7fcRnV9XKDgSsXBsCiwb4Ic789b6ALBvtgbKhzmx7DsixSi2uRXFQDVTs3QhBC7m4sy6KmUQ6FUoWqejme3xyP+7452eaAVcTnobpBDrlSu7JKo1yJbCObV01RWS/Huzvbl1dLyL2KZlrvAseSS7D49xhNniqgLpLNsiyqGxWa2x7s646YrApklum+4Y7q4QwLiRBvTwnFgz+eafMYLCUC1LR4LmMivGw0/2+UK5FbUQ8HqRg25iLO4+OzK/DK9su4XlQDAPB1sMAHU3thcIBDm8dKCLk77UrIx9cHk5FWUgcriQCWEiHyKhvada4mpQo/HU8HC2jVqf7yQDJOp+lPwWqL85nlKKuVwV5KaQKEmIKC1m6IZVk0yJUQ8XkQ8A1PhsuVKry0NUErYAWAgqpGTI9ww/BgJ1TWyzHI3x6BzpY4m16GBWsvaDUScLYS4/X71G/K/bxtMSLYEUfbuMlgRJAjHh3og7/icrHpQo7R4y0lQtQ0yvH6jis4eLUI9U1KCPkM7u/jjvce6AkL8c1fzZIaGR5dfV4rKM4orcPja85j5cMRiPK1gwO96RNyT9t7pRDPbYrXfF3dqNC6aG+vP85m4fnRgbAQC8CyLLaY8P7WFmkltRS0EmIiClq7mYNJRfjiQDKuFlTDUizAzChPvDQ+GBIhX+fY3Ip6vLMzEcU13Lv+9yUW4ctZEVq3RfvZY8/zQ/DDkXSU1skw0M8eD/XzhLW5Op2AYRj8OLcffjiahj/OZplck3XXpQK4WEvw4bQw1MgU2H2pQO+xlhIBzqWXY/H6WDS1CLblSlbd6EChxKo5fTW3b4vN5ZzFbVKyeHpDHAQ8BtMi3PH+1F6c3ydCyN3v+6OpbX6MVMRHrZ5OgM3qm5QorG6Ev6MUChVrdBOXhYiPRrkSShMzmDadz0F/X3tTh0zIPY1yWruREykleGJ9DK4WVAMAamQK/HYyA89vjtc5Nq+yAVNXncbBq8V6z6fkKMG7LTYX01adxpaYHBy6WnyjXqt2uoBEyMeysUE49cqoNs1gbr6QAxXLYuXDEVj5cDgCnSzAcBxX06jAD8fStALWlvZcLtBa0jOWP6ZQsdgam4v321l3kRBy52t+32wThkGEp43BQyxEfLjc2Hgl5PPQx8DxAh6DL2b2aVO+/jkD1V4IIdooaO1GfjiaBq5WD/sSi5BarM7lVKlYHLlWjAVrLhhtBzgu1EXr65jMcizfmoCK+pszBWkldXh09XnUym7OZLIsC6WKhUTIx5r5UfBq0UVLZCBdoaZRgaLqRvB5DFQsi5TiOrRnuxTLAlktSmsFO0tNety22FxUN5pWyoYQcnfxtNXt9mdMrUyBUDcrvD0lFG7W3BUB5kZ7a6Ur/W9sEPg83cvxKB9b7F82DBN6ueK+3m4mj8FOyp3HTwjRRekB3UiSgZmCxPxq+Nhb4NmN8dibWGj0XC5WErw0PhhZZXU4cq0YIgEfx5O5Z2XL65rw76V8TOnjhi/3J+PPmBxUNyrQ39cOL44NwpHlIxCbVYGaRjl6uFhi3FfHUadnSW3IZ0cwIsgRhdWNpr1oDjwG8HO8Gai625pBJODpnZltJlOoUFIjg5WkbZUTCCF3vhl93fF/+5Pb/LjYrAp8OC0Mjw70wdcHk7H2dCZqGhWQigWYG+2N5eOCtI4fHuSIDYsG4IejaUjMr4KbjRkeHeiDGX3dwTDqYNbf0bQLbQCYFenZ5jETcq+ioLUb8bA1Q2U990yhp60Z/r1cYFLAOinMBe8/0AurT2Xg+xazt1xL9c3yKhvxzIY4rQ1Y5zPKMe+389j61ED097XT3P7kcH98eYD7w4FlgSPXS8AxEWGy0SHOsJSofzW/PZSCL/Q8V2tWEgHcqGEBIfccpYpFQm5lux5rZ6Ge6eTzGLw4LhhLRgaguFoGR0sxzETcOfLRfvaI9tOfhxrqZoVoPzucTS83+NxedmZ4ZADVqybEVBS0diPzB/li+dYEzvte23HFpO4plhIB/u/BPriQWY5VR9K07jO0VC8V8TkrBjQpVfj6YDL8HaW4kFkOOwsRZkV54sOpvbD2dCbSS2uh5JgAbW8ZVSGPwYGkIkR9eBAPhLth83nTd+ouHOKn90OGEHJnyyqrw6ojqTiZUgqpRIAZfT3w+GBfiAQ8vLsrEfuT9Of3A+rUpiaON6tZUdoznYl51dh0IRvZ5fXwsDHDI9He6Odta9IYG5qUaFKoYG0uxPeP9MOyLfE4llyq9/j+vvbg3coVPiH3GIZlubIoSVdgWRYf7bmK305mcAZ9VhKBwRIu5iI+fnk0EoMDHPDcpnjsSsg36XlDXa0wu78n3vwnkfN+BroB79JRAfjfuGDM/PEMzmcank3obLbmQjw13B9PDPWjDwBC7kK5FfV44LtTOtVMJvZywVtTQjHk0yNQGrlSfqifB45cL9Y0GWCgfs9kGGBooCMe7OeBLw8kIzFfO02LYYAvZ/bBtAgPvecurZXhvV1J+O9KAeRKFs5WYsjkKlQaqTTQx8Ma/zw7xOAxhJCbaKa1G0ktrsWWCzl6ZymN1RzkM0BfL/WMQKmeMliAus1rdaMcYgEP9/dxwysTQ5CQU6n3eK7hfH80DY9Ee8PXwcKkoJXHAGNCnJGQU4kiA2Nrjx/n9cMAKhlDyF3r1xMZnOX3/rtSiD4eNkYDVgCY0scNH0zrhf2JhXhvdxJKapo0ufn/XSnE3iuFnO91LAt8sPsqgp0t8ce5bFwrqIa5SACFSoVamQLBzpaIz65EeovNo0XVpr3H2VrQJixC2oKC1m7km8OpRgPTMHdrXM6r4ryvRqbEt4dT0M/bFhZi/cvkUjEfXzzUG8HOVrCVivDPxTxcL6xp01gVKnVN1Z7uVkCM8eOFPAZ9PGxwIKmoTc9jik3nsiloJeQudi5D/4VxQbXxjlf9fe0wJMABTUoVvjucipIa3QDYUNhbVteE+1edgoKj+OqVvHaU2rrh4SjahEVIW1DQ2o2cTtWf+wQAIgEPz4zwxzMb4vS+wX5/VJ3HKhboL02VV9mIRb/HAlDXFVS0MwH1s73XIeSbthwvU7L4v/3X2/wcIgEPcoXK4AcKV6WClKIarDqSinMZ5bAxF2FmpAdmRnqivK4JjpZiakJAyB1EZOB9JsBRih4ulrjGceFtKVFXAHh2ZAAu5VXh7X+u4HpRbbvGwBWw3oonh/lhfE8X4wcSQjQop7UbGfrpYeRU6J81eHywD7bG5GrVVL3bPTHUFxcyy3Exh3t2GQCeHO6HVyfe7A2eWlyDaatOo6bV90nIZyBXsrAUCzB3oDdeHBtktE0uIaRrffzfVfx0LF3v/QIeMDrEBekltUgpVgekPAaY3d8L7z3QC3weg5MppZi/5ny7L9A72gujA/HC2CDjBxJCtNBMazfibW+hN2id098TPvbm90zAKhHw8NhgHzw5zA+/vJ+h9zgriQDzB/lo3fb9kTSdgBVQt4kF1J3GfjiaBhXLagW7hJDuJTarwmDACgAKFbAvsRAjghzxyYwwlNTIEOZhA3ebm+XvPtt3rVsErI6WYrx3f09MDHPt6qEQckeiaaZuhGfgp8HnMUhpx7LWmBAnzO7viegWdVZNYWg57nbwcbDAqxNDIBaod/fqo2KBQ1eL0dCi2YGh/LeW/jiThfqme+MigJA7kakVUADgaHIJpGIhJvRy1QpYaxrluJSrf6XGkI4uRlJWKwOPYXAqtRQyBXeDFkKIfhS0diMOFvrrsP5xLht/nMtu8zmtzYQ4nlyKsyYGcgDgbWeOpg7O32qra4U1UKlYWIgFGBnkqPe4WpkCb/x9BVEfHsDGG98fOxN35NY1KVFY1f7OXYSQzqVQGe6C11paifaFvUrF4nxGOdp7DW7qe4mpVCzw5B+xeOTXcxj08WHsvWK8WQwh5CYKWruRmQZ2krYn89jZUoztcXnIqzS+u7YZn2Hw+qQQzt7at5O7jRl4PAYJOZVIK6kzenytTInXdlzG2fQyg9/HlsxFfDhbcfcbJ4R0vTEhzm063s/RQvP/RrkSj64+j4XrYtCea3ARn9HUdO0MZXVNeG5THNJL2rcxjJB7EQWt3Ui0nz1enxQCkQmbg/gG1sx5jLqQtoW47SnLzlZi9HK3gr2eGQZP29vTJvXRgd6oqpfj0dXnkVVeb/Lj/jibhUf6e0FgQtD9yACvdn2PCCG3x7BAR0zqZdoO+6GBDujhYqX5+reTGThppCKLIbdjtUmuZPHFftPaVBPTHT16FAzDoLKyEgCwdu1a2NjYdPrzZmZmgmEYXLx4sdOfi2EY/P33353+PN0NBa3dzBPD/PD0CD+jxyn1TL1O7u2Ki2+Pw2cP9tYqdt1auIc15+35VY2Y9ctZFHM0APCyNTNY3aAjCPkMnhjqiyeG+uHvi3moMtJRprWCqkbweAwcpPqX9SRCHhYP88PLE3rc6nAJIbdAqWJR36SAviI2PB6Dx4f4mnSuj6aHaX2986Lp+bBd6d/LBfiyHeUAuxOlisWZtDL8czEPZ9LKTGr2cDvNmjULycl0cdDRWl8c3A40zdTNKFUsNp3PaffjS2pksJIIAQBedubI1jNLWV6vf9krp5w7MO3otyGxgAcfBws4SkXwdZBicIA9Bvjaw9ZChKoGOdadyWzzOcPc1cG4VCIA9HSl+X5OBEaFUH1EQrqKQqnClweSseFcNqoa5PB1sMCzIwMwo59uq9QcE1daWq+uNN5BG52+PZKKhyI94Wln3tVDabO9Vwrw7q4kFLTYH+BqLcHbU0IxoVf3qJJgZmYGM7Pbs0rYXcjlcgiFwq4eRoejmdZuplam4JzlNFWj/OYb9aKh3DMUzlZi9HLjnmk1pMDETUvuNhKTqg/IFCpcL6xBhJct/BwtsPF8DlZsv4R/LxVgyYZYpJuQy9qSgMegSaFCbkU9yuv0z9Au3Zyg94OwSaFCRmkdKg0E9YSQ9mtSqHD/dyfx/dE0zUpKRmkdXtyagL/icnWOD3K2NHpOazMhXFrlp48MduI8lsfApBSs24llgcPXirt6GG2290oBnv4jTuezobCqEU//EYe9Vwo67bllMhmWLl0KJycnSCQSDBkyBBcuXOA8lis94IMPPoCTkxMsLS2xaNEivPLKKwgPD9fcr1Kp8N5778HDwwNisRjh4eHYu3ev1jnOnz+PiIgISCQSREZGIj4+Xue5k5KSMGnSJEilUjg7O2PevHkoLb2ZtjJixAgsXboUL7/8Muzs7ODi4oJ33nlH6xwpKSkYNmwYJBIJQkNDceDAAa37m9MS/vzzT4wYMQISiQR//PGHSa8hNzcXDz/8MOzs7GBhYYHIyEicO3cOmZmZ4PF4iInRbnn57bffwtvbGxkZGRg5ciQAwNbWFgzDYP78+QAAlmXx2Wefwc/PD2ZmZujTpw+2bdvG+bNpq+71l0sgFQvgZKm/ioClxPDk+MgeN9+oB/lxl7kqqpYh0FlqUt5nS6bWOcyrbMSDHDMm+nx/NA3v7krC8eQS7E8qwpKNcTiZWtamsTWPb+P5bEz8+gSszfRfYdbKFPjpeJrO7evPZGLQJ4cx8vOjiPzgIJZuim9zegIhxLA3/76MpALuttHfHUnVSRXo5W6NcE8bg+f0tTfHvsRCvLQ1AW/+fQUxmeV4eoS/VumrZktHB+LxwT7tHX6nERnoYtgdKVUs3t2VxLkC13zbu7uSOi1V4OWXX8b27duxbt06xMXFISAgAOPHj0d5ufFKORs2bMCHH36ITz/9FLGxsfDy8sIPP/ygdczKlSvxxRdf4PPPP8elS5cwfvx43H///UhJSQEA1NXVYfLkyQgODkZsbCzeeecdLF++XOscBQUFGD58OMLDwxETE4O9e/eiqKgIM2fO1Dpu3bp1sLCwwLlz5/DZZ5/hvffe0wSmKpUK06dPB5/Px9mzZ/Hjjz9ixYoVnK9rxYoVWLp0Ka5evYrx48cbfQ21tbUYPnw48vPzsXPnTiQkJODll1+GSqWCj48PxowZgzVr1mg9x5o1azB//nx4eXlh+/btAIDr16+joKAAK1euBAC88cYbWLNmDX744QckJiZi2bJlmDt3Lo4dO2b0Z2MMdcTqhn46loaP/7umc/uUPm7o72OHN/+5wvk4iYCHs6+Nho25CIn5VXhs9Xm9u19H9XDC/EE++HTvNSTmt793tj7WZgIoVCzqZB27RMfAtDQFbztzgxu4/BwscHj5CM3X/1zMw/ObL+ocNzzIEesW9G/zOAkhuqrq5Yj88ICm0QeXa+9P0GmzXNMox+BPDqO6kbuuMlcq1LMjA/D4YB9sOJeNM2llsLMQ4aFID4wIdoJKxeK+b0/gqp7g+XYT8dXv3R1dYqsznUkrw+xfzho9btMT0Rjob9+hz11XVwdbW1usXbsWc+bMAaBeDvfx8cELL7yAqKgojBw5EhUVFbCxscHatWvxwgsvaHIvo6OjERkZie+++05zziFDhqC2tlazicrd3R1LlizBa6+9pjmmf//+iIqKwqpVq/Dzzz/j1VdfRU5ODszN1WkdP/74I55++mnEx8cjPDwcb731Fs6dO4d9+/ZpzpGbmwtPT09cv34dQUFBGDFiBJRKJU6cOKH1PKNGjcInn3yC/fv3Y9KkScjMzISHh3oyaO/evZg4cSJ27NiBqVOnIjMzE76+vvj666/x/PPPa85jymtYvnw5MjMzYWenO8n1559/4qmnnkJBQQHEYjESEhIQERGB9PR0+Pj44OjRo1rf5+afjYODAw4fPoyBAwdqzrVo0SLU19dj48aNbfpZt3ZnXdrdI54Y6ofl44JgY66eLTQT8jF/kA8+f6g3JoW56L0i/9+4YNiYi7DzYh6mrjplsFyLXKnCsCBH7H5uCL6bHWFwPNJ27LCvalBgdpRXmx9nDM/E2WFjFQcsW83E/nKCu+vOseQSPP1HLD7acxXXCjs+uCfkXpJbWW8wYLW3EEHM8f5mKRHi8IsjIBXzde4T8hnO3P3vjqSitLYJS0cHYtPiaKx6pC9G3EgZ4PEYvDk51ORx8xjA16Fz8k35PAYfTw+7owJWACiuMS1dzNTj2iItLQ1yuRyDBw/W3CYUCtG/f39cvXrV6OOvX7+O/v21JyNafl1dXY38/Hyt8wPA4MGDNee/evUq+vTpowlYAWgFaQAQGxuLI0eOQCqVav716NFD8xqa9e7dW+txrq6uKC4u1jyPl5eXJmDlep5mkZGRbXoNFy9eREREBGfACgBTp06FQCDAjh07AACrV6/GyJEj4ePjw3k8oE6HaGxsxNixY7Ve9++//671mtuLNmJ1Qzweg2dHBWLRUD8UVTfCQSrWlGYSS/n4/KE+ePHPi1pv/pPCXLBgsA9+PZGOD/41/kc7NlRd/5BhGPT3s4OAx3Au//s6WGBCTxf8cIz7l43PA5Qc9b/dbcywYkIP7E8qRLaejV3t0VFLTQ/2ddf6OsNA/ux/NwqA/3w8He8/0BPzBvp0yBgIudd42JhDJOChScHdNOCxQT5g9JTzc7AU49CLI/DxnqvYc6UQShULf0cLJBvoFPjflQIEu3DnxA7yd8DH08PwyX/XDKYB9XCxxKMDvfH6Du4VrtZ4jLqJgClG9XDCu/f3vCM3YDlZmlbj2tTj2qJ5gbj17wrLsnp/f1rjeqwpxzTfZsoitUqlwpQpU/Dpp5/q3OfqenOTWusNUwzDQHWjsYYp42pmYWGhc5uh12Bsc5pIJMK8efOwZs0aTJ8+HRs3bsTXX39t8DHN4/7333/h7q79OSsW6099NBXNtHZjEiEf3vYWOrVE7+/jhpMrRuGN+0KwdHQgtj89EKvm9IVMocLXB1OMnrefty0e6nezAL+TpQRzo705j80orcOWmBzOjjIMgGAn7g+Ep0f4Qyjg4cVxwXrHYWMuxKA2LBtJRR3z6zoy2BFzBmi/XlM/NN7dlaSZOUguqsHBpCJkl5leR5aQe5m1uRCzIrmbf0R622LJyAC9j1WpWFibCTEi2AmPDfTGW5NDUW0k59xYXDG7vxfOvTYaq+dHQqBn82hGaR2+OZRicvUUFQvOXNrWJoW54Od5/e7IgBUA+vvawdVaAn0hIgN1FYH+bWwhboqAgACIRCKcPHlSc5tcLkdMTAxCQkKMPj44OBjnz5/Xuq3lhiMrKyu4ublpnR8ATp8+rTl/aGgoEhIS0NBwc1Lm7FntdIm+ffsiMTERPj4+CAgI0PrHFWByCQ0NRXZ2NvLzb5ZwO3PmjNHHmfIaevfujYsXLxrMA160aBEOHjyI77//HnK5HNOnT9fcJxKpVweUyptpgKGhoRCLxcjOztZ5zZ6epjX+MYRmWu9QzlYSLBqqXc/1Um4VamXcOV/NBvnbY/X8KJ2csTcnh8Ldxgy/ncxAYbX2ck55HXeaAQsgqVA7J4zHAAsG++KRAerUgPv7uOFqQQ1+OZGumSW1MxdiXKgLMsvrUNNgeLwt1TapwDDGP4jcrCVQqFi9VRjcbcx0On6Fe9rgWqHx/DaFisU/8fk4llyiVbh8Sh83/N+DvXW+r4QQbW9ODoWAz2Dz+Rw0yJWwEPExN9obKyb00En/UapY/HgsDWtOZaC0tglCHgN5G1ZbPO3MoFSxBjv8SYR8+DtKodCTtiBTqFCop3weF0dLMY4sH45/LxfgqwPJWitNXnZmuC/MDWN7OiPC08bkWcHuiM9j8PaUUDz9R5zOXoPmV/X2lNBO6a5oYWGBp59+Gi+99BLs7Ozg5eWFzz77DPX19Vi4cCESEhIMPv65557DE088gcjISAwaNAhbtmzBpUuX4Od38zP1pZdewttvvw1/f3+Eh4djzZo1uHjxIjZs2AAAmDNnDl5//XUsXLgQb7zxBjIzM/H5559rPc+SJUvwyy+/YPbs2XjppZfg4OCA1NRUbN68Gb/88gv4fOOfF2PGjEFwcDAeffRRfPHFF6iursbrr79u0vfJ2GuYPXs2PvroI0ydOhUff/wxXF1dER8fDzc3N00KQkhICKKjo7FixQosWLBAa3bW29sbDMNg9+7dmDRpEszMzGBpaYnly5dj2bJlUKlUGDJkCKqrq3H69GlIpVI89thjJo1dHwpa7yKGdswDgIWYj8EB9nh/dxJcrSV4sJ8nXKzVSzd8HoMnhvkhp6Iev5/JavcYVCzw68kMuNuaYXQPZ+yIz4NcqcIn08PAMAzqZAp8ezgFm2PaV4vWUMDKAHh2VACeHuGPX46n4ys9s85/X8zHuw/00nozHeBnh80XTBvT9rhcnQB3V0I+HKQivD2lp0nnIORe0tCkxN7EAhRVyxDuaYO3Jodi+bhglNTI4GIt0Xux9+KfF/F3iyYBbQlYAWD51kv46kAKvpoVbnDGz9XaDPYWIpRxXKCL+Dw0ceVA6TF/kA9EAj6mRXhgarg74rIrkFZSB39HKfp63dmBamsTernih7l9deq0utyGOq2ffPIJVCoV5s2bh5qaGkRGRmLfvn2wtbU1+thHHnkE6enpWL58ORobGzFz5kzMnz9fa/Z16dKlqK6uxosvvoji4mKEhoZi586dCAwMBABIpVLs2rULTz31FCIiIhAaGopPP/0UM2bM0JzDzc0Np06dwooVKzB+/HjIZDJ4e3tjwoQJ4PFMWznk8XjYsWMHFi5ciP79+8PHxwfffPMNJkyYYPSxxl6DSCTC/v378eKLL2LSpElQKBQIDQ3FqlWrtM6zcOFCnD59GgsWLNC63d3dHe+++y5eeeUVPP7443j00Uexdu1avP/++3BycsLHH3+M9PR02NjYoG/fvlobwtqLqgfcRViWxeRvT3JWA3CyFEPAY5Df4o1FIuTh10ejMCTQQXPbk+tjsC+x6JbHIuAxULGsVm5XpLctrhfWoMbIbPCtmBnpgc8e7IPYrHLM+EH/EsrV9ybATHTzg7KmUY7ojw6hrqn91Q6kYgHi3hx7x5WuIaQzXcqtxONrLmgFhIMD7PHLo5EwF+mfN8koqcXIL269RA6g/ts88fJI2BrY7GTqfoCWHC3FKLmxoiPi8/BItBfeuK9zZhe7M6WKxfmMchTXNMLJUp0ScKd9D8aOHQsXFxesX7++q4fS7Xz44YfYvHkzLl++3NVDoZlWAEjKr8a605nIKKtDoJMUjw/2QUCrXE2WZXE2vRy7L+VDrlRhTIgzRoc4d6s/TIZh8NH0MDz4w2mdHbpOlmJcaRXMNspVeGlbAk68PBKCG8W2wz1tOyRo5drUFZNVYfAxQc5S5Fc2Gk1xMCTjRuvaEFcrWIoFnAFybw9rrYAVUH+ovToxBO/uSmzzbE6zWpkC9U0KiAR31i5gQjqLUsViycY4nRnMU6llWHkoBa9O5M4/bGhSYvYv5zpsHLUyBbbG5GDxcH+9xywc4osdcXlILDCtSoibtQRbnhwIlgXyqxoQ6CSFvfTWN5rcifg8psPLWnWm+vp6/Pjjjxg/fjz4fD42bdqEgwcP6hTtv9fV1tbi6tWr+Pbbb/H+++939XAAUNCKw9eK8OT6WE2Qdz6jHNtic7FuQX9E+6n/CFmWxfu7r2L1qQzN4/6MycX4ns5YNaevJuDrDg5dLeYsKdM6YG1WUNWI5zbFY5C/PUprZVAoWThKxSipbX9XrvYKc7fB4qF2WL7tUrvP0XyxYS4S4LnRAfhoj3a9Wz6Pwf/GBmndFpddgdf+umxSTqshDNTfTxtzCloJAYBz6WV620Jvj81FsLMlNp7LRmmtDH29bfH0cH84WUmw9pRubv2tWnsmE08M89O7PF9ULUOSiWXtVkwIxtxob1g2t8y2vzM3U92rGIbBnj178MEHH0AmkyE4OBjbt2/HmDFjunpo3cqzzz6LTZs2YerUqTqpAV3lnk4PUKlYjPziKLI4dn+HuVtj13NDAADx2RWY9v1pznNEetvif2ODMCjAgfP+223C18dvOfgCAB97c2Te5l3xfywcgC8OXEd8dmW7Hi8S8LD7uSEIcrZEXmUD9l4pxKXcSqQW16K0VoZQVys8NdwfA/xuzggU1zRi9OfHOixloTk9gZB73eFrRXhuU7zeBiNcZfb4DGCgjOst27BoAFytJfjpWDouZJbDzkKEWVGeeLCfB+KzKzH9B+73+dbm9PfCR9PDOm+ghBBO9/RMa0ZZHWfACgCX86pQUiODo6UYe2/U6eQSk1WBOb+ew0vjgw2Wa7ldOirJP7OsHktG+uO3Exlo1FNT0VzEx/anBuKJ9bHIrbi1WqyPDfLG4AB7LPuzfefp6WaF1+8LQZCzJdaeysB7u5O08mmfGxXAWX5ra0xuh+bYJpm4tMiFZVmwrOkNFAjpLliWRWxWBQqqGhHmboV1Z7Kw5lSmwcdw1Vy+1YCVzzBQGpiH+e9KIXZezNN01kovrUNMVgWuFdYgWk/bay5/X8zDB1N70d8qIbfZPR20GipPxDBt6wX95YFkPNjPA85WHV9IuS0m9XLBVY7AScADbMxFBrtktdYoVyHQWYrLedyBWH2TEkeTS2AlEULAa4SlRAA+j9H7HDZmQrw4LghfHEhGZf3N+op8HoPYrAqsO52FEBdLzcYGU00Nd0OwiyUCHKVILqrBO7uSdI759nAqBvrZa2bEWZbF4WvF2Hwhu03PZYyNWdtTA2plCny+7zq2x+WiTqbA4AAHLB8XjD5G+q0T0h3klNdj8fpYzvcdfcQCHmR6Lobby85CBHsLEVKK9TcbuJRbydkKds2pDCjaUCGgvkkJhYqFiIJWQm6rezo9AAAe/OE05wahUT2csHp+FADD6QEtfTI9DA/37/jWpW1RJ1Ng7m/ndJbYP5jaCxN7uWDzhRxsj81Feqn+DlDNIjxtEJ9TafQ4U/2+IArDgpxQWd+EiStPaJVIaSYV81HfpDS5o0xLYgEPo0OcsOcy98z4jL4e+GJmH1zMqcTTf8RyPn9rAh6DQf726Olmha2xuSYF/VE+tnhtYgh6ulsbvPBpaFLij7OZ+OZQqs5sr5mQj53PDkagM3fzBkK6A5Zl8cCqU7iUW9Wmx40LdcL+pOIOHYtIwMBcJNC6IG5JyGdgbSbU+zd8fx837EzI57yvtQG+dtjyJHcrTUJI5+k+O4i6yCczesOl1eyoh60Zhgc5YntsLkpq1HUF5w/yMXqu7lDqyEIswJbFA/HN7AjMivTE4mF+2PvCUMyN9oa9VIwlIwPw9cPhJp2r1MhmrLbOMUjF6k0L+5OK9AaMtTIlzEUC9LjRetFSLMDCIb7o42Ft9PwyhcpgKkedTIGj14sx/ftTRgNWGzMh9iwdghfGBCIuuxI/HEtHaW0TpGIBmidXzEXcM/UXMisw7YfTGPTJIaw+mcHZhk+mUGLub+fw4Z5rnOkJDXIlfjmRfmPZtRx/XshBTGa5Sa0DCbldkgqq2xywAup6zh62xrtGtUWTgtUbsAKAlUSIBgMl7YYHO0KopytWS0I+g5fG6+/0RwjpPPd0egAABDhJcWT5COy+lI/MsjrkVzTgvyuFeHtnIgD1G9SLY4MQ6CSFh60Z8iobOAvcS4Q8jA5xvs2jVwc/uxMKcOR6McyEfEyNcMcgf3vc38cN9/dx43xMbw8bPDbQG+uMNBHgKlvVUlvDp7MZZejrbYtLuZUGj6uVKXCtsAZBzlK890AvRPvZ43JuFR759Szn0l5LhoYc7mWDJ9fHGp3FHehnj7emhCK7vB6f70/WGZu7rQT/LBkCWzMhIt4/oHdMpbVNeG93EqRiAWZGabev251QgFgjJcDisyvx4I9ntI6L8LLBr49G3rOldUj30tZUnmbF1TL8OLcfFqy9oLdzXUfjahzQjGEAEY/BnAFe2Hw+R2/qgp+DBb6b0xehbladNUxCiAH3fNAKqGt7ylUq7E8s0smHkitZfLL3usHHC3gMPp3R22hHqo7WKFfi0dXncT7jZt/grbG5eHK4n976hzWNchRVy/DyhGCM6OGEtacycSy5hPNYro0SzV6dGIxP915v0zK+7Y1SUK1ntvVJLqrF/NXnMSvKE4n51ejtYQM7CxH4PAZ7rxSiQW56I4A+HtYorZEZzaOTCHhYMSEYv53MwP5E7lnbvIpGxGdXYmigg9EgGgB+Op6mE7Tq+563VNUg1/l9jM+uxOs7ruDHef2MPp6Qzhbmbt3mjlEAcOnGRtcTK0bi2Y1xOKAnVcDLzhzZ5e2rYmImVOfNmvIexbLAc5svAlC3oh4R7IiBfnYorW1CQm4VLMUCTO/rgUlhLndVRytC7jT3dNBaUNWAJRviENfOEks25kIsGOSD6f084GF7++v0bbmQoxWwNvvpWDqmR3gg2OVmPmSTQoWP9lzF5gvZaJSrYCHi49FBPpge4a43gCqvk4HPY3SC17cmhyIht7JNAauQz2BCLxecTS+DvVRs8kaMRoVKZ0b4iaG+mNzbFVtjcw0+ls9jYC0RoJe7Nd6aEmqwQ1azcC8bTP/htNHXll/ZgJMpxgNPQL1DmWVZrQ87C7HxntP60jP2JxWioq7JYHcfQm4He6kYjw/xwU/H0rVub92LnsufMTkIcrHEoavcAautuRC/PhaJxb/HtKv8nlQiQEON6RtPm6lY4Oj1EkyLcMeTw7u+Igwh5KZ7NmhlWRZP/xGHi7ew0aiyXo5Fw/wMtiLsTPv0zAQCwP7EQq2g9aM9V7H2dKbm67omJX44moZHBujfOKaOKW9+9DhKRfjlsSg4Worx/r+6O/QBQCxgIFPoflzJlSwmtdh8xee1vybjryczsOWJgTiRUmqwALlSxaK8Xo7jKaW4/7tTqDfSotXb3gxpxXUmBeNfHUhGZYP+/LmW/B2lOrMzU8Pdsel8DufxZkI+Fg/zw8pDKZz3q1j1LCwFraQ7eGVCD3jYmmPD2SwUVDWit4c1lowMwKojqTiRUqr3cWV1TTiRXKL3742BurXqhF4uEAv4uF5Yg7omBS7mVKLGhBWO8jZUSuGy5UIOHgh3v6VzEEI6VtfvHOoiifnVtxSwAupe05dzq7psc4yhFrLN9QMb5UoU1zRi03nu0k67LxVgdA8nk56vpLYJJTUyXC+s5szrBcAZsDZruflJqWp/TUaWBa4X1+DfpUPw7MgA8E1YrjMWsD493A9fzQo3uROYqQErACwe5qdz2wA/eywdHahz++woT1x4fbTB1AceY/hnT8jtxDAM5kV7Y+8Lw5Dw9jisXzgAoW5WSNLTha9ZtJ+9Tjvllsrr5fgzJhc/HkvHj8fS8EC4G5Qq1qSAFQDCPKwR5m58A6fe5zeQA0sI6Rr37ExroQnljppJhDw0ynWXspuUKsz6+Sxm9/fER9PCbnuu06QwV70zGY5SERatu4DD14rBsvqX6qoa5HhzcihULIsj140vd8dkluNynv7dwpYSgckfKrfCXMiHvVSM5eODEeZhjec2xrc5r67ZiGBHrJgYgtTiW+8k1pKjpRhLRvjjoX4enPf/b2wQpkW4Y39iIXiMOn3C084csVnl+Pl4OudjAPVM6w9HU/HR9N4dOl5COsrf8XkGNz65WkswL9obEiEPFiI+6oxcVMoUKiz78yLn+zAXhgGeHxOEi9kVBt+vDK329Pc1vdkAIeT2uGdnWnu5W5s0W/VAuBt2PDMIkd42eo/ZdD4HR00I+DrajL4eGKVnlvTl7Zdx8GoxVAYCVkBdkNvNxszkHby1MgVOp5XpvX9yb1eTztOMAWAladu1k7mIj7E9b1ZqGN/TBSdWjMSrE3tgzgBPA4/k1tzJzN9RilDXW98VHOVji6PLR+D0K6Mwf7CvwYsZXwcLPDncH08M84OnnToveldCgdHn2Hg+B/+37xqVwCLdUnqJ/jrQ7jYS/PXMIDhaimEpEeKrWeEmlQs0FrDyeQwYBujjaYPVj0Uh2NkS3x5ONfgYKzMhxoXqVn3h8xgM7iatuQkhN92zQauFmI++XjYGj3liqB9WPhwBHsNDcpH+LisAsOuSaUWpO5JIwMMvj0bit8ciMbu/Z7uqFywY7AM+j0GikaU8QL0szTMQgPk5WODJoX4wM9BprDUWwJg2lAoTCXj4alY4rCTar9XZSoInh/vjw6lh8LHXvynOsUWpKKlYgI+mhSHKRz2jwjAMvpjZBw5S7VxRQ+fTHoMYL08IxvqFA+DjYAEhX/vP61JuJV7amoBHfj2Lj/ZcRU55HcpqZahv0p6Zlps4Y7zqSBqOXO/YAu2EdIRAZ6ne++7r7QZX65s1Wsf1dMGpFaPw5uRQPDfSv92pL0oVi3APa4wNcUJPdyvsvVJoND+9ol6OQf72Ou9ZShWL5VsT2rQiRwjpfHddegDLsjibXo7/rhSAZYEJvVwwyN9eM9vV0KTEykPJ+O1EOoytNK0+mY4nhvrimQ2xRksbyW+1aXY78XkMRoc4g2Whd2NPSwIeA4WKha25EI8P8kGktx1+Op5m0rK+p6057A1s/vF1sMAD359uUykqAAaX75o5WorxzAh/3N/HzWCNUoZh4GVnrne38axIDwwOdIRMoUSUjx0sxNp/AiGuVjj20kjsvpSP7PJ6hLhaYVyoCxauu2BwUwmfAYYGOmBiL1fO9sD/XMzDsi0XNR+ip1LL8OuJdKhYdW705D6ueGtyKGzMRRgT6owN50xrL7stNhejetz++sCEcEkrqcWvJ9IRk1kBkYCHplYVQixEfMwd4K3zOEdLMRYO8QUApJfW49/LuqsNPMZwHWYAiM+pQnxOFX45kYHpEaZtovrhWBrne1ZNowIbz2fjf2ODTDoPIaTz3VVBK8uyeOPvK1of+OvPZuGhfh747MHeiMuuwKJ1Magw0DWlJSULvLc7EWkGlrqajQx2bPe4O0KGCW1ZAeDxwT6YFeWJzeez8f2xNHx5kHuHOpes8nq4WkvAMODciMVjGFS1YYNSMw9bM4P9wu0tRDj04nCd2VV9RAL9M705lQ0Y6G+vc3tmaR0u5lTCyVKMaD97zIrSrqrw0bQwzPn1LHLKGzjPq2SBbbF52HulCFufGoiQFmkGMoUS7+9O0vnAbf66SanCX3F5yCmvx59PDkR/bzvNxYUxx66XYPK3JzCxlyvmD/LRCcIJuV2uFlTjoR/PoJajwxugzjNdOMQXXkZWLlZM6IHYrAqtyiAiPg+zojyx/qzhhijNqhrkJq9CFFXrT41KKzG8wkYIub3uqk+4M2llnDNUW2NzMb6nM17dccXkgLXZ3itFJh13Oq0M0/tyb7i5HQIMLMc1Ewt4mN3fCy9tu2S0G5M+x1NL8eHUMLz5zxWt+q2Lh/nhqJEPCX1FyB8b6I3yOnUR79akIj42PhGN6gY5Np3LRkZpHYprZFCoWPTzssUj0V5waDXzOjzYEQevcv/c/rmYj37etnh0oA8AQKFU4ZW/LmNbi5qvfg4W+OWxSPg73vyeetqZ48Cy4diXWIi04lrYmIvw/u4knXzhWpkCXx1Ixs+PRmpuu5xbpbffeUsXMisQm1UBG3OhSQEroC5ddiWvGlfyqnHoahE2Lx7YLdoJk3vPlweS9QasgPpC97sjqZga4Q4/R/3vV1725tj7wlBsuZCDy3lVcLMxw8NRnvBzlGKgvz2+O5yKpALj6UyZZfXwsDVDbgX3haYp/B0s2v1YQkjHu6uC1r/i8/Tet/Z0ZrtaDpoaPOyIz8PyccFwsTat21NHGxboiB4ulrhWyL0D3tPODB9ODUNqcW27A1YAqG6QY84AL4zq4YT/rhSgSaHC6BBnBDhJEWfkvG/cF4LVpzI0S/fNjQvmr41Bfx879POyRWy2+hwiPg+jQpzw+YO9sSM+D2/vTNSZqTyeXIJN57Ox7emBWs0dZvR1x9aYHL090T/YfRWTwlzhIBXj5xPpWgEroG4G8NT6WOxfNkxrE5VEyNfUbXzrnyt6N7i1bNZQJ1NgZ4Lp+c5bLuTobSpgTFx2JfYmFupt38vlbHoZVh1JRUJOJVytzTB3oDfmDvCirj/dUPMF0V9xuaiVKTAs0BH/GxeEnm7tL+vUkU6l6k+faaZige1xuXhpfA+9x7Asi/zKRkT62GH+YB+IW6ycTApzxaQwVyzZEMeZQtBabkUDHuznAYVShUPXivWmQHFVEZCKBXi4v/461oSQ2++uCVob5UocTNI/K2pqqZT2UqpYpBTXdFnQCgD/GxeEn4+lIzarAiwAf0cLzI32RpSPHUJdrcDjMfhQT1MAUzXvqHWxluDxwb5a9z3YzwMxegLXKX1cMW+gN+ZGe2N/UhGe3xyv1RHrfGY5nK3E2Pv8UNTLlfC0NYNIwEdpbRPe2pmoty5sYXUjvjmUgs8e7KO5zVwkwMYnojHisyMo5Si706RU4fC1YsyM9MRmPXnAKcW12J9YiPG9uKsh6Ls4ANTLoN8cSsGx5BIkF9W0qQSYsS5fxpxMKTE5aD2VWopHV5/XzJhXN9bgzb+vIL+yASsm6A8qyO3HsiwWrL2g1QHv0LVinMsox85nBxucubxdpGKB0XrIAAyudl3Jq8KyLRc16UIOUhFevy8E0yK0V7GeHO6HvYmFBltNN4vLqsChF4fjwz1X8euJDM5jlCxgYybU1F8Oc7fGuw/0hJuNGefxhJCucUcGra1bYgLAzoR8gwXfH+rngbjsija1Hm3Nx17/Bh9AvVGpK8RkluP5zReRV6leBrOUCPDcqEA8MVS73FJRdeMt5Wj5OlhgdpT+mYeZkZ6Iz67ElpibgaCAx2DJSH88PzoIDKMuSROXXcHZwrWoWobD14pxMacSB68WQcWqy+MYq+p0kKMNpFQsgJ1UxBm0AupSWwBQZmBW86VtlxDqZq0pRdWSu43+i5NGuQpfHkg2POhOYmreL6Du6sX1of/byQw8MdQP+ZUNuJhTCWcrCUYEO+pUQyCdq7RWhvVnsnA2vQwqFYsLHBeEtTIFfj2ZgY+mhXXBCLXN6OeBH46mGT3OxYr7b6dWpsCjq89rFfUvrW3C//5MgJedBfp522pu33gu26SAFQBKamV4e2cifj9jOB+2skGOv54eCGdrM7hTsEpIt3THBK0sy2LzhRz8ciIdGaV1CHSS4qnh/po80pjMcr2PdbOW4MFIT6SW1OKXVlfaDABbCyHK64znuhoKWEf1cIJPF+Q/VdXL8fiaC6hpkUtW06jAR3uuop+3Dfp5q8s5/RWXi5e3XTKa7sCVd8rnMXhsoDeWjAyAtbn+oIjHY/Dpg72xcKgvTqWWwkoixPheLpC22hyUUqR/lvKHY2laM5N5lcZLzoj4PBy5XozjySWQigV4INwdAU5STOjliuQi3Y1mIgEPw4MdcTq1FL6OFriSx50fV92owHeHU/Hpg7pF/MeEOGNH/O0vc2bMtAh3NMqVEAt4Bpf4WZbVpGK01qRQ4Yl1MVr3u9uYYfX8KK3WwKTzFFY1YsYPpzUXooZcyq3s/AGZYOmoQOxKyDeaQ/rvpXw8NypA5/fz30v5nF2oWBb442yWVtBqSom+ZjWNCqMBa7ODV4vxMq0yENJt3TFB6y8n0vHRnmuar5OLavG/PxPQIFfikQHeBssgjQl1Bp/H4LVJIQhxtcLmCzkor2uCl505jicXmxSwGjIy2BFfzQy/pXO0198X87QC1pY2nM1GP287/BWXg//9ecngefwdLbBiQg84WIrx7s5Ezaao/j52eH9qrzYFK0HOlghy1n+8r4NUb/et9nTTEvIZPL7mgubrbw+n4r0HeuKJob44er1YJ7e1p6sVRn9xzKTn+jM2B8OCHNDTzRq25iJN0F7Zxg19nY3PACOCnTB/7QWU1Mjg62CBJ4b6Qizg40JmOeylIjzYzxO+Ny6sGIaBg1SsN8+7dUCbV9mAZzbE4uD/hlO+aydpuYL047E0kwJWQP/M5e1mJuJj4WAfvLv7qsHjrhfVIqO0TielwVCwm1Jcg4ScSoS4WqGmUQ6lqnPSvTo7jYwQcmvuiKD1Sl4VVh7iLs303eFUzIr0xIP9PPDz8XSdJSOGAR6+saTNMAym9/XA9L4eqGmUY8BHh8CxSt1m4Z42BmcgO1PLsjCtFVQ14nxGOV40ErACwFtTemJ4kLps1z/PDkFhVSN4PMDJsu0fiDnl9ahqkCPI2ZJzJ/vcaC9sOJelkyJgJuS3ucaru40Zcjg+7N7dlYSxoc7488mB2HO5AKdSS3E5twrJxbWIz6k0+fwsCyzZGA9AneowMcwVHzzQq83j7Eg+9uZwspKgn7cN7MzFMBfzcbWgGn+cvVk5I6O0Dq/tuKL1uB+PpePLmX00m8nm9Pfi/LsS8hnOusNpJXWIy65AP2875FbUIzG/Gq7WEoS5W1Mg20altTJsPp+NizmVyKtoQFZ5PeRKFYYHOeLFccFtahrxSLRu3dPbIbO0DnsTC8GywPiezvBzlBrsvteSim3Oo5bj20Mp2H2pwOBF5JW8ajyw6hQsxHw0NClvKc3LkCgfW+MHEUK6TLcOWouqG/HcxnicN7D0X1DViJJaGfwdpfhyZh+8vuOKpuyKuYiP1yeFIKmgGrsu5cPPwQKTe7vBTMTHf1cKTdo0YIoj10vw/JiuKUDdx0P/zuHentb46ViaSR8k/1zM0wStANq1oSy3oh4vbb2EM+nqNq8OUhGWjwvW2YHr5yjFmsej8PY/iZoNF329bDAk0AHfHOJuuxjuYY3SuibUyhSwMxch0scWo3o44beTGZwzUkoVi/8uF2LBEF9M7+sBIZ+H7XH6q0uYQqFisSshH+V1MrwzpSc++NfwjFJnGRPqjDfuC9V8XdUgx/u7jW+wU6pYvL7jCnzszWFjLsKSkQHIKa/XqroR6mqF1JJa6Gv+W1rbhBXbLuHP2BxNrnEfD2v8OK+fVpeju0lifhWO3Ug9aa46cSvSS2ox86eznFUiDl5Vb65yNOE5hDwG/f3sTDq2o606kor/23dd8/Wne6/B2UpssOZpM39HC/g7SiFXqjDrxzO4amBTY2t1ss67WOzpZoWxHC1dCSHdR7cOWpdsiNO7G72ZRMjTtC99INwdo0OccSK5BCwAb3tzLP49Viuo+epAMjY8EY0KPRt02qOivuPO1VZjQpzR081KJ8fLxlyIR6O98eCPZ0w6D9fGqLZQqljMX3MBqS2aBJTWNuGVvy7D2VqCkcFOWscP8nfA/mXDkF1eDyGfBzcbM9Q0yrHhbDbKWv1s+AyDKwXVUNyY/ausl6O6UYHFw/whNzDuqoab52lL2SljTqWWoUmpwmMDvbHOxFy5jnSyVWeu9JJak5c1a2UKPLDqNACgt4c1Pp3RG8+PCcTlvCq4WkvgaWuO+WvOI6lAN5Dg8xjEZ2lvtAOAhNwqPLsxHtufHtTOV9Q9qVQsXvnrEv6MuVnR4YPdV/H5zD5tKivW2sf/XTNY1qymUYEgZynAUUHKXMRDT1crXMiqhFzF4lRqGSZ/exJPDvfDqxND2j0mQF2erapBDmcrid5Wqk0KFd7fncRZ5N+UgJUB8MqEHmAYBq9sT9AbsOprYGJMXy8bXMqtMrlUIQDYWYhwfx83LBsTBAFtNiSkW+u2QWtSfrXRgBVQl1kyF918GVKxABPD1GWK5v56TmcWLr+qEa/vuNyhJX1udeblVgj4PGxYNACf77+Ofy7mQyZXYWQPR/jaW2DGD2cMpg+0NLqHk9FjVCoWx1NKcK2wBp625hgb6qxZ/j+RUqIVsLa05lSmTtAKqNM17CxEyCyth4DHwMlKgvULB+Dl7QmazVHuNmZokCt1NmiU1srw8Z6rcLExA/TUY2253NhownJ+X28bxGVVGj0OUC+Vv3N/T0T52uGPs1lILa6FUsXCSiJEVrn+DXsdoXl2WqFUYVtsLlaf4i7jY8yl3CrM/fUcjr40AveFueKbQ6n47shZvS2JlSoWmy5wB+mxWRW4VliNHi5WnPffiXbE52kFrIC6XNryPxMw0M8ejpam/90fSy7B2lMZyC6vN6nDnkLFYliQI44na+d+1zepcIHjd/SnY+kYF+qs2XjZFrUyBd7blYi/L+ajSaGCm7UEz48JxMgeTvh833X8e6kAChWLcT1dUNMgx9Fk7nx0U7AAPvz3KuKzKw2ufLQnYAWAsromvDopxKSVB0Cd2vX3ksHtezJCyG3XbYPWwmrDmxAYBpjc2w2vTwrlvL+0VoaTeopdn04rg6uNBBN7ueC/K4W3PNaJvVxu+Ry3wsZchA+mhuGDqWFgWRYrD6Xg6za0Zx3oZ4/JvQ3PHJXXNeGx1edxOe9mgOhuY4b1C/vDz1GKHAOBWnbZzQ/pI9eL8d3hVCTmV0Ei5KNOpoBcyYLHABPDXPHx9DDsenYIUotrIVeq0KRgMfX7U5znPXy9GDMMdCFrubFjVA8nnEjRX/zcTMjD+w/0wtN/xCHbhKAzwFEKhmEwubeb1vfu4z1X8dPxdKOPvxXe9uZgWRZLN8djz+Vb+/0tq2vC3xfz4SgV46uDxst0VTXozzssrpahR9f+KXSoHXqalTQpVdhzuQCPDfIx6Tybzmfj1b8ut+m5rxbU4Ke5ffHUMD/8FZ+L7XF5RgO5XQkF7QpaX9gcr1U2Lr+qESu2X4ajpfZGvV0dtFqRWV6P748ZL43VHgWVjVgw2Af9vG2xdFO80b/lxwf7dMo4CCGdo9sGraGu1pqOSa35O1pg7eP9OetnNjO23P3KtstYMNQHfb1ssflCtkmzH/p42HZ+Ll9aSS12JxRAoVJhTIgz+njacB7XIFfiNz0FtJsFOErhZCWGkM/D+J4umN7X3Wjrzw92J2kFrIB6R/myLRfx3Zy+Bncw21mIAAAHkoqweH2M5sO35ZK2igX+vVSAoqpGKFkW8dmVsDYTYkyI/hlgBtCZiWqpZWHwWVGe+Ds+j7NVLAA0yFXYcC4bW58aiJWHUrDvSiHkShWqOTaHDAlwQKjbzRlFmUI9iysW8OFh4HeyrXp7WOFSrm5pn4VDfHEuo/yWA9ZmWaV1OKSn7a2pRHweerrdPbOsAFDXpD9AN9SutCWZQqmV+2mqJoUKT/0Rh6MvjUBto9KkmUeuFsnGpBbXctY5BtCuDoJdTcmyYFnA3kKEnArDAau1mVCzKZEQcmfotkGri7UEs6I8sfFcttbtDAO8MTnUYMAKqGuzGmprevh6MQ5fL8Yn08Ow5/mhGPjxYc4agabYdakAE/R0TuoIPx1Lw8f/3Sz39e3hVMwZ4IUPp/bS2bWdU96gtwQWAOx4ehAivPXvkK1qkOOvuFwkF9XCx94cD/bzgFQiwO5L3C0TE3KrMPSzIwDUTQ24dgDHZ1di8jcnkFpSa/TDt2VKSFWDHNvj8mAu4nNumjMXCVCs54OVz2Mwu8UGMHORAJsWR2PLhRz8cDSN83Ebz2XjyWF++GhamKZY+58XcvD1wWTkVzVCyGdwX5gr3n2gFwD1hcRH/17F4evFYACMDXXG86MDYWsuNNj1xxQ8BvhyZji+OpiC/y4XQMUCVhIBnhrhjzn9vfBZOwIhfcxEfJP7sztailFaI9PZpjWlj6vBsnN3omGBjojPruS8z8PWjLPJSWvXCmra/b4iU6iwPTYXKcWmbVQaG9L2TUT6Unq6UqSPLWIy29dqelQPJ/B4DDJK64y+18gVXVcBhBDSPt02aAWA9+7vCTdrCf44m42imkb09rDBC6MDOfMjW2MYBm9NDsXc384ZLI/y8X/XMDXCHUtGBnDmQekr/9NSc1cllmWRXloHuVKFICdL8HitA8p6xGVXwEEqRrSfvd7NDoA6X1GuZJFdXq8VsDbbeC4bI4IcMa6n9nqss5VY75ilYgFCDMyGpZXU4uGfz2rNsKw6koqfH400aRZHX8kaFsCVNhQDb62+SQmJkKc1M2tvIdLZsNWMzzBY+XC4Tm1Zc5EAjw/21dvKEQDOpZfD2/5mk4iZUZ6Y3tcdBVWNsDITajb9ldXKMKvFDnAWwL7EIsRmVSDQSYqYrFvrvjYrygsBTpZYNacvSmpkKK2VwcfeAmYidR92CxHfyBlMw2PUF0GmeqS/F348nqaz8evo9RJU1cu7rPRbZ3hskA92JuQjo1R3Feb5zRex+lQmVs2JgIeBTnhWZvq/HwKeugRfbFa53pWe4hoZfB2kRleC/BwstKp/mMrPseMaorRsTNLDxVK9inU+G22Z/w1wMMeU3q7ILq9HsQkbu1qykggQ7mGN06ml8LIzvvrl3IUttwkh7dOtg1YBn4dnRwXi2VGBBmc1FEoVFCoWEqH2B3kfTxvwGEZTE5BLVYMccdkVWDDYB1YSAX47mYH0kjoEOEnx5HA/jAh2wqXcSqQV1+KdXdzJ/YHOllh5MBnb4vI0uZ1eduZ49/6eGNnDCUoVizf+voLNF7I1V/8+9ub4+dFIBDlbgmVZxGRV4Nj1EsRkleN6YQ2qGxVQqlg4G9js8U9Cvk7QamMuwgPh7tjG0cP+4ShPne9RS+/sTNRZEqxuVOC9XUno42Gtd2n9dnh5fA/UyhTIrahHiIsVzqSXYX+SviVtFq4GPpAsJfp/7Q9dK8L94W463yd3GzMcTynBhnPZKK6Rgc+Acwd4aW0TSmsNz6xJxQJYmwk4u33ZmgsxN9obS0cHam5ztBTrbPp5INwdXx5I1hsYW5sJ4e9ogdTiWtQ3KfXupjY1sBYJePC0NcO2uFzOSgVldU3YFpeLhUN8jZ5LpWLxV3wetsXmoLpBgcEB9lg4xK9dZdZuVa1MgX8u5uF6YQ287Mwxva+HJp3FzkKE7U8PwrrTmdgel6szG52QU4nFv8fi36VD9L43+TpYoK+XDeI4Zmyn93XHZw/2xvdHU/HZXu6Z83BPG0zoKcFBA+kb7jYSbH1qoM5FsimCnC0R7mmDiybWLuYx6vcYrtnjJqUKQh6DH+b2w5hQZyiUKsRmVeC6gQ54raWV1uPtnaZtomrJUiJAdaMC/7dfnZctNpLuBAAifsdc+BFCbp9uHbS2xPWhUNUgxyf/XcPf8XlokCvRz9sWL40PRrSfPQAY/LBuqaK+CVll9XiwnwceivTUuX9ooCMG+ztgb2IhzqZr14yVCHlYz1H2KLu8Hk+uV3+gHU8pxabz2mkOmWX1WPx7DDYtjsZT62P1BoRFBvLKZHrKHL33QE/IlSrsSsiHilXP6DwU6WGwPWFVvVzvRqWkgmp8PSscL2+/hKaO6MbQDqFuVpqf6/dHUw0ErICSBWb9dBZ/LBqgeUxLfo4WetNG9iUW4ZkNcVg9PwqJ+VX4bO91HE8pAY/hzq9uj1qZAj1cLDE4wAF7LheCZVmM6+mM50YFwsfewqTgw9POHFE+djiXwV3D+MNpvTQbxCrqmrA9LhcpRbWwsxChRqbAwauFKKzi/t2SigWwsxBhWoQ7Hgh3w5cHkrH7UoHR2b60EtOWmt/454pW2k9SQTV2JRRgx5JBt7XWa3ZZPR7++Qzyq25ePHx7OBXrF/ZHbw8bAOrAddnYIOzVs2EzqaBa03BBn69mheOx1ee12kD387bVbCKdHeWF9WeyUFClfRET7GyJSWGukAj5WPlwOD7975pmrO42Zghzt8aoHk6cF1ltsfqxSAz+9DAaTCib9vp9IQhxscLCdRc4j5erWPx7uQBjQp2xP6moTQEroK86sHGtV3lMKeFHM62E3HnumKC1NZZlsWjdBVxokfsUm1WBR387j7+eGYRe7tZwkIoQ5CxFcpH+D1Mhn8GSDeqOR6GuVvh4ehjnJicej8Ga+f3x47E07ErIR4NcCSdLscHZxyalCuvPZOJ0OndgkVlWbzBgNWa0nk1K5iIBVj4cgVcnhiCnoh4+9hZGy/MYmo0GgDAPa/z9zGD8ejIdiXnVSC+tNZo2YYrmJUUriQDBLpZaP89mPVwsMcBXHRQolCqsPmm8xJNCxWLlwRREL9YNWgVGgsLD14qx90ohXtqaoMkPVra3Bo8eMVkV+PyhPvh0Rm/siM/D2tOZePDHM+jpZoWnR/hjkL+D0XNUNejPmy1oMYtrayHCoqF+OHKtGE/+EWv0wuP50YF4YpgfACC1uEZvPnNrvvbcS80NTUqcSCmBQsXC1Uqik6cOqDu7/XI8A29NUQdyxTWNyClvgI+9OVioLwI9bc3bVGbKmHd3JWoFrID6e/rytkv47/mhWhfKBVX6c35bB5utedtb4OD/huPI9RLklNcjxNUK0X52mvPbWoiw9amB+OpACvYnFkLAZ3Bfb1f8b2ywJhh9INwd94W5IrOsDlYSIZw6sHWrnVSMH+b2wxO/x5iQCtWEpzfEGQxwmy8Ij7ahq9et0Ldh15gZfWkTFiF3mjs2aD2XUc4Z4DQpVfjlRDpWPhwBhmGwYkIPLF4fq/dNreWbdFJBNeb9dg6Hl4/grL1qJuJj2dggLBur7n417MYGJEO2x+fB0EJVewPWSG9bTIsw/KbrYi0xecnV1kKE/j52nN3HApyk8HOwAMMw+HJmOL49lIIvDhgvj2TI2FAnvDAmCCEuVqhulKO6QY5J35zQOU7IZ/DVrHDNB3x1o8Lo8nuzC3o6qZnSmvbzfdcMbmjrCKW1Muy5UqC1NHwipRQnU0sxs58HxvV0wYhgJ725z+rfUe6ZrHMZZVg01FfzfWtSqEyeKW9Z2/jINdNrcnItyR5IKsLyrQmaANvQBcOJlBI0NCnx+t+X8c/FfChVLJrjRpZtzgF1x3sP9DJ5ZlGuVGHtKfXyfq1MgaGBDnhmRADsLEQ4rCeoulZYg7QbKULN+nja6F2JyDCh8oiAz9PptlTTKMe+xCJUNcgxyN8eX8zsA6CP3nPUyhQQ8fmdsuFtRLATDr84Alsu5GBbXC4K9QTil/OqDF4sAYD3jU2yZrcw+2sKBsDLE4LxqZ7UCkPmRnvdUoMIQkjXuGPbf7TuAKXvvtEhzvjzyWiM7+kMX3tzhLlbYVqEG0YEc29aqG5UcOaDcjFlV3CdTIl6PbMSbclAC3SSYkyIE4YFOeL9qb3wx6IBt7QkyOWtKaGwapXvKblRv7TlrFNH1LZtaFKhp5s1eDwGNuYibI3NRS1Hi0a5ktUKPq0kAjhIRSY9h72e42ZGeRrcBAdAaynXVEK+6T9RcxEfnrZm+P6Ibr1KlgW2xORi4boYjP/6OPI52tQCwJwBXpy3A+p2oMdbBFknUkpMLmH0z8U8k5oxtHagVd5lQVUDlmyI0wpyDKXrWEoEeGdnIv6Ky9NcZLLszULzChWLP2Ny8WGL9rkqFYvqRjkUHBsFWZbFvF/P4cM9V3GtsAa5FQ3YdD4H074/hZyKeoO7yxUq7fMtGRkAfb8yXxxIxnk9aRr6HEsuwcCPD2P51gS8vzsJE1eewIt/JnBeXJfWyvDU+lj0ff8Ahv3fEQz77Aj+uXhrLYlbU6pY/HelADsT8lFq4PfEUK44oK7uMn+wD5QqFlIDueMd4cF+Hnh6RAD6GaiG0trTw/1wYNkwfDA1zGjlB0JI93PHBq2GaqN6trqvn7cdfpoXiSMvjcSu54biq1kRBmcBsspMq9ka5WPam6VSxXIGqLP6e8LGxN3WL40Pxq+PReH3Bf0xL9q7wwNWAOjlbo39y4Zj6ehATOzlgqeG+2P/C8Mx0F93if1Wtd7ElGwg9+16i/xTAZ+HBSZs9gEAR6kYb/59BefSy8CyLCrqmlArUyDI2RJfzuxjsDZtW9pANuNaWnWQijh/9k8N98ex5FKj9T5Ti2v1Fqaf2MsF0b76cyn/istFbFY5pn9/CgvXxRh8npYq6uWaJd7UEtNzEluXJfsrLq9NtUMn9HLBX/HGLxj/jMlBdUMTNpzLwtDPjqD3O/vR/6ND+OpAsiZ4ValYLFwXg7McwWRpbRO2XMhBtB/3987b3hxBTtqVJ6L97DEv2lvvmP5v3zWoTPydqZUp8OyGOJ2f/fa4XHxzKBmFVY2a18Gy6texN7FQs2kur7IBz2++iBMp6lnwRrkS22Jz8dqOy1h5MAW5RuqTclmx/RI+2nMN2eX1en/3B/nb4+Eo/RdKZkI+vpoZjgG+dvjfnxexiuOCrDU+j8Frk3pgxzOD0KNVtQ9AvTFRnyUjAwAAL44NMpry06ynuzUCnXWfhxByZ7hj0wNG9XCCp50Zcsp1Z6EeNaFTTaCzpd4Zw0An097Ulo4OxOm0MpOS/lkATpZiKFUs7KUizO7vhZmRnjhoYEMRoJ5ZfHFcsE6VgM7iYi3B/26kP+gzKcwFSQXtL2EFABFe2gG/t558SEC9A7ulp4b5Q6Fk8dvJDFQ1yGEm5EEs5KOyVW3UK/nVuJJfjfVns+AoFaGktgl8HoOxIc545/6e+G/pEIz+8vgtvQ5Dloz0x//GBuNUaim+P5qKxPxqeNiaY/4gb3jZmePR386bdJ5jySUormnUSWtgGAa93K05AzMAKKhswNxfz6OhHbOmNmZCsCyL/9rQwGBEq5JLXNUVmrXOQ5we4Y4hAY74aI9uebfWZAoV1p3O0kpRKa9rwspDKVh/Jguv3RcCMyEPh6/pz6k8l16Ozx7sjTm/nNVqICES8PDeA704N8NZiPW/XV7IrMDyrQn4YmYfozN4+xML9aaerDyUipWHUuFiJcGEXi7g8xgk6NnZ/8uJDIS6WuHhn89qWvuqz5EMT1tzWJsLMSnMFY8N9NGUSuPy/q4kvatLZkI+Ap2luC/MFY8N8oFEyMeEni7Ym6j9e2EpEWDnksHwdZQiNqsC/1w0rXvW/X3csHiYPwBgy+KB+OVEOvYlFoLHqPN6RwQ74JFfzut8v2ZFecLnxvvCoAAHbHkyGj8cTcPlvCqU1zXpzc11t7l9G/0IIR3vjg1ahXwe1j3eH8u2XNTkhZoJ+YjwskFBZSNqZQpIDXzIzOnvhbWnMnQ6Hjlaig22Bm0pwssWW58aiFVHUhGfXQmGAYoM1BYsrpFh8+JozY72HfG5evMzhwc54rlRAejlbt0ps6q34vHBvjh4tVinTE6IqyWuFhifmbMUC/DEUO3Z0jn9vfD7mUydckrWZkJMb/Xz4PEYLB0diMXD/FBU3QgHqRhmQv6NLlEFWH9Wt5pDyY3vs1LFYm9iIVKKa7D3hWHwc7BAOkcdTkCdy1te16RJA/G2M4OfoxQnUkpNmolNK64Dn8dgWJAjhrUK6B784TTkbZjN3Xg2Cy+MDda5fUigA37VszFNxaJdAWukty18HCygUrEm5/X6O1pg3sCbM5GpxTVgDCTAPDvSH+425iiva4KrjQThnjZwtBRDKhYYnX22MRNi8wXdzVwAUF7fhOVbEzhn7VqytRCil7s19r4wDBvPZePajZJXj0R7wd9RyvmYPh7WBs/5V3weXKwl6OVujZHBTnoDRX31jFsqrG7E2tOZBo9JLqzBVweTtQJWQP1zzyqvB8qBS7lVOHytGBsWDYCQr7uycPhaEX47pX9jo4plsfPZIVq3fTM7Ar+dzMD2uFzUNMrRx8MG/bxtkVfZCC97Cxwz0KWuJQsRH0evF6PPu/sxLtQZy8YGYfn4YCwfr/49r6xvwqbzOQh1s0JuRT2KamRQ3AhGj1wrwl9xuZr3hn7edvj1MfXM+ebz2XiFY3Wij4c1wvV0EiSE3BkYlu3gbdG3WW5FPb45lIKdCflaAY+zlRgbn4jW+wEEAFfyqvDe7iRNPtrQQAe8PaWn1gaMtqioa8KYL4/pLXoPqHMZtz89CCGuVnh/dxJ+0xNwDA9yxLoF/ds1jttBplDiv8uFOJlaCiuJENP7usNeKsLMn87ozH6HuFiiqlGOmkb1RpjnRwfpFP4HgNNppXjz7yua0ko93azwyfTeCDMSLLT01PpYnVkgfb5/pC+SCqrw3WHuZcxhQQ4Y1cMJW87noEmpgljAQ1KroNxSLNAb2E3s5YIf5vbTub1JoULQG/+ZNMaWvp0dgSmtNo+oVCweX3tBJ1AIcpZCKhZw1gcF1DnS9/dxw9HkEsS26ELm52iBdS1aJD/y61mcSi3TOyapWIBHBnhieoQn7KQiSMUCPL853mBJMncbM+x+bggOJBXhs33XNBdukd62CHWzwu8cJeRaCnCUItVIeS0hjzF4UbDy4fA2t/BcsiEW/5o482wlEeCb2REYwdEIJbW4BmM6aIafzzAmVbb4/pG+mBSm27XvyfUx2Jeo/2flaWeGEy+P4rxPrlTh5W2XsCP+Zn6tn4MFRoc44RcjraS5uNuY4d+lQ2BjLkJhVSMe/PG00U5tGxcNwKAA7UobLMti5aEU/HQsXXPRNsDXDisfjuiSWsCEkI5zxwStLMsiLrsSuRX16OlmjQAnKb4/korP91/XWyA92s8OmxcPNHruirom8BimQ7r5XC+swbTvT3G2HW02qocTVs+Pwq8n0vFBi00lLc3u74WPp4fd8nhut+Zg9kx6GaRiAR4f5AMPIy13W2ruKsZnGHjbm7d5s4SxD+GWnh8diAf7eWDY/x3h3JQT5m6Ny3ntb6gQ7GyJD6f1QqSPdu6kSsUi7J19qDPwO8JFyOeBx7BwsTbD7CgvzIzyhI2ZEHKVCn+czcbuS/loUqgwJsQZCwb74t3d6k1NXP7vwd54KNITLMsiNqsCVwuq4WFnjmGBjlqb1OKyKzDnl7M6M+Cz+3tiwWBflNc14d1dSZp0EWcrMedqg1jAg5WZEGNDnfHcqACkFtdiHkd6hLedOWZGeWLd6UwU18hgLRGCBYvqRgV4jOnNEAyZ0tsV38yOaNPvVmpxLcZ8eaxNz2Mm5OP0K6Nga6G7IfDVvy7r1G7uTC3fT5QqFmW1MliZCfHob+c5K4Y0e3ViDzw53J/zPn1VRHwdzJFd3tCuMlTNz/f6jsvYwFEarbUxIU749bEozvuqG+W4ml8NR0sx/AxMXhBC7hx3RHpAYVUjFq+PwaUW5aGsbnRAMeRsejkuZJYjyNlS036TC9eHSnsFu1jivQd6YfnWBL3HHLleDIVShel9PfD1wRSd5VAeAzxiYGd4dyYW8DE1wh1TjZTj0odhGIOz48ZM6OVictDqbW8OTztzvDOlJ97ZlagVuA72t8epNP0zjC35Olhwtvq8XlSD2b+cxebFA7V2OPN4DGb08zA6o9ia/MbmnKyyenyy9xo+2XsN3vbmWDoqEAuH+Op0o3psoI+mdFRLTpZiTeMBhmFgZSaEUsWisr4JjXKlVu5mXy9b7HhmMN76+wou5VVBqWLh62CBsaHOEPB5eGzNea2AVl96jEyhwt9LBmtyCt/YcYXzuKzyegQ6SXH21dGolythfiM15tlNcdjThvxaBkBvji5uE3u5tDlgBdSlu9qqQa7E7kv5mDfQR+e+D6f2gojPw7ozmW0+b3uIBTysO52J/y4X4GphzY1ccD78nfTnkkf52MLJUozkohoEcWxe2nwhh/NxGaX1eGaEP346nt7mwDX+xsqAoQ5gLRmq8mElEWIAR3MRQsid644IWl/YEq8VsAIwGrA2e+jHMxAJeJge4Y63p/Q0uCGho4wIcjA4K6SeMWNgZyHCmsej8OKfCci+0f7V3kKEt+/viV7upi+Jk5sm93bD7oQCHDKwCQdQl+5pXi59bJAPhgU5YufFfNQ3KTA6xBnHkotNDlp7e1jj+dGBeGHLRZ375EoWq46kYvV87dmglyf0QGpxLU6b+Bz6ZJXV48WtCRDwGZ3l7j6eNvh2dgTe352kKYDfx8Manz/UB2YiPpQqFq/+dQl/xtzchGMpScSPc/thcIsl1zNpZbjQIoUgpbgWC9bGYESwI2dLV33KamWaoDXHwA737PJ68HiMVk66oU1VXCaGueDLmeHYHpeLg0lFEAl4uL+POyb2ctEKWGtlChxIKtS0kw24sQmzuLoRPx9Px7HkEpiL+O1ualBex13TlMdj8NaUUKSW1BhMv+goOy/mobzVRsUGuRJX8qohEfJ0fo4WIj4uZFZoamGPCXHGt7MjtN4/y+r05+9HeNni1IpR2H0pH0eumf631Px9FvBMK2wTTJUACLmndPugNaO0Tqd1als1KVTYfCEHjXIlvn44ooNGpt/xlFKDy5hTertpdidH+djh6PIRSMitRJNChXAvG4gF3Wvj1Z1EyOfhp3n9cPBqEfYnFUHE58HZSoJtsbmaovn9vG3x6YzeWhvc8isbcDJVnd/598U8kytIAMB9Ya4GNzxx9XWXigXYsGgA3t+dhNWnMk1+Ln1+OJqGB8LdUSdToKy2Cc7WYogFfEwKc8WYHk7YEZ+H2iYFxoW6aPJVt1zI0QpYAfUmoaf+iMXZV0fDQixAo1yJbw6ncD7nmTYG3AVVDZr2qMEuVno71aUW695uKIkp1NVKq5rF0EAHfDQtDBIhH48M8MYjA7RLVdXKFPjzQg52JuTjSl6V1qa62f09sWxMEKZ9f1qryUJ7DfDTTQ3Zm1iIfy7mIbmoFmW1MpM6OjFoX4tTPgP4OFgYbMHbKFdharg7YrLKYSbkQ6ZQIrtVXvrBq0X4dO81vHN/T1wtqMaqI6kGLsoZzca6RUP9cOia6bPUD/dXt9Ge3McVPx1LN3isgMdgUasNnYSQu1u3D1oNlc1pq50J+XhlYkinJ+MbKlwv5DN4ZWIPrdt4PEanBBRpPwGfhwm9XDGh182NJ0tHByK1uFZd1L9Vju2ZtDI8uvq8JnAoqpYZrALR0rQId4wJccY5A8Xl7S1E+O1kBi7lVsLFSoJZUZ7wc5SCYRg4d1A7zutFNXj7nyv4MyYXDXIlbM2FmBnpCRdrCX47kYHcGwHYB/9excx+nvhwWi9si+Ve3q1pVOBAUhGmRrgjraRWp5RYM1NKvbX03eE0jO+p/pk8MdQXe68UcJYm2nwhB0MCHTQpDFcLqvV28uIx6koAH08Pg52FCD72Fpyb/JpV1csx86czuK6nLvCm8zkoqmpsc8DKZ4DWL2VEsKOm/TCgztdevjUBf8Wb1hiAz2PgZiNBsLMVFg/zw/u7k0zKsWYA2FmIMKGXC+YP9MHYr41v+ro/3BVfPxyOnPJ6DNXT6W9bbC6mRbjh4Z/PGbxIWzDYVzNjmlZSizNppk06MADSSurQ080az44MwNn0cq1yXzxG3bxAqVK3dl4xsQe9bxJyj+n2QWsPF0tYiPgmb1qRivmcnZWAG6Vgyuo6PWgd2cMJ5iI+52asJ4b6dWj/dGIaPo/RG8ysOpLaptw7IZ/Bw1FeGN/TBYMD7MEwDAb42iHASco5S1hSI8P7u5M0X68+lYHv5vTF+J4uGB3ijI//M16b1BhzER/rWuTIVtTL8dNx3ZkqdbetHPg4WBhsx9l8n71Fx/2uXs6rQp1MAQuxAL09bLBsTBA+28fdgvP3M1maoPWPs1l6ZxlVLHAqtQynUsvw+UN9DAasgPp7ry9gbXY6Xf8M8twBXhALeEgsqEZyUa2mHJqSBbzszaFQqiAVC/BAuLtWG11A3Xra1IAVUG+Y+r8H+2hK5DmZ+L7BAiira8KGc9koM7HlcXOdZEOTBLUyBb49nKo3YA1ylmL+IF88HOWB06mlOJZsehe25nG/uzMR43s6w1IixLanBuJAUhEuZJbDQSrG9L7usDYToqFJCTsLEXW0IuQe1O07YllKhHh6BPfu1Zb4PAbPjw7EmVdHw9OOu4A0n8foFKrvDFYS9cxP6xnXcE8bPHOjiwvpPi7lVrbp+AAnS7w/tReGBDpoPjh5PAa/PhqpVR9UwGMQ4GiBylbBoVzJ4vUdl9GkUCHASYrFw/x0nqOHiyUejvKE+EbXLj7PUNVToKGNlQg2X8g22P5ycIA6UHKxlmB0D92yTfq4GbggNBfxNa8HAGzM9W+AbNm6tjnf25jP913nbOfakikbfAzl6QY6W+LNKT0R4CjVaeOcXVaPwf4O2L9sOJaMDNBJ8zHWSISLlUS9gVSmUMKhHRe7exML4WtvuHrHyGBHOFmKsf5sFrbF5mr9jFryd7TAydRSzvsA4LlRAYj0tsG8385jzq/n8NPx9DYF6YA62L54YzOWkM/DpDBXvD2lJ5aMDICrtRnMRQLYS8UUsBJyj+r2M62Aul2fs5UE685kIreiAR42Zqiob0JepXpzSU83K3w8PUyTL7d4mD/e/Ft3d/K0CHc4ddByrDEPhLsj3NMG2+PyUFHXhAF+dhjf04WzwDfpWi7WElQ3Gq772dKCwT6ct/s4WOC/54ciPqcSpTUyhHvaYPj/HeU8trS2CTFZ5Rjk74BXJ/bAQD97bI/LRZ1MgeFBjngo0hO7EvJx6FoRSmqaAJbF2FBnjAh2xK6EApzNKAPLqjfMTO7tii0xxtuftlRSI9Mb6JqL+PB1uFnB4aUJwUgqqNZs5jJkdn8vnEgp5SyjNKOvBwQtfv/DDGw27OV2874QVyucSNEfLDUrrG5EZlm9wTrLt/L3xzDA5N6uuJxXqbcc09/xeYj0sYOlRIBhQY5am8n4/LYFWiGuVmBZFo+uPo/jySVo79ArG+R6V6CCnKV4dmQARn1xzOisaFaZ/havAPB/+5JNvsAwxFB7ZULIve2OCFoZhsFDkZ54KNJTcxvLssgoVXcc8rLTruc5d4AXmhQq/HA0DaW1MpgJ+ZgV5amTS9rZvO0tjLZEJV1vXrQ33vwnUef2ICcpPOzMceR6MVhWXWZtycgAPNhPf8c0hmHQt0WenaEJoea5U4ZhMLKHE0bemNGsrG/CPxfz8FqLslBKFtifVIT6JiU2LY5GUXUjiqob4Wgpxod6av0a0stNfxvj+iYl4rMrEOljh/zKBsxffQGF1cYDVkB9AfDVw+FYsOaC1jL80EAHzBnghW8OpaCuSR2YD/Szx4hgRxy9rt0YQchn8FSL1RVriWn1kxkGsDIz/JbW39eOc2OcKdxtzGBtJsTkb0/qTVeQq1is2H4JgHqz3UfTw3D/jYYQpTWmLdUD6te8fFwQZrdoM9s8idzWTVkV9XKsfDgcO+LzdL7XyUW1WLIxnjNgtRDxIRbyEeQsxdWCGoPpJDzG9BlxQ7zszNHnxuQDIYS0dkcErVwYhtFbMJphGCwc4otHB3qjuEYGW3MhzEV37EslneyRAd7IrWzAmpOZaLoRGfTxsMZ3c/rC084cBVUNKKttgr+jtM0l0yb0cuEs8O9oKdZZns+rbMCbf1/RBMlcTqaWIim/GqFuVsgorcOYL461uUkBjwHOZ1YaPKZ549O3h1NMDlgB4HxGOR6K9MR/zw/FmfQy5FbUI8TVCpfzqjDpmxOa1/XTsXRM6OmCbx4Ox8pDqdgak4PqRgX6+9rhf2ODNO02G5qU+Ok4d8ey1gQ8BvNXX8Bjg7wxM9KTcwk520BdT2OGBjrgm0MpJs04A+oc0GVbLqK3uzXspSLsuVxg8nNVNcrx2o7LnKX9WKg39xnqvNdaXFYFUvRUa9D3861rUuKtKaFwtTbDo6t1G0G0ZEpKOFdprZYsxQJ8NauPprIKIYS0dldHckI+T1MXkhB9eDwGr04MweKhfricVwVHSzFCXa00QY+rtRlcrdv3e/TS+GDEZFZozUKJ+Dx8PC1Maxm0SaHC3F/PcTYpaC29tBYBTlI8tym+TQGrSMCDp62ZwfJHgHpGua+3La4X1uDPC21LO2gOpHg8RlPrtaCqAW/9k6gTiO9NLMTwYEe8OTkUb9wXAqWK1UofYFkWm85nmVyTWa5kkVRQjRXbLyOvogH/Gxesc0xMi3qzbSHi8zA00BHPbYxr0+OUKhbbYnMxyUhZNC6GKlhU1JsesALqC/n2lPBasf0yfIzkxJrq84f6YNWRNFy9UZ7Mw1aCSWFuUKlYuNmYYVqEe4c2eiGE3H3u6qCVkLawl4o5e8XfCldrM+x5fij+istFQk4VXKzFmBXpBa9WgcCBpCKTAlYA8HeU4nRaqdEcRFdrCWobFXC0FGPOAC88PtgXIz7nLmfU0qwbG8CWboo3qa99S5E+upu7/r1UoLc6w66EfMzu7wWGYSBokfPJsixe23Gl3a1Ofz6RjoVD/HRaM9tZCNtVRq9JqcIr2y/plLUyRVldE1ytJRDyGc4SX+1hZyFCqYmVAQBgsL8dNp3PbnOZMsBw1ylTPT7YB5N7u+G+MFdcyqvCwaQi8BkGkT52GORvT7OrhBCTUNBKSCeTigV4dKAPMFD/MVylsrgMDXRAiKuVSQHu86MDUdekbiX6z8V81MmUKKg0vrQ9L9oHifnVBktDcdUldbcxw+wo3fbDTQZ29HMFUTKFEp/8d63dASugrgAwd/U5zB/og6kR7ppKHjMjPfFBO3KAAdO78LXW39cWthYiTO7thh1t3E3Plb9qJRHgyWF++HCP6aXSnt10UW/AOiTAATGZ5WhsR0BrCJ9hMDzYEXP6e2F0iBOqGuT4fN91bDqfrbWhK9rPDqvnR1EKFyHEKNqmSUg34OtouBSbkM9gargbvpvTFwAQ7WdvcJd1sIsldibk4/3dSYjPrsTlvCp8dTAZQiM72CeFucDL3hw1RgK0lQ9H4OkR/vC0M4OzlRhzo72w/elBnMu7o3s46z3PmBDt+5oUKixYewFrjHQJa05XNfR6LudW4cWtCXjxz4tgb8wYzx/kg4cMbKTraD3drDApzBUVdU24mNP21IQlI/3hbHWz1JW/owV+XzgAPm0s3acvYB0e5IBvZ0dgel93zvtvxeuTQrB6fhTGhDqjrK4JD3x3EuvPZulUIDibXo7vDqd2+PMTQu4+DMu2cf2PENLhZAolxnx5DDmt2mfyGeDHuZHo72cHazPtpe5VR1Lxf62K8zMMMD3CA8ODHLB080WTn5/HqGch35oSCnORAJmltRjx+THOY0NdLbHn+WEmnxsA3t2VqBOIhrpaYcuT0bBsUR1gR3wulm1JMHiuEcGO+HVeJMAAXx5IxvdHjW/UWr8wCkMDb6Z+HL5WjGVbLhrcEc/F1J37FmI+5g7wxthQZ6SW1OLn42lIL2nbMvvCIb54474QKFQsruRVQSTgaXKtn90Yh92XTN/YpU8vNysUVDea3ITAVEIeg7OvjYa9VB1wf/hvEn45kaH3eDdrCU6/OrpDx0AIufvQegwh3YBYwMeGhdF4dcclnEpVd2TydbDAG/eFYHQI90zlkpEBCHW1wsbz2SipkaGfty0WDPGFu40Z3tuVxPkYLv28bfDbY1Faxf7/u6K/EH57Ws++NTkUg/wd8Hd8HuqaFBga6IAeLla4XliDPp42mvqph64WGz3X/EE+ENyYZX5+TCCyy+uNBnAL1sZg1Zy+GNfTBTK5Eq/9ddloCSeuNNzxPZ1RI1PgVGoZeAwwqod62ftC5s1Z1Ak9XfD+1J549a/L+OlH3a5k+nw4rReqGxRQKFUY29MZPVysAKhnk5vblapULE6lluqtBNBWV/Kr2/wYGzMB6uUqva11AeCJYb6agBUADhr5uba1AgYh5N5EQSsh3YSXvTk2LIpGcXUj6puU8LIzN7pBpWV915akEtP/tC/lVuks2RrarNSepRmGYTA21BljQ51xMqUUK7Zf0uxmd7YS48OpYRgT6my0sPzwIEfsiM/Dm/9cgZOlBLP7e+Hb2RFYNjYIz2+K1xuEyZUsnt0Uj5Mvj8Rzm+KNlvFSsbp5u1525ujjaaNpj6ti1U0ivpoVjiaFCplldQhytlRfbPx92Wig1pKtuRCDA+xxMbsKTQoV7Di6hZXWyjB/zXlcyWt7oNmRonzskFVej2Q9gXO0nx1eGq9dE1tg7Pc42LHDxkcIuXtR0EpIN9MRXdumhrvh28Mpeuu9tiRXsjiTVoYpN4rgA4bbsUYaaP/KsiwS89Xds0LdrHRKzuVXNmDR7xe06nUWVcvwxO8x6OVhjf7ednrPLRbwcDqtVLMDP6e8AbFZFUgtrsUrE3vgqRH+eHZjvN7HNylU+OZQCs5l6Hbr4vL5Q31wtbAGxdWNiPCyhYNUhCWtzn8xpxKPrT6PQy8OR/CNFr4yhRLbY9u24aqiXo6Rnx/T/LwEPAYvjgvWamH91j9XbmvAOsDXDv6OFtibWITyuiZN9YMDBoLxPh7W+GlupE6N3Cl93PDlgWTOx9iaC7F0dGCHjp0QcneioJWQu5CfoxQfTQvDm39fMdh6s1nLdqMADHaNGujvwHl7UXUjntkQh9gbtVB5DPBgPw98OC1Ms/y/NSaXs8A8C/XGqcu5VRDwGM4x69tM9OuJdCwY4oNJvVwxu38pNp3P0Tt2UwNWG3MhJoa5Ylrfm5u2HtNTYD+7vB6HrxXDUiJAUXUjfB0s2lyTFYDWBYZCxeLTvdcQ7mmDgf72qGqQY1+i/pSNjhbhZYNJYa6Y3tcd7z7QC89ujDP6/F52Zpg30Bs7L+VjWKADvO1vbhZbNNQXJzna+44MdsQH08KonjYhxCQUtBJyl5p9o9TQvsQiyBUqZJfXYe3pLJ3jHC3FmkYAzWINFOFPK67V6eYFAEs3xWs9TsUCf8bkwtXaDMtutDPON6HAvSlBduvjYzMrMDHMFR9P7w25UoVtemY6y03sIvXaxBBIhNrdz3Ir9G+ken3HZa26qZYSgdEKDKbYGpODgf72qJMp9Na67Qzx2ZWIz67EryfT8dujUSblGmeXN2D5VnULW4YBFgxWbyRjGAbmIgE2LY7G4WvFOJNWBltzIab1dYeHbcc0LiCE3BsoaCXkLuZkKcG8aG8AQKNciYzSehxLvtl/3koiwKo5fXVySe2lYuTraVdqL9XNt0wtrtE7i7nhXDZeGBMIhmHQy90KW2La+2r0a7mJbGIvV71Ba1ldExgGnGkTHrZm6ONpg0ejvTHAz17n/lA3a73dxFoX+u+IgBW42fnKxUoCPwcLpJvYgKKj5JQ34OuDyW2+kGBZ4LeTGYjwssHk3uq0Ez7vZl4zIYS0B9VpJeQeIRHysfbxKPz55EC8ND4Yn83ojdOvjkZ/X9080of7e3Kew9VagmFBuptmig20HC2tlUHFqoPmYBcruFiL9R7bHl525lqvwdNO/+yduYiPd6f0ROt9QYuH+eHEyyOxak5fzoAVABYP9YOIb/pbprWZQOd52mqgv3osPB6Dlyf0uOXztcexlBK4GshxNmRbbNvaABNCiCEUtBJyD2EYBv197bBkZABmRnnq5LI2mx3lhfmDfLSCJHcbM/z6WKQmP7WlEFcrvTv/e7hYYt3pTER/fAgzfzqDwioZHDlma9vD1lyIUDcrfPjvVSTcyMMNdJIizN2a83hfBwvsvlyAYUGOmB3liVcn9sDB/w3Da5NCdDYPtRbmYY21C6LQ18sGAGAm5GN4EHd+LwDUNymxfHww530etmYQtWiMwBWM+jpYYFaLDmMTerlg/cL+RissdDQeGE16Bxd7joYSzSrr21YHlxBCDKHmAoQQvfIqGxCTWQ5bcxEG+dtDYGCmccW2/2/vTsOjLM89gP9nzySTTCbJZN/ITshKTFjCGhYBRQqIStFTKS1wFD0HrdUuXp5ztbXHWqnSWi2V6ikqiBZxOYqyKHtAIBACyBbIvpB9IclklvMhJCaZdyaTZCYzwf/vm+/7zjsPfpjrz8P93Hc+3jthfgjKUyFBc8fw+3BG+XngiblxiNaqcPxaLd48fB3Xa/vWmT45Jw6PzYrF1Rst+NE/jqO0/rsa2u7T773dOS4A2TF+mBqrxZhBTJlq0xkgk4jQ2NaJSb/fJziqNi1MjQ/WTkbm7/agXiC8PTQxAkaTCW2dBixND8Gp4gZ8kl+OToMJOQn+mJ8ciAgfD2g9FThZVIfNh67hdEkDym0YxTtYsf4qVDd3CPauXZYRiheXpeKLc5V442AhrlS3IFqrwsrsSEyJ1eJKdTOWvnZU8L2Pzow2a39FRDRUDK1EZBe//vAs3j5WbJd3TY72RavOgPgAFVZMiECIRgkfdznEYhHadAbctfGgxfrOvU9OR7RWhU6DEXsvVKOsoQ25hbXYfd766feHJ0fiuYWJgjuu7Z0G7DhVhn3fVkEhleCetGDMTQyASCTCH3Z9a3EqV3a0Lw5frRW813+61tLxofjDvSnYmVeGF7+4iMqmdohEQHKwGgXljYLDDoZrUpQvfnXXWIwL9sLfD17D7z+/0KfeN9zHHdvXTEKglfIAk8mER945hc8LKvtcD1a7Yee6bPh7Dr+FGxERwINYRGQnh6/WDPsdIgDr58RZ7du56UCh1QNJ7x4rRphGCb3RBJ3eiHa9EXnFlrshdHvryHWkh3tjUVpIzzWTqauH7RPbz/QZSPB/ZyuwPCscv1+SjKfujIdSJsGGPZfMDnhZCqyA+ZCGf50qhYdCgn8e/a7Dg8kE5Jc1Drj2oSqpv4mzZY3YdrwE750oNlv/gqRAq4EV6Co52bg8HW/nFmFnXhlaOvSYEe+P1dOiGFiJyK6400pENtMbjKhq7oC3UgaPXvWwV2+0YNZL+4f9/r+uSMeC5GCrz+S89DUKLZziH66psX7YsmoCgK6Rqc/syMf2E5YPE330aDYCvNww9+X9aGoT7hjQf0fVGnu1yrIXuVSM3F/Mgo+VulUiopHCnVYissnW48XYuPcyKhrbIZeKsSQ9BM/enQgPhRQ78yxPgJJJuoYF2PLX4//YdhpSsRhzxwVafKZDYDiBvTT1quncVVBhNbACwJ4LVdDpjRYDK9DVsaBVZ1tNb5uNz40Und6I0yX1yElgmyoicj52DyCiAX18phy/2HEWFbd6t+r0Rmz7pgTr3zsNwHpf0qxIH7y2Yjxi/FUArM+h7zSYsP6902jpsPy+mQmOm1PfPWRBpzfiuU/OD/i8TCI2m/LU36yxAfj7v92BmfFaJIV44Z5UyzvJ/l5DbwcWMIzPWtO7By4RkTMxtBLRgDYdED5o9OX5KhTeaDGbqNXbvKRAzEsKwu7105D37GxEaa2f0m/VGfBOrvnkrm6PzoxB8BD7hlprzyQRi3B3ahAA4KPTZbjRbLn3bLe7UoLgYyXUKaRirMuJwZzEALy5MgufPjYVG5en44cTws2e9VMp8PSdCRDqvOUmG/inOifef8BnBisuQIX0MG+7v5eIaCgYWoloQJerWizeu3qjFTkJ/siOMW/KnxDoiSXjQwF0HdgprGnFJSvv6rZh9yVcqmoWvBekVuLjx6Zg/ew4jAv2svFP0GVeUiCWpIcI3jMYTVj0l8N4ff9VfHGuUvCZ3u5JDUaUnwfuyxQexCAVi/De6kmIC/A0u/fbRUn4w9IUZEZqEBegwsOTI/HRumwsSg/Bqz8cj0hf9553LEwNxqGf5+DNhzPxywUJmCAwDCIxyBNbvzFvN2bJ5GjhAQq9hfu4468rMgbsX0tENFJ4EIuIBjTv5QP4tlI4RO55Yhpi/D3R3mnAn/ddxpajRWi6VS6QEOiJ5xaOQ6DaDR+eKkV+aSO+7jVG1poYfxXadAboDEbMHuuPBydG4GRRPRpvdmJyjC/GBXkh+4WvUNuqG/hlt7y0LBULU4Px8p5LeDv3u3X2lxGhwcmigTsOrJ0ejafnxeNPuy/h1a+vwnCrL5W3uwx/Xp6OqbFDK2UwGk2oam6Hh0IKLzeZ2b2vL1VjV0ElRBAhOVSNX+8sGNT7n56XgNqWDrxx6Fqf6z7uMqyeHo2EQE9MifGz2peXiGikMbQS0YD+dbIUT75/xuz6zHgt3lyZBQDo0BswZ8MBFNf1bfgvFYtgMJpsPkFvq7Qwb5y+NQXLVi8sTe6ZMvWXfZfxxy8vCT53R4QGJ2wIrRKxCIeenokgtRJVTe04fKUG7nIpZsRr4SaTDGptQ7Xhy4vYuO+Kzc/LJCJ89bMZCPFW4tP8Cmw/UYIbzR3QuMuRFuaNe9KCMTZocDvYREQjgX+NJqIBLc0IxXMLE3tqQqViERalBeOV5ek9z+wqqDQLrAC6Ogc4YE2DDawA8PS/zmL5plzUtHQITqnq1tCmw8z4gXdJDUYTcgu7erEGeLlhyfhQzEsKHLHACnT9/x2MToMJf9l3BSKRCJmRPhjj54HL1S04WliL1/ZfxfxXDuLZnQXgfgYRuRq2vCIim6zMHoMfTghHWX0bfDzkZqfKrdW9CpFLxFDIxGjTGQYdvAZird/p0cJarHv3FFZNicLmfv883u1KdSua2/RICPS0WBbR810KmdX7jlRSdxMHL1se6iA0uhYAdpwqg8lkwvsnSwUnbW3JLcKkaF8sSA6y53KJiIaFO61EZDOFVIIorUqwDdIYP+tdAfqL9HPHZ49PFTwtP1xpYd54/cHxsFSSmVtYh2ith9WT8VXNHZBYac8FdJ34nxpnuXOCI3UajHho8zGctTAx644IjWBgBQCdwYj3TggH1m4fWum9S0TkDAytRGQXC5KDEDSIVlQ3dQaseOOYxWA1HPmljZgep4XByhyC8+VNkEut/wSeK2/CgxPN21N1WzMtCgrpyJUC9Lbv22pcrzUvxwCAlFA1HpkRPaz339S5zmQuIiKAoZWI7EQpl+Dtn0xAVuR3LZkUVkJhaX2bYA2sPXi7ywacwLXjVCmOXbM+GAAA8ooboHEXrqR6/2SJ02o/r9dYHmWr0xvx4enh7ZROj3PcEAcioqFgaCUiu4nWqrB97SQceSYHW1ZloUNvv5Gra6ZFIS5AZdOzyzJCoZRLEGhlStS+i7a13jpX3oT6m8K7jpeqWlB+a0rYSBPq/9otNkCF/Vb+fAqp9bKH+ABPLM+yvMNMROQMDK1EZHfB3kr4WJk+NRSniuttGkwgFYvg4yGHSCTCM/PH2nUNQpQj2Cmgt2lxWsHWVCIRUFRz02IPWgDo0FveHX48Jwbb106Cp5vzDpgREQlhaCWiYcsvbcCP/nEcMb/8DON/sxvPf3YBoRol/D0t73T2JxGLYO3cky3N/oGuFlC/2lmAvOJ6/CA9BHEBgzsgNhhaT7ndw7mtJGIRnl+cBJWib+mCyQTkWzicZYvUMG+olQysROR6GFqJaFguVTXj/r/lYv+lG9AbTahr1WHTgUKsezcPz8xPsLk7wGePT8HfH8qweH8wXbFMJmDr8WJ06A0oqXPcP9/faNbhrcPCbbMczWQy4dmPCtDSYd8DU0q5c3aOiYgGwtBKRMPyt/2FaOs0mF0/eLkG4T7u2PbTiZifFIjEIC8sTg/BwlTz3p+/WjAW8YFeyI7VQq0UPvQ02M5YlU0d2FVQKbg2e3ph10U0tVseVOAoBWVNKChrsus7RUCfg3RERK6EwwWIaFjySxss3jtT2ohVU8ZgQpRvn+trpjViz4UqyCRi3JUchMhbPV7dZBI8e/c4PPXBGbPT/4M9o58W5o1z5fYNdULaOg3IvVqLueMCHf5dvdW0dtj9nZ5uUkgtNbclInIyhlYiGpZAtRsuVwsfkAr0Eu7bmhSiRlKIWvDevRmhGOPnji1Hi1De0A6TyYRvbKxn7ebjIceDE8Kx61zloD7X/dnEYC8csjJpqj9n/JN6SogacokYOmvNaAfp3oxQu72LiMje+FdqIhqWBydGCF4P9HLD7ET/Ib0zI8IHLz+Qju1rJ2F8hGbQn9eq5PBTKbAoLWTQh4pevDcFbz6cid8tTsIdERrIJNYLE7SeCkzst5M8EnxVCqyaOmZQn7kr2fpu8H/OjhvOkoiIHIqhlYiG5c5xgfjF/IQ+rZ+itR54c2WmXaZFZQ6hxvJiVQsOXqmBWinD//44C9Ha7zoIaD0ViLfQ49TfU4FpcVrIJGKsmBCBD/59Mnavn46pscKjWj3kErxyfxpkTvon9Z/fGY8FAwTRbt7uMjy/OBkad+EQP2dsALzYNYCIXJjI5KxxLkR0W2lq70RecQO83KRIC/OGyNa2AQMwGE14YNNRfHO9b4mARCyCwUpLgZ/NjcO6nFgAXSftz1c0oUNvRHKIGhcrm7F8Uy6ae528F4uAjcvTcXdKsOD76lt1aO804Fx5E/JK6qG9tZOrcVLLq27Hr9Xhvr8dtfqMRCzCn+5Pwz2pwfgwrxRPbO9bM+ztLsP2NZOsDiwgInI2hlYicnmtHXq8vv8qPj5Tjo5OI2aN9UdmpA/Wbz9tcVzrS8tSsVSgRtNkMuF67U2U1rdi/8UanK9oQqhGiQcyw3GiqA47TpWhVafH1FgtHpkRjVCNu4P/dMNjMpmw4o1jOHK1ts91mUSE7Bg/RGtVeCAzDLG9AumZkga8c6wIFY3tSA5R46FJEQhSK0d66UREg8LQSkSj1vsnSvDUB/lm1/1Uchz4+Uy4y/ueNS0oa8QzO/J7WkVF+XngvxeNw5QYP6zZchJfnq/q87zWU4FP1k1BoFr4QJmraO3QY8PuS9hxqhRN7XpMjfXDE3PikBLq7eylERHZDUMrEY1qb+cW4YVd36L51tjSKK0HNj6QbtadoL5Vh5kvfY2Gm317qiqkYvxxWQoe23pa8P2rp0XhlwscPw7WXkwmk91KM4iIXAlDKxGNem06A/JK6qFSSJEcohYMbZsPXcNvPj0v+PnMCI3Ftlrdh7O+uV4HLzcZ7s0IxYoJ4exnSkQ0wtinlYhGPaVcgsnRwif8uxXXtlq8Z20UanVzBz44Wdrz32fLugYjbP5RJuRSBlciopHCX1wi+l6ItXIyPtJvcIetDl6uQdbvduPzsxXDXRYREdmIoZWIvhd+kB4iOKHL002Km7rBT5VqaNPjsa15uFjZbI/lERHRABhaieh7QaWQYtvqiZgRr0V3yWtWpA/e+ckE1N/UDemdeqMJW48X23GVRERkCWtaieh7I9LPA2+tzEJzeycMRhO83bsGAySFqJFf2jikd5Y3tNlziUREZAF3Wonoe8fTTdYTWAHgx9lj4C4f2sjZ5H6ttYiIyDEYWonoey/GX4VtqydC4y4b1Of8VAosnxDuoFUREVFvDK1ERABSQr2x4b40m5+P8vPA+2snwU+lcNyiiIioB0MrEdEtMxP88dsfJEGttL7jKpOIsPnhTIzx8xihlRERESdiERH1095pwDfX67D7fBX2XqhGZVM7DMaun0ofDzmeX5yMeUmBTlnbhYomvPrVFZwsqu8qT8gKxwOZYRCLObqViG5vDK1ERAPo0Btw/FodACAzUoPzFc04cqUGaqUMd6UEw8dDPsAb7KOgrBHLXj+Ktk5Dn+srsyPx3MJxI7IGIiJnYWglIrJRp8GIx7fm4fOCyp5rUrEI85MCsWZ6NJIc3Elg7ZaT2HWu0uy6WAQcfiYHQWqlQ7+fiMiZWNNKRGSjt3OL+gRWoGvAwCf5Fbj7z4fw7M4COHIf4Ni1WsHrRhNw4nq9w76XiMgVMLQSEdloZ16Z1ftbcouw90K1w75fY6UMYaRKFIiInIWhlYjIRq06w4DPfHym3GHff/8dYYLXw3yUmBjl67DvJSJyBQytREQ2mh6nHfCZ/oek7GnVlDFYMj6kzzWpWITyhnYsevUQPjtb4bDvJiJyNoZWIiIbrZ4WhRBv64edchL8Hfb9UokYG+5Lw94np2NOYgCArppag9GEgrImPPLOKXx02noJAxHRaMXQSkRkowAvN+x8NBuPzogWHECQEaHB4vQQgU/afx1HrtQI3ntlz2WHHgYjInIWtrwiIhoCvcGIj06X49P8cnQaTJiTGID77giDUi5x+HefKWnAolcPW7yf/19z4eVmfaoXEdFoI3X2AoiIRiOpRIylGaFYmhE64t/t76WASAQIbTl4ukmhlDk+OBMRjTSWBxARjTJBaiVmJQQI3lueFQ6ZhD/tRHT74S8bEdEo9OK9KZga69fz3yIRsDg9BE/OjXPiqoiIHIc1rUREo9iV6maU1LUhNkCFUI27s5dDROQwDK1ERERE5PJYHkBERERELo+hlYiIiIhcHkMrEREREbk8hlYiIiIicnkMrURERETk8hhaiYiIiMjlMbQSERERkctjaCUiIiIil8fQSkREREQuj6GViIiIiFweQysRERERuTyGViKiEWA0mlBU24rqpnZnL4WIaFSSOnsBRES3u33fVuE3n17AtZpWAMDkaF/8z5IUhPu6O3llRESjh8hkMpmcvQgiotGiub0TnxdUorZFh8xIDTIiNBCJRBafP1vaiMV/PQy9se9PbZiPEnuemA6FVOLoJRMR3Ra400pEZKNjhbX4yT9PoLld33NtZrwWrz2YATeZcPh868h1s8AKACV1bfjiXBXuSQ122HqJiG4nrGklIrKBTm/Eo+/m9QmsAPDVxRvYdKDQ4ueu1bRYvFd4w/I9IiLqizutREQ2OHTlBmpaOgTvfZhXhsdnxQIAals6sOlgIfZeqIZcIoZEbLl0IMZf5ZC1EhHdjhhaiYhs0H+HVeheY1snlr1+FIW3Dlx1EwHoXyAQ6euOuYmBdl4lEdHti+UBREQ2mBTtC6mFXdNpcX4AgG3Hi80CK9AVWEO8lQAAkairDvadn06EXMqfYCIiW3GnlYjIBv6ebnhkZgw27r3c57rGXYbHcrpKA45crbX4+flJAVg1NQpuUgk0HnKHrpWI6HbE0EpEZKP1s2ORGOSJrcdLUNeqQ0aEBqumjEGYT1e/VbVSZvGzGg8FgtTKkVoqEdFth31aiYjs5ODlG3ho83Gz61KxCF8/NQOhGg4TICIaKhZUERHZydRYLdbPjkPv0lc3mRgv3ZfKwEpENEzcaSUisrPyhjZ8dbGr5dXcxECo3S2XDRARkW0YWomIiIjI5bE8gIiIiIhcHkMrEREREbk8hlYiIiIicnkMrURERETk8hhaiYiIiMjlMbQSERERkctjaCUiIiIil8fQSkREREQuj6GViIiIiFweQysRERERuTyGViIiIiJyeQytREREROTyGFqJiIiIyOUxtBIRERGRy2NoJSIiIiKXx9BKRERERC6PoZWIiIiIXB5DKxERERG5PIZWIiIiInJ5DK1ERERE5PIYWomIiIjI5TG0EhEREZHLY2glIiIiIpf3/wCyj0JZCeI2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nathanlevy/mambaforge/envs/phenospace_23/lib/python3.11/site-packages/anndata/_core/anndata.py:1222: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  df[key] = c\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nathanlevy/mambaforge/envs/phenospace_23/lib/python3.11/site-packages/anndata/_core/anndata.py:1222: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  df[key] = c\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nathanlevy/mambaforge/envs/phenospace_23/lib/python3.11/site-packages/anndata/_core/anndata.py:1222: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  df[key] = c\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nathanlevy/mambaforge/envs/phenospace_23/lib/python3.11/site-packages/anndata/_core/anndata.py:1222: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  df[key] = c\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nathanlevy/mambaforge/envs/phenospace_23/lib/python3.11/site-packages/anndata/_core/anndata.py:1222: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  df[key] = c\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nathanlevy/mambaforge/envs/phenospace_23/lib/python3.11/site-packages/anndata/_core/anndata.py:1222: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
      "  df[key] = c\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for sample in adata.obs.donor_slice.unique().tolist()[:]:\n",
    "    sc.pl.spatial(\n",
    "        adata=adata[\n",
    "            adata.obs[\"niche_type\"].isin([\"oligodendrocyte\"])\n",
    "            & (adata.obs[\"donor_slice\"] == sample)\n",
    "        ],\n",
    "        spot_size=40,\n",
    "        color=[\"niche_type\"],\n",
    "        ncols=1,\n",
    "        frameon=False,\n",
    "        title=sample,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([34948, 87720,   964])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the threshold\n",
    "threshold = 0.3333\n",
    "\n",
    "# Check which values are greater than the threshold\n",
    "greater_than_threshold = adata.obsm[\"neighborhood_composition\"][:, niches] > threshold\n",
    "\n",
    "# Sum the True values in each row (axis=1) to count the number of values greater than the threshold\n",
    "count_greater = np.sum(greater_than_threshold, axis=1)\n",
    "\n",
    "# Get the value counts for the counts\n",
    "value_counts = np.bincount(count_greater)\n",
    "\n",
    "value_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neuron</th>\n",
       "      <th>astrocyte</th>\n",
       "      <th>oligodendrocyte</th>\n",
       "      <th>medium spiny neuron</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1052</th>\n",
       "      <td>0.521739</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121503</th>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121525</th>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122028</th>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122034</th>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123054</th>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>964 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          neuron  astrocyte  oligodendrocyte  medium spiny neuron\n",
       "1010    0.350000   0.000000         0.450000                  0.0\n",
       "1014    0.380952   0.047619         0.333333                  0.0\n",
       "1018    0.333333   0.047619         0.380952                  0.0\n",
       "1026    0.428571   0.047619         0.333333                  0.0\n",
       "1052    0.521739   0.043478         0.347826                  0.0\n",
       "...          ...        ...              ...                  ...\n",
       "121503  0.043478   0.347826         0.434783                  0.0\n",
       "121525  0.047619   0.333333         0.476190                  0.0\n",
       "122028  0.043478   0.391304         0.347826                  0.0\n",
       "122034  0.050000   0.350000         0.500000                  0.0\n",
       "123054  0.050000   0.450000         0.350000                  0.0\n",
       "\n",
       "[964 rows x 4 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhood_composition.iloc[:, niches][count_greater == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "endothelial cell                  0.093888\n",
       "oligodendrocyte precursor cell    0.045545\n",
       "neuron                            0.202483\n",
       "astrocyte                         0.100572\n",
       "inhibitory interneuron            0.155010\n",
       "pericyte                          0.044983\n",
       "oligodendrocyte                   0.118568\n",
       "microglial cell                   0.045717\n",
       "vascular leptomeningeal cell      0.014415\n",
       "medium spiny neuron               0.172585\n",
       "macrophage                        0.004027\n",
       "T cell                            0.000365\n",
       "cell                              0.001842\n",
       "dtype: float64"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhood_composition.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1584113/2154981468.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"niche_assignment\"] = df.idxmax(axis=1)\n"
     ]
    }
   ],
   "source": [
    "neighborhood_composition = pd.DataFrame(\n",
    "    adata.obsm[\"neighborhood_composition\"], columns=cell_types\n",
    ")\n",
    "neighborhood_composition_niches = neighborhood_composition.iloc[:, niches]\n",
    "\n",
    "max_ct = neighborhood_composition_niches.max(axis=1)\n",
    "\n",
    "df = neighborhood_composition_niches  # .copy()\n",
    "\n",
    "# Create a new column with the name of the column containing the maximum value for each row\n",
    "df[\"niche_assignment\"] = df.idxmax(axis=1)\n",
    "\n",
    "# Set 'max_column' to 'unknown' for rows where the maximum value is less than the threshold\n",
    "df.loc[max_ct < 0.2, \"niche_assignment\"] = \"unknown\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "niche_assignment\n",
       "neuron                 42502\n",
       "medium spiny neuron    31410\n",
       "unknown                24769\n",
       "oligodendrocyte        14529\n",
       "astrocyte              10422\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"niche_assignment\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cell_niches(\n",
    "    adata: AnnData,\n",
    "    cell_types_to_include: list[str],\n",
    "    treshold: float = 0.2,\n",
    "    niche_type_key: str = \"niche_type\",\n",
    "    niche_composition_key: str = \"niche_composition\",\n",
    "):\n",
    "    composition_subet = neighborhood_composition[cell_types_to_include]\n",
    "\n",
    "    # for each cell, get the cell type with the highest proportion in its neighborhood\n",
    "\n",
    "    max_ct = composition_subet.max(axis=1)\n",
    "\n",
    "    # Create a new column with the name of the column containing the maximum value for each row\n",
    "    composition_subet[\"niche_assignment\"] = composition_subet.idxmax(axis=1)\n",
    "\n",
    "    # Set 'max_column' to 'unknown' for rows where the maximum value is less than the threshold\n",
    "    composition_subet.loc[max_ct < treshold, \"niche_assignment\"] = \"unknown\"\n",
    "\n",
    "    adata.obs[niche_type_key] = composition_subet[\"niche_assignment\"].values\n",
    "\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1584113/3149659337.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  composition_subet[\"niche_assignment\"] = composition_subet.idxmax(axis=1)\n"
     ]
    }
   ],
   "source": [
    "get_cell_niches(\n",
    "    adata,\n",
    "    cell_types_to_include=[\n",
    "        \"neuron\",\n",
    "        \"oligodendrocyte\",\n",
    "        \"astrocyte\",\n",
    "        \"medium spiny neuron\",\n",
    "    ],\n",
    "    niche_composition_key=\"neighborhood_composition\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "niche_type\n",
       "neuron                 42502\n",
       "medium spiny neuron    31410\n",
       "unknown                24769\n",
       "oligodendrocyte        14731\n",
       "astrocyte              10220\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs[\"niche_type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "stat = [\"mean\", \"std\"]\n",
    "multi_idx = pd.MultiIndex.from_product(\n",
    "    [neighborhood_composition.columns, stat], names=[\"cell type\", \"stat\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['endothelial cell',\n",
       " 'oligodendrocyte precursor cell',\n",
       " 'neuron',\n",
       " 'astrocyte',\n",
       " 'inhibitory interneuron',\n",
       " 'pericyte',\n",
       " 'oligodendrocyte',\n",
       " 'microglial cell',\n",
       " 'vascular leptomeningeal cell',\n",
       " 'medium spiny neuron',\n",
       " 'macrophage',\n",
       " 'T cell',\n",
       " 'cell']"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhood_composition.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell type</th>\n",
       "      <th>stat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">endothelial cell</th>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">oligodendrocyte precursor cell</th>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">neuron</th>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">astrocyte</th>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">inhibitory interneuron</th>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cluster                                A    B    C    D\n",
       "cell type                      stat                    \n",
       "endothelial cell               mean  NaN  NaN  NaN  NaN\n",
       "                               std   NaN  NaN  NaN  NaN\n",
       "oligodendrocyte precursor cell mean  NaN  NaN  NaN  NaN\n",
       "                               std   NaN  NaN  NaN  NaN\n",
       "neuron                         mean  NaN  NaN  NaN  NaN\n",
       "                               std   NaN  NaN  NaN  NaN\n",
       "astrocyte                      mean  NaN  NaN  NaN  NaN\n",
       "                               std   NaN  NaN  NaN  NaN\n",
       "inhibitory interneuron         mean  NaN  NaN  NaN  NaN\n",
       "                               std   NaN  NaN  NaN  NaN"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    data=[], index=multi_idx, columns=pd.Index([\"A\", \"B\", \"C\", \"D\"], name=\"cluster\")\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>cell type</th>\n",
       "      <th colspan=\"2\" halign=\"left\">endothelial cell</th>\n",
       "      <th colspan=\"2\" halign=\"left\">oligodendrocyte precursor cell</th>\n",
       "      <th colspan=\"2\" halign=\"left\">neuron</th>\n",
       "      <th colspan=\"2\" halign=\"left\">astrocyte</th>\n",
       "      <th colspan=\"2\" halign=\"left\">inhibitory interneuron</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">vascular leptomeningeal cell</th>\n",
       "      <th colspan=\"2\" halign=\"left\">medium spiny neuron</th>\n",
       "      <th colspan=\"2\" halign=\"left\">macrophage</th>\n",
       "      <th colspan=\"2\" halign=\"left\">T cell</th>\n",
       "      <th colspan=\"2\" halign=\"left\">cell</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stat</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>...</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.541291</td>\n",
       "      <td>1.310127</td>\n",
       "      <td>-0.594181</td>\n",
       "      <td>-0.066922</td>\n",
       "      <td>0.048902</td>\n",
       "      <td>-0.023037</td>\n",
       "      <td>-1.318195</td>\n",
       "      <td>-0.546703</td>\n",
       "      <td>-0.450539</td>\n",
       "      <td>-0.415695</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.824921</td>\n",
       "      <td>0.650109</td>\n",
       "      <td>-2.087075</td>\n",
       "      <td>-0.748056</td>\n",
       "      <td>0.151622</td>\n",
       "      <td>1.068552</td>\n",
       "      <td>0.312916</td>\n",
       "      <td>0.154385</td>\n",
       "      <td>-0.515064</td>\n",
       "      <td>-0.754032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.024503</td>\n",
       "      <td>0.317852</td>\n",
       "      <td>-0.949025</td>\n",
       "      <td>-0.101161</td>\n",
       "      <td>0.144671</td>\n",
       "      <td>-0.856898</td>\n",
       "      <td>-0.101895</td>\n",
       "      <td>-0.281181</td>\n",
       "      <td>-0.325689</td>\n",
       "      <td>-1.276665</td>\n",
       "      <td>...</td>\n",
       "      <td>0.664228</td>\n",
       "      <td>-0.463626</td>\n",
       "      <td>-0.758068</td>\n",
       "      <td>0.261649</td>\n",
       "      <td>-1.152322</td>\n",
       "      <td>0.684155</td>\n",
       "      <td>-0.097495</td>\n",
       "      <td>-1.816570</td>\n",
       "      <td>-0.513995</td>\n",
       "      <td>-1.066933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.402902</td>\n",
       "      <td>0.906653</td>\n",
       "      <td>0.980003</td>\n",
       "      <td>-1.279017</td>\n",
       "      <td>-0.108951</td>\n",
       "      <td>-0.098775</td>\n",
       "      <td>-0.299767</td>\n",
       "      <td>1.175900</td>\n",
       "      <td>0.976180</td>\n",
       "      <td>1.052884</td>\n",
       "      <td>...</td>\n",
       "      <td>1.725901</td>\n",
       "      <td>0.953601</td>\n",
       "      <td>-0.117405</td>\n",
       "      <td>0.097911</td>\n",
       "      <td>1.601058</td>\n",
       "      <td>-0.144762</td>\n",
       "      <td>1.668261</td>\n",
       "      <td>-1.723381</td>\n",
       "      <td>0.668928</td>\n",
       "      <td>0.064071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "cell type endothelial cell           oligodendrocyte precursor cell            \\\n",
       "stat                  mean       std                           mean       std   \n",
       "A                 1.541291  1.310127                      -0.594181 -0.066922   \n",
       "B                -1.024503  0.317852                      -0.949025 -0.101161   \n",
       "C                 0.402902  0.906653                       0.980003 -1.279017   \n",
       "\n",
       "cell type    neuron           astrocyte           inhibitory interneuron  \\\n",
       "stat           mean       std      mean       std                   mean   \n",
       "A          0.048902 -0.023037 -1.318195 -0.546703              -0.450539   \n",
       "B          0.144671 -0.856898 -0.101895 -0.281181              -0.325689   \n",
       "C         -0.108951 -0.098775 -0.299767  1.175900               0.976180   \n",
       "\n",
       "cell type            ... vascular leptomeningeal cell            \\\n",
       "stat            std  ...                         mean       std   \n",
       "A         -0.415695  ...                    -1.824921  0.650109   \n",
       "B         -1.276665  ...                     0.664228 -0.463626   \n",
       "C          1.052884  ...                     1.725901  0.953601   \n",
       "\n",
       "cell type medium spiny neuron           macrophage              T cell  \\\n",
       "stat                     mean       std       mean       std      mean   \n",
       "A                   -2.087075 -0.748056   0.151622  1.068552  0.312916   \n",
       "B                   -0.758068  0.261649  -1.152322  0.684155 -0.097495   \n",
       "C                   -0.117405  0.097911   1.601058 -0.144762  1.668261   \n",
       "\n",
       "cell type                cell            \n",
       "stat            std      mean       std  \n",
       "A          0.154385 -0.515064 -0.754032  \n",
       "B         -1.816570 -0.513995 -1.066933  \n",
       "C         -1.723381  0.668928  0.064071  \n",
       "\n",
       "[3 rows x 26 columns]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    np.random.randn(3, len(multi_idx)), index=[\"A\", \"B\", \"C\"], columns=multi_idx\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -1.318195\n",
       "B   -0.101895\n",
       "C   -0.299767\n",
       "Name: (astrocyte, mean), dtype: float64"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"astrocyte\", \"mean\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell type</th>\n",
       "      <th>stat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">endothelial cell</th>\n",
       "      <th>mean</th>\n",
       "      <td>1.541291</td>\n",
       "      <td>-1.024503</td>\n",
       "      <td>0.402902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.310127</td>\n",
       "      <td>0.317852</td>\n",
       "      <td>0.906653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">oligodendrocyte precursor cell</th>\n",
       "      <th>mean</th>\n",
       "      <td>-0.594181</td>\n",
       "      <td>-0.949025</td>\n",
       "      <td>0.980003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>-0.066922</td>\n",
       "      <td>-0.101161</td>\n",
       "      <td>-1.279017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">neuron</th>\n",
       "      <th>mean</th>\n",
       "      <td>0.048902</td>\n",
       "      <td>0.144671</td>\n",
       "      <td>-0.108951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>-0.023037</td>\n",
       "      <td>-0.856898</td>\n",
       "      <td>-0.098775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">astrocyte</th>\n",
       "      <th>mean</th>\n",
       "      <td>-1.318195</td>\n",
       "      <td>-0.101895</td>\n",
       "      <td>-0.299767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>-0.546703</td>\n",
       "      <td>-0.281181</td>\n",
       "      <td>1.175900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">inhibitory interneuron</th>\n",
       "      <th>mean</th>\n",
       "      <td>-0.450539</td>\n",
       "      <td>-0.325689</td>\n",
       "      <td>0.976180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>-0.415695</td>\n",
       "      <td>-1.276665</td>\n",
       "      <td>1.052884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">pericyte</th>\n",
       "      <th>mean</th>\n",
       "      <td>-2.113228</td>\n",
       "      <td>0.737442</td>\n",
       "      <td>0.001419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>-1.401720</td>\n",
       "      <td>-0.483496</td>\n",
       "      <td>-0.761579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">oligodendrocyte</th>\n",
       "      <th>mean</th>\n",
       "      <td>1.516984</td>\n",
       "      <td>0.806349</td>\n",
       "      <td>1.095467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>-0.747409</td>\n",
       "      <td>1.857976</td>\n",
       "      <td>-0.181204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">microglial cell</th>\n",
       "      <th>mean</th>\n",
       "      <td>0.690587</td>\n",
       "      <td>-0.299780</td>\n",
       "      <td>-0.759387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>-0.205107</td>\n",
       "      <td>-0.721792</td>\n",
       "      <td>0.182227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">vascular leptomeningeal cell</th>\n",
       "      <th>mean</th>\n",
       "      <td>-1.824921</td>\n",
       "      <td>0.664228</td>\n",
       "      <td>1.725901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.650109</td>\n",
       "      <td>-0.463626</td>\n",
       "      <td>0.953601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">medium spiny neuron</th>\n",
       "      <th>mean</th>\n",
       "      <td>-2.087075</td>\n",
       "      <td>-0.758068</td>\n",
       "      <td>-0.117405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>-0.748056</td>\n",
       "      <td>0.261649</td>\n",
       "      <td>0.097911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">macrophage</th>\n",
       "      <th>mean</th>\n",
       "      <td>0.151622</td>\n",
       "      <td>-1.152322</td>\n",
       "      <td>1.601058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.068552</td>\n",
       "      <td>0.684155</td>\n",
       "      <td>-0.144762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">T cell</th>\n",
       "      <th>mean</th>\n",
       "      <td>0.312916</td>\n",
       "      <td>-0.097495</td>\n",
       "      <td>1.668261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.154385</td>\n",
       "      <td>-1.816570</td>\n",
       "      <td>-1.723381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">cell</th>\n",
       "      <th>mean</th>\n",
       "      <td>-0.515064</td>\n",
       "      <td>-0.513995</td>\n",
       "      <td>0.668928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>-0.754032</td>\n",
       "      <td>-1.066933</td>\n",
       "      <td>0.064071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            A         B         C\n",
       "cell type                      stat                              \n",
       "endothelial cell               mean  1.541291 -1.024503  0.402902\n",
       "                               std   1.310127  0.317852  0.906653\n",
       "oligodendrocyte precursor cell mean -0.594181 -0.949025  0.980003\n",
       "                               std  -0.066922 -0.101161 -1.279017\n",
       "neuron                         mean  0.048902  0.144671 -0.108951\n",
       "                               std  -0.023037 -0.856898 -0.098775\n",
       "astrocyte                      mean -1.318195 -0.101895 -0.299767\n",
       "                               std  -0.546703 -0.281181  1.175900\n",
       "inhibitory interneuron         mean -0.450539 -0.325689  0.976180\n",
       "                               std  -0.415695 -1.276665  1.052884\n",
       "pericyte                       mean -2.113228  0.737442  0.001419\n",
       "                               std  -1.401720 -0.483496 -0.761579\n",
       "oligodendrocyte                mean  1.516984  0.806349  1.095467\n",
       "                               std  -0.747409  1.857976 -0.181204\n",
       "microglial cell                mean  0.690587 -0.299780 -0.759387\n",
       "                               std  -0.205107 -0.721792  0.182227\n",
       "vascular leptomeningeal cell   mean -1.824921  0.664228  1.725901\n",
       "                               std   0.650109 -0.463626  0.953601\n",
       "medium spiny neuron            mean -2.087075 -0.758068 -0.117405\n",
       "                               std  -0.748056  0.261649  0.097911\n",
       "macrophage                     mean  0.151622 -1.152322  1.601058\n",
       "                               std   1.068552  0.684155 -0.144762\n",
       "T cell                         mean  0.312916 -0.097495  1.668261\n",
       "                               std   0.154385 -1.816570 -1.723381\n",
       "cell                           mean -0.515064 -0.513995  0.668928\n",
       "                               std  -0.754032 -1.066933  0.064071"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_counts = np.array([1, 2, 4, 6, 3, 0], dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_counts_prop = cell_counts / cell_counts.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax(x, temperature=2):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp((x - x.max(axis=1).reshape(-1, 1)) / temperature)\n",
    "\n",
    "    softmax = e_x / e_x.sum(axis=1).reshape(-1, 1)\n",
    "    return softmax.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.006, 0.015, 0.112, 0.825, 0.041, 0.002]], dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "softmax(cell_counts.reshape(1, -1), temperature=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.   , 0.   , 0.018, 0.979, 0.002, 0.   ]], dtype=float32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "softmax(cell_counts.reshape(1, -1), temperature=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -2.7725728 ,  -2.0794337 ,  -1.3862903 ,  -0.98082656,\n",
       "        -1.6739712 , -13.815511  ], dtype=float32)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(cell_counts_prop + 1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy(neighborhood_composition.loc[0], neighborhood_composition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "entropy(neighborhood_composition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.6502e-02, 9.8330e-01, 1.9796e-04])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.distributions import Categorical, Dirichlet, Normal, MixtureSameFamily\n",
    "\n",
    "m = Dirichlet(torch.tensor([0.7, 10, 0.2]))\n",
    "m.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.0642, 0.9174, 0.0183])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(-inf)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.log_prob(torch.tensor([0.7, 0., 0.3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mix = Categorical(torch.ones(5,))\n",
    "comp = Normal(torch.randn(5,), torch.rand(5,))\n",
    "gmm = MixtureSameFamily(mix, comp)\n",
    "gmm.sample()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-6.5607, -0.5870, -2.1469, -1.8581, -2.3643])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmm.log_prob(torch.randn(5,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MixtureSameFamily(\n",
       "  Categorical(probs: torch.Size([5]), logits: torch.Size([5])),\n",
       "  Normal(loc: torch.Size([5]), scale: torch.Size([5])))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(-0.4473)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.5086,  1.1555, -0.0626, -0.0883],\n",
       "        [-2.6111,  1.4322, -1.6264, -0.7931],\n",
       "        [ 1.1172,  0.4605,  0.2723,  1.2255],\n",
       "        [ 1.3661, -2.1044,  0.6153,  0.6707]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# random 4x4 matrix\n",
    "x = torch.randn(4, 4)\n",
    "\n",
    "z = Normal(x, 1)\n",
    "x\n",
    "\n",
    "#sample 100 times and get the mean\n",
    "z.sample((1000,)).mean(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#implement log_loss between two vectors\n",
    "\n",
    "def log_loss(y_true, y_pred):\n",
    "    return -(y_true* np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/home/nathanlevy/spatial-scvi/ground_truth_niches.ipynb Cell 54\u001b[0m line \u001b[0;36m3\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bprivate/home/nathanlevy/spatial-scvi/ground_truth_niches.ipynb#Y104sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# test it\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bprivate/home/nathanlevy/spatial-scvi/ground_truth_niches.ipynb#Y104sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m y_true \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m])\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bprivate/home/nathanlevy/spatial-scvi/ground_truth_niches.ipynb#Y104sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m y_pred \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([\u001b[39m0.1\u001b[39m, \u001b[39m0.9\u001b[39m, \u001b[39m0.2\u001b[39m, \u001b[39m0.8\u001b[39m])\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bprivate/home/nathanlevy/spatial-scvi/ground_truth_niches.ipynb#Y104sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5'>6</a>\u001b[0m log_loss(y_true, y_pred)\u001b[39m.\u001b[39mmean()\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "# test it\n",
    "\n",
    "y_true = np.array([0, 1, 0, 1])\n",
    "y_pred = np.array([0.1, 0.9, 0.2, 0.8])\n",
    "\n",
    "log_loss(y_true, y_pred).mean()"
   ]
  }
 ],
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    "version": 3
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